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[NPU] npu support `transpose` (#31486)

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ascendrc
Meiyim 5 years ago committed by GitHub
parent 125201ee56
commit 7875bcb8f7
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4 changed files with 304 additions and 0 deletions
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4
paddle/fluid/operators/CMakeLists.txt
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@ -167,6 +167,10 @@ set(GLOB_OP_LIB ${OP_LIBRARY} CACHE INTERNAL "Global OP library")
add_subdirectory(benchmark)
cc_test(op_debug_string_test SRCS op_debug_string_test.cc DEPS elementwise_add_op)
if (WITH_ASCEND_CL)
cc_test(transpose_op_npu_test SRCS transpose_op_npu_test.cc DEPS op_registry transpose_op scope device_context enforce executor)
endif()
if(WITH_MKLDNN)
include(mkldnn/inplace_op_tests.cmake)

83
paddle/fluid/operators/transpose_op_npu.cc
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@ -0,0 +1,83 @@
/* Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
Licensed 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. */
#ifdef PADDLE_WITH_ASCEND_CL
#include <memory>
#include <string>
#include <iostream>
#include "paddle/fluid/operators/npu_op_runner.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/expand_op.h"
namespace paddle {
namespace operators {
template <typename DeviceContext, typename T>
class TransposeNPUKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto* x = ctx.Input<framework::LoDTensor>("X");
auto* out = ctx.Output<framework::LoDTensor>("Out");
std::vector<int> axis = ctx.Attr<std::vector<int>>("axis");
framework::NPUAttributeMap attr_input = {{"perm", axis}};
out->mutable_data<T>(ctx.device_context().GetPlace());
auto runner = NpuOpRunner("TransposeD", {*x}, {*out}, attr_input);
auto stream = ctx.template device_context<paddle::platform::NPUDeviceContext>().stream();
runner.Run(stream);
}
};
template <typename T>
class TransposeGradNPUKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext &ctx) const override {
auto* out_grad = ctx.Input<framework::LoDTensor>(framework::GradVarName("Out"));
auto* x_grad = ctx.Output<framework::LoDTensor>(framework::GradVarName("X"));
std::vector<int> axis = ctx.Attr<std::vector<int>>("axis");
std::vector<int> reversed_axis(axis);
for (size_t i = 0; i < axis.size(); i++) {
reversed_axis[axis[i]] = i;
}
framework::NPUAttributeMap attr_input = {{"perm", reversed_axis}};
auto runner = NpuOpRunner("TransposeD", {*out_grad}, {*x_grad}, attr_input);
auto stream = ctx.template device_context<paddle::platform::NPUDeviceContext>().stream();
runner.Run(stream);
}
};
}
}
namespace ops = paddle::operators;
REGISTER_OP_NPU_KERNEL(transpose,
ops::TransposeNPUKernel<paddle::platform::NPUDeviceContext, float>,
ops::TransposeNPUKernel<paddle::platform::NPUDeviceContext, paddle::platform::float16>,
ops::TransposeNPUKernel<paddle::platform::NPUDeviceContext, int>,
ops::TransposeNPUKernel<paddle::platform::NPUDeviceContext, uint8_t>,
ops::TransposeNPUKernel<paddle::platform::NPUDeviceContext, int8_t>
);
REGISTER_OP_NPU_KERNEL(transpose_grad,
ops::TransposeGradNPUKernel<float>,
ops::TransposeGradNPUKernel<paddle::platform::float16>,
ops::TransposeGradNPUKernel<int>,
ops::TransposeGradNPUKernel<uint8_t>,
ops::TransposeGradNPUKernel<int8_t>
);
#endif

