未验证 提交 3c957af1 编写于 作者: T tensor-tang 提交者: GitHub

Merge pull request #14080 from tensor-tang/refine/jit/crf2

Refine/jit/crf decoding
......@@ -301,6 +301,7 @@ op_library(flatten_op DEPS reshape_op)
op_library(sequence_pad_op DEPS sequence_padding)
op_library(unstack_op DEPS stack_op)
op_library(fake_quantize_op DEPS memory)
op_library(crf_decoding_op DEPS jit_kernel)
op_library(fusion_lstm_op DEPS jit_kernel)
if (WITH_GPU)
op_library(conv_op DEPS vol2col depthwise_conv im2col)
......
......@@ -16,6 +16,7 @@ limitations under the License. */
#include <limits>
#include "paddle/fluid/framework/eigen.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/math/jit_kernel.h"
#include "paddle/fluid/operators/math/math_function.h"
namespace paddle {
......@@ -69,9 +70,6 @@ class CRFDecodingOpKernel : public framework::OpKernel<T> {
auto emission_dims = emission_weights.dims();
const size_t seq_len = emission_dims[0];
const size_t tag_num = emission_dims[1];
const size_t state_trans_base_idx = 2;
const T* x = emission_weights.data<T>();
const T* w = transition_weights.data<T>();
int64_t* path = decoded_path->data<int64_t>();
......@@ -84,221 +82,10 @@ class CRFDecodingOpKernel : public framework::OpKernel<T> {
Tensor track;
int* track_value =
track.mutable_data<int>(emission_dims, platform::CPUPlace());
#ifdef __AVX__
// It use the AVX or AVX512 instruction to deal the data as the vector of 8 or
// 16 elements per iteration. Then it can implement the parallel processing.
// Only optimize for float type.
#ifdef __AVX512F__
size_t step_size = 16;
#else
size_t step_size = 8;
#endif
if (std::is_same<T, float>::value && (tag_num >= step_size)) {
size_t steps = tag_num / step_size;
size_t remain = tag_num % step_size;
int last_offset = static_cast<int>(remain) - static_cast<int>(step_size);
// Setup the alpha initial value.
size_t i_offset = 0;
for (size_t i = 0; i <= steps; ++i) {
#ifdef __AVX512F__
// Declare the variable for the content of weights, input and alpha
// values.
__m512 w_content, x_content, alpha_content;
// Load the relevant data into the variables from un-aligned address.
w_content = _mm512_loadu_ps((const float*)(w + i_offset));
x_content = _mm512_loadu_ps((const float*)(x + i_offset));
alpha_content = _mm512_add_ps(w_content, x_content);
// Save the alpha value.
_mm512_storeu_ps(reinterpret_cast<float*>(alpha_value + i_offset),
alpha_content);
#else
// Declare the variable for the content of weights, input and alpha
// values.
__m256 w_content, x_content, alpha_content;
// Load the relevant data into the variables from un-aligned address.
w_content = _mm256_loadu_ps((const float*)(w + i_offset));
x_content = _mm256_loadu_ps((const float*)(x + i_offset));
alpha_content = _mm256_add_ps(w_content, x_content);
// Save the alpha value.
_mm256_storeu_ps(reinterpret_cast<float*>(alpha_value + i_offset),
alpha_content);
#endif
i_offset += step_size;
if (i == steps - 1) {
if (remain > 0) {
i_offset += last_offset;
} else {
break;
}
}
}
// Use the column-major strategy to get the location of maximum score.
size_t seq_offset = 0;
for (size_t k = 1; k < seq_len; ++k) {
size_t j_offset = 0;
for (size_t j = 0; j <= steps; ++j) {
#ifdef __AVX512F__
// Initialize the variables of maximum score and location.
__m512 max_score = _mm512_set1_ps(-std::numeric_limits<T>::max());
__m512i max_j = _mm512_setzero_si512();
#else
// Initialize the variables of maximum score and location.
