a0615d020a
This commit is part of the migration of towards the new STEA syntax/design. In particular, this commit includes the following changes:
* Renaming compiler-internal functions/methods:
* `SparseTensorEncodingAttr::{getDimLevelType => getLvlTypes}`
* `Merger::{getDimLevelType => getLvlType}` (for consistency)
* `sparse_tensor::{getDimLevelType => buildLevelType}` (to help reduce confusion vs actual getter methods)
* Renaming external facets to match:
* the STEA parser and printer
* the C and Python bindings
* PyTACO
However, the actual renaming of the `DimLevelType` itself (along with all the "dlt" names) will be handled in a separate commit.
Reviewed By: aartbik
Differential Revision: https://reviews.llvm.org/D150330
100 lines
3.7 KiB
Python
100 lines
3.7 KiB
Python
# RUN: %PYTHON %s | FileCheck %s
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from mlir.ir import *
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from mlir.dialects import sparse_tensor as st
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def run(f):
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print("\nTEST:", f.__name__)
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f()
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return f
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# CHECK-LABEL: TEST: testEncodingAttr1D
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@run
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def testEncodingAttr1D():
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with Context() as ctx:
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parsed = Attribute.parse('#sparse_tensor.encoding<{'
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' lvlTypes = [ "compressed" ],'
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' posWidth = 16,'
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' crdWidth = 32'
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'}>')
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# CHECK: #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ], posWidth = 16, crdWidth = 32 }>
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print(parsed)
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casted = st.EncodingAttr(parsed)
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# CHECK: equal: True
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print(f"equal: {casted == parsed}")
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# CHECK: lvl_types: [<DimLevelType.compressed: 8>]
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print(f"lvl_types: {casted.lvl_types}")
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# CHECK: dim_ordering: None
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print(f"dim_ordering: {casted.dim_ordering}")
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# CHECK: pos_width: 16
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print(f"pos_width: {casted.pos_width}")
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# CHECK: crd_width: 32
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print(f"crd_width: {casted.crd_width}")
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created = st.EncodingAttr.get(casted.lvl_types, None, None, 0, 0)
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# CHECK: #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>
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print(created)
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# CHECK: created_equal: False
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print(f"created_equal: {created == casted}")
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# Verify that the factory creates an instance of the proper type.
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# CHECK: is_proper_instance: True
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print(f"is_proper_instance: {isinstance(created, st.EncodingAttr)}")
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# CHECK: created_pos_width: 0
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print(f"created_pos_width: {created.pos_width}")
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# CHECK-LABEL: TEST: testEncodingAttr2D
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@run
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def testEncodingAttr2D():
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with Context() as ctx:
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parsed = Attribute.parse('#sparse_tensor.encoding<{'
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' lvlTypes = [ "dense", "compressed" ],'
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' dimOrdering = affine_map<(d0, d1) -> (d1, d0)>,'
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' posWidth = 8,'
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' crdWidth = 32'
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'}>')
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# CHECK: #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed" ], dimOrdering = affine_map<(d0, d1) -> (d1, d0)>, posWidth = 8, crdWidth = 32 }>
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print(parsed)
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casted = st.EncodingAttr(parsed)
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# CHECK: equal: True
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print(f"equal: {casted == parsed}")
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# CHECK: lvl_types: [<DimLevelType.dense: 4>, <DimLevelType.compressed: 8>]
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print(f"lvl_types: {casted.lvl_types}")
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# CHECK: dim_ordering: (d0, d1) -> (d1, d0)
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print(f"dim_ordering: {casted.dim_ordering}")
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# CHECK: pos_width: 8
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print(f"pos_width: {casted.pos_width}")
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# CHECK: crd_width: 32
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print(f"crd_width: {casted.crd_width}")
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created = st.EncodingAttr.get(casted.lvl_types, casted.dim_ordering,
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casted.higher_ordering, 8, 32)
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# CHECK: #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed" ], dimOrdering = affine_map<(d0, d1) -> (d1, d0)>, posWidth = 8, crdWidth = 32 }>
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print(created)
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# CHECK: created_equal: True
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print(f"created_equal: {created == casted}")
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# CHECK-LABEL: TEST: testEncodingAttrOnTensorType
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@run
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def testEncodingAttrOnTensorType():
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with Context() as ctx, Location.unknown():
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encoding = st.EncodingAttr(
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Attribute.parse('#sparse_tensor.encoding<{'
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' lvlTypes = [ "compressed" ], '
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' posWidth = 64,'
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' crdWidth = 32'
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'}>'))
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tt = RankedTensorType.get((1024,), F32Type.get(), encoding=encoding)
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# CHECK: tensor<1024xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ], posWidth = 64, crdWidth = 32 }>>
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print(tt)
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# CHECK: #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ], posWidth = 64, crdWidth = 32 }>
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print(tt.encoding)
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assert tt.encoding == encoding
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