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| # Code generated by scarb DO NOT EDIT. | ||
| version = 1 | ||
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| [[package]] | ||
| name = "alexandria_data_structures" | ||
| version = "0.1.0" | ||
| source = "git+https://github.com/keep-starknet-strange/alexandria.git?rev=f37d73d#f37d73d8a8248e4d8dc65de3949333e30bda022f" | ||
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| [[package]] | ||
| name = "cubit" | ||
| version = "1.2.0" | ||
| source = "git+https://github.com/raphaelDkhn/cubit.git#e6331ebf98c5d5f442a0e5edefe0b367c8e270d9" | ||
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| [[package]] | ||
| name = "orion" | ||
| version = "0.1.2" | ||
| dependencies = [ | ||
| "alexandria_data_structures", | ||
| "cubit", | ||
| ] |
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| [package] | ||
| name = "orion" | ||
| version = "0.1.2" | ||
| version = "0.1.5" | ||
| description = "ONNX Runtime in Cairo for verifiable ML inference using STARK" | ||
| homepage = "https://github.com/gizatechxyz/orion" | ||
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| [dependencies] | ||
| alexandria_data_structures = { git = "https://github.com/keep-starknet-strange/alexandria.git", rev = "f37d73d" } | ||
| cubit = { git = "https://github.com/influenceth/cubit.git" } | ||
| cubit = { git = "https://github.com/raphaelDkhn/cubit.git" } | ||
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| [scripts] | ||
| sierra = "cairo-compile . -r" | ||
| docgen = "cd docgen && cargo run" | ||
| nodegen = "python3 nodegen/node/__init__.py" | ||
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| [workspace] | ||
| members = ["tests/"] | ||
| nodegen = "python3 nodegen/node/__init__.py" |
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| #tensor.where | ||
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| ```rust | ||
| fn where(self: @Tensor<T>, x: @Tensor<T>, y: @Tensor<T>) -> Tensor<T>; | ||
| ``` | ||
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| Computes a new tensor by selecting values from tensor x (resp. y) at | ||
| indices where the condition is 1 (resp. 0). | ||
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| ## Args | ||
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| * `self`(`@Tensor<T>`) - The condition tensor | ||
| * `x`(`@Tensor<T>`) - The first input tensor | ||
| * `y`(`@Tensor<T>`) - The second input tensor | ||
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| ## Panics | ||
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| * Panics if the shapes are not equal or broadcastable | ||
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| ## Returns | ||
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| Return a new `Tensor<T>` of the same shape as the input with elements | ||
| chosen from x or y depending on the condition. | ||
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| ## Example | ||
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| ```rust | ||
| use array::{ArrayTrait, SpanTrait}; | ||
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| use orion::operators::tensor::{TensorTrait, Tensor, U32Tensor}; | ||
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| fn where_example() -> Tensor<u32> { | ||
| let tensor_cond = TensorTrait::<u32>::new( | ||
| shape: array![2, 2].span(), data: array![0, 1, 0, 1].span(), | ||
| ); | ||
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| let tensor_x = TensorTrait::<u32>::new( | ||
| shape: array![2, 2].span(), data: array![2, 4, 6, 8].span(), | ||
| ); | ||
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| let tensor_y = TensorTrait::<u32>::new( | ||
| shape: array![2, 2].span(), data: array![1, 3, 5, 9].span(), | ||
| ); | ||
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| return tensor_cond.where(@tensor_1, @tensor_2); | ||
| } | ||
| >>> [1,4,5,8] | ||
| ``` |
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| import numpy as np | ||
| from nodegen.node import RunAll | ||
| from ..helpers import make_node, make_test, to_fp, Tensor, Dtype, FixedImpl | ||
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| class Where(RunAll): | ||
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| @staticmethod | ||
| def where_u32(): | ||
| def default(): | ||
| cond = np.random.choice([1, 0], (3, 3, 3)).astype(np.uint32) | ||
| x = np.random.randint(0, 6, (3, 3, 3)).astype(np.uint32) | ||
| y = np.random.randint(0, 6, (3, 3, 3)).astype(np.uint32) | ||
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| z = np.where(cond, x, y).astype(x.dtype) | ||
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| cond = Tensor(Dtype.U32, cond.shape, cond.flatten()) | ||