__m256 max_score = _mm256_set1_ps(-std::numeric_limits<T>::max());
__m256i max_j = _mm256_set1_epi32(0);
#endif
// Calculate the offset of transition_weights.
size_t trans_offset = state_trans_base_idx * tag_num + j_offset;
for (size_t i = 0; i < tag_num; ++i) {
#ifdef __AVX512F__
// Initalize the content of alpha variable with related offset.
__m512 alpha_content =
_mm512_set1_ps(*(const float*)(alpha_value + seq_offset + i));
// Obtain the content of weights from un-aligned address.
__m512 w_content =
_mm512_loadu_ps((const float*)(w + trans_offset));
__m512 score_v = _mm512_add_ps(alpha_content, w_content);
__mmask16 mask = _mm512_cmp_ps_mask(score_v, max_score, _CMP_GT_OS);
// According to the mask value, it update the index of the max_score
// location.
max_j = _mm512_mask_set1_epi32(max_j, mask, i);
// Update the max_score value.
max_score = _mm512_max_ps(max_score, score_v);
#else
// Initalize the content of alpha variable with related offset.
__m256 alpha_content = _mm256_broadcast_ss(
(const float*)(alpha_value + seq_offset + i));
// Obtain the content of weights from un-aligned address.
__m256 w_content =
_mm256_loadu_ps((const float*)(w + trans_offset));
__m256 score_v = _mm256_add_ps(alpha_content, w_content);
__m256 mask = _mm256_cmp_ps(score_v, max_score, _CMP_GT_OS);
#ifdef __AVX2__
// According to the mask value, it update the index of the max_score
// location.
max_j = _mm256_or_si256(
_mm256_andnot_si256((__m256i)mask, max_j),
_mm256_and_si256((__m256i)mask, _mm256_set1_epi32(i)));
#else
__m128i lo_max_j = _mm256_extractf128_si256(max_j, 0);
__m128i hi_max_j = _mm256_extractf128_si256(max_j, 1);
__m128i lo_mask = _mm256_extractf128_si256((__m256i)mask, 0);
__m128i hi_mask = _mm256_extractf128_si256((__m256i)mask, 1);
lo_max_j = _mm_andnot_si128(lo_mask, lo_max_j);
hi_max_j = _mm_andnot_si128(hi_mask, hi_max_j);
lo_mask = _mm_and_si128(lo_mask, _mm_set1_epi32(i));
hi_mask = _mm_and_si128(hi_mask, _mm_set1_epi32(i));
lo_max_j = _mm_or_si128(lo_mask, lo_max_j);
hi_max_j = _mm_or_si128(hi_mask, hi_max_j);
// According to the mask value, it update the index of the max_score
// location.
max_j = _mm256_insertf128_si256(max_j, lo_max_j, 0);
max_j = _mm256_insertf128_si256(max_j, hi_max_j, 1);
#endif
// Update the max_score value.
max_score = _mm256_max_ps(max_score, score_v);
#endif
trans_offset += tag_num;
}
#ifdef __AVX512F__
// Update the alpha and track values.
__m512 x_content = _mm512_loadu_ps(
(const float*)(x + seq_offset + tag_num + j_offset));
max_score = _mm512_add_ps(max_score, x_content);
_mm512_storeu_ps(reinterpret_cast<float*>(alpha_value + seq_offset +
tag_num + j_offset),
max_score);
_mm512_storeu_si512(
reinterpret_cast<__m512i*>(track_value + seq_offset + tag_num +
j_offset),
max_j);
#else
// Update the alpha and track values.
