| # Copyright (c) Microsoft Corporation. | |
| # Licensed under the MIT License. | |
| from onnxscript import opset15 as op | |
| from onnxscript.onnx_types import FLOAT | |
| # tensor inputs can have ONNX-like type annotations | |
| def gemm(A: FLOAT[2048, 124], W: FLOAT[124, 4096], Bias: FLOAT[4096]) -> FLOAT[2048, 4096]: | |
| return op.MatMul(A, W) + Bias | |
| # tensors and attributes distinguished by their types | |
| def scale(A: FLOAT[...], alpha: float, beta: float) -> FLOAT[...]: | |
| return alpha * A + beta | |
| # can return multiple-values | |
| def prodsum(A: FLOAT["N"], B: FLOAT["N"]) -> (FLOAT["N"], FLOAT["N"]): | |
| prod = A * B | |
| sum = A + B | |
| return prod, sum | |
| # can call ops/functions that return multiple-values | |
| def dropout_eg(A: FLOAT[...]) -> FLOAT[...]: | |
| output, mask = op.Dropout(A, 0.7, True, seed=1729) | |
| return output | |
| # will rename variable assigned multiple times | |
| def renaming(A: FLOAT["N"]) -> FLOAT["N"]: | |
| T = op.Abs(A) | |
| T = op.Neg(T) | |
| return T | |