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Commit 3dc5db72 authored by Jun Liu's avatar Jun Liu
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Merge branch 'amd-develop' into amd-master

parents b924e330 e547c141
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Showing with 1327 additions and 100 deletions
include/ck/tensor_operation/gpu/device/device_cgemm.hpp
View file @ 3dc5db72
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include "device_base.hpp"
......@@ -31,13 +31,13 @@ struct DeviceCGemm : public BaseOperator
CElementwiseOperation c_element_op,
ck::index_t KBatch = 1) = 0;
virtual std::unique_ptr<BaseInvoker> MakeInvokerPointer() = 0;
virtual std::unique_ptr<BaseInvoker> MakeInvokerPointer() = 0;
virtual std::size_t GetWorkspaceSize(index_t MRaw,
index_t NRaw,
index_t KRaw,
index_t StrideA,
index_t StrideB,
index_t StrideC) = 0;
index_t StrideC) const = 0;
};
template <typename AElementwiseOperation,
......
include/ck/tensor_operation/gpu/device/impl/device_cgemm_4gemm_xdl_cshuffle.hpp
View file @ 3dc5db72
......@@ -598,10 +598,26 @@ struct DeviceCGemm_4Gemm_Xdl_CShuffle
[[maybe_unused]] index_t K,
[[maybe_unused]] index_t StrideA,
[[maybe_unused]] index_t StrideB,
index_t StrideC) override
index_t StrideC) const override
{
return 2 * sizeof(CDataType) * GetCElementSpaceSize(M, N, StrideC);
}
std::size_t GetWorkSpaceSize(const BaseArgument* base_arg) const override
{
const auto* parg = dynamic_cast<const Argument*>(base_arg);
if(!parg)
{
std::ostringstream err;
err << "Provided argument pointer is not of an Argument class!"
<< " In " << __FILE__ << ":" << __LINE__ << ", in function: " << __func__;
throw std::runtime_error(err.str());
}
return GetWorkspaceSize(
parg->M, parg->N, parg->K, parg->StrideA, parg->StrideB, parg->StrideC);
}
};
} // namespace device
......
include/ck/tensor_operation/gpu/device/impl/device_grouped_gemm_multi_abd_xdl_fixed_nk.hpp
View file @ 3dc5db72
......@@ -64,7 +64,7 @@ __global__ void
const index_t N = gemm_desc_ptr[group_id].N;
const index_t K = gemm_desc_ptr[group_id].K;
if(M * N * K == 0)
if(M == 0 || N == 0 || K == 0)
return;
const auto StrideAs = gemm_desc_ptr[group_id].StrideAs;
......
include/ck/tensor_operation/gpu/device/impl/device_grouped_gemm_multiple_d_splitk_xdl_cshuffle_two_stage.hpp
View file @ 3dc5db72
......@@ -345,7 +345,7 @@ struct DeviceGroupedGemmMultipleDSplitKXdlCShuffleTwoStage
const index_t N = gemm_descs[i].N_;
const index_t K = gemm_descs[i].K_;
if(M * N * K == 0)
if(M == 0 || N == 0 || K == 0)
{
skipped_group_count_++;
continue;
......
include/ck/tensor_operation/gpu/device/impl/device_grouped_gemm_multiple_d_xdl_cshuffle_tile_loop.hpp
View file @ 3dc5db72
......@@ -109,7 +109,7 @@ __global__ void
N = gemm_desc_ptr[group_id].N;
K = gemm_desc_ptr[group_id].K;
if(M * N * K == 0)
if(M == 0 || N == 0 || K == 0)
{
grid_size_grp = 0;
continue;
......
include/ck/tensor_operation/gpu/device/impl/device_grouped_gemm_xdl_fixed_nk.hpp
View file @ 3dc5db72
......@@ -68,7 +68,7 @@ __global__ void
const index_t N = gemm_desc_ptr[group_id].N;
const index_t K = gemm_desc_ptr[group_id].K;
if(M * N * K == 0)
if(M == 0 || N == 0 || K == 0)
return;
const auto StrideA = gemm_desc_ptr[group_id].StrideA;
......
include/ck/tensor_operation/gpu/element/unary_element_wise_operation.hpp
View file @ 3dc5db72
......@@ -419,6 +419,12 @@ struct UnaryAbs
y = ck::math::abs(x);
};
template <>
__host__ __device__ void operator()(f8_t& y, const f8_t& x) const
{
y = ck::type_convert<f8_t>(ck::math::abs(ck::type_convert<float>(x)));
};
};
struct UnarySqrt
......
include/ck/tensor_operation/gpu/warp/dpp_gemm.hpp
View file @ 3dc5db72
......@@ -324,55 +324,55 @@ struct DppSelector
static constexpr auto GetDpp();
template <>
static constexpr auto GetDpp<half_t, 8, 32>()
constexpr auto GetDpp<half_t, 8, 32>()
{
return DppInstr::dpp8_f16_8x32x2;
}
template <>
static constexpr auto GetDpp<half_t, 8, 16>()
constexpr auto GetDpp<half_t, 8, 16>()
{
return DppInstr::dpp8_f16_8x16x2;
}
template <>
static constexpr auto GetDpp<half_t, 16, 16>()
constexpr auto GetDpp<half_t, 16, 16>()
{
return DppInstr::dpp8_f16_16x16x2;
}
template <>
static constexpr auto GetDpp<half_t, 32, 8>()
constexpr auto GetDpp<half_t, 32, 8>()
{
return DppInstr::dpp8_f16_32x8x2;
}
template <>
static constexpr auto GetDpp<half_t, 1, 32>()
constexpr auto GetDpp<half_t, 1, 32>()
{
return DppInstr::dpp8_f16_1x32x2;
}
template <>
static constexpr auto GetDpp<half_t, 2, 32>()
constexpr auto GetDpp<half_t, 2, 32>()
{
return DppInstr::dpp8_f16_2x32x2;
}
template <>
static constexpr auto GetDpp<half_t, 2, 16>()
constexpr auto GetDpp<half_t, 2, 16>()
{
return DppInstr::dpp8_f16_2x16x2;
}
template <>
static constexpr auto GetDpp<half_t, 4, 16>()
constexpr auto GetDpp<half_t, 4, 16>()
{
return DppInstr::dpp8_f16_4x16x2;
}
template <>
static constexpr auto GetDpp<half_t, 4, 32>()
constexpr auto GetDpp<half_t, 4, 32>()
{
return DppInstr::dpp8_f16_4x32x2;
}
......
