support excluded_layers for amp.decorate (#52871) (#54022)

python/paddle/amp/auto_cast.py

@@ -199,47 +199,95 @@ def _is_gpu_bfloat16_supported():
     return prop[0] >= 8 and cuda_version_check
 
 
+def need_keep_fp32(layer, dtype):
+    need_keep_fp32 = False
+    # Highest prority. Because all the layers except BN will use bfloat16 params in bfoat16 training,
+    # here we provide a option to keep fp32 param.
+    if not layer._cast_to_low_precison:
+        need_keep_fp32 = True
+    # The BN layers will keep fp32
+    elif isinstance(
+        layer,
+        (
+            paddle.nn.BatchNorm,
+            paddle.nn.BatchNorm1D,
+            paddle.nn.BatchNorm2D,
+            paddle.nn.BatchNorm3D,
+            paddle.nn.SyncBatchNorm,
+        ),
+    ):
+        need_keep_fp32 = True
+    # layer._dtype is used to set params dtype. BF16 will use bf16 params.
+    elif (layer._dtype == 'float16') or (
+        (dtype == 'float16')
+        and isinstance(
+            layer,
+            (
+                paddle.nn.LayerNorm,
+                paddle.nn.InstanceNorm1D,
+                paddle.nn.InstanceNorm2D,
+                paddle.nn.InstanceNorm3D,
+            ),
+        )
+    ):
+        need_keep_fp32 = True
+
+    return need_keep_fp32
+
+
+def set_excluded_layers(models, excluded_layers):
+    excluded_layers_instances = []
+    excluded_layers_types = []
+    error_message = "excluded_layers must be either a nn.Layer instance/type or a list of nn.Layer instances/types."
+    if excluded_layers is None:
+        excluded_layers = []
+    elif isinstance(excluded_layers, paddle.nn.Layer):
+        excluded_layers_instances = [excluded_layers]
+    elif isinstance(excluded_layers, type) and issubclass(
+        excluded_layers, paddle.nn.Layer
+    ):
+        excluded_layers_types = [excluded_layers]
+    elif isinstance(excluded_layers, list):
+        for item in excluded_layers:
+            if isinstance(item, paddle.nn.Layer):
+                excluded_layers_instances.append(item)
+            elif issubclass(item, paddle.nn.Layer):
+                excluded_layers_types.append(item)
+            else:
+                raise TypeError(error_message)
+    else:
+        raise TypeError(error_message)
+
+    for idx in range(len(excluded_layers_instances)):
+        for layer in excluded_layers_instances[idx].sublayers(
+            include_self=True
+        ):
+            layer._cast_to_low_precison = False
+    for idx in range(len(models)):
+        for layer in models[idx].sublayers(include_self=True):
+            if type(layer) in excluded_layers_types:
+                layer._cast_to_low_precison = False
+
+
 @dygraph_only
-def pure_fp16_initialize(models):
+def amp_initialize(models, dtype, excluded_layers):
+    set_excluded_layers(models, excluded_layers)
     for idx in range(len(models)):
         for layer in models[idx].sublayers(include_self=True):
-            layer._casted_by_pure_fp16 = True
-            if (layer._dtype == 'float16') or isinstance(
-                layer,
-                (
-                    paddle.nn.BatchNorm,
-                    paddle.nn.BatchNorm1D,
-                    paddle.nn.BatchNorm2D,
-                    paddle.nn.BatchNorm3D,
-                    paddle.nn.LayerNorm,
-                    paddle.nn.SyncBatchNorm,
-                    paddle.nn.InstanceNorm1D,
-                    paddle.nn.InstanceNorm2D,
-                    paddle.nn.InstanceNorm3D,
-                ),
-            ):
+            if need_keep_fp32(layer, dtype):
                 continue
-            if isinstance(
+            if dtype == "float16" and isinstance(
                 layer,
                 (
                     paddle.incubate.nn.FusedFeedForward,
                     paddle.incubate.nn.FusedMultiHeadAttention,
                 ),
             ):
-                layer._amp_decorate(dtype='float16')
+                layer._amp_decorate(dtype=dtype)
                 continue
-            layer._to_impl(
-                dtype='float16', include_sublayers=False, floating_only=True
-            )
-    return models
 
-
-@dygraph_only
-def pure_bf16_initialize(models):
-    for idx in range(len(models)):
-        for layer in models[idx].sublayers(include_self=True):
             layer._to_impl(
-                dtype='bfloat16', include_sublayers=False, floating_only=True
+                dtype=dtype, include_sublayers=False, floating_only=True
             )
     return models
 
