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refactor(dnn/fallback): refactor im2col 

GitOrigin-RevId: b58770211e
tags/v0.4.0
Megvii Engine Team Xinran Xu 6 years ago
parent
86a3445e8e
commit
c4dfdbd26d
12 changed files with 2185 additions and 710 deletions
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  1. +349
    -686
      dnn/src/fallback/conv_bias/im2col/algos.cpp
  2. +1
    -2
      dnn/src/fallback/conv_bias/im2col/algos.h
  3. +473
    -0
      dnn/src/fallback/conv_bias/im2col/factory.h
  4. +259
    -0
      dnn/src/fallback/conv_bias/im2col/strategy_base.h
  5. +379
    -0
      dnn/src/fallback/conv_bias/im2col/strategy_default.cpp
  6. +343
    -0
      dnn/src/fallback/conv_bias/im2col/strategy_nopack.cpp
  7. +349
    -0
      dnn/src/fallback/conv_bias/im2col/strategy_onlypacka.cpp
  8. +2
    -2
      dnn/src/fallback/convolution/img2col_helper.h
  9. +11
    -7
      dnn/src/x86/elemwise_helper/kimpl/op_unary_base.h
  10. +11
    -5
      dnn/src/x86/matrix_mul/algos.cpp
  11. +2
    -2
      dnn/src/x86/matrix_mul/algos.h
  12. +6
    -6
      dnn/test/x86/conv_bias.cpp

+ 349
- 686
dnn/src/fallback/conv_bias/im2col/algos.cpp
File diff suppressed because it is too large
View File


+ 1
- 2
dnn/src/fallback/conv_bias/im2col/algos.h View File

@@ -67,8 +67,7 @@ public:
} }
auto&& fm = param.filter_meta; auto&& fm = param.filter_meta;
auto OC = fm.ocpg, IC = fm.icpg; auto OC = fm.ocpg, IC = fm.icpg;
return (fm.spatial[0] == fm.spatial[1] && fm.spatial[0] == 1) ||
OC >= 32 || IC >= 32;
return OC >= 32 || IC >= 32;
} }


private: private:


+ 473
- 0
dnn/src/fallback/conv_bias/im2col/factory.h View File

@@ -0,0 +1,473 @@
/**
* \file dnn/src/fallback/conv_bias/im2col/factory.h
* MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
*
* Copyright (c) 2014-2020 Megvii Inc. All rights reserved.
*
* Unless required by applicable law or agreed to in writing,
* software distributed under the License is distributed on an
* "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
*/
#pragma once
#include <unordered_map>
#include "src/fallback/conv_bias/im2col/strategy_base.h"
#include "src/fallback/conv_bias/opr_impl.h"

#include "midout.h"

MIDOUT_DECL(megdnn_fallback_im2col_factory_make_strategy)

namespace megdnn {
namespace fallback {
namespace im2col {

enum class StrategyType : uint32_t {
FLOAT = 0,
#if __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
FLOAT_FP16 = 1,
#else
#if !MEGDNN_DISABLE_FLOAT16
FLOAT16_FLOAT16 = 2,
#endif
#endif
INT8x8x32 = 3,
INT8x8x16 = 4,
#if MEGDNN_AARCH64 || MEGDNN_ARMV7
QUINT8x8x32 = 5,
QUINT8x8x32x8 = 6,
#endif
QINT8x8x32 = 7,
QINT8x8x32x8 = 8
};

struct StrategyHashParam {
fallback::ConvBiasImpl::NCBKernSizeParam param;
param::ConvBias::Format format;
fallback::MatrixMulImpl::AlgoBase::PackMode packmode;
size_t block_m;
size_t block_n;
size_t block_k;
};

struct StrategyHashParamHash {
std::size_t operator()(const StrategyHashParam& sparam) const {
constexpr size_t base = 1; //! avoid hashkey is zero
std::size_t result =
static_cast<std::size_t>(sparam.param.src_type.enumv()) + base;
result = result ^
((static_cast<std::size_t>(sparam.param.dst_type.enumv()) +
base)
<< 3);
result = result ^
((static_cast<std::size_t>(sparam.param.filter_type.enumv()) +
base)
<< 6);
result = result ^
((static_cast<std::size_t>(sparam.param.bias_type.enumv()) +
base)
<< 9);
result = result ^
((static_cast<std::size_t>(sparam.format) + base) << 12);
result = result ^
((static_cast<std::size_t>(sparam.packmode) + base) << 15);
result = result ^
((static_cast<std::size_t>(sparam.block_m) + base) << 18);
result = result ^
((static_cast<std::size_t>(sparam.block_n) + base) << 22);
result = result ^
((static_cast<std::size_t>(sparam.block_k) + base) << 26);
return result;
};
};

struct StrategyHashParamEqual {
std::size_t operator()(const StrategyHashParam& param1,
const StrategyHashParam& param2) const {
bool flags = true;
flags = param1.param.src_type == param2.param.src_type && flags;
flags = param1.param.filter_type == param2.param.filter_type && flags;
flags = param1.param.bias_type == param2.param.bias_type && flags;
flags = param1.param.dst_type == param2.param.dst_type && flags;
flags = param1.format == param2.format && flags;
flags = param1.packmode == param2.packmode && flags;
flags = param1.block_m == param2.block_m && flags;
flags = param1.block_n == param2.block_n && flags;
flags = param1.block_k == param2.block_k && flags;
return flags;
};
};

class StrategyDelegationStorage {
std::mutex m_mtx;
std::unordered_map<StrategyHashParam, std::unique_ptr<StrategyBase>,
StrategyHashParamHash, StrategyHashParamEqual>
map_strategys;

public:
~StrategyDelegationStorage() = default;

template <typename Strategy>
Strategy* get(param::ConvBias::Format format,
fallback::MatrixMulImpl::AlgoBase* matmul_algo,
const fallback::ConvBiasImpl::NCBKernSizeParam& param,
StrategyType stype);
};

class Factory {
public:
static StrategyBase* get_im2col_strategy(
const fallback::ConvBiasImpl::NCBKernSizeParam& param,
fallback::MatrixMulImpl::AlgoBase* matmul_algo,
param::ConvBias::Format format) {
static StrategyDelegationStorage storage;
StrategyType strategytype = get_strategy_type(param);
return storage.get<StrategyBase>(format, matmul_algo, param,
strategytype);
}

static StrategyType get_strategy_type(
const fallback::ConvBiasImpl::NCBKernSizeParam& param) {
#define cb1(_dt, _post_ctype, _strategytype) \
if (param.filter_type.enumv() == DTypeTrait<_dt>::enumv) { \
return _strategytype; \
}

#define cb2(_i_src_type, _i_bias_type, _i_dst_type, _src_ctype, _bias_ctype, \
_dst_ctype, _strategytype) \
if (param.filter_type.enumv() == param.src_type.enumv() && \
param.src_type.enumv() == DTypeTrait<_i_src_type>::enumv && \
param.dst_type.enumv() == DTypeTrait<_i_dst_type>::enumv) { \
return _strategytype; \
}

cb1(dt_float32, dt_float32, StrategyType::FLOAT);
#if __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
cb1(dt_float16, __fp16, StrategyType::FLOAT_FP16);
#else
#if !MEGDNN_DISABLE_FLOAT16
cb1(dt_float16, dt_float16, StrategyType::FLOAT16_FLOAT16);
#endif
#endif

cb2(dt_int8, dt_int32, dt_int32, dt_int8, dt_int32, dt_int32,
StrategyType::INT8x8x32);

cb2(dt_int8, dt_int16, dt_int16, dt_int8, dt_int16, dt_int16,
StrategyType::INT8x8x16);

#if MEGDNN_AARCH64 || MEGDNN_ARMV7
cb2(dtype::Quantized8Asymm, dtype::QuantizedS32, dtype::QuantizedS32,
dt_uint8, dt_int32, dt_int32, StrategyType::QUINT8x8x32);

cb2(dtype::Quantized8Asymm, dtype::QuantizedS32, dtype::Quantized8Asymm,
dt_uint8, dt_int32, dt_uint8, StrategyType::QUINT8x8x32x8);
#endif
cb2(dtype::QuantizedS8, dtype::QuantizedS32, dtype::QuantizedS32,
dt_int8, dt_int32, dt_int32, StrategyType::QINT8x8x32);

cb2(dtype::QuantizedS8, dtype::QuantizedS32, dtype::QuantizedS8,
dt_int8, dt_int32, dt_int8, StrategyType::QINT8x8x32x8);
#undef cb1
#undef cb2
megdnn_throw("not support datatype in im2col strategy\n");
}

#define cb1(_packmode, _dt, _post_ctype, _postprocess_mode, _midout_tag) \
MIDOUT_BEGIN(megdnn_fallback_im2col_factory_make_strategy, \
midout_iv(_midout_tag)) { \
if (param.filter_type.enumv() == DTypeTrait<_dt>::enumv) { \
return std::make_unique< \
Strategy<_dt, _dt, _dt, _post_ctype, _post_ctype, \
_postprocess_mode, PackMode::_packmode>>(); \
} \
} \
MIDOUT_END(); \
return {};

#define cb2(_packmode, _i_src_type, _i_bias_type, _i_dst_type, _src_ctype, \
_bias_ctype, _dst_ctype, _postprocess_mode, _midout_tag) \
MIDOUT_BEGIN(megdnn_fallback_im2col_factory_make_strategy, \
midout_iv(_midout_tag)) { \
if (param.filter_type.enumv() == param.src_type.enumv() && \
param.src_type.enumv() == DTypeTrait<_i_src_type>::enumv && \
param.dst_type.enumv() == DTypeTrait<_i_dst_type>::enumv) { \
return std::make_unique< \
Strategy<_src_ctype, _bias_ctype, _dst_ctype, \
DTypeTrait<_i_bias_type>::ctype, \
DTypeTrait<_i_dst_type>::ctype, \
_postprocess_mode, PackMode::_packmode>>(); \
} \
} \
MIDOUT_END(); \
return {};

static std::unique_ptr<StrategyBase> make_default_strategy(
fallback::MatrixMulImpl::AlgoBase* matmul_algo,
const fallback::ConvBiasImpl::NCBKernSizeParam& param,
param::ConvBias::Format format, StrategyType strategytype) {
MEGDNN_MARK_USED_VAR(matmul_algo);
MEGDNN_MARK_USED_VAR(format);
switch (strategytype) {
case StrategyType::FLOAT:
cb1(DEFAULT, dt_float32, dt_float32, PostprocessMode::FLOAT,
"DefaultStrategyType::FLOAT"_hash);
break;
#if __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
case StrategyType::FLOAT_FP16:
cb1(DEFAULT, dt_float16, __fp16, PostprocessMode::FLOAT,
"DefaultStrategyType::FLOAT_FP16"_hash);
break;
#else
#if !MEGDNN_DISABLE_FLOAT16
case StrategyType::FLOAT16_FLOAT16:
cb1(DEFAULT, dt_float16, dt_float16,
PostprocessMode::NO_PROCESS,
"DefaultStrategyType::FLOAT16_FLOAT16"_hash);
break;
#endif
#endif
case StrategyType::INT8x8x32:
cb2(DEFAULT, dt_int8, dt_int32, dt_int32, dt_int8, dt_int32,
dt_int32, PostprocessMode::NO_PROCESS,
"DefaultStrategyType::INT8x8x32"_hash);
break;

case StrategyType::INT8x8x16:
cb2(DEFAULT, dt_int8, dt_int16, dt_int16, dt_int8, dt_int16,
dt_int16, PostprocessMode::NO_PROCESS,
"DefaultStrategyType::INT8x8x16"_hash);
break;
#if MEGDNN_AARCH64 || MEGDNN_ARMV7
case StrategyType::QUINT8x8x32:
cb2(DEFAULT, dtype::Quantized8Asymm, dtype::QuantizedS32,
dtype::QuantizedS32, dt_uint8, dt_int32, dt_int32,
PostprocessMode::NO_PROCESS,
"DefaultStrategyType::QUINT8x8x32"_hash);
break;

case StrategyType::QUINT8x8x32x8:
cb2(DEFAULT, dtype::Quantized8Asymm, dtype::QuantizedS32,
dtype::Quantized8Asymm, dt_uint8, dt_int32, dt_uint8,
PostprocessMode::QUANTIZED,
"DefaultStrategyType::QUINT8x8x32x8"_hash);
break;
#endif
case StrategyType::QINT8x8x32:
cb2(DEFAULT, dtype::QuantizedS8, dtype::QuantizedS32,
dtype::QuantizedS32, dt_int8, dt_int32, dt_int32,
PostprocessMode::NO_PROCESS,
"DefaultStrategyType::QINT8x8x32"_hash);
break;

