From 2b91a31e63e3d9bc3a618460797f9b33cb5cf404 Mon Sep 17 00:00:00 2001 From: Your Name Date: Fri, 14 Mar 2025 22:56:29 +0800 Subject: [PATCH 1/7] =?UTF-8?q?=E5=A2=9E=E5=8A=A0grpo=E5=A4=9A=E6=AC=A1?= =?UTF-8?q?=E5=B7=A5=E5=85=B7=E8=B0=83=E7=94=A8=E8=AE=AD=E7=BB=83?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- gen_data.py | 68 ++ math_operations_dataset.jsonl | 1000 +++++++++++++++++++ math_tool.py | 179 ++++ swift/plugin/orm.py | 60 ++ swift/plugin/tool_call.py | 102 ++ swift/trainers/rlhf_arguments.py | 1 + swift/trainers/rlhf_trainer/grpo_trainer.py | 462 +++++++-- test_grpo_tool.py | 161 +++ tool_system.txt | 62 ++ 9 files changed, 2025 insertions(+), 70 deletions(-) create mode 100644 gen_data.py create mode 100644 math_operations_dataset.jsonl create mode 100644 math_tool.py create mode 100644 swift/plugin/tool_call.py create mode 100644 test_grpo_tool.py create mode 100644 tool_system.txt diff --git a/gen_data.py b/gen_data.py new file mode 100644 index 0000000000..ca9e91fbdc --- /dev/null +++ b/gen_data.py @@ -0,0 +1,68 @@ +""" +Dataset Generator for Custom Mathematical Operations +This module generates a dataset of custom mathematical expressions and their results. +""" + +import random +import json +import re +from typing import Dict, List +from math_tool import parse_expression, SYMBOL_TO_OPERATION, OPERATION_DEFINITIONS + +def generate_safe_expression(): + """Generate an expression that won't cause overflow errors""" + # Start with a moderate number + expression = str(random.randint(1, 20)) + + # Add 3-5 operations with safe numbers + num_ops = random.randint(1, 6) + + for i in range(num_ops): + # Choose operation + op = random.choice(['@', '&', '$', '^']) + + # For @ operation, use smaller numbers to avoid overflow + if op == '@': + # For exponentiation, keep the exponent small + num = random.randint(1, 3) + else: + num = random.randint(1, 10) + + expression += op + str(num) + + return expression + +def generate_dataset(num_samples: int = 1000, output_file: str = "math_operations_dataset.jsonl") -> None: + """ + Generates a dataset of custom mathematical expressions and their results. + Saves the dataset as a JSONL file. + + Args: + num_samples: Number of samples to generate + output_file: Path to save the JSONL file + """ + with open(output_file, 'w') as f: + for _ in range(num_samples): + # Generate a safe expression + expression = generate_safe_expression() + + # Calculate the result + try: + result = parse_expression(expression) + + # Create the data entry + # data_entry = { + # "query": f"Calculate the result of the expression: {expression}", + # "answer": result + # } + data_entry = {"messages": [{"role": "user", "content": f"Calculate the result of the expression: {expression}"}],"response":result} + + # Write to JSONL file + f.write(json.dumps(data_entry) + '\n') + except Exception as e: + print(f"Skipping problematic expression {expression}: {e}") + continue + + print(f"Generated dataset with {num_samples} samples and saved to {output_file}") + +generate_dataset() \ No newline at end of file diff --git a/math_operations_dataset.jsonl b/math_operations_dataset.jsonl new file mode 100644 index 0000000000..f4706a6478 --- /dev/null +++ b/math_operations_dataset.jsonl @@ -0,0 +1,1000 @@ +{"messages": [{"role": "user", "content": "Calculate the result of the expression: 4@1"}], "response": 8} +{"messages": [{"role": "user", "content": "Calculate the result of the expression: 6^9^7&8@2"}], "response": 624.0} +{"messages": [{"role": "user", "content": "Calculate the result of the expression: 2@2&7^6&4"}], "response": 6.875} +{"messages": [{"role": "user", "content": "Calculate the result of the expression: 2@2$9&2"}], "response": 30.0} +{"messages": [{"role": "user", "content": "Calculate the result of the expression: 14&10&4^3&10"}], "response": 650890.5} +{"messages": [{"role": "user", "content": "Calculate the result of the expression: 14^1@1@3"}], 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19^10@1^1$7"}], "response": 98} +{"messages": [{"role": "user", "content": "Calculate the result of the expression: 11&1^9^5$3&1"}], "response": 13612.0} diff --git a/math_tool.py b/math_tool.py new file mode 100644 index 0000000000..56faa0d691 --- /dev/null +++ b/math_tool.py @@ -0,0 +1,179 @@ +""" +Custom Mathematical Operations Tool Module +This module provides a unified tool call interface for various mathematical operations. +""" + +import json +import re +import math +from typing import Tuple, Optional, Any, Dict + +# Define the custom mathematical operations with overflow protection +def operation_at(a: int, b: int) -> float: + """@ operation: Returns a raised to the power of b, then adds a*b + + Includes overflow protection for large numbers + """ + try: + # For very large exponents, use log approximation + if b > 100 or (a > 20 and b > 10): + # Fall back to a simpler calculation for very large values + return a * b * 2 # Simplified approximation + power_result = a ** b + product_result = a * b + return power_result + product_result + except OverflowError: + # If overflow occurs, return a simplified approximation + return a * b * 2 # Simplified approximation + +def operation_amp(a: int, b: int) -> float: + """& operation: Returns the average of a and b, multiplied by their absolute difference""" + avg = (a + b) / 2 + diff = abs(a - b) + return avg * diff + +def operation_dollar(a: int, b: int) -> float: + """$ operation: Returns a factorial-like sum of a repeated b times: a + (a-1) + (a-2) + ... + (a-b+1)""" + if b <= 0 or b > a: + return a + + # For large values, use arithmetic sequence sum formula + if b > 1000: + # Sum of arithmetic sequence: n/2 * (first + last) + n = min(b, a) + first = a + last = a - n + 1 + return n * (first + last) / 2 + + return sum(a - i for i in range(int(min(b, a)))) + +def operation_caret(a: int, b: int) -> float: + """^ operation: Returns a * b if both are even, a + b if both are odd, a - b otherwise""" + if a % 2 == 0 and b % 2 == 0: + return a * b + elif a % 2 == 1 and b % 2 == 1: + return a + b + else: + return a - b + +class TOOL_CALL: + def __call__(self, completion: str) -> Tuple[Any, bool, Optional[float]]: + raise NotImplementedError + +class MathOperation_Tool(TOOL_CALL): + """Unified tool for handling all mathematical operations""" + + def __init__(self): + self.operations = { + "at_operation": operation_at, + "amp_operation": operation_amp, + "dollar_operation": operation_dollar, + "caret_operation": operation_caret + } + + def __call__(self, completion: str) -> Tuple[float, bool, float]: + try: + # Check for required strict format + pattern = r'^(.*?)\n(.*?)$' + match = re.match(pattern, completion.strip(), re.DOTALL) + + if not match: + return "", True, 0 + + tool_content = match.group(2).strip() + + # Parse JSON from tool content + try: + tool_data = json.loads(tool_content) + except json.JSONDecodeError: + return "", True, 0 + + # Check if JSON has required fields + if not isinstance(tool_data, dict) or "tool" not in tool_data or "a" not in tool_data or "b" not in tool_data: + return "", True, 0 + + tool_name = tool_data["tool"] + + # Check if the requested operation exists + if tool_name not in self.operations: + return "", True, 0 + + # Get the operation function + operation_func = self.operations[tool_name] + + # Execute operation + try: + a, b = float(tool_data["a"]), float(tool_data["b"]) + result = operation_func(a, b) + return f"\n{result}\n", False, 0.2 + except (ValueError, TypeError): + return "", True, 0 + + except Exception as e: + print(f"Error in MathOperation_Tool: {e}") + return "", True, 0 + +# Parser for expressions with overflow protection +def parse_expression(expression: str) -> float: + """ + Parses and evaluates a custom mathematical expression. + Supports operations: @, &, $, ^ + Example: "11@2&1$44^2" + + Includes overflow protection for large numbers + """ + # Tokenize the expression - find all numbers and operators + tokens = re.findall(r'(\d+|\@|\&|\$|\^)', expression) + + # Process tokens + result = None + current_op = None + + for token in tokens: + if token in ['@', '&', '$', '^']: + current_op = token + else: + try: + num = int(token) + if result is None: + result = num + elif current_op == '@': + # Limit very large inputs for @ operation + if result > 10000 or num > 100: + result = result * num * 2 # Simplified approximation + else: + result = operation_at(result, num) + elif current_op == '&': + result = operation_amp(result, num) + elif current_op == '$': + result = operation_dollar(result, num) + elif current_op == '^': + result = operation_caret(result, num) + except (OverflowError, ValueError): + # Handle overflow by using a simplified calculation + if current_op == '@': + result = result * num * 2 # Simplified approximation + elif current_op == '&': + result = result * num # Simplified approximation + elif current_op == '$': + result = result + num # Simplified approximation + elif current_op == '^': + result = max(result, num) # Simplified approximation + + return result + +# Map symbols to operation names +SYMBOL_TO_OPERATION = { + '@': 'at_operation', + '&': 'amp_operation', + '$': 'dollar_operation', + '^': 'caret_operation' +} + +# Operation definitions for reference +OPERATION_DEFINITIONS = { + "@": "a@b = (a^b) + (a*b)", + "&": "a&b = ((a+b)/2) * |a-b|", + "$": "a$b = a + (a-1) + (a-2) + ... + (a-b+1)", + "^": "a^b = a*b if both even, a+b if both odd, a-b otherwise" +} \ No newline at end of file diff --git a/swift/plugin/orm.py b/swift/plugin/orm.py index 143bd703e7..4579eb7200 100644 --- a/swift/plugin/orm.py +++ b/swift/plugin/orm.py @@ -286,6 +286,65 @@ def __call__(self, completions, **kwargs) -> List[float]: matches = [re.match(pattern, content, re.DOTALL | re.MULTILINE) for content in completions] return [1.0 if match else 0.0 for match in matches] +import re +from typing import List + +class SimpleReward(ORM): + + def __call__(self, completions, **kwargs) -> List[float]: + """ + Reward function that checks if the completion has a specific format + and compares the extracted answer with the expected answer. + + Args: + completions: List of completion strings to evaluate + kwargs: Additional arguments, should include 'answer' key with expected float answer + + Returns: + List of scores: 0.0 for incorrect format, 0.2 for correct format but wrong answer, + 1.2 for correct format and correct answer + """ + # Format pattern to match ...... + format_pattern = r'^(.*?)\n(.*?)$' + scores = [] + expected_answer = kwargs.get("response", [0])[0] # Default to 0.0 if not provided + + for content in completions: + # Check if the format matches + match = re.match(format_pattern, content, re.DOTALL | re.MULTILINE) + + if not match: + # Incorrect format + scores.append(0.0) + else: + # Extract the answer + extracted_answer_str = match.group(1).strip() + + # Try to convert the extracted answer to float + try: + # Extract numeric part from the answer + # This regex finds all numbers (including decimals) in the string + number_matches = re.findall(r'-?\d+\.?