EngineX-Biren/enginex-biren-vllm
2
0
Fork 0
Code Issues Pull Requests Actions Projects Releases Wiki Activity

first commit

Browse Source
This commit is contained in:
Chranos
2026-03-10 13:31:25 +08:00
parent ba974cecfa
commit b62b889355
2604 changed files with 438977 additions and 0 deletions
Download Patch File Download Diff File Expand all files Collapse all files

365
vllm/model_executor/layers/fla/ops/solve_tril.py Normal file
View File

@@ -0,0 +1,365 @@
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
# SPDX-FileCopyrightText: Songlin Yang, Yu Zhang
#
# This file contains code copied from the flash-linear-attention project.
# The original source code was licensed under the MIT license and included
# the following copyright notice:
# Copyright (c) 2023-2025, Songlin Yang, Yu Zhang
# ruff: noqa: E501
from typing import Optional
import torch
from vllm.triton_utils import tl, triton
from .index import prepare_chunk_indices
from .utils import input_guard
@triton.heuristics({'IS_VARLEN': lambda args: args['cu_seqlens'] is not None})
@triton.autotune(
configs=[
triton.Config({}, num_warps=num_warps, num_stages=num_stages)
for num_warps in [1, 2, 4, 8] for num_stages in [2, 3, 4, 5]
],
key=['BT'],
)
@triton.jit(do_not_specialize=['T'])
def solve_tril_16x16_kernel(
A,
Ad,
cu_seqlens,
chunk_indices,
T,
H: tl.constexpr,
BT: tl.constexpr,
IS_VARLEN: tl.constexpr,
):
i_t, i_bh = tl.program_id(0), tl.program_id(1)
i_b, i_h = i_bh // H, i_bh % H
if IS_VARLEN:
i_n, i_t = tl.load(chunk_indices + i_t * 2).to(
tl.int32), tl.load(chunk_indices + i_t * 2 + 1).to(tl.int32)
bos, eos = tl.load(cu_seqlens + i_n).to(
tl.int32), tl.load(cu_seqlens + i_n + 1).to(tl.int32)
T = eos - bos
else:
bos, eos = i_b * T, i_b * T + T
A = A + (bos * H + i_h) * BT
Ad = Ad + (bos * H + i_h) * 16
offset = (i_t * 16) % BT
p_A = tl.make_block_ptr(A, (T, BT), (H * BT, 1), (i_t * 16, offset),
(16, 16), (1, 0))
p_Ai = tl.make_block_ptr(Ad, (T, 16), (H * 16, 1), (i_t * 16, 0), (16, 16),
(1, 0))
b_A = tl.load(p_A, boundary_check=(0, 1)).to(tl.float32)
b_A = -tl.where(
tl.arange(0, 16)[:, None] > tl.arange(0, 16)[None, :], b_A, 0)
o_i = tl.arange(0, 16)
for i in range(1, min(16, T - i_t * 16)):
b_a = -tl.load(A + (i_t * 16 + i) * H * BT + o_i + offset)
b_a = b_a + tl.sum(b_a[:, None] * b_A, 0)
mask = o_i == i
b_A = tl.where(mask[:, None], b_a, b_A)
b_A += o_i[:, None] == o_i[None, :]
tl.store(p_Ai,
b_A.to(p_Ai.dtype.element_ty, fp_downcast_rounding="rtne"),