case StrategyType::QINT8x8x32x8:
cb2(DEFAULT, dtype::QuantizedS8, dtype::QuantizedS32,
dtype::QuantizedS8, dt_int8, dt_int32, dt_int8,
PostprocessMode::QUANTIZED,
"DefaultStrategyType::QINT8x8x32x8"_hash);
break;
}
megdnn_throw("error not support strategy type ");
}

static std::unique_ptr<StrategyBase> make_nopack_strategy(
fallback::MatrixMulImpl::AlgoBase* matmul_algo,
const fallback::ConvBiasImpl::NCBKernSizeParam& param,
param::ConvBias::Format format, StrategyType strategytype) {
MEGDNN_MARK_USED_VAR(matmul_algo);
MEGDNN_MARK_USED_VAR(format);
switch (strategytype) {
case StrategyType::FLOAT:
cb1(NO_PACK, dt_float32, dt_float32, PostprocessMode::FLOAT,
"NoPackStrategyType::FLOAT"_hash);
break;
#if __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
case StrategyType::FLOAT_FP16:
cb1(NO_PACK, dt_float16, __fp16, PostprocessMode::FLOAT,
"NoPackStrategyType::FLOAT_FP16"_hash);
break;
#else
#if !MEGDNN_DISABLE_FLOAT16
case StrategyType::FLOAT16_FLOAT16:
cb1(NO_PACK, dt_float16, dt_float16, PostprocessMode::NO_PROCESS,
"NoPackStrategyType::FLOAT16_FLOAT16"_hash);
break;
#endif
#endif
case StrategyType::INT8x8x32:
cb2(NO_PACK, dt_int8, dt_int32, dt_int32, dt_int8, dt_int32,
dt_int32, PostprocessMode::NO_PROCESS,
"NoPackStrategyType::INT8x8x32"_hash);
break;

case StrategyType::INT8x8x16:
cb2(NO_PACK, dt_int8, dt_int16, dt_int16, dt_int8, dt_int16,
dt_int16, PostprocessMode::NO_PROCESS,
"NoPackStrategyType::INT8x8x16"_hash);
break;

#if MEGDNN_AARCH64 || MEGDNN_ARMV7
case StrategyType::QUINT8x8x32:
cb2(NO_PACK, dtype::Quantized8Asymm, dtype::QuantizedS32,
dtype::QuantizedS32, dt_uint8, dt_int32, dt_int32,
PostprocessMode::NO_PROCESS,
"NoPackStrategyType::QUINT8x8x32"_hash);
break;

case StrategyType::QUINT8x8x32x8:
cb2(NO_PACK, dtype::Quantized8Asymm, dtype::QuantizedS32,
dtype::Quantized8Asymm, dt_uint8, dt_int32, dt_uint8,
PostprocessMode::QUANTIZED,
"NoPackStrategyType::QUINT8x8x32x8"_hash);
break;
#endif
case StrategyType::QINT8x8x32:
cb2(NO_PACK, dtype::QuantizedS8, dtype::QuantizedS32,
dtype::QuantizedS32, dt_int8, dt_int32, dt_int32,
PostprocessMode::NO_PROCESS,
"NoPackStrategyType::QINT8x8x32"_hash);
break;

case StrategyType::QINT8x8x32x8:
cb2(NO_PACK, dtype::QuantizedS8, dtype::QuantizedS32,
dtype::QuantizedS8, dt_int8, dt_int32, dt_int8,
PostprocessMode::QUANTIZED,
"NoPackStrategyType::QINT8x8x32x8"_hash);
break;
}
megdnn_throw("error not support strategy type ");
}

static std::unique_ptr<StrategyBase> make_onlypacka_strategy(
fallback::MatrixMulImpl::AlgoBase* matmul_algo,
const fallback::ConvBiasImpl::NCBKernSizeParam& param,
param::ConvBias::Format format, StrategyType strategytype) {
MEGDNN_MARK_USED_VAR(matmul_algo);
MEGDNN_MARK_USED_VAR(format);
switch (strategytype) {
case StrategyType::FLOAT:
cb1(ONLY_PACKA, dt_float32, dt_float32, PostprocessMode::FLOAT,
"OnlyPackaStrategyType::FLOAT"_hash);
break;
#if __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
case StrategyType::FLOAT_FP16:
cb1(ONLY_PACKA, dt_float16, __fp16, PostprocessMode::FLOAT,
"OnlyPackaStrategyType::FLOAT_FP16"_hash);
break;
#else
#if !MEGDNN_DISABLE_FLOAT16
case StrategyType::FLOAT16_FLOAT16:
cb1(ONLY_PACKA, dt_float16, dt_float16,
PostprocessMode::NO_PROCESS,
"OnlyPackaStrategyType::FLOAT16_FLOAT16"_hash);
break;
#endif
#endif
case StrategyType::INT8x8x32:
cb2(ONLY_PACKA, dt_int8, dt_int32, dt_int32, dt_int8, dt_int32,
dt_int32, PostprocessMode::NO_PROCESS,
"OnlyPackaStrategyType::INT8x8x32"_hash);
break;

case StrategyType::INT8x8x16:
cb2(ONLY_PACKA, dt_int8, dt_int16, dt_int16, dt_int8, dt_int16,
dt_int16, PostprocessMode::NO_PROCESS,
"OnlyPackaStrategyType::INT8x8x16"_hash);
break;

#if MEGDNN_AARCH64 || MEGDNN_ARMV7
case StrategyType::QUINT8x8x32:
cb2(ONLY_PACKA, dtype::Quantized8Asymm, dtype::QuantizedS32,
dtype::QuantizedS32, dt_uint8, dt_int32, dt_int32,
PostprocessMode::NO_PROCESS,
"OnlyPackaStrategyType::QUINT8x8x32"_hash);
break;

case StrategyType::QUINT8x8x32x8:
cb2(ONLY_PACKA, dtype::Quantized8Asymm, dtype::QuantizedS32,
dtype::Quantized8Asymm, dt_uint8, dt_int32, dt_uint8,
PostprocessMode::QUANTIZED,
"OnlyPackaStrategyType::QUINT8x8x32x8"_hash);
break;
#endif
case StrategyType::QINT8x8x32:
cb2(ONLY_PACKA, dtype::QuantizedS8, dtype::QuantizedS32,
dtype::QuantizedS32, dt_int8, dt_int32, dt_int32,
PostprocessMode::NO_PROCESS,
"OnlyPackaStrategyType::QINT8x8x32"_hash);
break;

case StrategyType::QINT8x8x32x8:
cb2(ONLY_PACKA, dtype::QuantizedS8, dtype::QuantizedS32,
dtype::QuantizedS8, dt_int8, dt_int32, dt_int8,
PostprocessMode::QUANTIZED,
"OnlyPackaStrategyType::QINT8x8x32x8"_hash);
break;
}
megdnn_throw("error not support strategy type ");
}

#undef cb1
#undef cb2

static std::unique_ptr<StrategyBase> make_strategy(
param::ConvBias::Format format,
fallback::MatrixMulImpl::AlgoBase* matmul_algo,
fallback::MatrixMulImpl::AlgoBase::PackMode packmode,
const fallback::ConvBiasImpl::NCBKernSizeParam& param,
StrategyType stype) {
switch (packmode) {
case MatrixMulImpl::AlgoBase::PackMode::DEFAULT:
return make_default_strategy(matmul_algo, param, format, stype);
break;
case MatrixMulImpl::AlgoBase::PackMode::ONLY_PACKA:
return make_onlypacka_strategy(matmul_algo, param, format,
stype);
break;
case MatrixMulImpl::AlgoBase::PackMode::NO_PACK:
return make_nopack_strategy(matmul_algo, param, format, stype);
break;
default:
megdnn_throw(
"not support packmode except default onlypackA "
"nopack");
break;
}
megdnn_throw(
"factory make Strategy error please check your code");
}
};

template <typename Strategy>
Strategy* StrategyDelegationStorage::get(
param::ConvBias::Format format,
fallback::MatrixMulImpl::AlgoBase* matmul_algo,
const fallback::ConvBiasImpl::NCBKernSizeParam& param,
StrategyType stype) {
fallback::MatrixMulImpl::AlgoBase::PackMode packmode =
matmul_algo->packmode();
//! nopack mode block_m block_n block_k is zero
size_t block_m = 0, block_n = 0, block_k = 0;
if (packmode == fallback::MatrixMulImpl::AlgoBase::PackMode::DEFAULT ||
packmode == fallback::MatrixMulImpl::AlgoBase::PackMode::ONLY_PACKA) {
block_m = matmul_algo->get_inner_block_size().m;
block_n = matmul_algo->get_inner_block_size().n;
block_k = matmul_algo->get_inner_block_size().k;
}
StrategyHashParam sparam;
sparam.param = param;
sparam.format = format;
sparam.packmode = packmode;
sparam.block_m = block_m;
sparam.block_n = block_n;
sparam.block_k = block_k;
if (map_strategys.find(sparam) == map_strategys.end()) {
MEGDNN_LOCK_GUARD(m_mtx);
auto strategy = Factory::make_strategy(format, matmul_algo, packmode,
param, stype);
map_strategys[sparam] = std::move(strategy);
}
return static_cast<Strategy*>(map_strategys[sparam].get());
}
} // namespace im2col
} // namespace fallback
} // namespace megdnn

+ 259
- 0
dnn/src/fallback/conv_bias/im2col/strategy_base.h View File

@@ -0,0 +1,259 @@
/**
* \file dnn/src/fallback/conv_bias/im2col/strategy_base.h
* MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
*
* Copyright (c) 2014-2020 Megvii Inc. All rights reserved.
*
* Unless required by applicable law or agreed to in writing,
* software distributed under the License is distributed on an
* "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
*/

#pragma once
#include "src/fallback/conv_bias/opr_impl.h"
namespace megdnn {

using PackMode = fallback::MatrixMulImpl::AlgoBase::PackMode;

struct StrategyParam {
size_t batch_id;
size_t group_id;
size_t oc_tile_size;
size_t oc_cur_index;
size_t oc_end_index;
size_t ohw_cur_index;
size_t output_block_size;
size_t output_block_oc_size;
size_t ohw;
size_t block_m;
size_t block_n;
size_t block_k;
bool skip_copy_dst;
bool is_dst_8bit;
bool is_ohw_size_bigger;
};

class StrategyBase {
public:
StrategyBase() = default;
virtual ~StrategyBase() = default;
virtual void copy_padding_kern(
WorkspaceBundle bundle,
const fallback::ConvBiasImpl::NCBKernParam& param,
const fallback::ConvBiasImpl::NCBKernIndex& ncb_index) = 0;
virtual void packA_kern(
WorkspaceBundle bundle,
const fallback::ConvBiasImpl::NCBKernParam& param,
fallback::MatrixMulImpl::KernSizeParam matmulparam,
fallback::MatrixMulImpl::AlgoBase* matmul_algo,
const fallback::ConvBiasImpl::NCBKernIndex& ncb_index) = 0;

virtual void exec_im2col(
WorkspaceBundle bundle, WorkspaceBundle bundle_thread,
const StrategyParam& sparam,
const fallback::ConvBiasImpl::NCBKernParam& param,
fallback::MatrixMulImpl::KernParam matmul_param,
fallback::MatrixMulImpl::AlgoBase* matmul_algo) = 0;

virtual void exec_matmul(
const fallback::ConvBiasImpl::NCBKernParam& param,
const StrategyParam& sparam, WorkspaceBundle bundle,
WorkspaceBundle bundle_thread,
fallback::MatrixMulImpl::KernParam matmul_param,
fallback::MatrixMulImpl::AlgoBase* matmul_algo,
const fallback::ConvBiasImpl::NCBKernIndex& ncb_index) = 0;

virtual void exec_postprocess(
const fallback::ConvBiasImpl::NCBKernParam& param,
const StrategyParam& sparam, WorkspaceBundle bundle_thread) = 0;
};

template <typename src_ctype, typename bias_ctype, typename dst_ctype,
typename op_ctype, typename op_dtype,
megdnn::PostprocessMode postprocess_mode, PackMode packmode>
class Strategy;

template <typename src_ctype, typename bias_ctype, typename dst_ctype,
typename op_ctype, typename op_dtype,
megdnn::PostprocessMode postprocess_mode>
class Strategy<src_ctype, bias_ctype, dst_ctype, op_ctype, op_dtype,
postprocess_mode, PackMode::DEFAULT> : public StrategyBase {
public:
constexpr static size_t BUNDLE_PADDING_INDEX = 0;
constexpr static size_t BUNDLE_PACKA_INDEX = 1;
constexpr static size_t THREAD_BUNDLE_PACKB_INDEX = 0;
constexpr static size_t THREAD_BUNDLE_IM2COL_INDEX = 1;
constexpr static size_t THREAD_BUNDLE_BIAS_INDEX = 2;

