# Adapted from: https://github.com/vllm-project/vllm/tree/main/vllm/model_executor/layers/mamba/ops/ssd_bmm.py # SPDX-License-Identifier: Apache-2.0 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project # Copyright (c) 2024, Tri Dao, Albert Gu. # Adapted from https://github.com/state-spaces/mamba/blob/v2.2.4/mamba_ssm/ops/triton/ssd_bmm.py # ruff: noqa: E501,SIM102 import math import torch import triton import triton.language as tl @triton.jit def _bmm_chunk_fwd_kernel( # Pointers to matrices a_ptr, b_ptr, out_ptr, seq_idx_ptr, # Matrix dimensions seqlen, chunk_size, K, ngroups, stride_a_batch, stride_a_seqlen, stride_a_head, stride_ak, stride_b_batch, stride_b_seqlen, stride_b_head, stride_bk, stride_out_batch, stride_out_chunk, stride_out_head, stride_outm, stride_outn, stride_seq_idx_batch, stride_seq_idx_seqlen, # Meta-parameters IS_CAUSAL: tl.constexpr, dot_dtype: tl.constexpr, HAS_SEQ_IDX: tl.constexpr, BLOCK_SIZE_M: tl.constexpr = 16, BLOCK_SIZE_N: tl.constexpr = 16, BLOCK_SIZE_K: tl.constexpr = 16, ): pid_b = tl.program_id(axis=1) pid_ch = tl.program_id(axis=2).to(tl.int64) pid_c = pid_ch // ngroups pid_h = pid_ch - pid_c * ngroups num_pid_n = tl.cdiv(chunk_size, BLOCK_SIZE_N) pid_m = tl.program_id(axis=0) // num_pid_n pid_n = tl.program_id(axis=0) % num_pid_n if IS_CAUSAL: if pid_n * BLOCK_SIZE_N >= (pid_m + 1) * BLOCK_SIZE_M: return a_ptr += ( pid_b * stride_a_batch + pid_c * chunk_size * stride_a_seqlen + pid_h * stride_a_head ) b_ptr += ( pid_b * stride_b_batch + pid_c * chunk_size * stride_b_seqlen + pid_h * stride_b_head ) if HAS_SEQ_IDX: seq_idx_ptr += ( pid_b * stride_seq_idx_batch + pid_c * chunk_size * stride_seq_idx_seqlen ) offs_m = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M) offs_n = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N) offs_k = tl.arange(0, BLOCK_SIZE_K) a_ptrs = a_ptr + (offs_m[:, None] * stride_a_seqlen + offs_k[None, :] * stride_ak) b_ptrs = b_ptr + (offs_k[:, None] * stride_bk + offs_n[None, :] * stride_b_seqlen) chunk_size_limit = min(chunk_size, seqlen - pid_c * chunk_size) acc = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32) for k in range(0, tl.cdiv(K, BLOCK_SIZE_K)): a = tl.load( a_ptrs, mask=(offs_m[:, None] < chunk_size_limit) & (offs_k[None, :] < K - k * BLOCK_SIZE_K), other=0.0, ).to(dot_dtype) b = tl.load( b_ptrs, mask=(offs_k[:, None] < K - k * BLOCK_SIZE_K) & (offs_n[None, :] < chunk_size_limit), other=0.0, ).to(dot_dtype) acc += tl.dot(a, b) a_ptrs += BLOCK_SIZE_K * stride_ak b_ptrs += BLOCK_SIZE_K * stride_bk offs_m = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M) offs_n = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N) if HAS_SEQ_IDX: chunk_size_limit = min(chunk_size, seqlen - pid_c * chunk_size) seq_idx_m = tl.load( seq_idx_ptr + offs_m * stride_seq_idx_seqlen, mask=offs_m < chunk_size_limit, other=-1, ) seq_idx_n = tl.load( seq_idx_ptr + offs_n * stride_seq_idx_seqlen, mask=offs_n < chunk_size_limit, other=-2, ) acc = tl.where(seq_idx_m[:, None] == seq_idx_n[None, :], acc, 0.0) out = acc.to(out_ptr.dtype.element_ty) out_ptr += ( pid_b * stride_out_batch + pid_c * stride_out_chunk + pid_h * stride_out_head ) out_ptrs = out_ptr + (stride_outm * offs_m[:, None] + offs_n[None, :] * stride_outn) tl.store( out_ptrs, out, mask=(offs_m[:, None] < chunk_size) & (offs_n[None, :] < chunk_size), ) def _bmm_chunk_fwd(a, b, chunk_size, seq_idx=None, causal=False, output_dtype=None): """ Argument: a: (batch, seqlen, k) or (batch, seqlen, ngroups, k) b: (batch, seqlen, k) or (batch, seqlen, ngroups, k) seq_idx: (batch, seqlen) or None. out[i, j] for seq_idx[i] != seq_idx[j] will be zeroed out. causal: if True, then out[i, j] for i > j will be arbitrary, only out[i, j] for i <= j are guaranteed to be correct. Return: out: (batch, nchunks, chunk_size, chunk_size) or (batch, nchunks, ngroups, chunk_size, chunk_size) """ # Check constraints. has_groups = a.dim() == 4 if not has_groups: batch, seqlen, k = a.shape else: batch, seqlen, ngroups, k = a.shape assert b.shape == a.shape if seq_idx is not None: assert seq_idx.shape == (batch, seqlen) if a.stride(-1) != 1 and a.stride(1) != 1: a = a.contiguous() if b.stride(-1) != 1 and b.stride(1) != 1: b = b.contiguous() nchunks = math.ceil(seqlen / chunk_size) # Allocates output. out_dtype = a.dtype if output_dtype is None else output_dtype out = torch.empty( ( (batch, nchunks, chunk_size, chunk_size) if not has_groups else (batch, nchunks, ngroups, chunk_size, chunk_size) ), device=a.device, dtype=out_dtype, ) dot_dtype = ( tl.bfloat16 if a.dtype == torch.bfloat16 or b.dtype == torch.bfloat16 else ( tl.float16 if a.dtype == torch.float16 or b.dtype == torch.float16 else tl.float32 ) ) grid = lambda META: ( triton.cdiv(chunk_size, META["BLOCK_SIZE_M"]) * triton.cdiv(chunk_size, META["BLOCK_SIZE_N"]), batch, nchunks if not has_groups else nchunks * ngroups, ) with torch.cuda.device(a.device.index): _bmm_chunk_fwd_kernel[grid]( a, b, out, seq_idx, seqlen, chunk_size, k, ngroups if has_groups else 1, a.stride(0), a.stride(1), 0 if not has_groups else a.stride(2), a.stride(-1), b.stride(0), b.stride(1), 0 if not has_groups else b.stride(2), b.stride(-1), out.stride(0), out.stride(1), 0 if not has_groups else out.stride(2), out.stride(-2), out.stride(-1), *( (seq_idx.stride(0), seq_idx.stride(1)) if seq_idx is not None else (0, 0) ), causal, dot_dtype, HAS_SEQ_IDX=seq_idx is not None, ) return out