Skip to content

P
Paddle

BaiXuePrincess / Paddle
与 Fork 源项目一致

Fork自 PaddlePaddle / Paddle

通知 1
Star 1
Fork 0
P
Paddle
未验证 提交 91631492 编写于 作者: H houj04 提交者： GitHub
浏览文件
操作

[XPU] move rnn op to phi. (#45822)

上级 a9cc0274
隐藏空白更改
内联 并排
Showing with 623 addition and 571 deletion
paddle/fluid/operators/rnn_op_xpu.cc 已删除 100644 → 0
浏览文件 @ a9cc0274
/* Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#ifdef PADDLE_WITH_XPU
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/utils.h"
#include "paddle/fluid/platform/device/device_wrapper.h"
#include "paddle/fluid/platform/device/xpu/xpu_header.h"
#include "paddle/fluid/platform/device_context.h"
#include "paddle/phi/kernels/funcs/math_function.h"
namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
using DDim = framework::DDim;
using TensorList = std::vector<framework::Tensor>;
template <typename TensorType, typename T>
void reset_parameter_vector(const std::vector<TensorType>& raw_params_vec,
const int& num_layers,
const bool& is_bidirec,
std::vector<std::vector<T*>>* params_vec) {
// the parameter raw seuquence is [FWhi, FWhh, BWhi, BWhh] * num_layers
// + [FBhi, FBhh, BBhi, BBhh] * num_layers, we will reset the parameter to
// ([FWhi, FWhh, FBhi, FBhh] + [BWhi, BWhh, BBhi, BBhh]) * num_layers
const int& direction_num = is_bidirec ? 2 : 1;
const int& layer_weight_size = 4 * direction_num;
const int& all_weight_size = num_layers * layer_weight_size;
const int& bias_start_idx = all_weight_size / 2;
for (int i = 0; i < num_layers; i++) {
params_vec->at(i).resize(layer_weight_size);
for (int j = 0; j < layer_weight_size; j++) {
int k = j % 4;
const int& section = j / 4;
int tensor_idx = i * 2 * direction_num + section * 2 + k % 2;
if (k >= 2) {
tensor_idx += bias_start_idx;
}
using remove_cv_t = typename std::remove_cv<T>::type;
params_vec->at(i)[j] =
raw_params_vec[tensor_idx]->template data<remove_cv_t>();
}
}
}
template <typename DeviceContext, typename T>
class RnnXPUKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
// Input
auto* input = ctx.Input<Tensor>("Input");
auto pre_state = ctx.MultiInput<Tensor>("PreState");
auto weight_list = ctx.MultiInput<framework::Tensor>("WeightList");
bool has_seq_length = ctx.HasInput("SequenceLength");
// Output
auto state = ctx.MultiOutput<Tensor>("State");
auto* output = ctx.Output<Tensor>("Out");
auto* dropout_mask = ctx.Output<Tensor>("DropoutState");
auto* reserve_data = ctx.Output<Tensor>("Reserve");
// Attributes
const int& num_layers = ctx.Attr<int>("num_layers");
const bool& is_bidirec = ctx.Attr<bool>("is_bidirec");
const int& hidden_size = ctx.Attr<int>("hidden_size");
const std::string& mode = ctx.Attr<std::string>("mode");
const Tensor* sequence_length = nullptr;
if (has_seq_length) {
sequence_length = ctx.Input<Tensor>("SequenceLength");
}
if (dropout_mask->IsInitialized()) {
if (dropout_mask->numel() != output->numel()) dropout_mask->clear();
}
dropout_mask->mutable_data<uint8_t>(output->dims(), ctx.GetPlace());
auto& dev_ctx = ctx.template device_context<DeviceContext>();
phi::funcs::SetConstant<platform::XPUDeviceContext, uint8_t> ones;
ones(dev_ctx, dropout_mask, static_cast<uint8_t>(1));
PADDLE_ENFORCE_EQ(
mode,
"LSTM",
platform::errors::InvalidArgument(
"XPU only support LSTM mode now, current mode is %s", mode));
auto init_h = pre_state[0];
auto init_c = pre_state[1];
auto last_h = state[0];
auto last_c = state[1];
// check shape
const int& seq_len = input->dims()[0]; // time_step
const int& batch_size = input->dims()[1];
const int& input_dim = input->dims()[2];
const int& direction_num = is_bidirec ? 2 : 1;
PADDLE_ENFORCE_EQ(
init_h->dims()[0],
num_layers * direction_num,
platform::errors::InvalidArgument("The num_layers of in RNN layer must"
" be the same as first dim of init "
"hidden, but received num_layers:%d,"
" dim:%d",
num_layers,
init_h->dims()[0]));
PADDLE_ENFORCE_EQ(
init_c->dims()[0],
num_layers * direction_num,
platform::errors::InvalidArgument(
"The num_layers of in RNN layer must"
" be the same as first dim of cell state hidden, but received"
" num_layers:%d, dim:%d",
num_layers,
init_c->dims()[0]));
// weightlist
std::vector<std::vector<const T*>> parameter_lists;
parameter_lists.resize(num_layers);
reset_parameter_vector(
weight_list, num_layers, is_bidirec, &parameter_lists);
// init the output and allocate the memory
output->mutable_data<T>(ctx.GetPlace());
last_h->mutable_data<T>(ctx.GetPlace());
last_c->mutable_data<T>(ctx.GetPlace());
int gate_num = 4;
int hidden_data_idx = (num_layers - 1);
