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提交 d1fbf50b 编写于 作者: D dangqingqing
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Add unit testing for forwad implementation.

上级 2a8dbd13
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Showing with 233 addition and 186 deletion
paddle/operators/CMakeLists.txt
浏览文件 @ d1fbf50b
......@@ -127,7 +127,7 @@ op_library(softmax_with_cross_entropy_op DEPS cross_entropy softmax)
op_library(sum_op DEPS net_op)
op_library(pool_op DEPS pooling)
op_library(pool_with_index_op DEPS pooling)
op_library(lstm_op DEPS sequence2batch)
op_library(lstm_op DEPS sequence2batch lstm_compute math_function)
list(REMOVE_ITEM GENERAL_OPS ${DEPS_OPS})
foreach(src ${GENERAL_OPS})
......
paddle/operators/lstm_op.cc
浏览文件 @ d1fbf50b
......@@ -44,7 +44,7 @@ class LSTMOp : public framework::OperatorWithKernel {
"should be the same.");
}
int frame_size = x_dims[1];
int frame_size = x_dims[1] / 4;
auto w_dims = ctx->GetInputDim("Weight");
PADDLE_ENFORCE_EQ(w_dims.size(), 2,
"The rank of Input(Weight) should be 2.");
......@@ -71,9 +71,9 @@ class LSTMOp : public framework::OperatorWithKernel {
"4 * %d if diable peepholes connection",
frame_size);
}
ctx->SetOutputDim("Hidden", x_dims);
ctx->SetOutputDim("Cell", x_dims);
ctx->SetOutputDim("Batch", x_dims);
ctx->SetOutputDim("Hidden", {x_dims[0], frame_size});
ctx->SetOutputDim("Cell", {x_dims[0], frame_size});
ctx->SetOutputDim("BatchGate", x_dims);
ctx->ShareLoD("Input", "Hidden");
ctx->ShareLoD("Input", "Cell");
}
......
paddle/operators/lstm_op.h
浏览文件 @ d1fbf50b
......@@ -52,9 +52,14 @@ class LSTMKernel : public framework::OpKernel<T> {
to_batch(ctx.device_context(), *input, *batch_gate, is_reverse);
auto in_dims = input->dims();
int frame_size = in_dims[1];
int frame_size = in_dims[1] / 4;
framework::DDim dims({in_dims[0], frame_size});
if (bias) {
// framework::Tensor cpu_t;
// cpu_t.mutable_data<T>(in_dims, platform::CPUPlace());
// cpu_t.CopyFrom<T>(*batch_gate, platform::CPUPlace(),
// ctx.device_context());
Eigen::array<int, 2> extents({{1, 4 * frame_size}});
Eigen::array<int, 2> offsets({{0, 0}});
auto b = EigenMatrix<T>::From(*bias);
......@@ -76,15 +81,14 @@ class LSTMKernel : public framework::OpKernel<T> {
lstm_value.prevStateValue = nullptr;
framework::LoDTensor batch_out;
batch_out.mutable_data<T>(in_dims, ctx.GetPlace());
batch_out.mutable_data<T>(dims, ctx.GetPlace());
framework::LoDTensor batch_cell;
batch_cell.mutable_data<T>(in_dims, ctx.GetPlace());
batch_cell.mutable_data<T>(dims, ctx.GetPlace());
framework::LoDTensor batch_cell_pre_act;
batch_cell_pre_act.mutable_data<T>(in_dims, ctx.GetPlace());
batch_cell_pre_act.mutable_data<T>(dims, ctx.GetPlace());
auto batch_lod = batch_gate->lod()[0];
int num_batch = batch_lod.size() - 1;
auto gate_act = ctx.Attr<std::string>("gateActivation");
auto cell_act = ctx.Attr<std::string>("cellActivation");
auto cand_act = ctx.Attr<std::string>("candidateActivation");
......@@ -125,9 +129,12 @@ class LSTMKernel : public framework::OpKernel<T> {
// restore the output hidden in LoDTensor from the batch hidden
to_seq(ctx.device_context(), batch_out, *hidden_out);
batch_out.set_lod(batch_gate->lod());
batch_cell.set_lod(batch_gate->lod());
// restore the output cell state in LoDTensor from the batch cell
to_seq(ctx.device_context(), batch_cell, *cell_out);
auto t = framework::EigenVector<T>::Flatten(*batch_gate);
t.device(ctx.GetEigenDevice<Place>()) = t.constant(static_cast<T>(0));
}
};
......
