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Posted to commits@tvm.apache.org by tq...@apache.org on 2026/06/19 14:54:59 UTC
(tvm-site) branch asf-site updated: deploying docs (apache/tvm@1b900a386892408af6494e39014ca03bc8e2f1e6)
This is an automated email from the ASF dual-hosted git repository.
tqchen pushed a commit to branch asf-site
in repository https://gitbox.apache.org/repos/asf/tvm-site.git
The following commit(s) were added to refs/heads/asf-site by this push:
new 6e2c94f9772 deploying docs (apache/tvm@1b900a386892408af6494e39014ca03bc8e2f1e6)
6e2c94f9772 is described below
commit 6e2c94f9772a8698ff8fa5cca9e8d6a1ddfb9059
Author: tvm-bot <95...@users.noreply.github.com>
AuthorDate: Fri Jun 19 14:54:51 2026 +0000
deploying docs (apache/tvm@1b900a386892408af6494e39014ca03bc8e2f1e6)
---
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.../mix_python_and_tvm_with_pymodule.zip | Bin 39023 -> 39023 bytes
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.../bring_your_own_codegen.zip | Bin 18423 -> 36685 bytes
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diff --git a/docs/_downloads/2729a585e9deb6295e6d7badc73087d7/bring_your_own_codegen.ipynb b/docs/_downloads/2729a585e9deb6295e6d7badc73087d7/bring_your_own_codegen.ipynb
index 44c811d778f..9f0dc3e392d 100644
--- a/docs/_downloads/2729a585e9deb6295e6d7badc73087d7/bring_your_own_codegen.ipynb
+++ b/docs/_downloads/2729a585e9deb6295e6d7badc73087d7/bring_your_own_codegen.ipynb
@@ -15,21 +15,21 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "\n\n# Bring Your Own Codegen: NPU Backend Example\n\nThis tutorial shows how to integrate a custom hardware backend with TVM's\nBYOC framework, using the bundled example NPU backend (CPU emulation, no\nreal hardware required) as the worked example. You will see the key\nconcepts needed to offload operations to a custom accelerator: pattern\nregistration, graph partitioning, codegen, and runtime dispatch.\n\nNPUs are purpose-built accelerators designed around a fixed set of oper [...]
+ "\n\n# Bring Your Own Codegen\n\nTVM's Bring Your Own Codegen (BYOC) framework lets you offload parts of a model\nto a custom backend -- a hardware accelerator, an inference library, or your own\nkernels -- while TVM compiles the rest. This tutorial has two parts:\n\n- **How BYOC works** -- we teach the flow with a bundled, hardware-free *example\n NPU* backend and then drive the **same flow** on a real production backend,\n NVIDIA TensorRT. Both run a small, hand-written mod [...]
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "## Overview of the BYOC Flow\n\nThe BYOC framework lets you plug a custom backend into TVM's compilation\npipeline in four steps:\n\n1. **Register patterns** - describe which sequences of Relax ops the\n backend can handle.\n2. **Partition the graph** - group matched ops into composite functions.\n3. **Run codegen** - lower composite functions to backend-specific\n representation (JSON graph for the example NPU).\n4. **Execute** - the runtime dispatches composite functions t [...]
+ "## Overview of the BYOC flow\n\nBYOC plugs a custom backend into TVM's compilation pipeline in four steps:\n\n1. **Register patterns** - describe which sequences of Relax ops the backend\n can handle.\n2. **Partition the graph** - group matched ops into composite functions.\n3. **Run codegen** - lower each composite to the backend's representation\n (a JSON graph for both backends in this tutorial).\n4. **Execute** - the runtime dispatches each composite to the backend.\n\nS [...]
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "## Step 1: Import the backend to register its patterns\n\nImporting the module is enough to register all supported patterns with\nTVM's pattern registry.\n\n"
+ "## Step 1: Import the backends to register their patterns\n\nImporting a backend module registers its patterns with TVM's global registry.\nPattern registration is independent of the C++ build -- only codegen and the\nruntime require the backend to be compiled in -- so we probe each backend and\nguard the build-and-run cells accordingly.\n\n"
]
},
{
@@ -40,14 +40,14 @@
},
"outputs": [],
"source": [
- "import numpy as np\n\nimport tvm\nimport tvm.relax.backend.contrib.example_npu # registers patterns\nfrom tvm import relax\nfrom tvm.relax.backend.pattern_registry import get_patterns_with_prefix\nfrom tvm.relax.transform import FuseOpsByPattern, MergeCompositeFunctions, RunCodegen\nfrom tvm.script import relax as R\n\nhas_example_npu_codegen = tvm.get_global_func(\"relax.ext.example_npu\", True)\nhas_example_npu_runtime = tvm.get_global_func(\"runtime.ExampleNPUJSONRuntimeCrea [...]
+ "import os\nimport tempfile\n\nimport numpy as np\n\nimport tvm\nimport tvm.relax.backend.contrib.example_npu\nfrom tvm import relax\nfrom tvm.relax.backend.contrib.tensorrt import partition_for_tensorrt\nfrom tvm.relax.backend.pattern_registry import get_patterns_with_prefix\nfrom tvm.relax.transform import FuseOpsByPattern, MergeCompositeFunctions, RunCodegen\nfrom tvm.script import relax as R\n\nhas_example_npu_codegen = tvm.get_global_func(\"relax.ext.example_npu\", True)\nha [...]
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "## Step 2: Define a model\n\nWe use a simple MatMul + ReLU module to illustrate the flow.\n\n"
+ "## Step 2: Define the model\n\nA single convolution followed by a ReLU. This one model is used for both\nbackends.\n\n"
]
},
{
@@ -58,14 +58,14 @@
},
"outputs": [],
"source": [
- "@tvm.script.ir_module\nclass MatmulReLU:\n @R.function\n def main(\n x: R.Tensor((2, 4), \"float32\"),\n w: R.Tensor((4, 8), \"float32\"),\n ) -> R.Tensor((2, 8), \"float32\"):\n with R.dataflow():\n y = relax.op.matmul(x, w)\n z = relax.op.nn.relu(y)\n R.output(z)\n return z"
+ "@tvm.script.ir_module\nclass ConvReLU:\n @R.function\n def main(\n data: R.Tensor((1, 3, 32, 32), \"float32\"),\n weight: R.Tensor((16, 3, 3, 3), \"float32\"),\n ) -> R.Tensor((1, 16, 30, 30), \"float32\"):\n with R.dataflow():\n conv = relax.op.nn.conv2d(data, weight)\n out = relax.op.nn.relu(conv)\n R.output(out)\n return out"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "## Step 3: Partition the graph\n\n``FuseOpsByPattern`` groups ops that match a registered pattern into\ncomposite functions, controlled by two flags:\n\n- ``bind_constants=False`` keeps weights as function arguments instead\n of baking them in, so the host stays in charge of parameter\n ownership.\n- ``annotate_codegen=True`` tags each composite with its backend name\n (``example_npu``); without this tag, ``RunCodegen`` has no way to\n route the composite to a backend.\n\n`` [...]
+ "## Step 3: Partition for the example NPU\n\n``FuseOpsByPattern`` groups ops matching a registered pattern into composite\nfunctions; ``MergeCompositeFunctions`` then consolidates adjacent composites\nbound for the same backend into a single external call. Two flags steer\npartitioning:\n\n- ``bind_constants=False`` keeps weights as function arguments, so the host\n stays in charge of the parameters. (TensorRT below makes the opposite\n choice: it binds weights as constants b [...]
]
},
{
@@ -76,14 +76,14 @@
},
"outputs": [],
"source": [
- "mod = MatmulReLU\nmod = FuseOpsByPattern(patterns, bind_constants=False, annotate_codegen=True)(mod)\nmod = MergeCompositeFunctions()(mod)\nprint(\"After partitioning:\")\nprint(mod)"
+ "npu_patterns = get_patterns_with_prefix(\"example_npu\")\nnpu_mod = FuseOpsByPattern(npu_patterns, bind_constants=False, annotate_codegen=True)(ConvReLU)\nnpu_mod = MergeCompositeFunctions()(npu_mod)\nprint(\"After partitioning for the example NPU:\")\nprint(npu_mod)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "## Step 4: Run codegen\n\n``RunCodegen`` lowers each annotated composite function to the backend's\nserialization format. For the example NPU this produces a JSON graph\nthat the C++ runtime can execute.\n\nSteps 4 and 5 require TVM to be built with ``USE_EXAMPLE_NPU_CODEGEN=ON``\nand ``USE_EXAMPLE_NPU_RUNTIME=ON``.\n\n"
+ "## Step 4: Codegen, build and run on the example NPU\n\n``RunCodegen`` invokes each annotated composite's backend codegen, replacing it\nwith the backend runtime module (here, the NPU's JSON graph); ``relax.build``\nthen compiles the remaining host-side program and links everything. Because\nthe runtime is a stub that computes nothing, we assert on the output *shape*\nonly -- the values are uninitialized.\n\n"
]
},
{
@@ -94,14 +94,14 @@
},
"outputs": [],
"source": [
- "if has_example_npu:\n mod = RunCodegen()(mod)\n print(\"After codegen:\")\n print(mod)\n\n ######################################################################\n # Step 5: Build and run\n # ---------------------\n #\n # Build the module for the host target, create a virtual machine, and\n # execute the compiled function.\n\n np.random.seed(0)\n x_np = np.random.randn(2, 4).astype(\"float32\")\n w_np = np.random.randn(4, 8).astype(\"float32\" [...]
+ "np.random.seed(0)\ndata_np = np.random.randn(1, 3, 32, 32).astype(\"float32\")\nweight_np = np.random.randn(16, 3, 3, 3).astype(\"float32\")\n\nif has_example_npu:\n npu_mod = RunCodegen()(npu_mod)\n\n with tvm.transform.PassContext(opt_level=3):\n npu_exec = relax.build(npu_mod, tvm.target.Target(\"llvm\"))\n\n npu_vm = relax.VirtualMachine(npu_exec, tvm.cpu())\n npu_out = npu_vm[\"main\"](\n tvm.runtime.tensor(data_np, tvm.cpu()), tvm.runtime.tensor(w [...]
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "## Step 6: Conv2D + ReLU\n\nThe same flow applies to convolution workloads. Because the fused\n``conv2d + relu`` pattern is registered after the standalone\n``conv2d`` pattern in ``patterns.py`` (later entries have higher\npriority), both ops are offloaded as a single composite function.\n\n"
+ "## The same flow on a real backend: TensorRT\n\nSteps 1-4 above are the whole mechanism. Aiming them at a real backend\nchanges very little, so rather than repeat the walkthrough, here is only what\ndiffers for NVIDIA TensorRT:\n\n- **Partition in one call.** ``partition_for_tensorrt`` bundles the\n ``FuseOpsByPattern`` + ``MergeCompositeFunctions`` you ran by hand, using\n TensorRT's own pattern table.\n- **Weights become constants** (``bind_constants=True``): TensorRT bakes [...]
]
},
{
@@ -112,14 +112,100 @@
},
"outputs": [],
"source": [
- "@tvm.script.ir_module\nclass Conv2dReLU:\n @R.function\n def main(\n x: R.Tensor((1, 3, 32, 32), \"float32\"),\n w: R.Tensor((16, 3, 3, 3), \"float32\"),\n ) -> R.Tensor((1, 16, 30, 30), \"float32\"):\n with R.dataflow():\n y = relax.op.nn.conv2d(x, w)\n z = relax.op.nn.relu(y)\n R.output(z)\n return z\n\n\nif has_example_npu:\n mod2 = Conv2dReLU\n mod2 = FuseOpsByPattern(patterns, bind_constants=False, [...]
+ "trt_mod = relax.transform.BindParams(\"main\", {\"weight\": weight_np})(ConvReLU)\ntrt_mod = partition_for_tensorrt(trt_mod)\nprint(\"After partition_for_tensorrt:\")\nprint(trt_mod)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "## Next steps\n\nTo build a real NPU backend using this example as a starting point:\n\n- Replace ``example_npu_runtime.cc`` with your hardware SDK calls.\n- Extend ``patterns.py`` with the ops your hardware supports.\n- Add a C++ codegen under ``src/relax/backend/contrib/`` if your\n hardware requires a non-JSON serialization format.\n- Add your cmake module under ``cmake/modules/contrib/`` following\n the pattern in ``cmake/modules/contrib/ExampleNPU.cmake``.\n\n"
+ "Build for CUDA, run on the GPU, and compare against the CPU reference.\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "if has_tensorrt and has_cuda:\n dev = tvm.cuda(0)\n with tvm.transform.PassContext(opt_level=3):\n trt_exec = relax.build(RunCodegen()(trt_mod), \"cuda\")\n trt_out = relax.VirtualMachine(trt_exec, dev)[\"main\"](tvm.runtime.tensor(data_np, dev)).numpy()\n\n cpu_mod = relax.transform.LegalizeOps()(\n relax.transform.BindParams(\"main\", {\"weight\": weight_np})(ConvReLU)\n )\n cpu_exec = relax.build(cpu_mod, \"llvm\")\n cpu_out = relax.VirtualM [...]
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "A real backend also exposes knobs the stub does not. Setting ``use_fp16``\nthrough the ``relax.ext.tensorrt.options`` config lets TensorRT pick FP16\nkernels, trading a little accuracy for speed; nothing else about the flow\nchanges. (Other options are environment-driven: ``TVM_TENSORRT_USE_INT8``\nenables INT8 with calibration, ``TVM_TENSORRT_MAX_WORKSPACE_SIZE`` caps the\nbuild workspace, and ``TVM_TENSORRT_CACHE_DIR`` caches built engines to disk\nfor reuse across runs.)\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "if has_tensorrt and has_cuda:\n fp16_mod = partition_for_tensorrt(\n relax.transform.BindParams(\"main\", {\"weight\": weight_np})(ConvReLU)\n )\n with tvm.transform.PassContext(\n opt_level=3, config={\"relax.ext.tensorrt.options\": {\"use_fp16\": True}}\n ):\n fp16_exec = relax.build(RunCodegen()(fp16_mod), \"cuda\")\n fp16_out = relax.VirtualMachine(fp16_exec, tvm.cuda(0))[\"main\"](\n tvm.runtime.tensor(data_np, tvm.cuda(0))\n ). [...]
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Example NPU vs TensorRT at a glance\n\nThe same four-step flow, two backends:\n\n========= ============================== ==================================\nAspect Example NPU (teaching stub) TensorRT (real backend)\n========= ============================== ==================================\nRuntime logs decisions, no compute builds and runs an nvinfer engine\nOutput uninitialized (check shape) real values (cross-checked vs CPU)\nWeights ``bin [...]
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Deploying a PyTorch model with TensorRT\n\nEverything above used a hand-written ``IRModule`` so each op was visible. In\npractice you start from a trained model. This final section runs the *same*\n``partition_for_tensorrt`` flow on a real PyTorch ``nn.Module``, end to end:\nexport it, import it into Relax with the PyTorch frontend (the weights come in\nas constants -- exactly what TensorRT bakes into its engine), partition, build\nfor CUDA, and check the GPU result against [...]
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "try:\n import torch\n from torch import nn\n\n has_torch = True\nexcept ImportError:\n has_torch = False\n\nif has_torch and has_tensorrt and has_cuda:\n from tvm.relax.frontend.torch import from_exported_program\n\n class SmallConvNet(nn.Module):\n def __init__(self):\n super().__init__()\n self.conv1 = nn.Conv2d(3, 8, 3)\n self.conv2 = nn.Conv2d(8, 16, 3)\n self.pool = nn.MaxPool2d(2)\n\n def forward(s [...]
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Real deployment builds once and reuses the artifact. Export the compiled\nmodule to a shared library, then load and run it later -- in a fresh process,\nwith no PyTorch and no rebuild needed.\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "if has_torch and has_tensorrt and has_cuda:\n with tempfile.TemporaryDirectory() as tmpdir:\n lib_path = os.path.join(tmpdir, \"deployed_trt.so\")\n torch_exec.export_library(lib_path)\n loaded = tvm.runtime.load_module(lib_path)\n reran = relax.VirtualMachine(loaded, torch_dev)[\"main\"](\n tvm.runtime.tensor(example_input.numpy(), torch_dev)\n )[0].numpy()\n np.testing.assert_allclose(reran, torch_ref, rtol=1e-2, atol=1e- [...]
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Notes for real deployments\n\n- **Operator coverage and fallback.** TensorRT offloads only the ops in its\n pattern table (see ``python/tvm/relax/backend/contrib/tensorrt.py``);\n anything unsupported simply stays on the host. Print the partitioned module\n and look for the ``Codegen: \"tensorrt\"`` functions to see what was offloaded.\n- **Dynamic shapes.** The builder sets up an optimization profile for a dynamic\n leading (batch) dimension, so the integration can serv [...]
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Next steps\n\nTo build your own backend using the example NPU as a starting point:\n\n- Replace the stub runtime in\n ``src/runtime/extra/contrib/example_npu/example_npu_runtime.cc`` with your\n hardware SDK calls.\n- Extend ``patterns.py`` with the ops your hardware supports.\n- Add a C++ codegen under ``src/relax/backend/contrib/`` if your backend needs\n a non-JSON serialization format.\n- Add a CMake module under ``cmake/modules/contrib/`` following\n ``ExampleNPU.cma [...]
]
}
],
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diff --git a/docs/_downloads/c4ca99367570f8682036b6e670ef0dd9/bring_your_own_codegen.py b/docs/_downloads/c4ca99367570f8682036b6e670ef0dd9/bring_your_own_codegen.py
index b6039e49303..a0d4534cc49 100644
--- a/docs/_downloads/c4ca99367570f8682036b6e670ef0dd9/bring_your_own_codegen.py
+++ b/docs/_downloads/c4ca99367570f8682036b6e670ef0dd9/bring_your_own_codegen.py
@@ -18,55 +18,72 @@
"""
.. _tutorial-bring-your-own-codegen:
-Bring Your Own Codegen: NPU Backend Example
-===========================================
-
-This tutorial shows how to integrate a custom hardware backend with TVM's
-BYOC framework, using the bundled example NPU backend (CPU emulation, no
-real hardware required) as the worked example. You will see the key
-concepts needed to offload operations to a custom accelerator: pattern
-registration, graph partitioning, codegen, and runtime dispatch.
-
-NPUs are purpose-built accelerators designed around a fixed set of operations
-common in neural network inference, such as matrix multiplication, convolution,
-and activation functions.
-The example backend's runtime is a *stub*: it logs the dispatch decisions an
+Bring Your Own Codegen
+======================
+
+TVM's Bring Your Own Codegen (BYOC) framework lets you offload parts of a model
+to a custom backend -- a hardware accelerator, an inference library, or your own
+kernels -- while TVM compiles the rest. This tutorial has two parts:
+
+- **How BYOC works** -- we teach the flow with a bundled, hardware-free *example
+ NPU* backend and then drive the **same flow** on a real production backend,
+ NVIDIA TensorRT. Both run a small, hand-written model so every step is
+ visible; the only thing that changes between them is the backend, and that
+ contrast is the lesson.
+- **Deploying a real model** -- we then put it to work, taking an actual PyTorch
+ ``nn.Module`` from export through TensorRT and running it on the GPU.
+
+The example NPU is a teaching stub: its runtime logs the dispatch decisions an
NPU would make (memory tier, execution engine, fusion) but performs no real
-computation, so output buffers are uninitialized. Assertions in this tutorial
-therefore check shapes, not values. When you replace the runtime with your
-hardware SDK calls, the same flow produces real results.
-
-**Prerequisites**: Build TVM with ``USE_EXAMPLE_NPU_CODEGEN=ON`` and
-``USE_EXAMPLE_NPU_RUNTIME=ON``.
+computation, so its output buffers are left uninitialized. We therefore check
+*shapes*, not values, in the NPU sections -- its job is to make every BYOC step
+visible with nothing hidden. TensorRT then runs the identical flow for real, so
+we cross-check its result against a reference.
+
+**Prerequisites**: the example NPU sections need TVM built with
+``USE_EXAMPLE_NPU_CODEGEN=ON`` and ``USE_EXAMPLE_NPU_RUNTIME=ON``; the TensorRT
+sections need ``USE_TENSORRT_CODEGEN=ON``, ``USE_TENSORRT_RUNTIME=ON`` and
+``USE_CUDA=ON`` plus a CUDA GPU and a matching TensorRT install (from NVIDIA's
+``pip install tensorrt`` packages or the TensorRT archive); the final deployment
+section also needs PyTorch. Each section degrades gracefully when its backend is
+unavailable.
"""
######################################################################
-# Overview of the BYOC Flow
+# Overview of the BYOC flow
# -------------------------
#
-# The BYOC framework lets you plug a custom backend into TVM's compilation
-# pipeline in four steps:
+# BYOC plugs a custom backend into TVM's compilation pipeline in four steps:
#
-# 1. **Register patterns** - describe which sequences of Relax ops the
-# backend can handle.
+# 1. **Register patterns** - describe which sequences of Relax ops the backend
+# can handle.
# 2. **Partition the graph** - group matched ops into composite functions.
-# 3. **Run codegen** - lower composite functions to backend-specific
-# representation (JSON graph for the example NPU).
-# 4. **Execute** - the runtime dispatches composite functions to the
-# registered backend runtime.
+# 3. **Run codegen** - lower each composite to the backend's representation
+# (a JSON graph for both backends in this tutorial).
+# 4. **Execute** - the runtime dispatches each composite to the backend.
+#
+# Steps 1 and 2 are pure Python and run anywhere; steps 3 and 4 need the
+# backend's codegen and runtime compiled into TVM, which is why the
+# build-and-run cells below are guarded.
######################################################################
-# Step 1: Import the backend to register its patterns
-# ---------------------------------------------------
+# Step 1: Import the backends to register their patterns
+# ------------------------------------------------------
#
-# Importing the module is enough to register all supported patterns with
-# TVM's pattern registry.
+# Importing a backend module registers its patterns with TVM's global registry.
+# Pattern registration is independent of the C++ build -- only codegen and the
+# runtime require the backend to be compiled in -- so we probe each backend and
+# guard the build-and-run cells accordingly.
+
+import os
+import tempfile
import numpy as np
import tvm
-import tvm.relax.backend.contrib.example_npu # registers patterns
+import tvm.relax.backend.contrib.example_npu
from tvm import relax
+from tvm.relax.backend.contrib.tensorrt import partition_for_tensorrt
from tvm.relax.backend.pattern_registry import get_patterns_with_prefix
from tvm.relax.transform import FuseOpsByPattern, MergeCompositeFunctions, RunCodegen
from tvm.script import relax as R
@@ -75,148 +92,289 @@ has_example_npu_codegen = tvm.get_global_func("relax.ext.example_npu", True)
has_example_npu_runtime = tvm.get_global_func("runtime.ExampleNPUJSONRuntimeCreate", True)
has_example_npu = has_example_npu_codegen and has_example_npu_runtime
-target = tvm.target.Target("llvm")
-
-patterns = get_patterns_with_prefix("example_npu")
-print("Registered patterns:", [p.name for p in patterns])
+has_tensorrt_codegen = tvm.get_global_func("relax.ext.tensorrt", True) is not None
+_is_trt_runtime_enabled = tvm.get_global_func("relax.is_tensorrt_runtime_enabled", True)
+has_tensorrt = (
+ has_tensorrt_codegen and _is_trt_runtime_enabled is not None and _is_trt_runtime_enabled()
+)
+has_cuda = tvm.cuda(0).exist
######################################################################
-# Step 2: Define a model
-# ----------------------
+# Step 2: Define the model
+# ------------------------
#
-# We use a simple MatMul + ReLU module to illustrate the flow.
+# A single convolution followed by a ReLU. This one model is used for both
+# backends.
@tvm.script.ir_module
-class MatmulReLU:
+class ConvReLU:
@R.function
def main(
- x: R.Tensor((2, 4), "float32"),
- w: R.Tensor((4, 8), "float32"),
- ) -> R.Tensor((2, 8), "float32"):
+ data: R.Tensor((1, 3, 32, 32), "float32"),
+ weight: R.Tensor((16, 3, 3, 3), "float32"),
+ ) -> R.Tensor((1, 16, 30, 30), "float32"):
with R.dataflow():
- y = relax.op.matmul(x, w)
- z = relax.op.nn.relu(y)
- R.output(z)
- return z
+ conv = relax.op.nn.conv2d(data, weight)
+ out = relax.op.nn.relu(conv)
+ R.output(out)
+ return out
######################################################################
-# Step 3: Partition the graph
-# ---------------------------
-#
-# ``FuseOpsByPattern`` groups ops that match a registered pattern into
-# composite functions, controlled by two flags:
-#
-# - ``bind_constants=False`` keeps weights as function arguments instead
-# of baking them in, so the host stays in charge of parameter
-# ownership.
-# - ``annotate_codegen=True`` tags each composite with its backend name
-# (``example_npu``); without this tag, ``RunCodegen`` has no way to
-# route the composite to a backend.
-#
-# ``MergeCompositeFunctions`` then consolidates adjacent composites
-# that target the same backend so each group becomes a single external
-# call. Note that consolidation depends on the patterns themselves: an
-# ``op_a + op_b`` chain only collapses into one composite if a fused
-# pattern (e.g. ``matmul_relu_fused``) was registered for it; otherwise
-# each op stays as its own composite even when both target the same
-# backend.
-
-mod = MatmulReLU
-mod = FuseOpsByPattern(patterns, bind_constants=False, annotate_codegen=True)(mod)
-mod = MergeCompositeFunctions()(mod)
-print("After partitioning:")
-print(mod)
-
-######################################################################
-# Step 4: Run codegen
-# -------------------
+# Step 3: Partition for the example NPU
+# -------------------------------------
+#
+# ``FuseOpsByPattern`` groups ops matching a registered pattern into composite
+# functions; ``MergeCompositeFunctions`` then consolidates adjacent composites
+# bound for the same backend into a single external call. Two flags steer
+# partitioning:
#
-# ``RunCodegen`` lowers each annotated composite function to the backend's
-# serialization format. For the example NPU this produces a JSON graph
-# that the C++ runtime can execute.
+# - ``bind_constants=False`` keeps weights as function arguments, so the host
+# stays in charge of the parameters. (TensorRT below makes the opposite
+# choice: it binds weights as constants because it bakes them into its engine.)
+# - ``annotate_codegen=True`` wraps each matched composite in a function tagged
+# with the backend name -- the tag ``RunCodegen`` routes on. (The follow-up
+# ``MergeCompositeFunctions`` also attaches this tag when it groups composites,
+# which is why ``partition_for_tensorrt`` below can leave the flag off.)
#
-# Steps 4 and 5 require TVM to be built with ``USE_EXAMPLE_NPU_CODEGEN=ON``
-# and ``USE_EXAMPLE_NPU_RUNTIME=ON``.
+# The example NPU registers a fused ``conv2d + relu`` pattern with higher
+# priority than the standalone ``conv2d`` pattern, so the two ops collapse into a
+# single ``example_npu.conv2d_relu_fused`` composite -- look for it in the
+# printed module.
-if has_example_npu:
- mod = RunCodegen()(mod)
- print("After codegen:")
- print(mod)
+npu_patterns = get_patterns_with_prefix("example_npu")
+npu_mod = FuseOpsByPattern(npu_patterns, bind_constants=False, annotate_codegen=True)(ConvReLU)
+npu_mod = MergeCompositeFunctions()(npu_mod)
+print("After partitioning for the example NPU:")
+print(npu_mod)
- ######################################################################
- # Step 5: Build and run
- # ---------------------
- #
- # Build the module for the host target, create a virtual machine, and
- # execute the compiled function.
+######################################################################
+# Step 4: Codegen, build and run on the example NPU
+# -------------------------------------------------
+#
+# ``RunCodegen`` invokes each annotated composite's backend codegen, replacing it
+# with the backend runtime module (here, the NPU's JSON graph); ``relax.build``
+# then compiles the remaining host-side program and links everything. Because
+# the runtime is a stub that computes nothing, we assert on the output *shape*
+# only -- the values are uninitialized.
- np.random.seed(0)
- x_np = np.random.randn(2, 4).astype("float32")
- w_np = np.random.randn(4, 8).astype("float32")
+np.random.seed(0)
+data_np = np.random.randn(1, 3, 32, 32).astype("float32")
+weight_np = np.random.randn(16, 3, 3, 3).astype("float32")
- with tvm.transform.PassContext(opt_level=3):
- built = relax.build(mod, target)
+if has_example_npu:
+ npu_mod = RunCodegen()(npu_mod)
- vm = relax.VirtualMachine(built, tvm.cpu())
- result = vm["main"](tvm.runtime.tensor(x_np, tvm.cpu()), tvm.runtime.tensor(w_np, tvm.cpu()))
+ with tvm.transform.PassContext(opt_level=3):
+ npu_exec = relax.build(npu_mod, tvm.target.Target("llvm"))
- assert result.numpy().shape == (2, 8)
- print("Execution completed. Output shape:", result.numpy().shape)
+ npu_vm = relax.VirtualMachine(npu_exec, tvm.cpu())
+ npu_out = npu_vm["main"](
+ tvm.runtime.tensor(data_np, tvm.cpu()), tvm.runtime.tensor(weight_np, tvm.cpu())
+ )
+ assert npu_out.numpy().shape == (1, 16, 30, 30)
+ print("Example NPU run completed. Output shape:", npu_out.numpy().shape)
+else:
+ print("Example NPU backend unavailable; skipping its build and run.")
######################################################################
-# Step 6: Conv2D + ReLU
-# ---------------------
+# The same flow on a real backend: TensorRT
+# -----------------------------------------
+#
+# Steps 1-4 above are the whole mechanism. Aiming them at a real backend
+# changes very little, so rather than repeat the walkthrough, here is only what
+# differs for NVIDIA TensorRT:
#
-# The same flow applies to convolution workloads. Because the fused
-# ``conv2d + relu`` pattern is registered after the standalone
-# ``conv2d`` pattern in ``patterns.py`` (later entries have higher
-# priority), both ops are offloaded as a single composite function.
+# - **Partition in one call.** ``partition_for_tensorrt`` bundles the
+# ``FuseOpsByPattern`` + ``MergeCompositeFunctions`` you ran by hand, using
+# TensorRT's own pattern table.
+# - **Weights become constants** (``bind_constants=True``): TensorRT bakes them
+# into the engine it builds, so bind the parameters before partitioning.
+# - **Real values.** TensorRT actually computes, so we build for CUDA, run on
+# the GPU, and cross-check against a plain CPU build -- not just the shape.
+
+trt_mod = relax.transform.BindParams("main", {"weight": weight_np})(ConvReLU)
+trt_mod = partition_for_tensorrt(trt_mod)
+print("After partition_for_tensorrt:")
+print(trt_mod)
+######################################################################
+# Build for CUDA, run on the GPU, and compare against the CPU reference.
-@tvm.script.ir_module
-class Conv2dReLU:
- @R.function
- def main(
- x: R.Tensor((1, 3, 32, 32), "float32"),
- w: R.Tensor((16, 3, 3, 3), "float32"),
- ) -> R.Tensor((1, 16, 30, 30), "float32"):
- with R.dataflow():
- y = relax.op.nn.conv2d(x, w)
- z = relax.op.nn.relu(y)
- R.output(z)
- return z
+if has_tensorrt and has_cuda:
+ dev = tvm.cuda(0)
+ with tvm.transform.PassContext(opt_level=3):
+ trt_exec = relax.build(RunCodegen()(trt_mod), "cuda")
+ trt_out = relax.VirtualMachine(trt_exec, dev)["main"](tvm.runtime.tensor(data_np, dev)).numpy()
+ cpu_mod = relax.transform.LegalizeOps()(
+ relax.transform.BindParams("main", {"weight": weight_np})(ConvReLU)
+ )
+ cpu_exec = relax.build(cpu_mod, "llvm")
+ cpu_out = relax.VirtualMachine(cpu_exec, tvm.cpu())["main"](
+ tvm.runtime.tensor(data_np, tvm.cpu())
+ ).numpy()
-if has_example_npu:
- mod2 = Conv2dReLU
- mod2 = FuseOpsByPattern(patterns, bind_constants=False, annotate_codegen=True)(mod2)
- mod2 = MergeCompositeFunctions()(mod2)
- mod2 = RunCodegen()(mod2)
+ np.testing.assert_allclose(trt_out, cpu_out, rtol=1e-2, atol=1e-2)
+ print("TensorRT output shape:", trt_out.shape, "- matches the CPU reference.")
+else:
+ print("TensorRT/CUDA unavailable; skipping the GPU build and run.")
+
+######################################################################
+# A real backend also exposes knobs the stub does not. Setting ``use_fp16``
+# through the ``relax.ext.tensorrt.options`` config lets TensorRT pick FP16
+# kernels, trading a little accuracy for speed; nothing else about the flow
+# changes. (Other options are environment-driven: ``TVM_TENSORRT_USE_INT8``
+# enables INT8 with calibration, ``TVM_TENSORRT_MAX_WORKSPACE_SIZE`` caps the
+# build workspace, and ``TVM_TENSORRT_CACHE_DIR`` caches built engines to disk
+# for reuse across runs.)
+
+if has_tensorrt and has_cuda:
+ fp16_mod = partition_for_tensorrt(
+ relax.transform.BindParams("main", {"weight": weight_np})(ConvReLU)
+ )
+ with tvm.transform.PassContext(
+ opt_level=3, config={"relax.ext.tensorrt.options": {"use_fp16": True}}
+ ):
+ fp16_exec = relax.build(RunCodegen()(fp16_mod), "cuda")
+ fp16_out = relax.VirtualMachine(fp16_exec, tvm.cuda(0))["main"](
+ tvm.runtime.tensor(data_np, tvm.cuda(0))
+ ).numpy()
+
+ np.testing.assert_allclose(fp16_out, cpu_out, rtol=5e-2, atol=5e-2)
+ print("TensorRT FP16 output shape:", fp16_out.shape, "- matches within FP16 tolerance.")
+else:
+ print("TensorRT/CUDA unavailable; skipping the FP16 build.")
+
+######################################################################
+# Example NPU vs TensorRT at a glance
+# -----------------------------------
+#
+# The same four-step flow, two backends:
+#
+# ========= ============================== ==================================
+# Aspect Example NPU (teaching stub) TensorRT (real backend)
+# ========= ============================== ==================================
+# Runtime logs decisions, no compute builds and runs an nvinfer engine
+# Output uninitialized (check shape) real values (cross-checked vs CPU)
+# Weights ``bind_constants=False`` ``bind_constants=True`` (baked in)
+# Partition two passes, by hand ``partition_for_tensorrt`` one call
+# ========= ============================== ==================================
+######################################################################
+# Deploying a PyTorch model with TensorRT
+# ---------------------------------------
+#
+# Everything above used a hand-written ``IRModule`` so each op was visible. In
+# practice you start from a trained model. This final section runs the *same*
+# ``partition_for_tensorrt`` flow on a real PyTorch ``nn.Module``, end to end:
+# export it, import it into Relax with the PyTorch frontend (the weights come in
+# as constants -- exactly what TensorRT bakes into its engine), partition, build
+# for CUDA, and check the GPU result against PyTorch's own output. Beyond the
+# frontend import, the only difference is that the imported program returns its
+# outputs as a tuple, so we index ``[0]`` for the single result tensor; the
+# partition-build-run flow is otherwise unchanged.
+#
+# This section additionally requires PyTorch.
+
+try:
+ import torch
+ from torch import nn
+
+ has_torch = True
+except ImportError:
+ has_torch = False
+
+if has_torch and has_tensorrt and has_cuda:
+ from tvm.relax.frontend.torch import from_exported_program
+
+ class SmallConvNet(nn.Module):
+ def __init__(self):
+ super().__init__()
+ self.conv1 = nn.Conv2d(3, 8, 3)
+ self.conv2 = nn.Conv2d(8, 16, 3)
+ self.pool = nn.MaxPool2d(2)
+
+ def forward(self, x):
+ x = torch.relu(self.conv1(x))
+ x = self.pool(x)
+ x = torch.relu(self.conv2(x))
+ return x
+
+ torch_model = SmallConvNet().eval()
+ example_input = torch.randn(1, 3, 32, 32)
+ with torch.no_grad():
+ torch_ref = torch_model(example_input).numpy()
+ exported = torch.export.export(torch_model, (example_input,))
+
+ torch_mod = from_exported_program(exported)
+ torch_mod = partition_for_tensorrt(torch_mod)
+ print("After importing and partitioning the PyTorch model:")
+ print(torch_mod)
+
+ torch_dev = tvm.cuda(0)
with tvm.transform.PassContext(opt_level=3):
- built2 = relax.build(mod2, target)
+ torch_exec = relax.build(RunCodegen()(torch_mod), "cuda")
+ deployed = relax.VirtualMachine(torch_exec, torch_dev)["main"](
+ tvm.runtime.tensor(example_input.numpy(), torch_dev)
+ )[0].numpy()
- x2_np = np.random.randn(1, 3, 32, 32).astype("float32")
- w2_np = np.random.randn(16, 3, 3, 3).astype("float32")
+ np.testing.assert_allclose(deployed, torch_ref, rtol=1e-2, atol=1e-2)
+ print("Deployed PyTorch model on TensorRT; output", deployed.shape, "matches PyTorch.")
+else:
+ print("PyTorch / TensorRT / CUDA unavailable; skipping the deployment example.")
- vm2 = relax.VirtualMachine(built2, tvm.cpu())
- result2 = vm2["main"](
- tvm.runtime.tensor(x2_np, tvm.cpu()), tvm.runtime.tensor(w2_np, tvm.cpu())
- )
- assert result2.numpy().shape == (1, 16, 30, 30)
- print("Conv2dReLU output shape:", result2.numpy().shape)
+######################################################################
+# Real deployment builds once and reuses the artifact. Export the compiled
+# module to a shared library, then load and run it later -- in a fresh process,
+# with no PyTorch and no rebuild needed.
+
+if has_torch and has_tensorrt and has_cuda:
+ with tempfile.TemporaryDirectory() as tmpdir:
+ lib_path = os.path.join(tmpdir, "deployed_trt.so")
+ torch_exec.export_library(lib_path)
+ loaded = tvm.runtime.load_module(lib_path)
+ reran = relax.VirtualMachine(loaded, torch_dev)["main"](
+ tvm.runtime.tensor(example_input.numpy(), torch_dev)
+ )[0].numpy()
+ np.testing.assert_allclose(reran, torch_ref, rtol=1e-2, atol=1e-2)
+ print("Reloaded the exported library and reran; output", reran.shape, "still matches.")
+else:
+ print("PyTorch / TensorRT / CUDA unavailable; skipping the export/reload step.")
+
+######################################################################
+# Notes for real deployments
+# --------------------------
+#
+# - **Operator coverage and fallback.** TensorRT offloads only the ops in its
+# pattern table (see ``python/tvm/relax/backend/contrib/tensorrt.py``);
+# anything unsupported simply stays on the host. Print the partitioned module
+# and look for the ``Codegen: "tensorrt"`` functions to see what was offloaded.
+# - **Dynamic shapes.** The builder sets up an optimization profile for a dynamic
+# leading (batch) dimension, so the integration can serve a model exported with
+# a symbolic batch size.
+# - **Engine build cost.** Building a TensorRT engine is slow the first time (it
+# is not a hang). Set ``TVM_TENSORRT_CACHE_DIR`` to cache built engines to
+# disk and skip the rebuild on later runs.
######################################################################
# Next steps
# ----------
#
-# To build a real NPU backend using this example as a starting point:
+# To build your own backend using the example NPU as a starting point:
#
-# - Replace ``example_npu_runtime.cc`` with your hardware SDK calls.
+# - Replace the stub runtime in
+# ``src/runtime/extra/contrib/example_npu/example_npu_runtime.cc`` with your
+# hardware SDK calls.
# - Extend ``patterns.py`` with the ops your hardware supports.
-# - Add a C++ codegen under ``src/relax/backend/contrib/`` if your
-# hardware requires a non-JSON serialization format.
-# - Add your cmake module under ``cmake/modules/contrib/`` following
-# the pattern in ``cmake/modules/contrib/ExampleNPU.cmake``.
+# - Add a C++ codegen under ``src/relax/backend/contrib/`` if your backend needs
+# a non-JSON serialization format.
+# - Add a CMake module under ``cmake/modules/contrib/`` following
+# ``ExampleNPU.cmake``.
+#
+# For a complete real-backend implementation to study, see the TensorRT
+# integration: the pattern table and ``partition_for_tensorrt`` in
+# ``python/tvm/relax/backend/contrib/tensorrt.py``, the codegen in
+# ``src/relax/backend/contrib/tensorrt/``, and the runtime in
+# ``src/runtime/extra/contrib/tensorrt/``.
diff --git a/docs/_downloads/f69380821f417ef2210f45503d81bded/cross_compilation_and_rpc.zip b/docs/_downloads/f69380821f417ef2210f45503d81bded/cross_compilation_and_rpc.zip
index c87d4e56652..73c3b1bc413 100644
Binary files a/docs/_downloads/f69380821f417ef2210f45503d81bded/cross_compilation_and_rpc.zip and b/docs/_downloads/f69380821f417ef2210f45503d81bded/cross_compilation_and_rpc.zip differ
diff --git a/docs/_downloads/f69433a4a80715725df90d1386679956/customize_opt.zip b/docs/_downloads/f69433a4a80715725df90d1386679956/customize_opt.zip
index c4702e4107c..0855e96fe10 100644
Binary files a/docs/_downloads/f69433a4a80715725df90d1386679956/customize_opt.zip and b/docs/_downloads/f69433a4a80715725df90d1386679956/customize_opt.zip differ
diff --git a/docs/_sources/deep_dive/tensor_ir/tutorials/sg_execution_times.rst.txt b/docs/_sources/deep_dive/tensor_ir/tutorials/sg_execution_times.rst.txt
index 2ba8b819d7d..eee9399e339 100644
--- a/docs/_sources/deep_dive/tensor_ir/tutorials/sg_execution_times.rst.txt
+++ b/docs/_sources/deep_dive/tensor_ir/tutorials/sg_execution_times.rst.txt
@@ -6,7 +6,7 @@
Computation times
=================
-**00:00.896** total execution time for 4 files **from deep_dive/tensor_ir/tutorials**:
+**00:00.902** total execution time for 4 files **from deep_dive/tensor_ir/tutorials**:
.. container::
@@ -33,10 +33,10 @@ Computation times
- Time
- Mem (MB)
* - :ref:`sphx_glr_deep_dive_tensor_ir_tutorials_tir_creation.py` (``tir_creation.py``)
- - 00:00.382
+ - 00:00.388
- 0.0
* - :ref:`sphx_glr_deep_dive_tensor_ir_tutorials_tir_transformation.py` (``tir_transformation.py``)
- - 00:00.354
+ - 00:00.355
- 0.0
* - :ref:`sphx_glr_deep_dive_tensor_ir_tutorials_dlight_gpu_scheduling.py` (``dlight_gpu_scheduling.py``)
- 00:00.152
diff --git a/docs/_sources/deep_dive/tensor_ir/tutorials/tir_creation.rst.txt b/docs/_sources/deep_dive/tensor_ir/tutorials/tir_creation.rst.txt
index f1ef0fbe35b..11fc8c1bf31 100644
--- a/docs/_sources/deep_dive/tensor_ir/tutorials/tir_creation.rst.txt
+++ b/docs/_sources/deep_dive/tensor_ir/tutorials/tir_creation.rst.txt
@@ -322,17 +322,17 @@ Now let's check the runtime dynamic shape inference:
.. code-block:: none
- [[0.11088739 0.37859002 0.25887144 0.47898802]
- [0.6578438 1.0262297 0.49454063 0.97019464]
- [0.66448474 1.0079705 0.6300473 0.6706697 ]
- [0.758688 1.8438771 0.9797324 2.2190166 ]]
- [[37.943485 34.777294 33.82679 ... 36.445374 35.011868 35.313328]
- [33.63442 32.13715 30.743587 ... 33.137264 31.472588 30.236576]
- [34.64113 32.362938 29.226406 ... 31.654057 31.532555 33.139896]
+ [[0.8231925 0.74687773 0.61615294 1.0123911 ]
+ [0.7160674 0.45636386 0.6850383 0.6155127 ]
+ [1.005914 0.96243465 0.6228967 1.653728 ]
+ [1.002558 1.1868575 0.73863345 0.7221608 ]]
+ [[31.91493 36.009766 31.308624 ... 33.880024 35.974846 34.691463]
+ [29.066854 34.266792 31.542082 ... 31.765827 35.56741 33.41558 ]
+ [30.660873 35.36475 34.314415 ... 32.7977 36.34936 35.827236]
...
- [29.656555 28.751762 30.90607 ... 31.368315 29.983944 30.143927]
- [38.789223 33.203655 31.96145 ... 34.770775 32.493507 34.027424]
- [35.072197 29.962208 30.019306 ... 31.849218 30.205173 30.428644]]
+ [26.84151 30.554972 29.886185 ... 30.959145 31.836 31.593372]
+ [26.493942 32.096836 28.361483 ... 30.639572 32.666187 32.601116]
+ [28.802551 35.955505 32.694454 ... 32.20954 36.73205 34.373337]]
diff --git a/docs/_sources/deep_dive/tensor_ir/tutorials/tir_transformation.rst.txt b/docs/_sources/deep_dive/tensor_ir/tutorials/tir_transformation.rst.txt
index 6bde5c430d4..1492c3688cb 100644
--- a/docs/_sources/deep_dive/tensor_ir/tutorials/tir_transformation.rst.txt
+++ b/docs/_sources/deep_dive/tensor_ir/tutorials/tir_transformation.rst.txt
@@ -120,7 +120,7 @@ original implementation.
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 2.7603 2.7603 2.7603 2.7603 0.0000
+ 2.7551 2.7551 2.7551 2.7551 0.0000
@@ -294,7 +294,7 @@ action involves reordering these two loops.
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 0.8639 0.8639 0.8639 0.8639 0.0000
+ 0.8647 0.8647 0.8647 0.8647 0.0000
@@ -424,7 +424,7 @@ from the reduction update via the **decompose_reduction** primitive.
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 0.3379 0.3379 0.3379 0.3379 0.0000
+ 0.3382 0.3382 0.3382 0.3382 0.0000
diff --git a/docs/_sources/get_started/tutorials/ir_module.rst.txt b/docs/_sources/get_started/tutorials/ir_module.rst.txt
index 66fce11af70..acc6cd002fe 100644
--- a/docs/_sources/get_started/tutorials/ir_module.rst.txt
+++ b/docs/_sources/get_started/tutorials/ir_module.rst.txt
@@ -696,8 +696,8 @@ We can deploy the IRModule on CPU by specifying the target as ``llvm``.
.. code-block:: none
- [[ 0.24591367 -0.07023893 0.07478634 -0.08000526 -0.06134555 -0.06156536
- 0.12414779 -0.08805142 0.02194053 -0.03948892]]
+ [[ 0.14707375 0.15686896 -0.07788884 0.00588775 -0.03346043 -0.15874758
+ 0.16355126 -0.26740512 -0.04383552 -0.10067607]]
@@ -763,8 +763,8 @@ Now we can compile the IRModule on GPU, the similar way as we did on CPU.
.. code-block:: none
- [[ 0.24591374 -0.07023892 0.07478635 -0.0800053 -0.06134555 -0.06156533
- 0.1241478 -0.08805143 0.02194051 -0.03948891]]
+ [[ 0.14707379 0.156869 -0.07788893 0.00588775 -0.0334604 -0.15874766
+ 0.16355133 -0.26740506 -0.04383563 -0.1006761 ]]
diff --git a/docs/_sources/get_started/tutorials/quick_start.rst.txt b/docs/_sources/get_started/tutorials/quick_start.rst.txt
index 23ac6198b6a..eba84ef4a0c 100644
--- a/docs/_sources/get_started/tutorials/quick_start.rst.txt
+++ b/docs/_sources/get_started/tutorials/quick_start.rst.txt
@@ -224,8 +224,8 @@ different devices.
.. code-block:: none
- [[24839.627 24788.988 25688.764 24910.75 24252.566 26164.816 24886.838
- 25099.838 24203.76 24559.404]]
+ [[26245.865 25315.145 26411.287 24533.736 24396.021 24099.643 23988.234
+ 24814.387 24646.775 26737.93 ]]
diff --git a/docs/_sources/get_started/tutorials/sg_execution_times.rst.txt b/docs/_sources/get_started/tutorials/sg_execution_times.rst.txt
index ee8b24bb8c7..9afa50767cb 100644
--- a/docs/_sources/get_started/tutorials/sg_execution_times.rst.txt
+++ b/docs/_sources/get_started/tutorials/sg_execution_times.rst.txt
@@ -6,7 +6,7 @@
Computation times
=================
-**00:05.905** total execution time for 2 files **from get_started/tutorials**:
+**00:06.302** total execution time for 2 files **from get_started/tutorials**:
.. container::
@@ -33,7 +33,7 @@ Computation times
- Time
- Mem (MB)
* - :ref:`sphx_glr_get_started_tutorials_ir_module.py` (``ir_module.py``)
- - 00:05.722
+ - 00:06.120
- 0.0
* - :ref:`sphx_glr_get_started_tutorials_quick_start.py` (``quick_start.py``)
- 00:00.182
diff --git a/docs/_sources/how_to/tutorials/bring_your_own_codegen.rst.txt b/docs/_sources/how_to/tutorials/bring_your_own_codegen.rst.txt
index 37dcf6550af..9be491f771b 100644
--- a/docs/_sources/how_to/tutorials/bring_your_own_codegen.rst.txt
+++ b/docs/_sources/how_to/tutorials/bring_your_own_codegen.rst.txt
@@ -18,61 +18,78 @@
.. _tutorial-bring-your-own-codegen:
-Bring Your Own Codegen: NPU Backend Example
-===========================================
-
-This tutorial shows how to integrate a custom hardware backend with TVM's
-BYOC framework, using the bundled example NPU backend (CPU emulation, no
-real hardware required) as the worked example. You will see the key
-concepts needed to offload operations to a custom accelerator: pattern
-registration, graph partitioning, codegen, and runtime dispatch.
-
-NPUs are purpose-built accelerators designed around a fixed set of operations
-common in neural network inference, such as matrix multiplication, convolution,
-and activation functions.
-The example backend's runtime is a *stub*: it logs the dispatch decisions an
+Bring Your Own Codegen
+======================
+
+TVM's Bring Your Own Codegen (BYOC) framework lets you offload parts of a model
+to a custom backend -- a hardware accelerator, an inference library, or your own
+kernels -- while TVM compiles the rest. This tutorial has two parts:
+
+- **How BYOC works** -- we teach the flow with a bundled, hardware-free *example
+ NPU* backend and then drive the **same flow** on a real production backend,
+ NVIDIA TensorRT. Both run a small, hand-written model so every step is
+ visible; the only thing that changes between them is the backend, and that
+ contrast is the lesson.
+- **Deploying a real model** -- we then put it to work, taking an actual PyTorch
+ ``nn.Module`` from export through TensorRT and running it on the GPU.
+
+The example NPU is a teaching stub: its runtime logs the dispatch decisions an
NPU would make (memory tier, execution engine, fusion) but performs no real
-computation, so output buffers are uninitialized. Assertions in this tutorial
-therefore check shapes, not values. When you replace the runtime with your
-hardware SDK calls, the same flow produces real results.
-
-**Prerequisites**: Build TVM with ``USE_EXAMPLE_NPU_CODEGEN=ON`` and
-``USE_EXAMPLE_NPU_RUNTIME=ON``.
-
-.. GENERATED FROM PYTHON SOURCE LINES 44-57
-
-Overview of the BYOC Flow
+computation, so its output buffers are left uninitialized. We therefore check
+*shapes*, not values, in the NPU sections -- its job is to make every BYOC step
+visible with nothing hidden. TensorRT then runs the identical flow for real, so
+we cross-check its result against a reference.
+
+**Prerequisites**: the example NPU sections need TVM built with
+``USE_EXAMPLE_NPU_CODEGEN=ON`` and ``USE_EXAMPLE_NPU_RUNTIME=ON``; the TensorRT
+sections need ``USE_TENSORRT_CODEGEN=ON``, ``USE_TENSORRT_RUNTIME=ON`` and
+``USE_CUDA=ON`` plus a CUDA GPU and a matching TensorRT install (from NVIDIA's
+``pip install tensorrt`` packages or the TensorRT archive); the final deployment
+section also needs PyTorch. Each section degrades gracefully when its backend is
+unavailable.
+
+.. GENERATED FROM PYTHON SOURCE LINES 53-68
+
+Overview of the BYOC flow
-------------------------
-The BYOC framework lets you plug a custom backend into TVM's compilation
-pipeline in four steps:
+BYOC plugs a custom backend into TVM's compilation pipeline in four steps:
-1. **Register patterns** - describe which sequences of Relax ops the
- backend can handle.
+1. **Register patterns** - describe which sequences of Relax ops the backend
+ can handle.
2. **Partition the graph** - group matched ops into composite functions.
-3. **Run codegen** - lower composite functions to backend-specific
- representation (JSON graph for the example NPU).
-4. **Execute** - the runtime dispatches composite functions to the
- registered backend runtime.
+3. **Run codegen** - lower each composite to the backend's representation
+ (a JSON graph for both backends in this tutorial).
+4. **Execute** - the runtime dispatches each composite to the backend.
-.. GENERATED FROM PYTHON SOURCE LINES 59-64
+Steps 1 and 2 are pure Python and run anywhere; steps 3 and 4 need the
+backend's codegen and runtime compiled into TVM, which is why the
+build-and-run cells below are guarded.
-Step 1: Import the backend to register its patterns
----------------------------------------------------
+.. GENERATED FROM PYTHON SOURCE LINES 70-77
-Importing the module is enough to register all supported patterns with
-TVM's pattern registry.
+Step 1: Import the backends to register their patterns
+------------------------------------------------------
-.. GENERATED FROM PYTHON SOURCE LINES 64-83
+Importing a backend module registers its patterns with TVM's global registry.
+Pattern registration is independent of the C++ build -- only codegen and the
+runtime require the backend to be compiled in -- so we probe each backend and
+guard the build-and-run cells accordingly.
+
+.. GENERATED FROM PYTHON SOURCE LINES 77-102
.. code-block:: Python
+ import os
+ import tempfile
+
import numpy as np
import tvm
- import tvm.relax.backend.contrib.example_npu # registers patterns
+ import tvm.relax.backend.contrib.example_npu
from tvm import relax
+ from tvm.relax.backend.contrib.tensorrt import partition_for_tensorrt
from tvm.relax.backend.pattern_registry import get_patterns_with_prefix
from tvm.relax.transform import FuseOpsByPattern, MergeCompositeFunctions, RunCodegen
from tvm.script import relax as R
@@ -81,49 +98,46 @@ TVM's pattern registry.
has_example_npu_runtime = tvm.get_global_func("runtime.ExampleNPUJSONRuntimeCreate", True)
has_example_npu = has_example_npu_codegen and has_example_npu_runtime
- target = tvm.target.Target("llvm")
-
- patterns = get_patterns_with_prefix("example_npu")
- print("Registered patterns:", [p.name for p in patterns])
-
-
-
+ has_tensorrt_codegen = tvm.get_global_func("relax.ext.tensorrt", True) is not None
+ _is_trt_runtime_enabled = tvm.get_global_func("relax.is_tensorrt_runtime_enabled", True)
+ has_tensorrt = (
+ has_tensorrt_codegen and _is_trt_runtime_enabled is not None and _is_trt_runtime_enabled()
+ )
+ has_cuda = tvm.cuda(0).exist
-.. rst-class:: sphx-glr-script-out
- .. code-block:: none
- Registered patterns: ['example_npu.conv2d_relu_fused', 'example_npu.matmul_relu_fused', 'example_npu.depthwise_conv2d', 'example_npu.conv2d', 'example_npu.conv1d', 'example_npu.matmul', 'example_npu.dense', 'example_npu.avg_pool2d', 'example_npu.max_pool2d', 'example_npu.batch_norm', 'example_npu.softmax', 'example_npu.gelu', 'example_npu.relu', 'example_npu.divide', 'example_npu.subtract', 'example_npu.multiply', 'example_npu.add', 'example_npu.dequantize', 'example_npu.quantize']
-.. GENERATED FROM PYTHON SOURCE LINES 84-88
+.. GENERATED FROM PYTHON SOURCE LINES 103-108
-Step 2: Define a model
-----------------------
+Step 2: Define the model
+------------------------
-We use a simple MatMul + ReLU module to illustrate the flow.
+A single convolution followed by a ReLU. This one model is used for both
+backends.
-.. GENERATED FROM PYTHON SOURCE LINES 88-104
+.. GENERATED FROM PYTHON SOURCE LINES 108-124
.. code-block:: Python
@tvm.script.ir_module
- class MatmulReLU:
+ class ConvReLU:
@R.function
def main(
- x: R.Tensor((2, 4), "float32"),
- w: R.Tensor((4, 8), "float32"),
- ) -> R.Tensor((2, 8), "float32"):
+ data: R.Tensor((1, 3, 32, 32), "float32"),
+ weight: R.Tensor((16, 3, 3, 3), "float32"),
+ ) -> R.Tensor((1, 16, 30, 30), "float32"):
with R.dataflow():
- y = relax.op.matmul(x, w)
- z = relax.op.nn.relu(y)
- R.output(z)
- return z
+ conv = relax.op.nn.conv2d(data, weight)
+ out = relax.op.nn.relu(conv)
+ R.output(out)
+ return out
@@ -133,39 +147,39 @@ We use a simple MatMul + ReLU module to illustrate the flow.
-.. GENERATED FROM PYTHON SOURCE LINES 105-125
+.. GENERATED FROM PYTHON SOURCE LINES 125-145
-Step 3: Partition the graph
----------------------------
+Step 3: Partition for the example NPU
+-------------------------------------
-``FuseOpsByPattern`` groups ops that match a registered pattern into
-composite functions, controlled by two flags:
+``FuseOpsByPattern`` groups ops matching a registered pattern into composite
+functions; ``MergeCompositeFunctions`` then consolidates adjacent composites
+bound for the same backend into a single external call. Two flags steer
+partitioning:
-- ``bind_constants=False`` keeps weights as function arguments instead
- of baking them in, so the host stays in charge of parameter
- ownership.
-- ``annotate_codegen=True`` tags each composite with its backend name
- (``example_npu``); without this tag, ``RunCodegen`` has no way to
- route the composite to a backend.
+- ``bind_constants=False`` keeps weights as function arguments, so the host
+ stays in charge of the parameters. (TensorRT below makes the opposite
+ choice: it binds weights as constants because it bakes them into its engine.)
+- ``annotate_codegen=True`` wraps each matched composite in a function tagged
+ with the backend name -- the tag ``RunCodegen`` routes on. (The follow-up
+ ``MergeCompositeFunctions`` also attaches this tag when it groups composites,
+ which is why ``partition_for_tensorrt`` below can leave the flag off.)
-``MergeCompositeFunctions`` then consolidates adjacent composites
-that target the same backend so each group becomes a single external
-call. Note that consolidation depends on the patterns themselves: an
-``op_a + op_b`` chain only collapses into one composite if a fused
-pattern (e.g. ``matmul_relu_fused``) was registered for it; otherwise
-each op stays as its own composite even when both target the same
-backend.
+The example NPU registers a fused ``conv2d + relu`` pattern with higher
+priority than the standalone ``conv2d`` pattern, so the two ops collapse into a
+single ``example_npu.conv2d_relu_fused`` composite -- look for it in the
+printed module.
-.. GENERATED FROM PYTHON SOURCE LINES 125-132
+.. GENERATED FROM PYTHON SOURCE LINES 145-152
.. code-block:: Python
- mod = MatmulReLU
- mod = FuseOpsByPattern(patterns, bind_constants=False, annotate_codegen=True)(mod)
- mod = MergeCompositeFunctions()(mod)
- print("After partitioning:")
- print(mod)
+ npu_patterns = get_patterns_with_prefix("example_npu")
+ npu_mod = FuseOpsByPattern(npu_patterns, bind_constants=False, annotate_codegen=True)(ConvReLU)
+ npu_mod = MergeCompositeFunctions()(npu_mod)
+ print("After partitioning for the example NPU:")
+ print(npu_mod)
@@ -175,156 +189,425 @@ backend.
.. code-block:: none
- After partitioning:
+ After partitioning for the example NPU:
# from tvm.script import ir as I
# from tvm.script import relax as R
@I.ir_module
class Module:
@R.function
- def fused_relax_matmul_relax_nn_relu_example_npu_example_npu(x: R.Tensor((2, 4), dtype="float32"), w: R.Tensor((4, 8), dtype="float32")) -> R.Tensor((2, 8), dtype="float32"):
+ def fused_relax_nn_conv2d_relax_nn_relu_example_npu_example_npu(data: R.Tensor((1, 3, 32, 32), dtype="float32"), weight: R.Tensor((16, 3, 3, 3), dtype="float32")) -> R.Tensor((1, 16, 30, 30), dtype="float32"):
R.func_attr({"Codegen": "example_npu"})
# from tvm.script import relax as R
@R.function
- def local_func(x_1: R.Tensor((2, 4), dtype="float32"), w_1: R.Tensor((4, 8), dtype="float32")) -> R.Tensor((2, 8), dtype="float32"):
- R.func_attr({"Composite": "example_npu.matmul_relu_fused"})
- y: R.Tensor((2, 8), dtype="float32") = R.matmul(x_1, w_1, out_dtype="void")
- gv: R.Tensor((2, 8), dtype="float32") = R.nn.relu(y)
+ def local_func(data_1: R.Tensor((1, 3, 32, 32), dtype="float32"), weight_1: R.Tensor((16, 3, 3, 3), dtype="float32")) -> R.Tensor((1, 16, 30, 30), dtype="float32"):
+ R.func_attr({"Composite": "example_npu.conv2d_relu_fused"})
+ conv: R.Tensor((1, 16, 30, 30), dtype="float32") = R.nn.conv2d(data_1, weight_1, strides=[1, 1], padding=[0, 0, 0, 0], dilation=[1, 1], groups=1, data_layout="NCHW", kernel_layout="OIHW", out_layout="NCHW", out_dtype="void")
+ gv: R.Tensor((1, 16, 30, 30), dtype="float32") = R.nn.relu(conv)
return gv
- output: R.Tensor((2, 8), dtype="float32") = local_func(x, w)
+ output: R.Tensor((1, 16, 30, 30), dtype="float32") = local_func(data, weight)
return output
@R.function
- def main(x: R.Tensor((2, 4), dtype="float32"), w: R.Tensor((4, 8), dtype="float32")) -> R.Tensor((2, 8), dtype="float32"):
+ def main(data: R.Tensor((1, 3, 32, 32), dtype="float32"), weight: R.Tensor((16, 3, 3, 3), dtype="float32")) -> R.Tensor((1, 16, 30, 30), dtype="float32"):
cls = Module
with R.dataflow():
- gv: R.Tensor((2, 8), dtype="float32") = cls.fused_relax_matmul_relax_nn_relu_example_npu_example_npu(x, w)
+ gv: R.Tensor((1, 16, 30, 30), dtype="float32") = cls.fused_relax_nn_conv2d_relax_nn_relu_example_npu_example_npu(data, weight)
R.output(gv)
return gv
-.. GENERATED FROM PYTHON SOURCE LINES 133-142
+.. GENERATED FROM PYTHON SOURCE LINES 153-161
-Step 4: Run codegen
--------------------
+Step 4: Codegen, build and run on the example NPU
+-------------------------------------------------
-``RunCodegen`` lowers each annotated composite function to the backend's
-serialization format. For the example NPU this produces a JSON graph
-that the C++ runtime can execute.
+``RunCodegen`` invokes each annotated composite's backend codegen, replacing it
+with the backend runtime module (here, the NPU's JSON graph); ``relax.build``
+then compiles the remaining host-side program and links everything. Because
+the runtime is a stub that computes nothing, we assert on the output *shape*
+only -- the values are uninitialized.
-Steps 4 and 5 require TVM to be built with ``USE_EXAMPLE_NPU_CODEGEN=ON``
-and ``USE_EXAMPLE_NPU_RUNTIME=ON``.
-
-.. GENERATED FROM PYTHON SOURCE LINES 142-168
+.. GENERATED FROM PYTHON SOURCE LINES 161-181
.. code-block:: Python
+ np.random.seed(0)
+ data_np = np.random.randn(1, 3, 32, 32).astype("float32")
+ weight_np = np.random.randn(16, 3, 3, 3).astype("float32")
+
if has_example_npu:
- mod = RunCodegen()(mod)
- print("After codegen:")
- print(mod)
+ npu_mod = RunCodegen()(npu_mod)
- ######################################################################
- # Step 5: Build and run
- # ---------------------
- #
- # Build the module for the host target, create a virtual machine, and
- # execute the compiled function.
+ with tvm.transform.PassContext(opt_level=3):
+ npu_exec = relax.build(npu_mod, tvm.target.Target("llvm"))
- np.random.seed(0)
- x_np = np.random.randn(2, 4).astype("float32")
- w_np = np.random.randn(4, 8).astype("float32")
+ npu_vm = relax.VirtualMachine(npu_exec, tvm.cpu())
+ npu_out = npu_vm["main"](
+ tvm.runtime.tensor(data_np, tvm.cpu()), tvm.runtime.tensor(weight_np, tvm.cpu())
+ )
+ assert npu_out.numpy().shape == (1, 16, 30, 30)
+ print("Example NPU run completed. Output shape:", npu_out.numpy().shape)
+ else:
+ print("Example NPU backend unavailable; skipping its build and run.")
- with tvm.transform.PassContext(opt_level=3):
- built = relax.build(mod, target)
- vm = relax.VirtualMachine(built, tvm.cpu())
- result = vm["main"](tvm.runtime.tensor(x_np, tvm.cpu()), tvm.runtime.tensor(w_np, tvm.cpu()))
- assert result.numpy().shape == (2, 8)
- print("Execution completed. Output shape:", result.numpy().shape)
+.. rst-class:: sphx-glr-script-out
+ .. code-block:: none
+
+ Example NPU backend unavailable; skipping its build and run.
+.. GENERATED FROM PYTHON SOURCE LINES 182-196
-.. GENERATED FROM PYTHON SOURCE LINES 169-176
+The same flow on a real backend: TensorRT
+-----------------------------------------
-Step 6: Conv2D + ReLU
----------------------
+Steps 1-4 above are the whole mechanism. Aiming them at a real backend
+changes very little, so rather than repeat the walkthrough, here is only what
+differs for NVIDIA TensorRT:
-The same flow applies to convolution workloads. Because the fused
-``conv2d + relu`` pattern is registered after the standalone
-``conv2d`` pattern in ``patterns.py`` (later entries have higher
-priority), both ops are offloaded as a single composite function.
+- **Partition in one call.** ``partition_for_tensorrt`` bundles the
+ ``FuseOpsByPattern`` + ``MergeCompositeFunctions`` you ran by hand, using
+ TensorRT's own pattern table.
+- **Weights become constants** (``bind_constants=True``): TensorRT bakes them
+ into the engine it builds, so bind the parameters before partitioning.
+- **Real values.** TensorRT actually computes, so we build for CUDA, run on
+ the GPU, and cross-check against a plain CPU build -- not just the shape.
-.. GENERATED FROM PYTHON SOURCE LINES 176-211
+.. GENERATED FROM PYTHON SOURCE LINES 196-202
.. code-block:: Python
+ trt_mod = relax.transform.BindParams("main", {"weight": weight_np})(ConvReLU)
+ trt_mod = partition_for_tensorrt(trt_mod)
+ print("After partition_for_tensorrt:")
+ print(trt_mod)
- @tvm.script.ir_module
- class Conv2dReLU:
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+ After partition_for_tensorrt:
+ # from tvm.script import ir as I
+ # from tvm.script import relax as R
+
+ @I.ir_module
+ class Module:
@R.function
- def main(
- x: R.Tensor((1, 3, 32, 32), "float32"),
- w: R.Tensor((16, 3, 3, 3), "float32"),
- ) -> R.Tensor((1, 16, 30, 30), "float32"):
+ def fused_relax_nn_conv2d_relax_nn_relu_tensorrt(data: R.Tensor((1, 3, 32, 32), dtype="float32")) -> R.Tensor((1, 16, 30, 30), dtype="float32"):
+ R.func_attr({"Codegen": "tensorrt"})
+ # from tvm.script import relax as R
+
+ @R.function
+ def gv(data_1: R.Tensor((1, 3, 32, 32), dtype="float32")) -> R.Tensor((1, 16, 30, 30), dtype="float32"):
+ R.func_attr({"Composite": "tensorrt.nn.conv2d"})
+ with R.dataflow():
+ gv_1: R.Tensor((1, 16, 30, 30), dtype="float32") = R.nn.conv2d(data_1, metadata["relax.expr.Constant"][0], strides=[1, 1], padding=[0, 0, 0, 0], dilation=[1, 1], groups=1, data_layout="NCHW", kernel_layout="OIHW", out_layout="NCHW", out_dtype="void")
+ R.output(gv_1)
+ return gv_1
+
+ lv: R.Tensor((1, 16, 30, 30), dtype="float32") = gv(data)
+ # from tvm.script import relax as R
+
+ @R.function
+ def gv1(lv_1: R.Tensor((1, 16, 30, 30), dtype="float32")) -> R.Tensor((1, 16, 30, 30), dtype="float32"):
+ R.func_attr({"Composite": "tensorrt.nn.relu"})
+ with R.dataflow():
+ gv_1: R.Tensor((1, 16, 30, 30), dtype="float32") = R.nn.relu(lv_1)
+ R.output(gv_1)
+ return gv_1
+
+ gv_1: R.Tensor((1, 16, 30, 30), dtype="float32") = gv1(lv)
+ return gv_1
+
+ @R.function
+ def main(data: R.Tensor((1, 3, 32, 32), dtype="float32")) -> R.Tensor((1, 16, 30, 30), dtype="float32"):
+ cls = Module
with R.dataflow():
- y = relax.op.nn.conv2d(x, w)
- z = relax.op.nn.relu(y)
- R.output(z)
- return z
+ gv: R.Tensor((1, 16, 30, 30), dtype="float32") = cls.fused_relax_nn_conv2d_relax_nn_relu_tensorrt(data)
+ R.output(gv)
+ return gv
+ # Metadata omitted. Use show_meta=True in script() method to show it.
