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