305 lines
11 KiB
Python
305 lines
11 KiB
Python
#!/usr/bin/env python
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''' Contains the handler function that will be called by the serverless worker. '''
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# Start the vLLM serving layer on our RunPod worker.
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from typing import Generator
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from metrics import vllm_log_system_stats
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from templates import DEFAULT_TEMPLATE, LLAMA2_TEMPLATE
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from vllm import AsyncLLMEngine, SamplingParams, AsyncEngineArgs
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from vllm.utils import random_uuid
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import runpod
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import os
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# Prepare the model and tokenizer
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MODEL_NAME = os.environ.get('MODEL_NAME')
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MODEL_BASE_PATH = os.environ.get('MODEL_BASE_PATH', '/runpod-volume/')
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STREAMING = os.environ.get('STREAMING', False) == 'True'
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TOKENIZER = os.environ.get('TOKENIZER', None)
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USE_FULL_METRICS = os.environ.get('USE_FULL_METRICS', False)
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if not MODEL_NAME:
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print("Error: The model has not been provided.")
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# Tensor parallelism
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try:
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NUM_GPU_SHARD = int(os.environ.get('NUM_GPU_SHARD', 1))
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except ValueError:
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print("Error: NUM_GPU_SHARD should be an integer. Using default value of 1.")
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NUM_GPU_SHARD = 1
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# Prepare the engine's arguments
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engine_args = AsyncEngineArgs(
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model=f"{MODEL_BASE_PATH}{MODEL_NAME.split('/')[1]}",
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tokenizer=TOKENIZER,
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tokenizer_mode="auto",
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tensor_parallel_size=NUM_GPU_SHARD,
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dtype="auto",
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seed=0,
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max_num_batched_tokens=8192,
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disable_log_stats=False,
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# max_num_seqs=256,
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)
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# Create the vLLM asynchronous engine
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llm = AsyncLLMEngine.from_engine_args(engine_args)
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# Incorporate metrics tracking
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llm.engine._log_system_stats = lambda x, y: vllm_log_system_stats(
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llm.engine, x, y)
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def concurrency_controller() -> bool:
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# Calculate pending sequences
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total_pending_sequences = len(llm.engine.scheduler.waiting) + len(llm.engine.scheduler.swapped)
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print("Total pending sequences in vLLM queue: {}".format(total_pending_sequences))
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# Enable auto-scaling if pending sequences exist
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return total_pending_sequences > 0
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def prepare_metrics() -> dict:
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# The vLLM metrics are updated every 5 seconds, see metrics.py for the _LOGGING_INTERVAL_SEC field.
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if hasattr(llm.engine, 'metrics'):
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return llm.engine.metrics
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else:
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return {}
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# Validation
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def validate_sampling_params(sampling_params):
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def validate_int(value, default):
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try:
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return int(value)
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except (TypeError, ValueError):
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return default
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def validate_float(value, default):
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try:
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return float(value)
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except (TypeError, ValueError):
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return default
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def validate_bool(value, default):
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if isinstance(value, bool):
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return value
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return default
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n = validate_int(sampling_params.get('n'), 1)
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best_of = validate_int(sampling_params.get('best_of'), None)
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presence_penalty = validate_float(
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sampling_params.get('presence_penalty'), 0.0)
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frequency_penalty = validate_float(
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sampling_params.get('frequency_penalty'), 0.0)
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temperature = validate_float(sampling_params.get('temperature'), 1.0)
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top_p = validate_float(sampling_params.get('top_p'), 1.0)
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top_k = validate_int(sampling_params.get('top_k'), -1)
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use_beam_search = validate_bool(
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sampling_params.get('use_beam_search'), False)
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stop = sampling_params.get('stop', None)
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ignore_eos = validate_bool(sampling_params.get('ignore_eos'), False)
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max_tokens = validate_int(sampling_params.get('max_tokens'), 256)
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logprobs = validate_float(sampling_params.get('logprobs'), None)
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return {
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'n': n,
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'best_of': best_of,
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'presence_penalty': presence_penalty,
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'frequency_penalty': frequency_penalty,
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'temperature': temperature,
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'top_p': top_p,
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'top_k': top_k,
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'use_beam_search': use_beam_search,
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'stop': stop,
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'ignore_eos': ignore_eos,
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'max_tokens': max_tokens,
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'logprobs': logprobs,
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}
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def validate_and_set_sampling_params(sampling_params):
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"""
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Validates the given sampling parameters and creates a SamplingParams object.
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If no sampling parameters are provided, defaults are used.
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"""
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if sampling_params:
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validated_params = validate_sampling_params(sampling_params)
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# https://github.com/vllm-project/vllm/blob/main/vllm/sampling_params.py#L7
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return SamplingParams(**validated_params)
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return SamplingParams()
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async def handler_streaming(job: dict) -> Generator[dict[str, list], None, None]:
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'''
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This is the handler function that will be called by the serverless worker.
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'''
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print("Job received by handler: {}".format(job))
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# Retrieve the job input.
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job_input = job['input']
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# Utilize the built-in llama2 template if a llama2 base model is being employed.
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llama_models = ["llama-2-7b-chat-hf", "llama-2-13b-chat-hf", "llama-2-70b-chat-hf", "elinas/chronos-13b-v2"]
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if any(model_name.lower() in MODEL_NAME.lower() for model_name in llama_models):
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template = LLAMA2_TEMPLATE
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else:
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template = DEFAULT_TEMPLATE
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# Create the prompt using the template.
