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“fc374821ddb9d40daaaf443c3d78ac2d3643ce03”上不存在“paddle/fluid/memory/detail/memory_block_desc.cc”
未验证 提交 8f08f160 编写于 作者: L Leo Chen 提交者： GitHub
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Revert "[NPU] add npu kernel for mean Op (#31562)" (#31665) 

This reverts commit 468ac699.
上级 468ac699
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paddle/fluid/operators/CMakeLists.txt
浏览文件 @ 8f08f160
......@@ -184,6 +184,4 @@ endif()
if(WITH_ASCEND_CL)
cc_test(gelu_op_npu_test SRCS gelu_op_npu_test.cc DEPS op_registry gelu_op scope device_context enforce executor)
cc_test(mean_op_npu_test SRCS mean_op_npu_test.cc DEPS op_registry mean_op scope device_context enforce executor)
endif()
paddle/fluid/operators/mean_op_npu.cc 已删除 100644 → 0
浏览文件 @ 468ac699
/* 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. */
#include "paddle/fluid/operators/mean_op.h"
#include "paddle/fluid/platform/float16.h"
#include "paddle/fluid/operators/npu_op_runner.h"
namespace paddle {
namespace operators {
template <typename DeviceContext, typename T>
class MeanNPUKernel : 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");
auto reduce_ndim = x->dims().size();
std::vector<int> axes;
for (auto i = 0; i < reduce_ndim; ++i) {
axes.push_back(i);
}
framework::NPUAttributeMap attr_input = {
{"keep_dims", false},
{"axes", axes}};
std::vector<int64_t> out_dims;
out_dims.push_back(1);
out->Resize(framework::make_ddim(out_dims));
out->mutable_data<T>(ctx.GetPlace());
Tensor reduced_out(x->type());
std::vector<int64_t> reduced_dout_dims;
reduced_dout_dims.push_back(1);
reduced_out.Resize(framework::make_ddim(reduced_dout_dims));
reduced_out.mutable_data<T>(ctx.GetPlace());
auto runner = NpuOpRunner("ReduceMeanD",
{*x},
{*out},
attr_input);
auto stream =
ctx.template device_context<
paddle::platform::NPUDeviceContext>()
.stream();
runner.Run(stream);
}
};
template <typename DeviceContext, typename T>
class MeanGradNPUKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
auto stream =
context.template device_context<
paddle::platform::NPUDeviceContext>()
.stream();
auto grad = context.Input<Tensor>(framework::GradVarName("Out"));
PADDLE_ENFORCE_EQ(grad->numel(), 1,
platform::errors::InvalidArgument(
"Mean Gradient Input Tensor len should be 1. But "
"received Out@Grad's elements num is %d.",
grad->numel()));
auto IG = context.Output<Tensor>(framework::GradVarName("X"));
IG->mutable_data<T>(context.GetPlace());
// ones
Tensor ones(grad->type());
std::vector<int64_t> dout_dims;
for (auto i = 0; i < IG->dims().size(); ++i) {
dout_dims.push_back(IG->dims()[i]);
}
ones.Resize(framework::make_ddim(dout_dims));
ones.mutable_data<T>(context.GetPlace());
auto runner_ones = NpuOpRunner("OnesLike", {*IG}, {ones}, {});
runner_ones.Run(stream);
// means
Tensor mean_tensor(grad->type());
mean_tensor.Resize({1});
mean_tensor.mutable_data<T>(context.GetPlace());
std::vector<float> mean_vec;
mean_vec.push_back(1.0/static_cast<float>(IG->numel()));
framework::TensorFromVector(mean_vec,
context.device_context(),
&mean_tensor);
// means mul ones
Tensor mean_ma(grad->type());
mean_ma.Resize(framework::make_ddim(dout_dims));
