| { |
| "fixtureArrays": { |
| "onnx_backend_input_data": [0.54881352186203, 0.7151893377304077, 0.6027633547782898, 0.5448831915855408, 0.42365479469299316, 0.6458941102027893, 0.4375872015953064, 0.891772985458374, 0.9636627435684204, 0.3834415078163147, 0.7917250394821167, 0.5288949012756348, 0.5680445432662964, 0.9255966544151306, 0.07103605568408966, 0.08712930232286453, 0.020218396559357643, 0.832619845867157, 0.7781567573547363, 0.8700121641159058, 0.978618323802948, 0.7991585731506348, 0.4614793658256531, 0.7805292010307312] |
| }, |
| "cases": [ |
| { |
| "name": "2d_to_3d", |
| "provenance": { |
| "notes": "A nonzero target shape makes allowzero=1 executable while the empty allowzero fixture separately locks the mode's zero-preserving shape semantics." |
| }, |
| "attrs": { "allowzero": 1 }, |
| "inputs": { |
| "data": { |
| "dtype": "float32", |
| "shape": [3, 8], |
| "data": { "kind": "fillFloat32", "sinStep": 0.13, "cosStep": 0.29 } |
| }, |
| "shape": { "dtype": "int32", "shape": [3], "data": { "kind": "values", "values": [2, 3, 4] } } |
| }, |
| "outputs": { "reshaped": { "dtype": "float32", "shape": [2, 3, 4], "tolerance": 0.000001 } } |
| }, |
| { |
| "name": "backend_flatten_to_1d", |
| "inputs": { |
| "data": { |
| "dtype": "float32", |
| "shape": [2, 3, 4], |
| "data": { "kind": "fillFloat32", "sinStep": 0.11, "cosStep": 0.23 } |
| }, |
| "shape": { "dtype": "int32", "shape": [1], "data": { "kind": "values", "values": [24] } } |
| }, |
| "outputs": { "reshaped": { "dtype": "float32", "shape": [24], "tolerance": 0.000001 } } |
| }, |
| { |
| "name": "ort_bool_2d_to_3d", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/tensor/tensor_op_test.cc", |
| "test": "TensorOpTest.Reshape", |
| "notes": "Same reshape copy pattern with an ONNX-valid bool payload." |
| }, |
| "inputs": { |
| "data": { "dtype": "bool", "shape": [2, 3], "data": { "kind": "values", "values": [1, 0, 1, 1, 0, 0] } }, |
| "shape": { "dtype": "int32", "shape": [3], "data": { "kind": "values", "values": [1, 3, 2] } } |
| }, |
| "outputs": { "reshaped": { "dtype": "bool", "shape": [1, 3, 2], "tolerance": 0 } } |
| }, |
| { |
| "name": "ort_int16_2d_to_3d", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/tensor/tensor_op_test.cc", |
| "test": "TensorOpTest.Reshape", |
| "notes": "Same reshape copy pattern with ONNX-valid int16 payloads, including min/max sentinels." |
| }, |
| "inputs": { |
| "data": { |
| "dtype": "int16", |
| "shape": [2, 3], |
| "data": { "kind": "values", "values": [-32768, -1, 0, 1, 1234, 32767] } |
| }, |
| "shape": { "dtype": "int32", "shape": [3], "data": { "kind": "values", "values": [1, 3, 2] } } |
| }, |
| "outputs": { "reshaped": { "dtype": "int16", "shape": [1, 3, 2], "tolerance": 0 } } |
| }, |
| { |
| "name": "f16_rank1_to_rank4", |
| "inputs": { |
| "data": { |
| "dtype": "float16", |
| "shape": [6], |
| "data": { "kind": "values", "values": [1.0, -2.0, 3.5, -4.5, 5.0, -6.0] } |
| }, |
| "shape": { "dtype": "int32", "shape": [4], "data": { "kind": "values", "values": [1, 2, 1, 3] } } |
| }, |
| "outputs": { "reshaped": { "dtype": "float16", "shape": [1, 2, 1, 3], "tolerance": 0.001 } } |
| }, |
| { |
| "name": "uint32_rank2_to_rank4", |
| "inputs": { |
| "data": { "dtype": "uint32", "shape": [2, 3], "data": { "kind": "values", "values": [1, 0, 2, 0, 3, 0] } }, |
| "shape": { "dtype": "int32", "shape": [4], "data": { "kind": "values", "values": [1, 2, 3, 1] } } |
