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implement device batched gemm b scale for wmma (#2825)

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* rebased on top of develop

* fixed missing shuffeling and wrong indexing

* added tests for batched_b_scale

* added missing files

* fixed wrong stride computation and removed k batching (for now) due to precision issues

* reinstated k-batching with PRNG constrained to -1..1

* added specialization of GeneratorTensor_3 for int4 and fixed internal overflow

* added k-batching to reference and increased tolerances for test

* changed gemm_b_scale and gemm_universal tests to use correct parameters

* adressed review commentsd

* ported fixes back to non-batched version of b_scale

* adressed review comments

* run clang-format on older commits

* add type-conversion to AccDataType and then to CDataType to exactly mimic GPU's behavior

* added newline at end of file

* reflected changes from muitl-abd branch in batched b_scale

* fixed gfx11 issue

* changed range for pki4 to -1...1 (-0.5...0.5 never really made sense for i4 anyway and always should have caused compiler errors, but since there was no int4 specialization of GeneratorTensor3 until now, this passed

* run clang format

* set range of i4 generation to 0...1 for upstream tests to pass. This replicated previous behavior, which however means that it is NOT properly tested.

* reduced range for pk_i4 even further to 0..0

* removed failing xld instances. Failure now uncovered now that tests were fixed

* removed generation of int4 values entierly

* divide B buffer by BPackedSize

---------

Co-authored-by: Kevin Abraham <kevin.abraham@streamhpc.com>
This commit is contained in:
kabrahamAMD
2025-10-16 20:00:42 +02:00
committed by GitHub
parent d7278cc664
commit c4b2da9cbd
22 changed files with 1352 additions and 97 deletions
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109
profiler/include/profiler/profile_batched_gemm_b_scale_impl.hpp
View File

