.

train_sft_ds.py

@@ -31,6 +31,28 @@ def print_once(*args, **kwargs):
     if is_main_process():
         print(*args, **kwargs, flush=True)
 
+class DebugTrainer(Trainer):
+    def training_step(self, model, inputs, num_items_in_batch=None):
+        if not hasattr(self, "_dbg_printed"):
+            rank = int(os.environ.get("RANK", "0"))
+            host = socket.gethostname()
+            ids = inputs["input_ids"]
+            msk = inputs["attention_mask"]
+            labs = inputs["labels"]
+            print(f"[step0] ids={ids.device} mask={msk.device} labs={labs.device} "
+                f"supervised={(labs!=-100).sum().item()}",
+                flush=True)
+            print(
+                f"[step0][host={host} RANK={rank}] "
+                f"input_ids.shape={tuple(ids.shape)} "
+                f"attention_mask.shape={tuple(msk.shape)} "
+                f"labels.shape={tuple(labs.shape)} "
+                f"num_items_in_batch={num_items_in_batch}",
+                flush=True
+            )
+            self._dbg_printed = True
+        return super().training_step(model, inputs, num_items_in_batch)
+    
 
 # ----------------- 日志回调 -----------------
 class CsvLossLogger(TrainerCallback):
@@ -90,6 +112,16 @@ class QwenChatSFTDataset(IterableDataset):
         self.seq_len = seq_len
 
     def __iter__(self) -> Iterator[Dict[str, torch.Tensor]]:
+
+        # >>> DEBUG BEGIN
+        dbg_on = os.environ.get("DBG_SAMPLES", "0") == "1"
+        if not hasattr(self, "_dbg_seen"): self._dbg_seen = 0
+        dbg_limit = int(os.environ.get("DBG_SAMPLE_LIMIT", "3"))
+        rank = int(os.environ.get("RANK", "0"))
+        lrank = int(os.environ.get("LOCAL_RANK", "-1"))
+        host = socket.gethostname()
+        # >>> DEBUG END
+
         for ex in self.ex_iter:
             msgs = ex.get("messages", None)
             if not msgs or not isinstance(msgs, list):
@@ -168,6 +200,23 @@ class QwenChatSFTDataset(IterableDataset):
             assert len(labels)    == self.seq_len
             assert len(attn_mask) == self.seq_len
 
+            # >>> DEBUG PRINT（此时变量已定义）
+            if dbg_on and self._dbg_seen < dbg_limit:
+                sup_tok = sum(1 for v in labels if v != -100)
+                print(
+                    f"[sample][host={host} RANK={rank} LRank={lrank}] "
+                    f"rendered_len={len(rendered)} toks={len(input_ids)} sup_toks={sup_tok} "
+                    f"seq_len={self.seq_len} pad_id={pad_id}",
+                    flush=True
+                )
+                if sup_tok == 0:
+                    print(
+                        f"[WARN][host={host} RANK={rank}] sample has 0 supervised tokens -> would be skipped",
+                        flush=True
+                    )
+                self._dbg_seen += 1
+            # <<< DEBUG PRINT
+
             yield {
                 "input_ids": torch.tensor(input_ids, dtype=torch.long),
                 "attention_mask": torch.tensor(attn_mask, dtype=torch.long),
@@ -184,9 +233,9 @@ class SFTDataCollator:
         assert self.tok.pad_token_id is not None
 
     def __call__(self, features):
-        if not features:
-            raise RuntimeError(f"EMPTY BATCH in collator on rank={os.environ.get('RANK','0')}. "
-                           f"Check sampler/sharding & make eval size >= world_size * per_device_eval_batch_size.")
+        # if not features:
+        #     raise RuntimeError(f"EMPTY BATCH in collator on rank={os.environ.get('RANK','0')}. "
+        #                    f"Check sampler/sharding & make eval size >= world_size * per_device_eval_batch_size.")
         
