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Merged
BBuf merged 1 commit into from
Feb 9, 2026
Merged

[Sync] Add maybe launch and SBO for latest main #21

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26 changes: 21 additions & 5 deletions csrc/apis/gemm.hpp
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Original file line number Diff line number Diff line change
Expand Up @@ -219,7 +219,8 @@ static void m_grouped_fp8_fp4_gemm_nn_contiguous(const std::pair<torch::Tensor,
d, grouped_layout, recipe, recipe_a, recipe_b, compiled_dims, disable_ue8m0_cast, use_psum_layout, std::nullopt);
}

static void m_grouped_fp8_fp4_gemm_nt_masked(const std::pair<torch::Tensor, torch::Tensor>& a,
static std::tuple<c10::optional<int64_t>, c10::optional<int64_t>> m_grouped_fp8_fp4_gemm_nt_masked(
const std::pair<torch::Tensor, torch::Tensor>& a,
const std::pair<torch::Tensor, torch::Tensor>& b,
const torch::Tensor& d,
const torch::Tensor& masked_m,
Expand All @@ -228,7 +229,10 @@ static void m_grouped_fp8_fp4_gemm_nt_masked(const std::pair<torch::Tensor, torc
std::optional<std::tuple<int, int>> recipe_a,
std::optional<std::tuple<int, int>> recipe_b,
const std::string& compiled_dims,
const bool& disable_ue8m0_cast) {
const bool& disable_ue8m0_cast,
const int& max_block_n,
const bool& enable_overlap,
const c10::optional<torch::Tensor>& signal) {
// Shape must be `[G, M, K] @ [G, N, K].mT`
const auto& major_a = get_major_type_ab(a.first);
const auto& major_b = get_major_type_ab(b.first);
Expand All @@ -255,17 +259,28 @@ static void m_grouped_fp8_fp4_gemm_nt_masked(const std::pair<torch::Tensor, torc
a.second, b.second, m, n, k, recipe, recipe_a, recipe_b, num_groups, num_groups, disable_ue8m0_cast);

// Dispatch implementation
std::optional<std::pair<int, int>> overlap_result = std::nullopt;
if (arch_major == 9 and sfa.scalar_type() == torch::kFloat) {
const auto& major_sfb = get_major_type_ab(sfb);
sm90_m_grouped_fp8_gemm_masked_1d2d(a.first, sfa, b.first, sfb, d, masked_m,
num_groups, m, n, k, expected_m, major_a, major_b, major_sfb, compiled_dims);
overlap_result = sm90_m_grouped_fp8_gemm_masked_1d2d(a.first, sfa, b.first, sfb, d, masked_m,
num_groups, m, n, k, expected_m, major_a, major_b, major_sfb, compiled_dims,
max_block_n, enable_overlap, signal);
} else if (arch_major == 10 and sfa.scalar_type() == torch::kInt) {
DG_HOST_ASSERT(not enable_overlap);
sm100_m_grouped_fp8_fp4_gemm_masked_1d1d(a.first, sfa, b.first, sfb, d, masked_m,
num_groups, m, n, k, expected_m, gran_k_a, gran_k_b,
major_a, major_b, compiled_dims);
} else {
DG_HOST_UNREACHABLE("Unsupported architecture or scaling factor types");
}

if (!overlap_result) {
return std::make_tuple(c10::nullopt, c10::nullopt);
}
return std::make_tuple(
c10::optional<int64_t>(overlap_result->first),
c10::optional<int64_t>(overlap_result->second)
);
}

static void k_grouped_fp8_gemm_tn_contiguous(const std::pair<torch::Tensor, torch::Tensor>& a,
Expand Down Expand Up @@ -640,7 +655,8 @@ static void register_apis(pybind11::module_& m) {
py::arg("a"), py::arg("b"), py::arg("d"), py::arg("masked_m"),
py::arg("expected_m"), py::arg("recipe") = std::nullopt,
py::arg("recipe_a") = std::nullopt, py::arg("recipe_b") = std::nullopt,
py::arg("compiled_dims") = "nk", py::arg("disable_ue8m0_cast") = false);
py::arg("compiled_dims") = "nk", py::arg("disable_ue8m0_cast") = false,
py::arg("max_block_n") = 256, py::arg("enable_overlap") = false, py::arg("signal") = std::nullopt);
m.def("k_grouped_fp8_gemm_tn_contiguous", &k_grouped_fp8_gemm_tn_contiguous,
py::arg("a"), py::arg("b"), py::arg("d"), py::arg("ks"),
py::arg("ks_tensor"), py::arg("c") = std::nullopt,
Expand Down
6 changes: 6 additions & 0 deletions csrc/apis/runtime.hpp
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Expand Up @@ -14,6 +14,12 @@ static void register_apis(pybind11::module_& m) {
m.def("get_num_sms", [&]() {
return device_runtime->get_num_sms();
});
m.def("set_compile_mode", [&](const int& new_compile_mode) {
device_runtime->set_compile_mode(new_compile_mode);
});
m.def("get_compile_mode", [&]() {
return device_runtime->get_compile_mode();
});
m.def("set_tc_util", [&](const int& new_tc_util) {
device_runtime->set_tc_util(new_tc_util);
});
Expand Down
10 changes: 10 additions & 0 deletions csrc/jit/device_runtime.hpp
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Expand Up @@ -12,6 +12,7 @@ namespace deep_gemm {
class DeviceRuntime {
int num_sms = 0, tc_util = 0;
std::shared_ptr<cudaDeviceProp> cached_prop;
int compile_mode = 0;