include/ck/tensor_operation/gpu/warp/wmma_gemm.hpp
View file @ 3dc5db72
......@@ -415,7 +415,7 @@ struct WmmaSelector
static constexpr auto GetWmma();
template <>
static constexpr auto GetWmma<half_t, half_t, float, 16, 16>()
constexpr auto GetWmma<half_t, half_t, float, 16, 16>()
{
#ifdef __gfx12__
return WmmaInstr::wmma_f32_16x16x16_f16_gfx12;
......@@ -425,7 +425,7 @@ struct WmmaSelector
}
template <>
static constexpr auto GetWmma<bhalf_t, bhalf_t, float, 16, 16>()
constexpr auto GetWmma<bhalf_t, bhalf_t, float, 16, 16>()
{
#ifdef __gfx12__
return WmmaInstr::wmma_f32_16x16x16_bf16_gfx12;
......@@ -435,19 +435,19 @@ struct WmmaSelector
}
template <>
static constexpr auto GetWmma<half_t, half_t, half_t, 16, 16>()
constexpr auto GetWmma<half_t, half_t, half_t, 16, 16>()
{
return WmmaInstr::wmma_f16_16x16x16_f16;
}
template <>
static constexpr auto GetWmma<bhalf_t, bhalf_t, bhalf_t, 16, 16>()
constexpr auto GetWmma<bhalf_t, bhalf_t, bhalf_t, 16, 16>()
{
return WmmaInstr::wmma_bf16_16x16x16_bf16;
}
template <>
static constexpr auto GetWmma<int8_t, int8_t, int, 16, 16>()
constexpr auto GetWmma<int8_t, int8_t, int, 16, 16>()
{
#ifdef __gfx12__
return WmmaInstr::wmma_i32_16x16x16_iu8_gfx12;
......@@ -458,7 +458,7 @@ struct WmmaSelector
#ifdef CK_EXPERIMENTAL_BIT_INT_EXTENSION_INT4
template <>
static constexpr auto GetWmma<int4_t, int4_t, int, 16, 16>()
constexpr auto GetWmma<int4_t, int4_t, int, 16, 16>()
{
return WmmaInstr::wmma_i32_16x16x16_iu4;
}
......
include/ck/tensor_operation/gpu/warp/xdlops_gemm.hpp
View file @ 3dc5db72
......@@ -651,97 +651,97 @@ struct MfmaSelector
static constexpr auto GetMfma();
template <>
static constexpr auto GetMfma<double, 16, 16>()
constexpr auto GetMfma<double, 16, 16>()
{
return MfmaInstr::mfma_f64_16x16x4f64;
}
template <>
static constexpr auto GetMfma<float, 64, 64>()
constexpr auto GetMfma<float, 64, 64>()
{
return MfmaInstr::mfma_f32_32x32x1xf32;
}
template <>
static constexpr auto GetMfma<float, 32, 64>()
constexpr auto GetMfma<float, 32, 64>()
{
return MfmaInstr::mfma_f32_32x32x1xf32;
}
template <>
static constexpr auto GetMfma<float, 16, 64>()
constexpr auto GetMfma<float, 16, 64>()
{
return MfmaInstr::mfma_f32_16x16x1xf32;
}
template <>
static constexpr auto GetMfma<float, 8, 64>()
constexpr auto GetMfma<float, 8, 64>()
{
return MfmaInstr::mfma_f32_4x4x1xf32;
}
template <>
static constexpr auto GetMfma<float, 4, 64>()
constexpr auto GetMfma<float, 4, 64>()
{
return MfmaInstr::mfma_f32_4x4x1xf32;
}
template <>
static constexpr auto GetMfma<float, 32, 32>()
constexpr auto GetMfma<float, 32, 32>()
{
return MfmaInstr::mfma_f32_32x32x2xf32;
}
template <>
static constexpr auto GetMfma<float, 16, 16>()
constexpr auto GetMfma<float, 16, 16>()
{
return MfmaInstr::mfma_f32_16x16x4xf32;
}
template <>
static constexpr auto GetMfma<half_t, 64, 64>()
constexpr auto GetMfma<half_t, 64, 64>()
{
return MfmaInstr::mfma_f32_32x32x4f16;
}
template <>
static constexpr auto GetMfma<half_t, 32, 64>()
constexpr auto GetMfma<half_t, 32, 64>()
{
return MfmaInstr::mfma_f32_32x32x4f16;
}
template <>
static constexpr auto GetMfma<half_t, 32, 32>()
constexpr auto GetMfma<half_t, 32, 32>()
{
return MfmaInstr::mfma_f32_32x32x8f16;
}
template <>
static constexpr auto GetMfma<half_t, 16, 16>()
constexpr auto GetMfma<half_t, 16, 16>()
{
return MfmaInstr::mfma_f32_16x16x16f16;
}
template <>
static constexpr auto GetMfma<half_t, 16, 64>()
constexpr auto GetMfma<half_t, 16, 64>()
{
return MfmaInstr::mfma_f32_16x16x4f16;
}
template <>
static constexpr auto GetMfma<half_t, 8, 64>()
constexpr auto GetMfma<half_t, 8, 64>()
{
return MfmaInstr::mfma_f32_4x4x4f16;
}
template <>
static constexpr auto GetMfma<half_t, 4, 64>()
constexpr auto GetMfma<half_t, 4, 64>()
