mirror of
https://github.com/ROCm/composable_kernel.git
synced 2026-04-20 06:49:15 +00:00
implement device batched gemm b scale for wmma (#2825)
* 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
@@ -264,7 +264,7 @@ struct GeneratorTensor_2<ck::pk_i4_t>
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{
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int hi = std::rand() % (max_value - min_value) + min_value + 8;
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int lo = std::rand() % (max_value - min_value) + min_value + 8;
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ck::pk_i4_t r = ((hi << 4) + lo) & 0xff;
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ck::pk_i4_t r = (((hi & 0xf) << 4) + (lo & 0xf));
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return r;
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}
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};
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@@ -436,6 +436,22 @@ struct GeneratorTensor_3<ck::f4x2_pk_t>
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}
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};
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template <>
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struct GeneratorTensor_3<ck::pk_i4_t>
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{
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int min_value = 0;
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int max_value = 1;
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template <typename... Is>
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ck::pk_i4_t operator()(Is...)
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{
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int hi = std::rand() % (max_value - min_value) + min_value + 8;
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int lo = std::rand() % (max_value - min_value) + min_value + 8;
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ck::pk_i4_t r = (((hi & 0xf) << 4) + (lo & 0xf));
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return r;
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}
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};
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template <>
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struct GeneratorTensor_3<ck::f6x32_pk_t>
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{
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@@ -0,0 +1,836 @@
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// SPDX-License-Identifier: MIT
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// Copyright (c) 2025, Advanced Micro Devices, Inc. All rights reserved.
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#pragma once
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#include <iostream>
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#include <sstream>
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#include "ck/utility/common_header.hpp"
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#include "ck/tensor_description/tensor_descriptor.hpp"
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#include "ck/tensor_description/tensor_descriptor_helper.hpp"
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#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
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#include "ck/tensor_operation/gpu/device/device_batched_gemm.hpp"
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#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
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#include "ck/tensor_operation/gpu/grid/gridwise_gemm_wmma_cshuffle_v3_b_scale.hpp"
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#include "ck/host_utility/device_prop.hpp"
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#include "ck/host_utility/kernel_launch.hpp"
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#include "ck/host_utility/flush_cache.hpp"
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namespace ck {
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namespace tensor_operation {
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namespace device {
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template <typename GridwiseGemm,
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typename ComputePtrOffsetOfStridedBatch,
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bool HasMainKBlockLoop,
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InMemoryDataOperationEnum CGlobalMemoryDataOperation,
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index_t MinimumOccupancy = 1,
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TailNumber TailNum = TailNumber::Full>
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__global__ void
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#if CK_USE_LAUNCH_BOUNDS
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__launch_bounds__(CK_MAX_THREAD_PER_BLOCK, MinimumOccupancy)
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#endif
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kernel_batched_gemm_b_scale_wmma_cshuffle_v3(
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typename GridwiseGemm::Argument karg, // This works for now but it actually receives a
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// DeviceBatchedGemm_Wmma_CShuffleV3::Argument
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// argument through implicit conversion to base class!
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const ComputePtrOffsetOfStridedBatch compute_ptr_offset_of_batch)
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{
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#if(defined(__gfx11__) || defined(__gfx12__))
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#if defined(__gfx11__)
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// gfx11 does not support *_atomic_pk_add_f16/bf16 instructions
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using c_data_type = remove_cvref_t<remove_pointer_t<decltype(karg.p_e_grid)>>;
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if constexpr(!(CGlobalMemoryDataOperation == InMemoryDataOperationEnum::AtomicAdd &&
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(std::is_same_v<c_data_type, ck::half_t> ||
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std::is_same_v<c_data_type, ck::bhalf_t>)))
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{
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#endif
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// The normal approach to batching would be to increase the grid size by just stretching out
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// the grid Z dimension (which is the outermost dimension), but this depends on lower level
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// functions not directly using the Z dimension for other calculations. As it turns out, k
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// batching does rely directly on blockIdx.Z through SplitKBatchOffset. Therefore, for now
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// we will use the grid Y dimension for batching. This may be a bit fragile.