         def _to_list(x): return x.tolist() if isinstance(x, torch.Tensor) else list(x)
         input_ids   = [_to_list(f["input_ids"])      for f in features]
@@ -206,6 +255,27 @@ class SFTDataCollator:
             batch_inp.append(torch.tensor(inp + [pad_id]*pad_len, dtype=torch.long))
             batch_attn.append(torch.tensor(msk + [0]*pad_len, dtype=torch.long))
             batch_lab.append(torch.tensor(lab + [-100]*pad_len, dtype=torch.long))
+
+        # >>> DEBUG BEGIN
+        dbg_on = os.environ.get("DBG_COLLATE", "0") == "1"
+        if dbg_on:
+            rank = int(os.environ.get("RANK", "0"))
+            host = socket.gethostname()
+            bs = len(features)
+            first_len = len(input_ids[0]) if bs > 0 else None
+            print(
+                f"[collate][host={host} RANK={rank}] features={bs} "
+                f"target_len={target_len} first_len={first_len}",
+                flush=True
+            )
+        # 额外严苛校验：防止空 batch 继续往下走
+        if not features:
+            raise RuntimeError(
+                f"[FATAL][RANK={os.environ.get('RANK','?')}] Empty batch reached collator. "
+                f"Check dataset sharding/streaming."
+            )
+        # >>> DEBUG END
+
         return {
             "input_ids": torch.stack(batch_inp, dim=0),
             "attention_mask": torch.stack(batch_attn, dim=0),
@@ -290,13 +360,6 @@ def main():
     local_rank = int(os.environ.get("LOCAL_RANK", str(args.local_rank)))
     dbg(f"pre-init: world_size={world_size}, rank={rank}, local_rank={local_rank}")
 
-    if world_size > 1 and dist.is_available() and not dist.is_initialized():
-        backend = "nccl" if torch.cuda.is_available() else "gloo"
-        dbg(f"init_process_group backend={backend} via env://")
-        dist.init_process_group(backend=backend, init_method="env://")
-    else:
-        dbg(f"skip init_process_group: world_size>1? {world_size>1}, dist_available={dist.is_available()}, already_init={dist.is_initialized()}")
-
     if torch.cuda.is_available() and local_rank >= 0:
         torch.cuda.set_device(local_rank)
         dbg(f"set_device({local_rank}); current_device={torch.cuda.current_device()} "
@@ -304,6 +367,13 @@ def main():
     else:
         dbg("no cuda or invalid local_rank; not calling set_device")
 
+    if world_size > 1 and dist.is_available() and not dist.is_initialized():
+        backend = "nccl" if torch.cuda.is_available() else "gloo"
+        dbg(f"init_process_group backend={backend} via env://")
+        dist.init_process_group(backend=backend, init_method="env://")
+    else:
+        dbg(f"skip init_process_group: world_size>1? {world_size>1}, dist_available={dist.is_available()}, already_init={dist.is_initialized()}")
+
     
     if dist.is_available() and dist.is_initialized():
         try:
@@ -399,7 +469,11 @@ def main():
     local_ok = has_one_sample(probe_stream)
 
     if dist.is_available() and dist.is_initialized():
-        t = torch.tensor(local_ok, device=("cuda" if torch.cuda.is_available() else "cpu"))
+        # t = torch.tensor(local_ok, device=("cuda" if torch.cuda.is_available() else "cpu"))
+        t = torch.tensor(
+            local_ok,
+            device=(f"cuda:{local_rank}" if torch.cuda.is_available() and local_rank >= 0 else "cpu")
+        )
         dist.all_reduce(t, op=dist.ReduceOp.MIN)
         if t.item() == 0:
             if is_main_process():
@@ -533,14 +607,25 @@ def main():
         **ta_kwargs,
     )
 
-    trainer = Trainer(
+
+    trainer = DebugTrainer(
         model=model,
         args=training_args,
         train_dataset=train_stream,
         eval_dataset=eval_dataset,
-        processing_class=tokenizer,
+        tokenizer=tokenizer,
+        # processing_class=tokenizer,
         data_collator=data_collator
     )
+
+    # trainer = Trainer(
+    #     model=model,
+    #     args=training_args,
+    #     train_dataset=train_stream,
+    #     eval_dataset=eval_dataset,
+    #     processing_class=tokenizer,
+    #     data_collator=data_collator
+    # )
     trainer.add_callback(CsvLossLogger(csv_path=os.path.join(args.output_dir, "loss.csv")))
 
     # 无共享盘：各节点本地 output_dir 下是否已有 checkpoint-*