277 lines
14 KiB
MLIR
277 lines
14 KiB
MLIR
// RUN: onnf-opt --shape-inference %s -split-input-file | FileCheck %s
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//===----------------------------------------------------------------------===//
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/// Test the default behavior of transpose when no information for the
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/// permutation of the axes is provided and when a permutation is provided.
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//===----------------------------------------------------------------------===//
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func @test_default_transpose(%arg0 : tensor<5x5x1x32xf32>) -> tensor<*xf32> {
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%0 = "onnx.Transpose"(%arg0) : (tensor<5x5x1x32xf32>) -> tensor<*xf32>
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"std.return"(%0) : (tensor<*xf32>) -> ()
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// CHECK-LABEL: test_default_transpose
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// CHECK: [[RES:%.+]] = "onnx.Transpose"(%arg0) : (tensor<5x5x1x32xf32>) -> tensor<32x1x5x5xf32>
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// CHECK: return [[RES]] : tensor<32x1x5x5xf32>
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}
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/// Test shape inference for transposition when perm attribute is specified.
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func @test_transpose(%arg0 : tensor<5x5x1x32xf32>) -> tensor<*xf32> {
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%0 = "onnx.Transpose"(%arg0) {perm = [2, 0, 3, 1]} : (tensor<5x5x1x32xf32>) -> tensor<*xf32>
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"std.return"(%0) : (tensor<*xf32>) -> ()
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// CHECK-LABEL: test_transpose
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// CHECK: [[RES_ATTR:%.+]] = "onnx.Transpose"(%arg0) {perm = [2, 0, 3, 1]} : (tensor<5x5x1x32xf32>) -> tensor<1x5x32x5xf32>
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// CHECK: return [[RES_ATTR]] : tensor<1x5x32x5xf32>
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}
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//===----------------------------------------------------------------------===//
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/// Test the shape inferencing scheme for the matmul operation.
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//===----------------------------------------------------------------------===//
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/// MatMul: 1-D x 1-D
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func @test_matmul_1(%arg0 : tensor<32xf32>, %arg1 : tensor<32xf32>) -> tensor<*xf32> {
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%0 = "onnx.MatMul"(%arg0, %arg1) : (tensor<32xf32>, tensor<32xf32>) -> tensor<*xf32>
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"std.return"(%0) : (tensor<*xf32>) -> ()
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// CHECK-LABEL: test_matmul_1
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// CHECK: [[RES1:%.+]] = "onnx.MatMul"(%arg0, %arg1) : (tensor<32xf32>, tensor<32xf32>) -> tensor<1xf32>
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// CHECK: return [[RES1]] : tensor<1xf32>
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}
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/// MatMul: K-D x 2-D (K > 2)
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func @test_matmul_2(%arg0 : tensor<16x?x64x42xf32>, %arg1 : tensor<42x32xf32>) -> tensor<*xf32> {
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%0 = "onnx.MatMul"(%arg0, %arg1) : (tensor<16x?x64x42xf32>, tensor<42x32xf32>) -> tensor<*xf32>
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"std.return"(%0) : (tensor<*xf32>) -> ()
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// CHECK-LABEL: test_matmul_2
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// CHECK: [[RES2:%.+]] = "onnx.MatMul"(%arg0, %arg1) : (tensor<16x?x64x42xf32>, tensor<42x32xf32>) -> tensor<16x?x64x32xf32>
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// CHECK: return [[RES2]] : tensor<16x?x64x32xf32>
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}
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/// MatMul: 2-D x K-D (K > 2)
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func @test_matmul_3(%arg0 : tensor<64x42xf32>, %arg1 : tensor<16x?x42x32xf32>) -> tensor<*xf32> {
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%0 = "onnx.MatMul"(%arg0, %arg1) : (tensor<64x42xf32>, tensor<16x?x42x32xf32>) -> tensor<*xf32>
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"std.return"(%0) : (tensor<*xf32>) -> ()
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// CHECK-LABEL: test_matmul_3
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// CHECK: [[RES3:%.+]] = "onnx.MatMul"(%arg0, %arg1) : (tensor<64x42xf32>, tensor<16x?x42x32xf32>) -> tensor<16x?x64x32xf32>
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// CHECK: return [[RES3]] : tensor<16x?x64x32xf32>
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}
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/// MatMul: 2-D x K-D (K > 2)
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func @test_matmul_4(%arg0 : tensor<64x42xf32>, %arg1 : tensor<?x?x?x?xf32>) -> tensor<*xf32> {
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%0 = "onnx.MatMul"(%arg0, %arg1) : (tensor<64x42xf32>, tensor<?x?x?x?xf32>) -> tensor<*xf32>
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"std.return"(%0) : (tensor<*xf32>) -> ()
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// CHECK-LABEL: test_matmul_4
