This commit is contained in:
josc146
579
finetune/lora/v6/fla/ops/utils.py
vendored
Normal file
579
finetune/lora/v6/fla/ops/utils.py
vendored
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@@ -0,0 +1,579 @@
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# -*- coding: utf-8 -*-
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# Copyright (c) 2023-2024, Yu Zhang, Songlin Yang
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from typing import Optional
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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 fla.utils import contiguous
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@triton.autotune(
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configs=[
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triton.Config({'BT': 16}, num_warps=2),
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triton.Config({'BT': 16}, num_warps=4),
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triton.Config({'BT': 16}, num_warps=8),
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triton.Config({'BT': 32}, num_warps=2),
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triton.Config({'BT': 32}, num_warps=4),
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triton.Config({'BT': 32}, num_warps=8),
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triton.Config({'BT': 64}, num_warps=2),
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triton.Config({'BT': 64}, num_warps=4),
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triton.Config({'BT': 64}, num_warps=8),
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],
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key=['S']
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)
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@triton.jit
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def logcumsumexp_fwd_kernel(
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s,
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z,
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s_s_h,
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s_s_t,
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s_s_d,
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T: tl.constexpr,
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S: tl.constexpr,
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BT: tl.constexpr
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):
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i_bh = tl.program_id(0)
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o_i = tl.arange(0, BT)
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m_s = tl.where(o_i[:, None] >= o_i[None, :], 1., 0.)
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b_mp = tl.full([S,], float('-inf'), dtype=tl.float32)
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b_zp = tl.zeros([S,], dtype=tl.float32)
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for i_t in range(tl.cdiv(T, BT)):
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p_s = tl.make_block_ptr(s + i_bh * s_s_h, (T, S), (s_s_t, s_s_d), (i_t * BT, 0), (BT, S), (1, 0))
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p_z = tl.make_block_ptr(z + i_bh * s_s_h, (T, S), (s_s_t, s_s_d), (i_t * BT, 0), (BT, S), (1, 0))
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# [BT, S]
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b_s = tl.load(p_s, boundary_check=(0, 1)).to(tl.float32)
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# [S,]
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b_mc = tl.max(b_s, 0)
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# workaround for compiler bugs
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if i_t > 0:
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b_mc = tl.maximum(b_mp, b_mc)
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b_zp = b_zp * tl.exp(b_mp - b_mc)
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# [BT, S]
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b_s = tl.exp(b_s - b_mc)
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b_z = tl.dot(m_s, b_s, allow_tf32=False) + b_zp
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# [S,]
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b_zc = tl.max(b_z, 0)
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b_mp = b_mc
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b_zp = b_zc
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# [BT, BS]
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# small eps to prevent underflows
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b_z = tl.log(tl.where(b_z != 0, b_z, 1e-20)) + b_mc
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tl.store(p_z, b_z.to(p_z.dtype.element_ty), boundary_check=(0, 1))
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@triton.autotune(
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configs=[
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triton.Config({}, num_warps=2),
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triton.Config({}, num_warps=4),
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triton.Config({}, num_warps=8),
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],
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key=['S']
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)
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@triton.jit
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def softmax_fwd_kernel(
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s,
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p,
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s_s_h,
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s_s_t,
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s_s_d,
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T: tl.constexpr,
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S: tl.constexpr,
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BT: tl.constexpr
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):
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i_t, i_bh = tl.program_id(0), tl.program_id(1)
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p_s = tl.make_block_ptr(s + i_bh * s_s_h, (T, S), (s_s_t, s_s_d), (i_t * BT, 0), (BT, S), (1, 0))
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p_p = tl.make_block_ptr(p + i_bh * s_s_h, (T, S), (s_s_t, s_s_d), (i_t * BT, 0), (BT, S), (1, 0))
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# [BT, S]
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b_s = tl.load(p_s, boundary_check=(0, 1)).to(tl.float32)
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# [BT]
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b_m = tl.max(b_s, 1)
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# [BT, BS]
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b_s = tl.exp(b_s - b_m[:, None])
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b_z = tl.sum(b_s, 1)
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b_p = tl.where(b_s != 0, b_s / b_z[:, None], 0.)
