405 lines
15 KiB
Python
Vendored
405 lines
15 KiB
Python
Vendored
# -*- coding: utf-8 -*-
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# Copyright (c) 2023, 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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# on-the-fly computation without materializing hidden statets into HBMs
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@triton.jit
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def fused_recurrent_gla_fwd_kernel(
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# B: batch_size, H: n_heads, T: seq_len, D: d_head
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q, # query [B, H, L, D_head_K]
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k, # key [B, H, L, D_head_K]
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v, # value [B, H, L, D_head_V]
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gk, # log gate [B, H, L, D_head_K]
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gv, # log gate [B, H, L, D_head_V]
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o, # output [B, H, L, D_head_V]
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# initial hidden state initialization [B, H, D_head_K, D_head_V]
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initial_state,
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final_state, # final hidden state [B, H, D_head_K, D_head_V]
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s_qk_h, # stride size: L * D_head_K
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s_qk_t, # stride size: D_head_K
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s_qk_d, # stride size: 1
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s_vo_h, # stride size: L * D_head_V
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s_vo_t, # stride size: D_head_V
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s_vo_d, # stride size: 1
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B, # batch size
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H, # n_heads
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T, # seq_len
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scale, # D_head_K ** -0.5
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BK: tl.constexpr, # BLOCK SIZE along the K dimension
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BV: tl.constexpr, # BLOCK SIZE along the V dimension
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DK: tl.constexpr, # D_head_K
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DV: tl.constexpr, # D_head_V
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USE_INITIAL_STATE: tl.constexpr, # whether to use initial state
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STORE_FINAL_STATE: tl.constexpr, # whether to store final state
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REVERSE: tl.constexpr, # whether to do autoregressive modeling in the reverse direction
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USE_GK: tl.constexpr, # whether to use gk
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USE_GV: tl.constexpr, # whether to use gv
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):
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# indices
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i_v, i_k, i_bh = tl.program_id(0), tl.program_id(1), tl.program_id(2)
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p_q = q + i_bh * s_qk_h + i_k * BK + \
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tl.arange(0, BK) + ((T-1) * DK if REVERSE else 0)
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p_k = k + i_bh * s_qk_h + i_k * BK + \
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tl.arange(0, BK) + ((T-1) * DK if REVERSE else 0)
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p_v = v + i_bh * s_vo_h + i_v * BV + \
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tl.arange(0, BV) + ((T-1) * DV if REVERSE else 0)
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p_o = o + (i_bh + i_k * B * H) * s_vo_h + i_v * BV + \
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tl.arange(0, BV) + ((T-1) * DV if REVERSE else 0)
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if USE_GK:
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p_gk = gk + i_bh * s_qk_h + i_k * BK + \
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tl.arange(0, BK) + ((T-1) * DK if REVERSE else 0)
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if USE_GV:
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p_gv = gv + i_bh * s_vo_h + i_v * BV + \
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tl.arange(0, BV) + ((T-1) * DV if REVERSE else 0)
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mask_bk = (i_k * BK + tl.arange(0, BK)) < DK
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mask_bv = (i_v * BV + tl.arange(0, BV)) < DV
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h = tl.zeros([BV, BK], dtype=tl.float32)
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mask_kv = mask_bk[None, :] & mask_bv[:, None]
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if USE_INITIAL_STATE:
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p_init_s = initial_state + i_bh * DK * DV + \
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(i_k * BK + tl.arange(0, BK)[None, :]) * \
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DV + (i_v * BV + tl.arange(0, BV)[:, None])
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h += tl.load(p_init_s, mask=mask_kv, other=0).to(tl.float32)
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for _ in range(0, T):
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_k = tl.load(p_k, mask=mask_bk, other=0).to(tl.float32)
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_v = tl.load(p_v, mask=mask_bv, other=0).to(tl.float32)
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_q = tl.load(p_q, mask=mask_bk, other=0).to(tl.float32) * scale
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if USE_GK:
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_gk = tl.load(p_gk, mask=mask_bk, other=0).to(tl.float32)
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h = h * _gk[None, :]
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if USE_GV:
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_gv = tl.load(p_gv, mask=mask_bv, other=0).to(tl.float32)
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h = h * _gv[:, None]
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h += _k[None, :] * _v[:, None]
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_o = h * _q[None, :]
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_o = tl.sum(_o, axis=1)
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tl.store(p_o, _o.to(p_o.dtype.element_ty), mask=mask_bv)
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p_q += -DK if REVERSE else DK
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p_k += -DK if REVERSE else DK
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p_o += -DV if REVERSE else DV
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p_v += -DV if REVERSE else DV
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if USE_GK:
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p_gk += -DK if REVERSE else DK