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__shared__ char p_shared[GridwiseGemm::GetSharedMemoryNumberOfByte()];
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const index_t g_idx = amd_wave_read_first_lane(blockIdx.y);
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const long_index_t a_batch_offset =
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amd_wave_read_first_lane(compute_ptr_offset_of_batch.GetAPtrOffset(g_idx));
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const long_index_t b_batch_offset =
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amd_wave_read_first_lane(compute_ptr_offset_of_batch.GetBPtrOffset(g_idx));
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const long_index_t c_batch_offset =
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amd_wave_read_first_lane(compute_ptr_offset_of_batch.GetCPtrOffset(g_idx));
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const long_index_t b_scale_batch_offset =
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amd_wave_read_first_lane(compute_ptr_offset_of_batch.GetScaleBPtrOffset(g_idx));
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auto splitk_batch_offset = typename GridwiseGemm::SplitKBatchOffset(karg, blockIdx.z);
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// shift A matrices pointer for splitk
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typename GridwiseGemm::AsGridPointer p_as_grid_shift;
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static_for<0, GridwiseGemm::NumATensor, 1>{}([&](auto i) {
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using ADataType_ =
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remove_cvref_t<tuple_element_t<i.value, typename GridwiseGemm::AsDataType_>>;
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p_as_grid_shift(i) = static_cast<const ADataType_*>(karg.p_as_grid[i]) +
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splitk_batch_offset.a_k_split_offset[i] + a_batch_offset;
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});
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// shift B matrices pointer for splitk
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typename GridwiseGemm::BsGridPointer p_bs_grid_shift;
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static_for<0, GridwiseGemm::NumBTensor, 1>{}([&](auto i) {
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using BDataType_ =
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remove_cvref_t<tuple_element_t<i.value, typename GridwiseGemm::BsDataType_>>;
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p_bs_grid_shift(i) = static_cast<const BDataType_*>(karg.p_bs_grid[i]) +
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splitk_batch_offset.b_k_split_offset[i] + b_batch_offset;
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});
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GridwiseGemm::template Run<HasMainKBlockLoop, CGlobalMemoryDataOperation, TailNum>(
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p_as_grid_shift,
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p_bs_grid_shift,
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karg.p_ds_grid,
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karg.p_e_grid + splitk_batch_offset.c_reduce_offset + c_batch_offset,
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karg.p_b_scale_grid + b_scale_batch_offset + splitk_batch_offset.scale_k_split_offset,
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p_shared,
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karg,
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karg.a_element_op,
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karg.b_element_op,
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karg.cde_element_op);
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#if defined(__gfx11__)
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}
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#endif
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#else
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ignore = karg;
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ignore = compute_ptr_offset_of_batch;
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#endif
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}
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/// @brief \"Universal\" Batched GEMM operation without SplitK support.
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///
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/// @par Overview
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/// This GEMM operation implements the following mathematical equation:
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/// C{G,M,N} = C_op(A_op(A{G,M,K}) * B_op(B{G,K,N}))
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/// Where A, B are input tensors and C is the output tensor. The A/B/C_op are
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/// elementwise operations applied to the A, B, and C tensors, respectively.
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/// The \"universal\" gemm comes with multiple pipelines optimized for different usage
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/// scenarios. That's why it's called \"universal\". It's universal through its design
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/// and versatilty.
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///
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/// @note This Kernel implementation currently does not support the SplitK algorithm.
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///
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/// @tparam ALayout A tensor data layout.
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/// @tparam BLayout B tensor data layout.
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/// @tparam CLayout C tensor data layout.
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/// @tparam ADataType A tensor data type.
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/// @tparam BDataType B tensor data type.
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/// @tparam CDataType C tensor data type.
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/// @tparam AccDataType The accumulation data type related to the hardware
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/// matrix-multiplication instruction.
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/// @tparam CShuffleDataType The data type used to store matrix-multiplication results into
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/// LDS memory during \"CShuffle\" data layout optimization.
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/// @tparam AElementwiseOperation Elementwise operation applied to the A input tensor elements.
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/// @tparam BElementwiseOperation Elementwise operation applied to the B input tensor elements.
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/// @tparam CElementwiseOperation Elementwise operation applied to the C output tensor
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/// (after GEMM).
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/// @tparam GemmSpec Determines used "padding" version.
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/// @tparam BlockSize The number of threads within workgroup.
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/// @tparam MPerBlock The input/output data tile size in the M dimension.
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/// @tparam NPerBlock The input/output data tile size in the N dimension.
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/// @tparam KPerBlock The input data tile size in the K dimension.
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/// @tparam AK1 The vector load size from global memory for A tensor.
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/// @tparam BK1 The vector load size from global memory for B tensor.
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/// @tparam MPerWmma M size of Wave Matrix Multiply Accumulate (WMMA) instruction.
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/// @tparam NPerWmma N size of Wave Matrix Multiply Accumulate (WMMA) instruction.
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/// @tparam MRepeat The number of iterations in the M dimension over output tile per wavefront.
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/// @tparam NRepeat The number of iterations in the N dimension over output tile per wavefront.
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/// @tparam ABlockTransferThreadClusterLengths_AK0_M_AK1 Spatial thread distribution over the input
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/// data. Can be interpreted as the answer
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/// to the question, "How many threads can be
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/// arranged on each input data axis?"
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/// @tparam ABlockTransferThreadClusterArrangeOrder The order of thread spatial distribution over
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/// the input tensor dimension. Can be interpreted
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/// as the answer to the question: "In which
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/// order to spread threads through tensor axes?".
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/// @tparam ABlockTransferSrcAccessOrder The order of accessing input tensor axes. Can be
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/// interpreted as the answer to the question "Which dimension
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/// to read first? And which next?" etc.
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/// @tparam ABlockTransferSrcVectorDim The index of axis on which we could do vectorized memory
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/// access - the one with contiguous memory.
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/// @tparam ABlockTransferSrcScalarPerVector The size of vector access instruction - the number of
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/// elements accessed per thread per instruction.
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/// @tparam ABlockTransferDstScalarPerVector_AK1 The size of vectorized store into LDS memory.
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/// @tparam ABlockLdsExtraM Whether to use padding for LDS or not. With
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/// universal GEMM there's no need for padding.
