abfa950d86
Introduce the exp and log function in OpDSL. Add the soft plus operator to test the emitted IR in Python and C++. Reviewed By: nicolasvasilache Differential Revision: https://reviews.llvm.org/D105420
325 lines
14 KiB
Python
325 lines
14 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 matmul_mono(
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A=TensorDef(T, S.M, S.K),
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B=TensorDef(T, S.K, S.N),
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C=TensorDef(T, S.M, S.N, output=True)):
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domain(D.m, D.n, D.k)
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C[D.m, D.n] += A[D.m, D.k] * B[D.k, D.n]
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@linalg_structured_op
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def matmul_poly(
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A=TensorDef(T1, S.M, S.K),
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B=TensorDef(T2, S.K, S.N),
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C=TensorDef(U, S.M, S.N, output=True)):
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domain(D.m, D.n, D.k)
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C[D.m, D.n] += cast(U, A[D.m, D.k]) * cast(U, B[D.k, D.n])
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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=AttributeDef(S.SH, S.SW),
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dilations=AttributeDef(S.DH, S.DW)):
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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] += 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]) * cast(U, K[D.kh, D.kw, D.c])
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@linalg_structured_op
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def pooling_max_poly(
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I=TensorDef(T1, S.N, S.H, S.W, S.C),
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K=TensorDef(T2, S.KH, S.KW, index_dims=[D.kh, D.kw]),
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O=TensorDef(U, S.N, S.OH, S.OW, S.C, output=True),
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strides=AttributeDef(S.SH, S.SW),
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dilations=AttributeDef(S.DH, S.DW)):
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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] = ReduceFn.max(D.kh, D.kw)(
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cast(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]))
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@linalg_structured_op
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def pooling_min_poly(
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I=TensorDef(T1, S.N, S.H, S.W, S.C),
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K=TensorDef(T2, S.KH, S.KW, index_dims=[D.kh, D.kw]),
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O=TensorDef(U, S.N, S.OH, S.OW, S.C, output=True),
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strides=AttributeDef(S.SH, S.SW),
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dilations=AttributeDef(S.DH, S.DW)):
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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] = ReduceFn.min(D.kh, D.kw)(
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cast(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]))
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@linalg_structured_op
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def fill_rng_poly(
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min=ScalarDef(F64),
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max=ScalarDef(F64),
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seed=ScalarDef(I32),
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O=TensorDef(T, S.M, S.N, output=True)):
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multiplier = cast(I32, const(1103515245))
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increment = cast(I32, const(12345))
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rand1 = (cast(I32, index(D.m)) + seed) * multiplier + increment
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rand2 = (cast(I32, index(D.n)) + rand1) * multiplier + increment
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inv_range = cast(F64, const(2.3283064e-10))
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offset = cast(F64, const(2147483647))
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scaling = (max - min) * inv_range
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O[D.m, D.n] = cast(T, (offset + cast(F64, rand2)) * scaling + min)
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@linalg_structured_op
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def soft_plus_poly(
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I=TensorDef(T, S.M, S.N), O=TensorDef(U, S.M, S.N, output=True)):
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O[D.m, D.n] = \
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PrimFn.log(cast(U, const(1.0)) + cast(U, PrimFn.exp(I[D.m, D.n])))
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with Context() as ctx, Location.unknown():
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module = Module.create()
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f16 = F16Type.get()
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f32 = F32Type.get()
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f64 = F64Type.get()
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i8 = IntegerType.get_signless(8)
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i16 = IntegerType.get_signless(16)
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i32 = IntegerType.get_signless(32)
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with InsertionPoint(module.body):
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# Multiplication indexing maps. We verify only the indexing maps of the
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# first multiplication and then do additional tests on casting and body
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# generation behavior.
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# CHECK: #[[$MUL_MAP_A:.+]] = affine_map<(d0, d1, d2) -> (d0, d2)>
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# CHECK: #[[$MUL_MAP_B:.+]] = affine_map<(d0, d1, d2) -> (d2, d1)>
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# CHECK: #[[$MUL_MAP_C:.+]] = affine_map<(d0, d1, d2) -> (d0, d1)>
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# Convolution indexing maps.
