move some auto_parallel args into class AutoTrainingArguments

llm/auto_parallel/gpt-3/run_pretrain_auto.py

@@ -26,7 +26,12 @@
 import paddle.distributed as dist
 
 from paddlenlp.ops import Topology
-from paddlenlp.trainer import PdArgumentParser, TrainingArguments, get_last_checkpoint
+from paddlenlp.trainer import (
+    AutoTrainingArguments,
+    PdArgumentParser,
+    TrainingArguments,
+    get_last_checkpoint,
+)
 from paddlenlp.trainer.auto_trainer import AutoTrainer
 from paddlenlp.trainer.trainer_utils import IntervalStrategy, _get_distributed_seeds
 from paddlenlp.transformers import (
@@ -60,7 +65,7 @@ def docstring_decorator(fn):
 
 @dataclass
 @add_start_docstrings(TrainingArguments.__doc__)
-class PreTrainingArguments(TrainingArguments):
+class PreTrainingArguments(AutoTrainingArguments):
     min_learning_rate: float = field(
         default=1e-5,
         metadata={"help": "Minimum learning rate deacyed to."},
@@ -77,12 +82,6 @@ class PreTrainingArguments(TrainingArguments):
             "help": "Enable fused linear grad add strategy, which will reduce elementwise add for grad accumulation in the backward of nn.Linear ."
         },
     )
-    fused_linear_param_grad_add: bool = field(
-        default=False,
-        metadata={
-            "help": "Enable fused_linear_param_grad pass, which should replace add_n_op with add_op for gradients accumulation."
-        },
-    )
     job_schedule_profiler_start: int = field(
         default=-1,
         metadata={"help": "The step to start job_schedule_profiler."},
@@ -124,11 +123,6 @@ def __post_init__(self):
             self.save_strategy = IntervalStrategy.NO
             self.evaluation_strategy = IntervalStrategy.NO
 
-        if self.fused_linear_param_grad_add:
-            fused_passes = self.strategy.fused_passes
-            fused_passes.enable = True
-            fused_passes.fused_passes_list.append("fused_linear_param_grad_add_pass")
-
         logger.info(self.strategy)
 
 

llm/auto_parallel/llama/run_pretrain_auto.py

@@ -28,7 +28,12 @@
 from paddle.distributed import fleet
 
 from paddlenlp.ops import Topology
-from paddlenlp.trainer import PdArgumentParser, TrainingArguments, get_last_checkpoint
+from paddlenlp.trainer import (
+    AutoTrainingArguments,
+    PdArgumentParser,
+    TrainingArguments,
+    get_last_checkpoint,
+)
 from paddlenlp.trainer.auto_trainer import AutoTrainer
 from paddlenlp.trainer.trainer_utils import IntervalStrategy, _get_distributed_seeds
 from paddlenlp.transformers import (
@@ -63,7 +68,7 @@ def docstring_decorator(fn):
 
 @dataclass
 @add_start_docstrings(TrainingArguments.__doc__)
-class PreTrainingArguments(TrainingArguments):
+class PreTrainingArguments(AutoTrainingArguments):
     min_learning_rate: float = field(
         default=1e-5,
         metadata={"help": "Minimum learning rate deacyed to."},
@@ -80,22 +85,6 @@ class PreTrainingArguments(TrainingArguments):
             "help": "Enable fused linear grad add strategy, which will reduce elementwise add for grad accumulation in the backward of nn.Linear ."
         },
     )
-    fused_linear_param_grad_add: bool = field(
-        default=False,
-        metadata={
-            "help": "Enable fused_linear_param_grad pass, which should replace add_n_op with add_op for gradients accumulation."
-        },
-    )
-    fuse_allreduce_split_to_reducescatter: bool = field(
-        default=False,
-        metadata={"help": "Enable fuse_allreduce_split_to_reducescatter pass."},
-    )
-    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."},
@@ -137,21 +126,6 @@ def __post_init__(self):
             self.save_strategy = IntervalStrategy.NO
             self.evaluation_strategy = IntervalStrategy.NO
 
-        if self.fused_linear_param_grad_add:
-            fused_passes = self.strategy.fused_passes
-            fused_passes.enable = True
-            fused_passes.fused_passes_list.append("fused_linear_param_grad_add_pass")
-
-        if self.fuse_allreduce_split_to_reducescatter:
-            fused_passes = self.strategy.fused_passes
-            fused_passes.enable = True
-            fused_passes.fused_passes_list.append("fuse_allreduce_split_to_reducescatter_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)
 
 

llm/auto_parallel/llama/run_pretrain_auto_static.py

@@ -32,6 +32,7 @@
 
 from paddlenlp.ops import Topology
 from paddlenlp.trainer import (
+    AutoTrainingArguments,
     PdArgumentParser,
     Trainer,
     TrainingArguments,
@@ -88,7 +89,7 @@ def exec_mode_guard():
 
