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Posted to commits@tvm.apache.org by tq...@apache.org on 2020/09/03 03:49:21 UTC
[incubator-tvm-site] branch asf-site updated: Docs build at Wed Sep
2 20:49:07 PDT 2020
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/incubator-tvm-site.git
The following commit(s) were added to refs/heads/asf-site by this push:
new 747d265 Docs build at Wed Sep 2 20:49:07 PDT 2020
747d265 is described below
commit 747d265664c05d8baf6395b1396cb5bdc86b5ece
Author: tqchen <tq...@octoml.ai>
AuthorDate: Wed Sep 2 20:49:07 2020 -0700
Docs build at Wed Sep 2 20:49:07 PDT 2020
---
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1286 files changed, 139692 insertions(+), 130024 deletions(-)
diff --git a/docs/_downloads/289640e7ded85c57931fc0537d443c5f/relay_pass_infra.ipynb b/docs/_downloads/13509e02380dbdb802e80921620e9b5c/use_pass_infra.ipynb
similarity index 76%
rename from docs/_downloads/289640e7ded85c57931fc0537d443c5f/relay_pass_infra.ipynb
rename to docs/_downloads/13509e02380dbdb802e80921620e9b5c/use_pass_infra.ipynb
index c76d6d5..18254dc 100644
--- a/docs/_downloads/289640e7ded85c57931fc0537d443c5f/relay_pass_infra.ipynb
+++ b/docs/_downloads/13509e02380dbdb802e80921620e9b5c/use_pass_infra.ipynb
@@ -15,7 +15,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "\n\nHow to Use Relay Pass Infra\n===========================\n**Author**: `Zhi Chen <https://github.com/zhiics>`_\n\nAs the number of optimization passes increases in Relay, it becomes intractable to\nexecute them and maintain their dependencies manually. Therefore, we have\nintroduced an infrastructure to manage the optimization passes.\n\nThe optimizations of a Relay program could be applied at various granularity,\nnamely function-level and module-level using :py:class:`tvm.r [...]
+ "\n\nHow to Use TVM Pass Infra\n=========================\n**Author**: `Zhi Chen <https://github.com/zhiics>`_\n\nAs the number of optimization passes increases in Relay/tir, it becomes intractable to\nexecute them and maintain their dependencies manually. Therefore, we have\nintroduced an infrastructure to manage the optimization passes and make it\napplicable to different layers of the IR in the TVM stack.\n\nThe optimizations of a Relay/tir program could be applied at various [...]
]
},
{
@@ -33,7 +33,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "Create An Example Relay Program\n-------------------------------\nFirst of all, we create a simple Relay program for the tutorial. This program\nwill be used by various optimizations of the examples in this tutorial.\n\n"
+ "Create An Example Relay Program\n-------------------------------\nFirst of all, we create a simple Relay program for the tutorial. This program\nwill be used by various optimizations of the examples in this tutorial.\nSimilarly, users can write a tir primitive function and apply the tir passes.\n\n"
]
},
{
@@ -44,7 +44,7 @@
},
"outputs": [],
"source": [
- "def example():\n shape = (1, 64, 54, 54)\n c_data = np.empty(shape).astype(\"float32\")\n c = relay.const(c_data)\n weight = relay.var('weight', shape=(64, 64, 3, 3))\n x = relay.var(\"x\", relay.TensorType((1, 64, 56, 56), \"float32\"))\n conv = relay.nn.conv2d(x, weight)\n y = relay.add(c, c)\n y = relay.multiply(y, relay.const(2, \"float32\"))\n y = relay.add(conv, y)\n z = relay.add(y, c)\n z1 = relay.add(y, c)\n z2 = relay.add(z, z1)\n [...]
+ "def example():\n shape = (1, 64, 54, 54)\n c_data = np.empty(shape).astype(\"float32\")\n c = relay.const(c_data)\n weight = relay.var('weight', shape=(64, 64, 3, 3))\n x = relay.var(\"x\", relay.TensorType((1, 64, 56, 56), \"float32\"))\n conv = relay.nn.conv2d(x, weight)\n y = relay.add(c, c)\n y = relay.multiply(y, relay.const(2, \"float32\"))\n y = relay.add(conv, y)\n z = relay.add(y, c)\n z1 = relay.add(y, c)\n z2 = relay.add(z, z1)\n [...]
]
},
{
@@ -141,7 +141,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "From the transformed Relay program, we can see that there are still two\nidentical addition operations. This is because `EliminateCommonSubexpr`\nwas not actually performed. The reason is because only the passes that have\noptimization level less or equal to 2 will be executed by default under\n:py:class:`tvm.transform.Sequential`. The pass infra,\nhowever, provides a configuration interface\nfor users to customize the optimization level that they want to execute.\n\n"
+ "From the transformed Relay program, we can see that there are still two\nidentical addition operations. This is because ``EliminateCommonSubexpr``\nwas not actually performed. The reason is because only the passes that have\noptimization level less or equal to 2 will be executed by default under\n:py:class:`tvm.transform.Sequential`. The pass infra,\nhowever, provides a configuration interface\nfor users to customize the optimization level that they want to execute.\n\n"
]
},
{
@@ -213,7 +213,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "Debug a Pass\n------------\nRelay provides users a plug-and-play style debugging pass that print the IR\nafter a certain pass is done. For example, we can print out the IR on the\ncompletion of constant folding and fusion by adding the debugging pass after\nthem.\n\n"
+ "Debug a Pass\n------------\nTVM provides users a plug-and-play style debugging pass that print the IR\nafter a certain pass is done through a special pass (``PrintIR``) to dump the IR of the\nwhole module. A slightly modified version of the sequential pass example\ncould be like the following to enable IR dumping for ``FoldConstant`` optimization.\n\n"
]
},
{
@@ -224,7 +224,14 @@
},
"outputs": [],
"source": [
- "f = example()\nmod = tvm.IRModule.from_expr(f)\nseq = tvm.transform.Sequential([relay.transform.FoldConstant(),\n tvm.transform.PrintIR(),\n relay.transform.EliminateCommonSubexpr(),\n relay.transform.FuseOps(),\n tvm.transform.PrintIR()])\nwith tvm.transform.PassContext(opt_level=3):\n mod = seq(mod)\n\nprint(\"done\")"
+ "f = example()\nmod = tvm.IRModule.from_expr(f)\nseq = tvm.transform.Sequential([relay.transform.FoldConstant(),\n tvm.transform.PrintIR(),\n relay.transform.EliminateCommonSubexpr(),\n relay.transform.FuseOps(),\n relay.transform.AlterOpLayout()])\n\n# By inserting the ``PrintIR`` pass after ``FoldConstant``, the pass infra will\n# dump out the module IR wh [...]
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Summary\n-------\nThis tutorial has covered how we can write and invoke passes in TVM more\nconveniently using the pass infra. Different ways of invoking a pass are also\ndisucssed. Using :py:class:`tvm.transform.Sequential` can largely help\nusers to ease the work of handling multiple optimization passes and their\ndependencies. In addition, an example is provided to illustrate\nhow we can debug a pass using the ``PrintIR`` and tracing.\n\n"
]
}
],
diff --git a/docs/_downloads/38606228ff7130fbd6473b7c0625ddcd/deploy_model_on_android.ipynb b/docs/_downloads/38606228ff7130fbd6473b7c0625ddcd/deploy_model_on_android.ipynb
index 0733d45..dc10671 100644
--- a/docs/_downloads/38606228ff7130fbd6473b7c0625ddcd/deploy_model_on_android.ipynb
+++ b/docs/_downloads/38606228ff7130fbd6473b7c0625ddcd/deploy_model_on_android.ipynb
@@ -47,7 +47,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "Register Android device to RPC Tracker\n--------------------------------------\nNow we can register our Android device to the tracker.\n\nFollow this `readme page <https://github.com/apache/incubator-tvm/tree/master/apps/android_rpc>`_ to\ninstall TVM RPC APK on the android device.\n\nHere is an example of config.mk. I enabled OpenCL and Vulkan.\n\n\n.. code-block:: bash\n\n APP_ABI = arm64-v8a\n\n APP_PLATFORM = android-24\n\n # whether enable OpenCL during compile\n USE_OP [...]
+ "Register Android device to RPC Tracker\n--------------------------------------\nNow we can register our Android device to the tracker.\n\nFollow this `readme page <https://github.com/apache/incubator-tvm/tree/master/apps/android_rpc>`_ to\ninstall TVM RPC APK on the android device.\n\nHere is an example of config.mk. I enabled OpenCL and Vulkan.\n\n\n.. code-block:: bash\n\n APP_ABI = arm64-v8a\n\n APP_PLATFORM = android-24\n\n # whether enable OpenCL during compile\n USE_OP [...]
]
},
{
diff --git a/docs/_downloads/3961fdfa7abff1b6dc996faa43b4c40f/deploy_model_on_android.py b/docs/_downloads/3961fdfa7abff1b6dc996faa43b4c40f/deploy_model_on_android.py
index 2e3e389..fe16dac 100644
--- a/docs/_downloads/3961fdfa7abff1b6dc996faa43b4c40f/deploy_model_on_android.py
+++ b/docs/_downloads/3961fdfa7abff1b6dc996faa43b4c40f/deploy_model_on_android.py
@@ -145,7 +145,7 @@ from tvm.contrib.download import download_testdata
#
# .. code-block:: bash
#
-# /opt/android-sdk-linux/ndk-bundle/build/tools/make-standalone-toolchain.sh \
+# $ANDROID_NDK_HOME/build/tools/make-standalone-toolchain.sh \
# --platform=android-24 --use-llvm --arch=arm64 --install-dir=/opt/android-toolchain-arm64
# export TVM_NDK_CC=/opt/android-toolchain-arm64/bin/aarch64-linux-android-g++
#
diff --git a/docs/_downloads/66258823cf03d1875c9ea84a82131101/deploy_detection.ipynb b/docs/_downloads/66258823cf03d1875c9ea84a82131101/deploy_detection.ipynb
deleted file mode 100644
index 8ce07ae..0000000
--- a/docs/_downloads/66258823cf03d1875c9ea84a82131101/deploy_detection.ipynb
+++ /dev/null
@@ -1,169 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "%matplotlib inline"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\nDeploy Pretrained Vision Detection Model from Darknet on VTA\n============================================================\n**Author**: `Hua Jiang <https://github.com/huajsj>`_\n\nThis tutorial provides an end-to-end demo, on how to run Darknet YoloV3-tiny\ninference onto the VTA accelerator design to perform Image detection tasks.\nIt showcases Relay as a front end compiler that can perform quantization (VTA\nonly supports int8/32 inference) as well as graph packing (in order [...]
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Install dependencies\n--------------------\nTo use the autotvm package in tvm, we need to install some extra dependencies.\n(change \"3\" to \"2\" if you use python2):\n\n.. code-block:: bash\n\npip3 install \"Pillow<7\"\n\nYOLO-V3-tiny Model with Darknet parsing have dependancy with CFFI and CV2 library,\nwe need to install CFFI and CV2 before executing this script.\n\npip3 install \"Pillow<7\"\n\npip3 install cffi\npip3 install opencv-python\n\nNow return to the python code. I [...]
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "from __future__ import absolute_import, print_function\n\nimport sys\nimport os\nimport time\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport tvm\nimport vta\nfrom tvm import rpc, autotvm, relay\nfrom tvm.relay.testing import yolo_detection, darknet\nfrom tvm.relay.testing.darknet import __darknetffi__\nfrom tvm.contrib import graph_runtime, graph_runtime, util\nfrom tvm.contrib.download import download_testdata\nfrom vta.testing import simulator\nfrom vta.top import [...]
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Download yolo net configure file, weight file, darknet library file based on\nModel Name\n----------------------------------------------------------------------------\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "MODEL_NAME = 'yolov3-tiny'\nREPO_URL = 'https://github.com/dmlc/web-data/blob/master/darknet/'\n\ncfg_path = download_testdata('https://github.com/pjreddie/darknet/blob/master/cfg/'\n + MODEL_NAME + '.cfg' + '?raw=true',\n MODEL_NAME + '.cfg',\n module=\"darknet\")\nweights_path = download_testdata('https://pjreddie.com/media/files/'\n + MODEL_NAME + '.weights' + ' [...]
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Download yolo categories and illustration front.\n------------------------------------------------\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "coco_path = download_testdata(REPO_URL + 'data/' + 'coco.names' + '?raw=true',\n 'coco.names',\n module='data')\nfont_path = download_testdata(REPO_URL + 'data/' + 'arial.ttf' + '?raw=true',\n 'arial.ttf',\n module='data')\nwith open(coco_path) as f:\n content = f.readlines()\nnames = [x.strip() for x in content]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Define the platform and model targets.\n--------------------------------------\nExecute on CPU vs. VTA, and define the model.\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "# Load VTA parameters from the 3rdparty/vta-hw/config/vta_config.json file\nenv = vta.get_env()\n# Set ``device=arm_cpu`` to run inference on the CPU\n# or ``device=vta`` to run inference on the FPGA.\ndevice = \"vta\"\ntarget = env.target if device == \"vta\" else env.target_vta_cpu\n\npack_dict = {\n \"yolov3-tiny\": [\"nn.max_pool2d\", \"cast\", 4, 185],\n}\n\n# Name of Darknet model to compile\n# The ``start_pack`` and ``stop_pack`` labels indicate where\n# to start and e [...]
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Obtain an execution remote.\n---------------------------\nWhen target is 'pynq' or other FPGA backend, reconfigure FPGA and runtime.\nOtherwise, if target is 'sim', execute locally.\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "if env.TARGET not in [\"sim\", \"tsim\"]:\n # Get remote from tracker node if environment variable is set.\n # To set up the tracker, you'll need to follow the \"Auto-tuning\n # a convolutional network for VTA\" tutorial.\n tracker_host = os.environ.get(\"TVM_TRACKER_HOST\", None)\n tracker_port = os.environ.get(\"TVM_TRACKER_PORT\", None)\n # Otherwise if you have a device you want to program directly from\n # the host, make sure you've set the variables be [...]
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Build the inference graph runtime.\n----------------------------------\nUsing Darknet library load downloaded vision model and compile with Relay.\nThe compilation steps are:\n\n1. Front end translation from Darknet into Relay module.\n2. Apply 8-bit quantization: here we skip the first conv layer,\n and dense layer which will both be executed in fp32 on the CPU.\n3. Perform graph packing to alter the data layout for tensorization.\n4. Perform constant folding to reduce number [...]
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "# Load pre-configured AutoTVM schedules\nwith autotvm.tophub.context(target):\n net = __darknetffi__.dlopen(darknet_lib_path).load_network(cfg_path.encode('utf-8'),\n weights_path.encode('utf-8'),\n 0)\n dshape = (env.BATCH, net.c, net.h, net.w)\n dtype = 'float32'\n\n # Measure build start time\n build_start = time.time()\n\n # Start fron [...]
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Perform image detection inference.\n----------------------------------\nWe run detect on an downloaded image\nDownload test image\n\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "[neth, netw] = dshape[2:]\ntest_image = 'person.jpg'\nimg_url = REPO_URL + 'data/' + test_image + '?raw=true'\nimg_path = download_testdata(img_url, test_image, \"data\")\ndata = darknet.load_image(img_path, neth, netw).transpose(1, 2, 0)\n\n# Prepare test image for inference\nplt.imshow(data)\nplt.show()\ndata = data.transpose((2, 0, 1))\ndata = data[np.newaxis, :]\ndata = np.repeat(data, env.BATCH, axis=0)\n\n# Set the network parameters and inputs\nm.set_input('data', data)\n [...]
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.6.10"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
\ No newline at end of file
diff --git a/docs/_downloads/8246644805c8dfcb0b33ca356cc1fafc/deploy_ssd_gluoncv.ipynb b/docs/_downloads/8246644805c8dfcb0b33ca356cc1fafc/deploy_ssd_gluoncv.ipynb
index 8c1219f..00b35bf 100644
--- a/docs/_downloads/8246644805c8dfcb0b33ca356cc1fafc/deploy_ssd_gluoncv.ipynb
+++ b/docs/_downloads/8246644805c8dfcb0b33ca356cc1fafc/deploy_ssd_gluoncv.ipynb
@@ -26,7 +26,7 @@
},
"outputs": [],
"source": [
- "import tvm\nfrom tvm import te\n\nfrom matplotlib import pyplot as plt\nfrom tvm.relay.testing.config import ctx_list\nfrom tvm import relay\nfrom tvm.contrib import graph_runtime\nfrom tvm.contrib.download import download_testdata\nfrom gluoncv import model_zoo, data, utils"
+ "import tvm\nfrom tvm import te\n\nfrom matplotlib import pyplot as plt\nfrom tvm import relay\nfrom tvm.contrib import graph_runtime\nfrom tvm.contrib.download import download_testdata\nfrom gluoncv import model_zoo, data, utils"
]
},
{
@@ -44,7 +44,7 @@
},
"outputs": [],
"source": [
- "supported_model = [\n 'ssd_512_resnet50_v1_voc',\n 'ssd_512_resnet50_v1_coco',\n 'ssd_512_resnet101_v2_voc',\n 'ssd_512_mobilenet1.0_voc',\n 'ssd_512_mobilenet1.0_coco',\n 'ssd_300_vgg16_atrous_voc'\n 'ssd_512_vgg16_atrous_coco',\n]\n\nmodel_name = supported_model[0]\ndshape = (1, 3, 512, 512)\ntarget_list = ctx_list()"
+ "supported_model = [\n 'ssd_512_resnet50_v1_voc',\n 'ssd_512_resnet50_v1_coco',\n 'ssd_512_resnet101_v2_voc',\n 'ssd_512_mobilenet1.0_voc',\n 'ssd_512_mobilenet1.0_coco',\n 'ssd_300_vgg16_atrous_voc'\n 'ssd_512_vgg16_atrous_coco',\n]\n\nmodel_name = supported_model[0]\ndshape = (1, 3, 512, 512)"
]
},
{
@@ -98,7 +98,7 @@
},
"outputs": [],
"source": [
- "def run(lib, ctx):\n # Build TVM runtime\n m = graph_runtime.GraphModule(lib['default'](ctx))\n tvm_input = tvm.nd.array(x.asnumpy(), ctx=ctx)\n m.set_input('data', tvm_input)\n # execute\n m.run()\n # get outputs\n class_IDs, scores, bounding_boxs = m.get_output(0), m.get_output(1), m.get_output(2)\n return class_IDs, scores, bounding_boxs\n\nfor target, ctx in target_list:\n lib = build(target)\n class_IDs, scores, bounding_boxs = run(lib, ctx)"
+ "def run(lib, ctx):\n # Build TVM runtime\n m = graph_runtime.GraphModule(lib['default'](ctx))\n tvm_input = tvm.nd.array(x.asnumpy(), ctx=ctx)\n m.set_input('data', tvm_input)\n # execute\n m.run()\n # get outputs\n class_IDs, scores, bounding_boxs = m.get_output(0), m.get_output(1), m.get_output(2)\n return class_IDs, scores, bounding_boxs\n\nfor target in [\"llvm\", \"cuda\"]:\n ctx = tvm.context(target, 0)\n if ctx.exist:\n lib = build( [...]
]
},
{
diff --git a/docs/_downloads/52b04835c256bb81bbf4187f18950a0a/relay_pass_infra.py b/docs/_downloads/9fc6ca792ca6e47e4bdc761ee1b98501/use_pass_infra.py
similarity index 76%
rename from docs/_downloads/52b04835c256bb81bbf4187f18950a0a/relay_pass_infra.py
rename to docs/_downloads/9fc6ca792ca6e47e4bdc761ee1b98501/use_pass_infra.py
index ae7f544..4b842b9 100644
--- a/docs/_downloads/52b04835c256bb81bbf4187f18950a0a/relay_pass_infra.py
+++ b/docs/_downloads/9fc6ca792ca6e47e4bdc761ee1b98501/use_pass_infra.py
@@ -16,26 +16,28 @@
# under the License.
# pylint: disable=line-too-long
"""
-.. _tutorial-relay-pass-infra:
+.. _tutorial-use-pass-infra:
-How to Use Relay Pass Infra
-===========================
+How to Use TVM Pass Infra
+=========================
**Author**: `Zhi Chen <https://github.com/zhiics>`_
-As the number of optimization passes increases in Relay, it becomes intractable to
+As the number of optimization passes increases in Relay/tir, it becomes intractable to
execute them and maintain their dependencies manually. Therefore, we have
-introduced an infrastructure to manage the optimization passes.
-
-The optimizations of a Relay program could be applied at various granularity,
-namely function-level and module-level using :py:class:`tvm.relay.transform.FunctionPass`
-and :py:class:`tvm.relay.transform.ModulePass`
-respectively. Or users can rely on py:class:`tvm.transform.Sequential` to apply a sequence of passes
-on a Relay program where the dependencies between passes can be resolved by the
+introduced an infrastructure to manage the optimization passes and make it
+applicable to different layers of the IR in the TVM stack.
+
+The optimizations of a Relay/tir program could be applied at various granularity,
+namely function-level and module-level using :py:class:`tvm.relay.transform.FunctionPass`/
+:py:class:`tvm.tir.transform.PrimFuncPass` and :py:class:`tvm.transform.ModulePass`
+respectively. Or users can rely on :py:class:`tvm.transform.Sequential` to apply a sequence of passes
+on a Relay/tir program where the dependencies between passes can be resolved by the
pass infra. For more details about each type of these passes, please refer to
-the :ref:`relay-pass-infra`
+the :ref:`pass-infra`
-This tutorial demostrates how developers can use the Relay pass infra to perform
-a certain optimization and create an optimization pipeline.
+This tutorial mainly demostrates how developers can use the pass infra to perform
+a certain optimization and create an optimization pipeline for a Relay program.
+The same approach can be used for tir as well.
"""
import numpy as np
@@ -48,6 +50,7 @@ import tvm.relay as relay
# -------------------------------
# First of all, we create a simple Relay program for the tutorial. This program
# will be used by various optimizations of the examples in this tutorial.
+# Similarly, users can write a tir primitive function and apply the tir passes.
def example():
shape = (1, 64, 54, 54)
@@ -62,7 +65,7 @@ def example():
z = relay.add(y, c)
z1 = relay.add(y, c)
z2 = relay.add(z, z1)
- return relay.Function([x], z2)
+ return relay.Function([x, weight], z2)
###############################################################################
# Let us register layout alteration for a conv2d op so that we can apply the
@@ -153,7 +156,7 @@ print(mod1)
###############################################################################
# From the transformed Relay program, we can see that there are still two
-# identical addition operations. This is because `EliminateCommonSubexpr`
+# identical addition operations. This is because ``EliminateCommonSubexpr``
# was not actually performed. The reason is because only the passes that have
# optimization level less or equal to 2 will be executed by default under
# :py:class:`tvm.transform.Sequential`. The pass infra,
@@ -230,10 +233,10 @@ print(mod3)
##############################################################################
# Debug a Pass
# ------------
-# Relay provides users a plug-and-play style debugging pass that print the IR
-# after a certain pass is done. For example, we can print out the IR on the
-# completion of constant folding and fusion by adding the debugging pass after
-# them.
+# TVM provides users a plug-and-play style debugging pass that print the IR
+# after a certain pass is done through a special pass (``PrintIR``) to dump the IR of the
+# whole module. A slightly modified version of the sequential pass example
+# could be like the following to enable IR dumping for ``FoldConstant`` optimization.
f = example()
mod = tvm.IRModule.from_expr(f)
@@ -241,8 +244,39 @@ seq = tvm.transform.Sequential([relay.transform.FoldConstant(),
tvm.transform.PrintIR(),
relay.transform.EliminateCommonSubexpr(),
relay.transform.FuseOps(),
- tvm.transform.PrintIR()])
-with tvm.transform.PassContext(opt_level=3):
- mod = seq(mod)
+ relay.transform.AlterOpLayout()])
+
+# By inserting the ``PrintIR`` pass after ``FoldConstant``, the pass infra will
+# dump out the module IR when ``FoldConstant`` is done. Users can plug in this
+# pass after any pass they want to debug for viewing the optimization effect.
+#
+# There is a more flexible debugging mechanism also exposed by the build configuration
+# object. One can pass a tracing function which can be used to execute arbitrary code
+# before and/or after each pass. A tracing function will receive a :py::class:`tvm.IRModule`,
+# a :py:class:`tvm.transform.PassInfo` object,
+# and a boolean indicating whether you are executing before, or after a pass.
+# An example is below.
+
+def print_ir(mod, info, is_before):
+ """Print the name of the pass, the IR, only before passes execute."""
+ if is_before:
+ print("Running pass: {}", info)
+ print(mod)
+
+with tvm.transform.PassContext(opt_level=3, trace=print_ir):
+ with tvm.target.create("llvm"):
+ # Perform the optimizations.
+ mod = seq(mod)
+print(mod)
print("done")
+
+##############################################################################
+# Summary
+# -------
+# This tutorial has covered how we can write and invoke passes in TVM more
+# conveniently using the pass infra. Different ways of invoking a pass are also
+# disucssed. Using :py:class:`tvm.transform.Sequential` can largely help
+# users to ease the work of handling multiple optimization passes and their
+# dependencies. In addition, an example is provided to illustrate
+# how we can debug a pass using the ``PrintIR`` and tracing.
diff --git a/docs/_downloads/ca08de6c440df207921d807474d26f06/deploy_ssd_gluoncv.py b/docs/_downloads/ca08de6c440df207921d807474d26f06/deploy_ssd_gluoncv.py
index 46162e1..3643c8d 100644
--- a/docs/_downloads/ca08de6c440df207921d807474d26f06/deploy_ssd_gluoncv.py
+++ b/docs/_downloads/ca08de6c440df207921d807474d26f06/deploy_ssd_gluoncv.py
@@ -27,7 +27,6 @@ import tvm
from tvm import te
from matplotlib import pyplot as plt
-from tvm.relay.testing.config import ctx_list
from tvm import relay
from tvm.contrib import graph_runtime
from tvm.contrib.download import download_testdata
@@ -70,7 +69,6 @@ supported_model = [
model_name = supported_model[0]
dshape = (1, 3, 512, 512)
-target_list = ctx_list()
######################################################################
# Download and pre-process demo image
@@ -105,9 +103,11 @@ def run(lib, ctx):
class_IDs, scores, bounding_boxs = m.get_output(0), m.get_output(1), m.get_output(2)
return class_IDs, scores, bounding_boxs
-for target, ctx in target_list:
- lib = build(target)
- class_IDs, scores, bounding_boxs = run(lib, ctx)
+for target in ["llvm", "cuda"]:
+ ctx = tvm.context(target, 0)
+ if ctx.exist:
+ lib = build(target)
+ class_IDs, scores, bounding_boxs = run(lib, ctx)
######################################################################
# Display result
diff --git a/docs/_downloads/cd8ac9c09164cc04dd9ecd131c536680/micro_tflite.ipynb b/docs/_downloads/cd8ac9c09164cc04dd9ecd131c536680/micro_tflite.ipynb
index 6937630..22e3fa2 100644
--- a/docs/_downloads/cd8ac9c09164cc04dd9ecd131c536680/micro_tflite.ipynb
+++ b/docs/_downloads/cd8ac9c09164cc04dd9ecd131c536680/micro_tflite.ipynb
@@ -80,7 +80,7 @@
},
"outputs": [],
"source": [
- "input_tensor = \"dense_4_input\"\ninput_shape = (1,)\ninput_dtype = \"float32\"\n\nmod, params = relay.frontend.from_tflite(tflite_model,\n shape_dict={input_tensor: input_shape},\n dtype_dict={input_tensor: input_dtype})\n\n# %%\n# Running on device\n# ----------------------------------------------\n#\n# Setup the device config which is what will be used to communicate\n# with the microcontroller (a [...]
+ "input_tensor = \"dense_4_input\"\ninput_shape = (1,)\ninput_dtype = \"float32\"\n\nmod, params = relay.frontend.from_tflite(tflite_model,\n shape_dict={input_tensor: input_shape},\n dtype_dict={input_tensor: input_dtype})\n\n# %%\n# Running on device\n# ----------------------------------------------\n#\n# Setup the device config which is what will be used to communicate\n# with the microcontroller (a [...]
]
},
{
diff --git a/docs/_downloads/ed4fcfc19b39f91a14d817676480a454/deploy_detection.py b/docs/_downloads/ed4fcfc19b39f91a14d817676480a454/deploy_detection.py
deleted file mode 100644
index 5039488..0000000
--- a/docs/_downloads/ed4fcfc19b39f91a14d817676480a454/deploy_detection.py
+++ /dev/null
@@ -1,330 +0,0 @@
-# Licensed to the Apache Software Foundation (ASF) under one
-# or more contributor license agreements. See the NOTICE file
-# distributed with this work for additional information
-# regarding copyright ownership. The ASF licenses this file
-# to you under the Apache License, Version 2.0 (the
-# "License"); you may not use this file except in compliance
-# with the License. You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing,
-# software distributed under the License is distributed on an
-# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-# KIND, either express or implied. See the License for the
-# specific language governing permissions and limitations
-# under the License.
-"""
-Deploy Pretrained Vision Detection Model from Darknet on VTA
-============================================================
-**Author**: `Hua Jiang <https://github.com/huajsj>`_
-
-This tutorial provides an end-to-end demo, on how to run Darknet YoloV3-tiny
-inference onto the VTA accelerator design to perform Image detection tasks.
-It showcases Relay as a front end compiler that can perform quantization (VTA
-only supports int8/32 inference) as well as graph packing (in order to enable
-tensorization in the core) to massage the compute graph for the hardware target.
-"""
-
-######################################################################
-# Install dependencies
-# --------------------
-# To use the autotvm package in tvm, we need to install some extra dependencies.
-# (change "3" to "2" if you use python2):
-#
-# .. code-block:: bash
-#
-# pip3 install "Pillow<7"
-#
-# YOLO-V3-tiny Model with Darknet parsing have dependancy with CFFI and CV2 library,
-# we need to install CFFI and CV2 before executing this script.
-#
-# pip3 install "Pillow<7"
-#
-# pip3 install cffi
-# pip3 install opencv-python
-#
-# Now return to the python code. Import packages.
-
-from __future__ import absolute_import, print_function
-
-import sys
-import os
-import time
-import matplotlib.pyplot as plt
-import numpy as np
-import tvm
-import vta
-from tvm import rpc, autotvm, relay
-from tvm.relay.testing import yolo_detection, darknet
-from tvm.relay.testing.darknet import __darknetffi__
-from tvm.contrib import graph_runtime, graph_runtime, util
-from tvm.contrib.download import download_testdata
-from vta.testing import simulator
-from vta.top import graph_pack
-# Make sure that TVM was compiled with RPC=1
-assert tvm.runtime.enabled("rpc")
-
-##############################################################################
-# Download yolo net configure file, weight file, darknet library file based on
-# Model Name
-# ----------------------------------------------------------------------------
-MODEL_NAME = 'yolov3-tiny'
-REPO_URL = 'https://github.com/dmlc/web-data/blob/master/darknet/'
-
-cfg_path = download_testdata('https://github.com/pjreddie/darknet/blob/master/cfg/'
- + MODEL_NAME + '.cfg' + '?raw=true',
- MODEL_NAME + '.cfg',
- module="darknet")
-weights_path = download_testdata('https://pjreddie.com/media/files/'
- + MODEL_NAME + '.weights' + '?raw=true',
- MODEL_NAME + '.weights',
- module="darknet")
-
-if sys.platform in ['linux', 'linux2']:
- darknet_lib_path = download_testdata(REPO_URL + 'lib/' + 'libdarknet2.0.so' + '?raw=true',
- 'libdarknet2.0.so',
- module="darknet")
-elif sys.platform == 'darwin':
- darknet_lib_path = download_testdata(REPO_URL+'lib_osx/'+'libdarknet_mac2.0.so'+'?raw=true',
- 'libdarknet_mac2.0.so',
- module="darknet")
-else:
- raise NotImplementedError("Darknet lib is not supported on {} platform"
- .format(sys.platform))
-
-##################################################
-# Download yolo categories and illustration front.
-# ------------------------------------------------
-coco_path = download_testdata(REPO_URL + 'data/' + 'coco.names' + '?raw=true',
- 'coco.names',
- module='data')
-font_path = download_testdata(REPO_URL + 'data/' + 'arial.ttf' + '?raw=true',
- 'arial.ttf',
- module='data')
-with open(coco_path) as f:
- content = f.readlines()
-names = [x.strip() for x in content]
-
-########################################
-# Define the platform and model targets.
-# --------------------------------------
-# Execute on CPU vs. VTA, and define the model.
-
-# Load VTA parameters from the 3rdparty/vta-hw/config/vta_config.json file
-env = vta.get_env()
-# Set ``device=arm_cpu`` to run inference on the CPU
-# or ``device=vta`` to run inference on the FPGA.
-device = "vta"
-target = env.target if device == "vta" else env.target_vta_cpu
-
-pack_dict = {
- "yolov3-tiny": ["nn.max_pool2d", "cast", 4, 185],
-}
-
-# Name of Darknet model to compile
-# The ``start_pack`` and ``stop_pack`` labels indicate where
-# to start and end the graph packing relay pass: in other words
-# where to start and finish offloading to VTA.
-# the number 4 indicate the the ``start_pack`` index is 4, the
-# number 185 indicate the ``stop_pack index`` is 185, by using
-# name and index number, here we can located to correct place
-# where to start/end when there are multiple ``nn.max_pool2d``
-# or ``cast``, print(mod.astext(show_meta_data=False)) can help
-# to find operator name and index information.
-assert MODEL_NAME in pack_dict
-
-#############################
-# Obtain an execution remote.
-# ---------------------------
-# When target is 'pynq' or other FPGA backend, reconfigure FPGA and runtime.
-# Otherwise, if target is 'sim', execute locally.
-
-if env.TARGET not in ["sim", "tsim"]:
- # Get remote from tracker node if environment variable is set.
- # To set up the tracker, you'll need to follow the "Auto-tuning
- # a convolutional network for VTA" tutorial.
- tracker_host = os.environ.get("TVM_TRACKER_HOST", None)
- tracker_port = os.environ.get("TVM_TRACKER_PORT", None)
- # Otherwise if you have a device you want to program directly from
- # the host, make sure you've set the variables below to the IP of
- # your board.
- device_host = os.environ.get("VTA_RPC_HOST", "192.168.2.99")
- device_port = os.environ.get("VTA_RPC_PORT", "9091")
- if not tracker_host or not tracker_port:
- remote = rpc.connect(device_host, int(device_port))
- else:
- remote = autotvm.measure.request_remote(env.TARGET,
- tracker_host,
- int(tracker_port),
- timeout=10000)
- # Reconfigure the JIT runtime and FPGA.
- # You can program the FPGA with your own custom bitstream
- # by passing the path to the bitstream file instead of None.
- reconfig_start = time.time()
- vta.reconfig_runtime(remote)
- vta.program_fpga(remote, bitstream=None)
- reconfig_time = time.time() - reconfig_start
- print("Reconfigured FPGA and RPC runtime in {0:.2f}s!".format(reconfig_time))
-
-# In simulation mode, host the RPC server locally.
-else:
- remote = rpc.LocalSession()
-
-# Get execution context from remote
-ctx = remote.ext_dev(0) if device == "vta" else remote.cpu(0)
-
-####################################
-# Build the inference graph runtime.
-# ----------------------------------
-# Using Darknet library load downloaded vision model and compile with Relay.
-# The compilation steps are:
-#
-# 1. Front end translation from Darknet into Relay module.
-# 2. Apply 8-bit quantization: here we skip the first conv layer,
-# and dense layer which will both be executed in fp32 on the CPU.
-# 3. Perform graph packing to alter the data layout for tensorization.
-# 4. Perform constant folding to reduce number of operators (e.g. eliminate batch norm multiply).
-# 5. Perform relay build to object file.
-# 6. Load the object file onto remote (FPGA device).
-# 7. Generate graph runtime, `m`.
-#
-
-# Load pre-configured AutoTVM schedules
-with autotvm.tophub.context(target):
- net = __darknetffi__.dlopen(darknet_lib_path).load_network(cfg_path.encode('utf-8'),
- weights_path.encode('utf-8'),
- 0)
- dshape = (env.BATCH, net.c, net.h, net.w)
- dtype = 'float32'
-
- # Measure build start time
- build_start = time.time()
-
- # Start front end compilation
- mod, params = relay.frontend.from_darknet(net, dtype=dtype, shape=dshape)
-
- if target.device_name == "vta":
- # Perform quantization in Relay
- # Note: We set opt_level to 3 in order to fold batch norm
- with tvm.transform.PassContext(opt_level=3):
- with relay.quantize.qconfig(global_scale=33.0,
- skip_conv_layers=[0],
- store_lowbit_output=True,
- round_for_shift=True):
- mod = relay.quantize.quantize(mod, params=params)
- # Perform graph packing and constant folding for VTA target
- mod = graph_pack(
- mod["main"],
- env.BATCH,
- env.BLOCK_OUT,
- env.WGT_WIDTH,
- start_name=pack_dict[MODEL_NAME][0],
- stop_name=pack_dict[MODEL_NAME][1],
- start_name_idx=pack_dict[MODEL_NAME][2],
- stop_name_idx=pack_dict[MODEL_NAME][3])
- else:
- mod = mod["main"]
-
- # Compile Relay program with AlterOpLayout disabled
- with vta.build_config(disabled_pass={"AlterOpLayout"}):
- graph, lib, params = relay.build(
- mod,
- target=target,
- params=params,
- target_host=env.target_host)
-
- # Measure Relay build time
- build_time = time.time() - build_start
- print(MODEL_NAME + " inference graph built in {0:.2f}s!".format(build_time))
-
- # Send the inference library over to the remote RPC server
- temp = util.tempdir()
- lib.save(temp.relpath("graphlib.o"))
- remote.upload(temp.relpath("graphlib.o"))
- lib = remote.load_module("graphlib.o")
-
- # Graph runtime
- m = graph_runtime.create(graph, lib, ctx)
-
-####################################
-# Perform image detection inference.
