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ebfc1e6 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 | # Copyright 2025 the LlamaFactory team.
#
# 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 sys
from functools import partial
import torch
import torch.distributed as dist
import torch.nn.functional as F
import transformers
from ....accelerator.interface import Dim, DistributedInterface
from ....utils import logging
from ....utils.plugin import BasePlugin
from ....utils.types import ModelOutput
from .ulysses import (
UlyssesAttention,
get_ulysses_sequence_parallel_group,
get_ulysses_sequence_parallel_rank,
get_ulysses_sequence_parallel_world_size,
set_ulysses_sequence_parallel_group,
)
logger = logging.get_logger(__name__)
class SequenceParallelModelPlugin(BasePlugin):
def __call__(self, model, model_args):
return super().__call__(model, model_args)
class SequenceParallelLossPlugin(BasePlugin):
def __call__(self, model, inputs, *args, **kwargs):
return super().__call__(model, inputs, *args, **kwargs)
def new_flash_attn_forward(
query_states,
key_states,
value_states,
attention_mask,
sequence_parallel_size=1,
dropout=0,
deterministic=False,
is_causal=True,
group=None,
mode="ulysses",
attn_fn=None,
target_dtype=None,
**kwargs,
):
if mode == "ulysses":
dist_attn = UlyssesAttention(sequence_process_group=group, attn_fn=attn_fn)
attn_output = dist_attn(
query_states,
key_states,
value_states,
attention_mask,
query_length=query_states.shape[1] * sequence_parallel_size,
deterministic=deterministic,
dropout_p=dropout,
causal=is_causal,
position_ids=kwargs.get("position_ids", None),
target_dtype=target_dtype,
)
else:
raise NotImplementedError("Other sequence parallel modes are to be implemented.")
return attn_output
@SequenceParallelModelPlugin("ulysses").register()
def apply_sequence_parallel(model, model_args):
# Replace _flash_attention_forward with new_flash_attn_forward
module = sys.modules[model.__module__]
cp_size = model_args.get("cp_size", 1)
set_ulysses_sequence_parallel_group(DistributedInterface().get_group(Dim.CP))
try:
num_attention_heads, num_key_value_heads = model.config.num_attention_heads, model.config.num_attention_heads
except AttributeError:
num_attention_heads, num_key_value_heads = (
model.config.text_config.num_attention_heads,
model.config.text_config.num_key_value_heads,
)
assert num_attention_heads % cp_size == 0, "num_attention_heads must be divisible by cp_size"
assert num_key_value_heads % cp_size == 0 or cp_size % num_key_value_heads == 0, (
"num_key_value_heads must be divisible by cp_size"
)
origin_attn = transformers.modeling_flash_attention_utils._flash_attention_forward
new_flash_attention_forward = partial(
new_flash_attn_forward,
group=get_ulysses_sequence_parallel_group(),
mode="ulysses",
attn_fn=origin_attn,
sequence_parallel_size=cp_size,
)
for module_name, module in list(sys.modules.items()):
try:
if (
hasattr(module, "__file__")
and "transformers" in module.__file__
and getattr(module._flash_attention_forward, "__name__", "") == "_flash_attention_forward"
):
module._flash_attention_forward = new_flash_attention_forward
logger.info_rank0(
f"Replaced _flash_attention_forward in module {module_name} with new_flash_attn_forward for sequence parallel."
)
except (AttributeError, TypeError):
continue
def padding_and_split_data(data, device_mesh=None):
if device_mesh is not None:
cp_size = device_mesh["cp"].size()
cp_rank = device_mesh["cp"].get_local_rank()
cp_group = device_mesh["cp"].get_group()
for k, v in data.items():
if isinstance(v, torch.Tensor) and v.ndim > 1:
data_len = torch.tensor(v.shape[-1], device=v.device, dtype=torch.int64)
global_data_len = [torch.empty_like(data_len) for _ in range(cp_size)]
dist.all_gather(global_data_len, data_len, group=cp_group)
max_data_len = max(global_data_len)
pad_size = max_data_len - v.shape[-1] + (cp_size - max_data_len % cp_size) % cp_size
if k == "labels":
pad_value = -100
elif k == "loss_weights":
pad_value = 0.0
else:
pad_value = 0
pad_data = F.pad(v, (0, pad_size), value=pad_value)
data[k] = torch.chunk(pad_data, chunks=cp_size, dim=-1)[cp_rank].contiguous()
return data
@SequenceParallelLossPlugin("sequence_parallel_loss").register()
def sequence_parallel_loss(model, model_inputs):
device_mesh = DistributedInterface().get_device_mesh(Dim.CP)
model_inputs = {
k: v.to(dist.get_rank(), non_blocking=True) for k, v in model_inputs.items() if isinstance(v, torch.Tensor)
}
model_inputs = padding_and_split_data(model_inputs, device_mesh)
batch_size, _ = model_inputs["labels"].shape
outputs: ModelOutput = model(**model_inputs)
logits = outputs.logits.float()
labels = model_inputs["labels"]
cp_group = get_ulysses_sequence_parallel_group()
cp_world_size = get_ulysses_sequence_parallel_world_size(cp_group)
cp_rank = get_ulysses_sequence_parallel_rank(cp_group)
# use all_gather to collect labels from all sequence parallel processes
global_labels = [torch.empty_like(labels) for _ in range(cp_world_size)]
dist.all_gather(global_labels, labels, group=cp_group)
labels = torch.cat(global_labels, dim=1).contiguous()
shift_labels = labels[..., 1:].contiguous()
shift_labels = F.pad(shift_labels, (0, 1), value=-100)
shift_labels = torch.chunk(shift_labels, chunks=cp_world_size, dim=1)[cp_rank].contiguous()
# use all_gather to collect loss_weights from all sequence parallel processes
loss_weights = model_inputs["loss_weights"]
global_loss_weights = [torch.empty_like(loss_weights) for _ in range(cp_world_size)]
dist.all_gather(global_loss_weights, loss_weights, group=cp_group)
shift_loss_weights = torch.cat(global_loss_weights, dim=1).contiguous()
shift_loss_weights = shift_loss_weights[..., 1:].contiguous()
shift_logits = logits.view(-1, logits.size(-1)).contiguous()
shift_labels = shift_labels.view(-1).contiguous()
# use all_gather to collect log_probs from all sequence parallel processes
log_probs = -F.cross_entropy(shift_logits, shift_labels, reduction="none").view(batch_size, -1)
global_log_probs = dist.nn.all_gather(log_probs, group=cp_group)
global_log_probs = torch.cat(global_log_probs, dim=1).contiguous()
log_probs = global_log_probs[..., :-1].contiguous()
loss = (-log_probs * shift_loss_weights).sum() / (shift_loss_weights.sum() + 1e-6)
return loss
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