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/// @tparam BBlockTransferThreadClusterLengths_BK0_N_BK1 Spatial thread distribution over the input
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/// data. Can be interpreted as the answer
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/// to the question: "How many threads to
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/// arrange on each input data axis?"
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/// @tparam BBlockTransferThreadClusterArrangeOrder The order of thread spatial distribution over
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/// the input tensor dimension. Can be interpreted
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/// as the answer to the question: "In which
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/// order to spread threads through tensor axes?".
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/// @tparam BBlockTransferSrcAccessOrder he order of accessing input tensor axes. Can be
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/// interpreted as the answer to the question "Which dimension
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/// to read first? And which next?" etc.
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/// @tparam BBlockTransferSrcVectorDim The index of axis on which we could do vectorized memory
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/// access - the one with contiguous memory.
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/// @tparam BBlockTransferSrcScalarPerVector The size of vector access instruction - the number of
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/// elements accessed per thread per instruction.
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/// @tparam BBlockTransferDstScalarPerVector_BK1 The size of vectorized store into LDS memory.
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/// @tparam BBlockLdsExtraN Whether to use padding for LDS or not. With
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/// universal GEMM there's no need for padding.
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/// @tparam CShuffleMRepeatPerShuffle The number of matrix-multiplication instructions
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/// results to process per wave per iteration of CShuffle
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/// in M dimension.
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/// @tparam CShuffleNRepeatPerShuffle The number of matrix-multiplication instructions
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/// results to process per wave per iteration of CShuffle
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/// in N dimension.
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/// @tparam CShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock The spatial
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/// thread distribution used for storing data into output
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/// tensor across output data layout dimensions.
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/// @tparam CShuffleBlockTransferScalarPerVector_NPerBlock The size of vectorized memory access.
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/// Used when storing data to output tensor.
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/// @tparam BlkGemmPipeSched The version of blockwise-gemm pipeline scheduler (interwave or
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/// intrawave).
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/// @tparam BlkGemmPipelineVer The version of blockwise-gemm pipeline.
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/// @tparam ComputeTypeA Data type used for A input of hardware matrix-multiplication
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/// instructions.
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/// @tparam ComputeTypeB Data type used for B input of hardware matrix-multiplication
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/// instructions.
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/// @tparam PermuteA Whether the A input tensor has gridwise-gemm friendly data layout
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/// in global memory. Currently not supported!
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/// @tparam PermuteB Whether the B input tensor has gridwise-gemm friendly data layout
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/// in global memory (pre-shuffled). Currently not supported!
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template <typename ALayout,
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typename BLayout,
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typename CLayout,
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typename ADataType,
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typename BDataType,
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typename BScaleDataType,
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typename CDataType,
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typename AccDataType,
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typename CShuffleDataType,
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typename AElementwiseOperation,
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typename BElementwiseOperation,
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typename CElementwiseOperation,
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GemmSpecialization GemmSpec,
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index_t BlockSize,
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index_t ScaleBlockN, // scale block for N
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index_t ScaleBlockK, // scale block for K
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index_t MPerBlock,
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index_t NPerBlock,
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index_t KPerBlock,
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index_t AK1,
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index_t BK1,
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index_t MPerWmma,
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index_t NPerWmma,
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index_t MRepeat,
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index_t NRepeat,
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typename ABlockTransferThreadClusterLengths_AK0_M_AK1,
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typename ABlockTransferThreadClusterArrangeOrder,
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typename ABlockTransferSrcAccessOrder,
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index_t ABlockTransferSrcVectorDim,
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index_t ABlockTransferSrcScalarPerVector,
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index_t ABlockTransferDstScalarPerVector_AK1,
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bool ABlockLdsExtraM,
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typename BBlockTransferThreadClusterLengths_BK0_N_BK1,
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typename BBlockTransferThreadClusterArrangeOrder,
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typename BBlockTransferSrcAccessOrder,
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index_t BBlockTransferSrcVectorDim,
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index_t BBlockTransferSrcScalarPerVector,
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index_t BBlockTransferDstScalarPerVector_BK1,
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bool BBlockLdsExtraN,
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index_t CShuffleMRepeatPerShuffle,
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index_t CShuffleNRepeatPerShuffle,
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typename CShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
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index_t CShuffleBlockTransferScalarPerVector_NPerBlock,
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BlockGemmPipelineScheduler BlkGemmPipeSched = BlockGemmPipelineScheduler::Intrawave,
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BlockGemmPipelineVersion BlkGemmPipelineVer = BlockGemmPipelineVersion::v1,
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typename ComputeTypeA = CDataType,
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typename ComputeTypeB = ComputeTypeA,
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bool PermuteA = false,
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bool PermuteB = false>
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struct DeviceBatchedGemm_Wmma_CShuffleV3_BScale
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: public DeviceBatchedGemmV2BScale<ALayout,
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BLayout,
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CLayout,
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ADataType,
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BDataType,
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BScaleDataType,
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CDataType,
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ScaleBlockN,
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ScaleBlockK,
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AElementwiseOperation,
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BElementwiseOperation,
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CElementwiseOperation>
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{
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// We are inheriting from DeviceBatchedGemm and this base class does not support permuteA and
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// permuteB arguments so for now we are not including this functionality.
