392 lines
21 KiB
C++
392 lines
21 KiB
C++
#pragma once
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#include <torch/python.h>
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#include "../../jit/compiler.hpp"
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#include "../../jit/device_runtime.hpp"
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#include "../../jit/kernel_runtime.hpp"
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#include "../../utils/exception.hpp"
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#include "../../utils/format.hpp"
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#include "../../utils/math.hpp"
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#include "../heuristics/sm100.hpp"
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#include "runtime_utils.hpp"
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namespace deep_gemm {
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class SM100BF16GemmRuntime final: public LaunchRuntime<SM100BF16GemmRuntime> {
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public:
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struct Args {
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int m, n, k, num_groups;
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const std::string& compiled_dims;
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GemmConfig gemm_config;
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LaunchArgs launch_args;
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void* grouped_layout;
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CUtensorMap tensor_map_a;
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CUtensorMap tensor_map_b;
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CUtensorMap tensor_map_cd;
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};
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static std::string generate_impl(const Args& args) {
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return fmt::format(R"(
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#include <deep_gemm/impls/sm100_bf16_gemm.cuh>
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using namespace deep_gemm;
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static void __instantiate_kernel() {{
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auto ptr = reinterpret_cast<void*>(&sm100_bf16_gemm_impl<
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{}, {},
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{}, {}, {},
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{}, {}, {},
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{},
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{}, {}, {},
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{},
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{}, {},
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{}, {},
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{},
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{}, {}, {},
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{}
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>);
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}};
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)",
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to_string(args.gemm_config.major_a), to_string(args.gemm_config.major_b),
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get_compiled_dim(args.m, 'm', args.compiled_dims), get_compiled_dim(args.n, 'n', args.compiled_dims), get_compiled_dim(args.k, 'k', args.compiled_dims),
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args.gemm_config.block_m, args.gemm_config.block_n, args.gemm_config.block_k,
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args.num_groups,
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args.gemm_config.smem_config.swizzle_a_mode, args.gemm_config.smem_config.swizzle_b_mode, args.gemm_config.smem_config.swizzle_cd_mode,
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args.gemm_config.num_stages,
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args.gemm_config.thread_config.num_non_epilogue_threads, args.gemm_config.thread_config.num_epilogue_threads,
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args.gemm_config.multicast_config.num_multicast, args.gemm_config.multicast_config.is_multicast_on_a,
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args.gemm_config.num_sms,
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to_string(args.gemm_config.gemm_type), args.gemm_config.with_accumulation, to_string(args.gemm_config.cd_dtype),
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args.gemm_config.tc_util);
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}
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static void launch_impl(const KernelHandle& kernel, const LaunchConfigHandle& config, Args args) {
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// TODO: optimize `args` copy
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DG_CUDA_UNIFIED_CHECK(launch_kernel(kernel, config,
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args.grouped_layout, args.m, args.n, args.k,
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args.tensor_map_a, args.tensor_map_b,
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args.tensor_map_cd));
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}
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};
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static void sm100_bf16_gemm(const torch::Tensor& a,
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const torch::Tensor& b,
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const std::optional<torch::Tensor>& c,
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const torch::Tensor& d,
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const int& m, const int& n, const int& k,
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const cute::UMMA::Major& major_a, const cute::UMMA::Major& major_b,
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const std::string& compiled_dims) {
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const auto& config = get_best_config<SM100ArchSpec>(
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GemmType::Normal, KernelType::KernelNoSF,
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m, n, k, 1, major_a, major_b,
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a.scalar_type(), b.scalar_type(),
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d.scalar_type(), c.has_value(),
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device_runtime->get_num_sms());
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const auto& tensor_map_a = make_tma_a_desc(major_a, a, m, k,
