未验证 提交 acca0352 编写于 作者: Q qipengh 提交者: GitHub

[MLU]add dropout op (#42274)

上级 89951472
/* Copyright (c) 2022 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/framework/op_registry.h"
#include "paddle/fluid/operators/mlu/mlu_baseop.h"
namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
template <typename T>
class DropoutMLUKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto* x = ctx.Input<Tensor>("X");
auto* out = ctx.Output<Tensor>("Out");
auto dropout_prob = ctx.Attr<float>("dropout_prob");
auto is_test = ctx.Attr<bool>("is_test");
auto* seed_tensor =
ctx.HasInput("Seed") ? ctx.Input<Tensor>("Seed") : nullptr;
auto dropout_implementation =
ctx.Attr<std::string>("dropout_implementation");
const bool is_upscale = (dropout_implementation == "upscale_in_train");
out->mutable_data<T>(ctx.GetPlace());
MLUCnnlTensorDesc x_desc(*x);
MLUCnnlTensorDesc out_desc(*out);
if (!is_test) {
// exec dropout op for training only.
int seed_data = 0;
if (seed_tensor) {
if (platform::is_mlu_place(seed_tensor->place())) {
memory::Copy(platform::CPUPlace(), &seed_data, seed_tensor->place(),
seed_tensor->data<int>(), sizeof(int));
} else {
seed_data = *(seed_tensor->data<int>());
}
} else {
seed_data = ctx.Attr<bool>("fix_seed") ? ctx.Attr<int>("seed") : 0;
}
auto* mask = ctx.Output<Tensor>("Mask");
mask->mutable_data<uint8_t>(ctx.GetPlace());
MLUCnnlTensorDesc mask_desc(*mask);
// Special case when dropout_prob is 1.0
if (dropout_prob == 1.0f) {
auto value_t = static_cast<T>(0.0f);
MLUCnnl::Fill(ctx, CNNL_POINTER_MODE_HOST, &value_t, out_desc.get(),
GetBasePtr(out));
MLUCnnl::Fill(ctx, CNNL_POINTER_MODE_HOST, &value_t, mask_desc.get(),
GetBasePtr(mask));
return;
}
// create mlu random generator
const int device_id = ctx.GetPlace().GetDeviceId();
auto mlu_gen_random = GetMLURandomGenerator(ctx, device_id, seed_data);
const float prob = is_upscale ? dropout_prob : 0.0f;
MLUCnnl::FusedDropout(
ctx, mlu_gen_random->get(), x_desc.get(), GetBasePtr(x), prob,
GetBasePtr(&(mlu_gen_random->get_state())), mask_desc.get(),
GetBasePtr(mask), out_desc.get(), GetBasePtr(out));
} else {
// exec dropout op for inference only.
if (is_upscale) {
framework::TensorCopy(
*x, ctx.GetPlace(),
ctx.template device_context<platform::MLUDeviceContext>(), out);
} else {
float scale = static_cast<T>(1.0f - dropout_prob);
Tensor scale_tensor(x->dtype());
scale_tensor.mutable_data<T>({1}, ctx.GetPlace());
MLUCnnlTensorDesc scale_desc(scale_tensor);
MLUCnnl::Fill(ctx, CNNL_POINTER_MODE_HOST, &scale, scale_desc.get(),
GetBasePtr(&scale_tensor));
auto data_type = ToCnnlDataType<T>();
MLUCnnlOpTensorDesc op_tensor_desc(CNNL_OP_TENSOR_MUL, data_type,
CNNL_NOT_PROPAGATE_NAN);
MLUCnnl::OpTensor(ctx, op_tensor_desc.get(), x_desc.get(),
GetBasePtr(x), scale_desc.get(),
GetBasePtr(&scale_tensor), out_desc.get(),
GetBasePtr(out), data_type);
}
}
}
};
template <typename T>
class DropoutGradMLUKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
PADDLE_ENFORCE_EQ(!ctx.Attr<bool>("is_test"), true,
platform::errors::InvalidArgument(
"GradOp is only callable when is_test is false"));
auto* grad_x = ctx.Output<Tensor>(framework::GradVarName("X"));
auto* grad_out = ctx.Input<Tensor>(framework::GradVarName("Out"));
auto* mask = ctx.Input<Tensor>("Mask");
auto dropout_prob = ctx.Attr<float>("dropout_prob");
auto dropout_impl = ctx.Attr<std::string>("dropout_implementation");
