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提交 dfd1eee7 编写于 作者: G Guo Sheng 提交者： Tao Luo
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Add seq2seq api related code (#19820)

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paddle/fluid/API.spec
浏览文件 @ dfd1eee7
......@@ -306,6 +306,7 @@ paddle.fluid.layers.deformable_roi_pooling (ArgSpec(args=['input', 'rois', 'tran
paddle.fluid.layers.filter_by_instag (ArgSpec(args=['ins', 'ins_tag', 'filter_tag', 'is_lod'], varargs=None, keywords=None, defaults=None), ('document', '7703a2088af8de4128b143ff1164ca4a'))
paddle.fluid.layers.shard_index (ArgSpec(args=['input', 'index_num', 'nshards', 'shard_id', 'ignore_value'], varargs=None, keywords=None, defaults=(-1,)), ('document', '3c6b30e9cd57b38d4a5fa1ade887f779'))
paddle.fluid.layers.hard_swish (ArgSpec(args=['x', 'threshold', 'scale', 'offset', 'name'], varargs=None, keywords=None, defaults=(6.0, 6.0, 3.0, None)), ('document', 'bd763b9ca99239d624c3cb4626e3627a'))
paddle.fluid.layers.gather_tree (ArgSpec(args=['ids', 'parents'], varargs=None, keywords=None, defaults=None), ('document', '201b54fa7512305078c70a6610beaead'))
paddle.fluid.layers.mse_loss (ArgSpec(args=['input', 'label'], varargs=None, keywords=None, defaults=None), ('document', '88b967ef5132567396062d5d654b3064'))
paddle.fluid.layers.uniform_random (ArgSpec(args=['shape', 'dtype', 'min', 'max', 'seed'], varargs=None, keywords=None, defaults=('float32', -1.0, 1.0, 0)), ('document', '34e7c1ff0263baf9551000b6bb3bc47e'))
paddle.fluid.layers.data (ArgSpec(args=['name', 'shape', 'append_batch_size', 'dtype', 'lod_level', 'type', 'stop_gradient'], varargs=None, keywords=None, defaults=(True, 'float32', 0, VarType.LOD_TENSOR, True)), ('document', '9d7806e31bdf727c1a23b8782a09b545'))
......@@ -318,11 +319,11 @@ paddle.fluid.layers.create_tensor (ArgSpec(args=['dtype', 'name', 'persistable']
paddle.fluid.layers.create_parameter (ArgSpec(args=['shape', 'dtype', 'name', 'attr', 'is_bias', 'default_initializer'], varargs=None, keywords=None, defaults=(None, None, False, None)), ('document', '727aa63c061919bee38547fb126d9428'))
paddle.fluid.layers.create_global_var (ArgSpec(args=['shape', 'value', 'dtype', 'persistable', 'force_cpu', 'name'], varargs=None, keywords=None, defaults=(False, False, None)), ('document', 'fa7f74cfb940521cc9fdffabc83debbf'))
paddle.fluid.layers.cast (ArgSpec(args=['x', 'dtype'], varargs=None, keywords=None, defaults=None), ('document', '45df178cbd8c302f92c30ebdaaa6fa8a'))
paddle.fluid.layers.tensor_array_to_tensor (ArgSpec(args=['input', 'axis', 'name'], varargs=None, keywords=None, defaults=(1, None)), ('document', 'dd7d2f1e12a8a4225d017209866e5621'))
paddle.fluid.layers.tensor_array_to_tensor (ArgSpec(args=['input', 'axis', 'name', 'use_stack'], varargs=None, keywords=None, defaults=(1, None, False)), ('document', '4aa82374218ccf593bb8011df79c71e3'))
paddle.fluid.layers.concat (ArgSpec(args=['input', 'axis', 'name'], varargs=None, keywords=None, defaults=(0, None)), ('document', 'ec7d6e716fb29ef1e73e1e3efa5ca46b'))
paddle.fluid.layers.sums (ArgSpec(args=['input', 'out'], varargs=None, keywords=None, defaults=(None,)), ('document', '5df743d578638cd2bbb9369499b44af4'))
paddle.fluid.layers.assign (ArgSpec(args=['input', 'output'], varargs=None, keywords=None, defaults=(None,)), ('document', '8bd94aef4e123986d9a8c29f67b5532b'))
paddle.fluid.layers.fill_constant_batch_size_like (ArgSpec(args=['input', 'shape', 'dtype', 'value', 'input_dim_idx', 'output_dim_idx'], varargs=None, keywords=None, defaults=(0, 0)), ('document', '37a288e4400f6d5510e982827461c11b'))
paddle.fluid.layers.fill_constant_batch_size_like (ArgSpec(args=['input', 'shape', 'dtype', 'value', 'input_dim_idx', 'output_dim_idx', 'force_cpu'], varargs=None, keywords=None, defaults=(0, 0, False)), ('document', '2bb57637664173fee5f654e55896aec6'))
paddle.fluid.layers.fill_constant (ArgSpec(args=['shape', 'dtype', 'value', 'force_cpu', 'out'], varargs=None, keywords=None, defaults=(False, None)), ('document', '66e1e468666dd47e5b2715226cebeac0'))
paddle.fluid.layers.argmin (ArgSpec(args=['x', 'axis'], varargs=None, keywords=None, defaults=(0,)), ('document', '53629e27597e5dfb7020aac5bc639ebb'))
paddle.fluid.layers.argmax (ArgSpec(args=['x', 'axis'], varargs=None, keywords=None, defaults=(0,)), ('document', 'd9a89fbedbaebd5f65897ac75ee636f3'))
......@@ -467,6 +468,39 @@ paddle.fluid.layers.MultivariateNormalDiag.entropy (ArgSpec(args=['self'], varar
paddle.fluid.layers.MultivariateNormalDiag.kl_divergence (ArgSpec(args=['self', 'other'], varargs=None, keywords=None, defaults=None), ('document', 'd9190d29dbd54c81f747a6436c35f062'))
paddle.fluid.layers.MultivariateNormalDiag.log_prob (ArgSpec(args=['self', 'value'], varargs=None, keywords=None, defaults=None), ('document', 'c0edd2e2fc76711477b32dc4da9de768'))
paddle.fluid.layers.MultivariateNormalDiag.sample (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '08a2bbcaa20ee176ee7ec3d05737a0f6'))
paddle.fluid.layers.RNNCell ('paddle.fluid.layers.rnn.RNNCell', ('document', '2c3a2d3ecb4a3cec130395e7df0bd5c9'))
paddle.fluid.layers.RNNCell.__init__
paddle.fluid.layers.RNNCell.call (ArgSpec(args=['self', 'inputs', 'states'], varargs=None, keywords='kwargs', defaults=None), ('document', '3ac714b638258c520d66f682be67b658'))
paddle.fluid.layers.RNNCell.get_initial_states (ArgSpec(args=['self', 'batch_ref', 'shape', 'dtype', 'init_value'], varargs=None, keywords=None, defaults=(None, None, 0)), ('document', '003d1b4c99128f798ac0b0eecc81c489'))
paddle.fluid.layers.GRUCell ('paddle.fluid.layers.rnn.GRUCell', ('document', '7b2902a91258c4688a879805290adc00'))
paddle.fluid.layers.GRUCell.__init__ (ArgSpec(args=['self', 'hidden_size', 'param_attr', 'bias_attr', 'gate_activation', 'activation', 'dtype', 'name'], varargs=None, keywords=None, defaults=(None, None, None, None, 'float32', 'GRUCell')), ('document', '3624a6c93b4a999d0d809eb1a66d272e'))
paddle.fluid.layers.GRUCell.call (ArgSpec(args=['self', 'inputs', 'states'], varargs=None, keywords=None, defaults=None), ('document', '6094ab09a56c732c76abb5105327ea54'))
paddle.fluid.layers.GRUCell.get_initial_states (ArgSpec(args=['self', 'batch_ref', 'shape', 'dtype', 'init_value'], varargs=None, keywords=None, defaults=(None, None, 0)), ('document', '003d1b4c99128f798ac0b0eecc81c489'))
paddle.fluid.layers.LSTMCell ('paddle.fluid.layers.rnn.LSTMCell', ('document', '5cbd87bce446ba0f50398ce2772d43e9'))
paddle.fluid.layers.LSTMCell.__init__ (ArgSpec(args=['self', 'hidden_size', 'param_attr', 'bias_attr', 'gate_activation', 'activation', 'forget_bias', 'dtype', 'name'], varargs=None, keywords=None, defaults=(None, None, None, None, 1.0, 'float32', 'LSTMCell')), ('document', '9015961869b436d2739a0347618028e3'))
paddle.fluid.layers.LSTMCell.call (ArgSpec(args=['self', 'inputs', 'states'], varargs=None, keywords=None, defaults=None), ('document', '9c84a477021e4a7d0a497c1e6a31be2d'))
paddle.fluid.layers.LSTMCell.get_initial_states (ArgSpec(args=['self', 'batch_ref', 'shape', 'dtype', 'init_value'], varargs=None, keywords=None, defaults=(None, None, 0)), ('document', '003d1b4c99128f798ac0b0eecc81c489'))
paddle.fluid.layers.Decoder ('paddle.fluid.layers.rnn.Decoder', ('document', '23838bd065fddca1557a6a3368d9e365'))
paddle.fluid.layers.Decoder.__init__
paddle.fluid.layers.Decoder.finalize (ArgSpec(args=['self', 'outputs', 'final_states', 'sequence_lengths'], varargs=None, keywords=None, defaults=None), ('document', 'cab7fc752a05db18e99258473f50359d'))
paddle.fluid.layers.Decoder.initialize (ArgSpec(args=['self', 'inits'], varargs=None, keywords=None, defaults=None), ('document', '68cf1846fb58056dbe5a524f1ca9dff5'))
paddle.fluid.layers.Decoder.step (ArgSpec(args=['self', 'time', 'inputs', 'states'], varargs=None, keywords=None, defaults=None), ('document', '151d0229930b9654689f86c85f7c4c3f'))
paddle.fluid.layers.BeamSearchDecoder ('paddle.fluid.layers.rnn.BeamSearchDecoder', ('document', 'd7ef0c9229bfe73e0daefcfda24a2635'))
paddle.fluid.layers.BeamSearchDecoder.OutputWrapper ('paddle.fluid.layers.rnn.OutputWrapper', ('document', 'a7141ebf1fb097fa71006cdd35bdc219'))
paddle.fluid.layers.BeamSearchDecoder.OutputWrapper.__init__
paddle.fluid.layers.BeamSearchDecoder.OutputWrapper.count T.count(value) -> integer -- return number of occurrences of value
paddle.fluid.layers.BeamSearchDecoder.OutputWrapper.index T.index(value, [start, [stop]]) -> integer -- return first index of value.
