# coding=utf-8 # Copyright 2024 The HuggingFace Inc. team. # # 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. """Convert SigLIP checkpoints from the original repository. URL: https://github.com/google-research/big_vision/tree/main """ import argparse import collections from pathlib import Path import numpy as np import requests import torch from huggingface_hub import hf_hub_download from numpy import load from PIL import Image from transformers import SiglipConfig, SiglipImageProcessor, SiglipModel, SiglipProcessor, SiglipTokenizer from transformers.utils import logging logging.set_verbosity_info() logger = logging.get_logger(__name__) model_name_to_checkpoint = { # base checkpoints "siglip-base-patch16-224": "/Users/nielsrogge/Documents/SigLIP/webli_en_b16_224_63724782.npz", "siglip-base-patch16-256": "/Users/nielsrogge/Documents/SigLIP/webli_en_b16_256_60500360.npz", "siglip-base-patch16-384": "/Users/nielsrogge/Documents/SigLIP/webli_en_b16_384_68578854.npz", "siglip-base-patch16-512": "/Users/nielsrogge/Documents/SigLIP/webli_en_b16_512_68580893.npz", # large checkpoints "siglip-large-patch16-256": "/Users/nielsrogge/Documents/SigLIP/webli_en_l16_256_60552751.npz", "siglip-large-patch16-384": "/Users/nielsrogge/Documents/SigLIP/webli_en_l16_384_63634585.npz", # multilingual checkpoint "siglip-base-patch16-256-i18n": "/Users/nielsrogge/Documents/SigLIP/webli_i18n_b16_256_66117334.npz", # so400m checkpoints "siglip-so400m-patch14-384": "/Users/nielsrogge/Documents/SigLIP/webli_en_so400m_384_58765454.npz", } model_name_to_image_size = { "siglip-base-patch16-224": 224, "siglip-base-patch16-256": 256, "siglip-base-patch16-384": 384, "siglip-base-patch16-512": 512, "siglip-large-patch16-256": 256, "siglip-large-patch16-384": 384, "siglip-base-patch16-256-i18n": 256, "siglip-so400m-patch14-384": 384, } def get_siglip_config(model_name): config = SiglipConfig() vocab_size = 250000 if "i18n" in model_name else 32000 image_size = model_name_to_image_size[model_name] patch_size = 16 if "patch16" in model_name else 14 # size of the architecture config.vision_config.image_size = image_size config.vision_config.patch_size = patch_size config.text_config.vocab_size = vocab_size if "base" in model_name: pass elif "large" in model_name: config.text_config.hidden_size = 1024 config.text_config.intermediate_size = 4096 config.text_config.num_hidden_layers = 24 config.text_config.num_attention_heads = 16 config.vision_config.hidden_size = 1024 config.vision_config.intermediate_size = 4096 config.vision_config.num_hidden_layers = 24 config.vision_config.num_attention_heads = 16 elif "so400m" in model_name: config.text_config.hidden_size = 1152 config.text_config.intermediate_size = 4304 config.text_config.num_hidden_layers = 27 config.text_config.num_attention_heads = 16 config.vision_config.hidden_size = 1152 config.vision_config.intermediate_size = 4304 config.vision_config.num_hidden_layers = 27 config.vision_config.num_attention_heads = 16 else: raise ValueError("Model not supported") return config def create_rename_keys(config): rename_keys = [] # fmt: off # vision encoder rename_keys.append(("params/img/embedding/kernel", "vision_model.embeddings.patch_embedding.weight")) rename_keys.append(("params/img/embedding/bias", "vision_model.embeddings.patch_embedding.bias")) rename_keys.append(("params/img/pos_embedding", "vision_model.embeddings.position_embedding.weight")) for i in range(config.vision_config.num_hidden_layers): rename_keys.append((f"params/img/Transformer/encoderblock_{i}/LayerNorm_0/scale", f"vision_model.encoder.layers.