boundary_check=(0, 1))
@triton.heuristics({'IS_VARLEN': lambda args: args['cu_seqlens'] is not None})
@triton.autotune(
configs=[
triton.Config({}, num_warps=num_warps, num_stages=num_stages)
for num_warps in [1, 2, 4, 8] for num_stages in [2, 3, 4, 5]
],
key=['H', 'BT', 'IS_VARLEN'],
)
@triton.jit(do_not_specialize=['T'])
def merge_16x16_to_32x32_inverse_kernel(A, Ad, Ai, cu_seqlens, chunk_indices,
T, H: tl.constexpr, BT: tl.constexpr,
IS_VARLEN: tl.constexpr):
i_t, i_bh = tl.program_id(0), tl.program_id(1)
i_b, i_h = i_bh // H, i_bh % H
if IS_VARLEN:
i_n, i_t = tl.load(chunk_indices + i_t * 2).to(
tl.int32), tl.load(chunk_indices + i_t * 2 + 1).to(tl.int32)
bos, eos = tl.load(cu_seqlens + i_n).to(
tl.int32), tl.load(cu_seqlens + i_n + 1).to(tl.int32)
T = eos - bos
else:
bos, eos = i_b * T, i_b * T + T
A += (bos * H + i_h) * 32
Ad += (bos * H + i_h) * 16
Ai += (bos * H + i_h) * 32
p_A_21 = tl.make_block_ptr(A, (T, 32), (H * 32, 1), (i_t * 32 + 16, 0),
(16, 16), (1, 0))
p_Ad_11 = tl.make_block_ptr(Ad, (T, 16), (H * 16, 1), (i_t * 32, 0),
(16, 16), (1, 0))
p_Ad_22 = tl.make_block_ptr(Ad, (T, 16), (H * 16, 1), (i_t * 32 + 16, 0),
(16, 16), (1, 0))
p_Ai_11 = tl.make_block_ptr(Ai, (T, 32), (H * 32, 1), (i_t * 32, 0),
(16, 16), (1, 0))
p_Ai_22 = tl.make_block_ptr(Ai, (T, 32), (H * 32, 1), (i_t * 32 + 16, 16),
(16, 16), (1, 0))
p_Ai_21 = tl.make_block_ptr(Ai, (T, 32), (H * 32, 1), (i_t * 32 + 16, 0),
(16, 16), (1, 0))
A_21 = tl.load(p_A_21, boundary_check=(0, 1)).to(tl.float32)
Ai_11 = tl.load(p_Ad_11, boundary_check=(0, 1)).to(tl.float32)
Ai_22 = tl.load(p_Ad_22, boundary_check=(0, 1)).to(tl.float32)
Ai_21 = -tl.dot(tl.dot(Ai_22, A_21, input_precision='ieee'),
Ai_11,
input_precision='ieee')
tl.store(p_Ai_11,
Ai_11.to(p_Ai_11.dtype.element_ty, fp_downcast_rounding="rtne"),
boundary_check=(0, 1))
tl.store(p_Ai_22,
Ai_22.to(p_Ai_22.dtype.element_ty, fp_downcast_rounding="rtne"),
boundary_check=(0, 1))
tl.store(p_Ai_21,
Ai_21.to(p_Ai_21.dtype.element_ty, fp_downcast_rounding="rtne"),
boundary_check=(0, 1))
@triton.heuristics({'IS_VARLEN': lambda args: args['cu_seqlens'] is not None})
@triton.autotune(
configs=[
triton.Config({}, num_warps=num_warps, num_stages=num_stages)
for num_warps in [2, 4, 8] for num_stages in [2, 3, 4, 5]
],
key=['H', 'BT', 'IS_VARLEN'],
)
@triton.jit(do_not_specialize=['T'])
def merge_16x16_to_64x64_inverse_kernel(A, Ad, Ai, cu_seqlens, chunk_indices,
T, H: tl.constexpr, BT: tl.constexpr,