__m256 x_content = _mm256_loadu_ps(
(const float*)(x + seq_offset + tag_num + j_offset));
max_score = _mm256_add_ps(max_score, x_content);
_mm256_storeu_ps(reinterpret_cast<float*>(alpha_value + seq_offset +
tag_num + j_offset),
max_score);
_mm256_storeu_si256(
reinterpret_cast<__m256i*>(track_value + seq_offset + tag_num +
j_offset),
max_j);
#endif
// Calculate the offset of next step
j_offset += step_size;
if (j == steps - 1) {
if (remain > 0) {
j_offset += last_offset;
} else {
break;
}
}
}
seq_offset += tag_num;
}
} else {
for (size_t i = 0; i < tag_num; ++i) alpha_value[i] = w[i] + x[i];
for (size_t k = 1; k < seq_len; ++k) {
for (size_t i = 0; i < tag_num; ++i) {
T max_score = -std::numeric_limits<T>::max();
int max_j = 0;
for (size_t j = 0; j < tag_num; ++j) {
T score = alpha_value[(k - 1) * tag_num + j] +
w[(j + state_trans_base_idx) * tag_num + i];
if (score > max_score) {
max_score = score;
max_j = j;
}
}
alpha_value[k * tag_num + i] = max_score + x[k * tag_num + i];
track_value[k * tag_num + i] = max_j;
}
}
}
#else
for (size_t i = 0; i < tag_num; ++i) alpha_value[i] = w[i] + x[i];
for (size_t k = 1; k < seq_len; ++k) {
for (size_t i = 0; i < tag_num; ++i) {
T max_score = -std::numeric_limits<T>::max();
int max_j = 0;
for (size_t j = 0; j < tag_num; ++j) {
T score = alpha_value[(k - 1) * tag_num + j] +
w[(j + state_trans_base_idx) * tag_num + i];
if (score > max_score) {
max_score = score;
max_j = j;
}
}
alpha_value[k * tag_num + i] = max_score + x[k * tag_num + i];
track_value[k * tag_num + i] = max_j;
}
}
#endif
const auto& ker = math::jitkernel::KernelPool::Instance()
.template Get<math::jitkernel::CRFDecodeKernel<T>>(
static_cast<int>(tag_num));
ker->Compute(static_cast<int>(seq_len), x, w, alpha_value, track_value);
T max_score = -std::numeric_limits<T>::max();
int max_i = 0;
for (size_t i = 0; i < tag_num; ++i) {
......
......@@ -76,6 +76,6 @@ endif()
cc_test(concat_test SRCS concat_test.cc DEPS concat_and_split)
cc_test(cpu_vec_test SRCS cpu_vec_test.cc DEPS blas cpu_info)
cc_library(jit_kernel
SRCS jit_kernel.cc jit_kernel_blas.cc jit_kernel_exp.cc jit_kernel_rnn.cc
SRCS jit_kernel.cc jit_kernel_blas.cc jit_kernel_exp.cc jit_kernel_rnn.cc jit_kernel_crf_decode.cc
DEPS cpu_info cblas)
cc_test(jit_kernel_test SRCS jit_kernel_test.cc DEPS jit_kernel)
......@@ -151,6 +151,13 @@ class GRUKernel : public Kernel {
virtual void ComputeHtPart2(T *gates, const T *ht_1, T *ht) const = 0;
};
template <typename T>
class CRFDecodeKernel : public Kernel {
public:
virtual void Compute(const int seq_len, const T *x, const T *w, T *alpha,
int *track) const = 0;
};
} // namespace jitkernel
} // namespace math
} // namespace operators
......
