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Merge pull request #3 from STEVENTAN100/main

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Fixed code in experiments/module-summarization.
This commit is contained in:
Yuling Shi
2025-10-21 16:31:49 +08:00
committed by GitHub
parent 71248b737f 5478b117c8
commit c6a87287da
2 changed files with 11 additions and 7 deletions
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12
experiments/module-summarization/code_compressor.py
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@@ -213,7 +213,7 @@ class CodeCompressor:
self.load_model(model_name, device_map, model_config) self.load_model(model_name, device_map, model_config)
logger.debug("Initializing Entropy chunking...") logger.debug("Initializing Entropy chunking...")
self.ppl_chunking = EntropyChunking() self.entropy_chunking = EntropyChunking()
# Add caching system for model outputs and token information # Add caching system for model outputs and token information
self.cache = { self.cache = {
@@ -576,7 +576,9 @@ class CodeCompressor:
start_time = time.time() start_time = time.time()
if not context_list: if not context_list:
return [], [], [] # Always return a 4-tuple: (selected_contexts, used_indices, dynamic_ratio, demonstrations_sort)
# Keep API consistent for callers that unpack 4 values
return [], [], [], []
# Get token counts for each context # Get token counts for each context
logger.debug("Calculating token lengths for contexts") logger.debug("Calculating token lengths for contexts")
@@ -588,7 +590,9 @@ class CodeCompressor:
logger.debug(f"All contexts fit within budget ({total_tokens} <= {target_token})") logger.debug(f"All contexts fit within budget ({total_tokens} <= {target_token})")
end_time = time.time() end_time = time.time()
logger.debug(f"Context budget control completed in {end_time - start_time:.2f} seconds") logger.debug(f"Context budget control completed in {end_time - start_time:.2f} seconds")
return context_list, list(range(len(context_list))), [0.0] * len(context_list) # Build a default demonstrations_sort with zero scores to preserve structure
demonstrations_sort = list(zip(range(len(context_list)), [0.0] * len(context_list)))
return context_list, list(range(len(context_list))), [0.0] * len(context_list), demonstrations_sort
# Rank contexts by relevance if question is provided # Rank contexts by relevance if question is provided
logger.debug("Ranking contexts by relevance") logger.debug("Ranking contexts by relevance")
@@ -1883,4 +1887,4 @@ if __name__ == "__main__":
min_lines_for_fine_grained=5, min_lines_for_fine_grained=5,
importance_beta=0.5 importance_beta=0.5
) )
logger.info(f"Compressed code (using {result_cond['fine_grained_method_used']}): \n{result_cond['compressed_code']}") logger.info(f"Compressed code (using {result_cond['fine_grained_method_used']}): \n{result_cond['compressed_code']}")

6
experiments/module-summarization/main.py
View File

@@ -19,7 +19,7 @@ from itertools import cycle
class LLMGenerator: class LLMGenerator:
def __init__(self, model_name, device, **model_args): def __init__(self, model_name, device, **model_args):
# Create a vllm LLM instance # Create a vllm LLM instance
engine_args = EngineArgs(model=model_name, device=device, **model_args) engine_args = EngineArgs(model=model_name, gpu_memory_utilization=0.8, device=device, **model_args)
self.model = LLM(**vars(engine_args)) self.model = LLM(**vars(engine_args))
self.model_name = model_name self.model_name = model_name
self.device = device self.device = device
@@ -58,7 +58,7 @@ class LLMGenerator:
class LLMScorer: class LLMScorer:
def __init__(self, model_name, device, **model_args): def __init__(self, model_name, device, **model_args):
# Create a vllm LLM instance # Create a vllm LLM instance
engine_args = EngineArgs(model=model_name, device=device, **model_args) engine_args = EngineArgs(model=model_name, gpu_memory_utilization=0.8, device=device, **model_args)
self.model = LLM(**vars(engine_args)) self.model = LLM(**vars(engine_args))
self.model_name = model_name self.model_name = model_name
self.device = device self.device = device
@@ -1315,4 +1315,4 @@ def run_documentation_task(
if __name__ == "__main__": if __name__ == "__main__":
fire.Fire(run_documentation_task) fire.Fire(run_documentation_task)