/* * Modified by Neural Magic * Copyright (C) Marlin.2024 Elias Frantar * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. */ /* * Adapted from https://github.com/IST-DASLab/marlin */ #ifndef MARLIN_NAMESPACE_NAME #define MARLIN_NAMESPACE_NAME marlin_moe_wna16 #endif #include "quantization/gptq_marlin/marlin.cuh" #include "quantization/gptq_marlin/marlin_dtypes.cuh" #include "core/scalar_type.hpp" #define STATIC_ASSERT_SCALAR_TYPE_VALID(scalar_t) \ static_assert(std::is_same::value || \ std::is_same::value, \ "only float16 and bfloat16 is supported"); namespace MARLIN_NAMESPACE_NAME { #if defined(__CUDA_ARCH__) && __CUDA_ARCH__ < 800 template shared // fetch pipeline const bool has_act_order, // whether act_order is enabled const bool has_zp, // whether zero-points are enabled const int group_blocks, // number of consecutive 16x16 blocks // with a separate quantization scale const bool is_zp_float // is zero point of float16 type? > __global__ void Marlin( const int4* __restrict__ A, // fp16 input matrix of shape mxk const int4* __restrict__ B, // 4bit quantized weight matrix of shape kxn int4* __restrict__ C, // fp16 output buffer of shape mxn int4* __restrict__ C_tmp, // fp32 tmp output buffer (for reduce) const int4* __restrict__ scales_ptr, // fp16 quantization scales of shape // (k/groupsize)xn const int4* __restrict__ zp_ptr, // 4bit packed zero-points of shape // (k/groupsize)x(n/pack_factor) const int* __restrict__ g_idx, // int32 group indices of shape k const int32_t* __restrict__ sorted_token_ids_ptr, // moe sorted_ids const int32_t* __restrict__ expert_ids_ptr, // moe expert ids const int32_t* __restrict__ num_tokens_past_padded_ptr, // moe num tokens const float* __restrict__ topk_weights_ptr, // moe top weights int top_k, // num of experts per token bool mul_topk_weights, // mul topk weights or not bool is_ep, // expert parallelism int num_groups, // number of scale groups per output channel int prob_m, // batch dimension m int prob_n, // output dimension n int prob_k, // reduction dimension k int* locks, // extra global storage for barrier synchronization bool use_atomic_add, // whether to use atomic add to reduce bool use_fp32_reduce // whether to use fp32 global reduce ) {} } // namespace MARLIN_NAMESPACE_NAME #else // m16n8k16 tensor core mma instruction with fp16 inputs and fp32 // output/accumulation. template __device__ inline void mma(const typename ScalarType::FragA& a_frag, const typename ScalarType::FragB& frag_b, typename ScalarType::FragC& frag_c) { const uint32_t* a = reinterpret_cast(&a_frag); const uint32_t* b = reinterpret_cast(&frag_b); float* c = reinterpret_cast(&frag_c); if constexpr (std::is_same::value) { asm volatile( "mma.sync.aligned.m16n8k16.row.col.f32.f16.f16.f32 " "{%0,%1,%2,%3}, {%4,%5,%6,%7}, {%8,%9}, {%10,%11,%12,%13};\n" : "=f"(c[0]), "=f"(c[1]), "=f"(c[2]), "=f"(c[3]) : "r"(a[0]), "r"(a[1]), "r"(a[2]), "r"(a[3]), "r"(b[0]), "r"(b[1]), "f"(c[0]), "f"(c[1]), "f"(c[2]), "f"(c[3])); } else if constexpr (std::is_same::value) { asm volatile( "mma.sync.aligned.m16n8k16.row.col.f32.bf16.bf16.f32 " "{%0,%1,%2,%3}, {%4,%5,%6,%7}, {%8,%9}, {%10,%11,%12,%13};\n" : "=f"(c[0]), "=f"(c[1]), "=f"(c[2]), "=f"(c[3]) : "r"(a[0]), "r"(a[1]), "r"(a[2]), "r"(a[3]), "r"(b[0]), "r"(b[1]), "f"(c[0]), "f"(c[1]), "f"(c[2]), "f"(c[3])); } else { STATIC_ASSERT_SCALAR_TYPE_VALID(scalar_t); } } template __device__ inline void mma_trans( const typename ScalarType::FragA& a_frag, const typename ScalarType::FragB& frag_b, const typename ScalarType::FragB& frag_b2, typename ScalarType::FragC& frag_c) { const uint32_t* a = reinterpret_cast(&a_frag); const uint32_t* b = reinterpret_cast(&frag_b); const uint32_t* b2 = reinterpret_cast(&frag_b2); float* c = reinterpret_cast(&frag_c); if constexpr (std::is_same::value) { asm volatile( "mma.sync.aligned.m16n8k16.row.col.f32.f16.f16.f32 " "{%0,%1,%2,%3}, {%4,%5,%6,%7}, {%8,%9}, {%10,%11,%12,%13};\n" : "=f"(c[0]), "=f"(c[1]), "=f"(c[2]), "=f"(c[3]) : "r"(b[0]), "r"(b2[0]), "r"(b[1]), "r"(b2[1]), "r"(a[0]), "r"(a[1]), "f"(c[0]), "f"(c[1]), "f"(c[2]), "f"(c[3])); } else if constexpr (std::is_same::value) { asm volatile( "mma.sync.aligned.m16n8k16.row.col.f32.bf16.bf16.f32 " "{%0,%1,%2,%3}, {%4,%5,%6,%7}, {%8,%9}, {%10,%11,%12,%13};\n" : "=f"(c[0]), "=f"(c[1]), "=f"(c[2]), "=f"(c[3]) : "r"(b[0]), "r"(b2[0]), "r"(b[1]), "r"(b2[1]), "r"(a[0]), "r"(a[1]), "f"(c[0]), "f"(c[1]), "f"(c[2]), "f"(c[3])); } else { STATIC_ASSERT_SCALAR_TYPE_VALID(scalar_t); } } // Instruction for loading a full 16x16 matrix fragment of operand A from shared // memory, directly in tensor core layout. template __device__ inline void ldsm(typename ScalarType::FragA& frag_a, const void* smem_ptr) { uint32_t* a = reinterpret_cast(&frag_a); uint32_t smem = static_cast(__cvta_generic_to_shared(smem_ptr)); if constexpr (count == 4) { asm volatile( "ldmatrix.sync.aligned.m8n8.x4.shared.b16 {%0,%1,%2,%3}, [%4];\n" : "=r"(a[0]), "=r"(a[1]), "=r"(a[2]), "=r"(a[3]) : "r"(smem)); } else if constexpr (count == 2) { asm volatile("ldmatrix.sync.aligned.m8n8.x2.shared.b16 {%0,%1}, [%2];\n" : "=r"(a[0]), "=r"(a[1]) : "r"(smem)); } else if constexpr (count == 1) { asm volatile("ldmatrix.sync.aligned.m8n8.x1.shared.b16 {%0}, [%1];\n" : "=r"(a[0]) : "r"(smem)); } else { static_assert(count == 1 || count == 2 || count == 4, "invalid count"); } } // Lookup-table based 3-input logical operation; explicitly used for // dequantization as the compiler does not seem to automatically recognize it in // all cases. template __device__ inline int lop3(int a, int b, int c) { int res; asm volatile("lop3.b32 %0, %1, %2, %3, %4;\n" : "=r"(res) : "r"(a), "r"(b), "r"(c), "n"(lut)); return res; } // Constructs destination register by taking bytes from 2 sources (based on // mask) template __device__ inline uint32_t prmt(uint32_t a) { uint32_t res; asm volatile("prmt.b32 %0, %1, %2, %3;\n" : "=r"(res) : "r"(a), "n"(start_byte), "n"(mask)); return res; } template __device__ inline typename ScalarType::FragB dequant( int q, typename ScalarType::FragB& frag_b); // // Efficiently dequantize 4bit values packed in an int32 value into a full // B-fragment of 4 fp16 values. We mostly follow the strategy in the link below, // with some small changes: // - FP16: // https://github.com/NVIDIA/FasterTransformer/blob/release/v5.3_tag/src/fastertransformer/cutlass_extensions/include/cutlass_extensions/interleaved_numeric_conversion.h#L215-L287 // - BF16: // https://github.com/NVIDIA/FasterTransformer/blob/release/v5.3_tag/src/fastertransformer/cutlass_extensions/include/cutlass_extensions/interleaved_numeric_conversion.h#L327-L385 // template <> __device__ inline typename ScalarType::FragB dequant( int q, typename ScalarType::FragB& frag_b) { const int LO = 0x000f000f; const int HI = 0x00f000f0; const int EX = 0x64006400; // Guarantee that the `(a & b) | c` operations are LOP3s. int lo = lop3 < (0xf0 & 0xcc) | 0xaa > (q, LO, EX); int hi = lop3 < (0xf0 & 0xcc) | 0xaa > (q, HI, EX); // We want signed int4 outputs, hence we fuse the `-8` symmetric zero point // directly into `SUB` and `ADD`. const int SUB = 0x64086408; const int MUL = 0x2c002c00; const int ADD = 0xd480d480; frag_b[0] = __hsub2(*reinterpret_cast(&lo), *reinterpret_cast(&SUB)); frag_b[1] = __hfma2(*reinterpret_cast(&hi), *reinterpret_cast(&MUL), *reinterpret_cast(&ADD)); return frag_b; } template <> __device__ inline typename ScalarType::FragB dequant(int q, typename ScalarType::FragB& frag_b) { static constexpr uint32_t MASK = 0x000f000f; static constexpr uint32_t EX = 0x43004300; // Guarantee that the `(a & b) | c` operations are LOP3s. int lo = lop3 < (0xf0 & 0xcc) | 0xaa > (q, MASK, EX); q >>= 4; int hi = lop3 < (0xf0 & 0xcc) | 0xaa > (q, MASK, EX); static constexpr uint32_t MUL = 0x3F803F80; static constexpr uint32_t ADD = 0xC308C308; frag_b[0] = __hfma2(*reinterpret_cast(&lo), *reinterpret_cast(&MUL), *reinterpret_cast(&ADD)); frag_b[1] = __hfma2(*reinterpret_cast(&hi), *reinterpret_cast(&MUL), *reinterpret_cast(&ADD)); return frag_b; } // // Fast Int8ToFp16/Int8ToBf16: Efficiently dequantize 8bit int values to fp16 or // bf16 Reference: // - FP16: // https://github.com/NVIDIA/FasterTransformer/blob/release/v5.3_tag/src/fastertransformer/cutlass_extensions/include/cutlass_extensions/interleaved_numeric_conversion.h#L53-L85 // - BF16: // https://github.com/NVIDIA/FasterTransformer/blob/release/v5.3_tag/src/fastertransformer/cutlass_extensions/include/cutlass_extensions/interleaved_numeric_conversion.h#L125-L175 // template <> __device__ inline typename ScalarType::FragB dequant( int q, typename ScalarType::FragB& frag_b) { static constexpr uint32_t mask_for_elt_01 = 0x5250; static constexpr uint32_t mask_for_elt_23 = 0x5351; static constexpr uint32_t start_byte_for_fp16 = 0x64646464; uint32_t lo = prmt(q); uint32_t hi = prmt(q); static constexpr uint32_t I8s_TO_F16s_MAGIC_NUM = 0x64806480; frag_b[0] = __hsub2(*reinterpret_cast(&lo), *reinterpret_cast(&I8s_TO_F16s_MAGIC_NUM)); frag_b[1] = __hsub2(*reinterpret_cast(&hi), *reinterpret_cast(&I8s_TO_F16s_MAGIC_NUM)); return frag_b; } template <> __device__ inline typename ScalarType::FragB dequant(int q, typename ScalarType::FragB& frag_b) { float fp32_intermediates[4]; uint32_t* fp32_intermediates_casted = reinterpret_cast(fp32_intermediates); static constexpr uint32_t fp32_base = 0x4B000000; fp32_intermediates_casted[0] = __byte_perm(q, fp32_base, 0x7650); fp32_intermediates_casted[1] = __byte_perm(q, fp32_base, 0x7652); fp32_intermediates_casted[2] = __byte_perm(q, fp32_base, 0x7651); fp32_intermediates_casted[3] = __byte_perm(q, fp32_base, 0x7653); fp32_intermediates[0] -= 8388736.f; fp32_intermediates[1] -= 8388736.f; fp32_intermediates[2] -= 8388736.f; fp32_intermediates[3] -= 8388736.f; uint32_t* bf16_result_ptr = reinterpret_cast(&frag_b); bf16_result_ptr[0] = __byte_perm(fp32_intermediates_casted[0], fp32_intermediates_casted[1], 0x7632); bf16_result_ptr[1] = __byte_perm(fp32_intermediates_casted[2], fp32_intermediates_casted[3], 0x7632); return frag_b; } // Multiply dequantized values by the corresponding quantization scale; used // only for grouped quantization. template __device__ inline void scale(typename ScalarType::FragB& frag_b, typename ScalarType::FragS& frag_s, int i) { using scalar_t2 = typename ScalarType::scalar_t2; scalar_t2 s = ScalarType::num2num2(reinterpret_cast(&frag_s)[i]); frag_b[0] = __hmul2(frag_b[0], s); frag_b[1] = __hmul2(frag_b[1], s); } template __device__ inline void scale_and_sub( typename ScalarType::FragB& frag_b, scalar_t s, scalar_t zp) { using scalar_t2 = typename ScalarType::scalar_t2; scalar_t2 s2 = ScalarType::num2num2(s); scalar_t2 zp2 = ScalarType::num2num2(zp); frag_b[0] = __hfma2(frag_b[0], s2, __hneg2(zp2)); frag_b[1] = __hfma2(frag_b[1], s2, __hneg2(zp2)); } template __device__ inline void sub_zp(typename ScalarType::FragB& frag_b, typename ScalarType::scalar_t2& frag_zp, int i) { using scalar_t2 = typename ScalarType::scalar_t2; scalar_t2 zp = ScalarType::num2num2(reinterpret_cast(&frag_zp)[i]); frag_b[0] = __hsub2(frag_b[0], zp); frag_b[1] = __hsub2(frag_b[1], zp); } // Same as above, but for act_order (each K is multiplied individually) template __device__ inline void scale4(typename ScalarType::FragB& frag_b, typename ScalarType::FragS& frag_s_1, typename ScalarType::FragS& frag_s_2, typename ScalarType::FragS& frag_s_3, typename ScalarType::FragS& frag_s_4, int i) { using scalar_t2 = typename ScalarType::scalar_t2; scalar_t2 s_val_1_2; s_val_1_2.x = reinterpret_cast(&frag_s_1)[i]; s_val_1_2.y = reinterpret_cast(&frag_s_2)[i]; scalar_t2 s_val_3_4; s_val_3_4.x = reinterpret_cast(&frag_s_3)[i]; s_val_3_4.y = reinterpret_cast(&frag_s_4)[i]; frag_b[0] = __hmul2(frag_b[0], s_val_1_2); frag_b[1] = __hmul2(frag_b[1], s_val_3_4); } // Given 2 floats multiply by 2 scales (halves) template __device__ inline void scale_float(float* c, typename ScalarType::FragS& s) { scalar_t* s_ptr = reinterpret_cast(&s); c[0] = __fmul_rn(c[0], ScalarType::num2float(s_ptr[0])); c[1] = __fmul_rn(c[1], ScalarType::num2float(s_ptr[1])); } // Wait until barrier reaches `count`, then lock for current threadblock. __device__ inline void barrier_acquire(int* lock, int count) { if (threadIdx.x == 0) { int state = -1; do // Guarantee that subsequent writes by this threadblock will be visible // globally. asm volatile("ld.global.acquire.gpu.b32 %0, [%1];\n" : "=r"(state) : "l"(lock)); while (state != count); } __syncthreads(); } // Release barrier and increment visitation count. __device__ inline void barrier_release(int* lock, bool reset = false) { __syncthreads(); if (threadIdx.x == 0) { if (reset) { lock[0] = 0; return; } int val = 1; // Make sure that all writes since acquiring this barrier are visible // globally, while releasing the barrier. asm volatile("fence.acq_rel.gpu;\n"); asm volatile("red.relaxed.gpu.global.add.s32 [%0], %1;\n" : : "l"(lock), "r"(val)); } } // Wait until value of lock to be negative, and then add 1 __device__ inline void wait_negative_and_add(int* lock) { if (threadIdx.x == 0) { int state = 0; do // Guarantee that subsequent writes by this threadblock will be visible // globally. asm volatile("ld.global.acquire.gpu.b32 %0, [%1];\n" : "=r"(state) : "l"(lock)); while (state >= 0); atomicAdd(lock, 1); } __syncthreads(); } template shared // fetch pipeline const bool has_act_order, // whether act_order is enabled const bool has_zp, // whether zero-points are enabled const int group_blocks, // number of consecutive 16x16 blocks // with a separate quantization scale const bool is_zp_float // is zero point of float16 type? > __global__ void Marlin( const int4* __restrict__ A, // fp16 input matrix of shape mxk const int4* __restrict__ B, // 4bit quantized weight matrix of shape kxn int4* __restrict__ C, // fp16 output buffer of shape mxn int4* __restrict__ C_tmp, // fp32 tmp output buffer (for reduce) const int4* __restrict__ scales_ptr, // fp16 quantization scales of shape // (k/groupsize)xn const int4* __restrict__ zp_ptr, // 4bit packed zero-points of shape // (k/groupsize)x(n/pack_factor) const int* __restrict__ g_idx, // int32 group indices of shape k const int32_t* __restrict__ sorted_token_ids_ptr, // moe sorted_ids const int32_t* __restrict__ expert_ids_ptr, // moe expert ids const int32_t* __restrict__ num_tokens_past_padded_ptr, // moe num tokens const float* __restrict__ topk_weights_ptr, // moe top weights int top_k, // num of experts per token bool mul_topk_weights, // mul topk weights or not bool is_ep, // expert parallelism int num_groups, // number of scale groups per output channel int prob_m, // batch dimension m int prob_n, // output dimension n int prob_k, // reduction dimension k int* locks, // extra