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[GPU] Layout API improvement to replace direct named dimension usages from tensor (#9056)

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Vladimir Paramuzov 2022-01-13 17:02:13 +03:00 committed by GitHub
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336
src/plugins/intel_gpu/include/intel_gpu/runtime/format.hpp Normal file
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// Copyright (C) 2018-2021 Intel Corporation
// SPDX-License-Identifier: Apache-2.0
//
#pragma once
#include <map>
#include <list>
#include <cstdint>
#include <numeric>
#include <algorithm>
#include <vector>
#include <string>
#include <utility>
#include <stdexcept>
namespace cldnn {
/// @addtogroup cpp_api C++ API
/// @{
/// @addtogroup cpp_memory Memory description and management
/// @{
/// @brief Format information helper class.
struct format_traits {
/// @brief String representation of a format.
std::string str;
/// @brief Number of batch dimensions in a format.
size_t batch_num;
/// @brief Number of feature map/channel dimensions in a format.
size_t feature_num;
/// @brief Number of spatial (x,y) dimensions in a format.
size_t spatial_num;
/// @brief Number of groups in a format.
size_t group_num;
/// @brief Dimensions order. Default {0, 1, 2, ... rank }
std::vector<size_t> _order;
/// @brief Dimensions changing order from rare to often.
std::string order;
/// @brief Dimensions order for internal storage.
std::string internal_order;
/// @brief Block sizes as a vector of pairs of dimension number and block size ordered from rare to often.
std::vector<std::pair<size_t, int>> block_sizes;
/// @brief Characters representing batch dimensions in an order.
static const char* batch_chars() { return "bno"; }
/// @brief Characters representing feature map/channel dimensions in an order.
static const char* feature_chars() { return "fic"; }
/// @brief Characters representing spatial dimensions in an order.
static const char* spatial_chars() { return "xyzhsw"; }
/// @brief Characters representing group dimensions in an order.
static const char* group_chars() { return "g"; }
/// @brief Checks if @p c represents batch dimension.
static bool is_batch_char(char c) { return std::string(batch_chars()).find_first_of(c) != std::string::npos; }
/// @brief Checks if @p c represents feature map/channel dimension.
static bool is_feature_char(char c) { return std::string(feature_chars()).find_first_of(c) != std::string::npos; }
/// @brief Checks if @p c represents spatial dimension.
static bool is_spatial_char(char c) { return std::string(spatial_chars()).find_first_of(c) != std::string::npos; }
/// @brief Checks if @p c represents group dimensions.
static bool is_group_char(char c) { return std::string(group_chars()).find_first_of(c) != std::string::npos; }
};
/// @brief Represents memory formats (orders).
/// @n In CNN most of data is described as 4 dimensional blocks. In GPU plugin we describe memory with 4 letters
/// - b - number of blocks in batch. For weights formats: output features - conv, neurons - inner product
/// - f - number of feature maps, features or channels. For weights formats: input features - conv, inputs, inner product
/// - x - spatial, width
/// - y - spatial, height
/// /n
/// For explanation how each format type is implemented in memory we will use naming shown bellow:
struct format {
enum type : int32_t {
// Data formats
bfyx, ///< the most common format for activations in clDNN.
bfzyx, ///< format for 5d data tensors
bfwzyx, ///< batch, feature, 4D spatial
yxfb, ///< batch first, feature and than spatials
byxf, ///< used in bitmaps, input from user i.e b images of RGB format
fyxb, ///< format not used inside clDNN, but supported in reorder as extension
///< for user provided formats.
b_fs_yx_fsv16, ///< format used for blocked convolution
b_fs_yx_fsv32, ///< format used for blocked int8 convolution
b_fs_zyx_fsv16, ///< format used for 3D blocked convolution (features blocked by 16)
b_fs_zyx_fsv32, ///< format used for blocked int8 3d convolution
bs_fs_zyx_bsv16_fsv16, ///< format used for 3D blocked convolution (batch and features blocked by 16)
bs_fs_yx_bsv16_fsv16, ///< format used for 2D blocked convolution (batch and features blocked by 16)
bs_fs_yx_bsv4_fsv4, ///< format used for 2D blocked convolution (batch and features blocked by 4)
bs_fs_yx_bsv8_fsv4, ///< format used for 2D blocked convolution (batch and features blocked by 8 and 4)
bs_fs_yx_bsv4_fsv2, ///< format used for 2D blocked convolution (batch blocked by 4, features blocked by 2)
bs_fs_zyx_bsv4_fsv4, ///< format used for 3D blocked convolution (batch and features blocked by 4)
bs_fs_zyx_bsv4_fsv2, ///< format used for 3D blocked convolution (batch blocked by 4, features blocked by 2)
bs_fs_yx_bsv32_fsv32, ///< format used for big batches (batch and features blocked by 32)
bs_fs_yx_bsv32_fsv16, ///< format used for big batches (batch blocked by 32, features blocked by 16)
bs_fs_zyx_bsv32_fsv32, ///< format used for big batches (batch and features blocked by 32)
bs_fs_zyx_bsv32_fsv16, ///< format used for big batches (batch blocked by 32, features blocked by 16)
fs_b_yx_fsv32, ///< format for input for fp16 primitives
b_fs_yx_fsv4, ///< format for input for IMAD convolutions
bs_xs_xsv8_bsv8, ///< format used only for fully connected
bs_xs_xsv8_bsv16, ///< format used only for fully connected
bs_x_bsv16, ///< format used only for fully connected weights fp16 batch=1 : bs - batch slice
///< (responses slice), bsv16 - 16 values of single batch slice, x - flattened plane of (fyx)
b_fs_yx_32fp, ///< format for data for binary convolutions
winograd_2x3_s1_data, ///< format used for input for winograd convolution, F(2,3) -- filter 3x3 with stride 1
nv12, ///< format for media nv12 input
image_2d_rgba, ///< format for image2d RGBA, always allocates memory for 4 feature maps (even when only 3 are used)
// Weights formats
oiyx, ///< the most common format for 2D weights
ioyx, ///< 2D weights format for deconvolutions
yxio, ///< format used 2D weights
oizyx, ///< the most common format for 3D convolution
iozyx, ///< 3D weights format for deconvolutions
iyxo,
oyxi,
os_iyx_osv16, ///< format used only for convolution weights
o_is_yx_isv16, ///< format used only for convolution weights
os_yxi_osv16, ///< format used only for convolution weights
os_is_yx_osv16_isv16, ///< format used for convolution i8 weights
os_is_zyx_osv32_isv16,
os_is_zyx_osv64_isv16,
os_zyxi_osv16, ///< format used for weights for 3D convolution
os_is_yx_isv16_osv16, ///< format used for blocked convolution
os_is_zyx_isv16_osv16, ///< format used for weights for blocked 3D convolution
is_os_zyx_isv16_osv16, ///< format used for weights for blocked 3D deconvolution
is_os_yx_isv16_osv16, ///< format used for weights for blocked deconvolution
os_is_yx_isv8_osv16_isv2, ///< format used for weights for blocked 2D convolution
os_is_zyx_isv8_osv16_isv2, ///< format used for weights for blocked 3D convolution
///< os - output feature maps slice, i - input feature maps,
///< yx - spatials, sv16 - 16 values of single slice.
os_iyx_osv32, ///< format used only for convolution weights:
///< os - output feature maps slice, i - input feature maps,
///< yx - spatials, sv32 - 32 values of single slice.
os_iyx_osv64, ///< format used only for convolution weights:
///< os - output feature maps slice, i - input feature maps,
///< yx - spatials, sv64 - 64 values of single slice.
image_2d_weights_c4_fyx_b, ///< image format for weights, width size is f*y*x/4
///< (4-channels filled with fyx data), height is b
image_2d_weights_c1_b_fyx, ///< image format for weights, width size is b,
///< height is f*y*x, single channel
winograd_2x3_s1_weights, ///< format used for weights for winograd non-fused
///< convolution, F(2,3) -- filter 3x3 with stride 1
winograd_2x3_s1_fused_weights, ///< format used for weights for winograd fused
///< convolution, F(2,3) -- filter 3x3 with stride 1
winograd_6x3_s1_fused_weights, ///< format used for weights for winograd fused
///< convolution, F(6,3) -- filter 3x3 with stride 1
image_2d_weights_winograd_6x3_s1_fbxyb, ///< image format used for weights for winograd fused
///< convolution, F(6,3) -- filter 3x3 with stride 1
image_2d_weights_winograd_6x3_s1_xfbyb, ///< image format used for weights for winograd fused
///< convolution, F(6,3) -- filter 3x3 with stride 1
os_is_yx_isa8_osv8_isv4, ///< format for weights for MMAD convolution
os_is_zyx_isa8_osv8_isv4, ///< format for weights for MMAD convolution
os_is_yx_isa8_osv16_isv4, ///< format for weights for fully connected MMAD
os_is_zyx_isa8_osv16_isv4, ///< format for weights for fully connected MMAD
os_is_yx_isa8_osv8_isv4_swizzled_by_4, ///< format for weights for MMAD convolution
os_is_yx_osa4_isa8_osv8_isv4_swizzled_by_4, ///< format for weights for MMAD fsv32 convolution
os_is_zyx_osa4_isa8_osv8_isv4_swizzled_by_4, ///< format for weights for MMAD fsv32 convolution
os_is_yx_osa4_isa8_osv8_isv2, ///< format for weights for MMAD fsv32 convolution
os_is_zyx_osa4_isa8_osv8_isv2, ///< format for weights for MMAD fsv32 convolution
os_is_zyx_osa4_isa8_osv8_isv4, ///< format for weights for MMAD fsv32 convolution
os_is_yx_osa4_isa8_osv8_isv4, ///< format for weights for MMAD fsv32 convolution
os_is_yx_osa2_isa8_osv8_isv2,
os_is_yx_osa2_isa8_osv16_isv2,
os_is_yx_osa2_isa8_osv16_isv4,
is_os_yx_isa2_osa8_isv8_osv2,
is_os_yx_isa4_osa8_isv8_osv4,
is_o_yx_isv32, ///< format for weights for 1x1 MMAD convolutions
is_o32_yx_isv32_swizzled_by_4, ///< format for weights for 1x1 MMAD convolutions
os_is_y_x8_osv8_isv4, ///< format for weights for 1x1 MMAD convolutions
os_is_y_x8_osv8_isv4_swizzled_by_4, ///< format for weights for 1x1 MMAD convolutions
os_is_yx_osv16_isv4, ///< format for weights for IMAD convolutions
os_is_yx_osv8_isv4, ///< format used for convolution i8 weights
os_is_yx_osv8_isv2, ///< format used for convolution i8 weights
os_is_zyx_osv16_isv16, ///< format for weights for IMAD convolutions
os_is_yx_osv32_isv4_swizzled_by_2, ///< format for weights for IMAD convolutions
os_is_yx_osv32_isv4, ///< format for weights for IMAD convolutions
os_is_zyx_osv32_isv4, ///< format for weights for IMAD convolutions
os_is_yx_osv32_isv32p, ///< format for weights for binary convolutions
lstm_weights_dio, ///< dynamic_lstm, direction,
///< than IO (I - input size, O - 4 * hidden_size)
os_is_osv32_isv32_swizzled_by_4, ///< format for weights for 1x1 IMAD convolution
os_iyx_osv32__ai32,
iy_xs_os_xsv2_osv8__ao32,
iy_xs_os_xsv2_osv16__ao32,
i_yxs_os_yxsv2_osv16,
os_i_yxs_osv4_yxsv4,
os_i_osv16__ai8, ///< format used only for fully connected weights
os_i_osv8__ai8, ///< format used only for fully connected weights
goiyx, ///< format used for weights for 2D convolution
gioyx, ///< format used for weights for 2D deconvolution
gyxio, ///< format used for weights for 2D convolution
goizyx, ///< format used for weights for 3D convolution
giozyx, ///< format used for weights for 3D deconvolution
g_os_iyx_osv16, ///< format used for weights for 2D convolution
g_os_iyx_osv32, ///< format used for weights for 2D convolution
gs_oiyx_gsv16, ///< format used for weights for 2D convolution
gs_oizyx_gsv16, ///< format used for weights for 3D convolution
gs_oiyx_gsv32, ///< format used for weights for 2D convolution
g_is_os_zyx_isv16_osv16, ///< format used for grouped weights for blocked 3D deconvolution
g_os_is_yx_osv16_isv4,
g_os_is_zyx_osv16_isv16,
g_is_os_yx_isv16_osv16,
g_os_is_zyx_isv8_osv16_isv2,
g_os_is_yx_isv8_osv16_isv2,
g_os_is_zyx_isv16_osv16,
g_os_zyx_is_osv16_isv4, ///< format for imad deconvolution
g_os_zyx_is_osv16_isv16, ///< format for imad deconvolution
g_os_zyx_is_osv16_isv32, ///< format for imad deconvolution
g_os_zyx_is_osv32_isv4, ///< format for imad deconvolution
g_os_zyx_is_osv32_isv16, ///< format for imad deconvolution
g_os_zyx_is_osv32_isv32, ///< format for imad deconvolution
g_os_is_yx_isv16_osv16,
gs_oi_yxs_gsv4_yxsv4,
gs_oi_yxs_gsv16_yxsv4,
gs_oi_yxs_gsv32_yxsv4,
gi_yxs_os_yxsv2_osv16,
giy_xs_os_xsv2_osv8__ao32,
giy_xs_os_xsv2_osv16__ao32,
g_os_is_yx_osa4_isa8_osv8_isv4,
g_os_is_yx_osa4_isa8_osv8_isv2,
g_os_is_yx_osa2_isa8_osv16_isv2,
g_os_is_yx_osa2_isa8_osv16_isv4,
g_os_is_zyx_osa4_isa8_osv8_isv2,
g_os_is_zyx_osa4_isa8_osv8_isv4,
format_num, ///< number of format types
any = -1
};
/// @brief Get format traits for particular @p format::type
static const format_traits& traits(type fmt);
/// @brief Returns number of batch dimensions for a @p format.
