mirror of https://github.com/llvm/torch-mlir
42 lines
2.5 KiB
MLIR
42 lines
2.5 KiB
MLIR
// RUN: npcomp-opt -torch-inline-global-slots -split-input-file %s | FileCheck %s
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// CHECK-NOT: @readonly
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torch.global_slot "private" @readonly : !numpy.ndarray<*:!numpy.any_dtype> {
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%cst = constant dense<0.0> : tensor<1xf32>
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%0 = numpy.create_array_from_tensor %cst : (tensor<1xf32>) -> !numpy.ndarray<*:!numpy.any_dtype>
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torch.global_slot.init %0 : !numpy.ndarray<*:!numpy.any_dtype>
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}
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// CHECK-LABEL: torch.global_slot @public
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torch.global_slot @public : !numpy.ndarray<*:!numpy.any_dtype> {
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%cst = constant dense<0.0> : tensor<2xf32>
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%0 = numpy.create_array_from_tensor %cst : (tensor<2xf32>) -> !numpy.ndarray<*:!numpy.any_dtype>
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torch.global_slot.init %0 : !numpy.ndarray<*:!numpy.any_dtype>
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}
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// CHECK-LABEL: torch.global_slot "private" @mutated
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torch.global_slot "private" @mutated : !numpy.ndarray<*:!numpy.any_dtype> {
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%cst = constant dense<0.0> : tensor<3xf32>
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%0 = numpy.create_array_from_tensor %cst : (tensor<3xf32>) -> !numpy.ndarray<*:!numpy.any_dtype>
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torch.global_slot.init %0 : !numpy.ndarray<*:!numpy.any_dtype>
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}
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// CHECK-LABEL: func @forward() -> (!numpy.ndarray<*:!numpy.any_dtype>, !numpy.ndarray<*:!numpy.any_dtype>, !numpy.ndarray<*:!numpy.any_dtype>) {
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func @forward() -> (!numpy.ndarray<*:!numpy.any_dtype>, !numpy.ndarray<*:!numpy.any_dtype>, !numpy.ndarray<*:!numpy.any_dtype>) {
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// Inlined.
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// CHECK: %[[CST:.*]] = constant dense<0.000000e+00> : tensor<1xf32>
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// CHECK: %[[ARRAY_CST:.*]] = numpy.create_array_from_tensor %[[CST]] : (tensor<1xf32>) -> !numpy.ndarray<*:!numpy.any_dtype>
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%0 = torch.global_slot.get @readonly : !numpy.ndarray<*:!numpy.any_dtype>
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// Not inlined: potentially mutated by externals.
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// CHECK: %[[PUBLIC:.*]] = torch.global_slot.get @public : !numpy.ndarray<*:!numpy.any_dtype>
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%1 = torch.global_slot.get @public : !numpy.ndarray<*:!numpy.any_dtype>
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// Not inlined: potentially mutated internally.
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// CHECK: torch.global_slot.set @mutated = %[[ARRAY_CST]] : !numpy.ndarray<*:!numpy.any_dtype>
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// CHECK: %[[MUTATED:.*]] = torch.global_slot.get @mutated : !numpy.ndarray<*:!numpy.any_dtype>
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torch.global_slot.set @mutated = %0 : !numpy.ndarray<*:!numpy.any_dtype>
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%2 = torch.global_slot.get @mutated : !numpy.ndarray<*:!numpy.any_dtype>
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// CHECK: return %[[ARRAY_CST]], %[[PUBLIC]], %[[MUTATED]] : !numpy.ndarray<*:!numpy.any_dtype>, !numpy.ndarray<*:!numpy.any_dtype>, !numpy.ndarray<*:!numpy.any_dtype>
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return %0, %1, %2 : !numpy.ndarray<*:!numpy.any_dtype>, !numpy.ndarray<*:!numpy.any_dtype>, !numpy.ndarray<*:!numpy.any_dtype>
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}
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