| }, |
| "outputs": { "reshaped": { "dtype": "uint32", "shape": [1, 2, 3, 1], "tolerance": 0 } } |
| }, |
| { |
| "name": "scalar_to_scalar_empty_shape", |
| "inputs": { |
| "data": { "dtype": "float32", "shape": [], "data": { "kind": "values", "values": [-7.25] } }, |
| "shape": { "dtype": "int32", "shape": [0], "data": { "kind": "values", "values": [] } } |
| }, |
| "outputs": { "reshaped": { "dtype": "float32", "shape": [], "tolerance": 0.000001 } } |
| }, |
| { |
| "name": "ort_zero_dim_copies_input_same_rank", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/tensor/tensor_op_test.cc", |
| "test": "TensorOpTest.Reshape_WithOutAllowZero", |
| "notes": "ONNX default allowzero=0 behavior: zero in requested shape copies the corresponding input dimension." |
| }, |
| "inputs": { |
| "data": { "dtype": "float32", "shape": [2, 3], "data": { "kind": "constant", "value": 1.0 } }, |
| "shape": { "dtype": "int32", "shape": [2], "data": { "kind": "values", "values": [0, 3] } } |
| }, |
| "outputs": { "reshaped": { "dtype": "float32", "shape": [2, 3], "tolerance": 0 } } |
| }, |
| { |
| "name": "ort_diff_rank_without_zero_shape", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/tensor/tensor_op_test.cc", |
| "test": "TensorOpTest.Reshape_WithOutAllowZeroToDiffRank" |
| }, |
| "inputs": { |
| "data": { "dtype": "float32", "shape": [2, 3, 12], "data": { "kind": "constant", "value": 1.0 } }, |
| "shape": { "dtype": "int32", "shape": [4], "data": { "kind": "values", "values": [2, 3, 3, 4] } } |
| }, |
| "outputs": { "reshaped": { "dtype": "float32", "shape": [2, 3, 3, 4], "tolerance": 0 } } |
| }, |
| { |
| "name": "zero_dim_copies_input_dim", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/tensor/tensor_op_test.cc", |
| "test": "TensorOpTest.Reshape_WithOutAllowZeroToDiffRankOneZero", |
| "notes": "ONNX default allowzero=0 behavior: zero in requested shape copies the corresponding input dimension." |
| }, |
| "inputs": { |
| "data": { |
| "dtype": "float32", |
| "shape": [2, 3, 12], |
| "data": { "kind": "fillFloat32", "sinStep": 0.05, "cosStep": 0.17 } |
| }, |
| "shape": { "dtype": "int32", "shape": [4], "data": { "kind": "values", "values": [0, 3, 3, 4] } } |
| }, |
| "outputs": { "reshaped": { "dtype": "float32", "shape": [2, 3, 3, 4], "tolerance": 0.000001 } } |
| }, |
| { |
| "name": "two_zero_dims_copy_input_dims", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/tensor/tensor_op_test.cc", |
| "test": "TensorOpTest.Reshape_WithOutAllowZeroToDiffRankTwoZeroes", |
| "notes": "ONNX default allowzero=0 behavior: multiple zero dimensions copy from the input shape." |
| }, |
| "inputs": { |
| "data": { |
| "dtype": "float32", |
| "shape": [2, 3, 12], |
| "data": { "kind": "fillFloat32", "sinStep": 0.07, "cosStep": 0.13 } |
| }, |
| "shape": { "dtype": "int32", "shape": [4], "data": { "kind": "values", "values": [0, 0, 3, 4] } } |
| }, |
| "outputs": { "reshaped": { "dtype": "float32", "shape": [2, 3, 3, 4], "tolerance": 0.000001 } } |
| }, |
| { |
| "name": "empty_input_to_rank3", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/tensor/tensor_op_test.cc", |
| "test": "TensorOpTest.ReshapeWithEmptyInput" |
| }, |
| "inputs": { |
| "data": { "dtype": "float32", "shape": [0, 10], "data": { "kind": "values", "values": [] } }, |
| "shape": { "dtype": "int32", "shape": [3], "data": { "kind": "values", "values": [0, 10, 1] } } |
