[NPU] Add flatten_param_grads for Trainer to improve NPU performance (#…

…4426)

docs/trainer.md

@@ -548,10 +548,15 @@ Trainer 是一个简单，但功能完整的 Paddle训练和评估模块，并
                         是否从断点重启恢复训练，(可选，默认为 None)
                         The path to a folder with a valid checkpoint for your
                         model. (default: None)
-                        
+
   --skip_memory_metrics
                        是否跳过内存profiler检测。（可选，默认为True，跳过）
                        Whether or not to skip adding of memory profiler reports
                        to metrics.(default:True)
 
+  --flatten_param_grads
+                       是否在优化器中使用flatten_param_grads策略，该策略将素有参数摊平后输入Optimizer更新。目前该策略仅在NPU设备上生效。（可选，默认为False）
+                       Whether use flatten_param_grads method in optimizer,
+                       only used on NPU devices.(default:False)
+
 ```

paddlenlp/trainer/plugins/npu_plugin.py

@@ -0,0 +1,127 @@
+# Copyright 2020-present the HuggingFace Inc. team.
+# Copyright (c) 2022 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.
+
+import types
+
+import numpy as np
+import paddle
+from paddle.fluid.layer_helper import LayerHelper
+
+from ...utils.log import logger
+
+
+def npu_accelerate_plugin(optimizer):
+    """npu_accelerate_plugin uses the flatten_param_grads method to speed up the performance of the model on NPU devices.
+    flatten_param_grads method will be added to `step` function of optimizer.
+
+    Args:
+        optimizer (`paddle.optimizer.Optimizer`):
+            The Optimizer whose `step` method will be modified.
+    """
+    optimizer.step = types.MethodType(_optimizer_step_with_flatten_param_grads, optimizer)
+
+
+def _optimizer_step_with_flatten_param_grads(optimizer):
+    if not isinstance(optimizer._param_groups[0], dict):
+        params_grads = []
+        for param in optimizer._param_groups:
+            if param.stop_gradient:
+                continue
+            if param._grad_ivar() is not None:
+                grad_var = param._grad_ivar()
+                params_grads.append((param, grad_var))
+
+        # currently, only support ClipGradByGlobalNorm and without regularization.
+        if isinstance(params_grads, list) and optimizer.regularization is None:
+            if optimizer._grad_clip is None or isinstance(optimizer._grad_clip, paddle.nn.ClipGradByGlobalNorm):
+                params_grads = _flatten_param_grads(optimizer, params_grads)
+
+        optimizer._apply_optimize(
+            loss=None,
+            startup_program=None,
+            params_grads=params_grads,
+            param_group_idx=0,
+        )
+    else:
+        raise RuntimeError("flatten_param_grads is not supported when _param_groups[0] is dict.")
+
+
+def _flatten_param_grads(optimizer, params_grads):
+    optimizer.helper = LayerHelper(optimizer.__class__.__name__)
+    need_flatten_params = []
+    need_flatten_grads = []
+    for p, g in params_grads:
+        if g is None:
+            continue
+        g.persistable = True
+        if getattr(p, "need_clip", True) is False or getattr(p, "regularizer", None) is not None:
+            logger.warning(
+                f"flatten_param_grads=True will be discarded since paramter {p.name}'s need_clip is False or "
+                "the regularizer is set."
+            )
+            return params_grads
+
+        need_flatten_params.append(p)
+        need_flatten_grads.append(g)
+
+    shape = [np.prod(p.shape) for p in need_flatten_params]
+
+    flatten_param = optimizer.helper.create_global_variable(
+        name="flatten_param",
+        persistable=True,
+        dtype=need_flatten_params[0].dtype,
+        shape=[np.sum(shape)],
+        belong_to_optimizer=True,
+    )
+
+    flatten_grad = optimizer.helper.create_global_variable(
+        name="flatten_grad",
+        persistable=True,
+        dtype=need_flatten_grads[0].dtype,
+        shape=[np.sum(shape)],
+        belong_to_optimizer=True,
+    )
+
+    flatten_param.stop_gradient = False
+    # In the final state of the dynamic graph, the `coalesce_tensor` op
+    # does not support passing the output as an input into the op in
+    # temporary, so _legacy_C_ops is temporarily used here.
+    # `use_align` is set to false, which is different from the behavior
+    # under static graphs. `use_align` can be set to true after calling
+    # the coalesce_tensor op of the final state (_C_ops).
+    paddle._legacy_C_ops.coalesce_tensor(
+        need_flatten_params,
+        need_flatten_params,
+        flatten_param,
+        "copy_data",
+        True,
+        "use_align",
+        False,
+        "dtype",
+        need_flatten_params[0].dtype,
+    )
+
+    paddle._legacy_C_ops.coalesce_tensor(
+        need_flatten_grads,
+        need_flatten_grads,
+        flatten_grad,
+        "copy_data",
+        True,
+        "use_align",
+        False,
+        "dtype",
+        need_flatten_grads[0].dtype,
+    )
+    return [(flatten_param, flatten_grad)]

paddlenlp/trainer/trainer.py

@@ -594,6 +594,11 @@ def train(
         self._total_loss_scalar = 0.0
         self._globalstep_last_logged = self.state.global_step
 
+        if self.args.device == "npu" and self.args.flatten_param_grads:
+            from .plugins.npu_plugin import npu_accelerate_plugin
+
+            npu_accelerate_plugin(self.optimizer)
+
         for epoch in range(epochs_trained, num_train_epochs):
             if isinstance(train_dataloader, paddle.io.DataLoader) and isinstance(
                 train_dataloader.batch_sampler, DistributedBatchSampler

paddlenlp/trainer/training_args.py

@@ -263,6 +263,8 @@ class TrainingArguments:
             The path to a folder with a valid checkpoint for your model. This argument is not directly used by
             [`Trainer`], it's intended to be used by your training/evaluation scripts instead. See the [example
             scripts](https://github.com/PaddlePaddle/PaddleNLP/tree/develop/examples) for more details.
+        flatten_param_grads (`bool`, *optional*):
+            Whether use flatten_param_grads method in optimizer, only used on NPU devices. Default is `False`.
     """
 
     output_dir: str = field(
@@ -496,6 +498,10 @@ class TrainingArguments:
     skip_memory_metrics: bool = field(
         default=True, metadata={"help": "Whether or not to skip adding of memory profiler reports to metrics."}
     )
+    flatten_param_grads: Optional[bool] = field(
+        default=False,
+        metadata={"help": "Whether use flatten_param_grads method in optimizer, only used on NPU devices."},
+    )
 
     def __post_init__(self):
         env_local_rank = int(os.environ.get("PADDLE_RANK_IN_NODE", -1))
@@ -624,6 +630,9 @@ def __post_init__(self):
                 "Both warmup_ratio and warmup_steps given, warmup_steps will override any effect of warmup_ratio during training"
             )
 
+        if self.flatten_param_grads and self.device != "npu":
+            raise ValueError("flatten_param_grads can only be used on npu devices in temporary.")
+
     def __str__(self):
         self_as_dict = asdict(self)
         self_as_dict = {k: f"<{k.upper()}>" if k.endswith("_token") else v for k, v in self_as_dict.items()}