paddle.fluid.layers.BeamSearchDecoder.StateWrapper ('paddle.fluid.layers.rnn.StateWrapper', ('document', '157731f37c88ea01bc746653125a41c8'))
paddle.fluid.layers.BeamSearchDecoder.StateWrapper.__init__
paddle.fluid.layers.BeamSearchDecoder.StateWrapper.count T.count(value) -> integer -- return number of occurrences of value
paddle.fluid.layers.BeamSearchDecoder.StateWrapper.index T.index(value, [start, [stop]]) -> integer -- return first index of value.
paddle.fluid.layers.BeamSearchDecoder.__init__ (ArgSpec(args=['self', 'cell', 'start_token', 'end_token', 'beam_size', 'embedding_fn', 'output_fn'], varargs=None, keywords=None, defaults=(None, None)), ('document', '68951eaed573ec47c17a43155514b2f1'))
paddle.fluid.layers.BeamSearchDecoder.finalize (ArgSpec(args=['self', 'outputs', 'final_states', 'sequence_lengths'], varargs=None, keywords=None, defaults=None), ('document', '9a7f0a8fc5802bf860f2ac960466fb45'))
paddle.fluid.layers.BeamSearchDecoder.initialize (ArgSpec(args=['self', 'initial_cell_states'], varargs=None, keywords=None, defaults=None), ('document', '01ee508a9615e2483fe6ddcf14d5fa25'))
paddle.fluid.layers.BeamSearchDecoder.step (ArgSpec(args=['self', 'time', 'inputs', 'states'], varargs=None, keywords='kwargs', defaults=None), ('document', '35ee583c3c0fe7cceeafa289ed3374bd'))
paddle.fluid.layers.BeamSearchDecoder.tile_beam_merge_with_batch (ArgSpec(args=['x', 'beam_size'], varargs=None, keywords=None, defaults=None), ('document', 'ce7ffacba6f56f57acbf5d4dd82fe04d'))
paddle.fluid.layers.rnn (ArgSpec(args=['cell', 'inputs', 'initial_states', 'sequence_length', 'time_major', 'is_reverse'], varargs=None, keywords='kwargs', defaults=(None, None, False, False)), ('document', 'c36ade777ff43d2ba5542079b66a012b'))
paddle.fluid.layers.dynamic_decode (ArgSpec(args=['decoder', 'inits', 'max_step_num', 'output_time_major'], varargs=None, keywords='kwargs', defaults=(None, None, False)), ('document', '55b44de9d290c0c2ad8fdd635e6ab575'))
paddle.fluid.contrib.InitState ('paddle.fluid.contrib.decoder.beam_search_decoder.InitState', ('document', '3afd1f84232718e628e9e566941c5f05'))
paddle.fluid.contrib.InitState.__init__ (ArgSpec(args=['self', 'init', 'shape', 'value', 'init_boot', 'need_reorder', 'dtype'], varargs=None, keywords=None, defaults=(None, None, 0.0, None, False, 'float32')), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.contrib.StateCell ('paddle.fluid.contrib.decoder.beam_search_decoder.StateCell', ('document', 'ecd0066c02867d445d7b461e28220c50'))
......
paddle/fluid/operators/assign_op.cc
浏览文件 @ dfd1eee7
......@@ -154,10 +154,12 @@ REGISTER_OPERATOR(assign, ops::AssignOp, ops::AssignGradMaker,
ops::AssignOpProtoMaker, ops::AssignOpInplaceInferer);
REGISTER_OP_CPU_KERNEL_FUNCTOR(assign, float, ops::AssignKernel, double,
ops::AssignKernel, int, ops::AssignKernel,
int64_t, ops::AssignKernel);
int64_t, ops::AssignKernel, bool,
ops::AssignKernel);
#ifdef PADDLE_WITH_CUDA
REGISTER_OP_CUDA_KERNEL_FUNCTOR(assign, float, ops::AssignKernel, double,
ops::AssignKernel, int, ops::AssignKernel,
int64_t, ops::AssignKernel);
int64_t, ops::AssignKernel, bool,
ops::AssignKernel);
#endif
paddle/fluid/operators/fill_constant_batch_size_like_op.cc
浏览文件 @ dfd1eee7
......@@ -38,6 +38,11 @@ class FillConstantBatchSizeLikeOpMaker : public BatchSizeLikeOpMaker {
.SetDefault(framework::proto::VarType::FP32);
AddAttr<float>("value", "default 0. The value to be filled")
.SetDefault(0.0f);
AddAttr<bool>("force_cpu",
"(bool, default false) Force fill output variable to cpu "
"memory. Otherwise, fill output variable to the running "
"device")
.SetDefault(false);
AddComment(R"DOC(
This function creates a tensor of specified *shape*, *dtype* and batch size,
and initializes this with a constant supplied in *value*. The batch size is
......@@ -65,4 +70,6 @@ REGISTER_OP_CPU_KERNEL(
ops::FillConstantBatchSizeLikeOpKernel<paddle::platform::CPUDeviceContext,
int>,
ops::FillConstantBatchSizeLikeOpKernel<paddle::platform::CPUDeviceContext,
int64_t>);
int64_t>,
ops::FillConstantBatchSizeLikeOpKernel<paddle::platform::CPUDeviceContext,
bool>);
paddle/fluid/operators/fill_constant_batch_size_like_op.cu.cc
浏览文件 @ dfd1eee7
......@@ -25,4 +25,6 @@ REGISTER_OP_CUDA_KERNEL(
ops::FillConstantBatchSizeLikeOpKernel<paddle::platform::CUDADeviceContext,
int>,
ops::FillConstantBatchSizeLikeOpKernel<paddle::platform::CUDADeviceContext,
int64_t>);
int64_t>,
ops::FillConstantBatchSizeLikeOpKernel<paddle::platform::CUDADeviceContext,
bool>);
paddle/fluid/operators/fill_constant_batch_size_like_op.h
浏览文件 @ dfd1eee7
......@@ -23,6 +23,11 @@ template <typename DeviceContext, typename T>
class FillConstantBatchSizeLikeOpKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto data_type =
static_cast<framework::proto::VarType::Type>(ctx.Attr<int>("dtype"));
auto value = ctx.Attr<float>("value");
auto force_cpu = ctx.Attr<bool>("force_cpu");
auto* out = ctx.Output<framework::Tensor>("Out");
auto* in = ctx.Input<framework::LoDTensor>("Input");
if (in->lod().size() && ctx.Attr<int>("input_dim_idx") == 0) {
......@@ -32,12 +37,16 @@ class FillConstantBatchSizeLikeOpKernel : public framework::OpKernel<T> {
odims[output_dim_idx] = static_cast<int>(in->lod().back().size()) - 1;
out->mutable_data<T>(odims, ctx.GetPlace());
}
out->mutable_data<T>(ctx.GetPlace());
auto value = ctx.Attr<float>("value");
math::SetConstant<DeviceContext, T> setter;
setter(ctx.template device_context<DeviceContext>(), out,
static_cast<T>(value));
if (force_cpu) {
out->mutable_data(platform::CPUPlace(), data_type);
} else {
out->mutable_data(ctx.GetPlace(), data_type);
}
platform::DeviceContextPool& pool = platform::DeviceContextPool::Instance();
auto& dev_ctx = *pool.Get(ctx.GetPlace());
math::set_constant(dev_ctx, out, value);
}
};
......
