[mlir][OpDSL] Fix OpDSL tests after https://reviews.llvm.org/D114680.
Update the shapes of the convolution / pooling tests that where detected after enabling verification during printing (https://reviews.llvm.org/D114680). Also split the emit_structured_generic.py file that previously contained all tests into multiple separate files to simplify debugging.
Reviewed By: stellaraccident
Differential Revision: https://reviews.llvm.org/D114731
GitOrigin-RevId: 0d0371f58ff0e4289bdff9ef70f7f6fb0277c3d0
diff --git a/test/python/dialects/linalg/opdsl/emit_convolution.py b/test/python/dialects/linalg/opdsl/emit_convolution.py
new file mode 100644
index 0000000..44b3c77
--- /dev/null
+++ b/test/python/dialects/linalg/opdsl/emit_convolution.py
@@ -0,0 +1,58 @@
+# RUN: %PYTHON %s | FileCheck %s
+
+from mlir.ir import *
+from mlir.dialects import builtin
+from mlir.dialects import linalg
+from mlir.dialects import std
+
+from mlir.dialects.linalg.opdsl.lang import *
+
+T1 = TV.T1
+T2 = TV.T2
+
+
+@linalg_structured_op
+def conv_poly(
+ I=TensorDef(T1, S.N, S.IH, S.IW, S.C),
+ K=TensorDef(T2, S.KH, S.KW, S.C),
+ O=TensorDef(U, S.N, S.OH, S.OW, S.C, output=True),
+ strides=AttributeDef(S.SH, S.SW),
+ dilations=AttributeDef(S.DH, S.DW)):
+ domain(D.n, D.oh, D.ow, D.kh, D.kw, D.c)
+ O[D.n, D.oh, D.ow, D.c] += cast(
+ U, I[D.n, D.oh * S.SH + D.kh * S.DH, D.ow * S.SW + D.kw * S.DW,
+ D.c]) * cast(U, K[D.kh, D.kw, D.c])
+
+
+with Context() as ctx, Location.unknown():
+ module = Module.create()
+ f32 = F32Type.get()
+ i32 = IntegerType.get_signless(32)
+ with InsertionPoint(module.body):
+
+ # Convolution indexing maps.
+ # CHECK: #[[$CONV_MAP_I:.+]] = affine_map<(d0, d1, d2, d3, d4, d5) -> (d0, d1 * 2 + d3, d2 * 4 + d4 * 2, d5)>
+ # CHECK: #[[$CONV_MAP_K:.+]] = affine_map<(d0, d1, d2, d3, d4, d5) -> (d3, d4, d5)>
+ # CHECK: #[[$CONV_MAP_O:.+]] = affine_map<(d0, d1, d2, d3, d4, d5) -> (d0, d1, d2, d5)>
+
+ # CHECK-LABEL: @test_f32i32_conv
+ # CHECK: linalg.generic
+ # CHECK-SAME: indexing_maps = [#[[$CONV_MAP_I]], #[[$CONV_MAP_K]], #[[$CONV_MAP_O]]]
+ # CHECK-SAME: iterator_types = ["parallel", "parallel", "parallel", "reduction", "reduction", "parallel"]
+ # CHECK: ^{{.*}}(%[[IN:.+]]: f32, %[[FILTER:.+]]: f32, %[[OUT:.+]]: i32)
+ # CHECK-NEXT: %[[IN_CAST:.+]] = arith.fptosi %[[IN:.+]] : f32 to i32
+ # CHECK-NEXT: %[[FILTER_CAST:.+]] = arith.fptosi %[[FILTER:.+]] : f32 to i32
+ # CHECK-NEXT: %[[PROD:.+]] = arith.muli %[[IN_CAST]], %[[FILTER_CAST]] : i32
+ # CHECK-NEXT: %[[SUM:.+]] = arith.addi %[[OUT]], %[[PROD]] : i32
+ # CHECK-NEXT: linalg.yield %[[SUM]] : i32
+ # CHECK-NEXT: -> tensor<1x2x4x1xi32>
+ @builtin.FuncOp.from_py_func(
+ RankedTensorType.get((1, 4, 16, 1), f32),
+ RankedTensorType.get((2, 2, 1), f32),
+ RankedTensorType.get((1, 2, 4, 1), i32))
+ def test_f32i32_conv(input, filter, init_result):
+ return conv_poly(
+ input, filter, outs=[init_result], strides=[2, 4], dilations=[1, 2])
+
+
+print(module)
diff --git a/test/python/dialects/linalg/opdsl/emit_matmul.py b/test/python/dialects/linalg/opdsl/emit_matmul.py
new file mode 100644
index 0000000..978cddc
--- /dev/null
+++ b/test/python/dialects/linalg/opdsl/emit_matmul.py
@@ -0,0 +1,176 @@
+# RUN: %PYTHON %s | FileCheck %s
+
+from mlir.ir import *
+from mlir.dialects import builtin
+from mlir.dialects import linalg
+from mlir.dialects import std
+
+from mlir.dialects.linalg.opdsl.lang import *
+
+T1 = TV.T1
+T2 = TV.T2
+
+
+@linalg_structured_op
+def matmul_mono(
+ A=TensorDef(T, S.M, S.K),
+ B=TensorDef(T, S.K, S.N),
+ C=TensorDef(T, S.M, S.N, output=True)):
+ domain(D.m, D.n, D.k)
+ C[D.m, D.n] += A[D.m, D.k] * B[D.k, D.n]
+
+
+@linalg_structured_op
+def matmul_poly(
+ A=TensorDef(T1, S.M, S.K),
+ B=TensorDef(T2, S.K, S.N),
+ C=TensorDef(U, S.M, S.N, output=True)):
+ domain(D.m, D.n, D.k)
+ C[D.m, D.n] += cast(U, A[D.m, D.k]) * cast(U, B[D.k, D.n])
+
+
+@linalg_structured_op
+def matmul_unsigned_poly(
+ A=TensorDef(T1, S.M, S.K),
+ B=TensorDef(T2, S.K, S.N),
+ C=TensorDef(U, S.M, S.N, output=True)):
+ domain(D.m, D.n, D.k)
+ C[D.m, D.n] += cast_unsigned(U, A[D.m, D.k]) * cast_unsigned(U, B[D.k, D.n])
+
+
+with Context() as ctx, Location.unknown():
+ module = Module.create()
+ f16 = F16Type.get()
+ f32 = F32Type.get()
+ f64 = F64Type.get()
+ i8 = IntegerType.get_signless(8)
+ i16 = IntegerType.get_signless(16)
+ i32 = IntegerType.get_signless(32)
+ with InsertionPoint(module.body):
+
+ # Multiplication indexing maps. We verify only the indexing maps of the
+ # first multiplication and then do additional tests on casting and body
+ # generation behavior.
