[shardformer] support pipeline base vit model (#4284)

* Feature/vit support (#4182)

* [shardformer] added tests

* [shardformer] vit test finish and support

* fix attention dropout

* support base vit pipeline

* support vit downstream model

* fix vit shard test

* modify hidden states return type

---------

Co-authored-by: Kun Lin <81014421+klhhhhh@users.noreply.github.com>

colossalai/shardformer/modeling/vit.py

@@ -0,0 +1,337 @@
+import logging
+from typing import Dict, List, Optional, Set, Tuple, Union
+
+import torch
+from transformers.models.vit.modeling_vit import BaseModelOutput, ViTEncoder
+
+from colossalai.pipeline.stage_manager import PipelineStageManager
+
+
+def _encoder_forward(
+    encoder: ViTEncoder,
+    start_idx: int,
+    end_idx: int,
+    hidden_states: torch.Tensor,
+    head_mask: Optional[torch.Tensor] = None,
+    return_dict: bool = True,
+    stage_manager: PipelineStageManager = None,
+) -> Union[tuple, BaseModelOutput]:
+
+    for i in range(start_idx, end_idx):
+        layer_module = encoder.layer[i]
+
+        layer_head_mask = head_mask[i] if head_mask is not None else None
+
+        if encoder.gradient_checkpointing and encoder.training:
+
+            def create_custom_forward(module):
+
+                def custom_forward(*inputs):
+                    return module(*inputs, False)
+
+                return custom_forward
+
+            layer_outputs = torch.utils.checkpoint.checkpoint(
+                create_custom_forward(layer_module),
+                hidden_states,
+                layer_head_mask,
+            )
+        else:
+            layer_outputs = layer_module(hidden_states, layer_head_mask, False)
+
+        hidden_states = layer_outputs[0]
+    if not stage_manager.is_last_stage():
+        return hidden_states
+    else:
+        if not return_dict:
+            return tuple(hidden_states)
+        return BaseModelOutput(
+            last_hidden_state=hidden_states,
+            hidden_states=None,
+            attentions=None,
+        )
+
+
+def ViTModel_pipeline_forward(stage_manager: PipelineStageManager, stage_index: List[int]):
+
+    from transformers.models.vit.modeling_vit import BaseModelOutputWithPooling
+
+    def pp_forward(
+        self,
+        pixel_values: Optional[torch.Tensor] = None,
+        bool_masked_pos: Optional[torch.BoolTensor] = None,
+        head_mask: Optional[torch.Tensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        interpolate_pos_encoding: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        hidden_states: Optional[torch.FloatTensor] = None,
+    ) -> Union[Tuple, BaseModelOutputWithPooling]:
+        r"""
+            bool_masked_pos (`torch.BoolTensor` of shape `(batch_size, num_patches)`, *optional*):
+                Boolean masked positions. Indicates which patches are masked (1) and which aren't (0).
+            """
+
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (output_hidden_states
+                                if output_hidden_states is not None else self.config.output_hidden_states)
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        if output_attentions is not None:
+            logging.warning('Non-empty output_attentions is not supported for pipeline models at the moment.')
+            output_attentions = None
+        if output_hidden_states is not None:
+            logging.warning('Non-empty output_hidden_states is not supported for pipeline models at the moment.')
+            output_hidden_states = None
+
+        # Prepare head mask if needed
+        # 1.0 in head_mask indicate we keep the head
+        # attention_probs has shape bsz x n_heads x N x N
+        # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
+        # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
+        head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
+
+        if stage_manager.is_first_stage():
+            if pixel_values is None:
+                raise ValueError("You have to specify pixel_values")
+
+            # TODO: maybe have a cleaner way to cast the input (from `ImageProcessor` side?)
+            expected_dtype = self.embeddings.patch_embeddings.projection.weight.dtype
+            if pixel_values.dtype != expected_dtype:
+                pixel_values = pixel_values.to(expected_dtype)
+
+            embedding_output = self.embeddings(pixel_values,
+                                               bool_masked_pos=bool_masked_pos,
+                                               interpolate_pos_encoding=interpolate_pos_encoding)
+        else:
+            assert hidden_states is not None, f"Current stage is {stage_manager.stage}, hidden_states should not be None"
+
+        # Go through encoder
+        if not stage_manager.is_last_stage():
+            hidden_states = _encoder_forward(