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// CHECK: [[RES4:%.+]] = "onnx.MatMul"(%arg0, %arg1) : (tensor<64x42xf32>, tensor<?x?x?x?xf32>) -> tensor<?x?x64x?xf32>
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// CHECK: return [[RES4]] : tensor<?x?x64x?xf32>
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}
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/// MatMul: K1-D x K2-D (K1 > 2, K2 > 2)
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func @test_matmul_5(%arg0 : tensor<16x?x?x42xf32>, %arg1 : tensor<32x?x64x42x32xf32>) -> tensor<*xf32> {
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%0 = "onnx.MatMul"(%arg0, %arg1) : (tensor<16x?x?x42xf32>, tensor<32x?x64x42x32xf32>) -> tensor<*xf32>
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"std.return"(%0) : (tensor<*xf32>) -> ()
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// CHECK-LABEL: test_matmul_5
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// CHECK: [[RES5:%.+]] = "onnx.MatMul"(%arg0, %arg1) : (tensor<16x?x?x42xf32>, tensor<32x?x64x42x32xf32>) -> tensor<32x16x64x?x32xf32>
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// CHECK: return [[RES5]] : tensor<32x16x64x?x32xf32>
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}
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/// MatMul: 1-D x 2-D
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func @test_matmul_6(%arg0 : tensor<32xf32>, %arg1 : tensor<32x64xf32>) -> tensor<*xf32> {
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%0 = "onnx.MatMul"(%arg0, %arg1) : (tensor<32xf32>, tensor<32x64xf32>) -> tensor<*xf32>
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"std.return"(%0) : (tensor<*xf32>) -> ()
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// CHECK-LABEL: test_matmul_6
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// CHECK: [[RES6:%.+]] = "onnx.MatMul"(%arg0, %arg1) : (tensor<32xf32>, tensor<32x64xf32>) -> tensor<64xf32>
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// CHECK: return [[RES6]] : tensor<64xf32>
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}
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/// MatMul: 2-D x 1-D
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func @test_matmul_7(%arg0 : tensor<32x64xf32>, %arg1 : tensor<64xf32>) -> tensor<*xf32> {
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%0 = "onnx.MatMul"(%arg0, %arg1) : (tensor<32x64xf32>, tensor<64xf32>) -> tensor<*xf32>
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"std.return"(%0) : (tensor<*xf32>) -> ()
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// CHECK-LABEL: test_matmul_7
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// CHECK: [[RES7:%.+]] = "onnx.MatMul"(%arg0, %arg1) : (tensor<32x64xf32>, tensor<64xf32>) -> tensor<32xf32>
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// CHECK: return [[RES7]] : tensor<32xf32>
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}
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/// MatMul: 2-D x 2-D
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func @test_matmul_8(%arg0 : tensor<32x64xf32>, %arg1 : tensor<64x128xf32>) -> tensor<*xf32> {
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%0 = "onnx.MatMul"(%arg0, %arg1) : (tensor<32x64xf32>, tensor<64x128xf32>) -> tensor<*xf32>
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"std.return"(%0) : (tensor<*xf32>) -> ()
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// CHECK-LABEL: test_matmul_8
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// CHECK: [[RES8:%.+]] = "onnx.MatMul"(%arg0, %arg1) : (tensor<32x64xf32>, tensor<64x128xf32>) -> tensor<32x128xf32>
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// CHECK: return [[RES8]] : tensor<32x128xf32>
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}
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/// MatMul: 1-D x N-D
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func @test_matmul_9(%arg0 : tensor<42xf32>, %arg1 : tensor<?x42x32xf32>) -> tensor<*xf32> {
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%0 = "onnx.MatMul"(%arg0, %arg1) : (tensor<42xf32>, tensor<?x42x32xf32>) -> tensor<*xf32>
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"std.return"(%0) : (tensor<*xf32>) -> ()
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// CHECK-LABEL: test_matmul_9
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// CHECK: [[RES1:%.+]] = "onnx.MatMul"(%arg0, %arg1) : (tensor<42xf32>, tensor<?x42x32xf32>) -> tensor<?x32xf32>
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// CHECK: return [[RES1]] : tensor<?x32xf32>
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}
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/// MatMul: N-D x 1-D
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func @test_matmul_10(%arg0 : tensor<?x42x32xf32>, %arg1 : tensor<32xf32>) -> tensor<*xf32> {
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%0 = "onnx.MatMul"(%arg0, %arg1) : (tensor<?x42x32xf32>, tensor<32xf32>) -> tensor<*xf32>
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"std.return"(%0) : (tensor<*xf32>) -> ()
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// CHECK-LABEL: test_matmul_10
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// CHECK: [[RES1:%.+]] = "onnx.MatMul"(%arg0, %arg1) : (tensor<?x42x32xf32>, tensor<32xf32>) -> tensor<?x42xf32>
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// CHECK: return [[RES1]] : tensor<?x42xf32>
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}
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//===----------------------------------------------------------------------===//
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/// Test shape inference for ConvNoBias operation and all its attributes.
