fix(api): test LoRA blending code
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@ -1,22 +1,15 @@
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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 Any, Dict, List, Literal, Optional, Tuple, Union
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import numpy as np
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import torch
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from onnx import ModelProto, NodeProto, 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 onnx import ModelProto, NodeProto, TensorProto, load, numpy_helper
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from onnx.external_data_helper import set_external_data
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from onnxruntime import OrtValue
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from scipy import interpolate
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from ...server.context import ServerContext
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from ..utils import ConversionContext, load_tensor
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from ..utils import load_tensor
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logger = getLogger(__name__)
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@ -161,6 +154,245 @@ def kernel_slice(x: int, y: int, shape: Tuple[int, int, int, int]) -> Tuple[int,
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)
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def blend_weights_loha(
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key: str, lora_prefix: str, lora_model: Dict, dtype
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) -> Tuple[str, np.ndarray]:
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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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return base_key, np_weights
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def blend_weights_lora(
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key: str, lora_prefix: str, lora_model: Dict, dtype
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) -> Tuple[str, np.ndarray]:
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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("blending weights for LoRA keys: %s, %s, %s", key, up_key, alpha_key)
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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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(up_weight.squeeze(3).squeeze(2) @ down_weight.squeeze(3).squeeze(2))
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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((up_weight.shape[0], down_weight.shape[1], *kernel))
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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) @ 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((up_weight.shape[0], down_weight.shape[1], *kernel))
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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] @ 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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# TODO: should this be None?
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np_weights = np.zeros((1, 1, 1, 1))
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return base_key, np_weights
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def blend_node_conv_gemm(weight_node, weights) -> TensorProto:
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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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# TODO: test if this can be replaced with interpolation, simply reshaping is pretty sus
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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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return numpy_helper.from_array(blended.astype(onnx_weights.dtype), weight_node.name)