static size_t batch_num(type fmt) { return traits(fmt).batch_num; }
/// @brief Returns number of feature dimensions for a @p format.
static size_t feature_num(type fmt) { return traits(fmt).feature_num; }
/// @brief Returns number of spatial dimensions for a @p format.
static size_t spatial_num(type fmt) { return traits(fmt).spatial_num; }
/// @brief Returns number of group dimensions for a @p format.
static size_t group_num(type fmt) { return traits(fmt).group_num; }
/// @brief Returns an order of dimensions for a @ format.
static const std::string& order(type fmt) { return traits(fmt).order; }
/// @brief Returns an internal orders of dimensions for a @p format.
static const std::string& internal_order(type fmt) { return traits(fmt).internal_order; }
/// @brief Returns block sizes for @p format.
static const std::vector<std::pair<size_t, int>>& block_sizes(type fmt) { return traits(fmt).block_sizes; }
/// @brief Returns number of dimensions contained within a @p format
static size_t dimension(type fmt) { return order(fmt).size(); }
/// @brief Checks if @p format is a winograd format
static bool is_winograd(type fmt) {
return (fmt == winograd_2x3_s1_data ||
fmt == winograd_2x3_s1_weights ||
fmt == winograd_2x3_s1_fused_weights ||
fmt == winograd_6x3_s1_fused_weights ||
fmt == image_2d_weights_winograd_6x3_s1_fbxyb ||
fmt == image_2d_weights_winograd_6x3_s1_xfbyb); }
/// @brief Checks if @p format is of image2d type
static bool is_image_2d(type fmt) {
return (fmt == image_2d_weights_c4_fyx_b ||
fmt == image_2d_weights_c1_b_fyx ||
fmt == image_2d_weights_winograd_6x3_s1_fbxyb ||
fmt == image_2d_weights_winograd_6x3_s1_xfbyb ||
fmt == nv12 ||
fmt == image_2d_rgba);
}
/// @brief Checks if @p format is weights format
static bool is_weights_format(type fmt) {
const auto internal_order = traits(fmt).internal_order;
const auto weights_chars = { "o", "i" };
for (const auto& c : weights_chars) {
if (internal_order.find_first_of(c) != std::string::npos) {
return true;
}
}
return false;
}
/// @brief Checks if @p format is simple data format
static bool is_simple_data_format(type fmt) {
return (fmt == yxfb || fmt == byxf ||
fmt == bfyx || fmt == fyxb ||
fmt == bfzyx || fmt == bfwzyx);
}
/// @brief Checks if @p format is of grouped type
static bool is_grouped(type fmt) { return group_num(fmt) != 0; }
/// @brief Checks if @p format is of image type
static bool is_image(type fmt) { return (is_image_2d(fmt)); }
/// @brief Checks if @p format is blocked format
static bool is_blocked(type fmt) { return !(block_sizes(fmt).empty()); }
/// @brief Checks if @p format is nv12 format
static bool is_nv12(type fmt) { return (fmt == nv12); }
/// @brief Returns number of batch dimensions.
size_t batch_num() const { return traits(value).batch_num; }
/// @brief Returns number of feature dimensions.
size_t feature_num() const { return traits(value).feature_num; }
/// @brief Returns number of spatial dimensions.
size_t spatial_num() const { return traits(value).spatial_num; }
/// @brief Returns number of group dimensions.
size_t group_num() const { return traits(value).group_num; }
/// @brief Returns an order of dimensions in form of string.
const std::string& order() const { return traits(value).order; }
/// @brief Returns an internal orders of dimensions form of string.
const std::string& internal_order() const { return traits(value).internal_order; }
/// @brief Returns block sizes as vector of pairs of dimension and block size for that dimension.
const std::vector<std::pair<size_t, int>>& block_sizes() const { return traits(value).block_sizes; }
/// @brief Returns number of dimensions contained within this format
size_t dimension() const { return order(value).size(); }
/// @brief Checks if @p format is a winograd format
bool is_winograd() const { return is_winograd(value); }
/// @brief Checks if @p format is of image 2d type
bool is_image_2d() const { return is_image_2d(value); }
/// @brief Checks if @p format is of image type
bool is_image() const { return is_image(value); }
/// @brief Checks if @p format is blocked format
bool is_blocked() { return is_blocked(value); }
/// @brief Checks if @p format is a nv12 format
bool is_nv12() const { return is_nv12(value); }
/// @brief Transforms dimension from internal order to external order
size_t internal_to_external(size_t idx) const {
auto index = order().find_first_of(internal_order()[idx]);
if (index == std::string::npos)
throw std::invalid_argument("Internal dimension index does not map to external index.");
return index;
}
type value;
/// @brief Implicit conversion from format::type.
constexpr format(type t) : value(t) {}
/// @brief Implicit conversion to format::type.
constexpr operator type() const { return value; }
std::string to_string() const;
};
/// @}
/// @}
} // namespace cldnn

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src/plugins/intel_gpu/include/intel_gpu/runtime/layout.hpp
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@ -497,13 +497,35 @@ struct layout {
/// Number of bytes needed to store this layout
size_t bytes_count() const { return data_type_traits::size_of(data_type) * get_linear_size(); }
bool has_fused_format(data_types const& dt, cldnn::format const& fmt) const {
return (data_type == dt && format == fmt);
}
size_t get_rank() const;
auto fused_format() const -> decltype(fuse(data_type, format)) {
return fuse(data_type, format);
}
size_t get_spatial_rank() const;
tensor::value_type get_dim(size_t idx) const;
tensor::value_type batch() const;
tensor::value_type feature() const;
tensor::value_type spatial(size_t spatial_idx) const;
tensor::value_type group() const;
tensor::value_type ofm() const;
tensor::value_type ifm() const;
std::vector<tensor::value_type> get_dims() const;
std::vector<tensor::value_type> get_padded_dims() const;
std::vector<tensor::value_type> get_ordered_dims() const;
std::vector<size_t> get_dims_order() const;
layout convert_to_weights_layout(bool is_grouped) const;
std::string to_string() const;
};
/// @}

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src/plugins/intel_gpu/include/intel_gpu/runtime/tensor.hpp
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@ -4,6 +4,7 @@
#pragma once
#include "format.hpp"
#include "compounds.hpp"
#include "utils.hpp"
@ -26,457 +27,6 @@ namespace cldnn {
/// @addtogroup cpp_memory Memory description and management
/// @{
/// @brief Format information helper class.
struct format_traits {
/// @brief Number of batch dimensions in a format.
size_t batch_num;
/// @brief Number of feature map/channel dimensions in a format.
size_t feature_num;
/// @brief Number of spatial (x,y) dimensions in a format.
size_t spatial_num;
/// @brief Number of groups in a format.
size_t group_num;
/// @brief Dimensions changing order from rare to often.
std::string order;
/// @brief Dimensions order for internal storage.
std::string internal_order;
/// @brief Block sizes as a vector of pairs of dimension number and block size ordered from rare to often.
std::vector<std::pair<size_t, int>> block_sizes;
/// @brief Characters representing batch dimensions in an order.
static const char* batch_chars() { return "bno"; }
/// @brief Characters representing feature map/channel dimensions in an order.
static const char* feature_chars() { return "fic"; }
/// @brief Characters representing spatial dimensions in an order.
static const char* spatial_chars() { return "xyzhsw"; }
/// @brief Characters representing group dimensions in an order.
static const char* group_chars() { return "g"; }
/// @brief Checks if @p c represents batch dimension.
static bool is_batch_char(char c) { return std::string(batch_chars()).find_first_of(c) != std::string::npos; }
/// @brief Checks if @p c represents feature map/channel dimension.
static bool is_feature_char(char c) { return std::string(feature_chars()).find_first_of(c) != std::string::npos; }
/// @brief Checks if @p c represents spatial dimension.
static bool is_spatial_char(char c) { return std::string(spatial_chars()).find_first_of(c) != std::string::npos; }
/// @brief Checks if @p c represents group dimensions.
static bool is_group_char(char c) { return std::string(group_chars()).find_first_of(c) != std::string::npos; }
};
/// @brief Represents memory formats (orders).
/// @n In CNN most of data is described as 4 dimensional blocks. In Intel(R) clDNN library we describe memory with 4 letters
/// - b - number of blocks in batch. For weights formats: output features - conv, neurons - inner product
/// - f - number of feature maps, features or channels. For weights formats: input features - conv, inputs, inner product
/// - x - spatial, width
/// - y - spatial, height
/// /n
/// For explanation how each format type is implemented in memory we will use naming shown bellow (b=2,f=3,y=3,x=3):
struct format {
enum type : int32_t {
// Data formats
bfyx, ///< the most common format for activations in clDNN.
bfzyx, ///< format for 5d data tensors
bfwzyx, ///< batch, feature, 4D spatial
yxfb, ///< batch first, feature and than spatials
byxf, ///< used in bitmaps, input from user i.e b images of RGB format
fyxb, ///< format not used inside clDNN, but supported in reorder as extension
///< for user provided formats.
b_fs_yx_fsv16, ///< format used for blocked convolution
b_fs_yx_fsv32, ///< format used for blocked int8 convolution
b_fs_zyx_fsv16, ///< format used for 3D blocked convolution (features blocked by 16)
b_fs_zyx_fsv32, ///< format used for blocked int8 3d convolution
bs_fs_zyx_bsv16_fsv16, ///< format used for 3D blocked convolution (batch and features blocked by 16)
bs_fs_yx_bsv16_fsv16, ///< format used for 2D blocked convolution (batch and features blocked by 16)
bs_fs_yx_bsv4_fsv4, ///< format used for 2D blocked convolution (batch and features blocked by 4)
bs_fs_yx_bsv8_fsv4, ///< format used for 2D blocked convolution (batch and features blocked by 8 and 4)
bs_fs_yx_bsv4_fsv2, ///< format used for 2D blocked convolution (batch blocked by 4, features blocked by 2)
bs_fs_zyx_bsv4_fsv4, ///< format used for 3D blocked convolution (batch and features blocked by 4)
bs_fs_zyx_bsv4_fsv2, ///< format used for 3D blocked convolution (batch blocked by 4, features blocked by 2)
bs_fs_yx_bsv32_fsv32, ///< format used for big batches (batch and features blocked by 32)
bs_fs_yx_bsv32_fsv16, ///< format used for big batches (batch blocked by 32, features blocked by 16)
bs_fs_zyx_bsv32_fsv32, ///< format used for big batches (batch and features blocked by 32)
bs_fs_zyx_bsv32_fsv16, ///< format used for big batches (batch blocked by 32, features blocked by 16)
fs_b_yx_fsv32, ///< format for input for fp16 primitives
b_fs_yx_fsv4, ///< format for input for IMAD convolutions
bs_xs_xsv8_bsv8, ///< format used only for fully connected weights: bs - batch slice,
///< xs - x slice, bsv8 - 8 values of single slice.
bs_xs_xsv8_bsv16, ///< format used only for fully connected weights: bs - batch slice,
///< xs - x slice, bsv16 - 16 values of single slice.
bs_x_bsv16, ///< format used only for fully connected weights fp16 batch=1 : bs - batch slice
///< (responses slice), bsv16 - 16 values of single batch slice, x - flattened plane of (fyx)
b_fs_yx_32fp, ///< format for data for binary convolutions
winograd_2x3_s1_data, ///< format used for input for winograd convolution, F(2,3) -- filter 3x3 with stride 1
nv12, ///< format for media nv12 input
image_2d_rgba, ///< format for image2d RGBA, always allocates memory for 4 feature maps (even when only 3 are used)
// Weights formats
oiyx, ///< the most common format for 2D weights
ioyx, ///< 2D weights format for deconvolutions
yxio, ///< format used 2D weights
oizyx, ///< the most common format for 3D convolution
iozyx, ///< 3D weights format for deconvolutions
iyxo,
os_iyx_osv16, ///< format used only for convolution weights
o_is_yx_isv16, ///< format used only for convolution weights
os_yxi_osv16, ///< format used only for convolution weights
os_is_yx_osv16_isv16, ///< format used for convolution i8 weights
os_is_zyx_osv32_isv16,
os_is_zyx_osv64_isv16,
os_zyxi_osv16, ///< format used for weights for 3D convolution
os_is_yx_isv16_osv16, ///< format used for blocked convolution
os_is_zyx_isv16_osv16, ///< format used for weights for blocked 3D convolution
is_os_zyx_isv16_osv16, ///< format used for weights for blocked 3D deconvolution
is_os_yx_isv16_osv16, ///< format used for weights for blocked deconvolution
os_is_yx_isv8_osv16_isv2, ///< format used for weights for blocked 2D convolution
os_is_zyx_isv8_osv16_isv2, ///< format used for weights for blocked 3D convolution
///< os - output feature maps slice, i - input feature maps,
///< yx - spatials, sv16 - 16 values of single slice.
os_iyx_osv32, ///< format used only for convolution weights:
///< os - output feature maps slice, i - input feature maps,
///< yx - spatials, sv32 - 32 values of single slice.
os_iyx_osv64, ///< format used only for convolution weights:
///< os - output feature maps slice, i - input feature maps,
///< yx - spatials, sv64 - 64 values of single slice.