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//===----------------------------------------------------------------------===//
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/// Default and required attributes for 1-D convolution.
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func @test_conv_no_bias_0(%arg0 : tensor<1x2x32xf32>, %arg1 : tensor<5x2x6xf32>) -> tensor<*xf32> {
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%0 = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", group = 1 : i64} : (tensor<1x2x32xf32>, tensor<5x2x6xf32>) -> tensor<*xf32>
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"std.return"(%0) : (tensor<*xf32>) -> ()
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// CHECK-LABEL: test_conv_no_bias_0
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// CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", group = 1 : i64} : (tensor<1x2x32xf32>, tensor<5x2x6xf32>) -> tensor<1x5x27xf32>
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// CHECK: return [[RES_ATTR]] : tensor<1x5x27xf32>
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}
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/// Default and required attributes.
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func @test_conv_no_bias_1(%arg0 : tensor<1x2x32x64xf32>, %arg1 : tensor<5x2x6x7xf32>) -> tensor<*xf32> {
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%0 = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", group = 1 : i64} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<*xf32>
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"std.return"(%0) : (tensor<*xf32>) -> ()
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// CHECK-LABEL: test_conv_no_bias_1
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// CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", group = 1 : i64} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<1x5x27x58xf32>
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// CHECK: return [[RES_ATTR]] : tensor<1x5x27x58xf32>
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}
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/// kernel_shape attribute.
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func @test_conv_no_bias_2(%arg0 : tensor<1x2x32x64xf32>, %arg1 : tensor<5x2x6x7xf32>) -> tensor<*xf32> {
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%0 = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", group = 1 : i64, kernel_shape = [8, 9]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<*xf32>
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"std.return"(%0) : (tensor<*xf32>) -> ()
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// CHECK-LABEL: test_conv_no_bias_2
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// CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", group = 1 : i64, kernel_shape = [8, 9]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<1x5x25x56xf32>
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// CHECK: return [[RES_ATTR]] : tensor<1x5x25x56xf32>
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}
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/// pads attribute.
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/// Use pads to make output size equal to input size by adding K - 1 to the result.
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func @test_conv_no_bias_3(%arg0 : tensor<1x2x32x64xf32>, %arg1 : tensor<5x2x6x10xf32>) -> tensor<*xf32> {
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%0 = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", group = 1 : i64, pads = [2, 4, 3, 5]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x10xf32>) -> tensor<*xf32>
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"std.return"(%0) : (tensor<*xf32>) -> ()
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// CHECK-LABEL: test_conv_no_bias_3
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// CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", group = 1 : i64, pads = [2, 4, 3, 5]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x10xf32>) -> tensor<1x5x32x64xf32>
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// CHECK: return [[RES_ATTR]] : tensor<1x5x32x64xf32>
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}
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/// auto_pad set to SAME_UPPER and SAME_LOWER.
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func @test_conv_no_bias_4(%arg0 : tensor<1x2x32x64xf32>, %arg1 : tensor<5x2x6x10xf32>) -> tensor<*xf32> {
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%0 = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "SAME_UPPER", group = 1 : i64} : (tensor<1x2x32x64xf32>, tensor<5x2x6x10xf32>) -> tensor<*xf32>
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"std.return"(%0) : (tensor<*xf32>) -> ()
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// CHECK-LABEL: test_conv_no_bias_4
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// CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "SAME_UPPER", group = 1 : i64} : (tensor<1x2x32x64xf32>, tensor<5x2x6x10xf32>) -> tensor<1x5x32x64xf32>
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// CHECK: return [[RES_ATTR]] : tensor<1x5x32x64xf32>
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}
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func @test_conv_no_bias_5(%arg0 : tensor<1x2x32x64xf32>, %arg1 : tensor<5x2x6x10xf32>) -> tensor<*xf32> {
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%0 = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "SAME_LOWER", group = 1 : i64} : (tensor<1x2x32x64xf32>, tensor<5x2x6x10xf32>) -> tensor<*xf32>
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"std.return"(%0) : (tensor<*xf32>) -> ()
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// CHECK-LABEL: test_conv_no_bias_5
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// CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "SAME_LOWER", group = 1 : i64} : (tensor<1x2x32x64xf32>, tensor<5x2x6x10xf32>) -> tensor<1x5x32x64xf32>
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// CHECK: return [[RES_ATTR]] : tensor<1x5x32x64xf32>
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}
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/// auto_pad set to VALID.