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if USE_GV:
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p_gv += -DV if REVERSE else DV
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if STORE_FINAL_STATE:
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p_final_s = final_state + i_bh * DK * DV + \
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(i_k * BK + tl.arange(0, BK)[None, :]) * \
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DV + (i_v * BV + tl.arange(0, BV)[:, None])
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tl.store(p_final_s, h.to(p_final_s.dtype.element_ty), mask=mask_kv)
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# Similar to Algorithm1 of https://arxiv.org/abs/2006.16236
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@triton.jit
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def fused_recurrent_gla_bwd_kernel(
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# B: batch_size, H: n_heads, T: seq_len, D: d_head
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# NV: number of split in the V dimension. NK: number of split in the K dimension
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q, # query [B, H, L, D_head_K]
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k, # key [B, H, L, D_head_V]
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v, # value [B, H, L, D_head_V]
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gk, # log gate [B, H, L, D_head_K] \alpha
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gv, # log gate [B, H, L, D_head_V] \bete
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do, # gradient of output [B, H, L, D_head_V]
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dq, # gradient of query [NV, B, H, L, D_head_K]
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dk, # gradient of key [NV, B, H, L, D_head_K]
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dv, # gradient of value [NK, B, H, L, D_head_V]
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# initial hidden state initialization [B, H, D_head_K, D_head_V]
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initial_state,
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s_qk_h, # stride size: L * D_head_K
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s_qk_t, # stride size: D_head_K
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s_qk_d, # stride size: 1
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s_vo_h, # stride size: L * D_head_V
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s_vo_t, # stride size: D_head_V
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s_vo_d, # stride size: 1
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B, # batch_size
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H, # n_heads
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T, # seq_len
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scale, # D_head_K ** -0.5
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BK: tl.constexpr, # BLOCK SIZE along the K dimension
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BV: tl.constexpr, # BLOCK SIZE along the V dimension
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DK: tl.constexpr, # D_head_K
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DV: tl.constexpr, # D_head_V
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USE_INITIAL_STATE: tl.constexpr, # whether to use initial state
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REVERSE: tl.constexpr, # whether to do autoregressive modeling in the reverse direction
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USE_GK: tl.constexpr, # whether to use gk
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USE_GV: tl.constexpr, # whether to use gv
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):
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i_v, i_k, i_bh = tl.program_id(0), tl.program_id(1), tl.program_id(2)
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p_q = q + i_bh * s_qk_h + i_k * BK + \
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tl.arange(0, BK) + ((T-1) * DK if REVERSE else 0)
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p_k = k + i_bh * s_qk_h + i_k * BK + \
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tl.arange(0, BK) + ((T-1) * DK if REVERSE else 0)
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p_v = v + i_bh * s_vo_h + i_v * BV + \
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tl.arange(0, BV) + ((T-1) * DV if REVERSE else 0)
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p_do = do + i_bh * s_vo_h + i_v * BV + \
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tl.arange(0, BV) + ((T-1) * DV if REVERSE else 0)
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p_dq = dq + (i_bh + i_v * B * H) * s_qk_h + i_k * BK + \
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tl.arange(0, BK) + ((T-1) * DK if REVERSE else 0)
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if USE_GK:
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p_gk = gk + i_bh * s_qk_h + i_k * BK + \
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tl.arange(0, BK) + ((T-1) * DK if REVERSE else 0)
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if USE_GV:
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p_gv = gv + i_bh * s_vo_h + i_v * BV + \
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tl.arange(0, BV) + ((T-1) * DV if REVERSE else 0)
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mask_bk = i_k * BK + tl.arange(0, BK) < DK
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mask_bv = i_v * BV + tl.arange(0, BV) < DV
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mask_kv = mask_bk[:, None] & mask_bv[None, :]
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h = tl.zeros([BK, BV], dtype=tl.float32)
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if USE_INITIAL_STATE:
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p_init_s = initial_state + i_bh * DK * DV + \
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(i_k * BK + tl.arange(0, BK)[:, None]) * \
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DV + (i_v * BV + tl.arange(0, BV)[None, :])
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h += tl.load(p_init_s, mask=mask_kv, other=0).to(tl.float32)
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for i in range(0, T):
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_k = tl.load(p_k, mask=mask_bk, other=0).to(tl.float32)
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_v = tl.load(p_v, mask=mask_bv, other=0).to(tl.float32)
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_do = tl.load(p_do, mask=mask_bv, other=0).to(tl.float32)
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if USE_GK:
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_gk = tl.load(p_gk, mask=mask_bk, other=0).to(tl.float32)
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h = h * _gk[:, None]
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if USE_GV:
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_gv = tl.load(p_gv, mask=mask_bv, other=0).to(tl.float32)
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h = h * _gv[None, :]