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static_assert(PermuteA == false,
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"Permute A functionality not supported by DeviceBatchedGemm operations.\n");
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static_assert(PermuteB == false,
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"Permute B functionality not supported by DeviceBatchedGemm operations.\n");
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|
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struct ComputePtrOffsetOfStridedBatch
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{
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ComputePtrOffsetOfStridedBatch(index_t BatchStrideA,
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index_t BatchStrideB,
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index_t BatchStrideC,
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index_t BatchStrideScaleB)
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: BatchStrideA_(BatchStrideA),
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BatchStrideB_(BatchStrideB),
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BatchStrideC_(BatchStrideC),
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BatchStrideScaleB_(BatchStrideScaleB)
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{
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}
|
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|
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__host__ __device__ constexpr long_index_t GetAPtrOffset(index_t g_idx) const
|
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{
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return g_idx * static_cast<long_index_t>(BatchStrideA_);
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}
|
||||
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__host__ __device__ constexpr long_index_t GetBPtrOffset(index_t g_idx) const
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{
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return g_idx * static_cast<long_index_t>(BatchStrideB_) / GridwiseGemm::BPackedSize;
|
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}
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|
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__host__ __device__ constexpr long_index_t GetCPtrOffset(index_t g_idx) const
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{
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return g_idx * static_cast<long_index_t>(BatchStrideC_);
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||||
}
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__host__ __device__ constexpr long_index_t GetScaleBPtrOffset(index_t g_idx) const
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{
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return g_idx * static_cast<long_index_t>(BatchStrideScaleB_);
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}
|
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|
||||
private:
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index_t BatchStrideA_;
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index_t BatchStrideB_;
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||||
index_t BatchStrideC_;
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index_t BatchStrideScaleB_;
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};
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|
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// GridwiseGemm
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using GridwiseGemm = GridwiseGemm_wmma_cshuffle_v3_b_scale<
|
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ALayout,
|
||||
BLayout,
|
||||
Tuple<>, // DsLayout
|
||||
CLayout,
|
||||
Tuple<ADataType>,
|
||||
Tuple<BDataType>,
|
||||
BScaleDataType,
|
||||
AccDataType,
|
||||
CShuffleDataType,
|
||||
Tuple<>, // DsDataType
|
||||
CDataType,
|
||||
AElementwiseOperation,
|
||||
BElementwiseOperation,
|
||||
CElementwiseOperation,
|
||||
GemmSpec,
|
||||
BlockSize,
|
||||
ScaleBlockN,
|
||||
ScaleBlockK,
|
||||
MPerBlock,
|
||||
NPerBlock,
|
||||
KPerBlock,
|
||||
AK1,
|
||||
BK1,
|
||||
MPerWmma,
|
||||
NPerWmma,
|
||||
MRepeat,
|
||||
NRepeat,
|
||||
ABlockTransferThreadClusterLengths_AK0_M_AK1,
|
||||
ABlockTransferThreadClusterArrangeOrder,
|
||||
ABlockTransferSrcAccessOrder,
|
||||
ABlockTransferSrcVectorDim,
|
||||
ABlockTransferSrcScalarPerVector,
|
||||
ABlockTransferDstScalarPerVector_AK1,
|
||||
false,
|
||||
ABlockLdsExtraM,
|
||||
BBlockTransferThreadClusterLengths_BK0_N_BK1,
|
||||
BBlockTransferThreadClusterArrangeOrder,
|
||||
BBlockTransferSrcAccessOrder,
|
||||
BBlockTransferSrcVectorDim,
|
||||
BBlockTransferSrcScalarPerVector,
|
||||
BBlockTransferDstScalarPerVector_BK1,
|
||||
false,
|
||||
BBlockLdsExtraN,
|
||||
CShuffleMRepeatPerShuffle,
|
||||
CShuffleNRepeatPerShuffle,
|
||||
CShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
|
||||
Sequence<CShuffleBlockTransferScalarPerVector_NPerBlock>,
|
||||
BlkGemmPipeSched,
|
||||
BlkGemmPipelineVer,
|
||||
ComputeTypeA,
|
||||
ComputeTypeB,
|
||||
PermuteA, // PermuteA not supported by DeviceBatchedGemm base class.
|
||||
PermuteB>; // PermuteB not supported by DeviceBatchedGemm base class.