\d*', extracted_answer_str) + + if number_matches: + # Take the first number found in the answer + extracted_answer = float(number_matches[0]) + + # Compare with expected answer with some tolerance for floating point + if abs(extracted_answer - expected_answer) < 1e-6: + # Correct format and correct answer + scores.append(1.2) + else: + # Correct format but wrong answer + scores.append(0.2) + else: + # No numeric value found in the answer + scores.append(0.2) + except (ValueError, TypeError): + # Failed to convert to float + scores.append(0.2) + + return scores class ReActFormat(ORM): @@ -384,4 +443,5 @@ def __call__(self, completions, **kwargs) -> List[float]: 'react_format': ReActFormat, 'cosine': CosineReward, 'repetition': RepetitionPenalty, + 'simplereward':SimpleReward } diff --git a/swift/plugin/tool_call.py b/swift/plugin/tool_call.py new file mode 100644 index 0000000000..2022f65a1c --- /dev/null +++ b/swift/plugin/tool_call.py @@ -0,0 +1,102 @@ +from typing import Union, Tuple, Optional + +class TOOL_CALL: + + def __call__(self, completion:str) -> Tuple[str, bool, Optional[int]]: + raise NotImplementedError + + +""" +Search module for RL training loop. +This module provides functions to search through vectorized documents and retrieve question-answer pairs. +""" + +import json +import re +from typing import Tuple, Optional +import traceback + +# Load the vectorstore when module is imported +try: + vectorstore = load_vectorstore() + if vectorstore is None: + print("Warning: FAISS vectorstore could not be loaded.") +except Exception as e: + print(f"Error loading vectorstore: {e}") + vectorstore = None + +def search(query: str, results: int = 5): + """ + Search for relevant chunks using similarity search. + + Args: + query: The search query + return_type: Return as string or list (default: str) + results: Number of results to return (default: 5) + + Returns: + Results as string or list depending on return_type + """ + if vectorstore is None: + raise ValueError("Vectorstore not loaded. Please ensure FAISS index exists.") + + search_results = vectorstore.similarity_search(query, k=results) + + result_dict = {} + for idx, result in enumerate(search_results, start=1): + result_dict[idx] = result.page_content + + result_json = json.dumps(result_dict,indent=2,ensure_ascii=False) + return f"\n{result_json}\n" + +class TOOL_CALL: + def __call__(self, completion: str) -> Tuple[str, bool, Optional[float]]: + raise NotImplementedError + +class Search_Tool(TOOL_CALL): + def __call__(self, completion: str) -> Tuple[str, bool, Optional[float]]: + """ + Checks if the completion strictly follows the format xxxxxx + and if the tool_call contains valid JSON with "tool" and "arg" fields. + + Args: + completion: The text completion to check + + Returns: + Tuple containing: + - search result or empty string + - boolean indicating if there was an error + - score (0.2 if successful, 0 if error) + """ + try: + # Check for required strict format using regex + pattern = r'^(.*?)(.*?)$' + match = re.match(pattern, completion.strip(), re.DOTALL) + + if not match: + return "", True, 0 + + tool_content = match.group(2).strip() + + # Parse JSON from tool_call content + try: + tool_data = json.loads(tool_content) + except json.JSONDecodeError: + return "", True, 0 + + # Check if JSON has required fields + if not isinstance(tool_data, dict) or "tool" not in tool_data or "arg" not in tool_data: + return "", True, 0 + + # Check if the tool is "search" + if tool_data["tool"] != "search": + return "", True, 0 + + # Execute search with the provided argument + search_result = search(tool_data["arg"]) + return search_result, False, 0.2 + + except Exception as e: + print(f"Error in Search_Tool: {e}") + traceback.print_exc() + return "", True, 0 \ No newline at end of file diff --git a/swift/trainers/rlhf_arguments.py b/swift/trainers/rlhf_arguments.py index b4add66cfd..ae29942f75 100644 --- a/swift/trainers/rlhf_arguments.py +++ b/swift/trainers/rlhf_arguments.py @@ -45,6 +45,7 @@ class PPOConfig(SwiftArgumentsMixin, HfPPOConfig): @dataclass class GRPOConfig(GRPOArgumentsMixin, SwiftArgumentsMixin, HfGRPOConfig): stop_words: List[str] = field(default_factory=list) + is_reward_tool_call = True #是否额外单独计算每个tool call的format得分 def __post_init__(self): from swift.llm.argument.base_args.model_args import ModelArguments diff --git a/swift/trainers/rlhf_trainer/grpo_trainer.py b/swift/trainers/rlhf_trainer/grpo_trainer.py index 86d850fcaa..c294f2f41a 100644 --- a/swift/trainers/rlhf_trainer/grpo_trainer.py +++ b/swift/trainers/rlhf_trainer/grpo_trainer.py @@ -1,5 +1,6 @@ # Copyright (c) Alibaba, Inc. and its affiliates. # Part of the implementation is borrowed from huggingface/trl. +from builtins import property import concurrent.futures import inspect import os @@ -13,8 +14,8 @@ from math import ceil from queue import Queue from types import MethodType -from typing import Any, Callable, Dict, List, Optional, Union - +from typing import Any, Callable, Dict, List, Optional, Tuple, Union +from math_tool import MathOperation_Tool import numpy as np import torch import torch.nn as nn @@ -88,7 +89,8 @@ class DataCache: outputs: List[Dict] = field(default_factory=list) distributed_idx: List[List] = field(default_factory=list) - +def tool_call(): + pass class GRPOTrainer(RLHFTrainerMixin, SwiftMixin, HFGRPOTrainer): executor = concurrent.futures.ThreadPoolExecutor(max_workers=1) @@ -97,10 +99,29 @@ def __init__(self, ref_model: Optional[Union[PreTrainedModel, nn.Module]] = None, reward_model: Optional[Union[PreTrainedModel, nn.Module]] = None, reward_funcs: Optional[List[Union[str, Callable]]] = None, + tool_call:Optional[Callable] = None, *_args, **kwargs): from swift.trainers.rlhf_arguments import GRPOConfig args: GRPOConfig = kwargs['args'] + #add tool call + self.tool_call = MathOperation_Tool() + args.tool_call_weight = None + self.reward_weights = torch.ones(1, dtype=torch.float32) + self.is_reward_tool_call = True #add GRPO config + # In the __init__ method, after initializing reward_weights: + if self.is_reward_tool_call and self.tool_call is not None: + # Add a weight for tool call rewards + if args.tool_call_weight is not None: + self.reward_weights = torch.cat([ + self.reward_weights, + torch.tensor([args.tool_call_weight], dtype=torch.float32) + ]) + else: + self.reward_weights = torch.cat([ + self.reward_weights, + torch.ones(1, dtype=torch.float32) + ]) self.args = args self.queue = None self.train_queue = Queue() @@ -691,72 +712,103 @@ def _fast_infer(self, inputs): def old_policy(self): return self.num_iterations > 1 or self.args.async_generate - def _generate_and_score_completions( - self, inputs: dict[str, Union[torch.Tensor, Any]]) -> dict[str, Union[torch.Tensor, Any]]: - + def _generate_and_score_completions(self, inputs: dict[str, Union[torch.Tensor, Any]]) -> dict[str, Union[torch.Tensor, Any]]: device = self.accelerator.device - # Generate completions using either vLLM or regular generation - if self.args.use_vllm or self.args.use_lmdeploy: - inputs, outputs = self._fast_infer(inputs) - # Broadcast the completions from the main process to all processes, ensuring each process receives its - # corresponding slice. - # outputs = broadcast_object_list(outputs, from_process=0) - else: - # Regular generation path - is_multimodal = self.model.model_meta.is_multimodal - if is_multimodal: - models = self.template.remove_post_encode_hook() - with unwrap_model_for_generation( - self.model_wrapped, self.accelerator, gather_deepspeed3_params=self.args.ds3_gather_for_generation): - # same reference - outputs = self.engine.infer(inputs, self.request_config, use_tqdm=False) - self.model.train() - if is_multimodal: - self.template.register_post_encode_hook(models) - - # Slice to keep only the local part of the data + total_tool_calls = 0 + # 确定当前进程的数据切片 process_slice = slice( self.accelerator.process_index * len(inputs), (self.accelerator.process_index + 1) * len(inputs), ) - if self.args.use_vllm or self.args.use_lmdeploy: - outputs = outputs[process_slice] - - for i, output in enumerate(outputs): - messages = inputs[i]['messages'] - InferRequest.remove_response(messages) - messages.append({'role': 'assistant', 'content': output.choices[0].message.content}) + # Process tool calls if tool_call function is available + if self.tool_call is not None: + # Deep copy inputs to preserve the original prompt for tool calling + tool_inputs = deepcopy(inputs) + tool_inputs, total_tool_calls = self._process_tool_calls(tool_inputs) + + # Replace original inputs with tool-processed inputs + inputs = tool_inputs + + else: + if self.args.use_vllm or self.args.use_lmdeploy: + inputs, outputs = self._fast_infer(inputs) + else: + # Regular generation path + is_multimodal = self.model.model_meta.is_multimodal + if is_multimodal: + models = self.template.remove_post_encode_hook() + with unwrap_model_for_generation( + self.model_wrapped, self.accelerator, gather_deepspeed3_params=self.args.ds3_gather_for_generation): + outputs = self.engine.infer(inputs, self.request_config, use_tqdm=False) + self.model.train() + if is_multimodal: + self.template.register_post_encode_hook(models) + + # # Slice to keep only the local part of the data + # process_slice = slice( + # self.accelerator.process_index * len(inputs), + # (self.accelerator.process_index + 1) * len(inputs), + # ) + if self.args.use_vllm or self.args.use_lmdeploy: + outputs = outputs[process_slice] + + # Add final assistant responses to message history + for i, output in enumerate(outputs): + messages = inputs[i]['messages'] + InferRequest.remove_response(messages) + messages.append({'role': 'assistant', 'content': output.choices[0].message.content}) + + # Now process all inputs with all messages for training from copy import copy template = copy(self.template) with self._template_context(template): batched_inputs = [template.encode(infer_request) for infer_request in inputs] - outputs = to_device(template.data_collator(batched_inputs), self.model.device) - - # we only need to compute the logits for the completion tokens - labels = outputs.pop('labels') - logits_to_keep = (labels.shape[-1] - (torch.ne(labels, -100).int().argmax(-1))).max().item() - outputs['logits_to_keep'] = logits_to_keep - outputs['completion_mask'] = labels[:, -logits_to_keep:] != -100 - + encoded_outputs = to_device(template.data_collator(batched_inputs), self.model.device) + + # Extract labels and create mask for all assistant outputs + labels = encoded_outputs.pop('labels') + # Identify all positions where labels != -100, which captures all assistant outputs + completion_mask = (labels != -100) + + # Ensure we're not exceeding max completion length + if completion_mask.sum(1).max() > self.args.max_completion_length: + # Truncate to max length if needed + truncated_mask = torch.zeros_like(completion_mask) + for i in range(len(truncated_mask)): + # Find all indices where labels != -100 + indices = torch.where(completion_mask[i])[0] + # Take only up to max_completion_length indices + indices = indices[:self.args.max_completion_length] + # Set those indices to True in the truncated mask + truncated_mask[i, indices] = True + completion_mask = truncated_mask + + # Add to outputs + encoded_outputs['completion_mask'] = completion_mask + encoded_outputs['logits_to_keep'] = completion_mask.shape[1] # Use full length + encoded_outputs['labels'] = labels # Put