optimization.py

"""
"""

from typing import Any
from typing import Callable
from typing import ParamSpec
import spaces
import torch
from torch.utils._pytree import tree_map

P = ParamSpec('P')

TRANSFORMER_IMAGE_SEQ_LENGTH_DIM = torch.export.Dim('image_seq_length', min=64, max=16384)
TRANSFORMER_TEXT_SEQ_LENGTH_DIM = torch.export.Dim('text_seq_length', min=64, max=512)

TRANSFORMER_DYNAMIC_SHAPES = {
    'hidden_states': {
        1: TRANSFORMER_IMAGE_SEQ_LENGTH_DIM,
    },
    'encoder_hidden_states': {
        1: TRANSFORMER_TEXT_SEQ_LENGTH_DIM,
    },
    'img_ids': {
        1: TRANSFORMER_IMAGE_SEQ_LENGTH_DIM,
    },
    'txt_ids': {
        1: 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():

        with spaces.aoti_capture(pipeline.transformer) as call:
            pipeline(*args, **kwargs)

        dynamic_shapes = tree_map(lambda t: None, call.kwargs)
        dynamic_shapes |= TRANSFORMER_DYNAMIC_SHAPES
        
        exported = torch.export.export(
            mod=pipeline.transformer,
            args=call.args,
            kwargs=call.kwargs,
            dynamic_shapes=dynamic_shapes,
        )

        return spaces.aoti_compile(exported, INDUCTOR_CONFIGS)

    spaces.aoti_apply(compile_transformer(), pipeline.transformer)