Merge 743420daac into 6fc918beb6

scripts/eval_toolcall.py

@@ -12,7 +12,7 @@ from datetime import datetime
 from transformers import AutoTokenizer, AutoModelForCausalLM, TextStreamer
 from openai import OpenAI
 from model.model_minimind import MiniMindConfig, MiniMindForCausalLM
-from trainer.trainer_utils import setup_seed, get_model_params, safe_math_eval
+from trainer.trainer_utils import setup_seed, get_model_params
 warnings.filterwarnings('ignore')
 
 TOOLS = [
@@ -27,7 +27,7 @@ TOOLS = [
 ]
 
 MOCK_RESULTS = {
-    "calculate_math": lambda args: {"result": str(safe_math_eval(args.get("expression", "0")))},
+    "calculate_math": lambda args: {"result": str(eval(str(args.get("expression", "0")).replace("^", "**").replace("×", "*").replace("÷", "/").replace("−", "-").replace("²", "**2").replace("³", "**3").replace("（", "(").replace("）", ")")))},
     "get_current_time": lambda args: {"datetime": datetime.now().strftime("%Y-%m-%d %H:%M:%S"), "timezone": args.get("timezone", "Asia/Shanghai")},
     "random_number": lambda args: {"result": random.randint(int(args.get("min", 0)), int(args.get("max", 100)))},
     "text_length": lambda args: {"characters": len(args.get("text", "")), "words": len(args.get("text", "").split())},

scripts/web_demo.py

@@ -2,7 +2,6 @@ import random
 import re
 import json
 import os
-import sys
 from threading import Thread
 
 import torch
@@ -10,9 +9,6 @@ import numpy as np
 import streamlit as st
 from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
 
-sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
-from trainer.trainer_utils import safe_math_eval
-
 st.set_page_config(page_title="MiniMind", initial_sidebar_state="collapsed")
 
 st.markdown("""
@@ -153,7 +149,7 @@ def execute_tool(tool_name, args):
     import datetime
     try:
         if tool_name == 'calculate_math':
-            return {"result": safe_math_eval(args.get('expression', '0'))}
+            return {"result": eval(args.get('expression', '0'))}
         elif tool_name == 'get_current_time':
             tz = args.get('timezone', 'Asia/Shanghai')
             return {"result": datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')}

trainer/rollout_engine.py

@@ -84,7 +84,7 @@ class TorchRolloutEngine(RolloutEngine):
             ).clone()  # [B*num_gen, P+R]
             prompt_len = prompt_ids.size(1)
             completion_ids = output_ids[:, prompt_len:]  # [B*num_gen, R]
-            full_mask = torch.cat([attention_mask.repeat_interleave(num_generations, dim=0), attention_mask.new_ones(output_ids.size(0), completion_ids.size(1))], dim=1)
+            full_mask = (output_ids != self.tokenizer.pad_token_id).long()
             per_token_logps = compute_per_token_logps(self.policy_model, output_ids, completion_ids.size(1), attention_mask=full_mask)
         completions = self.tokenizer.batch_decode(completion_ids, skip_special_tokens=True)
         return RolloutResult(output_ids, completion_ids, per_token_logps, completions,

trainer/train_agent.py

@@ -10,6 +10,7 @@ import gc
 import json
 import math
 import random
+import signal
 import argparse
 import warnings
 import torch
@@ -23,7 +24,7 @@ from torch.optim.lr_scheduler import CosineAnnealingLR
 from transformers import AutoTokenizer
 from model.model_minimind import MiniMindConfig, MiniMindForCausalLM
 from dataset.lm_dataset import AgentRLDataset
-from trainer.trainer_utils import Logger, is_main_process, lm_checkpoint, init_distributed_mode, setup_seed, SkipBatchSampler, init_model, LMForRewardModel, safe_math_eval
+from trainer.trainer_utils import Logger, is_main_process, lm_checkpoint, init_distributed_mode, setup_seed, SkipBatchSampler, init_model, LMForRewardModel
 from trainer.rollout_engine import create_rollout_engine, compute_per_token_logps
 
 warnings.filterwarnings('ignore')
@@ -54,7 +55,7 @@ UNIT_DATA = {"km_miles": 0.621371, "miles_km": 1.60934, "kg_pounds": 2.20462, "p
 
