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import torch | ||
import inspect | ||
import re | ||
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import numpy as np | ||
import torch | ||
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from pi.models.unet import UNet2DConditionModel | ||
import torch_mlir | ||
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unet = UNet2DConditionModel( | ||
**{ | ||
"block_out_channels": (32, 64), | ||
"down_block_types": ("CrossAttnDownBlock2D", "DownBlock2D"), | ||
"up_block_types": ("UpBlock2D", "CrossAttnUpBlock2D"), | ||
"cross_attention_dim": 32, | ||
"attention_head_dim": 8, | ||
"out_channels": 4, | ||
"in_channels": 4, | ||
"layers_per_block": 2, | ||
"sample_size": 32, | ||
} | ||
) | ||
unet.eval() | ||
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batch_size = 4 | ||
num_channels = 4 | ||
sizes = (32, 32) | ||
from pi.lazy_importer.run_lazy_imports import do_package_imports, do_hand_imports | ||
from pi.lazy_importer import lazy_imports | ||
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def floats_tensor(shape, scale=1.0, rng=None, name=None): | ||
# | ||
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def floats_tensor(shape, scale=1.0, rng=None, name=None): | ||
total_dims = 1 | ||
for dim in shape: | ||
total_dims *= dim | ||
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values = [] | ||
for _ in range(total_dims): | ||
values.append(np.random.random() * scale) | ||
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return torch.tensor(data=values, dtype=torch.float).view(shape).contiguous() | ||
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noise = floats_tensor((batch_size, num_channels) + sizes) | ||
time_step = torch.tensor([10]) | ||
encoder_hidden_states = floats_tensor((batch_size, 4, 32)) | ||
def run( | ||
CTor, | ||
down_block_types=("CrossAttnDownBlock2D", "ResnetDownsampleBlock2D"), | ||
up_block_types=("UpBlock2D", "ResnetUpsampleBlock2D"), | ||
): | ||
unet = CTor( | ||
**{ | ||
"block_out_channels": (32, 64), | ||
"down_block_types": down_block_types, | ||
"up_block_types": up_block_types, | ||
"cross_attention_dim": 32, | ||
"attention_head_dim": 8, | ||
"out_channels": 4, | ||
"in_channels": 4, | ||
"layers_per_block": 2, | ||
"sample_size": 32, | ||
} | ||
) | ||
unet.eval() | ||
batch_size = 4 | ||
num_channels = 4 | ||
sizes = (32, 32) | ||
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noise = floats_tensor((batch_size, num_channels) + sizes) | ||
time_step = torch.tensor([10]) | ||
encoder_hidden_states = floats_tensor((batch_size, 4, 32)) | ||
output = unet(noise, time_step, encoder_hidden_states) | ||
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def make_linearized(): | ||
def filter(ret): | ||
try: | ||
MODULE_TARGET = lambda x: re.match( | ||
r"(huggingface|torch|diffusers)", inspect.getmodule(x).__package__ | ||
) | ||
return MODULE_TARGET(ret) | ||
except: | ||
return None | ||
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lazy_imports.MODULE_TARGET = filter | ||
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def _inner(): | ||
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from diffusers import UNet2DConditionModel | ||
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run( | ||
UNet2DConditionModel, | ||
down_block_types=("CrossAttnDownBlock2D", "ResnetDownsampleBlock2D"), | ||
up_block_types=("UpBlock2D", "ResnetUpsampleBlock2D"), | ||
) | ||
run( | ||
UNet2DConditionModel, | ||
down_block_types=("DownBlock2D", "AttnDownBlock2D"), | ||
up_block_types=("CrossAttnUpBlock2D", "ResnetUpsampleBlock2D"), | ||
) | ||
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prefix = "from pi.models.unet.prologue import CONFIG_NAME, LORA_WEIGHT_NAME" | ||
name = "unet_linearized" | ||
do_package_imports(_inner, prefix, name) | ||
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output = unet(noise, time_step, encoder_hidden_states) | ||
print(output) | ||
def run_linearized(): | ||
from pi.models.unet import linearized | ||
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traced = torch.jit.trace(unet, (noise, time_step, encoder_hidden_states), strict=False) | ||
frozen = torch.jit.freeze(traced) | ||
print(frozen.graph) | ||
run(linearized.UNet2DConditionModel) | ||
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module = torch_mlir.compile( | ||
frozen, | ||
(noise, time_step, encoder_hidden_states), | ||
use_tracing=True, | ||
output_type=torch_mlir.OutputType.RAW, | ||
) | ||
with open("unet.mlir", "w") as f: | ||
f.write(str(module)) | ||
if __name__ == "__main__": | ||
make_linearized() |
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