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27 lines
1.0 KiB
27 lines
1.0 KiB
4 months ago
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import torch
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from torch.testing import assert_close
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from colossalai.accelerator import get_accelerator
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from colossalai.quantization.fp8 import cast_from_fp8, cast_from_fp8_pipeline, cast_to_fp8, cast_to_fp8_pipeline
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from colossalai.testing import parameterize
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@parameterize("shape", [(100, 10), (10, 100), (3, 7), (2, 1), (1, 2), (2, 2), (4, 2), (5,), (4,), (2,)])
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@parameterize("dtype", [torch.bfloat16, torch.float16, torch.float32])
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@parameterize("fp8_format", ["e4m3", "e5m2"])
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def test_fp8_cast(shape, dtype, fp8_format):
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x = torch.rand(shape, dtype=dtype, device=get_accelerator().get_current_device())
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ret, scale_inv = cast_to_fp8(x, fp8_format=fp8_format)
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out = cast_from_fp8(ret, scale_inv, x.dtype)
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assert_close(out, x, rtol=0.1, atol=0.1)
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if x.size(-1) % 2 == 0:
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inp_dict = {"hidden_states": x.clone()}
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cast_to_fp8_pipeline(inp_dict)
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cast_from_fp8_pipeline(inp_dict)
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assert_close(inp_dict["hidden_states"], x, rtol=0.1, atol=0.1)
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if __name__ == "__main__":
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test_fp8_cast()
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