3655069234
This commit moves FuncOp out of the builtin dialect, and into the Func dialect. This move has been planned in some capacity from the moment we made FuncOp an operation (years ago). This commit handles the functional aspects of the move, but various aspects are left untouched to ease migration: func::FuncOp is re-exported into mlir to reduce the actual API churn, the assembly format still accepts the unqualified `func`. These temporary measures will remain for a little while to simplify migration before being removed. Differential Revision: https://reviews.llvm.org/D121266
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 func
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from mlir.dialects import linalg
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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_signed(
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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_signed(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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@func.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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