paddle/operators/math/CMakeLists.txt
浏览文件 @ d1fbf50b
add_subdirectory(detail)
if(WITH_GPU)
nv_library(math_function SRCS math_function.cc math_function.cu im2col.cc im2col.cu DEPS cblas device_context operator)
nv_test(math_function_test SRCS math_function_test.cc DEPS math_function tensor)
......@@ -6,7 +8,7 @@ if(WITH_GPU)
nv_library(pooling SRCS pooling.cc pooling.cu DEPS device_context)
nv_library(vol2col SRCS vol2col.cc vol2col.cu DEPS device_context)
nv_library(sequence2batch SRCS sequence2batch.cc sequence2batch.cu DEPS device_context)
nv_library(lstm_compute SRCS lstm_compute.cc lstm_compute.cu DEPS device_context)
nv_library(lstm_compute SRCS lstm_compute.cc lstm_compute.cu DEPS device_context activation_functions)
else()
cc_library(math_function SRCS math_function.cc im2col.cc DEPS cblas device_context operator)
cc_test(math_function_test SRCS math_function_test.cc DEPS math_function tensor)
......@@ -14,7 +16,7 @@ else()
cc_library(cross_entropy SRCS cross_entropy.cc DEPS operator)
cc_library(pooling SRCS pooling.cc DEPS device_context)
cc_library(sequence2batch SRCS sequence2batch.cc DEPS device_context)
cc_library(lstm_compute SRCS lstm_compute.cc DEPS device_context)
cc_library(lstm_compute SRCS lstm_compute.cc DEPS device_context activation_functions)
endif()
cc_test(im2col_test SRCS im2col_test.cc DEPS math_function tensor)
......
paddle/operators/math/detail/hl_avx_functions.cc
浏览文件 @ d1fbf50b
......@@ -14,10 +14,12 @@ limitations under the License. */
#include <immintrin.h>
#include "hl_functions.h"
// TODO(qingqing) refine this dependence
#include "paddle/cuda/src/avx_mathfun.h"
namespace hppl {
extern __m256 exp(__m256 a);
__m256 exp(__m256 a) { return exp256_ps(a); }
__m256 relu(const __m256 a) {
__m256 tmp = _mm256_set1_ps(0.0f);
......
paddle/operators/math/detail/hl_cpu_functions.cc 0 → 100644
浏览文件 @ d1fbf50b
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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 <math.h>
#include "hl_functions.h"
namespace hppl {
namespace typef {
float relu(const float a) {
return a > static_cast<float>(0.0) ? a : static_cast<float>(0.0);
}
float sigmoid(const float a) {
const float min = SIGMOID_THRESHOLD_MIN;
const float max = SIGMOID_THRESHOLD_MAX;
float tmp = (a < min) ? min : ((a > max) ? max : a);
return static_cast<float>(1.0) / (static_cast<float>(1.0) + exp(-tmp));
}
float tanh(const float a) {
float tmp = -2.0 * a;
tmp = (tmp > EXP_MAX_INPUT) ? EXP_MAX_INPUT : tmp;
return (2.0 / (1.0 + exp(tmp))) - 1.0;
}
float linear(const float a) { return a; }
float relu(const float a, const float b) { return a * (b > 0.0 ? 1.0 : 0.0); }
float sigmoid(const float a, const float b) {
return a * b * (static_cast<float>(1) - b);
}
float tanh(const float a, const float b) {
return a * (static_cast<float>(1) - b * b);
}
float linear(const float a, const float b) { return a; }
} // namespace typef
namespace typed {
double relu(const double a) {
return a > static_cast<double>(0.0) ? a : static_cast<double>(0.0);
}
double sigmoid(const double a) {
const double min = SIGMOID_THRESHOLD_MIN;
const double max = SIGMOID_THRESHOLD_MAX;
double tmp = (a < min) ? min : ((a > max) ? max : a);
return static_cast<double>(1.0) / (static_cast<double>(1.0) + exp(-tmp));
