mirror of https://github.com/llvm/torch-mlir
68 lines
2.7 KiB
MLIR
68 lines
2.7 KiB
MLIR
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// RUN: npcomp-run-mlir %s \
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// RUN: -invoke conv_2d_nchw \
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// RUN: -arg-value="dense<0.0> : tensor<2x1x1x1xf32>" \
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// RUN: -arg-value="dense<0.0> : tensor<1x1x1x1xf32>" \
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// RUN: -shared-libs=%npcomp_runtime_shlib 2>&1 \
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// RUN: | FileCheck %s --check-prefix=BATCH
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// RUN: npcomp-run-mlir %s \
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// RUN: -invoke conv_2d_nchw \
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// RUN: -arg-value="dense<0.0> : tensor<1x2x1x1xf32>" \
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// RUN: -arg-value="dense<0.0> : tensor<2x2x1x1xf32>" \
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// RUN: -shared-libs=%npcomp_runtime_shlib 2>&1 \
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// RUN: | FileCheck %s --check-prefix=SAME_CHANNELS
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// RUN: npcomp-run-mlir %s \
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// RUN: -invoke conv_2d_nchw \
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// RUN: -arg-value="dense<0.0> : tensor<1x2x1x1xf32>" \
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// RUN: -arg-value="dense<0.0> : tensor<1x2x1x1xf32>" \
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// RUN: -shared-libs=%npcomp_runtime_shlib 2>&1 \
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// RUN: | FileCheck %s --check-prefix=DIFFERENT_CHANNELS
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// RUN: npcomp-run-mlir %s \
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// RUN: -invoke conv_2d_nchw \
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// RUN: -arg-value="dense<0.0> : tensor<1x1x2x2xf32>" \
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// RUN: -arg-value="dense<0.0> : tensor<1x1x1x1xf32>" \
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// RUN: -shared-libs=%npcomp_runtime_shlib 2>&1 \
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// RUN: | FileCheck %s --check-prefix=TINY_SQUARE
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// RUN: npcomp-run-mlir %s \
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// RUN: -invoke conv_2d_nchw \
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// RUN: -arg-value="dense<0.0> : tensor<1x1x32x32xf32>" \
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// RUN: -arg-value="dense<0.0> : tensor<1x1x32x32xf32>" \
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// RUN: -shared-libs=%npcomp_runtime_shlib 2>&1 \
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// RUN: | FileCheck %s --check-prefix=HUGE_SQUARE
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// RUN: npcomp-run-mlir %s \
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// RUN: -invoke conv_2d_nchw \
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// RUN: -arg-value="dense<0.0> : tensor<1x1x2x2xf32>" \
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// RUN: -arg-value="dense<0.0> : tensor<1x1x0x0xf32>" \
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// RUN: -shared-libs=%npcomp_runtime_shlib 2>&1 \
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// RUN: | FileCheck %s --check-prefix=ZERO_KH_KW
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// RUN: npcomp-run-mlir %s \
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// RUN: -invoke conv_2d_nchw \
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// RUN: -arg-value="dense<0.0> : tensor<1x1x0x0xf32>" \
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// RUN: -arg-value="dense<0.0> : tensor<1x1x0x0xf32>" \
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// RUN: -shared-libs=%npcomp_runtime_shlib 2>&1 \
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// RUN: | FileCheck %s --check-prefix=ZERO_H_W
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// BATCH: output #0: dense<0.000000e+00> : tensor<2x1x1x1xf32>
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// SAME_CHANNELS: output #0: dense<0.000000e+00> : tensor<1x2x1x1xf32>
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// DIFFERENT_CHANNELS: output #0: dense<0.000000e+00> : tensor<1x1x1x1xf32>
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// TINY_SQUARE: output #0: dense<0.000000e+00> : tensor<1x1x2x2xf32>
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// HUGE_SQUARE: output #0: dense<0.000000e+00> : tensor<1x1x1x1xf32>
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// ZERO_KH_KW: output #0: dense<0.000000e+00> : tensor<1x1x3x3xf32>
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// ZERO_H_W: output #0: dense<0.000000e+00> : tensor<1x1x1x1xf32>
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func @conv_2d_nchw(%arg0: tensor<?x?x?x?xf32>, %arg1: tensor<?x?x?x?xf32>) -> tensor<?x?x?x?xf32> {
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%0 = tcf.conv_2d_nchw %arg0, %arg1 : (tensor<?x?x?x?xf32>, tensor<?x?x?x?xf32>) -> tensor<?x?x?x?xf32>
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return %0 : tensor<?x?x?x?xf32>
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}
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