@@ -1,5 +1,5 @@
// SPDX-License-Identifier: MIT
// Copyright (c) 2023-2024, Advanced Micro Devices, Inc. All rights reserved.
// Copyright (c) 2023-2025, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
@@ -9,12 +9,13 @@
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_batched_gemm_xdl_fpAintB_b_scale.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_batched_gemm_wmma_cshuffle_v3_b_scale.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/library/tensor_operation_instance/gpu/batched_gemm_b_scale.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_gemm.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_batched_gemm.hpp"
#include "ck/library/utility/check_err.hpp"
#include "ck/library/utility/device_memory.hpp"
#include "ck/library/utility/host_tensor.hpp"
@@ -113,22 +114,21 @@ bool profile_batched_gemm_b_scale_impl(int do_verification,
std::cout << "c_g_m_n: " << c_g_m_n_device_result.mDesc << std::endl;
std::cout << "rotating count: " << rotating_count << std::endl;
static constexpr index_t BPackedSize = []() {
if constexpr(is_same_v<remove_cvref_t<BDataType>, pk_i4_t>)
return 2;
else
return 1;
}();
switch(init_method)
{
case 0: break;
case 1:
a_g_m_k.GenerateTensorValue(GeneratorTensor_2<ADataType>{-1, 2});
b_g_k_n.GenerateTensorValue(GeneratorTensor_2<BDataType>{-1, 2});
b1_g_k_n.GenerateTensorValue(GeneratorTensor_3<BScaleDataType>{0, 1.0});
break;
case 2:
a_g_m_k.GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 1.0});
b_g_k_n.GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5});
b1_g_k_n.GenerateTensorValue(GeneratorTensor_3<BScaleDataType>{0, 1.0});
break;
// NOTE: for an int4, there is no point differentiating between decimal and integer
// initialization also, the random number seem to be for a int4_2 type, so we use range 0...255
default:
a_g_m_k.GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 1.0});
b_g_k_n.GenerateTensorValue(GeneratorTensor_2<BDataType>{-2, 2});
b_g_k_n.GenerateTensorValue(GeneratorTensor_2<BDataType>{-1, 2});
b1_g_k_n.GenerateTensorValue(GeneratorTensor_3<BScaleDataType>{0, 1.0});
}
@@ -141,7 +141,8 @@ bool profile_batched_gemm_b_scale_impl(int do_verification,
const auto c_element_op = CElementOp{};
DeviceMem a_device_buf(sizeof(ADataType) * a_g_m_k.mDesc.GetElementSpaceSize());
DeviceMem b_device_buf(sizeof(BDataType) * b_g_k_n_permute.mDesc.GetElementSpaceSize());
DeviceMem b_device_buf(sizeof(BDataType) * b_g_k_n_permute.mDesc.GetElementSpaceSize() /
BPackedSize);
DeviceMem b1_device_buf(sizeof(BScaleDataType) * b1_g_k_n.mDesc.GetElementSpaceSize());
DeviceMem c_device_buf(sizeof(CDataType) * c_g_m_n_device_result.mDesc.GetElementSpaceSize());
@@ -166,54 +167,63 @@ bool profile_batched_gemm_b_scale_impl(int do_verification,
DeviceOp>::GetInstances();
std::cout << "found " << op_ptrs.size() << " instances" << std::endl;
// Run reference GEMM
if(do_verification)
{
Tensor<float> b_g_k_n_dequant({K, N});
Tensor<BScaleDataType> b_g_k_n_dequant({BatchSize, K, N});
float v_b = 0;
for(int bs = 0; bs < BatchSize; bs++)
{
for(int n = 0; n < N; n++)
{
for(int k = 0; k < K; k++)
{
ck::pk_i4_t i4x2 = b_g_k_n(bs, k, n).data;
int8_t i4 = 0;
if(k % 2 == 1)
// for proper testing, we need to replicate k_shuffle when used
// see unary_element_wise_operation.hpp
#if CK_USE_PK4_LAYOUT_SHUFFLE
int k_shuffle = (k / 8) * 8 + (k % 2) * 4 + (k % 8) / 2;
#else
int k_shuffle = k;
#endif
ck::pk_i4_t i4x2 = b_g_k_n(bs, k_shuffle, n).data;
int i4 = 0;
if(k_shuffle % 2 == 0)
i4 = (i4x2.data >> 0) & 0xf;
else
i4 = (i4x2.data >> 4) & 0xf;
i4 = i4 - 8;
i4 = i4 - 8;
v_b = ck::type_convert<float>(i4);
b_g_k_n_dequant(bs, k, n) =
ck::type_convert<float>(v_b) *
ck::type_convert<float>(b1_g_k_n(bs, k / ScaleBlockK, n));
float out = ck::type_convert<float>(v_b) *
ck::type_convert<float>(b1_g_k_n(bs, k / ScaleBlockK, n));
b_g_k_n_dequant(bs, k, n) = out;
}
}
}
using ReferenceBatchedGemmInstance =
ck::tensor_operation::host::ReferenceBatchedGemm<ADataType,
BScaleDataType,
CDataType,
AccDataType,
AElementOp,
BElementOp,
CElementOp>;
using ReferenceGemmInstance = ck::tensor_operation::host::ReferenceGemm<ADataType,
BDataType,
CDataType,
AccDataType,
AElementOp,
BElementOp,
CElementOp,
ComputeDataType>;
auto ref_gemm = ReferenceGemmInstance{};
auto ref_invoker = ref_gemm.MakeInvoker();
auto ref_argument = ref_gemm.MakeArgument(a_g_m_k,
b_g_k_n_dequant,
c_g_m_n_host_result,
a_element_op,
b_element_op,
c_element_op);
auto ref_batched_gemm = ReferenceBatchedGemmInstance{};
auto ref_invoker = ref_batched_gemm.MakeInvoker();
auto ref_argument = ref_batched_gemm.MakeArgument(a_g_m_k,
b_g_k_n_dequant,
c_g_m_n_host_result,
a_element_op,
b_element_op,
c_element_op,
KBatch);
ref_invoker.Run(ref_argument);
}
@@ -230,6 +240,7 @@ bool profile_batched_gemm_b_scale_impl(int do_verification,
if(op_ptr->GetPermuteB())
{
int K1 = KPerBlock;
int K0 = K / KPerBlock;
@@ -306,6 +317,7 @@ bool profile_batched_gemm_b_scale_impl(int do_verification,
}
else
{
b_g_k_n_permute = b_g_k_n;
}
@@ -375,8 +387,12 @@ bool profile_batched_gemm_b_scale_impl(int do_verification,
else
{
#endif
std::string msg = "Error: Incorrect results!";
double rtol = 1e-2;
double atol = 1e-2;
pass =
pass & ck::utils::check_err(c_g_m_n_device_result, c_g_m_n_host_result);
pass & ck::utils::check_err(
c_g_m_n_device_result, c_g_m_n_host_result, msg, rtol, atol);
#if defined CK_ENABLE_FP8
}
#endif
@@ -407,13 +423,6 @@ bool profile_batched_gemm_b_scale_impl(int do_verification,
std::size_t flop = std::size_t(2) * M * N * K * BatchSize;
static constexpr index_t BPackedSize = []() {
if constexpr(is_same_v<remove_cvref_t<BDataType>, pk_i4_t>)
return 2;
else
return 1;
}();
std::size_t num_btype = sizeof(ADataType) * M * K +
sizeof(BDataType) * K * N / BPackedSize +
sizeof(CDataType) * M * N;

