1095 lines
57 KiB
MLIR
1095 lines
57 KiB
MLIR
// RUN: onnx-mlir-opt --shape-inference %s -split-input-file | FileCheck %s
|
|
|
|
// -----
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
/// Test the default behavior of transpose when no information for the
|
|
/// permutation of the axes is provided and when a permutation is provided.
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
func @test_default_transpose(%arg0 : tensor<5x5x1x32xf32>) -> tensor<*xf32> {
|
|
%0 = "onnx.Transpose"(%arg0) : (tensor<5x5x1x32xf32>) -> tensor<*xf32>
|
|
"std.return"(%0) : (tensor<*xf32>) -> ()
|
|
|
|
// CHECK-LABEL: test_default_transpose
|
|
// CHECK: [[RES:%.+]] = "onnx.Transpose"(%arg0) {perm = [3, 2, 1, 0]} : (tensor<5x5x1x32xf32>) -> tensor<32x1x5x5xf32>
|
|
// CHECK: return [[RES]] : tensor<32x1x5x5xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
/// Test shape inference for transposition when perm attribute is specified.
|
|
|
|
func @test_transpose(%arg0 : tensor<5x5x1x32xf32>) -> tensor<*xf32> {
|
|
%0 = "onnx.Transpose"(%arg0) {perm = [2, 0, 3, 1]} : (tensor<5x5x1x32xf32>) -> tensor<*xf32>
|
|
"std.return"(%0) : (tensor<*xf32>) -> ()
|
|
|
|
// CHECK-LABEL: test_transpose
|
|
// CHECK: [[RES_ATTR:%.+]] = "onnx.Transpose"(%arg0) {perm = [2, 0, 3, 1]} : (tensor<5x5x1x32xf32>) -> tensor<1x5x32x5xf32>
|
|
// CHECK: return [[RES_ATTR]] : tensor<1x5x32x5xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
/// Test the shape inferencing scheme for the matmul operation.
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
/// MatMul: 1-D x 1-D
|
|
|
|
func @test_matmul_1(%arg0 : tensor<32xf32>, %arg1 : tensor<32xf32>) -> tensor<*xf32> {
|
|
%0 = "onnx.MatMul"(%arg0, %arg1) : (tensor<32xf32>, tensor<32xf32>) -> tensor<*xf32>
|
|
"std.return"(%0) : (tensor<*xf32>) -> ()
|
|
|
|
// CHECK-LABEL: test_matmul_1
|
|
// CHECK: [[RES1:%.+]] = "onnx.MatMul"(%arg0, %arg1) : (tensor<32xf32>, tensor<32xf32>) -> tensor<1xf32>
|
|
// CHECK: return [[RES1]] : tensor<1xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
/// MatMul: K-D x 2-D (K > 2)
|
|
|
|
func @test_matmul_2(%arg0 : tensor<16x?x64x42xf32>, %arg1 : tensor<42x32xf32>) -> tensor<*xf32> {
|
|
%0 = "onnx.MatMul"(%arg0, %arg1) : (tensor<16x?x64x42xf32>, tensor<42x32xf32>) -> tensor<*xf32>
|
|
"std.return"(%0) : (tensor<*xf32>) -> ()
|
|
|
|
// CHECK-LABEL: test_matmul_2
|
|
// CHECK: [[RES2:%.+]] = "onnx.MatMul"(%arg0, %arg1) : (tensor<16x?x64x42xf32>, tensor<42x32xf32>) -> tensor<16x?x64x32xf32>
|
|
// CHECK: return [[RES2]] : tensor<16x?x64x32xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
/// MatMul: 2-D x K-D (K > 2)
|
|
|
|
func @test_matmul_3(%arg0 : tensor<64x42xf32>, %arg1 : tensor<16x?x42x32xf32>) -> tensor<*xf32> {
|
|
%0 = "onnx.MatMul"(%arg0, %arg1) : (tensor<64x42xf32>, tensor<16x?x42x32xf32>) -> tensor<*xf32>
|
|
"std.return"(%0) : (tensor<*xf32>) -> ()
|
|
|
|
// CHECK-LABEL: test_matmul_3
|
|
// CHECK: [[RES3:%.+]] = "onnx.MatMul"(%arg0, %arg1) : (tensor<64x42xf32>, tensor<16x?x42x32xf32>) -> tensor<16x?x64x32xf32>
|
|
// CHECK: return [[RES3]] : tensor<16x?x64x32xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
/// MatMul: 2-D x K-D (K > 2)
|
|
|
|
func @test_matmul_4(%arg0 : tensor<64x42xf32>, %arg1 : tensor<?x?x?x?xf32>) -> tensor<*xf32> {
|
|
%0 = "onnx.MatMul"(%arg0, %arg1) : (tensor<64x42xf32>, tensor<?x?x?x?xf32>) -> tensor<*xf32>
|
|
"std.return"(%0) : (tensor<*xf32>) -> ()
|
|
|
|
// CHECK-LABEL: test_matmul_4
|
|
// CHECK: [[RES4:%.+]] = "onnx.MatMul"(%arg0, %arg1) : (tensor<64x42xf32>, tensor<?x?x?x?xf32>) -> tensor<?x?x64x?xf32>
|
|
// CHECK: return [[RES4]] : tensor<?x?x64x?xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
/// MatMul: K1-D x K2-D (K1 > 2, K2 > 2)
|
|
|
|
func @test_matmul_5(%arg0 : tensor<16x?x?x42xf32>, %arg1 : tensor<32x?x64x42x32xf32>) -> tensor<*xf32> {
|
|
%0 = "onnx.MatMul"(%arg0, %arg1) : (tensor<16x?x?x42xf32>, tensor<32x?x64x42x32xf32>) -> tensor<*xf32>
|
|
"std.return"(%0) : (tensor<*xf32>) -> ()
|
|
|
|
// CHECK-LABEL: test_matmul_5
|
|
// CHECK: [[RES5:%.+]] = "onnx.MatMul"(%arg0, %arg1) : (tensor<16x?x?x42xf32>, tensor<32x?x64x42x32xf32>) -> tensor<32x16x64x?x32xf32>
|
|
// CHECK: return [[RES5]] : tensor<32x16x64x?x32xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
/// MatMul: 1-D x 2-D
|
|
|
|
func @test_matmul_6(%arg0 : tensor<32xf32>, %arg1 : tensor<32x64xf32>) -> tensor<*xf32> {
|
|
%0 = "onnx.MatMul"(%arg0, %arg1) : (tensor<32xf32>, tensor<32x64xf32>) -> tensor<*xf32>
|
|
"std.return"(%0) : (tensor<*xf32>) -> ()
|
|
|
|
// CHECK-LABEL: test_matmul_6
|
|
// CHECK: [[RES6:%.+]] = "onnx.MatMul"(%arg0, %arg1) : (tensor<32xf32>, tensor<32x64xf32>) -> tensor<64xf32>
|
|
// CHECK: return [[RES6]] : tensor<64xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
/// MatMul: 2-D x 1-D
|
|
|
|
func @test_matmul_7(%arg0 : tensor<32x64xf32>, %arg1 : tensor<64xf32>) -> tensor<*xf32> {
|
|
%0 = "onnx.MatMul"(%arg0, %arg1) : (tensor<32x64xf32>, tensor<64xf32>) -> tensor<*xf32>
|
|
"std.return"(%0) : (tensor<*xf32>) -> ()
|
|
|
|
// CHECK-LABEL: test_matmul_7
|
|
// CHECK: [[RES7:%.+]] = "onnx.MatMul"(%arg0, %arg1) : (tensor<32x64xf32>, tensor<64xf32>) -> tensor<32xf32>
|
|
// CHECK: return [[RES7]] : tensor<32xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
/// MatMul: 2-D x 2-D
|
|
|
|
func @test_matmul_8(%arg0 : tensor<32x64xf32>, %arg1 : tensor<64x128xf32>) -> tensor<*xf32> {
|
|
%0 = "onnx.MatMul"(%arg0, %arg1) : (tensor<32x64xf32>, tensor<64x128xf32>) -> tensor<*xf32>
|
|
"std.return"(%0) : (tensor<*xf32>) -> ()
|
|
|
|
// CHECK-LABEL: test_matmul_8
|
|
// CHECK: [[RES8:%.+]] = "onnx.MatMul"(%arg0, %arg1) : (tensor<32x64xf32>, tensor<64x128xf32>) -> tensor<32x128xf32>
|
|
// CHECK: return [[RES8]] : tensor<32x128xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
/// MatMul: 1-D x N-D
|
|
|
|
func @test_matmul_9(%arg0 : tensor<42xf32>, %arg1 : tensor<?x42x32xf32>) -> tensor<*xf32> {
|
|
%0 = "onnx.MatMul"(%arg0, %arg1) : (tensor<42xf32>, tensor<?x42x32xf32>) -> tensor<*xf32>
|
|
"std.return"(%0) : (tensor<*xf32>) -> ()
|
|
|
|
// CHECK-LABEL: test_matmul_9
|
|
// CHECK: [[RES1:%.+]] = "onnx.MatMul"(%arg0, %arg1) : (tensor<42xf32>, tensor<?x42x32xf32>) -> tensor<?x32xf32>
|
|
// CHECK: return [[RES1]] : tensor<?x32xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
/// MatMul: N-D x 1-D
|
|
|
|
func @test_matmul_10(%arg0 : tensor<?x42x32xf32>, %arg1 : tensor<32xf32>) -> tensor<*xf32> {
|
|
%0 = "onnx.MatMul"(%arg0, %arg1) : (tensor<?x42x32xf32>, tensor<32xf32>) -> tensor<*xf32>
|
|
"std.return"(%0) : (tensor<*xf32>) -> ()
|
|
|
|
// CHECK-LABEL: test_matmul_10
|
|
// CHECK: [[RES1:%.+]] = "onnx.MatMul"(%arg0, %arg1) : (tensor<?x42x32xf32>, tensor<32xf32>) -> tensor<?x42xf32>
|
|
// CHECK: return [[RES1]] : tensor<?x42xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
/// Test shape inference for Conv (first with no bias) operation and all its attributes.