Strategy();

void copy_padding_kern(
WorkspaceBundle bundle,
const fallback::ConvBiasImpl::NCBKernParam& param,
const fallback::ConvBiasImpl::NCBKernIndex& ncb_index) override;

void packA_kern(
WorkspaceBundle bundle,
const fallback::ConvBiasImpl::NCBKernParam& param,
fallback::MatrixMulImpl::KernSizeParam matmulparam,
fallback::MatrixMulImpl::AlgoBase* matmul_algo,
const fallback::ConvBiasImpl::NCBKernIndex& ncb_index) override;
void exec_im2col(WorkspaceBundle bundle, WorkspaceBundle bundle_thread,
const StrategyParam& sparam,
const fallback::ConvBiasImpl::NCBKernParam& param,
fallback::MatrixMulImpl::KernParam matmul_param,
fallback::MatrixMulImpl::AlgoBase* matmul_algo) override;

void exec_matmul(
const fallback::ConvBiasImpl::NCBKernParam& param,
const StrategyParam& sparam, WorkspaceBundle bundle,
WorkspaceBundle bundle_thread,
fallback::MatrixMulImpl::KernParam matmul_param,
fallback::MatrixMulImpl::AlgoBase* matmul_algo,
const fallback::ConvBiasImpl::NCBKernIndex& ncb_index) override;
void exec_postprocess(const fallback::ConvBiasImpl::NCBKernParam& param,
const StrategyParam& sparam,
WorkspaceBundle bundle_thread) override;

void copy_dst(const fallback::ConvBiasImpl::NCBKernParam& param,
const void* matmul_dst, const StrategyParam& sparam);

void copy_bias(const fallback::ConvBiasImpl::NCBKernParam& param,
WorkspaceBundle bundle_thread, const StrategyParam& sparam);

void* get_bias_temp_ptr(const fallback::ConvBiasImpl::NCBKernParam& param,
const WorkspaceBundle& bundle_thread);
void* get_matmul_dst_ptr(const fallback::ConvBiasImpl::NCBKernParam& param,
const WorkspaceBundle& bundle_thread,
const StrategyParam& sparam);
};

template <typename src_ctype, typename bias_ctype, typename dst_ctype,
typename op_ctype, typename op_dtype,
megdnn::PostprocessMode postprocess_mode>
class Strategy<src_ctype, bias_ctype, dst_ctype, op_ctype, op_dtype,
postprocess_mode, PackMode::NO_PACK> : public StrategyBase {
public:
constexpr static size_t BUNDLE_PADDING_INDEX = 0;
constexpr static size_t BUNDLE_PACKA_INDEX = 1;
constexpr static size_t THREAD_BUNDLE_IM2COL_INDEX = 0;
constexpr static size_t THREAD_BUNDLE_MATMULDST_INDEX = 1;
constexpr static size_t THREAD_BUNDLE_BIAS_INDEX = 2;
constexpr static size_t THREAD_BUNDLE_MATCOMP_INDEX = 3;

Strategy();

void copy_padding_kern(
WorkspaceBundle bundle,
const fallback::ConvBiasImpl::NCBKernParam& param,
const fallback::ConvBiasImpl::NCBKernIndex& ncb_index) override;

void packA_kern(
WorkspaceBundle bundle,
const fallback::ConvBiasImpl::NCBKernParam& param,
fallback::MatrixMulImpl::KernSizeParam matmulparam,
fallback::MatrixMulImpl::AlgoBase* matmul_algo,
const fallback::ConvBiasImpl::NCBKernIndex& ncb_index) override;

void exec_matmul(
const fallback::ConvBiasImpl::NCBKernParam& param,
const StrategyParam& sparam, WorkspaceBundle bundle,
WorkspaceBundle bundle_thread,
fallback::MatrixMulImpl::KernParam matmul_param,
fallback::MatrixMulImpl::AlgoBase* matmul_algo,
const fallback::ConvBiasImpl::NCBKernIndex& ncb_index) override;

void* get_matmul_dst_ptr(const fallback::ConvBiasImpl::NCBKernParam& param,
const WorkspaceBundle& bundle_thread,
const StrategyParam& sparam);

inline void* get_bias_temp_ptr(
const fallback::ConvBiasImpl::NCBKernParam& param,
const WorkspaceBundle& bundle_thread) {
bias_ctype* bias_tmp_ptr =
param.bias_mode == megdnn::BiasMode::BIAS
? static_cast<bias_ctype*>(
bundle_thread.get(THREAD_BUNDLE_BIAS_INDEX))
: nullptr;
return bias_tmp_ptr;
}

void exec_im2col(WorkspaceBundle bundle, WorkspaceBundle bundle_thread,
const StrategyParam& sparam,
const fallback::ConvBiasImpl::NCBKernParam& param,
fallback::MatrixMulImpl::KernParam matmul_param,
fallback::MatrixMulImpl::AlgoBase* matmul_algo) override;
void exec_postprocess(const fallback::ConvBiasImpl::NCBKernParam& param,
const StrategyParam& sparam,
WorkspaceBundle bundle_thread) override;
void copy_dst(const fallback::ConvBiasImpl::NCBKernParam& param,
const void* matmul_dst, const StrategyParam& sparam);

void copy_bias(const fallback::ConvBiasImpl::NCBKernParam& param,
WorkspaceBundle bundle_thread, const StrategyParam& sparam);
};

template <typename src_ctype, typename bias_ctype, typename dst_ctype,
typename op_ctype, typename op_dtype,
megdnn::PostprocessMode postprocess_mode>
class Strategy<src_ctype, bias_ctype, dst_ctype, op_ctype, op_dtype,
postprocess_mode, PackMode::ONLY_PACKA> : public StrategyBase {
public:
constexpr static size_t BUNDLE_PADDING_INDEX = 0;
constexpr static size_t BUNDLE_PACKA_INDEX = 1;
constexpr static size_t THREAD_BUNDLE_PACKB_INDEX = 0;
constexpr static size_t THREAD_BUNDLE_IM2COL_INDEX = 1;
constexpr static size_t THREAD_BUNDLE_MATMULDST_INDEX = 2;
constexpr static size_t THREAD_BUNDLE_BIAS_INDEX = 3;

Strategy();

void copy_padding_kern(
WorkspaceBundle bundle,
const fallback::ConvBiasImpl::NCBKernParam& param,
const fallback::ConvBiasImpl::NCBKernIndex& ncb_index) override;

void packA_kern(
WorkspaceBundle bundle,
const fallback::ConvBiasImpl::NCBKernParam& param,
fallback::MatrixMulImpl::KernSizeParam matmulparam,
fallback::MatrixMulImpl::AlgoBase* matmul_algo,
const fallback::ConvBiasImpl::NCBKernIndex& ncb_index) override;

void exec_im2col(WorkspaceBundle bundle, WorkspaceBundle bundle_thread,
const StrategyParam& sparam,
const fallback::ConvBiasImpl::NCBKernParam& param,
fallback::MatrixMulImpl::KernParam matmul_param,
fallback::MatrixMulImpl::AlgoBase* matmul_algo) override;

void exec_matmul(
const fallback::ConvBiasImpl::NCBKernParam& param,
const StrategyParam& sparam, WorkspaceBundle bundle,
WorkspaceBundle bundle_thread,
fallback::MatrixMulImpl::KernParam matmul_param,
fallback::MatrixMulImpl::AlgoBase* matmul_algo,
const fallback::ConvBiasImpl::NCBKernIndex& ncb_index) override;

void* get_matmul_dst_ptr(const fallback::ConvBiasImpl::NCBKernParam& param,
const WorkspaceBundle& bundle_thread,
const StrategyParam& sparam);
inline void* get_bias_temp_ptr(
const fallback::ConvBiasImpl::NCBKernParam& param,
const WorkspaceBundle& bundle_thread) {
bias_ctype* bias_tmp_ptr =
param.bias_mode == megdnn::BiasMode::BIAS
? static_cast<bias_ctype*>(
bundle_thread.get(THREAD_BUNDLE_BIAS_INDEX))
: nullptr;
return bias_tmp_ptr;
}
void exec_postprocess(const fallback::ConvBiasImpl::NCBKernParam& param,
const StrategyParam& sparam,
WorkspaceBundle bundle_thread) override;
void copy_dst(const fallback::ConvBiasImpl::NCBKernParam& param,
const void* matmul_dst, const StrategyParam& sparam);

void copy_bias(const fallback::ConvBiasImpl::NCBKernParam& param,
WorkspaceBundle bundle_thread, const StrategyParam& sparam);
};
} // namespace megdnn

+ 379
- 0
dnn/src/fallback/conv_bias/im2col/strategy_default.cpp View File

@@ -0,0 +1,379 @@
/**
* \file dnn/src/fallback/conv_bias/im2col/strategy_default.cpp
* MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
*
* Copyright (c) 2014-2020 Megvii Inc. All rights reserved.
*
* Unless required by applicable law or agreed to in writing,
* software distributed under the License is distributed on an
* "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
*/
#include "megdnn/opr_param_defs.h"
#include "src/common/utils.h"
#include "src/fallback/conv_bias/im2col/strategy_base.h"
#include "src/fallback/convolution/img2col_helper.h"
#if MEGDNN_X86
#include "src/x86/conv_bias/postprocess_helper.h"
#endif

using namespace megdnn;
#if MEGDNN_X86
using namespace x86;
#endif
namespace megdnn {

template <typename src_ctype, typename bias_ctype, typename dst_ctype,
typename op_ctype, typename op_dtype,
megdnn::PostprocessMode postprocess_mode>
Strategy<src_ctype, bias_ctype, dst_ctype, op_ctype, op_dtype,
postprocess_mode,PackMode::DEFAULT>::Strategy()
: StrategyBase() {}

template <typename src_ctype, typename bias_ctype, typename dst_ctype,
typename op_ctype, typename op_dtype,
megdnn::PostprocessMode postprocess_mode>
void Strategy<src_ctype, bias_ctype, dst_ctype, op_ctype, op_dtype,
postprocess_mode,PackMode::DEFAULT>::
copy_padding_kern(
WorkspaceBundle bundle,
const fallback::ConvBiasImpl::NCBKernParam& param,
const fallback::ConvBiasImpl::NCBKernIndex& ncb_index) {
UNPACK_CONV_F32_NCB_KERN_SIZES(param);
MEGDNN_MARK_USED_VAR(N);
MEGDNN_MARK_USED_VAR(OC);
MEGDNN_MARK_USED_VAR(OH);
MEGDNN_MARK_USED_VAR(OW);
MEGDNN_MARK_USED_VAR(FH);
MEGDNN_MARK_USED_VAR(FW);
MEGDNN_MARK_USED_VAR(SH);
MEGDNN_MARK_USED_VAR(SW);

size_t IW2 = IW + 2 * PW;
size_t IH2 = IH + 2 * PH;
size_t batch_id = ncb_index.ndrange_id[0];
size_t group_id = ncb_index.ndrange_id[1];
size_t channel_id = ncb_index.ndrange_id[2];

size_t padding_group_size = IH2 * IW2 * IC;
size_t workspace_channel_offset = IH2 * IW2 * channel_id;
size_t workspace_group_offset = group_id * padding_group_size;
size_t workspace_batch_offset =
param.filter_meta.group * batch_id * padding_group_size;
bundle.set(param.workspace_ptr);

src_ctype src_zp = static_cast<src_ctype>(0);
if (param.src_type.enumv() == DTypeEnum::Quantized8Asymm) {
src_zp = param.src_type.param<dtype::Quantized8Asymm>().zero_point;
}
src_ctype* src = const_cast<src_ctype*>(
param.src<src_ctype>(batch_id, group_id, channel_id));
src_ctype* src2;
src2 = static_cast<src_ctype*>(bundle.get(BUNDLE_PADDING_INDEX)) +
workspace_group_offset + workspace_batch_offset +
workspace_channel_offset;
src_ctype* src2_ptr = src2;
const src_ctype* src_ptr = src;
if (PH != 0) {
std::memset(src2_ptr, src_zp, sizeof(src_ctype) * PH * IW2);
src2_ptr += PH * IW2;
}
rep(ih, IH) {
if (PW != 0)
rep(pw, PW) * (src2_ptr++) = src_zp;
std::memcpy(src2_ptr, src_ptr, sizeof(src_ctype) * IW);
src2_ptr += IW;
src_ptr += IW;
if (PW != 0)
rep(pw, PW) * (src2_ptr++) = src_zp;
}
if (PH != 0) {
std::memset(src2_ptr, src_zp, sizeof(src_ctype) * PH * IW2);
src2_ptr += PH * IW2;
}
}

