[MoE Refactor][3/N] Deprecate cutlass block quant fp8 (b200) (#30990)
Signed-off-by: Robert Shaw <robshaw@redhat.com> Co-authored-by: Robert Shaw <robshaw@redhat.com>
This commit is contained in:
Robert Shaw
@@ -1,373 +0,0 @@
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#include "core/registration.h"
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#include <torch/all.h>
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#include <cutlass/arch/arch.h>
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#include <ATen/cuda/CUDAContext.h>
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#include <c10/cuda/CUDAGuard.h>
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#include <c10/cuda/CUDAStream.h>
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#include "cute/tensor.hpp"
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#include "cutlass/tensor_ref.h"
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#include "cutlass/epilogue/collective/default_epilogue.hpp"
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#include "cutlass/epilogue/thread/linear_combination.h"
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#include "cutlass/gemm/dispatch_policy.hpp"
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#include "cutlass/gemm/group_array_problem_shape.hpp"
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#include "cutlass/gemm/collective/collective_builder.hpp"
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#include "cutlass/epilogue/collective/collective_builder.hpp"
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#include "cutlass/gemm/device/gemm_universal_adapter.h"
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#include "cutlass/gemm/kernel/gemm_universal.hpp"
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#include "cutlass/util/command_line.h"
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#include "cutlass/util/distribution.h"
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#include "cutlass/util/host_tensor.h"
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#include "cutlass/util/packed_stride.hpp"
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#include "cutlass/util/tensor_view_io.h"
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#include "cutlass/util/reference/device/gemm.h"
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#include "cutlass/util/reference/device/tensor_compare.h"
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#include "cutlass/util/reference/host/tensor_fill.h"
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#include "cutlass/util/reference/host/gett.hpp"
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#include "cutlass/util/reference/host/tensor_norm.h"
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#include "cutlass/util/reference/host/tensor_compare.h"
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#include <cassert>
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using namespace cute;
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template <typename ElementAB, typename ElementC, typename ElementAccumulator,
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typename LayoutSFA, typename LayoutSFB, typename ScaleConfig>
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__global__ void get_ggemm_starts(
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int32_t* expert_offsets, ElementAB** a_offsets, ElementAB** b_offsets,
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ElementC** out_offsets, ElementAccumulator** a_scale_offsets,
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ElementAccumulator** b_scale_offsets, ElementAB* a_base_as_int,
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ElementAB* b_base_as_int, ElementC* out_base_as_int,
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ElementAccumulator* a_scale_base_as_int,
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ElementAccumulator* b_scale_base_as_int, LayoutSFA* layout_sfa_base_as_int,
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LayoutSFB* layout_sfb_base_as_int, int* problem_sizes) {
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int expert_id = threadIdx.x;
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if (expert_id >= gridDim.x * blockDim.x) {
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return;
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}
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int m = problem_sizes[expert_id * 3];
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int n = problem_sizes[expert_id * 3 + 1];
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int k = problem_sizes[expert_id * 3 + 2];
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int32_t expert_offset = expert_offsets[expert_id];
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int a_stride = expert_offset * k;
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int b_stride = expert_id * k * n;
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int a_scale_stride = expert_offset * k / 128;
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int b_scale_stride = expert_id * k * n / 128 / 128;
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a_offsets[expert_id] = a_base_as_int + a_stride;
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b_offsets[expert_id] = b_base_as_int + b_stride;
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out_offsets[expert_id] = out_base_as_int + expert_offset * n;
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a_scale_offsets[expert_id] = a_scale_base_as_int + a_scale_stride;
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b_scale_offsets[expert_id] = b_scale_base_as_int + b_scale_stride;
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LayoutSFA* layout_sfa_ptr = layout_sfa_base_as_int + expert_id;
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LayoutSFB* layout_sfb_ptr = layout_sfb_base_as_int + expert_id;
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*layout_sfa_ptr =
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ScaleConfig::tile_atom_to_shape_SFA(cute::make_shape(m, n, k, 1));
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*layout_sfb_ptr =
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ScaleConfig::tile_atom_to_shape_SFB(cute::make_shape(m, n, k, 1));
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}
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#define __CALL_GET_STARTS_KERNEL(TENSOR_C_TYPE, C_TYPE, LayoutSFA, LayoutSFB, \