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
/// Default and required attributes for 1-D convolution.
|
|
|
|
func @test_conv_no_bias_0(%arg0 : tensor<1x2x32xf32>, %arg1 : tensor<5x2x6xf32>) -> tensor<*xf32> {
|
|
%cst = constant unit
|
|
%0 = "onnx.Conv"(%arg0, %arg1, %cst) {auto_pad = "NOTSET", group = 1 : i64} : (tensor<1x2x32xf32>, tensor<5x2x6xf32>, none) -> tensor<*xf32>
|
|
"std.return"(%0) : (tensor<*xf32>) -> ()
|
|
|
|
// CHECK-LABEL: test_conv_no_bias_0
|
|
// CHECK: [[RES_ATTR:%.+]] = "onnx.Conv"(%arg0, %arg1, %cst) {auto_pad = "NOTSET", dilations = [1], group = 1 : i64, kernel_shape = [6], pads = [0, 0], strides = [1]} : (tensor<1x2x32xf32>, tensor<5x2x6xf32>, none) -> tensor<1x5x27xf32>
|
|
// CHECK: return [[RES_ATTR]] : tensor<1x5x27xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
/// Default and required attributes.
|
|
|
|
func @test_conv_no_bias_1(%arg0 : tensor<1x2x32x64xf32>, %arg1 : tensor<5x2x6x7xf32>) -> tensor<*xf32> {
|
|
%cst = constant unit
|
|
%0 = "onnx.Conv"(%arg0, %arg1, %cst) {auto_pad = "NOTSET", group = 1 : i64} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>, none) -> tensor<*xf32>
|
|
"std.return"(%0) : (tensor<*xf32>) -> ()
|
|
|
|
// CHECK-LABEL: test_conv_no_bias_1
|
|
// CHECK: [[RES_ATTR:%.+]] = "onnx.Conv"(%arg0, %arg1, %cst) {auto_pad = "NOTSET", dilations = [1, 1], group = 1 : i64, kernel_shape = [6, 7], pads = [0, 0, 0, 0], strides = [1, 1]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>, none) -> tensor<1x5x27x58xf32>
|
|
// CHECK: return [[RES_ATTR]] : tensor<1x5x27x58xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
/// kernel_shape attribute.
|
|
|
|
func @test_conv_no_bias_2(%arg0 : tensor<1x2x32x64xf32>, %arg1 : tensor<5x2x6x7xf32>) -> tensor<*xf32> {
|
|
%cst = constant unit
|
|
%0 = "onnx.Conv"(%arg0, %arg1, %cst) {auto_pad = "NOTSET", group = 1 : i64, kernel_shape = [8, 9]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>, none) -> tensor<*xf32>
|
|
"std.return"(%0) : (tensor<*xf32>) -> ()
|
|
|
|
// CHECK-LABEL: test_conv_no_bias_2
|
|
// CHECK: [[RES_ATTR:%.+]] = "onnx.Conv"(%arg0, %arg1, %cst) {auto_pad = "NOTSET", dilations = [1, 1], group = 1 : i64, kernel_shape = [8, 9], pads = [0, 0, 0, 0], strides = [1, 1]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>, none) -> tensor<1x5x25x56xf32>
|
|
// CHECK: return [[RES_ATTR]] : tensor<1x5x25x56xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
/// pads attribute.
|
|
/// Use pads to make output size equal to input size by adding K - 1 to the result.
|
|
|
|
func @test_conv_no_bias_3(%arg0 : tensor<1x2x32x64xf32>, %arg1 : tensor<5x2x6x10xf32>) -> tensor<*xf32> {
|
|
%cst = constant unit
|
|
%0 = "onnx.Conv"(%arg0, %arg1, %cst) {auto_pad = "NOTSET", group = 1 : i64, pads = [2, 4, 3, 5]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x10xf32>, none) -> tensor<*xf32>
|
|
"std.return"(%0) : (tensor<*xf32>) -> ()
|
|
|
|
// CHECK-LABEL: test_conv_no_bias_3
|
|
// CHECK: [[RES_ATTR:%.+]] = "onnx.Conv"(%arg0, %arg1, %cst) {auto_pad = "NOTSET", dilations = [1, 1], group = 1 : i64, kernel_shape = [6, 10], pads = [2, 4, 3, 5], strides = [1, 1]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x10xf32>, none) -> tensor<1x5x32x64xf32>
|
|
// CHECK: return [[RES_ATTR]] : tensor<1x5x32x64xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
/// auto_pad set to SAME_UPPER and SAME_LOWER.
|
|
|
|
func @test_conv_no_bias_4(%arg0 : tensor<1x2x32x64xf32>, %arg1 : tensor<5x2x6x10xf32>) -> tensor<*xf32> {
|
|
%cst = constant unit
|
|
%0 = "onnx.Conv"(%arg0, %arg1, %cst) {auto_pad = "SAME_UPPER", group = 1 : i64} : (tensor<1x2x32x64xf32>, tensor<5x2x6x10xf32>, none) -> tensor<*xf32>
|
|
"std.return"(%0) : (tensor<*xf32>) -> ()
|
|
|
|
// CHECK-LABEL: test_conv_no_bias_4
|
|
// CHECK: [[RES_ATTR:%.+]] = "onnx.Conv"(%arg0, %arg1, %cst) {auto_pad = "NOTSET", dilations = [1, 1], group = 1 : i64, kernel_shape = [6, 10], pads = [2, 4, 3, 5], strides = [1, 1]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x10xf32>, none) -> tensor<1x5x32x64xf32>
|
|
// CHECK: return [[RES_ATTR]] : tensor<1x5x32x64xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
func @test_conv_no_bias_5(%arg0 : tensor<1x2x32x64xf32>, %arg1 : tensor<5x2x6x10xf32>) -> tensor<*xf32> {
|
|
%cst = constant unit
|
|
%0 = "onnx.Conv"(%arg0, %arg1, %cst) {auto_pad = "SAME_LOWER", group = 1 : i64} : (tensor<1x2x32x64xf32>, tensor<5x2x6x10xf32>, none) -> tensor<*xf32>
|
|
"std.return"(%0) : (tensor<*xf32>) -> ()
|
|
|
|
// CHECK-LABEL: test_conv_no_bias_5
|
|
// CHECK: [[RES_ATTR:%.+]] = "onnx.Conv"(%arg0, %arg1, %cst) {auto_pad = "NOTSET", dilations = [1, 1], group = 1 : i64, kernel_shape = [6, 10], pads = [3, 5, 2, 4], strides = [1, 1]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x10xf32>, none) -> tensor<1x5x32x64xf32>
|
|
// CHECK: return [[RES_ATTR]] : tensor<1x5x32x64xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
/// auto_pad set to VALID.
|
|
|
|
func @test_conv_no_bias_6(%arg0 : tensor<1x2x32x64xf32>, %arg1 : tensor<5x2x6x10xf32>) -> tensor<*xf32> {
|
|
%cst = constant unit
|
|
%0 = "onnx.Conv"(%arg0, %arg1, %cst) {auto_pad = "VALID", group = 1 : i64} : (tensor<1x2x32x64xf32>, tensor<5x2x6x10xf32>, none) -> tensor<*xf32>
|
|
"std.return"(%0) : (tensor<*xf32>) -> ()
|
|
|
|
// CHECK-LABEL: test_conv_no_bias_6
|
|
// CHECK: [[RES_ATTR:%.+]] = "onnx.Conv"(%arg0, %arg1, %cst) {auto_pad = "NOTSET", dilations = [1, 1], group = 1 : i64, kernel_shape = [6, 10], pads = [0, 0, 0, 0], strides = [1, 1]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x10xf32>, none) -> tensor<1x5x27x55xf32>
|
|
// CHECK: return [[RES_ATTR]] : tensor<1x5x27x55xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
/// With strides attribute.
|
|
|
|
func @test_conv_no_bias_7(%arg0 : tensor<1x2x32x64xf32>, %arg1 : tensor<5x2x6x7xf32>) -> tensor<*xf32> {
|
|
%cst = constant unit
|
|
%0 = "onnx.Conv"(%arg0, %arg1, %cst) {auto_pad = "NOTSET", group = 1 : i64, strides = [2, 3]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>, none) -> tensor<*xf32>
|
|
"std.return"(%0) : (tensor<*xf32>) -> ()
|
|
|
|
// CHECK-LABEL: test_conv_no_bias_7
|
|
// CHECK: [[RES_ATTR:%.+]] = "onnx.Conv"(%arg0, %arg1, %cst) {auto_pad = "NOTSET", dilations = [1, 1], group = 1 : i64, kernel_shape = [6, 7], pads = [0, 0, 0, 0], strides = [2, 3]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>, none) -> tensor<1x5x14x20xf32>
|
|
// CHECK: return [[RES_ATTR]] : tensor<1x5x14x20xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
/// auto_pad set to SAME_UPPER with strides attribute.
|
|
/// The auto_pad will pas as if stride is equal to 1.
|
|
|
|
func @test_conv_no_bias_8(%arg0 : tensor<1x2x32x64xf32>, %arg1 : tensor<5x2x6x7xf32>) -> tensor<*xf32> {
|
|
%cst = constant unit
|
|
%0 = "onnx.Conv"(%arg0, %arg1, %cst) {auto_pad = "SAME_UPPER", group = 1 : i64, strides = [2, 3]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>, none) -> tensor<*xf32>
|
|
"std.return"(%0) : (tensor<*xf32>) -> ()
|
|
|
|
// CHECK-LABEL: test_conv_no_bias_8
|
|
// CHECK: [[RES_ATTR:%.+]] = "onnx.Conv"(%arg0, %arg1, %cst) {auto_pad = "NOTSET", dilations = [1, 1], group = 1 : i64, kernel_shape = [6, 7], pads = [2, 3, 2, 3], strides = [2, 3]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>, none) -> tensor<1x5x16x22xf32>
|
|
// CHECK: return [[RES_ATTR]] : tensor<1x5x16x22xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
/// dilations attribute.
|
|
|
|
func @test_conv_no_bias_9(%arg0 : tensor<1x2x32x64xf32>, %arg1 : tensor<5x2x6x7xf32>) -> tensor<*xf32> {
|
|
%cst = constant unit
|
|
%0 = "onnx.Conv"(%arg0, %arg1, %cst) {auto_pad = "NOTSET", group = 1 : i64, dilations = [2, 3]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>, none) -> tensor<*xf32>
|
|
"std.return"(%0) : (tensor<*xf32>) -> ()
|
|
|
|
// CHECK-LABEL: test_conv_no_bias_9
|
|
// CHECK: [[RES_ATTR:%.+]] = "onnx.Conv"(%arg0, %arg1, %cst) {auto_pad = "NOTSET", dilations = [2, 3], group = 1 : i64, kernel_shape = [6, 7], pads = [0, 0, 0, 0], strides = [1, 1]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>, none) -> tensor<1x5x22x46xf32>
|
|
// CHECK: return [[RES_ATTR]] : tensor<1x5x22x46xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
/// dilations attribute with stride.
|
|
|
|
func @test_conv_no_bias_10(%arg0 : tensor<1x2x32x64xf32>, %arg1 : tensor<5x2x6x7xf32>) -> tensor<*xf32> {
|
|
%cst = constant unit
|
|
%0 = "onnx.Conv"(%arg0, %arg1, %cst) {auto_pad = "NOTSET", group = 1 : i64, dilations = [2, 3], strides = [2, 2]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>, none) -> tensor<*xf32>
|
|
"std.return"(%0) : (tensor<*xf32>) -> ()
|
|
|
|
// CHECK-LABEL: test_conv_no_bias_10
|
|
// CHECK: [[RES_ATTR:%.+]] = "onnx.Conv"(%arg0, %arg1, %cst) {auto_pad = "NOTSET", dilations = [2, 3], group = 1 : i64, kernel_shape = [6, 7], pads = [0, 0, 0, 0], strides = [2, 2]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>, none) -> tensor<1x5x11x23xf32>
|
|
// CHECK: return [[RES_ATTR]] : tensor<1x5x11x23xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
/// dilations attribute with auto_pad set to SAME_UPPER.
|
|
|
|
func @test_conv_no_bias_11(%arg0 : tensor<1x2x32x64xf32>, %arg1 : tensor<5x2x6x7xf32>) -> tensor<*xf32> {
|
|
%cst = constant unit
|
|
%0 = "onnx.Conv"(%arg0, %arg1, %cst) {auto_pad = "SAME_UPPER", group = 1 : i64, dilations = [2, 3]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>, none) -> tensor<*xf32>
|
|
"std.return"(%0) : (tensor<*xf32>) -> ()
|
|
|
|
// CHECK-LABEL: test_conv_no_bias_11
|
|
// CHECK: [[RES_ATTR:%.+]] = "onnx.Conv"(%arg0, %arg1, %cst) {auto_pad = "NOTSET", dilations = [2, 3], group = 1 : i64, kernel_shape = [6, 7], pads = [5, 9, 5, 9], strides = [1, 1]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>, none) -> tensor<1x5x32x64xf32>
|
|
// CHECK: return [[RES_ATTR]] : tensor<1x5x32x64xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
// Test convolution with bias input.
|
|
|
|
func @test_conv_12(%arg0 : tensor<1x2x32xf32>, %arg1 : tensor<5x2x6xf32>, %arg2 : tensor<5xf32>) -> tensor<*xf32> {
|
|
%0 = "onnx.Conv"(%arg0, %arg1, %arg2) {auto_pad = "NOTSET", group = 1 : i64} : (tensor<1x2x32xf32>, tensor<5x2x6xf32>, tensor<5xf32>) -> tensor<*xf32>
|
|
"std.return"(%0) : (tensor<*xf32>) -> ()
|
|
|
|
// CHECK-LABEL: test_conv_12
|
|
// CHECK: [[RES_ATTR:%.+]] = "onnx.Conv"(%arg0, %arg1, %arg2) {auto_pad = "NOTSET", dilations = [1], group = 1 : i64, kernel_shape = [6], pads = [0, 0], strides = [1]} : (tensor<1x2x32xf32>, tensor<5x2x6xf32>, tensor<5xf32>) -> tensor<1x5x27xf32>
|
|
// CHECK: return [[RES_ATTR]] : tensor<1x5x27xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
/// Test shape inference for PadConstantValuePad.
