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# coding=utf-8
# Copyright 2021 The HuggingFace Inc. 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.
import argparse
import os.path
import torch
from open_clip import create_model
from transformers import CLIPConfig, CLIPVisionConfig, CLIPTextConfig, CLIPModel
def copy_attn_layer(hf_attn_layer, pt_attn_layer):
assert(hf_attn_layer.num_heads == pt_attn_layer.num_heads)
q_proj, k_proj, v_proj = pt_attn_layer.in_proj_weight.chunk(3, dim=0)
q_proj_bias, k_proj_bias, v_proj_bias = pt_attn_layer.in_proj_bias.chunk(3, dim=0)
hf_attn_layer.q_proj.weight.copy_(q_proj)
hf_attn_layer.q_proj.bias.copy_(q_proj_bias)
hf_attn_layer.k_proj.weight.copy_(k_proj)
hf_attn_layer.k_proj.bias.copy_(k_proj_bias)
hf_attn_layer.v_proj.weight.copy_(v_proj)
hf_attn_layer.v_proj.bias.copy_(v_proj_bias)
hf_attn_layer.out_proj.weight.copy_(pt_attn_layer.out_proj.weight)
hf_attn_layer.out_proj.bias.copy_(pt_attn_layer.out_proj.bias)
def copy_mlp(hf_mlp, pt_mlp):
copy_linear(hf_mlp.fc1, pt_mlp.c_fc)
copy_linear(hf_mlp.fc2, pt_mlp.c_proj)
def copy_linear(hf_linear, pt_linear):
hf_linear.weight.copy_(pt_linear.weight)
hf_linear.bias.copy_(pt_linear.bias)
def copy_layer(hf_layer, pt_layer):
# copy layer norms
copy_linear(hf_layer.layer_norm1, pt_layer.ln_1)
copy_linear(hf_layer.layer_norm2, pt_layer.ln_2)
# copy MLP
copy_mlp(hf_layer.mlp, pt_layer.mlp)
# copy attn
copy_attn_layer(hf_layer.self_attn, pt_layer.attn)
def copy_layers(hf_layers, pt_layers):
for hf_layer, pt_layer in zip(hf_layers, pt_layers):
copy_layer(hf_layer, pt_layer)
def copy_encoder(hf_encoder, pt_model):
# copy embeds
hf_encoder.embeddings.token_embedding.weight.copy_(pt_model.token_embedding.weight)
hf_encoder.embeddings.position_embedding.weight.copy_(pt_model.positional_embedding)
# copy layer norm
copy_linear(hf_encoder.final_layer_norm, pt_model.ln_final)
# copy hidden layers
copy_layers(hf_encoder.encoder.layers, pt_model.transformer.resblocks)
def copy_text_model_and_projection(hf_model, pt_model):
# copy projection
hf_model.text_projection.weight.copy_(pt_model.text_projection.T)
# copy text encoder
copy_encoder(hf_model.text_model, pt_model)
def copy_vison_model_and_projection(hf_model, pt_model):
# copy projection
hf_model.visual_projection.weight.copy_(pt_model.visual.proj.T)
# copy layer norms
copy_linear(hf_model.vision_model.pre_layrnorm, pt_model.visual.ln_pre)
copy_linear(hf_model.vision_model.post_layernorm, pt_model.visual.ln_post)
# copy embeds
hf_model.vision_model.embeddings.patch_embedding.weight.copy_(pt_model.visual.conv1.weight)
hf_model.vision_model.embeddings.class_embedding.copy_(pt_model.visual.class_embedding)
hf_model.vision_model.embeddings.position_embedding.weight.copy_(pt_model.visual.positional_embedding)
# copy encoder
copy_layers(hf_model.vision_model.encoder.layers, pt_model.visual.transformer.resblocks)
@torch.no_grad()
def convert_clip_checkpoint(model, pretrained, pytorch_dump_folder_path, config_path=None):
"""
Copy/paste/tweak model's weights to transformers design.
