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Srinath Avadhanula
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lit test updates
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-18
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‎test/python_lit/fx_import/basic_test.py‎

Lines changed: 2 additions & 3 deletions
Original file line numberDiff line numberDiff line change
@@ -24,18 +24,17 @@ def run(f):
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@run
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# CHECK-LABEL: test_import_frozen_exported_program
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# CHECK: func.func @main(%[[ARG0:[a-zA-Z0-9]+]]: !torch.vtensor<[3,4],f32>) -> !torch.vtensor<[3,4],f32>
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# CHECK-DAG: %[[a:.+]] = torch.vtensor.literal(dense_resource<torch_tensor_1_4_torch.float32> : tensor<1x4xf32>) : !torch.vtensor<[1,4],f32>
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# CHECK-DAG: %[[tanh:.+]] = torch.aten.tanh %[[ARG0]]
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# CHECK-DAG: %[[a:.+]] = torch.aten.rand{{.*}} -> !torch.vtensor<[1,4],f32>
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# CHECK-DAG: %[[b:.+]] = torch.vtensor.literal(dense_resource<torch_tensor_3_1_torch.float32> : tensor<3x1xf32>) : !torch.vtensor<[3,1],f32>
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# CHECK-DAG: %[[p:.+]] = torch.vtensor.literal(dense<{{.*>+}} : tensor<1x1xf32>) : !torch.vtensor<[1,1],f32>
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# CHECK-DAG: %[[tanh:.+]] = torch.aten.tanh %[[ARG0]]
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# CHECK-DAG: %[[mul_a:.+]] = torch.aten.mul.Tensor %[[tanh]], %[[a]]
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# CHECK-DAG: %[[mul_b:.+]] = torch.aten.mul.Tensor %[[mul_a]], %[[b]]
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# CHECK-DAG: %[[mul_p:.+]] = torch.aten.mul.Tensor %[[mul_b]], %[[p]]
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# CHECK: return %[[mul_p]]
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#
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# Validate dialect resources exist.
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# CHECK: dialect_resources:
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# CHECK-DAG: torch_tensor_1_4_torch.float32
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# CHECK-DAG: torch_tensor_3_1_torch.float32
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def test_import_frozen_exported_program():
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# Tests the basic structural premises of import_frozen_exported_program,

