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Posted to commits@tvm.apache.org by dr...@apache.org on 2022/06/14 17:56:19 UTC
[tvm-site] branch asf-site updated: Add v0.8.0 docs
This is an automated email from the ASF dual-hosted git repository.
driazati pushed a commit to branch asf-site
in repository https://gitbox.apache.org/repos/asf/tvm-site.git
The following commit(s) were added to refs/heads/asf-site by this push:
new 7375daba3 Add v0.8.0 docs
7375daba3 is described below
commit 7375daba39369beb56fb352c33afed7f959eb25e
Author: driazati <dr...@users.noreply.github.com>
AuthorDate: Tue Jun 14 10:55:51 2022 -0700
Add v0.8.0 docs
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.../tune_relay_cuda.py | 384 +
.../067cf39a44d9f315a39f8a7547c556d8/install.py | 50 +
.../tune_sparse_x86.py | 324 +
.../deploy_sparse.ipynb | 187 +
.../0e2f38fcb1a1fb3e636e5953aa600dee/from_mxnet.py | 146 +
.../opt_gemm.ipynb | 309 +
.../reduction.ipynb | 248 +
.../convolution_opt.py | 455 +
.../from_paddle.py | 117 +
.../tune_alu_vta.ipynb | 126 +
.../tune_network_arm.py | 439 +
.../deploy_prequantized_tflite.ipynb | 319 +
.../intrin_math.ipynb | 169 +
.../matrix_multiply.ipynb | 311 +
.../from_pytorch.ipynb | 162 +
.../deploy_model_on_android.py | 360 +
.../tvmc_command_line_driver.py | 510 +
.../from_tflite.ipynb | 151 +
.../auto_scheduler_matmul_x86.ipynb | 205 +
.../286e7f77f494a25312ac88e3f234822e/extern_op.py | 139 +
.../tune_sparse_x86.ipynb | 205 +
.../2a0982f8ca0176cb17713d28286536e4/reduction.py | 196 +
.../2a4c6a9cfa43e8afef159a2bf1b99108/install.ipynb | 57 +
.../autotvm_relay_x86.ipynb | 348 +
.../micro_tflite.py | 318 +
.../introduction.py | 134 +
.../tune_relay_arm.py | 416 +
.../autotvm_matmul_x86.ipynb | 222 +
.../deploy_object_detection_pytorch.ipynb | 162 +
.../3a9b1d387f618487c8ccf6b8b78ae179/intro_topi.py | 151 +
.../from_coreml.py | 114 +
.../tensorize.ipynb | 241 +
.../opt_conv_cuda.py | 249 +
.../relay_quick_start.ipynb | 144 +
.../deploy_model_on_rasp.py | 230 +
.../tensor_expr_get_started.py | 903 ++
.../428c6201e29ce74e73c6b41eee589f62/tensorize.py | 318 +
.../tensor_expr_get_started.ipynb | 592 +
.../from_mxnet.ipynb | 162 +
.../matrix_multiply_opt.ipynb | 176 +
.../tune_network_arm.ipynb | 161 +
.../from_caffe2.ipynb | 133 +
.../use_pass_infra.py | 273 +
.../deploy_prequantized_tflite.py | 262 +
.../autotvm_relay_x86.py | 477 +
.../micro_tflite.ipynb | 169 +
.../tensor_ir_blitz_course.py | 191 +
.../tune_network_mali.ipynb | 154 +
.../tune_conv2d_layer_cuda.ipynb | 205 +
.../intro_topi.ipynb | 230 +
.../tune_relay_mobile_gpu.py | 414 +
.../deploy_detection.py | 320 +
.../deploy_detection.ipynb | 169 +
.../tune_network_mali.py | 359 +
.../tune_relay_x86.py | 298 +
.../tuple_inputs.py | 122 +
.../tune_conv2d_cuda.py | 244 +
.../tune_relay_mobile_gpu.ipynb | 175 +
.../tune_alu_vta.py | 321 +
.../729378592a96230b4f7be71b44da43a4/scan.ipynb | 169 +
.../tune_conv2d_cuda.ipynb | 115 +
.../opt_conv_tensorcore.py | 411 +
.../opt_conv_tensorcore.ipynb | 165 +
.../cross_compilation_and_rpc.py | 265 +
.../from_darknet.py | 202 +
.../deploy_object_detection_pytorch.py | 153 +
.../from_onnx.ipynb | 151 +
.../deploy_quantized.py | 165 +
.../micro_reference_vm.py | 159 +
.../deploy_model_on_rasp.ipynb | 201 +
.../micro_reference_vm.ipynb | 43 +
.../use_pass_infra.ipynb | 241 +
.../from_tensorflow.py | 252 +
.../matrix_multiply_opt.py | 372 +
.../build_gcn.ipynb | 223 +
.../vta_get_started.ipynb | 373 +
.../from_tensorflow.ipynb | 216 +
.../extern_op.ipynb | 133 +
.../opt_conv_cuda.ipynb | 151 +
.../8c7d8fd6a4b93bcff1f5573943dd02f4/scan.py | 154 +
.../autotvm_matmul_x86.py | 377 +
.../tune_relay_x86.ipynb | 115 +
.../deploy_classification.ipynb | 133 +
.../96137df89d8034b548f407123ec50ce9/opt_gemm.py | 393 +
.../deploy_sparse.py | 362 +
.../micro_autotune.py | 255 +
.../deploy_classification.py | 306 +
.../introduction.ipynb | 50 +
.../tuple_inputs.ipynb | 122 +
.../deploy_quantized.ipynb | 144 +
.../from_paddle.ipynb | 144 +
.../from_tflite.py | 192 +
.../a7aff5918e1b86809a5bd1da8bef7229/tedd.ipynb | 165 +
.../from_coreml.ipynb | 144 +
.../tune_network_x86.ipynb | 147 +
.../tune_network_cuda.ipynb | 147 +
.../bring_your_own_datatypes.ipynb | 349 +
.../tune_relay_vta.ipynb | 186 +
.../convolution_opt.ipynb | 194 +
.../schedule_primitives.ipynb | 284 +
.../tune_relay_arm.ipynb | 168 +
.../deploy_prequantized.ipynb | 287 +
.../c23f7654585d9b0fa2129e1765b2a8f2/from_keras.py | 136 +
.../c253040abc62eace272e406b7e1a4df5/tedd.py | 160 +
.../from_keras.ipynb | 144 +
.../tensor_ir_blitz_course.ipynb | 223 +
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.../relay_quick_start.py | 155 +
.../deploy_ssd_gluoncv.py | 131 +
.../use_pass_instrument.py | 372 +
.../tune_relay_cuda.ipynb | 179 +
.../vta_get_started.py | 403 +
.../low_level_custom_pass.ipynb | 140 +
.../tune_relay_vta.py | 512 +
.../using_external_lib.py | 561 +
.../intrin_math.py | 172 +
.../deploy_ssd_gluoncv.ipynb | 144 +
.../schedule_primitives.py | 210 +
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.../matrix_multiply.py | 472 +
.../from_caffe2.py | 145 +
.../tune_conv2d_layer_cuda.py | 209 +
.../tune_network_x86.py | 344 +
.../auto_scheduler_matmul_x86.py | 214 +
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.../bring_your_own_datatypes.py | 408 +
.../deploy_model_on_android.ipynb | 190 +
.../tvmc_command_line_driver.ipynb | 141 +
.../cross_compilation_and_rpc.ipynb | 179 +
.../use_pass_instrument.ipynb | 342 +
.../micro_autotune.ipynb | 162 +
.../from_pytorch.py | 171 +
.../from_darknet.ipynb | 144 +
.../deploy_prequantized.py | 240 +
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docs/v0.8.0/_sources/arch/index.rst.txt | 426 +
docs/v0.8.0/_sources/arch/inferbound.rst.txt | 763 +
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docs/v0.8.0/_sources/arch/microtvm_design.rst.txt | 351 +
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docs/v0.8.0/tutorial/autotvm_relay_x86.html | 996 ++
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6063 files changed, 1467298 insertions(+)
diff --git a/docs/v0.8.0/_downloads/0387f07dee851b2b8c6b73e3e88c3140/tune_relay_cuda.py b/docs/v0.8.0/_downloads/0387f07dee851b2b8c6b73e3e88c3140/tune_relay_cuda.py
new file mode 100644
index 000000000..b2af2e13f
--- /dev/null
+++ b/docs/v0.8.0/_downloads/0387f07dee851b2b8c6b73e3e88c3140/tune_relay_cuda.py
@@ -0,0 +1,384 @@
+# 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.
+"""
+Auto-tuning a Convolutional Network for NVIDIA GPU
+==================================================
+**Author**: `Lianmin Zheng <https://github.com/merrymercy>`_, `Eddie Yan <https://github.com/eqy/>`_
+
+Auto-tuning for specific devices and workloads is critical for getting the
+best performance. This is a tutorial on how to tune a whole convolutional
+network for NVIDIA GPU.
+
+The operator implementation for NVIDIA GPU in TVM is written in template form.
+The template has many tunable knobs (tile factor, unrolling, etc).
+We will tune all convolution and depthwise convolution operators
+in the neural network. After tuning, we produce a log file which stores
+the best knob values for all required operators. When the TVM compiler compiles
+these operators, it will query this log file to get the best knob values.
+
+We also released pre-tuned parameters for some NVIDIA GPUs. You can go to
+`NVIDIA GPU Benchmark <https://github.com/apache/tvm/wiki/Benchmark#nvidia-gpu>`_
+to see the results.
+
+Note that this tutorial will not run on Windows or recent versions of macOS. To
+get it to run, you will need to wrap the body of this tutorial in a :code:`if
+__name__ == "__main__":` block.
+"""
+
+######################################################################
+# 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 --user psutil xgboost tornado cloudpickle
+#
+# To make TVM run faster during tuning, it is recommended to use cython
+# as FFI of tvm. In the root directory of tvm, execute:
+#
+# .. code-block:: bash
+#
+# pip3 install --user cython
+# sudo make cython3
+#
+# Now return to python code. Import packages.
+
+import os
+
+import numpy as np
+
+import tvm
+from tvm import relay, autotvm
+import tvm.relay.testing
+from tvm.autotvm.tuner import XGBTuner, GATuner, RandomTuner, GridSearchTuner
+import tvm.contrib.graph_executor as runtime
+
+#################################################################
+# Define Network
+# --------------
+# First we need to define the network in relay frontend API.
+# We can load some pre-defined network from :code:`tvm.relay.testing`.
+# We can also load models from MXNet, ONNX and TensorFlow.
+
+
+def get_network(name, batch_size):
+ """Get the symbol definition and random weight of a network"""
+ input_shape = (batch_size, 3, 224, 224)
+ output_shape = (batch_size, 1000)
+
+ if "resnet" in name:
+ n_layer = int(name.split("-")[1])
+ mod, params = relay.testing.resnet.get_workload(
+ num_layers=n_layer, batch_size=batch_size, dtype=dtype
+ )
+ elif "vgg" in name:
+ n_layer = int(name.split("-")[1])
+ mod, params = relay.testing.vgg.get_workload(
+ num_layers=n_layer, batch_size=batch_size, dtype=dtype
+ )
+ elif name == "mobilenet":
+ mod, params = relay.testing.mobilenet.get_workload(batch_size=batch_size, dtype=dtype)
+ elif name == "squeezenet_v1.1":
+ mod, params = relay.testing.squeezenet.get_workload(
+ batch_size=batch_size, version="1.1", dtype=dtype
+ )
+ elif name == "inception_v3":
+ input_shape = (batch_size, 3, 299, 299)
+ mod, params = relay.testing.inception_v3.get_workload(batch_size=batch_size, dtype=dtype)
+ elif name == "mxnet":
+ # an example for mxnet model
+ from mxnet.gluon.model_zoo.vision import get_model
+
+ block = get_model("resnet18_v1", pretrained=True)
+ mod, params = relay.frontend.from_mxnet(block, shape={"data": input_shape}, dtype=dtype)
+ net = mod["main"]
+ net = relay.Function(
+ net.params, relay.nn.softmax(net.body), None, net.type_params, net.attrs
+ )
+ mod = tvm.IRModule.from_expr(net)
+ else:
+ raise ValueError("Unsupported network: " + name)
+
+ return mod, params, input_shape, output_shape
+
+
+###########################################
+# Set Tuning Options
+# ------------------
+# Before tuning, we apply some configurations.
+
+#### DEVICE CONFIG ####
+target = tvm.target.cuda()
+
+#### TUNING OPTION ####
+network = "resnet-18"
+log_file = "%s.log" % network
+dtype = "float32"
+
+tuning_option = {
+ "log_filename": log_file,
+ "tuner": "xgb",
+ "n_trial": 2000,
+ "early_stopping": 600,
+ "measure_option": autotvm.measure_option(
+ builder=autotvm.LocalBuilder(timeout=10),
+ runner=autotvm.LocalRunner(number=20, repeat=3, timeout=4, min_repeat_ms=150),
+ ),
+}
+
+####################################################################
+#
+# .. note:: How to set tuning options
+#
+# In general, the default value provided here works well.
+#
+# If you have large time budget, you can set :code:`n_trial`, :code:`early_stopping` larger,
+# which makes the tuning runs longer.
+#
+# If you have multiple devices, you can use all of them for measurement to
+# accelerate the tuning process. (see the 'Scale up measurement` section below).
+#
+
+###################################################################
+# Begin Tuning
+# ------------
+# Now we can extract tuning tasks from the network and begin tuning.
+# Here, we provide a simple utility function to tune a list of tasks.
+# This function is just an initial implementation which tunes them in sequential order.
+# We will introduce a more sophisticated tuning scheduler in the future.
+
+# You can skip the implementation of this function for this tutorial.
+def tune_tasks(
+ tasks,
+ measure_option,
+ tuner="xgb",
+ n_trial=1000,
+ early_stopping=None,
+ log_filename="tuning.log",
+ use_transfer_learning=True,
+):
+ # create tmp log file
+ tmp_log_file = log_filename + ".tmp"
+ if os.path.exists(tmp_log_file):
+ os.remove(tmp_log_file)
+
+ for i, tsk in enumerate(reversed(tasks)):
+ prefix = "[Task %2d/%2d] " % (i + 1, len(tasks))
+
+ # create tuner
+ if tuner == "xgb" or tuner == "xgb-rank":
+ tuner_obj = XGBTuner(tsk, loss_type="rank")
+ elif tuner == "ga":
+ tuner_obj = GATuner(tsk, pop_size=100)
+ elif tuner == "random":
+ tuner_obj = RandomTuner(tsk)
+ elif tuner == "gridsearch":
+ tuner_obj = GridSearchTuner(tsk)
+ else:
+ raise ValueError("Invalid tuner: " + tuner)
+
+ if use_transfer_learning:
+ if os.path.isfile(tmp_log_file):
+ tuner_obj.load_history(autotvm.record.load_from_file(tmp_log_file))
+
+ # do tuning
+ tsk_trial = min(n_trial, len(tsk.config_space))
+ tuner_obj.tune(
+ n_trial=tsk_trial,
+ early_stopping=early_stopping,
+ measure_option=measure_option,
+ callbacks=[
+ autotvm.callback.progress_bar(tsk_trial, prefix=prefix),
+ autotvm.callback.log_to_file(tmp_log_file),
+ ],
+ )
+
+ # pick best records to a cache file
+ autotvm.record.pick_best(tmp_log_file, log_filename)
+ os.remove(tmp_log_file)
+
+
+########################################################################
+# Finally, we launch tuning jobs and evaluate the end-to-end performance.
+
+
+def tune_and_evaluate(tuning_opt):
+ # extract workloads from relay program
+ print("Extract tasks...")
+ mod, params, input_shape, out_shape = get_network(network, batch_size=1)
+ tasks = autotvm.task.extract_from_program(
+ mod["main"], target=target, params=params, ops=(relay.op.get("nn.conv2d"),)
+ )
+
+ # run tuning tasks
+ print("Tuning...")
+ tune_tasks(tasks, **tuning_opt)
+
+ # compile kernels with history best records
+ with autotvm.apply_history_best(log_file):
+ print("Compile...")
+ with tvm.transform.PassContext(opt_level=3):
+ lib = relay.build_module.build(mod, target=target, params=params)
+
+ # load parameters
+ dev = tvm.device(str(target), 0)
+ module = runtime.GraphModule(lib["default"](dev))
+ data_tvm = tvm.nd.array((np.random.uniform(size=input_shape)).astype(dtype))
+ module.set_input("data", data_tvm)
+
+ # evaluate
+ print("Evaluate inference time cost...")
+ print(module.benchmark(dev, number=1, repeat=600))
+
+
+# We do not run the tuning in our webpage server since it takes too long.
+# Uncomment the following line to run it by yourself.
+
+# tune_and_evaluate(tuning_option)
+
+######################################################################
+# Sample Output
+# -------------
+# The tuning needs to compile many programs and extract feature from them.
+# So a high performance CPU is recommended. One sample output is listed below.
+# It takes about 4 hours to get the following output on a 32T AMD Ryzen Threadripper.
+# The tuning target is NVIDIA 1080 Ti.
+# (You can see some errors during compilation. If the tuning is not stuck, it is okay.)
+#
+# .. code-block:: bash
+#
+# Extract tasks...
+# Tuning...
+# [Task 1/12] Current/Best: 541.83/3570.66 GFLOPS | Progress: (960/2000) | 1001.31 s Done.
+# [Task 2/12] Current/Best: 0.56/ 803.33 GFLOPS | Progress: (704/2000) | 608.08 s Done.
+# [Task 3/12] Current/Best: 103.69/1141.25 GFLOPS | Progress: (768/2000) | 702.13 s Done.
+# [Task 4/12] Current/Best: 2905.03/3925.15 GFLOPS | Progress: (864/2000) | 745.94 sterminate called without an active exception
+# [Task 4/12] Current/Best: 2789.36/3925.15 GFLOPS | Progress: (1056/2000) | 929.40 s Done.
