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Merged
reyoung merged 4 commits into from
May 4, 2018
Merged

Polish MatMul, clean copy & paste code #10371

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9d7279b
Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into…
reyoung May 3, 2018
815d888
Clean MatMul
reyoung May 3, 2018
d0785ce
Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into…
reyoung May 3, 2018
ef6ea79
Clean and extract blas
reyoung May 4, 2018
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2 changes: 1 addition & 1 deletion paddle/fluid/operators/bilinear_tensor_product_op.h
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Original file line number Diff line number Diff line change
Expand Up @@ -16,7 +16,7 @@ limitations under the License. */

#include "paddle/fluid/framework/eigen.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/math/math_function.h"
#include "paddle/fluid/operators/math/blas.h"

namespace paddle {
namespace operators {
Expand Down
16 changes: 7 additions & 9 deletions paddle/fluid/operators/conv_op.h
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Expand Up @@ -17,9 +17,9 @@ limitations under the License. */
#include <vector>
#include "paddle/fluid/framework/eigen.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/math/blas.h"
#include "paddle/fluid/operators/math/depthwise_conv.h"
#include "paddle/fluid/operators/math/im2col.h"
#include "paddle/fluid/operators/math/math_function.h"
#include "paddle/fluid/operators/math/vol2col.h"

namespace paddle {
Expand Down Expand Up @@ -161,6 +161,7 @@ class GemmConvKernel : public framework::OpKernel<T> {
math::Im2ColFunctor<math::ColFormat::kCFO, DeviceContext, T> im2col;

auto& dev_ctx = context.template device_context<DeviceContext>();
auto blas = math::GetBlas<DeviceContext, T>(dev_ctx);
for (int i = 0; i < batch_size; i++) {
Tensor in_batch = input->Slice(i, i + 1).Resize(input_shape);
Tensor out_batch = output->Slice(i, i + 1).Resize(output_matrix_shape);
Expand All @@ -186,8 +187,7 @@ class GemmConvKernel : public framework::OpKernel<T> {
// gemm
Tensor out_slice = out_batch.Slice(g * out_step, (g + 1) * out_step);
Tensor filter_slice = filter.Slice(g * out_step, (g + 1) * out_step);
math::matmul<DeviceContext, T>(dev_ctx, filter_slice, false, col_matrix,
false, T(1.0), &out_slice, T(0.0));
blas.MatMul(filter_slice, col_matrix, &out_slice);
}
}
}
Expand Down Expand Up @@ -274,6 +274,7 @@ class GemmConvGradKernel : public framework::OpKernel<T> {

math::SetConstant<DeviceContext, T> set_zero;
auto& dev_ctx = context.template device_context<DeviceContext>();
auto blas = math::GetBlas<DeviceContext, T>(dev_ctx);

if (input_grad) {
input_grad->mutable_data<T>(context.GetPlace());
Expand Down Expand Up @@ -303,9 +304,7 @@ class GemmConvGradKernel : public framework::OpKernel<T> {
col_matrix.ShareDataWith(in_grad_slice);
col_matrix.Resize(col_matrix_shape);
}
math::matmul<DeviceContext, T>(dev_ctx, filter_slice, true,
out_grad_slice, false, T(1.0),
&col_matrix, T(0.0));
blas.MatMul(filter_slice, true, out_grad_slice, false, &col_matrix);

if (is_expand && data_dim == 2U) {
col2im(dev_ctx, col, dilations, strides,
Expand Down Expand Up @@ -352,9 +351,8 @@ class GemmConvGradKernel : public framework::OpKernel<T> {
// gemm
Tensor filter_grad_slice =
filter_grad_.Slice(g * out_step, (g + 1) * out_step);
math::matmul<DeviceContext, T>(dev_ctx, out_grad_slice, false,
col_matrix, true, T(1.0),
&filter_grad_slice, T(1.0));
blas.MatMul(out_grad_slice, false, col_matrix, true,
&filter_grad_slice);
}
}
}
Expand Down
16 changes: 6 additions & 10 deletions paddle/fluid/operators/conv_transpose_op.h
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Expand Up @@ -16,8 +16,8 @@ limitations under the License. */
#include <vector>
#include "paddle/fluid/framework/eigen.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/math/blas.h"
#include "paddle/fluid/operators/math/im2col.h"
#include "paddle/fluid/operators/math/math_function.h"
#include "paddle/fluid/operators/math/vol2col.h"

namespace paddle {
Expand Down Expand Up @@ -118,6 +118,7 @@ class GemmConvTransposeKernel : public framework::OpKernel<T> {
output->mutable_data<T>(context.GetPlace());
math::SetConstant<DeviceContext, T> set_zero;
auto& dev_ctx = context.template device_context<DeviceContext>();
auto blas = math::GetBlas<DeviceContext, T>(dev_ctx);
set_zero(dev_ctx, output, static_cast<T>(0));

math::Col2ImFunctor<math::ColFormat::kCFO, DeviceContext, T> col2im;
Expand All @@ -134,9 +135,7 @@ class GemmConvTransposeKernel : public framework::OpKernel<T> {