- if has_example_npu:
- mod2 = Conv2dReLU
- mod2 = FuseOpsByPattern(patterns, bind_constants=False, annotate_codegen=True)(mod2)
- mod2 = MergeCompositeFunctions()(mod2)
- mod2 = RunCodegen()(mod2)
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 203-204
+
+Build for CUDA, run on the GPU, and compare against the CPU reference.
+
+.. GENERATED FROM PYTHON SOURCE LINES 204-224
+
+.. code-block:: Python
+
+
+ if has_tensorrt and has_cuda:
+ dev = tvm.cuda(0)
with tvm.transform.PassContext(opt_level=3):
- built2 = relax.build(mod2, target)
+ trt_exec = relax.build(RunCodegen()(trt_mod), "cuda")
+ trt_out = relax.VirtualMachine(trt_exec, dev)["main"](tvm.runtime.tensor(data_np, dev)).numpy()
+
+ cpu_mod = relax.transform.LegalizeOps()(
+ relax.transform.BindParams("main", {"weight": weight_np})(ConvReLU)
+ )
+ cpu_exec = relax.build(cpu_mod, "llvm")
+ cpu_out = relax.VirtualMachine(cpu_exec, tvm.cpu())["main"](
+ tvm.runtime.tensor(data_np, tvm.cpu())
+ ).numpy()
+
+ np.testing.assert_allclose(trt_out, cpu_out, rtol=1e-2, atol=1e-2)
+ print("TensorRT output shape:", trt_out.shape, "- matches the CPU reference.")
+ else:
+ print("TensorRT/CUDA unavailable; skipping the GPU build and run.")
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+ TensorRT/CUDA unavailable; skipping the GPU build and run.
+
- x2_np = np.random.randn(1, 3, 32, 32).astype("float32")
- w2_np = np.random.randn(16, 3, 3, 3).astype("float32")
- vm2 = relax.VirtualMachine(built2, tvm.cpu())
- result2 = vm2["main"](
- tvm.runtime.tensor(x2_np, tvm.cpu()), tvm.runtime.tensor(w2_np, tvm.cpu())
+
+.. GENERATED FROM PYTHON SOURCE LINES 225-232
+
+A real backend also exposes knobs the stub does not. Setting ``use_fp16``
+through the ``relax.ext.tensorrt.options`` config lets TensorRT pick FP16
+kernels, trading a little accuracy for speed; nothing else about the flow
+changes. (Other options are environment-driven: ``TVM_TENSORRT_USE_INT8``
+enables INT8 with calibration, ``TVM_TENSORRT_MAX_WORKSPACE_SIZE`` caps the
+build workspace, and ``TVM_TENSORRT_CACHE_DIR`` caches built engines to disk
+for reuse across runs.)
+
+.. GENERATED FROM PYTHON SOURCE LINES 232-250
+
+.. code-block:: Python
+
+
+ if has_tensorrt and has_cuda:
+ fp16_mod = partition_for_tensorrt(
+ relax.transform.BindParams("main", {"weight": weight_np})(ConvReLU)
)
- assert result2.numpy().shape == (1, 16, 30, 30)
- print("Conv2dReLU output shape:", result2.numpy().shape)
+ with tvm.transform.PassContext(
+ opt_level=3, config={"relax.ext.tensorrt.options": {"use_fp16": True}}
+ ):
+ fp16_exec = relax.build(RunCodegen()(fp16_mod), "cuda")
+ fp16_out = relax.VirtualMachine(fp16_exec, tvm.cuda(0))["main"](
+ tvm.runtime.tensor(data_np, tvm.cuda(0))
+ ).numpy()
+
+ np.testing.assert_allclose(fp16_out, cpu_out, rtol=5e-2, atol=5e-2)
+ print("TensorRT FP16 output shape:", fp16_out.shape, "- matches within FP16 tolerance.")
+ else:
+ print("TensorRT/CUDA unavailable; skipping the FP16 build.")
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+ TensorRT/CUDA unavailable; skipping the FP16 build.
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 251-264
+
+Example NPU vs TensorRT at a glance
+-----------------------------------
+
+The same four-step flow, two backends:
+
+========= ============================== ==================================
+Aspect Example NPU (teaching stub) TensorRT (real backend)
+========= ============================== ==================================
+Runtime logs decisions, no compute builds and runs an nvinfer engine
+Output uninitialized (check shape) real values (cross-checked vs CPU)
+Weights ``bind_constants=False`` ``bind_constants=True`` (baked in)
+Partition two passes, by hand ``partition_for_tensorrt`` one call
+========= ============================== ==================================
+
+.. GENERATED FROM PYTHON SOURCE LINES 266-280
+
+Deploying a PyTorch model with TensorRT
+---------------------------------------
+
+Everything above used a hand-written ``IRModule`` so each op was visible. In
+practice you start from a trained model. This final section runs the *same*
+``partition_for_tensorrt`` flow on a real PyTorch ``nn.Module``, end to end:
+export it, import it into Relax with the PyTorch frontend (the weights come in
+as constants -- exactly what TensorRT bakes into its engine), partition, build
+for CUDA, and check the GPU result against PyTorch's own output. Beyond the
+frontend import, the only difference is that the imported program returns its
+outputs as a tuple, so we index ``[0]`` for the single result tensor; the
+partition-build-run flow is otherwise unchanged.
+
+This section additionally requires PyTorch.
+
+.. GENERATED FROM PYTHON SOURCE LINES 280-328
+
+.. code-block:: Python
+
+
+ try:
+ import torch
+ from torch import nn
+
+ has_torch = True
+ except ImportError:
+ has_torch = False
+
+ if has_torch and has_tensorrt and has_cuda:
+ from tvm.relax.frontend.torch import from_exported_program
+
+ class SmallConvNet(nn.Module):
+ def __init__(self):
+ super().__init__()
+ self.conv1 = nn.Conv2d(3, 8, 3)
+ self.conv2 = nn.Conv2d(8, 16, 3)
+ self.pool = nn.MaxPool2d(2)
+
+ def forward(self, x):
+ x = torch.relu(self.conv1(x))
+ x = self.pool(x)
+ x = torch.relu(self.conv2(x))
+ return x
+
+ torch_model = SmallConvNet().eval()
+ example_input = torch.randn(1, 3, 32, 32)
+ with torch.no_grad():
+ torch_ref = torch_model(example_input).numpy()
+ exported = torch.export.export(torch_model, (example_input,))
+
+ torch_mod = from_exported_program(exported)
+ torch_mod = partition_for_tensorrt(torch_mod)
+ print("After importing and partitioning the PyTorch model:")
+ print(torch_mod)
+
+ torch_dev = tvm.cuda(0)
+ with tvm.transform.PassContext(opt_level=3):
+ torch_exec = relax.build(RunCodegen()(torch_mod), "cuda")
+ deployed = relax.VirtualMachine(torch_exec, torch_dev)["main"](
+ tvm.runtime.tensor(example_input.numpy(), torch_dev)
+ )[0].numpy()
+
+ np.testing.assert_allclose(deployed, torch_ref, rtol=1e-2, atol=1e-2)
+ print("Deployed PyTorch model on TensorRT; output", deployed.shape, "matches PyTorch.")
+ else:
+ print("PyTorch / TensorRT / CUDA unavailable; skipping the deployment example.")
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+
+ PyTorch / TensorRT / CUDA unavailable; skipping the deployment example.
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 329-332
+
+Real deployment builds once and reuses the artifact. Export the compiled
+module to a shared library, then load and run it later -- in a fresh process,
+with no PyTorch and no rebuild needed.
+
+.. GENERATED FROM PYTHON SOURCE LINES 332-346
+
+.. code-block:: Python
+
+
+ if has_torch and has_tensorrt and has_cuda:
+ with tempfile.TemporaryDirectory() as tmpdir:
+ lib_path = os.path.join(tmpdir, "deployed_trt.so")
+ torch_exec.export_library(lib_path)
+ loaded = tvm.runtime.load_module(lib_path)
+ reran = relax.VirtualMachine(loaded, torch_dev)["main"](
+ tvm.runtime.tensor(example_input.numpy(), torch_dev)
+ )[0].numpy()
+ np.testing.assert_allclose(reran, torch_ref, rtol=1e-2, atol=1e-2)
+ print("Reloaded the exported library and reran; output", reran.shape, "still matches.")
+ else:
+ print("PyTorch / TensorRT / CUDA unavailable; skipping the export/reload step.")
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ .. code-block:: none
+ PyTorch / TensorRT / CUDA unavailable; skipping the export/reload step.
+.. GENERATED FROM PYTHON SOURCE LINES 347-360
+Notes for real deployments
+--------------------------
+- **Operator coverage and fallback.** TensorRT offloads only the ops in its
+ pattern table (see ``python/tvm/relax/backend/contrib/tensorrt.py``);
+ anything unsupported simply stays on the host. Print the partitioned module
+ and look for the ``Codegen: "tensorrt"`` functions to see what was offloaded.
+- **Dynamic shapes.** The builder sets up an optimization profile for a dynamic
+ leading (batch) dimension, so the integration can serve a model exported with
+ a symbolic batch size.
+- **Engine build cost.** Building a TensorRT engine is slow the first time (it
+ is not a hang). Set ``TVM_TENSORRT_CACHE_DIR`` to cache built engines to
+ disk and skip the rebuild on later runs.
-.. GENERATED FROM PYTHON SOURCE LINES 212-223
+.. GENERATED FROM PYTHON SOURCE LINES 362-381
Next steps
----------
-To build a real NPU backend using this example as a starting point:
+To build your own backend using the example NPU as a starting point:
-- Replace ``example_npu_runtime.cc`` with your hardware SDK calls.
+- Replace the stub runtime in
+ ``src/runtime/extra/contrib/example_npu/example_npu_runtime.cc`` with your
+ hardware SDK calls.
- Extend ``patterns.py`` with the ops your hardware supports.
-- Add a C++ codegen under ``src/relax/backend/contrib/`` if your
- hardware requires a non-JSON serialization format.
-- Add your cmake module under ``cmake/modules/contrib/`` following
- the pattern in ``cmake/modules/contrib/ExampleNPU.cmake``.
+- Add a C++ codegen under ``src/relax/backend/contrib/`` if your backend needs
+ a non-JSON serialization format.
+- Add a CMake module under ``cmake/modules/contrib/`` following
+ ``ExampleNPU.cmake``.
+
+For a complete real-backend implementation to study, see the TensorRT
+integration: the pattern table and ``partition_for_tensorrt`` in
+``python/tvm/relax/backend/contrib/tensorrt.py``, the codegen in
+``src/relax/backend/contrib/tensorrt/``, and the runtime in
+``src/runtime/extra/contrib/tensorrt/``.
.. _sphx_glr_download_how_to_tutorials_bring_your_own_codegen.py:
diff --git a/docs/_sources/how_to/tutorials/cross_compilation_and_rpc.rst.txt b/docs/_sources/how_to/tutorials/cross_compilation_and_rpc.rst.txt
index 6e9f4a8a960..92060b4c6b4 100644
--- a/docs/_sources/how_to/tutorials/cross_compilation_and_rpc.rst.txt
+++ b/docs/_sources/how_to/tutorials/cross_compilation_and_rpc.rst.txt
@@ -267,7 +267,7 @@ device and returns the measured cost. Network overhead is excluded.
.. code-block:: none
- 1.13e-07 secs/op
+ 1.05e-07 secs/op
@@ -857,8 +857,8 @@ This workflow is applicable to various deployment scenarios:
Converted PyTorch model to Relax:
- Number of parameters: 4
Using local target for demonstration
- Exported library to: /tmp/tmp4lmyjg3g/model_deployed.so
- Saved parameters to: /tmp/tmp4lmyjg3g/model_params.npz
+ Exported library to: /tmp/tmpvnfep_p3/model_deployed.so
+ Saved parameters to: /tmp/tmpvnfep_p3/model_params.npz
RPC workflow (works for any remote device):
==================================================
diff --git a/docs/_sources/how_to/tutorials/customize_opt.rst.txt b/docs/_sources/how_to/tutorials/customize_opt.rst.txt
index 697272bf6a2..84850b6ba1d 100644
--- a/docs/_sources/how_to/tutorials/customize_opt.rst.txt
+++ b/docs/_sources/how_to/tutorials/customize_opt.rst.txt
@@ -426,8 +426,8 @@ We can build and deploy the optimized model to the TVM runtime.
.. code-block:: none
- [[23461.07 25317.805 23688.432 25397.043 25643.918 25524.94 25856.713
- 24760.25 24296.184 25380.734]]
+ [[25302.758 26301.07 25925.635 24935.379 25223.234 24493.31 24251.89
+ 24630.104 25003.176 23884.219]]
diff --git a/docs/_sources/how_to/tutorials/e2e_opt_model.rst.txt b/docs/_sources/how_to/tutorials/e2e_opt_model.rst.txt
index 468921ab3db..f09b4cb9f23 100644
--- a/docs/_sources/how_to/tutorials/e2e_opt_model.rst.txt
+++ b/docs/_sources/how_to/tutorials/e2e_opt_model.rst.txt
@@ -54,7 +54,7 @@ PyTorch.
.. code-block:: none
Downloading: "https://download.pytorch.org/models/resnet18-f37072fd.pth" to /workspace/.cache/torch/hub/checkpoints/resnet18-f37072fd.pth
- 0%| | 0.00/44.7M [00:00<?, ?B/s] 67%|██████▋ | 29.8M/44.7M [00:00<00:00, 311MB/s] 100%|██████████| 44.7M/44.7M [00:00<00:00, 336MB/s]
+ 0%| | 0.00/44.7M [00:00<?, ?B/s] 65%|██████▌ | 29.2M/44.7M [00:00<00:00, 306MB/s] 100%|██████████| 44.7M/44.7M [00:00<00:00, 337MB/s]
diff --git a/docs/_sources/how_to/tutorials/index.rst.txt b/docs/_sources/how_to/tutorials/index.rst.txt
index 09c66929673..f59d99e30ba 100644
--- a/docs/_sources/how_to/tutorials/index.rst.txt
+++ b/docs/_sources/how_to/tutorials/index.rst.txt
@@ -13,7 +13,7 @@ HOW TO
.. raw:: html
- <div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to integrate a custom hardware backend with TVM's BYOC framework, using the bundled example NPU backend (CPU emulation, no real hardware required) as the worked example. You will see the key concepts needed to offload operations to a custom accelerator: pattern registration, graph partitioning, codegen, and runtime dispatch.">
+ <div class="sphx-glr-thumbcontainer" tooltip="TVM's Bring Your Own Codegen (BYOC) framework lets you offload parts of a model to a custom backend -- a hardware accelerator, an inference library, or your own kernels -- while TVM compiles the rest. This tutorial has two parts:">
.. only:: html
@@ -24,7 +24,7 @@ HOW TO
.. raw:: html
- <div class="sphx-glr-thumbnail-title">Bring Your Own Codegen: NPU Backend Example</div>
+ <div class="sphx-glr-thumbnail-title">Bring Your Own Codegen</div>
</div>
diff --git a/docs/_sources/how_to/tutorials/sg_execution_times.rst.txt b/docs/_sources/how_to/tutorials/sg_execution_times.rst.txt
index 233acc0796d..ecc6c3d1a5c 100644
--- a/docs/_sources/how_to/tutorials/sg_execution_times.rst.txt
+++ b/docs/_sources/how_to/tutorials/sg_execution_times.rst.txt
@@ -6,7 +6,7 @@
Computation times
=================
-**00:16.819** total execution time for 8 files **from how_to/tutorials**:
+**00:16.742** total execution time for 8 files **from how_to/tutorials**:
.. container::
@@ -33,22 +33,22 @@ Computation times
- Time
- Mem (MB)
* - :ref:`sphx_glr_how_to_tutorials_optimize_llm.py` (``optimize_llm.py``)
- - 00:11.563
+ - 00:11.451
- 0.0
* - :ref:`sphx_glr_how_to_tutorials_import_model.py` (``import_model.py``)
- - 00:03.659
+ - 00:03.654
- 0.0
* - :ref:`sphx_glr_how_to_tutorials_e2e_opt_model.py` (``e2e_opt_model.py``)
- - 00:00.646
+ - 00:00.629
- 0.0
* - :ref:`sphx_glr_how_to_tutorials_cross_compilation_and_rpc.py` (``cross_compilation_and_rpc.py``)
- - 00:00.565
+ - 00:00.570
- 0.0
* - :ref:`sphx_glr_how_to_tutorials_customize_opt.py` (``customize_opt.py``)
- - 00:00.331
+ - 00:00.388
- 0.0
* - :ref:`sphx_glr_how_to_tutorials_bring_your_own_codegen.py` (``bring_your_own_codegen.py``)
- - 00:00.048
+ - 00:00.043
- 0.0
* - :ref:`sphx_glr_how_to_tutorials_mix_python_and_tvm_with_pymodule.py` (``mix_python_and_tvm_with_pymodule.py``)
- 00:00.005
diff --git a/docs/_sources/sg_execution_times.rst.txt b/docs/_sources/sg_execution_times.rst.txt
index 3c8eebe832f..a41a6ae86c2 100644
--- a/docs/_sources/sg_execution_times.rst.txt
+++ b/docs/_sources/sg_execution_times.rst.txt
@@ -6,7 +6,7 @@
Computation times
=================
-**00:23.898** total execution time for 16 files **from all galleries**:
+**00:24.225** total execution time for 16 files **from all galleries**:
.. container::
@@ -33,28 +33,28 @@ Computation times
- Time
- Mem (MB)
* - :ref:`sphx_glr_how_to_tutorials_optimize_llm.py` (``../how_to/tutorials/optimize_llm.py``)
- - 00:11.563
+ - 00:11.451
- 0.0
* - :ref:`sphx_glr_get_started_tutorials_ir_module.py` (``../get_started/tutorials/ir_module.py``)
- - 00:05.722
+ - 00:06.120
- 0.0
* - :ref:`sphx_glr_how_to_tutorials_import_model.py` (``../how_to/tutorials/import_model.py``)
- - 00:03.659
+ - 00:03.654
- 0.0
* - :ref:`sphx_glr_how_to_tutorials_e2e_opt_model.py` (``../how_to/tutorials/e2e_opt_model.py``)
- - 00:00.646
+ - 00:00.629
- 0.0
* - :ref:`sphx_glr_how_to_tutorials_cross_compilation_and_rpc.py` (``../how_to/tutorials/cross_compilation_and_rpc.py``)
- - 00:00.565
+ - 00:00.570
+ - 0.0
+ * - :ref:`sphx_glr_how_to_tutorials_customize_opt.py` (``../how_to/tutorials/customize_opt.py``)
+ - 00:00.388
- 0.0
* - :ref:`sphx_glr_deep_dive_tensor_ir_tutorials_tir_creation.py` (``../deep_dive/tensor_ir/tutorials/tir_creation.py``)
- - 00:00.382
+ - 00:00.388
- 0.0
* - :ref:`sphx_glr_deep_dive_tensor_ir_tutorials_tir_transformation.py` (``../deep_dive/tensor_ir/tutorials/tir_transformation.py``)
- - 00:00.354
- - 0.0
- * - :ref:`sphx_glr_how_to_tutorials_customize_opt.py` (``../how_to/tutorials/customize_opt.py``)
- - 00:00.331
+ - 00:00.355
- 0.0
* - :ref:`sphx_glr_deep_dive_relax_tutorials_relax_creation.py` (``../deep_dive/relax/tutorials/relax_creation.py``)
- 00:00.210
@@ -69,7 +69,7 @@ Computation times
- 00:00.068
- 0.0
* - :ref:`sphx_glr_how_to_tutorials_bring_your_own_codegen.py` (``../how_to/tutorials/bring_your_own_codegen.py``)
- - 00:00.048
+ - 00:00.043
- 0.0
* - :ref:`sphx_glr_deep_dive_tensor_ir_tutorials_meta_schedule.py` (``../deep_dive/tensor_ir/tutorials/meta_schedule.py``)
- 00:00.007
diff --git a/docs/arch/codegen.html b/docs/arch/codegen.html
index 168de92c890..9815bbed810 100644
--- a/docs/arch/codegen.html
+++ b/docs/arch/codegen.html
@@ -192,7 +192,7 @@
<li class="toctree-l1"><a class="reference internal" href="../how_to/tutorials/cross_compilation_and_rpc.html">Cross Compilation and RPC</a></li>
<li class="toctree-l1"><a class="reference internal" href="../how_to/tutorials/export_and_load_executable.html">Export and Load Relax Executables</a></li>
<li class="toctree-l1"><a class="reference internal" href="../how_to/tutorials/mix_python_and_tvm_with_pymodule.html">Mix Python/PyTorch with TVM Using BasePyModule</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../how_to/tutorials/bring_your_own_codegen.html">Bring Your Own Codegen: NPU Backend Example</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../how_to/tutorials/bring_your_own_codegen.html">Bring Your Own Codegen</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">Deep Dive</span></p>
<ul class="current">
diff --git a/docs/arch/device_target_interactions.html b/docs/arch/device_target_interactions.html
index f3069415534..b3fc8019504 100644
--- a/docs/arch/device_target_interactions.html
+++ b/docs/arch/device_target_interactions.html
@@ -192,7 +192,7 @@
<li class="toctree-l1"><a class="reference internal" href="../how_to/tutorials/cross_compilation_and_rpc.html">Cross Compilation and RPC</a></li>
<li class="toctree-l1"><a class="reference internal" href="../how_to/tutorials/export_and_load_executable.html">Export and Load Relax Executables</a></li>
<li class="toctree-l1"><a class="reference internal" href="../how_to/tutorials/mix_python_and_tvm_with_pymodule.html">Mix Python/PyTorch with TVM Using BasePyModule</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../how_to/tutorials/bring_your_own_codegen.html">Bring Your Own Codegen: NPU Backend Example</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../how_to/tutorials/bring_your_own_codegen.html">Bring Your Own Codegen</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">Deep Dive</span></p>
<ul class="current">
diff --git a/docs/arch/external_library_dispatch.html b/docs/arch/external_library_dispatch.html
index 70c174266d7..88bb1768509 100644
--- a/docs/arch/external_library_dispatch.html
+++ b/docs/arch/external_library_dispatch.html
@@ -192,7 +192,7 @@
<li class="toctree-l1"><a class="reference internal" href="../how_to/tutorials/cross_compilation_and_rpc.html">Cross Compilation and RPC</a></li>
<li class="toctree-l1"><a class="reference internal" href="../how_to/tutorials/export_and_load_executable.html">Export and Load Relax Executables</a></li>
<li class="toctree-l1"><a class="reference internal" href="../how_to/tutorials/mix_python_and_tvm_with_pymodule.html">Mix Python/PyTorch with TVM Using BasePyModule</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../how_to/tutorials/bring_your_own_codegen.html">Bring Your Own Codegen: NPU Backend Example</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../how_to/tutorials/bring_your_own_codegen.html">Bring Your Own Codegen</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">Deep Dive</span></p>
<ul class="current">
diff --git a/docs/arch/fusion.html b/docs/arch/fusion.html
index 2d19d108820..85be5205a09 100644
--- a/docs/arch/fusion.html
+++ b/docs/arch/fusion.html
@@ -192,7 +192,7 @@
<li class="toctree-l1"><a class="reference internal" href="../how_to/tutorials/cross_compilation_and_rpc.html">Cross Compilation and RPC</a></li>
<li class="toctree-l1"><a class="reference internal" href="../how_to/tutorials/export_and_load_executable.html">Export and Load Relax Executables</a></li>
<li class="toctree-l1"><a class="reference internal" href="../how_to/tutorials/mix_python_and_tvm_with_pymodule.html">Mix Python/PyTorch with TVM Using BasePyModule</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../how_to/tutorials/bring_your_own_codegen.html">Bring Your Own Codegen: NPU Backend Example</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../how_to/tutorials/bring_your_own_codegen.html">Bring Your Own Codegen</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">Deep Dive</span></p>
<ul class="current">
diff --git a/docs/arch/index.html b/docs/arch/index.html
index f9b23d4f33b..931141be3c1 100644
--- a/docs/arch/index.html
+++ b/docs/arch/index.html
@@ -46,7 +46,7 @@
<link rel="index" title="Index" href="../genindex.html" />
<link rel="search" title="Search" href="../search.html" />
<link rel="next" title="Operator Fusion" href="fusion.html" />
- <link rel="prev" title="Bring Your Own Codegen: NPU Backend Example" href="../how_to/tutorials/bring_your_own_codegen.html" />
+ <link rel="prev" title="Bring Your Own Codegen" href="../how_to/tutorials/bring_your_own_codegen.html" />
</head>
<body class="wy-body-for-nav">
@@ -192,7 +192,7 @@
<li class="toctree-l1"><a class="reference internal" href="../how_to/tutorials/cross_compilation_and_rpc.html">Cross Compilation and RPC</a></li>
<li class="toctree-l1"><a class="reference internal" href="../how_to/tutorials/export_and_load_executable.html">Export and Load Relax Executables</a></li>
<li class="toctree-l1"><a class="reference internal" href="../how_to/tutorials/mix_python_and_tvm_with_pymodule.html">Mix Python/PyTorch with TVM Using BasePyModule</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../how_to/tutorials/bring_your_own_codegen.html">Bring Your Own Codegen: NPU Backend Example</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../how_to/tutorials/bring_your_own_codegen.html">Bring Your Own Codegen</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">Deep Dive</span></p>
<ul class="current">
@@ -731,7 +731,7 @@ and then integrate it into the IRModule.</p>
<a href="fusion.html" class="btn btn-neutral float-right" title="Operator Fusion" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right"></span></a>
- <a href="../how_to/tutorials/bring_your_own_codegen.html" class="btn btn-neutral float-left" title="Bring Your Own Codegen: NPU Backend Example" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left"></span> Previous</a>
+ <a href="../how_to/tutorials/bring_your_own_codegen.html" class="btn btn-neutral float-left" title="Bring Your Own Codegen" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left"></span> Previous</a>
</div>
diff --git a/docs/arch/introduction_to_module_serialization.html b/docs/arch/introduction_to_module_serialization.html
index 597c3d9f27d..fa057d525d8 100644
--- a/docs/arch/introduction_to_module_serialization.html
+++ b/docs/arch/introduction_to_module_serialization.html
@@ -192,7 +192,7 @@
<li class="toctree-l1"><a class="reference internal" href="../how_to/tutorials/cross_compilation_and_rpc.html">Cross Compilation and RPC</a></li>
<li class="toctree-l1"><a class="reference internal" href="../how_to/tutorials/export_and_load_executable.html">Export and Load Relax Executables</a></li>
<li class="toctree-l1"><a class="reference internal" href="../how_to/tutorials/mix_python_and_tvm_with_pymodule.html">Mix Python/PyTorch with TVM Using BasePyModule</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../how_to/tutorials/bring_your_own_codegen.html">Bring Your Own Codegen: NPU Backend Example</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../how_to/tutorials/bring_your_own_codegen.html">Bring Your Own Codegen</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">Deep Dive</span></p>
<ul class="current">
diff --git a/docs/arch/pass_infra.html b/docs/arch/pass_infra.html
index 7871f356af8..87ba2a69f72 100644
--- a/docs/arch/pass_infra.html
+++ b/docs/arch/pass_infra.html
@@ -192,7 +192,7 @@
<li class="toctree-l1"><a class="reference internal" href="../how_to/tutorials/cross_compilation_and_rpc.html">Cross Compilation and RPC</a></li>
<li class="toctree-l1"><a class="reference internal" href="../how_to/tutorials/export_and_load_executable.html">Export and Load Relax Executables</a></li>
<li class="toctree-l1"><a class="reference internal" href="../how_to/tutorials/mix_python_and_tvm_with_pymodule.html">Mix Python/PyTorch with TVM Using BasePyModule</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../how_to/tutorials/bring_your_own_codegen.html">Bring Your Own Codegen: NPU Backend Example</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../how_to/tutorials/bring_your_own_codegen.html">Bring Your Own Codegen</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">Deep Dive</span></p>
<ul class="current">
diff --git a/docs/arch/relax_vm.html b/docs/arch/relax_vm.html
index 0f829eb2b04..0fb6dd5c554 100644
--- a/docs/arch/relax_vm.html
+++ b/docs/arch/relax_vm.html
@@ -192,7 +192,7 @@
<li class="toctree-l1"><a class="reference internal" href="../how_to/tutorials/cross_compilation_and_rpc.html">Cross Compilation and RPC</a></li>
<li class="toctree-l1"><a class="reference internal" href="../how_to/tutorials/export_and_load_executable.html">Export and Load Relax Executables</a></li>
<li class="toctree-l1"><a class="reference internal" href="../how_to/tutorials/mix_python_and_tvm_with_pymodule.html">Mix Python/PyTorch with TVM Using BasePyModule</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../how_to/tutorials/bring_your_own_codegen.html">Bring Your Own Codegen: NPU Backend Example</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../how_to/tutorials/bring_your_own_codegen.html">Bring Your Own Codegen</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">Deep Dive</span></p>
<ul class="current">
diff --git a/docs/arch/runtime.html b/docs/arch/runtime.html
index 7bfaf2fab31..2f8307c58c7 100644
--- a/docs/arch/runtime.html
+++ b/docs/arch/runtime.html
@@ -192,7 +192,7 @@
<li class="toctree-l1"><a class="reference internal" href="../how_to/tutorials/cross_compilation_and_rpc.html">Cross Compilation and RPC</a></li>
<li class="toctree-l1"><a class="reference internal" href="../how_to/tutorials/export_and_load_executable.html">Export and Load Relax Executables</a></li>
<li class="toctree-l1"><a class="reference internal" href="../how_to/tutorials/mix_python_and_tvm_with_pymodule.html">Mix Python/PyTorch with TVM Using BasePyModule</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../how_to/tutorials/bring_your_own_codegen.html">Bring Your Own Codegen: NPU Backend Example</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../how_to/tutorials/bring_your_own_codegen.html">Bring Your Own Codegen</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">Deep Dive</span></p>
<ul class="current">
diff --git a/docs/arch/runtimes/vulkan.html b/docs/arch/runtimes/vulkan.html
index 25d9aa241cb..2824c832b06 100644
--- a/docs/arch/runtimes/vulkan.html
+++ b/docs/arch/runtimes/vulkan.html
@@ -192,7 +192,7 @@
<li class="toctree-l1"><a class="reference internal" href="../../how_to/tutorials/cross_compilation_and_rpc.html">Cross Compilation and RPC</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../how_to/tutorials/export_and_load_executable.html">Export and Load Relax Executables</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../how_to/tutorials/mix_python_and_tvm_with_pymodule.html">Mix Python/PyTorch with TVM Using BasePyModule</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../how_to/tutorials/bring_your_own_codegen.html">Bring Your Own Codegen: NPU Backend Example</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../how_to/tutorials/bring_your_own_codegen.html">Bring Your Own Codegen</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">Deep Dive</span></p>
<ul class="current">
diff --git a/docs/arch/tvmscript.html b/docs/arch/tvmscript.html
index aa5c13c47bc..8801b1ccbc0 100644
--- a/docs/arch/tvmscript.html
+++ b/docs/arch/tvmscript.html
@@ -192,7 +192,7 @@
<li class="toctree-l1"><a class="reference internal" href="../how_to/tutorials/cross_compilation_and_rpc.html">Cross Compilation and RPC</a></li>
<li class="toctree-l1"><a class="reference internal" href="../how_to/tutorials/export_and_load_executable.html">Export and Load Relax Executables</a></li>
<li class="toctree-l1"><a class="reference internal" href="../how_to/tutorials/mix_python_and_tvm_with_pymodule.html">Mix Python/PyTorch with TVM Using BasePyModule</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../how_to/tutorials/bring_your_own_codegen.html">Bring Your Own Codegen: NPU Backend Example</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../how_to/tutorials/bring_your_own_codegen.html">Bring Your Own Codegen</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">Deep Dive</span></p>
<ul class="current">
diff --git a/docs/commit_hash b/docs/commit_hash
index 695d0e6e288..2331b47edf3 100644
--- a/docs/commit_hash
+++ b/docs/commit_hash
@@ -1 +1 @@
-7bd73e5ad22e359aff273165eddc47ff9c5373ee
+1b900a386892408af6494e39014ca03bc8e2f1e6
diff --git a/docs/contribute/ci.html b/docs/contribute/ci.html
index 34c0f34169a..80a1c23c8ad 100644
--- a/docs/contribute/ci.html
+++ b/docs/contribute/ci.html
@@ -192,7 +192,7 @@
<li class="toctree-l1"><a class="reference internal" href="../how_to/tutorials/cross_compilation_and_rpc.html">Cross Compilation and RPC</a></li>
<li class="toctree-l1"><a class="reference internal" href="../how_to/tutorials/export_and_load_executable.html">Export and Load Relax Executables</a></li>
<li class="toctree-l1"><a class="reference internal" href="../how_to/tutorials/mix_python_and_tvm_with_pymodule.html">Mix Python/PyTorch with TVM Using BasePyModule</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../how_to/tutorials/bring_your_own_codegen.html">Bring Your Own Codegen: NPU Backend Example</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../how_to/tutorials/bring_your_own_codegen.html">Bring Your Own Codegen</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">Deep Dive</span></p>
<ul>
diff --git a/docs/contribute/code_guide.html b/docs/contribute/code_guide.html
index 0aaccef03e0..cba29c6e7af 100644
--- a/docs/contribute/code_guide.html
+++ b/docs/contribute/code_guide.html
@@ -192,7 +192,7 @@
<li class="toctree-l1"><a class="reference internal" href="../how_to/tutorials/cross_compilation_and_rpc.html">Cross Compilation and RPC</a></li>
<li class="toctree-l1"><a class="reference internal" href="../how_to/tutorials/export_and_load_executable.html">Export and Load Relax Executables</a></li>
<li class="toctree-l1"><a class="reference internal" href="../how_to/tutorials/mix_python_and_tvm_with_pymodule.html">Mix Python/PyTorch with TVM Using BasePyModule</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../how_to/tutorials/bring_your_own_codegen.html">Bring Your Own Codegen: NPU Backend Example</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../how_to/tutorials/bring_your_own_codegen.html">Bring Your Own Codegen</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">Deep Dive</span></p>
<ul>
diff --git a/docs/contribute/code_review.html b/docs/contribute/code_review.html
index 32128253e95..a392ab942e6 100644
--- a/docs/contribute/code_review.html
+++ b/docs/contribute/code_review.html
@@ -192,7 +192,7 @@
<li class="toctree-l1"><a class="reference internal" href="../how_to/tutorials/cross_compilation_and_rpc.html">Cross Compilation and RPC</a></li>
<li class="toctree-l1"><a class="reference internal" href="../how_to/tutorials/export_and_load_executable.html">Export and Load Relax Executables</a></li>
<li class="toctree-l1"><a class="reference internal" href="../how_to/tutorials/mix_python_and_tvm_with_pymodule.html">Mix Python/PyTorch with TVM Using BasePyModule</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../how_to/tutorials/bring_your_own_codegen.html">Bring Your Own Codegen: NPU Backend Example</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../how_to/tutorials/bring_your_own_codegen.html">Bring Your Own Codegen</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">Deep Dive</span></p>
<ul>
diff --git a/docs/contribute/committer_guide.html b/docs/contribute/committer_guide.html
index 92d59339731..7756a8e50df 100644
--- a/docs/contribute/committer_guide.html
+++ b/docs/contribute/committer_guide.html
@@ -192,7 +192,7 @@
<li class="toctree-l1"><a class="reference internal" href="../how_to/tutorials/cross_compilation_and_rpc.html">Cross Compilation and RPC</a></li>
<li class="toctree-l1"><a class="reference internal" href="../how_to/tutorials/export_and_load_executable.html">Export and Load Relax Executables</a></li>
<li class="toctree-l1"><a class="reference internal" href="../how_to/tutorials/mix_python_and_tvm_with_pymodule.html">Mix Python/PyTorch with TVM Using BasePyModule</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../how_to/tutorials/bring_your_own_codegen.html">Bring Your Own Codegen: NPU Backend Example</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../how_to/tutorials/bring_your_own_codegen.html">Bring Your Own Codegen</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">Deep Dive</span></p>
<ul>
diff --git a/docs/contribute/community.html b/docs/contribute/community.html
index 80a081e7453..dfdcd8f70aa 100644
--- a/docs/contribute/community.html
+++ b/docs/contribute/community.html
@@ -192,7 +192,7 @@
<li class="toctree-l1"><a class="reference internal" href="../how_to/tutorials/cross_compilation_and_rpc.html">Cross Compilation and RPC</a></li>
<li class="toctree-l1"><a class="reference internal" href="../how_to/tutorials/export_and_load_executable.html">Export and Load Relax Executables</a></li>
<li class="toctree-l1"><a class="reference internal" href="../how_to/tutorials/mix_python_and_tvm_with_pymodule.html">Mix Python/PyTorch with TVM Using BasePyModule</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../how_to/tutorials/bring_your_own_codegen.html">Bring Your Own Codegen: NPU Backend Example</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../how_to/tutorials/bring_your_own_codegen.html">Bring Your Own Codegen</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">Deep Dive</span></p>
<ul>
diff --git a/docs/contribute/document.html b/docs/contribute/document.html
index b0de6173162..ac716dc0249 100644
--- a/docs/contribute/document.html
+++ b/docs/contribute/document.html
@@ -192,7 +192,7 @@
<li class="toctree-l1"><a class="reference internal" href="../how_to/tutorials/cross_compilation_and_rpc.html">Cross Compilation and RPC</a></li>
<li class="toctree-l1"><a class="reference internal" href="../how_to/tutorials/export_and_load_executable.html">Export and Load Relax Executables</a></li>
<li class="toctree-l1"><a class="reference internal" href="../how_to/tutorials/mix_python_and_tvm_with_pymodule.html">Mix Python/PyTorch with TVM Using BasePyModule</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../how_to/tutorials/bring_your_own_codegen.html">Bring Your Own Codegen: NPU Backend Example</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../how_to/tutorials/bring_your_own_codegen.html">Bring Your Own Codegen</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">Deep Dive</span></p>
<ul>
diff --git a/docs/contribute/error_handling.html b/docs/contribute/error_handling.html
index 68ea06b0aa8..c859d588e6e 100644
--- a/docs/contribute/error_handling.html
+++ b/docs/contribute/error_handling.html
@@ -192,7 +192,7 @@
<li class="toctree-l1"><a class="reference internal" href="../how_to/tutorials/cross_compilation_and_rpc.html">Cross Compilation and RPC</a></li>
<li class="toctree-l1"><a class="reference internal" href="../how_to/tutorials/export_and_load_executable.html">Export and Load Relax Executables</a></li>
<li class="toctree-l1"><a class="reference internal" href="../how_to/tutorials/mix_python_and_tvm_with_pymodule.html">Mix Python/PyTorch with TVM Using BasePyModule</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../how_to/tutorials/bring_your_own_codegen.html">Bring Your Own Codegen: NPU Backend Example</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../how_to/tutorials/bring_your_own_codegen.html">Bring Your Own Codegen</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">Deep Dive</span></p>
<ul>
diff --git a/docs/contribute/git_howto.html b/docs/contribute/git_howto.html
index b620fb95bdf..ccf2b6a3bdf 100644
--- a/docs/contribute/git_howto.html
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+++ b/docs/deep_dive/tensor_ir/tutorials/sg_execution_times.html
@@ -190,7 +190,7 @@
<li class="toctree-l1"><a class="reference internal" href="../../../how_to/tutorials/cross_compilation_and_rpc.html">Cross Compilation and RPC</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../how_to/tutorials/export_and_load_executable.html">Export and Load Relax Executables</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../how_to/tutorials/mix_python_and_tvm_with_pymodule.html">Mix Python/PyTorch with TVM Using BasePyModule</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../../how_to/tutorials/bring_your_own_codegen.html">Bring Your Own Codegen: NPU Backend Example</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../how_to/tutorials/bring_your_own_codegen.html">Bring Your Own Codegen</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">Deep Dive</span></p>
<ul>
@@ -297,7 +297,7 @@
<section id="computation-times">
<span id="sphx-glr-deep-dive-tensor-ir-tutorials-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Link to this heading"></a></h1>
-<p><strong>00:00.896</strong> total execution time for 4 files <strong>from deep_dive/tensor_ir/tutorials</strong>:</p>
+<p><strong>00:00.902</strong> total execution time for 4 files <strong>from deep_dive/tensor_ir/tutorials</strong>:</p>
<div class="docutils container">
<style scoped>
<link href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/5.3.0/css/bootstrap.min.css" rel="stylesheet" />
@@ -319,11 +319,11 @@ $(document).ready( function () {
</thead>
<tbody>
<tr class="row-even"><td><p><a class="reference internal" href="tir_creation.html#sphx-glr-deep-dive-tensor-ir-tutorials-tir-creation-py"><span class="std std-ref">TensorIR Creation</span></a> (<code class="docutils literal notranslate"><span class="pre">tir_creation.py</span></code>)</p></td>
-<td><p>00:00.382</p></td>
+<td><p>00:00.388</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="tir_transformation.html#sphx-glr-deep-dive-tensor-ir-tutorials-tir-transformation-py"><span class="std std-ref">Transformation</span></a> (<code class="docutils literal notranslate"><span class="pre">tir_transformation.py</span></code>)</p></td>
-<td><p>00:00.354</p></td>
+<td><p>00:00.355</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="dlight_gpu_scheduling.html#sphx-glr-deep-dive-tensor-ir-tutorials-dlight-gpu-scheduling-py"><span class="std std-ref">DLight: Rule-Based GPU Scheduling</span></a> (<code class="docutils literal notranslate"><span class="pre">dlight_gpu_scheduling.py</span></code>)</p></td>
diff --git a/docs/deep_dive/tensor_ir/tutorials/tir_creation.html b/docs/deep_dive/tensor_ir/tutorials/tir_creation.html
index d112ac67177..341b267b2f7 100644
--- a/docs/deep_dive/tensor_ir/tutorials/tir_creation.html
+++ b/docs/deep_dive/tensor_ir/tutorials/tir_creation.html
@@ -193,7 +193,7 @@
<li class="toctree-l1"><a class="reference internal" href="../../../how_to/tutorials/cross_compilation_and_rpc.html">Cross Compilation and RPC</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../how_to/tutorials/export_and_load_executable.html">Export and Load Relax Executables</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../how_to/tutorials/mix_python_and_tvm_with_pymodule.html">Mix Python/PyTorch with TVM Using BasePyModule</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../../how_to/tutorials/bring_your_own_codegen.html">Bring Your Own Codegen: NPU Backend Example</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../how_to/tutorials/bring_your_own_codegen.html">Bring Your Own Codegen</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">Deep Dive</span></p>
<ul>
@@ -496,17 +496,17 @@ be used to ascertain the shape and data type of a TensorIR.</p>
<span class="nb">print</span><span class="p">(</span><span class="n">evaluate_dynamic_shape</span><span class="p">(</span><span class="n">dyn_shape_lib</span><span class="p">,</span> <span class="n">m</span><span class="o">=</span><span class="mi">64</span><span class="p">,</span> <span class="n">n</span><span class="o">=</span><span class="mi">64</span><span class="p">,</span> <a href="../../../reference/api/python/tirx/tirx.html#tvm.tirx.IterVar" title="tvm.tirx.IterVar" class="sphx-gl [...]
</pre></div>
</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[[0.11088739 0.37859002 0.25887144 0.47898802]
- [0.6578438 1.0262297 0.49454063 0.97019464]
- [0.66448474 1.0079705 0.6300473 0.6706697 ]
- [0.758688 1.8438771 0.9797324 2.2190166 ]]
-[[37.943485 34.777294 33.82679 ... 36.445374 35.011868 35.313328]
- [33.63442 32.13715 30.743587 ... 33.137264 31.472588 30.236576]
- [34.64113 32.362938 29.226406 ... 31.654057 31.532555 33.139896]
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[[0.8231925 0.74687773 0.61615294 1.0123911 ]
+ [0.7160674 0.45636386 0.6850383 0.6155127 ]
+ [1.005914 0.96243465 0.6228967 1.653728 ]
+ [1.002558 1.1868575 0.73863345 0.7221608 ]]
+[[31.91493 36.009766 31.308624 ... 33.880024 35.974846 34.691463]
+ [29.066854 34.266792 31.542082 ... 31.765827 35.56741 33.41558 ]
+ [30.660873 35.36475 34.314415 ... 32.7977 36.34936 35.827236]
...
- [29.656555 28.751762 30.90607 ... 31.368315 29.983944 30.143927]
- [38.789223 33.203655 31.96145 ... 34.770775 32.493507 34.027424]
- [35.072197 29.962208 30.019306 ... 31.849218 30.205173 30.428644]]
+ [26.84151 30.554972 29.886185 ... 30.959145 31.836 31.593372]
+ [26.493942 32.096836 28.361483 ... 30.639572 32.666187 32.601116]
+ [28.802551 35.955505 32.694454 ... 32.20954 36.73205 34.373337]]
</pre></div>
</div>
</section>
diff --git a/docs/deep_dive/tensor_ir/tutorials/tir_transformation.html b/docs/deep_dive/tensor_ir/tutorials/tir_transformation.html
index 31dd7d6a715..292e463b3fa 100644
--- a/docs/deep_dive/tensor_ir/tutorials/tir_transformation.html
+++ b/docs/deep_dive/tensor_ir/tutorials/tir_transformation.html
@@ -192,7 +192,7 @@
<li class="toctree-l1"><a class="reference internal" href="../../../how_to/tutorials/cross_compilation_and_rpc.html">Cross Compilation and RPC</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../how_to/tutorials/export_and_load_executable.html">Export and Load Relax Executables</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../how_to/tutorials/mix_python_and_tvm_with_pymodule.html">Mix Python/PyTorch with TVM Using BasePyModule</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../../how_to/tutorials/bring_your_own_codegen.html">Bring Your Own Codegen: NPU Backend Example</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../how_to/tutorials/bring_your_own_codegen.html">Bring Your Own Codegen</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">Deep Dive</span></p>
<ul>
@@ -374,7 +374,7 @@ original implementation.</p>
</div>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 2.7603 2.7603 2.7603 2.7603 0.0000
+ 2.7551 2.7551 2.7551 2.7551 0.0000
</pre></div>
</div>
<section id="initialization-schedule">
@@ -472,7 +472,7 @@ class Module:
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 0.8639 0.8639 0.8639 0.8639 0.0000
+ 0.8647 0.8647 0.8647 0.8647 0.0000
</pre></div>
</div>
</section>
@@ -568,7 +568,7 @@ class Module:
Execution time summary:
mean (ms) median (ms) max (ms) min (ms) std (ms)
- 0.3379 0.3379 0.3379 0.3379 0.0000
+ 0.3382 0.3382 0.3382 0.3382 0.0000
</pre></div>
</div>
</section>
diff --git a/docs/errors.html b/docs/errors.html
index e4f994b69e8..7b1e3cb62c5 100644
--- a/docs/errors.html
+++ b/docs/errors.html
@@ -197,7 +197,7 @@
<li class="toctree-l1"><a class="reference internal" href="how_to/tutorials/cross_compilation_and_rpc.html">Cross Compilation and RPC</a></li>
<li class="toctree-l1"><a class="reference internal" href="how_to/tutorials/export_and_load_executable.html">Export and Load Relax Executables</a></li>
<li class="toctree-l1"><a class="reference internal" href="how_to/tutorials/mix_python_and_tvm_with_pymodule.html">Mix Python/PyTorch with TVM Using BasePyModule</a></li>
-<li class="toctree-l1"><a class="reference internal" href="how_to/tutorials/bring_your_own_codegen.html">Bring Your Own Codegen: NPU Backend Example</a></li>
+<li class="toctree-l1"><a class="reference internal" href="how_to/tutorials/bring_your_own_codegen.html">Bring Your Own Codegen</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">Deep Dive</span></p>
<ul>
diff --git a/docs/genindex.html b/docs/genindex.html
index d8416f3a88f..ac82bd8fc2d 100644
--- a/docs/genindex.html
+++ b/docs/genindex.html
@@ -189,7 +189,7 @@
<li class="toctree-l1"><a class="reference internal" href="how_to/tutorials/cross_compilation_and_rpc.html">Cross Compilation and RPC</a></li>
<li class="toctree-l1"><a class="reference internal" href="how_to/tutorials/export_and_load_executable.html">Export and Load Relax Executables</a></li>
<li class="toctree-l1"><a class="reference internal" href="how_to/tutorials/mix_python_and_tvm_with_pymodule.html">Mix Python/PyTorch with TVM Using BasePyModule</a></li>
-<li class="toctree-l1"><a class="reference internal" href="how_to/tutorials/bring_your_own_codegen.html">Bring Your Own Codegen: NPU Backend Example</a></li>
+<li class="toctree-l1"><a class="reference internal" href="how_to/tutorials/bring_your_own_codegen.html">Bring Your Own Codegen</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">Deep Dive</span></p>
<ul>
@@ -1073,9 +1073,11 @@
</li>
<li><a href="reference/api/python/relax/op.html#tvm.relax.op.clip">clip() (in module tvm.relax.op)</a>
</li>
- <li><a href="reference/api/python/meta_schedule.html#tvm.s_tir.meta_schedule.Mutator.clone">clone() (tvm.s_tir.meta_schedule.Mutator method)</a>
+ <li><a href="reference/api/python/arith.html#tvm.arith.Analyzer.clone">clone() (tvm.arith.Analyzer method)</a>
<ul>
+ <li><a href="reference/api/python/meta_schedule.html#tvm.s_tir.meta_schedule.Mutator.clone">(tvm.s_tir.meta_schedule.Mutator method)</a>
+</li>
<li><a href="reference/api/python/meta_schedule.html#tvm.s_tir.meta_schedule.Postproc.clone">(tvm.s_tir.meta_schedule.Postproc method)</a>
</li>
<li><a href="reference/api/python/meta_schedule.html#tvm.s_tir.meta_schedule.ScheduleRule.clone">(tvm.s_tir.meta_schedule.ScheduleRule method)</a>
@@ -1177,14 +1179,14 @@
</li>
<li><a href="reference/api/python/topi.html#tvm.topi.nn.conv">conv() (in module tvm.topi.nn)</a>
</li>
+ </ul></td>
+ <td style="width: 33%; vertical-align: top;"><ul>
<li><a href="reference/api/python/relax/op.html#tvm.relax.op.nn.conv1d">conv1d() (in module tvm.relax.op.nn)</a>
<ul>
<li><a href="reference/api/python/topi.html#tvm.topi.nn.conv1d">(in module tvm.topi.nn)</a>
</li>
</ul></li>
- </ul></td>
- <td style="width: 33%; vertical-align: top;"><ul>
<li><a href="reference/api/python/topi.html#tvm.topi.nn.conv1d_ncw">conv1d_ncw() (in module tvm.topi.nn)</a>
</li>
<li><a href="reference/api/python/topi.html#tvm.topi.testing.conv1d_ncw_python">conv1d_ncw_python() (in module tvm.topi.testing)</a>
diff --git a/docs/get_started/overview.html b/docs/get_started/overview.html
index 06e8565b851..7a35718ea00 100644
--- a/docs/get_started/overview.html
+++ b/docs/get_started/overview.html
@@ -197,7 +197,7 @@
<li class="toctree-l1"><a class="reference internal" href="../how_to/tutorials/cross_compilation_and_rpc.html">Cross Compilation and RPC</a></li>
<li class="toctree-l1"><a class="reference internal" href="../how_to/tutorials/export_and_load_executable.html">Export and Load Relax Executables</a></li>
<li class="toctree-l1"><a class="reference internal" href="../how_to/tutorials/mix_python_and_tvm_with_pymodule.html">Mix Python/PyTorch with TVM Using BasePyModule</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../how_to/tutorials/bring_your_own_codegen.html">Bring Your Own Codegen: NPU Backend Example</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../how_to/tutorials/bring_your_own_codegen.html">Bring Your Own Codegen</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">Deep Dive</span></p>
<ul>
diff --git a/docs/get_started/tutorials/index.html b/docs/get_started/tutorials/index.html
index 60347b4f530..f4b210e2990 100644
--- a/docs/get_started/tutorials/index.html
+++ b/docs/get_started/tutorials/index.html
@@ -190,7 +190,7 @@
<li class="toctree-l1"><a class="reference internal" href="../../how_to/tutorials/cross_compilation_and_rpc.html">Cross Compilation and RPC</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../how_to/tutorials/export_and_load_executable.html">Export and Load Relax Executables</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../how_to/tutorials/mix_python_and_tvm_with_pymodule.html">Mix Python/PyTorch with TVM Using BasePyModule</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../how_to/tutorials/bring_your_own_codegen.html">Bring Your Own Codegen: NPU Backend Example</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../how_to/tutorials/bring_your_own_codegen.html">Bring Your Own Codegen</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">Deep Dive</span></p>
<ul>
diff --git a/docs/get_started/tutorials/ir_module.html b/docs/get_started/tutorials/ir_module.html
index 967ffaeb15f..9fd77d6674f 100644
--- a/docs/get_started/tutorials/ir_module.html
+++ b/docs/get_started/tutorials/ir_module.html
@@ -208,7 +208,7 @@
<li class="toctree-l1"><a class="reference internal" href="../../how_to/tutorials/cross_compilation_and_rpc.html">Cross Compilation and RPC</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../how_to/tutorials/export_and_load_executable.html">Export and Load Relax Executables</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../how_to/tutorials/mix_python_and_tvm_with_pymodule.html">Mix Python/PyTorch with TVM Using BasePyModule</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../how_to/tutorials/bring_your_own_codegen.html">Bring Your Own Codegen: NPU Backend Example</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../how_to/tutorials/bring_your_own_codegen.html">Bring Your Own Codegen</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">Deep Dive</span></p>
<ul>
@@ -811,16 +811,16 @@ backends.</p>
<p>We can deploy the IRModule on CPU by specifying the target as <code class="docutils literal notranslate"><span class="pre">llvm</span></code>.</p>
<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><a href="../../reference/api/python/relax/relax.html#tvm.relax.VMExecutable" title="tvm.relax.VMExecutable" class="sphx-glr-backref-module-tvm-relax sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">exec</span></a> <span class="o">=</span> <a href="../../reference/api/python/driver.html#tvm.compile" title="tvm.compile" class="sphx-glr-backref-module-tvm sphx-glr-backref-type-py-func [...]
<span class="n">dev</span> <span class="o">=</span> <span class="n">tvm</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span>
-<span class="n">vm</span> <span class="o">=</span> <a href="../../reference/api/python/relax/relax.html#tvm.relax.VirtualMachine" title="tvm.relax.VirtualMachine" class="sphx-glr-backref-module-tvm-relax sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">relax</span><span class="o">.</span><span class="n">VirtualMachine</span></a><span class="p">(</span><a href="../../reference/api/python/relax/relax.html#tvm.relax.VMExecutable" title="tvm.relax.VMExecutable" class [...]
+<a href="../../reference/api/python/runtime/vm.html#tvm.runtime.vm.VirtualMachine" title="tvm.runtime.vm.VirtualMachine" class="sphx-glr-backref-module-tvm-runtime-vm sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">vm</span></a> <span class="o">=</span> <a href="../../reference/api/python/runtime/vm.html#tvm.runtime.vm.VirtualMachine" title="tvm.runtime.vm.VirtualMachine" class="sphx-glr-backref-module-tvm-runtime-vm sphx-glr-backref-type-py-class"><span class=" [...]
<span class="n">raw_data</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">784</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s2">"float32"</span><span class="p">)</span>
<span class="n">data</span> <span class="o">=</span> <span class="n">tvm</span><span class="o">.</span><span class="n">runtime</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="n">raw_data</span><span class="p">,</span> <span class="n">dev</span><span class="p">)</span>
-<span class="n">cpu_out</span> <span class="o">=</span> <span class="n">vm</span><span class="p">[</span><span class="s2">"main"</span><span class="p">](</span><span class="n">data</span><span class="p">,</span> <span class="o">*</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">params_from_torch</span></a><span class="p">[</ [...]
+<span class="n">cpu_out</span> <span class="o">=</span> <a href="../../reference/api/python/runtime/vm.html#tvm.runtime.vm.VirtualMachine" title="tvm.runtime.vm.VirtualMachine" class="sphx-glr-backref-module-tvm-runtime-vm sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">vm</span></a><span class="p">[</span><span class="s2">"main"</span><span class="p">](</span><span class="n">data</span><span class="p">,</span> <span class="o">*</span><a href="https:// [...]
<span class="nb">print</span><span class="p">(</span><span class="n">cpu_out</span><span class="p">)</span>
</pre></div>
</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[[ 0.24591367 -0.07023893 0.07478634 -0.08000526 -0.06134555 -0.06156536
- 0.12414779 -0.08805142 0.02194053 -0.03948892]]
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[[ 0.14707375 0.15686896 -0.07788884 0.00588775 -0.03346043 -0.15874758
+ 0.16355126 -0.26740512 -0.04383552 -0.10067607]]
</pre></div>
</div>
</section>
@@ -843,19 +843,19 @@ the details of <code class="docutils literal notranslate"><span class="pre">DLig
<p>Now we can compile the IRModule on GPU, the similar way as we did on CPU.</p>
<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><a href="../../reference/api/python/relax/relax.html#tvm.relax.VMExecutable" title="tvm.relax.VMExecutable" class="sphx-glr-backref-module-tvm-relax sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">exec</span></a> <span class="o">=</span> <a href="../../reference/api/python/driver.html#tvm.compile" title="tvm.compile" class="sphx-glr-backref-module-tvm sphx-glr-backref-type-py-func [...]
<span class="n">dev</span> <span class="o">=</span> <span class="n">tvm</span><span class="o">.</span><span class="n">device</span><span class="p">(</span><span class="s2">"cuda"</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
-<span class="n">vm</span> <span class="o">=</span> <a href="../../reference/api/python/relax/relax.html#tvm.relax.VirtualMachine" title="tvm.relax.VirtualMachine" class="sphx-glr-backref-module-tvm-relax sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">relax</span><span class="o">.</span><span class="n">VirtualMachine</span></a><span class="p">(</span><a href="../../reference/api/python/relax/relax.html#tvm.relax.VMExecutable" title="tvm.relax.VMExecutable" class [...]
+<a href="../../reference/api/python/runtime/vm.html#tvm.runtime.vm.VirtualMachine" title="tvm.runtime.vm.VirtualMachine" class="sphx-glr-backref-module-tvm-runtime-vm sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">vm</span></a> <span class="o">=</span> <a href="../../reference/api/python/runtime/vm.html#tvm.runtime.vm.VirtualMachine" title="tvm.runtime.vm.VirtualMachine" class="sphx-glr-backref-module-tvm-runtime-vm sphx-glr-backref-type-py-class"><span class=" [...]
<span class="c1"># Need to allocate data and params on GPU device</span>
<span class="n">data</span> <span class="o">=</span> <span class="n">tvm</span><span class="o">.</span><span class="n">runtime</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="n">raw_data</span><span class="p">,</span> <span class="n">dev</span><span class="p">)</span>
<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">gpu_params</span></a> <span class="o">=</span> <span class="p">[</span><span class="n">tvm</span><span class="o">.</span><span class="n">runtime</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="n">p</span><span class="p">,</span> <span class="n"> [...]
-<span class="n">gpu_out</span> <span class="o">=</span> <span class="n">vm</span><span class="p">[</span><span class="s2">"main"</span><span class="p">](</span><span class="n">data</span><span class="p">,</span> <span class="o">*</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">gpu_params</span></a><span class="p">)</span><s [...]
+<span class="n">gpu_out</span> <span class="o">=</span> <a href="../../reference/api/python/runtime/vm.html#tvm.runtime.vm.VirtualMachine" title="tvm.runtime.vm.VirtualMachine" class="sphx-glr-backref-module-tvm-runtime-vm sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">vm</span></a><span class="p">[</span><span class="s2">"main"</span><span class="p">](</span><span class="n">data</span><span class="p">,</span> <span class="o">*</span><a href="https:// [...]
<span class="nb">print</span><span class="p">(</span><span class="n">gpu_out</span><span class="p">)</span>
<span class="c1"># Check the correctness of the results</span>
<span class="k">assert</span> <span class="n">np</span><span class="o">.</span><span class="n">allclose</span><span class="p">(</span><span class="n">cpu_out</span><span class="p">,</span> <span class="n">gpu_out</span><span class="p">,</span> <span class="n">atol</span><span class="o">=</span><span class="mf">1e-3</span><span class="p">)</span>
</pre></div>
</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[[ 0.24591374 -0.07023892 0.07478635 -0.0800053 -0.06134555 -0.06156533
- 0.1241478 -0.08805143 0.02194051 -0.03948891]]
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[[ 0.14707379 0.156869 -0.07788893 0.00588775 -0.0334604 -0.15874766
+ 0.16355133 -0.26740506 -0.04383563 -0.1006761 ]]
</pre></div>
</div>
</section>
diff --git a/docs/get_started/tutorials/quick_start.html b/docs/get_started/tutorials/quick_start.html
index 2deea74adcc..334a4325331 100644
--- a/docs/get_started/tutorials/quick_start.html
+++ b/docs/get_started/tutorials/quick_start.html
@@ -202,7 +202,7 @@
<li class="toctree-l1"><a class="reference internal" href="../../how_to/tutorials/cross_compilation_and_rpc.html">Cross Compilation and RPC</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../how_to/tutorials/export_and_load_executable.html">Export and Load Relax Executables</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../how_to/tutorials/mix_python_and_tvm_with_pymodule.html">Mix Python/PyTorch with TVM Using BasePyModule</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../how_to/tutorials/bring_your_own_codegen.html">Bring Your Own Codegen: NPU Backend Example</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../how_to/tutorials/bring_your_own_codegen.html">Bring Your Own Codegen</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">Deep Dive</span></p>
<ul>
@@ -452,16 +452,16 @@ different devices.</p>
<a href="../../reference/api/python/target.html#tvm.target.Target" title="tvm.target.Target" class="sphx-glr-backref-module-tvm-target sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">target</span></a> <span class="o">=</span> <a href="../../reference/api/python/target.html#tvm.target.Target" title="tvm.target.Target" class="sphx-glr-backref-module-tvm-target sphx-glr-backref-type-py-class"><span class="n">tvm</span><span class="o">.</span><span class="n">target< [...]
<a href="../../reference/api/python/relax/relax.html#tvm.relax.VMExecutable" title="tvm.relax.VMExecutable" class="sphx-glr-backref-module-tvm-relax sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ex</span></a> <span class="o">=</span> <a href="../../reference/api/python/driver.html#tvm.compile" title="tvm.compile" class="sphx-glr-backref-module-tvm sphx-glr-backref-type-py-function"><span class="n">tvm</span><span class="o">.</span><span class="n">compile</span [...]
<span class="n">device</span> <span class="o">=</span> <span class="n">tvm</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span>
-<span class="n">vm</span> <span class="o">=</span> <a href="../../reference/api/python/relax/relax.html#tvm.relax.VirtualMachine" title="tvm.relax.VirtualMachine" class="sphx-glr-backref-module-tvm-relax sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">relax</span><span class="o">.</span><span class="n">VirtualMachine</span></a><span class="p">(</span><a href="../../reference/api/python/relax/relax.html#tvm.relax.VMExecutable" title="tvm.relax.VMExecutable" class [...]
+<a href="../../reference/api/python/runtime/vm.html#tvm.runtime.vm.VirtualMachine" title="tvm.runtime.vm.VirtualMachine" class="sphx-glr-backref-module-tvm-runtime-vm sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">vm</span></a> <span class="o">=</span> <a href="../../reference/api/python/runtime/vm.html#tvm.runtime.vm.VirtualMachine" title="tvm.runtime.vm.VirtualMachine" class="sphx-glr-backref-module-tvm-runtime-vm sphx-glr-backref-type-py-class"><span class=" [...]
<span class="n">data</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">784</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s2">"float32"</span><span class="p">)</span>
<span class="n">tvm_data</span> <span class="o">=</span> <span class="n">tvm</span><span class="o">.</span><span class="n">runtime</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">device</span><span class="p">)</span>
<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">params</span></a> <span class="o">=</span> <span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="o">*</span><span class="n">param</span><span class="o">.</sp [...]
<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">params</span></a> <span class="o">=</span> <span class="p">[</span><span class="n">tvm</span><span class="o">.</span><span class="n">runtime</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="n">param</span><span class="p">,</span> <span class="n"> [...]
-<span class="nb">print</span><span class="p">(</span><span class="n">vm</span><span class="p">[</span><span class="s2">"forward"</span><span class="p">](</span><span class="n">tvm_data</span><span class="p">,</span> <span class="o">*</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">params</span></a><span class="p">)</span><s [...]
+<span class="nb">print</span><span class="p">(</span><a href="../../reference/api/python/runtime/vm.html#tvm.runtime.vm.VirtualMachine" title="tvm.runtime.vm.VirtualMachine" class="sphx-glr-backref-module-tvm-runtime-vm sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">vm</span></a><span class="p">[</span><span class="s2">"forward"</span><span class="p">](</span><span class="n">tvm_data</span><span class="p">,</span> <span class="o">*</span><a href="http [...]
</pre></div>
</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[[24839.627 24788.988 25688.764 24910.75 24252.566 26164.816 24886.838
- 25099.838 24203.76 24559.404]]
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[[26245.865 25315.145 26411.287 24533.736 24396.021 24099.643 23988.234
+ 24814.387 24646.775 26737.93 ]]
</pre></div>
</div>
<p>Our goal is to bring machine learning to the application with any language of interest,
@@ -469,8 +469,8 @@ with the minimum runtime support.</p>
<ul>
<li><p>Each function in IRModule becomes a runnable function in the runtime. For example in LLM
cases, we can call <code class="docutils literal notranslate"><span class="pre">prefill</span></code> and <code class="docutils literal notranslate"><span class="pre">decode</span></code> functions directly.</p>
-<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">prefill_logits</span> <span class="o">=</span> <span class="n">vm</span><span class="p">[</span><span class="s2">"prefill"</span><span class="p">](</span><span class="n">inputs</span><span class="p">,</span> <span class="n">weight</span><span class="p">,</span> <span class="n">kv_cache</span><span class="p">)</span>
-<span class="n">decoded_logits</span> <span class="o">=</span> <span class="n">vm</span><span class="p">[</span><span class="s2">"decode"</span><span class="p">](</span><span class="n">inputs</span><span class="p">,</span> <span class="n">weight</span><span class="p">,</span> <span class="n">kv_cache</span><span class="p">)</span>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">prefill_logits</span> <span class="o">=</span> <a href="../../reference/api/python/runtime/vm.html#tvm.runtime.vm.VirtualMachine" title="tvm.runtime.vm.VirtualMachine" class="sphx-glr-backref-module-tvm-runtime-vm sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">vm</span></a><span class="p">[</span><span class="s2">"prefill"</span><span class="p">](</span> [...]
+<span class="n">decoded_logits</span> <span class="o">=</span> <a href="../../reference/api/python/runtime/vm.html#tvm.runtime.vm.VirtualMachine" title="tvm.runtime.vm.VirtualMachine" class="sphx-glr-backref-module-tvm-runtime-vm sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">vm</span></a><span class="p">[</span><span class="s2">"decode"</span><span class="p">](</span><span class="n">inputs</span><span class="p">,</span> <span class="n">weight</span>< [...]
</pre></div>
</div>
</li>
@@ -485,15 +485,15 @@ copy exchange with existing ecosystem (DLPack exchange with PyTorch)</p>
</li>
<li><p>TVM runtime works in non-python environments, so it works on settings such as mobile</p>
<div class="highlight-C++ notranslate"><div class="highlight"><pre><span></span><span class="c1">// C++ snippet</span>
-<span class="n">runtime</span><span class="o">::</span><span class="n">Module</span><span class="w"> </span><span class="n">vm</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><a href="../../reference/api/python/relax/relax.html#tvm.relax.VMExecutable" title="tvm.relax.VMExecutable" class="sphx-glr-backref-module-tvm-relax sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ex</span></a><span class="p">.</span><span class="n">GetFunction [...]
