fix startend_row_indics bug and docs

Author: cheng221

examples/README.md

@@ -20,18 +20,39 @@
 wget https://bj.bcebos.com/paddlenlp/datasets/examples/alpaca_demo.gz
 tar -xvf alpaca_demo.gz
 ```
+### 模型下载
+```bash
+# PaddleNLP/Qwen2-0.5B-Instruct
+aistudio download --model PaddleNLP/Qwen2-0.5B-Instruct  --local_dir PaddleNLP/Qwen2-0.5B-Instruct
+
+# baidu/ERNIE-4.5-0.3B-PT
+aistudio download --model PaddlePaddle/ERNIE-4.5-0.3B-PT --local_dir baidu/ERNIE-4.5-0.3B-PT
+
+# baidu/ERNIE-4.5-0.3B-PT
+aistudio download --model PaddlePaddle/ERNIE-4.5-21B-A3B-PT --local_dir baidu/ERNIE-4.5-21B-A3B-PT
+```
 
 ### 全参精调：SFT
 
 单卡
 ```bash
-# 需要12G显存左右
+# 微调Qwen2-0.5B-Instruct 需要12G显存左右
 python -u run_finetune.py ./config/qwen/sft_argument_qwen2_0p5b.json
+
+# 微调ERNIE-4.5-0.3B-PT
+python -u run_finetune.py ./config/ernie4_5/sft_argument_ernie4_5_0p3b.json
 ```
 
 多卡
 ```bash
+# SFT Qwen2-0.5B-Instruct
 python -u -m paddle.distributed.launch --devices "0,1,2,3,4,5,6,7" run_finetune.py ./config/qwen/sft_argument_qwen2_0p5b.json
+
+# SFT ERNIE-4.5-0.3B-PT
+python -u -m paddle.distributed.launch --devices "0,1,2,3,4,5,6,7" run_finetune.py ./config/ernie4_5/sft_argument_ernie4_5_0p3b.json
+
+# SFT ERNIE-4.5-21B-A3B-PT
+python -u -m paddle.distributed.launch --devices "0,1,2,3,4,5,6,7" run_finetune.py ./config/ernie4_5_moe/sft_argument_ernie4_5_21b_a3b.json
 ```
 
 ### LoRA

examples/config/ernie4_5/sft_argument_ernie4_5_0p3b.json

@@ -21,8 +21,6 @@
     "max_steps": 100,
     "evaluation_strategy": "epoch",
     "save_strategy": "epoch",
-    "src_length": 1024,
-    "max_length": 2048,
     "bf16": true,
     "fp16_opt_level": "O2",
     "do_train": true,
@@ -33,15 +31,17 @@
     "metric_for_best_model": "accuracy",
     "recompute": true,
     "save_total_limit": 1,
-    "tensor_parallel_degree": 2,
-    "pipeline_parallel_degree": 2,
+    "tensor_parallel_degree": 1,
+    "pipeline_parallel_degree": 1,
     "sharding": "stage2",
     "zero_padding": true,
     "flash_mask": true,
     "unified_checkpoint": true,
     "use_flash_attention": true,
-    "sequence_parallel": true,
+    "use_attn_mask_startend_row_indices": true,
+    "sequence_parallel": false,
     "report_to": "none",
     "convert_from_hf": true,
+    "save_to_hf": true,
     "pp_seg_method": "layer:DecoderLayer|EmptyLayer"
   }

examples/config/ernie4_5_moe/sft_argument_ernie4_5_21b_a3b.json

@@ -21,8 +21,6 @@
     "max_steps": 100,
     "evaluation_strategy": "epoch",
     "save_strategy": "epoch",
-    "src_length": 1024,
-    "max_length": 2048,
     "bf16": true,
     "fp16_opt_level": "O2",
     "do_train": true,
@@ -43,5 +41,6 @@
     "sequence_parallel": true,
     "report_to": "none",
     "convert_from_hf": true,
+    "save_to_hf": true,
     "pp_seg_method": "layer:DecoderLayer|EmptyLayer"
   }

examples/run_finetune.py

@@ -153,6 +153,13 @@ def main():
     logger.info(f"Final model config: {model_config}")
     logger.info("Creating model")
 
+    if model_args.flash_mask and model_args.use_attn_mask_startend_row_indices:
+        model_config._attn_implementation = "flashmask"
+    elif model_args.flash_mask and not model_args.use_attn_mask_startend_row_indices:
+        model_config._attn_implementation = "sdpa"
+    else:
+        model_config._attn_implementation = "eager"
+