grad_x->mutable_data<T>(ctx.GetPlace());
MLUCnnlTensorDesc grad_x_desc(*grad_x);
if (dropout_prob == 1.) {
auto value_t = static_cast<T>(0.0f);
MLUCnnl::Fill(ctx, CNNL_POINTER_MODE_HOST, &value_t, grad_x_desc.get(),
GetBasePtr(grad_x));
return;
}
// cast mask from uint8 to float32/float16
Tensor cast_mask(grad_x->dtype());
cast_mask.Resize(mask->dims());
cast_mask.mutable_data<T>(ctx.GetPlace());
MLUCnnlTensorDesc mask_desc(*mask);
MLUCnnlTensorDesc cast_mask_desc(cast_mask);
cnnlCastDataType_t cast_type =
GetCastDataType(framework::TransToProtoVarType(mask->dtype()),
framework::TransToProtoVarType(cast_mask.dtype()));
MLUCnnl::Cast(ctx, cast_type, mask_desc.get(), GetBasePtr(mask),
cast_mask_desc.get(), GetBasePtr(&cast_mask));
const bool is_upscale = (dropout_impl == "upscale_in_train");
const float scale = is_upscale ? (1.0f / (1.0f - dropout_prob)) : (1.0f);
auto data_type = ToCnnlDataType<T>();
MLUCnnlTensorDesc grad_out_desc(*grad_out);
MLUCnnlOpTensorDesc op_tensor_desc(CNNL_OP_TENSOR_MUL, data_type,
CNNL_NOT_PROPAGATE_NAN);
MLUCnnl::OpTensor(ctx, op_tensor_desc.get(), cast_mask_desc.get(),
GetBasePtr(&cast_mask), grad_out_desc.get(),
GetBasePtr(grad_out), grad_x_desc.get(),
GetBasePtr(grad_x), data_type, scale);
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
namespace plat = paddle::platform;
REGISTER_OP_MLU_KERNEL(dropout, ops::DropoutMLUKernel<float>,
ops::DropoutMLUKernel<plat::float16>);
REGISTER_OP_MLU_KERNEL(dropout_grad, ops::DropoutGradMLUKernel<float>,
ops::DropoutGradMLUKernel<plat::float16>);
...@@ -44,6 +44,32 @@ bool MLUSupportsCast(const VT::Type& src_type, const VT::Type& dst_type) { ...@@ -44,6 +44,32 @@ bool MLUSupportsCast(const VT::Type& src_type, const VT::Type& dst_type) {
return false; return false;
} }
const std::shared_ptr<MLUCnnlRandomGeneratorDesc>& GetMLURandomGenerator(
const ExecutionContext& ctx, const int64_t device_id, const int seed) {
static int64_t num_mlu_devices = -1;
static std::once_flag num_devices_init_flag;
static std::deque<std::once_flag> mlu_device_flags;
static std::vector<std::shared_ptr<MLUCnnlRandomGeneratorDesc>>
mlu_rand_generators;
std::call_once(num_devices_init_flag, []() {
num_mlu_devices = paddle::platform::GetMLUDeviceCount();
mlu_device_flags.resize(num_mlu_devices);
mlu_rand_generators.resize(num_mlu_devices);
});
if (device_id < 0) {
PADDLE_THROW(platform::errors::InvalidArgument(
"mlu device id shoule be greater than 0"));
}
std::call_once(mlu_device_flags[device_id], [&]() {
mlu_rand_generators[device_id].reset(
new MLUCnnlRandomGeneratorDesc(ctx, seed));
VLOG(4) << "device_id: " << device_id << ", initial seed: " << seed;
});
return mlu_rand_generators[device_id];
}
class MLUCnnlTensorDescPool { class MLUCnnlTensorDescPool {
public: public:
cnnlTensorDescriptor_t Pop() { cnnlTensorDescriptor_t Pop() {
...@@ -266,23 +292,32 @@ MLUCnnlPoolingDesc::~MLUCnnlPoolingDesc() { ...@@ -266,23 +292,32 @@ MLUCnnlPoolingDesc::~MLUCnnlPoolingDesc() {
} }
} }
MLUCnnlRandomGeneratorDesc::MLUCnnlRandomGeneratorDesc(const bool is_mlu200, MLUCnnlRandomGeneratorDesc::MLUCnnlRandomGeneratorDesc(
const int seed) { const ExecutionContext& ctx, const int seed) {
if (is_mlu200) { PADDLE_ENFORCE_MLU_SUCCESS(
PADDLE_ENFORCE_MLU_SUCCESS( cnnlRandCreateGenerator(&mlu_generator, CNNL_RAND_RNG_MTGP32));
cnnlRandCreateGenerator(&mlu_generator, CNNL_RAND_RNG_FAST)); PADDLE_ENFORCE_MLU_SUCCESS(
