121 lines
4.5 KiB
Python
121 lines
4.5 KiB
Python
# Copied and adapted from: https://github.com/hao-ai-lab/FastVideo
|
|
|
|
# SPDX-License-Identifier: Apache-2.0
|
|
# Adapted from vllm: https://github.com/vllm-project/vllm/blob/v0.7.3/vllm/forward_context.py
|
|
import time
|
|
from collections import defaultdict
|
|
from contextlib import contextmanager
|
|
from dataclasses import dataclass
|
|
from typing import TYPE_CHECKING, Optional, Type
|
|
|
|
import torch
|
|
|
|
from sglang.multimodal_gen.runtime.utils.logging_utils import init_logger
|
|
|
|
if TYPE_CHECKING:
|
|
from sglang.multimodal_gen.runtime.layers.attention import AttentionMetadata
|
|
from sglang.multimodal_gen.runtime.pipelines_core import Req
|
|
|
|
logger = init_logger(__name__)
|
|
|
|
# TODO(will): check if this is needed
|
|
# track_batchsize: bool = envs.SGLANG_DIFFUSION_LOG_BATCHSIZE_INTERVAL >= 0
|
|
track_batchsize: bool = False
|
|
last_logging_time: float = 0
|
|
forward_start_time: float = 0
|
|
# batchsize_logging_interval: float = envs.SGLANG_DIFFUSION_LOG_BATCHSIZE_INTERVAL
|
|
batchsize_logging_interval: float = 1000
|
|
batchsize_forward_time: defaultdict = defaultdict(list)
|
|
|
|
|
|
@dataclass
|
|
class ForwardContext:
|
|
current_timestep: int
|
|
# TODO(will): check this arg
|
|
# copy from vllm_config.compilation_config.static_forward_context
|
|
# attn_layers: Dict[str, Any]
|
|
# TODO: extend to support per-layer dynamic forward context
|
|
attn_metadata: "AttentionMetadata" # set dynamically for each forward pass
|
|
forward_batch: Optional["Req"] = None
|
|
attention_backend_cls: Optional[Type] = None
|
|
|
|
def set_attn_backend_cls(self, attention_backend_cls: Type):
|
|
if self.attention_backend_cls:
|
|
if self.attention_backend_cls != attention_backend_cls:
|
|
raise RuntimeError(
|
|
f"Different types of attention backend in a same context detected, previous: {self.attention_backend_cls}, new: {attention_backend_cls}"
|
|
)
|
|
else:
|
|
self.attention_backend_cls = attention_backend_cls
|
|
|
|
|
|
_forward_context: Optional["ForwardContext"] = None
|
|
|
|
|
|
def get_forward_context() -> "ForwardContext":
|
|
"""Get the current forward context."""
|
|
assert _forward_context is not None, (
|
|
"Forward context is not set. "
|
|
"Please use `set_forward_context` to set the forward context."
|
|
)
|
|
return _forward_context
|
|
|
|
|
|
# TODO(will): finalize the interface
|
|
@contextmanager
|
|
def set_forward_context(
|
|
current_timestep, attn_metadata, forward_batch: Optional["Req"] = None
|
|
):
|
|
"""A context manager that stores the current forward context,
|
|
can be attention metadata, etc.
|
|
Here we can inject common logic for every model forward pass.
|
|
"""
|
|
global forward_start_time
|
|
need_to_track_batchsize = track_batchsize and attn_metadata is not None
|
|
if need_to_track_batchsize:
|
|
forward_start_time = time.perf_counter()
|
|
global _forward_context
|
|
prev_context = _forward_context
|
|
_forward_context = ForwardContext(
|
|
current_timestep=current_timestep,
|
|
attn_metadata=attn_metadata,
|
|
forward_batch=forward_batch,
|
|
)
|
|
|
|
try:
|
|
yield
|
|
finally:
|
|
global last_logging_time, batchsize_logging_interval
|
|
if need_to_track_batchsize:
|
|
if hasattr(attn_metadata, "num_prefill_tokens"):
|
|
# for v0 attention backends
|
|
batchsize = (
|
|
attn_metadata.num_prefill_tokens + attn_metadata.num_decode_tokens
|
|
)
|
|
else:
|
|
# for v1 attention backends
|
|
batchsize = attn_metadata.num_input_tokens
|
|
now = time.perf_counter()
|
|
# time measurement is in milliseconds
|
|
batchsize_forward_time[batchsize].append((now - forward_start_time) * 1000)
|
|
if now - last_logging_time > batchsize_logging_interval:
|
|
last_logging_time = now
|
|
forward_stats = []
|
|
for bs, times in batchsize_forward_time.items():
|
|
if len(times) <= 1:
|
|
# can be cudagraph / profiling run
|
|
continue
|
|
medium = torch.quantile(torch.tensor(times), q=0.5).item()
|
|
medium = round(medium, 2)
|
|
forward_stats.append((bs, len(times), medium))
|
|
forward_stats.sort(key=lambda x: x[1], reverse=True)
|
|
if forward_stats:
|
|
logger.info(
|
|
(
|
|
"Batchsize forward time stats "
|
|
"(batchsize, count, median_time(ms)): %s"
|
|
),
|
|
forward_stats,
|
|
)
|
|
_forward_context = prev_context
|