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
/// Test Pad_1
|
|
func @test_Pad_1(%arg0 : tensor<16x13xf32>) -> tensor<*xf32> {
|
|
%cst = constant unit
|
|
%0 = "onnx.Pad"(%arg0, %cst, %cst) {constant_value = dense<0.000000e+00> : tensor<1xf32>, mode = "constant", pads = dense<[0, 2, 2, 4]> : tensor<4xi32>} : (tensor<16x13xf32>, none, none) -> tensor<*xf32>
|
|
"std.return"(%0) : (tensor<*xf32>) -> ()
|
|
|
|
// CHECK-LABEL: test_Pad_1
|
|
// CHECK-NEXT: [[NONE:%.+]] = constant unit
|
|
// CHECK: [[RES:%.+]] = "onnx.Pad"(%arg0, [[NONE]], [[NONE]]) {constant_value = dense<0.000000e+00> : tensor<1xf32>, mode = "constant", pads = dense<[0, 2, 2, 4]> : tensor<4xi32>} : (tensor<16x13xf32>, none, none) -> tensor<18x19xf32>
|
|
// CHECK: return [[RES]] : tensor<18x19xf32>
|
|
}
|
|
|
|
|
|
/// Test PadConstantValuePad_1
|
|
func @test_PadConstantValuePad_1(%arg0 : tensor<16x13xf32>) -> tensor<*xf32> {
|
|
%0 = "onnx.PadConstantValuePad"(%arg0) {constant_value = 0.000000e+00 : f32, mode = "constant", pads = [0, 0, 2, 0]} : (tensor<16x13xf32>) -> tensor<*xf32>
|
|
"std.return"(%0) : (tensor<*xf32>) -> ()
|
|
|
|
// CHECK-LABEL: test_PadConstantValuePad_1
|
|
// CHECK: [[RES:%.+]] = "onnx.PadConstantValuePad"(%arg0) {constant_value = 0.000000e+00 : f32, mode = "constant", pads = [0, 0, 2, 0]} : (tensor<16x13xf32>) -> tensor<18x13xf32>
|
|
// CHECK: return [[RES]] : tensor<18x13xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
/// Test PadConstantPad_1
|
|
func @test_PadConstantPad_1(%arg0 : tensor<16x13xf32>, %arg1 : tensor<*xf32>) -> tensor<*xf32> {
|
|
%0 = "onnx.PadConstantPad"(%arg0, %arg1) {mode = "constant", pads = [0, 3, 2, 1]} : (tensor<16x13xf32>, tensor<*xf32>) -> tensor<*xf32>
|
|
"std.return"(%0) : (tensor<*xf32>) -> ()
|
|
// CHECK-LABEL: test_PadConstantPad_1
|
|
// CHECK: [[RES:%.+]] = "onnx.PadConstantPad"(%arg0, %arg1) {mode = "constant", pads = [0, 3, 2, 1]} : (tensor<16x13xf32>, tensor<*xf32>) -> tensor<18x17xf32>
|
|
// CHECK: return [[RES]] : tensor<18x17xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
/// Test PadConstantPad_2
|
|
func @test_PadConstantPad_2(%arg0 : tensor<16x?xf32>, %arg1 : tensor<*xf32>) -> tensor<*xf32> {
|
|
%0 = "onnx.PadConstantPad"(%arg0, %arg1) {mode = "constant", pads = [0, 3, 2, 1]} : (tensor<16x?xf32>, tensor<*xf32>) -> tensor<*xf32>
|
|
"std.return"(%0) : (tensor<*xf32>) -> ()
|
|
|
|
// CHECK-LABEL: test_PadConstantPad_2
|
|
// CHECK: [[RES:%.+]] = "onnx.PadConstantPad"(%arg0, %arg1) {mode = "constant", pads = [0, 3, 2, 1]} : (tensor<16x?xf32>, tensor<*xf32>) -> tensor<18x?xf32>
|
|
// CHECK: return [[RES]] : tensor<18x?xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
/// Test for constant op.
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
/// Test ConstantOp shape inference for 1-D dense tensor.
|
|
func @test_constant_dense_1d_value() -> tensor<*xf32> {
|
|
%0 = "onnx.Constant"() {value = dense<[0.0, 1.0, 2.0]> : tensor<3xf32>} : () -> tensor<*xf32>
|
|
"std.return"(%0) : (tensor<*xf32>) -> ()
|
|
|
|
// CHECK-LABEL: test_constant_dense_1d_value
|
|
// CHECK: [[RES:%.+]] = "onnx.Constant"() {value = dense<[0.000000e+00, 1.000000e+00, 2.000000e+00]> : tensor<3xf32>} : () -> tensor<3xf32>
|
|
// CHECK: return [[RES]] : tensor<3xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
/// Test ConstantOp shape inference for 2-D dense tensor.
|
|
func @test_constant_dense_2d_value() -> tensor<*xf32> {
|
|
%0 = "onnx.Constant"() {value = dense<[[0.0, 0.0], [1.0, 1.1], [2.0, 2.1]]> : tensor<3x2xf32>} : () -> tensor<*xf32>
|
|
"std.return"(%0) : (tensor<*xf32>) -> ()
|
|
|
|
// CHECK-LABEL: test_constant_dense_2d_value
|
|
// CHECK: [[RES:%.+]] = "onnx.Constant"() {value = dense<{{\[}}[0.000000e+00, 0.000000e+00], [1.000000e+00, 1.100000e+00], [2.000000e+00, 2.100000e+00{{\]}}]> : tensor<3x2xf32>} : () -> tensor<3x2xf32>
|
|
// CHECK: return [[RES]] : tensor<3x2xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
/// Test ConstantOp shape inference for 1-D sparse tensor.
|
|
func @test_constant_sparse_1d_value() -> tensor<*xf32> {
|
|
%0 = "onnx.Constant"() {sparse_value = sparse<[[0]], [1.0]> : tensor<3xf32>} : () -> tensor<*xf32>
|
|
"std.return"(%0) : (tensor<*xf32>) -> ()
|
|
|
|
// CHECK-LABEL: test_constant_sparse_1d_value
|
|
// CHECK: [[RES:%.+]] = "onnx.Constant"() {sparse_value = sparse<0, 1.000000e+00> : tensor<3xf32>} : () -> tensor<3xf32>
|
|
// CHECK: return [[RES]] : tensor<3xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
/// Test ConstantOp shape inference for 2-D sparse tensor.
|
|
func @test_constant_sparse_2d_value() -> tensor<*xf32> {
|
|
%0 = "onnx.Constant"() {sparse_value = sparse<[[0, 1]], [2.0]> : tensor<3x2xf32>} : () -> tensor<*xf32>
|
|
"std.return"(%0) : (tensor<*xf32>) -> ()
|
|
|
|
// CHECK-LABEL: test_constant_sparse_2d_value
|
|
// CHECK: [[RES:%.+]] = "onnx.Constant"() {sparse_value = sparse<{{\[}}[0, 1{{\]}}], 2.000000e+00> : tensor<3x2xf32>} : () -> tensor<3x2xf32>
|
|
// CHECK: return [[RES]] : tensor<3x2xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
/// Test the default behavior of Average Pool with no padding (pad are set but shoud be ignored)
|
|
func @test_default_averagepool(%arg0 : tensor<5x5x32x32xf32>) -> tensor<*xf32> {
|
|
%0 = "onnx.AveragePool"(%arg0) {auto_pad = "VALID", ceil_mode = 0, kernel_shape = [3,3], pads = [1, 1, 1, 1] } : (tensor<5x5x32x32xf32>) -> tensor<*xf32>
|
|
"std.return"(%0) : (tensor<*xf32>) -> ()
|
|
|
|
// CHECK-LABEL: test_default_averagepool
|
|
// CHECK: [[RES:%.+]] = "onnx.AveragePool"(%arg0) {auto_pad = "NOTSET", ceil_mode = 0 : i64, kernel_shape = [3, 3], pads = [0, 0, 0, 0], strides = [1, 1]} : (tensor<5x5x32x32xf32>) -> tensor<5x5x30x30xf32>
|
|
// CHECK: return [[RES]] : tensor<5x5x30x30xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
/// Test the default behavior of Average Pool with no padding (pad are not set, default to zero)
|
|
func @test_default_averagepool_defpad(%arg0 : tensor<5x5x32x32xf32>) -> tensor<*xf32> {
|
|
%0 = "onnx.AveragePool"(%arg0) {auto_pad = "NOTSET", ceil_mode = 0, kernel_shape = [3,3]} : (tensor<5x5x32x32xf32>) -> tensor<*xf32>
|
|
"std.return"(%0) : (tensor<*xf32>) -> ()
|
|
|
|
// CHECK-LABEL: test_default_averagepool_defpad
|
|
// CHECK: [[RES:%.+]] = "onnx.AveragePool"(%arg0) {auto_pad = "NOTSET", ceil_mode = 0 : i64, kernel_shape = [3, 3], pads = [0, 0, 0, 0], strides = [1, 1]} : (tensor<5x5x32x32xf32>) -> tensor<5x5x30x30xf32>
|
|
// CHECK: return [[RES]] : tensor<5x5x30x30xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
/// Test the default behavior of Average Pool with uniform padding
