File size: 13,759 Bytes
d91766b | 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 | import os
from dataclasses import dataclass, field
from diffulex.engine.request import DllmReq
from diffulex.logger import get_logger
logger = get_logger(__name__)
def decode_token_ids_robust(tokenizer, token_ids: list[int] | None, *, skip_special_tokens: bool = False) -> str:
if not token_ids:
return ""
try:
return tokenizer.decode(token_ids, skip_special_tokens=skip_special_tokens)
except TypeError:
tokens = tokenizer.convert_ids_to_tokens(token_ids)
safe = [t if t is not None else "" for t in tokens]
return tokenizer.convert_tokens_to_string(safe)
@dataclass
class ReqStep:
step_id: int
step_time: float
is_prefill: bool
num_generated_tokens: int
running_token_ids: list[int]
block_size: int
buffer_bids: list[int]
block_trace: list[dict] = field(default_factory=list)
def to_dict(self) -> dict:
return dict(
step_id=self.step_id,
step_time=self.step_time,
is_prefill=self.is_prefill,
num_generated_tokens=self.num_generated_tokens,
running_token_ids=self.running_token_ids,
block_size=self.block_size,
buffer_bids=self.buffer_bids,
block_trace=self.block_trace,
)
@dataclass
class ReqTrajectory:
req_id: int
token_ids: list[int]
trajectory: list[ReqStep]
is_truncated: bool
max_new_tokens_reached: bool
max_model_len_reached: bool
max_nfe_reached: bool
max_repetition_run_reached: bool
eos_token_generated: bool
completion_reason: str | None = None
text: str = None
# Generation-only tokens including content after EOS (when applicable); see DllmReq.full_response.
full_token_ids: list[int] = field(default_factory=list)
full_text: str | None = None
def to_dict(self) -> dict:
return dict(
req_id=self.req_id,
token_ids=self.token_ids,
trajectory=[step.to_dict() for step in self.trajectory],
is_truncated=self.is_truncated,
max_new_tokens_reached=self.max_new_tokens_reached,
max_model_len_reached=self.max_model_len_reached,
max_nfe_reached=self.max_nfe_reached,
max_repetition_run_reached=self.max_repetition_run_reached,
eos_token_generated=self.eos_token_generated,
completion_reason=self.completion_reason,
text=self.text,
)
class GenerationOutputs:
"""Accumulates generation outputs."""
def __init__(self, num_prompts: int):
self.trajectories: list[ReqTrajectory] = [
ReqTrajectory(
req_id=req_id,
token_ids=[],
trajectory=[],
is_truncated=False,
max_new_tokens_reached=False,
max_model_len_reached=False,
max_nfe_reached=False,
max_repetition_run_reached=False,
eos_token_generated=False,
completion_reason=None,
)
for req_id in range(num_prompts)
]
self._batch_step_count = 0
self._batch_total_time = 0.0
self._batch_generated_tokens = 0
self._prefill_batch_time = 0.0
self._prefill_batch_tokens = 0
self._decode_batch_time = 0.0
self._decode_batch_tokens = 0
@property
def batch_step_count(self) -> int:
return self._batch_step_count
@staticmethod
def _mean(values: list[float]) -> float:
return sum(values) / len(values) if values else 0.0
@property
def tpf(self) -> float:
per_req_tpf = []
for trajectory in self.trajectories:
if not trajectory.trajectory:
continue
num_generated_tokens = sum(step.num_generated_tokens for step in trajectory.trajectory)
per_req_tpf.append(num_generated_tokens / len(trajectory.trajectory))
return self._mean(per_req_tpf)
@property
def ttft(self) -> float:
per_req_ttft = []
for trajectory in self.trajectories:
elapsed = 0.0
for step in trajectory.trajectory:
elapsed += step.step_time
if step.num_generated_tokens > 0:
per_req_ttft.append(elapsed)
break
return self._mean(per_req_ttft)
@property
def tpot(self) -> float:
per_req_tpot = []