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SM100ArchSpec::get_ab_load_block_m(config.multicast_config, config.block_m),
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config.block_k,
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static_cast<int>(a.stride(get_non_contiguous_dim(major_a))), 1,
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config.smem_config.swizzle_a_mode);
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const auto& tensor_map_b = make_tma_b_desc(major_b, b, n, k,
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SM100ArchSpec::get_ab_load_block_n(config.multicast_config, config.block_n),
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config.block_k,
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static_cast<int>(b.stride(get_non_contiguous_dim(major_b))), 1,
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config.smem_config.swizzle_b_mode);
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const auto& tensor_map_cd = make_tma_cd_desc(d, m, n,
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SM100ArchSpec::get_cd_store_block_m(config.block_m),
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SM100ArchSpec::get_cd_store_block_n(config.block_n),
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static_cast<int>(d.stride(-2)), 1,
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config.smem_config.swizzle_cd_mode);
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// Launch
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const SM100BF16GemmRuntime::Args& args = {
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.m = m, .n = n, .k = k,
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.num_groups = 1,
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.compiled_dims = compiled_dims,
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.gemm_config = config,
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.launch_args = LaunchArgs(config.num_sms, config.thread_config.num_threads,
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config.smem_config.smem_size,
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config.multicast_config.num_multicast),
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.grouped_layout = nullptr,
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.tensor_map_a = tensor_map_a,
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.tensor_map_b = tensor_map_b,
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.tensor_map_cd = tensor_map_cd
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};
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const auto& code = SM100BF16GemmRuntime::generate(args);
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const auto& runtime = compiler->build("sm100_bf16_gemm", code);
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SM100BF16GemmRuntime::launch(runtime, args);
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}
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static void sm100_m_grouped_bf16_gemm_contiguous(const torch::Tensor& a,
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const torch::Tensor& b,
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const torch::Tensor& d,
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const torch::Tensor& grouped_layout,
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const int& num_groups, const int& m, const int& n, const int& k,
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const cute::UMMA::Major& major_a, const cute::UMMA::Major& major_b,
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const std::string& compiled_dims,
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const bool& use_psum_layout,
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const std::optional<int>& expected_m_for_psum_layout) {
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const auto& gemm_type = use_psum_layout ? GemmType::MGroupedContiguousWithPsumLayout : GemmType::MGroupedContiguous;
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// NOTES: If actual M is dynamic, estimate config via `num_groups` and `expected_m`.
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// Otherwise, treat the contiguous layout as a whole.
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const auto& m_for_config = expected_m_for_psum_layout.has_value() ? expected_m_for_psum_layout.value() : m;
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const auto& num_groups_for_config = expected_m_for_psum_layout.has_value() ? num_groups : 1;
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const auto& config = get_best_config<SM100ArchSpec>(
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gemm_type, KernelType::KernelNoSF,
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// NOTES: `num_groups` is 1, since the contiguous layout is seen as a whole
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m_for_config, n, k, num_groups_for_config, major_a, major_b,
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a.scalar_type(), b.scalar_type(),
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d.scalar_type(), false,
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device_runtime->get_num_sms());
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const auto& tensor_map_a = make_tma_a_desc(major_a, a, m, k,
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SM100ArchSpec::get_ab_load_block_m(config.multicast_config, config.block_m),
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config.block_k,
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static_cast<int>(a.stride(get_non_contiguous_dim(major_a))), 1,
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config.smem_config.swizzle_a_mode);
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const auto& tensor_map_b = make_tma_b_desc(major_b, b, n, k,
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SM100ArchSpec::get_ab_load_block_n(config.multicast_config, config.block_n),
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config.block_k,
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static_cast<int>(b.stride(get_non_contiguous_dim(major_b))), num_groups,
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config.smem_config.swizzle_b_mode);
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const auto& tensor_map_cd = make_tma_cd_desc(d, m, n,
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SM100ArchSpec::get_cd_store_block_m(config.block_m),
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SM100ArchSpec::get_cd_store_block_n(config.block_n),
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static_cast<int>(d.stride(-2)), 1,
