Pipeline example (#43)

* added hf pipeline example

* pipeline example added + doc updated

README.md

@@ -92,8 +92,48 @@ if __name__ == "__main__":
     'This is a valid json string for http request:{ "request": { "method": "GET", "headers": [], "content": "Content","type": "application" }}
     'This is a valid json string for shopping cart:{ "name": "MyCart", "price": 0, "value": 1 }
     """
+```
+
+Alternatively, you can use `transformers-cfg` to perform grammar-constrained decoding with huggingface pipeline.
+
+<details>
+<summary>Click here to see an example, or check it out in `examples/pipeline_json.py` </summary>
 
+```python
+# Load model and tokenizer
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+tokenizer.pad_token = tokenizer.eos_token
+# Load model to defined device
+model = AutoModelForCausalLM.from_pretrained(model_id).to(device)
+
+# Load grammar
+with open(f"examples/grammars/json.ebnf", "r") as file:
+    grammar_str = file.read()
+
+grammar = IncrementalGrammarConstraint(grammar_str, "root", tokenizer)
+grammar_processor = GrammarConstrainedLogitsProcessor(grammar)
+
+# Initialize pipeline
+pipe = pipeline(
+    "text-generation",
+    model=model,
+    tokenizer=tokenizer,
+    device_map="auto",
+    max_length=50,
+    batch_size=2,
+)
+
+generations = pipe(
+    [
+        "This is a valid json string for http request: ",
+        "This is a valid json string for shopping cart: ",
+    ],
+    do_sample=False,
+    logits_processor=[grammar_processor],
+)
 ```
+</details>
+
 
 ## 💡Why should I use transformers-CFG?
 

examples/pipeline_json.py

@@ -0,0 +1,74 @@
+import torch
+import argparse
+from transformers import pipeline
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from transformers_cfg.grammar_utils import IncrementalGrammarConstraint
+from transformers_cfg.generation.logits_process import GrammarConstrainedLogitsProcessor
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description="Generate json strings with huggingface pipelining"
+    )
+    parser.add_argument(
+        "--model-id",
+        type=str,
+        default="/dlabdata1/llm_hub/Mistral-7B-v0.1",
+        help="Model ID",
+    )
+    parser.add_argument("--device", type=str, help="Device to put the model on")
+    return parser.parse_args()
+
+
+def main():
+    args = parse_args()
+    model_id = args.model_id
+
+    # Detect if GPU is available, otherwise use CPU
+    device = torch.device(
+        args.device or ("cuda" if torch.cuda.is_available() else "cpu")
+    )
+    print(f"Using device: {device}")
+
+    # Load model and tokenizer
+    tokenizer = AutoTokenizer.from_pretrained(model_id)
+    tokenizer.pad_token = tokenizer.eos_token
+    # Load model to defined device
+    model = AutoModelForCausalLM.from_pretrained(model_id).to(device)
+
+    # Load grammar
+    with open(f"examples/grammars/json.ebnf", "r") as file:
+        grammar_str = file.read()
+
+    grammar = IncrementalGrammarConstraint(grammar_str, "root", tokenizer)
+    grammar_processor = GrammarConstrainedLogitsProcessor(grammar)
+
+    # Initialize pipeline
+    pipe = pipeline(
+        "text-generation",
+        model=model,
+        tokenizer=tokenizer,
+        device_map="auto",
+        max_length=50,
+        batch_size=2,
+    )
+    # # outputs = pipe("This is a valid json string for http request:", do_sample=False, max_length=50)
+    generations = pipe(
+        [
+            "This is a valid json string for http request: ",
+            "This is a valid json string for shopping cart: ",
+        ],
+        do_sample=False,
+        logits_processor=[grammar_processor],
+    )
+
+    print(generations)
+
+    """
+    This is a valid json string for http request: {"name":"John","age":30,"city":"New York"}
+    This is a valid json string for shopping cart: {"items":[{"id":"1","quantity":"1"},{"id":"2","quantity":"2"}]}
+    """
+
+
+if __name__ == "__main__":
+    main()