e9085d0d25
The revision renames the following OpDSL functions: ``` TypeFn.cast -> TypeFn.cast_signed BinaryFn.min -> BinaryFn.min_signed BinaryFn.max -> BinaryFn.max_signed ``` The corresponding enum values on the C++ side are renamed accordingly: ``` #linalg.type_fn<cast> -> #linalg.type_fn<cast_signed> #linalg.binary_fn<min> -> #linalg.binary_fn<min_signed> #linalg.binary_fn<max> -> #linalg.binary_fn<max_signed> ``` Depends On D120110 Reviewed By: aartbik Differential Revision: https://reviews.llvm.org/D120562
193 lines
6.8 KiB
Python
193 lines
6.8 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 import arith
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from mlir.dialects.linalg.opdsl.lang import *
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def run(f):
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print("\nTEST:", f.__name__)
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f()
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return f
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# CHECK-LABEL: TEST: testInitTensor
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@run
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def testInitTensor():
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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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with InsertionPoint(module.body):
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# CHECK-LABEL: func @static_sizes
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# CHECK: %0 = linalg.init_tensor [3, 4] : tensor<3x4xf32>
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@builtin.FuncOp.from_py_func()
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def static_sizes():
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return linalg.InitTensorOp([3, 4], f32)
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# CHECK-LABEL: func @dynamic_sizes
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# CHECK: %0 = linalg.init_tensor [%arg0, %arg1] : tensor<?x?xf32>
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@builtin.FuncOp.from_py_func(IndexType.get(), IndexType.get())
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def dynamic_sizes(d0, d1):
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return linalg.InitTensorOp([d0, d1], f32)
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# CHECK-LABEL: func @zero_d
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# CHECK: %0 = linalg.init_tensor [] : tensor<f32>
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@builtin.FuncOp.from_py_func()
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def zero_d():
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return linalg.InitTensorOp([], f32)
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print(module)
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# CHECK-LABEL: TEST: testInitTensorStaticSizesAttribute
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@run
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def testInitTensorStaticSizesAttribute():
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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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with InsertionPoint(module.body):
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op = linalg.InitTensorOp([3, 4], f32)
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# CHECK: [3, 4]
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print(op.attributes["static_sizes"])
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# CHECK-LABEL: TEST: testFill
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@run
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def testFill():
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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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with InsertionPoint(module.body):
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# CHECK-LABEL: func @fill_tensor
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# CHECK-SAME: %[[OUT:[0-9a-z]+]]: tensor<12x?xf32>
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# CHECK-NEXT: %[[CST:.*]] = arith.constant 0.0{{.*}} : f32
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# CHECK-NEXT: %[[RES:.*]] = linalg.fill(%[[CST]], %[[OUT]]) : f32, tensor<12x?xf32> -> tensor<12x?xf32>
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# CHECK-NEXT: return %[[RES]] : tensor<12x?xf32>
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@builtin.FuncOp.from_py_func(RankedTensorType.get((12, -1), f32))
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def fill_tensor(out):
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zero = arith.ConstantOp(value=FloatAttr.get(f32, 0.), result=f32).result
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return linalg.FillOp(output=out, value=zero).result
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# CHECK-LABEL: func @fill_buffer
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# CHECK-SAME: %[[OUT:[0-9a-z]+]]: memref<12x?xf32>
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# CHECK-NEXT: %[[CST:.*]] = arith.constant 0.0{{.*}} : f32
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# CHECK-NEXT: linalg.fill(%[[CST]], %[[OUT]]) : f32, memref<12x?xf32>
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# CHECK-NEXT: return
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@builtin.FuncOp.from_py_func(MemRefType.get((12, -1), f32))
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def fill_buffer(out):
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zero = arith.ConstantOp(value=FloatAttr.get(f32, 0.), result=f32).result
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linalg.FillOp(output=out, value=zero)
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print(module)
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# CHECK-LABEL: TEST: testNamedStructuredOpCustomForm
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@run
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def testNamedStructuredOpCustomForm():
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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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with InsertionPoint(module.body):
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@builtin.FuncOp.from_py_func(
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RankedTensorType.get((4, 8), f32), RankedTensorType.get((4, 8), f32))
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def named_form(lhs, rhs):
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init_result = linalg.InitTensorOp([4, 8], f32)
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# Check for the named form with custom format
