未验证 提交 e9553612 编写于 作者: T tensor-tang 提交者: GitHub

Merge pull request #12737 from tensor-tang/feature/op/fusion_lstm

add fusion lstm 
...@@ -15,8 +15,7 @@ limitations under the License. */ ...@@ -15,8 +15,7 @@ limitations under the License. */
#include "paddle/fluid/operators/fc_op.h" #include "paddle/fluid/operators/fc_op.h"
#include <vector> #include <vector>
#include "paddle/fluid/operators/math/blas.h" #include "paddle/fluid/operators/math/blas.h"
#include "paddle/fluid/operators/math/fc_compute.h"
DECLARE_int32(paddle_num_threads);
namespace paddle { namespace paddle {
namespace operators { namespace operators {
...@@ -110,13 +109,8 @@ void FCOpMaker::Make() { ...@@ -110,13 +109,8 @@ void FCOpMaker::Make() {
AddComment(R"DOC( AddComment(R"DOC(
Fully Connected Operator. Fully Connected Operator.
The fully connected operation calculates the output based on the input, weights and bias attribute. The fully connected operation calculates the output based on the input, weights and bias.
The size of each dimension of the parameters checked in the infer-shape. The size of each dimension of the parameters checked in the infer-shape.
The matrix of bias is generated by the mkldnn framework, when the bias_attr is True.
Additional parametrs are use_mkldnn and bias_attr.
The input(X) size and output(Out) size may be diffrent.
The fully connected layer only supports MKLDNN version
)DOC"); )DOC");
} }
...@@ -133,26 +127,15 @@ class FCOpKernel : public framework::OpKernel<T> { ...@@ -133,26 +127,15 @@ class FCOpKernel : public framework::OpKernel<T> {
auto in_dims = input->dims(); auto in_dims = input->dims();
auto w_dims = w->dims(); auto w_dims = w->dims();
auto& dev_ctx = ctx.template device_context<platform::CPUDeviceContext>();
auto blas = math::GetBlas<platform::CPUDeviceContext, T>(dev_ctx);
const T* input_data = input->data<T>(); const T* input_data = input->data<T>();
const T* w_data = w->data<T>(); const T* w_data = w->data<T>();
T* output_data = output->mutable_data<T>(ctx.GetPlace()); T* output_data = output->mutable_data<T>(ctx.GetPlace());
auto blas = math::GetBlas<platform::CPUDeviceContext, T>(ctx);
math::FCCompute<platform::CPUDeviceContext, T>(
blas, in_dims[0], w_dims[1], w_dims[0], input_data, w_data, output_data,
bias ? bias->data<T>() : NULL);
blas.GEMM(CblasNoTrans, CblasNoTrans, in_dims[0], w_dims[1], w_dims[0], // TODO(TJ): fuse act
static_cast<T>(1), input_data, w_data, static_cast<T>(0),
output_data);
if (bias) {
const T* bias_data = bias->data<T>();
#ifdef PADDLE_WITH_MKLML
#pragma omp parallel for if (FLAGS_paddle_num_threads > 1)
#endif
for (int bs = 0; bs < in_dims[0]; bs++) {
blas.AXPY(w_dims[1], static_cast<T>(1), bias_data,
output_data + bs * w_dims[1]);
}
}
} }
}; };
......