| }, |
| "outputs": { "reshaped": { "dtype": "float32", "shape": [0, 10, 1], "tolerance": 0 } } |
| }, |
| { |
| "name": "ort_empty_shape_output_scalar", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/tensor/tensor_op_test.cc", |
| "test": "TensorOpTest.ReshapeWithEmptyDim" |
| }, |
| "inputs": { |
| "data": { "dtype": "float32", "shape": [1, 1, 1], "data": { "kind": "constant", "value": 1.0 } }, |
| "shape": { "dtype": "int32", "shape": [0], "data": { "kind": "values", "values": [] } } |
| }, |
| "outputs": { "reshaped": { "dtype": "float32", "shape": [], "tolerance": 0 } } |
| }, |
| { |
| "name": "ort_six_dim_new_shape", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/tensor/tensor_op_test.cc", |
| "test": "TensorOpTest.ReshapeSixDimNewShape" |
| }, |
| "inputs": { |
| "data": { "dtype": "float32", "shape": [8, 8, 8], "data": { "kind": "constant", "value": 1.0 } }, |
| "shape": { "dtype": "int32", "shape": [6], "data": { "kind": "values", "values": [2, 4, 4, 2, 8, 1] } } |
| }, |
| "outputs": { "reshaped": { "dtype": "float32", "shape": [2, 4, 4, 2, 8, 1], "tolerance": 0 } } |
| }, |
| { |
| "name": "ort_six_dim_input_shape_to_rank3", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/tensor/tensor_op_test.cc", |
| "test": "TensorOpTest.ReshapeSixDimInputShape" |
| }, |
| "inputs": { |
| "data": { "dtype": "float32", "shape": [2, 4, 4, 2, 8, 1], "data": { "kind": "constant", "value": 1.0 } }, |
| "shape": { "dtype": "int32", "shape": [3], "data": { "kind": "values", "values": [8, 8, 8] } } |
| }, |
| "outputs": { "reshaped": { "dtype": "float32", "shape": [8, 8, 8], "tolerance": 0 } } |
| }, |
| { |
| "name": "ort_basic_reshape_int32_dtype_path", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/tensor/tensor_op_test.cc", |
| "test": "TensorOpTest.Reshape", |
| "notes": "A basic Reshape transform uses supported int32 payload storage." |
| }, |
| "inputs": { |
| "data": { "dtype": "int32", "shape": [2, 3], "data": { "kind": "values", "values": [1, 2, 3, 4, 5, 6] } }, |
| "shape": { "dtype": "int32", "shape": [3], "data": { "kind": "values", "values": [1, 3, 2] } } |
| }, |
| "outputs": { "reshaped": { "dtype": "int32", "shape": [1, 3, 2], "tolerance": 0 } } |
| }, |
| { |
| "name": "ort_unknown_dim_inferred", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/tensor/tensor_op_test.cc", |
| "test": "TensorOpTest.Reshape_UnknownDimWithoutAllowZero", |
| "notes": "The int32 shape tensor exactly represents the source ONNX int64 values." |
| }, |
| "inputs": { |
| "data": { |
| "dtype": "float32", |
| "shape": [2, 3], |
| "data": { "kind": "values", "values": [1.0, 2.0, 3.0, 4.0, 5.0, 6.0] } |
| }, |
| "shape": { "dtype": "int32", "shape": [2], "data": { "kind": "values", "values": [-1, 6] } } |
| }, |
| "outputs": { "reshaped": { "dtype": "float32", "shape": [1, 6], "tolerance": 0 } } |
| }, |
| { |
| "name": "ort_empty_input_dynamic_shape_success", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/tensor/tensor_op_test.cc", |
| "test": "TensorOpTest.ReshapeWithEmptyInputAndDynamicShape", |
| "notes": "Input shape {1,0} with requested shape {1,1,-1} infers the zero-length dimension and produces {1,1,0}." |
| }, |
| "inputs": { |
| "data": { "dtype": "float32", "shape": [1, 0], "data": { "kind": "values", "values": [] } }, |
| "shape": { "dtype": "int32", "shape": [3], "data": { "kind": "values", "values": [1, 1, -1] } } |