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# CHECK: #[[$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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# Pooling indexing maps.
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# CHECK: #[[$POOL_MAP_K:.+]] = affine_map<(d0, d1, d2, d3, d4, d5) -> (d3, d4)>
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# CHECK-LABEL: func @test_matmul_mono
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# CHECK-SAME: %[[A:.+]]: tensor<4x16xf32>
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# CHECK-SAME: %[[B:.+]]: tensor<16x8xf32>
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# CHECK: %[[INITC:.+]] = linalg.init_tensor [4, 8] : tensor<4x8xf32>
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# CHECK: linalg.generic
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# CHECK-SAME: indexing_maps = [#[[$MUL_MAP_A]], #[[$MUL_MAP_B]], #[[$MUL_MAP_C]]]
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# CHECK-SAME: iterator_types = ["parallel", "parallel", "reduction"]
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# CHECK-SAME: ins(%[[A]], %[[B]]
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# CHECK-SAME: outs(%[[INITC]]
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@builtin.FuncOp.from_py_func(
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RankedTensorType.get((4, 16), f32), RankedTensorType.get((16, 8), f32))
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def test_matmul_mono(lhs, rhs):
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init_result = linalg.InitTensorOp([4, 8], f32)
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return matmul_mono(lhs, rhs, outs=[init_result.result])
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# CHECK-LABEL: @test_i8i8i32_matmul
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# CHECK: ^{{.*}}(%[[A_ARG:.+]]: i8, %[[B_ARG:.+]]: i8, %[[C_ARG:.+]]: i32)
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# CHECK-NEXT: %[[A_CAST:.+]] = sexti %[[A_ARG]] : i8 to i32
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# CHECK-NEXT: %[[B_CAST:.+]] = sexti %[[B_ARG]] : i8 to i32
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# CHECK-NEXT: %[[MUL:.+]] = muli %[[A_CAST]], %[[B_CAST]] : i32
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# CHECK-NEXT: %[[ADD:.+]] = addi %[[C_ARG]], %[[MUL]] : i32
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# CHECK-NEXT: linalg.yield %[[ADD]] : i32
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# CHECK-NEXT: -> tensor<4x8xi32>
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@builtin.FuncOp.from_py_func(
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RankedTensorType.get((4, 16), i8), RankedTensorType.get((16, 8), i8),
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RankedTensorType.get((4, 8), i32))
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def test_i8i8i32_matmul(lhs, rhs, init_result):
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return matmul_poly(lhs, rhs, outs=[init_result])
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# CHECK-LABEL: @test_i8i16i32_matmul
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# CHECK: ^{{.*}}(%[[A_ARG:.+]]: i8, %[[B_ARG:.+]]: i16, %[[C_ARG:.+]]: i32)
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# CHECK-NEXT: %[[A_CAST:.+]] = sexti %[[A_ARG]] : i8 to i32
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# CHECK-NEXT: %[[B_CAST:.+]] = sexti %[[B_ARG]] : i16 to i32
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# CHECK-NEXT: %[[MUL:.+]] = muli %[[A_CAST]], %[[B_CAST]] : i32
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# CHECK-NEXT: %[[ADD:.+]] = addi %[[C_ARG]], %[[MUL]] : i32
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# CHECK-NEXT: linalg.yield %[[ADD]] : i32
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# CHECK-NEXT: -> tensor<4x8xi32>
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@builtin.FuncOp.from_py_func(
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RankedTensorType.get((4, 16), i8), RankedTensorType.get((16, 8), i16),
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RankedTensorType.get((4, 8), i32))
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def test_i8i16i32_matmul(lhs, rhs, init_result):
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return matmul_poly(lhs, rhs, outs=[init_result])
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# CHECK-LABEL: @test_i32i32i16_matmul
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# CHECK: ^{{.*}}(%[[A_ARG:.+]]: i32, %[[B_ARG:.+]]: i32, %[[C_ARG:.+]]: i16)
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# CHECK-NEXT: %[[A_CAST:.+]] = trunci %[[A_ARG]] : i32 to i16
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# CHECK-NEXT: %[[B_CAST:.+]] = trunci %[[B_ARG]] : i32 to i16
