506 lines
19 KiB
Python
506 lines
19 KiB
Python
from argparse import ArgumentParser
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from logging import getLogger
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from os import path
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from typing import Dict, List, Literal, Tuple, Union
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import numpy as np
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import torch
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from onnx import ModelProto, load, numpy_helper
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from onnx.checker import check_model
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from onnx.external_data_helper import (
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convert_model_to_external_data,
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set_external_data,
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write_external_data_tensors,
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)
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from onnxruntime import InferenceSession, OrtValue, SessionOptions
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from ...server.context import ServerContext
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from ..utils import ConversionContext, load_tensor
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logger = getLogger(__name__)
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def sum_weights(a: np.ndarray, b: np.ndarray) -> np.ndarray:
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logger.trace("summing weights with shapes: %s + %s", a.shape, b.shape)
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# if they are the same, simply add them
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if len(a.shape) == len(b.shape):
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return a + b
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# get the kernel size from the tensor with the higher rank
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if len(a.shape) > len(b.shape):
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kernel = a.shape[-2:]
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hr = a
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lr = b
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else:
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kernel = b.shape[-2:]
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hr = b
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lr = a
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if kernel == (1, 1):
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lr = np.expand_dims(lr, axis=(2, 3))
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return hr + lr
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def buffer_external_data_tensors(
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model: ModelProto,
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) -> Tuple[ModelProto, List[Tuple[str, OrtValue]]]:
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external_data = []
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for tensor in model.graph.initializer:
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name = tensor.name
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logger.trace("externalizing tensor: %s", name)
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if tensor.HasField("raw_data"):
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npt = numpy_helper.to_array(tensor)
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orv = OrtValue.ortvalue_from_numpy(npt)
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external_data.append((name, orv))
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# mimic set_external_data
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set_external_data(tensor, location="foo.bin")
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tensor.name = name
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tensor.ClearField("raw_data")
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return (model, external_data)
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def fix_initializer_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 fix_node_name(key: str):
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fixed_name = fix_initializer_name(key.replace("/", "_"))
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if fixed_name[0] == "_":
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return fixed_name[1:]
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else:
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return fixed_name
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def kernel_slice(x: int, y: int, shape: Tuple[int, int, int, int]) -> Tuple[int, int]:
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return (
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min(x, shape[2] - 1),
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min(y, shape[3] - 1),
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)
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def blend_loras(
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_conversion: ServerContext,
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base_name: Union[str, ModelProto],
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loras: List[Tuple[str, float]],
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model_type: Literal["text_encoder", "unet"],
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):
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# always load to CPU for blending
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device = torch.device("cpu")
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dtype = torch.float32
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base_model = base_name if isinstance(base_name, ModelProto) else load(base_name)
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lora_models = [load_tensor(name, map_location=device) for name, _weight in loras]
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if model_type == "text_encoder":
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lora_prefix = "lora_te_"
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else:
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lora_prefix = f"lora_{model_type}_"
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blended: Dict[str, np.ndarray] = {}
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for (lora_name, lora_weight), lora_model in zip(loras, lora_models):
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logger.debug("blending LoRA from %s with weight of %s", lora_name, lora_weight)
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if lora_model is None:
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logger.warning("unable to load tensor for LoRA")
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continue
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for key in lora_model.keys():
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if ".hada_w1_a" in key and lora_prefix in key:
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# LoHA
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base_key = key[: key.index(".hada_w1_a")].replace(lora_prefix, "")
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t1_key = key.replace("hada_w1_a", "hada_t1")
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t2_key = key.replace("hada_w1_a", "hada_t2")
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w1b_key = key.replace("hada_w1_a", "hada_w1_b")
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w2a_key = key.replace("hada_w1_a", "hada_w2_a")
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w2b_key = key.replace("hada_w1_a", "hada_w2_b")
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alpha_key = key[: key.index("hada_w1_a")] + "alpha"
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logger.trace(
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"blending weights for LoHA keys: %s, %s, %s, %s, %s",
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key,
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w1b_key,
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w2a_key,
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w2b_key,
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alpha_key,
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)