{
return MfmaInstr::mfma_f32_4x4x4f16;
}
template <>
static constexpr auto GetMfma<bhalf_t, 32, 32>()
constexpr auto GetMfma<bhalf_t, 32, 32>()
{
#if defined(CK_USE_AMD_MFMA_BF16_1K_OP)
return MfmaInstr::mfma_f32_32x32x8bf16_1k;
......@@ -751,7 +751,7 @@ struct MfmaSelector
}
template <>
static constexpr auto GetMfma<bhalf_t, 16, 16>()
constexpr auto GetMfma<bhalf_t, 16, 16>()
{
#if defined(CK_USE_AMD_MFMA_BF16_1K_OP)
return MfmaInstr::mfma_f32_16x16x16bf16_1k;
......@@ -762,72 +762,72 @@ struct MfmaSelector
#if defined(CK_USE_AMD_MFMA_GFX940)
template <>
static constexpr auto GetMfma<int8_t, 32, 32>()
constexpr auto GetMfma<int8_t, 32, 32>()
{
return MfmaInstr::mfma_i32_32x32x16i8;
}
template <>
static constexpr auto GetMfma<int8_t, 16, 16>()
constexpr auto GetMfma<int8_t, 16, 16>()
{
return MfmaInstr::mfma_i32_16x16x32i8;
}
#else
template <>
static constexpr auto GetMfma<int8_t, 32, 32>()
constexpr auto GetMfma<int8_t, 32, 32>()
{
return MfmaInstr::mfma_i32_32x32x8i8;
}
template <>
static constexpr auto GetMfma<int8_t, 16, 16>()
constexpr auto GetMfma<int8_t, 16, 16>()
{
return MfmaInstr::mfma_i32_16x16x16i8;
}
#endif
template <>
static constexpr auto GetMfma<f8_t, 32, 32>()
constexpr auto GetMfma<f8_t, 32, 32>()
{
return MfmaInstr::mfma_f32_32x32x16f8f8;
}
template <>
static constexpr auto GetMfma<f8_t, 16, 16>()
constexpr auto GetMfma<f8_t, 16, 16>()
{
return MfmaInstr::mfma_f32_16x16x32f8f8;
}
template <>
static constexpr auto GetMfma<bf8_t, 32, 32>()
constexpr auto GetMfma<bf8_t, 32, 32>()
{
return MfmaInstr::mfma_f32_32x32x16bf8bf8;
}
template <>
static constexpr auto GetMfma<bf8_t, 16, 16>()
constexpr auto GetMfma<bf8_t, 16, 16>()
{
return MfmaInstr::mfma_f32_16x16x32bf8bf8;
}
template <>
static constexpr auto GetMfma<f8_t, 32, 32, bf8_t>()
constexpr auto GetMfma<f8_t, 32, 32, bf8_t>()
{
return MfmaInstr::mfma_f32_32x32x16f8bf8;
}
template <>
static constexpr auto GetMfma<f8_t, 16, 16, bf8_t>()
constexpr auto GetMfma<f8_t, 16, 16, bf8_t>()
{
return MfmaInstr::mfma_f32_16x16x32f8bf8;
}
template <>
static constexpr auto GetMfma<bf8_t, 32, 32, f8_t>()
constexpr auto GetMfma<bf8_t, 32, 32, f8_t>()
{
return MfmaInstr::mfma_f32_32x32x16bf8f8;
}
template <>
static constexpr auto GetMfma<bf8_t, 16, 16, f8_t>()
constexpr auto GetMfma<bf8_t, 16, 16, f8_t>()
{
return MfmaInstr::mfma_f32_16x16x32bf8f8;
}
......
include/ck/utility/data_type.hpp
View file @ 3dc5db72
This diff is collapsed.
include/ck/utility/math_v2.hpp
View file @ 3dc5db72
......@@ -80,6 +80,8 @@ static inline __host__ bool isnan(half_t x)
return (xx & 0x7FFF) > 0x7C00;
};
static inline __host__ bool isnan(f8_t x) { return (x & 0x80); };
#ifdef CK_EXPERIMENTAL_BIT_INT_EXTENSION_INT4
static inline __host__ bool isnan(int4_t x)
{
......@@ -529,6 +531,8 @@ static inline __device__ bool isnan(half_t x)
return (xx & 0x7FFF) > 0x7C00;
};
static inline __device__ bool isnan(f8_t x) { return (x & 0x80); };
static inline __device__ half_t sqrt(half_t x)
{
return static_cast<half_t>(__builtin_amdgcn_sqrtf(static_cast<float>(x)));
......
include/ck_tile/core/container/array.hpp
View file @ 3dc5db72
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include <initializer_list>
#include <vector>
#include "ck_tile/core/config.hpp"
#include "ck_tile/core/numeric/integer.hpp"
......@@ -236,6 +237,16 @@ CK_TILE_HOST_DEVICE constexpr bool operator!=(const array<T, Size>& a, const arr
return !(a == b);
}
template <typename T, index_t N, typename X>
CK_TILE_HOST_DEVICE constexpr auto to_array(const std::vector<X>& x)
{
array<T, N> arr;
static_for<0, N, 1>{}([&x, &arr](auto i) { arr(i) = x[i]; });
return arr;
}
template <typename T, index_t N, typename X>
CK_TILE_HOST_DEVICE constexpr auto to_array(const X& x)
{
......
