Merge branch 'develop' of https://github.com/PaddlePaddle/PaddleNLP i…

…nto develop

README.md

@@ -32,7 +32,7 @@
 
 * **2024.04.24 [PaddleNLP v2.8](https://github.com/PaddlePaddle/PaddleNLP/releases/tag/v2.8.0)**：自研极致收敛的RsLoRA+算法，大幅提升PEFT训练收敛速度以及训练效果；引入高性能生成加速到RLHF PPO算法，打破 PPO 训练中生成速度瓶颈，PPO训练性能大幅领先。通用化支持 FastFNN、FusedQKV等多个大模型训练性能优化方式，大模型训练更快、更稳定。
 
-* **2024.01.04 [PaddleNLP v2.7](https://github.com/PaddlePaddle/PaddleNLP/releases/tag/v2.7.1)**： 大模型体验全面升级，统一工具链大模型入口。统一预训练、精调、压缩、推理以及部署等环节的实现代码，到 `PaddleNLP/llm`目录。全新大[模型工具链文档](https://paddlenlp.readthedocs.io/zh/latest/llm/finetune.html)，一站式指引用户从大模型入门到业务部署上线。全断点存储机制 Unified Checkpoint，大大提高大模型存储的通用性。高效微调升级，支持了高效微调+LoRA同时使用，支持了QLoRA等算法。
+* **2024.01.04 [PaddleNLP v2.7](https://github.com/PaddlePaddle/PaddleNLP/releases/tag/v2.7.1)**： 大模型体验全面升级，统一工具链大模型入口。统一预训练、精调、压缩、推理以及部署等环节的实现代码，到 `PaddleNLP/llm`目录。全新[大模型工具链文档](https://paddlenlp.readthedocs.io/zh/latest/llm/finetune.html)，一站式指引用户从大模型入门到业务部署上线。全断点存储机制 Unified Checkpoint，大大提高大模型存储的通用性。高效微调升级，支持了高效微调+LoRA同时使用，支持了QLoRA等算法。
 
 * **2023.08.15 [PaddleNLP v2.6](https://github.com/PaddlePaddle/PaddleNLP/releases/tag/v2.6.0)**： 发布[全流程大模型工具链](./llm)，涵盖预训练，精调，压缩，推理以及部署等各个环节，为用户提供端到端的大模型方案和一站式的开发体验；内置[4D并行分布式Trainer](./docs/trainer.md)，[高效微调算法LoRA/Prefix Tuning](./llm#33-lora), [自研INT8/INT4量化算法](./llm#6-量化)等等；全面支持[LLaMA 1/2](./llm/llama), [BLOOM](.llm/bloom), [ChatGLM 1/2](./llm/chatglm), [GLM](./llm/glm), [OPT](./llm/opt)等主流大模型
 

llm/README.md

@@ -56,7 +56,7 @@ PaddleNLP将飞桨4D并行策略加入到Trainer API中， 用户只需修改Tra
 
 此项目支持了LLaMA、GPT-3、BaiChuan、Qwen 等大模型的预训练。用户切换配置config文件，即可一键运行。
 
-数据详细制作流程可参考[此处](https://paddlenlp.readthedocs.io/zh/latest/pretraining/dataset.html) : https://paddlenlp.readthedocs.io/zh/latest/pretraining/dataset.html
+数据详细制作流程可参考[此处](https://paddlenlp.readthedocs.io/zh/latest/llm/pretraining/dataset.html) : https://paddlenlp.readthedocs.io/zh/latest/llm/pretraining/dataset.html
 