+                encoder=self.encoder,
+                start_idx=stage_index[0],
+                end_idx=stage_index[1],
+                hidden_states=embedding_output,
+                head_mask=head_mask,
+                return_dict=return_dict,
+                stage_manager=stage_manager,
+            )
+            return {'hidden_states': hidden_states}
+        else:
+            encoder_outputs = _encoder_forward(
+                encoder=self.encoder,
+                start_idx=stage_index[0],
+                end_idx=stage_index[1],
+                hidden_states=hidden_states,
+                head_mask=head_mask,
+                return_dict=return_dict,
+                stage_manager=stage_manager,
+            )
+
+        # Go through rest layers
+        sequence_output = encoder_outputs[0]
+        sequence_output = self.layernorm(sequence_output)
+        pooled_output = self.pooler(sequence_output) if self.pooler is not None else None
+
+        if not return_dict:
+            head_outputs = (sequence_output, pooled_output) if pooled_output is not None else (sequence_output,)
+            return head_outputs + encoder_outputs[1:]
+
+        return BaseModelOutputWithPooling(
+            last_hidden_state=sequence_output,
+            pooler_output=pooled_output,
+            hidden_states=encoder_outputs.hidden_states,
+            attentions=encoder_outputs.attentions,
+        )
+
+    return pp_forward
+
+
+def ViTForImageClassification_pipeline_forward(stage_manager: PipelineStageManager, stage_index: List[int]):
+
+    from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
+    from transformers.models.vit.modeling_vit import ImageClassifierOutput
+
+    def pp_forward(
+        self,
+        pixel_values: Optional[torch.Tensor] = None,
+        head_mask: Optional[torch.Tensor] = None,
+        labels: Optional[torch.Tensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        interpolate_pos_encoding: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        hidden_states: Optional[torch.FloatTensor] = None,
+    ) -> Union[tuple, ImageClassifierOutput]:
+        r"""
+        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+            Labels for computing the image classification/regression loss. Indices should be in `[0, ...,
+            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+        """
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        if not stage_manager.is_first_stage():
+            assert hidden_states is not None, f"Current stage is {stage_manager.stage}, hidden_states should not be None"
+
+        outputs = self.vit(
+            pixel_values,
+            head_mask=head_mask,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            interpolate_pos_encoding=interpolate_pos_encoding,
+            return_dict=return_dict,
+            hidden_states=hidden_states,
+        )
+
+        # not last stage, return hidden_states
+        if not stage_manager.is_last_stage():
+            return outputs
+        else:
+            sequence_output = outputs[0]
+
+        # last stage
+        logits = self.classifier(sequence_output[:, 0, :])
+
+        loss = None
+        if labels is not None:
+            # move labels to correct device to enable model parallelism
+            labels = labels.to(logits.device)
+            if self.config.problem_type is None:
+                if self.num_labels == 1:
+                    self.config.problem_type = "regression"
+                elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
+                    self.config.problem_type = "single_label_classification"
+                else:
+                    self.config.problem_type = "multi_label_classification"
+
+            if self.config.problem_type == "regression":
+                loss_fct = MSELoss()
+                if self.num_labels == 1:
+                    loss = loss_fct(logits.squeeze(), labels.squeeze())
+                else:
+                    loss = loss_fct(logits, labels)
+            elif self.config.problem_type == "single_label_classification":
+                loss_fct = CrossEntropyLoss()
+                loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+            elif self.config.problem_type == "multi_label_classification":
+                loss_fct = BCEWithLogitsLoss()
+                loss = loss_fct(logits, labels)
+
+        if not return_dict:
+            output = (logits,) + outputs[1:]
+            return ((loss,) + output) if loss is not None else output
+
+        return ImageClassifierOutput(
+            loss=loss,
+            logits=logits,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+    return pp_forward
+
+
+def ViTForMaskedImageModeling_pipeline_forward(stage_manager: PipelineStageManager, stage_index: List[int]):