hidden_data_idx += (gate_num + 1) * num_layers;
const int& block_size = direction_num * seq_len * batch_size * hidden_size;
reserve_data->Resize({hidden_data_idx, block_size});
reserve_data->mutable_data<T>(ctx.GetPlace());
// get ptr from tensor
auto x = input->data<T>();
auto init_h_ptr = init_h->data<T>();
auto init_c_ptr = init_c->data<T>();
auto y = output->data<T>();
auto last_h_ptr = last_h->data<T>();
auto last_c_ptr = last_c->data<T>();
auto i_f_g_o_ptr = reserve_data->data<T>();
auto c_ptr =
i_f_g_o_ptr + num_layers * block_size * 4; // 4 for i_f_g_o offset
auto hidden_data_ptr =
c_ptr + num_layers * block_size * 1; // 1 for c offset
std::vector<int> seq_len_tensor(batch_size, seq_len);
if (has_seq_length) {
seq_len_tensor = operators::GetDataFromTensor(sequence_length);
}
int state_offset = pre_state[0]->dims()[1] * pre_state[0]->dims()[2];
const T* cur_input_ptr = nullptr;
int cur_xdim = -1;
T* cur_output_ptr = y;
for (int i = 0; i < num_layers; i++) {
auto i_f_g_o = i_f_g_o_ptr + i * block_size * 4;
auto c = c_ptr + i * block_size;
cur_output_ptr = y;
if (i < num_layers - 1 && num_layers > 1) {
cur_output_ptr = hidden_data_ptr + i * block_size;
}
if (i == 0) {
cur_input_ptr = x;
cur_xdim = input_dim;
} else {
cur_input_ptr = hidden_data_ptr + (i - 1) * block_size;
cur_xdim = is_bidirec ? 2 * hidden_size : hidden_size;
}
auto h_0 = init_h_ptr + direction_num * i * state_offset;
auto c_0 = init_c_ptr + direction_num * i * state_offset;
auto last_h = last_h_ptr + direction_num * i * state_offset;
auto last_c = last_c_ptr + direction_num * i * state_offset;
auto w_x = parameter_lists[i][0];
auto w_h = parameter_lists[i][1];
auto b_x = parameter_lists[i][2];
auto b_h = parameter_lists[i][3];
if (is_bidirec) {
auto bw_x = parameter_lists[i][4];
auto bw_h = parameter_lists[i][5];
auto bb_x = parameter_lists[i][6];
auto bb_h = parameter_lists[i][7];
int r = xpu::bilstm_train<T, T, int16_t>(
dev_ctx.x_context(),
(const T*)cur_input_ptr,
(const T*)h_0,
(const T*)c_0,
(const T*)w_x,
(const T*)w_h,
(const T*)b_x,
(const T*)b_h,
(const T*)bw_x,
(const T*)bw_h,
(const T*)bb_x,
(const T*)bb_h,
reinterpret_cast<T*>(cur_output_ptr),
reinterpret_cast<T*>(last_h),
reinterpret_cast<T*>(last_c),
batch_size,
cur_xdim,
hidden_size,
seq_len,
seq_len_tensor,
nullptr,
nullptr,
nullptr,
nullptr,
nullptr,
nullptr,
reinterpret_cast<T*>(i_f_g_o),
reinterpret_cast<T*>(c));
PADDLE_ENFORCE_XDNN_SUCCESS(r, "bilstm_train");
} else {
int r =
xpu::lstm_train<T, T, int16_t>(dev_ctx.x_context(),
(const T*)cur_input_ptr,
(const T*)h_0,
(const T*)c_0,
(const T*)w_x,
(const T*)w_h,
(const T*)b_x,
(const T*)b_h,
reinterpret_cast<T*>(cur_output_ptr),
reinterpret_cast<T*>(last_h),
reinterpret_cast<T*>(last_c),
batch_size,
cur_xdim,
hidden_size,
seq_len,
seq_len_tensor,
nullptr,
nullptr,
nullptr,
nullptr,
reinterpret_cast<T*>(i_f_g_o),
reinterpret_cast<T*>(c),
xpu::Activation_t::TANH,
xpu::Activation_t::SIGMOID);
PADDLE_ENFORCE_XDNN_SUCCESS(r, "lstm_train");
}
}
}
};
template <typename DeviceContext, typename T>
class RnnXPUGradKernel : public framework::OpKernel<T> {
using XPUTyp = typename XPUTypeTrait<T>::Type;
public:
void Compute(const framework::ExecutionContext& ctx) const override {
// get the tensor pointer for the input
auto* input = ctx.Input<Tensor>("Input");
auto pre_state = ctx.MultiInput<Tensor>("PreState");
auto weight_list = ctx.MultiInput<framework::Tensor>("WeightList");
auto* output = ctx.Input<Tensor>("Out");
auto* reserve_data = ctx.Input<Tensor>("Reserve");
const int& num_layers = ctx.Attr<int>("num_layers");
const bool& is_bidirec = ctx.Attr<bool>("is_bidirec");
const float& dropout_prob = ctx.Attr<float>("dropout_prob");
const int& hidden_size = ctx.Attr<int>("hidden_size");
const std::string& mode = ctx.Attr<std::string>("mode");
bool has_seq_length = ctx.HasInput("SequenceLength");
const Tensor* sequence_length = nullptr;
if (has_seq_length) {
sequence_length = ctx.Input<Tensor>("SequenceLength");
}
PADDLE_ENFORCE_EQ(
mode,
"LSTM",
platform::errors::InvalidArgument(
"XPU only support LSTM mode now, current mode is %s", mode));
auto init_h = pre_state[0];
auto init_c = pre_state[1];
auto output_grad = ctx.Input<Tensor>(framework::GradVarName("Out"));
auto state_grad = ctx.MultiInput<Tensor>(framework::GradVarName("State"));
auto last_h_grad = state_grad[0];
auto last_c_grad = state_grad[1];
// get the tensor pointer for the output
auto* input_grad = ctx.Output<Tensor>(framework::GradVarName("Input"));