-# ----------------------------------
-# We run detect on an downloaded image
-# Download test image
-[neth, netw] = dshape[2:]
-test_image = 'person.jpg'
-img_url = REPO_URL + 'data/' + test_image + '?raw=true'
-img_path = download_testdata(img_url, test_image, "data")
-data = darknet.load_image(img_path, neth, netw).transpose(1, 2, 0)
-
-# Prepare test image for inference
-plt.imshow(data)
-plt.show()
-data = data.transpose((2, 0, 1))
-data = data[np.newaxis, :]
-data = np.repeat(data, env.BATCH, axis=0)
-
-# Set the network parameters and inputs
-m.set_input('data', data)
-m.set_input(**params)
-
-# Perform inference and gather execution statistics
-# More on: :py:method:`tvm.runtime.Module.time_evaluator`
-num = 4 # number of times we run module for a single measurement
-rep = 3 # number of measurements (we derive std dev from this)
-timer = m.module.time_evaluator("run", ctx, number=num, repeat=rep)
-
-if env.TARGET in ["sim", "tsim"]:
- simulator.clear_stats()
- timer()
- sim_stats = simulator.stats()
- print("\nExecution statistics:")
- for k, v in sim_stats.items():
- # Since we execute the workload many times, we need to normalize stats
- # Note that there is always one warm up run
- # Therefore we divide the overall stats by (num * rep + 1)
- print("\t{:<16}: {:>16}".format(k, v // (num * rep + 1)))
-else:
- tcost = timer()
- std = np.std(tcost.results) * 1000
- mean = tcost.mean * 1000
- print("\nPerformed inference in %.2fms (std = %.2f) for %d samples" % (mean, std, env.BATCH))
- print("Average per sample inference time: %.2fms" % (mean/env.BATCH))
-
-# Get detection results from out
-thresh = 0.5
-nms_thresh = 0.45
-tvm_out = []
-for i in range(2):
- layer_out = {}
- layer_out['type'] = 'Yolo'
- # Get the yolo layer attributes (n, out_c, out_h, out_w, classes, total)
- layer_attr = m.get_output(i*4+3).asnumpy()
- layer_out['biases'] = m.get_output(i*4+2).asnumpy()
- layer_out['mask'] = m.get_output(i*4+1).asnumpy()
- out_shape = (layer_attr[0], layer_attr[1]//layer_attr[0],
- layer_attr[2], layer_attr[3])
- layer_out['output'] = m.get_output(i*4).asnumpy().reshape(out_shape)
- layer_out['classes'] = layer_attr[4]
- tvm_out.append(layer_out)
- thresh = 0.560
-
-# Show detection results
-img = darknet.load_image_color(img_path)
-_, im_h, im_w = img.shape
-dets = yolo_detection.fill_network_boxes((netw, neth),
- (im_w, im_h),
- thresh,
- 1,
- tvm_out)
-last_layer = net.layers[net.n - 1]
-yolo_detection.do_nms_sort(dets, last_layer.classes, nms_thresh)
-yolo_detection.draw_detections(font_path,
- img,
- dets,
- thresh,
- names,
- last_layer.classes)
-plt.imshow(img.transpose(1, 2, 0))
-plt.show()
diff --git a/docs/_downloads/fd012fa7b67f4e333acce1d25a8e62bc/micro_tflite.py b/docs/_downloads/fd012fa7b67f4e333acce1d25a8e62bc/micro_tflite.py
index 9838df7..0b0af57 100644
--- a/docs/_downloads/fd012fa7b67f4e333acce1d25a8e62bc/micro_tflite.py
+++ b/docs/_downloads/fd012fa7b67f4e333acce1d25a8e62bc/micro_tflite.py
@@ -152,7 +152,7 @@ mod, params = relay.frontend.from_tflite(tflite_model,
#
# Setup the device config which is what will be used to communicate
# with the microcontroller (a STM32F746 Discovery board)
-TARGET = 'c -device=micro_dev'
+TARGET = 'c --system-lib --runtime=c'
dev_config = micro.device.arm.stm32f746xx.generate_config("127.0.0.1", 6666)
######################################################################
diff --git a/docs/_images/sphx_glr_deploy_detection_001.png b/docs/_images/sphx_glr_deploy_detection_001.png
deleted file mode 100644
index 4433e44..0000000
Binary files a/docs/_images/sphx_glr_deploy_detection_001.png and /dev/null differ
diff --git a/docs/_images/sphx_glr_deploy_detection_thumb.png b/docs/_images/sphx_glr_deploy_detection_thumb.png
deleted file mode 100644
index 7520e7a..0000000
Binary files a/docs/_images/sphx_glr_deploy_detection_thumb.png and /dev/null differ
diff --git a/docs/_images/sphx_glr_relay_pass_infra_thumb.png b/docs/_images/sphx_glr_use_pass_infra_thumb.png
similarity index 100%
rename from docs/_images/sphx_glr_relay_pass_infra_thumb.png
rename to docs/_images/sphx_glr_use_pass_infra_thumb.png
diff --git a/docs/_sources/contribute/code_guide.rst.txt b/docs/_sources/contribute/code_guide.rst.txt
index c932e93..c0b022b 100644
--- a/docs/_sources/contribute/code_guide.rst.txt
+++ b/docs/_sources/contribute/code_guide.rst.txt
@@ -82,6 +82,20 @@ Python Code Styles
- Stick to language features as in ``python 3.5``
+Writing Python Tests
+--------------------
+We use `pytest <https://docs.pytest.org/en/stable/>`_ for all python testing. ``tests/python`` contains all the tests.
+
+If you want your test to run over a variety of targets, use the :py:func:`tvm.testing.parametrize_targets` decorator. For example:
+
+.. code:: python
+
+ @tvm.testing.parametrize_targets
+ def test_mytest(target, ctx):
+ ...
+
+will run `test_mytest` with `target="llvm"`, `target="cuda"`, and few others. This also ensures that your test is run on the correct hardware by the CI. If you only want to test against a couple targets use `@tvm.testing.parametrize_targets("target_1", "target_2")`. If you want to test on a single target, use the associated decorator from :py:func:`tvm.testing`. For example, CUDA tests use the `@tvm.testing.requires_cuda` decorator.
+
Handle Integer Constant Expression
----------------------------------
We often need to handle constant integer expressions in TVM. Before we do so, the first question we want to ask is that is it really necessary to get a constant integer. If symbolic expression also works and let the logic flow, we should use symbolic expression as much as possible. So the generated code works for shapes that are not known ahead of time.
diff --git a/docs/_sources/contribute/pull_request.rst.txt b/docs/_sources/contribute/pull_request.rst.txt
index 128ae80..935f2d5 100644
--- a/docs/_sources/contribute/pull_request.rst.txt
+++ b/docs/_sources/contribute/pull_request.rst.txt
@@ -32,8 +32,22 @@ This is a quick guide to submit a pull request, please also refer to the detaile
.. code:: bash
- # Reproduce the lint procedure in the CI.
+ # Run all lint steps.
+ docker/lint.sh
+
+ # To run steps individually, specify their step names on the command-line. An incorrectly
+ # spelled step name causes the tool to print all available steps.
+ docker/lint.sh <step_name> ...
+
+ # While the lint commands used should be identical to those run in CI, this command reproduces
+ # the CI lint procedure exactly (typically helpful for debugging lint script errors).
docker/bash.sh tvmai/ci-lint ./tests/scripts/task_lint.sh
+
+ When the clang-format lint check fails, run git-clang-format as follows to automatically reformat
+ your code:
+
+ .. code:: bash
+
# Run clang-format check for all the files that changed since upstream/master
docker/bash.sh tvmai/ci-lint ./tests/lint/git-clang-format.sh upstream/master
diff --git a/docs/_sources/deploy/arm_compute_lib.rst.txt b/docs/_sources/deploy/arm_compute_lib.rst.txt
index 6dc8df0..e3399c5 100644
--- a/docs/_sources/deploy/arm_compute_lib.rst.txt
+++ b/docs/_sources/deploy/arm_compute_lib.rst.txt
@@ -15,8 +15,9 @@
specific language governing permissions and limitations
under the License.
-Relay Arm|reg| Compute Library Integration
-==========================================
+Relay Arm :sup:`®` Compute Library Integration
+==============================================
+**Author**: `Luke Hutton <https://github.com/lhutton1>`_
Introduction
------------
@@ -26,6 +27,35 @@ and GPU's. Currently the integration offloads operators to ACL to use hand-craft
routines in the library. By offloading select operators from a relay graph to ACL we can achieve
a performance boost on such devices.
+Installing Arm Compute Library
+------------------------------
+
+Before installing Arm Compute Library, it is important to know what architecture to build for. One way
+to determine this is to use `lscpu` and look for the "Model name" of the CPU. You can then use this to
+determine the architecture by looking online.
+
+We recommend two different ways to build and install ACL:
+
+* Use the script located at `docker/install/ubuntu_install_arm_compute_library.sh`. You can use this
+ script for building ACL from source natively or for cross-compiling the library on an x86 machine.
+ You may need to change the architecture of the device you wish to compile for by altering the
+ `target_arch` variable. Binaries will be built from source and installed to the location denoted by
+ `install_path`.
+* Alternatively, you can download and use pre-built binaries from:
+ https://github.com/ARM-software/ComputeLibrary/releases. When using this package, you will need to
+ select the binaries for the architecture you require and make sure they are visible to cmake. This
+ can be done like so:
+
+ .. code:: bash
+
+ cd <acl-prebuilt-package>/lib
+ mv ./linux-<architecture-to-build-for>-neon/* .
+
+
+In both cases you will need to set USE_ARM_COMPUTE_LIB_GRAPH_RUNTIME to the path where the ACL package
+is located. Cmake will look in /path-to-acl/ along with /path-to-acl/lib and /path-to-acl/build for the
+required binaries. See the section below for more information on how to use these configuration options.
+
Building with ACL support
-------------------------
@@ -42,6 +72,20 @@ to compile an ACL module on an x86 machine and then run the module on a remote A
need to use USE_ARM_COMPUTE_LIB=ON on the x86 machine and USE_ARM_COMPUTE_LIB_GRAPH_RUNTIME=ON on the remote
AArch64 device.
+By default both options are set to OFF. Using USE_ARM_COMPUTE_LIB_GRAPH_RUNTIME=ON will mean that ACL
+binaries are searched for by cmake in the default locations
+(see https://cmake.org/cmake/help/v3.4/command/find_library.html). In addition to this,
+/path-to-tvm-project/acl/ will also be searched. It is likely that you will need to set your own path to
+locate ACL. This can be done by specifying a path in the place of ON.
+
+These flags should be set in your config.cmake file. For example:
+
+.. code:: cmake
+
+ set(USE_ARM_COMPUTE_LIB ON)
+ set(USE_ARM_COMPUTE_LIB_GRAPH_RUNTIME /path/to/acl)
+
+
Usage
-----
@@ -74,7 +118,7 @@ max_pool2d operator).
Annotate and partition the graph for ACL.
-..code:: python
+.. code:: python
from tvm.relay.op.contrib.arm_compute_lib import partition_for_arm_compute_lib
module = partition_for_arm_compute_lib(module)
@@ -100,7 +144,7 @@ Export the module.
Run Inference. This must be on an Arm device. If compiling on x86 device and running on AArch64,
consider using the RPC mechanism. Tutorials for using the RPC mechanism:
-https://tvm.apache.org/docs/tutorials/cross_compilation_and_rpc.html#sphx-glr-tutorials-cross-compilation-and-rpc-py
+https://tvm.apache.org/docs/tutorials/get_started/cross_compilation_and_rpc.html
.. code:: python
@@ -118,29 +162,76 @@ More examples
The example above only shows a basic example of how ACL can be used for offloading a single
Maxpool2D. If you would like to see more examples for each implemented operator and for
networks refer to the tests: `tests/python/contrib/test_arm_compute_lib`. Here you can modify
-`infrastructure.py` to use the remote device you have setup.
+`test_config.json` to configure how a remote device is created in `infrastructure.py` and,
+as a result, how runtime tests will be run.
+
+An example configuration for `test_config.json`:
+
+* connection_type - The type of RPC connection. Options: local, tracker, remote.
+* host - The host device to connect to.
+* port - The port to use when connecting.
+* target - The target to use for compilation.
+* device_key - The device key when connecting via a tracker.
+* cross_compile - Path to cross compiler when connecting from a non-arm platform e.g. aarch64-linux-gnu-g++.
+
+.. code:: json
+
+ {
+ "connection_type": "local",
+ "host": "localhost",
+ "port": 9090,
+ "target": "llvm -mtriple=aarch64-linux-gnu -mattr=+neon",
+ "device_key": "",
+ "cross_compile": ""
+ }
Operator support
----------------
-+--------------+-------------------------------------------------------------------------+
-| Relay Node | Remarks |
-+==============+=========================================================================+
-| nn.conv2d | fp32: |
-| | Simple: nn.conv2d |
-| | Composite: nn.pad?, nn.conv2d, nn.bias_add?, nn.relu? |
-| | |
-| | (only groups = 1 supported) |
-+--------------+-------------------------------------------------------------------------+
-| qnn.conv2d | uint8: |
-| | Composite: nn.pad?, nn.conv2d, nn.bias_add?, nn.relu?, qnn.requantize |
-| | |
-| | (only groups = 1 supported) |
-+--------------+-------------------------------------------------------------------------+
-| nn.maxpool2d | fp32, uint8 |
-+--------------+-------------------------------------------------------------------------+
-| reshape | fp32, uint8 |
-+--------------+-------------------------------------------------------------------------+
++----------------------+-------------------------------------------------------------------------+
+| Relay Node | Remarks |
++======================+=========================================================================+
+| nn.conv2d | fp32: |
+| | Simple: nn.conv2d |
+| | Composite: nn.pad?, nn.conv2d, nn.bias_add?, nn.relu? |
+| | |
+| | (only groups = 1 supported) |
++----------------------+-------------------------------------------------------------------------+
+| qnn.conv2d | uint8: |
+| | Composite: nn.pad?, nn.conv2d, nn.bias_add?, nn.relu?, qnn.requantize |
+| | |
+| | (only groups = 1 supported) |
++----------------------+-------------------------------------------------------------------------+
+| nn.dense | fp32: |
+| | Simple: nn.dense |
+| | Composite: nn.dense, nn.bias_add? |
++----------------------+-------------------------------------------------------------------------+
+| qnn.dense | uint8: |
+| | Composite: qnn.dense, nn.bias_add?, qnn.requantize |
++----------------------+-------------------------------------------------------------------------+
+| nn.max_pool2d | fp32, uint8 |
++----------------------+-------------------------------------------------------------------------+
+| nn.global_max_pool2d | fp32, uint8 |
++----------------------+-------------------------------------------------------------------------+
+| nn.avg_pool2d | fp32: |
+| | Simple: nn.avg_pool2d |
+| | |
+| | uint8: |
+| | Composite: cast(int32), nn.avg_pool2d, cast(uint8) |
++----------------------+-------------------------------------------------------------------------+
+| nn.global_avg_pool2d | fp32: |
+| | Simple: nn.global_avg_pool2d |
+| | |
+| | uint8: |
+| | Composite: cast(int32), nn.avg_pool2d, cast(uint8) |
++----------------------+-------------------------------------------------------------------------+
+| power(of 2) + | A special case for L2 pooling. |
+| nn.avg_pool2d + | |
+| sqrt | fp32: |
+| | Composite: power(of 2), nn.avg_pool2d, sqrt |
++----------------------+-------------------------------------------------------------------------+
+| reshape | fp32, uint8 |
++----------------------+-------------------------------------------------------------------------+
.. note::
A composite operator is a series of operators that map to a single Arm Compute Library operator. You can view this
@@ -155,12 +246,12 @@ what needs to be changed and where, it will not however dive into the complexiti
individual operator. This is left to the developer.
There are a series of files we need to make changes to:
+
* `python/relay/op/contrib/arm_compute_lib.py` In this file we define the operators we wish to offload using the
-`op.register` decorator. This will mean the annotation pass recognizes this operator as ACL
-offloadable.
+ `op.register` decorator. This will mean the annotation pass recognizes this operator as ACL offloadable.
* `src/relay/backend/contrib/arm_compute_lib/codegen.cc` Implement `Create[OpName]JSONNode` method. This is where we
-declare how the operator should be represented by JSON. This will be used to create the ACL module.
-* `src/runtime/contrib/arm_compute_lib/acl_kernel.h` Implement `Create[OpName]Layer` method. This is where we
-define how the JSON representation can be used to create an ACL function. We simply define how to
-translate from the JSON representation to ACL API.
+ declare how the operator should be represented by JSON. This will be used to create the ACL module.
+* `src/runtime/contrib/arm_compute_lib/acl_runtime.cc` Implement `Create[OpName]Layer` method. This is where we
+ define how the JSON representation can be used to create an ACL function. We simply define how to
+ translate from the JSON representation to ACL API.
* `tests/python/contrib/test_arm_compute_lib` Add unit tests for the given operator.
diff --git a/docs/_sources/dev/hybrid_script.rst.txt b/docs/_sources/dev/hybrid_script.rst.txt
index 7bb5e23..939cf05 100644
--- a/docs/_sources/dev/hybrid_script.rst.txt
+++ b/docs/_sources/dev/hybrid_script.rst.txt
@@ -91,3 +91,10 @@ Math Intrinsics
So far, these math intrinsics, ``log``, ``exp``, ``sigmoid``, ``tanh``, ``power``, and ``popcount``, are supported.
Math intrinsics will be imported by the decorator. Most of the intrinsics are borrowed by library implementation
except ``popcount`` and ``sigmoid``. I implemented them manually.
+
+
+Casting
+~~~~~~~
+
+You can cast values by using the keywords ``uint8``, ``uint16`` ``uint32``, ``uint64``, ``int8``, ``int16``, ``int32``, ``int64``,
+``float16``, ``float32``, ``float64``.
diff --git a/docs/_sources/dev/index.rst.txt b/docs/_sources/dev/index.rst.txt
index c448cb0..2e577df 100644
--- a/docs/_sources/dev/index.rst.txt
+++ b/docs/_sources/dev/index.rst.txt
@@ -295,6 +295,11 @@ The following code snippet gives an example of PassContext configuration.
Op is the common class to represent all system-defined primitive operator/intrinsics.
Developers can register new Ops as well as their additional attributes(e.g. whether the Op is elementwise) to the system.
+.. toctree::
+ :maxdepth: 1
+
+ pass_infra
+
tvm/target
----------
@@ -353,7 +358,6 @@ memory(for memory optimization).
relay_intro
relay_op_strategy
- relay_pass_infra
convert_layout
diff --git a/docs/_sources/dev/relay_pass_infra.rst.txt b/docs/_sources/dev/pass_infra.rst.txt
similarity index 67%
rename from docs/_sources/dev/relay_pass_infra.rst.txt
rename to docs/_sources/dev/pass_infra.rst.txt
index 446a91b..6fd150d 100644
--- a/docs/_sources/dev/relay_pass_infra.rst.txt
+++ b/docs/_sources/dev/pass_infra.rst.txt
@@ -15,24 +15,28 @@
specific language governing permissions and limitations
under the License.
-.. _relay-pass-infra:
+.. _pass-infra:
-Relay Pass Infrastructure
-=========================
+Pass Infrastructure
+===================
-Relay features a series of optimization passes which improve performance metrics
+Both Relay and TVM IR contain a series of optimization passes which improve performance metrics
of models such as mean inference, memory footprint, or power consumption for
specific devices. There is a suite of standard optimizations as well as machine
learning-specific optimizations including constant folding, dead code
-elimination, operator layout alteration, and operator fusion, etc. Each of these
-passes is structured as a Relay-to-Relay transformation on the abstract syntax
-tree (AST) using the analysis result collected during and/or before traversal.
+elimination, operator layout alteration, operator fusion, buffer handling, and
+loop transformation, etc. Each of these passes is structured as a ir-to-ir
+transformation using the analysis result collected during and/or before traversal.
-However, as Relay evolves quickly, the need for a more systematic and efficient
-way to manage these passes is becoming apparent. This doc describes the design of
-such an infra that takes the advantage of the way production compilers are used to
-manage the optimization passes and the style modern deep learning frameworks
-adopted to build up layers.
+However, as TVM evolves quickly, the need for a more systematic and efficient
+way to manage these passes is becoming apparent. In addition, a generic
+framework that manages the passes across different layers of the TVM stack (e.g.
+Relay and tir) paves the way for developers to quickly prototype and plug the
+implemented passes into the system.
+
+This doc describes the design of such an infra that takes the advantage of the
+way production compilers are used to manage the optimization passes and the style
+modern deep learning frameworks adopted to build up layers.
For example, many existing production compilers, such as GCC and LLVM, employ
pass managers to effectively manage the execution of passes. Initially managing
@@ -88,10 +92,10 @@ needs to be executed when running under a user-provided optimization level. The
.. code:: c++
- class PassInfoNode : public RelayNode {
- std::string name;
+ class PassInfoNode : public Object {
+ String name;
int opt_level;
- std::vector<std::string> required;
+ Array<String> required;
};
PassContext
@@ -111,17 +115,16 @@ This class is designed for users to conveniently write the Python ``with``
syntax to perform optimizations under a certain configuration. In addition, the
users can obtain the context that is available within a certain program scope in
a thread-safe way through ``PassContext::Current()``, since a thread-local store
-``RelayPassContextThreadLocalStore`` is used to hold the created pass context
+``PassContextThreadLocalStore`` is used to hold the created pass context
objects. Examples will be provided later to show how we can use both the C++ and
Python APIs to create a compilation pipeline using pass context.
.. code:: c++
- class PassContextNode : public RelayNode {
+ class PassContextNode : public Object {
public:
ErrorReporter err_reporter;
int opt_level{2};
- int fallback_device{static_cast<int>(kDLCPU)};
tvm::Array<tvm::Expr> required_pass;
tvm::Array<tvm::Expr> disabled_pass;
};
@@ -142,32 +145,32 @@ Python APIs to create a compilation pipeline using pass context.
friend class tvm::With<PassContext>;
};
- struct RelayPassContextThreadLocalEntry {
+ struct PassContextThreadLocalEntry {
/*! \brief The default pass context. */
PassContext default_context;
/*! \brief The current pass context. */
std::stack<PassContext> context_stack;
- RelayPassContextThreadLocalEntry() {
+ PassContextThreadLocalEntry() {
default_context = PassContext(make_node<PassContextNode>());
}
};
/*! \brief The thread-local store to hold the pass context. */
- typedef dmlc::ThreadLocalStore<RelayPassContextThreadLocalEntry>
- RelayPassContextThreadLocalStore;
+ typedef dmlc::ThreadLocalStore<PassContextThreadLocalEntry>
+ PassContextThreadLocalStore;
Pass Constructs
^^^^^^^^^^^^^^^
The pass infra is designed in a hierarchical manner, and it could work at
-different granularities of Relay programs. A pure virtual class ``PassNode`` is
+different granularities of Relay/tir programs. A pure virtual class ``PassNode`` is
introduced to serve as the base of the different optimization passes. This class
contains several virtual methods that must be implemented by the
-subclasses at the level of modules, functions, or sequences of passes..
+subclasses at the level of modules, functions, or sequences of passes.
.. code:: c++
- class PassNode : RelayNode {
+ class PassNode : Object {
virtual PassInfo Info() const = 0;
virtual Module operator()(const IRModule& mod
const PassContext& pass_ctx) const = 0;
@@ -192,7 +195,8 @@ Module level passes are geared mainly for global and inter-procedural
optimizations (IPO), which are similar to the module pass used in LLVM. Some
typical passes in Relay that need the global picture of a module, such as
A-normal form conversion and lambda lifting, etc., fall into this set. At this
-level, users can even add and/or delete functions in a module.
+level, users can even add and/or delete functions in a module. Note that all
+passes
.. code:: c++
@@ -215,13 +219,14 @@ Function-Level Passes
^^^^^^^^^^^^^^^^^^^^^
Function-level passes are used to implement various intra-function level
-optimizations for a given Relay module. It fetches one function at a time from
+optimizations for a given Relay/tir module. It fetches one function at a time from
the function list of a module for optimization and yields a rewritten Relay
-function. Most of Relay's passes can be classified into this category, such as
-common subexpression elimination and inference simplification, etc.
+``Function`` or tir ``PrimFunc``. Most of passes can be classified into this category, such as
+common subexpression elimination and inference simplification in Relay as well as vectorization
+and flattening storage in tir, etc.
-Note that the scope of passes at this level is a Relay function. Therefore, we
-cannot add or delete a function through these passes as they are not aware of
+Note that the scope of passes at this level is either a Relay function or a tir primitive function.
+Therefore, we cannot add or delete a function through these passes as they are not aware of
the global information.
.. code:: c++
@@ -312,74 +317,25 @@ favorably use Python APIs to create a specific pass object.
.. code:: c++
- FunctionPass CreateFunctionPass(std::string name,
- int opt_level,
- PassFunc pass_func);
-
- ModulePass CreateModulePass(std::string name,
- int opt_level,
- PassFunc pass_func);
-
- SequentialPass CreateSequentialPass(std::string name,
- int opt_level,
- Array<Pass> passes,
- Array<tvm::Expr> disabled);
-
-C++ Sequential Example
-^^^^^^^^^^^^^^^^^^^^^^
-
-Let's now take an example to illustrate how the pass infra works on
-``SequentialPass``. For illustrative purpose, only a code snippet is provided.
-First, we create a simple Relay program, ``y = f(x)``. Then, we build a module
-based on the function. After creating the module, we instantiate a sequential
-pass object which contains some standard Relay optimization passes, including
-type inference, dead code elimination, common subexpression elimination, and
-layout alteration.
-
-Finally, a pass context is constructed and the passes will be executed
-sequentially. During the execution of these passes, the pass dependency will be
-resolved automatically as we have encoded the dependent passes during
-registration.
-
-.. code:: c++
-
- // Create a simple Relay program.
- auto tensor_type = relay::TensorType({}, tvm::Bool());
- auto x = relay::Var("x", relay::Type());
- auto f = relay::Function(tvm::Array<relay::Var>{ x }, x, relay::Type(), {});
+ Pass CreateFunctionPass(
+ const runtime::TypedPackedFunc<PrimFunc(PrimFunc, IRModule, PassContext)>& pass_func,
+ int opt_level,
+ String name,
+ Array<String> required);
- auto y = relay::Var("y", tensor_type);
- auto call = relay::Call(f, tvm::Array<relay::Expr>{ y });
- auto fx = relay::Function(tvm::Array<relay::Var>{ y }, call, relay::Type(), {});
+ Pass CreatePrimFuncPass(
+ const runtime::TypedPackedFunc<PrimFunc(PrimFunc, IRModule, PassContext)>& pass_func,
+ int opt_level,
+ String name,
+ Array<String> required);
- // Create a module for optimization.
- auto mod = IRModule::FromExpr(fx);
+ Pass CreateModulePass(
+ const runtime::TypedPackedFunc<PrimFunc(PrimFunc, IRModule, PassContext)>& pass_func,
+ int opt_level,
+ String name,
+ Array<String> required);
- // Create a sequential pass.
- tvm::Array<relay::transform::Pass> pass_seqs{
- relay::transform::InferType(),
- relay::transform::DeadCodeElimination(),
- relay::transform::EliminateCommonSubexpr(),
- relay::transform::AlterOpLayout()
- };
- relay::transform::Pass seq = relay::transform::Sequential(pass_seqs);
-
- // Create a pass context for the optimization.
- auto ctx = relay::transform::PassContext::Create();
- ctx->opt_level = 2;
- ctx->fallback_device = kDLCPU;
-
- // Use the Python with syntax to execute the sequence of optimizations.
- tvm::With<relay::transform::PassContext> scope(ctx);
- mod = seq(mod);
-
- // View the updated module.
- LOG(INFO) << relay::AsText(mod) << std::endl;
-
-Other types of passes should be directly invoked for execution on a module. For
-example, users can directly apply const folding pass on a given module, ``mod
-= transform::FoldConstant()(mod)``. However, it is users' responsibility to
-execute the required passes explicitly.
+ Pass Sequential(tvm::Array<Pass> passes, PassInfo pass_info);
Pass Registration
~~~~~~~~~~~~~~~~~
@@ -400,7 +356,7 @@ In order to register this pass to the pass infra, we first need to decide at
which level this pass will be performed. As const folding happens on individual
functions, we should intuitively create a ``FunctionPass`` for it through
``CreateFunctionPass``. The ``pass_func`` is returned as a packed function that
-invokes the ``Expr`` to ``Expr`` API on each function in a Relay module. ``{}``
+invokes the ``Expr`` to ``Expr`` API on each function in a `IRModule`. ``{}``
indicates that no prerequisite is required for this pass. Otherwise, the pass
developer has to identify and list them.
@@ -414,8 +370,8 @@ Python when needed.
namespace transform {
Pass FoldConstant() {
- runtime::TypedPackedFunc<Function(Function, Module, PassContext)> pass_func =
- [=](Function f, Module m, PassContext pc) {
+ runtime::TypedPackedFunc<Function(Function, IRModule, PassContext)> pass_func =
+ [=](Function f, IRModule m, PassContext pc) {
return Downcast<Function>(FoldConstant(f));
};
return CreateFunctionPass(pass_func, 2, "FoldConstant", {});
@@ -438,7 +394,8 @@ Python Frontend
Only some simple APIs are needed for the frontend side. For example, we can
provide users the following APIs to create and execute a pass (full
-implementation is provided in `python/tvm/relay/transform.py`_). The backend
+implementation is provided in `python/tvm/relay/transform.py`_ and
+`python/tvm/ir/transform.py`_). The backend
receives the information and decides which function it should use to create
a Pass object.
@@ -452,13 +409,13 @@ a certain scope.
.. code:: python
- @register_relay_node
- class PassContext(RelayNode):
+ @tvm._ffi.register_object("transform.PassContext")
+ class PassContext(tvm.runtime.Object):
def __enter__(self):
_transform.EnterPassContext(self)
return self
- def __exit__(self, ptype, value, trace):
+ def __exit__(self, ptype, value, trace, config):
_transform.ExitPassContext(self)
@staticmethod
@@ -466,10 +423,19 @@ a certain scope.
"""Return the current pass context."""
return _transform.GetCurrentPassContext()
-A ``PassContext`` object can be instantiated through the ``build_config`` API
-which was used by Relay to configure the compilation options, including the
-optimization level, fallback device for heterogeneous execution, and
-required/disabled passes.
+A ``PassContext`` is used to configure the compilation options, including the
+optimization level and required/disabled passes. It can also take a dictionary
+of configs so that different passes can conveniently fetch the passed data, such
+as fallback device info and step/depth for loop unrolling, etc. In order to
+enable fetching the required config, the key must be registered through
+``TVM_REGISTER_PASS_CONFIG_OPTION``. For example, the following is used by the
+loop unrolling pass
+
+.. code:: c++
+
+ TVM_REGISTER_PASS_CONFIG_OPTION("tir.UnrollLoop", UnrollLoopConfig);
+
+Please refer to `src/tir/transforms/unroll_loop.cc`_ for more details.
Pass Objects
^^^^^^^^^^^^
@@ -493,7 +459,8 @@ example, ``module_pass``, ``function_pass``, and ``sequential`` are provided to
users so that they can customize their own pass or pass pipeline.
For all the passes that are implemented in the C++ backend, we provide
-a corresponding Python API in `python/tvm/relay/transform.py`_. For instance,
+corresponding Python APIs in `python/tvm/ir/transform.py`_ and
+`python/tvm/relay/transform.py`_, respectively. For instance,
const folding has a Python API like the following:
.. code:: python
@@ -555,95 +522,9 @@ instance, an example function-level pass could be written as the following:
Alternatively, users can also directly register a pass without using the
-decorators and then invoke it. Let's use ``Sequential`` to demo this scenario.
-
-Python Sequential Example
-^^^^^^^^^^^^^^^^^^^^^^^^^
-
-This example not only illustrates how users can directly create a sequential
-pass using Python APIs (this could be applied to module- and function-level
-passes as well), but also explains how we can build an optimization pipeline
-using ``Sequential`` associated with other types of passes.
-
-.. code:: python
-
- # Create a simple Relay program.
- shape = (1, 2, 3)
- c_data = np.array(shape).astype("float32")
- tp = relay.TensorType(shape, "float32")
- c = relay.const(c_data)
- x = relay.var("x", tp)
- y = relay.add(c, c)
- y = relay.multiply(y, relay.const(2, "float32"))
- y = relay.add(x, y)
- z = relay.add(y, c)
- z1 = relay.add(y, c)
- z2 = relay.add(z, z1)
- func = relay.Function([x], z2)
-
- # Customize the optimization pipeline.
- seq = tvm.transform.Sequential([
- relay.transform.InferType(),
- relay.transform.FoldConstant(),
- relay.transform.EliminateCommonSubexpr(),
- relay.transform.AlterOpLayout()
- ])
-
- # Create a module to perform optimizations.
- mod = relay.Module({"main": func})
-
- # Users can disable any passes that they don't want to execute by providing
- # a list, e.g. disabled_pass=["EliminateCommonSubexpr"].
- with relay.build_config(opt_level=3):
- with tvm.target.create("llvm"):
- # Perform the optimizations.
- mod = seq(mod)
-
-Debugging
-~~~~~~~~~
-
-The pass infra provides a special pass (``PrintIR``) to dump the IR of the
-whole module after applying a certain pass. A slightly modified version of the
-sequential pass example could be like the following to enable IR dumping for
-``FoldConstant`` optimization.
-
-.. code:: python
-
- seq = tvm.transform.Sequential([
- relay.transform.InferType(),
- relay.transform.FoldConstant(),
- transform.PrintIR(),
- relay.transform.EliminateCommonSubexpr(),
- relay.transform.AlterOpLayout()
- ])
-
-By inserting the ``PrintIR`` pass after ``FoldConstant``, the pass infra will
-dump out the module IR when ``FoldConstant`` is done. Users can plug in this
-pass after any pass they want to debug for viewing the optimization effect.
-
-There is a more flexible debugging mechanism also exposed by the build configuration
-object. One can pass a tracing function which can be used to execute arbitrary code
-before and/or after each pass. A tracing function will receive a ``IRModule``, ``PassInfo``,
-and a boolean indicating whether you are executing before, or after a pass.
-An example is below.
-
-.. code:: python
-
- def print_ir(mod, info, is_before):
- """Print the name of the pass, the IR, only before passes execute."""
- if is_before:
- print(f"Running pass: {}", info)
- print(mod)
-
- with relay.build_config(opt_level=3, trace=print_ir):
- with tvm.target.create("llvm"):
- # Perform the optimizations.
- mod = seq(mod)
-
-
-For more pass infra related examples in Python and C++, please refer to
-`tests/python/relay/test_pass_manager.py`_ and
-`tests/cpp/relay_transform_sequential.cc`_, respectively.
+decorators and then invoke it. For more examples about how to customize your own
+optimization pipeline and debug Relay and tir passes, please refer to the
+`use pass infra`_ tutorial.
.. _Sequential: https://pytorch.org/docs/stable/nn.html?highlight=sequential#torch.nn.Sequential
@@ -659,8 +540,10 @@ For more pass infra related examples in Python and C++, please refer to
.. _python/tvm/relay/transform.py: https://github.com/apache/incubator-tvm/blob/master/python/tvm/relay/transform.py
-.. _tests/python/relay/test_pass_manager.py: https://github.com/apache/incubator-tvm/blob/master/tests/python/relay/test_pass_manager.py
+.. _include/tvm/relay/transform.h: https://github.com/apache/incubator-tvm/blob/master/include/tvm/relay/transform.h
+
+.. _python/tvm/ir/transform.py: https://github.com/apache/incubator-tvm/blob/master/python/tvm/ir/transform.py
-.. _tests/cpp/relay_transform_sequential.cc: https://github.com/apache/incubator-tvm/blob/master/tests/cpp/relay_transform_sequential.cc
+.. _src/tir/transforms/unroll_loop.cc: https://github.com/apache/incubator-tvm/blob/master/src/tir/transforms/unroll_loop.cc
-.. _include/tvm/relay/transform.h: https://github.com/apache/incubator-tvm/blob/master/include/tvm/relay/transform.h
+.. _use pass infra: https://github.com/apache/incubator-tvm/blob/master/tutorials/dev/use_pass_infra.py
diff --git a/docs/_sources/dev/relay_add_pass.rst.txt b/docs/_sources/dev/relay_add_pass.rst.txt
index fc26559..e1a5e7e 100644
--- a/docs/_sources/dev/relay_add_pass.rst.txt
+++ b/docs/_sources/dev/relay_add_pass.rst.txt
@@ -30,7 +30,7 @@ compiler passes.
At a high level, there are two key components to writing a pass:
- Creating one or more C++ classes that traverse the program
-- Wrapping the traversal implementation and its metadata in the pass manager API so it can neatly interface with the :ref:`relay-pass-infra`
+- Wrapping the traversal implementation and its metadata in the pass manager API so it can neatly interface with the :ref:`pass-infra`
To begin, we'll give an overview of the key mechanisms for writing a compiler
pass. Then, we'll walk through a concrete example of the constant-folding
@@ -335,7 +335,7 @@ class that takes an expression and internally creates and uses a
Registering a Pass with the Pass Manager
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-*Note: please see the documentation on the :ref:`relay-pass-infra` for more specific detail on this subject.*
+*Note: please see the documentation on the :ref:`pass-infra` for more specific detail on this subject.*
With the AST traversers written, the pass can be registered to become a TVM
API endpoint with the following code:
@@ -395,7 +395,7 @@ the below code applies both the ``FoldConstant`` and ``ToANormalForm`` passes
new_mod = seq(mod)
More detail about registration can be found in :ref:`tvm-runtime-system` and more
-information about the pass manager interface can be found in :ref:`relay-pass-infra`.
+information about the pass manager interface can be found in :ref:`pass-infra`.
Relay's standard passes are listed in `include/tvm/relay/transform.h`_ and implemented
in `src/relay/pass/`_.
diff --git a/docs/_sources/dev/virtual_machine.rst.txt b/docs/_sources/dev/virtual_machine.rst.txt
index 059878f..ae6cac2 100644
--- a/docs/_sources/dev/virtual_machine.rst.txt
+++ b/docs/_sources/dev/virtual_machine.rst.txt
@@ -110,7 +110,7 @@ InvokePacked
Invoke the packed function denoted by ``packed_index``. The ``arity``
and ``output_size`` are used to inform the VM how many inputs and
outputs to expect. ``packed_args`` stores the list of argument registers. Note ``Index``
-is an alais of ``int64_t``, and it will be used in other instructions as well.
+is an alias of ``int64_t``, and it will be used in other instructions as well.
AllocTensor
^^^^^^^^^^^
diff --git a/docs/_sources/install/docker.rst.txt b/docs/_sources/install/docker.rst.txt
index f49c75e..e1e6570 100644
--- a/docs/_sources/install/docker.rst.txt
+++ b/docs/_sources/install/docker.rst.txt
@@ -37,8 +37,7 @@ We can then use the following command to launch a docker image.
