360 lines
12 KiB
Python
360 lines
12 KiB
Python
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# -*- coding: utf-8 -*-
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# Copyright (c) 2023, Yu Zhang, Songlin Yang
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from typing import Tuple
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import torch
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import triton
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import triton.language as tl
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from torch.cuda.amp import custom_bwd, custom_fwd
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from fla.utils import contiguous
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@torch.jit.script
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def normalize_output(q, k, o):
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k = k.transpose(-2, -1)
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k = k.cumsum(-1)
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k = k.transpose(-2, -1)
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z = (q * k).sum(-1, keepdim=True)
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return o / (z + 1e-5)
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@triton.jit
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def chunk_linear_attn_fwd_kernel_h(
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k,
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v,
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h,
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initial_state, # initial state of the chunk [B, H, D_head_K, D_head_V]
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final_state, # final state of the chunk [B, H, D_head_K, D_head_V]
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s_qk_h,
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s_qk_t,
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s_qk_d,
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s_vo_h,
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s_vo_t,
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s_vo_d,
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s_h_h,
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s_h_t,
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H: tl.constexpr,
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T: tl.constexpr,
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K: tl.constexpr,
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V: tl.constexpr,
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BT: tl.constexpr,
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BK: tl.constexpr,
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BV: tl.constexpr,
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NT: tl.constexpr,
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USE_INITIAL_STATE: tl.constexpr,
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STORE_FINAL_STATE: tl.constexpr
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):
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i_k, i_v, i_bh = tl.program_id(0), tl.program_id(1), tl.program_id(2)
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# [BK, BV]
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b_h = tl.zeros([BK, BV], dtype=tl.float32)
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if USE_INITIAL_STATE:
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p_h0 = tl.make_block_ptr(initial_state + i_bh * K * V, (K, V), (V, 1), (i_k * BK, i_v * BV), (BK, BV), (1, 0))
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b_h = tl.load(p_h0, boundary_check=(0, 1)).to(tl.float32)
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for i_t in range(NT):
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p_k = tl.make_block_ptr(k + i_bh * s_qk_h, (K, T), (s_qk_d, s_qk_t), (i_k * BK, i_t * BT), (BK, BT), (0, 1))
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p_v = tl.make_block_ptr(v + i_bh * s_vo_h, (T, V), (s_vo_t, s_vo_d), (i_t * BT, i_v * BV), (BT, BV), (1, 0))
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p_h = tl.make_block_ptr(h + i_bh * s_h_h + i_t * K * V, (K, V), (s_h_t, 1), (i_k * BK, i_v * BV), (BK, BV), (1, 0))
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tl.store(p_h, b_h.to(p_h.dtype.element_ty), boundary_check=(0, 1))
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# [BK, BT]
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b_k = tl.load(p_k, boundary_check=(0, 1))
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# [BT, BV]
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b_v = tl.load(p_v, boundary_check=(0, 1))
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# [BK, BV]
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b_h += tl.dot(b_k, b_v, allow_tf32=False)
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if STORE_FINAL_STATE:
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p_ht = tl.make_block_ptr(final_state + i_bh * K * V, (K, V), (V, 1), (i_k * BK, i_v * BV), (BK, BV), (1, 0))
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tl.store(p_ht, b_h.to(p_ht.dtype.element_ty), boundary_check=(0, 1))
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@triton.jit
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def chunk_linear_attn_fwd_kernel_o(
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q,
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k,
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v,
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h,
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o,
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s_qk_h,
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s_qk_t,
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s_qk_d,
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s_vo_h,
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s_vo_t,
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s_vo_d,
