Use monotonic time where appropriate (#1249)
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@@ -121,7 +121,7 @@ class Scheduler:
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blocks_to_copy: Dict[int, List[int]] = {}
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# Fix the current time.
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now = time.time()
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now = time.monotonic()
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# Join waiting sequences if possible.
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if not self.swapped:
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@@ -417,7 +417,8 @@ class AsyncLLMEngine:
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request.
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"""
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# Preprocess the request.
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arrival_time = time.time()
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# This should not be used for logging, as it is monotonic time.
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arrival_time = time.monotonic()
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try:
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stream = await self.add_request(request_id,
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@@ -256,10 +256,10 @@ class LLMEngine:
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prompt_token_ids: The token IDs of the prompt. If None, we
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use the tokenizer to convert the prompts to token IDs.
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arrival_time: The arrival time of the request. If None, we use
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the current time.
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the current monotonic time.
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"""
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if arrival_time is None:
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arrival_time = time.time()
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arrival_time = time.monotonic()
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if prompt_token_ids is None:
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assert prompt is not None
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prompt_token_ids = self.tokenizer.encode(prompt)
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@@ -568,7 +568,7 @@ class LLMEngine:
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prompt_run: bool,
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num_batched_tokens: int,
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) -> None:
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now = time.time()
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now = time.monotonic()
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# Log the number of batched input tokens.
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if prompt_run:
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self.num_prompt_tokens.append((now, num_batched_tokens))
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@@ -210,7 +210,7 @@ async def create_chat_completion(request: ChatCompletionRequest,
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model_name = request.model
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request_id = f"cmpl-{random_uuid()}"
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created_time = int(time.time())
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created_time = int(time.monotonic())
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try:
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sampling_params = SamplingParams(
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n=request.n,
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@@ -411,7 +411,7 @@ async def create_completion(request: CompletionRequest, raw_request: Request):
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if error_check_ret is not None:
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return error_check_ret
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created_time = int(time.time())
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created_time = int(time.monotonic())
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try:
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sampling_params = SamplingParams(
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n=request.n,
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