auto weight_grad_list = ctx.MultiOutput<framework::Tensor>(
framework::GradVarName("WeightList"));
auto pre_state_grad =
ctx.MultiOutput<Tensor>(framework::GradVarName("PreState"));
Tensor* init_h_grad = nullptr;
Tensor* init_c_grad = nullptr;
if (pre_state_grad.size() > 0) { // has gradient
init_h_grad = pre_state_grad[0];
init_c_grad = pre_state_grad[1];
}
// check shape
const int& seq_len = input->dims()[0];
const int& batch_size = input->dims()[1];
const int& input_dim = input->dims()[2];
const int& direction_num = is_bidirec ? 2 : 1;
PADDLE_ENFORCE_EQ(
init_h->dims()[0],
num_layers * direction_num,
platform::errors::InvalidArgument("The num_layers of in RNN layer must"
" be the same as first dim of init "
"hidden, but received num_layers:%d,"
" dim:%d",
num_layers,
init_h->dims()[0]));
PADDLE_ENFORCE_EQ(
init_c->dims()[0],
num_layers * direction_num,
platform::errors::InvalidArgument(
"The num_layers of in RNN layer must"
" be the same as first dim of cell state hidden, but received"
" num_layers:%d, dim:%d",
num_layers,
init_c->dims()[0]));
std::vector<std::vector<const T*>> parameter_lists;
parameter_lists.resize(num_layers);
reset_parameter_vector(
weight_list, num_layers, is_bidirec, &parameter_lists);
for (unsigned int i = 0; i < weight_grad_list.size(); ++i) {
weight_grad_list[i]->mutable_data<T>(ctx.GetPlace());
}
std::vector<std::vector<T*>> parameter_lists_grad;
parameter_lists_grad.resize(num_layers);
reset_parameter_vector(
weight_grad_list, num_layers, is_bidirec, &parameter_lists_grad);
// allocate the memory and initization the input_grad
input_grad->mutable_data<T>(input->dims(), ctx.GetPlace());
auto& dev_ctx = ctx.template device_context<DeviceContext>();
phi::funcs::SetConstant<platform::XPUDeviceContext, T> zero;
zero(dev_ctx, input_grad, static_cast<T>(0.0));
Tensor a, b;
Tensor* dynamic_grad_pre_h = &a;
Tensor* dynamic_grad_pre_c = &b;
if (init_h_grad) {
init_h_grad->mutable_data<T>(last_h_grad->dims(), ctx.GetPlace());
zero(dev_ctx, init_h_grad, static_cast<T>(0.0));
} else {
dynamic_grad_pre_h->Resize(last_h_grad->dims());
dynamic_grad_pre_h->mutable_data<T>(ctx.GetPlace());
zero(dev_ctx, dynamic_grad_pre_h, static_cast<T>(0.0));
init_h_grad = dynamic_grad_pre_h;
}
if (init_c_grad) {
init_c_grad->mutable_data<T>(last_c_grad->dims(), ctx.GetPlace());
} else {
dynamic_grad_pre_c->Resize(last_h_grad->dims());
dynamic_grad_pre_c->mutable_data<T>(ctx.GetPlace());
init_c_grad = dynamic_grad_pre_c;
}
Tensor temp_input_grad_1, temp_input_grad_2;
T* input_grad_1_ptr = nullptr;
T* input_grad_2_ptr = nullptr;
if (num_layers >= 2) {
temp_input_grad_1.Resize(output_grad->dims());
input_grad_1_ptr = temp_input_grad_1.mutable_data<T>(ctx.GetPlace());
}
if (num_layers >= 3) {
temp_input_grad_2.Resize(output_grad->dims());
input_grad_2_ptr = temp_input_grad_2.mutable_data<T>(ctx.GetPlace());
}
// get ptr from tensor
auto x = input->data<T>();
auto init_h_ptr = init_h->data<T>();
auto init_c_ptr = init_c->data<T>();
auto y = output->data<T>();
auto y_grad = output_grad->data<T>();
auto last_h_grad_ptr = last_h_grad->data<T>();
auto last_c_grad_ptr = last_c_grad->data<T>();
auto x_grad = input_grad->data<T>();
auto init_h_grad_ptr = init_h_grad->data<T>();
auto init_c_grad_ptr = init_c_grad->data<T>();
const int& block_size = direction_num * seq_len * batch_size * hidden_size;
auto i_f_g_o_ptr = reserve_data->data<T>();
auto c_ptr = i_f_g_o_ptr + num_layers * block_size * 4;
auto hidden_data_ptr = c_ptr + num_layers * block_size * 1;
int state_offset = pre_state[0]->dims()[1] * pre_state[0]->dims()[2];
std::vector<int> seq_len_tensor(batch_size, seq_len);
if (has_seq_length) {
seq_len_tensor = operators::GetDataFromTensor(sequence_length);
}
for (int i = num_layers - 1; i >= 0; --i) {
// the layer input output had saved, just use the data
auto w_x = parameter_lists[i][0];
auto w_h = parameter_lists[i][1];
auto bw_x = parameter_lists[i][4];
auto bw_h = parameter_lists[i][5];
auto i_f_g_o = i_f_g_o_ptr + i * block_size * 4;
auto c = c_ptr + i * block_size;
Tensor layer_input_t;
auto layer_input = x;
if (i > 0) {
layer_input_t.Resize(output->dims());
layer_input = layer_input_t.mutable_data<T>(ctx.GetPlace());
float scale = static_cast<float>(1.0f - dropout_prob);
auto hidden_data = hidden_data_ptr + (i - 1) * block_size;
int r = xpu::scale(dev_ctx.x_context(),
reinterpret_cast<const XPUTyp*>(hidden_data),
const_cast<XPUTyp*>(layer_input),
output->numel(),
false,
scale,
0.0f);
PADDLE_ENFORCE_XDNN_SUCCESS(r, "scale");
} else {