template <typename src_ctype, typename bias_ctype, typename dst_ctype,
typename op_ctype, typename op_dtype,
megdnn::PostprocessMode postprocess_mode>
void Strategy<src_ctype, bias_ctype, dst_ctype, op_ctype, op_dtype,
postprocess_mode,PackMode::DEFAULT>::
packA_kern(WorkspaceBundle bundle,
const fallback::ConvBiasImpl::NCBKernParam& param,
fallback::MatrixMulImpl::KernSizeParam matmulparam,
fallback::MatrixMulImpl::AlgoBase* matmul_algo,
const fallback::ConvBiasImpl::NCBKernIndex& ncb_index) {
bundle.set(param.workspace_ptr);
fallback::MatrixMulImpl::KernParam matmul_param;
size_t group_id = ncb_index.ndrange_id[0];
static_cast<fallback::MatrixMulImpl::KernSizeParam&>(matmul_param) =
matmulparam;
size_t packA_group_size = matmul_algo->get_bundle(matmul_param).get_size(0);
size_t packed_per_oc_block_size =
round_up(matmul_param.K, matmul_algo->get_inner_block_size().k) *
matmul_algo->get_inner_block_size().m *
matmul_algo->get_packA_type_size();
size_t a_panel_offset = ncb_index.ndrange_id[1] * packed_per_oc_block_size;
int8_t* a_panel = static_cast<int8_t*>(bundle.get(BUNDLE_PACKA_INDEX)) +
group_id * packA_group_size + a_panel_offset;
matmul_param.A_ptr =
const_cast<src_ctype*>(param.filter<src_ctype>(group_id));
matmul_algo->pack_A(matmul_param, a_panel, ncb_index.ndrange_id[1],
matmul_algo->get_inner_block_size().m);
}

template <typename src_ctype, typename bias_ctype, typename dst_ctype,
typename op_ctype, typename op_dtype,
megdnn::PostprocessMode postprocess_mode>
void Strategy<src_ctype, bias_ctype, dst_ctype, op_ctype, op_dtype,
postprocess_mode,PackMode::DEFAULT>::
exec_im2col(WorkspaceBundle bundle, WorkspaceBundle bundle_thread,
const StrategyParam& sparam,
const fallback::ConvBiasImpl::NCBKernParam& param,
fallback::MatrixMulImpl::KernParam matmul_param,
fallback::MatrixMulImpl::AlgoBase* matmul_algo
) {
size_t m_sh = param.filter_meta.stride[0];
size_t m_sw = param.filter_meta.stride[1];
size_t m_oc = param.filter_meta.ocpg;
size_t m_oh = param.osz[0];
size_t m_ow = param.osz[1];
size_t m_ic = param.filter_meta.icpg;
size_t m_ih = param.isz[0] + param.filter_meta.padding[0] * 2;
size_t m_iw = param.isz[1] + param.filter_meta.padding[1] * 2;
size_t m_fh = param.filter_meta.spatial[0];
size_t m_fw = param.filter_meta.spatial[1];
size_t m_is_xcorr = !param.filter_meta.should_flip;

size_t input_offset =
m_ih * m_iw * m_ic *
(sparam.group_id + param.filter_meta.group * sparam.batch_id) *
sizeof(src_ctype);

src_ctype* src2 = reinterpret_cast<src_ctype*>(
reinterpret_cast<uintptr_t>(bundle.get(BUNDLE_PADDING_INDEX)) +
input_offset);
bool is_phpwzero = param.filter_meta.padding[0] == 0 &&
param.filter_meta.padding[1] == 0;
if (is_phpwzero) {
src2 = const_cast<src_ctype*>(
param.src<src_ctype>(sparam.batch_id, sparam.group_id));
}
src_ctype* im2col_dst = static_cast<src_ctype*>(
bundle_thread.get(THREAD_BUNDLE_IM2COL_INDEX));
if (m_sh == 1 && m_sw == 1) {
if (m_is_xcorr) {
img2col<true>(src2, im2col_dst, m_oc, m_oh, m_ow, m_ic, m_ih, m_iw,
m_fh, m_fw, sparam.ohw_cur_index,
sparam.output_block_size);
} else {
img2col<false>(src2, im2col_dst, m_oc, m_oh, m_ow, m_ic, m_ih, m_iw,
m_fh, m_fw, sparam.ohw_cur_index,
sparam.output_block_size);
}
} else {
if (m_is_xcorr) {
img2col_stride<true>(src2, im2col_dst, m_oc, m_oh, m_ow, m_ic, m_ih,
m_iw, m_fh, m_fw, m_sh, m_sw,
sparam.ohw_cur_index,
sparam.output_block_size);
} else {
img2col_stride<false>(src2, im2col_dst, m_oc, m_oh, m_ow, m_ic,
m_ih, m_iw, m_fh, m_fw, m_sh, m_sw,
sparam.ohw_cur_index,
sparam.output_block_size);
}
}
matmul_param.M = sparam.output_block_oc_size;
matmul_param.N = sparam.output_block_size;
matmul_param.LDB = sparam.output_block_size;
matmul_param.LDC = sparam.output_block_size;
matmul_param.B_ptr = im2col_dst;

src_ctype* b_panel =
reinterpret_cast<src_ctype*>(reinterpret_cast<uintptr_t>(
bundle_thread.get(THREAD_BUNDLE_PACKB_INDEX)));
matmul_algo->pack_B(matmul_param, b_panel, 0, matmul_param.N);
}

template <typename src_ctype, typename bias_ctype, typename dst_ctype,
typename op_ctype, typename op_dtype,
megdnn::PostprocessMode postprocess_mode>
void* Strategy<src_ctype, bias_ctype, dst_ctype, op_ctype, op_dtype,
postprocess_mode,PackMode::DEFAULT>::
get_matmul_dst_ptr(const fallback::ConvBiasImpl::NCBKernParam& param,
const WorkspaceBundle& bundle_thread,
const StrategyParam& sparam) {
if (sparam.is_dst_8bit || !sparam.is_ohw_size_bigger) {
return static_cast<void*>(
bundle_thread.get(THREAD_BUNDLE_IM2COL_INDEX));
} else {
bias_ctype* dst =
param.dst<bias_ctype>(sparam.batch_id, sparam.group_id) +
sparam.oc_cur_index * sparam.ohw;
return static_cast<void*>(dst);
}
}

template <typename src_ctype, typename bias_ctype, typename dst_ctype,
typename op_ctype, typename op_dtype,
megdnn::PostprocessMode postprocess_mode>
void Strategy<src_ctype, bias_ctype, dst_ctype, op_ctype, op_dtype,
postprocess_mode,PackMode::DEFAULT>::
exec_matmul(const fallback::ConvBiasImpl::NCBKernParam& param,
const StrategyParam& sparam, WorkspaceBundle bundle,
WorkspaceBundle bundle_thread,
fallback::MatrixMulImpl::KernParam matmul_param,
fallback::MatrixMulImpl::AlgoBase* matmul_algo,
const fallback::ConvBiasImpl::NCBKernIndex& ncb_index) {
size_t packA_per_oc_block_size =
round_up(matmul_param.K, matmul_algo->get_inner_block_size().k) *
sparam.oc_tile_size * matmul_algo->get_packA_type_size();
size_t packA_group_size = matmul_algo->get_bundle(matmul_param).get_size(0);
size_t a_panel_offset = ncb_index.ndrange_id[1] * packA_group_size +
ncb_index.ndrange_id[3] * packA_per_oc_block_size;

void* matmul_dst = get_matmul_dst_ptr(param, bundle_thread, sparam);

src_ctype* a_panel = reinterpret_cast<src_ctype*>(
reinterpret_cast<uintptr_t>(bundle.get(BUNDLE_PACKA_INDEX)) +
a_panel_offset);
src_ctype* b_panel =
reinterpret_cast<src_ctype*>(reinterpret_cast<uintptr_t>(
bundle_thread.get(THREAD_BUNDLE_PACKB_INDEX)));

matmul_param.M = sparam.output_block_oc_size;
matmul_param.N = sparam.output_block_size;
matmul_param.LDB = sparam.output_block_size;
matmul_param.LDC = sparam.output_block_size;
matmul_param.C_ptr = matmul_dst;

auto matmul_kern_naked = matmul_algo->get_kern_naked(matmul_param);
matmul_kern_naked(matmul_param, a_panel, b_panel);
}

template <typename src_ctype, typename bias_ctype, typename dst_ctype,
typename op_ctype, typename op_dtype,
megdnn::PostprocessMode postprocess_mode>
void Strategy<src_ctype, bias_ctype, dst_ctype, op_ctype, op_dtype,
postprocess_mode,PackMode::DEFAULT>::
exec_postprocess(const fallback::ConvBiasImpl::NCBKernParam& param,
const StrategyParam& sparam,
WorkspaceBundle bundle_thread) {
copy_bias(param, bundle_thread, sparam);
void* matmul_dst = get_matmul_dst_ptr(param, bundle_thread, sparam);

const bias_ctype* bias_ptr = static_cast<const bias_ctype*>(
param.bias<bias_ctype>(sparam.batch_id, sparam.group_id));
void* bias_temp_ptr = get_bias_temp_ptr(param, bundle_thread);
void* bias_preprocess_ptr = const_cast<void*>(
param.bias_mode == megdnn::BiasMode::BIAS
? bias_temp_ptr
: static_cast<void*>(const_cast<bias_ctype*>(
bias_ptr + sparam.oc_cur_index)));

PostProcess<op_ctype, op_dtype, postprocess_mode>::run(
matmul_dst, bias_preprocess_ptr, matmul_dst, param.bias_mode,
param.nonlineMode, param.bias_type, param.dst_type, 1_z,
sparam.output_block_oc_size, 1_z, sparam.output_block_size);
copy_dst(param, matmul_dst, sparam);
}

template <typename src_ctype, typename bias_ctype, typename dst_ctype,
typename op_ctype, typename op_dtype,
megdnn::PostprocessMode postprocess_mode>
void Strategy<src_ctype, bias_ctype, dst_ctype, op_ctype, op_dtype,
postprocess_mode,PackMode::DEFAULT>::
copy_dst(const fallback::ConvBiasImpl::NCBKernParam& param,
const void* matmul_dst, const StrategyParam& sparam) {
if (!sparam.skip_copy_dst) {
dst_ctype* dst_tmp_ptr =
reinterpret_cast<dst_ctype*>(const_cast<void*>(matmul_dst));
dst_ctype* dst =
param.dst<dst_ctype>(sparam.batch_id, sparam.group_id) +
sparam.oc_cur_index * sparam.ohw + sparam.ohw_cur_index;
for (size_t oc = 0; oc < sparam.output_block_oc_size; oc++) {
std::memcpy(dst, dst_tmp_ptr,
sizeof(dst_ctype) * sparam.output_block_size);
dst_tmp_ptr += sparam.output_block_size;
dst += sparam.ohw;
}
}
}

template <typename src_ctype, typename bias_ctype, typename dst_ctype,
typename op_ctype, typename op_dtype,
megdnn::PostprocessMode postprocess_mode>
void* Strategy<src_ctype, bias_ctype, dst_ctype, op_ctype, op_dtype,
postprocess_mode,PackMode::DEFAULT>::
get_bias_temp_ptr(const fallback::ConvBiasImpl::NCBKernParam& param,
const WorkspaceBundle& bundle_thread) {
bias_ctype* bias_tmp_ptr =
param.bias_mode == megdnn::BiasMode::BIAS
? static_cast<bias_ctype*>(
bundle_thread.get(THREAD_BUNDLE_BIAS_INDEX))
: nullptr;
return bias_tmp_ptr;
}

template <typename src_ctype, typename bias_ctype, typename dst_ctype,
typename op_ctype, typename op_dtype,
megdnn::PostprocessMode postprocess_mode>
void Strategy<src_ctype, bias_ctype, dst_ctype, op_ctype, op_dtype,
postprocess_mode,PackMode::DEFAULT>::
copy_bias(const fallback::ConvBiasImpl::NCBKernParam& param,
WorkspaceBundle bundle_thread, const StrategyParam& sparam) {
const bias_ctype* bias_ptr = static_cast<const bias_ctype*>(
param.bias<bias_ctype>(sparam.batch_id, sparam.group_id));
bias_ctype* bias_temp_ptr =
static_cast<bias_ctype*>(get_bias_temp_ptr(param, bundle_thread));
if (param.bias_mode == megdnn::BiasMode::BIAS) {
bias_ctype* copy_dst = bias_temp_ptr;
const bias_ctype* copy_src = bias_ptr +
sparam.oc_cur_index * sparam.ohw +
sparam.ohw_cur_index;
for (size_t oc = sparam.oc_cur_index; oc < sparam.oc_end_index; oc++) {
std::memcpy(copy_dst, copy_src,
sizeof(bias_ctype) * sparam.output_block_size);
copy_dst += sparam.output_block_size;
copy_src += sparam.ohw;
}
}
}

