Arm backend: remove some xpassing xfails

backends/arm/test/models/test_resnet18.py

@@ -79,9 +79,6 @@ def test_resnet_u55_INT(per_channel_quantization):
 
 
 @pytest.mark.slow
-@pytest.mark.xfail(
-    reason="For resnet18 for Ethos-U85, the SRAM memory footprint is very high. The compiler team is investigating."
-)
 @common.XfailIfNoCorstone320
 @common.parametrize("per_channel_quantization", quant_test_data)
 def test_resnet_u85_INT(per_channel_quantization):

backends/arm/test/models/test_torch_functions.py

@@ -128,7 +128,6 @@ def test_torch_fns_FP(test_data):
         "Requires dynamic output shape.",
         "topk": "NotImplementedError: No registered serialization name for <class 'torch.return_types.topk'> found",
         "sort": "NotImplementedError: No registered serialization name for <class 'torch.return_types.sort'> found",
-        "t": "MLETORCH-855: Issue with Quantization folding.",
     },
     strict=False,
 )

backends/arm/test/ops/test_cosh.py

@@ -76,9 +76,6 @@ def test_cosh_u55_INT(test_data: Tuple):
 @common.parametrize(
     "test_data",
     test_data_suite,
-    xfails={
-        "ones_4D": "MLBEDSW-11046 - Incorrect output for TABLE followed by RESHAPE"
-    },
     strict=False,
 )
 def test_cosh_u85_INT(test_data: Tuple):

backends/arm/test/ops/test_mean_dim.py

@@ -286,15 +286,7 @@ def test_mean_dim_tosa_INT(test_data):
     pipeline.run()
 
 
-xfails = {
-    "rank5_01234": "Rank 5 graph input currently not supported in EthosUBackend (passes since CHW are all averaged over so data order does not matter in this case)",
-    "rank5_234": "Rank 5 graph input currently not supported in EthosUBackend (passes since CHW are all averaged over so data order does not matter in this case)",
-    "rank5_12": "Rank 5 graph input currently not supported in EthosUBackend",
-    "rank5_2": "Rank 5 graph input currently not supported in EthosUBackend",
-}
-
-
-@common.parametrize("test_data", MeanDim.test_data_suite, xfails=xfails, strict=False)
+@common.parametrize("test_data", MeanDim.test_data_suite)
 @common.XfailIfNoCorstone300
 def test_mean_dim_u55_INT(test_data):
     test_data, dim, keep_dim = test_data()
@@ -313,7 +305,7 @@ def test_mean_dim_u55_INT(test_data):
     pipeline.run()
 
 
-@common.parametrize("test_data", MeanDim.test_data_suite, xfails=xfails, strict=False)
+@common.parametrize("test_data", MeanDim.test_data_suite)
 @common.XfailIfNoCorstone320
 def test_mean_dim_u85_INT(test_data):
     test_data, dim, keep_dim = test_data()

backends/arm/test/ops/test_ne.py

@@ -159,9 +159,6 @@ def test_ne_scalar_u55_INT(test_module):
 @common.parametrize(
     "test_module",
     test_data_tensor,
-    xfails={
-        "ne_tensor_rank4_randn": "MLETORCH-517: Batch size > 1 not fully supported",
-    },
     strict=False,
 )
 @common.XfailIfNoCorstone320
@@ -179,7 +176,6 @@ def test_ne_tensor_u85_INT(test_module):
     "test_module",
     test_data_scalar,
     xfails={
-        "ne_scalar_rank4_randn": "MLETORCH-517: Batch size > 1 not fully supported",
         "ne_scalar_rank4_randn_1batch": "MLETORCH-847: Boolean ne result unstable on U85",
     },
     strict=False,

backends/arm/test/ops/test_pixel_shuffling.py

@@ -6,8 +6,6 @@
 
 from typing import Tuple
 
-import pytest
-
 import torch
 
 from executorch.backends.arm.constants import MAX_RANK
@@ -192,9 +190,12 @@ def test_pixel_unshuffle_u55_INT(test_data: input_t1):
     pipeline.run()
 
 
-@common.parametrize("test_data", PixelUnShuffle.test_data_generators)
+@common.parametrize(
+    "test_data",
+    PixelUnShuffle.test_data_generators,
+    xfails={"rand_4d": "MLETORCH-1424: rand test fails"},
+)
 @common.XfailIfNoCorstone320
-@pytest.mark.xfail(reason="MLETORCH-1424: rand test fails")
 def test_pixel_unshuffle_u85_INT(test_data: input_t1):
     pipeline = EthosU85PipelineINT[input_t1](
         PixelUnShuffle(),
@@ -219,9 +220,12 @@ def test_pixel_shuffle_u55_INT(test_data: input_t1):
     pipeline.run()
 