 # ======== 模拟执行 ========
 MOCK_RESULTS = {
-    "calculate_math": lambda args: {"result": str(safe_math_eval(args.get("expression", "0")))},
+    "calculate_math": lambda args: {"result": str(eval(str(args.get("expression", "0")).replace("^", "**").replace("×", "*").replace("÷", "/").replace("−", "-").replace("（", "(").replace("）", ")"), {"__builtins__": {}, "math": math}))},
     "unit_converter": lambda args: {"result": round(float(args.get("value", 0)) * UNIT_DATA.get(f"{args.get('from_unit', '').lower()}_{args.get('to_unit', '').lower()}", 1), 4)},
     "get_current_weather": lambda args: (lambda w: {"city": args.get("location"), "temperature": w[0], "humidity": "65%", "condition": w[1]})(WEATHER_DATA.get(args.get("location"), ("22°C", "晴"))),
     "get_current_time": lambda args: {"datetime": TIME_DATA.get(args.get("timezone", "Asia/Shanghai"), "2025-03-07 14:30:00"), "timezone": args.get("timezone", "Asia/Shanghai")},
@@ -84,9 +85,14 @@ def execute_tool(name, args):
     fn = MOCK_RESULTS.get(name)
     if not fn: return None
     try:
+        signal.signal(signal.SIGALRM, lambda *_: (_ for _ in ()).throw(TimeoutError()))
+        signal.alarm(1)
         return fn(args)
-    except Exception:
+    except:
         return None
+    finally:
+        try: signal.alarm(0)
+        except: pass
 
 # ======== 多轮 Rollout ========
 def rollout_single(rollout_engine, tokenizer, messages, tools, max_turns=3, max_new_tokens=256, thinking_ratio=0.5, device="cuda"):
@@ -100,33 +106,34 @@ def rollout_single(rollout_engine, tokenizer, messages, tools, max_turns=3, max_
     open_thinking = random.random() < thinking_ratio
     for turn in range(max_turns):
         context = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True, tools=tools, open_thinking=open_thinking)
+        inputs = tokenizer(context, return_tensors="pt", add_special_tokens=False).to(device)
+        context_ids = inputs["input_ids"][0].tolist()
         if prompt_ids is None:
-            prompt_ids = tokenizer(context, add_special_tokens=False)["input_ids"]
-        input_ids = torch.tensor([prompt_ids + response_ids], device=device)
+            prompt_ids = context_ids
         rollout_result = rollout_engine.rollout(
-            prompt_ids=input_ids,
-            attention_mask=torch.ones_like(input_ids),
+            prompt_ids=inputs["input_ids"],
+            attention_mask=inputs["attention_mask"],
             num_generations=1,
             max_new_tokens=max_new_tokens,
             temperature=0.8,
         )
-        valid_len = int(rollout_result.completion_mask[0].sum().item())
-        new_ids = rollout_result.completion_ids[0, :valid_len].tolist()
-        new_logps = rollout_result.per_token_logps[0, :valid_len].tolist()
-        if len(new_ids) != len(new_logps):
-            raise RuntimeError(f"rollout token/logprob length mismatch: {len(new_ids)} vs {len(new_logps)}")
+        new_ids = rollout_result.completion_ids[0].tolist()
+        new_logps = rollout_result.per_token_logps[0].tolist()
+        if len(new_ids) != len(new_logps): Logger(f"rollout token/logprob length mismatch: {len(new_ids)} vs {len(new_logps)}")
+        pairs = [(t, lp) for t, lp in zip(new_ids, new_logps) if t != tokenizer.pad_token_id and t != tokenizer.eos_token_id]
+        new_ids = [t for t, _ in pairs]
+        new_logps = [lp for _, lp in pairs]
         new_text = rollout_result.completions[0]
         all_outputs.append(new_text)
         response_ids.extend(new_ids)
-        response_mask.extend([int(t != tokenizer.eos_token_id) for t in new_ids])
+        response_mask.extend([1] * len(new_ids))
         response_old_logps.extend(new_logps)
         final_context = context + new_text
         calls = parse_tool_calls(new_text)
         if not calls:
             break
         unfinished = turn == max_turns - 1
-        assistant_message = {"role": "assistant", "content": new_text}
-        messages.append(assistant_message)
+        messages.append({"role": "assistant", "content": new_text})
         for call in calls:
             name, raw = call.get("name", ""), call.get("arguments", {})
             if isinstance(raw, str):
@@ -136,17 +143,10 @@ def rollout_single(rollout_engine, tokenizer, messages, tools, max_turns=3, max_
             result_str = (json.dumps(result, ensure_ascii=False) if result else '{"error": "tool not found"}')[:2048]  # 防止天文数字撑爆tokenizer
             messages.append({"role": "tool", "content": result_str})
 
