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https://github.com/deepseek-ai/DeepGEMM
synced 2025-05-02 06:50:55 +00:00
Remove unaligned predicates
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3497428a5e
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@ -27,6 +27,17 @@ __device__ __host__ constexpr int get_num_threads_per_sm(int block_m) {
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return (block_m == 64 ? 1 : 2) * kNumMathThreadsPerGroup + kNumTMAThreads;
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}
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template <int kNumFormerIters, int kGap, int kEnd>
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__device__ __host__ void outer_launch_k_iterations(const auto& inner_launch_k_iterations, const auto& func, int num_former_iters) {
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if (num_former_iters == kNumFormerIters) {
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inner_launch_k_iterations(func, cute::Int<kNumFormerIters>{});
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return;
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}
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if constexpr (kNumFormerIters + kGap <= kEnd)
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outer_launch_k_iterations<kNumFormerIters + kGap, kGap, kEnd>(inner_launch_k_iterations, func, num_former_iters);
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}
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template <uint32_t SHAPE_N, uint32_t SHAPE_K,
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uint32_t BLOCK_M, uint32_t BLOCK_N, uint32_t BLOCK_K,
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uint32_t kNumGroups, uint32_t kNumStages,
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@ -129,15 +140,19 @@ fp8_gemm_kernel(__nv_bfloat16* gmem_d, float* scales_b, int* grouped_layout,
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// For pipeline unrolling
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struct DivisibleK {};
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struct NotDivisibleK {};
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auto launch_k_iterations = [](const auto& func) {
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auto launch_k_iterations = [](const auto& func, int num_former_iters) {
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constexpr int kGap = gcd(BLOCK_K, BLOCK_N) / 8;
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constexpr int kEnd = BLOCK_K / 8;
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outer_launch_k_iterations<0, kGap, kEnd>([](const auto& func, auto num_former_iters_type) {
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if constexpr (SHAPE_K % kFullKOfAllStages == 0) {
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for (int k_iter = 0; k_iter < kNumIterations; ++ k_iter)
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func(k_iter, DivisibleK{});
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func(k_iter, DivisibleK{}, num_former_iters_type);
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} else {
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for (int k_iter = 0; k_iter < kNumIterations - 1; ++ k_iter)
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func(k_iter, DivisibleK{});
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func(kNumIterations - 1, NotDivisibleK{});
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func(k_iter, DivisibleK{}, num_former_iters_type);
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func(kNumIterations - 1, NotDivisibleK{}, num_former_iters_type);
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}
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}, func, num_former_iters);
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};
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// Register reconfigurations
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@ -156,7 +171,7 @@ fp8_gemm_kernel(__nv_bfloat16* gmem_d, float* scales_b, int* grouped_layout,
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if (threadIdx.x == kNumMathThreads) {
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// Persistently schedule over blocks
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while (scheduler.get_next_block(m_block_idx, n_block_idx)) {
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launch_k_iterations([&](int k_iter, auto type) {
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launch_k_iterations([&](int k_iter, auto type, auto _) {
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constexpr bool kHasDivisibleStages = std::is_same_v<decltype(type), DivisibleK>;
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constexpr int kNumInnerStages = kHasDivisibleStages ? kNumStages : (SHAPE_K % kFullKOfAllStages) / BLOCK_K;
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DG_STATIC_ASSERT(kNumInnerStages != 0, "Invalid number of inner stages");
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@ -193,7 +208,7 @@ fp8_gemm_kernel(__nv_bfloat16* gmem_d, float* scales_b, int* grouped_layout,
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empty_barriers[s]->wait((scheduler.current_iter * kNumIterations + k_iter + 1) & 1);
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full_barriers[s]->arrive();
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}
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});
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}, 0);
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}