 
-@common.parametrize("test_data", PixelShuffle.test_data_generators)
+@common.parametrize(
+    "test_data",
+    PixelShuffle.test_data_generators,
+    xfails={"rand_4d": "MLETORCH-1424: rand test fails"},
+)
 @common.XfailIfNoCorstone320
-@pytest.mark.xfail(reason="MLETORCH-1424: rand test fails")
 def test_pixel_shuffle_u85_INT(test_data: input_t1):
     pipeline = EthosU85PipelineINT[input_t1](
         PixelShuffle(),

backends/arm/test/ops/test_pow.py

@@ -62,10 +62,10 @@ class Pow_TensorScalar(torch.nn.Module):
 
     test_data = {
         # Test whole number exponents
-        "exp_minus_three": lambda: (torch.randn((10, 5)), -3.0),
-        "exp_minus_one": lambda: (torch.randn((42,)), -1.0),
-        "exp_zero": lambda: (torch.randn((1, 2, 3, 7)), 0.0),
-        "exp_one": lambda: (torch.randn((1, 4, 6, 2)), 1.0),
+        "exp_minus_three": lambda: (torch.randn((10, 5)).relu() + 0.1, -3.0),
+        "exp_minus_one": lambda: (torch.randn((42,)).relu() + 0.1, -1.0),
+        "exp_zero": lambda: (torch.randn((1, 2, 3, 7)).relu(), 0.0),
+        "exp_one": lambda: (torch.randn((1, 4, 6, 2)).relu(), 1.0),
         "exp_two": lambda: (torch.randn((1, 2, 3, 6)), 2.0),
         # Test decimal exponent (base must be non-negative)
         "non_neg_base_exp_pos_decimal": lambda: (
@@ -117,11 +117,7 @@ def test_pow_tensor_tensor_vgf_FP(test_data: Pow_TensorTensor.input_t):
 
 
 x_fail = {
-    "exp_minus_three": "TOSA constraints: If x == 0 and y ⇐ 0, the result is undefined.",
-    "exp_minus_one": "TOSA constraints: If x == 0 and y ⇐ 0, the result is undefined.",
-    "exp_zero": "TOSA constraints: If x == 0 and y ⇐ 0, the result is undefined.",
-    "exp_one": "TOSA constraints: If x == 0 and y ⇐ 0, the result is undefined.",
-    "exp_two": "TOSA constraints: If x == 0 and y ⇐ 0, the result is undefined.",
+    "exp_two": "TOSA constraints: If x <0 .",
     "non_neg_base_exp_pos_decimal": "TOSA constraints: If x == 0 and y ⇐ 0, the result is undefined.",
 }
 
@@ -138,7 +134,7 @@ def test_pow_tensor_scalar_tosa_FP(test_data: Pow_TensorScalar.input_t):
     pipeline.run()
 
 
-@common.parametrize("test_data", Pow_TensorScalar.test_data, x_fail, strict=False)
+@common.parametrize("test_data", Pow_TensorScalar.test_data, strict=False)
 def test_pow_tensor_scalar_tosa_INT(test_data: Pow_TensorScalar.input_t):
     base, exp = test_data()
     pipeline = TosaPipelineINT[Pow_TensorScalar.input_t](

backends/arm/test/ops/test_unflatten.py

@@ -57,13 +57,7 @@ def test_unflatten_int_tosa_INT(test_data: test_data_t):
     pipeline.run()
 
 
-xfails = {
-    "rand_3d_batch3": "Batch size > 1 currently not supported for FVP tests",
-    "randn_4d_dim1": "Batch size > 1 currently not supported for FVP tests",
-}
-
-
-@common.parametrize("test_data", Unflatten.test_data, xfails=xfails, strict=False)
+@common.parametrize("test_data", Unflatten.test_data, strict=False)
 @common.XfailIfNoCorstone300
 def test_unflatten_int_u55_INT(test_data: test_data_t):
     module, inputs = test_data()
@@ -75,7 +69,7 @@ def test_unflatten_int_u55_INT(test_data: test_data_t):
     pipeline.run()
 
 
-@common.parametrize("test_data", Unflatten.test_data, xfails=xfails, strict=False)
+@common.parametrize("test_data", Unflatten.test_data, strict=False)
 @common.XfailIfNoCorstone320
 def test_unflatten_int_u85_INT(test_data: test_data_t):
     module, inputs = test_data()