labels back for proper processing + + # Continue with the existing logic for computing logps and rewards with torch.no_grad(): if self.old_policy: - outputs['old_per_token_logps'] = self._get_per_token_logps(self.model, outputs) + encoded_outputs['old_per_token_logps'] = self._get_per_token_logps(self.model, encoded_outputs) else: - outputs['old_per_token_logps'] = None + encoded_outputs['old_per_token_logps'] = None if self.beta == 0.0: ref_per_token_logps = None elif self.ref_model is not None: - ref_per_token_logps = self._get_per_token_logps(self.ref_model, outputs) + ref_per_token_logps = self._get_per_token_logps(self.ref_model, encoded_outputs) else: with self.accelerator.unwrap_model(self.model).disable_adapter(): - ref_per_token_logps = self._get_per_token_logps(self.model, outputs) + ref_per_token_logps = self._get_per_token_logps(self.model, encoded_outputs) + # Calculate rewards rewards_per_func = torch.zeros((len(inputs), len(self.reward_funcs)), device=device) completions = [example['messages'][-1]['content'] for example in inputs] for i, (reward_func, reward_template) in enumerate(zip(self.reward_funcs, self.reward_templates)): - if isinstance(reward_func, nn.Module): # Module instead of PretrainedModel for compat with compiled models + if isinstance(reward_func, nn.Module): with self._template_context(reward_template): batched_inputs = [reward_template.encode(infer_request) for infer_request in inputs] reward_inputs = to_device(reward_template.data_collator(batched_inputs), reward_func.device) @@ -764,11 +816,19 @@ def _generate_and_score_completions( with torch.inference_mode(): rewards_per_func[:, i] = reward_func(**reward_inputs).logits[:, 0] else: - # Repeat all input columns (but "messages" and "completion") to match the number of generations reward_kwargs = RowPreprocessor.rows_to_batched(inputs) output_reward_func = reward_func(completions, **reward_kwargs) rewards_per_func[:, i] = torch.tensor(output_reward_func, dtype=torch.float32, device=device) + # Add tool call rewards if enabled + if self.is_reward_tool_call and self.tool_call is not None: + tool_rewards = torch.tensor( + [inputs[i].get('_tool_call_reward', 0.0) for i in range(len(inputs))], + dtype=torch.float32, + device=device + ).unsqueeze(1) + rewards_per_func = torch.cat([rewards_per_func, tool_rewards], dim=1) + rewards_per_func = gather(rewards_per_func) # Apply weights to each reward function's output and sum rewards = (rewards_per_func * self.reward_weights.to(device).unsqueeze(0)).sum(dim=1) @@ -783,47 +843,65 @@ def _generate_and_score_completions( advantages = (rewards - mean_grouped_rewards) / (std_grouped_rewards + 1e-4) advantages = advantages[process_slice] - # Log the metrics + # Log metrics mode = 'eval' if self.control.should_evaluate else 'train' - completion_length = self.accelerator.gather_for_metrics(outputs['completion_mask'].sum(1)).float().mean().item() + completion_length = self.accelerator.gather_for_metrics(encoded_outputs['completion_mask'].sum(1)).float().mean().item() self._metrics[mode]['completion_length'].append(completion_length) - # clip ratio + + # Add tool call metrics + if self.tool_call is not None: + avg_tool_calls = self.accelerator.gather_for_metrics( + torch.tensor([total_tool_calls / len(inputs)], device=device)).mean().item() + self._metrics[mode]['tool_call_nums'].append(avg_tool_calls) + + # Other existing metrics response_clip_ratio = torch.gt( - self.accelerator.gather_for_metrics(outputs['completion_mask'].sum(1)), + self.accelerator.gather_for_metrics(encoded_outputs['completion_mask'].sum(1)), self.args.max_completion_length).float().mean().item() self._metrics[mode]['response_clip_ratio'].append(response_clip_ratio) + reward_per_func = rewards_per_func.mean(0) for i, reward_func in enumerate(self.reward_funcs): - if isinstance(reward_func, nn.Module): # Module instead of PretrainedModel for compat with compiled models + if isinstance(reward_func, nn.Module): reward_func_name = reward_func.config._name_or_path.split('/')[-1] else: if inspect.isfunction(reward_func): - reward_func_name = reward_func.__name__ # function + reward_func_name = reward_func.__name__ else: - reward_func_name = reward_func.__class__.__name__ # method + reward_func_name = reward_func.__class__.__name__ self._metrics[mode][f'rewards/{reward_func_name}'].append(reward_per_func[i].item()) + # Add tool call reward metric if enabled + if self.is_reward_tool_call and self.tool_call is not None: + self._metrics[mode]['rewards/tool_call'].append(reward_per_func[-1].item()) + self._metrics[mode]['reward'].append(rewards.mean().item()) self._metrics[mode]['reward_std'].append(std_grouped_rewards.mean().item()) - outputs.update({ + encoded_outputs.update({ 'ref_per_token_logps': ref_per_token_logps, 'advantages': advantages, }) + # Log completions if self.log_completions and self.state.global_step % self.args.logging_steps == 0: - # For logging table = { 'step': [str(self.state.global_step)] * len(rewards), 'messages': [inputs['messages'][:-1] for inputs in gather_object(inputs)], 'completion': gather_object(completions), 'reward': rewards.tolist(), } + + if self.tool_call is not None: + # Log tool call counts for each prompt + tool_call_counts = [inputs[i].get('_tool_call_count', 0) for i in range(len(inputs))] + table['tool_call_nums'] = tool_call_counts * (len(rewards) // len(inputs)) + self.jsonl_writer.append(table) if 'wandb' in self.args.report_to and wandb.run is not None and self.accelerator.is_main_process: import pandas as pd df = pd.DataFrame(table) wandb.log({'completions': wandb.Table(dataframe=df)}) - return outputs + return encoded_outputs @profiling_decorator def compute_loss(self, model, inputs, return_outputs=False, num_items_in_batch=None): @@ -867,24 +945,50 @@ def compute_loss(self, model, inputs, return_outputs=False, num_items_in_batch=N @profiling_decorator def _get_per_token_logps(self, model, inputs): from trl.trainer.utils import selective_log_softmax - logits_to_keep = inputs['logits_to_keep'] + + # Extract input_ids and completion_mask input_ids = inputs['input_ids'] + completion_mask = inputs.get('completion_mask') + + # Get unwrapped model unwrapped_model = self.accelerator.unwrap_model(model) parameters = inspect.signature(unwrapped_model.forward).parameters - if not unwrapped_model.model_meta.is_multimodal and 'logits_to_keep' in parameters: - # save memory - return super()._get_per_token_logps(model, input_ids, inputs['attention_mask'], logits_to_keep) - inputs = { - k: v - for k, v in inputs.items() if k not in - ['logits_to_keep', 'completion_mask', 'ref_per_token_logps', 'advantages', 'old_per_token_logps'] + + if not unwrapped_model.model_meta.is_multimodal and 'completion_mask' in parameters: + # If model supports direct masking, use that + return super()._get_per_token_logps(model, input_ids, inputs['attention_mask'], completion_mask) + + # Otherwise, compute full logits and mask afterwards + forward_inputs = { + k: v for k, v in inputs.items() + if k not in ['completion_mask', 'logits_to_keep', 'ref_per_token_logps', + 'advantages', 'old_per_token_logps'] } - logits = model(**inputs).logits - # exclude the last logit: it corresponds to the next token pred - logits = logits[:, -(logits_to_keep + 1):-1, :] + + # Get logits from model + logits = model(**forward_inputs).logits logits = logits / self.temperature - input_ids = input_ids[:, -logits_to_keep:] - return selective_log_softmax(logits, input_ids) # compute logprobs for the input tokens + + # Find positions corresponding to completion tokens (where completion_mask is True) + # Create a mask that selects these positions + all_positions = torch.where(completion_mask) + batch_indices = all_positions[0] + seq_indices = all_positions[1] + + # Get the corresponding logits and input IDs + selected_logits = logits[batch_indices, seq_indices - 1] # -1 because logits predict next token + selected_tokens = input_ids[batch_indices, seq_indices] + + # Calculate log probabilities for selected tokens + log_probs = selective_log_softmax(selected_logits.unsqueeze(1), selected_tokens.unsqueeze(1)).squeeze(1) + + # Create output tensor with same shape as completion_mask, filled with zeros + result = torch.zeros_like(completion_mask, dtype=torch.float) + + # Place log probs at appropriate positions + result[batch_indices, seq_indices] = log_probs + + return result def evaluation_loop(self, dataloader, *args, **kwargs): self.queue = self.eval_queue @@ -896,3 +1000,221 @@ def evaluation_loop(self, dataloader, *args, **kwargs): output.metrics.update(metrics) self.queue = self.train_queue return output + def _process_tool_calls(self, inputs: List[Dict]) -> Tuple[List[Dict], int]: + """ + Args: + inputs: List of input dictionaries with message history + + Returns: + Updated inputs after tool calling process and total number of tool calls + """ + # Track tool call metrics for this batch + tool_call_counts = [0] * len(inputs) + tool_call_rewards = [0.0] * len(inputs) + assistant_tokens_used = [0] * len(inputs) + total_tool_calls = 0 + + # Process inputs until all are finished + active_indices = list(range(len(inputs))) + + while active_indices: + # Generate responses for active inputs with adjusted max_tokens + active_inputs = [inputs[i] for i in active_indices] + + # Create a copy of the request config with adjusted max_tokens for each input + adjusted_request_configs = [] + for idx in active_indices: + tokens_used = assistant_tokens_used[idx] + available_tokens = max(1, self.args.max_completion_length - tokens_used) + + # Create custom request config with adjusted max_tokens + config = copy(self.request_config) + config.max_tokens = available_tokens + adjusted_request_configs.append(config) + + # Generate responses with adjusted token limits + if self.args.use_vllm or self.args.use_lmdeploy: + active_inputs = [inputs[i] for i in active_indices] + _, active_outputs = self._fast_infer_with_custom_configs(active_inputs, adjusted_request_configs) + else: + is_multimodal = self.model.model_meta.is_multimodal + if is_multimodal: + models = self.template.remove_post_encode_hook() + with unwrap_model_for_generation( + self.model_wrapped, self.accelerator, gather_deepspeed3_params=self.args.ds3_gather_for_generation): + active_outputs = [] + for i, input_data in enumerate(active_inputs): + # Use custom config for each input + output = self.engine.infer([input_data], adjusted_request_configs[i], use_tqdm=False) + active_outputs.extend(output) + self.model.train() + if is_multimodal: + self.template.register_post_encode_hook(models) + + # Process tool calls asynchronously + futures = [] + executor = concurrent.futures.ThreadPoolExecutor(max_workers=len(active_indices)) + + # Start all tool calls in parallel + for i, idx in enumerate(active_indices): + completion = active_outputs[i].choices[0].message.content + tokens_used = len(self.tokenizer(completion).input_ids) + + # Add assistant response to message history + messages = active_inputs[i]['messages'] + InferRequest.remove_response(messages) + messages.append({'role': 'assistant', 'content': completion}) + + # Update token count + assistant_tokens_used[idx] += tokens_used + + futures.append((executor.submit(self.tool_call, completion), idx)) + + # Process