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tl.store(p_p, b_p.to(p_p.dtype.element_ty), boundary_check=(0, 1))
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@triton.autotune(
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configs=[
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triton.Config({}, num_warps=2),
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triton.Config({}, num_warps=4),
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triton.Config({}, num_warps=8),
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],
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key=['S']
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)
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@triton.jit
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def softmax_bwd_kernel(
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p,
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dp,
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ds,
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s_s_h,
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s_s_t,
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s_s_d,
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T: tl.constexpr,
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S: tl.constexpr,
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BT: tl.constexpr
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):
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i_t, i_bh = tl.program_id(0), tl.program_id(1)
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p_p = tl.make_block_ptr(p + i_bh * s_s_h, (T, S), (s_s_t, s_s_d), (i_t * BT, 0), (BT, S), (1, 0))
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p_dp = tl.make_block_ptr(dp + i_bh * s_s_h, (T, S), (s_s_t, s_s_d), (i_t * BT, 0), (BT, S), (1, 0))
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p_ds = tl.make_block_ptr(ds + i_bh * s_s_h, (T, S), (s_s_t, s_s_d), (i_t * BT, 0), (BT, S), (1, 0))
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# [BT, BS]
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b_p = tl.load(p_p, boundary_check=(0, 1)).to(tl.float32)
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b_dp = tl.load(p_dp, boundary_check=(0, 1)).to(tl.float32)
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# [BT,]
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b_pp = tl.sum(b_p * b_dp, 1)
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# [BT, BS]
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b_ds = b_p * b_dp - b_p * b_pp[:, None]
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tl.store(p_ds, b_ds.to(p_ds.dtype.element_ty), boundary_check=(0, 1))
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@triton.autotune(
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configs=[
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triton.Config({'BS': 32}, num_warps=2),
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triton.Config({'BS': 32}, num_warps=4),
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triton.Config({'BS': 32}, num_warps=8),
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triton.Config({'BS': 64}, num_warps=2),
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triton.Config({'BS': 64}, num_warps=4),
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triton.Config({'BS': 64}, num_warps=8),
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triton.Config({'BS': 128}, num_warps=2),
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triton.Config({'BS': 128}, num_warps=4),
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triton.Config({'BS': 128}, num_warps=8),
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],
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key=['S']
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)
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@triton.jit
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def recurrent_cumsum_fwd_kernel(
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s,
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z,
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s_s_h,
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s_s_t,
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T: tl.constexpr,
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S: tl.constexpr,
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BS: tl.constexpr
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):
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i_s, i_bh = tl.program_id(0), tl.program_id(1)
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o_s = i_s * BS + tl.arange(0, BS)
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mask = o_s < S
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b_z = tl.zeros([BS], dtype=tl.float32)
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for i_t in range(0, T):
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# [BS]
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b_s = tl.load(s + i_bh * s_s_h + i_t * s_s_t + o_s, mask=mask, other=0).to(tl.float32)
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b_z = b_z + b_s
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tl.store(z + i_bh * s_s_h + i_t * s_s_t + o_s, b_z.to(s.dtype.element_ty), mask=mask)
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@triton.autotune(
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configs=[
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triton.Config({'BS': 32}, num_warps=2),
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triton.Config({'BS': 32}, num_warps=4),
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triton.Config({'BS': 32}, num_warps=8),
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triton.Config({'BS': 64}, num_warps=2),
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triton.Config({'BS': 64}, num_warps=4),
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triton.Config({'BS': 64}, num_warps=8),
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triton.Config({'BS': 128}, num_warps=2),
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triton.Config({'BS': 128}, num_warps=4),
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triton.Config({'BS': 128}, num_warps=8),
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],
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key=['S']
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)
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@triton.jit
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def recurrent_cumsum_bwd_kernel(
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ds,