// cuBLASLt utils
static constexpr size_t kCublasLtWorkspaceSize = 32 * 1024 * 1024;
Expand Down Expand Up @@ -80,6 +81,15 @@ class DeviceRuntime {
return num_sms;
}

void set_compile_mode(const int& new_compile_mode) {
DG_HOST_ASSERT(0 <= new_compile_mode and new_compile_mode <= 1);
compile_mode = new_compile_mode;
}

int get_compile_mode() {
return compile_mode;
}

int get_l2_cache_size() {
return get_prop()->l2CacheSize;
}
Expand Down
9 changes: 7 additions & 2 deletions csrc/jit_kernels/heuristics/common.hpp
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Expand Up @@ -65,11 +65,13 @@ struct GemmConfig {
cute::UMMA::Major major_b;
bool with_accumulation;
int block_m, block_n, block_k;
int signal_threshold;
int num_stages, num_last_stages;

// Templated device configs
int num_sms;
int tc_util;
bool enable_overlap;

// Structured configs
MulticastConfig multicast_config;
Expand Down Expand Up @@ -154,7 +156,8 @@ static GemmConfig get_best_config(const GemmType& gemm_type, const KernelType& k
const cute::UMMA::Major& major_a, const cute::UMMA::Major& major_b,
const at::ScalarType& a_dtype, const at::ScalarType& b_dtype,
const at::ScalarType& cd_dtype,
const bool& with_accumulation, const int& num_sms) {
const bool& with_accumulation, const int& num_sms,
const int& max_block_n = 256, const bool& enable_overlap = false) {
const auto mma_kind = (a_dtype == torch::kBFloat16 ? MmaKind::BF16 : MmaKind::MXFP8FP4);
if (mma_kind == MmaKind::BF16) {
DG_HOST_ASSERT(a_dtype == torch::kBFloat16 and b_dtype == torch::kBFloat16);
Expand All @@ -170,7 +173,7 @@ static GemmConfig get_best_config(const GemmType& gemm_type, const KernelType& k
block_ms = std::vector{get_mk_alignment_for_contiguous_layout()};
if (gemm_type == GemmType::MGroupedMasked or gemm_type == GemmType::MGroupedContiguousWithPsumLayout)
block_ms = std::vector{64, 128}; // Exclude 256 for performance
auto block_ns = ArchSpec::get_block_n_candidates(kernel_type, cd_dtype);
auto block_ns = ArchSpec::get_block_n_candidates(kernel_type, cd_dtype, max_block_n);

// NOTES: TMA copy .b4x16_p64 only supports Swizzle 128B
// TODO: Optimize it
Expand Down Expand Up @@ -297,10 +300,12 @@ static GemmConfig get_best_config(const GemmType& gemm_type, const KernelType& k
.block_m = best_block_m,
.block_n = best_block_n,
.block_k = block_k,
.signal_threshold = ceil_div(n, best_block_n),
.num_stages = best_num_stages,
.num_last_stages = ceil_div(k, block_k) % best_num_stages,
.num_sms = num_min_sms,
.tc_util = device_runtime->get_tc_util(),
.enable_overlap = enable_overlap,
.multicast_config = best_multicast_config,
// ReSharper disable once CppLocalVariableMightNotBeInitialized
.smem_config = best_smem_config,
Expand Down
4 changes: 2 additions & 2 deletions csrc/jit_kernels/heuristics/sm100.hpp
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Expand Up @@ -22,11 +22,11 @@ struct SM100ArchSpec {
return candidates;
}