image_2d_weights_c4_fyx_b, ///< image format for weights, width size is f*y*x/4
///< (4-channels filled with fyx data), height is b
image_2d_weights_c1_b_fyx, ///< image format for weights, width size is b,
///< height is f*y*x, single channel
winograd_2x3_s1_weights, ///< format used for weights for winograd non-fused
///< convolution, F(2,3) -- filter 3x3 with stride 1
winograd_2x3_s1_fused_weights, ///< format used for weights for winograd fused
///< convolution, F(2,3) -- filter 3x3 with stride 1
winograd_6x3_s1_fused_weights, ///< format used for weights for winograd fused
///< convolution, F(6,3) -- filter 3x3 with stride 1
image_2d_weights_winograd_6x3_s1_fbxyb, ///< image format used for weights for winograd fused
///< convolution, F(6,3) -- filter 3x3 with stride 1
image_2d_weights_winograd_6x3_s1_xfbyb, ///< image format used for weights for winograd fused
///< convolution, F(6,3) -- filter 3x3 with stride 1
os_is_yx_isa8_osv8_isv4, ///< format for weights for MMAD convolution
os_is_zyx_isa8_osv8_isv4, ///< format for weights for MMAD convolution
os_is_yx_isa8_osv16_isv4, ///< format for weights for fully connected MMAD
os_is_zyx_isa8_osv16_isv4, ///< format for weights for fully connected MMAD
os_is_yx_isa8_osv8_isv4_swizzled_by_4, ///< format for weights for MMAD convolution
os_is_yx_osa4_isa8_osv8_isv4_swizzled_by_4, ///< format for weights for MMAD fsv32 convolution
os_is_zyx_osa4_isa8_osv8_isv4_swizzled_by_4, ///< format for weights for MMAD fsv32 convolution
os_is_yx_osa4_isa8_osv8_isv2, ///< format for weights for MMAD fsv32 convolution
os_is_zyx_osa4_isa8_osv8_isv2, ///< format for weights for MMAD fsv32 convolution
os_is_zyx_osa4_isa8_osv8_isv4, ///< format for weights for MMAD fsv32 convolution
os_is_yx_osa4_isa8_osv8_isv4, ///< format for weights for MMAD fsv32 convolution
os_is_yx_osa2_isa8_osv8_isv2,
os_is_yx_osa2_isa8_osv16_isv2,
os_is_yx_osa2_isa8_osv16_isv4,
is_os_yx_isa2_osa8_isv8_osv2,
is_os_yx_isa4_osa8_isv8_osv4, ///< format for weights for MMAD fsv32 convolution
is_o_yx_isv32, ///< format for weights for 1x1 MMAD convolutions
is_o32_yx_isv32_swizzled_by_4, ///< format for weights for 1x1 MMAD convolutions
os_is_y_x8_osv8_isv4, ///< format for weights for 1x1 MMAD convolutions
os_is_y_x8_osv8_isv4_swizzled_by_4, ///< format for weights for 1x1 MMAD convolutions
os_is_yx_osv16_isv4, ///< format for weights for IMAD convolutions
os_is_yx_osv8_isv4, ///< format used for convolution i8 weights
os_is_yx_osv8_isv2, ///< format used for convolution i8 weights
os_is_zyx_osv16_isv16, ///< format for weights for IMAD convolutions
os_is_yx_osv32_isv4_swizzled_by_2, ///< format for weights for IMAD convolutions
os_is_yx_osv32_isv4, ///< format for weights for IMAD convolutions
os_is_zyx_osv32_isv4, ///< format for weights for IMAD convolutions
os_is_yx_osv32_isv32p, ///< format for weights for binary convolutions
lstm_weights_dio, ///< dynamic_lstm, direction,
///< than IO (I - input size, O - 4 * hidden_size)
os_is_osv32_isv32_swizzled_by_4, ///< format for weights for 1x1 IMAD convolution
os_iyx_osv32__ai32,
iy_xs_os_xsv2_osv8__ao32,
iy_xs_os_xsv2_osv16__ao32,
i_yxs_os_yxsv2_osv16,
os_i_yxs_osv4_yxsv4,
goiyx, ///< format used for weights for 2D convolution
gioyx, ///< format used for weights for 2D deconvolution
yxiog, ///< format used for weights for 2D convolution
gyxio, ///< format used for weights for 2D convolution
goizyx, ///< format used for weights for 3D convolution
giozyx, ///< format used for weights for 3D deconvolution
g_os_iyx_osv16, ///< format used for weights for 2D convolution
g_os_iyx_osv32, ///< format used for weights for 2D convolution
gs_oiyx_gsv16, ///< format used for weights for 2D convolution
gs_oizyx_gsv16, ///< format used for weights for 3D convolution
gs_oiyx_gsv32, ///< format used for weights for 2D convolution
g_is_os_zyx_isv16_osv16, ///< format used for grouped weights for blocked 3D deconvolution
g_os_is_yx_osv16_isv4,
g_os_is_zyx_osv16_isv16,
g_is_os_yx_isv16_osv16,
g_os_is_zyx_isv8_osv16_isv2,
g_os_is_yx_isv8_osv16_isv2,
g_os_is_zyx_isv16_osv16,
g_os_zyx_is_osv16_isv4, ///< format for imad deconvolution
g_os_zyx_is_osv16_isv16, ///< format for imad deconvolution
g_os_zyx_is_osv16_isv32, ///< format for imad deconvolution
g_os_zyx_is_osv32_isv4, ///< format for imad deconvolution
g_os_zyx_is_osv32_isv16, ///< format for imad deconvolution
g_os_zyx_is_osv32_isv32, ///< format for imad deconvolution
g_os_is_yx_isv16_osv16,
gs_oi_yxs_gsv4_yxsv4,
gs_oi_yxs_gsv16_yxsv4,
gs_oi_yxs_gsv32_yxsv4,
gi_yxs_os_yxsv2_osv16,
giy_xs_os_xsv2_osv8__ao32,
giy_xs_os_xsv2_osv16__ao32,
g_os_is_yx_osa4_isa8_osv8_isv4,
g_os_is_yx_osa4_isa8_osv8_isv2,
g_os_is_yx_osa2_isa8_osv16_isv2,
g_os_is_yx_osa2_isa8_osv16_isv4,
g_os_is_zyx_osa4_isa8_osv8_isv2,
g_os_is_zyx_osa4_isa8_osv8_isv4,
format_num, ///< number of format types
any = -1
};
/// @brief Get format traits for particular @p format::type
static const format_traits& traits(type fmt) {
static const std::map<type, format_traits> traits {
// B - number of Batch dimensions
// F - number of Feature dimensions
// S - number of Spatial dimensions
// G - number of Group dimensions
// Order - dims changing order from rare to often
// Inner order - dims order for internal storage in _sizes array
// Block sizes - vector of pairs of dimension number (by inner order) and block size ordered from rare to often
// Format B F S G Order Inner order Block sizes
{ yxfb, { 1, 1, 2, 0, "yxfb", "bfxy?", {}}},
{ byxf, { 1, 1, 2, 0, "byxf", "bfxy?", {}}},
{ bfyx, { 1, 1, 2, 0, "bfyx", "bfxy?", {}}},
{ fyxb, { 1, 1, 2, 0, "fyxb", "bfxy?", {}}},
{ b_fs_yx_fsv16, { 1, 1, 2, 0, "bfyx", "bfxy", {{1, 16}}}},
{ b_fs_yx_fsv32, { 1, 1, 2, 0, "bfyx", "bfxy", {{1, 32}}}},
{ b_fs_zyx_fsv32, { 1, 1, 3, 0, "bfzyx", "bfxyz", {{1, 32}}}},
{ bs_xs_xsv8_bsv8, { 1, 1, 1, 0, "bx", "b?x??", {{2, 8}, {0, 8}}}},
{ bs_xs_xsv8_bsv16, { 1, 1, 1, 0, "bx", "b?x??", {{2, 8}, {0, 16}}}},
{ bs_x_bsv16, { 1, 1, 1, 0, "bx", "b?x??", {{0, 16}}}},
{ winograd_2x3_s1_data, { 1, 1, 2, 0, "bxyf", "bfxy?", {}}},
{ b_fs_yx_fsv4, { 1, 1, 2, 0, "bfyx", "bfxy?", {{1, 4}}}},
{ bfzyx, { 1, 1, 3, 0, "bfzyx", "bfxyz", {}}},
{ bfwzyx, { 1, 1, 4, 0, "bfwzyx", "bfxyzw", {}}},
{ fs_b_yx_fsv32, { 1, 1, 2, 0, "fbyx", "bfxy?", {{1, 32}}}},
{ b_fs_yx_32fp, { 1, 1, 2, 0, "bfyx", "bfxy?", {}}},
{ b_fs_zyx_fsv16, { 1, 1, 3, 0, "bfzyx", "bfxyz", {{1, 16}}}},
{ bs_fs_zyx_bsv16_fsv16, { 1, 1, 3, 0, "bfzyx", "bfxyz", {{0, 16 }, {1, 16}}}},
{ bs_fs_yx_bsv16_fsv16, { 1, 1, 2, 0, "bfyx", "bfxy?", {{0, 16 }, {1, 16}}}},
{ bs_fs_yx_bsv4_fsv4, { 1, 1, 2, 0, "bfyx", "bfxy?", {{0, 4 }, {1, 4}}}},
{ bs_fs_yx_bsv8_fsv4, { 1, 1, 2, 0, "bfyx", "bfxy?", {{0, 8 }, {1, 4}}}},
{ bs_fs_yx_bsv4_fsv2, { 1, 1, 2, 0, "bfyx", "bfxy?", {{0, 4 }, {1, 2}}}},
{ bs_fs_zyx_bsv4_fsv4, { 1, 1, 3, 0, "bfzyx", "bfxyz", {{0, 4 }, {1, 4}}}},
{ bs_fs_zyx_bsv4_fsv2, { 1, 1, 3, 0, "bfzyx", "bfxyz", {{0, 4 }, {1, 2}}}},
{ bs_fs_zyx_bsv32_fsv32, { 1, 1, 3, 0, "bfzyx", "bfxyz", {{0, 32 }, {1, 32}}}},
{ bs_fs_zyx_bsv32_fsv16, { 1, 1, 3, 0, "bfzyx", "bfxyz", {{0, 32 }, {1, 16}}}},
{ bs_fs_yx_bsv32_fsv32, { 1, 1, 2, 0, "bfyx", "bfxy?", {{0, 32 }, {1, 32}}}},
{ bs_fs_yx_bsv32_fsv16, { 1, 1, 2, 0, "bfyx", "bfxy?", {{0, 32 }, {1, 16}}}},
{ nv12, { 1, 1, 2, 0, "bfyx", "bfxy?", {}}},
{ image_2d_rgba, { 1, 1, 2, 0, "bfyx", "bfxy?", {}}},
{ oiyx, { 1, 1, 2, 0, "oiyx", "oixy", {}}},
{ ioyx, { 1, 1, 2, 0, "ioyx", "oixy", {}}},
{ iyxo, { 1, 1, 2, 0, "iyxo", "oixy", {}}},
{ yxio, { 1, 1, 2, 0, "yxio", "oixy?", {}}},
{ oizyx, { 1, 1, 3, 0, "oizyx", "oixyz", {}}},
{ iozyx, { 1, 1, 3, 0, "iozyx", "oixyz", {}}},
{ os_is_yx_isv16_osv16, { 1, 1, 2, 0, "oiyx", "oixy", {{1, 16}, {0, 16}}}},
{ o_is_yx_isv16, { 1, 1, 2, 0, "oiyx", "oixy?", {{1, 16}}}},
{ os_yxi_osv16, { 1, 1, 2, 0, "oyxi", "oixy?", {{0, 16}}}},
{ os_iyx_osv16, { 1, 1, 2, 0, "oiyx", "oixy?", {{0, 16}}}},
{ os_iyx_osv32, { 1, 1, 2, 0, "oiyx", "oixy?", {{0, 32}}}},
{ os_iyx_osv64, { 1, 1, 2, 0, "oiyx", "oixy?", {{0, 64}}}},
{ winograd_2x3_s1_weights, { 1, 1, 2, 0, "oiyx", "oixy?", {}}},
{ winograd_2x3_s1_fused_weights, { 1, 1, 2, 0, "xyio", "oixy?", {}}},
{ winograd_6x3_s1_fused_weights, { 1, 1, 2, 0, "xyio", "oixy?", {}}},
{ image_2d_weights_winograd_6x3_s1_fbxyb, { 1, 1, 2, 0, "xyio", "oixy?", {}}},
{ image_2d_weights_winograd_6x3_s1_xfbyb, { 1, 1, 2, 0, "xyio", "oixy?", {}}},
{ image_2d_weights_c4_fyx_b, { 1, 1, 2, 0, "oiyx", "oixy?", {}}},
{ image_2d_weights_c1_b_fyx, { 1, 1, 2, 0, "oiyx", "oixy?", {}}},
{ lstm_weights_dio, { 1, 1, 2, 0, "oixy", "oixy?", {}}},
{ os_is_yx_isa8_osv8_isv4, { 1, 1, 2, 0, "oiyx", "oixy?", {}}},
{ os_is_yx_isa8_osv16_isv4, { 1, 1, 2, 0, "oiyx", "oixy?", {}}},
{ os_is_yx_isa8_osv8_isv4_swizzled_by_4, { 1, 1, 2, 0, "oiyx", "oixy?", {}}},
{ os_is_yx_osa4_isa8_osv8_isv2, { 1, 1, 2, 0, "oiyx", "oixy?", {{0, 32}, {1, 16}}}},
{ os_is_yx_osa4_isa8_osv8_isv4, { 1, 1, 2, 0, "oiyx", "oixy", {{0, 32}, {1, 32}}}},
{ os_is_zyx_osa4_isa8_osv8_isv2, { 1, 1, 3, 0, "oizyx", "oixyz", {{0, 32}, {1, 16}}}},
{ os_is_zyx_osa4_isa8_osv8_isv4, { 1, 1, 3, 0, "oizyx", "oixyz", {{0, 32}, {1, 32}}}},
{ os_is_yx_osa2_isa8_osv8_isv2, { 1, 1, 2, 0, "oiyx", "oixy?", {{0, 16}, {1, 16}}}},
