mirror of https://github.com/llvm/torch-mlir
33 lines
1.3 KiB
MLIR
33 lines
1.3 KiB
MLIR
// RUN: npcomp-run-mlir %s \
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// RUN: -invoke matmul \
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// RUN: -arg-value="dense<[[1.0, 0.0, 1.0], [1.0, 1.0, 1.0]]> : tensor<2x3xf32>" \
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// RUN: -arg-value="dense<[[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]]> : tensor<3x2xf32>" \
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// RUN: -shared-libs=%npcomp_runtime_shlib 2>&1 \
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// RUN: | FileCheck %s
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// RUN: npcomp-run-mlir %s \
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// RUN: -invoke matmul \
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// RUN: -arg-value="dense<0.0> : tensor<2x3xf32>" \
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// RUN: -arg-value="dense<0.0> : tensor<3x2xf32>" \
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// RUN: -shared-libs=%npcomp_runtime_shlib 2>&1 \
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// RUN: | FileCheck %s --check-prefix=ZEROS
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// Basic correctness check:
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// [1 0 1] * [1 2] = [6 8]
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// [1 1 1] [3 4] [9 12]
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// [5 6]
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// CHECK: output #0: dense<[
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// CHECK-SAME: [6.000000e+00, 8.000000e+00], [9.000000e+00, 1.200000e+01]
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// CHECK-SAME: ]> : tensor<2x2xf32>
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// Check with zeros as well. The result should be identically zeros.
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// If any uninitialized data sneaks in (even very small values that would be
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// rounding errors for the test case above), it will show up here.
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// ZEROS: output #0: dense<0.000000e+00> : tensor<2x2xf32>
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func @matmul(%arg0: tensor<?x?xf32>, %arg1: tensor<?x?xf32>) -> tensor<?x?xf32> {
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%0 = tcf.matmul %arg0, %arg1 : (tensor<?x?xf32>, tensor<?x?xf32>) -> tensor<?x?xf32>
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return %0 : tensor<?x?xf32>
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}
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