File size: 13,094 Bytes
62dca4c | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 | from abc import ABC, abstractmethod
from dataclasses import dataclass
from typing import List, Optional
import torch
import torch.distributed as dist
import torch.nn as nn
from sglang.srt.configs.model_config import ModelConfig
from sglang.srt.managers.schedule_batch import Req, ScheduleBatch
from sglang.srt.managers.scheduler import Scheduler
from sglang.srt.mem_cache.cache_init_params import CacheInitParams
from sglang.srt.mem_cache.radix_cache import RadixCache
from sglang.srt.model_executor.forward_batch_info import CaptureHiddenMode, ForwardBatch
from sglang.srt.sampling.sampling_params import SamplingParams
from sglang.srt.server_args import ServerArgs
from sglang.srt.speculative.spec_info import SpeculativeAlgorithm
from sglang.srt.utils import require_mlp_sync, require_mlp_tp_gather
from transformers import AutoModelForCausalLM
from specforge.distributed import get_tp_device_mesh, get_tp_group
from specforge.utils import padding
from .sglang_backend import SGLangRunner
@dataclass
class DFlashTargetOutput:
hidden_states: torch.Tensor # [batch, seq_len, hidden_size]
input_ids: torch.Tensor # [batch, seq_len]
attention_mask: torch.Tensor # [batch, seq_len]
loss_mask: torch.Tensor # [batch, seq_len]
class DFlashTargetModel(ABC):
"""
Abstract base class for DFlash target model backend.
"""
def __init__(self):
self.capture_layer_ids = None
@classmethod
@abstractmethod
def from_pretrained(
cls,
pretrained_model_name_or_path: str,
torch_dtype: torch.dtype = None,
device: str = None,
cache_dir: Optional[str] = None,
**kwargs,
) -> "DFlashTargetModel":
"""Initialize the target model backend."""
@abstractmethod
def generate_dflash_data(
self,
input_ids: torch.Tensor,
attention_mask: torch.Tensor,
loss_mask: torch.Tensor,
) -> DFlashTargetOutput:
"""Generate context hidden states for DFlash training."""
def set_capture_layers(self, layer_ids: List[int]) -> None:
"""Set which layers' hidden states to capture."""
self.capture_layer_ids = layer_ids
class SGLangDFlashTargetModel(DFlashTargetModel):
def __init__(self, model_runner: SGLangRunner):
super().__init__()
self.model_runner = model_runner
@classmethod
def from_pretrained(
cls,
pretrained_model_name_or_path: str,
torch_dtype: torch.dtype = None,
device: str = None,
cache_dir: Optional[str] = None,
trust_remote_code: bool = False,
**kwargs,
) -> "SGLangDFlashTargetModel":
tp_size = dist.get_world_size(get_tp_group())
server_args = ServerArgs(
model_path=pretrained_model_name_or_path,
trust_remote_code=trust_remote_code,
dtype=torch_dtype,
enable_return_hidden_states=True, # Critical for DFlash
disable_cuda_graph=True,
tp_size=tp_size,
pp_size=1,
**kwargs,
)
tp_rank = dist.get_rank(get_tp_group())
moe_ep_rank = tp_rank // (server_args.tp_size // server_args.ep_size)
model_config = ModelConfig.from_server_args(server_args)
model_runner = SGLangRunner(
model_config=model_config,
mem_fraction_static=server_args.mem_fraction_static,
gpu_id=torch.cuda.current_device(),
tp_rank=dist.get_rank(get_tp_group()),
tp_size=server_args.tp_size,
moe_ep_rank=moe_ep_rank,
moe_ep_size=server_args.ep_size,
pp_rank=0,
pp_size=1,
server_args=server_args,
nccl_port=None,
)
return cls(model_runner)
def set_capture_layers(self, layer_ids: List[int]) -> None:
super().set_capture_layers(layer_ids)