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dz,
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s_s_h,
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s_s_t,
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T: tl.constexpr,
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S: tl.constexpr,
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BS: tl.constexpr
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):
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i_s, i_bh = tl.program_id(0), tl.program_id(1)
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o_s = i_s * BS + tl.arange(0, BS)
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mask = o_s < S
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b_ds = tl.zeros([BS], dtype=tl.float32)
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for i_t in range(T - 1, -1, -1):
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# [BS]
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b_dz = tl.load(dz + i_bh * s_s_h + i_t * s_s_t + o_s, mask=mask, other=0).to(tl.float32)
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b_ds = b_ds + b_dz
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tl.store(ds + i_bh * s_s_h + i_t * s_s_t + o_s, b_ds.to(ds.dtype.element_ty), mask=mask)
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@triton.autotune(
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configs=[
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triton.Config({'BT': 16}, num_warps=2),
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triton.Config({'BT': 16}, num_warps=4),
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triton.Config({'BT': 16}, num_warps=8),
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triton.Config({'BT': 32}, num_warps=2),
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triton.Config({'BT': 32}, num_warps=4),
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triton.Config({'BT': 32}, num_warps=8),
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triton.Config({'BT': 64}, num_warps=2),
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triton.Config({'BT': 64}, num_warps=4),
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triton.Config({'BT': 64}, num_warps=8),
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],
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key=['S']
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)
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@triton.jit
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def chunk_cumsum_fwd_kernel(
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s,
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z,
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s_s_h,
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s_s_t,
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s_s_d,
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T: tl.constexpr,
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S: tl.constexpr,
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BT: tl.constexpr,
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BS: tl.constexpr
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):
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i_s, i_bh = tl.program_id(0), tl.program_id(1)
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o_i = tl.arange(0, BT)
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m_s = tl.where(o_i[:, None] >= o_i[None, :], 1., 0.)
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b_z = tl.zeros([BS], dtype=tl.float32)
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for i_t in range(tl.cdiv(T, BT)):
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p_s = tl.make_block_ptr(s + i_bh * s_s_h, (T, S), (s_s_t, s_s_d), (i_t * BT, i_s * BS), (BT, BS), (1, 0))
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p_z = tl.make_block_ptr(z + i_bh * s_s_h, (T, S), (s_s_t, s_s_d), (i_t * BT, i_s * BS), (BT, BS), (1, 0))
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# [BT, BS]
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b_s = tl.load(p_s, boundary_check=(0, 1)).to(tl.float32)
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b_c = b_z[None, :] + tl.dot(m_s, b_s, allow_tf32=False)
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tl.store(p_z, b_c.to(p_z.dtype.element_ty), boundary_check=(0, 1))
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if i_t >= 0:
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b_z += tl.sum(b_s, 0)
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@triton.autotune(
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configs=[
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triton.Config({'BT': 16}, num_warps=2),
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triton.Config({'BT': 16}, num_warps=4),
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triton.Config({'BT': 16}, num_warps=8),
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triton.Config({'BT': 32}, num_warps=2),
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triton.Config({'BT': 32}, num_warps=4),
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triton.Config({'BT': 32}, num_warps=8),
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triton.Config({'BT': 64}, num_warps=2),
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triton.Config({'BT': 64}, num_warps=4),
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triton.Config({'BT': 64}, num_warps=8),
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],
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key=['S']
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)
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@triton.jit
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def chunk_cumsum_bwd_kernel(
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ds,
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dz,
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s_s_h,
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s_s_t,
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s_s_d,
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T: tl.constexpr,
|
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S: tl.constexpr,
|
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BT: tl.constexpr,
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BS: tl.constexpr
|
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):
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i_s, i_bh = tl.program_id(0), tl.program_id(1)
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o_i = tl.arange(0, BT)
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m_s = tl.where(o_i[:, None] <= o_i[None, :], 1., 0.)