|
|
func @test_default_averagepool_pad(%arg0 : tensor<5x5x32x32xf32>) -> tensor<*xf32> {
|
|
%0 = "onnx.AveragePool"(%arg0) {auto_pad = "NOTSET", ceil_mode = 0, kernel_shape = [3,3], pads = [1, 1, 1, 1] } : (tensor<5x5x32x32xf32>) -> tensor<*xf32>
|
|
"std.return"(%0) : (tensor<*xf32>) -> ()
|
|
|
|
// CHECK-LABEL: test_default_averagepool_pad
|
|
// CHECK: [[RES:%.+]] = "onnx.AveragePool"(%arg0) {auto_pad = "NOTSET", ceil_mode = 0 : i64, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]} : (tensor<5x5x32x32xf32>) -> tensor<5x5x32x32xf32>
|
|
// CHECK: return [[RES]] : tensor<5x5x32x32xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
/// Test the default behavior of Average Pool with non uniform padding
|
|
func @test_default_averagepool_pad_nonunif(%arg0 : tensor<5x5x32x32xf32>) -> tensor<*xf32> {
|
|
%0 = "onnx.AveragePool"(%arg0) {auto_pad = "NOTSET", ceil_mode = 0, kernel_shape = [5,3], pads = [2, 1, 1, 0] } : (tensor<5x5x32x32xf32>) -> tensor<*xf32>
|
|
"std.return"(%0) : (tensor<*xf32>) -> ()
|
|
|
|
// CHECK-LABEL: test_default_averagepool_pad_nonunif
|
|
// CHECK: [[RES:%.+]] = "onnx.AveragePool"(%arg0) {auto_pad = "NOTSET", ceil_mode = 0 : i64, kernel_shape = [5, 3], pads = [2, 1, 1, 0], strides = [1, 1]} : (tensor<5x5x32x32xf32>) -> tensor<5x5x31x31xf32>
|
|
// CHECK: return [[RES]] : tensor<5x5x31x31xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
/// Test the default behavior of Average Pool with non uniform padding
|
|
func @test_default_averagepool_strides(%arg0 : tensor<5x5x32x32xf32>) -> tensor<*xf32> {
|
|
%0 = "onnx.AveragePool"(%arg0) {auto_pad = "NOTSET", ceil_mode = 0, kernel_shape = [3,3], pads = [1, 1, 1, 1], strides = [2, 2] } : (tensor<5x5x32x32xf32>) -> tensor<*xf32>
|
|
"std.return"(%0) : (tensor<*xf32>) -> ()
|
|
|
|
// CHECK-LABEL: test_default_averagepool_strides
|
|
// CHECK: [[RES:%.+]] = "onnx.AveragePool"(%arg0) {auto_pad = "NOTSET", ceil_mode = 0 : i64, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]} : (tensor<5x5x32x32xf32>) -> tensor<5x5x16x16xf32>
|
|
// CHECK: return [[RES]] : tensor<5x5x16x16xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
/// Test the default behavior of Average Pool with non uniform padding
|
|
func @test_default_averagepool_strides_nonunifpad(%arg0 : tensor<5x5x30x32xf32>) -> tensor<*xf32> {
|
|
%0 = "onnx.AveragePool"(%arg0) {auto_pad = "NOTSET", ceil_mode = 0, kernel_shape = [2,2], pads = [1, 0, 0, 0], strides = [2, 2] } : (tensor<5x5x30x32xf32>) -> tensor<*xf32>
|
|
"std.return"(%0) : (tensor<*xf32>) -> ()
|
|
|
|
// CHECK-LABEL: test_default_averagepool_strides_nonunifpad
|
|
// CHECK: [[RES:%.+]] = "onnx.AveragePool"(%arg0) {auto_pad = "NOTSET", ceil_mode = 0 : i64, kernel_shape = [2, 2], pads = [1, 0, 0, 0], strides = [2, 2]} : (tensor<5x5x30x32xf32>) -> tensor<5x5x15x16xf32>
|
|
// CHECK: return [[RES]] : tensor<5x5x15x16xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
/// Test the default behavior of Average Pool with non uniform padding
|
|
func @test_default_averagepool_strides_nonunifpad_ceil(%arg0 : tensor<5x5x30x32xf32>) -> tensor<*xf32> {
|
|
%0 = "onnx.AveragePool"(%arg0) {auto_pad = "NOTSET", ceil_mode = 1, kernel_shape = [2,2], pads = [1, 0, 0, 0], strides = [2, 2] } : (tensor<5x5x30x32xf32>) -> tensor<*xf32>
|
|
"std.return"(%0) : (tensor<*xf32>) -> ()
|
|
|
|
// CHECK-LABEL: test_default_averagepool_strides_nonunifpad_ceil
|
|
// CHECK: [[RES:%.+]] = "onnx.AveragePool"(%arg0) {auto_pad = "NOTSET", ceil_mode = 1 : i64, kernel_shape = [2, 2], pads = [1, 0, 0, 0], strides = [2, 2]} : (tensor<5x5x30x32xf32>) -> tensor<5x5x16x16xf32>
|
|
// CHECK: return [[RES]] : tensor<5x5x16x16xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
/// Test the reshape op inference when constants are present.
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
func @test_reshape_dynamic(%arg0 : tensor<5x5x1x32xf32>, %arg1 : tensor<4xi32>) -> tensor<*xf32> {
|
|
%0 = "onnx.Reshape"(%arg0, %arg1) : (tensor<5x5x1x32xf32>, tensor<4xi32>) -> tensor<*xf32>
|
|
"std.return"(%0) : (tensor<*xf32>) -> ()
|
|
|
|
// CHECK-LABEL: test_reshape_dynamic
|
|
// CHECK: [[RES:%.+]] = "onnx.Reshape"(%arg0, %arg1) : (tensor<5x5x1x32xf32>, tensor<4xi32>) -> tensor<?x?x?x?xf32>
|
|
// CHECK: return [[RES]] : tensor<?x?x?x?xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
func @test_reshape_1(%arg0 : tensor<5x5x1x32xf32>) -> tensor<*xf32> {
|
|
%0 = "onnx.Constant"() {value = dense<[5, 5, 16, 2]> : tensor<4xi32> } : () -> tensor<4xi32>
|
|
%1 = "onnx.Reshape"(%arg0, %0) : (tensor<5x5x1x32xf32>, tensor<4xi32>) -> tensor<*xf32>
|
|
"std.return"(%1) : (tensor<*xf32>) -> ()
|
|
|
|
// CHECK-LABEL: test_reshape_1
|
|
// CHECK: [[RES:%.+]] = "onnx.Reshape"(%arg0, %0) : (tensor<5x5x1x32xf32>, tensor<4xi32>) -> tensor<5x5x16x2xf32>
|
|
// CHECK: return [[RES]] : tensor<5x5x16x2xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
func @test_reshape_2(%arg0 : tensor<5x5x1x32xf32>) -> tensor<*xf32> {
|
|
%0 = "onnx.Constant"() {value = dense<[-1, 16, 2]> : tensor<3xi32> } : () -> tensor<3xi32>
|
|
%1 = "onnx.Reshape"(%arg0, %0) : (tensor<5x5x1x32xf32>, tensor<3xi32>) -> tensor<*xf32>
|
|
"std.return"(%1) : (tensor<*xf32>) -> ()
|
|
|
|
// CHECK-LABEL: test_reshape_2
|
|
// CHECK: [[RES:%.+]] = "onnx.Reshape"(%arg0, %0) : (tensor<5x5x1x32xf32>, tensor<3xi32>) -> tensor<25x16x2xf32>
|
|
// CHECK: return [[RES]] : tensor<25x16x2xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
func @test_reshape_3(%arg0 : tensor<5x5x1x32xf32>) -> tensor<*xf32> {
|
|
%0 = "onnx.Constant"() {value = dense<[-1, 0, 2]> : tensor<3xi32> } : () -> tensor<3xi32>
|
|
%1 = "onnx.Reshape"(%arg0, %0) : (tensor<5x5x1x32xf32>, tensor<3xi32>) -> tensor<*xf32>
|
|
"std.return"(%1) : (tensor<*xf32>) -> ()
|
|
|
|
// CHECK-LABEL: test_reshape_3
|
|
// CHECK: [[RES:%.+]] = "onnx.Reshape"(%arg0, %0) : (tensor<5x5x1x32xf32>, tensor<3xi32>) -> tensor<80x5x2xf32>
|
|
// CHECK: return [[RES]] : tensor<80x5x2xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
/// Test the flatten op inference.
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
func @test_flatten_1(%arg0 : tensor<5x2x3x4xf32>) -> tensor<*xf32> {
|
|
%1 = "onnx.Flatten"(%arg0) {axis = 1 : i64} : (tensor<5x2x3x4xf32>) -> tensor<*xf32>
|
|
"std.return"(%1) : (tensor<*xf32>) -> ()
|
|
|
|
// CHECK-LABEL: test_flatten_1
|
|
// CHECK: [[RES:%.+]] = "onnx.Flatten"(%arg0) {axis = 1 : i64} : (tensor<5x2x3x4xf32>) -> tensor<5x24xf32>
|
|
// CHECK: return [[RES]] : tensor<5x24xf32>
|
|
}
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
/// Test the reshape op inference when concat are present.
