[TOSA] Add legalization for avg_pool with count_include_pad=True #4273
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sjarus
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Jul 16, 2025
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sjarus
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Please add an e2e test under projects/pt1/python/torch_mlir_e2e_test/test_suite/pooling.py as well.
sahas3
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Jul 17, 2025
| // CHECK: %[[VAL_11:.*]] = tosa.const_shape {values = dense<[0, 0, 0, 0, 1, 1, 1, 1]> : tensor<8xindex>} : () -> !tosa.shape<8> | ||
| // CHECK: %[[VAL_12:.*]] = "tosa.const"() <{values = dense<0.000000e+00> : tensor<1xf32>}> : () -> tensor<1xf32> | ||
| // CHECK: %[[VAL_13:.*]] = tosa.pad %[[VAL_1]], %[[VAL_11]], %[[VAL_12]] : (tensor<1x192x35x35xf32>, !tosa.shape<8>, tensor<1xf32>) -> tensor<1x192x37x37xf32> | ||
| // CHECK: %[[VAL_14:.*]] = tosa.transpose %[[VAL_13]] {perms = array<i32: 0, 2, 3, 1>} : (tensor<1x192x37x37xf32>) -> tensor<1x37x37x192xf32> |
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Thanks for the change.
I think it'll be better to pad after transposing the input data -- the transpose operations for back-to-back pool (or conv -> pool pattern) ops will be optimized out leading to better performance.
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I reworked it as you suggested. In newer LLVM, the pad isn't folded into pooling anymore, so we can produce the optimal chain.
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Before this patch, the `avg_pool2d` and `avg_pool1d` legalizations lacked support for pooling with count_include_pad=True. This patch introduces that support. Signed-off-by: Vitalii Shutov <[email protected]> Change-Id: I73fa26a58379e2c021929ade81c983ff91c59667
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Before this patch, the
avg_pool2dandavg_pool1dlegalizations lacked support for pooling with count_include_pad=True. This patch introduces that support.