# 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 class CLIPVisionTransformer(Module): def __init__(self, image_size, num_channels, patch_size, hidden_size, num_attention_heads, max_position_embeddings, norm_epsilon, intermediate_size, hidden_act, num_hidden_layers, require_ln_f, mapping: Mapping, dtype) -> None: super().__init__() self.hidden_size = hidden_size self.dtype = dtype self.mapping = mapping self.embeddings = CLIPVisionEmbeddings(image_size=image_size, num_channels=num_channels, patch_size=patch_size, hidden_size=hidden_size, dtype=self.dtype) self.pre_layernorm = LayerNorm(normalized_shape=self.hidden_size, eps=norm_epsilon, dtype=self.dtype) self.encoder = CLIPEncoder( 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, num_hidden_layers=num_hidden_layers, mapping=self.mapping, dtype=self.dtype) self.ln_f = None if require_ln_f: self.ln_f = LayerNorm(normalized_shape=self.hidden_size, eps=norm_epsilon, dtype=self.dtype) def forward(self, pixel_values): hidden_states = self.embeddings(pixel_values) hidden_states = self.pre_layernorm(hidden_states) hidden_states = self.encoder(inputs_embeds=hidden_states) if self.ln_f is None: return hidden_states return self.ln_f(hidden_states)