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config.smem_config.swizzle_cd_mode);
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// Launch
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const SM100BF16GemmRuntime::Args& args = {
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.m = m, .n = n, .k = k,
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.num_groups = num_groups,
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.compiled_dims = compiled_dims,
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.gemm_config = config,
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.launch_args = LaunchArgs(config.num_sms, config.thread_config.num_threads,
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config.smem_config.smem_size,
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config.multicast_config.num_multicast),
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.grouped_layout = grouped_layout.data_ptr(),
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.tensor_map_a = tensor_map_a,
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.tensor_map_b = tensor_map_b,
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.tensor_map_cd = tensor_map_cd
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};
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const auto& code = SM100BF16GemmRuntime::generate(args);
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const auto& runtime = compiler->build("sm100_bf16_m_grouped_gemm_contiguous", code);
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SM100BF16GemmRuntime::launch(runtime, args);
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}
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static void sm100_m_grouped_bf16_gemm_masked(const torch::Tensor& a,
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const torch::Tensor& b,
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const torch::Tensor& d,
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const torch::Tensor& masked_m,
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const int& num_groups, const int& m, const int& n, const int& k,
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const int& expected_m,
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const cute::UMMA::Major& major_a, const cute::UMMA::Major& major_b,
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const std::string& compiled_dims) {
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const auto& config = get_best_config<SM100ArchSpec>(
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GemmType::MGroupedMasked, KernelType::KernelNoSF,
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expected_m, n, k, num_groups, major_a, major_b,
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a.scalar_type(), b.scalar_type(),
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d.scalar_type(), false,
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device_runtime->get_num_sms());
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const auto& tensor_map_a = make_tma_a_desc(major_a, a, m, k,
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SM100ArchSpec::get_ab_load_block_m(config.multicast_config, config.block_m),
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config.block_k,
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static_cast<int>(a.stride(get_non_contiguous_dim(major_a))), num_groups,
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config.smem_config.swizzle_a_mode);
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const auto& tensor_map_b = make_tma_b_desc(major_b, b, n, k,
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SM100ArchSpec::get_ab_load_block_n(config.multicast_config, config.block_n),
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config.block_k,
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static_cast<int>(b.stride(get_non_contiguous_dim(major_b))), num_groups,
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config.smem_config.swizzle_b_mode);
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const auto& tensor_map_cd = make_tma_cd_desc(d, m, n,
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SM100ArchSpec::get_cd_store_block_m(config.block_m),
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SM100ArchSpec::get_cd_store_block_n(config.block_n),
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static_cast<int>(d.stride(-2)), num_groups,
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config.smem_config.swizzle_cd_mode);
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// Launch
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const SM100BF16GemmRuntime::Args& args = {
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.m = m, .n = n, .k = k,
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.num_groups = num_groups,
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.compiled_dims = compiled_dims,
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.gemm_config = config,
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.launch_args = LaunchArgs(config.num_sms, config.thread_config.num_threads,
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config.smem_config.smem_size,
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config.multicast_config.num_multicast),
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.grouped_layout = masked_m.data_ptr(),
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.tensor_map_a = tensor_map_a,
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.tensor_map_b = tensor_map_b,
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.tensor_map_cd = tensor_map_cd
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};
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const auto& code = SM100BF16GemmRuntime::generate(args);
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const auto& runtime = compiler->build("sm100_bf16_m_grouped_gemm_masked", code);
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SM100BF16GemmRuntime::launch(runtime, args);
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}
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static void sm100_bf16_k_grouped_gemm(const torch::Tensor& a,
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const torch::Tensor& b,
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const std::optional<torch::Tensor>& c,
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const torch::Tensor& d,
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const int& m, const int& n,
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const std::vector<int>& ks, const torch::Tensor& ks_tensor,
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const cute::UMMA::Major& major_a, const cute::UMMA::Major& major_b,