mean_ma.mutable_data<T>(context.GetPlace());
auto runner_mul_1 = NpuOpRunner("Mul", {mean_tensor, ones}, {mean_ma}, {});
runner_mul_1.Run(stream);
// and mul grad
auto runner_mul_2 = NpuOpRunner("Mul", {mean_ma, *grad}, {*IG}, {});
runner_mul_2.Run(stream);
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
namespace plat = paddle::platform;
REGISTER_OP_NPU_KERNEL(
mean,
ops::MeanNPUKernel<paddle::platform::NPUDeviceContext, int>,
ops::MeanNPUKernel<paddle::platform::NPUDeviceContext, float>,
ops::MeanNPUKernel<paddle::platform::NPUDeviceContext, double>,
ops::MeanNPUKernel<paddle::platform::NPUDeviceContext, plat::float16>)
REGISTER_OP_NPU_KERNEL(
mean_grad,
ops::MeanGradNPUKernel<paddle::platform::NPUDeviceContext, int>,
ops::MeanGradNPUKernel<paddle::platform::NPUDeviceContext, float>,
ops::MeanGradNPUKernel<paddle::platform::NPUDeviceContext, double>,
ops::MeanGradNPUKernel<paddle::platform::NPUDeviceContext, plat::float16>)
paddle/fluid/operators/mean_op_npu_test.cc 已删除 100644 → 0
浏览文件 @ 468ac699
/* 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 <thread> // NOLINT
#include <vector>
#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(mean);
USE_OP_DEVICE_KERNEL(mean, NPU);
USE_OP(mean_grad);
USE_OP_DEVICE_KERNEL(mean_grad, NPU);
template <typename T>
void Compare(f::Scope* scope, const p::DeviceContext& ctx,
std::string op_type) {
// init
auto x = scope->Var("X");
auto tensor_x = x->GetMutable<f::LoDTensor>();
std::vector<T> init;
init.push_back(static_cast<T>(1.0));
init.push_back(static_cast<T>(2.0));
init.push_back(static_cast<T>(3.0));
init.push_back(static_cast<T>(4.0));
TensorFromVector(init, ctx, tensor_x);
tensor_x->Resize({4});
ctx.Wait();
auto place = ctx.GetPlace();
auto out = scope->Var("Out");
auto tensor_out = out->GetMutable<f::LoDTensor>();
auto op = f::OpRegistry::CreateOp(op_type,
{{"X", {"X"}}},
{{"Out", {"Out"}}},
{});
op->Run(*scope, place);
std::vector<float> out_vec;
TensorToVector(*tensor_out, ctx, &out_vec);
ctx.Wait();
EXPECT_EQ((uint32_t)out_vec.size(), (uint32_t)1);
EXPECT_EQ((float)out_vec[0], (float)2.5);
}
template <typename T>
void CompareGrad(f::Scope* scope, const p::DeviceContext& ctx,
std::string op_type) {
// init
auto dout = scope->Var("DOut");
auto tensor_dout = dout->GetMutable<f::LoDTensor>();
float dvalue = 2.0;
tensor_dout->Resize({1});
std::vector<T> init_dout;
init_dout.push_back(static_cast<T>(dvalue));
TensorFromVector(init_dout, ctx, tensor_dout);
ctx.Wait();
auto x = scope->Var("X");
auto tensor_x = x->GetMutable<f::LoDTensor>();
tensor_x->Resize({4});
auto dx = scope->Var("DX");
auto tensor_dx = dx->GetMutable<f::LoDTensor>();
tensor_dx->Resize({4});
ctx.Wait();
auto op = f::OpRegistry::CreateOp(op_type,
{{"Out@GRAD", {"DOut"}},
{"X", {"X"}}},
{{"X@GRAD", {"DX"}}},
{});
auto place = ctx.GetPlace();
op->Run(*scope, place);
std::vector<float> out_vec;
TensorToVector(*tensor_dx, ctx, &out_vec);
ctx.Wait();
EXPECT_EQ((uint32_t)out_vec.size(), (uint32_t)4);
EXPECT_EQ((float)out_vec[0], (float)1.0/dvalue);
EXPECT_EQ((float)out_vec[1], (float)1.0/dvalue);
EXPECT_EQ((float)out_vec[2], (float)1.0/dvalue);
EXPECT_EQ((float)out_vec[3], (float)1.0/dvalue);
}
TEST(mean, NPU_fp32) {
