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提交 894a2407 编写于 作者: M Megvii Engine Team 提交者： huangxinda
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feat(dnn/cuda): add relayout format kernel for nchw <-> nhwc 

GitOrigin-RevId: e11f3e54085929ab9919fe2070fcc9e633755b69
上级 43c59204
隐藏空白更改
内联 并排
Showing with 660 addition and 63 deletion
dnn/scripts/opr_param_defs.py
浏览文件 @ 894a2407
......@@ -1001,7 +1001,9 @@ Note: NCHW_NCHW4_WEIGHT will auto pad oc and ic, you should remove oc in later o
'NCHW_NCHW4_WEIGHT',
'NCHW_NCHW64',
'NCHW64_NCHW',
)
'NCHW_NHWC',
'NHWC_NCHW',
)
)
(pdef('RelayoutFormat', 'Change the tensor layout format', version=1).
......
dnn/src/common/relayout_format.cpp
浏览文件 @ 894a2407
......@@ -268,6 +268,22 @@ void RelayoutFormat::deduce_layout_fwd(const TensorLayout& src,
dst[2] = src[2];
dst[3] = src[3];
break;
case Param::Mode::NCHW_NHWC:
megdnn_assert(src.ndim == 4);
dst.ndim = 4;
dst[0] = src[0];
dst[1] = src[2];
dst[2] = src[3];
dst[3] = src[1];
break;
case Param::Mode::NHWC_NCHW:
megdnn_assert(src.ndim == 4);
dst.ndim = 4;
dst[0] = src[0];
dst[1] = src[3];
dst[2] = src[1];
dst[3] = src[2];
break;
default:
megdnn_assert(0, "Invalid RelayoutFormat Mode");
break;
......@@ -375,6 +391,10 @@ void RelayoutFormat::deduce_format(TensorFormat src, TensorFormat& dst) {
case Param::Mode::NCHW64_NCHW:
dst = src;
break;
case Param::Mode::NCHW_NHWC:
case Param::Mode::NHWC_NCHW:
dst = src;
break;
default:
megdnn_throw("Invalid relayout format mode");
break;
......@@ -666,6 +686,14 @@ void RelayoutFormat::deduce_exec_layout(const TensorLayout& src,
exec_src = src.dimshuffle({0, 1, 4, 2, 3});
exec_dst = dst;
break;
case Param::Mode::NCHW_NHWC:
exec_src = src.dimshuffle({0, 2, 3, 1});
exec_dst = dst;
break;
case Param::Mode::NHWC_NCHW:
exec_src = src.dimshuffle({0, 3, 1, 2});
exec_dst = dst;
break;
default:
megdnn_assert(0, "Invalid RelayoutFormat Mode");
}
......
dnn/src/cuda/pooling/pooling2d_qint.cu
浏览文件 @ 894a2407
......@@ -505,7 +505,7 @@ void megdnn::cuda::pooling2d::do_pooling2d_int8_cdiv4hwn4(const int8_t* d_src,
void megdnn::cuda::pooling2d::do_pooling2d_int8_ncdiv4hw4(
const int8_t* d_src, int8_t* d_dst, const Param& param,
cudaStream_t stream, uint32_t mode, bool uint_case, int zero_point) {
cudaStream_t stream, uint32_t mode, bool /* uint_case */, int zero_point) {
using Mode = megdnn::param_enumv::Pooling::Mode;
void (*kern)(const int8_t* __restrict__, int8_t* __restrict__, Param param,
int zero_point);
......@@ -545,7 +545,7 @@ void megdnn::cuda::pooling2d::do_pooling2d_int8_ncdiv4hw4(
void megdnn::cuda::pooling2d::do_pooling2d_int8_ncdiv32hw32(
const int8_t* d_src, int8_t* d_dst, const Param& param,
cudaStream_t stream, uint32_t mode, bool uint_case, int zero_point) {
cudaStream_t stream, uint32_t mode, bool /* uint_case */, int zero_point) {
using Mode = megdnn::param_enumv::Pooling::Mode;
void (*kern)(const int8_t* __restrict__, int8_t* __restrict__, Param param,
int zero_point);
......
dnn/src/cuda/relayout_format/opr_impl.cpp
浏览文件 @ 894a2407
......@@ -33,7 +33,9 @@ void RelayoutFormatImpl::exec(_megdnn_tensor_in src, _megdnn_tensor_out dst,
Param::Mode::
NCHW_NCHW4_IC_SMALL_CONV_DENSE_WEIGHT ||
param().mode == Param::Mode::NCHW_NCHW64 ||
param().mode == Param::Mode::NCHW64_NCHW,
param().mode == Param::Mode::NCHW64_NCHW ||
param().mode == Param::Mode::NCHW_NHWC ||
param().mode == Param::Mode::NHWC_NCHW,
"relayout format of cuda only support NCHW4->CHWN4 or "
"CHWN4->NCHW4 or NCHW->NCHW4");
if ((param().mode == param::RelayoutFormat::Mode::NCHW4_CHWN4 ||
......@@ -82,7 +84,9 @@ void RelayoutFormatImpl::exec(_megdnn_tensor_in src, _megdnn_tensor_out dst,
{src.raw_ptr, exec_src_layout}, {dst.raw_ptr, exec_dst_layout});
}
bool is_trans_4bits = (param().mode == Param::Mode::NCHW_NCHW64 ||
param().mode == Param::Mode::NCHW64_NCHW) &&
param().mode == Param::Mode::NCHW64_NCHW ||
param().mode == Param::Mode::NCHW_NHWC ||
param().mode == Param::Mode::NHWC_NCHW) &&
(src_dtype.enumv() == DTypeEnum::QuantizedS4 ||
src_dtype.enumv() == DTypeEnum::Quantized4Asymm);
bool is_nchw_nchw4 = param().mode == Param::Mode::NCHW_NCHW4 ||
......