 为了方便用户运行测试本模型，本项目提供了处理好的100k条doc的训练样本：
 ```shell

llm/data.py

@@ -90,7 +90,7 @@ def tokenize_example(tokenizer, example, data_args):
     return tokenized_source, tokenized_target_input_ids
 
 
-def tokenize_rounds_example(tokenizer, example, data_args):
+def tokenize_rounds_example(tokenizer, example, data_args, **kwargs):
     """tokenize multi-rounds examples with chat_template.json
 
     Args:
@@ -117,7 +117,7 @@ def tokenize_rounds_example(tokenizer, example, data_args):
 
     # 1. only tokenize input_ids
     conversation_result: list[tuple[list[int], list[int]]] = tokenizer.encode_chat_inputs(
-        conversations, context_data=context_data
+        conversations, context_data=context_data, **kwargs
     )
     system_ids = conversation_result.pop("system", []) or []
 

llm/llama/auto_parallel/run_pretrain_auto.py

@@ -86,6 +86,12 @@ class PreTrainingArguments(TrainingArguments):
             "help": "Enable fused_linear_param_grad pass, which should replace add_n_op with add_op for gradients accumulation."
         },
     )
+    eliminate_transpose: bool = field(
+        default=False,
+        metadata={
+            "help": "Enable eliminate_transpose pass, which should replace transpose with reshape when sequence parallel is enabled."
+        },
+    )
     job_schedule_profiler_start: int = field(
         default=-1,
         metadata={"help": "The step to start job_schedule_profiler."},
@@ -132,6 +138,11 @@ def __post_init__(self):
             fused_passes.enable = True
             fused_passes.fused_passes_list.append("fused_linear_param_grad_add_pass")
 
+        if self.eliminate_transpose:
+            fused_passes = self.strategy.fused_passes
+            fused_passes.enable = True
+            fused_passes.fused_passes_list.append("eliminate_transpose")
+
         logger.info(self.strategy)
 
 
@@ -574,7 +585,11 @@ def fn(layer):
     # Create the learning_rate sheduler and optimizer
     if training_args.decay_steps is None:
         training_args.decay_steps = training_args.max_steps
-    warmup_steps = training_args.warmup_ratio * training_args.max_steps
+
+    if training_args.warmup_steps > 0:
+        warmup_steps = training_args.warmup_steps
+    else:
+        warmup_steps = training_args.warmup_ratio * training_args.max_steps
 
     lr_scheduler = None
     if training_args.lr_scheduler_type.value == "cosine":

llm/run_pretrain.py

@@ -46,6 +46,7 @@
 )
 from paddlenlp.utils.batch_sampler import DistributedBatchSampler
 from paddlenlp.utils.log import logger
+from paddlenlp.utils.tools import get_env_device
 
 
 def add_start_docstrings(*docstr):
@@ -218,6 +219,10 @@ class ModelArguments:
         default=False,
         metadata={"help": "recompute_use_reentrant"},
     )
+    num_hidden_layers: Optional[int] = field(
+        default=None,
+        metadata={"help": "num_hidden_layers."},
+    )
 
 
 def create_pretrained_dataset(
@@ -451,6 +456,9 @@ def main():
     if model_args.no_recompute_layers is not None:
         model_args.no_recompute_layers.sort()
 
+    config.num_hidden_layers = (
+        model_args.num_hidden_layers if model_args.num_hidden_layers is not None else config.num_hidden_layers
+    )
     config.use_flash_attention = model_args.use_flash_attention
     config.use_fused_rms_norm = model_args.use_fused_rms_norm
     config.use_fast_layer_norm = model_args.use_fast_layer_norm
@@ -483,6 +491,16 @@ def main():
         config.num_attention_heads % config.sep_parallel_degree == 0
     ), f"num_attention_heads:{config.num_attention_heads} must be divisible by sep_parallel_degree {config.sep_parallel_degree}"
 
+    if get_env_device() == "xpu" and training_args.gradient_accumulation_steps > 1:
+        try:
+            from paddle_xpu.layers.nn.linear import LinearConfig  # noqa: F401
+
+            LinearConfig.enable_accumulate_steps_opt()
+            LinearConfig.set_accumulate_steps(training_args.gradient_accumulation_steps)
+        except ImportError:
+            # It's OK, not use accumulate_steps optimization
+            pass
+
     print("Final pre-training config:", config)
 
     # Set the dtype for loading model

llm/utils.py

@@ -125,9 +125,11 @@ def get_lora_target_modules(model):
             ".*v_proj.*",
             ".*k_proj.*",
             ".*o_proj.*",
+            ".*qkv_proj.*",
             ".*gate_proj.*",
             ".*down_proj.*",
             ".*up_proj.*",
+            ".*gate_up_fused_proj.*",
         ]
     elif model.base_model_prefix == "opt":
         target_modules = [

paddlenlp/taskflow/text_feature_extraction.py

@@ -228,8 +228,7 @@ def _parse_batch(batch_examples):
                 )
             else:
                 tokenized_inputs = self._tokenizer(
-                    text=[""] * len(batch_examples),
-                    text_pair=batch_examples,
+                    text=batch_examples,
                     padding="max_length",
                     truncation=True,
                     max_seq_len=self.max_seq_len,

paddlenlp/transformers/linear_utils.py

@@ -0,0 +1,59 @@
+# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+This file is used for replacing Paddle's native Linear implementations with vendors' customized implementations
+"""
+
+import paddle.distributed.fleet.meta_parallel as mpu
+from paddle import nn
+from paddle.distributed.fleet.utils import sequence_parallel_utils
+
+from paddlenlp.transformers.mc2_parallel_linear import (
+    MC2ColumnSeqParallelLinear,
+    MC2RowSeqParallelLinear,
+)
+from paddlenlp.utils.tools import get_env_device
+
+Linear = nn.Linear
+ColumnParallelLinear = mpu.ColumnParallelLinear
+RowParallelLinear = mpu.RowParallelLinear
+ColumnSequenceParallelLinear = sequence_parallel_utils.ColumnSequenceParallelLinear
+RowSequenceParallelLinear = sequence_parallel_utils.RowSequenceParallelLinear
+
+if get_env_device() == "npu":
+    if MC2ColumnSeqParallelLinear is not None and MC2RowSeqParallelLinear is not None:
+        ColumnSequenceParallelLinear = MC2ColumnSeqParallelLinear
+        RowSequenceParallelLinear = MC2RowSeqParallelLinear
+elif get_env_device() == "xpu":
+    try:
+        from paddle_xpu.layers.nn import ColumnParallelLinear as XPUColumnParallelLinear
+        from paddle_xpu.layers.nn import Linear as XPULinear
+        from paddle_xpu.layers.nn import RowParallelLinear as XPURowParallelLinear
+        from paddle_xpu.layers.nn.sequence_parallel import (
+            XPUColumnSequenceParallelLinear,
+            XPURowSequenceParallelLinear,
+        )
+
+        Linear = XPULinear
+        ColumnParallelLinear = XPUColumnParallelLinear
+        RowParallelLinear = XPURowParallelLinear
+        ColumnSequenceParallelLinear = XPUColumnSequenceParallelLinear
+        RowSequenceParallelLinear = XPURowSequenceParallelLinear
+    except ImportError:
+        # If paddle_xpu is not installed, just use Paddle's native Linear implementations
+        pass
+else:
+    # By default, use Paddle's native Linear implementations
+    pass