@@ -522,6 +570,7 @@ def amp_decorate(
     master_weight=None,
     save_dtype=None,
     master_grad=False,
+    excluded_layers=None,
 ):
     """
     Decorate models and optimizers for auto-mixed-precision. When level is O1(amp), the decorate will do nothing.
@@ -590,6 +639,8 @@ def amp_decorate(
         raise ValueError(
             "level should be O1 or O2, O1 represent AMP train mode, O2 represent Pure fp16 train mode."
         )
+    if not (dtype in ['float16', 'bfloat16']):
+        raise ValueError("dtype only support float16 or bfloat16.")
 
     if level == 'O1':
         if optimizers is None:
@@ -609,12 +660,9 @@ def amp_decorate(
         raise TypeError(
             "models must be either a single model or a list of models."
         )
-    if dtype == 'float16':
-        models = pure_fp16_initialize(models=models)
-    elif dtype == 'bfloat16':
-        models = pure_bf16_initialize(models=models)
-    else:
-        raise TypeError("dtype only support float16 or bfloat16.")
+
+    # initialize parameters of the model.
+    amp_initialize(models=models, dtype=dtype, excluded_layers=excluded_layers)
 
     if optimizers is not None:
         # check optimizers
@@ -741,6 +789,7 @@ def decorate(
     master_weight=None,
     save_dtype=None,
     master_grad=False,
+    excluded_layers=None,
 ):
     """
     Decorate models and optimizers for auto-mixed-precision. When level is O1(amp), the decorate will do nothing.
@@ -757,8 +806,10 @@ def decorate(
         master_weight(bool, optinal): For level='O2', whether to use multi-precision during weight updating. If master_weight is None, in O2 level optimizer will use multi-precision. Default is None.
         save_dtype(float, optional): The save model parameter dtype when use `paddle.save` or `paddle.jit.save`,it should be float16, bfloat16, float32, float64 or None.
              The save_dtype will not change model parameters dtype, it just change the state_dict dtype. When save_dtype is None, the save dtype is same as model dtype. Default is None.
-        master_grad(bool, optional): For level='O2', whether to use FP32 weight gradients for calculations such as gradient clipping, weight decay, and weight updates. If it is enabled, the weight
-             gradients will be FP32 dtype after the backpropagation. Default is False.
+        master_grad(bool, optional): For level='O2', whether to use float32 weight gradients for calculations such as gradient clipping, weight decay, and weight updates. If master_grad is enabled, the weight
+             gradients will be float32 dtype after the backpropagation. Default is False, there is only float16 weight gradients.
+        excluded_layers(Layer|list of Layer, optional): Specify the layers not to be decorated. The weights of these layers will always keep float32 when level is O2. `excluded_layers` can be specified as
+             an Layer instance/type or a list of Layer instances/types. Default is None, the weights of the whole model will be casted to float16 or bfloat16.
 
     Examples:
 
@@ -808,5 +859,12 @@ def decorate(
             print(output.dtype) # FP16
     """
     return amp_decorate(
-        models, optimizers, level, dtype, master_weight, save_dtype, master_grad
+        models,
+        optimizers,
+        level,
+        dtype,
+        master_weight,
+        save_dtype,
+        master_grad,
+        excluded_layers,
     )

python/paddle/nn/layer/layers.py

@@ -401,7 +401,8 @@ def __init__(self, name_scope=None, dtype="float32"):
         self._forward_pre_hooks = collections.OrderedDict()
         self._forward_post_hooks = collections.OrderedDict()
 
-        self._casted_by_pure_fp16 = False
+        # only used in AMP Training
+        self._cast_to_low_precison = True
 
         self._state_dict_hooks = collections.OrderedDict()
         # Records orignal functions after @to_static to support to rollback

