ColossalAI/colossalai/nn/layer/parallel_2d/_vit.py

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2021-10-28 16:21:23 +00:00
#!/usr/bin/env python
# -*- encoding: utf-8 -*-
import math
import torch
from torch import nn as nn, Tensor, distributed as dist
from colossalai.context import seed, ParallelMode
from colossalai.core import global_context as gpc
from colossalai.nn.layer._common_utils import divide, ACT2FN
from colossalai.nn.layer.parallel_2d._utils import assert_summa_initialization, get_summa_dim_from_env
from colossalai.nn.layer.vanilla_vision_transformer.layers import to_2tuple
from colossalai.registry import LAYERS
from colossalai.utils import checkpoint
from colossalai.utils import get_current_device
from ._operation import _ViT_Split_Input_2D
from .layers import Linear2D
from .._common_utils import set_tensor_parallel_attribute
from ..base_layer import ParallelLayer
@LAYERS.register_module
class ViTMLP2D(ParallelLayer):
"""MLP layer for 2D parallel Vision Transformer
:param in_features: size of each input sample
:type in_features: int
:param mlp_ratio: hidden size of MLP divided by embedding dim
:type mlp_ratio: int
:param act_func: activation function, defaults to 'gelu'
:type act_func: str, optional
:param dropout_prob: dropout probability, defaults to 0.
:type dropout_prob: float, optional
:param dtype: The dtype of parameters, defaults to None
:type dtype: torch.dtype, optional
:param checkpoint: whether to checkpoint the layer, defaults to False
:type checkpoint: bool, optional
"""
def __init__(self,
in_features: int,
mlp_ratio: int,
act_func: str = 'gelu',
dropout_prob: float = 0.,
dtype=None,
checkpoint: bool = False
):
super().__init__()
assert_summa_initialization()
self.summa_dim = get_summa_dim_from_env()
self.in_features = in_features
self.mlp_ratio = mlp_ratio
self.checkpoint = checkpoint
# Project to mlp_ratio * h.
self.dense_1 = Linear2D(
self.in_features,
self.mlp_ratio * self.in_features,
dtype=dtype,
)
self.act = ACT2FN[act_func]
# Project back to h.
self.dense_2 = Linear2D(
self.mlp_ratio * self.in_features,
self.in_features,
dtype=dtype,
)
self.dropout = nn.Dropout(dropout_prob)
def _forward(self, hidden_states: Tensor) -> Tensor:
intermediate_output = self.dense_1(hidden_states)
intermediate_output = self.act(intermediate_output)
with seed(ParallelMode.TENSOR):
intermediate_output = self.dropout(intermediate_output)
output = self.dense_2(intermediate_output)
with seed(ParallelMode.TENSOR):
output = self.dropout(output)
return output
def _checkpoint_forward(self, hidden_states: Tensor) -> Tensor:
return checkpoint(self._forward, hidden_states)
def forward(self, hidden_states: Tensor) -> Tensor:
if self.checkpoint:
return self._checkpoint_forward(hidden_states)
else:
return self._forward(hidden_states)
@LAYERS.register_module
class ViTSelfAttention2D(ParallelLayer):
"""Self-attention layer for 2D parallel Vision Transformer
:param hidden_size: hidden size
:type hidden_size: int
:param num_attention_heads: number of attention heads
:type num_attention_heads: int
:param attention_dropout_prob: dropout probability for attention layers
:type attention_dropout_prob: float
:param hidden_dropout_prob: dropout probability for hidden layers
:type hidden_dropout_prob: float
:param dtype: dtype of parameters, defaults to None
:type dtype: torch.dtype, optional
:param checkpoint: whether to checkpoint the layer, defaults to False
:type checkpoint: bool, optional
"""
def __init__(self,
hidden_size: int,
num_attention_heads: int,
attention_dropout_prob: float,
hidden_dropout_prob: float,
dtype=None,
checkpoint: bool = False
):
super().__init__()
assert_summa_initialization()