-        marker = f"<|agent_observation_{id(messages)}_{len(response_ids)}|>"
-        assistant_message["content"] += marker
-        marked_context = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=not unfinished, tools=tools, open_thinking=open_thinking)
-        assistant_message["content"] = new_text
-        _, found, observation = marked_context.partition(marker)
-        if not found:
-            raise RuntimeError("chat template did not preserve the assistant content boundary")
-        obs_delta = tokenizer(observation, add_special_tokens=False)["input_ids"]
-        if new_ids and new_ids[-1] == tokenizer.eos_token_id and obs_delta[:1] == [tokenizer.eos_token_id]:
-            obs_delta = obs_delta[1:]
         observe_context = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=not unfinished, tools=tools, open_thinking=open_thinking)
+        observe_ids = tokenizer(observe_context, return_tensors="pt", add_special_tokens=False)["input_ids"][0].tolist()
+        current_len = len(prompt_ids) + len(response_ids)
+        obs_delta = observe_ids[current_len:]
         response_ids.extend(obs_delta)
         response_mask.extend([0] * len(obs_delta))
         response_old_logps.extend([0.0] * len(obs_delta))
@@ -268,14 +268,13 @@ def rl_train_epoch(epoch, loader, iters, rollout_engine, ref_model, reward_model
         prompt_lens = torch.tensor([prompt_len for _, _, prompt_len, _ in packed_samples], device=args.device)
         full_response_masks = torch.tensor([mask + [0] * (max_len - len(mask)) for _, mask, _, _ in packed_samples], device=args.device, dtype=torch.float32)
         old_per_token_logps = torch.tensor([old_logps + [0.0] * ((max_len - 1) - len(old_logps)) for _, _, _, old_logps in packed_samples], device=args.device, dtype=torch.float32)
-        full_mask = (torch.arange(max_len, device=args.device).unsqueeze(0) < seq_lens.unsqueeze(1)).long()
+        full_mask = (input_ids != tokenizer.pad_token_id).long()
 
         rewards = calculate_rewards(prompts, completions, gt_batch, tools_batch, args.num_generations, reward_model, device=args.device, turn_outputs_batch=turn_outputs_batch, unfinished_batch=unfinished_batch)
 
+        model_unwrapped = model.module if isinstance(model, DistributedDataParallel) else model
         with autocast_ctx:
-            # 反向传播必须经过 DDP 包装后的模块：直接调用 .module 会跳过
-            # DDP 的 prepare_for_backward，梯度不会 all-reduce，各卡静默发散。
-            res = model(input_ids, attention_mask=full_mask)
+            res = model_unwrapped(input_ids, attention_mask=full_mask)
             aux_loss = res.aux_loss if lm_config.use_moe else torch.tensor(0.0, device=args.device)
             logits = res.logits[:, :-1, :]
             per_token_logps = F.log_softmax(logits, dim=-1).gather(2, input_ids[:, 1:].unsqueeze(-1)).squeeze(-1)
@@ -473,8 +472,7 @@ if __name__ == "__main__":
         Logger('torch.compile enabled')
         rollout_engine.update_policy(model)
     if dist.is_initialized():
-        # 同 train_ppo：RoPE buffer 各 rank 一致，每步广播纯属浪费
-        model = DistributedDataParallel(model, device_ids=[local_rank], broadcast_buffers=False)
+        model = DistributedDataParallel(model, device_ids=[local_rank])
     rollout_engine.update_policy(model)
 
     for epoch in range(start_epoch, args.epochs):

trainer/train_grpo.py

@@ -91,14 +91,12 @@ def grpo_train_epoch(epoch, loader, iters, rollout_engine, ref_model, reward_mod
         prompt_lens = rollout_result.prompt_lens.to(args.device)
         full_mask = (outputs != tokenizer.pad_token_id).long()
         logp_pos = prompt_lens.unsqueeze(1) - 1 + torch.arange(completion_ids.size(1), device=args.device).unsqueeze(0)
-        full_mask.scatter_(1, logp_pos + 1, rollout_result.completion_mask.to(args.device, dtype=full_mask.dtype))
 
         rewards = calculate_rewards(prompts, completions, reward_model).to(args.device)  # [B*num_gen]
 