/* 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. */
#include "paddle/fluid/operators/math/jit_kernel.h"
#include <limits>
#include <string>
#include "paddle/fluid/operators/math/jit_kernel_macro.h"
#ifdef __AVX__
#include <immintrin.h>
#endif
namespace paddle {
namespace operators {
namespace math {
namespace jitkernel {
namespace jit = platform::jit;
/* CRF Decode JitKernel */
template <typename T, platform::jit::cpu_isa_t isa, jit_block>
class CRFDecodeKernelImpl : public CRFDecodeKernel<T> {
public:
explicit CRFDecodeKernelImpl(int tag_num) : CRFDecodeKernel<T>() {
this->num_ = tag_num;
}
void Compute(const int seq_len, const T* x, const T* w, T* alpha,
int* track) const override {
constexpr int state_trans_base_idx = 2;
for (int i = 0; i < this->num_; ++i) {
alpha[i] = w[i] + x[i];
}
for (int k = 1; k < seq_len; ++k) {
for (int i = 0; i < this->num_; ++i) {
T max_score = -std::numeric_limits<T>::max();
int max_j = 0;
for (int j = 0; j < this->num_; ++j) {
T score = alpha[(k - 1) * this->num_ + j] +
w[(j + state_trans_base_idx) * this->num_ + i];
if (score > max_score) {
max_score = score;
max_j = j;
}
}
alpha[k * this->num_ + i] = max_score + x[k * this->num_ + i];
track[k * this->num_ + i] = max_j;
}
}
}
};
#define INIT_ALPHA(step_size) \
/* Setup the alpha initial value.*/ \
int i_offset = 0; \
int last_offset = this->rest_ - step_size; \
for (int i = 0; i <= this->end_; ++i) { \
/* weights, input and alpha values. */ \
__m256 w_content, x_content, alpha_content; \
/* Load the relevant data into the variables from un-aligned address.*/ \
w_content = _mm256_loadu_ps(w + i_offset); \
x_content = _mm256_loadu_ps(x + i_offset); \
alpha_content = _mm256_add_ps(w_content, x_content); \
_mm256_storeu_ps(alpha + i_offset, alpha_content); \
i_offset += step_size; \
if (i == this->end_ - 1) { \
if (this->rest_ > 0) { \
i_offset += last_offset; \
} else { \
break; \
} \
} \
}
#define UPDATE_ALPHA(step_size) \
/* Update the alpha and track values. */ \
__m256 x_content = _mm256_loadu_ps(x + seq_offset + this->num_ + j_offset); \
max_score = _mm256_add_ps(max_score, x_content); \
_mm256_storeu_ps(alpha + seq_offset + this->num_ + j_offset, max_score); \
_mm256_storeu_si256( \
reinterpret_cast<__m256i*>(track + seq_offset + this->num_ + j_offset), \
max_j); \
/* Calculate the offset of next step*/ \
j_offset += step_size; \
if (j == this->end_ - 1) { \
if (this->rest_ > 0) { \
j_offset += last_offset; \
} else { \
break; \
} \
}
#define INTRIAVX_FLOAT(block) \
template <> \
CRFDecodeKernelImpl<float, jit::avx, block>::CRFDecodeKernelImpl( \
int tag_num) \
: CRFDecodeKernel<float>() { \
this->num_ = tag_num; \
this->end_ = this->num_ / AVX_FLOAT_BLOCK; \
this->rest_ = this->num_ % AVX_FLOAT_BLOCK; \
} \
template <> \
void CRFDecodeKernelImpl<float, jit::avx, block>::Compute( \
const int seq_len, const float* x, const float* w, float* alpha, \
int* track) const { \
INIT_ALPHA(AVX_FLOAT_BLOCK) \
/* Use the column-major strategy to get the location of maximum score.*/ \
int seq_offset = 0; \
constexpr int state_trans_base_idx = 2; \
for (int k = 1; k < seq_len; ++k) { \
int j_offset = 0; \
for (int j = 0; j <= this->end_; ++j) { \
/* Initialize the variables of maximum score and location.*/ \
