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# Copyright 2025 Bytedance Ltd. and/or its affiliates
# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved.
#
# 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.
import torch
from megatron.core import parallel_state as mpu
from megatron.core.packed_seq_params import PackedSeqParams
def preprocess_packed_seqs(input_ids: torch.Tensor, attention_mask: torch.Tensor, pre_process: bool = True) -> tuple[torch.Tensor, PackedSeqParams]:
"""
Preprocess packed sequences
CP splits sequence into CP*2 chunks, and each GPU gets 2 chunks (GPU0 gets first and last chunks, GPU1 gets second and second last chunks, and so on), this is for load balancing with causal masking.
See https://github.com/NVIDIA/TransformerEngine/issues/1368
"""
batch_size = input_ids.shape[0]
seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
tp_size = mpu.get_tensor_model_parallel_world_size()
cp_size = mpu.get_context_parallel_world_size()
cp_rank = mpu.get_context_parallel_rank()
align_size = tp_size * cp_size * 2 if cp_size > 1 else tp_size
pad_size = (align_size - seqlens_in_batch % align_size) % align_size
seqlens_in_batch_padded = seqlens_in_batch + pad_size
cu_seqlens = torch.zeros(batch_size + 1, dtype=torch.int32, device=input_ids.device)
cu_seqlens[1:] = torch.cumsum(seqlens_in_batch, dim=0)
cu_seqlens_padded = torch.zeros(batch_size + 1, dtype=torch.int32, device=input_ids.device)
cu_seqlens_padded[1:] = torch.cumsum(seqlens_in_batch_padded, dim=0)
max_seqlen_in_batch = seqlens_in_batch_padded.max().item()
shape = list(input_ids.shape[1:])
shape[0] = seqlens_in_batch_padded.sum().item() // cp_size
if pre_process:
input_ids_rmpad = torch.zeros(shape, dtype=input_ids.dtype, device=input_ids.device)
for i in range(batch_size):
if cp_size <= 1:
seqlen = seqlens_in_batch[i]
input_ids_rmpad[cu_seqlens_padded[i] : cu_seqlens_padded[i] + seqlen] = input_ids[i, attention_mask[i]]
continue
seqlen = seqlens_in_batch_padded[i] // cp_size
half_seqlen = seqlen // 2
start_idx = cu_seqlens_padded[i] // cp_size
# split to 2 chunks
d = input_ids[i, attention_mask[i]]
input_ids_rmpad[start_idx : start_idx + half_seqlen] = d[half_seqlen * cp_rank : half_seqlen * (cp_rank + 1)]
remain_start = seqlens_in_batch_padded[i] - half_seqlen * (cp_rank + 1)
remain_end = seqlens_in_batch_padded[i] - half_seqlen * cp_rank
remain_end = min(remain_end, d.shape[0])
remain_len = remain_end - remain_start
if remain_len > 0:
input_ids_rmpad[start_idx + half_seqlen : start_idx + half_seqlen + remain_len] = d[remain_start:remain_end]
packed_seq_params = PackedSeqParams(
qkv_format="thd",
cu_seqlens_q=cu_seqlens_padded,
max_seqlen_q=max_seqlen_in_batch,
cu_seqlens_kv=cu_seqlens_padded,
max_seqlen_kv=max_seqlen_in_batch,
cu_seqlens_q_padded=cu_seqlens_padded,
cu_seqlens_kv_padded=cu_seqlens_padded,
)
if pre_process:
return input_ids_rmpad.unsqueeze(0), packed_seq_params
else:
return input_ids, packed_seq_params
def postprocess_packed_seqs(
output: torch.Tensor,
packed_seq_params: PackedSeqParams,
attention_mask: torch.Tensor,
batch_size: int,
seq_len: int,
post_process: bool = True,
) -> torch.Tensor:
"""
Postprocess packed sequences
"""
if not post_process:
return output
shape = [batch_size, seq_len] + list(output.shape[2:]) # 1,packed, dim -> batch_size, seq_len, dim
output_new = torch.zeros(shape, dtype=output.dtype, device=output.device)
cp_size = mpu.get_context_parallel_world_size()
# all gather output across context parallel group
if cp_size > 1:
# output shape: [1, packed_len, hidden_dim]
# need to gather across cp group and concatenate in sequence dimension
output_list = [torch.empty_like(output) for _ in range(cp_size)]
torch.distributed.all_gather(output_list, output.detach(), group=mpu.get_context_parallel_group())
output_list[mpu.get_context_parallel_rank()] = output
else:
output_list = [output]
for i in range(batch_size):
if cp_size <= 1:
s = attention_mask[i].sum().item()
output_new[i, attention_mask[i]] = output[0][packed_seq_params.cu_seqlens_q_padded[i] : packed_seq_params.cu_seqlens_q_padded[i] + s]
continue
s_len_padded_chunk = (packed_seq_params.cu_seqlens_q_padded[i + 1] - packed_seq_params.cu_seqlens_q_padded[i]) // cp_size
half_seqlen = s_len_padded_chunk // 2
s_len = attention_mask[i].sum().item()
s_len_padded = s_len_padded_chunk * cp_size
tmp = torch.empty(s_len_padded, *output.shape[2:], device=output.device)
for j in range(cp_size):
o = output_list[j][0]
# split to 2 chunks
packed_start_idx = packed_seq_params.cu_seqlens_q_padded[i] // cp_size
o0, o1 = (
o[packed_start_idx : packed_start_idx + half_seqlen],
o[packed_start_idx + half_seqlen : packed_start_idx + s_len_padded_chunk],
)
tmp[j * half_seqlen : (j + 1) * half_seqlen] = o0
tmp[s_len_padded - (j + 1) * half_seqlen : s_len_padded - j * half_seqlen] = o1
output_new[i, attention_mask[i]] = tmp[:s_len]
return output_new
def remove_left_padding(
input_ids: torch.Tensor,
attention_mask: torch.Tensor,
position_ids: torch.Tensor,
sequence_parallel: bool = False,
pre_process: bool = True,
):
"""
Remove left padding from input_ids, attention_mask and position_ids
return new_input_ids, new_attention_mask, new_position_ids
"""
assert attention_mask.ndim == 2
assert position_ids.ndim == 2
cp_size = mpu.get_context_parallel_world_size()
assert cp_size == 1, "Context parallel size without seq_pack is not supported"
batch_size = input_ids.shape[0]
shape = list(input_ids.shape) # batch_size, seq_len,...
seq_lens = attention_mask.sum(dim=1)
seq_len = seq_lens.max().item()
if sequence_parallel:
sp_world_size = mpu.get_tensor_model_parallel_world_size()
pad_size = (sp_world_size - seq_len % sp_world_size) % sp_world_size
seq_len = seq_len + pad_size
shape[1] = seq_len
if pre_process:
new_input_ids = torch.zeros(dtype=input_ids.dtype, device=input_ids.device, size=shape)
new_attention_mask = torch.zeros(dtype=attention_mask.dtype, device=attention_mask.device, size=(batch_size, seq_len))
new_position_ids = torch.zeros(dtype=position_ids.dtype, device=position_ids.device, size=(batch_size, seq_len))
for i in range(batch_size):
if pre_process:
new_input_ids[i, : seq_lens[i]] = input_ids[i, attention_mask[i]]
new_attention_mask[i, : seq_lens[i]] = attention_mask[i, attention_mask[i]]
new_position_ids[i, : seq_lens[i]] = position_ids[i, attention_mask[i]]
if pre_process:
return new_input_ids, new_attention_mask, new_position_ids
else:
return input_ids, new_attention_mask, new_position_ids
def recover_left_padding(
result,
attention_mask: torch.Tensor,
original_attention_mask: torch.Tensor,
origin_seqlen: int,
post_process: bool = True,
):
"""
Recover left padding from result
return result
"""
if not post_process:
return result
shape = list(result.shape)
batch_size = shape[0]
shape[1] = origin_seqlen
new_result = torch.zeros(dtype=result.dtype, device=result.device, size=shape)
for i in range(batch_size):
new_result[i, original_attention_mask[i]] = result[i, attention_mask[i]]
return new_result
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