self.summa_dim = get_summa_dim_from_env()
self.hidden_size = hidden_size
self.num_attention_heads = divide(num_attention_heads, self.summa_dim)
self.attention_head_size = divide(hidden_size, num_attention_heads)
self.all_head_size = self.num_attention_heads * self.attention_head_size
self.checkpoint = checkpoint
self.query_key_value = Linear2D(
hidden_size,
3 * hidden_size,
dtype=dtype,
)
self.attention_dropout = nn.Dropout(attention_dropout_prob)
self.dense = Linear2D(
hidden_size,
hidden_size,
dtype=dtype,
)
self.dropout = nn.Dropout(hidden_dropout_prob)
self.softmax = nn.Softmax(dim=-1)
def _forward(self, hidden_states: Tensor) -> Tensor:
query_key_value = self.query_key_value(hidden_states)
new_qkv_shape = query_key_value.shape[:-1] + \
(self.num_attention_heads, 3 * self.attention_head_size)
query_key_value = query_key_value.view(new_qkv_shape)
query_key_value = query_key_value.permute((0, 2, 1, 3))
query_layer, key_layer, value_layer = torch.chunk(
query_key_value, 3, dim=-1)
attention_scores = torch.matmul(
query_layer, key_layer.transpose(-1, -2))
attention_scores = attention_scores / \
math.sqrt(self.attention_head_size)
attention_probs = self.softmax(attention_scores)
with seed(ParallelMode.TENSOR):
attention_probs = self.attention_dropout(attention_probs)
context_layer = torch.matmul(attention_probs, value_layer)
context_layer = context_layer.transpose(1, 2)
new_context_layer_shape = context_layer.size()[
:-2] + (self.all_head_size,)
context_layer = context_layer.reshape(new_context_layer_shape)
output = self.dense(context_layer)
with seed(ParallelMode.TENSOR):
output = self.dropout(output)
return output
def _checkpoint_forward(self, hidden_states: Tensor) -> Tensor:
return checkpoint(self._forward, hidden_states)
def forward(self, hidden_states: Tensor) -> Tensor:
if self.checkpoint:
return self._checkpoint_forward(hidden_states)
else:
return self._forward(hidden_states)
@LAYERS.register_module
class ViTHead2D(ParallelLayer):
"""Output layer for 2D parallel Vision Transformer
:param hidden_size: hidden size
:type hidden_size: int
:param num_classes: number of classes
:type num_classes: int
:param dtype: dtype of parameters, defaults to None
:type dtype: torch.dtype, optional
"""
def __init__(self,
hidden_size,
num_classes,
dtype=None,
):
super().__init__()
assert_summa_initialization()
self.summa_dim = get_summa_dim_from_env()
self.linear = Linear2D(
hidden_size,
num_classes,
dtype=dtype,
)
def forward(self, x: Tensor) -> Tensor:
x = x[:, 0]
x = self.linear(x)
return x
@LAYERS.register_module
class ViTPatchEmbedding2D(ParallelLayer):
""" 2D Image to Patch Embedding
:param img_size: iamge size
:type img_size: int
:param patch_size: patch size
:type patch_size: int
:param embed_dim: dimension of embedding
:type embed_dim: int
:param in_chans: number of channels of input image, defaults to 3
:type in_chans: int, optional
:param flatten: whether to flatten output tensor, defaults to True
:type flatten: bool, optional
"""
def __init__(self,
img_size,
patch_size,
embed_dim,
in_chans=3,
flatten=True):
super().__init__()
img_size = to_2tuple(img_size)
patch_size = to_2tuple(patch_size)
assert_summa_initialization()
self.summa_dim = get_summa_dim_from_env()
self.img_size = img_size
self.patch_size = patch_size
self.grid_size = (img_size[0] // patch_size[0],
img_size[1] // patch_size[1])
self.num_patches = self.grid_size[0] * self.grid_size[1]
self.flatten = flatten
self.embed_dim = embed_dim // self.summa_dim
with seed(ParallelMode.TENSOR):
# ensure the partitions are initialized differently
self.proj = nn.Conv2d(in_chans,
self.embed_dim,
kernel_size=patch_size,
stride=patch_size
)
# sync
self._broadcast_conv_params()