}
double tanh(const double a) {
double tmp = -2.0 * a;
tmp = (tmp > EXP_MAX_INPUT) ? EXP_MAX_INPUT : tmp;
return (2.0 / (1.0 + exp(tmp))) - 1.0;
}
double linear(const double a) { return a; }
double relu(const double a, const double b) {
return a * (b > 0.0 ? 1.0 : 0.0);
}
double sigmoid(const double a, const double b) {
return a * b * (static_cast<double>(1) - b);
}
double tanh(const double a, const double b) {
return a * (static_cast<double>(1) - b * b);
}
double linear(const double a, const double b) { return a; }
} // namespace typed
} // namespace hppl
paddle/operators/math/detail/hl_functions.h
浏览文件 @ d1fbf50b
......@@ -34,83 +34,28 @@ limitations under the License. */
#ifndef __NVCC__
namespace hppl {
namespace typef {
/*
* forward activation
*/
float relu(const float a) {
return a > static_cast<float>(0.0) ? a : static_cast<float>(0.0);
}
float sigmoid(const float a) {
const float min = SIGMOID_THRESHOLD_MIN;
const float max = SIGMOID_THRESHOLD_MAX;
float tmp = (a < min) ? min : ((a > max) ? max : a);
return static_cast<float>(1.0) / (static_cast<float>(1.0) + exp(-tmp));
}
float tanh(const float a) {
float tmp = -2.0 * a;
tmp = (tmp > EXP_MAX_INPUT) ? EXP_MAX_INPUT : tmp;
return (2.0 / (1.0 + exp(tmp))) - 1.0;
}
float linear(const float a) { return a; }
/*
* backward activation
*/
float relu(const float a, const float b) { return a * (b > 0.0 ? 1.0 : 0.0); }
float relu(const float a);
float sigmoid(const float a);
float tanh(const float a);
float linear(const float a);
float sigmoid(const float a, const float b) {
return a * b * (static_cast<float>(1) - b);
}
float relu(const float a, const float b);
float sigmoid(const float a, const float b);
float tanh(const float a, const float b);
float linear(const float a, const float b);
float tanh(const float a, const float b) {
return a * (static_cast<float>(1) - b * b);
}
float linear(const float a, const float b) { return a; }
} // namespace typef
namespace typed {
/*
* forward activation
*/
double relu(const double a) {
return a > static_cast<double>(0.0) ? a : static_cast<double>(0.0);
}
double sigmoid(const double a) {
const double min = SIGMOID_THRESHOLD_MIN;
const double max = SIGMOID_THRESHOLD_MAX;
double tmp = (a < min) ? min : ((a > max) ? max : a);
return static_cast<double>(1.0) / (static_cast<double>(1.0) + exp(-tmp));
}
double tanh(const double a) {
double tmp = -2.0 * a;
tmp = (tmp > EXP_MAX_INPUT) ? EXP_MAX_INPUT : tmp;
return (2.0 / (1.0 + exp(tmp))) - 1.0;
}
double linear(const double a) { return a; }
/*
* backward activation
*/
double relu(const double a, const double b) {
return a * (b > 0.0 ? 1.0 : 0.0);
}
double sigmoid(const double a, const double b) {
return a * b * (static_cast<double>(1) - b);
}
double tanh(const double a, const double b) {
return a * (static_cast<double>(1) - b * b);
}
double linear(const double a, const double b) { return a; }
double relu(const double a);
double sigmoid(const double a);
double tanh(const double a);
double linear(const double a);
double relu(const double a, const double b);
double sigmoid(const double a, const double b);
double tanh(const double a, const double b);
double linear(const double a, const double b);
} // namespace typed
} // namespace hppl
......