test/amp/test_amp_decorate.py

@@ -0,0 +1,166 @@
+# Copyright (c) 2023 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 unittest
+
+import paddle
+import paddle.nn.functional as F
+
+
+class ConvBNLayer(paddle.nn.Layer):
+    def __init__(
+        self,
+        num_channels,
+        num_filters,
+        filter_size,
+        stride=1,
+        groups=1,
+        act=None,
+    ):
+        super().__init__()
+
+        self._conv = paddle.nn.Conv2D(
+            in_channels=num_channels,
+            out_channels=num_filters,
+            kernel_size=filter_size,
+            stride=stride,
+            padding=(filter_size - 1) // 2,
+            groups=groups,
+            bias_attr=None,
+        )
+
+        self._batch_norm = paddle.nn.BatchNorm(num_filters, act=act)
+
+    def forward(self, inputs):
+        y = self._conv(inputs)
+        y = self._batch_norm(y)
+
+        return y
+
+
+class Model(paddle.nn.Layer):
+    def __init__(
+        self, input_channel, hidden_size, fp16_conv=True, fp16_linear=True
+    ):
+        super().__init__()
+        self.conv = ConvBNLayer(input_channel, 8, 3)
+        self.linear = paddle.nn.Linear(8, hidden_size)
+        self.layernorm = paddle.nn.Sequential(
+            paddle.nn.LayerNorm(hidden_size),
+            paddle.nn.LayerNorm(hidden_size),
+        )
+        self.fp16_conv = fp16_conv
+        self.fp16_linear = fp16_linear
+
+    def forward(self, inputs):
+        with paddle.amp.auto_cast(enable=self.fp16_conv):
+            if not self.fp16_conv:
+                inputs = inputs.astype('float32')
+            x = self.conv(inputs)
+        with paddle.amp.auto_cast(enable=self.fp16_linear):
+            if not self.fp16_linear:
+                x = x.astype('float32')
+            x = self.linear(x)
+        x = F.relu(x)
+        x = self.layernorm(x)
+        return x
+
+
+class TestAMPDecorate(unittest.TestCase):
+    def check_results(self, fp32_layers=[], fp16_layers=[]):
+        for idx in range(len(fp32_layers)):
+            for layer in fp32_layers[idx].sublayers(include_self=False):
+                self.assertEqual(layer.weight.dtype, paddle.float32)
+                self.assertEqual(layer.bias.dtype, paddle.float32)
+
+        for idx in range(len(fp16_layers)):
+            for layer in fp16_layers[idx].sublayers(include_self=False):
+                self.assertEqual(layer.weight.dtype, paddle.float16)
+                self.assertEqual(layer.bias.dtype, paddle.float16)
+
+    def test_excluded_layers(self):
+        if not paddle.amp.is_float16_supported():
+            return
+        model = Model(4, 8, fp16_conv=False)
+        model = paddle.amp.decorate(
+            models=model,
+            level='O2',
+            dtype='float16',
+            excluded_layers=model.conv,
+        )
+        with paddle.amp.auto_cast(level='O2'):
+            out = model(paddle.rand(shape=[2, 4, 8, 8], dtype='float32'))
+        self.check_results(
+            fp32_layers=[model.conv, model.layernorm],
+            fp16_layers=[model.linear],
+        )
+
+    def test_excluded_layers_attr_list(self):
+        if not paddle.amp.is_float16_supported():
+            return
+        model = Model(4, 8, fp16_conv=False, fp16_linear=False)
+        model = paddle.amp.decorate(
+            models=model,
+            level='O2',
+            dtype='float16',
+            excluded_layers=[model.conv, model.linear],
+        )
+
+        with paddle.amp.auto_cast(level='O2'):
+            out = model(paddle.rand(shape=[2, 4, 8, 8], dtype='float32'))
+
+        self.check_results(
+            fp32_layers=[model.conv, model.linear, model.layernorm]
+        )
+
+    def test_excluded_layers_attr_types(self):
+        if not paddle.amp.is_float16_supported():
+            return
+        model = Model(4, 8)
+        model = paddle.amp.decorate(
+            models=model,
+            level='O2',
+            dtype='float16',
+            excluded_layers=[paddle.nn.Conv2D, model.linear],
+        )
+
+        with paddle.amp.auto_cast(level='O2'):
+            out = model(paddle.rand(shape=[2, 4, 8, 8], dtype='float16'))
+
+        self.check_results(
+            fp32_layers=[model.conv, model.linear, model.layernorm]
+        )
+
+    def test_excluded_layers_attr_none(self):
+        if not paddle.amp.is_float16_supported():
+            return
+        model = Model(4, 8)
+        model = paddle.amp.decorate(
+            models=model,
+            level='O2',
+            dtype='float16',
+            excluded_layers=None,
+        )
+
+        with paddle.amp.auto_cast(level='O2'):
+            out = model(paddle.rand(shape=[2, 4, 8, 8], dtype='float16'))
+
+        self.check_results(
+            fp32_layers=[model.layernorm, model.conv._batch_norm],
+            fp16_layers=[model.conv._conv, model.linear],
+        )
+
+
+if __name__ == '__main__':
+    unittest.main()