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[PyTorch]Add PyTorchTVM: compile torchscript to tvm and export as pyt…
…orch_op (#8777) * add pt_op * add compile api * perf: support set_output_zero_copy * fix: cpu device_id mismatch * fix: pt_class test script * refactor: unify namespace to tvm.contrib.torch * add ASF header * build: set pt tvmdsoop default off * build: remove unset_log_macros.h * refactor: change header order * refactor: fix python code format * style: resolve pylint issues * style: add blank line * style: fix pylint invalid_name * trigger CI * test: add more test scripts * style: add empty lines * test: update test for trace tvm module * style: fix linting issues * style: remove single quote * style: disable pylint invalid-name * trigger CI * trigger CI Co-authored-by: kongroo <imjcqt@gmail.com>
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# 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. | ||
cmake_minimum_required(VERSION 3.2) | ||
project(pt_tvmdsoop C CXX) | ||
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set(BUILD_PT_TVMDSOOP_ONLY ON) | ||
set(CMAKE_CURRENT_SOURCE_DIR ${TVM_ROOT}) | ||
set(CMAKE_CURRENT_BINARY_DIR ${TVM_ROOT}/build) | ||
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include_directories(SYSTEM ${TVM_ROOT}/3rdparty/dlpack/include/) | ||
include_directories(SYSTEM ${TVM_ROOT}/3rdparty/dmlc-core/include/) | ||
include_directories(${TVM_ROOT}/include) | ||
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link_directories(${TVM_ROOT}/build) | ||
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include(${TVM_ROOT}/cmake/utils/Utils.cmake) | ||
include(${TVM_ROOT}/cmake/utils/FindCUDA.cmake) | ||
include(${TVM_ROOT}/cmake/modules/CUDA.cmake) | ||
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include(${TVM_ROOT}/cmake/modules/contrib/PT_TVMDSOOP.cmake) |
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#!/bin/bash | ||
# Licensed to the Apache Software Foundation (ASF) under one | ||
# or more contributor license agreements. See the NOTICE file | ||
# distributed with this work for additional information | ||
# regarding copyright ownership. The ASF licenses this file | ||
# to you under the Apache License, Version 2.0 (the | ||
# "License"); you may not use this file except in compliance | ||
# with the License. You may obtain a copy of the License at | ||
# | ||
# http://www.apache.org/licenses/LICENSE-2.0 | ||
# | ||
# Unless required by applicable law or agreed to in writing, | ||
# software distributed under the License is distributed on an | ||
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY | ||
# KIND, either express or implied. See the License for the | ||
# specific language governing permissions and limitations | ||
# under the License. | ||
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TVM_ROOT=$(cd $(dirname $0)/../..; pwd) | ||
echo "TVM_ROOT=${TVM_ROOT}" | ||
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export PYTHONPATH=${TVM_ROOT}/python | ||
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if [ ! -f $TVM_ROOT/build/libtvm.so ]; then | ||
echo "$TVM_ROOT/build/libtvm.so missing" | ||
exit 1 | ||
fi | ||
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if [ ! -f $TVM_ROOT/build/libtvm_runtime.so ]; then | ||
echo "$TVM_ROOT/build/libtvm_runtime.so missing" | ||
exit 1 | ||
fi | ||
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python3 -c "import tvm; print(tvm.runtime.enabled('gpu'))" | grep -e 1 | ||
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if [ "$?" -eq 0 ]; then | ||
echo "Build PT_TVMDSOOP with gpu support and execute tests" | ||
CMAKE_OPTIONS="-DUSE_CUDA=ON -DUSE_CUDNN=ON -DPython3_EXECUTABLE=python3 -DTVM_ROOT=${TVM_ROOT}" | ||
mkdir -p build | ||
cd build; cmake .. ${CMAKE_OPTIONS} && make | ||
cp *.so $TVM_ROOT/build/ | ||
cd .. | ||
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LD_LIBRARY_PATH=${TVM_ROOT}/build:./build:$LD_LIBRARY_PATH python3 -m pytest -v ./tests | ||
fi | ||
