[Trainer] update sequence parallel

llm/README.md

@@ -367,7 +367,7 @@ python -m paddle.distributed.launch --gpus "0,1,2,3,4,5,6,7" ./predict/flask_ser
 
 #### 7.2 大模型服务化部署工具
 
-该部署工具是基于英伟达Triton框架专为服务器场景的大模型服务化部署而设计。它提供了支持gRPC、HTTP协议的服务接口，以及流式Token输出能力。底层推理引擎支持连续批处理、weight only int8、后训练量化（PTQ）等加速优化策略，为用户带来易用且高性能的部署体验。
+该部署工具是基于英伟达 Triton 框架专为服务器场景的大模型服务化部署而设计。它提供了支持 gRPC、HTTP 协议的服务接口，以及流式 Token 输出能力。底层推理引擎支持连续批处理、weight only int8、后训练量化（PTQ）等加速优化策略，为用户带来易用且高性能的部署体验。
 
 基于预编译镜像部署，本节以 Meta-Llama-3-8B-Instruct-A8W8C8 为例，更多模型请参考[LLaMA](https://github.com/PaddlePaddle/PaddleNLP/blob/develop/llm/docs/predict/llama.md)、[Qwen](https://github.com/PaddlePaddle/PaddleNLP/blob/develop/llm/docs/predict/qwen.md)、[Mixtral](https://github.com/PaddlePaddle/PaddleNLP/blob/develop/llm/docs/predict/mixtral.md), 更细致的模型推理、量化教程可以参考[大模型推理教程](https://github.com/PaddlePaddle/PaddleNLP/blob/develop/llm/docs/predict/inference.md)：
 

llm/alignment/dpo/run_dpo.py

@@ -43,7 +43,6 @@
     LlamaForCausalLMPipe,
     Qwen2ForCausalLM,
     Qwen2ForCausalLMPipe,
-    register_sequence_parallel_allreduce_hooks,
 )
 from paddlenlp.transformers.configuration_utils import LlmMetaConfig
 from paddlenlp.trl import (
@@ -154,10 +153,6 @@ def main():
     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.")
 
-    if training_args.sequence_parallel:
-        register_sequence_parallel_allreduce_hooks(
-            model, training_args.gradient_accumulation_steps, training_args.fuse_sequence_parallel_allreduce
-        )
     if model_args.tokenizer_name_or_path is not None:
         tokenizer = AutoTokenizer.from_pretrained(model_args.tokenizer_name_or_path)
     else:

llm/alignment/kto/run_kto.py

@@ -38,7 +38,6 @@
     LlamaForCausalLM,
     LlamaForCausalLMPipe,
     Qwen2ForCausalLM,
-    register_sequence_parallel_allreduce_hooks,
 )
 from paddlenlp.transformers.configuration_utils import LlmMetaConfig
 from paddlenlp.trl import (
@@ -140,10 +139,6 @@ def main():
     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.")
 
-    if training_args.sequence_parallel:
-        register_sequence_parallel_allreduce_hooks(
-            model, training_args.gradient_accumulation_steps, training_args.fuse_sequence_parallel_allreduce
-        )
     if model_args.tokenizer_name_or_path is not None:
         tokenizer = AutoTokenizer.from_pretrained(model_args.tokenizer_name_or_path)
     else:

llm/alignment/rm/flashmask/reward_argument.py

@@ -86,7 +86,3 @@ class ModelArgument:
         default=1,
         metadata={"help": "virtual_pp_degree"},
     )
-    sequence_parallel: bool = field(
-        default=False,
-        metadata={"help": "whether to use sequence parallel"},
-    )

llm/alignment/rm/flashmask/run_reward.py

@@ -35,11 +35,7 @@
     get_last_checkpoint,
     set_seed,
 )
-from paddlenlp.transformers import (
-    AutoConfig,
-    AutoTokenizer,
-    register_sequence_parallel_allreduce_hooks,
-)
+from paddlenlp.transformers import AutoConfig, AutoTokenizer
 from paddlenlp.utils.log import logger
 
 
@@ -126,10 +122,6 @@ def main():
         logger.warning("`flash_mask` must use with zero padding and flash attention.")
         model.config.use_flash_attention = True
 
-    if model_args.sequence_parallel:
-        register_sequence_parallel_allreduce_hooks(
-            model, training_args.gradient_accumulation_steps, training_args.fuse_sequence_parallel_allreduce
-        )
     if model_args.tokenizer_name_or_path is not None:
         tokenizer = AutoTokenizer.from_pretrained(model_args.tokenizer_name_or_path)
     else:

llm/auto_parallel/gpt-3/run_pretrain_auto.py

@@ -225,14 +225,6 @@ class ModelArguments:
     hidden_dropout_prob: float = field(default=0.1, metadata={"help": "The hidden dropout prob."})
     attention_probs_dropout_prob: float = field(default=0.1, metadata={"help": "The attention hidden dropout prob."})
 