// col_matrix = filter * input_batch
// of shape (c * k_h * k_w, h * w) or (c * k_d * k_h * k_w, d * h * w)
math::matmul<DeviceContext, T>(dev_ctx, filter, true, input_batch, false,
static_cast<T>(1.0), &col_matrix,
static_cast<T>(0.0));
blas.MatMul(filter, true, input_batch, false, &col_matrix);

if (data_dim == 2U) {
// col2im: col_matrix -> dy
Expand Down Expand Up @@ -213,6 +212,7 @@ class GemmConvTransposeGradKernel : public framework::OpKernel<T> {
// im2col + gemm (similar to conv-forward)
// input need to compute gradient
auto& dev_ctx = context.template device_context<DeviceContext>();
auto blas = math::GetBlas<DeviceContext, T>(dev_ctx);
if (input_grad || filter_grad) {
Tensor col;
col.mutable_data<T>(col_shape, context.GetPlace());
Expand Down Expand Up @@ -267,9 +267,7 @@ class GemmConvTransposeGradKernel : public framework::OpKernel<T> {
// or
// (m, c * k_d * k_h * k_w) * (c * k_d * k_h * k_w, d * h * w) -> (m,
// d, h, w)
math::matmul<DeviceContext, T>(
dev_ctx, filter, false, col_matrix, false, static_cast<T>(1.0),
&input_grad_batch, static_cast<T>(0.0));
blas.MatMul(filter, false, col_matrix, false, &input_grad_batch);
}
if (filter_grad) {
// input batch
Expand All @@ -279,9 +277,7 @@ class GemmConvTransposeGradKernel : public framework::OpKernel<T> {
// or
// (m, d * h * w) * (d * h * w, c * k_d * k_h * k_w) -> (m, c * k_d *
// k_h * k_w)
math::matmul<DeviceContext, T>(dev_ctx, in_batch, false, col_matrix,
true, static_cast<T>(1.0),
&filter_grad_, static_cast<T>(1.0));
blas.MatMul(in_batch, false, col_matrix, true, &filter_grad_);
}
}
}
Expand Down
5 changes: 2 additions & 3 deletions paddle/fluid/operators/gru_unit_op.h
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Expand Up @@ -14,11 +14,10 @@ limitations under the License. */

#pragma once

#include "paddle/fluid/operators/activation_op.h"
#include "paddle/fluid/operators/math/math_function.h"

#include "paddle/fluid/framework/eigen.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/activation_op.h"
#include "paddle/fluid/operators/math/blas.h"

namespace paddle {
namespace operators {
Expand Down
14 changes: 7 additions & 7 deletions paddle/fluid/operators/layer_norm_op.h
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Expand Up @@ -15,8 +15,8 @@ limitations under the License. */
#pragma once
#include "paddle/fluid/framework/eigen.h"
#include "paddle/fluid/framework/op_registry.h"

#include "paddle/fluid/operators/elementwise_op_function.h"
#include "paddle/fluid/operators/math/blas.h"
#include "paddle/fluid/operators/math/math_function.h"

namespace paddle {
Expand Down Expand Up @@ -46,9 +46,9 @@ class RowwiseMean2D<platform::CUDADeviceContext, T> {
}
void operator()(const platform::CUDADeviceContext& context,
const framework::Tensor& input, framework::Tensor* out) {
math::gemv<platform::CUDADeviceContext, T>(
context, false, left_, right_, 1., input.data<T>(), divisor_.data<T>(),
0., out->data<T>());
math::GetBlas<platform::CUDADeviceContext, T>(context).GEMV(
false, left_, right_, 1., input.data<T>(), divisor_.data<T>(), 0.,
out->data<T>());
}

private:
Expand Down Expand Up @@ -93,9 +93,9 @@ class ColwiseSum2D<platform::CUDADeviceContext, T> {

void operator()(const platform::CUDADeviceContext& context,
const framework::Tensor& input, framework::Tensor* out) {
math::gemv<platform::CUDADeviceContext, T>(
context, true, left_, right_, 1., input.data<T>(), divisor_.data<T>(),
0., out->data<T>());
math::GetBlas<platform::CUDADeviceContext, T>(context).GEMV(
true, left_, right_, 1., input.data<T>(), divisor_.data<T>(), 0.,
out->data<T>());
}