"""
if config_path is not None:
config = CLIPConfig.from_pretrained(config_path)
else:
config = CLIPConfig(
projection_dim=512,
text_config_dict=dict(hidden_act='gelu'),
vision_config_dict=dict(hidden_act='gelu'))
#CLIPVisionConfig()
#CLIPTextConfig()
# L14
# config = CLIPConfig(
# projection_dim=768,
# text_config_dict=dict(
# hidden_act='gelu',
# hidden_size=768,
# intermediate_size=3072,
# num_attention_heads=12,
# ),
# vision_config_dict=dict(
# hidden_act='gelu',
# num_hidden_layers=24,
# patch_size=14,
# hidden_size=1024,
# intermediate_size=4096,
# num_attention_heads=16,
# ))
## H14
#
# config = CLIPConfig(
# projection_dim=1024,
# text_config_dict=dict(
# hidden_act='gelu',
# hidden_size=1024,
# intermediate_size=4096,
# num_attention_heads=16,
# num_hidden_layers=24,
# ),
# vision_config_dict=dict(
# hidden_act='gelu',
# num_hidden_layers=32,
# patch_size=14,
# hidden_size=1280,
# intermediate_size=5120,
# num_attention_heads=16,
# ))
## B16 / B16 plus
config = CLIPConfig(
projection_dim=512,
text_config_dict=dict(
hidden_act='gelu',
),
vision_config_dict=dict(
hidden_act='gelu',
num_hidden_layers=12,
patch_size=16
))
# config = CLIPConfig(
# projection_dim=640,
# text_config_dict=dict(
# hidden_act='gelu',
# hidden_size=640,
# intermediate_size=2560,
# num_attention_heads=10,
# ),
# vision_config_dict=dict(
# hidden_act='gelu',
# num_hidden_layers=12,
# patch_size=16,
# hidden_size=896,
# num_attention_heads=14,
# intermediate_size=3584,
# image_size=240,
# ))
# ## g14
# config = CLIPConfig(
# projection_dim=1024,
# text_config_dict=dict(
# hidden_act='gelu',
# hidden_size=1024,
# intermediate_size=4096,
# num_attention_heads=16,
# num_hidden_layers=24,
# ),
# vision_config_dict=dict(
# hidden_act='gelu',
# num_hidden_layers=40,
# patch_size=14,
# hidden_size=1408,
# intermediate_size=6144,
# num_attention_heads=16,
# ))
print(config)
hf_model = CLIPModel(config).eval()
print(hf_model)
pt_model = create_model(model, pretrained=pretrained, precision='fp32')
pt_model = pt_model.eval()
print(pt_model)
copy_text_model_and_projection(hf_model, pt_model)
copy_vison_model_and_projection(hf_model, pt_model)
hf_model.logit_scale = pt_model.logit_scale
input_ids = torch.arange(0, 77).unsqueeze(0)
pixel_values = torch.randn(1, 3, 224, 224)
hf_image_embed = hf_model.get_image_features(pixel_values)
hf_text_embed = hf_model.get_text_features(input_ids)
pt_image_embed = pt_model.encode_image(pixel_values)
pt_text_embed = pt_model.encode_text(input_ids)
print((pt_image_embed - hf_image_embed).sum())
print((pt_text_embed - hf_text_embed).sum())
print((pt_text_embed - hf_text_embed).max(), (pt_text_embed - hf_text_embed).min())
assert torch.allclose(hf_image_embed, pt_image_embed, atol=1e-4)
assert torch.allclose(hf_text_embed, pt_text_embed, atol=1e-4)
hf_logits_per_image, hf_logits_per_text = hf_model(
input_ids=input_ids, pixel_values=pixel_values, return_dict=False
)[:2]
pt_image_features, pt_text_features, logit_scale = pt_model(pixel_values, input_ids)
pt_logits_per_image = pt_image_features @ pt_text_features.T * logit_scale
pt_logits_per_text = pt_logits_per_image.T
assert torch.allclose(hf_logits_per_image, pt_logits_per_image, atol=1e-4)
assert torch.allclose(hf_logits_per_text, pt_logits_per_text, atol=1e-4)
if os.path.exists(pretrained):
pretrained = os.path.splitext(os.path.basename(pretrained))[0]
hf_model.save_pretrained(f'{model}-{pretrained}')
torch.save(pt_model.state_dict(), f'{model}-{pretrained}/open_clip_pytorch_model.bin')
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
parser.add_argument("--model", default=None, type=str, help="Path to fairseq checkpoint")
parser.add_argument("--pretrained", default=None, type=str, help="Path to fairseq checkpoint")
parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
args = parser.parse_args()
convert_clip_checkpoint(args.model, args.pretrained, args.pytorch_dump_folder_path, args.config_path)
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