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w1a_weight = lora_model[key].to(dtype=dtype)
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w1b_weight = lora_model[w1b_key].to(dtype=dtype)
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w2a_weight = lora_model[w2a_key].to(dtype=dtype)
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w2b_weight = lora_model[w2b_key].to(dtype=dtype)
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t1_weight = lora_model.get(t1_key, None)
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t2_weight = lora_model.get(t2_key, None)
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dim = w1b_weight.size()[0]
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alpha = lora_model.get(alpha_key, dim).to(dtype).numpy()
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if t1_weight is not None and t2_weight is not None:
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t1_weight = t1_weight.to(dtype=dtype)
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t2_weight = t2_weight.to(dtype=dtype)
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logger.trace(
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"composing weights for LoHA node: (%s, %s, %s) * (%s, %s, %s)",
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t1_weight.shape,
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w1a_weight.shape,
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w1b_weight.shape,
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t2_weight.shape,
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w2a_weight.shape,
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w2b_weight.shape,
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)
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weights_1 = torch.einsum(
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"i j k l, j r, i p -> p r k l",
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t1_weight,
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w1b_weight,
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w1a_weight,
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)
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weights_2 = torch.einsum(
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"i j k l, j r, i p -> p r k l",
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t2_weight,
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w2b_weight,
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w2a_weight,
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)
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weights = weights_1 * weights_2
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np_weights = weights.numpy() * (alpha / dim)
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else:
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logger.trace(
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"blending weights for LoHA node: (%s @ %s) * (%s @ %s)",
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w1a_weight.shape,
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w1b_weight.shape,
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w2a_weight.shape,
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w2b_weight.shape,
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)
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weights = (w1a_weight @ w1b_weight) * (w2a_weight @ w2b_weight)
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np_weights = weights.numpy() * (alpha / dim)
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np_weights *= lora_weight
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if base_key in blended:
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logger.trace(
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"summing LoHA weights: %s + %s",
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blended[base_key].shape,
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np_weights.shape,
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)
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blended[base_key] += sum_weights(blended[base_key], np_weights)
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else:
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blended[base_key] = np_weights
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elif ".lora_down" in key and lora_prefix in key:
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# LoRA or LoCON
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base_key = key[: key.index(".lora_down")].replace(lora_prefix, "")
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mid_key = key.replace("lora_down", "lora_mid")
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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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logger.trace(
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"blending weights for LoRA keys: %s, %s, %s", key, up_key, alpha_key
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)
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down_weight = lora_model[key].to(dtype=dtype)
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up_weight = lora_model[up_key].to(dtype=dtype)
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mid_weight = None
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if mid_key in lora_model:
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mid_weight = lora_model[mid_key].to(dtype=dtype)
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dim = down_weight.size()[0]
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alpha = lora_model.get(alpha_key, dim)
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if not isinstance(alpha, int):
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alpha = alpha.to(dtype).numpy()
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kernel = down_weight.shape[-2:]
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if mid_weight is not None:
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kernel = mid_weight.shape[-2:]
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if len(down_weight.size()) == 2:
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# blend for nn.Linear
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logger.trace(
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"blending weights for Linear node: (%s @ %s) * %s",
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down_weight.shape,
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up_weight.shape,
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alpha,
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)
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weights = up_weight @ down_weight
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np_weights = weights.numpy() * (alpha / dim)
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elif len(down_weight.size()) == 4 and kernel == (
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1,
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1,
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):
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# blend for nn.Conv2d 1x1
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logger.trace(
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"blending weights for Conv 1x1 node: %s, %s, %s",
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down_weight.shape,
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up_weight.shape,
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alpha,
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)
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weights = (
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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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np_weights = weights.numpy() * (alpha / dim)
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elif len(down_weight.size()) == 4 and kernel == (
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3,
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3,
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):
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if mid_weight is not None:
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# blend for nn.Conv2d 3x3 with CP decomp
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logger.trace(
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"composing weights for Conv 3x3 node: %s, %s, %s, %s",
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down_weight.shape,