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
func @test_concat_1(%arg0 : tensor<5x5x1x32xf32>, %arg1 : tensor<5x5x3x32xf32>, %arg2 : tensor<5x5x5x32xf32>) -> tensor<*xf32> {
|
|
%1 = "onnx.Concat"(%arg0, %arg1, %arg2) { axis = 2 } : (tensor<5x5x1x32xf32>, tensor<5x5x3x32xf32>, tensor<5x5x5x32xf32>) -> tensor<*xf32>
|
|
"std.return"(%1) : (tensor<*xf32>) -> ()
|
|
|
|
// CHECK-LABEL: test_concat_1
|
|
// CHECK: [[RES:%.+]] = "onnx.Concat"(%arg0, %arg1, %arg2) {axis = 2 : i64} : (tensor<5x5x1x32xf32>, tensor<5x5x3x32xf32>, tensor<5x5x5x32xf32>) -> tensor<5x5x9x32xf32>
|
|
// CHECK: return [[RES]] : tensor<5x5x9x32xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
func @test_concat_2(%arg0 : tensor<5x1x32xf32>, %arg1 : tensor<5x3x32xf32>, %arg2 : tensor<5x5x32xf32>) -> tensor<*xf32> {
|
|
%1 = "onnx.Concat"(%arg0, %arg1, %arg2) { axis = 1 } : (tensor<5x1x32xf32>, tensor<5x3x32xf32>, tensor<5x5x32xf32>) -> tensor<*xf32>
|
|
"std.return"(%1) : (tensor<*xf32>) -> ()
|
|
|
|
// CHECK-LABEL: test_concat_2
|
|
// CHECK: [[RES:%.+]] = "onnx.Concat"(%arg0, %arg1, %arg2) {axis = 1 : i64} : (tensor<5x1x32xf32>, tensor<5x3x32xf32>, tensor<5x5x32xf32>) -> tensor<5x9x32xf32>
|
|
// CHECK: return [[RES]] : tensor<5x9x32xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
func @test_concat_3(%arg0 : tensor<5x1x32xf32>, %arg1 : tensor<5x3x32xf32>, %arg2 : tensor<5x5x32xf32>) -> tensor<*xf32> {
|
|
%1 = "onnx.Concat"(%arg0, %arg1, %arg2) { axis = -2 } : (tensor<5x1x32xf32>, tensor<5x3x32xf32>, tensor<5x5x32xf32>) -> tensor<*xf32>
|
|
"std.return"(%1) : (tensor<*xf32>) -> ()
|
|
|
|
// CHECK-LABEL: test_concat_3
|
|
// CHECK: [[RES:%.+]] = "onnx.Concat"(%arg0, %arg1, %arg2) {axis = 1 : i64} : (tensor<5x1x32xf32>, tensor<5x3x32xf32>, tensor<5x5x32xf32>) -> tensor<5x9x32xf32>
|
|
// CHECK: return [[RES]] : tensor<5x9x32xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
func @test_rnn_all_results(%arg0: tensor<4x3x2xf32>, %arg1: tensor<1x12x2xf32>, %arg2: tensor<1x12x3xf32>) -> tensor<*xf32> {
|
|
%cst = constant unit
|
|
%Y, %Y_h = "onnx.RNN"(%arg0, %arg1, %arg2, %cst, %cst, %cst) {hidden_size = 3 : i64} : (tensor<4x3x2xf32>, tensor<1x12x2xf32>, tensor<1x12x3xf32>, none, none, none) -> (tensor<*xf32>, tensor<*xf32>)
|
|
return %Y_h : tensor<*xf32>
|
|
|
|
// CHECK-LABEL: test_rnn_all_results
|
|
// CHECK: %{{.*}}, [[RES:%.+]] = "onnx.RNN"(%arg0, %arg1, %arg2, %cst, %cst, %cst) {hidden_size = 3 : i64} : (tensor<4x3x2xf32>, tensor<1x12x2xf32>, tensor<1x12x3xf32>, none, none, none) -> (tensor<4x1x3x3xf32>, tensor<1x3x3xf32>)
|
|
// CHECK: return [[RES]] : tensor<1x3x3xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
func @test_rnn_no_results(%arg0: tensor<4x3x2xf32>, %arg1: tensor<1x12x2xf32>, %arg2: tensor<1x12x3xf32>) -> () {
|
|
%cst = constant unit
|
|
%Y, %Y_h = "onnx.RNN"(%arg0, %arg1, %arg2, %cst, %cst, %cst) {hidden_size = 3 : i64} : (tensor<4x3x2xf32>, tensor<1x12x2xf32>, tensor<1x12x3xf32>, none, none, none) -> (none, none)
|
|
return
|
|
|
|
// CHECK-LABEL: test_rnn_no_results
|
|
// CHECK: %{{.*}}, [[RES:%.+]] = "onnx.RNN"(%arg0, %arg1, %arg2, %cst, %cst, %cst) {hidden_size = 3 : i64} : (tensor<4x3x2xf32>, tensor<1x12x2xf32>, tensor<1x12x3xf32>, none, none, none) -> (none, none)
|
|
// CHECK: return
|
|
}
|
|
|
|
// -----
|
|
|
|
func @test_rnn_missing_first_result(%arg0: tensor<4x3x2xf32>, %arg1: tensor<1x12x2xf32>, %arg2: tensor<1x12x3xf32>) -> tensor<*xf32> {
|
|
%cst = constant unit
|
|
%Y, %Y_h = "onnx.RNN"(%arg0, %arg1, %arg2, %cst, %cst, %cst) {hidden_size = 3 : i64} : (tensor<4x3x2xf32>, tensor<1x12x2xf32>, tensor<1x12x3xf32>, none, none, none) -> (none, tensor<*xf32>)
|
|
return %Y_h : tensor<*xf32>
|
|
|
|
// CHECK-LABEL: test_rnn_missing_first_result
|
|
// CHECK: %{{.*}}, [[RES:%.+]] = "onnx.RNN"(%arg0, %arg1, %arg2, %cst, %cst, %cst) {hidden_size = 3 : i64} : (tensor<4x3x2xf32>, tensor<1x12x2xf32>, tensor<1x12x3xf32>, none, none, none) -> (none, tensor<1x3x3xf32>)
|
|
// CHECK: return [[RES]] : tensor<1x3x3xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
func @test_rnn_missing_trailing_result(%arg0: tensor<4x3x2xf32>, %arg1: tensor<1x12x2xf32>, %arg2: tensor<1x12x3xf32>) -> () {
|
|
%cst = constant unit
|
|
%Y, %Y_h = "onnx.RNN"(%arg0, %arg1, %arg2, %cst, %cst, %cst) {hidden_size = 3 : i64} : (tensor<4x3x2xf32>, tensor<1x12x2xf32>, tensor<1x12x3xf32>, none, none, none) -> (tensor<*xf32>, none)
|
|
return
|
|
|
|
// CHECK-LABEL: test_rnn_missing_trailing_result
|
|
// CHECK: %{{.*}}, [[RES:%.+]] = "onnx.RNN"(%arg0, %arg1, %arg2, %cst, %cst, %cst) {hidden_size = 3 : i64} : (tensor<4x3x2xf32>, tensor<1x12x2xf32>, tensor<1x12x3xf32>, none, none, none) -> (tensor<4x1x3x3xf32>, none)
|
|
// CHECK: return
|
|
}
|
|
|
|
// -----
|
|
|
|
func @test_rnn_all_results_no_hidden_size(%arg0: tensor<4x3x2xf32>, %arg1: tensor<1x12x2xf32>, %arg2: tensor<1x12x3xf32>) -> tensor<*xf32> {
|
|
%cst = constant unit
|
|
%Y, %Y_h = "onnx.RNN"(%arg0, %arg1, %arg2, %cst, %cst, %cst) : (tensor<4x3x2xf32>, tensor<1x12x2xf32>, tensor<1x12x3xf32>, none, none, none) -> (tensor<*xf32>, tensor<*xf32>)
|
|
return %Y_h : tensor<*xf32>
|
|
|
|
// CHECK-LABEL: test_rnn_all_results_no_hidden_size
|
|
// CHECK: %{{.*}}, [[RES:%.+]] = "onnx.RNN"(%arg0, %arg1, %arg2, %cst, %cst, %cst) {hidden_size = 3 : i64} : (tensor<4x3x2xf32>, tensor<1x12x2xf32>, tensor<1x12x3xf32>, none, none, none) -> (tensor<4x1x3x3xf32>, tensor<1x3x3xf32>)
|
|
// CHECK: return [[RES]] : tensor<1x3x3xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
func @test_rnn_all_results_unknown_dims(%arg0: tensor<?x?x?xf32>, %arg1: tensor<?x?x?xf32>, %arg2: tensor<?x?x?xf32>) -> tensor<*xf32> {
|
|
%cst = constant unit
|
|
%Y, %Y_h = "onnx.RNN"(%arg0, %arg1, %arg2, %cst, %cst, %cst) : (tensor<?x?x?xf32>, tensor<?x?x?xf32>, tensor<?x?x?xf32>, none, none, none) -> (tensor<*xf32>, tensor<*xf32>)
|
|
return %Y_h : tensor<*xf32>
|
|
|
|
// CHECK-LABEL: test_rnn_all_results_unknown_dims
|
|
// CHECK: %{{.*}}, [[RES:%.+]] = "onnx.RNN"(%arg0, %arg1, %arg2, %cst, %cst, %cst) : (tensor<?x?x?xf32>, tensor<?x?x?xf32>, tensor<?x?x?xf32>, none, none, none) -> (tensor<?x1x?x?xf32>, tensor<1x?x?xf32>)
|
|
// CHECK: return [[RES]] : tensor<1x?x?xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
func @test_gru_all_results(%arg0: tensor<4x3x2xf32>, %arg1: tensor<1x12x2xf32>, %arg2: tensor<1x12x3xf32>) -> tensor<*xf32> {
|
|
%cst = constant unit
|
|
%Y, %Y_h = "onnx.GRU"(%arg0, %arg1, %arg2, %cst, %cst, %cst) {hidden_size = 3 : i64} : (tensor<4x3x2xf32>, tensor<1x12x2xf32>, tensor<1x12x3xf32>, none, none, none) -> (tensor<*xf32>, tensor<*xf32>)
|
|
return %Y_h : tensor<*xf32>
|
|
|
|
// CHECK-LABEL: test_gru_all_results
|
|
// CHECK: %{{.*}}, [[RES:%.+]] = "onnx.GRU"(%arg0, %arg1, %arg2, %cst, %cst, %cst) {hidden_size = 3 : i64} : (tensor<4x3x2xf32>, tensor<1x12x2xf32>, tensor<1x12x3xf32>, none, none, none) -> (tensor<4x1x3x3xf32>, tensor<1x3x3xf32>)
|
|
// CHECK: return [[RES]] : tensor<1x3x3xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
func @test_gru_no_results(%arg0: tensor<4x3x2xf32>, %arg1: tensor<1x12x2xf32>, %arg2: tensor<1x12x3xf32>) -> () {
|
|
%cst = constant unit
|
|
%Y, %Y_h = "onnx.GRU"(%arg0, %arg1, %arg2, %cst, %cst, %cst) {hidden_size = 3 : i64} : (tensor<4x3x2xf32>, tensor<1x12x2xf32>, tensor<1x12x3xf32>, none, none, none) -> (none, none)
|
|
return
|
|
|
|
// CHECK-LABEL: test_gru_no_results
|
|
// CHECK: %{{.*}}, [[RES:%.+]] = "onnx.GRU"(%arg0, %arg1, %arg2, %cst, %cst, %cst) {hidden_size = 3 : i64} : (tensor<4x3x2xf32>, tensor<1x12x2xf32>, tensor<1x12x3xf32>, none, none, none) -> (none, none)
|
|
// CHECK: return
|
|
}
|
|
|
|
// -----
|
|
|
|
func @test_gru_missing_first_result(%arg0: tensor<4x3x2xf32>, %arg1: tensor<1x12x2xf32>, %arg2: tensor<1x12x3xf32>) -> tensor<*xf32> {
|
|
%cst = constant unit
|
|
%Y, %Y_h = "onnx.GRU"(%arg0, %arg1, %arg2, %cst, %cst, %cst) {hidden_size = 3 : i64} : (tensor<4x3x2xf32>, tensor<1x12x2xf32>, tensor<1x12x3xf32>, none, none, none) -> (none, tensor<*xf32>)
|
|
return %Y_h : tensor<*xf32>
|
|
|
|
// CHECK-LABEL: test_gru_missing_first_result
|
|
// CHECK: %{{.*}}, [[RES:%.+]] = "onnx.GRU"(%arg0, %arg1, %arg2, %cst, %cst, %cst) {hidden_size = 3 : i64} : (tensor<4x3x2xf32>, tensor<1x12x2xf32>, tensor<1x12x3xf32>, none, none, none) -> (none, tensor<1x3x3xf32>)
|
|
// CHECK: return [[RES]] : tensor<1x3x3xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
func @test_gru_missing_trailing_result(%arg0: tensor<4x3x2xf32>, %arg1: tensor<1x12x2xf32>, %arg2: tensor<1x12x3xf32>) -> () {
|
|
%cst = constant unit
|
|