include/ck_tile/core/container/thread_buffer.hpp
View file @ 3dc5db72
......@@ -58,7 +58,7 @@ struct thread_buffer {
template <index_t I> CK_TILE_HOST_DEVICE constexpr const auto& at() const { return get(I); }
template <index_t I> CK_TILE_HOST_DEVICE constexpr auto& at(number<I>) { return get(I); }
template <index_t I> CK_TILE_HOST_DEVICE constexpr const auto& at(number<I>) const { return get(I); }
template <typename X_,
typename std::enable_if<has_same_scalar_type<value_type, X_>::value, bool>::type = false>
CK_TILE_HOST_DEVICE constexpr auto _get_as() const
......
include/ck_tile/host.hpp
View file @ 3dc5db72
......@@ -5,6 +5,8 @@
#include "ck_tile/host/arg_parser.hpp"
#include "ck_tile/host/check_err.hpp"
#include "ck_tile/host/convolution_host_tensor_descriptor_helper.hpp"
#include "ck_tile/host/convolution_parameter.hpp"
#include "ck_tile/host/device_memory.hpp"
#include "ck_tile/host/fill.hpp"
#include "ck_tile/host/hip_check_error.hpp"
......
include/ck_tile/host/arg_parser.hpp
View file @ 3dc5db72
......@@ -50,12 +50,22 @@ class ArgParser
}
return *this;
}
void print()
void print() const
{
// find max key length
std::string::size_type max_key_length = 11;
for(auto& key : keys)
{
if(max_key_length < key.length())
{
max_key_length = key.length();
}
}
printf("args:\n");
for(auto& key : keys)
{
auto value = input_map[key];
auto value = input_map.at(key);
std::vector<std::string> help_text_lines;
size_t pos = 0;
for(size_t next_pos = value.help_text.find('\n', pos); next_pos != std::string::npos;)
......@@ -69,8 +79,7 @@ class ArgParser
std::string(value.help_text.begin() + pos, value.help_text.end()));
std::string default_value = std::string("(default:") + value.value + std::string(")");
std::cout << std::setw(2) << std::setw(12 - value.name.length()) << "-" << key
std::cout << std::setw(1 + max_key_length - value.name.length()) << "-" << key
<< std::setw(4) << " " << help_text_lines[0] << " " << default_value
<< std::endl;
......@@ -78,7 +87,8 @@ class ArgParser
help_next_line != help_text_lines.end();
++help_next_line)
{
std::cout << std::setw(17) << " " << *help_next_line << std::endl;
std::cout << std::setw(1 + max_key_length + 4) << " " << *help_next_line
<< std::endl;
}
}
}
......
include/ck_tile/host/convolution_host_tensor_descriptor_helper.hpp 0 → 100644
View file @ 3dc5db72
// SPDX-License-Identifier: MIT
// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include "ck_tile/ops/common/tensor_layout.hpp"
#include "ck_tile/host/convolution_parameter.hpp"
#include "ck_tile/host/host_tensor.hpp"
namespace ck_tile {
namespace conv {
namespace detail {
template <typename OldLayout>
CK_TILE_HOST std::vector<std::size_t> get_layout_transpose_gnchw_to_old()
{
if constexpr(std::is_same_v<OldLayout, ck_tile::tensor_layout::convolution::GNCW> ||
std::is_same_v<OldLayout, ck_tile::tensor_layout::convolution::GKCX> ||
std::is_same_v<OldLayout, ck_tile::tensor_layout::convolution::GNKW>)
{
return {0, 1, 2, 3};
}
else if constexpr(std::is_same_v<OldLayout, ck_tile::tensor_layout::convolution::GNCHW> ||
std::is_same_v<OldLayout, ck_tile::tensor_layout::convolution::GKCYX> ||
std::is_same_v<OldLayout, ck_tile::tensor_layout::convolution::GNKHW>)
{
return {0, 1, 2, 3, 4};
}
else if constexpr(std::is_same_v<OldLayout, ck_tile::tensor_layout::convolution::GNCDHW> ||
std::is_same_v<OldLayout, ck_tile::tensor_layout::convolution::GKCZYX> ||
std::is_same_v<OldLayout, ck_tile::tensor_layout::convolution::GNKDHW>)
{
return {0, 1, 2, 3, 4, 5};
}
if constexpr(std::is_same_v<OldLayout, ck_tile::tensor_layout::convolution::GNWC> ||
std::is_same_v<OldLayout, ck_tile::tensor_layout::convolution::GKXC> ||
std::is_same_v<OldLayout, ck_tile::tensor_layout::convolution::GNWK>)
{
return {0, 1, 3, 2};
}
else if constexpr(std::is_same_v<OldLayout, ck_tile::tensor_layout::convolution::GNHWC> ||
std::is_same_v<OldLayout, ck_tile::tensor_layout::convolution::GKYXC> ||
std::is_same_v<OldLayout, ck_tile::tensor_layout::convolution::GNHWK>)
{
return {0, 1, 4, 2, 3};
}
else if constexpr(std::is_same_v<OldLayout, ck_tile::tensor_layout::convolution::GNDHWC> ||
std::is_same_v<OldLayout, ck_tile::tensor_layout::convolution::GKZYXC> ||
std::is_same_v<OldLayout, ck_tile::tensor_layout::convolution::GNDHWK>)
{
return {0, 1, 5, 2, 3, 4};
}
else if constexpr(std::is_same_v<OldLayout, ck_tile::tensor_layout::convolution::NWGC> ||
std::is_same_v<OldLayout, ck_tile::tensor_layout::convolution::KXGC> ||