paddle/fluid/operators/fill_constant_op.cu.cc
浏览文件 @ dfd1eee7
......@@ -19,4 +19,5 @@ REGISTER_OP_CUDA_KERNEL(fill_constant, ops::FillConstantKernel<float>,
ops::FillConstantKernel<double>,
ops::FillConstantKernel<int64_t>,
ops::FillConstantKernel<int>,
ops::FillConstantKernel<bool>,
ops::FillConstantKernel<paddle::platform::float16>);
paddle/fluid/operators/gather_nd_op.cc
浏览文件 @ dfd1eee7
......@@ -60,8 +60,13 @@ class GatherNdOp : public framework::OperatorWithKernel {
protected:
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext& ctx) const override {
return framework::OpKernelType(ctx.Input<Tensor>("X")->type(),
ctx.device_context());
auto* x = ctx.Input<Tensor>("X");
const auto& x_type = x->type();
return framework::OpKernelType(
x_type,
x_type == framework::proto::VarType::BOOL
? x->place() // to be consistent with compare and logical ops
: ctx.device_context().GetPlace());
}
};
......@@ -173,7 +178,7 @@ REGISTER_OPERATOR(gather_nd_grad, ops::GatherNdGradOp,
REGISTER_OP_CPU_KERNEL(gather_nd, ops::GatherNdOpKernel<float>,
ops::GatherNdOpKernel<double>,
ops::GatherNdOpKernel<int64_t>,
ops::GatherNdOpKernel<int>,
ops::GatherNdOpKernel<int>, ops::GatherNdOpKernel<bool>,
ops::GatherNdOpKernel<uint8_t>);
REGISTER_OP_CPU_KERNEL(gather_nd_grad, ops::GatherNdGradOpKernel<float>,
......
paddle/fluid/operators/gather_nd_op.cu
浏览文件 @ dfd1eee7
......@@ -95,6 +95,7 @@ REGISTER_OP_CUDA_KERNEL(gather_nd, ops::GatherNdOpCUDAKernel<CUDA, float>,
ops::GatherNdOpCUDAKernel<CUDA, double>,
ops::GatherNdOpCUDAKernel<CUDA, int64_t>,
ops::GatherNdOpCUDAKernel<CUDA, int>,
ops::GatherNdOpCUDAKernel<CUDA, bool>,
ops::GatherNdOpCUDAKernel<CUDA, plat::float16>);
REGISTER_OP_CUDA_KERNEL(gather_nd_grad,
......
paddle/fluid/operators/gather_tree_op.cc 0 → 100644
浏览文件 @ dfd1eee7
/* Copyright (c) 2019 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/gather_tree_op.h"
namespace paddle {
namespace operators {
class GatherTreeOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("Ids"),
"Input(Ids) of GatherTreeOp should not be null.");
PADDLE_ENFORCE(ctx->HasInput("Parents"),
"Input(Parents) of GatherTreeOp should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Out"),
"Output(Out) of GatherTreeOp should not be null.");
auto ids_dims = ctx->GetInputDim("Ids");
auto parents_dims = ctx->GetInputDim("Parents");
PADDLE_ENFORCE(ids_dims == parents_dims,
"The shape of Input(Parents) must be same with the shape of "
"Input(Ids).");
ctx->SetOutputDim("Out", ids_dims);
}
protected:
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext& ctx) const override {
return framework::OpKernelType(ctx.Input<Tensor>("Ids")->type(),
ctx.device_context());
}
};
class GatherTreeOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("Ids",
"The Tensor with shape [length, batch_size, beam_size] containing "
"the selected ids of all time steps.");
AddInput("Parents",
"The Tensor has the same shape as Ids and contains the parents "
"corresponding to selected ids when searching among beams.");
AddOutput(
"Out",
"A Tensor with shape [length, batch_size, beam_size] containing the "
"full sequences. The sequences is collected by backtracing from the "
"last time step of Ids.");
AddComment(R"DOC(
GatherTree Operator.
Backtrace from the last time step and generate the full sequences by collecting beam search
selected ids.
)DOC");
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OPERATOR(gather_tree, ops::GatherTreeOp, ops::GatherTreeOpMaker);
REGISTER_OP_CPU_KERNEL(gather_tree, ops::GatherTreeOpKernel<int32_t>,
ops::GatherTreeOpKernel<int64_t>);
paddle/fluid/operators/gather_tree_op.cu 0 → 100644
浏览文件 @ dfd1eee7
/* Copyright (c) 2019 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 <algorithm>
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/gather_tree_op.h"
namespace paddle {
namespace operators {
#define CUDA_1D_KERNEL_LOOP(i, n) \
for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < (n); \
i += blockDim.x * gridDim.x)
template <typename T>
__global__ void GatherTree(const T *ids_data, const T *parents_data,
T *out_data, const int64_t max_length,
const int64_t batch_size, const int64_t beam_size) {
CUDA_1D_KERNEL_LOOP(i, batch_size * beam_size) {
int batch = i / beam_size;
int beam = i % beam_size;
auto idx =
(max_length - 1) * batch_size * beam_size + batch * beam_size + beam;
out_data[idx] = ids_data[idx];
auto parent = parents_data[idx];
for (int step = max_length - 2; step >= 0; step--) {
idx = step * batch_size * beam_size + batch * beam_size;
out_data[idx + beam] = ids_data[idx + parent];
parent = parents_data[idx + parent];
}
}
}
template <typename T>
class GatherTreeOpCUDAKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext &ctx) const override {
auto *ids = ctx.Input<Tensor>("Ids");
auto *parents = ctx.Input<Tensor>("Parents");
auto *out = ctx.Output<Tensor>("Out");
const auto *ids_data = ids->data<T>();
const auto *parents_data = parents->data<T>();
auto *out_data = out->mutable_data<T>(ctx.GetPlace());
auto &ids_dims = ids->dims();
int64_t max_length = ids_dims[0];
int64_t batch_size = ids_dims[1];
int64_t beam_size = ids_dims[2];
auto &dev_ctx = ctx.cuda_device_context();
const int block = 512;
int max_threads =
std::min(static_cast<int64_t>(dev_ctx.GetMaxPhysicalThreadCount()),
batch_size * beam_size);
const int grid = std::max(max_threads / block, 1);
GatherTree<<<grid, block>>>(ids_data, parents_data, out_data, max_length,
batch_size, beam_size);
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP_CUDA_KERNEL(gather_tree, ops::GatherTreeOpCUDAKernel<int32_t>,
ops::GatherTreeOpCUDAKernel<int64_t>);
paddle/fluid/operators/gather_tree_op.h 0 → 100644
浏览文件 @ dfd1eee7
/* Copyright (c) 2019 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. */
#pragma once
#include "paddle/fluid/framework/eigen.h"
#include "paddle/fluid/framework/op_registry.h"
namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
template <typename T>
class GatherTreeOpKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext &ctx) const override {
auto *ids = ctx.Input<Tensor>("Ids");
auto *parents = ctx.Input<Tensor>("Parents");
auto *out = ctx.Output<Tensor>("Out");
const auto *ids_data = ids->data<T>();
const auto *parents_data = parents->data<T>();
auto *out_data = out->mutable_data<T>(ctx.GetPlace());
auto &ids_dims = ids->dims();
auto max_length = ids_dims[0];
auto batch_size = ids_dims[1];
auto beam_size = ids_dims[2];
for (int batch = 0; batch < batch_size; batch++) {
for (int beam = 0; beam < beam_size; beam++) {
auto idx = (max_length - 1) * batch_size * beam_size +
batch * beam_size + beam;
out_data[idx] = ids_data[idx];
auto parent = parents_data[idx];
for (int step = max_length - 2; step >= 0; step--) {
idx = step * batch_size * beam_size + batch * beam_size;
out_data[idx + beam] = ids_data[idx + parent];
parent = parents_data[idx + parent];
}
}
}
}
};
} // namespace operators
} // namespace paddle
