update

generate-responses.py

@@ -2,18 +2,12 @@
 # requires-python = ">=3.10"
 # dependencies = [
 #     "datasets",
-#     "flashinfer-python",
 #     "huggingface-hub[hf_transfer]",
 #     "torch",
 #     "transformers",
 #     "vllm",
 # ]
 #
-# [[tool.uv.index]]
-# url = "https://flashinfer.ai/whl/cu126/torch2.6"
-#
-# [[tool.uv.index]]
-# url = "https://wheels.vllm.ai/nightly"
 # ///
 """
 Generate responses for prompts in a dataset using vLLM for efficient GPU inference.
@@ -60,7 +54,9 @@ from vllm import LLM, SamplingParams
 # Enable HF Transfer for faster downloads
 os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "1"
 
-logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+logging.basicConfig(
+    level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s"
+)
 logger = logging.getLogger(__name__)
 
 
@@ -68,15 +64,17 @@ def check_gpu_availability() -> int:
     """Check if CUDA is available and return the number of GPUs."""
     if not cuda.is_available():
         logger.error("CUDA is not available. This script requires a GPU.")
-        logger.error("Please run on a machine with NVIDIA GPU or use HF Jobs with GPU flavor.")
+        logger.error(
+            "Please run on a machine with NVIDIA GPU or use HF Jobs with GPU flavor."
+        )
         sys.exit(1)
-    
+
     num_gpus = cuda.device_count()
     for i in range(num_gpus):
         gpu_name = cuda.get_device_name(i)
         gpu_memory = cuda.get_device_properties(i).total_memory / 1024**3
         logger.info(f"GPU {i}: {gpu_name} with {gpu_memory:.1f} GB memory")
-    
+
     return num_gpus
 
 
@@ -167,7 +165,7 @@ def main(
 ):
     """
     Main generation pipeline.
-    
+
     Args:
         src_dataset_hub_id: Input dataset on Hugging Face Hub
         output_dataset_hub_id: Where to save results on Hugging Face Hub
@@ -185,30 +183,34 @@ def main(
         hf_token: Hugging Face authentication token
     """
     generation_start_time = datetime.now().isoformat()
-    
+
     # GPU check and configuration
     num_gpus = check_gpu_availability()
     if tensor_parallel_size is None:
         tensor_parallel_size = num_gpus
-        logger.info(f"Auto-detected {num_gpus} GPU(s), using tensor_parallel_size={tensor_parallel_size}")
+        logger.info(
+            f"Auto-detected {num_gpus} GPU(s), using tensor_parallel_size={tensor_parallel_size}"
+        )
     else:
         logger.info(f"Using specified tensor_parallel_size={tensor_parallel_size}")
         if tensor_parallel_size > num_gpus:
-            logger.warning(f"Requested {tensor_parallel_size} GPUs but only {num_gpus} available")
-    
+            logger.warning(
+                f"Requested {tensor_parallel_size} GPUs but only {num_gpus} available"
+            )
+
     # Authentication - try multiple methods
     HF_TOKEN = hf_token or os.environ.get("HF_TOKEN") or get_token()
-    
+
     if not HF_TOKEN:
         logger.error("No HuggingFace token found. Please provide token via:")
         logger.error("  1. --hf-token argument")
         logger.error("  2. HF_TOKEN environment variable")
         logger.error("  3. Run 'huggingface-cli login' or use login() in Python")
         sys.exit(1)
-    
+
     logger.info("HuggingFace token found, authenticating...")
     login(token=HF_TOKEN)
-    
+
     # Initialize vLLM
     logger.info(f"Loading model: {model_id}")
     llm = LLM(
@@ -216,11 +218,11 @@ def main(
         tensor_parallel_size=tensor_parallel_size,
         gpu_memory_utilization=gpu_memory_utilization,
     )
-    
+
     # Load tokenizer for chat template
     logger.info("Loading tokenizer...")
     tokenizer = AutoTokenizer.from_pretrained(model_id)
-    
+
     # Create sampling parameters
     sampling_params = SamplingParams(
         temperature=temperature,
@@ -230,18 +232,20 @@ def main(
         max_tokens=max_tokens,
         repetition_penalty=repetition_penalty,
     )
-    
+
     # Load dataset
     logger.info(f"Loading dataset: {src_dataset_hub_id}")
     dataset = load_dataset(src_dataset_hub_id, split="train")
     total_examples = len(dataset)
     logger.info(f"Dataset loaded with {total_examples:,} examples")
-    
+
     # Validate messages column
     if messages_column not in dataset.column_names:
-        logger.error(f"Column '{messages_column}' not found. Available columns: {dataset.column_names}")
+        logger.error(
+            f"Column '{messages_column}' not found. Available columns: {dataset.column_names}"
+        )
         sys.exit(1)
-    
+
     # Process messages and apply chat template
     logger.info("Applying chat template to messages...")
     prompts = []
@@ -249,29 +253,27 @@ def main(
         messages = example[messages_column]
         # Apply chat template
         prompt = tokenizer.apply_chat_template(
-            messages,
-            tokenize=False,
-            add_generation_prompt=True
+            messages, tokenize=False, add_generation_prompt=True
         )
         prompts.append(prompt)
-    
+
     # Generate responses - vLLM handles batching internally
     logger.info(f"Starting generation for {len(prompts):,} prompts...")
     logger.info("vLLM will handle batching and scheduling automatically")
-    
+
     outputs = llm.generate(prompts, sampling_params)
-    
+
     # Extract generated text
     logger.info("Extracting generated responses...")
     responses = []
     for output in outputs:
         response = output.outputs[0].text.strip()
         responses.append(response)
-    
+
     # Add responses to dataset
     logger.info("Adding responses to dataset...")
     dataset = dataset.add_column(output_column, responses)
-    
+
     # Create dataset card
     logger.info("Creating dataset card...")
     card_content = create_dataset_card(
@@ -283,17 +285,19 @@ def main(
         num_examples=total_examples,
         generation_time=generation_start_time,
     )
-    
+
     # Push dataset to hub
     logger.info(f"Pushing dataset to: {output_dataset_hub_id}")
     dataset.push_to_hub(output_dataset_hub_id, token=HF_TOKEN)
-    
+
     # Push dataset card
     card = DatasetCard(card_content)
     card.push_to_hub(output_dataset_hub_id, token=HF_TOKEN)
-    
+
     logger.info("✅ Generation complete!")
-    logger.info(f"Dataset available at: https://huggingface.co/datasets/{output_dataset_hub_id}")
+    logger.info(
+        f"Dataset available at: https://huggingface.co/datasets/{output_dataset_hub_id}"
+    )
 