-<span class="n">vm</span><span class="p">.</span><span class="n">GetFunction</span><span class="p">(</span><span class="s">"init"</span><span class="p">)(...);</span>
-<span class="n">Tensor</span><span class="w"> </span><span class="n">out</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">vm</span><span class="p">.</span><span class="n">GetFunction</span><span class="p">(</span><span class="s">"prefill"</span><span class="p">)(</span><span class="n">data</span><span class="p">,</span><span class="w"> </span><span class="n">weight</span><span class="p">,</span><span class="w"> </span><span class="n" [...]
+<span class="n">runtime</span><span class="o">::</span><span class="n">Module</span><span class="w"> </span><a href="../../reference/api/python/runtime/vm.html#tvm.runtime.vm.VirtualMachine" title="tvm.runtime.vm.VirtualMachine" class="sphx-glr-backref-module-tvm-runtime-vm sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">vm</span></a><span class="w"> </span><span class="o">=</span><span class="w"> </span><a href="../../reference/api/python/relax/relax.html#tvm.r [...]
+<a href="../../reference/api/python/runtime/vm.html#tvm.runtime.vm.VirtualMachine" title="tvm.runtime.vm.VirtualMachine" class="sphx-glr-backref-module-tvm-runtime-vm sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">vm</span></a><span class="p">.</span><span class="n">GetFunction</span><span class="p">(</span><span class="s">"init"</span><span class="p">)(...);</span>
+<span class="n">Tensor</span><span class="w"> </span><span class="n">out</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><a href="../../reference/api/python/runtime/vm.html#tvm.runtime.vm.VirtualMachine" title="tvm.runtime.vm.VirtualMachine" class="sphx-glr-backref-module-tvm-runtime-vm sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">vm</span></a><span class="p">.</span><span class="n">GetFunction</span><span class="p">(</span><span [...]
</pre></div>
</div>
<div class="highlight-Java notranslate"><div class="highlight"><pre><span></span><span class="c1">// Java snippet</span>
-<span class="n">Module</span><span class="w"> </span><span class="n">vm</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><a href="../../reference/api/python/relax/relax.html#tvm.relax.VMExecutable" title="tvm.relax.VMExecutable" class="sphx-glr-backref-module-tvm-relax sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ex</span></a><span class="p">.</span><span class="na">getFunction</span><span class="p">(</span><span class="s">"l [...]
-<span class="n">vm</span><span class="p">.</span><span class="na">getFunction</span><span class="p">(</span><span class="s">"init"</span><span class="p">).</span><span class="na">pushArg</span><span class="p">(...).</span><span class="na">invoke</span><span class="p">;</span>
-<span class="n">Tensor</span><span class="w"> </span><span class="n">out</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">vm</span><span class="p">.</span><span class="na">getFunction</span><span class="p">(</span><span class="s">"prefill"</span><span class="p">).</span><span class="na">pushArg</span><span class="p">(</span><span class="n">data</span><span class="p">).</span><span class="na">pushArg</span><span class="p">(</span><spa [...]
+<span class="n">Module</span><span class="w"> </span><a href="../../reference/api/python/runtime/vm.html#tvm.runtime.vm.VirtualMachine" title="tvm.runtime.vm.VirtualMachine" class="sphx-glr-backref-module-tvm-runtime-vm sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">vm</span></a><span class="w"> </span><span class="o">=</span><span class="w"> </span><a href="../../reference/api/python/relax/relax.html#tvm.relax.VMExecutable" title="tvm.relax.VMExecutable" class [...]
+<a href="../../reference/api/python/runtime/vm.html#tvm.runtime.vm.VirtualMachine" title="tvm.runtime.vm.VirtualMachine" class="sphx-glr-backref-module-tvm-runtime-vm sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">vm</span></a><span class="p">.</span><span class="na">getFunction</span><span class="p">(</span><span class="s">"init"</span><span class="p">).</span><span class="na">pushArg</span><span class="p">(...).</span><span class="na">invoke</span>< [...]
+<span class="n">Tensor</span><span class="w"> </span><span class="n">out</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><a href="../../reference/api/python/runtime/vm.html#tvm.runtime.vm.VirtualMachine" title="tvm.runtime.vm.VirtualMachine" class="sphx-glr-backref-module-tvm-runtime-vm sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">vm</span></a><span class="p">.</span><span class="na">getFunction</span><span class="p">(</span><spa [...]
</pre></div>
</div>
</li>
diff --git a/docs/get_started/tutorials/sg_execution_times.html b/docs/get_started/tutorials/sg_execution_times.html
index 16d0a306434..c4b51ef2c86 100644
--- a/docs/get_started/tutorials/sg_execution_times.html
+++ b/docs/get_started/tutorials/sg_execution_times.html
@@ -190,7 +190,7 @@
<li class="toctree-l1"><a class="reference internal" href="../../how_to/tutorials/cross_compilation_and_rpc.html">Cross Compilation and RPC</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../how_to/tutorials/export_and_load_executable.html">Export and Load Relax Executables</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../how_to/tutorials/mix_python_and_tvm_with_pymodule.html">Mix Python/PyTorch with TVM Using BasePyModule</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../how_to/tutorials/bring_your_own_codegen.html">Bring Your Own Codegen: NPU Backend Example</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../how_to/tutorials/bring_your_own_codegen.html">Bring Your Own Codegen</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">Deep Dive</span></p>
<ul>
@@ -297,7 +297,7 @@
<section id="computation-times">
<span id="sphx-glr-get-started-tutorials-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Link to this heading"></a></h1>
-<p><strong>00:05.905</strong> total execution time for 2 files <strong>from get_started/tutorials</strong>:</p>
+<p><strong>00:06.302</strong> total execution time for 2 files <strong>from get_started/tutorials</strong>:</p>
<div class="docutils container">
<style scoped>
<link href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/5.3.0/css/bootstrap.min.css" rel="stylesheet" />
@@ -319,7 +319,7 @@ $(document).ready( function () {
</thead>
<tbody>
<tr class="row-even"><td><p><a class="reference internal" href="ir_module.html#sphx-glr-get-started-tutorials-ir-module-py"><span class="std std-ref">IRModule</span></a> (<code class="docutils literal notranslate"><span class="pre">ir_module.py</span></code>)</p></td>
-<td><p>00:05.722</p></td>
+<td><p>00:06.120</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="quick_start.html#sphx-glr-get-started-tutorials-quick-start-py"><span class="std std-ref">Quick Start</span></a> (<code class="docutils literal notranslate"><span class="pre">quick_start.py</span></code>)</p></td>
diff --git a/docs/how_to/tutorials/bring_your_own_codegen.html b/docs/how_to/tutorials/bring_your_own_codegen.html
index 79d36c8340d..887aa3319c0 100644
--- a/docs/how_to/tutorials/bring_your_own_codegen.html
+++ b/docs/how_to/tutorials/bring_your_own_codegen.html
@@ -10,7 +10,7 @@
<meta name="viewport" content="width=device-width, initial-scale=1.0">
- <title>Bring Your Own Codegen: NPU Backend Example — tvm 0.25.dev0 documentation</title>
+ <title>Bring Your Own Codegen — tvm 0.25.dev0 documentation</title>
@@ -192,13 +192,16 @@
<li class="toctree-l1"><a class="reference internal" href="cross_compilation_and_rpc.html">Cross Compilation and RPC</a></li>
<li class="toctree-l1"><a class="reference internal" href="export_and_load_executable.html">Export and Load Relax Executables</a></li>
<li class="toctree-l1"><a class="reference internal" href="mix_python_and_tvm_with_pymodule.html">Mix Python/PyTorch with TVM Using BasePyModule</a></li>
-<li class="toctree-l1 current"><a class="current reference internal" href="#">Bring Your Own Codegen: NPU Backend Example</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="#overview-of-the-byoc-flow">Overview of the BYOC Flow</a></li>
-<li class="toctree-l2"><a class="reference internal" href="#step-1-import-the-backend-to-register-its-patterns">Step 1: Import the backend to register its patterns</a></li>
-<li class="toctree-l2"><a class="reference internal" href="#step-2-define-a-model">Step 2: Define a model</a></li>
-<li class="toctree-l2"><a class="reference internal" href="#step-3-partition-the-graph">Step 3: Partition the graph</a></li>
-<li class="toctree-l2"><a class="reference internal" href="#step-4-run-codegen">Step 4: Run codegen</a></li>
-<li class="toctree-l2"><a class="reference internal" href="#step-6-conv2d-relu">Step 6: Conv2D + ReLU</a></li>
+<li class="toctree-l1 current"><a class="current reference internal" href="#">Bring Your Own Codegen</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="#overview-of-the-byoc-flow">Overview of the BYOC flow</a></li>
+<li class="toctree-l2"><a class="reference internal" href="#step-1-import-the-backends-to-register-their-patterns">Step 1: Import the backends to register their patterns</a></li>
+<li class="toctree-l2"><a class="reference internal" href="#step-2-define-the-model">Step 2: Define the model</a></li>
+<li class="toctree-l2"><a class="reference internal" href="#step-3-partition-for-the-example-npu">Step 3: Partition for the example NPU</a></li>
+<li class="toctree-l2"><a class="reference internal" href="#step-4-codegen-build-and-run-on-the-example-npu">Step 4: Codegen, build and run on the example NPU</a></li>
+<li class="toctree-l2"><a class="reference internal" href="#the-same-flow-on-a-real-backend-tensorrt">The same flow on a real backend: TensorRT</a></li>
+<li class="toctree-l2"><a class="reference internal" href="#example-npu-vs-tensorrt-at-a-glance">Example NPU vs TensorRT at a glance</a></li>
+<li class="toctree-l2"><a class="reference internal" href="#deploying-a-pytorch-model-with-tensorrt">Deploying a PyTorch model with TensorRT</a></li>
+<li class="toctree-l2"><a class="reference internal" href="#notes-for-real-deployments">Notes for real deployments</a></li>
<li class="toctree-l2"><a class="reference internal" href="#next-steps">Next steps</a></li>
</ul>
</li>
@@ -279,7 +282,7 @@
<li><a href="../../index.html">Docs</a> <span class="br-arrow">></span></li>
- <li>Bring Your Own Codegen: NPU Backend Example</li>
+ <li>Bring Your Own Codegen</li>
@@ -310,46 +313,63 @@
<p class="admonition-title">Note</p>
<p>You can click <a class="reference internal" href="#sphx-glr-download-how-to-tutorials-bring-your-own-codegen-py"><span class="std std-ref">here</span></a> to run the Jupyter notebook locally.</p>
</div>
-<section class="sphx-glr-example-title" id="bring-your-own-codegen-npu-backend-example">
-<span id="tutorial-bring-your-own-codegen"></span><span id="sphx-glr-how-to-tutorials-bring-your-own-codegen-py"></span><h1>Bring Your Own Codegen: NPU Backend Example<a class="headerlink" href="#bring-your-own-codegen-npu-backend-example" title="Link to this heading"></a></h1>
-<p>This tutorial shows how to integrate a custom hardware backend with TVM’s
-BYOC framework, using the bundled example NPU backend (CPU emulation, no
-real hardware required) as the worked example. You will see the key
-concepts needed to offload operations to a custom accelerator: pattern
-registration, graph partitioning, codegen, and runtime dispatch.</p>
-<p>NPUs are purpose-built accelerators designed around a fixed set of operations
-common in neural network inference, such as matrix multiplication, convolution,
-and activation functions.
-The example backend’s runtime is a <em>stub</em>: it logs the dispatch decisions an
+<section class="sphx-glr-example-title" id="bring-your-own-codegen">
+<span id="tutorial-bring-your-own-codegen"></span><span id="sphx-glr-how-to-tutorials-bring-your-own-codegen-py"></span><h1>Bring Your Own Codegen<a class="headerlink" href="#bring-your-own-codegen" title="Link to this heading"></a></h1>
+<p>TVM’s Bring Your Own Codegen (BYOC) framework lets you offload parts of a model
+to a custom backend – a hardware accelerator, an inference library, or your own
+kernels – while TVM compiles the rest. This tutorial has two parts:</p>
+<ul class="simple">
+<li><p><strong>How BYOC works</strong> – we teach the flow with a bundled, hardware-free <em>example
+NPU</em> backend and then drive the <strong>same flow</strong> on a real production backend,
+NVIDIA TensorRT. Both run a small, hand-written model so every step is
+visible; the only thing that changes between them is the backend, and that
+contrast is the lesson.</p></li>
+<li><p><strong>Deploying a real model</strong> – we then put it to work, taking an actual PyTorch
+<code class="docutils literal notranslate"><span class="pre">nn.Module</span></code> from export through TensorRT and running it on the GPU.</p></li>
+</ul>
+<p>The example NPU is a teaching stub: its runtime logs the dispatch decisions an
NPU would make (memory tier, execution engine, fusion) but performs no real
-computation, so output buffers are uninitialized. Assertions in this tutorial
-therefore check shapes, not values. When you replace the runtime with your
-hardware SDK calls, the same flow produces real results.</p>
-<p><strong>Prerequisites</strong>: Build TVM with <code class="docutils literal notranslate"><span class="pre">USE_EXAMPLE_NPU_CODEGEN=ON</span></code> and
-<code class="docutils literal notranslate"><span class="pre">USE_EXAMPLE_NPU_RUNTIME=ON</span></code>.</p>
+computation, so its output buffers are left uninitialized. We therefore check
+<em>shapes</em>, not values, in the NPU sections – its job is to make every BYOC step
+visible with nothing hidden. TensorRT then runs the identical flow for real, so
+we cross-check its result against a reference.</p>
+<p><strong>Prerequisites</strong>: the example NPU sections need TVM built with
+<code class="docutils literal notranslate"><span class="pre">USE_EXAMPLE_NPU_CODEGEN=ON</span></code> and <code class="docutils literal notranslate"><span class="pre">USE_EXAMPLE_NPU_RUNTIME=ON</span></code>; the TensorRT
+sections need <code class="docutils literal notranslate"><span class="pre">USE_TENSORRT_CODEGEN=ON</span></code>, <code class="docutils literal notranslate"><span class="pre">USE_TENSORRT_RUNTIME=ON</span></code> and
+<code class="docutils literal notranslate"><span class="pre">USE_CUDA=ON</span></code> plus a CUDA GPU and a matching TensorRT install (from NVIDIA’s
+<code class="docutils literal notranslate"><span class="pre">pip</span> <span class="pre">install</span> <span class="pre">tensorrt</span></code> packages or the TensorRT archive); the final deployment
+section also needs PyTorch. Each section degrades gracefully when its backend is
+unavailable.</p>
<section id="overview-of-the-byoc-flow">
-<h2>Overview of the BYOC Flow<a class="headerlink" href="#overview-of-the-byoc-flow" title="Link to this heading"></a></h2>
-<p>The BYOC framework lets you plug a custom backend into TVM’s compilation
-pipeline in four steps:</p>
+<h2>Overview of the BYOC flow<a class="headerlink" href="#overview-of-the-byoc-flow" title="Link to this heading"></a></h2>
+<p>BYOC plugs a custom backend into TVM’s compilation pipeline in four steps:</p>
<ol class="arabic simple">
-<li><p><strong>Register patterns</strong> - describe which sequences of Relax ops the
-backend can handle.</p></li>
+<li><p><strong>Register patterns</strong> - describe which sequences of Relax ops the backend
+can handle.</p></li>
<li><p><strong>Partition the graph</strong> - group matched ops into composite functions.</p></li>
-<li><p><strong>Run codegen</strong> - lower composite functions to backend-specific
-representation (JSON graph for the example NPU).</p></li>
-<li><p><strong>Execute</strong> - the runtime dispatches composite functions to the
-registered backend runtime.</p></li>
+<li><p><strong>Run codegen</strong> - lower each composite to the backend’s representation
+(a JSON graph for both backends in this tutorial).</p></li>
+<li><p><strong>Execute</strong> - the runtime dispatches each composite to the backend.</p></li>
</ol>
+<p>Steps 1 and 2 are pure Python and run anywhere; steps 3 and 4 need the
+backend’s codegen and runtime compiled into TVM, which is why the
+build-and-run cells below are guarded.</p>
</section>
-<section id="step-1-import-the-backend-to-register-its-patterns">
-<h2>Step 1: Import the backend to register its patterns<a class="headerlink" href="#step-1-import-the-backend-to-register-its-patterns" title="Link to this heading"></a></h2>
-<p>Importing the module is enough to register all supported patterns with
-TVM’s pattern registry.</p>
-<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
+<section id="step-1-import-the-backends-to-register-their-patterns">
+<h2>Step 1: Import the backends to register their patterns<a class="headerlink" href="#step-1-import-the-backends-to-register-their-patterns" title="Link to this heading"></a></h2>
+<p>Importing a backend module registers its patterns with TVM’s global registry.
+Pattern registration is independent of the C++ build – only codegen and the
+runtime require the backend to be compiled in – so we probe each backend and
+guard the build-and-run cells accordingly.</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">os</span>
+<span class="kn">import</span><span class="w"> </span><span class="nn">tempfile</span>
+
+<span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">tvm</span>
-<span class="kn">import</span><span class="w"> </span><span class="nn">tvm.relax.backend.contrib.example_npu</span> <span class="c1"># registers patterns</span>
+<span class="kn">import</span><span class="w"> </span><span class="nn">tvm.relax.backend.contrib.example_npu</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">tvm</span><span class="w"> </span><span class="kn">import</span> <span class="n">relax</span>
+<span class="kn">from</span><span class="w"> </span><span class="nn">tvm.relax.backend.contrib.tensorrt</span><span class="w"> </span><span class="kn">import</span> <span class="n">partition_for_tensorrt</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">tvm.relax.backend.pattern_registry</span><span class="w"> </span><span class="kn">import</span> <a href="../../reference/api/python/relax/backend.html#tvm.relax.backend.get_patterns_with_prefix" title="tvm.relax.backend.get_patterns_with_prefix" class="sphx-glr-backref-module-tvm-relax-backend sphx-glr-backref-type-py-function"><span class="n">get_patterns_with_prefix</span></a>
<span class="kn">from</span><span class="w"> </span><span class="nn">tvm.relax.transform</span><span class="w"> </span><span class="kn">import</span> <a href="../../reference/api/python/relax/transform.html#tvm.relax.transform.FuseOpsByPattern" title="tvm.relax.transform.FuseOpsByPattern" class="sphx-glr-backref-module-tvm-relax-transform sphx-glr-backref-type-py-function"><span class="n">FuseOpsByPattern</span></a><span class="p">,</span> <a href="../../reference/api/python/relax/transf [...]
<span class="kn">from</span><span class="w"> </span><span class="nn">tvm.script</span><span class="w"> </span><span class="kn">import</span> <span class="n">relax</span> <span class="k">as</span> <span class="n">R</span>
@@ -358,177 +378,386 @@ TVM’s pattern registry.</p>
<span class="n">has_example_npu_runtime</span> <span class="o">=</span> <span class="n">tvm</span><span class="o">.</span><span class="n">get_global_func</span><span class="p">(</span><span class="s2">"runtime.ExampleNPUJSONRuntimeCreate"</span><span class="p">,</span> <span class="kc">True</span><span class="p">)</span>
<span class="n">has_example_npu</span> <span class="o">=</span> <span class="n">has_example_npu_codegen</span> <span class="ow">and</span> <span class="n">has_example_npu_runtime</span>
-<a href="../../reference/api/python/target.html#tvm.target.Target" title="tvm.target.Target" class="sphx-glr-backref-module-tvm-target sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">target</span></a> <span class="o">=</span> <a href="../../reference/api/python/target.html#tvm.target.Target" title="tvm.target.Target" class="sphx-glr-backref-module-tvm-target sphx-glr-backref-type-py-class"><span class="n">tvm</span><span class="o">.</span><span class="n">target< [...]
-
-<span class="n">patterns</span> <span class="o">=</span> <a href="../../reference/api/python/relax/backend.html#tvm.relax.backend.get_patterns_with_prefix" title="tvm.relax.backend.get_patterns_with_prefix" class="sphx-glr-backref-module-tvm-relax-backend sphx-glr-backref-type-py-function"><span class="n">get_patterns_with_prefix</span></a><span class="p">(</span><span class="s2">"example_npu"</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="s2">"Registered patterns:"</span><span class="p">,</span> <span class="p">[</span><span class="n">p</span><span class="o">.</span><span class="n">name</span> <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">patterns</span><span class="p">])</span>
-</pre></div>
-</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Registered patterns: ['example_npu.conv2d_relu_fused', 'example_npu.matmul_relu_fused', 'example_npu.depthwise_conv2d', 'example_npu.conv2d', 'example_npu.conv1d', 'example_npu.matmul', 'example_npu.dense', 'example_npu.avg_pool2d', 'example_npu.max_pool2d', 'example_npu.batch_norm', 'example_npu.softmax&# [...]
+<a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">has_tensorrt_codegen</span></a> <span class="o">=</span> <span class="n">tvm</span><span class="o">.</span><span class="n">get_global_func</span><span class="p">(</span><span class="s2">"relax.ext.tensorrt"</span><span class="p">,</span> <span class="kc">True</span><span class="p"> [...]
+<span class="n">_is_trt_runtime_enabled</span> <span class="o">=</span> <span class="n">tvm</span><span class="o">.</span><span class="n">get_global_func</span><span class="p">(</span><span class="s2">"relax.is_tensorrt_runtime_enabled"</span><span class="p">,</span> <span class="kc">True</span><span class="p">)</span>
+<a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">has_tensorrt</span></a> <span class="o">=</span> <span class="p">(</span>
+ <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">has_tensorrt_codegen</span></a> <span class="ow">and</span> <span class="n">_is_trt_runtime_enabled</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">_is_trt_runtime_enabled</span><span class="p">()</span>
+<span class="p">)</span>
+<a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">has_cuda</span></a> <span class="o">=</span> <span class="n">tvm</span><span class="o">.</span><span class="n">cuda</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">exist</span>
</pre></div>
</div>
</section>
-<section id="step-2-define-a-model">
-<h2>Step 2: Define a model<a class="headerlink" href="#step-2-define-a-model" title="Link to this heading"></a></h2>
-<p>We use a simple MatMul + ReLU module to illustrate the flow.</p>
+<section id="step-2-define-the-model">
+<h2>Step 2: Define the model<a class="headerlink" href="#step-2-define-the-model" title="Link to this heading"></a></h2>
+<p>A single convolution followed by a ReLU. This one model is used for both
+backends.</p>
<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="nd">@tvm</span><span class="o">.</span><span class="n">script</span><span class="o">.</span><span class="n">ir_module</span>
-<span class="k">class</span><span class="w"> </span><span class="nc">MatmulReLU</span><span class="p">:</span>
+<span class="k">class</span><span class="w"> </span><span class="nc">ConvReLU</span><span class="p">:</span>
<span class="nd">@R</span><span class="o">.</span><span class="n">function</span>
<span class="k">def</span><span class="w"> </span><span class="nf">main</span><span class="p">(</span>
- <span class="n">x</span><span class="p">:</span> <span class="n">R</span><span class="o">.</span><span class="n">Tensor</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">),</span> <span class="s2">"float32"</span><span class="p">),</span>
- <span class="n">w</span><span class="p">:</span> <span class="n">R</span><span class="o">.</span><span class="n">Tensor</span><span class="p">((</span><span class="mi">4</span><span class="p">,</span> <span class="mi">8</span><span class="p">),</span> <span class="s2">"float32"</span><span class="p">),</span>
- <span class="p">)</span> <span class="o">-></span> <span class="n">R</span><span class="o">.</span><span class="n">Tensor</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span> <span class="mi">8</span><span class="p">),</span> <span class="s2">"float32"</span><span class="p">):</span>
+ <span class="n">data</span><span class="p">:</span> <span class="n">R</span><span class="o">.</span><span class="n">Tensor</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">32</span><span class="p">,</span> <span class="mi">32</span><span class="p">),</span> <span class="s2">"float32"</span><span class="p">),</span>
+ <span class="n">weight</span><span class="p">:</span> <span class="n">R</span><span class="o">.</span><span class="n">Tensor</span><span class="p">((</span><span class="mi">16</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="s2">"float32"</span><span class="p">),</span>
+ <span class="p">)</span> <span class="o">-></span> <span class="n">R</span><span class="o">.</span><span class="n">Tensor</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">16</span><span class="p">,</span> <span class="mi">30</span><span class="p">,</span> <span class="mi">30</span><span class="p">),</span> <span class="s2">"float32"</span><span class="p">):</span>
<span class="k">with</span> <span class="n">R</span><span class="o">.</span><span class="n">dataflow</span><span class="p">():</span>
- <span class="n">y</span> <span class="o">=</span> <a href="../../reference/api/python/relax/op.html#tvm.relax.op.matmul" title="tvm.relax.op.matmul" class="sphx-glr-backref-module-tvm-relax-op sphx-glr-backref-type-py-function"><span class="n">relax</span><span class="o">.</span><span class="n">op</span><span class="o">.</span><span class="n">matmul</span></a><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">w</span><span class="p">)</span>
- <span class="n">z</span> <span class="o">=</span> <a href="../../reference/api/python/relax/op.html#tvm.relax.op.nn.relu" title="tvm.relax.op.nn.relu" class="sphx-glr-backref-module-tvm-relax-op-nn sphx-glr-backref-type-py-function"><span class="n">relax</span><span class="o">.</span><span class="n">op</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">relu</span></a><span class="p">(</span><span class="n">y</span><span class="p">)</span>
- <span class="n">R</span><span class="o">.</span><span class="n">output</span><span class="p">(</span><span class="n">z</span><span class="p">)</span>
- <span class="k">return</span> <span class="n">z</span>
+ <span class="n">conv</span> <span class="o">=</span> <a href="../../reference/api/python/relax/op.html#tvm.relax.op.nn.conv2d" title="tvm.relax.op.nn.conv2d" class="sphx-glr-backref-module-tvm-relax-op-nn sphx-glr-backref-type-py-function"><span class="n">relax</span><span class="o">.</span><span class="n">op</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">conv2d</span></a><span class="p">(</span><span class="n">data</span><span [...]
+ <span class="n">out</span> <span class="o">=</span> <a href="../../reference/api/python/relax/op.html#tvm.relax.op.nn.relu" title="tvm.relax.op.nn.relu" class="sphx-glr-backref-module-tvm-relax-op-nn sphx-glr-backref-type-py-function"><span class="n">relax</span><span class="o">.</span><span class="n">op</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">relu</span></a><span class="p">(</span><span class="n">conv</span><span class=" [...]
+ <span class="n">R</span><span class="o">.</span><span class="n">output</span><span class="p">(</span><span class="n">out</span><span class="p">)</span>
+ <span class="k">return</span> <span class="n">out</span>
</pre></div>
</div>
</section>
-<section id="step-3-partition-the-graph">
-<h2>Step 3: Partition the graph<a class="headerlink" href="#step-3-partition-the-graph" title="Link to this heading"></a></h2>
-<p><code class="docutils literal notranslate"><span class="pre">FuseOpsByPattern</span></code> groups ops that match a registered pattern into
-composite functions, controlled by two flags:</p>
+<section id="step-3-partition-for-the-example-npu">
+<h2>Step 3: Partition for the example NPU<a class="headerlink" href="#step-3-partition-for-the-example-npu" title="Link to this heading"></a></h2>
+<p><code class="docutils literal notranslate"><span class="pre">FuseOpsByPattern</span></code> groups ops matching a registered pattern into composite
+functions; <code class="docutils literal notranslate"><span class="pre">MergeCompositeFunctions</span></code> then consolidates adjacent composites
+bound for the same backend into a single external call. Two flags steer
+partitioning:</p>
<ul class="simple">
-<li><p><code class="docutils literal notranslate"><span class="pre">bind_constants=False</span></code> keeps weights as function arguments instead
-of baking them in, so the host stays in charge of parameter
-ownership.</p></li>
-<li><p><code class="docutils literal notranslate"><span class="pre">annotate_codegen=True</span></code> tags each composite with its backend name
-(<code class="docutils literal notranslate"><span class="pre">example_npu</span></code>); without this tag, <code class="docutils literal notranslate"><span class="pre">RunCodegen</span></code> has no way to
-route the composite to a backend.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">bind_constants=False</span></code> keeps weights as function arguments, so the host
+stays in charge of the parameters. (TensorRT below makes the opposite
+choice: it binds weights as constants because it bakes them into its engine.)</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">annotate_codegen=True</span></code> wraps each matched composite in a function tagged
+with the backend name – the tag <code class="docutils literal notranslate"><span class="pre">RunCodegen</span></code> routes on. (The follow-up
+<code class="docutils literal notranslate"><span class="pre">MergeCompositeFunctions</span></code> also attaches this tag when it groups composites,
+which is why <code class="docutils literal notranslate"><span class="pre">partition_for_tensorrt</span></code> below can leave the flag off.)</p></li>
</ul>
-<p><code class="docutils literal notranslate"><span class="pre">MergeCompositeFunctions</span></code> then consolidates adjacent composites
-that target the same backend so each group becomes a single external
-call. Note that consolidation depends on the patterns themselves: an
-<code class="docutils literal notranslate"><span class="pre">op_a</span> <span class="pre">+</span> <span class="pre">op_b</span></code> chain only collapses into one composite if a fused
-pattern (e.g. <code class="docutils literal notranslate"><span class="pre">matmul_relu_fused</span></code>) was registered for it; otherwise
-each op stays as its own composite even when both target the same
-backend.</p>
-<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">mod</span> <span class="o">=</span> <span class="n">MatmulReLU</span>
-<span class="n">mod</span> <span class="o">=</span> <a href="../../reference/api/python/relax/transform.html#tvm.relax.transform.FuseOpsByPattern" title="tvm.relax.transform.FuseOpsByPattern" class="sphx-glr-backref-module-tvm-relax-transform sphx-glr-backref-type-py-function"><span class="n">FuseOpsByPattern</span></a><span class="p">(</span><span class="n">patterns</span><span class="p">,</span> <span class="n">bind_constants</span><span class="o">=</span><span class="kc">False</span>< [...]
-<span class="n">mod</span> <span class="o">=</span> <a href="../../reference/api/python/relax/transform.html#tvm.relax.transform.MergeCompositeFunctions" title="tvm.relax.transform.MergeCompositeFunctions" class="sphx-glr-backref-module-tvm-relax-transform sphx-glr-backref-type-py-function"><span class="n">MergeCompositeFunctions</span></a><span class="p">()(</span><span class="n">mod</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="s2">"After partitioning:"</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">mod</span><span class="p">)</span>
+<p>The example NPU registers a fused <code class="docutils literal notranslate"><span class="pre">conv2d</span> <span class="pre">+</span> <span class="pre">relu</span></code> pattern with higher
+priority than the standalone <code class="docutils literal notranslate"><span class="pre">conv2d</span></code> pattern, so the two ops collapse into a
+single <code class="docutils literal notranslate"><span class="pre">example_npu.conv2d_relu_fused</span></code> composite – look for it in the
+printed module.</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">npu_patterns</span> <span class="o">=</span> <a href="../../reference/api/python/relax/backend.html#tvm.relax.backend.get_patterns_with_prefix" title="tvm.relax.backend.get_patterns_with_prefix" class="sphx-glr-backref-module-tvm-relax-backend sphx-glr-backref-type-py-function"><span class="n">get_patterns_with_prefix</span></a><span class="p">(</span><span class="s2">"example_npu" [...]
+<span class="n">npu_mod</span> <span class="o">=</span> <a href="../../reference/api/python/relax/transform.html#tvm.relax.transform.FuseOpsByPattern" title="tvm.relax.transform.FuseOpsByPattern" class="sphx-glr-backref-module-tvm-relax-transform sphx-glr-backref-type-py-function"><span class="n">FuseOpsByPattern</span></a><span class="p">(</span><span class="n">npu_patterns</span><span class="p">,</span> <span class="n">bind_constants</span><span class="o">=</span><span class="kc">False [...]
+<span class="n">npu_mod</span> <span class="o">=</span> <a href="../../reference/api/python/relax/transform.html#tvm.relax.transform.MergeCompositeFunctions" title="tvm.relax.transform.MergeCompositeFunctions" class="sphx-glr-backref-module-tvm-relax-transform sphx-glr-backref-type-py-function"><span class="n">MergeCompositeFunctions</span></a><span class="p">()(</span><span class="n">npu_mod</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">"After partitioning for the example NPU:"</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">npu_mod</span><span class="p">)</span>
</pre></div>
</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>After partitioning:
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>After partitioning for the example NPU:
# from tvm.script import ir as I
# from tvm.script import relax as R
@I.ir_module
class Module:
@R.function
- def fused_relax_matmul_relax_nn_relu_example_npu_example_npu(x: R.Tensor((2, 4), dtype="float32"), w: R.Tensor((4, 8), dtype="float32")) -> R.Tensor((2, 8), dtype="float32"):
+ def fused_relax_nn_conv2d_relax_nn_relu_example_npu_example_npu(data: R.Tensor((1, 3, 32, 32), dtype="float32"), weight: R.Tensor((16, 3, 3, 3), dtype="float32")) -> R.Tensor((1, 16, 30, 30), dtype="float32"):
R.func_attr({"Codegen": "example_npu"})
# from tvm.script import relax as R
@R.function
- def local_func(x_1: R.Tensor((2, 4), dtype="float32"), w_1: R.Tensor((4, 8), dtype="float32")) -> R.Tensor((2, 8), dtype="float32"):
- R.func_attr({"Composite": "example_npu.matmul_relu_fused"})
- y: R.Tensor((2, 8), dtype="float32") = R.matmul(x_1, w_1, out_dtype="void")
- gv: R.Tensor((2, 8), dtype="float32") = R.nn.relu(y)
+ def local_func(data_1: R.Tensor((1, 3, 32, 32), dtype="float32"), weight_1: R.Tensor((16, 3, 3, 3), dtype="float32")) -> R.Tensor((1, 16, 30, 30), dtype="float32"):
+ R.func_attr({"Composite": "example_npu.conv2d_relu_fused"})
+ conv: R.Tensor((1, 16, 30, 30), dtype="float32") = R.nn.conv2d(data_1, weight_1, strides=[1, 1], padding=[0, 0, 0, 0], dilation=[1, 1], groups=1, data_layout="NCHW", kernel_layout="OIHW", out_layout="NCHW", out_dtype="void")
+ gv: R.Tensor((1, 16, 30, 30), dtype="float32") = R.nn.relu(conv)
return gv
- output: R.Tensor((2, 8), dtype="float32") = local_func(x, w)
+ output: R.Tensor((1, 16, 30, 30), dtype="float32") = local_func(data, weight)
return output
@R.function
- def main(x: R.Tensor((2, 4), dtype="float32"), w: R.Tensor((4, 8), dtype="float32")) -> R.Tensor((2, 8), dtype="float32"):
+ def main(data: R.Tensor((1, 3, 32, 32), dtype="float32"), weight: R.Tensor((16, 3, 3, 3), dtype="float32")) -> R.Tensor((1, 16, 30, 30), dtype="float32"):
cls = Module
with R.dataflow():
- gv: R.Tensor((2, 8), dtype="float32") = cls.fused_relax_matmul_relax_nn_relu_example_npu_example_npu(x, w)
+ gv: R.Tensor((1, 16, 30, 30), dtype="float32") = cls.fused_relax_nn_conv2d_relax_nn_relu_example_npu_example_npu(data, weight)
R.output(gv)
return gv
</pre></div>
</div>
</section>
-<section id="step-4-run-codegen">
-<h2>Step 4: Run codegen<a class="headerlink" href="#step-4-run-codegen" title="Link to this heading"></a></h2>
-<p><code class="docutils literal notranslate"><span class="pre">RunCodegen</span></code> lowers each annotated composite function to the backend’s
-serialization format. For the example NPU this produces a JSON graph
-that the C++ runtime can execute.</p>
-<p>Steps 4 and 5 require TVM to be built with <code class="docutils literal notranslate"><span class="pre">USE_EXAMPLE_NPU_CODEGEN=ON</span></code>
-and <code class="docutils literal notranslate"><span class="pre">USE_EXAMPLE_NPU_RUNTIME=ON</span></code>.</p>
-<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="k">if</span> <span class="n">has_example_npu</span><span class="p">:</span>
- <span class="n">mod</span> <span class="o">=</span> <a href="../../reference/api/python/relax/transform.html#tvm.relax.transform.RunCodegen" title="tvm.relax.transform.RunCodegen" class="sphx-glr-backref-module-tvm-relax-transform sphx-glr-backref-type-py-function"><span class="n">RunCodegen</span></a><span class="p">()(</span><span class="n">mod</span><span class="p">)</span>
- <span class="nb">print</span><span class="p">(</span><span class="s2">"After codegen:"</span><span class="p">)</span>
- <span class="nb">print</span><span class="p">(</span><span class="n">mod</span><span class="p">)</span>
-
- <span class="c1">######################################################################</span>
- <span class="c1"># Step 5: Build and run</span>
- <span class="c1"># ---------------------</span>
- <span class="c1">#</span>
- <span class="c1"># Build the module for the host target, create a virtual machine, and</span>
- <span class="c1"># execute the compiled function.</span>
-
- <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
- <span class="n">x_np</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s2">"float32"</span><span class="p">)</span>
- <span class="n">w_np</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">8</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s2">"float32"</span><span class="p">)</span>
+<section id="step-4-codegen-build-and-run-on-the-example-npu">
+<h2>Step 4: Codegen, build and run on the example NPU<a class="headerlink" href="#step-4-codegen-build-and-run-on-the-example-npu" title="Link to this heading"></a></h2>
+<p><code class="docutils literal notranslate"><span class="pre">RunCodegen</span></code> invokes each annotated composite’s backend codegen, replacing it
+with the backend runtime module (here, the NPU’s JSON graph); <code class="docutils literal notranslate"><span class="pre">relax.build</span></code>
+then compiles the remaining host-side program and links everything. Because
+the runtime is a stub that computes nothing, we assert on the output <em>shape</em>
+only – the values are uninitialized.</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
+<span class="n">data_np</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">32</span><span class="p">,</span> <span class="mi">32</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><spa [...]
+<span class="n">weight_np</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">16</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><sp [...]
- <span class="k">with</span> <a href="../../reference/api/python/transform.html#tvm.ir.transform.PassContext" title="tvm.ir.transform.PassContext" class="sphx-glr-backref-module-tvm-ir-transform sphx-glr-backref-type-py-class"><span class="n">tvm</span><span class="o">.</span><span class="n">transform</span><span class="o">.</span><span class="n">PassContext</span></a><span class="p">(</span><span class="n">opt_level</span><span class="o">=</span><span class="mi">3</span><span class=" [...]
- <span class="n">built</span> <span class="o">=</span> <a href="../../reference/api/python/relax/relax.html#tvm.relax.build" title="tvm.relax.build" class="sphx-glr-backref-module-tvm-relax sphx-glr-backref-type-py-function"><span class="n">relax</span><span class="o">.</span><span class="n">build</span></a><span class="p">(</span><span class="n">mod</span><span class="p">,</span> <a href="../../reference/api/python/target.html#tvm.target.Target" title="tvm.target.Target" class="s [...]
+<span class="k">if</span> <span class="n">has_example_npu</span><span class="p">:</span>
+ <span class="n">npu_mod</span> <span class="o">=</span> <a href="../../reference/api/python/relax/transform.html#tvm.relax.transform.RunCodegen" title="tvm.relax.transform.RunCodegen" class="sphx-glr-backref-module-tvm-relax-transform sphx-glr-backref-type-py-function"><span class="n">RunCodegen</span></a><span class="p">()(</span><span class="n">npu_mod</span><span class="p">)</span>
- <span class="n">vm</span> <span class="o">=</span> <a href="../../reference/api/python/relax/relax.html#tvm.relax.VirtualMachine" title="tvm.relax.VirtualMachine" class="sphx-glr-backref-module-tvm-relax sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">relax</span><span class="o">.</span><span class="n">VirtualMachine</span></a><span class="p">(</span><span class="n">built</span><span class="p">,</span> <span class="n">tvm</span><span class="o">.</span><span [...]
- <span class="n">result</span> <span class="o">=</span> <span class="n">vm</span><span class="p">[</span><span class="s2">"main"</span><span class="p">](</span><span class="n">tvm</span><span class="o">.</span><span class="n">runtime</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="n">x_np</span><span class="p">,</span> <span class="n">tvm</span><span class="o">.</span><span class="n">cpu</span><span class="p">()),</span> <span [...]
+ <span class="k">with</span> <a href="../../reference/api/python/transform.html#tvm.ir.transform.PassContext" title="tvm.ir.transform.PassContext" class="sphx-glr-backref-module-tvm-ir-transform sphx-glr-backref-type-py-class"><span class="n">tvm</span><span class="o">.</span><span class="n">transform</span><span class="o">.</span><span class="n">PassContext</span></a><span class="p">(</span><span class="n">opt_level</span><span class="o">=</span><span class="mi">3</span><span class=" [...]
+ <span class="n">npu_exec</span> <span class="o">=</span> <a href="../../reference/api/python/relax/relax.html#tvm.relax.build" title="tvm.relax.build" class="sphx-glr-backref-module-tvm-relax sphx-glr-backref-type-py-function"><span class="n">relax</span><span class="o">.</span><span class="n">build</span></a><span class="p">(</span><span class="n">npu_mod</span><span class="p">,</span> <a href="../../reference/api/python/target.html#tvm.target.Target" title="tvm.target.Target" c [...]
- <span class="k">assert</span> <span class="n">result</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span><span class="o">.</span><span class="n">shape</span> <span class="o">==</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">8</span><span class="p">)</span>
- <span class="nb">print</span><span class="p">(</span><span class="s2">"Execution completed. Output shape:"</span><span class="p">,</span> <span class="n">result</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
+ <span class="n">npu_vm</span> <span class="o">=</span> <a href="../../reference/api/python/runtime/vm.html#tvm.runtime.vm.VirtualMachine" title="tvm.runtime.vm.VirtualMachine" class="sphx-glr-backref-module-tvm-runtime-vm sphx-glr-backref-type-py-class"><span class="n">relax</span><span class="o">.</span><span class="n">VirtualMachine</span></a><span class="p">(</span><span class="n">npu_exec</span><span class="p">,</span> <span class="n">tvm</span><span class="o">.</span><span class [...]
+ <span class="n">npu_out</span> <span class="o">=</span> <span class="n">npu_vm</span><span class="p">[</span><span class="s2">"main"</span><span class="p">](</span>
+ <span class="n">tvm</span><span class="o">.</span><span class="n">runtime</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="n">data_np</span><span class="p">,</span> <span class="n">tvm</span><span class="o">.</span><span class="n">cpu</span><span class="p">()),</span> <span class="n">tvm</span><span class="o">.</span><span class="n">runtime</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class [...]
+ <span class="p">)</span>
+ <span class="k">assert</span> <span class="n">npu_out</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span><span class="o">.</span><span class="n">shape</span> <span class="o">==</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">16</span><span class="p">,</span> <span class="mi">30</span><span class="p">,</span> <span class="mi">30</span><span class="p">)</span>
+ <span class="nb">print</span><span class="p">(</span><span class="s2">"Example NPU run completed. Output shape:"</span><span class="p">,</span> <span class="n">npu_out</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
+<span class="k">else</span><span class="p">:</span>
+ <span class="nb">print</span><span class="p">(</span><span class="s2">"Example NPU backend unavailable; skipping its build and run."</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Example NPU backend unavailable; skipping its build and run.
</pre></div>
</div>
</section>
-<section id="step-6-conv2d-relu">
-<h2>Step 6: Conv2D + ReLU<a class="headerlink" href="#step-6-conv2d-relu" title="Link to this heading"></a></h2>
-<p>The same flow applies to convolution workloads. Because the fused
-<code class="docutils literal notranslate"><span class="pre">conv2d</span> <span class="pre">+</span> <span class="pre">relu</span></code> pattern is registered after the standalone
-<code class="docutils literal notranslate"><span class="pre">conv2d</span></code> pattern in <code class="docutils literal notranslate"><span class="pre">patterns.py</span></code> (later entries have higher
-priority), both ops are offloaded as a single composite function.</p>
-<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="nd">@tvm</span><span class="o">.</span><span class="n">script</span><span class="o">.</span><span class="n">ir_module</span>
-<span class="k">class</span><span class="w"> </span><span class="nc">Conv2dReLU</span><span class="p">:</span>
- <span class="nd">@R</span><span class="o">.</span><span class="n">function</span>
- <span class="k">def</span><span class="w"> </span><span class="nf">main</span><span class="p">(</span>
- <span class="n">x</span><span class="p">:</span> <span class="n">R</span><span class="o">.</span><span class="n">Tensor</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">32</span><span class="p">,</span> <span class="mi">32</span><span class="p">),</span> <span class="s2">"float32"</span><span class="p">),</span>
- <span class="n">w</span><span class="p">:</span> <span class="n">R</span><span class="o">.</span><span class="n">Tensor</span><span class="p">((</span><span class="mi">16</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="s2">"float32"</span><span class="p">),</span>
- <span class="p">)</span> <span class="o">-></span> <span class="n">R</span><span class="o">.</span><span class="n">Tensor</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">16</span><span class="p">,</span> <span class="mi">30</span><span class="p">,</span> <span class="mi">30</span><span class="p">),</span> <span class="s2">"float32"</span><span class="p">):</span>
- <span class="k">with</span> <span class="n">R</span><span class="o">.</span><span class="n">dataflow</span><span class="p">():</span>
- <span class="n">y</span> <span class="o">=</span> <a href="../../reference/api/python/relax/op.html#tvm.relax.op.nn.conv2d" title="tvm.relax.op.nn.conv2d" class="sphx-glr-backref-module-tvm-relax-op-nn sphx-glr-backref-type-py-function"><span class="n">relax</span><span class="o">.</span><span class="n">op</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">conv2d</span></a><span class="p">(</span><span class="n">x</span><span class= [...]
- <span class="n">z</span> <span class="o">=</span> <a href="../../reference/api/python/relax/op.html#tvm.relax.op.nn.relu" title="tvm.relax.op.nn.relu" class="sphx-glr-backref-module-tvm-relax-op-nn sphx-glr-backref-type-py-function"><span class="n">relax</span><span class="o">.</span><span class="n">op</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">relu</span></a><span class="p">(</span><span class="n">y</span><span class="p">)</span>
- <span class="n">R</span><span class="o">.</span><span class="n">output</span><span class="p">(</span><span class="n">z</span><span class="p">)</span>
- <span class="k">return</span> <span class="n">z</span>
+<section id="the-same-flow-on-a-real-backend-tensorrt">
+<h2>The same flow on a real backend: TensorRT<a class="headerlink" href="#the-same-flow-on-a-real-backend-tensorrt" title="Link to this heading"></a></h2>
+<p>Steps 1-4 above are the whole mechanism. Aiming them at a real backend
+changes very little, so rather than repeat the walkthrough, here is only what
+differs for NVIDIA TensorRT:</p>
+<ul class="simple">
+<li><p><strong>Partition in one call.</strong> <code class="docutils literal notranslate"><span class="pre">partition_for_tensorrt</span></code> bundles the
+<code class="docutils literal notranslate"><span class="pre">FuseOpsByPattern</span></code> + <code class="docutils literal notranslate"><span class="pre">MergeCompositeFunctions</span></code> you ran by hand, using
+TensorRT’s own pattern table.</p></li>
+<li><p><strong>Weights become constants</strong> (<code class="docutils literal notranslate"><span class="pre">bind_constants=True</span></code>): TensorRT bakes them
+into the engine it builds, so bind the parameters before partitioning.</p></li>
+<li><p><strong>Real values.</strong> TensorRT actually computes, so we build for CUDA, run on
+the GPU, and cross-check against a plain CPU build – not just the shape.</p></li>
+</ul>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">trt_mod</span> <span class="o">=</span> <a href="../../reference/api/python/relax/transform.html#tvm.relax.transform.BindParams" title="tvm.relax.transform.BindParams" class="sphx-glr-backref-module-tvm-relax-transform sphx-glr-backref-type-py-function"><span class="n">relax</span><span class="o">.</span><span class="n">transform</span><span class="o">.</span><span class="n">BindParams</spa [...]
+<span class="n">trt_mod</span> <span class="o">=</span> <span class="n">partition_for_tensorrt</span><span class="p">(</span><span class="n">trt_mod</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">"After partition_for_tensorrt:"</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">trt_mod</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>After partition_for_tensorrt:
+# from tvm.script import ir as I
+# from tvm.script import relax as R
+@I.ir_module
+class Module:
+ @R.function
+ def fused_relax_nn_conv2d_relax_nn_relu_tensorrt(data: R.Tensor((1, 3, 32, 32), dtype="float32")) -> R.Tensor((1, 16, 30, 30), dtype="float32"):
+ R.func_attr({"Codegen": "tensorrt"})
+ # from tvm.script import relax as R
-<span class="k">if</span> <span class="n">has_example_npu</span><span class="p">:</span>
- <span class="n">mod2</span> <span class="o">=</span> <span class="n">Conv2dReLU</span>
- <span class="n">mod2</span> <span class="o">=</span> <a href="../../reference/api/python/relax/transform.html#tvm.relax.transform.FuseOpsByPattern" title="tvm.relax.transform.FuseOpsByPattern" class="sphx-glr-backref-module-tvm-relax-transform sphx-glr-backref-type-py-function"><span class="n">FuseOpsByPattern</span></a><span class="p">(</span><span class="n">patterns</span><span class="p">,</span> <span class="n">bind_constants</span><span class="o">=</span><span class="kc">False</s [...]
- <span class="n">mod2</span> <span class="o">=</span> <a href="../../reference/api/python/relax/transform.html#tvm.relax.transform.MergeCompositeFunctions" title="tvm.relax.transform.MergeCompositeFunctions" class="sphx-glr-backref-module-tvm-relax-transform sphx-glr-backref-type-py-function"><span class="n">MergeCompositeFunctions</span></a><span class="p">()(</span><span class="n">mod2</span><span class="p">)</span>
- <span class="n">mod2</span> <span class="o">=</span> <a href="../../reference/api/python/relax/transform.html#tvm.relax.transform.RunCodegen" title="tvm.relax.transform.RunCodegen" class="sphx-glr-backref-module-tvm-relax-transform sphx-glr-backref-type-py-function"><span class="n">RunCodegen</span></a><span class="p">()(</span><span class="n">mod2</span><span class="p">)</span>
+ @R.function
+ def gv(data_1: R.Tensor((1, 3, 32, 32), dtype="float32")) -> R.Tensor((1, 16, 30, 30), dtype="float32"):
+ R.func_attr({"Composite": "tensorrt.nn.conv2d"})
+ with R.dataflow():
+ gv_1: R.Tensor((1, 16, 30, 30), dtype="float32") = R.nn.conv2d(data_1, metadata["relax.expr.Constant"][0], strides=[1, 1], padding=[0, 0, 0, 0], dilation=[1, 1], groups=1, data_layout="NCHW", kernel_layout="OIHW", out_layout="NCHW", out_dtype="void")
+ R.output(gv_1)
+ return gv_1
+
+ lv: R.Tensor((1, 16, 30, 30), dtype="float32") = gv(data)
+ # from tvm.script import relax as R
- <span class="k">with</span> <a href="../../reference/api/python/transform.html#tvm.ir.transform.PassContext" title="tvm.ir.transform.PassContext" class="sphx-glr-backref-module-tvm-ir-transform sphx-glr-backref-type-py-class"><span class="n">tvm</span><span class="o">.</span><span class="n">transform</span><span class="o">.</span><span class="n">PassContext</span></a><span class="p">(</span><span class="n">opt_level</span><span class="o">=</span><span class="mi">3</span><span class=" [...]
- <span class="n">built2</span> <span class="o">=</span> <a href="../../reference/api/python/relax/relax.html#tvm.relax.build" title="tvm.relax.build" class="sphx-glr-backref-module-tvm-relax sphx-glr-backref-type-py-function"><span class="n">relax</span><span class="o">.</span><span class="n">build</span></a><span class="p">(</span><span class="n">mod2</span><span class="p">,</span> <a href="../../reference/api/python/target.html#tvm.target.Target" title="tvm.target.Target" class= [...]
+ @R.function
+ def gv1(lv_1: R.Tensor((1, 16, 30, 30), dtype="float32")) -> R.Tensor((1, 16, 30, 30), dtype="float32"):
+ R.func_attr({"Composite": "tensorrt.nn.relu"})
+ with R.dataflow():
+ gv_1: R.Tensor((1, 16, 30, 30), dtype="float32") = R.nn.relu(lv_1)
+ R.output(gv_1)
+ return gv_1
+
+ gv_1: R.Tensor((1, 16, 30, 30), dtype="float32") = gv1(lv)
+ return gv_1
+
+ @R.function
+ def main(data: R.Tensor((1, 3, 32, 32), dtype="float32")) -> R.Tensor((1, 16, 30, 30), dtype="float32"):
+ cls = Module
+ with R.dataflow():
+ gv: R.Tensor((1, 16, 30, 30), dtype="float32") = cls.fused_relax_nn_conv2d_relax_nn_relu_tensorrt(data)
+ R.output(gv)
+ return gv
- <span class="n">x2_np</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">32</span><span class="p">,</span> <span class="mi">32</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><s [...]
- <span class="n">w2_np</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">16</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><sp [...]
+# Metadata omitted. Use show_meta=True in script() method to show it.
+</pre></div>
+</div>
+<p>Build for CUDA, run on the GPU, and compare against the CPU reference.</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">has_tensorrt</span></a> <span class="ow">and</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx- [...]
+ <span class="n">dev</span> <span class="o">=</span> <span class="n">tvm</span><span class="o">.</span><span class="n">cuda</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
+ <span class="k">with</span> <a href="../../reference/api/python/transform.html#tvm.ir.transform.PassContext" title="tvm.ir.transform.PassContext" class="sphx-glr-backref-module-tvm-ir-transform sphx-glr-backref-type-py-class"><span class="n">tvm</span><span class="o">.</span><span class="n">transform</span><span class="o">.</span><span class="n">PassContext</span></a><span class="p">(</span><span class="n">opt_level</span><span class="o">=</span><span class="mi">3</span><span class=" [...]
+ <span class="n">trt_exec</span> <span class="o">=</span> <a href="../../reference/api/python/relax/relax.html#tvm.relax.build" title="tvm.relax.build" class="sphx-glr-backref-module-tvm-relax sphx-glr-backref-type-py-function"><span class="n">relax</span><span class="o">.</span><span class="n">build</span></a><span class="p">(</span><a href="../../reference/api/python/relax/transform.html#tvm.relax.transform.RunCodegen" title="tvm.relax.transform.RunCodegen" class="sphx-glr-backr [...]
+ <span class="n">trt_out</span> <span class="o">=</span> <a href="../../reference/api/python/runtime/vm.html#tvm.runtime.vm.VirtualMachine" title="tvm.runtime.vm.VirtualMachine" class="sphx-glr-backref-module-tvm-runtime-vm sphx-glr-backref-type-py-class"><span class="n">relax</span><span class="o">.</span><span class="n">VirtualMachine</span></a><span class="p">(</span><span class="n">trt_exec</span><span class="p">,</span> <span class="n">dev</span><span class="p">)[</span><span cla [...]
- <span class="n">vm2</span> <span class="o">=</span> <a href="../../reference/api/python/relax/relax.html#tvm.relax.VirtualMachine" title="tvm.relax.VirtualMachine" class="sphx-glr-backref-module-tvm-relax sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">relax</span><span class="o">.</span><span class="n">VirtualMachine</span></a><span class="p">(</span><span class="n">built2</span><span class="p">,</span> <span class="n">tvm</span><span class="o">.</span><spa [...]
- <span class="n">result2</span> <span class="o">=</span> <span class="n">vm2</span><span class="p">[</span><span class="s2">"main"</span><span class="p">](</span>
- <span class="n">tvm</span><span class="o">.</span><span class="n">runtime</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="n">x2_np</span><span class="p">,</span> <span class="n">tvm</span><span class="o">.</span><span class="n">cpu</span><span class="p">()),</span> <span class="n">tvm</span><span class="o">.</span><span class="n">runtime</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class=" [...]
+ <span class="n">cpu_mod</span> <span class="o">=</span> <a href="../../reference/api/python/relax/transform.html#tvm.relax.transform.LegalizeOps" title="tvm.relax.transform.LegalizeOps" class="sphx-glr-backref-module-tvm-relax-transform sphx-glr-backref-type-py-function"><span class="n">relax</span><span class="o">.</span><span class="n">transform</span><span class="o">.</span><span class="n">LegalizeOps</span></a><span class="p">()(</span>
+ <a href="../../reference/api/python/relax/transform.html#tvm.relax.transform.BindParams" title="tvm.relax.transform.BindParams" class="sphx-glr-backref-module-tvm-relax-transform sphx-glr-backref-type-py-function"><span class="n">relax</span><span class="o">.</span><span class="n">transform</span><span class="o">.</span><span class="n">BindParams</span></a><span class="p">(</span><span class="s2">"main"</span><span class="p">,</span> <span class="p">{</span><span class= [...]
+ <span class="p">)</span>
+ <span class="n">cpu_exec</span> <span class="o">=</span> <a href="../../reference/api/python/relax/relax.html#tvm.relax.build" title="tvm.relax.build" class="sphx-glr-backref-module-tvm-relax sphx-glr-backref-type-py-function"><span class="n">relax</span><span class="o">.</span><span class="n">build</span></a><span class="p">(</span><span class="n">cpu_mod</span><span class="p">,</span> <span class="s2">"llvm"</span><span class="p">)</span>
+ <span class="n">cpu_out</span> <span class="o">=</span> <a href="../../reference/api/python/runtime/vm.html#tvm.runtime.vm.VirtualMachine" title="tvm.runtime.vm.VirtualMachine" class="sphx-glr-backref-module-tvm-runtime-vm sphx-glr-backref-type-py-class"><span class="n">relax</span><span class="o">.</span><span class="n">VirtualMachine</span></a><span class="p">(</span><span class="n">cpu_exec</span><span class="p">,</span> <span class="n">tvm</span><span class="o">.</span><span clas [...]
+ <span class="n">tvm</span><span class="o">.</span><span class="n">runtime</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="n">data_np</span><span class="p">,</span> <span class="n">tvm</span><span class="o">.</span><span class="n">cpu</span><span class="p">())</span>
+ <span class="p">)</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span>
+
+ <span class="n">np</span><span class="o">.</span><span class="n">testing</span><span class="o">.</span><span class="n">assert_allclose</span><span class="p">(</span><span class="n">trt_out</span><span class="p">,</span> <span class="n">cpu_out</span><span class="p">,</span> <span class="n">rtol</span><span class="o">=</span><span class="mf">1e-2</span><span class="p">,</span> <span class="n">atol</span><span class="o">=</span><span class="mf">1e-2</span><span class="p">)</span>
+ <span class="nb">print</span><span class="p">(</span><span class="s2">"TensorRT output shape:"</span><span class="p">,</span> <span class="n">trt_out</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="s2">"- matches the CPU reference."</span><span class="p">)</span>
+<span class="k">else</span><span class="p">:</span>
+ <span class="nb">print</span><span class="p">(</span><span class="s2">"TensorRT/CUDA unavailable; skipping the GPU build and run."</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>TensorRT/CUDA unavailable; skipping the GPU build and run.
+</pre></div>
+</div>
+<p>A real backend also exposes knobs the stub does not. Setting <code class="docutils literal notranslate"><span class="pre">use_fp16</span></code>
+through the <code class="docutils literal notranslate"><span class="pre">relax.ext.tensorrt.options</span></code> config lets TensorRT pick FP16
+kernels, trading a little accuracy for speed; nothing else about the flow
+changes. (Other options are environment-driven: <code class="docutils literal notranslate"><span class="pre">TVM_TENSORRT_USE_INT8</span></code>
+enables INT8 with calibration, <code class="docutils literal notranslate"><span class="pre">TVM_TENSORRT_MAX_WORKSPACE_SIZE</span></code> caps the
+build workspace, and <code class="docutils literal notranslate"><span class="pre">TVM_TENSORRT_CACHE_DIR</span></code> caches built engines to disk
+for reuse across runs.)</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">has_tensorrt</span></a> <span class="ow">and</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx- [...]
+ <span class="n">fp16_mod</span> <span class="o">=</span> <span class="n">partition_for_tensorrt</span><span class="p">(</span>
+ <a href="../../reference/api/python/relax/transform.html#tvm.relax.transform.BindParams" title="tvm.relax.transform.BindParams" class="sphx-glr-backref-module-tvm-relax-transform sphx-glr-backref-type-py-function"><span class="n">relax</span><span class="o">.</span><span class="n">transform</span><span class="o">.</span><span class="n">BindParams</span></a><span class="p">(</span><span class="s2">"main"</span><span class="p">,</span> <span class="p">{</span><span class= [...]
<span class="p">)</span>
- <span class="k">assert</span> <span class="n">result2</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span><span class="o">.</span><span class="n">shape</span> <span class="o">==</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">16</span><span class="p">,</span> <span class="mi">30</span><span class="p">,</span> <span class="mi">30</span><span class="p">)</span>
- <span class="nb">print</span><span class="p">(</span><span class="s2">"Conv2dReLU output shape:"</span><span class="p">,</span> <span class="n">result2</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
+ <span class="k">with</span> <a href="../../reference/api/python/transform.html#tvm.ir.transform.PassContext" title="tvm.ir.transform.PassContext" class="sphx-glr-backref-module-tvm-ir-transform sphx-glr-backref-type-py-class"><span class="n">tvm</span><span class="o">.</span><span class="n">transform</span><span class="o">.</span><span class="n">PassContext</span></a><span class="p">(</span>
+ <span class="n">opt_level</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">config</span><span class="o">=</span><span class="p">{</span><span class="s2">"relax.ext.tensorrt.options"</span><span class="p">:</span> <span class="p">{</span><span class="s2">"use_fp16"</span><span class="p">:</span> <span class="kc">True</span><span class="p">}}</span>
+ <span class="p">):</span>
+ <span class="n">fp16_exec</span> <span class="o">=</span> <a href="../../reference/api/python/relax/relax.html#tvm.relax.build" title="tvm.relax.build" class="sphx-glr-backref-module-tvm-relax sphx-glr-backref-type-py-function"><span class="n">relax</span><span class="o">.</span><span class="n">build</span></a><span class="p">(</span><a href="../../reference/api/python/relax/transform.html#tvm.relax.transform.RunCodegen" title="tvm.relax.transform.RunCodegen" class="sphx-glr-back [...]
+ <span class="n">fp16_out</span> <span class="o">=</span> <a href="../../reference/api/python/runtime/vm.html#tvm.runtime.vm.VirtualMachine" title="tvm.runtime.vm.VirtualMachine" class="sphx-glr-backref-module-tvm-runtime-vm sphx-glr-backref-type-py-class"><span class="n">relax</span><span class="o">.</span><span class="n">VirtualMachine</span></a><span class="p">(</span><span class="n">fp16_exec</span><span class="p">,</span> <span class="n">tvm</span><span class="o">.</span><span cl [...]
+ <span class="n">tvm</span><span class="o">.</span><span class="n">runtime</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="n">data_np</span><span class="p">,</span> <span class="n">tvm</span><span class="o">.</span><span class="n">cuda</span><span class="p">(</span><span class="mi">0</span><span class="p">))</span>
+ <span class="p">)</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span>
+
+ <span class="n">np</span><span class="o">.</span><span class="n">testing</span><span class="o">.</span><span class="n">assert_allclose</span><span class="p">(</span><span class="n">fp16_out</span><span class="p">,</span> <span class="n">cpu_out</span><span class="p">,</span> <span class="n">rtol</span><span class="o">=</span><span class="mf">5e-2</span><span class="p">,</span> <span class="n">atol</span><span class="o">=</span><span class="mf">5e-2</span><span class="p">)</span>
+ <span class="nb">print</span><span class="p">(</span><span class="s2">"TensorRT FP16 output shape:"</span><span class="p">,</span> <span class="n">fp16_out</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="s2">"- matches within FP16 tolerance."</span><span class="p">)</span>
+<span class="k">else</span><span class="p">:</span>
+ <span class="nb">print</span><span class="p">(</span><span class="s2">"TensorRT/CUDA unavailable; skipping the FP16 build."</span><span class="p">)</span>
</pre></div>
</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>TensorRT/CUDA unavailable; skipping the FP16 build.
+</pre></div>
+</div>
+</section>
+<section id="example-npu-vs-tensorrt-at-a-glance">
+<h2>Example NPU vs TensorRT at a glance<a class="headerlink" href="#example-npu-vs-tensorrt-at-a-glance" title="Link to this heading"></a></h2>
+<p>The same four-step flow, two backends:</p>
+<table class="docutils align-default">
+<thead>
+<tr class="row-odd"><th class="head"><p>Aspect</p></th>
+<th class="head"><p>Example NPU (teaching stub)</p></th>
+<th class="head"><p>TensorRT (real backend)</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p>Runtime</p></td>
+<td><p>logs decisions, no compute</p></td>
+<td><p>builds and runs an nvinfer engine</p></td>
+</tr>
+<tr class="row-odd"><td><p>Output</p></td>
+<td><p>uninitialized (check shape)</p></td>
+<td><p>real values (cross-checked vs CPU)</p></td>
+</tr>
+<tr class="row-even"><td><p>Weights</p></td>
+<td><p><code class="docutils literal notranslate"><span class="pre">bind_constants=False</span></code></p></td>
+<td><p><code class="docutils literal notranslate"><span class="pre">bind_constants=True</span></code> (baked in)</p></td>
+</tr>
+<tr class="row-odd"><td><p>Partition</p></td>
+<td><p>two passes, by hand</p></td>
+<td><p><code class="docutils literal notranslate"><span class="pre">partition_for_tensorrt</span></code> one call</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+<section id="deploying-a-pytorch-model-with-tensorrt">
+<h2>Deploying a PyTorch model with TensorRT<a class="headerlink" href="#deploying-a-pytorch-model-with-tensorrt" title="Link to this heading"></a></h2>
+<p>Everything above used a hand-written <code class="docutils literal notranslate"><span class="pre">IRModule</span></code> so each op was visible. In
+practice you start from a trained model. This final section runs the <em>same</em>
+<code class="docutils literal notranslate"><span class="pre">partition_for_tensorrt</span></code> flow on a real PyTorch <code class="docutils literal notranslate"><span class="pre">nn.Module</span></code>, end to end:
+export it, import it into Relax with the PyTorch frontend (the weights come in
+as constants – exactly what TensorRT bakes into its engine), partition, build
+for CUDA, and check the GPU result against PyTorch’s own output. Beyond the
+frontend import, the only difference is that the imported program returns its
+outputs as a tuple, so we index <code class="docutils literal notranslate"><span class="pre">[0]</span></code> for the single result tensor; the
+partition-build-run flow is otherwise unchanged.</p>
+<p>This section additionally requires PyTorch.</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="k">try</span><span class="p">:</span>
+ <span class="kn">import</span><span class="w"> </span><span class="nn">torch</span>
+ <span class="kn">from</span><span class="w"> </span><span class="nn">torch</span><span class="w"> </span><span class="kn">import</span> <span class="n">nn</span>
+
+ <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">has_torch</span></a> <span class="o">=</span> <span class="kc">True</span>
+<span class="k">except</span> <span class="ne">ImportError</span><span class="p">:</span>
+ <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">has_torch</span></a> <span class="o">=</span> <span class="kc">False</span>
+
+<span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">has_torch</span></a> <span class="ow">and</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">has_tensorrt</spa [...]