| }, |
| "outputs": { "reshaped": { "dtype": "float32", "shape": [1, 1, 0], "tolerance": 0 } } |
| }, |
| { |
| "name": "onnx_backend_reshape_allowzero_reordered", |
| "inputs": { |
| "data": { "dtype": "float32", "shape": [0, 3, 4], "data": { "kind": "values", "values": [] } }, |
| "shape": { "dtype": "int32", "shape": [3], "data": { "kind": "values", "values": [3, 4, 0] } } |
| }, |
| "outputs": { "reshaped": { "dtype": "float32", "shape": [3, 4, 0] } }, |
| "provenance": { |
| "source": "cmake/external/onnx/onnx/backend/test/data/node/test_reshape_allowzero_reordered", |
| "notes": "The integer shape tensor exactly represents the source ONNX int64 values. With `allowzero=1`, the declared output shape is [3,4,0], so the empty copy performs no element reads." |
| }, |
| "attrs": { "allowzero": 1 } |
| }, |
| { |
| "name": "onnx_backend_reshape_extended_dims", |
| "inputs": { |
| "data": { |
| "dtype": "float32", |
| "shape": [2, 3, 4], |
| "data": { "kind": "values", "values": { "$ref": "#/fixtureArrays/onnx_backend_input_data" } } |
| }, |
| "shape": { "dtype": "int32", "shape": [4], "data": { "kind": "values", "values": [2, 3, 2, 2] } } |
| }, |
| "outputs": { "reshaped": { "dtype": "float32", "shape": [2, 3, 2, 2] } }, |
| "provenance": { |
| "source": "cmake/external/onnx/onnx/backend/test/data/node/test_reshape_extended_dims", |
| "notes": "The integer shape tensor exactly represents the source ONNX int64 values." |
| } |
| }, |
| { |
| "name": "onnx_backend_reshape_one_dim", |
| "inputs": { |
| "data": { |
| "dtype": "float32", |
| "shape": [2, 3, 4], |
| "data": { "kind": "values", "values": { "$ref": "#/fixtureArrays/onnx_backend_input_data" } } |
| }, |
| "shape": { "dtype": "int32", "shape": [1], "data": { "kind": "values", "values": [24] } } |
| }, |
| "outputs": { "reshaped": { "dtype": "float32", "shape": [24] } }, |
| "provenance": { |
| "source": "cmake/external/onnx/onnx/backend/test/data/node/test_reshape_one_dim", |
| "notes": "The integer shape tensor exactly represents the source ONNX int64 values." |
| } |
| }, |
| { |
| "name": "onnx_backend_reshape_reduced_dims", |
| "inputs": { |
| "data": { |
| "dtype": "float32", |
| "shape": [2, 3, 4], |
| "data": { "kind": "values", "values": { "$ref": "#/fixtureArrays/onnx_backend_input_data" } } |
| }, |
| "shape": { "dtype": "int32", "shape": [2], "data": { "kind": "values", "values": [2, 12] } } |
| }, |
| "outputs": { "reshaped": { "dtype": "float32", "shape": [2, 12] } }, |
| "provenance": { |
| "source": "cmake/external/onnx/onnx/backend/test/data/node/test_reshape_reduced_dims", |
| "notes": "The integer shape tensor exactly represents the source ONNX int64 values." |
| } |
| }, |
| { |
| "name": "onnx_backend_reshape_reordered_all_dims", |
| "inputs": { |
| "data": { |
| "dtype": "float32", |
| "shape": [2, 3, 4], |
| "data": { "kind": "values", "values": { "$ref": "#/fixtureArrays/onnx_backend_input_data" } } |
| }, |
| "shape": { "dtype": "int32", "shape": [3], "data": { "kind": "values", "values": [4, 2, 3] } } |
| }, |
| "outputs": { "reshaped": { "dtype": "float32", "shape": [4, 2, 3] } }, |
| "provenance": { |
| "source": "cmake/external/onnx/onnx/backend/test/data/node/test_reshape_reordered_all_dims", |
| "notes": "The integer shape tensor exactly represents the source ONNX int64 values." |
| } |
| }, |
| { |
