Move passing tests to ALLOWLIST_OP (#168)

* Add passing tests to ALLOWLIST_OP

* Remove tab indentation

Author: DenisVieriu97

test/test_mps.py

@@ -8260,7 +8260,7 @@ class TestConsistency(TestCase):
         'bitwise_or': ['b8', 'i16', 'i32', 'i64', 'u8'],
         'bitwise_right_shift': ['i16', 'i32', 'i64', 'u8'],
         'bitwise_xor': ['b8', 'i16', 'i32', 'i64', 'u8'],
-        'block_diag': ['b8', 'f16', 'f32', 'i16', 'i32', 'i64'],
+        'block_diag': ['b8', 'f16', 'f32', 'i16', 'i32', 'i64', 'u8'],
         'bmm': ['f32'],
         'broadcast_shapes': ['f32'],
         'ceil': ['f32', 'int32', 'int64', 'f16'],
@@ -8283,10 +8283,10 @@ class TestConsistency(TestCase):
         'cumsum': ['f16', 'f32', 'int16', 'int32'],
         'deg2rad': ['b8', 'f16', 'f32', 'i16', 'i32', 'i64', 'u8'],
         'diag': ['f32', 'i32'],
-        'diag_embed': ['b8', 'f16', 'f32', 'i16', 'i32', 'i64'],
+        'diag_embed': ['b8', 'f16', 'f32', 'i16', 'i32', 'i64', 'u8'],
         'diagflat': ['f32', 'i32'],
-        'diagonal_scatter': ['b8', 'f16', 'f32', 'i16', 'i32', 'i64'],
-        'diff': ['b8', 'f16', 'f32', 'i16', 'i32', 'i64', 'u8'],
+        'diagonal_scatter': ['b8', 'f16', 'f32', 'i16', 'i32', 'i64', 'u8'],
+        'diff': ['f16', 'f32', 'i16', 'i32', 'i64'],
         'dist': ['f32'],
         'dot': ['f32', 'i16', 'i32', 'i64', 'u8'],
         'equal': ['b8', 'f16', 'f32', 'i16', 'i32', 'i64', 'u8'],
@@ -8325,6 +8325,7 @@ class TestConsistency(TestCase):
         'linalg.vector_norm': ['f16', 'f32'],
         'linspace': ['f16', 'f32', 'i16', 'i32', 'i64', 'u8'],
         'log': ['b8', 'f32', 'i16', 'i32', 'u8'],
+        'log': ['f16', 'f32', 'i16', 'i32', 'i64', 'u8'],
         'log10': ['b8', 'f32', 'i16', 'i32', 'u8'],
         'log1p': ['b8', 'f32', 'i16', 'i32', 'u8'],
         'log2': ['b8', 'f32', 'i16', 'i32', 'u8'],
@@ -8374,14 +8375,15 @@ class TestConsistency(TestCase):
         'nn.functional.layer_norm': ['f32'],
         'nn.functional.leaky_relu': ['f32'],
         'nn.functional.linear': ['f32'],
-        'nn.functional.local_response_norm': ['f32'],
+        'nn.functional.local_response_norm': ['f32', 'i64'],
         'nn.functional.margin_ranking_loss': ['f32', 'i16', 'i32'],
         'nn.functional.max_pool1d': ['f32'],
         'nn.functional.max_pool2d': ['f32'],
         'max_pool2d_with_indices_backward': ['f32'],
         'nn.functional.mse_loss': ['f16', 'f32'],
         'nn.functional.normalize': ['f32'],
-        'nn.functional.pad': ['b8', 'f16', 'f32', 'i16', 'i32', 'i64'],
+        'nn.functional.pad': ['b8', 'f16', 'f32', 'i16', 'i32', 'i64', 'u8'],
+        'nn.functional.padcircular': ['b8', 'f16', 'f32', 'i16', 'i32', 'i64', 'u8'],
         'nn.functional.pairwise_distance': ['f16',
                                             'f32',
                                             'i16',
@@ -8419,16 +8421,14 @@ class TestConsistency(TestCase):
         'rot90': ['f16', 'f32', 'i16', 'i32', 'i64', 'u8'],
         'round': ['f32', 'f16', 'i16', 'i32', 'i64'],
         'rsqrt': ['b8', 'f32', 'i16', 'i32', 'u8'],
-        'scatter': ['b8', 'f16', 'f32', 'i16', 'i32', 'i64', 'u8'],
-        'scatter_add': ['b8', 'f16', 'f32', 'i16', 'i32', 'i64', 'u8'],
-        'select_scatter': ['b8', 'f16', 'f32', 'i16', 'i32', 'i64'],
+        'select_scatter': ['b8', 'f16', 'f32', 'i16', 'i32', 'i64', 'u8'],
         'sgn': ['b8', 'f16', 'f32', 'i16', 'i32', 'i64', 'u8'],
         'short': ['i16'],
         'sigmoid': ['b8', 'f16', 'f32', 'i16', 'i32', 'u8'],
         'sign': ['b8', 'f16', 'f32', 'i16', 'i32', 'u8', 'i64'],
         'sin': ['b8', 'f32', 'i16', 'i32', 'u8'],
         'sinh': ['b8', 'f32', 'i16', 'i32', 'u8'],
-        'slice_scatter': ['b8', 'f16', 'f32', 'i16', 'i32', 'i64'],
+        'slice_scatter': ['b8', 'f16', 'f32', 'i16', 'i32', 'i64', 'u8'],
         'softmax': ['f32'],
         'special.ndtr': ['b8', 'f32', 'i16', 'i32', 'i64', 'u8'],
         'split': ['b8', 'f16', 'f32', 'i16', 'i32', 'i64', 'u8'],
@@ -8478,6 +8478,8 @@ class TestConsistency(TestCase):
         'masked.mean': ['b8', 'f16', 'f32', 'i16', 'i32', 'i64', 'u8'],
         'masked.prod': ['b8', 'f16', 'f32', 'i16', 'i32', 'i64', 'u8'],
         'masked.sum': ['b8', 'f16', 'f32', 'i16', 'i32', 'i64', 'u8'],
+        'nn.functional.nll_loss': ['f32'],
+        'byte': ['b8', 'i16', 'i32', 'i64', 'u8'],
     }
 