} else { cnnlRandSetPseudoRandomGeneratorSeed(mlu_generator, seed));
PADDLE_ENFORCE_MLU_SUCCESS( size_t workspace_size;
cnnlRandCreateGenerator(&mlu_generator, CNNL_RAND_RNG_MTGP32)); PADDLE_ENFORCE_MLU_SUCCESS(
PADDLE_ENFORCE_MLU_SUCCESS( cnnlRandGetMTGP32StateSize(mlu_generator, &workspace_size));
cnnlRandSetPseudoRandomGeneratorSeed(mlu_generator, seed));
} auto& dev_ctx = GetDevCtxFromCTX(ctx);
mlu_state = ctx.AllocateTmpTensor<int8_t, MLUDeviceContext>(
{static_cast<int64_t>(workspace_size)}, dev_ctx);
void* mlu_state_ptr = mlu_state.mutable_data(ctx.GetPlace());
cnnlHandle_t handle = GetHandleFromCTX(ctx);
PADDLE_ENFORCE_MLU_SUCCESS(cnnlRandMakeMTGP32KernelState(
handle, mlu_state_ptr, nullptr, nullptr, seed));
} }
const cnnlRandGenerator_t MLUCnnlRandomGeneratorDesc::get() const { const cnnlRandGenerator_t MLUCnnlRandomGeneratorDesc::get() const {
return mlu_generator; return mlu_generator;
} }
Tensor& MLUCnnlRandomGeneratorDesc::get_state() { return mlu_state; }
MLUCnnlRandomGeneratorDesc::~MLUCnnlRandomGeneratorDesc() { MLUCnnlRandomGeneratorDesc::~MLUCnnlRandomGeneratorDesc() {
if (mlu_generator) { if (mlu_generator) {
PADDLE_ENFORCE_MLU_SUCCESS(cnnlRandDestroyGenerator(mlu_generator)); PADDLE_ENFORCE_MLU_SUCCESS(cnnlRandDestroyGenerator(mlu_generator));
...@@ -947,6 +982,26 @@ MLUCnnlTrigonDesc::~MLUCnnlTrigonDesc() { ...@@ -947,6 +982,26 @@ MLUCnnlTrigonDesc::~MLUCnnlTrigonDesc() {
workspace_ptr, workspace_size, beta_ptr, output_desc, output)); workspace_ptr, workspace_size, beta_ptr, output_desc, output));
} }
/* static */ void MLUCnnl::MulAx(const ExecutionContext& ctx,
const cnnlTensorDescriptor_t alpha_desc,
const void* alpha,
const cnnlTensorDescriptor_t output_desc,
void* output) {
cnnlHandle_t handle = GetHandleFromCTX(ctx);
size_t workspace_size;
PADDLE_ENFORCE_MLU_SUCCESS(
cnnlGetAxWorkspaceSize(handle, alpha_desc, output_desc, &workspace_size));
auto& dev_ctx = GetDevCtxFromCTX(ctx);
Tensor workspace = ctx.AllocateTmpTensor<int8_t, MLUDeviceContext>(
{static_cast<int64_t>(workspace_size)}, dev_ctx);
void* workspace_ptr = workspace.mutable_data(ctx.GetPlace());
PADDLE_ENFORCE_MLU_SUCCESS(cnnlAx_v2(handle, alpha_desc, alpha, output_desc,
output, workspace_ptr, workspace_size));
}
/* static */ void MLUCnnl::BiasAddGrad( /* static */ void MLUCnnl::BiasAddGrad(
const ExecutionContext& ctx, const int axis, const ExecutionContext& ctx, const int axis,
const cnnlTensorDescriptor_t out_backprop_desc, const void* out_backprop, const cnnlTensorDescriptor_t out_backprop_desc, const void* out_backprop,
...@@ -959,12 +1014,23 @@ MLUCnnlTrigonDesc::~MLUCnnlTrigonDesc() { ...@@ -959,12 +1014,23 @@ MLUCnnlTrigonDesc::~MLUCnnlTrigonDesc() {
/* static */ void MLUCnnl::RandomUniform( /* static */ void MLUCnnl::RandomUniform(
const ExecutionContext& ctx, const int num, const cnnlDataType_t data_type, const ExecutionContext& ctx, const int num, const cnnlDataType_t data_type,
const cnnlRandGenerator_t mlu_generator, const float min, const float max, const cnnlRandGenerator_t mlu_generator, void* mlu_state, void* output) {
void* output) {
cnnlHandle_t handle = GetHandleFromCTX(ctx); cnnlHandle_t handle = GetHandleFromCTX(ctx);
PADDLE_ENFORCE_MLU_SUCCESS(cnnlRandGenerateUniform( PADDLE_ENFORCE_MLU_SUCCESS(cnnlRandGenerateUniform(
