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# Copyright (c) 2025 NVIDIA CORPORATION.
# Licensed under the MIT license.
# Adapted from https://github.com/NVlabs/VILA/tree/main under the Apache 2.0 license.
# LICENSE is in incl_licenses directory.
# Copyright 2024 NVIDIA CORPORATION & AFFILIATES
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# SPDX-License-Identifier: Apache-2.0
# Adopted from https://github.com/zhuzilin/ring-flash-attention.
# Implementation refers to Ring Attention Paper: https://arxiv.org/abs/2310.01889
import torch
import triton
import triton.language as tl
@triton.jit
def flatten_kernel(
# pointers to matrices
OUT,
LSE,
CU_SEQLENS,
# strides
stride_out_nheads,
stride_out_seqlen,
stride_lse_batch,
stride_lse_nheads,
stride_lse_seqlen,
# meta-parameters
BLOCK_M: tl.constexpr,
):
pid_m = tl.program_id(axis=0)
pid_batch = tl.program_id(axis=1)
pid_head = tl.program_id(axis=2)
start_idx = tl.load(CU_SEQLENS + pid_batch)
seqlen = tl.load(CU_SEQLENS + pid_batch + 1) - start_idx
LSE = LSE + pid_batch * stride_lse_batch + pid_head * stride_lse_nheads
OUT = OUT + pid_head * stride_out_nheads + start_idx * stride_out_seqlen
rm = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)
LSE = LSE + rm[:, None] * stride_lse_seqlen
x = tl.load(LSE, mask=rm[:, None] < seqlen, other=0.0)
OUT = OUT + rm[:, None] * stride_out_seqlen
tl.store(OUT, x, mask=rm[:, None] < seqlen)
def flatten_varlen_lse(lse, cu_seqlens):
"""
Arguments:
lse: (batch_size, nheads, max_seqlen)
cu_seqlens: (batch_size + 1,)
Return:
flatten_lse: (nheads, total_seqlen)
"""
total_seqlen = cu_seqlens[-1]
batch_size, nheads, max_seqlen = lse.shape
output = torch.empty((nheads, total_seqlen), dtype=lse.dtype, device=lse.device)
grid = lambda META: (triton.cdiv(max_seqlen, META["BLOCK_M"]), batch_size, nheads)
BLOCK_M = 4
with torch.cuda.device(lse.device.index):
flatten_kernel[grid](
output,
lse,
cu_seqlens,
# strides
output.stride(0),
output.stride(1),
lse.stride(0),
lse.stride(1),
lse.stride(2),
BLOCK_M,
)
return output
@triton.jit
def unflatten_kernel(
# pointers to matrices
OUT,
LSE,
CU_SEQLENS,
# strides
stride_out_batch,
stride_out_nheads,
stride_out_seqlen,
stride_lse_seqlen,
stride_lse_nheads,
# meta-parameters
BLOCK_M: tl.constexpr,
):
pid_m = tl.program_id(axis=0)
pid_batch = tl.program_id(axis=1)
pid_head = tl.program_id(axis=2)
start_idx = tl.load(CU_SEQLENS + pid_batch)
seqlen = tl.load(CU_SEQLENS + pid_batch + 1) - start_idx
LSE = LSE + pid_head * stride_lse_nheads + start_idx * stride_lse_seqlen
OUT = OUT + pid_batch * stride_out_batch + pid_head * stride_out_nheads
rm = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)
LSE = LSE + rm[:, None] * stride_lse_seqlen
x = tl.load(LSE, mask=rm[:, None] < seqlen, other=0.0)
OUT = OUT + rm[:, None] * stride_out_seqlen
tl.store(OUT, x, mask=rm[:, None] < seqlen)
def unflatten_varlen_lse(lse, cu_seqlens, max_seqlen: int):
"""
Arguments:
lse: (total_seqlen, nheads, 1)
cu_seqlens: (batch_size + 1,)
max_seqlen: int
Return:
unflatten_lse: (batch_size, nheads, max_seqlen)
"""
lse = lse.unsqueeze(dim=-1)
batch_size = len(cu_seqlens) - 1
nheads = lse.shape[1]
output = torch.empty(
(batch_size, nheads, max_seqlen),
dtype=lse.dtype,
device=lse.device,
)
grid = lambda META: (triton.cdiv(max_seqlen, META["BLOCK_M"]), batch_size, nheads)
BLOCK_M = 4
with torch.cuda.device(lse.device.index):
unflatten_kernel[grid](
output,
lse,
cu_seqlens,
# strides
output.stride(0),
output.stride(1),
output.stride(2),
lse.stride(0),
lse.stride(1),
BLOCK_M,
)
return output
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