[LLM] Support prefix tuning and lora for qwen2

llm/qwen/lora_argument_qwen2_7b.json

@@ -0,0 +1,32 @@
+{
+    "model_name_or_path": "Qwen/Qwen2-7B",
+    "dataset_name_or_path": "./data",
+    "output_dir": "./checkpoints/qwen2_7b__lora_ckpts",
+    "per_device_train_batch_size": 4,
+    "gradient_accumulation_steps": 4,
+    "per_device_eval_batch_size": 8,
+    "eval_accumulation_steps": 16,
+    "num_train_epochs": 3,
+    "learning_rate": 3e-04,
+    "warmup_steps": 30,
+    "logging_steps": 1,
+    "evaluation_strategy": "epoch",
+    "save_strategy": "epoch",
+    "src_length": 2048,
+    "max_length": 4096,
+    "bf16": true,
+    "fp16_opt_level": "O2",
+    "do_train": true,
+    "do_eval": true,
+    "disable_tqdm": true,
+    "load_best_model_at_end": true,
+    "eval_with_do_generation": false,
+    "metric_for_best_model": "accuracy",
+    "recompute": true,
+    "save_total_limit": 1,
+    "tensor_parallel_degree": 1,
+    "pipeline_parallel_degree": 1,
+    "lora": true,
+    "zero_padding": false,
+    "use_flash_attention": false
+}

llm/qwen/pretrain-qwen1.5_7b-tp2sd4_stage2.json

@@ -0,0 +1,41 @@
+{
+    "model_name_or_path": "Qwen/Qwen1.5-7B",
+    "tokenizer_name_or_path": "Qwen/Qwen1.5-7B",
+    "input_dir": "./data",
+    "output_dir": "./checkpoints/qwen1.5_7b_pretrain_ckpts",
+    "per_device_train_batch_size": 2,
+    "gradient_accumulation_steps": 8,
+    "per_device_eval_batch_size": 2,
+    "tensor_parallel_degree": 2,
+    "pipeline_parallel_degree": 1,
+    "sharding_parallel_degree": 4,
+    "sharding": "stage2",
+    "virtual_pp_degree": 1,
+    "sequence_parallel": 0,   
+    "use_flash_attention": true,
+    "use_fused_rms_norm": true,
+    "use_fused_rope": true,
+    "max_seq_length": 4096,
+    "learning_rate": 3e-05,
+    "min_learning_rate": 3e-06,
+    "warmup_steps": 30,
+    "logging_steps": 1,
+    "max_steps": 10000,
+    "save_steps": 5000,
+    "eval_steps": 1000,
+    "weight_decay": 0.01,
+    "bf16": true,
+    "fp16_opt_level": "O2",
+    "warmup_ratio": 0.01,
+    "max_grad_norm": 1.0,
+    "dataloader_num_workers": 1,
+    "continue_training": 1,
+    "do_train": true,
+    "do_eval": true,
+    "do_predict": true,
+    "disable_tqdm": true,
+    "recompute": true,
+    "distributed_dataloader": 1,
+    "recompute_granularity": "full",
+    "save_total_limit": 2
+  }

llm/qwen/pretrain-qwen2_7b-tp2sd4_stage2.json

@@ -0,0 +1,41 @@
+{
+    "model_name_or_path": "Qwen/Qwen2-7B",
+    "tokenizer_name_or_path": "Qwen/Qwen2-7B",
+    "input_dir": "./data",
+    "output_dir": "./checkpoints/qwen2_7b_pretrain_ckpts",
+    "per_device_train_batch_size": 2,
+    "gradient_accumulation_steps": 8,
+    "per_device_eval_batch_size": 2,
+    "tensor_parallel_degree": 2,
+    "pipeline_parallel_degree": 1,
+    "sharding_parallel_degree": 4,
+    "sharding": "stage2",
+    "virtual_pp_degree": 1,
+    "sequence_parallel": 0,   
+    "use_flash_attention": true,
+    "use_fused_rms_norm": true,
+    "use_fused_rope": true,
+    "max_seq_length": 4096,
+    "learning_rate": 3e-05,
+    "min_learning_rate": 3e-06,
+    "warmup_steps": 30,
+    "logging_steps": 1,
+    "max_steps": 10000,
+    "save_steps": 5000,
+    "eval_steps": 1000,
+    "weight_decay": 0.01,
+    "bf16": true,
+    "fp16_opt_level": "O2",
+    "warmup_ratio": 0.01,
+    "max_grad_norm": 1.0,
+    "dataloader_num_workers": 1,
+    "continue_training": 1,
+    "do_train": true,
+    "do_eval": true,
+    "do_predict": true,
+    "disable_tqdm": true,
+    "recompute": false,
+    "distributed_dataloader": 1,
+    "recompute_granularity": "full",
+    "save_total_limit": 2
+  }