std::is_same_v<OldLayout, ck_tile::tensor_layout::convolution::NWGK>)
{
return {2, 0, 3, 1};
}
else if constexpr(std::is_same_v<OldLayout, ck_tile::tensor_layout::convolution::NHWGC> ||
std::is_same_v<OldLayout, ck_tile::tensor_layout::convolution::KYXGC> ||
std::is_same_v<OldLayout, ck_tile::tensor_layout::convolution::NHWGK>)
{
return {3, 0, 4, 1, 2};
}
else if constexpr(std::is_same_v<OldLayout, ck_tile::tensor_layout::convolution::NDHWGC> ||
std::is_same_v<OldLayout, ck_tile::tensor_layout::convolution::KZYXGC> ||
std::is_same_v<OldLayout, ck_tile::tensor_layout::convolution::NDHWGK>)
{
return {4, 0, 5, 1, 2, 3};
}
else
{
printf("%s\n", __func__);
throw std::runtime_error("wrong! unsupported layout");
}
}
} // namespace detail
// make tensor descriptor for packed input tensor, and order the dimension in the order of GNCHW
// regardless of physical layout
template <typename InLayout>
CK_TILE_HOST HostTensorDescriptor
make_input_host_tensor_descriptor_g_n_c_wis_packed(const ck_tile::conv::ConvParam& param)
{
std::vector<std::size_t> physical_lengths;
if constexpr(std::is_same_v<InLayout, ck_tile::tensor_layout::convolution::GNCW> ||
std::is_same_v<InLayout, ck_tile::tensor_layout::convolution::GNCHW> ||
std::is_same_v<InLayout, ck_tile::tensor_layout::convolution::GNCDHW>)
{
physical_lengths = std::vector<std::size_t>{static_cast<std::size_t>(param.G_),
static_cast<std::size_t>(param.N_),
static_cast<std::size_t>(param.C_)};
physical_lengths.insert(physical_lengths.end(),
param.input_spatial_lengths_.begin(),
param.input_spatial_lengths_.begin() + param.num_dim_spatial_);
}
else if constexpr(std::is_same_v<InLayout, ck_tile::tensor_layout::convolution::GNWC> ||
std::is_same_v<InLayout, ck_tile::tensor_layout::convolution::GNHWC> ||
std::is_same_v<InLayout, ck_tile::tensor_layout::convolution::GNDHWC>)
{
physical_lengths = std::vector<std::size_t>{static_cast<std::size_t>(param.G_),
static_cast<std::size_t>(param.N_),
static_cast<std::size_t>(param.C_)};
physical_lengths.insert(physical_lengths.begin() + 2,
param.input_spatial_lengths_.begin(),
param.input_spatial_lengths_.begin() + param.num_dim_spatial_);
}
else if constexpr(std::is_same_v<InLayout, ck_tile::tensor_layout::convolution::NWGC> ||
std::is_same_v<InLayout, ck_tile::tensor_layout::convolution::NHWGC> ||
std::is_same_v<InLayout, ck_tile::tensor_layout::convolution::NDHWGC>)
{
physical_lengths = std::vector<std::size_t>{static_cast<std::size_t>(param.N_),
static_cast<std::size_t>(param.G_),
static_cast<std::size_t>(param.C_)};
physical_lengths.insert(physical_lengths.begin() + 1,
param.input_spatial_lengths_.begin(),
param.input_spatial_lengths_.begin() + param.num_dim_spatial_);
}
else
{
printf("%s\n", __func__);
printf("%s\n", InLayout::name);
throw std::runtime_error("wrong! unsupported layout");
}
return transpose_host_tensor_descriptor_given_new2old(
HostTensorDescriptor(physical_lengths),
detail::get_layout_transpose_gnchw_to_old<InLayout>());
}
// make tensor descriptor for packed weight tensor, and order the dimension in the order of GKCYX
// regardless of physical layout
template <typename WeiLayout>
CK_TILE_HOST HostTensorDescriptor
make_weight_host_tensor_descriptor_g_k_c_xs_packed(const ck_tile::conv::ConvParam& param)
{
std::vector<std::size_t> physical_lengths;
if constexpr(std::is_same_v<WeiLayout, ck_tile::tensor_layout::convolution::KXC> ||
std::is_same_v<WeiLayout, ck_tile::tensor_layout::convolution::KYXC> ||
std::is_same_v<WeiLayout, ck_tile::tensor_layout::convolution::KZYXC>)
{
if(param.G_ != 1)
{
throw std::runtime_error("wrong! G != 1");
}
physical_lengths = std::vector<std::size_t>{static_cast<std::size_t>(param.K_),
static_cast<std::size_t>(param.C_)};
physical_lengths.insert(physical_lengths.end(),
param.filter_spatial_lengths_.begin(),
param.filter_spatial_lengths_.begin() + param.num_dim_spatial_);
}
else if constexpr(std::is_same_v<WeiLayout, ck_tile::tensor_layout::convolution::GKCX> ||
std::is_same_v<WeiLayout, ck_tile::tensor_layout::convolution::GKCYX> ||
std::is_same_v<WeiLayout, ck_tile::tensor_layout::convolution::GKCZYX>)
{
physical_lengths = std::vector<std::size_t>{static_cast<std::size_t>(param.G_),
static_cast<std::size_t>(param.K_),
static_cast<std::size_t>(param.C_)};
physical_lengths.insert(physical_lengths.end(),
param.filter_spatial_lengths_.begin(),
param.filter_spatial_lengths_.begin() + param.num_dim_spatial_);
}
else if constexpr(std::is_same_v<WeiLayout, ck_tile::tensor_layout::convolution::GKXC> ||