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#!/usr/bin/env python | ||
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# 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. | ||
"""Test script for torch module""" | ||
import torch | ||
import time | ||
import tvm | ||
from tvm.contrib.torch import compile | ||
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class Model(torch.nn.Module): | ||
def __init__(self): | ||
super().__init__() | ||
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def forward(self, x: torch.Tensor): | ||
return x * x | ||
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model = Model() | ||
x = torch.rand([1, 3, 224, 224]) | ||
model_jit = torch.jit.trace(model, x) | ||
print(model_jit.graph) | ||
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print("run torchscript...") | ||
for i in range(20): | ||
t = time.time() | ||
model_jit(x) | ||
print(time.time() - t) | ||
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option = { | ||
"input_infos": [ | ||
("x", (1, 3, 224, 224)), | ||
], | ||
"default_dtype": "float16", | ||
"export_dir": "pytorch_compiled", | ||
"num_outputs": 1, | ||
"tuning_n_trials": 1, # set zero to skip tuning | ||
"tuning_log_file": "tuning.log", | ||
"target": "llvm", | ||
"device": tvm.cpu(), | ||
} | ||
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pytorch_tvm_module = compile(model_jit, option) | ||
torch.jit.script(pytorch_tvm_module).save("model_tvm.pt") | ||
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print("Run PyTorch...") | ||
for i in range(20): | ||
t = time.time() | ||
outputs = pytorch_tvm_module.forward([x.cpu()]) | ||
print(1000 * (time.time() - t)) | ||
print(outputs[0].shape) |
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#!/usr/bin/env python | ||
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# 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. | ||
"""Test script for torch module""" | ||
import torch | ||
import time | ||
from torchvision.models import resnet50 | ||
import tvm | ||
from tvm.contrib.torch import compile | ||
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model = resnet50().half().cuda() | ||
x = torch.rand([1, 3, 224, 224]).half().cuda() | ||
model_jit = torch.jit.trace(model, x) | ||
print(model_jit.graph) | ||
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print("run torchscript...") | ||
for i in range(20): | ||
t = time.time() | ||
model_jit(x) | ||
torch.cuda.synchronize() | ||
print(time.time() - t) | ||
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option = { | ||
"input_infos": [ | ||
("x", (1, 3, 224, 224)), | ||
], | ||
"default_dtype": "float16", | ||
"export_dir": "pytorch_compiled", | ||
"num_outputs": 1, | ||
"tuning_n_trials": 1, # set zero to skip tuning | ||
"tuning_log_file": "tuning.log", | ||
"target": "cuda", | ||
"device": tvm.cuda(0), | ||
} | ||
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pytorch_tvm_module = compile(model_jit, option) | ||
torch.jit.script(pytorch_tvm_module).save("model_tvm.pt") | ||
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print("Run PyTorch...") | ||
for i in range(20): | ||
t = time.time() | ||
outputs = pytorch_tvm_module.forward([x]) | ||
torch.cuda.synchronize() | ||
print(1000 * (time.time() - t)) | ||
print(outputs[0].shape) |
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#!/usr/bin/env python | ||
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# 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. | ||
"""Test script for torch module""" | ||
import tempfile | ||
import os | ||
import logging | ||
import torch | ||
import numpy as np | ||
import tvm | ||
import tvm.testing | ||
from tvm import te, relay | ||
import tvm.contrib.torch | ||
from tvm.contrib import graph_runtime | ||
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TVM_ASSETS = ["mod.so", "graph.json", "params"] | ||
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def test_use_pt_graph_module(): | ||
"""main test function""" | ||
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def build_export_graph(device): | ||