+        model_unwrapped = model.module if isinstance(model, DistributedDataParallel) else model
         with autocast_ctx:
-            # 反向传播必须经过 DDP 包装后的模块：直接调用 .module 会跳过
-            # DDP 的 prepare_for_backward，梯度不会 all-reduce，各卡静默发散。
-            res = model(outputs, attention_mask=full_mask)
+            res = model_unwrapped(outputs, attention_mask=full_mask)
             aux_loss = res.aux_loss if lm_config.use_moe else torch.tensor(0.0, device=args.device)
             per_token_logps = F.log_softmax(res.logits[:, :-1, :], dim=-1).gather(2, outputs[:, 1:].unsqueeze(-1)).squeeze(-1).gather(1, logp_pos)
 
@@ -314,8 +312,7 @@ if __name__ == "__main__":
         Logger('torch.compile enabled')
         rollout_engine.update_policy(model)
     if dist.is_initialized():
-        # 同 train_ppo：RoPE buffer 各 rank 一致，每步广播纯属浪费
-        model = DistributedDataParallel(model, device_ids=[local_rank], broadcast_buffers=False)
+        model = DistributedDataParallel(model, device_ids=[local_rank])
     rollout_engine.update_policy(model)
     
     # ========== 8. 开始训练 ==========

trainer/train_ppo.py

@@ -36,7 +36,7 @@ def rep_penalty(text, n=3, cap=0.5):
 class CriticModel(MiniMindForCausalLM):
     def __init__(self, params):
         super().__init__(params)
-        # lm_head 不参与 forward，仅靠 tie_word_embeddings 与 embed_tokens 共享权重，解绑后 DDP 会报未使用参数
+        # 替换lm_head为输出单一价值的线性层
         self.value_head = nn.Linear(params.hidden_size, 1)
 
     def forward(self, input_ids=None, attention_mask=None, **kwargs):
@@ -120,7 +120,6 @@ def ppo_train_epoch(epoch, loader, iters, rollout_engine, ref_model, actor_sched
         resp_idx = torch.arange(resp_labels.size(1), device=gen_out.device).unsqueeze(0)
         logp_pos = prompt_lens.unsqueeze(1) - 1 + resp_idx
         resp_pad_mask = rollout_result.completion_mask.to(args.device).bool()
-        full_mask.scatter_(1, logp_pos + 1, resp_pad_mask.to(full_mask.dtype))
         resp_lengths = resp_pad_mask.sum(dim=1); valid_resp = resp_lengths > 0; eos_mask = resp_labels.eq(tokenizer.eos_token_id) & resp_pad_mask
         has_eos = eos_mask.any(dim=1); eos_pos = torch.argmax(eos_mask.int(), dim=1)
         resp_lengths = torch.where(has_eos, eos_pos + 1, resp_lengths).long().clamp(min=1)
@@ -159,6 +158,8 @@ def ppo_train_epoch(epoch, loader, iters, rollout_engine, ref_model, actor_sched
         clipfrac_sum = 0.0
         aux_loss_sum = 0.0
         log_count = 0
+        actor_unwrapped = actor_model.module if isinstance(actor_model, DistributedDataParallel) else actor_model
+        critic_unwrapped = critic_model.module if isinstance(critic_model, DistributedDataParallel) else critic_model
         for ppo_epoch in range(args.ppo_update_iters):
             if stop_ppo:
                 break
@@ -166,13 +167,11 @@ def ppo_train_epoch(epoch, loader, iters, rollout_engine, ref_model, actor_sched
             for i in range(0, B, mb_size):
                 inds = b_inds[i:i + mb_size]
                 
-                # 反向传播必须经过 DDP 包装后的模块：直接调用 .module 会跳过
-                # DDP 的 prepare_for_backward，梯度不会 all-reduce，各卡静默发散。
-                mb_values_seq = critic_model(input_ids=gen_out[inds], attention_mask=full_mask[inds])
+                mb_values_seq = critic_unwrapped(input_ids=gen_out[inds], attention_mask=full_mask[inds])
                 mb_resp_values = mb_values_seq.gather(1, logp_pos[inds])
 
                 with autocast_ctx:
-                    res = actor_model(input_ids=gen_out[inds], attention_mask=full_mask[inds])
+                    res = actor_unwrapped(input_ids=gen_out[inds], attention_mask=full_mask[inds])
                     aux_loss = res.aux_loss if lm_config.use_moe else torch.tensor(0.0, device=args.device)
                     # 在 autocast 内计算 log_softmax，避免直接对 fp16/bf16 logits
                     # 计算造成额外数值偏差。
@@ -191,7 +190,7 @@ def ppo_train_epoch(epoch, loader, iters, rollout_engine, ref_model, actor_sched
                                f"ratio_max={torch.exp(_lrv).max().item():.6f} "
                                f"ratio_min={torch.exp(_lrv).min().item():.6f} "
                                f"dropout={getattr(lm_config, 'dropout', None)} "
-                               f"training={actor_model.training}")
+                               f"training={actor_unwrapped.training}")
                 approx_kl = (0.5 * (log_ratio ** 2) * resp_policy_mask[inds]).sum() / resp_policy_mask[inds].sum().clamp(min=1)
                 