/path/to/tvm/docker/bash.sh <image-name>
-Here the image-name can be a local docker image name, e.g. `tvm.ci_cpu` after you have done
-the local build. Or a pre-built third party image (`tvmai/demo-cpu` or `tvmai/ci-gpu`).
+Here the image-name can be a local docker image name, e.g. `tvm.ci_cpu` after you have done the local build.
This auxiliary script does the following things:
@@ -65,10 +64,22 @@ Note that on macOS, because we use bridge network, jupyter notebook will be repo
at an URL like ``http://{container_hostname}:8888/?token=...``. You should replace the ``container_hostname``
with ``localhost`` when pasting it into browser.
-You can find some un-official prebuilt images in `<https://hub.docker.com/r/tvmai/>`_.
-Note that these are convenience images and are not part of the ASF release.
Docker Source
-------------
Check out `The docker source <https://github.com/apache/incubator-tvm/tree/master/docker>`_ if you are interested in
building your own docker images.
+
+
+Run the following command to build the docker image.
+
+.. code:: bash
+
+ /path/to/tvm/docker/build.sh <image-name>
+
+
+You can find some un-official thirdparty prebuilt images.
+These images are used for test purposes and are NOT of the ASF release.
+
+
+`<https://hub.docker.com/r/tlpack/>`_.
diff --git a/docs/_sources/install/from_source.rst.txt b/docs/_sources/install/from_source.rst.txt
index 26aec77..9fe6c5e 100644
--- a/docs/_sources/install/from_source.rst.txt
+++ b/docs/_sources/install/from_source.rst.txt
@@ -213,13 +213,6 @@ like ``virtualenv``.
pip3 install --user tornado psutil xgboost
- * If you want to build tvm to compile a model, you must use Python 3 and run the following
-
- .. code:: bash
-
- sudo apt install antlr4
- pip3 install --user mypy orderedset antlr4-python3-runtime
-
Install Contrib Libraries
-------------------------
diff --git a/docs/_sources/tutorials/autotvm/sg_execution_times.rst.txt b/docs/_sources/tutorials/autotvm/sg_execution_times.rst.txt
index 4839155..6a44793 100644
--- a/docs/_sources/tutorials/autotvm/sg_execution_times.rst.txt
+++ b/docs/_sources/tutorials/autotvm/sg_execution_times.rst.txt
@@ -5,11 +5,11 @@
Computation times
=================
-**00:49.255** total execution time for **tutorials_autotvm** files:
-
-- **00:27.486**: :ref:`sphx_glr_tutorials_autotvm_tune_simple_template.py` (``tune_simple_template.py``)
-- **00:21.211**: :ref:`sphx_glr_tutorials_autotvm_tune_conv2d_cuda.py` (``tune_conv2d_cuda.py``)
-- **00:00.143**: :ref:`sphx_glr_tutorials_autotvm_tune_relay_x86.py` (``tune_relay_x86.py``)
-- **00:00.143**: :ref:`sphx_glr_tutorials_autotvm_tune_relay_cuda.py` (``tune_relay_cuda.py``)
-- **00:00.136**: :ref:`sphx_glr_tutorials_autotvm_tune_relay_mobile_gpu.py` (``tune_relay_mobile_gpu.py``)
-- **00:00.136**: :ref:`sphx_glr_tutorials_autotvm_tune_relay_arm.py` (``tune_relay_arm.py``)
+**00:53.528** total execution time for **tutorials_autotvm** files:
+
+- **00:29.137**: :ref:`sphx_glr_tutorials_autotvm_tune_simple_template.py` (``tune_simple_template.py``)
+- **00:23.762**: :ref:`sphx_glr_tutorials_autotvm_tune_conv2d_cuda.py` (``tune_conv2d_cuda.py``)
+- **00:00.166**: :ref:`sphx_glr_tutorials_autotvm_tune_relay_cuda.py` (``tune_relay_cuda.py``)
+- **00:00.155**: :ref:`sphx_glr_tutorials_autotvm_tune_relay_mobile_gpu.py` (``tune_relay_mobile_gpu.py``)
+- **00:00.155**: :ref:`sphx_glr_tutorials_autotvm_tune_relay_x86.py` (``tune_relay_x86.py``)
+- **00:00.154**: :ref:`sphx_glr_tutorials_autotvm_tune_relay_arm.py` (``tune_relay_arm.py``)
diff --git a/docs/_sources/tutorials/autotvm/tune_conv2d_cuda.rst.txt b/docs/_sources/tutorials/autotvm/tune_conv2d_cuda.rst.txt
index 9d1a408..cc061f4 100644
--- a/docs/_sources/tutorials/autotvm/tune_conv2d_cuda.rst.txt
+++ b/docs/_sources/tutorials/autotvm/tune_conv2d_cuda.rst.txt
@@ -234,26 +234,26 @@ for this template
7 unroll_explicit: OtherOption([0, 1]) len=2
)
Get devices for measurement successfully!
- No: 1 GFLOPS: 0.00/0.00 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f38a36cfee1]\n [bt] (3) /workspace/build/libtvm.so(+0x5d0037) [0x7f38a2c67037]\n [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3e6) [0x7f38a2c66ad6]\n [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
- No: 2 GFLOPS: 0.00/0.00 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f38a36cfee1]\n [bt] (3) /workspace/build/libtvm.so(+0x5d0037) [0x7f38a2c67037]\n [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3e6) [0x7f38a2c66ad6]\n [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
- No: 3 GFLOPS: 0.00/0.00 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f38a36cfee1]\n [bt] (3) /workspace/build/libtvm.so(+0x5d0037) [0x7f38a2c67037]\n [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3e6) [0x7f38a2c66ad6]\n [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
- No: 4 GFLOPS: 0.00/0.00 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f38a36cfee1]\n [bt] (3) /workspace/build/libtvm.so(+0x5d0037) [0x7f38a2c67037]\n [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3e6) [0x7f38a2c66ad6]\n [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
- No: 5 GFLOPS: 0.00/0.00 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f38a36cfee1]\n [bt] (3) /workspace/build/libtvm.so(+0x5d0037) [0x7f38a2c67037]\n [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3e6) [0x7f38a2c66ad6]\n [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
- No: 6 GFLOPS: 0.00/0.00 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f38a36cfee1]\n [bt] (3) /workspace/build/libtvm.so(+0x5d0037) [0x7f38a2c67037]\n [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3e6) [0x7f38a2c66ad6]\n [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
- No: 7 GFLOPS: 0.00/0.00 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f38a36cfee1]\n [bt] (3) /workspace/build/libtvm.so(+0x5d0037) [0x7f38a2c67037]\n [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3e6) [0x7f38a2c66ad6]\n [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
- No: 8 GFLOPS: 0.00/0.00 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f38a36cfee1]\n [bt] (3) /workspace/build/libtvm.so(+0x5d0037) [0x7f38a2c67037]\n [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3e6) [0x7f38a2c66ad6]\n [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
- No: 9 GFLOPS: 0.00/0.00 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f38a36cfee1]\n [bt] (3) /workspace/build/libtvm.so(+0x5d0037) [0x7f38a2c67037]\n [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3e6) [0x7f38a2c66ad6]\n [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
- No: 10 GFLOPS: 0.00/0.00 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f38a36cfee1]\n [bt] (3) /workspace/build/libtvm.so(+0x5d0037) [0x7f38a2c67037]\n [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3e6) [0x7f38a2c66ad6]\n [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
- No: 11 GFLOPS: 0.00/0.00 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f38a36cfee1]\n [bt] (3) /workspace/build/libtvm.so(+0x5d0037) [0x7f38a2c67037]\n [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3e6) [0x7f38a2c66ad6]\n [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
- No: 12 GFLOPS: 70.55/70.55 result: MeasureResult(costs=(0.00328131475,), error_no=0, all_cost=1.6126303672790527, timestamp=1596419638.176739) [('tile_f', [-1, 1, 1, 1]), ('tile_y', [-1, 1, 1, 7]), ('tile_x', [-1, 1, 1, 1]), ('tile_rc', [-1, 16, 1]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 1, 3]), ('auto_unroll_max_step', 512), ('unroll_explicit', 1)],None,8533140
- No: 13 GFLOPS: 0.00/70.55 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f38a36cfee1]\n [bt] (3) /workspace/build/libtvm.so(+0x5d0037) [0x7f38a2c67037]\n [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3e6) [0x7f38a2c66ad6]\n [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
- No: 14 GFLOPS: 0.00/70.55 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f38a36cfee1]\n [bt] (3) /workspace/build/libtvm.so(+0x5d0037) [0x7f38a2c67037]\n [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3e6) [0x7f38a2c66ad6]\n [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
- No: 15 GFLOPS: 0.00/70.55 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f38a36cfee1]\n [bt] (3) /workspace/build/libtvm.so(+0x5d0037) [0x7f38a2c67037]\n [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3e6) [0x7f38a2c66ad6]\n [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
- No: 16 GFLOPS: 0.00/70.55 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f38a36cfee1]\n [bt] (3) /workspace/build/libtvm.so(+0x5d0037) [0x7f38a2c67037]\n [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3e6) [0x7f38a2c66ad6]\n [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
- No: 17 GFLOPS: 0.00/70.55 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f38a36cfee1]\n [bt] (3) /workspace/build/libtvm.so(+0x5d0037) [0x7f38a2c67037]\n [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3e6) [0x7f38a2c66ad6]\n [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
- No: 18 GFLOPS: 0.00/70.55 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f38a36cfee1]\n [bt] (3) /workspace/build/libtvm.so(+0x5d0037) [0x7f38a2c67037]\n [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3e6) [0x7f38a2c66ad6]\n [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
- No: 19 GFLOPS: 739.67/739.67 result: MeasureResult(costs=(0.00031298046559633024,), error_no=0, all_cost=1.9273707866668701, timestamp=1596419642.162234) [('tile_f', [-1, 2, 1, 1]), ('tile_y', [-1, 1, 7, 1]), ('tile_x', [-1, 1, 7, 1]), ('tile_rc', [-1, 32, 1]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 1)],None,9699801
- No: 20 GFLOPS: 7.10/739.67 result: MeasureResult(costs=(0.03259615375,), error_no=0, all_cost=1.5985333919525146, timestamp=1596419643.0097616) [('tile_f', [-1, 2, 1, 16]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 7, 1, 1]), ('tile_rc', [-1, 2, 32]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 1, 1]), ('auto_unroll_max_step', 512), ('unroll_explicit', 1)],None,7502165
+ No: 1 GFLOPS: 0.00/0.00 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7fc651973821]\n [bt] (3) /workspace/build/libtvm.so(+0x668a17) [0x7fc650e76a17]\n [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3e6) [0x7fc650e764b6]\n [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
+ No: 2 GFLOPS: 0.00/0.00 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7fc651973821]\n [bt] (3) /workspace/build/libtvm.so(+0x668a17) [0x7fc650e76a17]\n [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3e6) [0x7fc650e764b6]\n [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
+ No: 3 GFLOPS: 0.00/0.00 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7fc651973821]\n [bt] (3) /workspace/build/libtvm.so(+0x668a17) [0x7fc650e76a17]\n [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3e6) [0x7fc650e764b6]\n [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
+ No: 4 GFLOPS: 0.00/0.00 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7fc651973821]\n [bt] (3) /workspace/build/libtvm.so(+0x668a17) [0x7fc650e76a17]\n [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3e6) [0x7fc650e764b6]\n [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
+ No: 5 GFLOPS: 0.00/0.00 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7fc651973821]\n [bt] (3) /workspace/build/libtvm.so(+0x668a17) [0x7fc650e76a17]\n [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3e6) [0x7fc650e764b6]\n [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
+ No: 6 GFLOPS: 0.00/0.00 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7fc651973821]\n [bt] (3) /workspace/build/libtvm.so(+0x668a17) [0x7fc650e76a17]\n [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3e6) [0x7fc650e764b6]\n [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
+ No: 7 GFLOPS: 0.00/0.00 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7fc651973821]\n [bt] (3) /workspace/build/libtvm.so(+0x668a17) [0x7fc650e76a17]\n [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3e6) [0x7fc650e764b6]\n [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
+ No: 8 GFLOPS: 0.00/0.00 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7fc651973821]\n [bt] (3) /workspace/build/libtvm.so(+0x668a17) [0x7fc650e76a17]\n [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3e6) [0x7fc650e764b6]\n [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
+ No: 9 GFLOPS: 0.00/0.00 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7fc651973821]\n [bt] (3) /workspace/build/libtvm.so(+0x668a17) [0x7fc650e76a17]\n [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3e6) [0x7fc650e764b6]\n [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
+ No: 10 GFLOPS: 0.00/0.00 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7fc651973821]\n [bt] (3) /workspace/build/libtvm.so(+0x668a17) [0x7fc650e76a17]\n [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3e6) [0x7fc650e764b6]\n [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
+ No: 11 GFLOPS: 0.00/0.00 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7fc651973821]\n [bt] (3) /workspace/build/libtvm.so(+0x668a17) [0x7fc650e76a17]\n [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3e6) [0x7fc650e764b6]\n [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
+ No: 12 GFLOPS: 69.63/69.63 result: MeasureResult(costs=(0.0033246283750000003,), error_no=0, all_cost=1.6560664176940918, timestamp=1599097909.3491123) [('tile_f', [-1, 1, 1, 1]), ('tile_y', [-1, 1, 1, 7]), ('tile_x', [-1, 1, 1, 1]), ('tile_rc', [-1, 16, 1]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 1, 3]), ('auto_unroll_max_step', 512), ('unroll_explicit', 1)],None,8533140
+ No: 13 GFLOPS: 0.00/69.63 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7fc651973821]\n [bt] (3) /workspace/build/libtvm.so(+0x668a17) [0x7fc650e76a17]\n [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3e6) [0x7fc650e764b6]\n [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
+ No: 14 GFLOPS: 0.00/69.63 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7fc651973821]\n [bt] (3) /workspace/build/libtvm.so(+0x668a17) [0x7fc650e76a17]\n [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3e6) [0x7fc650e764b6]\n [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
+ No: 15 GFLOPS: 0.00/69.63 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7fc651973821]\n [bt] (3) /workspace/build/libtvm.so(+0x668a17) [0x7fc650e76a17]\n [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3e6) [0x7fc650e764b6]\n [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
+ No: 16 GFLOPS: 0.00/69.63 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7fc651973821]\n [bt] (3) /workspace/build/libtvm.so(+0x668a17) [0x7fc650e76a17]\n [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3e6) [0x7fc650e764b6]\n [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
+ No: 17 GFLOPS: 0.00/69.63 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7fc651973821]\n [bt] (3) /workspace/build/libtvm.so(+0x668a17) [0x7fc650e76a17]\n [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3e6) [0x7fc650e764b6]\n [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
+ No: 18 GFLOPS: 0.00/69.63 result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7fc651973821]\n [bt] (3) /workspace/build/libtvm.so(+0x668a17) [0x7fc650e76a17]\n [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3e6) [0x7fc650e764b6]\n [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
+ No: 19 GFLOPS: 692.74/692.74 result: MeasureResult(costs=(0.00033418439013452913,), error_no=0, all_cost=2.1232569217681885, timestamp=1599097914.050044) [('tile_f', [-1, 2, 1, 1]), ('tile_y', [-1, 1, 7, 1]), ('tile_x', [-1, 1, 7, 1]), ('tile_rc', [-1, 32, 1]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 1)],None,9699801
+ No: 20 GFLOPS: 7.06/692.74 result: MeasureResult(costs=(0.03278169025,), error_no=0, all_cost=1.757389783859253, timestamp=1599097915.0128677) [('tile_f', [-1, 2, 1, 16]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 7, 1, 1]), ('tile_rc', [-1, 2, 32]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 1, 1]), ('auto_unroll_max_step', 512), ('unroll_explicit', 1)],None,7502165
@@ -307,7 +307,7 @@ and measure running time.
Best config:
[('tile_f', [-1, 2, 1, 1]), ('tile_y', [-1, 1, 7, 1]), ('tile_x', [-1, 1, 7, 1]), ('tile_rc', [-1, 32, 1]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 1)],None,9699801
- Time cost of this operator: 0.000359
+ Time cost of this operator: 0.000361
diff --git a/docs/_sources/tutorials/autotvm/tune_simple_template.rst.txt b/docs/_sources/tutorials/autotvm/tune_simple_template.rst.txt
index 9353a5e..4ecc782 100644
--- a/docs/_sources/tutorials/autotvm/tune_simple_template.rst.txt
+++ b/docs/_sources/tutorials/autotvm/tune_simple_template.rst.txt
@@ -361,16 +361,16 @@ used to get the best config later.
.. code-block:: none
Get devices for measurement successfully!
- No: 1 GFLOPS: 7.09/7.09 result: MeasureResult(costs=(0.037853168799999996,), error_no=0, all_cost=1.04099702835083, timestamp=1596419606.6558206) [('tile_y', [-1, 8]), ('tile_x', [-1, 8])],None,33
- No: 2 GFLOPS: 2.30/7.09 result: MeasureResult(costs=(0.116738172,), error_no=0, all_cost=2.374105215072632, timestamp=1596419608.9016356) [('tile_y', [-1, 8]), ('tile_x', [-1, 2])],None,13
- No: 3 GFLOPS: 7.49/7.49 result: MeasureResult(costs=(0.035850838,), error_no=0, all_cost=1.4166457653045654, timestamp=1596419609.8355696) [('tile_y', [-1, 16]), ('tile_x', [-1, 128])],None,74
- No: 4 GFLOPS: 4.48/7.49 result: MeasureResult(costs=(0.0599121328,), error_no=0, all_cost=1.4389441013336182, timestamp=1596419611.1620698) [('tile_y', [-1, 16]), ('tile_x', [-1, 32])],None,54
- No: 5 GFLOPS: 6.08/7.49 result: MeasureResult(costs=(0.0441534014,), error_no=0, all_cost=1.1245598793029785, timestamp=1596419612.2320726) [('tile_y', [-1, 128]), ('tile_x', [-1, 8])],None,37
- No: 6 GFLOPS: 6.47/7.49 result: MeasureResult(costs=(0.041497002399999997,), error_no=0, all_cost=1.107459306716919, timestamp=1596419613.2634501) [('tile_y', [-1, 64]), ('tile_x', [-1, 8])],None,36
- No: 7 GFLOPS: 27.33/27.33 result: MeasureResult(costs=(0.009821894000000001,), error_no=0, all_cost=0.6573705673217773, timestamp=1596419613.7893965) [('tile_y', [-1, 1]), ('tile_x', [-1, 128])],None,70
- No: 8 GFLOPS: 21.47/27.33 result: MeasureResult(costs=(0.012502513000000002,), error_no=0, all_cost=0.706108570098877, timestamp=1596419614.3581583) [('tile_y', [-1, 4]), ('tile_x', [-1, 512])],None,92
- No: 9 GFLOPS: 0.83/27.33 result: MeasureResult(costs=(0.3229418878,), error_no=0, all_cost=5.547542572021484, timestamp=1596419619.885717) [('tile_y', [-1, 512]), ('tile_x', [-1, 2])],None,19
- No: 10 GFLOPS: 0.00/27.33 result: MeasureResult(costs=(RuntimeError('Traceback (most recent call last):\n [bt] (5) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f38a36cfee1]\n [bt] (4) /workspace/build/libtvm.so(+0x106bc12) [0x7f38a3702c12]\n [bt] (3) /workspace/build/libtvm.so(tvm::runtime::RPCWrappedFunc::operator()(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const+0x26b) [0x7f38a3705d2b]\n [bt] (2) /workspace/build/libtvm.so(tvm::runtime::RPCClientSession::Call [...]
+ No: 1 GFLOPS: 6.72/6.72 result: MeasureResult(costs=(0.0399487232,), error_no=0, all_cost=0.9529943466186523, timestamp=1599097874.9568658) [('tile_y', [-1, 8]), ('tile_x', [-1, 8])],None,33
+ No: 2 GFLOPS: 2.20/6.72 result: MeasureResult(costs=(0.1221858156,), error_no=0, all_cost=2.2919247150421143, timestamp=1599097877.3494487) [('tile_y', [-1, 8]), ('tile_x', [-1, 2])],None,13
+ No: 3 GFLOPS: 7.14/7.14 result: MeasureResult(costs=(0.037595030200000004,), error_no=0, all_cost=1.4327435493469238, timestamp=1599097878.3681812) [('tile_y', [-1, 16]), ('tile_x', [-1, 128])],None,74
+ No: 4 GFLOPS: 4.28/7.14 result: MeasureResult(costs=(0.062721577,), error_no=0, all_cost=1.4620404243469238, timestamp=1599097879.794526) [('tile_y', [-1, 16]), ('tile_x', [-1, 32])],None,54
+ No: 5 GFLOPS: 6.10/7.14 result: MeasureResult(costs=(0.0440382272,), error_no=0, all_cost=1.0699217319488525, timestamp=1599097880.9146302) [('tile_y', [-1, 128]), ('tile_x', [-1, 8])],None,37
+ No: 6 GFLOPS: 6.17/7.14 result: MeasureResult(costs=(0.0435387002,), error_no=0, all_cost=1.103325605392456, timestamp=1599097882.0777445) [('tile_y', [-1, 64]), ('tile_x', [-1, 8])],None,36
+ No: 7 GFLOPS: 25.21/25.21 result: MeasureResult(costs=(0.010645947999999999,), error_no=0, all_cost=0.6406195163726807, timestamp=1599097882.6758382) [('tile_y', [-1, 1]), ('tile_x', [-1, 128])],None,70
+ No: 8 GFLOPS: 20.94/25.21 result: MeasureResult(costs=(0.0128166258,), error_no=0, all_cost=0.5873754024505615, timestamp=1599097883.2995389) [('tile_y', [-1, 4]), ('tile_x', [-1, 512])],None,92
+ No: 9 GFLOPS: 0.71/25.21 result: MeasureResult(costs=(0.3807563502,), error_no=0, all_cost=6.390629291534424, timestamp=1599097889.7905786) [('tile_y', [-1, 512]), ('tile_x', [-1, 2])],None,19
+ No: 10 GFLOPS: 0.00/25.21 result: MeasureResult(costs=(RuntimeError('Traceback (most recent call last):\n [bt] (5) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7fc651973821]\n [bt] (4) /workspace/build/libtvm.so(+0x119ab32) [0x7fc6519a8b32]\n [bt] (3) /workspace/build/libtvm.so(tvm::runtime::RPCWrappedFunc::operator()(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const+0x26b) [0x7fc6519aa12b]\n [bt] (2) /workspace/build/libtvm.so(tvm::runtime::RPCClientSession::Call [...]
diff --git a/docs/_sources/tutorials/dev/low_level_custom_pass.rst.txt b/docs/_sources/tutorials/dev/low_level_custom_pass.rst.txt
index 8ffdf2b..118c461 100644
--- a/docs/_sources/tutorials/dev/low_level_custom_pass.rst.txt
+++ b/docs/_sources/tutorials/dev/low_level_custom_pass.rst.txt
@@ -74,16 +74,16 @@ our customized lowering pass to manipulate the IR directly instead of using sche
primfn(a_1: handle, b_1: handle, c_1: handle) -> ()
attr = {"global_symbol": "main", "tir.noalias": True}
- buffers = {c: Buffer(c_2: handle, float32, [128], []),
- b: Buffer(b_2: handle, float32, [128], []),
- a: Buffer(a_2: handle, float32, [128], [])}
+ buffers = {c: Buffer(c_2: Pointer(float32), float32, [128], []),
+ b: Buffer(b_2: Pointer(float32), float32, [128], []),
+ a: Buffer(a_2: Pointer(float32), float32, [128], [])}
buffer_map = {a_1: a, b_1: b, c_1: c} {
for (i: int32, 0, 128) {
c_2[i] = ((float32*)a_2[i] + (float32*)b_2[i])
}
}
- // meta data omitted. you can use show_meta_data=True to include meta data
+
@@ -217,16 +217,16 @@ Thus, a good place to put this transformation pass is just after Phase 1.
primfn(a_1: handle, b_1: handle, c_1: handle) -> ()
attr = {"global_symbol": "main", "tir.noalias": True}
- buffers = {c: Buffer(c_2: handle, float32, [128], []),
- b: Buffer(b_2: handle, float32, [128], []),
- a: Buffer(a_2: handle, float32, [128], [])}
+ buffers = {c: Buffer(c_2: Pointer(float32), float32, [128], []),
+ b: Buffer(b_2: Pointer(float32), float32, [128], []),
+ a: Buffer(a_2: Pointer(float32), float32, [128], [])}
buffer_map = {a_1: a, b_1: b, c_1: c} {
for (i.outer: int32, 0, 16) {
c_2[ramp((i.outer*8), 1, 8)] = ((float32x8*)a_2[ramp((i.outer*8), 1, 8)] + (float32x8*)b_2[ramp((i.outer*8), 1, 8)])
}
}
- // meta data omitted. you can use show_meta_data=True to include meta data
+
diff --git a/docs/_sources/tutorials/dev/sg_execution_times.rst.txt b/docs/_sources/tutorials/dev/sg_execution_times.rst.txt
index 8df7f88..aaae757 100644
--- a/docs/_sources/tutorials/dev/sg_execution_times.rst.txt
+++ b/docs/_sources/tutorials/dev/sg_execution_times.rst.txt
@@ -5,7 +5,7 @@
Computation times
=================
-**00:00.440** total execution time for **tutorials_dev** files:
+**00:00.554** total execution time for **tutorials_dev** files:
-- **00:00.304**: :ref:`sphx_glr_tutorials_dev_relay_pass_infra.py` (``relay_pass_infra.py``)
-- **00:00.136**: :ref:`sphx_glr_tutorials_dev_low_level_custom_pass.py` (``low_level_custom_pass.py``)
+- **00:00.385**: :ref:`sphx_glr_tutorials_dev_use_pass_infra.py` (``use_pass_infra.py``)
+- **00:00.169**: :ref:`sphx_glr_tutorials_dev_low_level_custom_pass.py` (``low_level_custom_pass.py``)
diff --git a/docs/_sources/tutorials/dev/relay_pass_infra.rst.txt b/docs/_sources/tutorials/dev/use_pass_infra.rst.txt
similarity index 53%
rename from docs/_sources/tutorials/dev/relay_pass_infra.rst.txt
rename to docs/_sources/tutorials/dev/use_pass_infra.rst.txt
index f813330..334c3a5 100644
--- a/docs/_sources/tutorials/dev/relay_pass_infra.rst.txt
+++ b/docs/_sources/tutorials/dev/use_pass_infra.rst.txt
@@ -1,32 +1,34 @@
.. note::
:class: sphx-glr-download-link-note
- Click :ref:`here <sphx_glr_download_tutorials_dev_relay_pass_infra.py>` to download the full example code
+ Click :ref:`here <sphx_glr_download_tutorials_dev_use_pass_infra.py>` to download the full example code
.. rst-class:: sphx-glr-example-title
-.. _sphx_glr_tutorials_dev_relay_pass_infra.py:
+.. _sphx_glr_tutorials_dev_use_pass_infra.py:
-.. _tutorial-relay-pass-infra:
+.. _tutorial-use-pass-infra:
-How to Use Relay Pass Infra
-===========================
+How to Use TVM Pass Infra
+=========================
**Author**: `Zhi Chen <https://github.com/zhiics>`_
-As the number of optimization passes increases in Relay, it becomes intractable to
+As the number of optimization passes increases in Relay/tir, it becomes intractable to
execute them and maintain their dependencies manually. Therefore, we have
-introduced an infrastructure to manage the optimization passes.
-
-The optimizations of a Relay program could be applied at various granularity,
-namely function-level and module-level using :py:class:`tvm.relay.transform.FunctionPass`
-and :py:class:`tvm.relay.transform.ModulePass`
-respectively. Or users can rely on py:class:`tvm.transform.Sequential` to apply a sequence of passes
-on a Relay program where the dependencies between passes can be resolved by the
+introduced an infrastructure to manage the optimization passes and make it
+applicable to different layers of the IR in the TVM stack.
+
+The optimizations of a Relay/tir program could be applied at various granularity,
+namely function-level and module-level using :py:class:`tvm.relay.transform.FunctionPass`/
+:py:class:`tvm.tir.transform.PrimFuncPass` and :py:class:`tvm.transform.ModulePass`
+respectively. Or users can rely on :py:class:`tvm.transform.Sequential` to apply a sequence of passes
+on a Relay/tir program where the dependencies between passes can be resolved by the
pass infra. For more details about each type of these passes, please refer to
-the :ref:`relay-pass-infra`
+the :ref:`pass-infra`
-This tutorial demostrates how developers can use the Relay pass infra to perform
-a certain optimization and create an optimization pipeline.
+This tutorial mainly demostrates how developers can use the pass infra to perform
+a certain optimization and create an optimization pipeline for a Relay program.
+The same approach can be used for tir as well.
.. code-block:: default
@@ -47,6 +49,7 @@ Create An Example Relay Program
-------------------------------
First of all, we create a simple Relay program for the tutorial. This program
will be used by various optimizations of the examples in this tutorial.
+Similarly, users can write a tir primitive function and apply the tir passes.
.. code-block:: default
@@ -65,7 +68,7 @@ will be used by various optimizations of the examples in this tutorial.
z = relay.add(y, c)
z1 = relay.add(y, c)
z2 = relay.add(z, z1)
- return relay.Function([x], z2)
+ return relay.Function([x, weight], z2)
@@ -143,7 +146,7 @@ Manually Apply Optimization Passes
add(%2, %3) /* ty=Tensor[(1, 64, 54, 54), float32] */
}
- // meta data omitted. you can use show_meta_data=True to include meta data
+
@@ -173,7 +176,7 @@ eliminate the common expressions that used by `z` and `z1`.
add(%2, %2) /* ty=Tensor[(1, 64, 54, 54), float32] */
}
- // meta data omitted. you can use show_meta_data=True to include meta data
+
@@ -219,7 +222,7 @@ opt level 0 will not allow operators to be fused together. Users can pass the
%6(%5) /* ty=Tensor[(1, 64, 54, 54), float32] */
}
- // meta data omitted. you can use show_meta_data=True to include meta data
+
@@ -278,12 +281,12 @@ pass.
%4(%x, %weight, meta[relay.Constant][0] /* ty=Tensor[(1, 64, 54, 54), float32] */ /* ty=Tensor[(1, 64, 54, 54), float32] */, meta[relay.Constant][1] /* ty=Tensor[(1, 64, 54, 54), float32] */ /* ty=Tensor[(1, 64, 54, 54), float32] */) /* ty=Tensor[(1, 64, 54, 54), float32] */
}
- // meta data omitted. you can use show_meta_data=True to include meta data
+
From the transformed Relay program, we can see that there are still two
-identical addition operations. This is because `EliminateCommonSubexpr`
+identical addition operations. This is because ``EliminateCommonSubexpr``
was not actually performed. The reason is because only the passes that have
optimization level less or equal to 2 will be executed by default under
:py:class:`tvm.transform.Sequential`. The pass infra,
@@ -318,7 +321,7 @@ for users to customize the optimization level that they want to execute.
%3(%x, %weight, meta[relay.Constant][0] /* ty=Tensor[(1, 64, 54, 54), float32] */ /* ty=Tensor[(1, 64, 54, 54), float32] */, meta[relay.Constant][1] /* ty=Tensor[(1, 64, 54, 54), float32] */ /* ty=Tensor[(1, 64, 54, 54), float32] */) /* ty=Tensor[(1, 64, 54, 54), float32] */
}
- // meta data omitted. you can use show_meta_data=True to include meta data
+
@@ -359,7 +362,7 @@ identical addition operations.
%4(%x, %weight, meta[relay.Constant][0] /* ty=Tensor[(1, 64, 54, 54), float32] */ /* ty=Tensor[(1, 64, 54, 54), float32] */, meta[relay.Constant][1] /* ty=Tensor[(1, 64, 54, 54), float32] */ /* ty=Tensor[(1, 64, 54, 54), float32] */) /* ty=Tensor[(1, 64, 54, 54), float32] */
}
- // meta data omitted. you can use show_meta_data=True to include meta data
+
@@ -401,7 +404,7 @@ alteration pass falls in such category.
%3(%x, %weight, meta[relay.Constant][0] /* ty=Tensor[(1, 64, 54, 54), float32] */ /* ty=Tensor[(1, 64, 54, 54), float32] */, meta[relay.Constant][1] /* ty=Tensor[(1, 64, 54, 54), float32] */ /* ty=Tensor[(1, 64, 54, 54), float32] */) /* ty=Tensor[(1, 64, 54, 54), float32] */
}
- // meta data omitted. you can use show_meta_data=True to include meta data
+
def @main(%x: Tensor[(1, 64, 56, 56), float32], %weight: Tensor[(64, 64, 3, 3), float32]) -> Tensor[(1, 64, 54, 54), float32] {
%0 = layout_transform(%x, src_layout="NCHW", dst_layout="NCHW16c") /* ty=Tensor[(1, 4, 56, 56, 16), float32] */;
%1 = nn.conv2d(%0, %weight, padding=[0, 0, 0, 0], data_layout="NCHW16c") /* ty=Tensor[(1, 4, 54, 54, 16), float32] */;
@@ -416,7 +419,7 @@ alteration pass falls in such category.
layout_transform(%9, src_layout="NCHW16c", dst_layout="NCHW") /* ty=Tensor[(1, 64, 54, 54), float32] */
}
- // meta data omitted. you can use show_meta_data=True to include meta data
+
@@ -480,16 +483,16 @@ customized pass.
add(%6, %7) /* ty=Tensor[(1, 64, 54, 54), float32] */
}
- // meta data omitted. you can use show_meta_data=True to include meta data
+
Debug a Pass
------------
-Relay provides users a plug-and-play style debugging pass that print the IR
-after a certain pass is done. For example, we can print out the IR on the
-completion of constant folding and fusion by adding the debugging pass after
-them.
+TVM provides users a plug-and-play style debugging pass that print the IR
+after a certain pass is done through a special pass (``PrintIR``) to dump the IR of the
+whole module. A slightly modified version of the sequential pass example
+could be like the following to enable IR dumping for ``FoldConstant`` optimization.
.. code-block:: default
@@ -501,27 +504,254 @@ them.
tvm.transform.PrintIR(),
relay.transform.EliminateCommonSubexpr(),
relay.transform.FuseOps(),
- tvm.transform.PrintIR()])
- with tvm.transform.PassContext(opt_level=3):
- mod = seq(mod)
+ relay.transform.AlterOpLayout()])
+
+ # By inserting the ``PrintIR`` pass after ``FoldConstant``, the pass infra will
+ # dump out the module IR when ``FoldConstant`` is done. Users can plug in this
+ # pass after any pass they want to debug for viewing the optimization effect.
+ #
+ # There is a more flexible debugging mechanism also exposed by the build configuration
+ # object. One can pass a tracing function which can be used to execute arbitrary code
+ # before and/or after each pass. A tracing function will receive a :py::class:`tvm.IRModule`,
+ # a :py:class:`tvm.transform.PassInfo` object,
+ # and a boolean indicating whether you are executing before, or after a pass.
+ # An example is below.
+
+ def print_ir(mod, info, is_before):
+ """Print the name of the pass, the IR, only before passes execute."""
+ if is_before:
+ print("Running pass: {}", info)
+ print(mod)
+
+ with tvm.transform.PassContext(opt_level=3, trace=print_ir):
+ with tvm.target.create("llvm"):
+ # Perform the optimizations.