+
+    import math
+
+    import torch.nn as nn
+    from transformers.models.vit.modeling_vit import ImageClassifierOutput, MaskedImageModelingOutput
+
+    def pp_forward(
+        self,
+        pixel_values: Optional[torch.Tensor] = None,
+        bool_masked_pos: Optional[torch.BoolTensor] = None,
+        head_mask: Optional[torch.Tensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        interpolate_pos_encoding: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        hidden_states: Optional[torch.FloatTensor] = None,
+    ) -> Union[tuple, ImageClassifierOutput]:
+        r"""
+        bool_masked_pos (`torch.BoolTensor` of shape `(batch_size, num_patches)`):
+            Boolean masked positions. Indicates which patches are masked (1) and which aren't (0).
+
+        Returns:
+
+        Examples:
+        ```python
+        >>> from transformers import AutoImageProcessor, ViTForMaskedImageModeling
+        >>> import torch
+        >>> from PIL import Image
+        >>> import requests
+
+        >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+        >>> image = Image.open(requests.get(url, stream=True).raw)
+
+        >>> image_processor = AutoImageProcessor.from_pretrained("google/vit-base-patch16-224-in21k")
+        >>> model = ViTForMaskedImageModeling.from_pretrained("google/vit-base-patch16-224-in21k")
+
+        >>> num_patches = (model.config.image_size // model.config.patch_size) ** 2
+        >>> pixel_values = image_processor(images=image, return_tensors="pt").pixel_values
+        >>> # create random boolean mask of shape (batch_size, num_patches)
+        >>> bool_masked_pos = torch.randint(low=0, high=2, size=(1, num_patches)).bool()
+
+        >>> outputs = model(pixel_values, bool_masked_pos=bool_masked_pos)
+        >>> loss, reconstructed_pixel_values = outputs.loss, outputs.reconstruction
+        >>> list(reconstructed_pixel_values.shape)
+        [1, 3, 224, 224]
+        ```"""
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        if bool_masked_pos is not None and (self.config.patch_size != self.config.encoder_stride):
+            raise ValueError(
+                "When `bool_masked_pos` is provided, `patch_size` must be equal to `encoder_stride` to ensure that "
+                "the reconstructed image has the same dimensions as the input."
+                f"Got `patch_size` = {self.config.patch_size} and `encoder_stride` = {self.config.encoder_stride}.")
+
+        if not stage_manager.is_first_stage():
+            assert hidden_states is not None, f"Current stage is {stage_manager.stage}, hidden_states should not be None"
+
+        outputs = self.vit(pixel_values,
+                           bool_masked_pos=bool_masked_pos,
+                           head_mask=head_mask,
+                           output_attentions=output_attentions,
+                           output_hidden_states=output_hidden_states,
+                           interpolate_pos_encoding=interpolate_pos_encoding,
+                           return_dict=return_dict,
+                           hidden_states=hidden_states)
+        if not stage_manager.is_last_stage():
+            return outputs
+        else:
+            sequence_output = outputs[0]
+
+        # Reshape to (batch_size, num_channels, height, width)
+        sequence_output = sequence_output[:, 1:]
+        batch_size, sequence_length, num_channels = sequence_output.shape
+        height = width = math.floor(sequence_length**0.5)
+        sequence_output = sequence_output.permute(0, 2, 1).reshape(batch_size, num_channels, height, width)
+
+        # Reconstruct pixel values
+        reconstructed_pixel_values = self.decoder(sequence_output)
+
+        masked_im_loss = None
+        if bool_masked_pos is not None:
+            size = self.config.image_size // self.config.patch_size
+            bool_masked_pos = bool_masked_pos.reshape(-1, size, size)
+            mask = (bool_masked_pos.repeat_interleave(self.config.patch_size,
+                                                      1).repeat_interleave(self.config.patch_size,
+                                                                           2).unsqueeze(1).contiguous())
+            reconstruction_loss = nn.functional.l1_loss(pixel_values, reconstructed_pixel_values, reduction="none")
+            masked_im_loss = (reconstruction_loss * mask).sum() / (mask.sum() + 1e-5) / self.config.num_channels
+
+        if not return_dict:
+            output = (reconstructed_pixel_values,) + outputs[1:]
+            return ((masked_im_loss,) + output) if masked_im_loss is not None else output
+
+        return MaskedImageModelingOutput(
+            loss=masked_im_loss,
+            reconstruction=reconstructed_pixel_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+    return pp_forward