for trajectory in self.trajectories:
total_time = sum(step.step_time for step in trajectory.trajectory)
total_generated_tokens = sum(step.num_generated_tokens for step in trajectory.trajectory)
if total_generated_tokens <= 1:
continue
elapsed = 0.0
ttft = None
for step in trajectory.trajectory:
elapsed += step.step_time
if step.num_generated_tokens > 0:
ttft = elapsed
break
if ttft is not None:
per_req_tpot.append((total_time - ttft) / (total_generated_tokens - 1))
return self._mean(per_req_tpot)
@property
def throughput(self) -> float:
return self._batch_generated_tokens / self._batch_total_time if self._batch_total_time > 0 else 0
@property
def e2e_total_time(self) -> float:
return sum(sum(step.step_time for step in trajectory.trajectory) for trajectory in self.trajectories)
@property
def e2e_throughput(self) -> float:
total_tokens = sum(len(trajectory.token_ids) for trajectory in self.trajectories)
batch_time = max(self._batch_total_time, 0.001)
return total_tokens / batch_time
@property
def prefill_throughput(self) -> float:
return self._prefill_batch_tokens / self._prefill_batch_time if self._prefill_batch_time > 0 else 0
@property
def decode_throughput(self) -> float:
return self._decode_batch_tokens / self._decode_batch_time if self._decode_batch_time > 0 else 0
@property
def avg_e2e_tps(self) -> float:
"""Mean of per-sample TPS (tokens/total_time), matching dInfer's np.mean(tpss)."""
per_sample = []
for trajectory in self.trajectories:
total_time = sum(step.step_time for step in trajectory.trajectory)
total_tokens = len(trajectory.token_ids)
if total_time > 0 and total_tokens > 0:
per_sample.append(total_tokens / total_time)
return self._mean(per_sample)
@property
def avg_decode_tps(self) -> float:
"""Mean of per-sample decode TPS (decode_tokens / decode_time)."""
per_sample = []
for trajectory in self.trajectories:
decode_time = sum(step.step_time for step in trajectory.trajectory if not step.is_prefill)
decode_tokens = sum(step.num_generated_tokens for step in trajectory.trajectory if not step.is_prefill)
if decode_time > 0 and decode_tokens > 0:
per_sample.append(decode_tokens / decode_time)
return self._mean(per_sample)
@property
def total_time(self) -> float:
return self._batch_total_time
def record_step(self, reqs: list[DllmReq], step_time: float, req_id_to_prompt_id: dict[int, int] | None = None):
if reqs:
self._batch_step_count += 1
self._batch_total_time += step_time
has_prefill = False
has_decode = False
prefill_tokens_this_step = 0
decode_tokens_this_step = 0
generated_tokens_this_step = 0
for req in reqs:
generated_tokens_this_step += req.new_tokens
running_sequence = req.running_sequence
if req.is_prefilling:
has_prefill = True
prefill_tokens_this_step += len(running_sequence or [])
else:
has_decode = True
decode_tokens_this_step += req.new_tokens
self._batch_generated_tokens += generated_tokens_this_step
self._prefill_batch_tokens += prefill_tokens_this_step
self._decode_batch_tokens += decode_tokens_this_step
if has_prefill:
self._prefill_batch_time += step_time
if has_decode:
self._decode_batch_time += step_time
for req in reqs:
prompt_idx = (req_id_to_prompt_id or {}).get(req.req_id, req.req_id)
if prompt_idx >= len(self.trajectories):
continue
cur_trajectory = self.trajectories[prompt_idx]
step_id = len(cur_trajectory.trajectory)
# Per-block trace: mask ratio and active status
block_trace = []
if os.environ.get("DIFFULEX_SAVE_TRACE", "1") != "0":
for block in req.dllm_block_buffer.dllm_blocks:
block_trace.append({
"block_id": block.block_id,
"is_active": block.is_active,
"is_dummy": block.is_dummy,