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# CHECK-NEXT: %[[MUL:.+]] = muli %[[A_CAST]], %[[B_CAST]] : i16
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# CHECK-NEXT: %[[ADD:.+]] = addi %[[C_ARG]], %[[MUL]] : i16
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# CHECK-NEXT: linalg.yield %[[ADD]] : i16
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# CHECK-NEXT: -> tensor<4x8xi16>
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@builtin.FuncOp.from_py_func(
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RankedTensorType.get((4, 16), i32), RankedTensorType.get((16, 8), i32),
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RankedTensorType.get((4, 8), i16))
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def test_i32i32i16_matmul(lhs, rhs, init_result):
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return matmul_poly(lhs, rhs, outs=[init_result])
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# CHECK-LABEL: @test_i8i8f32_matmul
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# CHECK: ^{{.*}}(%[[A_ARG:.+]]: i8, %[[B_ARG:.+]]: i8, %[[C_ARG:.+]]: f32)
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# CHECK-NEXT: %[[A_CAST:.+]] = sitofp %[[A_ARG]] : i8 to f32
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# CHECK-NEXT: %[[B_CAST:.+]] = sitofp %[[B_ARG]] : i8 to f32
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# CHECK-NEXT: %[[MUL:.+]] = mulf %[[A_CAST]], %[[B_CAST]] : f32
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# CHECK-NEXT: %[[ADD:.+]] = addf %[[C_ARG]], %[[MUL]] : f32
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# CHECK-NEXT: linalg.yield %[[ADD]] : f32
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# CHECK-NEXT: -> tensor<4x8xf32>
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@builtin.FuncOp.from_py_func(
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RankedTensorType.get((4, 16), i8), RankedTensorType.get((16, 8), i8),
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RankedTensorType.get((4, 8), f32))
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def test_i8i8f32_matmul(lhs, rhs, init_result):
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return matmul_poly(lhs, rhs, outs=[init_result])
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# CHECK-LABEL: @test_f16f16f32_matmul
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# CHECK: ^{{.*}}(%[[A_ARG:.+]]: f16, %[[B_ARG:.+]]: f16, %[[C_ARG:.+]]: f32)
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# CHECK-NEXT: %[[A_CAST:.+]] = fpext %[[A_ARG]] : f16 to f32
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# CHECK-NEXT: %[[B_CAST:.+]] = fpext %[[B_ARG]] : f16 to f32
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# CHECK-NEXT: %[[MUL:.+]] = mulf %[[A_CAST]], %[[B_CAST]] : f32
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# CHECK-NEXT: %[[ADD:.+]] = addf %[[C_ARG]], %[[MUL]] : f32
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# CHECK-NEXT: linalg.yield %[[ADD]] : f32
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# CHECK-NEXT: -> tensor<4x8xf32>
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@builtin.FuncOp.from_py_func(
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RankedTensorType.get((4, 16), f16), RankedTensorType.get((16, 8), f16),
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RankedTensorType.get((4, 8), f32))
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def test_f16f16f32_matmul(lhs, rhs, init_result):
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return matmul_poly(lhs, rhs, outs=[init_result])
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# CHECK-LABEL: @test_f64f64f32_matmul
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# CHECK: ^{{.*}}(%[[A_ARG:.+]]: f64, %[[B_ARG:.+]]: f64, %[[C_ARG:.+]]: f32)
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# CHECK-NEXT: %[[A_CAST:.+]] = fptrunc %[[A_ARG]] : f64 to f32
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# CHECK-NEXT: %[[B_CAST:.+]] = fptrunc %[[B_ARG]] : f64 to f32
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# CHECK-NEXT: %[[MUL:.+]] = mulf %[[A_CAST]], %[[B_CAST]] : f32
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# CHECK-NEXT: %[[ADD:.+]] = addf %[[C_ARG]], %[[MUL]] : f32
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# CHECK-NEXT: linalg.yield %[[ADD]] : f32
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# CHECK-NEXT: -> tensor<4x8xf32>
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@builtin.FuncOp.from_py_func(
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RankedTensorType.get((4, 16), f64), RankedTensorType.get((16, 8), f64),
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RankedTensorType.get((4, 8), f32))
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def test_f64f64f32_matmul(lhs, rhs, init_result):
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return matmul_poly(lhs, rhs, outs=[init_result])
