[Benchmark] [Diffusion] [Enhancement] Random dataset (#1657)
Signed-off-by: dengyunyang <584797741@qq.com> Signed-off-by: Samit <285365963@qq.com> Signed-off-by: bjf-frz <frz123db@gmail.com> Co-authored-by: Samit <285365963@qq.com> Co-authored-by: bjf-frz <frz123db@gmail.com>
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@@ -1,3 +1,4 @@
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import asyncio
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import base64
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import mimetypes
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import os
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@@ -206,7 +207,140 @@ async def async_request_openai_images(
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return output
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async def async_request_v1_videos(
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input: RequestFuncInput,
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session: aiohttp.ClientSession,
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pbar: tqdm | None = None,
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) -> RequestFuncOutput:
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output = RequestFuncOutput()
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output.start_time = time.perf_counter()
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files = dict(input.extra_body)
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if input.prompt:
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files.setdefault("prompt", input.prompt)
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if input.width and input.height:
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files.setdefault("height", input.height)
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files.setdefault("width", input.width)
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if input.num_frames:
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files.setdefault("num_frames", input.num_frames)
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if input.num_inference_steps:
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files.setdefault("num_inference_steps", input.num_inference_steps)
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if input.seed is not None:
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files.setdefault("seed", input.seed)
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if input.fps:
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files.setdefault("fps", input.fps)
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form = aiohttp.FormData()
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for k, v in files.items():
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form.add_field(k, str(v))
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image_file = None
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if input.image_paths and len(input.image_paths) > 0:
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image_path = input.image_paths[0]
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image_file = open(image_path, "rb")
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form.add_field(
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"input_reference",
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image_file,
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filename=os.path.basename(image_path),
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content_type="application/octet-stream",
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)
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job_id = None
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job_status = None
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poll_json = {}
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resp_json = {}
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try:
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# invoke a post request (POST /v1/videos)
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async with session.post(input.api_url, data=form) as response:
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if response.status == 200:
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resp_json = await response.json()
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job_id = resp_json.get("id")
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job_status = resp_json.get("status")
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if not job_id or not job_status:
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output.error = "API response missing job 'id' or 'status' field."
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output.success = False
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return output
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else:
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output.error = f"HTTP {response.status}: {await response.text()}"
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output.success = False
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return output
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# invoke a poll request (GET /v1/videos/{video_id})
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poll_interval = 2.0 # Unit(s)
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timeout_seconds = 600.0
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deadline = time.perf_counter() + timeout_seconds
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job_url = f"{input.api_url}/{job_id}"
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while job_status not in {"completed", "failed"}:
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await asyncio.sleep(poll_interval)
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async with session.get(job_url) as poll_response:
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if poll_response.status != 200:
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output.error = f"Polling failed HTTP {poll_response.status}: {await poll_response.text()}"
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output.success = False
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return output
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poll_json = await poll_response.json()
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job_status = poll_json.get("status")
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if time.perf_counter() >= deadline:
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output.error = f"Timed out waiting for video job {job_id} to complete."
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output.success = False
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return output
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if job_status == "failed":
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output.error = f"Video job failed: {poll_json}"
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output.success = False
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return output
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# invoke a get request (GET /v1/videos/{video_id}/content)
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content_url = f"{job_url}/content"
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async with session.get(content_url) as content_response:
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if content_response.status != 200:
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output.error = (
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f"Content retrieval failed HTTP {content_response.status}: {await content_response.text()}"
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)
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output.success = False
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return output
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video_bytes = await content_response.read()
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output.response_body = video_bytes
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output.success = True
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if "peak_memory_mb" in poll_json:
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output.peak_memory_mb = poll_json["peak_memory_mb"]
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elif "peak_memory_mb" in resp_json:
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output.peak_memory_mb = resp_json["peak_memory_mb"]
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except Exception as e:
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output.error = str(e)
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output.success = False
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finally:
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if image_file is not None:
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image_file.close()
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if job_id is not None:
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try:
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async with session.delete(f"{input.api_url}/{job_id}") as _:
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pass
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except Exception as e:
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print(f"Failed to clean up video job {job_id}: {e}")
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output.latency = time.perf_counter() - output.start_time
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if output.success and input.slo_ms is not None:
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output.slo_achieved = (output.latency * 1000.0) <= float(input.slo_ms)
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if pbar:
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pbar.update(1)
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return output
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backends_function_mapping = {
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"vllm-omni": (async_request_chat_completions, "/v1/chat/completions"),
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"openai": (async_request_openai_images, "/v1/images/generations"),
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"2i": {
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"vllm-omni": (async_request_chat_completions, "/v1/chat/completions"),
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"openai": (async_request_openai_images, "/v1/images/generations"),
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},
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"2v": {
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"v1/videos": (async_request_v1_videos, "/v1/videos"),
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},
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}
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