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# CHECK: linalg.elemwise_unary
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# CHECK-SAME: cast = #linalg.type_fn<cast_signed>
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# CHECK-SAME: fun = #linalg.unary_fn<exp>
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# CHECK-SAME: ins(%{{.*}} : tensor<4x8xf32>) outs(%{{.*}} : tensor<4x8xf32>)
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unary_result = linalg.elemwise_unary(lhs, outs=[init_result.result])
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# CHECK: linalg.elemwise_binary
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# CHECK-SAME: cast = #linalg.type_fn<cast_unsigned>
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# CHECK-SAME: fun = #linalg.binary_fn<mul>
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# CHECK-SAME: ins(%{{.*}}, %{{.*}} : tensor<4x8xf32>, tensor<4x8xf32>) outs(%{{.*}} : tensor<4x8xf32>)
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# CHECK: return
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binary_result = linalg.elemwise_binary(
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lhs,
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rhs,
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outs=[init_result.result],
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fun=BinaryFn.mul,
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cast=TypeFn.cast_unsigned)
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return unary_result, binary_result
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print(module)
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# CHECK-LABEL: TEST: testNamedStructuredOpGenericForm
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@run
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def testNamedStructuredOpGenericForm():
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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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with InsertionPoint(module.body):
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@builtin.FuncOp.from_py_func(
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RankedTensorType.get((4, 16), f32), RankedTensorType.get((16, 8),
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f32))
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def named_form(lhs, rhs):
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init_result = linalg.InitTensorOp([4, 8], f32)
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# CHECK: "linalg.matmul"(%{{.*}})
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# CHECK-NEXT: ^bb0(%{{.*}}: f32, %{{.*}}: f32, %{{.*}}: f32):
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# CHECK-NEXT: arith.mulf{{.*}} (f32, f32) -> f32
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# CHECK-NEXT: arith.addf{{.*}} (f32, f32) -> f32
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# CHECK-NEXT: linalg.yield{{.*}} (f32) -> ()
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# CHECK-NEXT: cast = #linalg.type_fn<cast_signed>
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# CHECK-SAME: operand_segment_sizes = dense<[2, 1]> : vector<2xi32>
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# CHECK-SAME: (tensor<4x16xf32>, tensor<16x8xf32>, tensor<4x8xf32>) -> tensor<4x8xf32>
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return linalg.matmul(lhs, rhs, outs=[init_result.result])
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module.operation.print(print_generic_op_form=True)
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# CHECK-LABEL: TEST: testNamedStructuredAsGenericOp
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@run
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def testNamedStructuredAsGenericOp():
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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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with InsertionPoint(module.body):
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@builtin.FuncOp.from_py_func(
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RankedTensorType.get((4, 16), f32), RankedTensorType.get((16, 8),
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f32))
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def generic_form(lhs, rhs):
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init_result = linalg.InitTensorOp([4, 8], f32)
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# CHECK: linalg.generic
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return linalg.matmul(
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lhs, rhs, outs=[init_result.result], emit_generic=True)
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print(module)
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# CHECK-LABEL: TEST: testOpResultFromOtherOp
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@run
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def testOpResultFromOtherOp():
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with Context(), Location.unknown():
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module = Module.create()
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f32 = F32Type.get()
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with InsertionPoint(module.body):
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@builtin.FuncOp.from_py_func(
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RankedTensorType.get((4, 16), f32), RankedTensorType.get((16, 8),
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f32))
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def pass_an_op_directly(arg0, arg1):
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one = arith.ConstantOp(F32Type.get(), 1.0)
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# CHECK: %[[LHS:.*]] = linalg.fill
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lhs = linalg.FillOp(arg0, one)
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# CHECK: %[[RHS:.*]] = linalg.fill
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rhs = linalg.FillOp(arg1, one)
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# CHECK: %[[INIT:.*]] = linalg.init_tensor
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init = linalg.InitTensorOp([4, 8], f32)
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# CHECK: linalg.matmul
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# CHECK: ins(%[[LHS]], %[[RHS]]
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# CHECK: outs(%[[INIT]]
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return linalg.matmul(lhs, rhs, outs=init)
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print(module)
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