global storage for barrier synchronization bool use_atomic_add, // whether to use atomic add to reduce bool use_fp32_reduce // whether to use fp32 global reduce ) { // Each threadblock processes one "stripe" of the B matrix with (roughly) the // same size, which might involve multiple column "slices" (of width 16 * // `thread_n_blocks`). Stripes are defined as shown in the 3x3 matrix 5 SM // example: // 0 1 3 // 0 2 3 // 1 2 4 // While this kind of partitioning makes things somewhat more complicated, it // ensures good utilization of all SMs for many kinds of shape and GPU // configurations, while requiring as few slow global cross-threadblock // reductions as possible. using Dtype = ScalarType; using scalar_t2 = typename ScalarType::scalar_t2; using FragA = typename ScalarType::FragA; using FragB = typename ScalarType::FragB; using FragC = typename ScalarType::FragC; using FragS = typename ScalarType::FragS; using FragZP = typename ScalarType::FragZP; extern __shared__ int4 sh[]; static constexpr auto w_type = vllm::ScalarType::from_id(w_type_id); constexpr int pack_factor = 32 / w_type.size_bits(); static_assert(thread_m_blocks == 1 || !m_block_size_8); constexpr int moe_block_size = m_block_size_8 ? 8 : (16 * thread_m_blocks); const int group_size = (!has_act_order && group_blocks == -1) ? prob_k : prob_k / num_groups; const int scales_expert_stride = prob_n * prob_k / group_size / 8; const int zp_expert_stride = is_zp_float ? prob_n * prob_k / group_size / 8 : prob_n * prob_k / group_size / (pack_factor * 4); // parallel: num valid moe blocks int num_tokens_past_padded = num_tokens_past_padded_ptr[0]; int parallel = num_tokens_past_padded / moe_block_size; int num_valid_blocks = parallel; if (is_ep) { for (int i = 0; i < parallel; i++) { if (expert_ids_ptr[i] == -1) num_valid_blocks--; } } int num_invalid_blocks = parallel - num_valid_blocks; parallel = num_valid_blocks; int k_tiles = prob_k / 16 / thread_k_blocks; int n_tiles = prob_n / 16 / thread_n_blocks; int iters = div_ceil(k_tiles * n_tiles * parallel, gridDim.x); if constexpr (!has_act_order && group_blocks != -1) { if (group_blocks >= thread_k_blocks) { // Ensure that the number of tiles in each stripe is a multiple of the // groupsize; this avoids an annoying special case where a stripe starts // in the middle of group. iters = (group_blocks / thread_k_blocks) * div_ceil(iters, (group_blocks / thread_k_blocks)); } } int slice_row = (iters * blockIdx.x) % k_tiles; int slice_col_par = (iters * blockIdx.x) / k_tiles; int slice_col = slice_col_par; int slice_iters; // number of threadblock tiles in the current slice int slice_count = 0; // total number of active threadblocks in the current slice int slice_idx; // index of threadblock in current slice; numbered bottom to // top int par_id = 0; int block_id = -1; int64_t expert_id = 0; // use int64 to avoid computation result overflow int old_expert_id = 0; int64_t B_expert_off = 0; int4* sh_block_sorted_ids_int4 = sh; int32_t* sh_block_sorted_ids = reinterpret_cast(sh_block_sorted_ids_int4); int4* sh_block_topk_weights_int4 = sh_block_sorted_ids_int4 + moe_block_size / 4; scalar_t2* sh_block_topk_weights = reinterpret_cast(sh_block_topk_weights_int4); int4* sh_new = sh_block_topk_weights_int4 + moe_block_size / 4; int32_t block_num_valid_tokens = 0; int32_t locks_off = 0; // We can easily implement parallel problem execution by just remapping // indices and advancing global pointers if (slice_col_par >= n_tiles) { slice_col = slice_col_par % n_tiles; par_id = slice_col_par / n_tiles; } if (parallel * n_tiles >= gridDim.x) { // when parallel * n_tiles >= sms // then there are at most $sms$ conflict tile blocks locks_off = blockIdx.x; } else { locks_off = (iters * blockIdx.x) / k_tiles - 1; } // read moe block data given block_id // block_sorted_ids / block_num_valid_tokens / block_topk_weights auto read_moe_block_data = [&](int block_id) { block_num_valid_tokens = moe_block_size; #pragma unroll for (int i = 0; i < moe_block_size / 4; i++) { int4 sorted_token_ids_int4 = reinterpret_cast( sorted_token_ids_ptr)[block_id * moe_block_size / 4 + i]; int* sorted_token_ids = reinterpret_cast(&sorted_token_ids_int4); #pragma unroll for (int j = 0; j < 4; j++) { if (sorted_token_ids[j] >= prob_m * top_k) { block_num_valid_tokens = i * 4 + j; break; } } if (block_num_valid_tokens != moe_block_size) break; } __syncthreads(); int tid4 = threadIdx.x / 4; if (threadIdx.x % 4 == 0 && threadIdx.x < block_num_valid_tokens) { sh_block_sorted_ids_int4[tid4] = reinterpret_cast( sorted_token_ids_ptr)[block_id * moe_block_size / 4 + tid4]; if (mul_topk_weights) { #pragma unroll for (int i = 0; i < 4; i++) { sh_block_topk_weights[tid4 * 4 + i] = Dtype::num2num2(Dtype::float2num( topk_weights_ptr[sh_block_sorted_ids[tid4 * 4 + i]])); } } } __syncthreads(); }; // when move to next moe block, find the next block_id and expert_id // and then read moe block data auto update_next_moe_block_data = [&]() { if (par_id >= parallel) return; old_expert_id = expert_id; if (num_invalid_blocks > 0) { int skip_count = block_id == -1 ? par_id : 0; block_id++; for (int i = block_id; i < num_tokens_past_padded / moe_block_size; i++) { expert_id = expert_ids_ptr[i]; if (expert_id != -1) { if (skip_count == 0) { block_id = i; break; }; skip_count--; }; } } else { block_id = par_id; expert_id = expert_ids_ptr[block_id]; } B_expert_off = expert_id * prob_n * prob_k / (pack_factor * 4); scales_ptr += (expert_id - old_expert_id) * scales_expert_stride; if constexpr (has_zp) { zp_ptr += (expert_id - old_expert_id) * zp_expert_stride; } if constexpr (has_act_order) { g_idx += (expert_id - old_expert_id) * prob_k; } read_moe_block_data(block_id); }; // Compute all information about the current slice which is required for // synchronization. auto init_slice = [&](bool first_init = false) { slice_iters = iters * (blockIdx.x + 1) - (k_tiles * slice_col_par + slice_row); if (slice_iters < 0 || slice_col_par >= n_tiles * parallel) slice_iters = 0; if (slice_iters == 0) return; if (slice_row + slice_iters > k_tiles) slice_iters = k_tiles - slice_row; slice_count = 1; slice_idx = 0; int col_first = iters * div_ceil(k_tiles * slice_col_par, iters); if (col_first <= k_tiles * (slice_col_par + 1)) { int col_off = col_first - k_tiles * slice_col_par; slice_count = div_ceil(k_tiles - col_off, iters); if (col_off > 0) slice_count++; int delta_first = iters * blockIdx.x - col_first; if (delta_first < 0 || (col_off == 0 && delta_first == 0)) slice_idx = slice_count - 1; else { slice_idx = slice_count - 1 - delta_first / iters; if (col_off > 0) slice_idx--; } } if (parallel * n_tiles >= gridDim.x) { if (slice_count > 1 && slice_idx == slice_count - 1) { locks_off++; } } else { locks_off++; } if (first_init && use_atomic_add && slice_count > 1 && slice_idx == 0) { constexpr int threads_per_m = 16 * thread_n_blocks / 8; int m_per_thread = div_ceil(block_num_valid_tokens, threads / threads_per_m); for (int i = 0; i < m_per_thread; i++) { int row = threads / threads_per_m * i + threadIdx.x / threads_per_m; if (row < block_num_valid_tokens) { int64_t sorted_row = sh_block_sorted_ids[row]; int col = slice_col * 16 * thread_n_blocks / 8 + threadIdx.x % threads_per_m; C[sorted_row * prob_n / 8 + col] = {0, 0, 0, 0}; } } // After write zero to output, write a negative value to lock. // Every