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func @test_conv_no_bias_6(%arg0 : tensor<1x2x32x64xf32>, %arg1 : tensor<5x2x6x10xf32>) -> tensor<*xf32> {
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%0 = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "VALID", group = 1 : i64} : (tensor<1x2x32x64xf32>, tensor<5x2x6x10xf32>) -> tensor<*xf32>
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"std.return"(%0) : (tensor<*xf32>) -> ()
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// CHECK-LABEL: test_conv_no_bias_6
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// CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "VALID", group = 1 : i64} : (tensor<1x2x32x64xf32>, tensor<5x2x6x10xf32>) -> tensor<1x5x27x55xf32>
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// CHECK: return [[RES_ATTR]] : tensor<1x5x27x55xf32>
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}
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/// With strides attribute.
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func @test_conv_no_bias_7(%arg0 : tensor<1x2x32x64xf32>, %arg1 : tensor<5x2x6x7xf32>) -> tensor<*xf32> {
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%0 = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", group = 1 : i64, strides = [2, 3]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<*xf32>
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"std.return"(%0) : (tensor<*xf32>) -> ()
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// CHECK-LABEL: test_conv_no_bias_7
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// CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", group = 1 : i64, strides = [2, 3]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<1x5x14x20xf32>
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// CHECK: return [[RES_ATTR]] : tensor<1x5x14x20xf32>
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}
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/// auto_pad set to SAME_UPPER with strides attribute.
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/// The auto_pad will pas as if stride is equal to 1.
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func @test_conv_no_bias_8(%arg0 : tensor<1x2x32x64xf32>, %arg1 : tensor<5x2x6x7xf32>) -> tensor<*xf32> {
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%0 = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "SAME_UPPER", group = 1 : i64, strides = [2, 3]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<*xf32>
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"std.return"(%0) : (tensor<*xf32>) -> ()
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// CHECK-LABEL: test_conv_no_bias_8
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// CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "SAME_UPPER", group = 1 : i64, strides = [2, 3]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<1x5x16x22xf32>
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// CHECK: return [[RES_ATTR]] : tensor<1x5x16x22xf32>
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}
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/// dilations attribute.
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func @test_conv_no_bias_9(%arg0 : tensor<1x2x32x64xf32>, %arg1 : tensor<5x2x6x7xf32>) -> tensor<*xf32> {
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%0 = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", group = 1 : i64, dilations = [2, 3]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<*xf32>
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"std.return"(%0) : (tensor<*xf32>) -> ()
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// CHECK-LABEL: test_conv_no_bias_9
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// CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", dilations = [2, 3], group = 1 : i64} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<1x5x20x42xf32>
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// CHECK: return [[RES_ATTR]] : tensor<1x5x20x42xf32>
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}
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/// dilations attribute with stride.
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func @test_conv_no_bias_10(%arg0 : tensor<1x2x32x64xf32>, %arg1 : tensor<5x2x6x7xf32>) -> tensor<*xf32> {
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%0 = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", group = 1 : i64, dilations = [2, 3], strides = [2, 2]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<*xf32>
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"std.return"(%0) : (tensor<*xf32>) -> ()
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// CHECK-LABEL: test_conv_no_bias_10
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// CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", dilations = [2, 3], group = 1 : i64, strides = [2, 2]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<1x5x10x21xf32>
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// CHECK: return [[RES_ATTR]] : tensor<1x5x10x21xf32>
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}
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/// dilations attribute with auto_pad set to SAME_UPPER.
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func @test_conv_no_bias_11(%arg0 : tensor<1x2x32x64xf32>, %arg1 : tensor<5x2x6x7xf32>) -> tensor<*xf32> {
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%0 = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "SAME_UPPER", group = 1 : i64, dilations = [2, 3]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<*xf32>
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"std.return"(%0) : (tensor<*xf32>) -> ()
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// CHECK-LABEL: test_conv_no_bias_11
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// CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "SAME_UPPER", dilations = [2, 3], group = 1 : i64} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<1x5x32x64xf32>
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// CHECK: return [[RES_ATTR]] : tensor<1x5x32x64xf32>
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}
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