#define INSTANTIAL_CLASS(_src_ctype, _bias_ctype, _dst_ctype, _op_ctype, \
_op_dtype, _postprocess_mode) \
template class Strategy<_src_ctype, _bias_ctype, _dst_ctype, _op_ctype, \
_op_dtype, _postprocess_mode, PackMode::DEFAULT>;

INSTANTIAL_CLASS(dt_float32, dt_float32, dt_float32, dt_float32, dt_float32,
megdnn::PostprocessMode::FLOAT)

#if __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
INSTANTIAL_CLASS(dt_float16, dt_float16, dt_float16, __fp16, __fp16,
megdnn::PostprocessMode::FLOAT)
#else
#if !MEGDNN_DISABLE_FLOAT16
INSTANTIAL_CLASS(dt_float16, dt_float16, dt_float16, dt_float16, dt_float16,
megdnn::PostprocessMode::NO_PROCESS)
#endif
#endif

#if MEGDNN_AARCH64 || MEGDNN_ARMV7
//! x86 do not have uint8 matmul so only armv7 armv8 support uint8
INSTANTIAL_CLASS(dt_uint8, dt_int32, dt_uint8, dt_qint32, dt_quint8,
megdnn::PostprocessMode::QUANTIZED)
INSTANTIAL_CLASS(dt_uint8, dt_int32, dt_int32, dt_qint32, dt_qint32,
megdnn::PostprocessMode::NO_PROCESS)
#endif

INSTANTIAL_CLASS(dt_int8, dt_int32, dt_int8, dt_qint32, dt_qint8,
megdnn::PostprocessMode::QUANTIZED)
INSTANTIAL_CLASS(dt_int8, dt_int32, dt_int32, dt_int32, dt_int32,
megdnn::PostprocessMode::NO_PROCESS)
INSTANTIAL_CLASS(dt_int8, dt_int16, dt_int16, dt_int16, dt_int16,
megdnn::PostprocessMode::NO_PROCESS)
INSTANTIAL_CLASS(dt_int8, dt_int32, dt_int32, dt_qint32, dt_qint32,
megdnn::PostprocessMode::NO_PROCESS)

#undef INSTANTIAL_CLASS
} // namespace megdnn

+ 343
- 0
dnn/src/fallback/conv_bias/im2col/strategy_nopack.cpp View File

@@ -0,0 +1,343 @@
/**
* \file dnn/src/fallback/conv_bias/im2col/strategy_nopack.cpp
* MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
*
* Copyright (c) 2014-2020 Megvii Inc. All rights reserved.
*
* Unless required by applicable law or agreed to in writing,
* software distributed under the License is distributed on an
* "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
*/

#include "megdnn/opr_param_defs.h"
#include "src/common/utils.h"
#include "src/fallback/conv_bias/im2col/strategy_base.h"
#include "src/fallback/convolution/img2col_helper.h"
#if MEGDNN_X86
#include "src/x86/conv_bias/postprocess_helper.h"
#endif

using namespace megdnn;
#if MEGDNN_X86
using namespace x86;
#endif
namespace megdnn {
template <typename src_ctype, typename bias_ctype, typename dst_ctype,
typename op_ctype, typename op_dtype,
megdnn::PostprocessMode postprocess_mode>
Strategy<src_ctype, bias_ctype, dst_ctype, op_ctype, op_dtype,
postprocess_mode,PackMode::NO_PACK>::Strategy()
: StrategyBase() {}

template <typename src_ctype, typename bias_ctype, typename dst_ctype,
typename op_ctype, typename op_dtype,
megdnn::PostprocessMode postprocess_mode>
void Strategy<src_ctype, bias_ctype, dst_ctype, op_ctype, op_dtype,
postprocess_mode,PackMode::NO_PACK>::
copy_padding_kern(
WorkspaceBundle bundle,
const fallback::ConvBiasImpl::NCBKernParam& param,
const fallback::ConvBiasImpl::NCBKernIndex& ncb_index) {
UNPACK_CONV_F32_NCB_KERN_SIZES(param);
MEGDNN_MARK_USED_VAR(N);
MEGDNN_MARK_USED_VAR(OC);
MEGDNN_MARK_USED_VAR(OH);
MEGDNN_MARK_USED_VAR(OW);
MEGDNN_MARK_USED_VAR(FH);
MEGDNN_MARK_USED_VAR(FW);
MEGDNN_MARK_USED_VAR(SH);
MEGDNN_MARK_USED_VAR(SW);

size_t IW2 = IW + 2 * PW;
size_t IH2 = IH + 2 * PH;
size_t batch_id = ncb_index.ndrange_id[0];
size_t group_id = ncb_index.ndrange_id[1];
size_t channel_id = ncb_index.ndrange_id[2];

size_t padding_group_size = IH2 * IW2 * IC;
size_t workspace_channel_offset = IH2 * IW2 * channel_id;
size_t workspace_group_offset = group_id * padding_group_size;
size_t workspace_batch_offset =
param.filter_meta.group * batch_id * padding_group_size;
bundle.set(param.workspace_ptr);

src_ctype src_zp = static_cast<src_ctype>(0);
if (param.src_type.enumv() == DTypeEnum::Quantized8Asymm) {
src_zp = param.src_type.param<dtype::Quantized8Asymm>().zero_point;
}
src_ctype* src = const_cast<src_ctype*>(
param.src<src_ctype>(batch_id, group_id, channel_id));
src_ctype* src2;
src2 = static_cast<src_ctype*>(bundle.get(BUNDLE_PADDING_INDEX)) +
workspace_group_offset + workspace_batch_offset +
workspace_channel_offset;
src_ctype* src2_ptr = src2;
const src_ctype* src_ptr = src;
if (PH != 0) {
std::memset(src2_ptr, src_zp, sizeof(src_ctype) * PH * IW2);
src2_ptr += PH * IW2;
}
rep(ih, IH) {
if (PW != 0)
rep(pw, PW) * (src2_ptr++) = src_zp;
std::memcpy(src2_ptr, src_ptr, sizeof(src_ctype) * IW);
src2_ptr += IW;
src_ptr += IW;
if (PW != 0)
rep(pw, PW) * (src2_ptr++) = src_zp;
}
if (PH != 0) {
std::memset(src2_ptr, src_zp, sizeof(src_ctype) * PH * IW2);
src2_ptr += PH * IW2;
}
}

template <typename src_ctype, typename bias_ctype, typename dst_ctype,
typename op_ctype, typename op_dtype,
megdnn::PostprocessMode postprocess_mode>
void Strategy<src_ctype, bias_ctype, dst_ctype, op_ctype, op_dtype,
postprocess_mode,PackMode::NO_PACK>::
packA_kern(WorkspaceBundle bundle,
const fallback::ConvBiasImpl::NCBKernParam& param,
fallback::MatrixMulImpl::KernSizeParam matmulparam,
fallback::MatrixMulImpl::AlgoBase* matmul_algo,
const fallback::ConvBiasImpl::NCBKernIndex& ncb_index) {
MEGDNN_MARK_USED_VAR(bundle);
MEGDNN_MARK_USED_VAR(param);
MEGDNN_MARK_USED_VAR(matmulparam);
MEGDNN_MARK_USED_VAR(matmul_algo);
MEGDNN_MARK_USED_VAR(ncb_index);
megdnn_throw(
"nopack mode should not call packA_kern please check your code");
}

template <typename src_ctype, typename bias_ctype, typename dst_ctype,
typename op_ctype, typename op_dtype,
megdnn::PostprocessMode postprocess_mode>
void* Strategy<src_ctype, bias_ctype, dst_ctype, op_ctype, op_dtype,
postprocess_mode,PackMode::NO_PACK>::
get_matmul_dst_ptr(const fallback::ConvBiasImpl::NCBKernParam& param,
const WorkspaceBundle& bundle_thread,
const StrategyParam& sparam) {
if (sparam.is_dst_8bit || !sparam.is_ohw_size_bigger) {
return static_cast<bias_ctype*>(
bundle_thread.get(THREAD_BUNDLE_MATMULDST_INDEX));
} else {
bias_ctype* dst =
param.dst<bias_ctype>(sparam.batch_id, sparam.group_id) +
sparam.oc_cur_index * sparam.ohw;
return static_cast<void*>(dst);
}
}

template <typename src_ctype, typename bias_ctype, typename dst_ctype,
typename op_ctype, typename op_dtype,
megdnn::PostprocessMode postprocess_mode>
void Strategy<src_ctype, bias_ctype, dst_ctype, op_ctype, op_dtype,
postprocess_mode,PackMode::NO_PACK>::
exec_matmul(const fallback::ConvBiasImpl::NCBKernParam& param,
const StrategyParam& sparam, WorkspaceBundle bundle,
WorkspaceBundle bundle_thread,
fallback::MatrixMulImpl::KernParam matmul_param,
fallback::MatrixMulImpl::AlgoBase* matmul_algo,
const fallback::ConvBiasImpl::NCBKernIndex& ncb_index) {
MEGDNN_MARK_USED_VAR(bundle);
MEGDNN_MARK_USED_VAR(ncb_index);
matmul_param.workspace_ptr = bundle_thread.get(THREAD_BUNDLE_MATCOMP_INDEX);
void* matmul_dst = get_matmul_dst_ptr(param, bundle_thread, sparam);

src_ctype* im2col_dst = static_cast<src_ctype*>(
bundle_thread.get(THREAD_BUNDLE_IM2COL_INDEX));
const void* filter = param.filter<src_ctype>(sparam.group_id) +
sparam.oc_cur_index * param.filter_meta.icpg *
param.filter_meta.spatial[0] *
param.filter_meta.spatial[1];
matmul_param.M = sparam.output_block_oc_size;
matmul_param.N = sparam.output_block_size;
matmul_param.LDB = sparam.output_block_size;
matmul_param.LDC = sparam.output_block_size;
matmul_param.A_ptr = filter;
matmul_param.B_ptr = im2col_dst;
matmul_param.C_ptr = matmul_dst;
auto matmul_kern = matmul_algo->get_kern(matmul_param);
matmul_kern(matmul_param);
}

template <typename src_ctype, typename bias_ctype, typename dst_ctype,
typename op_ctype, typename op_dtype,
megdnn::PostprocessMode postprocess_mode>
void Strategy<src_ctype, bias_ctype, dst_ctype, op_ctype, op_dtype,
postprocess_mode,PackMode::NO_PACK>::
exec_im2col(WorkspaceBundle bundle, WorkspaceBundle bundle_thread,
const StrategyParam& sparam,
const fallback::ConvBiasImpl::NCBKernParam& param,
fallback::MatrixMulImpl::KernParam matmul_param,
fallback::MatrixMulImpl::AlgoBase* matmul_algo
) {
MEGDNN_MARK_USED_VAR(matmul_param);
MEGDNN_MARK_USED_VAR(matmul_algo);
size_t m_sh = param.filter_meta.stride[0];
size_t m_sw = param.filter_meta.stride[1];
size_t m_oc = param.filter_meta.ocpg;
size_t m_oh = param.osz[0];
size_t m_ow = param.osz[1];
size_t m_ic = param.filter_meta.icpg;
size_t m_ih = param.isz[0] + param.filter_meta.padding[0] * 2;
size_t m_iw = param.isz[1] + param.filter_meta.padding[1] * 2;
size_t m_fh = param.filter_meta.spatial[0];
size_t m_fw = param.filter_meta.spatial[1];
size_t m_is_xcorr = !param.filter_meta.should_flip;

size_t input_offset =
m_ih * m_iw * m_ic *
(sparam.group_id + param.filter_meta.group * sparam.batch_id) *
sizeof(src_ctype);

src_ctype* src2 = reinterpret_cast<src_ctype*>(
reinterpret_cast<uintptr_t>(bundle.get(BUNDLE_PADDING_INDEX)) +
input_offset);