paddle/operators/math/detail/lstm_gpu_kernel.h
浏览文件 @ d1fbf50b
......@@ -19,6 +19,8 @@ limitations under the License. */
#include "paddle/platform/cuda_helper.h"
#include "paddle/platform/device_context.h"
#include <glog/logging.h>
namespace paddle {
namespace operators {
namespace math {
......@@ -29,11 +31,10 @@ namespace detail {
* grid(frameBlocks, batchBlocks)
*/
template <class T, class Op, bool isBatch>
__global__ void KeLstmForward(
Op op, LstmMetaValue<T> value, int frameSize, int batchSize,
typename hppl::ForwardActType<T>::type active_node,
typename hppl::ForwardActType<T>::type active_gate,
typename hppl::ForwardActType<T>::type active_state) {
__global__ void KeLstmForward(Op op, LstmMetaValue<T> value, int frameSize,
int batchSize, activation_mode_t active_node,
activation_mode_t active_gate,
activation_mode_t active_state) {
const int frameIdx = blockIdx.x * blockDim.x + threadIdx.x;
if (frameIdx >= frameSize) return;
......@@ -69,8 +70,10 @@ __global__ void KeLstmForward(
rPrevState = value.prevStateValue[frameIdx];
}
hppl::gpu::ForwardAct<T> act;
op(rValueIn, rValueIg, rValueFg, rValueOg, rPrevState, rState, rStateAtv,
rOut, rCheckI, rCheckF, rCheckO, active_node, active_gate, active_state);
rOut, rCheckI, rCheckF, rCheckO, act(active_node), act(active_gate),
act(active_state));
value.gateValue[frameIdx] = rValueIn;
value.gateValue[frameIdx + frameSize] = rValueIg;
......@@ -87,11 +90,11 @@ __global__ void KeLstmForward(
* grid(frameBlocks, batchBlocks)
*/
template <class T, class Op, bool isBatch>
__global__ void KeLstmBackward(
Op op, LstmMetaValue<T> value, LstmMetaGrad<T> grad, int frameSize,
int batchSize, typename hppl::BackwardActType<T>::type active_node,
typename hppl::BackwardActType<T>::type active_gate,
typename hppl::BackwardActType<T>::type active_state) {
__global__ void KeLstmBackward(Op op, LstmMetaValue<T> value,
LstmMetaGrad<T> grad, int frameSize,
int batchSize, activation_mode_t active_node,
activation_mode_t active_gate,
activation_mode_t active_state) {
const int frameIdx = blockIdx.x * blockDim.x + threadIdx.x;
if (frameIdx >= frameSize) return;
......@@ -142,10 +145,11 @@ __global__ void KeLstmBackward(
rPrevState = value.prevStateValue[frameIdx];
}
hppl::gpu::BackwardAct<T> act;
op(rValueIn, rValueIg, rValueFg, rValueOg, rGradIn, rGradIg, rGradFg, rGradOg,
rPrevState, rPrevStateGrad, rState, rStateGrad, rStateAtv, rOutputGrad,
rCheckI, rCheckF, rCheckO, rCheckIGrad, rCheckFGrad, rCheckOGrad,
active_node, active_gate, active_state);
act(active_node), act(active_gate), act(active_state));
grad.gateGrad[frameIdx] = rGradIn;
grad.gateGrad[frameIdx + frameSize] = rGradIg;
......@@ -196,22 +200,16 @@ void gpu_lstm_forward(const platform::DeviceContext& context, Op op,
grid = dim3((frameSize + 32 - 1) / 32, (batchSize + 32 - 1) / 32);
}
using type = typename hppl::ForwardActType<T>::type;
hppl::gpu::ForwardAct<T> act;
type act_node = act(active_node);
type act_gate = act(active_gate);
type act_state = act(active_state);
auto stream =
reinterpret_cast<const platform::CUDADeviceContext&>(context).stream();
if (batchSize == 1) {
KeLstmForward<T, Op,
/* isBatch= */ false><<<grid, threads, 0, stream>>>(
op, value, frameSize, batchSize, act_node, act_gate, act_state);
op, value, frameSize, batchSize, active_node, active_gate, active_gate);
} else {
KeLstmForward<T, Op,
/* isBatch= */ true><<<grid, threads, 0, stream>>>(
op, value, frameSize, batchSize, act_node, act_gate, act_state);
op, value, frameSize, batchSize, active_node, active_gate, active_gate);
}
}
......@@ -235,22 +233,18 @@ void gpu_lstm_backward(const platform::DeviceContext& context, Op op,
grid = dim3((frameSize + 32 - 1) / 32, (batchSize + 32 - 1) / 32);
}
using type = typename hppl::BackwardActType<T>::type;
hppl::gpu::BackwardAct<T> act;
type act_node = act(active_node);
type act_gate = act(active_gate);
type act_state = act(active_state);
auto stream =
reinterpret_cast<const platform::CUDADeviceContext&>(context).stream();
if (batchSize == 1) {
KeLstmBackward<T, Op,
/* isBatch= */ false><<<grid, threads, 0, stream>>>(
op, value, grad, frameSize, batchSize, act_node, act_gate, act_state);
op, value, grad, frameSize, batchSize, active_node, active_gate,
active_state);
} else {
KeLstmBackward<T, Op,
/* isBatch= */ true><<<grid, threads, 0, stream>>>(
op, value, grad, frameSize, batchSize, act_node, act_gate, act_state);
op, value, grad, frameSize, batchSize, active_node, active_gate,
active_state);
}
}
......