static std::vector<int> get_block_n_candidates(const KernelType& kernel_type, const at::ScalarType& cd_dtype) {
static std::vector<int> get_block_n_candidates(const KernelType& kernel_type, const at::ScalarType& cd_dtype, const int& max_block_n) {
// 16 is for better SM usage
// Stride 32 is due to low-performance swizzle-16/32B
std::vector<int> candidates = {16};
for (int i = 32; i <= 256; i += 32)
for (int i = 32; i <= max_block_n; i += 32)
candidates.push_back(i);
return candidates;
}
Expand Down
4 changes: 2 additions & 2 deletions csrc/jit_kernels/heuristics/sm90.hpp
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Expand Up @@ -21,7 +21,7 @@ struct SM90ArchSpec {
return candidates;
}

static std::vector<int> get_block_n_candidates(const KernelType& kernel_type, const at::ScalarType& cd_dtype) {
static std::vector<int> get_block_n_candidates(const KernelType& kernel_type, const at::ScalarType& cd_dtype, const int& max_block_n) {
int start = 16;

// Avoid bank conflicts for 1D1D kernel FP32 output
Expand All @@ -32,7 +32,7 @@ struct SM90ArchSpec {
}

// Push the strided options
for (int i = start; i <= 256; i += 16)
for (int i = start; i <= max_block_n; i += 16)
candidates.push_back(i);
return candidates;
}
Expand Down
9 changes: 9 additions & 0 deletions csrc/jit_kernels/impls/runtime_utils.hpp
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Expand Up @@ -5,6 +5,7 @@

#include "../heuristics/sm90.hpp"
#include "../../jit/handle.hpp"
#include "../../jit/device_runtime.hpp"
#include "../../utils/math.hpp"
#include "../../utils/system.hpp"
#include "../../utils/exception.hpp"
Expand Down Expand Up @@ -234,4 +235,12 @@ static CUtensorMap make_tma_sf_desc(const cute::UMMA::Major& major,
allow_tf32);
}

#ifndef MAYBE_LAUNCH
#define MAYBE_LAUNCH(EXPR) do { \
if (device_runtime->get_compile_mode() == 0) { \
(EXPR); \
} \
} while (0)
#endif

} // namespace deep_gemm
12 changes: 6 additions & 6 deletions csrc/jit_kernels/impls/sm100_bf16_gemm.hpp
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Expand Up @@ -118,7 +118,7 @@ static void sm100_bf16_gemm(const torch::Tensor& a,
};
const auto& code = SM100BF16GemmRuntime::generate(args);
const auto& runtime = compiler->build("sm100_bf16_gemm", code);
SM100BF16GemmRuntime::launch(runtime, args);
MAYBE_LAUNCH(SM100BF16GemmRuntime::launch(runtime, args));
}

static void sm100_m_grouped_bf16_gemm_contiguous(const torch::Tensor& a,
Expand Down Expand Up @@ -177,7 +177,7 @@ static void sm100_m_grouped_bf16_gemm_contiguous(const torch::Tensor& a,
};
const auto& code = SM100BF16GemmRuntime::generate(args);
const auto& runtime = compiler->build("sm100_bf16_m_grouped_gemm_contiguous", code);
SM100BF16GemmRuntime::launch(runtime, args);
MAYBE_LAUNCH(SM100BF16GemmRuntime::launch(runtime, args));
}

static void sm100_m_grouped_bf16_gemm_masked(const torch::Tensor& a,
Expand Down Expand Up @@ -227,7 +227,7 @@ static void sm100_m_grouped_bf16_gemm_masked(const torch::Tensor& a,
};
const auto& code = SM100BF16GemmRuntime::generate(args);
const auto& runtime = compiler->build("sm100_bf16_m_grouped_gemm_masked", code);
SM100BF16GemmRuntime::launch(runtime, args);
MAYBE_LAUNCH(SM100BF16GemmRuntime::launch(runtime, args));
}

static void sm100_bf16_k_grouped_gemm(const torch::Tensor& a,
Expand Down Expand Up @@ -289,7 +289,7 @@ static void sm100_bf16_k_grouped_gemm(const torch::Tensor& a,
};
const auto& code = SM100BF16GemmRuntime::generate(args);
const auto& runtime = compiler->build("sm100_bf16_k_grouped_gemm", code);
SM100BF16GemmRuntime::launch(runtime, args);
MAYBE_LAUNCH(SM100BF16GemmRuntime::launch(runtime, args));
}