results and update inputs + new_active_indices = [] + for future, idx in futures: + tool_result, finish, tool_reward = future.result() + + if not finish: + # Add user message with tool result + inputs[idx]['messages'].append({'role': 'user', 'content': tool_result}) + new_active_indices.append(idx) + + # Update tracking variables + tool_call_counts[idx] += 1 + total_tool_calls += 1 + if self.is_reward_tool_call and not finish: #remove last reponse + tool_call_rewards[idx] += tool_reward + + executor.shutdown() + active_indices = new_active_indices + + # Break if no active indices remain + if not active_indices: + break + + # Store tool call metrics in inputs for later use + for i in range(len(inputs)): + inputs[i]['_tool_call_count'] = tool_call_counts[i] + inputs[i]['_tool_call_reward'] = tool_call_rewards[i] + inputs[i]['_assistant_tokens_used'] = assistant_tokens_used[i] + + return inputs, total_tool_calls + def _fast_infer_with_custom_configs(self, inputs, request_configs): + """ + Similar to _fast_infer but uses a different request_config for each input. + + Args: + inputs: List of input dicts + request_configs: List of request configs (same length as inputs) + + Returns: + inputs, outputs tuple + """ + if self.args.sleep_level > 0 and self.infer_rank >= 0: + if self.args.offload_model: + self.offload_model() + if self.args.offload_optimizer: + self.offload_optimizer() + if self.args.gc_collect_after_offload: + gc_collect() + self.engine.engine.wake_up() + + # First, have main process load weights if needed + if self.state.global_step != self._last_loaded_step: + self._move_model_to_vllm_lmdeploy() + self._last_loaded_step = self.state.global_step + + # Gather all prompts + all_inputs = gather_object(inputs) + all_configs = gather_object(request_configs) + + # Distribute inputs to different workers + distributed_idx = self.round_robin(len(all_inputs), get_node_setting()[1] * self.args.num_infer_workers) + + if self.infer_rank >= 0: + _input_slice = np.array(all_inputs)[distributed_idx[self.infer_rank]] + _config_slice = np.array(all_configs)[distributed_idx[self.infer_rank]] + + if self.args.async_generate: + # Modified async_infer to use custom configs + self.async_infer_with_configs(inputs, _input_slice, _config_slice, distributed_idx) + data_cache = self.queue.get() + inputs = data_cache.inputs + outputs = data_cache.outputs + distributed_idx = data_cache.distributed_idx + else: + with set_device_context(self.infer_device): + outputs = [] + # Process each input with its own config + for i in range(len(_input_slice)): + request_config = _config_slice[i] + if self.args.tensor_parallel_size > 1: + request_config.seed += self.state.global_step + output = self.engine.infer([_input_slice[i]], request_config, use_tqdm=False) + outputs.extend(output) + + if self.args.tensor_parallel_size > 1: + if self.infer_rank_tp_0 < 0: + outputs = [] + else: + _outputs = [] + for tp_idx in range(self.args.tensor_parallel_size): + for prompt_idx in range(len(outputs)): + output = deepcopy(outputs[prompt_idx]) + output.choices = [output.choices[tp_idx]] + _outputs.append(output) + outputs = _outputs + else: + if self.args.async_generate: + self.queue.put(DataCache(inputs, [], distributed_idx)) + data_cache = self.queue.get() + inputs = data_cache.inputs + distributed_idx = data_cache.distributed_idx + outputs = [] + + outputs = gather_object(outputs) + outputs = self.reorder_outputs(outputs, distributed_idx) + + if self.args.sleep_level > 0 and self.infer_rank >= 0: + self.engine.engine.sleep(level=self.args.sleep_level) + if self.args.gc_collect_after_offload: + gc_collect() + if self.args.offload_model: + self.load_model() + if self.args.offload_optimizer: + self.load_optimizer() + + return inputs, outputs + def async_infer_with_configs(self, inputs, inputs_slice, configs_slice, distributed_idx): + """ + Asynchronous inference with custom configs for each input. + + Args: + inputs: Original inputs + inputs_slice: Slice of inputs for this worker + configs_slice: Slice of configs corresponding to inputs_slice + distributed_idx: Distribution of indices among workers + """ + def infer_task(): + with set_device_context(self.infer_device): + results = [] + # Process each input with its own config + for i in range(len(inputs_slice)): + result = self.engine.infer( + infer_requests=[inputs_slice[i]], + request_config=configs_slice[i], + use_tqdm=False) + results.extend(result) + return results + + future: Future = self.executor.submit(infer_task) + + def done(_self): + self.queue.put(DataCache(inputs, _self.result(), distributed_idx)) + + future.add_done_callback(done) + @property + def tokenizer(self): + """Get the tokenizer from the template.""" + return self.processing_class \ No newline at end of file diff --git a/test_grpo_tool.py b/test_grpo_tool.py new file mode 100644 index 0000000000..3c2efffd7b --- /dev/null +++ b/test_grpo_tool.py @@ -0,0 +1,161 @@ +# Copyright (c) Alibaba, Inc. and its affiliates. +import os +os.environ["CUDA_VISIBLE_DEVICES"] = "2" +from typing import List, Union + +from swift.utils import get_logger, get_model_parameter_info +from swift.llm.argument import RLHFArguments +from swift.llm.train.kto import prepare_kto_dataset +from swift.llm.train.sft import SwiftSft +logger = get_logger() + + +class SwiftRLHF(SwiftSft): + args_class = RLHFArguments + args: args_class + + def _prepare_model_tokenizer(self): + from swift.llm.infer.utils import prepare_adapter + args = self.args + for key in ['ref', 'reward', 'value']: + origin_key = key + setattr(self, f'{key}_model', None) + if key == 'value': + if args.rlhf_type == 'ppo': + key = 'reward' + else: + continue + model_id_or_path = getattr(args, f'{key}_model') + if model_id_or_path is None: + continue + model_type = getattr(args, f'{key}_model_type') + model_revision = getattr(args, f'{key}_model_revision') + adapters = args.adapters if key == 'ref' else args.reward_adapters + if origin_key == 'ref': + task_type = args.task_type + num_labels = None + else: + task_type = 'seq_cls' + num_labels = 1 + # Be aware of the unexpected behavior caused by double monkey patching. + model, processor = args.get_model_processor( + model=model_id_or_path, + model_type=model_type, + model_revision=model_revision, + task_type=task_type, + num_labels=num_labels) + + model = prepare_adapter(args, model, adapters) + if origin_key in {'ref', 'reward'}: + model.requires_grad_(False).eval() + else: + model = self.prepare_model(args, model, task_type=task_type) + logger.info(f'value_model: {model}') + model_parameter_info = get_model_parameter_info(model) + self.train_msg['value_model_parameter_info'] = model_parameter_info + logger.info(f'value_model_parameter_info: {model_parameter_info}') + setattr(self, f'{origin_key}_model', model) + if origin_key == 'reward' and args.rlhf_type == 'grpo': + reward_template = self.args.get_template(processor) + if reward_template.use_model: + reward_template.model = model + self.reward_template = reward_template + + super()._prepare_model_tokenizer() + + def _prepare_template(self) -> None: + args = self.args + super()._prepare_template() + model_mapping = {'kto': 'kto', 'ppo': 'pt', 'grpo': 'pt'} + self.template.set_mode(model_mapping.get(args.rlhf_type, 'rlhf')) + + if args.rlhf_type == 'ppo': + args.training_args.stop_token_id = self.template.template_meta.stop_token_id + + def _get_dataset(self): + args = self.args + train_dataset, val_dataset = super()._get_dataset() + if args.rlhf_type == 'kto': + train_dataset, val_dataset = prepare_kto_dataset(args, train_dataset, val_dataset) + return train_dataset, val_dataset + + def _get_trainer_kwargs(self): + trainer_kwargs = {} + for key in ['ref', 'reward', 'value']: + key = f'{key}_model' + model = getattr(self, key, None) + if model or self.args.rlhf_type == 'ppo': + trainer_kwargs[key] = model + if hasattr(self, 'reward_template'): + trainer_kwargs['reward_template'] = self.reward_template + if self.args.rlhf_type == 'grpo': + trainer_kwargs['reward_funcs'] = self.args.reward_funcs + return trainer_kwargs + + +if __name__ == '__main__': + """ +CUDA_VISIBLE_DEVICES=0 \ +swift rlhf \ + --rlhf_type grpo \ + --model Qwen/Qwen2.5-7B-Instruct \ + --reward_funcs accuracy format \ + --train_type lora \ + --lora_rank 8 \ + --lora_alpha 32 \ + --target_modules all-linear \ + --torch_dtype bfloat16 \ + --dataset 'AI-MO/NuminaMath-TIR#1000' \ + --max_completion_length 1024 \ + --num_train_epochs 1 \ + --per_device_train_batch_size 4 \ + --per_device_eval_batch_size 4 \ + --learning_rate 1e-5 \ + --gradient_accumulation_steps 1 \ + --eval_steps 100 \ + --save_steps 100 \ + --save_total_limit 2 \ + --logging_steps 5 \ + --max_length 2048 \ + --output_dir output \ + --warmup_ratio 0.05 \ + --dataloader_num_workers 4 \ + --dataset_num_proc 4 \ + --num_generations 4 \ + --temperature 0.9 \ + --system 'examples/train/grpo/prompt.txt' \ + --log_completions true + """ + args = RLHFArguments( + rlhf_type = 'grpo', + model = '~/.cache/modelscope/hub/qwen/Qwen2.5-7B-Instruct', + reward_funcs = ['simplereward'], + train_type = 'lora', + lora_rank = 8, + lora_alpha = 32, + target_modules = 'all-linear', + torch_dtype = 'bfloat16', + dataset = 'math_operations_dataset.jsonl', + max_completion_length = 1024, + num_train_epochs = 1, + per_device_train_batch_size = 4, + per_device_eval_batch_size = 4, + learning_rate = 1e-5, + gradient_accumulation_steps = 1, + eval_steps = 100, + save_steps = 100, + save_total_limit = 2, + logging_steps = 1, + max_length = 2048, + output_dir = 'output', + warmup_ratio = 0.05, + dataloader_num_workers = 4, + dataset_num_proc = 4, + num_generations = 4, + temperature = 0.9, + log_completions = True, + system = 'tool_system.txt', + loss_scale="default" + + ) + SwiftRLHF(args).run() diff --git a/tool_system.txt b/tool_system.txt new file mode 100644 index 0000000000..bb473290f9 --- /dev/null +++ b/tool_system.txt @@ -0,0 +1,62 @@ +你是一个计算助手，擅长多次调用计算工具来处理**定义新运算**，下面是你可以使用的工具: + +{ + "type": "function", + "function": { + "name": "math_operation", + "description": "Perform custom mathematical operations between two integers", + "parameters": { + "type": "object", + "properties": { + "tool": { + "type": "string", + "enum": ["at_operation", "amp_operation", "dollar_operation", "caret_operation"], + "description": "The type of mathematical operation to perform" + }, + "a": { + "type": "float", + "description": "The first float operand" + }, + "b": { + "type": "float", + "description": "The second float operand" + } + }, + "required": ["tool", "a", "b"] + } + } +} + +这些工具对应的运算定义如下: +1. at_operation (@): a@b +2. amp_operation (&): a&b +3. dollar_operation ($): a$b +4. caret_operation (^): a^b +当你调用工具时你的输出格式需要满足: +``` + +你的推理过程 + + +{ +"tool":"at_operation", +"a":5, +"b":2 +} + +``` +注意: +- 每次只能调用一个工具 +- 工具参数必须是有效的JSON格式 +- 必须包含和标签 + +当你计算完成时，需要满足格式: +``` + +总结整合之前的推理然后给出答案 + + +19.0 //最终答案只有一个浮点数 + +``` +**这是定义的新运算，你从只能从左到右依次计算，每轮对话只能调用一个工具**,然后使用返回的结果作为下一个操作的第一个操作数. \ 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Module -This module provides a unified tool call interface for various mathematical operations. -""" - -import json -import re -import math -from typing import Tuple, Optional, Any, Dict - -# Define the custom mathematical operations with overflow protection -def operation_at(a: int, b: int) -> float: - """@ operation: Returns a raised to the power of b, then adds a*b - - Includes overflow protection for large numbers - """ - try: - # For very large exponents, use log approximation - if b > 100 or (a > 20 and b > 10): - # Fall back to a simpler calculation for very large values - return a * b * 2 # Simplified approximation - power_result = a ** b - product_result = a * b - return power_result + product_result - except OverflowError: - # If overflow occurs, return a simplified approximation - return a * b * 2 # Simplified approximation - -def operation_amp(a: int, b: int) -> float: - """& operation: Returns the average of a and b, multiplied by their absolute difference""" - avg = (a + b) / 2 - diff = abs(a - b) - return avg * diff - -def operation_dollar(a: int, b: int) -> float: - """$ operation: Returns a factorial-like sum of a repeated b times: a + (a-1) + (a-2) + ... + (a-b+1)""" - if b <= 0 or b > a: - return a - - # For large values, use arithmetic sequence sum formula - if b > 1000: - # Sum of arithmetic sequence: n/2 * (first + last) - n = min(b, a) - first = a - last = a - n + 1 - return n * (first + last) / 2 - - return sum(a - i for i in range(int(min(b, a)))) - -def operation_caret(a: int, b: int) -> float: - """^ operation: Returns a * b if both are even, a + b if both are odd, a - b otherwise""" - if a % 2 == 0 and b % 2 == 0: - return a * b - elif a % 2 == 1 and b % 2 == 1: - return a + b - else: - return a - b - -class TOOL_CALL: - def __call__(self, completion: str) -> Tuple[Any, bool, Optional[float]]: - raise NotImplementedError - -class MathOperation_Tool(TOOL_CALL): - """Unified tool for handling all mathematical operations""" - - def __init__(self): - self.operations = { - "at_operation": operation_at, - "amp_operation": operation_amp, - "dollar_operation": operation_dollar, - "caret_operation": operation_caret - } - - def __call__(self, completion: str) -> Tuple[float, bool, float]: - try: - # Check for required strict format - pattern = r'^(.*?)\n(.*?)$' - match = re.match(pattern, completion.strip(), re.DOTALL) - - if not match: - return "", True, 0 - - tool_content = match.group(2).strip() - - # Parse JSON from tool content - try: - tool_data = json.loads(tool_content) - except json.JSONDecodeError: - return "", True, 0 - - # Check if JSON has required fields - if not isinstance(tool_data, dict) or "tool" not in tool_data or "a" not in tool_data or "b" not in tool_data: - return "", True, 0 - - tool_name = tool_data["tool"] - - # Check if the requested operation exists - if tool_name not in self.operations: - return "", True, 0 - - # Get the operation function - operation_func = self.operations[tool_name] - - # Execute operation - try: - a, b = float(tool_data["a"]), float(tool_data["b"]) - result = operation_func(a, b) - return f"\n{result}\n", False, 0.2 - except (ValueError, TypeError): - return "", True, 0 - - except Exception as e: - print(f"Error in MathOperation_Tool: {e}") - return "", True, 0 - -# Parser for expressions with overflow protection -def parse_expression(expression: str) -> float: - """ - Parses and evaluates a custom mathematical expression. - Supports operations: @, &, $, ^ - Example: "11@2&1$44^2" - - Includes overflow protection for large numbers - """ - # Tokenize the expression - find all numbers and operators - tokens = re.findall(r'(\d+|\@|\&|\$|\^)', expression) - - # Process tokens - result = None - current_op = None - - for token in tokens: - if token in ['@', '&', '$', '^']: - current_op = token - else: - try: - num = int(token) - if result is None: - result = num - elif current_op == '@': - # Limit very large inputs for @ operation - if result > 10000 or num > 100: - result = result * num * 2 # Simplified approximation - else: - result = operation_at(result, num) - elif current_op == '&': - result = operation_amp(result, num) - elif current_op == '$': - result = operation_dollar(result, num) - elif current_op == '^': - result = operation_caret(result, num) - except (OverflowError, ValueError): - # Handle overflow by using a simplified calculation - if current_op == '@': - result = result * num * 2 # Simplified approximation - elif current_op == '&': - result = result * num # Simplified approximation - elif current_op == '$': - result = result + num # Simplified approximation - elif current_op == '^': - result = max(result, num) # Simplified approximation - - return result - -# Map symbols to operation names -SYMBOL_TO_OPERATION = { - '@': 'at_operation', - '&': 'amp_operation', - '$': 'dollar_operation', - '^': 'caret_operation' -} - -# Operation definitions for reference -OPERATION_DEFINITIONS = { - "@": "a@b = (a^b) + (a*b)", - "&": "a&b = ((a+b)/2) * |a-b|", - "$": "a$b = a + (a-1) + (a-2) + ... + (a-b+1)", - "^": "a^b = a*b if both even, a+b if both odd, a-b otherwise" -} \ No newline at end of file diff --git a/swift/plugin/orm.py b/swift/plugin/orm.py index 4579eb7200..4021515741 100644 --- a/swift/plugin/orm.py +++ b/swift/plugin/orm.py @@ -286,65 +286,6 @@ def __call__(self, completions, **kwargs) -> List[float]: matches = [re.match(pattern, content, re.DOTALL | re.MULTILINE) for content in completions] return [1.0 if match else 0.0 for match in matches] -import re -from typing import List - -class SimpleReward(ORM): - - def __call__(self, completions, **kwargs) -> List[float]: - """ - Reward function that checks if the completion has a specific format - and compares the extracted answer with the expected answer. - - Args: - completions: List of completion strings to evaluate - kwargs: Additional arguments, should include 'answer' key with expected float answer - - Returns: - List of scores: 0.0 for incorrect format, 0.2 for correct format but wrong answer, - 1.2 for correct format and correct answer - """ - # Format pattern to match ...... - format_pattern = r'^(.*?)\n(.*?)$' - scores = [] - expected_answer = kwargs.get("response", [0])[0] # Default to 0.0 if not provided - - for content in completions: - # Check if the format matches - match = re.match(format_pattern, content, re.DOTALL | re.MULTILINE) - - if not match: - # Incorrect format - scores.append(0.0) - else: - # Extract the answer - extracted_answer_str = match.group(1).strip() - - # Try to convert the extracted answer to float - try: - # Extract numeric part from the answer - # This regex finds all numbers (including decimals) in the string - number_matches = re.findall(r'-?\d+\.?\d*', extracted_answer_str) - - if number_matches: - # Take the first number found in the answer - extracted_answer = float(number_matches[0]) - - # Compare with expected answer with some tolerance for floating point - if abs(extracted_answer - expected_answer) < 1e-6: - # Correct format and correct answer - scores.append(1.2) - else: - # Correct format but wrong answer - scores.append(0.2) - else: - # No numeric value found in the answer - scores.append(0.2) - except (ValueError, TypeError): - # Failed to convert to float - scores.append(0.2) - - return scores class ReActFormat(ORM): @@ -442,6 +383,5 @@ def __call__(self, completions, **kwargs) -> List[float]: 'format': Format, 'react_format': ReActFormat, 'cosine': CosineReward, - 'repetition': RepetitionPenalty, - 'simplereward':SimpleReward + 'repetition': RepetitionPenalty } diff --git a/swift/plugin/tool_call.py b/swift/plugin/tool_call.py index 2022f65a1c..25b3936f94 100644 --- a/swift/plugin/tool_call.py +++ b/swift/plugin/tool_call.py @@ -1,102 +1,9 @@ -from typing import Union, Tuple, Optional - +from typing import Tuple,Any, Optional class TOOL_CALL: - - def __call__(self, completion:str) -> Tuple[str, bool, Optional[int]]: + def __call__(self, completion: str) -> Tuple[Any, bool, Optional[float]]: raise NotImplementedError -""" -Search module for RL training loop. -This module provides functions to search through vectorized documents and retrieve question-answer pairs. -""" - -import json -import re -from typing import Tuple, Optional -import traceback - -# Load the vectorstore when module is imported -try: - vectorstore = load_vectorstore() - if vectorstore is None: - print("Warning: FAISS vectorstore could not be loaded.") -except Exception as e: - print(f"Error loading vectorstore: {e}") - vectorstore = None - -def search(query: str, results: int = 5): - """ - Search for relevant chunks using similarity search. - - Args: - query: The search query - return_type: Return as string or list (default: str) - results: Number of results to return (default: 5) - - Returns: - Results as string or list depending on return_type - """ - if vectorstore is None: - raise ValueError("Vectorstore not loaded. Please ensure FAISS index exists.") - - search_results = vectorstore.similarity_search(query, k=results) +tools = { - result_dict = {} - for idx, result in enumerate(search_results, start=1): - result_dict[idx] = result.page_content - - result_json = json.dumps(result_dict,indent=2,ensure_ascii=False) - return f"\n{result_json}\n" - -class TOOL_CALL: - def __call__(self, completion: str) -> Tuple[str, bool, Optional[float]]: - raise NotImplementedError - -class Search_Tool(TOOL_CALL): - def __call__(self, completion: str) -> Tuple[str, bool, Optional[float]]: - """ - Checks if the completion strictly follows the format xxxxxx - and if the tool_call contains valid JSON with "tool" and "arg" fields. - - Args: - completion: The text completion to check - - Returns: - Tuple containing: - - search result or empty string - - boolean indicating if there was an error - - score (0.2 if successful, 0 if error) - """ - try: - # Check for required strict format using regex - pattern = r'^(.*?)(.*?)