/* Copyright (c) 2016 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/fusion_lstm_op.h"
#include <string>
#include "paddle/fluid/operators/math/blas.h"
#include "paddle/fluid/operators/math/detail/activation_functions.h"
#include "paddle/fluid/operators/math/fc_compute.h"
#include "paddle/fluid/operators/math/lstm_compute.h"
#include "paddle/fluid/operators/math/sequence2batch.h"
namespace paddle {
namespace operators {
void FusionLSTMOp::InferShape(framework::InferShapeContext* ctx) const {
PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) of LSTM should not be null.");
PADDLE_ENFORCE(ctx->HasInput("WeightX"),
"Input(WeightX) of LSTM should not be null.");
PADDLE_ENFORCE(ctx->HasInput("WeightH"),
"Input(WeightH) of LSTM should not be null.");
PADDLE_ENFORCE(ctx->HasInput("Bias"),
"Input(Bias) of LSTM should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("XX"),
"Output(XX) of LSTM should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Hidden"),
"Output(Hidden) of LSTM should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Cell"),
"Output(Cell) of LSTM should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("BatchedGate"),
"Output(BatchedGate) of LSTM should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("BatchCellPreAct"),
"Output(BatchedGate) of LSTM should not be null.");
auto x_dims = ctx->GetInputDim("X");
PADDLE_ENFORCE_EQ(x_dims.size(), 2, "Input(X)'s rank must be 2.");
if (ctx->HasInput("H0")) {
PADDLE_ENFORCE(ctx->HasInput("C0"),
"Input(Cell) and Input(Hidden) of LSTM should not "
"be null at the same time.");
auto h_dims = ctx->GetInputDim("H0");
auto c_dims = ctx->GetInputDim("C0");
PADDLE_ENFORCE(h_dims == c_dims,
"The dimension of Input(H0) and Input(C0) "
"should be the same.");
}
auto wx_dims = ctx->GetInputDim("WeightX");
PADDLE_ENFORCE_EQ(wx_dims.size(), 2,
"The rank of Input(WeightX) should be 2.");
PADDLE_ENFORCE_EQ(wx_dims[0], x_dims[1],
"The first dimension of Input(WeightX) "
"should be %d.",
x_dims[1]);
int frame_size = wx_dims[1] / 4;
auto wh_dims = ctx->GetInputDim("WeightH");
PADDLE_ENFORCE_EQ(wh_dims.size(), 2,
"The rank of Input(WeightH) should be 2.");
PADDLE_ENFORCE_EQ(wh_dims[0], frame_size,
"The first dimension of Input(WeightH) "
"should be %d.",
frame_size);
PADDLE_ENFORCE_EQ(wh_dims[1], 4 * frame_size,
"The second dimension of Input(WeightH) "
"should be 4 * %d.",
frame_size);
auto b_dims = ctx->GetInputDim("Bias");
PADDLE_ENFORCE_EQ(b_dims.size(), 2, "The rank of Input(Bias) should be 2.");
PADDLE_ENFORCE_EQ(b_dims[0], 1,
"The first dimension of Input(Bias) should be 1.");
PADDLE_ENFORCE(!ctx->Attrs().Get<bool>("use_peepholes"),
"Do not support peephole yet.");
PADDLE_ENFORCE_EQ(b_dims[1], 4 * frame_size,
"The second dimension of Input(Bias) should be "
"4 * %d if disable peepholes connection",
frame_size);
framework::DDim out_dims({x_dims[0], frame_size});
ctx->SetOutputDim("Hidden", out_dims);
ctx->SetOutputDim("Cell", out_dims);
ctx->SetOutputDim("BatchedGate", {x_dims[0], wx_dims[1]});
ctx->SetOutputDim("BatchCellPreAct", out_dims);
ctx->ShareLoD("X", "Hidden");
ctx->ShareLoD("X", "Cell");
int xx_width = x_dims[1] > wx_dims[1] ? wx_dims[1] : x_dims[1];
ctx->SetOutputDim("XX", {x_dims[0], xx_width});
ctx->ShareLoD("X", "XX");
}
framework::OpKernelType FusionLSTMOp::GetExpectedKernelType(
const framework::ExecutionContext& ctx) const {
return framework::OpKernelType(
framework::ToDataType(ctx.Input<framework::LoDTensor>("X")->type()),
ctx.device_context());
}
void FusionLSTMOpMaker::Make() {
AddInput("X",
"(LoDTensor) the input is a LodTensor, which support "
"variable-time length input sequence. The underlying tensor in "
"this LoDTensor is a matrix with shape (T X M), where T is the "
"total time steps in this mini-batch, M is the dim size of x.");
AddInput("WeightX",
"(Tensor) the learnable weights of X."
" - The shape is (M x 4D), where M is the dim size of x, D is the "
"hidden size. "
" - Weight = {W_cx, W_ix, W_fx, W_ox}");
AddInput("WeightH",
"(Tensor) same as LSTMOp, the learnable hidden-hidden weights."
" - The shape is (D x 4D), where D is the hidden size. "
" - Weight = {W_ch, W_ih, W_fh, W_oh}");
AddInput("Bias",
"(Tensor) the learnable weights. Almost same as LSTMOp"
"Note: we should add the fc bias into this (1x4D) in bias."