paddle/operators/math/lstm_compute.cc
浏览文件 @ d1fbf50b
......@@ -72,6 +72,8 @@ struct LstmUnitGradFunctor<platform::CPUPlace, T> {
};
template class LstmUnitFunctor<platform::CPUPlace, float>;
template class LstmUnitFunctor<platform::CPUPlace, double>;
template class LstmUnitGradFunctor<platform::CPUPlace, float>;
template class LstmUnitGradFunctor<platform::CPUPlace, double>;
} // namespace math
......
paddle/operators/math/lstm_compute.cu
浏览文件 @ d1fbf50b
......@@ -26,18 +26,9 @@ struct LstmUnitFunctor<platform::GPUPlace, T> {
LstmMetaValue<T> value, int frame_size, int batch_size,
std::string gate_act, std::string cell_act,
std::string cand_act) {
for (int b = 0; b < batch_size; b++) {
detail::gpu_lstm_forward(context, detail::forward::lstm<T>(), value,
frame_size, batch_size, ActiveType(cand_act),
ActiveType(gate_act), ActiveType(cell_act));
value.gateValue += frame_size * 4;
value.stateValue += frame_size;
value.stateActiveValue += frame_size;
value.outputValue += frame_size;
if (value.prevStateValue) {
value.prevStateValue += frame_size;
}
}
detail::gpu_lstm_forward<T>(context, detail::forward::lstm<T>(), value,
frame_size, batch_size, ActiveType(cand_act),
ActiveType(gate_act), ActiveType(cell_act));
}
};
......@@ -47,32 +38,15 @@ struct LstmUnitGradFunctor<platform::GPUPlace, T> {
LstmMetaValue<T> value, LstmMetaGrad<T> grad,
int frame_size, int batch_size, std::string gate_act,
std::string cell_act, std::string cand_act) {
for (int b = 0; b < batch_size; b++) {
detail::gpu_lstm_backward(context, detail::backward::lstm<T>(), value,
grad, frame_size, batch_size,
ActiveType(cand_act), ActiveType(gate_act),
ActiveType(cell_act));
value.gateValue += frame_size * 4;
value.stateValue += frame_size;
value.stateActiveValue += frame_size;
value.outputValue += frame_size;
if (value.prevStateValue) {
value.prevStateValue += frame_size;
}
grad.gateGrad += frame_size * 4;
grad.stateGrad += frame_size;
grad.stateActiveGrad += frame_size;
grad.outputGrad += frame_size;
if (grad.prevStateGrad) {
grad.prevStateGrad += frame_size;
}
}
detail::gpu_lstm_backward(context, detail::backward::lstm<T>(), value, grad,
frame_size, batch_size, ActiveType(cand_act),
ActiveType(gate_act), ActiveType(cell_act));
}
};
template class LstmUnitFunctor<platform::GPUPlace, float>;
template class LstmUnitFunctor<platform::GPUPlace, double>;
template class LstmUnitGradFunctor<platform::GPUPlace, float>;
template class LstmUnitGradFunctor<platform::GPUPlace, double>;
} // namespace math
......
paddle/operators/math/lstm_compute.h
浏览文件 @ d1fbf50b
......@@ -53,7 +53,7 @@ struct LstmMetaGrad {
T *checkOgGrad;
};
activation_mode_t ActiveType(const std::string &type) {
inline activation_mode_t ActiveType(const std::string &type) {
if (type == "sigmoid") {
return HL_ACTIVATION_SIGMOID;
} else if (type == "relu") {
......