handle, mlu_generator, data_type, nullptr, num, min, max, output)); handle, mlu_generator, data_type, mlu_state, num, 0, 1, output));
}
/* static */ void MLUCnnl::FusedDropout(
const ExecutionContext& ctx, const cnnlRandGenerator_t generator,
const cnnlTensorDescriptor_t input_desc, const void* input, const float p,
void* state, const cnnlTensorDescriptor_t mask_desc, const void* mask,
const cnnlTensorDescriptor_t output_desc, void* output) {
cnnlHandle_t handle = GetHandleFromCTX(ctx);
PADDLE_ENFORCE_MLU_SUCCESS(cnnlFusedDropout_v2(handle, generator, input_desc,
input, p, state, mask_desc,
mask, output_desc, output));
} }
/* static */ void MLUCnnl::TopK( /* static */ void MLUCnnl::TopK(
......
...@@ -273,14 +273,19 @@ class MLUCnnlPoolingDesc { ...@@ -273,14 +273,19 @@ class MLUCnnlPoolingDesc {
class MLUCnnlRandomGeneratorDesc { class MLUCnnlRandomGeneratorDesc {
public: public:
MLUCnnlRandomGeneratorDesc(const bool is_mlu200, const int seed); MLUCnnlRandomGeneratorDesc(const ExecutionContext& ctx, const int seed);
const cnnlRandGenerator_t get() const; const cnnlRandGenerator_t get() const;
Tensor& get_state();
~MLUCnnlRandomGeneratorDesc(); ~MLUCnnlRandomGeneratorDesc();
private: private:
Tensor mlu_state;
cnnlRandGenerator_t mlu_generator = nullptr; cnnlRandGenerator_t mlu_generator = nullptr;
}; };
const std::shared_ptr<MLUCnnlRandomGeneratorDesc>& GetMLURandomGenerator(
const ExecutionContext& ctx, const int64_t device_id, const int seed);
class MLUCnnlReduceDesc { class MLUCnnlReduceDesc {
public: public:
MLUCnnlReduceDesc(const MLUCnnlReduceDesc& desc) = delete; MLUCnnlReduceDesc(const MLUCnnlReduceDesc& desc) = delete;
...@@ -537,7 +542,13 @@ class MLUCnnl { ...@@ -537,7 +542,13 @@ class MLUCnnl {
static void RandomUniform(const ExecutionContext& ctx, const int num, static void RandomUniform(const ExecutionContext& ctx, const int num,
const cnnlDataType_t data_type, const cnnlDataType_t data_type,
const cnnlRandGenerator_t mlu_generator, const cnnlRandGenerator_t mlu_generator,
const float min, const float max, void* output); void* mlu_state, void* output);
static void FusedDropout(
const ExecutionContext& ctx, const cnnlRandGenerator_t generator,
const cnnlTensorDescriptor_t input_desc, const void* input, const float p,
void* state, const cnnlTensorDescriptor_t mask_desc, const void* mask,
const cnnlTensorDescriptor_t output_desc, void* output);
static void Cumsum(const ExecutionContext& ctx, const int axis, static void Cumsum(const ExecutionContext& ctx, const int axis,
const bool exclusive, const bool reverse, const bool exclusive, const bool reverse,
...@@ -709,6 +720,10 @@ class MLUCnnl { ...@@ -709,6 +720,10 @@ class MLUCnnl {
const void* in0, const cnnlTensorDescriptor_t in1_desc, const void* in1, const void* in0, const cnnlTensorDescriptor_t in1_desc, const void* in1,
const cnnlTensorDescriptor_t output_desc, void* output); const cnnlTensorDescriptor_t output_desc, void* output);
static void MulAx(const ExecutionContext& ctx,
const cnnlTensorDescriptor_t alpha_desc, const void* alpha,
const cnnlTensorDescriptor_t output_desc, void* output);
static void OpTensor(const ExecutionContext& ctx, static void OpTensor(const ExecutionContext& ctx,
const cnnlOpTensorDescriptor_t op_tensor_desc, const cnnlOpTensorDescriptor_t op_tensor_desc,
const cnnlTensorDescriptor_t a_desc, const void* a, const cnnlTensorDescriptor_t a_desc, const void* a,
......
