ColossalAI/colossalai/shardformer/modeling/vit.py

382 lines
16 KiB
Python

import math
from typing import List, Optional, Tuple, Union
import torch
from transformers.models.vit.modeling_vit import BaseModelOutput, ViTEncoder
from transformers.utils import logging
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).
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
logger = logging.get_logger(__name__)
# Preprocess passed in arguments
if output_attentions:
logger.warning_once("output_attentions=True is not supported for pipeline models at the moment.")
output_attentions = False
if output_hidden_states:
logger.warning_once("output_hidden_states=True is not supported for pipeline models at the moment.")
output_hidden_states = False
# 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(FoolPlayer): 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
)
hidden_states = embedding_output
else:
assert (
hidden_states is not None
), f"Current stage is {stage_manager.stage}, hidden_states should not be None"
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,
)
if not stage_manager.is_last_stage():
return {"hidden_states": encoder_outputs}
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
def get_vit_flash_self_attention_forward():
from transformers.models.vit.modeling_vit import ViTSelfAttention
from colossalai.kernel.cuda_native import ColoAttention
def transpose_for_scores(x: torch.Tensor, num_attention_heads, attention_head_size) -> torch.Tensor:
new_x_shape = x.size()[:-1] + (num_attention_heads, attention_head_size)
x = x.view(new_x_shape)
return x
def forward(
self: ViTSelfAttention,
hidden_states: torch.Tensor,
head_mask: Optional[torch.Tensor] = None,
output_attentions: bool = False,
) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]:
mixed_query_layer = self.query(hidden_states)
key_layer = transpose_for_scores(self.key(hidden_states), self.num_attention_heads, self.attention_head_size)
value_layer = transpose_for_scores(
self.value(hidden_states), self.num_attention_heads, self.attention_head_size
)
query_layer = transpose_for_scores(mixed_query_layer, self.num_attention_heads, self.attention_head_size)
scale = 1.0 / math.sqrt(self.attention_head_size)
attention = ColoAttention(
embed_dim=self.all_head_size, num_heads=self.num_attention_heads, dropout=self.dropout.p, scale=scale
)
context_layer = attention(query_layer, key_layer, value_layer)
outputs = (context_layer,)
return outputs
return forward
def get_jit_fused_vit_output_forward():
from transformers.models.vit.modeling_vit import ViTOutput
def forward(self: ViTOutput, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
hidden_states = self.dense(hidden_states)
hidden_states = self.dropout_add(hidden_states, input_tensor, self.dropout.p, self.dropout.training)
return hidden_states
return forward