"input-hidden bias weight and peephole connections weight if "
"setting `use_peepholes` True. "
"1. `use_peepholes = False` "
" - The shape is (1 x 4D). "
" - Bias = {b_c, b_i, b_f, b_o}."
"2. `use_peepholes = True` "
" - The shape is (1 x 7D). "
" - Bias = {b_c, b_i, b_f, b_o, W_ic, W_fc, W_oc}.");
AddInput("H0",
"(Tensor, optional) (same as LSTMOp) the initial hidden state is an "
"optional "
"input. This is a tensor with shape (N x D), where N is the "
"batch size and D is the hidden size.")
.AsDispensable();
AddInput("C0",
"(Tensor, optional) (same as LSTMOp) (the initial cell state is an "
"optional "
"input. This is a tensor with shape (N x D), where N is the "
"batch size. `H0` and `C0` can be NULL but only at the same time.")
.AsDispensable();
AddOutput("Hidden",
"(LoDTensor) (same as LSTMOp) the hidden state of LSTM operator. "
"The shape is (T x D), and lod is the same with the `Input`.");
AddOutput("Cell",
"(LoDTensor) (same as LSTMOp) the cell state of LSTM operator. "
"The shape is (T x D), and lod is the same with the `Input`.");
AddOutput("XX",
"(LoDTensor) the result after X * WeightX (size is T x 4D)"
" or batched_X (size is T x M), this will be automatically chosen,"
" where T is the total time steps in this mini-batch,"
" D is the hidden size, M is the dim size of x input.")
.AsIntermediate();
AddOutput("BatchedGate", "(LoDTensor) (same as LSTMOp).").AsIntermediate();
AddOutput("BatchCellPreAct", "(LoDTensor) (same as LSTMOp).")
.AsIntermediate();
AddAttr<bool>("use_peepholes",
"(bool, defalut: True) "
"whether to enable diagonal/peephole connections.")
.SetDefault(true);
AddAttr<bool>("is_reverse",
"(bool, defalut: False) "
"whether to compute reversed LSTM.")
.SetDefault(false);
AddAttr<std::string>("gate_activation",
"(string, default: sigmoid)"
"The activation for input gate, forget gate and output "
"gate, `sigmoid` by default.")
.SetDefault("sigmoid")
.InEnum({"sigmoid", "tanh", "relu", "identity"});
AddAttr<std::string>("cell_activation",
"(string, default: tanh)"
"The activation for cell output, `tanh` by defalut.")
.SetDefault("tanh")
.InEnum({"sigmoid", "tanh", "relu", "identity"});
AddAttr<std::string>("candidate_activation",
"(string, default: tanh)"
"The activation for candidate hidden state, "
"`tanh` by default.")
.SetDefault("tanh")
.InEnum({"sigmoid", "tanh", "relu", "identity"});
AddComment(R"DOC(
Fusion Long-Short Term Memory (LSTM) Operator.
This operator fuse the X into LSTM, more details can refer to LSTM op.
)DOC");
}
template <typename DeviceContext, typename T>
inline void ReorderInitState(const DeviceContext& ctx,
const framework::Tensor& src,
framework::Vector<size_t> index_lod,
framework::Tensor* dst, bool indexed_src) {
math::CopyMatrixRowsFunctor<DeviceContext, T> row_shuffle;
dst->mutable_data<T>(src.dims(), ctx.GetPlace());
// TODO(TJ): check mem copy perf
row_shuffle(ctx, src, index_lod, dst, indexed_src);
}
template <typename DeviceContext, typename T>
class FuisonLSTMKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto* x = ctx.Input<LoDTensor>("X");
auto* wx = ctx.Input<Tensor>("WeightX");
auto* wh = ctx.Input<Tensor>("WeightH");
auto* bias = ctx.Input<Tensor>("Bias");
auto* hidden_t0 = ctx.Input<Tensor>("H0");
auto* cell_t0 = ctx.Input<Tensor>("C0");
auto* xx = ctx.Output<LoDTensor>("XX");
auto* batched_gate = ctx.Output<LoDTensor>("BatchedGate");
auto* hidden_out = ctx.Output<LoDTensor>("Hidden");
auto* cell_out = ctx.Output<LoDTensor>("Cell");