colossalai/shardformer/policies/auto_policy.py

@@ -75,6 +75,14 @@ _POLICY_LIST = {
     "transformers.models.gpt2.modeling_gpt2.GPT2ForSequenceClassification":
         PolicyLocation(file_name="gpt2", class_name="GPT2ForSequenceClassificationPolicy"),
 
+    # ViT
+    "transformers.models.vit.modeling_vit.ViTModel":
+        PolicyLocation(file_name="vit", class_name="ViTModelPolicy"),
+    "transformers.models.vit.modeling_vit.ViTForImageClassification":
+        PolicyLocation(file_name="vit", class_name="ViTForImageClassificationPolicy"),
+    "transformers.models.vit.modeling_vit.ViTForMaskedImageModeling":
+        PolicyLocation(file_name="vit", class_name="ViTForMaskedImageModelingPolicy"),
+
     # OPT
     "transformers.models.opt.modeling_opt.OPTModel":
         PolicyLocation(file_name="opt", class_name="OPTModelPolicy"),

colossalai/shardformer/policies/vit.py

@@ -1,12 +1,18 @@
-from typing import Dict, Union
+from functools import partial
+from typing import Callable, Dict, List, Union
 
 import torch.nn as nn
 
-from colossalai.shardformer.layer import DropoutForReplicatedInput, FusedLayerNorm, Linear1D_Col, Linear1D_Row
+import colossalai.shardformer.layer as col_nn
 
+from ..modeling.vit import (
+    ViTForImageClassification_pipeline_forward,
+    ViTForMaskedImageModeling_pipeline_forward,
+    ViTModel_pipeline_forward,
+)
 from .base_policy import ModulePolicyDescription, Policy, SubModuleReplacementDescription
 
-__all__ = ['ViTPolicy']
+__all__ = ['ViTPolicy', 'ViTModelPolicy', 'ViTForImageClassificationPolicy', 'ViTForMaskedImageModelingPolicy']
 
 
 class ViTPolicy(Policy):
@@ -15,96 +21,203 @@ class ViTPolicy(Policy):
         pass
 
     def preprocess(self):
-        # Resize embedding
-        vocab_size = self.model.config.vocab_size
-        world_size = self.shard_config.tensor_parallel_size
-
-        if vocab_size % world_size != 0:
-            new_vocab_size = vocab_size + world_size - vocab_size % world_size
-            self.model.resize_token_embeddings(new_vocab_size)
-
         return self.model
 
     def module_policy(self) -> Dict[Union[str, nn.Module], ModulePolicyDescription]:
         from transformers.models.vit.modeling_vit import ViTEmbeddings, ViTLayer
 
-        base_policy = {
+        policy = {}
-            ViTEmbeddings:
+
-                ModulePolicyDescription(sub_module_replacement=[
+        if self.shard_config.enable_tensor_parallelism:
+            policy[ViTEmbeddings] = ModulePolicyDescription(attribute_replacement={},
+                                                            param_replacement=[],
+                                                            sub_module_replacement=[
                                                                 SubModuleReplacementDescription(
                                                                     suffix="dropout",
-                        target_module=DropoutForReplicatedInput,
+                                                                    target_module=col_nn.DropoutForReplicatedInput,
                                                                 )
-                ]),
+                                                            ])
-            ViTLayer:
+
-                ModulePolicyDescription(attribute_replacement={
+            policy[ViTLayer] = ModulePolicyDescription(attribute_replacement={
                 "attention.attention.num_attention_heads":
                     self.model.config.num_attention_heads // self.shard_config.tensor_parallel_size,
                 "attention.attention.all_head_size":
                     self.model.config.hidden_size // self.shard_config.tensor_parallel_size,
             },
+                                                       param_replacement=[],
                                                        sub_module_replacement=[
                                                            SubModuleReplacementDescription(
                                                                suffix="attention.attention.query",
-                                                target_module=Linear1D_Col,
+                                                               target_module=col_nn.Linear1D_Col,
                                                            ),
