d50571ab07
Index attributes had no default value, which means the attribute values had to be set on the operation. This revision adds a default parameter to `IndexAttrDef`. After the change, every index attribute has to define a default value. For example, we may define the following strides attribute: ``` ``` When using the operation the default stride is used if the strides attribute is not set. The mechanism is implemented using `DefaultValuedAttr`. Additionally, the revision uses the naming index attribute instead of attribute more consistently, which is a preparation for follow up revisions that will introduce function attributes. Depends On D119125 Reviewed By: stellaraccident Differential Revision: https://reviews.llvm.org/D119126
60 lines
2.3 KiB
Python
60 lines
2.3 KiB
Python
# RUN: %PYTHON %s | FileCheck %s
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from mlir.ir import *
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from mlir.dialects import builtin
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from mlir.dialects import linalg
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from mlir.dialects import std
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from mlir.dialects.linalg.opdsl.lang import *
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T1 = TV.T1
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T2 = TV.T2
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@linalg_structured_op
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def conv_poly(
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I=TensorDef(T1, S.N, S.IH, S.IW, S.C),
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K=TensorDef(T2, S.KH, S.KW, S.C),
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O=TensorDef(U, S.N, S.OH, S.OW, S.C, output=True),
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strides=IndexAttrDef(S.SH, S.SW, default=[1, 1]),
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dilations=IndexAttrDef(S.DH, S.DW, default=[1, 2])):
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domain(D.n, D.oh, D.ow, D.kh, D.kw, D.c)
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O[D.n, D.oh, D.ow, D.c] += TypeFn.cast(
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U, I[D.n, D.oh * S.SH + D.kh * S.DH, D.ow * S.SW + D.kw * S.DW,
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D.c]) * TypeFn.cast(U, K[D.kh, D.kw, D.c])
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with Context() as ctx, Location.unknown():
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module = Module.create()
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f32 = F32Type.get()
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i32 = IntegerType.get_signless(32)
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with InsertionPoint(module.body):
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# Convolution indexing maps.
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# CHECK: #[[$CONV_MAP_I:.+]] = affine_map<(d0, d1, d2, d3, d4, d5) -> (d0, d1 * 2 + d3, d2 * 4 + d4 * 2, d5)>
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# CHECK: #[[$CONV_MAP_K:.+]] = affine_map<(d0, d1, d2, d3, d4, d5) -> (d3, d4, d5)>
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# CHECK: #[[$CONV_MAP_O:.+]] = affine_map<(d0, d1, d2, d3, d4, d5) -> (d0, d1, d2, d5)>
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# CHECK-LABEL: @test_f32i32_conv
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# CHECK: linalg.generic
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# CHECK-SAME: indexing_maps = [#[[$CONV_MAP_I]], #[[$CONV_MAP_K]], #[[$CONV_MAP_O]]]
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# CHECK-SAME: iterator_types = ["parallel", "parallel", "parallel", "reduction", "reduction", "parallel"]
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# CHECK: ^{{.*}}(%[[IN:.+]]: f32, %[[FILTER:.+]]: f32, %[[OUT:.+]]: i32)
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# CHECK-NEXT: %[[IN_CAST:.+]] = arith.fptosi %[[IN:.+]] : f32 to i32
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# CHECK-NEXT: %[[FILTER_CAST:.+]] = arith.fptosi %[[FILTER:.+]] : f32 to i32
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# CHECK-NEXT: %[[PROD:.+]] = arith.muli %[[IN_CAST]], %[[FILTER_CAST]] : i32
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# CHECK-NEXT: %[[SUM:.+]] = arith.addi %[[OUT]], %[[PROD]] : i32
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# CHECK-NEXT: linalg.yield %[[SUM]] : i32
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# CHECK-NEXT: -> tensor<1x2x4x1xi32>
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@builtin.FuncOp.from_py_func(
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RankedTensorType.get((1, 4, 16, 1), f32),
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RankedTensorType.get((2, 2, 1), f32),
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RankedTensorType.get((1, 2, 4, 1), i32))
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def test_f32i32_conv(input, filter, init_result):
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# Use default dilations and set non-default strides.
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return conv_poly(
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input, filter, outs=[init_result], strides=[2, 4])
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print(module)
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