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s_h_h,
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s_h_t,
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scale,
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H: tl.constexpr,
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T: tl.constexpr,
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K: tl.constexpr,
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V: tl.constexpr,
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BT: tl.constexpr,
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BK: tl.constexpr,
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BV: tl.constexpr
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):
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i_v, i_t, i_bh = tl.program_id(0), tl.program_id(1), tl.program_id(2)
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o_i = tl.arange(0, BT)
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m_s = o_i[:, None] >= o_i[None, :]
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b_o = tl.zeros([BT, BV], dtype=tl.float32)
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b_s = tl.zeros([BT, BT], dtype=tl.float32)
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for i_k in range(tl.cdiv(K, BK)):
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p_q = tl.make_block_ptr(q + i_bh * s_qk_h, (T, K), (s_qk_t, s_qk_d), (i_t * BT, i_k * BK), (BT, BK), (1, 0))
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p_k = tl.make_block_ptr(k + i_bh * s_qk_h, (K, T), (s_qk_d, s_qk_t), (i_k * BK, i_t * BT), (BK, BT), (0, 1))
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p_h = tl.make_block_ptr(h + i_bh * s_h_h + i_t * K * V, (K, V), (s_h_t, 1), (i_k * BK, i_v * BV), (BK, BV), (1, 0))
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# [BT, BK]
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b_q = tl.load(p_q, boundary_check=(0, 1))
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# [BK, BT]
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b_k = tl.load(p_k, boundary_check=(0, 1))
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# [BK, BV]
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b_h = tl.load(p_h, boundary_check=(0, 1))
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b_o += tl.dot(b_q, b_h, allow_tf32=False)
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b_s += tl.dot(b_q, b_k, allow_tf32=False)
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b_s = tl.where(m_s, b_s, 0)
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p_v = tl.make_block_ptr(v + i_bh * s_vo_h, (T, V), (s_vo_t, s_vo_d), (i_t * BT, i_v * BV), (BT, BV), (1, 0))
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b_v = tl.load(p_v, boundary_check=(0, 1))
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b_o = (b_o + tl.dot(b_s.to(b_v.dtype), b_v, allow_tf32=False)) * scale
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p_o = tl.make_block_ptr(o + i_bh * s_vo_h, (T, V), (s_vo_t, s_vo_d), (i_t * BT, i_v * BV), (BT, BV), (1, 0))
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tl.store(p_o, b_o.to(p_o.dtype.element_ty), boundary_check=(0, 1))
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@triton.jit
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def chunk_linear_attn_bwd_kernel_dh(
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q,
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do,
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dh,
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s_qk_h,
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s_qk_t,
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s_qk_d,
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s_vo_h,
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s_vo_t,
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s_vo_d,
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s_h_h,
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s_h_t,
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scale,
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H: tl.constexpr,
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T: tl.constexpr,
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K: tl.constexpr,
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V: tl.constexpr,
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BT: tl.constexpr,
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BK: tl.constexpr,
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BV: tl.constexpr,
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NT: tl.constexpr
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):
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i_k, i_v, i_bh = tl.program_id(0), tl.program_id(1), tl.program_id(2)
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# [BK, BV]
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b_dh = tl.zeros([BK, BV], dtype=tl.float32)
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for i_t in range(NT - 1, -1, -1):
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p_q = tl.make_block_ptr(q + i_bh * s_qk_h, (K, T), (s_qk_d, s_qk_t), (i_k * BK, i_t * BT), (BK, BT), (0, 1))
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p_do = tl.make_block_ptr(do + i_bh * s_vo_h, (T, V), (s_vo_t, s_vo_d), (i_t * BT, i_v * BV), (BT, BV), (1, 0))
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p_dh = tl.make_block_ptr(dh + i_bh * s_h_h + i_t * K * V, (K, V), (s_h_t, 1), (i_k * BK, i_v * BV), (BK, BV), (1, 0))
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tl.store(p_dh, b_dh.to(p_dh.dtype.element_ty), boundary_check=(0, 1))
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# [BK, BT]
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b_q = tl.load(p_q, boundary_check=(0, 1))
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b_q = (b_q * scale).to(b_q.dtype)
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# [BT, V]