+ <span class="kn">from</span><span class="w"> </span><span class="nn">tvm.relax.frontend.torch</span><span class="w"> </span><span class="kn">import</span> <a href="../../reference/api/python/relax/frontend.html#tvm.relax.frontend.torch.from_exported_program" title="tvm.relax.frontend.torch.from_exported_program" class="sphx-glr-backref-module-tvm-relax-frontend-torch sphx-glr-backref-type-py-function"><span class="n">from_exported_program</span></a>
+
+ <span class="k">class</span><span class="w"> </span><span class="nc">SmallConvNet</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
+ <span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+ <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
+ <span class="bp">self</span><span class="o">.</span><span class="n">conv1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
+ <span class="bp">self</span><span class="o">.</span><span class="n">conv2</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">16</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
+ <span class="bp">self</span><span class="o">.</span><span class="n">pool</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">MaxPool2d</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
+
+ <span class="k">def</span><span class="w"> </span><span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
+ <span class="n">x</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">conv1</span><span class="p">(</span><span class="n">x</span><span class="p">))</span>
+ <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">pool</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+ <span class="n">x</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">conv2</span><span class="p">(</span><span class="n">x</span><span class="p">))</span>
+ <span class="k">return</span> <span class="n">x</span>
+
+ <span class="n">torch_model</span> <span class="o">=</span> <span class="n">SmallConvNet</span><span class="p">()</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span>
+ <span class="n">example_input</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">32</span><span class="p">,</span> <span class="mi">32</span><span class="p">)</span>
+ <span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">():</span>
+ <span class="n">torch_ref</span> <span class="o">=</span> <span class="n">torch_model</span><span class="p">(</span><span class="n">example_input</span><span class="p">)</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span>
+ <span class="n">exported</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">export</span><span class="o">.</span><span class="n">export</span><span class="p">(</span><span class="n">torch_model</span><span class="p">,</span> <span class="p">(</span><span class="n">example_input</span><span class="p">,))</span>
+
+ <span class="n">torch_mod</span> <span class="o">=</span> <a href="../../reference/api/python/relax/frontend.html#tvm.relax.frontend.torch.from_exported_program" title="tvm.relax.frontend.torch.from_exported_program" class="sphx-glr-backref-module-tvm-relax-frontend-torch sphx-glr-backref-type-py-function"><span class="n">from_exported_program</span></a><span class="p">(</span><span class="n">exported</span><span class="p">)</span>
+ <span class="n">torch_mod</span> <span class="o">=</span> <span class="n">partition_for_tensorrt</span><span class="p">(</span><span class="n">torch_mod</span><span class="p">)</span>
+ <span class="nb">print</span><span class="p">(</span><span class="s2">"After importing and partitioning the PyTorch model:"</span><span class="p">)</span>
+ <span class="nb">print</span><span class="p">(</span><span class="n">torch_mod</span><span class="p">)</span>
+
+ <span class="n">torch_dev</span> <span class="o">=</span> <span class="n">tvm</span><span class="o">.</span><span class="n">cuda</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
+ <span class="k">with</span> <a href="../../reference/api/python/transform.html#tvm.ir.transform.PassContext" title="tvm.ir.transform.PassContext" class="sphx-glr-backref-module-tvm-ir-transform sphx-glr-backref-type-py-class"><span class="n">tvm</span><span class="o">.</span><span class="n">transform</span><span class="o">.</span><span class="n">PassContext</span></a><span class="p">(</span><span class="n">opt_level</span><span class="o">=</span><span class="mi">3</span><span class=" [...]
+ <span class="n">torch_exec</span> <span class="o">=</span> <a href="../../reference/api/python/relax/relax.html#tvm.relax.build" title="tvm.relax.build" class="sphx-glr-backref-module-tvm-relax sphx-glr-backref-type-py-function"><span class="n">relax</span><span class="o">.</span><span class="n">build</span></a><span class="p">(</span><a href="../../reference/api/python/relax/transform.html#tvm.relax.transform.RunCodegen" title="tvm.relax.transform.RunCodegen" class="sphx-glr-bac [...]
+ <span class="n">deployed</span> <span class="o">=</span> <a href="../../reference/api/python/runtime/vm.html#tvm.runtime.vm.VirtualMachine" title="tvm.runtime.vm.VirtualMachine" class="sphx-glr-backref-module-tvm-runtime-vm sphx-glr-backref-type-py-class"><span class="n">relax</span><span class="o">.</span><span class="n">VirtualMachine</span></a><span class="p">(</span><span class="n">torch_exec</span><span class="p">,</span> <span class="n">torch_dev</span><span class="p">)[</span> [...]
+ <span class="n">tvm</span><span class="o">.</span><span class="n">runtime</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="n">example_input</span><span class="o">.</span><span class="n">numpy</span><span class="p">(),</span> <span class="n">torch_dev</span><span class="p">)</span>
+ <span class="p">)[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span>
+
+ <span class="n">np</span><span class="o">.</span><span class="n">testing</span><span class="o">.</span><span class="n">assert_allclose</span><span class="p">(</span><span class="n">deployed</span><span class="p">,</span> <span class="n">torch_ref</span><span class="p">,</span> <span class="n">rtol</span><span class="o">=</span><span class="mf">1e-2</span><span class="p">,</span> <span class="n">atol</span><span class="o">=</span><span class="mf">1e-2</span><span class="p">)</span>
+ <span class="nb">print</span><span class="p">(</span><span class="s2">"Deployed PyTorch model on TensorRT; output"</span><span class="p">,</span> <span class="n">deployed</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="s2">"matches PyTorch."</span><span class="p">)</span>
+<span class="k">else</span><span class="p">:</span>
+ <span class="nb">print</span><span class="p">(</span><span class="s2">"PyTorch / TensorRT / CUDA unavailable; skipping the deployment example."</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>PyTorch / TensorRT / CUDA unavailable; skipping the deployment example.
+</pre></div>
+</div>
+<p>Real deployment builds once and reuses the artifact. Export the compiled
+module to a shared library, then load and run it later – in a fresh process,
+with no PyTorch and no rebuild needed.</p>
+<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">has_torch</span></a> <span class="ow">and</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr [...]
+ <span class="k">with</span> <a href="https://docs.python.org/3/library/tempfile.html#tempfile.TemporaryDirectory" title="tempfile.TemporaryDirectory" class="sphx-glr-backref-module-tempfile sphx-glr-backref-type-py-class"><span class="n">tempfile</span><span class="o">.</span><span class="n">TemporaryDirectory</span></a><span class="p">()</span> <span class="k">as</span> <span class="n">tmpdir</span><span class="p">:</span>
+ <span class="n">lib_path</span> <span class="o">=</span> <a href="https://docs.python.org/3/library/os.path.html#os.path.join" title="os.path.join" class="sphx-glr-backref-module-os-path sphx-glr-backref-type-py-function"><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span></a><span class="p">(</span><span class="n">tmpdir</span><span class="p">,</span> <span class="s2">"deployed_trt.so"</span><s [...]
+ <span class="n">torch_exec</span><span class="o">.</span><span class="n">export_library</span><span class="p">(</span><span class="n">lib_path</span><span class="p">)</span>
+ <span class="n">loaded</span> <span class="o">=</span> <span class="n">tvm</span><span class="o">.</span><span class="n">runtime</span><span class="o">.</span><span class="n">load_module</span><span class="p">(</span><span class="n">lib_path</span><span class="p">)</span>
+ <span class="n">reran</span> <span class="o">=</span> <a href="../../reference/api/python/runtime/vm.html#tvm.runtime.vm.VirtualMachine" title="tvm.runtime.vm.VirtualMachine" class="sphx-glr-backref-module-tvm-runtime-vm sphx-glr-backref-type-py-class"><span class="n">relax</span><span class="o">.</span><span class="n">VirtualMachine</span></a><span class="p">(</span><span class="n">loaded</span><span class="p">,</span> <span class="n">torch_dev</span><span class="p">)[</span><sp [...]
+ <span class="n">tvm</span><span class="o">.</span><span class="n">runtime</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="n">example_input</span><span class="o">.</span><span class="n">numpy</span><span class="p">(),</span> <span class="n">torch_dev</span><span class="p">)</span>
+ <span class="p">)[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span>
+ <span class="n">np</span><span class="o">.</span><span class="n">testing</span><span class="o">.</span><span class="n">assert_allclose</span><span class="p">(</span><span class="n">reran</span><span class="p">,</span> <span class="n">torch_ref</span><span class="p">,</span> <span class="n">rtol</span><span class="o">=</span><span class="mf">1e-2</span><span class="p">,</span> <span class="n">atol</span><span class="o">=</span><span class="mf">1e-2</span><span class="p">)</span>
+ <span class="nb">print</span><span class="p">(</span><span class="s2">"Reloaded the exported library and reran; output"</span><span class="p">,</span> <span class="n">reran</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="s2">"still matches."</span><span class="p">)</span>
+<span class="k">else</span><span class="p">:</span>
+ <span class="nb">print</span><span class="p">(</span><span class="s2">"PyTorch / TensorRT / CUDA unavailable; skipping the export/reload step."</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>PyTorch / TensorRT / CUDA unavailable; skipping the export/reload step.
+</pre></div>
+</div>
+</section>
+<section id="notes-for-real-deployments">
+<h2>Notes for real deployments<a class="headerlink" href="#notes-for-real-deployments" title="Link to this heading"></a></h2>
+<ul class="simple">
+<li><p><strong>Operator coverage and fallback.</strong> TensorRT offloads only the ops in its
+pattern table (see <code class="docutils literal notranslate"><span class="pre">python/tvm/relax/backend/contrib/tensorrt.py</span></code>);
+anything unsupported simply stays on the host. Print the partitioned module
+and look for the <code class="docutils literal notranslate"><span class="pre">Codegen:</span> <span class="pre">"tensorrt"</span></code> functions to see what was offloaded.</p></li>
+<li><p><strong>Dynamic shapes.</strong> The builder sets up an optimization profile for a dynamic
+leading (batch) dimension, so the integration can serve a model exported with
+a symbolic batch size.</p></li>
+<li><p><strong>Engine build cost.</strong> Building a TensorRT engine is slow the first time (it
+is not a hang). Set <code class="docutils literal notranslate"><span class="pre">TVM_TENSORRT_CACHE_DIR</span></code> to cache built engines to
+disk and skip the rebuild on later runs.</p></li>
+</ul>
</section>
<section id="next-steps">
<h2>Next steps<a class="headerlink" href="#next-steps" title="Link to this heading"></a></h2>
-<p>To build a real NPU backend using this example as a starting point:</p>
+<p>To build your own backend using the example NPU as a starting point:</p>
<ul class="simple">
-<li><p>Replace <code class="docutils literal notranslate"><span class="pre">example_npu_runtime.cc</span></code> with your hardware SDK calls.</p></li>
+<li><p>Replace the stub runtime in
+<code class="docutils literal notranslate"><span class="pre">src/runtime/extra/contrib/example_npu/example_npu_runtime.cc</span></code> with your
+hardware SDK calls.</p></li>
<li><p>Extend <code class="docutils literal notranslate"><span class="pre">patterns.py</span></code> with the ops your hardware supports.</p></li>
-<li><p>Add a C++ codegen under <code class="docutils literal notranslate"><span class="pre">src/relax/backend/contrib/</span></code> if your
-hardware requires a non-JSON serialization format.</p></li>
-<li><p>Add your cmake module under <code class="docutils literal notranslate"><span class="pre">cmake/modules/contrib/</span></code> following
-the pattern in <code class="docutils literal notranslate"><span class="pre">cmake/modules/contrib/ExampleNPU.cmake</span></code>.</p></li>
+<li><p>Add a C++ codegen under <code class="docutils literal notranslate"><span class="pre">src/relax/backend/contrib/</span></code> if your backend needs
+a non-JSON serialization format.</p></li>
+<li><p>Add a CMake module under <code class="docutils literal notranslate"><span class="pre">cmake/modules/contrib/</span></code> following
+<code class="docutils literal notranslate"><span class="pre">ExampleNPU.cmake</span></code>.</p></li>
</ul>
+<p>For a complete real-backend implementation to study, see the TensorRT
+integration: the pattern table and <code class="docutils literal notranslate"><span class="pre">partition_for_tensorrt</span></code> in
+<code class="docutils literal notranslate"><span class="pre">python/tvm/relax/backend/contrib/tensorrt.py</span></code>, the codegen in
+<code class="docutils literal notranslate"><span class="pre">src/relax/backend/contrib/tensorrt/</span></code>, and the runtime in
+<code class="docutils literal notranslate"><span class="pre">src/runtime/extra/contrib/tensorrt/</span></code>.</p>
<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-how-to-tutorials-bring-your-own-codegen-py">
<div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
<p><a class="reference download internal" download="" href="../../_downloads/2729a585e9deb6295e6d7badc73087d7/bring_your_own_codegen.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">bring_your_own_codegen.ipynb</span></code></a></p>
diff --git a/docs/how_to/tutorials/cross_compilation_and_rpc.html b/docs/how_to/tutorials/cross_compilation_and_rpc.html
index 5bb19f415e8..6504a4e0454 100644
--- a/docs/how_to/tutorials/cross_compilation_and_rpc.html
+++ b/docs/how_to/tutorials/cross_compilation_and_rpc.html
@@ -198,7 +198,7 @@
</li>
<li class="toctree-l1"><a class="reference internal" href="export_and_load_executable.html">Export and Load Relax Executables</a></li>
<li class="toctree-l1"><a class="reference internal" href="mix_python_and_tvm_with_pymodule.html">Mix Python/PyTorch with TVM Using BasePyModule</a></li>
-<li class="toctree-l1"><a class="reference internal" href="bring_your_own_codegen.html">Bring Your Own Codegen: NPU Backend Example</a></li>
+<li class="toctree-l1"><a class="reference internal" href="bring_your_own_codegen.html">Bring Your Own Codegen</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">Deep Dive</span></p>
<ul>
@@ -474,7 +474,7 @@ device and returns the measured cost. Network overhead is excluded.</p>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="n">cost</span><span class="si">:</span><span class="s2">g</span><span class="si">}</span><span class="s2"> secs/op"</span><span class="p">)</span>
</pre></div>
</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>1.13e-07 secs/op
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>1.05e-07 secs/op
</pre></div>
</div>
<dl>
@@ -990,8 +990,8 @@ for ONNX models. Simply replace <code class="docutils literal notranslate"><span
Converted PyTorch model to Relax:
- Number of parameters: 4
Using local target for demonstration
-Exported library to: /tmp/tmp4lmyjg3g/model_deployed.so
-Saved parameters to: /tmp/tmp4lmyjg3g/model_params.npz
+Exported library to: /tmp/tmpvnfep_p3/model_deployed.so
+Saved parameters to: /tmp/tmpvnfep_p3/model_params.npz
RPC workflow (works for any remote device):
==================================================
diff --git a/docs/how_to/tutorials/customize_opt.html b/docs/how_to/tutorials/customize_opt.html
index 64a9d816e96..cb55d02e686 100644
--- a/docs/how_to/tutorials/customize_opt.html
+++ b/docs/how_to/tutorials/customize_opt.html
@@ -204,7 +204,7 @@
<li class="toctree-l1"><a class="reference internal" href="cross_compilation_and_rpc.html">Cross Compilation and RPC</a></li>
<li class="toctree-l1"><a class="reference internal" href="export_and_load_executable.html">Export and Load Relax Executables</a></li>
<li class="toctree-l1"><a class="reference internal" href="mix_python_and_tvm_with_pymodule.html">Mix Python/PyTorch with TVM Using BasePyModule</a></li>
-<li class="toctree-l1"><a class="reference internal" href="bring_your_own_codegen.html">Bring Your Own Codegen: NPU Backend Example</a></li>
+<li class="toctree-l1"><a class="reference internal" href="bring_your_own_codegen.html">Bring Your Own Codegen</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">Deep Dive</span></p>
<ul>
@@ -613,16 +613,16 @@ pushing the performance to the limit. The current optimization may not be the be
<p>We can build and deploy the optimized model to the TVM runtime.</p>
<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><a href="../../reference/api/python/relax/relax.html#tvm.relax.VMExecutable" title="tvm.relax.VMExecutable" class="sphx-glr-backref-module-tvm-relax sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ex</span></a> <span class="o">=</span> <a href="../../reference/api/python/driver.html#tvm.compile" title="tvm.compile" class="sphx-glr-backref-module-tvm sphx-glr-backref-type-py-functi [...]
<span class="n">dev</span> <span class="o">=</span> <span class="n">tvm</span><span class="o">.</span><span class="n">device</span><span class="p">(</span><span class="s2">"cuda"</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
-<span class="n">vm</span> <span class="o">=</span> <a href="../../reference/api/python/relax/relax.html#tvm.relax.VirtualMachine" title="tvm.relax.VirtualMachine" class="sphx-glr-backref-module-tvm-relax sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">relax</span><span class="o">.</span><span class="n">VirtualMachine</span></a><span class="p">(</span><a href="../../reference/api/python/relax/relax.html#tvm.relax.VMExecutable" title="tvm.relax.VMExecutable" class [...]
+<a href="../../reference/api/python/runtime/vm.html#tvm.runtime.vm.VirtualMachine" title="tvm.runtime.vm.VirtualMachine" class="sphx-glr-backref-module-tvm-runtime-vm sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">vm</span></a> <span class="o">=</span> <a href="../../reference/api/python/runtime/vm.html#tvm.runtime.vm.VirtualMachine" title="tvm.runtime.vm.VirtualMachine" class="sphx-glr-backref-module-tvm-runtime-vm sphx-glr-backref-type-py-class"><span class=" [...]
<span class="c1"># Need to allocate data and params on GPU device</span>
<span class="n">data</span> <span class="o">=</span> <span class="n">tvm</span><span class="o">.</span><span class="n">runtime</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="o">*</span><a href="https://docs.python.org/3/library/stdtypes.html#tuple" title="builtins.tuple" class="sphx-glr-ba [...]
<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">gpu_params</span></a> <span class="o">=</span> <span class="p">[</span><span class="n">tvm</span><span class="o">.</span><span class="n">runtime</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n"> [...]
-<span class="n">gpu_out</span> <span class="o">=</span> <span class="n">vm</span><span class="p">[</span><span class="s2">"forward"</span><span class="p">](</span><span class="n">data</span><span class="p">,</span> <span class="o">*</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">gpu_params</span></a><span class="p">)</span [...]
+<span class="n">gpu_out</span> <span class="o">=</span> <a href="../../reference/api/python/runtime/vm.html#tvm.runtime.vm.VirtualMachine" title="tvm.runtime.vm.VirtualMachine" class="sphx-glr-backref-module-tvm-runtime-vm sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">vm</span></a><span class="p">[</span><span class="s2">"forward"</span><span class="p">](</span><span class="n">data</span><span class="p">,</span> <span class="o">*</span><a href="https [...]
<span class="nb">print</span><span class="p">(</span><span class="n">gpu_out</span><span class="p">)</span>
</pre></div>
</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[[23461.07 25317.805 23688.432 25397.043 25643.918 25524.94 25856.713
- 24760.25 24296.184 25380.734]]
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[[25302.758 26301.07 25925.635 24935.379 25223.234 24493.31 24251.89
+ 24630.104 25003.176 23884.219]]
</pre></div>
</div>
</section>
diff --git a/docs/how_to/tutorials/e2e_opt_model.html b/docs/how_to/tutorials/e2e_opt_model.html
index 0d2ffae78a7..39af36c9f5d 100644
--- a/docs/how_to/tutorials/e2e_opt_model.html
+++ b/docs/how_to/tutorials/e2e_opt_model.html
@@ -201,7 +201,7 @@
<li class="toctree-l1"><a class="reference internal" href="cross_compilation_and_rpc.html">Cross Compilation and RPC</a></li>
<li class="toctree-l1"><a class="reference internal" href="export_and_load_executable.html">Export and Load Relax Executables</a></li>
<li class="toctree-l1"><a class="reference internal" href="mix_python_and_tvm_with_pymodule.html">Mix Python/PyTorch with TVM Using BasePyModule</a></li>
-<li class="toctree-l1"><a class="reference internal" href="bring_your_own_codegen.html">Bring Your Own Codegen: NPU Backend Example</a></li>
+<li class="toctree-l1"><a class="reference internal" href="bring_your_own_codegen.html">Bring Your Own Codegen</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">Deep Dive</span></p>
<ul>
@@ -332,8 +332,8 @@ PyTorch.</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Downloading: "https://download.pytorch.org/models/resnet18-f37072fd.pth" to /workspace/.cache/torch/hub/checkpoints/resnet18-f37072fd.pth
0%| | 0.00/44.7M [00:00<?, ?B/s]
- 67%|██████▋ | 29.8M/44.7M [00:00<00:00, 311MB/s]
-100%|██████████| 44.7M/44.7M [00:00<00:00, 336MB/s]
+ 65%|██████▌ | 29.2M/44.7M [00:00<00:00, 306MB/s]
+100%|██████████| 44.7M/44.7M [00:00<00:00, 337MB/s]
</pre></div>
</div>
</section>
@@ -434,7 +434,7 @@ We skip this step in the CI environment.</p>
<span class="n">mod</span> <span class="o">=</span> <span class="n">tvm</span><span class="o">.</span><span class="n">s_tir</span><span class="o">.</span><span class="n">transform</span><span class="o">.</span><span class="n">DefaultGPUSchedule</span><span class="p">()(</span><span class="n">mod</span><span class="p">)</span>
<span class="n">ex</span> <span class="o">=</span> <a href="../../reference/api/python/driver.html#tvm.compile" title="tvm.compile" class="sphx-glr-backref-module-tvm sphx-glr-backref-type-py-function"><span class="n">tvm</span><span class="o">.</span><span class="n">compile</span></a><span class="p">(</span><span class="n">mod</span><span class="p">,</span> <a href="../../reference/api/python/target.html#tvm.target.Target" title="tvm.target.Target" class="sphx-glr-backref-module-tvm [...]
<span class="n">dev</span> <span class="o">=</span> <span class="n">tvm</span><span class="o">.</span><span class="n">device</span><span class="p">(</span><span class="s2">"cuda"</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
- <span class="n">vm</span> <span class="o">=</span> <a href="../../reference/api/python/relax/relax.html#tvm.relax.VirtualMachine" title="tvm.relax.VirtualMachine" class="sphx-glr-backref-module-tvm-relax sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">relax</span><span class="o">.</span><span class="n">VirtualMachine</span></a><span class="p">(</span><span class="n">ex</span><span class="p">,</span> <span class="n">dev</span><span class="p">)</span>
+ <span class="n">vm</span> <span class="o">=</span> <a href="../../reference/api/python/runtime/vm.html#tvm.runtime.vm.VirtualMachine" title="tvm.runtime.vm.VirtualMachine" class="sphx-glr-backref-module-tvm-runtime-vm sphx-glr-backref-type-py-class"><span class="n">relax</span><span class="o">.</span><span class="n">VirtualMachine</span></a><span class="p">(</span><span class="n">ex</span><span class="p">,</span> <span class="n">dev</span><span class="p">)</span>
<span class="c1"># Need to allocate data and params on GPU device</span>
<span class="n">gpu_data</span> <span class="o">=</span> <span class="n">tvm</span><span class="o">.</span><span class="n">runtime</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">224< [...]
<span class="n">gpu_params</span> <span class="o">=</span> <span class="p">[</span><span class="n">tvm</span><span class="o">.</span><span class="n">runtime</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="n">p</span><span class="p">,</span> <span class="n">dev</span><span class="p">)</span> <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">params</span><span class="p">[</span><span class="s2" [...]
diff --git a/docs/how_to/tutorials/export_and_load_executable.html b/docs/how_to/tutorials/export_and_load_executable.html
index 7374773bb04..f552c703ce5 100644
--- a/docs/how_to/tutorials/export_and_load_executable.html
+++ b/docs/how_to/tutorials/export_and_load_executable.html
@@ -202,7 +202,7 @@
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="mix_python_and_tvm_with_pymodule.html">Mix Python/PyTorch with TVM Using BasePyModule</a></li>
-<li class="toctree-l1"><a class="reference internal" href="bring_your_own_codegen.html">Bring Your Own Codegen: NPU Backend Example</a></li>
+<li class="toctree-l1"><a class="reference internal" href="bring_your_own_codegen.html">Bring Your Own Codegen</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">Deep Dive</span></p>
<ul>
@@ -446,7 +446,7 @@ runtime module directly.</p>
<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">RUN_EXAMPLE</span></a><span class="p">:</span>
<span class="n">loaded_rt_mod</span> <span class="o">=</span> <span class="n">tvm</span><span class="o">.</span><span class="n">runtime</span><span class="o">.</span><span class="n">load_module</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">library_path</span><span class="p">))</span>
<span class="n">dev</span> <span class="o">=</span> <span class="n">tvm</span><span class="o">.</span><span class="n">cpu</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
- <span class="n">vm</span> <span class="o">=</span> <a href="../../reference/api/python/relax/relax.html#tvm.relax.VirtualMachine" title="tvm.relax.VirtualMachine" class="sphx-glr-backref-module-tvm-relax sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">relax</span><span class="o">.</span><span class="n">VirtualMachine</span></a><span class="p">(</span><span class="n">loaded_rt_mod</span><span class="p">,</span> <span class="n">dev</span><span class="p">)</span>
+ <span class="n">vm</span> <span class="o">=</span> <a href="../../reference/api/python/runtime/vm.html#tvm.runtime.vm.VirtualMachine" title="tvm.runtime.vm.VirtualMachine" class="sphx-glr-backref-module-tvm-runtime-vm sphx-glr-backref-type-py-class"><span class="n">relax</span><span class="o">.</span><span class="n">VirtualMachine</span></a><span class="p">(</span><span class="n">loaded_rt_mod</span><span class="p">,</span> <span class="n">dev</span><span class="p">)</span>
<span class="c1"># Prepare input data</span>
<span class="n">input_tensor</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">28</span><span class="p">,</span> <span class="mi">28</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">f [...]
@@ -527,7 +527,7 @@ of how to reload and run the model. Save this as <code class="docutils literal n
<span class="c1"># Step 2: Create Virtual Machine</span>
<span class="n">device</span> <span class="o">=</span> <span class="n">tvm</span><span class="o">.</span><span class="n">cpu</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
-<span class="n">vm</span> <span class="o">=</span> <a href="../../reference/api/python/relax/relax.html#tvm.relax.VirtualMachine" title="tvm.relax.VirtualMachine" class="sphx-glr-backref-module-tvm-relax sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">relax</span><span class="o">.</span><span class="n">VirtualMachine</span></a><span class="p">(</span><span class="n">lib</span><span class="p">,</span> <span class="n">device</span><span class="p">)</span>
+<span class="n">vm</span> <span class="o">=</span> <a href="../../reference/api/python/runtime/vm.html#tvm.runtime.vm.VirtualMachine" title="tvm.runtime.vm.VirtualMachine" class="sphx-glr-backref-module-tvm-runtime-vm sphx-glr-backref-type-py-class"><span class="n">relax</span><span class="o">.</span><span class="n">VirtualMachine</span></a><span class="p">(</span><span class="n">lib</span><span class="p">,</span> <span class="n">device</span><span class="p">)</span>
<span class="c1"># Step 3: Load parameters from the .npz file</span>
<span class="n">params_npz</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s2">"relax_export_artifacts/model_params.npz"</span><span class="p">)</span>
@@ -562,7 +562,7 @@ To run on GPU instead of CPU, make the following changes:</p>
</li>
<li><p><strong>Use GPU device in the script</strong>:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">device</span> <span class="o">=</span> <span class="n">tvm</span><span class="o">.</span><span class="n">cuda</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span> <span class="c1"># Use CUDA device instead of CPU</span>
-<span class="n">vm</span> <span class="o">=</span> <a href="../../reference/api/python/relax/relax.html#tvm.relax.VirtualMachine" title="tvm.relax.VirtualMachine" class="sphx-glr-backref-module-tvm-relax sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">relax</span><span class="o">.</span><span class="n">VirtualMachine</span></a><span class="p">(</span><span class="n">lib</span><span class="p">,</span> <span class="n">device</span><span class="p">)</span>
+<span class="n">vm</span> <span class="o">=</span> <a href="../../reference/api/python/runtime/vm.html#tvm.runtime.vm.VirtualMachine" title="tvm.runtime.vm.VirtualMachine" class="sphx-glr-backref-module-tvm-runtime-vm sphx-glr-backref-type-py-class"><span class="n">relax</span><span class="o">.</span><span class="n">VirtualMachine</span></a><span class="p">(</span><span class="n">lib</span><span class="p">,</span> <span class="n">device</span><span class="p">)</span>
<span class="c1"># Load parameters to GPU</span>
<span class="n">params</span> <span class="o">=</span> <span class="p">[</span><span class="n">tvm</span><span class="o">.</span><span class="n">runtime</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="n">params_npz</span><span class="p">[</span><span class="sa">f</span><span class="s2">"p_</span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s2">"</span><span class="p">],</span> <span class= [...]
@@ -625,7 +625,7 @@ for a comprehensive guide on:</p>
<span class="c1"># Step 4: Load and run on remote device</span>
<span class="n">lib</span> <span class="o">=</span> <span class="n">remote</span><span class="o">.</span><span class="n">load_module</span><span class="p">(</span><span class="s2">"mlp_arm.so"</span><span class="p">)</span>
-<span class="n">vm</span> <span class="o">=</span> <a href="../../reference/api/python/relax/relax.html#tvm.relax.VirtualMachine" title="tvm.relax.VirtualMachine" class="sphx-glr-backref-module-tvm-relax sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">relax</span><span class="o">.</span><span class="n">VirtualMachine</span></a><span class="p">(</span><span class="n">lib</span><span class="p">,</span> <span class="n">remote</span><span class="o">.</span><span cla [...]
+<span class="n">vm</span> <span class="o">=</span> <a href="../../reference/api/python/runtime/vm.html#tvm.runtime.vm.VirtualMachine" title="tvm.runtime.vm.VirtualMachine" class="sphx-glr-backref-module-tvm-runtime-vm sphx-glr-backref-type-py-class"><span class="n">relax</span><span class="o">.</span><span class="n">VirtualMachine</span></a><span class="p">(</span><span class="n">lib</span><span class="p">,</span> <span class="n">remote</span><span class="o">.</span><span class="n">cpu</ [...]
<span class="c1"># ... prepare input and params, then run inference</span>
</pre></div>
</div>
diff --git a/docs/how_to/tutorials/import_model.html b/docs/how_to/tutorials/import_model.html
index bf683b39989..9ab7a705375 100644
--- a/docs/how_to/tutorials/import_model.html
+++ b/docs/how_to/tutorials/import_model.html
@@ -212,7 +212,7 @@
<li class="toctree-l1"><a class="reference internal" href="cross_compilation_and_rpc.html">Cross Compilation and RPC</a></li>
<li class="toctree-l1"><a class="reference internal" href="export_and_load_executable.html">Export and Load Relax Executables</a></li>
<li class="toctree-l1"><a class="reference internal" href="mix_python_and_tvm_with_pymodule.html">Mix Python/PyTorch with TVM Using BasePyModule</a></li>
-<li class="toctree-l1"><a class="reference internal" href="bring_your_own_codegen.html">Bring Your Own Codegen: NPU Backend Example</a></li>
+<li class="toctree-l1"><a class="reference internal" href="bring_your_own_codegen.html">Bring Your Own Codegen</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">Deep Dive</span></p>
<ul>
@@ -530,13 +530,13 @@ shown below.</p>
<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">mod_compiled</span> <span class="o">=</span> <a href="../../reference/api/python/relax/relax.html#tvm.relax.get_pipeline" title="tvm.relax.get_pipeline" class="sphx-glr-backref-module-tvm-relax sphx-glr-backref-type-py-function"><span class="n">relax</span><span class="o">.</span><span class="n">get_pipeline</span></a><span class="p">(</span><span class="s2">"zero"</span><span cla [...]
<a href="../../reference/api/python/relax/relax.html#tvm.relax.VMExecutable" title="tvm.relax.VMExecutable" class="sphx-glr-backref-module-tvm-relax sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">exec_module</span></a> <span class="o">=</span> <a href="../../reference/api/python/driver.html#tvm.compile" title="tvm.compile" class="sphx-glr-backref-module-tvm sphx-glr-backref-type-py-function"><span class="n">tvm</span><span class="o">.</span><span class="n">comp [...]
<span class="n">dev</span> <span class="o">=</span> <span class="n">tvm</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span>
-<span class="n">vm</span> <span class="o">=</span> <a href="../../reference/api/python/relax/relax.html#tvm.relax.VirtualMachine" title="tvm.relax.VirtualMachine" class="sphx-glr-backref-module-tvm-relax sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">relax</span><span class="o">.</span><span class="n">VirtualMachine</span></a><span class="p">(</span><a href="../../reference/api/python/relax/relax.html#tvm.relax.VMExecutable" title="tvm.relax.VMExecutable" class [...]
+<a href="../../reference/api/python/runtime/vm.html#tvm.runtime.vm.VirtualMachine" title="tvm.runtime.vm.VirtualMachine" class="sphx-glr-backref-module-tvm-runtime-vm sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">vm</span></a> <span class="o">=</span> <a href="../../reference/api/python/runtime/vm.html#tvm.runtime.vm.VirtualMachine" title="tvm.runtime.vm.VirtualMachine" class="sphx-glr-backref-module-tvm-runtime-vm sphx-glr-backref-type-py-class"><span class=" [...]
<span class="c1"># Run inference</span>
<span class="n">input_data</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">32</span><span class="p">,</span> <span class="mi">32</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><s [...]
<span class="n">tvm_input</span> <span class="o">=</span> <span class="n">tvm</span><span class="o">.</span><span class="n">runtime</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="n">input_data</span><span class="p">,</span> <span class="n">dev</span><span class="p">)</span>
<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tvm_params</span></a> <span class="o">=</span> <span class="p">[</span><span class="n">tvm</span><span class="o">.</span><span class="n">runtime</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="n">p</span><span class="p">,</span> <span class="n"> [...]
-<span class="n">tvm_out</span> <span class="o">=</span> <span class="n">vm</span><span class="p">[</span><span class="s2">"main"</span><span class="p">](</span><span class="n">tvm_input</span><span class="p">,</span> <span class="o">*</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tvm_params</span></a><span class="p">)</sp [...]
+<span class="n">tvm_out</span> <span class="o">=</span> <a href="../../reference/api/python/runtime/vm.html#tvm.runtime.vm.VirtualMachine" title="tvm.runtime.vm.VirtualMachine" class="sphx-glr-backref-module-tvm-runtime-vm sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">vm</span></a><span class="p">[</span><span class="s2">"main"</span><span class="p">](</span><span class="n">tvm_input</span><span class="p">,</span> <span class="o">*</span><a href="htt [...]
<span class="c1"># Compare with PyTorch</span>
<span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">():</span>
diff --git a/docs/how_to/tutorials/index.html b/docs/how_to/tutorials/index.html
index 77a7e6194bc..79eb7b760d5 100644
--- a/docs/how_to/tutorials/index.html
+++ b/docs/how_to/tutorials/index.html
@@ -190,7 +190,7 @@
<li class="toctree-l1"><a class="reference internal" href="cross_compilation_and_rpc.html">Cross Compilation and RPC</a></li>
<li class="toctree-l1"><a class="reference internal" href="export_and_load_executable.html">Export and Load Relax Executables</a></li>
<li class="toctree-l1"><a class="reference internal" href="mix_python_and_tvm_with_pymodule.html">Mix Python/PyTorch with TVM Using BasePyModule</a></li>
-<li class="toctree-l1"><a class="reference internal" href="bring_your_own_codegen.html">Bring Your Own Codegen: NPU Backend Example</a></li>
+<li class="toctree-l1"><a class="reference internal" href="bring_your_own_codegen.html">Bring Your Own Codegen</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">Deep Dive</span></p>
<ul>
@@ -297,9 +297,9 @@
<section id="how-to">
<h1>HOW TO<a class="headerlink" href="#how-to" title="Link to this heading"></a></h1>
-<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to integrate a custom hardware backend with TVM's BYOC framework, using the bundled example NPU backend (CPU emulation, no real hardware required) as the worked example. You will see the key concepts needed to offload operations to a custom accelerator: pattern registration, graph partitioning, codegen, and runtime dispatch."><img alt="" src="../../_images/sphx_glr_bring_your_own_ [...]
-<p><a class="reference internal" href="bring_your_own_codegen.html"><span class="doc">Bring Your Own Codegen: NPU Backend Example</span></a></p>
- <div class="sphx-glr-thumbnail-title">Bring Your Own Codegen: NPU Backend Example</div>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="TVM's Bring Your Own Codegen (BYOC) framework lets you offload parts of a model to a custom backend -- a hardware accelerator, an inference library, or your own kernels -- while TVM compiles the rest. This tutorial has two parts:"><img alt="" src="../../_images/sphx_glr_bring_your_own_codegen_thumb.png" />
+<p><a class="reference internal" href="bring_your_own_codegen.html"><span class="doc">Bring Your Own Codegen</span></a></p>
+ <div class="sphx-glr-thumbnail-title">Bring Your Own Codegen</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial introduces cross compilation and remote device execution with RPC in TVM."><img alt="" src="../../_images/sphx_glr_cross_compilation_and_rpc_thumb.png" />
<p><a class="reference internal" href="cross_compilation_and_rpc.html"><span class="doc">Cross Compilation and RPC</span></a></p>
<div class="sphx-glr-thumbnail-title">Cross Compilation and RPC</div>
diff --git a/docs/how_to/tutorials/mix_python_and_tvm_with_pymodule.html b/docs/how_to/tutorials/mix_python_and_tvm_with_pymodule.html
index c49439f24e9..200158d7e0b 100644
--- a/docs/how_to/tutorials/mix_python_and_tvm_with_pymodule.html
+++ b/docs/how_to/tutorials/mix_python_and_tvm_with_pymodule.html
@@ -45,7 +45,7 @@
<script type="text/javascript" src="../../_static/js/tlcpack_theme.js"></script>
<link rel="index" title="Index" href="../../genindex.html" />
<link rel="search" title="Search" href="../../search.html" />
- <link rel="next" title="Bring Your Own Codegen: NPU Backend Example" href="bring_your_own_codegen.html" />
+ <link rel="next" title="Bring Your Own Codegen" href="bring_your_own_codegen.html" />
<link rel="prev" title="Export and Load Relax Executables" href="export_and_load_executable.html" />
</head>
@@ -202,7 +202,7 @@
<li class="toctree-l2"><a class="reference internal" href="#summary">Summary</a></li>
</ul>
</li>
-<li class="toctree-l1"><a class="reference internal" href="bring_your_own_codegen.html">Bring Your Own Codegen: NPU Backend Example</a></li>
+<li class="toctree-l1"><a class="reference internal" href="bring_your_own_codegen.html">Bring Your Own Codegen</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">Deep Dive</span></p>
<ul>
@@ -772,7 +772,7 @@ tensors at call time, so the same module handles different sizes without recompi
<div class="rst-footer-buttons" role="navigation" aria-label="footer navigation">
- <a href="bring_your_own_codegen.html" class="btn btn-neutral float-right" title="Bring Your Own Codegen: NPU Backend Example" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right"></span></a>
+ <a href="bring_your_own_codegen.html" class="btn btn-neutral float-right" title="Bring Your Own Codegen" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right"></span></a>
<a href="export_and_load_executable.html" class="btn btn-neutral float-left" title="Export and Load Relax Executables" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left"></span> Previous</a>
diff --git a/docs/how_to/tutorials/optimize_llm.html b/docs/how_to/tutorials/optimize_llm.html
index 7cec203820f..be25beff513 100644
--- a/docs/how_to/tutorials/optimize_llm.html
+++ b/docs/how_to/tutorials/optimize_llm.html
@@ -207,7 +207,7 @@
<li class="toctree-l1"><a class="reference internal" href="cross_compilation_and_rpc.html">Cross Compilation and RPC</a></li>
<li class="toctree-l1"><a class="reference internal" href="export_and_load_executable.html">Export and Load Relax Executables</a></li>
<li class="toctree-l1"><a class="reference internal" href="mix_python_and_tvm_with_pymodule.html">Mix Python/PyTorch with TVM Using BasePyModule</a></li>
-<li class="toctree-l1"><a class="reference internal" href="bring_your_own_codegen.html">Bring Your Own Codegen: NPU Backend Example</a></li>
+<li class="toctree-l1"><a class="reference internal" href="bring_your_own_codegen.html">Bring Your Own Codegen</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">Deep Dive</span></p>
<ul>
@@ -728,7 +728,7 @@ is designed specifically for the LLMs.</p>
<span class="k">with</span> <a href="../../reference/api/python/target.html#tvm.target.Target" title="tvm.target.Target" class="sphx-glr-backref-module-tvm-target sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">target</span></a><span class="p">:</span>
<a href="../../reference/api/python/relax/relax.html#tvm.relax.VMExecutable" title="tvm.relax.VMExecutable" class="sphx-glr-backref-module-tvm-relax sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ex</span></a> <span class="o">=</span> <a href="../../reference/api/python/driver.html#tvm.compile" title="tvm.compile" class="sphx-glr-backref-module-tvm sphx-glr-backref-type-py-function"><span class="n">tvm</span><span class="o">.</span><span class="n">compile</ [...]
- <span class="n">vm</span> <span class="o">=</span> <a href="../../reference/api/python/relax/relax.html#tvm.relax.VirtualMachine" title="tvm.relax.VirtualMachine" class="sphx-glr-backref-module-tvm-relax sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">relax</span><span class="o">.</span><span class="n">VirtualMachine</span></a><span class="p">(</span><a href="../../reference/api/python/relax/relax.html#tvm.relax.VMExecutable" title="tvm.relax.VMExecutable" c [...]
+ <a href="../../reference/api/python/runtime/vm.html#tvm.runtime.vm.VirtualMachine" title="tvm.runtime.vm.VirtualMachine" class="sphx-glr-backref-module-tvm-runtime-vm sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">vm</span></a> <span class="o">=</span> <a href="../../reference/api/python/runtime/vm.html#tvm.runtime.vm.VirtualMachine" title="tvm.runtime.vm.VirtualMachine" class="sphx-glr-backref-module-tvm-runtime-vm sphx-glr-backref-type-py-class"><span cla [...]
</pre></div>
</div>
</section>
@@ -826,7 +826,7 @@ the model documentation for the correct tokenization and prompt format.</p>
key and value tensors for the attention layer. Apache TVM provides a PagedKVCache to store the
key and value tensors. We create the PagedKVCache with the specified parameters.</p>
<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="k">if</span> <span class="ow">not</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">IS_IN_CI</span></a><span class="p">:</span>
- <span class="n">kv_cache</span> <span class="o">=</span> <span class="n">vm</span><span class="p">[</span><span class="s2">"create_tir_paged_kv_cache"</span><span class="p">](</span>
+ <span class="n">kv_cache</span> <span class="o">=</span> <a href="../../reference/api/python/runtime/vm.html#tvm.runtime.vm.VirtualMachine" title="tvm.runtime.vm.VirtualMachine" class="sphx-glr-backref-module-tvm-runtime-vm sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">vm</span></a><span class="p">[</span><span class="s2">"create_tir_paged_kv_cache"</span><span class="p">](</span>
<a href="https://docs.python.org/3/library/stdtypes.html#tuple" title="builtins.tuple" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class"><span class="n">Shape</span></a><span class="p">([</span><span class="mi">1</span><span class="p">]),</span> <span class="c1"># max_batch_size=1</span>
<a href="https://docs.python.org/3/library/stdtypes.html#tuple" title="builtins.tuple" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class"><span class="n">Shape</span></a><span class="p">([</span><span class="mi">2048</span><span class="p">]),</span> <span class="c1"># max_total_seq_len=2048</span>
<a href="https://docs.python.org/3/library/stdtypes.html#tuple" title="builtins.tuple" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class"><span class="n">Shape</span></a><span class="p">([</span><span class="mi">2048</span><span class="p">]),</span> <span class="c1"># prefill_chunk_size=2048</span>
@@ -843,7 +843,7 @@ compiled in the Relax IRModule to embed the tokens into the hidden states.</p>
<span class="k">def</span><span class="w"> </span><span class="nf">embed</span><span class="p">(</span><span class="n">tokens</span><span class="p">,</span> <span class="n">params</span><span class="p">):</span>
- <span class="n">_embed</span> <span class="o">=</span> <span class="n">vm</span><span class="p">[</span><span class="s2">"embed"</span><span class="p">](</span><span class="n">tokens</span><span class="p">,</span> <span class="n">params</span><span class="p">)</span>
+ <span class="n">_embed</span> <span class="o">=</span> <a href="../../reference/api/python/runtime/vm.html#tvm.runtime.vm.VirtualMachine" title="tvm.runtime.vm.VirtualMachine" class="sphx-glr-backref-module-tvm-runtime-vm sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">vm</span></a><span class="p">[</span><span class="s2">"embed"</span><span class="p">](</span><span class="n">tokens</span><span class="p">,</span> <span class="n">params</span><span [...]
<span class="c1"># Reshape hidden from [seq_len, hidden_size] to [1, seq_len, hidden_size]</span>
<span class="n">_embed</span> <span class="o">=</span> <span class="n">nd_view_func</span><span class="p">(</span><span class="n">_embed</span><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#tuple" title="builtins.tuple" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class"><span class="n">Shape</span></a><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="n">_embed</span><span class="o">.</span>< [...]
<span class="k">return</span> <span class="n">_embed</span>
@@ -866,7 +866,7 @@ and <cite>end_forward_func</cite> to end the forward pass.</p>
<span class="n">add_sequence_func</span><span class="p">(</span><span class="n">kv_cache</span><span class="p">,</span> <span class="n">seq_id</span><span class="p">)</span>
<span class="n">hidden_states</span> <span class="o">=</span> <span class="n">embed</span><span class="p">(</span><span class="n">tokens</span><span class="p">,</span> <span class="n">params</span><span class="p">)</span>
<span class="n">begin_forward_func</span><span class="p">(</span><span class="n">kv_cache</span><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#tuple" title="builtins.tuple" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class"><span class="n">Shape</span></a><span class="p">([</span><span class="n">seq_id</span><span class="p">]),</span> <a href="https://docs.python.org/3/library/stdtypes.html#tuple" title="builtins.tuple" class="s [...]
- <span class="n">logits</span><span class="p">,</span> <span class="n">kv_cache</span> <span class="o">=</span> <span class="n">vm</span><span class="p">[</span><span class="s2">"prefill"</span><span class="p">](</span><span class="n">hidden_states</span><span class="p">,</span> <span class="n">kv_cache</span><span class="p">,</span> <span class="n">params</span><span class="p">)</span>
+ <span class="n">logits</span><span class="p">,</span> <span class="n">kv_cache</span> <span class="o">=</span> <a href="../../reference/api/python/runtime/vm.html#tvm.runtime.vm.VirtualMachine" title="tvm.runtime.vm.VirtualMachine" class="sphx-glr-backref-module-tvm-runtime-vm sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">vm</span></a><span class="p">[</span><span class="s2">"prefill"</span><span class="p">](</span><span class="n">hidden_states</ [...]
<span class="n">end_forward_func</span><span class="p">(</span><span class="n">kv_cache</span><span class="p">)</span>
</pre></div>
</div>
@@ -898,7 +898,7 @@ IRModule to generate the token.</p>
<span class="n">tokens</span> <span class="o">=</span> <span class="n">tvm</span><span class="o">.</span><span class="n">runtime</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">last_token</span><span class="p">])</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s2">"int32"< [...]
<span class="n">hidden_states</span> <span class="o">=</span> <span class="n">embed</span><span class="p">(</span><span class="n">tokens</span><span class="p">,</span> <span class="n">params</span><span class="p">)</span>
<span class="n">begin_forward_func</span><span class="p">(</span><span class="n">kv_cache</span><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#tuple" title="builtins.tuple" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class"><span class="n">Shape</span></a><span class="p">([</span><span class="n">seq_id</span><span class="p">]),</span> <a href="https://docs.python.org/3/library/stdtypes.html#tuple" title="builtins.tuple" clas [...]
- <span class="n">logits</span><span class="p">,</span> <span class="n">kv_cache</span> <span class="o">=</span> <span class="n">vm</span><span class="p">[</span><span class="s2">"decode"</span><span class="p">](</span><span class="n">hidden_states</span><span class="p">,</span> <span class="n">kv_cache</span><span class="p">,</span> <span class="n">params</span><span class="p">)</span>
+ <span class="n">logits</span><span class="p">,</span> <span class="n">kv_cache</span> <span class="o">=</span> <a href="../../reference/api/python/runtime/vm.html#tvm.runtime.vm.VirtualMachine" title="tvm.runtime.vm.VirtualMachine" class="sphx-glr-backref-module-tvm-runtime-vm sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">vm</span></a><span class="p">[</span><span class="s2">"decode"</span><span class="p">](</span><span class="n">hidden_state [...]
<span class="n">end_forward_func</span><span class="p">(</span><span class="n">kv_cache</span><span class="p">)</span>
<span class="n">last_token</span> <span class="o">=</span> <span class="n">sample_token</span><span class="p">(</span><span class="n">logits</span><span class="p">)</span>
diff --git a/docs/how_to/tutorials/sg_execution_times.html b/docs/how_to/tutorials/sg_execution_times.html
index a5b207631d4..349e594bfd9 100644
--- a/docs/how_to/tutorials/sg_execution_times.html
+++ b/docs/how_to/tutorials/sg_execution_times.html
@@ -190,7 +190,7 @@
<li class="toctree-l1"><a class="reference internal" href="cross_compilation_and_rpc.html">Cross Compilation and RPC</a></li>
<li class="toctree-l1"><a class="reference internal" href="export_and_load_executable.html">Export and Load Relax Executables</a></li>
<li class="toctree-l1"><a class="reference internal" href="mix_python_and_tvm_with_pymodule.html">Mix Python/PyTorch with TVM Using BasePyModule</a></li>
-<li class="toctree-l1"><a class="reference internal" href="bring_your_own_codegen.html">Bring Your Own Codegen: NPU Backend Example</a></li>
+<li class="toctree-l1"><a class="reference internal" href="bring_your_own_codegen.html">Bring Your Own Codegen</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">Deep Dive</span></p>
<ul>
@@ -297,7 +297,7 @@
<section id="computation-times">
<span id="sphx-glr-how-to-tutorials-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Link to this heading"></a></h1>
-<p><strong>00:16.819</strong> total execution time for 8 files <strong>from how_to/tutorials</strong>:</p>
+<p><strong>00:16.742</strong> total execution time for 8 files <strong>from how_to/tutorials</strong>:</p>
<div class="docutils container">
<style scoped>
<link href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/5.3.0/css/bootstrap.min.css" rel="stylesheet" />
@@ -319,27 +319,27 @@ $(document).ready( function () {
</thead>
<tbody>
<tr class="row-even"><td><p><a class="reference internal" href="optimize_llm.html#sphx-glr-how-to-tutorials-optimize-llm-py"><span class="std std-ref">Optimize Large Language Model</span></a> (<code class="docutils literal notranslate"><span class="pre">optimize_llm.py</span></code>)</p></td>
-<td><p>00:11.563</p></td>
+<td><p>00:11.451</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="import_model.html#sphx-glr-how-to-tutorials-import-model-py"><span class="std std-ref">Importing Models from ML Frameworks</span></a> (<code class="docutils literal notranslate"><span class="pre">import_model.py</span></code>)</p></td>
-<td><p>00:03.659</p></td>
+<td><p>00:03.654</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="e2e_opt_model.html#sphx-glr-how-to-tutorials-e2e-opt-model-py"><span class="std std-ref">End-to-End Optimize Model</span></a> (<code class="docutils literal notranslate"><span class="pre">e2e_opt_model.py</span></code>)</p></td>
-<td><p>00:00.646</p></td>
+<td><p>00:00.629</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="cross_compilation_and_rpc.html#sphx-glr-how-to-tutorials-cross-compilation-and-rpc-py"><span class="std std-ref">Cross Compilation and RPC</span></a> (<code class="docutils literal notranslate"><span class="pre">cross_compilation_and_rpc.py</span></code>)</p></td>
-<td><p>00:00.565</p></td>
+<td><p>00:00.570</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="customize_opt.html#sphx-glr-how-to-tutorials-customize-opt-py"><span class="std std-ref">Customize Optimization</span></a> (<code class="docutils literal notranslate"><span class="pre">customize_opt.py</span></code>)</p></td>
-<td><p>00:00.331</p></td>
+<td><p>00:00.388</p></td>
<td><p>0.0</p></td>
</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="bring_your_own_codegen.html#sphx-glr-how-to-tutorials-bring-your-own-codegen-py"><span class="std std-ref">Bring Your Own Codegen: NPU Backend Example</span></a> (<code class="docutils literal notranslate"><span class="pre">bring_your_own_codegen.py</span></code>)</p></td>
-<td><p>00:00.048</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="bring_your_own_codegen.html#sphx-glr-how-to-tutorials-bring-your-own-codegen-py"><span class="std std-ref">Bring Your Own Codegen</span></a> (<code class="docutils literal notranslate"><span class="pre">bring_your_own_codegen.py</span></code>)</p></td>
+<td><p>00:00.043</p></td>
<td><p>0.0</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="mix_python_and_tvm_with_pymodule.html#sphx-glr-how-to-tutorials-mix-python-and-tvm-with-pymodule-py"><span class="std std-ref">Mix Python/PyTorch with TVM Using BasePyModule</span></a> (<code class="docutils literal notranslate"><span class="pre">mix_python_and_tvm_with_pymodule.py</span></code>)</p></td>
diff --git a/docs/index.html b/docs/index.html
index 78b68926908..aac31640c5b 100644
--- a/docs/index.html
+++ b/docs/index.html
@@ -192,7 +192,7 @@
<li class="toctree-l1"><a class="reference internal" href="how_to/tutorials/cross_compilation_and_rpc.html">Cross Compilation and RPC</a></li>
<li class="toctree-l1"><a class="reference internal" href="how_to/tutorials/export_and_load_executable.html">Export and Load Relax Executables</a></li>
<li class="toctree-l1"><a class="reference internal" href="how_to/tutorials/mix_python_and_tvm_with_pymodule.html">Mix Python/PyTorch with TVM Using BasePyModule</a></li>
-<li class="toctree-l1"><a class="reference internal" href="how_to/tutorials/bring_your_own_codegen.html">Bring Your Own Codegen: NPU Backend Example</a></li>
+<li class="toctree-l1"><a class="reference internal" href="how_to/tutorials/bring_your_own_codegen.html">Bring Your Own Codegen</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">Deep Dive</span></p>
<ul>
@@ -328,7 +328,7 @@ driving its costs down.</p>
<li class="toctree-l1"><a class="reference internal" href="how_to/tutorials/cross_compilation_and_rpc.html">Cross Compilation and RPC</a></li>
<li class="toctree-l1"><a class="reference internal" href="how_to/tutorials/export_and_load_executable.html">Export and Load Relax Executables</a></li>
<li class="toctree-l1"><a class="reference internal" href="how_to/tutorials/mix_python_and_tvm_with_pymodule.html">Mix Python/PyTorch with TVM Using BasePyModule</a></li>
-<li class="toctree-l1"><a class="reference internal" href="how_to/tutorials/bring_your_own_codegen.html">Bring Your Own Codegen: NPU Backend Example</a></li>
+<li class="toctree-l1"><a class="reference internal" href="how_to/tutorials/bring_your_own_codegen.html">Bring Your Own Codegen</a></li>
</ul>
</div>
<div class="toctree-wrapper compound">
diff --git a/docs/install/docker.html b/docs/install/docker.html
index 37beea33da2..f04a6b06536 100644
--- a/docs/install/docker.html
+++ b/docs/install/docker.html
@@ -199,7 +199,7 @@
<li class="toctree-l1"><a class="reference internal" href="../how_to/tutorials/cross_compilation_and_rpc.html">Cross Compilation and RPC</a></li>
<li class="toctree-l1"><a class="reference internal" href="../how_to/tutorials/export_and_load_executable.html">Export and Load Relax Executables</a></li>
<li class="toctree-l1"><a class="reference internal" href="../how_to/tutorials/mix_python_and_tvm_with_pymodule.html">Mix Python/PyTorch with TVM Using BasePyModule</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../how_to/tutorials/bring_your_own_codegen.html">Bring Your Own Codegen: NPU Backend Example</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../how_to/tutorials/bring_your_own_codegen.html">Bring Your Own Codegen</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">Deep Dive</span></p>
<ul>
diff --git a/docs/install/from_source.html b/docs/install/from_source.html
index 20a3ad377f3..d7338214b83 100644
--- a/docs/install/from_source.html
+++ b/docs/install/from_source.html
@@ -220,7 +220,7 @@
<li class="toctree-l1"><a class="reference internal" href="../how_to/tutorials/cross_compilation_and_rpc.html">Cross Compilation and RPC</a></li>
<li class="toctree-l1"><a class="reference internal" href="../how_to/tutorials/export_and_load_executable.html">Export and Load Relax Executables</a></li>
<li class="toctree-l1"><a class="reference internal" href="../how_to/tutorials/mix_python_and_tvm_with_pymodule.html">Mix Python/PyTorch with TVM Using BasePyModule</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../how_to/tutorials/bring_your_own_codegen.html">Bring Your Own Codegen: NPU Backend Example</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../how_to/tutorials/bring_your_own_codegen.html">Bring Your Own Codegen</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">Deep Dive</span></p>
<ul>
diff --git a/docs/install/index.html b/docs/install/index.html
index b003e26d417..804b59d023a 100644
--- a/docs/install/index.html
+++ b/docs/install/index.html
@@ -196,7 +196,7 @@
<li class="toctree-l1"><a class="reference internal" href="../how_to/tutorials/cross_compilation_and_rpc.html">Cross Compilation and RPC</a></li>
<li class="toctree-l1"><a class="reference internal" href="../how_to/tutorials/export_and_load_executable.html">Export and Load Relax Executables</a></li>
<li class="toctree-l1"><a class="reference internal" href="../how_to/tutorials/mix_python_and_tvm_with_pymodule.html">Mix Python/PyTorch with TVM Using BasePyModule</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../how_to/tutorials/bring_your_own_codegen.html">Bring Your Own Codegen: NPU Backend Example</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../how_to/tutorials/bring_your_own_codegen.html">Bring Your Own Codegen</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">Deep Dive</span></p>
<ul>
diff --git a/docs/objects.inv b/docs/objects.inv
index f2bb25365db..ae6fa0c81b1 100644
Binary files a/docs/objects.inv and b/docs/objects.inv differ
diff --git a/docs/py-modindex.html b/docs/py-modindex.html
index b38384d0e7d..322035ed5a5 100644
--- a/docs/py-modindex.html
+++ b/docs/py-modindex.html
@@ -192,7 +192,7 @@
<li class="toctree-l1"><a class="reference internal" href="how_to/tutorials/cross_compilation_and_rpc.html">Cross Compilation and RPC</a></li>
<li class="toctree-l1"><a class="reference internal" href="how_to/tutorials/export_and_load_executable.html">Export and Load Relax Executables</a></li>
<li class="toctree-l1"><a class="reference internal" href="how_to/tutorials/mix_python_and_tvm_with_pymodule.html">Mix Python/PyTorch with TVM Using BasePyModule</a></li>
-<li class="toctree-l1"><a class="reference internal" href="how_to/tutorials/bring_your_own_codegen.html">Bring Your Own Codegen: NPU Backend Example</a></li>
+<li class="toctree-l1"><a class="reference internal" href="how_to/tutorials/bring_your_own_codegen.html">Bring Your Own Codegen</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">Deep Dive</span></p>
<ul>
diff --git a/docs/reference/api/doxygen/analyzer_8h_source.html b/docs/reference/api/doxygen/analyzer_8h_source.html
index f36f25ea8d8..2e3a07b2598 100644
--- a/docs/reference/api/doxygen/analyzer_8h_source.html
+++ b/docs/reference/api/doxygen/analyzer_8h_source.html
@@ -181,354 +181,363 @@ $(function() {
<div class="line"><a name="l00181"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1ConstIntBoundAnalyzer.html#ab8bf22547cf1df0a28fc4ee98841ab89"> 181</a></span>  <span class="keyword">friend</span> <span class="keyword">class </span><a class="code" href="classtvm_1_1arith_1_1ConstraintContext.html">ConstraintContext</a>;</div>
<div class="line"><a name="l00182"></a><span class="lineno"> 182</span>  <span class="keyword">explicit</span> <a class="code" href="classtvm_1_1arith_1_1ConstIntBoundAnalyzer.html">ConstIntBoundAnalyzer</a>(<a class="code" href="classtvm_1_1arith_1_1AnalyzerObj.html">AnalyzerObj</a>* parent);</div>
<div class="line"><a name="l00183"></a><span class="lineno"> 183</span>  TVM_DLL ~<a class="code" href="classtvm_1_1arith_1_1ConstIntBoundAnalyzer.html">ConstIntBoundAnalyzer</a>();</div>
-<div class="line"><a name="l00190"></a><span class="lineno"> 190</span>  std::function<void()> EnterConstraint(<span class="keyword">const</span> <a class="code" href="classtvm_1_1PrimExpr.html">PrimExpr</a>& constraint);</div>
-<div class="line"><a name="l00191"></a><span class="lineno"> 191</span>  <span class="keyword">struct </span>Entry;</div>
-<div class="line"><a name="l00192"></a><span class="lineno"> 192</span>  <span class="keyword">class </span>Impl;</div>
-<div class="line"><a name="l00194"></a><span class="lineno"> 194</span>  Impl* impl_;</div>
-<div class="line"><a name="l00195"></a><span class="lineno"> 195</span> };</div>
-<div class="line"><a name="l00196"></a><span class="lineno"> 196</span>  </div>
-<div class="line"><a name="l00209"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1ModularSetNode.html"> 209</a></span> <span class="keyword">class </span><a class="code" href="classtvm_1_1arith_1_1ModularSetNode.html">ModularSetNode</a> : <span class="keyword">public</span> ffi::Object {</div>
-<div class="line"><a name="l00210"></a><span class="lineno"> 210</span>  <span class="keyword">public</span>:</div>
-<div class="line"><a name="l00212"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1ModularSetNode.html#a0531d08d8529454c726a9a20a2002ee6"> 212</a></span>  int64_t <a class="code" href="classtvm_1_1arith_1_1ModularSetNode.html#a0531d08d8529454c726a9a20a2002ee6">coeff</a>;</div>
-<div class="line"><a name="l00214"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1ModularSetNode.html#afe63e5586712774be5935e86121431ad"> 214</a></span>  int64_t <a class="code" href="classtvm_1_1arith_1_1ModularSetNode.html#afe63e5586712774be5935e86121431ad">base</a>;</div>
-<div class="line"><a name="l00215"></a><span class="lineno"> 215</span>  </div>
-<div class="line"><a name="l00216"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1ModularSetNode.html#a680e547835e5062f0c88e0f982bcc805"> 216</a></span>  <span class="keyword">static</span> <span class="keywordtype">void</span> <a class="code" href="classtvm_1_1arith_1_1ModularSetNode.html#a680e547835e5062f0c88e0f982bcc805">RegisterReflection</a>() {</div>
-<div class="line"><a name="l00217"></a><span class="lineno"> 217</span>  <span class="keyword">namespace </span>refl = tvm::ffi::reflection;</div>
-<div class="line"><a name="l00218"></a><span class="lineno"> 218</span>  refl::ObjectDef<ModularSetNode>()</div>
-<div class="line"><a name="l00219"></a><span class="lineno"> 219</span>  .def_ro(<span class="stringliteral">"coeff"</span>, &<a class="code" href="classtvm_1_1arith_1_1ModularSetNode.html#a0531d08d8529454c726a9a20a2002ee6">ModularSetNode::coeff</a>)</div>
-<div class="line"><a name="l00220"></a><span class="lineno"> 220</span>  .def_ro(<span class="stringliteral">"base"</span>, &<a class="code" href="classtvm_1_1arith_1_1ModularSetNode.html#afe63e5586712774be5935e86121431ad">ModularSetNode::base</a>);</div>
-<div class="line"><a name="l00221"></a><span class="lineno"> 221</span>  }</div>
-<div class="line"><a name="l00222"></a><span class="lineno"> 222</span>  </div>
-<div class="line"><a name="l00223"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1ModularSetNode.html#ac08e3992efa956f31feaf12cd360b578"> 223</a></span>  <span class="keyword">static</span> constexpr TVMFFISEqHashKind <a class="code" href="classtvm_1_1arith_1_1ModularSetNode.html#ac08e3992efa956f31feaf12cd360b578">_type_s_eq_hash_kind</a> = kTVMFFISEqHashKindTreeNode;</div>
-<div class="line"><a name="l00224"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1ModularSetNode.html#ab5e5f8aa92fe0e785bd24ae47f29fd4e"> 224</a></span>  <a class="code" href="classtvm_1_1arith_1_1ModularSetNode.html#ab5e5f8aa92fe0e785bd24ae47f29fd4e">TVM_FFI_DECLARE_OBJECT_INFO_FINAL</a>(<span class="stringliteral">"arith.ModularSet"</span>, <a class="code" href="classtvm_1_1arith_1_1ModularSetNode.html">ModularSetNode</a>, ffi::Object);</div>
-<div class="line"><a name="l00225"></a><span class="lineno"> 225</span> };</div>
-<div class="line"><a name="l00226"></a><span class="lineno"> 226</span>  </div>
-<div class="line"><a name="l00231"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1ModularSet.html"> 231</a></span> <span class="keyword">class </span><a class="code" href="classtvm_1_1arith_1_1ModularSet.html">ModularSet</a> : <span class="keyword">public</span> ffi::ObjectRef {</div>
-<div class="line"><a name="l00232"></a><span class="lineno"> 232</span>  <span class="keyword">public</span>:</div>
-<div class="line"><a name="l00233"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1ModularSet.html#a9f54896d98169246c6a24cc338fde500"> 233</a></span>  TVM_DLL <a class="code" href="classtvm_1_1arith_1_1ModularSet.html#a9f54896d98169246c6a24cc338fde500">ModularSet</a>(int64_t coeff, int64_t base);</div>
-<div class="line"><a name="l00234"></a><span class="lineno"> 234</span>  </div>
-<div class="line"><a name="l00235"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1ModularSet.html#a476321601d927da2e672e36eec8ec444"> 235</a></span>  <a class="code" href="classtvm_1_1arith_1_1ModularSet.html#a476321601d927da2e672e36eec8ec444">TVM_FFI_DEFINE_OBJECT_REF_METHODS_NULLABLE</a>(<a class="code" href="classtvm_1_1arith_1_1ModularSet.html">ModularSet</a>, ffi::ObjectRef, <a class="code" href="classtvm_1_1arith_1_1ModularSetNode.html">ModularSetNode</a [...]
-<div class="line"><a name="l00236"></a><span class="lineno"> 236</span> };</div>
-<div class="line"><a name="l00237"></a><span class="lineno"> 237</span>  </div>
-<div class="line"><a name="l00241"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1ModularSetAnalyzer.html"> 241</a></span> <span class="keyword">class </span><a class="code" href="classtvm_1_1arith_1_1ModularSetAnalyzer.html">ModularSetAnalyzer</a> {</div>
-<div class="line"><a name="l00242"></a><span class="lineno"> 242</span>  <span class="keyword">public</span>:</div>
-<div class="line"><a name="l00248"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1ModularSetAnalyzer.html#a57c3c62ebc821f5f01bc2b43f06488da"> 248</a></span>  TVM_DLL <a class="code" href="classtvm_1_1arith_1_1ModularSet.html">ModularSet</a> <a class="code" href="classtvm_1_1arith_1_1ModularSetAnalyzer.html#a57c3c62ebc821f5f01bc2b43f06488da">operator()</a>(<span class="keyword">const</span> <a class="code" href="classtvm_1_1PrimExpr.html">PrimExpr</a>& expr [...]
-<div class="line"><a name="l00256"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1ModularSetAnalyzer.html#a04156fac580981f3005af3b8e676720d"> 256</a></span>  TVM_DLL <span class="keywordtype">void</span> <a class="code" href="classtvm_1_1arith_1_1ModularSetAnalyzer.html#a04156fac580981f3005af3b8e676720d">Update</a>(<span class="keyword">const</span> <a class="code" href="classtvm_1_1tirx_1_1Var.html">Var</a>& <a class="code" href="namespacetvm_1_1te.html#a [...]
-<div class="line"><a name="l00257"></a><span class="lineno"> 257</span>  </div>
-<div class="line"><a name="l00258"></a><span class="lineno"> 258</span>  <span class="keyword">private</span>:</div>
-<div class="line"><a name="l00259"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1ModularSetAnalyzer.html#ab502b81f91a3692606541f1ec44911de"> 259</a></span>  <span class="keyword">friend</span> <span class="keyword">class </span><a class="code" href="classtvm_1_1arith_1_1AnalyzerObj.html">AnalyzerObj</a>;</div>
-<div class="line"><a name="l00260"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1ModularSetAnalyzer.html#ab8bf22547cf1df0a28fc4ee98841ab89"> 260</a></span>  <span class="keyword">friend</span> <span class="keyword">class </span><a class="code" href="classtvm_1_1arith_1_1ConstraintContext.html">ConstraintContext</a>;</div>
-<div class="line"><a name="l00261"></a><span class="lineno"> 261</span>  <span class="keyword">explicit</span> <a class="code" href="classtvm_1_1arith_1_1ModularSetAnalyzer.html">ModularSetAnalyzer</a>(<a class="code" href="classtvm_1_1arith_1_1AnalyzerObj.html">AnalyzerObj</a>* parent);</div>
-<div class="line"><a name="l00262"></a><span class="lineno"> 262</span>  TVM_DLL ~<a class="code" href="classtvm_1_1arith_1_1ModularSetAnalyzer.html">ModularSetAnalyzer</a>();</div>
-<div class="line"><a name="l00269"></a><span class="lineno"> 269</span>  std::function<void()> EnterConstraint(<span class="keyword">const</span> <a class="code" href="classtvm_1_1PrimExpr.html">PrimExpr</a>& constraint);</div>
-<div class="line"><a name="l00270"></a><span class="lineno"> 270</span>  <span class="keyword">struct </span>Entry;</div>
-<div class="line"><a name="l00271"></a><span class="lineno"> 271</span>  <span class="keyword">class </span>Impl;</div>
-<div class="line"><a name="l00273"></a><span class="lineno"> 273</span>  Impl* impl_;</div>
-<div class="line"><a name="l00274"></a><span class="lineno"> 274</span> };</div>
-<div class="line"><a name="l00275"></a><span class="lineno"> 275</span>  </div>
-<div class="line"><a name="l00279"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1RewriteSimplifier.html"> 279</a></span> <span class="keyword">class </span><a class="code" href="classtvm_1_1arith_1_1RewriteSimplifier.html">RewriteSimplifier</a> {</div>
-<div class="line"><a name="l00280"></a><span class="lineno"> 280</span>  <span class="keyword">public</span>:</div>
-<div class="line"><a name="l00286"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1RewriteSimplifier.html#af91d307afc0c857751171a68d6d2b043"> 286</a></span>  TVM_DLL <a class="code" href="classtvm_1_1PrimExpr.html">PrimExpr</a> <a class="code" href="classtvm_1_1arith_1_1RewriteSimplifier.html#af91d307afc0c857751171a68d6d2b043">operator()</a>(<span class="keyword">const</span> <a class="code" href="classtvm_1_1PrimExpr.html">PrimExpr</a>& expr);</div>
-<div class="line"><a name="l00287"></a><span class="lineno"> 287</span>  </div>
-<div class="line"><a name="l00295"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1RewriteSimplifier.html#a5e6752c0702dc2d3e4235797d9d3ac7b"> 295</a></span>  TVM_DLL <span class="keywordtype">void</span> <a class="code" href="classtvm_1_1arith_1_1RewriteSimplifier.html#a5e6752c0702dc2d3e4235797d9d3ac7b">Update</a>(<span class="keyword">const</span> <a class="code" href="classtvm_1_1tirx_1_1Var.html">Var</a>& <a class="code" href="namespacetvm_1_1te.html#ae0 [...]