__m256 max_score = _mm256_set1_ps(-std::numeric_limits<float>::max()); \
__m256i max_j = _mm256_set1_epi32(0); \
/* Calculate the offset of transition_weights.*/ \
int trans_offset = state_trans_base_idx * this->num_ + j_offset; \
for (int i = 0; i < this->num_; ++i) { \
/* Initalize the content of alpha variable with related offset.*/ \
__m256 alpha_content = _mm256_broadcast_ss(alpha + seq_offset + i); \
/* Obtain the content of weights from un-aligned address.*/ \
__m256 w_content = _mm256_loadu_ps(w + trans_offset); \
__m256 score_v = _mm256_add_ps(alpha_content, w_content); \
__m256 mask = _mm256_cmp_ps(score_v, max_score, _CMP_GT_OS); \
/* According to the mask value, update the index of the max_score.*/ \
/* AVX instructions.*/ \
__m128i lo_max_j = _mm256_extractf128_si256(max_j, 0); \
__m128i hi_max_j = _mm256_extractf128_si256(max_j, 1); \
__m128i lo_mask = _mm256_extractf128_si256((__m256i)mask, 0); \
__m128i hi_mask = _mm256_extractf128_si256((__m256i)mask, 1); \
lo_max_j = _mm_andnot_si128(lo_mask, lo_max_j); \
hi_max_j = _mm_andnot_si128(hi_mask, hi_max_j); \
lo_mask = _mm_and_si128(lo_mask, _mm_set1_epi32(i)); \
hi_mask = _mm_and_si128(hi_mask, _mm_set1_epi32(i)); \
lo_max_j = _mm_or_si128(lo_mask, lo_max_j); \
hi_max_j = _mm_or_si128(hi_mask, hi_max_j); \
max_j = _mm256_insertf128_si256(max_j, lo_max_j, 0); \
max_j = _mm256_insertf128_si256(max_j, hi_max_j, 1); \
/* AVX done*/ \
/* Update the max_score value.*/ \
max_score = _mm256_max_ps(max_score, score_v); \
trans_offset += this->num_; \
} \
UPDATE_ALPHA(AVX_FLOAT_BLOCK) \
} \
seq_offset += this->num_; \
} \
}
#define INTRIAVX2_FLOAT(isa, block) \
template <> \
CRFDecodeKernelImpl<float, isa, block>::CRFDecodeKernelImpl(int tag_num) \
: CRFDecodeKernel<float>() { \
this->num_ = tag_num; \
this->end_ = this->num_ / AVX2_FLOAT_BLOCK; \
this->rest_ = this->num_ % AVX2_FLOAT_BLOCK; \
} \
template <> \
void CRFDecodeKernelImpl<float, isa, block>::Compute( \
const int seq_len, const float* x, const float* w, float* alpha, \
int* track) const { \
INIT_ALPHA(AVX2_FLOAT_BLOCK) \
/* Use the column-major strategy to get the location of maximum score.*/ \
int seq_offset = 0; \
constexpr int state_trans_base_idx = 2; \
for (int k = 1; k < seq_len; ++k) { \
int j_offset = 0; \
for (int j = 0; j <= this->end_; ++j) { \
/* Initialize the variables of maximum score and location.*/ \
__m256 max_score = _mm256_set1_ps(-std::numeric_limits<float>::max()); \
__m256i max_j = _mm256_set1_epi32(0); \
/* Calculate the offset of transition_weights.*/ \
int trans_offset = state_trans_base_idx * this->num_ + j_offset; \
for (int i = 0; i < this->num_; ++i) { \
/* Initalize the content of alpha variable with related offset.*/ \
__m256 alpha_content = _mm256_broadcast_ss(alpha + seq_offset + i); \
/* Obtain the content of weights from un-aligned address.*/ \
__m256 w_content = _mm256_loadu_ps(w + trans_offset); \
__m256 score_v = _mm256_add_ps(alpha_content, w_content); \
__m256 mask = _mm256_cmp_ps(score_v, max_score, _CMP_GT_OS); \
/* According to the mask value, update the index of the max_score.*/ \
/* AVX2 instructions.*/ \
max_j = _mm256_or_si256( \
_mm256_andnot_si256((__m256i)mask, max_j), \
