biondizzle/DeepGEMM
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

Make various updates and fixes (#198)

Browse Source
This commit is contained in:
Ray Wang
2025-09-25 16:19:07 +08:00
committed by GitHub
parent 79f48ee15a
commit 3f71de7aa9
45 changed files with 3281 additions and 1060 deletions
Download Patch File Download Diff File Expand all files Collapse all files

114
csrc/apis/gemm.hpp
View File

@@ -1,5 +1,6 @@
#pragma once
#include "../jit_kernels/impls/sm90_fp8_gemm_1d1d.hpp"
#include "../jit_kernels/impls/sm90_fp8_gemm_1d2d.hpp"
#include "../jit_kernels/impls/sm90_bf16_gemm.hpp"
#include "../jit_kernels/impls/sm100_fp8_gemm_1d1d.hpp"
@@ -52,13 +53,18 @@ static void fp8_gemm_nt(const std::pair<torch::Tensor, torch::Tensor>& a,
// Transform SFA and SFB into compute-required layout
if (not recipe.has_value())
recipe = get_default_recipe(a.second.scalar_type(), b.second.scalar_type());
DG_HOST_ASSERT(recipe.value() == std::make_tuple(1, 1, 128) or recipe.value() == std::make_tuple(1, 128, 128));
const auto& sfa = layout::transform_sf_into_required_layout(a.second, m, k, recipe.value(), std::nullopt, true, disable_ue8m0_cast);
const auto& sfb = layout::transform_sf_into_required_layout(b.second, n, k, recipe.value(), std::nullopt, false, disable_ue8m0_cast);
// Dispatch into different implements
const auto& arch_major = device_runtime->get_arch_major();
if (arch_major == 9 and sfa.scalar_type() == torch::kFloat) {
sm90_fp8_gemm_1d2d(a.first, sfa, b.first, sfb, c, d, m, n, k, major_a, major_b, compiled_dims);
if (std::get<1>(recipe.value()) == 1) {
sm90_fp8_gemm_1d1d(a.first, sfa, b.first, sfb, c, d, m, n, k, major_a, major_b, compiled_dims);
} else {
sm90_fp8_gemm_1d2d(a.first, sfa, b.first, sfb, c, d, m, n, k, major_a, major_b, compiled_dims);
}
} else if (arch_major == 10 and sfa.scalar_type() == torch::kInt) {
sm100_fp8_gemm_1d1d(a.first, sfa, b.first, sfb, c, d, m, n, k, major_a, major_b, compiled_dims);
} else if (arch_major == 10 and sfa.scalar_type() == torch::kFloat) {
@@ -261,6 +267,60 @@ static void k_grouped_fp8_gemm_tn_contiguous(const std::pair<torch::Tensor, torc
}
}
static void k_grouped_fp8_gemm_nt_contiguous(const std::pair<torch::Tensor, torch::Tensor>& a,
const std::pair<torch::Tensor, torch::Tensor>& b,
const torch::Tensor& d,
const std::vector<int>& ks,
const torch::Tensor& ks_tensor,
const std::optional<torch::Tensor>& c,
const std::tuple<int, int, int>& recipe,
const std::string& compiled_dims) {
// Must be 1D1D kernel
DG_HOST_ASSERT(recipe == std::make_tuple(1, 1, 128));
// Shape checks
const auto& [num_groups, m, n] = get_shape<3>(d);
const auto& sum_mk = a.first.numel();
const auto& sum_nk = b.first.numel();
int sum_k = 0;
for (const auto& k: ks)
sum_k += k;
DG_HOST_ASSERT(sum_mk == m * sum_k);
DG_HOST_ASSERT(sum_nk == n * sum_k);
// Contiguity checks
DG_HOST_ASSERT(a.first.is_contiguous());
DG_HOST_ASSERT(b.first.is_contiguous());
DG_HOST_ASSERT(d.is_contiguous());
if (c.has_value()) {
DG_HOST_ASSERT(c.value().scalar_type() == torch::kFloat);
DG_HOST_ASSERT(c.value().is_contiguous());
}
// Do nothing if empty
if (std::accumulate(ks.begin(), ks.end(), 0) == 0)
return;
// Transform SF with padding
const auto& sfa = layout::transform_k_grouped_sf_into_required_layout(a.second, ks, ks_tensor, recipe);
const auto& sfb = layout::transform_k_grouped_sf_into_required_layout(b.second, ks, ks_tensor, recipe);
// Allocate tensormap buffer