dnn/src/cuda/relayout_format/relayout_format.cpp
浏览文件 @ 894a2407
......@@ -66,6 +66,22 @@ void relayout_format::RelayoutFormatFast::exec(const TensorND& src,
return relayout_format_cuda_nchwx_nchw(src, dst, stream, src_scale,
dst_scale, src_zero_point,
dst_zero_point);
} else if (mode == RelayoutFormat::Param::Mode::NCHW_NHWC) {
#define CHECK(dt) \
megdnn_assert(dt.enumv() == DTypeEnum::Quantized4Asymm || \
dt.enumv() == DTypeEnum::QuantizedS4)
CHECK(src.layout.dtype);
CHECK(dst.layout.dtype);
return relayout_format_cuda_nchw_nhwc(src, dst, stream, src_scale,
dst_scale, src_zero_point,
dst_zero_point);
} else if (mode == RelayoutFormat::Param::Mode::NHWC_NCHW) {
CHECK(src.layout.dtype);
CHECK(dst.layout.dtype);
return relayout_format_cuda_nhwc_nchw(src, dst, stream, src_scale,
dst_scale, src_zero_point,
dst_zero_point);
#undef CHECK
} else if (mode == RelayoutFormat::Param::Mode::NCHW_NCHW4_WEIGHT) {
return relayout_format_cuda_nchw_nchw4_weight(src, dst, stream);
} else if (mode == RelayoutFormat::Param::Mode::NCHW4_NCHW) {
......
dnn/src/cuda/relayout_format/relayout_format_kern.cuh
浏览文件 @ 894a2407
......@@ -20,8 +20,17 @@ namespace relayout_format {
namespace internal {
using namespace memory;
struct LayoutType {
static constexpr uint32_t NCHWx = 0;
static constexpr uint32_t NHWC = 1;
};
template <typename Type_, int pack_size_, int chan_blk_, int width_,
int size_nbits_>
int size_nbits_, uint32_t layout_type_ = LayoutType::NCHWx>
class TensorIteratorOverChannel;
template <typename Type_, int pack_size_, int chan_blk_, int width_,
int size_nbits_, uint32_t layout_type_>
class TensorIteratorOverChannel {
public:
using Type = Type_;
......@@ -116,6 +125,98 @@ private:
template <typename Type_, int pack_size_, int chan_blk_, int width_,
int size_nbits_>
class TensorIteratorOverChannel<Type_, pack_size_, chan_blk_, width_,
size_nbits_, LayoutType::NHWC> {
public:
using Type = Type_;
static constexpr int pack_size = pack_size_;
static constexpr int chan_blk = chan_blk_;
static constexpr int width = width_;
static constexpr int size_nbits = size_nbits_;
static constexpr int elements_in_type =
chan_blk * width * size_nbits / (8 * sizeof(Type));
static constexpr int pack_size_in_type =
pack_size * size_nbits / (8 * sizeof(Type));
static constexpr int pack_size_in_byte = pack_size_in_type * sizeof(Type);
using AccessType = array_wrapper<Type, pack_size_in_type>;
using Fragment = array_wrapper<Type, elements_in_type>;
MEGDNN_HOST TensorIteratorOverChannel()
: pointer{nullptr}, hw_stride_in_elements{0}, channel{0} {}
MEGDNN_HOST TensorIteratorOverChannel(Type* pointer_,
int hw_stride_in_elements_,
int channel_, int, int)
: pointer{pointer_},
hw_stride_in_elements{hw_stride_in_elements_},
channel{channel_} {}
MEGDNN_DEVICE __forceinline__ void initialize(int c_idx, int hw_idx) {
pointer += c_idx * size_nbits / (8 * sizeof(Type)) +
hw_idx * hw_stride_in_elements;
channel -= c_idx;
}
MEGDNN_DEVICE __forceinline__ void add_pointer_offset(
size_t offset_in_type) {
pointer += offset_in_type;
}
MEGDNN_DEVICE __forceinline__ void load(Fragment& frag, int zero_point) {
AccessType* frag_ptr = reinterpret_cast<AccessType*>(&frag);
Type* pointer_ = pointer;
#pragma unroll
for (int i = 0; i < width; ++i) {
#pragma unroll
for (int j = 0; j < chan_blk; j += pack_size) {
int frag_idx = i * (chan_blk / pack_size) + (j / pack_size);
bool guard = j < channel;
global_load<AccessType, pack_size_in_byte>(
frag_ptr[frag_idx],
reinterpret_cast<void*>(
pointer_ + j * size_nbits / (8 * sizeof(Type))),
guard, zero_point);
}
pointer_ += hw_stride_in_elements;
}
}
MEGDNN_DEVICE __forceinline__ void store(const Fragment& frag) {
const AccessType* frag_ptr = reinterpret_cast<const AccessType*>(&frag);
Type* pointer_ = pointer;
#pragma unroll
for (int i = 0; i < width; ++i) {
#pragma unroll
for (int j = 0; j < chan_blk; j += pack_size) {
int frag_idx = i * (chan_blk / pack_size) + (j / pack_size);
bool guard = j < channel;
global_store<AccessType, pack_size_in_byte>(
frag_ptr[frag_idx],
reinterpret_cast<void*>(
pointer_ + j * size_nbits / (8 * sizeof(Type))),
guard);
}
pointer_ += hw_stride_in_elements;
}
}
MEGDNN_DEVICE __forceinline__ void advance() {
pointer += chan_blk * size_nbits / (8 * sizeof(Type));
channel -= chan_blk;
}
private:
Type* pointer;
int hw_stride_in_elements;
int channel;
};
template <typename Type_, int pack_size_, int chan_blk_, int width_,
int size_nbits_, uint32_t layout_type_ = LayoutType::NCHWx>
class MaskedTensorIteratorOverChannel;
template <typename Type_, int pack_size_, int chan_blk_, int width_,
int size_nbits_, uint32_t layout_type_>
class MaskedTensorIteratorOverChannel {
public:
using Type = Type_;
......@@ -243,24 +344,143 @@ private:
size_t stride[lane_size_in_type / pack_size_in_type];