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h += _k[:, None] * _v[None, :]
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_d_q = h * _do[None, :]
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d_q = tl.sum(_d_q, axis=1) * scale
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tl.store(p_dq, d_q.to(p_dq.dtype.element_ty), mask=mask_bk)
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p_k += -DK if REVERSE else DK
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p_v += -DV if REVERSE else DV
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p_q += -DK if REVERSE else DK
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p_do += -DV if REVERSE else DV
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p_dq += -DK if REVERSE else DK
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if USE_GK:
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p_gk += -DK if REVERSE else DK
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if USE_GV:
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p_gv += -DV if REVERSE else DV
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# sync threads
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tl.debug_barrier()
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p_q = q + i_bh * s_qk_h + i_k * BK + \
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tl.arange(0, BK) + ((T - 1) * DK if not REVERSE else 0)
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p_k = k + i_bh * s_qk_h + i_k * BK + \
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tl.arange(0, BK) + ((T - 1) * DK if not REVERSE else 0)
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p_do = do + i_bh * s_vo_h + i_v * BV + \
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tl.arange(0, BV) + ((T - 1) * DV if not REVERSE else 0)
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p_v = v + i_bh * s_vo_h + i_v * BV + \
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tl.arange(0, BV) + ((T - 1) * DV if not REVERSE else 0)
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p_dk = dk + (i_bh + i_v * B * H) * s_qk_h + i_k * \
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BK + tl.arange(0, BK) + ((T - 1) * DK if not REVERSE else 0)
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p_dv = dv + (i_bh + i_k * B * H) * s_vo_h + i_v * \
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BV + tl.arange(0, BV) + ((T - 1) * DV if not REVERSE else 0)
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if USE_GK:
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p_gk = gk + i_bh * s_qk_h + i_k * BK + \
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tl.arange(0, BK) + ((T - 1) * DK if not REVERSE else 0)
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if USE_GV:
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p_gv = gv + i_bh * s_vo_h + i_v * BV + \
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tl.arange(0, BV) + ((T - 1) * DV if not REVERSE else 0)
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d_h = tl.zeros([BK, BV], dtype=tl.float32)
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for _ in range(T):
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_do = tl.load(p_do, mask=mask_bv, other=0).to(tl.float32)
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_q = tl.load(p_q, mask=mask_bk, other=0).to(tl.float32) * scale
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_k = tl.load(p_k, mask=mask_bk, other=0).to(tl.float32)
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_v = tl.load(p_v, mask=mask_bv, other=0).to(tl.float32)
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d_h += _q[:, None] * _do[None, :]
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d_k = tl.sum(d_h * _v[None, :], axis=1)
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d_v = tl.sum(d_h * _k[:, None], axis=0)
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if USE_GK:
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_gk = tl.load(p_gk, mask=mask_bk, other=0).to(tl.float32)
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d_h *= _gk[:, None]
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if USE_GV:
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_gv = tl.load(p_gv, mask=mask_bv, other=0).to(tl.float32)
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d_h *= _gv[None, :]
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tl.store(p_dk, d_k.to(p_dk.dtype.element_ty), mask=mask_bk)
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tl.store(p_dv, d_v.to(p_dv.dtype.element_ty), mask=mask_bv)
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p_do += DV if REVERSE else -DV
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p_q += DK if REVERSE else -DK
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p_k += DK if REVERSE else -DK
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p_v += DV if REVERSE else -DV
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p_dk += DK if REVERSE else -DK
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p_dv += DV if REVERSE else -DV
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if USE_GK:
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p_gk += DK if REVERSE else -DK
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if USE_GV:
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p_gv += DV if REVERSE else -DV
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class FusedRecurrentGLAFunction(torch.autograd.Function):
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@staticmethod
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@contiguous
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@custom_fwd
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def forward(ctx, q, k, v, gk, gv, scale=None, initial_state=None, output_final_state=False, reverse=False):
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batch_size, n_heads, seq_len, d_head_qk = q.shape
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d_head_v = v.shape[-1]
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# default scale
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if scale is None:
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scale = d_head_qk ** -0.5
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if gk is not None:
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gk = gk.float().exp()
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if gv is not None:
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gv = gv.float().exp()
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BK, BV = min(d_head_qk, 32), min(d_head_v, 32)
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NK, NV = triton.cdiv(d_head_qk, BK), triton.cdiv(d_head_v, BV)
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num_stages = 1
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num_warps = 1
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o = q.new_empty(NK, batch_size, n_heads, seq_len,
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d_head_v, dtype=torch.float32)
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if output_final_state:
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final_state = q.new_empty(batch_size, n_heads, d_head_qk, d_head_v)
