mirror of https://github.com/hpcaitech/ColossalAI
526 lines
19 KiB
Python
526 lines
19 KiB
Python
"""
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Fused Attention
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===============
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This is a Triton implementation of the Flash Attention algorithm
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(see: Dao et al., https://arxiv.org/pdf/2205.14135v2.pdf; Rabe and Staats https://arxiv.org/pdf/2112.05682v2.pdf; Triton https://github.com/openai/triton)
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"""
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import math
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import os
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import subprocess
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import torch
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def triton_cuda_check():
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cuda_home = os.getenv("CUDA_HOME", default="/usr/local/cuda")
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cuda_version = subprocess.check_output([os.path.join(cuda_home, "bin/nvcc"), "--version"]).decode().strip()
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cuda_version = cuda_version.split('release ')[1]
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cuda_version = cuda_version.split(',')[0]
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cuda_version = cuda_version.split('.')
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if len(cuda_version) == 2 and \
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(int(cuda_version[0]) == 11 and int(cuda_version[1]) >= 4) or \
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int(cuda_version[0]) > 11:
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return True
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return False
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try:
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import triton
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import triton.language as tl
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if triton_cuda_check():
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HAS_TRITON = True
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else:
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print("triton requires cuda >= 11.4")
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HAS_TRITON = False
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except ImportError:
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print('please install triton from https://github.com/openai/triton')
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HAS_TRITON = False
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try:
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from flash_attn.flash_attention import FlashAttention
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from flash_attn.flash_attn_interface import (
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flash_attn_unpadded_func,
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flash_attn_unpadded_kvpacked_func,
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flash_attn_unpadded_qkvpacked_func,
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)
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HAS_FLASH_ATTN = True
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except ImportError:
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HAS_FLASH_ATTN = False
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print('please install flash_attn from https://github.com/HazyResearch/flash-attention')
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try:
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from xformers.ops.fmha import memory_efficient_attention
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HAS_MEM_EFF_ATTN = True
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except ImportError:
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HAS_MEM_EFF_ATTN = False
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print('please install xformers from https://github.com/facebookresearch/xformers')
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if HAS_TRITON:
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@triton.jit
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def _fwd_kernel(
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Q,
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K,
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V,
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sm_scale,
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TMP,
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L,
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M, # NOTE: TMP is a scratchpad buffer to workaround a compiler bug
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Out,
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stride_qz,
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stride_qh,
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stride_qm,
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stride_qk,
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stride_kz,
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stride_kh,
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stride_kn,
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stride_kk,
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stride_vz,
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stride_vh,
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stride_vk,
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stride_vn,
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stride_oz,
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stride_oh,
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stride_om,
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stride_on,
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Z,
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H,
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N_CTX,
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BLOCK_M: tl.constexpr,
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BLOCK_DMODEL: tl.constexpr,
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BLOCK_N: tl.constexpr,
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):
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start_m = tl.program_id(0)
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off_hz = tl.program_id(1)
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# initialize offsets
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offs_m = start_m * BLOCK_M + tl.arange(0, BLOCK_M)
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offs_n = tl.arange(0, BLOCK_N)
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offs_d = tl.arange(0, BLOCK_DMODEL)
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off_q = off_hz * stride_qh + offs_m[:, None] * stride_qm + offs_d[None, :] * stride_qk
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off_k = off_hz * stride_qh + offs_n[:, None] * stride_kn + offs_d[None, :] * stride_kk
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off_v = off_hz * stride_qh + offs_n[:, None] * stride_qm + offs_d[None, :] * stride_qk
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# Initialize pointers to Q, K, V
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q_ptrs = Q + off_q
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k_ptrs = K + off_k
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v_ptrs = V + off_v
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# initialize pointer to m and l
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t_ptrs = TMP + off_hz * N_CTX + offs_m
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m_i = tl.zeros([BLOCK_M], dtype=tl.float32) - float("inf")
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l_i = tl.zeros([BLOCK_M], dtype=tl.float32)
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acc = tl.zeros([BLOCK_M, BLOCK_DMODEL], dtype=tl.float32)
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# load q: it will stay in SRAM throughout
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q = tl.load(q_ptrs)
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# loop over k, v and update accumulator