|
||||
|
||||
// Argument
|
||||
struct Argument : public GridwiseGemm::Argument
|
||||
{
|
||||
__host__ Argument(const ADataType* p_a_grid_,
|
||||
const BDataType* p_b_grid_,
|
||||
CDataType* p_c_grid_,
|
||||
index_t M_,
|
||||
index_t N_,
|
||||
index_t K_,
|
||||
index_t StrideA_,
|
||||
index_t StrideB_,
|
||||
index_t StrideC_,
|
||||
index_t StrideScaleB_,
|
||||
index_t BatchStrideA_,
|
||||
index_t BatchStrideB_,
|
||||
index_t BatchStrideC_,
|
||||
index_t BatchStrideScaleB_,
|
||||
const BScaleDataType* p_b_scale_grid_,
|
||||
index_t Batch_,
|
||||
index_t k_batch_,
|
||||
AElementwiseOperation a_element_op_,
|
||||
BElementwiseOperation b_element_op_,
|
||||
CElementwiseOperation c_element_op_,
|
||||
bool is_reduce_ = false)
|
||||
: GridwiseGemm::Argument(std::array<const void*, 1>{p_a_grid_},
|
||||
std::array<const void*, 1>{p_b_grid_},
|
||||
std::array<const void*, 0>{}, // p_ds_grid_
|
||||
p_c_grid_,
|
||||
M_,
|
||||
N_,
|
||||
K_,
|
||||
std::array<index_t, 1>{StrideA_},
|
||||
std::array<index_t, 1>{StrideB_},
|
||||
std::array<index_t, 0>{}, // StrideDs_
|
||||
StrideC_,
|
||||
StrideScaleB_,
|
||||
p_b_scale_grid_,
|
||||
k_batch_,
|
||||
a_element_op_,
|
||||
b_element_op_,
|
||||
c_element_op_,
|
||||
is_reduce_),
|
||||
Batch(Batch_),
|
||||
compute_ptr_offset_of_batch{
|
||||
BatchStrideA_, BatchStrideB_, BatchStrideC_, BatchStrideScaleB_}
|
||||
{
|
||||
}
|
||||
|
||||
index_t Batch;
|
||||
ComputePtrOffsetOfStridedBatch compute_ptr_offset_of_batch;
|
||||
};
|
||||
|
||||
/// @brief Helper structure responsible for kernel invocation.
|
||||
///
|
||||
/// @paragraph The `Invoker` class is responsible for preparation and invocation of actual GPU
|
||||
/// kernel function. It usually determines the launched grid size prepares kernel
|
||||
/// arguments as well as perform specific kernel configuration selection based on
|
||||
/// runtime arguments.
|
||||
///
|
||||
/// @note If appropriately configured it may measure kernel execution time.
|
||||
///
|
||||
struct Invoker : public BaseInvoker
|
||||
{
|
||||
/// @brief This function issues GPU kernel execution.
|
||||
/// @param arg The GPU kernel arguments.
|
||||
/// @param stream_config The HIP stream configuration helper structure.
|
||||
/// @return The kernel's average execution time (if time measurement is
|
||||
/// enabled).
|
||||
float Run(const Argument& arg, const StreamConfig& stream_config = StreamConfig{})
|
||||
{
|
||||
if(stream_config.log_level_ > 0)
|
||||
{
|
||||
arg.Print();
|
||||
GridwiseGemm::BlockwiseGemmPipe::HotLoopInstList::Print();
|
||||
}
|
||||
|
||||
if(!GridwiseGemm::CheckValidity(arg))
|
||||
{
|
||||
throw std::runtime_error("wrong! GridwiseGemm has invalid setting");
|
||||
}
|
||||
|
||||
index_t gdx, gdy, gdz;
|
||||
std::tie(gdx, gdy, gdz) = GridwiseGemm::CalculateGridSize(arg.M, arg.N, arg.KBatch);
|
||||
|
||||
// The normal approach to batching would be to increase the grid size by just stretching
|
||||
// out the grid Z dimension (which is the outermost dimension), but this depends on
|
||||
// lower level functions not directly using the Z dimension for other calculations. As
|
||||
// it turns out, k batching does rely directly on blockIdx.Z through SplitKBatchOffset.
|
||||
// Therefore, for now we will use the grid Y dimension for batching. This may be a bit
|
||||
// fragile.
|
||||
gdy *= arg.Batch;
|
||||
|
||||
float ave_time = 0;
|
||||
|
||||
index_t k_grain = arg.KBatch * KPerBlock;
|
||||
index_t K_split = (arg.K + k_grain - 1) / k_grain * KPerBlock;
|
||||
|
||||
const bool has_main_k_block_loop = GridwiseGemm::CalculateHasMainKBlockLoop(K_split);
|
||||
|
||||
const auto Run = [&](const auto& kernel) {
|
||||
if(stream_config.flush_cache)
|
||||
{
|
||||
Argument arg_ = arg;
|
||||
|
||||
const auto a_grid_desc_ak0_m_ak1 = GridwiseGemm::MakeAsGridDescriptor_AK0_M_AK1(
|
||||
arg_.M, arg_.MPadded, arg_.K, arg_.KPadded, arg_.StrideAs, arg_.AK0);
|
||||
const auto b_grid_desc_bk0_n_bk1 = GridwiseGemm::MakeBsGridDescriptor_BK0_N_BK1(
|
||||
arg_.K, arg_.KPadded, arg_.N, arg_.NPadded, arg_.StrideBs, arg_.BK0);
|
||||
|
||||
// Packed sizes are 1 for all implemented data types but we include it anyway
|
||||
// for future compatibility.
|
||||
// Note: the grid descriptors and size_a / size_b do *not* take batching into
|
||||
// account, so we have to manually multiply overall buffer sizes for rotating
|
||||
// memory by batch.