bool is_phpwzero = param.filter_meta.padding[0] == 0 &&
param.filter_meta.padding[1] == 0;
if (is_phpwzero) {
src2 = const_cast<src_ctype*>(
param.src<src_ctype>(sparam.batch_id, sparam.group_id));
}
src_ctype* im2col_dst = static_cast<src_ctype*>(
bundle_thread.get(THREAD_BUNDLE_IM2COL_INDEX));
if (m_sh == 1 && m_sw == 1) {
if (m_is_xcorr) {
img2col<true>(src2, im2col_dst, m_oc, m_oh, m_ow, m_ic, m_ih, m_iw,
m_fh, m_fw, sparam.ohw_cur_index,
sparam.output_block_size);
} else {
img2col<false>(src2, im2col_dst, m_oc, m_oh, m_ow, m_ic, m_ih, m_iw,
m_fh, m_fw, sparam.ohw_cur_index,
sparam.output_block_size);
}
} else {
if (m_is_xcorr) {
img2col_stride<true>(src2, im2col_dst, m_oc, m_oh, m_ow, m_ic, m_ih,
m_iw, m_fh, m_fw, m_sh, m_sw,
sparam.ohw_cur_index,
sparam.output_block_size);
} else {
img2col_stride<false>(src2, im2col_dst, m_oc, m_oh, m_ow, m_ic,
m_ih, m_iw, m_fh, m_fw, m_sh, m_sw,
sparam.ohw_cur_index,
sparam.output_block_size);
}
}
}

template <typename src_ctype, typename bias_ctype, typename dst_ctype,
typename op_ctype, typename op_dtype,
megdnn::PostprocessMode postprocess_mode>
void Strategy<src_ctype, bias_ctype, dst_ctype, op_ctype, op_dtype,
postprocess_mode,PackMode::NO_PACK>::
exec_postprocess(const fallback::ConvBiasImpl::NCBKernParam& param,
const StrategyParam& sparam,
WorkspaceBundle bundle_thread) {
copy_bias(param, bundle_thread, sparam);
void* matmul_dst = get_matmul_dst_ptr(param, bundle_thread, sparam);

const bias_ctype* bias_ptr = static_cast<const bias_ctype*>(
param.bias<bias_ctype>(sparam.batch_id, sparam.group_id));
bias_ctype* bias_temp_ptr =
static_cast<bias_ctype*>(get_bias_temp_ptr(param, bundle_thread));
PostProcess<op_ctype, op_dtype, postprocess_mode>::run(
matmul_dst,
const_cast<void*>(
param.bias_mode == megdnn::BiasMode::BIAS
? bias_temp_ptr
: static_cast<void*>(const_cast<bias_ctype*>(
bias_ptr + sparam.oc_cur_index))),
matmul_dst, param.bias_mode, param.nonlineMode, param.bias_type,
param.dst_type, 1_z, sparam.output_block_oc_size, 1_z,
sparam.output_block_size);
copy_dst(param, matmul_dst, sparam);
}

template <typename src_ctype, typename bias_ctype, typename dst_ctype,
typename op_ctype, typename op_dtype,
megdnn::PostprocessMode postprocess_mode>
void Strategy<src_ctype, bias_ctype, dst_ctype, op_ctype, op_dtype,
postprocess_mode,PackMode::NO_PACK>::
copy_dst(const fallback::ConvBiasImpl::NCBKernParam& param,
const void* matmul_dst, const StrategyParam& sparam) {
if (!sparam.skip_copy_dst) {
dst_ctype* dst_tmp_ptr =
reinterpret_cast<dst_ctype*>(const_cast<void*>(matmul_dst));
dst_ctype* dst =
param.dst<dst_ctype>(sparam.batch_id, sparam.group_id) +
sparam.oc_cur_index * sparam.ohw + sparam.ohw_cur_index;
for (size_t oc = 0; oc < sparam.output_block_oc_size; oc++) {
std::memcpy(dst, dst_tmp_ptr,
sizeof(dst_ctype) * sparam.output_block_size);
dst_tmp_ptr += sparam.output_block_size;
dst += sparam.ohw;
}
}
}

template <typename src_ctype, typename bias_ctype, typename dst_ctype,
typename op_ctype, typename op_dtype,
megdnn::PostprocessMode postprocess_mode>
void Strategy<src_ctype, bias_ctype, dst_ctype, op_ctype, op_dtype,
postprocess_mode,PackMode::NO_PACK>::
copy_bias(const fallback::ConvBiasImpl::NCBKernParam& param,
WorkspaceBundle bundle_thread, const StrategyParam& sparam) {
const bias_ctype* bias_ptr = static_cast<const bias_ctype*>(
param.bias<bias_ctype>(sparam.batch_id, sparam.group_id));
bias_ctype* bias_temp_ptr =
static_cast<bias_ctype*>(get_bias_temp_ptr(param, bundle_thread));
if (param.bias_mode == megdnn::BiasMode::BIAS) {
bias_ctype* copy_dst = bias_temp_ptr;
const bias_ctype* copy_src = bias_ptr +
sparam.oc_cur_index * sparam.ohw +
sparam.ohw_cur_index;
for (size_t oc = sparam.oc_cur_index; oc < sparam.oc_end_index; oc++) {
std::memcpy(copy_dst, copy_src,
sizeof(bias_ctype) * sparam.output_block_size);
copy_dst += sparam.output_block_size;
copy_src += sparam.ohw;
}
}
}

#define INSTANTIAL_CLASS(_src_ctype, _bias_ctype, _dst_ctype, _op_ctype, \
_op_dtype, _postprocess_mode) \
template class Strategy<_src_ctype, _bias_ctype, _dst_ctype, _op_ctype, \
_op_dtype, _postprocess_mode, PackMode::NO_PACK>;

INSTANTIAL_CLASS(dt_float32, dt_float32, dt_float32, dt_float32, dt_float32,
megdnn::PostprocessMode::FLOAT)

#if __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
INSTANTIAL_CLASS(dt_float16, dt_float16, dt_float16, __fp16, __fp16,
megdnn::PostprocessMode::FLOAT)
#else
#if !MEGDNN_DISABLE_FLOAT16
INSTANTIAL_CLASS(dt_float16, dt_float16, dt_float16, dt_float16, dt_float16,
megdnn::PostprocessMode::NO_PROCESS)
#endif
#endif

#if MEGDNN_AARCH64 || MEGDNN_ARMV7
//! x86 do not have uint8 matmul so only armv7 armv8 support uint8
INSTANTIAL_CLASS(dt_uint8, dt_int32, dt_uint8, dt_qint32, dt_quint8,
megdnn::PostprocessMode::QUANTIZED)
INSTANTIAL_CLASS(dt_uint8, dt_int32, dt_int32, dt_qint32, dt_qint32,
megdnn::PostprocessMode::NO_PROCESS)
#endif

INSTANTIAL_CLASS(dt_int8, dt_int32, dt_int8, dt_qint32, dt_qint8,
megdnn::PostprocessMode::QUANTIZED)
INSTANTIAL_CLASS(dt_int8, dt_int32, dt_int32, dt_int32, dt_int32,
megdnn::PostprocessMode::NO_PROCESS)
INSTANTIAL_CLASS(dt_int8, dt_int16, dt_int16, dt_int16, dt_int16,
megdnn::PostprocessMode::NO_PROCESS)
INSTANTIAL_CLASS(dt_int8, dt_int32, dt_int32, dt_qint32, dt_qint32,
megdnn::PostprocessMode::NO_PROCESS)

} // namespace megdnn

+ 349
- 0
dnn/src/fallback/conv_bias/im2col/strategy_onlypacka.cpp View File

@@ -0,0 +1,349 @@
/**
* \file dnn/src/fallback/conv_bias/im2col/algos.cpp
* MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
*
* Copyright (c) 2014-2020 Megvii Inc. All rights reserved.
*
* Unless required by applicable law or agreed to in writing,
* software distributed under the License is distributed on an
* "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
*/

#include "megdnn/opr_param_defs.h"
#include "src/fallback/conv_bias/im2col/strategy_base.h"
#include "src/fallback/convolution/img2col_helper.h"
#if MEGDNN_X86
#include "src/x86/conv_bias/postprocess_helper.h"
#endif

using namespace megdnn;
#if MEGDNN_X86
using namespace x86;
#endif
namespace megdnn {
template <typename src_ctype, typename bias_ctype, typename dst_ctype,
typename op_ctype, typename op_dtype,
megdnn::PostprocessMode postprocess_mode>
Strategy<src_ctype, bias_ctype, dst_ctype, op_ctype, op_dtype,
postprocess_mode,PackMode::ONLY_PACKA>::Strategy()
: StrategyBase() {}

template <typename src_ctype, typename bias_ctype, typename dst_ctype,
typename op_ctype, typename op_dtype,
megdnn::PostprocessMode postprocess_mode>
void Strategy<src_ctype, bias_ctype, dst_ctype, op_ctype, op_dtype,
postprocess_mode,PackMode::ONLY_PACKA>::
copy_padding_kern(
WorkspaceBundle bundle,
const fallback::ConvBiasImpl::NCBKernParam& param,
const fallback::ConvBiasImpl::NCBKernIndex& ncb_index) {
UNPACK_CONV_F32_NCB_KERN_SIZES(param);
MEGDNN_MARK_USED_VAR(N);
MEGDNN_MARK_USED_VAR(OC);
MEGDNN_MARK_USED_VAR(OH);
MEGDNN_MARK_USED_VAR(OW);
MEGDNN_MARK_USED_VAR(FH);
MEGDNN_MARK_USED_VAR(FW);
MEGDNN_MARK_USED_VAR(SH);
MEGDNN_MARK_USED_VAR(SW);

size_t IW2 = IW + 2 * PW;
size_t IH2 = IH + 2 * PH;
size_t batch_id = ncb_index.ndrange_id[0];
size_t group_id = ncb_index.ndrange_id[1];
size_t channel_id = ncb_index.ndrange_id[2];

size_t padding_group_size = IH2 * IW2 * IC;
size_t workspace_channel_offset = IH2 * IW2 * channel_id;
size_t workspace_group_offset = group_id * padding_group_size;
size_t workspace_batch_offset =
param.filter_meta.group * batch_id * padding_group_size;
bundle.set(param.workspace_ptr);

src_ctype src_zp = static_cast<src_ctype>(0);
if (param.src_type.enumv() == DTypeEnum::Quantized8Asymm) {
src_zp = param.src_type.param<dtype::Quantized8Asymm>().zero_point;
}
src_ctype* src = const_cast<src_ctype*>(
param.src<src_ctype>(batch_id, group_id, channel_id));
src_ctype* src2;
src2 = static_cast<src_ctype*>(bundle.get(BUNDLE_PADDING_INDEX)) +
workspace_group_offset + workspace_batch_offset +
workspace_channel_offset;
src_ctype* src2_ptr = src2;
const src_ctype* src_ptr = src;
if (PH != 0) {
std::memset(src2_ptr, src_zp, sizeof(src_ctype) * PH * IW2);
src2_ptr += PH * IW2;
}
rep(ih, IH) {
if (PW != 0)
rep(pw, PW) * (src2_ptr++) = src_zp;
std::memcpy(src2_ptr, src_ptr, sizeof(src_ctype) * IW);
src2_ptr += IW;
src_ptr += IW;
if (PW != 0)
rep(pw, PW) * (src2_ptr++) = src_zp;
}
if (PH != 0) {
std::memset(src2_ptr, src_zp, sizeof(src_ctype) * PH * IW2);
src2_ptr += PH * IW2;
}
}

template <typename src_ctype, typename bias_ctype, typename dst_ctype,
typename op_ctype, typename op_dtype,
megdnn::PostprocessMode postprocess_mode>
void Strategy<src_ctype, bias_ctype, dst_ctype, op_ctype, op_dtype,
postprocess_mode,PackMode::ONLY_PACKA>::
packA_kern(WorkspaceBundle bundle,
const fallback::ConvBiasImpl::NCBKernParam& param,
fallback::MatrixMulImpl::KernSizeParam matmulparam,
fallback::MatrixMulImpl::AlgoBase* matmul_algo,
const fallback::ConvBiasImpl::NCBKernIndex& ncb_index) {
bundle.set(param.workspace_ptr);
fallback::MatrixMulImpl::KernParam matmul_param;
static_cast<fallback::MatrixMulImpl::KernSizeParam&>(matmul_param) =
matmulparam;
size_t OC = param.filter_meta.ocpg;
size_t oc_tile_size = matmul_param.M;
size_t group_id = ncb_index.ndrange_id[0];
size_t output_block_oc_size =
std::min(oc_tile_size, OC - ncb_index.ndrange_id[1] * oc_tile_size);
size_t oc_cur_index = ncb_index.ndrange_id[1] * oc_tile_size;
size_t packA_group_size =
bundle.get_size(BUNDLE_PACKA_INDEX) / param.filter_meta.group;
size_t a_panel_offset = ncb_index.ndrange_id[1] *
matmul_algo->get_bundle(matmul_param).get_size(0);
int8_t* a_panel = static_cast<int8_t*>(bundle.get(BUNDLE_PACKA_INDEX)) +
group_id * packA_group_size + a_panel_offset;
matmul_param.A_ptr =
const_cast<src_ctype*>(param.filter<src_ctype>(group_id)) +
oc_cur_index * matmul_param.K;
matmul_param.M = output_block_oc_size;
matmul_algo->pack_A(matmul_param, a_panel, 0_z, 0_z);
}