+# [Task 5/12] Current/Best: 89.06/1076.24 GFLOPS | Progress: (704/2000) | 601.73 s Done.
+# [Task 6/12] Current/Best: 40.39/2129.02 GFLOPS | Progress: (1088/2000) | 1125.76 s Done.
+# [Task 7/12] Current/Best: 4090.53/5007.02 GFLOPS | Progress: (800/2000) | 903.90 s Done.
+# [Task 8/12] Current/Best: 4.78/1272.28 GFLOPS | Progress: (768/2000) | 749.14 s Done.
+# [Task 9/12] Current/Best: 1391.45/2325.08 GFLOPS | Progress: (992/2000) | 1084.87 s Done.
+# [Task 10/12] Current/Best: 1995.44/2383.59 GFLOPS | Progress: (864/2000) | 862.60 s Done.
+# [Task 11/12] Current/Best: 4093.94/4899.80 GFLOPS | Progress: (224/2000) | 240.92 sterminate called without an active exception
+# [Task 11/12] Current/Best: 3487.98/4909.91 GFLOPS | Progress: (480/2000) | 534.96 sterminate called without an active exception
+# [Task 11/12] Current/Best: 4636.84/4912.17 GFLOPS | Progress: (1184/2000) | 1381.16 sterminate called without an active exception
+# [Task 11/12] Current/Best: 50.12/4912.17 GFLOPS | Progress: (1344/2000) | 1602.81 s Done.
+# [Task 12/12] Current/Best: 3581.31/4286.30 GFLOPS | Progress: (736/2000) | 943.52 s Done.
+# Compile...
+# Evaluate inference time cost...
+# Mean inference time (std dev): 1.07 ms (0.05 ms)
+#
+# As a reference baseline, the time cost of MXNet + TensorRT on resnet-18 is 1.30ms. So we are a little faster.
+
+######################################################################
+#
+# .. note:: **Experiencing Difficulties?**
+#
+# The auto tuning module is error-prone. If you always see " 0.00/ 0.00 GFLOPS",
+# then there must be something wrong.
+#
+# First, make sure you set the correct configuration of your device.
+# Then, you can print debug information by adding these lines in the beginning
+# of the script. It will print every measurement result, where you can find useful
+# error messages.
+#
+# .. code-block:: python
+#
+# import logging
+# logging.getLogger('autotvm').setLevel(logging.DEBUG)
+#
+# Finally, always feel free to ask our community for help on https://discuss.tvm.apache.org
+
+#################################################################
+# .. _tutorials-autotvm-scale-up-rpc-tracker:
+
+#################################################################
+# Scale up measurement by using multiple devices
+# ----------------------------------------------
+# If you have multiple devices, you can use all of them for measurement.
+# TVM uses the RPC Tracker to manage distributed devices.
+# The RPC Tracker is a centralized controller node. We can register all devices to
+# the tracker. For example, if we have 10 GPU cards, we can register all of them
+# to the tracker, and run 10 measurements in parallel, accelerating the tuning process.
+#
+# To start an RPC tracker, run this command on the host machine. The tracker is
+# required during the whole tuning process, so we need to open a new terminal for
+# this command:
+#
+# .. code-block:: bash
+#
+# python -m tvm.exec.rpc_tracker --host=0.0.0.0 --port=9190
+#
+# The expected output is
+#
+# .. code-block:: bash
+#
+# INFO:RPCTracker:bind to 0.0.0.0:9190
+#
+# Then open another new terminal for the RPC server. We need to start one dedicated server
+# for each device. We use a string key to distinguish the types of devices.
+# You can pick a name you like.
+# (Note: For rocm backend, there are some internal errors with the compiler,
+# we need to add `--no-fork` to the argument list.)
+#
+# .. code-block:: bash
+#
+# python -m tvm.exec.rpc_server --tracker=127.0.0.1:9190 --key=1080ti
+#
+# After registering devices, we can confirm it by querying rpc_tracker
+#
+# .. code-block:: bash
+#
+# python -m tvm.exec.query_rpc_tracker --host=127.0.0.1 --port=9190
+#
+# For example, if we have four 1080ti, two titanx and one gfx900, the output can be
+#
+# .. code-block:: bash
+#
+# Queue Status
+# ----------------------------------
+# key total free pending
+# ----------------------------------
+# 1080ti 4 4 0
+# titanx 2 2 0
+# gfx900 1 1 0
+# ----------------------------------
+#
+# Finally, we need to change the tuning option to use RPCRunner. Use the code below
+# to replace the corresponding part above.
+
+tuning_option = {
+ "log_filename": log_file,
+ "tuner": "xgb",
+ "n_trial": 2000,
+ "early_stopping": 600,
+ "measure_option": autotvm.measure_option(
+ builder=autotvm.LocalBuilder(timeout=10),
+ runner=autotvm.RPCRunner(
+ "1080ti", # change the device key to your key
+ "127.0.0.1",
+ 9190,
+ number=20,
+ repeat=3,
+ timeout=4,
+ min_repeat_ms=150,
+ ),
+ ),
+}
diff --git a/docs/v0.8.0/_downloads/067cf39a44d9f315a39f8a7547c556d8/install.py b/docs/v0.8.0/_downloads/067cf39a44d9f315a39f8a7547c556d8/install.py
new file mode 100644
index 000000000..67ce093b9
--- /dev/null
+++ b/docs/v0.8.0/_downloads/067cf39a44d9f315a39f8a7547c556d8/install.py
@@ -0,0 +1,50 @@
+# 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.
+"""
+Installing TVM
+==============
+**Authors**:
+`Jocelyn Shiue <https://github.com/>`_,
+`Chris Hoge <https://github.com/hogepodge>`_
+
+Depending on your needs and your working environment, there are a few different
+methods for installing TVM. These include:
+
+* Installing from source
+* Installing from third-party binary package.
+"""
+
+################################################################################
+# Installing from Source
+# ----------------------
+# Installing from source is the recommended method for installing TVM. It will
+# allow you to enable specific features such as GPU support, microcontroller
+# support (microTVM), and a debugging runtime, and other features. You will also
+# want to install from source if you want to actively contribute to the TVM
+# project. The full instructions are on the :ref:`Install TVM From Source
+# <install-from-source>` page.
+
+################################################################################
+# Installing From Binary Packages
+# --------------------------------
+# You may install convenient third-party binary package distributions to
+# quickly try things out. TLCPack is a thirdparty volunteer community that
+# builds binary packages from TVM source. It offers support matrix with
+# instructions to install on different platforms, with different features.
+# Checkout `TLCPack <https://tlcpack.ai>`_ to learn more. Note that the
+# thirdparty binary packages could contain additional licensing terms for
+# the hardware drivers that are bundled with it.
diff --git a/docs/v0.8.0/_downloads/07733b6b2cc4df026fce525285e8f538/tune_sparse_x86.py b/docs/v0.8.0/_downloads/07733b6b2cc4df026fce525285e8f538/tune_sparse_x86.py
new file mode 100644
index 000000000..55ee76ef6
--- /dev/null
+++ b/docs/v0.8.0/_downloads/07733b6b2cc4df026fce525285e8f538/tune_sparse_x86.py
@@ -0,0 +1,324 @@
+# 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.
+"""
+Auto-scheduling Sparse Matrix Multiplication on CPU with Custom Sketch Rule
+===========================================================================
+**Author**: `Chengfan Jia <https://github.com/jcf94/>`_
+
+This is a tutorial on how to use the auto-scheduler to tune a sparse matrix multiplication for
+CPUs.
+
+Auto-scheduler is designed to explore the schedule with best performance for a given computation
+declaration automatically. While sometimes, we may have a demand to try some special ops which may
+not been well-supported by auto-scheduler's default sketch rules and result in poor performance.
+Fortunately, auto-scheduler currently allows user to provide a CustomSketch to cover these cases.
+
+We use sparse matrix multiplication as an example in this tutorial to demonstrate how to implement
+and plug a custom sketch rule to the auto-scheduler's search policy.
+
+Note that this tutorial will not run on Windows or recent versions of macOS. To
+get it to run, you will need to wrap the body of this tutorial in a :code:`if
+__name__ == "__main__":` block.
+"""
+
+import os
+
+import numpy as np
+import tvm
+import tvm.testing
+from tvm import te, auto_scheduler, runtime, topi
+from tvm.auto_scheduler import _ffi_api
+from tvm.topi.utils import get_const_tuple
+from tvm.topi.sparse.utils import random_bsr_matrix
+
+######################################################################
+# Define the computation
+# ^^^^^^^^^^^^^^^^^^^^^^
+# To begin with, let us define the computation of a sparse matmul with several relu and bias add.
+# The function should return the list of input/output tensors.
+# From these tensors, the auto-scheduler can get the whole computational graph.
+
+
+@auto_scheduler.register_workload
+def sparse_dense(M, N, K, w_data_shape, w_indices_shape, w_indptr_shape, dtype):
+ X = te.placeholder(shape=(M, K), dtype=dtype)
+ W_data = te.placeholder(shape=w_data_shape, dtype=dtype)
+ W_indices = te.placeholder(shape=w_indices_shape, dtype="int32")
+ W_indptr = te.placeholder(shape=w_indptr_shape, dtype="int32")
+ B = te.placeholder(shape=(M, N), dtype=dtype)
+
+ out = topi.nn.sparse_dense(topi.nn.relu(X), W_data, W_indices, W_indptr)
+ out = te.compute((M, N), lambda i, j: out[i, j] + B[i, j], name="BiasAdd")
+ out = topi.nn.relu(out)
+
+ return [X, W_data, W_indices, W_indptr, B, out]
+
+
+######################################################################
+# Special step for sparse workload
+# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+# During schedule tuning, auto-scheduler will use random inputs to measure the performance of a
+# generated schedule. While we cannot directly use a random array as the input of a sparse op, for
+# the "indices" and "indptr" array are meaningful for the computation.
+#
+# To solve this problem, we register these as special buffers, and load them when process program
+# measuring.
+# See the `tvm.auto_scheduler.measure.py` for more details.
+
+# Define the basic shapes of this sparse computation
+M = 128
+K = 256
+N = 512
+BS_R = 16
+BS_C = 1
+density = 0.6
+
+# Generate the test data with numpy
+X_np = np.random.randn(M, K).astype("float32")
+X_np = np.maximum(np.zeros((M, K), dtype="float32"), X_np) # Relu
+W_sp_np = random_bsr_matrix(N, K, BS_R, BS_C, density=density, dtype="float32")
+W_np = W_sp_np.todense()
+Y_np = X_np @ W_np.T # Process the matrix multiplication
+B_np = np.random.randn(M, N).astype("float32")
+Y_np = Y_np + B_np # Bias add
+Y_np = np.maximum(np.zeros((M, N), dtype="float32"), Y_np) # Relu
+
+######################################################################
+# Create the search task
+# ^^^^^^^^^^^^^^^^^^^^^^
+# We then create a search task with M=N=K=512 and dtype="float32"
+# If your machine supports avx instructions, you can
+#
+# - replace "llvm" below with "llvm -mcpu=core-avx2" to enable AVX2
+# - replace "llvm" below with "llvm -mcpu=skylake-avx512" to enable AVX-512
+
+target = tvm.target.Target("llvm")
+
+# Register the sparse data to task inputs
+prefix = "sparse_dense_bsr_%d_%d_%d_%d_%d_%d_" % (
+ N,
+ K,
+ BS_R,
+ BS_C,
+ W_sp_np.indices.shape[0],
+ W_sp_np.indptr.shape[0],
+)
+task = tvm.auto_scheduler.SearchTask(
+ func=sparse_dense,
+ args=(M, N, K, W_sp_np.data.shape, W_sp_np.indices.shape, W_sp_np.indptr.shape, "float32"),
+ target=target,
+ task_inputs={
+ prefix + "W_data": runtime.ndarray.array(W_sp_np.data),
+ prefix + "W_indices": runtime.ndarray.array(W_sp_np.indices),
+ prefix + "W_indptr": runtime.ndarray.array(W_sp_np.indptr),
+ },
+ task_inputs_save_to_file=True,
+)
+
+# Inspect the computational graph
+print("Computational DAG:")
+print(task.compute_dag)
+
+######################################################################
+# Write the custom sketch for sparse dense op
+# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+# Before tuning, we will need to define the CustomSketchRule for the sparse dense op.
+#
+# CustomSketchRule consists of two parts: the condition function and the apply function.
+#
+# - condition function: describe when to apply this sketch rule. For example, we can only apply
+# the rule to the sparse ops by matching their name and tag.
+# - apply function: describe how to generate the initial sketch. You can implement it using
+# auto-scheduler provided loop state APIs.
+
+
+def meet_condition_func(search_policy, state, stage_id):
+ state = auto_scheduler.loop_state.State(state, search_policy.search_task.compute_dag)
+ if state.stages[stage_id].op.tag in [
+ "sparse_dense_sp_rhs_bsrmm",
+ "sparse_dense_sp_rhs_bsrmm_block",
+ ]:
+ return auto_scheduler.PreloadCustomSketchRule.APPLY_AND_SKIP_REST
+ else:
+ return auto_scheduler.PreloadCustomSketchRule.PASS
+
+
+def apply_func(search_policy, state, stage_id):
+ ret = []
+ s0 = auto_scheduler.loop_state.State(state, search_policy.search_task.compute_dag)
+ if s0.stages[stage_id].op.tag == "sparse_dense_sp_rhs_bsrmm_block":
+ return [s0.state_object, stage_id - 1]
+
+ sparse_dense = s0.stages[stage_id].op
+ sparse_dense_block = s0.stages[stage_id - 1].op
+ assert sparse_dense.tag == "sparse_dense_sp_rhs_bsrmm"
+ assert sparse_dense_block.tag == "sparse_dense_sp_rhs_bsrmm_block"
+
+ # Set the default consumer of compute block
+ consumer = sparse_dense
+
+ # If sparse dense has a single elementwise consumer
+ # We can compute inline the sparse_dense output stage
+ consumers = _ffi_api.SearchPolicyUtilsGetConsumers(
+ search_policy.search_task, s0.state_object, stage_id
+ )
+ if len(consumers) == 1:
+ consumer_id = int(consumers.items()[0][0])
+ if _ffi_api.SearchPolicyUtilsIsElementwiseMatch(
+ search_policy.search_task, s0.state_object, stage_id, consumer_id
+ ):
+ consumer = s0.stages[consumer_id].op
+ s0.compute_inline(sparse_dense)
+
+ i, nb_j, j, row_offset, c = s0[sparse_dense_block].iters
+ m, n = s0[consumer].iters
+ i0, i1, i2 = s0.split(sparse_dense_block, i, [None, None])
+ m0, m1 = s0.follow_split(consumer, m, len(s0.transform_steps) - 1, 1)
+ j0, j1 = s0.split(sparse_dense_block, nb_j, [None])
+ n0, n1 = s0.follow_split(consumer, n, len(s0.transform_steps) - 1, 1)
+ s0.reorder(sparse_dense_block, [i0, j0, i1, j1, row_offset, i2, j, c])
+ s0.reorder(consumer, [m0, n0, m1, n1])
+ s0.compute_at(sparse_dense_block, consumer, n0)
+
+ ret.append([s0.state_object, stage_id - 2])
+
+ return ret
+
+
+######################################################################
+# Next, we set parameters for the auto-scheduler with the custom sketch plugged in.
+#
+# * :code:`num_measure_trials` is the number of measurement trials we can use during the search.
+# We only make 10 trials in this tutorial for a fast demonstration. In practice, 1000 is a
+# good value for the search to converge. You can do more trials according to your time budget.
+# * In addition, we use :code:`RecordToFile` to dump measurement records into a file
+# `sparse_dense.json`.
+# The measurement records can be used to query the history best, resume the search,
+# and do more analyses later.
+# * see :any:`auto_scheduler.TuningOptions` for more parameters
+# * Here, we need to create a :code:`auto_scheduler.SketchPolicy` object, and add the custom sketch
+# rule as a `init_search_callbacks`.
+
+log_file = "sparse_dense.json"
+tune_option = auto_scheduler.TuningOptions(
+ num_measure_trials=10,
+ measure_callbacks=[auto_scheduler.RecordToFile(log_file)],
+ verbose=2,
+)
+
+search_policy = auto_scheduler.SketchPolicy(
+ task,
+ program_cost_model=auto_scheduler.XGBModel(),
+ init_search_callbacks=[
+ auto_scheduler.PreloadCustomSketchRule(meet_condition_func, apply_func, "SparseDense")
+ ],
+)
+
+######################################################################
+# Run the search
+# ^^^^^^^^^^^^^^
+# Now we get all inputs ready.
+# We can kick off the search and let the auto-scheduler do its magic.
+# After some measurement trials, we can load the best schedule from the log
+# file and apply it.
+
+# Run auto-tuning (search)
+# Notice: We do not run the tuning in our webpage server since it takes too long.
+# Uncomment the following line to run it by yourself.
+task.tune(tune_option, search_policy)
+
+# Apply the best schedule
+sch, args = task.apply_best(log_file)
+
+######################################################################
+# We can lower the schedule to see the IR after auto-scheduling.
+# The auto-scheduler correctly performs optimizations including multi-level tiling,
+# layout transformation, parallelization, vectorization, unrolling, and operator fusion.
+
+print("Lowered TIR:")
+print(tvm.lower(sch, args, simple_mode=True))
+
+######################################################################
+# Check correctness and evaluate performance
+# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+# We build the binary and check its correctness and performance.
+
+func = tvm.build(sch, args, target)
+
+dev = tvm.cpu()
+
+X_tvm = tvm.nd.array(X_np, device=dev)
+W_data_tvm = tvm.nd.array(W_sp_np.data, device=dev)
+W_indices_tvm = tvm.nd.array(W_sp_np.indices, device=dev)
+W_indptr_tvm = tvm.nd.array(W_sp_np.indptr, device=dev)
+B_tvm = tvm.nd.array(B_np, device=dev)
+Y_tvm = tvm.nd.empty(Y_np.shape, device=dev)
+
+func(X_tvm, W_data_tvm, W_indices_tvm, W_indptr_tvm, B_tvm, Y_tvm)
+
+# Check results
+tvm.testing.assert_allclose(Y_np, Y_tvm.numpy(), atol=1e-4, rtol=1e-4)
+
+# Evaluate execution time.