layer_input = x;
}
auto layer_output = y;
if (i == num_layers - 1) {
layer_output = y;
} else {
layer_output = hidden_data_ptr + i * block_size;
}
const T* cur_input_ptr = nullptr;
if (i == num_layers - 1) {
cur_input_ptr = y_grad;
} else if (i % 2 != 0) {
cur_input_ptr = input_grad_2_ptr;
} else {
cur_input_ptr = input_grad_1_ptr;
}
T* cur_output_ptr = nullptr;
int cur_xdim = -1;
if (i == 0) {
cur_output_ptr = x_grad;
cur_xdim = input_dim;
} else if (i % 2 != 0) {
cur_output_ptr = input_grad_1_ptr;
cur_xdim = is_bidirec ? 2 * hidden_size : hidden_size;
} else {
cur_output_ptr = input_grad_2_ptr;
cur_xdim = is_bidirec ? 2 * hidden_size : hidden_size;
}
auto w_x_grad = parameter_lists_grad[i][0];
auto w_h_grad = parameter_lists_grad[i][1];
auto b_x_grad = parameter_lists_grad[i][2];
auto b_h_grad = parameter_lists_grad[i][3];
auto h_0 = init_h_ptr + direction_num * i * state_offset;
auto c_0 = init_c_ptr + direction_num * i * state_offset;
auto h_0_grad = init_h_grad_ptr + direction_num * i * state_offset;
auto c_0_grad = init_c_grad_ptr + direction_num * i * state_offset;
auto h_t_grad = last_h_grad_ptr + direction_num * i * state_offset;
auto c_t_grad = last_c_grad_ptr + direction_num * i * state_offset;
if (is_bidirec) {
auto bw_x_grad = parameter_lists_grad[i][4];
auto bw_h_grad = parameter_lists_grad[i][5];
auto bb_x_grad = parameter_lists_grad[i][6];
auto bb_h_grad = parameter_lists_grad[i][7];
int r = xpu::bilstm_grad<T, T, int16_t>(
dev_ctx.x_context(),
(const T*)layer_input,
(const T*)h_0,
(const T*)c_0,
(const T*)w_x,
(const T*)w_h,
(const T*)bw_x,
(const T*)bw_h,
(const T*)layer_output,
(const T*)cur_input_ptr,
(const T*)h_t_grad,
(const T*)c_t_grad,
reinterpret_cast<T*>(cur_output_ptr),
reinterpret_cast<T*>(h_0_grad),
reinterpret_cast<T*>(c_0_grad),
w_x_grad,
w_h_grad,
b_x_grad,
b_h_grad,
bw_x_grad,
bw_h_grad,
bb_x_grad,
bb_h_grad,
batch_size,
cur_xdim,
hidden_size,
seq_len,
seq_len_tensor,
nullptr,
nullptr,
nullptr,
nullptr,
nullptr,
nullptr,
i_f_g_o,
c);
PADDLE_ENFORCE_XDNN_SUCCESS(r, "bilstm_grad");
} else {
int r =
xpu::lstm_grad<T, T, int16_t>(dev_ctx.x_context(),
(const T*)layer_input,
(const T*)h_0,
(const T*)c_0,
(const T*)w_x,
(const T*)w_h,
(const T*)layer_output,
(const T*)cur_input_ptr,
(const T*)h_t_grad,
(const T*)c_t_grad,
reinterpret_cast<T*>(cur_output_ptr),
reinterpret_cast<T*>(h_0_grad),
reinterpret_cast<T*>(c_0_grad),
w_x_grad,
w_h_grad,
b_x_grad,
b_h_grad,
batch_size,
cur_xdim,
hidden_size,
seq_len,
seq_len_tensor,
nullptr,
nullptr,
nullptr,
nullptr,
i_f_g_o,
c);
PADDLE_ENFORCE_XDNN_SUCCESS(r, "lstm_grad");
}
}
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP_XPU_KERNEL(
rnn, ops::RnnXPUKernel<paddle::platform::XPUDeviceContext, float>);
REGISTER_OP_XPU_KERNEL(
rnn_grad, ops::RnnXPUGradKernel<paddle::platform::XPUDeviceContext, float>);
#endif // PADDLE_WITH_XPU
paddle/fluid/platform/device/npu/CMakeLists.txt
浏览文件 @ 91631492
...@@ -31,3 +31,9 @@ if(WITH_ASCEND_CL) ...@@ -31,3 +31,9 @@ if(WITH_ASCEND_CL)
SRCS npu_op_runner.cc SRCS npu_op_runner.cc
DEPS operator npu_info) DEPS operator npu_info)
endif() endif()
# every source file that includes "dnnl.h" must depends on mkldnn
# or, the first one should depends on mkldnn
if(WITH_MKLDNN)
add_dependencies(npu_collective_helper mkldnn)
endif()
paddle/phi/kernels/funcs/math_function.cc
浏览文件 @ 91631492
...@@ -67,6 +67,19 @@ template struct SetConstant<paddle::platform::XPUDeviceContext, ...@@ -67,6 +67,19 @@ template struct SetConstant<paddle::platform::XPUDeviceContext,
phi::dtype::complex<float>>; phi::dtype::complex<float>>;
template struct SetConstant<paddle::platform::XPUDeviceContext, template struct SetConstant<paddle::platform::XPUDeviceContext,
phi::dtype::complex<double>>; phi::dtype::complex<double>>;
template struct SetConstant<phi::XPUContext, phi::dtype::float16>;
template struct SetConstant<phi::XPUContext, phi::dtype::bfloat16>;
template struct SetConstant<phi::XPUContext, float>;
template struct SetConstant<phi::XPUContext, double>;
template struct SetConstant<phi::XPUContext, uint8_t>;
template struct SetConstant<phi::XPUContext, int16_t>;
template struct SetConstant<phi::XPUContext, int>;
template struct SetConstant<phi::XPUContext, int64_t>;
template struct SetConstant<phi::XPUContext, bool>;
template struct SetConstant<phi::XPUContext, phi::dtype::complex<float>>;
template struct SetConstant<phi::XPUContext, phi::dtype::complex<double>>;
#endif #endif
#define DEFINE_CPU_TRANS(RANK) \ #define DEFINE_CPU_TRANS(RANK) \
......