paddle/fluid/operators/reduce_ops/reduce_all_op.cc
浏览文件 @ dfd1eee7
......@@ -14,7 +14,9 @@
#include "paddle/fluid/operators/reduce_ops/reduce_all_op.h"
REGISTER_REDUCE_OP_WITHOUT_GRAD(reduce_all);
// kernel's device type is decided by input tensor place, to be consistent with
// compare and logical ops
REGISTER_REDUCE_OP_WITHOUT_GRAD(reduce_all, UseInputPlace);
REGISTER_OP_CPU_KERNEL(reduce_all,
ops::ReduceKernel<paddle::platform::CPUDeviceContext,
bool, ops::AllFunctor>);
paddle/fluid/operators/reduce_ops/reduce_any_op.cc
浏览文件 @ dfd1eee7
......@@ -14,7 +14,9 @@
#include "paddle/fluid/operators/reduce_ops/reduce_any_op.h"
REGISTER_REDUCE_OP_WITHOUT_GRAD(reduce_any);
// kernel's device type is decided by input tensor place, to be consistent with
// compare and logical ops
REGISTER_REDUCE_OP_WITHOUT_GRAD(reduce_any, UseInputPlace);
REGISTER_OP_CPU_KERNEL(reduce_any,
ops::ReduceKernel<paddle::platform::CPUDeviceContext,
bool, ops::AnyFunctor>);
paddle/fluid/operators/reduce_ops/reduce_op.h
浏览文件 @ dfd1eee7
......@@ -223,6 +223,19 @@ class ReduceOp : public framework::OperatorWithKernel {
}
};
class ReduceOpUseInputPlace : public ReduceOp {
public:
using ReduceOp::ReduceOp;
protected:
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext& ctx) const override {
framework::OpKernelType kt = OperatorWithKernel::GetExpectedKernelType(ctx);
kt.place_ = ctx.Input<framework::LoDTensor>("X")->place();
return kt;
}
};
class ReduceGradOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
......@@ -313,11 +326,11 @@ namespace ops = paddle::operators;
paddle::framework::DefaultGradOpDescMaker<true>); \
REGISTER_OPERATOR(op_name##_grad, ops::ReduceGradOp)
#define REGISTER_REDUCE_OP_WITHOUT_GRAD(op_name) \
#define REGISTER_REDUCE_OP_WITHOUT_GRAD(op_name, ...) \
class __##op_name##Maker__ : public ops::ReduceOpMaker { \
protected: \
virtual std::string GetName() const { return #op_name; } \
virtual std::string GetOpType() const { return "Reduce " #op_name; } \
}; \
REGISTER_OPERATOR(op_name, ops::ReduceOp, __##op_name##Maker__, \
REGISTER_OPERATOR(op_name, ops::ReduceOp##__VA_ARGS__, __##op_name##Maker__, \
paddle::framework::EmptyGradOpMaker);
paddle/fluid/operators/tensor_array_to_tensor_op.cc
浏览文件 @ dfd1eee7
......@@ -120,11 +120,18 @@ class LoDTensorArray2TensorOp : public framework::OperatorBase {
out.Resize(out_dims);
LodTensorArray2LodTensorVector(scope, base_name, Input("X"), &names);
// Invoke concat Op
auto concat_op = framework::OpRegistry::CreateOp(
auto use_stack = Attr<bool>("use_stack");
// Invoke concat Op or stack Op
auto op =
use_stack
? framework::OpRegistry::CreateOp("stack", {{"X", names}},
{{"Y", {Output("Out")}}}, attrs)
: framework::OpRegistry::CreateOp(
"concat", {{"X", names}}, {{"Out", {Output("Out")}}}, attrs);
concat_op->Run(scope, place);
op->Run(scope, place);
}
};
......@@ -139,17 +146,32 @@ class LoDTensorArray2TensorOpMaker : public framework::OpProtoAndCheckerMaker {
AddAttr<int>("axis",
"The axis along which the input tensors will be concatenated.")
.SetDefault(0);
AddAttr<bool>("use_stack",
"Act as concat_op or stack_op. For stack mode, all tensors "
"in the tensor array must have the same shape.")
.SetDefault(false);
AddComment(R"DOC(
tensor_array_to_tensor Operator.
Concatenate the input LoDTensorArray along dimension axis to the output Tensor.
If use concat mode, concatenate all tensors in the input LoDTensorArray along
axis into the output Tensor.
Examples:
Input = {[1,2], [3,4], [5,6]}
axis = 0
Output = [1,2,3,4,5,6]
OutputIndex = [2,2,2]
If use stack mode, stack all tensors in the input LoDTensorArray along axis into
the output Tensor.
Examples:
Input = {[1,2], [3,4], [5,6]}
axis = 0
Output = [[1,2],
[3,4],
[5,6]]
OutputIndex = [1,1,1]
OutputIndex = [2,2,2]
)DOC");
}
......@@ -157,12 +179,34 @@ Examples:
class LoDTensorArray2TensorOpInferShape : public framework::InferShapeBase {
public:
void operator()(framework::InferShapeContext *ctx) const override {}
void operator()(framework::InferShapeContext *ctx) const override {
// in runtime, shape is determined by RunImpl
if (ctx->IsRuntime()) return;
auto dims = ctx->GetInputDim("X");
// if the shape is empty
if (dims == framework::make_ddim({0UL})) return;
// otherwise, suppose the shape of array is the shape of tensor in the
// array, which is consistent with what tensor_array_read_write dose
auto axis = ctx->Attrs().Get<int>("axis");
auto use_stack = ctx->Attrs().Get<bool>("use_stack");
if (use_stack) {
auto dim_vec = framework::vectorize<int>(dims);
// use -1 for the stack dim size
dim_vec.insert(dim_vec.begin() + axis, -1);
dims = framework::make_ddim(dim_vec);
} else {
// use -1 for the concat dim size
dims[axis] = -1;
}
ctx->SetOutputDim("Out", dims);
}
};
class LoDTensorArray2TensorGradInferShape : public framework::InferShapeBase {
public:
void operator()(framework::InferShapeContext *context) const override {}
void operator()(framework::InferShapeContext *ctx) const override {
ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("X"));
}
};
class LoDTensorArray2TensorGradInferVarType
......@@ -204,11 +248,18 @@ class LoDTensorArray2TensorGradOp : public framework::OperatorBase {
LodTensorVectorResizeFromLodTensorArray(scope, "grad_name", Input("X"),
&grad_names);
auto concat_grad_op = framework::OpRegistry::CreateOp(
auto use_stack = Attr<bool>("use_stack");
auto grad_op =
use_stack
? framework::OpRegistry::CreateOp(
"stack_grad", {{"X", names}, {"Y@GRAD", {dout_name}}},
{{"X@GRAD", grad_names}}, attrs)
: framework::OpRegistry::CreateOp(
"concat_grad", {{"X", names}, {"Out@GRAD", {dout_name}}},
{{"X@GRAD", grad_names}}, attrs);
concat_grad_op->Run(scope, place);
grad_op->Run(scope, place);
LodTensorArrayCreateFromLodTensorArray(scope, Input("X"), dx_name);
auto &grad_inx =
......
python/paddle/fluid/layers/__init__.py
浏览文件 @ dfd1eee7
......@@ -35,6 +35,7 @@ from .metric_op import *
from .learning_rate_scheduler import *
from .collective import *
from .distributions import *
from . import rnn
__all__ = []
__all__ += nn.__all__
......@@ -47,3 +48,6 @@ __all__ += detection.__all__
__all__ += metric_op.__all__
__all__ += learning_rate_scheduler.__all__
__all__ += distributions.__all__
__all__ += rnn.__all__
from .rnn import *
python/paddle/fluid/layers/nn.py
浏览文件 @ dfd1eee7
......@@ -221,6 +221,7 @@ __all__ = [
'filter_by_instag',
'shard_index',
'hard_swish',
'gather_tree',
'mse_loss',
'uniform_random',
]
......@@ -16994,6 +16995,81 @@ def hard_swish(x, threshold=6.0, scale=6.0, offset=3.0, name=None):
return out
def gather_tree(ids, parents):
"""
To be used after beam search. After beam search, we get selected ids at
each time step and the corresponding parents in the search tree. Both ids
and parents have the layout :attr:`[max_time, batch_size, beam_size]`. Then
:attr:`gather_tree` is used to backtrace from the last time step and
generate the full sequences by collecting selected ids.