};
template <typename Type_, int pack_size_, int chan_blk_, int width_,
int size_nbits_>
class MaskedTensorIteratorOverChannel<Type_, pack_size_, chan_blk_, width_,
size_nbits_, LayoutType::NHWC> {
public:
using Type = Type_;
static constexpr int pack_size = pack_size_;
static constexpr int chan_blk = chan_blk_;
static constexpr int width = width_;
static constexpr int size_nbits = size_nbits_;
static constexpr int elements_in_type =
chan_blk * width * size_nbits / (8 * sizeof(Type));
static constexpr int lane_size_in_type =
(width * pack_size * size_nbits) / (8 * sizeof(Type));
static constexpr int pack_size_in_type =
pack_size * size_nbits / (8 * sizeof(Type));
static constexpr int pack_size_in_byte = pack_size_in_type * sizeof(Type);
static constexpr int accesses = elements_in_type / pack_size_in_type;
static constexpr int mask_size = (accesses + 32 - 1) / 32;
using AccessType = array_wrapper<Type, pack_size_in_type>;
using Fragment = array_wrapper<Type, elements_in_type>;
MEGDNN_HOST MaskedTensorIteratorOverChannel()
: pointer{nullptr}, hw_stride_in_elements{0}, channel{0} {}
MEGDNN_HOST MaskedTensorIteratorOverChannel(Type* pointer_,
int hw_stride_in_elements_,
int channel_, int bound_,
int div_)
: pointer{pointer_},
hw_stride_in_elements{hw_stride_in_elements_},
channel{channel_},
bound{bound_},
div{uint32_t(div_)} {}
MEGDNN_DEVICE __forceinline__ void initialize(int c_idx, int hw_idx) {
pointer += c_idx * size_nbits / (8 * sizeof(Type));
channel -= c_idx;
#pragma unroll
for (int i = 0; i < mask_size; ++i) {
mask[i] = 0;
}
#pragma unroll
for (int i = 0; i < width; ++i) {
int offset = hw_idx + i;
int h = (int)((uint32_t)(offset) / div);
int w = (int)((uint32_t)(offset) % div);
stride[i] = (h * bound + w) * hw_stride_in_elements;
#pragma unroll
for (int j = 0; j < chan_blk; j += pack_size) {
bool guard = (j < channel) && (w < bound);
int index = i * (chan_blk / pack_size) + (j / pack_size);
int mask_index = (index >> 5);
int mask_shift = (index & 0x1f);
mask[mask_index] |= (guard << mask_shift);
}
}
}
MEGDNN_DEVICE __forceinline__ void add_pointer_offset(
size_t offset_in_type) {
pointer += offset_in_type;
}
MEGDNN_DEVICE __forceinline__ void load(Fragment& frag, int zero_point) {
AccessType* frag_ptr = reinterpret_cast<AccessType*>(&frag);
#pragma unroll
for (int i = 0; i < width; ++i) {
Type* pointer_ = pointer + stride[i];
#pragma unroll
for (int j = 0; j < chan_blk; j+= pack_size) {
int frag_idx = i * (chan_blk / pack_size) + (j / pack_size);
int mask_index = (frag_idx >> 5);
int mask_shift = (frag_idx & 0x1f);
bool guard = (mask[mask_index] & (1 << mask_shift));
global_load<AccessType, pack_size_in_byte>(
frag_ptr[frag_idx],
reinterpret_cast<void*>(
pointer_ + j * size_nbits / (8 * sizeof(Type))),
guard, zero_point);
}
}
}
MEGDNN_DEVICE __forceinline__ void store(const Fragment& frag) {
const AccessType* frag_ptr = reinterpret_cast<const AccessType*>(&frag);
#pragma unroll
for (int i = 0; i < width; ++i) {
Type* pointer_ = pointer + stride[i];
#pragma unroll
for (int j = 0; j < chan_blk; j+= pack_size) {
int frag_idx = i * (chan_blk / pack_size) + (j / pack_size);
int mask_index = (frag_idx >> 5);
int mask_shift = (frag_idx & 0x1f);
bool guard = (mask[mask_index] & (1 << mask_shift));
global_store<AccessType, pack_size_in_byte>(
frag_ptr[frag_idx],
reinterpret_cast<void*>(
pointer_ + j * size_nbits / (8 * sizeof(Type))),
guard);
}
}
}
MEGDNN_DEVICE __forceinline__ void advance() {
pointer += chan_blk * size_nbits / (8 * sizeof(Type));
channel -= chan_blk;
}
private:
Type* pointer;
int hw_stride_in_elements;
int channel;
int bound;
Uint32Fastdiv div;
uint32_t mask[mask_size];
size_t stride[width];
};
template <bool padding_, typename Type_, int pack_size_, int chan_blk_,
int width_, int size_nbits_>
int width_, int size_nbits_,
uint32_t layout_type_ = LayoutType::NCHWx>
struct TensorIteratorPolicy;
template <typename Type_, int pack_size_, int chan_blk_, int width_,
int size_nbits_>
int size_nbits_, uint32_t layout_type_>
struct TensorIteratorPolicy<true, Type_, pack_size_, chan_blk_, width_,
size_nbits_> {
size_nbits_, layout_type_> {
using TensorIterator =
MaskedTensorIteratorOverChannel<Type_, pack_size_, chan_blk_,
width_, size_nbits_>;
width_, size_nbits_, layout_type_>;
};
template <typename Type_, int pack_size_, int chan_blk_, int width_,
int size_nbits_>
int size_nbits_, uint32_t layout_type_>
struct TensorIteratorPolicy<false, Type_, pack_size_, chan_blk_, width_,
size_nbits_> {
size_nbits_, layout_type_> {
using TensorIterator =
TensorIteratorOverChannel<Type_, pack_size_, chan_blk_, width_,
size_nbits_>;
size_nbits_, layout_type_>;
};
template <typename SrcIterator_, typename DstIterator_, typename Transpose_,
......
