From acca0352dd37c64896fbe3ef8a41e08277b950d8 Mon Sep 17 00:00:00 2001 From: qipengh Date: Wed, 27 Apr 2022 11:47:54 +0800 Subject: [PATCH] [MLU]add dropout op (#42274) --- paddle/fluid/operators/dropout_op_mlu.cc | 165 +++++++++++ paddle/fluid/operators/mlu/mlu_baseop.cc | 94 +++++- paddle/fluid/operators/mlu/mlu_baseop.h | 19 +- .../unittests/mlu/test_dropout_op_mlu.py | 273 ++++++++++++++++++ 4 files changed, 535 insertions(+), 16 deletions(-) create mode 100644 paddle/fluid/operators/dropout_op_mlu.cc create mode 100644 python/paddle/fluid/tests/unittests/mlu/test_dropout_op_mlu.py diff --git a/paddle/fluid/operators/dropout_op_mlu.cc b/paddle/fluid/operators/dropout_op_mlu.cc new file mode 100644 index 00000000000..b88974a51ce --- /dev/null +++ b/paddle/fluid/operators/dropout_op_mlu.cc @@ -0,0 +1,165 @@ +/* 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 +class DropoutMLUKernel : public framework::OpKernel { + public: + void Compute(const framework::ExecutionContext& ctx) const override { + auto* x = ctx.Input("X"); + auto* out = ctx.Output("Out"); + auto dropout_prob = ctx.Attr("dropout_prob"); + auto is_test = ctx.Attr("is_test"); + auto* seed_tensor = + ctx.HasInput("Seed") ? ctx.Input("Seed") : nullptr; + auto dropout_implementation = + ctx.Attr("dropout_implementation"); + + const bool is_upscale = (dropout_implementation == "upscale_in_train"); + + out->mutable_data(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(), sizeof(int)); + } else { + seed_data = *(seed_tensor->data()); + } + } else { + seed_data = ctx.Attr("fix_seed") ? ctx.Attr("seed") : 0; + } + + auto* mask = ctx.Output("Mask"); + mask->mutable_data(ctx.GetPlace()); + MLUCnnlTensorDesc mask_desc(*mask); + // Special case when dropout_prob is 1.0 + if (dropout_prob == 1.0f) { + auto value_t = static_cast(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(), out); + } else { + float scale = static_cast(1.0f - dropout_prob); + Tensor scale_tensor(x->dtype()); + scale_tensor.mutable_data({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(); + 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 +class DropoutGradMLUKernel : public framework::OpKernel { + public: + void Compute(const framework::ExecutionContext& ctx) const override { + PADDLE_ENFORCE_EQ(!ctx.Attr("is_test"), true, + platform::errors::InvalidArgument( + "GradOp is only callable when is_test is false")); + auto* grad_x = ctx.Output(framework::GradVarName("X")); + auto* grad_out = ctx.Input(framework::GradVarName("Out")); + auto* mask = ctx.Input("Mask"); + auto dropout_prob = ctx.Attr("dropout_prob"); + auto dropout_impl = ctx.Attr("dropout_implementation"); + + grad_x->mutable_data(ctx.GetPlace()); + MLUCnnlTensorDesc grad_x_desc(*grad_x); + + if (dropout_prob == 1.) { + auto value_t = static_cast(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(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(); + 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, + ops::DropoutMLUKernel); + +REGISTER_OP_MLU_KERNEL(dropout_grad, ops::DropoutGradMLUKernel, + ops::DropoutGradMLUKernel); diff --git a/paddle/fluid/operators/mlu/mlu_baseop.cc b/paddle/fluid/operators/mlu/mlu_baseop.cc index 793aa2644b5..eacab468005 100644 --- a/paddle/fluid/operators/mlu/mlu_baseop.cc +++ b/paddle/fluid/operators/mlu/mlu_baseop.cc @@ -44,6 +44,32 @@ bool MLUSupportsCast(const VT::Type& src_type, const VT::Type& dst_type) { return false; } +const std::shared_ptr& 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 mlu_device_flags; + static std::vector> + 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 { public: cnnlTensorDescriptor_t Pop() { @@ -266,23 +292,32 @@ MLUCnnlPoolingDesc::~MLUCnnlPoolingDesc() { } } -MLUCnnlRandomGeneratorDesc::MLUCnnlRandomGeneratorDesc(const bool is_mlu200, - const int seed) { - if (is_mlu200) { - PADDLE_ENFORCE_MLU_SUCCESS( - cnnlRandCreateGenerator(&mlu_generator, CNNL_RAND_RNG_FAST)); - } else { - PADDLE_ENFORCE_MLU_SUCCESS( - cnnlRandCreateGenerator(&mlu_generator, CNNL_RAND_RNG_MTGP32)); - PADDLE_ENFORCE_MLU_SUCCESS( - cnnlRandSetPseudoRandomGeneratorSeed(mlu_generator, seed)); - } +MLUCnnlRandomGeneratorDesc::MLUCnnlRandomGeneratorDesc( + const ExecutionContext& ctx, const int seed) { + PADDLE_ENFORCE_MLU_SUCCESS( + cnnlRandCreateGenerator(&mlu_generator, CNNL_RAND_RNG_MTGP32)); + PADDLE_ENFORCE_MLU_SUCCESS( + cnnlRandSetPseudoRandomGeneratorSeed(mlu_generator, seed)); + size_t workspace_size; + PADDLE_ENFORCE_MLU_SUCCESS( + cnnlRandGetMTGP32StateSize(mlu_generator, &workspace_size)); + + auto& dev_ctx = GetDevCtxFromCTX(ctx); + mlu_state = ctx.AllocateTmpTensor( + {static_cast(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 { return mlu_generator; } +Tensor& MLUCnnlRandomGeneratorDesc::get_state() { return mlu_state; } + MLUCnnlRandomGeneratorDesc::~MLUCnnlRandomGeneratorDesc() { if (mlu_generator) { PADDLE_ENFORCE_MLU_SUCCESS(cnnlRandDestroyGenerator(mlu_generator)); @@ -947,6 +982,26 @@ MLUCnnlTrigonDesc::~MLUCnnlTrigonDesc() { 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( + {static_cast(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( const ExecutionContext& ctx, const int axis, const cnnlTensorDescriptor_t out_backprop_desc, const void* out_backprop, @@ -959,12 +1014,23 @@ MLUCnnlTrigonDesc::~MLUCnnlTrigonDesc() { /* static */ void MLUCnnl::RandomUniform( const ExecutionContext& ctx, const int num, const cnnlDataType_t data_type, - const cnnlRandGenerator_t mlu_generator, const float min, const float max, - void* output) { + const cnnlRandGenerator_t mlu_generator, void* mlu_state, void* output) { cnnlHandle_t handle = GetHandleFromCTX(ctx); 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( diff --git a/paddle/fluid/operators/mlu/mlu_baseop.h b/paddle/fluid/operators/mlu/mlu_baseop.h index 9948c45e246..572b7aa2bbd 100644 --- a/paddle/fluid/operators/mlu/mlu_baseop.h +++ b/paddle/fluid/operators/mlu/mlu_baseop.h @@ -273,14 +273,19 @@ class MLUCnnlPoolingDesc { class MLUCnnlRandomGeneratorDesc { public: - MLUCnnlRandomGeneratorDesc(const bool is_mlu200, const int seed); + MLUCnnlRandomGeneratorDesc(const ExecutionContext& ctx, const int seed); const cnnlRandGenerator_t get() const; + Tensor& get_state(); ~MLUCnnlRandomGeneratorDesc(); private: + Tensor mlu_state; cnnlRandGenerator_t mlu_generator = nullptr; }; +const std::shared_ptr& GetMLURandomGenerator( + const ExecutionContext& ctx, const int64_t device_id, const int seed); + class MLUCnnlReduceDesc { public: MLUCnnlReduceDesc(const MLUCnnlReduceDesc& desc) = delete; @@ -537,7 +542,13 @@ class MLUCnnl { static void RandomUniform(const ExecutionContext& ctx, const int num, const cnnlDataType_t data_type, 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, const bool exclusive, const bool reverse, @@ -709,6 +720,10 @@ class MLUCnnl { const void* in0, const cnnlTensorDescriptor_t in1_desc, const void* in1, 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, const cnnlOpTensorDescriptor_t op_tensor_desc, const cnnlTensorDescriptor_t a_desc, const void* a, diff --git a/python/paddle/fluid/tests/unittests/mlu/test_dropout_op_mlu.py b/python/paddle/fluid/tests/unittests/mlu/test_dropout_op_mlu.py new file mode 100644 index 00000000000..f8984f5c6df --- /dev/null +++ b/python/paddle/fluid/tests/unittests/mlu/test_dropout_op_mlu.py @@ -0,0 +1,273 @@ +# 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() -- GitLab