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def blend_node_matmul(matmul_node, weights, matmul_key) -> TensorProto:
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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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t_weights = weights.transpose()
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if weights.shape != onnx_weights.shape and t_weights.shape != onnx_weights.shape:
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logger.warning(
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"weight shapes do not match for %s: %s vs %s",
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matmul_key,
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weights.shape,
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onnx_weights.shape,
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)
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t_weights = interp_to_match(weights, onnx_weights).transpose()
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blended = onnx_weights + t_weights
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logger.trace("blended weight shape: %s, %s", blended.shape, onnx_weights.dtype)
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# replace the original initializer
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return numpy_helper.from_array(blended.astype(onnx_weights.dtype), matmul_node.name)
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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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@ -194,205 +426,41 @@ def blend_loras(
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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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base_key, np_weights = blend_weights_loha(
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key, lora_prefix, lora_model, dtype
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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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np_weights = 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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blended[base_key] = sum_weights(blended[base_key], np_weights)
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else:
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logger.trace(
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"adding LoHA weights: %s",
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np_weights.shape,
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)
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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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base_key, np_weights = blend_weights_lora(
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key, lora_prefix, lora_model, dtype
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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] = (
|
||||
up_weight.squeeze(3).squeeze(2)
|
||||
@ mid_weight[:, :, w, h]
|
||||
) @ down_weight.squeeze(3).squeeze(2)
|
||||
|
||||
np_weights = weights.numpy() * (alpha / dim)
|
||||
else:
|
||||
# blend for nn.Conv2d 3x3
|
||||
logger.trace(
|
||||
"blending weights for Conv 3x3 node: %s, %s, %s",
|
||||
down_weight.shape,
|
||||
up_weight.shape,
|
||||
alpha,
|
||||
)
|
||||
weights = torch.zeros(
|
||||
(up_weight.shape[0], down_weight.shape[1], *kernel)
|
||||
)
|
||||
|
||||
for w in range(kernel[0]):
|
||||
for h in range(kernel[1]):
|
||||
down_w, down_h = kernel_slice(w, h, down_weight.shape)
|
||||
up_w, up_h = kernel_slice(w, h, up_weight.shape)
|
||||
|
||||
weights[:, :, w, h] = (
|
||||
up_weight[:, :, up_w, up_h]
|
||||
@ down_weight[:, :, down_w, down_h]
