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Optimize GeGLU layer in Gemma (#2975)

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This commit is contained in:
Woosuk Kwon
2024-02-21 20:17:52 -08:00
committed by GitHub
parent 93dc5a2870
commit fd5dcc5c81
6 changed files with 107 additions and 76 deletions
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50
tests/kernels/test_activation.py
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@@ -1,7 +1,10 @@
from typing import Type
import pytest
import torch
from vllm.model_executor.layers.activation import FastGELU, NewGELU, SiluAndMul
from vllm.model_executor.layers.activation import (FastGELU, GeluAndMul,
NewGELU, SiluAndMul)
from allclose_default import get_default_atol, get_default_rtol
DTYPES = [torch.half, torch.bfloat16, torch.float]
@@ -13,13 +16,15 @@ CUDA_DEVICES = [
]
@pytest.mark.parametrize("activation", [SiluAndMul, GeluAndMul])
@pytest.mark.parametrize("num_tokens", NUM_TOKENS)
@pytest.mark.parametrize("d", D)
@pytest.mark.parametrize("dtype", DTYPES)
@pytest.mark.parametrize("seed", SEEDS)
@pytest.mark.parametrize("device", CUDA_DEVICES)
@torch.inference_mode()
def test_silu_and_mul(
def test_act_and_mul(
activation: Type[torch.nn.Module],
num_tokens: int,
d: int,
dtype: torch.dtype,
@@ -31,22 +36,23 @@ def test_silu_and_mul(
torch.cuda.manual_seed(seed)
torch.set_default_device(device)
x = torch.randn(num_tokens, 2 * d, dtype=dtype)
layer = SiluAndMul()
layer = activation()
out = layer(x)
ref_out = layer._forward(x)
assert torch.allclose(out,
ref_out,
atol=get_default_atol(out),
rtol=get_default_rtol(out))
# The SiLU and GELU implementations are equivalent to the native PyTorch
# implementations, so we can do exact comparison.
assert torch.allclose(out, ref_out, atol=0.0, rtol=0.0)
@pytest.mark.parametrize("activation", [FastGELU, NewGELU])
@pytest.mark.parametrize("num_tokens", NUM_TOKENS)
@pytest.mark.parametrize("d", D)
@pytest.mark.parametrize("dtype", DTYPES)
@pytest.mark.parametrize("seed", SEEDS)
@pytest.mark.parametrize("device", CUDA_DEVICES)
@torch.inference_mode()
def test_gelu_new(
def test_activation(
activation: Type[torch.nn.Module],
num_tokens: int,
d: int,
dtype: torch.dtype,
@@ -58,33 +64,7 @@ def test_gelu_new(
torch.cuda.manual_seed(seed)
torch.set_default_device(device)
x = torch.randn(num_tokens, d, dtype=dtype)
layer = NewGELU()
out = layer(x)
ref_out = layer._forward(x)
assert torch.allclose(out,
ref_out,
atol=get_default_atol(out),
rtol=get_default_rtol(out))
@pytest.mark.parametrize("num_tokens", NUM_TOKENS)
@pytest.mark.parametrize("d", D)
@pytest.mark.parametrize("dtype", DTYPES)
@pytest.mark.parametrize("seed", SEEDS)
@pytest.mark.parametrize("device", CUDA_DEVICES)
def test_gelu_fast(
num_tokens: int,
d: int,
dtype: torch.dtype,
seed: int,
device: str,
) -> None:
torch.random.manual_seed(seed)
if torch.cuda.is_available():
torch.cuda.manual_seed(seed)
torch.set_default_device(device)
x = torch.randn(num_tokens, d, dtype=dtype)
layer = FastGELU()
layer = activation()
out = layer(x)
ref_out = layer._forward(x)
assert torch.allclose(out,