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for start_n in range(0, (start_m + 1) * BLOCK_M, BLOCK_N):
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start_n = tl.multiple_of(start_n, BLOCK_N)
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# -- compute qk ----
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k = tl.load(k_ptrs + start_n * stride_kn)
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qk = tl.zeros([BLOCK_M, BLOCK_N], dtype=tl.float32)
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qk += tl.dot(q, k, trans_b=True)
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qk *= sm_scale
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qk += tl.where(offs_m[:, None] >= (start_n + offs_n[None, :]), 0, float("-inf"))
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# -- compute m_ij, p, l_ij
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m_ij = tl.max(qk, 1)
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p = tl.exp(qk - m_ij[:, None])
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l_ij = tl.sum(p, 1)
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# -- update m_i and l_i
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m_i_new = tl.maximum(m_i, m_ij)
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alpha = tl.exp(m_i - m_i_new)
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beta = tl.exp(m_ij - m_i_new)
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l_i_new = alpha * l_i + beta * l_ij
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# -- update output accumulator --
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# scale p
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p_scale = beta / l_i_new
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p = p * p_scale[:, None]
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# scale acc
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acc_scale = l_i / l_i_new * alpha
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tl.store(t_ptrs, acc_scale)
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acc_scale = tl.load(t_ptrs) # BUG: have to store and immediately load
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acc = acc * acc_scale[:, None]
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# update acc
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v = tl.load(v_ptrs + start_n * stride_vk)
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p = p.to(tl.float16)
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acc += tl.dot(p, v)
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# update m_i and l_i
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l_i = l_i_new
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m_i = m_i_new
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# rematerialize offsets to save registers
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start_m = tl.program_id(0)
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offs_m = start_m * BLOCK_M + tl.arange(0, BLOCK_M)
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# write back l and m
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l_ptrs = L + off_hz * N_CTX + offs_m
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m_ptrs = M + off_hz * N_CTX + offs_m
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tl.store(l_ptrs, l_i)
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tl.store(m_ptrs, m_i)
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# initialize pointers to output
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offs_n = tl.arange(0, BLOCK_DMODEL)
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off_o = off_hz * stride_oh + offs_m[:, None] * stride_om + offs_n[None, :] * stride_on
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out_ptrs = Out + off_o
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tl.store(out_ptrs, acc)
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@triton.jit
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def _bwd_preprocess(
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Out,
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DO,
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L,
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NewDO,
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Delta,
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BLOCK_M: tl.constexpr,
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D_HEAD: tl.constexpr,
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):
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off_m = tl.program_id(0) * BLOCK_M + tl.arange(0, BLOCK_M)
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off_n = tl.arange(0, D_HEAD)
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# load
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o = tl.load(Out + off_m[:, None] * D_HEAD + off_n[None, :]).to(tl.float32)
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do = tl.load(DO + off_m[:, None] * D_HEAD + off_n[None, :]).to(tl.float32)
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denom = tl.load(L + off_m).to(tl.float32)
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# compute
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do = do / denom[:, None]
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delta = tl.sum(o * do, axis=1)
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# write-back
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tl.store(NewDO + off_m[:, None] * D_HEAD + off_n[None, :], do)
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tl.store(Delta + off_m, delta)
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@triton.jit
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def _bwd_kernel(
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Q,
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K,
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V,
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sm_scale,
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Out,
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DO,
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DQ,
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DK,
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DV,
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L,
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M,
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D,
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stride_qz,
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stride_qh,
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stride_qm,
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stride_qk,
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stride_kz,
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stride_kh,
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stride_kn,
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stride_kk,
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stride_vz,
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stride_vh,
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stride_vk,
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stride_vn,
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Z,
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H,
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N_CTX,
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num_block,
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BLOCK_M: tl.constexpr,
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BLOCK_DMODEL: tl.constexpr,
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BLOCK_N: tl.constexpr,
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):
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off_hz = tl.program_id(0)
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off_z = off_hz // H
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off_h = off_hz % H
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# offset pointers for batch/head
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Q += off_z * stride_qz + off_h * stride_qh
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K += off_z * stride_qz + off_h * stride_qh
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V += off_z * stride_qz + off_h * stride_qh
