Spaces:
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"""
"""
from typing import Any
from typing import Callable
from typing import ParamSpec
import spaces
import torch
from torch.utils._pytree import tree_map
P = ParamSpec('P')
TRANSFORMER_IMAGE_SEQ_LENGTH_DIM = torch.export.Dim('image_seq_length', min=64, max=16384)
TRANSFORMER_TEXT_SEQ_LENGTH_DIM = torch.export.Dim('text_seq_length', min=64, max=512)
TRANSFORMER_DYNAMIC_SHAPES = {
'hidden_states': {
1: TRANSFORMER_IMAGE_SEQ_LENGTH_DIM,
},
'encoder_hidden_states': {
1: TRANSFORMER_TEXT_SEQ_LENGTH_DIM,
},
'img_ids': {
1: TRANSFORMER_IMAGE_SEQ_LENGTH_DIM,
},
'txt_ids': {
1: TRANSFORMER_TEXT_SEQ_LENGTH_DIM,
},
}
INDUCTOR_CONFIGS = {
'conv_1x1_as_mm': True,
'epilogue_fusion': False,
'coordinate_descent_tuning': True,
'coordinate_descent_check_all_directions': True,
#'max_autotune': True,
#'triton.cudagraphs': True,
}
def optimize_pipeline_(pipeline: Callable[P, Any], *args: P.args, **kwargs: P.kwargs):
@spaces.GPU(duration=1500)
def compile_transformer():
with spaces.aoti_capture(pipeline.transformer) as call:
pipeline(*args, **kwargs)
dynamic_shapes = tree_map(lambda t: None, call.kwargs)
dynamic_shapes |= TRANSFORMER_DYNAMIC_SHAPES
exported = torch.export.export(
mod=pipeline.transformer,
args=call.args,
kwargs=call.kwargs,
dynamic_shapes=dynamic_shapes,
)
return spaces.aoti_compile(exported, INDUCTOR_CONFIGS)
spaces.aoti_apply(compile_transformer(), pipeline.transformer) |