mirror of https://github.com/llvm/torch-mlir
54 lines
2.0 KiB
MLIR
54 lines
2.0 KiB
MLIR
// RUN: npcomp-opt -npcomp-verify-backend-contract -split-input-file -verify-diagnostics -allow-unregistered-dialect %s | FileCheck %s
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// CHECK: func @mm
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func @mm(%arg0: tensor<?x?xf32>, %arg1: tensor<?x?xf32>) -> tensor<?x?xf32> attributes {iree.module.export} {
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%c0 = constant 0 : index
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%c1 = constant 1 : index
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%cst = constant 0.000000e+00 : f32
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%0 = memref.dim %arg0, %c0 : tensor<?x?xf32>
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%1 = memref.dim %arg0, %c1 : tensor<?x?xf32>
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%2 = memref.dim %arg1, %c0 : tensor<?x?xf32>
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%3 = memref.dim %arg1, %c1 : tensor<?x?xf32>
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%4 = cmpi eq, %1, %2 : index
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assert %4, "mismatching contracting dimension for aten.mm"
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%5 = linalg.init_tensor [%0, %3] : tensor<?x?xf32>
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%6 = linalg.fill(%cst, %5) : f32, tensor<?x?xf32> -> tensor<?x?xf32>
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%7 = linalg.matmul ins(%arg0, %arg1 : tensor<?x?xf32>, tensor<?x?xf32>) outs(%6 : tensor<?x?xf32>) -> tensor<?x?xf32>
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return %7 : tensor<?x?xf32>
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}
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// -----
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// Basic check of error reporting.
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// expected-error@+1 {{Module does not conform to npcomp's backend contract.}}
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module {
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func @disallowed() {
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// expected-error@+1 {{failed to legalize operation 'unknown_dialect.unknown_op'}}
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"unknown_dialect.unknown_op"() : () -> ()
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return
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}
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}
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// -----
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// TODO: Improve these errors to give more exact reporting.
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//
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// The reporting we inherit from dialect conversion is not precise.
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// For example, here we want it to explicitly call out that
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// `tensor<?x!numpy.any_dtype>` is the problem here, which suggests
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// that type inference didn't succeed, or insufficient type information
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// was available.
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//
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// Ultimately, the output of this pass needs to be conveyed to the user
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// in an understandable way, such as suggesting a particular place where
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// a shape annotation is needed.
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// expected-error@+1 {{Module does not conform to npcomp's backend contract.}}
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module {
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func @disallowed(%arg0: tensor<?x!numpy.any_dtype>) -> tensor<?x!numpy.any_dtype> {
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// expected-error@+1 {{failed to legalize operation 'std.return'}}
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return %arg0 : tensor<?x!numpy.any_dtype>
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}
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}
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