Merge d3de9e8d1f into 4cc6253d5c

demo_function_calling.py

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+"""
+DeepSeek-V3 Function Calling Demo
+
+This script demonstrates how to use function calling with the DeepSeek-V3 model
+after applying the fix for the chat template.
+"""
+
+from transformers import AutoTokenizer
+import json
+import argparse
+
+
+def get_current_temperature(location: str) -> float:
+    """
+    Get the current temperature at a location.
+
+    Args:
+        location: Get the location of the temperature in the format of "city, country"
+    Returns:
+        Displays the current temperature at the specified location as a floating point number.
+    """
+    # This is a mock function that would normally call a weather API
+    print(f"Getting temperature for {location}")
+    return 22.0
+
+
+def get_current_time(timezone: str) -> str:
+    """
+    Get the current time in a specific timezone.
+
+    Args:
+        timezone: The timezone to get the current time for (e.g., "UTC", "America/New_York")
+    Returns:
+        The current time as a string.
+    """
+    # This is a mock function that would normally get the current time
+    print(f"Getting time for timezone {timezone}")
+    return "12:30 PM"
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        description="Test DeepSeek-V3 function calling")
+    parser.add_argument("--model_path", type=str, required=True,
+                        help="Path to the DeepSeek-V3 model")
+    args = parser.parse_args()
+
+    print(f"Loading tokenizer from {args.model_path}")
+    tokenizer = AutoTokenizer.from_pretrained(
+        args.model_path, trust_remote_code=True)
+
+    # Example 1: Simple weather query
+    print("\nExample 1: Weather query")
+    tool_call = {"name": "get_current_temperature",
+                 "arguments": {"location": "Paris, France"}}
+    messages = [
+        {"role": "system", "content": "You are a helpful assistant that provides weather information."},
+        {"role": "user", "content": "What is the temperature in Paris now?"},
+        {"role": "assistant", "tool_calls": [
+            {"type": "function", "function": tool_call}]},
+        {"role": "user", "content": "Thanks for checking! Is that warm for this time of year?"}
+    ]
+
+    print("Processing chat template...")
+    chat_input = tokenizer.apply_chat_template(
+        messages,
+        tools=[get_current_temperature],
+        add_generation_prompt=True,
+        tokenize=False,
+        tools_in_user_message=False
+    )
+
+    print("\nGenerated chat template:")
+    print("-" * 50)
+    print(chat_input)
+    print("-" * 50)
+
+    # Example 2: Multiple tool calls
+    print("\nExample 2: Multiple function calls")
+    messages = [
+        {"role": "system", "content": "You are a helpful assistant."},
+        {"role": "user", "content": "What's the time in New York and temperature in Tokyo?"},
+        {"role": "assistant", "tool_calls": [
+            {"type": "function", "function": {"name": "get_current_time",
+                                              "arguments": {"timezone": "America/New_York"}}},
+            {"type": "function", "function": {
+                "name": "get_current_temperature", "arguments": {"location": "Tokyo, Japan"}}}
+        ]},
+    ]
+
+    print("Processing chat template...")
+    chat_input = tokenizer.apply_chat_template(
+        messages,
+        tools=[get_current_time, get_current_temperature],
+        add_generation_prompt=True,
+        tokenize=False,
+        tools_in_user_message=False
+    )
+
+    print("\nGenerated chat template:")
+    print("-" * 50)
+    print(chat_input)
+    print("-" * 50)
+
+
+if __name__ == "__main__":
+    main()