+evaluator = func.time_evaluator(func.entry_name, dev, min_repeat_ms=500)
+print(
+ "Execution time of this operator: %.3f ms"
+ % (
+ np.median(evaluator(X_tvm, W_data_tvm, W_indices_tvm, W_indptr_tvm, B_tvm, Y_tvm).results)
+ * 1000
+ )
+)
+
+######################################################################
+# .. note:: Tuning result example
+#
+# .. code-block:: c
+#
+# ----------------------------------------------------------------------
+# Lowered TIR:
+# primfn(placeholder_5: handle, placeholder_6: handle, placeholder_7: handle, placeholder_8: handle, placeholder_9: handle, compute_1: handle) -> ()
+# attr = {"global_symbol": "main", "tir.noalias": True}
+# buffers = {placeholder_2: Buffer(placeholder_10: Pointer(float32), float32, [9831, 16, 1], []),
+# placeholder_4: Buffer(placeholder_11: Pointer(int32), int32, [33], []),
+# placeholder_3: Buffer(placeholder_12: Pointer(float32), float32, [512, 512], []),
+# compute: Buffer(compute_2: Pointer(float32), float32, [512, 512], []),
+# placeholder_1: Buffer(placeholder_13: Pointer(float32), float32, [512, 512], []),
+# placeholder: Buffer(placeholder_14: Pointer(int32), int32, [9831], [])}
+# buffer_map = {placeholder_7: placeholder, placeholder_9: placeholder_1, placeholder_6: placeholder_2, compute_1: compute, placeholder_5: placeholder_3, placeholder_8: placeholder_4} {
+# for (i0.outer.i1.outer.fused: int32, 0, 1024) "parallel" {
+# attr [compute_3: Pointer(float32)] "storage_scope" = "global";
+# allocate(compute_3, float32, [256]) {
+# for (nb_j.inner: int32, 0, 2) {
+# for (i.inner.init: int32, 0, 8) {
+# for (j.init: int32, 0, 16) {
+# compute_3[(((i.inner.init*32) + (nb_j.inner*16)) + j.init)] = 0f32
+# }
+# }
+# for (elem_idx: int32, 0, ((int32*)placeholder_11[(((floormod(i0.outer.i1.outer.fused, 16)*2) + nb_j.inner) + 1)] - (int32*)placeholder_11[((floormod(i0.outer.i1.outer.fused, 16)*2) + nb_j.inner)])) {
+# for (i.inner: int32, 0, 8) {
+# for (j: int32, 0, 16) {
+# compute_3[(((i.inner*32) + (nb_j.inner*16)) + j)] = ((float32*)compute_3[(((i.inner*32) + (nb_j.inner*16)) + j)] + ((float32*)placeholder_10[((((int32*)placeholder_11[((floormod(i0.outer.i1.outer.fused, 16)*2) + nb_j.inner)]*16) + (elem_idx*16)) + j)]*max((float32*)placeholder_12[(((floordiv(i0.outer.i1.outer.fused, 16)*4096) + (i.inner*512)) + (int32*)placeholder_14[((int32*)placeholder_11[((floormod(i0.outer.i1.outer.fused, 16)*2) + nb_j.inner)] + elem_idx)])], 0f32)))
+# }
+# }
+# }
+# }
+# for (i0.inner: int32, 0, 8) {
+# compute_2[ramp((((floordiv(i0.outer.i1.outer.fused, 16)*4096) + (i0.inner*512)) + (floormod(i0.outer.i1.outer.fused, 16)*32)), 1, 32)] = max(((float32x32*)compute_3[ramp((i0.inner*32), 1, 32)] + (float32x32*)placeholder_13[ramp((((floordiv(i0.outer.i1.outer.fused, 16)*4096) + (i0.inner*512)) + (floormod(i0.outer.i1.outer.fused, 16)*32)), 1, 32)]), broadcast(0f32, 32))
+# }
+# }
+# }
+# }
diff --git a/docs/v0.8.0/_downloads/0b60295044fd20226a0d5adc52b50b2f/deploy_sparse.ipynb b/docs/v0.8.0/_downloads/0b60295044fd20226a0d5adc52b50b2f/deploy_sparse.ipynb
new file mode 100644
index 000000000..56554967d
--- /dev/null
+++ b/docs/v0.8.0/_downloads/0b60295044fd20226a0d5adc52b50b2f/deploy_sparse.ipynb
@@ -0,0 +1,187 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "%matplotlib inline"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\nDeploy a Hugging Face Pruned Model on CPU\n=========================================\n**Author**: `Josh Fromm <https://github.com/jwfromm>`_\n\nThis tutorial demonstrates how to take any pruned model, in this case `PruneBert\nfrom Hugging Face\n<https://huggingface.co/huggingface/prunebert-base-uncased-6-finepruned-w-distil-squad>`_,\nand use TVM to leverage the model's sparsity support to produce real speedups. Although\nthe primary purpose of this tutorial is to realize spee [...]
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Load Required Modules\n---------------------\nOther than TVM, scipy, the latest transformers, and\ntensorflow 2.2+ are required.\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "import os\nimport tvm\nimport time\nimport itertools\nimport numpy as np\nimport tensorflow as tf\nfrom tvm import relay, runtime\nfrom tvm.contrib import graph_executor\nfrom tvm.relay import data_dep_optimization as ddo\nfrom tensorflow.python.framework.convert_to_constants import (\n convert_variables_to_constants_v2,\n)\nimport scipy.sparse as sp\n\n\n# Ask tensorflow to limit its GPU memory to what's actually needed\n# instead of gobbling everything that's available.\n# [...]
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Configure Settings\n------------------\nLet's start by defining some parameters that define the type of model\nand sparsity to run.\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "# The name of the transformer model to download and run.\nname = \"huggingface/prunebert-base-uncased-6-finepruned-w-distil-squad\"\n# The number of batches in an input.\nbatch_size = 1\n# The length of each input sequence.\nseq_len = 128\n# TVM platform identifier. Note that best cpu performance can be achieved by setting -mcpu\n# appropriately for your specific machine. CUDA and ROCm are also supported.\ntarget = \"llvm\"\n# Which device to run on. Should be one of tvm.cpu() o [...]
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Download and Convert Transformers Model\n---------------------------------------\nNow we'll grab a model from the transformers module, download it,\nconvert it into a TensorFlow graphdef in preperation for converting that graphdef into\na relay graph that we can optimize and deploy.\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "def load_keras_model(module, name, seq_len, batch_size, report_runtime=True):\n model = module.from_pretrained(name)\n dummy_input = tf.keras.Input(shape=[seq_len], batch_size=batch_size, dtype=\"int32\")\n dummy_out = model(dummy_input) # Propagate shapes through the keras model.\n if report_runtime:\n np_input = np.random.uniform(size=[batch_size, seq_len], low=0, high=seq_len).astype(\n \"int32\"\n )\n start = time.time()\n [...]
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Convert to Relay Graph\n----------------------\nWe now have all the tooling to get a transformers model in the right format\nfor relay conversion. Let's import it! In the following function we\nsave the imported graph in relay's json format so that we dont have\nto reimport from tensorflow each time this script is run.\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "def import_graphdef(\n name,\n batch_size,\n seq_len,\n save_relay=True,\n relay_file=\"model.json\",\n relay_params=\"model.params\",\n):\n abs_path = os.path.dirname(os.path.abspath(__file__))\n shape_dict = {\"input_1\": (batch_size, seq_len)}\n relay_file = (\"%s_%d_%d_%s\" % (name, batch_size, seq_len, relay_file)).replace(\"/\", \"_\")\n relay_params = (\"%s_%d_%d_%s\" % (name, batch_size, seq_len, relay_params)).replace(\"/\", \"_\")\n if [...]
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Run the Dense Graph\n-------------------\nLet's run the default version of the imported model. Note that even if\nthe weights are sparse, we won't see any speedup because we are using\nregular dense matrix multiplications on these dense (but mostly zero)\ntensors instead of sparse aware kernels.\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "def run_relay_graph(mod, params, shape_dict, target, dev):\n with relay.build_config(opt_level=3):\n lib = relay.build(mod, target=target, params=params)\n input_shape = shape_dict[\"input_1\"]\n dummy_data = np.random.uniform(size=input_shape, low=0, high=input_shape[1]).astype(\"int32\")\n\n m = graph_executor.GraphModule(lib[\"default\"](dev))\n m.set_input(0, dummy_data)\n m.run()\n tvm_output = m.get_output(0)\n\n print(m.benchmark(dev, repeat [...]
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Run the Sparse Graph\n--------------------\nNext we'll convert the graph into a sparse representation and generate\nfake sparse weights if needed. Then we'll use the same benchmarking\nscript as dense to see how much faster we go! We apply a few relay passes\nto the graph to get it leveraging sparsity. First we use\n`simplify_fc_transpose` to use transposes on the weights of dense layers\ninto the parameters. This makes it easier to convert to matrix multiplies\nto sparse versio [...]
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "def random_bsr_matrix(M, N, BS_R, BS_C, density, dtype=\"float32\"):\n Y = np.zeros((M, N), dtype=dtype)\n assert M % BS_R == 0\n assert N % BS_C == 0\n nnz = int(density * M * N)\n num_blocks = int(nnz / (BS_R * BS_C)) + 1\n candidate_blocks = np.asarray(list(itertools.product(range(0, M, BS_R), range(0, N, BS_C))))\n assert candidate_blocks.shape[0] == M // BS_R * N // BS_C\n chosen_blocks = candidate_blocks[\n np.random.choice(candidate_blocks.s [...]
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Run All the Code!\n-----------------\nAnd that's it! Now we'll simply call all the needed function to benchmark\nthe model according to the set parameters. Note that to run this code\nyou'll need to uncomment the last line first.\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "def benchmark():\n mod, params, shape_dict = import_graphdef(name, batch_size, seq_len)\n run_dense(mod, params, shape_dict, target, dev)\n if measure_sparse:\n gen_weights = \"prune\" not in name\n run_sparse(mod, params, shape_dict, target, dev, bs_r, sparsity, gen_weights)\n\n\n# benchmark()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Sample Output\n-------------\nFor reference, below is the output of the script when run on an AMD CPU\nand shows about a 2.5X speedup from using sparsity.\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "# Dense Model Benchmark:\n# Cannot find config for target=llvm, workload=('dense_nopack.x86', ('TENSOR', (1, 768), 'float32'), ('TENSOR', (2, 768), 'float32'), None, 'float32'). A fallback configuration is used, which may bring great performance regression.\n# Cannot find config for target=llvm, workload=('dense_nopack.x86', ('TENSOR', (1, 768), 'float32'), ('TENSOR', (768, 768), 'float32'), None, 'float32'). A fallback configuration is used, which may bring great performance re [...]
+ ]
+ }
+ ],
+ "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": "3.6.9"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/docs/v0.8.0/_downloads/0e2f38fcb1a1fb3e636e5953aa600dee/from_mxnet.py b/docs/v0.8.0/_downloads/0e2f38fcb1a1fb3e636e5953aa600dee/from_mxnet.py
new file mode 100644
index 000000000..027e9e6eb
--- /dev/null
+++ b/docs/v0.8.0/_downloads/0e2f38fcb1a1fb3e636e5953aa600dee/from_mxnet.py
@@ -0,0 +1,146 @@
+# 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.
+"""
+.. _tutorial-from-mxnet:
+
+Compile MXNet Models
+====================
+**Author**: `Joshua Z. Zhang <https://zhreshold.github.io/>`_, \
+ `Kazutaka Morita <https://github.com/kazum>`_
+
+This article is an introductory tutorial to deploy mxnet models with Relay.
+
+For us to begin with, mxnet module is required to be installed.
+
+A quick solution is
+
+.. code-block:: bash
+
+ pip install mxnet --user
+
+or please refer to official installation guide.
+https://mxnet.apache.org/versions/master/install/index.html
+"""
+# some standard imports
+import mxnet as mx
+import tvm
+import tvm.relay as relay
+import numpy as np
+
+######################################################################
+# Download Resnet18 model from Gluon Model Zoo
+# ---------------------------------------------
+# In this section, we download a pretrained imagenet model and classify an image.
+from tvm.contrib.download import download_testdata
+from mxnet.gluon.model_zoo.vision import get_model
+from PIL import Image
+from matplotlib import pyplot as plt
+
+block = get_model("resnet18_v1", pretrained=True)
+img_url = "https://github.com/dmlc/mxnet.js/blob/main/data/cat.png?raw=true"
+img_name = "cat.png"
+synset_url = "".join(
+ [
+ "https://gist.githubusercontent.com/zhreshold/",
+ "4d0b62f3d01426887599d4f7ede23ee5/raw/",
+ "596b27d23537e5a1b5751d2b0481ef172f58b539/",
+ "imagenet1000_clsid_to_human.txt",
+ ]
+)
+synset_name = "imagenet1000_clsid_to_human.txt"
+img_path = download_testdata(img_url, "cat.png", module="data")
+synset_path = download_testdata(synset_url, synset_name, module="data")
+with open(synset_path) as f:
+ synset = eval(f.read())
+image = Image.open(img_path).resize((224, 224))
+plt.imshow(image)
+plt.show()
+
+
+def transform_image(image):
+ image = np.array(image) - np.array([123.0, 117.0, 104.0])
+ image /= np.array([58.395, 57.12, 57.375])
+ image = image.transpose((2, 0, 1))
+ image = image[np.newaxis, :]
+ return image
+
+
+x = transform_image(image)
+print("x", x.shape)
+
+######################################################################
+# Compile the Graph
+# -----------------
+# Now we would like to port the Gluon model to a portable computational graph.
+# It's as easy as several lines.
+# We support MXNet static graph(symbol) and HybridBlock in mxnet.gluon
+shape_dict = {"data": x.shape}
+mod, params = relay.frontend.from_mxnet(block, shape_dict)
+## we want a probability so add a softmax operator
+func = mod["main"]
+func = relay.Function(func.params, relay.nn.softmax(func.body), None, func.type_params, func.attrs)
+
+######################################################################
+# now compile the graph
+target = "cuda"
+with tvm.transform.PassContext(opt_level=3):
+ lib = relay.build(func, target, params=params)
+
+######################################################################
+# Execute the portable graph on TVM
+# ---------------------------------
+# Now, we would like to reproduce the same forward computation using TVM.
+from tvm.contrib import graph_executor
+
+dev = tvm.cuda(0)
+dtype = "float32"
+m = graph_executor.GraphModule(lib["default"](dev))
+# set inputs
+m.set_input("data", tvm.nd.array(x.astype(dtype)))
+# execute
+m.run()
+# get outputs
+tvm_output = m.get_output(0)
+top1 = np.argmax(tvm_output.numpy()[0])
+print("TVM prediction top-1:", top1, synset[top1])
+
+######################################################################
+# Use MXNet symbol with pretrained weights
+# ----------------------------------------
+# MXNet often use `arg_params` and `aux_params` to store network parameters
+# separately, here we show how to use these weights with existing API
+def block2symbol(block):
+ data = mx.sym.Variable("data")
+ sym = block(data)
+ args = {}
+ auxs = {}
+ for k, v in block.collect_params().items():
+ args[k] = mx.nd.array(v.data().asnumpy())
+ return sym, args, auxs
+
+
+mx_sym, args, auxs = block2symbol(block)
+# usually we would save/load it as checkpoint
+mx.model.save_checkpoint("resnet18_v1", 0, mx_sym, args, auxs)
+# there are 'resnet18_v1-0000.params' and 'resnet18_v1-symbol.json' on disk
+
+######################################################################
+# for a normal mxnet model, we start from here
+mx_sym, args, auxs = mx.model.load_checkpoint("resnet18_v1", 0)
+# now we use the same API to get Relay computation graph
+mod, relay_params = relay.frontend.from_mxnet(mx_sym, shape_dict, arg_params=args, aux_params=auxs)
+# repeat the same steps to run this model using TVM
diff --git a/docs/v0.8.0/_downloads/0f8d36b3ffd04a5a08089dc671eb788e/opt_gemm.ipynb b/docs/v0.8.0/_downloads/0f8d36b3ffd04a5a08089dc671eb788e/opt_gemm.ipynb
new file mode 100644
index 000000000..aeeb82dad
--- /dev/null
+++ b/docs/v0.8.0/_downloads/0f8d36b3ffd04a5a08089dc671eb788e/opt_gemm.ipynb
@@ -0,0 +1,309 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "%matplotlib inline"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n\nHow to optimize GEMM on CPU\n===========================\n**Author**: `Jian Weng <https://github.com/were>`_, `Ruofei Yu <https://github.com/yuruofeifei>`_\n\n(TL;DR) TVM provides abstract interfaces which allows users to depict an algorithm and the\nalgorithm's implementing organization (the so-called schedule) separately. Typically, writing\nalgorithm in high-performance schedule breaks the algorithm's readability and modularity. Also,\ntrying various seemingly [...]
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Preparation and Baseline\n------------------------\nIn this tutorial, we will demo how to use TVM to optimize matrix multiplication.\nBefore actually demonstrating, we first define these variables.\nThen we write a baseline implementation, the simplest way to write a matrix multiplication in TVM.\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "import tvm\nimport tvm.testing\nfrom tvm import te\nimport numpy\nimport timeit\n\n# The size of the matrix\n# (M, K) x (K, N)\n# You are free to try out different shapes, sometimes TVM optimization outperforms numpy with MKL.\nM = 1024\nK = 1024\nN = 1024\n\n# The default tensor type in tvm\ndtype = \"float32\"\n\n# using Intel AVX2(Advanced Vector Extensions) ISA for SIMD\n# To get the best performance, please change the following line\n# to llvm -mcpu=core-avx2, or specific t [...]