f::Scope scope;
p::NPUDeviceContext ctx(p::NPUPlace(0));
Compare<float>(&scope, ctx, "mean");
}
TEST(mean_grad, NPU_fp32) {
f::Scope scope;
p::NPUDeviceContext ctx(p::NPUPlace(0));
CompareGrad<float>(&scope, ctx, "mean_grad");
}
python/paddle/fluid/tests/unittests/npu/test_mean_op_npu.py 已删除 100644 → 0
浏览文件 @ 468ac699
# 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
import paddle
import paddle.fluid as fluid
from paddle.fluid import core
paddle.enable_static()
SEED = 2021
@unittest.skipIf(not paddle.is_compiled_with_npu(),
"core is not compiled with NPU")
class TestMean(OpTest):
def setUp(self):
self.set_npu()
self.place = paddle.NPUPlace(0)
self.op_type = "mean"
self.init_dtype()
x = np.random.random([3, 3]).astype(self.dtype)
self.inputs = {'X': x}
self.attrs = {}
np_out = np.mean(x)
self.outputs = {'Out': np_out}
def set_npu(self):
self.__class__.use_npu = True
self.__class__.no_need_check_grad = True
def init_dtype(self):
self.dtype = np.float32
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 TestMeanFP16(OpTest):
def setUp(self):
self.set_npu()
self.place = paddle.NPUPlace(0)
self.op_type = "mean"
self.init_dtype()
x = np.random.random([3, 3]).astype(self.dtype)
self.inputs = {'X': x}
self.attrs = {}
np_out = np.mean(x)
self.outputs = {'Out': np_out}
def set_npu(self):
self.__class__.use_npu = True
self.__class__.no_need_check_grad = True
def init_dtype(self):
self.dtype = np.float16
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 TestMeanNet(unittest.TestCase):
def _test(self, run_npu=True):
main_prog = paddle.static.Program()
startup_prog = paddle.static.Program()
main_prog.random_seed = SEED
startup_prog.random_seed = SEED
np.random.seed(SEED)
a_np = np.random.random(size=(32, 32)).astype('float32')
b_np = np.random.random(size=(32, 32)).astype('float32')
label_np = np.random.randint(2, size=(32, 1)).astype('int64')
with paddle.static.program_guard(main_prog, startup_prog):
a = paddle.static.data(name="a", shape=[32, 32], dtype='float32')
b = paddle.static.data(name="b", shape=[32, 32], dtype='float32')
label = paddle.static.data(
name="label", shape=[32, 1], dtype='int64')
c = paddle.multiply(a, b)
d = paddle.sqrt(c)
fc_1 = fluid.layers.fc(input=d, size=128)
prediction = fluid.layers.fc(input=fc_1, size=2, act='sigmoid')
cost = fluid.layers.cross_entropy(input=prediction, label=label)
loss = fluid.layers.mean(cost)
sgd = fluid.optimizer.SGD(learning_rate=0.01)
sgd.minimize(loss)
if run_npu:
place = paddle.NPUPlace(0)
else:
place = paddle.CPUPlace()
exe = paddle.static.Executor(place)
exe.run(startup_prog)
print("Start run on {}".format(place))
for epoch in range(100):
pred_res, loss_res = exe.run(
main_prog,
feed={"a": a_np,
"b": b_np,
"label": label_np},
fetch_list=[prediction, loss])
if epoch % 10 == 0:
print("Epoch {} | Prediction[0]: {}, Loss: {}".format(
epoch, pred_res[0], loss_res))
return pred_res, loss_res
def test_npu(self):
cpu_pred, cpu_loss = self._test(False)
npu_pred, npu_loss = self._test(True)
self.assertTrue(np.allclose(npu_pred, cpu_pred))
self.assertTrue(np.allclose(npu_loss, cpu_loss))
if __name__ == '__main__':
unittest.main()
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