static void sm100_bf16_bhr_hdr_bhd(const torch::Tensor& tensor_a,
Expand Down Expand Up @@ -337,7 +337,7 @@ static void sm100_bf16_bhr_hdr_bhd(const torch::Tensor& tensor_a,
};
const auto& code = SM100BF16GemmRuntime::generate(args);
const auto& runtime = compiler->build("sm100_bf16_bhr_hdr_bhd", code);
SM100BF16GemmRuntime::launch(runtime, args);
MAYBE_LAUNCH(SM100BF16GemmRuntime::launch(runtime, args));
}

static void sm100_bf16_bhd_hdr_bhr(const torch::Tensor& tensor_a,
Expand Down Expand Up @@ -385,7 +385,7 @@ static void sm100_bf16_bhd_hdr_bhr(const torch::Tensor& tensor_a,
};
const auto& code = SM100BF16GemmRuntime::generate(args);
const auto& runtime = compiler->build("sm100_bf16_bhd_hdr_bhr", code);
SM100BF16GemmRuntime::launch(runtime, args);
MAYBE_LAUNCH(SM100BF16GemmRuntime::launch(runtime, args));
}

} // namespace deep_gemm
10 changes: 5 additions & 5 deletions csrc/jit_kernels/impls/sm100_fp8_gemm_1d1d.hpp
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Expand Up @@ -141,7 +141,7 @@ static void sm100_fp8_fp4_gemm_1d1d(const torch::Tensor& a, const torch::Tensor&
};
const auto& code = SM100FP8FP4Gemm1D1DRuntime::generate(args);
const auto& runtime = compiler->build("sm100_fp8_fp4_gemm_1d1d", code);
SM100FP8FP4Gemm1D1DRuntime::launch(runtime, args);
MAYBE_LAUNCH(SM100FP8FP4Gemm1D1DRuntime::launch(runtime, args));
}

static void sm100_m_grouped_fp8_fp4_gemm_contiguous_1d1d(const torch::Tensor& a, const torch::Tensor& sfa,
Expand Down Expand Up @@ -210,7 +210,7 @@ static void sm100_m_grouped_fp8_fp4_gemm_contiguous_1d1d(const torch::Tensor& a,
};
const auto& code = SM100FP8FP4Gemm1D1DRuntime::generate(args);
const auto& runtime = compiler->build("sm100_m_grouped_fp8_fp4_gemm_contiguous_1d1d", code);
SM100FP8FP4Gemm1D1DRuntime::launch(runtime, args);
MAYBE_LAUNCH(SM100FP8FP4Gemm1D1DRuntime::launch(runtime, args));
}

static void sm100_m_grouped_fp8_fp4_gemm_masked_1d1d(const torch::Tensor& a, const torch::Tensor& sfa,
Expand Down Expand Up @@ -271,7 +271,7 @@ static void sm100_m_grouped_fp8_fp4_gemm_masked_1d1d(const torch::Tensor& a, con
};
const auto& code = SM100FP8FP4Gemm1D1DRuntime::generate(args);
const auto& runtime = compiler->build("sm100_m_grouped_fp8_fp4_gemm_masked_1d1d", code);
SM100FP8FP4Gemm1D1DRuntime::launch(runtime, args);
MAYBE_LAUNCH(SM100FP8FP4Gemm1D1DRuntime::launch(runtime, args));
}

static void sm100_k_grouped_fp8_gemm_1d1d(const torch::Tensor& a, const torch::Tensor& sfa,
Expand Down Expand Up @@ -342,7 +342,7 @@ static void sm100_k_grouped_fp8_gemm_1d1d(const torch::Tensor& a, const torch::T
};
const auto& code = SM100FP8FP4Gemm1D1DRuntime::generate(args);
const auto& runtime = compiler->build("sm100_k_grouped_fp8_gemm_1d1d", code);
SM100FP8FP4Gemm1D1DRuntime::launch(runtime, args);
MAYBE_LAUNCH(SM100FP8FP4Gemm1D1DRuntime::launch(runtime, args));
}

static void sm100_fp8_bmm(const torch::Tensor& a, const torch::Tensor& sfa,
Expand Down Expand Up @@ -410,7 +410,7 @@ static void sm100_fp8_bmm(const torch::Tensor& a, const torch::Tensor& sfa,
};
const auto& code = SM100FP8FP4Gemm1D1DRuntime::generate(args);
const auto& runtime = compiler->build("sm100_fp8_gemm_1d1d", code);
SM100FP8FP4Gemm1D1DRuntime::launch(runtime, args);
MAYBE_LAUNCH(SM100FP8FP4Gemm1D1DRuntime::launch(runtime, args));
}

