remove pad custom op (#4801)

examples/models/flamingo/export_preprocess_lib.py

@@ -15,10 +15,6 @@
 from torch.export import Dim, ExportedProgram
 from torchtune.models.clip.inference._transforms import _CLIPImageTransform
 
-from .passes.replace_custom_ops_with_aten_ops_pass import (
-    ReplaceCustomOpsWithAtenOpsPass,
-)
-
 
 def get_example_inputs() -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
     image = torch.ones(3, 800, 600)
@@ -59,7 +55,6 @@ def export_preprocess(
     )
 
     # Replace non-exportable ops with custom ops.
-    image_transform_model.pad = torch.ops.preprocess.pad.default
     image_transform_model.tile_crop = torch.ops.preprocess.tile_crop.default
 
     # Export.
@@ -80,8 +75,6 @@ def lower_to_executorch_preprocess(
     edge_program = to_edge(
         exported_program, compile_config=EdgeCompileConfig(_check_ir_validity=False)
     )
-    # Replace custom ops with aten ops.
-    edge_program = edge_program.transform([ReplaceCustomOpsWithAtenOpsPass()])
 
     et_program = edge_program.to_executorch(ExecutorchBackendConfig())
     return et_program

extension/llm/custom_ops/preprocess_custom_ops.py

@@ -7,61 +7,12 @@
 # pyre-unsafe
 
 
-from typing import List
-
 import torch
 
 from torch.library import impl, Library
 
 preprocess_op_lib = Library("preprocess", "DEF")
 
-# Register and define pad and out variant.
-# Note: pad doesn't require an explicit meta kernel because
-# CompositeExplicitAutograd automatically registers the implementation to meta,
-# and meta kernels do not go through functionalization. The implementation
-# does not export due to issues during functionalization.
-# See: https://github.com/pytorch/pytorch/issues/120288
-preprocess_op_lib.define("pad(Tensor image, SymInt[] padding) -> Tensor")
-
-
-@impl(preprocess_op_lib, "pad", dispatch_key="CompositeExplicitAutograd")
-def pad_impl(
-    image: torch.Tensor,
-    padding: List[int],
-) -> torch.Tensor:
-    output = torch.empty(
-        [image.shape[0], image.shape[1] + padding[3], image.shape[2] + padding[1]],
-        dtype=image.dtype,
-        device=image.device,
-        requires_grad=False,
-    )
-    output = torch.fill(output, 0)
-    output.narrow(1, 0, image.shape[1]).narrow(2, 0, image.shape[2]).copy_(image)
-    return output
-
-
-preprocess_op_lib.define(
-    "pad.out(Tensor image, SymInt[] padding, *, Tensor(a!) out) -> Tensor(a!)"
-)
-
-
-@impl(preprocess_op_lib, "pad.out", dispatch_key="CompositeExplicitAutograd")
-def pad_out_impl(
-    image: torch.Tensor,
-    padding: List[int],
-    out: torch.Tensor,
-) -> torch.Tensor:
-    out = torch.empty(
-        [image.shape[0], image.shape[1] + padding[3], image.shape[2] + padding[1]],
-        dtype=image.dtype,
-        device=image.device,
-        requires_grad=False,
-    )
-    out = torch.fill(out, 0)
-    out.narrow(1, 0, image.shape[1]).narrow(2, 0, image.shape[2]).copy_(image)
-    return out
-
-
 # Register and define tile_crop and out variant.
 preprocess_op_lib.define("tile_crop(Tensor input, int tile_size) -> Tensor")