|
||||
std::array<std::size_t, 1> size_as_buffers;
|
||||
size_as_buffers[0] = a_grid_desc_ak0_m_ak1[Number<0>{}].GetElementSpaceSize() *
|
||||
sizeof(ADataType) / GridwiseGemm::APackedSize * arg_.Batch;
|
||||
|
||||
std::array<std::size_t, 1> size_bs_buffers;
|
||||
size_bs_buffers[0] = b_grid_desc_bk0_n_bk1[Number<0>{}].GetElementSpaceSize() *
|
||||
sizeof(BDataType) / GridwiseGemm::BPackedSize * arg_.Batch;
|
||||
|
||||
ck::utility::RotatingMemWrapperMultiABD<Argument,
|
||||
Tuple<ADataType>,
|
||||
Tuple<BDataType>,
|
||||
Tuple<>>
|
||||
rotating_mem(arg_,
|
||||
stream_config.rotating_count,
|
||||
size_as_buffers,
|
||||
size_bs_buffers,
|
||||
std::array<std::size_t, 0>{});
|
||||
rotating_mem.Print();
|
||||
|
||||
auto run_flush_cache = [&]() {
|
||||
ck::utility::flush_icache();
|
||||
rotating_mem.Next();
|
||||
// clear c mem
|
||||
if(arg_.KBatch > 1)
|
||||
// Note: we multiply by batch since we want to clear the C matrix for
|
||||
// the whole batch. Untested since we don't have k batching ATM.
|
||||
// Note: This seems incorrect for non-contiguous memory layouts for C
|
||||
// (padding, gaps).
|
||||
HIP_CHECK_ERROR(
|
||||
hipMemsetAsync(arg_.p_e_grid,
|
||||
0,
|
||||
arg_.Batch * arg_.M * arg_.N * sizeof(CDataType),
|
||||
stream_config.stream_id_));
|
||||
};
|
||||
|
||||
ave_time = ck::utility::launch_and_time_kernel_with_preprocess<false>(
|
||||
stream_config,
|
||||
run_flush_cache,
|
||||
kernel,
|
||||
dim3(gdx, gdy, gdz),
|
||||
dim3(BlockSize),
|
||||
0,
|
||||
arg_,
|
||||
arg_.compute_ptr_offset_of_batch);
|
||||
}
|
||||
else
|
||||
{
|
||||
auto clear_workspace = [&]() {
|
||||
// clear c mem
|
||||
if(arg.KBatch > 1)
|
||||
// Note: we multiply by batch since we want to clear the C matrix for
|
||||
// the whole batch. Untested since we don't have k batching ATM.
|
||||
// Note: This seems incorrect for non-contiguous memory layouts for C
|
||||
// (padding, gaps).
|
||||
HIP_CHECK_ERROR(
|
||||
hipMemsetAsync(arg.p_e_grid,
|
||||
0,
|
||||
arg.Batch * arg.M * arg.N * sizeof(CDataType),
|
||||
stream_config.stream_id_));
|
||||
};
|
||||
|
||||
ave_time = ck::utility::launch_and_time_kernel_with_preprocess<false>(
|
||||
stream_config,
|
||||
clear_workspace,
|
||||
kernel,
|
||||
dim3(gdx, gdy, gdz),
|
||||
dim3(BlockSize),
|
||||
0,
|
||||
arg,
|
||||
arg.compute_ptr_offset_of_batch);
|
||||
}
|
||||
};
|
||||
|
||||
constexpr index_t minimum_occupancy = []() {
|
||||
if constexpr(BlkGemmPipeSched == BlockGemmPipelineScheduler::Interwave)
|
||||
{
|
||||
return 2;
|
||||
}
|
||||
else if constexpr(BlkGemmPipelineVer == BlockGemmPipelineVersion::v3)
|
||||
{
|
||||
return (MPerBlock * NPerBlock / BlockSize <= 128) ? 2 : 1;
|
||||
}
|
||||
else
|
||||
{
|
||||
return 1;
|
||||
}
|
||||
}();
|
||||
|
||||
if(has_main_k_block_loop)
|
||||
{
|
||||
// Tail number always full
|
||||
if constexpr(BlkGemmPipelineVer == BlockGemmPipelineVersion::v1 ||
|
||||
BlkGemmPipelineVer == BlockGemmPipelineVersion::v3)
|
||||
{
|
||||
if(arg.KBatch > 1)
|
||||
{
|
||||
const auto kernel = kernel_batched_gemm_b_scale_wmma_cshuffle_v3<
|
||||
GridwiseGemm,
|
||||
ComputePtrOffsetOfStridedBatch,
|
||||
true,
|
||||
InMemoryDataOperationEnum::AtomicAdd,
|
||||
minimum_occupancy>;
|
||||
Run(kernel);
|
||||
}
|
||||
else
|
||||
{
|
||||
const auto kernel = kernel_batched_gemm_b_scale_wmma_cshuffle_v3<
|
||||
GridwiseGemm,
|
||||
remove_reference_t<ComputePtrOffsetOfStridedBatch>,
|
||||
true,
|
||||
InMemoryDataOperationEnum::Set,
|
||||
minimum_occupancy>;
|
||||
Run(kernel);
|
||||
}
|
||||
}
|
||||
else
|
||||
{
|
||||