+ # CHECK: #[[$MUL_MAP_A:.+]] = affine_map<(d0, d1, d2) -> (d0, d2)>
+ # CHECK: #[[$MUL_MAP_B:.+]] = affine_map<(d0, d1, d2) -> (d2, d1)>
+ # CHECK: #[[$MUL_MAP_C:.+]] = affine_map<(d0, d1, d2) -> (d0, d1)>
+
+ # CHECK-LABEL: func @test_matmul_mono
+ # CHECK-SAME: %[[A:.+]]: tensor<4x16xf32>
+ # CHECK-SAME: %[[B:.+]]: tensor<16x8xf32>
+ # CHECK: %[[INITC:.+]] = linalg.init_tensor [4, 8] : tensor<4x8xf32>
+ # CHECK: linalg.generic
+ # CHECK-SAME: indexing_maps = [#[[$MUL_MAP_A]], #[[$MUL_MAP_B]], #[[$MUL_MAP_C]]]
+ # CHECK-SAME: iterator_types = ["parallel", "parallel", "reduction"]
+ # CHECK-SAME: ins(%[[A]], %[[B]]
+ # CHECK-SAME: outs(%[[INITC]]
+ @builtin.FuncOp.from_py_func(
+ RankedTensorType.get((4, 16), f32), RankedTensorType.get((16, 8), f32))
+ def test_matmul_mono(lhs, rhs):
+ init_result = linalg.InitTensorOp([4, 8], f32)
+ return matmul_mono(lhs, rhs, outs=[init_result.result])
+
+ # CHECK-LABEL: @test_i8i8i32_matmul
+ # CHECK: ^{{.*}}(%[[A_ARG:.+]]: i8, %[[B_ARG:.+]]: i8, %[[C_ARG:.+]]: i32)
+ # CHECK-NEXT: %[[A_CAST:.+]] = arith.extsi %[[A_ARG]] : i8 to i32
+ # CHECK-NEXT: %[[B_CAST:.+]] = arith.extsi %[[B_ARG]] : i8 to i32
+ # CHECK-NEXT: %[[MUL:.+]] = arith.muli %[[A_CAST]], %[[B_CAST]] : i32
+ # CHECK-NEXT: %[[ADD:.+]] = arith.addi %[[C_ARG]], %[[MUL]] : i32
+ # CHECK-NEXT: linalg.yield %[[ADD]] : i32
+ # CHECK-NEXT: -> tensor<4x8xi32>
+ @builtin.FuncOp.from_py_func(
+ RankedTensorType.get((4, 16), i8), RankedTensorType.get((16, 8), i8),
+ RankedTensorType.get((4, 8), i32))
+ def test_i8i8i32_matmul(lhs, rhs, init_result):
+ return matmul_poly(lhs, rhs, outs=[init_result])
+
+ # CHECK-LABEL: @test_i8i8i32_matmul_unsigned
+ # CHECK: = arith.extui
+ # CHECK: = arith.extui
+ @builtin.FuncOp.from_py_func(
+ RankedTensorType.get((4, 16), i8), RankedTensorType.get((16, 8), i8),
+ RankedTensorType.get((4, 8), i32))
+ def test_i8i8i32_matmul_unsigned(lhs, rhs, init_result):
+ return matmul_unsigned_poly(lhs, rhs, outs=[init_result])
+
+ # CHECK-LABEL: @test_i8i16i32_matmul
+ # CHECK: ^{{.*}}(%[[A_ARG:.+]]: i8, %[[B_ARG:.+]]: i16, %[[C_ARG:.+]]: i32)
+ # CHECK-NEXT: %[[A_CAST:.+]] = arith.extsi %[[A_ARG]] : i8 to i32
+ # CHECK-NEXT: %[[B_CAST:.+]] = arith.extsi %[[B_ARG]] : i16 to i32
+ # CHECK-NEXT: %[[MUL:.+]] = arith.muli %[[A_CAST]], %[[B_CAST]] : i32
+ # CHECK-NEXT: %[[ADD:.+]] = arith.addi %[[C_ARG]], %[[MUL]] : i32
+ # CHECK-NEXT: linalg.yield %[[ADD]] : i32
+ # CHECK-NEXT: -> tensor<4x8xi32>
+ @builtin.FuncOp.from_py_func(
+ RankedTensorType.get((4, 16), i8), RankedTensorType.get((16, 8), i16),
+ RankedTensorType.get((4, 8), i32))
+ def test_i8i16i32_matmul(lhs, rhs, init_result):
+ return matmul_poly(lhs, rhs, outs=[init_result])
+
+ # CHECK-LABEL: @test_i32i32i16_matmul
+ # CHECK: ^{{.*}}(%[[A_ARG:.+]]: i32, %[[B_ARG:.+]]: i32, %[[C_ARG:.+]]: i16)
+ # CHECK-NEXT: %[[A_CAST:.+]] = arith.trunci %[[A_ARG]] : i32 to i16
+ # CHECK-NEXT: %[[B_CAST:.+]] = arith.trunci %[[B_ARG]] : i32 to i16
+ # CHECK-NEXT: %[[MUL:.+]] = arith.muli %[[A_CAST]], %[[B_CAST]] : i16
+ # CHECK-NEXT: %[[ADD:.+]] = arith.addi %[[C_ARG]], %[[MUL]] : i16
+ # CHECK-NEXT: linalg.yield %[[ADD]] : i16
+ # CHECK-NEXT: -> tensor<4x8xi16>
+ @builtin.FuncOp.from_py_func(
+ RankedTensorType.get((4, 16), i32), RankedTensorType.get((16, 8), i32),
+ RankedTensorType.get((4, 8), i16))
+ def test_i32i32i16_matmul(lhs, rhs, init_result):
+ return matmul_poly(lhs, rhs, outs=[init_result])
+
+ # CHECK-LABEL: @test_i8i8f32_matmul
+ # CHECK: ^{{.*}}(%[[A_ARG:.+]]: i8, %[[B_ARG:.+]]: i8, %[[C_ARG:.+]]: f32)
+ # CHECK-NEXT: %[[A_CAST:.+]] = arith.sitofp %[[A_ARG]] : i8 to f32
+ # CHECK-NEXT: %[[B_CAST:.+]] = arith.sitofp %[[B_ARG]] : i8 to f32
+ # CHECK-NEXT: %[[MUL:.+]] = arith.mulf %[[A_CAST]], %[[B_CAST]] : f32
+ # CHECK-NEXT: %[[ADD:.+]] = arith.addf %[[C_ARG]], %[[MUL]] : f32
+ # CHECK-NEXT: linalg.yield %[[ADD]] : f32
+ # CHECK-NEXT: -> tensor<4x8xf32>
+ @builtin.FuncOp.from_py_func(
+ RankedTensorType.get((4, 16), i8), RankedTensorType.get((16, 8), i8),
+ RankedTensorType.get((4, 8), f32))
+ def test_i8i8f32_matmul(lhs, rhs, init_result):
+ return matmul_poly(lhs, rhs, outs=[init_result])
+