"""relay build & export graph""" | ||
x = relay.var("x", shape=(10, 5)) | ||
y = relay.var("y", shape=(1, 5)) | ||
z = relay.add(x, y) | ||
z = relay.exp(z) | ||
func = relay.Function([x, y], z) | ||
x_data = np.random.rand(10, 5).astype("float32") | ||
y_data = np.random.rand(1, 5).astype("float32") | ||
params = {"y": y_data} | ||
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pt_device = torch.device(device) | ||
if pt_device.type == "cuda": | ||
target = "cuda" | ||
ctx = tvm.cuda(pt_device.index) | ||
else: | ||
target = "llvm" | ||
ctx = tvm.cpu(0) | ||
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graph, lib, params = relay.build(tvm.IRModule.from_expr(func), target=target, params=params) | ||
mod = graph_runtime.create(graph, lib, device=ctx) | ||
mod.set_input(**params) | ||
mod.set_input(x=x_data) | ||
mod.run() | ||
res = mod.get_output(0).asnumpy() | ||
ref_res = np.exp(y_data + x_data) | ||
tvm.testing.assert_allclose(res, ref_res, atol=1e-5, rtol=1e-5) | ||
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# export to tempdir | ||
export_dir = tempfile.mkdtemp("tvm_export") | ||
lib.export_library(os.path.join(export_dir, TVM_ASSETS[0])) | ||
with open(os.path.join(export_dir, TVM_ASSETS[1]), "w") as fout: | ||
fout.write(graph) | ||
with open(os.path.join(export_dir, TVM_ASSETS[2]), "wb") as fout: | ||
fout.write(relay.save_param_dict(params)) | ||
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return export_dir | ||
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def test_pt_run(device, trace=True, to_device=None): | ||
"""test add lib with Pytorch wrapper""" | ||
print("\n############## Test on device:", device, "#################") | ||
export_dir = build_export_graph(device) | ||
engine = tvm.contrib.torch.GraphModule(num_inputs=2, num_outputs=1).to(device) | ||
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x = np.random.rand(10, 5).astype("float32") | ||
y = np.random.rand(1, 5).astype("float32") | ||
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expect = np.exp(y + x) | ||
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def get_inputs_by_device(device): | ||
inps = [torch.Tensor(x), torch.Tensor(y)] | ||
if device == "cpu": | ||
return inps | ||
else: | ||
device_type, device_id = device.split(":") | ||
assert device_type == "cuda" | ||
return [inp.cuda(int(device_id)) for inp in inps] | ||
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assets = [os.path.join(export_dir, i) for i in TVM_ASSETS] | ||
engine.init((x.shape, y.shape), *assets) | ||
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outputs = engine.forward(get_inputs_by_device(device)) | ||
tvm.testing.assert_allclose(outputs[0].cpu(), expect, atol=1e-5, rtol=1e-5) | ||
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if trace: | ||
print("\n################ Test trace and load #################") | ||
scripted = torch.jit.script(engine) | ||
scripted_dir = tempfile.mkdtemp("scripted") | ||
scripted_path = os.path.join(scripted_dir, "model.pt") | ||
scripted.save(scripted_path) | ||
loaded = torch.jit.load(scripted_path) | ||
outputs = loaded.forward(get_inputs_by_device(device)) | ||
tvm.testing.assert_allclose(outputs[0].cpu(), expect, atol=1e-5, rtol=1e-5) | ||
del scripted | ||
del loaded | ||
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if to_device: | ||
print( | ||
"\n################ Test move from [{}] to [{}] #################".format( | ||
device, to_device | ||
) | ||
) | ||
engine = engine.to(to_device) | ||
outputs = engine.forward(get_inputs_by_device(to_device)) | ||
tvm.testing.assert_allclose(outputs[0].cpu(), expect, atol=1e-5, rtol=1e-5) | ||
del engine | ||
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test_pt_run(device="cuda:0", trace=True, to_device="cuda:1") | ||
test_pt_run(device="cpu", trace=True) | ||
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if __name__ == "__main__": | ||
test_use_pt_graph_module() |
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