dnn/src/cuda/relayout_format/relayout_format_nchw_nhwc.cu 0 → 100644
浏览文件 @ 894a2407
/**
* \file dnn/src/cuda/relayout_format/relayout_format_nchw_nhwc.cu
* MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
*
* Copyright (c) 2014-2021 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 "src/cuda/query_blocksize.cuh"
#include "src/cuda/relayout_format/relayout_format_kern.cuh"
using namespace megdnn;
using namespace cuda;
using namespace relayout_format;
using namespace internal;
namespace {
template <int pack_w>
struct rwtype_helper;
template <>
struct rwtype_helper<2> {
using InnerDtype = char;
};
template <>
struct rwtype_helper<8> {
using InnerDtype = unsigned;
};
} // namespace
void relayout_format::relayout_format_cuda_nchw_nhwc(
const TensorND& src, const TensorND& dst, const cudaStream_t& stream,
const float src_scale, const float dst_scale,
const uint8_t src_zero_point, const uint8_t dst_zero_point) {
auto&& stype = src.layout.dtype;
auto&& dtype = dst.layout.dtype;
auto& src_layout = src.layout;
auto& dst_layout = dst.layout;
int n = src.layout[0];
int ic = src.layout[1];
int h = src.layout[2];
int w = src.layout[3];
int w_pad = DIVUP(w, 2) * 2;
int hw = h * w_pad;
int n_stride_src = src_layout.stride[0];
int ic_stride = src_layout.stride[1];
int n_stride_dst = dst_layout.stride[0];
int hw_stride = dst_layout.stride[2];
static constexpr int chan_blk = 8;
static constexpr int pack_oc = 8;
int problem_size = n * DIVUP(ic, chan_blk) * hw;
int oc = dst.layout[3];
bool same_scale = src_scale == dst_scale;
bool padding = w % 2 != 0;
#define DISPATCH_RAW(_padding, _same_scale, _pack_w, _src_type, _dst_type, \
_src_c_type, _dst_c_type, _size_nbits) \
if (padding == _padding && same_scale == _same_scale && \
hw % _pack_w == 0 && stype.enumv().ev == DTypeEnum::Ev::_src_type && \
dtype.enumv().ev == DTypeEnum::Ev::_dst_type) { \
using InnerDtype_ = typename rwtype_helper<_pack_w>::InnerDtype; \
using SrcIterator_ = \
TensorIteratorOverChannel<InnerDtype_, 1, chan_blk, _pack_w, \
_size_nbits>; \
using DstIterator_ = typename TensorIteratorPolicy< \
_padding, _dst_c_type, pack_oc, chan_blk, _pack_w, \
_size_nbits, LayoutType::NHWC>::TensorIterator; \
using CudaPostProcess_ = \
CudaPostProcess<dtype::_src_type, dtype::_dst_type, \
_same_scale>; \
using Transpose_ = \
Translayout<_pack_w, chan_blk, InnerDtype_, dtype::_src_type, \
dtype::_dst_type, _same_scale>; \
using RelayoutProblem_ = \
RelayoutProblem<SrcIterator_, DstIterator_, Transpose_, \
CudaPostProcess_>; \
n_stride_src = n_stride_src * _size_nbits / (8 * sizeof(InnerDtype_)); \
ic_stride = ic_stride * _size_nbits / (8 * sizeof(InnerDtype_)); \
n_stride_dst = n_stride_dst * _size_nbits / (8 * sizeof(_dst_c_type)); \
hw_stride = hw_stride * _size_nbits / (8 * sizeof(_dst_c_type)); \
typename RelayoutProblem_::Param param{ \
SrcIterator_{(InnerDtype_*)src.raw_ptr, ic_stride, ic, w, \
w_pad}, \
DstIterator_{(_dst_c_type*)dst.raw_ptr, hw_stride, oc, w, \
w_pad}, \
CudaPostProcess_{src_scale, src_zero_point, dst_scale, \
dst_zero_point}, \
n_stride_src, \
n_stride_dst, \
n, \
ic, \
hw, \
src_zero_point}; \
auto kernel = relayout_kern<RelayoutProblem_>; \
int nr_threads = query_blocksize_for_kernel(kernel); \
nr_threads = std::min(nr_threads, DIVUP(problem_size, _pack_w)); \
const dim3 block_dim(DIVUP(problem_size, nr_threads* _pack_w)); \
const dim3 thread_dim(nr_threads); \
return kernel<<<block_dim, thread_dim, 0, stream>>>(param); \
}
#define DISPATCH_4BITS(_src_type, _dst_type) \
DISPATCH_RAW(true, true, 8, _src_type, _dst_type, char, char, 4); \
DISPATCH_RAW(true, false, 8, _src_type, _dst_type, char, char, 4); \
DISPATCH_RAW(true, true, 2, _src_type, _dst_type, char, char, 4); \
DISPATCH_RAW(true, false, 2, _src_type, _dst_type, char, char, 4); \
DISPATCH_RAW(false, true, 8, _src_type, _dst_type, char, char, 4); \
DISPATCH_RAW(false, false, 8, _src_type, _dst_type, char, char, 4); \
DISPATCH_RAW(false, true, 2, _src_type, _dst_type, char, char, 4); \
DISPATCH_RAW(false, false, 2, _src_type, _dst_type, char, char, 4);
DISPATCH_4BITS(QuantizedS4, QuantizedS4);
DISPATCH_4BITS(Quantized4Asymm, Quantized4Asymm);
#undef DISPATCH_4BITS
#undef DISPATCH_RAW
megdnn_assert(false,
"Unsupported data type(src:%s, dst:%s) or image size(%dx%d).",
stype.name(), dtype.name(), h, w);
}
void relayout_format::relayout_format_cuda_nhwc_nchw(
const TensorND& src, const TensorND& dst, const cudaStream_t& stream,