# Copyright (c) 2022 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, skip_check_grad_ci
import paddle
import paddle.fluid as fluid
paddle.enable_static()
SEED = 2022
class TestDropoutOp(OpTest):
def setUp(self):
self.op_type = "dropout"
self.set_mlu()
self.init_dtype()
self.inputs = {'X': np.random.random((32, 64)).astype(self.dtype)}
self.attrs = {
'dropout_prob': 0.0,
'fix_seed': True,
'is_test': False,
'dropout_implementation': 'upscale_in_train'
}
self.outputs = {
'Out': self.inputs['X'],
'Mask': np.ones((32, 64)).astype('uint8')
}
def init_dtype(self):
self.dtype = np.float32
def set_mlu(self):
self.__class__.use_mlu = True
self.place = paddle.device.MLUPlace(0)
def test_check_output(self):
self.check_output_with_place(self.place)
def test_check_grad_normal(self):
self.check_grad_with_place(self.place, ['X'], 'Out')
class TestDropoutOpInput1d(TestDropoutOp):
# change input shape
def setUp(self):
self.op_type = "dropout"
self.set_mlu()
self.init_dtype()
self.inputs = {'X': np.random.random((3, 62)).astype(self.dtype)}
self.attrs = {
'dropout_prob': 0.0,
'fix_seed': True,
'is_test': False,
'dropout_implementation': 'upscale_in_train'
}
self.outputs = {
'Out': self.inputs['X'],
'Mask': np.ones((3, 62)).astype('uint8')
}
class TestDropoutOpInput1d_1(TestDropoutOp):
# the input is 1-D
def setUp(self):
self.op_type = "dropout"
self.set_mlu()
self.init_dtype()
self.inputs = {'X': np.random.random((2000)).astype(self.dtype)}
self.attrs = {
'dropout_prob': 0.0,
'fix_seed': True,
'is_test': False,
'dropout_implementation': 'upscale_in_train'
}
self.outputs = {
'Out': self.inputs['X'],
'Mask': np.ones((2000)).astype('uint8')
}
class TestDropoutOp2(TestDropoutOp):
# the dropout_prob is 1.0
def setUp(self):
self.op_type = "dropout"
self.set_mlu()
self.init_dtype()
self.inputs = {'X': np.random.random((32, 64)).astype(self.dtype)}
self.attrs = {
'dropout_prob': 1.0,
'fix_seed': True,
'is_test': False,
'dropout_implementation': 'upscale_in_train'
}
self.outputs = {
'Out': np.zeros((32, 64)).astype('float32'),
'Mask': np.zeros((32, 64)).astype('uint8')
}
class TestDropoutOp3(TestDropoutOp):
# the input dim is 3
def setUp(self):
self.op_type = "dropout"
self.set_mlu()
self.init_dtype()
self.inputs = {'X': np.random.random((32, 64, 2)).astype(self.dtype)}
self.attrs = {
'dropout_prob': 0.0,
'fix_seed': True,
'is_test': False,
'dropout_implementation': 'upscale_in_train'
}
self.outputs = {
'Out': self.inputs['X'],
'Mask': np.ones((32, 64, 2)).astype('uint8')
}
@skip_check_grad_ci(reason="For inference, check_grad is not required.")
