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""" |
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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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import spaces |
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import torch |
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from torch.utils._pytree import tree_map_only |
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from optimization_utils import capture_component_call |
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from optimization_utils import aoti_compile |
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P = ParamSpec('P') |
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TRANSFORMER_HIDDEN_DIM = torch.export.Dim('hidden', min=4096, max=8212) |
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TRANSFORMER_DYNAMIC_SHAPES = { |
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'hidden_states': {1: TRANSFORMER_HIDDEN_DIM}, |
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'img_ids': {0: TRANSFORMER_HIDDEN_DIM}, |
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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 capture_component_call(pipeline, 'transformer') as call: |
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pipeline(*args, **kwargs) |
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dynamic_shapes = tree_map_only((torch.Tensor, bool), lambda t: None, call.kwargs) |
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dynamic_shapes |= TRANSFORMER_DYNAMIC_SHAPES |
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pipeline.transformer.fuse_qkv_projections() |
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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 aoti_compile(exported, INDUCTOR_CONFIGS) |
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transformer_config = pipeline.transformer.config |
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pipeline.transformer = compile_transformer() |
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pipeline.transformer.config = transformer_config |
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