feat: vllm timings patch
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"""
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Disk-edit patch for vLLM nightly-07351e0883470724dd5a7e9730ed10e01fc99d08:
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inject llama.cpp-compatible `timings` into chat/completion API responses.
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Adds `timings` to:
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- /v1/chat/completions non-streaming responses
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- /v1/chat/completions streaming final usage chunk
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- /v1/completions non-streaming responses
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- /v1/completions streaming final usage chunk
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The `timings` object matches llama.cpp fields consumed by llama-swap:
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prompt_n, prompt_ms, prompt_per_second,
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predicted_n, predicted_ms, predicted_per_second, cache_n
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Usage, before `exec vllm serve`:
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python3 /patches/patch_timings.py
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"""
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import logging
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import os
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import sys
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log = logging.getLogger("patch_timings")
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log.setLevel(logging.INFO)
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if not log.handlers:
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log.addHandler(logging.StreamHandler())
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PATCH_TAG = "# [patch_timings]"
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TIMINGS_HELPER = f'''
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{PATCH_TAG}
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def _compute_timings(metrics, num_prompt, num_gen, num_cached=None):
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"""Compute llama.cpp-compatible timings from RequestStateStats."""
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t = {{
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"prompt_n": num_prompt,
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"prompt_ms": 0.0,
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"prompt_per_second": 0.0,
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"predicted_n": num_gen,
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"predicted_ms": 0.0,
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"predicted_per_second": 0.0,
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"cache_n": num_cached if num_cached is not None else -1,
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}}
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if metrics is None:
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return t
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if metrics.first_token_ts > 0 and metrics.scheduled_ts > 0:
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ps = metrics.first_token_ts - metrics.scheduled_ts
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if ps > 0:
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t["prompt_ms"] = ps * 1000.0
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t["prompt_per_second"] = num_prompt / ps
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if metrics.last_token_ts > 0 and metrics.first_token_ts > 0:
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ds = metrics.last_token_ts - metrics.first_token_ts
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if ds > 0:
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t["predicted_ms"] = ds * 1000.0
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t["predicted_per_second"] = num_gen / ds
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return t
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'''
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def _find_vllm_dir():
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"""Auto-discover vLLM install directory."""
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try:
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import vllm
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return os.path.dirname(vllm.__file__)
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except ImportError:
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pass
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for path in [
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"/usr/local/lib/python3.12/dist-packages/vllm",
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"/usr/lib/python3.12/site-packages/vllm",
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]:
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if os.path.isdir(path):
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return path
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return None
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def _read(path):
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with open(path, "r") as f:
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return f.read()
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def _write(path, content):
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with open(path, "w") as f:
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f.write(content)
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def _replace_once(content, old, new, label):
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count = content.count(old)
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if count != 1:
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raise RuntimeError(f"{label}: anchor matched {count} times")
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return content.replace(old, new, 1)
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def _patch_protocol(path, label, replacements):
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if not os.path.exists(path):
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log.error(" %s: file not found: %s", label, path)
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return False
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content = _read(path)
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if PATCH_TAG in content:
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log.info(" %s: already patched, skipping", label)
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return True
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try:
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for old, new in replacements:
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content = _replace_once(content, old, new, label)
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except RuntimeError as e:
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log.error(" %s", e)
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return False
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_write(path, content)
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log.info(" %s: patched successfully", label)
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return True
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def _patch_chat_protocol(vllm_dir):
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path = os.path.join(vllm_dir, "entrypoints/openai/chat_completion/protocol.py")
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return _patch_protocol(path, "chat_completion/protocol.py", [
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(
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''' kv_transfer_params: dict[str, Any] | None = Field(\n default=None, description="KVTransfer parameters."\n )\n''',
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''' kv_transfer_params: dict[str, Any] | None = Field(\n default=None, description="KVTransfer parameters."\n )\n\n # llama.cpp-compatible per-request timings # [patch_timings]\n timings: dict[str, Any] | None = None\n''',
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),
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(
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''' # not part of the OpenAI spec but for tracing the tokens\n prompt_token_ids: list[int] | None = None\n''',
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''' # not part of the OpenAI spec but for tracing the tokens\n prompt_token_ids: list[int] | None = None\n\n # llama.cpp-compatible per-request timings # [patch_timings]\n timings: dict[str, Any] | None = None\n''',
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),
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])
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def _patch_completion_protocol(vllm_dir):
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path = os.path.join(vllm_dir, "entrypoints/openai/completion/protocol.py")
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return _patch_protocol(path, "completion/protocol.py", [
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(
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''' kv_transfer_params: dict[str, Any] | None = Field(\n default=None, description="KVTransfer parameters."\n )\n''',
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''' kv_transfer_params: dict[str, Any] | None = Field(\n default=None, description="KVTransfer parameters."\n )\n\n # llama.cpp-compatible per-request timings # [patch_timings]\n timings: dict[str, Any] | None = None\n''',
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),
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(
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'''class CompletionStreamResponse(OpenAIBaseModel):\n id: str = Field(default_factory=lambda: f"cmpl-{random_uuid()}")\n object: str = "text_completion"\n created: int = Field(default_factory=lambda: int(time.time()))\n model: str\n choices: list[CompletionResponseStreamChoice]\n usage: UsageInfo | None = Field(default=None)\n''',
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'''class CompletionStreamResponse(OpenAIBaseModel):\n id: str = Field(default_factory=lambda: f"cmpl-{random_uuid()}")\n object: str = "text_completion"\n created: int = Field(default_factory=lambda: int(time.time()))\n model: str\n choices: list[CompletionResponseStreamChoice]\n usage: UsageInfo | None = Field(default=None)\n\n # llama.cpp-compatible per-request timings # [patch_timings]\n timings: dict[str, Any] | None = None\n''',
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),
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])
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def _patch_chat_serving(vllm_dir):
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path = os.path.join(vllm_dir, "entrypoints/openai/chat_completion/serving.py")
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label = "chat_completion/serving.py"
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if not os.path.exists(path):
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log.error(" %s: file not found: %s", label, path)
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return False
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content = _read(path)
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if PATCH_TAG in content:
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log.info(" %s: already patched, skipping", label)
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return True
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try:
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# Helper Function
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content = _replace_once(
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content,
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"class OpenAIServingChat(OpenAIServing):",
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TIMINGS_HELPER + "\n\nclass OpenAIServingChat(OpenAIServing):",
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label,
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)
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# Streaming Last Result Capture - first streaming loop only.
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content = _replace_once(
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content,
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" async for res in result_generator:\n if res.prompt_token_ids is not None:",
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f" async for res in result_generator:\n _last_stream_res = res {PATCH_TAG}\n if res.prompt_token_ids is not None:",
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label,
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)
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# Streaming Final Usage Chunk - pinned image has no system_fingerprint arg.
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content = _replace_once(
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content,
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''' final_usage_chunk = ChatCompletionStreamResponse(\n id=request_id,\n object=chunk_object_type,\n created=created_time,\n choices=[],\n model=model_name,\n usage=final_usage,\n )\n''',
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f''' final_usage_chunk = ChatCompletionStreamResponse(\n id=request_id,\n object=chunk_object_type,\n created=created_time,\n choices=[],\n model=model_name,\n usage=final_usage,\n )\n # Inject Timings {PATCH_TAG}\n try:\n _s_cached = _last_stream_res.num_cached_tokens\n final_usage_chunk.timings = _compute_timings(\n _last_stream_res.metrics,\n num_prompt_tokens, completion_tokens, _s_cached,\n )\n except NameError:\n pass\n''',
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label,
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)
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# Non-Streaming Response - pinned image has no system_fingerprint arg.
