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Allow partitioning quantized linear for FP32-only partition #7284
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Allow partitioning quantized linear for FP32-only partition #7284
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🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/7284
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This pull request was exported from Phabricator. Differential Revision: D67011716 |
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…7284) Summary: Pull Request resolved: pytorch#7284 Add overwrite precision of linear op in partitioning. When using legacy_mode, we will test we don't partition [add]mm given, (1) We can't assume that weights are always static (non param). (2) Alternatively, when lowering [add]mm to xnn::bmm we can't support bias. (2)(a) Only lowering non-bias [add]mm, which is only exposed on legacy_path deemed low ROI. Added tests to make sure we see this behavior Differential Revision: D67011716
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This pull request was exported from Phabricator. Differential Revision: D67011716 |
…7284) Summary: Pull Request resolved: pytorch#7284 Add overwrite precision of linear op in partitioning. When using legacy_mode, we will test we don't partition [add]mm given, (1) We can't assume that weights are always static (non param). (2) Alternatively, when lowering [add]mm to xnn::bmm we can't support bias. (2)(a) Only lowering non-bias [add]mm, which is only exposed on legacy_path deemed low ROI. Added tests to make sure we see this behavior Differential Revision: D67011716
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This pull request was exported from Phabricator. Differential Revision: D67011716 |
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…7284) Summary: Add overwrite precision of linear op in partitioning. When using legacy_mode, we will test we don't partition [add]mm given, (1) We can't assume that weights are always static (non param). (2) Alternatively, when lowering [add]mm to xnn::bmm we can't support bias. (2)(a) Only lowering non-bias [add]mm, which is only exposed on legacy_path deemed low ROI. Added tests to make sure we see this behavior Reviewed By: mcr229 Differential Revision: D67011716
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This pull request was exported from Phabricator. Differential Revision: D67011716 |
…7284) Summary: Add overwrite precision of linear op in partitioning. When using legacy_mode, we will test we don't partition [add]mm given, (1) We can't assume that weights are always static (non param). (2) Alternatively, when lowering [add]mm to xnn::bmm we can't support bias. (2)(a) Only lowering non-bias [add]mm, which is only exposed on legacy_path deemed low ROI. Added tests to make sure we see this behavior Reviewed By: mcr229 Differential Revision: D67011716
adf9fc1 to
8b47c4d
Compare
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This pull request was exported from Phabricator. Differential Revision: D67011716 |
…7284) Summary: Add overwrite precision of linear op in partitioning. When using legacy_mode, we will test we don't partition [add]mm given, (1) We can't assume that weights are always static (non param). (2) Alternatively, when lowering [add]mm to xnn::bmm we can't support bias. (2)(a) Only lowering non-bias [add]mm, which is only exposed on legacy_path deemed low ROI. Added tests to make sure we see this behavior Reviewed By: mcr229 Differential Revision: D67011716
8b47c4d to
9b1bb48
Compare
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This pull request was exported from Phabricator. Differential Revision: D67011716 |
Summary:
Add overwrite precision of linear op in partitioning.
When using legacy_mode, we will test we don't partition [add]mm given,
(1) We can't assume that weights are always static (non param).
(2) Alternatively, when lowering [add]mm to xnn::bmm we can't support bias.
(2)(a) Only lowering non-bias [add]mm, which is only exposed on legacy_path deemed low ROI.
Added tests to make sure we see this behavior
Differential Revision: D67011716