emit_pooling.py | Explore in Territory

# RUN: %PYTHON %s | FileCheck %s

from mlir.ir import *
from mlir.dialects import builtin
from mlir.dialects import func
from mlir.dialects import linalg

from mlir.dialects.linalg.opdsl.lang import *

T1 = TV.T1
T2 = TV.T2


@linalg_structured_op
def pooling_poly(
    I=TensorDef(T1, S.N, S.H, S.W, S.C),
    K=TensorDef(T2, S.KH, S.KW, index_dims=[D.kh, D.kw]),
    O=TensorDef(U, S.N, S.OH, S.OW, S.C, output=True),
    reduce=BinaryFnAttrDef(default=BinaryFn.max_signed),
    cast=TypeFnAttrDef(default=TypeFn.cast_signed),
    strides=IndexAttrDef(S.SH, S.SW, default=[1, 1]),
    dilations=IndexAttrDef(S.DH, S.DW, default=[1, 1]),
):
    domain(D.n, D.oh, D.ow, D.kh, D.kw, D.c)
    O[D.n, D.oh, D.ow, D.c] = reduce[D.kh, D.kw](
        cast(U, I[D.n, D.oh * S.SH + D.kh * S.DH, D.ow * S.SW + D.kw * S.DW, D.c])
    )


with Context() as ctx, Location.unknown():
    module = Module.create()
    f32 = F32Type.get()
    i32 = IntegerType.get_signless(32)
    with InsertionPoint(module.body):

        # Pooling indexing maps.
        # CHECK: #[[$POOL_MAP_I:.+]] = affine_map<(d0, d1, d2, d3, d4, d5) -> (d0, d1 * 2 + d3, d2 * 4 + d4 * 2, d5)>
        # CHECK: #[[$POOL_MAP_K:.+]] = affine_map<(d0, d1, d2, d3, d4, d5) -> (d3, d4)>
        # CHECK: #[[$POOL_MAP_O:.+]] = affine_map<(d0, d1, d2, d3, d4, d5) -> (d0, d1, d2, d5)>

        # CHECK-LABEL: @test_f32i32_max_pooling
        # CHECK: linalg.generic
        # CHECK-SAME: indexing_maps = [#[[$POOL_MAP_I]], #[[$POOL_MAP_K]], #[[$POOL_MAP_O]]]
        # CHECK-SAME: iterator_types = ["parallel", "parallel", "parallel", "reduction", "reduction", "parallel"]
        # CHECK:      ^{{.*}}(%[[IN:.+]]: f32, %[[SHAPE:.+]]: f32, %[[OUT:.+]]: i32)
        # CHECK-NEXT:   %[[IN_CAST:.+]] = arith.fptosi %[[IN:.+]] : f32 to i32
        # CHECK-NEXT:   %[[MAX:.+]] = arith.maxsi %[[OUT]], %[[IN_CAST:.+]] : i32
        # CHECK-NEXT:   linalg.yield %[[MAX]] : i32
        # CHECK-NEXT: -> tensor<1x2x4x1xi32>
        @func.FuncOp.from_py_func(
            RankedTensorType.get((1, 4, 16, 1), f32),
            RankedTensorType.get((2, 2), f32),
            RankedTensorType.get((1, 2, 4, 1), i32),
        )
        def test_f32i32_max_pooling(input, shape, init_result):
            return pooling_poly(
                input, shape, outs=[init_result], strides=[2, 4], dilations=[1, 2]
            )

        # CHECK-LABEL: @test_f32i32_max_unsigned_pooling
        # CHECK:   = arith.fptoui
        # CHECK:   = arith.maxui
        @func.FuncOp.from_py_func(
            RankedTensorType.get((1, 4, 16, 1), f32),
            RankedTensorType.get((2, 2), f32),
            RankedTensorType.get((1, 2, 4, 1), i32),
        )
        def test_f32i32_max_unsigned_pooling(input, shape, init_result):
            return pooling_poly(
                input,
                shape,
                outs=[init_result],
                reduce=BinaryFn.max_unsigned,
                cast=TypeFn.cast_unsigned,
                strides=[2, 4],
                dilations=[1, 2],
            )

        # CHECK-LABEL: @test_f32f32_max_pooling
        # CHECK: linalg.generic
        # CHECK-SAME: indexing_maps = [#[[$POOL_MAP_I]], #[[$POOL_MAP_K]], #[[$POOL_MAP_O]]]
        # CHECK-SAME: iterator_types = ["parallel", "parallel", "parallel", "reduction", "reduction", "parallel"]
        # CHECK:      ^{{.*}}(%[[IN:.+]]: f32, %[[SHAPE:.+]]: f32, %[[OUT:.+]]: f32)
        # CHECK-NEXT:   %[[MAX:.+]] = arith.maximumf %[[OUT]], %[[IN:.+]] : f32
        # CHECK-NEXT:   linalg.yield %[[MAX]] : f32
        # CHECK-NEXT: -> tensor<1x2x4x1xf32>
        @func.FuncOp.from_py_func(
            RankedTensorType.get((1, 4, 16, 1), f32),
            RankedTensorType.get((2, 2), f32),
            RankedTensorType.get((1, 2, 4, 1), f32),
        )
        def test_f32f32_max_pooling(input, shape, init_result):
            return pooling_poly(
                input, shape, outs=[init_result], strides=[2, 4], dilations=[1, 2]
            )

        # CHECK-LABEL: @test_f32i32_min_pooling
        # CHECK:   = arith.fptosi
        # CHECK:   = arith.minsi
        @func.FuncOp.from_py_func(
            RankedTensorType.get((1, 4, 16, 1), f32),
            RankedTensorType.get((2, 2), f32),
            RankedTensorType.get((1, 2, 4, 1), i32),
        )
        def test_f32i32_min_pooling(input, shape, init_result):
            return pooling_poly(
                input,
                shape,
                outs=[init_result],
                reduce=BinaryFn.min_signed,
                strides=[2, 4],
                dilations=[1, 2],
            )

        # CHECK-LABEL: @test_f32i32_min_unsigned_pooling
        # CHECK:   = arith.fptoui
        # CHECK:   = arith.minui
        @func.FuncOp.from_py_func(
            RankedTensorType.get((1, 4, 16, 1), f32),
            RankedTensorType.get((2, 2), f32),
            RankedTensorType.get((1, 2, 4, 1), i32),
        )
        def test_f32i32_min_unsigned_pooling(input, shape, init_result):
            return pooling_poly(
                input,
                shape,
                outs=[init_result],
                reduce=BinaryFn.min_unsigned,
                cast=TypeFn.cast_unsigned,
                strides=[2, 4],
                dilations=[1, 2],
            )

        # CHECK-LABEL: @test_f32f32_min_pooling
        # CHECK:   = arith.minimumf
        @func.FuncOp.from_py_func(
            RankedTensorType.get((1, 4, 16, 1), f32),
            RankedTensorType.get((2, 2), f32),
            RankedTensorType.get((1, 2, 4, 1), f32),
        )
        def test_f32f32_min_pooling(input, shape, init_result):
            return pooling_poly(
                input,
                shape,
                outs=[init_result],
                reduce=BinaryFn.min_signed,
                strides=[2, 4],
                dilations=[1, 2],
            )


print(module)
llvm/mlir/test/python/dialects/linalg/opdsl/emit_pooling.py