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b_ds = tl.zeros([BS], dtype=tl.float32)
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for i_t in range(tl.cdiv(T, BT) - 1, -1, -1):
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p_ds = tl.make_block_ptr(ds + i_bh * s_s_h, (T, S), (s_s_t, s_s_d), (i_t * BT, i_s * BS), (BT, BS), (1, 0))
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p_dz = tl.make_block_ptr(dz + i_bh * s_s_h, (T, S), (s_s_t, s_s_d), (i_t * BT, i_s * BS), (BT, BS), (1, 0))
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# [BT, BS]
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b_dz = tl.load(p_dz, boundary_check=(0, 1)).to(tl.float32)
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b_c = b_ds[None, :] + tl.dot(m_s, b_dz, allow_tf32=False)
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tl.store(p_ds, b_c.to(p_ds.dtype.element_ty), boundary_check=(0, 1))
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if i_t >= 0:
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b_ds += tl.sum(b_dz, 0)
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@contiguous
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def chunk_cumsum_fwd(
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s: torch.Tensor,
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dtype: Optional[torch.dtype] = None,
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) -> torch.Tensor:
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B, H, T, S = s.shape
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BS = 32
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dtype = dtype or s.dtype
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grid = (triton.cdiv(S, BS), B * H)
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z = torch.empty_like(s, dtype=dtype)
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chunk_cumsum_fwd_kernel[grid](
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s, z,
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s.stride(1), s.stride(2), s.stride(3),
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T=T, S=S, BS=BS
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)
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return z
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@contiguous
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def chunk_cumsum_bwd(
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dz: torch.Tensor,
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dtype: Optional[torch.dtype] = None,
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) -> torch.Tensor:
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B, H, T, S = dz.shape
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BS = 32
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dtype = dtype or dz.dtype
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grid = (triton.cdiv(S, BS), B * H)
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ds = torch.empty_like(dz, dtype=dtype)
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chunk_cumsum_bwd_kernel[grid](
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ds, dz,
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ds.stride(1), ds.stride(2), ds.stride(3),
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T=T, S=S, BS=BS
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)
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return ds
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class CumsumFunction(torch.autograd.Function):
|
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@staticmethod
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def forward(ctx, s, dtype):
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z = chunk_cumsum_fwd(s, dtype)
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ctx.dtype = dtype
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return z
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@staticmethod
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def backward(ctx, dz):
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ds = chunk_cumsum_bwd(dz, ctx.dtype)
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return ds, None
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def cumsum(
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s: torch.Tensor,
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dtype: Optional[torch.dtype] = None,
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) -> torch.Tensor:
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return CumsumFunction.apply(s, dtype)
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@triton.autotune(
|
||||
