1944c4f76b
The "Dim" prefix is a legacy left-over that no longer makes sense, since we have a very strict "Dimension" vs. "Level" definition for sparse tensor types and their storage.
116 lines
3.9 KiB
Python
116 lines
3.9 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(
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"#sparse_tensor.encoding<{"
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" map = (d0) -> (d0 : compressed),"
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" posWidth = 16,"
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" crdWidth = 32"
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"}>"
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)
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# CHECK: #sparse_tensor.encoding<{ map = (d0) -> (d0 : 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: [<LevelType.compressed: 8>]
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print(f"lvl_types: {casted.lvl_types}")
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# CHECK: dim_to_lvl: (d0) -> (d0)
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print(f"dim_to_lvl: {casted.dim_to_lvl}")
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# CHECK: lvl_to_dim: (d0) -> (d0)
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print(f"lvl_to_dim: {casted.lvl_to_dim}")
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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<{ map = (d0) -> (d0 : 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(
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"#sparse_tensor.encoding<{"
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" map = (d0, d1) -> (d1 : dense, d0 : compressed),"
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" posWidth = 8,"
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" crdWidth = 32"
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"}>"
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)
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# CHECK: #sparse_tensor.encoding<{ map = (d0, d1) -> (d1 : dense, d0 : compressed), 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: [<LevelType.dense: 4>, <LevelType.compressed: 8>]
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print(f"lvl_types: {casted.lvl_types}")
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# CHECK: dim_to_lvl: (d0, d1) -> (d1, d0)
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print(f"dim_to_lvl: {casted.dim_to_lvl}")
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# CHECK: lvl_to_dim: (d0, d1) -> (d1, d0)
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print(f"lvl_to_dim: {casted.lvl_to_dim}")
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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(
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casted.lvl_types,
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casted.dim_to_lvl,
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casted.lvl_to_dim,
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8,
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32,
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)
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# CHECK: #sparse_tensor.encoding<{ map = (d0, d1) -> (d1 : dense, d0 : compressed), 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(
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"#sparse_tensor.encoding<{"
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" map = (d0) -> (d0 : compressed), "
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" posWidth = 64,"
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" crdWidth = 32"
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"}>"
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)
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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<{ map = (d0) -> (d0 : compressed), posWidth = 64, crdWidth = 32 }>>
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print(tt)
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# CHECK: #sparse_tensor.encoding<{ map = (d0) -> (d0 : compressed), posWidth = 64, crdWidth = 32 }>
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print(tt.encoding)
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assert tt.encoding == encoding
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