IS_VARLEN: tl.constexpr):
i_t, i_bh = tl.program_id(0), tl.program_id(1)
i_b, i_h = i_bh // H, i_bh % H
if IS_VARLEN:
i_n, i_t = tl.load(chunk_indices + i_t * 2).to(
tl.int32), tl.load(chunk_indices + i_t * 2 + 1).to(tl.int32)
bos, eos = tl.load(cu_seqlens + i_n).to(
tl.int32), tl.load(cu_seqlens + i_n + 1).to(tl.int32)
T = eos - bos
else:
bos, eos = i_b * T, i_b * T + T
A += (bos * H + i_h) * 64
Ad += (bos * H + i_h) * 16
Ai += (bos * H + i_h) * 64
p_A_21 = tl.make_block_ptr(A, (T, 64), (H * 64, 1), (i_t * 64 + 16, 0),
(16, 16), (1, 0))
p_A_32 = tl.make_block_ptr(A, (T, 64), (H * 64, 1), (i_t * 64 + 32, 16),
(16, 16), (1, 0))
p_A_31 = tl.make_block_ptr(A, (T, 64), (H * 64, 1), (i_t * 64 + 32, 0),
(16, 16), (1, 0))
p_A_43 = tl.make_block_ptr(A, (T, 64), (H * 64, 1), (i_t * 64 + 48, 32),
(16, 16), (1, 0))
p_A_42 = tl.make_block_ptr(A, (T, 64), (H * 64, 1), (i_t * 64 + 48, 16),
(16, 16), (1, 0))
p_A_41 = tl.make_block_ptr(A, (T, 64), (H * 64, 1), (i_t * 64 + 48, 0),
(16, 16), (1, 0))
p_Ad_11 = tl.make_block_ptr(Ad, (T, 16), (H * 16, 1), (i_t * 64, 0),
(16, 16), (1, 0))
p_Ad_22 = tl.make_block_ptr(Ad, (T, 16), (H * 16, 1), (i_t * 64 + 16, 0),
(16, 16), (1, 0))
p_Ad_33 = tl.make_block_ptr(Ad, (T, 16), (H * 16, 1), (i_t * 64 + 32, 0),
(16, 16), (1, 0))
p_Ad_44 = tl.make_block_ptr(Ad, (T, 16), (H * 16, 1), (i_t * 64 + 48, 0),
(16, 16), (1, 0))
A_21 = tl.load(p_A_21, boundary_check=(0, 1)).to(tl.float32)
A_32 = tl.load(p_A_32, boundary_check=(0, 1)).to(tl.float32)
A_31 = tl.load(p_A_31, boundary_check=(0, 1)).to(tl.float32)
A_43 = tl.load(p_A_43, boundary_check=(0, 1)).to(tl.float32)
A_42 = tl.load(p_A_42, boundary_check=(0, 1)).to(tl.float32)
A_41 = tl.load(p_A_41, boundary_check=(0, 1)).to(tl.float32)
Ai_11 = tl.load(p_Ad_11, boundary_check=(0, 1)).to(tl.float32)
Ai_22 = tl.load(p_Ad_22, boundary_check=(0, 1)).to(tl.float32)
Ai_33 = tl.load(p_Ad_33, boundary_check=(0, 1)).to(tl.float32)
Ai_44 = tl.load(p_Ad_44, boundary_check=(0, 1)).to(tl.float32)
Ai_21 = -tl.dot(tl.dot(Ai_22, A_21, input_precision='ieee'),
Ai_11,
input_precision='ieee')
Ai_32 = -tl.dot(tl.dot(Ai_33, A_32, input_precision='ieee'),
Ai_22,
input_precision='ieee')
Ai_43 = -tl.dot(tl.dot(Ai_44, A_43, input_precision='ieee'),
Ai_33,
input_precision='ieee')
Ai_31 = -tl.dot(Ai_33,
tl.dot(A_31, Ai_11, input_precision='ieee') +
tl.dot(A_32, Ai_21, input_precision='ieee'),
input_precision='ieee')
Ai_42 = -tl.dot(Ai_44,
tl.dot(A_42, Ai_22, input_precision='ieee') +