+ mod = seq(mod)
+ print(mod)
print("done")
+
.. rst-class:: sphx-glr-script-out
Out:
.. code-block:: none
+ Running pass: {} The meta data of the pass: pass name: FoldConstantopt_level: 2required passes: [
+ ]
+
+ def @main(%x: Tensor[(1, 64, 56, 56), float32], %weight: Tensor[(64, 64, 3, 3), float32]) -> Tensor[(1, 64, 54, 54), float32] {
+ %0 = nn.conv2d(%x, %weight, padding=[0, 0, 0, 0]) /* ty=Tensor[(1, 64, 54, 54), float32] */;
+ %1 = add(meta[relay.Constant][0] /* ty=Tensor[(1, 64, 54, 54), float32] */ /* ty=Tensor[(1, 64, 54, 54), float32] */, meta[relay.Constant][0] /* ty=Tensor[(1, 64, 54, 54), float32] */ /* ty=Tensor[(1, 64, 54, 54), float32] */) /* ty=Tensor[(1, 64, 54, 54), float32] */;
+ %2 = multiply(%1, 2f /* ty=float32 */) /* ty=Tensor[(1, 64, 54, 54), float32] */;
+ %3 = add(%0, %2) /* ty=Tensor[(1, 64, 54, 54), float32] */;
+ %4 = add(%3, meta[relay.Constant][0] /* ty=Tensor[(1, 64, 54, 54), float32] */ /* ty=Tensor[(1, 64, 54, 54), float32] */) /* ty=Tensor[(1, 64, 54, 54), float32] */;
+ %5 = add(%3, meta[relay.Constant][0] /* ty=Tensor[(1, 64, 54, 54), float32] */ /* ty=Tensor[(1, 64, 54, 54), float32] */) /* ty=Tensor[(1, 64, 54, 54), float32] */;
+ add(%4, %5) /* ty=Tensor[(1, 64, 54, 54), float32] */
+ }
+
+
+ Running pass: {} The meta data of the pass: pass name: InferTypeopt_level: 0required passes: [
+ ]
+
+ def @main() -> Tensor[(1, 64, 54, 54), float32] {
+ add(meta[relay.Constant][0] /* ty=Tensor[(1, 64, 54, 54), float32] */ /* ty=Tensor[(1, 64, 54, 54), float32] */, meta[relay.Constant][0] /* ty=Tensor[(1, 64, 54, 54), float32] */ /* ty=Tensor[(1, 64, 54, 54), float32] */) /* ty=Tensor[(1, 64, 54, 54), float32] */
+ }
+
+
+ Running pass: {} The meta data of the pass: pass name: FuseOpsopt_level: 1required passes: [
+ InferType, ]
+
+ def @main() -> Tensor[(1, 64, 54, 54), float32] {
+ add(meta[relay.Constant][0] /* ty=Tensor[(1, 64, 54, 54), float32] */ /* ty=Tensor[(1, 64, 54, 54), float32] */, meta[relay.Constant][0] /* ty=Tensor[(1, 64, 54, 54), float32] */ /* ty=Tensor[(1, 64, 54, 54), float32] */) /* ty=Tensor[(1, 64, 54, 54), float32] */
+ }
+
+
+ Running pass: {} The meta data of the pass: pass name: ToANormalFormopt_level: 1required passes: [
+ ]
+
+ def @main() -> Tensor[(1, 64, 54, 54), float32] {
+ %0 = fn (%p0: Tensor[(1, 64, 54, 54), float32], Primitive=1) -> Tensor[(1, 64, 54, 54), float32] {
+ add(%p0, %p0) /* ty=Tensor[(1, 64, 54, 54), float32] */
+ };
+ %0(meta[relay.Constant][0] /* ty=Tensor[(1, 64, 54, 54), float32] */ /* ty=Tensor[(1, 64, 54, 54), float32] */) /* ty=Tensor[(1, 64, 54, 54), float32] */
+ }
+
+
+ Running pass: {} The meta data of the pass: pass name: InferTypeopt_level: 0required passes: [
+ ]
+
+ def @main() -> Tensor[(1, 64, 54, 54), float32] {
+ let %x: Tensor[(1, 64, 54, 54), float32] = meta[relay.Constant][0] /* ty=Tensor[(1, 64, 54, 54), float32] */ /* ty=Tensor[(1, 64, 54, 54), float32] */;
+ let %x1: fn (Tensor[(1, 64, 54, 54), float32]) -> Tensor[(1, 64, 54, 54), float32] = fn (%p0: Tensor[(1, 64, 54, 54), float32], Primitive=1) -> Tensor[(1, 64, 54, 54), float32] {
+ add(%p0, %p0) /* ty=Tensor[(1, 64, 54, 54), float32] */
+ };
+ let %x2: Tensor[(1, 64, 54, 54), float32] = %x1(%x) /* ty=Tensor[(1, 64, 54, 54), float32] */;
+ %x2
+ }
+
+
+ Running pass: {} The meta data of the pass: pass name: InferTypeopt_level: 0required passes: [
+ ]
+
+ def @main() -> Tensor[(1, 64, 54, 54), float32] {
+ multiply(meta[relay.Constant][0] /* ty=Tensor[(1, 64, 54, 54), float32] */ /* ty=Tensor[(1, 64, 54, 54), float32] */, 2f /* ty=float32 */) /* ty=Tensor[(1, 64, 54, 54), float32] */
+ }
+
+
+ Running pass: {} The meta data of the pass: pass name: FuseOpsopt_level: 1required passes: [
+ InferType, ]
+
+ def @main() -> Tensor[(1, 64, 54, 54), float32] {
+ multiply(meta[relay.Constant][0] /* ty=Tensor[(1, 64, 54, 54), float32] */ /* ty=Tensor[(1, 64, 54, 54), float32] */, 2f /* ty=float32 */) /* ty=Tensor[(1, 64, 54, 54), float32] */
+ }
+
+
+ Running pass: {} The meta data of the pass: pass name: ToANormalFormopt_level: 1required passes: [
+ ]
+
+ def @main() -> Tensor[(1, 64, 54, 54), float32] {
+ %0 = fn (%p0: Tensor[(1, 64, 54, 54), float32], %p1: float32, Primitive=1) -> Tensor[(1, 64, 54, 54), float32] {
+ multiply(%p0, %p1) /* ty=Tensor[(1, 64, 54, 54), float32] */
+ };
+ %0(meta[relay.Constant][0] /* ty=Tensor[(1, 64, 54, 54), float32] */ /* ty=Tensor[(1, 64, 54, 54), float32] */, 2f /* ty=float32 */) /* ty=Tensor[(1, 64, 54, 54), float32] */
+ }
+
+
+ Running pass: {} The meta data of the pass: pass name: InferTypeopt_level: 0required passes: [
+ ]
+
+ def @main() -> Tensor[(1, 64, 54, 54), float32] {
+ let %x: Tensor[(1, 64, 54, 54), float32] = meta[relay.Constant][0] /* ty=Tensor[(1, 64, 54, 54), float32] */ /* ty=Tensor[(1, 64, 54, 54), float32] */;
+ let %x1: float32 = 2f /* ty=float32 */;
+ let %x2: fn (Tensor[(1, 64, 54, 54), float32], float32) -> Tensor[(1, 64, 54, 54), float32] = fn (%p0: Tensor[(1, 64, 54, 54), float32], %p1: float32, Primitive=1) -> Tensor[(1, 64, 54, 54), float32] {
+ multiply(%p0, %p1) /* ty=Tensor[(1, 64, 54, 54), float32] */
+ };
+ let %x3: Tensor[(1, 64, 54, 54), float32] = %x2(%x, %x1) /* ty=Tensor[(1, 64, 54, 54), float32] */;
+ %x3
+ }
+
+
+ Running pass: {} The meta data of the pass: pass name: PrintIRopt_level: 0required passes: [
+ ]
+
+ def @main(%x: Tensor[(1, 64, 56, 56), float32], %weight: Tensor[(64, 64, 3, 3), float32]) -> Tensor[(1, 64, 54, 54), float32] {
+ %0 = nn.conv2d(%x, %weight, padding=[0, 0, 0, 0]) /* ty=Tensor[(1, 64, 54, 54), float32] */;
+ %1 = add(%0, meta[relay.Constant][0] /* ty=Tensor[(1, 64, 54, 54), float32] */ /* ty=Tensor[(1, 64, 54, 54), float32] */) /* ty=Tensor[(1, 64, 54, 54), float32] */;
+ %2 = add(%1, meta[relay.Constant][1] /* ty=Tensor[(1, 64, 54, 54), float32] */ /* ty=Tensor[(1, 64, 54, 54), float32] */) /* ty=Tensor[(1, 64, 54, 54), float32] */;
+ %3 = add(%1, meta[relay.Constant][1] /* ty=Tensor[(1, 64, 54, 54), float32] */ /* ty=Tensor[(1, 64, 54, 54), float32] */) /* ty=Tensor[(1, 64, 54, 54), float32] */;
+ add(%2, %3) /* ty=Tensor[(1, 64, 54, 54), float32] */
+ }
+
+
+ Running pass: {} The meta data of the pass: pass name: InferTypeopt_level: 0required passes: [
+ ]
+
+ def @main(%x: Tensor[(1, 64, 56, 56), float32], %weight: Tensor[(64, 64, 3, 3), float32]) -> Tensor[(1, 64, 54, 54), float32] {
+ %0 = nn.conv2d(%x, %weight, padding=[0, 0, 0, 0]) /* ty=Tensor[(1, 64, 54, 54), float32] */;
+ %1 = add(%0, meta[relay.Constant][0] /* ty=Tensor[(1, 64, 54, 54), float32] */ /* ty=Tensor[(1, 64, 54, 54), float32] */) /* ty=Tensor[(1, 64, 54, 54), float32] */;
+ %2 = add(%1, meta[relay.Constant][1] /* ty=Tensor[(1, 64, 54, 54), float32] */ /* ty=Tensor[(1, 64, 54, 54), float32] */) /* ty=Tensor[(1, 64, 54, 54), float32] */;
+ %3 = add(%1, meta[relay.Constant][1] /* ty=Tensor[(1, 64, 54, 54), float32] */ /* ty=Tensor[(1, 64, 54, 54), float32] */) /* ty=Tensor[(1, 64, 54, 54), float32] */;
+ add(%2, %3) /* ty=Tensor[(1, 64, 54, 54), float32] */
+ }
+
+
+ Running pass: {} The meta data of the pass: pass name: EliminateCommonSubexpropt_level: 3required passes: [
+ InferType, ]
+
+ def @main(%x: Tensor[(1, 64, 56, 56), float32], %weight: Tensor[(64, 64, 3, 3), float32]) -> Tensor[(1, 64, 54, 54), float32] {
+ %0 = nn.conv2d(%x, %weight, padding=[0, 0, 0, 0]) /* ty=Tensor[(1, 64, 54, 54), float32] */;
+ %1 = add(%0, meta[relay.Constant][0] /* ty=Tensor[(1, 64, 54, 54), float32] */ /* ty=Tensor[(1, 64, 54, 54), float32] */) /* ty=Tensor[(1, 64, 54, 54), float32] */;
+ %2 = add(%1, meta[relay.Constant][1] /* ty=Tensor[(1, 64, 54, 54), float32] */ /* ty=Tensor[(1, 64, 54, 54), float32] */) /* ty=Tensor[(1, 64, 54, 54), float32] */;
+ %3 = add(%1, meta[relay.Constant][1] /* ty=Tensor[(1, 64, 54, 54), float32] */ /* ty=Tensor[(1, 64, 54, 54), float32] */) /* ty=Tensor[(1, 64, 54, 54), float32] */;
+ add(%2, %3) /* ty=Tensor[(1, 64, 54, 54), float32] */
+ }
+
+
+ Running pass: {} The meta data of the pass: pass name: InferTypeopt_level: 0required passes: [
+ ]
+
+ def @main(%x: Tensor[(1, 64, 56, 56), float32], %weight: Tensor[(64, 64, 3, 3), float32]) -> Tensor[(1, 64, 54, 54), float32] {
+ %0 = nn.conv2d(%x, %weight, padding=[0, 0, 0, 0]) /* ty=Tensor[(1, 64, 54, 54), float32] */;
+ %1 = add(%0, meta[relay.Constant][0] /* ty=Tensor[(1, 64, 54, 54), float32] */ /* ty=Tensor[(1, 64, 54, 54), float32] */) /* ty=Tensor[(1, 64, 54, 54), float32] */;
+ %2 = add(%1, meta[relay.Constant][1] /* ty=Tensor[(1, 64, 54, 54), float32] */ /* ty=Tensor[(1, 64, 54, 54), float32] */) /* ty=Tensor[(1, 64, 54, 54), float32] */;
+ add(%2, %2) /* ty=Tensor[(1, 64, 54, 54), float32] */
+ }
+
+
+ Running pass: {} The meta data of the pass: pass name: FuseOpsopt_level: 1required passes: [
+ InferType, ]
+
+ def @main(%x: Tensor[(1, 64, 56, 56), float32], %weight: Tensor[(64, 64, 3, 3), float32]) -> Tensor[(1, 64, 54, 54), float32] {
+ %0 = nn.conv2d(%x, %weight, padding=[0, 0, 0, 0]) /* ty=Tensor[(1, 64, 54, 54), float32] */;
+ %1 = add(%0, meta[relay.Constant][0] /* ty=Tensor[(1, 64, 54, 54), float32] */ /* ty=Tensor[(1, 64, 54, 54), float32] */) /* ty=Tensor[(1, 64, 54, 54), float32] */;
+ %2 = add(%1, meta[relay.Constant][1] /* ty=Tensor[(1, 64, 54, 54), float32] */ /* ty=Tensor[(1, 64, 54, 54), float32] */) /* ty=Tensor[(1, 64, 54, 54), float32] */;
+ add(%2, %2) /* ty=Tensor[(1, 64, 54, 54), float32] */
+ }
+
+
+ Running pass: {} The meta data of the pass: pass name: InferTypeopt_level: 0required passes: [
+ ]
+
+ def @main(%x: Tensor[(1, 64, 56, 56), float32], %weight: Tensor[(64, 64, 3, 3), float32]) -> Tensor[(1, 64, 54, 54), float32] {
+ %3 = fn (%p0: Tensor[(1, 64, 56, 56), float32], %p1: Tensor[(64, 64, 3, 3), float32], %p2: Tensor[(1, 64, 54, 54), float32], %p3: Tensor[(1, 64, 54, 54), float32], Primitive=1) -> Tensor[(1, 64, 54, 54), float32] {
+ %0 = nn.conv2d(%p0, %p1, padding=[0, 0, 0, 0]) /* ty=Tensor[(1, 64, 54, 54), float32] */;
+ %1 = add(%0, %p2) /* ty=Tensor[(1, 64, 54, 54), float32] */;
+ %2 = add(%1, %p3) /* ty=Tensor[(1, 64, 54, 54), float32] */;
+ add(%2, %2) /* ty=Tensor[(1, 64, 54, 54), float32] */
+ };
+ %3(%x, %weight, meta[relay.Constant][0] /* ty=Tensor[(1, 64, 54, 54), float32] */ /* ty=Tensor[(1, 64, 54, 54), float32] */, meta[relay.Constant][1] /* ty=Tensor[(1, 64, 54, 54), float32] */ /* ty=Tensor[(1, 64, 54, 54), float32] */) /* ty=Tensor[(1, 64, 54, 54), float32] */
+ }
+
+
+ Running pass: {} The meta data of the pass: pass name: AlterOpLayoutopt_level: 3required passes: [
+ InferType, ]
+
+ def @main(%x: Tensor[(1, 64, 56, 56), float32], %weight: Tensor[(64, 64, 3, 3), float32]) -> Tensor[(1, 64, 54, 54), float32] {
+ %3 = fn (%p0: Tensor[(1, 64, 56, 56), float32], %p1: Tensor[(64, 64, 3, 3), float32], %p2: Tensor[(1, 64, 54, 54), float32], %p3: Tensor[(1, 64, 54, 54), float32], Primitive=1) -> Tensor[(1, 64, 54, 54), float32] {
+ %0 = nn.conv2d(%p0, %p1, padding=[0, 0, 0, 0]) /* ty=Tensor[(1, 64, 54, 54), float32] */;
+ %1 = add(%0, %p2) /* ty=Tensor[(1, 64, 54, 54), float32] */;
+ %2 = add(%1, %p3) /* ty=Tensor[(1, 64, 54, 54), float32] */;
+ add(%2, %2) /* ty=Tensor[(1, 64, 54, 54), float32] */
+ };
+ %3(%x, %weight, meta[relay.Constant][0] /* ty=Tensor[(1, 64, 54, 54), float32] */ /* ty=Tensor[(1, 64, 54, 54), float32] */, meta[relay.Constant][1] /* ty=Tensor[(1, 64, 54, 54), float32] */ /* ty=Tensor[(1, 64, 54, 54), float32] */) /* ty=Tensor[(1, 64, 54, 54), float32] */
+ }
+
+
+ def @main(%x: Tensor[(1, 64, 56, 56), float32], %weight: Tensor[(64, 64, 3, 3), float32]) -> Tensor[(1, 64, 54, 54), float32] {
+ %7 = fn (%p0: Tensor[(1, 64, 56, 56), float32], %p1: Tensor[(64, 64, 3, 3), float32], %p2: Tensor[(1, 64, 54, 54), float32], %p3: Tensor[(1, 64, 54, 54), float32], Primitive=1) -> Tensor[(1, 64, 54, 54), float32] {
+ %0 = layout_transform(%p0, src_layout="NCHW", dst_layout="NCHW16c") /* ty=Tensor[(1, 4, 56, 56, 16), float32] */;
+ %1 = nn.conv2d(%0, %p1, padding=[0, 0, 0, 0], data_layout="NCHW16c") /* ty=Tensor[(1, 4, 54, 54, 16), float32] */;
+ %2 = layout_transform(%p2, src_layout="NCHW", dst_layout="NCHW16c") /* ty=Tensor[(1, 4, 54, 54, 16), float32] */;
+ %3 = add(%1, %2) /* ty=Tensor[(1, 4, 54, 54, 16), float32] */;
+ %4 = layout_transform(%p3, src_layout="NCHW", dst_layout="NCHW16c") /* ty=Tensor[(1, 4, 54, 54, 16), float32] */;
+ %5 = add(%3, %4) /* ty=Tensor[(1, 4, 54, 54, 16), float32] */;
+ %6 = add(%5, %5) /* ty=Tensor[(1, 4, 54, 54, 16), float32] */;
+ layout_transform(%6, src_layout="NCHW16c", dst_layout="NCHW") /* ty=Tensor[(1, 64, 54, 54), float32] */
+ };
+ %7(%x, %weight, meta[relay.Constant][0] /* ty=Tensor[(1, 64, 54, 54), float32] */ /* ty=Tensor[(1, 64, 54, 54), float32] */, meta[relay.Constant][1] /* ty=Tensor[(1, 64, 54, 54), float32] */ /* ty=Tensor[(1, 64, 54, 54), float32] */) /* ty=Tensor[(1, 64, 54, 54), float32] */
+ }
+
+
done
+Summary
+-------
+This tutorial has covered how we can write and invoke passes in TVM more
+conveniently using the pass infra. Different ways of invoking a pass are also
+disucssed. Using :py:class:`tvm.transform.Sequential` can largely help
+users to ease the work of handling multiple optimization passes and their
+dependencies. In addition, an example is provided to illustrate
+how we can debug a pass using the ``PrintIR`` and tracing.
+
-.. _sphx_glr_download_tutorials_dev_relay_pass_infra.py:
+.. _sphx_glr_download_tutorials_dev_use_pass_infra.py:
.. only :: html
@@ -533,13 +763,13 @@ them.
.. container:: sphx-glr-download
- :download:`Download Python source code: relay_pass_infra.py <relay_pass_infra.py>`
+ :download:`Download Python source code: use_pass_infra.py <use_pass_infra.py>`
.. container:: sphx-glr-download
- :download:`Download Jupyter notebook: relay_pass_infra.ipynb <relay_pass_infra.ipynb>`
+ :download:`Download Jupyter notebook: use_pass_infra.ipynb <use_pass_infra.ipynb>`
.. only:: html
diff --git a/docs/_sources/tutorials/frontend/deploy_model_on_android.rst.txt b/docs/_sources/tutorials/frontend/deploy_model_on_android.rst.txt
index ef8d312..92ab7ce 100644
--- a/docs/_sources/tutorials/frontend/deploy_model_on_android.rst.txt
+++ b/docs/_sources/tutorials/frontend/deploy_model_on_android.rst.txt
@@ -142,7 +142,7 @@ Here is an example of config.mk. I enabled OpenCL and Vulkan.
.. code-block:: bash
- /opt/android-sdk-linux/ndk-bundle/build/tools/make-standalone-toolchain.sh \
+ $ANDROID_NDK_HOME/build/tools/make-standalone-toolchain.sh \
--platform=android-24 --use-llvm --arch=arm64 --install-dir=/opt/android-toolchain-arm64
export TVM_NDK_CC=/opt/android-toolchain-arm64/bin/aarch64-linux-android-g++
@@ -413,7 +413,7 @@ Execute on TVM
TVM prediction top-1: tiger cat
Evaluate inference time cost...
- Mean inference time (std dev): 4.78 ms (0.81 ms)
+ Mean inference time (std dev): 17.66 ms (0.04 ms)
diff --git a/docs/_sources/tutorials/frontend/deploy_prequantized.rst.txt b/docs/_sources/tutorials/frontend/deploy_prequantized.rst.txt
index b433745..b5dfdc4 100644
--- a/docs/_sources/tutorials/frontend/deploy_prequantized.rst.txt
+++ b/docs/_sources/tutorials/frontend/deploy_prequantized.rst.txt
@@ -245,15 +245,6 @@ qnn.quantize, qnn.dequantize, qnn.requantize, and qnn.conv2d etc.
-.. rst-class:: sphx-glr-script-out
-
- Out:
-
- .. code-block:: none
-
- ANTLR runtime and generated code versions disagree: 4.8!=4.7.2
- ANTLR runtime and generated code versions disagree: 4.8!=4.7.2
-
Compile and run the Relay module
@@ -351,7 +342,7 @@ Here we give an example of how to measure performance of TVM compiled models.
.. code-block:: none
- Elapsed average ms: 11.722627030000002
+ Elapsed average ms: 15.2029395
diff --git a/docs/_sources/tutorials/frontend/deploy_prequantized_tflite.rst.txt b/docs/_sources/tutorials/frontend/deploy_prequantized_tflite.rst.txt
index 71d858b..3fa51ce 100644
--- a/docs/_sources/tutorials/frontend/deploy_prequantized_tflite.rst.txt
+++ b/docs/_sources/tutorials/frontend/deploy_prequantized_tflite.rst.txt
@@ -359,7 +359,7 @@ Here we give an example of how to measure performance of TVM compiled models.
.. code-block:: none
- Elapsed average ms: 30.09917093
+ Elapsed average ms: 46.384218929999996
@@ -392,7 +392,7 @@ Here we give an example of how to measure performance of TVM compiled models.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 57.708 seconds)
+ **Total running time of the script:** ( 2 minutes 10.602 seconds)
.. _sphx_glr_download_tutorials_frontend_deploy_prequantized_tflite.py:
diff --git a/docs/_sources/tutorials/frontend/deploy_quantized.rst.txt b/docs/_sources/tutorials/frontend/deploy_quantized.rst.txt
index f5ba934..412c244 100644
--- a/docs/_sources/tutorials/frontend/deploy_quantized.rst.txt
+++ b/docs/_sources/tutorials/frontend/deploy_quantized.rst.txt
@@ -209,15 +209,6 @@ We create a Relay VM to build and execute the model.
-.. rst-class:: sphx-glr-script-out
-
- Out:
-
- .. code-block:: none
-
- ANTLR runtime and generated code versions disagree: 4.8!=4.7.2
- ANTLR runtime and generated code versions disagree: 4.8!=4.7.2
-
diff --git a/docs/_sources/tutorials/frontend/deploy_ssd_gluoncv.rst.txt b/docs/_sources/tutorials/frontend/deploy_ssd_gluoncv.rst.txt
index b121041..1a7a0f9 100644
--- a/docs/_sources/tutorials/frontend/deploy_ssd_gluoncv.rst.txt
+++ b/docs/_sources/tutorials/frontend/deploy_ssd_gluoncv.rst.txt
@@ -22,7 +22,6 @@ We will use GluonCV pre-trained SSD model and convert it to Relay IR
from tvm import te
from matplotlib import pyplot as plt
- from tvm.relay.testing.config import ctx_list
from tvm import relay
from tvm.contrib import graph_runtime
from tvm.contrib.download import download_testdata
@@ -74,7 +73,6 @@ Preliminary and Set parameters
model_name = supported_model[0]
dshape = (1, 3, 512, 512)
- target_list = ctx_list()
@@ -154,9 +152,11 @@ Create TVM runtime and do inference
class_IDs, scores, bounding_boxs = m.get_output(0), m.get_output(1), m.get_output(2)
return class_IDs, scores, bounding_boxs
- for target, ctx in target_list:
- lib = build(target)
- class_IDs, scores, bounding_boxs = run(lib, ctx)
+ for target in ["llvm", "cuda"]:
+ ctx = tvm.context(target, 0)
+ if ctx.exist:
+ lib = build(target)
+ class_IDs, scores, bounding_boxs = run(lib, ctx)
@@ -168,45 +168,6 @@ Create TVM runtime and do inference
.. code-block:: none
- Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -thread_warp_size=32, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 3, 512, 512), 'float32'), ('TENSOR', (64, 3, 7, 7), 'float32'), (2, 2), (3, 3, 3, 3), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -thread_warp_size=32, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 64, 128, 128), 'float32'), ('TENSOR', (64, 64, 1, 1), 'float32'), (1, 1), (0, 0, 0, 0), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -thread_warp_size=32, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 64, 128, 128), 'float32'), ('TENSOR', (64, 64, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -thread_warp_size=32, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 64, 128, 128), 'float32'), ('TENSOR', (256, 64, 1, 1), 'float32'), (1, 1), (0, 0, 0, 0), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -thread_warp_size=32, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 256, 128, 128), 'float32'), ('TENSOR', (64, 256, 1, 1), 'float32'), (1, 1), (0, 0, 0, 0), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -thread_warp_size=32, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 256, 128, 128), 'float32'), ('TENSOR', (128, 256, 1, 1), 'float32'), (2, 2), (0, 0, 0, 0), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -thread_warp_size=32, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 128, 64, 64), 'float32'), ('TENSOR', (128, 128, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -thread_warp_size=32, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 128, 64, 64), 'float32'), ('TENSOR', (512, 128, 1, 1), 'float32'), (1, 1), (0, 0, 0, 0), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -thread_warp_size=32, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 256, 128, 128), 'float32'), ('TENSOR', (512, 256, 1, 1), 'float32'), (2, 2), (0, 0, 0, 0), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -thread_warp_size=32, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 512, 64, 64), 'float32'), ('TENSOR', (128, 512, 1, 1), 'float32'), (1, 1), (0, 0, 0, 0), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -thread_warp_size=32, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 512, 64, 64), 'float32'), ('TENSOR', (256, 512, 1, 1), 'float32'), (2, 2), (0, 0, 0, 0), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -thread_warp_size=32, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 256, 32, 32), 'float32'), ('TENSOR', (256, 256, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -thread_warp_size=32, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 256, 32, 32), 'float32'), ('TENSOR', (1024, 256, 1, 1), 'float32'), (1, 1), (0, 0, 0, 0), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -thread_warp_size=32, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 512, 64, 64), 'float32'), ('TENSOR', (1024, 512, 1, 1), 'float32'), (2, 2), (0, 0, 0, 0), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -thread_warp_size=32, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 1024, 32, 32), 'float32'), ('TENSOR', (256, 1024, 1, 1), 'float32'), (1, 1), (0, 0, 0, 0), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -thread_warp_size=32, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 1024, 32, 32), 'float32'), ('TENSOR', (84, 1024, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -thread_warp_size=32, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 1024, 32, 32), 'float32'), ('TENSOR', (512, 1024, 1, 1), 'float32'), (2, 2), (0, 0, 0, 0), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -thread_warp_size=32, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 512, 16, 16), 'float32'), ('TENSOR', (512, 512, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -thread_warp_size=32, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 512, 16, 16), 'float32'), ('TENSOR', (2048, 512, 1, 1), 'float32'), (1, 1), (0, 0, 0, 0), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -thread_warp_size=32, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 1024, 32, 32), 'float32'), ('TENSOR', (2048, 1024, 1, 1), 'float32'), (2, 2), (0, 0, 0, 0), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -thread_warp_size=32, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 2048, 16, 16), 'float32'), ('TENSOR', (512, 2048, 1, 1), 'float32'), (1, 1), (0, 0, 0, 0), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -thread_warp_size=32, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 2048, 16, 16), 'float32'), ('TENSOR', (126, 2048, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -thread_warp_size=32, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 512, 16, 16), 'float32'), ('TENSOR', (512, 512, 3, 3), 'float32'), (2, 2), (1, 1, 1, 1), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -thread_warp_size=32, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 512, 8, 8), 'float32'), ('TENSOR', (126, 512, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -thread_warp_size=32, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 512, 8, 8), 'float32'), ('TENSOR', (512, 512, 1, 1), 'float32'), (1, 1), (0, 0, 0, 0), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -thread_warp_size=32, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 512, 8, 8), 'float32'), ('TENSOR', (512, 512, 3, 3), 'float32'), (2, 2), (1, 1, 1, 1), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -thread_warp_size=32, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 512, 4, 4), 'float32'), ('TENSOR', (126, 512, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -thread_warp_size=32, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 512, 4, 4), 'float32'), ('TENSOR', (256, 512, 1, 1), 'float32'), (1, 1), (0, 0, 0, 0), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -thread_warp_size=32, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 256, 4, 4), 'float32'), ('TENSOR', (256, 256, 3, 3), 'float32'), (2, 2), (1, 1, 1, 1), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -thread_warp_size=32, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 256, 2, 2), 'float32'), ('TENSOR', (84, 256, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -thread_warp_size=32, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 256, 2, 2), 'float32'), ('TENSOR', (256, 256, 1, 1), 'float32'), (1, 1), (0, 0, 0, 0), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -thread_warp_size=32, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 256, 2, 2), 'float32'), ('TENSOR', (256, 256, 3, 3), 'float32'), (2, 2), (1, 1, 1, 1), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -thread_warp_size=32, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 256, 1, 1), 'float32'), ('TENSOR', (84, 256, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -thread_warp_size=32, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 1024, 32, 32), 'float32'), ('TENSOR', (16, 1024, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -thread_warp_size=32, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 2048, 16, 16), 'float32'), ('TENSOR', (24, 2048, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -thread_warp_size=32, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 512, 8, 8), 'float32'), ('TENSOR', (24, 512, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -thread_warp_size=32, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 512, 4, 4), 'float32'), ('TENSOR', (24, 512, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -thread_warp_size=32, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 256, 2, 2), 'float32'), ('TENSOR', (16, 256, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
- Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -thread_warp_size=32, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 256, 1, 1), 'float32'), ('TENSOR', (16, 256, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
Cannot find config for target=llvm -keys=cpu, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 3, 512, 512), 'float32'), ('TENSOR', (64, 3, 7, 7), 'float32'), (2, 2), (3, 3, 3, 3), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
Cannot find config for target=llvm -keys=cpu, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 64, 128, 128), 'float32'), ('TENSOR', (64, 64, 1, 1), 'float32'), (1, 1), (0, 0, 0, 0), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
Cannot find config for target=llvm -keys=cpu, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 64, 128, 128), 'float32'), ('TENSOR', (64, 64, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
@@ -285,6 +246,45 @@ Create TVM runtime and do inference
Cannot find config for target=llvm -keys=cpu, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 64, 4, 4, 8), 'float32'), ('TENSOR', (3, 64, 3, 3, 8, 8), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'NCHW8c', 'NCHW8c', 'float32'). A fallback configuration is used, which may bring great performance regression.
Cannot find config for target=llvm -keys=cpu, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 32, 2, 2, 8), 'float32'), ('TENSOR', (2, 32, 3, 3, 8, 8), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'NCHW8c', 'NCHW8c', 'float32'). A fallback configuration is used, which may bring great performance regression.
Cannot find config for target=llvm -keys=cpu, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 32, 1, 1, 8), 'float32'), ('TENSOR', (2, 32, 3, 3, 8, 8), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'NCHW8c', 'NCHW8c', 'float32'). A fallback configuration is used, which may bring great performance regression.
+ Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -thread_warp_size=32, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 3, 512, 512), 'float32'), ('TENSOR', (64, 3, 7, 7), 'float32'), (2, 2), (3, 3, 3, 3), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
+ Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -thread_warp_size=32, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 64, 128, 128), 'float32'), ('TENSOR', (64, 64, 1, 1), 'float32'), (1, 1), (0, 0, 0, 0), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
+ Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -thread_warp_size=32, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 64, 128, 128), 'float32'), ('TENSOR', (64, 64, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
+ Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -thread_warp_size=32, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 64, 128, 128), 'float32'), ('TENSOR', (256, 64, 1, 1), 'float32'), (1, 1), (0, 0, 0, 0), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
+ Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -thread_warp_size=32, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 256, 128, 128), 'float32'), ('TENSOR', (64, 256, 1, 1), 'float32'), (1, 1), (0, 0, 0, 0), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
+ Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -thread_warp_size=32, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 256, 128, 128), 'float32'), ('TENSOR', (128, 256, 1, 1), 'float32'), (2, 2), (0, 0, 0, 0), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
+ Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -thread_warp_size=32, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 128, 64, 64), 'float32'), ('TENSOR', (128, 128, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
+ Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -thread_warp_size=32, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 128, 64, 64), 'float32'), ('TENSOR', (512, 128, 1, 1), 'float32'), (1, 1), (0, 0, 0, 0), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
+ Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -thread_warp_size=32, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 256, 128, 128), 'float32'), ('TENSOR', (512, 256, 1, 1), 'float32'), (2, 2), (0, 0, 0, 0), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
+ Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -thread_warp_size=32, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 512, 64, 64), 'float32'), ('TENSOR', (128, 512, 1, 1), 'float32'), (1, 1), (0, 0, 0, 0), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
+ Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -thread_warp_size=32, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 512, 64, 64), 'float32'), ('TENSOR', (256, 512, 1, 1), 'float32'), (2, 2), (0, 0, 0, 0), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
+ Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -thread_warp_size=32, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 256, 32, 32), 'float32'), ('TENSOR', (256, 256, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
+ Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -thread_warp_size=32, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 256, 32, 32), 'float32'), ('TENSOR', (1024, 256, 1, 1), 'float32'), (1, 1), (0, 0, 0, 0), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
+ Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -thread_warp_size=32, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 512, 64, 64), 'float32'), ('TENSOR', (1024, 512, 1, 1), 'float32'), (2, 2), (0, 0, 0, 0), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
+ Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -thread_warp_size=32, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 1024, 32, 32), 'float32'), ('TENSOR', (256, 1024, 1, 1), 'float32'), (1, 1), (0, 0, 0, 0), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
+ Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -thread_warp_size=32, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 1024, 32, 32), 'float32'), ('TENSOR', (84, 1024, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
+ Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -thread_warp_size=32, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 1024, 32, 32), 'float32'), ('TENSOR', (512, 1024, 1, 1), 'float32'), (2, 2), (0, 0, 0, 0), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
+ Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -thread_warp_size=32, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 512, 16, 16), 'float32'), ('TENSOR', (512, 512, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
+ Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -thread_warp_size=32, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 512, 16, 16), 'float32'), ('TENSOR', (2048, 512, 1, 1), 'float32'), (1, 1), (0, 0, 0, 0), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
+ Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -thread_warp_size=32, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 1024, 32, 32), 'float32'), ('TENSOR', (2048, 1024, 1, 1), 'float32'), (2, 2), (0, 0, 0, 0), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
+ Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -thread_warp_size=32, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 2048, 16, 16), 'float32'), ('TENSOR', (512, 2048, 1, 1), 'float32'), (1, 1), (0, 0, 0, 0), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
+ Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -thread_warp_size=32, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 2048, 16, 16), 'float32'), ('TENSOR', (126, 2048, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
+ Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -thread_warp_size=32, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 512, 16, 16), 'float32'), ('TENSOR', (512, 512, 3, 3), 'float32'), (2, 2), (1, 1, 1, 1), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
+ Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -thread_warp_size=32, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 512, 8, 8), 'float32'), ('TENSOR', (126, 512, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
+ Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -thread_warp_size=32, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 512, 8, 8), 'float32'), ('TENSOR', (512, 512, 1, 1), 'float32'), (1, 1), (0, 0, 0, 0), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
+ Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -thread_warp_size=32, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 512, 8, 8), 'float32'), ('TENSOR', (512, 512, 3, 3), 'float32'), (2, 2), (1, 1, 1, 1), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
+ Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -thread_warp_size=32, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 512, 4, 4), 'float32'), ('TENSOR', (126, 512, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
+ Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -thread_warp_size=32, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 512, 4, 4), 'float32'), ('TENSOR', (256, 512, 1, 1), 'float32'), (1, 1), (0, 0, 0, 0), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
+ Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -thread_warp_size=32, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 256, 4, 4), 'float32'), ('TENSOR', (256, 256, 3, 3), 'float32'), (2, 2), (1, 1, 1, 1), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
+ Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -thread_warp_size=32, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 256, 2, 2), 'float32'), ('TENSOR', (84, 256, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
+ Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -thread_warp_size=32, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 256, 2, 2), 'float32'), ('TENSOR', (256, 256, 1, 1), 'float32'), (1, 1), (0, 0, 0, 0), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
+ Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -thread_warp_size=32, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 256, 2, 2), 'float32'), ('TENSOR', (256, 256, 3, 3), 'float32'), (2, 2), (1, 1, 1, 1), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
+ Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -thread_warp_size=32, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 256, 1, 1), 'float32'), ('TENSOR', (84, 256, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
+ Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -thread_warp_size=32, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 1024, 32, 32), 'float32'), ('TENSOR', (16, 1024, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
+ Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -thread_warp_size=32, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 2048, 16, 16), 'float32'), ('TENSOR', (24, 2048, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
+ Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -thread_warp_size=32, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 512, 8, 8), 'float32'), ('TENSOR', (24, 512, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
+ Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -thread_warp_size=32, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 512, 4, 4), 'float32'), ('TENSOR', (24, 512, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
+ Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -thread_warp_size=32, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 256, 2, 2), 'float32'), ('TENSOR', (16, 256, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
+ Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -thread_warp_size=32, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 256, 1, 1), 'float32'), ('TENSOR', (16, 256, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
@@ -307,6 +307,11 @@ Display result
+.. rst-class:: sphx-glr-timing
+
+ **Total running time of the script:** ( 1 minutes 23.470 seconds)
+
+
.. _sphx_glr_download_tutorials_frontend_deploy_ssd_gluoncv.py:
diff --git a/docs/_sources/tutorials/frontend/from_onnx.rst.txt b/docs/_sources/tutorials/frontend/from_onnx.rst.txt
index adae6d8..a5cf3dc 100644
--- a/docs/_sources/tutorials/frontend/from_onnx.rst.txt
+++ b/docs/_sources/tutorials/frontend/from_onnx.rst.txt
@@ -125,7 +125,7 @@ Compile the model with relay
.. code-block:: none
- /workspace/docs/../python/tvm/relay/frontend/onnx.py:2409: UserWarning: Mismatched attribute type in ' : kernel_shape'
+ /workspace/docs/../python/tvm/relay/frontend/onnx.py:2420: UserWarning: Mismatched attribute type in ' : kernel_shape'
==> Context: Bad node spec: input: "1" input: "2" output: "11" op_type: "Conv" attribute { name: "kernel_shape" ints: 5 ints: 5 } attribute { name: "strides" ints: 1 ints: 1 } attribute { name: "pads" ints: 2 ints: 2 ints: 2 ints: 2 } attribute { name: "dilations" ints: 1 ints: 1 } attribute { name: "group" i: 1 }
warnings.warn(str(e))
@@ -151,7 +151,7 @@ Execute on TVM
.. code-block:: none
-
...47%, 0.01 MB, 47 KB/s, 0 seconds passed
...94%, 0.02 MB, 94 KB/s, 0 seconds passed
...100%, 0.02 MB, 140 KB/s, 0 seconds passed
+
...47%, 0.01 MB, 654 KB/s, 0 seconds passed
...94%, 0.02 MB, 1246 KB/s, 0 seconds passed
...100%, 0.02 MB, 1828 KB/s, 0 seconds passed
Cannot find config for target=llvm -keys=cpu, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 32, 224, 224), 'float32'), ('TENSOR', (9, 32, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
Cannot find config for target=llvm -keys=cpu, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 64, 224, 224), 'float32'), ('TENSOR', (32, 64, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
Cannot find config for target=llvm -keys=cpu, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 1, 224, 224), 'float32'), ('TENSOR', (64, 1, 5, 5), 'float32'), (1, 1), (2, 2, 2, 2), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
diff --git a/docs/_sources/tutorials/frontend/from_pytorch.rst.txt b/docs/_sources/tutorials/frontend/from_pytorch.rst.txt
index 0b360f6..2de794a 100644
--- a/docs/_sources/tutorials/frontend/from_pytorch.rst.txt
+++ b/docs/_sources/tutorials/frontend/from_pytorch.rst.txt
@@ -128,15 +128,6 @@ Convert PyTorch graph to Relay graph. The input name can be arbitrary.
