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gemm/Conv xdlops + dlops quantization (#625)

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* Add conv perlayer quantization

* Add gemm_dlops quantization

* Support int8 for innerproduct

* Refine gemm dlops int8 kernel parameter

* Support gfx908(MI100) and gfx90a(MI200)

* clang-format

* Rename example number

* Support different layout for d tensor

* Add conv dlops perchannel quantization example

* Move to example 40

* Extract the common code for different platform (dlops and xdlops)

* Move ot subfolder. Prepare to add other op of quantization

* Refine the quantization instance library

* Add conv dl instances and client example

* Remove unnecessary type

* Add gemm quantization instance

* Add external api and client example

* Refine num_bytes

* Separete different layout to different cpp

* Add more xdl instances

* Revert "Remove unnecessary type"

This reverts commit 820869182f.

* Remove CShuffleDataType in dlops
Let acc and CShuffleDataType be the same in xdlops

---------

Co-authored-by: zjing14 <zhangjing14@gmail.com>
This commit is contained in:
rocking5566
2023-03-16 04:29:40 +08:00
committed by GitHub
parent a2d5ca8e95
commit 16dc18e0f9
60 changed files with 3186 additions and 717 deletions
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12
include/ck/tensor_operation/gpu/device/device_grouped_conv_fwd_dl_multiple_d_nhwc_kyxc_nhwk.hpp
View File

@@ -134,7 +134,8 @@ __global__ void
const Block2CTileMap block_2_ctile_map,
const ComputePtrOffsetOfBatch compute_ptr_offset_of_batch)
{
#if(!defined(__HIP_DEVICE_COMPILE__) || defined(__gfx906__) || defined(__gfx1030__))
#if(!defined(__HIP_DEVICE_COMPILE__) || defined(__gfx906__) || defined(__gfx1030__) || \
defined(__gfx90a__) || defined(__gfx908__))
// offset base pointer for each work-group
const index_t num_blocks_per_batch =
__builtin_amdgcn_readfirstlane(get_grid_size() / batch_count);
@@ -314,9 +315,8 @@ struct DeviceGroupedConvFwdDlMultipleD_NHWC_KYXC_NHWK
const auto in_gemmm_gemmk_desc =
matrix_padder.PadADescriptor_M_K(in_gemmmraw_gemmkraw_desc);
const auto M = in_gemmm_gemmk_desc.GetLength(I0);
const auto K = in_gemmm_gemmk_desc.GetLength(I1);
const auto M = in_gemmm_gemmk_desc.GetLength(I0);
const auto K = in_gemmm_gemmk_desc.GetLength(I1);
const auto AK0 = K / K1;
return transform_tensor_descriptor(
@@ -709,7 +709,8 @@ struct DeviceGroupedConvFwdDlMultipleD_NHWC_KYXC_NHWK
namespace ctc = tensor_layout::convolution;
// check device
if(!(ck::get_device_name() == "gfx906" || ck::get_device_name() == "gfx1030"))
if(!(ck::get_device_name() == "gfx906" || ck::get_device_name() == "gfx1030" ||
ck::get_device_name() == "gfx90a" || ck::get_device_name() == "gfx908"))
{
return false;
}
@@ -834,6 +835,7 @@ struct DeviceGroupedConvFwdDlMultipleD_NHWC_KYXC_NHWK
{
return false;
}
// check Gridwise GEMM
return GridwiseGemm::CheckValidity(
arg.a_grid_desc_ak0_m_ak1_, arg.b_grid_desc_bk0_n_bk1_, arg.e_grid_desc_m_n_);

4
include/ck/tensor_operation/gpu/device/impl/device_gemm_multiple_d_dl.hpp
View File

@@ -51,7 +51,7 @@ __global__ void
const Block2CTileMap block_2_ctile_map)
{
#if(!defined(__HIP_DEVICE_COMPILE__) || defined(__gfx906__) || defined(__gfx908__) || \
defined(__gfx1030__))
defined(__gfx90a__) || defined(__gfx1030__))
constexpr index_t shared_block_size =
GridwiseGemm::GetSharedMemoryNumberOfByte() / sizeof(ABDataType);
@@ -552,7 +552,7 @@ struct DeviceGemmMultipleD_Dl : public DeviceGemmMultipleD<ALayout,
static bool IsSupportedArgument(const Argument& arg)
{
if(ck::get_device_name() == "gfx906" || ck::get_device_name() == "gfx908" ||
ck::get_device_name() == "gfx1030")
ck::get_device_name() == "gfx90a" || ck::get_device_name() == "gfx1030")
{
return GridwiseGemm::CheckValidity(
arg.a_grid_desc_k0_m_k1_, arg.b_grid_desc_k0_n_k1_, arg.e_grid_desc_m_n_);