# Note: We need to ensure SGLang supports custom capture layers.
# Eagle3 implementation uses `set_eagle3_layers_to_capture`.
# For DFlash, we might need to rely on `output_hidden_states=True` returning all layers
# and then filtering, OR implementing `set_custom_layers_to_capture` in SGLang patch.
# Assuming we can use the same mechanism or general mechanism if available.
# If SGLang doesn't support selective capture easily, we might get all and select later.
# But for memory efficiency, selective capture is better.
# Checking Eagle3 implementation again: it calls `model.set_eagle3_layers_to_capture`.
# This implies SGLang model wrapper has this method patched.
# We will try to use a similar approach or assume we get full hidden states.
# For now, let's assume we capture what's needed.
if hasattr(self.model_runner.model, "set_eagle3_layers_to_capture"):
self.model_runner.model.set_eagle3_layers_to_capture(layer_ids)
@torch.no_grad
def _extend(self, reqs):
# Similar to Eagle3 _extend but simplified for just hidden states
cache_params = CacheInitParams(
disable=False,
req_to_token_pool=self.model_runner.req_to_token_pool,
token_to_kv_pool_allocator=self.model_runner.token_to_kv_pool_allocator,
page_size=self.model_runner.server_args.page_size,
)
tree_cache = RadixCache(cache_params)
batch = ScheduleBatch.init_new(
reqs=reqs,
req_to_token_pool=self.model_runner.req_to_token_pool,
token_to_kv_pool_allocator=self.model_runner.token_to_kv_pool_allocator,
tree_cache=tree_cache,
model_config=self.model_runner.model_config,
enable_overlap=False,
spec_algorithm=SpeculativeAlgorithm.NONE,
)
batch.prepare_for_extend()
if require_mlp_sync(self.model_runner.server_args):
Scheduler.prepare_mlp_sync_batch_raw(
batch,
dp_size=self.model_runner.server_args.dp_size,
attn_tp_size=1,
tp_group=self.model_runner.tp_group,
get_idle_batch=None,
disable_cuda_graph=self.model_runner.server_args.disable_cuda_graph,
spec_algorithm=SpeculativeAlgorithm.NONE,
speculative_num_draft_tokens=None,
require_mlp_tp_gather=require_mlp_tp_gather(
self.model_runner.server_args
),
disable_overlap_schedule=self.model_runner.server_args.disable_overlap_schedule,
offload_tags=set(),
)
model_worker_batch = batch.get_model_worker_batch()
forward_batch = ForwardBatch.init_new(model_worker_batch, self.model_runner)
forward_batch.capture_hidden_mode = CaptureHiddenMode.FULL
output, _ = self.model_runner.forward(forward_batch)
# Eagle3 output has aux_hidden_states.
# We need to check what SGLang returns. Typically it returns 'hidden_states' or 'aux_hidden_states'.
# Assuming it aligns with Eagle3 patch.
input_lens = [len(req.origin_input_ids) for req in reqs]
# Split per request
if (
hasattr(output, "aux_hidden_states")
and output.aux_hidden_states is not None
):
hidden_states_list = torch.split(
output.aux_hidden_states, input_lens, dim=0
)
elif hasattr(output, "hidden_states") and output.hidden_states is not None:
hidden_states_list = torch.split(output.hidden_states, input_lens, dim=0)
else:
raise ValueError("SGLang output does not contain hidden states.")
