Merge pull request #674 from Bob17293729/zongbo-dev

code exection class and test cases

examples/tool_inference.py

@@ -0,0 +1,45 @@
+import os
+import argparse
+from lmflow.args import InferencerArguments
+from lmflow.args import ModelArguments
+from lmflow.args import DatasetArguments
+from lmflow.models import hf_decoder_model
+from lmflow.pipeline.inferencer import ToolInferencer
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--gpu', type=str, default='0', 
+                        help='gpu id, currently speculative inference only support single gpu')
+    parser.add_argument('--model', type=str, default='codellama/CodeLlama-7b-instruct-hf',
+                        help='target code generation model name or path you  \
+                            currently only supports huggingface decoder only models')
+    params = parser.parse_args()
+    os.environ["CUDA_VISIBLE_DEVICES"] = params.gpu
+
+    model_args = ModelArguments(model_name_or_path=params.model)
+    model = hf_decoder_model.HFDecoderModel(model_args)
+    inferencer_args = InferencerArguments()
+    data_args = DatasetArguments()
+
+    toolinf = ToolInferencer(model_args, data_args, inferencer_args)
+
+    while True:
+        try:
+            text = input("Tool Inference: ")
+            toolinf_res = toolinf.inference(model, text)
+            toolinf_res = toolinf_res.replace("<s>","")
+            toolinf_res = toolinf_res.replace("</s>","")
+            print('\n\nResult:')
+            print(toolinf_res)
+            print('\n\n')
+            run_code = input("Run code? (y/n): ")
+            if run_code == 'y':
+                toolinf.code_exec(toolinf_res)
+            if run_code == 'n':
+                continue
+
+
+        except EOFError:
+            break
+
+if __name__ == '__main__':
+    main()

scripts/run_tool.sh

@@ -0,0 +1,3 @@
+model="gorilla-llm/gorilla-7b-hf-delta-v1"
+python examples/tool_inference.py \
+    --model ${model} \

src/lmflow/pipeline/inferencer.py

@@ -15,6 +15,7 @@
 import logging
 from typing import Dict, List
 from concurrent.futures import ThreadPoolExecutor
+import subprocess
 
 from transformers import AutoConfig
 import torch.distributed as dist
@@ -553,3 +554,88 @@ def speculative_sampling(input_ids: torch.Tensor,
 
     def stream_inference(self):
         raise NotImplementedError("Streaming output for SpeculativeInferencer is not supported yet")
+
+class ToolInferencer(Inferencer):
+    """
+    Initializes the `ToolInferencer` class with given arguments.
+
+    Parameters
+    ------------
+    model_args : ModelArguments object.
+        Contains the arguments required to load the model.
+
+    data_args : DatasetArguments object.
+        Contains the arguments required to load the dataset.
+
+    inferencer_args : InferencerArguments object.
+        Contains the arguments required to perform inference.
+
+
+    """
+    def __init__(self, model_args, data_args, inferencer_args):
+        super().__init__(model_args, data_args, inferencer_args)
+
+        self.model = HFDecoderModel(self.model_args)
+
+    def inference(
+        self,
+        model: HFDecoderModel,
+        input: str,
+        max_new_tokens: int=1024,
+    ):
+        """
+        Perform inference for a model
+
+        Parameters
+        ------------
+        model : HFDecoderModel object.
+            TunableModel to perform inference
+
+        input : str.
+            The input text (i.e., the prompt) for the model. 
+            
+        max_new_tokens : int.
+            The maximum number of tokens to be generated by the model.
+
+        Returns:
+
+        output : str.
+            The output text generated by the model.
+        """
+        if self.inferencer_args.device == "gpu":
+            input_id = model.encode(input, return_tensors="pt").to(device=self.local_rank)
+        elif self.inferencer_args.device == "cpu":
+            input_id = model.encode(input, return_tensors="pt").to(device='cpu')
+        logger.debug(f"input_id: {input_id}")
+        input_length = input_id.shape[1]
+        output_id = model.inference(
+            input_id,
+            use_accelerator=True,
+            max_new_tokens=max_new_tokens,
+            # pad_token_id=model.tokenizer.eos_token_id,
+        )
+        # logger.debug(f"output: {output_id}")
+        output = model.decode(output_id[0])
+        output = output.replace(input,"")
+        return output
+
+    def code_exec(self, code):
+        # Execute the code
+        result = subprocess.run(["python", "-c", code], capture_output=True, text=True)
+
+        # Print the result
+        if result.returncode == 0:
+            print("Successfully Executed, the result is:")
+            print(result.stdout)
+            return result.stdout
+        else:
+            print("Error:")
+            print(result.stderr)
+            return result
+
+
+
+
+
+
+

tests/models/test_tool_inferencer.py

@@ -0,0 +1,36 @@
+from lmflow.pipeline.inferencer import ToolInferencer
+import unittest
+from lmflow.args import InferencerArguments
+from lmflow.args import ModelArguments
+from lmflow.args import DatasetArguments
+from lmflow.models import hf_decoder_model
+
+CODE_1 = "print(\"hello world\")"
+RES_1 = "hello world\n"
+CODE_2 = "b=a+1\nprint(b)"
+RES_2 = """Traceback (most recent call last):
+  File "<string>", line 1, in <module>
+NameError: name 'a' is not defined
+"""
+
+class ToolInferencerTest(unittest.TestCase):
+    def set_up(self):
+        model_args = ModelArguments(model_name_or_path="codellama/CodeLlama-7b-instruct-hf")
+        model = hf_decoder_model.HFDecoderModel(model_args)
+        inferencer_args = InferencerArguments()
+        data_args = DatasetArguments()
+        self.toolinf = ToolInferencer(model_args, data_args, inferencer_args)
+
+    def test_code_exec_1(self,code=CODE_1, expected_output=RES_1):
+
+        toolinf_res = self.toolinf.code_exec(code)
+        self.assertEqual(toolinf_res, expected_output)
+
+    def test_code_exec_2(self,code=CODE_2):
+        toolinf_res = self.toolinf.code_exec(code)
+        self.assertNotEqual(toolinf_res.returncode, 0)
+
+unittest.main()
+
+
+