tl.dot(A_43, Ai_32, input_precision='ieee'),
input_precision='ieee')
Ai_41 = -tl.dot(Ai_44,
tl.dot(A_41, Ai_11, input_precision='ieee') +
tl.dot(A_42, Ai_21, input_precision='ieee') +
tl.dot(A_43, Ai_31, input_precision='ieee'),
input_precision='ieee')
p_Ai_11 = tl.make_block_ptr(Ai, (T, 64), (H * 64, 1), (i_t * 64, 0),
(16, 16), (1, 0))
p_Ai_22 = tl.make_block_ptr(Ai, (T, 64), (H * 64, 1), (i_t * 64 + 16, 16),
(16, 16), (1, 0))
p_Ai_33 = tl.make_block_ptr(Ai, (T, 64), (H * 64, 1), (i_t * 64 + 32, 32),
(16, 16), (1, 0))
p_Ai_44 = tl.make_block_ptr(Ai, (T, 64), (H * 64, 1), (i_t * 64 + 48, 48),
(16, 16), (1, 0))
p_Ai_21 = tl.make_block_ptr(Ai, (T, 64), (H * 64, 1), (i_t * 64 + 16, 0),
(16, 16), (1, 0))
p_Ai_31 = tl.make_block_ptr(Ai, (T, 64), (H * 64, 1), (i_t * 64 + 32, 0),
(16, 16), (1, 0))
p_Ai_32 = tl.make_block_ptr(Ai, (T, 64), (H * 64, 1), (i_t * 64 + 32, 16),
(16, 16), (1, 0))
p_Ai_41 = tl.make_block_ptr(Ai, (T, 64), (H * 64, 1), (i_t * 64 + 48, 0),
(16, 16), (1, 0))
p_Ai_42 = tl.make_block_ptr(Ai, (T, 64), (H * 64, 1), (i_t * 64 + 48, 16),
(16, 16), (1, 0))
p_Ai_43 = tl.make_block_ptr(Ai, (T, 64), (H * 64, 1), (i_t * 64 + 48, 32),
(16, 16), (1, 0))
tl.store(p_Ai_11,
Ai_11.to(p_Ai_11.dtype.element_ty, fp_downcast_rounding="rtne"),
boundary_check=(0, 1))
tl.store(p_Ai_22,
Ai_22.to(p_Ai_22.dtype.element_ty, fp_downcast_rounding="rtne"),
boundary_check=(0, 1))
tl.store(p_Ai_33,
Ai_33.to(p_Ai_33.dtype.element_ty, fp_downcast_rounding="rtne"),
boundary_check=(0, 1))
tl.store(p_Ai_44,
Ai_44.to(p_Ai_44.dtype.element_ty, fp_downcast_rounding="rtne"),
boundary_check=(0, 1))
tl.store(p_Ai_21,
Ai_21.to(p_Ai_21.dtype.element_ty, fp_downcast_rounding="rtne"),
boundary_check=(0, 1))
tl.store(p_Ai_31,
Ai_31.to(p_Ai_31.dtype.element_ty, fp_downcast_rounding="rtne"),
boundary_check=(0, 1))
tl.store(p_Ai_32,
Ai_32.to(p_Ai_32.dtype.element_ty, fp_downcast_rounding="rtne"),
boundary_check=(0, 1))
tl.store(p_Ai_41,
Ai_41.to(p_Ai_41.dtype.element_ty, fp_downcast_rounding="rtne"),
boundary_check=(0, 1))
tl.store(p_Ai_42,
Ai_42.to(p_Ai_42.dtype.element_ty, fp_downcast_rounding="rtne"),
boundary_check=(0, 1))
tl.store(p_Ai_43,
Ai_43.to(p_Ai_43.dtype.element_ty, fp_downcast_rounding="rtne"),
boundary_check=(0, 1))
fill_zeros = tl.zeros((16, 16), dtype=tl.float32)
p_Ai_12 = tl.make_block_ptr(Ai, (T, 64), (H * 64, 1), (i_t * 64, 16),
(16, 16), (1, 0))
p_Ai_13 = tl.make_block_ptr(Ai, (T, 64), (H * 64, 1), (i_t * 64, 32),
(16, 16), (1, 0))