// `4` means the double buffering for both A and B operands (2 * 2)
const auto& num_sms = device_runtime->get_num_sms();
const auto& tensor_map_buffer = torch::empty({num_sms * 4 * static_cast<int>(sizeof(CUtensorMap))},
a.first.options().dtype(torch::kByte));
// Dispatch implementation
const auto& arch_major = device_runtime->get_arch_major();
if (arch_major == 9) {
sm90_fp8_k_grouped_gemm_1d1d(a.first, sfa, b.first, sfb, c, d, m, n, ks, ks_tensor, tensor_map_buffer,
cute::UMMA::Major::K, cute::UMMA::Major::K, compiled_dims);
} else {
DG_HOST_UNREACHABLE("Unsupported architecture");
}
}
static void bf16_gemm_nt(const torch::Tensor& a,
const torch::Tensor& b,
const torch::Tensor& d,
@@ -403,6 +463,43 @@ static void m_grouped_bf16_gemm_nt_masked(const torch::Tensor& a, const torch::T
}
}
static void cublaslt_gemm_nt(const torch::Tensor& a, const torch::Tensor& b,
const torch::Tensor& d, const std::optional<torch::Tensor>& c) {
// Shape must be `[M, K] @ [N, K].T`
const auto& major_a = get_major_type_ab(a);
const auto& major_b = get_major_type_ab(b);
// Type and shape checks
const auto& [m , k ] = get_shape<2>(a);
const auto& [n , k_] = get_shape<2>(b);
const auto& [m_, n_] = get_shape<2>(d);
DG_HOST_ASSERT(m == m_ and n == n_ and k == k_);
if (c.has_value())
DG_HOST_ASSERT(c.value().scalar_type() == d.scalar_type());
// Do nothing if the problem is empty
if (m == 0 or n == 0)
return;
cublaslt_gemm(a, b, c, d, m, n, k, major_a, major_b);
}
static void cublaslt_gemm_nn(const torch::Tensor& a, const torch::Tensor& b,
const torch::Tensor& d, const std::optional<torch::Tensor>& c) {
cublaslt_gemm_nt(a, b.transpose(0, 1), d, c);
}
static void cublaslt_gemm_tn(const torch::Tensor& a, const torch::Tensor& b,
const torch::Tensor& d, const std::optional<torch::Tensor>& c) {
cublaslt_gemm_nt(a.transpose(0, 1), b.transpose(0, 1), d, c);
}
static void cublaslt_gemm_tt(const torch::Tensor& a, const torch::Tensor& b,
const torch::Tensor& d, const std::optional<torch::Tensor>& c) {
cublaslt_gemm_nt(a.transpose(0, 1), b, d, c);
}
static void register_apis(pybind11::module_& m) {
// FP8 GEMMs
m.def("fp8_gemm_nt", &fp8_gemm_nt,
@@ -442,6 +539,11 @@ static void register_apis(pybind11::module_& m) {
py::arg("ks_tensor"), py::arg("c") = std::nullopt,
py::arg("recipe") = std::make_tuple(1, 1, 128),
py::arg("compiled_dims") = "mn");
m.def("k_grouped_fp8_gemm_nt_contiguous", &k_grouped_fp8_gemm_nt_contiguous,
py::arg("a"), py::arg("b"), py::arg("d"), py::arg("ks"),
py::arg("ks_tensor"), py::arg("c") = std::nullopt,
py::arg("recipe") = std::make_tuple(1, 1, 128),
py::arg("compiled_dims") = "mn");
// BF16 GEMMs
m.def("bf16_gemm_nt", &bf16_gemm_nt,
@@ -466,6 +568,16 @@ static void register_apis(pybind11::module_& m) {
m.def("m_grouped_bf16_gemm_nt_masked", &m_grouped_bf16_gemm_nt_masked,
py::arg("a"), py::arg("b"), py::arg("d"), py::arg("masked_m"),
py::arg("expected_m"), py::arg("compiled_dims") = "nk");
// cuBLASLt GEMMs
m.def("cublaslt_gemm_nt", &cublaslt_gemm_nt,
py::arg("a"), py::arg("b"), py::arg("d"), py::arg("c") = std::nullopt);
m.def("cublaslt_gemm_nn", &cublaslt_gemm_nn,
py::arg("a"), py::arg("b"), py::arg("d"), py::arg("c") = std::nullopt);
m.def("cublaslt_gemm_tn", &cublaslt_gemm_tn,
py::arg("a"), py::arg("b"), py::arg("d"), py::arg("c") = std::nullopt);
m.def("cublaslt_gemm_tt", &cublaslt_gemm_tt,
py::arg("a"), py::arg("b"), py::arg("d"), py::arg("c") = std::nullopt);
}
} // namespace deep_gemm::gemm