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ScaleConfig) \
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else if (out_tensors.dtype() == TENSOR_C_TYPE) { \
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get_ggemm_starts<cutlass::float_e4m3_t, C_TYPE, float, LayoutSFA, \
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LayoutSFB, ScaleConfig><<<1, num_experts, 0, stream>>>( \
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static_cast<int32_t*>(expert_offsets.data_ptr()), \
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static_cast<cutlass::float_e4m3_t**>(a_ptrs.data_ptr()), \
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static_cast<cutlass::float_e4m3_t**>(b_ptrs.data_ptr()), \
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static_cast<C_TYPE**>(out_ptrs.data_ptr()), \
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static_cast<float**>(a_scales_ptrs.data_ptr()), \
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static_cast<float**>(b_scales_ptrs.data_ptr()), \
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static_cast<cutlass::float_e4m3_t*>(a_tensors.data_ptr()), \
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static_cast<cutlass::float_e4m3_t*>(b_tensors.data_ptr()), \
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static_cast<C_TYPE*>(out_tensors.data_ptr()), \
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static_cast<float*>(a_scales.data_ptr()), \
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static_cast<float*>(b_scales.data_ptr()), \
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reinterpret_cast<LayoutSFA*>(layout_sfa.data_ptr()), \
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reinterpret_cast<LayoutSFB*>(layout_sfb.data_ptr()), \
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static_cast<int*>(problem_sizes.data_ptr())); \
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}
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template <typename LayoutSFA, typename LayoutSFB, typename ScaleConfig>
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void run_get_ggemm_starts(
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torch::Tensor const& expert_offsets, torch::Tensor& a_ptrs,
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torch::Tensor& b_ptrs, torch::Tensor& out_ptrs,
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torch::Tensor& a_scales_ptrs, torch::Tensor& b_scales_ptrs,
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torch::Tensor const& a_tensors, torch::Tensor const& b_tensors,
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torch::Tensor out_tensors, torch::Tensor const& a_scales,
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torch::Tensor const& b_scales, torch::Tensor const& layout_sfa,
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torch::Tensor const& layout_sfb, torch::Tensor const& problem_sizes) {
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TORCH_CHECK(a_tensors.dtype() == torch::kFloat8_e4m3fn);
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TORCH_CHECK(b_tensors.dtype() == torch::kFloat8_e4m3fn);
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TORCH_CHECK(a_scales.dtype() == torch::kFloat32);
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TORCH_CHECK(b_scales.dtype() == torch::kFloat32);
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TORCH_CHECK(out_tensors.size(1) % 128 == 0 or out_tensors.size(0) % 128 == 0);
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TORCH_CHECK(a_tensors.size(1) % 128 == 0 or a_tensors.size(0) % 128 == 0);
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int num_experts = (int)expert_offsets.size(0);
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auto stream = at::cuda::getCurrentCUDAStream(a_tensors.device().index());
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if (false) {
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}
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__CALL_GET_STARTS_KERNEL(torch::kBFloat16, cutlass::bfloat16_t, LayoutSFA,
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LayoutSFB, ScaleConfig)
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__CALL_GET_STARTS_KERNEL(torch::kFloat16, cutlass::half_t, LayoutSFA,
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LayoutSFB, ScaleConfig)
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else {
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TORCH_CHECK(false, "Unsupported output tensor type");
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}
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}
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template <typename OutType, typename ScheduleConfig, typename LayoutD>
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void run_blockwise_scaled_group_mm(
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torch::Tensor& out_ptrs, const torch::Tensor& a_ptrs,
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const torch::Tensor& b_ptrs, const torch::Tensor& a_scales_ptrs,
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const torch::Tensor& b_scales_ptrs, const torch::Tensor& stride_a,
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const torch::Tensor& stride_b, const torch::Tensor& stride_c,
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const torch::Tensor& layout_sfa, const torch::Tensor& layout_sfb,
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const torch::Tensor& problem_sizes, const torch::Tensor& expert_offsets) {
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using ProblemShape = cutlass::gemm::GroupProblemShape<Shape<int, int, int>>;
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// Types
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using ElementA = cutlass::float_e4m3_t;
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using ElementB = cutlass::float_e4m3_t;
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using ElementC = OutType;
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using ElementD = ElementC;
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using ElementAccumulator = float;
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using LayoutA = cutlass::layout::RowMajor;
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using LayoutB = cutlass::layout::ColumnMajor;
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using LayoutC = LayoutD;