SM that processes the same slice would wait for // the negative value, and then atomicAdd 1 to it. // After all SMs are processed, the lock value would back to 0 again. __syncthreads(); if (threadIdx.x == 0) locks[locks_off] = 1 - slice_count; } if (slice_col == n_tiles) { slice_col = 0; par_id++; update_next_moe_block_data(); } }; update_next_moe_block_data(); init_slice(true); // A sizes/strides // stride of the A matrix in global memory int a_gl_stride = prob_k / 8; // stride of an A matrix tile in shared memory constexpr int a_sh_stride = 16 * thread_k_blocks / 8; // delta between subsequent A tiles in global memory constexpr int a_gl_rd_delta_o = 16 * thread_k_blocks / 8; // between subsequent accesses within a tile int a_gl_rd_delta_i = a_gl_stride * (threads / a_gl_rd_delta_o); // between shared memory writes constexpr int a_sh_wr_delta = a_sh_stride * (threads / a_gl_rd_delta_o); // between shared memory tile reads constexpr int a_sh_rd_delta_o = 2 * ((threads / 32) / (thread_n_blocks / 4)); // within a shared memory tile constexpr int a_sh_rd_delta_i = a_sh_stride * 16; // overall size of a tile constexpr int a_sh_stage = a_sh_stride * (16 * thread_m_blocks); // number of shared write iterations for a tile constexpr int a_sh_wr_iters = div_ceil(a_sh_stage, a_sh_wr_delta); // B sizes/strides int b_gl_stride = 16 * prob_n / (pack_factor * 4); constexpr int b_sh_stride = ((thread_n_blocks * 16) * 16 / pack_factor) / 4; constexpr int b_thread_vecs = w_type.size_bits() == 4 ? 1 : 2; constexpr int b_sh_stride_threads = b_sh_stride / b_thread_vecs; int b_gl_rd_delta_o = b_gl_stride * thread_k_blocks; int b_gl_rd_delta_i = b_gl_stride * (threads / b_sh_stride_threads); constexpr int b_sh_wr_delta = threads * b_thread_vecs; constexpr int b_sh_rd_delta = threads * b_thread_vecs; constexpr int b_sh_stage = b_sh_stride * thread_k_blocks; constexpr int b_sh_wr_iters = b_sh_stage / b_sh_wr_delta; // Scale sizes/strides without act_order int s_gl_stride = prob_n / 8; constexpr int s_sh_stride = 16 * thread_n_blocks / 8; constexpr int s_tb_groups = !has_act_order && group_blocks != -1 && group_blocks < thread_k_blocks ? thread_k_blocks / group_blocks : 1; constexpr int s_sh_stage = s_tb_groups * s_sh_stride; int s_gl_rd_delta = s_gl_stride; // Scale size/strides with act_order constexpr int tb_k = 16 * thread_k_blocks; constexpr int g_idx_stage = has_act_order ? (tb_k * sizeof(int)) / 16 : 0; // constexpr int act_s_row_stride = 1; // int act_s_col_stride = act_s_row_stride * num_groups; int act_s_col_stride = 1; int act_s_col_warp_stride = act_s_col_stride * 8; int tb_n_warps = thread_n_blocks / 4; int act_s_col_tb_stride = act_s_col_warp_stride * tb_n_warps; // Zero-points sizes/strides int zp_gl_stride = is_zp_float ? prob_n / 8 : (prob_n / pack_factor) / 4; constexpr int zp_sh_stride = is_zp_float ? 16 * thread_n_blocks / 8 : ((16 * thread_n_blocks) / pack_factor) / 4; constexpr int zp_tb_groups = s_tb_groups; constexpr int zp_sh_stage = has_zp ? zp_tb_groups * zp_sh_stride : 0; int zp_gl_rd_delta = zp_gl_stride; // Global A read index of current thread. int a_gl_rd = a_gl_stride * (threadIdx.x / a_gl_rd_delta_o) + (threadIdx.x % a_gl_rd_delta_o); a_gl_rd += a_gl_rd_delta_o * slice_row; // Shared write index of current thread. int a_sh_wr = a_sh_stride * (threadIdx.x / a_gl_rd_delta_o) + (threadIdx.x % a_gl_rd_delta_o); // Shared read index. int a_sh_rd = a_sh_stride * ((threadIdx.x % 32) % (16 / (m_block_size_8 ? 2 : 1))) + (threadIdx.x % 32) / (16 / (m_block_size_8 ? 2 : 1)); a_sh_rd += 2 * ((threadIdx.x / 32) / (thread_n_blocks / 4)); int b_gl_rd = b_gl_stride * (threadIdx.x / b_sh_stride_threads) + (threadIdx.x % b_sh_stride_threads) * b_thread_vecs; b_gl_rd += b_sh_stride * slice_col; b_gl_rd += b_gl_rd_delta_o * slice_row; int b_sh_wr = threadIdx.x * b_thread_vecs; int b_sh_rd = threadIdx.x * b_thread_vecs; // For act_order constexpr int k_iter_size = tb_k / b_sh_wr_iters; int slice_k_start = tb_k * slice_row; int slice_k_finish = slice_k_start + tb_k * slice_iters; int slice_k_start_shared_fetch = slice_k_start; int slice_n_offset = act_s_col_tb_stride * slice_col; // No act_order int s_gl_rd; if constexpr (!has_act_order) { if constexpr (group_blocks == -1) { s_gl_rd = s_sh_stride * slice_col + threadIdx.x; } else { s_gl_rd = s_gl_stride * ((thread_k_blocks * slice_row) / group_blocks) + s_sh_stride * slice_col + threadIdx.x; } } int s_sh_wr = threadIdx.x; bool s_sh_wr_pred = threadIdx.x < s_sh_stride; // Zero-points int zp_gl_rd; if constexpr (has_zp) { if constexpr (group_blocks == -1) { zp_gl_rd = zp_sh_stride * slice_col + threadIdx.x; } else { zp_gl_rd = zp_gl_stride * ((thread_k_blocks * slice_row) / group_blocks) + zp_sh_stride * slice_col + threadIdx.x; } } int zp_sh_wr = threadIdx.x; bool zp_sh_wr_pred = threadIdx.x < zp_sh_stride; // We use a different scale layout for grouped and column-wise quantization as // we scale a `half2` tile in column-major layout in the former and in // row-major in the latter case. int s_sh_rd; if constexpr (group_blocks != -1) s_sh_rd = 8 * ((threadIdx.x / 32) % (thread_n_blocks / 4)) + (threadIdx.x % 32) / 4; else if constexpr (group_blocks == -1 && (m_block_size_8 || has_zp)) s_sh_rd = 8 * ((threadIdx.x / 32) % (thread_n_blocks / 4)) + (threadIdx.x % 32) / 8; else s_sh_rd = 8 * ((threadIdx.x / 32) % (thread_n_blocks / 4)) + (threadIdx.x % 32) % 4; // Zero-points have the same read layout as the scales // (without column-wise case) constexpr int num_col_threads = 8; constexpr int num_row_threads = 4; constexpr int num_ints_per_thread = 8 / pack_factor; int zp_sh_rd; if constexpr (has_zp) { if constexpr (is_zp_float) { if constexpr (group_blocks != -1) { zp_sh_rd = 8 * ((threadIdx.x / 32) % (thread_n_blocks / 4)) + (threadIdx.x % 32) / 4; } } else { zp_sh_rd = num_ints_per_thread * num_col_threads * ((threadIdx.x / 32) % (thread_n_blocks / 4)) + num_ints_per_thread * ((threadIdx.x % 32) / num_row_threads); } } // To ensure that writing and reading A tiles to/from shared memory, the // latter in fragment format, is fully bank conflict free, we need to use a // rather fancy XOR-based layout. The key here is that neither reads nor // writes of the 16-byte `int4` blocks of 8 consecutive threads involve the // same shared memory banks. Further, it seems (based on NSight-Compute) that // each warp must also write a consecutive memory segment? auto transform_a = [&](int i) { int row = i / a_gl_rd_delta_o; return a_gl_rd_delta_o * row + (i % a_gl_rd_delta_o) ^ row; }; // Since the computation of this remapping is non-trivial and, due to our main // loop unrolls, all shared memory accesses are static, we simply precompute // both transformed reads and writes. int a_sh_wr_trans[a_sh_wr_iters]; #pragma unroll for (int i = 0; i < a_sh_wr_iters; i++) a_sh_wr_trans[i] = transform_a(a_sh_wr_delta * i + a_sh_wr); int a_sh_rd_trans[b_sh_wr_iters][thread_m_blocks]; #pragma unroll for (int i = 0; i < b_sh_wr_iters; i++) { #pragma unroll for (int j = 0; j < thread_m_blocks; j++) a_sh_rd_trans[i][j] = transform_a(a_sh_rd_delta_o * i + a_sh_rd_delta_i * j + a_sh_rd); } // Since B-accesses have non-constant stride they have to be computed at // runtime; we break dependencies between subsequent accesses with a tile by // maintining multiple pointers (we have enough registers), a tiny // optimization. const