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else:
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final_state = None
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grid = (NV, NK, batch_size * n_heads)
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fused_recurrent_gla_fwd_kernel[grid](
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q, k, v, gk, gv, o, 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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batch_size, n_heads, seq_len, scale,
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DK=d_head_qk, DV=d_head_v, BK=BK, BV=BV,
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USE_INITIAL_STATE=initial_state is not None,
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STORE_FINAL_STATE=final_state is not None,
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USE_GK=gk is not None,
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USE_GV=gv is not None,
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REVERSE=reverse,
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num_warps=num_warps,
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num_stages=num_stages
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)
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o = o.sum(0)
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ctx.save_for_backward(q, k, v, gk, gv, initial_state, o)
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ctx.scale = scale
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ctx.reverse = reverse
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# we do not need the gradient of the final state from the next chunk
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# similiar to Trunctated BPTT
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if final_state is not None:
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final_state = final_state.detach()
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return o.to(q.dtype), final_state
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@staticmethod
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@contiguous
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@custom_bwd
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def backward(ctx, do, d_final_state=None):
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q, k, v, gk, gv, initial_state, o = ctx.saved_tensors
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batch_size, n_heads, seq_len, d_head_qk = q.shape
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d_head_v = v.shape[-1]
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scale = ctx.scale
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BK, BV = min(d_head_qk, 32), min(d_head_v, 32)
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NK, NV = triton.cdiv(d_head_qk, BK), triton.cdiv(d_head_v, BV)
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num_stages = 1
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num_warps = 1
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dq = q.new_empty(NV, batch_size, n_heads, seq_len,
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d_head_qk, dtype=torch.float32)
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dk = q.new_empty(NV, batch_size, n_heads, seq_len,
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d_head_qk, dtype=torch.float32)
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dv = q.new_empty(NK, batch_size, n_heads, seq_len,
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d_head_v, dtype=torch.float32)
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grid = (NV, NK, batch_size * n_heads)
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fused_recurrent_gla_bwd_kernel[grid](
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q, k, v, gk, gv, do, dq, dk, dv, initial_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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batch_size, n_heads, seq_len, scale,
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DK=d_head_qk, DV=d_head_v, BK=BK, BV=BV,
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num_warps=num_warps,
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num_stages=num_stages,
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USE_INITIAL_STATE=initial_state is not None,
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REVERSE=ctx.reverse,
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USE_GK=gk is not None,
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USE_GV=gv is not None
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)
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dq = dq.sum(0)
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dk = dk.sum(0)
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dv = dv.sum(0)
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if gk is not None:
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_dgk = dq * q.float() - dk * k.float()
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if ctx.reverse:
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dgk = _dgk.cumsum(-2)
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else:
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_dgk_cumsum = _dgk.cumsum(-2)
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dgk = _dgk + _dgk_cumsum[:, :, -1, None] - _dgk_cumsum
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else:
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dgk = None
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if gv is not None:
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_dgv = do.float() * o.float() - dv * v.float()
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if ctx.reverse:
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dgv = _dgv.cumsum(-2)
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else:
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_dgv_cumsum = _dgv.cumsum(-2)
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dgv = _dgv + _dgv_cumsum[:, :, -1, None] - _dgv_cumsum
|
|
else:
|
|
dgv = None
|
|
|
|
return dq.to(q.dtype), dk.to(k.dtype), dv.to(v.dtype), dgk, dgv, None, None, None, None
|
|
|
|
|
|
# if scale is None, use d_head_qk ** -0.5 by default. Otherwise specify the scale yourself. e.g. scale = 1.0
|
|
def fused_recurrent_gla(
|
|
q: torch.Tensor,
|
|
k: torch.Tensor,
|
|
v: torch.Tensor,
|
|
gk: torch.Tensor = None,
|
|
gv: torch.Tensor = None,
|
|
scale: int = -1,
|
|
initial_state: torch.Tensor = None,
|
|
output_final_state: bool = False,
|
|
causal: 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()
|
|
if causal:
|
|
o, final_state = FusedRecurrentGLAFunction.apply(q, k, v, gk, gv, scale, initial_state, output_final_state)
|
|
return o, final_state
|
|
else:
|
|
# do not support initial_state yet. looks very strange for bidirectional modeling
|
|
assert initial_state is None
|
|
assert output_final_state is False
|
|
o, final_state = FusedRecurrentGLAFunction.apply(
|
|
q, k, v, gk, gv, scale, initial_state, output_final_state, False)
|
|
o_reversed, final_state = FusedRecurrentGLAFunction.apply(
|
|
q, k, v, gk, gv, scale, initial_state, output_final_state, True)
|
|
return [o, o_reversed]
|