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b_do = tl.load(p_do, boundary_check=(0, 1))
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# [BK, BV]
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b_dh += tl.dot(b_q, b_do.to(b_q.dtype), allow_tf32=False)
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@triton.jit
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def chunk_linear_attn_bwd_kernel_dqkv(
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q,
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k,
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v,
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h,
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do,
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dh,
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dq,
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dk,
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dv,
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s_qk_h,
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s_qk_t,
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s_qk_d,
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s_vo_h,
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s_vo_t,
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s_vo_d,
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s_h_h,
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s_h_t,
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scale,
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H: tl.constexpr,
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T: tl.constexpr,
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K: tl.constexpr,
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V: tl.constexpr,
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BT: tl.constexpr,
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BK: tl.constexpr,
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BV: tl.constexpr,
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NT: tl.constexpr
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):
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i_k, i_t, i_bh = tl.program_id(0), tl.program_id(1), tl.program_id(2)
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n_bh = tl.num_programs(2)
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o_i = tl.arange(0, BT)
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p_q = tl.make_block_ptr(q + i_bh * s_qk_h, (K, T), (s_qk_d, s_qk_t), (i_k * BK, i_t * BT), (BK, BT), (0, 1))
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p_k = tl.make_block_ptr(k + i_bh * s_qk_h, (T, K), (s_qk_t, s_qk_d), (i_t * BT, i_k * BK), (BT, BK), (1, 0))
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b_q = tl.load(p_q, boundary_check=(0, 1))
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b_k = tl.load(p_k, boundary_check=(0, 1))
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b_s = tl.dot(b_k, b_q, allow_tf32=False) * scale
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b_s = tl.where(o_i[:, None] <= o_i[None, :], b_s, 0)
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b_dq = tl.zeros([BT, BK], dtype=tl.float32)
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b_dk = tl.zeros([BT, BK], dtype=tl.float32)
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b_ds = tl.zeros([BT, BT], dtype=tl.float32)
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for i_v in range(tl.cdiv(V, BV)):
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p_v = tl.make_block_ptr(v + i_bh * s_vo_h, (T, V), (s_vo_t, s_vo_d), (i_t * BT, i_v * BV), (BT, BV), (1, 0))
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p_h = tl.make_block_ptr(h + i_bh * s_h_h, (V, NT * K), (1, s_h_t), (i_v * BV, i_t * K + i_k * BK), (BV, BK), (0, 1))
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p_do = tl.make_block_ptr(do + i_bh * s_vo_h, (T, V), (s_vo_t, s_vo_d), (i_t * BT, i_v * BV), (BT, BV), (1, 0))
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p_dh = tl.make_block_ptr(dh + i_bh * s_h_h, (NT * K, V), (s_h_t, 1), (i_t * K + i_k * BK, i_v * BV), (BK, BV), (1, 0))
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p_dv = tl.make_block_ptr(dv + (i_k*n_bh+i_bh)*s_vo_h, (T, V), (s_vo_t, s_vo_d), (i_t * BT, i_v * BV), (BT, BV), (1, 0))
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# [BT, BV]
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b_v = tl.load(p_v, boundary_check=(0, 1))
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b_do = tl.load(p_do, boundary_check=(0, 1))
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# [BV, BK]
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b_h = tl.load(p_h, boundary_check=(0, 1))
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# [BK, BV]
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b_dh = tl.load(p_dh, boundary_check=(0, 1))
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# [BT, BT]
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b_ds += tl.dot(b_do, tl.trans(b_v), allow_tf32=False)
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# [BT, BK]
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b_dq += tl.dot(b_do, b_h, allow_tf32=False) * scale
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b_dk += tl.dot(b_v, tl.trans(b_dh), allow_tf32=False)
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# [BT, BV]
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b_dv = tl.dot(b_k, b_dh, allow_tf32=False) + tl.dot(b_s.to(b_q.dtype), b_do, allow_tf32=False)
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tl.store(p_dv, b_dv.to(p_dv.dtype.element_ty), boundary_check=(0, 1))
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# [BT, BT]
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b_ds = tl.where(o_i[:, None] >= o_i[None, :], b_ds * scale, 0).to(b_q.dtype)
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# [BT, BK]
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b_dq += tl.dot(b_ds, b_k, allow_tf32=False)
