243 lines
7.8 KiB
Python
243 lines
7.8 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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# Copyright (c) 2024, Tri Dao, Albert Gu.
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# Adapted from https://github.com/state-spaces/mamba/blob/v2.2.4/mamba_ssm/ops/triton/ssd_bmm.py
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# ruff: noqa: E501,SIM102
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import math
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import torch
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from vllm.triton_utils import tl, triton
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@triton.autotune(
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configs=[
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triton.Config(
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{
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'BLOCK_SIZE_M': 128,
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'BLOCK_SIZE_N': 256,
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'BLOCK_SIZE_K': 64
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},
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num_stages=3,
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num_warps=8),
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triton.Config(
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{
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'BLOCK_SIZE_M': 64,
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'BLOCK_SIZE_N': 256,
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'BLOCK_SIZE_K': 32
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},
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num_stages=4,
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num_warps=4),
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triton.Config(
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{
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'BLOCK_SIZE_M': 128,
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'BLOCK_SIZE_N': 128,
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'BLOCK_SIZE_K': 32
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},
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num_stages=4,
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num_warps=4),
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triton.Config(
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{
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'BLOCK_SIZE_M': 128,
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'BLOCK_SIZE_N': 64,
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'BLOCK_SIZE_K': 32
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},
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num_stages=4,
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num_warps=4),
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triton.Config(
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{
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'BLOCK_SIZE_M': 64,
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'BLOCK_SIZE_N': 128,
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'BLOCK_SIZE_K': 32
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},
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num_stages=4,
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num_warps=4),
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triton.Config(
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{
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'BLOCK_SIZE_M': 128,
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'BLOCK_SIZE_N': 32,
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'BLOCK_SIZE_K': 32
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},
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num_stages=4,
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num_warps=4),
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triton.Config(
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{
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'BLOCK_SIZE_M': 64,
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'BLOCK_SIZE_N': 32,
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'BLOCK_SIZE_K': 32
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},
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num_stages=5,
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num_warps=2),
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triton.Config(
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{
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'BLOCK_SIZE_M': 32,
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'BLOCK_SIZE_N': 64,
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'BLOCK_SIZE_K': 32
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},
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num_stages=5,
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num_warps=2),
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triton.Config(
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{
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'BLOCK_SIZE_M': 64,
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'BLOCK_SIZE_N': 64,
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'BLOCK_SIZE_K': 32
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},
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num_stages=4,
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num_warps=2),
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],
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key=['chunk_size', 'K', 'IS_CAUSAL'],
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)
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@triton.jit
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def _bmm_chunk_fwd_kernel(
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# Pointers to matrices
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a_ptr,
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b_ptr,
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out_ptr,
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seq_idx_ptr,
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# Matrix dimensions
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seqlen,
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chunk_size: tl.constexpr,
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K: tl.constexpr,
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ngroups: tl.constexpr,
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stride_a_seqlen: tl.int64,
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stride_a_head: tl.int64,
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stride_ak: tl.constexpr,
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stride_b_seqlen: tl.int64,
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stride_b_head: tl.int64,
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stride_bk: tl.constexpr,
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stride_out_chunk: tl.int64,
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stride_out_head: tl.int64,
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stride_outm: tl.int64,
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stride_outn: tl.constexpr,
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stride_seq_idx_seqlen: tl.constexpr,
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# Meta-parameters
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IS_CAUSAL: tl.constexpr,
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dot_dtype: tl.constexpr,
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BLOCK_SIZE_M: tl.constexpr,
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BLOCK_SIZE_N: tl.constexpr,
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BLOCK_SIZE_K: tl.constexpr,
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):
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pid_ch = tl.program_id(axis=1).to(tl.int64)
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pid_c = pid_ch // ngroups
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pid_h = pid_ch - pid_c * ngroups
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num_pid_n = tl.cdiv(chunk_size, BLOCK_SIZE_N)
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pid_m = tl.program_id(axis=0) // num_pid_n
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pid_n = tl.program_id(axis=0) % num_pid_n
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if IS_CAUSAL:
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if pid_n * BLOCK_SIZE_N >= (pid_m + 1) * BLOCK_SIZE_M:
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return
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a_ptr += pid_c * chunk_size * stride_a_seqlen + pid_h * stride_a_head
