forked from ccf-ai-infra/Intro-ops
32 lines
1.4 KiB
Python
32 lines
1.4 KiB
Python
from __future__ import annotations
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import torch
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def correctness_cases():
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return [
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{"name": "rowwise_1x128", "shape": (1, 128), "dtype": torch.float32, "atol": 1e-5, "rtol": 1e-5},
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{"name": "rowwise_16x128", "shape": (16, 128), "dtype": torch.float32, "atol": 1e-5, "rtol": 1e-5},
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{"name": "rowwise_16x256", "shape": (16, 256), "dtype": torch.float32, "atol": 1e-5, "rtol": 1e-5},
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{"name": "rowwise_32x128", "shape": (32, 128), "dtype": torch.float32, "atol": 1e-5, "rtol": 1e-5},
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]
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def api_error_cases():
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return [
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{"name": "wrong_dim", "shape": (16, 16), "dtype": torch.float32, "dim": 0},
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{"name": "wrong_dtype", "shape": (16, 16), "dtype": torch.float16, "dim": 1},
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{"name": "wrong_output_shape", "shape": (16, 16), "dtype": torch.float32, "out_shape": (16, 15), "dim": 1},
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{"name": "non_contiguous", "shape": (16, 16), "dtype": torch.float32, "dim": 1},
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{"name": "wrong_rank", "shape": (16,), "dtype": torch.float32, "dim": 1},
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]
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def benchmark_cases():
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return [
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{"name": "rowwise_128x1024", "shape": (128, 1024), "dtype": torch.float32},
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{"name": "rowwise_1024x1024", "shape": (1024, 1024), "dtype": torch.float32},
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{"name": "rowwise_1024x4096", "shape": (1024, 4096), "dtype": torch.float32},
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{"name": "rowwise_4096x1024", "shape": (4096, 1024), "dtype": torch.float32},
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]
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