%Y, %Y_h = "onnx.GRU"(%arg0, %arg1, %arg2, %cst, %cst, %cst) {hidden_size = 3 : i64} : (tensor<4x3x2xf32>, tensor<1x12x2xf32>, tensor<1x12x3xf32>, none, none, none) -> (tensor<*xf32>, none)
|
|
return
|
|
|
|
// CHECK-LABEL: test_gru_missing_trailing_result
|
|
// CHECK: %{{.*}}, [[RES:%.+]] = "onnx.GRU"(%arg0, %arg1, %arg2, %cst, %cst, %cst) {hidden_size = 3 : i64} : (tensor<4x3x2xf32>, tensor<1x12x2xf32>, tensor<1x12x3xf32>, none, none, none) -> (tensor<4x1x3x3xf32>, none)
|
|
// CHECK: return
|
|
}
|
|
|
|
// -----
|
|
|
|
func @test_gru_all_results_no_hidden_size(%arg0: tensor<4x3x2xf32>, %arg1: tensor<1x12x2xf32>, %arg2: tensor<1x12x3xf32>) -> tensor<*xf32> {
|
|
%cst = constant unit
|
|
%Y, %Y_h = "onnx.GRU"(%arg0, %arg1, %arg2, %cst, %cst, %cst) : (tensor<4x3x2xf32>, tensor<1x12x2xf32>, tensor<1x12x3xf32>, none, none, none) -> (tensor<*xf32>, tensor<*xf32>)
|
|
return %Y_h : tensor<*xf32>
|
|
|
|
// CHECK-LABEL: test_gru_all_results_no_hidden_size
|
|
// CHECK: %{{.*}}, [[RES:%.+]] = "onnx.GRU"(%arg0, %arg1, %arg2, %cst, %cst, %cst) {hidden_size = 3 : i64} : (tensor<4x3x2xf32>, tensor<1x12x2xf32>, tensor<1x12x3xf32>, none, none, none) -> (tensor<4x1x3x3xf32>, tensor<1x3x3xf32>)
|
|
// CHECK: return [[RES]] : tensor<1x3x3xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
func @test_gru_all_results_unknown_dims(%arg0: tensor<?x?x?xf32>, %arg1: tensor<?x?x?xf32>, %arg2: tensor<?x?x?xf32>) -> tensor<*xf32> {
|
|
%cst = constant unit
|
|
%Y, %Y_h = "onnx.GRU"(%arg0, %arg1, %arg2, %cst, %cst, %cst) : (tensor<?x?x?xf32>, tensor<?x?x?xf32>, tensor<?x?x?xf32>, none, none, none) -> (tensor<*xf32>, tensor<*xf32>)
|
|
return %Y_h : tensor<*xf32>
|
|
|
|
// CHECK-LABEL: test_gru_all_results_unknown_dims
|
|
// CHECK: %{{.*}}, [[RES:%.+]] = "onnx.GRU"(%arg0, %arg1, %arg2, %cst, %cst, %cst) : (tensor<?x?x?xf32>, tensor<?x?x?xf32>, tensor<?x?x?xf32>, none, none, none) -> (tensor<?x1x?x?xf32>, tensor<1x?x?xf32>)
|
|
// CHECK: return [[RES]] : tensor<1x?x?xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
func @test_lstm_all_results(%arg0: tensor<4x3x2xf32>, %arg1: tensor<1x12x2xf32>, %arg2: tensor<1x12x3xf32>) -> tensor<*xf32> {
|
|
%cst = constant unit
|
|
%Y, %Y_h, %Y_c = "onnx.LSTM"(%arg0, %arg1, %arg2, %cst, %cst, %cst, %cst, %cst) {hidden_size = 3 : i64} : (tensor<4x3x2xf32>, tensor<1x12x2xf32>, tensor<1x12x3xf32>, none, none, none, none, none) -> (tensor<*xf32>, tensor<*xf32>, tensor<*xf32>)
|
|
return %Y_h : tensor<*xf32>
|
|
|
|
// CHECK-LABEL: test_lstm_all_results
|
|
// CHECK: %{{.*}}, [[RES:%.+]], %{{.*}} = "onnx.LSTM"(%arg0, %arg1, %arg2, %cst, %cst, %cst, %cst, %cst) {hidden_size = 3 : i64} : (tensor<4x3x2xf32>, tensor<1x12x2xf32>, tensor<1x12x3xf32>, none, none, none, none, none) -> (tensor<4x1x3x3xf32>, tensor<1x3x3xf32>, tensor<1x3x3xf32>)
|
|
// CHECK: return [[RES]] : tensor<1x3x3xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
func @test_lstm_no_results(%arg0: tensor<4x3x2xf32>, %arg1: tensor<1x12x2xf32>, %arg2: tensor<1x12x3xf32>) -> () {
|
|
%cst = constant unit
|
|
%Y, %Y_h, %Y_c = "onnx.LSTM"(%arg0, %arg1, %arg2, %cst, %cst, %cst, %cst, %cst) {hidden_size = 3 : i64} : (tensor<4x3x2xf32>, tensor<1x12x2xf32>, tensor<1x12x3xf32>, none, none, none, none, none) -> (none, none, none)
|
|
return
|
|
|
|
// CHECK-LABEL: test_lstm_no_results
|
|
// CHECK: %{{.*}}, [[RES:%.+]], %{{.*}} = "onnx.LSTM"(%arg0, %arg1, %arg2, %cst, %cst, %cst, %cst, %cst) {hidden_size = 3 : i64} : (tensor<4x3x2xf32>, tensor<1x12x2xf32>, tensor<1x12x3xf32>, none, none, none, none, none) -> (none, none, none)
|
|
// CHECK: return
|
|
}
|
|
|
|
// -----
|
|
|
|
func @test_lstm_missing_first_result(%arg0: tensor<4x3x2xf32>, %arg1: tensor<1x12x2xf32>, %arg2: tensor<1x12x3xf32>) -> tensor<*xf32> {
|
|
%cst = constant unit
|
|
%Y, %Y_h, %Y_c = "onnx.LSTM"(%arg0, %arg1, %arg2, %cst, %cst, %cst, %cst, %cst) {hidden_size = 3 : i64} : (tensor<4x3x2xf32>, tensor<1x12x2xf32>, tensor<1x12x3xf32>, none, none, none, none, none) -> (none, tensor<*xf32>, tensor<*xf32>)
|
|
return %Y_h : tensor<*xf32>
|
|
|
|
// CHECK-LABEL: test_lstm_missing_first_result
|
|
// CHECK: %{{.*}}, [[RES:%.+]], %{{.*}} = "onnx.LSTM"(%arg0, %arg1, %arg2, %cst, %cst, %cst, %cst, %cst) {hidden_size = 3 : i64} : (tensor<4x3x2xf32>, tensor<1x12x2xf32>, tensor<1x12x3xf32>, none, none, none, none, none) -> (none, tensor<1x3x3xf32>, tensor<1x3x3xf32>)
|
|
// CHECK: return [[RES]] : tensor<1x3x3xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
func @test_lstm_missing_trailing_result(%arg0: tensor<4x3x2xf32>, %arg1: tensor<1x12x2xf32>, %arg2: tensor<1x12x3xf32>) -> tensor<*xf32> {
|
|
%cst = constant unit
|
|
%Y, %Y_h, %Y_c = "onnx.LSTM"(%arg0, %arg1, %arg2, %cst, %cst, %cst, %cst, %cst) {hidden_size = 3 : i64} : (tensor<4x3x2xf32>, tensor<1x12x2xf32>, tensor<1x12x3xf32>, none, none, none, none, none) -> (tensor<*xf32>, tensor<*xf32>, none)
|
|
return %Y_h : tensor<*xf32>
|
|
|
|
// CHECK-LABEL: test_lstm_missing_trailing_result
|
|
// CHECK: %{{.*}}, [[RES:%.+]], %{{.*}} = "onnx.LSTM"(%arg0, %arg1, %arg2, %cst, %cst, %cst, %cst, %cst) {hidden_size = 3 : i64} : (tensor<4x3x2xf32>, tensor<1x12x2xf32>, tensor<1x12x3xf32>, none, none, none, none, none) -> (tensor<4x1x3x3xf32>, tensor<1x3x3xf32>, none)
|
|
// CHECK: return [[RES]] : tensor<1x3x3xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
func @test_lstm_all_results_no_hidden_size(%arg0: tensor<4x3x2xf32>, %arg1: tensor<1x12x2xf32>, %arg2: tensor<1x12x3xf32>) -> tensor<*xf32> {
|
|
%cst = constant unit
|
|
%Y, %Y_h, %Y_c = "onnx.LSTM"(%arg0, %arg1, %arg2, %cst, %cst, %cst, %cst, %cst) : (tensor<4x3x2xf32>, tensor<1x12x2xf32>, tensor<1x12x3xf32>, none, none, none, none, none) -> (tensor<*xf32>, tensor<*xf32>, tensor<*xf32>)
|
|
return %Y_h : tensor<*xf32>
|
|
|
|
// CHECK-LABEL: test_lstm_all_results_no_hidden_size
|
|
// CHECK: %{{.*}}, [[RES:%.+]], %{{.*}} = "onnx.LSTM"(%arg0, %arg1, %arg2, %cst, %cst, %cst, %cst, %cst) {hidden_size = 3 : i64} : (tensor<4x3x2xf32>, tensor<1x12x2xf32>, tensor<1x12x3xf32>, none, none, none, none, none) -> (tensor<4x1x3x3xf32>, tensor<1x3x3xf32>, tensor<1x3x3xf32>)
|
|
// CHECK: return [[RES]] : tensor<1x3x3xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
func @test_lstm_all_results_unknown_dims(%arg0: tensor<?x?x?xf32>, %arg1: tensor<?x?x?xf32>, %arg2: tensor<?x?x?xf32>) -> tensor<*xf32> {
|
|
%cst = constant unit
|
|
%Y, %Y_h, %Y_c = "onnx.LSTM"(%arg0, %arg1, %arg2, %cst, %cst, %cst, %cst, %cst) : (tensor<?x?x?xf32>, tensor<?x?x?xf32>, tensor<?x?x?xf32>, none, none, none, none, none) -> (tensor<*xf32>, tensor<*xf32>, tensor<*xf32>)
|
|
return %Y_h : tensor<*xf32>
|
|
|
|
// CHECK-LABEL: test_lstm_all_results_unknown_dims
|
|
// CHECK: %{{.*}}, [[RES:%.+]], %{{.*}} = "onnx.LSTM"(%arg0, %arg1, %arg2, %cst, %cst, %cst, %cst, %cst) : (tensor<?x?x?xf32>, tensor<?x?x?xf32>, tensor<?x?x?xf32>, none, none, none, none, none) -> (tensor<?x1x?x?xf32>, tensor<1x?x?xf32>, tensor<1x?x?xf32>)
|
|
// CHECK: return [[RES]] : tensor<1x?x?xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
func @test_split_1(%arg0 : tensor<16x32x64xf32>) -> tensor<*xf32> {
|
|
%0, %1 = "onnx.Split"(%arg0) { axis = 1 } : (tensor<16x32x64xf32>) -> (tensor<*xf32>, tensor<*xf32>)
|
|
"std.return"(%0) : (tensor<*xf32>) -> ()
|
|
|
|
// CHECK-LABEL: test_split_1
|
|
// CHECK: [[RES:%.+]]:2 = "onnx.Split"(%arg0) {axis = 1 : i64, split = [16, 16]} : (tensor<16x32x64xf32>) -> (tensor<16x16x64xf32>, tensor<16x16x64xf32>)
|
|
// CHECK: return [[RES]]#0 : tensor<16x16x64xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
func @test_split_2(%arg0 : tensor<16x32x64xf32>) -> tensor<*xf32> {
|
|
%0, %1 = "onnx.Split"(%arg0) { axis = -2 } : (tensor<16x32x64xf32>) -> (tensor<*xf32>, tensor<*xf32>)
|
|
"std.return"(%0) : (tensor<*xf32>) -> ()
|
|
|
|
// CHECK-LABEL: test_split_2
|
|
// CHECK: [[RES:%.+]]:2 = "onnx.Split"(%arg0) {axis = 1 : i64, split = [16, 16]} : (tensor<16x32x64xf32>) -> (tensor<16x16x64xf32>, tensor<16x16x64xf32>)
|
|
// CHECK: return [[RES]]#0 : tensor<16x16x64xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
func @test_split_3(%arg0 : tensor<16x32x64xf32>) -> tensor<*xf32> {
|
|
%0, %1 = "onnx.Split"(%arg0) { axis = 1, split = [2, 30]} : (tensor<16x32x64xf32>) -> (tensor<*xf32>, tensor<*xf32>)
|
|
"std.return"(%0) : (tensor<*xf32>) -> ()
|
|
|
|
// CHECK-LABEL: test_split_3
|
|
// CHECK: [[RES:%.+]]:2 = "onnx.Split"(%arg0) {axis = 1 : i64, split = [2, 30]} : (tensor<16x32x64xf32>) -> (tensor<16x2x64xf32>, tensor<16x30x64xf32>)
|
|
// CHECK: return [[RES]]#0 : tensor<16x2x64xf32>
|
|
}
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
/// Test the cast op inference.