std::is_same_v<WeiLayout, ck_tile::tensor_layout::convolution::GKYXC> ||
std::is_same_v<WeiLayout, ck_tile::tensor_layout::convolution::GKZYXC>)
{
physical_lengths = std::vector<std::size_t>{static_cast<std::size_t>(param.G_),
static_cast<std::size_t>(param.K_),
static_cast<std::size_t>(param.C_)};
physical_lengths.insert(physical_lengths.begin() + 2,
param.filter_spatial_lengths_.begin(),
param.filter_spatial_lengths_.begin() + param.num_dim_spatial_);
}
else if constexpr(std::is_same_v<WeiLayout, ck_tile::tensor_layout::convolution::KXGC> ||
std::is_same_v<WeiLayout, ck_tile::tensor_layout::convolution::KYXGC> ||
std::is_same_v<WeiLayout, ck_tile::tensor_layout::convolution::KZYXGC>)
{
physical_lengths = std::vector<std::size_t>{static_cast<std::size_t>(param.K_),
static_cast<std::size_t>(param.G_),
static_cast<std::size_t>(param.C_)};
physical_lengths.insert(physical_lengths.begin() + 1,
param.filter_spatial_lengths_.begin(),
param.filter_spatial_lengths_.begin() + param.num_dim_spatial_);
}
else
{
printf("%s\n", __func__);
printf("%s\n", WeiLayout::name);
throw std::runtime_error("wrong! unsupported layout");
}
return transpose_host_tensor_descriptor_given_new2old(
HostTensorDescriptor(physical_lengths),
detail::get_layout_transpose_gnchw_to_old<WeiLayout>());
}
// make tensor descriptor for packed output tensor, and order the dimension in the order of GNKHW
// regardless of physical layout
template <typename OutLayout>
CK_TILE_HOST HostTensorDescriptor
make_output_host_tensor_descriptor_g_n_k_wos_packed(const ck_tile::conv::ConvParam& param)
{
std::vector<std::size_t> physical_lengths;
if constexpr(std::is_same_v<OutLayout, ck_tile::tensor_layout::convolution::GNKW> ||
std::is_same_v<OutLayout, ck_tile::tensor_layout::convolution::GNKHW> ||
std::is_same_v<OutLayout, ck_tile::tensor_layout::convolution::GNKDHW>)
{
physical_lengths = std::vector<std::size_t>{static_cast<std::size_t>(param.G_),
static_cast<std::size_t>(param.N_),
static_cast<std::size_t>(param.K_)};
physical_lengths.insert(physical_lengths.end(),
param.output_spatial_lengths_.begin(),
param.output_spatial_lengths_.begin() + param.num_dim_spatial_);
}
// separate from legacy code above
else if constexpr(std::is_same_v<OutLayout, ck_tile::tensor_layout::convolution::GNWK> ||
std::is_same_v<OutLayout, ck_tile::tensor_layout::convolution::GNHWK> ||
std::is_same_v<OutLayout, ck_tile::tensor_layout::convolution::GNDHWK>)
{
physical_lengths = std::vector<std::size_t>{static_cast<std::size_t>(param.G_),
static_cast<std::size_t>(param.N_),
static_cast<std::size_t>(param.K_)};
physical_lengths.insert(physical_lengths.begin() + 2,
param.output_spatial_lengths_.begin(),
param.output_spatial_lengths_.begin() + param.num_dim_spatial_);
}
else if constexpr(std::is_same_v<OutLayout, ck_tile::tensor_layout::convolution::NWGK> ||
std::is_same_v<OutLayout, ck_tile::tensor_layout::convolution::NHWGK> ||
std::is_same_v<OutLayout, ck_tile::tensor_layout::convolution::NDHWGK>)
{
physical_lengths = std::vector<std::size_t>{static_cast<std::size_t>(param.N_),
static_cast<std::size_t>(param.G_),
static_cast<std::size_t>(param.K_)};
physical_lengths.insert(physical_lengths.begin() + 1,
param.output_spatial_lengths_.begin(),
param.output_spatial_lengths_.begin() + param.num_dim_spatial_);
}
else
{
printf("%s\n", __func__);
printf("%s\n", OutLayout::name);
throw std::runtime_error("wrong! unsupported layout");
}
return transpose_host_tensor_descriptor_given_new2old(
HostTensorDescriptor(physical_lengths),
detail::get_layout_transpose_gnchw_to_old<OutLayout>());
}
} // namespace conv
} // namespace ck_tile
include/ck_tile/host/convolution_parameter.hpp 0 → 100644
View file @ 3dc5db72
// SPDX-License-Identifier: MIT
// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include <cstdlib>
#include <numeric>
#include <iterator>
#include <vector>
namespace ck_tile {
namespace conv {
struct ConvParam
{
ConvParam(ck_tile::index_t n_dim,
ck_tile::index_t group_count,
ck_tile::index_t n_batch,
ck_tile::index_t n_out_channels,
ck_tile::index_t n_in_channels,
const std::vector<ck_tile::index_t>& filters_len,
const std::vector<ck_tile::index_t>& input_len,
const std::vector<ck_tile::index_t>& strides,
const std::vector<ck_tile::index_t>& dilations,
const std::vector<ck_tile::index_t>& left_pads,
const std::vector<ck_tile::index_t>& right_pads)
: num_dim_spatial_(static_cast<ck_tile::long_index_t>(n_dim)),
G_(static_cast<ck_tile::long_index_t>(group_count)),