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content = _replace_once(
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content,
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''' response = ChatCompletionResponse(\n id=request_id,\n created=created_time,\n model=model_name,\n choices=choices,\n usage=usage,\n prompt_logprobs=clamp_prompt_logprobs(final_res.prompt_logprobs),\n prompt_token_ids=(\n final_res.prompt_token_ids if request.return_token_ids else None\n ),\n kv_transfer_params=final_res.kv_transfer_params,\n )\n''',
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f''' response = ChatCompletionResponse(\n id=request_id,\n created=created_time,\n model=model_name,\n choices=choices,\n usage=usage,\n prompt_logprobs=clamp_prompt_logprobs(final_res.prompt_logprobs),\n prompt_token_ids=(\n final_res.prompt_token_ids if request.return_token_ids else None\n ),\n kv_transfer_params=final_res.kv_transfer_params,\n )\n\n # Inject Timings {PATCH_TAG}\n _cached = final_res.num_cached_tokens\n response.timings = _compute_timings(\n final_res.metrics, num_prompt_tokens, num_generated_tokens,\n _cached,\n )\n''',
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label,
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)
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except RuntimeError as e:
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log.error(" %s", e)
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return False
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_write(path, content)
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log.info(" %s: patched successfully", label)
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return True
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def _patch_completion_serving(vllm_dir):
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path = os.path.join(vllm_dir, "entrypoints/openai/completion/serving.py")
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label = "completion/serving.py"
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if not os.path.exists(path):
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log.error(" %s: file not found: %s", label, path)
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return False
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content = _read(path)
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if PATCH_TAG in content:
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log.info(" %s: already patched, skipping", label)
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return True
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try:
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# Helper Function
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content = _replace_once(
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content,
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"class OpenAIServingCompletion(OpenAIServing):",
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TIMINGS_HELPER + "\n\nclass OpenAIServingCompletion(OpenAIServing):",
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label,
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)
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# Streaming Last Result Capture.
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content = _replace_once(
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content,
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" async for prompt_idx, res in result_generator:\n prompt_token_ids = res.prompt_token_ids",
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f" async for prompt_idx, res in result_generator:\n _last_comp_res = res {PATCH_TAG}\n prompt_token_ids = res.prompt_token_ids",
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label,
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)
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# Streaming Final Usage Chunk - pinned image has no system_fingerprint arg.
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content = _replace_once(
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content,
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''' final_usage_chunk = CompletionStreamResponse(\n id=request_id,\n created=created_time,\n model=model_name,\n choices=[],\n usage=final_usage_info,\n )\n''',
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f''' final_usage_chunk = CompletionStreamResponse(\n id=request_id,\n created=created_time,\n model=model_name,\n choices=[],\n usage=final_usage_info,\n )\n # Inject Timings {PATCH_TAG}\n try:\n _sc_cached = _last_comp_res.num_cached_tokens\n final_usage_chunk.timings = _compute_timings(\n _last_comp_res.metrics,\n total_prompt_tokens, total_completion_tokens,\n _sc_cached,\n )\n except NameError:\n pass\n''',
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label,
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)
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# Non-Streaming Response - pinned image has no system_fingerprint arg.
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content = _replace_once(
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content,
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''' return CompletionResponse(\n id=request_id,\n created=created_time,\n model=model_name,\n choices=choices,\n usage=usage,\n kv_transfer_params=kv_transfer_params,\n )\n''',
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f''' _comp_response = CompletionResponse( {PATCH_TAG}\n id=request_id,\n created=created_time,\n model=model_name,\n choices=choices,\n usage=usage,\n kv_transfer_params=kv_transfer_params,\n )\n # Inject Timings {PATCH_TAG}\n if last_final_res is not None:\n _comp_cached = last_final_res.num_cached_tokens\n _comp_response.timings = _compute_timings(\n last_final_res.metrics, num_prompt_tokens,\n num_generated_tokens, _comp_cached,\n )\n return _comp_response\n''',
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label,
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)
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except RuntimeError as e:
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log.error(" %s", e)
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return False
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_write(path, content)
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log.info(" %s: patched successfully", label)
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return True
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def apply():
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vllm_dir = _find_vllm_dir()
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if vllm_dir is None:
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log.error("Could not find vLLM installation directory")
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return False
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log.info("Found vLLM at: %s", vllm_dir)
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ok = True
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ok &= _patch_chat_protocol(vllm_dir)
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ok &= _patch_chat_serving(vllm_dir)
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ok &= _patch_completion_protocol(vllm_dir)
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ok &= _patch_completion_serving(vllm_dir)
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return ok
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if __name__ == "__main__":
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if apply():
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print("patch_timings: All patches applied successfully")
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else:
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print("patch_timings: Some patches failed!", file=sys.stderr)
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sys.exit(1)
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