Spaces:
Running
on
Zero
Running
on
Zero
File size: 2,177 Bytes
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from typing import Any
from typing import Callable
from typing import ParamSpec
import spaces
import torch
from spaces.zero.torch.aoti import ZeroGPUCompiledModel
from spaces.zero.torch.aoti import ZeroGPUWeights
from torch.utils._pytree import tree_map
P = ParamSpec('P')
TRANSFORMER_IMAGE_SEQ_LENGTH_DIM = torch.export.Dim('image_seq_length', min=1024, max=16384)
TRANSFORMER_TEXT_SEQ_LENGTH_DIM = torch.export.Dim('text_seq_length', min=64, max=1024)
TRANSFORMER_DYNAMIC_SHAPES = {
'hidden_states': {
1: TRANSFORMER_IMAGE_SEQ_LENGTH_DIM,
},
'encoder_hidden_states': {
1: TRANSFORMER_TEXT_SEQ_LENGTH_DIM,
},
'encoder_hidden_states_mask': {
1: TRANSFORMER_TEXT_SEQ_LENGTH_DIM,
},
'image_rotary_emb': (
{0: TRANSFORMER_IMAGE_SEQ_LENGTH_DIM}, # vid_freqs
{0: TRANSFORMER_TEXT_SEQ_LENGTH_DIM}, # txt_freqs
),
}
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):
blocks = pipeline.transformer.transformer_blocks
@spaces.GPU(duration=1200)
def compile_block():
block = blocks[0]
with spaces.aoti_capture(block) as call:
pipeline(*args, **kwargs)
dynamic_shapes = tree_map(lambda t: None, call.kwargs)
# Only merge keys that exist in call.kwargs
for key, value in TRANSFORMER_DYNAMIC_SHAPES.items():
if key in call.kwargs:
dynamic_shapes[key] = value
with torch.no_grad():
exported = torch.export.export(
mod=block,
args=call.args,
kwargs=call.kwargs,
dynamic_shapes=dynamic_shapes,
)
return spaces.aoti_compile(exported, INDUCTOR_CONFIGS).archive_file
archive_file = compile_block()
for block in blocks:
weights = ZeroGPUWeights(block.state_dict())
block.forward = ZeroGPUCompiledModel(archive_file, weights) |