const float src_scale, const float dst_scale,
const uint8_t src_zero_point, const uint8_t dst_zero_point) {
auto&& stype = src.layout.dtype;
auto&& dtype = dst.layout.dtype;
auto& src_layout = src.layout;
auto& dst_layout = dst.layout;
int n = src.layout[0];
int h = src.layout[1];
int w = src.layout[2];
int ic = src.layout[3];
int w_pad = DIVUP(w, 2) * 2;
int hw = h * w_pad;
int n_stride_src = src_layout.stride[0];
int hw_stride = src_layout.stride[2];
int n_stride_dst = dst_layout.stride[0];
int oc_stride = dst_layout.stride[1];
static constexpr int chan_blk = 8;
static constexpr int pack_oc = 8;
int problem_size = n * DIVUP(ic, chan_blk) * hw;
int oc = dst.layout[1];
bool same_scale = src_scale == dst_scale;
bool padding = w % 2 != 0;
#define DISPATCH_RAW(_padding, _same_scale, _pack_w, _src_type, _dst_type, \
_src_c_type, _dst_c_type, _size_nbits) \
if (padding == _padding && same_scale == _same_scale && \
hw % _pack_w == 0 && stype.enumv().ev == DTypeEnum::Ev::_src_type && \
dtype.enumv().ev == DTypeEnum::Ev::_dst_type) { \
using SrcIterator_ = typename TensorIteratorPolicy< \
_padding, _src_c_type, pack_oc, chan_blk, _pack_w, \
_size_nbits, LayoutType::NHWC>::TensorIterator; \
using InnerDtype_ = typename rwtype_helper<_pack_w>::InnerDtype; \
using DstIterator_ = \
TensorIteratorOverChannel<InnerDtype_, 1, chan_blk, _pack_w, \
_size_nbits>; \
using CudaPostProcess_ = \
CudaPostProcess<dtype::_src_type, dtype::_dst_type, \
_same_scale>; \
using Transpose_ = \
Translayout<chan_blk, _pack_w, _src_c_type, dtype::_src_type, \
dtype::_dst_type, _same_scale>; \
using RelayoutProblem_ = \
RelayoutProblem<SrcIterator_, DstIterator_, Transpose_, \
CudaPostProcess_>; \
n_stride_src = n_stride_src * _size_nbits / (8 * sizeof(_src_c_type)); \
hw_stride = hw_stride * _size_nbits / (8 * sizeof(_src_c_type)); \
n_stride_dst = n_stride_dst * _size_nbits / (8 * sizeof(InnerDtype_)); \
oc_stride = oc_stride * _size_nbits / (8 * sizeof(InnerDtype_)); \
typename RelayoutProblem_::Param param{ \
SrcIterator_{(_src_c_type*)src.raw_ptr, hw_stride, ic, w, \
w_pad}, \
DstIterator_{(InnerDtype_*)dst.raw_ptr, oc_stride, oc, w, \
w_pad}, \
CudaPostProcess_{src_scale, src_zero_point, dst_scale, \
dst_zero_point}, \
n_stride_src, \
n_stride_dst, \
n, \
ic, \
hw, \
src_zero_point}; \
auto kernel = relayout_kern<RelayoutProblem_>; \
int nr_threads = query_blocksize_for_kernel(kernel); \
nr_threads = std::min(nr_threads, DIVUP(problem_size, _pack_w)); \
const dim3 block_dim(DIVUP(problem_size, nr_threads* _pack_w)); \
const dim3 thread_dim(nr_threads); \
return kernel<<<block_dim, thread_dim, 0, stream>>>(param); \
}
#define DISPATCH_4BITS(_src_type, _dst_type) \
DISPATCH_RAW(true, true, 8, _src_type, _dst_type, char, char, 4); \
DISPATCH_RAW(true, false, 8, _src_type, _dst_type, char, char, 4); \
DISPATCH_RAW(true, true, 2, _src_type, _dst_type, char, char, 4); \
DISPATCH_RAW(true, false, 2, _src_type, _dst_type, char, char, 4); \
DISPATCH_RAW(false, true, 8, _src_type, _dst_type, char, char, 4); \
DISPATCH_RAW(false, false, 8, _src_type, _dst_type, char, char, 4); \
DISPATCH_RAW(false, true, 2, _src_type, _dst_type, char, char, 4); \
DISPATCH_RAW(false, false, 2, _src_type, _dst_type, char, char, 4);
DISPATCH_4BITS(QuantizedS4, QuantizedS4);
DISPATCH_4BITS(Quantized4Asymm, Quantized4Asymm);
#undef DISPATCH_4BITS
#undef DISPATCH_RAW
megdnn_assert(false,
"Unsupported data type(src:%s, dst:%s) or image size(%dx%d).",
stype.name(), dtype.name(), h, w);
}
dnn/src/cuda/relayout_format/translayout.cuh
浏览文件 @ 894a2407
......@@ -42,8 +42,9 @@ struct enable_qtype_b4 {
static constexpr bool val_dst =
std::is_same<dt_dst, dtype::QuantizedS4>::value ||
std::is_same<dt_dst, dtype::Quantized4Asymm>::value;
using type = typename std::enable_if<std::is_same<dt_src, dt_dst>::value &&
val_src && val_dst>::type;
static constexpr bool value =
std::is_same<dt_src, dt_dst>::value && val_src && val_dst;
using type = typename std::enable_if<value>::type;
};
// The input fragment is stored in RowMajor order. The translayout operator
......@@ -393,26 +394,32 @@ struct Translayout<2, 8, SrcType, DnnSrcType_, DnnDstType_, same_scale,
using Fragment = array_wrapper<SrcType, elements_in_type>;
static inline __device__ void trans(
Fragment& dst, const Fragment& src,
CudaPostProcess<DnnSrcType, DnnDstType, same_scale>& post_process,
const char zero_point) {
CudaPostProcess<DnnSrcType, DnnDstType, same_scale>& post_process) {