{ os_is_yx_osa2_isa8_osv16_isv2, { 1, 1, 2, 0, "oiyx", "oixy", {{0, 32}, {1, 16}}}},
{ os_is_yx_osa2_isa8_osv16_isv4, { 1, 1, 2, 0, "oiyx", "oixy", {{0, 32}, {1, 32}}}},
{ os_is_zyx_isa8_osv8_isv4, { 1, 1, 3, 0, "oizyx", "oixyz", {{1, 8}, {0, 8}, {1, 4}}}},
{ os_is_zyx_isa8_osv16_isv4, { 1, 1, 3, 0, "oizyx", "oixyz", {{1, 8}, {0, 16}, {1, 4}}}},
{ os_is_yx_osa4_isa8_osv8_isv4_swizzled_by_4, { 1, 1, 2, 0, "oiyx", "oixy?", {{0, 32}, {1, 32}}}},
{ os_is_zyx_osa4_isa8_osv8_isv4_swizzled_by_4, { 1, 1, 3, 0, "oizyx", "oixyz", {{0, 32}, {1, 32}}}},
{ is_os_yx_isa2_osa8_isv8_osv2, { 1, 1, 2, 0, "ioyx", "ioxy?", {{1, 16}, {0, 16}}}},
{ is_os_yx_isa4_osa8_isv8_osv4, { 1, 1, 2, 0, "ioyx", "ioxy?", {{1, 32}, {0, 32}}}},
{ is_o_yx_isv32, { 1, 1, 2, 0, "oyxi", "oixy?", {{1, 32}}}},
{ is_o32_yx_isv32_swizzled_by_4, { 1, 1, 2, 0, "oyxi", "oixy?", {}}},
{ os_is_y_x8_osv8_isv4, { 1, 1, 2, 0, "oyxi", "oixy?", {}}},
{ os_is_y_x8_osv8_isv4_swizzled_by_4, { 1, 1, 2, 0, "oyxi", "oixy?", {}}},
{ os_is_yx_osv16_isv4, { 1, 1, 2, 0, "oixy", "oixy?", {{0, 16}, {1, 4}}}},
{ os_is_yx_osv8_isv4, { 1, 1, 2, 0, "oiyx", "oixy", {{1, 4}, {0, 8}}}},
{ os_is_yx_osv8_isv2, { 1, 1, 2, 0, "oiyx", "oixy", {{1, 2}, {0, 8}}}},
{ os_is_zyx_osv16_isv16, { 1, 1, 3, 0, "oizyx", "oixyz", {{0, 16}, {1, 16}}}},
{ os_is_yx_osv32_isv4_swizzled_by_2, { 1, 1, 2, 0, "oixy", "oixy?", {{0, 32}, {1, 4}}}},
{ os_is_yx_osv32_isv4, { 1, 1, 2, 0, "oixy", "oixy?", {{0, 32}, {1, 4}}}},
{ os_is_zyx_osv32_isv4, { 1, 1, 3, 0, "oizyx", "oixyz", {{0, 32}, {1, 4}}}},
{ os_is_yx_osv32_isv32p, { 1, 1, 1, 0, "oixy", "oixy?", {}}},
{ os_is_zyx_isv16_osv16, { 1, 1, 3, 0, "oizyx", "oixyz", {{0, 16}, {1, 16}}}},
{ is_os_zyx_isv16_osv16, { 1, 1, 3, 0, "iozyx", "oixyz", {{1, 16}, {0, 16}}}},
{ is_os_yx_isv16_osv16, { 1, 1, 2, 0, "ioyx", "oixyz", {{1, 16}, {0, 16}}}},
{ os_is_osv32_isv32_swizzled_by_4, { 1, 1, 0, 0, "oixy", "oixy?", {{0, 32}, {1, 32}}}},
{ os_is_zyx_isv8_osv16_isv2, { 1, 1, 3, 0, "oizyx", "oixyz", {{1, 8}, {0, 16}, {1, 2}}}},
{ os_zyxi_osv16, { 1, 1, 3, 0, "ozyxi", "oixyz", {{0, 16}}}},
{ os_is_yx_isv8_osv16_isv2, { 1, 1, 2, 0, "oizyx", "oixyz", {{1, 8}, {0, 16}, {1, 2}}}},
{ os_is_yx_osv16_isv16, { 1, 1, 2, 0, "oiyx", "oixy", {{1, 16}, {0, 16}}}},
{ os_is_zyx_osv32_isv16, { 1, 1, 3, 0, "oizyx", "oixyz", {{0, 32}, {1, 16}}}},
{ os_is_zyx_osv64_isv16, { 1, 1, 3, 0, "oizyx", "oixyz", {{0, 64}, {1, 16}}}},
{ os_iyx_osv32__ai32, { 1, 1, 2, 0, "oiyx", "oixy", {{0, 32}}}},
{ i_yxs_os_yxsv2_osv16, { 1, 1, 2, 0, "iyxo", "oixy", {{0, 16}}}},
{ iy_xs_os_xsv2_osv8__ao32, { 1, 1, 2, 0, "iyxo", "oixy", {{2, 2}, {0, 8}}}},
{ iy_xs_os_xsv2_osv16__ao32, { 1, 1, 2, 0, "iyxo", "oixy", {{2, 2}, {0, 16}}}},
{ os_i_yxs_osv4_yxsv4, { 1, 1, 2, 0, "oiyx", "oixy", {{0, 4}}}},
{ goiyx, { 1, 1, 2, 1, "goiyx", "oixy??g", {}}},
{ gioyx, { 1, 1, 2, 1, "gioyx", "oixy??g", {}}},
{ goizyx, { 1, 1, 3, 1, "goizyx", "oixyz?g", {}}},
{ giozyx, { 1, 1, 3, 1, "giozyx", "oixyz?g", {}}},
{ g_os_iyx_osv16, { 1, 1, 2, 1, "goiyx", "oixy??g", {{0, 16}}}},
{ g_os_iyx_osv32, { 1, 1, 2, 1, "goiyx", "oixy??g", {{0, 32}}}},
{ gs_oiyx_gsv16, { 1, 1, 2, 1, "goiyx", "oixy??g", {{6, 16}}}},
{ gs_oizyx_gsv16, { 1, 1, 3, 1, "goizyx", "oixyz?g", {{6, 16}}}},
{ gs_oiyx_gsv32, { 1, 1, 2, 1, "goiyx", "oixy??g", {{6, 32}}}},
{ gyxio, { 1, 1, 2, 1, "gyxio", "oixy??g", {}}},
{ g_is_os_zyx_isv16_osv16, { 1, 1, 3, 1, "giozyx", "oixyz?g", {{1, 16}, {0, 16}}}},
{ g_is_os_yx_isv16_osv16, { 1, 1, 2, 1, "gioyx", "oixy??g", {{1, 16}, {0, 16}}}},
{ g_os_is_zyx_isv8_osv16_isv2, { 1, 1, 3, 1, "goizyx", "oixyz?g", {{1, 8}, {0, 16}, {1, 2}}}},
{ g_os_is_yx_isv8_osv16_isv2, { 1, 1, 2, 1, "goiyx", "oixy??g", {{1, 8}, {0, 16}, {1, 2}}}},
{ g_os_is_zyx_isv16_osv16, { 1, 1, 3, 1, "goizyx", "oixyz?g", {{0, 16}, {1, 16}}}},
{ g_os_is_yx_osv16_isv4, { 1, 1, 2, 1, "goixy", "oixy??g", {{0, 16}, {1, 4}}}},
{ g_os_is_zyx_osv16_isv16, { 1, 1, 3, 1, "goizyx", "oixyz?g", {{0, 16}, {1, 16}}}},
{ g_os_zyx_is_osv16_isv4, { 1, 1, 3, 1, "gozyxi", "oixyz?g", {{0, 16}, {1, 4}}}},
{ g_os_zyx_is_osv16_isv16, { 1, 1, 3, 1, "gozyxi", "oixyz?g", {{0, 16}, {1, 16}}}},
{ g_os_zyx_is_osv16_isv32, { 1, 1, 3, 1, "gozyxi", "oixyz?g", {{0, 16}, {1, 32}}}},
{ g_os_zyx_is_osv32_isv4, { 1, 1, 3, 1, "gozyxi", "oixyz?g", {{0, 32}, {1, 4}}}},
{ g_os_zyx_is_osv32_isv16, { 1, 1, 3, 1, "gozyxi", "oixyz?g", {{0, 32}, {1, 16}}}},
{ g_os_zyx_is_osv32_isv32, { 1, 1, 3, 1, "gozyxi", "oixyz?g", {{0, 32}, {1, 32}}}},
{ g_os_is_yx_osa4_isa8_osv8_isv4, { 1, 1, 2, 1, "goiyx", "oixy??g", {{0, 32}, {1, 32}}}},
{ g_os_is_zyx_osa4_isa8_osv8_isv4, { 1, 1, 3, 1, "goizyx", "oixyz?g", {{0, 32}, {1, 32}}}},
{ g_os_is_yx_osa4_isa8_osv8_isv2, { 1, 1, 2, 1, "goiyx", "oixy??g", {{0, 32}, {1, 16}}}},
{ g_os_is_zyx_osa4_isa8_osv8_isv2, { 1, 1, 3, 1, "goizyx", "oixyz?g", {{0, 32}, {1, 16}}}},
{ g_os_is_yx_osa2_isa8_osv16_isv4, { 1, 1, 2, 1, "goiyx", "oixy??g", {{0, 32}, {1, 32}}}},
{ g_os_is_yx_osa2_isa8_osv16_isv2, { 1, 1, 2, 1, "goiyx", "oixy??g", {{0, 32}, {1, 16}}}},
{ gs_oi_yxs_gsv4_yxsv4, { 1, 1, 2, 1, "goiyx", "oixy??g", {{6, 4}}}},
{ gs_oi_yxs_gsv16_yxsv4, { 1, 1, 2, 1, "goiyx", "oixy??g", {{6, 16}}}},
{ gs_oi_yxs_gsv32_yxsv4, { 1, 1, 2, 1, "goiyx", "oixy??g", {{6, 32}}}},
{ g_os_is_yx_isv16_osv16, { 1, 1, 2, 1, "goiyx", "oixy??g", {{1, 16}, {0, 16}}}},
{ gi_yxs_os_yxsv2_osv16, { 1, 1, 2, 1, "giyxo", "oixy??g", {{0, 16}}}},
{ giy_xs_os_xsv2_osv8__ao32, { 1, 1, 2, 1, "giyxo", "oixy??g", {{2, 2}, {0, 8}}}},
{ giy_xs_os_xsv2_osv16__ao32, { 1, 1, 2, 1, "giyxo", "oixy??g", {{2, 2}, {0, 16}}}},
};
if (traits.find(fmt) == traits.end()) {
throw std::runtime_error("[clDNN] Format description is missing in fmt traits");
}
return traits.at(fmt);
}
/// @brief Returns number of batch dimensions for a @p format.
static size_t batch_num(type fmt) { return traits(fmt).batch_num; }
/// @brief Returns number of feature dimensions for a @p format.
static size_t feature_num(type fmt) { return traits(fmt).feature_num; }
/// @brief Returns number of spatial dimensions for a @p format.
static size_t spatial_num(type fmt) { return traits(fmt).spatial_num; }
/// @brief Returns number of group dimensions for a @p format.
static size_t group_num(type fmt) { return traits(fmt).group_num; }
/// @brief Returns an order of dimensions for a @ format.
static const std::string& order(type fmt) { return traits(fmt).order; }
/// @brief Returns an internal orders of dimensions for a @p format.
static const std::string& internal_order(type fmt) { return traits(fmt).internal_order; }
/// @brief Returns block sizes for @p format.
static const std::vector<std::pair<size_t, int>>& block_sizes(type fmt) { return traits(fmt).block_sizes; }
/// @brief Returns number of dimensions contained within a @p format
static size_t dimension(type fmt) { return order(fmt).size(); }
/// @brief Checks if @p format is a winograd format
static bool is_winograd(type fmt) {
return (fmt == winograd_2x3_s1_data ||
fmt == winograd_2x3_s1_weights ||
fmt == winograd_2x3_s1_fused_weights ||
fmt == winograd_6x3_s1_fused_weights ||
fmt == image_2d_weights_winograd_6x3_s1_fbxyb ||
fmt == image_2d_weights_winograd_6x3_s1_xfbyb); }
/// @brief Checks if @p format is of image2d type
static bool is_image_2d(type fmt) {
return (fmt == image_2d_weights_c4_fyx_b ||
fmt == image_2d_weights_c1_b_fyx ||
fmt == image_2d_weights_winograd_6x3_s1_fbxyb ||
fmt == image_2d_weights_winograd_6x3_s1_xfbyb ||
fmt == nv12 ||
fmt == image_2d_rgba);
}
/// @brief Checks if @p format is weights format
static bool is_weights_format(type fmt) {
const auto internal_order = traits(fmt).internal_order;
const auto weights_chars = { "o", "i" };
for (const auto& c : weights_chars) {
if (internal_order.find_first_of(c) != std::string::npos) {
return true;
}
}
return false;
}
/// @brief Checks if @p format is simple data format
static bool is_simple_data_format(type fmt) {
return (fmt == yxfb || fmt == byxf ||
fmt == bfyx || fmt == fyxb ||
fmt == bfzyx || fmt == bfwzyx);
}
/// @brief Checks if @p format is of grouped type
static bool is_grouped(type fmt) { return group_num(fmt) != 0; }
/// @brief Checks if @p format is of image type
static bool is_image(type fmt) { return (is_image_2d(fmt)); }
/// @brief Checks if @p format is blocked format
static bool is_blocked(type fmt) { return !(block_sizes(fmt).empty()); }
/// @brief Checks if @p format is nv12 format
static bool is_nv12(type fmt) { return (fmt == nv12); }
/// @brief Returns number of batch dimensions.
size_t batch_num() const { return traits(value).batch_num; }
/// @brief Returns number of feature dimensions.
size_t feature_num() const { return traits(value).feature_num; }
/// @brief Returns number of spatial dimensions.
size_t spatial_num() const { return traits(value).spatial_num; }
/// @brief Returns number of group dimensions.
size_t group_num() const { return traits(value).group_num; }
/// @brief Returns an order of dimensions in form of string.
const std::string& order() const { return traits(value).order; }
/// @brief Returns an internal orders of dimensions form of string.
const std::string& internal_order() const { return traits(value).internal_order; }
/// @brief Returns block sizes as vector of pairs of dimension and block size for that dimension.