_mm256_and_si256((__m256i)mask, _mm256_set1_epi32(i))); \
/* Update the max_score value.*/ \
max_score = _mm256_max_ps(max_score, score_v); \
trans_offset += this->num_; \
} \
UPDATE_ALPHA(AVX2_FLOAT_BLOCK) \
} \
seq_offset += this->num_; \
} \
}
#define INTRIAVX512_FLOAT(block) \
template <> \
CRFDecodeKernelImpl<float, jit::avx512f, block>::CRFDecodeKernelImpl( \
int tag_num) \
: CRFDecodeKernel<float>() { \
this->num_ = tag_num; \
this->end_ = this->num_ / AVX512_FLOAT_BLOCK; \
this->rest_ = this->num_ % AVX512_FLOAT_BLOCK; \
} \
template <> \
void CRFDecodeKernelImpl<float, jit::avx512f, block>::Compute( \
const int seq_len, const float* x, const float* w, float* alpha, \
int* track) const { \
INIT_ALPHA(AVX512_FLOAT_BLOCK) \
/* Use the column-major strategy to get the location of maximum score.*/ \
int seq_offset = 0; \
constexpr int state_trans_base_idx = 2; \
for (int k = 1; k < seq_len; ++k) { \
int j_offset = 0; \
for (int j = 0; j <= this->end_; ++j) { \
/* Initialize the variables of maximum score and location.*/ \
__m512 max_score = _mm512_set1_ps(-std::numeric_limits<float>::max()); \
__m512i max_j = _mm512_setzero_si512(); \
/* Calculate the offset of transition_weights.*/ \
int trans_offset = state_trans_base_idx * this->num_ + j_offset; \
for (int i = 0; i < this->num_; ++i) { \
/* Initalize the content of alpha variable with related offset.*/ \
__m512 alpha_content = _mm512_set1_ps(*(alpha + seq_offset + i)); \
/* Obtain the content of weights from un-aligned address.*/ \
__m512 w_content = _mm512_loadu_ps(w + trans_offset); \
__m512 score_v = _mm512_add_ps(alpha_content, w_content); \
__mmask16 mask = _mm512_cmp_ps_mask(score_v, max_score, _CMP_GT_OS); \
/* AVX512 instructions.*/ \
max_j = _mm512_mask_set1_epi32(max_j, mask, i); \
/* Update the max_score value.*/ \
max_score = _mm512_max_ps(max_score, score_v); \
trans_offset += this->num_; \
} \
/* Update the alpha and track values.*/ \
__m512 x_content = \
_mm512_loadu_ps(x + seq_offset + this->num_ + j_offset); \
max_score = _mm512_add_ps(max_score, x_content); \
_mm512_storeu_ps(alpha + seq_offset + this->num_ + j_offset, \
max_score); \
_mm512_storeu_si512(reinterpret_cast<__m512i*>(track + seq_offset + \
this->num_ + j_offset), \
max_j); \
/* Calculate the offset of next step*/ \
j_offset += AVX512_FLOAT_BLOCK; \
if (j == this->end_ - 1) { \
if (this->rest_ > 0) { \
j_offset += last_offset; \
} else { \
break; \
} \
} \
} \
seq_offset += this->num_; \
} \
}
#ifdef __AVX__
INTRIAVX_FLOAT(kEQ8);
INTRIAVX_FLOAT(kGT8LT16);
INTRIAVX_FLOAT(kEQ16);
INTRIAVX_FLOAT(kGT16);
#endif
#ifdef __AVX2__
INTRIAVX2_FLOAT(jit::avx2, kEQ8);
INTRIAVX2_FLOAT(jit::avx2, kGT8LT16);
INTRIAVX2_FLOAT(jit::avx2, kEQ16);
INTRIAVX2_FLOAT(jit::avx2, kGT16);
#endif
#ifdef __AVX512F__
INTRIAVX2_FLOAT(jit::avx512f, kEQ8);
INTRIAVX2_FLOAT(jit::avx512f, kGT8LT16);
INTRIAVX512_FLOAT(kEQ16);
INTRIAVX512_FLOAT(kGT16);
#endif
#undef INTRIAVX512_FLOAT
#undef INTRIAVX2_FLOAT
#undef INTRIAVX_FLOAT
#undef INIT_ALPHA
#undef UPDATE_ALPHA
REGISTER_JITKERNEL(crf_decode, CRFDecodeKernel);
} // namespace jitkernel
} // namespace math
} // namespace operators
} // namespace paddle
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