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": "contiguous_1k_fp32", "shape": (1024,), "dtype": torch.float32, "atol": 0, "rtol": 0},
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{"name": "contiguous_1k_fp16", "shape": (1024,), "dtype": torch.float16, "atol": 0, "rtol": 0},
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{"name": "contiguous_1536_fp32", "shape": (1536,), "dtype": torch.float32, "atol": 0, "rtol": 0},
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{"name": "contiguous_1536_fp16", "shape": (1536,), "dtype": torch.float16, "atol": 0, "rtol": 0},
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]
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def api_error_cases():
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return [
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{"name": "shape_mismatch", "shape": (16,), "out_shape": (8,), "dtype": torch.float32},
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{"name": "dtype_mismatch", "shape": (16,), "dtype": torch.float32, "out_dtype": torch.float16},
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{"name": "non_contiguous", "shape": (4, 4), "dtype": torch.float32},
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]
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def benchmark_cases():
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return [
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{"name": "contiguous_1m_fp16", "shape": (1 << 20,), "dtype": torch.float16},
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{"name": "contiguous_4m_fp16", "shape": (4 << 20,), "dtype": torch.float16},
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{"name": "contiguous_16m_fp16", "shape": (16 << 20,), "dtype": torch.float16},
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{"name": "contiguous_64m_fp16", "shape": (64 << 20,), "dtype": torch.float16},
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{"name": "contiguous_1m_fp32", "shape": (1 << 20,), "dtype": torch.float32},
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{"name": "contiguous_4m_fp32", "shape": (4 << 20,), "dtype": torch.float32},
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{"name": "contiguous_16m_fp32", "shape": (16 << 20,), "dtype": torch.float32},
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{"name": "contiguous_64m_fp32", "shape": (64 << 20,), "dtype": torch.float32},
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]
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