const std::vector<std::pair<size_t, int>>& block_sizes() const { return traits(value).block_sizes; }
/// @brief Returns number of dimensions contained within this format
size_t dimension() const { return order(value).size(); }
/// @brief Checks if @p format is a winograd format
bool is_winograd() const { return is_winograd(value); }
/// @brief Checks if @p format is of image 2d type
bool is_image_2d() const { return is_image_2d(value); }
/// @brief Checks if @p format is of image type
bool is_image() const { return is_image(value); }
/// @brief Checks if @p format is blocked format
bool is_blocked() { return is_blocked(value); }
/// @brief Checks if @p format is a nv12 format
bool is_nv12() const { return is_nv12(value); }
/// @brief Transforms dimension from internal order to external order
size_t internal_to_external(size_t idx) const {
auto index = order().find_first_of(internal_order()[idx]);
if (index == std::string::npos)
throw std::invalid_argument("Internal dimension index does not map to external index.");
return index;
}
type value;
/// @brief Implicit conversion from format::type.
constexpr format(type t) : value(t) {}
/// @brief Implicit conversion to format::type.
constexpr operator type() const { return value; }
};
constexpr int32_t tensor_batch_dim_max = 1;
constexpr int32_t tensor_feature_dim_max = 1;
constexpr int32_t tensor_spatial_dim_max = 4;
@ -923,6 +473,8 @@ public:
// skip f and y, z for the formats that do not have it
if (((new_fmt == format::bs_xs_xsv8_bsv8) ||
(new_fmt == format::bs_xs_xsv8_bsv16) ||
(new_fmt == format::os_i_osv8__ai8) ||
(new_fmt == format::os_i_osv16__ai8) ||
(new_fmt == format::bs_x_bsv16)) &&
((c == 'f') ||
(c == 'y') ||

32
src/plugins/intel_gpu/include/intel_gpu/runtime/utils.hpp
View File

@ -9,6 +9,8 @@
#include <stddef.h>
#include <memory>
#include <stdexcept>
#include <algorithm>
#include <vector>
namespace cldnn {
@ -139,6 +141,36 @@ inline derived_type& downcast(base_type& base) {
throw std::runtime_error("downcast failed with unhadnled exception");
}
template <typename T>
inline bool all_ones(const std::vector<T> vec) {
return std::all_of(vec.begin(), vec.end(), [](const T& val) { return val == 1; });
}
template <typename T>
inline bool all_zeroes(const std::vector<T> vec) {
return std::all_of(vec.begin(), vec.end(), [](const T& val) { return val == 0; });
}
template <typename T>
inline bool any_one(const std::vector<T> vec) {
return std::any_of(vec.begin(), vec.end(), [](const T& val) { return val == 1; });
}
template <typename T>
inline bool any_zero(const std::vector<T> vec) {
return std::any_of(vec.begin(), vec.end(), [](const T& val) { return val == 0; });
}
template <typename T>
inline bool any_not_one(const std::vector<T> vec) {
return std::any_of(vec.begin(), vec.end(), [](const T& val) { return val != 1; });
}
template <typename T>
inline bool any_not_zero(const std::vector<T> vec) {
return std::any_of(vec.begin(), vec.end(), [](const T& val) { return val != 0; });
}
/// @}
/// @endcond
/// @}

201
src/plugins/intel_gpu/src/graph/include/to_string_utils.h
View File

@ -60,206 +60,7 @@ inline std::string dt_to_str(data_types dt) {
}
inline std::string fmt_to_str(format fmt) {
switch (fmt.value) {
case format::yxfb:
return "yxfb";
case format::byxf:
return "byxf";
case format::bfyx:
return "bfyx";
case format::fyxb:
return "fyxb";
case format::b_fs_yx_fsv16:
return "b_fs_yx_fsv16";
case format::b_fs_yx_fsv32:
return "b_fs_yx_fsv32";
case format::b_fs_zyx_fsv32:
return "b_fs_zyx_fsv32";
case format::bs_xs_xsv8_bsv8:
return "bs_xs_xsv8_bsv8";
case format::bs_xs_xsv8_bsv16:
return "bs_xs_xsv8_bsv16";
case format::bs_x_bsv16:
return "bs_x_bsv16";
case format::winograd_2x3_s1_data:
return "winograd_2x3_s1_data";
case format::b_fs_yx_fsv4:
return "b_fs_yx_fsv4";
case format::b_fs_yx_32fp:
return "b_fs_yx_32fp";
case format::bfzyx:
return "bfzyx";
case format::bfwzyx:
return "bfwzyx";
case format::fs_b_yx_fsv32:
return "fs_b_yx_fsv32";
case format::bs_fs_yx_bsv16_fsv16:
return "bs_fs_yx_bsv16_fsv16";
case format::bs_fs_yx_bsv32_fsv16:
return "bs_fs_yx_bsv32_fsv16";
case format::bs_fs_yx_bsv4_fsv2:
return "bs_fs_yx_bsv4_fsv2";
case format::bs_fs_yx_bsv4_fsv4:
return "bs_fs_yx_bsv4_fsv4";
case format::bs_fs_yx_bsv8_fsv4:
return "bs_fs_yx_bsv8_fsv4";
case format::bs_fs_yx_bsv32_fsv32:
return "bs_fs_yx_bsv32_fsv32";
case format::b_fs_zyx_fsv16:
return "b_fs_zyx_fsv16";
case format::bs_fs_zyx_bsv16_fsv16:
return "bs_fs_zyx_bsv16_fsv16";
case format::image_2d_rgba:
return "image_2d_rgba";
case format::oiyx:
return "oiyx";
case format::ioyx:
return "ioyx";
case format::yxio:
return "yxio";
case format::oizyx:
return "oizyx";
case format::iozyx:
return "iozyx";
case format::winograd_2x3_s1_weights:
return "winograd_2x3_s1_weights";
case format::winograd_2x3_s1_fused_weights:
return "winograd_2x3_s1_fused_weights";
case format::winograd_6x3_s1_fused_weights:
return "winograd_6x3_s1_fused_weights";
case format::image_2d_weights_c4_fyx_b:
return "image_2d_weights_c4_fyx_b";
case format::image_2d_weights_c1_b_fyx:
return "image_2d_weights_c1_b_fyx";
case format::image_2d_weights_winograd_6x3_s1_fbxyb:
return "image_2d_weights_winograd_6x3_s1_fbxyb";
case format::image_2d_weights_winograd_6x3_s1_xfbyb:
return "image_2d_weights_winograd_6x3_s1_xfbyb";
case format::os_yxi_osv16:
return "os_yxi_osv16";
case format::o_is_yx_isv16:
return "o_is_yx_isv16";
case format::os_iyx_osv16:
return "os_iyx_osv16";
case format::os_is_yx_osv16_isv16:
return "os_is_yx_osv16_isv16";
case format::os_iyx_osv32:
return "os_iyx_osv32";
case format::os_iyx_osv64:
return "os_iyx_osv64";
case format::is_o_yx_isv32:
return "is_o_yx_isv32";
case format::os_is_yx_isv16_osv16:
return "os_is_yx_isv16_osv16";
case format::os_is_yx_isa8_osv8_isv4:
return "os_is_yx_isa8_osv8_isv4";
case format::os_is_yx_isa8_osv16_isv4:
return "os_is_yx_isa8_osv16_isv4";
case format::os_is_zyx_isa8_osv8_isv4:
return "os_is_zyx_isa8_osv8_isv4";
case format::os_is_zyx_isa8_osv16_isv4:
return "os_is_zyx_isa8_osv16_isv4";
case format::os_is_yx_osa4_isa8_osv8_isv2:
return "os_is_yx_osa4_isa8_osv8_isv2";
case format::os_is_zyx_osa4_isa8_osv8_isv2:
return "os_is_zyx_osa4_isa8_osv8_isv2";
case format::os_is_zyx_osa4_isa8_osv8_isv4:
return "os_is_zyx_osa4_isa8_osv8_isv4";
case format::g_os_is_yx_osa4_isa8_osv8_isv2:
return "g_os_is_yx_osa4_isa8_osv8_isv2";
case format::g_os_is_yx_osa4_isa8_osv8_isv4:
return "g_os_is_yx_osa4_isa8_osv8_isv4";
case format::g_os_is_zyx_osa4_isa8_osv8_isv4:
return "g_os_is_zyx_osa4_isa8_osv8_isv4";
case format::g_os_is_zyx_osa4_isa8_osv8_isv2:
return "g_os_is_zyx_osa4_isa8_osv8_isv2";
case format::os_is_yx_osa4_isa8_osv8_isv4:
return "os_is_yx_osa4_isa8_osv8_isv4";
case format::os_is_yx_osa4_isa8_osv8_isv4_swizzled_by_4:
return "os_is_yx_osa4_isa8_osv8_isv4_swizzled_by_4";
case format::os_is_zyx_osa4_isa8_osv8_isv4_swizzled_by_4:
return "os_is_zyx_osa4_isa8_osv8_isv4_swizzled_by_4";
case format::os_is_yx_isa8_osv8_isv4_swizzled_by_4:
return "os_is_yx_isa8_osv8_isv4_swizzled_by_4";
case format::is_o32_yx_isv32_swizzled_by_4:
return "is_o32_yx_isv32_swizzled_by_4";
case format::os_is_yx_osv8_isv2:
return "os_is_yx_osv8_isv2";
case format::os_is_yx_osv8_isv4:
return "os_is_yx_osv8_isv4";
case format::os_is_yx_osv16_isv4:
return "os_is_yx_osv16_isv4";
case format::os_is_yx_osv32_isv4_swizzled_by_2:
return "os_is_yx_osv32_isv4_swizzled_by_2";
case format::os_is_yx_osv32_isv4:
return "os_is_yx_osv32_isv4";
case format::os_is_zyx_osv32_isv4:
return "os_is_zyx_osv32_isv4";
case format::os_is_y_x8_osv8_isv4:
return "os_is_y_x8_osv8_isv4";
case format::os_is_yx_osv32_isv32p:
return "os_is_yx_osv32_isv32p";
case format::os_is_zyx_isv16_osv16:
return "os_is_zyx_isv16_osv16";
case format::is_os_zyx_isv16_osv16:
return "is_os_zyx_isv16_osv16";
case format::is_os_yx_isv16_osv16:
return "is_os_yx_isv16_osv16";
case format::os_is_osv32_isv32_swizzled_by_4:
return "os_is_osv32_isv32_swizzled_by_4";
case format::os_is_zyx_isv8_osv16_isv2:
return "os_is_zyx_isv8_osv16_isv2";
case format::os_zyxi_osv16:
return "os_zyxi_osv16";
case format::goiyx:
return "goiyx";
case format::goizyx:
return "goizyx";
case format::gioyx:
return "gioyx";
case format::giozyx:
return "giozyx";
case format::g_os_iyx_osv16:
return "g_os_iyx_osv16";
case format::g_os_iyx_osv32:
return "g_os_iyx_osv32";
case format::gs_oiyx_gsv16:
return "gs_oiyx_gsv16";
case format::gs_oiyx_gsv32:
return "gs_oiyx_gsv32";
case format::g_is_os_zyx_isv16_osv16:
return "g_is_os_zyx_isv16_osv16";
case format::g_is_os_yx_isv16_osv16:
return "g_is_os_yx_isv16_osv16";
case format::g_os_is_zyx_isv8_osv16_isv2:
return "g_os_is_zyx_isv8_osv16_isv2";
case format::g_os_is_yx_isv8_osv16_isv2:
return "g_os_is_yx_isv8_osv16_isv2";
case format::g_os_is_zyx_isv16_osv16:
return "g_os_is_zyx_isv16_osv16";
case format::g_os_is_yx_osv16_isv4:
return "g_os_is_yx_osv16_isv4";
case format::g_os_is_zyx_osv16_isv16:
return "g_os_is_zyx_osv16_isv16";