+ # CHECK-LABEL: @test_i8i8f32_matmul_unsigned
+ # CHECK: = arith.uitofp
+ # CHECK: = arith.uitofp
+ @builtin.FuncOp.from_py_func(
+ RankedTensorType.get((4, 16), i8), RankedTensorType.get((16, 8), i8),
+ RankedTensorType.get((4, 8), f32))
+ def test_i8i8f32_matmul_unsigned(lhs, rhs, init_result):
+ return matmul_unsigned_poly(lhs, rhs, outs=[init_result])
+
+ # CHECK-LABEL: @test_f16f16f32_matmul
+ # CHECK: ^{{.*}}(%[[A_ARG:.+]]: f16, %[[B_ARG:.+]]: f16, %[[C_ARG:.+]]: f32)
+ # CHECK-NEXT: %[[A_CAST:.+]] = arith.extf %[[A_ARG]] : f16 to f32
+ # CHECK-NEXT: %[[B_CAST:.+]] = arith.extf %[[B_ARG]] : f16 to f32
+ # CHECK-NEXT: %[[MUL:.+]] = arith.mulf %[[A_CAST]], %[[B_CAST]] : f32
+ # CHECK-NEXT: %[[ADD:.+]] = arith.addf %[[C_ARG]], %[[MUL]] : f32
+ # CHECK-NEXT: linalg.yield %[[ADD]] : f32
+ # CHECK-NEXT: -> tensor<4x8xf32>
+ @builtin.FuncOp.from_py_func(
+ RankedTensorType.get((4, 16), f16), RankedTensorType.get((16, 8), f16),
+ RankedTensorType.get((4, 8), f32))
+ def test_f16f16f32_matmul(lhs, rhs, init_result):
+ return matmul_poly(lhs, rhs, outs=[init_result])
+
+ # CHECK-LABEL: @test_f64f64f32_matmul
+ # CHECK: ^{{.*}}(%[[A_ARG:.+]]: f64, %[[B_ARG:.+]]: f64, %[[C_ARG:.+]]: f32)
+ # CHECK-NEXT: %[[A_CAST:.+]] = arith.truncf %[[A_ARG]] : f64 to f32
+ # CHECK-NEXT: %[[B_CAST:.+]] = arith.truncf %[[B_ARG]] : f64 to f32
+ # CHECK-NEXT: %[[MUL:.+]] = arith.mulf %[[A_CAST]], %[[B_CAST]] : f32
+ # CHECK-NEXT: %[[ADD:.+]] = arith.addf %[[C_ARG]], %[[MUL]] : f32
+ # CHECK-NEXT: linalg.yield %[[ADD]] : f32
+ # CHECK-NEXT: -> tensor<4x8xf32>
+ @builtin.FuncOp.from_py_func(
+ RankedTensorType.get((4, 16), f64), RankedTensorType.get((16, 8), f64),
+ RankedTensorType.get((4, 8), f32))
+ def test_f64f64f32_matmul(lhs, rhs, init_result):
+ return matmul_poly(lhs, rhs, outs=[init_result])
+
+
+print(module)
diff --git a/test/python/dialects/linalg/opdsl/emit_misc.py b/test/python/dialects/linalg/opdsl/emit_misc.py
new file mode 100644
index 0000000..69d44a6
--- /dev/null
+++ b/test/python/dialects/linalg/opdsl/emit_misc.py
@@ -0,0 +1,93 @@
+# RUN: %PYTHON %s | FileCheck %s
+
+from mlir.ir import *
+from mlir.dialects import builtin
+from mlir.dialects import linalg
+from mlir.dialects import std
+
+from mlir.dialects.linalg.opdsl.lang import *
+
+# This tests miscellaneous features of the emitter that are not tested by the
+# matmul, convolution, or, pooling tests. The features include:
+# - constant defined in the body
+# - fix/predefined types
+# - exponential functions
+# - custom op names.
+
+@linalg_structured_op
+def fill_rng_poly(
+ min=ScalarDef(F64),
+ max=ScalarDef(F64),
+ seed=ScalarDef(I32),
+ O=TensorDef(T, S.M, S.N, output=True)):
+ multiplier = cast(I32, const(1103515245))
+ increment = cast(I32, const(12345))
+ rand1 = (cast(I32, index(D.m)) + seed) * multiplier + increment
+ rand2 = (cast(I32, index(D.n)) + rand1) * multiplier + increment
+ inv_range = cast(F64, const(2.3283064e-10))
+ offset = cast(F64, const(2147483647))
+ scaling = (max - min) * inv_range
+ O[D.m, D.n] = cast(T, (offset + cast(F64, rand2)) * scaling + min)
+
+
+@linalg_structured_op
+def soft_plus_poly(
+ I=TensorDef(T, S.M, S.N), O=TensorDef(U, S.M, S.N, output=True)):
+ O[D.m, D.n] = \
+ PrimFn.log(cast(U, const(1.0)) + cast(U, PrimFn.exp(I[D.m, D.n])))
+
+
+@linalg_structured_op(op_name="custom_op_name")
+def non_default_op_name(I=TensorDef(T, S.N), O=TensorDef(T, S.N, output=True)):
+ O[D.n] = I[D.n]
+
+
+with Context() as ctx, Location.unknown():
+ module = Module.create()
+ f32 = F32Type.get()
+ f64 = F64Type.get()
+ i32 = IntegerType.get_signless(32)
+ with InsertionPoint(module.body):
+
+ # CHECK-LABEL: @test_i32_fill_rng
+ # CHECK: ^{{.*}}(%[[MIN:.+]]: f64, %[[MAX:.+]]: f64, %[[SEED:.+]]: i32, %{{.*}}
+ # CHECK-DAG: %[[IDX0:.+]] = linalg.index 0 : index
+ # CHECK-DAG: %[[IDX0_CAST:.+]] = arith.index_cast %[[IDX0]] : index to i32
+ # CHECK-DAG: %[[RND0:.+]] = arith.addi %[[IDX0_CAST]], %[[SEED]] : i32
+ # CHECK-DAG: %[[CST0:.+]] = arith.constant 1103515245 : i64
+ # CHECK-DAG: %[[CST0_CAST:.+]] = arith.trunci %[[CST0]] : i64 to i32
+ # Skip the remaining random number computation and match the scaling logic.