bool is_reverse = ctx.Attr<bool>("is_reverse");
T* xx_data = xx->mutable_data<T>(ctx.GetPlace());
T* batched_gate_data = batched_gate->mutable_data<T>(ctx.GetPlace());
hidden_out->mutable_data<T>(ctx.GetPlace());
cell_out->mutable_data<T>(ctx.GetPlace());
const T* x_data = x->data<T>();
const T* wx_data = wx->data<T>();
auto x_dims = x->dims();
auto wx_dims = wx->dims();
math::LoDTensor2BatchFunctor<DeviceContext, T> to_batch;
auto& dev_ctx = ctx.template device_context<DeviceContext>();
auto blas = math::GetBlas<DeviceContext, T>(dev_ctx);
if (x_dims[1] > wx_dims[1]) {
math::FCCompute<DeviceContext, T>(blas, x_dims[0], wx_dims[1], x_dims[1],
x_data, wx_data, xx_data,
bias->data<T>());
to_batch(dev_ctx, *xx, batched_gate, true, is_reverse);
} else {
to_batch(dev_ctx, *x, xx, true, is_reverse);
batched_gate->set_lod(xx->lod());
math::FCCompute<DeviceContext, T>(blas, x_dims[0], wx_dims[1], x_dims[1],
xx_data, wx_data, batched_gate_data,
bias->data<T>());
}
int frame_size = static_cast<int>(wx_dims[1] / 4);
framework::DDim out_dims({x_dims[0], frame_size});
math::LstmMetaValue<T> lstm_value;
// no peephole
lstm_value.check_ig = nullptr;
lstm_value.check_fg = nullptr;
lstm_value.check_og = nullptr;
lstm_value.prev_state_value = nullptr;
Tensor ordered_c0;
framework::Vector<size_t> order(batched_gate->lod()[2]);
if (cell_t0) {
// Since the batch computing for LSTM reorders the input sequence
// according to their length. The initialized cell state also needs
// to reorder.
ReorderInitState<DeviceContext, T>(dev_ctx, *cell_t0, order, &ordered_c0,
true);
lstm_value.prev_state_value = ordered_c0.data<T>();
}
// Use the local variable as here.
LoDTensor batch_hidden, batch_cell;
auto* batch_cell_pre_act = ctx.Output<LoDTensor>("BatchCellPreAct");
batch_hidden.mutable_data<T>(out_dims, ctx.GetPlace());
batch_cell.mutable_data<T>(out_dims, ctx.GetPlace());
batch_cell_pre_act->mutable_data<T>(out_dims, ctx.GetPlace());
auto batch_starts = batched_gate->lod()[0];
size_t max_seq_len = batch_starts.size() - 1;
auto gate_act = math::detail::GetActivationType(
ctx.Attr<std::string>("gate_activation"));
auto cell_act = math::detail::GetActivationType(
ctx.Attr<std::string>("cell_activation"));
auto cand_act = math::detail::GetActivationType(
ctx.Attr<std::string>("candidate_activation"));
for (size_t n = 0; n < max_seq_len; n++) {
int bstart = static_cast<int>(batch_starts[n]);
int bend = static_cast<int>(batch_starts[n + 1]);
Tensor gate_t = batched_gate->Slice(bstart, bend);
Tensor out_t = batch_hidden.Slice(bstart, bend);
Tensor cell_t = batch_cell.Slice(bstart, bend);
Tensor cell_pre_act_t = batch_cell_pre_act->Slice(bstart, bend);
int cur_batch_size = bend - bstart;
if (n > 0) {
int pre_h_start = static_cast<int>(batch_starts[n - 1]);
int pre_h_end = pre_h_start + cur_batch_size;
auto pre_hidden_t = batch_hidden.Slice(pre_h_start, pre_h_end);
// TODO(TJ): use gemm directly
blas.MatMul(pre_hidden_t, false, *wh, false, static_cast<T>(1.0),
&gate_t, static_cast<T>(1.0));
} else if (hidden_t0) {
// TODO(TJ): move h0 outside for
// If n == 0 and there is no initialized hidden state, that is to say
// the H0 is zeros, the calculation W_h * H0 will be skiped.
// If n == 0 and there is initialized hidden state, calculate W_h * H0.
// Since the batch computing for LSTM reorders the input sequence
// according to their length. The initialized hidden state also needs
// to reorder.
