apply lint
This commit is contained in:
parent
91210ee236
commit
45166f281e
GPG Key ID:
3EED7B957D362AF1
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@ -1,7 +1,7 @@
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from argparse import ArgumentParser
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from logging import getLogger
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from typing import Dict, List, Literal, Tuple
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from os import path
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from typing import Dict, List, Literal, Tuple
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import numpy as np
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import torch
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@ -12,7 +12,7 @@ from onnx.external_data_helper import (
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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 OrtValue, InferenceSession, SessionOptions
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from onnxruntime import InferenceSession, OrtValue, SessionOptions
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from safetensors.torch import load_file
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from onnx_web.convert.utils import ConversionContext
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@ -25,7 +25,9 @@ logger = getLogger(__name__)
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###
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def buffer_external_data_tensors(model: ModelProto) -> Tuple[ModelProto, List[Tuple[str, OrtValue]]]:
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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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@ -74,17 +76,19 @@ def merge_lora(
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lora_prefix = f"lora_{dest_type}_"
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blended: Dict[str, np.ndarray] = {}
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for lora_name, lora_model, lora_weight in zip(lora_names, lora_models, lora_weights):
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for lora_name, lora_model, lora_weight in zip(
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lora_names, lora_models, lora_weights
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):
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logger.info("blending LoRA from %s with weight of %s", lora_name, lora_weight)
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for key in lora_model.keys():
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if ".lora_down" in key and lora_prefix in key:
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base_key = key[: key.index(".lora_down")].replace(
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lora_prefix, ""
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)
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base_key = key[: key.index(".lora_down")].replace(lora_prefix, "")
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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.info("blending weights for keys: %s, %s, %s", key, up_key, alpha_key)
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logger.info(
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"blending weights for keys: %s, %s, %s", key, up_key, alpha_key
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)
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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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@ -95,12 +99,22 @@ def merge_lora(
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try:
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if len(up_weight.size()) == 2:
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# blend for nn.Linear
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logger.info("blending weights for Linear node: %s, %s, %s", down_weight.shape, up_weight.shape, alpha)
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logger.info(
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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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np_weights = weights.numpy() * (alpha / dim)
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elif len(up_weight.size()) == 4 and up_weight.shape[-2:] == (1, 1):
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# blend for nn.Conv2d 1x1
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logger.info("blending weights for Conv node: %s, %s, %s", down_weight.shape, up_weight.shape, alpha)
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logger.info(
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"blending weights for Conv 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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@ -109,10 +123,14 @@ def merge_lora(
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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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np_weights = weights.numpy() * (alpha / dim)
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else:
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# TODO: add support for Conv2d 3x3
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logger.warning("unknown LoRA node type at %s: %s", base_key, up_weight.shape[-2:])
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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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@ -122,15 +140,13 @@ def merge_lora(
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blended[base_key] = np_weights
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except Exception:
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logger.exception(
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"error blending weights for key %s", base_key
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)
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logger.exception("error blending weights for key %s", base_key)
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logger.info(
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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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list(blended.keys()),
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)
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fixed_initializer_names = [
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@ -138,17 +154,19 @@ def merge_lora(
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]
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# logger.info("fixed initializer names: %s", fixed_initializer_names)
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fixed_node_names = [
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fix_node_name(node.name) for node in base_model.graph.node
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]
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fixed_node_names = [fix_node_name(node.name) for node in base_model.graph.node]
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# logger.info("fixed node names: %s", fixed_node_names)
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for base_key, weights in blended.items():
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conv_key = base_key + "_Conv"
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matmul_key = base_key + "_MatMul"
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logger.info("key %s has conv: %s, matmul: %s", base_key, conv_key in fixed_node_names, matmul_key in fixed_node_names)
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logger.info(
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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:
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conv_idx = fixed_node_names.index(conv_key)
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@ -166,7 +184,11 @@ def merge_lora(
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# blending
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base_weights = numpy_helper.to_array(weight_node)
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logger.info("found blended weights for conv: %s, %s", weights.shape, base_weights.shape)
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logger.info(
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"found blended weights for conv: %s, %s",
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weights.shape,
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base_weights.shape,
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)
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blended = base_weights.squeeze((3, 2)) + weights.squeeze((3, 2))
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blended = np.expand_dims(blended, (2, 3))
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@ -191,7 +213,11 @@ def merge_lora(
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# blending
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base_weights = numpy_helper.to_array(matmul_node)