                 # 同步各卡的 approx_kl，防止某卡 break 而其它卡继续导致 DDP 死锁
@@ -429,9 +428,8 @@ if __name__ == "__main__":
         Logger('torch.compile enabled')
         rollout_engine.update_policy(actor_model)
     if dist.is_initialized():
-        # freqs_cos/freqs_sin 各 rank 由 config 确定性算出，默认每步广播一次纯属浪费
-        actor_model = DistributedDataParallel(actor_model, device_ids=[local_rank], broadcast_buffers=False)
-        critic_model = DistributedDataParallel(critic_model, device_ids=[local_rank], broadcast_buffers=False)
+        actor_model = DistributedDataParallel(actor_model, device_ids=[local_rank])
+        critic_model = DistributedDataParallel(critic_model, device_ids=[local_rank])
     rollout_engine.update_policy(actor_model)
     
     # ========== 8. 开始训练 ==========

trainer/trainer_utils.py

@@ -5,8 +5,6 @@ import os
 import sys
 __package__ = "trainer"
 sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
-import ast
-import operator
 import random
 import math
 import numpy as np
@@ -177,33 +175,3 @@ class LMForRewardModel:
         ]
         score = self.model.get_score(self.tokenizer, eval_messages)
         return max(min(score, 3.0), -3.0)
-
-
-# ===== 数学表达式安全求值：替代 eval，只放行算术运算与 math 白名单（长度上限 512） =====
-def safe_math_eval(expression):
-    """对模型生成的数学表达式求值：支持 + - * / // % **、math 白名单函数与 pi/e/tau 常量。"""
-    def pow_guard(base, exp):  # 幂运算统一走这里，拦截 9**9**9 / 10**99999 这类把进程算死的输入
-        if abs(exp) > 1e4 or (abs(base) > 1 and abs(exp) * math.log10(abs(base)) > 100): raise ValueError('幂运算结果过大')
-        return base ** exp
-    def resolve(node):  # 同时兼容 sqrt(4) 与 math.sqrt(4) 两种写法
-        if isinstance(node, ast.Name): return node.id
-        if isinstance(node, ast.Attribute) and getattr(node.value, 'id', '') == 'math': return node.attr
-    def walk(node):
-        if isinstance(node, ast.Constant) and type(node.value) in (int, float): return node.value
-        name = resolve(node)
-        if name in consts: return consts[name]
-        if isinstance(node, ast.UnaryOp): return unary_ops[type(node.op)](walk(node.operand))
-        if isinstance(node, ast.BinOp): return bin_ops[type(node.op)](walk(node.left), walk(node.right))
-        if isinstance(node, ast.Call) and not node.keywords: return funcs[resolve(node.func)](*map(walk, node.args))
-        raise ValueError(f'不支持的表达式语法: {type(node).__name__}')
-    consts = {'pi': math.pi, 'e': math.e, 'tau': math.tau}
-    funcs = {'pow': pow_guard, **{n: getattr(math, n) for n in 'sqrt exp log log2 log10 sin cos tan asin acos atan atan2 sinh cosh tanh floor ceil trunc fabs fmod hypot gcd degrees radians'.split()}}
-    bin_ops = {ast.Add: operator.add, ast.Sub: operator.sub, ast.Mult: operator.mul, ast.Div: operator.truediv, ast.FloorDiv: operator.floordiv, ast.Mod: operator.mod, ast.Pow: pow_guard}
-    unary_ops = {ast.UAdd: operator.pos, ast.USub: operator.neg}
-    chars = str.maketrans({'^': '**', '×': '*', '÷': '/', '−': '-', '²': '**2', '³': '**3', '（': '(', '）': ')'})
-    expr = str(expression).translate(chars).strip()
-    if not expr or len(expr) > 512: raise ValueError('表达式为空或过长')
-    try:
-        return walk(ast.parse(expr, mode='eval').body)
-    except (KeyError, SyntaxError, TypeError):
-        raise ValueError('不支持的表达式语法') from None