N_(static_cast<ck_tile::long_index_t>(n_batch)),
K_(static_cast<ck_tile::long_index_t>(n_out_channels)),
C_(static_cast<ck_tile::long_index_t>(n_in_channels)),
filter_spatial_lengths_(num_dim_spatial_),
input_spatial_lengths_(num_dim_spatial_),
output_spatial_lengths_(num_dim_spatial_),
conv_filter_strides_(num_dim_spatial_),
conv_filter_dilations_(num_dim_spatial_),
input_left_pads_(num_dim_spatial_),
input_right_pads_(num_dim_spatial_)
{
if(static_cast<ck_tile::index_t>(filter_spatial_lengths_.size()) != num_dim_spatial_ ||
static_cast<ck_tile::index_t>(input_spatial_lengths_.size()) != num_dim_spatial_ ||
static_cast<ck_tile::index_t>(conv_filter_strides_.size()) != num_dim_spatial_ ||
static_cast<ck_tile::index_t>(conv_filter_dilations_.size()) != num_dim_spatial_ ||
static_cast<ck_tile::index_t>(input_left_pads_.size()) != num_dim_spatial_ ||
static_cast<ck_tile::index_t>(input_right_pads_.size()) != num_dim_spatial_)
{
throw(std::runtime_error(
"ConvParam::ConvParam: "
"parameter size is different from number of declared dimensions!"));
}
for(ck_tile::index_t i = 0; i < num_dim_spatial_; ++i)
{
filter_spatial_lengths_[i] = static_cast<ck_tile::long_index_t>(filters_len[i]);
input_spatial_lengths_[i] = static_cast<ck_tile::long_index_t>(input_len[i]);
conv_filter_strides_[i] = static_cast<ck_tile::long_index_t>(strides[i]);
conv_filter_dilations_[i] = static_cast<ck_tile::long_index_t>(dilations[i]);
input_left_pads_[i] = static_cast<ck_tile::long_index_t>(left_pads[i]);
input_right_pads_[i] = static_cast<ck_tile::long_index_t>(right_pads[i]);
// XEff = (X - 1) * conv_dilation_w + 1;
// Wo = (Wi + in_left_pad_w + in_right_pad_w - XEff) / conv_stride_w + 1;
const ck_tile::long_index_t x_eff =
(filter_spatial_lengths_[i] - 1) * conv_filter_dilations_[i] + 1;
output_spatial_lengths_[i] =
(input_spatial_lengths_[i] + input_left_pads_[i] + input_right_pads_[i] - x_eff) /
conv_filter_strides_[i] +
1;
}
}
ConvParam(ck_tile::long_index_t n_dim,
ck_tile::long_index_t group_count,
ck_tile::long_index_t n_batch,
ck_tile::long_index_t n_out_channels,
ck_tile::long_index_t n_in_channels,
const std::vector<ck_tile::long_index_t>& filters_len,
const std::vector<ck_tile::long_index_t>& input_len,
const std::vector<ck_tile::long_index_t>& strides,
const std::vector<ck_tile::long_index_t>& dilations,
const std::vector<ck_tile::long_index_t>& left_pads,
const std::vector<ck_tile::long_index_t>& right_pads)
: num_dim_spatial_(n_dim),
G_(group_count),
N_(n_batch),
K_(n_out_channels),
C_(n_in_channels),
filter_spatial_lengths_(filters_len),
input_spatial_lengths_(input_len),
output_spatial_lengths_(num_dim_spatial_),
conv_filter_strides_(strides),
conv_filter_dilations_(dilations),
input_left_pads_(left_pads),
input_right_pads_(right_pads)
{
if(static_cast<ck_tile::index_t>(filter_spatial_lengths_.size()) != num_dim_spatial_ ||
static_cast<ck_tile::index_t>(input_spatial_lengths_.size()) != num_dim_spatial_ ||
static_cast<ck_tile::index_t>(conv_filter_strides_.size()) != num_dim_spatial_ ||
static_cast<ck_tile::index_t>(conv_filter_dilations_.size()) != num_dim_spatial_ ||
static_cast<ck_tile::index_t>(input_left_pads_.size()) != num_dim_spatial_ ||
static_cast<ck_tile::index_t>(input_right_pads_.size()) != num_dim_spatial_)
{
throw(std::runtime_error(
"ConvParam::ConvParam: "
"parameter size is different from number of declared dimensions!"));
}
for(ck_tile::index_t i = 0; i < num_dim_spatial_; ++i)
{
// XEff = (X - 1) * conv_dilation_w + 1;
// Wo = (Wi + in_left_pad_w + in_right_pad_w - XEff) / conv_stride_w + 1;
const ck_tile::long_index_t x_eff =
(filter_spatial_lengths_[i] - 1) * conv_filter_dilations_[i] + 1;
output_spatial_lengths_[i] =
(input_spatial_lengths_[i] + input_left_pads_[i] + input_right_pads_[i] - x_eff) /
conv_filter_strides_[i] +
1;
}
}
ck_tile::long_index_t num_dim_spatial_;
ck_tile::long_index_t G_;
ck_tile::long_index_t N_;
ck_tile::long_index_t K_;
ck_tile::long_index_t C_;
std::vector<ck_tile::long_index_t> filter_spatial_lengths_;
std::vector<ck_tile::long_index_t> input_spatial_lengths_;
std::vector<ck_tile::long_index_t> output_spatial_lengths_;
std::vector<ck_tile::long_index_t> conv_filter_strides_;
std::vector<ck_tile::long_index_t> conv_filter_dilations_;
std::vector<ck_tile::long_index_t> input_left_pads_;
std::vector<ck_tile::long_index_t> input_right_pads_;
std::vector<ck_tile::long_index_t> GetOutputSpatialLengths() const
{
return output_spatial_lengths_;
}
std::size_t GetFlops() const