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logger.info("found blended weights for matmul: %s, %s", weights.shape, base_weights.shape)
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logger.info(
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"found blended weights for matmul: %s, %s",
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weights.shape,
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base_weights.shape,
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)
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blended = base_weights + weights.transpose()
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logger.info("blended weight shape: %s", blended.shape)
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@ -208,7 +234,7 @@ def merge_lora(
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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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len(base_model.graph.node),
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)
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return base_model
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@ -219,11 +245,16 @@ if __name__ == "__main__":
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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)
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parser.add_argument("--lora_weights", nargs='+', type=float)
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parser.add_argument("--lora_models", nargs="+", type=str)
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parser.add_argument("--lora_weights", nargs="+", type=float)
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args = parser.parse_args()
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logger.info("merging %s with %s with weights: %s", args.lora_models, args.base, args.lora_weights)
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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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blend_model = merge_lora(args.base, args.lora_models, args.type, args.lora_weights)
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if args.dest is None or args.dest == "" or args.dest == "ort":
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@ -234,10 +265,18 @@ if __name__ == "__main__":
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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(bare_model.SerializeToString(), sess_options=opts, providers=["CPUExecutionProvider"])
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logger.info("successfully loaded blended model: %s", [i.name for i in sess.get_inputs()])
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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"],
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)
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logger.info(
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"successfully loaded blended model: %s", [i.name for i in sess.get_inputs()]
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)
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else:
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convert_model_to_external_data(blend_model, all_tensors_to_one_file=True, location=f"lora-{args.type}.pb")
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convert_model_to_external_data(
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blend_model, all_tensors_to_one_file=True, location=f"lora-{args.type}.pb"
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)
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bare_model = write_external_data_tensors(blend_model, args.dest)
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dest_file = path.join(args.dest, f"lora-{args.type}.onnx")
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@ -37,7 +37,7 @@ try:
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except ImportError:
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from ..diffusers.stub_scheduler import StubScheduler as UniPCMultistepScheduler
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from ..convert.diffusion.lora import merge_lora, buffer_external_data_tensors
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from ..convert.diffusion.lora import buffer_external_data_tensors, merge_lora
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from ..params import DeviceParams, Size
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from ..server import ServerContext
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from ..utils import run_gc
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@ -118,7 +118,10 @@ def get_loras_from_prompt(prompt: str) -> Tuple[str, List[str]]:
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name, weight = next_match.groups()
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loras.append(name)
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# remove this match and look for another
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remaining_prompt = remaining_prompt[:next_match.start()] + remaining_prompt[next_match.end():]
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remaining_prompt = (
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remaining_prompt[: next_match.start()]
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+ remaining_prompt[next_match.end() :]
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)
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next_match = lora_expr.search(remaining_prompt)
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return (remaining_prompt, loras)
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@ -244,15 +247,23 @@ def load_pipeline(
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)
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# test LoRA blending
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lora_models = [path.join(server.model_path, "lora", f"{i}.safetensors") for i in loras]
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lora_models = [
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path.join(server.model_path, "lora", f"{i}.safetensors") for i in loras
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]
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logger.info("blending base model %s with LoRA models: %s", model, lora_models)
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# blend and load text encoder
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blended_text_encoder = merge_lora(path.join(model, "text_encoder", "model.onnx"), lora_models, "text_encoder")
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(text_encoder_model, text_encoder_data) = buffer_external_data_tensors(blended_text_encoder)
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blended_text_encoder = merge_lora(
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path.join(model, "text_encoder", "model.onnx"), lora_models, "text_encoder"
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)
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(text_encoder_model, text_encoder_data) = buffer_external_data_tensors(
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blended_text_encoder
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)
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text_encoder_names, text_encoder_values = zip(*text_encoder_data)
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text_encoder_opts = SessionOptions()
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text_encoder_opts.add_external_initializers(list(text_encoder_names), list(text_encoder_values))
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text_encoder_opts.add_external_initializers(
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list(text_encoder_names), list(text_encoder_values)
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)
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components["text_encoder"] = OnnxRuntimeModel(
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OnnxRuntimeModel.load_model(
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text_encoder_model.SerializeToString(),
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@ -262,7 +273,9 @@ def load_pipeline(
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)
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# blend and load unet
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blended_unet = merge_lora(path.join(model, "unet", "model.onnx"), lora_models, "unet")
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blended_unet = merge_lora(
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path.join(model, "unet", "model.onnx"), lora_models, "unet"
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)
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(unet_model, unet_data) = buffer_external_data_tensors(blended_unet)
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unet_names, unet_values = zip(*unet_data)
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unet_opts = SessionOptions()
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