class TestDropoutOpInference(OpTest):
# is_test = True
def setUp(self):
self.op_type = "dropout"
self.set_mlu()
self.init_dtype()
self.inputs = {'X': np.random.random((32, 64)).astype(self.dtype)}
self.attrs = {
'dropout_prob': 0.35,
'fix_seed': True,
'is_test': True,
'dropout_implementation': 'upscale_in_train'
}
self.outputs = {'Out': self.inputs['X']}
def init_dtype(self):
self.dtype = np.float32
def set_mlu(self):
self.__class__.use_mlu = True
self.place = paddle.device.MLUPlace(0)
def test_check_output(self):
self.check_output_with_place(self.place)
@skip_check_grad_ci(reason="For inference, check_grad is not required.")
class TestDropoutOpInference2(TestDropoutOpInference):
def setUp(self):
self.op_type = "dropout"
self.set_mlu()
self.init_dtype()
self.inputs = {'X': np.random.random((32, 64, 3)).astype(self.dtype)}
self.attrs = {
'dropout_prob': 0.75,
'is_test': True,
'dropout_implementation': 'upscale_in_train'
}
self.outputs = {'Out': self.inputs['X']}
class TestDropoutOpWithSeed(TestDropoutOp):
# the seed is a Tensor
def setUp(self):
self.op_type = "dropout"
self.set_mlu()
self.init_dtype()
self.inputs = {
"X": np.random.random((32, 64)).astype(self.dtype),
"Seed": np.asarray(
[125], dtype="int32")
}
self.attrs = {
'dropout_prob': 0.0,
'is_test': False,
'dropout_implementation': 'upscale_in_train'
}
self.outputs = {
'Out': self.inputs['X'],
'Mask': np.ones((32, 64)).astype('uint8')
}
class TestDropoutOpFp16(TestDropoutOp):
# float16
def init_dtype(self):
self.dtype = np.float16
def set_mlu(self):
self.__class__.use_mlu = True
self.place = paddle.device.MLUPlace(0)
self.__class__.no_need_check_grad = True
class TestDropoutAPI(unittest.TestCase):
def setUp(self):
np.random.seed(123)
self.places = [fluid.CPUPlace(), paddle.device.MLUPlace(0)]
def check_static_result(self, place):
with fluid.program_guard(fluid.Program(), fluid.Program()):
input = fluid.data(name="input", shape=[40, 40], dtype="float32")
res1 = paddle.nn.functional.dropout(
x=input, p=0., training=False, mode='upscale_in_train')
res2 = paddle.nn.functional.dropout(
x=input, p=0., axis=0, training=True, mode='upscale_in_train')
res3 = paddle.nn.functional.dropout(
x=input, p=0., axis=0, training=False, mode='upscale_in_train')
res4 = paddle.nn.functional.dropout(
x=input,
p=0.,
axis=[0, 1],
training=True,
mode='upscale_in_train')
res5 = paddle.nn.functional.dropout(
x=input,
p=0.,
axis=[0, 1],
training=False,
mode='upscale_in_train')
res6 = paddle.nn.functional.dropout(
x=input, p=1., training=True, mode='upscale_in_train')
res7 = paddle.fluid.layers.dropout(
x=input,
dropout_prob=0.,
dropout_implementation='upscale_in_train')
res8 = paddle.nn.functional.dropout(
x=input,
p=0.,
axis=(0, 1),
training=False,
mode='upscale_in_train')
in_np = np.random.random([40, 40]).astype("float32")
res_np = in_np
res_np2 = np.zeros_like(in_np)
exe = fluid.Executor(place)
res_list = [res1, res2, res3, res4, res5, res7, res8]
for res in res_list:
fetches = exe.run(fluid.default_main_program(),
feed={"input": in_np},
fetch_list=[res])
self.assertTrue(np.allclose(fetches[0], res_np))
fetches2 = exe.run(fluid.default_main_program(),
feed={"input": in_np},
fetch_list=[res6])
self.assertTrue(np.allclose(fetches2[0], res_np2))
def test_static(self):
for place in self.places:
self.check_static_result(place=place)
if __name__ == '__main__':
unittest.main()
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