‎test/python_lit/fx_import/custom_op_test.py‎

Lines changed: 7 additions & 7 deletions
Original file line numberDiff line numberDiff line change
@@ -26,15 +26,15 @@ def run(f):
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# CHECK-SAME: %[[ARG0:[a-zA-Z0-9]+]]: !torch.vtensor<[?,?,3],f32>,
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# CHECK-SAME: %[[ARG1:[a-zA-Z0-9]+]]: !torch.vtensor<[?,?,3],f32>,
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# CHECK-SAME: %[[ARG2:[a-zA-Z0-9]+]]: !torch.vtensor<[?,?,3],f32>) -> !torch.vtensor<[?,?,3],f32> {
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# CHECK: %[[S0:.+]] = torch.symbolic_int "s0" {min_val = 5, max_val = 10} : !torch.int
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# CHECK: %[[S1:.+]] = torch.symbolic_int "s1" {min_val = {{[0-9]+}}, max_val = 100} : !torch.int
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# CHECK: %[[S2:.+]] = torch.symbolic_int "s3" {min_val = {{[0-9]+}}, max_val = 50} : !torch.int
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# CHECK: %[[S3:.+]] = torch.symbolic_int "s5" {min_val = {{[0-9]+}}, max_val = {{[0-9]+}}} : !torch.int
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# CHECK: torch.bind_symbolic_shape %[[ARG0]], [%[[S0]], %[[S1]]], affine_map<()[s0, s1] -> (s0, s1, 3)> : !torch.vtensor<[?,?,3],f32>
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# CHECK: %[[S0:.+]] = torch.symbolic_int "s{{.*}}" {min_val = 5, max_val = 10} : !torch.int
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# CHECK: %[[S1:.+]] = torch.symbolic_int "s{{.*}}" {min_val = {{[0-9]+}}, max_val = 100} : !torch.int
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# CHECK: %[[S2:.+]] = torch.symbolic_int "s{{.*}}" {min_val = {{[0-9]+}}, max_val = 50} : !torch.int
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# CHECK: %[[S3:.+]] = torch.symbolic_int "s{{.*}}" {min_val = {{[0-9]+}}, max_val = {{[0-9]+}}} : !torch.int
33+
# CHECK: torch.bind_symbolic_shape %[[ARG0]], [%[[S1]], %[[S0]]], affine_map<()[s0, s1] -> (s1, s0, 3)> : !torch.vtensor<[?,?,3],f32>
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# CHECK: torch.bind_symbolic_shape %[[ARG1]], [%[[S0]], %[[S2]]], affine_map<()[s0, s1] -> (s0, s1, 3)> : !torch.vtensor<[?,?,3],f32>
35-
# CHECK: torch.bind_symbolic_shape %[[ARG2]], [%[[S0]], %[[S3]]], affine_map<()[s0, s1] -> (s0, s1, 3)> : !torch.vtensor<[?,?,3],f32>
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# CHECK: torch.bind_symbolic_shape %[[ARG2]], [%[[S3]], %[[S0]]], affine_map<()[s0, s1] -> (s1, s0, 3)> : !torch.vtensor<[?,?,3],f32>
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# CHECK: %[[OP:.+]] = torch.operator "torch.my_custom_library.tanh_sigmoid_cat_op"(%[[ARG0]], %[[ARG1]], %[[ARG2]]) : (!torch.vtensor<[?,?,3],f32>, !torch.vtensor<[?,?,3],f32>, !torch.vtensor<[?,?,3],f32>) -> !torch.vtensor<[?,?,3],f32>
37-
# CHECK: torch.bind_symbolic_shape %[[OP]], [%[[S0]], %[[S1]], %[[S2]], %[[S3]]], affine_map<()[s0, s1, s2, s3] -> (s0, s2 + s3 + s1 * 2, 3)> : !torch.vtensor<[?,?,3],f32>
37+
# CHECK: torch.bind_symbolic_shape %[[OP]], [%[[S1]], %[[S3]], %[[S0]], %[[S2]]], affine_map<()[s0, s1, s2, s3] -> (s2, s1 + s3 + s0 * 2, 3)> : !torch.vtensor<[?,?,3],f32>
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# CHECK: return %[[OP]] : !torch.vtensor<[?,?,3],f32>
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def test_tanh_sigmoid_cat_custom_op():
4040