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up_weight.shape,
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mid_weight.shape,
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alpha,
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)
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weights = torch.zeros(
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(up_weight.shape[0], down_weight.shape[1], *kernel)
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)
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for w in range(kernel[0]):
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for h in range(kernel[1]):
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weights[:, :, w, h] = (
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up_weight.squeeze(3).squeeze(2)
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@ mid_weight[:, :, w, h]
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) @ down_weight.squeeze(3).squeeze(2)
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np_weights = weights.numpy() * (alpha / dim)
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else:
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# blend for nn.Conv2d 3x3
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logger.trace(
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"blending weights for Conv 3x3 node: %s, %s, %s",
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down_weight.shape,
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up_weight.shape,
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alpha,
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)
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weights = torch.zeros(
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(up_weight.shape[0], down_weight.shape[1], *kernel)
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)
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for w in range(kernel[0]):
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for h in range(kernel[1]):
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down_w, down_h = kernel_slice(w, h, down_weight.shape)
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up_w, up_h = kernel_slice(w, h, up_weight.shape)
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weights[:, :, w, h] = (
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up_weight[:, :, up_w, up_h]
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@ down_weight[:, :, down_w, down_h]
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)
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np_weights = weights.numpy() * (alpha / dim)
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else:
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logger.warning(
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"unknown LoRA node type at %s: %s",
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base_key,
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up_weight.shape[-2:],
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)
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continue
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np_weights *= lora_weight
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if base_key in blended:
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logger.trace(
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"summing weights: %s + %s",
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blended[base_key].shape,
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np_weights.shape,
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)
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blended[base_key] = sum_weights(blended[base_key], np_weights)
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else:
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blended[base_key] = np_weights
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logger.trace(
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"updating %s of %s initializers: %s",
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len(blended.keys()),
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len(base_model.graph.initializer),
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list(blended.keys()),
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)
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fixed_initializer_names = [
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fix_initializer_name(node.name) for node in base_model.graph.initializer
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]
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logger.trace("fixed initializer names: %s", fixed_initializer_names)
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fixed_node_names = [fix_node_name(node.name) for node in base_model.graph.node]
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logger.trace("fixed node names: %s", fixed_node_names)
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unmatched_keys = []
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for base_key, weights in blended.items():
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conv_key = base_key + "_Conv"
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gemm_key = base_key + "_Gemm"
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matmul_key = base_key + "_MatMul"
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logger.trace(
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"key %s has conv: %s, matmul: %s",
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base_key,
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conv_key in fixed_node_names,
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matmul_key in fixed_node_names,
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)
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if conv_key in fixed_node_names or gemm_key in fixed_node_names:
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if conv_key in fixed_node_names:
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conv_idx = fixed_node_names.index(conv_key)
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conv_node = base_model.graph.node[conv_idx]
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logger.trace(
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"found conv node %s using %s", conv_node.name, conv_node.input
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)
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else:
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conv_idx = fixed_node_names.index(gemm_key)
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conv_node = base_model.graph.node[conv_idx]
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logger.trace(
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"found gemm node %s using %s", conv_node.name, conv_node.input
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)
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# find weight initializer
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weight_name = [n for n in conv_node.input if ".weight" in n][0]
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weight_name = fix_initializer_name(weight_name)
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weight_idx = fixed_initializer_names.index(weight_name)
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weight_node = base_model.graph.initializer[weight_idx]
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logger.trace("found weight initializer: %s", weight_node.name)
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# blending
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onnx_weights = numpy_helper.to_array(weight_node)
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logger.trace(
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"found blended weights for conv: %s, %s",
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onnx_weights.shape,
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weights.shape,
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)
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if onnx_weights.shape[-2:] == (1, 1):
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if weights.shape[-2:] == (1, 1):
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blended = onnx_weights.squeeze((3, 2)) + weights.squeeze((3, 2))
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else:
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blended = onnx_weights.squeeze((3, 2)) + weights