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// Alignments
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static constexpr int AlignmentA = 128 / cutlass::sizeof_bits<ElementA>::value;
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static constexpr int AlignmentB = 128 / cutlass::sizeof_bits<ElementB>::value;
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static constexpr int AlignmentC = 128 / cutlass::sizeof_bits<ElementC>::value;
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using ArchTag = cutlass::arch::Sm100;
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using OperatorClass = cutlass::arch::OpClassTensorOp;
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using CollectiveEpilogue =
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typename cutlass::epilogue::collective::CollectiveBuilder<
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ArchTag, OperatorClass, typename ScheduleConfig::MmaTileShape,
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typename ScheduleConfig::ClusterShape,
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cutlass::epilogue::collective::EpilogueTileAuto, ElementAccumulator,
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ElementAccumulator, void, LayoutC*, AlignmentC, ElementD, LayoutC*,
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AlignmentC, typename ScheduleConfig::EpilogueSchedule>::CollectiveOp;
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using CollectiveMainloop =
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typename cutlass::gemm::collective::CollectiveBuilder<
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ArchTag, OperatorClass, ElementA,
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cute::tuple<LayoutA*, typename ScheduleConfig::LayoutSFA*>,
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AlignmentA, ElementB,
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cute::tuple<LayoutB*, typename ScheduleConfig::LayoutSFB*>,
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AlignmentB, ElementAccumulator, typename ScheduleConfig::MmaTileShape,
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typename ScheduleConfig::ClusterShape,
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cutlass::gemm::collective::StageCountAutoCarveout<static_cast<int>(
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sizeof(typename CollectiveEpilogue::SharedStorage))>,
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typename ScheduleConfig::KernelSchedule>::CollectiveOp;
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using GemmKernel =
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cutlass::gemm::kernel::GemmUniversal<ProblemShape, CollectiveMainloop,
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CollectiveEpilogue, void>;
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using Gemm = cutlass::gemm::device::GemmUniversalAdapter<GemmKernel>;
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using StrideA = typename Gemm::GemmKernel::InternalStrideA;
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using StrideB = typename Gemm::GemmKernel::InternalStrideB;
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using StrideC = typename Gemm::GemmKernel::InternalStrideC;
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using StrideD = typename Gemm::GemmKernel::InternalStrideD;
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using UnderlyingProblemShape = ProblemShape::UnderlyingProblemShape;
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int num_experts = (int)expert_offsets.size(0);
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Gemm gemm_op;
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// Mainloop Arguments
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typename GemmKernel::MainloopArguments mainloop_args{
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static_cast<const ElementA**>(a_ptrs.data_ptr()),
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static_cast<StrideA*>(stride_a.data_ptr()),
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static_cast<const ElementB**>(b_ptrs.data_ptr()),
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static_cast<StrideB*>(stride_b.data_ptr()),
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static_cast<const ElementAccumulator**>(a_scales_ptrs.data_ptr()),
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reinterpret_cast<typename ScheduleConfig::LayoutSFA*>(
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layout_sfa.data_ptr()),
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static_cast<const ElementAccumulator**>(b_scales_ptrs.data_ptr()),
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reinterpret_cast<typename ScheduleConfig::LayoutSFB*>(
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layout_sfb.data_ptr())};
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int device_id = a_ptrs.device().index();
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static const cutlass::KernelHardwareInfo hw_info{
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device_id, cutlass::KernelHardwareInfo::query_device_multiprocessor_count(
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device_id)};
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// Epilogue Arguments
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typename GemmKernel::EpilogueArguments epilogue_args{
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{}, // epilogue.thread
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nullptr,
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static_cast<StrideC*>(stride_c.data_ptr()),
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static_cast<ElementD**>(out_ptrs.data_ptr()),
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static_cast<StrideC*>(stride_c.data_ptr())};
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UnderlyingProblemShape* problem_sizes_as_shapes =
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static_cast<UnderlyingProblemShape*>(problem_sizes.data_ptr());
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// Gemm Arguments
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typename GemmKernel::Arguments args{
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cutlass::gemm::GemmUniversalMode::kGrouped,
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{num_experts, problem_sizes_as_shapes, nullptr},
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mainloop_args,