| "name": "onnx_backend_reshape_reordered_last_dims", |
| "inputs": { |
| "data": { |
| "dtype": "float32", |
| "shape": [2, 3, 4], |
| "data": { "kind": "values", "values": { "$ref": "#/fixtureArrays/onnx_backend_input_data" } } |
| }, |
| "shape": { "dtype": "int32", "shape": [3], "data": { "kind": "values", "values": [2, 4, 3] } } |
| }, |
| "outputs": { "reshaped": { "dtype": "float32", "shape": [2, 4, 3] } }, |
| "provenance": { |
| "source": "cmake/external/onnx/onnx/backend/test/data/node/test_reshape_reordered_last_dims", |
| "notes": "The integer shape tensor exactly represents the source ONNX int64 values." |
| } |
| }, |
| { |
| "name": "onnx_backend_reshape_zero_dim", |
| "inputs": { |
| "data": { |
| "dtype": "float32", |
| "shape": [2, 3, 4], |
| "data": { "kind": "values", "values": { "$ref": "#/fixtureArrays/onnx_backend_input_data" } } |
| }, |
| "shape": { "dtype": "int32", "shape": [4], "data": { "kind": "values", "values": [2, 0, 4, 1] } } |
| }, |
| "outputs": { "reshaped": { "dtype": "float32", "shape": [2, 3, 4, 1] } }, |
| "provenance": { |
| "source": "cmake/external/onnx/onnx/backend/test/data/node/test_reshape_zero_dim", |
| "notes": "The integer shape tensor exactly represents the source ONNX int64 values." |
| } |
| }, |
| { |
| "name": "onnx_backend_negative_dim_infer_middle", |
| "provenance": { |
| "source": "cmake/external/onnx/onnx/backend/test/data/node/test_reshape_negative_dim", |
| "notes": "Projects signed int64 shape metadata to int32 storage." |
| }, |
| "inputs": { |
| "data": { |
| "dtype": "float32", |
| "shape": [2, 3, 4], |
| "data": { "kind": "values", "values": { "$ref": "#/fixtureArrays/onnx_backend_input_data" } } |
| }, |
| "shape": { "dtype": "int32", "shape": [3], "data": { "kind": "values", "values": [2, -1, 2] } } |
| }, |
| "outputs": { "reshaped": { "dtype": "float32", "shape": [2, 6, 2], "tolerance": 0.000001 } } |
| }, |
| { |
| "name": "onnx_backend_negative_dim_infer_leading", |
| "provenance": { |
| "source": "cmake/external/onnx/onnx/backend/test/data/node/test_reshape_negative_extended_dims", |
| "notes": "Projects signed int64 shape metadata to int32 storage." |
| }, |
| "inputs": { |
| "data": { |
| "dtype": "float32", |
| "shape": [2, 3, 4], |
| "data": { "kind": "values", "values": { "$ref": "#/fixtureArrays/onnx_backend_input_data" } } |
| }, |
| "shape": { "dtype": "int32", "shape": [4], "data": { "kind": "values", "values": [-1, 2, 3, 4] } } |
| }, |
| "outputs": { "reshaped": { "dtype": "float32", "shape": [1, 2, 3, 4], "tolerance": 0.000001 } } |
| }, |
| { |
| "name": "onnx_backend_zero_copy_and_negative_dim", |
| "provenance": { |
| "source": "cmake/external/onnx/onnx/backend/test/data/node/test_reshape_zero_and_negative_dim", |
| "notes": "Projects signed int64 shape metadata to int32 storage." |
| }, |
| "inputs": { |
| "data": { |
| "dtype": "float32", |
| "shape": [2, 3, 4], |
| "data": { "kind": "values", "values": { "$ref": "#/fixtureArrays/onnx_backend_input_data" } } |
| }, |
| "shape": { "dtype": "int32", "shape": [4], "data": { "kind": "values", "values": [2, 0, 1, -1] } } |
| }, |
| "outputs": { "reshaped": { "dtype": "float32", "shape": [2, 3, 1, 4], "tolerance": 0.000001 } } |
| }, |
| { |
| "name": "ort_int8_unknown_dim_edge_values", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/tensor/tensor_op_test.cc", |
| "test": "TensorOpTest.Reshape_UnknownDimWithoutAllowZero", |