paddle/phi/kernels/xpu/rnn_grad_kernel.cc 0 → 100644
浏览文件 @ 91631492
// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include "paddle/phi/kernels/rnn_grad_kernel.h"
#include "paddle/fluid/operators/utils.h"
#include "paddle/phi/backends/xpu/enforce_xpu.h"
#include "paddle/phi/core/kernel_registry.h"
#include "paddle/phi/kernels/funcs/math_function.h"
#include "paddle/phi/kernels/xpu/rnn_util.h"
namespace phi {
template <typename T, typename Context>
void RnnGradKernel(const Context& dev_ctx,
const DenseTensor& x,
const std::vector<const DenseTensor*>& pre_state,
const std::vector<const DenseTensor*>& weight_list,
const paddle::optional<DenseTensor>& sequence_length,
const DenseTensor& out,
const DenseTensor& dropout_state,
const DenseTensor& reserve,
const DenseTensor& out_grad,
const std::vector<const DenseTensor*>& state_grad,
float dropout_prob,
bool is_bidirec,
int input_size,
int hidden_size,
int num_layers,
const std::string& mode,
int seed,
bool is_test,
DenseTensor* x_grad,
std::vector<DenseTensor*> pre_state_grad,
std::vector<DenseTensor*> weight_grad_list) {
using XPUTyp = typename XPUTypeTrait<T>::Type;
PADDLE_ENFORCE_EQ(
mode,
"LSTM",
errors::InvalidArgument(
"XPU only support LSTM mode now, current mode is %s", mode));
auto init_h = pre_state[0];
auto init_c = pre_state[1];
auto last_h_grad = state_grad[0];
auto last_c_grad = state_grad[1];
// get the tensor pointer for the output
DenseTensor* init_h_grad = nullptr;
DenseTensor* init_c_grad = nullptr;
if (pre_state_grad.size() > 0) { // has gradient
init_h_grad = pre_state_grad[0];
init_c_grad = pre_state_grad[1];
}
// check shape
const int& seq_len = x.dims()[0];
const int& batch_size = x.dims()[1];
const int& input_dim = x.dims()[2];
const int& direction_num = is_bidirec ? 2 : 1;
PADDLE_ENFORCE_EQ(
init_h->dims()[0],
num_layers * direction_num,
errors::InvalidArgument("The num_layers of in RNN layer must"
" be the same as first dim of init "
"hidden, but received num_layers:%d,"
" dim:%d",
num_layers,
init_h->dims()[0]));
PADDLE_ENFORCE_EQ(
init_c->dims()[0],
num_layers * direction_num,
errors::InvalidArgument(
"The num_layers of in RNN layer must"
" be the same as first dim of cell state hidden, but received"
" num_layers:%d, dim:%d",
num_layers,
init_c->dims()[0]));
std::vector<std::vector<const T*>> parameter_lists;
parameter_lists.resize(num_layers);
reset_parameter_vector(weight_list, num_layers, is_bidirec, &parameter_lists);
for (unsigned int i = 0; i < weight_grad_list.size(); ++i) {
dev_ctx.template Alloc<T>(weight_grad_list[i]);
}
std::vector<std::vector<T*>> parameter_lists_grad;
parameter_lists_grad.resize(num_layers);
reset_parameter_vector(
weight_grad_list, num_layers, is_bidirec, &parameter_lists_grad);
// allocate the memory and initization the x_grad
x_grad->Resize(x.dims());
dev_ctx.template Alloc<T>(x_grad);
phi::funcs::SetConstant<phi::XPUContext, T> zero;
zero(dev_ctx, x_grad, static_cast<T>(0.0));
DenseTensor a, b;
DenseTensor* dynamic_grad_pre_h = &a;
DenseTensor* dynamic_grad_pre_c = &b;
if (init_h_grad) {
init_h_grad->Resize(last_h_grad->dims());
dev_ctx.template Alloc<T>(init_h_grad);
zero(dev_ctx, init_h_grad, static_cast<T>(0.0));
} else {
dynamic_grad_pre_h->Resize(last_h_grad->dims());
dev_ctx.template Alloc<T>(dynamic_grad_pre_h);
zero(dev_ctx, dynamic_grad_pre_h, static_cast<T>(0.0));
init_h_grad = dynamic_grad_pre_h;
}
if (init_c_grad) {
init_c_grad->Resize(last_c_grad->dims());
dev_ctx.template Alloc<T>(init_c_grad);
} else {
dynamic_grad_pre_c->Resize(last_h_grad->dims());
dev_ctx.template Alloc<T>(dynamic_grad_pre_c);
init_c_grad = dynamic_grad_pre_c;
}
DenseTensor temp_input_grad_1, temp_input_grad_2;
T* input_grad_1_ptr = nullptr;
T* input_grad_2_ptr = nullptr;
if (num_layers >= 2) {
temp_input_grad_1.Resize(x_grad->dims());
input_grad_1_ptr = dev_ctx.template Alloc<T>(&temp_input_grad_1);
