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