[shardformer] Add layernorm

colossalai/shardformer/layer/__init__.py

@@ -1,10 +1,11 @@
 from .dropout import Dropout1D
 from .embedding import Embedding1D, VocabParallelEmbedding1D
+from .layernorm import LayerNorm1D
 from .linear import Linear1D_Col, Linear1D_Row
 from .linear_conv import LinearConv1D_Col, LinearConv1D_Row
 from .loss import cross_entropy_1d
 
 __all__ = [
     "Embedding1D", "VocabParallelEmbedding1D", "Linear1D_Col", "Linear1D_Row", "LinearConv1D_Col", "LinearConv1D_Row",
-    "Dropout1D", "cross_entropy_1d"
+    "Dropout1D", "cross_entropy_1d", 'LayerNorm1D'
 ]

colossalai/shardformer/layer/layernorm.py

@@ -0,0 +1,89 @@
+#!/usr/bin/env python
+# -*- encoding: utf-8 -*-
+
+from typing import List, Union
+
+import torch
+import torch.nn as nn
+from torch.distributed import ProcessGroup
+
+from colossalai.kernel import LayerNorm
+from colossalai.nn import init as init
+
+from .parallel_module import ParallelModule
+
+__all__ = ['LayerNorm1D']
+
+Fast_LN = None
+try:
+    from apex.contrib.layer_norm.layer_norm import FastLayerNorm
+    Fast_LN = FastLayerNorm
+except ImportError:
+    pass
+
+
+class LayerNorm1D(ParallelModule):
+    r"""
+    Layer Normalization for colossalai
+
+    Args:
+        normalized_shape (int): input shape from an expected input of size.
+            :math:`[* \times \text{normalized_shape}[0] \times \text{normalized_shape}[1]
+            \times \ldots \times \text{normalized_shape}[-1]]`
+            If a single integer is used, it is treated as a singleton list, and this module will
+            normalize over the last dimension which is expected to be of that specific size.
+        eps (float): a value added to the denominator for numerical stability, defaults to 1e-05.
+        bias (bool, optional): Whether to add a bias, defaults to ``True``.
+        dtype (:class:`torch.dtype`, optional): The dtype of parameters, defaults to None.
+    """
+
+    _fast_ln_supported_sizes = [
+        1024, 1536, 2048, 2304, 3072, 3840, 4096, 5120, 6144, 8192, 10240, 12288, 12800, 15360, 16384, 18432, 20480,
+        24576, 25600, 30720, 32768, 40960, 49152, 65536
+    ]
+
+    def __init__(self,
+                 normalized_shape: int,
+                 eps: int = 1e-05,
+                 bias: bool = True,
+                 dtype: torch.dtype = None,
+                 device: torch.device = None):
+        super().__init__()
+        if Fast_LN is not None and normalized_shape in self._fast_ln_supported_sizes:
+            norm = Fast_LN(normalized_shape, eps=eps).to(dtype)
+        else:
+            norm = None
+            try:
+                from apex.normalization import FusedLayerNorm
+                norm = FusedLayerNorm(normalized_shape, eps=eps).to(dtype)
+            except ImportError:
+                norm = LayerNorm(normalized_shape, eps=eps, device=device, dtype=dtype)
+        self.norm = norm
+
+    @staticmethod
+    def from_native_module(module: nn.LayerNorm, process_group: Union[ProcessGroup, List[ProcessGroup]], *args,
+                           **kwargs) -> ParallelModule:
+        r"""
+        Convert a native pytorch layer norm module to colossalai layer norm module
+        """
+        normalized_shape = module.normalized_shape
+        eps = module.eps
+        bias = module.bias is not None
+        dtype = module.weight.dtype
+        device = module.weight.device
+
+        # ensure only one process group is passed
+        if isinstance(process_group, (list, tuple)):
+            assert len(process_group) == 1, \
+                f'Expected only one process group, got {len(process_group)}.'
+            process_group = process_group[0]
+
+        # create layer norm
+        layer_norm = LayerNorm1D(normalized_shape, eps=eps, bias=bias, device=device, dtype=dtype).norm
+
+        with torch.no_grad():
+            # copy weight and bias
+            layer_norm.weight.copy_(module.weight)
+            if bias:
+                layer_norm.bias.copy_(module.bias)
+        return layer_norm

colossalai/shardformer/policies/bert.py

@@ -1,8 +1,14 @@
 import torch.nn as nn
-from transformers.models.bert.modeling_bert import BertEmbeddings, BertLayer, BertLMPredictionHead
+from transformers.models.bert.modeling_bert import (
+    BertEmbeddings,
+    BertForMultipleChoice,
+    BertForSequenceClassification,
+    BertForTokenClassification,
+    BertLayer,
+    BertLMPredictionHead,
+)
 
 import colossalai.shardformer.layer as col_nn
-from colossalai.shardformer.layer.dropout import Dropout1D
 