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const std::string& compiled_dims) {
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DG_HOST_ASSERT(major_a == cute::UMMA::Major::MN and major_b == cute::UMMA::Major::MN);
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int sum_k = 0;
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for (const auto& k: ks) {
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sum_k += k;
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DG_HOST_ASSERT(k % 128 == 0);
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}
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const auto& num_groups = static_cast<int>(ks.size());
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// Get config using max K for better performance
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const auto& max_k = *std::max_element(ks.begin(), ks.end());
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const auto& config = get_best_config<SM100ArchSpec>(
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GemmType::KGroupedContiguous, KernelType::KernelNoSF,
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m, n, max_k, num_groups, cute::UMMA::Major::MN, cute::UMMA::Major::MN,
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a.scalar_type(), b.scalar_type(),
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d.scalar_type(), c.has_value(),
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device_runtime->get_num_sms());
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// Create tensor descriptors
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const auto& tensor_map_a = make_tma_a_desc(cute::UMMA::Major::MN, a, m, sum_k,
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SM100ArchSpec::get_ab_load_block_m(config.multicast_config, config.block_m),
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config.block_k,
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static_cast<int>(a.stride(0)), 1,
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config.smem_config.swizzle_a_mode);
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const auto& tensor_map_b = make_tma_b_desc(cute::UMMA::Major::MN, b, n, sum_k,
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SM100ArchSpec::get_ab_load_block_n(config.multicast_config, config.block_n),
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config.block_k,
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static_cast<int>(b.stride(0)), 1,
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config.smem_config.swizzle_b_mode);
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const auto& tensor_map_cd = make_tma_cd_desc(d, m, n,
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SM100ArchSpec::get_cd_store_block_m(config.block_m),
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SM100ArchSpec::get_cd_store_block_n(config.block_n),
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static_cast<int>(d.stride(1)), num_groups,
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config.smem_config.swizzle_cd_mode);
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// Launch kernel
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const SM100BF16GemmRuntime::Args& args = {
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.m = m, .n = n, .k = sum_k,
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.num_groups = num_groups,
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.compiled_dims = compiled_dims,
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.gemm_config = config,
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.launch_args = LaunchArgs(config.num_sms, config.thread_config.num_threads,
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config.smem_config.smem_size,
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config.multicast_config.num_multicast),
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.grouped_layout = ks_tensor.data_ptr(),
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.tensor_map_a = tensor_map_a,
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.tensor_map_b = tensor_map_b,
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.tensor_map_cd = tensor_map_cd
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};
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const auto& code = SM100BF16GemmRuntime::generate(args);
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const auto& runtime = compiler->build("sm100_bf16_k_grouped_gemm", code);
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SM100BF16GemmRuntime::launch(runtime, args);
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}
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static void sm100_bf16_bhr_hdr_bhd(const torch::Tensor& tensor_a,
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const torch::Tensor& tensor_b,
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const torch::Tensor& tensor_d,
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const int& b, const int& h, const int& r, const int& d,
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const std::string& compiled_dims = "nk") {
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const auto& config = get_best_config<SM100ArchSpec>(
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GemmType::Batched, KernelType::KernelNoSF,
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b, d, r, h, cute::UMMA::Major::K, cute::UMMA::Major::K,
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tensor_a.scalar_type(), tensor_b.scalar_type(),
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tensor_d.scalar_type(), false,
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device_runtime->get_num_sms());
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const int& load_block_m = SM100ArchSpec::get_ab_load_block_m(config.multicast_config, config.block_m);
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const auto& tensor_map_a = make_tma_3d_desc(tensor_a, r, b, h,
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config.block_k, load_block_m, 1,
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tensor_a.stride(0), tensor_a.stride(1),
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config.smem_config.swizzle_a_mode);
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const int& load_block_n = SM100ArchSpec::get_ab_load_block_n(config.multicast_config, config.block_n);
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const auto& tensor_map_b = make_tma_3d_desc(tensor_b, r, d, h,
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config.block_k, load_block_n, 1,
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tensor_b.stride(1), tensor_b.stride(0),