"num_mask_tokens": block.num_mask_tokens,
"mask_ratio": block.num_mask_tokens / max(block.block_size, 1),
"progress": block.progress,
"block_status": str(block.status) if hasattr(block, "status") else "?",
})
cur_trajectory.trajectory.append(
ReqStep(
step_id=step_id,
step_time=step_time,
is_prefill=req.is_prefilling,
num_generated_tokens=req.new_tokens,
running_token_ids=(
req.running_sequence.copy() if req.running_sequence is not None else []
),
block_size=req.block_size,
buffer_bids=[block.block_id for block in req.dllm_block_buffer.dllm_blocks],
block_trace=block_trace,
)
)
cur_trajectory.token_ids = req.truncated_response.copy() if req.truncated_response else []
cur_trajectory.full_token_ids = list(req.full_response)
cur_trajectory.is_truncated = req.is_truncated
cur_trajectory.max_new_tokens_reached = req.max_new_tokens_reached
cur_trajectory.max_model_len_reached = req.max_model_len_reached
cur_trajectory.max_nfe_reached = req.max_nfe_reached
cur_trajectory.max_repetition_run_reached = req.max_repetition_run_reached
cur_trajectory.eos_token_generated = req.eos_token_generated
cur_trajectory.completion_reason = req.completion_reason
def postfix(self) -> dict:
return dict(
tpf=f"{self.tpf:.2f}tok/step",
ttft=f"{self.ttft:.2f}s",
tpot=f"{self.tpot:.2f}s",
e2eps=f"{self.e2e_throughput:.2f}tok/s",
ptps=f"{self.prefill_throughput:.2f}tok/s",
dtps=f"{self.decode_throughput:.2f}tok/s",
)
def fast_postfix(self) -> dict:
"""Lightweight postfix using pre-accumulated counters — O(1) per call."""
steps = max(self._batch_step_count, 1)
elapsed = max(self._batch_total_time, 0.001)
decode_elapsed = max(self._decode_batch_time, 0.001)
return dict(
tpf=f"{self._batch_generated_tokens / steps:.2f}tok/step",
dtps=f"{self._decode_batch_tokens / decode_elapsed:.2f}tok/s",
e2eps=f"{self._batch_generated_tokens / elapsed:.2f}tok/s",
)
def log_summary(self):
logger.info("--------------------------------")
logger.info("Generation Outputs Summary:")
logger.info("--------------------------------")
logger.info(f"Total Tokens: {sum(len(trajectory.token_ids) for trajectory in self.trajectories)} toks")
logger.info(f"Total NFEs: {self.batch_step_count} nfes (steps)")
logger.info(f"Total Time: {self.total_time} sec")
logger.info(f"E2E Time: {self.e2e_total_time} sec")
logger.info(f"TPF: {self.tpf:.2f} tok/step")
logger.info(f"TTFT: {self.ttft:.2f} sec")
logger.info(f"TPOT: {self.tpot:.2f} sec")
logger.info(f"E2E Throughput: {self.e2e_throughput:.2f} tok/sec")
logger.info(f"Prefill Throughput: {self.prefill_throughput:.2f} tok/sec")
logger.info(f"Decode Throughput: {self.decode_throughput:.2f} tok/sec")
logger.info(f"Avg E2E TPS (per-sample mean): {self.avg_e2e_tps:.2f} tok/sec")
logger.info(f"Avg Decode TPS (per-sample mean): {self.avg_decode_tps:.2f} tok/sec")
logger.info("--------------------------------")
def convert_to_text(self, tokenizer):
eos = getattr(tokenizer, "eos_token", None) or ""
for trajectory in self.trajectories:
gen_full = trajectory.full_token_ids if trajectory.full_token_ids else trajectory.token_ids
raw_full = decode_token_ids_robust(tokenizer, gen_full)
trajectory.full_text = raw_full
raw_trunc = decode_token_ids_robust(tokenizer, trajectory.token_ids)
trajectory.text = raw_trunc.split(eos)[0] if eos else raw_trunc
def to_benchmark_format(self) -> list[dict]:
"""Convert to list of dicts expected by diffulex_bench: text, token_ids, nfe."""
return [
dict(
text=t.text or "",
full_text=(t.full_text if t.full_text is not None else t.text or ""),
token_ids=t.token_ids if t.token_ids is not None else [],
nfe=len(t.trajectory),
)
for t in self.trajectories
]
|