-<div class="line"><a name="l00296"></a><span class="lineno"> 296</span>  </div>
-<div class="line"><a name="l00303"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1RewriteSimplifier.html#a01dda385f4e0bb0e1155c76c2d28cb96"> 303</a></span>  TVM_DLL std::function<void()> <a class="code" href="classtvm_1_1arith_1_1RewriteSimplifier.html#a01dda385f4e0bb0e1155c76c2d28cb96">EnterConstraint</a>(<span class="keyword">const</span> <a class="code" href="classtvm_1_1PrimExpr.html">PrimExpr</a>& constraint);</div>
-<div class="line"><a name="l00304"></a><span class="lineno"> 304</span>  </div>
-<div class="line"><a name="l00317"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1RewriteSimplifier.html#a6705af5099418b968ded40d79102ad4e"> 317</a></span>  <span class="keyword">enum</span> <a class="code" href="classtvm_1_1arith_1_1RewriteSimplifier.html#a6705af5099418b968ded40d79102ad4e">Extension</a> {</div>
-<div class="line"><a name="l00318"></a><span class="lineno"> 318</span>  <span class="comment">// No extensions enabled</span></div>
-<div class="line"><a name="l00319"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1RewriteSimplifier.html#a6705af5099418b968ded40d79102ad4ea237e1cb407cd81dfc9608651eee87b3b"> 319</a></span>  <a class="code" href="classtvm_1_1arith_1_1RewriteSimplifier.html#a6705af5099418b968ded40d79102ad4ea237e1cb407cd81dfc9608651eee87b3b">kNone</a> = 0,</div>
-<div class="line"><a name="l00320"></a><span class="lineno"> 320</span>  </div>
-<div class="line"><a name="l00321"></a><span class="lineno"> 321</span>  <span class="comment">/* When simplifying an inequality, attempt to use scope-based knowns.</span></div>
-<div class="line"><a name="l00322"></a><span class="lineno"> 322</span> <span class="comment"> *</span></div>
-<div class="line"><a name="l00323"></a><span class="lineno"> 323</span> <span class="comment"> * Example:</span></div>
-<div class="line"><a name="l00324"></a><span class="lineno"> 324</span> <span class="comment"> * if_then_else(i<j && j<k, i<k, false) => if_then_else(i<j && j<k, true, false)</span></div>
-<div class="line"><a name="l00325"></a><span class="lineno"> 325</span> <span class="comment"> */</span></div>
-<div class="line"><a name="l00326"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1RewriteSimplifier.html#a6705af5099418b968ded40d79102ad4ea775c43159049079d4a71cd788f4d1568"> 326</a></span>  <a class="code" href="classtvm_1_1arith_1_1RewriteSimplifier.html#a6705af5099418b968ded40d79102ad4ea775c43159049079d4a71cd788f4d1568">kTransitivelyProveInequalities</a> = (1 << 0),</div>
-<div class="line"><a name="l00327"></a><span class="lineno"> 327</span>  </div>
-<div class="line"><a name="l00328"></a><span class="lineno"> 328</span>  <span class="comment">/* When simplifying a boolean expression, convert to an AND of ORs</span></div>
-<div class="line"><a name="l00329"></a><span class="lineno"> 329</span> <span class="comment"> * (conjunctive normal form).</span></div>
-<div class="line"><a name="l00330"></a><span class="lineno"> 330</span> <span class="comment"> *</span></div>
-<div class="line"><a name="l00331"></a><span class="lineno"> 331</span> <span class="comment"> * Example:</span></div>
-<div class="line"><a name="l00332"></a><span class="lineno"> 332</span> <span class="comment"> * (a && b) || c => (a || c) && (b || c)</span></div>
-<div class="line"><a name="l00333"></a><span class="lineno"> 333</span> <span class="comment"> */</span></div>
-<div class="line"><a name="l00334"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1RewriteSimplifier.html#a6705af5099418b968ded40d79102ad4eafd77a96599dad627a7c1d88292aa578e"> 334</a></span>  <a class="code" href="classtvm_1_1arith_1_1RewriteSimplifier.html#a6705af5099418b968ded40d79102ad4eafd77a96599dad627a7c1d88292aa578e">kConvertBooleanToAndOfOrs</a> = (1 << 1),</div>
-<div class="line"><a name="l00335"></a><span class="lineno"> 335</span>  </div>
-<div class="line"><a name="l00336"></a><span class="lineno"> 336</span>  <span class="comment">/* When simplifying a boolean AND or a boolean OR, simplify each</span></div>
-<div class="line"><a name="l00337"></a><span class="lineno"> 337</span> <span class="comment"> * branch under the assumption that the other branch does not</span></div>
-<div class="line"><a name="l00338"></a><span class="lineno"> 338</span> <span class="comment"> * already dominate the result. That is, simplify each branch of</span></div>
-<div class="line"><a name="l00339"></a><span class="lineno"> 339</span> <span class="comment"> * (A && B) under the assumption that the other branch is true,</span></div>
-<div class="line"><a name="l00340"></a><span class="lineno"> 340</span> <span class="comment"> * and simplify each branch of (A || B) under the assumption that</span></div>
-<div class="line"><a name="l00341"></a><span class="lineno"> 341</span> <span class="comment"> * the other branch is false.</span></div>
-<div class="line"><a name="l00342"></a><span class="lineno"> 342</span> <span class="comment"> *</span></div>
-<div class="line"><a name="l00343"></a><span class="lineno"> 343</span> <span class="comment"> * Example:</span></div>
-<div class="line"><a name="l00344"></a><span class="lineno"> 344</span> <span class="comment"> * (n < 10) && (n < 5) => (n < 10)</span></div>
-<div class="line"><a name="l00345"></a><span class="lineno"> 345</span> <span class="comment"> * (n < 10) || (n < 5) => (n < 5)</span></div>
-<div class="line"><a name="l00346"></a><span class="lineno"> 346</span> <span class="comment"> */</span></div>
-<div class="line"><a name="l00347"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1RewriteSimplifier.html#a6705af5099418b968ded40d79102ad4ea254331b07f4bdf970d7f28f480da637b"> 347</a></span>  <a class="code" href="classtvm_1_1arith_1_1RewriteSimplifier.html#a6705af5099418b968ded40d79102ad4ea254331b07f4bdf970d7f28f480da637b">kApplyConstraintsToBooleanBranches</a> = (1 << 2),</div>
-<div class="line"><a name="l00348"></a><span class="lineno"> 348</span>  </div>
-<div class="line"><a name="l00349"></a><span class="lineno"> 349</span>  <span class="comment">/* Special handling for expressions `(A+B)*C < (A*B)*D`</span></div>
-<div class="line"><a name="l00350"></a><span class="lineno"> 350</span> <span class="comment"> *</span></div>
-<div class="line"><a name="l00351"></a><span class="lineno"> 351</span> <span class="comment"> * Expressions of the form `(A+B)*C < (A*B)*D` can occur occur</span></div>
-<div class="line"><a name="l00352"></a><span class="lineno"> 352</span> <span class="comment"> * when comparing the number of operations required for two</span></div>
-<div class="line"><a name="l00353"></a><span class="lineno"> 353</span> <span class="comment"> * different orderings in which matrix multiplications can be</span></div>
-<div class="line"><a name="l00354"></a><span class="lineno"> 354</span> <span class="comment"> * performed. Proving or disproving this conditional allows an</span></div>
-<div class="line"><a name="l00355"></a><span class="lineno"> 355</span> <span class="comment"> * optimal order of execution to be selected, even for dynamic</span></div>
-<div class="line"><a name="l00356"></a><span class="lineno"> 356</span> <span class="comment"> * argument shapes.</span></div>
-<div class="line"><a name="l00357"></a><span class="lineno"> 357</span> <span class="comment"> *</span></div>
-<div class="line"><a name="l00358"></a><span class="lineno"> 358</span> <span class="comment"> * The default behavior of `ConstIntBounds` assumes that each term</span></div>
-<div class="line"><a name="l00359"></a><span class="lineno"> 359</span> <span class="comment"> * in an expression is independent, and is insufficient to prove</span></div>
-<div class="line"><a name="l00360"></a><span class="lineno"> 360</span> <span class="comment"> * these inequalities. For example, the maximum value of `(A+B)*C</span></div>
-<div class="line"><a name="l00361"></a><span class="lineno"> 361</span> <span class="comment"> * - (A*B)*D` is determined by taking the maximum value of</span></div>
-<div class="line"><a name="l00362"></a><span class="lineno"> 362</span> <span class="comment"> * `(A+B)*C` and subtracting the minimum value of `(A*B)*D`.</span></div>
-<div class="line"><a name="l00363"></a><span class="lineno"> 363</span> <span class="comment"> * While this algorithm can be applied in all cases, the bound it</span></div>
-<div class="line"><a name="l00364"></a><span class="lineno"> 364</span> <span class="comment"> * provides is looser than strictly required.</span></div>
-<div class="line"><a name="l00365"></a><span class="lineno"> 365</span> <span class="comment"> *</span></div>
-<div class="line"><a name="l00366"></a><span class="lineno"> 366</span> <span class="comment"> * This extension adds a check for this case. When `A`, `B`, `C`,</span></div>
-<div class="line"><a name="l00367"></a><span class="lineno"> 367</span> <span class="comment"> * and `D` are all positive values, as is the case for tensor</span></div>
-<div class="line"><a name="l00368"></a><span class="lineno"> 368</span> <span class="comment"> * shapes, the inequality can be written as `1/A + 1/B < D/C`. If</span></div>
-<div class="line"><a name="l00369"></a><span class="lineno"> 369</span> <span class="comment"> * this inequality holds for the minimum values of `A`, `B`, and</span></div>
-<div class="line"><a name="l00370"></a><span class="lineno"> 370</span> <span class="comment"> * `D`, along with the maximum value of `C`, then the inequality</span></div>
-<div class="line"><a name="l00371"></a><span class="lineno"> 371</span> <span class="comment"> * holds for all values.</span></div>
-<div class="line"><a name="l00372"></a><span class="lineno"> 372</span> <span class="comment"> *</span></div>
-<div class="line"><a name="l00373"></a><span class="lineno"> 373</span> <span class="comment"> * This extension requires little to no performance overhead, and</span></div>
-<div class="line"><a name="l00374"></a><span class="lineno"> 374</span> <span class="comment"> * may be enabled by default in future releases.</span></div>
-<div class="line"><a name="l00375"></a><span class="lineno"> 375</span> <span class="comment"> */</span></div>
-<div class="line"><a name="l00376"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1RewriteSimplifier.html#a6705af5099418b968ded40d79102ad4eaf2a72591e015241f111c915110ad0b23"> 376</a></span>  <a class="code" href="classtvm_1_1arith_1_1RewriteSimplifier.html#a6705af5099418b968ded40d79102ad4eaf2a72591e015241f111c915110ad0b23">kComparisonOfProductAndSum</a> = (1 << 3),</div>
-<div class="line"><a name="l00377"></a><span class="lineno"> 377</span>  };</div>
-<div class="line"><a name="l00378"></a><span class="lineno"> 378</span>  </div>
-<div class="line"><a name="l00384"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1RewriteSimplifier.html#a44b7c12d6a5d9adfc4ee4cee8d935e38"> 384</a></span>  TVM_DLL <span class="keywordtype">void</span> <a class="code" href="classtvm_1_1arith_1_1RewriteSimplifier.html#a44b7c12d6a5d9adfc4ee4cee8d935e38">SetEnabledExtensions</a>(<a class="code" href="classtvm_1_1arith_1_1RewriteSimplifier.html#a6705af5099418b968ded40d79102ad4e">Extension</a> flags);</div>
-<div class="line"><a name="l00385"></a><span class="lineno"> 385</span>  </div>
-<div class="line"><a name="l00387"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1RewriteSimplifier.html#a2066e6d9a5b71d482c73542d0ca8c7a3"> 387</a></span>  TVM_DLL <a class="code" href="classtvm_1_1arith_1_1RewriteSimplifier.html#a6705af5099418b968ded40d79102ad4e">Extension</a> <a class="code" href="classtvm_1_1arith_1_1RewriteSimplifier.html#a2066e6d9a5b71d482c73542d0ca8c7a3">GetEnabledExtensions</a>() <span class="keyword">const</span>;</div>
-<div class="line"><a name="l00388"></a><span class="lineno"> 388</span>  </div>
-<div class="line"><a name="l00390"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1RewriteSimplifier.html#a418674df0487f883db23b9ebe208f598"> 390</a></span>  TVM_DLL ffi::ObjectRef <a class="code" href="classtvm_1_1arith_1_1RewriteSimplifier.html#a418674df0487f883db23b9ebe208f598">GetStatsCounters</a>() <span class="keyword">const</span>;</div>
-<div class="line"><a name="l00391"></a><span class="lineno"> 391</span>  </div>
-<div class="line"><a name="l00393"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1RewriteSimplifier.html#aef5b3e9793a5b459c9f457efef179e4f"> 393</a></span>  TVM_DLL <span class="keywordtype">void</span> <a class="code" href="classtvm_1_1arith_1_1RewriteSimplifier.html#aef5b3e9793a5b459c9f457efef179e4f">ResetStatsCounters</a>();</div>
-<div class="line"><a name="l00394"></a><span class="lineno"> 394</span>  </div>
-<div class="line"><a name="l00409"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1RewriteSimplifier.html#a95dd28018de6b7aa1a5a7ee76f3ba178"> 409</a></span>  TVM_DLL <span class="keywordtype">void</span> <a class="code" href="classtvm_1_1arith_1_1RewriteSimplifier.html#a95dd28018de6b7aa1a5a7ee76f3ba178">SetMaximumRewriteSteps</a>(int64_t <a class="code" href="namespacetvm_1_1tirx.html#ae97ec08fd3edb5b1a83e46171c4a7939">maximum</a>);</div>
-<div class="line"><a name="l00410"></a><span class="lineno"> 410</span>  </div>
-<div class="line"><a name="l00411"></a><span class="lineno"> 411</span>  <span class="keyword">private</span>:</div>
-<div class="line"><a name="l00412"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1RewriteSimplifier.html#ab502b81f91a3692606541f1ec44911de"> 412</a></span>  <span class="keyword">friend</span> <span class="keyword">class </span><a class="code" href="classtvm_1_1arith_1_1AnalyzerObj.html">AnalyzerObj</a>;</div>
-<div class="line"><a name="l00413"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1RewriteSimplifier.html#ab8bf22547cf1df0a28fc4ee98841ab89"> 413</a></span>  <span class="keyword">friend</span> <span class="keyword">class </span><a class="code" href="classtvm_1_1arith_1_1ConstraintContext.html">ConstraintContext</a>;</div>
-<div class="line"><a name="l00414"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1RewriteSimplifier.html#abd092d074e3063da6d7bb0c67115a33f"> 414</a></span>  <span class="keyword">friend</span> <span class="keyword">class </span><a class="code" href="classtvm_1_1arith_1_1CanonicalSimplifier.html">CanonicalSimplifier</a>;</div>
-<div class="line"><a name="l00415"></a><span class="lineno"> 415</span>  <span class="keyword">explicit</span> <a class="code" href="classtvm_1_1arith_1_1RewriteSimplifier.html">RewriteSimplifier</a>(<a class="code" href="classtvm_1_1arith_1_1AnalyzerObj.html">AnalyzerObj</a>* parent);</div>
-<div class="line"><a name="l00416"></a><span class="lineno"> 416</span>  TVM_DLL ~<a class="code" href="classtvm_1_1arith_1_1RewriteSimplifier.html">RewriteSimplifier</a>();</div>
-<div class="line"><a name="l00417"></a><span class="lineno"> 417</span>  <span class="keyword">class </span>Impl;</div>
-<div class="line"><a name="l00419"></a><span class="lineno"> 419</span>  Impl* impl_;</div>
-<div class="line"><a name="l00420"></a><span class="lineno"> 420</span> };</div>
-<div class="line"><a name="l00421"></a><span class="lineno"> 421</span>  </div>
-<div class="line"><a name="l00425"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1CanonicalSimplifier.html"> 425</a></span> <span class="keyword">class </span><a class="code" href="classtvm_1_1arith_1_1CanonicalSimplifier.html">CanonicalSimplifier</a> {</div>
-<div class="line"><a name="l00426"></a><span class="lineno"> 426</span>  <span class="keyword">public</span>:</div>
-<div class="line"><a name="l00432"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1CanonicalSimplifier.html#a2ed4ed7990499ee188e69967afb511c6"> 432</a></span>  TVM_DLL <a class="code" href="classtvm_1_1PrimExpr.html">PrimExpr</a> <a class="code" href="classtvm_1_1arith_1_1CanonicalSimplifier.html#a2ed4ed7990499ee188e69967afb511c6">operator()</a>(<span class="keyword">const</span> <a class="code" href="classtvm_1_1PrimExpr.html">PrimExpr</a>& expr);</div>
-<div class="line"><a name="l00433"></a><span class="lineno"> 433</span>  </div>
-<div class="line"><a name="l00441"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1CanonicalSimplifier.html#a790c032e12c7d93e9e940297b860c509"> 441</a></span>  TVM_DLL <span class="keywordtype">void</span> <a class="code" href="classtvm_1_1arith_1_1CanonicalSimplifier.html#a790c032e12c7d93e9e940297b860c509">Update</a>(<span class="keyword">const</span> <a class="code" href="classtvm_1_1tirx_1_1Var.html">Var</a>& <a class="code" href="namespacetvm_1_1te.html [...]
-<div class="line"><a name="l00442"></a><span class="lineno"> 442</span>  </div>
-<div class="line"><a name="l00443"></a><span class="lineno"> 443</span>  <span class="keyword">private</span>:</div>
-<div class="line"><a name="l00444"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1CanonicalSimplifier.html#ab502b81f91a3692606541f1ec44911de"> 444</a></span>  <span class="keyword">friend</span> <span class="keyword">class </span><a class="code" href="classtvm_1_1arith_1_1AnalyzerObj.html">AnalyzerObj</a>;</div>
-<div class="line"><a name="l00445"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1CanonicalSimplifier.html#ab8bf22547cf1df0a28fc4ee98841ab89"> 445</a></span>  <span class="keyword">friend</span> <span class="keyword">class </span><a class="code" href="classtvm_1_1arith_1_1ConstraintContext.html">ConstraintContext</a>;</div>
-<div class="line"><a name="l00446"></a><span class="lineno"> 446</span>  <span class="keyword">explicit</span> <a class="code" href="classtvm_1_1arith_1_1CanonicalSimplifier.html">CanonicalSimplifier</a>(<a class="code" href="classtvm_1_1arith_1_1AnalyzerObj.html">AnalyzerObj</a>* parent);</div>
-<div class="line"><a name="l00447"></a><span class="lineno"> 447</span>  TVM_DLL ~<a class="code" href="classtvm_1_1arith_1_1CanonicalSimplifier.html">CanonicalSimplifier</a>();</div>
-<div class="line"><a name="l00448"></a><span class="lineno"> 448</span>  <span class="keyword">class </span>Impl;</div>
-<div class="line"><a name="l00450"></a><span class="lineno"> 450</span>  Impl* impl_;</div>
-<div class="line"><a name="l00451"></a><span class="lineno"> 451</span> };</div>
-<div class="line"><a name="l00452"></a><span class="lineno"> 452</span>  </div>
-<div class="line"><a name="l00458"></a><span class="lineno"><a class="line" href="namespacetvm_1_1arith.html#a1d555a0cc363f669a49fad7e5f7d69d0"> 458</a></span> <span class="keyword">enum class</span> <a class="code" href="namespacetvm_1_1arith.html#a1d555a0cc363f669a49fad7e5f7d69d0">CompareResult</a> : int {</div>
-<div class="line"><a name="l00459"></a><span class="lineno"> 459</span>  <a class="code" href="namespacetvm_1_1arith.html#a1d555a0cc363f669a49fad7e5f7d69d0af9f8d006118de5ad04dd7c000b8f3b91">kInconsistent</a> = 0,</div>
-<div class="line"><a name="l00460"></a><span class="lineno"> 460</span>  <a class="code" href="namespacetvm_1_1arith.html#a1d555a0cc363f669a49fad7e5f7d69d0a7db2fe13b438b1005c70fcc9ab826b3b">kEQ</a> = 1,</div>
-<div class="line"><a name="l00461"></a><span class="lineno"> 461</span>  <a class="code" href="namespacetvm_1_1arith.html#a1d555a0cc363f669a49fad7e5f7d69d0add0f23f162dcf68bf7bc761ac13040ae">kLT</a> = 2,</div>
-<div class="line"><a name="l00462"></a><span class="lineno"> 462</span>  <a class="code" href="namespacetvm_1_1arith.html#a1d555a0cc363f669a49fad7e5f7d69d0a1893df55d7fc768f2336fdcb7c7b9c37">kLE</a> = 3,</div>
-<div class="line"><a name="l00463"></a><span class="lineno"> 463</span>  <a class="code" href="namespacetvm_1_1arith.html#a1d555a0cc363f669a49fad7e5f7d69d0aa3d18adb9a64157d6708f8ba39dfcdbc">kGT</a> = 4,</div>
-<div class="line"><a name="l00464"></a><span class="lineno"> 464</span>  <a class="code" href="namespacetvm_1_1arith.html#a1d555a0cc363f669a49fad7e5f7d69d0ac0275be4f85005f8ef340d3d5be8d361">kGE</a> = 5,</div>
-<div class="line"><a name="l00465"></a><span class="lineno"> 465</span>  <a class="code" href="namespacetvm_1_1arith.html#a1d555a0cc363f669a49fad7e5f7d69d0a03e3ad128c41e04acc6a0e59fdf11a58">kNE</a> = 6,</div>
-<div class="line"><a name="l00466"></a><span class="lineno"> 466</span>  <a class="code" href="namespacetvm_1_1arith.html#aca8806e355ad3dd5f1df9c1eca9aac9da8812c1a077255594d23bc1c2f3af3979">kUnknown</a> = 7</div>
-<div class="line"><a name="l00467"></a><span class="lineno"> 467</span> };</div>
-<div class="line"><a name="l00468"></a><span class="lineno"> 468</span>  </div>
-<div class="line"><a name="l00469"></a><span class="lineno"><a class="line" href="namespacetvm_1_1arith.html#ae300654bf1788008de90f1e77fbaa7fe"> 469</a></span> <span class="keyword">inline</span> constexpr <a class="code" href="namespacetvm_1_1arith.html#a1d555a0cc363f669a49fad7e5f7d69d0">CompareResult</a> <a class="code" href="namespacetvm_1_1arith.html#ae300654bf1788008de90f1e77fbaa7fe">operator&</a>(<a class="code" href="namespacetvm_1_1arith.html#a1d555a0cc363f669a49fad7e5f [...]
-<div class="line"><a name="l00470"></a><span class="lineno"> 470</span>  <span class="keywordflow">return</span> <a class="code" href="namespacetvm_1_1arith.html#a1d555a0cc363f669a49fad7e5f7d69d0">CompareResult</a>(<span class="keyword">static_cast<</span><span class="keywordtype">int</span><span class="keyword">></span>(lhs) & <span class="keyword">static_cast<</span><span class="keywordtype">int</span><span class="keyword">></span>(rhs));</div>
-<div class="line"><a name="l00471"></a><span class="lineno"> 471</span> }</div>
-<div class="line"><a name="l00472"></a><span class="lineno"><a class="line" href="namespacetvm_1_1arith.html#a660ff1fdbb08fea19a922157cadad7a7"> 472</a></span> <span class="keyword">inline</span> constexpr <a class="code" href="namespacetvm_1_1arith.html#a1d555a0cc363f669a49fad7e5f7d69d0">CompareResult</a> <a class="code" href="namespacetvm_1_1arith.html#a660ff1fdbb08fea19a922157cadad7a7">operator|</a>(<a class="code" href="namespacetvm_1_1arith.html#a1d555a0cc363f669a49fad7e5f7d69 [...]
-<div class="line"><a name="l00473"></a><span class="lineno"> 473</span>  <span class="keywordflow">return</span> <a class="code" href="namespacetvm_1_1arith.html#a1d555a0cc363f669a49fad7e5f7d69d0">CompareResult</a>(<span class="keyword">static_cast<</span><span class="keywordtype">int</span><span class="keyword">></span>(lhs) | <span class="keyword">static_cast<</span><span class="keywordtype">int</span><span class="keyword">></span>(rhs));</div>
-<div class="line"><a name="l00474"></a><span class="lineno"> 474</span> }</div>
-<div class="line"><a name="l00475"></a><span class="lineno"> 475</span>  </div>
-<div class="line"><a name="l00482"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1TransitiveComparisonAnalyzer.html"> 482</a></span> <span class="keyword">class </span><a class="code" href="classtvm_1_1arith_1_1TransitiveComparisonAnalyzer.html">TransitiveComparisonAnalyzer</a> {</div>
-<div class="line"><a name="l00483"></a><span class="lineno"> 483</span>  <span class="keyword">public</span>:</div>
-<div class="line"><a name="l00484"></a><span class="lineno"> 484</span>  <span class="comment">/* \brief Using previously specified knowns, compare the expressions provided</span></div>
-<div class="line"><a name="l00485"></a><span class="lineno"> 485</span> <span class="comment"> *</span></div>
-<div class="line"><a name="l00486"></a><span class="lineno"> 486</span> <span class="comment"> * \param lhs The left-hand side of the comparison</span></div>
-<div class="line"><a name="l00487"></a><span class="lineno"> 487</span> <span class="comment"> *</span></div>
-<div class="line"><a name="l00488"></a><span class="lineno"> 488</span> <span class="comment"> * \param rhs The right-hand side of the comparison</span></div>
+<div class="line"><a name="l00184"></a><span class="lineno"> 184</span>  <span class="keywordtype">void</span> CopyFrom(<span class="keyword">const</span> <a class="code" href="classtvm_1_1arith_1_1ConstIntBoundAnalyzer.html">ConstIntBoundAnalyzer</a>& other);</div>
+<div class="line"><a name="l00191"></a><span class="lineno"> 191</span>  std::function<void()> EnterConstraint(<span class="keyword">const</span> <a class="code" href="classtvm_1_1PrimExpr.html">PrimExpr</a>& constraint);</div>
+<div class="line"><a name="l00192"></a><span class="lineno"> 192</span>  <span class="keyword">struct </span>Entry;</div>
+<div class="line"><a name="l00193"></a><span class="lineno"> 193</span>  <span class="keyword">class </span>Impl;</div>
+<div class="line"><a name="l00195"></a><span class="lineno"> 195</span>  Impl* impl_;</div>
+<div class="line"><a name="l00196"></a><span class="lineno"> 196</span> };</div>
+<div class="line"><a name="l00197"></a><span class="lineno"> 197</span>  </div>
+<div class="line"><a name="l00210"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1ModularSetNode.html"> 210</a></span> <span class="keyword">class </span><a class="code" href="classtvm_1_1arith_1_1ModularSetNode.html">ModularSetNode</a> : <span class="keyword">public</span> ffi::Object {</div>
+<div class="line"><a name="l00211"></a><span class="lineno"> 211</span>  <span class="keyword">public</span>:</div>
+<div class="line"><a name="l00213"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1ModularSetNode.html#a0531d08d8529454c726a9a20a2002ee6"> 213</a></span>  int64_t <a class="code" href="classtvm_1_1arith_1_1ModularSetNode.html#a0531d08d8529454c726a9a20a2002ee6">coeff</a>;</div>
+<div class="line"><a name="l00215"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1ModularSetNode.html#afe63e5586712774be5935e86121431ad"> 215</a></span>  int64_t <a class="code" href="classtvm_1_1arith_1_1ModularSetNode.html#afe63e5586712774be5935e86121431ad">base</a>;</div>
+<div class="line"><a name="l00216"></a><span class="lineno"> 216</span>  </div>
+<div class="line"><a name="l00217"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1ModularSetNode.html#a680e547835e5062f0c88e0f982bcc805"> 217</a></span>  <span class="keyword">static</span> <span class="keywordtype">void</span> <a class="code" href="classtvm_1_1arith_1_1ModularSetNode.html#a680e547835e5062f0c88e0f982bcc805">RegisterReflection</a>() {</div>
+<div class="line"><a name="l00218"></a><span class="lineno"> 218</span>  <span class="keyword">namespace </span>refl = tvm::ffi::reflection;</div>
+<div class="line"><a name="l00219"></a><span class="lineno"> 219</span>  refl::ObjectDef<ModularSetNode>()</div>
+<div class="line"><a name="l00220"></a><span class="lineno"> 220</span>  .def_ro(<span class="stringliteral">"coeff"</span>, &<a class="code" href="classtvm_1_1arith_1_1ModularSetNode.html#a0531d08d8529454c726a9a20a2002ee6">ModularSetNode::coeff</a>)</div>
+<div class="line"><a name="l00221"></a><span class="lineno"> 221</span>  .def_ro(<span class="stringliteral">"base"</span>, &<a class="code" href="classtvm_1_1arith_1_1ModularSetNode.html#afe63e5586712774be5935e86121431ad">ModularSetNode::base</a>);</div>
+<div class="line"><a name="l00222"></a><span class="lineno"> 222</span>  }</div>
+<div class="line"><a name="l00223"></a><span class="lineno"> 223</span>  </div>
+<div class="line"><a name="l00224"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1ModularSetNode.html#ac08e3992efa956f31feaf12cd360b578"> 224</a></span>  <span class="keyword">static</span> constexpr TVMFFISEqHashKind <a class="code" href="classtvm_1_1arith_1_1ModularSetNode.html#ac08e3992efa956f31feaf12cd360b578">_type_s_eq_hash_kind</a> = kTVMFFISEqHashKindTreeNode;</div>
+<div class="line"><a name="l00225"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1ModularSetNode.html#ab5e5f8aa92fe0e785bd24ae47f29fd4e"> 225</a></span>  <a class="code" href="classtvm_1_1arith_1_1ModularSetNode.html#ab5e5f8aa92fe0e785bd24ae47f29fd4e">TVM_FFI_DECLARE_OBJECT_INFO_FINAL</a>(<span class="stringliteral">"arith.ModularSet"</span>, <a class="code" href="classtvm_1_1arith_1_1ModularSetNode.html">ModularSetNode</a>, ffi::Object);</div>
+<div class="line"><a name="l00226"></a><span class="lineno"> 226</span> };</div>
+<div class="line"><a name="l00227"></a><span class="lineno"> 227</span>  </div>
+<div class="line"><a name="l00232"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1ModularSet.html"> 232</a></span> <span class="keyword">class </span><a class="code" href="classtvm_1_1arith_1_1ModularSet.html">ModularSet</a> : <span class="keyword">public</span> ffi::ObjectRef {</div>
+<div class="line"><a name="l00233"></a><span class="lineno"> 233</span>  <span class="keyword">public</span>:</div>
+<div class="line"><a name="l00234"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1ModularSet.html#a9f54896d98169246c6a24cc338fde500"> 234</a></span>  TVM_DLL <a class="code" href="classtvm_1_1arith_1_1ModularSet.html#a9f54896d98169246c6a24cc338fde500">ModularSet</a>(int64_t coeff, int64_t base);</div>
+<div class="line"><a name="l00235"></a><span class="lineno"> 235</span>  </div>
+<div class="line"><a name="l00236"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1ModularSet.html#a476321601d927da2e672e36eec8ec444"> 236</a></span>  <a class="code" href="classtvm_1_1arith_1_1ModularSet.html#a476321601d927da2e672e36eec8ec444">TVM_FFI_DEFINE_OBJECT_REF_METHODS_NULLABLE</a>(<a class="code" href="classtvm_1_1arith_1_1ModularSet.html">ModularSet</a>, ffi::ObjectRef, <a class="code" href="classtvm_1_1arith_1_1ModularSetNode.html">ModularSetNode</a [...]
+<div class="line"><a name="l00237"></a><span class="lineno"> 237</span> };</div>
+<div class="line"><a name="l00238"></a><span class="lineno"> 238</span>  </div>
+<div class="line"><a name="l00242"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1ModularSetAnalyzer.html"> 242</a></span> <span class="keyword">class </span><a class="code" href="classtvm_1_1arith_1_1ModularSetAnalyzer.html">ModularSetAnalyzer</a> {</div>
+<div class="line"><a name="l00243"></a><span class="lineno"> 243</span>  <span class="keyword">public</span>:</div>
+<div class="line"><a name="l00249"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1ModularSetAnalyzer.html#a57c3c62ebc821f5f01bc2b43f06488da"> 249</a></span>  TVM_DLL <a class="code" href="classtvm_1_1arith_1_1ModularSet.html">ModularSet</a> <a class="code" href="classtvm_1_1arith_1_1ModularSetAnalyzer.html#a57c3c62ebc821f5f01bc2b43f06488da">operator()</a>(<span class="keyword">const</span> <a class="code" href="classtvm_1_1PrimExpr.html">PrimExpr</a>& expr [...]
+<div class="line"><a name="l00257"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1ModularSetAnalyzer.html#a04156fac580981f3005af3b8e676720d"> 257</a></span>  TVM_DLL <span class="keywordtype">void</span> <a class="code" href="classtvm_1_1arith_1_1ModularSetAnalyzer.html#a04156fac580981f3005af3b8e676720d">Update</a>(<span class="keyword">const</span> <a class="code" href="classtvm_1_1tirx_1_1Var.html">Var</a>& <a class="code" href="namespacetvm_1_1te.html#a [...]
+<div class="line"><a name="l00258"></a><span class="lineno"> 258</span>  </div>
+<div class="line"><a name="l00259"></a><span class="lineno"> 259</span>  <span class="keyword">private</span>:</div>
+<div class="line"><a name="l00260"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1ModularSetAnalyzer.html#ab502b81f91a3692606541f1ec44911de"> 260</a></span>  <span class="keyword">friend</span> <span class="keyword">class </span><a class="code" href="classtvm_1_1arith_1_1AnalyzerObj.html">AnalyzerObj</a>;</div>
+<div class="line"><a name="l00261"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1ModularSetAnalyzer.html#ab8bf22547cf1df0a28fc4ee98841ab89"> 261</a></span>  <span class="keyword">friend</span> <span class="keyword">class </span><a class="code" href="classtvm_1_1arith_1_1ConstraintContext.html">ConstraintContext</a>;</div>
+<div class="line"><a name="l00262"></a><span class="lineno"> 262</span>  <span class="keyword">explicit</span> <a class="code" href="classtvm_1_1arith_1_1ModularSetAnalyzer.html">ModularSetAnalyzer</a>(<a class="code" href="classtvm_1_1arith_1_1AnalyzerObj.html">AnalyzerObj</a>* parent);</div>
+<div class="line"><a name="l00263"></a><span class="lineno"> 263</span>  TVM_DLL ~<a class="code" href="classtvm_1_1arith_1_1ModularSetAnalyzer.html">ModularSetAnalyzer</a>();</div>
+<div class="line"><a name="l00264"></a><span class="lineno"> 264</span>  <span class="keywordtype">void</span> CopyFrom(<span class="keyword">const</span> <a class="code" href="classtvm_1_1arith_1_1ModularSetAnalyzer.html">ModularSetAnalyzer</a>& other);</div>
+<div class="line"><a name="l00271"></a><span class="lineno"> 271</span>  std::function<void()> EnterConstraint(<span class="keyword">const</span> <a class="code" href="classtvm_1_1PrimExpr.html">PrimExpr</a>& constraint);</div>
+<div class="line"><a name="l00272"></a><span class="lineno"> 272</span>  <span class="keyword">struct </span>Entry;</div>
+<div class="line"><a name="l00273"></a><span class="lineno"> 273</span>  <span class="keyword">class </span>Impl;</div>
+<div class="line"><a name="l00275"></a><span class="lineno"> 275</span>  Impl* impl_;</div>
+<div class="line"><a name="l00276"></a><span class="lineno"> 276</span> };</div>
+<div class="line"><a name="l00277"></a><span class="lineno"> 277</span>  </div>
+<div class="line"><a name="l00281"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1RewriteSimplifier.html"> 281</a></span> <span class="keyword">class </span><a class="code" href="classtvm_1_1arith_1_1RewriteSimplifier.html">RewriteSimplifier</a> {</div>
+<div class="line"><a name="l00282"></a><span class="lineno"> 282</span>  <span class="keyword">public</span>:</div>
+<div class="line"><a name="l00288"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1RewriteSimplifier.html#af91d307afc0c857751171a68d6d2b043"> 288</a></span>  TVM_DLL <a class="code" href="classtvm_1_1PrimExpr.html">PrimExpr</a> <a class="code" href="classtvm_1_1arith_1_1RewriteSimplifier.html#af91d307afc0c857751171a68d6d2b043">operator()</a>(<span class="keyword">const</span> <a class="code" href="classtvm_1_1PrimExpr.html">PrimExpr</a>& expr);</div>
+<div class="line"><a name="l00289"></a><span class="lineno"> 289</span>  </div>
+<div class="line"><a name="l00297"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1RewriteSimplifier.html#a5e6752c0702dc2d3e4235797d9d3ac7b"> 297</a></span>  TVM_DLL <span class="keywordtype">void</span> <a class="code" href="classtvm_1_1arith_1_1RewriteSimplifier.html#a5e6752c0702dc2d3e4235797d9d3ac7b">Update</a>(<span class="keyword">const</span> <a class="code" href="classtvm_1_1tirx_1_1Var.html">Var</a>& <a class="code" href="namespacetvm_1_1te.html#ae0 [...]
+<div class="line"><a name="l00298"></a><span class="lineno"> 298</span>  </div>
+<div class="line"><a name="l00305"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1RewriteSimplifier.html#a01dda385f4e0bb0e1155c76c2d28cb96"> 305</a></span>  TVM_DLL std::function<void()> <a class="code" href="classtvm_1_1arith_1_1RewriteSimplifier.html#a01dda385f4e0bb0e1155c76c2d28cb96">EnterConstraint</a>(<span class="keyword">const</span> <a class="code" href="classtvm_1_1PrimExpr.html">PrimExpr</a>& constraint);</div>
+<div class="line"><a name="l00306"></a><span class="lineno"> 306</span>  </div>
+<div class="line"><a name="l00319"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1RewriteSimplifier.html#a6705af5099418b968ded40d79102ad4e"> 319</a></span>  <span class="keyword">enum</span> <a class="code" href="classtvm_1_1arith_1_1RewriteSimplifier.html#a6705af5099418b968ded40d79102ad4e">Extension</a> {</div>
+<div class="line"><a name="l00320"></a><span class="lineno"> 320</span>  <span class="comment">// No extensions enabled</span></div>
+<div class="line"><a name="l00321"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1RewriteSimplifier.html#a6705af5099418b968ded40d79102ad4ea237e1cb407cd81dfc9608651eee87b3b"> 321</a></span>  <a class="code" href="classtvm_1_1arith_1_1RewriteSimplifier.html#a6705af5099418b968ded40d79102ad4ea237e1cb407cd81dfc9608651eee87b3b">kNone</a> = 0,</div>
+<div class="line"><a name="l00322"></a><span class="lineno"> 322</span>  </div>
+<div class="line"><a name="l00323"></a><span class="lineno"> 323</span>  <span class="comment">/* When simplifying an inequality, attempt to use scope-based knowns.</span></div>
+<div class="line"><a name="l00324"></a><span class="lineno"> 324</span> <span class="comment"> *</span></div>
+<div class="line"><a name="l00325"></a><span class="lineno"> 325</span> <span class="comment"> * Example:</span></div>
+<div class="line"><a name="l00326"></a><span class="lineno"> 326</span> <span class="comment"> * if_then_else(i<j && j<k, i<k, false) => if_then_else(i<j && j<k, true, false)</span></div>
+<div class="line"><a name="l00327"></a><span class="lineno"> 327</span> <span class="comment"> */</span></div>
+<div class="line"><a name="l00328"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1RewriteSimplifier.html#a6705af5099418b968ded40d79102ad4ea775c43159049079d4a71cd788f4d1568"> 328</a></span>  <a class="code" href="classtvm_1_1arith_1_1RewriteSimplifier.html#a6705af5099418b968ded40d79102ad4ea775c43159049079d4a71cd788f4d1568">kTransitivelyProveInequalities</a> = (1 << 0),</div>
+<div class="line"><a name="l00329"></a><span class="lineno"> 329</span>  </div>
+<div class="line"><a name="l00330"></a><span class="lineno"> 330</span>  <span class="comment">/* When simplifying a boolean expression, convert to an AND of ORs</span></div>
+<div class="line"><a name="l00331"></a><span class="lineno"> 331</span> <span class="comment"> * (conjunctive normal form).</span></div>
+<div class="line"><a name="l00332"></a><span class="lineno"> 332</span> <span class="comment"> *</span></div>
+<div class="line"><a name="l00333"></a><span class="lineno"> 333</span> <span class="comment"> * Example:</span></div>
+<div class="line"><a name="l00334"></a><span class="lineno"> 334</span> <span class="comment"> * (a && b) || c => (a || c) && (b || c)</span></div>
+<div class="line"><a name="l00335"></a><span class="lineno"> 335</span> <span class="comment"> */</span></div>
+<div class="line"><a name="l00336"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1RewriteSimplifier.html#a6705af5099418b968ded40d79102ad4eafd77a96599dad627a7c1d88292aa578e"> 336</a></span>  <a class="code" href="classtvm_1_1arith_1_1RewriteSimplifier.html#a6705af5099418b968ded40d79102ad4eafd77a96599dad627a7c1d88292aa578e">kConvertBooleanToAndOfOrs</a> = (1 << 1),</div>
+<div class="line"><a name="l00337"></a><span class="lineno"> 337</span>  </div>
+<div class="line"><a name="l00338"></a><span class="lineno"> 338</span>  <span class="comment">/* When simplifying a boolean AND or a boolean OR, simplify each</span></div>
+<div class="line"><a name="l00339"></a><span class="lineno"> 339</span> <span class="comment"> * branch under the assumption that the other branch does not</span></div>
+<div class="line"><a name="l00340"></a><span class="lineno"> 340</span> <span class="comment"> * already dominate the result. That is, simplify each branch of</span></div>
+<div class="line"><a name="l00341"></a><span class="lineno"> 341</span> <span class="comment"> * (A && B) under the assumption that the other branch is true,</span></div>
+<div class="line"><a name="l00342"></a><span class="lineno"> 342</span> <span class="comment"> * and simplify each branch of (A || B) under the assumption that</span></div>
+<div class="line"><a name="l00343"></a><span class="lineno"> 343</span> <span class="comment"> * the other branch is false.</span></div>
+<div class="line"><a name="l00344"></a><span class="lineno"> 344</span> <span class="comment"> *</span></div>
+<div class="line"><a name="l00345"></a><span class="lineno"> 345</span> <span class="comment"> * Example:</span></div>
+<div class="line"><a name="l00346"></a><span class="lineno"> 346</span> <span class="comment"> * (n < 10) && (n < 5) => (n < 10)</span></div>
+<div class="line"><a name="l00347"></a><span class="lineno"> 347</span> <span class="comment"> * (n < 10) || (n < 5) => (n < 5)</span></div>
+<div class="line"><a name="l00348"></a><span class="lineno"> 348</span> <span class="comment"> */</span></div>
+<div class="line"><a name="l00349"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1RewriteSimplifier.html#a6705af5099418b968ded40d79102ad4ea254331b07f4bdf970d7f28f480da637b"> 349</a></span>  <a class="code" href="classtvm_1_1arith_1_1RewriteSimplifier.html#a6705af5099418b968ded40d79102ad4ea254331b07f4bdf970d7f28f480da637b">kApplyConstraintsToBooleanBranches</a> = (1 << 2),</div>
+<div class="line"><a name="l00350"></a><span class="lineno"> 350</span>  </div>
+<div class="line"><a name="l00351"></a><span class="lineno"> 351</span>  <span class="comment">/* Special handling for expressions `(A+B)*C < (A*B)*D`</span></div>
+<div class="line"><a name="l00352"></a><span class="lineno"> 352</span> <span class="comment"> *</span></div>
+<div class="line"><a name="l00353"></a><span class="lineno"> 353</span> <span class="comment"> * Expressions of the form `(A+B)*C < (A*B)*D` can occur occur</span></div>
+<div class="line"><a name="l00354"></a><span class="lineno"> 354</span> <span class="comment"> * when comparing the number of operations required for two</span></div>
+<div class="line"><a name="l00355"></a><span class="lineno"> 355</span> <span class="comment"> * different orderings in which matrix multiplications can be</span></div>
+<div class="line"><a name="l00356"></a><span class="lineno"> 356</span> <span class="comment"> * performed. Proving or disproving this conditional allows an</span></div>
+<div class="line"><a name="l00357"></a><span class="lineno"> 357</span> <span class="comment"> * optimal order of execution to be selected, even for dynamic</span></div>
+<div class="line"><a name="l00358"></a><span class="lineno"> 358</span> <span class="comment"> * argument shapes.</span></div>
+<div class="line"><a name="l00359"></a><span class="lineno"> 359</span> <span class="comment"> *</span></div>
+<div class="line"><a name="l00360"></a><span class="lineno"> 360</span> <span class="comment"> * The default behavior of `ConstIntBounds` assumes that each term</span></div>
+<div class="line"><a name="l00361"></a><span class="lineno"> 361</span> <span class="comment"> * in an expression is independent, and is insufficient to prove</span></div>
+<div class="line"><a name="l00362"></a><span class="lineno"> 362</span> <span class="comment"> * these inequalities. For example, the maximum value of `(A+B)*C</span></div>
+<div class="line"><a name="l00363"></a><span class="lineno"> 363</span> <span class="comment"> * - (A*B)*D` is determined by taking the maximum value of</span></div>
+<div class="line"><a name="l00364"></a><span class="lineno"> 364</span> <span class="comment"> * `(A+B)*C` and subtracting the minimum value of `(A*B)*D`.</span></div>
+<div class="line"><a name="l00365"></a><span class="lineno"> 365</span> <span class="comment"> * While this algorithm can be applied in all cases, the bound it</span></div>
+<div class="line"><a name="l00366"></a><span class="lineno"> 366</span> <span class="comment"> * provides is looser than strictly required.</span></div>
+<div class="line"><a name="l00367"></a><span class="lineno"> 367</span> <span class="comment"> *</span></div>
+<div class="line"><a name="l00368"></a><span class="lineno"> 368</span> <span class="comment"> * This extension adds a check for this case. When `A`, `B`, `C`,</span></div>
+<div class="line"><a name="l00369"></a><span class="lineno"> 369</span> <span class="comment"> * and `D` are all positive values, as is the case for tensor</span></div>
+<div class="line"><a name="l00370"></a><span class="lineno"> 370</span> <span class="comment"> * shapes, the inequality can be written as `1/A + 1/B < D/C`. If</span></div>
+<div class="line"><a name="l00371"></a><span class="lineno"> 371</span> <span class="comment"> * this inequality holds for the minimum values of `A`, `B`, and</span></div>
+<div class="line"><a name="l00372"></a><span class="lineno"> 372</span> <span class="comment"> * `D`, along with the maximum value of `C`, then the inequality</span></div>
+<div class="line"><a name="l00373"></a><span class="lineno"> 373</span> <span class="comment"> * holds for all values.</span></div>
+<div class="line"><a name="l00374"></a><span class="lineno"> 374</span> <span class="comment"> *</span></div>
+<div class="line"><a name="l00375"></a><span class="lineno"> 375</span> <span class="comment"> * This extension requires little to no performance overhead, and</span></div>
+<div class="line"><a name="l00376"></a><span class="lineno"> 376</span> <span class="comment"> * may be enabled by default in future releases.</span></div>
+<div class="line"><a name="l00377"></a><span class="lineno"> 377</span> <span class="comment"> */</span></div>
+<div class="line"><a name="l00378"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1RewriteSimplifier.html#a6705af5099418b968ded40d79102ad4eaf2a72591e015241f111c915110ad0b23"> 378</a></span>  <a class="code" href="classtvm_1_1arith_1_1RewriteSimplifier.html#a6705af5099418b968ded40d79102ad4eaf2a72591e015241f111c915110ad0b23">kComparisonOfProductAndSum</a> = (1 << 3),</div>
+<div class="line"><a name="l00379"></a><span class="lineno"> 379</span>  };</div>
+<div class="line"><a name="l00380"></a><span class="lineno"> 380</span>  </div>
+<div class="line"><a name="l00386"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1RewriteSimplifier.html#a44b7c12d6a5d9adfc4ee4cee8d935e38"> 386</a></span>  TVM_DLL <span class="keywordtype">void</span> <a class="code" href="classtvm_1_1arith_1_1RewriteSimplifier.html#a44b7c12d6a5d9adfc4ee4cee8d935e38">SetEnabledExtensions</a>(<a class="code" href="classtvm_1_1arith_1_1RewriteSimplifier.html#a6705af5099418b968ded40d79102ad4e">Extension</a> flags);</div>
+<div class="line"><a name="l00387"></a><span class="lineno"> 387</span>  </div>
+<div class="line"><a name="l00389"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1RewriteSimplifier.html#a2066e6d9a5b71d482c73542d0ca8c7a3"> 389</a></span>  TVM_DLL <a class="code" href="classtvm_1_1arith_1_1RewriteSimplifier.html#a6705af5099418b968ded40d79102ad4e">Extension</a> <a class="code" href="classtvm_1_1arith_1_1RewriteSimplifier.html#a2066e6d9a5b71d482c73542d0ca8c7a3">GetEnabledExtensions</a>() <span class="keyword">const</span>;</div>
+<div class="line"><a name="l00390"></a><span class="lineno"> 390</span>  </div>
+<div class="line"><a name="l00392"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1RewriteSimplifier.html#a418674df0487f883db23b9ebe208f598"> 392</a></span>  TVM_DLL ffi::ObjectRef <a class="code" href="classtvm_1_1arith_1_1RewriteSimplifier.html#a418674df0487f883db23b9ebe208f598">GetStatsCounters</a>() <span class="keyword">const</span>;</div>
+<div class="line"><a name="l00393"></a><span class="lineno"> 393</span>  </div>
+<div class="line"><a name="l00395"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1RewriteSimplifier.html#aef5b3e9793a5b459c9f457efef179e4f"> 395</a></span>  TVM_DLL <span class="keywordtype">void</span> <a class="code" href="classtvm_1_1arith_1_1RewriteSimplifier.html#aef5b3e9793a5b459c9f457efef179e4f">ResetStatsCounters</a>();</div>
+<div class="line"><a name="l00396"></a><span class="lineno"> 396</span>  </div>
+<div class="line"><a name="l00411"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1RewriteSimplifier.html#a95dd28018de6b7aa1a5a7ee76f3ba178"> 411</a></span>  TVM_DLL <span class="keywordtype">void</span> <a class="code" href="classtvm_1_1arith_1_1RewriteSimplifier.html#a95dd28018de6b7aa1a5a7ee76f3ba178">SetMaximumRewriteSteps</a>(int64_t <a class="code" href="namespacetvm_1_1tirx.html#ae97ec08fd3edb5b1a83e46171c4a7939">maximum</a>);</div>
+<div class="line"><a name="l00412"></a><span class="lineno"> 412</span>  </div>
+<div class="line"><a name="l00413"></a><span class="lineno"> 413</span>  <span class="keyword">private</span>:</div>
+<div class="line"><a name="l00414"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1RewriteSimplifier.html#ab502b81f91a3692606541f1ec44911de"> 414</a></span>  <span class="keyword">friend</span> <span class="keyword">class </span><a class="code" href="classtvm_1_1arith_1_1AnalyzerObj.html">AnalyzerObj</a>;</div>
+<div class="line"><a name="l00415"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1RewriteSimplifier.html#ab8bf22547cf1df0a28fc4ee98841ab89"> 415</a></span>  <span class="keyword">friend</span> <span class="keyword">class </span><a class="code" href="classtvm_1_1arith_1_1ConstraintContext.html">ConstraintContext</a>;</div>
+<div class="line"><a name="l00416"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1RewriteSimplifier.html#abd092d074e3063da6d7bb0c67115a33f"> 416</a></span>  <span class="keyword">friend</span> <span class="keyword">class </span><a class="code" href="classtvm_1_1arith_1_1CanonicalSimplifier.html">CanonicalSimplifier</a>;</div>
+<div class="line"><a name="l00417"></a><span class="lineno"> 417</span>  <span class="keyword">explicit</span> <a class="code" href="classtvm_1_1arith_1_1RewriteSimplifier.html">RewriteSimplifier</a>(<a class="code" href="classtvm_1_1arith_1_1AnalyzerObj.html">AnalyzerObj</a>* parent);</div>
+<div class="line"><a name="l00418"></a><span class="lineno"> 418</span>  TVM_DLL ~<a class="code" href="classtvm_1_1arith_1_1RewriteSimplifier.html">RewriteSimplifier</a>();</div>
+<div class="line"><a name="l00419"></a><span class="lineno"> 419</span>  <span class="keywordtype">void</span> CopyFrom(<span class="keyword">const</span> <a class="code" href="classtvm_1_1arith_1_1RewriteSimplifier.html">RewriteSimplifier</a>& other);</div>
+<div class="line"><a name="l00420"></a><span class="lineno"> 420</span>  <span class="keyword">class </span>Impl;</div>
+<div class="line"><a name="l00422"></a><span class="lineno"> 422</span>  Impl* impl_;</div>
+<div class="line"><a name="l00423"></a><span class="lineno"> 423</span> };</div>
+<div class="line"><a name="l00424"></a><span class="lineno"> 424</span>  </div>
+<div class="line"><a name="l00428"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1CanonicalSimplifier.html"> 428</a></span> <span class="keyword">class </span><a class="code" href="classtvm_1_1arith_1_1CanonicalSimplifier.html">CanonicalSimplifier</a> {</div>
+<div class="line"><a name="l00429"></a><span class="lineno"> 429</span>  <span class="keyword">public</span>:</div>
+<div class="line"><a name="l00435"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1CanonicalSimplifier.html#a2ed4ed7990499ee188e69967afb511c6"> 435</a></span>  TVM_DLL <a class="code" href="classtvm_1_1PrimExpr.html">PrimExpr</a> <a class="code" href="classtvm_1_1arith_1_1CanonicalSimplifier.html#a2ed4ed7990499ee188e69967afb511c6">operator()</a>(<span class="keyword">const</span> <a class="code" href="classtvm_1_1PrimExpr.html">PrimExpr</a>& expr);</div>
+<div class="line"><a name="l00436"></a><span class="lineno"> 436</span>  </div>
+<div class="line"><a name="l00444"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1CanonicalSimplifier.html#a790c032e12c7d93e9e940297b860c509"> 444</a></span>  TVM_DLL <span class="keywordtype">void</span> <a class="code" href="classtvm_1_1arith_1_1CanonicalSimplifier.html#a790c032e12c7d93e9e940297b860c509">Update</a>(<span class="keyword">const</span> <a class="code" href="classtvm_1_1tirx_1_1Var.html">Var</a>& <a class="code" href="namespacetvm_1_1te.html [...]
+<div class="line"><a name="l00445"></a><span class="lineno"> 445</span>  </div>
+<div class="line"><a name="l00446"></a><span class="lineno"> 446</span>  <span class="keyword">private</span>:</div>
+<div class="line"><a name="l00447"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1CanonicalSimplifier.html#ab502b81f91a3692606541f1ec44911de"> 447</a></span>  <span class="keyword">friend</span> <span class="keyword">class </span><a class="code" href="classtvm_1_1arith_1_1AnalyzerObj.html">AnalyzerObj</a>;</div>
+<div class="line"><a name="l00448"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1CanonicalSimplifier.html#ab8bf22547cf1df0a28fc4ee98841ab89"> 448</a></span>  <span class="keyword">friend</span> <span class="keyword">class </span><a class="code" href="classtvm_1_1arith_1_1ConstraintContext.html">ConstraintContext</a>;</div>
+<div class="line"><a name="l00449"></a><span class="lineno"> 449</span>  <span class="keyword">explicit</span> <a class="code" href="classtvm_1_1arith_1_1CanonicalSimplifier.html">CanonicalSimplifier</a>(<a class="code" href="classtvm_1_1arith_1_1AnalyzerObj.html">AnalyzerObj</a>* parent);</div>
+<div class="line"><a name="l00450"></a><span class="lineno"> 450</span>  TVM_DLL ~<a class="code" href="classtvm_1_1arith_1_1CanonicalSimplifier.html">CanonicalSimplifier</a>();</div>
+<div class="line"><a name="l00451"></a><span class="lineno"> 451</span>  <span class="keywordtype">void</span> CopyFrom(<span class="keyword">const</span> <a class="code" href="classtvm_1_1arith_1_1CanonicalSimplifier.html">CanonicalSimplifier</a>& other);</div>
+<div class="line"><a name="l00452"></a><span class="lineno"> 452</span>  <span class="keyword">class </span>Impl;</div>
+<div class="line"><a name="l00454"></a><span class="lineno"> 454</span>  Impl* impl_;</div>
+<div class="line"><a name="l00455"></a><span class="lineno"> 455</span> };</div>
+<div class="line"><a name="l00456"></a><span class="lineno"> 456</span>  </div>
+<div class="line"><a name="l00462"></a><span class="lineno"><a class="line" href="namespacetvm_1_1arith.html#a1d555a0cc363f669a49fad7e5f7d69d0"> 462</a></span> <span class="keyword">enum class</span> <a class="code" href="namespacetvm_1_1arith.html#a1d555a0cc363f669a49fad7e5f7d69d0">CompareResult</a> : int {</div>
+<div class="line"><a name="l00463"></a><span class="lineno"> 463</span>  <a class="code" href="namespacetvm_1_1arith.html#a1d555a0cc363f669a49fad7e5f7d69d0af9f8d006118de5ad04dd7c000b8f3b91">kInconsistent</a> = 0,</div>
+<div class="line"><a name="l00464"></a><span class="lineno"> 464</span>  <a class="code" href="namespacetvm_1_1arith.html#a1d555a0cc363f669a49fad7e5f7d69d0a7db2fe13b438b1005c70fcc9ab826b3b">kEQ</a> = 1,</div>
+<div class="line"><a name="l00465"></a><span class="lineno"> 465</span>  <a class="code" href="namespacetvm_1_1arith.html#a1d555a0cc363f669a49fad7e5f7d69d0add0f23f162dcf68bf7bc761ac13040ae">kLT</a> = 2,</div>
+<div class="line"><a name="l00466"></a><span class="lineno"> 466</span>  <a class="code" href="namespacetvm_1_1arith.html#a1d555a0cc363f669a49fad7e5f7d69d0a1893df55d7fc768f2336fdcb7c7b9c37">kLE</a> = 3,</div>
+<div class="line"><a name="l00467"></a><span class="lineno"> 467</span>  <a class="code" href="namespacetvm_1_1arith.html#a1d555a0cc363f669a49fad7e5f7d69d0aa3d18adb9a64157d6708f8ba39dfcdbc">kGT</a> = 4,</div>
+<div class="line"><a name="l00468"></a><span class="lineno"> 468</span>  <a class="code" href="namespacetvm_1_1arith.html#a1d555a0cc363f669a49fad7e5f7d69d0ac0275be4f85005f8ef340d3d5be8d361">kGE</a> = 5,</div>
+<div class="line"><a name="l00469"></a><span class="lineno"> 469</span>  <a class="code" href="namespacetvm_1_1arith.html#a1d555a0cc363f669a49fad7e5f7d69d0a03e3ad128c41e04acc6a0e59fdf11a58">kNE</a> = 6,</div>
+<div class="line"><a name="l00470"></a><span class="lineno"> 470</span>  <a class="code" href="namespacetvm_1_1arith.html#aca8806e355ad3dd5f1df9c1eca9aac9da8812c1a077255594d23bc1c2f3af3979">kUnknown</a> = 7</div>
+<div class="line"><a name="l00471"></a><span class="lineno"> 471</span> };</div>
+<div class="line"><a name="l00472"></a><span class="lineno"> 472</span>  </div>
+<div class="line"><a name="l00473"></a><span class="lineno"><a class="line" href="namespacetvm_1_1arith.html#ae300654bf1788008de90f1e77fbaa7fe"> 473</a></span> <span class="keyword">inline</span> constexpr <a class="code" href="namespacetvm_1_1arith.html#a1d555a0cc363f669a49fad7e5f7d69d0">CompareResult</a> <a class="code" href="namespacetvm_1_1arith.html#ae300654bf1788008de90f1e77fbaa7fe">operator&</a>(<a class="code" href="namespacetvm_1_1arith.html#a1d555a0cc363f669a49fad7e5f [...]
+<div class="line"><a name="l00474"></a><span class="lineno"> 474</span>  <span class="keywordflow">return</span> <a class="code" href="namespacetvm_1_1arith.html#a1d555a0cc363f669a49fad7e5f7d69d0">CompareResult</a>(<span class="keyword">static_cast<</span><span class="keywordtype">int</span><span class="keyword">></span>(lhs) & <span class="keyword">static_cast<</span><span class="keywordtype">int</span><span class="keyword">></span>(rhs));</div>
+<div class="line"><a name="l00475"></a><span class="lineno"> 475</span> }</div>
+<div class="line"><a name="l00476"></a><span class="lineno"><a class="line" href="namespacetvm_1_1arith.html#a660ff1fdbb08fea19a922157cadad7a7"> 476</a></span> <span class="keyword">inline</span> constexpr <a class="code" href="namespacetvm_1_1arith.html#a1d555a0cc363f669a49fad7e5f7d69d0">CompareResult</a> <a class="code" href="namespacetvm_1_1arith.html#a660ff1fdbb08fea19a922157cadad7a7">operator|</a>(<a class="code" href="namespacetvm_1_1arith.html#a1d555a0cc363f669a49fad7e5f7d69 [...]
+<div class="line"><a name="l00477"></a><span class="lineno"> 477</span>  <span class="keywordflow">return</span> <a class="code" href="namespacetvm_1_1arith.html#a1d555a0cc363f669a49fad7e5f7d69d0">CompareResult</a>(<span class="keyword">static_cast<</span><span class="keywordtype">int</span><span class="keyword">></span>(lhs) | <span class="keyword">static_cast<</span><span class="keywordtype">int</span><span class="keyword">></span>(rhs));</div>
+<div class="line"><a name="l00478"></a><span class="lineno"> 478</span> }</div>
+<div class="line"><a name="l00479"></a><span class="lineno"> 479</span>  </div>
+<div class="line"><a name="l00486"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1TransitiveComparisonAnalyzer.html"> 486</a></span> <span class="keyword">class </span><a class="code" href="classtvm_1_1arith_1_1TransitiveComparisonAnalyzer.html">TransitiveComparisonAnalyzer</a> {</div>
+<div class="line"><a name="l00487"></a><span class="lineno"> 487</span>  <span class="keyword">public</span>:</div>
+<div class="line"><a name="l00488"></a><span class="lineno"> 488</span>  <span class="comment">/* \brief Using previously specified knowns, compare the expressions provided</span></div>
<div class="line"><a name="l00489"></a><span class="lineno"> 489</span> <span class="comment"> *</span></div>
-<div class="line"><a name="l00490"></a><span class="lineno"> 490</span> <span class="comment"> * \param propagate_inequalities If true, attempt to find a sequence</span></div>
-<div class="line"><a name="l00491"></a><span class="lineno"> 491</span> <span class="comment"> * of transitive inequalities that allow the lhs and rhs to be</span></div>
-<div class="line"><a name="l00492"></a><span class="lineno"> 492</span> <span class="comment"> * compared. If false, only use the known comparison that have been</span></div>
-<div class="line"><a name="l00493"></a><span class="lineno"> 493</span> <span class="comment"> * directly provided. Using `propagate_inequalities = false` is</span></div>
-<div class="line"><a name="l00494"></a><span class="lineno"> 494</span> <span class="comment"> * roughly equivalent to comparing against all known inequality</span></div>
-<div class="line"><a name="l00495"></a><span class="lineno"> 495</span> <span class="comment"> * expressions using `ExprDeepEqual`, but also allows for constant</span></div>
-<div class="line"><a name="l00496"></a><span class="lineno"> 496</span> <span class="comment"> * offsets on either side of the inequality.</span></div>
-<div class="line"><a name="l00497"></a><span class="lineno"> 497</span> <span class="comment"> *</span></div>
-<div class="line"><a name="l00498"></a><span class="lineno"> 498</span> <span class="comment"> * \return The most specific result that can be proven about the</span></div>
-<div class="line"><a name="l00499"></a><span class="lineno"> 499</span> <span class="comment"> * comparison. If nothing can be proven, returns kUnknown.</span></div>
-<div class="line"><a name="l00500"></a><span class="lineno"> 500</span> <span class="comment"> */</span></div>
-<div class="line"><a name="l00501"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1TransitiveComparisonAnalyzer.html#ad00ac744402de84c7c3d6f2f46a6f7c7"> 501</a></span>  TVM_DLL <a class="code" href="namespacetvm_1_1arith.html#a1d555a0cc363f669a49fad7e5f7d69d0">CompareResult</a> <a class="code" href="classtvm_1_1arith_1_1TransitiveComparisonAnalyzer.html#ad00ac744402de84c7c3d6f2f46a6f7c7">TryCompare</a>(<span class="keyword">const</span> <a class="code" href="cl [...]
-<div class="line"><a name="l00502"></a><span class="lineno"> 502</span>  <span class="keywordtype">bool</span> propagate_inequalities = <span class="keyword">true</span>);</div>
-<div class="line"><a name="l00503"></a><span class="lineno"> 503</span>  </div>
-<div class="line"><a name="l00510"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1TransitiveComparisonAnalyzer.html#ab397bc32c73753205e70d63b71738779"> 510</a></span>  TVM_DLL <span class="keywordtype">void</span> <a class="code" href="classtvm_1_1arith_1_1TransitiveComparisonAnalyzer.html#ab397bc32c73753205e70d63b71738779">Bind</a>(<span class="keyword">const</span> <a class="code" href="classtvm_1_1tirx_1_1Var.html">Var</a>& <a class="code" href="namespa [...]
-<div class="line"><a name="l00511"></a><span class="lineno"> 511</span>  </div>
-<div class="line"><a name="l00518"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1TransitiveComparisonAnalyzer.html#a1a57e873676402922a7af5b8e1991f00"> 518</a></span>  TVM_DLL <span class="keywordtype">void</span> <a class="code" href="classtvm_1_1arith_1_1TransitiveComparisonAnalyzer.html#a1a57e873676402922a7af5b8e1991f00">Bind</a>(<span class="keyword">const</span> <a class="code" href="classtvm_1_1tirx_1_1Var.html">Var</a>& <a class="code" href="namespa [...]
