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annotate asyncio_threads/inference/main.py @ 134:75e7aac67fa2 websocket-blog

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Closing blog branch since it is written
author June Park <parkjune1995@gmail.com>
date Fri, 09 Jan 2026 09:30:30 -0800
parents 46daba6e3cf4
children
Ignore whitespace changes - Everywhere: Within whitespace: At end of lines:
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48
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1 from typing import List, Dict, Any, Optional
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2
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3 class UserRequest:
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4 """Represents an incoming user request."""
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5 def __init__(self, request_id: int, prompt: str):
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6 self.request_id = request_id
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7 self.prompt = prompt
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8 self.output_tokens: List[str] = [] # Stores tokens as they are generated
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9
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10 def __repr__(self):
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11 return f"Request(ID={self.request_id}, Prompt='{self.prompt[:20]}...', Tokens={len(self.output_tokens)})"
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12
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13 # --- Mock LLM Interface ---
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14 def mock_inference_call(prompts: List[str], batch_id: int) -> Dict[str, Any]:
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15 """
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16 Simulates the call to the underlying LLM/GPU.
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17
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18 In a real scenario, this returns generated tokens and associated metadata.
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19 For this mock, we return a flat list of tokens, one for each request
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20 in the batch, and the batch ID for verification.
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21
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22 The length of the tokens list MUST equal the length of the prompts list.
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23 """
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24 print(f" [INFERENCE] Running Batch {batch_id} with {len(prompts)} requests...")
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25 results = {
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26 'batch_id': batch_id,
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27 # Simulate generating a single new token for each request in the batch
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28 'generated_tokens': [
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29 f"token_{i+1}_of_{batch_id}" for i, _ in enumerate(prompts)
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30 ]
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31 }
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32 return results
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33 # -------------------------
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34
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35
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36 class BatchInferenceEngine:
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37 """
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38 A simplified inference engine that handles request batching and
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39 token-level result distribution.
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40 """
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41 def __init__(self, batch_size: int = 4):
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42 self.batch_size = batch_size
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43 self.request_queue: List[UserRequest] = []
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44 self.next_batch_id = 1
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45
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46 def enqueue_request(self, request: UserRequest) -> None:
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47 """
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48 Adds a new request to the queue and triggers batch processing if
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49 the batch size is reached.
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50 """
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51 # --- YOUR CODE HERE ---
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52 # 1. Add the request to the queue.
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53 # 2. Check if the queue size meets or exceeds self.batch_size.
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54 # 3. If so, call self._process_batch().
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55 # ----------------------
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56 self.request_queue.append(request)
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57 while len(self.request_queue) > self.batch_size:
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58 self._process_batch()
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59
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60
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61 def _process_batch(self) -> None:
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62 """
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63 Executes the inference call for the current batch and distributes
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64 the results back to the individual requests.
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65 """
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66 batch = self.request_queue[:self.batch_size]
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67 prompts = map(lambda x : x.prompt, batch)
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68 results = mock_inference_call(list(prompts), self.next_batch_id)
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69 for request in self.request_queue:
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70 request.output_tokens = results["generated_tokens"]
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71 self.request_queue[self.batch_size:]
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72 self.next_batch_id += 1
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73
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74 def get_results(self, request_id: int) -> Optional[List[str]]:
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75 """
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76 In a real system, this would retrieve results from a separate
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77 completed-requests store. For this mock, assume we can only
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78 retrieve results for requests that have been fully processed and
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79 are no longer in the queue.
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80 """
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81 # For simplicity, assume all requests that have been processed
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82 # by a batch call have completed their generation for this *single step*
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83 # of the mock. If you want to make this more realistic, feel free to
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84 # expand the UserRequest class to include a 'is_complete' flag.
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85
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86 # For the provided mock structure, we'll just check the queue:
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87
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88 for req in self.request_queue:
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89 if req.request_id == request_id:
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90 # If it's still in the queue, it hasn't been processed yet
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91 return None
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92
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93 # In a complete system, you'd look up the request ID in a completed-requests map.
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94 # For this simplified version, let's just return a simulated result for
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95 # requests that *would* have been processed:
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96
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97 # If the request ID is less than the ID of the requests that would be
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98 # processed in the *next* batch, we simulate a complete token output.
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99 if request_id < self.next_batch_id * self.batch_size:
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100 # Simple simulation:
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101 return [f"token_X_of_{batch_num}" for batch_num in range(1, self.next_batch_id)]
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102
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103 return None
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104
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105
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106 def main():
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107 pass