                                                            SubModuleReplacementDescription(
                                                                suffix="attention.attention.key",
-                                                target_module=Linear1D_Col,
+                                                               target_module=col_nn.Linear1D_Col,
                                                            ),
                                                            SubModuleReplacementDescription(
                                                                suffix="attention.attention.value",
-                                                target_module=Linear1D_Col,
+                                                               target_module=col_nn.Linear1D_Col,
                                                            ),
                                                            SubModuleReplacementDescription(
                                                                suffix="attention.attention.dropout",
-                                                target_module=DropoutForParallelInput,
+                                                               target_module=col_nn.DropoutForParallelInput,
                                                            ),
                                                            SubModuleReplacementDescription(
                                                                suffix="attention.output.dense",
-                                                target_module=Linear1D_Row,
+                                                               target_module=col_nn.Linear1D_Row,
                                                            ),
                                                            SubModuleReplacementDescription(
                                                                suffix="attention.output.dropout",
-                                                target_module=DropoutForParallelInput,
+                                                               target_module=col_nn.DropoutForReplicatedInput,
                                                            ),
                                                            SubModuleReplacementDescription(
                                                                suffix="intermediate.dense",
-                                                target_module=Linear1D_Col,
+                                                               target_module=col_nn.Linear1D_Col,
                                                            ),
                                                            SubModuleReplacementDescription(
                                                                suffix="output.dense",
-                                                target_module=Linear1D_Row,
+                                                               target_module=col_nn.Linear1D_Row,
                                                            ),
                                                            SubModuleReplacementDescription(
                                                                suffix="output.dropout",
-                                                target_module=DropoutForParallelInput,
+                                                               target_module=col_nn.DropoutForReplicatedInput,
                                                            ),
-                                        ]),
-        }
-
-        # optimization configuration
-        if self.shard_config.enable_fused_normalization:
-            base_policy[ViTAttention].sub_module_replacement.extend([
-                SubModuleReplacementDescription(
-                    suffix="layernorm_before",
-                    target_module=FusedLayerNorm,
-                ),
-                SubModuleReplacementDescription(
-                    suffix="layernorm_after",
-                    target_module=FusedLayerNorm,
-                )
                                                        ])
-            base_policy[ViTModel].sub_module_replacement.append(
-                SubModuleReplacementDescription(
-                    suffix="layernorm",
-                    target_module=FusedLayerNorm,
-                ))
 