self.model_runner.req_to_token_pool.clear()
self.model_runner.token_to_kv_pool_allocator.clear()
return hidden_states_list
@torch.no_grad()
def generate_dflash_data(
self,
input_ids: torch.Tensor,
attention_mask: torch.Tensor,
loss_mask: torch.Tensor,
) -> DFlashTargetOutput:
sampling_params = SamplingParams(temperature=0, max_new_tokens=1)
reqs, data_cache = [], []
if isinstance(input_ids, torch.Tensor):
input_ids_list = torch.split(input_ids, 1, dim=0)
attn_mask_list = torch.split(attention_mask, 1, dim=0)
loss_mask_list = torch.split(loss_mask, 1, dim=0)
for idx, (curr_ids, curr_attn, curr_loss) in enumerate(
zip(input_ids_list, attn_mask_list, loss_mask_list)
):
req = Req(
rid=str(idx),
origin_input_text="",
origin_input_ids=curr_ids.view(-1).tolist(),
sampling_params=sampling_params,
)
req.fill_ids = req.origin_input_ids
req.extend_input_len = len(req.fill_ids) - len(req.prefix_indices)
data_cache.append((curr_ids, curr_attn, curr_loss))
reqs.append(req)
hidden_states_list = self._extend(reqs)
# Stack back to batch
hidden_states = torch.cat([h.unsqueeze(0) for h in hidden_states_list], dim=0)
input_ids = torch.cat([d[0] for d in data_cache], dim=0)
attention_mask = torch.cat([d[1] for d in data_cache], dim=0)
loss_mask = torch.cat([d[2] for d in data_cache], dim=0)
# Padding might be needed if batching varied lengths (but usually fixed length training)
hidden_states = padding(hidden_states, left=False)
input_ids = padding(input_ids, left=False)
return DFlashTargetOutput(
hidden_states=hidden_states,
input_ids=input_ids,
attention_mask=attention_mask,
loss_mask=loss_mask,
)
class HFDFlashTargetModel(DFlashTargetModel):
def __init__(self, model: nn.Module):
super().__init__()
self.model = model
@classmethod
def from_pretrained(
cls,
pretrained_model_name_or_path: str,
torch_dtype: torch.dtype = None,
device: str = None,
cache_dir: Optional[str] = None,
trust_remote_code: bool = True,
**kwargs,
) -> "HFDFlashTargetModel":
tp_size = get_tp_group().size()
if tp_size > 1:
device_kwargs = {
"tp_plan": "auto",
"tp_size": tp_size,
"device_mesh": get_tp_device_mesh(),
}
else:
device_kwargs = {
"device_map": device,
}
target_model = AutoModelForCausalLM.from_pretrained(
pretrained_model_name_or_path,
torch_dtype=torch_dtype,
cache_dir=cache_dir,
output_hidden_states=True,
trust_remote_code=trust_remote_code,
attn_implementation="flash_attention_2",
**device_kwargs,
**kwargs,
).eval()
return cls(target_model)
@torch.no_grad()
def generate_dflash_data(
self,
input_ids: torch.Tensor,
attention_mask: torch.Tensor,
loss_mask: torch.Tensor,
) -> DFlashTargetOutput:
outputs = self.model(
input_ids=input_ids,
attention_mask=attention_mask,
output_hidden_states=True,
use_cache=False,
)
# Extract selected layers
# outputs.hidden_states is a tuple of (L+1) tensors
# Indices in self.capture_layer_ids correspond to 0-based index of transformer layers.
# outputs.hidden_states[0] is embedding output (usually).
# Typically hidden_states[i+1] is output of layer i.
offset = 1
selected = []
if self.capture_layer_ids is not None:
for idx in self.capture_layer_ids:
selected.append(outputs.hidden_states[idx + offset])
hidden_states = torch.cat(selected, dim=-1)
else:
# Fallback if no layers specified (maybe return last?)
hidden_states = outputs.hidden_states[-1]
return DFlashTargetOutput(
hidden_states=hidden_states,
input_ids=input_ids,
attention_mask=attention_mask,
loss_mask=loss_mask,
)
def get_dflash_target_model(
pretrained_model_name_or_path: str,
backend: str = "sglang",
torch_dtype: torch.dtype = None,
device: str = None,
cache_dir: Optional[str] = None,
**kwargs,
) -> DFlashTargetModel:
if backend == "sglang":
return SGLangDFlashTargetModel.from_pretrained(
pretrained_model_name_or_path=pretrained_model_name_or_path,
torch_dtype=torch_dtype,
device=device,
cache_dir=cache_dir,
**kwargs,
)
elif backend == "hf":
return HFDFlashTargetModel.from_pretrained(
pretrained_model_name_or_path=pretrained_model_name_or_path,
torch_dtype=torch_dtype,
device=device,
cache_dir=cache_dir,
**kwargs,
)
else:
raise ValueError(f"Invalid backend: {backend}")
|