 
 if __name__ == "__main__":
@@ -319,90 +323,89 @@ Examples:
   
   # Using environment variable for token
   HF_TOKEN=hf_xxx uv run generate-responses.py input-dataset output-dataset
-            """
+            """,
         )
-        
+
         parser.add_argument(
             "src_dataset_hub_id",
-            help="Input dataset on Hugging Face Hub (e.g., username/dataset-name)"
+            help="Input dataset on Hugging Face Hub (e.g., username/dataset-name)",
         )
         parser.add_argument(
-            "output_dataset_hub_id",
-            help="Output dataset name on Hugging Face Hub"
+            "output_dataset_hub_id", help="Output dataset name on Hugging Face Hub"
         )
         parser.add_argument(
             "--model-id",
             type=str,
             default="Qwen/Qwen3-30B-A3B-Instruct-2507-FP8",
-            help="Model to use for generation (default: Qwen3-30B-A3B-Instruct-2507-FP8)"
+            help="Model to use for generation (default: Qwen3-30B-A3B-Instruct-2507-FP8)",
         )
         parser.add_argument(
             "--messages-column",
             type=str,
             default="messages",
-            help="Column containing chat messages (default: messages)"
+            help="Column containing chat messages (default: messages)",
         )
         parser.add_argument(
             "--output-column",
             type=str,
             default="response",
-            help="Column name for generated responses (default: response)"
+            help="Column name for generated responses (default: response)",
         )
         parser.add_argument(
             "--temperature",
             type=float,
             default=0.7,
-            help="Sampling temperature (default: 0.7)"
+            help="Sampling temperature (default: 0.7)",
         )
         parser.add_argument(
             "--top-p",
             type=float,
             default=0.8,
-            help="Top-p sampling parameter (default: 0.8)"
+            help="Top-p sampling parameter (default: 0.8)",
         )
         parser.add_argument(
             "--top-k",
             type=int,
             default=20,
-            help="Top-k sampling parameter (default: 20)"
+            help="Top-k sampling parameter (default: 20)",
         )
         parser.add_argument(
             "--min-p",
             type=float,
             default=0.0,
-            help="Minimum probability threshold (default: 0.0)"
+            help="Minimum probability threshold (default: 0.0)",
         )
         parser.add_argument(
             "--max-tokens",
             type=int,
             default=16384,
-            help="Maximum tokens to generate (default: 16384)"
+            help="Maximum tokens to generate (default: 16384)",
         )
         parser.add_argument(
             "--repetition-penalty",
             type=float,
             default=1.0,
-            help="Repetition penalty (default: 1.0)"
+            help="Repetition penalty (default: 1.0)",
         )
         parser.add_argument(
             "--gpu-memory-utilization",
             type=float,
             default=0.90,
-            help="GPU memory utilization factor (default: 0.90)"
+            help="GPU memory utilization factor (default: 0.90)",
         )
         parser.add_argument(
             "--tensor-parallel-size",
             type=int,
-            help="Number of GPUs to use (default: auto-detect)"
+            help="Number of GPUs to use (default: auto-detect)",
         )
         parser.add_argument(
             "--hf-token",
             type=str,
-            help="Hugging Face token (can also use HF_TOKEN env var)"
+            help="Hugging Face token (can also use HF_TOKEN env var)",
         )
-        
+
         args = parser.parse_args()
-        
+
         main(
             src_dataset_hub_id=args.src_dataset_hub_id,
             output_dataset_hub_id=args.output_dataset_hub_id,
@@ -439,4 +442,4 @@ Example HF Jobs command with multi-GPU:
         --model-id Qwen/Qwen3-30B-A3B-Instruct-2507-FP8 \\
         --temperature 0.7 \\
         --max-tokens 16384
-        """)
+        """)