p_Ai_14 = tl.make_block_ptr(Ai, (T, 64), (H * 64, 1), (i_t * 64, 48),
(16, 16), (1, 0))
p_Ai_23 = tl.make_block_ptr(Ai, (T, 64), (H * 64, 1), (i_t * 64 + 16, 32),
(16, 16), (1, 0))
p_Ai_24 = tl.make_block_ptr(Ai, (T, 64), (H * 64, 1), (i_t * 64 + 16, 48),
(16, 16), (1, 0))
p_Ai_34 = tl.make_block_ptr(Ai, (T, 64), (H * 64, 1), (i_t * 64 + 32, 48),
(16, 16), (1, 0))
tl.store(p_Ai_12,
fill_zeros.to(p_Ai_12.dtype.element_ty,
fp_downcast_rounding="rtne"),
boundary_check=(0, 1))
tl.store(p_Ai_13,
fill_zeros.to(p_Ai_13.dtype.element_ty,
fp_downcast_rounding="rtne"),
boundary_check=(0, 1))
tl.store(p_Ai_14,
fill_zeros.to(p_Ai_14.dtype.element_ty,
fp_downcast_rounding="rtne"),
boundary_check=(0, 1))
tl.store(p_Ai_23,
fill_zeros.to(p_Ai_23.dtype.element_ty,
fp_downcast_rounding="rtne"),
boundary_check=(0, 1))
tl.store(p_Ai_24,
fill_zeros.to(p_Ai_24.dtype.element_ty,
fp_downcast_rounding="rtne"),
boundary_check=(0, 1))
tl.store(p_Ai_34,
fill_zeros.to(p_Ai_34.dtype.element_ty,
fp_downcast_rounding="rtne"),
boundary_check=(0, 1))
@input_guard
def solve_tril(A: torch.Tensor,
cu_seqlens: Optional[torch.Tensor] = None,
output_dtype: torch.dtype = torch.float) -> torch.Tensor:
"""
Compute the inverse of the lower triangular matrix
A should be strictly lower triangular, i.e., A.triu() == 0.
Args:
A (torch.Tensor):
[B, T, H, K]
cu_seqlens (torch.Tensor):
The cumulative sequence lengths of the input tensor.
Default: None.
output_dtype (torch.dtype):
The dtype of the output tensor. Default: `torch.float`
Returns:
(I + A)^-1 with the same shape as A
"""
assert A.shape[-1] in [16, 32, 64]
B, T, H, BT = A.shape
Ad = torch.empty(B,
T,
H,
16,
device=A.device,
dtype=torch.float if BT != 16 else output_dtype)
chunk_indices = prepare_chunk_indices(
cu_seqlens, 16) if cu_seqlens is not None else None
NT = len(chunk_indices) if cu_seqlens is not None else triton.cdiv(T, 16)
solve_tril_16x16_kernel[NT, B * H](
A=A,
Ad=Ad,
cu_seqlens=cu_seqlens,
chunk_indices=chunk_indices,
T=T,
H=H,
BT=BT,
)
if BT == 16:
return Ad
Ai = torch.empty(B, T, H, BT, device=A.device, dtype=output_dtype)
merge_fn = merge_16x16_to_32x32_inverse_kernel if BT == 32 else merge_16x16_to_64x64_inverse_kernel
chunk_indices = prepare_chunk_indices(
cu_seqlens, BT) if cu_seqlens is not None else None
NT = len(chunk_indices) if cu_seqlens is not None else triton.cdiv(T, BT)
merge_fn[NT, B * H](
A=A,
Ad=Ad,
Ai=Ai,
cu_seqlens=cu_seqlens,
chunk_indices=chunk_indices,
T=T,
H=H,
BT=BT,
)
return Ai