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# RUN: %PYTHON %s | FileCheck %s
from mlir.ir import *
from mlir.dialects import sparse_tensor as st
def run(f):
print("\nTEST:", f.__name__)
f()
return f
# CHECK-LABEL: TEST: testEncodingAttr1D
@run
def testEncodingAttr1D():
with Context() as ctx:
parsed = Attribute.parse('#sparse_tensor.encoding<{'
' dimLevelType = [ "compressed" ],'
' posWidth = 16,'
' crdWidth = 32'
'}>')
# CHECK: #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], posWidth = 16, crdWidth = 32 }>
print(parsed)
casted = st.EncodingAttr(parsed)
# CHECK: equal: True
print(f"equal: {casted == parsed}")
# CHECK: dim_level_types: [<DimLevelType.compressed: 8>]
print(f"dim_level_types: {casted.dim_level_types}")
# CHECK: dim_ordering: None
print(f"dim_ordering: {casted.dim_ordering}")
# CHECK: pos_width: 16
print(f"pos_width: {casted.pos_width}")
# CHECK: crd_width: 32
print(f"crd_width: {casted.crd_width}")
created = st.EncodingAttr.get(casted.dim_level_types, None, None, 0, 0)
# CHECK: #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>
print(created)
# CHECK: created_equal: False
print(f"created_equal: {created == casted}")
# Verify that the factory creates an instance of the proper type.
# CHECK: is_proper_instance: True
print(f"is_proper_instance: {isinstance(created, st.EncodingAttr)}")
# CHECK: created_pos_width: 0
print(f"created_pos_width: {created.pos_width}")
# CHECK-LABEL: TEST: testEncodingAttr2D
@run
def testEncodingAttr2D():
with Context() as ctx:
parsed = Attribute.parse('#sparse_tensor.encoding<{'
' dimLevelType = [ "dense", "compressed" ],'
' dimOrdering = affine_map<(d0, d1) -> (d1, d0)>,'
' posWidth = 8,'
' crdWidth = 32'
'}>')
# CHECK: #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], dimOrdering = affine_map<(d0, d1) -> (d1, d0)>, posWidth = 8, crdWidth = 32 }>
print(parsed)
casted = st.EncodingAttr(parsed)
# CHECK: equal: True
print(f"equal: {casted == parsed}")
# CHECK: dim_level_types: [<DimLevelType.dense: 4>, <DimLevelType.compressed: 8>]
print(f"dim_level_types: {casted.dim_level_types}")
# CHECK: dim_ordering: (d0, d1) -> (d1, d0)
print(f"dim_ordering: {casted.dim_ordering}")
# CHECK: pos_width: 8
print(f"pos_width: {casted.pos_width}")
# CHECK: crd_width: 32
print(f"crd_width: {casted.crd_width}")
created = st.EncodingAttr.get(casted.dim_level_types, casted.dim_ordering,
casted.higher_ordering, 8, 32)
# CHECK: #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], dimOrdering = affine_map<(d0, d1) -> (d1, d0)>, posWidth = 8, crdWidth = 32 }>
print(created)
# CHECK: created_equal: True
print(f"created_equal: {created == casted}")
# CHECK-LABEL: TEST: testEncodingAttrOnTensorType
@run
def testEncodingAttrOnTensorType():
with Context() as ctx, Location.unknown():
encoding = st.EncodingAttr(
Attribute.parse('#sparse_tensor.encoding<{'
' dimLevelType = [ "compressed" ], '
' posWidth = 64,'
' crdWidth = 32'
'}>'))
tt = RankedTensorType.get((1024,), F32Type.get(), encoding=encoding)
# CHECK: tensor<1024xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], posWidth = 64, crdWidth = 32 }>>
print(tt)
# CHECK: #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], posWidth = 64, crdWidth = 32 }>
print(tt.encoding)
assert tt.encoding == encoding
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