191 lines
6.2 KiB
Python
Executable File
191 lines
6.2 KiB
Python
Executable File
import torch
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from backend.utils import load_torch_file
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from backend.state_dict import transformers_convert, state_dict_prefix_replace
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from backend import operations, memory_management
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from backend.patcher.base import ModelPatcher
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from transformers import modeling_utils, CLIPVisionConfig, CLIPVisionModelWithProjection
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CLIP_VISION_G = {
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"attention_dropout": 0.0,
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"dropout": 0.0,
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"hidden_act": "gelu",
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"hidden_size": 1664,
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"image_size": 224,
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"initializer_factor": 1.0,
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"initializer_range": 0.02,
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"intermediate_size": 8192,
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"layer_norm_eps": 1e-05,
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"model_type": "clip_vision_model",
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"num_attention_heads": 16,
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"num_channels": 3,
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"num_hidden_layers": 48,
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"patch_size": 14,
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"projection_dim": 1280,
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"torch_dtype": "float32"
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}
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CLIP_VISION_H = {
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"attention_dropout": 0.0,
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"dropout": 0.0,
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"hidden_act": "gelu",
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"hidden_size": 1280,
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"image_size": 224,
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"initializer_factor": 1.0,
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"initializer_range": 0.02,
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"intermediate_size": 5120,
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"layer_norm_eps": 1e-05,
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"model_type": "clip_vision_model",
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"num_attention_heads": 16,
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"num_channels": 3,
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"num_hidden_layers": 32,
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"patch_size": 14,
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"projection_dim": 1024,
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"torch_dtype": "float32"
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}
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CLIP_VISION_VITL = {
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"attention_dropout": 0.0,
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"dropout": 0.0,
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"hidden_act": "quick_gelu",
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"hidden_size": 1024,
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"image_size": 224,
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"initializer_factor": 1.0,
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"initializer_range": 0.02,
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"intermediate_size": 4096,
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"layer_norm_eps": 1e-05,
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"model_type": "clip_vision_model",
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"num_attention_heads": 16,
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"num_channels": 3,
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"num_hidden_layers": 24,
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"patch_size": 14,
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"projection_dim": 768,
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"torch_dtype": "float32"
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}
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class Output:
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def __getitem__(self, key):
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return getattr(self, key)
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def __setitem__(self, key, item):
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setattr(self, key, item)
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def clip_preprocess(image, size=224):
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mean = torch.tensor([0.48145466, 0.4578275, 0.40821073], device=image.device, dtype=image.dtype)
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std = torch.tensor([0.26862954, 0.26130258, 0.27577711], device=image.device, dtype=image.dtype)
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image = image.movedim(-1, 1)
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if not (image.shape[2] == size and image.shape[3] == size):
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scale = (size / min(image.shape[2], image.shape[3]))
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image = torch.nn.functional.interpolate(image, size=(round(scale * image.shape[2]), round(scale * image.shape[3])), mode="bicubic", antialias=True)
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h = (image.shape[2] - size) // 2
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w = (image.shape[3] - size) // 2
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image = image[:, :, h:h + size, w:w + size]
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image = torch.clip((255. * image), 0, 255).round() / 255.0
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return (image - mean.view([3, 1, 1])) / std.view([3, 1, 1])
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class ClipVisionModel:
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def __init__(self, config):
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config = CLIPVisionConfig(**config)
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self.load_device = memory_management.text_encoder_device()
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self.offload_device = memory_management.text_encoder_offload_device()
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if memory_management.should_use_fp16(self.load_device, prioritize_performance=False):
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self.dtype = torch.float16
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else:
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self.dtype = torch.float32
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with operations.using_forge_operations():
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with modeling_utils.no_init_weights():
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self.model = CLIPVisionModelWithProjection(config)
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self.model.to(self.dtype)
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self.patcher = ModelPatcher(
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self.model,
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load_device=self.load_device,
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offload_device=self.offload_device
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)
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def load_sd(self, sd):
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return self.model.load_state_dict(sd, strict=False)
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def get_sd(self):
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return self.model.state_dict()
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def encode_image(self, image):
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memory_management.load_model_gpu(self.patcher)
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pixel_values = clip_preprocess(image.to(self.load_device))
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outputs = self.model(pixel_values=pixel_values, output_hidden_states=True)
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o = Output()
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o["last_hidden_state"] = outputs.last_hidden_state.to(memory_management.intermediate_device())
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o["penultimate_hidden_states"] = outputs.hidden_states[-2].to(memory_management.intermediate_device())
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o["image_embeds"] = outputs.image_embeds.to(memory_management.intermediate_device())
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return o
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def convert_to_transformers(sd, prefix):
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sd_k = sd.keys()
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if "{}transformer.resblocks.0.attn.in_proj_weight".format(prefix) in sd_k:
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keys_to_replace = {
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"{}class_embedding".format(prefix): "vision_model.embeddings.class_embedding",
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"{}conv1.weight".format(prefix): "vision_model.embeddings.patch_embedding.weight",
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"{}positional_embedding".format(prefix): "vision_model.embeddings.position_embedding.weight",
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"{}ln_post.bias".format(prefix): "vision_model.post_layernorm.bias",
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"{}ln_post.weight".format(prefix): "vision_model.post_layernorm.weight",
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"{}ln_pre.bias".format(prefix): "vision_model.pre_layrnorm.bias",
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"{}ln_pre.weight".format(prefix): "vision_model.pre_layrnorm.weight",
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}
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for x in keys_to_replace:
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if x in sd_k:
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sd[keys_to_replace[x]] = sd.pop(x)
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if "{}proj".format(prefix) in sd_k:
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sd['visual_projection.weight'] = sd.pop("{}proj".format(prefix)).transpose(0, 1)
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sd = transformers_convert(sd, prefix, "vision_model.", 48)
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else:
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replace_prefix = {prefix: ""}
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sd = state_dict_prefix_replace(sd, replace_prefix)
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return sd
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def load_clipvision_from_sd(sd, prefix="", convert_keys=False):
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if convert_keys:
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sd = convert_to_transformers(sd, prefix)
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if "vision_model.encoder.layers.47.layer_norm1.weight" in sd:
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config = CLIP_VISION_G
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elif "vision_model.encoder.layers.30.layer_norm1.weight" in sd:
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config = CLIP_VISION_H
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elif "vision_model.encoder.layers.22.layer_norm1.weight" in sd:
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config = CLIP_VISION_VITL
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else:
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return None
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clip = ClipVisionModel(config)
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m, u = clip.load_sd(sd)
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if len(m) > 0:
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print("extra clip vision:", m)
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u = set(u)
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keys = list(sd.keys())
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for k in keys:
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if k not in u:
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t = sd.pop(k)
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del t
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return clip
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def load(ckpt_path):
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sd = load_torch_file(ckpt_path)
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if "visual.transformer.resblocks.0.attn.in_proj_weight" in sd:
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return load_clipvision_from_sd(sd, prefix="visual.", convert_keys=True)
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else:
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return load_clipvision_from_sd(sd)
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