Here is an example:
.. code-block:: text
Given:
ids = [[[2 2]
[6 1]]
[[3 9]
[6 1]]
[[0 1]
[9 0]]]
parents = [[[0 0]
[1 1]]
[[1 0]
[1 0]]
[[0 0]
[0 1]]]
Then:
gather_tree(ids, parents)
= [[[2 2]
[1 6]]
[[3 3]
[6 1]]
[[0 1]
[9 0]]]
Args:
ids(Variable): A Tensor with shape :attr:`[length, batch_size, beam_size]`
and data type :attr:`int32` or :attr:`int64`. It contains the selected
ids of all time steps.
parents(Variable): A Tensor with the same shape and data type as :attr:`ids`,
It contains the parents corresponding to selected ids when searching
among beams.
Returns:
Variable: A Tensor with the same shape and data type as :attr:`ids`. \
It contains the full sequences. The sequences are collected from \
:attr:`ids` by backtracing according to :attr:`parents`.
Examples:
.. code-block:: python
import paddle.fluid as fluid
ids = fluid.layers.data(name='ids',
shape=[5, 2, 2],
dtype='int64',
append_batch_size=False)
parents = fluid.layers.data(name='parents',
shape=[5, 2, 2],
dtype='int64',
append_batch_size=False)
final_sequences = fluid.layers.gather_tree(ids, parents)
"""
helper = LayerHelper('gather_tree', **locals())
out = helper.create_variable_for_type_inference(dtype=ids.dtype)
helper.append_op(
type="gather_tree",
inputs={"Ids": ids,
"Parents": parents},
outputs={"Out": out})
return out
def mse_loss(input, label):
"""
This op accepts input predications and target label and returns the mean square error.
......
python/paddle/fluid/layers/rnn.py 0 → 100644
浏览文件 @ dfd1eee7
此差异已折叠。
python/paddle/fluid/layers/tensor.py
浏览文件 @ dfd1eee7
......@@ -273,50 +273,85 @@ def concat(input, axis=0, name=None):
return out
def tensor_array_to_tensor(input, axis=1, name=None):
def tensor_array_to_tensor(input, axis=1, name=None, use_stack=False):
"""
This OP concatenates the input LodTensorArray along the axis.
This function concatenates or stacks all tensors in the input LoDTensorArray
along the axis mentioned and returns that as the output.
For Example:
.. code-block:: text
Case 1:
Given:
input.data = {[[0.6, 0.1, 0.3],
[0.5, 0.3, 0.2]],
[[1.3],
[1.8]],
[[2.3, 2.1],
[2.5, 2.4]]}
axis = 1, use_stack = False
Then:
output.data = [[0.6, 0.1, 0.3, 1.3, 2.3, 2.1],
[0.5, 0.3, 0.2, 1.8, 2.5, 2.4]]
output_index.data = [3, 1, 2]
Case 2:
Given:
input.data = {[[0.6, 0.1],
[0.5, 0.3]],
[[0.3, 1.3],
[0.2, 1.8]],
[[2.3, 2.1],
[2.5, 2.4]]}
axis = 1, use_stack = True
Then:
output.data = [[[0.6, 0.1]
[0.3, 1.3]
[2.3, 2.1],
[[0.5, 0.3]
[0.2, 1.8]
[2.5, 2.4]]]
output_index.data = [2, 2, 2]
Args:
input(Variable): A LodTensorArray with data type float32, float64, int32,
int64.
axis(int, optional): Axis to compute indices along. The effective range
is [-R, R), where R is Rank(x). when axis<0, it works the same way
as axis+R. Default is 1.
name (str, optional): The default value is None. Normally there is no
need for user to set this property. For more information, please
refer to :ref:`api_guide_Name`.
input(Variable): A LodTensorArray variable.
axis(int): The axis along which the tensors in attr::`input` will be
concatenated or stacked.
name(str|None): A name for this layer(optional). If set None, the layer
will be named automatically.
use_stack(bool): Act as concat_op or stack_op. For stack mode, all
tensors in the tensor array must have the same shape.
Returns:
Variable: A LoDTensor with the same data type as input's
Variable: The input LodTensorArray items' dims along the axis.
Variable: The concatenated or stacked tensor variable.
Variable: A 1-D tensor variable with int32 data type. The data in this \
tensor contains all input including tensors' sizes along the axis.
Examples:
.. code-block:: python
import paddle.fluid as fluid
import numpy as np
place = fluid.CPUPlace()
x1 = fluid.data(name="x", shape=[2,2], lod_level=0)
tmp = fluid.layers.fill_constant(shape=[2,3], dtype="float32", value=1)
x_arr = fluid.layers.create_array(dtype="float32")
c0 = fluid.layers.fill_constant(shape=[1], dtype='int64', value=0)
fluid.layers.array_write(x=tmp, i=c0, array=x_arr)
c1 = fluid.layers.fill_constant(shape=[1], dtype='int64', value=1)
fluid.layers.array_write(x=x1, i=c1, array=x_arr)
output, output_index = fluid.layers.tensor_array_to_tensor(input=x_arr, axis=1)
exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())
feedx = fluid.LoDTensor()
feedx.set(np.array([[1.3,-2.4],[0,4]]).astype("float32"), place)
res = exe.run(fluid.default_main_program(), feed={'x':feedx}, fetch_list=[output], return_numpy=False)
print(np.array(res[0]))
# [[ 1. 1. 1. 1.3 -2.4]
# [ 1. 1. 1. 0. 4. ]]
x0 = fluid.layers.assign(np.random.rand(2, 2).astype("float32"))
x1 = fluid.layers.assign(np.random.rand(2, 2).astype("float32"))
i = fluid.layers.fill_constant(shape=[1], dtype="int64", value=0)
array = fluid.layers.create_array(dtype='float32')
fluid.layers.array_write(x0, i, array)
fluid.layers.array_write(x1, i + 1, array)
output, output_index = fluid.layers.tensor_array_to_tensor(input=array)
"""
helper = LayerHelper('tensor_array_to_tensor', **locals())
out = helper.create_variable_for_type_inference(dtype=helper.input_dtype())
......@@ -326,7 +361,8 @@ def tensor_array_to_tensor(input, axis=1, name=None):
inputs={'X': input},
outputs={'Out': [out],
'OutIndex': [out_index]},
attrs={'axis': axis})
attrs={'axis': axis,
'use_stack': use_stack})
return out, out_index
......@@ -517,7 +553,8 @@ def fill_constant_batch_size_like(input,
dtype,
value,
input_dim_idx=0,
output_dim_idx=0):
output_dim_idx=0,
force_cpu=False):
"""
This OP creates a Tesnor accroding the shape and dtype, and initializes the
Tensor with the constants provided in ``value``. When the input is LoDTensor
......@@ -537,6 +574,7 @@ def fill_constant_batch_size_like(input,
The default value is 0.
output_dim_idx(int): Used to specify which dimension of Tensor is created to be set
the value of batch_size of input Tensor. The default value is 0.
force_cpu(bool): data should be on CPU if it's true, defalut value is False.
Returns:
Variable: Tensor which will be created according to dtype.
......@@ -562,7 +600,8 @@ def fill_constant_batch_size_like(input,
'dtype': out.dtype,
'value': float(value),
'input_dim_idx': input_dim_idx,
'output_dim_idx': output_dim_idx
'output_dim_idx': output_dim_idx,
'force_cpu': force_cpu or force_init_on_cpu()
})
out.stop_gradient = True
return out
......
python/paddle/fluid/layers/utils.py
浏览文件 @ dfd1eee7
......@@ -13,6 +13,8 @@
# limitations under the License.
from __future__ import print_function
import collections
import six
import numpy as np
......@@ -59,3 +61,173 @@ def convert_to_list(value, n, name, dtype=np.int):
"including element " + str(single_value) + " of type" + " "
+ str(type(single_value)))
return value_list
def is_sequence(seq):
"""
Whether `seq` is an entry or nested structure
"""
if isinstance(seq, dict):
return True
return (isinstance(seq, collections.Sequence) and
not isinstance(seq, six.string_types))
def _sorted(dict_):
"""
Returns a sorted list of the dict keys, with error if keys not sortable.