Tensor ordered_h0;
ReorderInitState<DeviceContext, T>(dev_ctx, *hidden_t0, order,
&ordered_h0, true);
// TODO(TJ): use gemm directly
blas.MatMul(ordered_h0, false, *wh, false, static_cast<T>(1.0), &gate_t,
static_cast<T>(1.0));
}
lstm_value.gate_value = gate_t.data<T>();
lstm_value.output_value = out_t.data<T>();
lstm_value.state_value = cell_t.data<T>();
lstm_value.state_active_value = cell_pre_act_t.data<T>();
math::LstmUnitFunctor<DeviceContext, T>::compute(
dev_ctx, lstm_value, frame_size, cur_batch_size, gate_act, cell_act,
cand_act);
lstm_value.prev_state_value = lstm_value.state_value;
}
math::Batch2LoDTensorFunctor<DeviceContext, T> to_seq;
batch_hidden.set_lod(batched_gate->lod());
// restore the output hidden in LoDTensor from the batch hidden
to_seq(dev_ctx, batch_hidden, hidden_out);
batch_cell.set_lod(batched_gate->lod());
// restore the output cell state in LoDTensor from the batch cell
to_seq(dev_ctx, batch_cell, cell_out);
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OPERATOR(fusion_lstm, ops::FusionLSTMOp, ops::FusionLSTMOpMaker,
paddle::framework::DefaultGradOpDescMaker<true>);
REGISTER_OP_CPU_KERNEL(
fusion_lstm,
ops::FuisonLSTMKernel<paddle::platform::CPUDeviceContext, float>,
ops::FuisonLSTMKernel<paddle::platform::CPUDeviceContext, double>);
/* Copyright (c) 2016 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 <string>
#include "paddle/fluid/framework/op_registry.h"
namespace paddle {
namespace operators {
using LoDTensor = framework::LoDTensor;
using Tensor = framework::Tensor;
class FusionLSTMOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override;
protected:
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext& ctx) const override;
};
class FusionLSTMOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override;
};
} // namespace operators
} // namespace paddle
/* Copyright (c) 2016 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/operators/math/blas.h"
DECLARE_int32(paddle_num_threads);
namespace paddle {
namespace operators {
namespace math {
template <typename DeviceContext, typename T>
inline void FCCompute(const BlasT<DeviceContext, T>& blas, const int M,
const int N, const int K, const T* X, const T* W, T* Y,
const T* B = NULL) {
blas.GEMM(CblasNoTrans, CblasNoTrans, M, N, K, static_cast<T>(1), X, W,
static_cast<T>(0), Y);
if (B) {
#ifdef PADDLE_WITH_MKLML
#pragma omp parallel for if (FLAGS_paddle_num_threads > 1)
#endif
for (int i = 0; i < M; i++) {
blas.AXPY(N, static_cast<T>(1), B, Y + i * N);
}
}
}
} // namespace math
} // namespace operators
} // namespace paddle
...@@ -64,27 +64,47 @@ class TestFCOp(OpTest): ...@@ -64,27 +64,47 @@ class TestFCOp(OpTest):
self.check_output() self.check_output()
class TestFCOpBiasBoth(TestFCOp): class TestFCOpNoBias(TestFCOp):
def init_shapes(self, mb, ic, oc, h, w): def init_shapes(self, mb, ic, oc, h, w):
for with_bias in {True, False}: self.with_bias = False
self.with_bias = with_bias self.matrix = MatrixGenerate(mb, ic, oc, h, w)
self.matrix = MatrixGenerate(mb, ic, oc, h, w)
class TestFCOp1(TestFCOpBiasBoth): class TestFCOpWithBias(TestFCOp):
def init_shapes(self, mb, ic, oc, h, w):
self.with_bias = True
self.matrix = MatrixGenerate(mb, ic, oc, h, w)
class TestFCOp1(TestFCOpNoBias):
def init_op_type(self): def init_op_type(self):
self.init_shapes(2, 8, 10, 1, 1) self.init_shapes(2, 8, 10, 1, 1)
class TestFCOp2(TestFCOpBiasBoth): class TestFCOp2(TestFCOpNoBias):
def init_op_type(self): def init_op_type(self):
self.init_shapes(4, 5, 6, 2, 2) self.init_shapes(4, 5, 6, 2, 2)
