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Add NumpyPublicFunctionsToTensor pass. * Rewrites public function signatures to operate on tensors (vs ndarray). * Most of our backends presume immutable tensors at public function boundaries.
2020-07-09 13:51:54 +08:00
// RUN: npcomp-opt -split-input-file %s -verify-diagnostics -allow-unregistered-dialect -numpy-public-functions-to-tensor | FileCheck --dump-input=fail %s
// CHECK-LABEL: legalConversion
module @legalConversion {
// CHECK: @f(%arg0: tensor<3x?xf32>, %arg1: i32, %arg2: tensor<*xf32>) -> (i32, tensor<3x?xf32>, tensor<*xf32>)
func @f(%arg0: !numpy.ndarray<[3,?]:f32>, %arg1: i32, %arg2: !numpy.ndarray<*:f32>) ->
(i32, !numpy.ndarray<[3,?]:f32>, !numpy.ndarray<*:f32>) {
// CHECK: %[[CREATE0:.+]] = numpy.create_array_from_tensor %arg0
// CHECK: %[[CREATE1:.+]] = numpy.create_array_from_tensor %arg2
// CHECK: %[[R0:.+]] = "unfoldable_arg0"(%[[CREATE0]]) : (!numpy.ndarray<[3,?]:f32>) -> !numpy.ndarray<[3,?]:f32>
// CHECK: %[[R1:.+]] = "unfoldable_arg1"(%[[CREATE1]]) : (!numpy.ndarray<*:f32>) -> !numpy.ndarray<*:f32>
%0 = "unfoldable_arg0"(%arg0) : (!numpy.ndarray<[3,?]:f32>) -> !numpy.ndarray<[3,?]:f32>
%1 = "unfoldable_arg1"(%arg2) : (!numpy.ndarray<*:f32>) -> !numpy.ndarray<*:f32>
// CHECK: %[[COPY0:.+]] = numpy.copy_to_tensor %[[R0]]
// CHECK: %[[COPY1:.+]] = numpy.copy_to_tensor %[[R1]]
// CHECK: return %arg1, %[[COPY0]], %[[COPY1]] : i32, tensor<3x?xf32>, tensor<*xf32>
return %arg1, %0, %1 : i32, !numpy.ndarray<[3,?]:f32>, !numpy.ndarray<*:f32>
}
}
// -----
// CHECK-LABEL: @nonPublic
module @nonPublic {
// CHECK: @f(%arg0: !numpy.ndarray<[3,?]:f32>) -> !numpy.ndarray<[3,?]:f32>
Bump llvm-project to be7352c00d51f4358db3a23ed6a077f7cb48eafd - TensorFromElementsOp -> tensor::FromElementsOp - `cmpi "eq", ...` -> `cmpi eq, ...`. Same for `cmpf` - syntax change for private func ops - some changes to the python bindings
2021-01-21 10:08:28 +08:00
func private @f(%arg0: !numpy.ndarray<[3,?]:f32>) -> (!numpy.ndarray<[3,?]:f32>) {
Add NumpyPublicFunctionsToTensor pass. * Rewrites public function signatures to operate on tensors (vs ndarray). * Most of our backends presume immutable tensors at public function boundaries.
2020-07-09 13:51:54 +08:00
return %arg0 : !numpy.ndarray<[3,?]:f32>
}
}
// -----
// CHECK-LABEL: @called
module @called {
// CHECK: @f(%arg0: !numpy.ndarray<*:f32>) -> !numpy.ndarray<*:f32>
// expected-warning @+1 {{unimplemented: cannot convert}}
func @f(%arg0: !numpy.ndarray<*:f32>) -> !numpy.ndarray<*:f32> {
return %arg0 : !numpy.ndarray<*:f32>
}
Bump llvm-project to be7352c00d51f4358db3a23ed6a077f7cb48eafd - TensorFromElementsOp -> tensor::FromElementsOp - `cmpi "eq", ...` -> `cmpi eq, ...`. Same for `cmpf` - syntax change for private func ops - some changes to the python bindings
2021-01-21 10:08:28 +08:00
func private @caller(%arg0: !numpy.ndarray<*:f32>) -> !numpy.ndarray<*:f32> {
Add NumpyPublicFunctionsToTensor pass. * Rewrites public function signatures to operate on tensors (vs ndarray). * Most of our backends presume immutable tensors at public function boundaries.
2020-07-09 13:51:54 +08:00
%0 = call @f(%arg0) : (!numpy.ndarray<*:f32>) -> !numpy.ndarray<*:f32>
return %0 : !numpy.ndarray<*:f32>
}
}