{i}.layer_norm1.weight")) rename_keys.append((f"params/img/Transformer/encoderblock_{i}/LayerNorm_0/bias", f"vision_model.encoder.layers.{i}.layer_norm1.bias")) rename_keys.append((f"params/img/Transformer/encoderblock_{i}/LayerNorm_1/scale", f"vision_model.encoder.layers.{i}.layer_norm2.weight")) rename_keys.append((f"params/img/Transformer/encoderblock_{i}/LayerNorm_1/bias", f"vision_model.encoder.layers.{i}.layer_norm2.bias")) rename_keys.append((f"params/img/Transformer/encoderblock_{i}/MlpBlock_0/Dense_0/kernel", f"vision_model.encoder.layers.{i}.mlp.fc1.weight")) rename_keys.append((f"params/img/Transformer/encoderblock_{i}/MlpBlock_0/Dense_0/bias", f"vision_model.encoder.layers.{i}.mlp.fc1.bias")) rename_keys.append((f"params/img/Transformer/encoderblock_{i}/MlpBlock_0/Dense_1/kernel", f"vision_model.encoder.layers.{i}.mlp.fc2.weight")) rename_keys.append((f"params/img/Transformer/encoderblock_{i}/MlpBlock_0/Dense_1/bias", f"vision_model.encoder.layers.{i}.mlp.fc2.bias")) rename_keys.append((f"params/img/Transformer/encoderblock_{i}/MultiHeadDotProductAttention_0/key/kernel", f"vision_model.encoder.layers.{i}.self_attn.k_proj.weight")) rename_keys.append((f"params/img/Transformer/encoderblock_{i}/MultiHeadDotProductAttention_0/key/bias", f"vision_model.encoder.layers.{i}.self_attn.k_proj.bias")) rename_keys.append((f"params/img/Transformer/encoderblock_{i}/MultiHeadDotProductAttention_0/value/kernel", f"vision_model.encoder.layers.{i}.self_attn.v_proj.weight")) rename_keys.append((f"params/img/Transformer/encoderblock_{i}/MultiHeadDotProductAttention_0/value/bias", f"vision_model.encoder.layers.{i}.self_attn.v_proj.bias")) rename_keys.append((f"params/img/Transformer/encoderblock_{i}/MultiHeadDotProductAttention_0/query/kernel", f"vision_model.encoder.layers.{i}.self_attn.q_proj.weight")) rename_keys.append((f"params/img/Transformer/encoderblock_{i}/MultiHeadDotProductAttention_0/query/bias", f"vision_model.encoder.layers.{i}.self_attn.q_proj.bias")) rename_keys.append((f"params/img/Transformer/encoderblock_{i}/MultiHeadDotProductAttention_0/out/kernel", f"vision_model.encoder.layers.{i}.self_attn.out_proj.weight")) rename_keys.append((f"params/img/Transformer/encoderblock_{i}/MultiHeadDotProductAttention_0/out/bias", f"vision_model.encoder.layers.{i}.self_attn.out_proj.bias")) rename_keys.append(("params/img/Transformer/encoder_norm/scale", "vision_model.post_layernorm.weight")) rename_keys.append(("params/img/Transformer/encoder_norm/bias", "vision_model.post_layernorm.bias")) rename_keys.append(("params/img/MAPHead_0/probe", "vision_model.head.probe")) rename_keys.append(("params/img/MAPHead_0/LayerNorm_0/scale", "vision_model.head.layernorm.weight")) rename_keys.append(("params/img/MAPHead_0/LayerNorm_0/bias", "vision_model.head.layernorm.bias")) rename_keys.append(("params/img/MAPHead_0/MlpBlock_0/Dense_0/kernel", "vision_model.head.mlp.fc1.weight")) rename_keys.append(("params/img/MAPHead_0/MlpBlock_0/Dense_0/bias", "vision_model.head.mlp.fc1.bias")) rename_keys.append(("params/img/MAPHead_0/MlpBlock_0/Dense_1/kernel", "vision_model.head.mlp.fc2.weight")) rename_keys.append(("params/img/MAPHead_0/MlpBlock_0/Dense_1/bias", "vision_model.head.mlp.fc2.bias")) rename_keys.append(("params/img/MAPHead_0/MultiHeadDotProductAttention_0/out/kernel", "vision_model.head.attention.out_proj.weight")) rename_keys.append(("params/img/MAPHead_0/MultiHeadDotProductAttention_0/out/bias", "vision_model.head.attention.out_proj.bias")) # text encoder rename_keys.append(("params/txt/Embed_0/embedding", "text_model.embeddings.token_embedding.weight")) rename_keys.append(("params/txt/pos_embedding", "text_model.embeddings.position_embedding.weight")) for i in range(config.text_config.num_hidden_layers): rename_keys.append((f"params/txt/Encoder_0/encoderblock_{i}/LayerNorm_0/scale", f"text_model.encoder.layers.