class TestFCOp4(TestFCOpBiasBoth): class TestFCOp4(TestFCOpNoBias):
def init_op_type(self): def init_op_type(self):
self.init_shapes(1, 32, 64, 3, 3) self.init_shapes(1, 32, 64, 3, 3)
class TestFCOpWithBias1(TestFCOpWithBias):
def init_op_type(self):
self.init_shapes(3, 8, 10, 2, 1)
class TestFCOpWithBias2(TestFCOpWithBias):
def init_op_type(self):
self.init_shapes(4, 5, 6, 2, 2)
class TestFCOpWithBias3(TestFCOpWithBias):
def init_op_type(self):
self.init_shapes(1, 64, 32, 3, 3)
if __name__ == "__main__": if __name__ == "__main__":
unittest.main() unittest.main()
# Copyright (c) 2018 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
from test_lstm_op import lstm, ACTIVATION
def fc(x, w, b):
return np.dot(x, w) + b
def fusion_lstm(
x, # T x M
lod, # 1 x N
wx=None, # M x 4D
bx=None, # 1 x 4D
h0=None, # N x D
c0=None, # N x D
w_h=None, # D x 4D
w_b=None, # 1 x 4D
w_c=None, # 1 x 3D
is_reverse=False,
act_gate=None,
act_cell=None,
act_cand=None):
return lstm(
fc(x, wx, bx), lod, h0, c0, w_h, w_b, w_c, is_reverse, act_gate,
act_cell, act_cand)
class TestLstmOp(OpTest):
def set_argument(self):
self.lod = [[2, 3, 2]]
def setUp(self):
self.op_type = 'fusion_lstm'
self.lod = [[2, 3, 2]]
self.M = 8
self.D = 16
self.has_initial_state = False
self.is_reverse = False
self.act_gate = 'sigmoid'
self.act_cell = 'tanh'
self.act_cand = 'tanh'
self.use_peepholes = False
self.set_argument()
T = sum(self.lod[0])
bs = len(self.lod[0])
x = np.random.normal(size=(T, self.M)).astype('float64')
if self.has_initial_state:
h0 = np.random.normal(size=(bs, self.D)).astype('float64')
c0 = np.random.normal(size=(bs, self.D)).astype('float64')
else:
h0 = np.zeros((bs, self.D)).astype('float64')
c0 = np.zeros((bs, self.D)).astype('float64')
wh = np.random.normal(size=(self.D, 4 * self.D)).astype('float64')
if self.use_peepholes:
b = np.random.normal(size=(1, 7 * self.D)).astype('float64')
else:
b = np.random.normal(size=(1, 4 * self.D)).astype('float64')
w_b = np.copy(b[:, 0:4 * self.D])
w_c = b[:, 4 * self.D:] if self.use_peepholes else None
# this is the weight of fc
wx = np.random.normal(size=(self.M, 4 * self.D)).astype('float64')
# this is the bias of fc
# and it should be manually added into the bias of this fusion LSTM
bx = np.random.normal(size=(1, 4 * self.D)).astype('float64')
b[0, 0:4 * self.D] += bx[0, :]
h, c = fusion_lstm(x, self.lod, wx, bx, h0, c0, wh, w_b, w_c,
self.is_reverse, ACTIVATION[self.act_gate],
ACTIVATION[self.act_cell], ACTIVATION[self.act_cand])
self.inputs = {
'X': (x, self.lod),
'WeightX': wx,
'WeightH': wh,
'Bias': b
}
if self.has_initial_state:
self.inputs['H0'] = h0
self.inputs['C0'] = c0
self.outputs = {
'Hidden': (h, self.lod),
'Cell': (c, self.lod),
}
self.attrs = {
'use_peepholes': self.use_peepholes,
'is_reverse': self.is_reverse,
'gate_activation': self.act_gate,
'cell_activation': self.act_cell,
'candidate_activation': self.act_cand
}
def test_check_output(self):
self.check_output(atol=1e-8)
class TestLstmOpInitReverse(TestLstmOp):
def set_argument(self):
self.has_initial_state = True
self.is_reverse = True
class TestLstmOpMD1(TestLstmOp):
def set_argument(self):
self.M = 36
self.D = 8
class TestLstmOpMD2(TestLstmOp):
def set_argument(self):
self.M = 8
self.D = 8
class TestLstmOpMD3(TestLstmOp):
def set_argument(self):
self.M = 15
self.D = 3
class TestLstmOpBS1(TestLstmOp):
def set_argument(self):
self.lod = [[3]]
self.D = 16
if __name__ == '__main__':
unittest.main()
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