paddle/operators/math/sequence2batch.h
浏览文件 @ d1fbf50b
......@@ -59,7 +59,7 @@ class LoDTensor2BatchFunctor {
};
std::vector<SeqInfo> seq_info;
for (size_t seq_id = 0; seq_id < lod.size(); ++seq_id) {
for (size_t seq_id = 0; seq_id < lod.size() - 1; ++seq_id) {
int length = lod[seq_id + 1] - lod[seq_id];
seq_info.emplace_back(lod[seq_id], length, seq_id);
}
......@@ -83,10 +83,11 @@ class LoDTensor2BatchFunctor {
// The batch number represents batch size after rearranging the
// input LodTensor. It is also the maximum length of input sequence.
auto batch_lods = batch.lod();
if (batch_lods.size() == 0) {
batch_lods.resize(2);
}
paddle::framework::LoD batch_lods;
batch_lods.push_back(std::vector<size_t>{0});
batch_lods.push_back(std::vector<size_t>{0});
// batch_lods[0] is the start positions for batch LoDTensor
int num_batch = (size_t)seq_info[0].length;
batch_lods[0].resize(num_batch + 1);
......@@ -115,6 +116,7 @@ class LoDTensor2BatchFunctor {
}
batch_starts[n + 1] = batch_id;
}
batch.set_lod(batch_lods);
CopyMatrixRowsFunctor<Place, T> to_batch;
to_batch(context, lod_tensor, seq2batch_idx, batch, true);
......@@ -130,12 +132,13 @@ class Batch2LoDTensorFunctor {
auto in_lod = batch.lod();
PADDLE_ENFORCE_EQ(in_lod.size(), 2UL,
"The LoD size of input `batch` should be 2.");
auto out_lod = lod_tensor.lod();
PADDLE_ENFORCE_EQ(out_lod[0][0], out_lod[1].size());
PADDLE_ENFORCE_EQ(out_lod[0][0], lod_tensor.dims()[0]);
PADDLE_ENFORCE_EQ(out_lod[0][0], batch.dims()[0]);
auto out_lod = lod_tensor.lod()[0];
auto num = out_lod[out_lod.size() - 1];
PADDLE_ENFORCE_EQ(num, lod_tensor.dims()[0]);
PADDLE_ENFORCE_EQ(num, in_lod[1].size());
PADDLE_ENFORCE_EQ(num, batch.dims()[0]);
CopyMatrixRowsFunctor<Place, T> to_seq;
size_t* index = out_lod[1].data();
size_t* index = in_lod[1].data();
to_seq(context, batch, index, lod_tensor, false);
}
};
......
python/paddle/v2/framework/tests/test_lstm_op.py
浏览文件 @ d1fbf50b
......@@ -2,17 +2,26 @@ import unittest
import numpy as np
from op_test import OpTest
SIGMOID_THRESHOLD_MIN = -40.0
SIGMOID_THRESHOLD_MAX = 13.0
EXP_MAX_INPUT = 40.0
def identity(x):
return x
def sigmoid(x):
return 1. / (1. + np.exp(-x))
y = np.copy(x)
y[x < SIGMOID_THRESHOLD_MIN] = SIGMOID_THRESHOLD_MIN
y[x > SIGMOID_THRESHOLD_MAX] = SIGMOID_THRESHOLD_MAX
return 1. / (1. + np.exp(-y))
def tanh(x):
return 2. * sigmoid(2. * x) - 1.
y = -2. * x
y[y > EXP_MAX_INPUT] = EXP_MAX_INPUT
return (2. / (1. + np.exp(y))) - 1.