int intermediate[8][2];
transform_b4x2_to_int8<signedness>(intermediate[0],
reinterpret_cast<uint8_t&>(src[0]));
transform_b4x2_to_int8<signedness>(intermediate[1],
reinterpret_cast<uint8_t&>(src[1]));
transform_b4x2_to_int8<signedness>(intermediate[2],
reinterpret_cast<uint8_t&>(src[2]));
transform_b4x2_to_int8<signedness>(intermediate[3],
reinterpret_cast<uint8_t&>(src[3]));
transform_b4x2_to_int8<signedness>(intermediate[4],
reinterpret_cast<uint8_t&>(src[4]));
transform_b4x2_to_int8<signedness>(intermediate[5],
reinterpret_cast<uint8_t&>(src[5]));
transform_b4x2_to_int8<signedness>(intermediate[6],
reinterpret_cast<uint8_t&>(src[6]));
transform_b4x2_to_int8<signedness>(intermediate[7],
reinterpret_cast<uint8_t&>(src[7]));
transform_b4x2_to_int8<signedness>(
intermediate[0],
reinterpret_cast<const uint8_t&>(src[0 * col_in_type]));
transform_b4x2_to_int8<signedness>(
intermediate[1],
reinterpret_cast<const uint8_t&>(src[1 * col_in_type]));
transform_b4x2_to_int8<signedness>(
intermediate[2],
reinterpret_cast<const uint8_t&>(src[2 * col_in_type]));
transform_b4x2_to_int8<signedness>(
intermediate[3],
reinterpret_cast<const uint8_t&>(src[3 * col_in_type]));
transform_b4x2_to_int8<signedness>(
intermediate[4],
reinterpret_cast<const uint8_t&>(src[4 * col_in_type]));
transform_b4x2_to_int8<signedness>(
intermediate[5],
reinterpret_cast<const uint8_t&>(src[5 * col_in_type]));
transform_b4x2_to_int8<signedness>(
intermediate[6],
reinterpret_cast<const uint8_t&>(src[6 * col_in_type]));
transform_b4x2_to_int8<signedness>(
intermediate[7],
reinterpret_cast<const uint8_t&>(src[7 * col_in_type]));
int* dst_frag = reinterpret_cast<int*>(&dst);
auto pack = [&](int idx) -> int {
return transform_int8_to_b4x8<signedness>(
......@@ -445,25 +452,24 @@ struct Translayout<8, 8, SrcType, DnnSrcType_, DnnDstType_, same_scale,
using Fragment = array_wrapper<SrcType, elements_in_type>;
static inline __device__ void trans(
Fragment& dst, const Fragment& src,
CudaPostProcess<DnnSrcType, DnnDstType, same_scale>& post_process,
const char zero_point) {
CudaPostProcess<DnnSrcType, DnnDstType, same_scale>& post_process) {
int intermediate[8][8];
transform_b4x8_to_int8<signedness>(
intermediate[0], reinterpret_cast<const int&>(src[0]));
intermediate[0], reinterpret_cast<const int&>(src[0 * col_in_type]));
transform_b4x8_to_int8<signedness>(
intermediate[1], reinterpret_cast<const int&>(src[1]));
intermediate[1], reinterpret_cast<const int&>(src[1 * col_in_type]));
transform_b4x8_to_int8<signedness>(
intermediate[2], reinterpret_cast<const int&>(src[2]));
intermediate[2], reinterpret_cast<const int&>(src[2 * col_in_type]));
transform_b4x8_to_int8<signedness>(
intermediate[3], reinterpret_cast<const int&>(src[3]));
intermediate[3], reinterpret_cast<const int&>(src[3 * col_in_type]));
transform_b4x8_to_int8<signedness>(
intermediate[4], reinterpret_cast<const int&>(src[4]));
intermediate[4], reinterpret_cast<const int&>(src[4 * col_in_type]));
transform_b4x8_to_int8<signedness>(
intermediate[5], reinterpret_cast<const int&>(src[5]));
intermediate[5], reinterpret_cast<const int&>(src[5 * col_in_type]));
transform_b4x8_to_int8<signedness>(
intermediate[6], reinterpret_cast<const int&>(src[6]));
intermediate[6], reinterpret_cast<const int&>(src[6 * col_in_type]));
transform_b4x8_to_int8<signedness>(
intermediate[7], reinterpret_cast<const int&>(src[7]));
intermediate[7], reinterpret_cast<const int&>(src[7 * col_in_type]));
int* dst_frag = reinterpret_cast<int*>(&dst);
auto pack = [&](int idx) {
return transform_int8_to_b4x8<signedness>(
......@@ -502,13 +508,12 @@ struct Translayout<8, 2, SrcType, DnnSrcType_, DnnDstType_, same_scale,
using Fragment = array_wrapper<SrcType, elements_in_type>;
static inline __device__ void trans(
Fragment& dst, const Fragment& src,
CudaPostProcess<DnnSrcType, DnnDstType, same_scale>& post_process,
const char zero_point) {
CudaPostProcess<DnnSrcType, DnnDstType, same_scale>& post_process) {
int intermediate[2][8];
transform_b4x8_to_int8<signedness>(
intermediate[0], reinterpret_cast<const int&>(src[0]));
intermediate[0], reinterpret_cast<const int&>(src[0 * col_in_type]));
transform_b4x8_to_int8<signedness>(
intermediate[1], reinterpret_cast<const int&>(src[1]));
intermediate[1], reinterpret_cast<const int&>(src[1 * col_in_type]));
int* dst_frag = reinterpret_cast<int*>(&dst);
dst_frag[0] = transform_int8_to_b4x8<signedness>(
post_process(intermediate[0][0]),
......
