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examples/pytorch/continuous_batching_simple.py

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+# Copyright 2025 The HuggingFace Inc. team
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import argparse
+import time
+
+import datasets
+import torch
+
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from transformers.generation import GenerationConfig
+from transformers.utils import is_torch_accelerator_available
+
+
+MODEL_ID = "Qwen/Qwen3-4B-Instruct-2507"
+DISPLAYED_SAMPLES = 3
+
+
+if __name__ == "__main__":
+    # Parse args
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--num-blocks", "-n", type=int, default=None)
+    parser.add_argument("--max-batch-tokens", "-b", type=int, default=None)
+    parser.add_argument("--attn", type=str, default="kernels-community/flash-attn2", help="Attention implementation")
+    parser.add_argument("--samples", type=int, default=500)
+    parser.add_argument("--max-new-tokens", type=int, default=32)
+
+    args = parser.parse_args()
+
+    device = torch.accelerator.current_accelerator() if is_torch_accelerator_available() else "cuda"
+    device_map = "cpu" if device is None else device.type
+
+    # Prepare model
+    model = AutoModelForCausalLM.from_pretrained(
+        MODEL_ID,
+        attn_implementation=args.attn,
+        device_map=device_map,
+        dtype=torch.bfloat16,
+    )
+    model = model.eval()
+
+    # Prepare tokenizer and dataset
+    tokenizer = AutoTokenizer.from_pretrained(MODEL_ID, padding_side="left")
+    dataset = datasets.load_dataset("openai/gsm8k", "socratic", split="test")
+    dataset = dataset.select(range(args.samples))
+    tokenized_datasets = dataset.map(lambda x: tokenizer(x["question"]), batched=True)
+    simple_batch_inputs = [item["input_ids"] for item in tokenized_datasets]
+
+    # Prepare generation config
+    generation_config = GenerationConfig(
+        max_new_tokens=args.max_new_tokens,
+        use_cuda_graph=False,  # Not supported for simple version
+        eos_token_id=tokenizer.eos_token_id,
+        pad_token_id=tokenizer.pad_token_id,
+        do_sample=False,
+        num_blocks=args.num_blocks,
+        max_batch_tokens=args.max_batch_tokens,
+    )
+
+    # Warmup iterations
+    _ = model.generate_batch(
+        inputs=simple_batch_inputs[: min(5, args.samples)],
+        generation_config=generation_config,
+    )
+
+    # Actual batch generation
+    print("--- Running CB Generation Example ---")
+    start_time = time.time()
+    batch_outputs = model.generate_batch(
+        inputs=simple_batch_inputs,
+        generation_config=generation_config,
+    )
+    end_time = time.time()
+    print("Done with batch generation.")
+
+    # Decode outputs
+    token_count = 0
+    for i, request in enumerate(batch_outputs):
+        input_text = tokenizer.decode(batch_outputs[request].prompt_ids, skip_special_tokens=True)
+        # Try to decode the output
+        try:
+            output_text = tokenizer.decode(batch_outputs[request].generated_tokens, skip_special_tokens=True)
+            token_count += len(batch_outputs[request].generated_tokens[1:])
+        except Exception as e:
+            print(f"Decoding failed for request {request}: {e}")
+            continue
+
+        # Display sample if asked
+        if i < DISPLAYED_SAMPLES:
+            print("-" * 20)
+            print(f"{request} Input:  {input_text}")
+            if len(output_text) > 0:
+                print(f"{request} Output: {output_text}")
+            else:
+                print(f"[WARN] {request} Output was empty!")
+
+    # Compute stats and maybe print them
+    gen_time = end_time - start_time
+    tok_per_sec = token_count / gen_time
+    print("-" * 20)
+    print("--- Finished CB Generation Example ---\n")
+    print(f"CB generation took: {gen_time:.2f} seconds for {token_count} tokens. {tok_per_sec:.2f}tok/s")