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In TVM, we can always inspect lower level IR to debug or optimize our schedule.\nHere is the generated IR using our baseline schedule.\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "print(tvm.lower(s, [A, B, C], simple_mode=True))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Blocking\n--------\nA important trick to enhance the cache hit rate is blocking --- data chunk will be computed\nblock by block. The memory access inside the block is a small neighbourhood which is with high\nmemory locality. In this tutorial, I picked up 32 as the blocking factor. So the block will\nfill 32 * 32 * sizeof(float) which is 4KB in the cache whose total size is 32KB (L1 data cache)\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "bn = 32\nkfactor = 4\ns = te.create_schedule(C.op)\n\n# Blocking by loop tiling\nmo, no, mi, ni = s[C].tile(C.op.axis[0], C.op.axis[1], bn, bn)\n(kaxis,) = s[C].op.reduce_axis\nko, ki = s[C].split(kaxis, factor=kfactor)\n\n# Hoist reduction domain outside the blocking loop\ns[C].reorder(mo, no, ko, ki, mi, ni)\n\nfunc = tvm.build(s, [A, B, C], target=target, name=\"mmult\")\nassert func\n\nc = tvm.nd.array(numpy.zeros((M, N), dtype=dtype), dev)\nfunc(a, b, c)\ntvm.testing.assert [...]
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Here is the generated IR after blocking.\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "print(tvm.lower(s, [A, B, C], simple_mode=True))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Vectorization\n-------------\nAnother important trick is vectorization. When the memory access pattern is uniform,\nthe compiler can detect this pattern and pass the continuous memory to vector processor. In TVM,\nwe can use `vectorize` interface to hint the compiler this pattern, so that we can accelerate it\nvastly.\n\nIn this tutorial, we chose to vectorize the inner loop row data since it is cache friendly.\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "s = te.create_schedule(C.op)\nmo, no, mi, ni = s[C].tile(C.op.axis[0], C.op.axis[1], bn, bn)\n(kaxis,) = s[C].op.reduce_axis\nko, ki = s[C].split(kaxis, factor=kfactor)\n\ns[C].reorder(mo, no, ko, ki, mi, ni)\n\n# Vectorization\ns[C].vectorize(ni)\n\nfunc = tvm.build(s, [A, B, C], target=target, name=\"mmult\")\nassert func\n\nc = tvm.nd.array(numpy.zeros((M, N), dtype=dtype), dev)\nfunc(a, b, c)\ntvm.testing.assert_allclose(c.numpy(), answer, rtol=1e-5)\n\nevaluator = func.time [...]
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Here is the generated IR after vectorization.\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "print(tvm.lower(s, [A, B, C], simple_mode=True))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Loop Permutation\n----------------\nIf we look at the above IR, we can see the inner loop row data is vectorized for both B and C.\nNext we will look at the access pattern of A. In current schedule, A is accessed column by column\nwhich is not cache friendly. If we change the nested loop order of ki and inner axes mi,\nthe access pattern for A matrix is more cache friendly.\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "s = te.create_schedule(C.op)\nmo, no, mi, ni = s[C].tile(C.op.axis[0], C.op.axis[1], bn, bn)\n(kaxis,) = s[C].op.reduce_axis\nko, ki = s[C].split(kaxis, factor=kfactor)\n\n# re-ordering\ns[C].reorder(mo, no, ko, mi, ki, ni)\ns[C].vectorize(ni)\n\nfunc = tvm.build(s, [A, B, C], target=target, name=\"mmult\")\nassert func\n\nc = tvm.nd.array(numpy.zeros((M, N), dtype=dtype), dev)\nfunc(a, b, c)\ntvm.testing.assert_allclose(c.numpy(), answer, rtol=1e-5)\n\nevaluator = func.time_eva [...]
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Here is the generated IR after loop permutation.\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "print(tvm.lower(s, [A, B, C], simple_mode=True))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Array Packing\n-------------\nAnother important trick is array packing. The trick is to reorder the storage of a multi-\ndimensional array so that it is accessed sequentially after it is flattened and stored in one-\ndimensional memory.\n\n\n\n :align: center\n\nNOTE: This figure is a general illustration of how array packing works.\n\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can use array packing to address the access pattern for B. Observe the array access pattern of\nB after flattening which is not sequential as we iterate over the K dimension. We can reorder B\nwith dimensions [K][N] so that it has dimensions [N/bn][K][bn] where bn is the blocking factor and\nalso the vector size for B in the inner loop. This reorder splits N into two dimensions ---\nbigN (N/bn) and littleN (bn) --- and the new dimensions [N/bn][K][bn] match the indexing of B [...]
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "# We have to re-write the algorithm slightly.\npackedB = te.compute(\n (N / bn, K, bn), lambda bigN, k, littleN: B[k, bigN * bn + littleN], name=\"packedB\"\n)\nC = te.compute(\n (M, N),\n lambda m, n: te.sum(A[m, k] * packedB[n // bn, k, tvm.tir.indexmod(n, bn)], axis=k),\n name=\"C\",\n)\n\ns = te.create_schedule(C.op)\n\nmo, no, mi, ni = s[C].tile(C.op.axis[0], C.op.axis[1], bn, bn)\n(kaxis,) = s[C].op.reduce_axis\nko, ki = s[C].split(kaxis, factor=kfactor)\n\ns[C [...]
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Here is the generated IR after array packing.\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "print(tvm.lower(s, [A, B, C], simple_mode=True))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Write cache for blocks\n----------------------\nAfter blocking, the program will write result to C block by block, the access pattern\nis not sequential. So we can use a sequential cache array to hold the block results and\nwrite to C when all the block results are ready.\n\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "s = te.create_schedule(C.op)\n\n# Allocate write cache\nCC = s.cache_write(C, \"global\")\n\nmo, no, mi, ni = s[C].tile(C.op.axis[0], C.op.axis[1], bn, bn)\n\n# Write cache is computed at no\ns[CC].compute_at(s[C], no)\n\n# New inner axes\nmc, nc = s[CC].op.axis\n\n(kaxis,) = s[CC].op.reduce_axis\nko, ki = s[CC].split(kaxis, factor=kfactor)\ns[CC].reorder(ko, mc, ki, nc)\ns[CC].vectorize(nc)\n\n# TODO: Add separate optimization step to discuss loop unrolloing\n# unrolling is a l [...]
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Here is the generated IR after blocking.\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "print(tvm.lower(s, [A, B, C], simple_mode=True))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Parallel\n--------\nFuthermore, we can also utilize multi-core processors to do the thread-level parallelization.\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "s = te.create_schedule(C.op)\n\nCC = s.cache_write(C, \"global\")\n\nmo, no, mi, ni = s[C].tile(C.op.axis[0], C.op.axis[1], bn, bn)\n\ns[CC].compute_at(s[C], no)\n\nmc, nc = s[CC].op.axis\n\n(kaxis,) = s[CC].op.reduce_axis\nko, ki = s[CC].split(kaxis, factor=kfactor)\ns[CC].reorder(ko, mc, ki, nc)\ns[CC].vectorize(nc)\ns[CC].unroll(ki)\n\n# parallel\ns[C].parallel(mo)\n\nbigN, _, littleN = s[packedB].op.axis\ns[packedB].vectorize(littleN)\ns[packedB].parallel(bigN)\n\nfunc = tvm [...]
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Here is the generated IR after parallelization.\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "print(tvm.lower(s, [A, B, C], simple_mode=True))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Summary\n-------\nAfter applying the above simple optimizations with only 18 lines of code,\nour generated code can achieve 60% of the `numpy` performance with MKL.\nNote that the outputs on the web page reflect the running times on a non-exclusive\nDocker container, thereby they are *unreliable*. It is highly encouraged to run the\ntutorial by yourself to observe the performance gain acheived by TVM.\n\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": "3.6.9"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/docs/v0.8.0/_downloads/10d831d158490a9ee3abd1901806fc11/reduction.ipynb b/docs/v0.8.0/_downloads/10d831d158490a9ee3abd1901806fc11/reduction.ipynb
new file mode 100644
index 000000000..98af79124
--- /dev/null
+++ b/docs/v0.8.0/_downloads/10d831d158490a9ee3abd1901806fc11/reduction.ipynb
@@ -0,0 +1,248 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "%matplotlib inline"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\nReduction\n=========\n**Author**: `Tianqi Chen <https://tqchen.github.io>`_\n\nThis is an introduction material on how to do reduction in TVM.\nAssociative reduction operators like sum/max/min are typical\nconstruction blocks of linear algebra operations.\n\nIn this tutorial, we will demonstrate how to do reduction in TVM.\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "from __future__ import absolute_import, print_function\n\nimport tvm\nimport tvm.testing\nfrom tvm import te\nimport numpy as np"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Describe Sum of Rows\n--------------------\nAssume we want to compute sum of rows as our example.\nIn numpy semantics this can be written as :code:`B = numpy.sum(A, axis=1)`\n\nThe following lines describe the row sum operation.\nTo create a reduction formula, we declare a reduction axis using\n:any:`te.reduce_axis`. :any:`te.reduce_axis` takes in the range of reductions.\n:any:`te.sum` takes in the expression to be reduced as well as the reduction\naxis and compute the sum of v [...]
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "n = te.var(\"n\")\nm = te.var(\"m\")\nA = te.placeholder((n, m), name=\"A\")\nk = te.reduce_axis((0, m), \"k\")\nB = te.compute((n,), lambda i: te.sum(A[i, k], axis=k), name=\"B\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Schedule the Reduction\n----------------------\nThere are several ways to schedule a reduction.\nBefore doing anything, let us print out the IR code of default schedule.\n\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "s = te.create_schedule(B.op)\nprint(tvm.lower(s, [A, B], simple_mode=True))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "You can find that the IR code is quite like the C code.\nThe reduction axis is similar to a normal axis, it can be splitted.\n\nIn the following code we split both the row axis of B as well\naxis by different factors. The result is a nested reduction.\n\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "ko, ki = s[B].split(B.op.reduce_axis[0], factor=16)\nxo, xi = s[B].split(B.op.axis[0], factor=32)\nprint(tvm.lower(s, [A, B], simple_mode=True))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "If we are building a GPU kernel, we can bind the rows of B to GPU threads.\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "s[B].bind(xo, te.thread_axis(\"blockIdx.x\"))\ns[B].bind(xi, te.thread_axis(\"threadIdx.x\"))\nprint(tvm.lower(s, [A, B], simple_mode=True))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Reduction Factoring and Parallelization\n---------------------------------------\nOne problem of building a reduction is that we cannot simply\nparallelize over the reduction axis. We need to divide the computation\nof the reduction, store the local reduction result in a temporal array\nbefore doing a reduction over the temp array.\n\nThe rfactor primitive does such rewrite of the computation.\nIn the following schedule, the result of B is written to a temporary\nresult B.rf. Th [...]
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "s = te.create_schedule(B.op)\nko, ki = s[B].split(B.op.reduce_axis[0], factor=16)\nBF = s.rfactor(B, ki)\nprint(tvm.lower(s, [A, B], simple_mode=True))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The scheduled operator of B also get rewritten to be sum over\nthe first axis of reduced result of B.f\n\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "print(s[B].op.body)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Cross Thread Reduction\n----------------------\nWe can now parallelize over the factored axis.\nHere the reduction axis of B is marked to be a thread.\nTVM allows reduction axis to be marked as thread if it is the only\naxis in reduction and cross thread reduction is possible in the device.\n\nThis is indeed the case after the factoring.\nWe can directly compute BF at the reduction axis as well.\nThe final generated kernel will divide the rows by blockIdx.x and threadIdx.y\ncolu [...]
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "xo, xi = s[B].split(s[B].op.axis[0], factor=32)\ns[B].bind(xo, te.thread_axis(\"blockIdx.x\"))\ns[B].bind(xi, te.thread_axis(\"threadIdx.y\"))\ntx = te.thread_axis(\"threadIdx.x\")\ns[B].bind(s[B].op.reduce_axis[0], tx)\ns[BF].compute_at(s[B], s[B].op.reduce_axis[0])\ns[B].set_store_predicate(tx.var.equal(0))\nfcuda = tvm.build(s, [A, B], \"cuda\")\nprint(fcuda.imported_modules[0].get_source())"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Verify the correctness of result kernel by comparing it to numpy.\n\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "nn = 128\ndev = tvm.cuda(0)\na = tvm.nd.array(np.random.uniform(size=(nn, nn)).astype(A.dtype), dev)\nb = tvm.nd.array(np.zeros(nn, dtype=B.dtype), dev)\nfcuda(a, b)\ntvm.testing.assert_allclose(b.numpy(), np.sum(a.numpy(), axis=1), rtol=1e-4)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Describe Convolution via 2D Reduction\n-------------------------------------\nIn TVM, we can describe convolution via 2D reduction in a simple way.\nHere is an example for 2D convolution with filter size = [3, 3] and strides = [1, 1].\n\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "n = te.var(\"n\")\nInput = te.placeholder((n, n), name=\"Input\")\nFilter = te.placeholder((3, 3), name=\"Filter\")\ndi = te.reduce_axis((0, 3), name=\"di\")\ndj = te.reduce_axis((0, 3), name=\"dj\")\nOutput = te.compute(\n (n - 2, n - 2),\n lambda i, j: te.sum(Input[i + di, j + dj] * Filter[di, dj], axis=[di, dj]),\n name=\"Output\",\n)\ns = te.create_schedule(Output.op)\nprint(tvm.lower(s, [Input, Filter, Output], simple_mode=True))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\nDefine General Commutative Reduction Operation\n----------------------------------------------\nBesides the built-in reduction operations like :any:`te.sum`,\n:any:`tvm.te.min` and :any:`tvm.te.max`, you can also define your\ncommutative reduction operation by :any:`te.comm_reducer`.\n\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "n = te.var(\"n\")\nm = te.var(\"m\")\nproduct = te.comm_reducer(lambda x, y: x * y, lambda t: tvm.tir.const(1, dtype=t), name=\"product\")\nA = te.placeholder((n, m), name=\"A\")\nk = te.reduce_axis((0, m), name=\"k\")\nB = te.compute((n,), lambda i: product(A[i, k], axis=k), name=\"B\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<div class=\"alert alert-info\"><h4>Note</h4><p>Sometimes we would like to perform reduction that involves multiple\n values like :code:`argmax`, which can be done by tuple inputs.\n See `reduction-with-tuple-inputs` for more detail.</p></div>\n\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Summary\n-------\nThis tutorial provides a walk through of reduction schedule.\n\n- Describe reduction with reduce_axis.\n- Use rfactor to factor out axis if we need parallelism.\n- Define new reduction operation by :any:`te.comm_reducer`\n\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": "3.6.9"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/docs/v0.8.0/_downloads/13ef71e33eaef0855c6e883d9ec5d632/convolution_opt.py b/docs/v0.8.0/_downloads/13ef71e33eaef0855c6e883d9ec5d632/convolution_opt.py
new file mode 100644
index 000000000..8eff3d423
--- /dev/null
+++ b/docs/v0.8.0/_downloads/13ef71e33eaef0855c6e883d9ec5d632/convolution_opt.py
@@ -0,0 +1,455 @@
+# 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.
+"""
+2D Convolution Optimization
+===========================
+**Author**: `Thierry Moreau <https://homes.cs.washington.edu/~moreau/>`_
+
+This tutorial provides an overview on how to use TVM to map a 2D convolution
+workload efficiently on the VTA design.
+We recommend covering the :ref:`vta-mat-mult-opt` tutorial first.
+
+2D convolution is dominant in most computer vision deep neural networks.
+In this tutorial, we will demonstrate TVM schedule optimizations to map
+2D convolution operators in NCHW layout onto VTA.
+We also introduce the notion of latency hiding, which allows us to
+maximize VTA's compute and memory resource utilization.
+"""
+
+######################################################################
+# RPC Setup
+# ---------
+# We start by programming the Pynq's FPGA and building its RPC runtime.
+
+from __future__ import absolute_import, print_function
+
+import os
+import tvm
+import tvm.testing
+from tvm import te
+import vta
+import numpy as np
+
+from tvm import rpc
+from tvm.contrib import utils
+from vta.testing import simulator
+
+# Load VTA parameters from the 3rdparty/vta-hw/config/vta_config.json file
+env = vta.get_env()
+
+# We read the Pynq RPC host IP address and port number from the OS environment
+host = os.environ.get("VTA_RPC_HOST", "192.168.2.99")
+port = int(os.environ.get("VTA_RPC_PORT", "9091"))
+
+# We configure both the bitstream and the runtime system on the Pynq
+# to match the VTA configuration specified by the vta_config.json file.
+if env.TARGET == "pynq":
+
+ # Make sure that TVM was compiled with RPC=1
+ assert tvm.runtime.enabled("rpc")
+ remote = rpc.connect(host, port)
+
+ # Reconfigure the JIT runtime
+ vta.reconfig_runtime(remote)
+
+ # Program the FPGA with a pre-compiled VTA bitstream.
+ # You can program the FPGA with your own custom bitstream
+ # by passing the path to the bitstream file instead of None.
+ vta.program_fpga(remote, bitstream=None)
+
+# In simulation mode, host the RPC server locally.
+elif env.TARGET in ["sim", "tsim"]:
+ remote = rpc.LocalSession()
+
+######################################################################
+# Computation Declaration
+# -----------------------
+# As a first step, we need to describe our 2D convolution computation
+# in NCHW format.
+#
+# We define the 2D convolution shape by the batch size,
+# spatial dimensions, input channels, output channels, kernel dimensions,
+# kernel dimensions, padding dimensions, and stride dimensions.
+#
+# We pick the shape of the 9th convolutional layer of the ResNet-18
+# architecture as our convolution workload parameters.
+#
+# We've added extra operators to the 2D convolution that apply
+# shifting and clipping to the output in order to mimic a fixed-point
+# convolution followed by a rectified linear activation.