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prompt = template(job_input['prompt'])
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# Validate and set sampling parameters
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sampling_params = validate_and_set_sampling_params(job_input.get('sampling_params', None))
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# Print job input and sampling parameters
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print("Job Input:", job_input)
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print("Sampling Parameters:", sampling_params)
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# Send request to VLLM
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request_id = random_uuid()
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results_generator = llm.generate(prompt, sampling_params, request_id)
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# Keep track of the stream's information to perform the appropriate chunking.
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class Tracker():
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def __init__(self):
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self.positions = None
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self.stream_index = 0
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def inc_stream_idx(self):
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self.stream_index +=1
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tracker = Tracker()
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def extract_next_chunk(request_output):
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"""
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Extracts and processes generated chunks and token counts from the request output.
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Args:
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request_output (CompletionOutput): The output of a language model request.
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Returns:
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tuple: A tuple containing two lists - chunk_outputs (extracted chunks) and num_output_tokens (generated token counts).
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"""
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chunk_outputs = [] # List to store extracted chunks
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num_output_tokens = [] # List to store generated token counts
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# Iterate over each completion in the request output
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for idx, completion in enumerate(request_output.outputs):
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# Extract the current chunk position from the tracker
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chunk_pos = tracker.positions[idx]['chunk_pos']
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# Append the chunk to the output
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chunk_outputs.append(completion.text[chunk_pos:])
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# Update the chunk position in the tracker
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tracker.positions[idx]['chunk_pos'] = len(completion.text)
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# Calculate the number of generated tokens in the current completion
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num_generated_tokens = len(completion.token_ids) - tracker.positions[idx]['token_pos']
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# Append the token count to the output
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num_output_tokens.append(num_generated_tokens)
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# Update the token position in the tracker
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tracker.positions[idx]['token_pos'] = len(completion.token_ids)
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return chunk_outputs, num_output_tokens
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async for request_output in results_generator:
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# Initialize chunk positions if not already done
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if tracker.positions is None:
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tracker.positions = [{'chunk_pos': 0, 'token_pos': 0}] * len(request_output.outputs)
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# Metrics for the vLLM serverless worker
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runpod_metrics = prepare_metrics() if USE_FULL_METRICS else {}
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# Number of generated sequences
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num_seqs = sampling_params.n
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# Extract the next chunk from the output
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text_outputs, output_tokens = extract_next_chunk(request_output)
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# Record job input and token counts
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runpod_metrics['job_input'] = job_input
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runpod_metrics['input_tokens'] = len(request_output.prompt_token_ids) * num_seqs
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runpod_metrics['output_tokens'] = output_tokens
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# Store the scenario type and stream index
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runpod_metrics['scenario'] = 'stream'
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runpod_metrics['stream_index'] = tracker.stream_index
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# Increment the index within the stream
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tracker.inc_stream_idx()
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ret = {
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"text": text_outputs,
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"metrics": runpod_metrics,
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"final_output": [output.text for output in request_output.outputs] # Temporary, for debugging purposes.
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}
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yield ret
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async def handler(job: dict) -> dict[str, list]:
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'''
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This is the handler function that will be called by the serverless worker.
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'''
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print("Job received by handler: {}".format(job))
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# Retrieve the job input.
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job_input = job['input']
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# Utilize the built-in llama2 template if a llama2 base model is being employed.
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llama_models = ["llama-2-7b-chat-hf", "llama-2-13b-chat-hf", "llama-2-70b-chat-hf", "elinas/chronos-13b-v2"]
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if any(model_name.lower() in MODEL_NAME.lower() for model_name in llama_models):
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template = LLAMA2_TEMPLATE
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else:
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template = DEFAULT_TEMPLATE
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# Create the prompt using the template.
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prompt = template(job_input['prompt'])
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# Validate and set sampling parameters
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sampling_params = validate_and_set_sampling_params(job_input.get('sampling_params', None))
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# Print job input and sampling parameters
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print("Job Input:", job_input)
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print("Sampling Parameters:", sampling_params)
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# Send request to VLLM
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request_id = random_uuid()
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results_generator = llm.generate(prompt, sampling_params, request_id)
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# Get the final generated output
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final_output = None
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async for request_output in results_generator:
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final_output = request_output
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# Extract prompt and text outputs
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prompt = final_output.prompt
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text_outputs = [output.text for output in final_output.outputs]
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# Number of generated sequences
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num_seqs = sampling_params.n
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# Prepare metrics if full metrics are enabled
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runpod_metrics = prepare_metrics() if USE_FULL_METRICS else {}
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# Record job input and token counts
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runpod_metrics['job_input'] = job_input
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runpod_metrics['input_tokens'] = len(final_output.prompt_token_ids) * num_seqs
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runpod_metrics['output_tokens'] = sum([len(output.token_ids) for output in final_output.outputs])
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# Store the scenario type
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runpod_metrics['scenario'] = 'batch'
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ret = {
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"text": text_outputs,
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"metrics": runpod_metrics
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}
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return ret
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# Start the serverless worker with appropriate settings
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if STREAMING:
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print("Starting the vLLM serverless worker with streaming enabled.")
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runpod.serverless.start({"handler": handler_streaming, "concurrency_controller": concurrency_controller, "return_aggregate_stream": True})
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else:
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print("Starting the vLLM serverless worker with streaming disabled.")
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runpod.serverless.start({"handler": handler, "concurrency_controller": concurrency_controller})
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