Delete optimization.py

optimization.py

@@ -1,70 +0,0 @@
-"""
-"""
-
-from typing import Any
-from typing import Callable
-from typing import ParamSpec
-from torchao.quantization import quantize_
-from torchao.quantization import Float8DynamicActivationFloat8WeightConfig
-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')
-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():
-
-        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
-
-        # quantize_(pipeline.transformer, Float8DynamicActivationFloat8WeightConfig())
-        
-        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)