configs=[
|
||||
triton.Config({'BS': 32}, num_warps=2),
|
||||
triton.Config({'BS': 32}, num_warps=4),
|
||||
triton.Config({'BS': 32}, num_warps=8),
|
||||
triton.Config({'BS': 64}, num_warps=2),
|
||||
triton.Config({'BS': 64}, num_warps=4),
|
||||
triton.Config({'BS': 64}, num_warps=8),
|
||||
triton.Config({'BS': 128}, num_warps=2),
|
||||
triton.Config({'BS': 128}, num_warps=4),
|
||||
triton.Config({'BS': 128}, num_warps=8),
|
||||
],
|
||||
key=['S']
|
||||
)
|
||||
@triton.jit
|
||||
def recurrent_reversed_cumsum_fwd_kernel(
|
||||
s,
|
||||
z,
|
||||
s_s_h,
|
||||
s_s_t,
|
||||
T: tl.constexpr,
|
||||
S: tl.constexpr,
|
||||
BS: tl.constexpr
|
||||
):
|
||||
i_s, i_bh = tl.program_id(0), tl.program_id(1)
|
||||
|
||||
o_s = i_s * BS + tl.arange(0, BS)
|
||||
mask = o_s < S
|
||||
|
||||
b_z = tl.zeros([BS], dtype=tl.float32)
|
||||
for i_t in range(T - 1, -1, -1):
|
||||
# [BS]
|
||||
b_s = tl.load(s + i_bh * s_s_h + i_t * s_s_t + o_s, mask=mask, other=0).to(tl.float32)
|
||||
b_z = b_z + b_s
|
||||
|
||||
tl.store(z + i_bh * s_s_h + i_t * s_s_t + o_s, b_z.to(s.dtype.element_ty), mask=mask)
|
||||
|
||||
|
||||
@triton.autotune(
|
||||
configs=[
|
||||
triton.Config({'BS': 32}, num_warps=2),
|
||||
triton.Config({'BS': 32}, num_warps=4),
|
||||
triton.Config({'BS': 32}, num_warps=8),
|
||||
triton.Config({'BS': 64}, num_warps=2),
|
||||
triton.Config({'BS': 64}, num_warps=4),
|
||||
triton.Config({'BS': 64}, num_warps=8),
|
||||
triton.Config({'BS': 128}, num_warps=2),
|
||||
triton.Config({'BS': 128}, num_warps=4),
|
||||
triton.Config({'BS': 128}, num_warps=8),
|
||||
],
|
||||
key=['S']
|
||||
)
|
||||
@triton.jit
|
||||
def recurrent_reversed_cumsum_bwd_kernel(
|
||||
ds,
|
||||
dz,
|
||||
s_s_h,
|
||||
s_s_t,
|
||||
T: tl.constexpr,
|
||||
S: tl.constexpr,
|
||||
BS: tl.constexpr
|
||||
):
|
||||
i_s, i_bh = tl.program_id(0), tl.program_id(1)
|
||||
|
||||
o_s = i_s * BS + tl.arange(0, BS)
|
||||
mask = o_s < S
|
||||
|
||||
b_ds = tl.zeros([BS], dtype=tl.float32)
|
||||
for i_t in range(0, T):
|
||||
# [BS]
|
||||
b_dz = tl.load(dz + i_bh * s_s_h + i_t * s_s_t + o_s, mask=mask, other=0).to(tl.float32)
|
||||
b_ds = b_ds + b_dz
|
||||
|
||||
tl.store(ds + i_bh * s_s_h + i_t * s_s_t + o_s, b_ds.to(ds.dtype.element_ty), mask=mask)
|
||||
|
||||
|
||||
@triton.autotune(
|
||||
configs=[
|
||||
triton.Config({'BT': 16}, num_warps=2),
|
||||
triton.Config({'BT': 16}, num_warps=4),
|
||||
triton.Config({'BT': 16}, num_warps=8),
|
||||
triton.Config({'BT': 32}, num_warps=2),
|
||||
triton.Config({'BT': 32}, num_warps=4),
|
||||
triton.Config({'BT': 32}, num_warps=8),
|
||||
triton.Config({'BT': 64}, num_warps=2),
|
||||
triton.Config({'BT': 64}, num_warps=4),
|
||||
triton.Config({'BT': 64}, num_warps=8),
|
||||
],
|
||||
key=['S']
|
||||
)
|
||||
@triton.jit
|
||||
def chunk_reversed_cumsum_fwd_kernel(
|
||||
s,
|
||||
z,
|
||||
s_s_h,
|
||||
s_s_t,
|
||||
s_s_d,
|
||||
T: tl.constexpr,
|
||||
S: tl.constexpr,
|
||||
BT: tl.constexpr,
|
||||
BS: tl.constexpr
|
||||
):
|
||||
i_s, i_bh = tl.program_id(0), tl.program_id(1)
|
||||
o_i = tl.arange(0, BT)
|
||||
m_s = tl.where(o_i[:, None] <= o_i[None, :], 1., 0.)
|
||||
|
||||
b_z = tl.zeros([BS], dtype=tl.float32)
|
||||
for i_t in range(tl.cdiv(T, BT) - 1, -1, -1):
|
||||
p_s = tl.make_block_ptr(s + i_bh * s_s_h, (T, S), (s_s_t, s_s_d), (i_t * BT, i_s * BS), (BT, BS), (1, 0))
|
||||
p_z = tl.make_block_ptr(z + i_bh * s_s_h, (T, S), (s_s_t, s_s_d), (i_t * BT, i_s * BS), (BT, BS), (1, 0))
|
||||
# [BT, BS]
|
||||
b_s = tl.load(p_s, boundary_check=(0, 1)).to(tl.float32)
|
||||
b_c = b_z[None, :] + tl.dot(m_s, b_s, allow_tf32=False)
|
||||
tl.store(p_z, b_c.to(p_z.dtype.element_ty), boundary_check=(0, 1))
|
||||
|
||||
if i_t >= 0:
|
||||
b_z += tl.sum(b_s, 0)
|
||||
|
||||
|
||||
@triton.autotune(
|
||||
configs=[
|
||||
triton.Config({'BT': 16}, num_warps=2),
|
||||
triton.Config({'BT': 16}, num_warps=4),
|
||||
triton.Config({'BT': 16}, num_warps=8),
|
||||
triton.Config({'BT': 32}, num_warps=2),
|
||||
triton.Config({'BT': 32}, num_warps=4),
|
||||
triton.Config({'BT': 32}, num_warps=8),
|
||||
triton.Config({'BT': 64}, num_warps=2),
|
||||
triton.Config({'BT': 64}, num_warps=4),
|
||||
triton.Config({'BT': 64}, num_warps=8),
|
||||
],
|
||||
key=['S']
|
||||
)
|
||||
@triton.jit
|
||||
def chunk_reversed_cumsum_bwd_kernel(
|
||||
ds,
|
||||
dz,
|
||||
s_s_h,
|
||||
s_s_t,
|
||||
s_s_d,
|
||||
T: tl.constexpr,
|
||||
S: tl.constexpr,
|
||||
BT: tl.constexpr,
|
||||
BS: tl.constexpr
|
||||
):
|
||||
i_s, i_bh = tl.program_id(0), tl.program_id(1)
|
||||
o_i = tl.arange(0, BT)
|
||||
m_s = tl.where(o_i[:, None] >= o_i[None, :], 1., 0.)