"""
try:
return sorted(six.iterkeys(dict_))
except TypeError:
raise TypeError("nest only supports dicts with sortable keys.")
def _yield_value(iterable):
if isinstance(iterable, dict):
# Iterate through dictionaries in a deterministic order by sorting the
# keys. Notice this means that we ignore the original order of `OrderedDict`
# instances. This is intentional, to avoid potential bugs caused by mixing
# ordered and plain dicts (e.g., flattening a dict but using a
# corresponding `OrderedDict` to pack it back).
for key in _sorted(iterable):
yield iterable[key]
else:
for value in iterable:
yield value
def _yield_flat_nest(nest):
for n in _yield_value(nest):
if is_sequence(n):
for ni in _yield_flat_nest(n):
yield ni
else:
yield n
def flatten(nest):
"""
Traverse all entries in the nested structure and put them into an list.
"""
if is_sequence(nest):
return list(_yield_flat_nest(nest))
else:
return [nest]
def _sequence_like(instance, args):
"""
Convert the sequence `args` to the same type as `instance`.
"""
if isinstance(instance, dict):
# Pack dictionaries in a deterministic order by sorting the keys.
# Notice this means that we ignore the original order of `OrderedDict`
# instances. This is intentional, to avoid potential bugs caused by mixing
# ordered and plain dicts (e.g., flattening a dict but using a
# corresponding `OrderedDict` to pack it back).
result = dict(zip(_sorted(instance), args))
return type(instance)((key, result[key])
for key in six.iterkeys(instance))
elif (isinstance(instance, tuple) and hasattr(instance, "_fields") and
isinstance(instance._fields, collections.Sequence) and
all(isinstance(f, six.string_types) for f in instance._fields)):
# This is a namedtuple
return type(instance)(*args)
else:
# Not a namedtuple
return type(instance)(args)
def _packed_nest_with_indices(structure, flat, index):
"""
Helper function for pack_sequence_as.
"""
packed = []
for s in _yield_value(structure):
if is_sequence(s):
new_index, child = _packed_nest_with_indices(s, flat, index)
packed.append(_sequence_like(s, child))
index = new_index
else:
packed.append(flat[index])
index += 1
return index, packed
def pack_sequence_as(structure, flat_sequence):
"""
Pack a given flattened sequence into a given structure.
"""
if not is_sequence(flat_sequence):
raise TypeError("flat_sequence must be a sequence")
if not is_sequence(structure):
if len(flat_sequence) != 1:
raise ValueError(
"Structure is a scalar but len(flat_sequence) == %d > 1" %
len(flat_sequence))
return flat_sequence[0]
flat_structure = flatten(structure)
if len(flat_structure) != len(flat_sequence):
raise ValueError(
"Could not pack sequence. Structure had %d elements, but flat_sequence "
"had %d elements. Structure: %s, flat_sequence: %s." %
(len(flat_structure), len(flat_sequence), structure, flat_sequence))
_, packed = _packed_nest_with_indices(structure, flat_sequence, 0)
return _sequence_like(structure, packed)
def map_structure(func, *structure):
"""
Apply `func` to each entry in `structure` and return a new structure.
"""
flat_structure = [flatten(s) for s in structure]
entries = zip(*flat_structure)
return pack_sequence_as(structure[0], [func(*x) for x in entries])
def _recursive_assert_same_structure(nest1, nest2, check_types):
"""
Helper function for `assert_same_structure`.
"""
is_sequence_nest1 = is_sequence(nest1)
if is_sequence_nest1 != is_sequence(nest2):
raise ValueError(
"The two structures don't have the same nested structure.\n\n"
"First structure: %s\n\nSecond structure: %s." % (nest1, nest2))
if not is_sequence_nest1:
return # finished checking
if check_types:
type_nest1 = type(nest1)
type_nest2 = type(nest2)
if type_nest1 != type_nest2:
raise TypeError(
"The two structures don't have the same sequence type. First "
"structure has type %s, while second structure has type %s." %
(type_nest1, type_nest2))
if isinstance(nest1, dict):
keys1 = set(six.iterkeys(nest1))
keys2 = set(six.iterkeys(nest2))
if keys1 != keys2:
raise ValueError(
"The two dictionaries don't have the same set of keys. First "
"structure has keys {}, while second structure has keys {}."
.format(keys1, keys2))
nest1_as_sequence = [n for n in _yield_value(nest1)]
nest2_as_sequence = [n for n in _yield_value(nest2)]
for n1, n2 in zip(nest1_as_sequence, nest2_as_sequence):
_recursive_assert_same_structure(n1, n2, check_types)
def assert_same_structure(nest1, nest2, check_types=True):
"""
Confirm two nested structures with the same structure.
"""
len_nest1 = len(flatten(nest1)) if is_sequence(nest1) else 1
len_nest2 = len(flatten(nest2)) if is_sequence(nest2) else 1
if len_nest1 != len_nest2:
raise ValueError("The two structures don't have the same number of "
"elements.\n\nFirst structure (%i elements): %s\n\n"
"Second structure (%i elements): %s" %
(len_nest1, nest1, len_nest2, nest2))
_recursive_assert_same_structure(nest1, nest2, check_types)
python/paddle/fluid/tests/unittests/test_gather_tree_op.py 0 → 100644
浏览文件 @ dfd1eee7
# Copyright (c) 2019 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 unittest
import numpy as np
from op_test import OpTest
import paddle.fluid as fluid
class TestGatherTreeOp(OpTest):
def setUp(self):
self.op_type = "gather_tree"
max_length, batch_size, beam_size = 5, 2, 2
ids = np.random.randint(
0, high=10, size=(max_length, batch_size, beam_size))
parents = np.random.randint(
0, high=beam_size, size=(max_length, batch_size, beam_size))
self.inputs = {"Ids": ids, "Parents": parents}
self.outputs = {'Out': self.backtrace(ids, parents)}
def test_check_output(self):
self.check_output()
@staticmethod
def backtrace(ids, parents):
out = np.zeros_like(ids)
(max_length, batch_size, beam_size) = ids.shape
for batch in range(batch_size):
for beam in range(beam_size):
out[max_length - 1, batch, beam] = ids[max_length - 1, batch,
beam]
parent = parents[max_length - 1, batch, beam]
for step in range(max_length - 2, -1, -1):
out[step, batch, beam] = ids[step, batch, parent]
parent = parents[step, batch, parent]
return out
class TestGatherTreeOpAPI(OpTest):
def test_case(self):
ids = fluid.layers.data(
name='ids', shape=[5, 2, 2], dtype='int64', append_batch_size=False)
parents = fluid.layers.data(
name='parents',
shape=[5, 2, 2],
dtype='int64',
append_batch_size=False)
final_sequences = fluid.layers.gather_tree(ids, parents)
if __name__ == "__main__":
unittest.main()
python/paddle/fluid/tests/unittests/test_rnn_cell_api.py 0 → 100644
浏览文件 @ dfd1eee7
