96b6f475dd
Co-authored-by: Jiang Li <jiang1.li@intel.com> Co-authored-by: Kunshang Ji <kunshang.ji@intel.com>
54 lines
1.9 KiB
Python
54 lines
1.9 KiB
Python
import pytest
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import torch
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from vllm.model_executor.layers.layernorm import RMSNorm
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DTYPES = [torch.half, torch.bfloat16, torch.float]
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NUM_TOKENS = [7, 83, 4096] # Arbitrary values for testing
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HIDDEN_SIZES = [768, 5120, 8192] # Arbitrary values for testing
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ADD_RESIDUAL = [False, True]
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SEEDS = [0]
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CUDA_DEVICES = [
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f"cuda:{i}" for i in range(1 if torch.cuda.device_count() == 1 else 2)
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]
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@pytest.mark.parametrize("num_tokens", NUM_TOKENS)
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@pytest.mark.parametrize("hidden_size", HIDDEN_SIZES)
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@pytest.mark.parametrize("add_residual", ADD_RESIDUAL)
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@pytest.mark.parametrize("dtype", DTYPES)
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@pytest.mark.parametrize("seed", SEEDS)
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@pytest.mark.parametrize("device", CUDA_DEVICES)
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@torch.inference_mode()
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def test_rms_norm(
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num_tokens: int,
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hidden_size: int,
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add_residual: bool,
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dtype: torch.dtype,
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seed: int,
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device: str,
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) -> None:
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torch.random.manual_seed(seed)
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if torch.cuda.is_available():
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torch.cuda.manual_seed(seed)
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torch.set_default_device(device)
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layer = RMSNorm(hidden_size).to(dtype=dtype)
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layer.weight.data.normal_(mean=1.0, std=0.1)
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scale = 1 / (2 * hidden_size)
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x = torch.randn(num_tokens, hidden_size, dtype=dtype)
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x *= scale
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residual = torch.randn_like(x) * scale if add_residual else None
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# NOTE(woosuk): The reference implementation should be executed first
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# because the custom kernel is in-place.
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ref_out = layer._forward(x, residual)
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out = layer(x, residual)
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# NOTE(woosuk): LayerNorm operators (including RMS) typically have larger
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# numerical errors than other operators because they involve reductions.
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# Therefore, we use a larger tolerance.
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if add_residual:
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assert torch.allclose(out[0], ref_out[0], atol=1e-2, rtol=1e-2)
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assert torch.allclose(out[1], ref_out[1], atol=1e-2, rtol=1e-2)
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else:
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assert torch.allclose(out, ref_out, atol=1e-2, rtol=1e-2)
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