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b_dk += tl.trans(tl.dot(b_q, b_ds, allow_tf32=False))
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p_dq = tl.make_block_ptr(dq + i_bh * s_qk_h, (T, K), (s_qk_t, s_qk_d), (i_t * BT, i_k * BK), (BT, BK), (1, 0))
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p_dk = tl.make_block_ptr(dk + i_bh * s_qk_h, (T, K), (s_qk_t, s_qk_d), (i_t * BT, i_k * BK), (BT, BK), (1, 0))
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tl.store(p_dq, b_dq.to(p_dq.dtype.element_ty), boundary_check=(0, 1))
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tl.store(p_dk, b_dk.to(p_dk.dtype.element_ty), boundary_check=(0, 1))
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class ChunkLinearAttentionFunction(torch.autograd.Function):
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@staticmethod
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@custom_fwd
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@contiguous
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def forward(ctx, q, k, v, scale, initial_state, output_final_state):
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B, H, T, K, V = *q.shape, v.shape[-1]
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BT = 64
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BK, BV = min(64, triton.next_power_of_2(K)), min(64, triton.next_power_of_2(V))
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NT, NK, NV = triton.cdiv(T, BT), triton.cdiv(K, BK), triton.cdiv(V, BV)
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num_stages = 1
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num_warps = 4 if BK == 64 else 2
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ctx.scale = scale
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final_state = None
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if output_final_state:
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final_state = q.new_empty(B, H, K, V, dtype=torch.float32, requires_grad=False)
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h = q.new_empty(B, H, NT * K, V)
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grid = (NK, NV, B * H)
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chunk_linear_attn_fwd_kernel_h[grid](
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k, v, h, initial_state, final_state,
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q.stride(1), q.stride(2), q.stride(3),
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v.stride(1), v.stride(2), v.stride(3),
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h.stride(1), h.stride(2),
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H=H, T=T, K=K, V=V, BT=BT, BK=BK, BV=BV, NT=NT,
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USE_INITIAL_STATE=initial_state is not None,
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STORE_FINAL_STATE=output_final_state,
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num_warps=num_warps,
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num_stages=num_stages
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)
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grid = (NV, NT, B * H)
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o = torch.empty_like(v)
|
||
|
|
chunk_linear_attn_fwd_kernel_o[grid](
|
||
|
|
q, k, v, h, o,
|
||
|
|
q.stride(1), q.stride(2), q.stride(3),
|
||
|
|
v.stride(1), v.stride(2), v.stride(3),
|
||
|
|
h.stride(1), h.stride(2),
|
||
|
|
scale,
|
||
|
|
H=H, T=T, K=K, V=V, BT=BT, BK=BK, BV=BV,
|
||
|
|
num_warps=num_warps,
|
||
|
|
num_stages=num_stages
|
||
|
|
)
|
||
|
|
|
||
|
|
ctx.save_for_backward(q, k, v, h)
|
||
|
|
return o.to(q.dtype), final_state
|
||
|
|
|
||
|
|
@staticmethod
|
||
|
|
@custom_bwd
|
||
|
|
@contiguous
|
||
|
|
def backward(ctx, do, d_ht=None):
|
||
|
|
q, k, v, h = ctx.saved_tensors
|
||
|
|
|
||
|
|
B, H, T, K, V = *q.shape, v.shape[-1]
|
||
|
|
BT = 64
|
||
|
|
BK, BV = min(64, triton.next_power_of_2(K)), min(32 if q.dtype == torch.float32 else 64, triton.next_power_of_2(V))
|
||
|
|
NT, NK, NV = triton.cdiv(T, BT), triton.cdiv(K, BK), triton.cdiv(V, BV)
|
||
|
|
num_stages = 1
|
||
|
|
num_warps = 4 if BK == 64 else 2
|
||
|
|
scale = ctx.scale
|
||
|
|
|
||
|
|
dh = q.new_empty(B, H, NT * K, V)
|
||
|
|
grid = (NK, NV, B * H)
|
||
|
|
chunk_linear_attn_bwd_kernel_dh[grid](
|
||
|
|
q, do, dh,
|
||
|
|
q.stride(1), q.stride(2), q.stride(3),
|
||
|
|
v.stride(1), v.stride(2), v.stride(3),
|
||
|
|
dh.stride(1), dh.stride(2),
|
||
|
|
scale,
|
||
|
|
H=H, T=T, K=K, V=V, BT=BT, BK=BK, BV=BV, NT=NT,
|
||
|
|
num_warps=num_warps,
|
||
|
|
num_stages=num_stages
|
||
|
|
)
|
||
|
|
|
||
|
|
grid = (NK, NT, B * H)
|
||
|
|
dq = torch.empty_like(q)
|
||
|
|
dk = torch.empty_like(k)
|
||
|
|
dv = v.new_empty(NK, *v.shape)
|
||
|
|
num_stages = 1
|
||
|
|
num_warps = 4 if BK == 64 else 2
|
||
|
|
chunk_linear_attn_bwd_kernel_dqkv[grid](
|
||
|
|
q, k, v, h, do, dh, dq, dk, dv,
|
||
|
|
q.stride(1), q.stride(2), q.stride(3),
|
||
|
|
v.stride(1), v.stride(2), v.stride(3),
|
||
|
|
dh.stride(1), dh.stride(2),
|
||
|
|
scale,
|
||
|
|
H=H, T=T, K=K, V=V, BT=BT, BK=BK, BV=BV, NT=NT,
|
||
|
|
num_warps=num_warps,
|
||
|
|
num_stages=num_stages
|
||
|
|
)
|
||
|
|
dv = dv.sum(0)
|
||
|
|
return dq.to(q.dtype), dk.to(k.dtype), dv.to(v.dtype), None, None, None
|
||
|
|
|
||
|
|
|
||
|
|
def chunk_linear_attn(
|
||
|
|
q: torch.Tensor,
|
||
|
|
k: torch.Tensor,
|
||
|
|
v: torch.Tensor,
|
||
|
|
scale: float = -1,
|
||
|
|
initial_state: torch.Tensor = None,
|
||
|
|
output_final_state: bool = False,
|
||
|
|
normalize: bool = True
|
||
|
|
) -> Tuple[torch.Tensor, torch.Tensor]:
|
||
|
|
if scale == -1:
|
||
|
|
scale = q.shape[-1] ** -0.5
|
||
|
|
if initial_state is not None:
|
||
|
|
initial_state = initial_state.detach()
|
||
|
|
o, final_state = ChunkLinearAttentionFunction.apply(q, k, v, scale, initial_state, output_final_state)
|
||
|
|
|
||
|
|
if normalize:
|
||
|
|
o = normalize_output(q * scale, k, o)
|
||
|
|
|
||
|
|
return o, final_state
|