335 lines
14 KiB
Python
Vendored
335 lines
14 KiB
Python
Vendored
# -*- 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 packaging import version
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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_chunk_retention_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_V]
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v, # value [B, H, L, D_head_V]
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o, # output [B, H, L, D_head_V]
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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, # 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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BT: tl.constexpr, # BLOCK SIZE along the sequence dimension, a.k.a. chunk size
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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,
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STORE_FINAL_STATE: tl.constexpr,
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CHECK: tl.constexpr
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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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i_h = i_bh % H
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o_i = tl.arange(0, BT)
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# decay rate given the head index
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b_b = tl.math.log2(1 - tl.math.pow(2, -5 - i_h * 1.0))
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# d_b: overall decay for the entire chunk
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# d_o: cumulative decay from the start of the chunk
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# d_h: cumulative decay from the end of the chunk
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d_b, d_o, d_h = tl.math.exp2(BT * b_b), tl.math.exp2((o_i + 1) * b_b), tl.math.exp2((BT - o_i - 1) * b_b)
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# [BT, BT]
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m_s = o_i[:, None] >= o_i[None, :]
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d_s = tl.where(m_s, tl.math.exp2((o_i[:, None] - o_i[None, :]) * b_b), 0)
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# [BK, BV]
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b_h = tl.zeros([BK, BV], dtype=tl.float32)
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# make block pointers
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p_q = tl.make_block_ptr(q + i_bh * s_qk_h, (T, DK), (s_qk_t, s_qk_d), (0, i_k * BK), (BT, BK), (1, 0))
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p_k = tl.make_block_ptr(k + i_bh * s_qk_h, (DK, T), (s_qk_d, s_qk_t), (i_k * BK, 0), (BK, BT), (0, 1))
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p_v = tl.make_block_ptr(v + i_bh * s_vo_h, (T, DV), (s_vo_t, s_vo_d), (0, i_v * BV), (BT, BV), (1, 0))
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p_o = tl.make_block_ptr(o + (i_bh+i_k*B*H) * s_vo_h, (T, DV), (s_vo_t, s_vo_d), (0, i_v * BV), (BT, BV), (1, 0))
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if USE_INITIAL_STATE:
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p_h = tl.make_block_ptr(initial_state + i_bh * DK * DV, (DK, DV), (DV, 1), (i_k * BK, i_v * BV), (BK, BV), (1, 0))
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b_h = tl.load(p_h, boundary_check=(0, 1)).to(tl.float32)
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NT = tl.cdiv(T, BT)
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for i in range(0, NT):
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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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# [BT, BK]
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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_k.dtype)
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# [BT, BT]
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b_s = tl.dot(b_q, b_k, allow_tf32=False) * d_s
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# [BT, BV]
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b_o = tl.dot(b_s.to(b_q.dtype), b_v, allow_tf32=False)
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if CHECK and i == 0:
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b_o += tl.dot(b_q, b_h.to(b_q.dtype), allow_tf32=False) * d_o[:, None]
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b_h = d_b * b_h + tl.dot(b_k, (b_v * d_h[:, None]).to(b_k.dtype), allow_tf32=False)
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else:
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b_o += tl.dot(b_q, b_h.to(b_q.dtype), allow_tf32=False) * d_o[:, None]
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if i == NT - 1 and (T % BT) != 0:
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d_b = tl.math.exp2((T % BT) * b_b)
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d_h = tl.math.exp2(((T % BT) - o_i - 1) * b_b)
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b_h = d_b * b_h + tl.dot(b_k, (b_v * d_h[:, None]).to(b_k.dtype), allow_tf32=False)
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tl.store(p_o, b_o.to(p_o.dtype.element_ty), boundary_check=(0, 1))
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p_q = tl.advance(p_q, (BT, 0))
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p_k = tl.advance(p_k, (0, BT))
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p_v = tl.advance(p_v, (BT, 0))
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p_o = tl.advance(p_o, (BT, 0))
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if STORE_FINAL_STATE:
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p_final = tl.make_block_ptr(final_state + i_bh * DK * DV, (DK, DV), (DV, 1), (i_k * BK, i_v * BV), (BK, BV), (1, 0))
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tl.store(p_final, b_h.to(p_final.dtype.element_ty), boundary_check=(0, 1))
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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_chunk_retention_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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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_state, # initial state of the chunk [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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BT: tl.constexpr, # BLOCK SIZE along the sequence dimension, a.k.a. chunk size
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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,
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CHECK: tl.constexpr