| x = Tensor(Dtype.U32, x.shape, x.flatten()) | ||
| y = Tensor(Dtype.U32, y.shape, y.flatten()) | ||
| z = Tensor(Dtype.U32, z.shape, z.flatten()) | ||
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| name = "where_u32" | ||
| make_node([cond, x, y], [z], name) | ||
| make_test([cond, x, y], z, "input_0.where(@input_1,@input_2)", name) | ||
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| def broadcast(): | ||
| cond = np.random.choice([1, 0], (1, 1)).astype(np.uint32) | ||
| x = np.random.randint(0, 6, (2, 2)).astype(np.uint32) | ||
| y = np.random.randint(0, 6, (1, 2)).astype(np.uint32) | ||
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| z = np.where(cond, x, y).astype(x.dtype) | ||
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| cond = Tensor(Dtype.U32, cond.shape, cond.flatten()) | ||
| x = Tensor(Dtype.U32, x.shape, x.flatten()) | ||
| y = Tensor(Dtype.U32, y.shape, y.flatten()) | ||
| z = Tensor(Dtype.U32, z.shape, z.flatten()) | ||
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| name = "where_u32_broadcast" | ||
| make_node([cond, x, y], [z], name) | ||
| make_test([cond, x, y], z, "input_0.where(@input_1,@input_2)", name) | ||
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| default() | ||
| broadcast() | ||
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| @staticmethod | ||
| def where_i32(): | ||
| def default(): | ||
| cond = np.random.choice([1, 0], (3, 3, 3)).astype(np.int32) | ||
| x = np.random.randint(0, 6, (3, 3, 3)).astype(np.int32) | ||
| y = np.random.randint(0, 6, (3, 3, 3)).astype(np.int32) | ||
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| z = np.where(cond, x, y).astype(x.dtype) | ||
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| cond = Tensor(Dtype.I32, cond.shape, cond.flatten()) | ||
| x = Tensor(Dtype.I32, x.shape, x.flatten()) | ||
| y = Tensor(Dtype.I32, y.shape, y.flatten()) | ||
| z = Tensor(Dtype.I32, z.shape, z.flatten()) | ||
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| name = "where_i32" | ||
| make_node([cond, x, y], [z], name) | ||
| make_test([cond, x, y], z, "input_0.where(@input_1,@input_2)", name) | ||
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| def broadcast(): | ||
| cond = np.random.choice([1, 0], (1, 1)).astype(np.int32) | ||
| x = np.random.randint(0, 6, (2, 2)).astype(np.int32) | ||
| y = np.random.randint(0, 6, (1, 2)).astype(np.int32) | ||
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| z = np.where(cond, x, y).astype(x.dtype) | ||
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| cond = Tensor(Dtype.I32, cond.shape, cond.flatten()) | ||
| x = Tensor(Dtype.I32, x.shape, x.flatten()) | ||
| y = Tensor(Dtype.I32, y.shape, y.flatten()) | ||
| z = Tensor(Dtype.I32, z.shape, z.flatten()) | ||
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| name = "where_i32_broadcast" | ||
| make_node([cond, x, y], [z], name) | ||
| make_test([cond, x, y], z, "input_0.where(@input_1,@input_2)", name) | ||
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| default() | ||
| broadcast() | ||
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| @staticmethod | ||
| def where_i8(): | ||
| def default(): | ||
| cond = np.random.choice([1, 0], (3, 3, 3)).astype(np.int8) | ||
| x = np.random.randint(0, 6, (3, 3, 3)).astype(np.int8) | ||
| y = np.random.randint(0, 6, (3, 3, 3)).astype(np.int8) | ||
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| z = np.where(cond, x, y).astype(x.dtype) | ||
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| cond = Tensor(Dtype.I8, cond.shape, cond.flatten()) | ||
| x = Tensor(Dtype.I8, x.shape, x.flatten()) | ||
| y = Tensor(Dtype.I8, y.shape, y.flatten()) | ||
| z = Tensor(Dtype.I8, z.shape, z.flatten()) | ||
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| name = "where_i8" | ||
| make_node([cond, x, y], [z], name) | ||
| make_test([cond, x, y], z, "input_0.where(@input_1,@input_2)", name) | ||
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| def broadcast(): | ||
| cond = np.random.choice([1, 0], (1, 1)).astype(np.int8) | ||
| x = np.random.randint(0, 6, (2, 2)).astype(np.int8) | ||
| y = np.random.randint(0, 6, (1, 2)).astype(np.int8) | ||
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| z = np.where(cond, x, y).astype(x.dtype) | ||
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| cond = Tensor(Dtype.I8, cond.shape, cond.flatten()) | ||
| x = Tensor(Dtype.I8, x.shape, x.flatten()) | ||
| y = Tensor(Dtype.I8, y.shape, y.flatten()) | ||
| z = Tensor(Dtype.I8, z.shape, z.flatten()) | ||
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| name = "where_i8_broadcast" | ||
| make_node([cond, x, y], [z], name) | ||