private:
Expand Down
34 changes: 15 additions & 19 deletions paddle/fluid/operators/lstm_op.h
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Expand Up @@ -15,9 +15,9 @@ limitations under the License. */
#pragma once
#include <string>
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/math/blas.h"
#include "paddle/fluid/operators/math/detail/activation_functions.h"
#include "paddle/fluid/operators/math/lstm_compute.h"
#include "paddle/fluid/operators/math/math_function.h"
#include "paddle/fluid/operators/math/sequence2batch.h"

namespace paddle {
Expand Down Expand Up @@ -114,6 +114,7 @@ class LSTMKernel : public framework::OpKernel<T> {
auto cand_act = math::detail::GetActivationType(
ctx.Attr<std::string>("candidate_activation"));

auto blas = math::GetBlas<DeviceContext, T>(device_ctx);
for (size_t n = 0; n < num_batch; n++) {
int bstart = static_cast<int>(batch_starts[n]);
int bend = static_cast<int>(batch_starts[n + 1]);
Expand All @@ -129,9 +130,8 @@ class LSTMKernel : public framework::OpKernel<T> {
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);
math::matmul<DeviceContext, T>(device_ctx, pre_hidden_t, false, *weight,
false, static_cast<T>(1.0), &gate_t,
static_cast<T>(1.0));
blas.MatMul(pre_hidden_t, false, *weight, false, static_cast<T>(1.0),
&gate_t, static_cast<T>(1.0));
} else if (hidden_t0) {
// 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.
Expand All @@ -143,9 +143,8 @@ class LSTMKernel : public framework::OpKernel<T> {
Tensor ordered_h0;
ReorderInitState<DeviceContext, T>(device_ctx, *hidden_t0, order,
&ordered_h0, true);
math::matmul<DeviceContext, T>(device_ctx, ordered_h0, false, *weight,
false, static_cast<T>(1.0), &gate_t,
static_cast<T>(1.0));
blas.MatMul(ordered_h0, false, *weight, false, static_cast<T>(1.0),
&gate_t, static_cast<T>(1.0));
}

lstm_value.gate_value = gate_t.data<T>();
Expand Down Expand Up @@ -282,6 +281,7 @@ class LSTMGradKernel : public framework::OpKernel<T> {

auto batch_starts = batch_gate->lod()[0];
size_t num_batch = batch_starts.size() - 1;
auto blas = math::GetBlas<DeviceContext, T>(device_ctx);
for (int n = static_cast<int>(num_batch) - 1; n >= 0; n--) {
int bstart = static_cast<int>(batch_starts[n]);
int bend = static_cast<int>(batch_starts[n + 1]);
Expand Down Expand Up @@ -320,29 +320,25 @@ class LSTMGradKernel : public framework::OpKernel<T> {
int pre_h_start = static_cast<int>(batch_starts[n - 1]);
int pre_h_end = pre_h_start + cur_batch_size;
auto pre_hidden_g = batch_hidden_g.Slice(pre_h_start, pre_h_end);
math::matmul<DeviceContext, T>(device_ctx, gate_g, false, *weight, true,
static_cast<T>(1.0), &pre_hidden_g,
static_cast<T>(1.0));
blas.MatMul(gate_g, false, *weight, true, static_cast<T>(1.0),
&pre_hidden_g, static_cast<T>(1.0));
if (weight_g) {
/* backward weight */
auto pre_hidden = batch_hidden.Slice(pre_h_start, pre_h_end);
math::matmul<DeviceContext, T>(device_ctx, pre_hidden, true, gate_g,
false, static_cast<T>(1.0), weight_g,
static_cast<T>(1.0));
blas.MatMul(pre_hidden, true, gate_g, false, static_cast<T>(1.0),
weight_g, static_cast<T>(1.0));
}
} else {
if (h0 && weight_g) {
ReorderInitState<DeviceContext, T>(device_ctx, *h0, order,
&ordered_h0, true);
math::matmul<DeviceContext, T>(device_ctx, ordered_h0, true, gate_g,
false, static_cast<T>(1.0), weight_g,
static_cast<T>(1.0));
blas.MatMul(ordered_h0, true, gate_g, false, static_cast<T>(1.0),
weight_g, static_cast<T>(1.0));
}
if (h0 && h0_g) {
ordered_h0_g.mutable_data<T>(h0_g->dims(), ctx.GetPlace());
math::matmul<DeviceContext, T>(device_ctx, gate_g, false, *weight,
true, static_cast<T>(1.0),
&ordered_h0_g, static_cast<T>(0.0));
blas.MatMul(gate_g, false, *weight, true, static_cast<T>(1.0),
&ordered_h0_g, static_cast<T>(0.0));
}
}
}
Expand Down
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