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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]], #[[$CONV_MAP_O]]]
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# CHECK-SAME: iterator_types = ["parallel", "parallel", "parallel", "reduction", "reduction", "parallel"]
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# CHECK: ^{{.*}}(%[[IN:.+]]: f32, %[[FILTER:.+]]: f32, %[[OUT:.+]]: i32)
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# CHECK-NEXT: %[[IN_CAST:.+]] = fptosi %[[IN:.+]] : f32 to i32
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# CHECK-NEXT: %[[FILTER_CAST:.+]] = fptosi %[[FILTER:.+]] : f32 to i32
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# CHECK-NEXT: %[[PROD:.+]] = muli %[[IN_CAST]], %[[FILTER_CAST]] : i32
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# CHECK-NEXT: %[[SUM:.+]] = addi %[[OUT]], %[[PROD]] : i32
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# CHECK-NEXT: linalg.yield %[[SUM]] : i32
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# CHECK-NEXT: -> tensor<2x4xi32>
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@builtin.FuncOp.from_py_func(
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RankedTensorType.get((4, 16), f32), RankedTensorType.get((2, 2, 1),
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f32),
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RankedTensorType.get((2, 4), i32))
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def test_f32i32_conv(input, filter, init_result):
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return conv_poly(
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input, filter, outs=[init_result], strides=[2, 4], dilations=[1, 2])
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# CHECK-LABEL: @test_f32i32_max_pooling
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# CHECK: linalg.generic
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# CHECK-SAME: indexing_maps = [#[[$CONV_MAP_I]], #[[$POOL_MAP_K]], #[[$CONV_MAP_O]]]
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# CHECK-SAME: iterator_types = ["parallel", "parallel", "parallel", "reduction", "reduction", "parallel"]
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# CHECK: ^{{.*}}(%[[IN:.+]]: f32, %[[SHAPE:.+]]: f32, %[[OUT:.+]]: i32)
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# CHECK-NEXT: %[[IN_CAST:.+]] = fptosi %[[IN:.+]] : f32 to i32
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# CHECK-NEXT: %[[COND:.+]] = cmpi sgt, %[[OUT]], %[[IN_CAST:.+]] : i32
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# CHECK-NEXT: %[[MAX:.+]] = select %[[COND]], %[[OUT]], %[[IN_CAST:.+]] : i32
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# CHECK-NEXT: linalg.yield %[[MAX]] : i32
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# CHECK-NEXT: -> tensor<2x4xi32>
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@builtin.FuncOp.from_py_func(
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RankedTensorType.get((4, 16), f32), RankedTensorType.get((2, 2), f32),
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RankedTensorType.get((2, 4), i32))
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def test_f32i32_max_pooling(input, shape, init_result):
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return pooling_max_poly(
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input, shape, outs=[init_result], strides=[2, 4], dilations=[1, 2])
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# CHECK-LABEL: @test_f32f32_max_pooling
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# CHECK: linalg.generic
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# CHECK-SAME: indexing_maps = [#[[$CONV_MAP_I]], #[[$POOL_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, %[[SHAPE:.+]]: f32, %[[OUT:.+]]: f32)
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# CHECK-NEXT: %[[COND:.+]] = cmpf ogt, %[[OUT]], %[[IN:.+]] : f32
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# CHECK-NEXT: %[[MAX:.+]] = select %[[COND]], %[[OUT]], %[[IN:.+]] : f32
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# CHECK-NEXT: linalg.yield %[[MAX]] : f32
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# CHECK-NEXT: -> tensor<2x4xf32>
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@builtin.FuncOp.from_py_func(
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RankedTensorType.get((4, 16), f32), RankedTensorType.get((2, 2), f32),
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RankedTensorType.get((2, 4), f32))
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def test_f32f32_max_pooling(input, shape, init_result):
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return pooling_max_poly(
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input, shape, outs=[init_result], strides=[2, 4], dilations=[1, 2])