-<div class="line"><a name="l00519"></a><span class="lineno"> 519</span>  </div>
-<div class="line"><a name="l00526"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1TransitiveComparisonAnalyzer.html#ab0c67cf201be8b57460cc155e5ff352a"> 526</a></span>  TVM_DLL std::function<void()> <a class="code" href="classtvm_1_1arith_1_1TransitiveComparisonAnalyzer.html#ab0c67cf201be8b57460cc155e5ff352a">EnterConstraint</a>(<span class="keyword">const</span> <a class="code" href="classtvm_1_1PrimExpr.html">PrimExpr</a>& constraint);</div>
-<div class="line"><a name="l00527"></a><span class="lineno"> 527</span>  </div>
-<div class="line"><a name="l00528"></a><span class="lineno"> 528</span>  <span class="keyword">private</span>:</div>
-<div class="line"><a name="l00529"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1TransitiveComparisonAnalyzer.html#ab502b81f91a3692606541f1ec44911de"> 529</a></span>  <span class="keyword">friend</span> <span class="keyword">class </span><a class="code" href="classtvm_1_1arith_1_1AnalyzerObj.html">AnalyzerObj</a>;</div>
-<div class="line"><a name="l00530"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1TransitiveComparisonAnalyzer.html#ab8bf22547cf1df0a28fc4ee98841ab89"> 530</a></span>  <span class="keyword">friend</span> <span class="keyword">class </span><a class="code" href="classtvm_1_1arith_1_1ConstraintContext.html">ConstraintContext</a>;</div>
-<div class="line"><a name="l00531"></a><span class="lineno"> 531</span>  <a class="code" href="classtvm_1_1arith_1_1TransitiveComparisonAnalyzer.html">TransitiveComparisonAnalyzer</a>();</div>
-<div class="line"><a name="l00532"></a><span class="lineno"> 532</span>  TVM_DLL ~<a class="code" href="classtvm_1_1arith_1_1TransitiveComparisonAnalyzer.html">TransitiveComparisonAnalyzer</a>();</div>
-<div class="line"><a name="l00533"></a><span class="lineno"> 533</span>  <span class="keyword">class </span>Impl;</div>
-<div class="line"><a name="l00535"></a><span class="lineno"> 535</span>  std::unique_ptr<Impl> impl_;</div>
-<div class="line"><a name="l00536"></a><span class="lineno"> 536</span> };</div>
-<div class="line"><a name="l00537"></a><span class="lineno"> 537</span>  </div>
-<div class="line"><a name="l00541"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1IntSetAnalyzer.html"> 541</a></span> <span class="keyword">class </span><a class="code" href="classtvm_1_1arith_1_1IntSetAnalyzer.html">IntSetAnalyzer</a> {</div>
-<div class="line"><a name="l00542"></a><span class="lineno"> 542</span>  <span class="keyword">public</span>:</div>
-<div class="line"><a name="l00551"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1IntSetAnalyzer.html#ab84b89220625f2b2bc7c69c860e42155"> 551</a></span>  TVM_DLL <a class="code" href="classtvm_1_1arith_1_1IntSet.html">IntSet</a> <a class="code" href="classtvm_1_1arith_1_1IntSetAnalyzer.html#ab84b89220625f2b2bc7c69c860e42155">operator()</a>(<span class="keyword">const</span> <a class="code" href="classtvm_1_1PrimExpr.html">PrimExpr</a>& expr, <span class="k [...]
-<div class="line"><a name="l00552"></a><span class="lineno"> 552</span>  </div>
-<div class="line"><a name="l00561"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1IntSetAnalyzer.html#af4274caf7f5a6af64fbafc2f2465f591"> 561</a></span>  TVM_DLL <a class="code" href="classtvm_1_1arith_1_1IntSet.html">IntSet</a> <a class="code" href="classtvm_1_1arith_1_1IntSetAnalyzer.html#af4274caf7f5a6af64fbafc2f2465f591">operator()</a>(<span class="keyword">const</span> <a class="code" href="classtvm_1_1PrimExpr.html">PrimExpr</a>& expr);</div>
-<div class="line"><a name="l00562"></a><span class="lineno"> 562</span>  </div>
-<div class="line"><a name="l00570"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1IntSetAnalyzer.html#a9a201c941931807b4b88eedc4f8c03f4"> 570</a></span>  TVM_DLL <span class="keywordtype">void</span> <a class="code" href="classtvm_1_1arith_1_1IntSetAnalyzer.html#a9a201c941931807b4b88eedc4f8c03f4">Update</a>(<span class="keyword">const</span> <a class="code" href="classtvm_1_1tirx_1_1Var.html">Var</a>& <a class="code" href="namespacetvm_1_1te.html#ae0c71f84 [...]
-<div class="line"><a name="l00571"></a><span class="lineno"> 571</span>  </div>
-<div class="line"><a name="l00579"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1IntSetAnalyzer.html#ab0ded402c93fc89cba57015b6f3f4d8d"> 579</a></span>  TVM_DLL <span class="keywordtype">void</span> <a class="code" href="classtvm_1_1arith_1_1IntSetAnalyzer.html#ab0ded402c93fc89cba57015b6f3f4d8d">Bind</a>(<span class="keyword">const</span> <a class="code" href="classtvm_1_1tirx_1_1Var.html">Var</a>& <a class="code" href="namespacetvm_1_1te.html#ae0c71f8471 [...]
-<div class="line"><a name="l00580"></a><span class="lineno"> 580</span>  </div>
-<div class="line"><a name="l00581"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1IntSetAnalyzer.html#a096433dd7bde50f73eed4854af8b6111"> 581</a></span>  std::function<void()> <a class="code" href="classtvm_1_1arith_1_1IntSetAnalyzer.html#a096433dd7bde50f73eed4854af8b6111">EnterConstraint</a>(<span class="keyword">const</span> <a class="code" href="classtvm_1_1PrimExpr.html">PrimExpr</a>& constraint);</div>
-<div class="line"><a name="l00582"></a><span class="lineno"> 582</span>  </div>
-<div class="line"><a name="l00583"></a><span class="lineno"> 583</span>  <span class="keyword">private</span>:</div>
-<div class="line"><a name="l00584"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1IntSetAnalyzer.html#ab502b81f91a3692606541f1ec44911de"> 584</a></span>  <span class="keyword">friend</span> <span class="keyword">class </span><a class="code" href="classtvm_1_1arith_1_1AnalyzerObj.html">AnalyzerObj</a>;</div>
-<div class="line"><a name="l00585"></a><span class="lineno"> 585</span>  <span class="keyword">explicit</span> <a class="code" href="classtvm_1_1arith_1_1IntSetAnalyzer.html">IntSetAnalyzer</a>(<a class="code" href="classtvm_1_1arith_1_1AnalyzerObj.html">AnalyzerObj</a>* parent);</div>
-<div class="line"><a name="l00586"></a><span class="lineno"> 586</span>  TVM_DLL ~<a class="code" href="classtvm_1_1arith_1_1IntSetAnalyzer.html">IntSetAnalyzer</a>();</div>
-<div class="line"><a name="l00587"></a><span class="lineno"> 587</span>  <span class="keyword">class </span>Impl;</div>
-<div class="line"><a name="l00589"></a><span class="lineno"> 589</span>  Impl* impl_;</div>
-<div class="line"><a name="l00590"></a><span class="lineno"> 590</span> };</div>
-<div class="line"><a name="l00591"></a><span class="lineno"> 591</span>  </div>
-<div class="line"><a name="l00592"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1Z3Prover.html"> 592</a></span> <span class="keyword">class </span><a class="code" href="classtvm_1_1arith_1_1Z3Prover.html">Z3Prover</a> {</div>
-<div class="line"><a name="l00593"></a><span class="lineno"> 593</span>  <span class="keyword">public</span>:</div>
-<div class="line"><a name="l00601"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1Z3Prover.html#a6e21274c18c5310f1aee546f73177bf0"> 601</a></span>  TVM_DLL <span class="keywordtype">void</span> <a class="code" href="classtvm_1_1arith_1_1Z3Prover.html#a6e21274c18c5310f1aee546f73177bf0">Bind</a>(<span class="keyword">const</span> <a class="code" href="classtvm_1_1tirx_1_1Var.html">Var</a>& <a class="code" href="namespacetvm_1_1te.html#ae0c71f84710b436cbe0b32 [...]
-<div class="line"><a name="l00602"></a><span class="lineno"> 602</span>  </div>
-<div class="line"><a name="l00610"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1Z3Prover.html#af3c439f0bba8a33014cb208ff98ff034"> 610</a></span>  TVM_DLL <span class="keywordtype">void</span> <a class="code" href="classtvm_1_1arith_1_1Z3Prover.html#af3c439f0bba8a33014cb208ff98ff034">Bind</a>(<span class="keyword">const</span> <a class="code" href="classtvm_1_1tirx_1_1Var.html">Var</a>& <a class="code" href="namespacetvm_1_1te.html#ae0c71f84710b436cbe0b32 [...]
-<div class="line"><a name="l00611"></a><span class="lineno"> 611</span>  </div>
-<div class="line"><a name="l00617"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1Z3Prover.html#aa2463d9d04c02b57cbcee80bb208e2a8"> 617</a></span>  TVM_DLL <span class="keywordtype">bool</span> <a class="code" href="classtvm_1_1arith_1_1Z3Prover.html#aa2463d9d04c02b57cbcee80bb208e2a8">IsEnabled</a>() <span class="keyword">const</span>;</div>
-<div class="line"><a name="l00618"></a><span class="lineno"> 618</span>  </div>
-<div class="line"><a name="l00625"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1Z3Prover.html#a79fef83abf68eb50042bc81bf1ef0a9c"> 625</a></span>  TVM_DLL <span class="keywordtype">bool</span> <a class="code" href="classtvm_1_1arith_1_1Z3Prover.html#a79fef83abf68eb50042bc81bf1ef0a9c">CanProve</a>(<span class="keyword">const</span> <a class="code" href="classtvm_1_1PrimExpr.html">PrimExpr</a>& expr);</div>
-<div class="line"><a name="l00626"></a><span class="lineno"> 626</span>  </div>
-<div class="line"><a name="l00633"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1Z3Prover.html#af925259ab605048261b6e0ee6752eebc"> 633</a></span>  std::function<void()> <a class="code" href="classtvm_1_1arith_1_1Z3Prover.html#af925259ab605048261b6e0ee6752eebc">EnterConstraint</a>(<span class="keyword">const</span> <a class="code" href="classtvm_1_1PrimExpr.html">PrimExpr</a>& constraint);</div>
-<div class="line"><a name="l00634"></a><span class="lineno"> 634</span>  </div>
-<div class="line"><a name="l00641"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1Z3Prover.html#add91f63efd092fa65a95d0735f6e2045"> 641</a></span>  ffi::String <a class="code" href="classtvm_1_1arith_1_1Z3Prover.html#add91f63efd092fa65a95d0735f6e2045">GetSMTLIB2</a>(<span class="keyword">const</span> ffi::Optional<PrimExpr> expr);</div>
-<div class="line"><a name="l00642"></a><span class="lineno"> 642</span>  </div>
-<div class="line"><a name="l00648"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1Z3Prover.html#ab8a69dd18ec8594480472c159472f77b"> 648</a></span>  ffi::String <a class="code" href="classtvm_1_1arith_1_1Z3Prover.html#ab8a69dd18ec8594480472c159472f77b">GetStats</a>();</div>
-<div class="line"><a name="l00649"></a><span class="lineno"> 649</span>  </div>
-<div class="line"><a name="l00655"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1Z3Prover.html#a52f045f47f9f868d7372402ea574341c"> 655</a></span>  <span class="keywordtype">void</span> <a class="code" href="classtvm_1_1arith_1_1Z3Prover.html#a52f045f47f9f868d7372402ea574341c">SetTimeoutMs</a>(<span class="keywordtype">unsigned</span> timeout_ms);</div>
-<div class="line"><a name="l00656"></a><span class="lineno"> 656</span>  </div>
-<div class="line"><a name="l00662"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1Z3Prover.html#afebe6e21034ded48a7dbf9d625ed87c3"> 662</a></span>  <span class="keywordtype">void</span> <a class="code" href="classtvm_1_1arith_1_1Z3Prover.html#afebe6e21034ded48a7dbf9d625ed87c3">SetRLimit</a>(<span class="keywordtype">unsigned</span> rlimit);</div>
-<div class="line"><a name="l00663"></a><span class="lineno"> 663</span>  </div>
-<div class="line"><a name="l00670"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1Z3Prover.html#a35d81090c4816ac6202c878cea54d98f"> 670</a></span>  ffi::String <a class="code" href="classtvm_1_1arith_1_1Z3Prover.html#a35d81090c4816ac6202c878cea54d98f">GetModel</a>(<span class="keyword">const</span> <a class="code" href="classtvm_1_1PrimExpr.html">PrimExpr</a>& expr);</div>
-<div class="line"><a name="l00671"></a><span class="lineno"> 671</span>  </div>
-<div class="line"><a name="l00683"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1Z3Prover.html#ac2bbbfc80009dc0276ef52720baa8130"> 683</a></span>  TVM_DLL int64_t <a class="code" href="classtvm_1_1arith_1_1Z3Prover.html#ac2bbbfc80009dc0276ef52720baa8130">CountSatisfyingValues</a>(<span class="keyword">const</span> <a class="code" href="classtvm_1_1tirx_1_1Var.html">Var</a>& <a class="code" href="namespacetvm_1_1te.html#ae0c71f84710b436cbe0b32289d0838f4">v [...]
-<div class="line"><a name="l00684"></a><span class="lineno"> 684</span>  int64_t min_consecutive = 1);</div>
-<div class="line"><a name="l00685"></a><span class="lineno"> 685</span>  </div>
-<div class="line"><a name="l00686"></a><span class="lineno"> 686</span>  <span class="keyword">private</span>:</div>
-<div class="line"><a name="l00687"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1Z3Prover.html#ab502b81f91a3692606541f1ec44911de"> 687</a></span>  <span class="keyword">friend</span> <span class="keyword">class </span><a class="code" href="classtvm_1_1arith_1_1AnalyzerObj.html">AnalyzerObj</a>;</div>
-<div class="line"><a name="l00688"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1Z3Prover.html#a88d594816df596eed10643082b0d0805"> 688</a></span>  <span class="keyword">friend</span> <span class="keyword">class </span><a class="code" href="classtvm_1_1arith_1_1Analyzer.html">Analyzer</a>;</div>
-<div class="line"><a name="l00689"></a><span class="lineno"> 689</span>  <span class="keyword">explicit</span> <a class="code" href="classtvm_1_1arith_1_1Z3Prover.html">Z3Prover</a>(<a class="code" href="classtvm_1_1arith_1_1AnalyzerObj.html">AnalyzerObj</a>* parent);</div>
-<div class="line"><a name="l00690"></a><span class="lineno"> 690</span>  TVM_DLL ~<a class="code" href="classtvm_1_1arith_1_1Z3Prover.html">Z3Prover</a>();</div>
-<div class="line"><a name="l00691"></a><span class="lineno"> 691</span>  <span class="keywordtype">void</span> CopyFrom(<span class="keyword">const</span> <a class="code" href="classtvm_1_1arith_1_1Z3Prover.html">Z3Prover</a>& other);</div>
-<div class="line"><a name="l00692"></a><span class="lineno"> 692</span>  <span class="keyword">class </span>Impl;</div>
-<div class="line"><a name="l00693"></a><span class="lineno"> 693</span>  Impl* impl_;</div>
-<div class="line"><a name="l00694"></a><span class="lineno"> 694</span> };</div>
-<div class="line"><a name="l00695"></a><span class="lineno"> 695</span>  </div>
-<div class="line"><a name="l00706"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1AnalyzerObj.html"> 706</a></span> <span class="keyword">class </span>TVM_DLL <a class="code" href="classtvm_1_1arith_1_1AnalyzerObj.html">AnalyzerObj</a> : <span class="keyword">public</span> ffi::Object {</div>
-<div class="line"><a name="l00707"></a><span class="lineno"> 707</span>  <span class="keyword">public</span>:</div>
-<div class="line"><a name="l00709"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1AnalyzerObj.html#a7007e17a46f88e125eb649ffd40a1d8b"> 709</a></span>  <a class="code" href="classtvm_1_1arith_1_1ConstIntBoundAnalyzer.html">ConstIntBoundAnalyzer</a> <a class="code" href="classtvm_1_1arith_1_1AnalyzerObj.html#a7007e17a46f88e125eb649ffd40a1d8b">const_int_bound</a>;</div>
-<div class="line"><a name="l00711"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1AnalyzerObj.html#ad1cb5737d9af424de0ad43ec794d254f"> 711</a></span>  <a class="code" href="classtvm_1_1arith_1_1ModularSetAnalyzer.html">ModularSetAnalyzer</a> <a class="code" href="classtvm_1_1arith_1_1AnalyzerObj.html#ad1cb5737d9af424de0ad43ec794d254f">modular_set</a>;</div>
-<div class="line"><a name="l00713"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1AnalyzerObj.html#a945243b84edc40b89cd326e7d3cc52da"> 713</a></span>  <a class="code" href="classtvm_1_1arith_1_1RewriteSimplifier.html">RewriteSimplifier</a> <a class="code" href="classtvm_1_1arith_1_1AnalyzerObj.html#a945243b84edc40b89cd326e7d3cc52da">rewrite_simplify</a>;</div>
-<div class="line"><a name="l00715"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1AnalyzerObj.html#afcf97e818f5778d64ceb6e5e0fa59de7"> 715</a></span>  <a class="code" href="classtvm_1_1arith_1_1CanonicalSimplifier.html">CanonicalSimplifier</a> <a class="code" href="classtvm_1_1arith_1_1AnalyzerObj.html#afcf97e818f5778d64ceb6e5e0fa59de7">canonical_simplify</a>;</div>
-<div class="line"><a name="l00717"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1AnalyzerObj.html#a9b3fbbc422505fa18cff90ab81d591ab"> 717</a></span>  <a class="code" href="classtvm_1_1arith_1_1IntSetAnalyzer.html">IntSetAnalyzer</a> <a class="code" href="classtvm_1_1arith_1_1AnalyzerObj.html#a9b3fbbc422505fa18cff90ab81d591ab">int_set</a>;</div>
-<div class="line"><a name="l00719"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1AnalyzerObj.html#aabac046e3ed1abf7f96b446114cafa62"> 719</a></span>  <a class="code" href="classtvm_1_1arith_1_1TransitiveComparisonAnalyzer.html">TransitiveComparisonAnalyzer</a> <a class="code" href="classtvm_1_1arith_1_1AnalyzerObj.html#aabac046e3ed1abf7f96b446114cafa62">transitive_comparisons</a>;</div>
-<div class="line"><a name="l00721"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1AnalyzerObj.html#a730c77bc237767905c43be5e6c6545f6"> 721</a></span>  <a class="code" href="classtvm_1_1arith_1_1Z3Prover.html">Z3Prover</a> <a class="code" href="classtvm_1_1arith_1_1AnalyzerObj.html#a730c77bc237767905c43be5e6c6545f6">z3_prover</a>;</div>
-<div class="line"><a name="l00723"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1AnalyzerObj.html#a61718b9bdfcc01483cf70d159c4f12c8"> 723</a></span>  <a class="code" href="classtvm_1_1arith_1_1AnalyzerObj.html#a61718b9bdfcc01483cf70d159c4f12c8">AnalyzerObj</a>();</div>
-<div class="line"><a name="l00739"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1AnalyzerObj.html#a855fc72735981c7ceeb89e5ac851c7fe"> 739</a></span>  <span class="keywordtype">void</span> <a class="code" href="classtvm_1_1arith_1_1AnalyzerObj.html#a855fc72735981c7ceeb89e5ac851c7fe">MarkGlobalNonNegValue</a>(<span class="keyword">const</span> <a class="code" href="classtvm_1_1PrimExpr.html">PrimExpr</a>& value);</div>
-<div class="line"><a name="l00752"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1AnalyzerObj.html#aa784e4543c60e2a458ab7d4aaafb23eb"> 752</a></span>  <span class="keywordtype">void</span> <a class="code" href="classtvm_1_1arith_1_1AnalyzerObj.html#aa784e4543c60e2a458ab7d4aaafb23eb">Bind</a>(<span class="keyword">const</span> <a class="code" href="classtvm_1_1tirx_1_1Var.html">Var</a>& <a class="code" href="namespacetvm_1_1te.html#ae0c71f84710b436cbe0b3228 [...]
-<div class="line"><a name="l00765"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1AnalyzerObj.html#a7b48ee7d8d2d88687f7eaba43ff7b6bd"> 765</a></span>  <span class="keywordtype">void</span> <a class="code" href="classtvm_1_1arith_1_1AnalyzerObj.html#a7b48ee7d8d2d88687f7eaba43ff7b6bd">Bind</a>(<span class="keyword">const</span> <a class="code" href="classtvm_1_1tirx_1_1Var.html">Var</a>& <a class="code" href="namespacetvm_1_1te.html#ae0c71f84710b436cbe0b3228 [...]
-<div class="line"><a name="l00774"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1AnalyzerObj.html#a7adfc0b82669145611d8638adbf3e432"> 774</a></span>  <span class="keywordtype">void</span> <a class="code" href="classtvm_1_1arith_1_1AnalyzerObj.html#a7adfc0b82669145611d8638adbf3e432">Bind</a>(<span class="keyword">const</span> ffi::Map<Var, Range>& variables, <span class="keywordtype">bool</span> allow_override = <span class="keyword">false</span>);</div>
-<div class="line"><a name="l00787"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1AnalyzerObj.html#a33b289b63c0a91e11b00e98fdbfe2d53"> 787</a></span>  <span class="keywordtype">bool</span> <a class="code" href="classtvm_1_1arith_1_1AnalyzerObj.html#a33b289b63c0a91e11b00e98fdbfe2d53">CanProveGreaterEqual</a>(<span class="keyword">const</span> <a class="code" href="classtvm_1_1PrimExpr.html">PrimExpr</a>& expr, int64_t lower_bound);</div>
-<div class="line"><a name="l00800"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1AnalyzerObj.html#a59475536e8c1078ad57cd5089cf4ffd7"> 800</a></span>  <span class="keywordtype">bool</span> <a class="code" href="classtvm_1_1arith_1_1AnalyzerObj.html#a59475536e8c1078ad57cd5089cf4ffd7">CanProveLess</a>(<span class="keyword">const</span> <a class="code" href="classtvm_1_1PrimExpr.html">PrimExpr</a>& expr, int64_t upper_bound);</div>
-<div class="line"><a name="l00810"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1AnalyzerObj.html#ab90111060d30e07408083c5b28f58a68"> 810</a></span>  <span class="keywordtype">bool</span> <a class="code" href="classtvm_1_1arith_1_1AnalyzerObj.html#ab90111060d30e07408083c5b28f58a68">CanProveEqual</a>(<span class="keyword">const</span> <a class="code" href="classtvm_1_1PrimExpr.html">PrimExpr</a>& lhs, <span class="keyword">const</span> <a class="code" href [...]
-<div class="line"><a name="l00827"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1AnalyzerObj.html#a3d3b62509fecace627f8843256dc8c02"> 827</a></span>  <span class="keywordtype">bool</span> <a class="code" href="classtvm_1_1arith_1_1AnalyzerObj.html#a3d3b62509fecace627f8843256dc8c02">CanProveLessEqualThanSymbolicShapeValue</a>(<span class="keyword">const</span> <a class="code" href="classtvm_1_1PrimExpr.html">PrimExpr</a>& lhs, <span class="keyword">const</ [...]
-<div class="line"><a name="l00840"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1AnalyzerObj.html#a6dfebc27ddce54ca2f10b71c2391d607"> 840</a></span>  <span class="keywordtype">bool</span> <a class="code" href="classtvm_1_1arith_1_1AnalyzerObj.html#a6dfebc27ddce54ca2f10b71c2391d607">CanProve</a>(<span class="keyword">const</span> <a class="code" href="classtvm_1_1PrimExpr.html">PrimExpr</a>& cond, <a class="code" href="namespacetvm_1_1arith.html#a038b0205b [...]
-<div class="line"><a name="l00841"></a><span class="lineno"> 841</span>  </div>
-<div class="line"><a name="l00855"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1AnalyzerObj.html#a19f9b83800c85ab898a23220fbf000b1"> 855</a></span>  <a class="code" href="classtvm_1_1PrimExpr.html">PrimExpr</a> <a class="code" href="classtvm_1_1arith_1_1AnalyzerObj.html#a19f9b83800c85ab898a23220fbf000b1">Simplify</a>(<span class="keyword">const</span> <a class="code" href="classtvm_1_1PrimExpr.html">PrimExpr</a>& expr, <span class="keywordtype">int</span [...]
-<div class="line"><a name="l00856"></a><span class="lineno"> 856</span>  </div>
-<div class="line"><a name="l00866"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1AnalyzerObj.html#ae26f9e1ceb0f7c8d0f1a038837529ae8"> 866</a></span>  <span class="keyword">static</span> constexpr <span class="keywordtype">bool</span> _type_mutable = <span class="keyword">true</span>;</div>
-<div class="line"><a name="l00867"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1AnalyzerObj.html#ae7f9b32e8df29a1230396bee65ca42c1"> 867</a></span>  <a class="code" href="classtvm_1_1arith_1_1AnalyzerObj.html#ae7f9b32e8df29a1230396bee65ca42c1">TVM_FFI_DECLARE_OBJECT_INFO_FINAL</a>(<span class="stringliteral">"arith.Analyzer"</span>, <a class="code" href="classtvm_1_1arith_1_1AnalyzerObj.html">AnalyzerObj</a>, ffi::Object);</div>
-<div class="line"><a name="l00868"></a><span class="lineno"> 868</span> };</div>
-<div class="line"><a name="l00869"></a><span class="lineno"> 869</span>  </div>
-<div class="line"><a name="l00883"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1Analyzer.html"> 883</a></span> <span class="keyword">class </span><a class="code" href="classtvm_1_1arith_1_1Analyzer.html">Analyzer</a> : <span class="keyword">public</span> ffi::ObjectRef {</div>
-<div class="line"><a name="l00884"></a><span class="lineno"> 884</span>  <span class="keyword">public</span>:</div>
-<div class="line"><a name="l00886"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1Analyzer.html#ae8568eeee02c68849e468f8c19a2701d"> 886</a></span>  <a class="code" href="classtvm_1_1arith_1_1Analyzer.html#ae8568eeee02c68849e468f8c19a2701d">Analyzer</a>() : <a class="code" href="classtvm_1_1arith_1_1Analyzer.html">Analyzer</a>(ffi::make_object<<a class="code" href="classtvm_1_1arith_1_1AnalyzerObj.html">AnalyzerObj</a>>()) {}</div>
-<div class="line"><a name="l00887"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1Analyzer.html#a986c5877025fcf36b25a79fb58b8c2ed"> 887</a></span>  <span class="keyword">explicit</span> <a class="code" href="classtvm_1_1arith_1_1Analyzer.html#a986c5877025fcf36b25a79fb58b8c2ed">Analyzer</a>(ffi::ObjectPtr<AnalyzerObj> n) : ffi::ObjectRef(std::move(n)) {</div>
-<div class="line"><a name="l00888"></a><span class="lineno"> 888</span>  TVM_FFI_ICHECK(this->get() != <span class="keyword">nullptr</span>);</div>
-<div class="line"><a name="l00889"></a><span class="lineno"> 889</span>  }</div>
-<div class="line"><a name="l00890"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1Analyzer.html#ade37f587bfed0383436428182bef8a73"> 890</a></span>  <a class="code" href="classtvm_1_1arith_1_1Analyzer.html#ade37f587bfed0383436428182bef8a73">TVM_FFI_DEFINE_OBJECT_REF_METHODS_NOTNULLABLE</a>(<a class="code" href="classtvm_1_1arith_1_1Analyzer.html">Analyzer</a>, ffi::ObjectRef, <a class="code" href="classtvm_1_1arith_1_1AnalyzerObj.html">AnalyzerObj</a>);</div>
-<div class="line"><a name="l00891"></a><span class="lineno"> 891</span> };</div>
-<div class="line"><a name="l00892"></a><span class="lineno"> 892</span>  </div>
-<div class="line"><a name="l00909"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1ConstraintContext.html"> 909</a></span> <span class="keyword">class </span><a class="code" href="classtvm_1_1arith_1_1ConstraintContext.html">ConstraintContext</a> {</div>
-<div class="line"><a name="l00910"></a><span class="lineno"> 910</span>  <span class="keyword">private</span>:</div>
-<div class="line"><a name="l00911"></a><span class="lineno"> 911</span>  <span class="comment">// declare friend to enable with.</span></div>
-<div class="line"><a name="l00912"></a><span class="lineno"> 912</span>  <span class="keyword">friend</span> <span class="keyword">class </span><a class="code" href="classtvm_1_1With.html">With</a><<a class="code" href="classtvm_1_1arith_1_1ConstraintContext.html">ConstraintContext</a>>;</div>
-<div class="line"><a name="l00919"></a><span class="lineno"> 919</span>  <a class="code" href="classtvm_1_1arith_1_1ConstraintContext.html">ConstraintContext</a>(<span class="keyword">const</span> <a class="code" href="classtvm_1_1arith_1_1Analyzer.html">Analyzer</a>& analyzer, <a class="code" href="classtvm_1_1PrimExpr.html">PrimExpr</a> constraint)</div>
-<div class="line"><a name="l00920"></a><span class="lineno"> 920</span>  : <a class="code" href="classtvm_1_1arith_1_1ConstraintContext.html">ConstraintContext</a>(analyzer, std::move(constraint), <span class="keyword">false</span>) {}</div>
-<div class="line"><a name="l00928"></a><span class="lineno"> 928</span>  <a class="code" href="classtvm_1_1arith_1_1ConstraintContext.html">ConstraintContext</a>(<span class="keyword">const</span> <a class="code" href="classtvm_1_1arith_1_1Analyzer.html">Analyzer</a>& analyzer, <a class="code" href="classtvm_1_1PrimExpr.html">PrimExpr</a> constraint, <span class="keywordtype">bool</span> is_assume)</div>
-<div class="line"><a name="l00929"></a><span class="lineno"> 929</span>  : analyzer_(analyzer), constraint_(std::move(constraint)), is_assume_(is_assume) {}</div>
-<div class="line"><a name="l00937"></a><span class="lineno"> 937</span>  <a class="code" href="classtvm_1_1arith_1_1ConstraintContext.html">ConstraintContext</a>(<a class="code" href="classtvm_1_1arith_1_1AnalyzerObj.html">AnalyzerObj</a>* analyzer, <a class="code" href="classtvm_1_1PrimExpr.html">PrimExpr</a> constraint)</div>
-<div class="line"><a name="l00938"></a><span class="lineno"> 938</span>  : <a class="code" href="classtvm_1_1arith_1_1ConstraintContext.html">ConstraintContext</a>(ffi::GetRef<Analyzer>(analyzer), std::move(constraint), <span class="keyword">false</span>) {}</div>
-<div class="line"><a name="l00945"></a><span class="lineno"> 945</span>  <a class="code" href="classtvm_1_1arith_1_1ConstraintContext.html">ConstraintContext</a>(<a class="code" href="classtvm_1_1arith_1_1AnalyzerObj.html">AnalyzerObj</a>* analyzer, <a class="code" href="classtvm_1_1PrimExpr.html">PrimExpr</a> constraint, <span class="keywordtype">bool</span> is_assume)</div>
-<div class="line"><a name="l00946"></a><span class="lineno"> 946</span>  : <a class="code" href="classtvm_1_1arith_1_1ConstraintContext.html">ConstraintContext</a>(ffi::GetRef<Analyzer>(analyzer), std::move(constraint), is_assume) {}</div>
-<div class="line"><a name="l00947"></a><span class="lineno"> 947</span>  <span class="comment">// enter the scope.</span></div>
-<div class="line"><a name="l00948"></a><span class="lineno"> 948</span>  <span class="keywordtype">void</span> EnterWithScope();</div>
-<div class="line"><a name="l00949"></a><span class="lineno"> 949</span>  <span class="comment">// exit the scope.</span></div>
-<div class="line"><a name="l00950"></a><span class="lineno"> 950</span>  <span class="keywordtype">void</span> ExitWithScope();</div>
-<div class="line"><a name="l00952"></a><span class="lineno"> 952</span>  <a class="code" href="classtvm_1_1arith_1_1Analyzer.html">Analyzer</a> analyzer_;</div>
-<div class="line"><a name="l00954"></a><span class="lineno"> 954</span>  <a class="code" href="classtvm_1_1PrimExpr.html">PrimExpr</a> constraint_;</div>
-<div class="line"><a name="l00956"></a><span class="lineno"> 956</span>  std::vector<std::function<void()>> recovery_functions_;</div>
-<div class="line"><a name="l00958"></a><span class="lineno"> 958</span>  <span class="keywordtype">bool</span> is_assume_;</div>
-<div class="line"><a name="l00959"></a><span class="lineno"> 959</span> };</div>
-<div class="line"><a name="l00960"></a><span class="lineno"> 960</span>  </div>
-<div class="line"><a name="l00961"></a><span class="lineno"> 961</span> } <span class="comment">// namespace arith</span></div>
-<div class="line"><a name="l00962"></a><span class="lineno"> 962</span> } <span class="comment">// namespace tvm</span></div>
-<div class="line"><a name="l00963"></a><span class="lineno"> 963</span> <span class="preprocessor">#endif </span><span class="comment">// TVM_ARITH_ANALYZER_H_</span></div>
+<div class="line"><a name="l00490"></a><span class="lineno"> 490</span> <span class="comment"> * \param lhs The left-hand side of the comparison</span></div>
+<div class="line"><a name="l00491"></a><span class="lineno"> 491</span> <span class="comment"> *</span></div>
+<div class="line"><a name="l00492"></a><span class="lineno"> 492</span> <span class="comment"> * \param rhs The right-hand side of the comparison</span></div>
+<div class="line"><a name="l00493"></a><span class="lineno"> 493</span> <span class="comment"> *</span></div>
+<div class="line"><a name="l00494"></a><span class="lineno"> 494</span> <span class="comment"> * \param propagate_inequalities If true, attempt to find a sequence</span></div>
+<div class="line"><a name="l00495"></a><span class="lineno"> 495</span> <span class="comment"> * of transitive inequalities that allow the lhs and rhs to be</span></div>
+<div class="line"><a name="l00496"></a><span class="lineno"> 496</span> <span class="comment"> * compared. If false, only use the known comparison that have been</span></div>
+<div class="line"><a name="l00497"></a><span class="lineno"> 497</span> <span class="comment"> * directly provided. Using `propagate_inequalities = false` is</span></div>
+<div class="line"><a name="l00498"></a><span class="lineno"> 498</span> <span class="comment"> * roughly equivalent to comparing against all known inequality</span></div>
+<div class="line"><a name="l00499"></a><span class="lineno"> 499</span> <span class="comment"> * expressions using `ExprDeepEqual`, but also allows for constant</span></div>
+<div class="line"><a name="l00500"></a><span class="lineno"> 500</span> <span class="comment"> * offsets on either side of the inequality.</span></div>
+<div class="line"><a name="l00501"></a><span class="lineno"> 501</span> <span class="comment"> *</span></div>
+<div class="line"><a name="l00502"></a><span class="lineno"> 502</span> <span class="comment"> * \return The most specific result that can be proven about the</span></div>
+<div class="line"><a name="l00503"></a><span class="lineno"> 503</span> <span class="comment"> * comparison. If nothing can be proven, returns kUnknown.</span></div>
+<div class="line"><a name="l00504"></a><span class="lineno"> 504</span> <span class="comment"> */</span></div>
+<div class="line"><a name="l00505"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1TransitiveComparisonAnalyzer.html#ad00ac744402de84c7c3d6f2f46a6f7c7"> 505</a></span>  TVM_DLL <a class="code" href="namespacetvm_1_1arith.html#a1d555a0cc363f669a49fad7e5f7d69d0">CompareResult</a> <a class="code" href="classtvm_1_1arith_1_1TransitiveComparisonAnalyzer.html#ad00ac744402de84c7c3d6f2f46a6f7c7">TryCompare</a>(<span class="keyword">const</span> <a class="code" href="cl [...]
+<div class="line"><a name="l00506"></a><span class="lineno"> 506</span>  <span class="keywordtype">bool</span> propagate_inequalities = <span class="keyword">true</span>);</div>
+<div class="line"><a name="l00507"></a><span class="lineno"> 507</span>  </div>
+<div class="line"><a name="l00514"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1TransitiveComparisonAnalyzer.html#ab397bc32c73753205e70d63b71738779"> 514</a></span>  TVM_DLL <span class="keywordtype">void</span> <a class="code" href="classtvm_1_1arith_1_1TransitiveComparisonAnalyzer.html#ab397bc32c73753205e70d63b71738779">Bind</a>(<span class="keyword">const</span> <a class="code" href="classtvm_1_1tirx_1_1Var.html">Var</a>& <a class="code" href="namespa [...]
+<div class="line"><a name="l00515"></a><span class="lineno"> 515</span>  </div>
+<div class="line"><a name="l00522"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1TransitiveComparisonAnalyzer.html#a1a57e873676402922a7af5b8e1991f00"> 522</a></span>  TVM_DLL <span class="keywordtype">void</span> <a class="code" href="classtvm_1_1arith_1_1TransitiveComparisonAnalyzer.html#a1a57e873676402922a7af5b8e1991f00">Bind</a>(<span class="keyword">const</span> <a class="code" href="classtvm_1_1tirx_1_1Var.html">Var</a>& <a class="code" href="namespa [...]
+<div class="line"><a name="l00523"></a><span class="lineno"> 523</span>  </div>
+<div class="line"><a name="l00530"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1TransitiveComparisonAnalyzer.html#ab0c67cf201be8b57460cc155e5ff352a"> 530</a></span>  TVM_DLL std::function<void()> <a class="code" href="classtvm_1_1arith_1_1TransitiveComparisonAnalyzer.html#ab0c67cf201be8b57460cc155e5ff352a">EnterConstraint</a>(<span class="keyword">const</span> <a class="code" href="classtvm_1_1PrimExpr.html">PrimExpr</a>& constraint);</div>
+<div class="line"><a name="l00531"></a><span class="lineno"> 531</span>  </div>
+<div class="line"><a name="l00532"></a><span class="lineno"> 532</span>  <span class="keyword">private</span>:</div>
+<div class="line"><a name="l00533"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1TransitiveComparisonAnalyzer.html#ab502b81f91a3692606541f1ec44911de"> 533</a></span>  <span class="keyword">friend</span> <span class="keyword">class </span><a class="code" href="classtvm_1_1arith_1_1AnalyzerObj.html">AnalyzerObj</a>;</div>
+<div class="line"><a name="l00534"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1TransitiveComparisonAnalyzer.html#ab8bf22547cf1df0a28fc4ee98841ab89"> 534</a></span>  <span class="keyword">friend</span> <span class="keyword">class </span><a class="code" href="classtvm_1_1arith_1_1ConstraintContext.html">ConstraintContext</a>;</div>
+<div class="line"><a name="l00535"></a><span class="lineno"> 535</span>  <a class="code" href="classtvm_1_1arith_1_1TransitiveComparisonAnalyzer.html">TransitiveComparisonAnalyzer</a>();</div>
+<div class="line"><a name="l00536"></a><span class="lineno"> 536</span>  TVM_DLL ~<a class="code" href="classtvm_1_1arith_1_1TransitiveComparisonAnalyzer.html">TransitiveComparisonAnalyzer</a>();</div>
+<div class="line"><a name="l00537"></a><span class="lineno"> 537</span>  <span class="keywordtype">void</span> CopyFrom(<span class="keyword">const</span> <a class="code" href="classtvm_1_1arith_1_1TransitiveComparisonAnalyzer.html">TransitiveComparisonAnalyzer</a>& other);</div>
+<div class="line"><a name="l00538"></a><span class="lineno"> 538</span>  <span class="keyword">class </span>Impl;</div>
+<div class="line"><a name="l00540"></a><span class="lineno"> 540</span>  std::unique_ptr<Impl> impl_;</div>
+<div class="line"><a name="l00541"></a><span class="lineno"> 541</span> };</div>
+<div class="line"><a name="l00542"></a><span class="lineno"> 542</span>  </div>
+<div class="line"><a name="l00546"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1IntSetAnalyzer.html"> 546</a></span> <span class="keyword">class </span><a class="code" href="classtvm_1_1arith_1_1IntSetAnalyzer.html">IntSetAnalyzer</a> {</div>
+<div class="line"><a name="l00547"></a><span class="lineno"> 547</span>  <span class="keyword">public</span>:</div>
+<div class="line"><a name="l00556"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1IntSetAnalyzer.html#ab84b89220625f2b2bc7c69c860e42155"> 556</a></span>  TVM_DLL <a class="code" href="classtvm_1_1arith_1_1IntSet.html">IntSet</a> <a class="code" href="classtvm_1_1arith_1_1IntSetAnalyzer.html#ab84b89220625f2b2bc7c69c860e42155">operator()</a>(<span class="keyword">const</span> <a class="code" href="classtvm_1_1PrimExpr.html">PrimExpr</a>& expr, <span class="k [...]
+<div class="line"><a name="l00557"></a><span class="lineno"> 557</span>  </div>
+<div class="line"><a name="l00566"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1IntSetAnalyzer.html#af4274caf7f5a6af64fbafc2f2465f591"> 566</a></span>  TVM_DLL <a class="code" href="classtvm_1_1arith_1_1IntSet.html">IntSet</a> <a class="code" href="classtvm_1_1arith_1_1IntSetAnalyzer.html#af4274caf7f5a6af64fbafc2f2465f591">operator()</a>(<span class="keyword">const</span> <a class="code" href="classtvm_1_1PrimExpr.html">PrimExpr</a>& expr);</div>
+<div class="line"><a name="l00567"></a><span class="lineno"> 567</span>  </div>
+<div class="line"><a name="l00575"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1IntSetAnalyzer.html#a9a201c941931807b4b88eedc4f8c03f4"> 575</a></span>  TVM_DLL <span class="keywordtype">void</span> <a class="code" href="classtvm_1_1arith_1_1IntSetAnalyzer.html#a9a201c941931807b4b88eedc4f8c03f4">Update</a>(<span class="keyword">const</span> <a class="code" href="classtvm_1_1tirx_1_1Var.html">Var</a>& <a class="code" href="namespacetvm_1_1te.html#ae0c71f84 [...]
+<div class="line"><a name="l00576"></a><span class="lineno"> 576</span>  </div>
+<div class="line"><a name="l00584"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1IntSetAnalyzer.html#ab0ded402c93fc89cba57015b6f3f4d8d"> 584</a></span>  TVM_DLL <span class="keywordtype">void</span> <a class="code" href="classtvm_1_1arith_1_1IntSetAnalyzer.html#ab0ded402c93fc89cba57015b6f3f4d8d">Bind</a>(<span class="keyword">const</span> <a class="code" href="classtvm_1_1tirx_1_1Var.html">Var</a>& <a class="code" href="namespacetvm_1_1te.html#ae0c71f8471 [...]
+<div class="line"><a name="l00585"></a><span class="lineno"> 585</span>  </div>
+<div class="line"><a name="l00586"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1IntSetAnalyzer.html#a096433dd7bde50f73eed4854af8b6111"> 586</a></span>  std::function<void()> <a class="code" href="classtvm_1_1arith_1_1IntSetAnalyzer.html#a096433dd7bde50f73eed4854af8b6111">EnterConstraint</a>(<span class="keyword">const</span> <a class="code" href="classtvm_1_1PrimExpr.html">PrimExpr</a>& constraint);</div>
+<div class="line"><a name="l00587"></a><span class="lineno"> 587</span>  </div>
+<div class="line"><a name="l00588"></a><span class="lineno"> 588</span>  <span class="keyword">private</span>:</div>
+<div class="line"><a name="l00589"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1IntSetAnalyzer.html#ab502b81f91a3692606541f1ec44911de"> 589</a></span>  <span class="keyword">friend</span> <span class="keyword">class </span><a class="code" href="classtvm_1_1arith_1_1AnalyzerObj.html">AnalyzerObj</a>;</div>
+<div class="line"><a name="l00590"></a><span class="lineno"> 590</span>  <span class="keyword">explicit</span> <a class="code" href="classtvm_1_1arith_1_1IntSetAnalyzer.html">IntSetAnalyzer</a>(<a class="code" href="classtvm_1_1arith_1_1AnalyzerObj.html">AnalyzerObj</a>* parent);</div>
+<div class="line"><a name="l00591"></a><span class="lineno"> 591</span>  TVM_DLL ~<a class="code" href="classtvm_1_1arith_1_1IntSetAnalyzer.html">IntSetAnalyzer</a>();</div>
+<div class="line"><a name="l00592"></a><span class="lineno"> 592</span>  <span class="keywordtype">void</span> CopyFrom(<span class="keyword">const</span> <a class="code" href="classtvm_1_1arith_1_1IntSetAnalyzer.html">IntSetAnalyzer</a>& other);</div>
+<div class="line"><a name="l00593"></a><span class="lineno"> 593</span>  <span class="keyword">class </span>Impl;</div>
+<div class="line"><a name="l00595"></a><span class="lineno"> 595</span>  Impl* impl_;</div>
+<div class="line"><a name="l00596"></a><span class="lineno"> 596</span> };</div>
+<div class="line"><a name="l00597"></a><span class="lineno"> 597</span>  </div>
+<div class="line"><a name="l00598"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1Z3Prover.html"> 598</a></span> <span class="keyword">class </span><a class="code" href="classtvm_1_1arith_1_1Z3Prover.html">Z3Prover</a> {</div>
+<div class="line"><a name="l00599"></a><span class="lineno"> 599</span>  <span class="keyword">public</span>:</div>
+<div class="line"><a name="l00607"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1Z3Prover.html#a6e21274c18c5310f1aee546f73177bf0"> 607</a></span>  TVM_DLL <span class="keywordtype">void</span> <a class="code" href="classtvm_1_1arith_1_1Z3Prover.html#a6e21274c18c5310f1aee546f73177bf0">Bind</a>(<span class="keyword">const</span> <a class="code" href="classtvm_1_1tirx_1_1Var.html">Var</a>& <a class="code" href="namespacetvm_1_1te.html#ae0c71f84710b436cbe0b32 [...]
+<div class="line"><a name="l00608"></a><span class="lineno"> 608</span>  </div>
+<div class="line"><a name="l00616"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1Z3Prover.html#af3c439f0bba8a33014cb208ff98ff034"> 616</a></span>  TVM_DLL <span class="keywordtype">void</span> <a class="code" href="classtvm_1_1arith_1_1Z3Prover.html#af3c439f0bba8a33014cb208ff98ff034">Bind</a>(<span class="keyword">const</span> <a class="code" href="classtvm_1_1tirx_1_1Var.html">Var</a>& <a class="code" href="namespacetvm_1_1te.html#ae0c71f84710b436cbe0b32 [...]
+<div class="line"><a name="l00617"></a><span class="lineno"> 617</span>  </div>
+<div class="line"><a name="l00623"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1Z3Prover.html#aa2463d9d04c02b57cbcee80bb208e2a8"> 623</a></span>  TVM_DLL <span class="keywordtype">bool</span> <a class="code" href="classtvm_1_1arith_1_1Z3Prover.html#aa2463d9d04c02b57cbcee80bb208e2a8">IsEnabled</a>() <span class="keyword">const</span>;</div>
+<div class="line"><a name="l00624"></a><span class="lineno"> 624</span>  </div>
+<div class="line"><a name="l00631"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1Z3Prover.html#a79fef83abf68eb50042bc81bf1ef0a9c"> 631</a></span>  TVM_DLL <span class="keywordtype">bool</span> <a class="code" href="classtvm_1_1arith_1_1Z3Prover.html#a79fef83abf68eb50042bc81bf1ef0a9c">CanProve</a>(<span class="keyword">const</span> <a class="code" href="classtvm_1_1PrimExpr.html">PrimExpr</a>& expr);</div>
+<div class="line"><a name="l00632"></a><span class="lineno"> 632</span>  </div>
+<div class="line"><a name="l00639"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1Z3Prover.html#af925259ab605048261b6e0ee6752eebc"> 639</a></span>  std::function<void()> <a class="code" href="classtvm_1_1arith_1_1Z3Prover.html#af925259ab605048261b6e0ee6752eebc">EnterConstraint</a>(<span class="keyword">const</span> <a class="code" href="classtvm_1_1PrimExpr.html">PrimExpr</a>& constraint);</div>
+<div class="line"><a name="l00640"></a><span class="lineno"> 640</span>  </div>
+<div class="line"><a name="l00647"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1Z3Prover.html#add91f63efd092fa65a95d0735f6e2045"> 647</a></span>  ffi::String <a class="code" href="classtvm_1_1arith_1_1Z3Prover.html#add91f63efd092fa65a95d0735f6e2045">GetSMTLIB2</a>(<span class="keyword">const</span> ffi::Optional<PrimExpr> expr);</div>
+<div class="line"><a name="l00648"></a><span class="lineno"> 648</span>  </div>
+<div class="line"><a name="l00654"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1Z3Prover.html#ab8a69dd18ec8594480472c159472f77b"> 654</a></span>  ffi::String <a class="code" href="classtvm_1_1arith_1_1Z3Prover.html#ab8a69dd18ec8594480472c159472f77b">GetStats</a>();</div>
+<div class="line"><a name="l00655"></a><span class="lineno"> 655</span>  </div>
+<div class="line"><a name="l00661"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1Z3Prover.html#a52f045f47f9f868d7372402ea574341c"> 661</a></span>  <span class="keywordtype">void</span> <a class="code" href="classtvm_1_1arith_1_1Z3Prover.html#a52f045f47f9f868d7372402ea574341c">SetTimeoutMs</a>(<span class="keywordtype">unsigned</span> timeout_ms);</div>
+<div class="line"><a name="l00662"></a><span class="lineno"> 662</span>  </div>
+<div class="line"><a name="l00668"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1Z3Prover.html#afebe6e21034ded48a7dbf9d625ed87c3"> 668</a></span>  <span class="keywordtype">void</span> <a class="code" href="classtvm_1_1arith_1_1Z3Prover.html#afebe6e21034ded48a7dbf9d625ed87c3">SetRLimit</a>(<span class="keywordtype">unsigned</span> rlimit);</div>
+<div class="line"><a name="l00669"></a><span class="lineno"> 669</span>  </div>
+<div class="line"><a name="l00676"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1Z3Prover.html#a35d81090c4816ac6202c878cea54d98f"> 676</a></span>  ffi::String <a class="code" href="classtvm_1_1arith_1_1Z3Prover.html#a35d81090c4816ac6202c878cea54d98f">GetModel</a>(<span class="keyword">const</span> <a class="code" href="classtvm_1_1PrimExpr.html">PrimExpr</a>& expr);</div>
+<div class="line"><a name="l00677"></a><span class="lineno"> 677</span>  </div>
+<div class="line"><a name="l00689"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1Z3Prover.html#ac2bbbfc80009dc0276ef52720baa8130"> 689</a></span>  TVM_DLL int64_t <a class="code" href="classtvm_1_1arith_1_1Z3Prover.html#ac2bbbfc80009dc0276ef52720baa8130">CountSatisfyingValues</a>(<span class="keyword">const</span> <a class="code" href="classtvm_1_1tirx_1_1Var.html">Var</a>& <a class="code" href="namespacetvm_1_1te.html#ae0c71f84710b436cbe0b32289d0838f4">v [...]
+<div class="line"><a name="l00690"></a><span class="lineno"> 690</span>  int64_t min_consecutive = 1);</div>
+<div class="line"><a name="l00691"></a><span class="lineno"> 691</span>  </div>
+<div class="line"><a name="l00692"></a><span class="lineno"> 692</span>  <span class="keyword">private</span>:</div>
+<div class="line"><a name="l00693"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1Z3Prover.html#ab502b81f91a3692606541f1ec44911de"> 693</a></span>  <span class="keyword">friend</span> <span class="keyword">class </span><a class="code" href="classtvm_1_1arith_1_1AnalyzerObj.html">AnalyzerObj</a>;</div>
+<div class="line"><a name="l00694"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1Z3Prover.html#a88d594816df596eed10643082b0d0805"> 694</a></span>  <span class="keyword">friend</span> <span class="keyword">class </span><a class="code" href="classtvm_1_1arith_1_1Analyzer.html">Analyzer</a>;</div>
+<div class="line"><a name="l00695"></a><span class="lineno"> 695</span>  <span class="keyword">explicit</span> <a class="code" href="classtvm_1_1arith_1_1Z3Prover.html">Z3Prover</a>(<a class="code" href="classtvm_1_1arith_1_1AnalyzerObj.html">AnalyzerObj</a>* parent);</div>
+<div class="line"><a name="l00696"></a><span class="lineno"> 696</span>  TVM_DLL ~<a class="code" href="classtvm_1_1arith_1_1Z3Prover.html">Z3Prover</a>();</div>
+<div class="line"><a name="l00697"></a><span class="lineno"> 697</span>  <span class="keywordtype">void</span> CopyFrom(<span class="keyword">const</span> <a class="code" href="classtvm_1_1arith_1_1Z3Prover.html">Z3Prover</a>& other);</div>
+<div class="line"><a name="l00698"></a><span class="lineno"> 698</span>  <span class="keyword">class </span>Impl;</div>
+<div class="line"><a name="l00699"></a><span class="lineno"> 699</span>  Impl* impl_;</div>
+<div class="line"><a name="l00700"></a><span class="lineno"> 700</span> };</div>
+<div class="line"><a name="l00701"></a><span class="lineno"> 701</span>  </div>
+<div class="line"><a name="l00712"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1AnalyzerObj.html"> 712</a></span> <span class="keyword">class </span>TVM_DLL <a class="code" href="classtvm_1_1arith_1_1AnalyzerObj.html">AnalyzerObj</a> : <span class="keyword">public</span> ffi::Object {</div>
+<div class="line"><a name="l00713"></a><span class="lineno"> 713</span>  <span class="keyword">public</span>:</div>
+<div class="line"><a name="l00715"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1AnalyzerObj.html#a7007e17a46f88e125eb649ffd40a1d8b"> 715</a></span>  <a class="code" href="classtvm_1_1arith_1_1ConstIntBoundAnalyzer.html">ConstIntBoundAnalyzer</a> <a class="code" href="classtvm_1_1arith_1_1AnalyzerObj.html#a7007e17a46f88e125eb649ffd40a1d8b">const_int_bound</a>;</div>
+<div class="line"><a name="l00717"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1AnalyzerObj.html#ad1cb5737d9af424de0ad43ec794d254f"> 717</a></span>  <a class="code" href="classtvm_1_1arith_1_1ModularSetAnalyzer.html">ModularSetAnalyzer</a> <a class="code" href="classtvm_1_1arith_1_1AnalyzerObj.html#ad1cb5737d9af424de0ad43ec794d254f">modular_set</a>;</div>
+<div class="line"><a name="l00719"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1AnalyzerObj.html#a945243b84edc40b89cd326e7d3cc52da"> 719</a></span>  <a class="code" href="classtvm_1_1arith_1_1RewriteSimplifier.html">RewriteSimplifier</a> <a class="code" href="classtvm_1_1arith_1_1AnalyzerObj.html#a945243b84edc40b89cd326e7d3cc52da">rewrite_simplify</a>;</div>
+<div class="line"><a name="l00721"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1AnalyzerObj.html#afcf97e818f5778d64ceb6e5e0fa59de7"> 721</a></span>  <a class="code" href="classtvm_1_1arith_1_1CanonicalSimplifier.html">CanonicalSimplifier</a> <a class="code" href="classtvm_1_1arith_1_1AnalyzerObj.html#afcf97e818f5778d64ceb6e5e0fa59de7">canonical_simplify</a>;</div>
+<div class="line"><a name="l00723"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1AnalyzerObj.html#a9b3fbbc422505fa18cff90ab81d591ab"> 723</a></span>  <a class="code" href="classtvm_1_1arith_1_1IntSetAnalyzer.html">IntSetAnalyzer</a> <a class="code" href="classtvm_1_1arith_1_1AnalyzerObj.html#a9b3fbbc422505fa18cff90ab81d591ab">int_set</a>;</div>
+<div class="line"><a name="l00725"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1AnalyzerObj.html#aabac046e3ed1abf7f96b446114cafa62"> 725</a></span>  <a class="code" href="classtvm_1_1arith_1_1TransitiveComparisonAnalyzer.html">TransitiveComparisonAnalyzer</a> <a class="code" href="classtvm_1_1arith_1_1AnalyzerObj.html#aabac046e3ed1abf7f96b446114cafa62">transitive_comparisons</a>;</div>
+<div class="line"><a name="l00727"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1AnalyzerObj.html#a730c77bc237767905c43be5e6c6545f6"> 727</a></span>  <a class="code" href="classtvm_1_1arith_1_1Z3Prover.html">Z3Prover</a> <a class="code" href="classtvm_1_1arith_1_1AnalyzerObj.html#a730c77bc237767905c43be5e6c6545f6">z3_prover</a>;</div>
+<div class="line"><a name="l00729"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1AnalyzerObj.html#a61718b9bdfcc01483cf70d159c4f12c8"> 729</a></span>  <a class="code" href="classtvm_1_1arith_1_1AnalyzerObj.html#a61718b9bdfcc01483cf70d159c4f12c8">AnalyzerObj</a>();</div>
+<div class="line"><a name="l00745"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1AnalyzerObj.html#a855fc72735981c7ceeb89e5ac851c7fe"> 745</a></span>  <span class="keywordtype">void</span> <a class="code" href="classtvm_1_1arith_1_1AnalyzerObj.html#a855fc72735981c7ceeb89e5ac851c7fe">MarkGlobalNonNegValue</a>(<span class="keyword">const</span> <a class="code" href="classtvm_1_1PrimExpr.html">PrimExpr</a>& value);</div>
+<div class="line"><a name="l00758"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1AnalyzerObj.html#aa784e4543c60e2a458ab7d4aaafb23eb"> 758</a></span>  <span class="keywordtype">void</span> <a class="code" href="classtvm_1_1arith_1_1AnalyzerObj.html#aa784e4543c60e2a458ab7d4aaafb23eb">Bind</a>(<span class="keyword">const</span> <a class="code" href="classtvm_1_1tirx_1_1Var.html">Var</a>& <a class="code" href="namespacetvm_1_1te.html#ae0c71f84710b436cbe0b3228 [...]
+<div class="line"><a name="l00771"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1AnalyzerObj.html#a7b48ee7d8d2d88687f7eaba43ff7b6bd"> 771</a></span>  <span class="keywordtype">void</span> <a class="code" href="classtvm_1_1arith_1_1AnalyzerObj.html#a7b48ee7d8d2d88687f7eaba43ff7b6bd">Bind</a>(<span class="keyword">const</span> <a class="code" href="classtvm_1_1tirx_1_1Var.html">Var</a>& <a class="code" href="namespacetvm_1_1te.html#ae0c71f84710b436cbe0b3228 [...]
+<div class="line"><a name="l00780"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1AnalyzerObj.html#a7adfc0b82669145611d8638adbf3e432"> 780</a></span>  <span class="keywordtype">void</span> <a class="code" href="classtvm_1_1arith_1_1AnalyzerObj.html#a7adfc0b82669145611d8638adbf3e432">Bind</a>(<span class="keyword">const</span> ffi::Map<Var, Range>& variables, <span class="keywordtype">bool</span> allow_override = <span class="keyword">false</span>);</div>
+<div class="line"><a name="l00793"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1AnalyzerObj.html#a33b289b63c0a91e11b00e98fdbfe2d53"> 793</a></span>  <span class="keywordtype">bool</span> <a class="code" href="classtvm_1_1arith_1_1AnalyzerObj.html#a33b289b63c0a91e11b00e98fdbfe2d53">CanProveGreaterEqual</a>(<span class="keyword">const</span> <a class="code" href="classtvm_1_1PrimExpr.html">PrimExpr</a>& expr, int64_t lower_bound);</div>
+<div class="line"><a name="l00806"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1AnalyzerObj.html#a59475536e8c1078ad57cd5089cf4ffd7"> 806</a></span>  <span class="keywordtype">bool</span> <a class="code" href="classtvm_1_1arith_1_1AnalyzerObj.html#a59475536e8c1078ad57cd5089cf4ffd7">CanProveLess</a>(<span class="keyword">const</span> <a class="code" href="classtvm_1_1PrimExpr.html">PrimExpr</a>& expr, int64_t upper_bound);</div>
+<div class="line"><a name="l00816"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1AnalyzerObj.html#ab90111060d30e07408083c5b28f58a68"> 816</a></span>  <span class="keywordtype">bool</span> <a class="code" href="classtvm_1_1arith_1_1AnalyzerObj.html#ab90111060d30e07408083c5b28f58a68">CanProveEqual</a>(<span class="keyword">const</span> <a class="code" href="classtvm_1_1PrimExpr.html">PrimExpr</a>& lhs, <span class="keyword">const</span> <a class="code" href [...]
+<div class="line"><a name="l00833"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1AnalyzerObj.html#a3d3b62509fecace627f8843256dc8c02"> 833</a></span>  <span class="keywordtype">bool</span> <a class="code" href="classtvm_1_1arith_1_1AnalyzerObj.html#a3d3b62509fecace627f8843256dc8c02">CanProveLessEqualThanSymbolicShapeValue</a>(<span class="keyword">const</span> <a class="code" href="classtvm_1_1PrimExpr.html">PrimExpr</a>& lhs, <span class="keyword">const</ [...]
+<div class="line"><a name="l00846"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1AnalyzerObj.html#a6dfebc27ddce54ca2f10b71c2391d607"> 846</a></span>  <span class="keywordtype">bool</span> <a class="code" href="classtvm_1_1arith_1_1AnalyzerObj.html#a6dfebc27ddce54ca2f10b71c2391d607">CanProve</a>(<span class="keyword">const</span> <a class="code" href="classtvm_1_1PrimExpr.html">PrimExpr</a>& cond, <a class="code" href="namespacetvm_1_1arith.html#a038b0205b [...]
+<div class="line"><a name="l00847"></a><span class="lineno"> 847</span>  </div>
+<div class="line"><a name="l00861"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1AnalyzerObj.html#a19f9b83800c85ab898a23220fbf000b1"> 861</a></span>  <a class="code" href="classtvm_1_1PrimExpr.html">PrimExpr</a> <a class="code" href="classtvm_1_1arith_1_1AnalyzerObj.html#a19f9b83800c85ab898a23220fbf000b1">Simplify</a>(<span class="keyword">const</span> <a class="code" href="classtvm_1_1PrimExpr.html">PrimExpr</a>& expr, <span class="keywordtype">int</span [...]