+# We describe the TVM dataflow graph of the 2D convolution layer below:
+#
+# .. image:: https://raw.githubusercontent.com/uwsampl/web-data/main/vta/tutorial/conv2d_dataflow.png
+# :align: center
+#
+# This computation is intentionally too large to fit onto VTA's on-chip
+# buffers all at once. Therefore in the scheduling phase we'll
+# rely on computation blocking strategies to break the computation down into
+# manageable chunks.
+#
+# .. note::
+#
+# *Spatial padding*
+#
+# Note that we'll need to import the TOPI library to apply spatial padding
+# on the input feature map tensor.
+# Spatial padding facilitates blocking in the context of 2D convolutions
+# due to the fact that the same (x, y) spatial location of the input
+# feature map of any given layer is read more than once if the convolution
+# kernel window size is greater than one.
+# On CPUs, and GPUs, one way to increase efficiency of memory accesses
+# when parallelizing work is spatial packing, which requires data re-layout.
+# VTA load DMA engine can insert padding automatically so that the original
+# input feature map does not have to be re-packed in memory.
+#
+# We show the effect of VTA's on the fly spatial padding when data is being
+# loaded from DRAM into VTA's SRAM, following a 2D strided and padded memory
+# read.
+#
+# .. image:: https://raw.githubusercontent.com/uwsampl/web-data/main/vta/tutorial/padding.png
+# :align: center
+# :width: 480px
+
+from tvm import topi
+
+# 2D convolution layer dimensions taken from ResNet-18 architecture
+# (9th convolutional layer)
+batch_size = 1
+height = 14
+width = 14
+in_channels = 256
+out_channels = 256
+kernel_h = 3
+kernel_w = 3
+pad_h = 1
+pad_w = 1
+stride_h = 1
+stride_w = 1
+assert batch_size % env.BATCH == 0
+assert in_channels % env.BLOCK_IN == 0
+assert out_channels % env.BLOCK_OUT == 0
+
+# Input feature map: (N, IC, H, W, n, ic)
+data_shape = (
+ batch_size // env.BATCH,
+ in_channels // env.BLOCK_IN,
+ height,
+ width,
+ env.BATCH,
+ env.BLOCK_IN,
+)
+# Kernel: (OC, IC, H, W, oc, ic)
+kernel_shape = (
+ out_channels // env.BLOCK_OUT,
+ in_channels // env.BLOCK_IN,
+ kernel_h,
+ kernel_w,
+ env.BLOCK_OUT,
+ env.BLOCK_IN,
+)
+# Derive output feature map dimensions
+fout_height = (height + 2 * pad_h - kernel_h) // stride_h + 1
+fout_width = (width + 2 * pad_w - kernel_w) // stride_w + 1
+# Output feature map: (N, OC, H, W, n, oc)
+output_shape = (
+ batch_size // env.BATCH,
+ out_channels // env.BLOCK_OUT,
+ fout_height,
+ fout_width,
+ env.BATCH,
+ env.BLOCK_OUT,
+)
+
+# Convolution reduction axes
+dy = te.reduce_axis((0, kernel_h), name="dy")
+dx = te.reduce_axis((0, kernel_w), name="dx")
+ic = te.reduce_axis((0, in_channels // env.BLOCK_IN), name="ic")
+ic_tns = te.reduce_axis((0, env.BLOCK_IN), name="ic_tns")
+
+# Input placeholder tensors
+data = te.placeholder(data_shape, name="data", dtype=env.inp_dtype)
+kernel = te.placeholder(kernel_shape, name="kernel", dtype=env.wgt_dtype)
+
+# Copy buffers:
+# Apply spatial padding to input feature map
+data_buf = topi.nn.pad(data, [0, 0, pad_h, pad_w, 0, 0], name="data_buf")
+kernel_buf = te.compute(kernel_shape, lambda *i: kernel(*i), "kernel_buf")
+
+# Declare 2D convolution
+res_conv = te.compute(
+ output_shape,
+ lambda bo, co, i, j, bi, ci: te.sum(
+ data_buf[bo, ic, i * stride_h + dy, j * stride_w + dx, bi, ic_tns].astype(env.acc_dtype)
+ * kernel_buf[co, ic, dy, dx, ci, ic_tns].astype(env.acc_dtype),
+ axis=[ic, dy, dx, ic_tns],
+ ),
+ name="res_conv",
+)
+
+# Add shift stage for fix-point normalization
+res_shr = te.compute(output_shape, lambda *i: res_conv(*i) >> 8, name="res_shr")
+
+# Apply clipping between (0, input max value)
+inp_max = (1 << (env.INP_WIDTH - 1)) - 1
+res_max = te.compute(output_shape, lambda *i: tvm.te.max(res_shr(*i), 0), "res_max")
+res_min = te.compute(output_shape, lambda *i: tvm.te.min(res_max(*i), inp_max), "res_min")
+
+# Result Tensor
+res = te.compute(output_shape, lambda *i: res_min(*i).astype(env.inp_dtype), name="res")
+
+
+######################################################################
+# Scheduling the Computation
+# --------------------------
+# We'll look at a set of schedule transformations necessary to map the
+# 2D convolution onto VTA in an efficient fashion.
+# Those include:
+#
+# - Computation blocking
+# - Virtual threading to increase compute utilization
+# - Lowering to VTA hardware intrinsics
+
+# Create TVM schedule
+s = te.create_schedule(res.op)
+# Let's look at the default TVM schedule
+print(tvm.lower(s, [data, kernel, res], simple_mode=True))
+
+######################################################################
+# Blocking the Computation
+# ~~~~~~~~~~~~~~~~~~~~~~~~
+# The 2D convolution is by default too large for activations or kernel weights
+# to fit on VTA's on-chip buffers all at once.
+# We apply blocking along input channels, output channels, and along
+# the height spatial dimensions.
+# We don't apply blocking along the width spatial dimension since it's
+# the innermost dimension in the NCHW layout (and consequently to increase
+# locality, it's best not to block along the innermost dimension).
+
+# Let's define tiling sizes
+b_block = 1 // env.BATCH
+oc_block = 128 // env.BLOCK_OUT
+ic_block = 16 // env.BLOCK_IN
+h_block = 7
+w_block = 14
+
+# Tile the output tensor along the spatial and output channel dimensions
+# (since by default we are doing single batch inference, the split along
+# the batch dimension has no effect)
+b, oc, y, x, b_tns, oc_tns = s[res].op.axis
+b_out, b_inn = s[res].split(b, factor=b_block)
+oc_out, oc_inn = s[res].split(oc, factor=oc_block)
+y_out, y_inn = s[res].split(y, factor=h_block)
+x_out, x_inn = s[res].split(x, factor=w_block)
+s[res].reorder(b_out, oc_out, y_out, x_out, b_inn, oc_inn, y_inn, x_inn, b_tns, oc_tns)
+
+# Move intermediate computation into each output compute tile
+s[res_conv].compute_at(s[res], x_out)
+s[res_shr].compute_at(s[res], x_out)
+s[res_max].compute_at(s[res], x_out)
+s[res_min].compute_at(s[res], x_out)
+
+# Apply additional loop split along reduction axis (input channel)
+b_inn, oc_inn, y_inn, x_inn, b_tns, oc_tns = s[res_conv].op.axis
+ic_out, ic_inn = s[res_conv].split(ic, factor=ic_block)
+
+# Reorder axes.
+# 1) Group the VTA tensor axes in the inner most position: b_tns, oc_tns, ic_tns
+# to allow TVM to tensorize.
+# 2) We move the ic_out axis all the way out of the convolution loop to block
+# along the reduction axis.
+# 3) Now we re-order the block axes: b_inn, oc_inn, y_inn, x_inn, ic_inn, dy, dx.
+# VTA runtime/hardware requires us to write to a different output feature map
+# location for every VTA tensor operation.
+# This restriction requires us to order one of oc_inn, y_inn or x_inn right
+# before b_tns, since they all affect output feature map indexing.
+# Therefore, we choose to bring x_inn inside as shown below.
+s[res_conv].reorder(ic_out, b_inn, oc_inn, y_inn, ic_inn, dy, dx, x_inn, b_tns, oc_tns, ic_tns)
+
+######################################################################
+# Virtual Threading
+# ~~~~~~~~~~~~~~~~~
+# Virtual threading is a mechanism that increases task-level pipeline
+# parallelism in the VTA hardware design.
+# Put it another way, it increases compute resource utilization by hiding
+# memory access latency.
+#
+# In the implementation below, virtual threading distributes work across two
+# threads split along the output channel axis.
+# We show how work is split when computing the 2D convolution in the figure
+# below.
+#
+# .. image:: https://raw.githubusercontent.com/uwsampl/web-data/main/vta/tutorial/virtual_threading.png
+# :align: center
+# :width: 480px
+
+# VTA only supports 2 virtual threads
+v_threads = 2
+
+# Perform virtual thread split along output channel outer axis
+_, tx = s[res].split(oc_out, factor=v_threads)
+s[res].reorder(tx, b_out)
+s[res].bind(tx, te.thread_axis("cthread"))
+
+# Let's look at the current TVM schedule after blocking and virtual threading
+print(tvm.lower(s, [data, kernel, res], simple_mode=True))
+
+######################################################################
+# Lowering Copies to DMA Transfers
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# Next we set the buffer scopes to the corresponding on-chip VTA SRAM buffers.
+# We move the load loops into the 2D convolution computation loop to stage
+# memory loads such that they fit in the on-chip SRAM buffers.
+# Finally we annotate the load/store loop outer axes with the DMA copy pragma
+# to perform bulk memory transfers on VTA.
+
+# Set scope of SRAM buffers
+s[data_buf].set_scope(env.inp_scope)
+s[kernel_buf].set_scope(env.wgt_scope)
+s[res_conv].set_scope(env.acc_scope)
+s[res_shr].set_scope(env.acc_scope)
+s[res_min].set_scope(env.acc_scope)
+s[res_max].set_scope(env.acc_scope)
+
+# Block data and kernel cache reads
+s[data_buf].compute_at(s[res_conv], ic_out)
+s[kernel_buf].compute_at(s[res_conv], ic_out)
+
+# Use DMA copy pragma on DRAM->SRAM operations
+s[data_buf].pragma(s[data_buf].op.axis[0], env.dma_copy)
+s[kernel_buf].pragma(s[kernel_buf].op.axis[0], env.dma_copy)
+
+# Use DMA copy pragma on SRAM->DRAM operation in each result block
+# (this implies that these copies should be performed along b_inn,
+# or result axis 4)
+s[res].pragma(s[res].op.axis[4], env.dma_copy)
+
+######################################################################
+# Lowering Computation to VTA Compute Intrinsics
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# The last phase is to lower the computation loops down to VTA hardware
+# intrinsics by mapping the 2D convolution to tensor intrinsics,
+# and mapping the shift, and clipping computation to the vector ALU.
+
+# Apply tensorization over the batch tensor tile axis
+s[res_conv].tensorize(b_tns, env.gemm)
+
+# Add an ALU pragma over the shift and clipping operations
+s[res_shr].pragma(s[res_shr].op.axis[0], env.alu)
+s[res_min].pragma(s[res_min].op.axis[0], env.alu)
+s[res_max].pragma(s[res_max].op.axis[0], env.alu)
+
+# Let's look at the final lowered TVM schedule after lowering memory
+# loads/stores down to DMA copy intrinsics, and the computation down to
+# VTA compute intrinsics.
+print(vta.lower(s, [data, kernel, res], simple_mode=True))
+
+######################################################################
+# TVM Compilation and Verification
+# --------------------------------
+# After specifying the schedule, we can compile it into a TVM function.
+# We save the module so we can send it over RPC.
+# We run the function and verify it against a numpy implementation to
+# ensure correctness.
+
+# This library facilitates 2D convolution testing
+from tvm.topi.testing import conv2d_nchw_python
+
+# Compile the TVM module
+my_conv = vta.build(s, [data, kernel, res], "ext_dev", env.target_host, name="my_conv")
+temp = utils.tempdir()
+my_conv.save(temp.relpath("conv2d.o"))
+remote.upload(temp.relpath("conv2d.o"))
+f = remote.load_module("conv2d.o")
+
+# Get the remote device context
+ctx = remote.ext_dev(0)
+
+# Initialize the data and kernel arrays randomly in the int range
+# of (-128, 128] in NCHW layout
+data_np = np.random.randint(-128, 128, size=(batch_size, in_channels, height, width)).astype(
+ data.dtype
+)
+kernel_np = np.random.randint(
+ -128, 128, size=(out_channels, in_channels, kernel_h, kernel_w)
+).astype(kernel.dtype)
+
+# Apply packing to the data and kernel arrays from a 2D NCHW
+# to a 4D NCHWnc packed layout
+data_packed = data_np.reshape(
+ batch_size // env.BATCH, env.BATCH, in_channels // env.BLOCK_IN, env.BLOCK_IN, height, width
+).transpose((0, 2, 4, 5, 1, 3))
+
+kernel_packed = kernel_np.reshape(
+ out_channels // env.BLOCK_OUT,
+ env.BLOCK_OUT,
+ in_channels // env.BLOCK_IN,
+ env.BLOCK_IN,
+ kernel_h,
+ kernel_w,
+).transpose((0, 2, 4, 5, 1, 3))
+
+# Format the input/output arrays with tvm.nd.array to the DLPack standard
+data_nd = tvm.nd.array(data_packed, ctx)
+kernel_nd = tvm.nd.array(kernel_packed, ctx)
+res_nd = tvm.nd.array(np.zeros(output_shape).astype(res.dtype), ctx)
+
+# Clear stats
+if env.TARGET in ["sim", "tsim"]:
+ simulator.clear_stats()
+
+# Invoke the module to perform the computation
+f(data_nd, kernel_nd, res_nd)
+
+# Verify against numpy implementation
+res_ref = conv2d_nchw_python(
+ data_np.astype(env.acc_dtype),
+ kernel_np.astype(env.acc_dtype),
+ (stride_h, stride_w),
+ (pad_h, pad_w),
+).astype(env.acc_dtype)
+res_ref = res_ref >> env.INP_WIDTH
+res_ref = np.clip(res_ref, 0, inp_max)
+res_ref = res_ref.astype(res.dtype)
+res_ref = res_ref.reshape(
+ (
+ batch_size // env.BATCH,
+ env.BATCH,
+ out_channels // env.BLOCK_OUT,
+ env.BLOCK_OUT,
+ fout_height,
+ fout_width,
+ )
+).transpose((0, 2, 4, 5, 1, 3))
+tvm.testing.assert_allclose(res_ref, res_nd.numpy())
+
+# Print stats
+if env.TARGET in ["sim", "tsim"]:
+ sim_stats = simulator.stats()
+ print("Execution statistics:")
+ for k, v in sim_stats.items():
+ print("\t{:<16}: {:>16}".format(k, v))
+
+print("Successful 2D convolution test!")
+
+######################################################################
+# Summary
+# -------
+# This tutorial demonstrates how TVM scheduling primitives can be used to
+# lower 2D convolution onto hardware accelerator intrinsics, making
+# use of hardware specific optimizations, such as latency hiding with
+# virtual threading.
+#
diff --git a/docs/v0.8.0/_downloads/16269b77359771348d507395692524cf/from_paddle.py b/docs/v0.8.0/_downloads/16269b77359771348d507395692524cf/from_paddle.py
new file mode 100644
index 000000000..9d67cbcdf
--- /dev/null
+++ b/docs/v0.8.0/_downloads/16269b77359771348d507395692524cf/from_paddle.py
@@ -0,0 +1,117 @@
+# 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.
+"""
+Compile PaddlePaddle Models
+===========================
+**Author**: `Ziyuan Ma <https://github.com/ZiyuanMa/>`_
+
+This article is an introductory tutorial to deploy PaddlePaddle models with Relay.
+For us to begin with, PaddlePaddle>=2.1.3 is required to be installed.
+A quick solution is
+
+.. code-block:: bash
+
+ pip install paddlepaddle -i https://mirror.baidu.com/pypi/simple
+
+or please refer to official site.
+https://www.paddlepaddle.org.cn/install/quick?docurl=/documentation/docs/zh/install/pip/linux-pip.html
+"""
+import tarfile
+import paddle
+import numpy as np
+import tvm
+from tvm import relay
+from tvm.contrib.download import download_testdata
+
+######################################################################
+# Load pretrained ResNet50 model
+# ---------------------------------------------
+# We load a pretrained ResNet50 provided by PaddlePaddle.
+url = "https://bj.bcebos.com/x2paddle/models/paddle_resnet50.tar"
+model_path = download_testdata(url, "paddle_resnet50.tar", module="model")
+
+with tarfile.open(model_path) as tar:
+ names = tar.getnames()
+ for name in names:
+ tar.extract(name, "./")
+
+model = paddle.jit.load("./paddle_resnet50/model")
+
+######################################################################
+# Load a test image
+# ---------------------------------------------
+# A single cat dominates the examples!
+
+from PIL import Image
+import paddle.vision.transforms as T
+
+
+transforms = T.Compose(
+ [
+ T.Resize((256, 256)),
+ T.CenterCrop(224),
+ T.ToTensor(),
+ T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+ ]
+)
+
+img_url = "https://github.com/dmlc/mxnet.js/blob/main/data/cat.png?raw=true"
+img_path = download_testdata(img_url, "cat.png", module="data")
+img = Image.open(img_path).resize((224, 224))
+
+img = transforms(img)
+img = np.expand_dims(img, axis=0)
+
+######################################################################
+# Compile the model with relay
+# ---------------------------------------------
+
+target = "llvm"
+shape_dict = {"inputs": img.shape}
+mod, params = relay.frontend.from_paddle(model, shape_dict)
+
+with tvm.transform.PassContext(opt_level=3):
+ executor = relay.build_module.create_executor(
+ "graph", mod, tvm.cpu(0), target, params
+ ).evaluate()
+
+######################################################################
+# Execute on TVM
+# ---------------------------------------------
+dtype = "float32"
+tvm_output = executor(tvm.nd.array(img.astype(dtype))).numpy()
+
+######################################################################
+# Look up synset name
+# ---------------------------------------------
+# Look up prediction top 1 index in 1000 class synset.