+ # CHECK-DAG: %[[DIFF:.+]] = arith.subf %[[MAX]], %[[MIN]] : f64
+ # CHECK-DAG: %[[CST3:.+]] = arith.constant 2.3283063999999999E-10 : f64
+ # CHECK-DAG: %[[FACT:.+]] = arith.mulf %[[DIFF]], %[[CST3]] : f64
+ # CHECK-DAG: %[[RND4:.+]] = arith.mulf %{{.+}}, %[[FACT]] : f64
+ # CHECK-DAG: %[[RND5:.+]] = arith.addf %[[RND4]], %[[MIN]] : f64
+ # CHECK-DAG: %{{.*}} = arith.fptosi %[[RND5]] : f64 to i32
+ @builtin.FuncOp.from_py_func(f64, f64, i32,
+ RankedTensorType.get((4, 16), i32))
+ def test_i32_fill_rng(min, max, seed, init_result):
+ return fill_rng_poly(min, max, seed, outs=[init_result])
+
+ # CHECK-LABEL: @test_f32_soft_plus
+ # CHECK: ^{{.*}}(%[[IN:.+]]: f32, %[[OUT:.+]]: f32)
+ # CHECK-NEXT: %[[C1:.+]] = arith.constant 1.000000e+00 : f64
+ # CHECK-NEXT: %[[C1_CAST:.+]] = arith.truncf %[[C1]] : f64 to f32
+ # CHECK-NEXT: %[[EXP:.+]] = math.exp %[[IN]] : f32
+ # CHECK-NEXT: %[[SUM:.+]] = arith.addf %[[C1_CAST]], %[[EXP]] : f32
+ # CHECK-NEXT: %[[LOG:.+]] = math.log %[[SUM]] : f32
+ # CHECK-NEXT: linalg.yield %[[LOG]] : f32
+ # CHECK-NEXT: -> tensor<4x16xf32>
+ @builtin.FuncOp.from_py_func(
+ RankedTensorType.get((4, 16), f32), RankedTensorType.get((4, 16), f32))
+ def test_f32_soft_plus(input, init_result):
+ return soft_plus_poly(input, outs=[init_result])
+
+ # Just check that we don't assert out on name mismatch.
+ # CHECK-LABEL: @test_non_default_op_name
+ @builtin.FuncOp.from_py_func(
+ RankedTensorType.get((42,), f32), RankedTensorType.get((42,), f32))
+ def test_non_default_op_name(input, init_result):
+ return non_default_op_name(input, outs=[init_result])
+
+
+print(module)
diff --git a/test/python/dialects/linalg/opdsl/emit_pooling.py b/test/python/dialects/linalg/opdsl/emit_pooling.py
new file mode 100644
index 0000000..2bc8be3
--- /dev/null
+++ b/test/python/dialects/linalg/opdsl/emit_pooling.py
@@ -0,0 +1,154 @@
+# RUN: %PYTHON %s | FileCheck %s
+
+from mlir.ir import *
+from mlir.dialects import builtin
+from mlir.dialects import linalg
+from mlir.dialects import std
+
+from mlir.dialects.linalg.opdsl.lang import *
+
+T1 = TV.T1
+T2 = TV.T2
+
+
+@linalg_structured_op
+def pooling_max_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),
+ strides=AttributeDef(S.SH, S.SW),
+ dilations=AttributeDef(S.DH, S.DW)):
+ domain(D.n, D.oh, D.ow, D.kh, D.kw, D.c)
+ O[D.n, D.oh, D.ow, D.c] = ReduceFn.max(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]))
+
+
+@linalg_structured_op
+def pooling_max_unsigned_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),
+ strides=AttributeDef(S.SH, S.SW),
+ dilations=AttributeDef(S.DH, S.DW)):
+ domain(D.n, D.oh, D.ow, D.kh, D.kw, D.c)
+ O[D.n, D.oh, D.ow, D.c] = ReduceFn.max_unsigned(D.kh, D.kw)(
+ cast_unsigned(
+ U, I[D.n, D.oh * S.SH + D.kh * S.DH, D.ow * S.SW + D.kw * S.DW, D.c]))
+
+
+@linalg_structured_op
+def pooling_min_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),
+ strides=AttributeDef(S.SH, S.SW),
+ dilations=AttributeDef(S.DH, S.DW)):
+ domain(D.n, D.oh, D.ow, D.kh, D.kw, D.c)
+ O[D.n, D.oh, D.ow, D.c] = ReduceFn.min(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]))
+
+
+@linalg_structured_op
+def pooling_min_unsigned_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),
+ strides=AttributeDef(S.SH, S.SW),
+ dilations=AttributeDef(S.DH, S.DW)):
+ domain(D.n, D.oh, D.ow, D.kh, D.kw, D.c)
+ O[D.n, D.oh, D.ow, D.c] = ReduceFn.min_unsigned(D.kh, D.kw)(
+ cast_unsigned(
+ 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>
+ @builtin.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_max_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
+ @builtin.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_max_unsigned_poly(
+ input, shape, outs=[init_result], 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.maxf %[[OUT]], %[[IN:.+]] : f32
+ # CHECK-NEXT: linalg.yield %[[MAX]] : f32
+ # CHECK-NEXT: -> tensor<1x2x4x1xf32>
+ @builtin.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_max_poly(
+ input, shape, outs=[init_result], strides=[2, 4], dilations=[1, 2])
+
+ # CHECK-LABEL: @test_f32i32_min_pooling
+ # CHECK: = arith.fptosi
+ # CHECK: = arith.minsi
+ @builtin.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_min_poly(
+ input, shape, outs=[init_result], strides=[2, 4], dilations=[1, 2])
+
+ # CHECK-LABEL: @test_f32i32_min_unsigned_pooling
+ # CHECK: = arith.fptoui
+ # CHECK: = arith.minui
+ @builtin.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_min_unsigned_poly(
+ input, shape, outs=[init_result], strides=[2, 4], dilations=[1, 2])
+
+ # CHECK-LABEL: @test_f32f32_min_pooling
+ # CHECK: = arith.minf
+ @builtin.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_min_poly(
+ input, shape, outs=[init_result], strides=[2, 4], dilations=[1, 2])