{
// 2 * G * N * K * C * <output spatial lengths product> * <filter spatial lengths product>
return static_cast<std::size_t>(2) * G_ * N_ * K_ * C_ *
std::accumulate(std::begin(output_spatial_lengths_),
std::next(std::begin(output_spatial_lengths_), num_dim_spatial_),
1,
std::multiplies<>()) *
std::accumulate(std::begin(filter_spatial_lengths_),
std::next(std::begin(filter_spatial_lengths_), num_dim_spatial_),
1,
std::multiplies<>());
}
template <typename InDataType>
std::size_t GetInputByte() const
{
// sizeof(InDataType) * (G * N * C * <input spatial lengths product>) +
return sizeof(InDataType) *
(G_ * N_ * C_ *
std::accumulate(std::begin(input_spatial_lengths_),
std::next(std::begin(input_spatial_lengths_), num_dim_spatial_),
1,
std::multiplies<>()));
}
template <typename WeiDataType>
std::size_t GetWeightByte() const
{
// sizeof(WeiDataType) * (G * K * C * <filter spatial lengths product>) +
return sizeof(WeiDataType) *
(G_ * K_ * C_ *
std::accumulate(std::begin(filter_spatial_lengths_),
std::next(std::begin(filter_spatial_lengths_), num_dim_spatial_),
1,
std::multiplies<>()));
}
template <typename OutDataType>
std::size_t GetOutputByte() const
{
// sizeof(OutDataType) * (G * N * K * <output spatial lengths product>);
return sizeof(OutDataType) * (G_ * N_ * K_ *
std::accumulate(std::begin(output_spatial_lengths_),
std::end(output_spatial_lengths_),
static_cast<std::size_t>(1),
std::multiplies<std::size_t>()));
}
template <typename InDataType, typename WeiDataType, typename OutDataType>
std::size_t GetByte() const
{
return GetInputByte<InDataType>() + GetWeightByte<WeiDataType>() +
GetOutputByte<OutDataType>();
}
};
CK_TILE_HOST std::string get_conv_param_parser_helper_msg()
{
std::string msg;
msg += "Following arguments (depending on number of spatial dims):\n"
" Number of spatial dimensions (1=Conv1d, 2=Conv2d, 3=Conv3d)\n"
" G, N, K, C, \n"
" <filter spatial dimensions>, (ie Y, X for 2D)\n"
" <input image spatial dimensions>, (ie Hi, Wi for 2D)\n"
" <strides>, (ie Sy, Sx for 2D)\n"
" <dilations>, (ie Dy, Dx for 2D)\n"
" <left padding>, (ie LeftPy, LeftPx for 2D)\n"
" <right padding>, (ie RightPy, RightPx for 2D)\n";
return msg;
}
CK_TILE_HOST ck_tile::conv::ConvParam
parse_conv_param(int num_dim_spatial, int arg_idx, char* const argv[])
{
const ck_tile::long_index_t G = std::stol(argv[arg_idx++]);
const ck_tile::long_index_t N = std::stol(argv[arg_idx++]);
const ck_tile::long_index_t K = std::stol(argv[arg_idx++]);
const ck_tile::long_index_t C = std::stol(argv[arg_idx++]);
std::vector<ck_tile::long_index_t> filter_spatial_lengths(num_dim_spatial);
std::vector<ck_tile::long_index_t> input_spatial_lengths(num_dim_spatial);
std::vector<ck_tile::long_index_t> conv_filter_strides(num_dim_spatial);
std::vector<ck_tile::long_index_t> conv_filter_dilations(num_dim_spatial);
std::vector<ck_tile::long_index_t> input_left_pads(num_dim_spatial);
std::vector<ck_tile::long_index_t> input_right_pads(num_dim_spatial);
for(int i = 0; i < num_dim_spatial; ++i)
{
filter_spatial_lengths[i] = std::stol(argv[arg_idx++]);
}
for(int i = 0; i < num_dim_spatial; ++i)
{
input_spatial_lengths[i] = std::stol(argv[arg_idx++]);
}
for(int i = 0; i < num_dim_spatial; ++i)
{
conv_filter_strides[i] = std::stol(argv[arg_idx++]);
}
for(int i = 0; i < num_dim_spatial; ++i)
{
conv_filter_dilations[i] = std::stol(argv[arg_idx++]);
}
for(int i = 0; i < num_dim_spatial; ++i)
{
input_left_pads[i] = std::stol(argv[arg_idx++]);
}
for(int i = 0; i < num_dim_spatial; ++i)
{
input_right_pads[i] = std::stol(argv[arg_idx++]);
}
return ck_tile::conv::ConvParam{num_dim_spatial,
G,
N,
K,
C,
filter_spatial_lengths,
input_spatial_lengths,
conv_filter_strides,
conv_filter_dilations,
input_left_pads,
input_right_pads};
}
} // namespace conv
} // namespace ck_tile
include/ck_tile/host/host_tensor.hpp
View file @ 3dc5db72
......@@ -176,7 +176,20 @@ struct HostTensorDescriptor
return std::inner_product(iss.begin(), iss.end(), mStrides.begin(), std::size_t{0});
}
friend std::ostream& operator<<(std::ostream& os, const HostTensorDescriptor& desc);
friend std::ostream& operator<<(std::ostream& os, const HostTensorDescriptor& desc)
{
os << "dim " << desc.get_num_of_dimension() << ", ";
os << "lengths {";
LogRange(os, desc.get_lengths(), ", ");
os << "}, ";
os << "strides {";
LogRange(os, desc.get_strides(), ", ");
os << "}";
return os;
}
private:
std::vector<std::size_t> mLens;
......
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