96b6f475dd
Co-authored-by: Jiang Li <jiang1.li@intel.com> Co-authored-by: Kunshang Ji <kunshang.ji@intel.com>
84 lines
2.4 KiB
Python
84 lines
2.4 KiB
Python
import pytest
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import torch
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from vllm.model_executor.layers.activation import FastGELU, NewGELU, SiluAndMul
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DTYPES = [torch.half, torch.bfloat16, torch.float]
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NUM_TOKENS = [7, 83, 2048] # Arbitrary values for testing
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D = [512, 4096, 5120, 13824] # Arbitrary values for testing
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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("d", D)
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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_silu_and_mul(
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num_tokens: int,
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d: int,
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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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x = torch.randn(num_tokens, 2 * d, dtype=dtype)
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layer = SiluAndMul()
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out = layer(x)
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ref_out = layer._forward(x)
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assert torch.allclose(out, ref_out, atol=1e-5, rtol=1e-5)
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@pytest.mark.parametrize("num_tokens", NUM_TOKENS)
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@pytest.mark.parametrize("d", D)
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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_gelu_new(
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num_tokens: int,
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d: int,
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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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x = torch.randn(num_tokens, d, dtype=dtype)
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layer = NewGELU()
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out = layer(x)
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ref_out = layer._forward(x)
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assert torch.allclose(out, ref_out, atol=1e-5, rtol=1e-5)
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@pytest.mark.parametrize("num_tokens", NUM_TOKENS)
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@pytest.mark.parametrize("d", D)
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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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def test_gelu_fast(
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num_tokens: int,
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d: int,
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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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x = torch.randn(num_tokens, d, dtype=dtype)
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layer = FastGELU()
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out = layer(x)
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ref_out = layer._forward(x)
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assert torch.allclose(out, ref_out, atol=1e-5, rtol=1e-5)
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