int4* B_ptr[b_sh_wr_iters]; #pragma unroll for (int i = 0; i < b_sh_wr_iters; i++) B_ptr[i] = B + b_gl_rd_delta_i * i + b_gl_rd; // Shared memory storage for global fetch pipelines. int4* sh_a = sh_new; int4* sh_b = sh_a + (stages * a_sh_stage); int4* sh_g_idx = sh_b + (stages * b_sh_stage); int4* sh_zp = sh_g_idx + (stages * g_idx_stage); int4* sh_s = sh_zp + (stages * zp_sh_stage); int4* sh_red = sh_b; // Register storage for double buffer of shared memory reads. FragA frag_a[2][thread_m_blocks]; I4 frag_b_quant[2][b_thread_vecs]; FragC frag_c[thread_m_blocks][4][2]; FragS frag_s[2][4]; // No act-order FragS act_frag_s[2][4][4]; // For act-order int frag_qzp[2][num_ints_per_thread]; // Zero-points FragZP frag_zp; // Zero-points in fp16 FragZP frag_zpf[2]; // Zero-points in fp16 in HQQ // Zero accumulators. auto zero_accums = [&]() { #pragma unroll for (int i = 0; i < thread_m_blocks * 4 * 2 * 4; i++) reinterpret_cast(frag_c)[i] = 0; }; int sh_first_group_id = -1; int sh_num_groups = -1; constexpr int sh_max_num_groups = 32; auto fetch_act_order_scales_to_shared = [&](bool is_async, int first_group_id, int last_group_id) { sh_first_group_id = first_group_id; sh_num_groups = last_group_id - first_group_id + 1; if (sh_num_groups < sh_max_num_groups) { sh_num_groups = sh_max_num_groups; } if (sh_first_group_id + sh_num_groups > num_groups) { sh_num_groups = num_groups - sh_first_group_id; } int row_offset = first_group_id * s_gl_stride; if (is_async) { for (int i = 0; i < sh_num_groups; i++) { if (threadIdx.x < s_sh_stride) { cp_async4_pred(&sh_s[(i * s_sh_stride) + threadIdx.x], &scales_ptr[row_offset + (i * s_gl_stride) + slice_n_offset + threadIdx.x]); } } } else { for (int i = 0; i < sh_num_groups; i++) { if (threadIdx.x < s_sh_stride) { sh_s[(i * s_sh_stride) + threadIdx.x] = scales_ptr[row_offset + (i * s_gl_stride) + slice_n_offset + threadIdx.x]; } } } }; // Asynchronously fetch the next A, B and s tile from global to the next // shared memory pipeline location. int a_remaining_load_count_in_slice = stages; auto fetch_to_shared = [&](int pipe, int a_off, bool pred = true) { if (pred) { int4* sh_a_stage = sh_a + a_sh_stage * pipe; if (prob_k > thread_k_blocks * 16 * stages || slice_col == 0 || a_remaining_load_count_in_slice > 0) { a_remaining_load_count_in_slice--; #pragma unroll for (int i = 0; i < a_sh_wr_iters; i++) { int a_idx = a_gl_rd_delta_i * i + a_gl_rd + a_gl_rd_delta_o * a_off; int row = a_idx / a_gl_stride; int64_t sorted_row = 0; if (!m_block_size_8 || row < 8) sorted_row = sh_block_sorted_ids[row] / top_k; int64_t true_idx = sorted_row * a_gl_stride + a_idx % a_gl_stride; cp_async4_pred(&sh_a_stage[a_sh_wr_trans[i]], &A[true_idx], row < block_num_valid_tokens); } } int4* sh_b_stage = sh_b + b_sh_stage * pipe; #pragma unroll for (int i = 0; i < b_sh_wr_iters; i++) { #pragma unroll for (int j = 0; j < b_thread_vecs; j++) { cp_async4(&sh_b_stage[b_sh_wr_delta * i + b_sh_wr + j], B_ptr[i] + j + B_expert_off); } B_ptr[i] += b_gl_rd_delta_o; } if constexpr (has_act_order) { // Fetch g_idx thread-block portion int full_pipe = a_off; int cur_k = slice_k_start_shared_fetch + tb_k * full_pipe; if (cur_k < prob_k && cur_k < slice_k_finish) { int4* sh_g_idx_stage = sh_g_idx + g_idx_stage * pipe; int4 const* cur_g_idx_stage_ptr = reinterpret_cast(&g_idx[cur_k]); if (threadIdx.x < g_idx_stage) { cp_async4_pred(&sh_g_idx_stage[threadIdx.x], &cur_g_idx_stage_ptr[threadIdx.x]); } } } else { if constexpr (group_blocks != -1) { int4* sh_s_stage = sh_s + s_sh_stage * pipe; if constexpr (group_blocks >= thread_k_blocks) { // Only fetch scales if this tile starts a new group if (pipe % (group_blocks / thread_k_blocks) == 0) { if (s_sh_wr_pred) { cp_async4(&sh_s_stage[s_sh_wr], &scales_ptr[s_gl_rd]); } s_gl_rd += s_gl_rd_delta; } } else { for (int i = 0; i < s_tb_groups; i++) { if (s_sh_wr_pred) { cp_async4(&sh_s_stage[i * s_sh_stride + s_sh_wr], &scales_ptr[s_gl_rd]); } s_gl_rd += s_gl_rd_delta; } } } if constexpr (has_zp && group_blocks != -1) { int4* sh_zp_stage = sh_zp + zp_sh_stage * pipe; if constexpr (group_blocks >= thread_k_blocks) { // Only fetch zero-points if this tile starts a new group if (pipe % (group_blocks / thread_k_blocks) == 0) { if (zp_sh_wr_pred) { cp_async4(&sh_zp_stage[zp_sh_wr], &zp_ptr[zp_gl_rd]); } zp_gl_rd += zp_gl_rd_delta; } } else { for (int i = 0; i < zp_tb_groups; i++) { if (zp_sh_wr_pred) { cp_async4(&sh_zp_stage[i * zp_sh_stride + zp_sh_wr], &zp_ptr[zp_gl_rd]); } zp_gl_rd += zp_gl_rd_delta; } } } } } // Insert a fence even when we are winding down the pipeline to ensure that // waiting is also correct at this point. cp_async_fence(); }; auto fetch_col_zp_to_shared = [&]() { if (zp_sh_wr_pred) { cp_async4(&sh_zp[zp_sh_wr], &zp_ptr[zp_gl_rd]); } }; auto fetch_col_scale_to_shared = [&]() { if (s_sh_wr_pred) { cp_async4(&sh_s[s_sh_wr], &scales_ptr[s_gl_rd]); } }; // Wait until the next thread tile has been loaded to shared memory. auto wait_for_stage = [&]() { // We only have `stages - 2` active fetches since we are double buffering // and can only issue the next fetch when it is guaranteed that the previous // shared memory load is fully complete (as it may otherwise be // overwritten). cp_async_wait(); __syncthreads(); }; // Load the next sub-tile from the current location in the shared memory pipe // into the current register buffer. auto fetch_to_registers = [&](int k, int pipe) { int4* sh_a_stage = sh_a + a_sh_stage * pipe; #pragma unroll for (int i = 0; i < thread_m_blocks; i++) ldsm( frag_a[k % 2][i], &sh_a_stage[a_sh_rd_trans[k % b_sh_wr_iters][i]]); int4* sh_b_stage = sh_b + b_sh_stage * pipe; #pragma unroll for (int i = 0; i < b_thread_vecs; i++) { frag_b_quant[k % 2][i] = *reinterpret_cast( &sh_b_stage[b_sh_rd_delta * (k % b_sh_wr_iters) + b_sh_rd + i]); } }; bool is_same_group[stages]; int same_group_id[stages]; auto init_same_group = [&](int pipe) { if constexpr (!has_act_order) { return; } int4* sh_g_idx_stage = sh_g_idx + g_idx_stage * pipe; int* sh_g_idx_int_ptr = reinterpret_cast(sh_g_idx_stage); int group_id_1 = sh_g_idx_int_ptr[0]; int group_id_2 = sh_g_idx_int_ptr[tb_k - 1]; is_same_group[pipe] = group_id_1 == group_id_2; same_group_id[pipe] = group_id_1; }; auto fetch_scales_to_registers = [&](int k, int full_pipe) { int pipe = full_pipe % stages; if constexpr (!has_act_order) { // No act-order case if constexpr (group_blocks == -1) { // load only when starting a new slice if (k == 0 && full_pipe == 0) { reinterpret_cast(&frag_s)[0] = sh_s[s_sh_rd]; reinterpret_cast(&frag_s)[1] = sh_s[s_sh_rd + 4]; } } else if constexpr (group_blocks != -1) { if constexpr (group_blocks >= thread_k_blocks) { int4* sh_s_stage = sh_s + s_sh_stage * ((group_blocks / thread_k_blocks) * (pipe / (group_blocks / thread_k_blocks))); reinterpret_cast(&frag_s[k % 2])[0] = sh_s_stage[s_sh_rd]; } else { int warp_id = threadIdx.x / 32; int n_warps = thread_n_blocks / 4; int warp_row = warp_id / n_warps; int cur_k = warp_row * 16; cur_k += k_iter_size * (k % b_sh_wr_iters); int k_blocks = cur_k / 16; int cur_group_id = k_blocks / group_blocks; int4* sh_s_stage = sh_s + s_sh_stage * pipe; reinterpret_cast(&frag_s[k % 2])[0] = sh_s_stage[s_sh_rd + cur_group_id * s_sh_stride]; } } return; } // Act-order case // Determine K of the "current" thread-block int cur_k = slice_k_start + tb_k * full_pipe; if (cur_k >= prob_k || cur_k >= slice_k_finish) { return; } // Reset (to current thread-block) since we read g_idx portion from the // shared memory cur_k = 0; // Progress to current iteration cur_k += k_iter_size * (k % b_sh_wr_iters); // Determine "position" inside the thread-block (based on warp and // thread-id) int warp_id = threadIdx.x / 32; int n_warps = thread_n_blocks / 4; // Each warp processes 4 16-size tiles over N int warp_row = warp_id / n_warps; int warp_col = warp_id % n_warps; cur_k += warp_row * 16; int th_id = threadIdx.x % 32; cur_k += (th_id % 4) * 2; // Due to tensor-core layout for fp16 B matrix int s_col_shift = /*slice_n_offset +*/ (act_s_col_warp_stride * warp_col) + (th_id / 4) * act_s_col_stride; if (is_same_group[pipe]) { if (k % 2 == 0) { *(reinterpret_cast(&(act_frag_s[k % 2][0][0]))) = sh_s[(same_group_id[pipe] - sh_first_group_id) * s_sh_stride + s_col_shift]; } else { *(reinterpret_cast(&(act_frag_s[k % 2][0][0]))) = *(reinterpret_cast(&(act_frag_s[(k - 1) % 2][0][0]))); } for (int i = 1; i < 4; i++) { *(reinterpret_cast(&(act_frag_s[k % 2][i][0]))) = *(reinterpret_cast(&(act_frag_s[k % 2][0][0]))); } return; } int4* sh_g_idx_stage = sh_g_idx + g_idx_stage * pipe; int* sh_g_idx_int_ptr = reinterpret_cast(sh_g_idx_stage); constexpr int k_frag_offsets[4] = {0, 1, 8, 9}; // Tensor core offsets per thread #pragma unroll for (int i = 0; i < 4; i++) { int actual_k = cur_k + k_frag_offsets[i]; int group_id = sh_g_idx_int_ptr[actual_k]; int rel_group_id = group_id - sh_first_group_id; *(reinterpret_cast(&(act_frag_s[k % 2][i][0]))) = sh_s[rel_group_id * s_sh_stride + s_col_shift]; } }; auto fetch_zp_to_registers = [&](int k, int full_pipe) { // This code does not handle group_blocks == 0, // which signifies act_order. // has_zp implies AWQ, which doesn't have act_order, static_assert(!has_zp || group_blocks != 0); if constexpr (has_zp && !is_zp_float) { int pipe = full_pipe % stages; if constexpr (group_blocks == -1) { // load only when starting a new slice if (k == 0 && full_pipe == 0) { #pragma unroll for (int i = 0; i < num_ints_per_thread; i++) { frag_qzp[k % 2][i] = (reinterpret_cast(sh_zp))[zp_sh_rd + i]; } } } else if constexpr (group_blocks >= thread_k_blocks) { int4* sh_zp_stage = sh_zp + zp_sh_stage * ((group_blocks / thread_k_blocks) * (pipe / (group_blocks / thread_k_blocks))); for (int i = 0; i < num_ints_per_thread; i++) { frag_qzp[k % 2][i] = (reinterpret_cast(sh_zp_stage))[zp_sh_rd + i]; } } else { int warp_id = threadIdx.x / 32; int n_warps = thread_n_blocks / 4; int warp_row = warp_id / n_warps; int cur_k = warp_row * 16; cur_k += k_iter_size * (k % b_sh_wr_iters); int k_blocks = cur_k / 16; int cur_group_id = 0; // Suppress bogus and persistent divide-by-zero warning #pragma nv_diagnostic push #pragma nv_diag_suppress divide_by_zero cur_group_id = k_blocks / group_blocks; #pragma nv_diagnostic pop int4* sh_zp_stage = sh_zp + zp_sh_stage * pipe; sh_zp_stage += cur_group_id * zp_sh_stride; for (int i = 0; i < num_ints_per_thread; i++) { frag_qzp[k % 2][i] = (reinterpret_cast(sh_zp_stage))[zp_sh_rd + i]; } } } else if constexpr (has_zp && is_zp_float) { int pipe = full_pipe % stages; if constexpr (group_blocks != -1) { if constexpr (group_blocks >= thread_k_blocks) { int4* sh_zp_stage = sh_zp + zp_sh_stage * ((group_blocks / thread_k_blocks) * (pipe / (group_blocks / thread_k_blocks))); reinterpret_cast(&frag_zpf[k % 2])[0] = sh_zp_stage[zp_sh_rd]; } else { int warp_id = threadIdx.x / 32; int n_warps = thread_n_blocks / 4; int warp_row = warp_id / n_warps; int cur_k = warp_row * 16; cur_k += k_iter_size * (k % b_sh_wr_iters); int k_blocks = cur_k / 16; // Suppress bogus and persistent divide-by-zero warning #pragma nv_diagnostic push #pragma nv_diag_suppress divide_by_zero int cur_group_id = k_blocks / group_blocks; #pragma nv_diagnostic pop int4* sh_zp_stage = sh_zp + zp_sh_stage * pipe; reinterpret_cast(&frag_zpf[k % 2])[0] = sh_zp_stage[zp_sh_rd + cur_group_id * zp_sh_stride]; } } } }; // Execute the actual tensor core matmul of a sub-tile. bool is_first_matmul_in_slice = true; auto matmul = [&](int k) { int k2 = k % 2; const bool is_new_zp = ((group_blocks != -1) && (group_blocks < thread_k_blocks || k == 0)) || (group_blocks == -1 && is_first_matmul_in_slice); if constexpr (has_zp && !is_zp_float) { if (is_new_zp) { if constexpr (group_blocks == -1) is_first_matmul_in_slice = false; FragB frag_zp_0; FragB frag_zp_1; int zp_quant_0, zp_quant_1; if constexpr (w_type.size_bits() == 4) { zp_quant_0 = frag_qzp[k2][0]; zp_quant_1 = zp_quant_0 >> 8; } else { static_assert(w_type.size_bits() == 8); zp_quant_0 = frag_qzp[k2][0]; zp_quant_1 = frag_qzp[k2][1]; } dequant(zp_quant_0, frag_zp_0); dequant(zp_quant_1, frag_zp_1); frag_zp[0] = frag_zp_0[0]; frag_zp[1] = frag_zp_0[1]; frag_zp[2] = frag_zp_1[0]; frag_zp[3] = frag_zp_1[1]; } } // We have the m dimension as the inner loop in order to encourage overlapping // dequantization and matmul operations. #pragma unroll for (int j = 0; j < 4; j++) { FragB frag_b0; FragB frag_b1; int b_quant_0, b_quant_1; if constexpr (w_type.size_bits() == 4) { b_quant_0 = frag_b_quant[k2][0][j]; b_quant_1 = b_quant_0 >> 8; } else { static_assert(w_type.size_bits() == 8); int* frag_b_quant_ptr = reinterpret_cast(frag_b_quant[k2]); b_quant_0 = frag_b_quant_ptr[j * 2 + 0]; b_quant_1 = frag_b_quant_ptr[j * 2 + 1]; } dequant(b_quant_0, frag_b0); dequant(b_quant_1, frag_b1); // Apply scale to frag_b0 if constexpr (has_act_order) { static_assert(group_blocks != -1); scale4(frag_b0, act_frag_s[k2][0][j], act_frag_s[k2][1][j], act_frag_s[k2][2][j], act_frag_s[k2][3][j], 0); scale4(frag_b1, act_frag_s[k2][0][j], act_frag_s[k2][1][j], act_frag_s[k][2][j], act_frag_s[k2][3][j], 1); } else if constexpr (has_zp && !is_zp_float && group_blocks == -1) { int idx = (threadIdx.x / 4) % 2; scalar_t2 s2 = Dtype::nums2num2( reinterpret_cast(&frag_s[j / 2][j % 2 * 2 + 0])[idx], reinterpret_cast(&frag_s[j / 2][j % 2 * 2 + 1])[idx]); if (is_new_zp) frag_zp[j] = __hmul2(frag_zp[j], s2); scale_and_sub(frag_b0, s2.x, frag_zp[j].x); scale_and_sub(frag_b1, s2.y, frag_zp[j].y); } else if constexpr (has_zp && !is_zp_float && group_blocks != -1) { if (is_new_zp) frag_zp[j] = __hmul2(frag_zp[j], *reinterpret_cast(&frag_s[k2][j])); scale_and_sub(frag_b0, frag_s[k % 2][j][0].x, frag_zp[j].x); scale_and_sub(frag_b1, frag_s[k % 2][j][0].y, frag_zp[j].y); } else if constexpr (has_zp && is_zp_float && group_blocks != -1) { if (is_new_zp) frag_zpf[k2][j] = __hmul2( frag_zpf[k2][j], *reinterpret_cast(&frag_s[k2][j])); scale_and_sub(frag_b0, frag_s[k2][j].x, frag_zpf[k2][j].x); scale_and_sub(frag_b1, frag_s[k2][j].y, frag_zpf[k2][j].y); } else if constexpr (group_blocks != -1) { scale(frag_b0, frag_s[k2][j], 0); scale(frag_b1, frag_s[k2][j], 1); } #pragma unroll for (int i = 0; i < thread_m_blocks; i++) { if constexpr (m_block_size_8) { mma_trans(frag_a[k2][i], frag_b0, frag_b1, frag_c[i][j][0]); } else { mma(frag_a[k2][i], frag_b0, frag_c[i][j][0]); mma(frag_a[k2][i], frag_b1, frag_c[i][j][1]); } } } }; // Since we slice across the k dimension of a tile in order to increase the // number of warps while keeping the n dimension of a tile reasonable, we have // multiple warps that accumulate their partial sums of the same output // location; which we have to reduce over in the end. We do in shared memory. auto thread_block_reduce = [&]() { constexpr int red_off = threads / b_sh_stride_threads / 2; if (red_off >= 1) { int red_idx = threadIdx.x / b_sh_stride_threads; constexpr int red_sh_stride = b_sh_stride_threads * 4 * 2; constexpr int red_sh_delta = b_sh_stride_threads; int red_sh_rd = red_sh_stride * (threadIdx.x / b_sh_stride_threads) + (threadIdx.x % b_sh_stride_threads); // Parallel logarithmic shared memory reduction. We make sure to avoid any // unnecessary read or write iterations, e.g., for two warps we write only // once by warp 1 and read only once by warp 0. #pragma unroll for (int m_block = 0; m_block < thread_m_blocks; m_block++) { #pragma unroll for (int i = red_off; i > 0; i /= 2) { if (i <= red_idx && red_idx < 2 * i) { #pragma unroll for (int j = 0; j < 4 * 2; j += (m_block_size_8 ? 