}
if (num_layers >= 3) {
temp_input_grad_2.Resize(x_grad->dims());
input_grad_2_ptr = dev_ctx.template Alloc<T>(&temp_input_grad_2);
}
// get ptr from tensor
auto x_data = x.data<T>();
auto init_h_ptr = init_h->data<T>();
auto init_c_ptr = init_c->data<T>();
auto y = out.data<T>();
auto y_grad = out_grad.data<T>();
auto last_h_grad_ptr = last_h_grad->data<T>();
auto last_c_grad_ptr = last_c_grad->data<T>();
auto x_grad_data = x_grad->data<T>();
auto init_h_grad_ptr = init_h_grad->data<T>();
auto init_c_grad_ptr = init_c_grad->data<T>();
const int& block_size = direction_num * seq_len * batch_size * hidden_size;
auto i_f_g_o_ptr = reserve.data<T>();
auto c_ptr = i_f_g_o_ptr + num_layers * block_size * 4;
auto hidden_data_ptr = c_ptr + num_layers * block_size * 1;
int state_offset = pre_state[0]->dims()[1] * pre_state[0]->dims()[2];
bool has_seq_length = sequence_length.is_initialized();
std::vector<int> seq_len_tensor(batch_size, seq_len);
if (has_seq_length) {
seq_len_tensor =
paddle::operators::GetDataFromTensor<int>(sequence_length.get_ptr());
}
for (int i = num_layers - 1; i >= 0; --i) {
// the layer input output had saved, just use the data
auto w_x = parameter_lists[i][0];
auto w_h = parameter_lists[i][1];
auto bw_x = parameter_lists[i][4];
auto bw_h = parameter_lists[i][5];
auto i_f_g_o = i_f_g_o_ptr + i * block_size * 4;
auto c = c_ptr + i * block_size;
DenseTensor layer_input_t;
auto layer_input = x_data;
if (i > 0) {
layer_input_t.Resize(out.dims());
layer_input = dev_ctx.template Alloc<T>(&layer_input_t);
float scale = static_cast<float>(1.0f - dropout_prob);
auto hidden_data = hidden_data_ptr + (i - 1) * block_size;
int r = xpu::scale(dev_ctx.x_context(),
reinterpret_cast<const XPUTyp*>(hidden_data),
const_cast<XPUTyp*>(layer_input),
out.numel(),
false,
scale,
0.0f);
PADDLE_ENFORCE_XDNN_SUCCESS(r, "scale");
} else {
layer_input = x_data;
}
auto layer_output = y;
if (i == num_layers - 1) {
layer_output = y;
} else {
layer_output = hidden_data_ptr + i * block_size;
}
const T* cur_input_ptr = nullptr;
if (i == num_layers - 1) {
cur_input_ptr = y_grad;
} else if (i % 2 != 0) {
cur_input_ptr = input_grad_2_ptr;
} else {
cur_input_ptr = input_grad_1_ptr;
}
T* cur_output_ptr = nullptr;
int cur_xdim = -1;
if (i == 0) {
cur_output_ptr = x_grad_data;
cur_xdim = input_dim;
} else if (i % 2 != 0) {
cur_output_ptr = input_grad_1_ptr;
cur_xdim = is_bidirec ? 2 * hidden_size : hidden_size;
} else {
cur_output_ptr = input_grad_2_ptr;
cur_xdim = is_bidirec ? 2 * hidden_size : hidden_size;
}
auto w_x_grad = parameter_lists_grad[i][0];
auto w_h_grad = parameter_lists_grad[i][1];
auto b_x_grad = parameter_lists_grad[i][2];
auto b_h_grad = parameter_lists_grad[i][3];
auto h_0 = init_h_ptr + direction_num * i * state_offset;
auto c_0 = init_c_ptr + direction_num * i * state_offset;
auto h_0_grad = init_h_grad_ptr + direction_num * i * state_offset;
auto c_0_grad = init_c_grad_ptr + direction_num * i * state_offset;
auto h_t_grad = last_h_grad_ptr + direction_num * i * state_offset;
auto c_t_grad = last_c_grad_ptr + direction_num * i * state_offset;
if (is_bidirec) {
auto bw_x_grad = parameter_lists_grad[i][4];
auto bw_h_grad = parameter_lists_grad[i][5];
auto bb_x_grad = parameter_lists_grad[i][6];
auto bb_h_grad = parameter_lists_grad[i][7];
int r =
xpu::bilstm_grad<T, T, int16_t>(dev_ctx.x_context(),
(const T*)layer_input,
(const T*)h_0,
(const T*)c_0,
(const T*)w_x,
(const T*)w_h,
(const T*)bw_x,
(const T*)bw_h,
(const T*)layer_output,
(const T*)cur_input_ptr,
(const T*)h_t_grad,
(const T*)c_t_grad,
reinterpret_cast<T*>(cur_output_ptr),
reinterpret_cast<T*>(h_0_grad),
reinterpret_cast<T*>(c_0_grad),
w_x_grad,
w_h_grad,
b_x_grad,
b_h_grad,
bw_x_grad,
bw_h_grad,
bb_x_grad,
bb_h_grad,
batch_size,
cur_xdim,
hidden_size,
seq_len,
seq_len_tensor,
nullptr,
nullptr,
nullptr,
nullptr,
nullptr,
nullptr,