docs/function_calling.md

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+# Function Calling with DeepSeek-V3
+
+This document provides guidance on using function calling with DeepSeek-V3 models.
+
+## Overview
+
+Function calling allows the model to call external functions through a structured interface. It's particularly useful for:
+
+- Retrieving real-time information (weather, time, data from APIs)
+- Performing calculations
+- Executing actions based on user requests
+
+## Usage with Transformers
+
+DeepSeek-V3 supports function calling through the Hugging Face Transformers library. The example below demonstrates how to use this feature:
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+# Define your function
+def get_weather(location: str) -> str:
+    """Get the weather for a location."""
+    # In a real application, this would call a weather API
+    return f"Sunny, 22°C in {location}"
+
+# Load the model and tokenizer
+tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/DeepSeek-V3", trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained("deepseek-ai/DeepSeek-V3", trust_remote_code=True)
+
+# Create a conversation with function calling
+messages = [
+    {"role": "system", "content": "You are a helpful assistant."},
+    {"role": "user", "content": "What's the weather like in Tokyo?"},
+    {"role": "assistant", "tool_calls": [
+        {"type": "function", "function": {"name": "get_weather", "arguments": {"location": "Tokyo, Japan"}}}
+    ]},
+    {"role": "user", "content": "Thanks! And what about New York?"}
+]
+
+# Apply the chat template
+inputs = tokenizer.apply_chat_template(
+    messages, 
+    tools=[get_weather],
+    add_generation_prompt=True, 
+    tokenize=True,
+    tools_in_user_message=False
+)
+
+# Generate a response
+output_ids = model.generate(inputs, max_new_tokens=100)
+response = tokenizer.decode(output_ids[0], skip_special_tokens=True)
+print(response)
+```
+
+## Function Definitions
+
+Functions must have type annotations and docstrings following the OpenAI format:
+
+```python
+def function_name(param1: type, param2: type) -> return_type:
+    """
+    Brief description of what the function does.
+    
+    Args:
+        param1: Description of parameter 1
+        param2: Description of parameter 2
+    Returns:
+        Description of what is returned
+    """
+    # Function implementation
+    pass
+```
+
+## Limitations
+
+- Function parameters must be JSON-serializable types
+- Function return values should also be JSON-serializable
+- Complex object types are not directly supported
+
+For more advanced use cases, please refer to the Hugging Face documentation on function calling.

test_fix.py

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+from transformers import AutoTokenizer
+import json
+import os
+import sys
+
+# Function to test
+
+
+def get_current_temperature(location: str) -> float:
+    """
+    Get the current temperature at a location.
+
+    Args:
+        location: Get the location of the temperature in the format of "city, country"
+    Returns:
+        Displays the current temperature at the specified location as a floating point number (in the specified unit).
+    """
+    return 22.0
+
+
+def test_with_original_tokenizer(model_path):
+    print("Testing with original tokenizer...")
+    try:
+        tokenizer = AutoTokenizer.from_pretrained(
+            model_path, trust_remote_code=True)
+
+        tool_call = {"name": "get_current_temperature",
+                     "arguments": {"location": "Paris, France"}}
+        messages = [
+            {"role": "system", "content": "You are a robot that responds to weather queries."},
+            {"role": "user", "content": "What is the temperature in Paris now?"},
+            {"role": "assistant", "tool_calls": [
+                {"type": "function", "function": tool_call}]},
+        ]
+
+        inputs = tokenizer.apply_chat_template(
+            messages,
+            tools=[get_current_temperature],
+            add_generation_prompt=False,
+            tokenize=False,
+            tools_in_user_message=False
+        )
+        print("Success with original tokenizer!")
+        print(inputs)
+        return True
+    except Exception as e:
+        print(f"Error with original tokenizer: {e}")
+        return False
+
+
+def test_with_fixed_tokenizer(model_path, fixed_config_path):
+    print("Testing with fixed tokenizer config...")
+    try:
+        # Read the original tokenizer files
+        tokenizer_json_path = os.path.join(model_path, "tokenizer.json")
+        if not os.path.exists(tokenizer_json_path):
+            print(f"Error: tokenizer.json not found at {tokenizer_json_path}")
+            return False
+
+        # Copy the tokenizer.json and use our fixed config
+        fixed_dir = "fixed_tokenizer"
+        os.makedirs(fixed_dir, exist_ok=True)
+
+        # Copy tokenizer.json
+        import shutil
+        shutil.copy(tokenizer_json_path, os.path.join(
+            fixed_dir, "tokenizer.json"))
+
+        # Create fixed tokenizer_config.json
+        with open(fixed_config_path, 'r') as f:
+            fixed_config = f.read()
+
+        with open(os.path.join(fixed_dir, "tokenizer_config.json"), 'w') as f:
+            f.write(fixed_config)
+
+        # Load the fixed tokenizer
+        tokenizer = AutoTokenizer.from_pretrained(
+            fixed_dir, trust_remote_code=True)
+
+        tool_call = {"name": "get_current_temperature",
+                     "arguments": {"location": "Paris, France"}}
+        messages = [
+            {"role": "system", "content": "You are a robot that responds to weather queries."},
+            {"role": "user", "content": "What is the temperature in Paris now?"},
+            {"role": "assistant", "tool_calls": [
+                {"type": "function", "function": tool_call}]},
+        ]
+
+        inputs = tokenizer.apply_chat_template(
+            messages,
+            tools=[get_current_temperature],
+            add_generation_prompt=False,
+            tokenize=False,
+            tools_in_user_message=False
+        )
+        print("Success with fixed tokenizer!")
+        print(inputs)
+        return True
+    except Exception as e:
+        print(f"Error with fixed tokenizer: {e}")
+        return False
+
+
+def main():
+    if len(sys.argv) < 2:
+        print("Usage: python test_fix.py /path/to/DeepSeek-V3 /path/to/fixed_config.json")
+        return
+
+    model_path = sys.argv[1]
+    fixed_config_path = sys.argv[2] if len(
+        sys.argv) > 2 else "tokenizer_config.json"
+
+    # Test with original tokenizer (should fail)
+    original_success = test_with_original_tokenizer(model_path)
+
+    # Test with fixed tokenizer (should succeed)
+    fixed_success = test_with_fixed_tokenizer(model_path, fixed_config_path)
+
+    if not original_success and fixed_success:
+        print("\n✅ Fix was successful! The issue has been resolved.")
+    else:
+        print("\n❌ Testing did not confirm the fix. Please check the logs above.")
+
+
+if __name__ == "__main__":
+    main()