|
||||
|
||||
b_ds = tl.zeros([BS], dtype=tl.float32)
|
||||
for i_t in range(tl.cdiv(T, BT)):
|
||||
p_ds = tl.make_block_ptr(ds + i_bh * s_s_h, (T, S), (s_s_t, s_s_d), (i_t * BT, i_s * BS), (BT, BS), (1, 0))
|
||||
p_dz = tl.make_block_ptr(dz + i_bh * s_s_h, (T, S), (s_s_t, s_s_d), (i_t * BT, i_s * BS), (BT, BS), (1, 0))
|
||||
# [BT, BS]
|
||||
b_dz = tl.load(p_dz, boundary_check=(0, 1)).to(tl.float32)
|
||||
b_c = b_ds[None, :] + tl.dot(m_s, b_dz, allow_tf32=False)
|
||||
tl.store(p_ds, b_c.to(p_ds.dtype.element_ty), boundary_check=(0, 1))
|
||||
|
||||
if i_t >= 0:
|
||||
b_ds += tl.sum(b_dz, 0)
|
||||
|
||||
|
||||
@contiguous
|
||||
def chunk_reversed_cumsum_fwd(
|
||||
s: torch.Tensor,
|
||||
dtype: Optional[torch.dtype] = None,
|
||||
) -> torch.Tensor:
|
||||
B, H, T, S = s.shape
|
||||
BS = 32
|
||||
|
||||
dtype = dtype or s.dtype
|
||||
grid = (triton.cdiv(S, BS), B * H)
|
||||
z = torch.empty_like(s, dtype=dtype)
|
||||
chunk_reversed_cumsum_fwd_kernel[grid](
|
||||
s, z,
|
||||
s.stride(1), s.stride(2), s.stride(3),
|
||||
T=T, S=S, BS=BS
|
||||
)
|
||||
return z
|
||||
|
||||
|
||||
@contiguous
|
||||
def chunk_reversed_cumsum_bwd(
|
||||
dz: torch.Tensor,
|
||||
dtype: Optional[torch.dtype] = None,
|
||||
) -> torch.Tensor:
|
||||
B, H, T, S = dz.shape
|
||||
BS = 32
|
||||
|
||||
dtype = dtype or dz.dtype
|
||||
grid = (triton.cdiv(S, BS), B * H)
|
||||
ds = torch.empty_like(dz, dtype=dtype)
|
||||
chunk_reversed_cumsum_bwd_kernel[grid](
|
||||
ds, dz,
|
||||
ds.stride(1), ds.stride(2), ds.stride(3),
|
||||
T=T, S=S, BS=BS
|
||||
)
|
||||
return ds
|
||||
|
||||
|
||||
class ReversedCumsumFunction(torch.autograd.Function):
|
||||
|
||||
@staticmethod
|
||||
def forward(ctx, s, dtype):
|
||||
z = chunk_reversed_cumsum_fwd(s, dtype)
|
||||
ctx.dtype = dtype
|
||||
return z
|
||||
|
||||
@staticmethod
|
||||
def backward(ctx, dz):
|
||||
ds = chunk_reversed_cumsum_bwd(dz, ctx.dtype)
|
||||
return ds, None
|
||||
|
||||
|
||||
def reversed_cumsum(
|
||||
s: torch.Tensor,
|
||||
dtype: Optional[torch.dtype] = None,
|
||||
) -> torch.Tensor:
|
||||
return CumsumFunction.apply(s, dtype)
|
||||
Reference in New Issue
Block a user