+
+synset_url = "".join(
+ [
+ "https://gist.githubusercontent.com/zhreshold/",
+ "4d0b62f3d01426887599d4f7ede23ee5/raw/",
+ "596b27d23537e5a1b5751d2b0481ef172f58b539/",
+ "imagenet1000_clsid_to_human.txt",
+ ]
+)
+synset_name = "imagenet1000_clsid_to_human.txt"
+synset_path = download_testdata(synset_url, synset_name, module="data")
+with open(synset_path) as f:
+ synset = f.readlines()
+
+top1 = np.argmax(tvm_output[0])
+print(f"TVM prediction top-1 id: {top1}, class name: {synset[top1]}")
diff --git a/docs/v0.8.0/_downloads/178b6f23dffc01ac92f2cf95f41a5679/tune_alu_vta.ipynb b/docs/v0.8.0/_downloads/178b6f23dffc01ac92f2cf95f41a5679/tune_alu_vta.ipynb
new file mode 100644
index 000000000..49f839dfc
--- /dev/null
+++ b/docs/v0.8.0/_downloads/178b6f23dffc01ac92f2cf95f41a5679/tune_alu_vta.ipynb
@@ -0,0 +1,126 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "%matplotlib inline"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\nAuto-tuning a ALU fused op on VTA\n---------------------------------\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "import os\nfrom mxnet.gluon.model_zoo import vision\nimport numpy as np\nfrom PIL import Image\n\nfrom tvm import topi\nimport tvm\nfrom tvm import te\nfrom tvm import rpc, autotvm, relay\nfrom tvm.contrib import download\nfrom tvm.autotvm.measure.measure_methods import request_remote\nfrom tvm.autotvm.tuner import XGBTuner, GATuner, RandomTuner, GridSearchTuner\nfrom tvm.autotvm import record\n\nimport vta\nfrom vta.testing import simulator\nfrom vta.top import graph_pack\nimpo [...]
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Compile network\n---------------\nPerform vta-specific compilation with Relay from a Gluon model\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "def compile_network(env, target, model, start_pack, stop_pack):\n\n # Populate the shape and data type dictionary\n dtype_dict = {\"data\": \"float32\"}\n shape_dict = {\"data\": (env.BATCH, 3, 224, 224)}\n\n # Get off the shelf gluon model, and convert to relay\n gluon_model = vision.get_model(model, pretrained=True)\n mod, params = relay.frontend.from_mxnet(gluon_model, shape_dict)\n\n # Update shape and type dictionary\n shape_dict.update({k: v.shape f [...]
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Set Tuning Options\n------------------\nBefore tuning, we should apply some configurations.\nHere we use an Pynq-Z1 board as an example.\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "# Tracker host and port can be set by your environment\ntracker_host = os.environ.get(\"TVM_TRACKER_HOST\", \"0.0.0.0\")\ntracker_port = int(os.environ.get(\"TVM_TRACKER_PORT\", 9190))\n\n# Load VTA parameters from the vta/config/vta_config.json file\nenv = vta.get_env()\n\n# This target is used for cross compilation. You can query it by :code:`gcc -v` on your device.\n# Set ``device=arm_cpu`` to run inference on the CPU\n# or ``device=vta`` to run inference on the FPGA.\ndevice [...]
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Register VTA-specific tuning tasks\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "def register_vta_tuning_tasks():\n from tvm.autotvm.task import TaskExtractEnv\n\n @tvm.te.tag_scope(tag=topi.tag.ELEMWISE)\n def my_clip(x, a_min, a_max):\n \"\"\"Unlike topi's current clip, put min and max into two stages.\"\"\"\n const_min = tvm.tir.const(a_min, x.dtype)\n const_max = tvm.tir.const(a_max, x.dtype)\n x = te.compute(x.shape, lambda *i: tvm.te.min(x(*i), const_max), name=\"clipA\")\n x = te.compute(x.shape, lambda *i: [...]
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Finally, we launch tuning jobs and evaluate the end-to-end performance.\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "def tune_and_evaluate(tuning_opt):\n\n if env.TARGET != \"intelfocl\":\n print(\"ALU only op only available for intelfocl target\")\n return\n\n # Register VTA tuning tasks\n register_vta_tuning_tasks()\n\n # Perform task extraction on Relay program\n print(\"Extract tasks...\")\n relay_prog, params = compile_network(env, target, network, start_pack, stop_pack)\n mod = tvm.IRModule.from_expr(relay_prog)\n tasks = autotvm.task.extract_from_pr [...]
+ ]
+ }
+ ],
+ "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": "3.6.9"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/docs/v0.8.0/_downloads/17b139d609f9480c7eeda2da1f90f28c/tune_network_arm.py b/docs/v0.8.0/_downloads/17b139d609f9480c7eeda2da1f90f28c/tune_network_arm.py
new file mode 100644
index 000000000..1619a55dc
--- /dev/null
+++ b/docs/v0.8.0/_downloads/17b139d609f9480c7eeda2da1f90f28c/tune_network_arm.py
@@ -0,0 +1,439 @@
+# 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.
+"""
+Auto-scheduling a Neural Network for ARM CPU
+=============================================
+**Author**: `Thierry Moreau <https://github.com/tmoreau89>_`, \
+ `Lianmin Zheng <https://github.com/merrymercy>_`, \
+ `Chengfan Jia <https://github.com/jcf94/>`_
+
+Auto-tuning for specific devices and workloads is critical for getting the
+best performance. This is a tutorial on how to tune a whole neural
+network for ARM CPU with the auto-scheduler via RPC.
+
+To auto-tune a neural network, we partition the network into small subgraphs and
+tune them independently. Each subgraph is treated as one search task.
+A task scheduler slices the time and dynamically allocates time resources to
+these tasks. The task scheduler predicts the impact of each task on the end-to-end
+execution time and prioritizes the one that can reduce the execution time the most.
+
+For each subgraph, we use the compute declaration in :code:`tvm/python/topi` to
+get the computational DAG in the tensor expression form.
+We then use the auto-scheduler to construct a search space of this DAG and search
+for good schedules (low-level optimizations).
+
+Different from the template-based :ref:`autotvm <tutorials-autotvm-sec>` which relies on
+manual templates to define the search space, the auto-scheduler does not require any
+schedule templates. In other words, the auto-scheduler only uses the compute declarations
+in :code:`tvm/python/topi` and does not use existing schedule templates.
+
+Note that this tutorial will not run on Windows or recent versions of macOS. To
+get it to run, you will need to wrap the body of this tutorial in a :code:`if
+__name__ == "__main__":` block.
+"""
+
+import numpy as np
+import os
+
+import tvm
+from tvm import relay, auto_scheduler
+from tvm.relay import data_dep_optimization as ddo
+import tvm.relay.testing
+from tvm.contrib import graph_executor
+from tvm.contrib.utils import tempdir
+
+#################################################################
+# Define a Network
+# ----------------
+# First, we need to define the network with relay frontend API.
+# We can load some pre-defined network from :code:`tvm.relay.testing`.
+# We can also load models from MXNet, ONNX, PyTorch, and TensorFlow
+# (see :ref:`front end tutorials<tutorial-frontend>`).
+#
+# For convolutional neural networks, although auto-scheduler can work correctly
+# with any layout, we found the best performance is typically achieved with NHWC layout.
+# We also implemented more optimizations for NHWC layout with the auto-scheduler.
+# So it is recommended to convert your models to NHWC layout to use the auto-scheduler.
+# You can use :ref:`ConvertLayout <convert-layout-usage>` pass to do the layout conversion in TVM.
+
+
+def get_network(name, batch_size, layout="NHWC", dtype="float32", use_sparse=False):
+ """Get the symbol definition and random weight of a network"""
+
+ # auto-scheduler prefers NHWC layout
+ if layout == "NHWC":
+ image_shape = (224, 224, 3)
+ elif layout == "NCHW":
+ image_shape = (3, 224, 224)
+ else:
+ raise ValueError("Invalid layout: " + layout)
+
+ input_shape = (batch_size,) + image_shape
+ output_shape = (batch_size, 1000)
+
+ if name.startswith("resnet-"):
+ n_layer = int(name.split("-")[1])
+ mod, params = relay.testing.resnet.get_workload(
+ num_layers=n_layer,
+ batch_size=batch_size,
+ layout=layout,
+ dtype=dtype,
+ image_shape=image_shape,
+ )
+ elif name.startswith("resnet3d-"):
+ n_layer = int(name.split("-")[1])
+ mod, params = relay.testing.resnet.get_workload(
+ num_layers=n_layer,
+ batch_size=batch_size,
+ layout=layout,
+ dtype=dtype,
+ image_shape=image_shape,
+ )
+ elif name == "mobilenet":
+ mod, params = relay.testing.mobilenet.get_workload(
+ batch_size=batch_size, layout=layout, dtype=dtype, image_shape=image_shape
+ )
+ elif name == "squeezenet_v1.1":
+ assert layout == "NCHW", "squeezenet_v1.1 only supports NCHW layout"
+ mod, params = relay.testing.squeezenet.get_workload(
+ version="1.1",
+ batch_size=batch_size,
+ dtype=dtype,
+ image_shape=image_shape,
+ )
+ elif name == "inception_v3":
+ input_shape = (batch_size, 3, 299, 299) if layout == "NCHW" else (batch_size, 299, 299, 3)
+ mod, params = relay.testing.inception_v3.get_workload(batch_size=batch_size, dtype=dtype)
+ elif name == "mxnet":
+ # an example for mxnet model
+ from mxnet.gluon.model_zoo.vision import get_model
+
+ assert layout == "NCHW"
+
+ block = get_model("resnet50_v1", pretrained=True)
+ mod, params = relay.frontend.from_mxnet(block, shape={"data": input_shape}, dtype=dtype)
+ net = mod["main"]
+ net = relay.Function(
+ net.params, relay.nn.softmax(net.body), None, net.type_params, net.attrs
+ )
+ mod = tvm.IRModule.from_expr(net)
+ elif name == "mlp":
+ mod, params = relay.testing.mlp.get_workload(
+ batch_size=batch_size, dtype=dtype, image_shape=image_shape, num_classes=1000
+ )
+ else:
+ raise ValueError("Network not found.")
+
+ if use_sparse:
+ from tvm.topi.sparse.utils import convert_model_dense_to_sparse
+
+ mod, params = convert_model_dense_to_sparse(mod, params, random_params=True)
+
+ return mod, params, input_shape, output_shape
+
+
+#################################################################
+# Start RPC Tracker
+# -----------------
+# TVM uses RPC session to communicate with ARM boards.
+# During tuning, the tuner will send the generated code to the board and
+# measure the speed of code on the board.
+#
+# To scale up the tuning, TVM uses RPC Tracker to manage distributed devices.
+# The RPC Tracker is a centralized controller node. We can register all devices to
+# the tracker. For example, if we have 10 phones, we can register all of them
+# to the tracker, and run 10 measurements in parallel, accelerating the tuning process.
+#
+# To start an RPC tracker, run this command on the host machine. The tracker is
+# required during the whole tuning process, so we need to open a new terminal for
+# this command:
+#
+# .. code-block:: bash
+#
+# python -m tvm.exec.rpc_tracker --host=0.0.0.0 --port=9190
+#
+# The expected output is
+#
+# .. code-block:: bash
+#
+# INFO:RPCTracker:bind to 0.0.0.0:9190
+
+#################################################################
+# Register Devices to RPC Tracker
+# -----------------------------------
+# Now we can register our devices to the tracker. The first step is to
+# build the TVM runtime for the ARM devices.
+#
+# * For Linux:
+# Follow this section :ref:`build-tvm-runtime-on-device` to build
+# the TVM runtime on the device. Then register the device to tracker by
+#
+# .. code-block:: bash
+#
+# python -m tvm.exec.rpc_server --tracker=[HOST_IP]:9190 --key=rasp4b-64
+#
+# (replace :code:`[HOST_IP]` with the IP address of your host machine)
+#
+# * For Android:
+# Follow this `readme page <https://github.com/apache/tvm/tree/main/apps/android_rpc>`_ to
+# install the TVM RPC APK on the android device. Make sure you can pass the android rpc test.
+# Then you have already registered your device. During tuning, you have to go to developer option
+# and enable "Keep screen awake during changing" and charge your phone to make it stable.
+#
+# After registering devices, we can confirm it by querying rpc_tracker
+#
+# .. code-block:: bash
+#
+# python -m tvm.exec.query_rpc_tracker --host=0.0.0.0 --port=9190
+#
+# For example, if we have 2 Huawei mate10 pro, 11 Raspberry Pi 4B with 64bit OS, and 2 rk3399,
+# the output can be
+#
+# .. code-block:: bash
+#
+# Queue Status
+# ----------------------------------
+# key total free pending
+# ----------------------------------
+# mate10pro 2 2 0
+# rk3399 2 2 0
+# rasp4b-64 11 11 0
+# ----------------------------------
+#
+# You can register multiple devices to the tracker to accelerate the measurement in tuning.
+
+###########################################
+# Set Tuning Options
+# ------------------
+# Before tuning, we should apply some configurations. Here I use a Raspberry Pi 4b 4GB board
+# as example with a 64bit OS (Ubuntu 20.04). In your setting, you should modify the target
+# and device_key accordingly.
+# set :code:`use_ndk` to True if you use android phone.
+
+#### DEVICE CONFIG ####
+
+# Replace "aarch64-linux-gnu" with the correct target of your board.
+# This target is used for cross compilation. You can query it by :code:`gcc -v` on your device.
+# FIXME(tmoreau89, merrymercy): We leave '-device=arm_cpu' out of the target string
+# because we're sharing x86 op strategy.
+target = tvm.target.Target("llvm -mtriple=aarch64-linux-gnu -mattr=+neon")
+
+# Also replace this with the device key, rpc host and rpc port in your tracker
+device_key = "rasp4b-64"
+rpc_host = "127.0.0.1"
+rpc_port = 9190
+
+# Set this to True if you use ndk tools for cross compiling
+# And also set the environment variable below to point to the cross compiler
+use_ndk = False
+# os.environ["TVM_NDK_CC"] = "/usr/bin/aarch64-linux-gnu-g++"
+
+#### TUNING OPTION ####
+network = "mobilenet"
+use_sparse = False
+batch_size = 1
+layout = "NHWC"
+dtype = "float32"
+log_file = "%s-%s-B%d-%s.json" % (network, layout, batch_size, target.kind.name)
+
+#################################################################
+# Extract Search Tasks
+# --------------------
+# Next, we extract the search tasks and their weights from a network.
+# The weight of a task is the number of appearances of the task's subgraph
+# in the whole network.
+# By using the weight, we can approximate the end-to-end latency of the network
+# as :code:`sum(latency[t] * weight[t])`, where :code:`latency[t]` is the
+# latency of a task and :code:`weight[t]` is the weight of the task.
+# The task scheduler will just optimize this objective.
+
+# Extract tasks from the network
+print("Get model...")
+mod, params, input_shape, output_shape = get_network(
+ network, batch_size, layout, dtype=dtype, use_sparse=use_sparse
+)
+print("Extract tasks...")
+tasks, task_weights = auto_scheduler.extract_tasks(mod["main"], params, target)
+
+for idx, task in enumerate(tasks):
+ print("========== Task %d (workload key: %s) ==========" % (idx, task.workload_key))
+ print(task.compute_dag)
+
+
+#################################################################
+# Tuning and Evaluation
+# ---------------------
+# Now, we set some options for tuning and launch the search tasks
+#
+# * :code:`num_measure_trials` is the number of measurement trials we can use during the tuning.
+# You can set it to a small number (e.g., 200) for a fast demonstrative run.
+# In practice, we recommend setting it around :code:`800 * len(tasks)`,
+# which is typically enough for the search to converge.
+# For example, there are 29 tasks in resnet-50, so we can set it as 20000.
+# You can adjust this parameter according to your time budget.
+# * In addition, we use :code:`RecordToFile` to dump measurement records into a log file,
+# The measurement records can be used to query the history best, resume the search,
+# and do more analyses later.
+# * see :any:`auto_scheduler.TuningOptions`,
+# :any:`auto_scheduler.LocalRunner` for more parameters.
+#
+# After auto-tuning, we can compile the network with the best schedules we found.
+# All measurement records are dumped into the log file during auto-tuning,
+# so we can read the log file and load the best schedules.
+
+
+def tune_and_evaluate():
+ print("Begin tuning...")
+ tuner = auto_scheduler.TaskScheduler(tasks, task_weights)
+ tune_option = auto_scheduler.TuningOptions(
+ num_measure_trials=200, # change this to 20000 to achieve the best performance
+ builder=auto_scheduler.LocalBuilder(build_func="ndk" if use_ndk else "default"),
+ runner=auto_scheduler.RPCRunner(
+ device_key,
+ host=rpc_host,
+ port=rpc_port,
+ timeout=30,
+ repeat=1,
+ min_repeat_ms=200,
+ enable_cpu_cache_flush=True,
+ ),
+ measure_callbacks=[auto_scheduler.RecordToFile(log_file)],
+ )
+
+ tuner.tune(tune_option)
+
+ # Compile with the history best
+ print("Compile...")
+ with auto_scheduler.ApplyHistoryBest(log_file):
+ with tvm.transform.PassContext(
+ opt_level=3, config={"relay.backend.use_auto_scheduler": True}
+ ):
+ lib = relay.build(mod, target=target, params=params)
+
+ # Export library
+ tmp = tempdir()
+ if use_ndk:
+ from tvm.contrib import ndk
+
+ filename = "net.so"
+ lib.export_library(tmp.relpath(filename), ndk.create_shared)
+ else:
+ filename = "net.tar"
+ lib.export_library(tmp.relpath(filename))
+
+ # Upload module to device
+ print("Upload...")