tokenizer_config.json

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+{
+  "add_bos_token": false,
+  "add_eos_token": false,
+  "bos_token": {
+    "__type": "AddedToken",
+    "content": "<｜begin of sentence｜>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "chat_template": "{% set ns = namespace(found=false, is_first=false, is_tool=false) %}\n{%- for message in messages %}\n  {%- if message['role'] == 'system' %}\n    {{ '<｜begin of sentence｜><｜system｜>' + message['content'] + '<｜end of sentence｜>' }}\n  {%- elif message['role'] == 'user' %}\n    {%- if message.get('content') and message.get('tool_results') %}\n      {{ '<｜begin of sentence｜><｜User｜>' + message['content'] }}\n      {%- for result in message['tool_results'] %}\n        {{ '\n<｜tool result begin｜>' + result['name'] + '\n```json\n' + result['result'] + '\n```<｜tool result end｜>' }}\n      {%- endfor %}\n      {{ '<｜end of sentence｜>' }}\n    {%- elif message.get('content') %}\n      {{ '<｜begin of sentence｜><｜User｜>' + message['content'] + '<｜end of sentence｜>' }}\n    {%- endif %}\n  {%- elif message['role'] == 'assistant' %}\n    {%- if message.get('content') is none or message.get('content') == '' %}\n      {%- set ns.is_tool = false -%}\n      {%- for tool in message.get('tool_calls', []) %}\n        {%- if not ns.is_first %}\n          {{ '<｜Assistant｜><｜tool calls begin｜><｜tool call begin｜>' + tool['type'] + '<｜tool sep｜>' + tool['function']['name'] + '\n```json\n' + (tool['function']['arguments']|tojson) + '\n```<｜tool call end｜>' }}\n          {%- set ns.is_first = true -%}\n        {%- else %}\n          {{ '\n<｜tool call begin｜>' + tool['type'] + '<｜tool sep｜>' + tool['function']['name'] + '\n```json\n' + (tool['function']['arguments']|tojson) + '\n```<｜tool call end｜>' }}\n          {{ '<｜tool calls end｜><｜end of sentence｜>' }}\n        {%- endif %}\n      {%- endfor %}\n    {%- else %}\n      {{ '<｜begin of sentence｜><｜Assistant｜>' + message['content'] + '<｜end of sentence｜>' }}\n    {%- endif %}\n  {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n  {{ '<｜begin of sentence｜><｜Assistant｜>' }}\n{%- endif %}\n",
+  "clean_up_tokenization_spaces": true,
+  "model_max_length": 131072,
+  "eos_token": {
+    "__type": "AddedToken",
+    "content": "<｜end of sentence｜>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "tokenizer_class": "CodeLlamaTokenizer",
+  "pad_token": null,
+  "special_tokens_map_file": null,
+  "tokenizer_file": "tokenizer.json",
+  "unk_token": {
+    "__type": "AddedToken",
+    "content": "<｜unknown｜>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  }
+}