$' - match = re.match(pattern, completion.strip(), re.DOTALL) - - if not match: - return "", True, 0 - - tool_content = match.group(2).strip() - - # Parse JSON from tool_call content - try: - tool_data = json.loads(tool_content) - except json.JSONDecodeError: - return "", True, 0 - - # Check if JSON has required fields - if not isinstance(tool_data, dict) or "tool" not in tool_data or "arg" not in tool_data: - return "", True, 0 - - # Check if the tool is "search" - if tool_data["tool"] != "search": - return "", True, 0 - - # Execute search with the provided argument - search_result = search(tool_data["arg"]) - return search_result, False, 0.2 - - except Exception as e: - print(f"Error in Search_Tool: {e}") - traceback.print_exc() - return "", True, 0 \ No newline at end of file +} \ No newline at end of file diff --git a/swift/trainers/arguments.py b/swift/trainers/arguments.py index 62417c19e7..cdf40a3021 100644 --- a/swift/trainers/arguments.py +++ b/swift/trainers/arguments.py @@ -2,7 +2,7 @@ import os from dataclasses import dataclass from functools import wraps -from typing import Any, Dict, Literal, Optional, Union +from typing import Any, Dict, Literal, Optional, Union, Callable import torch import torch.utils.checkpoint @@ -104,6 +104,9 @@ class GRPOArgumentsMixin: offload_optimizer: bool = False offload_model: bool = False gc_collect_after_offload: bool = False + is_reward_tool_call:bool = True #是否额外单独计算每个tool call的format得分 + tool_call_weight:float = 1.0 + tool_call:str = None @dataclass diff --git a/swift/trainers/rlhf_arguments.py b/swift/trainers/rlhf_arguments.py index ae29942f75..e55e686049 100644 --- a/swift/trainers/rlhf_arguments.py +++ b/swift/trainers/rlhf_arguments.py @@ -1,5 +1,5 @@ from dataclasses import dataclass, field -from typing import List, Optional +from typing import List, Optional,Callable from trl import CPOConfig as HfCPOConfig from trl import DPOConfig as HfDPOConfig @@ -45,7 +45,6 @@ class PPOConfig(SwiftArgumentsMixin, HfPPOConfig): @dataclass class GRPOConfig(GRPOArgumentsMixin, SwiftArgumentsMixin, HfGRPOConfig): stop_words: List[str] = field(default_factory=list) - is_reward_tool_call = True #是否额外单独计算每个tool call的format得分 def __post_init__(self): from swift.llm.argument.base_args.model_args import ModelArguments diff --git a/swift/trainers/rlhf_trainer/grpo_trainer.py b/swift/trainers/rlhf_trainer/grpo_trainer.py index c294f2f41a..1f79cb0733 100644 --- a/swift/trainers/rlhf_trainer/grpo_trainer.py +++ b/swift/trainers/rlhf_trainer/grpo_trainer.py @@ -31,7 +31,7 @@ get_node_setting, is_lmdeploy_available, is_vllm_available, is_wandb_available) from ..mixin import SwiftMixin from .rlhf_mixin import RLHFTrainerMixin - +from swift.plugin.tool_call import tools try: from trl.extras.profiling import profiling_decorator except ImportError: @@ -99,16 +99,16 @@ def __init__(self, ref_model: Optional[Union[PreTrainedModel, nn.Module]] = None, reward_model: Optional[Union[PreTrainedModel, nn.Module]] = None, reward_funcs: Optional[List[Union[str, Callable]]] = None, - tool_call:Optional[Callable] = None, *_args, **kwargs): from swift.trainers.rlhf_arguments import GRPOConfig args: GRPOConfig = kwargs['args'] #add tool call - self.tool_call = MathOperation_Tool() - args.tool_call_weight = None - self.reward_weights = torch.ones(1, dtype=torch.float32) - self.is_reward_tool_call = True #add GRPO config + print(args) + self.tool_call = tools[args.tool_call] + args.tool_call_weight = args.tool_call_weight + self.reward_weights = torch.ones(1, dtype=torch.float32) #通过配置 + self.is_reward_tool_call = args.is_reward_tool_call #add GRPO config # In the __init__ method, after initializing reward_weights: if self.is_reward_tool_call and self.tool_call is not None: # Add a weight for tool call rewards @@ -873,7 +873,7 @@ def _generate_and_score_completions(self, inputs: dict[str, Union[torch.Tensor, # Add tool call reward metric if enabled if self.is_reward_tool_call and self.tool_call is not None: - self._metrics[mode]['rewards/tool_call'].append(reward_per_func[-1].item()) + self._metrics[mode]['rewards/tool_call'].append(reward_per_func[-1].item()) #todo 应该用反射记录每个tool call的reward self._metrics[mode]['reward'].append(rewards.mean().item()) self._metrics[mode]['reward_std'].append(std_grouped_rewards.mean().item()) diff --git a/test_grpo_tool.py b/test_grpo_tool.py deleted file mode 100644 index 3c2efffd7b..0000000000 --- a/test_grpo_tool.py +++ /dev/null @@ -1,161 +0,0 @@ -# Copyright (c) Alibaba, Inc. and its affiliates. -import os -os.environ["CUDA_VISIBLE_DEVICES"] = "2" -from typing import List, Union - -from swift.utils import get_logger, get_model_parameter_info -from swift.llm.argument import RLHFArguments -from swift.llm.train.kto import prepare_kto_dataset -from swift.llm.train.sft import SwiftSft -logger = get_logger() - - -class SwiftRLHF(SwiftSft): - args_class = RLHFArguments - args: args_class - - def _prepare_model_tokenizer(self): - from swift.llm.infer.utils import prepare_adapter - args = self.args - for key in ['ref', 'reward', 'value']: - origin_key = key - setattr(self, f'{key}_model', None) - if key == 'value': - if args.rlhf_type == 'ppo': - key = 'reward' - else: - continue - model_id_or_path = getattr(args, f'{key}_model') - if model_id_or_path is None: - continue - model_type = getattr(args, f'{key}_model_type') - model_revision = getattr(args, f'{key}_model_revision') - adapters = args.adapters if key == 'ref' else args.reward_adapters - if origin_key == 'ref': - task_type = args.task_type - num_labels = None - else: - task_type = 'seq_cls' - num_labels = 1 - # Be aware of the unexpected behavior caused by double monkey patching. - model, processor = args.get_model_processor( - model=model_id_or_path, - model_type=model_type, - model_revision=model_revision, - task_type=task_type, - num_labels=num_labels) - - model = prepare_adapter(args, model, adapters) - if origin_key in {'ref', 'reward'}: - model.requires_grad_(False).eval() - else: - model = self.prepare_model(args, model, task_type=task_type) - logger.info(f'value_model: {model}') - model_parameter_info = get_model_parameter_info(model) - self.train_msg['value_model_parameter_info'] = model_parameter_info - logger.info(f'value_model_parameter_info: {model_parameter_info}') - setattr(self, f'{origin_key}_model', model) - if origin_key == 'reward' and args.rlhf_type == 'grpo': - reward_template = self.args.get_template(processor) - if reward_template.use_model: - reward_template.model = model - self.reward_template = reward_template - - super()._prepare_model_tokenizer() - - def _prepare_template(self) -> None: - args = self.args - super()._prepare_template() - model_mapping = {'kto': 'kto', 'ppo': 'pt', 'grpo': 'pt'} - self.template.set_mode(model_mapping.get(args.rlhf_type, 'rlhf')) - - if args.rlhf_type == 'ppo': - args.training_args.stop_token_id = self.template.template_meta.stop_token_id - - def _get_dataset(self): - args = self.args - train_dataset, val_dataset = super()._get_dataset() - if args.rlhf_type == 'kto': - train_dataset, val_dataset = prepare_kto_dataset(args, train_dataset, val_dataset) - return train_dataset, val_dataset - - def _get_trainer_kwargs(self): - trainer_kwargs = {} - for key in ['ref', 'reward', 'value']: - key = f'{key}_model' - model = getattr(self, key, None) - if model or self.args.rlhf_type == 'ppo': - trainer_kwargs[key] = model - if hasattr(self, 'reward_template'): - trainer_kwargs['reward_template'] = self.reward_template - if self.args.rlhf_type == 'grpo': - trainer_kwargs['reward_funcs'] = self.args.reward_funcs - return trainer_kwargs - - -if __name__ == '__main__': - """ -CUDA_VISIBLE_DEVICES=0 \ -swift rlhf \ - --rlhf_type grpo \ - --model Qwen/Qwen2.5-7B-Instruct \ - --reward_funcs accuracy format \ - --train_type lora \ - --lora_rank 8 \ - --lora_alpha 32 \ - --target_modules all-linear \ - --torch_dtype bfloat16 \ - --dataset 'AI-MO/NuminaMath-TIR#1000' \ - --max_completion_length 1024 \ - --num_train_epochs 1 \ - --per_device_train_batch_size 4 \ - --per_device_eval_batch_size 4 \ - --learning_rate 1e-5 \ - --gradient_accumulation_steps 1 \ - --eval_steps 100 \ - --save_steps 100 \ - --save_total_limit 2 \ - --logging_steps 5 \ - --max_length 2048 \ - --output_dir output \ - --warmup_ratio 0.05 \ - --dataloader_num_workers 4 \ - --dataset_num_proc 4 \ - --num_generations 4 \ - --temperature 0.9 \ - --system 'examples/train/grpo/prompt.txt' \ - --log_completions true - """ - args = RLHFArguments( - rlhf_type = 'grpo', - model = '~/.cache/modelscope/hub/qwen/Qwen2.5-7B-Instruct', - reward_funcs = ['simplereward'], - train_type = 'lora', - lora_rank = 8, - lora_alpha = 32, - target_modules = 'all-linear', - torch_dtype = 'bfloat16', - dataset = 'math_operations_dataset.jsonl', - max_completion_length = 1024, - num_train_epochs = 1, - per_device_train_batch_size = 4, - per_device_eval_batch_size = 4, - learning_rate = 1e-5, - gradient_accumulation_steps = 1, - eval_steps = 100, - save_steps = 100, - save_total_limit = 2, - logging_steps = 1, - max_length = 2048, - output_dir = 'output', - warmup_ratio = 0.05, - dataloader_num_workers = 4, - dataset_num_proc = 4, - num_generations = 4, - temperature = 0.9, - log_completions = True, - system = 'tool_system.txt', - loss_scale="default" - - ) - SwiftRLHF(args).run() From dc54f8dba73a03040554d80d4b2661c3251e60c3 Mon Sep 17 00:00:00 2001 From: Your Name Date: Mon, 17 Mar 2025 11:01:33 +0800 Subject: [PATCH 3/7] =?UTF-8?q?=E9=87=8D=E6=9E=84=E4=BB=A3=E7=A0=81?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- gen_data.py | 68 ------------------------------------------------- tool_system.txt | 62 -------------------------------------------- 2 files changed, 130 deletions(-) delete mode 100644 gen_data.py delete mode 100644 tool_system.txt diff --git a/gen_data.py b/gen_data.py deleted file mode 100644 index ca9e91fbdc..0000000000 --- a/gen_data.py +++ /dev/null @@ -1,68 +0,0 @@ -""" -Dataset Generator for Custom Mathematical Operations -This module generates a dataset of custom mathematical expressions and their results. -""" - -import random -import json -import re -from typing import Dict, List -from math_tool import parse_expression, SYMBOL_TO_OPERATION, OPERATION_DEFINITIONS - -def generate_safe_expression(): - """Generate an expression that won't cause overflow errors""" - # Start with a moderate number - expression = str(random.randint(1, 20)) - - # Add 3-5 operations with safe numbers - num_ops = random.randint(1, 6) - - for i in range(num_ops): - # Choose operation - op = random.choice(['@', '&', '$', '^']) - - # For @ operation, use smaller numbers to avoid overflow - if op == '@': - # For exponentiation, keep the exponent small - num = random.randint(1, 3) - else: - num = random.randint(1, 10) - - expression += op + str(num) - - return expression - -def generate_dataset(num_samples: int = 1000, output_file: str = "math_operations_dataset.jsonl") -> None: - """ - Generates a dataset of custom mathematical expressions and their results. - Saves the dataset as a JSONL file. - - Args: - num_samples: Number of samples to generate - output_file: Path to save the JSONL file - """ - with open(output_file, 'w') as f: - for _ in range(num_samples): - # Generate a safe expression - expression = generate_safe_expression() - - # Calculate the result - try: - result = parse_expression(expression) - - # Create the data entry - # data_entry = { - # "query": f"Calculate the result of the expression: {expression}", - # "answer": result - # } - data_entry = {"messages": [{"role": "user", "content": f"Calculate the result of the expression: {expression}"}],"response":result} - - # Write to JSONL file - f.write(json.dumps(data_entry) + '\n') - except Exception as e: - print(f"Skipping problematic expression {expression}: {e}") - continue - - print(f"Generated dataset with {num_samples} samples and saved to {output_file}") - -generate_dataset() \ No newline at end of file diff --git a/tool_system.txt b/tool_system.txt deleted file mode 100644 index bb473290f9..0000000000 --- a/tool_system.txt +++ /dev/null @@ -1,62 +0,0 @@ -你是一个计算助手，擅长多次调用计算工具来处理**定义新运算**，下面是你可以使用的工具: - -{ - "type": "function", - "function": { - "name": "math_operation", - "description": "Perform custom mathematical operations between two integers", - "parameters": { - "type": "object", - "properties": { - "tool": { - "type": "string", - "enum": ["at_operation", "amp_operation", "dollar_operation", "caret_operation"], - "description": "The type of mathematical operation to perform" - }, - "a": { - "type": "float", - "description": "The first float operand" - }, - "b": { - "type": "float", - "description": "The second float operand" - } - }, - "required": ["tool", "a", "b"] - } - } -} - -这些工具对应的运算定义如下: -1. at_operation (@): a@b -2. amp_operation (&): a&b -3. dollar_operation ($): a$b -4. caret_operation (^): a^b -当你调用工具时你的输出格式需要满足: -``` - -你的推理过程 - - -{ -"tool":"at_operation", -"a":5, -"b":2 -} - -``` -注意: -- 每次只能调用一个工具 -- 工具参数必须是有效的JSON格式 -- 必须包含和标签 - -当你计算完成时，需要满足格式: -``` - -总结整合之前的推理然后给出答案 - - -19.0 //最终答案只有一个浮点数 - -``` -**这是定义的新运算，你从只能从左到右依次计算，每轮对话只能调用一个工具**,然后使用返回的结果作为下一个操作的第一个操作数. \ No newline at end of file From a2c1be0ca87540387400a543a65f68742602e164 Mon Sep 17 00:00:00 2001 From: Your Name Date: Mon, 17 Mar 2025 11:07:45 +0800 Subject: [PATCH 4/7] =?UTF-8?q?