137 lines
5.2 KiB
Python
137 lines
5.2 KiB
Python
from itertools import groupby
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from logging import getLogger
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from os import path
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from sys import argv
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from typing import List, Literal, Tuple
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import torch
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from onnx import TensorProto, load, numpy_helper
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from onnx.checker import check_model
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from onnx.external_data_helper import convert_model_to_external_data, write_external_data_tensors
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from safetensors.torch import load_file
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# from ..utils import ConversionContext
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logger = getLogger(__name__)
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###
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# everything in this file is still super experimental and may not produce valid ONNX models
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###
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def fix_name(key: str):
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# lora_unet_up_blocks_3_attentions_2_transformer_blocks_0_attn2_to_out_0.lora_down.weight
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# lora, unet, up_block.3.attentions.2.transformer_blocks.0.attn2.to_out.0
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return key.replace(".", "_")
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def merge_lora(base_name: str, lora_names: str, dest_path: str, dest_type: Literal["text_encoder", "unet"]):
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base_model = load(base_name)
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lora_models = [load_file(name) for name in lora_names.split(",")]
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lora_nodes: List[Tuple[int, TensorProto]] = []
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fixed_initialized_names = [fix_name(node.name) for node in base_model.graph.initializer]
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logger.info("fixed initializer names: %s", fixed_initialized_names)
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if dest_type == "text_encoder":
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lora_prefix = "lora_te_"
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elif dest_type == "unet":
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lora_prefix = "lora_unet_"
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else:
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lora_prefix = "lora_"
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for i in range(len(fixed_initialized_names)):
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base_key = fixed_initialized_names[i]
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base_node = base_model.graph.initializer[i]
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updates = []
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for lora_model in lora_models:
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for key in lora_model.keys():
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if ".lora_down" in key:
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original_key = key[: key.index(".lora_down")].replace(lora_prefix, "")
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bias_key = original_key + "_bias"
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weight_key = original_key + "_weight"
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if bias_key.startswith(base_key):
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print("found bias key:", base_key, bias_key)
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if weight_key == base_key:
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print("down for key:", base_key, weight_key)
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up_key = key.replace("lora_down", "lora_up")
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alpha_key = key[: key.index("lora_down")] + "alpha"
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down_weight = lora_model[key].to(dtype=torch.float32)
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up_weight = lora_model[up_key].to(dtype=torch.float32)
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dim = down_weight.size()[0]
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alpha = lora_model.get(alpha_key).numpy() or dim
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np_vals = numpy_helper.to_array(base_node)
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print("before shape", np_vals.shape, up_weight.shape, down_weight.shape)
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try:
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if len(up_weight.size()) == 2:
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squoze = up_weight @ down_weight
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print(squoze.shape)
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np_vals = np_vals + (squoze.numpy() * (alpha / dim))
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else:
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squoze = (
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(
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up_weight.squeeze(3).squeeze(2)
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@ down_weight.squeeze(3).squeeze(2)
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)
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.unsqueeze(2)
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.unsqueeze(3)
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)
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print(squoze.shape)
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np_vals = np_vals + (alpha * squoze.numpy())
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print("after shape", np_vals.shape)
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updates.append(np_vals)
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break
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except Exception as e:
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logger.exception("error blending weights with key %s", weight_key)
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if len(updates) == 0:
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logger.debug("no lora found for key %s", base_key)
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else:
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# blend updates together and append to lora_nodes
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logger.info("blending %s updated weights for key %s", len(updates), base_key)
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# TODO: allow individual alphas
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np_vals = sum(updates) / len(updates)
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retensor = numpy_helper.from_array(np_vals, base_node.name)
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logger.info("created new tensor with %s bytes", len(retensor.raw_data))
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# TypeError: does not support assignment
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lora_nodes.append((i, retensor))
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logger.info("updating %s of %s nodes", len(lora_nodes), len(base_model.graph.initializer))
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for idx, node in lora_nodes:
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del base_model.graph.initializer[idx]
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base_model.graph.initializer.insert(idx, node)
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# save it back to disk
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# TODO: save to memory instead
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convert_model_to_external_data(base_model, all_tensors_to_one_file=True, location=f"lora-{dest_type}-external.pb")
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bare_model = write_external_data_tensors(base_model, dest_path)
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dest_file = path.join(dest_path, f"lora-{dest_type}.onnx")
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with open(dest_file, "wb") as model_file:
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model_file.write(bare_model.SerializeToString())
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logger.info("model saved, checking...")
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check_model(dest_file)
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logger.info("model successfully exported")
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if __name__ == "__main__":
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merge_lora(*argv[1:])
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