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# CHECK-LABEL: @test_f32i32_min_pooling
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# CHECK: = cmpi slt,
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@builtin.FuncOp.from_py_func(
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RankedTensorType.get((4, 16), f32), RankedTensorType.get((2, 2), f32),
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RankedTensorType.get((2, 4), i32))
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def test_f32i32_min_pooling(input, shape, init_result):
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return pooling_min_poly(
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input, shape, outs=[init_result], strides=[2, 4], dilations=[1, 2])
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# CHECK-LABEL: @test_f32f32_min_pooling
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# CHECK: = cmpf olt,
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@builtin.FuncOp.from_py_func(
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RankedTensorType.get((4, 16), f32), RankedTensorType.get((2, 2), f32),
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RankedTensorType.get((2, 4), f32))
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def test_f32f32_min_pooling(input, shape, init_result):
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return pooling_min_poly(
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input, shape, outs=[init_result], strides=[2, 4], dilations=[1, 2])
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# CHECK-LABEL: @test_i32_fill_rng
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# CHECK: ^{{.*}}(%[[MIN:.+]]: f64, %[[MAX:.+]]: f64, %[[SEED:.+]]: i32, %{{.*}}
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# CHECK-DAG: %[[IDX0:.+]] = linalg.index 0 : index
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# CHECK-DAG: %[[IDX0_CAST:.+]] = index_cast %[[IDX0]] : index to i32
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# CHECK-DAG: %[[RND0:.+]] = addi %[[IDX0_CAST]], %[[SEED]] : i32
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# CHECK-DAG: %[[CST0:.+]] = constant 1103515245 : i64
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# CHECK-DAG: %[[CST0_CAST:.+]] = trunci %[[CST0]] : i64 to i32
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# Skip the remaining random number computation and match the scaling logic.
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# CHECK-DAG: %[[DIFF:.+]] = subf %[[MAX]], %[[MIN]] : f64
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# CHECK-DAG: %[[CST3:.+]] = constant 2.3283063999999999E-10 : f64
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# CHECK-DAG: %[[FACT:.+]] = mulf %[[DIFF]], %[[CST3]] : f64
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# CHECK-DAG: %[[RND4:.+]] = mulf %{{.+}}, %[[FACT]] : f64
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# CHECK-DAG: %[[RND5:.+]] = addf %[[RND4]], %[[MIN]] : f64
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# CHECK-DAG: %{{.*}} = fptosi %[[RND5]] : f64 to i32
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@builtin.FuncOp.from_py_func(f64, f64, i32,
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RankedTensorType.get((4, 16), i32))
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def test_i32_fill_rng(min, max, seed, init_result):
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return fill_rng_poly(min, max, seed, outs=[init_result])
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# CHECK-LABEL: @test_f32_soft_plus
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# CHECK: ^{{.*}}(%[[IN:.+]]: f32, %[[OUT:.+]]: f32)
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# CHECK-NEXT: %[[C1:.+]] = constant 1.000000e+00 : f64
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# CHECK-NEXT: %[[C1_CAST:.+]] = fptrunc %[[C1]] : f64 to f32
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# CHECK-NEXT: %[[EXP:.+]] = math.exp %[[IN]] : f32
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# CHECK-NEXT: %[[SUM:.+]] = addf %[[C1_CAST]], %[[EXP]] : f32
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# CHECK-NEXT: %[[LOG:.+]] = math.log %[[SUM]] : f32
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# CHECK-NEXT: linalg.yield %[[LOG]] : f32
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# CHECK-NEXT: -> tensor<4x16xf32>
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@builtin.FuncOp.from_py_func(
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RankedTensorType.get((4, 16), f32), RankedTensorType.get((4, 16), f32))
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def test_f32_soft_plus(input, init_result):
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return soft_plus_poly(input, outs=[init_result])
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print(module)
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