# Copyright (c) 2019 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 unittest
import numpy
import paddle.fluid as fluid
import paddle.fluid.layers as layers
import paddle.fluid.core as core
from paddle.fluid.executor import Executor
from paddle.fluid import framework
from paddle.fluid.layers.rnn import LSTMCell, GRUCell, RNNCell
from paddle.fluid.layers import rnn as dynamic_rnn
from paddle.fluid import contrib
from paddle.fluid.contrib.layers import basic_lstm
import paddle.fluid.layers.utils as utils
import numpy as np
class TestLSTMCell(unittest.TestCase):
def setUp(self):
self.batch_size = 4
self.input_size = 16
self.hidden_size = 16
def test_run(self):
inputs = fluid.data(
name='inputs', shape=[None, self.input_size], dtype='float32')
pre_hidden = fluid.data(
name='pre_hidden', shape=[None, self.hidden_size], dtype='float32')
pre_cell = fluid.data(
name='pre_cell', shape=[None, self.hidden_size], dtype='float32')
cell = LSTMCell(self.hidden_size)
lstm_hidden_new, lstm_states_new = cell(inputs, [pre_hidden, pre_cell])
lstm_unit = contrib.layers.rnn_impl.BasicLSTMUnit(
"basicLSTM", self.hidden_size, None, None, None, None, 1.0,
"float32")
lstm_hidden, lstm_cell = lstm_unit(inputs, pre_hidden, pre_cell)
if core.is_compiled_with_cuda():
place = core.CUDAPlace(0)
else:
place = core.CPUPlace()
exe = Executor(place)
exe.run(framework.default_startup_program())
inputs_np = np.random.uniform(
-0.1, 0.1, (self.batch_size, self.input_size)).astype('float32')
pre_hidden_np = np.random.uniform(
-0.1, 0.1, (self.batch_size, self.hidden_size)).astype('float32')
pre_cell_np = np.random.uniform(
-0.1, 0.1, (self.batch_size, self.hidden_size)).astype('float32')
param_names = [[
"LSTMCell/BasicLSTMUnit_0.w_0", "basicLSTM/BasicLSTMUnit_0.w_0"
], ["LSTMCell/BasicLSTMUnit_0.b_0", "basicLSTM/BasicLSTMUnit_0.b_0"]]
for names in param_names:
param = np.array(fluid.global_scope().find_var(names[0]).get_tensor(
))
param = np.random.uniform(
-0.1, 0.1, size=param.shape).astype('float32')
fluid.global_scope().find_var(names[0]).get_tensor().set(param,
place)
fluid.global_scope().find_var(names[1]).get_tensor().set(param,
place)
out = exe.run(feed={
'inputs': inputs_np,
'pre_hidden': pre_hidden_np,
'pre_cell': pre_cell_np
},
fetch_list=[lstm_hidden_new, lstm_hidden])
self.assertTrue(np.allclose(out[0], out[1], rtol=1e-4, atol=0))
class TestGRUCell(unittest.TestCase):
def setUp(self):
self.batch_size = 4
self.input_size = 16
self.hidden_size = 16
def test_run(self):
inputs = fluid.data(
name='inputs', shape=[None, self.input_size], dtype='float32')
pre_hidden = layers.data(
name='pre_hidden',
shape=[None, self.hidden_size],
append_batch_size=False,
dtype='float32')
cell = GRUCell(self.hidden_size)
gru_hidden_new, _ = cell(inputs, pre_hidden)
gru_unit = contrib.layers.rnn_impl.BasicGRUUnit(
"basicGRU", self.hidden_size, None, None, None, None, "float32")
gru_hidden = gru_unit(inputs, pre_hidden)
if core.is_compiled_with_cuda():
place = core.CUDAPlace(0)
else:
place = core.CPUPlace()
exe = Executor(place)
exe.run(framework.default_startup_program())
inputs_np = np.random.uniform(
-0.1, 0.1, (self.batch_size, self.input_size)).astype('float32')
pre_hidden_np = np.random.uniform(
-0.1, 0.1, (self.batch_size, self.hidden_size)).astype('float32')
param_names = [
["GRUCell/BasicGRUUnit_0.w_0", "basicGRU/BasicGRUUnit_0.w_0"],
["GRUCell/BasicGRUUnit_0.w_1", "basicGRU/BasicGRUUnit_0.w_1"],
["GRUCell/BasicGRUUnit_0.b_0", "basicGRU/BasicGRUUnit_0.b_0"],
["GRUCell/BasicGRUUnit_0.b_1", "basicGRU/BasicGRUUnit_0.b_1"]
]
for names in param_names:
param = np.array(fluid.global_scope().find_var(names[0]).get_tensor(
))
param = np.random.uniform(
-0.1, 0.1, size=param.shape).astype('float32')
fluid.global_scope().find_var(names[0]).get_tensor().set(param,
place)
fluid.global_scope().find_var(names[1]).get_tensor().set(param,
place)
out = exe.run(feed={'inputs': inputs_np,
'pre_hidden': pre_hidden_np},
fetch_list=[gru_hidden_new, gru_hidden])
self.assertTrue(np.allclose(out[0], out[1], rtol=1e-4, atol=0))
class TestRnn(unittest.TestCase):
def setUp(self):
self.batch_size = 4
self.input_size = 16
self.hidden_size = 16
self.seq_len = 4
def test_run(self):
inputs_basic_lstm = fluid.data(
name='inputs_basic_lstm',
shape=[None, None, self.input_size],
dtype='float32')
sequence_length = fluid.data(
name="sequence_length", shape=[None], dtype='int64')
inputs_dynamic_rnn = layers.transpose(inputs_basic_lstm, perm=[1, 0, 2])
cell = LSTMCell(self.hidden_size, name="LSTMCell_for_rnn")
output, final_state = dynamic_rnn(
cell=cell,
inputs=inputs_dynamic_rnn,
sequence_length=sequence_length,
is_reverse=False)
output_new = layers.transpose(output, perm=[1, 0, 2])
rnn_out, last_hidden, last_cell = basic_lstm(inputs_basic_lstm, None, None, self.hidden_size, num_layers=1, \
batch_first = False, bidirectional=False, sequence_length=sequence_length, forget_bias = 1.0)
if core.is_compiled_with_cuda():
place = core.CUDAPlace(0)
else:
place = core.CPUPlace()
exe = Executor(place)
exe.run(framework.default_startup_program())
inputs_basic_lstm_np = np.random.uniform(
-0.1, 0.1,
(self.seq_len, self.batch_size, self.input_size)).astype('float32')
sequence_length_np = np.ones(
self.batch_size, dtype='int64') * self.seq_len
inputs_np = np.random.uniform(
-0.1, 0.1, (self.batch_size, self.input_size)).astype('float32')
pre_hidden_np = np.random.uniform(
-0.1, 0.1, (self.batch_size, self.hidden_size)).astype('float32')
pre_cell_np = np.random.uniform(
-0.1, 0.1, (self.batch_size, self.hidden_size)).astype('float32')
param_names = [[
"LSTMCell_for_rnn/BasicLSTMUnit_0.w_0",
"basic_lstm_layers_0/BasicLSTMUnit_0.w_0"
], [
"LSTMCell_for_rnn/BasicLSTMUnit_0.b_0",
"basic_lstm_layers_0/BasicLSTMUnit_0.b_0"
]]
for names in param_names:
param = np.array(fluid.global_scope().find_var(names[0]).get_tensor(
))
param = np.random.uniform(
-0.1, 0.1, size=param.shape).astype('float32')
fluid.global_scope().find_var(names[0]).get_tensor().set(param,
place)
fluid.global_scope().find_var(names[1]).get_tensor().set(param,
place)
out = exe.run(feed={
'inputs_basic_lstm': inputs_basic_lstm_np,
'sequence_length': sequence_length_np,
'inputs': inputs_np,
'pre_hidden': pre_hidden_np,
'pre_cell': pre_cell_np
},
fetch_list=[output_new, rnn_out])
self.assertTrue(np.allclose(out[0], out[1], rtol=1e-4))
class TestRnnUtil(unittest.TestCase):
"""
Test cases for rnn apis' utility methods for coverage.
