c8683ae305
Co-authored-by: jianyingzhu <53300651@qq.com> Co-authored-by: Jianying <53503712+jianyingzhu@users.noreply.github.com> Co-authored-by: Mick <mickjagger19@icloud.com>
129 lines
3.9 KiB
Python
129 lines
3.9 KiB
Python
import os
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import torch
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from sglang.multimodal_gen.runtime.utils.logging_utils import init_logger
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logger = init_logger(__name__)
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class SGLDiffusionProfiler:
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"""
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A wrapper around torch.profiler to simplify usage in pipelines.
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Supports both full profiling and scheduled profiling.
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1. if profile_all_stages is on: profile all stages, including all denoising steps
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2. otherwise, if num_profiled_timesteps is specified: profile {num_profiled_timesteps} denoising steps. profile all steps if num_profiled_timesteps==-1
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"""
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_instance = None
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def __init__(
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self,
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request_id: str | None = None,
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rank: int = 0,
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full_profile: bool = False,
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num_steps: int | None = None,
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num_inference_steps: int | None = None,
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log_dir: str = "./logs",
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):
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self.request_id = request_id or "profile_trace"
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self.rank = rank
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self.full_profile = full_profile
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self.log_dir = log_dir
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try:
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os.makedirs(self.log_dir, exist_ok=True)
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except OSError:
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pass
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activities = [torch.profiler.ProfilerActivity.CPU]
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if torch.cuda.is_available():
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activities.append(torch.profiler.ProfilerActivity.CUDA)
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common_torch_profiler_args = dict(
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activities=activities,
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record_shapes=True,
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with_stack=True,
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on_trace_ready=None,
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)
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if self.full_profile:
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# profile all stages
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self.profiler = torch.profiler.profile(**common_torch_profiler_args)
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self.profile_mode_id = "full stages"
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else:
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# profile denoising stage only
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warmup = 1
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num_actual_steps = num_inference_steps if num_steps == -1 else num_steps
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num_active_steps = num_actual_steps + warmup
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self.profiler = torch.profiler.profile(
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**common_torch_profiler_args,
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schedule=torch.profiler.schedule(
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skip_first=0,
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wait=0,
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warmup=warmup,
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active=num_active_steps,
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repeat=1,
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),
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)
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self.profile_mode_id = f"{num_actual_steps} steps"
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logger.info(f"Profiling request: {request_id} for {self.profile_mode_id}...")
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self.has_stopped = False
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SGLDiffusionProfiler._instance = self
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self.start()
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def start(self):
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logger.info("Starting Profiler...")
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self.profiler.start()
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def _step(self):
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self.profiler.step()
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def step_stage(self):
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if self.full_profile:
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self._step()
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def step_denoising_step(self):
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if not self.full_profile:
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self._step()
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@classmethod
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def get_instance(cls) -> "SGLDiffusionProfiler":
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return cls._instance
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def stop(self, export_trace: bool = True, dump_rank: int | None = None):
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if self.has_stopped:
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return
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self.has_stopped = True
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logger.info("Stopping Profiler...")
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if torch.cuda.is_available():
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torch.cuda.synchronize()
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self.profiler.stop()
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if export_trace:
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self._export_trace(dump_rank)
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SGLDiffusionProfiler._instance = None
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def _export_trace(self, dump_rank: int | None = None):
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if dump_rank is None:
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dump_rank = self.rank
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try:
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os.makedirs(self.log_dir, exist_ok=True)
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sanitized_profile_mode_id = self.profile_mode_id.replace(" ", "_")
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trace_path = os.path.abspath(
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os.path.join(
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self.log_dir,
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f"{self.request_id}-{sanitized_profile_mode_id}-global-rank{dump_rank}.trace.json.gz",
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)
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)
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logger.info(f"Saving profiler traces to: {trace_path}")
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self.profiler.export_chrome_trace(trace_path)
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except Exception as e:
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logger.error(f"Failed to export trace: {e}")
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