|
||||
)
|
||||
|
||||
np_weights = weights.numpy() * (alpha / dim)
|
||||
else:
|
||||
logger.warning(
|
||||
"unknown LoRA node type at %s: %s",
|
||||
base_key,
|
||||
up_weight.shape[-2:],
|
||||
)
|
||||
continue
|
||||
|
||||
np_weights *= lora_weight
|
||||
np_weights = np_weights * lora_weight
|
||||
if base_key in blended:
|
||||
logger.trace(
|
||||
"summing weights: %s + %s",
|
||||
"summing LoRA weights: %s + %s",
|
||||
blended[base_key].shape,
|
||||
np_weights.shape,
|
||||
)
|
||||
blended[base_key] = sum_weights(blended[base_key], np_weights)
|
||||
else:
|
||||
logger.trace(
|
||||
"adding LoRA weights: %s",
|
||||
np_weights.shape,
|
||||
)
|
||||
blended[base_key] = np_weights
|
||||
|
||||
# rewrite node names for XL
|
||||
|
|
@ -400,7 +468,7 @@ def blend_loras(
|
|||
nodes = list(base_model.graph.node)
|
||||
blended = fix_xl_names(blended, nodes)
|
||||
|
||||
logger.trace(
|
||||
logger.debug(
|
||||
"updating %s of %s initializers",
|
||||
len(blended.keys()),
|
||||
len(base_model.graph.initializer),
|
||||
|
|
@ -409,10 +477,7 @@ def blend_loras(
|
|||
fixed_initializer_names = [
|
||||
fix_initializer_name(node.name) for node in base_model.graph.initializer
|
||||
]
|
||||
logger.trace("fixed initializer names: %s", fixed_initializer_names)
|
||||
|
||||
fixed_node_names = [fix_node_name(node.name) for node in base_model.graph.node]
|
||||
logger.trace("fixed node names: %s", fixed_node_names)
|
||||
|
||||
unmatched_keys = []
|
||||
for base_key, weights in blended.items():
|
||||
|
|
@ -421,9 +486,10 @@ def blend_loras(
|
|||
matmul_key = base_key + "_MatMul"
|
||||
|
||||
logger.trace(
|
||||
"key %s has conv: %s, matmul: %s",
|
||||
"key %s has conv: %s, gemm: %s, matmul: %s",
|
||||
base_key,
|
||||
conv_key in fixed_node_names,
|
||||
gemm_key in fixed_node_names,
|
||||
matmul_key in fixed_node_names,
|
||||
)
|
||||
|
||||
|
|
@ -449,38 +515,9 @@ def blend_loras(
|
|||
weight_node = base_model.graph.initializer[weight_idx]
|
||||
logger.trace("found weight initializer: %s", weight_node.name)
|
||||
|
||||
# blending
|
||||
onnx_weights = numpy_helper.to_array(weight_node)
|
||||
logger.trace(
|
||||
"found blended weights for conv: %s, %s",
|
||||
onnx_weights.shape,
|
||||
weights.shape,
|
||||
)
|
||||
# replace the previous node
|
||||
updated_node = blend_node_conv_gemm(weight_node, weights)
|
||||
|
||||
if onnx_weights.shape[-2:] == (1, 1):
|
||||
if weights.shape[-2:] == (1, 1):
|
||||
blended = onnx_weights.squeeze((3, 2)) + weights.squeeze((3, 2))
|
||||
else:
|
||||
blended = onnx_weights.squeeze((3, 2)) + weights
|
||||
|
||||
blended = np.expand_dims(blended, (2, 3))
|
||||
else:
|
||||
if onnx_weights.shape != weights.shape:
|
||||
logger.warning(
|
||||
"reshaping weights for mismatched Conv node: %s, %s",
|
||||
onnx_weights.shape,
|
||||
weights.shape,
|
||||
)
|
||||
blended = onnx_weights + weights.reshape(onnx_weights.shape)
|
||||
else:
|
||||
blended = onnx_weights + weights
|
||||
|
||||
logger.trace("blended weight shape: %s", blended.shape)
|
||||
|
||||
# replace the original initializer
|
||||
updated_node = numpy_helper.from_array(
|
||||
blended.astype(onnx_weights.dtype), weight_node.name
|
||||
)
|
||||
del base_model.graph.initializer[weight_idx]
|
||||
base_model.graph.initializer.insert(weight_idx, updated_node)
|
||||
elif matmul_key in fixed_node_names:
|
||||
|
|
@ -497,36 +534,9 @@ def blend_loras(
|
|||
matmul_node = base_model.graph.initializer[matmul_idx]
|
||||
logger.trace("found matmul initializer: %s", matmul_node.name)
|
||||
|
||||
# blending
|
||||
onnx_weights = numpy_helper.to_array(matmul_node)
|
||||
logger.trace(
|
||||
"found blended weights for matmul: %s, %s",
|
||||
weights.shape,
|
||||
onnx_weights.shape,
|
||||
)
|
||||
# replace the previous node
|
||||
updated_node = blend_node_matmul(matmul_node, weights, matmul_key)