61
include/ck/tensor_operation/gpu/element/quantization_operation.hpp
View File

@@ -1,6 +1,7 @@
#pragma once
#include "ck/utility/data_type.hpp"
// #include "ck/utility/get_id.hpp"
namespace ck {
namespace tensor_operation {
@@ -17,18 +18,27 @@ struct Activation_Mul_Clamp
__host__ __device__ constexpr void operator()(int8_t& y, const int32_t& x) const
{
float x_fp32 = ck::type_convert<float>(x);
activationOp_(x_fp32, x_fp32);
float y_fp32 = math::clamp(requantScale_ * x_fp32, -128.f, 127.f);
y = ck::type_convert<int8_t>(y_fp32);
float y_fp32 = ck::type_convert<float>(x);
activationOp_(y_fp32, y_fp32);
y_fp32 = math::clamp(requantScale_ * y_fp32, -128.f, 127.f);
y = ck::type_convert<int8_t>(y_fp32);
}
__host__ __device__ constexpr void operator()(float& y, const int32_t& x) const
__device__ constexpr void operator()(int32_t& y, const int32_t& x) const
{
// We might type_convert to int8 after lambda in someplace
float x_fp32 = ck::type_convert<float>(x);
activationOp_(x_fp32, x_fp32);
y = math::clamp(requantScale_ * x_fp32, -128.f, 127.f);
// CAUSION - We might type_convert to int8 in threadwise copy
// eg. GridwiseGemmDlMultipleD_km_kn_mn
float y_fp32 = ck::type_convert<float>(x);
activationOp_(y_fp32, y_fp32);
y_fp32 = math::clamp(requantScale_ * y_fp32, -128.f, 127.f);
y = ck::type_convert<int32_t>(y_fp32);
}
__host__ constexpr void operator()(float& y, const float& x) const
{
// CAUSION - We might float in & float out in reference code
activationOp_(y, x);
y = math::clamp(requantScale_ * y, -128.f, 127.f);
}
float requantScale_;
@@ -51,6 +61,17 @@ struct Activation_Mul2_Clamp
y = ck::type_convert<int8_t>(y_fp32);
}
__device__ constexpr void
operator()(int32_t& y, const int32_t& x, const float& requantScale) const
{
// CAUSION - We might type_convert to int8 in threadwise copy
// eg. GridwiseGemmDlMultipleD_km_kn_mn
float y_fp32 = ck::type_convert<float>(x);
activationOp_(y_fp32, y_fp32);
y_fp32 = math::clamp(requantScale * y_fp32, -128.f, 127.f);
y = ck::type_convert<int32_t>(y_fp32);
}
Activation activationOp_;
};
@@ -72,6 +93,17 @@ struct Add_Activation_Mul_Clamp
y = ck::type_convert<int8_t>(y_fp32);
}
__host__ __device__ constexpr void
operator()(int32_t& y, const int32_t& x, const int32_t& bias) const
{
// CAUSION - We might type_convert to int8 in threadwise copy
// eg. GridwiseGemmDlMultipleD_km_kn_mn
float y_fp32 = ck::type_convert<float>(x + bias);
activationOp_(y_fp32, y_fp32);
y_fp32 = math::clamp(requantScale_ * y_fp32, -128.f, 127.f);
y = ck::type_convert<int32_t>(y_fp32);
}
float requantScale_;
Activation activationOp_;
};
@@ -92,6 +124,17 @@ struct Add_Activation_Mul2_Clamp
y = ck::type_convert<int8_t>(y_fp32);
}
__host__ __device__ constexpr void
operator()(int32_t& y, const int32_t& x, const int32_t& bias, const float& requantScale) const
{
// CAUSION - We might type_convert to int8 in threadwise copy
// eg. GridwiseGemmDlMultipleD_km_kn_mn
float y_fp32 = ck::type_convert<float>(x + bias);
activationOp_(y_fp32, y_fp32);
y_fp32 = math::clamp(requantScale * y_fp32, -128.f, 127.f);
y = ck::type_convert<int32_t>(y_fp32);
}
Activation activationOp_;
};

4
include/ck/tensor_operation/gpu/grid/gridwise_gemm_dl_multiple_d.hpp
View File

@@ -185,8 +185,10 @@ struct GridwiseGemmDlMultipleD_km_kn_mn
return b_grid_desc_k0_n0_n1_k1;
}
// E desc for destination in blockwise copy
template <typename CGridDesc_M_N_>
__host__ __device__ static constexpr auto
MakeCGridDescriptor_M0_M10_M11_N0_N10_N11(const CGridDesc_M_N& c_grid_desc_m_n)
MakeCGridDescriptor_M0_M10_M11_N0_N10_N11(const CGridDesc_M_N_& c_grid_desc_m_n)
{
const auto M = c_grid_desc_m_n.GetLength(I0);
const auto N = c_grid_desc_m_n.GetLength(I1);