template <typename src_ctype, typename bias_ctype, typename dst_ctype,
typename op_ctype, typename op_dtype,
megdnn::PostprocessMode postprocess_mode>
void* Strategy<src_ctype, bias_ctype, dst_ctype, op_ctype, op_dtype,
postprocess_mode,PackMode::ONLY_PACKA>::
get_matmul_dst_ptr(const fallback::ConvBiasImpl::NCBKernParam& param,
const WorkspaceBundle& bundle_thread,
const StrategyParam& sparam) {
if (sparam.is_dst_8bit || !sparam.is_ohw_size_bigger) {
return static_cast<void*>(
bundle_thread.get(THREAD_BUNDLE_MATMULDST_INDEX));
} else {
bias_ctype* dst =
param.dst<bias_ctype>(sparam.batch_id, sparam.group_id) +
sparam.oc_cur_index * sparam.ohw;
return static_cast<void*>(dst);
}
}

template <typename src_ctype, typename bias_ctype, typename dst_ctype,
typename op_ctype, typename op_dtype,
megdnn::PostprocessMode postprocess_mode>
void Strategy<src_ctype, bias_ctype, dst_ctype, op_ctype, op_dtype,
postprocess_mode,PackMode::ONLY_PACKA>::
exec_matmul(const fallback::ConvBiasImpl::NCBKernParam& param,
const StrategyParam& sparam, WorkspaceBundle bundle,
WorkspaceBundle bundle_thread,
fallback::MatrixMulImpl::KernParam matmul_param,
fallback::MatrixMulImpl::AlgoBase* matmul_algo,
const fallback::ConvBiasImpl::NCBKernIndex& ncb_index) {
size_t packA_group_size =
bundle.get_size(BUNDLE_PACKA_INDEX) / param.filter_meta.group;
size_t a_panel_offset = ncb_index.ndrange_id[3] *
matmul_algo->get_bundle(matmul_param).get_size(0);
a_panel_offset = sparam.group_id * packA_group_size + a_panel_offset;

void* matmul_dst = get_matmul_dst_ptr(param, bundle_thread, sparam);

src_ctype* a_panel = reinterpret_cast<src_ctype*>(
reinterpret_cast<uintptr_t>(bundle.get(BUNDLE_PACKA_INDEX)) +
a_panel_offset);
src_ctype* b_panel = nullptr;

src_ctype* im2col_dst = static_cast<src_ctype*>(
bundle_thread.get(THREAD_BUNDLE_IM2COL_INDEX));

matmul_param.M = sparam.output_block_oc_size;
matmul_param.N = sparam.output_block_size;
matmul_param.LDB = sparam.output_block_size;
matmul_param.LDC = sparam.output_block_size;
matmul_param.B_ptr = im2col_dst;
matmul_param.C_ptr = matmul_dst;

auto matmul_kern = matmul_algo->get_kern_naked(matmul_param);
matmul_kern(matmul_param, a_panel, b_panel);
}

template <typename src_ctype, typename bias_ctype, typename dst_ctype,
typename op_ctype, typename op_dtype,
megdnn::PostprocessMode postprocess_mode>
void Strategy<src_ctype, bias_ctype, dst_ctype, op_ctype, op_dtype,
postprocess_mode,PackMode::ONLY_PACKA>::
exec_im2col(WorkspaceBundle bundle, WorkspaceBundle bundle_thread,
const StrategyParam& sparam,
const fallback::ConvBiasImpl::NCBKernParam& param,
fallback::MatrixMulImpl::KernParam matmul_param,
fallback::MatrixMulImpl::AlgoBase* matmul_algo
) {
MEGDNN_MARK_USED_VAR(matmul_param);
MEGDNN_MARK_USED_VAR(matmul_algo);
size_t m_sh = param.filter_meta.stride[0];
size_t m_sw = param.filter_meta.stride[1];
size_t m_oc = param.filter_meta.ocpg;
size_t m_oh = param.osz[0];
size_t m_ow = param.osz[1];
size_t m_ic = param.filter_meta.icpg;
size_t m_ih = param.isz[0] + param.filter_meta.padding[0] * 2;
size_t m_iw = param.isz[1] + param.filter_meta.padding[1] * 2;
size_t m_fh = param.filter_meta.spatial[0];
size_t m_fw = param.filter_meta.spatial[1];
size_t m_is_xcorr = !param.filter_meta.should_flip;

size_t input_offset =
m_ih * m_iw * m_ic *
(sparam.group_id + param.filter_meta.group * sparam.batch_id) *
sizeof(src_ctype);

src_ctype* src2 = reinterpret_cast<src_ctype*>(
reinterpret_cast<uintptr_t>(bundle.get(BUNDLE_PADDING_INDEX)) +
input_offset);
bool is_phpwzero = param.filter_meta.padding[0] == 0 &&
param.filter_meta.padding[1] == 0;
if (is_phpwzero) {
src2 = const_cast<src_ctype*>(
param.src<src_ctype>(sparam.batch_id, sparam.group_id));
}
src_ctype* im2col_dst = static_cast<src_ctype*>(
bundle_thread.get(THREAD_BUNDLE_IM2COL_INDEX));
if (m_sh == 1 && m_sw == 1) {
if (m_is_xcorr) {
img2col<true>(src2, im2col_dst, m_oc, m_oh, m_ow, m_ic, m_ih, m_iw,
m_fh, m_fw, sparam.ohw_cur_index,
sparam.output_block_size);
} else {
img2col<false>(src2, im2col_dst, m_oc, m_oh, m_ow, m_ic, m_ih, m_iw,
m_fh, m_fw, sparam.ohw_cur_index,
sparam.output_block_size);
}
} else {
if (m_is_xcorr) {
img2col_stride<true>(src2, im2col_dst, m_oc, m_oh, m_ow, m_ic, m_ih,
m_iw, m_fh, m_fw, m_sh, m_sw,
sparam.ohw_cur_index,
sparam.output_block_size);
} else {
img2col_stride<false>(src2, im2col_dst, m_oc, m_oh, m_ow, m_ic,
m_ih, m_iw, m_fh, m_fw, m_sh, m_sw,
sparam.ohw_cur_index,
sparam.output_block_size);
}
}
}

template <typename src_ctype, typename bias_ctype, typename dst_ctype,
typename op_ctype, typename op_dtype,
megdnn::PostprocessMode postprocess_mode>
void Strategy<src_ctype, bias_ctype, dst_ctype, op_ctype, op_dtype,
postprocess_mode,PackMode::ONLY_PACKA>::
exec_postprocess(const fallback::ConvBiasImpl::NCBKernParam& param,
const StrategyParam& sparam,
WorkspaceBundle bundle_thread) {
void* matmul_dst = get_matmul_dst_ptr(param, bundle_thread, sparam);

const bias_ctype* bias_ptr = static_cast<const bias_ctype*>(
param.bias<bias_ctype>(sparam.batch_id, sparam.group_id));
bias_ctype* bias_temp_ptr =
static_cast<bias_ctype*>(get_bias_temp_ptr(param, bundle_thread));

if (param.bias_mode == megdnn::BiasMode::BIAS) {
bias_ctype* copy_dst = bias_temp_ptr;
const bias_ctype* copy_src = bias_ptr +
sparam.oc_cur_index * sparam.ohw +
sparam.ohw_cur_index;
for (size_t oc = sparam.oc_cur_index; oc < sparam.oc_end_index; oc++) {
std::memcpy(copy_dst, copy_src,
sizeof(bias_ctype) * sparam.output_block_size);
copy_dst += sparam.output_block_size;
copy_src += sparam.ohw;
}
}

PostProcess<op_ctype, op_dtype, postprocess_mode>::run(
matmul_dst,
const_cast<void*>(
param.bias_mode == megdnn::BiasMode::BIAS
? bias_temp_ptr
: static_cast<void*>(const_cast<bias_ctype*>(
bias_ptr + sparam.oc_cur_index))),
matmul_dst, param.bias_mode, param.nonlineMode, param.bias_type,
param.dst_type, 1_z, sparam.output_block_oc_size, 1_z,
sparam.output_block_size);
copy_dst(param, matmul_dst, sparam);
}

template <typename src_ctype, typename bias_ctype, typename dst_ctype,
typename op_ctype, typename op_dtype,
megdnn::PostprocessMode postprocess_mode>
void Strategy<src_ctype, bias_ctype, dst_ctype, op_ctype, op_dtype,
postprocess_mode,PackMode::ONLY_PACKA>::
copy_dst(const fallback::ConvBiasImpl::NCBKernParam& param,
const void* matmul_dst, const StrategyParam& sparam) {
if (!sparam.skip_copy_dst) {
dst_ctype* dst_tmp_ptr =
reinterpret_cast<dst_ctype*>(const_cast<void*>(matmul_dst));
dst_ctype* dst =
param.dst<dst_ctype>(sparam.batch_id, sparam.group_id) +
sparam.oc_cur_index * sparam.ohw + sparam.ohw_cur_index;
for (size_t oc = 0; oc < sparam.output_block_oc_size; oc++) {
std::memcpy(dst, dst_tmp_ptr,
sizeof(dst_ctype) * sparam.output_block_size);
dst_tmp_ptr += sparam.output_block_size;
dst += sparam.ohw;
}
}
}

#define INSTANTIAL_CLASS(_src_ctype, _bias_ctype, _dst_ctype, _op_ctype, \
_op_dtype, _postprocess_mode) \
template class Strategy<_src_ctype, _bias_ctype, _dst_ctype, \
_op_ctype, _op_dtype, _postprocess_mode,PackMode::ONLY_PACKA>;

INSTANTIAL_CLASS(dt_float32, dt_float32, dt_float32, dt_float32, dt_float32,
megdnn::PostprocessMode::FLOAT)

#if __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
INSTANTIAL_CLASS(dt_float16, dt_float16, dt_float16, __fp16, __fp16,
megdnn::PostprocessMode::FLOAT)
#else
#if !MEGDNN_DISABLE_FLOAT16
INSTANTIAL_CLASS(dt_float16, dt_float16, dt_float16, dt_float16, dt_float16,
megdnn::PostprocessMode::NO_PROCESS)
#endif
#endif

#if MEGDNN_AARCH64 || MEGDNN_ARMV7
//! x86 do not have uint8 matmul so only armv7 armv8 support uint8
INSTANTIAL_CLASS(dt_uint8, dt_int32, dt_uint8, dt_qint32, dt_quint8,
megdnn::PostprocessMode::QUANTIZED)
INSTANTIAL_CLASS(dt_uint8, dt_int32, dt_int32, dt_qint32, dt_qint32,
megdnn::PostprocessMode::NO_PROCESS)
#endif

INSTANTIAL_CLASS(dt_int8, dt_int32, dt_int8, dt_qint32, dt_qint8,
megdnn::PostprocessMode::QUANTIZED)
INSTANTIAL_CLASS(dt_int8, dt_int32, dt_int32, dt_int32, dt_int32,
megdnn::PostprocessMode::NO_PROCESS)
INSTANTIAL_CLASS(dt_int8, dt_int16, dt_int16, dt_int16, dt_int16,
megdnn::PostprocessMode::NO_PROCESS)
INSTANTIAL_CLASS(dt_int8, dt_int32, dt_int32, dt_qint32, dt_qint32,
megdnn::PostprocessMode::NO_PROCESS)

#undef INSTANTIAL_CLASS
} // namespace megdnn

+ 2
- 2
dnn/src/fallback/convolution/img2col_helper.h View File

@@ -8,7 +8,6 @@
* software distributed under the License is distributed on an * software distributed under the License is distributed on an
* "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
*/ */
#include <cstddef>
#include "src/common/utils.h" #include "src/common/utils.h"


namespace { namespace {
@@ -42,7 +41,8 @@ void img2col_stride(const dtype* __restrict src, dtype* __restrict dst,
} }
} }


//! add for im2col matmul multithread
//!add for im2col matmul multithread

template <bool is_xcorr, typename dtype> template <bool is_xcorr, typename dtype>
void img2col_stride(const dtype* __restrict src, dtype* __restrict dst, void img2col_stride(const dtype* __restrict src, dtype* __restrict dst,
const int OC, const int OH, const int OW, const int IC, const int OC, const int OH, const int OW, const int IC,