 
@@ -8591,7 +8593,7 @@ class TestConsistency(TestCase):
         'nn.functional.kl_div': ['f32'],
         'nn.functional.l1_loss': ['f16', 'f32'],
         'nn.functional.leaky_relu': ['f32'],
-        'nn.functional.local_response_norm': ['f32'],
+        'nn.functional.local_response_norm': ['f32', 'i64'],
         'nn.functional.margin_ranking_loss': ['f32'],
         'nn.functional.max_pool1d': ['f32'],
         'nn.functional.max_pool2d': ['f32'],
@@ -8674,15 +8676,14 @@ class TestConsistency(TestCase):
         'stft': [torch.float32], 'var': [torch.float16],
         # + forward when requires_grad=True or running backward
         'nn.functional.embedding': [torch.float32, torch.float16],
+        'index_select': [torch.float16],
         '__rpow__': [torch.int64],
 
         'as_strided_scatter': [torch.uint8],
         'atan2': [torch.int64],
         'bfloat16': None,
         'block_diag': [torch.uint8],
-        'byte': None,
         'chalf': None,
-        'diag_embed': [torch.uint8],
         'diagonal_scatter': [torch.uint8],
         'index_add': None,
         'linalg.inv': [torch.float32],
@@ -8693,12 +8694,9 @@ class TestConsistency(TestCase):
         'nn.functional.conv_transpose2d': [torch.int64],
         'nn.functional.conv_transpose3d': [torch.int64, torch.float32],
         'nn.functional.huber_loss': [torch.float16],
-        'nn.functional.local_response_norm': [torch.int64],
-        'nn.functional.padcircular': [torch.uint8],
+        'nn.functional.softplus': [torch.float32],
         'pow': [torch.int64],
-        'select_scatter': [torch.uint8],
         'sigmoid': [torch.int64],
-        'slice_scatter': [torch.uint8],
         'square': [torch.bool, torch.int16, torch.int32, torch.int64, torch.uint8],  # moved from section below
 
         # failure in average pooling when both ceilMode and includeZeroPadToAverage are True