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DO += off_z * stride_qz + off_h * stride_qh
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DQ += off_z * stride_qz + off_h * stride_qh
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DK += off_z * stride_qz + off_h * stride_qh
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DV += off_z * stride_qz + off_h * stride_qh
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for start_n in range(0, num_block):
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lo = start_n * BLOCK_M
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# initialize row/col offsets
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offs_qm = lo + tl.arange(0, BLOCK_M)
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offs_n = start_n * BLOCK_M + tl.arange(0, BLOCK_M)
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offs_m = tl.arange(0, BLOCK_N)
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offs_k = tl.arange(0, BLOCK_DMODEL)
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# initialize pointers to value-like data
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q_ptrs = Q + (offs_qm[:, None] * stride_qm + offs_k[None, :] * stride_qk)
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k_ptrs = K + (offs_n[:, None] * stride_kn + offs_k[None, :] * stride_kk)
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v_ptrs = V + (offs_n[:, None] * stride_qm + offs_k[None, :] * stride_qk)
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do_ptrs = DO + (offs_qm[:, None] * stride_qm + offs_k[None, :] * stride_qk)
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dq_ptrs = DQ + (offs_qm[:, None] * stride_qm + offs_k[None, :] * stride_qk)
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# pointer to row-wise quantities in value-like data
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D_ptrs = D + off_hz * N_CTX
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m_ptrs = M + off_hz * N_CTX
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# initialize dv amd dk
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dv = tl.zeros([BLOCK_M, BLOCK_DMODEL], dtype=tl.float32)
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dk = tl.zeros([BLOCK_M, BLOCK_DMODEL], dtype=tl.float32)
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# k and v stay in SRAM throughout
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k = tl.load(k_ptrs)
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v = tl.load(v_ptrs)
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# loop over rows
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for start_m in range(lo, num_block * BLOCK_M, BLOCK_M):
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offs_m_curr = start_m + offs_m
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# load q, k, v, do on-chip
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q = tl.load(q_ptrs)
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# recompute p = softmax(qk, dim=-1).T
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# NOTE: `do` is pre-divided by `l`; no normalization here
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qk = tl.dot(q, k, trans_b=True)
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qk = tl.where(offs_m_curr[:, None] >= (offs_n[None, :]), qk, float("-inf"))
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m = tl.load(m_ptrs + offs_m_curr)
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p = tl.exp(qk * sm_scale - m[:, None])
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# compute dv
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do = tl.load(do_ptrs)
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dv += tl.dot(p.to(tl.float16), do, trans_a=True)
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# compute dp = dot(v, do)
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Di = tl.load(D_ptrs + offs_m_curr)
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dp = tl.zeros([BLOCK_M, BLOCK_N], dtype=tl.float32) - Di[:, None]
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dp += tl.dot(do, v, trans_b=True)
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# compute ds = p * (dp - delta[:, None])
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ds = p * dp * sm_scale
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# compute dk = dot(ds.T, q)
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dk += tl.dot(ds.to(tl.float16), q, trans_a=True)
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# # compute dq
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dq = tl.load(dq_ptrs, eviction_policy="evict_last")
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dq += tl.dot(ds.to(tl.float16), k)
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tl.store(dq_ptrs, dq, eviction_policy="evict_last")
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# # increment pointers
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dq_ptrs += BLOCK_M * stride_qm
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q_ptrs += BLOCK_M * stride_qm
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do_ptrs += BLOCK_M * stride_qm
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# write-back
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dv_ptrs = DV + (offs_n[:, None] * stride_qm + offs_k[None, :] * stride_qk)
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dk_ptrs = DK + (offs_n[:, None] * stride_kn + offs_k[None, :] * stride_kk)
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tl.store(dv_ptrs, dv)
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tl.store(dk_ptrs, dk)
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class _TritonFlashAttention(torch.autograd.Function):
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@staticmethod
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def forward(ctx, q, k, v, sm_scale):
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BLOCK = 128
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# shape constraints
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Lq, Lk, Lv = q.shape[-1], k.shape[-1], v.shape[-1]
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assert Lq == Lk and Lk == Lv
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assert Lk in {16, 32, 64, 128}
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o = torch.empty_like(q)
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grid = (triton.cdiv(q.shape[2], BLOCK), q.shape[0] * q.shape[1])
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tmp = torch.empty((q.shape[0] * q.shape[1], q.shape[2]), device=q.device, dtype=torch.float32)
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L = torch.empty((q.shape[0] * q.shape[1], q.shape[2]), device=q.device, dtype=torch.float32)
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m = torch.empty((q.shape[0] * q.shape[1], q.shape[2]), device=q.device, dtype=torch.float32)
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num_warps = 4 if Lk <= 64 else 8
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_fwd_kernel[grid](
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q,
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k,
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v,
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sm_scale,
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tmp,
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L,
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m,
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o,
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q.stride(0),
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q.stride(1),
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q.stride(2),
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q.stride(3),
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k.stride(0),
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k.stride(1),
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k.stride(2),
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k.stride(3),
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v.stride(0),
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v.stride(1),
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v.stride(2),
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v.stride(3),
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o.stride(0),