throw std::runtime_error("Pipeline not implemented");
|
||||
}
|
||||
}
|
||||
else
|
||||
{
|
||||
// Tail number always 1
|
||||
if constexpr(BlkGemmPipelineVer == BlockGemmPipelineVersion::v1)
|
||||
{
|
||||
if(arg.KBatch > 1)
|
||||
{
|
||||
const auto kernel = kernel_batched_gemm_b_scale_wmma_cshuffle_v3<
|
||||
GridwiseGemm,
|
||||
ComputePtrOffsetOfStridedBatch,
|
||||
false,
|
||||
InMemoryDataOperationEnum::AtomicAdd,
|
||||
minimum_occupancy>;
|
||||
Run(kernel);
|
||||
}
|
||||
else
|
||||
{
|
||||
const auto kernel = kernel_batched_gemm_b_scale_wmma_cshuffle_v3<
|
||||
GridwiseGemm,
|
||||
remove_reference_t<ComputePtrOffsetOfStridedBatch>,
|
||||
false,
|
||||
InMemoryDataOperationEnum::Set,
|
||||
minimum_occupancy>;
|
||||
Run(kernel);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
return ave_time;
|
||||
}
|
||||
|
||||
// polymorphic
|
||||
float Run(const BaseArgument* p_arg,
|
||||
const StreamConfig& stream_config = StreamConfig{}) override
|
||||
{
|
||||
return Run(*dynamic_cast<const Argument*>(p_arg), stream_config);
|
||||
}
|
||||
};
|
||||
|
||||
static constexpr bool IsValidCompilationParameter()
|
||||
{
|
||||
// TODO: properly implement this check
|
||||
return true;
|
||||
}
|
||||
|
||||
static bool IsSupportedArgument(const Argument& arg)
|
||||
{
|
||||
if(!ck::is_gfx11_supported() && !ck::is_gfx12_supported())
|
||||
{
|
||||
return false;
|
||||
}
|
||||
|
||||
if constexpr(std::is_same_v<CDataType, ck::half_t> ||
|
||||
std::is_same_v<CDataType, ck::bhalf_t>)
|
||||
{
|
||||
if(arg.KBatch > 1 && ck::is_gfx11_supported())
|
||||
{
|
||||
// gfx11 does not support *_atomic_pk_add_f16/bf16 instructions
|
||||
return false;
|
||||
}
|
||||
}
|
||||
|
||||
if constexpr(std::is_same_v<ComputeTypeA, f8_t> || std::is_same_v<ComputeTypeA, bf8_t> ||
|
||||
std::is_same_v<ComputeTypeB, f8_t> || std::is_same_v<ComputeTypeB, bf8_t>)
|
||||
{
|
||||
if(ck::is_gfx11_supported())
|
||||
{
|
||||
return false;
|
||||
}
|
||||
}
|
||||
|
||||
if((arg.K % AK1 != 0 || arg.K % BK1 != 0) && !(GemmSpec == GemmSpecialization::MKPadding ||
|
||||
GemmSpec == GemmSpecialization::NKPadding ||
|
||||
GemmSpec == GemmSpecialization::MNKPadding ||
|
||||
GemmSpec == GemmSpecialization::KPadding))
|
||||
{
|
||||
return false;
|
||||
}
|
||||
|
||||
return GridwiseGemm::CheckValidity(arg);
|
||||
}
|
||||
|
||||
// polymorphic
|
||||
bool IsSupportedArgument(const BaseArgument* p_arg) override
|
||||
{
|
||||
return IsSupportedArgument(*dynamic_cast<const Argument*>(p_arg));
|
||||
}
|
||||
|
||||
index_t GetKPerBlock() override { return KPerBlock; }
|
||||
bool GetPermuteB() override { return PermuteB; }
|
||||
|
||||
static auto MakeArgument(const ADataType* p_a,
|
||||
const BDataType* p_b,
|
||||
CDataType* p_c,
|
||||
index_t M,
|
||||
index_t N,
|
||||
index_t K,
|
||||
index_t StrideA,
|
||||
index_t StrideB,
|
||||
index_t StrideC,
|
||||
index_t StrideScaleB,
|
||||
index_t BatchStrideA,
|
||||
index_t BatchStrideB,
|
||||
index_t BatchStrideC,
|
||||
index_t BatchStrideScaleB,
|
||||
const BScaleDataType* p_b_scale,
|
||||
index_t Batch,
|
||||
AElementwiseOperation,
|
||||
BElementwiseOperation,
|
||||
CElementwiseOperation,
|
||||
index_t KBatch = 1)
|
||||
{
|
||||
return Argument{p_a,
|
||||
p_b,
|
||||
p_c,
|
||||
M,
|
||||
N,
|
||||
K,
|
||||
StrideA,
|
||||
StrideB,
|
||||
StrideC,
|
||||
StrideScaleB,
|
||||
BatchStrideA,
|
||||
BatchStrideB,
|
||||
BatchStrideC,
|
||||
BatchStrideScaleB,
|
||||
p_b_scale,
|
||||
Batch,
|
||||
KBatch};
|
||||
}
|
||||
|
||||
static auto MakeInvoker() { return Invoker{}; }
|
||||
|