-    sequence_parallel: bool = field(
-        default=False,
-        metadata={"help": "whether to use sequence parallel"},
-    )
-    fuse_sequence_parallel_allreduce: bool = field(
-        default=False,
-        metadata={"help": "whether to use fuse sequence parallel allreduce"},
-    )
     use_fused_rope: Optional[bool] = field(
         default=False,
         metadata={"help": "Enable rope fusion or not."},
@@ -502,8 +494,8 @@ def main():
     config.fuse_attention_ffn = model_args.fuse_attention_ffn
     config.recompute_granularity = model_args.recompute_granularity
     config.virtual_pp_degree = model_args.virtual_pp_degree
-    config.sequence_parallel = model_args.sequence_parallel
-    config.fuse_sequence_parallel_allreduce = model_args.fuse_sequence_parallel_allreduce
+    config.sequence_parallel = training_args.sequence_parallel
+    config.fuse_sequence_parallel_allreduce = training_args.fuse_sequence_parallel_allreduce
     config.use_fused_rope = model_args.use_fused_rope
     config.no_recompute_layers = model_args.no_recompute_layers
     config.pp_recompute_interval = model_args.pp_recompute_interval

llm/auto_parallel/llama/run_pretrain_auto.py

@@ -221,14 +221,6 @@ class ModelArguments:
             "help": "Pre-training from existing paddlenlp model weights. Default False and model will train from scratch. If set True, the model_name_or_path argument must exist in the paddlenlp models."
         },
     )
-    sequence_parallel: bool = field(
-        default=False,
-        metadata={"help": "whether to use sequence parallel"},
-    )
-    fuse_sequence_parallel_allreduce: bool = field(
-        default=False,
-        metadata={"help": "whether to use fuse sequence parallel allreduce"},
-    )
     use_fused_rope: Optional[bool] = field(
         default=False,
         metadata={"help": "Enable rope fusion or not."},
@@ -534,8 +526,8 @@ def main():
     config.fuse_attention_ffn = model_args.fuse_attention_ffn
     config.recompute_granularity = model_args.recompute_granularity
     config.virtual_pp_degree = model_args.virtual_pp_degree
-    config.sequence_parallel = model_args.sequence_parallel
-    config.fuse_sequence_parallel_allreduce = model_args.fuse_sequence_parallel_allreduce
+    config.sequence_parallel = training_args.sequence_parallel
+    config.fuse_sequence_parallel_allreduce = training_args.fuse_sequence_parallel_allreduce
     config.use_fused_rope = model_args.use_fused_rope
     config.no_recompute_layers = model_args.no_recompute_layers
     config.pp_recompute_interval = model_args.pp_recompute_interval

llm/auto_parallel/qwen/run_pretrain_3D_auto.py

@@ -225,14 +225,6 @@ class ModelArguments:
             "help": "Pre-training from existing paddlenlp model weights. Default False and model will train from scratch. If set True, the model_name_or_path argument must exist in the paddlenlp models."
         },
     )
-    sequence_parallel: bool = field(
-        default=False,
-        metadata={"help": "whether to use sequence parallel"},
-    )
-    fuse_sequence_parallel_allreduce: bool = field(
-        default=False,
-        metadata={"help": "whether to use fuse sequence parallel allreduce"},
-    )
     use_fused_rope: Optional[bool] = field(
         default=False,
         metadata={"help": "Enable rope fusion or not."},
@@ -513,8 +505,8 @@ def main():
     config.fuse_attention_ffn = model_args.fuse_attention_ffn
     config.recompute_granularity = model_args.recompute_granularity
     config.virtual_pp_degree = model_args.virtual_pp_degree
-    config.sequence_parallel = model_args.sequence_parallel
-    config.fuse_sequence_parallel_allreduce = model_args.fuse_sequence_parallel_allreduce
+    config.sequence_parallel = training_args.sequence_parallel
+    config.fuse_sequence_parallel_allreduce = training_args.fuse_sequence_parallel_allreduce
     config.use_fused_rope = model_args.use_fused_rope
     config.no_recompute_layers = model_args.no_recompute_layers
     config.pp_recompute_interval = model_args.pp_recompute_interval

llm/run_finetune.py

@@ -58,7 +58,6 @@
     LlamaTokenizer,
     Qwen2ForCausalLM,
     Qwen2ForCausalLMPipe,
-    register_sequence_parallel_allreduce_hooks,
 )
 from paddlenlp.transformers.configuration_utils import LlmMetaConfig
 from paddlenlp.trl import DataConfig, ModelConfig, SFTConfig, SFTTrainer
@@ -231,10 +230,6 @@ def neft_post_hook(module, input, output):
         else:
             raise NotImplementedError("Only support neftune for model with get_input_embeddings")
 