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
func @test_cast_1(%arg0 : tensor<2x3x4xf32>) -> tensor<*xf32> {
|
|
%1 = "onnx.Cast"(%arg0) {to = 1} : (tensor<2x3x4xf32>) -> tensor<*xf32>
|
|
"std.return"(%1) : (tensor<*xf32>) -> ()
|
|
|
|
// CHECK-LABEL: test_cast_1
|
|
// CHECK: [[RES:%.+]] = "onnx.Cast"(%arg0) {to = 1 : i64} : (tensor<2x3x4xf32>) -> tensor<2x3x4xf32>
|
|
// CHECK: return [[RES]] : tensor<2x3x4xf32>
|
|
}
|
|
|
|
func @test_cast_2(%arg0 : tensor<2x3x4xf32>) -> tensor<*xui8> {
|
|
%1 = "onnx.Cast"(%arg0) {to = 2} : (tensor<2x3x4xf32>) -> tensor<*xui8>
|
|
"std.return"(%1) : (tensor<*xui8>) -> ()
|
|
|
|
// CHECK-LABEL: test_cast_2
|
|
// CHECK: [[RES:%.+]] = "onnx.Cast"(%arg0) {to = 2 : i64} : (tensor<2x3x4xf32>) -> tensor<2x3x4xi8>
|
|
// CHECK: return [[RES]] : tensor<2x3x4xi8>
|
|
}
|
|
|
|
func @test_cast_3(%arg0 : tensor<2x3x4xf32>) -> tensor<*xsi8> {
|
|
%1 = "onnx.Cast"(%arg0) {to = 3} : (tensor<2x3x4xf32>) -> tensor<*xsi8>
|
|
"std.return"(%1) : (tensor<*xsi8>) -> ()
|
|
|
|
// CHECK-LABEL: test_cast_3
|
|
// CHECK: [[RES:%.+]] = "onnx.Cast"(%arg0) {to = 3 : i64} : (tensor<2x3x4xf32>) -> tensor<2x3x4xi8>
|
|
// CHECK: return [[RES]] : tensor<2x3x4xi8>
|
|
}
|
|
|
|
func @test_cast_10(%arg0 : tensor<2x3x4xf32>) -> tensor<*xf16> {
|
|
%1 = "onnx.Cast"(%arg0) {to = 10} : (tensor<2x3x4xf32>) -> tensor<*xf16>
|
|
"std.return"(%1) : (tensor<*xf16>) -> ()
|
|
|
|
// CHECK-LABEL: test_cast_10
|
|
// CHECK: [[RES:%.+]] = "onnx.Cast"(%arg0) {to = 10 : i64} : (tensor<2x3x4xf32>) -> tensor<2x3x4xf16>
|
|
// CHECK: return [[RES]] : tensor<2x3x4xf16>
|
|
}
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
/// Test the quantization op inferences.
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
func @test_dyn_quantize_linear_1(%arg0 : tensor<5x2x3x4xf32>) -> tensor<*xi8> {
|
|
%1:3 = "onnx.DynamicQuantizeLinear"(%arg0) {} : (tensor<5x2x3x4xf32>) -> (tensor<*xi8>, tensor<*xi8>, tensor<*xi8>)
|
|
"std.return"(%1#0) {} : (tensor<*xi8>) -> ()
|
|
|
|
// CHECK-LABEL: test_dyn_quantize_linear_1
|
|
// CHECK: [[RES:%.+]], {{.*}}, {{.*}} = "onnx.DynamicQuantizeLinear"(%arg0) : (tensor<5x2x3x4xf32>) -> (tensor<5x2x3x4xi8>, tensor<i8>, tensor<i8>)
|
|
// CHECK: return [[RES]] : tensor<5x2x3x4xi8>
|
|
}
|
|
|
|
func @test_quantize_linear_1(%arg0 : tensor<5x2x3x4xf32>, %arg1 : tensor<i8>, %arg2 : tensor<i8>) -> tensor<*xi8> {
|
|
%1 = "onnx.QuantizeLinear"(%arg0, %arg1, %arg2) {} : (tensor<5x2x3x4xf32>, tensor<i8>, tensor<i8>) -> tensor<*xi8>
|
|
"std.return"(%1) {} : (tensor<*xi8>) -> ()
|
|
|
|
// CHECK-LABEL: test_quantize_linear_1
|
|
// CHECK: [[RES:%.+]] = "onnx.QuantizeLinear"(%arg0, %arg1, %arg2) : (tensor<5x2x3x4xf32>, tensor<i8>, tensor<i8>) -> tensor<5x2x3x4xi8>
|
|
// CHECK: return [[RES]] : tensor<5x2x3x4xi8>
|
|
}
|
|
|
|
func @test_dequantize_linear_1(%arg0 : tensor<5x2x3x4xi8>, %arg1 : tensor<i8>, %arg2 : tensor<i8>) -> tensor<*xf32> {
|
|
%1 = "onnx.DequantizeLinear"(%arg0, %arg1, %arg2) {} : (tensor<5x2x3x4xi8>, tensor<i8>, tensor<i8>) -> tensor<*xf32>
|
|
"std.return"(%1) {} : (tensor<*xf32>) -> ()
|
|
|
|
// CHECK-LABEL: test_dequantize_linear_1
|
|
// CHECK: [[RES:%.+]] = "onnx.DequantizeLinear"(%arg0, %arg1, %arg2) : (tensor<5x2x3x4xi8>, tensor<i8>, tensor<i8>) -> tensor<5x2x3x4xf32>
|
|
// CHECK: return [[RES]] : tensor<5x2x3x4xf32>
|
|
}
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
/// Test shape inference for ConvInteger operation and all its attributes.
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
/// Default and required attributes for 1-D convolution.
|
|
|
|
func @test_convinteger_0(%arg0 : tensor<1x2x32xi8>, %arg1 : tensor<5x2x6xi8>, %arg2 : tensor<i8>, %arg3 : tensor<i8>) -> tensor<*xi32> {
|
|
%0 = "onnx.ConvInteger"(%arg0, %arg1, %arg2, %arg3) {auto_pad = "NOTSET", group = 1 : i64} : (tensor<1x2x32xi8>, tensor<5x2x6xi8>, tensor<i8>, tensor<i8>) -> tensor<*xi32>
|
|
"std.return"(%0) : (tensor<*xi32>) -> ()
|
|
|
|
// CHECK-LABEL: test_convinteger_0
|
|
// CHECK: [[RES_ATTR:%.+]] = "onnx.ConvInteger"(%arg0, %arg1, %arg2, %arg3) {auto_pad = "NOTSET", dilations = [1], group = 1 : i64, kernel_shape = [6], pads = [0, 0], strides = [1]} : (tensor<1x2x32xi8>, tensor<5x2x6xi8>, tensor<i8>, tensor<i8>) -> tensor<1x5x27xi32>
|
|
// CHECK: return [[RES_ATTR]] : tensor<1x5x27xi32>
|
|
}
|
|
|
|
/// Default and required attributes.
|
|
|
|
func @test_convinteger_1(%arg0 : tensor<1x2x32x64xi8>, %arg1 : tensor<5x2x6x7xi8>, %arg2 : tensor<i8>, %arg3 : tensor<i8>) -> tensor<*xi32> {
|
|
%0 = "onnx.ConvInteger"(%arg0, %arg1, %arg2, %arg3) {auto_pad = "NOTSET", group = 1 : i64} : (tensor<1x2x32x64xi8>, tensor<5x2x6x7xi8>, tensor<i8>, tensor<i8>) -> tensor<*xi32>
|
|
"std.return"(%0) : (tensor<*xi32>) -> ()
|
|
|
|
// CHECK-LABEL: test_convinteger_1
|
|
// CHECK: [[RES_ATTR:%.+]] = "onnx.ConvInteger"(%arg0, %arg1, %arg2, %arg3) {auto_pad = "NOTSET", dilations = [1, 1], group = 1 : i64, kernel_shape = [6, 7], pads = [0, 0, 0, 0], strides = [1, 1]} : (tensor<1x2x32x64xi8>, tensor<5x2x6x7xi8>, tensor<i8>, tensor<i8>) -> tensor<1x5x27x58xi32>
|
|
// CHECK: return [[RES_ATTR]] : tensor<1x5x27x58xi32>
|
|
}
|
|
|
|
/// kernel_shape attribute.
|
|
|
|
func @test_convinteger_2(%arg0 : tensor<1x2x32x64xi8>, %arg1 : tensor<5x2x6x7xi8>, %arg2 : tensor<i8>, %arg3 : tensor<i8>) -> tensor<*xi32> {
|
|
%0 = "onnx.ConvInteger"(%arg0, %arg1, %arg2, %arg3) {auto_pad = "NOTSET", group = 1 : i64, kernel_shape = [8, 9]} : (tensor<1x2x32x64xi8>, tensor<5x2x6x7xi8>, tensor<i8>, tensor<i8>) -> tensor<*xi32>
|
|
"std.return"(%0) : (tensor<*xi32>) -> ()
|
|
|
|
// CHECK-LABEL: test_convinteger_2
|
|
// CHECK: [[RES_ATTR:%.+]] = "onnx.ConvInteger"(%arg0, %arg1, %arg2, %arg3) {auto_pad = "NOTSET", dilations = [1, 1], group = 1 : i64, kernel_shape = [8, 9], pads = [0, 0, 0, 0], strides = [1, 1]} : (tensor<1x2x32x64xi8>, tensor<5x2x6x7xi8>, tensor<i8>, tensor<i8>) -> tensor<1x5x25x56xi32>
|
|
// CHECK: return [[RES_ATTR]] : tensor<1x5x25x56xi32>
|
|
}
|
|
|
|
/// pads attribute.
|
|
/// Use pads to make output size equal to input size by adding K - 1 to the result.
|
|
|
|
func @test_convinteger_3(%arg0 : tensor<1x2x32x64xi8>, %arg1 : tensor<5x2x6x10xi8>, %arg2 : tensor<i8>, %arg3 : tensor<i8>) -> tensor<*xi32> {
|
|
%0 = "onnx.ConvInteger"(%arg0, %arg1, %arg2, %arg3) {auto_pad = "NOTSET", group = 1 : i64, pads = [2, 4, 3, 5]} : (tensor<1x2x32x64xi8>, tensor<5x2x6x10xi8>, tensor<i8>, tensor<i8>) -> tensor<*xi32>
|
|
"std.return"(%0) : (tensor<*xi32>) -> ()
|
|
|
|
// CHECK-LABEL: test_convinteger_3
|
|
// CHECK: [[RES_ATTR:%.+]] = "onnx.ConvInteger"(%arg0, %arg1, %arg2, %arg3) {auto_pad = "NOTSET", dilations = [1, 1], group = 1 : i64, kernel_shape = [6, 10], pads = [2, 4, 3, 5], strides = [1, 1]} : (tensor<1x2x32x64xi8>, tensor<5x2x6x10xi8>, tensor<i8>, tensor<i8>) -> tensor<1x5x32x64xi32>
|
|
// CHECK: return [[RES_ATTR]] : tensor<1x5x32x64xi32>
|
|
}
|
|
|
|
/// auto_pad set to SAME_UPPER and SAME_LOWER.