llm/qwen/pt_argument_qwen2_7b.json

@@ -0,0 +1,33 @@
+{
+    "model_name_or_path": "Qwen/Qwen2-7B",
+    "dataset_name_or_path": "./data",
+    "output_dir": "./checkpoints/qwen2_7b_pt_ckpts",
+    "per_device_train_batch_size": 4,
+    "gradient_accumulation_steps": 4,
+    "per_device_eval_batch_size": 8,
+    "eval_accumulation_steps": 16,
+    "num_train_epochs": 3,
+    "learning_rate": 3e-02,
+    "warmup_steps": 30,
+    "logging_steps": 1,
+    "evaluation_strategy": "epoch",
+    "save_strategy": "epoch",
+    "src_length": 2048,
+    "max_length": 4096,
+    "bf16": true,
+    "fp16_opt_level": "O2",
+    "do_train": true,
+    "do_eval": true,
+    "disable_tqdm": true,
+    "load_best_model_at_end": true,
+    "eval_with_do_generation": false,
+    "metric_for_best_model": "accuracy",
+    "recompute": true,
+    "save_total_limit": 1,
+    "tensor_parallel_degree": 1,
+    "pipeline_parallel_degree": 1,
+    "prefix_tuning": true,
+    "zero_padding": false,
+    "use_flash_attention": false
+  }
+

llm/qwen/sft_argument_qwen2_7b.json

@@ -0,0 +1,31 @@
+{
+    "model_name_or_path": "Qwen/Qwen2-7B",
+    "dataset_name_or_path": "./data",
+    "output_dir": "./checkpoints/qwen2-7b_sft_ckpts",
+    "per_device_train_batch_size": 1,
+    "gradient_accumulation_steps": 4,
+    "per_device_eval_batch_size": 8,
+    "eval_accumulation_steps":16,
+    "num_train_epochs": 3,
+    "learning_rate": 3e-05,
+    "warmup_steps": 30,
+    "logging_steps": 1,
+    "evaluation_strategy": "epoch",
+    "save_strategy": "epoch",
+    "src_length": 2048,
+    "max_length": 4096,
+    "bf16": true,
+    "fp16_opt_level": "O2",
+    "do_train": true,
+    "do_eval": true,
+    "disable_tqdm": true,
+    "load_best_model_at_end": true,
+    "eval_with_do_generation": false,
+    "metric_for_best_model": "accuracy",
+    "recompute": true,
+    "save_total_limit": 1,
+    "tensor_parallel_degree": 4,
+    "pipeline_parallel_degree": 1,
+    "zero_padding": false,
+    "use_flash_attention": false
+  }

llm/utils.py

@@ -35,6 +35,7 @@
     ChatGLMv2Tokenizer,
     LlamaForCausalLMPipe,
     PretrainedConfig,
+    Qwen2ForCausalLMPipe,
 )
 from paddlenlp.transformers.tokenizer_utils import PretrainedTokenizer
 from paddlenlp.utils.log import logger
@@ -67,7 +68,7 @@ def get_prefix_tuning_params(model):
         num_hidden_layers = model.config.num_layers
         hidden_size = model.config.hidden_size
         postprocess_past_key_value = chatglm_postprocess_past_key_value
-        multi_query_group_num = model.config.multi_query_group_num
+        multi_query_group_num = model.config.multi_query_group_num  # num_key_value_heads
     elif model.base_model_prefix == "bloom":
         from paddlenlp.peft.prefix import bloom_postprocess_past_key_value
 
@@ -92,6 +93,14 @@ def get_prefix_tuning_params(model):
         hidden_size = model.config.hidden_size
         postprocess_past_key_value = qwen_postprocess_past_key_value
         multi_query_group_num = None
+    elif model.base_model_prefix == "qwen2":
+        from paddlenlp.peft.prefix import qwen_postprocess_past_key_value
+
+        num_attention_heads = model.config.num_attention_heads
+        num_hidden_layers = model.config.num_hidden_layers
+        hidden_size = model.config.hidden_size
+        postprocess_past_key_value = qwen_postprocess_past_key_value
+        multi_query_group_num = model.config.num_key_value_heads  # num_key_value_heads
     else:
         raise ValueError(f"Unknown base_model_prefix: {model.base_model_prefix}. ")
     return dict(
@@ -150,6 +159,16 @@ def get_lora_target_modules(model):
             ".*mlp.w2.*",
             ".*mlp.c_proj.*",
         ]
+    elif model.base_model_prefix == "qwen2" or isinstance(model, Qwen2ForCausalLMPipe):
+        target_modules = [
+            ".*q_proj.*",
+            ".*k_proj.*",
+            ".*v_proj.*",
+            ".*o_proj.*",
+            ".*gate_proj.*",
+            ".*down_proj.*",
+            ".*up_proj.*",
+        ]
     elif model.base_model_prefix == "mixtral":
         target_modules = [
             ".*q_proj.*",

paddlenlp/transformers/model_utils.py

@@ -2356,6 +2356,17 @@
                 )
                 pass
 
+        # Note:
+        # 1. PipelineLayer will create parameters for each layer and
+        # call `_synchronize_shared_weights()` to synchronize the shared parameters.
+        # 2. When setting the model `state_dict`, `_synchronize_shared_weights` will be called to
+        # synchronize the shared parameters.
+        # However, when state dict only contains the one piece of shared parameters, the shared parameters
+        # will be different from the original shared parameters.
+
+        if isinstance(model, PipelineLayer):
+            model._synchronize_shared_weights()
+
         if paddle.in_dynamic_mode():
             return model
 

paddlenlp/transformers/qwen2/__init__.py

@@ -15,4 +15,5 @@
 
 from .configuration import *
 from .modeling import *
+from .modeling_pp import *
 from .tokenizer import *

paddlenlp/transformers/qwen2/configuration.py

@@ -150,6 +150,9 @@ def __init__(
         self.eos_token_id = eos_token_id
 
         super().__init__(
+            pad_token_id=pad_token_id,
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
             tie_word_embeddings=tie_word_embeddings,
             **kwargs,
         )