-.. rst-class:: sphx-glr-script-out
-
- Out:
-
- .. code-block:: none
-
- ANTLR runtime and generated code versions disagree: 4.8!=4.7.2
- ANTLR runtime and generated code versions disagree: 4.8!=4.7.2
-
Relay Build
diff --git a/docs/_sources/tutorials/frontend/from_tensorflow.rst.txt b/docs/_sources/tutorials/frontend/from_tensorflow.rst.txt
index fdb4b07..06ab917 100644
--- a/docs/_sources/tutorials/frontend/from_tensorflow.rst.txt
+++ b/docs/_sources/tutorials/frontend/from_tensorflow.rst.txt
@@ -195,8 +195,6 @@ Results:
.. code-block:: none
- ANTLR runtime and generated code versions disagree: 4.8!=4.7.2
- ANTLR runtime and generated code versions disagree: 4.8!=4.7.2
/workspace/docs/../python/tvm/relay/frontend/tensorflow.py:2661: UserWarning: Ignore the passed shape. Shape in graphdef will be used for operator DecodeJpeg/contents.
"will be used for operator %s." % node.name)
/workspace/docs/../python/tvm/relay/frontend/tensorflow.py:686: UserWarning: DecodeJpeg: It's a pass through, please handle preprocessing before input
diff --git a/docs/_sources/tutorials/frontend/sg_execution_times.rst.txt b/docs/_sources/tutorials/frontend/sg_execution_times.rst.txt
index cf7c24a..24f8180 100644
--- a/docs/_sources/tutorials/frontend/sg_execution_times.rst.txt
+++ b/docs/_sources/tutorials/frontend/sg_execution_times.rst.txt
@@ -5,23 +5,23 @@
Computation times
=================
-**05:28.927** total execution time for **tutorials_frontend** files:
+**06:58.557** total execution time for **tutorials_frontend** files:
-- **01:57.708**: :ref:`sphx_glr_tutorials_frontend_deploy_prequantized_tflite.py` (``deploy_prequantized_tflite.py``)
-- **00:31.096**: :ref:`sphx_glr_tutorials_frontend_deploy_ssd_gluoncv.py` (``deploy_ssd_gluoncv.py``)
-- **00:27.286**: :ref:`sphx_glr_tutorials_frontend_deploy_prequantized.py` (``deploy_prequantized.py``)
-- **00:25.435**: :ref:`sphx_glr_tutorials_frontend_from_tensorflow.py` (``from_tensorflow.py``)
-- **00:19.997**: :ref:`sphx_glr_tutorials_frontend_from_tflite.py` (``from_tflite.py``)
-- **00:17.655**: :ref:`sphx_glr_tutorials_frontend_deploy_quantized.py` (``deploy_quantized.py``)
-- **00:17.600**: :ref:`sphx_glr_tutorials_frontend_from_darknet.py` (``from_darknet.py``)
-- **00:12.539**: :ref:`sphx_glr_tutorials_frontend_from_caffe2.py` (``from_caffe2.py``)
-- **00:10.911**: :ref:`sphx_glr_tutorials_frontend_deploy_model_on_rasp.py` (``deploy_model_on_rasp.py``)
-- **00:09.774**: :ref:`sphx_glr_tutorials_frontend_deploy_model_on_android.py` (``deploy_model_on_android.py``)
-- **00:08.931**: :ref:`sphx_glr_tutorials_frontend_from_pytorch.py` (``from_pytorch.py``)
-- **00:08.328**: :ref:`sphx_glr_tutorials_frontend_from_keras.py` (``from_keras.py``)
-- **00:07.266**: :ref:`sphx_glr_tutorials_frontend_from_coreml.py` (``from_coreml.py``)
-- **00:06.399**: :ref:`sphx_glr_tutorials_frontend_from_mxnet.py` (``from_mxnet.py``)
-- **00:04.607**: :ref:`sphx_glr_tutorials_frontend_build_gcn.py` (``build_gcn.py``)
-- **00:01.752**: :ref:`sphx_glr_tutorials_frontend_using_external_lib.py` (``using_external_lib.py``)
-- **00:01.497**: :ref:`sphx_glr_tutorials_frontend_from_onnx.py` (``from_onnx.py``)
-- **00:00.145**: :ref:`sphx_glr_tutorials_frontend_deploy_sparse.py` (``deploy_sparse.py``)
+- **02:10.602**: :ref:`sphx_glr_tutorials_frontend_deploy_prequantized_tflite.py` (``deploy_prequantized_tflite.py``)
+- **01:23.470**: :ref:`sphx_glr_tutorials_frontend_deploy_ssd_gluoncv.py` (``deploy_ssd_gluoncv.py``)
+- **00:31.979**: :ref:`sphx_glr_tutorials_frontend_deploy_prequantized.py` (``deploy_prequantized.py``)
+- **00:28.920**: :ref:`sphx_glr_tutorials_frontend_from_tensorflow.py` (``from_tensorflow.py``)
+- **00:22.131**: :ref:`sphx_glr_tutorials_frontend_from_tflite.py` (``from_tflite.py``)
+- **00:20.266**: :ref:`sphx_glr_tutorials_frontend_from_darknet.py` (``from_darknet.py``)
+- **00:19.613**: :ref:`sphx_glr_tutorials_frontend_deploy_quantized.py` (``deploy_quantized.py``)
+- **00:14.318**: :ref:`sphx_glr_tutorials_frontend_from_caffe2.py` (``from_caffe2.py``)
+- **00:12.623**: :ref:`sphx_glr_tutorials_frontend_deploy_model_on_android.py` (``deploy_model_on_android.py``)
+- **00:11.926**: :ref:`sphx_glr_tutorials_frontend_deploy_model_on_rasp.py` (``deploy_model_on_rasp.py``)
+- **00:09.563**: :ref:`sphx_glr_tutorials_frontend_from_pytorch.py` (``from_pytorch.py``)
+- **00:09.231**: :ref:`sphx_glr_tutorials_frontend_from_keras.py` (``from_keras.py``)
+- **00:08.213**: :ref:`sphx_glr_tutorials_frontend_from_coreml.py` (``from_coreml.py``)
+- **00:06.999**: :ref:`sphx_glr_tutorials_frontend_from_mxnet.py` (``from_mxnet.py``)
+- **00:05.069**: :ref:`sphx_glr_tutorials_frontend_build_gcn.py` (``build_gcn.py``)
+- **00:01.982**: :ref:`sphx_glr_tutorials_frontend_using_external_lib.py` (``using_external_lib.py``)
+- **00:01.492**: :ref:`sphx_glr_tutorials_frontend_from_onnx.py` (``from_onnx.py``)
+- **00:00.160**: :ref:`sphx_glr_tutorials_frontend_deploy_sparse.py` (``deploy_sparse.py``)
diff --git a/docs/_sources/tutorials/frontend/using_external_lib.rst.txt b/docs/_sources/tutorials/frontend/using_external_lib.rst.txt
index 1655ec3..b0053ae 100644
--- a/docs/_sources/tutorials/frontend/using_external_lib.rst.txt
+++ b/docs/_sources/tutorials/frontend/using_external_lib.rst.txt
@@ -105,18 +105,18 @@ By setting the logging level to DEBUG, the result of Relay graph compilation wil
.. code-block:: none
DEBUG:autotvm:Finish loading 688 records
- INFO:compile_engine:Use implementation injective.cpu for op add
- INFO:compile_engine:Use implementation injective.cpu for op sqrt
- INFO:compile_engine:Use implementation injective.cpu for op divide
- INFO:compile_engine:Use implementation injective.cpu for op multiply
- INFO:compile_engine:Use implementation injective.cpu for op expand_dims
- INFO:compile_engine:Use implementation injective.cpu for op negative
- INFO:compile_engine:Use implementation injective.cpu for op add
+ INFO:compile_engine:Using injective.cpu for add based on highest priority (10)
+ INFO:compile_engine:Using injective.cpu for sqrt based on highest priority (10)
+ INFO:compile_engine:Using injective.cpu for divide based on highest priority (10)
+ INFO:compile_engine:Using injective.cpu for multiply based on highest priority (10)
+ INFO:compile_engine:Using injective.cpu for expand_dims based on highest priority (10)
+ INFO:compile_engine:Using injective.cpu for negative based on highest priority (10)
+ INFO:compile_engine:Using injective.cpu for add based on highest priority (10)
WARNING:autotvm:Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -thread_warp_size=32, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 3, 224, 224), 'float32'), ('TENSOR', (16, 3, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
- INFO:compile_engine:Use implementation conv2d_nchw.cuda for op nn.conv2d
- INFO:compile_engine:Use implementation injective.cuda for op multiply
- INFO:compile_engine:Use implementation injective.cuda for op add
- INFO:compile_engine:Use implementation injective.cuda for op nn.relu
+ INFO:compile_engine:Using conv2d_nchw.cuda for nn.conv2d based on highest priority (10)
+ INFO:compile_engine:Using injective.cuda for multiply based on highest priority (10)
+ INFO:compile_engine:Using injective.cuda for add based on highest priority (10)
+ INFO:compile_engine:Using injective.cuda for nn.relu based on highest priority (10)
diff --git a/docs/_sources/tutorials/get_started/cross_compilation_and_rpc.rst.txt b/docs/_sources/tutorials/get_started/cross_compilation_and_rpc.rst.txt
index 4d27b6a..64e0f5e 100644
--- a/docs/_sources/tutorials/get_started/cross_compilation_and_rpc.rst.txt
+++ b/docs/_sources/tutorials/get_started/cross_compilation_and_rpc.rst.txt
@@ -235,7 +235,7 @@ device and returns the measured cost. Network overhead is excluded.
.. code-block:: none
- 1.894e-07 secs/op
+ 1.292e-07 secs/op
diff --git a/docs/_sources/tutorials/get_started/relay_quick_start.rst.txt b/docs/_sources/tutorials/get_started/relay_quick_start.rst.txt
index 19be2c9..ee6359e 100644
--- a/docs/_sources/tutorials/get_started/relay_quick_start.rst.txt
+++ b/docs/_sources/tutorials/get_started/relay_quick_start.rst.txt
@@ -84,7 +84,7 @@ to show the network structure.
.. code-block:: none
- v0.0.4
+ #[version = "0.0.5"]
def @main(%data: Tensor[(1, 3, 224, 224), float32], %bn_data_gamma: Tensor[(3), float32], %bn_data_beta: Tensor[(3), float32], %bn_data_moving_mean: Tensor[(3), float32], %bn_data_moving_var: Tensor[(3), float32], %conv0_weight: Tensor[(64, 3, 7, 7), float32], %bn0_gamma: Tensor[(64), float32], %bn0_beta: Tensor[(64), float32], %bn0_moving_mean: Tensor[(64), float32], %bn0_moving_var: Tensor[(64), float32], %stage1_unit1_bn1_gamma: Tensor[(64), float32], %stage1_unit1_bn1_beta: Tenso [...]
%0 = nn.batch_norm(%data, %bn_data_gamma, %bn_data_beta, %bn_data_moving_mean, %bn_data_moving_var, epsilon=2e-05f, scale=False) /* ty=(Tensor[(1, 3, 224, 224), float32], Tensor[(3), float32], Tensor[(3), float32]) */;
%1 = %0.0;
@@ -223,7 +223,7 @@ in this example. Then the machine code will be generated as the module library.
.. code-block:: none
-
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...77%, 0.30 MB, 7035 KB/s, 0 seconds passed
...79%, 0.31 MB, 7200 KB/s, 0 seconds passed
...81%, 0.32 MB, 7326 KB/s, 0 seconds
passed
...83%, 0.33 MB, 7488 KB/s, 0 seconds passed
...85%, 0.34 MB, 7583 KB/s, 0 seconds passed
...87%, 0.34 MB, 7743 KB/s, 0 seconds passed
...89%, 0.35 MB, 7824 KB/s, 0 seconds passed
...91%, 0.36 MB, 7980 KB/s, 0 seconds passed
...93%, 0.37 MB, 8130 KB/s, 0 seconds passed
...95%, 0.38 MB, 8286 KB/s, 0 seconds passed
...97%, 0.38 MB, 8381 KB/s, 0 seconds passed
...99%, 0.39 MB, 8533 KB/s, 0 seconds passed
...100%, 0.40 MB, 8658 KB/s, 0 seconds passed
Cannot find config for target=cuda -keys=cuda,gpu -max_num_threads=1024 -model=unknown -thread_warp_size=32, workload=('dense_small_batch.cuda', ('TENSOR', (1, 512), 'float32'), ('TENSOR', (1000, 512), 'float32'), None, 'float32'). A fallback configuration is used, which may bring great performance regression.
diff --git a/docs/_sources/tutorials/get_started/sg_execution_times.rst.txt b/docs/_sources/tutorials/get_started/sg_execution_times.rst.txt
index ab65508..fe2274d 100644
--- a/docs/_sources/tutorials/get_started/sg_execution_times.rst.txt
+++ b/docs/_sources/tutorials/get_started/sg_execution_times.rst.txt
@@ -5,8 +5,8 @@
Computation times
=================
-**00:09.442** total execution time for **tutorials_get_started** files:
+**00:10.063** total execution time for **tutorials_get_started** files:
-- **00:09.035**: :ref:`sphx_glr_tutorials_get_started_relay_quick_start.py` (``relay_quick_start.py``)
-- **00:00.296**: :ref:`sphx_glr_tutorials_get_started_tensor_expr_get_started.py` (``tensor_expr_get_started.py``)
-- **00:00.111**: :ref:`sphx_glr_tutorials_get_started_cross_compilation_and_rpc.py` (``cross_compilation_and_rpc.py``)
+- **00:09.575**: :ref:`sphx_glr_tutorials_get_started_relay_quick_start.py` (``relay_quick_start.py``)
+- **00:00.342**: :ref:`sphx_glr_tutorials_get_started_tensor_expr_get_started.py` (``tensor_expr_get_started.py``)
+- **00:00.146**: :ref:`sphx_glr_tutorials_get_started_cross_compilation_and_rpc.py` (``cross_compilation_and_rpc.py``)
diff --git a/docs/_sources/tutorials/get_started/tensor_expr_get_started.rst.txt b/docs/_sources/tutorials/get_started/tensor_expr_get_started.rst.txt
index eb64516..629e56d 100644
--- a/docs/_sources/tutorials/get_started/tensor_expr_get_started.rst.txt
+++ b/docs/_sources/tutorials/get_started/tensor_expr_get_started.rst.txt
@@ -254,12 +254,12 @@ The following code fetches the device module and prints the content code.
.. code-block:: none
-----GPU code-----
- extern "C" __global__ void myadd_kernel0(void* __restrict__ C, void* __restrict__ A, void* __restrict__ B, int n, int stride, int stride1, int stride2) {
+ extern "C" __global__ void myadd_kernel0(float* __restrict__ C, float* __restrict__ A, float* __restrict__ B, int n, int stride, int stride1, int stride2) {
if (((int)blockIdx.x) < (n >> 6)) {
- ((float*)C)[((((((int)blockIdx.x) * 64) + ((int)threadIdx.x)) * stride2))] = (((float*)A)[((((((int)blockIdx.x) * 64) + ((int)threadIdx.x)) * stride))] + ((float*)B)[((((((int)blockIdx.x) * 64) + ((int)threadIdx.x)) * stride1))]);
+ C[((((((int)blockIdx.x) * 64) + ((int)threadIdx.x)) * stride2))] = (A[((((((int)blockIdx.x) * 64) + ((int)threadIdx.x)) * stride))] + B[((((((int)blockIdx.x) * 64) + ((int)threadIdx.x)) * stride1))]);
} else {
if (((((int)blockIdx.x) * 64) + ((int)threadIdx.x)) < n) {
- ((float*)C)[((((((int)blockIdx.x) * 64) + ((int)threadIdx.x)) * stride2))] = (((float*)A)[((((((int)blockIdx.x) * 64) + ((int)threadIdx.x)) * stride))] + ((float*)B)[((((((int)blockIdx.x) * 64) + ((int)threadIdx.x)) * stride1))]);
+ C[((((((int)blockIdx.x) * 64) + ((int)threadIdx.x)) * stride2))] = (A[((((((int)blockIdx.x) * 64) + ((int)threadIdx.x)) * stride))] + B[((((((int)blockIdx.x) * 64) + ((int)threadIdx.x)) * stride1))]);
}
}
}
diff --git a/docs/_sources/tutorials/index.rst.txt b/docs/_sources/tutorials/index.rst.txt
index 40a8f65..d571d7a 100644
--- a/docs/_sources/tutorials/index.rst.txt
+++ b/docs/_sources/tutorials/index.rst.txt
@@ -887,13 +887,13 @@ Developer Tutorials
.. raw:: html
- <div class="sphx-glr-thumbcontainer" tooltip="As the number of optimization passes increases in Relay, it becomes intractable to execute them...">
+ <div class="sphx-glr-thumbcontainer" tooltip="As the number of optimization passes increases in Relay/tir, it becomes intractable to execute ...">
.. only:: html
- .. figure:: /tutorials/dev/images/thumb/sphx_glr_relay_pass_infra_thumb.png
+ .. figure:: /tutorials/dev/images/thumb/sphx_glr_use_pass_infra_thumb.png
- :ref:`sphx_glr_tutorials_dev_relay_pass_infra.py`
+ :ref:`sphx_glr_tutorials_dev_use_pass_infra.py`
.. raw:: html
@@ -903,7 +903,7 @@ Developer Tutorials
.. toctree::
:hidden:
- /tutorials/dev/relay_pass_infra
+ /tutorials/dev/use_pass_infra
.. raw:: html
<div style='clear:both'></div>
diff --git a/docs/_sources/tutorials/language/intrin_math.rst.txt b/docs/_sources/tutorials/language/intrin_math.rst.txt
index f7584fb..869e9d9 100644
--- a/docs/_sources/tutorials/language/intrin_math.rst.txt
+++ b/docs/_sources/tutorials/language/intrin_math.rst.txt
@@ -69,12 +69,12 @@ In the following example, we use :any:`tvm.tir.call_pure_extern` to call
.. code-block:: none
- extern "C" __global__ void myexp_kernel0(void* __restrict__ B, void* __restrict__ A, int n, int stride, int stride1) {
+ extern "C" __global__ void myexp_kernel0(float* __restrict__ B, float* __restrict__ A, int n, int stride, int stride1) {
if (((int)blockIdx.x) < (n >> 6)) {
- ((float*)B)[((((((int)blockIdx.x) * 64) + ((int)threadIdx.x)) * stride1))] = __expf(((float*)A)[((((((int)blockIdx.x) * 64) + ((int)threadIdx.x)) * stride))]);
+ B[((((((int)blockIdx.x) * 64) + ((int)threadIdx.x)) * stride1))] = __expf(A[((((((int)blockIdx.x) * 64) + ((int)threadIdx.x)) * stride))]);
} else {
if (((((int)blockIdx.x) * 64) + ((int)threadIdx.x)) < n) {
- ((float*)B)[((((((int)blockIdx.x) * 64) + ((int)threadIdx.x)) * stride1))] = __expf(((float*)A)[((((((int)blockIdx.x) * 64) + ((int)threadIdx.x)) * stride))]);
+ B[((((((int)blockIdx.x) * 64) + ((int)threadIdx.x)) * stride1))] = __expf(A[((((((int)blockIdx.x) * 64) + ((int)threadIdx.x)) * stride))]);
}
}
}
@@ -119,12 +119,12 @@ The following code use te.exp instead, which create an intrinsic call
.. code-block:: none
- extern "C" __global__ void myexp_kernel0(void* __restrict__ B, void* __restrict__ A, int n, int stride, int stride1) {
+ extern "C" __global__ void myexp_kernel0(float* __restrict__ B, float* __restrict__ A, int n, int stride, int stride1) {
if (((int)blockIdx.x) < (n >> 6)) {
- ((float*)B)[((((((int)blockIdx.x) * 64) + ((int)threadIdx.x)) * stride1))] = __expf(((float*)A)[((((((int)blockIdx.x) * 64) + ((int)threadIdx.x)) * stride))]);
+ B[((((((int)blockIdx.x) * 64) + ((int)threadIdx.x)) * stride1))] = __expf(A[((((((int)blockIdx.x) * 64) + ((int)threadIdx.x)) * stride))]);
} else {
if (((((int)blockIdx.x) * 64) + ((int)threadIdx.x)) < n) {
- ((float*)B)[((((((int)blockIdx.x) * 64) + ((int)threadIdx.x)) * stride1))] = __expf(((float*)A)[((((((int)blockIdx.x) * 64) + ((int)threadIdx.x)) * stride))]);
+ B[((((((int)blockIdx.x) * 64) + ((int)threadIdx.x)) * stride1))] = __expf(A[((((((int)blockIdx.x) * 64) + ((int)threadIdx.x)) * stride))]);
}
}
}
@@ -153,12 +153,12 @@ The same te.exp can also be used for float64 data types.
.. code-block:: none
- __kernel void myexp_kernel0(__global void* restrict B, __global void* restrict A, int n, int stride, int stride1) {
+ __kernel void myexp_kernel0(__global float* restrict B, __global float* restrict A, int n, int stride, int stride1) {
if (((int)get_group_id(0)) < (n >> 6)) {
- ((__global float*)B)[((((((int)get_group_id(0)) * 64) + ((int)get_local_id(0))) * stride1))] = exp(((__global float*)A)[((((((int)get_group_id(0)) * 64) + ((int)get_local_id(0))) * stride))]);
+ B[((((((int)get_group_id(0)) * 64) + ((int)get_local_id(0))) * stride1))] = exp(A[((((((int)get_group_id(0)) * 64) + ((int)get_local_id(0))) * stride))]);
} else {
if (((((int)get_group_id(0)) * 64) + ((int)get_local_id(0))) < n) {
- ((__global float*)B)[((((((int)get_group_id(0)) * 64) + ((int)get_local_id(0))) * stride1))] = exp(((__global float*)A)[((((((int)get_group_id(0)) * 64) + ((int)get_local_id(0))) * stride))]);
+ B[((((((int)get_group_id(0)) * 64) + ((int)get_local_id(0))) * stride1))] = exp(A[((((((int)get_group_id(0)) * 64) + ((int)get_local_id(0))) * stride))]);
}
}
}
@@ -228,12 +228,12 @@ fast math version :code:`__expf`.
.. code-block:: none
- extern "C" __global__ void myexp_kernel0(void* __restrict__ B, void* __restrict__ A, int n, int stride, int stride1) {
+ extern "C" __global__ void myexp_kernel0(float* __restrict__ B, float* __restrict__ A, int n, int stride, int stride1) {
if (((int)blockIdx.x) < (n >> 6)) {
- ((float*)B)[((((((int)blockIdx.x) * 64) + ((int)threadIdx.x)) * stride1))] = expf(((float*)A)[((((((int)blockIdx.x) * 64) + ((int)threadIdx.x)) * stride))]);
+ B[((((((int)blockIdx.x) * 64) + ((int)threadIdx.x)) * stride1))] = expf(A[((((((int)blockIdx.x) * 64) + ((int)threadIdx.x)) * stride))]);
} else {
if (((((int)blockIdx.x) * 64) + ((int)threadIdx.x)) < n) {
- ((float*)B)[((((((int)blockIdx.x) * 64) + ((int)threadIdx.x)) * stride1))] = expf(((float*)A)[((((((int)blockIdx.x) * 64) + ((int)threadIdx.x)) * stride))]);
+ B[((((((int)blockIdx.x) * 64) + ((int)threadIdx.x)) * stride1))] = expf(A[((((((int)blockIdx.x) * 64) + ((int)threadIdx.x)) * stride))]);
}
}
}
@@ -293,12 +293,12 @@ The following example add an intrinsic :code:`mylog` to the system.
.. code-block:: none
- extern "C" __global__ void mylog_kernel0(void* __restrict__ B, void* __restrict__ A, int n, int stride, int stride1) {
+ extern "C" __global__ void mylog_kernel0(float* __restrict__ B, float* __restrict__ A, int n, int stride, int stride1) {
if (((int)blockIdx.x) < (n >> 6)) {
- ((float*)B)[((((((int)blockIdx.x) * 64) + ((int)threadIdx.x)) * stride1))] = logf(((float*)A)[((((((int)blockIdx.x) * 64) + ((int)threadIdx.x)) * stride))]);
+ B[((((((int)blockIdx.x) * 64) + ((int)threadIdx.x)) * stride1))] = logf(A[((((((int)blockIdx.x) * 64) + ((int)threadIdx.x)) * stride))]);
} else {
if (((((int)blockIdx.x) * 64) + ((int)threadIdx.x)) < n) {
- ((float*)B)[((((((int)blockIdx.x) * 64) + ((int)threadIdx.x)) * stride1))] = logf(((float*)A)[((((((int)blockIdx.x) * 64) + ((int)threadIdx.x)) * stride))]);
+ B[((((((int)blockIdx.x) * 64) + ((int)threadIdx.x)) * stride1))] = logf(A[((((((int)blockIdx.x) * 64) + ((int)threadIdx.x)) * stride))]);
}
}
}
diff --git a/docs/_sources/tutorials/language/reduction.rst.txt b/docs/_sources/tutorials/language/reduction.rst.txt
index 5938d1e..fc891cc 100644
--- a/docs/_sources/tutorials/language/reduction.rst.txt
+++ b/docs/_sources/tutorials/language/reduction.rst.txt
@@ -94,8 +94,8 @@ Before doing anything, let us print out the IR code of default schedule.
primfn(A_1: handle, B_1: handle) -> ()
attr = {"global_symbol": "main", "tir.noalias": True}
- buffers = {B: Buffer(B_2: handle, float32, [n: int32], [stride: int32], type="auto"),
- A: Buffer(A_2: handle, float32, [n, m: int32], [stride_1: int32, stride_2: int32], type="auto")}
+ buffers = {B: Buffer(B_2: Pointer(float32), float32, [n: int32], [stride: int32], type="auto"),
+ A: Buffer(A_2: Pointer(float32), float32, [n, m: int32], [stride_1: int32, stride_2: int32], type="auto")}
buffer_map = {A_1: A, B_1: B} {
for (i: int32, 0, n) {
B_2[(i*stride)] = 0f32
@@ -105,7 +105,7 @@ Before doing anything, let us print out the IR code of default schedule.
}
}
- // meta data omitted. you can use show_meta_data=True to include meta data
+
@@ -135,17 +135,17 @@ axis by different factors. The result is a nested reduction.
primfn(A_1: handle, B_1: handle) -> ()
attr = {"global_symbol": "main", "tir.noalias": True}
- buffers = {B: Buffer(B_2: handle, float32, [n: int32], [stride: int32], type="auto"),
- A: Buffer(A_2: handle, float32, [n, m: int32], [stride_1: int32, stride_2: int32], type="auto")}
+ buffers = {B: Buffer(B_2: Pointer(float32), float32, [n: int32], [stride: int32], type="auto"),
+ A: Buffer(A_2: Pointer(float32), float32, [n, m: int32], [stride_1: int32, stride_2: int32], type="auto")}
buffer_map = {A_1: A, B_1: B} {
for (i.outer: int32, 0, floordiv((n + 31), 32)) {
for (i.inner: int32, 0, 32) {
if @tir.likely((((i.outer*32) + i.inner) < n), dtype=bool) {
B_2[(((i.outer*32) + i.inner)*stride)] = 0f32
}
- for (k.outer: int32, 0, floordiv((m + 15), 16)) {
- for (k.inner: int32, 0, 16) {
- if @tir.likely((((i.outer*32) + i.inner) < n), dtype=bool) {
+ if @tir.likely((((i.outer*32) + i.inner) < n), dtype=bool) {
+ for (k.outer: int32, 0, floordiv((m + 15), 16)) {
+ for (k.inner: int32, 0, 16) {
if @tir.likely((((k.outer*16) + k.inner) < m), dtype=bool) {
B_2[(((i.outer*32) + i.inner)*stride)] = ((float32*)B_2[(((i.outer*32) + i.inner)*stride)] + (float32*)A_2[((((i.outer*32) + i.inner)*stride_1) + (((k.outer*16) + k.inner)*stride_2))])
}
@@ -156,7 +156,7 @@ axis by different factors. The result is a nested reduction.
}
}
- // meta data omitted. you can use show_meta_data=True to include meta data
+
@@ -181,8 +181,8 @@ If we are building a GPU kernel, we can bind the rows of B to GPU threads.
primfn(A_1: handle, B_1: handle) -> ()
attr = {"global_symbol": "main", "tir.noalias": True}
- buffers = {B: Buffer(B_2: handle, float32, [n: int32], [stride: int32], type="auto"),
- A: Buffer(A_2: handle, float32, [n, m: int32], [stride_1: int32, stride_2: int32], type="auto")}
+ buffers = {B: Buffer(B_2: Pointer(float32), float32, [n: int32], [stride: int32], type="auto"),
+ A: Buffer(A_2: Pointer(float32), float32, [n, m: int32], [stride_1: int32, stride_2: int32], type="auto")}
buffer_map = {A_1: A, B_1: B} {
attr [IterVar(blockIdx.x: int32, (nullptr), "ThreadIndex", "blockIdx.x")] "thread_extent" = floordiv((n + 31), 32);
attr [IterVar(threadIdx.x: int32, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 32 {
@@ -201,7 +201,7 @@ If we are building a GPU kernel, we can bind the rows of B to GPU threads.
}
}
- // meta data omitted. you can use show_meta_data=True to include meta data
+
@@ -237,10 +237,10 @@ result B.rf. The factored dimension becomes the first dimension of B.rf.
primfn(A_1: handle, B_1: handle) -> ()
attr = {"global_symbol": "main", "tir.noalias": True}
- buffers = {B: Buffer(B_2: handle, float32, [n: int32], [stride: int32], type="auto"),
- A: Buffer(A_2: handle, float32, [n, m: int32], [stride_1: int32, stride_2: int32], type="auto")}
+ buffers = {B: Buffer(B_2: Pointer(float32), float32, [n: int32], [stride: int32], type="auto"),
+ A: Buffer(A_2: Pointer(float32), float32, [n, m: int32], [stride_1: int32, stride_2: int32], type="auto")}
buffer_map = {A_1: A, B_1: B} {
- attr [B.rf: handle] "storage_scope" = "global";
+ attr [B.rf: Pointer(float32)] "storage_scope" = "global";
allocate(B.rf, float32, [(n*16)]) {
for (k.inner: int32, 0, 16) {
for (i: int32, 0, n) {
@@ -261,7 +261,7 @@ result B.rf. The factored dimension becomes the first dimension of B.rf.
}
}
- // meta data omitted. you can use show_meta_data=True to include meta data
+
@@ -284,7 +284,7 @@ the first axis of reduced result of B.f
.. code-block:: none
- [reduce(combiner=comm_reducer(result=[(x + y)], lhs=[x], rhs=[y], identity_element=[0f]), source=[B.rf[k.inner.v, ax0]], axis=[iter_var(k.inner.v, range(min=0, ext=16))], where=(bool)1, value_index=0)]
+ [reduce(combiner=comm_reducer(result=[(x + y)], lhs=[x], rhs=[y], identity_element=[0f]), source=[B.rf[k.inner.v, ax0]], init=[], axis=[iter_var(k.inner.v, range(min=0, ext=16))], where=(bool)1, value_index=0)]
@@ -324,19 +324,19 @@ columns by threadIdx.x and finally do a cross thread reduction over threadIdx.x
.. code-block:: none
- extern "C" __global__ void default_function_kernel0(void* __restrict__ A, void* __restrict__ B, int m, int n, int stride, int stride1, int stride2) {
+ extern "C" __global__ void default_function_kernel0(float* __restrict__ A, float* __restrict__ B, int m, int n, int stride, int stride1, int stride2) {
float B_rf[1];
__shared__ float red_buf0[512];
B_rf[(0)] = 0.000000e+00f;
for (int k_outer = 0; k_outer < (m >> 4); ++k_outer) {
if (((((int)blockIdx.x) * 32) + ((int)threadIdx.y)) < n) {
- B_rf[(0)] = (B_rf[(0)] + ((float*)A)[(((((((int)blockIdx.x) * 32) + ((int)threadIdx.y)) * stride) + (((k_outer * 16) + ((int)threadIdx.x)) * stride1)))]);
+ B_rf[(0)] = (B_rf[(0)] + A[(((((((int)blockIdx.x) * 32) + ((int)threadIdx.y)) * stride) + (((k_outer * 16) + ((int)threadIdx.x)) * stride1)))]);
}
}
for (int k_outer1 = 0; k_outer1 < (((m & 15) + 15) >> 4); ++k_outer1) {
if (((((int)blockIdx.x) * 32) + ((int)threadIdx.y)) < n) {
if (((((m >> 4) * 16) + (k_outer1 * 16)) + ((int)threadIdx.x)) < m) {
- B_rf[(0)] = (B_rf[(0)] + ((float*)A)[(((((((int)blockIdx.x) * 32) + ((int)threadIdx.y)) * stride) + (((((m >> 4) * 16) + (k_outer1 * 16)) + ((int)threadIdx.x)) * stride1)))]);
+ B_rf[(0)] = (B_rf[(0)] + A[(((((((int)blockIdx.x) * 32) + ((int)threadIdx.y)) * stride) + (((((m >> 4) * 16) + (k_outer1 * 16)) + ((int)threadIdx.x)) * stride1)))]);
}
}
}
@@ -351,7 +351,7 @@ columns by threadIdx.x and finally do a cross thread reduction over threadIdx.x
}
__syncthreads();
if (((int)threadIdx.x) == 0) {
- ((float*)B)[((((((int)blockIdx.x) * 32) + ((int)threadIdx.y)) * stride2))] = ((volatile float*)red_buf0)[((((int)threadIdx.y) * 16))];
+ B[((((((int)blockIdx.x) * 32) + ((int)threadIdx.y)) * stride2))] = ((volatile float*)red_buf0)[((((int)threadIdx.y) * 16))];
}
}
@@ -412,9 +412,9 @@ Here is an example for 2D convolution with filter size = [3, 3] and strides = [1
primfn(Input_1: handle, Filter_1: handle, Output_1: handle) -> ()
attr = {"global_symbol": "main", "tir.noalias": True}
- buffers = {Output: Buffer(Output_2: handle, float32, [(n: int32 - 2), (n - 2)], []),
- Filter: Buffer(Filter_2: handle, float32, [3, 3], []),
- Input: Buffer(Input_2: handle, float32, [n, n], [stride: int32, stride_1: int32], type="auto")}
+ buffers = {Output: Buffer(Output_2: Pointer(float32), float32, [(n: int32 - 2), (n - 2)], []),
+ Filter: Buffer(Filter_2: Pointer(float32), float32, [3, 3], []),
+ Input: Buffer(Input_2: Pointer(float32), float32, [n, n], [stride: int32, stride_1: int32], type="auto")}
buffer_map = {Input_1: Input, Filter_1: Filter, Output_1: Output} {
for (i: int32, 0, (n - 2)) {
for (j: int32, 0, (n - 2)) {
@@ -428,7 +428,7 @@ Here is an example for 2D convolution with filter size = [3, 3] and strides = [1
}
}
- // meta data omitted. you can use show_meta_data=True to include meta data
+
diff --git a/docs/_sources/tutorials/language/scan.rst.txt b/docs/_sources/tutorials/language/scan.rst.txt
index f63c38a..24567b7 100644
--- a/docs/_sources/tutorials/language/scan.rst.txt
+++ b/docs/_sources/tutorials/language/scan.rst.txt
@@ -100,8 +100,8 @@ To split on the time iteration, user can schedule on scan_op.scan_axis instead.
primfn(X_1: handle, scan_1: handle) -> ()
attr = {"global_symbol": "main", "tir.noalias": True}
- buffers = {scan: Buffer(scan_2: handle, float32, [m: int32, n: int32], [stride: int32, stride_1: int32], type="auto"),
- X: Buffer(X_2: handle, float32, [m, n], [stride_2: int32, stride_3: int32], type="auto")}
+ buffers = {scan: Buffer(scan_2: Pointer(float32), float32, [m: int32, n: int32], [stride: int32, stride_1: int32], type="auto"),
+ X: Buffer(X_2: Pointer(float32), float32, [m, n], [stride_2: int32, stride_3: int32], type="auto")}
buffer_map = {X_1: X, scan_1: scan} {
attr [IterVar(blockIdx.x: int32, (nullptr), "ThreadIndex", "blockIdx.x")] "thread_extent" = floordiv((n + 255), 256);
attr [IterVar(threadIdx.x: int32, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 256;
@@ -117,7 +117,7 @@ To split on the time iteration, user can schedule on scan_op.scan_axis instead.
}
}
- // meta data omitted. you can use show_meta_data=True to include meta data
+
@@ -199,10 +199,10 @@ the body of scan to be compute_at locations outside the scan loop.
primfn(X_1: handle, scan_1: handle) -> ()
attr = {"global_symbol": "main", "tir.noalias": True}
- buffers = {scan: Buffer(scan_2: handle, float32, [m: int32, n: int32], [stride: int32, stride_1: int32], type="auto"),
- X: Buffer(X_2: handle, float32, [m, n], [stride_2: int32, stride_3: int32], type="auto")}
+ buffers = {scan: Buffer(scan_2: Pointer(float32), float32, [m: int32, n: int32], [stride: int32, stride_1: int32], type="auto"),
+ X: Buffer(X_2: Pointer(float32), float32, [m, n], [stride_2: int32, stride_3: int32], type="auto")}
buffer_map = {X_1: X, scan_1: scan} {
- attr [s1: handle] "storage_scope" = "global";
+ attr [s1: Pointer(float32)] "storage_scope" = "global";
allocate(s1, float32, [32]) {
for (i: int32, 0, n) {
scan_2[(i*stride_1)] = (float32*)X_2[(i*stride_3)]
@@ -224,7 +224,7 @@ the body of scan to be compute_at locations outside the scan loop.
}
}
- // meta data omitted. you can use show_meta_data=True to include meta data
+
@@ -266,9 +266,9 @@ The following example demonstrates how we can build recurrence with two states.
primfn(X_1: handle, scan.v0_1: handle, scan.v1_1: handle) -> ()
attr = {"global_symbol": "main", "tir.noalias": True}
- buffers = {scan.v1: Buffer(scan.v1_2: handle, float32, [m: int32, l: int32], [stride: int32, stride_1: int32], type="auto"),
- scan.v0: Buffer(scan.v0_2: handle, float32, [m, n: int32], [stride_2: int32, stride_3: int32], type="auto"),
- X: Buffer(X_2: handle, float32, [m, n], [stride_4: int32, stride_5: int32], type="auto")}
+ buffers = {scan.v1: Buffer(scan.v1_2: Pointer(float32), float32, [m: int32, l: int32], [stride: int32, stride_1: int32], type="auto"),
+ scan.v0: Buffer(scan.v0_2: Pointer(float32), float32, [m, n: int32], [stride_2: int32, stride_3: int32], type="auto"),
+ X: Buffer(X_2: Pointer(float32), float32, [m, n], [stride_4: int32, stride_5: int32], type="auto")}
buffer_map = {X_1: X, scan.v0_1: scan.v0, scan.v1_1: scan.v1} {
for (i: int32, 0, n) {
scan.v0_2[(i*stride_3)] = (float32*)X_2[(i*stride_5)]
@@ -286,7 +286,7 @@ The following example demonstrates how we can build recurrence with two states.