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epilogue_args,
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hw_info};
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at::cuda::CUDAGuard device_guard{(char)a_ptrs.device().index()};
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const cudaStream_t stream =
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at::cuda::getCurrentCUDAStream(a_ptrs.get_device());
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auto can_implement_status = gemm_op.can_implement(args);
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TORCH_CHECK(can_implement_status == cutlass::Status::kSuccess,
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"Failed to implement GEMM");
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size_t workspace_size = gemm_op.get_workspace_size(args);
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auto const workspace_options =
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torch::TensorOptions().dtype(torch::kUInt8).device(a_ptrs.device());
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auto workspace = torch::empty(workspace_size, workspace_options);
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auto status = gemm_op.initialize(args, workspace.data_ptr(), stream);
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TORCH_CHECK(status == cutlass::Status::kSuccess, "Failed to initialize GEMM");
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status = gemm_op.run(stream);
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TORCH_CHECK(status == cutlass::Status::kSuccess, "Failed to run GEMM");
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}
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template <typename OutType>
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void blockwise_scaled_group_mm_dispatch_shape(
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torch::Tensor& output, const torch::Tensor& a, const torch::Tensor& b,
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const torch::Tensor& scales_a, const torch::Tensor& scales_b,
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const torch::Tensor& problem_sizes, const torch::Tensor& expert_offsets) {
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struct MmaConfig {
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using ElementA = cutlass::float_e4m3_t;
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using KernelSchedule =
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cutlass::gemm::KernelPtrArrayTmaWarpSpecializedBlockwise1SmSm100;
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using EpilogueSchedule = cutlass::epilogue::PtrArrayTmaWarpSpecialized1Sm;
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using ScaleConfig = cutlass::detail::Sm100BlockwiseScaleConfig<
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1, 128, 128, cute::UMMA::Major::K, cute::UMMA::Major::K>;
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using LayoutSFA = decltype(ScaleConfig::deduce_layoutSFA());
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using LayoutSFB = decltype(ScaleConfig::deduce_layoutSFB());
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using LayoutC = cutlass::layout::RowMajor;
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using MmaTileShape = Shape<_128, _128, _128>;
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using ClusterShape = Shape<_1, _1, _1>;
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};
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int num_experts = (int)expert_offsets.size(0);
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auto a_ptrs = torch::empty(
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{num_experts},
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torch::TensorOptions().dtype(torch::kInt64).device(a.device()));
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auto b_ptrs = torch::empty(
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{num_experts},
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torch::TensorOptions().dtype(torch::kInt64).device(a.device()));
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auto out_ptrs = torch::empty(
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{num_experts},
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torch::TensorOptions().dtype(torch::kInt64).device(a.device()));
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auto a_scales_ptrs = torch::empty(
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{num_experts},
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torch::TensorOptions().dtype(torch::kInt64).device(a.device()));
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auto b_scales_ptrs = torch::empty(
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{num_experts},
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torch::TensorOptions().dtype(torch::kInt64).device(a.device()));
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auto layout_sfa = torch::empty(
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{num_experts, 5},
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torch::TensorOptions().dtype(torch::kInt32).device(a.device()));
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auto layout_sfb = torch::empty(
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{num_experts, 5},
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torch::TensorOptions().dtype(torch::kInt32).device(a.device()));
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auto stride_a = torch::full(
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{num_experts}, a.size(1),
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torch::TensorOptions().dtype(torch::kInt64).device(a.device()));
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auto stride_b = torch::full(
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{num_experts}, a.size(1),
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torch::TensorOptions().dtype(torch::kInt64).device(a.device()));
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auto stride_c = torch::full(
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{num_experts}, output.size(1),
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torch::TensorOptions().dtype(torch::kInt64).device(a.device()));
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torch::TensorOptions options_int =
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torch::TensorOptions().dtype(torch::kInt64).device(a.device());
