70 lines
1.8 KiB
Python
70 lines
1.8 KiB
Python
"""
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"""
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from typing import Any
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from typing import Callable
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from typing import ParamSpec
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from torchao.quantization import quantize_
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from torchao.quantization import Float8DynamicActivationFloat8WeightConfig
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import spaces
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import torch
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from torch.utils._pytree import tree_map
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P = ParamSpec('P')
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TRANSFORMER_IMAGE_SEQ_LENGTH_DIM = torch.export.Dim('image_seq_length')
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TRANSFORMER_TEXT_SEQ_LENGTH_DIM = torch.export.Dim('text_seq_length')
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TRANSFORMER_DYNAMIC_SHAPES = {
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'hidden_states': {
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1: TRANSFORMER_IMAGE_SEQ_LENGTH_DIM,
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},
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'encoder_hidden_states': {
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1: TRANSFORMER_TEXT_SEQ_LENGTH_DIM,
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},
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'encoder_hidden_states_mask': {
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1: TRANSFORMER_TEXT_SEQ_LENGTH_DIM,
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},
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'image_rotary_emb': ({
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0: TRANSFORMER_IMAGE_SEQ_LENGTH_DIM,
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}, {
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0: TRANSFORMER_TEXT_SEQ_LENGTH_DIM,
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}),
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}
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INDUCTOR_CONFIGS = {
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'conv_1x1_as_mm': True,
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'epilogue_fusion': False,
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'coordinate_descent_tuning': True,
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'coordinate_descent_check_all_directions': True,
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'max_autotune': True,
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'triton.cudagraphs': True,
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}
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def optimize_pipeline_(pipeline: Callable[P, Any], *args: P.args, **kwargs: P.kwargs):
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@spaces.GPU(duration=1500)
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def compile_transformer():
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with spaces.aoti_capture(pipeline.transformer) as call:
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pipeline(*args, **kwargs)
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dynamic_shapes = tree_map(lambda t: None, call.kwargs)
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dynamic_shapes |= TRANSFORMER_DYNAMIC_SHAPES
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# quantize_(pipeline.transformer, Float8DynamicActivationFloat8WeightConfig())
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exported = torch.export.export(
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mod=pipeline.transformer,
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args=call.args,
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kwargs=call.kwargs,
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dynamic_shapes=dynamic_shapes,
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)
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return spaces.aoti_compile(exported, INDUCTOR_CONFIGS)
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spaces.aoti_apply(compile_transformer(), pipeline.transformer) |