+ remote = auto_scheduler.utils.request_remote(device_key, rpc_host, rpc_port, timeout=10000)
+ remote.upload(tmp.relpath(filename))
+ rlib = remote.load_module(filename)
+
+ # Create graph executor
+ dev = remote.cpu()
+ module = graph_executor.GraphModule(rlib["default"](dev))
+ data_tvm = tvm.nd.array((np.random.uniform(size=input_shape)).astype(dtype))
+ module.set_input("data", data_tvm)
+
+ # Evaluate
+ print("Evaluate inference time cost...")
+ print(module.benchmark(dev, repeat=3, min_repeat_ms=500))
+
+
+# We do not run the tuning in our webpage server since the server doesn't have a Raspberry Pi,
+# or device tracker running.
+# Uncomment the following line to run it by yourself.
+
+# tune_and_evaluate()
+
+
+######################################################################
+# .. note:: Explaining the printed information during tuning
+#
+# During the tuning, a lot of information will be printed on the console.
+# They are used for debugging purposes. The most important info is the output
+# of the task scheduler. The following table is a sample output.
+#
+# .. code-block:: c
+#
+# ----------------------------------------------------------------------
+# ------------------------------ [ Task Scheduler ]
+# ----------------------------------------------------------------------
+# | ID | Latency (ms) | Speed (GFLOPS) | Trials |
+# -------------------------------------------------
+# | 0 | 0.013 | 0.31 | 64 |
+# | 1 | 0.845 | 2.43 | 448 |
+# | 2 | 0.046 | -0.00 | 64 |
+# | 3 | 4.194 | 24.53 | 2112 |
+# | 4 | 0.109 | 9.21 | 64 |
+# | 5 | 1.759 | 29.27 | 896 |
+# | 6 | 0.083 | 6.01 | 64 |
+# | 7 | 3.084 | 33.38 | 7680 |
+# | 8 | 0.136 | 14.78 | 384 |
+# | 9 | 1.349 | 38.23 | 768 |
+# | 10 | 0.133 | 7.55 | 128 |
+# | 11 | 2.747 | 37.56 | 1536 |
+# | 12 | 0.338 | 11.87 | 192 |
+# | 13 | 1.295 | 40.00 | 704 |
+# | 14 | 0.482 | 4.16 | 256 |
+# | 15 | 2.686 | 38.56 | 1344 |
+# | 16 | 0.884 | 9.08 | 448 |
+# | 17 | 1.332 | 39.18 | 704 |
+# | 18 | 1.045 | 3.84 | 576 |
+# | 19 | 1.391 | 38.09 | 704 |
+# | 20 | 0.777 | 10.34 | 448 |
+# | 21 | 0.739 | 30.97 | 448 |
+# -------------------------------------------------
+# Estimated total latency: 38.347 ms Trials: 19992 Used time : 19260 s Next ID: 3
+#
+# This table lists the latency and (estimated) speed of all tasks.
+# It also lists the allocation of measurement trials for all tasks.
+# The last line prints the total weighted latency of these tasks,
+# which can be a rough estimation of the end-to-end execution time
+# of the network.
+# The last line also prints the total number of measurement trials,
+# total time spent on auto-tuning and the id of the next task to tune.
+#
+# There will also be some "dmlc::Error"s errors, because the
+# auto-scheduler will try some invalid schedules.
+# You can safely ignore them if the tuning can continue, because these
+# errors are isolated from the main process.
+#
+
+######################################################################
+# .. note:: Terminate the tuning earlier
+#
+# You can terminate the tuning earlier by forcibly killing this process.
+# As long as you get at least one valid schedule for each task in the log file,
+# you should be able to do the compilation (the secion below).
+#
+
+#################################################################
+# Other Tips
+# ----------
+# 1. During the tuning, the auto-scheduler needs to compile many programs and
+# extract feature from them. This part is CPU-intensive,
+# so a high-performance CPU with many cores is recommended for faster search.
+# 2. You can use :code:`python3 -m tvm.auto_scheduler.measure_record --mode distill -i log.json`
+# to distill the large log file and only save the best useful records.
+# 3. You can resume a search from the previous log file. You just need to
+# add a new argument :code:`load_log_file` when creating the task scheduler
+# in function :code:`run_tuning`. Say,
+# :code:`tuner = auto_scheduler.TaskScheduler(tasks, task_weights, load_log_file=log_file)`
+# 4. If you have multiple target CPUs, you can use all of them for measurements to
+# parallelize the measurements. Check this :ref:`section <tutorials-autotvm-scale-up-rpc-tracker>`
+# to learn how to use the RPC Tracker and RPC Server.
+# To use the RPC Tracker in auto-scheduler, replace the runner in :code:`TuningOptions`
+# with :any:`auto_scheduler.RPCRunner`.
diff --git a/docs/v0.8.0/_downloads/1a26d790f7b98309d730181290dae3ee/deploy_prequantized_tflite.ipynb b/docs/v0.8.0/_downloads/1a26d790f7b98309d730181290dae3ee/deploy_prequantized_tflite.ipynb
new file mode 100644
index 000000000..d64f71f38
--- /dev/null
+++ b/docs/v0.8.0/_downloads/1a26d790f7b98309d730181290dae3ee/deploy_prequantized_tflite.ipynb
@@ -0,0 +1,319 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "%matplotlib inline"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\nDeploy a Framework-prequantized Model with TVM - Part 3 (TFLite)\n================================================================\n**Author**: `Siju Samuel <https://github.com/siju-samuel>`_\n\nWelcome to part 3 of the Deploy Framework-Prequantized Model with TVM tutorial.\nIn this part, we will start with a Quantized TFLite graph and then compile and execute it via TVM.\n\n\nFor more details on quantizing the model using TFLite, readers are encouraged to\ngo through `Convert [...]
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Necessary imports\n-----------------\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "import os\n\nimport numpy as np\nimport tflite\n\nimport tvm\nfrom tvm import relay"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Download pretrained Quantized TFLite model\n------------------------------------------\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "# Download mobilenet V2 TFLite model provided by Google\nfrom tvm.contrib.download import download_testdata\n\nmodel_url = (\n \"https://storage.googleapis.com/download.tensorflow.org/models/\"\n \"tflite_11_05_08/mobilenet_v2_1.0_224_quant.tgz\"\n)\n\n# Download model tar file and extract it to get mobilenet_v2_1.0_224.tflite\nmodel_path = download_testdata(\n model_url, \"mobilenet_v2_1.0_224_quant.tgz\", module=[\"tf\", \"official\"]\n)\nmodel_dir = os.path.dirname(m [...]
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Utils for downloading and extracting zip files\n----------------------------------------------\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "def extract(path):\n import tarfile\n\n if path.endswith(\"tgz\") or path.endswith(\"gz\"):\n dir_path = os.path.dirname(path)\n tar = tarfile.open(path)\n tar.extractall(path=dir_path)\n tar.close()\n else:\n raise RuntimeError(\"Could not decompress the file: \" + path)\n\n\nextract(model_path)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Load a test image\n-----------------\n\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Get a real image for e2e testing\n--------------------------------\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "def get_real_image(im_height, im_width):\n from PIL import Image\n\n repo_base = \"https://github.com/dmlc/web-data/raw/main/tensorflow/models/InceptionV1/\"\n img_name = \"elephant-299.jpg\"\n image_url = os.path.join(repo_base, img_name)\n img_path = download_testdata(image_url, img_name, module=\"data\")\n image = Image.open(img_path).resize((im_height, im_width))\n x = np.array(image).astype(\"uint8\")\n data = np.reshape(x, (1, im_height, im_width, 3 [...]
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Load a tflite model\n-------------------\n\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now we can open mobilenet_v2_1.0_224.tflite\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "tflite_model_file = os.path.join(model_dir, \"mobilenet_v2_1.0_224_quant.tflite\")\ntflite_model_buf = open(tflite_model_file, \"rb\").read()\n\n# Get TFLite model from buffer\ntry:\n import tflite\n\n tflite_model = tflite.Model.GetRootAsModel(tflite_model_buf, 0)\nexcept AttributeError:\n import tflite.Model\n\n tflite_model = tflite.Model.Model.GetRootAsModel(tflite_model_buf, 0)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Lets run TFLite pre-quantized model inference and get the TFLite prediction.\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "def run_tflite_model(tflite_model_buf, input_data):\n \"\"\"Generic function to execute TFLite\"\"\"\n try:\n from tensorflow import lite as interpreter_wrapper\n except ImportError:\n from tensorflow.contrib import lite as interpreter_wrapper\n\n input_data = input_data if isinstance(input_data, list) else [input_data]\n\n interpreter = interpreter_wrapper.Interpreter(model_content=tflite_model_buf)\n interpreter.allocate_tensors()\n\n input_d [...]
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Lets run TVM compiled pre-quantized model inference and get the TVM prediction.\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "def run_tvm(lib):\n from tvm.contrib import graph_executor\n\n rt_mod = graph_executor.GraphModule(lib[\"default\"](tvm.cpu(0)))\n rt_mod.set_input(\"input\", data)\n rt_mod.run()\n tvm_res = rt_mod.get_output(0).numpy()\n tvm_pred = np.squeeze(tvm_res).argsort()[-5:][::-1]\n return tvm_pred, rt_mod"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "TFLite inference\n----------------\n\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Run TFLite inference on the quantized model.\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "tflite_res = run_tflite_model(tflite_model_buf, data)\ntflite_pred = np.squeeze(tflite_res).argsort()[-5:][::-1]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "TVM compilation and inference\n-----------------------------\n\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We use the TFLite-Relay parser to convert the TFLite pre-quantized graph into Relay IR. Note that\nfrontend parser call for a pre-quantized model is exactly same as frontend parser call for a FP32\nmodel. We encourage you to remove the comment from print(mod) and inspect the Relay module. You\nwill see many QNN operators, like, Requantize, Quantize and QNN Conv2D.\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "dtype_dict = {\"input\": data.dtype.name}\nshape_dict = {\"input\": data.shape}\n\nmod, params = relay.frontend.from_tflite(tflite_model, shape_dict=shape_dict, dtype_dict=dtype_dict)\n# print(mod)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Lets now the compile the Relay module. We use the \"llvm\" target here. Please replace it with the\ntarget platform that you are interested in.\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "target = \"llvm\"\nwith tvm.transform.PassContext(opt_level=3):\n lib = relay.build_module.build(mod, target=target, params=params)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Finally, lets call inference on the TVM compiled module.\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "tvm_pred, rt_mod = run_tvm(lib)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Accuracy comparison\n-------------------\n\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Print the top-5 labels for MXNet and TVM inference.\nChecking the labels because the requantize implementation is different between\nTFLite and Relay. This cause final output numbers to mismatch. So, testing accuracy via labels.\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "print(\"TVM Top-5 labels:\", tvm_pred)\nprint(\"TFLite Top-5 labels:\", tflite_pred)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Measure performance\n-------------------\nHere we give an example of how to measure performance of TVM compiled models.\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "n_repeat = 100 # should be bigger to make the measurement more accurate\ndev = tvm.cpu(0)\nprint(rt_mod.benchmark(dev, number=1, repeat=n_repeat))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<div class=\"alert alert-info\"><h4>Note</h4><p>Unless the hardware has special support for fast 8 bit instructions, quantized models are\n not expected to be any faster than FP32 models. Without fast 8 bit instructions, TVM does\n quantized convolution in 16 bit, even if the model itself is 8 bit.\n\n For x86, the best performance can be achieved on CPUs with AVX512 instructions set.\n In this case, TVM utilizes the fastest available 8 bit instructions for the given target. [...]
+ ]
+ }
+ ],
+ "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": "3.6.9"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/docs/v0.8.0/_downloads/1e482ba1190961191e3a0bdbd0585faa/intrin_math.ipynb b/docs/v0.8.0/_downloads/1e482ba1190961191e3a0bdbd0585faa/intrin_math.ipynb
new file mode 100644
index 000000000..d1f0b008a
--- /dev/null
+++ b/docs/v0.8.0/_downloads/1e482ba1190961191e3a0bdbd0585faa/intrin_math.ipynb
@@ -0,0 +1,169 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "%matplotlib inline"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\nIntrinsics and Math Functions\n=============================\n**Author**: `Tianqi Chen <https://tqchen.github.io>`_\n\nWhile TVM supports basic arithmetic operations. In many cases\nusually we will need more complicated builtin functions.\nFor example :code:`exp` to take the exponential of the function.\n\nThese functions are target system dependent and may have different\nnames of different target platforms. In this tutorial, we will learn\nhow we can invoke these target spec [...]
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "from __future__ import absolute_import, print_function\nimport numpy as np\n\nimport tvm\nfrom tvm import te\nfrom tvm.ir import register_op_attr, register_intrin_lowering"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Direct Declare Extern Math Call\n-------------------------------\nThe most straight-forward way to call target specific function is via\nextern function call construct in tvm.\nIn the following example, we use :any:`tvm.tir.call_pure_extern` to call\n:code:`__expf` function, which is only available under CUDA.\n\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "n = te.var(\"n\")\nA = te.placeholder((n,), name=\"A\")\nB = te.compute(A.shape, lambda i: tvm.tir.call_pure_extern(\"float32\", \"__expf\", A[i]), name=\"B\")\ns = te.create_schedule(B.op)\nnum_thread = 64\nbx, tx = s[B].split(B.op.axis[0], factor=num_thread)\ns[B].bind(bx, te.thread_axis(\"blockIdx.x\"))\ns[B].bind(tx, te.thread_axis(\"threadIdx.x\"))\nf = tvm.build(s, [A, B], \"cuda\", name=\"myexp\")\nprint(f.imported_modules[0].get_source())"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Unified Intrinsic Call\n----------------------\nThe above code verifies that direct external call can be used to\ncall into device specific functions.\nHowever, the above way only works for CUDA target with float type.\nIdeally, we want to write same code for any device and any data type.\n\nTVM intrinsic provides the user a mechanism to achieve this, and this\nis the recommended way to solve the problem.\nThe following code use te.exp instead, which create an intrinsic call\n:p [...]
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "n = te.var(\"n\")\nA = te.placeholder((n,), name=\"A\")\nB = te.compute(A.shape, lambda i: te.exp(A[i]), name=\"B\")\ns = te.create_schedule(B.op)\nnum_thread = 64\nbx, tx = s[B].split(B.op.axis[0], factor=num_thread)\ns[B].bind(bx, te.thread_axis(\"blockIdx.x\"))\ns[B].bind(tx, te.thread_axis(\"threadIdx.x\"))\nfcuda = tvm.build(s, [A, B], \"cuda\", name=\"myexp\")\nprint(fcuda.imported_modules[0].get_source())"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can find that the code works for both CUDA and opencl.\nThe same te.exp can also be used for float64 data types.\n\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "fopencl = tvm.build(s, [A, B], \"opencl\", name=\"myexp\")\nprint(fopencl.imported_modules[0].get_source())"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Intrinsic Lowering Rule\n-----------------------\nWhen :py:func:`tvm.te.exp` is called, TVM creates an intrinsic Call Expr.\nTVM uses transformation rules to transform the intrinsic\ncall to device specific extern calls.\n\nTVM also allows user to customize the rules during runtime.\nThe following example customizes CUDA lowering rule for :code:`exp`.\n\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "def my_cuda_math_rule(op):\n \"\"\"Customized CUDA intrinsic lowering rule\"\"\"\n assert isinstance(op, tvm.tir.Call)\n name = op.op.name\n assert name.startswith(\"tir.\")\n dispatch_name = name[4:]\n if op.dtype == \"float32\":\n # call float function\n return tvm.tir.call_pure_extern(\"float32\", \"%sf\" % dispatch_name, op.args[0])\n elif op.dtype == \"float64\":\n # call double function\n return tvm.tir.call_pure_extern(\"fl [...]
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Register the rule to TVM with override option to override existing rule.\nNotice the difference between the printed code from previous one:\nour new rule uses math function :code:`expf` instead of\nfast math version :code:`__expf`.\n\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "fcuda = tvm.build(s, [A, B], \"cuda\", name=\"myexp\")\nprint(fcuda.imported_modules[0].get_source())"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Add Your Own Intrinsic\n----------------------\nIf there is an intrinsic that is not provided by TVM.\nUser can easily add new intrinsic by using the intrinsic rule system.\nThe following example add an intrinsic :code:`mylog` to the system.\n\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "def mylog(x):\n \"\"\"customized log intrinsic function\"\"\"\n return tvm.tir.call_intrin(x.dtype, \"tir.mylog\", x)\n\n\ndef my_cuda_mylog_rule(op):\n \"\"\"CUDA lowering rule for log\"\"\"\n if op.dtype == \"float32\":\n return tvm.tir.call_pure_extern(\"float32\", \"logf\", op.args[0])\n elif op.dtype == \"float64\":\n return tvm.tir.call_pure_extern(\"float64\", \"log\", op.args[0])\n else:\n return op\n\n\n# new op registration is tri [...]
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Summary\n-------\n- TVM can call extern target dependent math function.\n- Use intrinsic to defined a unified interface for the functions.\n- For more intrinsics available in tvm, take a look at :any:`tvm.tir`\n- You can customize the intrinsic behavior by defining your own rules.\n\n\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": "3.6.9"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/docs/v0.8.0/_downloads/1ee0b869c5082223c5dfbb0fe4574252/matrix_multiply.ipynb b/docs/v0.8.0/_downloads/1ee0b869c5082223c5dfbb0fe4574252/matrix_multiply.ipynb
new file mode 100644
index 000000000..d4b767a63
--- /dev/null
+++ b/docs/v0.8.0/_downloads/1ee0b869c5082223c5dfbb0fe4574252/matrix_multiply.ipynb
@@ -0,0 +1,311 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "%matplotlib inline"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n\nSimple Matrix Multiply\n======================\n**Author**: `Thierry Moreau <https://homes.cs.washington.edu/~moreau/>`_\n\nIn this tutorial, we will build on top of the `vta-get-started` tutorial\nand introduce additional concepts required to implement matrix multiplication\non VTA with the TVM workflow.\n\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "RPC Setup\n---------\nWe start by programming the Pynq's FPGA and building its RPC runtime\nas we did in the VTA introductory tutorial.\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "from __future__ import absolute_import, print_function\n\nimport os\nimport tvm\nfrom tvm import te\nimport vta\nimport numpy as np\nfrom tvm import rpc\nfrom tvm.contrib import utils\nfrom vta.testing import simulator\n\n# Load VTA parameters from the 3rdparty/vta-hw/config/vta_config.json file\nenv = vta.get_env()\n\n# We read the Pynq RPC host IP address and port number from the OS environment\nhost = os.environ.get(\"VTA_RPC_HOST\", \"192.168.2.99\")\nport = int(os.environ.g [...]