}
}
- // meta data omitted. you can use show_meta_data=True to include meta data
+
diff --git a/docs/_sources/tutorials/language/schedule_primitives.rst.txt b/docs/_sources/tutorials/language/schedule_primitives.rst.txt
index c06efa3..36213b9 100644
--- a/docs/_sources/tutorials/language/schedule_primitives.rst.txt
+++ b/docs/_sources/tutorials/language/schedule_primitives.rst.txt
@@ -85,9 +85,9 @@ schedule computes tensor in a serial manner in a row-major order.
primfn(A_1: handle, B_1: handle, C_1: handle) -> ()
attr = {"global_symbol": "main", "tir.noalias": True}
- buffers = {C: Buffer(C_2: handle, float32, [m: int32, n: int32], [stride: int32, stride_1: int32], type="auto"),
- B: Buffer(B_2: handle, float32, [m, n], [stride_2: int32, stride_3: int32], type="auto"),
- A: Buffer(A_2: handle, float32, [m, n], [stride_4: int32, stride_5: int32], type="auto")}
+ buffers = {C: Buffer(C_2: Pointer(float32), float32, [m: int32, n: int32], [stride: int32, stride_1: int32], type="auto"),
+ B: Buffer(B_2: Pointer(float32), float32, [m, n], [stride_2: int32, stride_3: int32], type="auto"),
+ A: Buffer(A_2: Pointer(float32), float32, [m, n], [stride_4: int32, stride_5: int32], type="auto")}
buffer_map = {A_1: A, B_1: B, C_1: C} {
for (i: int32, 0, m) {
for (j: int32, 0, n) {
@@ -96,7 +96,7 @@ schedule computes tensor in a serial manner in a row-major order.
}
}
- // meta data omitted. you can use show_meta_data=True to include meta data
+
@@ -131,8 +131,8 @@ split
primfn(A_1: handle, B_1: handle) -> ()
attr = {"global_symbol": "main", "tir.noalias": True}
- buffers = {B: Buffer(B_2: handle, float32, [m: int32], [stride: int32], type="auto"),
- A: Buffer(A_2: handle, float32, [m], [stride_1: int32], type="auto")}
+ buffers = {B: Buffer(B_2: Pointer(float32), float32, [m: int32], [stride: int32], type="auto"),
+ A: Buffer(A_2: Pointer(float32), float32, [m], [stride_1: int32], type="auto")}
buffer_map = {A_1: A, B_1: B} {
for (i.outer: int32, 0, floordiv((m + 31), 32)) {
for (i.inner: int32, 0, 32) {
@@ -143,7 +143,7 @@ split
}
}
- // meta data omitted. you can use show_meta_data=True to include meta data
+
@@ -172,8 +172,8 @@ contrary with :code:`factor`.
primfn(A_1: handle, B_1: handle) -> ()
attr = {"global_symbol": "main", "tir.noalias": True}
- buffers = {B: Buffer(B_2: handle, float32, [m: int32], [stride: int32], type="auto"),
- A: Buffer(A_2: handle, float32, [m], [stride_1: int32], type="auto")}
+ buffers = {B: Buffer(B_2: Pointer(float32), float32, [m: int32], [stride: int32], type="auto"),
+ A: Buffer(A_2: Pointer(float32), float32, [m], [stride_1: int32], type="auto")}
buffer_map = {A_1: A, B_1: B} {
for (i.outer: int32, 0, 32) {
for (i.inner: int32, 0, floordiv((m + 31), 32)) {
@@ -184,7 +184,7 @@ contrary with :code:`factor`.
}
}
- // meta data omitted. you can use show_meta_data=True to include meta data
+
@@ -215,14 +215,14 @@ axises.
primfn(A_1: handle, B_1: handle) -> ()
attr = {"global_symbol": "main", "tir.noalias": True}
- buffers = {B: Buffer(B_2: handle, float32, [m: int32, n: int32], [stride: int32, stride_1: int32], type="auto"),
- A: Buffer(A_2: handle, float32, [m, n], [stride_2: int32, stride_3: int32], type="auto")}
+ buffers = {B: Buffer(B_2: Pointer(float32), float32, [m: int32, n: int32], [stride: int32, stride_1: int32], type="auto"),
+ A: Buffer(A_2: Pointer(float32), float32, [m, n], [stride_2: int32, stride_3: int32], type="auto")}
buffer_map = {A_1: A, B_1: B} {
for (i.outer: int32, 0, floordiv((m + 9), 10)) {
for (j.outer: int32, 0, floordiv((n + 4), 5)) {
for (i.inner: int32, 0, 10) {
- for (j.inner: int32, 0, 5) {
- if @tir.likely((((i.outer*10) + i.inner) < m), dtype=bool) {
+ if @tir.likely((((i.outer*10) + i.inner) < m), dtype=bool) {
+ for (j.inner: int32, 0, 5) {
if @tir.likely((((j.outer*5) + j.inner) < n), dtype=bool) {
B_2[((((i.outer*10) + i.inner)*stride) + (((j.outer*5) + j.inner)*stride_1))] = (float32*)A_2[((((i.outer*10) + i.inner)*stride_2) + (((j.outer*5) + j.inner)*stride_3))]
}
@@ -233,7 +233,7 @@ axises.
}
}
- // meta data omitted. you can use show_meta_data=True to include meta data
+
@@ -266,8 +266,8 @@ fuse
primfn(A_1: handle, B_1: handle) -> ()
attr = {"global_symbol": "main", "tir.noalias": True}
- buffers = {B: Buffer(B_2: handle, float32, [m: int32, n: int32], [stride: int32, stride_1: int32], type="auto"),
- A: Buffer(A_2: handle, float32, [m, n], [stride_2: int32, stride_3: int32], type="auto")}
+ buffers = {B: Buffer(B_2: Pointer(float32), float32, [m: int32, n: int32], [stride: int32, stride_1: int32], type="auto"),
+ A: Buffer(A_2: Pointer(float32), float32, [m, n], [stride_2: int32, stride_3: int32], type="auto")}
buffer_map = {A_1: A, B_1: B} {
for (i.outer: int32, 0, floordiv((m + 9), 10)) {
for (j.outer: int32, 0, floordiv((n + 4), 5)) {
@@ -282,7 +282,7 @@ fuse
}
}
- // meta data omitted. you can use show_meta_data=True to include meta data
+
@@ -315,14 +315,14 @@ reorder
primfn(A_1: handle, B_1: handle) -> ()
attr = {"global_symbol": "main", "tir.noalias": True}
- buffers = {B: Buffer(B_2: handle, float32, [m: int32, n: int32], [stride: int32, stride_1: int32], type="auto"),
- A: Buffer(A_2: handle, float32, [m, n], [stride_2: int32, stride_3: int32], type="auto")}
+ buffers = {B: Buffer(B_2: Pointer(float32), float32, [m: int32, n: int32], [stride: int32, stride_1: int32], type="auto"),
+ A: Buffer(A_2: Pointer(float32), float32, [m, n], [stride_2: int32, stride_3: int32], type="auto")}
buffer_map = {A_1: A, B_1: B} {
for (i.inner: int32, 0, 10) {
for (j.outer: int32, 0, floordiv((n + 4), 5)) {
for (i.outer: int32, 0, floordiv((m + 9), 10)) {
- for (j.inner: int32, 0, 5) {
- if @tir.likely((((i.outer*10) + i.inner) < m), dtype=bool) {
+ if @tir.likely((((i.outer*10) + i.inner) < m), dtype=bool) {
+ for (j.inner: int32, 0, 5) {
if @tir.likely((((j.outer*5) + j.inner) < n), dtype=bool) {
B_2[((((i.outer*10) + i.inner)*stride) + (((j.outer*5) + j.inner)*stride_1))] = (float32*)A_2[((((i.outer*10) + i.inner)*stride_2) + (((j.outer*5) + j.inner)*stride_3))]
}
@@ -333,7 +333,7 @@ reorder
}
}
- // meta data omitted. you can use show_meta_data=True to include meta data
+
@@ -366,8 +366,8 @@ in gpu programming.
primfn(A_1: handle, B_1: handle) -> ()
attr = {"global_symbol": "main", "tir.noalias": True}
- buffers = {B: Buffer(B_2: handle, float32, [n: int32], [stride: int32], type="auto"),
- A: Buffer(A_2: handle, float32, [n], [stride_1: int32], type="auto")}
+ buffers = {B: Buffer(B_2: Pointer(float32), float32, [n: int32], [stride: int32], type="auto"),
+ A: Buffer(A_2: Pointer(float32), float32, [n], [stride_1: int32], type="auto")}
buffer_map = {A_1: A, B_1: B} {
attr [IterVar(blockIdx.x: int32, (nullptr), "ThreadIndex", "blockIdx.x")] "thread_extent" = floordiv((n + 63), 64);
attr [IterVar(threadIdx.x: int32, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
@@ -376,7 +376,7 @@ in gpu programming.
}
}
- // meta data omitted. you can use show_meta_data=True to include meta data
+
@@ -407,19 +407,19 @@ tensors at the root separately by default.
primfn(A_1: handle, B_1: handle, C_1: handle) -> ()
attr = {"global_symbol": "main", "tir.noalias": True}
- buffers = {B: Buffer(B_2: handle, float32, [m: int32], [stride: int32], type="auto"),
- C: Buffer(C_2: handle, float32, [m], [stride_1: int32], type="auto"),
- A: Buffer(A_2: handle, float32, [m], [stride_2: int32], type="auto")}
+ buffers = {C: Buffer(C_2: Pointer(float32), float32, [m: int32], [stride: int32], type="auto"),
+ B: Buffer(B_2: Pointer(float32), float32, [m], [stride_1: int32], type="auto"),
+ A: Buffer(A_2: Pointer(float32), float32, [m], [stride_2: int32], type="auto")}
buffer_map = {A_1: A, B_1: B, C_1: C} {
for (i: int32, 0, m) {
- B_2[(i*stride)] = ((float32*)A_2[(i*stride_2)] + 1f32)
+ B_2[(i*stride_1)] = ((float32*)A_2[(i*stride_2)] + 1f32)
}
for (i_1: int32, 0, m) {
- C_2[(i_1*stride_1)] = ((float32*)B_2[(i_1*stride)]*2f32)
+ C_2[(i_1*stride)] = ((float32*)B_2[(i_1*stride_1)]*2f32)
}
}
- // meta data omitted. you can use show_meta_data=True to include meta data
+
@@ -449,9 +449,9 @@ of computation of `C`.
primfn(A_1: handle, B_1: handle, C_1: handle) -> ()
attr = {"global_symbol": "main", "tir.noalias": True}
- buffers = {C: Buffer(C_2: handle, float32, [m: int32], [stride: int32], type="auto"),
- B: Buffer(B_2: handle, float32, [m], [stride_1: int32], type="auto"),
- A: Buffer(A_2: handle, float32, [m], [stride_2: int32], type="auto")}
+ buffers = {C: Buffer(C_2: Pointer(float32), float32, [m: int32], [stride: int32], type="auto"),
+ B: Buffer(B_2: Pointer(float32), float32, [m], [stride_1: int32], type="auto"),
+ A: Buffer(A_2: Pointer(float32), float32, [m], [stride_2: int32], type="auto")}
buffer_map = {A_1: A, B_1: B, C_1: C} {
for (i: int32, 0, m) {
B_2[(i*stride_1)] = ((float32*)A_2[(i*stride_2)] + 1f32)
@@ -459,7 +459,7 @@ of computation of `C`.
}
}
- // meta data omitted. you can use show_meta_data=True to include meta data
+
@@ -492,16 +492,16 @@ tensor is required.
primfn(A_1: handle, B_1: handle, C_1: handle) -> ()
attr = {"global_symbol": "main", "tir.noalias": True}
- buffers = {C: Buffer(C_2: handle, float32, [m: int32], [stride: int32], type="auto"),
- B: Buffer(B_2: handle, float32, [m], [stride_1: int32], type="auto"),
- A: Buffer(A_2: handle, float32, [m], [stride_2: int32], type="auto")}
+ buffers = {B: Buffer(B_2: Pointer(float32), float32, [m: int32], [stride: int32], type="auto"),
+ C: Buffer(C_2: Pointer(float32), float32, [m], [stride_1: int32], type="auto"),
+ A: Buffer(A_2: Pointer(float32), float32, [m], [stride_2: int32], type="auto")}
buffer_map = {A_1: A, B_1: B, C_1: C} {
for (i: int32, 0, m) {
- C_2[(i*stride)] = (((float32*)A_2[(i*stride_2)] + 1f32)*2f32)
+ C_2[(i*stride_1)] = (((float32*)A_2[(i*stride_2)] + 1f32)*2f32)
}
}
- // meta data omitted. you can use show_meta_data=True to include meta data
+
@@ -533,9 +533,9 @@ compute_root
primfn(A_1: handle, B_1: handle, C_1: handle) -> ()
attr = {"global_symbol": "main", "tir.noalias": True}
- buffers = {C: Buffer(C_2: handle, float32, [m: int32], [stride: int32], type="auto"),
- B: Buffer(B_2: handle, float32, [m], [stride_1: int32], type="auto"),
- A: Buffer(A_2: handle, float32, [m], [stride_2: int32], type="auto")}
+ buffers = {C: Buffer(C_2: Pointer(float32), float32, [m: int32], [stride: int32], type="auto"),
+ B: Buffer(B_2: Pointer(float32), float32, [m], [stride_1: int32], type="auto"),
+ A: Buffer(A_2: Pointer(float32), float32, [m], [stride_2: int32], type="auto")}
buffer_map = {A_1: A, B_1: B, C_1: C} {
for (i: int32, 0, m) {
B_2[(i*stride_1)] = ((float32*)A_2[(i*stride_2)] + 1f32)
@@ -545,7 +545,7 @@ compute_root
}
}
- // meta data omitted. you can use show_meta_data=True to include meta data
+
diff --git a/docs/_sources/tutorials/language/sg_execution_times.rst.txt b/docs/_sources/tutorials/language/sg_execution_times.rst.txt
index c1b7b67..645c7b1 100644
--- a/docs/_sources/tutorials/language/sg_execution_times.rst.txt
+++ b/docs/_sources/tutorials/language/sg_execution_times.rst.txt
@@ -5,13 +5,13 @@
Computation times
=================
-**00:03.450** total execution time for **tutorials_language** files:
+**00:04.036** total execution time for **tutorials_language** files:
-- **00:01.225**: :ref:`sphx_glr_tutorials_language_intrin_math.py` (``intrin_math.py``)
-- **00:00.611**: :ref:`sphx_glr_tutorials_language_tensorize.py` (``tensorize.py``)
-- **00:00.524**: :ref:`sphx_glr_tutorials_language_reduction.py` (``reduction.py``)
-- **00:00.421**: :ref:`sphx_glr_tutorials_language_scan.py` (``scan.py``)
-- **00:00.211**: :ref:`sphx_glr_tutorials_language_extern_op.py` (``extern_op.py``)
-- **00:00.158**: :ref:`sphx_glr_tutorials_language_schedule_primitives.py` (``schedule_primitives.py``)
-- **00:00.157**: :ref:`sphx_glr_tutorials_language_tedd.py` (``tedd.py``)
-- **00:00.141**: :ref:`sphx_glr_tutorials_language_tuple_inputs.py` (``tuple_inputs.py``)
+- **00:01.485**: :ref:`sphx_glr_tutorials_language_intrin_math.py` (``intrin_math.py``)
+- **00:00.702**: :ref:`sphx_glr_tutorials_language_tensorize.py` (``tensorize.py``)
+- **00:00.567**: :ref:`sphx_glr_tutorials_language_reduction.py` (``reduction.py``)
+- **00:00.507**: :ref:`sphx_glr_tutorials_language_scan.py` (``scan.py``)
+- **00:00.256**: :ref:`sphx_glr_tutorials_language_extern_op.py` (``extern_op.py``)
+- **00:00.188**: :ref:`sphx_glr_tutorials_language_tedd.py` (``tedd.py``)
+- **00:00.182**: :ref:`sphx_glr_tutorials_language_schedule_primitives.py` (``schedule_primitives.py``)
+- **00:00.149**: :ref:`sphx_glr_tutorials_language_tuple_inputs.py` (``tuple_inputs.py``)
diff --git a/docs/_sources/tutorials/language/tensorize.rst.txt b/docs/_sources/tutorials/language/tensorize.rst.txt
index 003d3f7..04cb605 100644
--- a/docs/_sources/tutorials/language/tensorize.rst.txt
+++ b/docs/_sources/tutorials/language/tensorize.rst.txt
@@ -71,9 +71,9 @@ The following lines describe the computation :code:`A * B^T` in TVM.
primfn(A_1: handle, B_1: handle, C_1: handle) -> ()
attr = {"global_symbol": "main", "tir.noalias": True}
- buffers = {B: Buffer(B_2: handle, float32, [512, 64], []),
- C: Buffer(C_2: handle, float32, [1024, 512], []),
- A: Buffer(A_2: handle, float32, [1024, 64], [])}
+ buffers = {C: Buffer(C_2: Pointer(float32), float32, [1024, 512], []),
+ B: Buffer(B_2: Pointer(float32), float32, [512, 64], []),
+ A: Buffer(A_2: Pointer(float32), float32, [1024, 64], [])}
buffer_map = {A_1: A, B_1: B, C_1: C} {
for (i: int32, 0, 1024) {
for (j: int32, 0, 512) {
@@ -85,7 +85,7 @@ The following lines describe the computation :code:`A * B^T` in TVM.
}
}
- // meta data omitted. you can use show_meta_data=True to include meta data
+
@@ -120,9 +120,9 @@ Thus we break down the matmul loops to make the innermost loops a (16x64) GEMV.
primfn(A_1: handle, B_1: handle, C_1: handle) -> ()
attr = {"global_symbol": "main", "tir.noalias": True}
- buffers = {C: Buffer(C_2: handle, float32, [1024, 512], []),
- B: Buffer(B_2: handle, float32, [512, 64], []),
- A: Buffer(A_2: handle, float32, [1024, 64], [])}
+ buffers = {B: Buffer(B_2: Pointer(float32), float32, [512, 64], []),
+ C: Buffer(C_2: Pointer(float32), float32, [1024, 512], []),
+ A: Buffer(A_2: Pointer(float32), float32, [1024, 64], [])}
buffer_map = {A_1: A, B_1: B, C_1: C} {
for (i: int32, 0, 1024) {
for (j.outer: int32, 0, 32) {
@@ -136,7 +136,7 @@ Thus we break down the matmul loops to make the innermost loops a (16x64) GEMV.
}
}
- // meta data omitted. you can use show_meta_data=True to include meta data
+
@@ -236,9 +236,9 @@ such placeholder can be put to let TVM automatically bind the inferred value for
primfn(A_1: handle, B_1: handle, C_1: handle) -> ()
attr = {"global_symbol": "main", "tir.noalias": True}
- buffers = {C: Buffer(C_2: handle, float32, [1024, 512], []),
- B: Buffer(B_2: handle, float32, [512, 64], []),
- A: Buffer(A_2: handle, float32, [1024, 64], [])}
+ buffers = {C: Buffer(C_2: Pointer(float32), float32, [1024, 512], []),
+ B: Buffer(B_2: Pointer(float32), float32, [512, 64], []),
+ A: Buffer(A_2: Pointer(float32), float32, [1024, 64], [])}
buffer_map = {A_1: A, B_1: B, C_1: C} {
for (i: int32, 0, 1024) {
for (j.outer: int32, 0, 32) {
@@ -247,7 +247,7 @@ such placeholder can be put to let TVM automatically bind the inferred value for
}
}
- // meta data omitted. you can use show_meta_data=True to include meta data
+
@@ -306,12 +306,12 @@ The importing needs to happen before the tensorized GEMV being executed.
primfn(A_1: handle, B_1: handle, C_1: handle) -> ()
attr = {"global_symbol": "main", "tir.noalias": True}
- buffers = {C: Buffer(C_2: handle, float32, [1024, 512], []),
- B: Buffer(B_2: handle, float32, [512, 64], []),
- A: Buffer(A_2: handle, float32, [1024, 64], [])}
+ buffers = {C: Buffer(C_2: Pointer(float32), float32, [1024, 512], []),
+ B: Buffer(B_2: Pointer(float32), float32, [512, 64], []),
+ A: Buffer(A_2: Pointer(float32), float32, [1024, 64], [])}
buffer_map = {A_1: A, B_1: B, C_1: C} {
- attr [IterVar(i: int32, (nullptr), "DataPar", "")] "pragma_import_llvm" = "; ModuleID = '/tmp/tmpwb3x0ppk/input0.cc'
- source_filename = "/tmp/tmpwb3x0ppk/input0.cc"
+ attr [IterVar(i: int32, (nullptr), "DataPar", "")] "pragma_import_llvm" = "; ModuleID = '/tmp/tmp8w4jrxj5/input0.cc'
+ source_filename = "/tmp/tmp8w4jrxj5/input0.cc"
target datalayout = "e-m:e-i64:64-f80:128-n8:16:32:64-S128"
target triple = "x86_64-pc-linux-gnu"
@@ -409,7 +409,7 @@ The importing needs to happen before the tensorized GEMV being executed.
}
}
- // meta data omitted. you can use show_meta_data=True to include meta data
+
diff --git a/docs/_sources/tutorials/language/tuple_inputs.rst.txt b/docs/_sources/tutorials/language/tuple_inputs.rst.txt
index 135a6bc..1340ee4 100644
--- a/docs/_sources/tutorials/language/tuple_inputs.rst.txt
+++ b/docs/_sources/tutorials/language/tuple_inputs.rst.txt
@@ -64,20 +64,20 @@ together in the next schedule procedure.
primfn(A0_1: handle, A1_1: handle, B.v0_1: handle, B.v1_1: handle) -> ()
attr = {"global_symbol": "main", "tir.noalias": True}
- buffers = {B.v0: Buffer(B.v0_2: handle, float32, [m: int32, n: int32], [stride: int32, stride_1: int32], type="auto"),
- B.v1: Buffer(B.v1_2: handle, float32, [m, n], [stride_2: int32, stride_3: int32], type="auto"),
- A1: Buffer(A1_2: handle, float32, [m, n], [stride_4: int32, stride_5: int32], type="auto"),
- A0: Buffer(A0_2: handle, float32, [m, n], [stride_6: int32, stride_7: int32], type="auto")}
+ buffers = {B.v1: Buffer(B.v1_2: Pointer(float32), float32, [m: int32, n: int32], [stride: int32, stride_1: int32], type="auto"),
+ B.v0: Buffer(B.v0_2: Pointer(float32), float32, [m, n], [stride_2: int32, stride_3: int32], type="auto"),
+ A1: Buffer(A1_2: Pointer(float32), float32, [m, n], [stride_4: int32, stride_5: int32], type="auto"),
+ A0: Buffer(A0_2: Pointer(float32), float32, [m, n], [stride_6: int32, stride_7: int32], type="auto")}
buffer_map = {A0_1: A0, A1_1: A1, B.v0_1: B.v0, B.v1_1: B.v1} {
for (i: int32, 0, m) {
for (j: int32, 0, n) {
- B.v0_2[((i*stride) + (j*stride_1))] = ((float32*)A0_2[((i*stride_6) + (j*stride_7))] + 2f32)
- B.v1_2[((i*stride_2) + (j*stride_3))] = ((float32*)A1_2[((i*stride_4) + (j*stride_5))]*3f32)
+ B.v0_2[((i*stride_2) + (j*stride_3))] = ((float32*)A0_2[((i*stride_6) + (j*stride_7))] + 2f32)
+ B.v1_2[((i*stride) + (j*stride_1))] = ((float32*)A1_2[((i*stride_4) + (j*stride_5))]*3f32)
}
}
}
- // meta data omitted. you can use show_meta_data=True to include meta data
+
@@ -133,10 +133,10 @@ with :py:func:`te.comm_reducer` as below:
primfn(idx_1: handle, val_1: handle, T.v0_1: handle, T.v1_1: handle) -> ()
attr = {"global_symbol": "main", "tir.noalias": True}
- buffers = {T.v1: Buffer(T.v1_2: handle, int32, [m: int32], [stride: int32], type="auto"),
- T.v0: Buffer(T.v0_2: handle, int32, [m], [stride_1: int32], type="auto"),
- val: Buffer(val_2: handle, int32, [m, n: int32], [stride_2: int32, stride_3: int32], type="auto"),
- idx: Buffer(idx_2: handle, int32, [m, n], [stride_4: int32, stride_5: int32], type="auto")}
+ buffers = {T.v1: Buffer(T.v1_2: Pointer(int32), int32, [m: int32], [stride: int32], type="auto"),
+ T.v0: Buffer(T.v0_2: Pointer(int32), int32, [m], [stride_1: int32], type="auto"),
+ val: Buffer(val_2: Pointer(int32), int32, [m, n: int32], [stride_2: int32, stride_3: int32], type="auto"),
+ idx: Buffer(idx_2: Pointer(int32), int32, [m, n], [stride_4: int32, stride_5: int32], type="auto")}
buffer_map = {idx_1: idx, val_1: val, T.v0_1: T.v0, T.v1_1: T.v1} {
for (i: int32, 0, m) {
T.v0_2[(i*stride_1)] = -1
@@ -148,7 +148,7 @@ with :py:func:`te.comm_reducer` as below:
}
}
- // meta data omitted. you can use show_meta_data=True to include meta data
+
@@ -191,13 +191,13 @@ in terms of operation.
primfn(A0_1: handle, A1_1: handle, C_1: handle) -> ()
attr = {"global_symbol": "main", "tir.noalias": True}
- buffers = {C: Buffer(C_2: handle, float32, [m: int32, n: int32], [stride: int32, stride_1: int32], type="auto"),
- A1: Buffer(A1_2: handle, float32, [m, n], [stride_2: int32, stride_3: int32], type="auto"),
- A0: Buffer(A0_2: handle, float32, [m, n], [stride_4: int32, stride_5: int32], type="auto")}
+ buffers = {C: Buffer(C_2: Pointer(float32), float32, [m: int32, n: int32], [stride: int32, stride_1: int32], type="auto"),
+ A1: Buffer(A1_2: Pointer(float32), float32, [m, n], [stride_2: int32, stride_3: int32], type="auto"),
+ A0: Buffer(A0_2: Pointer(float32), float32, [m, n], [stride_4: int32, stride_5: int32], type="auto")}
buffer_map = {A0_1: A0, A1_1: A1, C_1: C} {
- attr [B.v0: handle] "storage_scope" = "global";
+ attr [B.v0: Pointer(float32)] "storage_scope" = "global";
allocate(B.v0, float32, [n]);
- attr [B.v1: handle] "storage_scope" = "global";
+ attr [B.v1: Pointer(float32)] "storage_scope" = "global";
allocate(B.v1, float32, [n]);
for (i: int32, 0, m) {
for (j: int32, 0, n) {
@@ -210,7 +210,7 @@ in terms of operation.
}
}
- // meta data omitted. you can use show_meta_data=True to include meta data
+
diff --git a/docs/_sources/tutorials/micro/micro_tflite.rst.txt b/docs/_sources/tutorials/micro/micro_tflite.rst.txt
index 668be1b..5ff5ed8 100644
--- a/docs/_sources/tutorials/micro/micro_tflite.rst.txt
+++ b/docs/_sources/tutorials/micro/micro_tflite.rst.txt
@@ -190,7 +190,7 @@ See : How do I inspect a .tflite file? `<https://www.tensorflow.org/lite/guide/f
#
# Setup the device config which is what will be used to communicate
# with the microcontroller (a STM32F746 Discovery board)
- TARGET = 'c -device=micro_dev'
+ TARGET = 'c --system-lib --runtime=c'
dev_config = micro.device.arm.stm32f746xx.generate_config("127.0.0.1", 6666)
diff --git a/docs/_sources/tutorials/micro/sg_execution_times.rst.txt b/docs/_sources/tutorials/micro/sg_execution_times.rst.txt
index 0bf985d..f09ec7a 100644
--- a/docs/_sources/tutorials/micro/sg_execution_times.rst.txt
+++ b/docs/_sources/tutorials/micro/sg_execution_times.rst.txt
@@ -5,6 +5,6 @@
Computation times
=================
-**00:00.147** total execution time for **tutorials_micro** files:
+**00:00.164** total execution time for **tutorials_micro** files:
-- **00:00.147**: :ref:`sphx_glr_tutorials_micro_micro_tflite.py` (``micro_tflite.py``)
+- **00:00.164**: :ref:`sphx_glr_tutorials_micro_micro_tflite.py` (``micro_tflite.py``)
diff --git a/docs/_sources/tutorials/optimize/opt_conv_cuda.rst.txt b/docs/_sources/tutorials/optimize/opt_conv_cuda.rst.txt
index 69bd38d..2080119 100644
--- a/docs/_sources/tutorials/optimize/opt_conv_cuda.rst.txt
+++ b/docs/_sources/tutorials/optimize/opt_conv_cuda.rst.txt
@@ -293,7 +293,7 @@ latency of convolution.
.. code-block:: none
- Convolution: 19.770489 ms
+ Convolution: 18.827409 ms
diff --git a/docs/_sources/tutorials/optimize/opt_conv_tensorcore.rst.txt b/docs/_sources/tutorials/optimize/opt_conv_tensorcore.rst.txt
index 4128075..9ef72a7 100644
--- a/docs/_sources/tutorials/optimize/opt_conv_tensorcore.rst.txt
+++ b/docs/_sources/tutorials/optimize/opt_conv_tensorcore.rst.txt
@@ -342,20 +342,20 @@ one time.
primfn(A_1: handle, W_1: handle, Conv_1: handle) -> ()
attr = {"global_symbol": "main", "tir.noalias": True}
- buffers = {Conv: Buffer(Conv_2: handle, float32, [16, 14, 14, 32, 16, 16], []),
- W: Buffer(W_2: handle, float16, [3, 3, 16, 32, 16, 16], []),
- A: Buffer(A_2: handle, float16, [16, 14, 14, 16, 16, 16], [])}
+ buffers = {Conv: Buffer(Conv_2: Pointer(float32), float32, [16, 14, 14, 32, 16, 16], []),
+ W: Buffer(W_2: Pointer(float16), float16, [3, 3, 16, 32, 16, 16], []),
+ A: Buffer(A_2: Pointer(float16), float16, [16, 14, 14, 16, 16, 16], [])}
buffer_map = {A_1: A, W_1: W, Conv_1: Conv} {
attr [IterVar(blockIdx.z: int32, (nullptr), "ThreadIndex", "blockIdx.z")] "thread_extent" = 196;
- attr [Conv.wmma.accumulator: handle] "storage_scope" = "wmma.accumulator";
+ attr [Conv.wmma.accumulator: Pointer(float32)] "storage_scope" = "wmma.accumulator";
allocate(Conv.wmma.accumulator, float32, [2048]);
- attr [Apad.shared: handle] "storage_scope" = "shared";
+ attr [Apad.shared: Pointer(float16)] "storage_scope" = "shared";
allocate(Apad.shared, float16, [12288]);
- attr [W.shared: handle] "storage_scope" = "shared";
+ attr [W.shared: Pointer(float16)] "storage_scope" = "shared";
allocate(W.shared, float16, [12288]);
- attr [Apad.shared.wmma.matrix_a: handle] "storage_scope" = "wmma.matrix_a";
+ attr [Apad.shared.wmma.matrix_a: Pointer(float16)] "storage_scope" = "wmma.matrix_a";
allocate(Apad.shared.wmma.matrix_a, float16, [512]);
- attr [W.shared.wmma.matrix_b: handle] "storage_scope" = "wmma.matrix_b";
+ attr [W.shared.wmma.matrix_b: Pointer(float16)] "storage_scope" = "wmma.matrix_b";
allocate(W.shared.wmma.matrix_b, float16, [1024]);
attr [IterVar(blockIdx.x: int32, (nullptr), "ThreadIndex", "blockIdx.x")] "thread_extent" = 2;
attr [IterVar(blockIdx.y: int32, (nullptr), "ThreadIndex", "blockIdx.y")] "thread_extent" = 4;
@@ -429,7 +429,7 @@ one time.
}
}
- // meta data omitted. you can use show_meta_data=True to include meta data
+
@@ -460,20 +460,20 @@ by mapping the 2D convolution to tensor intrinsics
primfn(A_1: handle, W_1: handle, Conv_1: handle) -> ()
attr = {"global_symbol": "main", "tir.noalias": True}
- buffers = {Conv: Buffer(Conv_2: handle, float32, [16, 14, 14, 32, 16, 16], []),
- W: Buffer(W_2: handle, float16, [3, 3, 16, 32, 16, 16], []),
- A: Buffer(A_2: handle, float16, [16, 14, 14, 16, 16, 16], [])}
+ buffers = {Conv: Buffer(Conv_2: Pointer(float32), float32, [16, 14, 14, 32, 16, 16], []),
+ W: Buffer(W_2: Pointer(float16), float16, [3, 3, 16, 32, 16, 16], []),
+ A: Buffer(A_2: Pointer(float16), float16, [16, 14, 14, 16, 16, 16], [])}
buffer_map = {A_1: A, W_1: W, Conv_1: Conv} {
attr [IterVar(blockIdx.z: int32, (nullptr), "ThreadIndex", "blockIdx.z")] "thread_extent" = 196;
- attr [Conv.wmma.accumulator: handle] "storage_scope" = "wmma.accumulator";
+ attr [Conv.wmma.accumulator: Pointer(float32)] "storage_scope" = "wmma.accumulator";
allocate(Conv.wmma.accumulator, float32, [2048]);
- attr [Apad.shared: handle] "storage_scope" = "shared";
+ attr [Apad.shared: Pointer(float16)] "storage_scope" = "shared";
allocate(Apad.shared, float16, [12288]);
- attr [W.shared: handle] "storage_scope" = "shared";
+ attr [W.shared: Pointer(float16)] "storage_scope" = "shared";
allocate(W.shared, float16, [12288]);
- attr [Apad.shared.wmma.matrix_a: handle] "storage_scope" = "wmma.matrix_a";
+ attr [Apad.shared.wmma.matrix_a: Pointer(float16)] "storage_scope" = "wmma.matrix_a";
allocate(Apad.shared.wmma.matrix_a, float16, [512]);
- attr [W.shared.wmma.matrix_b: handle] "storage_scope" = "wmma.matrix_b";
+ attr [W.shared.wmma.matrix_b: Pointer(float16)] "storage_scope" = "wmma.matrix_b";
allocate(W.shared.wmma.matrix_b, float16, [1024]);
attr [IterVar(blockIdx.x: int32, (nullptr), "ThreadIndex", "blockIdx.x")] "thread_extent" = 2;
attr [IterVar(blockIdx.y: int32, (nullptr), "ThreadIndex", "blockIdx.y")] "thread_extent" = 4;
@@ -525,7 +525,7 @@ by mapping the 2D convolution to tensor intrinsics
}
}
- // meta data omitted. you can use show_meta_data=True to include meta data
+
@@ -563,7 +563,7 @@ be able to run on our build server
.. code-block:: none
- conv2d with tensor core: 6.656447 ms
+ conv2d with tensor core: 6.651443 ms
diff --git a/docs/_sources/tutorials/optimize/opt_gemm.rst.txt b/docs/_sources/tutorials/optimize/opt_gemm.rst.txt
index 6cf0cbb..b8570aa 100644
--- a/docs/_sources/tutorials/optimize/opt_gemm.rst.txt
+++ b/docs/_sources/tutorials/optimize/opt_gemm.rst.txt
@@ -118,8 +118,8 @@ Then we write a baseline implementation, the simplest way to write a matrix mult
.. code-block:: none
- Numpy running time: 0.006930
- Baseline: 5.833613
+ Numpy running time: 0.010477
+ Baseline: 6.061385
@@ -144,9 +144,9 @@ Here is the generated IR using our baseline schedule.
primfn(A_1: handle, B_1: handle, C_1: handle) -> ()
attr = {"global_symbol": "main", "tir.noalias": True}
- buffers = {C: Buffer(C_2: handle, float32, [1024, 1024], []),
- B: Buffer(B_2: handle, float32, [1024, 1024], []),
- A: Buffer(A_2: handle, float32, [1024, 1024], [])}
+ buffers = {C: Buffer(C_2: Pointer(float32), float32, [1024, 1024], []),
+ B: Buffer(B_2: Pointer(float32), float32, [1024, 1024], []),
+ A: Buffer(A_2: Pointer(float32), float32, [1024, 1024], [])}
buffer_map = {A_1: A, B_1: B, C_1: C} {
for (x: int32, 0, 1024) {
for (y: int32, 0, 1024) {
@@ -158,7 +158,7 @@ Here is the generated IR using our baseline schedule.
}
}
- // meta data omitted. you can use show_meta_data=True to include meta data
+
@@ -206,7 +206,7 @@ fill 32 * 32 * sizeof(float) which is 4KB in the cache whose total size is 32KB
.. code-block:: none
- Opt1: 0.108537
+ Opt1: 0.174518
@@ -230,9 +230,9 @@ Here is the generated IR after blocking.
primfn(A_1: handle, B_1: handle, C_1: handle) -> ()
attr = {"global_symbol": "main", "tir.noalias": True}
- buffers = {C: Buffer(C_2: handle, float32, [1024, 1024], []),
- B: Buffer(B_2: handle, float32, [1024, 1024], []),
- A: Buffer(A_2: handle, float32, [1024, 1024], [])}
+ buffers = {C: Buffer(C_2: Pointer(float32), float32, [1024, 1024], []),
+ B: Buffer(B_2: Pointer(float32), float32, [1024, 1024], []),
+ A: Buffer(A_2: Pointer(float32), float32, [1024, 1024], [])}
buffer_map = {A_1: A, B_1: B, C_1: C} {
for (x.outer: int32, 0, 32) {
for (y.outer: int32, 0, 32) {
@@ -254,7 +254,7 @@ Here is the generated IR after blocking.