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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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i_h = i_bh % H
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o_i = tl.arange(0, BT)
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b_b = tl.math.log2(1 - tl.math.pow(2, -5 - i_h * 1.0))
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d_q, d_k = tl.math.exp2((o_i+1) * b_b) * scale, tl.math.exp2((BT - o_i - 1) * b_b)
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d_b = tl.math.exp2(BT * b_b)
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m_s = o_i[:, None] >= o_i[None, :]
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d_s = tl.where(m_s, tl.math.exp2((o_i[:, None] - o_i[None, :]) * b_b), 0) * scale
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# [BV, BK]
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b_h = tl.zeros([BV, BK], dtype=tl.float32)
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if USE_INITIAL_STATE:
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p_h = tl.make_block_ptr(initial_state + i_bh * DK * DV, (DV, DK), (1, DV), (i_v * BV, i_k * BK), (BV, BK), (0, 1))
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b_h = tl.load(p_h, boundary_check=(0, 1)).to(tl.float32)
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for i in range(0, tl.cdiv(T, BT)):
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p_k = tl.make_block_ptr(k + i_bh * s_qk_h, (T, DK), (s_qk_t, s_qk_d), (i * BT, i_k * BK), (BT, BK), (1, 0))
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p_v = tl.make_block_ptr(v + i_bh * s_vo_h, (DV, T), (s_vo_d, s_vo_t), (i_v * BV, i * BT), (BV, BT), (0, 1))
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p_do = tl.make_block_ptr(do + i_bh * s_vo_h, (T, DV), (s_vo_t, s_vo_d), (i * BT, i_v * BV), (BT, BV), (1, 0))
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p_dq = tl.make_block_ptr(dq + (i_bh + i_v*B*H) * s_qk_h, (T, DK), (s_qk_t, s_qk_d), (i*BT, i_k*BK), (BT, BK), (1, 0))
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# [BT, DK]
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b_k = tl.load(p_k, boundary_check=(0, 1))
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# [DV, BT]
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b_v = tl.load(p_v, boundary_check=(0, 1))
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# [BT, DV]
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b_do = tl.load(p_do, boundary_check=(0, 1))
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b_dd = (b_do * d_q[:, None]).to(b_do.dtype)
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# [BT, BT]
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b_ds = tl.dot(b_do, b_v, allow_tf32=False)
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b_ds = (b_ds * d_s).to(b_k.dtype)
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# [BT, DK]
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b_dq = tl.dot(b_ds, b_k, allow_tf32=False)
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# [DV, DK]
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if CHECK and i == 0:
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b_dq += tl.dot(b_dd, b_h.to(b_k.dtype), allow_tf32=False)
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b_h = d_b * b_h + tl.dot((b_v * d_k[None, :]).to(b_k.dtype), b_k, allow_tf32=False)
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else:
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b_dq += tl.dot(b_dd, b_h.to(b_k.dtype), allow_tf32=False)
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b_h = d_b * b_h + tl.dot((b_v * d_k[None, :]).to(b_k.dtype), b_k, allow_tf32=False)
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tl.store(p_dq, b_dq.to(p_dq.dtype.element_ty), boundary_check=(0, 1))
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# sync threads
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b_h = None
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tl.debug_barrier()
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d_s = tl.trans(d_s)
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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 in range(1, tl.cdiv(T, BT) + 1):
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p_q = tl.make_block_ptr(q + i_bh * s_qk_h, (DK, T), (s_qk_d, s_qk_t), (i_k * BK, T - i * BT), (BK, BT), (0, 1))
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p_k = tl.make_block_ptr(k + i_bh * s_qk_h, (T, DK), (s_qk_t, s_qk_d), (T - i * BT, i_k * BK), (BT, BK), (1, 0))
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p_v = tl.make_block_ptr(v + i_bh * s_vo_h, (T, DV), (s_vo_t, s_vo_d), (T - i * BT, i_v * BV), (BT, BV), (1, 0))
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p_do = tl.make_block_ptr(do + i_bh * s_vo_h, (T, DV), (s_vo_t, s_vo_d), (T - i * BT, i_v * BV), (BT, BV), (1, 0))
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p_dk = tl.make_block_ptr(dk + (i_bh+i_v*B*H) * s_qk_h, (T, DK), (s_qk_t, s_qk_d), (T - i*BT, i_k*BK), (BT, BK), (1, 0))
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p_dv = tl.make_block_ptr(dv + (i_bh+i_k*B*H) * s_vo_h, (T, DV), (s_vo_t, s_vo_d), (T - i*BT, i_v*BV), (BT, BV), (1, 0))
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# [DK, BT]
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b_q = tl.load(p_q, boundary_check=(0, 1))
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# [BT, DK]
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b_k = tl.load(p_k, boundary_check=(0, 1))
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# [BT, DV]
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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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b_dd = (b_do * d_q[:, None]).to(b_do.dtype)
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# [BT, BT]
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b_ds = tl.dot(b_v, tl.trans(b_do), allow_tf32=False)
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b_ds = (b_ds * d_s).to(b_k.dtype)
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# [BT, BT]
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b_s = tl.dot(b_k, b_q, allow_tf32=False) * d_s
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# [BT, DK]
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b_dk = tl.dot(b_ds, tl.trans(b_q), allow_tf32=False)
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# [BT, DV]
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b_dv = tl.dot(b_s.to(b_q.dtype), b_do, allow_tf32=False)
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if CHECK and i == 1:
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b_dk += tl.dot(b_v, tl.trans(b_dh).to(b_v.dtype), allow_tf32=False) * d_k[:, None]
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b_dv += tl.dot(b_k, b_dh.to(b_k.dtype), allow_tf32=False) * d_k[:, None]
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b_dh = d_b * b_dh + tl.dot(b_q, b_dd, allow_tf32=False)
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else:
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b_dk += tl.dot(b_v, tl.trans(b_dh).to(b_v.dtype), allow_tf32=False) * d_k[:, None]
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b_dv += tl.dot(b_k, b_dh.to(b_k.dtype), allow_tf32=False) * d_k[:, None]
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b_dh = d_b * b_dh + tl.dot(b_q, b_dd, allow_tf32=False)
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tl.store(p_dk, b_dk.to(p_dk.dtype.element_ty), boundary_check=(0, 1))
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tl.store(p_dv, b_dv.to(p_dv.dtype.element_ty), boundary_check=(0, 1))
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class FusedChunkRetentionFunction(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, initial_state, output_final_state):
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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 = d_head_qk ** -0.5
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BT = 64
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BK, BV = min(triton.next_power_of_2(d_head_qk), 64), min(triton.next_power_of_2(d_head_v), 64)
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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 = 4
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o = q.new_empty(NK, batch_size, n_heads, seq_len, d_head_v)
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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, dtype=torch.float32, requires_grad=False)
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else:
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final_state = None
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# the bug still exists even for Triton 2.2 on H100 GPUs
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# so we always enable initial checks
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CHECK = True
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if version.parse(triton.__version__) < version.parse('2.2.0'):
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import warnings
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warnings.warn(
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"Triton<2.2.0 detected for running this kernel, "
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"which is known to have some weird compiler issues (refer to https://github.com/openai/triton/issues/2852) "
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"that lead to significant precision loss. "
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"We've add some initial condition checks to resolve this, sadly at the sacrifice of the speed. "
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"For optimal performance, it is recommended to install Triton>=2.2.0 (if possible)."
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)
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CHECK = True
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grid = (NV, NK, batch_size * n_heads)
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fused_chunk_retention_fwd_kernel[grid](
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q, k, v, 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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BT=BT, 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=output_final_state,
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CHECK=CHECK,
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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, initial_state)
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ctx.CHECK = CHECK
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return o.to(q.dtype), final_state
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@staticmethod
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@custom_bwd
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@contiguous
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def backward(ctx, do, d_final_state=None):
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q, k, v, initial_state = 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 = d_head_qk ** -0.5
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BT = 64
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BK, BV = min(triton.next_power_of_2(d_head_qk), 64), min(triton.next_power_of_2(d_head_v), 64)
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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 = 4
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dq = q.new_empty(NV, batch_size, n_heads, seq_len, d_head_qk)
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dk = q.new_empty(NV, batch_size, n_heads, seq_len, d_head_qk)
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dv = q.new_empty(NK, batch_size, n_heads, seq_len, d_head_v)
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grid = (NV, NK, batch_size * n_heads)
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fused_chunk_retention_bwd_kernel[grid](
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q, k, v, 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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BT=BT, 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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CHECK=ctx.CHECK,
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num_warps=num_warps,
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num_stages=num_stages
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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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return dq.to(q.dtype), dk.to(k.dtype), dv.to(v.dtype), None, None
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def fused_chunk_retention(
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q: torch.Tensor,
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k: torch.Tensor,
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v: torch.Tensor,
|
|
initial_state: torch.Tensor = None,
|
|
output_final_state: bool = False
|
|
) -> Tuple[torch.Tensor, torch.Tensor]:
|
|
if initial_state is not None:
|
|
initial_state = initial_state.detach()
|
|
o, final_state = FusedChunkRetentionFunction.apply(q, k, v, initial_state, output_final_state)
|
|
return o, final_state
|