-    if training_args.sequence_parallel:
-        register_sequence_parallel_allreduce_hooks(
-            model, training_args.gradient_accumulation_steps, training_args.fuse_sequence_parallel_allreduce
-        )
     # Load tokenizer & dataset
     tokenizer = AutoTokenizer.from_pretrained(model_args.model_name_or_path, from_aistudio=model_args.from_aistudio)
     reft_layers = None

llm/run_pretrain.py

@@ -41,7 +41,6 @@
     AutoTokenizer,
     CosineAnnealingWithWarmupDecay,
     LinearAnnealingWithWarmupDecay,
-    register_sequence_parallel_allreduce_hooks,
 )
 from paddlenlp.transformers.configuration_utils import LlmMetaConfig, llmmetaclass
 from paddlenlp.utils.batch_sampler import DistributedBatchSampler
@@ -492,11 +491,6 @@ def main():
     else:
         model = model_class.from_config(config, dtype=dtype)
 
-    if training_args.sequence_parallel:
-        register_sequence_parallel_allreduce_hooks(
-            model, training_args.gradient_accumulation_steps, training_args.fuse_sequence_parallel_allreduce
-        )
-
     if training_args.recompute:
         model.recompute_enable()
 

llm/run_quantization.py

@@ -41,7 +41,6 @@
     LlamaTokenizer,
     Qwen2ForCausalLM,
     Qwen2ForCausalLMPipe,
-    register_sequence_parallel_allreduce_hooks,
 )
 from paddlenlp.transformers.configuration_utils import LlmMetaConfig
 from paddlenlp.trl import DataConfig, ModelConfig, QuantConfig, SFTConfig, SFTTrainer
@@ -162,10 +161,6 @@ def main():
     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.")
 
-    if training_args.sequence_parallel:
-        register_sequence_parallel_allreduce_hooks(
-            model, training_args.gradient_accumulation_steps, training_args.fuse_sequence_parallel_allreduce
-        )
     # Load tokenizer & dataset
     tokenizer = AutoTokenizer.from_pretrained(model_args.model_name_or_path, from_aistudio=model_args.from_aistudio)
     # init chat_template for tokenizer

paddlenlp/trainer/trainer.py

@@ -87,6 +87,12 @@
     from ..quantization.quantization_linear import QuantizationLinear
 except:
     QuantizationLinear = None
+try:
+    from paddle.distributed.fleet.utils.sequence_parallel_utils import (
+        register_sequence_parallel_allreduce_hooks,
+    )
+except:
+    pass
 from ..transformers.context_parallel_utils import split_inputs_sequence_dim_load_balance
 from ..transformers.model_utils import (
     PretrainedModel,
@@ -428,6 +434,11 @@
                     "We do not support skip_save_model_weight in peft model when using unified checkpoint, remove this config."
                 )
 
+        if args.sequence_parallel:
+            register_sequence_parallel_allreduce_hooks(
+                self.model, args.gradient_accumulation_steps, args.fuse_sequence_parallel_allreduce
+            )
+
         self.do_grad_scaling = False
         self.enable_autocast_context_manager = False
         if args.fp16 or args.bf16:

paddlenlp/trainer/training_args.py

@@ -669,6 +669,13 @@
             )
         },
     )
+    sequence_parallel: bool = field(
+        default=False,
+        metadata={"help": "Whether to enable sequence parallel."},
+    )
+    fuse_sequence_parallel_allreduce: bool = field(
+        default=False, metadata={"help": "Whether to use fuse sequence parallel allreduce."}
+    )
     sequence_parallel_config: str = field(
         default="",
         metadata={
@@ -1209,10 +1216,17 @@
                                     f"Found unknown pipeline mode config {x}, accpet config is disable_p2p_cache_shape, disable_partial_send_recv."
                                 )
 
+                    enable_partial_send_recv = "disable_partial_send_recv" not in pipeline_parallel_config
+                    if self.sequence_parallel and enable_partial_send_recv:
+                        logger.warning(
+                            "When use pipeline parallel and sequence parallel simultaneously, we should turn off partial send recv."
+                        )
+                        enable_partial_send_recv = False
+
                     strategy.pipeline_configs = {
                         "accumulate_steps": self.gradient_accumulation_steps,
                         "micro_batch_size": self.per_device_train_batch_size,
-                        "enable_partial_send_recv": "disable_partial_send_recv" not in pipeline_parallel_config,
+                        "enable_partial_send_recv": enable_partial_send_recv,
                         "p2p_cache_shape": False if "disable_p2p_cache_shape" in pipeline_parallel_config else True,
                         # "delay_scale_loss": True, Fix ME
                     }