dnn/src/cuda/warp_perspective/forward.cu
浏览文件 @ 894a2407
......@@ -508,7 +508,7 @@ struct KernCoreNHWC<ctype, OutputConverter, 8> {
"assert qu4 or q4");
constexpr bool signedness = std::is_same<ctype, dt_qint4>::value;
int8_t bval_4 = bval.as_storage() & 0xF;
const int bval_int = transform_int8_to_bit4x8<signedness>(
const int bval_int = transform_int8_to_b4x8<signedness>(
bval_4, bval_4, bval_4, bval_4, bval_4, bval_4, bval_4, bval_4);
int src_ori[4];
src_ori[0] = src0_ok ? *(int*)(src_ptr0 + offset) : bval_int;
......@@ -516,10 +516,10 @@ struct KernCoreNHWC<ctype, OutputConverter, 8> {
src_ori[2] = src2_ok ? *(int*)(src_ptr2 + offset) : bval_int;
src_ori[3] = src3_ok ? *(int*)(src_ptr3 + offset) : bval_int;
int src[4][8];
transform_bit4x8_to_int8<signedness>(src[0], src_ori[0]);
transform_bit4x8_to_int8<signedness>(src[1], src_ori[1]);
transform_bit4x8_to_int8<signedness>(src[2], src_ori[2]);
transform_bit4x8_to_int8<signedness>(src[3], src_ori[3]);
transform_b4x8_to_int8<signedness>(src[0], src_ori[0]);
transform_b4x8_to_int8<signedness>(src[1], src_ori[1]);
transform_b4x8_to_int8<signedness>(src[2], src_ori[2]);
transform_b4x8_to_int8<signedness>(src[3], src_ori[3]);
int res = pack_output_func<signedness>(output_converter, src[0], src[1],
src[2], src[3], w00, w01, w10,
w11);
......@@ -542,7 +542,7 @@ struct KernCoreNHWC<ctype, OutputConverter, 16> {
"assert qu4 or q4");
constexpr bool signedness = std::is_same<ctype, dt_qint4>::value;
int8_t bval_4 = bval.as_storage() & 0xF;
const int bval_int_temp = transform_int8_to_bit4x8<signedness>(
const int bval_int_temp = transform_int8_to_b4x8<signedness>(
bval_4, bval_4, bval_4, bval_4, bval_4, bval_4, bval_4, bval_4);
const int2 bval_int{bval_int_temp, bval_int_temp};
......@@ -552,15 +552,15 @@ struct KernCoreNHWC<ctype, OutputConverter, 16> {
src_ori[2] = src2_ok ? *(int2*)(src_ptr2 + offset) : bval_int;
src_ori[3] = src3_ok ? *(int2*)(src_ptr3 + offset) : bval_int;
int src[8][8];
transform_bit4x8_to_int8<signedness>(src[0], src_ori[0].x);
transform_bit4x8_to_int8<signedness>(src[1], src_ori[1].x);
transform_bit4x8_to_int8<signedness>(src[2], src_ori[2].x);
transform_bit4x8_to_int8<signedness>(src[3], src_ori[3].x);
transform_bit4x8_to_int8<signedness>(src[4], src_ori[0].y);
transform_bit4x8_to_int8<signedness>(src[5], src_ori[1].y);
transform_bit4x8_to_int8<signedness>(src[6], src_ori[2].y);
transform_bit4x8_to_int8<signedness>(src[7], src_ori[3].y);
transform_b4x8_to_int8<signedness>(src[0], src_ori[0].x);
transform_b4x8_to_int8<signedness>(src[1], src_ori[1].x);
transform_b4x8_to_int8<signedness>(src[2], src_ori[2].x);
transform_b4x8_to_int8<signedness>(src[3], src_ori[3].x);
transform_b4x8_to_int8<signedness>(src[4], src_ori[0].y);
transform_b4x8_to_int8<signedness>(src[5], src_ori[1].y);
transform_b4x8_to_int8<signedness>(src[6], src_ori[2].y);
transform_b4x8_to_int8<signedness>(src[7], src_ori[3].y);
int2 res;
res.x = pack_output_func<signedness>(output_converter, src[0], src[1],
......
