Fix op decomposition issue when multiple partitioners with conflicting expectations are run - #14458

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abhinaykukkadapu merged 1 commit into
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abhinaykukkadapu:export-D82936479
Sep 23, 2025
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Fix op decomposition issue when multiple partitioners with conflicting expectations are run#14458
abhinaykukkadapu merged 1 commit into
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abhinaykukkadapu:export-D82936479

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Differential Revision: D82936479

Context:
This diff enables sequential recipes targeting multiple backends (for example: CoreML.FP32 + XNNPack.FP32, with XNNPack as a fallback). I think, we had not tested lowering models to multiple backends, so this edge case was unhandled.

While lowering the Vision Transformer (ViT) model, I encountered issues similar to those previously seen with the SDPA op (discussion). Although a fix exists, it did not account for scenarios with multiple partitioners having conflicting decomposition requirements and op filtering for the no-decomp namespace.

Error scenarios

The core problem: if two partitioners request to preserve different ops (ex: XNNPack wants to preserve aten.max_pool2d, but QNN does not support it), the current logic unions all ops to preserve, causing errors if a backend cannot handle an op.

QNN + XNNPack

[2025-09-22T12:18:33.640-07:00] partition_list = capability_partitioner.propose_partitions()
[2025-09-22T12:18:33.640-07:00] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[2025-09-22T12:18:33.640-07:00] File "/data/users/abhinayk/fbsource/buck-out/v2/gen/fbcode/afd2a63214a057a8/executorch/export/tests/__test_target_recipes__/test_target_recipes#link-tree/torch/fx/passes/infra/partitioner.py", line 226, in propose_partitions
[2025-09-22T12:18:33.640-07:00] if self._is_node_supported(node) and node not in assignment:
[2025-09-22T12:18:33.640-07:00] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[2025-09-22T12:18:33.640-07:00] File "/data/users/abhinayk/fbsource/buck-out/v2/gen/fbcode/afd2a63214a057a8/executorch/export/tests/__test_target_recipes__/test_target_recipes#link-tree/torch/fx/passes/infra/partitioner.py", line 87, in _is_node_supported
[2025-09-22T12:18:33.640-07:00] return self.operator_support.is_node_supported(
[2025-09-22T12:18:33.640-07:00] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[2025-09-22T12:18:33.640-07:00] File "/data/users/abhinayk/fbsource/buck-out/v2/gen/fbcode/afd2a63214a057a8/executorch/export/tests/__test_target_recipes__/test_target_recipes#link-tree/executorch/backends/qualcomm/partition/qnn_partitioner.py", line 100, in is_node_supported
[2025-09-22T12:18:33.640-07:00] op_wrapper = self.node_visitors[node.target.__name__].define_node(
[2025-09-22T12:18:33.640-07:00] ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^
[2025-09-22T12:18:33.640-07:00] KeyError: 'aten.max_pool2d.default'

CoreML + XNNPack

[2025-09-22T11:01:47.263-07:00] ValueError: Cannot view a tensor with shape torch.Size([197, 1, 12, 64]) and strides (64, 151296, 12608, 1) as a tensor with shape (197, 768)!
[2025-09-22T11:01:47.263-07:00]
[2025-09-22T11:01:47.263-07:00] While executing %view_8 : [num_users=1] = call_function[target=torch.ops.aten.view.default](args = (%permute_6, [197, 768]), kwargs = {})
[2025-09-22T11:01:47.263-07:00] Original traceback:
[2025-09-22T11:01:47.263-07:00] File "/data/users/abhinayk/fbsource/buck-out/v2/gen/fbcode/afd2a63214a057a8/executorch/export/tests/__test_target_recipes__/test_target_recipes#link-tree/torchvision/models/vision_transformer.py", line 298, in forward
[2025-09-22T11:01:47.263-07:00] x = self.encoder(x)
[2025-09-22T11:01:47.263-07:00] File "/data/users/abhinayk/fbsource/buck-out/v2/gen/fbcode/afd2a63214a057a8/executorch/export/tests/__test_target_recipes__/test_target_recipes#link-tree/torchvision/models/vision_transformer.py", line 157, in forward
[2025-09-22T11:01:47.263-07:00] return self.ln(self.layers(self.dropout(input)))
[2025-09-22T11:01:47.263-07:00] File "/data/users/abhinayk/fbsource/buck-out/v2/gen/fbcode/afd2a63214a057a8/executorch/export/tests/__test_target_recipes__/test_target_recipes#link-tree/torchvision/models/vision_transformer.py", line 113, in forward
[2025-09-22T11:01:47.263-07:00] x, _ = self.self_attention(x, x, x, need_weights=False)

Note: Lowering to a single backend (CoreML, XNNPack, or QNN) works as expected, the issue is only hit when there are backend combinations with conflicting expectations.

Changes:

  • Decomposition skipping and filtering are now handled per partitioner, rather than globally.
  • Refactored code for readability by removing too many boolean + if/else checks which are hard to follow.

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abhinaykukkadapu added a commit to abhinaykukkadapu/executorch that referenced this pull request Sep 22, 2025
@abhinaykukkadapu@facebook-github-bot
Fix op decomposition issue when multiple partitioners with conflictin…
abf4d67
…g expectations are run (pytorch#14458)
Summary: Pull Request resolved: pytorch#14458
Differential Revision: D82936479
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abhinaykukkadapu marked this pull request as ready for review September 22, 2025 18:43
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abhinaykukkadapu added a commit to abhinaykukkadapu/executorch that referenced this pull request Sep 22, 2025
@abhinaykukkadapu@facebook-github-bot
Fix op decomposition issue when multiple partitioners with conflictin…
e593a22
…g expectations are run (pytorch#14458)
Summary:
Pull Request resolved: pytorch#14458
Context: I'm trying to enable sequential recipes targeting multiple backends (such as `CoreML.FP32 + XNNPack.FP32`, here xnnpack will be a fallback for the ops) and i don't think we've ever tested lowering a model to multiple backends or this edge case has never been hit.
I've hit this case when i tried to lower vision transformer model (VIT)
I've seen a similar problem occurred with SDPA op (discussed [here](https://fb.workplace.com/groups/pytorch.edge.users/permalink/1796069037930048/)) and has been fixed and i think the fix works but it didn't considered when there multiple partitioners in mind with *conflicting decomposition requirements and filter for op no decomp namespace*.
## Error (using both coreml + xnnpack)
```
[2025-09-22T11:01:47.263-07:00] ValueError: Cannot view a tensor with shape torch.Size([197, 1, 12, 64]) and strides (64, 151296, 12608, 1) as a tensor with shape (197, 768)!
[2025-09-22T11:01:47.263-07:00]
[2025-09-22T11:01:47.263-07:00] While executing %view_8 : [num_users=1] = call_function[target=torch.ops.aten.view.default](args = (%permute_6, [197, 768]), kwargs = {})
[2025-09-22T11:01:47.263-07:00] Original traceback:
[2025-09-22T11:01:47.263-07:00] File "/data/users/abhinayk/fbsource/buck-out/v2/gen/fbcode/afd2a63214a057a8/executorch/export/tests/__test_target_recipes__/test_target_recipes#link-tree/torchvision/models/vision_transformer.py", line 298, in forward
[2025-09-22T11:01:47.263-07:00] x = self.encoder(x)
[2025-09-22T11:01:47.263-07:00] File "/data/users/abhinayk/fbsource/buck-out/v2/gen/fbcode/afd2a63214a057a8/executorch/export/tests/__test_target_recipes__/test_target_recipes#link-tree/torchvision/models/vision_transformer.py", line 157, in forward
[2025-09-22T11:01:47.263-07:00] return self.ln(self.layers(self.dropout(input)))
[2025-09-22T11:01:47.263-07:00] File "/data/users/abhinayk/fbsource/buck-out/v2/gen/fbcode/afd2a63214a057a8/executorch/export/tests/__test_target_recipes__/test_target_recipes#link-tree/torchvision/models/vision_transformer.py", line 113, in forward
[2025-09-22T11:01:47.263-07:00] x, _ = self.self_attention(x, x, x, need_weights=False)
```
**Note**: Lowering to single backend works with both coreml or xnnpack, it is the combination that give this error.
Changes:
- The fix is to run decomposition filtering and skipping per partitioner rather than maintaining same rule in global scope.
- I've additionally refactored the code to make it more readable by removing multiple boolean checks.
Differential Revision: D82936479
@abhinaykukkadapu@facebook-github-bot
Fix op decomposition issue when multiple partitioners with conflictin…
04f413a
…g expectations are run (pytorch#14458)
Summary:
Context: I'm trying to enable sequential recipes targeting multiple backends (such as `CoreML.FP32 + XNNPack.FP32`, here xnnpack will be a fallback for the ops) and i don't think we've ever tested lowering a model to multiple backends or this edge case has never been hit.
I've hit this case when i tried to lower vision transformer model (VIT)
I've seen a similar problem occurred with SDPA op (discussed [here](https://fb.workplace.com/groups/pytorch.edge.users/permalink/1796069037930048/)) and has been fixed and i think the fix works but it didn't considered when there multiple partitioners in mind with *conflicting decomposition requirements and filter for op no decomp namespace*.
## Error with coreml + xnnpack
```
[2025-09-22T11:01:47.263-07:00] ValueError: Cannot view a tensor with shape torch.Size([197, 1, 12, 64]) and strides (64, 151296, 12608, 1) as a tensor with shape (197, 768)!
[2025-09-22T11:01:47.263-07:00]
[2025-09-22T11:01:47.263-07:00] While executing %view_8 : [num_users=1] = call_function[target=torch.ops.aten.view.default](args = (%permute_6, [197, 768]), kwargs = {})
[2025-09-22T11:01:47.263-07:00] Original traceback:
[2025-09-22T11:01:47.263-07:00] File "/data/users/abhinayk/fbsource/buck-out/v2/gen/fbcode/afd2a63214a057a8/executorch/export/tests/__test_target_recipes__/test_target_recipes#link-tree/torchvision/models/vision_transformer.py", line 298, in forward
[2025-09-22T11:01:47.263-07:00] x = self.encoder(x)
[2025-09-22T11:01:47.263-07:00] File "/data/users/abhinayk/fbsource/buck-out/v2/gen/fbcode/afd2a63214a057a8/executorch/export/tests/__test_target_recipes__/test_target_recipes#link-tree/torchvision/models/vision_transformer.py", line 157, in forward
[2025-09-22T11:01:47.263-07:00] return self.ln(self.layers(self.dropout(input)))
[2025-09-22T11:01:47.263-07:00] File "/data/users/abhinayk/fbsource/buck-out/v2/gen/fbcode/afd2a63214a057a8/executorch/export/tests/__test_target_recipes__/test_target_recipes#link-tree/torchvision/models/vision_transformer.py", line 113, in forward
[2025-09-22T11:01:47.263-07:00] x, _ = self.self_attention(x, x, x, need_weights=False)
```
## Error with QNN + XNNPACK
The core problem here is that if there are two partitioners, Backend A partitioner (asking to preserve ops x, y) and Backend B partitioner (asking to preserve ops z), assume Backend A doesn't understand Z and want to decompose, currently we `union` all the ops to preserve from multiple partitioner and it errors out
In this specific case, xnnpack asks to preserve `aten.max_pool2d` but QNN doesn't understand it.
```
[2025-09-22T12:18:33.640-07:00] partition_list = capability_partitioner.propose_partitions()
[2025-09-22T12:18:33.640-07:00] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[2025-09-22T12:18:33.640-07:00] File "/data/users/abhinayk/fbsource/buck-out/v2/gen/fbcode/afd2a63214a057a8/executorch/export/tests/__test_target_recipes__/test_target_recipes#link-tree/torch/fx/passes/infra/partitioner.py", line 226, in propose_partitions
[2025-09-22T12:18:33.640-07:00] if self._is_node_supported(node) and node not in assignment:
[2025-09-22T12:18:33.640-07:00] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[2025-09-22T12:18:33.640-07:00] File "/data/users/abhinayk/fbsource/buck-out/v2/gen/fbcode/afd2a63214a057a8/executorch/export/tests/__test_target_recipes__/test_target_recipes#link-tree/torch/fx/passes/infra/partitioner.py", line 87, in _is_node_supported
[2025-09-22T12:18:33.640-07:00] return self.operator_support.is_node_supported(
[2025-09-22T12:18:33.640-07:00] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[2025-09-22T12:18:33.640-07:00] File "/data/users/abhinayk/fbsource/buck-out/v2/gen/fbcode/afd2a63214a057a8/executorch/export/tests/__test_target_recipes__/test_target_recipes#link-tree/executorch/backends/qualcomm/partition/qnn_partitioner.py", line 100, in is_node_supported
[2025-09-22T12:18:33.640-07:00] op_wrapper = self.node_visitors[node.target.__name__].define_node(
[2025-09-22T12:18:33.640-07:00] ~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^
[2025-09-22T12:18:33.640-07:00] KeyError: 'aten.max_pool2d.default'
```
**Note**: Lowering to single backend works with both coreml or xnnpack or QNN, it is the combinations that hits this error.
Changes:
- The fix is to run decomposition filtering and skipping per partitioner rather than maintaining same rule in global scope.
- I've additionally refactored the code to make it more readable by removing multiple boolean checks.
Differential Revision: D82936479
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metascroy
metascroy approved these changes Sep 23, 2025
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Comment threadexir/program/_program.py
if not can_skip_using_EDGE_DO_NOT_DECOMP:
program = program.run_decompositions(_default_decomposition_table())
_restore_transformed_ops_to_aten_ops(program)
if can_skip_using_edge_do_not_decomp:

@metascroymetascroySep 23, 2025

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LGTM, but there is still a logical bug in ET's AOT code with the EDGE_DO_NOT_DECOMP namespace / preservation when it comes to SDPA (and maybe other ops).