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o.stride(1),
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o.stride(2),
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o.stride(3),
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q.shape[0],
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q.shape[1],
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q.shape[2],
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BLOCK_M=BLOCK,
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BLOCK_N=BLOCK,
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BLOCK_DMODEL=Lk,
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num_warps=num_warps,
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num_stages=1,
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)
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ctx.save_for_backward(q, k, v, o, L, m)
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ctx.BLOCK = BLOCK
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ctx.grid = grid
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ctx.sm_scale = sm_scale
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ctx.BLOCK_DMODEL = Lk
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return o
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@staticmethod
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def backward(ctx, do):
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q, k, v, o, l, m = ctx.saved_tensors
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do = do.contiguous()
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dq = torch.zeros_like(q, dtype=torch.float32)
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dk = torch.empty_like(k)
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dv = torch.empty_like(v)
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do_scaled = torch.empty_like(do)
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delta = torch.empty_like(l)
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_bwd_preprocess[(ctx.grid[0] * ctx.grid[1],)](
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o,
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do,
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l,
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do_scaled,
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delta,
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BLOCK_M=ctx.BLOCK,
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D_HEAD=ctx.BLOCK_DMODEL,
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)
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# NOTE: kernel currently buggy for other values of `num_warps`
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num_warps = 8
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_bwd_kernel[(ctx.grid[1],)](
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q,
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k,
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v,
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ctx.sm_scale,
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o,
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do_scaled,
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dq,
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dk,
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dv,
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l,
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m,
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delta,
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q.stride(0),
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q.stride(1),
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q.stride(2),
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q.stride(3),
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k.stride(0),
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k.stride(1),
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k.stride(2),
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k.stride(3),
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v.stride(0),
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v.stride(1),
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v.stride(2),
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v.stride(3),
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q.shape[0],
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q.shape[1],
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q.shape[2],
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ctx.grid[0],
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BLOCK_M=ctx.BLOCK,
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BLOCK_N=ctx.BLOCK,
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BLOCK_DMODEL=ctx.BLOCK_DMODEL,
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num_warps=num_warps,
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num_stages=1,
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)
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return dq, dk, dv, None
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def triton_flash_attention(q, k, v, sm_scale):
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"""
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Arguments:
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q: (batch, nheads, seq, headdim)
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k: (batch, nheads, seq, headdim)
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v: (batch, nheads, seq, headdim)
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sm_scale: float. The scaling of QK^T before applying softmax.
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Return:
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out: (batch, nheads, seq, headdim)
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"""
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if HAS_TRITON:
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return _TritonFlashAttention.apply(q, k, v, sm_scale)
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else:
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raise RuntimeError("Triton kernel requires CUDA 11.4+!")
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if HAS_FLASH_ATTN:
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from einops import rearrange
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class MaskedFlashAttention(torch.nn.Module):
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def __init__(self, num_attention_heads: int, attention_head_size: int, attention_dropout: float) -> None:
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super().__init__()
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self.num_attention_heads = num_attention_heads
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self.attention_head_size = attention_head_size
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self.attention_func = FlashAttention(softmax_scale=math.sqrt(attention_head_size),
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attention_dropout=attention_dropout)
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def forward(self, query_key_value: torch.Tensor, attention_mask: torch.Tensor, causal=False):
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if attention_mask.dtype is not torch.bool:
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attention_mask = attention_mask.bool()
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qkv = rearrange(query_key_value, 'b s (three h d) -> b s three h d', three=3, h=self.num_attention_heads)
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context, _ = self.attention_func(qkv, key_padding_mask=attention_mask, causal=causal)
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context = rearrange(context, 'b s h d -> b s (h d)')
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return context
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def flash_attention_qkv(qkv, sm_scale, batch_size, seq_len, dropout_p=0., causal=False):
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"""
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Arguments:
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qkv: (batch * seqlen, 3, nheads, headdim)
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batch_size: int.
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seq_len: int.