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Computation Declaration\n-----------------------\nIn this example we describe a simple matrix multiplication addition, which\nrequires multiple computation stages, as shown in the dataflow diagram below.\nFirst we describe the input tensors :code:`A` and :code:`B` that are living\nin main memory.\nSecond, we need to declare intermediate tensors :code:`A_buf` and\n:code:`B_buf`, which will live in VTA's on-chip buffers.\nHaving this extra computational stage allows us to explicit [...]
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Data Layout\n~~~~~~~~~~~\nWe describe the placeholder tensors :code:`A`, and :code:`B` in a tiled data\nformat to match the data layout requirements imposed by the VTA tensor core.\n\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<div class=\"alert alert-info\"><h4>Note</h4><p>**Data Tiling**\n\n One source of complexity when targeting accelerators is to make sure\n that the data layout matches the layout imposed by the accelerator design.\n VTA is designed around a *tensor core* that performs, one matrix-matrix\n operation per cycle between an activation matrix and a weight matrix,\n adding the result matrix to an accumulator matrix, as shown in the\n figure below.\n\n .. image:: https://raw.gith [...]
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<div class=\"alert alert-info\"><h4>Note</h4><p>**Data Types**\n\n It's important to not only match the inner-tile\n dimension of VTA's tensor core, but also to match the specific data types\n expected by VTA.\n VTA for now only supports fixed point data types, which integer width is\n specified in the :code:`vta_config.json` file by :code:`INP_WIDTH` and\n :code:`WGT_WIDTH` for the activations and weights data types respectively.\n In addition, the accumulator data type [...]
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "# Output channel factor m - total 16x16=256 output channels\nm = 16\n# Input channel factor n - total 16x16=256 input channels\nn = 16\n# Batch factor o (we use single batch inference)\no = 1\n# A placeholder tensor in tiled data format\nA = te.placeholder((o, n, env.BATCH, env.BLOCK_IN), name=\"A\", dtype=env.inp_dtype)\n# B placeholder tensor in tiled data format\nB = te.placeholder((m, n, env.BLOCK_OUT, env.BLOCK_IN), name=\"B\", dtype=env.wgt_dtype)\n# A copy buffer\nA_buf = [...]
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Matrix Multiplication\n~~~~~~~~~~~~~~~~~~~~~\nNow we're ready to describe the matrix multiplication result tensor :code:`C`,\nwith another compute operation.\nThe compute function takes the shape of the tensor, as well as a lambda\nfunction that describes the computation rule for each position of the tensor.\n\nIn order to implement matrix multiplication, the lambda function needs to\ninclude a reduction formula over the input channel dimension axes.\nTo create a reduction formu [...]
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "# Outer input feature reduction axis\nko = te.reduce_axis((0, n), name=\"ko\")\n# Inner input feature reduction axis\nki = te.reduce_axis((0, env.BLOCK_IN), name=\"ki\")\n# Describe the in-VTA matrix multiplication\nC_buf = te.compute(\n (o, m, env.BATCH, env.BLOCK_OUT),\n lambda bo, co, bi, ci: te.sum(\n A_buf[bo, ko, bi, ki].astype(env.acc_dtype) * B_buf[co, ko, ci, ki].astype(env.acc_dtype),\n axis=[ko, ki],\n ),\n name=\"C_buf\",\n)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Casting the Results\n~~~~~~~~~~~~~~~~~~~\nAfter the computation is done, we'll need to send the results computed by VTA\nback to main memory.\n\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<div class=\"alert alert-info\"><h4>Note</h4><p>**Memory Store Restrictions**\n\n One specificity of VTA is that it only supports DRAM stores in the narrow\n :code:`env.inp_dtype` data type format.\n This lets us reduce the data footprint for memory transfers, but also lets\n us quantize the wide accumulator data type down to a data format that\n matches the input activation data type.\n This means that in the context of neural network inference, the outputs\n of a given [...]
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "# Cast to output type, and send to main memory\nC = te.compute(\n (o, m, env.BATCH, env.BLOCK_OUT), lambda *i: C_buf(*i).astype(env.inp_dtype), name=\"C\"\n)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This concludes the computation declaration part of this tutorial.\n\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Scheduling the Computation\n--------------------------\nWhile the above lines describes the computation rule, we can obtain\n:code:`C` in many ways.\nTVM asks the user to provide an implementation of the computation called\n*schedule*.\n\nA schedule is a set of transformations to an original computation that\ntransforms the implementation of the computation without affecting\ncorrectness.\nThis simple VTA programming tutorial aims to demonstrate basic schedule\ntransformations t [...]
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Default Schedule\n~~~~~~~~~~~~~~~~\nAfter we construct the schedule, by default the schedule computes\n:code:`C` in the following way:\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "# Let's take a look at the generated schedule\ns = te.create_schedule(C.op)\nprint(tvm.lower(s, [A, B, C], simple_mode=True))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Although this schedule makes sense, it won't compile to VTA.\nIn order to obtain correct code generation, we need to apply scheduling\nprimitives and code annotation that will transform the schedule into\none that can be directly lowered onto VTA hardware intrinsics.\nThose include:\n\n - DMA copy operations which will take globally-scoped tensors and copy\n those into locally-scoped tensors.\n - Tensor operations that will perform the matrix multiplication.\n\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Buffer Scopes\n~~~~~~~~~~~~~\nFirst, we set the scope of the buffers to tell TVM that these buffers\nwill be living in the VTA's on-chip SRAM caches.\nBelow, we tell TVM that :code:`A_buf`, :code:`B_buf`, :code:`C_buf`\nwill respectively live in VTA's on-chip input, weight and accumulator\nmemory.\n\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<div class=\"alert alert-info\"><h4>Note</h4><p>**VTA's On-Chip SRAMs**\n\n VTA has three different memory scopes, each corresponding to different\n on-chip SRAM buffers.\n\n - :code:`env.inp_scope`: Input buffer, which is a read-only SRAM buffer\n that stores input matrices of shape :code:`(env.BATCH, env.BLOCK_IN)`\n of type :code:`env.inp_dtype`. The input buffer contains\n `2 ^ LOG_INP_BUFF_SIZE` matrix elements (as specified in the\n :code:`vta_config.js [...]
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "# Set the intermediate tensor's scope to VTA's on-chip buffers\ns[A_buf].set_scope(env.inp_scope)\ns[B_buf].set_scope(env.wgt_scope)\ns[C_buf].set_scope(env.acc_scope)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "DMA Transfers\n~~~~~~~~~~~~~\nWe need to schedule DMA transfers to move data living in DRAM to\nand from the VTA on-chip buffers.\nThis can be achieved using the :code:`compute_at` schedule primitive\nwhich nests the copying of the buffers into the computation loop\nthat performs the matrix multiplication.\n\nWe insert :code:`dma_copy` pragmas to indicate to the compiler\nthat the copy operations will be performed in bulk via DMA,\nwhich is common in hardware accelerators.\nFina [...]
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "# Move buffer copy into matrix multiply loop\ns[A_buf].compute_at(s[C_buf], ko)\ns[B_buf].compute_at(s[C_buf], ko)\n\n# Tag the buffer copies with the DMA pragma to insert a DMA transfer\ns[A_buf].pragma(s[A_buf].op.axis[0], env.dma_copy)\ns[B_buf].pragma(s[B_buf].op.axis[0], env.dma_copy)\ns[C].pragma(s[C].op.axis[0], env.dma_copy)\n\n# Let's take a look at the transformed schedule\nprint(tvm.lower(s, [A, B, C], simple_mode=True))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Tensorization\n~~~~~~~~~~~~~\nThe last step of the schedule transformation consists in applying\n*tensorization* to our schedule.\nTensorization is analogous to vectorization, but extends the concept\nto a higher-dimensional unit of computation.\nConsequently, tensorization imposes data layout constraints as discussed\nearlier when declaring the data layout input placeholders.\nWe've already arranged our tensors in a tiled format, so the next thing\nwe need to perform is loop re [...]
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "s[C_buf].reorder(\n ko, s[C_buf].op.axis[0], s[C_buf].op.axis[1], s[C_buf].op.axis[2], s[C_buf].op.axis[3], ki\n)\ns[C_buf].tensorize(s[C_buf].op.axis[2], env.gemm)\n\n# Let's take a look at the finalized schedule\nprint(vta.lower(s, [A, B, C], simple_mode=True))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This concludes the scheduling portion of this tutorial.\n\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "TVM Compilation\n---------------\nAfter we have finished specifying the schedule, we can compile it\ninto a TVM function.\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "# Build GEMM VTA kernel\nmy_gemm = vta.build(s, [A, B, C], \"ext_dev\", env.target_host, name=\"my_gemm\")\n\n# Write the compiled module into an object file.\ntemp = utils.tempdir()\nmy_gemm.save(temp.relpath(\"gemm.o\"))\n\n# Send the executable over RPC\nremote.upload(temp.relpath(\"gemm.o\"))\n\n# Load the compiled module\nf = remote.load_module(\"gemm.o\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Running the Function\n--------------------\nThe compiled TVM function uses a concise C API and can be invoked from\ncode language.\n\nTVM provides an array API in python to aid quick testing and prototyping.\nThe array API is based on `DLPack <https://github.com/dmlc/dlpack>`_ standard.\n\n- We first create a remote context (for remote execution on the Pynq).\n- Then :code:`tvm.nd.array` formats the data accordingly.\n- :code:`f()` runs the actual computation.\n- :code:`numpy()` [...]
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "# Get the remote device context\nctx = remote.ext_dev(0)\n\n# Initialize the A and B arrays randomly in the int range of (-128, 128]\nA_orig = np.random.randint(-128, 128, size=(o * env.BATCH, n * env.BLOCK_IN)).astype(A.dtype)\nB_orig = np.random.randint(-128, 128, size=(m * env.BLOCK_OUT, n * env.BLOCK_IN)).astype(B.dtype)\n\n# Apply packing to the A and B arrays from a 2D to a 4D packed layout\nA_packed = A_orig.reshape(o, env.BATCH, n, env.BLOCK_IN).transpose((0, 2, 1, 3))\n [...]
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Verifying Correctness\n---------------------\nCompute the reference result with numpy and assert that the output of the\nmatrix multiplication indeed is correct\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "# Compute reference result with numpy\nC_ref = np.dot(A_orig.astype(env.acc_dtype), B_orig.T.astype(env.acc_dtype)).astype(C.dtype)\nC_ref = C_ref.reshape(o, env.BATCH, m, env.BLOCK_OUT).transpose((0, 2, 1, 3))\nnp.testing.assert_equal(C_ref, C_nd.numpy())\n\n# Print stats\nif env.TARGET in [\"sim\", \"tsim\"]:\n sim_stats = simulator.stats()\n print(\"Execution statistics:\")\n for k, v in sim_stats.items():\n print(\"\\t{:<16}: {:>16}\".format(k, v))\n\nprint(\ [...]
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Summary\n-------\nThis tutorial showcases the TVM workflow to implement a simple matrix\nmultiplication example on VTA.\nThe general workflow includes:\n\n- Programming the FPGA with the VTA bitstream over RPC.\n- Describing matrix multiplication via a series of computations.\n- Describing how we want to perform the computation using schedule primitives.\n- Compiling the function to the VTA target.\n- Running the compiled module and verifying it against a numpy implementation.\n\n\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": "3.6.9"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/docs/v0.8.0/_downloads/1f4943aed1aa607b2775c18b1d71db10/from_pytorch.ipynb b/docs/v0.8.0/_downloads/1f4943aed1aa607b2775c18b1d71db10/from_pytorch.ipynb
new file mode 100644
index 000000000..9cc61ac0b
--- /dev/null
+++ b/docs/v0.8.0/_downloads/1f4943aed1aa607b2775c18b1d71db10/from_pytorch.ipynb
@@ -0,0 +1,162 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "%matplotlib inline"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\nCompile PyTorch Models\n======================\n**Author**: `Alex Wong <https://github.com/alexwong/>`_\n\nThis article is an introductory tutorial to deploy PyTorch models with Relay.\n\nFor us to begin with, PyTorch should be installed.\nTorchVision is also required since we will be using it as our model zoo.\n\nA quick solution is to install via pip\n\n.. code-block:: bash\n\n pip install torch==1.7.0\n pip install torchvision==0.8.1\n\nor please refer to official sit [...]
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "import tvm\nfrom tvm import relay\n\nimport numpy as np\n\nfrom tvm.contrib.download import download_testdata\n\n# PyTorch imports\nimport torch\nimport torchvision"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Load a pretrained PyTorch model\n-------------------------------\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "model_name = \"resnet18\"\nmodel = getattr(torchvision.models, model_name)(pretrained=True)\nmodel = model.eval()\n\n# We grab the TorchScripted model via tracing\ninput_shape = [1, 3, 224, 224]\ninput_data = torch.randn(input_shape)\nscripted_model = torch.jit.trace(model, input_data).eval()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Load a test image\n-----------------\nClassic cat example!\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "from PIL import Image\n\nimg_url = \"https://github.com/dmlc/mxnet.js/blob/main/data/cat.png?raw=true\"\nimg_path = download_testdata(img_url, \"cat.png\", module=\"data\")\nimg = Image.open(img_path).resize((224, 224))\n\n# Preprocess the image and convert to tensor\nfrom torchvision import transforms\n\nmy_preprocess = transforms.Compose(\n [\n transforms.Resize(256),\n transforms.CenterCrop(224),\n transforms.ToTensor(),\n transforms.Normalize(m [...]
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Import the graph to Relay\n-------------------------\nConvert PyTorch graph to Relay graph. The input name can be arbitrary.\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "input_name = \"input0\"\nshape_list = [(input_name, img.shape)]\nmod, params = relay.frontend.from_pytorch(scripted_model, shape_list)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Relay Build\n-----------\nCompile the graph to llvm target with given input specification.\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "target = tvm.target.Target(\"llvm\", host=\"llvm\")\ndev = tvm.cpu(0)\nwith tvm.transform.PassContext(opt_level=3):\n lib = relay.build(mod, target=target, params=params)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Execute the portable graph on TVM\n---------------------------------\nNow we can try deploying the compiled model on target.\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "from tvm.contrib import graph_executor\n\ndtype = \"float32\"\nm = graph_executor.GraphModule(lib[\"default\"](dev))\n# Set inputs\nm.set_input(input_name, tvm.nd.array(img.astype(dtype)))\n# Execute\nm.run()\n# Get outputs\ntvm_output = m.get_output(0)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Look up synset name\n-------------------\nLook up prediction top 1 index in 1000 class synset.\n\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "synset_url = \"\".join(\n [\n \"https://raw.githubusercontent.com/Cadene/\",\n \"pretrained-models.pytorch/master/data/\",\n \"imagenet_synsets.txt\",\n ]\n)\nsynset_name = \"imagenet_synsets.txt\"\nsynset_path = download_testdata(synset_url, synset_name, module=\"data\")\nwith open(synset_path) as f:\n synsets = f.readlines()\n\nsynsets = [x.strip() for x in synsets]\nsplits = [line.split(\" \") for line in synsets]\nkey_to_classname = {spl[0]: \" [...]
+ ]
+ }
+ ],
+ "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": "3.6.9"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/docs/v0.8.0/_downloads/21a9dd883b196be58ca1c5cd02700274/deploy_model_on_android.py b/docs/v0.8.0/_downloads/21a9dd883b196be58ca1c5cd02700274/deploy_model_on_android.py
new file mode 100644
index 000000000..c7b610d5d
--- /dev/null
+++ b/docs/v0.8.0/_downloads/21a9dd883b196be58ca1c5cd02700274/deploy_model_on_android.py
@@ -0,0 +1,360 @@
+# 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.
+
+"""
+.. _tutorial-deploy-model-on-android:
+
+Deploy the Pretrained Model on Android
+=======================================
+**Author**: `Tomohiro Kato <https://tkat0.github.io/>`_
+
+This is an example of using Relay to compile a keras model and deploy it on Android device.
+"""
+
+import os
+import numpy as np
+from PIL import Image
+import keras
+from keras.applications.mobilenet_v2 import MobileNetV2
+import tvm
+from tvm import te
+import tvm.relay as relay
+from tvm import rpc
+from tvm.contrib import utils, ndk, graph_executor as runtime
+from tvm.contrib.download import download_testdata
+
+
+######################################################################
+# Setup Environment
+# -----------------
+# Since there are many required packages for Android, it is recommended to use the official Docker Image.
+#
+# First, to build and run Docker Image, we can run the following command.
+#
+# .. code-block:: bash
+#
+# git clone --recursive https://github.com/apache/tvm tvm
+# cd tvm
+# docker build -t tvm.demo_android -f docker/Dockerfile.demo_android ./docker
+# docker run --pid=host -h tvm -v $PWD:/workspace \
+# -w /workspace -p 9190:9190 --name tvm -it tvm.demo_android bash
+#
+# You are now inside the container. The cloned TVM directory is mounted on /workspace.
+# At this time, mount the 9190 port used by RPC described later.
+#
+# .. note::
+#
+# Please execute the following steps in the container.
+# We can execute :code:`docker exec -it tvm bash` to open a new terminal in the container.
+#
+# Next we build the TVM.
+#
+# .. code-block:: bash
+#
+# mkdir build
+# cd build
+# cmake -DUSE_LLVM=llvm-config-8 \
+# -DUSE_RPC=ON \
+# -DUSE_SORT=ON \
+# -DUSE_VULKAN=ON \
+# -DUSE_GRAPH_EXECUTOR=ON \
+# ..
... 1499894 lines suppressed ...