}
}
- // meta data omitted. you can use show_meta_data=True to include meta data
+
@@ -300,7 +300,7 @@ In this tutorial, we chose to vectorize the inner loop row data since it is cach
.. code-block:: none
- Opt2: 0.115233
+ Opt2: 0.170520
@@ -324,9 +324,9 @@ Here is the generated IR after vectorization.
primfn(A_1: handle, B_1: handle, C_1: handle) -> ()
attr = {"global_symbol": "main", "tir.noalias": True}
- buffers = {C: Buffer(C_2: handle, float32, [1024, 1024], []),
- B: Buffer(B_2: handle, float32, [1024, 1024], []),
- A: Buffer(A_2: handle, float32, [1024, 1024], [])}
+ buffers = {C: Buffer(C_2: Pointer(float32), float32, [1024, 1024], []),
+ B: Buffer(B_2: Pointer(float32), float32, [1024, 1024], []),
+ A: Buffer(A_2: Pointer(float32), float32, [1024, 1024], [])}
buffer_map = {A_1: A, B_1: B, C_1: C} {
for (x.outer: int32, 0, 32) {
for (y.outer: int32, 0, 32) {
@@ -344,7 +344,7 @@ Here is the generated IR after vectorization.
}
}
- // meta data omitted. you can use show_meta_data=True to include meta data
+
@@ -389,7 +389,7 @@ the access pattern for A matrix is more cache friendly.
.. code-block:: none
- Opt3: 0.059706
+ Opt3: 0.071670
@@ -413,9 +413,9 @@ Here is the generated IR after loop permutation.
primfn(A_1: handle, B_1: handle, C_1: handle) -> ()
attr = {"global_symbol": "main", "tir.noalias": True}
- buffers = {C: Buffer(C_2: handle, float32, [1024, 1024], []),
- B: Buffer(B_2: handle, float32, [1024, 1024], []),
- A: Buffer(A_2: handle, float32, [1024, 1024], [])}
+ buffers = {C: Buffer(C_2: Pointer(float32), float32, [1024, 1024], []),
+ B: Buffer(B_2: Pointer(float32), float32, [1024, 1024], []),
+ A: Buffer(A_2: Pointer(float32), float32, [1024, 1024], [])}
buffer_map = {A_1: A, B_1: B, C_1: C} {
for (x.outer: int32, 0, 32) {
for (y.outer: int32, 0, 32) {
@@ -433,7 +433,7 @@ Here is the generated IR after loop permutation.
}
}
- // meta data omitted. you can use show_meta_data=True to include meta data
+
@@ -497,7 +497,7 @@ the corresponding value from the packed array.
.. code-block:: none
- Opt4: 0.060272
+ Opt4: 0.062465
@@ -521,11 +521,11 @@ Here is the generated IR after array packing.
primfn(A_1: handle, B_1: handle, C_1: handle) -> ()
attr = {"global_symbol": "main", "tir.noalias": True}
- buffers = {C: Buffer(C_2: handle, float32, [1024, 1024], []),
- B: Buffer(B_2: handle, float32, [1024, 1024], []),
- A: Buffer(A_2: handle, float32, [1024, 1024], [])}
+ buffers = {B: Buffer(B_2: Pointer(float32), float32, [1024, 1024], []),
+ C: Buffer(C_2: Pointer(float32), float32, [1024, 1024], []),
+ A: Buffer(A_2: Pointer(float32), float32, [1024, 1024], [])}
buffer_map = {A_1: A, B_1: B, C_1: C} {
- attr [packedB: handle] "storage_scope" = "global";
+ attr [packedB: Pointer(float32)] "storage_scope" = "global";
allocate(packedB, float32x32, [32768]) {
for (x: int32, 0, 32) "parallel" {
for (y: int32, 0, 1024) {
@@ -549,7 +549,7 @@ Here is the generated IR after array packing.
}
}
- // meta data omitted. you can use show_meta_data=True to include meta data
+
@@ -607,7 +607,7 @@ write to C when all the block results are ready.
.. code-block:: none
- Opt5: 0.058253
+ Opt5: 0.081238
@@ -631,13 +631,13 @@ Here is the generated IR after blocking.
primfn(A_1: handle, B_1: handle, C_1: handle) -> ()
attr = {"global_symbol": "main", "tir.noalias": True}
- buffers = {C: Buffer(C_2: handle, float32, [1024, 1024], []),
- B: Buffer(B_2: handle, float32, [1024, 1024], []),
- A: Buffer(A_2: handle, float32, [1024, 1024], [])}
+ buffers = {B: Buffer(B_2: Pointer(float32), float32, [1024, 1024], []),
+ C: Buffer(C_2: Pointer(float32), float32, [1024, 1024], []),
+ A: Buffer(A_2: Pointer(float32), float32, [1024, 1024], [])}
buffer_map = {A_1: A, B_1: B, C_1: C} {
- attr [packedB: handle] "storage_scope" = "global";
+ attr [packedB: Pointer(float32)] "storage_scope" = "global";
allocate(packedB, float32x32, [32768]);
- attr [C.global: handle] "storage_scope" = "global";
+ attr [C.global: Pointer(float32)] "storage_scope" = "global";
allocate(C.global, float32, [1024]) {
for (x: int32, 0, 32) "parallel" {
for (y: int32, 0, 1024) {
@@ -667,7 +667,7 @@ Here is the generated IR after blocking.
}
}
- // meta data omitted. you can use show_meta_data=True to include meta data
+
@@ -723,7 +723,7 @@ Futhermore, we can also utilize multi-core processors to do the thread-level par
.. code-block:: none
- Opt6: 0.015962
+ Opt6: 0.020309
@@ -747,11 +747,11 @@ Here is the generated IR after parallelization.
primfn(A_1: handle, B_1: handle, C_1: handle) -> ()
attr = {"global_symbol": "main", "tir.noalias": True}
- buffers = {C: Buffer(C_2: handle, float32, [1024, 1024], []),
- B: Buffer(B_2: handle, float32, [1024, 1024], []),
- A: Buffer(A_2: handle, float32, [1024, 1024], [])}
+ buffers = {C: Buffer(C_2: Pointer(float32), float32, [1024, 1024], []),
+ B: Buffer(B_2: Pointer(float32), float32, [1024, 1024], []),
+ A: Buffer(A_2: Pointer(float32), float32, [1024, 1024], [])}
buffer_map = {A_1: A, B_1: B, C_1: C} {
- attr [packedB: handle] "storage_scope" = "global";
+ attr [packedB: Pointer(float32)] "storage_scope" = "global";
allocate(packedB, float32x32, [32768]) {
for (x: int32, 0, 32) "parallel" {
for (y: int32, 0, 1024) {
@@ -759,7 +759,7 @@ Here is the generated IR after parallelization.
}
}
for (x.outer: int32, 0, 32) "parallel" {
- attr [C.global: handle] "storage_scope" = "global";
+ attr [C.global: Pointer(float32)] "storage_scope" = "global";
allocate(C.global, float32, [1024]);
for (y.outer: int32, 0, 32) {
for (x.c.init: int32, 0, 32) {
@@ -783,7 +783,7 @@ Here is the generated IR after parallelization.
}
}
- // meta data omitted. you can use show_meta_data=True to include meta data
+
diff --git a/docs/_sources/tutorials/optimize/sg_execution_times.rst.txt b/docs/_sources/tutorials/optimize/sg_execution_times.rst.txt
index 9f7db25..b1b6380 100644
--- a/docs/_sources/tutorials/optimize/sg_execution_times.rst.txt
+++ b/docs/_sources/tutorials/optimize/sg_execution_times.rst.txt
@@ -5,9 +5,9 @@
Computation times
=================
-**00:26.384** total execution time for **tutorials_optimize** files:
+**00:29.757** total execution time for **tutorials_optimize** files:
-- **00:24.629**: :ref:`sphx_glr_tutorials_optimize_opt_gemm.py` (``opt_gemm.py``)
-- **00:00.917**: :ref:`sphx_glr_tutorials_optimize_opt_conv_tensorcore.py` (``opt_conv_tensorcore.py``)
-- **00:00.685**: :ref:`sphx_glr_tutorials_optimize_opt_conv_cuda.py` (``opt_conv_cuda.py``)
-- **00:00.153**: :ref:`sphx_glr_tutorials_optimize_opt_matmul_auto_tensorcore.py` (``opt_matmul_auto_tensorcore.py``)
+- **00:27.804**: :ref:`sphx_glr_tutorials_optimize_opt_gemm.py` (``opt_gemm.py``)
+- **00:01.005**: :ref:`sphx_glr_tutorials_optimize_opt_conv_tensorcore.py` (``opt_conv_tensorcore.py``)
+- **00:00.754**: :ref:`sphx_glr_tutorials_optimize_opt_conv_cuda.py` (``opt_conv_cuda.py``)
+- **00:00.194**: :ref:`sphx_glr_tutorials_optimize_opt_matmul_auto_tensorcore.py` (``opt_matmul_auto_tensorcore.py``)
diff --git a/docs/_sources/tutorials/topi/intro_topi.rst.txt b/docs/_sources/tutorials/topi/intro_topi.rst.txt
index dcfeb87..d699190 100644
--- a/docs/_sources/tutorials/topi/intro_topi.rst.txt
+++ b/docs/_sources/tutorials/topi/intro_topi.rst.txt
@@ -74,9 +74,9 @@ and to examine the IR code in human readable format, we can do
primfn(A_1: handle) -> ()
attr = {"global_symbol": "main", "tir.noalias": True}
- buffers = {A: Buffer(A_2: handle, float32, [n: int32, m: int32], [stride: int32, stride_1: int32], type="auto")}
+ buffers = {A: Buffer(A_2: Pointer(float32), float32, [n: int32, m: int32], [stride: int32, stride_1: int32], type="auto")}
buffer_map = {A_1: A} {
- attr [B: handle] "storage_scope" = "global";
+ attr [B: Pointer(float32)] "storage_scope" = "global";
allocate(B, float32, [n]);
for (i: int32, 0, n) {
B[i] = 0f32
@@ -86,7 +86,7 @@ and to examine the IR code in human readable format, we can do
}
}
- // meta data omitted. you can use show_meta_data=True to include meta data
+
@@ -114,9 +114,9 @@ Fortunately, we can replace those two lines with simple :code:`topi.sum` much li
primfn(A_1: handle) -> ()
attr = {"global_symbol": "main", "tir.noalias": True}
- buffers = {A: Buffer(A_2: handle, float32, [n: int32, m: int32], [stride: int32, stride_1: int32], type="auto")}
+ buffers = {A: Buffer(A_2: Pointer(float32), float32, [n: int32, m: int32], [stride: int32, stride_1: int32], type="auto")}
buffer_map = {A_1: A} {
- attr [A_red: handle] "storage_scope" = "global";
+ attr [A_red: Pointer(float32)] "storage_scope" = "global";
allocate(A_red, float32, [n]);
for (ax0: int32, 0, n) {
A_red[ax0] = 0f32
@@ -126,7 +126,7 @@ Fortunately, we can replace those two lines with simple :code:`topi.sum` much li
}
}
- // meta data omitted. you can use show_meta_data=True to include meta data
+
@@ -184,13 +184,13 @@ we can schedule the following series of operations ending with :code:`topi.sum`
primfn(a_1: handle, b_1: handle) -> ()
attr = {"global_symbol": "main", "tir.noalias": True}
- buffers = {b: Buffer(b_2: handle, float32, [10, 10], []),
- a: Buffer(a_2: handle, float32, [100, 10, 10], [])}
+ buffers = {b: Buffer(b_2: Pointer(float32), float32, [10, 10], []),
+ a: Buffer(a_2: Pointer(float32), float32, [100, 10, 10], [])}
buffer_map = {a_1: a, b_1: b} {
- attr [T_divide_red: handle] "storage_scope" = "global";
+ attr [T_divide_red: Pointer(float32)] "storage_scope" = "global";
allocate(T_divide_red, float32, [1]);
attr [IterVar(threadIdx.x: int32, [0:1024], "ThreadIndex", "threadIdx.x")] "thread_extent" = 1024;
- attr [T_divide_red.rf: handle] "storage_scope" = "local";
+ attr [T_divide_red.rf: Pointer(float32)] "storage_scope" = "local";
allocate(T_divide_red.rf, float32, [1]);
attr [reduce_temp0: handle] "storage_scope" = "local";
allocate(reduce_temp0, float32, [1]) {
@@ -208,7 +208,7 @@ we can schedule the following series of operations ending with :code:`topi.sum`
}
}
- // meta data omitted. you can use show_meta_data=True to include meta data
+
@@ -230,7 +230,7 @@ As you can see, scheduled stages of computation have been accumulated and we can
.. code-block:: none
- [stage(a, 0x83da2ab0), stage(b, 0x9090f790), stage(T_add, 0x86d0bf20), stage(T_multiply, 0x88f3e0e0), stage(T_elemwise_sum, 0x7edb6330), stage(T_divide, 0x7f8347f0), stage(T_divide_red.rf, 0x80ff0c20), stage(T_divide_red, 0x7f85a5e0)]
+ [stage(a, 0x80353420), stage(b, 0x92595380), stage(T_add, 0x6c054870), stage(T_multiply, 0x742ca280), stage(T_elemwise_sum, 0x98f0fc80), stage(T_divide, 0x8e0e1aa0), stage(T_divide_red.rf, 0x61917c90), stage(T_divide_red, 0x956adf90)]
@@ -281,16 +281,16 @@ TOPI also provides common neural nets operations such as _softmax_ with optimize
primfn(tarray_1: handle) -> ()
attr = {"global_symbol": "main", "tir.noalias": True}
- buffers = {tarray: Buffer(tarray_2: handle, float32, [512, 512], [])}
+ buffers = {tarray: Buffer(tarray_2: Pointer(float32), float32, [512, 512], [])}
buffer_map = {tarray_1: tarray} {
- attr [T_softmax_norm: handle] "storage_scope" = "global";
+ attr [T_softmax_norm: Pointer(float32)] "storage_scope" = "global";
allocate(T_softmax_norm, float32x4, [65536]);
attr [IterVar(blockIdx.x: int32, (nullptr), "ThreadIndex", "blockIdx.x")] "thread_extent" = 512;
attr [normal_reduce_temp0: handle] "storage_scope" = "local";
allocate(normal_reduce_temp0, float32, [1]);
attr [reduce_temp0: handle] "storage_scope" = "local";
allocate(reduce_temp0, float32, [1]);
- attr [T_softmax_exp: handle] "storage_scope" = "warp";
+ attr [T_softmax_exp: Pointer(float32)] "storage_scope" = "warp";
allocate(T_softmax_exp, float32, [512]);
attr [normal_reduce_temp0_1: handle] "storage_scope" = "local";
allocate(normal_reduce_temp0_1, float32, [1]);
@@ -321,7 +321,7 @@ TOPI also provides common neural nets operations such as _softmax_ with optimize
}
}
- // meta data omitted. you can use show_meta_data=True to include meta data
+
@@ -362,17 +362,17 @@ We can fuse :code:`topi.nn.conv2d` and :code:`topi.nn.relu` together.
primfn(placeholder_2: handle, placeholder_3: handle) -> ()
attr = {"global_symbol": "main", "tir.noalias": True}
- buffers = {placeholder_1: Buffer(placeholder_4: handle, float32, [10, 3, 5, 5], []),
- placeholder: Buffer(placeholder_5: handle, float32, [1, 3, 224, 224], [])}
+ buffers = {placeholder_1: Buffer(placeholder_4: Pointer(float32), float32, [10, 3, 5, 5], []),
+ placeholder: Buffer(placeholder_5: Pointer(float32), float32, [1, 3, 224, 224], [])}
buffer_map = {placeholder_2: placeholder, placeholder_3: placeholder_1} {
- attr [compute: handle] "storage_scope" = "global";
+ attr [compute: Pointer(float32)] "storage_scope" = "global";
allocate(compute, float32, [501760]);
attr [IterVar(blockIdx.z: int32, (nullptr), "ThreadIndex", "blockIdx.z")] "thread_extent" = 5;
- attr [compute_1: handle] "storage_scope" = "local";
+ attr [compute_1: Pointer(float32)] "storage_scope" = "local";
allocate(compute_1, float32, [14]);
- attr [pad_temp.shared: handle] "storage_scope" = "shared";
+ attr [pad_temp.shared: Pointer(float32)] "storage_scope" = "shared";
allocate(pad_temp.shared, float32, [112]);
- attr [placeholder.shared: handle] "storage_scope" = "shared";
+ attr [placeholder.shared: Pointer(float32)] "storage_scope" = "shared";
allocate(placeholder.shared, float32, [2]);
attr [IterVar(blockIdx.y: int32, (nullptr), "ThreadIndex", "blockIdx.y")] "thread_extent" = 224;
attr [IterVar(blockIdx.x: int32, (nullptr), "ThreadIndex", "blockIdx.x")] "thread_extent" = 2;
@@ -569,7 +569,7 @@ We can fuse :code:`topi.nn.conv2d` and :code:`topi.nn.relu` together.
}
}
- // meta data omitted. you can use show_meta_data=True to include meta data
+
diff --git a/docs/_sources/tutorials/topi/sg_execution_times.rst.txt b/docs/_sources/tutorials/topi/sg_execution_times.rst.txt
index ad7d3a9..470e85a 100644
--- a/docs/_sources/tutorials/topi/sg_execution_times.rst.txt
+++ b/docs/_sources/tutorials/topi/sg_execution_times.rst.txt
@@ -5,6 +5,6 @@
Computation times
=================
-**00:00.482** total execution time for **tutorials_topi** files:
+**00:00.566** total execution time for **tutorials_topi** files:
-- **00:00.482**: :ref:`sphx_glr_tutorials_topi_intro_topi.py` (``intro_topi.py``)
+- **00:00.566**: :ref:`sphx_glr_tutorials_topi_intro_topi.py` (``intro_topi.py``)
diff --git a/docs/_sources/vta/tutorials/autotvm/sg_execution_times.rst.txt b/docs/_sources/vta/tutorials/autotvm/sg_execution_times.rst.txt
index 10dea85..316aa3d 100644
--- a/docs/_sources/vta/tutorials/autotvm/sg_execution_times.rst.txt
+++ b/docs/_sources/vta/tutorials/autotvm/sg_execution_times.rst.txt
@@ -5,6 +5,6 @@
Computation times
=================
-**00:05.278** total execution time for **vta_tutorials_autotvm** files:
+**00:06.714** total execution time for **vta_tutorials_autotvm** files:
-- **00:05.278**: :ref:`sphx_glr_vta_tutorials_autotvm_tune_relay_vta.py` (``tune_relay_vta.py``)
+- **00:06.714**: :ref:`sphx_glr_vta_tutorials_autotvm_tune_relay_vta.py` (``tune_relay_vta.py``)
diff --git a/docs/_sources/vta/tutorials/autotvm/tune_relay_vta.rst.txt b/docs/_sources/vta/tutorials/autotvm/tune_relay_vta.rst.txt
index 3a263b8..ca2b8b2 100644
--- a/docs/_sources/vta/tutorials/autotvm/tune_relay_vta.rst.txt
+++ b/docs/_sources/vta/tutorials/autotvm/tune_relay_vta.rst.txt
@@ -481,7 +481,7 @@ Finally, we launch tuning jobs and evaluate the end-to-end performance.
.. code-block:: none
Extract tasks...
-
...1%, 0.01 MB, 37 KB/s, 0 seconds passed
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...30%, 0.22 MB, 1010 KB/s, 0 seconds passed
...31%, 0.23 MB, 1045 KB/s, 0 seconds passed
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...33%, 0.24 MB, 1113 KB/s, 0 seconds passed
...34%, 0.25 MB, 1145 KB/s, 0 seconds passed
...35%, 0.26 MB, 1181 KB/s, 0 seconds passed
...36%, 0.27 MB, 1214 KB/s, 0 seconds passed
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...40%, 0.29 MB, 1316 KB/s, 0 seconds passed
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...43%, 0.31 MB, 1419 KB/s, 0 seconds passed
...44%, 0.32 MB, 1454 KB/s, 0 seconds passed
...45%, 0.33 MB, 1
487 KB/s, 0 seconds passed
...46%, 0.34 MB, 1521 KB/s, 0 seconds passed
...47%, 0.34 MB, 1552 KB/s, 0 seconds passed
...48%, 0.35 MB, 1587 KB/s, 0 seconds passed
...50%, 0.36 MB, 1620 KB/s, 0 seconds passed
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...55%, 0.40 MB, 1791 KB/s, 0 seconds passed
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...58%, 0.42 MB, 1891 KB/s, 0 seconds passed
...59%, 0.43 MB, 1925 KB/s, 0 seconds passed
...60%, 0.44 MB, 1957 KB/s, 0 seconds passed
...62%, 0.45 MB, 1991 KB/s, 0 seconds passed
...63%, 0.45 MB, 2024 KB/s, 0 seconds passed
...64%, 0.46 MB, 2058 KB/s, 0 seconds passed
...65%, 0.47 MB, 2092 KB/s, 0 seconds passed
...66%, 0.48 MB, 2126 KB/s, 0 seconds passed
...67%, 0.48 MB, 2158 KB/s, 0 secon
ds passed
...68%, 0.49 MB, 2192 KB/s, 0 seconds passed
...69%, 0.50 MB, 2224 KB/s, 0 seconds passed
...70%, 0.51 MB, 2258 KB/s, 0 seconds passed
...71%, 0.52 MB, 2290 KB/s, 0 seconds passed
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...88%, 0.63 MB, 2783 KB/s, 0 seconds passed
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...
90%, 0.65 MB, 2850 KB/s, 0 seconds passed
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...92%, 0.66 MB, 2915 KB/s, 0 seconds passed
...93%, 0.67 MB, 2948 KB/s, 0 seconds passed
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+
...1%, 0.01 MB, 312 KB/s, 0 seconds passed
...2%, 0.02 MB, 578 KB/s, 0 seconds passed
...3%, 0.02 MB, 844 KB/s, 0 seconds passed
...4%, 0.03 MB, 1076 KB/s, 0 seconds passed
...5%, 0.04 MB, 1326 KB/s, 0 seconds passed
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/s, 0 seconds passed
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sed
...45%, 0.33 MB, 7648 KB/s, 0 seconds passed
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...64%, 0.46 MB, 9292 KB/s, 0 seconds passed
...65%, 0.47 MB, 9357 KB/s, 0 seconds passed
...66%, 0.48 MB, 9497 KB/s, 0 seconds passed
...67%, 0
.48 MB, 9562 KB/s, 0 seconds passed
...68%, 0.49 MB, 9701 KB/s, 0 seconds passed
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...76%, 0.55 MB, 10294 KB/s, 0 seconds passed
...77%, 0.55 MB, 10424 KB/s, 0 seconds passed
...78%, 0.56 MB, 10550 KB/s, 0 seconds passed
...79%, 0.57 MB, 10679 KB/s, 0 seconds passed
...80%, 0.58 MB, 10727 KB/s, 0 seconds passed
...81%, 0.59 MB, 10856 KB/s, 0 seconds passed
...82%, 0.59 MB, 10879 KB/s, 0 seconds passed
...83%, 0.60 MB, 11005 KB/s, 0 seconds passed
...84%, 0.61 MB, 11129 KB/s, 0 seconds passed
...85%, 0.62 MB, 11255 KB/s, 0 seconds passed
...87%, 0.62 MB, 11335 KB/s, 0 seconds passed
...88%, 0.63 MB, 11459 KB/s, 0 seconds passed
...89%, 0.
64 MB, 11541 KB/s, 0 seconds passed
...90%, 0.65 MB, 11664 KB/s, 0 seconds passed
...91%, 0.66 MB, 11783 KB/s, 0 seconds passed
...92%, 0.66 MB, 11883 KB/s, 0 seconds passed
...93%, 0.67 MB, 12005 KB/s, 0 seconds passed
...94%, 0.68 MB, 12082 KB/s, 0 seconds passed
...95%, 0.69 MB, 12201 KB/s, 0 seconds passed
...96%, 0.70 MB, 12276 KB/s, 0 seconds passed
...97%, 0.70 MB, 12395 KB/s, 0 seconds passed
...99%, 0.71 MB, 12509 KB/s, 0 seconds passed
...100%, 0.72 MB, 12590 KB/s, 0 seconds passed
Extracted 10 conv2d tasks:
(1, 14, 14, 256, 512, 1, 1, 0, 0, 2, 2)
(1, 28, 28, 128, 256, 1, 1, 0, 0, 2, 2)
diff --git a/docs/_sources/vta/tutorials/frontend/deploy_classification.rst.txt b/docs/_sources/vta/tutorials/frontend/deploy_classification.rst.txt
index f9bca79..b04e9d7 100644
--- a/docs/_sources/vta/tutorials/frontend/deploy_classification.rst.txt
+++ b/docs/_sources/vta/tutorials/frontend/deploy_classification.rst.txt
@@ -243,8 +243,8 @@ The compilation steps are:
.. code-block:: none
-
...12%, 0.01 MB, 48 KB/s, 0 seconds passed
...25%, 0.02 MB, 97 KB/s, 0 seconds passed
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...77%, 0.05 MB, 289 KB/s, 0 seconds passed
...89%, 0.05 MB, 336 KB/s, 0 seconds passed
...100%, 0.06 MB, 383 KB/s, 0 seconds passed
- resnet18_v1 inference graph built in 5.85s!
+
...12%, 0.01 MB, 41 KB/s, 0 seconds passed
...25%, 0.02 MB, 83 KB/s, 0 seconds passed
...38%, 0.02 MB, 124 KB/s, 0 seconds passed
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...77%, 0.05 MB, 247 KB/s, 0 seconds passed
...89%, 0.05 MB, 287 KB/s, 0 seconds passed
...100%, 0.06 MB, 327 KB/s, 0 seconds passed
+ resnet18_v1 inference graph built in 7.56s!
diff --git a/docs/_sources/vta/tutorials/frontend/deploy_detection.rst.txt b/docs/_sources/vta/tutorials/frontend/deploy_detection.rst.txt
deleted file mode 100644
index 6182de0..0000000
--- a/docs/_sources/vta/tutorials/frontend/deploy_detection.rst.txt
+++ /dev/null
@@ -1,458 +0,0 @@
-.. note::
- :class: sphx-glr-download-link-note
-
- Click :ref:`here <sphx_glr_download_vta_tutorials_frontend_deploy_detection.py>` to download the full example code
-.. rst-class:: sphx-glr-example-title
-
-.. _sphx_glr_vta_tutorials_frontend_deploy_detection.py:
-
-
-Deploy Pretrained Vision Detection Model from Darknet on VTA
-============================================================
-**Author**: `Hua Jiang <https://github.com/huajsj>`_
-
-This tutorial provides an end-to-end demo, on how to run Darknet YoloV3-tiny
-inference onto the VTA accelerator design to perform Image detection tasks.
-It showcases Relay as a front end compiler that can perform quantization (VTA
-only supports int8/32 inference) as well as graph packing (in order to enable
-tensorization in the core) to massage the compute graph for the hardware target.
-
-Install dependencies
---------------------
-To use the autotvm package in tvm, we need to install some extra dependencies.
-(change "3" to "2" if you use python2):
-
-.. code-block:: bash
-
-pip3 install "Pillow<7"
-
-YOLO-V3-tiny Model with Darknet parsing have dependancy with CFFI and CV2 library,
-we need to install CFFI and CV2 before executing this script.
-
-pip3 install "Pillow<7"
-
-pip3 install cffi
-pip3 install opencv-python
-
-Now return to the python code. Import packages.
-
-
-.. code-block:: default
-
-
- from __future__ import absolute_import, print_function
-
- import sys
- import os
- import time
- import matplotlib.pyplot as plt
- import numpy as np
- import tvm
- import vta
- from tvm import rpc, autotvm, relay
- from tvm.relay.testing import yolo_detection, darknet
- from tvm.relay.testing.darknet import __darknetffi__
- from tvm.contrib import graph_runtime, graph_runtime, util
- from tvm.contrib.download import download_testdata
- from vta.testing import simulator
- from vta.top import graph_pack
- # Make sure that TVM was compiled with RPC=1
- assert tvm.runtime.enabled("rpc")
-
-
-
-
-
-
-
-Download yolo net configure file, weight file, darknet library file based on
-Model Name
-----------------------------------------------------------------------------
-
-
-.. code-block:: default
-
- MODEL_NAME = 'yolov3-tiny'
- REPO_URL = 'https://github.com/dmlc/web-data/blob/master/darknet/'
-
- cfg_path = download_testdata('https://github.com/pjreddie/darknet/blob/master/cfg/'
- + MODEL_NAME + '.cfg' + '?raw=true',
- MODEL_NAME + '.cfg',
- module="darknet")
- weights_path = download_testdata('https://pjreddie.com/media/files/'
- + MODEL_NAME + '.weights' + '?raw=true',
- MODEL_NAME + '.weights',
- module="darknet")
-
- if sys.platform in ['linux', 'linux2']:
- darknet_lib_path = download_testdata(REPO_URL + 'lib/' + 'libdarknet2.0.so' + '?raw=true',
- 'libdarknet2.0.so',
- module="darknet")
- elif sys.platform == 'darwin':
- darknet_lib_path = download_testdata(REPO_URL+'lib_osx/'+'libdarknet_mac2.0.so'+'?raw=true',
- 'libdarknet_mac2.0.so',
- module="darknet")
- else:
- raise NotImplementedError("Darknet lib is not supported on {} platform"
- .format(sys.platform))
-
-
-
-
-
-.. rst-class:: sphx-glr-script-out
-
- Out:
-
- .. code-block:: none
-
- File /workspace/.tvm_test_data/darknet/yolov3-tiny.cfg exists, skip.
- File /workspace/.tvm_test_data/darknet/yolov3-tiny.weights exists, skip.
- File /workspace/.tvm_test_data/darknet/libdarknet2.0.so exists, skip.
-
-
-
-Download yolo categories and illustration front.
-------------------------------------------------
-
-
-.. code-block:: default
-
- coco_path = download_testdata(REPO_URL + 'data/' + 'coco.names' + '?raw=true',
- 'coco.names',
- module='data')
- font_path = download_testdata(REPO_URL + 'data/' + 'arial.ttf' + '?raw=true',
- 'arial.ttf',
- module='data')
- with open(coco_path) as f:
- content = f.readlines()
- names = [x.strip() for x in content]
-
-
-
-
-
-.. rst-class:: sphx-glr-script-out
-
- Out:
-
- .. code-block:: none
-
- File /workspace/.tvm_test_data/data/coco.names exists, skip.
- File /workspace/.tvm_test_data/data/arial.ttf exists, skip.
-
-
-
-Define the platform and model targets.
---------------------------------------
-Execute on CPU vs. VTA, and define the model.
-
-
-.. code-block:: default
-
-
- # Load VTA parameters from the 3rdparty/vta-hw/config/vta_config.json file
- env = vta.get_env()
- # Set ``device=arm_cpu`` to run inference on the CPU
- # or ``device=vta`` to run inference on the FPGA.
- device = "vta"
- target = env.target if device == "vta" else env.target_vta_cpu
-
- pack_dict = {
- "yolov3-tiny": ["nn.max_pool2d", "cast", 4, 185],
- }
-
- # Name of Darknet model to compile
- # The ``start_pack`` and ``stop_pack`` labels indicate where
- # to start and end the graph packing relay pass: in other words
- # where to start and finish offloading to VTA.
- # the number 4 indicate the the ``start_pack`` index is 4, the
- # number 185 indicate the ``stop_pack index`` is 185, by using
- # name and index number, here we can located to correct place
- # where to start/end when there are multiple ``nn.max_pool2d``
- # or ``cast``, print(mod.astext(show_meta_data=False)) can help
- # to find operator name and index information.
- assert MODEL_NAME in pack_dict
-
-
-
-
-
-
-
-Obtain an execution remote.
----------------------------
-When target is 'pynq' or other FPGA backend, reconfigure FPGA and runtime.
-Otherwise, if target is 'sim', execute locally.
-
-
-.. code-block:: default
-
-
- if env.TARGET not in ["sim", "tsim"]:
- # Get remote from tracker node if environment variable is set.
- # To set up the tracker, you'll need to follow the "Auto-tuning
- # a convolutional network for VTA" tutorial.
- tracker_host = os.environ.get("TVM_TRACKER_HOST", None)
- tracker_port = os.environ.get("TVM_TRACKER_PORT", None)
- # Otherwise if you have a device you want to program directly from
- # the host, make sure you've set the variables below to the IP of
- # your board.
- device_host = os.environ.get("VTA_RPC_HOST", "192.168.2.99")
- device_port = os.environ.get("VTA_RPC_PORT", "9091")
- if not tracker_host or not tracker_port:
- remote = rpc.connect(device_host, int(device_port))
- else:
- remote = autotvm.measure.request_remote(env.TARGET,
- tracker_host,
- int(tracker_port),
- timeout=10000)
- # Reconfigure the JIT runtime and FPGA.
- # You can program the FPGA with your own custom bitstream
- # by passing the path to the bitstream file instead of None.
- reconfig_start = time.time()
- vta.reconfig_runtime(remote)
- vta.program_fpga(remote, bitstream=None)
- reconfig_time = time.time() - reconfig_start
- print("Reconfigured FPGA and RPC runtime in {0:.2f}s!".format(reconfig_time))
-
- # In simulation mode, host the RPC server locally.
- else:
- remote = rpc.LocalSession()
-
- # Get execution context from remote
- ctx = remote.ext_dev(0) if device == "vta" else remote.cpu(0)
-
-
-
-
-
-
-
-Build the inference graph runtime.
-----------------------------------
-Using Darknet library load downloaded vision model and compile with Relay.
-The compilation steps are:
-
-1. Front end translation from Darknet into Relay module.
-2. Apply 8-bit quantization: here we skip the first conv layer,
- and dense layer which will both be executed in fp32 on the CPU.
-3. Perform graph packing to alter the data layout for tensorization.
-4. Perform constant folding to reduce number of operators (e.g. eliminate batch norm multiply).
-5. Perform relay build to object file.
-6. Load the object file onto remote (FPGA device).
-7. Generate graph runtime, `m`.
-
-
-
-.. code-block:: default
-
-
- # Load pre-configured AutoTVM schedules
- with autotvm.tophub.context(target):
- net = __darknetffi__.dlopen(darknet_lib_path).load_network(cfg_path.encode('utf-8'),
- weights_path.encode('utf-8'),
- 0)
- dshape = (env.BATCH, net.c, net.h, net.w)
- dtype = 'float32'
-
- # Measure build start time
- build_start = time.time()
-
- # Start front end compilation
- mod, params = relay.frontend.from_darknet(net, dtype=dtype, shape=dshape)
-
- if target.device_name == "vta":
- # Perform quantization in Relay
- # Note: We set opt_level to 3 in order to fold batch norm
- with tvm.transform.PassContext(opt_level=3):
- with relay.quantize.qconfig(global_scale=33.0,
- skip_conv_layers=[0],
- store_lowbit_output=True,
- round_for_shift=True):
- mod = relay.quantize.quantize(mod, params=params)
- # Perform graph packing and constant folding for VTA target
- mod = graph_pack(
- mod["main"],
- env.BATCH,
- env.BLOCK_OUT,
- env.WGT_WIDTH,
- start_name=pack_dict[MODEL_NAME][0],
- stop_name=pack_dict[MODEL_NAME][1],
- start_name_idx=pack_dict[MODEL_NAME][2],
- stop_name_idx=pack_dict[MODEL_NAME][3])
- else:
- mod = mod["main"]
-
- # Compile Relay program with AlterOpLayout disabled
- with vta.build_config(disabled_pass={"AlterOpLayout"}):
- graph, lib, params = relay.build(
- mod,
- target=target,
- params=params,
- target_host=env.target_host)
-
- # Measure Relay build time
- build_time = time.time() - build_start
- print(MODEL_NAME + " inference graph built in {0:.2f}s!".format(build_time))
-
- # Send the inference library over to the remote RPC server
- temp = util.tempdir()
- lib.save(temp.relpath("graphlib.o"))
- remote.upload(temp.relpath("graphlib.o"))
- lib = remote.load_module("graphlib.o")
-
- # Graph runtime
- m = graph_runtime.create(graph, lib, ctx)
-
-
-
-
-
-.. rst-class:: sphx-glr-script-out
-
- Out:
-
- .. code-block:: none
-
- yolov3-tiny inference graph built in 7.33s!
-
-
-
-Perform image detection inference.
-----------------------------------
-We run detect on an downloaded image
-Download test image
-
-
-.. code-block:: default
-
- [neth, netw] = dshape[2:]
- test_image = 'person.jpg'
- img_url = REPO_URL + 'data/' + test_image + '?raw=true'
- img_path = download_testdata(img_url, test_image, "data")
- data = darknet.load_image(img_path, neth, netw).transpose(1, 2, 0)
-
- # Prepare test image for inference
- plt.imshow(data)
- plt.show()
- data = data.transpose((2, 0, 1))
- data = data[np.newaxis, :]
- data = np.repeat(data, env.BATCH, axis=0)
-
- # Set the network parameters and inputs
- m.set_input('data', data)
- m.set_input(**params)
-
- # Perform inference and gather execution statistics
- # More on: :py:method:`tvm.runtime.Module.time_evaluator`
- num = 4 # number of times we run module for a single measurement
- rep = 3 # number of measurements (we derive std dev from this)
- timer = m.module.time_evaluator("run", ctx, number=num, repeat=rep)
-
- if env.TARGET in ["sim", "tsim"]:
- simulator.clear_stats()
- timer()
- sim_stats = simulator.stats()
- print("\nExecution statistics:")
- for k, v in sim_stats.items():
- # Since we execute the workload many times, we need to normalize stats
- # Note that there is always one warm up run
- # Therefore we divide the overall stats by (num * rep + 1)
- print("\t{:<16}: {:>16}".format(k, v // (num * rep + 1)))
- else:
- tcost = timer()
- std = np.std(tcost.results) * 1000
- mean = tcost.mean * 1000
- print("\nPerformed inference in %.2fms (std = %.2f) for %d samples" % (mean, std, env.BATCH))
- print("Average per sample inference time: %.2fms" % (mean/env.BATCH))
-
- # Get detection results from out
- thresh = 0.5
- nms_thresh = 0.45
- tvm_out = []
- for i in range(2):
- layer_out = {}
- layer_out['type'] = 'Yolo'
- # Get the yolo layer attributes (n, out_c, out_h, out_w, classes, total)
- layer_attr = m.get_output(i*4+3).asnumpy()
- layer_out['biases'] = m.get_output(i*4+2).asnumpy()
- layer_out['mask'] = m.get_output(i*4+1).asnumpy()
- out_shape = (layer_attr[0], layer_attr[1]//layer_attr[0],
- layer_attr[2], layer_attr[3])
- layer_out['output'] = m.get_output(i*4).asnumpy().reshape(out_shape)
- layer_out['classes'] = layer_attr[4]
- tvm_out.append(layer_out)
- thresh = 0.560
-
- # Show detection results
- img = darknet.load_image_color(img_path)
- _, im_h, im_w = img.shape
- dets = yolo_detection.fill_network_boxes((netw, neth),
- (im_w, im_h),
- thresh,
- 1,
- tvm_out)
- last_layer = net.layers[net.n - 1]
- yolo_detection.do_nms_sort(dets, last_layer.classes, nms_thresh)
- yolo_detection.draw_detections(font_path,
- img,
- dets,
- thresh,
- names,
- last_layer.classes)
- plt.imshow(img.transpose(1, 2, 0))
- plt.show()
-
-
-
-.. image:: /vta/tutorials/frontend/images/sphx_glr_deploy_detection_001.png
- :class: sphx-glr-single-img
-
-
-.. rst-class:: sphx-glr-script-out
-
- Out:
-
- .. code-block:: none
-
- File /workspace/.tvm_test_data/data/person.jpg exists, skip.