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sm_scale: float. The scaling of QK^T before applying softmax.
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Default to 1 / sqrt(headdim).
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|
dropout_p: float.
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|
causal: bool. Whether to apply causal attention mask (e.g., for auto-regressive modeling).
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|
Return:
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|
out: (total, nheads, headdim).
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|
"""
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max_s = seq_len
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cu_seqlens = torch.arange(0, (batch_size + 1) * seq_len, step=seq_len, dtype=torch.int32, device=qkv.device)
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out = flash_attn_unpadded_qkvpacked_func(qkv,
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|
cu_seqlens,
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max_s,
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|
dropout_p,
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softmax_scale=sm_scale,
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|
causal=causal)
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|
return out
|
|
|
|
def flash_attention_q_kv(q, kv, sm_scale, batch_size, q_seqlen, kv_seqlen, dropout_p=0., causal=False):
|
|
"""
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|
Arguments:
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|
q: (batch * q_seqlen, nheads, headdim)
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|
kv: (batch * kv_seqlen, 2, nheads, headdim)
|
|
batch_size: int.
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|
seq_len: int.
|
|
sm_scale: float. The scaling of QK^T before applying softmax.
|
|
Default to 1 / sqrt(headdim).
|
|
dropout_p: float.
|
|
causal: bool. Whether to apply causal attention mask (e.g., for auto-regressive modeling).
|
|
Return:
|
|
out: (total, nheads, headdim).
|
|
"""
|
|
cu_seqlens_q = torch.arange(0, (batch_size + 1) * q_seqlen, step=q_seqlen, dtype=torch.int32, device=q.device)
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|
cu_seqlens_k = torch.arange(0, (batch_size + 1) * kv_seqlen,
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|
step=kv_seqlen,
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|
dtype=torch.int32,
|
|
device=kv.device)
|
|
out = flash_attn_unpadded_kvpacked_func(q, kv, cu_seqlens_q, cu_seqlens_k, q_seqlen, kv_seqlen, dropout_p,
|
|
sm_scale, causal)
|
|
return out
|
|
|
|
def flash_attention_q_k_v(q, k, v, sm_scale, batch_size, q_seqlen, kv_seqlen, dropout_p=0., causal=False):
|
|
"""
|
|
Arguments:
|
|
q: (batch * q_seqlen, nheads, headdim)
|
|
k: (batch * kv_seqlen, nheads, headdim)
|
|
v: (batch * kv_seqlen, nheads, headdim)
|
|
batch_size: int.
|
|
seq_len: int.
|
|
dropout_p: float. Dropout probability.
|
|
sm_scale: float. The scaling of QK^T before applying softmax.
|
|
Default to 1 / sqrt(headdim).
|
|
causal: bool. Whether to apply causal attention mask (e.g., for auto-regressive modeling).
|
|
Return:
|
|
out: (total, nheads, headdim).
|
|
"""
|
|
cu_seqlens_q = torch.arange(0, (batch_size + 1) * q_seqlen, step=q_seqlen, dtype=torch.int32, device=q.device)
|
|
cu_seqlens_kv = torch.arange(0, (batch_size + 1) * kv_seqlen,
|
|
step=kv_seqlen,
|
|
dtype=torch.int32,
|
|
device=k.device)
|
|
return flash_attn_unpadded_func(q, k, v, cu_seqlens_q, cu_seqlens_kv, q_seqlen, kv_seqlen, dropout_p, sm_scale,
|
|
causal)
|
|
|
|
|
|
if HAS_MEM_EFF_ATTN:
|
|
|
|
from einops import rearrange
|
|
from xformers.ops.fmha import LowerTriangularMask
|
|
|
|
class MemoryEfficientAttention(torch.nn.Module):
|
|
|
|
def __init__(self, hidden_size: int, num_attention_heads: int, attention_dropout: float = 0.0):
|
|
super().__init__()
|
|
attention_head_size = hidden_size // num_attention_heads
|
|
self.scale = 1 / attention_head_size**0.5
|
|
self.dropout = attention_dropout
|
|
|
|
def forward(self, query: torch.Tensor, key: torch.Tensor, value: torch.Tensor, attention_mask: torch.Tensor):
|
|
context = memory_efficient_attention(query, key, value, attention_mask, self.dropout, self.scale)
|
|
context = rearrange(context, 'b s h d -> b s (h d)')
|
|
return context
|