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blended = np.expand_dims(blended, (2, 3))
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else:
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if onnx_weights.shape != weights.shape:
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logger.warning(
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"reshaping weights for mismatched Conv node: %s, %s",
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onnx_weights.shape,
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weights.shape,
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)
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blended = onnx_weights + weights.reshape(onnx_weights.shape)
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else:
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blended = onnx_weights + weights
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logger.trace("blended weight shape: %s", blended.shape)
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# replace the original initializer
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updated_node = numpy_helper.from_array(
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blended.astype(onnx_weights.dtype), weight_node.name
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)
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del base_model.graph.initializer[weight_idx]
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base_model.graph.initializer.insert(weight_idx, updated_node)
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elif matmul_key in fixed_node_names:
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weight_idx = fixed_node_names.index(matmul_key)
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weight_node = base_model.graph.node[weight_idx]
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logger.trace(
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"found matmul node %s using %s", weight_node.name, weight_node.input
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)
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# find the MatMul initializer
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matmul_name = [n for n in weight_node.input if "MatMul" in n][0]
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matmul_idx = fixed_initializer_names.index(matmul_name)
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matmul_node = base_model.graph.initializer[matmul_idx]
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logger.trace("found matmul initializer: %s", matmul_node.name)
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# blending
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onnx_weights = numpy_helper.to_array(matmul_node)
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logger.trace(
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"found blended weights for matmul: %s, %s",
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weights.shape,
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onnx_weights.shape,
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)
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blended = onnx_weights + weights.transpose()
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logger.trace("blended weight shape: %s", blended.shape)
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# replace the original initializer
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updated_node = numpy_helper.from_array(
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blended.astype(onnx_weights.dtype), matmul_node.name
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)
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del base_model.graph.initializer[matmul_idx]
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base_model.graph.initializer.insert(matmul_idx, updated_node)
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else:
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unmatched_keys.append(base_key)
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logger.debug(
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"node counts: %s -> %s, %s -> %s",
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len(fixed_initializer_names),
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len(base_model.graph.initializer),
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len(fixed_node_names),
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len(base_model.graph.node),
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)
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if len(unmatched_keys) > 0:
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logger.warning("could not find nodes for some keys: %s", unmatched_keys)
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return base_model
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if __name__ == "__main__":
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context = ConversionContext.from_environ()
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parser = ArgumentParser()
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parser.add_argument("--base", type=str)
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parser.add_argument("--dest", type=str)
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parser.add_argument("--type", type=str, choices=["text_encoder", "unet"])
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parser.add_argument("--lora_models", nargs="+", type=str, default=[])
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parser.add_argument("--lora_weights", nargs="+", type=float, default=[])
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args = parser.parse_args()
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logger.info(
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"merging %s with %s with weights: %s",
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args.lora_models,
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args.base,
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args.lora_weights,
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)
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default_weight = 1.0 / len(args.lora_models)
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while len(args.lora_weights) < len(args.lora_models):
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args.lora_weights.append(default_weight)
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blend_model = blend_loras(
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context,
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args.base,
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list(zip(args.lora_models, args.lora_weights)),
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args.type,
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)
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if args.dest is None or args.dest == "" or args.dest == ":load":
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# convert to external data and save to memory
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(bare_model, external_data) = buffer_external_data_tensors(blend_model)
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logger.info("saved external data for %s nodes", len(external_data))
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external_names, external_values = zip(*external_data)
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opts = SessionOptions()
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opts.add_external_initializers(list(external_names), list(external_values))
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sess = InferenceSession(
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bare_model.SerializeToString(),
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sess_options=opts,
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providers=["CPUExecutionProvider"],
|
|
)
|
|
logger.info(
|
|
"successfully loaded blended model: %s", [i.name for i in sess.get_inputs()]
|
|
)
|
|
else:
|
|
convert_model_to_external_data(
|
|
blend_model, all_tensors_to_one_file=True, location=f"lora-{args.type}.pb"
|
|
)
|
|
bare_model = write_external_data_tensors(blend_model, args.dest)
|
|
dest_file = path.join(args.dest, f"lora-{args.type}.onnx")
|
|
|
|
with open(dest_file, "w+b") as model_file:
|
|
model_file.write(bare_model.SerializeToString())
|
|
|
|
logger.info("successfully saved blended model: %s", dest_file)
|
|
|
|
check_model(dest_file)
|
|
|
|
logger.info("checked blended model")
|