2 : 1)) { int red_sh_wr = red_sh_delta * j + (red_sh_rd - red_sh_stride * i); if (i < red_off) { float* c_rd = reinterpret_cast( &sh_red[red_sh_delta * j + red_sh_rd]); float* c_wr = reinterpret_cast(&sh_red[red_sh_wr]); #pragma unroll for (int k = 0; k < 4; k++) reinterpret_cast(frag_c)[4 * 2 * m_block + j][k] += c_rd[k] + c_wr[k]; } sh_red[red_sh_wr] = reinterpret_cast(&frag_c)[4 * 2 * m_block + j]; } } __syncthreads(); } if (red_idx == 0) { #pragma unroll for (int i = 0; i < 4 * 2; i += (m_block_size_8 ? 2 : 1)) { float* c_rd = reinterpret_cast(&sh_red[red_sh_delta * i + red_sh_rd]); #pragma unroll for (int j = 0; j < 4; j++) reinterpret_cast(frag_c)[4 * 2 * m_block + i][j] += c_rd[j]; } } __syncthreads(); } } }; // Since multiple threadblocks may process parts of the same column slice, we // finally have to globally reduce over the results. As the striped // partitioning minimizes the number of such reductions and our outputs are // usually rather small, we perform this reduction serially in L2 cache. auto global_reduce_fp16 = [&](bool first = false, bool last = false) { // We are very careful here to reduce directly in the output buffer to // maximize L2 cache utilization in this step. To do this, we write out // results in FP16 (but still reduce with FP32 compute). constexpr int active_threads = 32 * thread_n_blocks / 4; bool is_th_active = threadIdx.x < active_threads; if (!is_th_active) { return; } int c_gl_stride = prob_n / 8; int c_gl_wr_delta_o = 8 * c_gl_stride; int c_gl_wr_delta_i = 4 * (active_threads / 32); int c_gl_wr; if constexpr (m_block_size_8) { c_gl_wr = c_gl_stride * ((threadIdx.x % 4) * 2) + 4 * (threadIdx.x / 32) + (threadIdx.x % 32) / 8; c_gl_wr += (2 * thread_n_blocks) * slice_col; } else { c_gl_wr = c_gl_stride * ((threadIdx.x % 32) / 4) + 4 * (threadIdx.x / 32) + threadIdx.x % 4; c_gl_wr += (2 * thread_n_blocks) * slice_col; } constexpr int c_sh_wr_delta = active_threads; int c_sh_wr = threadIdx.x; if (!first) { #pragma unroll for (int i = 0; i < (m_block_size_8 ? 2 : thread_m_blocks * 4); i++) { int c_idx; if constexpr (m_block_size_8) c_idx = c_gl_wr + i * c_gl_stride + (threadIdx.x % 8) / 4 * c_gl_wr_delta_i; else c_idx = c_gl_wr + c_gl_wr_delta_o * (i / 2) + c_gl_wr_delta_i * (i % 2); if (c_idx / c_gl_stride < block_num_valid_tokens) { int64_t sorted_row = sh_block_sorted_ids[c_idx / c_gl_stride]; int64_t true_idx = sorted_row * c_gl_stride + c_idx % c_gl_stride; sh_red[c_sh_wr + c_sh_wr_delta * i] = C[true_idx]; } } } #pragma unroll for (int i = 0; i < (m_block_size_8 ? 2 : thread_m_blocks * 4); i++) { if (!first) { int4 c_red = sh_red[c_sh_wr + i * c_sh_wr_delta]; #pragma unroll for (int j = 0; j < 2 * 4; j++) { int delta = 0; if constexpr (m_block_size_8) { delta = j % 2 == 1 ? -2 : 0; } reinterpret_cast( &frag_c)[4 * 2 * 4 * (i / 4) + 4 * j + (i % 4) + delta] += Dtype::num2float(reinterpret_cast(&c_red)[j]); } } if (!last) { int4 c; #pragma unroll for (int j = 0; j < 2 * 4; j++) { int delta = 0; if constexpr (m_block_size_8) { delta = j % 2 == 1 ? -2 : 0; } reinterpret_cast(&c)[j] = Dtype::float2num(reinterpret_cast( &frag_c)[4 * 2 * 4 * (i / 4) + 4 * j + (i % 4) + delta]); } int c_idx; if constexpr (m_block_size_8) c_idx = c_gl_wr + i * c_gl_stride + (threadIdx.x % 8) / 4 * c_gl_wr_delta_i; else c_idx = c_gl_wr + c_gl_wr_delta_o * (i / 2) + c_gl_wr_delta_i * (i % 2); if (c_idx / c_gl_stride < block_num_valid_tokens) { int64_t sorted_row = sh_block_sorted_ids[c_idx / c_gl_stride]; int64_t true_idx = sorted_row * c_gl_stride + c_idx % c_gl_stride; C[true_idx] = c; } } } }; // Globally reduce over threadblocks that compute the same column block. // We use a tmp C buffer to reduce in full fp32 precision. auto global_reduce_fp32 = [&](bool first = false, bool last = false) { constexpr int tb_m = thread_m_blocks * 16; constexpr int tb_n = thread_n_blocks * 16; constexpr int c_size = tb_m * tb_n * sizeof(float) / 16; constexpr int active_threads = 32 * thread_n_blocks / 4; bool is_th_active = threadIdx.x < active_threads; constexpr int num_floats = thread_m_blocks * 4 * 2 * 4; constexpr int th_size = num_floats * sizeof(float) / 16; int c_cur_offset = locks_off * c_size; if (!is_th_active) { return; } if (!first) { float* frag_c_ptr = reinterpret_cast(&frag_c); #pragma unroll for (int k = 0; k < th_size; k++) { if constexpr (m_block_size_8) { if (k % 2) continue; } else { if (k / 8 * 16 + (threadIdx.x % 32) / 4 >= block_num_valid_tokens) continue; } sh_red[threadIdx.x] = C_tmp[c_cur_offset + active_threads * k + threadIdx.x]; float* sh_c_ptr = reinterpret_cast(&sh_red[threadIdx.x]); #pragma unroll for (int f = 0; f < 4; f++) { frag_c_ptr[k * 4 + f] += sh_c_ptr[f]; } } } if (!last) { int4* frag_c_ptr = reinterpret_cast(&frag_c); #pragma unroll for (int k = 0; k < th_size; k++) { if constexpr (m_block_size_8) { if (k % 2) continue; } else { if (k / 8 * 16 + (threadIdx.x % 32) / 4 >= block_num_valid_tokens) continue; } C_tmp[c_cur_offset + active_threads * k + threadIdx.x] = frag_c_ptr[k]; } } }; // Write out the reduce final result in the correct layout. We only actually // reshuffle matrix fragments in this step, the reduction above is performed // in fragment layout. auto write_result = [&]() { int c_gl_stride = prob_n / 8; constexpr int c_sh_stride = 2 * thread_n_blocks + 1; int c_gl_wr_delta = c_gl_stride * (threads / (2 * thread_n_blocks)); constexpr int c_sh_rd_delta = c_sh_stride * (threads / (2 * thread_n_blocks)); int c_gl_wr = c_gl_stride * (threadIdx.x / (2 * thread_n_blocks)) + (threadIdx.x % (2 * thread_n_blocks)); c_gl_wr += (2 * thread_n_blocks) * slice_col; int c_sh_wr; if constexpr (m_block_size_8) { c_sh_wr = (8 * c_sh_stride) * ((threadIdx.x % 32) % 4 * 2) + (threadIdx.x % 32) / 4; c_sh_wr += 64 * (threadIdx.x / 32); } else { c_sh_wr = (4 * c_sh_stride) * ((threadIdx.x % 32) / 4) + (threadIdx.x % 32) % 4; c_sh_wr += 32 * (threadIdx.x / 32); } int c_sh_rd = c_sh_stride * (threadIdx.x / (2 * thread_n_blocks)) + (threadIdx.x % (2 * thread_n_blocks)); // We first reorder in shared memory to guarantee the most efficient final // global write patterns auto write = [&](int idx, float c0, float c1, FragS& s) { scalar_t2 res = Dtype::nums2num2(Dtype::float2num(c0), Dtype::float2num(c1)); // For per-column quantization we finally apply the scale here (only for // 4-bit) if constexpr (!has_act_order && group_blocks == -1 && w_type.size_bits() == 4 && !has_zp) { res = __hmul2(res, s[0]); } if constexpr (m_block_size_8) { ((scalar_t*)sh_red)[idx] = res.x; ((scalar_t*)sh_red)[idx + 8 * c_sh_stride] = res.y; } else { ((scalar_t2*)sh_red)[idx] = res; } }; if (threadIdx.x / 32 < thread_n_blocks / 4) { #pragma unroll for (int i = 0; i < thread_m_blocks; i++) { #pragma unroll for (int j = 0; j < 4; j++) { if constexpr (m_block_size_8) { int wr = c_sh_wr + 16 * j; write(wr, frag_c[i][j][0][0], frag_c[i][j][0][1], frag_s[j / 2][2 * (j % 2) + 0]); write(wr + 8, frag_c[i][j][0][2], frag_c[i][j][0][3], frag_s[j / 2][2 * (j % 2) + 1]); } else { int wr = c_sh_wr + 8 * j; write(wr + (4 * c_sh_stride) * 0 + 0, frag_c[i][j][0][0], frag_c[i][j][0][1], frag_s[j / 2][2 * (j % 2) + 0]); write(wr + (4 * c_sh_stride) * 8 + 0, frag_c[i][j][0][2], frag_c[i][j][0][3], frag_s[j / 2][2 * (j % 2) + 0]); write(wr + (4 * c_sh_stride) * 0 + 4, frag_c[i][j][1][0], frag_c[i][j][1][1], frag_s[j / 2][2 * (j % 2) + 1]); write(wr + (4 * c_sh_stride) * 8 + 4, frag_c[i][j][1][2], frag_c[i][j][1][3], frag_s[j / 2][2 * (j % 2) + 1]); } } c_sh_wr += 16 * (4 * c_sh_stride); } } __syncthreads(); #pragma unroll for (int i = 0; i < div_ceil(16 * thread_m_blocks, threads / (2 * thread_n_blocks)); i++) { int row = c_gl_wr / c_gl_stride; if (row < block_num_valid_tokens) { int64_t sorted_row = sh_block_sorted_ids[row]; int64_t true_idx = sorted_row * c_gl_stride + c_gl_wr % c_gl_stride; scalar_t2 topk_weight_score; if (mul_topk_weights) topk_weight_score = sh_block_topk_weights[row]; if (use_atomic_add && slice_count > 1 || mul_topk_weights) { scalar_t2* C_half2 = reinterpret_cast(&C[true_idx]); scalar_t2* sh_red_half2 = reinterpret_cast(&sh_red[c_sh_rd]); #pragma unroll for (int a = 0; a < 4; a++) { scalar_t2 res = sh_red_half2[a]; if (mul_topk_weights) { res = __hmul2(res, topk_weight_score); } if (use_atomic_add && slice_count > 1) { atomicAdd(&C_half2[a], res); } else { C_half2[a] = res; }; } } else { C[true_idx] = sh_red[c_sh_rd]; } c_gl_wr += c_gl_wr_delta; c_sh_rd += c_sh_rd_delta; } } __syncthreads(); }; // Start global fetch and register load pipelines. auto start_pipes = [&]() { #pragma unroll for (int i = 0; i < stages - 1; i++) { if (has_act_order && i == 0) { int last_g_idx = slice_k_start + stages * tb_k * 2; if (last_g_idx >= prob_k) { last_g_idx = prob_k - 1; } fetch_act_order_scales_to_shared(true, g_idx[slice_k_start], g_idx[last_g_idx]); } if constexpr (has_zp && !is_zp_float && group_blocks == -1) { if (i == 0) { fetch_col_zp_to_shared(); fetch_col_scale_to_shared(); } } fetch_to_shared(i, i, i < slice_iters); } zero_accums(); wait_for_stage(); init_same_group(0); fetch_to_registers(0, 0); fetch_scales_to_registers(0, 0); fetch_zp_to_registers(0, 0); a_gl_rd += a_gl_rd_delta_o * (stages - 1); slice_k_start_shared_fetch += tb_k * (stages - 1); }; if (slice_iters) { start_pipes(); } // Main loop. while (slice_iters) { // We unroll over both the global fetch and the register load pipeline to // ensure all shared memory accesses are static. Note that both pipelines // have even length meaning that the next iteration will always start at // index 0. #pragma unroll for (int pipe = 0; pipe < stages;) { #pragma unroll for (int k = 0; k < b_sh_wr_iters; k++) { fetch_to_registers(k + 1, pipe % stages); fetch_scales_to_registers(k + 1, pipe); fetch_zp_to_registers(k + 1, pipe); if (k == b_sh_wr_iters - 2) { fetch_to_shared((pipe + stages - 1) % stages, pipe, slice_iters >= stages); pipe++; wait_for_stage(); init_same_group(pipe % stages); } matmul(k); } slice_iters--; if (slice_iters == 0) { break; } } a_remaining_load_count_in_slice = 0; a_gl_rd += a_gl_rd_delta_o * stages; slice_k_start += tb_k * stages; slice_k_start_shared_fetch += tb_k * stages; if constexpr (has_act_order) { int first_group_id = g_idx[slice_k_start]; int last_g_idx = slice_k_start + stages * tb_k * 2; if (last_g_idx >= prob_k) { last_g_idx = prob_k - 1; } int last_group_id = g_idx[last_g_idx]; if (last_group_id >= sh_first_group_id + sh_num_groups) { fetch_act_order_scales_to_shared(false, first_group_id, last_group_id); __syncthreads(); } } // Process results and, if necessary, proceed to the next column slice. // While this pattern may not be the most readable, other ways of writing // the loop seemed to noticeably worse performance after compilation. if (slice_iters == 0) { cp_async_wait<0>(); bool last = slice_idx == slice_count - 1; // For per-column scales, we only fetch them here in the final step before // write-out if constexpr (!has_act_order && group_blocks == -1 && !has_zp) { if (w_type.size_bits() == 8 || (last || use_atomic_add)) { if (s_sh_wr_pred) { cp_async4(&sh_s[s_sh_wr], &scales_ptr[s_gl_rd]); } cp_async_fence(); } } thread_block_reduce(); if constexpr (!has_act_order && group_blocks == -1 && !has_zp) { if (w_type.size_bits() == 8 || (last || use_atomic_add)) { cp_async_wait<0>(); __syncthreads(); if (threadIdx.x / 32 < thread_n_blocks / 4) { reinterpret_cast(&frag_s)[0] = sh_s[s_sh_rd + 0]; reinterpret_cast(&frag_s)[1] = sh_s[s_sh_rd + 4]; if constexpr (m_block_size_8) { int idx = (threadIdx.x / 4) % 2; scalar_t2* frag_s_half2 = reinterpret_cast(frag_s); #pragma unroll for (int i = 0; i < 8; i++) { frag_s_half2[i] = Dtype::num2num2( reinterpret_cast(&frag_s_half2[i])[idx]); } } } } } // For 8-bit channelwise, we apply the scale before the global reduction // that converts the fp32 results to fp16 (so that we avoid possible // overflow in fp16) if constexpr (!has_act_order && group_blocks == -1 && w_type.size_bits() == 8 && !has_zp) { if (threadIdx.x / 32 < thread_n_blocks / 4) { #pragma unroll for (int i = 0; i < thread_m_blocks; i++) { #pragma unroll for (int j = 0; j < 4; j++) { scale_float( reinterpret_cast(&frag_c[i][j][0][0]), frag_s[j / 2][2 * (j % 2) + 0]); scale_float( reinterpret_cast(&frag_c[i][j][0][2]), frag_s[j / 2][2 * (j % 2) + (m_block_size_8 ? 1 : 0)]); if constexpr (!m_block_size_8) { scale_float( reinterpret_cast(&frag_c[i][j][1][0]), frag_s[j / 2][2 * (j % 2) + 1]); scale_float( reinterpret_cast(&frag_c[i][j][1][2]), frag_s[j / 2][2 * (j % 2) + 1]); } } } } } if (slice_count > 1 && !use_atomic_add) { // only globally reduce if there is more than one block in a slice barrier_acquire(&locks[locks_off], slice_idx); if (use_fp32_reduce) { global_reduce_fp32(slice_idx == 0, last); } else { global_reduce_fp16(slice_idx == 0, last); } barrier_release(&locks[locks_off], last); } if (use_atomic_add && slice_count > 1 && slice_idx != 0) wait_negative_and_add(&locks[locks_off]); if (last || use_atomic_add) // only the last block in a slice actually writes the result write_result(); if (slice_row) a_remaining_load_count_in_slice = stages; slice_row = 0; slice_col_par++; slice_col++; is_first_matmul_in_slice = true; init_slice(); if (slice_iters) { a_gl_rd = a_gl_stride * (threadIdx.x / a_gl_rd_delta_o) + (threadIdx.x % a_gl_rd_delta_o); #pragma unroll for (int i = 0; i < b_sh_wr_iters; i++) B_ptr[i] += b_sh_stride - b_gl_rd_delta_o * k_tiles; if (slice_col == 0) { #pragma unroll for (int i = 0; i < b_sh_wr_iters; i++) B_ptr[i] -= b_gl_stride; } // Update slice k/n for scales loading if constexpr (has_act_order) { slice_k_start = tb_k * slice_row; slice_k_finish = slice_k_start + tb_k * slice_iters; slice_k_start_shared_fetch = slice_k_start; slice_n_offset = act_s_col_tb_stride * slice_col; } else { s_gl_rd = s_sh_stride * slice_col + threadIdx.x; zp_gl_rd = zp_sh_stride * slice_col + threadIdx.x; } start_pipes(); } } } } } // namespace MARLIN_NAMESPACE_NAME #endif