For CoreML, we get around it by skipping that path, but other backends (e.g., XNNPACK or QNN) will run into it if they preserve SDPA.

Perhaps the runtime time could take a look at this issue? cc @JacobSzwejbka@larryliu0820

@abhinaykukkadapu
abhinaykukkadapu merged commit b991271 into pytorch:mainSep 23, 2025
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abhinaykukkadapu deleted the export-D82936479 branch September 23, 2025 18:25
jirioc pushed a commit to nxp-upstream/executorch that referenced this pull request Dec 19, 2025
@abhinaykukkadapu@jirioc
Fix op decomposition issue when multiple partitioners with conflictin…
df951f6
…g expectations are run (pytorch#14458)
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Fix op decomposition issue when multiple partitioners with conflicting expectations are run - #14458
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abhinaykukkadapu merged 1 commit into
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abhinaykukkadapu:export-D82936479
Sep 23, 2025
Merged
Fix op decomposition issue when multiple partitioners with conflicting expectations are run#14458
abhinaykukkadapu merged 1 commit into
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abhinaykukkadapu:export-D82936479
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Differential Revision: D82936479
Context:

This diff enables sequential recipes targeting multiple backends (for example: CoreML.FP32 + XNNPack.FP32, with XNNPack as a fallback). I think, we had not tested lowering models to multiple backends, so this edge case was unhandled.
While lowering the Vision Transformer (ViT) model, I encountered issues similar to those previously seen with the SDPA op (discussion). Although a fix exists, it did not account for scenarios with multiple partitioners having conflicting decomposition requirements and op filtering for the no-decomp namespace.
Error scenarios
The core problem: if two partitioners request to preserve different ops (ex: XNNPack wants to preserve aten.max_pool2d, but QNN does not support it), the current logic unions all ops to preserve, causing errors if a backend cannot handle an op.
QNN + XNNPack
[2025-09-22T12:18:33.640-07:00] partition_list = capability_partitioner.propose_partitions()
[2025-09-22T12:18:33.640-07:00] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[2025-09-22T12:18:33.640-07:00] File "/data/users/abhinayk/fbsource/buck-out/v2/gen/fbcode/afd2a63214a057a8/executorch/export/tests/__test_target_recipes__/test_target_recipes#link-tree/torch/fx/passes/infra/partitioner.py", line 226, in propose_partitions
[2025-09-22T12:18:33.640-07:00] if self._is_node_supported(node) and node not in assignment:
[2025-09-22T12:18:33.640-07:00] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[2025-09-22T12:18:33.640-07:00] File "/data/users/abhinayk/fbsource/buck-out/v2/gen/fbcode/afd2a63214a057a8/executorch/export/tests/__test_target_recipes__/test_target_recipes#link-tree/torch/fx/passes/infra/partitioner.py", line 87, in _is_node_supported
[2025-09-22T12:18:33.640-07:00] return self.operator_support.is_node_supported(
[2025-09-22T12:18:33.640-07:00] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[2025-09-22T12:18:33.640-07:00] File "/data/users/abhinayk/fbsource/buck-out/v2/gen/fbcode/afd2a63214a057a8/executorch/export/tests/__test_target_recipes__/test_target_recipes#link-tree/executorch/backends/qualcomm/partition/qnn_partitioner.py", line 100, in is_node_supported
[2025-09-22T12:18:33.640-07:00] op_wrapper = self.node_visitors[node.target.__name__].define_node(
[2025-09-22T12:18:33.640-07:00] ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^
[2025-09-22T12:18:33.640-07:00] KeyError: 'aten.max_pool2d.default'

CoreML + XNNPack
[2025-09-22T11:01:47.263-07:00] ValueError: Cannot view a tensor with shape torch.Size([197, 1, 12, 64]) and strides (64, 151296, 12608, 1) as a tensor with shape (197, 768)!
[2025-09-22T11:01:47.263-07:00]
[2025-09-22T11:01:47.263-07:00] While executing %view_8 : [num_users=1] = call_function[target=torch.ops.aten.view.default](args = (%permute_6, [197, 768]), kwargs = {})
[2025-09-22T11:01:47.263-07:00] Original traceback:
[2025-09-22T11:01:47.263-07:00] File "/data/users/abhinayk/fbsource/buck-out/v2/gen/fbcode/afd2a63214a057a8/executorch/export/tests/__test_target_recipes__/test_target_recipes#link-tree/torchvision/models/vision_transformer.py", line 298, in forward
[2025-09-22T11:01:47.263-07:00] x = self.encoder(x)
[2025-09-22T11:01:47.263-07:00] File "/data/users/abhinayk/fbsource/buck-out/v2/gen/fbcode/afd2a63214a057a8/executorch/export/tests/__test_target_recipes__/test_target_recipes#link-tree/torchvision/models/vision_transformer.py", line 157, in forward
[2025-09-22T11:01:47.263-07:00] return self.ln(self.layers(self.dropout(input)))
[2025-09-22T11:01:47.263-07:00] File "/data/users/abhinayk/fbsource/buck-out/v2/gen/fbcode/afd2a63214a057a8/executorch/export/tests/__test_target_recipes__/test_target_recipes#link-tree/torchvision/models/vision_transformer.py", line 113, in forward
[2025-09-22T11:01:47.263-07:00] x, _ = self.self_attention(x, x, x, need_weights=False)

Note: Lowering to a single backend (CoreML, XNNPack, or QNN) works as expected, the issue is only hit when there are backend combinations with conflicting expectations.
Changes:
  • Decomposition skipping and filtering are now handled per partitioner, rather than globally.
  • Refactored code for readability by removing too many boolean + if/else checks which are hard to follow.
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🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/14458
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Fix op decomposition issue when multiple partitioners with conflictin…
abf4d67
…g expectations are run (pytorch#14458)
Summary: Pull Request resolved: pytorch#14458
Differential Revision: D82936479
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@abhinaykukkadapu@facebook-github-bot
Fix op decomposition issue when multiple partitioners with conflictin…
e593a22
…g expectations are run (pytorch#14458)
Summary:
Pull Request resolved: pytorch#14458
Context: I'm trying to enable sequential recipes targeting multiple backends (such as `CoreML.FP32 + XNNPack.FP32`, here xnnpack will be a fallback for the ops) and i don't think we've ever tested lowering a model to multiple backends or this edge case has never been hit.
I've hit this case when i tried to lower vision transformer model (VIT)
I've seen a similar problem occurred with SDPA op (discussed [here](https://fb.workplace.com/groups/pytorch.edge.users/permalink/1796069037930048/)) and has been fixed and i think the fix works but it didn't considered when there multiple partitioners in mind with *conflicting decomposition requirements and filter for op no decomp namespace*.
## Error (using both coreml + xnnpack)
```
[2025-09-22T11:01:47.263-07:00] ValueError: Cannot view a tensor with shape torch.Size([197, 1, 12, 64]) and strides (64, 151296, 12608, 1) as a tensor with shape (197, 768)!
[2025-09-22T11:01:47.263-07:00]
[2025-09-22T11:01:47.263-07:00] While executing %view_8 : [num_users=1] = call_function[target=torch.ops.aten.view.default](args = (%permute_6, [197, 768]), kwargs = {})
[2025-09-22T11:01:47.263-07:00] Original traceback:
[2025-09-22T11:01:47.263-07:00] File "/data/users/abhinayk/fbsource/buck-out/v2/gen/fbcode/afd2a63214a057a8/executorch/export/tests/__test_target_recipes__/test_target_recipes#link-tree/torchvision/models/vision_transformer.py", line 298, in forward
[2025-09-22T11:01:47.263-07:00] x = self.encoder(x)
[2025-09-22T11:01:47.263-07:00] File "/data/users/abhinayk/fbsource/buck-out/v2/gen/fbcode/afd2a63214a057a8/executorch/export/tests/__test_target_recipes__/test_target_recipes#link-tree/torchvision/models/vision_transformer.py", line 157, in forward
[2025-09-22T11:01:47.263-07:00] return self.ln(self.layers(self.dropout(input)))
[2025-09-22T11:01:47.263-07:00] File "/data/users/abhinayk/fbsource/buck-out/v2/gen/fbcode/afd2a63214a057a8/executorch/export/tests/__test_target_recipes__/test_target_recipes#link-tree/torchvision/models/vision_transformer.py", line 113, in forward
[2025-09-22T11:01:47.263-07:00] x, _ = self.self_attention(x, x, x, need_weights=False)
```
**Note**: Lowering to single backend works with both coreml or xnnpack, it is the combination that give this error.
Changes:
- The fix is to run decomposition filtering and skipping per partitioner rather than maintaining same rule in global scope.
- I've additionally refactored the code to make it more readable by removing multiple boolean checks.
Differential Revision: D82936479
@abhinaykukkadapu@facebook-github-bot
Fix op decomposition issue when multiple partitioners with conflictin…
04f413a
…g expectations are run (pytorch#14458)
Summary:
Context: I'm trying to enable sequential recipes targeting multiple backends (such as `CoreML.FP32 + XNNPack.FP32`, here xnnpack will be a fallback for the ops) and i don't think we've ever tested lowering a model to multiple backends or this edge case has never been hit.
I've hit this case when i tried to lower vision transformer model (VIT)
I've seen a similar problem occurred with SDPA op (discussed [here](https://fb.workplace.com/groups/pytorch.edge.users/permalink/1796069037930048/)) and has been fixed and i think the fix works but it didn't considered when there multiple partitioners in mind with *conflicting decomposition requirements and filter for op no decomp namespace*.
## Error with coreml + xnnpack
```
[2025-09-22T11:01:47.263-07:00] ValueError: Cannot view a tensor with shape torch.Size([197, 1, 12, 64]) and strides (64, 151296, 12608, 1) as a tensor with shape (197, 768)!
[2025-09-22T11:01:47.263-07:00]
[2025-09-22T11:01:47.263-07:00] While executing %view_8 : [num_users=1] = call_function[target=torch.ops.aten.view.default](args = (%permute_6, [197, 768]), kwargs = {})
[2025-09-22T11:01:47.263-07:00] Original traceback:
[2025-09-22T11:01:47.263-07:00] File "/data/users/abhinayk/fbsource/buck-out/v2/gen/fbcode/afd2a63214a057a8/executorch/export/tests/__test_target_recipes__/test_target_recipes#link-tree/torchvision/models/vision_transformer.py", line 298, in forward
[2025-09-22T11:01:47.263-07:00] x = self.encoder(x)
[2025-09-22T11:01:47.263-07:00] File "/data/users/abhinayk/fbsource/buck-out/v2/gen/fbcode/afd2a63214a057a8/executorch/export/tests/__test_target_recipes__/test_target_recipes#link-tree/torchvision/models/vision_transformer.py", line 157, in forward
[2025-09-22T11:01:47.263-07:00] return self.ln(self.layers(self.dropout(input)))
[2025-09-22T11:01:47.263-07:00] File "/data/users/abhinayk/fbsource/buck-out/v2/gen/fbcode/afd2a63214a057a8/executorch/export/tests/__test_target_recipes__/test_target_recipes#link-tree/torchvision/models/vision_transformer.py", line 113, in forward
[2025-09-22T11:01:47.263-07:00] x, _ = self.self_attention(x, x, x, need_weights=False)
```
## Error with QNN + XNNPACK
The core problem here is that if there are two partitioners, Backend A partitioner (asking to preserve ops x, y) and Backend B partitioner (asking to preserve ops z), assume Backend A doesn't understand Z and want to decompose, currently we `union` all the ops to preserve from multiple partitioner and it errors out
In this specific case, xnnpack asks to preserve `aten.max_pool2d` but QNN doesn't understand it.
```
[2025-09-22T12:18:33.640-07:00] partition_list = capability_partitioner.propose_partitions()
[2025-09-22T12:18:33.640-07:00] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[2025-09-22T12:18:33.640-07:00] File "/data/users/abhinayk/fbsource/buck-out/v2/gen/fbcode/afd2a63214a057a8/executorch/export/tests/__test_target_recipes__/test_target_recipes#link-tree/torch/fx/passes/infra/partitioner.py", line 226, in propose_partitions
[2025-09-22T12:18:33.640-07:00] if self._is_node_supported(node) and node not in assignment:
[2025-09-22T12:18:33.640-07:00] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[2025-09-22T12:18:33.640-07:00] File "/data/users/abhinayk/fbsource/buck-out/v2/gen/fbcode/afd2a63214a057a8/executorch/export/tests/__test_target_recipes__/test_target_recipes#link-tree/torch/fx/passes/infra/partitioner.py", line 87, in _is_node_supported
[2025-09-22T12:18:33.640-07:00] return self.operator_support.is_node_supported(
[2025-09-22T12:18:33.640-07:00] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[2025-09-22T12:18:33.640-07:00] File "/data/users/abhinayk/fbsource/buck-out/v2/gen/fbcode/afd2a63214a057a8/executorch/export/tests/__test_target_recipes__/test_target_recipes#link-tree/executorch/backends/qualcomm/partition/qnn_partitioner.py", line 100, in is_node_supported
[2025-09-22T12:18:33.640-07:00] op_wrapper = self.node_visitors[node.target.__name__].define_node(
[2025-09-22T12:18:33.640-07:00] ~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^
[2025-09-22T12:18:33.640-07:00] KeyError: 'aten.max_pool2d.default'
```
**Note**: Lowering to single backend works with both coreml or xnnpack or QNN, it is the combinations that hits this error.
Changes:
- The fix is to run decomposition filtering and skipping per partitioner rather than maintaining same rule in global scope.
- I've additionally refactored the code to make it more readable by removing multiple boolean checks.
Differential Revision: D82936479
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Comment threadexir/program/_program.py
 if not can_skip_using_EDGE_DO_NOT_DECOMP:
 program = program.run_decompositions(_default_decomposition_table())
 _restore_transformed_ops_to_aten_ops(program)
 if can_skip_using_edge_do_not_decomp:
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LGTM, but there is still a logical bug in ET's AOT code with the EDGE_DO_NOT_DECOMP namespace / preservation when it comes to SDPA (and maybe other ops).
For CoreML, we get around it by skipping that path, but other backends (e.g., XNNPACK or QNN) will run into it if they preserve SDPA.
Perhaps the runtime time could take a look at this issue? cc @JacobSzwejbka@larryliu0820
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Fix op decomposition issue when multiple partitioners with conflictin…
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…g expectations are run (pytorch#14458)
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Fix op decomposition issue when multiple partitioners with conflicting expectations are run - #14458
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Fix op decomposition issue when multiple partitioners with conflicting expectations are run#14458
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Differential Revision: D82936479
Context:

This diff enables sequential recipes targeting multiple backends (for example: CoreML.FP32 + XNNPack.FP32, with XNNPack as a fallback). I think, we had not tested lowering models to multiple backends, so this edge case was unhandled.
While lowering the Vision Transformer (ViT) model, I encountered issues similar to those previously seen with the SDPA op (discussion). Although a fix exists, it did not account for scenarios with multiple partitioners having conflicting decomposition requirements and op filtering for the no-decomp namespace.
Error scenarios
The core problem: if two partitioners request to preserve different ops (ex: XNNPack wants to preserve aten.max_pool2d, but QNN does not support it), the current logic unions all ops to preserve, causing errors if a backend cannot handle an op.
QNN + XNNPack
[2025-09-22T12:18:33.640-07:00] partition_list = capability_partitioner.propose_partitions()
[2025-09-22T12:18:33.640-07:00] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[2025-09-22T12:18:33.640-07:00] File "/data/users/abhinayk/fbsource/buck-out/v2/gen/fbcode/afd2a63214a057a8/executorch/export/tests/__test_target_recipes__/test_target_recipes#link-tree/torch/fx/passes/infra/partitioner.py", line 226, in propose_partitions
[2025-09-22T12:18:33.640-07:00] if self._is_node_supported(node) and node not in assignment:
[2025-09-22T12:18:33.640-07:00] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[2025-09-22T12:18:33.640-07:00] File "/data/users/abhinayk/fbsource/buck-out/v2/gen/fbcode/afd2a63214a057a8/executorch/export/tests/__test_target_recipes__/test_target_recipes#link-tree/torch/fx/passes/infra/partitioner.py", line 87, in _is_node_supported
[2025-09-22T12:18:33.640-07:00] return self.operator_support.is_node_supported(
[2025-09-22T12:18:33.640-07:00] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[2025-09-22T12:18:33.640-07:00] File "/data/users/abhinayk/fbsource/buck-out/v2/gen/fbcode/afd2a63214a057a8/executorch/export/tests/__test_target_recipes__/test_target_recipes#link-tree/executorch/backends/qualcomm/partition/qnn_partitioner.py", line 100, in is_node_supported
[2025-09-22T12:18:33.640-07:00] op_wrapper = self.node_visitors[node.target.__name__].define_node(
[2025-09-22T12:18:33.640-07:00] ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^
[2025-09-22T12:18:33.640-07:00] KeyError: 'aten.max_pool2d.default'

CoreML + XNNPack
[2025-09-22T11:01:47.263-07:00] ValueError: Cannot view a tensor with shape torch.Size([197, 1, 12, 64]) and strides (64, 151296, 12608, 1) as a tensor with shape (197, 768)!
[2025-09-22T11:01:47.263-07:00]
[2025-09-22T11:01:47.263-07:00] While executing %view_8 : [num_users=1] = call_function[target=torch.ops.aten.view.default](args = (%permute_6, [197, 768]), kwargs = {})
[2025-09-22T11:01:47.263-07:00] Original traceback:
[2025-09-22T11:01:47.263-07:00] File "/data/users/abhinayk/fbsource/buck-out/v2/gen/fbcode/afd2a63214a057a8/executorch/export/tests/__test_target_recipes__/test_target_recipes#link-tree/torchvision/models/vision_transformer.py", line 298, in forward
[2025-09-22T11:01:47.263-07:00] x = self.encoder(x)
[2025-09-22T11:01:47.263-07:00] File "/data/users/abhinayk/fbsource/buck-out/v2/gen/fbcode/afd2a63214a057a8/executorch/export/tests/__test_target_recipes__/test_target_recipes#link-tree/torchvision/models/vision_transformer.py", line 157, in forward
[2025-09-22T11:01:47.263-07:00] return self.ln(self.layers(self.dropout(input)))
[2025-09-22T11:01:47.263-07:00] File "/data/users/abhinayk/fbsource/buck-out/v2/gen/fbcode/afd2a63214a057a8/executorch/export/tests/__test_target_recipes__/test_target_recipes#link-tree/torchvision/models/vision_transformer.py", line 113, in forward
[2025-09-22T11:01:47.263-07:00] x, _ = self.self_attention(x, x, x, need_weights=False)

Note: Lowering to a single backend (CoreML, XNNPack, or QNN) works as expected, the issue is only hit when there are backend combinations with conflicting expectations.
Changes:
  • Decomposition skipping and filtering are now handled per partitioner, rather than globally.
  • Refactored code for readability by removing too many boolean + if/else checks which are hard to follow.
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🔗 Helpful Links
🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/14458
Note: Links to docs will display an error until the docs builds have been completed.
❌ 1 New Failure
As of commit 04f413a with merge base 99e4fbe (image):
NEW FAILURE - The following job has failed:
This comment was automatically generated by Dr. CI and updates every 15 minutes.
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Fix op decomposition issue when multiple partitioners with conflictin…
abf4d67
…g expectations are run (pytorch#14458)
Summary: Pull Request resolved: pytorch#14458
Differential Revision: D82936479
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@abhinaykukkadapu@facebook-github-bot
Fix op decomposition issue when multiple partitioners with conflictin…
e593a22
…g expectations are run (pytorch#14458)
Summary:
Pull Request resolved: pytorch#14458
Context: I'm trying to enable sequential recipes targeting multiple backends (such as `CoreML.FP32 + XNNPack.FP32`, here xnnpack will be a fallback for the ops) and i don't think we've ever tested lowering a model to multiple backends or this edge case has never been hit.
I've hit this case when i tried to lower vision transformer model (VIT)
I've seen a similar problem occurred with SDPA op (discussed [here](https://fb.workplace.com/groups/pytorch.edge.users/permalink/1796069037930048/)) and has been fixed and i think the fix works but it didn't considered when there multiple partitioners in mind with *conflicting decomposition requirements and filter for op no decomp namespace*.
## Error (using both coreml + xnnpack)
```
[2025-09-22T11:01:47.263-07:00] ValueError: Cannot view a tensor with shape torch.Size([197, 1, 12, 64]) and strides (64, 151296, 12608, 1) as a tensor with shape (197, 768)!
[2025-09-22T11:01:47.263-07:00]
[2025-09-22T11:01:47.263-07:00] While executing %view_8 : [num_users=1] = call_function[target=torch.ops.aten.view.default](args = (%permute_6, [197, 768]), kwargs = {})
[2025-09-22T11:01:47.263-07:00] Original traceback:
[2025-09-22T11:01:47.263-07:00] File "/data/users/abhinayk/fbsource/buck-out/v2/gen/fbcode/afd2a63214a057a8/executorch/export/tests/__test_target_recipes__/test_target_recipes#link-tree/torchvision/models/vision_transformer.py", line 298, in forward
[2025-09-22T11:01:47.263-07:00] x = self.encoder(x)
[2025-09-22T11:01:47.263-07:00] File "/data/users/abhinayk/fbsource/buck-out/v2/gen/fbcode/afd2a63214a057a8/executorch/export/tests/__test_target_recipes__/test_target_recipes#link-tree/torchvision/models/vision_transformer.py", line 157, in forward
[2025-09-22T11:01:47.263-07:00] return self.ln(self.layers(self.dropout(input)))
[2025-09-22T11:01:47.263-07:00] File "/data/users/abhinayk/fbsource/buck-out/v2/gen/fbcode/afd2a63214a057a8/executorch/export/tests/__test_target_recipes__/test_target_recipes#link-tree/torchvision/models/vision_transformer.py", line 113, in forward
[2025-09-22T11:01:47.263-07:00] x, _ = self.self_attention(x, x, x, need_weights=False)
```
**Note**: Lowering to single backend works with both coreml or xnnpack, it is the combination that give this error.
Changes:
- The fix is to run decomposition filtering and skipping per partitioner rather than maintaining same rule in global scope.
- I've additionally refactored the code to make it more readable by removing multiple boolean checks.
Differential Revision: D82936479
@abhinaykukkadapu@facebook-github-bot
Fix op decomposition issue when multiple partitioners with conflictin…
04f413a
…g expectations are run (pytorch#14458)
Summary:
Context: I'm trying to enable sequential recipes targeting multiple backends (such as `CoreML.FP32 + XNNPack.FP32`, here xnnpack will be a fallback for the ops) and i don't think we've ever tested lowering a model to multiple backends or this edge case has never been hit.
I've hit this case when i tried to lower vision transformer model (VIT)
I've seen a similar problem occurred with SDPA op (discussed [here](https://fb.workplace.com/groups/pytorch.edge.users/permalink/1796069037930048/)) and has been fixed and i think the fix works but it didn't considered when there multiple partitioners in mind with *conflicting decomposition requirements and filter for op no decomp namespace*.
## Error with coreml + xnnpack
```
[2025-09-22T11:01:47.263-07:00] ValueError: Cannot view a tensor with shape torch.Size([197, 1, 12, 64]) and strides (64, 151296, 12608, 1) as a tensor with shape (197, 768)!
[2025-09-22T11:01:47.263-07:00]
[2025-09-22T11:01:47.263-07:00] While executing %view_8 : [num_users=1] = call_function[target=torch.ops.aten.view.default](args = (%permute_6, [197, 768]), kwargs = {})
[2025-09-22T11:01:47.263-07:00] Original traceback:
[2025-09-22T11:01:47.263-07:00] File "/data/users/abhinayk/fbsource/buck-out/v2/gen/fbcode/afd2a63214a057a8/executorch/export/tests/__test_target_recipes__/test_target_recipes#link-tree/torchvision/models/vision_transformer.py", line 298, in forward
[2025-09-22T11:01:47.263-07:00] x = self.encoder(x)
[2025-09-22T11:01:47.263-07:00] File "/data/users/abhinayk/fbsource/buck-out/v2/gen/fbcode/afd2a63214a057a8/executorch/export/tests/__test_target_recipes__/test_target_recipes#link-tree/torchvision/models/vision_transformer.py", line 157, in forward
[2025-09-22T11:01:47.263-07:00] return self.ln(self.layers(self.dropout(input)))
[2025-09-22T11:01:47.263-07:00] File "/data/users/abhinayk/fbsource/buck-out/v2/gen/fbcode/afd2a63214a057a8/executorch/export/tests/__test_target_recipes__/test_target_recipes#link-tree/torchvision/models/vision_transformer.py", line 113, in forward
[2025-09-22T11:01:47.263-07:00] x, _ = self.self_attention(x, x, x, need_weights=False)
```
## Error with QNN + XNNPACK
The core problem here is that if there are two partitioners, Backend A partitioner (asking to preserve ops x, y) and Backend B partitioner (asking to preserve ops z), assume Backend A doesn't understand Z and want to decompose, currently we `union` all the ops to preserve from multiple partitioner and it errors out
In this specific case, xnnpack asks to preserve `aten.max_pool2d` but QNN doesn't understand it.
```
[2025-09-22T12:18:33.640-07:00] partition_list = capability_partitioner.propose_partitions()
[2025-09-22T12:18:33.640-07:00] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[2025-09-22T12:18:33.640-07:00] File "/data/users/abhinayk/fbsource/buck-out/v2/gen/fbcode/afd2a63214a057a8/executorch/export/tests/__test_target_recipes__/test_target_recipes#link-tree/torch/fx/passes/infra/partitioner.py", line 226, in propose_partitions
[2025-09-22T12:18:33.640-07:00] if self._is_node_supported(node) and node not in assignment:
[2025-09-22T12:18:33.640-07:00] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[2025-09-22T12:18:33.640-07:00] File "/data/users/abhinayk/fbsource/buck-out/v2/gen/fbcode/afd2a63214a057a8/executorch/export/tests/__test_target_recipes__/test_target_recipes#link-tree/torch/fx/passes/infra/partitioner.py", line 87, in _is_node_supported
[2025-09-22T12:18:33.640-07:00] return self.operator_support.is_node_supported(
[2025-09-22T12:18:33.640-07:00] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[2025-09-22T12:18:33.640-07:00] File "/data/users/abhinayk/fbsource/buck-out/v2/gen/fbcode/afd2a63214a057a8/executorch/export/tests/__test_target_recipes__/test_target_recipes#link-tree/executorch/backends/qualcomm/partition/qnn_partitioner.py", line 100, in is_node_supported
[2025-09-22T12:18:33.640-07:00] op_wrapper = self.node_visitors[node.target.__name__].define_node(
[2025-09-22T12:18:33.640-07:00] ~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^
[2025-09-22T12:18:33.640-07:00] KeyError: 'aten.max_pool2d.default'
```
**Note**: Lowering to single backend works with both coreml or xnnpack or QNN, it is the combinations that hits this error.
Changes:
- The fix is to run decomposition filtering and skipping per partitioner rather than maintaining same rule in global scope.
- I've additionally refactored the code to make it more readable by removing multiple boolean checks.
Differential Revision: D82936479
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metascroy
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Sep 23, 2025
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Comment threadexir/program/_program.py
 if not can_skip_using_EDGE_DO_NOT_DECOMP:
 program = program.run_decompositions(_default_decomposition_table())
 _restore_transformed_ops_to_aten_ops(program)
 if can_skip_using_edge_do_not_decomp:
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LGTM, but there is still a logical bug in ET's AOT code with the EDGE_DO_NOT_DECOMP namespace / preservation when it comes to SDPA (and maybe other ops).
For CoreML, we get around it by skipping that path, but other backends (e.g., XNNPACK or QNN) will run into it if they preserve SDPA.
Perhaps the runtime time could take a look at this issue? cc @JacobSzwejbka@larryliu0820
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Fix op decomposition issue when multiple partitioners with conflictin…
df951f6
…g expectations are run (pytorch#14458)
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Fix op decomposition issue when multiple partitioners with conflicting expectations are run - #14458
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Fix op decomposition issue when multiple partitioners with conflicting expectations are run#14458
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Differential Revision: D82936479
Context:

This diff enables sequential recipes targeting multiple backends (for example: CoreML.FP32 + XNNPack.FP32, with XNNPack as a fallback). I think, we had not tested lowering models to multiple backends, so this edge case was unhandled.
While lowering the Vision Transformer (ViT) model, I encountered issues similar to those previously seen with the SDPA op (discussion). Although a fix exists, it did not account for scenarios with multiple partitioners having conflicting decomposition requirements and op filtering for the no-decomp namespace.
Error scenarios
The core problem: if two partitioners request to preserve different ops (ex: XNNPack wants to preserve aten.max_pool2d, but QNN does not support it), the current logic unions all ops to preserve, causing errors if a backend cannot handle an op.
QNN + XNNPack
[2025-09-22T12:18:33.640-07:00] partition_list = capability_partitioner.propose_partitions()
[2025-09-22T12:18:33.640-07:00] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[2025-09-22T12:18:33.640-07:00] File "/data/users/abhinayk/fbsource/buck-out/v2/gen/fbcode/afd2a63214a057a8/executorch/export/tests/__test_target_recipes__/test_target_recipes#link-tree/torch/fx/passes/infra/partitioner.py", line 226, in propose_partitions
[2025-09-22T12:18:33.640-07:00] if self._is_node_supported(node) and node not in assignment:
[2025-09-22T12:18:33.640-07:00] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[2025-09-22T12:18:33.640-07:00] File "/data/users/abhinayk/fbsource/buck-out/v2/gen/fbcode/afd2a63214a057a8/executorch/export/tests/__test_target_recipes__/test_target_recipes#link-tree/torch/fx/passes/infra/partitioner.py", line 87, in _is_node_supported
[2025-09-22T12:18:33.640-07:00] return self.operator_support.is_node_supported(
[2025-09-22T12:18:33.640-07:00] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[2025-09-22T12:18:33.640-07:00] File "/data/users/abhinayk/fbsource/buck-out/v2/gen/fbcode/afd2a63214a057a8/executorch/export/tests/__test_target_recipes__/test_target_recipes#link-tree/executorch/backends/qualcomm/partition/qnn_partitioner.py", line 100, in is_node_supported
[2025-09-22T12:18:33.640-07:00] op_wrapper = self.node_visitors[node.target.__name__].define_node(
[2025-09-22T12:18:33.640-07:00] ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^
[2025-09-22T12:18:33.640-07:00] KeyError: 'aten.max_pool2d.default'

CoreML + XNNPack
[2025-09-22T11:01:47.263-07:00] ValueError: Cannot view a tensor with shape torch.Size([197, 1, 12, 64]) and strides (64, 151296, 12608, 1) as a tensor with shape (197, 768)!
[2025-09-22T11:01:47.263-07:00]
[2025-09-22T11:01:47.263-07:00] While executing %view_8 : [num_users=1] = call_function[target=torch.ops.aten.view.default](args = (%permute_6, [197, 768]), kwargs = {})
[2025-09-22T11:01:47.263-07:00] Original traceback:
[2025-09-22T11:01:47.263-07:00] File "/data/users/abhinayk/fbsource/buck-out/v2/gen/fbcode/afd2a63214a057a8/executorch/export/tests/__test_target_recipes__/test_target_recipes#link-tree/torchvision/models/vision_transformer.py", line 298, in forward
[2025-09-22T11:01:47.263-07:00] x = self.encoder(x)
[2025-09-22T11:01:47.263-07:00] File "/data/users/abhinayk/fbsource/buck-out/v2/gen/fbcode/afd2a63214a057a8/executorch/export/tests/__test_target_recipes__/test_target_recipes#link-tree/torchvision/models/vision_transformer.py", line 157, in forward
[2025-09-22T11:01:47.263-07:00] return self.ln(self.layers(self.dropout(input)))
[2025-09-22T11:01:47.263-07:00] File "/data/users/abhinayk/fbsource/buck-out/v2/gen/fbcode/afd2a63214a057a8/executorch/export/tests/__test_target_recipes__/test_target_recipes#link-tree/torchvision/models/vision_transformer.py", line 113, in forward
[2025-09-22T11:01:47.263-07:00] x, _ = self.self_attention(x, x, x, need_weights=False)

Note: Lowering to a single backend (CoreML, XNNPack, or QNN) works as expected, the issue is only hit when there are backend combinations with conflicting expectations.
Changes:
  • Decomposition skipping and filtering are now handled per partitioner, rather than globally.
  • Refactored code for readability by removing too many boolean + if/else checks which are hard to follow.
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@abhinaykukkadapu@facebook-github-bot
Fix op decomposition issue when multiple partitioners with conflictin…
abf4d67
…g expectations are run (pytorch#14458)
Summary: Pull Request resolved: pytorch#14458
Differential Revision: D82936479
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abhinaykukkadapu added a commit
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Sep 22, 2025
@abhinaykukkadapu@facebook-github-bot
Fix op decomposition issue when multiple partitioners with conflictin…
e593a22
…g expectations are run (pytorch#14458)
Summary:
Pull Request resolved: pytorch#14458
Context: I'm trying to enable sequential recipes targeting multiple backends (such as `CoreML.FP32 + XNNPack.FP32`, here xnnpack will be a fallback for the ops) and i don't think we've ever tested lowering a model to multiple backends or this edge case has never been hit.
I've hit this case when i tried to lower vision transformer model (VIT)
I've seen a similar problem occurred with SDPA op (discussed [here](https://fb.workplace.com/groups/pytorch.edge.users/permalink/1796069037930048/)) and has been fixed and i think the fix works but it didn't considered when there multiple partitioners in mind with *conflicting decomposition requirements and filter for op no decomp namespace*.
## Error (using both coreml + xnnpack)
```
[2025-09-22T11:01:47.263-07:00] ValueError: Cannot view a tensor with shape torch.Size([197, 1, 12, 64]) and strides (64, 151296, 12608, 1) as a tensor with shape (197, 768)!
[2025-09-22T11:01:47.263-07:00]
[2025-09-22T11:01:47.263-07:00] While executing %view_8 : [num_users=1] = call_function[target=torch.ops.aten.view.default](args = (%permute_6, [197, 768]), kwargs = {})
[2025-09-22T11:01:47.263-07:00] Original traceback:
[2025-09-22T11:01:47.263-07:00] File "/data/users/abhinayk/fbsource/buck-out/v2/gen/fbcode/afd2a63214a057a8/executorch/export/tests/__test_target_recipes__/test_target_recipes#link-tree/torchvision/models/vision_transformer.py", line 298, in forward
[2025-09-22T11:01:47.263-07:00] x = self.encoder(x)
[2025-09-22T11:01:47.263-07:00] File "/data/users/abhinayk/fbsource/buck-out/v2/gen/fbcode/afd2a63214a057a8/executorch/export/tests/__test_target_recipes__/test_target_recipes#link-tree/torchvision/models/vision_transformer.py", line 157, in forward
[2025-09-22T11:01:47.263-07:00] return self.ln(self.layers(self.dropout(input)))
[2025-09-22T11:01:47.263-07:00] File "/data/users/abhinayk/fbsource/buck-out/v2/gen/fbcode/afd2a63214a057a8/executorch/export/tests/__test_target_recipes__/test_target_recipes#link-tree/torchvision/models/vision_transformer.py", line 113, in forward
[2025-09-22T11:01:47.263-07:00] x, _ = self.self_attention(x, x, x, need_weights=False)
```
**Note**: Lowering to single backend works with both coreml or xnnpack, it is the combination that give this error.
Changes:
- The fix is to run decomposition filtering and skipping per partitioner rather than maintaining same rule in global scope.
- I've additionally refactored the code to make it more readable by removing multiple boolean checks.
Differential Revision: D82936479
@abhinaykukkadapu@facebook-github-bot
Fix op decomposition issue when multiple partitioners with conflictin…
04f413a
…g expectations are run (pytorch#14458)
Summary:
Context: I'm trying to enable sequential recipes targeting multiple backends (such as `CoreML.FP32 + XNNPack.FP32`, here xnnpack will be a fallback for the ops) and i don't think we've ever tested lowering a model to multiple backends or this edge case has never been hit.
I've hit this case when i tried to lower vision transformer model (VIT)
I've seen a similar problem occurred with SDPA op (discussed [here](https://fb.workplace.com/groups/pytorch.edge.users/permalink/1796069037930048/)) and has been fixed and i think the fix works but it didn't considered when there multiple partitioners in mind with *conflicting decomposition requirements and filter for op no decomp namespace*.
## Error with coreml + xnnpack
```
[2025-09-22T11:01:47.263-07:00] ValueError: Cannot view a tensor with shape torch.Size([197, 1, 12, 64]) and strides (64, 151296, 12608, 1) as a tensor with shape (197, 768)!
[2025-09-22T11:01:47.263-07:00]
[2025-09-22T11:01:47.263-07:00] While executing %view_8 : [num_users=1] = call_function[target=torch.ops.aten.view.default](args = (%permute_6, [197, 768]), kwargs = {})
[2025-09-22T11:01:47.263-07:00] Original traceback:
[2025-09-22T11:01:47.263-07:00] File "/data/users/abhinayk/fbsource/buck-out/v2/gen/fbcode/afd2a63214a057a8/executorch/export/tests/__test_target_recipes__/test_target_recipes#link-tree/torchvision/models/vision_transformer.py", line 298, in forward
[2025-09-22T11:01:47.263-07:00] x = self.encoder(x)
[2025-09-22T11:01:47.263-07:00] File "/data/users/abhinayk/fbsource/buck-out/v2/gen/fbcode/afd2a63214a057a8/executorch/export/tests/__test_target_recipes__/test_target_recipes#link-tree/torchvision/models/vision_transformer.py", line 157, in forward
[2025-09-22T11:01:47.263-07:00] return self.ln(self.layers(self.dropout(input)))
[2025-09-22T11:01:47.263-07:00] File "/data/users/abhinayk/fbsource/buck-out/v2/gen/fbcode/afd2a63214a057a8/executorch/export/tests/__test_target_recipes__/test_target_recipes#link-tree/torchvision/models/vision_transformer.py", line 113, in forward
[2025-09-22T11:01:47.263-07:00] x, _ = self.self_attention(x, x, x, need_weights=False)
```
## Error with QNN + XNNPACK
The core problem here is that if there are two partitioners, Backend A partitioner (asking to preserve ops x, y) and Backend B partitioner (asking to preserve ops z), assume Backend A doesn't understand Z and want to decompose, currently we `union` all the ops to preserve from multiple partitioner and it errors out
In this specific case, xnnpack asks to preserve `aten.max_pool2d` but QNN doesn't understand it.
```
[2025-09-22T12:18:33.640-07:00] partition_list = capability_partitioner.propose_partitions()
[2025-09-22T12:18:33.640-07:00] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[2025-09-22T12:18:33.640-07:00] File "/data/users/abhinayk/fbsource/buck-out/v2/gen/fbcode/afd2a63214a057a8/executorch/export/tests/__test_target_recipes__/test_target_recipes#link-tree/torch/fx/passes/infra/partitioner.py", line 226, in propose_partitions
[2025-09-22T12:18:33.640-07:00] if self._is_node_supported(node) and node not in assignment:
[2025-09-22T12:18:33.640-07:00] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[2025-09-22T12:18:33.640-07:00] File "/data/users/abhinayk/fbsource/buck-out/v2/gen/fbcode/afd2a63214a057a8/executorch/export/tests/__test_target_recipes__/test_target_recipes#link-tree/torch/fx/passes/infra/partitioner.py", line 87, in _is_node_supported
[2025-09-22T12:18:33.640-07:00] return self.operator_support.is_node_supported(
[2025-09-22T12:18:33.640-07:00] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[2025-09-22T12:18:33.640-07:00] File "/data/users/abhinayk/fbsource/buck-out/v2/gen/fbcode/afd2a63214a057a8/executorch/export/tests/__test_target_recipes__/test_target_recipes#link-tree/executorch/backends/qualcomm/partition/qnn_partitioner.py", line 100, in is_node_supported
[2025-09-22T12:18:33.640-07:00] op_wrapper = self.node_visitors[node.target.__name__].define_node(
[2025-09-22T12:18:33.640-07:00] ~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^
[2025-09-22T12:18:33.640-07:00] KeyError: 'aten.max_pool2d.default'
```
**Note**: Lowering to single backend works with both coreml or xnnpack or QNN, it is the combinations that hits this error.
Changes:
- The fix is to run decomposition filtering and skipping per partitioner rather than maintaining same rule in global scope.
- I've additionally refactored the code to make it more readable by removing multiple boolean checks.
Differential Revision: D82936479
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Comment threadexir/program/_program.py
 if not can_skip_using_EDGE_DO_NOT_DECOMP:
 program = program.run_decompositions(_default_decomposition_table())
 _restore_transformed_ops_to_aten_ops(program)
 if can_skip_using_edge_do_not_decomp:
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LGTM, but there is still a logical bug in ET's AOT code with the EDGE_DO_NOT_DECOMP namespace / preservation when it comes to SDPA (and maybe other ops).
For CoreML, we get around it by skipping that path, but other backends (e.g., XNNPACK or QNN) will run into it if they preserve SDPA.
Perhaps the runtime time could take a look at this issue? cc @JacobSzwejbka@larryliu0820
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Fix op decomposition issue when multiple partitioners with conflictin…
df951f6
…g expectations are run (pytorch#14458)
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Fix op decomposition issue when multiple partitioners with conflicting expectations are run - #14458
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Fix op decomposition issue when multiple partitioners with conflicting expectations are run#14458
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Differential Revision: D82936479
Context:

This diff enables sequential recipes targeting multiple backends (for example: CoreML.FP32 + XNNPack.FP32, with XNNPack as a fallback). I think, we had not tested lowering models to multiple backends, so this edge case was unhandled.
While lowering the Vision Transformer (ViT) model, I encountered issues similar to those previously seen with the SDPA op (discussion). Although a fix exists, it did not account for scenarios with multiple partitioners having conflicting decomposition requirements and op filtering for the no-decomp namespace.
Error scenarios
The core problem: if two partitioners request to preserve different ops (ex: XNNPack wants to preserve aten.max_pool2d, but QNN does not support it), the current logic unions all ops to preserve, causing errors if a backend cannot handle an op.
QNN + XNNPack
[2025-09-22T12:18:33.640-07:00] partition_list = capability_partitioner.propose_partitions()
[2025-09-22T12:18:33.640-07:00] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[2025-09-22T12:18:33.640-07:00] File "/data/users/abhinayk/fbsource/buck-out/v2/gen/fbcode/afd2a63214a057a8/executorch/export/tests/__test_target_recipes__/test_target_recipes#link-tree/torch/fx/passes/infra/partitioner.py", line 226, in propose_partitions
[2025-09-22T12:18:33.640-07:00] if self._is_node_supported(node) and node not in assignment:
[2025-09-22T12:18:33.640-07:00] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[2025-09-22T12:18:33.640-07:00] File "/data/users/abhinayk/fbsource/buck-out/v2/gen/fbcode/afd2a63214a057a8/executorch/export/tests/__test_target_recipes__/test_target_recipes#link-tree/torch/fx/passes/infra/partitioner.py", line 87, in _is_node_supported
[2025-09-22T12:18:33.640-07:00] return self.operator_support.is_node_supported(
[2025-09-22T12:18:33.640-07:00] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[2025-09-22T12:18:33.640-07:00] File "/data/users/abhinayk/fbsource/buck-out/v2/gen/fbcode/afd2a63214a057a8/executorch/export/tests/__test_target_recipes__/test_target_recipes#link-tree/executorch/backends/qualcomm/partition/qnn_partitioner.py", line 100, in is_node_supported
[2025-09-22T12:18:33.640-07:00] op_wrapper = self.node_visitors[node.target.__name__].define_node(
[2025-09-22T12:18:33.640-07:00] ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^
[2025-09-22T12:18:33.640-07:00] KeyError: 'aten.max_pool2d.default'

CoreML + XNNPack
[2025-09-22T11:01:47.263-07:00] ValueError: Cannot view a tensor with shape torch.Size([197, 1, 12, 64]) and strides (64, 151296, 12608, 1) as a tensor with shape (197, 768)!
[2025-09-22T11:01:47.263-07:00]
[2025-09-22T11:01:47.263-07:00] While executing %view_8 : [num_users=1] = call_function[target=torch.ops.aten.view.default](args = (%permute_6, [197, 768]), kwargs = {})
[2025-09-22T11:01:47.263-07:00] Original traceback:
[2025-09-22T11:01:47.263-07:00] File "/data/users/abhinayk/fbsource/buck-out/v2/gen/fbcode/afd2a63214a057a8/executorch/export/tests/__test_target_recipes__/test_target_recipes#link-tree/torchvision/models/vision_transformer.py", line 298, in forward
[2025-09-22T11:01:47.263-07:00] x = self.encoder(x)
[2025-09-22T11:01:47.263-07:00] File "/data/users/abhinayk/fbsource/buck-out/v2/gen/fbcode/afd2a63214a057a8/executorch/export/tests/__test_target_recipes__/test_target_recipes#link-tree/torchvision/models/vision_transformer.py", line 157, in forward
[2025-09-22T11:01:47.263-07:00] return self.ln(self.layers(self.dropout(input)))
[2025-09-22T11:01:47.263-07:00] File "/data/users/abhinayk/fbsource/buck-out/v2/gen/fbcode/afd2a63214a057a8/executorch/export/tests/__test_target_recipes__/test_target_recipes#link-tree/torchvision/models/vision_transformer.py", line 113, in forward
[2025-09-22T11:01:47.263-07:00] x, _ = self.self_attention(x, x, x, need_weights=False)

Note: Lowering to a single backend (CoreML, XNNPack, or QNN) works as expected, the issue is only hit when there are backend combinations with conflicting expectations.
Changes:
  • Decomposition skipping and filtering are now handled per partitioner, rather than globally.
  • Refactored code for readability by removing too many boolean + if/else checks which are hard to follow.
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🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/14458
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@abhinaykukkadapu@facebook-github-bot
Fix op decomposition issue when multiple partitioners with conflictin…
abf4d67
…g expectations are run (pytorch#14458)
Summary: Pull Request resolved: pytorch#14458
Differential Revision: D82936479
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Sep 22, 2025
@abhinaykukkadapu@facebook-github-bot
Fix op decomposition issue when multiple partitioners with conflictin…
e593a22
…g expectations are run (pytorch#14458)
Summary:
Pull Request resolved: pytorch#14458
Context: I'm trying to enable sequential recipes targeting multiple backends (such as `CoreML.FP32 + XNNPack.FP32`, here xnnpack will be a fallback for the ops) and i don't think we've ever tested lowering a model to multiple backends or this edge case has never been hit.
I've hit this case when i tried to lower vision transformer model (VIT)
I've seen a similar problem occurred with SDPA op (discussed [here](https://fb.workplace.com/groups/pytorch.edge.users/permalink/1796069037930048/)) and has been fixed and i think the fix works but it didn't considered when there multiple partitioners in mind with *conflicting decomposition requirements and filter for op no decomp namespace*.
## Error (using both coreml + xnnpack)
```
[2025-09-22T11:01:47.263-07:00] ValueError: Cannot view a tensor with shape torch.Size([197, 1, 12, 64]) and strides (64, 151296, 12608, 1) as a tensor with shape (197, 768)!
[2025-09-22T11:01:47.263-07:00]
[2025-09-22T11:01:47.263-07:00] While executing %view_8 : [num_users=1] = call_function[target=torch.ops.aten.view.default](args = (%permute_6, [197, 768]), kwargs = {})
[2025-09-22T11:01:47.263-07:00] Original traceback:
[2025-09-22T11:01:47.263-07:00] File "/data/users/abhinayk/fbsource/buck-out/v2/gen/fbcode/afd2a63214a057a8/executorch/export/tests/__test_target_recipes__/test_target_recipes#link-tree/torchvision/models/vision_transformer.py", line 298, in forward
[2025-09-22T11:01:47.263-07:00] x = self.encoder(x)
[2025-09-22T11:01:47.263-07:00] File "/data/users/abhinayk/fbsource/buck-out/v2/gen/fbcode/afd2a63214a057a8/executorch/export/tests/__test_target_recipes__/test_target_recipes#link-tree/torchvision/models/vision_transformer.py", line 157, in forward
[2025-09-22T11:01:47.263-07:00] return self.ln(self.layers(self.dropout(input)))
[2025-09-22T11:01:47.263-07:00] File "/data/users/abhinayk/fbsource/buck-out/v2/gen/fbcode/afd2a63214a057a8/executorch/export/tests/__test_target_recipes__/test_target_recipes#link-tree/torchvision/models/vision_transformer.py", line 113, in forward
[2025-09-22T11:01:47.263-07:00] x, _ = self.self_attention(x, x, x, need_weights=False)
```
**Note**: Lowering to single backend works with both coreml or xnnpack, it is the combination that give this error.
Changes:
- The fix is to run decomposition filtering and skipping per partitioner rather than maintaining same rule in global scope.
- I've additionally refactored the code to make it more readable by removing multiple boolean checks.
Differential Revision: D82936479
@abhinaykukkadapu@facebook-github-bot
Fix op decomposition issue when multiple partitioners with conflictin…
04f413a
…g expectations are run (pytorch#14458)
Summary:
Context: I'm trying to enable sequential recipes targeting multiple backends (such as `CoreML.FP32 + XNNPack.FP32`, here xnnpack will be a fallback for the ops) and i don't think we've ever tested lowering a model to multiple backends or this edge case has never been hit.
I've hit this case when i tried to lower vision transformer model (VIT)
I've seen a similar problem occurred with SDPA op (discussed [here](https://fb.workplace.com/groups/pytorch.edge.users/permalink/1796069037930048/)) and has been fixed and i think the fix works but it didn't considered when there multiple partitioners in mind with *conflicting decomposition requirements and filter for op no decomp namespace*.
## Error with coreml + xnnpack
```
[2025-09-22T11:01:47.263-07:00] ValueError: Cannot view a tensor with shape torch.Size([197, 1, 12, 64]) and strides (64, 151296, 12608, 1) as a tensor with shape (197, 768)!
[2025-09-22T11:01:47.263-07:00]
[2025-09-22T11:01:47.263-07:00] While executing %view_8 : [num_users=1] = call_function[target=torch.ops.aten.view.default](args = (%permute_6, [197, 768]), kwargs = {})
[2025-09-22T11:01:47.263-07:00] Original traceback:
[2025-09-22T11:01:47.263-07:00] File "/data/users/abhinayk/fbsource/buck-out/v2/gen/fbcode/afd2a63214a057a8/executorch/export/tests/__test_target_recipes__/test_target_recipes#link-tree/torchvision/models/vision_transformer.py", line 298, in forward
[2025-09-22T11:01:47.263-07:00] x = self.encoder(x)
[2025-09-22T11:01:47.263-07:00] File "/data/users/abhinayk/fbsource/buck-out/v2/gen/fbcode/afd2a63214a057a8/executorch/export/tests/__test_target_recipes__/test_target_recipes#link-tree/torchvision/models/vision_transformer.py", line 157, in forward
[2025-09-22T11:01:47.263-07:00] return self.ln(self.layers(self.dropout(input)))
[2025-09-22T11:01:47.263-07:00] File "/data/users/abhinayk/fbsource/buck-out/v2/gen/fbcode/afd2a63214a057a8/executorch/export/tests/__test_target_recipes__/test_target_recipes#link-tree/torchvision/models/vision_transformer.py", line 113, in forward
[2025-09-22T11:01:47.263-07:00] x, _ = self.self_attention(x, x, x, need_weights=False)
```
## Error with QNN + XNNPACK
The core problem here is that if there are two partitioners, Backend A partitioner (asking to preserve ops x, y) and Backend B partitioner (asking to preserve ops z), assume Backend A doesn't understand Z and want to decompose, currently we `union` all the ops to preserve from multiple partitioner and it errors out
In this specific case, xnnpack asks to preserve `aten.max_pool2d` but QNN doesn't understand it.
```
[2025-09-22T12:18:33.640-07:00] partition_list = capability_partitioner.propose_partitions()
[2025-09-22T12:18:33.640-07:00] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[2025-09-22T12:18:33.640-07:00] File "/data/users/abhinayk/fbsource/buck-out/v2/gen/fbcode/afd2a63214a057a8/executorch/export/tests/__test_target_recipes__/test_target_recipes#link-tree/torch/fx/passes/infra/partitioner.py", line 226, in propose_partitions
[2025-09-22T12:18:33.640-07:00] if self._is_node_supported(node) and node not in assignment:
[2025-09-22T12:18:33.640-07:00] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[2025-09-22T12:18:33.640-07:00] File "/data/users/abhinayk/fbsource/buck-out/v2/gen/fbcode/afd2a63214a057a8/executorch/export/tests/__test_target_recipes__/test_target_recipes#link-tree/torch/fx/passes/infra/partitioner.py", line 87, in _is_node_supported
[2025-09-22T12:18:33.640-07:00] return self.operator_support.is_node_supported(
[2025-09-22T12:18:33.640-07:00] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[2025-09-22T12:18:33.640-07:00] File "/data/users/abhinayk/fbsource/buck-out/v2/gen/fbcode/afd2a63214a057a8/executorch/export/tests/__test_target_recipes__/test_target_recipes#link-tree/executorch/backends/qualcomm/partition/qnn_partitioner.py", line 100, in is_node_supported
[2025-09-22T12:18:33.640-07:00] op_wrapper = self.node_visitors[node.target.__name__].define_node(
[2025-09-22T12:18:33.640-07:00] ~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^
[2025-09-22T12:18:33.640-07:00] KeyError: 'aten.max_pool2d.default'
```
**Note**: Lowering to single backend works with both coreml or xnnpack or QNN, it is the combinations that hits this error.
Changes:
- The fix is to run decomposition filtering and skipping per partitioner rather than maintaining same rule in global scope.
- I've additionally refactored the code to make it more readable by removing multiple boolean checks.
Differential Revision: D82936479
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Comment threadexir/program/_program.py
 if not can_skip_using_EDGE_DO_NOT_DECOMP:
 program = program.run_decompositions(_default_decomposition_table())
 _restore_transformed_ops_to_aten_ops(program)
 if can_skip_using_edge_do_not_decomp:
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LGTM, but there is still a logical bug in ET's AOT code with the EDGE_DO_NOT_DECOMP namespace / preservation when it comes to SDPA (and maybe other ops).
For CoreML, we get around it by skipping that path, but other backends (e.g., XNNPACK or QNN) will run into it if they preserve SDPA.
Perhaps the runtime time could take a look at this issue? cc @JacobSzwejbka@larryliu0820
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Fix op decomposition issue when multiple partitioners with conflictin…
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…g expectations are run (pytorch#14458)
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Fix op decomposition issue when multiple partitioners with conflicting expectations are run - #14458
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Fix op decomposition issue when multiple partitioners with conflicting expectations are run#14458
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Differential Revision: D82936479
Context:

This diff enables sequential recipes targeting multiple backends (for example: CoreML.FP32 + XNNPack.FP32, with XNNPack as a fallback). I think, we had not tested lowering models to multiple backends, so this edge case was unhandled.
While lowering the Vision Transformer (ViT) model, I encountered issues similar to those previously seen with the SDPA op (discussion). Although a fix exists, it did not account for scenarios with multiple partitioners having conflicting decomposition requirements and op filtering for the no-decomp namespace.
Error scenarios
The core problem: if two partitioners request to preserve different ops (ex: XNNPack wants to preserve aten.max_pool2d, but QNN does not support it), the current logic unions all ops to preserve, causing errors if a backend cannot handle an op.
QNN + XNNPack
[2025-09-22T12:18:33.640-07:00] partition_list = capability_partitioner.propose_partitions()
[2025-09-22T12:18:33.640-07:00] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[2025-09-22T12:18:33.640-07:00] File "/data/users/abhinayk/fbsource/buck-out/v2/gen/fbcode/afd2a63214a057a8/executorch/export/tests/__test_target_recipes__/test_target_recipes#link-tree/torch/fx/passes/infra/partitioner.py", line 226, in propose_partitions
[2025-09-22T12:18:33.640-07:00] if self._is_node_supported(node) and node not in assignment:
[2025-09-22T12:18:33.640-07:00] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[2025-09-22T12:18:33.640-07:00] File "/data/users/abhinayk/fbsource/buck-out/v2/gen/fbcode/afd2a63214a057a8/executorch/export/tests/__test_target_recipes__/test_target_recipes#link-tree/torch/fx/passes/infra/partitioner.py", line 87, in _is_node_supported
[2025-09-22T12:18:33.640-07:00] return self.operator_support.is_node_supported(
[2025-09-22T12:18:33.640-07:00] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[2025-09-22T12:18:33.640-07:00] File "/data/users/abhinayk/fbsource/buck-out/v2/gen/fbcode/afd2a63214a057a8/executorch/export/tests/__test_target_recipes__/test_target_recipes#link-tree/executorch/backends/qualcomm/partition/qnn_partitioner.py", line 100, in is_node_supported
[2025-09-22T12:18:33.640-07:00] op_wrapper = self.node_visitors[node.target.__name__].define_node(
[2025-09-22T12:18:33.640-07:00] ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^
[2025-09-22T12:18:33.640-07:00] KeyError: 'aten.max_pool2d.default'

CoreML + XNNPack
[2025-09-22T11:01:47.263-07:00] ValueError: Cannot view a tensor with shape torch.Size([197, 1, 12, 64]) and strides (64, 151296, 12608, 1) as a tensor with shape (197, 768)!
[2025-09-22T11:01:47.263-07:00]
[2025-09-22T11:01:47.263-07:00] While executing %view_8 : [num_users=1] = call_function[target=torch.ops.aten.view.default](args = (%permute_6, [197, 768]), kwargs = {})
[2025-09-22T11:01:47.263-07:00] Original traceback:
[2025-09-22T11:01:47.263-07:00] File "/data/users/abhinayk/fbsource/buck-out/v2/gen/fbcode/afd2a63214a057a8/executorch/export/tests/__test_target_recipes__/test_target_recipes#link-tree/torchvision/models/vision_transformer.py", line 298, in forward
[2025-09-22T11:01:47.263-07:00] x = self.encoder(x)
[2025-09-22T11:01:47.263-07:00] File "/data/users/abhinayk/fbsource/buck-out/v2/gen/fbcode/afd2a63214a057a8/executorch/export/tests/__test_target_recipes__/test_target_recipes#link-tree/torchvision/models/vision_transformer.py", line 157, in forward
[2025-09-22T11:01:47.263-07:00] return self.ln(self.layers(self.dropout(input)))
[2025-09-22T11:01:47.263-07:00] File "/data/users/abhinayk/fbsource/buck-out/v2/gen/fbcode/afd2a63214a057a8/executorch/export/tests/__test_target_recipes__/test_target_recipes#link-tree/torchvision/models/vision_transformer.py", line 113, in forward
[2025-09-22T11:01:47.263-07:00] x, _ = self.self_attention(x, x, x, need_weights=False)

Note: Lowering to a single backend (CoreML, XNNPack, or QNN) works as expected, the issue is only hit when there are backend combinations with conflicting expectations.
Changes:
  • Decomposition skipping and filtering are now handled per partitioner, rather than globally.
  • Refactored code for readability by removing too many boolean + if/else checks which are hard to follow.
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🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/14458
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Fix op decomposition issue when multiple partitioners with conflictin…
abf4d67
…g expectations are run (pytorch#14458)
Summary: Pull Request resolved: pytorch#14458
Differential Revision: D82936479
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@abhinaykukkadapu@facebook-github-bot
Fix op decomposition issue when multiple partitioners with conflictin…
e593a22
…g expectations are run (pytorch#14458)
Summary:
Pull Request resolved: pytorch#14458
Context: I'm trying to enable sequential recipes targeting multiple backends (such as `CoreML.FP32 + XNNPack.FP32`, here xnnpack will be a fallback for the ops) and i don't think we've ever tested lowering a model to multiple backends or this edge case has never been hit.
I've hit this case when i tried to lower vision transformer model (VIT)
I've seen a similar problem occurred with SDPA op (discussed [here](https://fb.workplace.com/groups/pytorch.edge.users/permalink/1796069037930048/)) and has been fixed and i think the fix works but it didn't considered when there multiple partitioners in mind with *conflicting decomposition requirements and filter for op no decomp namespace*.
## Error (using both coreml + xnnpack)
```
[2025-09-22T11:01:47.263-07:00] ValueError: Cannot view a tensor with shape torch.Size([197, 1, 12, 64]) and strides (64, 151296, 12608, 1) as a tensor with shape (197, 768)!
[2025-09-22T11:01:47.263-07:00]
[2025-09-22T11:01:47.263-07:00] While executing %view_8 : [num_users=1] = call_function[target=torch.ops.aten.view.default](args = (%permute_6, [197, 768]), kwargs = {})
[2025-09-22T11:01:47.263-07:00] Original traceback:
[2025-09-22T11:01:47.263-07:00] File "/data/users/abhinayk/fbsource/buck-out/v2/gen/fbcode/afd2a63214a057a8/executorch/export/tests/__test_target_recipes__/test_target_recipes#link-tree/torchvision/models/vision_transformer.py", line 298, in forward
[2025-09-22T11:01:47.263-07:00] x = self.encoder(x)
[2025-09-22T11:01:47.263-07:00] File "/data/users/abhinayk/fbsource/buck-out/v2/gen/fbcode/afd2a63214a057a8/executorch/export/tests/__test_target_recipes__/test_target_recipes#link-tree/torchvision/models/vision_transformer.py", line 157, in forward
[2025-09-22T11:01:47.263-07:00] return self.ln(self.layers(self.dropout(input)))
[2025-09-22T11:01:47.263-07:00] File "/data/users/abhinayk/fbsource/buck-out/v2/gen/fbcode/afd2a63214a057a8/executorch/export/tests/__test_target_recipes__/test_target_recipes#link-tree/torchvision/models/vision_transformer.py", line 113, in forward
[2025-09-22T11:01:47.263-07:00] x, _ = self.self_attention(x, x, x, need_weights=False)
```
**Note**: Lowering to single backend works with both coreml or xnnpack, it is the combination that give this error.
Changes:
- The fix is to run decomposition filtering and skipping per partitioner rather than maintaining same rule in global scope.
- I've additionally refactored the code to make it more readable by removing multiple boolean checks.
Differential Revision: D82936479
@abhinaykukkadapu@facebook-github-bot
Fix op decomposition issue when multiple partitioners with conflictin…
04f413a
…g expectations are run (pytorch#14458)
Summary:
Context: I'm trying to enable sequential recipes targeting multiple backends (such as `CoreML.FP32 + XNNPack.FP32`, here xnnpack will be a fallback for the ops) and i don't think we've ever tested lowering a model to multiple backends or this edge case has never been hit.
I've hit this case when i tried to lower vision transformer model (VIT)
I've seen a similar problem occurred with SDPA op (discussed [here](https://fb.workplace.com/groups/pytorch.edge.users/permalink/1796069037930048/)) and has been fixed and i think the fix works but it didn't considered when there multiple partitioners in mind with *conflicting decomposition requirements and filter for op no decomp namespace*.
## Error with coreml + xnnpack
```
[2025-09-22T11:01:47.263-07:00] ValueError: Cannot view a tensor with shape torch.Size([197, 1, 12, 64]) and strides (64, 151296, 12608, 1) as a tensor with shape (197, 768)!
[2025-09-22T11:01:47.263-07:00]
[2025-09-22T11:01:47.263-07:00] While executing %view_8 : [num_users=1] = call_function[target=torch.ops.aten.view.default](args = (%permute_6, [197, 768]), kwargs = {})
[2025-09-22T11:01:47.263-07:00] Original traceback:
[2025-09-22T11:01:47.263-07:00] File "/data/users/abhinayk/fbsource/buck-out/v2/gen/fbcode/afd2a63214a057a8/executorch/export/tests/__test_target_recipes__/test_target_recipes#link-tree/torchvision/models/vision_transformer.py", line 298, in forward
[2025-09-22T11:01:47.263-07:00] x = self.encoder(x)
[2025-09-22T11:01:47.263-07:00] File "/data/users/abhinayk/fbsource/buck-out/v2/gen/fbcode/afd2a63214a057a8/executorch/export/tests/__test_target_recipes__/test_target_recipes#link-tree/torchvision/models/vision_transformer.py", line 157, in forward
[2025-09-22T11:01:47.263-07:00] return self.ln(self.layers(self.dropout(input)))
[2025-09-22T11:01:47.263-07:00] File "/data/users/abhinayk/fbsource/buck-out/v2/gen/fbcode/afd2a63214a057a8/executorch/export/tests/__test_target_recipes__/test_target_recipes#link-tree/torchvision/models/vision_transformer.py", line 113, in forward
[2025-09-22T11:01:47.263-07:00] x, _ = self.self_attention(x, x, x, need_weights=False)
```
## Error with QNN + XNNPACK
The core problem here is that if there are two partitioners, Backend A partitioner (asking to preserve ops x, y) and Backend B partitioner (asking to preserve ops z), assume Backend A doesn't understand Z and want to decompose, currently we `union` all the ops to preserve from multiple partitioner and it errors out
In this specific case, xnnpack asks to preserve `aten.max_pool2d` but QNN doesn't understand it.
```
[2025-09-22T12:18:33.640-07:00] partition_list = capability_partitioner.propose_partitions()
[2025-09-22T12:18:33.640-07:00] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[2025-09-22T12:18:33.640-07:00] File "/data/users/abhinayk/fbsource/buck-out/v2/gen/fbcode/afd2a63214a057a8/executorch/export/tests/__test_target_recipes__/test_target_recipes#link-tree/torch/fx/passes/infra/partitioner.py", line 226, in propose_partitions
[2025-09-22T12:18:33.640-07:00] if self._is_node_supported(node) and node not in assignment:
[2025-09-22T12:18:33.640-07:00] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[2025-09-22T12:18:33.640-07:00] File "/data/users/abhinayk/fbsource/buck-out/v2/gen/fbcode/afd2a63214a057a8/executorch/export/tests/__test_target_recipes__/test_target_recipes#link-tree/torch/fx/passes/infra/partitioner.py", line 87, in _is_node_supported
[2025-09-22T12:18:33.640-07:00] return self.operator_support.is_node_supported(
[2025-09-22T12:18:33.640-07:00] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[2025-09-22T12:18:33.640-07:00] File "/data/users/abhinayk/fbsource/buck-out/v2/gen/fbcode/afd2a63214a057a8/executorch/export/tests/__test_target_recipes__/test_target_recipes#link-tree/executorch/backends/qualcomm/partition/qnn_partitioner.py", line 100, in is_node_supported
[2025-09-22T12:18:33.640-07:00] op_wrapper = self.node_visitors[node.target.__name__].define_node(
[2025-09-22T12:18:33.640-07:00] ~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^
[2025-09-22T12:18:33.640-07:00] KeyError: 'aten.max_pool2d.default'
```
**Note**: Lowering to single backend works with both coreml or xnnpack or QNN, it is the combinations that hits this error.
Changes:
- The fix is to run decomposition filtering and skipping per partitioner rather than maintaining same rule in global scope.
- I've additionally refactored the code to make it more readable by removing multiple boolean checks.
Differential Revision: D82936479
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Comment threadexir/program/_program.py
 if not can_skip_using_EDGE_DO_NOT_DECOMP:
 program = program.run_decompositions(_default_decomposition_table())
 _restore_transformed_ops_to_aten_ops(program)
 if can_skip_using_edge_do_not_decomp:
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LGTM, but there is still a logical bug in ET's AOT code with the EDGE_DO_NOT_DECOMP namespace / preservation when it comes to SDPA (and maybe other ops).
For CoreML, we get around it by skipping that path, but other backends (e.g., XNNPACK or QNN) will run into it if they preserve SDPA.
Perhaps the runtime time could take a look at this issue? cc @JacobSzwejbka@larryliu0820
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@abhinaykukkadapu@jirioc
Fix op decomposition issue when multiple partitioners with conflictin…
df951f6
…g expectations are run (pytorch#14458)
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Fix op decomposition issue when multiple partitioners with conflicting expectations are run - #14458
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Merged
Fix op decomposition issue when multiple partitioners with conflicting expectations are run#14458
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Differential Revision: D82936479
Context:

This diff enables sequential recipes targeting multiple backends (for example: CoreML.FP32 + XNNPack.FP32, with XNNPack as a fallback). I think, we had not tested lowering models to multiple backends, so this edge case was unhandled.
While lowering the Vision Transformer (ViT) model, I encountered issues similar to those previously seen with the SDPA op (discussion). Although a fix exists, it did not account for scenarios with multiple partitioners having conflicting decomposition requirements and op filtering for the no-decomp namespace.
Error scenarios
The core problem: if two partitioners request to preserve different ops (ex: XNNPack wants to preserve aten.max_pool2d, but QNN does not support it), the current logic unions all ops to preserve, causing errors if a backend cannot handle an op.
QNN + XNNPack
[2025-09-22T12:18:33.640-07:00] partition_list = capability_partitioner.propose_partitions()
[2025-09-22T12:18:33.640-07:00] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[2025-09-22T12:18:33.640-07:00] File "/data/users/abhinayk/fbsource/buck-out/v2/gen/fbcode/afd2a63214a057a8/executorch/export/tests/__test_target_recipes__/test_target_recipes#link-tree/torch/fx/passes/infra/partitioner.py", line 226, in propose_partitions
[2025-09-22T12:18:33.640-07:00] if self._is_node_supported(node) and node not in assignment:
[2025-09-22T12:18:33.640-07:00] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[2025-09-22T12:18:33.640-07:00] File "/data/users/abhinayk/fbsource/buck-out/v2/gen/fbcode/afd2a63214a057a8/executorch/export/tests/__test_target_recipes__/test_target_recipes#link-tree/torch/fx/passes/infra/partitioner.py", line 87, in _is_node_supported
[2025-09-22T12:18:33.640-07:00] return self.operator_support.is_node_supported(
[2025-09-22T12:18:33.640-07:00] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[2025-09-22T12:18:33.640-07:00] File "/data/users/abhinayk/fbsource/buck-out/v2/gen/fbcode/afd2a63214a057a8/executorch/export/tests/__test_target_recipes__/test_target_recipes#link-tree/executorch/backends/qualcomm/partition/qnn_partitioner.py", line 100, in is_node_supported
[2025-09-22T12:18:33.640-07:00] op_wrapper = self.node_visitors[node.target.__name__].define_node(
[2025-09-22T12:18:33.640-07:00] ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^
[2025-09-22T12:18:33.640-07:00] KeyError: 'aten.max_pool2d.default'

CoreML + XNNPack
[2025-09-22T11:01:47.263-07:00] ValueError: Cannot view a tensor with shape torch.Size([197, 1, 12, 64]) and strides (64, 151296, 12608, 1) as a tensor with shape (197, 768)!
[2025-09-22T11:01:47.263-07:00]
[2025-09-22T11:01:47.263-07:00] While executing %view_8 : [num_users=1] = call_function[target=torch.ops.aten.view.default](args = (%permute_6, [197, 768]), kwargs = {})
[2025-09-22T11:01:47.263-07:00] Original traceback:
[2025-09-22T11:01:47.263-07:00] File "/data/users/abhinayk/fbsource/buck-out/v2/gen/fbcode/afd2a63214a057a8/executorch/export/tests/__test_target_recipes__/test_target_recipes#link-tree/torchvision/models/vision_transformer.py", line 298, in forward
[2025-09-22T11:01:47.263-07:00] x = self.encoder(x)
[2025-09-22T11:01:47.263-07:00] File "/data/users/abhinayk/fbsource/buck-out/v2/gen/fbcode/afd2a63214a057a8/executorch/export/tests/__test_target_recipes__/test_target_recipes#link-tree/torchvision/models/vision_transformer.py", line 157, in forward
[2025-09-22T11:01:47.263-07:00] return self.ln(self.layers(self.dropout(input)))
[2025-09-22T11:01:47.263-07:00] File "/data/users/abhinayk/fbsource/buck-out/v2/gen/fbcode/afd2a63214a057a8/executorch/export/tests/__test_target_recipes__/test_target_recipes#link-tree/torchvision/models/vision_transformer.py", line 113, in forward
[2025-09-22T11:01:47.263-07:00] x, _ = self.self_attention(x, x, x, need_weights=False)

Note: Lowering to a single backend (CoreML, XNNPack, or QNN) works as expected, the issue is only hit when there are backend combinations with conflicting expectations.
Changes:
  • Decomposition skipping and filtering are now handled per partitioner, rather than globally.
  • Refactored code for readability by removing too many boolean + if/else checks which are hard to follow.
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@abhinaykukkadapu@facebook-github-bot
Fix op decomposition issue when multiple partitioners with conflictin…
abf4d67
…g expectations are run (pytorch#14458)
Summary: Pull Request resolved: pytorch#14458
Differential Revision: D82936479
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@abhinaykukkadapu@facebook-github-bot
Fix op decomposition issue when multiple partitioners with conflictin…
e593a22
…g expectations are run (pytorch#14458)
Summary:
Pull Request resolved: pytorch#14458
Context: I'm trying to enable sequential recipes targeting multiple backends (such as `CoreML.FP32 + XNNPack.FP32`, here xnnpack will be a fallback for the ops) and i don't think we've ever tested lowering a model to multiple backends or this edge case has never been hit.
I've hit this case when i tried to lower vision transformer model (VIT)
I've seen a similar problem occurred with SDPA op (discussed [here](https://fb.workplace.com/groups/pytorch.edge.users/permalink/1796069037930048/)) and has been fixed and i think the fix works but it didn't considered when there multiple partitioners in mind with *conflicting decomposition requirements and filter for op no decomp namespace*.
## Error (using both coreml + xnnpack)
```
[2025-09-22T11:01:47.263-07:00] ValueError: Cannot view a tensor with shape torch.Size([197, 1, 12, 64]) and strides (64, 151296, 12608, 1) as a tensor with shape (197, 768)!
[2025-09-22T11:01:47.263-07:00]
[2025-09-22T11:01:47.263-07:00] While executing %view_8 : [num_users=1] = call_function[target=torch.ops.aten.view.default](args = (%permute_6, [197, 768]), kwargs = {})
[2025-09-22T11:01:47.263-07:00] Original traceback:
[2025-09-22T11:01:47.263-07:00] File "/data/users/abhinayk/fbsource/buck-out/v2/gen/fbcode/afd2a63214a057a8/executorch/export/tests/__test_target_recipes__/test_target_recipes#link-tree/torchvision/models/vision_transformer.py", line 298, in forward
[2025-09-22T11:01:47.263-07:00] x = self.encoder(x)
[2025-09-22T11:01:47.263-07:00] File "/data/users/abhinayk/fbsource/buck-out/v2/gen/fbcode/afd2a63214a057a8/executorch/export/tests/__test_target_recipes__/test_target_recipes#link-tree/torchvision/models/vision_transformer.py", line 157, in forward
[2025-09-22T11:01:47.263-07:00] return self.ln(self.layers(self.dropout(input)))
[2025-09-22T11:01:47.263-07:00] File "/data/users/abhinayk/fbsource/buck-out/v2/gen/fbcode/afd2a63214a057a8/executorch/export/tests/__test_target_recipes__/test_target_recipes#link-tree/torchvision/models/vision_transformer.py", line 113, in forward
[2025-09-22T11:01:47.263-07:00] x, _ = self.self_attention(x, x, x, need_weights=False)
```
**Note**: Lowering to single backend works with both coreml or xnnpack, it is the combination that give this error.
Changes:
- The fix is to run decomposition filtering and skipping per partitioner rather than maintaining same rule in global scope.
- I've additionally refactored the code to make it more readable by removing multiple boolean checks.
Differential Revision: D82936479
@abhinaykukkadapu@facebook-github-bot
Fix op decomposition issue when multiple partitioners with conflictin…
04f413a
…g expectations are run (pytorch#14458)
Summary:
Context: I'm trying to enable sequential recipes targeting multiple backends (such as `CoreML.FP32 + XNNPack.FP32`, here xnnpack will be a fallback for the ops) and i don't think we've ever tested lowering a model to multiple backends or this edge case has never been hit.
I've hit this case when i tried to lower vision transformer model (VIT)
I've seen a similar problem occurred with SDPA op (discussed [here](https://fb.workplace.com/groups/pytorch.edge.users/permalink/1796069037930048/)) and has been fixed and i think the fix works but it didn't considered when there multiple partitioners in mind with *conflicting decomposition requirements and filter for op no decomp namespace*.
## Error with coreml + xnnpack
```
[2025-09-22T11:01:47.263-07:00] ValueError: Cannot view a tensor with shape torch.Size([197, 1, 12, 64]) and strides (64, 151296, 12608, 1) as a tensor with shape (197, 768)!
[2025-09-22T11:01:47.263-07:00]
[2025-09-22T11:01:47.263-07:00] While executing %view_8 : [num_users=1] = call_function[target=torch.ops.aten.view.default](args = (%permute_6, [197, 768]), kwargs = {})
[2025-09-22T11:01:47.263-07:00] Original traceback:
[2025-09-22T11:01:47.263-07:00] File "/data/users/abhinayk/fbsource/buck-out/v2/gen/fbcode/afd2a63214a057a8/executorch/export/tests/__test_target_recipes__/test_target_recipes#link-tree/torchvision/models/vision_transformer.py", line 298, in forward
[2025-09-22T11:01:47.263-07:00] x = self.encoder(x)
[2025-09-22T11:01:47.263-07:00] File "/data/users/abhinayk/fbsource/buck-out/v2/gen/fbcode/afd2a63214a057a8/executorch/export/tests/__test_target_recipes__/test_target_recipes#link-tree/torchvision/models/vision_transformer.py", line 157, in forward
[2025-09-22T11:01:47.263-07:00] return self.ln(self.layers(self.dropout(input)))
[2025-09-22T11:01:47.263-07:00] File "/data/users/abhinayk/fbsource/buck-out/v2/gen/fbcode/afd2a63214a057a8/executorch/export/tests/__test_target_recipes__/test_target_recipes#link-tree/torchvision/models/vision_transformer.py", line 113, in forward
[2025-09-22T11:01:47.263-07:00] x, _ = self.self_attention(x, x, x, need_weights=False)
```
## Error with QNN + XNNPACK
The core problem here is that if there are two partitioners, Backend A partitioner (asking to preserve ops x, y) and Backend B partitioner (asking to preserve ops z), assume Backend A doesn't understand Z and want to decompose, currently we `union` all the ops to preserve from multiple partitioner and it errors out
In this specific case, xnnpack asks to preserve `aten.max_pool2d` but QNN doesn't understand it.
```
[2025-09-22T12:18:33.640-07:00] partition_list = capability_partitioner.propose_partitions()
[2025-09-22T12:18:33.640-07:00] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[2025-09-22T12:18:33.640-07:00] File "/data/users/abhinayk/fbsource/buck-out/v2/gen/fbcode/afd2a63214a057a8/executorch/export/tests/__test_target_recipes__/test_target_recipes#link-tree/torch/fx/passes/infra/partitioner.py", line 226, in propose_partitions
[2025-09-22T12:18:33.640-07:00] if self._is_node_supported(node) and node not in assignment:
[2025-09-22T12:18:33.640-07:00] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[2025-09-22T12:18:33.640-07:00] File "/data/users/abhinayk/fbsource/buck-out/v2/gen/fbcode/afd2a63214a057a8/executorch/export/tests/__test_target_recipes__/test_target_recipes#link-tree/torch/fx/passes/infra/partitioner.py", line 87, in _is_node_supported
[2025-09-22T12:18:33.640-07:00] return self.operator_support.is_node_supported(
[2025-09-22T12:18:33.640-07:00] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[2025-09-22T12:18:33.640-07:00] File "/data/users/abhinayk/fbsource/buck-out/v2/gen/fbcode/afd2a63214a057a8/executorch/export/tests/__test_target_recipes__/test_target_recipes#link-tree/executorch/backends/qualcomm/partition/qnn_partitioner.py", line 100, in is_node_supported
[2025-09-22T12:18:33.640-07:00] op_wrapper = self.node_visitors[node.target.__name__].define_node(
[2025-09-22T12:18:33.640-07:00] ~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^
[2025-09-22T12:18:33.640-07:00] KeyError: 'aten.max_pool2d.default'
```
**Note**: Lowering to single backend works with both coreml or xnnpack or QNN, it is the combinations that hits this error.
Changes:
- The fix is to run decomposition filtering and skipping per partitioner rather than maintaining same rule in global scope.
- I've additionally refactored the code to make it more readable by removing multiple boolean checks.
Differential Revision: D82936479
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Comment threadexir/program/_program.py
 if not can_skip_using_EDGE_DO_NOT_DECOMP:
 program = program.run_decompositions(_default_decomposition_table())
 _restore_transformed_ops_to_aten_ops(program)
 if can_skip_using_edge_do_not_decomp:
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LGTM, but there is still a logical bug in ET's AOT code with the EDGE_DO_NOT_DECOMP namespace / preservation when it comes to SDPA (and maybe other ops).
For CoreML, we get around it by skipping that path, but other backends (e.g., XNNPACK or QNN) will run into it if they preserve SDPA.
Perhaps the runtime time could take a look at this issue? cc @JacobSzwejbka@larryliu0820
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Fix op decomposition issue when multiple partitioners with conflictin…
df951f6
…g expectations are run (pytorch#14458)
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Fix op decomposition issue when multiple partitioners with conflicting expectations are run - #14458
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Fix op decomposition issue when multiple partitioners with conflicting expectations are run#14458
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Differential Revision: D82936479
Context:

This diff enables sequential recipes targeting multiple backends (for example: CoreML.FP32 + XNNPack.FP32, with XNNPack as a fallback). I think, we had not tested lowering models to multiple backends, so this edge case was unhandled.
While lowering the Vision Transformer (ViT) model, I encountered issues similar to those previously seen with the SDPA op (discussion). Although a fix exists, it did not account for scenarios with multiple partitioners having conflicting decomposition requirements and op filtering for the no-decomp namespace.
Error scenarios
The core problem: if two partitioners request to preserve different ops (ex: XNNPack wants to preserve aten.max_pool2d, but QNN does not support it), the current logic unions all ops to preserve, causing errors if a backend cannot handle an op.
QNN + XNNPack
[2025-09-22T12:18:33.640-07:00] partition_list = capability_partitioner.propose_partitions()
[2025-09-22T12:18:33.640-07:00] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[2025-09-22T12:18:33.640-07:00] File "/data/users/abhinayk/fbsource/buck-out/v2/gen/fbcode/afd2a63214a057a8/executorch/export/tests/__test_target_recipes__/test_target_recipes#link-tree/torch/fx/passes/infra/partitioner.py", line 226, in propose_partitions
[2025-09-22T12:18:33.640-07:00] if self._is_node_supported(node) and node not in assignment:
[2025-09-22T12:18:33.640-07:00] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[2025-09-22T12:18:33.640-07:00] File "/data/users/abhinayk/fbsource/buck-out/v2/gen/fbcode/afd2a63214a057a8/executorch/export/tests/__test_target_recipes__/test_target_recipes#link-tree/torch/fx/passes/infra/partitioner.py", line 87, in _is_node_supported
[2025-09-22T12:18:33.640-07:00] return self.operator_support.is_node_supported(
[2025-09-22T12:18:33.640-07:00] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[2025-09-22T12:18:33.640-07:00] File "/data/users/abhinayk/fbsource/buck-out/v2/gen/fbcode/afd2a63214a057a8/executorch/export/tests/__test_target_recipes__/test_target_recipes#link-tree/executorch/backends/qualcomm/partition/qnn_partitioner.py", line 100, in is_node_supported
[2025-09-22T12:18:33.640-07:00] op_wrapper = self.node_visitors[node.target.__name__].define_node(
[2025-09-22T12:18:33.640-07:00] ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^
[2025-09-22T12:18:33.640-07:00] KeyError: 'aten.max_pool2d.default'

CoreML + XNNPack
[2025-09-22T11:01:47.263-07:00] ValueError: Cannot view a tensor with shape torch.Size([197, 1, 12, 64]) and strides (64, 151296, 12608, 1) as a tensor with shape (197, 768)!
[2025-09-22T11:01:47.263-07:00]
[2025-09-22T11:01:47.263-07:00] While executing %view_8 : [num_users=1] = call_function[target=torch.ops.aten.view.default](args = (%permute_6, [197, 768]), kwargs = {})
[2025-09-22T11:01:47.263-07:00] Original traceback:
[2025-09-22T11:01:47.263-07:00] File "/data/users/abhinayk/fbsource/buck-out/v2/gen/fbcode/afd2a63214a057a8/executorch/export/tests/__test_target_recipes__/test_target_recipes#link-tree/torchvision/models/vision_transformer.py", line 298, in forward
[2025-09-22T11:01:47.263-07:00] x = self.encoder(x)
[2025-09-22T11:01:47.263-07:00] File "/data/users/abhinayk/fbsource/buck-out/v2/gen/fbcode/afd2a63214a057a8/executorch/export/tests/__test_target_recipes__/test_target_recipes#link-tree/torchvision/models/vision_transformer.py", line 157, in forward
[2025-09-22T11:01:47.263-07:00] return self.ln(self.layers(self.dropout(input)))
[2025-09-22T11:01:47.263-07:00] File "/data/users/abhinayk/fbsource/buck-out/v2/gen/fbcode/afd2a63214a057a8/executorch/export/tests/__test_target_recipes__/test_target_recipes#link-tree/torchvision/models/vision_transformer.py", line 113, in forward
[2025-09-22T11:01:47.263-07:00] x, _ = self.self_attention(x, x, x, need_weights=False)

Note: Lowering to a single backend (CoreML, XNNPack, or QNN) works as expected, the issue is only hit when there are backend combinations with conflicting expectations.
Changes:
  • Decomposition skipping and filtering are now handled per partitioner, rather than globally.
  • Refactored code for readability by removing too many boolean + if/else checks which are hard to follow.
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🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/14458
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@abhinaykukkadapu@facebook-github-bot
Fix op decomposition issue when multiple partitioners with conflictin…
abf4d67
…g expectations are run (pytorch#14458)
Summary: Pull Request resolved: pytorch#14458
Differential Revision: D82936479
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@abhinaykukkadapu@facebook-github-bot
Fix op decomposition issue when multiple partitioners with conflictin…
e593a22
…g expectations are run (pytorch#14458)
Summary:
Pull Request resolved: pytorch#14458
Context: I'm trying to enable sequential recipes targeting multiple backends (such as `CoreML.FP32 + XNNPack.FP32`, here xnnpack will be a fallback for the ops) and i don't think we've ever tested lowering a model to multiple backends or this edge case has never been hit.
I've hit this case when i tried to lower vision transformer model (VIT)
I've seen a similar problem occurred with SDPA op (discussed [here](https://fb.workplace.com/groups/pytorch.edge.users/permalink/1796069037930048/)) and has been fixed and i think the fix works but it didn't considered when there multiple partitioners in mind with *conflicting decomposition requirements and filter for op no decomp namespace*.
## Error (using both coreml + xnnpack)
```
[2025-09-22T11:01:47.263-07:00] ValueError: Cannot view a tensor with shape torch.Size([197, 1, 12, 64]) and strides (64, 151296, 12608, 1) as a tensor with shape (197, 768)!
[2025-09-22T11:01:47.263-07:00]
[2025-09-22T11:01:47.263-07:00] While executing %view_8 : [num_users=1] = call_function[target=torch.ops.aten.view.default](args = (%permute_6, [197, 768]), kwargs = {})
[2025-09-22T11:01:47.263-07:00] Original traceback:
[2025-09-22T11:01:47.263-07:00] File "/data/users/abhinayk/fbsource/buck-out/v2/gen/fbcode/afd2a63214a057a8/executorch/export/tests/__test_target_recipes__/test_target_recipes#link-tree/torchvision/models/vision_transformer.py", line 298, in forward
[2025-09-22T11:01:47.263-07:00] x = self.encoder(x)
[2025-09-22T11:01:47.263-07:00] File "/data/users/abhinayk/fbsource/buck-out/v2/gen/fbcode/afd2a63214a057a8/executorch/export/tests/__test_target_recipes__/test_target_recipes#link-tree/torchvision/models/vision_transformer.py", line 157, in forward
[2025-09-22T11:01:47.263-07:00] return self.ln(self.layers(self.dropout(input)))
[2025-09-22T11:01:47.263-07:00] File "/data/users/abhinayk/fbsource/buck-out/v2/gen/fbcode/afd2a63214a057a8/executorch/export/tests/__test_target_recipes__/test_target_recipes#link-tree/torchvision/models/vision_transformer.py", line 113, in forward
[2025-09-22T11:01:47.263-07:00] x, _ = self.self_attention(x, x, x, need_weights=False)
```
**Note**: Lowering to single backend works with both coreml or xnnpack, it is the combination that give this error.
Changes:
- The fix is to run decomposition filtering and skipping per partitioner rather than maintaining same rule in global scope.
- I've additionally refactored the code to make it more readable by removing multiple boolean checks.
Differential Revision: D82936479
@abhinaykukkadapu@facebook-github-bot
Fix op decomposition issue when multiple partitioners with conflictin…
04f413a
…g expectations are run (pytorch#14458)
Summary:
Context: I'm trying to enable sequential recipes targeting multiple backends (such as `CoreML.FP32 + XNNPack.FP32`, here xnnpack will be a fallback for the ops) and i don't think we've ever tested lowering a model to multiple backends or this edge case has never been hit.
I've hit this case when i tried to lower vision transformer model (VIT)
I've seen a similar problem occurred with SDPA op (discussed [here](https://fb.workplace.com/groups/pytorch.edge.users/permalink/1796069037930048/)) and has been fixed and i think the fix works but it didn't considered when there multiple partitioners in mind with *conflicting decomposition requirements and filter for op no decomp namespace*.
## Error with coreml + xnnpack
```
[2025-09-22T11:01:47.263-07:00] ValueError: Cannot view a tensor with shape torch.Size([197, 1, 12, 64]) and strides (64, 151296, 12608, 1) as a tensor with shape (197, 768)!
[2025-09-22T11:01:47.263-07:00]
[2025-09-22T11:01:47.263-07:00] While executing %view_8 : [num_users=1] = call_function[target=torch.ops.aten.view.default](args = (%permute_6, [197, 768]), kwargs = {})
[2025-09-22T11:01:47.263-07:00] Original traceback:
[2025-09-22T11:01:47.263-07:00] File "/data/users/abhinayk/fbsource/buck-out/v2/gen/fbcode/afd2a63214a057a8/executorch/export/tests/__test_target_recipes__/test_target_recipes#link-tree/torchvision/models/vision_transformer.py", line 298, in forward
[2025-09-22T11:01:47.263-07:00] x = self.encoder(x)
[2025-09-22T11:01:47.263-07:00] File "/data/users/abhinayk/fbsource/buck-out/v2/gen/fbcode/afd2a63214a057a8/executorch/export/tests/__test_target_recipes__/test_target_recipes#link-tree/torchvision/models/vision_transformer.py", line 157, in forward
[2025-09-22T11:01:47.263-07:00] return self.ln(self.layers(self.dropout(input)))
[2025-09-22T11:01:47.263-07:00] File "/data/users/abhinayk/fbsource/buck-out/v2/gen/fbcode/afd2a63214a057a8/executorch/export/tests/__test_target_recipes__/test_target_recipes#link-tree/torchvision/models/vision_transformer.py", line 113, in forward
[2025-09-22T11:01:47.263-07:00] x, _ = self.self_attention(x, x, x, need_weights=False)
```
## Error with QNN + XNNPACK
The core problem here is that if there are two partitioners, Backend A partitioner (asking to preserve ops x, y) and Backend B partitioner (asking to preserve ops z), assume Backend A doesn't understand Z and want to decompose, currently we `union` all the ops to preserve from multiple partitioner and it errors out
In this specific case, xnnpack asks to preserve `aten.max_pool2d` but QNN doesn't understand it.
```
[2025-09-22T12:18:33.640-07:00] partition_list = capability_partitioner.propose_partitions()
[2025-09-22T12:18:33.640-07:00] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[2025-09-22T12:18:33.640-07:00] File "/data/users/abhinayk/fbsource/buck-out/v2/gen/fbcode/afd2a63214a057a8/executorch/export/tests/__test_target_recipes__/test_target_recipes#link-tree/torch/fx/passes/infra/partitioner.py", line 226, in propose_partitions
[2025-09-22T12:18:33.640-07:00] if self._is_node_supported(node) and node not in assignment:
[2025-09-22T12:18:33.640-07:00] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[2025-09-22T12:18:33.640-07:00] File "/data/users/abhinayk/fbsource/buck-out/v2/gen/fbcode/afd2a63214a057a8/executorch/export/tests/__test_target_recipes__/test_target_recipes#link-tree/torch/fx/passes/infra/partitioner.py", line 87, in _is_node_supported
[2025-09-22T12:18:33.640-07:00] return self.operator_support.is_node_supported(
[2025-09-22T12:18:33.640-07:00] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[2025-09-22T12:18:33.640-07:00] File "/data/users/abhinayk/fbsource/buck-out/v2/gen/fbcode/afd2a63214a057a8/executorch/export/tests/__test_target_recipes__/test_target_recipes#link-tree/executorch/backends/qualcomm/partition/qnn_partitioner.py", line 100, in is_node_supported
[2025-09-22T12:18:33.640-07:00] op_wrapper = self.node_visitors[node.target.__name__].define_node(
[2025-09-22T12:18:33.640-07:00] ~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^
[2025-09-22T12:18:33.640-07:00] KeyError: 'aten.max_pool2d.default'
```
**Note**: Lowering to single backend works with both coreml or xnnpack or QNN, it is the combinations that hits this error.
Changes:
- The fix is to run decomposition filtering and skipping per partitioner rather than maintaining same rule in global scope.
- I've additionally refactored the code to make it more readable by removing multiple boolean checks.
Differential Revision: D82936479
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metascroy
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Comment threadexir/program/_program.py
 if not can_skip_using_EDGE_DO_NOT_DECOMP:
 program = program.run_decompositions(_default_decomposition_table())
 _restore_transformed_ops_to_aten_ops(program)
 if can_skip_using_edge_do_not_decomp:
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LGTM, but there is still a logical bug in ET's AOT code with the EDGE_DO_NOT_DECOMP namespace / preservation when it comes to SDPA (and maybe other ops).
For CoreML, we get around it by skipping that path, but other backends (e.g., XNNPACK or QNN) will run into it if they preserve SDPA.
Perhaps the runtime time could take a look at this issue? cc @JacobSzwejbka@larryliu0820
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Fix op decomposition issue when multiple partitioners with conflictin…
df951f6
…g expectations are run (pytorch#14458)
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