-        return base_policy
+        return policy
 
     def new_model_class(self):
         return None
 
     def postprocess(self):
         return self.model
+
+    def get_held_layers(self) -> List[nn.Module]:
+        """Get pipeline layers for current stage."""
+        assert self.pipeline_stage_manager is not None, "pipeline_stage_manager is None"
+
+        if self.model.__class__.__name__ == 'ViTModel':
+            module = self.model
+        else:
+            module = self.model.vit
+        stage_manager = self.pipeline_stage_manager
+
+        held_layers = []
+        layers_per_stage = self.distribute_layers(len(module.encoder.layer), stage_manager.num_stages)
+        if stage_manager.is_first_stage():
+            held_layers.append(module.embeddings)
+        start_idx, end_idx = self.get_stage_index(layers_per_stage, stage_manager.stage)
+        held_layers.extend(module.encoder.layer[start_idx:end_idx])
+        return held_layers
+
+    def set_pipeline_forward(self, model_cls: nn.Module, pipeline_forward: Callable, policy: Dict):
+        if self.pipeline_stage_manager:
+            stage_manager = self.pipeline_stage_manager
+            if self.model.__class__.__name__ == 'ViTModel':
+                module = self.model
+            else:
+                module = self.model.vit
+
+            layers_per_stage = Policy.distribute_layers(len(module.encoder.layer), stage_manager.num_stages)
+            stage_index = Policy.get_stage_index(layers_per_stage, stage_manager.stage)
+            method_replacement = {'forward': pipeline_forward(stage_manager=stage_manager, stage_index=stage_index)}
+            self.append_or_create_method_replacement(description=method_replacement,
+                                                     policy=policy,
+                                                     target_key=model_cls)
+
+
+# ViTModel
+class ViTModelPolicy(ViTPolicy):
+
+    def __init__(self) -> None:
+        super().__init__()
+
+    def module_policy(self):
+        from transformers.models.vit.modeling_vit import ViTModel
+
+        policy = super().module_policy()
+
+        if self.shard_config.pipeline_stage_manager is not None:
+            self.set_pipeline_forward(model_cls=ViTModel, pipeline_forward=ViTModel_pipeline_forward, policy=policy)
+        return policy
+
+    def get_held_layers(self) -> List[nn.Module]:
+        held_layers = super().get_held_layers()
+        assert self.pipeline_stage_manager is not None, "pipeline_stage_manager is None"
+
+        module = self.model
+        stage_manager = self.pipeline_stage_manager
+        if stage_manager.is_last_stage():
+            held_layers.append(module.layernorm)
+            held_layers.append(module.pooler)
+
+        return held_layers
+
+
+# ViTForImageClassification
+class ViTForImageClassificationPolicy(ViTPolicy):
+
+    def module_policy(self):
+        from transformers.models.vit.modeling_vit import ViTForImageClassification, ViTModel
+
+        policy = super().module_policy()
+        if self.shard_config.enable_tensor_parallelism:
+            new_item = {
+                ViTForImageClassification:
+                    ModulePolicyDescription(sub_module_replacement=[
+                        SubModuleReplacementDescription(
+                            suffix="classifier", target_module=col_nn.Linear1D_Col, kwargs=dict(gather_output=True))
+                    ])
+            }
+            policy.update(new_item)
+
+        if self.shard_config.pipeline_stage_manager is not None:
+            self.set_pipeline_forward(model_cls=ViTModel, pipeline_forward=ViTModel_pipeline_forward, policy=policy)
+            self.set_pipeline_forward(model_cls=ViTForImageClassification,
+                                      pipeline_forward=ViTForImageClassification_pipeline_forward,
+                                      policy=policy)
+
+        return policy
+
+    def get_held_layers(self) -> List[nn.Module]:
+        held_layers = super().get_held_layers()
+        assert self.pipeline_stage_manager is not None, "pipeline_stage_manager is None"
+
+        module = self.model.vit
+        stage_manager = self.pipeline_stage_manager
+        if stage_manager.is_last_stage():
+            held_layers.append(module.layernorm)
+            held_layers.append(self.model.classifier)
+
+        return held_layers
+
+
+# ViTForMaskedImageModeling
+class ViTForMaskedImageModelingPolicy(ViTPolicy):
+
+    def __init__(self) -> None:
+        super().__init__()
+
+    def module_policy(self):
+        from transformers.models.vit.modeling_vit import ViTForMaskedImageModeling, ViTModel
+
+        policy = super().module_policy()
+
+        if self.shard_config.pipeline_stage_manager is not None:
+            self.set_pipeline_forward(model_cls=ViTModel, pipeline_forward=ViTModel_pipeline_forward, policy=policy)
+            self.set_pipeline_forward(model_cls=ViTForMaskedImageModeling,
+                                      pipeline_forward=ViTForMaskedImageModeling_pipeline_forward,
+                                      policy=policy)
+        return policy
+
+    def get_held_layers(self) -> List[nn.Module]:
+        held_layers = super().get_held_layers()
+        assert self.pipeline_stage_manager is not None, "pipeline_stage_manager is None"
+
+        module = self.model.vit
+        stage_manager = self.pipeline_stage_manager
+        if stage_manager.is_last_stage():
+            held_layers.append(module.layernorm)
+            held_layers.append(self.model.decoder)
+
+        return held_layers

tests/kit/model_zoo/transformers/__init__.py

@@ -5,3 +5,4 @@ from .gpt import *
 from .llama import *
 from .opt import *
 from .t5 import *
+from .vit import *