i_f_g_o,
c);
PADDLE_ENFORCE_XDNN_SUCCESS(r, "bilstm_grad");
} else {
int r =
xpu::lstm_grad<T, T, int16_t>(dev_ctx.x_context(),
(const T*)layer_input,
(const T*)h_0,
(const T*)c_0,
(const T*)w_x,
(const T*)w_h,
(const T*)layer_output,
(const T*)cur_input_ptr,
(const T*)h_t_grad,
(const T*)c_t_grad,
reinterpret_cast<T*>(cur_output_ptr),
reinterpret_cast<T*>(h_0_grad),
reinterpret_cast<T*>(c_0_grad),
w_x_grad,
w_h_grad,
b_x_grad,
b_h_grad,
batch_size,
cur_xdim,
hidden_size,
seq_len,
seq_len_tensor,
nullptr,
nullptr,
nullptr,
nullptr,
i_f_g_o,
c);
PADDLE_ENFORCE_XDNN_SUCCESS(r, "lstm_grad");
}
}
}
} // namespace phi
PD_REGISTER_KERNEL(rnn_grad, XPU, ALL_LAYOUT, phi::RnnGradKernel, float) {}
paddle/phi/kernels/xpu/rnn_kernel.cc 0 → 100644
浏览文件 @ 91631492
// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include "paddle/phi/kernels/rnn_kernel.h"
#include "paddle/fluid/operators/utils.h"
#include "paddle/phi/backends/xpu/enforce_xpu.h"
#include "paddle/phi/core/kernel_registry.h"
#include "paddle/phi/kernels/funcs/math_function.h"
#include "paddle/phi/kernels/xpu/rnn_util.h"
namespace phi {
template <typename T, typename Context>
void RnnKernel(const Context& dev_ctx,
const DenseTensor& x,
const std::vector<const DenseTensor*>& pre_state,
const std::vector<const DenseTensor*>& weight_list,
const paddle::optional<DenseTensor>& sequence_length,
float dropout_prob,
bool is_bidirec,
int input_size,
int hidden_size,
int num_layers,
const std::string& mode,
int seed,
bool is_test,
DenseTensor* out,
DenseTensor* dropout_state,
std::vector<DenseTensor*> state,
DenseTensor* reserve) {
using XPUTyp = typename XPUTypeTrait<T>::Type;
if (dropout_state->IsInitialized()) {
if (dropout_state->numel() != out->numel()) dropout_state->clear();
}
dropout_state->Resize(out->dims());
dev_ctx.template Alloc<T>(dropout_state);
phi::funcs::SetConstant<phi::XPUContext, uint8_t> ones;
ones(dev_ctx, dropout_state, static_cast<uint8_t>(1));
PADDLE_ENFORCE_EQ(
mode,
"LSTM",
errors::InvalidArgument(
"XPU only support LSTM mode now, current mode is %s", mode));
auto init_h = pre_state[0];
auto init_c = pre_state[1];
auto last_h = state[0];
auto last_c = state[1];
// check shape
const int& seq_len = x.dims()[0]; // time_step
const int& batch_size = x.dims()[1];
const int& input_dim = x.dims()[2];
const int& direction_num = is_bidirec ? 2 : 1;
PADDLE_ENFORCE_EQ(
init_h->dims()[0],
num_layers * direction_num,
errors::InvalidArgument("The num_layers of in RNN layer must"
" be the same as first dim of init "
"hidden, but received num_layers:%d,"
" dim:%d",
num_layers,
init_h->dims()[0]));
PADDLE_ENFORCE_EQ(
init_c->dims()[0],
num_layers * direction_num,
errors::InvalidArgument(
"The num_layers of in RNN layer must"
" be the same as first dim of cell state hidden, but received"
" num_layers:%d, dim:%d",
num_layers,
init_c->dims()[0]));
// weightlist
std::vector<std::vector<const T*>> parameter_lists;
parameter_lists.resize(num_layers);
reset_parameter_vector(weight_list, num_layers, is_bidirec, &parameter_lists);
// init the output and allocate the memory
dev_ctx.template Alloc<T>(out);
dev_ctx.template Alloc<T>(last_h);
dev_ctx.template Alloc<T>(last_c);
int gate_num = 4;
int hidden_data_idx = (num_layers - 1);
hidden_data_idx += (gate_num + 1) * num_layers;
const int& block_size = direction_num * seq_len * batch_size * hidden_size;
reserve->Resize({hidden_data_idx, block_size});
dev_ctx.template Alloc<T>(reserve);
// get ptr from tensor
auto x_data = x.data<T>();
auto init_h_ptr = init_h->data<T>();
auto init_c_ptr = init_c->data<T>();
auto y = out->data<T>();
auto last_h_ptr = last_h->data<T>();
auto last_c_ptr = last_c->data<T>();
auto i_f_g_o_ptr = reserve->data<T>();
auto c_ptr =
i_f_g_o_ptr + num_layers * block_size * 4; // 4 for i_f_g_o offset
auto hidden_data_ptr = c_ptr + num_layers * block_size * 1; // 1 for c offset