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run_get_ggemm_starts<typename MmaConfig::LayoutSFA,
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typename MmaConfig::LayoutSFB,
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typename MmaConfig::ScaleConfig>(
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expert_offsets, a_ptrs, b_ptrs, out_ptrs, a_scales_ptrs, b_scales_ptrs, a,
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b, output, scales_a, scales_b, layout_sfa, layout_sfb, problem_sizes);
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run_blockwise_scaled_group_mm<OutType, MmaConfig,
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typename MmaConfig::LayoutC>(
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out_ptrs, a_ptrs, b_ptrs, a_scales_ptrs, b_scales_ptrs, stride_a,
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stride_b, stride_c, layout_sfa, layout_sfb, problem_sizes,
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expert_offsets);
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}
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void cutlass_blockwise_scaled_grouped_mm(
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torch::Tensor& output, const torch::Tensor& a, const torch::Tensor& b,
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const torch::Tensor& scales_a, const torch::Tensor& scales_b,
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const torch::Tensor& problem_sizes, const torch::Tensor& expert_offsets) {
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TORCH_CHECK(problem_sizes.dim() == 2, "problem_sizes must be 2D tensor");
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TORCH_CHECK(problem_sizes.size(1) == 3,
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"problem_sizes must have shape (num_experts, 3)");
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TORCH_CHECK(problem_sizes.size(0) == expert_offsets.size(0),
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"Number of experts in problem_sizes must match expert_offsets");
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TORCH_CHECK(problem_sizes.dtype() == torch::kInt32,
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"problem_sizes must be int32");
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TORCH_CHECK(a.scalar_type() == torch::kFloat8_e4m3fn,
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"a must be kFloat8_e4m3fn");
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TORCH_CHECK(b.scalar_type() == torch::kFloat8_e4m3fn,
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"b must be kFloat8_e4m3fn");
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TORCH_CHECK(output.scalar_type() == torch::kBFloat16 ||
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output.scalar_type() == torch::kHalf,
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"output must be bfloat16 or half");
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TORCH_CHECK(scales_a.scalar_type() == torch::kFloat32,
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"scales_a must be float32");
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TORCH_CHECK(scales_b.scalar_type() == torch::kFloat32,
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"scales_b must be float32");
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TORCH_CHECK(expert_offsets.scalar_type() == torch::kInt32,
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"expert_offsets must be int32");
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TORCH_CHECK(output.dim() == 2, "output must be 2D tensor");
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TORCH_CHECK(a.dim() == 2, "a must be 2D tensor");
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TORCH_CHECK(b.dim() == 3, "b must be 3D tensor");
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TORCH_CHECK(scales_a.dim() == 2, "scales_a must be 2D tensor");
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TORCH_CHECK(scales_b.dim() == 3, "scales_b must be 3D tensor");
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TORCH_CHECK(problem_sizes.dim() == 2, "problem_sizes must be 2D tensor");
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TORCH_CHECK(problem_sizes.size(1) == 3,
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"problem_sizes must have shape (num_experts, 3)");
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TORCH_CHECK(problem_sizes.size(0) == expert_offsets.size(0),
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"Number of experts in problem_sizes must match expert_offsets");
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TORCH_CHECK(problem_sizes.dtype() == torch::kInt32,
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"problem_sizes must be int32");
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TORCH_CHECK(expert_offsets.dim() == 1, "expert_offsets must be 1D tensor");
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#if defined(ENABLE_CUTLASS_MOE_SM100) && ENABLE_CUTLASS_MOE_SM100
|
||||
if (output.scalar_type() == torch::kBFloat16) {
|
||||
blockwise_scaled_group_mm_dispatch_shape<cutlass::bfloat16_t>(
|
||||
output, a, b, scales_a, scales_b, problem_sizes, expert_offsets);
|
||||
} else if (output.scalar_type() == torch::kFloat16) {
|
||||
blockwise_scaled_group_mm_dispatch_shape<cutlass::half_t>(
|
||||
output, a, b, scales_a, scales_b, problem_sizes, expert_offsets);
|
||||
} else {
|
||||
TORCH_CHECK(false, "Unsupported output tensor type");
|
||||
}
|
||||
#endif
|
||||
}
|
||||
|
||||
TORCH_LIBRARY_IMPL_EXPAND(TORCH_EXTENSION_NAME, CUDA, m) {
|
||||
m.impl("cutlass_blockwise_scaled_grouped_mm",
|
||||
&cutlass_blockwise_scaled_grouped_mm);
|
||||
}
|
||||
@@ -416,13 +416,6 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, ops) {
|
||||
" Tensor alpha) -> ()");
|
||||
ops.impl("cutlass_scaled_fp4_mm", torch::kCUDA, &cutlass_scaled_fp4_mm);
|
||||
|
||||
// cutlass blockwise scaledgroup GEMM
|
||||
ops.def(
|
||||
"cutlass_blockwise_scaled_grouped_mm(Tensor! output, Tensor a, Tensor b, "
|
||||
"Tensor scales_a, Tensor scales_b, "
|
||||
"Tensor problem_sizes, Tensor expert_offsets) -> ()");
|
||||
// conditionally compiled so impl registration is in source file
|
||||
|
||||
// cutlass nvfp4 block scaled group GEMM
|
||||
ops.def(
|
||||
"cutlass_fp4_group_mm(Tensor! out, Tensor a, Tensor b,"
|
||||
|
||||
Reference in New Issue
Block a user