Spaces:
Running
on
Zero
Running
on
Zero
File size: 2,217 Bytes
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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
from spaces.zero.torch.aoti import ZeroGPUCompiledModel, ZeroGPUWeights
P = ParamSpec('P')
TRANSFORMER_IMAGE_SEQ_LENGTH_DIM = torch.export.Dim('image_seq_length')
TRANSFORMER_TEXT_SEQ_LENGTH_DIM = torch.export.Dim('text_seq_length')
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,
}, {
0: 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():
# Only capture what the first `transformer_block` sees.
with spaces.aoti_capture(pipeline.transformer.transformer_blocks[0]) as call:
pipeline(*args, **kwargs)
dynamic_shapes = tree_map(lambda t: None, call.kwargs)
dynamic_shapes |= TRANSFORMER_DYNAMIC_SHAPES
# Optionally quantize it.
# quantize_(pipeline.transformer, Float8DynamicActivationFloat8WeightConfig())
# Only export the first transformer block.
exported = torch.export.export(
mod=pipeline.transformer.transformer_blocks[0],
args=call.args,
kwargs=call.kwargs,
dynamic_shapes=dynamic_shapes,
)
return spaces.aoti_compile(exported, INDUCTOR_CONFIGS)
compiled = compile_transformer()
for block in pipeline.transformer.transformer_blocks:
weights = ZeroGPUWeights(block.state_dict())
compiled_block = ZeroGPUCompiledModel(compiled.archive_file, weights)
block.forward = compiled_block
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