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* Apply same ruff settings as in transformers See https://github.com/huggingface/transformers/blob/main/pyproject.toml Co-authored-by: Aaron Gokaslan <aaronGokaslan@gmail.com> * Apply new style rules * Style Co-authored-by: Aaron Gokaslan <aaronGokaslan@gmail.com> * style * remove list, ruff wouldn't auto fix. --------- Co-authored-by: Aaron Gokaslan <aaronGokaslan@gmail.com>
101 lines
3.7 KiB
Python
101 lines
3.7 KiB
Python
import json
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import os
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import torch
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from diffusers import UNet1DModel
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os.makedirs("hub/hopper-medium-v2/unet/hor32", exist_ok=True)
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os.makedirs("hub/hopper-medium-v2/unet/hor128", exist_ok=True)
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os.makedirs("hub/hopper-medium-v2/value_function", exist_ok=True)
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def unet(hor):
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if hor == 128:
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down_block_types = ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
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block_out_channels = (32, 128, 256)
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up_block_types = ("UpResnetBlock1D", "UpResnetBlock1D")
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elif hor == 32:
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down_block_types = ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
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block_out_channels = (32, 64, 128, 256)
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up_block_types = ("UpResnetBlock1D", "UpResnetBlock1D", "UpResnetBlock1D")
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model = torch.load(f"/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch")
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state_dict = model.state_dict()
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config = {
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"down_block_types": down_block_types,
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"block_out_channels": block_out_channels,
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"up_block_types": up_block_types,
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"layers_per_block": 1,
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"use_timestep_embedding": True,
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"out_block_type": "OutConv1DBlock",
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"norm_num_groups": 8,
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"downsample_each_block": False,
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"in_channels": 14,
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"out_channels": 14,
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"extra_in_channels": 0,
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"time_embedding_type": "positional",
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"flip_sin_to_cos": False,
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"freq_shift": 1,
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"sample_size": 65536,
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"mid_block_type": "MidResTemporalBlock1D",
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"act_fn": "mish",
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}
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hf_value_function = UNet1DModel(**config)
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print(f"length of state dict: {len(state_dict.keys())}")
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print(f"length of value function dict: {len(hf_value_function.state_dict().keys())}")
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mapping = dict(zip(model.state_dict().keys(), hf_value_function.state_dict().keys()))
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for k, v in mapping.items():
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state_dict[v] = state_dict.pop(k)
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hf_value_function.load_state_dict(state_dict)
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torch.save(hf_value_function.state_dict(), f"hub/hopper-medium-v2/unet/hor{hor}/diffusion_pytorch_model.bin")
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with open(f"hub/hopper-medium-v2/unet/hor{hor}/config.json", "w") as f:
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json.dump(config, f)
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def value_function():
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config = {
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"in_channels": 14,
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"down_block_types": ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D"),
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"up_block_types": (),
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"out_block_type": "ValueFunction",
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"mid_block_type": "ValueFunctionMidBlock1D",
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"block_out_channels": (32, 64, 128, 256),
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"layers_per_block": 1,
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"downsample_each_block": True,
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"sample_size": 65536,
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"out_channels": 14,
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"extra_in_channels": 0,
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"time_embedding_type": "positional",
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"use_timestep_embedding": True,
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"flip_sin_to_cos": False,
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"freq_shift": 1,
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"norm_num_groups": 8,
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"act_fn": "mish",
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}
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model = torch.load("/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch")
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state_dict = model
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hf_value_function = UNet1DModel(**config)
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print(f"length of state dict: {len(state_dict.keys())}")
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print(f"length of value function dict: {len(hf_value_function.state_dict().keys())}")
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mapping = dict(zip(state_dict.keys(), hf_value_function.state_dict().keys()))
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for k, v in mapping.items():
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state_dict[v] = state_dict.pop(k)
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hf_value_function.load_state_dict(state_dict)
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torch.save(hf_value_function.state_dict(), "hub/hopper-medium-v2/value_function/diffusion_pytorch_model.bin")
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with open("hub/hopper-medium-v2/value_function/config.json", "w") as f:
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json.dump(config, f)
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if __name__ == "__main__":
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unet(32)
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# unet(128)
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value_function()
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