# SPDX-FileCopyrightText: Copyright (c) 2022-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# 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.
from ...functional import arange, concat, expand, expand_dims, shape
from ...layers import MLP, BertAttention, Conv2d, Embedding, LayerNorm
from ...mapping import Mapping
from ...module import Module, ModuleList
from ...parameter import Parameter
# Adapted from https://github.com/huggingface/transformers/blob/v4.39.0/src/transformers/models/clip/modeling_clip.py#L164
class CLIPVisionEmbeddings(Module):
def __init__(self, image_size, num_channels, patch_size, hidden_size,
dtype):
super().__init__()
self.image_size = image_size
self.num_channels = num_channels
self.patch_size = patch_size
self.embed_dim = hidden_size
self.dtype = dtype
self.class_embedding = Parameter(shape=[
self.embed_dim,
],
dtype=self.dtype)
self.patch_embedding = Conv2d(in_channels=self.num_channels,
out_channels=self.embed_dim,
kernel_size=(self.patch_size,
self.patch_size),
stride=(self.patch_size, self.patch_size),
bias=False,
dtype=self.dtype)
self.num_patches = (self.image_size // self.patch_size)**2
self.num_positions = self.num_patches + 1
self.position_embedding = Embedding(self.num_positions,
self.embed_dim,
dtype=self.dtype)
def forward(self, pixel_values):
batch_size = shape(pixel_values, 0)
target_dtype = self.patch_embedding.weight.dtype
patch_embeds = self.patch_embedding(
pixel_values.cast(
dtype=target_dtype)) # shape = [*, width, grid, grid]
patch_embeds = patch_embeds.flatten(2).transpose(1, 2)
class_embeds = expand_dims(expand_dims(self.class_embedding.value, 0),
0)
expand_shape = concat(
[batch_size,
shape(class_embeds, -2),
shape(class_embeds, -1)])
class_embeds = expand(class_embeds,
expand_shape) # shape = [*, 1, grid, grid]
embeddings = concat([class_embeds, patch_embeds],
dim=1) # shape = [*, width + 1, grid, grid]
position_ids = arange(0, self.num_positions, dtype='int32')
position_embeds = self.position_embedding(position_ids)
position_embeds = expand_dims(position_embeds, 0)
expand_shape = concat([
batch_size,
shape(position_embeds, -2),
shape(position_embeds, -1)
])
position_embeds = expand(
position_embeds, expand_shape) # shape = [*, width + 1, grid, grid]
embeddings = embeddings + position_embeds
return embeddings
class CLIPEncoderLayer(Module):
def __init__(self, hidden_size, num_attention_heads,
max_position_embeddings, norm_epsilon, intermediate_size,
hidden_act, mapping: Mapping, dtype):
super().__init__()
self.hidden_size = hidden_size
self.dtype = dtype
self.mapping = mapping
self.input_layernorm = LayerNorm(normalized_shape=self.hidden_size,
eps=norm_epsilon,
dtype=self.dtype)
self.attention = BertAttention(
hidden_size=self.hidden_size,
num_attention_heads=num_attention_heads,
max_position_embeddings=max_position_embeddings,
attention_head_size=self.hidden_size // num_attention_heads,
num_kv_heads=num_attention_heads,
dtype=self.dtype,
tp_group=self.mapping.tp_group,
tp_size=self.mapping.tp_size,
tp_rank=self.mapping.tp_rank,
cp_group=self.mapping.cp_group,
cp_size=self.mapping.cp_size)
self.post_layernorm = LayerNorm(normalized_shape=self.hidden_size,
eps=norm_epsilon,
dtype=self.dtype)
self.mlp = MLP(hidden_size=self.hidden_size,
ffn_hidden_size=intermediate_size,
hidden_act=hidden_act,
dtype=self.dtype,
tp_group=self.mapping.tp_group,
tp_size=self.mapping.tp_size)
def forward(self, hidden_states):
residual = hidden_states
hidden_states = self.input_layernorm(hidden_states)
hidden_states = self.attention(hidden_states)
hidden_states = residual + hidden_states
residual = hidden_states
hidden_states = self.post_layernorm(hidden_states)
hidden_states = self.mlp(hidden_states)
hidden_states = residual + hidden_states
return hidden_states
class CLIPEncoder(Module):
def __init__(self, hidden_size, num_attention_heads,
max_position_embeddings, norm_epsilon, intermediate_size,
hidden_act, num_hidden_layers, mapping: Mapping, dtype):
super().__init__()
self.hidden_size = hidden_size
self.dtype = dtype
self.mapping = mapping
self.layers = ModuleList([
CLIPEncoderLayer(hidden_size=self.hidden_size,
num_attention_heads=num_attention_heads,
max_position_embeddings=max_position_embeddings,
norm_epsilon=norm_epsilon,
intermediate_size=intermediate_size,
hidden_act=hidden_act,
mapping=self.mapping,
dtype=self.dtype) for _ in range(num_hidden_layers)
])
def forward(self, inputs_embeds):
hidden_states = inputs_embeds
for layer in self.layers:
hidden_states = layer(hidden_states)
return hidden_states