self.proj.weight.register_hook(self._sync_grad_during_backward)
self.proj.bias.register_hook(self._sync_grad_during_backward)
def _set_tensor_parallel_attribute(self):
set_tensor_parallel_attribute(self.proj.weight)
set_tensor_parallel_attribute(self.proj.bias)
def _broadcast_conv_params(self) -> None:
self.to(get_current_device())
ranks_in_col = gpc.get_ranks_in_group(ParallelMode.PARALLEL_2D_COL)
dist.broadcast(self.proj.weight, src=ranks_in_col[0],
group=gpc.get_group(ParallelMode.PARALLEL_2D_COL))
dist.broadcast(self.proj.bias, src=ranks_in_col[0],
group=gpc.get_group(ParallelMode.PARALLEL_2D_COL))
def _sync_grad_during_backward(self, grad: Tensor) -> None:
dist.all_reduce(grad, group=gpc.get_group(
ParallelMode.PARALLEL_2D_COL))
grad = grad / self.summa_dim
return grad
def forward(self, x: Tensor) -> Tensor:
B, C, H, W = x.shape
assert H == self.img_size[0] and W == self.img_size[1], \
f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
x = self.proj(x)
if self.flatten:
x = x.flatten(2).transpose(1, 2) # BCHW -> BNC
return x
@LAYERS.register_module
class ViTTokenFuser2D(ParallelLayer):
"""
Fuse cls token and pos embedding to the input
:param img_size: image size
:type img_size: int
:param patch_size: patch size
:type patch_size: int
:param embed_dim: dimension of embedding
:type embed_dim: int
:param drop_rate: dropout probability, defaults to 0.
:type drop_rate: float, optional
"""
def __init__(self,
img_size,
patch_size,
embed_dim,
drop_rate=0.
):
super().__init__()
img_size = to_2tuple(img_size)
patch_size = to_2tuple(patch_size)
assert_summa_initialization()
self.summa_dim = get_summa_dim_from_env()
self.img_size = img_size
self.patch_size = patch_size
self.grid_size = (img_size[0] // patch_size[0],
img_size[1] // patch_size[1])
self.num_patches = self.grid_size[0] * self.grid_size[1]
self.embed_dim = embed_dim
self.cls_token = nn.Parameter(torch.zeros(
1, 1, self.embed_dim // self.summa_dim))
self.pos_embed = nn.Parameter(torch.zeros(
1, self.num_patches + 1, self.embed_dim // self.summa_dim))
# move to cuda before broadcast
self.to(get_current_device())
# sync param in both forward and backward
_cls_token = self.cls_token.view(-1)
_pos_embed = self.pos_embed.view(-1)
self._param = torch.cat([_cls_token, _pos_embed], dim=0)
self._broadcast_params(self._param)
self._param.register_hook(self._sync_grad_hook)
self.pos_drop = nn.Dropout(p=drop_rate)
self._set_tensor_parallel_attribute()
def _set_tensor_parallel_attribute(self):
set_tensor_parallel_attribute(self.cls_token)
set_tensor_parallel_attribute(self.pos_embed)
def _broadcast_params(self, param) -> None:
" broadcast to all column ranks for data consistency "
ranks_in_col = gpc.get_ranks_in_group(ParallelMode.PARALLEL_2D_COL)
col_group = gpc.get_group(ParallelMode.PARALLEL_2D_COL)
dist.broadcast(param, src=ranks_in_col[0],
group=col_group)
def _sync_grad_hook(self, grad) -> None:
dist.all_reduce(grad, group=gpc.get_group(
ParallelMode.PARALLEL_2D_COL))
grad = grad / self.summa_dim
return grad
def forward(self, x: Tensor) -> Tensor:
# stole cls_tokens impl from Phil Wang, thanks
cls_token = self.cls_token.expand(x.shape[0], -1, -1)
x = torch.cat((cls_token, x), dim=1)
with seed(ParallelMode.TENSOR):
x = self.pos_drop(x + self.pos_embed)
return x
@LAYERS.register_module
class ViTInputSplitter2D(ParallelLayer):
"""Split the input tensor for 2D parallel Vision Transformer
"""
def __init__(self):
super().__init__()
assert_summa_initialization()
self.summa_dim = get_summa_dim_from_env()
def forward(self, x: Tensor) -> Tensor:
batch_size = x.size(0)
return _ViT_Split_Input_2D.apply(
x,
batch_size,
self.summa_dim,
ParallelMode.PARALLEL_2D_COL
)