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config.smem_config.swizzle_b_mode);
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const int& store_block_m = SM100ArchSpec::get_cd_store_block_m(config.block_m);
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const int& store_block_n = SM100ArchSpec::get_cd_store_block_n(config.block_n);
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const auto& tensor_map_cd = make_tma_3d_desc(tensor_d, d, b, h,
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store_block_n, store_block_m, 1,
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tensor_d.stride(0), tensor_d.stride(1),
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config.smem_config.swizzle_cd_mode);
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// Launch
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const SM100BF16GemmRuntime::Args& args = {
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.m = b, .n = d, .k = r,
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.num_groups = h,
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.compiled_dims = compiled_dims,
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.gemm_config = config,
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.launch_args = LaunchArgs(config.num_sms, config.thread_config.num_threads,
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config.smem_config.smem_size,
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config.multicast_config.num_multicast),
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.grouped_layout = nullptr,
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.tensor_map_a = tensor_map_a,
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.tensor_map_b = tensor_map_b,
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.tensor_map_cd = tensor_map_cd
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};
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const auto& code = SM100BF16GemmRuntime::generate(args);
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const auto& runtime = compiler->build("sm100_bf16_bhr_hdr_bhd", code);
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SM100BF16GemmRuntime::launch(runtime, args);
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}
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static void sm100_bf16_bhd_hdr_bhr(const torch::Tensor& tensor_a,
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const torch::Tensor& tensor_b,
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const torch::Tensor& tensor_d,
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const int& b, const int& h, const int& r, const int& d,
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const std::string& compiled_dims = "nk") {
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const auto& config = get_best_config<SM100ArchSpec>(
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GemmType::Batched, KernelType::KernelNoSF,
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b, r, d, h, cute::UMMA::Major::K, cute::UMMA::Major::MN,
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tensor_a.scalar_type(), tensor_b.scalar_type(),
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tensor_d.scalar_type(), false,
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device_runtime->get_num_sms());
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const int& load_block_m = SM100ArchSpec::get_ab_load_block_m(config.multicast_config, config.block_m);
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const auto& tensor_map_a = make_tma_3d_desc(tensor_a, d, b, h,
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config.block_k, load_block_m, 1,
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tensor_a.stride(0), tensor_a.stride(1),
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config.smem_config.swizzle_a_mode);
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const int& load_block_n = SM100ArchSpec::get_ab_load_block_n(config.multicast_config, config.block_n);
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const auto& tensor_map_b = make_tma_3d_desc(tensor_b, r, d, h,
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load_block_n, config.block_k, 1,
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tensor_b.stride(1), tensor_b.stride(0),
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config.smem_config.swizzle_b_mode);
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const int& store_block_m = SM100ArchSpec::get_cd_store_block_m(config.block_m);
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const int& store_block_n = SM100ArchSpec::get_cd_store_block_n(config.block_n);
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const auto& tensor_map_cd = make_tma_3d_desc(tensor_d, r, b, h,
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store_block_n, store_block_m, 1,
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tensor_d.stride(0), tensor_d.stride(1),
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config.smem_config.swizzle_cd_mode);
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// Launch
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const SM100BF16GemmRuntime::Args& args = {
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.m = b, .n = r, .k = d,
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.num_groups = h,
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.compiled_dims = compiled_dims,
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.gemm_config = config,
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.launch_args = LaunchArgs(config.num_sms, config.thread_config.num_threads,
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config.smem_config.smem_size,
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config.multicast_config.num_multicast),
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.grouped_layout = nullptr,
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.tensor_map_a = tensor_map_a,
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.tensor_map_b = tensor_map_b,
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.tensor_map_cd = tensor_map_cd
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};
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const auto& code = SM100BF16GemmRuntime::generate(args);
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const auto& runtime = compiler->build("sm100_bf16_bhd_hdr_bhr", code);
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SM100BF16GemmRuntime::launch(runtime, args);
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}
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} // namespace deep_gemm
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