siglip-so400m-14-384-flash-attn2 / convert_siglip_to_hf.py

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	# 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)