|
||||
|
||||
t_weights = weights.transpose()
|
||||
if (
|
||||
weights.shape != onnx_weights.shape
|
||||
and t_weights.shape != onnx_weights.shape
|
||||
):
|
||||
logger.warning(
|
||||
"weight shapes do not match for %s: %s vs %s",
|
||||
matmul_key,
|
||||
weights.shape,
|
||||
onnx_weights.shape,
|
||||
)
|
||||
t_weights = interp_to_match(weights, onnx_weights).transpose()
|
||||
|
||||
blended = onnx_weights + t_weights
|
||||
logger.trace(
|
||||
"blended weight shape: %s, %s", blended.shape, onnx_weights.dtype
|
||||
)
|
||||
|
||||
# replace the original initializer
|
||||
updated_node = numpy_helper.from_array(
|
||||
blended.astype(onnx_weights.dtype), matmul_node.name
|
||||
)
|
||||
del base_model.graph.initializer[matmul_idx]
|
||||
base_model.graph.initializer.insert(matmul_idx, updated_node)
|
||||
else:
|
||||
|
|
@ -565,63 +575,3 @@ def interp_to_match(ref: np.ndarray, resize: np.ndarray) -> np.ndarray:
|
|||
logger.debug("weights after interpolation: %s", output.shape)
|
||||
|
||||
return output
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
context = ConversionContext.from_environ()
|
||||
parser = ArgumentParser()
|
||||
parser.add_argument("--base", type=str)
|
||||
parser.add_argument("--dest", type=str)
|
||||
parser.add_argument("--type", type=str, choices=["text_encoder", "unet"])
|
||||
parser.add_argument("--lora_models", nargs="+", type=str, default=[])
|
||||
parser.add_argument("--lora_weights", nargs="+", type=float, default=[])
|
||||
|
||||
args = parser.parse_args()
|
||||
logger.info(
|
||||
"merging %s with %s with weights: %s",
|
||||
args.lora_models,
|
||||
args.base,
|
||||
args.lora_weights,
|
||||
)
|
||||
|
||||
default_weight = 1.0 / len(args.lora_models)
|
||||
while len(args.lora_weights) < len(args.lora_models):
|
||||
args.lora_weights.append(default_weight)
|
||||
|
||||
blend_model = blend_loras(
|
||||
context,
|
||||
args.base,
|
||||
list(zip(args.lora_models, args.lora_weights)),
|
||||
args.type,
|
||||
)
|
||||
if args.dest is None or args.dest == "" or args.dest == ":load":
|
||||
# convert to external data and save to memory
|
||||
(bare_model, external_data) = buffer_external_data_tensors(blend_model)
|
||||
logger.info("saved external data for %s nodes", len(external_data))
|
||||
|
||||
external_names, external_values = zip(*external_data)
|
||||
opts = SessionOptions()
|
||||
opts.add_external_initializers(list(external_names), list(external_values))
|
||||
sess = InferenceSession(
|
||||
bare_model.SerializeToString(),
|
||||
sess_options=opts,
|
||||
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")
|
||||
|
|
|
|||
|
|
@ -0,0 +1,74 @@
|
|||
from argparse import ArgumentParser
|
||||
from onnx_web.convert.diffusion.lora import blend_loras, buffer_external_data_tensors
|
||||
from os import path
|
||||
from onnx.checker import check_model
|
||||
from onnx.external_data_helper import (
|
||||
convert_model_to_external_data,
|
||||
write_external_data_tensors,
|
||||
)
|
||||
from onnxruntime import InferenceSession, SessionOptions
|
||||
from logging import getLogger
|
||||
|
||||
from onnx_web.convert.utils import ConversionContext
|
||||
|
||||
logger = getLogger(__name__)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
context = ConversionContext.from_environ()
|
||||
parser = ArgumentParser()
|
||||
parser.add_argument("--base", type=str)
|
||||
parser.add_argument("--dest", type=str)
|
||||
parser.add_argument("--type", type=str, choices=["text_encoder", "unet"])
|
||||
parser.add_argument("--lora_models", nargs="+", type=str, default=[])
|
||||
parser.add_argument("--lora_weights", nargs="+", type=float, default=[])
|
||||
|
||||
args = parser.parse_args()
|
||||
logger.info(
|
||||
"merging %s with %s with weights: %s",
|
||||
args.lora_models,
|
||||
args.base,
|
||||
args.lora_weights,
|
||||
)
|
||||
|