 from .._utils import getattr_, setattr_
 from .basepolicy import ModulePolicyDescription, Policy, SubModuleReplacementDescription
@@ -24,7 +30,7 @@ def preprocess(self):
         return self.model
 
     def module_policy(self):
-        return {
+        base_policy = {
             BertLayer:
                 ModulePolicyDescription(
                     attribute_replacement={
@@ -53,10 +59,18 @@ def module_policy(self):
                             suffix="attention.self.value",
                             target_module=col_nn.Linear1D_Col,
                         ),
+                        SubModuleReplacementDescription(
+                            suffix="attention.self.dropout",
+                            target_module=col_nn.Dropout1D,
+                        ),
                         SubModuleReplacementDescription(
                             suffix="attention.output.dense",
                             target_module=col_nn.Linear1D_Row,
                         ),
+                        SubModuleReplacementDescription(
+                            suffix="attention.output.dropout",
+                            target_module=col_nn.Dropout1D,
+                        ),
                         SubModuleReplacementDescription(
                             suffix="intermediate.dense",
                             target_module=col_nn.Linear1D_Col,
@@ -66,12 +80,8 @@ def module_policy(self):
                             target_module=col_nn.Linear1D_Row,
                         ),
                         SubModuleReplacementDescription(
-                            suffix="attention.self.dropout",
-                            target_module=Dropout1D,
-                        ),
-                        SubModuleReplacementDescription(
-                            suffix="attention.output.dropout",
-                            target_module=Dropout1D,
+                            suffix="output.dropout",
+                            target_module=col_nn.Dropout1D,
                         )
                     ]),
             BertEmbeddings:
@@ -81,10 +91,32 @@ def module_policy(self):
                                             SubModuleReplacementDescription(
                                                 suffix="word_embeddings",
                                                 target_module=col_nn.VocabParallelEmbedding1D,
+                                            ),
+                                            SubModuleReplacementDescription(
+                                                suffix="dropout",
+                                                target_module=col_nn.Dropout1D,
                                             )
                                         ])
         }
 
+        if self.shard_config.fused_layernorm:
+            base_policy[BertLayer].sub_module_replacement.append(
+                SubModuleReplacementDescription(
+                    suffix="attention.output.LayerNorm",
+                    target_module=col_nn.LayerNorm1D,
+                ))
+            base_policy[BertLayer].sub_module_replacement.append(
+                SubModuleReplacementDescription(
+                    suffix="output.LayerNorm",
+                    target_module=col_nn.LayerNorm1D,
+                ))
+            base_policy[BertEmbeddings].sub_module_replacement.append(
+                SubModuleReplacementDescription(
+                    suffix="LayerNorm",
+                    target_module=col_nn.LayerNorm1D,
+                ),)
+        return base_policy
+
     def new_model_class(self):
         # do nothing
         return self.model
@@ -115,9 +147,15 @@ def module_policy(self):
                                         sub_module_replacement=[
                                             SubModuleReplacementDescription(suffix="decoder",
                                                                             target_module=col_nn.Linear1D_Col,
-                                                                            kwargs={"gather_output": True})
+                                                                            kwargs={"gather_output": True}),
                                         ])
         }
+        if self.shard_config.fused_layernorm:
+            addon_module[BertLMPredictionHead].sub_module_replacement.append(
+                SubModuleReplacementDescription(
+                    suffix="transform.LayerNorm",
+                    target_module=col_nn.LayerNorm1D,
+                ))
         module_policy.update(addon_module)
         return module_policy
 
@@ -146,9 +184,15 @@ def module_policy(self):
                                         sub_module_replacement=[
                                             SubModuleReplacementDescription(suffix="decoder",
                                                                             target_module=col_nn.Linear1D_Col,
-                                                                            kwargs={"gather_output": True})
+                                                                            kwargs={"gather_output": True}),
                                         ])
         }
+        if self.shard_config.fused_layernorm:
+            addon_module[BertLMPredictionHead].sub_module_replacement.append(
+                SubModuleReplacementDescription(
+                    suffix="transform.LayerNorm",
+                    target_module=col_nn.LayerNorm1D,
+                ))
         module_policy.update(addon_module)
         return module_policy
 
@@ -177,9 +221,15 @@ def module_policy(self):
                                         sub_module_replacement=[
                                             SubModuleReplacementDescription(suffix="decoder",
                                                                             target_module=col_nn.Linear1D_Col,
-                                                                            kwargs={"gather_output": True})
+                                                                            kwargs={"gather_output": True}),
                                         ])
         }
+        if self.shard_config.fused_layernorm:
+            addon_module[BertLMPredictionHead].sub_module_replacement.append(
+                SubModuleReplacementDescription(
+                    suffix="transform.LayerNorm",
+                    target_module=col_nn.LayerNorm1D,
+                ))
         module_policy.update(addon_module)
         return module_policy
 
@@ -199,13 +249,45 @@ class BertForSequenceClassificationPolicy(BertPolicy):
     def __init__(self) -> None:
         super().__init__()
 