std::vector<int> seq_len_tensor(batch_size, seq_len);
bool has_seq_length = sequence_length.is_initialized();
if (has_seq_length) {
seq_len_tensor =
paddle::operators::GetDataFromTensor<int>(sequence_length.get_ptr());
}
int state_offset = pre_state[0]->dims()[1] * pre_state[0]->dims()[2];
const T* cur_input_ptr = nullptr;
int cur_xdim = -1;
T* cur_output_ptr = y;
for (int i = 0; i < num_layers; i++) {
auto i_f_g_o = i_f_g_o_ptr + i * block_size * 4;
auto c = c_ptr + i * block_size;
cur_output_ptr = y;
if (i < num_layers - 1 && num_layers > 1) {
cur_output_ptr = hidden_data_ptr + i * block_size;
}
if (i == 0) {
cur_input_ptr = x_data;
cur_xdim = input_dim;
} else {
cur_input_ptr = hidden_data_ptr + (i - 1) * block_size;
cur_xdim = is_bidirec ? 2 * hidden_size : hidden_size;
}
auto h_0 = init_h_ptr + direction_num * i * state_offset;
auto c_0 = init_c_ptr + direction_num * i * state_offset;
auto last_h = last_h_ptr + direction_num * i * state_offset;
auto last_c = last_c_ptr + direction_num * i * state_offset;
auto w_x = parameter_lists[i][0];
auto w_h = parameter_lists[i][1];
auto b_x = parameter_lists[i][2];
auto b_h = parameter_lists[i][3];
if (is_bidirec) {
auto bw_x = parameter_lists[i][4];
auto bw_h = parameter_lists[i][5];
auto bb_x = parameter_lists[i][6];
auto bb_h = parameter_lists[i][7];
int r =
xpu::bilstm_train<T, T, int16_t>(dev_ctx.x_context(),
(const T*)cur_input_ptr,
(const T*)h_0,
(const T*)c_0,
(const T*)w_x,
(const T*)w_h,
(const T*)b_x,
(const T*)b_h,
(const T*)bw_x,
(const T*)bw_h,
(const T*)bb_x,
(const T*)bb_h,
reinterpret_cast<T*>(cur_output_ptr),
reinterpret_cast<T*>(last_h),
reinterpret_cast<T*>(last_c),
batch_size,
cur_xdim,
hidden_size,
seq_len,
seq_len_tensor,
nullptr,
nullptr,
nullptr,
nullptr,
nullptr,
nullptr,
reinterpret_cast<T*>(i_f_g_o),
reinterpret_cast<T*>(c));
PADDLE_ENFORCE_XDNN_SUCCESS(r, "bilstm_train");
} else {
int r =
xpu::lstm_train<T, T, int16_t>(dev_ctx.x_context(),
(const T*)cur_input_ptr,
(const T*)h_0,
(const T*)c_0,
(const T*)w_x,
(const T*)w_h,
(const T*)b_x,
(const T*)b_h,
reinterpret_cast<T*>(cur_output_ptr),
reinterpret_cast<T*>(last_h),
reinterpret_cast<T*>(last_c),
batch_size,
cur_xdim,
hidden_size,
seq_len,
seq_len_tensor,
nullptr,
nullptr,
nullptr,
nullptr,
reinterpret_cast<T*>(i_f_g_o),
reinterpret_cast<T*>(c),
xpu::Activation_t::TANH,
xpu::Activation_t::SIGMOID);
PADDLE_ENFORCE_XDNN_SUCCESS(r, "lstm_train");
}
}
}
} // namespace phi
PD_REGISTER_KERNEL(rnn, XPU, ALL_LAYOUT, phi::RnnKernel, float) {}
paddle/phi/kernels/xpu/rnn_util.h 0 → 100644
浏览文件 @ 91631492
// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#pragma once
#include <vector>
namespace phi {
template <typename TensorType, typename T>
void reset_parameter_vector(const std::vector<TensorType>& raw_params_vec,
const int& num_layers,
const bool& is_bidirec,
std::vector<std::vector<T*>>* params_vec) {
// the parameter raw seuquence is [FWhi, FWhh, BWhi, BWhh] * num_layers
// + [FBhi, FBhh, BBhi, BBhh] * num_layers, we will reset the parameter to
// ([FWhi, FWhh, FBhi, FBhh] + [BWhi, BWhh, BBhi, BBhh]) * num_layers
const int& direction_num = is_bidirec ? 2 : 1;
const int& layer_weight_size = 4 * direction_num;
const int& all_weight_size = num_layers * layer_weight_size;
const int& bias_start_idx = all_weight_size / 2;
for (int i = 0; i < num_layers; i++) {
params_vec->at(i).resize(layer_weight_size);
for (int j = 0; j < layer_weight_size; j++) {
int k = j % 4;
const int& section = j / 4;
int tensor_idx = i * 2 * direction_num + section * 2 + k % 2;
if (k >= 2) {
tensor_idx += bias_start_idx;
}
using remove_cv_t = typename std::remove_cv<T>::type;
params_vec->at(i)[j] =
raw_params_vec[tensor_idx]->template data<remove_cv_t>();
}
}
}
} // namespace phi
Markdown is supported
0% 或 .
You are about to add 0 people to the discussion. Proceed with caution.
先完成此消息的编辑！
想要评论请 注册 或