+
+
+print(module)
diff --git a/test/python/dialects/linalg/opdsl/emit_structured_generic.py b/test/python/dialects/linalg/opdsl/emit_structured_generic.py
deleted file mode 100644
index 115c227..0000000
--- a/test/python/dialects/linalg/opdsl/emit_structured_generic.py
+++ /dev/null
@@ -1,411 +0,0 @@
-# RUN: %PYTHON %s | FileCheck %s
-
-from mlir.ir import *
-from mlir.dialects import builtin
-from mlir.dialects import linalg
-from mlir.dialects import std
-
-from mlir.dialects.linalg.opdsl.lang import *
-
-T1 = TV.T1
-T2 = TV.T2
-
-
-@linalg_structured_op
-def matmul_mono(
- A=TensorDef(T, S.M, S.K),
- B=TensorDef(T, S.K, S.N),
- C=TensorDef(T, S.M, S.N, output=True)):
- domain(D.m, D.n, D.k)
- C[D.m, D.n] += A[D.m, D.k] * B[D.k, D.n]
-
-
-@linalg_structured_op
-def matmul_poly(
- A=TensorDef(T1, S.M, S.K),
- B=TensorDef(T2, S.K, S.N),
- C=TensorDef(U, S.M, S.N, output=True)):
- domain(D.m, D.n, D.k)
- C[D.m, D.n] += cast(U, A[D.m, D.k]) * cast(U, B[D.k, D.n])
-
-
-@linalg_structured_op
-def matmul_unsigned_poly(
- A=TensorDef(T1, S.M, S.K),
- B=TensorDef(T2, S.K, S.N),
- C=TensorDef(U, S.M, S.N, output=True)):
- domain(D.m, D.n, D.k)
- C[D.m, D.n] += cast_unsigned(U, A[D.m, D.k]) * cast_unsigned(U, B[D.k, D.n])
-
-
-@linalg_structured_op
-def conv_poly(
- I=TensorDef(T1, S.N, S.IH, S.IW, S.C),
- K=TensorDef(T2, S.KH, S.KW, S.C),
- O=TensorDef(U, S.N, S.OH, S.OW, S.C, output=True),
- strides=AttributeDef(S.SH, S.SW),
- dilations=AttributeDef(S.DH, S.DW)):
- domain(D.n, D.oh, D.ow, D.kh, D.kw, D.c)
- O[D.n, D.oh, D.ow, D.c] += cast(
- U, I[D.n, D.oh * S.SH + D.kh * S.DH, D.ow * S.SW + D.kw * S.DW,
- D.c]) * cast(U, K[D.kh, D.kw, D.c])
-
-
-@linalg_structured_op
-def pooling_max_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),
- strides=AttributeDef(S.SH, S.SW),
- dilations=AttributeDef(S.DH, S.DW)):
- domain(D.n, D.oh, D.ow, D.kh, D.kw, D.c)
- O[D.n, D.oh, D.ow, D.c] = ReduceFn.max(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]))
-
-
-@linalg_structured_op
-def pooling_max_unsigned_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),
- strides=AttributeDef(S.SH, S.SW),
- dilations=AttributeDef(S.DH, S.DW)):
- domain(D.n, D.oh, D.ow, D.kh, D.kw, D.c)
- O[D.n, D.oh, D.ow, D.c] = ReduceFn.max_unsigned(D.kh, D.kw)(
- cast_unsigned(
- U, I[D.n, D.oh * S.SH + D.kh * S.DH, D.ow * S.SW + D.kw * S.DW, D.c]))
-
-
-@linalg_structured_op
-def pooling_min_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),
- strides=AttributeDef(S.SH, S.SW),
- dilations=AttributeDef(S.DH, S.DW)):
- domain(D.n, D.oh, D.ow, D.kh, D.kw, D.c)
- O[D.n, D.oh, D.ow, D.c] = ReduceFn.min(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]))
-
-
-@linalg_structured_op
-def pooling_min_unsigned_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),
- strides=AttributeDef(S.SH, S.SW),
- dilations=AttributeDef(S.DH, S.DW)):
- domain(D.n, D.oh, D.ow, D.kh, D.kw, D.c)
- O[D.n, D.oh, D.ow, D.c] = ReduceFn.min_unsigned(D.kh, D.kw)(
- cast_unsigned(
- U, I[D.n, D.oh * S.SH + D.kh * S.DH, D.ow * S.SW + D.kw * S.DW, D.c]))
-
-
-@linalg_structured_op
-def fill_rng_poly(
- min=ScalarDef(F64),
- max=ScalarDef(F64),
- seed=ScalarDef(I32),
- O=TensorDef(T, S.M, S.N, output=True)):
- multiplier = cast(I32, const(1103515245))
- increment = cast(I32, const(12345))
- rand1 = (cast(I32, index(D.m)) + seed) * multiplier + increment
- rand2 = (cast(I32, index(D.n)) + rand1) * multiplier + increment
- inv_range = cast(F64, const(2.3283064e-10))
- offset = cast(F64, const(2147483647))
- scaling = (max - min) * inv_range
- O[D.m, D.n] = cast(T, (offset + cast(F64, rand2)) * scaling + min)
-
-
-@linalg_structured_op
-def soft_plus_poly(
- I=TensorDef(T, S.M, S.N), O=TensorDef(U, S.M, S.N, output=True)):
- O[D.m, D.n] = \
- PrimFn.log(cast(U, const(1.0)) + cast(U, PrimFn.exp(I[D.m, D.n])))
-
-
-@linalg_structured_op(op_name="custom_op_name")
-def non_default_op_name(I=TensorDef(T, S.N), O=TensorDef(T, S.N, output=True)):
- O[D.n] = I[D.n]
-
-
-with Context() as ctx, Location.unknown():
- module = Module.create()
- f16 = F16Type.get()
- f32 = F32Type.get()
- f64 = F64Type.get()
- i8 = IntegerType.get_signless(8)
- i16 = IntegerType.get_signless(16)
- i32 = IntegerType.get_signless(32)
- with InsertionPoint(module.body):
-
- # Multiplication indexing maps. We verify only the indexing maps of the
- # first multiplication and then do additional tests on casting and body
- # generation behavior.
- # CHECK: #[[$MUL_MAP_A:.+]] = affine_map<(d0, d1, d2) -> (d0, d2)>
- # CHECK: #[[$MUL_MAP_B:.+]] = affine_map<(d0, d1, d2) -> (d2, d1)>
- # CHECK: #[[$MUL_MAP_C:.+]] = affine_map<(d0, d1, d2) -> (d0, d1)>
-
- # Convolution indexing maps.