dnn/test/cuda/relayout_format.cpp
浏览文件 @ 894a2407
......@@ -325,6 +325,91 @@ TEST_F(CUDA, RELAYOUT_FORMAT_NCHW64_NCHW) {
}
}
TEST_F(CUDA, RELAYOUT_FORMAT_NCHW_NHWC) {
Checker<RelayoutFormat> checker(handle_cuda());
UniformIntRNG s4{-8, 7};
UniformIntRNG u4{0, 15};
param::RelayoutFormat param;
param.mode = param::RelayoutFormat::Mode::NCHW_NHWC;
for (size_t n : {1, 3}) {
for (size_t c : {8, 16}) {
for (size_t h : {7, 14, 16, 28}) {
for (size_t w : {2, 3, 7, 8, 16, 31}) {
checker.set_dtype(0, dtype::QuantizedS4{2.f})
.set_dtype(1, dtype::QuantizedS4{2.f})
.set_rng(0, &s4)
.set_param(param)
.execs({{n, c, h, w}, {}});
checker.set_dtype(0, dtype::Quantized4Asymm{1.2f, 8})
.set_dtype(1, dtype::Quantized4Asymm{1.2f, 4})
.set_rng(0, &u4)
.set_param(param)
.execs({{n, c, h, w}, {}});
checker.set_dtype(0, dtype::QuantizedS4{1.19990307f})
.set_dtype(1, dtype::QuantizedS4{1.f})
.set_rng(0, &s4)
.set_param(param)
.execs({{n, c, h, w}, {}});
checker.set_dtype(0, dtype::Quantized4Asymm{1.19990307f, 8})
.set_dtype(1, dtype::Quantized4Asymm{1.f, 4})
.set_rng(0, &u4)
.set_param(param)
.set_epsilon(1e-3)
.execs({{n, c, h, w}, {}});
}
}
}
}
checker.execs({{1, 256, 384, 640}, {}});
}
TEST_F(CUDA, RELAYOUT_FORMAT_NHWC_NCHW) {
Checker<RelayoutFormat> checker(handle_cuda());
UniformIntRNG s4{-8, 7};
UniformIntRNG u4{0, 15};
param::RelayoutFormat param;
param.mode = param::RelayoutFormat::Mode::NHWC_NCHW;
for (size_t n : {1, 3}) {
for (size_t c : {8, 16}) {
for (size_t h : {7, 14, 16, 28}) {
for (size_t w : {2, 3, 4, 7, 14, 16, 17}) {
checker.set_dtype(0, dtype::QuantizedS4{2.f})
.set_dtype(1, dtype::QuantizedS4{2.f})
.set_rng(0, &s4)
.set_param(param)
.set_epsilon(1e-3)
.execs({{n, h, w, c}, {}});
checker.set_dtype(0, dtype::Quantized4Asymm{1.2f, 4})
.set_dtype(1, dtype::Quantized4Asymm{1.2f, 8})
.set_rng(0, &u4)
.set_param(param)
.set_epsilon(1e-3)
.execs({{n, h, w, c}, {}});
checker.set_dtype(0, dtype::QuantizedS4{1.19990307f})
.set_dtype(1, dtype::QuantizedS4{1.f})
.set_rng(0, &s4)
.set_param(param)
.set_epsilon(1e-3)
.execs({{n, h, w, c}, {}});
checker.set_dtype(0, dtype::Quantized4Asymm{1.20211209f, 8})
.set_dtype(1, dtype::Quantized4Asymm{1.f, 4})
.set_rng(0, &u4)
.set_param(param)
.set_epsilon(1e-3)
.execs({{n, h, w, c}, {}});
}
}
}
}
checker.execs({{1, 384, 640, 256}, {}});
}
#if MEGDNN_WITH_BENCHMARK
TEST_F(CUDA, BENCHMARK_RELAYOUT_FORMAT) {
using Param = RelayoutFormat::Param;
......@@ -393,6 +478,7 @@ TEST_F(CUDA, BENCHMARK_RELAYOUT_FORMAT_QS4) {
}
};
printf("nchw -> nchw64\n");
{
TensorShapeArray shapes = {
{1, 64, 56, 56}, {16, 64, 56, 56}, {64, 64, 56, 56},
......@@ -403,6 +489,18 @@ TEST_F(CUDA, BENCHMARK_RELAYOUT_FORMAT_QS4) {
param.mode = param::RelayoutFormat::Mode::NCHW_NCHW64;
run(shapes, param);
}
printf("nchw -> nhwc\n");
{
TensorShapeArray shapes = {
{1, 64, 56, 56}, {16, 64, 56, 56}, {64, 64, 56, 56},
{1, 64, 56, 55}, {16, 64, 56, 55}, {64, 64, 56, 55},
{1, 256, 384, 640}, {16, 16, 384, 640},
};
Param param;
param.mode = param::RelayoutFormat::Mode::NCHW_NHWC;
run(shapes, param);
}
printf("nchw64 -> nchw\n");
{
TensorShapeArray shapes = {
{64, 1, 56, 56, 64},
......@@ -415,6 +513,19 @@ TEST_F(CUDA, BENCHMARK_RELAYOUT_FORMAT_QS4) {
param.mode = param::RelayoutFormat::Mode::NCHW64_NCHW;
run(shapes, param);
}
printf("nhwc -> nchw\n");
{
TensorShapeArray shapes = {
{64, 56, 56, 64},
{1, 7, 7, 64*32},
{16, 7, 7, 64*32},
{64, 7, 7, 64*32},
{1, 384, 640, 64*4},
};
Param param;
param.mode = param::RelayoutFormat::Mode::NHWC_NCHW;
run(shapes, param);
}
}
#endif
......
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