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// To safely deconstruct distributed shared barriers, we need another round of empty waits
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@ -246,7 +261,7 @@ fp8_gemm_kernel(__nv_bfloat16* gmem_d, float* scales_b, int* grouped_layout,
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};
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// Launch MMAs
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launch_k_iterations([&](int k_iter, auto type) {
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launch_k_iterations([&](int k_iter, auto type, auto num_former_iters_type) {
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constexpr bool kHasDivisibleStages = std::is_same_v<decltype(type), DivisibleK>;
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constexpr int kNumInnerStages = kHasDivisibleStages ? kNumStages : (SHAPE_K % kFullKOfAllStages) / BLOCK_K;
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DG_STATIC_ASSERT(kNumInnerStages != 0, "Invalid number of inner stages");
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@ -292,13 +307,21 @@ fp8_gemm_kernel(__nv_bfloat16* gmem_d, float* scales_b, int* grouped_layout,
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float scale_0_1, scale_1_1;
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if constexpr (not kMustUseUniformedScaleB)
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scale_0_1 = scale_a_0 * scale_b_1, scale_1_1 = scale_a_1 * scale_b_1;
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constexpr int kNumFormerIters = kMustUseUniformedScaleB ? WGMMA::kNumAccum / 4 : decltype(num_former_iters_type)::value;
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#pragma unroll
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for (int i = 0; i < WGMMA::kNumAccum / 4; ++ i) {
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bool predicate = kMustUseUniformedScaleB or i < num_former_iters;
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final_accum[i * 4 + 0] += (predicate ? scale_0_0 : scale_0_1) * accum[i * 4 + 0];
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final_accum[i * 4 + 1] += (predicate ? scale_0_0 : scale_0_1) * accum[i * 4 + 1];
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final_accum[i * 4 + 2] += (predicate ? scale_1_0 : scale_1_1) * accum[i * 4 + 2];
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final_accum[i * 4 + 3] += (predicate ? scale_1_0 : scale_1_1) * accum[i * 4 + 3];
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for (int i = 0; i < kNumFormerIters; ++ i) {
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final_accum[i * 4 + 0] += scale_0_0 * accum[i * 4 + 0];
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final_accum[i * 4 + 1] += scale_0_0 * accum[i * 4 + 1];
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final_accum[i * 4 + 2] += scale_1_0 * accum[i * 4 + 2];
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final_accum[i * 4 + 3] += scale_1_0 * accum[i * 4 + 3];
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}
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#pragma unroll
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for (int i = kNumFormerIters; i < WGMMA::kNumAccum / 4; ++ i) {
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final_accum[i * 4 + 0] += scale_0_1 * accum[i * 4 + 0];
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final_accum[i * 4 + 1] += scale_0_1 * accum[i * 4 + 1];
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final_accum[i * 4 + 2] += scale_1_1 * accum[i * 4 + 2];
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final_accum[i * 4 + 3] += scale_1_1 * accum[i * 4 + 3];
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}
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}
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@ -308,7 +331,7 @@ fp8_gemm_kernel(__nv_bfloat16* gmem_d, float* scales_b, int* grouped_layout,
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full_barriers[s]->wait((scheduler.current_iter * kNumIterations + k_iter) & 1);
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empty_barrier_arrive(s);
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}
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});
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}, num_former_iters);
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// Write back to shared memory using STSM
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DG_STATIC_ASSERT(WGMMA::kNumAccum % 4 == 0, "Invalid STSM x2 vectorization");
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@ -101,7 +101,7 @@ def build(name: str, arg_defs: tuple, code: str) -> Runtime:
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'--ptxas-options=--register-usage-level=10' + (',--verbose' if 'DG_PTXAS_VERBOSE' in os.environ else ''),
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# Suppress some unnecessary warnings, such as unused variables for certain `constexpr` branch cases
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'--diag-suppress=177,174,940']
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cxx_flags = ['-fPIC', '-O3', '-Wno-deprecated-declarations', '-Wno-abi']
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cxx_flags = ['-fPIC', '-O3', '-Wno-deprecated-declarations', '-Wno-abi', '-fconcepts']
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flags = [*nvcc_flags, f'--compiler-options={",".join(cxx_flags)}']
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include_dirs = [get_jit_include_dir()]
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