[inference] add hf_infer script for inference using huggingface backend

scripts/hf_infer.py

@@ -0,0 +1,160 @@
+# Copyright 2025 the LlamaFactory 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 gc
+import json
+from typing import Optional
+
+import torch
+import fire
+from tqdm import tqdm
+from transformers import (
+    AutoModelForCausalLM,
+    AutoTokenizer,
+    GenerationConfig,
+    Seq2SeqTrainingArguments,
+)
+from peft import PeftModel
+
+from llamafactory.data import get_dataset, get_template_and_fix_tokenizer
+from llamafactory.extras.constants import IGNORE_INDEX
+from llamafactory.hparams import get_infer_args
+from llamafactory.model import load_tokenizer
+
+
+def hf_infer(
+    model_name_or_path: str,
+    adapter_name_or_path: str = None,
+    dataset: str = "alpaca_en_demo",
+    dataset_dir: str = "data",
+    template: str = "default",
+    cutoff_len: int = 2048,
+    max_samples: Optional[int] = None,
+    save_name: str = "generated_predictions.jsonl",
+    temperature: float = 0.95,
+    top_p: float = 0.7,
+    top_k: int = 50,
+    max_new_tokens: int = 1024,
+    repetition_penalty: float = 1.0,
+    skip_special_tokens: bool = True,
+    default_system: Optional[str] = None,
+    enable_thinking: bool = True,
+    seed: Optional[int] = None,
+    batch_size: int = 4,
+    device: str = None,
+):
+    """
+    Perform batch generation using Hugging Face transformers backend.
+
+    Usage:
+        python hf_infer.py --model_name_or_path meta-llama/Llama-2-7b-hf --template llama --dataset alpaca_en_demo --batch_size 16 --save_name test.jsonl 
+    """
+    device = device or ("cuda" if torch.cuda.is_available() else "cpu")
+
+    model_args, data_args, _, generating_args = get_infer_args(
+        dict(
+            model_name_or_path=model_name_or_path,
+            adapter_name_or_path=adapter_name_or_path,
+            dataset=dataset,
+            dataset_dir=dataset_dir,
+            template=template,
+            cutoff_len=cutoff_len,
+            max_samples=max_samples,
+            preprocessing_num_workers=8,
+            default_system=default_system,
+            enable_thinking=enable_thinking,
+            temperature=temperature,
+            top_p=top_p,
+            top_k=top_k,
+            max_new_tokens=max_new_tokens,
+            repetition_penalty=repetition_penalty,
+        )
+    )
+
+    training_args = Seq2SeqTrainingArguments(output_dir="dummy_dir")
+    tokenizer_module = load_tokenizer(model_args)
+    tokenizer = tokenizer_module["tokenizer"]
+    template_obj = get_template_and_fix_tokenizer(tokenizer, data_args)
+    template_obj.mm_plugin.expand_mm_tokens = False
+
+    # --- Load model ---
+    model = AutoModelForCausalLM.from_pretrained(
+        model_name_or_path,
+        torch_dtype=torch.bfloat16,
+        device_map="auto",
+        trust_remote_code=True,
+    )
+    if adapter_name_or_path is not None:
+        model = PeftModel.from_pretrained(model, adapter_name_or_path)
+
+    model.eval()
+
+    # --- Load dataset ---
+    dataset_module = get_dataset(template_obj, model_args, data_args, training_args, "ppo", **tokenizer_module)
+    train_dataset = dataset_module["train_dataset"]
+
+    # --- Generation configuration ---
+    gen_cfg = GenerationConfig(
+        temperature=temperature,
+        top_p=top_p,
+        top_k=top_k,
+        repetition_penalty=repetition_penalty,
+        max_new_tokens=max_new_tokens,
+        do_sample=True,
+    )
+
+    all_prompts, all_preds, all_labels = [], [], []
+
+    for i in tqdm(range(0, len(train_dataset), batch_size), desc="Processing batched inference"):
+        batch = train_dataset[i : min(i + batch_size, len(train_dataset))]
+
+        input_ids = [torch.tensor(x) for x in batch["input_ids"]]
+        input_ids = torch.nn.utils.rnn.pad_sequence(
+            input_ids, batch_first=True, padding_value=tokenizer.pad_token_id
+        ).to(device)
+
+        # Generate
+        with torch.no_grad():
+            outputs = model.generate(
+                input_ids=input_ids,
+                **gen_cfg.to_dict(),
+            )
+
+        # Decode predictions
+        for j in range(len(batch["input_ids"])):
+            prompt = tokenizer.decode(batch["input_ids"][j], skip_special_tokens=skip_special_tokens)
+            label = tokenizer.decode(
+                list(filter(lambda x: x != IGNORE_INDEX, batch["labels"][j])),
+                skip_special_tokens=skip_special_tokens,
+            )
+            pred = tokenizer.decode(outputs[j][len(batch["input_ids"][j]) :], skip_special_tokens=skip_special_tokens)
+
+            all_prompts.append(prompt)
+            all_preds.append(pred)
+            all_labels.append(label)
+
+        gc.collect()
+
+    # Save all results
+    with open(save_name, "w", encoding="utf-8") as f:
+        for text, pred, label in zip(all_prompts, all_preds, all_labels):
+            f.write(json.dumps({"prompt": text, "predict": pred, "label": label}, ensure_ascii=False) + "\n")
+
+    print("*" * 70)
+    print(f"{len(all_prompts)} total generated results have been saved at {save_name}.")
+    print("*" * 70)
+
+
+if __name__ == "__main__":
+    fire.Fire(hf_infer)