‎test/python_lit/fx_import/symbolic_shape_expr_test.py‎

Lines changed: 8 additions & 8 deletions
Original file line numberDiff line numberDiff line change
@@ -26,20 +26,20 @@ def run(f):
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# CHECK-SAME: %[[ARG0:[a-zA-Z0-9]+]]: !torch.vtensor<[?,?,3],f32>,
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# CHECK-SAME: %[[ARG1:[a-zA-Z0-9]+]]: !torch.vtensor<[?,?,3],f32>,
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# CHECK-SAME: %[[ARG2:[a-zA-Z0-9]+]]: !torch.vtensor<[?,?,3],f32>) -> !torch.vtensor<[?,?,3],f32> {
29-
# CHECK: %[[S0:.+]] = torch.symbolic_int "s0" {min_val = 5, max_val = 10} : !torch.int
30-
# CHECK: %[[S1:.+]] = torch.symbolic_int "s1" {min_val = {{[0-9]+}}, max_val = 100} : !torch.int
31-
# CHECK: %[[S2:.+]] = torch.symbolic_int "s3" {min_val = {{[0-9]+}}, max_val = 50} : !torch.int
32-
# CHECK: %[[S3:.+]] = torch.symbolic_int "s5" {min_val = {{[0-9]+}}, max_val = {{[0-9]+}}} : !torch.int
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# CHECK: torch.bind_symbolic_shape %[[ARG0]], [%[[S0]], %[[S1]]], affine_map<()[s0, s1] -> (s0, s1, 3)> : !torch.vtensor<[?,?,3],f32>
29+
# CHECK: %[[S0:.+]] = torch.symbolic_int "s{{[0-9]+}}" {min_val = 5, max_val = 10} : !torch.int
30+
# CHECK: %[[S1:.+]] = torch.symbolic_int "s{{[0-9]+}}" {min_val = {{[0-9]+}}, max_val = 100} : !torch.int
31+
# CHECK: %[[S2:.+]] = torch.symbolic_int "s{{[0-9]+}}" {min_val = {{[0-9]+}}, max_val = 50} : !torch.int
32+
# CHECK: %[[S3:.+]] = torch.symbolic_int "s{{[0-9]+}}" {min_val = {{[0-9]+}}, max_val = {{[0-9]+}}} : !torch.int
33+
# CHECK: torch.bind_symbolic_shape %[[ARG0]], [%[[S1]], %[[S0]]], affine_map<()[s0, s1] -> (s1, s0, 3)> : !torch.vtensor<[?,?,3],f32>
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# CHECK: torch.bind_symbolic_shape %[[ARG1]], [%[[S0]], %[[S2]]], affine_map<()[s0, s1] -> (s0, s1, 3)> : !torch.vtensor<[?,?,3],f32>
35-
# CHECK: torch.bind_symbolic_shape %[[ARG2]], [%[[S0]], %[[S3]]], affine_map<()[s0, s1] -> (s0, s1, 3)> : !torch.vtensor<[?,?,3],f32>
35+
# CHECK: torch.bind_symbolic_shape %[[ARG2]], [%[[S3]], %[[S0]]], affine_map<()[s0, s1] -> (s1, s0, 3)> : !torch.vtensor<[?,?,3],f32>
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# CHECK: %[[TANH:.+]] = torch.aten.tanh %[[ARG0]] : !torch.vtensor<[?,?,3],f32> -> !torch.vtensor<[?,?,3],f32>
37-
# CHECK: torch.bind_symbolic_shape %[[TANH]], [%[[S0]], %[[S1]]], affine_map<()[s0, s1] -> (s0, s1, 3)> : !torch.vtensor<[?,?,3],f32>
37+
# CHECK: torch.bind_symbolic_shape %[[TANH]], [%[[S1]], %[[S0]]], affine_map<()[s0, s1] -> (s1, s0, 3)> : !torch.vtensor<[?,?,3],f32>
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# CHECK: %[[SIG:.+]] = torch.aten.sigmoid %[[ARG1]] : !torch.vtensor<[?,?,3],f32> -> !torch.vtensor<[?,?,3],f32>
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# CHECK: torch.bind_symbolic_shape %[[SIG]], [%[[S0]], %[[S2]]], affine_map<()[s0, s1] -> (s0, s1, 3)> : !torch.vtensor<[?,?,3],f32>
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# CHECK: %[[LIST:.+]] = torch.prim.ListConstruct %[[TANH]], %[[TANH]], %[[SIG]], %[[ARG2]] : (!torch.vtensor<[?,?,3],f32>, !torch.vtensor<[?,?,3],f32>, !torch.vtensor<[?,?,3],f32>, !torch.vtensor<[?,?,3],f32>) -> !torch.list<vtensor>
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# CHECK: %[[CAT:.+]] = torch.aten.cat %[[LIST]], {{.*}} : !torch.list<vtensor>, !torch.int -> !torch.vtensor<[?,?,3],f32>
42-
# CHECK: torch.bind_symbolic_shape %[[CAT]], [%[[S0]], %[[S1]], %[[S2]], %[[S3]]], affine_map<()[s0, s1, s2, s3] -> (s0, s2 + s3 + s1 * 2, 3)> : !torch.vtensor<[?,?,3],f32>
42+
# CHECK: torch.bind_symbolic_shape %[[CAT]], [%[[S1]], %[[S3]], %[[S0]], %[[S2]]], affine_map<()[s0, s1, s2, s3] -> (s2, s1 + s3 + s0 * 2, 3)> : !torch.vtensor<[?,?,3],f32>
4343
# CHECK: return %[[CAT]] : !torch.vtensor<[?,?,3],f32>
4444
def test_tanh_sigmoid_cat():
4545
class TanhSigmoidCat(nn.Module):

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