tests/kit/model_zoo/transformers/vit.py

@@ -0,0 +1,68 @@
+import torch
+import transformers
+
+from ..registry import ModelAttribute, model_zoo
+
+# ===============================
+# Register single-sentence VIT
+# ===============================
+
+config = transformers.ViTConfig(
+    num_hidden_layers=4,
+    # hidden_size=128,
+    # intermediate_size=256,
+    num_attention_heads=4)
+
+
+# define data gen function
+def data_gen():
+    pixel_values = torch.randn(1, 3, 224, 224)
+    return dict(pixel_values=pixel_values)
+
+
+def data_gen_for_image_classification():
+    data = data_gen()
+    data['labels'] = torch.tensor([0])
+    return data
+
+
+def data_gen_for_masked_image_modeling():
+    data = data_gen()
+    num_patches = (config.image_size // config.patch_size)**2
+    bool_masked_pos = torch.randint(low=0, high=2, size=(1, num_patches)).bool()
+    data['bool_masked_pos'] = bool_masked_pos
+    return data
+
+
+# define output transform function
+output_transform_fn = lambda x: x
+
+# function to get the loss
+loss_fn_for_vit_model = lambda x: x.pooler_output.mean()
+loss_fn_for_image_classification = lambda x: x.logits.mean()
+loss_fn_for_masked_image_modeling = lambda x: x.loss
+
+# register the following models
+# transformers.ViTModel,
+# transformers.ViTForMaskedImageModeling,
+# transformers.ViTForImageClassification,
+model_zoo.register(name='transformers_vit',
+                   model_fn=lambda: transformers.ViTModel(config),
+                   data_gen_fn=data_gen,
+                   output_transform_fn=output_transform_fn,
+                   loss_fn=loss_fn_for_vit_model,
+                   model_attribute=ModelAttribute(has_control_flow=True))
+
+model_zoo.register(name='transformers_vit_for_masked_image_modeling',
+                   model_fn=lambda: transformers.ViTForMaskedImageModeling(config),
+                   data_gen_fn=data_gen_for_masked_image_modeling,
+                   output_transform_fn=output_transform_fn,
+                   loss_fn=loss_fn_for_masked_image_modeling,
+                   model_attribute=ModelAttribute(has_control_flow=True))
+
+model_zoo.register(name='transformers_vit_for_image_classification',
+                   model_fn=lambda: transformers.ViTForImageClassification(config),
+                   data_gen_fn=data_gen_for_image_classification,
+                   output_transform_fn=output_transform_fn,
+                   loss_fn=loss_fn_for_image_classification,
+                   model_attribute=ModelAttribute(has_control_flow=True))

tests/test_shardformer/test_model/test_shard_vit.py

@@ -1,9 +1,18 @@
+import os
+
 import pytest
 import torch
 
 import colossalai
 from colossalai.logging import disable_existing_loggers
-from colossalai.testing import assert_hf_output_close, clear_cache_before_run, rerun_if_address_is_in_use, spawn
+from colossalai.tensor.d_tensor.api import is_customized_distributed_tensor, is_distributed_tensor
+from colossalai.testing import (
+    assert_hf_output_close,
+    clear_cache_before_run,
+    parameterize,
+    rerun_if_address_is_in_use,
+    spawn,
+)
 from tests.kit.model_zoo import model_zoo
 from tests.test_shardformer.test_model._utils import build_model, run_forward
 
@@ -12,44 +21,58 @@ def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transfo
     # check forward
     org_output, org_loss, shard_output, shard_loss = run_forward(org_model, sharded_model, data_gen_fn,
                                                                  output_transform_fn, loss_fn)
-    assert_hf_output_close(org_output, shard_output)
+    assert_hf_output_close(org_output, shard_output, atol=1e-3, rtol=1e-3)
-
     # do backward
     org_loss.backward()
     shard_loss.backward()
 
-    # check grad
+    assert torch.allclose(org_loss, shard_loss,
-    org_grad = org_model.encoder.layer[0].attention.attention.query.weight.grad
+                          atol=1e-5), f"shard model loss is not equal to orgin model loss\n{org_loss}\n{shard_loss}"
-    shard_grad = sharded_model.encoder.layer[0].attention.attention.query.weight.grad
 
+    # unwrap model
+    if org_model.__class__.__name__ == 'ViTModel':
+        vit_model = org_model
+        shard_vit_model = sharded_model
+    else:
+        vit_model = org_model.vit
+        shard_vit_model = sharded_model.vit
+
+    # check attention grad
+    org_grad = vit_model.encoder.layer[0].attention.attention.query.weight.grad
+    shard_grad = shard_vit_model.encoder.layer[0].attention.attention.query.weight.grad
+    shard_weight = shard_vit_model.encoder.layer[0].attention.attention.query.weight
+
+    if is_distributed_tensor(shard_weight) or is_customized_distributed_tensor(shard_weight):
         shard_grad_list = [torch.zeros([*shard_grad.shape]).to('cuda') for _ in range(2)]
         shard_grad = torch.distributed.all_gather(shard_grad_list, shard_grad)
         all_shard_grad = torch.cat(shard_grad_list, dim=0)
-
+    else:
-    assert torch.allclose(org_loss, shard_loss,
+        all_shard_grad = shard_grad
-                          atol=1e-5), f"shard model loss is not equal to orgin model loss\n{org_loss}\n{shard_loss}"
     assert torch.allclose(org_grad, all_shard_grad,
-                          atol=1e-5), f"shard model grad is not equal to orgin model grad\n{org_grad}\n{all_shard_grad}"
+                          atol=1e-5), f"shard model grad is not equal to orgin model grad\n{org_grad}\n{shard_grad}"
+
+
+@parameterize('enable_fused_normalization', [True, False])
+@parameterize('enable_tensor_parallelism', [True, False])
+def run_vit_test(enable_fused_normalization, enable_tensor_parallelism):
+    sub_model_zoo = model_zoo.get_sub_registry('transformers_vit')
+    for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items():
+        org_model, sharded_model = build_model(model_fn, enable_fused_normalization, enable_tensor_parallelism)
+        check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn)
+    torch.cuda.empty_cache()
 