def relu(x):
......@@ -35,7 +44,7 @@ def lstm(
g = np.dot(h_pre, w_h) # 1 x 4D
g = g + x
g = np.reshape(g, (1, g.size))
c, g_i, g_f, g_o = np.split(g, 4, axis=1)
c_tmp, g_i, g_f, g_o = np.split(g, 4, axis=1)
if w_c is None:
g_i = gate_act(g_i) # 1 x D
g_f = gate_act(g_f) # 1 x D
......@@ -43,7 +52,7 @@ def lstm(
w_ic, w_fc, w_oc = np.split(w_c, 3, axis=1)
g_i = gate_act(g_i + w_ic * c_pre) # 1 x D
g_f = gate_act(g_f + w_fc * c_pre) # 1 x D
c = g_f * c_pre + g_i * cand_act(c) # 1 x D
c = g_f * c_pre + g_i * cand_act(c_tmp) # 1 x D
if w_c is None:
g_o = gate_act(g_o) # 1 x D
......@@ -51,12 +60,14 @@ def lstm(
_, _, w_oc = np.split(w_c, 3, axis=1)
g_o = gate_act(g_o + w_oc * c) # 1 x D
h = g_o * cell_act(c)
return h, c
bg = np.concatenate((cand_act(c_tmp), g_i, g_f, g_o), axis=1)
return h, c, bg
offset = lod[0]
batch_size = len(offset) - 1
hidden = []
cell = []
gate = []
if w_b is not None:
input = input + np.tile(w_b, (offset[-1], 1))
for i in range(batch_size):
......@@ -64,44 +75,62 @@ def lstm(
seq_len = offset[i + 1] - offset[i]
x = input[offset[i]:offset[i + 1], :]
h_pre = h0[i] # 1 x D
c_pre = h0[i] # 1 x D
c_pre = c0[i] # 1 x D
for j in range(seq_len):
# compute one step
h_pre, c_pre = _step(x[j], w_h, w_c, h_pre, c_pre, gate_act,
cell_act, cand_act)
h_pre, c_pre, g_pre = _step(x[j], w_h, w_c, h_pre, c_pre, gate_act,
cell_act, cand_act)
hidden.append(h_pre.flatten())
cell.append(c_pre.flatten())
gate.append(g_pre.flatten())
hidden = np.array(hidden).astype("float64")
cell = np.array(cell).astype("float64")
gate = np.array(gate).astype("float64")
assert gate.shape == input.shape
assert hidden.shape == (input.shape[0], input.shape[1] / 4)
assert cell.shape == (input.shape[0], input.shape[1] / 4)
return hidden, cell
return hidden, cell, gate
class LstmUnitTest(OpTest):
def set_data(self):
lod = [[0, 2, 6, 9]]
shape = (9, 64)
x = np.random.normal(size=(9, 4 * 64)).astype("float64")
h0 = np.random.normal(size=(4, 64)).astype("float64")
c0 = np.random.normal(size=(4, 64)).astype("float64")
w = np.random.normal(size=(64, 4 * 64)).astype("float64")
b = np.random.normal(size=(1, 7 * 64)).astype("float64")
w_b = b[:, 4 * 64]
w_c = b[:, 4 * 64:]
h, c = lstm(x, lod, h0, c0, w, w_b, w_c, False, sigmoid, tanh, tanh)
self.inputs = {'Input': x, 'H0': h0, 'C0': c0, 'Weight': w, 'Bias': b}
self.inputs = {'Hidden': h, 'Cell': c}
D = 4
#lod = [[0, 2, 6, 9]]
lod = [[0, 1]]
shape = (1, D)
x = np.random.normal(size=(1, 4 * D)).astype("float64")
h0 = np.zeros((4, D)).astype("float64")
c0 = np.zeros((4, D)).astype("float64")
w = np.random.normal(size=(D, 4 * D)).astype("float64")
b = np.random.normal(size=(1, 7 * D)).astype("float64")
w_b = b[:, 0:4 * D]
w_c = b[:, 4 * D:]
#h, c, g = lstm(x, lod, h0, c0, w, w_b, w_c, False, sigmoid, tanh, tanh)
h, c, g = lstm(x, lod, h0, c0, w, w_b, w_c, False, identity, identity,
identity)
g_sort = np.zeros_like(x)
#idx = [2,6,0,3,7,1,4,8,5]
#for i, j in enumerate(idx):
# g_sort[i, :] = g[j, :]
self.inputs = {
'Input': (x, lod),
'H0': h0,
'C0': c0,
'Weight': w,
'Bias': b
}
self.outputs = {'Hidden': h, 'Cell': c, 'BatchGate': g_sort}
self.attrs = {
'usePeepholes': True,
'isReverse': False,
'gateActivation': 'sigmoid',
'cellActivation': 'tanh',
'candidateActivation': 'tanh'
'gateActivation': 'linear',
'cellActivation': 'linear',
'candidateActivation': 'linear'
}
def setUp(self):
......
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