143
paddle/fluid/operators/transpose_op_npu_test.cc
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@ -0,0 +1,143 @@
/* Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
Licensed 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. */
#ifndef _WIN32
#include <unistd.h>
#endif
#include <string>
#include <cmath>
#include <thread> // NOLINT
#include <vector>
#include <numeric>
#include <iostream>
#include "gtest/gtest.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/operator.h"
#include "paddle/fluid/framework/program_desc.h"
#include "paddle/fluid/operators/dropout_op.h"
#include "paddle/fluid/operators/math/math_function.h"
#include "paddle/fluid/string/printf.h"
namespace f = paddle::framework;
namespace p = paddle::platform;
namespace m = paddle::operators::math;
USE_OP(transpose);
USE_OP_DEVICE_KERNEL(transpose, NPU);
template <typename T>
void Compare(f::Scope* scope, const p::DeviceContext& ctx) {
// init
auto x = scope->Var("X");
auto out = scope->Var("Out");
auto* x_t = x->GetMutable<f::LoDTensor>();
auto* out_t = out->GetMutable<f::LoDTensor>();
auto place = ctx.GetPlace();
int dim0 = 2;
int dim1 = 3;
TensorFromVector(std::vector<T>({0, 1, 2, 3, 4, 5}), ctx, x_t);
ctx.Wait();
x_t->Resize({dim0, dim1});
out_t->Resize({dim0, dim1});
ctx.Wait();
out_t->mutable_data<T>(place);
ctx.Wait();
f::AttributeMap attrs = {
{"axis", std::vector<int>({1, 0})},
{"data_format", std::string("AnyLayout")}
};
auto op = f::OpRegistry::CreateOp("transpose", {{"X", {"X"}}},
{{"Out", {"Out"}}}, attrs);
ctx.Wait();
op->Run(*scope, place);
ctx.Wait();
std::vector<T> out_v;
TensorToVector(*out_t, ctx, &out_v);
ctx.Wait();
EXPECT_EQ(out_t->numel(), dim0 * dim1);
EXPECT_EQ(out_v[0], 0);
EXPECT_EQ(out_v[1], 3);
EXPECT_EQ(out_v[2], 1);
EXPECT_EQ(out_v[3], 4);
EXPECT_EQ(out_v[4], 2);
EXPECT_EQ(out_v[5], 5);
}
template <typename T>
void CompareGrad(f::Scope* scope, const p::DeviceContext& ctx) {
// init
auto x = scope->Var("X");
auto x_grad = scope->Var("X@GRAD");
auto out = scope->Var("Out");
auto out_grad = scope->Var("Out@GRAD");
auto* x_grad_t = x_grad->GetMutable<f::LoDTensor>();
auto* x_t = x->GetMutable<f::LoDTensor>();
auto* out_grad_t = out_grad->GetMutable<f::LoDTensor>();
auto* out_t = out->GetMutable<f::LoDTensor>();
int dim0 = 2;
int dim1 = 3;
auto place = ctx.GetPlace();
TensorFromVector(std::vector<T>({0, 1, 2, 3, 4, 5}), ctx, out_grad_t);
TensorFromVector(std::vector<T>({0, 1, 2, 3, 4, 5}), ctx, x_t);
ctx.Wait();
x_grad_t->Resize({dim0, dim1});
x_t->Resize({dim0, dim1});
out_grad_t->Resize({dim0, dim1});
out_t->Resize({dim0, dim1});
x_grad_t->mutable_data<T>(place);
out_t->mutable_data<T>(place);
ctx.Wait();
f::AttributeMap attrs = {
{"axis", std::vector<int>({1, 0})},
{"data_format", std::string("AnyLayout")}
};
auto op = f::OpRegistry::CreateOp(
"transpose_grad",
{{"Out@GRAD", {"Out@GRAD"}}, {"X", {"X"}}, {"Out", {"Out"}}},
{{"X@GRAD", {"X@GRAD"}}}, attrs);
op->Run(*scope, place);
ctx.Wait();
std::vector<T> out_v;
TensorToVector(*x_grad_t, ctx, &out_v);
ctx.Wait();
EXPECT_EQ(x_grad_t->numel(), dim0 * dim1);
EXPECT_EQ(out_v[0], 0);
EXPECT_EQ(out_v[1], 3);
EXPECT_EQ(out_v[2], 1);
EXPECT_EQ(out_v[3], 4);
EXPECT_EQ(out_v[4], 2);
EXPECT_EQ(out_v[5], 5);
}
TEST(transpose, NPU_fp32) {
f::Scope scope;
p::NPUDeviceContext ctx(p::NPUPlace(0));
Compare<float>(&scope, ctx);
}
TEST(transpose_grad, NPU_fp32) {
f::Scope scope;
p::NPUDeviceContext ctx(p::NPUPlace(0));
CompareGrad<float>(&scope, ctx);
}

74
python/paddle/fluid/tests/unittests/npu/test_transpose_op_npu.py
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@ -0,0 +1,74 @@
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed 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.
from __future__ import print_function
import numpy as np
import unittest
import sys
sys.path.append("..")
from op_test import OpTest, _set_use_system_allocator
import paddle
import paddle.fluid as fluid
paddle.enable_static()
@unittest.skipIf(not paddle.is_compiled_with_npu(),
"core is not compiled with NPU")
class TestTransposeOp(OpTest):
def setUp(self):
self.set_npu()
self.op_type = "transpose"
self.place = paddle.NPUPlace(0)
self.init_dtype()
self.init_input_output()
self.init_kernel_type()
self.init_axis()
self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(self.x)}
self.attrs = {'axis': [0, 2, 1, 3], 'data_format': 'AnyLayout'}
self.outputs = {'Out': self.out}
def set_npu(self):
self.__class__.use_npu = True
def init_kernel_type(self):
self.use_mkldnn = False
def init_input_output(self):
self.x = np.random.uniform(0.1, 1, [8, 512, 12, 64]).astype(self.dtype)
self.out = np.transpose(self.x, [0, 2, 1, 3])
def init_dtype(self):
self.dtype = np.float32
def init_axis(self):
self.axis = -1
def test_check_output(self):
self.check_output_with_place(self.place, check_dygraph=False)
@unittest.skipIf(not paddle.is_compiled_with_npu(),
"core is not compiled with NPU")
class TestTransposeOpFP16(TestTransposeOp):
no_need_check_grad = True
def init_dtype(self):
self.dtype = np.float16
if __name__ == '__main__':
unittest.main()
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