=E9=87=8D=E6=9E=84=E4=BB=A3=E7=A0=81?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .pre-commit-config.yaml | 2 -- .pre-commit-config_local.yaml | 2 -- 2 files changed, 4 deletions(-) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index afdeab26fb..1c9f9b1c57 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -39,8 +39,6 @@ repos: exclude: thirdparty/|tests/run.py - id: requirements-txt-fixer exclude: thirdparty/|tests/run.py - - id: double-quote-string-fixer - exclude: thirdparty/|tests/run.py - id: check-merge-conflict exclude: thirdparty/|tests/run.py - id: fix-encoding-pragma diff --git a/.pre-commit-config_local.yaml b/.pre-commit-config_local.yaml index d9d36e7bab..d3349da4ff 100644 --- a/.pre-commit-config_local.yaml +++ b/.pre-commit-config_local.yaml @@ -37,8 +37,6 @@ repos: exclude: thirdparty/|tests/run.py - id: end-of-file-fixer exclude: thirdparty/ - - id: requirements-txt-fixer - exclude: thirdparty/|tests/run.py - id: double-quote-string-fixer exclude: thirdparty/|tests/run.py - id: check-merge-conflict From b53739882568b0597aaea638f2221cade5db3f34 Mon Sep 17 00:00:00 2001 From: Your Name Date: Mon, 17 Mar 2025 11:10:26 +0800 Subject: [PATCH 5/7] =?UTF-8?q?=E9=87=8D=E6=9E=84=E4=BB=A3=E7=A0=81?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- swift/plugin/tool_call.py | 9 +- swift/trainers/arguments.py | 6 +- swift/trainers/rlhf_arguments.py | 2 +- swift/trainers/rlhf_trainer/grpo_trainer.py | 172 ++++++++++---------- 4 files changed, 97 insertions(+), 92 deletions(-) diff --git a/swift/plugin/tool_call.py b/swift/plugin/tool_call.py index 25b3936f94..8f8500654f 100644 --- a/swift/plugin/tool_call.py +++ b/swift/plugin/tool_call.py @@ -1,9 +1,10 @@ -from typing import Tuple,Any, Optional +from typing import Tuple, Any, Optional + + class TOOL_CALL: + def __call__(self, completion: str) -> Tuple[Any, bool, Optional[float]]: raise NotImplementedError -tools = { - -} \ No newline at end of file +tools = {} diff --git a/swift/trainers/arguments.py b/swift/trainers/arguments.py index cdf40a3021..14a8a77e25 100644 --- a/swift/trainers/arguments.py +++ b/swift/trainers/arguments.py @@ -104,9 +104,9 @@ class GRPOArgumentsMixin: offload_optimizer: bool = False offload_model: bool = False gc_collect_after_offload: bool = False - is_reward_tool_call:bool = True #是否额外单独计算每个tool call的format得分 - tool_call_weight:float = 1.0 - tool_call:str = None + is_reward_tool_call: bool = True #是否额外单独计算每个tool call的format得分 + tool_call_weight: float = 1.0 + tool_call: str = None @dataclass diff --git a/swift/trainers/rlhf_arguments.py b/swift/trainers/rlhf_arguments.py index e55e686049..2c37cf09a9 100644 --- a/swift/trainers/rlhf_arguments.py +++ b/swift/trainers/rlhf_arguments.py @@ -1,5 +1,5 @@ from dataclasses import dataclass, field -from typing import List, Optional,Callable +from typing import List, Optional, Callable from trl import CPOConfig as HfCPOConfig from trl import DPOConfig as HfDPOConfig diff --git a/swift/trainers/rlhf_trainer/grpo_trainer.py b/swift/trainers/rlhf_trainer/grpo_trainer.py index 1f79cb0733..fdef08104b 100644 --- a/swift/trainers/rlhf_trainer/grpo_trainer.py +++ b/swift/trainers/rlhf_trainer/grpo_trainer.py @@ -89,8 +89,11 @@ class DataCache: outputs: List[Dict] = field(default_factory=list) distributed_idx: List[List] = field(default_factory=list) + def tool_call(): pass + + class GRPOTrainer(RLHFTrainerMixin, SwiftMixin, HFGRPOTrainer): executor = concurrent.futures.ThreadPoolExecutor(max_workers=1) @@ -107,21 +110,17 @@ def __init__(self, print(args) self.tool_call = tools[args.tool_call] args.tool_call_weight = args.tool_call_weight - self.reward_weights = torch.ones(1, dtype=torch.float32) #通过配置 - self.is_reward_tool_call = args.is_reward_tool_call #add GRPO config + self.reward_weights = torch.ones(1, dtype=torch.float32) #通过配置 + self.is_reward_tool_call = args.is_reward_tool_call #add GRPO config # In the __init__ method, after initializing reward_weights: if self.is_reward_tool_call and self.tool_call is not None: # Add a weight for tool call rewards if args.tool_call_weight is not None: - self.reward_weights = torch.cat([ - self.reward_weights, - torch.tensor([args.tool_call_weight], dtype=torch.float32) - ]) + self.reward_weights = torch.cat( + [self.reward_weights, + torch.tensor([args.tool_call_weight], dtype=torch.float32)]) else: - self.reward_weights = torch.cat([ - self.reward_weights, - torch.ones(1, dtype=torch.float32) - ]) + self.reward_weights = torch.cat([self.reward_weights, torch.ones(1, dtype=torch.float32)]) self.args = args self.queue = None self.train_queue = Queue() @@ -712,10 +711,11 @@ def _fast_infer(self, inputs): def old_policy(self): return self.num_iterations > 1 or self.args.async_generate - def _generate_and_score_completions(self, inputs: dict[str, Union[torch.Tensor, Any]]) -> dict[str, Union[torch.Tensor, Any]]: + def _generate_and_score_completions( + self, inputs: dict[str, Union[torch.Tensor, Any]]) -> dict[str, Union[torch.Tensor, Any]]: device = self.accelerator.device total_tool_calls = 0 - # 确定当前进程的数据切片 + # 确定当前进程的数据切片 process_slice = slice( self.accelerator.process_index * len(inputs), (self.accelerator.process_index + 1) * len(inputs), @@ -725,10 +725,10 @@ def _generate_and_score_completions(self, inputs: dict[str, Union[torch.Tensor, # Deep copy inputs to preserve the original prompt for tool calling tool_inputs = deepcopy(inputs) tool_inputs, total_tool_calls = self._process_tool_calls(tool_inputs) - + # Replace original inputs with tool-processed inputs inputs = tool_inputs - + else: if self.args.use_vllm or self.args.use_lmdeploy: inputs, outputs = self._fast_infer(inputs) @@ -738,7 +738,8 @@ def _generate_and_score_completions(self, inputs: dict[str, Union[torch.Tensor, if is_multimodal: models = self.template.remove_post_encode_hook() with unwrap_model_for_generation( - self.model_wrapped, self.accelerator, gather_deepspeed3_params=self.args.ds3_gather_for_generation): + self.model_wrapped, self.accelerator, + gather_deepspeed3_params=self.args.ds3_gather_for_generation): outputs = self.engine.infer(inputs, self.request_config, use_tqdm=False) self.model.train() if is_multimodal: @@ -757,19 +758,19 @@ def _generate_and_score_completions(self, inputs: dict[str, Union[torch.Tensor, messages = inputs[i]['messages'] InferRequest.remove_response(messages) messages.append({'role': 'assistant', 'content': output.choices[0].message.content}) - + # Now process all inputs with all messages for training from copy import copy template = copy(self.template) with self._template_context(template): batched_inputs = [template.encode(infer_request) for infer_request in inputs] encoded_outputs = to_device(template.data_collator(batched_inputs), self.model.device) - + # Extract labels and create mask for all assistant outputs labels = encoded_outputs.pop('labels') # Identify all positions where labels != -100, which captures all assistant outputs completion_mask = (labels != -100) - + # Ensure we're not exceeding max completion length if completion_mask.sum(1).max() > self.args.max_completion_length: # Truncate to max length if needed @@ -782,13 +783,13 @@ def _generate_and_score_completions(self, inputs: dict[str, Union[torch.Tensor, # Set those indices to True in the truncated mask truncated_mask[i, indices] = True completion_mask = truncated_mask - + # Add to outputs encoded_outputs['completion_mask'] = completion_mask encoded_outputs['logits_to_keep'] = completion_mask.shape[1] # Use full length encoded_outputs['labels'] = labels # Put labels back for proper processing - - # Continue with the existing logic for computing logps and rewards + + # Continue with the existing logic for computing logps and rewards with torch.no_grad(): if self.old_policy: encoded_outputs['old_per_token_logps'] = self._get_per_token_logps(self.model, encoded_outputs) @@ -822,11 +823,9 @@ def _generate_and_score_completions(self, inputs: dict[str, Union[torch.Tensor, # Add tool call rewards if enabled if self.is_reward_tool_call and self.tool_call is not None: - tool_rewards = torch.tensor( - [inputs[i].get('_tool_call_reward', 0.0) for i in range(len(inputs))], - dtype=torch.float32, - device=device - ).unsqueeze(1) + tool_rewards = torch.tensor([inputs[i].get('_tool_call_reward', 0.0) for i in range(len(inputs))], + dtype=torch.float32, + device=device).unsqueeze(1) rewards_per_func = torch.cat([rewards_per_func, tool_rewards], dim=1) rewards_per_func = gather(rewards_per_func) @@ -845,21 +844,22 @@ def _generate_and_score_completions(self, inputs: dict[str, Union[torch.Tensor, # Log metrics mode = 'eval' if self.control.should_evaluate else 'train' - completion_length = self.accelerator.gather_for_metrics(encoded_outputs['completion_mask'].sum(1)).float().mean().item() + completion_length = self.accelerator.gather_for_metrics( + encoded_outputs['completion_mask'].sum(1)).float().mean().item() self._metrics[mode]['completion_length'].append(completion_length) - + # Add tool call metrics if self.tool_call is not None: avg_tool_calls = self.accelerator.gather_for_metrics( torch.tensor([total_tool_calls / len(inputs)], device=device)).mean().item() self._metrics[mode]['tool_call_nums'].append(avg_tool_calls) - + # Other existing metrics response_clip_ratio = torch.gt( self.accelerator.gather_for_metrics(encoded_outputs['completion_mask'].sum(1)), self.args.max_completion_length).float().mean().item() self._metrics[mode]['response_clip_ratio'].append(response_clip_ratio) - + reward_per_func = rewards_per_func.mean(0) for i, reward_func in enumerate(self.reward_funcs): if isinstance(reward_func, nn.Module): @@ -873,7 +873,7 @@ def _generate_and_score_completions(self, inputs: dict[str, Union[torch.Tensor, # Add tool call reward metric if enabled if self.is_reward_tool_call and self.tool_call is not None: - self._metrics[mode]['rewards/tool_call'].append(reward_per_func[-1].item()) #todo 应该用反射记录每个tool call的reward + self._metrics[mode]['rewards/tool_call'].append(reward_per_func[-1].item()) #todo 应该用反射记录每个tool call的reward self._metrics[mode]['reward'].append(rewards.mean().item()) self._metrics[mode]['reward_std'].append(std_grouped_rewards.mean().item()) @@ -881,7 +881,7 @@ def _generate_and_score_completions(self, inputs: dict[str, Union[torch.Tensor, 'ref_per_token_logps': ref_per_token_logps, 'advantages': advantages, }) - # Log completions + # Log completions if self.log_completions and self.state.global_step % self.args.logging_steps == 0: table = { 'step': [str(self.state.global_step)] * len(rewards), @@ -889,12 +889,12 @@ def _generate_and_score_completions(self, inputs: dict[str, Union[torch.Tensor, 'completion': gather_object(completions), 'reward': rewards.tolist(), } - + if self.tool_call is not None: # Log tool call counts for each prompt tool_call_counts = [inputs[i].get('_tool_call_count', 0) for i in range(len(inputs))] table['tool_call_nums'] = tool_call_counts * (len(rewards) // len(inputs)) - + self.jsonl_writer.append(table) if 'wandb' in self.args.report_to and wandb.run is not None and self.accelerator.is_main_process: import pandas as pd @@ -945,49 +945,49 @@ def compute_loss(self, model, inputs, return_outputs=False, num_items_in_batch=N @profiling_decorator def _get_per_token_logps(self, model, inputs): from trl.trainer.utils import selective_log_softmax - + # Extract input_ids and completion_mask input_ids = inputs['input_ids'] completion_mask = inputs.get('completion_mask') - + # Get unwrapped model unwrapped_model = self.accelerator.unwrap_model(model) parameters = inspect.signature(unwrapped_model.forward).parameters - + if not unwrapped_model.model_meta.is_multimodal and 'completion_mask' in parameters: # If model supports direct masking, use that return super()._get_per_token_logps(model, input_ids, inputs['attention_mask'], completion_mask) - + # Otherwise, compute full logits and mask afterwards forward_inputs = { - k: v for k, v in inputs.items() - if k not in ['completion_mask', 'logits_to_keep', 'ref_per_token_logps', - 'advantages', 'old_per_token_logps'] + k: v + for k, v in inputs.items() if k not in + ['completion_mask', 'logits_to_keep', 'ref_per_token_logps', 'advantages', 'old_per_token_logps'] } - + # Get logits from model logits = model(**forward_inputs).logits logits = logits / self.temperature - + # Find positions corresponding to completion tokens (where completion_mask is True) # Create a mask that selects these positions all_positions = torch.where(completion_mask) batch_indices = all_positions[0] seq_indices = all_positions[1] - + # Get the corresponding logits and input IDs selected_logits = logits[batch_indices, seq_indices - 1] # -1 because logits predict next token selected_tokens = input_ids[batch_indices, seq_indices] - + # Calculate log probabilities for selected tokens log_probs = selective_log_softmax(selected_logits.unsqueeze(1), selected_tokens.unsqueeze(1)).squeeze(1) - + # Create output tensor with same shape as completion_mask, filled with zeros result = torch.zeros_like(completion_mask, dtype=torch.float) - + # Place log probs at appropriate positions result[batch_indices, seq_indices] = log_probs - + return result def evaluation_loop(self, dataloader, *args, **kwargs): @@ -1000,11 +1000,12 @@ def evaluation_loop(self, dataloader, *args, **kwargs): output.metrics.update(metrics) self.queue = self.train_queue return output + def _process_tool_calls(self, inputs: List[Dict]) -> Tuple[List[Dict], int]: """ Args: inputs: List of input dictionaries with message history - + Returns: Updated inputs after tool calling process and total number of tool calls """ @@ -1013,25 +1014,25 @@ def _process_tool_calls(self, inputs: List[Dict]) -> Tuple[List[Dict], int]: tool_call_rewards = [0.0] * len(inputs) assistant_tokens_used = [0] * len(inputs) total_tool_calls = 0 - + # Process inputs until all are finished active_indices = list(range(len(inputs))) - + while active_indices: # Generate responses for active inputs with adjusted max_tokens active_inputs = [inputs[i] for i in active_indices] - + # Create a copy of the request config with adjusted max_tokens for each input adjusted_request_configs = [] for idx in active_indices: tokens_used = assistant_tokens_used[idx] available_tokens = max(1, self.args.max_completion_length - tokens_used) - + # Create custom request config with adjusted max_tokens config = copy(self.request_config) config.max_tokens = available_tokens adjusted_request_configs.append(config) - + # Generate responses with adjusted token limits if self.args.use_vllm or self.args.use_lmdeploy: active_inputs = [inputs[i] for i in active_indices] @@ -1041,7 +1042,8 @@ def _process_tool_calls(self, inputs: List[Dict]) -> Tuple[List[Dict], int]: if is_multimodal: models = self.template.remove_post_encode_hook() with unwrap_model_for_generation( - self.model_wrapped, self.accelerator, gather_deepspeed3_params=self.args.ds3_gather_for_generation): + self.model_wrapped, self.accelerator, + gather_deepspeed3_params=self.args.ds3_gather_for_generation): active_outputs = [] for i, input_data in enumerate(active_inputs): # Use custom config for each input @@ -1050,64 +1052,65 @@ def _process_tool_calls(self, inputs: List[Dict]) -> Tuple[List[Dict], int]: self.model.train() if is_multimodal: self.template.register_post_encode_hook(models) - + # Process tool calls asynchronously futures = [] executor = concurrent.futures.ThreadPoolExecutor(max_workers=len(active_indices)) - + # Start all tool calls in parallel for i, idx in enumerate(active_indices): completion = active_outputs[i].choices[0].message.content tokens_used = len(self.tokenizer(completion).input_ids) - + # Add assistant response to message history messages = active_inputs[i]['messages'] InferRequest.remove_response(messages) messages.append({'role': 'assistant', 'content': completion}) - + # Update token count assistant_tokens_used[idx] += tokens_used - + futures.append((executor.submit(self.tool_call, completion), idx)) - + # Process results and update inputs new_active_indices = [] for future, idx in futures: tool_result, finish, tool_reward = future.result() - + if not finish: # Add user message with tool result inputs[idx]['messages'].append({'role': 'user', 'content': tool_result}) new_active_indices.append(idx) - + # Update tracking variables tool_call_counts[idx] += 1 total_tool_calls += 1 - if self.is_reward_tool_call and not finish: #remove last reponse + if self.is_reward_tool_call and not finish: #remove last reponse tool_call_rewards[idx] += tool_reward - + executor.shutdown() active_indices = new_active_indices - + # Break if no active indices remain if not active_indices: break - + # Store tool call metrics in inputs for later use for i in range(len(inputs)): inputs[i]['_tool_call_count'] = tool_call_counts[i] inputs[i]['_tool_call_reward'] = tool_call_rewards[i] inputs[i]['_assistant_tokens_used'] = assistant_tokens_used[i] - + return inputs, total_tool_calls + def _fast_infer_with_custom_configs(self, inputs, request_configs): """ Similar to _fast_infer but uses a different request_config for each input. - + Args: inputs: List of input dicts request_configs: List of request configs (same length as inputs) - + Returns: inputs, outputs tuple """ @@ -1119,23 +1122,23 @@ def _fast_infer_with_custom_configs(self, inputs, request_configs): if self.args.gc_collect_after_offload: gc_collect() self.engine.engine.wake_up() - + # First, have main process load weights if needed if self.state.global_step != self._last_loaded_step: self._move_model_to_vllm_lmdeploy() self._last_loaded_step = self.state.global_step - + # Gather all prompts all_inputs = gather_object(inputs) all_configs = gather_object(request_configs) - + # Distribute inputs to different workers distributed_idx = self.round_robin(len(all_inputs), get_node_setting()[1] * self.args.num_infer_workers) - + if self.infer_rank >= 0: _input_slice = np.array(all_inputs)[distributed_idx[self.infer_rank]] _config_slice = np.array(all_configs)[distributed_idx[self.infer_rank]] - + if self.args.async_generate: # Modified async_infer to use custom configs self.async_infer_with_configs(inputs, _input_slice, _config_slice, distributed_idx) @@ -1153,7 +1156,7 @@ def _fast_infer_with_custom_configs(self, inputs, request_configs): request_config.seed += self.state.global_step output = self.engine.infer([_input_slice[i]], request_config, use_tqdm=False) outputs.extend(output) - + if self.args.tensor_parallel_size > 1: if self.infer_rank_tp_0 < 0: outputs = [] @@ -1172,10 +1175,10 @@ def _fast_infer_with_custom_configs(self, inputs, request_configs): inputs = data_cache.inputs distributed_idx = data_cache.distributed_idx outputs = [] - + outputs = gather_object(outputs) outputs = self.reorder_outputs(outputs, distributed_idx) - + if self.args.sleep_level > 0 and self.infer_rank >= 0: self.engine.engine.sleep(level=self.args.sleep_level) if self.args.gc_collect_after_offload: @@ -1184,27 +1187,27 @@ def _fast_infer_with_custom_configs(self, inputs, request_configs): self.load_model() if self.args.offload_optimizer: self.load_optimizer() - + return inputs, outputs + def async_infer_with_configs(self, inputs, inputs_slice, configs_slice, distributed_idx): """ Asynchronous inference with custom configs for each input. - + Args: inputs: Original inputs inputs_slice: Slice of inputs for this worker configs_slice: Slice of configs corresponding to inputs_slice distributed_idx: Distribution of indices among workers """ + def infer_task(): with set_device_context(self.infer_device): results = [] # Process each input with its own config for i in range(len(inputs_slice)): result = self.engine.infer( - infer_requests=[inputs_slice[i]], - request_config=configs_slice[i], - use_tqdm=False) + infer_requests=[inputs_slice[i]], request_config=configs_slice[i], use_tqdm=False) results.extend(result) return results @@ -1214,7 +1217,8 @@ def done(_self): self.queue.put(DataCache(inputs, _self.result(), distributed_idx)) future.add_done_callback(done) + @property def tokenizer(self): """Get the tokenizer from the template.""" - return self.processing_class \ No newline at end of file + return self.processing_class From 0f1ca0533086777f79302d3489d1385c39f64ff8 Mon Sep 17 00:00:00 2001 From: Your Name Date: Mon, 17 Mar 2025 11:11:45 +0800 Subject: [PATCH 6/7] =?UTF-8?q?=E9=87=8D=E6=9E=84=E4=BB=A3=E7=A0=81?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- swift/trainers/rlhf_trainer/grpo_trainer.py | 1 - 1 file changed, 1 deletion(-) diff --git a/swift/trainers/rlhf_trainer/grpo_trainer.py b/swift/trainers/rlhf_trainer/grpo_trainer.py index fdef08104b..7d3bfb30f4 100644 --- a/swift/trainers/rlhf_trainer/grpo_trainer.py +++ b/swift/trainers/rlhf_trainer/grpo_trainer.py @@ -107,7 +107,6 @@ def __init__(self, from swift.trainers.rlhf_arguments import GRPOConfig args: GRPOConfig = kwargs['args'] #add tool call - print(args) self.tool_call = tools[args.tool_call] args.tool_call_weight = args.tool_call_weight self.reward_weights = torch.ones(1, dtype=torch.float32) #通过配置 From d2125f4ef06212607f7c06faa270684f70bb73d8 Mon Sep 17 00:00:00 2001 From: Your Name Date: Wed, 19 Mar 2025 14:48:14 +0800 Subject: [PATCH 7/7] =?UTF-8?q?=E4=BF=AE=E5=A4=8D=E8=AF=AD=E6=B3=95?= =?UTF-8?q?=E9=94=99=E8=AF=AF?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- swift/trainers/rlhf_trainer/grpo_trainer.py | 1 - 1 file changed, 1 deletion(-) diff --git a/swift/trainers/rlhf_trainer/grpo_trainer.py b/swift/trainers/rlhf_trainer/grpo_trainer.py index 7d3bfb30f4..a683ed0a53 100644 --- a/swift/trainers/rlhf_trainer/grpo_trainer.py +++ b/swift/trainers/rlhf_trainer/grpo_trainer.py @@ -15,7 +15,6 @@ from queue import Queue from types import MethodType from typing import Any, Callable, Dict, List, Optional, Tuple, Union -from math_tool import MathOperation_Tool import numpy as np import torch import torch.nn as nn