case format::g_os_zyx_is_osv16_isv4:
return "g_os_zyx_is_osv16_isv4";
case format::g_os_zyx_is_osv16_isv16:
return "g_os_zyx_is_osv16_isv16";
case format::g_os_zyx_is_osv16_isv32:
return "g_os_zyx_is_osv16_isv32";
case format::g_os_zyx_is_osv32_isv4:
return "g_os_zyx_is_osv32_isv4";
case format::g_os_zyx_is_osv32_isv16:
return "g_os_zyx_is_osv32_isv16";
case format::g_os_zyx_is_osv32_isv32:
return "g_os_zyx_is_osv32_isv32";
case format::gs_oi_yxs_gsv32_yxsv4:
return "gs_oi_yxs_gsv32_yxsv4";
default:
return "unknown (" + std::to_string(fmt.value) + ")";
}
return fmt.to_string();
}
inline std::string type_to_str(std::shared_ptr<const primitive> primitive) { return primitive->type_string(); }

1
src/plugins/intel_gpu/src/kernel_selector/common/tensor_type.cpp
View File

@ -53,6 +53,7 @@ WeightsTensor::WeightsChannelArray WeightsTensor::weightsChannelArray {{
{ WeightsLayout::ioyx, { 0, 1, -1, 3, 2, -1 } },
{ WeightsLayout::oyxi, { 1, 2, -1, 0, 3, -1 } },
{ WeightsLayout::iyxo, { 1, 2, -1, 3, 0, -1 } },
{ WeightsLayout::oyxi, { 1, 2, -1, 0, 3, -1 } },
{ WeightsLayout::yxio, { 2, 3, -1, 1, 0, -1 } },
{ WeightsLayout::os_iyx_osv16, { 0, 1, -1, 2, 3, -1 } },
{ WeightsLayout::os_iyx_osv32, { 0, 1, -1, 2, 3, -1 } },

173
src/plugins/intel_gpu/src/runtime/format.cpp Normal file
View File

@ -0,0 +1,173 @@
// Copyright (C) 2018-2021 Intel Corporation
// SPDX-License-Identifier: Apache-2.0
//
#include "intel_gpu/runtime/format.hpp"
#include <list>
#include <vector>
#include <algorithm>
namespace cldnn {
const format_traits& format::traits(type fmt) {
#define FMT_TRAITS(fmt, ...) {fmt, {#fmt, __VA_ARGS__}}
static const std::map<type, format_traits> traits {
// B - number of Batch dimensions
// F - number of Feature dimensions
// S - number of Spatial dimensions
// G - number of Group dimensions
// Order - dims changing order from rare to often
// Inner order - dims order for internal storage in _sizes array
// Block sizes - vector of pairs of dimension number (by inner order) and block size ordered from rare to often
// Format B F S G Dims order Order Inner order Block sizes
FMT_TRAITS(yxfb, 1, 1, 2, 0, {2, 3, 1, 0}, "yxfb", "bfxy?", {}),
FMT_TRAITS(byxf, 1, 1, 2, 0, {0, 2, 3, 1}, "byxf", "bfxy?", {}),
FMT_TRAITS(bfyx, 1, 1, 2, 0, {0, 1, 2, 3}, "bfyx", "bfxy?", {}),
FMT_TRAITS(fyxb, 1, 1, 2, 0, {1, 2, 3, 0}, "fyxb", "bfxy?", {}),
FMT_TRAITS(b_fs_yx_fsv16, 1, 1, 2, 0, {0, 1, 2, 3}, "bfyx", "bfxy", {{1, 16}}),
FMT_TRAITS(b_fs_yx_fsv32, 1, 1, 2, 0, {0, 1, 2, 3}, "bfyx", "bfxy", {{1, 32}}),
FMT_TRAITS(b_fs_zyx_fsv32, 1, 1, 3, 0, {0, 1, 2, 3, 4}, "bfzyx", "bfxyz", {{1, 32}}),
FMT_TRAITS(bs_xs_xsv8_bsv8, 1, 0, 1, 0, {0, 1}, "bx", "b?x??", {{2, 8}, {0, 8}}),
FMT_TRAITS(bs_xs_xsv8_bsv16, 1, 0, 1, 0, {0, 1}, "bx", "b?x??", {{2, 8}, {0, 16}}),
FMT_TRAITS(bs_x_bsv16, 1, 1, 1, 0, {0, 1}, "bx", "b?x??", {{0, 16}}),
FMT_TRAITS(winograd_2x3_s1_data, 1, 1, 2, 0, {0, 2, 3, 1}, "bxyf", "bfxy?", {}),
FMT_TRAITS(b_fs_yx_fsv4, 1, 1, 2, 0, {0, 1, 2, 3}, "bfyx", "bfxy?", {{1, 4}}),
FMT_TRAITS(bfzyx, 1, 1, 3, 0, {0, 1, 2, 3, 4}, "bfzyx", "bfxyz", {}),
FMT_TRAITS(bfwzyx, 1, 1, 4, 0, {0, 1, 2, 3, 4, 5}, "bfwzyx", "bfxyzw", {}),
FMT_TRAITS(fs_b_yx_fsv32, 1, 1, 2, 0, {1, 0, 2, 3}, "fbyx", "bfxy?", {{1, 32}}),
FMT_TRAITS(b_fs_yx_32fp, 1, 1, 2, 0, {0, 1, 2, 3}, "bfyx", "bfxy?", {}),
FMT_TRAITS(b_fs_zyx_fsv16, 1, 1, 3, 0, {0, 1, 2, 3, 4}, "bfzyx", "bfxyz", {{1, 16}}),
FMT_TRAITS(bs_fs_zyx_bsv16_fsv16, 1, 1, 3, 0, {0, 1, 2, 3, 4}, "bfzyx", "bfxyz", {{0, 16 }, {1, 16}}),
FMT_TRAITS(bs_fs_yx_bsv16_fsv16, 1, 1, 2, 0, {0, 1, 2, 3}, "bfyx", "bfxy?", {{0, 16 }, {1, 16}}),
FMT_TRAITS(bs_fs_yx_bsv4_fsv4, 1, 1, 2, 0, {0, 1, 2, 3}, "bfyx", "bfxy?", {{0, 4 }, {1, 4}}),
FMT_TRAITS(bs_fs_yx_bsv8_fsv4, 1, 1, 2, 0, {0, 1, 2, 3}, "bfyx", "bfxy?", {{0, 8 }, {1, 4}}),
FMT_TRAITS(bs_fs_yx_bsv4_fsv2, 1, 1, 2, 0, {0, 1, 2, 3}, "bfyx", "bfxy?", {{0, 4 }, {1, 2}}),
FMT_TRAITS(bs_fs_zyx_bsv4_fsv4, 1, 1, 3, 0, {0, 1, 2, 3, 4}, "bfzyx", "bfxyz", {{0, 4 }, {1, 4}}),
FMT_TRAITS(bs_fs_zyx_bsv4_fsv2, 1, 1, 3, 0, {0, 1, 2, 3, 4}, "bfzyx", "bfxyz", {{0, 4 }, {1, 2}}),
FMT_TRAITS(bs_fs_zyx_bsv32_fsv32, 1, 1, 3, 0, {0, 1, 2, 3, 4}, "bfzyx", "bfxyz", {{0, 32 }, {1, 32}}),
FMT_TRAITS(bs_fs_zyx_bsv32_fsv16, 1, 1, 3, 0, {0, 1, 2, 3, 4}, "bfzyx", "bfxyz", {{0, 32 }, {1, 16}}),
FMT_TRAITS(bs_fs_yx_bsv32_fsv32, 1, 1, 2, 0, {0, 1, 2, 3}, "bfyx", "bfxy?", {{0, 32 }, {1, 32}}),
FMT_TRAITS(bs_fs_yx_bsv32_fsv16, 1, 1, 2, 0, {0, 1, 2, 3}, "bfyx", "bfxy?", {{0, 32 }, {1, 16}}),
FMT_TRAITS(nv12, 1, 1, 2, 0, {0, 1, 2, 3}, "bfyx", "bfxy?", {}),
FMT_TRAITS(image_2d_rgba, 1, 1, 2, 0, {0, 1, 2, 3}, "bfyx", "bfxy?", {}),
FMT_TRAITS(oiyx, 1, 1, 2, 0, {0, 1, 2, 3}, "oiyx", "oixy", {}),
FMT_TRAITS(ioyx, 1, 1, 2, 0, {1, 0, 2, 3}, "ioyx", "oixy", {}),
FMT_TRAITS(iyxo, 1, 1, 2, 0, {1, 2, 3, 0}, "iyxo", "oixy", {}),
FMT_TRAITS(oyxi, 1, 1, 2, 0, {0, 2, 3, 1}, "oyxi", "oixy", {}),
FMT_TRAITS(yxio, 1, 1, 2, 0, {2, 3, 1, 0}, "yxio", "oixy?", {}),
FMT_TRAITS(oizyx, 1, 1, 3, 0, {0, 1, 2, 3, 4}, "oizyx", "oixyz", {}),
FMT_TRAITS(iozyx, 1, 1, 3, 0, {1, 0, 2, 3, 4}, "iozyx", "oixyz", {}),
FMT_TRAITS(os_is_yx_isv16_osv16, 1, 1, 2, 0, {0, 1, 2, 3}, "oiyx", "oixy", {{1, 16}, {0, 16}}),
FMT_TRAITS(o_is_yx_isv16, 1, 1, 2, 0, {0, 1, 2, 3}, "oiyx", "oixy?", {{1, 16}}),
FMT_TRAITS(os_yxi_osv16, 1, 1, 2, 0, {0, 2, 3, 1}, "oyxi", "oixy?", {{0, 16}}),
FMT_TRAITS(os_iyx_osv16, 1, 1, 2, 0, {0, 1, 2, 3}, "oiyx", "oixy?", {{0, 16}}),
FMT_TRAITS(os_iyx_osv32, 1, 1, 2, 0, {0, 1, 2, 3}, "oiyx", "oixy?", {{0, 32}}),
FMT_TRAITS(os_iyx_osv64, 1, 1, 2, 0, {0, 1, 2, 3}, "oiyx", "oixy?", {{0, 64}}),
FMT_TRAITS(winograd_2x3_s1_weights, 1, 1, 2, 0, {0, 1, 2, 3}, "oiyx", "oixy?", {}),
FMT_TRAITS(winograd_2x3_s1_fused_weights, 1, 1, 2, 0, {3, 2, 1, 0}, "xyio", "oixy?", {}),
FMT_TRAITS(winograd_6x3_s1_fused_weights, 1, 1, 2, 0, {3, 2, 1, 0}, "xyio", "oixy?", {}),
FMT_TRAITS(image_2d_weights_winograd_6x3_s1_fbxyb, 1, 1, 2, 0, {3, 2, 1, 0}, "xyio", "oixy?", {}),
FMT_TRAITS(image_2d_weights_winograd_6x3_s1_xfbyb, 1, 1, 2, 0, {3, 2, 1, 0}, "xyio", "oixy?", {}),
FMT_TRAITS(image_2d_weights_c4_fyx_b, 1, 1, 2, 0, {0, 1, 2, 3}, "oiyx", "oixy?", {}),
FMT_TRAITS(image_2d_weights_c1_b_fyx, 1, 1, 2, 0, {0, 1, 2, 3}, "oiyx", "oixy?", {}),
FMT_TRAITS(lstm_weights_dio, 1, 1, 2, 0, {0, 1, 3, 2}, "oixy", "oixy?", {}),
FMT_TRAITS(os_is_yx_isa8_osv8_isv4, 1, 1, 2, 0, {0, 1, 2, 3}, "oiyx", "oixy?", {}),
FMT_TRAITS(os_is_yx_isa8_osv16_isv4, 1, 1, 2, 0, {0, 1, 2, 3}, "oiyx", "oixy?", {}),
FMT_TRAITS(os_is_yx_isa8_osv8_isv4_swizzled_by_4, 1, 1, 2, 0, {0, 1, 2, 3}, "oiyx", "oixy?", {}),
FMT_TRAITS(os_is_yx_osa4_isa8_osv8_isv2, 1, 1, 2, 0, {0, 1, 2, 3}, "oiyx", "oixy?", {{0, 32}, {1, 16}}),
FMT_TRAITS(os_is_yx_osa4_isa8_osv8_isv4, 1, 1, 2, 0, {0, 1, 2, 3}, "oiyx", "oixy", {{0, 32}, {1, 32}}),
FMT_TRAITS(os_is_zyx_osa4_isa8_osv8_isv2, 1, 1, 3, 0, {0, 1, 2, 3, 4}, "oizyx", "oixyz", {{0, 32}, {1, 16}}),
FMT_TRAITS(os_is_zyx_osa4_isa8_osv8_isv4, 1, 1, 3, 0, {0, 1, 2, 3, 4}, "oizyx", "oixyz", {{0, 32}, {1, 32}}),
FMT_TRAITS(os_is_yx_osa2_isa8_osv16_isv2, 1, 1, 2, 0, {0, 1, 2, 3}, "oiyx", "oixy", {{0, 32}, {1, 16}}),
FMT_TRAITS(os_is_yx_osa2_isa8_osv16_isv4, 1, 1, 2, 0, {0, 1, 2, 3}, "oiyx", "oixy", {{0, 32}, {1, 32}}),
FMT_TRAITS(os_is_yx_osa2_isa8_osv8_isv2, 1, 1, 2, 0, {0, 1, 2, 3}, "oiyx", "oixy", {{0, 16}, {1, 16}}),
FMT_TRAITS(os_is_zyx_isa8_osv8_isv4, 1, 1, 3, 0, {0, 1, 2, 3, 4}, "oizyx", "oixyz", {{1, 8}, {0, 8}, {1, 4}}),
FMT_TRAITS(os_is_zyx_isa8_osv16_isv4, 1, 1, 3, 0, {0, 1, 2, 3, 4}, "oizyx", "oixyz", {{1, 8}, {0, 16}, {1, 4}}),
FMT_TRAITS(os_is_yx_osa4_isa8_osv8_isv4_swizzled_by_4, 1, 1, 2, 0, {0, 1, 2, 3}, "oiyx", "oixy?", {{0, 32}, {1, 32}}),
FMT_TRAITS(os_is_zyx_osa4_isa8_osv8_isv4_swizzled_by_4, 1, 1, 3, 0, {0, 1, 2, 3, 4}, "oizyx", "oixyz", {{0, 32}, {1, 32}}),
FMT_TRAITS(is_os_yx_isa2_osa8_isv8_osv2, 1, 1, 2, 0, {1, 0, 2, 3}, "ioyx", "ioxy?", {{1, 16}, {0, 16}}),
FMT_TRAITS(is_os_yx_isa4_osa8_isv8_osv4, 1, 1, 2, 0, {1, 0, 2, 3}, "ioyx", "ioxy?", {{1, 32}, {0, 32}}),
FMT_TRAITS(is_o_yx_isv32, 1, 1, 2, 0, {1, 0, 2, 3}, "oyxi", "oixy?", {{1, 32}}),
FMT_TRAITS(is_o32_yx_isv32_swizzled_by_4, 1, 1, 2, 0, {0, 1, 2, 3}, "oyxi", "oixy?", {}),
FMT_TRAITS(os_is_y_x8_osv8_isv4, 1, 1, 2, 0, {0, 1, 2, 3}, "oyxi", "oixy?", {}),
FMT_TRAITS(os_is_y_x8_osv8_isv4_swizzled_by_4, 1, 1, 2, 0, {0, 1, 2, 3}, "oyxi", "oixy?", {}),
FMT_TRAITS(os_is_yx_osv16_isv4, 1, 1, 2, 0, {0, 1, 2, 3}, "oixy", "oixy?", {{0, 16}, {1, 4}}),
FMT_TRAITS(os_is_yx_osv8_isv4, 1, 1, 2, 0, {0, 1, 2, 3}, "oiyx", "oixy", {{1, 4}, {0, 8}}),
FMT_TRAITS(os_is_yx_osv8_isv2, 1, 1, 2, 0, {0, 1, 2, 3}, "oiyx", "oixy", {{1, 2}, {0, 8}}),
FMT_TRAITS(os_is_zyx_osv16_isv16, 1, 1, 3, 0, {0, 1, 2, 3, 4}, "oizyx", "oixyz", {{0, 16}, {1, 16}}),
FMT_TRAITS(os_is_yx_osv32_isv4_swizzled_by_2, 1, 1, 2, 0, {0, 1, 2, 3}, "oixy", "oixy?", {{0, 32}, {1, 4}}),
FMT_TRAITS(os_is_yx_osv32_isv4, 1, 1, 2, 0, {0, 1, 2, 3}, "oixy", "oixy?", {{0, 32}, {1, 4}}),
FMT_TRAITS(os_is_zyx_osv32_isv4, 1, 1, 3, 0, {0, 1, 2, 3, 4}, "oizyx", "oixyz", {{0, 32}, {1, 4}}),