- # CHECK: #[[$CONV_MAP_I:.+]] = affine_map<(d0, d1, d2, d3, d4, d5) -> (d0, d1 * 2 + d3, d2 * 4 + d4 * 2, d5)>
- # CHECK: #[[$CONV_MAP_K:.+]] = affine_map<(d0, d1, d2, d3, d4, d5) -> (d3, d4, d5)>
- # CHECK: #[[$CONV_MAP_O:.+]] = affine_map<(d0, d1, d2, d3, d4, d5) -> (d0, d1, d2, d5)>
-
- # Pooling indexing maps.
- # CHECK: #[[$POOL_MAP_K:.+]] = affine_map<(d0, d1, d2, d3, d4, d5) -> (d3, d4)>
-
- # CHECK-LABEL: func @test_matmul_mono
- # CHECK-SAME: %[[A:.+]]: tensor<4x16xf32>
- # CHECK-SAME: %[[B:.+]]: tensor<16x8xf32>
-
- # CHECK: %[[INITC:.+]] = linalg.init_tensor [4, 8] : tensor<4x8xf32>
- # CHECK: linalg.generic
- # CHECK-SAME: indexing_maps = [#[[$MUL_MAP_A]], #[[$MUL_MAP_B]], #[[$MUL_MAP_C]]]
- # CHECK-SAME: iterator_types = ["parallel", "parallel", "reduction"]
- # CHECK-SAME: ins(%[[A]], %[[B]]
- # CHECK-SAME: outs(%[[INITC]]
-
- @builtin.FuncOp.from_py_func(
- RankedTensorType.get((4, 16), f32), RankedTensorType.get((16, 8), f32))
- def test_matmul_mono(lhs, rhs):
- init_result = linalg.InitTensorOp([4, 8], f32)
- return matmul_mono(lhs, rhs, outs=[init_result.result])
-
- # CHECK-LABEL: @test_i8i8i32_matmul
- # CHECK: ^{{.*}}(%[[A_ARG:.+]]: i8, %[[B_ARG:.+]]: i8, %[[C_ARG:.+]]: i32)
- # CHECK-NEXT: %[[A_CAST:.+]] = arith.extsi %[[A_ARG]] : i8 to i32
- # CHECK-NEXT: %[[B_CAST:.+]] = arith.extsi %[[B_ARG]] : i8 to i32
- # CHECK-NEXT: %[[MUL:.+]] = arith.muli %[[A_CAST]], %[[B_CAST]] : i32
- # CHECK-NEXT: %[[ADD:.+]] = arith.addi %[[C_ARG]], %[[MUL]] : i32
- # CHECK-NEXT: linalg.yield %[[ADD]] : i32
- # CHECK-NEXT: -> tensor<4x8xi32>
- @builtin.FuncOp.from_py_func(
- RankedTensorType.get((4, 16), i8), RankedTensorType.get((16, 8), i8),
- RankedTensorType.get((4, 8), i32))
- def test_i8i8i32_matmul(lhs, rhs, init_result):
- return matmul_poly(lhs, rhs, outs=[init_result])
-
- # CHECK-LABEL: @test_i8i8i32_matmul_unsigned
- # CHECK: = arith.extui
- # CHECK: = arith.extui
- @builtin.FuncOp.from_py_func(
- RankedTensorType.get((4, 16), i8), RankedTensorType.get((16, 8), i8),
- RankedTensorType.get((4, 8), i32))
- def test_i8i8i32_matmul_unsigned(lhs, rhs, init_result):
- return matmul_unsigned_poly(lhs, rhs, outs=[init_result])
-
- # CHECK-LABEL: @test_i8i16i32_matmul
- # CHECK: ^{{.*}}(%[[A_ARG:.+]]: i8, %[[B_ARG:.+]]: i16, %[[C_ARG:.+]]: i32)
- # CHECK-NEXT: %[[A_CAST:.+]] = arith.extsi %[[A_ARG]] : i8 to i32
- # CHECK-NEXT: %[[B_CAST:.+]] = arith.extsi %[[B_ARG]] : i16 to i32
- # CHECK-NEXT: %[[MUL:.+]] = arith.muli %[[A_CAST]], %[[B_CAST]] : i32
- # CHECK-NEXT: %[[ADD:.+]] = arith.addi %[[C_ARG]], %[[MUL]] : i32
- # CHECK-NEXT: linalg.yield %[[ADD]] : i32
- # CHECK-NEXT: -> tensor<4x8xi32>
- @builtin.FuncOp.from_py_func(
- RankedTensorType.get((4, 16), i8), RankedTensorType.get((16, 8), i16),
- RankedTensorType.get((4, 8), i32))
- def test_i8i16i32_matmul(lhs, rhs, init_result):
- return matmul_poly(lhs, rhs, outs=[init_result])
-
- # CHECK-LABEL: @test_i32i32i16_matmul
- # CHECK: ^{{.*}}(%[[A_ARG:.+]]: i32, %[[B_ARG:.+]]: i32, %[[C_ARG:.+]]: i16)
- # CHECK-NEXT: %[[A_CAST:.+]] = arith.trunci %[[A_ARG]] : i32 to i16
- # CHECK-NEXT: %[[B_CAST:.+]] = arith.trunci %[[B_ARG]] : i32 to i16
- # CHECK-NEXT: %[[MUL:.+]] = arith.muli %[[A_CAST]], %[[B_CAST]] : i16
- # CHECK-NEXT: %[[ADD:.+]] = arith.addi %[[C_ARG]], %[[MUL]] : i16
- # CHECK-NEXT: linalg.yield %[[ADD]] : i16
- # CHECK-NEXT: -> tensor<4x8xi16>
- @builtin.FuncOp.from_py_func(
- RankedTensorType.get((4, 16), i32), RankedTensorType.get((16, 8), i32),
- RankedTensorType.get((4, 8), i16))
- def test_i32i32i16_matmul(lhs, rhs, init_result):
- return matmul_poly(lhs, rhs, outs=[init_result])
-
- # CHECK-LABEL: @test_i8i8f32_matmul
- # CHECK: ^{{.*}}(%[[A_ARG:.+]]: i8, %[[B_ARG:.+]]: i8, %[[C_ARG:.+]]: f32)
- # CHECK-NEXT: %[[A_CAST:.+]] = arith.sitofp %[[A_ARG]] : i8 to f32
- # CHECK-NEXT: %[[B_CAST:.+]] = arith.sitofp %[[B_ARG]] : i8 to f32
- # CHECK-NEXT: %[[MUL:.+]] = arith.mulf %[[A_CAST]], %[[B_CAST]] : f32
- # CHECK-NEXT: %[[ADD:.+]] = arith.addf %[[C_ARG]], %[[MUL]] : f32
- # CHECK-NEXT: linalg.yield %[[ADD]] : f32
- # CHECK-NEXT: -> tensor<4x8xf32>
- @builtin.FuncOp.from_py_func(
- RankedTensorType.get((4, 16), i8), RankedTensorType.get((16, 8), i8),
- RankedTensorType.get((4, 8), f32))
- def test_i8i8f32_matmul(lhs, rhs, init_result):