} // namespace deep_gemm
12 changes: 6 additions & 6 deletions csrc/jit_kernels/impls/sm90_bf16_gemm.hpp
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Expand Up @@ -119,7 +119,7 @@ static void sm90_bf16_gemm(const torch::Tensor& a,
};
const auto& code = SM90BF16GemmRuntime::generate(args);
const auto& runtime = compiler->build("sm90_bf16_gemm", code);
SM90BF16GemmRuntime::launch(runtime, args);
MAYBE_LAUNCH(SM90BF16GemmRuntime::launch(runtime, args));
}

static void sm90_m_grouped_bf16_gemm_contiguous(const torch::Tensor& a,
Expand Down Expand Up @@ -173,7 +173,7 @@ static void sm90_m_grouped_bf16_gemm_contiguous(const torch::Tensor& a,
};
const auto& code = SM90BF16GemmRuntime::generate(args);
const auto& runtime = compiler->build("sm90_m_grouped_bf16_gemm_contiguous", code);
SM90BF16GemmRuntime::launch(runtime, args);
MAYBE_LAUNCH(SM90BF16GemmRuntime::launch(runtime, args));
}

static void sm90_bf16_m_grouped_gemm_masked(const torch::Tensor& a,
Expand Down Expand Up @@ -228,7 +228,7 @@ static void sm90_bf16_m_grouped_gemm_masked(const torch::Tensor& a,
};
const auto& code = SM90BF16GemmRuntime::generate(args);
const auto& runtime = compiler->build("sm90_bf16_m_grouped_gemm_masked", code);
SM90BF16GemmRuntime::launch(runtime, args);
MAYBE_LAUNCH(SM90BF16GemmRuntime::launch(runtime, args));
}

static void sm90_bf16_k_grouped_gemm(const torch::Tensor& a,
Expand Down Expand Up @@ -290,7 +290,7 @@ static void sm90_bf16_k_grouped_gemm(const torch::Tensor& a,
};
const auto& code = SM90BF16GemmRuntime::generate(args);
const auto& runtime = compiler->build("sm90_bf16_k_grouped_gemm", code);
SM90BF16GemmRuntime::launch(runtime, args);
MAYBE_LAUNCH(SM90BF16GemmRuntime::launch(runtime, args));
}

static void sm90_bf16_bhr_hdr_bhd(const torch::Tensor& tensor_a,
Expand Down Expand Up @@ -337,7 +337,7 @@ static void sm90_bf16_bhr_hdr_bhd(const torch::Tensor& tensor_a,
};
const auto& code = SM90BF16GemmRuntime::generate(args);
const auto& runtime = compiler->build("sm90_bf16_bhr_hdr_bhd", code);
SM90BF16GemmRuntime::launch(runtime, args);
MAYBE_LAUNCH(SM90BF16GemmRuntime::launch(runtime, args));
}

static void sm90_bf16_bhd_hdr_bhr(const torch::Tensor& tensor_a,
Expand Down Expand Up @@ -384,7 +384,7 @@ static void sm90_bf16_bhd_hdr_bhr(const torch::Tensor& tensor_a,
};
const auto& code = SM90BF16GemmRuntime::generate(args);
const auto& runtime = compiler->build("sm90_bf16_bhd_hdr_bhr", code);
SM90BF16GemmRuntime::launch(runtime, args);
MAYBE_LAUNCH(SM90BF16GemmRuntime::launch(runtime, args));
}

} // namespace deep_gemm
4 changes: 2 additions & 2 deletions csrc/jit_kernels/impls/sm90_fp8_gemm_1d1d.hpp
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Expand Up @@ -136,7 +136,7 @@ static void sm90_fp8_gemm_1d1d(const torch::Tensor& a, const torch::Tensor& sfa,
const auto& code = SM90FP8Gemm1D1DRuntime::generate(args);
const auto& runtime = compiler->build("sm90_fp8_gemm_1d1d", code);

SM90FP8Gemm1D1DRuntime::launch(runtime, args);
MAYBE_LAUNCH(SM90FP8Gemm1D1DRuntime::launch(runtime, args));
}

static void sm90_k_grouped_fp8_gemm_1d1d(const torch::Tensor& a, const torch::Tensor& sfa,
Expand Down Expand Up @@ -212,7 +212,7 @@ static void sm90_k_grouped_fp8_gemm_1d1d(const torch::Tensor& a, const torch::Te
const auto& code = SM90FP8Gemm1D1DRuntime::generate(args);
const auto& runtime = compiler->build("sm90_fp8_gemm_1d1d", code);

SM90FP8Gemm1D1DRuntime::launch(runtime, args);
MAYBE_LAUNCH(SM90FP8Gemm1D1DRuntime::launch(runtime, args));
}

} // namespace deep_gemm
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