FMT_TRAITS(os_is_yx_osv32_isv32p, 1, 1, 1, 0, {0, 1, 2, 3}, "oixy", "oixy?", {}),
FMT_TRAITS(os_is_zyx_isv16_osv16, 1, 1, 3, 0, {0, 1, 2, 3, 4}, "oizyx", "oixyz", {{0, 16}, {1, 16}}),
FMT_TRAITS(is_os_zyx_isv16_osv16, 1, 1, 3, 0, {1, 0, 2, 3, 4}, "iozyx", "oixyz", {{1, 16}, {0, 16}}),
FMT_TRAITS(is_os_yx_isv16_osv16, 1, 1, 2, 0, {1, 0, 2, 3, 4}, "ioyx", "oixy", {{1, 16}, {0, 16}}),
FMT_TRAITS(os_is_osv32_isv32_swizzled_by_4, 1, 1, 0, 0, {0, 1, 2, 3}, "oixy", "oixy?", {{0, 32}, {1, 32}}),
FMT_TRAITS(os_is_zyx_isv8_osv16_isv2, 1, 1, 3, 0, {0, 1, 2, 3, 4}, "oizyx", "oixyz", {{1, 8}, {0, 16}, {1, 2}}),
FMT_TRAITS(os_zyxi_osv16, 1, 1, 3, 0, {0, 2, 3, 4, 1}, "ozyxi", "oixyz", {{0, 16}}),
FMT_TRAITS(os_is_yx_isv8_osv16_isv2, 1, 1, 2, 0, {0, 1, 2, 3}, "oiyx", "oixy", {{1, 8}, {0, 16}, {1, 2}}),
FMT_TRAITS(os_is_yx_osv16_isv16, 1, 1, 2, 0, {0, 1, 2, 3}, "oiyx", "oixy", {{1, 16}, {0, 16}}),
FMT_TRAITS(os_is_zyx_osv32_isv16, 1, 1, 3, 0, {0, 1, 2, 3, 4}, "oizyx", "oixyz", {{0, 32}, {1, 16}}),
FMT_TRAITS(os_is_zyx_osv64_isv16, 1, 1, 3, 0, {0, 1, 2, 3, 4}, "oizyx", "oixyz", {{0, 64}, {1, 16}}),
FMT_TRAITS(os_iyx_osv32__ai32, 1, 1, 2, 0, {0, 1, 2, 3}, "oiyx", "oixy", {{0, 32}}),
FMT_TRAITS(i_yxs_os_yxsv2_osv16, 1, 1, 2, 0, {1, 2, 3, 0}, "iyxo", "oixy", {{0, 16}}),
FMT_TRAITS(iy_xs_os_xsv2_osv8__ao32, 1, 1, 2, 0, {1, 2, 3, 0}, "iyxo", "oixy", {{2, 2}, {0, 8}}),
FMT_TRAITS(iy_xs_os_xsv2_osv16__ao32, 1, 1, 2, 0, {1, 2, 3, 0}, "iyxo", "oixy", {{2, 2}, {0, 16}}),
FMT_TRAITS(os_i_yxs_osv4_yxsv4, 1, 1, 2, 0, {0, 1, 2, 3}, "oiyx", "oixy", {{0, 4}}),
FMT_TRAITS(os_i_osv16__ai8, 1, 1, 0, 0, {0, 1}, "oi", "oi??", {{1, 8}, {0, 16}}),
FMT_TRAITS(os_i_osv8__ai8, 1, 1, 0, 0, {0, 1}, "oi", "oi??", {{1, 8}, {0, 8}}),
FMT_TRAITS(goiyx, 1, 1, 2, 1, {0, 1, 2, 3, 4}, "goiyx", "oixy??g", {}),
FMT_TRAITS(gioyx, 1, 1, 2, 1, {0, 2, 1, 3, 4}, "gioyx", "oixy??g", {}),
FMT_TRAITS(goizyx, 1, 1, 3, 1, {0, 1, 2, 3, 4, 5}, "goizyx", "oixyz?g", {}),
FMT_TRAITS(giozyx, 1, 1, 3, 1, {0, 2, 1, 3, 4, 5}, "giozyx", "oixyz?g", {}),
FMT_TRAITS(g_os_iyx_osv16, 1, 1, 2, 1, {0, 1, 2, 3, 4}, "goiyx", "oixy??g", {{0, 16}}),
FMT_TRAITS(g_os_iyx_osv32, 1, 1, 2, 1, {0, 1, 2, 3, 4}, "goiyx", "oixy??g", {{0, 32}}),
FMT_TRAITS(gs_oiyx_gsv16, 1, 1, 2, 1, {0, 1, 2, 3, 4}, "goiyx", "oixy??g", {{6, 16}}),
FMT_TRAITS(gs_oizyx_gsv16, 1, 1, 3, 1, {0, 1, 2, 3, 4, 5}, "goizyx", "oixyz?g", {{6, 16}}),
FMT_TRAITS(gs_oiyx_gsv32, 1, 1, 2, 1, {0, 1, 2, 3, 4}, "goiyx", "oixy??g", {{6, 32}}),
FMT_TRAITS(gyxio, 1, 1, 2, 1, {0, 3, 4, 2, 1}, "gyxio", "oixy??g", {}),
FMT_TRAITS(g_is_os_zyx_isv16_osv16, 1, 1, 3, 1, {0, 2, 1, 3, 4, 5}, "giozyx", "oixyz?g", {{1, 16}, {0, 16}}),
FMT_TRAITS(g_is_os_yx_isv16_osv16, 1, 1, 2, 1, {0, 2, 1, 3, 4}, "gioyx", "oixy??g", {{1, 16}, {0, 16}}),
FMT_TRAITS(g_os_is_zyx_isv8_osv16_isv2, 1, 1, 3, 1, {0, 1, 2, 3, 4, 5}, "goizyx", "oixyz?g", {{1, 8}, {0, 16}, {1, 2}}),
FMT_TRAITS(g_os_is_yx_isv8_osv16_isv2, 1, 1, 2, 1, {0, 1, 2, 3, 4}, "goiyx", "oixy??g", {{1, 8}, {0, 16}, {1, 2}}),
FMT_TRAITS(g_os_is_zyx_isv16_osv16, 1, 1, 3, 1, {0, 1, 2, 3, 4, 5}, "goizyx", "oixyz?g", {{0, 16}, {1, 16}}),
FMT_TRAITS(g_os_is_yx_osv16_isv4, 1, 1, 2, 1, {0, 1, 2, 3, 4}, "goixy", "oixy??g", {{0, 16}, {1, 4}}),
FMT_TRAITS(g_os_is_zyx_osv16_isv16, 1, 1, 3, 1, {0, 1, 2, 3, 4, 5}, "goizyx", "oixyz?g", {{0, 16}, {1, 16}}),
FMT_TRAITS(g_os_zyx_is_osv16_isv4, 1, 1, 3, 1, {0, 1, 2, 3, 4, 5}, "gozyxi", "oixyz?g", {{0, 16}, {1, 4}}),
FMT_TRAITS(g_os_zyx_is_osv16_isv16, 1, 1, 3, 1, {0, 1, 2, 3, 4, 5}, "gozyxi", "oixyz?g", {{0, 16}, {1, 16}}),
FMT_TRAITS(g_os_zyx_is_osv16_isv32, 1, 1, 3, 1, {0, 1, 2, 3, 4, 5}, "gozyxi", "oixyz?g", {{0, 16}, {1, 32}}),
FMT_TRAITS(g_os_zyx_is_osv32_isv4, 1, 1, 3, 1, {0, 1, 2, 3, 4, 5}, "gozyxi", "oixyz?g", {{0, 32}, {1, 4}}),
FMT_TRAITS(g_os_zyx_is_osv32_isv16, 1, 1, 3, 1, {0, 1, 2, 3, 4, 5}, "gozyxi", "oixyz?g", {{0, 32}, {1, 16}}),
FMT_TRAITS(g_os_zyx_is_osv32_isv32, 1, 1, 3, 1, {0, 1, 2, 3, 4, 5}, "gozyxi", "oixyz?g", {{0, 32}, {1, 32}}),
FMT_TRAITS(g_os_is_yx_osa4_isa8_osv8_isv4, 1, 1, 2, 1, {0, 1, 2, 3, 4}, "goiyx", "oixy??g", {{0, 32}, {1, 32}}),
FMT_TRAITS(g_os_is_zyx_osa4_isa8_osv8_isv4, 1, 1, 3, 1, {0, 1, 2, 3, 4, 5}, "goizyx", "oixyz?g", {{0, 32}, {1, 32}}),
FMT_TRAITS(g_os_is_yx_osa4_isa8_osv8_isv2, 1, 1, 2, 1, {0, 1, 2, 3, 4}, "goiyx", "oixy??g", {{0, 32}, {1, 16}}),
FMT_TRAITS(g_os_is_zyx_osa4_isa8_osv8_isv2, 1, 1, 3, 1, {0, 1, 2, 3, 4, 5}, "goizyx", "oixyz?g", {{0, 32}, {1, 16}}),
FMT_TRAITS(g_os_is_yx_osa2_isa8_osv16_isv4, 1, 1, 2, 1, {0, 1, 2, 3, 4}, "goiyx", "oixy??g", {{0, 32}, {1, 32}}),
FMT_TRAITS(g_os_is_yx_osa2_isa8_osv16_isv2, 1, 1, 2, 1, {0, 1, 2, 3, 4}, "goiyx", "oixy??g", {{0, 32}, {1, 16}}),
FMT_TRAITS(gs_oi_yxs_gsv4_yxsv4, 1, 1, 2, 1, {0, 1, 2, 3, 4}, "goiyx", "oixy??g", {{6, 4}}),
FMT_TRAITS(gs_oi_yxs_gsv16_yxsv4, 1, 1, 2, 1, {0, 1, 2, 3, 4}, "goiyx", "oixy??g", {{6, 16}}),
FMT_TRAITS(gs_oi_yxs_gsv32_yxsv4, 1, 1, 2, 1, {0, 1, 2, 3, 4}, "goiyx", "oixy??g", {{6, 32}}),
FMT_TRAITS(g_os_is_yx_isv16_osv16, 1, 1, 2, 1, {0, 1, 2, 3, 4}, "goiyx", "oixy??g", {{1, 16}, {0, 16}}),
FMT_TRAITS(gi_yxs_os_yxsv2_osv16, 1, 1, 2, 1, {0, 2, 3, 4, 1}, "giyxo", "oixy??g", {{0, 16}}),
FMT_TRAITS(giy_xs_os_xsv2_osv8__ao32, 1, 1, 2, 1, {0, 2, 3, 4, 1}, "giyxo", "oixy??g", {{2, 2}, {0, 8}}),
FMT_TRAITS(giy_xs_os_xsv2_osv16__ao32, 1, 1, 2, 1, {0, 2, 3, 4, 1}, "giyxo", "oixy??g", {{2, 2}, {0, 16}}),
};
if (traits.find(fmt) == traits.end()) {
throw std::runtime_error("[GPU] Format description is missing in fmt traits");
}
return traits.at(fmt);
}
std::string format::to_string() const {
if (value == any) {
return "any";
}
return traits(value).str;
}
} // namespace cldnn

172
src/plugins/intel_gpu/src/runtime/layout.cpp Normal file
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@ -0,0 +1,172 @@
// Copyright (C) 2018-2021 Intel Corporation
// SPDX-License-Identifier: Apache-2.0
//
#include "intel_gpu/runtime/layout.hpp"
#include <list>
#include <vector>
#include <algorithm>
namespace cldnn {
size_t layout::get_rank() const {
return format.dimension();
}
size_t layout::get_spatial_rank() const {
return format.spatial_num();
}
tensor::value_type layout::get_dim(size_t idx) const {
auto dims = get_dims();
return dims[idx];
}
tensor::value_type layout::batch() const {
auto dims = get_dims();
const size_t dim_idx = 0;
if (!format::is_weights_format(format) && dims[dim_idx] != size.batch[0]) {
throw std::runtime_error("batch mismatch: " + std::to_string(dims[dim_idx]) + " vs " + std::to_string(size.batch[0]));
}
return dims[dim_idx];
}
tensor::value_type layout::feature() const {
auto dims = get_dims();
const size_t dim_idx = 1;
if (!format::is_weights_format(format) && dims[dim_idx] != size.feature[0]) {
throw std::runtime_error("feature mismatch: " + std::to_string(dims[dim_idx]) + " vs " + std::to_string(size.feature[0]));
}
return dims[dim_idx];
}
tensor::value_type layout::spatial(size_t spatial_idx) const {
if (spatial_idx >= format.spatial_num() )
return 1;
auto dims = get_dims();
const size_t dim_idx = (format::is_grouped(format) ? 3 : 2) + (format.spatial_num() - 1 - spatial_idx);
if (dims[dim_idx] != size.spatial[spatial_idx]) {
throw std::runtime_error("spatials mismatch: " + std::to_string(dims[dim_idx]) + " vs " + std::to_string(size.spatial[spatial_idx]));
}
return dims[dim_idx];
}
tensor::value_type layout::group() const {
auto dims = get_dims();
if (!format::is_weights_format(format)) {
throw std::logic_error("[GPU] can't get group dimension for data layout");
}
if (!format::is_grouped(format))
return 1;
if (dims[0] != size.group[0]) {
throw std::runtime_error("groups mismatch: " + std::to_string(dims[0]) + " vs " + std::to_string(size.group[0]));
}
return dims[0];
}
tensor::value_type layout::ofm() const {
if (!format::is_weights_format(format)) {
throw std::logic_error("[GPU] can't get OFM dimension for data layout");
}
auto dims = get_dims();
const size_t dim_idx = format::is_grouped(format) ? 1 : 0;
return dims[dim_idx];
}
tensor::value_type layout::ifm() const {
if (!format::is_weights_format(format)) {
throw std::logic_error("[GPU] can't get IFM dimension for data layout");
}
auto dims = get_dims();
const size_t dim_idx = format::is_grouped(format) ? 2 : 1;
return dims[dim_idx];
}
static format get_default_format(size_t rank, bool is_weights, bool is_grouped) {
auto default_fmt = cldnn::format::bfyx;
if (is_weights) {
if (is_grouped) {
if (rank == 5) {
default_fmt = cldnn::format::goiyx;
} else if (rank == 6) {
default_fmt = cldnn::format::goizyx;
}
} else {
if (rank == 4) {
default_fmt = cldnn::format::oiyx;
} else if (rank == 5) {
default_fmt = cldnn::format::oizyx;
}
}
} else {
if (rank == 5) {
default_fmt = cldnn::format::bfzyx;
} else if (rank == 6) {
default_fmt = cldnn::format::bfwzyx;
}
}
return default_fmt;
}
std::vector<tensor::value_type> layout::get_dims() const {
auto default_fmt = get_default_format(format.dimension(), format::is_weights_format(format), format::is_grouped(format));