 
 def check_vit(rank, world_size, port):
     disable_existing_loggers()
     colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
-
+    run_vit_test()
-    sub_model_zoo = model_zoo.get_sub_registry('transformers_vit')
-    for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items():
-        org_model, sharded_model = build_model(world_size, model_fn)
-        check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn)
-
-    torch.cuda.empty_cache()
 
 
 @pytest.mark.dist
-@pytest.mark.skip
 @rerun_if_address_is_in_use()
 @clear_cache_before_run()
 def test_vit():
-    spawn(check_vit, 4)
+    spawn(check_vit, 2)
 
 
 if __name__ == "__main__":

tests/test_shardformer/test_model/test_shard_vit_pipeline.py

@@ -0,0 +1,74 @@
+import pytest
+import torch
+
+import colossalai
+from colossalai.cluster import ProcessGroupMesh
+from colossalai.logging import disable_existing_loggers
+from colossalai.pipeline.stage_manager import PipelineStageManager
+from colossalai.testing import clear_cache_before_run, parameterize, rerun_if_address_is_in_use, spawn
+from tests.kit.model_zoo import model_zoo
+from tests.test_shardformer.test_model._utils import build_pipeline_model
+
+
+def check_forward_backward(org_model, sharded_model, data_gen_fn, output_transform_fn, loss_fn):
+    # TODO: add tests for forward/backward later
+    pass
+
+
+@parameterize('enable_tensor_parallelism', [False])
+@parameterize('enable_fused_normalization', [False])
+@parameterize('use_lazy_init', [False])
+#TODO: merge this into test_shard_vit
+def run_vit_test(enable_fused_normalization, enable_tensor_parallelism, use_lazy_init):
+    DP_DIM, PP_DIM = 0, 1
+    DP_SIZE, PP_SIZE = 2, 2
+    pg_mesh = ProcessGroupMesh(DP_SIZE, PP_SIZE)
+    stage_manager = PipelineStageManager(pg_mesh, PP_DIM)
+
+    sub_model_zoo = model_zoo.get_sub_registry('transformers_vit')
+
+    for name, (model_fn, data_gen_fn, _, _, _) in sub_model_zoo.items():
+
+        inputs = data_gen_fn()
+        inputs = {k: v.cuda() for k, v in inputs.items()}
+        pixel_values = inputs['pixel_values']
+        batch_size = len(pixel_values)
+        hidden_size = 768
+        hidden_state_shape = (batch_size, 197, hidden_size)
+
+        if not stage_manager.is_first_stage():
+            # change inputs if not the first stage
+            hidden_states = torch.randn(*hidden_state_shape).cuda()
+            # inputs['pixel_values'] = None
+            inputs['hidden_states'] = hidden_states
+
+        _, sharded_model = build_pipeline_model(model_fn, stage_manager, enable_fused_normalization,
+                                                enable_tensor_parallelism, use_lazy_init)
+        sharded_model.train()
+
+        output = sharded_model(**inputs)
+        if stage_manager.is_last_stage():
+            if name != 'transformers_vit':
+                assert output.loss is not None
+        else:
+            assert output['hidden_states'].shape == hidden_state_shape, \
+            f'hidden_states shape is not correct, output:{output["hidden_states"].shape} is not equal to hidden_state:{hidden_state_shape}'
+
+    torch.cuda.empty_cache()
+
+
+def check_vit(rank, world_size, port):
+    disable_existing_loggers()
+    colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
+    run_vit_test()
+
+
+@pytest.mark.dist
+@rerun_if_address_is_in_use()
+@clear_cache_before_run()
+def test_vit():
+    spawn(check_vit, 4)
+
+
+if __name__ == "__main__":
+    test_vit()