"""
def test_case(self):
inputs = {"key1": 1, "key2": 2}
func = lambda x: x + 1
outputs = utils.map_structure(func, inputs)
utils.assert_same_structure(inputs, outputs)
try:
inputs["key3"] = 3
utils.assert_same_structure(inputs, outputs)
except ValueError as identifier:
pass
if __name__ == '__main__':
unittest.main()
python/paddle/fluid/tests/unittests/test_rnn_decode_api.py 0 → 100644
浏览文件 @ dfd1eee7
# Copyright (c) 2019 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 unittest
import numpy
import paddle.fluid as fluid
import paddle.fluid.layers as layers
import paddle.fluid.core as core
from paddle.fluid.executor import Executor
from paddle.fluid import framework
from paddle.fluid.layers.rnn import LSTMCell, GRUCell, RNNCell, BeamSearchDecoder, dynamic_decode
from paddle.fluid.layers import rnn as dynamic_rnn
from paddle.fluid import contrib
from paddle.fluid.contrib.layers import basic_lstm
import numpy as np
class EncoderCell(RNNCell):
def __init__(self, num_layers, hidden_size, dropout_prob=0.):
self.num_layers = num_layers
self.hidden_size = hidden_size
self.dropout_prob = dropout_prob
self.lstm_cells = []
for i in range(num_layers):
self.lstm_cells.append(LSTMCell(hidden_size))
def call(self, step_input, states):
new_states = []
for i in range(self.num_layers):
out, new_state = self.lstm_cells[i](step_input, states[i])
step_input = layers.dropout(
out, self.dropout_prob) if self.dropout_prob > 0 else out
new_states.append(new_state)
return step_input, new_states
@property
def state_shape(self):
return [cell.state_shape for cell in self.lstm_cells]
class DecoderCell(RNNCell):
def __init__(self, num_layers, hidden_size, dropout_prob=0.):
self.num_layers = num_layers
self.hidden_size = hidden_size
self.dropout_prob = dropout_prob
self.lstm_cells = []
for i in range(num_layers):
self.lstm_cells.append(LSTMCell(hidden_size))
def attention(self, hidden, encoder_output, encoder_padding_mask):
query = layers.fc(hidden,
size=encoder_output.shape[-1],
bias_attr=False)
attn_scores = layers.matmul(
layers.unsqueeze(query, [1]), encoder_output, transpose_y=True)
if encoder_padding_mask is not None:
attn_scores = layers.elementwise_add(attn_scores,
encoder_padding_mask)
attn_scores = layers.softmax(attn_scores)
attn_out = layers.squeeze(
layers.matmul(attn_scores, encoder_output), [1])
attn_out = layers.concat([attn_out, hidden], 1)
attn_out = layers.fc(attn_out, size=self.hidden_size, bias_attr=False)
return attn_out
def call(self,
step_input,
states,
encoder_output,
encoder_padding_mask=None):
lstm_states, input_feed = states
new_lstm_states = []
step_input = layers.concat([step_input, input_feed], 1)
for i in range(self.num_layers):
out, new_lstm_state = self.lstm_cells[i](step_input, lstm_states[i])
step_input = layers.dropout(
out, self.dropout_prob) if self.dropout_prob > 0 else out
new_lstm_states.append(new_lstm_state)
out = self.attention(step_input, encoder_output, encoder_padding_mask)
return out, [new_lstm_states, out]
class TestDynamicDecode(unittest.TestCase):
def setUp(self):
self.batch_size = 4
self.input_size = 16
self.hidden_size = 16
self.seq_len = 4
def test_run(self):
start_token = 0
end_token = 1
src_vocab_size = 10
trg_vocab_size = 10
num_layers = 1
hidden_size = self.hidden_size
beam_size = 8
max_length = self.seq_len
src = layers.data(name="src", shape=[-1, 1], dtype='int64')
src_len = layers.data(name="src_len", shape=[-1], dtype='int64')
trg = layers.data(name="trg", shape=[-1, 1], dtype='int64')
trg_len = layers.data(name="trg_len", shape=[-1], dtype='int64')
src_embeder = lambda x: fluid.embedding(
x,
size=[src_vocab_size, hidden_size],
param_attr=fluid.ParamAttr(name="src_embedding"))
trg_embeder = lambda x: fluid.embedding(
x,
size=[trg_vocab_size, hidden_size],
param_attr=fluid.ParamAttr(name="trg_embedding"))
# use basic_lstm
encoder_cell = EncoderCell(num_layers, hidden_size)
encoder_output, encoder_final_state = dynamic_rnn(
cell=encoder_cell,
inputs=src_embeder(src),
sequence_length=src_len,
is_reverse=False)
src_mask = layers.sequence_mask(
src_len, maxlen=layers.shape(src)[1], dtype='float32')
encoder_padding_mask = (src_mask - 1.0) * 1000000000
encoder_padding_mask = layers.unsqueeze(encoder_padding_mask, [1])
decoder_cell = DecoderCell(num_layers, hidden_size)
decoder_initial_states = [
encoder_final_state, decoder_cell.get_initial_states(
batch_ref=encoder_output, shape=[hidden_size])
]
decoder_output, _ = dynamic_rnn(
cell=decoder_cell,
inputs=trg_embeder(trg),
initial_states=decoder_initial_states,
sequence_length=None,
encoder_output=encoder_output,
encoder_padding_mask=encoder_padding_mask)
output_layer = lambda x: layers.fc(x,
size=trg_vocab_size,
num_flatten_dims=len(x.shape) - 1,
param_attr=fluid.ParamAttr(
name="output_w"),
bias_attr=False)
# inference
encoder_output = BeamSearchDecoder.tile_beam_merge_with_batch(
encoder_output, beam_size)
encoder_padding_mask = BeamSearchDecoder.tile_beam_merge_with_batch(
encoder_padding_mask, beam_size)
beam_search_decoder = BeamSearchDecoder(
decoder_cell,
start_token,
end_token,
beam_size,
embedding_fn=trg_embeder,
output_fn=output_layer)
outputs, _ = dynamic_decode(
beam_search_decoder,
inits=decoder_initial_states,
max_step_num=max_length,
encoder_output=encoder_output,
encoder_padding_mask=encoder_padding_mask)
if core.is_compiled_with_cuda():
place = core.CUDAPlace(0)
else:
place = core.CPUPlace()
exe = Executor(place)
exe.run(framework.default_startup_program())
src_np = np.random.randint(
0, src_vocab_size, (self.batch_size, max_length)).astype('int64')
src_len_np = np.ones(self.batch_size, dtype='int64') * max_length
trg_np = np.random.randint(
0, trg_vocab_size, (self.batch_size, max_length)).astype('int64')
trg_len_np = np.ones(self.batch_size, dtype='int64') * max_length
out = exe.run(feed={
'src': src_np,
'src_len': src_len_np,
'trg': trg_np,
'trg_len': trg_len_np
},
fetch_list=[outputs])
self.assertTrue(out[0].shape[0] == self.batch_size)
self.assertTrue(out[0].shape[1] <= max_length + 1)
self.assertTrue(out[0].shape[2] == beam_size)
if __name__ == '__main__':
unittest.main()
python/paddle/fluid/tests/unittests/test_tensor_array_to_tensor.py
浏览文件 @ dfd1eee7
......@@ -23,6 +23,8 @@ from paddle.fluid.executor import Executor
class TestLoDTensorArrayConcat(unittest.TestCase):
"""Test case for concat mode of tensor_array_to_tensor."""
def setUp(self):
self.op_type = "tensor_array_to_tensor"
self.attrs = {"axis": 0}
......@@ -138,5 +140,82 @@ class TestLoDTensorArrayConcat(unittest.TestCase):
numpy.array(random_grad[i + 1]))
class TestLoDTensorArrayStack(unittest.TestCase):
"""Test case for stack mode of tensor_array_to_tensor."""
def setUp(self):
self.op_type = "tensor_array_to_tensor"
self.attrs = {"axis": 1, "use_stack": True}
self.inputs = [
numpy.random.rand(2, 3, 4).astype("float32"),
numpy.random.rand(2, 3, 4).astype("float32"),
numpy.random.rand(2, 3, 4).astype("float32")
]
self.outputs = [
numpy.stack(
self.inputs, axis=self.attrs["axis"]), numpy.array(
[x.shape[self.attrs["axis"]] for x in self.inputs],
dtype="int32")
]
self.input_grads = [numpy.ones_like(x) for x in self.inputs]
self.set_program()
for var in self.program.list_vars():
# to avoid scope clearing after execution
var.persistable = True
def set_program(self):
self.program = fluid.Program()
with fluid.program_guard(self.program):
self.array = array = fluid.layers.create_array(dtype='float32')
idx = fluid.layers.fill_constant(shape=[1], dtype="int64", value=0)
for i, x in enumerate(self.inputs):
x = fluid.layers.assign(x)
fluid.layers.array_write(x, idx + i, array)
output, output_index = fluid.layers.tensor_array_to_tensor(
input=array, **self.attrs)
loss = fluid.layers.reduce_sum(output)
fluid.backward.append_backward(loss)
self.output_vars = [output, output_index]
def run_check(self, executor, scope):
executor.run(self.program, scope=scope)
for i, output in enumerate(self.outputs):
numpy.allclose(
numpy.array(scope.var(self.output_vars[i].name).get_tensor()),
output,
atol=0)
tensor_array_grad = scope.var(self.array.name).get_lod_tensor_array()
for i, input_grad in enumerate(self.input_grads):
numpy.allclose(
numpy.array(tensor_array_grad[i]), input_grad, atol=0)
def test_cpu(self):
scope = core.Scope()
place = core.CPUPlace()
executor = fluid.Executor(place)
self.run_check(executor, scope)
def test_gpu(self):
if core.is_compiled_with_cuda():
place = core.CUDAPlace(0)
scope = core.Scope()
executor = fluid.Executor(place)
self.run_check(executor, scope)
class TestTensorArrayToTensorAPI(unittest.TestCase):
def test_case(self):
x0 = fluid.layers.assign(numpy.random.rand(2, 3, 4).astype("float32"))
x1 = fluid.layers.assign(numpy.random.rand(2, 3, 4).astype("float32"))
i = fluid.layers.fill_constant(shape=[1], dtype="int64", value=0)
array = fluid.layers.create_array(dtype='float32')
fluid.layers.array_write(x0, i, array)
fluid.layers.array_write(x1, i + 1, array)
output, output_index = fluid.layers.tensor_array_to_tensor(
input=array, axis=1, use_stack=True)
output, output_index = fluid.layers.tensor_array_to_tensor(
input=array, axis=1, use_stack=False)
if __name__ == '__main__':
unittest.main()
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