return size.sizes(default_fmt);
}
std::vector<tensor::value_type> layout::get_padded_dims() const {
auto default_fmt = get_default_format(format.dimension(), format::is_weights_format(format), format::is_grouped(format));
auto padded_size = size.add(data_padding.lower_size()).add(data_padding.upper_size());
return padded_size.sizes(default_fmt);
}
static format to_weights_format(format f, bool is_grouped) {
if (format::is_weights_format(f))
return f;
switch (f) {
case format::bfyx:
return format::oiyx;
case format::fyxb:
return format::iyxo;
case format::byxf:
return format::oyxi;
case format::yxfb:
return format::yxio;
case format::bfzyx:
return is_grouped ? format::goiyx : format::oizyx;
case format::bfwzyx: {
if (!is_grouped)
throw std::runtime_error("Invalid conversion of data format to weights format. bfwzyx can't be non-grouped as 4D spatials are not supported");
return format::goizyx;
}
case format::bs_xs_xsv8_bsv8:
return format::os_i_osv8__ai8;
default:
throw std::invalid_argument("Unable to convert data format " + f.to_string() + " to weights format");
}
}
layout layout::convert_to_weights_layout(bool is_grouped) const {
auto dims = size.sizes(format);
auto fmt = to_weights_format(format, is_grouped);
return layout{data_type, fmt, tensor{fmt, dims}};
}
std::vector<tensor::value_type> layout::get_ordered_dims() const {
return size.sizes(format);
}
std::vector<size_t> layout::get_dims_order() const {
return format::traits(format)._order;
}
std::string layout::to_string() const {
// TODO: Extend with format/data-type info
return size.to_string();
}
} // namespace cldnn

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// Copyright (C) 2021 Intel Corporation
// SPDX-License-Identifier: Apache-2.0
//
#include "test_utils.h"
#include "intel_gpu/runtime/format.hpp"
TEST(format, to_string) {
typedef std::underlying_type<cldnn::format::type>::type format_underlying_type;
for (format_underlying_type i = 0; i < static_cast<format_underlying_type>(cldnn::format::format_num); i++) {
cldnn::format fmt = static_cast<cldnn::format::type>(i);
ASSERT_NO_THROW(fmt.to_string()) << "Can't convert to string format " << i;
}
}
TEST(format, traits) {
typedef std::underlying_type<cldnn::format::type>::type format_underlying_type;
for (format_underlying_type i = 0; i < static_cast<format_underlying_type>(cldnn::format::format_num); i++) {
cldnn::format fmt = static_cast<cldnn::format::type>(i);
ASSERT_NO_THROW(cldnn::format::traits(fmt)) << "Can't get traits for format " << i;
}
}

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// Copyright (C) 2021 Intel Corporation
// SPDX-License-Identifier: Apache-2.0
//
#include "test_utils.h"
#include "intel_gpu/runtime/layout.hpp"
using namespace cldnn;
using namespace ::tests;
struct layout_test_params {
data_types dt;
format fmt;
std::vector<tensor::value_type> size;
std::vector<tensor::value_type> expected_aligned_size;
std::vector<size_t> expected_order;
};
class data_layout_test : public testing::TestWithParam<layout_test_params> { };
TEST_P(data_layout_test, size_check) {
auto p = GetParam();
auto default_fmt = format::bfyx;
if (p.size.size() == 5) {
default_fmt = format::bfzyx;
} else if (p.size.size() == 6) {
default_fmt = format::bfwzyx;
}
ASSERT_FALSE(format::is_weights_format(p.fmt));
auto l = layout(p.dt, p.fmt, tensor{default_fmt, p.size});
size_t expected_count = std::accumulate(p.size.begin(), p.size.end(), 1, std::multiplies<size_t>());
size_t expected_bytes_count = std::accumulate(p.expected_aligned_size.begin(), p.expected_aligned_size.end(), 1, std::multiplies<size_t>()) *
data_type_traits::size_of(p.dt);
ASSERT_EQ(l.bytes_count(), expected_bytes_count);
ASSERT_EQ(l.count(), expected_count);
ASSERT_EQ(l.get_rank(), p.size.size());
ASSERT_EQ(l.batch(), p.size[0]);
ASSERT_EQ(l.feature(), p.size[1]);
if (p.size.size() == 6) {
ASSERT_EQ(l.spatial(0), p.size[5]);
ASSERT_EQ(l.spatial(1), p.size[4]);
ASSERT_EQ(l.spatial(2), p.size[3]);
ASSERT_EQ(l.spatial(3), p.size[2]);
} else if (p.size.size() == 5) {
ASSERT_EQ(l.spatial(0), p.size[4]);
ASSERT_EQ(l.spatial(1), p.size[3]);
ASSERT_EQ(l.spatial(2), p.size[2]);
ASSERT_EQ(l.spatial(3), 1);
} else if (p.size.size() == 4) {
ASSERT_EQ(l.spatial(0), p.size[3]);
ASSERT_EQ(l.spatial(1), p.size[2]);
ASSERT_EQ(l.spatial(2), 1);
ASSERT_EQ(l.spatial(3), 1);
}
auto dims = l.get_dims();
auto ordered_dims = l.get_ordered_dims();
ASSERT_EQ(dims, p.size);
ASSERT_EQ(l.get_dims_order(), p.expected_order);
ASSERT_EQ(l.get_dims_order().size(), dims.size());
for (auto& dim_idx : l.get_dims_order()) {
ASSERT_LT(dim_idx, ordered_dims.size());
}
for (size_t i = 0; i < l.get_rank(); i++) {
ASSERT_EQ(ordered_dims[i], dims[p.expected_order[i]]);
ASSERT_EQ(ordered_dims[i], p.size[p.expected_order[i]]);
}
}
INSTANTIATE_TEST_SUITE_P(smoke, data_layout_test,
testing::ValuesIn(std::vector<layout_test_params>{
{data_types::f32, format::bfyx, {2, 33, 3, 5}, {2, 33, 3, 5}, {0, 1, 2, 3}},
{data_types::f16, format::bfzyx, {2, 33, 3, 5, 4}, {2, 33, 3, 5, 4}, {0, 1, 2, 3, 4}},
{data_types::i8, format::bfwzyx, {2, 33, 3, 5, 4, 6}, {2, 33, 3, 5, 4, 6}, {0, 1, 2, 3, 4, 5}},
{data_types::u8, format::yxfb, {2, 33, 3, 5}, {2, 33, 3, 5}, {2, 3, 1, 0}},
{data_types::f32, format::byxf, {2, 33, 3, 5}, {2, 33, 3, 5}, {0, 2, 3, 1}},
{data_types::f32, format::fyxb, {2, 33, 3, 5}, {2, 33, 3, 5}, {1, 2, 3, 0}},
{data_types::f32, format::b_fs_yx_fsv16, {2, 33, 3, 5}, {2, 48, 3, 5}, {0, 1, 2, 3}},
{data_types::f32, format::b_fs_yx_fsv32, {2, 33, 3, 5}, {2, 64, 3, 5}, {0, 1, 2, 3}},
{data_types::f32, format::b_fs_zyx_fsv16, {2, 33, 3, 5, 6}, {2, 48, 3, 5, 6}, {0, 1, 2, 3, 4}},
{data_types::f32, format::b_fs_zyx_fsv32, {2, 33, 3, 5, 6}, {2, 64, 3, 5, 6}, {0, 1, 2, 3, 4}},
{data_types::f32, format::bs_fs_zyx_bsv16_fsv16, {2, 33, 3, 5, 6}, {16, 48, 3, 5, 6}, {0, 1, 2, 3, 4}},
{data_types::f32, format::bs_fs_yx_bsv16_fsv16, {2, 33, 3, 5}, {16, 48, 3, 5}, {0, 1, 2, 3}},
{data_types::f32, format::bs_fs_yx_bsv4_fsv4, {2, 33, 3, 5}, {4, 36, 3, 5}, {0, 1, 2, 3}},
}));
class weights_layout_test : public testing::TestWithParam<layout_test_params> { };
TEST_P(weights_layout_test, size_check) {
auto p = GetParam();
auto default_fmt = format::oiyx;
if (format::is_weights_format(p.fmt)) {
if (p.size.size() == 5) {
default_fmt = format::goiyx;
} else if (p.size.size() == 6) {
default_fmt = format::goizyx;
}
} else {
if (p.size.size() == 4) {
default_fmt = format::oiyx;
} else if (p.size.size() == 5) {
default_fmt = format::oizyx;
}
}
auto l = layout(p.dt, p.fmt, tensor{default_fmt, p.size});
size_t expected_count = std::accumulate(p.size.begin(), p.size.end(), 1, std::multiplies<size_t>());
size_t expected_bytes_count = std::accumulate(p.expected_aligned_size.begin(), p.expected_aligned_size.end(), 1, std::multiplies<size_t>()) *
data_type_traits::size_of(p.dt);
ASSERT_EQ(l.bytes_count(), expected_bytes_count);
ASSERT_EQ(l.count(), expected_count);
ASSERT_EQ(l.get_rank(), p.size.size());
if (format::is_weights_format(p.fmt)) {
if (format::is_grouped(p.fmt)) {
ASSERT_EQ(l.group(), p.size[0]);
ASSERT_EQ(l.ofm(), p.size[1]);
ASSERT_EQ(l.ifm(), p.size[2]);
if (p.size.size() == 6) {
ASSERT_EQ(l.spatial(0), p.size[5]);
ASSERT_EQ(l.spatial(1), p.size[4]);
ASSERT_EQ(l.spatial(2), p.size[3]);
} else if (p.size.size() == 5) {
ASSERT_EQ(l.spatial(0), p.size[4]);
ASSERT_EQ(l.spatial(1), p.size[3]);
}
} else {
ASSERT_EQ(l.ofm(), p.size[0]);
ASSERT_EQ(l.ifm(), p.size[1]);
if (p.size.size() == 6) {
ASSERT_EQ(l.spatial(0), p.size[4]);
ASSERT_EQ(l.spatial(1), p.size[3]);
ASSERT_EQ(l.spatial(2), p.size[2]);
} else if (p.size.size() == 5) {
ASSERT_EQ(l.spatial(0), p.size[3]);
ASSERT_EQ(l.spatial(1), p.size[2]);
}
}
}
auto dims = l.get_dims();
auto ordered_dims = l.get_ordered_dims();
ASSERT_EQ(dims, p.size);
ASSERT_EQ(l.get_dims_order(), p.expected_order);
ASSERT_EQ(l.get_dims_order().size(), dims.size());
for (auto& dim_idx : l.get_dims_order()) {
ASSERT_LT(dim_idx, ordered_dims.size());
}
for (size_t i = 0; i < l.get_rank(); i++) {
ASSERT_EQ(ordered_dims[i], dims[p.expected_order[i]]);
ASSERT_EQ(ordered_dims[i], p.size[p.expected_order[i]]);
}
}
INSTANTIATE_TEST_SUITE_P(smoke, weights_layout_test,
testing::ValuesIn(std::vector<layout_test_params>{
{data_types::f32, format::oiyx, {2, 15, 3, 5}, {2, 15, 3, 5}, {0, 1, 2, 3}},
{data_types::f32, format::ioyx, {2, 15, 3, 5}, {2, 15, 3, 5}, {1, 0, 2, 3}},
{data_types::f32, format::yxio, {2, 15, 3, 5}, {2, 15, 3, 5}, {2, 3, 1, 0}},
{data_types::f32, format::goiyx, {4, 2, 15, 3, 5}, {4, 2, 15, 3, 5}, {0, 1, 2, 3, 4}},
{data_types::f32, format::goizyx, {4, 2, 15, 3, 5, 6}, {4, 2, 15, 3, 5, 6}, {0, 1, 2, 3, 4, 5}},
{data_types::f32, format::giozyx, {4, 2, 15, 3, 5, 6}, {4, 2, 15, 3, 5, 6}, {0, 2, 1, 3, 4, 5}},
{data_types::f32, format::g_os_is_yx_osa2_isa8_osv16_isv2, {4, 2, 15, 3, 5}, {4, 32, 16, 3, 5}, {0, 1, 2, 3, 4}},
{data_types::f32, format::g_os_is_zyx_osa4_isa8_osv8_isv4, {4, 2, 15, 3, 5, 6}, {4, 32, 32, 3, 5, 6}, {0, 1, 2, 3, 4, 5}},
}));