- return matmul_poly(lhs, rhs, outs=[init_result])
-
- # CHECK-LABEL: @test_i8i8f32_matmul_unsigned
- # CHECK: = arith.uitofp
- # CHECK: = arith.uitofp
- @builtin.FuncOp.from_py_func(
- RankedTensorType.get((4, 16), i8), RankedTensorType.get((16, 8), i8),
- RankedTensorType.get((4, 8), f32))
- def test_i8i8f32_matmul_unsigned(lhs, rhs, init_result):
- return matmul_unsigned_poly(lhs, rhs, outs=[init_result])
-
- # CHECK-LABEL: @test_f16f16f32_matmul
- # CHECK: ^{{.*}}(%[[A_ARG:.+]]: f16, %[[B_ARG:.+]]: f16, %[[C_ARG:.+]]: f32)
- # CHECK-NEXT: %[[A_CAST:.+]] = arith.extf %[[A_ARG]] : f16 to f32
- # CHECK-NEXT: %[[B_CAST:.+]] = arith.extf %[[B_ARG]] : f16 to f32
- # CHECK-NEXT: %[[MUL:.+]] = arith.mulf %[[A_CAST]], %[[B_CAST]] : f32
- # CHECK-NEXT: %[[ADD:.+]] = arith.addf %[[C_ARG]], %[[MUL]] : f32
- # CHECK-NEXT: linalg.yield %[[ADD]] : f32
- # CHECK-NEXT: -> tensor<4x8xf32>
- @builtin.FuncOp.from_py_func(
- RankedTensorType.get((4, 16), f16), RankedTensorType.get((16, 8), f16),
- RankedTensorType.get((4, 8), f32))
- def test_f16f16f32_matmul(lhs, rhs, init_result):
- return matmul_poly(lhs, rhs, outs=[init_result])
-
- # CHECK-LABEL: @test_f64f64f32_matmul
- # CHECK: ^{{.*}}(%[[A_ARG:.+]]: f64, %[[B_ARG:.+]]: f64, %[[C_ARG:.+]]: f32)
- # CHECK-NEXT: %[[A_CAST:.+]] = arith.truncf %[[A_ARG]] : f64 to f32
- # CHECK-NEXT: %[[B_CAST:.+]] = arith.truncf %[[B_ARG]] : f64 to f32
- # CHECK-NEXT: %[[MUL:.+]] = arith.mulf %[[A_CAST]], %[[B_CAST]] : f32
- # CHECK-NEXT: %[[ADD:.+]] = arith.addf %[[C_ARG]], %[[MUL]] : f32
- # CHECK-NEXT: linalg.yield %[[ADD]] : f32
- # CHECK-NEXT: -> tensor<4x8xf32>
- @builtin.FuncOp.from_py_func(
- RankedTensorType.get((4, 16), f64), RankedTensorType.get((16, 8), f64),
- RankedTensorType.get((4, 8), f32))
- def test_f64f64f32_matmul(lhs, rhs, init_result):
- return matmul_poly(lhs, rhs, outs=[init_result])
-
- # CHECK-LABEL: @test_f32i32_conv
- # CHECK: linalg.generic
- # CHECK-SAME: indexing_maps = [#[[$CONV_MAP_I]], #[[$CONV_MAP_K]], #[[$CONV_MAP_O]]]
- # CHECK-SAME: iterator_types = ["parallel", "parallel", "parallel", "reduction", "reduction", "parallel"]
- # CHECK: ^{{.*}}(%[[IN:.+]]: f32, %[[FILTER:.+]]: f32, %[[OUT:.+]]: i32)
- # CHECK-NEXT: %[[IN_CAST:.+]] = arith.fptosi %[[IN:.+]] : f32 to i32
- # CHECK-NEXT: %[[FILTER_CAST:.+]] = arith.fptosi %[[FILTER:.+]] : f32 to i32
- # CHECK-NEXT: %[[PROD:.+]] = arith.muli %[[IN_CAST]], %[[FILTER_CAST]] : i32
- # CHECK-NEXT: %[[SUM:.+]] = arith.addi %[[OUT]], %[[PROD]] : i32
- # CHECK-NEXT: linalg.yield %[[SUM]] : i32
- # CHECK-NEXT: -> tensor<2x4xi32>
- @builtin.FuncOp.from_py_func(
- RankedTensorType.get((4, 16), f32), RankedTensorType.get((2, 2, 1),
- f32),
- RankedTensorType.get((2, 4), i32))
- def test_f32i32_conv(input, filter, init_result):
- return conv_poly(
- input, filter, outs=[init_result], strides=[2, 4], dilations=[1, 2])
-
- # CHECK-LABEL: @test_f32i32_max_pooling
- # CHECK: linalg.generic
- # CHECK-SAME: indexing_maps = [#[[$CONV_MAP_I]], #[[$POOL_MAP_K]], #[[$CONV_MAP_O]]]
- # CHECK-SAME: iterator_types = ["parallel", "parallel", "parallel", "reduction", "reduction", "parallel"]
- # CHECK: ^{{.*}}(%[[IN:.+]]: f32, %[[SHAPE:.+]]: f32, %[[OUT:.+]]: i32)
- # CHECK-NEXT: %[[IN_CAST:.+]] = arith.fptosi %[[IN:.+]] : f32 to i32
- # CHECK-NEXT: %[[MAX:.+]] = arith.maxsi %[[OUT]], %[[IN_CAST:.+]] : i32
- # CHECK-NEXT: linalg.yield %[[MAX]] : i32
- # CHECK-NEXT: -> tensor<2x4xi32>
- @builtin.FuncOp.from_py_func(
- RankedTensorType.get((4, 16), f32), RankedTensorType.get((2, 2), f32),
- RankedTensorType.get((2, 4), i32))
- def test_f32i32_max_pooling(input, shape, init_result):
- return pooling_max_poly(
- input, shape, outs=[init_result], strides=[2, 4], dilations=[1, 2])
-
- # CHECK-LABEL: @test_f32i32_max_unsigned_pooling
- # CHECK: = arith.fptoui
- # CHECK: = arith.maxui
- @builtin.FuncOp.from_py_func(
- RankedTensorType.get((4, 16), f32), RankedTensorType.get((2, 2), f32),
- RankedTensorType.get((2, 4), i32))
- def test_f32i32_max_unsigned_pooling(input, shape, init_result):
- return pooling_max_unsigned_poly(
- input, shape, outs=[init_result], strides=[2, 4], dilations=[1, 2])
-
- # CHECK-LABEL: @test_f32f32_max_pooling
- # CHECK: linalg.generic