+    def module_policy(self):
+        module_policy = super().module_policy()
+        addon_module = {
+            BertForSequenceClassification:
+                ModulePolicyDescription(attribute_replacement={},
+                                        param_replacement=[],
+                                        sub_module_replacement=[
+                                            SubModuleReplacementDescription(
+                                                suffix="dropout",
+                                                target_module=col_nn.Dropout1D,
+                                            )
+                                        ])
+        }
+        module_policy.update(addon_module)
+        return module_policy
+
 
 # BertForTokenClassification
 class BertForTokenClassificationPolicy(BertPolicy):
 
     def __init__(self) -> None:
         super().__init__()
 
+    def module_policy(self):
+        module_policy = super().module_policy()
+        addon_module = {
+            BertForTokenClassification:
+                ModulePolicyDescription(attribute_replacement={},
+                                        param_replacement=[],
+                                        sub_module_replacement=[
+                                            SubModuleReplacementDescription(
+                                                suffix="dropout",
+                                                target_module=col_nn.Dropout1D,
+                                            )
+                                        ])
+        }
+        module_policy.update(addon_module)
+        return module_policy
+
 
 # BertForNextSentencePrediction
 class BertForNextSentencePredictionPolicy(BertPolicy):
@@ -219,3 +301,19 @@ class BertForMultipleChoicePolicy(BertPolicy):
 
     def __init__(self) -> None:
         super().__init__()
+
+    def module_policy(self):
+        module_policy = super().module_policy()
+        addon_module = {
+            BertForMultipleChoice:
+                ModulePolicyDescription(attribute_replacement={},
+                                        param_replacement=[],
+                                        sub_module_replacement=[
+                                            SubModuleReplacementDescription(
+                                                suffix="dropout",
+                                                target_module=col_nn.Dropout1D,
+                                            )
+                                        ])
+        }
+        module_policy.update(addon_module)
+        return module_policy

colossalai/shardformer/shard/shard_config.py

@@ -11,15 +11,17 @@ class ShardConfig:
     The config for sharding the huggingface model
 
     Args:
-        data_parallel_size (int): The size of data parallel
         tensor_parallel_size (int): The size of tensor parallel
+        use_mixedfusedLN (bool): Whether to use the `MixedFusedLayerNorm`
+        data_parallel_size (int): The size of data parallel
         pipeline_parallel_size (int): The size of pipeline parallel
         tensor_parallel_mode (List): The mode of tensor parallel, choose from `['1d','2d','2.5d','3d']
         inference_only (bool): Whether to use the inference only mode, when setting to `True`, the model
             will not calculate the loss and just return the output.
         gather_output (bool): Whether to gather the output of the model of the last layer
     """
     tensor_parallel_size: int
+    fused_layernorm: bool = False
 
     # TODO: add support for tensor parallel
     # pipeline_parallel_size: int

tests/test_shardformer/test_layer/test_layernorm.py

@@ -0,0 +1,45 @@
+import torch
+import torch.distributed as dist
+import torch.nn as nn
+from torch.testing import assert_close
+
+import colossalai
+from colossalai.shardformer.layer import LayerNorm1D
+from colossalai.testing import rerun_if_address_is_in_use, spawn
+
+
+def check_layernorm_1d():
+    norm = nn.LayerNorm(128, 0.00001).cuda()
+    norm1d = LayerNorm1D.from_native_module(norm, process_group=None)
+
+    assert norm1d.weight.shape == torch.Size([128])
+
+    # ensure state dict is reversibly loadable
+    norm.load_state_dict(norm1d.state_dict())
+    norm1d.load_state_dict(norm.state_dict())
+
+    # check computation correctness
+    x = torch.rand(4, 128).cuda()
+    out = norm(x)
+    gather_out = norm1d(x)
+    assert_close(out, gather_out)
+
+    # check backward correctness
+    out.sum().backward()
+    gather_out.sum().backward()
+
+    assert_close(norm.weight.grad, norm1d.weight.grad)
+
+
+def run_dist(rank, world_size, port):
+    colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
+    check_layernorm_1d()
+
+
+@rerun_if_address_is_in_use()
+def test_layernorm_1d():
+    spawn(run_dist, nprocs=2)
+
+
+if __name__ == '__main__':
+    test_layernorm_1d()

tests/test_shardformer/test_layer/test_linearconv_1d.py

@@ -77,7 +77,7 @@ def check_linear_conv_1d_col():
     assert_close(target_grad, linear_conv_col.weight.grad)
 
 
-def check_linear_1d_row():
+def check_linear_conv_1d_row():
     linear = Conv1D(192, 48).cuda()
     linear_row = LinearConv1D_Row.from_native_module(linear, process_group=None, parallel_input=False)
 
@@ -103,7 +103,7 @@ def check_linear_1d_row():
 def run_dist(rank, world_size, port):
     colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
     check_linear_conv_1d_col()
-    check_linear_1d_row()
+    check_linear_conv_1d_row()
 
 
 @rerun_if_address_is_in_use()