|
|
|
|
func @test_convinteger_4(%arg0 : tensor<1x2x32x64xi8>, %arg1 : tensor<5x2x6x10xi8>, %arg2 : tensor<i8>, %arg3 : tensor<i8>) -> tensor<*xi32> {
|
|
%0 = "onnx.ConvInteger"(%arg0, %arg1, %arg2, %arg3) {auto_pad = "SAME_UPPER", group = 1 : i64} : (tensor<1x2x32x64xi8>, tensor<5x2x6x10xi8>, tensor<i8>, tensor<i8>) -> tensor<*xi32>
|
|
"std.return"(%0) : (tensor<*xi32>) -> ()
|
|
|
|
// CHECK-LABEL: test_convinteger_4
|
|
// CHECK: [[RES_ATTR:%.+]] = "onnx.ConvInteger"(%arg0, %arg1, %arg2, %arg3) {auto_pad = "NOTSET", dilations = [1, 1], group = 1 : i64, kernel_shape = [6, 10], pads = [2, 4, 3, 5], strides = [1, 1]} : (tensor<1x2x32x64xi8>, tensor<5x2x6x10xi8>, tensor<i8>, tensor<i8>) -> tensor<1x5x32x64xi32>
|
|
// CHECK: return [[RES_ATTR]] : tensor<1x5x32x64xi32>
|
|
}
|
|
|
|
func @test_convinteger_5(%arg0 : tensor<1x2x32x64xi8>, %arg1 : tensor<5x2x6x10xi8>, %arg2 : tensor<i8>, %arg3 : tensor<i8>) -> tensor<*xi32> {
|
|
%0 = "onnx.ConvInteger"(%arg0, %arg1, %arg2, %arg3) {auto_pad = "SAME_LOWER", group = 1 : i64} : (tensor<1x2x32x64xi8>, tensor<5x2x6x10xi8>, tensor<i8>, tensor<i8>) -> tensor<*xi32>
|
|
"std.return"(%0) : (tensor<*xi32>) -> ()
|
|
|
|
// CHECK-LABEL: test_convinteger_5
|
|
// CHECK: [[RES_ATTR:%.+]] = "onnx.ConvInteger"(%arg0, %arg1, %arg2, %arg3) {auto_pad = "NOTSET", dilations = [1, 1], group = 1 : i64, kernel_shape = [6, 10], pads = [3, 5, 2, 4], strides = [1, 1]} : (tensor<1x2x32x64xi8>, tensor<5x2x6x10xi8>, tensor<i8>, tensor<i8>) -> tensor<1x5x32x64xi32>
|
|
// CHECK: return [[RES_ATTR]] : tensor<1x5x32x64xi32>
|
|
}
|
|
|
|
/// auto_pad set to VALID.
|
|
|
|
func @test_convinteger_6(%arg0 : tensor<1x2x32x64xi8>, %arg1 : tensor<5x2x6x10xi8>, %arg2 : tensor<i8>, %arg3 : tensor<i8>) -> tensor<*xi32> {
|
|
%0 = "onnx.ConvInteger"(%arg0, %arg1, %arg2, %arg3) {auto_pad = "VALID", group = 1 : i64} : (tensor<1x2x32x64xi8>, tensor<5x2x6x10xi8>, tensor<i8>, tensor<i8>) -> tensor<*xi32>
|
|
"std.return"(%0) : (tensor<*xi32>) -> ()
|
|
|
|
// CHECK-LABEL: test_convinteger_6
|
|
// CHECK: [[RES_ATTR:%.+]] = "onnx.ConvInteger"(%arg0, %arg1, %arg2, %arg3) {auto_pad = "NOTSET", dilations = [1, 1], group = 1 : i64, kernel_shape = [6, 10], pads = [0, 0, 0, 0], strides = [1, 1]} : (tensor<1x2x32x64xi8>, tensor<5x2x6x10xi8>, tensor<i8>, tensor<i8>) -> tensor<1x5x27x55xi32>
|
|
// CHECK: return [[RES_ATTR]] : tensor<1x5x27x55xi32>
|
|
}
|
|
|
|
/// With strides attribute.
|
|
|
|
func @test_convinteger_7(%arg0 : tensor<1x2x32x64xi8>, %arg1 : tensor<5x2x6x7xi8>, %arg2 : tensor<i8>, %arg3 : tensor<i8>) -> tensor<*xi32> {
|
|
%0 = "onnx.ConvInteger"(%arg0, %arg1, %arg2, %arg3) {auto_pad = "NOTSET", group = 1 : i64, strides = [2, 3]} : (tensor<1x2x32x64xi8>, tensor<5x2x6x7xi8>, tensor<i8>, tensor<i8>) -> tensor<*xi32>
|
|
"std.return"(%0) : (tensor<*xi32>) -> ()
|
|
|
|
// CHECK-LABEL: test_convinteger_7
|
|
// CHECK: [[RES_ATTR:%.+]] = "onnx.ConvInteger"(%arg0, %arg1, %arg2, %arg3) {auto_pad = "NOTSET", dilations = [1, 1], group = 1 : i64, kernel_shape = [6, 7], pads = [0, 0, 0, 0], strides = [2, 3]} : (tensor<1x2x32x64xi8>, tensor<5x2x6x7xi8>, tensor<i8>, tensor<i8>) -> tensor<1x5x14x20xi32>
|
|
// CHECK: return [[RES_ATTR]] : tensor<1x5x14x20xi32>
|
|
}
|
|
|
|
/// auto_pad set to SAME_UPPER with strides attribute.
|
|
/// The auto_pad will pas as if stride is equal to 1.
|
|
|
|
func @test_convinteger_8(%arg0 : tensor<1x2x32x64xi8>, %arg1 : tensor<5x2x6x7xi8>, %arg2 : tensor<i8>, %arg3 : tensor<i8>) -> tensor<*xi32> {
|
|
%0 = "onnx.ConvInteger"(%arg0, %arg1, %arg2, %arg3) {auto_pad = "SAME_UPPER", group = 1 : i64, strides = [2, 3]} : (tensor<1x2x32x64xi8>, tensor<5x2x6x7xi8>, tensor<i8>, tensor<i8>) -> tensor<*xi32>
|
|
"std.return"(%0) : (tensor<*xi32>) -> ()
|
|
|
|
// CHECK-LABEL: test_convinteger_8
|
|
// CHECK: [[RES_ATTR:%.+]] = "onnx.ConvInteger"(%arg0, %arg1, %arg2, %arg3) {auto_pad = "NOTSET", dilations = [1, 1], group = 1 : i64, kernel_shape = [6, 7], pads = [2, 3, 2, 3], strides = [2, 3]} : (tensor<1x2x32x64xi8>, tensor<5x2x6x7xi8>, tensor<i8>, tensor<i8>) -> tensor<1x5x16x22xi32>
|
|
// CHECK: return [[RES_ATTR]] : tensor<1x5x16x22xi32>
|
|
}
|
|
|
|
/// dilations attribute.
|
|
|
|
func @test_convinteger_9(%arg0 : tensor<1x2x32x64xi8>, %arg1 : tensor<5x2x6x7xi8>, %arg2 : tensor<i8>, %arg3 : tensor<i8>) -> tensor<*xi32> {
|
|
%0 = "onnx.ConvInteger"(%arg0, %arg1, %arg2, %arg3) {auto_pad = "NOTSET", group = 1 : i64, dilations = [2, 3]} : (tensor<1x2x32x64xi8>, tensor<5x2x6x7xi8>, tensor<i8>, tensor<i8>) -> tensor<*xi32>
|
|
"std.return"(%0) : (tensor<*xi32>) -> ()
|
|
|
|
// CHECK-LABEL: test_convinteger_9
|
|
// CHECK: [[RES_ATTR:%.+]] = "onnx.ConvInteger"(%arg0, %arg1, %arg2, %arg3) {auto_pad = "NOTSET", dilations = [2, 3], group = 1 : i64, kernel_shape = [6, 7], pads = [0, 0, 0, 0], strides = [1, 1]} : (tensor<1x2x32x64xi8>, tensor<5x2x6x7xi8>, tensor<i8>, tensor<i8>) -> tensor<1x5x22x46xi32>
|
|
// CHECK: return [[RES_ATTR]] : tensor<1x5x22x46xi32>
|
|
}
|
|
|
|
/// dilations attribute with stride.
|
|
|
|
func @test_convinteger_10(%arg0 : tensor<1x2x32x64xi8>, %arg1 : tensor<5x2x6x7xi8>, %arg2 : tensor<i8>, %arg3 : tensor<i8>) -> tensor<*xi32> {
|
|
%0 = "onnx.ConvInteger"(%arg0, %arg1, %arg2, %arg3) {auto_pad = "NOTSET", group = 1 : i64, dilations = [2, 3], strides = [2, 2]} : (tensor<1x2x32x64xi8>, tensor<5x2x6x7xi8>, tensor<i8>, tensor<i8>) -> tensor<*xi32>
|
|
"std.return"(%0) : (tensor<*xi32>) -> ()
|
|
|
|
// CHECK-LABEL: test_convinteger_10
|
|
// CHECK: [[RES_ATTR:%.+]] = "onnx.ConvInteger"(%arg0, %arg1, %arg2, %arg3) {auto_pad = "NOTSET", dilations = [2, 3], group = 1 : i64, kernel_shape = [6, 7], pads = [0, 0, 0, 0], strides = [2, 2]} : (tensor<1x2x32x64xi8>, tensor<5x2x6x7xi8>, tensor<i8>, tensor<i8>) -> tensor<1x5x11x23xi32>
|
|
// CHECK: return [[RES_ATTR]] : tensor<1x5x11x23xi32>
|
|
}
|
|
|
|
/// dilations attribute with auto_pad set to SAME_UPPER.
|
|
|
|
func @test_convinteger_11(%arg0 : tensor<1x2x32x64xi8>, %arg1 : tensor<5x2x6x7xi8>, %arg2 : tensor<i8>, %arg3 : tensor<i8>) -> tensor<*xi32> {
|
|
%0 = "onnx.ConvInteger"(%arg0, %arg1, %arg2, %arg3) {auto_pad = "SAME_UPPER", group = 1 : i64, dilations = [2, 3]} : (tensor<1x2x32x64xi8>, tensor<5x2x6x7xi8>, tensor<i8>, tensor<i8>) -> tensor<*xi32>
|
|
"std.return"(%0) : (tensor<*xi32>) -> ()
|
|
|
|
// CHECK-LABEL: test_convinteger_11
|
|
// CHECK: [[RES_ATTR:%.+]] = "onnx.ConvInteger"(%arg0, %arg1, %arg2, %arg3) {auto_pad = "NOTSET", dilations = [2, 3], group = 1 : i64, kernel_shape = [6, 7], pads = [5, 9, 5, 9], strides = [1, 1]} : (tensor<1x2x32x64xi8>, tensor<5x2x6x7xi8>, tensor<i8>, tensor<i8>) -> tensor<1x5x32x64xi32>
|
|
// CHECK: return [[RES_ATTR]] : tensor<1x5x32x64xi32>
|
|
}
|