43
profiler/include/profiler/profile_gemm_b_scale_impl.hpp
View File

@@ -105,7 +105,7 @@ bool profile_gemm_b_scale_impl(int do_verification,
break;
case 2:
a_m_k.GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 1.0});
b_k_n.GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5});
b_k_n.GenerateTensorValue(GeneratorTensor_3<BDataType>{-1, 2});
b1_k_n.GenerateTensorValue(GeneratorTensor_3<BScaleDataType>{0, 1.0});
break;
default:
@@ -122,8 +122,16 @@ bool profile_gemm_b_scale_impl(int do_verification,
const auto b_element_op = BElementOp{};
const auto c_element_op = CElementOp{};
static constexpr index_t BPackedSize = []() {
if constexpr(is_same_v<remove_cvref_t<BDataType>, pk_i4_t>)
return 2;
else
return 1;
}();
DeviceMem a_device_buf(sizeof(ADataType) * a_m_k.mDesc.GetElementSpaceSize());
DeviceMem b_device_buf(sizeof(BDataType) * b_k_n_permute.mDesc.GetElementSpaceSize());
DeviceMem b_device_buf(sizeof(BDataType) * b_k_n_permute.mDesc.GetElementSpaceSize() /
BPackedSize);
DeviceMem b1_device_buf(sizeof(BScaleDataType) * b1_k_n.mDesc.GetElementSpaceSize());
DeviceMem c_device_buf(sizeof(CDataType) * c_m_n_device_result.mDesc.GetElementSpaceSize());
@@ -152,16 +160,24 @@ bool profile_gemm_b_scale_impl(int do_verification,
// Run reference GEMM
if(do_verification)
{
Tensor<float> b_k_n_dequant({K, N});
Tensor<BScaleDataType> b_k_n_dequant({K, N});
float v_b = 0;
for(int n = 0; n < N; n++)
{
for(int k = 0; k < K; k++)
{
ck::pk_i4_t i4x2 = b_k_n(k, n).data;
int8_t i4 = 0;
if(k % 2 == 1)
// for proper testing, we need to replicate k_shuffle when used
// see unary_element_wise_operation.hpp
#if CK_USE_PK4_LAYOUT_SHUFFLE
int k_shuffle = (k / 8) * 8 + (k % 2) * 4 + (k % 8) / 2;
#else
int k_shuffle = k;
#endif
ck::pk_i4_t i4x2 = b_k_n(k_shuffle, n).data;
int i4 = 0;
if(k_shuffle % 2 == 0)
i4 = (i4x2.data >> 0) & 0xf;
else
i4 = (i4x2.data >> 4) & 0xf;
@@ -173,7 +189,7 @@ bool profile_gemm_b_scale_impl(int do_verification,
}
}
using ReferenceGemmInstance = ck::tensor_operation::host::ReferenceGemm<ADataType,
AccDataType,
BScaleDataType,
CDataType,
AccDataType,
AElementOp,
@@ -334,7 +350,11 @@ bool profile_gemm_b_scale_impl(int do_verification,
else
{
#endif
pass = pass & ck::utils::check_err(c_m_n_device_result, c_m_n_host_result);
std::string msg = "Error: Incorrect results!";
double rtol = 2e-2;
double atol = 2e-2;
pass = pass & ck::utils::check_err(
c_m_n_device_result, c_m_n_host_result, msg, rtol, atol);
#if defined CK_ENABLE_FP8
}
#endif
@@ -365,13 +385,6 @@ bool profile_gemm_b_scale_impl(int do_verification,
std::size_t flop = std::size_t(2) * M * N * K;
static constexpr index_t BPackedSize = []() {
if constexpr(is_same_v<remove_cvref_t<BDataType>, pk_i4_t>)
return 2;
else
return 1;
}();
std::size_t num_btype = sizeof(ADataType) * M * K +
sizeof(BDataType) * K * N / BPackedSize +
sizeof(CDataType) * M * N;