 @dataclass
 @add_start_docstrings(TrainingArguments.__doc__)
-class PreTrainingArguments(TrainingArguments):
+class PreTrainingArguments(AutoTrainingArguments):
     min_learning_rate: float = field(
         default=1e-5,
         metadata={"help": "Minimum learning rate deacyed to."},
@@ -99,12 +100,6 @@ class PreTrainingArguments(TrainingArguments):
             "help": "The steps use to control the learing rate. If the step > decay_steps, will use the min_learning_rate."
         },
     )
-    fused_linear_param_grad_add: bool = field(
-        default=False,
-        metadata={
-            "help": "Enable fused_linear_param_grad pass, which should replace add_n_op with add_op for gradients accumulation."
-        },
-    )
     job_schedule_profiler_start: int = field(
         default=-1,
         metadata={"help": "The step to start job_schedule_profiler."},
@@ -127,10 +122,7 @@ class PreTrainingArguments(TrainingArguments):
     def __post_init__(self):
         super().__post_init__()
         assert self.enable_auto_parallel
-        if self.fused_linear_param_grad_add:
-            fused_passes = self.strategy.fused_passes
-            fused_passes.enable = True
-            fused_passes.fused_passes_list.append("fused_linear_param_grad_add_pass")
+
         logger.info(self.strategy)
 
 

llm/auto_parallel/qwen/run_pretrain_3D_auto.py

@@ -27,7 +27,12 @@
 import paddle.distributed as dist
 from paddle.distributed import fleet
 
-from paddlenlp.trainer import PdArgumentParser, TrainingArguments, get_last_checkpoint
+from paddlenlp.trainer import (
+    AutoTrainingArguments,
+    PdArgumentParser,
+    TrainingArguments,
+    get_last_checkpoint,
+)
 from paddlenlp.trainer.auto_trainer import AutoTrainer
 from paddlenlp.trainer.trainer_utils import IntervalStrategy
 from paddlenlp.transformers import (
@@ -61,7 +66,7 @@ def docstring_decorator(fn):
 
 @dataclass
 @add_start_docstrings(TrainingArguments.__doc__)
-class PreTrainingArguments(TrainingArguments):
+class PreTrainingArguments(AutoTrainingArguments):
     min_learning_rate: float = field(
         default=1e-5,
         metadata={"help": "Minimum learning rate deacyed to."},
@@ -78,12 +83,6 @@ class PreTrainingArguments(TrainingArguments):
             "help": "Enable fused linear grad add strategy, which will reduce elementwise add for grad accumulation in the backward of nn.Linear ."
         },
     )
-    fused_linear_param_grad_add: bool = field(
-        default=False,
-        metadata={
-            "help": "Enable fused_linear_param_grad pass, which should replace add_n_op with add_op for gradients accumulation."
-        },
-    )
     job_schedule_profiler_start: int = field(
         default=-1,
         metadata={"help": "The step to start job_schedule_profiler."},
@@ -133,11 +132,6 @@ def __post_init__(self):
             self.save_strategy = IntervalStrategy.NO
             self.evaluation_strategy = IntervalStrategy.NO
 
-        if self.fused_linear_param_grad_add:
-            fused_passes = self.strategy.fused_passes
-            fused_passes.enable = True
-            fused_passes.fused_passes_list.append("fused_linear_param_grad_add_pass")
-
         logger.info(self.strategy)
 
 

paddlenlp/trainer/__init__.py

@@ -13,6 +13,7 @@
 # limitations under the License.
 
 from .argparser import *
+from .auto_training_args import *
 from .compression_args import *
 from .plugins.timer import *
 from .trainer import *

paddlenlp/trainer/auto_training_args.py

@@ -0,0 +1,62 @@
+# 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.
+
+from dataclasses import dataclass, field
+
+from .training_args import TrainingArguments
+from .utils import add_start_docstrings
+
+
+@dataclass
+@add_start_docstrings(TrainingArguments.__doc__)
+class AutoTrainingArguments(TrainingArguments):
+    """
+    自动并行相关参数配置
+    """
+
+    fused_linear_param_grad_add: bool = field(
+        default=False,
+        metadata={
+            "help": "Enable fused_linear_param_grad pass, which should replace add_n_op with add_op for gradients accumulation."
+        },
+    )
+    fuse_allreduce_split_to_reducescatter: bool = field(
+        default=False,
+        metadata={"help": "Enable fuse_allreduce_split_to_reducescatter pass."},
+    )
+    eliminate_transpose: bool = field(
+        default=False,
+        metadata={
+            "help": "Enable eliminate_transpose pass, which should replace transpose with reshape when sequence parallel is enabled."
+        },
+    )
+
+    def __post_init__(self):
+        super().__post_init__()
+        assert self.enable_auto_parallel
+
+        if self.fused_linear_param_grad_add:
+            fused_passes = self.strategy.fused_passes
+            fused_passes.enable = True
+            fused_passes.fused_passes_list.append("fused_linear_param_grad_add_pass")
+
+        if self.fuse_allreduce_split_to_reducescatter:
+            fused_passes = self.strategy.fused_passes
+            fused_passes.enable = True
+            fused_passes.fused_passes_list.append("fuse_allreduce_split_to_reducescatter_pass")
+
+        if self.eliminate_transpose:
+            fused_passes = self.strategy.fused_passes
+            fused_passes.enable = True
+            fused_passes.fused_passes_list.append("eliminate_transpose")