||||
default_weight = 1.0 / len(args.lora_models)
|
||||
while len(args.lora_weights) < len(args.lora_models):
|
||||
args.lora_weights.append(default_weight)
|
||||
|
||||
blend_model = blend_loras(
|
||||
context,
|
||||
args.base,
|
||||
list(zip(args.lora_models, args.lora_weights)),
|
||||
args.type,
|
||||
)
|
||||
if args.dest is None or args.dest == "" or args.dest == ":load":
|
||||
# convert to external data and save to memory
|
||||
(bare_model, external_data) = buffer_external_data_tensors(blend_model)
|
||||
logger.info("saved external data for %s nodes", len(external_data))
|
||||
|
||||
external_names, external_values = zip(*external_data)
|
||||
opts = SessionOptions()
|
||||
opts.add_external_initializers(list(external_names), list(external_values))
|
||||
sess = InferenceSession(
|
||||
bare_model.SerializeToString(),
|
||||
sess_options=opts,
|
||||
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")
|
||||
|
|
@ -1,11 +1,16 @@
|
|||
import unittest
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
from onnx import GraphProto, ModelProto, NodeProto
|
||||
from onnx.numpy_helper import from_array
|
||||
|
||||
from onnx_web.convert.diffusion.lora import (
|
||||
blend_loras,
|
||||
blend_node_conv_gemm,
|
||||
blend_node_matmul,
|
||||
blend_weights_loha,
|
||||
blend_weights_lora,
|
||||
buffer_external_data_tensors,
|
||||
fix_initializer_name,
|
||||
fix_node_name,
|
||||
|
|
@ -151,6 +156,23 @@ class KernelSliceTests(unittest.TestCase):
|
|||
)
|
||||
|
||||
|
||||
class InterpToMatchTests(unittest.TestCase):
|
||||
def test_same_shape(self):
|
||||
ref = np.zeros((4, 4))
|
||||
resize = np.zeros((4, 4))
|
||||
self.assertEqual(interp_to_match(ref, resize).shape, (4, 4))
|
||||
|
||||
def test_different_one_dim(self):
|
||||
ref = np.zeros((4, 2))
|
||||
resize = np.zeros((4, 4))
|
||||
self.assertEqual(interp_to_match(ref, resize).shape, (4, 4))
|
||||
|
||||
def test_different_both_dims(self):
|
||||
ref = np.zeros((2, 2))
|
||||
resize = np.zeros((4, 4))
|
||||
self.assertEqual(interp_to_match(ref, resize).shape, (4, 4))
|
||||
|
||||
|
||||
class BlendLoRATests(unittest.TestCase):
|
||||
def test_blend_unet(self):
|
||||
"""
|
||||
|
|
@ -183,18 +205,131 @@ class BlendLoRATests(unittest.TestCase):
|
|||
pass
|
||||
|
||||
|
||||
class InterpToMatchTests(unittest.TestCase):
|
||||
def test_same_shape(self):
|
||||
ref = np.zeros((4, 4))
|
||||
resize = np.zeros((4, 4))
|
||||
self.assertEqual(interp_to_match(ref, resize).shape, (4, 4))
|
||||
class BlendWeightsLoHATests(unittest.TestCase):
|
||||
def test_blend_t1_t2(self):
|
||||
# blend einsum: i j k l, j r, i p -> p r k l
|
||||
i = 32
|
||||
j = 4
|
||||
k = 1
|
||||
l = 1
|
||||
p = 2
|
||||
r = 4
|
||||
|
||||
def test_different_one_dim(self):
|
||||
ref = np.zeros((4, 2))
|
||||
resize = np.zeros((4, 4))
|
||||
self.assertEqual(interp_to_match(ref, resize).shape, (4, 4))
|
||||
model = {
|
||||
"foo.hada_t1": torch.from_numpy(np.ones((i, j, k, l))),
|
||||
"foo.hada_t2": torch.from_numpy(np.ones((i, j, k, l))),
|
||||
"foo.hada_w1_a": torch.from_numpy(np.ones((i, p))),
|
||||
"foo.hada_w1_b": torch.from_numpy(np.ones((j, r))),
|
||||
"foo.hada_w2_a": torch.from_numpy(np.ones((i, p))),
|
||||
"foo.hada_w2_b": torch.from_numpy(np.ones((j, r))),
|
||||
"foo.alpha": torch.tensor(1),
|
||||
}
|
||||
key, result = blend_weights_loha("foo.hada_w1_a", "", model, torch.float32)
|
||||
self.assertEqual(result.shape, (p, r, k, l))
|
||||
|
||||
def test_different_both_dims(self):
|
||||
ref = np.zeros((2, 2))
|
||||
resize = np.zeros((4, 4))
|
||||
self.assertEqual(interp_to_match(ref, resize).shape, (4, 4))
|
||||
def test_blend_w1_w2(self):
|
||||
model = {
|
||||
"foo.hada_w1_a": torch.from_numpy(np.ones((4, 1))),
|
||||
"foo.hada_w1_b": torch.from_numpy(np.ones((1, 4))),
|
||||
"foo.hada_w2_a": torch.from_numpy(np.ones((4, 1))),