+<div class="line"><a name="l00862"></a><span class="lineno"> 862</span>  </div>
+<div class="line"><a name="l00881"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1AnalyzerObj.html#a55ea30504104fa309e0b5e0785086d0b"> 881</a></span>  <a class="code" href="classtvm_1_1arith_1_1Analyzer.html">Analyzer</a> <a class="code" href="classtvm_1_1arith_1_1AnalyzerObj.html#a55ea30504104fa309e0b5e0785086d0b">Clone</a>() <span class="keyword">const</span>;</div>
+<div class="line"><a name="l00882"></a><span class="lineno"> 882</span>  </div>
+<div class="line"><a name="l00892"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1AnalyzerObj.html#ae26f9e1ceb0f7c8d0f1a038837529ae8"> 892</a></span>  <span class="keyword">static</span> constexpr <span class="keywordtype">bool</span> _type_mutable = <span class="keyword">true</span>;</div>
+<div class="line"><a name="l00893"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1AnalyzerObj.html#ae7f9b32e8df29a1230396bee65ca42c1"> 893</a></span>  <a class="code" href="classtvm_1_1arith_1_1AnalyzerObj.html#ae7f9b32e8df29a1230396bee65ca42c1">TVM_FFI_DECLARE_OBJECT_INFO_FINAL</a>(<span class="stringliteral">"arith.Analyzer"</span>, <a class="code" href="classtvm_1_1arith_1_1AnalyzerObj.html">AnalyzerObj</a>, ffi::Object);</div>
+<div class="line"><a name="l00894"></a><span class="lineno"> 894</span> };</div>
+<div class="line"><a name="l00895"></a><span class="lineno"> 895</span>  </div>
+<div class="line"><a name="l00909"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1Analyzer.html"> 909</a></span> <span class="keyword">class </span><a class="code" href="classtvm_1_1arith_1_1Analyzer.html">Analyzer</a> : <span class="keyword">public</span> ffi::ObjectRef {</div>
+<div class="line"><a name="l00910"></a><span class="lineno"> 910</span>  <span class="keyword">public</span>:</div>
+<div class="line"><a name="l00912"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1Analyzer.html#ae8568eeee02c68849e468f8c19a2701d"> 912</a></span>  <a class="code" href="classtvm_1_1arith_1_1Analyzer.html#ae8568eeee02c68849e468f8c19a2701d">Analyzer</a>() : <a class="code" href="classtvm_1_1arith_1_1Analyzer.html">Analyzer</a>(ffi::make_object<<a class="code" href="classtvm_1_1arith_1_1AnalyzerObj.html">AnalyzerObj</a>>()) {}</div>
+<div class="line"><a name="l00913"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1Analyzer.html#a986c5877025fcf36b25a79fb58b8c2ed"> 913</a></span>  <span class="keyword">explicit</span> <a class="code" href="classtvm_1_1arith_1_1Analyzer.html#a986c5877025fcf36b25a79fb58b8c2ed">Analyzer</a>(ffi::ObjectPtr<AnalyzerObj> n) : ffi::ObjectRef(std::move(n)) {</div>
+<div class="line"><a name="l00914"></a><span class="lineno"> 914</span>  TVM_FFI_ICHECK(this->get() != <span class="keyword">nullptr</span>);</div>
+<div class="line"><a name="l00915"></a><span class="lineno"> 915</span>  }</div>
+<div class="line"><a name="l00916"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1Analyzer.html#ade37f587bfed0383436428182bef8a73"> 916</a></span>  <a class="code" href="classtvm_1_1arith_1_1Analyzer.html#ade37f587bfed0383436428182bef8a73">TVM_FFI_DEFINE_OBJECT_REF_METHODS_NOTNULLABLE</a>(<a class="code" href="classtvm_1_1arith_1_1Analyzer.html">Analyzer</a>, ffi::ObjectRef, <a class="code" href="classtvm_1_1arith_1_1AnalyzerObj.html">AnalyzerObj</a>);</div>
+<div class="line"><a name="l00917"></a><span class="lineno"> 917</span> };</div>
+<div class="line"><a name="l00918"></a><span class="lineno"> 918</span>  </div>
+<div class="line"><a name="l00935"></a><span class="lineno"><a class="line" href="classtvm_1_1arith_1_1ConstraintContext.html"> 935</a></span> <span class="keyword">class </span><a class="code" href="classtvm_1_1arith_1_1ConstraintContext.html">ConstraintContext</a> {</div>
+<div class="line"><a name="l00936"></a><span class="lineno"> 936</span>  <span class="keyword">private</span>:</div>
+<div class="line"><a name="l00937"></a><span class="lineno"> 937</span>  <span class="comment">// declare friend to enable with.</span></div>
+<div class="line"><a name="l00938"></a><span class="lineno"> 938</span>  <span class="keyword">friend</span> <span class="keyword">class </span><a class="code" href="classtvm_1_1With.html">With</a><<a class="code" href="classtvm_1_1arith_1_1ConstraintContext.html">ConstraintContext</a>>;</div>
+<div class="line"><a name="l00945"></a><span class="lineno"> 945</span>  <a class="code" href="classtvm_1_1arith_1_1ConstraintContext.html">ConstraintContext</a>(<span class="keyword">const</span> <a class="code" href="classtvm_1_1arith_1_1Analyzer.html">Analyzer</a>& analyzer, <a class="code" href="classtvm_1_1PrimExpr.html">PrimExpr</a> constraint)</div>
+<div class="line"><a name="l00946"></a><span class="lineno"> 946</span>  : <a class="code" href="classtvm_1_1arith_1_1ConstraintContext.html">ConstraintContext</a>(analyzer, std::move(constraint), <span class="keyword">false</span>) {}</div>
+<div class="line"><a name="l00954"></a><span class="lineno"> 954</span>  <a class="code" href="classtvm_1_1arith_1_1ConstraintContext.html">ConstraintContext</a>(<span class="keyword">const</span> <a class="code" href="classtvm_1_1arith_1_1Analyzer.html">Analyzer</a>& analyzer, <a class="code" href="classtvm_1_1PrimExpr.html">PrimExpr</a> constraint, <span class="keywordtype">bool</span> is_assume)</div>
+<div class="line"><a name="l00955"></a><span class="lineno"> 955</span>  : analyzer_(analyzer), constraint_(std::move(constraint)), is_assume_(is_assume) {}</div>
+<div class="line"><a name="l00963"></a><span class="lineno"> 963</span>  <a class="code" href="classtvm_1_1arith_1_1ConstraintContext.html">ConstraintContext</a>(<a class="code" href="classtvm_1_1arith_1_1AnalyzerObj.html">AnalyzerObj</a>* analyzer, <a class="code" href="classtvm_1_1PrimExpr.html">PrimExpr</a> constraint)</div>
+<div class="line"><a name="l00964"></a><span class="lineno"> 964</span>  : <a class="code" href="classtvm_1_1arith_1_1ConstraintContext.html">ConstraintContext</a>(ffi::GetRef<Analyzer>(analyzer), std::move(constraint), <span class="keyword">false</span>) {}</div>
+<div class="line"><a name="l00971"></a><span class="lineno"> 971</span>  <a class="code" href="classtvm_1_1arith_1_1ConstraintContext.html">ConstraintContext</a>(<a class="code" href="classtvm_1_1arith_1_1AnalyzerObj.html">AnalyzerObj</a>* analyzer, <a class="code" href="classtvm_1_1PrimExpr.html">PrimExpr</a> constraint, <span class="keywordtype">bool</span> is_assume)</div>
+<div class="line"><a name="l00972"></a><span class="lineno"> 972</span>  : <a class="code" href="classtvm_1_1arith_1_1ConstraintContext.html">ConstraintContext</a>(ffi::GetRef<Analyzer>(analyzer), std::move(constraint), is_assume) {}</div>
+<div class="line"><a name="l00973"></a><span class="lineno"> 973</span>  <span class="comment">// enter the scope.</span></div>
+<div class="line"><a name="l00974"></a><span class="lineno"> 974</span>  <span class="keywordtype">void</span> EnterWithScope();</div>
+<div class="line"><a name="l00975"></a><span class="lineno"> 975</span>  <span class="comment">// exit the scope.</span></div>
+<div class="line"><a name="l00976"></a><span class="lineno"> 976</span>  <span class="keywordtype">void</span> ExitWithScope();</div>
+<div class="line"><a name="l00978"></a><span class="lineno"> 978</span>  <a class="code" href="classtvm_1_1arith_1_1Analyzer.html">Analyzer</a> analyzer_;</div>
+<div class="line"><a name="l00980"></a><span class="lineno"> 980</span>  <a class="code" href="classtvm_1_1PrimExpr.html">PrimExpr</a> constraint_;</div>
+<div class="line"><a name="l00982"></a><span class="lineno"> 982</span>  std::vector<std::function<void()>> recovery_functions_;</div>
+<div class="line"><a name="l00984"></a><span class="lineno"> 984</span>  <span class="keywordtype">bool</span> is_assume_;</div>
+<div class="line"><a name="l00985"></a><span class="lineno"> 985</span> };</div>
+<div class="line"><a name="l00986"></a><span class="lineno"> 986</span>  </div>
+<div class="line"><a name="l00987"></a><span class="lineno"> 987</span> } <span class="comment">// namespace arith</span></div>
+<div class="line"><a name="l00988"></a><span class="lineno"> 988</span> } <span class="comment">// namespace tvm</span></div>
+<div class="line"><a name="l00989"></a><span class="lineno"> 989</span> <span class="preprocessor">#endif </span><span class="comment">// TVM_ARITH_ANALYZER_H_</span></div>
<div class="ttc" id="aclasstvm_1_1PrimExpr_html"><div class="ttname"><a href="classtvm_1_1PrimExpr.html">tvm::PrimExpr</a></div><div class="ttdoc">Reference to PrimExprNode.</div><div class="ttdef"><b>Definition:</b> expr.h:123</div></div>
<div class="ttc" id="aclasstvm_1_1Range_html"><div class="ttname"><a href="classtvm_1_1Range.html">tvm::Range</a></div><div class="ttdoc">Range container</div><div class="ttdef"><b>Definition:</b> expr.h:612</div></div>
<div class="ttc" id="aclasstvm_1_1With_html"><div class="ttname"><a href="classtvm_1_1With.html">tvm::With</a></div><div class="ttdoc">RAII wrapper function to enter and exit a context object similar to python's with syntax.</div><div class="ttdef"><b>Definition:</b> with_context.h:59</div></div>
-<div class="ttc" id="aclasstvm_1_1arith_1_1AnalyzerObj_html"><div class="ttname"><a href="classtvm_1_1arith_1_1AnalyzerObj.html">tvm::arith::AnalyzerObj</a></div><div class="ttdoc">Analyzer that contains bunch of sub-analyzers.</div><div class="ttdef"><b>Definition:</b> analyzer.h:706</div></div>
+<div class="ttc" id="aclasstvm_1_1arith_1_1AnalyzerObj_html"><div class="ttname"><a href="classtvm_1_1arith_1_1AnalyzerObj.html">tvm::arith::AnalyzerObj</a></div><div class="ttdoc">Analyzer that contains bunch of sub-analyzers.</div><div class="ttdef"><b>Definition:</b> analyzer.h:712</div></div>
<div class="ttc" id="aclasstvm_1_1arith_1_1AnalyzerObj_html_a19f9b83800c85ab898a23220fbf000b1"><div class="ttname"><a href="classtvm_1_1arith_1_1AnalyzerObj.html#a19f9b83800c85ab898a23220fbf000b1">tvm::arith::AnalyzerObj::Simplify</a></div><div class="ttdeci">PrimExpr Simplify(const PrimExpr &expr, int steps=2)</div><div class="ttdoc">Simplify expr.</div></div>
<div class="ttc" id="aclasstvm_1_1arith_1_1AnalyzerObj_html_a33b289b63c0a91e11b00e98fdbfe2d53"><div class="ttname"><a href="classtvm_1_1arith_1_1AnalyzerObj.html#a33b289b63c0a91e11b00e98fdbfe2d53">tvm::arith::AnalyzerObj::CanProveGreaterEqual</a></div><div class="ttdeci">bool CanProveGreaterEqual(const PrimExpr &expr, int64_t lower_bound)</div><div class="ttdoc">Whether can we prove expr >= val.</div></div>
<div class="ttc" id="aclasstvm_1_1arith_1_1AnalyzerObj_html_a3d3b62509fecace627f8843256dc8c02"><div class="ttname"><a href="classtvm_1_1arith_1_1AnalyzerObj.html#a3d3b62509fecace627f8843256dc8c02">tvm::arith::AnalyzerObj::CanProveLessEqualThanSymbolicShapeValue</a></div><div class="ttdeci">bool CanProveLessEqualThanSymbolicShapeValue(const PrimExpr &lhs, const PrimExpr &shape)</div><div class="ttdoc">Whether we can prove lhs is smaller than possibly symbolic shape.</div></div>
+<div class="ttc" id="aclasstvm_1_1arith_1_1AnalyzerObj_html_a55ea30504104fa309e0b5e0785086d0b"><div class="ttname"><a href="classtvm_1_1arith_1_1AnalyzerObj.html#a55ea30504104fa309e0b5e0785086d0b">tvm::arith::AnalyzerObj::Clone</a></div><div class="ttdeci">Analyzer Clone() const</div><div class="ttdoc">Deep-copy this analyzer into a new, independent Analyzer.</div></div>
<div class="ttc" id="aclasstvm_1_1arith_1_1AnalyzerObj_html_a59475536e8c1078ad57cd5089cf4ffd7"><div class="ttname"><a href="classtvm_1_1arith_1_1AnalyzerObj.html#a59475536e8c1078ad57cd5089cf4ffd7">tvm::arith::AnalyzerObj::CanProveLess</a></div><div class="ttdeci">bool CanProveLess(const PrimExpr &expr, int64_t upper_bound)</div><div class="ttdoc">Whether can we prove expr < val.</div></div>
<div class="ttc" id="aclasstvm_1_1arith_1_1AnalyzerObj_html_a61718b9bdfcc01483cf70d159c4f12c8"><div class="ttname"><a href="classtvm_1_1arith_1_1AnalyzerObj.html#a61718b9bdfcc01483cf70d159c4f12c8">tvm::arith::AnalyzerObj::AnalyzerObj</a></div><div class="ttdeci">AnalyzerObj()</div><div class="ttdoc">constructor</div></div>
<div class="ttc" id="aclasstvm_1_1arith_1_1AnalyzerObj_html_a6dfebc27ddce54ca2f10b71c2391d607"><div class="ttname"><a href="classtvm_1_1arith_1_1AnalyzerObj.html#a6dfebc27ddce54ca2f10b71c2391d607">tvm::arith::AnalyzerObj::CanProve</a></div><div class="ttdeci">bool CanProve(const PrimExpr &cond, ProofStrength strength=ProofStrength::kDefault)</div><div class="ttdoc">Whether can we prove condition.</div></div>
-<div class="ttc" id="aclasstvm_1_1arith_1_1AnalyzerObj_html_a7007e17a46f88e125eb649ffd40a1d8b"><div class="ttname"><a href="classtvm_1_1arith_1_1AnalyzerObj.html#a7007e17a46f88e125eb649ffd40a1d8b">tvm::arith::AnalyzerObj::const_int_bound</a></div><div class="ttdeci">ConstIntBoundAnalyzer const_int_bound</div><div class="ttdoc">sub-analyzer: const integer bound</div><div class="ttdef"><b>Definition:</b> analyzer.h:709</div></div>
-<div class="ttc" id="aclasstvm_1_1arith_1_1AnalyzerObj_html_a730c77bc237767905c43be5e6c6545f6"><div class="ttname"><a href="classtvm_1_1arith_1_1AnalyzerObj.html#a730c77bc237767905c43be5e6c6545f6">tvm::arith::AnalyzerObj::z3_prover</a></div><div class="ttdeci">Z3Prover z3_prover</div><div class="ttdoc">sub-analyzer using Z3</div><div class="ttdef"><b>Definition:</b> analyzer.h:721</div></div>
+<div class="ttc" id="aclasstvm_1_1arith_1_1AnalyzerObj_html_a7007e17a46f88e125eb649ffd40a1d8b"><div class="ttname"><a href="classtvm_1_1arith_1_1AnalyzerObj.html#a7007e17a46f88e125eb649ffd40a1d8b">tvm::arith::AnalyzerObj::const_int_bound</a></div><div class="ttdeci">ConstIntBoundAnalyzer const_int_bound</div><div class="ttdoc">sub-analyzer: const integer bound</div><div class="ttdef"><b>Definition:</b> analyzer.h:715</div></div>
+<div class="ttc" id="aclasstvm_1_1arith_1_1AnalyzerObj_html_a730c77bc237767905c43be5e6c6545f6"><div class="ttname"><a href="classtvm_1_1arith_1_1AnalyzerObj.html#a730c77bc237767905c43be5e6c6545f6">tvm::arith::AnalyzerObj::z3_prover</a></div><div class="ttdeci">Z3Prover z3_prover</div><div class="ttdoc">sub-analyzer using Z3</div><div class="ttdef"><b>Definition:</b> analyzer.h:727</div></div>
<div class="ttc" id="aclasstvm_1_1arith_1_1AnalyzerObj_html_a7adfc0b82669145611d8638adbf3e432"><div class="ttname"><a href="classtvm_1_1arith_1_1AnalyzerObj.html#a7adfc0b82669145611d8638adbf3e432">tvm::arith::AnalyzerObj::Bind</a></div><div class="ttdeci">void Bind(const ffi::Map< Var, Range > &variables, bool allow_override=false)</div><div class="ttdoc">Bind all the vars in the Map.</div></div>
<div class="ttc" id="aclasstvm_1_1arith_1_1AnalyzerObj_html_a7b48ee7d8d2d88687f7eaba43ff7b6bd"><div class="ttname"><a href="classtvm_1_1arith_1_1AnalyzerObj.html#a7b48ee7d8d2d88687f7eaba43ff7b6bd">tvm::arith::AnalyzerObj::Bind</a></div><div class="ttdeci">void Bind(const Var &var, const Range &range, bool allow_override=false)</div><div class="ttdoc">Notify all the sub-analyzers that var is created and bound to a range.</div></div>
<div class="ttc" id="aclasstvm_1_1arith_1_1AnalyzerObj_html_a855fc72735981c7ceeb89e5ac851c7fe"><div class="ttname"><a href="classtvm_1_1arith_1_1AnalyzerObj.html#a855fc72735981c7ceeb89e5ac851c7fe">tvm::arith::AnalyzerObj::MarkGlobalNonNegValue</a></div><div class="ttdeci">void MarkGlobalNonNegValue(const PrimExpr &value)</div><div class="ttdoc">Mark the value as non-negative value globally in analyzer.</div></div>
-<div class="ttc" id="aclasstvm_1_1arith_1_1AnalyzerObj_html_a945243b84edc40b89cd326e7d3cc52da"><div class="ttname"><a href="classtvm_1_1arith_1_1AnalyzerObj.html#a945243b84edc40b89cd326e7d3cc52da">tvm::arith::AnalyzerObj::rewrite_simplify</a></div><div class="ttdeci">RewriteSimplifier rewrite_simplify</div><div class="ttdoc">sub-analyzer rewrite simplify</div><div class="ttdef"><b>Definition:</b> analyzer.h:713</div></div>
-<div class="ttc" id="aclasstvm_1_1arith_1_1AnalyzerObj_html_a9b3fbbc422505fa18cff90ab81d591ab"><div class="ttname"><a href="classtvm_1_1arith_1_1AnalyzerObj.html#a9b3fbbc422505fa18cff90ab81d591ab">tvm::arith::AnalyzerObj::int_set</a></div><div class="ttdeci">IntSetAnalyzer int_set</div><div class="ttdoc">sub-analyzer: int set</div><div class="ttdef"><b>Definition:</b> analyzer.h:717</div></div>
+<div class="ttc" id="aclasstvm_1_1arith_1_1AnalyzerObj_html_a945243b84edc40b89cd326e7d3cc52da"><div class="ttname"><a href="classtvm_1_1arith_1_1AnalyzerObj.html#a945243b84edc40b89cd326e7d3cc52da">tvm::arith::AnalyzerObj::rewrite_simplify</a></div><div class="ttdeci">RewriteSimplifier rewrite_simplify</div><div class="ttdoc">sub-analyzer rewrite simplify</div><div class="ttdef"><b>Definition:</b> analyzer.h:719</div></div>
+<div class="ttc" id="aclasstvm_1_1arith_1_1AnalyzerObj_html_a9b3fbbc422505fa18cff90ab81d591ab"><div class="ttname"><a href="classtvm_1_1arith_1_1AnalyzerObj.html#a9b3fbbc422505fa18cff90ab81d591ab">tvm::arith::AnalyzerObj::int_set</a></div><div class="ttdeci">IntSetAnalyzer int_set</div><div class="ttdoc">sub-analyzer: int set</div><div class="ttdef"><b>Definition:</b> analyzer.h:723</div></div>
<div class="ttc" id="aclasstvm_1_1arith_1_1AnalyzerObj_html_aa784e4543c60e2a458ab7d4aaafb23eb"><div class="ttname"><a href="classtvm_1_1arith_1_1AnalyzerObj.html#aa784e4543c60e2a458ab7d4aaafb23eb">tvm::arith::AnalyzerObj::Bind</a></div><div class="ttdeci">void Bind(const Var &var, const PrimExpr &expr, bool allow_override=false)</div><div class="ttdoc">Notify all the sub-analyzers that var is created and binded to expr.</div></div>
-<div class="ttc" id="aclasstvm_1_1arith_1_1AnalyzerObj_html_aabac046e3ed1abf7f96b446114cafa62"><div class="ttname"><a href="classtvm_1_1arith_1_1AnalyzerObj.html#aabac046e3ed1abf7f96b446114cafa62">tvm::arith::AnalyzerObj::transitive_comparisons</a></div><div class="ttdeci">TransitiveComparisonAnalyzer transitive_comparisons</div><div class="ttdoc">sub-analyzer transitive comparisons</div><div class="ttdef"><b>Definition:</b> analyzer.h:719</div></div>
+<div class="ttc" id="aclasstvm_1_1arith_1_1AnalyzerObj_html_aabac046e3ed1abf7f96b446114cafa62"><div class="ttname"><a href="classtvm_1_1arith_1_1AnalyzerObj.html#aabac046e3ed1abf7f96b446114cafa62">tvm::arith::AnalyzerObj::transitive_comparisons</a></div><div class="ttdeci">TransitiveComparisonAnalyzer transitive_comparisons</div><div class="ttdoc">sub-analyzer transitive comparisons</div><div class="ttdef"><b>Definition:</b> analyzer.h:725</div></div>
<div class="ttc" id="aclasstvm_1_1arith_1_1AnalyzerObj_html_ab90111060d30e07408083c5b28f58a68"><div class="ttname"><a href="classtvm_1_1arith_1_1AnalyzerObj.html#ab90111060d30e07408083c5b28f58a68">tvm::arith::AnalyzerObj::CanProveEqual</a></div><div class="ttdeci">bool CanProveEqual(const PrimExpr &lhs, const PrimExpr &rhs)</div><div class="ttdoc">Whether can we prove lhs == rhs.</div></div>
-<div class="ttc" id="aclasstvm_1_1arith_1_1AnalyzerObj_html_ad1cb5737d9af424de0ad43ec794d254f"><div class="ttname"><a href="classtvm_1_1arith_1_1AnalyzerObj.html#ad1cb5737d9af424de0ad43ec794d254f">tvm::arith::AnalyzerObj::modular_set</a></div><div class="ttdeci">ModularSetAnalyzer modular_set</div><div class="ttdoc">sub-analyzer: modular set</div><div class="ttdef"><b>Definition:</b> analyzer.h:711</div></div>
+<div class="ttc" id="aclasstvm_1_1arith_1_1AnalyzerObj_html_ad1cb5737d9af424de0ad43ec794d254f"><div class="ttname"><a href="classtvm_1_1arith_1_1AnalyzerObj.html#ad1cb5737d9af424de0ad43ec794d254f">tvm::arith::AnalyzerObj::modular_set</a></div><div class="ttdeci">ModularSetAnalyzer modular_set</div><div class="ttdoc">sub-analyzer: modular set</div><div class="ttdef"><b>Definition:</b> analyzer.h:717</div></div>
<div class="ttc" id="aclasstvm_1_1arith_1_1AnalyzerObj_html_ae7f9b32e8df29a1230396bee65ca42c1"><div class="ttname"><a href="classtvm_1_1arith_1_1AnalyzerObj.html#ae7f9b32e8df29a1230396bee65ca42c1">tvm::arith::AnalyzerObj::TVM_FFI_DECLARE_OBJECT_INFO_FINAL</a></div><div class="ttdeci">TVM_FFI_DECLARE_OBJECT_INFO_FINAL("arith.Analyzer", AnalyzerObj, ffi::Object)</div></div>
-<div class="ttc" id="aclasstvm_1_1arith_1_1AnalyzerObj_html_afcf97e818f5778d64ceb6e5e0fa59de7"><div class="ttname"><a href="classtvm_1_1arith_1_1AnalyzerObj.html#afcf97e818f5778d64ceb6e5e0fa59de7">tvm::arith::AnalyzerObj::canonical_simplify</a></div><div class="ttdeci">CanonicalSimplifier canonical_simplify</div><div class="ttdoc">sub-analyzer canonical simplify</div><div class="ttdef"><b>Definition:</b> analyzer.h:715</div></div>
-<div class="ttc" id="aclasstvm_1_1arith_1_1Analyzer_html"><div class="ttname"><a href="classtvm_1_1arith_1_1Analyzer.html">tvm::arith::Analyzer</a></div><div class="ttdoc">Managed reference to AnalyzerObj.</div><div class="ttdef"><b>Definition:</b> analyzer.h:883</div></div>
-<div class="ttc" id="aclasstvm_1_1arith_1_1Analyzer_html_a986c5877025fcf36b25a79fb58b8c2ed"><div class="ttname"><a href="classtvm_1_1arith_1_1Analyzer.html#a986c5877025fcf36b25a79fb58b8c2ed">tvm::arith::Analyzer::Analyzer</a></div><div class="ttdeci">Analyzer(ffi::ObjectPtr< AnalyzerObj > n)</div><div class="ttdef"><b>Definition:</b> analyzer.h:887</div></div>
+<div class="ttc" id="aclasstvm_1_1arith_1_1AnalyzerObj_html_afcf97e818f5778d64ceb6e5e0fa59de7"><div class="ttname"><a href="classtvm_1_1arith_1_1AnalyzerObj.html#afcf97e818f5778d64ceb6e5e0fa59de7">tvm::arith::AnalyzerObj::canonical_simplify</a></div><div class="ttdeci">CanonicalSimplifier canonical_simplify</div><div class="ttdoc">sub-analyzer canonical simplify</div><div class="ttdef"><b>Definition:</b> analyzer.h:721</div></div>
+<div class="ttc" id="aclasstvm_1_1arith_1_1Analyzer_html"><div class="ttname"><a href="classtvm_1_1arith_1_1Analyzer.html">tvm::arith::Analyzer</a></div><div class="ttdoc">Managed reference to AnalyzerObj.</div><div class="ttdef"><b>Definition:</b> analyzer.h:909</div></div>
+<div class="ttc" id="aclasstvm_1_1arith_1_1Analyzer_html_a986c5877025fcf36b25a79fb58b8c2ed"><div class="ttname"><a href="classtvm_1_1arith_1_1Analyzer.html#a986c5877025fcf36b25a79fb58b8c2ed">tvm::arith::Analyzer::Analyzer</a></div><div class="ttdeci">Analyzer(ffi::ObjectPtr< AnalyzerObj > n)</div><div class="ttdef"><b>Definition:</b> analyzer.h:913</div></div>
<div class="ttc" id="aclasstvm_1_1arith_1_1Analyzer_html_ade37f587bfed0383436428182bef8a73"><div class="ttname"><a href="classtvm_1_1arith_1_1Analyzer.html#ade37f587bfed0383436428182bef8a73">tvm::arith::Analyzer::TVM_FFI_DEFINE_OBJECT_REF_METHODS_NOTNULLABLE</a></div><div class="ttdeci">TVM_FFI_DEFINE_OBJECT_REF_METHODS_NOTNULLABLE(Analyzer, ffi::ObjectRef, AnalyzerObj)</div></div>
-<div class="ttc" id="aclasstvm_1_1arith_1_1Analyzer_html_ae8568eeee02c68849e468f8c19a2701d"><div class="ttname"><a href="classtvm_1_1arith_1_1Analyzer.html#ae8568eeee02c68849e468f8c19a2701d">tvm::arith::Analyzer::Analyzer</a></div><div class="ttdeci">Analyzer()</div><div class="ttdoc">Default-construct a fresh analyzer (allocates an AnalyzerObj).</div><div class="ttdef"><b>Definition:</b> analyzer.h:886</div></div>
-<div class="ttc" id="aclasstvm_1_1arith_1_1CanonicalSimplifier_html"><div class="ttname"><a href="classtvm_1_1arith_1_1CanonicalSimplifier.html">tvm::arith::CanonicalSimplifier</a></div><div class="ttdoc">Canonical-form based simplifier.</div><div class="ttdef"><b>Definition:</b> analyzer.h:425</div></div>
+<div class="ttc" id="aclasstvm_1_1arith_1_1Analyzer_html_ae8568eeee02c68849e468f8c19a2701d"><div class="ttname"><a href="classtvm_1_1arith_1_1Analyzer.html#ae8568eeee02c68849e468f8c19a2701d">tvm::arith::Analyzer::Analyzer</a></div><div class="ttdeci">Analyzer()</div><div class="ttdoc">Default-construct a fresh analyzer (allocates an AnalyzerObj).</div><div class="ttdef"><b>Definition:</b> analyzer.h:912</div></div>
+<div class="ttc" id="aclasstvm_1_1arith_1_1CanonicalSimplifier_html"><div class="ttname"><a href="classtvm_1_1arith_1_1CanonicalSimplifier.html">tvm::arith::CanonicalSimplifier</a></div><div class="ttdoc">Canonical-form based simplifier.</div><div class="ttdef"><b>Definition:</b> analyzer.h:428</div></div>
<div class="ttc" id="aclasstvm_1_1arith_1_1CanonicalSimplifier_html_a2ed4ed7990499ee188e69967afb511c6"><div class="ttname"><a href="classtvm_1_1arith_1_1CanonicalSimplifier.html#a2ed4ed7990499ee188e69967afb511c6">tvm::arith::CanonicalSimplifier::operator()</a></div><div class="ttdeci">PrimExpr operator()(const PrimExpr &expr)</div><div class="ttdoc">analyze the expr</div></div>
<div class="ttc" id="aclasstvm_1_1arith_1_1CanonicalSimplifier_html_a790c032e12c7d93e9e940297b860c509"><div class="ttname"><a href="classtvm_1_1arith_1_1CanonicalSimplifier.html#a790c032e12c7d93e9e940297b860c509">tvm::arith::CanonicalSimplifier::Update</a></div><div class="ttdeci">void Update(const Var &var, const PrimExpr &new_expr, bool allow_override=false)</div><div class="ttdoc">Update binding of var to a new expression.</div></div>
<div class="ttc" id="aclasstvm_1_1arith_1_1ConstIntBoundAnalyzer_html"><div class="ttname"><a href="classtvm_1_1arith_1_1ConstIntBoundAnalyzer.html">tvm::arith::ConstIntBoundAnalyzer</a></div><div class="ttdoc">Analyzer to get constant integer bound over expression.</div><div class="ttdef"><b>Definition:</b> analyzer.h:135</div></div>
@@ -551,47 +560,47 @@ $(function() {
<div class="ttc" id="aclasstvm_1_1arith_1_1ConstIntBound_html_ac2368aa444fafe5f030ac2674148c5fa"><div class="ttname"><a href="classtvm_1_1arith_1_1ConstIntBound.html#ac2368aa444fafe5f030ac2674148c5fa">tvm::arith::ConstIntBound::kNegInf</a></div><div class="ttdeci">static constexpr const int64_t kNegInf</div><div class="ttdef"><b>Definition:</b> analyzer.h:128</div></div>
<div class="ttc" id="aclasstvm_1_1arith_1_1ConstIntBound_html_aefb417203cf5c5996d7ed664051aaba3"><div class="ttname"><a href="classtvm_1_1arith_1_1ConstIntBound.html#aefb417203cf5c5996d7ed664051aaba3">tvm::arith::ConstIntBound::ConstIntBound</a></div><div class="ttdeci">ConstIntBound(int64_t min_value, int64_t max_value)</div><div class="ttdoc">constructor by fields.</div></div>
<div class="ttc" id="aclasstvm_1_1arith_1_1ConstIntBound_html_af2dbef4ddb3a0895bab046d18ff85ab8"><div class="ttname"><a href="classtvm_1_1arith_1_1ConstIntBound.html#af2dbef4ddb3a0895bab046d18ff85ab8">tvm::arith::ConstIntBound::kPosInf</a></div><div class="ttdeci">static constexpr const int64_t kPosInf</div><div class="ttdef"><b>Definition:</b> analyzer.h:127</div></div>
-<div class="ttc" id="aclasstvm_1_1arith_1_1ConstraintContext_html"><div class="ttname"><a href="classtvm_1_1arith_1_1ConstraintContext.html">tvm::arith::ConstraintContext</a></div><div class="ttdoc">Constraint context.</div><div class="ttdef"><b>Definition:</b> analyzer.h:909</div></div>
-<div class="ttc" id="aclasstvm_1_1arith_1_1IntSetAnalyzer_html"><div class="ttname"><a href="classtvm_1_1arith_1_1IntSetAnalyzer.html">tvm::arith::IntSetAnalyzer</a></div><div class="ttdoc">Integer set analyzer.</div><div class="ttdef"><b>Definition:</b> analyzer.h:541</div></div>
+<div class="ttc" id="aclasstvm_1_1arith_1_1ConstraintContext_html"><div class="ttname"><a href="classtvm_1_1arith_1_1ConstraintContext.html">tvm::arith::ConstraintContext</a></div><div class="ttdoc">Constraint context.</div><div class="ttdef"><b>Definition:</b> analyzer.h:935</div></div>
+<div class="ttc" id="aclasstvm_1_1arith_1_1IntSetAnalyzer_html"><div class="ttname"><a href="classtvm_1_1arith_1_1IntSetAnalyzer.html">tvm::arith::IntSetAnalyzer</a></div><div class="ttdoc">Integer set analyzer.</div><div class="ttdef"><b>Definition:</b> analyzer.h:546</div></div>
<div class="ttc" id="aclasstvm_1_1arith_1_1IntSetAnalyzer_html_a096433dd7bde50f73eed4854af8b6111"><div class="ttname"><a href="classtvm_1_1arith_1_1IntSetAnalyzer.html#a096433dd7bde50f73eed4854af8b6111">tvm::arith::IntSetAnalyzer::EnterConstraint</a></div><div class="ttdeci">std::function< void()> EnterConstraint(const PrimExpr &constraint)</div></div>
<div class="ttc" id="aclasstvm_1_1arith_1_1IntSetAnalyzer_html_a9a201c941931807b4b88eedc4f8c03f4"><div class="ttname"><a href="classtvm_1_1arith_1_1IntSetAnalyzer.html#a9a201c941931807b4b88eedc4f8c03f4">tvm::arith::IntSetAnalyzer::Update</a></div><div class="ttdeci">void Update(const Var &var, const IntSet &new_interval_set, bool allow_override=false)</div><div class="ttdoc">Update binding of var to a new expression.</div></div>
<div class="ttc" id="aclasstvm_1_1arith_1_1IntSetAnalyzer_html_ab0ded402c93fc89cba57015b6f3f4d8d"><div class="ttname"><a href="classtvm_1_1arith_1_1IntSetAnalyzer.html#ab0ded402c93fc89cba57015b6f3f4d8d">tvm::arith::IntSetAnalyzer::Bind</a></div><div class="ttdeci">void Bind(const Var &var, const Range &new_range, bool allow_override=false)</div><div class="ttdoc">Update binding of var to a new expression.</div></div>
<div class="ttc" id="aclasstvm_1_1arith_1_1IntSetAnalyzer_html_ab84b89220625f2b2bc7c69c860e42155"><div class="ttname"><a href="classtvm_1_1arith_1_1IntSetAnalyzer.html#ab84b89220625f2b2bc7c69c860e42155">tvm::arith::IntSetAnalyzer::operator()</a></div><div class="ttdeci">IntSet operator()(const PrimExpr &expr, const ffi::Map< Var, IntSet > &dom_map)</div><div class="ttdoc">Find a symbolic integer set that contains all possible values of expr given the domain of each variab.. [...]
<div class="ttc" id="aclasstvm_1_1arith_1_1IntSetAnalyzer_html_af4274caf7f5a6af64fbafc2f2465f591"><div class="ttname"><a href="classtvm_1_1arith_1_1IntSetAnalyzer.html#af4274caf7f5a6af64fbafc2f2465f591">tvm::arith::IntSetAnalyzer::operator()</a></div><div class="ttdeci">IntSet operator()(const PrimExpr &expr)</div><div class="ttdoc">Find a symbolic integer set that contains all possible values of expr given the domain of each variab...</div></div>
<div class="ttc" id="aclasstvm_1_1arith_1_1IntSet_html"><div class="ttname"><a href="classtvm_1_1arith_1_1IntSet.html">tvm::arith::IntSet</a></div><div class="ttdoc">Managed reference to IntSetNode.</div><div class="ttdef"><b>Definition:</b> int_set.h:67</div></div>
-<div class="ttc" id="aclasstvm_1_1arith_1_1ModularSetAnalyzer_html"><div class="ttname"><a href="classtvm_1_1arith_1_1ModularSetAnalyzer.html">tvm::arith::ModularSetAnalyzer</a></div><div class="ttdoc">Analyzer to get modular information over expression.</div><div class="ttdef"><b>Definition:</b> analyzer.h:241</div></div>
+<div class="ttc" id="aclasstvm_1_1arith_1_1ModularSetAnalyzer_html"><div class="ttname"><a href="classtvm_1_1arith_1_1ModularSetAnalyzer.html">tvm::arith::ModularSetAnalyzer</a></div><div class="ttdoc">Analyzer to get modular information over expression.</div><div class="ttdef"><b>Definition:</b> analyzer.h:242</div></div>
<div class="ttc" id="aclasstvm_1_1arith_1_1ModularSetAnalyzer_html_a04156fac580981f3005af3b8e676720d"><div class="ttname"><a href="classtvm_1_1arith_1_1ModularSetAnalyzer.html#a04156fac580981f3005af3b8e676720d">tvm::arith::ModularSetAnalyzer::Update</a></div><div class="ttdeci">void Update(const Var &var, const ModularSet &info, bool allow_override=false)</div><div class="ttdoc">Update constant int bound information of var.</div></div>
<div class="ttc" id="aclasstvm_1_1arith_1_1ModularSetAnalyzer_html_a57c3c62ebc821f5f01bc2b43f06488da"><div class="ttname"><a href="classtvm_1_1arith_1_1ModularSetAnalyzer.html#a57c3c62ebc821f5f01bc2b43f06488da">tvm::arith::ModularSetAnalyzer::operator()</a></div><div class="ttdeci">ModularSet operator()(const PrimExpr &expr)</div><div class="ttdoc">analyze the expr</div></div>
-<div class="ttc" id="aclasstvm_1_1arith_1_1ModularSetNode_html"><div class="ttname"><a href="classtvm_1_1arith_1_1ModularSetNode.html">tvm::arith::ModularSetNode</a></div><div class="ttdoc">Range of a linear integer function. Use to do specify the possible index values.</div><div class="ttdef"><b>Definition:</b> analyzer.h:209</div></div>
-<div class="ttc" id="aclasstvm_1_1arith_1_1ModularSetNode_html_a0531d08d8529454c726a9a20a2002ee6"><div class="ttname"><a href="classtvm_1_1arith_1_1ModularSetNode.html#a0531d08d8529454c726a9a20a2002ee6">tvm::arith::ModularSetNode::coeff</a></div><div class="ttdeci">int64_t coeff</div><div class="ttdoc">linear co-efficient</div><div class="ttdef"><b>Definition:</b> analyzer.h:212</div></div>
-<div class="ttc" id="aclasstvm_1_1arith_1_1ModularSetNode_html_a680e547835e5062f0c88e0f982bcc805"><div class="ttname"><a href="classtvm_1_1arith_1_1ModularSetNode.html#a680e547835e5062f0c88e0f982bcc805">tvm::arith::ModularSetNode::RegisterReflection</a></div><div class="ttdeci">static void RegisterReflection()</div><div class="ttdef"><b>Definition:</b> analyzer.h:216</div></div>
+<div class="ttc" id="aclasstvm_1_1arith_1_1ModularSetNode_html"><div class="ttname"><a href="classtvm_1_1arith_1_1ModularSetNode.html">tvm::arith::ModularSetNode</a></div><div class="ttdoc">Range of a linear integer function. Use to do specify the possible index values.</div><div class="ttdef"><b>Definition:</b> analyzer.h:210</div></div>
+<div class="ttc" id="aclasstvm_1_1arith_1_1ModularSetNode_html_a0531d08d8529454c726a9a20a2002ee6"><div class="ttname"><a href="classtvm_1_1arith_1_1ModularSetNode.html#a0531d08d8529454c726a9a20a2002ee6">tvm::arith::ModularSetNode::coeff</a></div><div class="ttdeci">int64_t coeff</div><div class="ttdoc">linear co-efficient</div><div class="ttdef"><b>Definition:</b> analyzer.h:213</div></div>
+<div class="ttc" id="aclasstvm_1_1arith_1_1ModularSetNode_html_a680e547835e5062f0c88e0f982bcc805"><div class="ttname"><a href="classtvm_1_1arith_1_1ModularSetNode.html#a680e547835e5062f0c88e0f982bcc805">tvm::arith::ModularSetNode::RegisterReflection</a></div><div class="ttdeci">static void RegisterReflection()</div><div class="ttdef"><b>Definition:</b> analyzer.h:217</div></div>
<div class="ttc" id="aclasstvm_1_1arith_1_1ModularSetNode_html_ab5e5f8aa92fe0e785bd24ae47f29fd4e"><div class="ttname"><a href="classtvm_1_1arith_1_1ModularSetNode.html#ab5e5f8aa92fe0e785bd24ae47f29fd4e">tvm::arith::ModularSetNode::TVM_FFI_DECLARE_OBJECT_INFO_FINAL</a></div><div class="ttdeci">TVM_FFI_DECLARE_OBJECT_INFO_FINAL("arith.ModularSet", ModularSetNode, ffi::Object)</div></div>
-<div class="ttc" id="aclasstvm_1_1arith_1_1ModularSetNode_html_ac08e3992efa956f31feaf12cd360b578"><div class="ttname"><a href="classtvm_1_1arith_1_1ModularSetNode.html#ac08e3992efa956f31feaf12cd360b578">tvm::arith::ModularSetNode::_type_s_eq_hash_kind</a></div><div class="ttdeci">static constexpr TVMFFISEqHashKind _type_s_eq_hash_kind</div><div class="ttdef"><b>Definition:</b> analyzer.h:223</div></div>
-<div class="ttc" id="aclasstvm_1_1arith_1_1ModularSetNode_html_afe63e5586712774be5935e86121431ad"><div class="ttname"><a href="classtvm_1_1arith_1_1ModularSetNode.html#afe63e5586712774be5935e86121431ad">tvm::arith::ModularSetNode::base</a></div><div class="ttdeci">int64_t base</div><div class="ttdoc">The base.</div><div class="ttdef"><b>Definition:</b> analyzer.h:214</div></div>
-<div class="ttc" id="aclasstvm_1_1arith_1_1ModularSet_html"><div class="ttname"><a href="classtvm_1_1arith_1_1ModularSet.html">tvm::arith::ModularSet</a></div><div class="ttdoc">reference of ModularSetNode</div><div class="ttdef"><b>Definition:</b> analyzer.h:231</div></div>
+<div class="ttc" id="aclasstvm_1_1arith_1_1ModularSetNode_html_ac08e3992efa956f31feaf12cd360b578"><div class="ttname"><a href="classtvm_1_1arith_1_1ModularSetNode.html#ac08e3992efa956f31feaf12cd360b578">tvm::arith::ModularSetNode::_type_s_eq_hash_kind</a></div><div class="ttdeci">static constexpr TVMFFISEqHashKind _type_s_eq_hash_kind</div><div class="ttdef"><b>Definition:</b> analyzer.h:224</div></div>
+<div class="ttc" id="aclasstvm_1_1arith_1_1ModularSetNode_html_afe63e5586712774be5935e86121431ad"><div class="ttname"><a href="classtvm_1_1arith_1_1ModularSetNode.html#afe63e5586712774be5935e86121431ad">tvm::arith::ModularSetNode::base</a></div><div class="ttdeci">int64_t base</div><div class="ttdoc">The base.</div><div class="ttdef"><b>Definition:</b> analyzer.h:215</div></div>
+<div class="ttc" id="aclasstvm_1_1arith_1_1ModularSet_html"><div class="ttname"><a href="classtvm_1_1arith_1_1ModularSet.html">tvm::arith::ModularSet</a></div><div class="ttdoc">reference of ModularSetNode</div><div class="ttdef"><b>Definition:</b> analyzer.h:232</div></div>
<div class="ttc" id="aclasstvm_1_1arith_1_1ModularSet_html_a476321601d927da2e672e36eec8ec444"><div class="ttname"><a href="classtvm_1_1arith_1_1ModularSet.html#a476321601d927da2e672e36eec8ec444">tvm::arith::ModularSet::TVM_FFI_DEFINE_OBJECT_REF_METHODS_NULLABLE</a></div><div class="ttdeci">TVM_FFI_DEFINE_OBJECT_REF_METHODS_NULLABLE(ModularSet, ffi::ObjectRef, ModularSetNode)</div></div>
<div class="ttc" id="aclasstvm_1_1arith_1_1ModularSet_html_a9f54896d98169246c6a24cc338fde500"><div class="ttname"><a href="classtvm_1_1arith_1_1ModularSet.html#a9f54896d98169246c6a24cc338fde500">tvm::arith::ModularSet::ModularSet</a></div><div class="ttdeci">ModularSet(int64_t coeff, int64_t base)</div></div>
-<div class="ttc" id="aclasstvm_1_1arith_1_1RewriteSimplifier_html"><div class="ttname"><a href="classtvm_1_1arith_1_1RewriteSimplifier.html">tvm::arith::RewriteSimplifier</a></div><div class="ttdoc">Rewrite-rule based simplifier.</div><div class="ttdef"><b>Definition:</b> analyzer.h:279</div></div>
+<div class="ttc" id="aclasstvm_1_1arith_1_1RewriteSimplifier_html"><div class="ttname"><a href="classtvm_1_1arith_1_1RewriteSimplifier.html">tvm::arith::RewriteSimplifier</a></div><div class="ttdoc">Rewrite-rule based simplifier.</div><div class="ttdef"><b>Definition:</b> analyzer.h:281</div></div>
<div class="ttc" id="aclasstvm_1_1arith_1_1RewriteSimplifier_html_a01dda385f4e0bb0e1155c76c2d28cb96"><div class="ttname"><a href="classtvm_1_1arith_1_1RewriteSimplifier.html#a01dda385f4e0bb0e1155c76c2d28cb96">tvm::arith::RewriteSimplifier::EnterConstraint</a></div><div class="ttdeci">std::function< void()> EnterConstraint(const PrimExpr &constraint)</div><div class="ttdoc">Update the internal state to enter constraint.</div></div>
<div class="ttc" id="aclasstvm_1_1arith_1_1RewriteSimplifier_html_a2066e6d9a5b71d482c73542d0ca8c7a3"><div class="ttname"><a href="classtvm_1_1arith_1_1RewriteSimplifier.html#a2066e6d9a5b71d482c73542d0ca8c7a3">tvm::arith::RewriteSimplifier::GetEnabledExtensions</a></div><div class="ttdeci">Extension GetEnabledExtensions() const</div><div class="ttdoc">Return the currently enabled extensions.</div></div>
<div class="ttc" id="aclasstvm_1_1arith_1_1RewriteSimplifier_html_a418674df0487f883db23b9ebe208f598"><div class="ttname"><a href="classtvm_1_1arith_1_1RewriteSimplifier.html#a418674df0487f883db23b9ebe208f598">tvm::arith::RewriteSimplifier::GetStatsCounters</a></div><div class="ttdeci">ffi::ObjectRef GetStatsCounters() const</div><div class="ttdoc">Return the statistics counters.</div></div>
<div class="ttc" id="aclasstvm_1_1arith_1_1RewriteSimplifier_html_a44b7c12d6a5d9adfc4ee4cee8d935e38"><div class="ttname"><a href="classtvm_1_1arith_1_1RewriteSimplifier.html#a44b7c12d6a5d9adfc4ee4cee8d935e38">tvm::arith::RewriteSimplifier::SetEnabledExtensions</a></div><div class="ttdeci">void SetEnabledExtensions(Extension flags)</div><div class="ttdoc">Enable an optional extension or extensions.</div></div>
<div class="ttc" id="aclasstvm_1_1arith_1_1RewriteSimplifier_html_a5e6752c0702dc2d3e4235797d9d3ac7b"><div class="ttname"><a href="classtvm_1_1arith_1_1RewriteSimplifier.html#a5e6752c0702dc2d3e4235797d9d3ac7b">tvm::arith::RewriteSimplifier::Update</a></div><div class="ttdeci">void Update(const Var &var, const PrimExpr &new_expr, bool allow_override=false)</div><div class="ttdoc">Update binding of var to a new expression.</div></div>
-<div class="ttc" id="aclasstvm_1_1arith_1_1RewriteSimplifier_html_a6705af5099418b968ded40d79102ad4e"><div class="ttname"><a href="classtvm_1_1arith_1_1RewriteSimplifier.html#a6705af5099418b968ded40d79102ad4e">tvm::arith::RewriteSimplifier::Extension</a></div><div class="ttdeci">Extension</div><div class="ttdoc">Flags to enable more computationally-intensive simplifications.</div><div class="ttdef"><b>Definition:</b> analyzer.h:317</div></div>
-<div class="ttc" id="aclasstvm_1_1arith_1_1RewriteSimplifier_html_a6705af5099418b968ded40d79102ad4ea237e1cb407cd81dfc9608651eee87b3b"><div class="ttname"><a href="classtvm_1_1arith_1_1RewriteSimplifier.html#a6705af5099418b968ded40d79102ad4ea237e1cb407cd81dfc9608651eee87b3b">tvm::arith::RewriteSimplifier::kNone</a></div><div class="ttdeci">@ kNone</div><div class="ttdef"><b>Definition:</b> analyzer.h:319</div></div>
-<div class="ttc" id="aclasstvm_1_1arith_1_1RewriteSimplifier_html_a6705af5099418b968ded40d79102ad4ea254331b07f4bdf970d7f28f480da637b"><div class="ttname"><a href="classtvm_1_1arith_1_1RewriteSimplifier.html#a6705af5099418b968ded40d79102ad4ea254331b07f4bdf970d7f28f480da637b">tvm::arith::RewriteSimplifier::kApplyConstraintsToBooleanBranches</a></div><div class="ttdeci">@ kApplyConstraintsToBooleanBranches</div><div class="ttdef"><b>Definition:</b> analyzer.h:347</div></div>
-<div class="ttc" id="aclasstvm_1_1arith_1_1RewriteSimplifier_html_a6705af5099418b968ded40d79102ad4ea775c43159049079d4a71cd788f4d1568"><div class="ttname"><a href="classtvm_1_1arith_1_1RewriteSimplifier.html#a6705af5099418b968ded40d79102ad4ea775c43159049079d4a71cd788f4d1568">tvm::arith::RewriteSimplifier::kTransitivelyProveInequalities</a></div><div class="ttdeci">@ kTransitivelyProveInequalities</div><div class="ttdef"><b>Definition:</b> analyzer.h:326</div></div>
-<div class="ttc" id="aclasstvm_1_1arith_1_1RewriteSimplifier_html_a6705af5099418b968ded40d79102ad4eaf2a72591e015241f111c915110ad0b23"><div class="ttname"><a href="classtvm_1_1arith_1_1RewriteSimplifier.html#a6705af5099418b968ded40d79102ad4eaf2a72591e015241f111c915110ad0b23">tvm::arith::RewriteSimplifier::kComparisonOfProductAndSum</a></div><div class="ttdeci">@ kComparisonOfProductAndSum</div><div class="ttdef"><b>Definition:</b> analyzer.h:376</div></div>
-<div class="ttc" id="aclasstvm_1_1arith_1_1RewriteSimplifier_html_a6705af5099418b968ded40d79102ad4eafd77a96599dad627a7c1d88292aa578e"><div class="ttname"><a href="classtvm_1_1arith_1_1RewriteSimplifier.html#a6705af5099418b968ded40d79102ad4eafd77a96599dad627a7c1d88292aa578e">tvm::arith::RewriteSimplifier::kConvertBooleanToAndOfOrs</a></div><div class="ttdeci">@ kConvertBooleanToAndOfOrs</div><div class="ttdef"><b>Definition:</b> analyzer.h:334</div></div>
+<div class="ttc" id="aclasstvm_1_1arith_1_1RewriteSimplifier_html_a6705af5099418b968ded40d79102ad4e"><div class="ttname"><a href="classtvm_1_1arith_1_1RewriteSimplifier.html#a6705af5099418b968ded40d79102ad4e">tvm::arith::RewriteSimplifier::Extension</a></div><div class="ttdeci">Extension</div><div class="ttdoc">Flags to enable more computationally-intensive simplifications.</div><div class="ttdef"><b>Definition:</b> analyzer.h:319</div></div>
+<div class="ttc" id="aclasstvm_1_1arith_1_1RewriteSimplifier_html_a6705af5099418b968ded40d79102ad4ea237e1cb407cd81dfc9608651eee87b3b"><div class="ttname"><a href="classtvm_1_1arith_1_1RewriteSimplifier.html#a6705af5099418b968ded40d79102ad4ea237e1cb407cd81dfc9608651eee87b3b">tvm::arith::RewriteSimplifier::kNone</a></div><div class="ttdeci">@ kNone</div><div class="ttdef"><b>Definition:</b> analyzer.h:321</div></div>
+<div class="ttc" id="aclasstvm_1_1arith_1_1RewriteSimplifier_html_a6705af5099418b968ded40d79102ad4ea254331b07f4bdf970d7f28f480da637b"><div class="ttname"><a href="classtvm_1_1arith_1_1RewriteSimplifier.html#a6705af5099418b968ded40d79102ad4ea254331b07f4bdf970d7f28f480da637b">tvm::arith::RewriteSimplifier::kApplyConstraintsToBooleanBranches</a></div><div class="ttdeci">@ kApplyConstraintsToBooleanBranches</div><div class="ttdef"><b>Definition:</b> analyzer.h:349</div></div>
+<div class="ttc" id="aclasstvm_1_1arith_1_1RewriteSimplifier_html_a6705af5099418b968ded40d79102ad4ea775c43159049079d4a71cd788f4d1568"><div class="ttname"><a href="classtvm_1_1arith_1_1RewriteSimplifier.html#a6705af5099418b968ded40d79102ad4ea775c43159049079d4a71cd788f4d1568">tvm::arith::RewriteSimplifier::kTransitivelyProveInequalities</a></div><div class="ttdeci">@ kTransitivelyProveInequalities</div><div class="ttdef"><b>Definition:</b> analyzer.h:328</div></div>
+<div class="ttc" id="aclasstvm_1_1arith_1_1RewriteSimplifier_html_a6705af5099418b968ded40d79102ad4eaf2a72591e015241f111c915110ad0b23"><div class="ttname"><a href="classtvm_1_1arith_1_1RewriteSimplifier.html#a6705af5099418b968ded40d79102ad4eaf2a72591e015241f111c915110ad0b23">tvm::arith::RewriteSimplifier::kComparisonOfProductAndSum</a></div><div class="ttdeci">@ kComparisonOfProductAndSum</div><div class="ttdef"><b>Definition:</b> analyzer.h:378</div></div>
+<div class="ttc" id="aclasstvm_1_1arith_1_1RewriteSimplifier_html_a6705af5099418b968ded40d79102ad4eafd77a96599dad627a7c1d88292aa578e"><div class="ttname"><a href="classtvm_1_1arith_1_1RewriteSimplifier.html#a6705af5099418b968ded40d79102ad4eafd77a96599dad627a7c1d88292aa578e">tvm::arith::RewriteSimplifier::kConvertBooleanToAndOfOrs</a></div><div class="ttdeci">@ kConvertBooleanToAndOfOrs</div><div class="ttdef"><b>Definition:</b> analyzer.h:336</div></div>
<div class="ttc" id="aclasstvm_1_1arith_1_1RewriteSimplifier_html_a95dd28018de6b7aa1a5a7ee76f3ba178"><div class="ttname"><a href="classtvm_1_1arith_1_1RewriteSimplifier.html#a95dd28018de6b7aa1a5a7ee76f3ba178">tvm::arith::RewriteSimplifier::SetMaximumRewriteSteps</a></div><div class="ttdeci">void SetMaximumRewriteSteps(int64_t maximum)</div><div class="ttdoc">Set the maximum allowed number of rewrite steps.</div></div>
<div class="ttc" id="aclasstvm_1_1arith_1_1RewriteSimplifier_html_aef5b3e9793a5b459c9f457efef179e4f"><div class="ttname"><a href="classtvm_1_1arith_1_1RewriteSimplifier.html#aef5b3e9793a5b459c9f457efef179e4f">tvm::arith::RewriteSimplifier::ResetStatsCounters</a></div><div class="ttdeci">void ResetStatsCounters()</div><div class="ttdoc">Reset the statistics counters.</div></div>
<div class="ttc" id="aclasstvm_1_1arith_1_1RewriteSimplifier_html_af91d307afc0c857751171a68d6d2b043"><div class="ttname"><a href="classtvm_1_1arith_1_1RewriteSimplifier.html#af91d307afc0c857751171a68d6d2b043">tvm::arith::RewriteSimplifier::operator()</a></div><div class="ttdeci">PrimExpr operator()(const PrimExpr &expr)</div><div class="ttdoc">analyze the expr</div></div>
-<div class="ttc" id="aclasstvm_1_1arith_1_1TransitiveComparisonAnalyzer_html"><div class="ttname"><a href="classtvm_1_1arith_1_1TransitiveComparisonAnalyzer.html">tvm::arith::TransitiveComparisonAnalyzer</a></div><div class="ttdoc">Using previously specified knowns, compare the expressions provided.</div><div class="ttdef"><b>Definition:</b> analyzer.h:482</div></div>
+<div class="ttc" id="aclasstvm_1_1arith_1_1TransitiveComparisonAnalyzer_html"><div class="ttname"><a href="classtvm_1_1arith_1_1TransitiveComparisonAnalyzer.html">tvm::arith::TransitiveComparisonAnalyzer</a></div><div class="ttdoc">Using previously specified knowns, compare the expressions provided.</div><div class="ttdef"><b>Definition:</b> analyzer.h:486</div></div>
<div class="ttc" id="aclasstvm_1_1arith_1_1TransitiveComparisonAnalyzer_html_a1a57e873676402922a7af5b8e1991f00"><div class="ttname"><a href="classtvm_1_1arith_1_1TransitiveComparisonAnalyzer.html#a1a57e873676402922a7af5b8e1991f00">tvm::arith::TransitiveComparisonAnalyzer::Bind</a></div><div class="ttdeci">void Bind(const Var &var, const Range &range, bool allow_override=false)</div><div class="ttdoc">Bind a variable as being within a specified range.</div></div>
<div class="ttc" id="aclasstvm_1_1arith_1_1TransitiveComparisonAnalyzer_html_ab0c67cf201be8b57460cc155e5ff352a"><div class="ttname"><a href="classtvm_1_1arith_1_1TransitiveComparisonAnalyzer.html#ab0c67cf201be8b57460cc155e5ff352a">tvm::arith::TransitiveComparisonAnalyzer::EnterConstraint</a></div><div class="ttdeci">std::function< void()> EnterConstraint(const PrimExpr &constraint)</div><div class="ttdoc">Update the internal state to enter constraint.</div></div>
<div class="ttc" id="aclasstvm_1_1arith_1_1TransitiveComparisonAnalyzer_html_ab397bc32c73753205e70d63b71738779"><div class="ttname"><a href="classtvm_1_1arith_1_1TransitiveComparisonAnalyzer.html#ab397bc32c73753205e70d63b71738779">tvm::arith::TransitiveComparisonAnalyzer::Bind</a></div><div class="ttdeci">void Bind(const Var &var, const PrimExpr &expr, bool allow_override=false)</div><div class="ttdoc">Bind a variable as being equal to a known expression.</div></div>
<div class="ttc" id="aclasstvm_1_1arith_1_1TransitiveComparisonAnalyzer_html_ad00ac744402de84c7c3d6f2f46a6f7c7"><div class="ttname"><a href="classtvm_1_1arith_1_1TransitiveComparisonAnalyzer.html#ad00ac744402de84c7c3d6f2f46a6f7c7">tvm::arith::TransitiveComparisonAnalyzer::TryCompare</a></div><div class="ttdeci">CompareResult TryCompare(const PrimExpr &lhs, const PrimExpr &rhs, bool propagate_inequalities=true)</div></div>
-<div class="ttc" id="aclasstvm_1_1arith_1_1Z3Prover_html"><div class="ttname"><a href="classtvm_1_1arith_1_1Z3Prover.html">tvm::arith::Z3Prover</a></div><div class="ttdef"><b>Definition:</b> analyzer.h:592</div></div>
+<div class="ttc" id="aclasstvm_1_1arith_1_1Z3Prover_html"><div class="ttname"><a href="classtvm_1_1arith_1_1Z3Prover.html">tvm::arith::Z3Prover</a></div><div class="ttdef"><b>Definition:</b> analyzer.h:598</div></div>
<div class="ttc" id="aclasstvm_1_1arith_1_1Z3Prover_html_a35d81090c4816ac6202c878cea54d98f"><div class="ttname"><a href="classtvm_1_1arith_1_1Z3Prover.html#a35d81090c4816ac6202c878cea54d98f">tvm::arith::Z3Prover::GetModel</a></div><div class="ttdeci">ffi::String GetModel(const PrimExpr &expr)</div><div class="ttdoc">Get the Z3 model for the given expression if satisfiable.</div></div>
<div class="ttc" id="aclasstvm_1_1arith_1_1Z3Prover_html_a52f045f47f9f868d7372402ea574341c"><div class="ttname"><a href="classtvm_1_1arith_1_1Z3Prover.html#a52f045f47f9f868d7372402ea574341c">tvm::arith::Z3Prover::SetTimeoutMs</a></div><div class="ttdeci">void SetTimeoutMs(unsigned timeout_ms)</div><div class="ttdoc">Set timeout in milliseconds for Z3 prover.</div></div>
<div class="ttc" id="aclasstvm_1_1arith_1_1Z3Prover_html_a6e21274c18c5310f1aee546f73177bf0"><div class="ttname"><a href="classtvm_1_1arith_1_1Z3Prover.html#a6e21274c18c5310f1aee546f73177bf0">tvm::arith::Z3Prover::Bind</a></div><div class="ttdeci">void Bind(const Var &var, const Range &new_range, bool allow_override=false)</div><div class="ttdoc">Update binding of var to a new expression.</div></div>
@@ -609,7 +618,7 @@ $(function() {
<div class="ttc" id="anamespacetvm_1_1arith_html_a038b0205b1c66d27c9dff7703e359339"><div class="ttname"><a href="namespacetvm_1_1arith.html#a038b0205b1c66d27c9dff7703e359339">tvm::arith::ProofStrength</a></div><div class="ttdeci">ProofStrength</div><div class="ttdoc">The strength used in top-level condition proves.</div><div class="ttdef"><b>Definition:</b> analyzer.h:75</div></div>
<div class="ttc" id="anamespacetvm_1_1arith_html_a038b0205b1c66d27c9dff7703e359339a3f67e3bf6c5ae9efc290f80011f65b80"><div class="ttname"><a href="namespacetvm_1_1arith.html#a038b0205b1c66d27c9dff7703e359339a3f67e3bf6c5ae9efc290f80011f65b80">tvm::arith::ProofStrength::kSymbolicBound</a></div><div class="ttdeci">@ kSymbolicBound</div><div class="ttdoc">Prove using symbolic bound analysis.</div></div>
<div class="ttc" id="anamespacetvm_1_1arith_html_a038b0205b1c66d27c9dff7703e359339a6867faeaa475fda467e48267db2bb8a8"><div class="ttname"><a href="namespacetvm_1_1arith.html#a038b0205b1c66d27c9dff7703e359339a6867faeaa475fda467e48267db2bb8a8">tvm::arith::ProofStrength::kDefault</a></div><div class="ttdeci">@ kDefault</div><div class="ttdoc">default strength, can be used in.</div></div>
-<div class="ttc" id="anamespacetvm_1_1arith_html_a1d555a0cc363f669a49fad7e5f7d69d0"><div class="ttname"><a href="namespacetvm_1_1arith.html#a1d555a0cc363f669a49fad7e5f7d69d0">tvm::arith::CompareResult</a></div><div class="ttdeci">CompareResult</div><div class="ttdoc">Structure for representing result of known.</div><div class="ttdef"><b>Definition:</b> analyzer.h:458</div></div>
+<div class="ttc" id="anamespacetvm_1_1arith_html_a1d555a0cc363f669a49fad7e5f7d69d0"><div class="ttname"><a href="namespacetvm_1_1arith.html#a1d555a0cc363f669a49fad7e5f7d69d0">tvm::arith::CompareResult</a></div><div class="ttdeci">CompareResult</div><div class="ttdoc">Structure for representing result of known.</div><div class="ttdef"><b>Definition:</b> analyzer.h:462</div></div>
<div class="ttc" id="anamespacetvm_1_1arith_html_a1d555a0cc363f669a49fad7e5f7d69d0a03e3ad128c41e04acc6a0e59fdf11a58"><div class="ttname"><a href="namespacetvm_1_1arith.html#a1d555a0cc363f669a49fad7e5f7d69d0a03e3ad128c41e04acc6a0e59fdf11a58">tvm::arith::CompareResult::kNE</a></div><div class="ttdeci">@ kNE</div></div>
<div class="ttc" id="anamespacetvm_1_1arith_html_a1d555a0cc363f669a49fad7e5f7d69d0a1893df55d7fc768f2336fdcb7c7b9c37"><div class="ttname"><a href="namespacetvm_1_1arith.html#a1d555a0cc363f669a49fad7e5f7d69d0a1893df55d7fc768f2336fdcb7c7b9c37">tvm::arith::CompareResult::kLE</a></div><div class="ttdeci">@ kLE</div></div>
<div class="ttc" id="anamespacetvm_1_1arith_html_a1d555a0cc363f669a49fad7e5f7d69d0a7db2fe13b438b1005c70fcc9ab826b3b"><div class="ttname"><a href="namespacetvm_1_1arith.html#a1d555a0cc363f669a49fad7e5f7d69d0a7db2fe13b438b1005c70fcc9ab826b3b">tvm::arith::CompareResult::kEQ</a></div><div class="ttdeci">@ kEQ</div></div>
@@ -617,12 +626,12 @@ $(function() {
<div class="ttc" id="anamespacetvm_1_1arith_html_a1d555a0cc363f669a49fad7e5f7d69d0ac0275be4f85005f8ef340d3d5be8d361"><div class="ttname"><a href="namespacetvm_1_1arith.html#a1d555a0cc363f669a49fad7e5f7d69d0ac0275be4f85005f8ef340d3d5be8d361">tvm::arith::CompareResult::kGE</a></div><div class="ttdeci">@ kGE</div></div>
<div class="ttc" id="anamespacetvm_1_1arith_html_a1d555a0cc363f669a49fad7e5f7d69d0add0f23f162dcf68bf7bc761ac13040ae"><div class="ttname"><a href="namespacetvm_1_1arith.html#a1d555a0cc363f669a49fad7e5f7d69d0add0f23f162dcf68bf7bc761ac13040ae">tvm::arith::CompareResult::kLT</a></div><div class="ttdeci">@ kLT</div></div>
<div class="ttc" id="anamespacetvm_1_1arith_html_a1d555a0cc363f669a49fad7e5f7d69d0af9f8d006118de5ad04dd7c000b8f3b91"><div class="ttname"><a href="namespacetvm_1_1arith.html#a1d555a0cc363f669a49fad7e5f7d69d0af9f8d006118de5ad04dd7c000b8f3b91">tvm::arith::CompareResult::kInconsistent</a></div><div class="ttdeci">@ kInconsistent</div></div>
-<div class="ttc" id="anamespacetvm_1_1arith_html_a660ff1fdbb08fea19a922157cadad7a7"><div class="ttname"><a href="namespacetvm_1_1arith.html#a660ff1fdbb08fea19a922157cadad7a7">tvm::arith::operator|</a></div><div class="ttdeci">constexpr CompareResult operator|(CompareResult lhs, CompareResult rhs)</div><div class="ttdef"><b>Definition:</b> analyzer.h:472</div></div>
+<div class="ttc" id="anamespacetvm_1_1arith_html_a660ff1fdbb08fea19a922157cadad7a7"><div class="ttname"><a href="namespacetvm_1_1arith.html#a660ff1fdbb08fea19a922157cadad7a7">tvm::arith::operator|</a></div><div class="ttdeci">constexpr CompareResult operator|(CompareResult lhs, CompareResult rhs)</div><div class="ttdef"><b>Definition:</b> analyzer.h:476</div></div>
<div class="ttc" id="anamespacetvm_1_1arith_html_ac6a38da661cd3681eb85abe1cd810422"><div class="ttname"><a href="namespacetvm_1_1arith.html#ac6a38da661cd3681eb85abe1cd810422">tvm::arith::DivMode</a></div><div class="ttdeci">DivMode</div><div class="ttdef"><b>Definition:</b> analyzer.h:61</div></div>
<div class="ttc" id="anamespacetvm_1_1arith_html_ac6a38da661cd3681eb85abe1cd810422aa6da1e276448319d25de8fe181872432"><div class="ttname"><a href="namespacetvm_1_1arith.html#ac6a38da661cd3681eb85abe1cd810422aa6da1e276448319d25de8fe181872432">tvm::arith::kTruncDiv</a></div><div class="ttdeci">@ kTruncDiv</div><div class="ttdoc">Truncated division.</div><div class="ttdef"><b>Definition:</b> analyzer.h:63</div></div>
<div class="ttc" id="anamespacetvm_1_1arith_html_ac6a38da661cd3681eb85abe1cd810422aad0250170b362173e1e2a2e3a6f13d20"><div class="ttname"><a href="namespacetvm_1_1arith.html#ac6a38da661cd3681eb85abe1cd810422aad0250170b362173e1e2a2e3a6f13d20">tvm::arith::kFloorDiv</a></div><div class="ttdeci">@ kFloorDiv</div><div class="ttdoc">Floor division.</div><div class="ttdef"><b>Definition:</b> analyzer.h:65</div></div>
<div class="ttc" id="anamespacetvm_1_1arith_html_aca8806e355ad3dd5f1df9c1eca9aac9da8812c1a077255594d23bc1c2f3af3979"><div class="ttname"><a href="namespacetvm_1_1arith.html#aca8806e355ad3dd5f1df9c1eca9aac9da8812c1a077255594d23bc1c2f3af3979">tvm::arith::kUnknown</a></div><div class="ttdeci">@ kUnknown</div><div class="ttdef"><b>Definition:</b> int_set.h:51</div></div>
-<div class="ttc" id="anamespacetvm_1_1arith_html_ae300654bf1788008de90f1e77fbaa7fe"><div class="ttname"><a href="namespacetvm_1_1arith.html#ae300654bf1788008de90f1e77fbaa7fe">tvm::arith::operator&</a></div><div class="ttdeci">constexpr CompareResult operator&(CompareResult lhs, CompareResult rhs)</div><div class="ttdef"><b>Definition:</b> analyzer.h:469</div></div>
+<div class="ttc" id="anamespacetvm_1_1arith_html_ae300654bf1788008de90f1e77fbaa7fe"><div class="ttname"><a href="namespacetvm_1_1arith.html#ae300654bf1788008de90f1e77fbaa7fe">tvm::arith::operator&</a></div><div class="ttdeci">constexpr CompareResult operator&(CompareResult lhs, CompareResult rhs)</div><div class="ttdef"><b>Definition:</b> analyzer.h:473</div></div>
<div class="ttc" id="anamespacetvm_1_1te_html_ae0c71f84710b436cbe0b32289d0838f4"><div class="ttname"><a href="namespacetvm_1_1te.html#ae0c71f84710b436cbe0b32289d0838f4">tvm::te::var</a></div><div class="ttdeci">Var var(std::string name_hint, DataType t=DataType::Int(32))</div><div class="ttdoc">Construct a new Var expression.</div></div>
<div class="ttc" id="anamespacetvm_1_1tirx_html_ae97ec08fd3edb5b1a83e46171c4a7939"><div class="ttname"><a href="namespacetvm_1_1tirx.html#ae97ec08fd3edb5b1a83e46171c4a7939">tvm::tirx::maximum</a></div><div class="ttdeci">const Op & maximum()</div></div>
<div class="ttc" id="anamespacetvm_1_1topi_html_af30c02f3a3f37c7963b3af60fb9c72a1"><div class="ttname"><a href="namespacetvm_1_1topi.html#af30c02f3a3f37c7963b3af60fb9c72a1">tvm::topi::shape</a></div><div class="ttdeci">Tensor shape(const Tensor &src, DataType dtype, const std::string name="T_shape", const std::string tag=kInjective)</div><div class="ttdoc">Get the shape of input tensor.</div><div class="ttdef"><b>Definition:</b> transform.h:1983</div></div>
diff --git a/docs/reference/api/doxygen/axis__group__graph_8h_source.html b/docs/reference/api/doxygen/axis__group__graph_8h_source.html
index 62dacb48fe2..9882d41ff21 100644
--- a/docs/reference/api/doxygen/axis__group__graph_8h_source.html
+++ b/docs/reference/api/doxygen/axis__group__graph_8h_source.html
@@ -503,7 +503,7 @@ $(function() {
<div class="ttc" id="aclasstvm_1_1PrimExpr_html"><div class="ttname"><a href="classtvm_1_1PrimExpr.html">tvm::PrimExpr</a></div><div class="ttdoc">Reference to PrimExprNode.</div><div class="ttdef"><b>Definition:</b> expr.h:123</div></div>
<div class="ttc" id="aclasstvm_1_1Range_html_a91e7301ca1d135ca5f8ed199efbb9818"><div class="ttname"><a href="classtvm_1_1Range.html#a91e7301ca1d135ca5f8ed199efbb9818">tvm::Range::FromMinExtent</a></div><div class="ttdeci">static Range FromMinExtent(PrimExpr min, PrimExpr extent, Span span=Span())</div><div class="ttdoc">construct a new range with min and extent The corresponding constructor is removed,...</div></div>
<div class="ttc" id="aclasstvm_1_1RelaxExprNode_html"><div class="ttname"><a href="classtvm_1_1RelaxExprNode.html">tvm::RelaxExprNode</a></div><div class="ttdoc">Base node of all non-primitive expressions.</div><div class="ttdef"><b>Definition:</b> expr.h:415</div></div>
-<div class="ttc" id="aclasstvm_1_1arith_1_1Analyzer_html"><div class="ttname"><a href="classtvm_1_1arith_1_1Analyzer.html">tvm::arith::Analyzer</a></div><div class="ttdoc">Managed reference to AnalyzerObj.</div><div class="ttdef"><b>Definition:</b> analyzer.h:883</div></div>
+<div class="ttc" id="aclasstvm_1_1arith_1_1Analyzer_html"><div class="ttname"><a href="classtvm_1_1arith_1_1Analyzer.html">tvm::arith::Analyzer</a></div><div class="ttdoc">Managed reference to AnalyzerObj.</div><div class="ttdef"><b>Definition:</b> analyzer.h:909</div></div>
<div class="ttc" id="aclasstvm_1_1relax_1_1Call_html"><div class="ttname"><a href="classtvm_1_1relax_1_1Call.html">tvm::relax::Call</a></div><div class="ttdef"><b>Definition:</b> expr.h:180</div></div>
<div class="ttc" id="aclasstvm_1_1relax_1_1ConstantNode_html"><div class="ttname"><a href="classtvm_1_1relax_1_1ConstantNode.html">tvm::relax::ConstantNode</a></div><div class="ttdoc">Constant tensor.</div><div class="ttdef"><b>Definition:</b> expr.h:425</div></div>
<div class="ttc" id="aclasstvm_1_1relax_1_1VarNode_html"><div class="ttname"><a href="classtvm_1_1relax_1_1VarNode.html">tvm::relax::VarNode</a></div><div class="ttdoc">The variable class for all Relax bindings.</div><div class="ttdef"><b>Definition:</b> expr.h:344</div></div>
diff --git a/docs/reference/api/doxygen/block__builder_8h_source.html b/docs/reference/api/doxygen/block__builder_8h_source.html
index 6cf167d03bb..0a19261b9e4 100644
--- a/docs/reference/api/doxygen/block__builder_8h_source.html
+++ b/docs/reference/api/doxygen/block__builder_8h_source.html
@@ -176,7 +176,7 @@ $(function() {
<div class="ttc" id="aclasstvm_1_1IRModule_html"><div class="ttname"><a href="classtvm_1_1IRModule.html">tvm::IRModule</a></div><div class="ttdoc">Managed reference class to IRModuleNode.</div><div class="ttdef"><b>Definition:</b> module.h:255</div></div>
<div class="ttc" id="aclasstvm_1_1RelaxExpr_html"><div class="ttname"><a href="classtvm_1_1RelaxExpr.html">tvm::RelaxExpr</a></div><div class="ttdoc">Managed reference to RelaxExprNode.</div><div class="ttdef"><b>Definition:</b> expr.h:438</div></div>
<div class="ttc" id="aclasstvm_1_1UniqueNameSupply_html"><div class="ttname"><a href="classtvm_1_1UniqueNameSupply.html">tvm::UniqueNameSupply</a></div><div class="ttdoc">Managed reference class to UniqueNameSupplyNode.</div><div class="ttdef"><b>Definition:</b> unique_name_supply.h:115</div></div>
-<div class="ttc" id="aclasstvm_1_1arith_1_1Analyzer_html"><div class="ttname"><a href="classtvm_1_1arith_1_1Analyzer.html">tvm::arith::Analyzer</a></div><div class="ttdoc">Managed reference to AnalyzerObj.</div><div class="ttdef"><b>Definition:</b> analyzer.h:883</div></div>
+<div class="ttc" id="aclasstvm_1_1arith_1_1Analyzer_html"><div class="ttname"><a href="classtvm_1_1arith_1_1Analyzer.html">tvm::arith::Analyzer</a></div><div class="ttdoc">Managed reference to AnalyzerObj.</div><div class="ttdef"><b>Definition:</b> analyzer.h:909</div></div>
<div class="ttc" id="aclasstvm_1_1relax_1_1BindingBlock_html"><div class="ttname"><a href="classtvm_1_1relax_1_1BindingBlock.html">tvm::relax::BindingBlock</a></div><div class="ttdef"><b>Definition:</b> expr.h:683</div></div>
<div class="ttc" id="aclasstvm_1_1relax_1_1Binding_html"><div class="ttname"><a href="classtvm_1_1relax_1_1Binding.html">tvm::relax::Binding</a></div><div class="ttdef"><b>Definition:</b> expr.h:586</div></div>
<div class="ttc" id="aclasstvm_1_1relax_1_1BlockBuilderNode_html"><div class="ttname"><a href="classtvm_1_1relax_1_1BlockBuilderNode.html">tvm::relax::BlockBuilderNode</a></div><div class="ttdoc">A builder to build Relax binding blocks.</div><div class="ttdef"><b>Definition:</b> block_builder.h:65</div></div>
diff --git a/docs/reference/api/doxygen/bnn_8h_source.html b/docs/reference/api/doxygen/bnn_8h_source.html
index ba501c72b58..bf372a6ad82 100644
--- a/docs/reference/api/doxygen/bnn_8h_source.html
+++ b/docs/reference/api/doxygen/bnn_8h_source.html
@@ -175,7 +175,7 @@ $(function() {
<div class="ttc" id="aclasstvm_1_1IntImm_html"><div class="ttname"><a href="classtvm_1_1IntImm.html">tvm::IntImm</a></div><div class="ttdoc">Managed reference class to IntImmNode.</div><div class="ttdef"><b>Definition:</b> expr.h:508</div></div>
<div class="ttc" id="aclasstvm_1_1PrimExpr_html"><div class="ttname"><a href="classtvm_1_1PrimExpr.html">tvm::PrimExpr</a></div><div class="ttdoc">Reference to PrimExprNode.</div><div class="ttdef"><b>Definition:</b> expr.h:123</div></div>
<div class="ttc" id="aclasstvm_1_1Range_html"><div class="ttname"><a href="classtvm_1_1Range.html">tvm::Range</a></div><div class="ttdoc">Range container</div><div class="ttdef"><b>Definition:</b> expr.h:612</div></div>
-<div class="ttc" id="aclasstvm_1_1arith_1_1Analyzer_html"><div class="ttname"><a href="classtvm_1_1arith_1_1Analyzer.html">tvm::arith::Analyzer</a></div><div class="ttdoc">Managed reference to AnalyzerObj.</div><div class="ttdef"><b>Definition:</b> analyzer.h:883</div></div>
+<div class="ttc" id="aclasstvm_1_1arith_1_1Analyzer_html"><div class="ttname"><a href="classtvm_1_1arith_1_1Analyzer.html">tvm::arith::Analyzer</a></div><div class="ttdoc">Managed reference to AnalyzerObj.</div><div class="ttdef"><b>Definition:</b> analyzer.h:909</div></div>
<div class="ttc" id="aclasstvm_1_1runtime_1_1DataType_html_aef2fa30c095f37b20a301665bed80826"><div class="ttname"><a href="classtvm_1_1runtime_1_1DataType.html#aef2fa30c095f37b20a301665bed80826">tvm::runtime::DataType::UInt</a></div><div class="ttdeci">static DataType UInt(int bits, int lanes=1, bool is_scalable=false)</div><div class="ttdoc">Construct an uint type.</div><div class="ttdef"><b>Definition:</b> data_type.h:284</div></div>
<div class="ttc" id="aclasstvm_1_1te_1_1Tensor_html"><div class="ttname"><a href="classtvm_1_1te_1_1Tensor.html">tvm::te::Tensor</a></div><div class="ttdoc">Tensor structure representing a possible input, or intermediate computation result.</div><div class="ttdef"><b>Definition:</b> tensor.h:100</div></div>
<div class="ttc" id="aclasstvm_1_1tirx_1_1Var_html"><div class="ttname"><a href="classtvm_1_1tirx_1_1Var.html">tvm::tirx::Var</a></div><div class="ttdoc">a named variable in TIR</div><div class="ttdef"><b>Definition:</b> var.h:77</div></div>
diff --git a/docs/reference/api/doxygen/classtvm_1_1arith_1_1AnalyzerObj-members.html b/docs/reference/api/doxygen/classtvm_1_1arith_1_1AnalyzerObj-members.html
index 5b53783e0c7..5c77a4cb676 100644
--- a/docs/reference/api/doxygen/classtvm_1_1arith_1_1AnalyzerObj-members.html
+++ b/docs/reference/api/doxygen/classtvm_1_1arith_1_1AnalyzerObj-members.html
@@ -83,15 +83,16 @@ $(function() {
<tr class="even"><td class="entry"><a class="el" href="classtvm_1_1arith_1_1AnalyzerObj.html#a33b289b63c0a91e11b00e98fdbfe2d53">CanProveGreaterEqual</a>(const PrimExpr &expr, int64_t lower_bound)</td><td class="entry"><a class="el" href="classtvm_1_1arith_1_1AnalyzerObj.html">tvm::arith::AnalyzerObj</a></td><td class="entry"></td></tr>
<tr><td class="entry"><a class="el" href="classtvm_1_1arith_1_1AnalyzerObj.html#a59475536e8c1078ad57cd5089cf4ffd7">CanProveLess</a>(const PrimExpr &expr, int64_t upper_bound)</td><td class="entry"><a class="el" href="classtvm_1_1arith_1_1AnalyzerObj.html">tvm::arith::AnalyzerObj</a></td><td class="entry"></td></tr>
<tr class="even"><td class="entry"><a class="el" href="classtvm_1_1arith_1_1AnalyzerObj.html#a3d3b62509fecace627f8843256dc8c02">CanProveLessEqualThanSymbolicShapeValue</a>(const PrimExpr &lhs, const PrimExpr &shape)</td><td class="entry"><a class="el" href="classtvm_1_1arith_1_1AnalyzerObj.html">tvm::arith::AnalyzerObj</a></td><td class="entry"></td></tr>
- <tr><td class="entry"><a class="el" href="classtvm_1_1arith_1_1AnalyzerObj.html#a7007e17a46f88e125eb649ffd40a1d8b">const_int_bound</a></td><td class="entry"><a class="el" href="classtvm_1_1arith_1_1AnalyzerObj.html">tvm::arith::AnalyzerObj</a></td><td class="entry"></td></tr>
- <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1arith_1_1AnalyzerObj.html#a9b3fbbc422505fa18cff90ab81d591ab">int_set</a></td><td class="entry"><a class="el" href="classtvm_1_1arith_1_1AnalyzerObj.html">tvm::arith::AnalyzerObj</a></td><td class="entry"></td></tr>
- <tr><td class="entry"><a class="el" href="classtvm_1_1arith_1_1AnalyzerObj.html#a855fc72735981c7ceeb89e5ac851c7fe">MarkGlobalNonNegValue</a>(const PrimExpr &value)</td><td class="entry"><a class="el" href="classtvm_1_1arith_1_1AnalyzerObj.html">tvm::arith::AnalyzerObj</a></td><td class="entry"></td></tr>
- <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1arith_1_1AnalyzerObj.html#ad1cb5737d9af424de0ad43ec794d254f">modular_set</a></td><td class="entry"><a class="el" href="classtvm_1_1arith_1_1AnalyzerObj.html">tvm::arith::AnalyzerObj</a></td><td class="entry"></td></tr>
- <tr><td class="entry"><a class="el" href="classtvm_1_1arith_1_1AnalyzerObj.html#a945243b84edc40b89cd326e7d3cc52da">rewrite_simplify</a></td><td class="entry"><a class="el" href="classtvm_1_1arith_1_1AnalyzerObj.html">tvm::arith::AnalyzerObj</a></td><td class="entry"></td></tr>
- <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1arith_1_1AnalyzerObj.html#a19f9b83800c85ab898a23220fbf000b1">Simplify</a>(const PrimExpr &expr, int steps=2)</td><td class="entry"><a class="el" href="classtvm_1_1arith_1_1AnalyzerObj.html">tvm::arith::AnalyzerObj</a></td><td class="entry"></td></tr>
- <tr><td class="entry"><a class="el" href="classtvm_1_1arith_1_1AnalyzerObj.html#aabac046e3ed1abf7f96b446114cafa62">transitive_comparisons</a></td><td class="entry"><a class="el" href="classtvm_1_1arith_1_1AnalyzerObj.html">tvm::arith::AnalyzerObj</a></td><td class="entry"></td></tr>
- <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1arith_1_1AnalyzerObj.html#ae7f9b32e8df29a1230396bee65ca42c1">TVM_FFI_DECLARE_OBJECT_INFO_FINAL</a>("arith.Analyzer", AnalyzerObj, ffi::Object)</td><td class="entry"><a class="el" href="classtvm_1_1arith_1_1AnalyzerObj.html">tvm::arith::AnalyzerObj</a></td><td class="entry"></td></tr>
- <tr><td class="entry"><a class="el" href="classtvm_1_1arith_1_1AnalyzerObj.html#a730c77bc237767905c43be5e6c6545f6">z3_prover</a></td><td class="entry"><a class="el" href="classtvm_1_1arith_1_1AnalyzerObj.html">tvm::arith::AnalyzerObj</a></td><td class="entry"></td></tr>
+ <tr><td class="entry"><a class="el" href="classtvm_1_1arith_1_1AnalyzerObj.html#a55ea30504104fa309e0b5e0785086d0b">Clone</a>() const</td><td class="entry"><a class="el" href="classtvm_1_1arith_1_1AnalyzerObj.html">tvm::arith::AnalyzerObj</a></td><td class="entry"></td></tr>
+ <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1arith_1_1AnalyzerObj.html#a7007e17a46f88e125eb649ffd40a1d8b">const_int_bound</a></td><td class="entry"><a class="el" href="classtvm_1_1arith_1_1AnalyzerObj.html">tvm::arith::AnalyzerObj</a></td><td class="entry"></td></tr>
+ <tr><td class="entry"><a class="el" href="classtvm_1_1arith_1_1AnalyzerObj.html#a9b3fbbc422505fa18cff90ab81d591ab">int_set</a></td><td class="entry"><a class="el" href="classtvm_1_1arith_1_1AnalyzerObj.html">tvm::arith::AnalyzerObj</a></td><td class="entry"></td></tr>
+ <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1arith_1_1AnalyzerObj.html#a855fc72735981c7ceeb89e5ac851c7fe">MarkGlobalNonNegValue</a>(const PrimExpr &value)</td><td class="entry"><a class="el" href="classtvm_1_1arith_1_1AnalyzerObj.html">tvm::arith::AnalyzerObj</a></td><td class="entry"></td></tr>
+ <tr><td class="entry"><a class="el" href="classtvm_1_1arith_1_1AnalyzerObj.html#ad1cb5737d9af424de0ad43ec794d254f">modular_set</a></td><td class="entry"><a class="el" href="classtvm_1_1arith_1_1AnalyzerObj.html">tvm::arith::AnalyzerObj</a></td><td class="entry"></td></tr>
+ <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1arith_1_1AnalyzerObj.html#a945243b84edc40b89cd326e7d3cc52da">rewrite_simplify</a></td><td class="entry"><a class="el" href="classtvm_1_1arith_1_1AnalyzerObj.html">tvm::arith::AnalyzerObj</a></td><td class="entry"></td></tr>
+ <tr><td class="entry"><a class="el" href="classtvm_1_1arith_1_1AnalyzerObj.html#a19f9b83800c85ab898a23220fbf000b1">Simplify</a>(const PrimExpr &expr, int steps=2)</td><td class="entry"><a class="el" href="classtvm_1_1arith_1_1AnalyzerObj.html">tvm::arith::AnalyzerObj</a></td><td class="entry"></td></tr>
+ <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1arith_1_1AnalyzerObj.html#aabac046e3ed1abf7f96b446114cafa62">transitive_comparisons</a></td><td class="entry"><a class="el" href="classtvm_1_1arith_1_1AnalyzerObj.html">tvm::arith::AnalyzerObj</a></td><td class="entry"></td></tr>
+ <tr><td class="entry"><a class="el" href="classtvm_1_1arith_1_1AnalyzerObj.html#ae7f9b32e8df29a1230396bee65ca42c1">TVM_FFI_DECLARE_OBJECT_INFO_FINAL</a>("arith.Analyzer", AnalyzerObj, ffi::Object)</td><td class="entry"><a class="el" href="classtvm_1_1arith_1_1AnalyzerObj.html">tvm::arith::AnalyzerObj</a></td><td class="entry"></td></tr>
+ <tr class="even"><td class="entry"><a class="el" href="classtvm_1_1arith_1_1AnalyzerObj.html#a730c77bc237767905c43be5e6c6545f6">z3_prover</a></td><td class="entry"><a class="el" href="classtvm_1_1arith_1_1AnalyzerObj.html">tvm::arith::AnalyzerObj</a></td><td class="entry"></td></tr>
</table></div><!-- contents -->
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<hr class="footer"/><address class="footer"><small>
diff --git a/docs/reference/api/doxygen/classtvm_1_1arith_1_1AnalyzerObj.html b/docs/reference/api/doxygen/classtvm_1_1arith_1_1AnalyzerObj.html
index e9007407ec4..d12c154e9ee 100644
--- a/docs/reference/api/doxygen/classtvm_1_1arith_1_1AnalyzerObj.html
+++ b/docs/reference/api/doxygen/classtvm_1_1arith_1_1AnalyzerObj.html
@@ -82,13 +82,13 @@ $(function() {
<div class="dynheader">
Inheritance diagram for tvm::arith::AnalyzerObj:</div>
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+<div class="center"><iframe scrolling="no" frameborder="0" src="classtvm_1_1arith_1_1AnalyzerObj__inherit__graph.svg" width="267" height="522"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
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<div class="dynheader">
Collaboration diagram for tvm::arith::AnalyzerObj:</div>
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<table class="memberdecls">
@@ -127,6 +127,9 @@ Public Member Functions</h2></td></tr>
<tr class="memitem:a19f9b83800c85ab898a23220fbf000b1"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="classtvm_1_1arith_1_1AnalyzerObj.html#a19f9b83800c85ab898a23220fbf000b1">Simplify</a> (const <a class="el" href="classtvm_1_1PrimExpr.html">PrimExpr</a> &expr, int steps=2)</td></tr>
<tr class="memdesc:a19f9b83800c85ab898a23220fbf000b1"><td class="mdescLeft"> </td><td class="mdescRight">Simplify expr. <a href="classtvm_1_1arith_1_1AnalyzerObj.html#a19f9b83800c85ab898a23220fbf000b1">More...</a><br /></td></tr>
<tr class="separator:a19f9b83800c85ab898a23220fbf000b1"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a55ea30504104fa309e0b5e0785086d0b"><td class="memItemLeft" align="right" valign="top"><a class="el" href="classtvm_1_1arith_1_1Analyzer.html">Analyzer</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="classtvm_1_1arith_1_1AnalyzerObj.html#a55ea30504104fa309e0b5e0785086d0b">Clone</a> () const</td></tr>
+<tr class="memdesc:a55ea30504104fa309e0b5e0785086d0b"><td class="mdescLeft"> </td><td class="mdescRight">Deep-copy this analyzer into a new, independent <a class="el" href="classtvm_1_1arith_1_1Analyzer.html" title="Managed reference to AnalyzerObj.">Analyzer</a>. <a href="classtvm_1_1arith_1_1AnalyzerObj.html#a55ea30504104fa309e0b5e0785086d0b">More...</a><br /></td></tr>
+<tr class="separator:a55ea30504104fa309e0b5e0785086d0b"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:ae7f9b32e8df29a1230396bee65ca42c1"><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="classtvm_1_1arith_1_1AnalyzerObj.html#ae7f9b32e8df29a1230396bee65ca42c1">TVM_FFI_DECLARE_OBJECT_INFO_FINAL</a> ("arith.Analyzer", AnalyzerObj, ffi::Object)</td></tr>
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@@ -509,6 +512,28 @@ Static Public Attributes</h2></td></tr>
</dl>
<dl class="section return"><dt>Returns</dt><dd>Whether we can prove lhs <= shape. </dd></dl>
+</div>
+</div>
+<a id="a55ea30504104fa309e0b5e0785086d0b"></a>
+<h2 class="memtitle"><span class="permalink"><a href="#a55ea30504104fa309e0b5e0785086d0b">◆ </a></span>Clone()</h2>
+
+<div class="memitem">
+<div class="memproto">
+ <table class="memname">
+ <tr>
+ <td class="memname"><a class="el" href="classtvm_1_1arith_1_1Analyzer.html">Analyzer</a> tvm::arith::AnalyzerObj::Clone </td>
+ <td>(</td>
+ <td class="paramname"></td><td>)</td>
+ <td> const</td>
+ </tr>
+ </table>
+</div><div class="memdoc">
+
+<p>Deep-copy this analyzer into a new, independent <a class="el" href="classtvm_1_1arith_1_1Analyzer.html" title="Managed reference to AnalyzerObj.">Analyzer</a>. </p>
+<p>The returned analyzer carries the same accumulated facts (variable bounds, modular sets, rewrite/canonical bindings, integer-set domains, literal constraints and transitive comparisons) as this one, but owns its own state: binding or simplifying on either analyzer afterwards does not affect the other. This is the deep copy that handle-copying an <a class="el" href="classtvm_1_1arith_1_1Analyzer.html" title="Managed reference to AnalyzerObj.">Analyzer</a> does not provide.</p>
+<dl class="section note"><dt>Note</dt><dd>Do not call this while a <code><a class="el" href="classtvm_1_1With.html" title="RAII wrapper function to enter and exit a context object similar to python's with syntax.">With</a><<a class="el" href="classtvm_1_1arith_1_1ConstraintContext.html" title="Constraint context.">ConstraintContext</a>></code> scope is active on this analyzer. The clone would inherit the scoped constraints but not the recovery functions that pop them on scope e [...]