||||
// polymorphic
|
||||
std::unique_ptr<BaseArgument> MakeArgumentPointer(const void* p_a,
|
||||
const void* p_b,
|
||||
void* p_c,
|
||||
index_t M,
|
||||
index_t N,
|
||||
index_t K,
|
||||
index_t StrideA,
|
||||
index_t StrideB,
|
||||
index_t StrideC,
|
||||
index_t StrideScaleB,
|
||||
index_t BatchStrideA,
|
||||
index_t BatchStrideB,
|
||||
index_t BatchStrideC,
|
||||
index_t BatchStrideScaleB,
|
||||
const void* p_b_scale,
|
||||
index_t Batch,
|
||||
index_t KBatch,
|
||||
AElementwiseOperation a_element_op,
|
||||
BElementwiseOperation b_element_op,
|
||||
CElementwiseOperation c_element_op) override
|
||||
{
|
||||
return std::make_unique<Argument>(static_cast<const ADataType*>(p_a),
|
||||
static_cast<const BDataType*>(p_b),
|
||||
static_cast<CDataType*>(p_c),
|
||||
M,
|
||||
N,
|
||||
K,
|
||||
StrideA,
|
||||
StrideB,
|
||||
StrideC,
|
||||
StrideScaleB,
|
||||
BatchStrideA,
|
||||
BatchStrideB,
|
||||
BatchStrideC,
|
||||
BatchStrideScaleB,
|
||||
static_cast<const BScaleDataType*>(p_b_scale),
|
||||
Batch,
|
||||
KBatch,
|
||||
a_element_op,
|
||||
b_element_op,
|
||||
c_element_op);
|
||||
}
|
||||
|
||||
// polymorphic
|
||||
std::unique_ptr<BaseInvoker> MakeInvokerPointer() override
|
||||
{
|
||||
return std::make_unique<Invoker>(Invoker{});
|
||||
}
|
||||
|
||||
// polymorphic
|
||||
std::string GetTypeString() const override
|
||||
{
|
||||
auto str = std::stringstream();
|
||||
|
||||
std::map<BlockGemmPipelineScheduler, std::string> BlkGemmPipelineSchedulerToString{
|
||||
{BlockGemmPipelineScheduler::Intrawave, "Intrawave"},
|
||||
{BlockGemmPipelineScheduler::Interwave, "Interwave"}};
|
||||
|
||||
std::map<BlockGemmPipelineVersion, std::string> BlkGemmPipelineVersionToString{
|
||||
{BlockGemmPipelineVersion::v1, "v1"},
|
||||
{BlockGemmPipelineVersion::v2, "v2"},
|
||||
{BlockGemmPipelineVersion::v3, "v3"},
|
||||
{BlockGemmPipelineVersion::v4, "v4"},
|
||||
{BlockGemmPipelineVersion::v5, "v5"}};
|
||||
|
||||
// clang-format off
|
||||
str << "DeviceBatchedGemm_Wmma_CShuffleV3_BScale"
|
||||
<< "<"
|
||||
<< getGemmSpecializationString(GemmSpec) << ", "
|
||||
<< std::string(ALayout::name)[0]
|
||||
<< std::string(BLayout::name)[0]
|
||||
<< std::string(CLayout::name)[0]
|
||||
<< ">"
|
||||
<< " BlkSize: "
|
||||
<< BlockSize << ", "
|
||||
<< "BlkTile: "
|
||||
<< MPerBlock << "x" << NPerBlock << "x" << KPerBlock << ", "
|
||||
<< "WaveTile: "
|
||||
<< MPerWmma << "x"<<NPerWmma << ", "
|
||||
<< "WaveMap: "
|
||||
<< MRepeat << "x" << NRepeat << ", "
|
||||
<< "VmemReadVec: "
|
||||
<< ABlockTransferSrcScalarPerVector << "x" << BBlockTransferSrcScalarPerVector << ", "
|
||||
<< "BlkGemmPipelineScheduler: "
|
||||
<< BlkGemmPipelineSchedulerToString[BlkGemmPipeSched] << ", "
|
||||
<< "BlkGemmPipelineVersion: "
|
||||
<< BlkGemmPipelineVersionToString[BlkGemmPipelineVer] << ", "
|
||||
<< "BlkGemmPipelinePrefetchStages: "
|
||||
<< GridwiseGemm::BlockwiseGemmPipe::PrefetchStages << ", "
|
||||
<< "KPack: "
|
||||
<< GridwiseGemm::KPack;
|
||||
// clang-format on
|
||||
|
||||
return str.str();
|
||||
}
|
||||
REGISTER_EXTRA_PRINTING_METHODS
|
||||
};
|
||||
|
||||
} // namespace device
|
||||
} // namespace tensor_operation
|
||||
} // namespace ck
|
||||
@@ -222,6 +222,8 @@ struct GridwiseGemm_wmma_cshuffle_v3_b_scale
|
||||
using typename Base::AsGridPointer;
|
||||
using typename Base::BsGridPointer;
|
||||
using typename Base::DsGridPointer;
|
||||
using AsDataType_ = AsDataType;
|
||||
using BsDataType_ = BsDataType;
|
||||
|
||||
struct Problem
|
||||
{
|
||||
|
||||
Reference in New Issue
Block a user