|
||||
"foo.hada_w2_b": torch.from_numpy(np.ones((1, 4))),
|
||||
"foo.alpha": torch.tensor(1),
|
||||
}
|
||||
key, result = blend_weights_loha("foo.hada_w1_a", "", model, torch.float32)
|
||||
self.assertEqual(result.shape, (4, 4))
|
||||
|
||||
def test_blend_no_dim(self):
|
||||
"""
|
||||
model = {
|
||||
"foo.hada_w1_a": torch.from_numpy(np.ones((1, 4))),
|
||||
"foo.hada_w1_b": torch.from_numpy(np.ones((4, 1))),
|
||||
"foo.hada_w2_a": torch.from_numpy(np.ones((1, 4))),
|
||||
"foo.hada_w2_b": torch.from_numpy(np.ones((4, 1))),
|
||||
}
|
||||
result = blend_weights_loha("foo.hada_w1_a", "", model, torch.float32)
|
||||
self.assertEqual(result.shape, (4, 4))
|
||||
"""
|
||||
|
||||
class BlendWeightsLoRATests(unittest.TestCase):
|
||||
def test_blend_kernel_none(self):
|
||||
model = {
|
||||
"foo.lora_down": torch.from_numpy(np.ones((1, 4))),
|
||||
"foo.lora_up": torch.from_numpy(np.ones((4, 1))),
|
||||
"foo.alpha": 1,
|
||||
}
|
||||
key, result = blend_weights_lora("foo.lora_down", "", model, torch.float32)
|
||||
self.assertEqual(result.shape, (4, 4))
|
||||
|
||||
|
||||
def test_blend_kernel_1x1(self):
|
||||
model = {
|
||||
"foo.lora_down": torch.from_numpy(np.ones((1, 4, 1, 1))),
|
||||
"foo.lora_up": torch.from_numpy(np.ones((4, 1, 1, 1))),
|
||||
"foo.alpha": 1,
|
||||
}
|
||||
key, result = blend_weights_lora("foo.lora_down", "", model, torch.float32)
|
||||
self.assertEqual(result.shape, (4, 4, 1, 1))
|
||||
|
||||
def test_blend_kernel_3x3(self):
|
||||
model = {
|
||||
"foo.lora_down": torch.from_numpy(np.ones((1, 4, 3, 3))),
|
||||
"foo.lora_up": torch.from_numpy(np.ones((4, 1, 3, 3))),
|
||||
"foo.alpha": 1,
|
||||
}
|
||||
key, result = blend_weights_lora("foo.lora_down", "", model, torch.float32)
|
||||
self.assertEqual(result.shape, (4, 4, 3, 3))
|
||||
|
||||
def test_blend_kernel_3x3_cp_decomp(self):
|
||||
model = {
|
||||
"foo.lora_down": torch.from_numpy(np.ones((2, 4, 1, 1))),
|
||||
"foo.lora_mid": torch.from_numpy(np.ones((2, 2, 3, 3))),
|
||||
"foo.lora_up": torch.from_numpy(np.ones((4, 2, 1, 1))),
|
||||
"foo.alpha": 1,
|
||||
}
|
||||
key, result = blend_weights_lora("foo.lora_down", "", model, torch.float32)
|
||||
self.assertEqual(result.shape, (4, 4, 3, 3))
|
||||
|
||||
def test_blend_unknown(self):
|
||||
pass
|
||||
|
||||
|
||||
class BlendNodeConvGemmTests(unittest.TestCase):
|
||||
def test_blend_kernel_1x1_and_1x1(self):
|
||||
node = from_array(np.ones((4, 4, 1, 1)))
|
||||
result = blend_node_conv_gemm(node, np.ones((4, 4, 1, 1)))
|
||||
|
||||
self.assertEqual(result.dims, [4, 4, 1, 1])
|
||||
self.assertEqual(len(result.raw_data), 4 * 4 * 8)
|
||||
|
||||
def test_blend_kernel_1x1_and_none(self):
|
||||
node = from_array(np.ones((4, 4, 1, 1)))
|
||||
result = blend_node_conv_gemm(node, np.ones((4, 4)))
|
||||
|
||||
self.assertEqual(result.dims, [4, 4, 1, 1])
|
||||
self.assertEqual(len(result.raw_data), 4 * 4 * 8)
|
||||
|
||||
def test_blend_other_matching(self):
|
||||
node = from_array(np.ones((4, 4)))
|
||||
result = blend_node_conv_gemm(node, np.ones((4, 4)))
|
||||
|
||||
self.assertEqual(result.dims, [4, 4])
|
||||
self.assertEqual(len(result.raw_data), 4 * 4 * 8)
|
||||
|
||||
def test_blend_other_mismatched(self):
|
||||
pass
|
||||
|
||||
|
||||
class BlendNodeMatMulTests(unittest.TestCase):
|
||||
def test_blend_matching(self):
|
||||
node = from_array(np.ones((4, 4)))
|
||||
result = blend_node_matmul(node, np.ones((4, 4)), "test")
|
||||
|
||||
self.assertEqual(result.dims, [4, 4])
|
||||
self.assertEqual(len(result.raw_data), 4 * 4 * 8)
|
||||
|
||||
def test_blend_mismatched(self):
|
||||
node = from_array(np.ones((4, 4)))
|
||||
result = blend_node_matmul(node, np.ones((2, 2)), "test")
|
||||
|
||||
self.assertEqual(result.dims, [4, 4])
|
||||
self.assertEqual(len(result.raw_data), 4 * 4 * 8)
|
||||
|
|
|
|||
Loading…
Reference in New Issue