mirror of https://github.com/llvm/torch-mlir
54 lines
2.0 KiB
MLIR
54 lines
2.0 KiB
MLIR
// RUN: torch-mlir-opt -torch-verify-linalg-on-tensors-backend-contract -split-input-file -verify-diagnostics -allow-unregistered-dialect %s | FileCheck %s
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// CHECK: func.func @mm
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func.func @mm(%arg0: tensor<?x?xf32>, %arg1: tensor<?x?xf32>) -> tensor<?x?xf32> {
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%c0 = arith.constant 0 : index
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%c1 = arith.constant 1 : index
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%cst = arith.constant 0.000000e+00 : f32
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%0 = tensor.dim %arg0, %c0 : tensor<?x?xf32>
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%1 = tensor.dim %arg0, %c1 : tensor<?x?xf32>
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%2 = tensor.dim %arg1, %c0 : tensor<?x?xf32>
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%3 = tensor.dim %arg1, %c1 : tensor<?x?xf32>
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%4 = arith.cmpi eq, %1, %2 : index
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cf.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 ins(%cst : f32) outs(%5 : 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 the linalg-on-tensors backend contract.}}
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module {
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func.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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// `!torch.tensor` 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 the linalg-on-tensors backend contract.}}
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module {
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func.func @disallowed(%arg0: !torch.tensor) -> !torch.tensor {
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// expected-error@+1 {{failed to legalize operation 'func.return'}}
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return %arg0 : !torch.tensor
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}
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}
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