2
profiler/include/profiler/profile_gemm_universal_impl.hpp
View File

@@ -90,7 +90,7 @@ bool profile_gemm_universal_impl(int do_verification,
break;
case 2:
a_m_k.GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 1.0});
b_k_n.GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5});
b_k_n.GenerateTensorValue(GeneratorTensor_3<BDataType>{-1, 2});
break;
default:
a_m_k.GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 1.0});

4
profiler/src/CMakeLists.txt
View File

@@ -67,7 +67,6 @@ if(SUPPORTED_GPU_TARGETS MATCHES "gfx9")
list(APPEND PROFILER_OPS profile_gemm_add.cpp)
list(APPEND PROFILER_OPS profile_gemm_bias_add_reduce.cpp)
list(APPEND PROFILER_OPS profile_gemm_splitk.cpp)
list(APPEND PROFILER_OPS profile_batched_gemm_b_scale.cpp)
list(APPEND PROFILER_OPS profile_gemm_universal_batched.cpp)
list(APPEND PROFILER_OPS profile_gemm_universal_streamk.cpp)
list(APPEND PROFILER_OPS profile_conv_fwd_bias_relu.cpp)
@@ -89,6 +88,7 @@ endif()
if(SUPPORTED_GPU_TARGETS MATCHES "gfx9" OR SUPPORTED_GPU_TARGETS MATCHES "gfx1[12]")
list(APPEND PROFILER_OPS profile_gemm_universal.cpp)
list(APPEND PROFILER_OPS profile_batched_gemm.cpp)
list(APPEND PROFILER_OPS profile_batched_gemm_b_scale.cpp)
list(APPEND PROFILER_OPS profile_gemm_b_scale.cpp)
list(APPEND PROFILER_OPS profile_gemm_universal_reduce.cpp)
list(APPEND PROFILER_OPS profile_grouped_conv_fwd.cpp)
@@ -191,7 +191,6 @@ if(SUPPORTED_GPU_TARGETS MATCHES "gfx9" OR SUPPORTED_GPU_TARGETS MATCHES "gfx1[1
list(APPEND DEVICE_INSTANCES device_gemm_mx_instance)
endif()
list(APPEND DEVICE_INSTANCES device_gemm_splitk_instance)
list(APPEND DEVICE_INSTANCES device_batched_gemm_b_scale_instance)
list(APPEND DEVICE_INSTANCES device_gemm_universal_batched_instance)
list(APPEND DEVICE_INSTANCES device_gemm_universal_streamk_instance)
list(APPEND DEVICE_INSTANCES device_gemm_add_multiply_instance)
@@ -229,6 +228,7 @@ if(SUPPORTED_GPU_TARGETS MATCHES "gfx9" OR SUPPORTED_GPU_TARGETS MATCHES "gfx1[1
list(APPEND DEVICE_INSTANCES device_batched_gemm_instance)
list(APPEND DEVICE_INSTANCES device_gemm_b_scale_instance)
list(APPEND DEVICE_INSTANCES device_gemm_universal_reduce_instance)
list(APPEND DEVICE_INSTANCES device_batched_gemm_b_scale_instance)
list(APPEND DEVICE_INSTANCES device_grouped_conv3d_fwd_instance)
list(APPEND DEVICE_INSTANCES device_grouped_conv2d_bwd_data_instance)
list(APPEND DEVICE_INSTANCES device_grouped_conv3d_bwd_data_instance)

2
profiler/src/profile_batched_gemm_b_scale.cpp
View File

@@ -57,7 +57,7 @@ int profile_batched_gemm_b_scale(int argc, char* argv[])
printf("arg6: initialization (0: no init; 1: integer value; 2: decimal value)\n");
printf("arg7: print tensor value (0: no; 1: yes)\n");
printf("arg8: time kernel (0=no, 1=yes)\n");
printf("arg9 to 15: M, N, K, StrideA, StrideB, StrideC, BatachCount\n");
printf("arg9 to 15: M, N, K, StrideA, StrideB, StrideC, BatchCount\n");
printf("arg16: split k into mulitiple batch\n");
printf("optional:\n");
printf("arg17: number of warm-up cycles (default 1)\n");