| "notes": "Same inferred-dimension pattern with logical int8 edge values." |
| }, |
| "inputs": { |
| "data": { "dtype": "int8", "shape": [2, 3], "data": { "kind": "values", "values": [-128, -1, 0, 1, 2, 127] } }, |
| "shape": { "dtype": "int32", "shape": [3], "data": { "kind": "values", "values": [3, -1, 1] } } |
| }, |
| "outputs": { "reshaped": { "dtype": "int8", "shape": [3, 2, 1], "tolerance": 0 } } |
| }, |
| { |
| "name": "uint8_empty_shape_output_scalar", |
| "provenance": { |
| "source": "onnxruntime/test/providers/cpu/tensor/tensor_op_test.cc", |
| "test": "TensorOpTest.ReshapeWithEmptyDim", |
| "notes": "Empty shape tensor produces a scalar output for logical uint8 data." |
| }, |
| "inputs": { |
| "data": { "dtype": "uint8", "shape": [1, 1], "data": { "kind": "values", "values": [255] } }, |
| "shape": { "dtype": "int32", "shape": [0], "data": { "kind": "values", "values": [] } } |
| }, |
| "outputs": { "reshaped": { "dtype": "uint8", "shape": [], "tolerance": 0 } } |
| }, |
| { |
| "name": "f16_vec4_2x4_to_8", |
| "inputs": { |
| "data": { |
| "dtype": "float16", |
| "shape": [2, 4], |
| "data": { "kind": "values", "values": [1.0, -2.0, 3.5, -4.5, 5.0, -6.0, 7.25, -8.75] } |
| }, |
| "shape": { "dtype": "int32", "shape": [1], "data": { "kind": "values", "values": [8] } } |
| }, |
| "outputs": { "reshaped": { "dtype": "float16", "shape": [8], "tolerance": 0 } } |
| }, |
| { |
| "name": "scalar_copy_dispatch_clamp", |
| "provenance": { |
| "notes": "Odd numel forces the scalar copy variant; WORKGROUP_SIZE=4 makes ceil(numel/WG)=65536 exceed the 65535 workgroups-per-dimension limit, pinning the min(...,65535) dispatch clamp + grid-stride loop (some threads copy two elements)." |
| }, |
| "tunables": { "WORKGROUP_SIZE": 4 }, |
| "inputs": { |
| "data": { |
| "dtype": "float32", |
| "shape": [262141], |
| "data": { "kind": "fillFloat32", "sinStep": 0.17, "cosStep": 0.31 } |
| }, |
| "shape": { "dtype": "int32", "shape": [1], "data": { "kind": "values", "values": [262141] } } |
| }, |
| "outputs": { "reshaped": { "dtype": "float32", "shape": [262141], "tolerance": 0 } } |
| }, |
| { |
| "name": "int8_numel2_scalar_copy_minus1_dim", |
| "inputs": { |
| "data": { "dtype": "int8", "shape": [2], "data": { "kind": "values", "values": [-128, 127] } }, |
| "shape": { "dtype": "int32", "shape": [2], "data": { "kind": "values", "values": [1, -1] } } |
| }, |
| "outputs": { "reshaped": { "dtype": "int8", "shape": [1, 2], "tolerance": 0 } } |
| }, |
| { |
| "name": "uint8_numel3_with_zero_copy_dim", |
| "inputs": { |
| "data": { "dtype": "uint8", "shape": [3, 1], "data": { "kind": "values", "values": [0, 128, 255] } }, |
| "shape": { "dtype": "int32", "shape": [2], "data": { "kind": "values", "values": [0, 1] } } |
| }, |
| "outputs": { "reshaped": { "dtype": "uint8", "shape": [3, 1], "tolerance": 0 } } |
| }, |
| { |
| "name": "f16_numel6_rank1_to_rank3", |
| "inputs": { |
| "data": { |
| "dtype": "float16", |
| "shape": [6], |
| "data": { "kind": "values", "values": [-65504.0, -1.0, 0.0, 1.0, 100.0, 65504.0] } |
| }, |
| "shape": { "dtype": "int32", "shape": [3], "data": { "kind": "values", "values": [1, 2, 3] } } |
| }, |
| "outputs": { |
| "reshaped": { |
| "dtype": "float16", |
| "shape": [1, 2, 3], |
| "data": { "kind": "values", "values": [-65504.0, -1.0, 0.0, 1.0, 100.0, 65504.0] }, |
| "tolerance": 0 |
| } |
| } |
| } |
| ] |
| } |
|
|