| make_test([cond, x, y], z, "input_0.where(@input_1,@input_2)", name) | ||
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| default() | ||
| broadcast() | ||
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| @staticmethod | ||
| def where_fp8x23(): | ||
| def default(): | ||
| cond = np.random.choice([1, 0], (3, 3, 3)).astype(np.float64) | ||
| x = np.random.randint(0, 6, (3, 3, 3)).astype(np.float64) | ||
| y = np.random.randint(0, 6, (3, 3, 3)).astype(np.float64) | ||
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| z = np.where(cond, x, y).astype(x.dtype) | ||
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| cond = Tensor(Dtype.FP8x23, cond.shape, to_fp( | ||
| cond.flatten(), FixedImpl.FP8x23)) | ||
| x = Tensor(Dtype.FP8x23, x.shape, to_fp( | ||
| x.flatten(), FixedImpl.FP8x23)) | ||
| y = Tensor(Dtype.FP8x23, y.shape, to_fp( | ||
| y.flatten(), FixedImpl.FP8x23)) | ||
| z = Tensor(Dtype.FP8x23, z.shape, to_fp( | ||
| z.flatten(), FixedImpl.FP8x23)) | ||
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| name = "where_fp8x23" | ||
| make_node([cond, x, y], [z], name) | ||
| make_test([cond, x, y], z, "input_0.where(@input_1,@input_2)", name) | ||
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| def broadcast(): | ||
| cond = np.random.choice([1, 0], (1, 1)).astype(np.float64) | ||
| x = np.random.randint(0, 6, (2, 2)).astype(np.float64) | ||
| y = np.random.randint(0, 6, (1, 2)).astype(np.float64) | ||
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| z = np.where(cond, x, y).astype(x.dtype) | ||
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| cond = Tensor(Dtype.FP8x23, cond.shape, to_fp( | ||
| cond.flatten(), FixedImpl.FP8x23)) | ||
| x = Tensor(Dtype.FP8x23, x.shape, to_fp( | ||
| x.flatten(), FixedImpl.FP8x23)) | ||
| y = Tensor(Dtype.FP8x23, y.shape, to_fp( | ||
| y.flatten(), FixedImpl.FP8x23)) | ||
| z = Tensor(Dtype.FP8x23, z.shape, to_fp( | ||
| z.flatten(), FixedImpl.FP8x23)) | ||
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| name = "where_fp8x23_broadcast" | ||
| make_node([cond, x, y], [z], name) | ||
| make_test([cond, x, y], z, "input_0.where(@input_1,@input_2)", name) | ||
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| default() | ||
| broadcast() | ||
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| @staticmethod | ||
| def where_fp16x16(): | ||
| def default(): | ||
| cond = np.random.choice([1, 0], (3, 3, 3)).astype(np.float64) | ||
| x = np.random.randint(0, 6, (3, 3, 3)).astype(np.float64) | ||
| y = np.random.randint(0, 6, (3, 3, 3)).astype(np.float64) | ||
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| z = np.where(cond, x, y).astype(x.dtype) | ||
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| cond = Tensor(Dtype.FP16x16, cond.shape, to_fp( | ||
| cond.flatten(), FixedImpl.FP16x16)) | ||
| x = Tensor(Dtype.FP16x16, x.shape, to_fp( | ||
| x.flatten(), FixedImpl.FP16x16)) | ||
| y = Tensor(Dtype.FP16x16, y.shape, to_fp( | ||
| y.flatten(), FixedImpl.FP16x16)) | ||
| z = Tensor(Dtype.FP16x16, z.shape, to_fp( | ||
| z.flatten(), FixedImpl.FP16x16)) | ||
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| name = "where_fp16x16" | ||
| make_node([cond, x, y], [z], name) | ||
| make_test([cond, x, y], z, "input_0.where(@input_1,@input_2)", name) | ||
|
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| def broadcast(): | ||
| cond = np.random.choice([1, 0], (1, 1)).astype(np.float64) | ||
| x = np.random.randint(0, 6, (2, 2)).astype(np.float64) | ||
| y = np.random.randint(0, 6, (1, 2)).astype(np.float64) | ||
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| z = np.where(cond, x, y).astype(x.dtype) | ||
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| cond = Tensor(Dtype.FP16x16, cond.shape, to_fp( | ||
| cond.flatten(), FixedImpl.FP16x16)) | ||
| x = Tensor(Dtype.FP16x16, x.shape, to_fp( | ||
| x.flatten(), FixedImpl.FP16x16)) | ||
| y = Tensor(Dtype.FP16x16, y.shape, to_fp( | ||
| y.flatten(), FixedImpl.FP16x16)) | ||
| z = Tensor(Dtype.FP16x16, z.shape, to_fp( | ||
| z.flatten(), FixedImpl.FP16x16)) | ||
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| name = "where_fp16x16_broadcast" | ||
| make_node([cond, x, y], [z], name) | ||
| make_test([cond, x, y], z, "input_0.where(@input_1,@input_2)", name) | ||
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| default() | ||
| broadcast() |
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| Original file line number | Diff line number | Diff line change |
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| mod operators; | ||
| mod numbers; | ||
| mod utils; | ||
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| mod test_helper; |
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