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# Copyright 2018- The Hugging Face team. 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.
# ------------------------------------------------------------------------
# Modified from CLIP (https://github.com/huggingface/transformers)
# Copyright 2024 Jiachen Li
# ------------------------------------------------------------------------
import torch
import torch.nn as nn
from typing import Dict, Optional, Sequence, List
from transformers.activations import ACT2FN
from einops import rearrange, repeat, reduce, pack, unpack
class CLIPAttention(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.embed_dim = config.hidden_size
self.num_heads = config.num_attention_heads
self.head_dim = self.embed_dim // self.num_heads
if self.head_dim * self.num_heads != self.embed_dim:
raise ValueError(
f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:"
f" {self.num_heads})."
)
self.scale = self.head_dim**-0.5
self.dropout = config.attention_dropout
self.k_proj = nn.Linear(self.embed_dim, self.embed_dim)
self.v_proj = nn.Linear(self.embed_dim, self.embed_dim)
self.q_proj = nn.Linear(self.embed_dim, self.embed_dim)
self.out_proj = nn.Linear(self.embed_dim, self.embed_dim)
def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
def forward(
self,
hidden_states: torch.Tensor,
):
"""Input shape: Batch x Time x Channel"""
bsz, tgt_len, embed_dim = hidden_states.size()
# get query proj
query_states = self.q_proj(hidden_states) * self.scale
key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
proj_shape = (bsz * self.num_heads, -1, self.head_dim)
query_states = self._shape(query_states, tgt_len, bsz).view(*proj_shape)
key_states = key_states.view(*proj_shape)
value_states = value_states.view(*proj_shape)
src_len = key_states.size(1)
attn_weights = torch.bmm(query_states, key_states.transpose(1, 2))
if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len):
raise ValueError(
f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is"
f" {attn_weights.size()}"
)
attn_weights = nn.functional.softmax(attn_weights, dim=-1)
attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training)
attn_output = torch.bmm(attn_probs, value_states)
if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim):
raise ValueError(
f"`attn_output` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is"
f" {attn_output.size()}"
)
attn_output = attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim)
attn_output = attn_output.transpose(1, 2)
attn_output = attn_output.reshape(bsz, tgt_len, embed_dim)
attn_output = self.out_proj(attn_output)
return attn_output
class CLIPMLP(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.activation_fn = ACT2FN[config.hidden_act]
self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size)
self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size)
def forward(self, hidden_states):
hidden_states = self.fc1(hidden_states)
hidden_states = self.activation_fn(hidden_states)
hidden_states = self.fc2(hidden_states)
return hidden_states
class CLIPEncoderMoELayer(nn.Module):
def __init__(self, config):
super().__init__()
self.embed_dim = config.hidden_size
self.self_attn = CLIPAttention(config)
self.layer_norm1 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)
self.num_of_experts = config.num_of_experts
self.num_selected = config.num_selected
self.gate = nn.Linear(self.embed_dim, self.num_of_experts, bias=False)
self.experts = nn.ModuleList([CLIPMLP(config) for _ in range(self.num_of_experts)])
self.layer_norm2 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)
def forward(
self,
hidden_states
):
residual = hidden_states
hidden_states = self.layer_norm1(hidden_states)
hidden_states = self.self_attn(hidden_states)
hidden_states = residual + hidden_states
residual = hidden_states
hidden_states = self.layer_norm2(hidden_states)
gate_logits = self.gate(hidden_states)
router_z_loss = torch.logsumexp(gate_logits, dim = -1)
router_z_loss = torch.square(router_z_loss)
router_z_loss = router_z_loss.mean()
gate_softmax = nn.functional.softmax(gate_logits, dim=-1, dtype=torch.float).to(hidden_states.dtype)
density_1_proxy = reduce(gate_softmax, '... n e -> ... e', 'mean')
weights, selected_experts = torch.topk(gate_softmax, self.num_selected)
one_hot_gate_indices = nn.functional.one_hot(rearrange(selected_experts, '... k -> k ...'), self.num_of_experts).float()[0]
density_1 = reduce(one_hot_gate_indices, '... n e -> ... e', 'mean')
balance_loss = (density_1_proxy * density_1).mean() * float(self.num_of_experts ** 2)
weights = weights / torch.sum(weights, dim=-1, keepdim=True).to(hidden_states.dtype)
results = torch.zeros_like(hidden_states).to(hidden_states.device, hidden_states.dtype)
for b in range(hidden_states.shape[0]):
for i, expert in enumerate(self.experts):
token_idx, nth_expert = torch.where(selected_experts[b] == i)
results[b][token_idx] += weights[b][token_idx, nth_expert, None] * expert(hidden_states[b][token_idx])
#hidden_states = self.mlp(hidden_states)
hidden_states = residual + results
outputs = (hidden_states, balance_loss, router_z_loss)
return outputs
class CLIPEncoder(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.layers = nn.ModuleList([CLIPEncoderMoELayer(config) for _ in range(config.num_hidden_layers)])
def forward(
self,
inputs_embeds
):
encoder_states = ()
hidden_states = inputs_embeds
balance_losses = []
router_z_losses = []
for idx, encoder_layer in enumerate(self.layers):
encoder_states = encoder_states + (hidden_states,)
layer_outputs = encoder_layer(hidden_states)
hidden_states = layer_outputs[0]
balance_loss = layer_outputs[1]
balance_losses.append(balance_loss)
router_z_loss = layer_outputs[2]
router_z_losses.append(router_z_loss)
return encoder_states, balance_losses, router_z_losses
class CLIPVisionEmbeddings(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.embed_dim = config.hidden_size
self.image_size = config.image_size
self.patch_size = config.patch_size
self.class_embedding = nn.Parameter(torch.randn(self.embed_dim))
self.patch_embedding = nn.Conv2d(
in_channels=config.num_channels,
out_channels=self.embed_dim,
kernel_size=self.patch_size,
stride=self.patch_size,
bias=False,
)
self.num_patches = (self.image_size // self.patch_size) ** 2
self.num_positions = self.num_patches + 1
self.position_embedding = nn.Embedding(self.num_positions, self.embed_dim)
self.register_buffer("position_ids", torch.arange(self.num_positions).expand((1, -1)), persistent=False)
def forward(self, pixel_values):
batch_size = pixel_values.shape[0]
target_dtype = self.patch_embedding.weight.dtype
patch_embeds = self.patch_embedding(pixel_values.to(dtype=target_dtype)) # shape = [*, width, grid, grid]
patch_embeds = patch_embeds.flatten(2).transpose(1, 2)
class_embeds = self.class_embedding.expand(batch_size, 1, -1)
embeddings = torch.cat([class_embeds, patch_embeds], dim=1)
embeddings = embeddings + self.position_embedding(self.position_ids)
return embeddings
class CLIPSMoEVisionTransformer(nn.Module):
def __init__(self, config, num_experts=4, num_selected=2):
super().__init__()
self.config = config
embed_dim = config.hidden_size
config.num_of_experts = num_experts
config.num_selected = num_selected
self.embeddings = CLIPVisionEmbeddings(config)
self.pre_layrnorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)
self.encoder = CLIPEncoder(config)
#self.post_layernorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)
def forward(self, pixel_values):
hidden_states = self.embeddings(pixel_values)
hidden_states = self.pre_layrnorm(hidden_states)
encoder_outputs, balance_losses, router_z_losses = self.encoder(hidden_states)
return encoder_outputs[-1], torch.stack(balance_losses).mean(), torch.stack(router_z_losses).mean() |