34 lines
1.5 KiB
Python
34 lines
1.5 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": (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_144x1280", "shape": (144, 1280), "dtype": torch.float32},
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{"name": "rowwise_1008x1280", "shape": (1008, 1280), "dtype": torch.float32},
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{"name": "rowwise_1008x2304", "shape": (1008, 2304), "dtype": torch.float32},
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{"name": "rowwise_2032x2304", "shape": (2032, 2304), "dtype": torch.float32},
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{"name": "rowwise_784x3840", "shape": (784, 3840), "dtype": torch.float32},
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{"name": "rowwise_4080x1536", "shape": (4080, 1536), "dtype": torch.float32},
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]
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