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b_ptr += pid_c * chunk_size * stride_b_seqlen + pid_h * stride_b_head
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seq_idx_ptr += pid_c * chunk_size * stride_seq_idx_seqlen
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offs_m = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)
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offs_n = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)
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offs_k = tl.arange(0, BLOCK_SIZE_K)
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a_ptrs = a_ptr + (offs_m[:, None] * stride_a_seqlen +
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offs_k[None, :] * stride_ak)
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b_ptrs = b_ptr + (offs_k[:, None] * stride_bk +
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offs_n[None, :] * stride_b_seqlen)
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chunk_size_limit = min(chunk_size, seqlen - pid_c * chunk_size)
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acc = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32)
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# compute a * b.T
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for k in range(0, tl.cdiv(K, BLOCK_SIZE_K)):
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a = tl.load(a_ptrs,
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mask=(offs_m[:, None] < chunk_size_limit) &
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(offs_k[None, :] < K - k * BLOCK_SIZE_K),
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other=0.0).to(dot_dtype)
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b = tl.load(b_ptrs,
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mask=(offs_k[:, None] < K - k * BLOCK_SIZE_K) &
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(offs_n[None, :] < chunk_size_limit),
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other=0.0).to(dot_dtype)
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acc += tl.dot(a, b)
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a_ptrs += BLOCK_SIZE_K * stride_ak
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b_ptrs += BLOCK_SIZE_K * stride_bk
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offs_m = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)
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offs_n = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)
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# Zero out the results that are not from the same request
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# in the varlen batch
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seq_idx_m = tl.load(seq_idx_ptr + offs_m * stride_seq_idx_seqlen,
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mask=offs_m < chunk_size_limit,
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other=-1)
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seq_idx_n = tl.load(seq_idx_ptr + offs_n * stride_seq_idx_seqlen,
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mask=offs_n < chunk_size_limit,
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other=-2)
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acc = tl.where(seq_idx_m[:, None] == seq_idx_n[None, :], acc, 0.0)
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out = acc.to(out_ptr.dtype.element_ty)
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out_ptr += pid_c * stride_out_chunk + pid_h * stride_out_head
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out_ptrs = out_ptr + (stride_outm * offs_m[:, None] +
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offs_n[None, :] * stride_outn)
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tl.store(out_ptrs,
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out,
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mask=(offs_m[:, None] < chunk_size) &
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(offs_n[None, :] < chunk_size))
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def _bmm_chunk_fwd(a, b, chunk_size, seq_idx, causal=False, output_dtype=None):
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"""
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Argument:
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a: (seqlen, ngroups, k)
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b: (seqlen, ngroups, k)
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seq_idx: (seqlen,). out[i, j] for seq_idx[i] != seq_idx[j] will be zeroed out.
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causal: if True, then out[i, j] for i > j will be arbitrary, only out[i, j] for i <= j are
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guaranteed to be correct.
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Return:
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out: (nchunks, ngroups, chunk_size, chunk_size)
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"""
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seqlen, ngroups, k = a.shape
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assert b.shape == a.shape
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assert seq_idx is not None
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assert seq_idx.shape == (seqlen, )
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if a.stride(-1) != 1 and a.stride(0) != 1:
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a = a.contiguous()
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if b.stride(-1) != 1 and b.stride(0) != 1:
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b = b.contiguous()
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nchunks = math.ceil(seqlen / chunk_size)
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# Allocates output.
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out_dtype = a.dtype if output_dtype is None else output_dtype
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out = torch.empty((nchunks, ngroups, chunk_size, chunk_size),
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device=a.device,
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dtype=out_dtype)
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dot_dtype = (tl.bfloat16
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if a.dtype == torch.bfloat16 or b.dtype == torch.bfloat16 else
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(tl.float16 if a.dtype == torch.float16
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or b.dtype == torch.float16 else tl.float32))
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grid = lambda META: (triton.cdiv(
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chunk_size, META['BLOCK_SIZE_M']) * triton.cdiv(
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chunk_size, META['BLOCK_SIZE_N']), nchunks * ngroups)
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with torch.cuda.device(a.device.index):
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_bmm_chunk_fwd_kernel[grid](
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a_ptr=a,
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b_ptr=b,
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out_ptr=out,
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seq_idx_ptr=seq_idx,
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seqlen=seqlen,
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chunk_size=chunk_size,
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K=k,
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ngroups=ngroups,
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stride_a_seqlen=a.stride(0),
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stride_a_head=a.stride(1),
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stride_ak=a.stride(2),
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stride_b_seqlen=b.stride(0),
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stride_b_head=b.stride(1),
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stride_bk=b.stride(2),
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stride_out_chunk=out.stride(0),
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stride_out_head=out.stride(1),
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stride_outm=out.stride(-2),
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stride_outn=out.stride(-1),
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stride_seq_idx_seqlen=seq_idx.stride(0),
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IS_CAUSAL=causal,
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dot_dtype=dot_dtype,
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)
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return out
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