+ 11
- 7
dnn/src/x86/elemwise_helper/kimpl/op_unary_base.h View File

@@ -323,6 +323,7 @@ struct UnaryOpBase<SIMDType::NONE, dt_qint32, dt_qint8>
init(src_scale, dst_scale); init(src_scale, dst_scale);
} }
}; };

template <> template <>
struct UnaryOpBase<SIMDType::NONE, dt_qint32, dt_quint8> struct UnaryOpBase<SIMDType::NONE, dt_qint32, dt_quint8>
: OpBase<dt_qint32, dt_quint8> { : OpBase<dt_qint32, dt_quint8> {
@@ -330,20 +331,24 @@ struct UnaryOpBase<SIMDType::NONE, dt_qint32, dt_quint8>
using src_ctype = dt_qint32; using src_ctype = dt_qint32;
using dst_ctype = dt_quint8; using dst_ctype = dt_quint8;
float scale, scale_src, scale_dst; float scale, scale_src, scale_dst;
void init(float src_scale, float dst_scale) {
uint8_t dzp;
void init(float src_scale, float dst_scale, uint8_t dst_zp) {
scale_src = src_scale; scale_src = src_scale;
scale_dst = 1.f / dst_scale;
scale_dst = 1.0f / dst_scale;
dzp = dst_zp;
scale = src_scale / dst_scale; scale = src_scale / dst_scale;
} }
UnaryOpBase(DType src_dtype, DType dst_dtype) { UnaryOpBase(DType src_dtype, DType dst_dtype) {
float src_scale = src_dtype.param<dtype::QuantizedS32>().scale; float src_scale = src_dtype.param<dtype::QuantizedS32>().scale;
float dst_scale = dst_dtype.param<dtype::QuantizedS8>().scale;
init(src_scale, dst_scale);
float dst_scale = dst_dtype.param<dtype::Quantized8Asymm>().scale;
uint8_t dst_zp = dst_dtype.param<dtype::Quantized8Asymm>().zero_point;
init(src_scale, dst_scale, dst_zp);
} }
UnaryOpBase(float src_scale, float dst_scale) {
init(src_scale, dst_scale);
UnaryOpBase(float src_scale, float dst_scale, uint8_t dst_zp) {
init(src_scale, dst_scale, dst_zp);
} }
}; };

#define OP_BASE(_simd_type, _simd_target, _simd_data_type, _func_prefix) \ #define OP_BASE(_simd_type, _simd_target, _simd_data_type, _func_prefix) \
template <> \ template <> \
struct UnaryOpBase<_simd_type, dt_float32, dt_qint8> \ struct UnaryOpBase<_simd_type, dt_float32, dt_qint8> \
@@ -828,7 +833,6 @@ template <typename Op>
struct UnaryQuantizationOp<SIMDType::NONE, dt_qint32, dt_quint8, Op> struct UnaryQuantizationOp<SIMDType::NONE, dt_qint32, dt_quint8, Op>
: UnaryOpBase<SIMDType::NONE, dt_qint32, dt_quint8> { : UnaryOpBase<SIMDType::NONE, dt_qint32, dt_quint8> {
using UnaryOpBase<SIMDType::NONE, dt_qint32, dt_quint8>::UnaryOpBase; using UnaryOpBase<SIMDType::NONE, dt_qint32, dt_quint8>::UnaryOpBase;
constexpr static size_t SIMD_WIDTH = 8;
Op op; Op op;


void operator()(const dt_qint32& src, dt_quint8* dst) const { void operator()(const dt_qint32& src, dt_quint8* dst) const {


+ 11
- 5
dnn/src/x86/matrix_mul/algos.cpp View File

@@ -195,10 +195,10 @@ MatrixMulImpl::kern_t MatrixMulImpl::AlgoInt8x8x32Vnni::get_kern(
return int8x8x32_kern_vnni; return int8x8x32_kern_vnni;
} }


MEGDNN_REG_GEMM_FUNC_FOR_IM2COL_IMPL_PACKA(AlgoInt8x8x32Vnni,
megdnn_x86_matmul_kern, 5,
x86::matmul::gemm_int8_vnni_12x32x4,
dt_int8, dt_int32, dt_uint8);
MEGDNN_REG_GEMM_FUNC_FOR_IM2COL_IMPL_DETAIL(AlgoInt8x8x32Vnni,
megdnn_x86_matmul_kern, 5,
x86::matmul::gemm_int8_vnni_12x32x4,
dt_int8, dt_int32, dt_uint8);
#endif #endif


/* ===================== Int8 mkldnn algo ===================== */ /* ===================== Int8 mkldnn algo ===================== */
@@ -364,7 +364,9 @@ size_t MatrixMulImpl::AlgoInt8x8x32AVX2M4N16K2::get_workspace(
m, n, k, trans_a, trans_b, strategy, cacheline) m, n, k, trans_a, trans_b, strategy, cacheline)
.get_workspace_size(); .get_workspace_size();
} }

MEGDNN_REG_GEMM_FUNC_FOR_IM2COL_IMPL_DETAIL(
AlgoInt8x8x32AVX2M4N16K2, megdnn_x86_matmul_kern, 8,
x86::matmul::gemm_avx2_s8s8s32_4x16x2, dt_int8, dt_int32, dt_int16);


MatrixMulImpl::kern_t MatrixMulImpl::AlgoInt8x8x32AVX2M2N4K16::get_kern( MatrixMulImpl::kern_t MatrixMulImpl::AlgoInt8x8x32AVX2M2N4K16::get_kern(
const KernSizeParam&) const { const KernSizeParam&) const {
@@ -437,6 +439,10 @@ size_t MatrixMulImpl::AlgoInt8x8x32SSEM4N8K2::get_workspace(
.get_workspace_size(); .get_workspace_size();
} }


MEGDNN_REG_GEMM_FUNC_FOR_IM2COL_IMPL_DETAIL(
AlgoInt8x8x32SSEM4N8K2, megdnn_x86_matmul_kern, 9,
x86::matmul::gemm_sse_s8s8s32_4x8x2, dt_int8, dt_int32, dt_int16);

/*************************AlgoF32MK8_8x8********************/ /*************************AlgoF32MK8_8x8********************/
MatrixMulImpl::kern_t MatrixMulImpl::AlgoF32MK8_8x8::get_kern( MatrixMulImpl::kern_t MatrixMulImpl::AlgoF32MK8_8x8::get_kern(
const KernSizeParam&) const { const KernSizeParam&) const {


+ 2
- 2
dnn/src/x86/matrix_mul/algos.h View File

@@ -68,7 +68,7 @@ public:
size_t get_workspace(const KernSizeParam&) const override; size_t get_workspace(const KernSizeParam&) const override;
kern_t get_kern(const KernSizeParam&) const override; kern_t get_kern(const KernSizeParam&) const override;
void* type() const override { return sm_x86_algo_type; } void* type() const override { return sm_x86_algo_type; }
PackMode packmode() const override { return PackMode::NO_PACK; }
MEGDNN_REG_GEMM_FUNC_FOR_IM2COL();
}; };


class MatrixMulImpl::AlgoInt8x8x32SSEM4N8K2 : public AlgoBase { class MatrixMulImpl::AlgoInt8x8x32SSEM4N8K2 : public AlgoBase {
@@ -79,7 +79,7 @@ public:
size_t get_workspace(const KernSizeParam&) const override; size_t get_workspace(const KernSizeParam&) const override;
kern_t get_kern(const KernSizeParam&) const override; kern_t get_kern(const KernSizeParam&) const override;
void* type() const override { return sm_x86_algo_type; } void* type() const override { return sm_x86_algo_type; }
PackMode packmode() const override { return PackMode::NO_PACK; }
MEGDNN_REG_GEMM_FUNC_FOR_IM2COL();
}; };


class MatrixMulImpl::AlgoF32MK8_8x8 : public AlgoBase { class MatrixMulImpl::AlgoF32MK8_8x8 : public AlgoBase {


+ 6
- 6
dnn/test/x86/conv_bias.cpp View File

@@ -741,7 +741,7 @@ TEST_F(X86_MULTI_THREADS, CONV_BIAS_IM2COLMATMUL_INT8x8x32) {
TensorShape{oc, ic, kernel, kernel}, TensorShape{}); TensorShape{oc, ic, kernel, kernel}, TensorShape{});
}; };


for (size_t kernel : {1, 2, 3, 4, 5, 6, 7})
for (size_t kernel : {2, 3, 4, 5, 6, 7})
for (size_t ic : {1, 4, 8, 16}) for (size_t ic : {1, 4, 8, 16})
for (size_t oc : {1, 4, 8}) for (size_t oc : {1, 4, 8})
for (size_t p : {0, 2}) for (size_t p : {0, 2})
@@ -751,7 +751,7 @@ TEST_F(X86_MULTI_THREADS, CONV_BIAS_IM2COLMATMUL_INT8x8x32) {
run(oc, ic, size, size, kernel, p, nonline_mode); run(oc, ic, size, size, kernel, p, nonline_mode);
} }
//! test OC block //! test OC block
run(2046, 1, 8, 8, 1, 0, NonlineMode::IDENTITY);
run(2046, 1, 8, 8, 2, 0, NonlineMode::IDENTITY);


Checker<ConvBias> checker(handle()); Checker<ConvBias> checker(handle());
UniformIntRNG rng{-50, 50}; UniformIntRNG rng{-50, 50};
@@ -826,7 +826,7 @@ TEST_F(X86_MULTI_THREADS, CONV_BIAS_IM2COLMATMUL_FP32) {
(w + 2 * p - kernel) / param.stride_w + 1}); (w + 2 * p - kernel) / param.stride_w + 1});
}; };


for (size_t kernel : {1, 2, 3, 4, 5, 6, 7})
for (size_t kernel : {2, 3, 4, 5, 6, 7})
for (size_t ic : {1, 4, 8, 16}) for (size_t ic : {1, 4, 8, 16})
for (size_t oc : {1, 4, 8, 16, 300}) for (size_t oc : {1, 4, 8, 16, 300})
for (size_t p : {0, 2}) for (size_t p : {0, 2})
@@ -895,7 +895,7 @@ TEST_F(X86_MULTI_THREADS, CONV_BIAS_IM2COLMATMUL_FP32_PACKA) {
(w + 2 * param.pad_w - kernel) / 1 + 1}); (w + 2 * param.pad_w - kernel) / 1 + 1});
}; };


for (size_t kernel : {1, 2, 3, 4, 5, 6, 7})
for (size_t kernel : {2, 3, 4, 5, 6, 7})
for (size_t ic : {1, 4, 8, 16}) for (size_t ic : {1, 4, 8, 16})
for (size_t oc : {1, 4, 8, 16}) for (size_t oc : {1, 4, 8, 16})
for (size_t p : {0, 1}) for (size_t p : {0, 1})
@@ -945,7 +945,7 @@ TEST_F(X86_MULTI_THREADS, CONV_BIAS_IM2COLMATMUL_QINT8) {
TensorShape{1, oc, 1, 1}); TensorShape{1, oc, 1, 1});
}; };


for (size_t kernel : {1, 2, 3, 4, 5, 6, 7})
for (size_t kernel : {2, 3, 4, 5, 6, 7})
for (size_t ic : {1, 4, 8, 16}) for (size_t ic : {1, 4, 8, 16})
for (size_t oc : {1, 4, 8}) for (size_t oc : {1, 4, 8})
for (size_t p : {0, 2}) for (size_t p : {0, 2})
@@ -2183,7 +2183,7 @@ TEST_F(X86_BENCHMARK_MULTI_THREADS, BENCHMARK_CONVBIAS_IM2COL_INT8X8X32) {


std::vector<DType> data_type = {dtype::Int8(), dtype::Int8(), std::vector<DType> data_type = {dtype::Int8(), dtype::Int8(),
dtype::Int32(), dtype::Int32()}; dtype::Int32(), dtype::Int32()};
std::string algo_name = "IM2COLMATMUL:X86_INT8X8X32_AVX2_4X16X2";
std::string algo_name = "IM2COLMATMUL:X86_INT8X8X32_AVX2_4X16X2:192";
// std::string algo_name = "IM2COLMATMUL:X86_INT8X8X32_AVX2_2X4X16"; // std::string algo_name = "IM2COLMATMUL:X86_INT8X8X32_AVX2_2X4X16";
// printf("Benchmark IM2COLMATMUL:X86_INT8X8X32_AVX2_4X16X2 algo\n"); // printf("Benchmark IM2COLMATMUL:X86_INT8X8X32_AVX2_4X16X2 algo\n");
benchmark_impl(param, shapes_and_computation, algo_name, RUNS, benchmark_impl(param, shapes_and_computation, algo_name, RUNS,


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