{i}.layer_norm1.weight")) rename_keys.append((f"params/txt/Encoder_0/encoderblock_{i}/LayerNorm_0/bias", f"text_model.encoder.layers.{i}.layer_norm1.bias")) rename_keys.append((f"params/txt/Encoder_0/encoderblock_{i}/LayerNorm_1/scale", f"text_model.encoder.layers.{i}.layer_norm2.weight")) rename_keys.append((f"params/txt/Encoder_0/encoderblock_{i}/LayerNorm_1/bias", f"text_model.encoder.layers.{i}.layer_norm2.bias")) rename_keys.append((f"params/txt/Encoder_0/encoderblock_{i}/MlpBlock_0/Dense_0/kernel", f"text_model.encoder.layers.{i}.mlp.fc1.weight")) rename_keys.append((f"params/txt/Encoder_0/encoderblock_{i}/MlpBlock_0/Dense_0/bias", f"text_model.encoder.layers.{i}.mlp.fc1.bias")) rename_keys.append((f"params/txt/Encoder_0/encoderblock_{i}/MlpBlock_0/Dense_1/kernel", f"text_model.encoder.layers.{i}.mlp.fc2.weight")) rename_keys.append((f"params/txt/Encoder_0/encoderblock_{i}/MlpBlock_0/Dense_1/bias", f"text_model.encoder.layers.{i}.mlp.fc2.bias")) rename_keys.append((f"params/txt/Encoder_0/encoderblock_{i}/MultiHeadDotProductAttention_0/key/kernel", f"text_model.encoder.layers.{i}.self_attn.k_proj.weight")) rename_keys.append((f"params/txt/Encoder_0/encoderblock_{i}/MultiHeadDotProductAttention_0/key/bias", f"text_model.encoder.layers.{i}.self_attn.k_proj.bias")) rename_keys.append((f"params/txt/Encoder_0/encoderblock_{i}/MultiHeadDotProductAttention_0/value/kernel", f"text_model.encoder.layers.{i}.self_attn.v_proj.weight")) rename_keys.append((f"params/txt/Encoder_0/encoderblock_{i}/MultiHeadDotProductAttention_0/value/bias", f"text_model.encoder.layers.{i}.self_attn.v_proj.bias")) rename_keys.append((f"params/txt/Encoder_0/encoderblock_{i}/MultiHeadDotProductAttention_0/query/kernel", f"text_model.encoder.layers.{i}.self_attn.q_proj.weight")) rename_keys.append((f"params/txt/Encoder_0/encoderblock_{i}/MultiHeadDotProductAttention_0/query/bias", f"text_model.encoder.layers.{i}.self_attn.q_proj.bias")) rename_keys.append((f"params/txt/Encoder_0/encoderblock_{i}/MultiHeadDotProductAttention_0/out/kernel", f"text_model.encoder.layers.{i}.self_attn.out_proj.weight")) rename_keys.append((f"params/txt/Encoder_0/encoderblock_{i}/MultiHeadDotProductAttention_0/out/bias", f"text_model.encoder.layers.{i}.self_attn.out_proj.bias")) rename_keys.append(("params/txt/Encoder_0/encoder_norm/scale", "text_model.final_layer_norm.weight")) rename_keys.append(("params/txt/Encoder_0/encoder_norm/bias", "text_model.final_layer_norm.bias")) rename_keys.append(("params/txt/head/kernel", "text_model.head.weight")) rename_keys.append(("params/txt/head/bias", "text_model.head.bias")) # learned temperature and bias rename_keys.append(("params/t", "logit_scale")) rename_keys.append(("params/b", "logit_bias")) # fmt: on return rename_keys def rename_key(dct, old, new, config): val = dct.pop(old) if ("out_proj" in new or "v_proj" in new or "k_proj" in new or "q_proj" in new) and "vision" in new: val = val.reshape(-1, config.vision_config.hidden_size) if ("out_proj" in new or "v_proj" in new or "k_proj" in new or "q_proj" in new) and "text" in new: val = val.reshape(-1, config.text_config.hidden_size) if "patch_embedding.weight" in new: val = val.transpose(3, 2, 0, 1) elif new.endswith("weight") and "position_embedding" not in new and "token_embedding" not in new: val = val.T if "position_embedding" in new and "vision" in new: val = val.reshape(-1, config.vision_config.hidden_size) if "position_embedding" in new and "text" in new: val = val.reshape(-1, config.text_config.hidden_size) if new.endswith("bias"): val = val.reshape(-1) dct[new] = torch.from_numpy(val) def read_in_q_k_v_head(state_dict, config): # read in individual input projection layers key_proj_weight = ( state_dict.pop("params/img/MAPHead_0/MultiHeadDotProductAttention_0/key/kernel") .reshape(-1, config.vision_config.hidden_size) .T ) key_proj_bias = state_dict.pop("params/img/MAPHead_0/MultiHeadDotProductAttention_0/key/bias").reshape(-1) value_proj_weight = ( state_dict.pop("params/img/MAPHead_0/MultiHeadDotProductAttention_0/value/kernel") .reshape(-1, config.vision_config.hidden_size) .T ) value_proj_bias = state_dict.pop("params/img/MAPHead_0/MultiHeadDotProductAttention_0/value/bias").reshape(-1) query_proj_weight = ( state_dict.pop("params/img/MAPHead_0/MultiHeadDotProductAttention_0/query/kernel") .reshape(-1, config.vision_config.hidden_size) .T ) query_proj_bias = state_dict.pop("params/img/MAPHead_0/MultiHeadDotProductAttention_0/query/bias").reshape(-1) # next, add them to the state dict as a single matrix + vector state_dict["vision_model.head.attention.in_proj_weight"] = torch.from_numpy( np.concatenate([query_proj_weight, key_proj_weight, value_proj_weight], axis=0) ) state_dict["vision_model.head.attention.in_proj_bias"] = torch.from_numpy( np.concatenate([query_proj_bias, key_proj_bias, value_proj_bias], axis=0) ) # We will verify our results on an image of cute cats def prepare_img(): url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) return image def flatten_nested_dict(params, parent_key="", sep="/"): items = [] for k, v in params.items(): new_key = parent_key + sep + k if parent_key else k if isinstance(v, collections.abc.MutableMapping): items.extend(flatten_nested_dict(v, new_key, sep=sep).items()) else: items.append((new_key, v)) return dict(items) @torch.no_grad() def convert_siglip_checkpoint(model_name, pytorch_dump_folder_path, verify_logits=True, push_to_hub=False): """ Copy/paste/tweak model's weights to our SigLIP structure. """ # define default SigLIP configuration config = get_siglip_config(model_name) # get checkpoint checkpoint = model_name_to_checkpoint[model_name] # get vocab file if "i18n" in model_name: vocab_file = "/Users/nielsrogge/Documents/SigLIP/multilingual_vocab/sentencepiece.model" else: vocab_file = "/Users/nielsrogge/Documents/SigLIP/english_vocab/sentencepiece.model" # load original state dict data = load(checkpoint) state_dict = flatten_nested_dict(data) # remove and rename some keys rename_keys = create_rename_keys(config) for src, dest in rename_keys: rename_key(state_dict, src, dest, config) # qkv matrices of attention pooling head need special treatment read_in_q_k_v_head(state_dict, config) # load HuggingFace model model = SiglipModel(config).eval() model.load_state_dict(state_dict) # create processor # important: make tokenizer not return attention_mask since original one doesn't require it image_size = config.vision_config.image_size size = {"height": image_size, "width": image_size} image_processor = SiglipImageProcessor(size=size) tokenizer = SiglipTokenizer(vocab_file=vocab_file, model_input_names=["input_ids"]) processor = SiglipProcessor(image_processor=image_processor, tokenizer=tokenizer) # verify on dummy images and texts url_1 = "https://cdn.openai.com/multimodal-neurons/assets/apple/apple-ipod.jpg" image_1 = Image.open(requests.get(url_1, stream=True).raw).convert("RGB") url_2 = "https://cdn.openai.com/multimodal-neurons/assets/apple/apple-blank.jpg" image_2 = Image.open(requests.get(url_2, stream=True).raw).convert("RGB") texts = ["an apple", "a picture of an apple"] inputs = processor(images=[image_1, image_2], text=texts, return_tensors="pt", padding="max_length") # verify input_ids against original ones if image_size == 224: filename = "siglip_pixel_values.pt" elif image_size == 256: filename = "siglip_pixel_values_256.pt" elif image_size == 384: filename = "siglip_pixel_values_384.pt" elif image_size == 512: filename = "siglip_pixel_values_512.pt" else: raise ValueError("Image size not supported") filepath = hf_hub_download(repo_id="nielsr/test-image", filename=filename, repo_type="dataset") original_pixel_values = torch.load(filepath) filepath = hf_hub_download(repo_id="nielsr/test-image", filename="siglip_input_ids.pt", repo_type="dataset") original_input_ids = torch.load(filepath) if "i18n" not in model_name: assert inputs.input_ids.tolist() == original_input_ids.tolist() print("Mean of original pixel values:", original_pixel_values.mean()) print("Mean of new pixel values:", inputs.pixel_values.mean()) # note: we're testing with original pixel values here since we don't have exact pixel values with torch.no_grad(): outputs = model(input_ids=inputs.input_ids, pixel_values=original_pixel_values) # with torch.no_grad(): # outputs = model(input_ids=inputs.input_ids, pixel_values=inputs.pixel_values) print(outputs.logits_per_image[:3, :3]) probs = torch.sigmoid(outputs.logits_per_image) # these are the probabilities print(f"{probs[0][0]:.1%} that image 0 is '{texts[0]}'") print(f"{probs[0][1]:.1%} that image 0 is '{texts[1]}'") if verify_logits: if model_name == "siglip-base-patch16-224": expected_slice = torch.tensor( [[-2.9621, -2.1672], [-0.2713, 0.2910]], ) elif model_name == "siglip-base-patch16-256": expected_slice = torch.tensor( [[-3.1146, -1.9894], [-0.7312, 0.6387]], ) elif model_name == "siglip-base-patch16-384": expected_slice = torch.tensor( [[-2.8098, -2.1891], [-0.4242, 0.4102]], ) elif model_name == "siglip-base-patch16-512": expected_slice = torch.tensor( [[-2.7899, -2.2668], [-0.4295, -0.0735]], ) elif model_name == "siglip-large-patch16-256": expected_slice = torch.tensor( [[-1.5827, -0.5801], [-0.9153, 0.1363]], ) elif model_name == "siglip-large-patch16-384": expected_slice = torch.tensor( [[-2.1523, -0.2899], [-0.2959, 0.7884]], ) elif model_name == "siglip-so400m-patch14-384": expected_slice = torch.tensor([[-1.2441, -0.6649], [-0.7060, 0.7374]]) elif model_name == "siglip-base-patch16-256-i18n": expected_slice = torch.tensor( [[-0.9064, 0.1073], [-0.0299, 0.5304]], ) assert torch.allclose(outputs.logits_per_image[:3, :3], expected_slice, atol=1e-4) print("Looks ok!") if pytorch_dump_folder_path is not None: Path(pytorch_dump_folder_path).mkdir(exist_ok=True) print(f"Saving model {model_name} to {pytorch_dump_folder_path}") model.save_pretrained(pytorch_dump_folder_path) print(f"Saving processor to {pytorch_dump_folder_path}") processor.save_pretrained(pytorch_dump_folder_path) if push_to_hub: model.push_to_hub(f"nielsr/{model_name}") processor.push_to_hub(f"nielsr/{model_name}") if __name__ == "__main__": parser = argparse.ArgumentParser() # Required parameters parser.add_argument( "--model_name", default="siglip-base-patch16-224", type=str, choices=model_name_to_checkpoint.keys(), help="Name of the model you'd like to convert.", ) parser.add_argument( "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory." ) parser.add_argument( "--verify_logits", action="store_false", help="Whether to verify logits against the original implementation.", ) parser.add_argument( "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub." ) args = parser.parse_args() convert_siglip_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.verify_logits, args.push_to_hub)