+<dl class="section return"><dt>Returns</dt><dd>A new <a class="el" href="classtvm_1_1arith_1_1Analyzer.html" title="Managed reference to AnalyzerObj.">Analyzer</a> holding an independent copy of the facts. </dd></dl>
+
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+<text text-anchor="start" x="8" y="-150.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ MarkGlobalNonNegValue()</text>
+<text text-anchor="start" x="8" y="-139.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ Bind()</text>
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+<text text-anchor="start" x="8" y="-62.5" font-family="Helvetica,sans-Serif" font-size="10.00">ShapeValue()</text>
+<text text-anchor="start" x="8" y="-51.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ CanProve()</text>
+<text text-anchor="start" x="8" y="-40.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ Simplify()</text>
+<text text-anchor="start" x="8" y="-29.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ Clone()</text>
<text text-anchor="start" x="8" y="-18.5" font-family="Helvetica,sans-Serif" font-size="10.00">+ TVM_FFI_DECLARE_OBJECT</text>
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diff --git a/docs/reference/api/doxygen/constant__utils_8h_source.html b/docs/reference/api/doxygen/constant__utils_8h_source.html
index a909ac36849..d0ef5e1c1de 100644
--- a/docs/reference/api/doxygen/constant__utils_8h_source.html
+++ b/docs/reference/api/doxygen/constant__utils_8h_source.html
@@ -165,7 +165,7 @@ $(function() {
<div class="line"><a name="l00147"></a><span class="lineno"> 147</span> <span class="preprocessor">#endif </span><span class="comment">// TVM_TOPI_DETAIL_CONSTANT_UTILS_H_</span></div>
<div class="ttc" id="aanalyzer_8h_html"><div class="ttname"><a href="analyzer_8h.html">analyzer.h</a></div><div class="ttdoc">Algebra expression simplifications.</div></div>
<div class="ttc" id="aclasstvm_1_1IntImmNode_html"><div class="ttname"><a href="classtvm_1_1IntImmNode.html">tvm::IntImmNode</a></div><div class="ttdoc">Constant integer literals in the program.</div><div class="ttdef"><b>Definition:</b> expr.h:491</div></div>
-<div class="ttc" id="aclasstvm_1_1arith_1_1Analyzer_html"><div class="ttname"><a href="classtvm_1_1arith_1_1Analyzer.html">tvm::arith::Analyzer</a></div><div class="ttdoc">Managed reference to AnalyzerObj.</div><div class="ttdef"><b>Definition:</b> analyzer.h:883</div></div>
+<div class="ttc" id="aclasstvm_1_1arith_1_1Analyzer_html"><div class="ttname"><a href="classtvm_1_1arith_1_1Analyzer.html">tvm::arith::Analyzer</a></div><div class="ttdoc">Managed reference to AnalyzerObj.</div><div class="ttdef"><b>Definition:</b> analyzer.h:909</div></div>
<div class="ttc" id="anamespacetvm_1_1te_html"><div class="ttname"><a href="namespacetvm_1_1te.html">tvm::te</a></div><div class="ttdoc">Tensor expression language DSL.</div><div class="ttdef"><b>Definition:</b> extracted_task.h:32</div></div>
<div class="ttc" id="anamespacetvm_1_1tirx_html_a903411af1a444a50244da8dba662e8f9"><div class="ttname"><a href="namespacetvm_1_1tirx.html#a903411af1a444a50244da8dba662e8f9">tvm::tirx::is_const_int</a></div><div class="ttdeci">bool is_const_int(const PrimExpr &x, int64_t value)</div><div class="ttdoc">Check whether x is a constant integer expression.</div><div class="ttdef"><b>Definition:</b> op.h:956</div></div>
<div class="ttc" id="anamespacetvm_html"><div class="ttname"><a href="namespacetvm.html">tvm</a></div><div class="ttdoc">An object that builds and maintains block scope and StmtSref mapping for Dependence analysis.</div><div class="ttdef"><b>Definition:</b> analyzer.h:40</div></div>
diff --git a/docs/reference/api/doxygen/dilate_8h_source.html b/docs/reference/api/doxygen/dilate_8h_source.html
index 6d650012711..a3776251d87 100644
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@@ -154,7 +154,7 @@ $(function() {
<div class="line"><a name="l00108"></a><span class="lineno"> 108</span> <span class="preprocessor">#endif </span><span class="comment">// TVM_TOPI_NN_DILATE_H_</span></div>
<div class="ttc" id="aanalyzer_8h_html"><div class="ttname"><a href="analyzer_8h.html">analyzer.h</a></div><div class="ttdoc">Algebra expression simplifications.</div></div>
<div class="ttc" id="aclasstvm_1_1PrimExpr_html"><div class="ttname"><a href="classtvm_1_1PrimExpr.html">tvm::PrimExpr</a></div><div class="ttdoc">Reference to PrimExprNode.</div><div class="ttdef"><b>Definition:</b> expr.h:123</div></div>
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+<div class="ttc" id="aclasstvm_1_1arith_1_1Analyzer_html"><div class="ttname"><a href="classtvm_1_1arith_1_1Analyzer.html">tvm::arith::Analyzer</a></div><div class="ttdoc">Managed reference to AnalyzerObj.</div><div class="ttdef"><b>Definition:</b> analyzer.h:909</div></div>
<div class="ttc" id="aclasstvm_1_1te_1_1Tensor_html"><div class="ttname"><a href="classtvm_1_1te_1_1Tensor.html">tvm::te::Tensor</a></div><div class="ttdoc">Tensor structure representing a possible input, or intermediate computation result.</div><div class="ttdef"><b>Definition:</b> tensor.h:100</div></div>
<div class="ttc" id="anamespacetvm_1_1te_html"><div class="ttname"><a href="namespacetvm_1_1te.html">tvm::te</a></div><div class="ttdoc">Tensor expression language DSL.</div><div class="ttdef"><b>Definition:</b> extracted_task.h:32</div></div>
<div class="ttc" id="anamespacetvm_1_1te_html_ad98d13fdbc71aede147f419e218fc680"><div class="ttname"><a href="namespacetvm_1_1te.html#ad98d13fdbc71aede147f419e218fc680">tvm::te::compute</a></div><div class="ttdeci">Tensor compute(ffi::Array< PrimExpr > shape, FCompute fcompute, std::string name="tensor", std::string tag="", ffi::Map< ffi::String, ffi::Any > attrs={})</div><div class="ttdoc">Construct a new tensor by computing over shape, using the computat [...]
diff --git a/docs/reference/api/doxygen/functions_a.html b/docs/reference/api/doxygen/functions_a.html
index ec1773e8a92..8978316b0ad 100644
--- a/docs/reference/api/doxygen/functions_a.html
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@@ -482,7 +482,7 @@ $(function() {
, <a class="el" href="classtvm_1_1te_1_1BaseComputeOpNode.html#ae1421534602284de3d2721d53c5d8f1a">tvm::te::BaseComputeOpNode</a>
</li>
<li>Axis()
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+: <a class="el" href="classtvm_1_1tirx_1_1Axis.html#af48c986fa0e4ad84780c794b62fdc11a">tvm::tirx::Axis</a>
, <a class="el" href="classtvm_1_1tirx_1_1AxisAttrMap.html#a9c05b17b809103942e6d7089d28f6db9">tvm::tirx::AxisAttrMap< ValueType ></a>
, <a class="el" href="classtvm_1_1tirx_1_1AxisRegEntry.html#a9c05b17b809103942e6d7089d28f6db9">tvm::tirx::AxisRegEntry</a>
</li>
diff --git a/docs/reference/api/doxygen/functions_c.html b/docs/reference/api/doxygen/functions_c.html
index 4f78425cff0..3a4f0a10b7a 100644
--- a/docs/reference/api/doxygen/functions_c.html
+++ b/docs/reference/api/doxygen/functions_c.html
@@ -203,7 +203,8 @@ $(function() {
: <a class="el" href="structtvm_1_1relax_1_1MultiboxTransformLocAttrs.html#a72744968ebdca190d8c51ab5ac5d6e2c">tvm::relax::MultiboxTransformLocAttrs</a>
</li>
<li>Clone()
-: <a class="el" href="classtvm_1_1s__tir_1_1meta__schedule_1_1MutatorNode.html#aae3e2b8f500bcb9a417dfdd2cf2f9d88">tvm::s_tir::meta_schedule::MutatorNode</a>
+: <a class="el" href="classtvm_1_1arith_1_1AnalyzerObj.html#a55ea30504104fa309e0b5e0785086d0b">tvm::arith::AnalyzerObj</a>
+, <a class="el" href="classtvm_1_1s__tir_1_1meta__schedule_1_1MutatorNode.html#aae3e2b8f500bcb9a417dfdd2cf2f9d88">tvm::s_tir::meta_schedule::MutatorNode</a>
, <a class="el" href="classtvm_1_1s__tir_1_1meta__schedule_1_1PostprocNode.html#af5cf3bb5b41bb579661916a6f3ec1200">tvm::s_tir::meta_schedule::PostprocNode</a>
, <a class="el" href="classtvm_1_1s__tir_1_1meta__schedule_1_1PyMutatorNode.html#a20b39ff62a8f6963f0a07c9f1530d6e6">tvm::s_tir::meta_schedule::PyMutatorNode</a>
, <a class="el" href="classtvm_1_1s__tir_1_1meta__schedule_1_1PyPostprocNode.html#a515d23c155235a69cc331bcd6e288fc6">tvm::s_tir::meta_schedule::PyPostprocNode</a>
diff --git a/docs/reference/api/doxygen/functions_d.html b/docs/reference/api/doxygen/functions_d.html
index 43ca4893241..82146441970 100644
--- a/docs/reference/api/doxygen/functions_d.html
+++ b/docs/reference/api/doxygen/functions_d.html
@@ -111,7 +111,7 @@ $(function() {
: <a class="el" href="classtvm_1_1relax_1_1DataflowVarPattern.html#a03bea82d4b0b0cafc9e5a5c1ca9d598c">tvm::relax::DataflowVarPattern</a>
</li>
<li>DataType()
-: <a class="el" href="classtvm_1_1runtime_1_1DataType.html#a7c8b2990aabb75e5668e59f43f59e3b7">tvm::runtime::DataType</a>
+: <a class="el" href="classtvm_1_1runtime_1_1DataType.html#a6db427a47b8d503b10cd2aee6a39d3a9">tvm::runtime::DataType</a>
, <a class="el" href="classtvm_1_1runtime_1_1Tensor.html#ab406c6d1fcb05e2b5103131a92e44e71">tvm::runtime::Tensor</a>
, <a class="el" href="classtvm_1_1script_1_1printer_1_1LiteralDoc.html#a5d34a540368c29e2a8fd47f94a82ad35">tvm::script::printer::LiteralDoc</a>
</li>
diff --git a/docs/reference/api/doxygen/functions_func_c.html b/docs/reference/api/doxygen/functions_func_c.html
index 2803438d5c2..427f4f88f72 100644
--- a/docs/reference/api/doxygen/functions_func_c.html
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@@ -152,7 +152,8 @@ $(function() {
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</li>
<li>Clone()
-: <a class="el" href="classtvm_1_1s__tir_1_1meta__schedule_1_1MutatorNode.html#aae3e2b8f500bcb9a417dfdd2cf2f9d88">tvm::s_tir::meta_schedule::MutatorNode</a>
+: <a class="el" href="classtvm_1_1arith_1_1AnalyzerObj.html#a55ea30504104fa309e0b5e0785086d0b">tvm::arith::AnalyzerObj</a>
+, <a class="el" href="classtvm_1_1s__tir_1_1meta__schedule_1_1MutatorNode.html#aae3e2b8f500bcb9a417dfdd2cf2f9d88">tvm::s_tir::meta_schedule::MutatorNode</a>
, <a class="el" href="classtvm_1_1s__tir_1_1meta__schedule_1_1PostprocNode.html#af5cf3bb5b41bb579661916a6f3ec1200">tvm::s_tir::meta_schedule::PostprocNode</a>
, <a class="el" href="classtvm_1_1s__tir_1_1meta__schedule_1_1PyMutatorNode.html#a20b39ff62a8f6963f0a07c9f1530d6e6">tvm::s_tir::meta_schedule::PyMutatorNode</a>
, <a class="el" href="classtvm_1_1s__tir_1_1meta__schedule_1_1PyPostprocNode.html#a515d23c155235a69cc331bcd6e288fc6">tvm::s_tir::meta_schedule::PyPostprocNode</a>
diff --git a/docs/reference/api/doxygen/functions_func_g.html b/docs/reference/api/doxygen/functions_func_g.html
index f8cddc8b4e2..b6183cc133f 100644
--- a/docs/reference/api/doxygen/functions_func_g.html
+++ b/docs/reference/api/doxygen/functions_func_g.html
@@ -94,7 +94,7 @@ $(function() {
, <a class="el" href="structtvm_1_1runtime_1_1ThreadLocalDiscoWorker.html#a999c9c506f5019a424ed360ea2aa2ec1">tvm::runtime::ThreadLocalDiscoWorker</a>
, <a class="el" href="classtvm_1_1s__tir_1_1InstructionKind.html#a7c41f80d94a4cbbfc6823048f5f71abb">tvm::s_tir::InstructionKind</a>
, <a class="el" href="classtvm_1_1s__tir_1_1meta__schedule_1_1ProfilerNode.html#a990281a0a3d2b83fbbd4919189e22fda">tvm::s_tir::meta_schedule::ProfilerNode</a>
-, <a class="el" href="classtvm_1_1s__tir_1_1ScheduleNode.html#a9bfa004e3d543d3ca16ed818624ac789">tvm::s_tir::ScheduleNode</a>
+, <a class="el" href="classtvm_1_1s__tir_1_1ScheduleNode.html#abf82babe866a0014516d861c843b7b20">tvm::s_tir::ScheduleNode</a>
, <a class="el" href="classtvm_1_1script_1_1ir__builder_1_1IRBuilderNode.html#a84baa43243371ac9aa0c67aa1b5dd570">tvm::script::ir_builder::IRBuilderNode</a>
, <a class="el" href="classtvm_1_1SourceName.html#a44973bf3009230d58348f4877ec45cba">tvm::SourceName</a>
, <a class="el" href="classtvm_1_1TargetKind.html#afd5a207d10c4123cf0a6b267de05fad2">tvm::TargetKind</a>
@@ -110,12 +110,12 @@ $(function() {
: <a class="el" href="classtvm_1_1tirx_1_1SizeVar.html#a190d36c4ccf8f063dfca62bbc1b88d76">tvm::tirx::SizeVar</a>
</li>
<li>Get()
-: <a class="el" href="classtvm_1_1tirx_1_1SLayoutAxis.html#acb860a83436f0808377b84bd67a1b8ce">tvm::tirx::SLayoutAxis</a>
+: <a class="el" href="classtvm_1_1tirx_1_1SLayoutAxis.html#a184e4f24a25306a44d2f1221e3376bd4">tvm::tirx::SLayoutAxis</a>
, <a class="el" href="classtvm_1_1tirx_1_1TensorIntrin.html#a71bec3759a0b60fa217ff087db0cfb0c">tvm::tirx::TensorIntrin</a>
</li>
<li>get()
: <a class="el" href="classtvm_1_1tirx_1_1Var.html#afa14cb42999f93337038bb25ad247a38">tvm::tirx::Var</a>
-, <a class="el" href="classtvm_1_1With.html#a54ab86b1e1f78e9304d8b89c97eb07af">tvm::With< ContextType ></a>
+, <a class="el" href="classtvm_1_1With.html#acc09c3d117aa5500f8579e18866209f8">tvm::With< ContextType ></a>
</li>
<li>get_lanes_or_vscale_factor()
: <a class="el" href="classtvm_1_1runtime_1_1DataType.html#a0e7a2a8061a945809d84aef0bbda975a">tvm::runtime::DataType</a>
diff --git a/docs/reference/api/doxygen/functions_g.html b/docs/reference/api/doxygen/functions_g.html
index f0a4418bf78..6a0bdfbcbac 100644
--- a/docs/reference/api/doxygen/functions_g.html
+++ b/docs/reference/api/doxygen/functions_g.html
@@ -94,7 +94,7 @@ $(function() {
, <a class="el" href="structtvm_1_1runtime_1_1ThreadLocalDiscoWorker.html#a999c9c506f5019a424ed360ea2aa2ec1">tvm::runtime::ThreadLocalDiscoWorker</a>
, <a class="el" href="classtvm_1_1s__tir_1_1InstructionKind.html#a7c41f80d94a4cbbfc6823048f5f71abb">tvm::s_tir::InstructionKind</a>
, <a class="el" href="classtvm_1_1s__tir_1_1meta__schedule_1_1ProfilerNode.html#a990281a0a3d2b83fbbd4919189e22fda">tvm::s_tir::meta_schedule::ProfilerNode</a>
-, <a class="el" href="classtvm_1_1s__tir_1_1ScheduleNode.html#a9bfa004e3d543d3ca16ed818624ac789">tvm::s_tir::ScheduleNode</a>
+, <a class="el" href="classtvm_1_1s__tir_1_1ScheduleNode.html#a20a8aa623ce7126d503778e5c6d95ea0">tvm::s_tir::ScheduleNode</a>
, <a class="el" href="classtvm_1_1script_1_1ir__builder_1_1IRBuilderNode.html#a84baa43243371ac9aa0c67aa1b5dd570">tvm::script::ir_builder::IRBuilderNode</a>
, <a class="el" href="classtvm_1_1SourceName.html#a44973bf3009230d58348f4877ec45cba">tvm::SourceName</a>
, <a class="el" href="classtvm_1_1TargetKind.html#afd5a207d10c4123cf0a6b267de05fad2">tvm::TargetKind</a>
@@ -110,7 +110,7 @@ $(function() {
: <a class="el" href="classtvm_1_1tirx_1_1SizeVar.html#a190d36c4ccf8f063dfca62bbc1b88d76">tvm::tirx::SizeVar</a>
</li>
<li>Get()
-: <a class="el" href="classtvm_1_1tirx_1_1SLayoutAxis.html#acb860a83436f0808377b84bd67a1b8ce">tvm::tirx::SLayoutAxis</a>
+: <a class="el" href="classtvm_1_1tirx_1_1SLayoutAxis.html#afc00df2a3fb3363f3958ee3d3f9aef5d">tvm::tirx::SLayoutAxis</a>
, <a class="el" href="classtvm_1_1tirx_1_1TensorIntrin.html#a71bec3759a0b60fa217ff087db0cfb0c">tvm::tirx::TensorIntrin</a>
</li>
<li>get()
diff --git a/docs/reference/api/doxygen/functions_k.html b/docs/reference/api/doxygen/functions_k.html
index 95d0a1a38d7..72442ef8c89 100644
--- a/docs/reference/api/doxygen/functions_k.html
+++ b/docs/reference/api/doxygen/functions_k.html
@@ -159,13 +159,13 @@ $(function() {
, <a class="el" href="classtvm_1_1runtime_1_1vm_1_1VMExecutable.html#a3d765476b2f4291e3dcfcedd17189134">tvm::runtime::vm::VMExecutable</a>
, <a class="el" href="structtvm_1_1runtime_1_1vm_1_1VMFuncInfo.html#a28c496ea36dfce28b237b254c7dfbcdc">tvm::runtime::vm::VMFuncInfo</a>
, <a class="el" href="classtvm_1_1s__tir_1_1InstructionNode.html#a2675268a611112ca4cce2e05b40413a6">tvm::s_tir::InstructionNode</a>
+, <a class="el" href="classtvm_1_1script_1_1printer_1_1OperationDocNode.html#a9b996402fa4a7202d749c56ac044e810">tvm::script::printer::OperationDocNode</a>
</li>
<li>Kind
: <a class="el" href="classtvm_1_1script_1_1printer_1_1OperationDocNode.html#ab096bbea749ee994d75230cd8136afc2">tvm::script::printer::OperationDocNode</a>
</li>
<li>kind
-: <a class="el" href="classtvm_1_1script_1_1printer_1_1OperationDocNode.html#a9b996402fa4a7202d749c56ac044e810">tvm::script::printer::OperationDocNode</a>
-, <a class="el" href="classtvm_1_1TargetNode.html#ac19a4ee0f0ec7ea607ec746bc4551b71">tvm::TargetNode</a>
+: <a class="el" href="classtvm_1_1TargetNode.html#ac19a4ee0f0ec7ea607ec746bc4551b71">tvm::TargetNode</a>
, <a class="el" href="classtvm_1_1tirx_1_1DependencyNode.html#aad99641267be2c02bb453e4edbbdc606">tvm::tirx::DependencyNode</a>
, <a class="el" href="classtvm_1_1tirx_1_1ExecScopeNode.html#a4e8b0e6dc6c5c2ba2b1ea8bc060c8b25">tvm::tirx::ExecScopeNode</a>
, <a class="el" href="classtvm_1_1tirx_1_1ForNode.html#aafc5aa4abb12156c88d9e0e9fdc7b71e">tvm::tirx::ForNode</a>
diff --git a/docs/reference/api/doxygen/functions_s.html b/docs/reference/api/doxygen/functions_s.html
index 418a0ff7cf3..d793aca44ac 100644
--- a/docs/reference/api/doxygen/functions_s.html
+++ b/docs/reference/api/doxygen/functions_s.html
@@ -119,7 +119,7 @@ $(function() {
: <a class="el" href="classtvm_1_1s__tir_1_1SBlockRV.html#a501204e90d9c9d00183b3759f9736575">tvm::s_tir::SBlockRV</a>
</li>
<li>SBlockScope()
-: <a class="el" href="classtvm_1_1tirx_1_1SBlockScope.html#a50b2bfc76efaf5755bfcd4411e815bf4">tvm::tirx::SBlockScope</a>
+: <a class="el" href="classtvm_1_1tirx_1_1SBlockScope.html#a55225636094093610e648652acf2bf6d">tvm::tirx::SBlockScope</a>
</li>
<li>scale
: <a class="el" href="classtvm_1_1arith_1_1IterSplitExprNode.html#a93c760bb7a5af0c7c442dc879d7bc55a">tvm::arith::IterSplitExprNode</a>
@@ -215,7 +215,7 @@ $(function() {
: <a class="el" href="classtvm_1_1tirx_1_1StmtSRefNode.html#ac265725a1fe1dced81b8a7b28123e6dc">tvm::tirx::StmtSRefNode</a>
</li>
<li>SeqExpr()
-: <a class="el" href="classtvm_1_1relax_1_1SeqExpr.html#a94be04d43a731061bb6b7d1d040c7f6d">tvm::relax::SeqExpr</a>
+: <a class="el" href="classtvm_1_1relax_1_1SeqExpr.html#a1932bc9e3f41d1e614ee0e13380bb4bc">tvm::relax::SeqExpr</a>
</li>
<li>SeqExprFrame()
: <a class="el" href="classtvm_1_1script_1_1ir__builder_1_1relax_1_1SeqExprFrame.html#a0104b0133047ae523afeddf00afeda50">tvm::script::ir_builder::relax::SeqExprFrame</a>
@@ -230,10 +230,10 @@ $(function() {
, <a class="el" href="classtvm_1_1relax_1_1VarNode.html#acbd2d697e21b71078b023d7b34b145cf">tvm::relax::VarNode</a>
</li>
<li>Sequential()
-: <a class="el" href="classtvm_1_1transform_1_1Sequential.html#afa9559a293db6a374aba9a6251faf085">tvm::transform::Sequential</a>
+: <a class="el" href="classtvm_1_1transform_1_1Sequential.html#a69d4e7f2edc34e4c7fd3ee505cb2d191">tvm::transform::Sequential</a>
</li>
<li>SequentialSpan()
-: <a class="el" href="classtvm_1_1SequentialSpan.html#a513e3602ee070f406daa79b216af0c1b">tvm::SequentialSpan</a>
+: <a class="el" href="classtvm_1_1SequentialSpan.html#aa5adb345b82e0be21d8fbc2a13573846">tvm::SequentialSpan</a>
</li>
<li>session
: <a class="el" href="classtvm_1_1runtime_1_1DRefObj.html#a06d46f22a0da4d974bd209d8af9045bd">tvm::runtime::DRefObj</a>
@@ -389,7 +389,7 @@ $(function() {
: <a class="el" href="classtvm_1_1relax_1_1ShapePattern.html#a5d8b7240977200d306617a7d8afcd882">tvm::relax::ShapePattern</a>
</li>
<li>ShapeStructInfo()
-: <a class="el" href="classtvm_1_1relax_1_1ShapeStructInfo.html#afb48717685736cf1384392a0374aa3b2">tvm::relax::ShapeStructInfo</a>
+: <a class="el" href="classtvm_1_1relax_1_1ShapeStructInfo.html#acefe77c070d032725c260210797121f7">tvm::relax::ShapeStructInfo</a>
</li>
<li>ShapeType()
: <a class="el" href="classtvm_1_1relax_1_1ShapeType.html#a12718f38e937eabfc462f2b4a42af7ed">tvm::relax::ShapeType</a>
@@ -466,7 +466,7 @@ $(function() {
: <a class="el" href="classtvm_1_1tirx_1_1SizeVar.html#a18a105ca58b10c3bccda62125634abbc">tvm::tirx::SizeVar</a>
</li>
<li>SLayout()
-: <a class="el" href="classtvm_1_1tirx_1_1SLayout.html#a33961a815fbf438e38c526c3b0de2dc3">tvm::tirx::SLayout</a>
+: <a class="el" href="classtvm_1_1tirx_1_1SLayout.html#a7644f83479b37d999300b65e475dcc85">tvm::tirx::SLayout</a>
</li>
<li>Slice()
: <a class="el" href="classtvm_1_1te_1_1Tensor_1_1Slice.html#ab314819e8bcca6421e9a4f33e48578c3">tvm::te::Tensor::Slice</a>
diff --git a/docs/reference/api/doxygen/functions_t.html b/docs/reference/api/doxygen/functions_t.html
index c97d95cc6fb..4fc5f8398be 100644
--- a/docs/reference/api/doxygen/functions_t.html
+++ b/docs/reference/api/doxygen/functions_t.html
@@ -78,7 +78,7 @@ $(function() {
, <a class="el" href="classtvm_1_1s__tir_1_1meta__schedule_1_1TuningRecordNode.html#adea94babb5c769eb47619dc067119ca9">tvm::s_tir::meta_schedule::TuningRecordNode</a>
</li>
<li>Target()
-: <a class="el" href="classtvm_1_1Target.html#a58a5a1e042e265fe5a6973045226fe1a">tvm::Target</a>
+: <a class="el" href="classtvm_1_1Target.html#a1abb29217d8e394f0b88e28178c70bb0">tvm::Target</a>
</li>
<li>target
: <a class="el" href="classtvm_1_1tirx_1_1DispatchContextNode.html#a62f1f41ca0f1f62aeea52c32a43bc41a">tvm::tirx::DispatchContextNode</a>
@@ -138,7 +138,7 @@ $(function() {
: <a class="el" href="structtvm_1_1relax_1_1distributed_1_1Axis.html#af05e0215bba34ea04ed67a5bedf9f8d0">tvm::relax::distributed::Axis</a>
</li>
<li>Tensor()
-: <a class="el" href="classtvm_1_1runtime_1_1Tensor.html#ab491d1924c4088559f76244f25a8e916">tvm::runtime::Tensor</a>
+: <a class="el" href="classtvm_1_1runtime_1_1Tensor.html#aa9f29f654b805f7c4b3264ee5f676ab9">tvm::runtime::Tensor</a>
, <a class="el" href="classtvm_1_1te_1_1Tensor.html#a27b4276ab6a34d81f93ec4a1098873bc">tvm::te::Tensor</a>
</li>
<li>tensor_sinfo
diff --git a/docs/reference/api/doxygen/functions_u.html b/docs/reference/api/doxygen/functions_u.html
index c63d9394d7d..b8cb2f8d515 100644
--- a/docs/reference/api/doxygen/functions_u.html
+++ b/docs/reference/api/doxygen/functions_u.html
@@ -83,7 +83,7 @@ $(function() {
: <a class="el" href="classtvm_1_1UniqueNameSupply.html#a3fcf83b3620cbb767886e2749123c8df">tvm::UniqueNameSupply</a>
</li>
<li>UniqueNameSupplyNode()
-: <a class="el" href="classtvm_1_1UniqueNameSupplyNode.html#ae0b2820c583e1a4f6572f1cfb49f1b4b">tvm::UniqueNameSupplyNode</a>
+: <a class="el" href="classtvm_1_1UniqueNameSupplyNode.html#a619c93a5c28ab0cd1f0f27734ef4769b">tvm::UniqueNameSupplyNode</a>
</li>
<li>UnorderedTuplePattern()
: <a class="el" href="classtvm_1_1relax_1_1UnorderedTuplePattern.html#a3b2994f5227401cf555ee09e001605d0">tvm::relax::UnorderedTuplePattern</a>
diff --git a/docs/reference/api/doxygen/index__map_8h_source.html b/docs/reference/api/doxygen/index__map_8h_source.html
index 986ad655c0d..72fb4babf43 100644
--- a/docs/reference/api/doxygen/index__map_8h_source.html
+++ b/docs/reference/api/doxygen/index__map_8h_source.html
@@ -182,7 +182,7 @@ $(function() {
<div class="line"><a name="l00285"></a><span class="lineno"> 285</span> } <span class="comment">// namespace tvm</span></div>
<div class="line"><a name="l00286"></a><span class="lineno"> 286</span>  </div>
<div class="line"><a name="l00287"></a><span class="lineno"> 287</span> <span class="preprocessor">#endif </span><span class="comment">// TVM_TIR_INDEX_MAP_H_</span></div>
-<div class="ttc" id="aclasstvm_1_1arith_1_1Analyzer_html"><div class="ttname"><a href="classtvm_1_1arith_1_1Analyzer.html">tvm::arith::Analyzer</a></div><div class="ttdoc">Managed reference to AnalyzerObj.</div><div class="ttdef"><b>Definition:</b> analyzer.h:883</div></div>
+<div class="ttc" id="aclasstvm_1_1arith_1_1Analyzer_html"><div class="ttname"><a href="classtvm_1_1arith_1_1Analyzer.html">tvm::arith::Analyzer</a></div><div class="ttdoc">Managed reference to AnalyzerObj.</div><div class="ttdef"><b>Definition:</b> analyzer.h:909</div></div>
<div class="ttc" id="aclasstvm_1_1runtime_1_1Tensor_html"><div class="ttname"><a href="classtvm_1_1runtime_1_1Tensor.html">tvm::runtime::Tensor</a></div><div class="ttdoc">Managed Tensor. The array is backed by reference counted blocks.</div><div class="ttdef"><b>Definition:</b> tensor.h:49</div></div>
<div class="ttc" id="aclasstvm_1_1tirx_1_1IndexMapNode_html"><div class="ttname"><a href="classtvm_1_1tirx_1_1IndexMapNode.html">tvm::tirx::IndexMapNode</a></div><div class="ttdoc">Defines a mapping between two representations of indices into a buffer.</div><div class="ttdef"><b>Definition:</b> index_map.h:52</div></div>
<div class="ttc" id="aclasstvm_1_1tirx_1_1IndexMapNode_html_a1ed7757b999c66b71889901ddbb40d34"><div class="ttname"><a href="classtvm_1_1tirx_1_1IndexMapNode.html#a1ed7757b999c66b71889901ddbb40d34">tvm::tirx::IndexMapNode::_type_s_eq_hash_kind</a></div><div class="ttdeci">static constexpr TVMFFISEqHashKind _type_s_eq_hash_kind</div><div class="ttdef"><b>Definition:</b> index_map.h:188</div></div>
diff --git a/docs/reference/api/doxygen/int__set_8h_source.html b/docs/reference/api/doxygen/int__set_8h_source.html
index 40de2e191c2..f97aa4c4092 100644
--- a/docs/reference/api/doxygen/int__set_8h_source.html
+++ b/docs/reference/api/doxygen/int__set_8h_source.html
@@ -197,7 +197,7 @@ $(function() {
<div class="line"><a name="l00339"></a><span class="lineno"> 339</span> <span class="preprocessor">#endif </span><span class="comment">// TVM_ARITH_INT_SET_H_</span></div>
<div class="ttc" id="aclasstvm_1_1PrimExpr_html"><div class="ttname"><a href="classtvm_1_1PrimExpr.html">tvm::PrimExpr</a></div><div class="ttdoc">Reference to PrimExprNode.</div><div class="ttdef"><b>Definition:</b> expr.h:123</div></div>
<div class="ttc" id="aclasstvm_1_1Range_html"><div class="ttname"><a href="classtvm_1_1Range.html">tvm::Range</a></div><div class="ttdoc">Range container</div><div class="ttdef"><b>Definition:</b> expr.h:612</div></div>
-<div class="ttc" id="aclasstvm_1_1arith_1_1Analyzer_html"><div class="ttname"><a href="classtvm_1_1arith_1_1Analyzer.html">tvm::arith::Analyzer</a></div><div class="ttdoc">Managed reference to AnalyzerObj.</div><div class="ttdef"><b>Definition:</b> analyzer.h:883</div></div>
+<div class="ttc" id="aclasstvm_1_1arith_1_1Analyzer_html"><div class="ttname"><a href="classtvm_1_1arith_1_1Analyzer.html">tvm::arith::Analyzer</a></div><div class="ttdoc">Managed reference to AnalyzerObj.</div><div class="ttdef"><b>Definition:</b> analyzer.h:909</div></div>
<div class="ttc" id="aclasstvm_1_1arith_1_1IntSetNode_html"><div class="ttname"><a href="classtvm_1_1arith_1_1IntSetNode.html">tvm::arith::IntSetNode</a></div><div class="ttdoc">Base class of all Integer set containers. represent a set of integers in one dimension.</div><div class="ttdef"><b>Definition:</b> int_set.h:58</div></div>
<div class="ttc" id="aclasstvm_1_1arith_1_1IntSetNode_html_a51d92f0c7ad562c0041888cc8ef62c3a"><div class="ttname"><a href="classtvm_1_1arith_1_1IntSetNode.html#a51d92f0c7ad562c0041888cc8ef62c3a">tvm::arith::IntSetNode::TVM_FFI_DECLARE_OBJECT_INFO</a></div><div class="ttdeci">TVM_FFI_DECLARE_OBJECT_INFO("ir.IntSet", IntSetNode, ffi::Object)</div></div>
<div class="ttc" id="aclasstvm_1_1arith_1_1IntSet_html"><div class="ttname"><a href="classtvm_1_1arith_1_1IntSet.html">tvm::arith::IntSet</a></div><div class="ttdoc">Managed reference to IntSetNode.</div><div class="ttdef"><b>Definition:</b> int_set.h:67</div></div>
diff --git a/docs/reference/api/doxygen/iter__affine__map_8h_source.html b/docs/reference/api/doxygen/iter__affine__map_8h_source.html
index 9992b655d65..63143c886ea 100644
--- a/docs/reference/api/doxygen/iter__affine__map_8h_source.html
+++ b/docs/reference/api/doxygen/iter__affine__map_8h_source.html
@@ -260,7 +260,7 @@ $(function() {
<div class="ttc" id="aanalyzer_8h_html"><div class="ttname"><a href="analyzer_8h.html">analyzer.h</a></div><div class="ttdoc">Algebra expression simplifications.</div></div>
<div class="ttc" id="aclasstvm_1_1PrimExprNode_html"><div class="ttname"><a href="classtvm_1_1PrimExprNode.html">tvm::PrimExprNode</a></div><div class="ttdoc">Base node of all primitive expressions.</div><div class="ttdef"><b>Definition:</b> expr.h:92</div></div>
<div class="ttc" id="aclasstvm_1_1PrimExpr_html"><div class="ttname"><a href="classtvm_1_1PrimExpr.html">tvm::PrimExpr</a></div><div class="ttdoc">Reference to PrimExprNode.</div><div class="ttdef"><b>Definition:</b> expr.h:123</div></div>
-<div class="ttc" id="aclasstvm_1_1arith_1_1Analyzer_html"><div class="ttname"><a href="classtvm_1_1arith_1_1Analyzer.html">tvm::arith::Analyzer</a></div><div class="ttdoc">Managed reference to AnalyzerObj.</div><div class="ttdef"><b>Definition:</b> analyzer.h:883</div></div>
+<div class="ttc" id="aclasstvm_1_1arith_1_1Analyzer_html"><div class="ttname"><a href="classtvm_1_1arith_1_1Analyzer.html">tvm::arith::Analyzer</a></div><div class="ttdoc">Managed reference to AnalyzerObj.</div><div class="ttdef"><b>Definition:</b> analyzer.h:909</div></div>
<div class="ttc" id="aclasstvm_1_1arith_1_1IterMapExprNode_html"><div class="ttname"><a href="classtvm_1_1arith_1_1IterMapExprNode.html">tvm::arith::IterMapExprNode</a></div><div class="ttdoc">Base class of all iter map expressions.</div><div class="ttdef"><b>Definition:</b> iter_affine_map.h:67</div></div>
<div class="ttc" id="aclasstvm_1_1arith_1_1IterMapExprNode_html_ab8a4d68ae04e4269485c18f97cd3db21"><div class="ttname"><a href="classtvm_1_1arith_1_1IterMapExprNode.html#ab8a4d68ae04e4269485c18f97cd3db21">tvm::arith::IterMapExprNode::_type_child_slots</a></div><div class="ttdeci">static constexpr const uint32_t _type_child_slots</div><div class="ttdef"><b>Definition:</b> iter_affine_map.h:69</div></div>
<div class="ttc" id="aclasstvm_1_1arith_1_1IterMapExprNode_html_ada01f2d9516490f495b1bd35eb8671f9"><div class="ttname"><a href="classtvm_1_1arith_1_1IterMapExprNode.html#ada01f2d9516490f495b1bd35eb8671f9">tvm::arith::IterMapExprNode::TVM_FFI_DECLARE_OBJECT_INFO</a></div><div class="ttdeci">TVM_FFI_DECLARE_OBJECT_INFO("arith.IterMapExpr", IterMapExprNode, PrimExprNode)</div></div>
diff --git a/docs/reference/api/doxygen/pooling_8h_source.html b/docs/reference/api/doxygen/pooling_8h_source.html
index afff33314d2..7b17fe23be6 100644
--- a/docs/reference/api/doxygen/pooling_8h_source.html
+++ b/docs/reference/api/doxygen/pooling_8h_source.html
@@ -654,7 +654,7 @@ $(function() {
<div class="ttc" id="aclasstvm_1_1PrimExpr_html"><div class="ttname"><a href="classtvm_1_1PrimExpr.html">tvm::PrimExpr</a></div><div class="ttdoc">Reference to PrimExprNode.</div><div class="ttdef"><b>Definition:</b> expr.h:123</div></div>
<div class="ttc" id="aclasstvm_1_1PrimExpr_html_a18a8a0fef87b74b18fc254f49465cc56"><div class="ttname"><a href="classtvm_1_1PrimExpr.html#a18a8a0fef87b74b18fc254f49465cc56">tvm::PrimExpr::dtype</a></div><div class="ttdeci">DataType dtype() const</div><div class="ttdef"><b>Definition:</b> expr.h:137</div></div>
<div class="ttc" id="aclasstvm_1_1Range_html"><div class="ttname"><a href="classtvm_1_1Range.html">tvm::Range</a></div><div class="ttdoc">Range container</div><div class="ttdef"><b>Definition:</b> expr.h:612</div></div>
-<div class="ttc" id="aclasstvm_1_1arith_1_1Analyzer_html"><div class="ttname"><a href="classtvm_1_1arith_1_1Analyzer.html">tvm::arith::Analyzer</a></div><div class="ttdoc">Managed reference to AnalyzerObj.</div><div class="ttdef"><b>Definition:</b> analyzer.h:883</div></div>
+<div class="ttc" id="aclasstvm_1_1arith_1_1Analyzer_html"><div class="ttname"><a href="classtvm_1_1arith_1_1Analyzer.html">tvm::arith::Analyzer</a></div><div class="ttdoc">Managed reference to AnalyzerObj.</div><div class="ttdef"><b>Definition:</b> analyzer.h:909</div></div>
<div class="ttc" id="aclasstvm_1_1runtime_1_1DataType_html_ab45f13dd70d982d9f977c79b6f7fac98"><div class="ttname"><a href="classtvm_1_1runtime_1_1DataType.html#ab45f13dd70d982d9f977c79b6f7fac98">tvm::runtime::DataType::Int</a></div><div class="ttdeci">static DataType Int(int bits, int lanes=1)</div><div class="ttdoc">Construct an int type.</div><div class="ttdef"><b>Definition:</b> data_type.h:276</div></div>
<div class="ttc" id="aclasstvm_1_1te_1_1Tensor_html"><div class="ttname"><a href="classtvm_1_1te_1_1Tensor.html">tvm::te::Tensor</a></div><div class="ttdoc">Tensor structure representing a possible input, or intermediate computation result.</div><div class="ttdef"><b>Definition:</b> tensor.h:100</div></div>
<div class="ttc" id="aclasstvm_1_1tirx_1_1And_html"><div class="ttname"><a href="classtvm_1_1tirx_1_1And.html">tvm::tirx::And</a></div><div class="ttdoc">Managed reference to AndNode.</div><div class="ttdef"><b>Definition:</b> expr.h:429</div></div>
diff --git a/docs/reference/api/doxygen/relax_2analysis_8h_source.html b/docs/reference/api/doxygen/relax_2analysis_8h_source.html
index 110cf637d68..dbcc75ce004 100644
--- a/docs/reference/api/doxygen/relax_2analysis_8h_source.html
+++ b/docs/reference/api/doxygen/relax_2analysis_8h_source.html
@@ -263,7 +263,7 @@ $(function() {
<div class="ttc" id="aclasstvm_1_1PrimExpr_html"><div class="ttname"><a href="classtvm_1_1PrimExpr.html">tvm::PrimExpr</a></div><div class="ttdoc">Reference to PrimExprNode.</div><div class="ttdef"><b>Definition:</b> expr.h:123</div></div>
<div class="ttc" id="aclasstvm_1_1RelaxExpr_html"><div class="ttname"><a href="classtvm_1_1RelaxExpr.html">tvm::RelaxExpr</a></div><div class="ttdoc">Managed reference to RelaxExprNode.</div><div class="ttdef"><b>Definition:</b> expr.h:438</div></div>
<div class="ttc" id="aclasstvm_1_1Type_html"><div class="ttname"><a href="classtvm_1_1Type.html">tvm::Type</a></div><div class="ttdoc">Managed reference to TypeNode.</div><div class="ttdef"><b>Definition:</b> type.h:99</div></div>
-<div class="ttc" id="aclasstvm_1_1arith_1_1Analyzer_html"><div class="ttname"><a href="classtvm_1_1arith_1_1Analyzer.html">tvm::arith::Analyzer</a></div><div class="ttdoc">Managed reference to AnalyzerObj.</div><div class="ttdef"><b>Definition:</b> analyzer.h:883</div></div>
+<div class="ttc" id="aclasstvm_1_1arith_1_1Analyzer_html"><div class="ttname"><a href="classtvm_1_1arith_1_1Analyzer.html">tvm::arith::Analyzer</a></div><div class="ttdoc">Managed reference to AnalyzerObj.</div><div class="ttdef"><b>Definition:</b> analyzer.h:909</div></div>
<div class="ttc" id="aclasstvm_1_1relax_1_1BlockBuilder_html"><div class="ttname"><a href="classtvm_1_1relax_1_1BlockBuilder.html">tvm::relax::BlockBuilder</a></div><div class="ttdef"><b>Definition:</b> block_builder.h:257</div></div>
<div class="ttc" id="aclasstvm_1_1relax_1_1Call_html"><div class="ttname"><a href="classtvm_1_1relax_1_1Call.html">tvm::relax::Call</a></div><div class="ttdef"><b>Definition:</b> expr.h:180</div></div>
<div class="ttc" id="aclasstvm_1_1relax_1_1DataflowBlock_html"><div class="ttname"><a href="classtvm_1_1relax_1_1DataflowBlock.html">tvm::relax::DataflowBlock</a></div><div class="ttdef"><b>Definition:</b> expr.h:701</div></div>
diff --git a/docs/reference/api/doxygen/relax_2utils_8h_source.html b/docs/reference/api/doxygen/relax_2utils_8h_source.html
index 6c14ff59584..56739c78e6f 100644
--- a/docs/reference/api/doxygen/relax_2utils_8h_source.html
+++ b/docs/reference/api/doxygen/relax_2utils_8h_source.html
@@ -127,7 +127,7 @@ $(function() {
<div class="line"><a name="l00150"></a><span class="lineno"> 150</span> <span class="preprocessor">#endif </span><span class="comment">// TVM_RELAX_UTILS_H_</span></div>
<div class="ttc" id="aanalyzer_8h_html"><div class="ttname"><a href="analyzer_8h.html">analyzer.h</a></div><div class="ttdoc">Algebra expression simplifications.</div></div>
<div class="ttc" id="aclasstvm_1_1RelaxExpr_html"><div class="ttname"><a href="classtvm_1_1RelaxExpr.html">tvm::RelaxExpr</a></div><div class="ttdoc">Managed reference to RelaxExprNode.</div><div class="ttdef"><b>Definition:</b> expr.h:438</div></div>
-<div class="ttc" id="aclasstvm_1_1arith_1_1Analyzer_html"><div class="ttname"><a href="classtvm_1_1arith_1_1Analyzer.html">tvm::arith::Analyzer</a></div><div class="ttdoc">Managed reference to AnalyzerObj.</div><div class="ttdef"><b>Definition:</b> analyzer.h:883</div></div>
+<div class="ttc" id="aclasstvm_1_1arith_1_1Analyzer_html"><div class="ttname"><a href="classtvm_1_1arith_1_1Analyzer.html">tvm::arith::Analyzer</a></div><div class="ttdoc">Managed reference to AnalyzerObj.</div><div class="ttdef"><b>Definition:</b> analyzer.h:909</div></div>
<div class="ttc" id="aclasstvm_1_1relax_1_1Binding_html"><div class="ttname"><a href="classtvm_1_1relax_1_1Binding.html">tvm::relax::Binding</a></div><div class="ttdef"><b>Definition:</b> expr.h:586</div></div>
<div class="ttc" id="aclasstvm_1_1relax_1_1Call_html"><div class="ttname"><a href="classtvm_1_1relax_1_1Call.html">tvm::relax::Call</a></div><div class="ttdef"><b>Definition:</b> expr.h:180</div></div>
<div class="ttc" id="aclasstvm_1_1relax_1_1Function_html"><div class="ttname"><a href="classtvm_1_1relax_1_1Function.html">tvm::relax::Function</a></div><div class="ttdef"><b>Definition:</b> expr.h:838</div></div>
diff --git a/docs/reference/api/doxygen/s__tir_2analysis_8h_source.html b/docs/reference/api/doxygen/s__tir_2analysis_8h_source.html
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--- a/docs/reference/api/doxygen/s__tir_2analysis_8h_source.html
+++ b/docs/reference/api/doxygen/s__tir_2analysis_8h_source.html
@@ -167,7 +167,7 @@ $(function() {
<div class="line"><a name="l00210"></a><span class="lineno"> 210</span> } <span class="comment">// namespace tvm</span></div>
<div class="line"><a name="l00211"></a><span class="lineno"> 211</span> <span class="preprocessor">#endif </span><span class="comment">// TVM_S_TIR_ANALYSIS_H_</span></div>
<div class="ttc" id="aclasstvm_1_1IRModule_html"><div class="ttname"><a href="classtvm_1_1IRModule.html">tvm::IRModule</a></div><div class="ttdoc">Managed reference class to IRModuleNode.</div><div class="ttdef"><b>Definition:</b> module.h:255</div></div>
-<div class="ttc" id="aclasstvm_1_1arith_1_1Analyzer_html"><div class="ttname"><a href="classtvm_1_1arith_1_1Analyzer.html">tvm::arith::Analyzer</a></div><div class="ttdoc">Managed reference to AnalyzerObj.</div><div class="ttdef"><b>Definition:</b> analyzer.h:883</div></div>
+<div class="ttc" id="aclasstvm_1_1arith_1_1Analyzer_html"><div class="ttname"><a href="classtvm_1_1arith_1_1Analyzer.html">tvm::arith::Analyzer</a></div><div class="ttdoc">Managed reference to AnalyzerObj.</div><div class="ttdef"><b>Definition:</b> analyzer.h:909</div></div>
<div class="ttc" id="aclasstvm_1_1tirx_1_1BufferRegion_html"><div class="ttname"><a href="classtvm_1_1tirx_1_1BufferRegion.html">tvm::tirx::BufferRegion</a></div><div class="ttdoc">Managed reference to BufferRegionNode.</div><div class="ttdef"><b>Definition:</b> stmt.h:767</div></div>
<div class="ttc" id="aclasstvm_1_1tirx_1_1For_html"><div class="ttname"><a href="classtvm_1_1tirx_1_1For.html">tvm::tirx::For</a></div><div class="ttdoc">Managed reference to ForNode.</div><div class="ttdef"><b>Definition:</b> stmt.h:641</div></div>
<div class="ttc" id="aclasstvm_1_1tirx_1_1PrimFunc_html"><div class="ttname"><a href="classtvm_1_1tirx_1_1PrimFunc.html">tvm::tirx::PrimFunc</a></div><div class="ttdoc">Managed reference to PrimFuncNode.</div><div class="ttdef"><b>Definition:</b> function.h:129</div></div>
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