-
- Execution statistics:
- inp_load_nbytes : 25462784
- wgt_load_nbytes : 17558016
- acc_load_nbytes : 96128
- uop_load_nbytes : 5120
- out_store_nbytes: 3396224
- gemm_counter : 10578048
- alu_counter : 1061320
-
-
-
-
-.. _sphx_glr_download_vta_tutorials_frontend_deploy_detection.py:
-
-
-.. only :: html
-
- .. container:: sphx-glr-footer
- :class: sphx-glr-footer-example
-
-
-
- .. container:: sphx-glr-download
-
- :download:`Download Python source code: deploy_detection.py <deploy_detection.py>`
-
-
-
- .. container:: sphx-glr-download
-
- :download:`Download Jupyter notebook: deploy_detection.ipynb <deploy_detection.ipynb>`
-
-
-.. only:: html
-
- .. rst-class:: sphx-glr-signature
-
- `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_
diff --git a/docs/_sources/vta/tutorials/frontend/sg_execution_times.rst.txt b/docs/_sources/vta/tutorials/frontend/sg_execution_times.rst.txt
index 146e921..c075bc0 100644
--- a/docs/_sources/vta/tutorials/frontend/sg_execution_times.rst.txt
+++ b/docs/_sources/vta/tutorials/frontend/sg_execution_times.rst.txt
@@ -5,7 +5,6 @@
Computation times
=================
-**00:55.845** total execution time for **vta_tutorials_frontend** files:
+**00:26.123** total execution time for **vta_tutorials_frontend** files:
-- **00:33.300**: :ref:`sphx_glr_vta_tutorials_frontend_deploy_detection.py` (``deploy_detection.py``)
-- **00:22.545**: :ref:`sphx_glr_vta_tutorials_frontend_deploy_classification.py` (``deploy_classification.py``)
+- **00:26.123**: :ref:`sphx_glr_vta_tutorials_frontend_deploy_classification.py` (``deploy_classification.py``)
diff --git a/docs/_sources/vta/tutorials/index.rst.txt b/docs/_sources/vta/tutorials/index.rst.txt
index 40ea474..20f07c1 100644
--- a/docs/_sources/vta/tutorials/index.rst.txt
+++ b/docs/_sources/vta/tutorials/index.rst.txt
@@ -83,26 +83,6 @@ Compile Deep Learning Models
:hidden:
/vta/tutorials/frontend/deploy_classification
-
-.. raw:: html
-
- <div class="sphx-glr-thumbcontainer" tooltip="This tutorial provides an end-to-end demo, on how to run Darknet YoloV3-tiny inference onto the...">
-
-.. only:: html
-
- .. figure:: /vta/tutorials/frontend/images/thumb/sphx_glr_deploy_detection_thumb.png
-
- :ref:`sphx_glr_vta_tutorials_frontend_deploy_detection.py`
-
-.. raw:: html
-
- </div>
-
-
-.. toctree::
- :hidden:
-
- /vta/tutorials/frontend/deploy_detection
.. raw:: html
<div style='clear:both'></div>
diff --git a/docs/_sources/vta/tutorials/matrix_multiply.rst.txt b/docs/_sources/vta/tutorials/matrix_multiply.rst.txt
index 565e62e..17ddbdf 100644
--- a/docs/_sources/vta/tutorials/matrix_multiply.rst.txt
+++ b/docs/_sources/vta/tutorials/matrix_multiply.rst.txt
@@ -304,15 +304,15 @@ After we construct the schedule, by default the schedule computes
primfn(A_1: handle, B_1: handle, C_1: handle) -> ()
attr = {"global_symbol": "main", "tir.noalias": True}
- buffers = {B: Buffer(B_2: handle, int8, [16, 16, 16, 16], []),
- C: Buffer(C_2: handle, int8, [1, 16, 1, 16], []),
- A: Buffer(A_2: handle, int8, [1, 16, 1, 16], [])}
+ buffers = {C: Buffer(C_2: Pointer(int8), int8, [1, 16, 1, 16], []),
+ B: Buffer(B_2: Pointer(int8), int8, [16, 16, 16, 16], []),
+ A: Buffer(A_2: Pointer(int8), int8, [1, 16, 1, 16], [])}
buffer_map = {A_1: A, B_1: B, C_1: C} {
- attr [A_buf: handle] "storage_scope" = "global";
+ attr [A_buf: Pointer(int8)] "storage_scope" = "global";
allocate(A_buf, int8, [256]);
- attr [B_buf: handle] "storage_scope" = "global";
+ attr [B_buf: Pointer(int8)] "storage_scope" = "global";
allocate(B_buf, int8, [65536]);
- attr [C_buf: handle] "storage_scope" = "global";
+ attr [C_buf: Pointer(int32)] "storage_scope" = "global";
allocate(C_buf, int32, [256]) {
for (i1: int32, 0, 16) {
for (i3: int32, 0, 16) {
@@ -346,7 +346,7 @@ After we construct the schedule, by default the schedule computes
}
}
- // meta data omitted. you can use show_meta_data=True to include meta data
+
@@ -450,15 +450,15 @@ moving the copy operations into the matrix multiplication loop.
primfn(A_1: handle, B_1: handle, C_1: handle) -> ()
attr = {"global_symbol": "main", "tir.noalias": True}
- buffers = {C: Buffer(C_2: handle, int8, [1, 16, 1, 16], []),
- B: Buffer(B_2: handle, int8, [16, 16, 16, 16], []),
- A: Buffer(A_2: handle, int8, [1, 16, 1, 16], [])}
+ buffers = {C: Buffer(C_2: Pointer(int8), int8, [1, 16, 1, 16], []),
+ B: Buffer(B_2: Pointer(int8), int8, [16, 16, 16, 16], []),
+ A: Buffer(A_2: Pointer(int8), int8, [1, 16, 1, 16], [])}
buffer_map = {A_1: A, B_1: B, C_1: C} {
- attr [C_buf: handle] "storage_scope" = "local.acc_buffer";
+ attr [C_buf: Pointer(int32)] "storage_scope" = "local.acc_buffer";
allocate(C_buf, int32, [256]);
- attr [A_buf: handle] "storage_scope" = "local.inp_buffer";
+ attr [A_buf: Pointer(int8)] "storage_scope" = "local.inp_buffer";
allocate(A_buf, int8, [16]);
- attr [B_buf: handle] "storage_scope" = "local.wgt_buffer";
+ attr [B_buf: Pointer(int8)] "storage_scope" = "local.wgt_buffer";
allocate(B_buf, int8, [16]) {
for (co: int32, 0, 16) {
for (ci: int32, 0, 16) {
@@ -487,7 +487,7 @@ moving the copy operations into the matrix multiplication loop.
}
}
- // meta data omitted. you can use show_meta_data=True to include meta data
+
@@ -539,13 +539,13 @@ by the VTA runtime JIT compiler.
primfn(A_1: handle, B_1: handle, C_1: handle) -> ()
attr = {"global_symbol": "main", "tir.noalias": True}
- buffers = {C: Buffer(C_2: handle, int8, [1, 16, 1, 16], []),
- B: Buffer(B_2: handle, int8, [16, 16, 16, 16], []),
- A: Buffer(A_2: handle, int8, [1, 16, 1, 16], [])}
+ buffers = {C: Buffer(C_2: Pointer(int8), int8, [1, 16, 1, 16], []),
+ B: Buffer(B_2: Pointer(int8), int8, [16, 16, 16, 16], []),
+ A: Buffer(A_2: Pointer(int8), int8, [1, 16, 1, 16], [])}
buffer_map = {A_1: A, B_1: B, C_1: C} {
- attr [C_buf: handle] "storage_scope" = "local.acc_buffer";
- attr [A_buf: handle] "storage_scope" = "local.inp_buffer";
- attr [B_buf: handle] "storage_scope" = "local.wgt_buffer" {
+ attr [C_buf: Pointer(int32)] "storage_scope" = "local.acc_buffer";
+ attr [A_buf: Pointer(int8)] "storage_scope" = "local.inp_buffer";
+ attr [B_buf: Pointer(int8)] "storage_scope" = "local.wgt_buffer" {
attr [IterVar(vta: int32, (nullptr), "ThreadIndex", "vta")] "coproc_scope" = 2 {
attr [IterVar(vta, (nullptr), "ThreadIndex", "vta")] "coproc_uop_scope" = "VTAPushGEMMOp" {
@tir.call_extern("VTAUopLoopBegin", 16, 1, 0, 0, dtype=int32)
@@ -581,7 +581,7 @@ by the VTA runtime JIT compiler.
}
}
- // meta data omitted. you can use show_meta_data=True to include meta data
+
diff --git a/docs/_sources/vta/tutorials/optimize/convolution_opt.rst.txt b/docs/_sources/vta/tutorials/optimize/convolution_opt.rst.txt
index 0338842..e5ea46d 100644
--- a/docs/_sources/vta/tutorials/optimize/convolution_opt.rst.txt
+++ b/docs/_sources/vta/tutorials/optimize/convolution_opt.rst.txt
@@ -257,15 +257,15 @@ Those include:
primfn(data_1: handle, kernel_1: handle, res_1: handle) -> ()
attr = {"global_symbol": "main", "tir.noalias": True}
- buffers = {res: Buffer(res_2: handle, int8, [1, 16, 14, 14, 1, 16], []),
- kernel: Buffer(kernel_2: handle, int8, [16, 16, 3, 3, 16, 16], []),
- data: Buffer(data_2: handle, int8, [1, 16, 14, 14, 1, 16], [])}
+ buffers = {kernel: Buffer(kernel_2: Pointer(int8), int8, [16, 16, 3, 3, 16, 16], []),
+ res: Buffer(res_2: Pointer(int8), int8, [1, 16, 14, 14, 1, 16], []),
+ data: Buffer(data_2: Pointer(int8), int8, [1, 16, 14, 14, 1, 16], [])}
buffer_map = {data_1: data, kernel_1: kernel, res_1: res} {
- attr [data_buf: handle] "storage_scope" = "global";
+ attr [data_buf: Pointer(int8)] "storage_scope" = "global";
allocate(data_buf, int8, [65536]);
- attr [kernel_buf: handle] "storage_scope" = "global";
+ attr [kernel_buf: Pointer(int8)] "storage_scope" = "global";
allocate(kernel_buf, int8, [589824]);
- attr [res_conv: handle] "storage_scope" = "global";
+ attr [res_conv: Pointer(int32)] "storage_scope" = "global";
allocate(res_conv, int32, [50176]) {
for (i1: int32, 0, 16) {
for (i2: int32, 0, 16) {
@@ -346,7 +346,7 @@ Those include:
}
}
- // meta data omitted. you can use show_meta_data=True to include meta data
+
@@ -453,15 +453,15 @@ below.
primfn(data_1: handle, kernel_1: handle, res_1: handle) -> ()
attr = {"global_symbol": "main", "tir.noalias": True}
- buffers = {res: Buffer(res_2: handle, int8, [1, 16, 14, 14, 1, 16], []),
- kernel: Buffer(kernel_2: handle, int8, [16, 16, 3, 3, 16, 16], []),
- data: Buffer(data_2: handle, int8, [1, 16, 14, 14, 1, 16], [])}
+ buffers = {res: Buffer(res_2: Pointer(int8), int8, [1, 16, 14, 14, 1, 16], []),
+ kernel: Buffer(kernel_2: Pointer(int8), int8, [16, 16, 3, 3, 16, 16], []),
+ data: Buffer(data_2: Pointer(int8), int8, [1, 16, 14, 14, 1, 16], [])}
buffer_map = {data_1: data, kernel_1: kernel, res_1: res} {
- attr [data_buf: handle] "storage_scope" = "global";
+ attr [data_buf: Pointer(int8)] "storage_scope" = "global";
allocate(data_buf, int8, [65536]);
- attr [kernel_buf: handle] "storage_scope" = "global";
+ attr [kernel_buf: Pointer(int8)] "storage_scope" = "global";
allocate(kernel_buf, int8, [589824]);
- attr [res_conv: handle] "storage_scope" = "global";
+ attr [res_conv: Pointer(int32)] "storage_scope" = "global";
allocate(res_conv, int32, [25088]) {
for (i1: int32, 0, 16) {
for (i2: int32, 0, 16) {
@@ -558,7 +558,7 @@ below.
}
}
- // meta data omitted. you can use show_meta_data=True to include meta data
+
@@ -636,13 +636,13 @@ and mapping the shift, and clipping computation to the vector ALU.
primfn(data_1: handle, kernel_1: handle, res_1: handle) -> ()
attr = {"global_symbol": "main", "tir.noalias": True}
- buffers = {kernel: Buffer(kernel_2: handle, int8, [16, 16, 3, 3, 16, 16], []),
- res: Buffer(res_2: handle, int8, [1, 16, 14, 14, 1, 16], []),
- data: Buffer(data_2: handle, int8, [1, 16, 14, 14, 1, 16], [])}
+ buffers = {res: Buffer(res_2: Pointer(int8), int8, [1, 16, 14, 14, 1, 16], []),
+ kernel: Buffer(kernel_2: Pointer(int8), int8, [16, 16, 3, 3, 16, 16], []),
+ data: Buffer(data_2: Pointer(int8), int8, [1, 16, 14, 14, 1, 16], [])}
buffer_map = {data_1: data, kernel_1: kernel, res_1: res} {
- attr [res_conv: handle] "storage_scope" = "local.acc_buffer";
- attr [data_buf: handle] "storage_scope" = "local.inp_buffer";
- attr [kernel_buf: handle] "storage_scope" = "local.wgt_buffer" {
+ attr [res_conv: Pointer(int32)] "storage_scope" = "local.acc_buffer";
+ attr [data_buf: Pointer(int8)] "storage_scope" = "local.inp_buffer";
+ attr [kernel_buf: Pointer(int8)] "storage_scope" = "local.wgt_buffer" {
@tir.vta.coproc_dep_push(3, 2, dtype=int32)
@tir.vta.coproc_dep_push(3, 2, dtype=int32)
for (i2.outer: int32, 0, 2) {
@@ -736,7 +736,7 @@ and mapping the shift, and clipping computation to the vector ALU.
}
}
- // meta data omitted. you can use show_meta_data=True to include meta data
+
diff --git a/docs/_sources/vta/tutorials/optimize/matrix_multiply_opt.rst.txt b/docs/_sources/vta/tutorials/optimize/matrix_multiply_opt.rst.txt
index f114ffc..9d094d9 100644
--- a/docs/_sources/vta/tutorials/optimize/matrix_multiply_opt.rst.txt
+++ b/docs/_sources/vta/tutorials/optimize/matrix_multiply_opt.rst.txt
@@ -202,15 +202,15 @@ Those include:
primfn(data_1: handle, weight_1: handle, res_1: handle) -> ()
attr = {"global_symbol": "main", "tir.noalias": True}
- buffers = {weight: Buffer(weight_2: handle, int8, [64, 64, 16, 16], []),
- res: Buffer(res_2: handle, int8, [1, 64, 1, 16], []),
- data: Buffer(data_2: handle, int8, [1, 64, 1, 16], [])}
+ buffers = {res: Buffer(res_2: Pointer(int8), int8, [1, 64, 1, 16], []),
+ weight: Buffer(weight_2: Pointer(int8), int8, [64, 64, 16, 16], []),
+ data: Buffer(data_2: Pointer(int8), int8, [1, 64, 1, 16], [])}
buffer_map = {data_1: data, weight_1: weight, res_1: res} {
- attr [data_buf: handle] "storage_scope" = "global";
+ attr [data_buf: Pointer(int8)] "storage_scope" = "global";
allocate(data_buf, int8, [1024]);
- attr [weight_buf: handle] "storage_scope" = "global";
+ attr [weight_buf: Pointer(int8)] "storage_scope" = "global";
allocate(weight_buf, int8, [1048576]);
- attr [res_gem: handle] "storage_scope" = "global";
+ attr [res_gem: Pointer(int32)] "storage_scope" = "global";
allocate(res_gem, int32, [1024]) {
for (i1: int32, 0, 64) {
for (i3: int32, 0, 16) {
@@ -259,7 +259,7 @@ Those include:
}
}
- // meta data omitted. you can use show_meta_data=True to include meta data
+
@@ -364,15 +364,15 @@ below:
primfn(data_1: handle, weight_1: handle, res_1: handle) -> ()
attr = {"global_symbol": "main", "tir.noalias": True}
- buffers = {res: Buffer(res_2: handle, int8, [1, 64, 1, 16], []),
- weight: Buffer(weight_2: handle, int8, [64, 64, 16, 16], []),
- data: Buffer(data_2: handle, int8, [1, 64, 1, 16], [])}
+ buffers = {weight: Buffer(weight_2: Pointer(int8), int8, [64, 64, 16, 16], []),
+ res: Buffer(res_2: Pointer(int8), int8, [1, 64, 1, 16], []),
+ data: Buffer(data_2: Pointer(int8), int8, [1, 64, 1, 16], [])}
buffer_map = {data_1: data, weight_1: weight, res_1: res} {
- attr [data_buf: handle] "storage_scope" = "global";
+ attr [data_buf: Pointer(int8)] "storage_scope" = "global";
allocate(data_buf, int8, [1024]);
- attr [weight_buf: handle] "storage_scope" = "global";
+ attr [weight_buf: Pointer(int8)] "storage_scope" = "global";
allocate(weight_buf, int8, [1048576]);
- attr [res_gem: handle] "storage_scope" = "global";
+ attr [res_gem: Pointer(int32)] "storage_scope" = "global";
allocate(res_gem, int32, [256]) {
for (i1: int32, 0, 64) {
for (i3: int32, 0, 16) {
@@ -429,7 +429,7 @@ below:
}
}
- // meta data omitted. you can use show_meta_data=True to include meta data
+
@@ -507,13 +507,13 @@ and mapping the shift, and clipping computation to the vector ALU.
primfn(data_1: handle, weight_1: handle, res_1: handle) -> ()
attr = {"global_symbol": "main", "tir.noalias": True}
- buffers = {res: Buffer(res_2: handle, int8, [1, 64, 1, 16], []),
- weight: Buffer(weight_2: handle, int8, [64, 64, 16, 16], []),
- data: Buffer(data_2: handle, int8, [1, 64, 1, 16], [])}
+ buffers = {res: Buffer(res_2: Pointer(int8), int8, [1, 64, 1, 16], []),
+ weight: Buffer(weight_2: Pointer(int8), int8, [64, 64, 16, 16], []),
+ data: Buffer(data_2: Pointer(int8), int8, [1, 64, 1, 16], [])}
buffer_map = {data_1: data, weight_1: weight, res_1: res} {
- attr [res_gem: handle] "storage_scope" = "local.acc_buffer";
- attr [data_buf: handle] "storage_scope" = "local.inp_buffer";
- attr [weight_buf: handle] "storage_scope" = "local.wgt_buffer" {
+ attr [res_gem: Pointer(int32)] "storage_scope" = "local.acc_buffer";
+ attr [data_buf: Pointer(int8)] "storage_scope" = "local.inp_buffer";
+ attr [weight_buf: Pointer(int8)] "storage_scope" = "local.wgt_buffer" {
@tir.vta.coproc_dep_push(3, 2, dtype=int32)
for (i1.outer: int32, 0, 4) {
attr [IterVar(vta: int32, (nullptr), "ThreadIndex", "vta")] "coproc_scope" = 2 {
@@ -576,7 +576,7 @@ and mapping the shift, and clipping computation to the vector ALU.
}
}
- // meta data omitted. you can use show_meta_data=True to include meta data
+
diff --git a/docs/_sources/vta/tutorials/optimize/sg_execution_times.rst.txt b/docs/_sources/vta/tutorials/optimize/sg_execution_times.rst.txt
index 6369639..89def49 100644
--- a/docs/_sources/vta/tutorials/optimize/sg_execution_times.rst.txt
+++ b/docs/_sources/vta/tutorials/optimize/sg_execution_times.rst.txt
@@ -5,7 +5,7 @@
Computation times
=================
-**00:02.955** total execution time for **vta_tutorials_optimize** files:
+**00:03.190** total execution time for **vta_tutorials_optimize** files:
-- **00:02.576**: :ref:`sphx_glr_vta_tutorials_optimize_convolution_opt.py` (``convolution_opt.py``)
-- **00:00.379**: :ref:`sphx_glr_vta_tutorials_optimize_matrix_multiply_opt.py` (``matrix_multiply_opt.py``)
+- **00:02.764**: :ref:`sphx_glr_vta_tutorials_optimize_convolution_opt.py` (``convolution_opt.py``)
+- **00:00.426**: :ref:`sphx_glr_vta_tutorials_optimize_matrix_multiply_opt.py` (``matrix_multiply_opt.py``)
diff --git a/docs/_sources/vta/tutorials/sg_execution_times.rst.txt b/docs/_sources/vta/tutorials/sg_execution_times.rst.txt
index 863714c..16eaf68 100644
--- a/docs/_sources/vta/tutorials/sg_execution_times.rst.txt
+++ b/docs/_sources/vta/tutorials/sg_execution_times.rst.txt
@@ -5,7 +5,7 @@
Computation times
=================
-**00:00.696** total execution time for **vta_tutorials** files:
+**00:00.792** total execution time for **vta_tutorials** files:
-- **00:00.355**: :ref:`sphx_glr_vta_tutorials_vta_get_started.py` (``vta_get_started.py``)
-- **00:00.341**: :ref:`sphx_glr_vta_tutorials_matrix_multiply.py` (``matrix_multiply.py``)
+- **00:00.406**: :ref:`sphx_glr_vta_tutorials_vta_get_started.py` (``vta_get_started.py``)
+- **00:00.386**: :ref:`sphx_glr_vta_tutorials_matrix_multiply.py` (``matrix_multiply.py``)
diff --git a/docs/_sources/vta/tutorials/vta_get_started.rst.txt b/docs/_sources/vta/tutorials/vta_get_started.rst.txt
index bc383dd..dc98318 100644
--- a/docs/_sources/vta/tutorials/vta_get_started.rst.txt
+++ b/docs/_sources/vta/tutorials/vta_get_started.rst.txt
@@ -300,13 +300,13 @@ After we construct the schedule, by default the schedule computes
primfn(A_1: handle, B_1: handle, C_1: handle) -> ()
attr = {"global_symbol": "main", "tir.noalias": True}
- buffers = {C: Buffer(C_2: handle, int8, [1, 64, 1, 16], []),
- B: Buffer(B_2: handle, int32, [1, 64, 1, 16], []),
- A: Buffer(A_2: handle, int32, [1, 64, 1, 16], [])}
+ buffers = {C: Buffer(C_2: Pointer(int8), int8, [1, 64, 1, 16], []),
+ B: Buffer(B_2: Pointer(int32), int32, [1, 64, 1, 16], []),
+ A: Buffer(A_2: Pointer(int32), int32, [1, 64, 1, 16], [])}
buffer_map = {A_1: A, B_1: B, C_1: C} {
- attr [A_buf: handle] "storage_scope" = "global";
+ attr [A_buf: Pointer(int32)] "storage_scope" = "global";
allocate(A_buf, int32, [1024]);
- attr [B_buf: handle] "storage_scope" = "global";
+ attr [B_buf: Pointer(int32)] "storage_scope" = "global";
allocate(B_buf, int32, [1024]) {
for (i1: int32, 0, 64) {
for (i3: int32, 0, 16) {
@@ -331,7 +331,7 @@ After we construct the schedule, by default the schedule computes
}
}
- // meta data omitted. you can use show_meta_data=True to include meta data
+
@@ -423,11 +423,11 @@ with an :code:`env.alu` pragma.
primfn(A_1: handle, B_1: handle, C_1: handle) -> ()
attr = {"global_symbol": "main", "tir.noalias": True}
- buffers = {B: Buffer(B_2: handle, int32, [1, 64, 1, 16], []),
- C: Buffer(C_2: handle, int8, [1, 64, 1, 16], []),
- A: Buffer(A_2: handle, int32, [1, 64, 1, 16], [])}
+ buffers = {B: Buffer(B_2: Pointer(int32), int32, [1, 64, 1, 16], []),
+ C: Buffer(C_2: Pointer(int8), int8, [1, 64, 1, 16], []),
+ A: Buffer(A_2: Pointer(int32), int32, [1, 64, 1, 16], [])}
buffer_map = {A_1: A, B_1: B, C_1: C} {
- attr [A_buf: handle] "storage_scope" = "local.acc_buffer" {
+ attr [A_buf: Pointer(int32)] "storage_scope" = "local.acc_buffer" {
attr [IterVar(vta: int32, (nullptr), "ThreadIndex", "vta")] "coproc_scope" = 2 {
@tir.call_extern("VTALoadBuffer2D", @tir.tvm_thread_context(@tir.vta.command_handle(, dtype=handle), dtype=handle), A_2, 0, 64, 1, 64, 0, 0, 0, 0, 0, 3, dtype=int32)
@tir.call_extern("VTALoadBuffer2D", @tir.tvm_thread_context(@tir.vta.command_handle(, dtype=handle), dtype=handle), B_2, 0, 64, 1, 64, 0, 0, 0, 0, 64, 3, dtype=int32)
@@ -446,7 +446,7 @@ with an :code:`env.alu` pragma.
}
}
- // meta data omitted. you can use show_meta_data=True to include meta data
+
diff --git a/docs/api/doxygen/algorithm_8h.html b/docs/api/doxygen/algorithm_8h.html
index c5c2419..5ae7f0e 100644
--- a/docs/api/doxygen/algorithm_8h.html
+++ b/docs/api/doxygen/algorithm_8h.html
@@ -99,7 +99,7 @@ var searchBox = new SearchBox("searchBox", "search",false,'Search');
</div><div class="textblock"><div class="dynheader">
Include dependency graph for algorithm.h:</div>
<div class="dyncontent">
-<div class="center"><iframe scrolling="no" frameborder="0" src="algorithm_8h__incl.svg" width="4992" height="1515"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
+<div class="center"><iframe scrolling="no" frameborder="0" src="algorithm_8h__incl.svg" width="4878" height="1440"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
</div>
</div>
</div>
diff --git a/docs/api/doxygen/algorithm_8h__incl.svg b/docs/api/doxygen/algorithm_8h__incl.svg
index 83018f9..61da6d0 100644
--- a/docs/api/doxygen/algorithm_8h__incl.svg
+++ b/docs/api/doxygen/algorithm_8h__incl.svg
@@ -4,1468 +4,1468 @@
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-<polygon fill="midnightblue" stroke="midnightblue" points="1014.14,-188.867 1003.96,-191.799 1013.75,-195.856 1014.14,-188.867"/>
+<!-- Node20->Node24 -->
+<g id="edge53" class="edge"><title>Node20->Node24</title>
+<path fill="none" stroke="midnightblue" d="M1384.48,-241.542C1367.61,-239.286 1349.07,-236.923 1332,-235 1159.02,-215.519 1114.3,-223.743 942,-199 940.54,-198.79 939.056,-198.565 937.559,-198.328"/>
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-<!-- Node16->Node24 -->
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-<polygon fill="midnightblue" stroke="midnightblue" points="3571.11,-194.671 3580.97,-190.796 3570.84,-187.676 3571.11,-194.671"/>
+<!-- Node20->Node25 -->
+<g id="edge54" class="edge"><title>Node20->Node25</title>
+<path fill="none" stroke="midnightblue" d="M1497.61,-247.33C1750.03,-237.652 2768.83,-198.594 2961.48,-191.208"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="2961.9,-194.695 2971.76,-190.814 2961.63,-187.7 2961.9,-194.695"/>
</g>
-<!-- Node25 -->
-<g id="node25" class="node"><title>Node25</title>
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-<!-- Node16->Node25 -->
-<g id="edge47" class="edge"><title>Node16->Node25</title>
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-<polygon fill="midnightblue" stroke="midnightblue" points="1910.11,-198.576 1899.53,-198.142 1907.53,-205.082 1910.11,-198.576"/>
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-<!-- Node17->Node9 -->
-<g id="edge23" class="edge"><title>Node17->Node9</title>
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-<!-- Node17->Node12 -->
-<g id="edge24" class="edge"><title>Node17->Node12</title>
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-<!-- Node17->Node13 -->
-<g id="edge25" class="edge"><title>Node17->Node13</title>
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-<!-- Node17->Node14 -->
-<g id="edge26" class="edge"><title>Node17->Node14</title>
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-<!-- Node18->Node10 -->
-<g id="edge29" class="edge"><title>Node18->Node10</title>
-<path fill="none" stroke="midnightblue" d="M2080.05,-181.106C2169.55,-171.161 2334.61,-152.821 2430.23,-142.197"/>
-<polygon fill="midnightblue" stroke="midnightblue" points="2430.84,-145.65 2440.4,-141.067 2430.07,-138.693 2430.84,-145.65"/>
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-<!-- Node18->Node11 -->
-<g id="edge30" class="edge"><title>Node18->Node11</title>
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-<!-- Node18->Node12 -->
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-<!-- Node18->Node13 -->
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-<!-- Node18->Node16 -->
-<g id="edge35" class="edge"><title>Node18->Node16</title>
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-<polygon fill="midnightblue" stroke="midnightblue" points="2027.71,-225.821 2032.23,-235.399 2034.67,-225.088 2027.71,-225.821"/>
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-<!-- Node18->Node19 -->
-<g id="edge31" class="edge"><title>Node18->Node19</title>
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-<polygon fill="midnightblue" stroke="midnightblue" points="1952.8,-144.798 1942.42,-142.655 1949.2,-150.801 1952.8,-144.798"/>
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-<!-- Node18->Node20 -->
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-<!-- Node18->Node21 -->
-<g id="edge34" class="edge"><title>Node18->Node21</title>
-<path fill="none" stroke="midnightblue" d="M1956,-182.475C1829.22,-171.178 1544.07,-145.768 1445.94,-137.024"/>
-<polygon fill="midnightblue" stroke="midnightblue" points="1445.98,-133.514 1435.71,-136.112 1445.36,-140.486 1445.98,-133.514"/>
+<!-- Node26 -->
+<g id="node26" class="node"><title>Node26</title>
+<polygon fill="white" stroke="#bfbfbf" points="1420.5,-179.5 1420.5,-198.5 1461.5,-198.5 1461.5,-179.5 1420.5,-179.5"/>
+<text text-anchor="middle" x="1441" y="-186.5" font-family="Helvetica,sans-Serif" font-size="10.00">tuple</text>
+</g>
+<!-- Node20->Node26 -->
+<g id="edge57" class="edge"><title>Node20->Node26</title>
+<path fill="none" stroke="midnightblue" d="M1441,-235.399C1441,-227.466 1441,-217.458 1441,-208.858"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="1444.5,-208.658 1441,-198.658 1437.5,-208.658 1444.5,-208.658"/>
+</g>
+<!-- Node21->Node9 -->
+<g id="edge33" class="edge"><title>Node21->Node9</title>
+<path fill="none" stroke="midnightblue" d="M2290.93,-123.464C2248.74,-109.442 2160.63,-81.6313 2084,-67 1929.66,-37.5292 1744.05,-24.2035 1654.5,-19.1348"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="1654.5,-15.6296 1644.33,-18.5711 1654.12,-22.6189 1654.5,-15.6296"/>
+</g>
+<!-- Node21->Node12 -->
+<g id="edge34" class="edge"><title>Node21->Node12</title>
+<path fill="none" stroke="midnightblue" d="M2275.32,-123.481C2206.56,-109.419 2062.87,-81.4698 1940,-67 1675.03,-35.7946 1358.07,-22.9683 1217,-18.5226"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="1216.85,-15.0163 1206.75,-18.2044 1216.63,-22.0129 1216.85,-15.0163"/>
+</g>
+<!-- Node21->Node13 -->
+<g id="edge35" class="edge"><title>Node21->Node13</title>
+<path fill="none" stroke="midnightblue" d="M2327.89,-123.444C2348.66,-108.997 2393.42,-80.0678 2436,-67 2525.73,-39.4632 2814.74,-22.923 2911.84,-18.0332"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="2912.11,-21.5239 2921.93,-17.5324 2911.77,-14.5325 2912.11,-21.5239"/>
+</g>
+<!-- Node21->Node14 -->
+<g id="edge36" class="edge"><title>Node21->Node14</title>
+<path fill="none" stroke="midnightblue" d="M2308.56,-123.193C2301.47,-114.464 2290.9,-100.483 2284,-87 2275.36,-70.1249 2268.75,-49.453 2264.67,-34.8695"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="2268.01,-33.8283 2262.05,-25.0678 2261.25,-35.632 2268.01,-33.8283"/>
+</g>
+<!-- Node22->Node10 -->
+<g id="edge39" class="edge"><title>Node22->Node10</title>
+<path fill="none" stroke="midnightblue" d="M1386.59,-179.444C1438.41,-169.925 1522.13,-154.547 1577.62,-144.356"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="1578.43,-147.765 1587.63,-142.516 1577.16,-140.881 1578.43,-147.765"/>
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<!-- Node22->Node11 -->
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-<path fill="none" stroke="midnightblue" d="M2790.46,-179.368C2815.3,-160.653 2874.36,-116.151 2905.31,-92.8324"/>
-<polygon fill="midnightblue" stroke="midnightblue" points="2907.72,-95.4044 2913.6,-86.5914 2903.5,-89.8135 2907.72,-95.4044"/>
+<g id="edge40" class="edge"><title>Node22->Node11</title>
+<path fill="none" stroke="midnightblue" d="M1341.56,-179.274C1344.58,-165.105 1352.62,-137.248 1371,-123 1395.72,-103.833 1473.73,-91.456 1532.08,-84.6274"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="1532.83,-88.0655 1542.37,-83.4572 1532.04,-81.1104 1532.83,-88.0655"/>
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<!-- Node22->Node12 -->
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-<path fill="none" stroke="midnightblue" d="M2781.87,-179.23C2789.77,-156.527 2814.2,-95.6137 2857,-67 2889.54,-45.2496 2989.74,-30.951 3060.24,-23.2918"/>
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+<g id="edge38" class="edge"><title>Node22->Node12</title>
+<path fill="none" stroke="midnightblue" d="M1330.23,-179.495C1300.47,-153.589 1210.34,-75.1216 1166.9,-37.3093"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="1168.96,-34.4563 1159.12,-30.53 1164.36,-39.7359 1168.96,-34.4563"/>
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-<!-- Node22->Node15 -->
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-<polygon fill="midnightblue" stroke="midnightblue" points="1804.05,-13.1422 1794.02,-16.558 1803.99,-20.142 1804.05,-13.1422"/>
+<!-- Node22->Node13 -->
+<g id="edge42" class="edge"><title>Node22->Node13</title>
+<path fill="none" stroke="midnightblue" d="M1402.34,-180.082C1405.26,-179.715 1408.16,-179.353 1411,-179 1542.07,-162.695 1575.01,-159.929 1706,-143 1953.79,-110.978 2014.52,-93.1398 2263,-67 2512.16,-40.7882 2814.02,-23.4389 2911.79,-18.1821"/>
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<!-- Node22->Node17 -->
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-<polygon fill="midnightblue" stroke="midnightblue" points="3265.1,-144.975 3274.72,-140.537 3264.43,-138.007 3265.1,-144.975"/>
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-<!-- Node22->Node21 -->
-<g id="edge41" class="edge"><title>Node22->Node21</title>
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+<g id="edge41" class="edge"><title>Node22->Node17</title>
+<path fill="none" stroke="midnightblue" d="M1351.09,-179.324C1361.49,-171.185 1377.26,-158.839 1389.72,-149.087"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="1391.99,-151.753 1397.71,-142.834 1387.68,-146.241 1391.99,-151.753"/>
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+<!-- Node22->Node18 -->
+<g id="edge43" class="edge"><title>Node22->Node18</title>
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+<polygon fill="midnightblue" stroke="midnightblue" points="664.907,-134.689 654.676,-137.443 664.392,-141.67 664.907,-134.689"/>
+</g>
+<!-- Node22->Node19 -->
+<g id="edge44" class="edge"><title>Node22->Node19</title>
+<path fill="none" stroke="midnightblue" d="M1277.95,-181.351C1170.46,-169.835 953.134,-146.55 869.684,-137.609"/>
+<polygon fill="midnightblue" stroke="midnightblue" points="870.047,-134.128 859.731,-136.543 869.301,-141.088 870.047,-134.128"/>
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+<!-- Node22->Node20 -->
+<g id="edge45" class="edge"><title>Node22->Node20</title>
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+<!-- Node23->Node11 -->
+<g id="edge49" class="edge"><title>Node23->Node11</title>
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+<!-- Node23->Node12 -->
+<g id="edge47" class="edge"><title>Node23->Node12</title>
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+<polygon fill="midnightblue" stroke="midnightblue" points="1138.16,-41.3175 1138.11,-30.7228 1131.54,-39.0402 1138.16,-41.3175"/>
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+<!-- Node23->Node15 -->
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+<!-- Node23->Node19 -->
+<g id="edge51" class="edge"><title>Node23->Node19</title>
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+<polygon fill="midnightblue" stroke="midnightblue" points="870.292,-137.885 859.777,-139.177 868.803,-144.725 870.292,-137.885"/>
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+<!-- Node23->Node21 -->
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<!-- Node27->Node10 -->
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<!-- Node27->Node11 -->
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<!-- Node27->Node12 -->
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<!-- Node27->Node13 -->
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<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
<div class="textblock"><p>Algebra expression simplifications. </p>
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