     model_class = AutoModelForCausalLM
     if training_args.pipeline_parallel_degree > 1:
         if data_args.eval_with_do_generation and training_args.do_eval:
@@ -174,7 +181,6 @@ def main():
         logger.warning("`flash_mask` must use with zero padding and flash attention.")
         data_args.zero_padding = True
         model.config.use_flash_attention = True
-        model.config._attn_implementation = "flashmask"
 
     if model_args.flash_mask and not any(isinstance(model, cls) for cls in flash_mask_support_list):
         raise NotImplementedError(f"{model.__class__} not support flash mask.")

paddleformers/transformers/ernie4_5/modeling.py

@@ -834,7 +834,8 @@ def forward(
 
         # Pretrain & Eval must have labels
         assert labels is not None
-        return self.criterion(logits, labels, loss_mask)
+        loss, _ = self.criterion(logits, labels, loss_mask)
+        return loss, logits
 
 
 class Ernie4_5ForCausalLMPipe(GeneralModelForCausalLMPipe):

paddleformers/transformers/ernie4_5_moe/modeling.py

@@ -1157,7 +1157,8 @@ def forward(
         # Pretrain & Eval must have labels
         assert labels is not None
 
-        return self.criterion(logits, labels, loss_mask, router_loss=router_loss, mtp_logits=mtp_logits)
+        loss, _ = self.criterion(logits, labels, loss_mask, router_loss=router_loss, mtp_logits=mtp_logits)
+        return loss, logits
 
 
 class Ernie4_5_MoeForCausalLMPipe(GeneralModelForCausalLMPipe):

paddleformers/transformers/llama/fusion_ops.py

@@ -248,16 +248,14 @@ def fusion_flash_attention(
             else:
                 if attn_mask_startend_row_indices is not None:
                     assert alibi is None, "flashmask_attention or flash_attention_with_sparse_mask not support alibi"
-                    if len(attn_mask_startend_row_indices.shape) == 2:
-                        attn_mask_startend_row_indices = paddle.unsqueeze(attn_mask_startend_row_indices, axis=1)
 
                     if hasattr(F, "flashmask_attention"):
                         attn_output = no_recompute(
                             F.flashmask_attention,
                             query_states,
                             key_states,
                             value_states,
-                            startend_row_indices=attn_mask_startend_row_indices.unsqueeze(-1),
+                            startend_row_indices=attn_mask_startend_row_indices,
                             causal=True,
                             enable=skip_recompute,
                         )

paddleformers/utils/masking_utils.py

@@ -31,9 +31,9 @@ def _gen_from_sparse_attn_mask_indices(attn_mask_start_row_indices, dtype):
     Returns:
         paddle.Tensor: The dense attention mask recovered from attn_mask_start_row_indices.
     """
-    batch_size, _, max_seq_len = attn_mask_start_row_indices.shape
+    batch_size, _, max_seq_len, _ = attn_mask_start_row_indices.shape
     base = paddle.arange(max_seq_len, dtype="int32").unsqueeze(1).expand([batch_size, -1, max_seq_len]).unsqueeze(1)
-    mask_indices = attn_mask_start_row_indices.unsqueeze(1)
+    mask_indices = attn_mask_start_row_indices
 
     tril = paddle.tril(
         paddle.ones([max_seq_len, max_seq_len], dtype="bool").expand([batch_size, 1, max_seq_len, max_seq_len])