- # CHECK-SAME: indexing_maps = [#[[$CONV_MAP_I]], #[[$POOL_MAP_K]], #[[$CONV_MAP_O]]]
- # CHECK-SAME: iterator_types = ["parallel", "parallel", "parallel", "reduction", "reduction", "parallel"]
- # CHECK: ^{{.*}}(%[[IN:.+]]: f32, %[[SHAPE:.+]]: f32, %[[OUT:.+]]: f32)
- # CHECK-NEXT: %[[MAX:.+]] = arith.maxf %[[OUT]], %[[IN:.+]] : f32
- # CHECK-NEXT: linalg.yield %[[MAX]] : f32
- # CHECK-NEXT: -> tensor<2x4xf32>
- @builtin.FuncOp.from_py_func(
- RankedTensorType.get((4, 16), f32), RankedTensorType.get((2, 2), f32),
- RankedTensorType.get((2, 4), f32))
- def test_f32f32_max_pooling(input, shape, init_result):
- return pooling_max_poly(
- input, shape, outs=[init_result], strides=[2, 4], dilations=[1, 2])
-
- # CHECK-LABEL: @test_f32i32_min_pooling
- # CHECK: = arith.fptosi
- # CHECK: = arith.minsi
- @builtin.FuncOp.from_py_func(
- RankedTensorType.get((4, 16), f32), RankedTensorType.get((2, 2), f32),
- RankedTensorType.get((2, 4), i32))
- def test_f32i32_min_pooling(input, shape, init_result):
- return pooling_min_poly(
- input, shape, outs=[init_result], strides=[2, 4], dilations=[1, 2])
-
- # CHECK-LABEL: @test_f32i32_min_unsigned_pooling
- # CHECK: = arith.fptoui
- # CHECK: = arith.minui
- @builtin.FuncOp.from_py_func(
- RankedTensorType.get((4, 16), f32), RankedTensorType.get((2, 2), f32),
- RankedTensorType.get((2, 4), i32))
- def test_f32i32_min_unsigned_pooling(input, shape, init_result):
- return pooling_min_unsigned_poly(
- input, shape, outs=[init_result], strides=[2, 4], dilations=[1, 2])
-
- # CHECK-LABEL: @test_f32f32_min_pooling
- # CHECK: = arith.minf
- @builtin.FuncOp.from_py_func(
- RankedTensorType.get((4, 16), f32), RankedTensorType.get((2, 2), f32),
- RankedTensorType.get((2, 4), f32))
- def test_f32f32_min_pooling(input, shape, init_result):
- return pooling_min_poly(
- input, shape, outs=[init_result], strides=[2, 4], dilations=[1, 2])
-
- # CHECK-LABEL: @test_i32_fill_rng
- # CHECK: ^{{.*}}(%[[MIN:.+]]: f64, %[[MAX:.+]]: f64, %[[SEED:.+]]: i32, %{{.*}}
- # CHECK-DAG: %[[IDX0:.+]] = linalg.index 0 : index
- # CHECK-DAG: %[[IDX0_CAST:.+]] = arith.index_cast %[[IDX0]] : index to i32
- # CHECK-DAG: %[[RND0:.+]] = arith.addi %[[IDX0_CAST]], %[[SEED]] : i32
- # CHECK-DAG: %[[CST0:.+]] = arith.constant 1103515245 : i64
- # CHECK-DAG: %[[CST0_CAST:.+]] = arith.trunci %[[CST0]] : i64 to i32
- # Skip the remaining random number computation and match the scaling logic.
- # CHECK-DAG: %[[DIFF:.+]] = arith.subf %[[MAX]], %[[MIN]] : f64
- # CHECK-DAG: %[[CST3:.+]] = arith.constant 2.3283063999999999E-10 : f64
- # CHECK-DAG: %[[FACT:.+]] = arith.mulf %[[DIFF]], %[[CST3]] : f64
- # CHECK-DAG: %[[RND4:.+]] = arith.mulf %{{.+}}, %[[FACT]] : f64
- # CHECK-DAG: %[[RND5:.+]] = arith.addf %[[RND4]], %[[MIN]] : f64
- # CHECK-DAG: %{{.*}} = arith.fptosi %[[RND5]] : f64 to i32
- @builtin.FuncOp.from_py_func(f64, f64, i32,
- RankedTensorType.get((4, 16), i32))
- def test_i32_fill_rng(min, max, seed, init_result):
- return fill_rng_poly(min, max, seed, outs=[init_result])
-
- # CHECK-LABEL: @test_f32_soft_plus
- # CHECK: ^{{.*}}(%[[IN:.+]]: f32, %[[OUT:.+]]: f32)
- # CHECK-NEXT: %[[C1:.+]] = arith.constant 1.000000e+00 : f64
- # CHECK-NEXT: %[[C1_CAST:.+]] = arith.truncf %[[C1]] : f64 to f32
- # CHECK-NEXT: %[[EXP:.+]] = math.exp %[[IN]] : f32
- # CHECK-NEXT: %[[SUM:.+]] = arith.addf %[[C1_CAST]], %[[EXP]] : f32
- # CHECK-NEXT: %[[LOG:.+]] = math.log %[[SUM]] : f32
- # CHECK-NEXT: linalg.yield %[[LOG]] : f32
- # CHECK-NEXT: -> tensor<4x16xf32>
- @builtin.FuncOp.from_py_func(
- RankedTensorType.get((4, 16), f32), RankedTensorType.get((4, 16), f32))
- def test_f32_soft_plus(input, init_result):
- return soft_plus_poly(input, outs=[init_result])
-
- # Just check that we don't assert out on name mismatch.
- # CHECK-LABEL: @test_non_default_op_name
- @builtin.FuncOp.from_py_func(
- RankedTensorType.get((42,), f32), RankedTensorType.get((42,), f32))
- def test_non_default_op_name(input, init_result):
- return non_default_op_name(input, outs=[init_result])
-
-
-# TODO: Fix me! Conv and pooling ops above do not verify, which was uncovered
-# when switching to more robust module verification. For now, reverting to the
-# old behavior which does not verify on module print.
-print(module.operation.get_asm(assume_verified=True))
