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import pytest
import sgl_kernel
import torch
from sgl_kernel.utils import is_arch_support_pdl
def llama_rms_norm(x, w, eps=1e-6):
orig_dtype = x.dtype
x = x.float()
variance = x.pow(2).mean(dim=-1, keepdim=True)
x = x * torch.rsqrt(variance + eps)
x = x * w.float()
x = x.to(orig_dtype)
return x
def gemma_rms_norm(x, w, eps=1e-6):
orig_dtype = x.dtype
x = x.float()
variance = x.pow(2).mean(dim=-1, keepdim=True)
x = x * torch.rsqrt(variance + eps)
x = x * (1.0 + w.float())
x = x.to(orig_dtype)
return x
def gemma_fused_add_rms_norm(x, residual, w, eps=1e-6):
orig_dtype = x.dtype
x = x + residual
residual = x
x = x.float()
variance = x.pow(2).mean(dim=-1, keepdim=True)
x = x * torch.rsqrt(variance + eps)
x = x * (1.0 + w.float())
x = x.to(orig_dtype)
return x, residual
def fused_add_rms_norm(x, residual, weight, eps):
orig_dtype = x.dtype
x = x.to(torch.float32)
x = x + residual.to(torch.float32)
residual = x.to(orig_dtype)
variance = x.pow(2).mean(dim=-1, keepdim=True)
x = x * torch.rsqrt(variance + eps)
x = (x * weight.float()).to(orig_dtype)
return x, residual
@pytest.mark.parametrize("batch_size", [1, 19, 99, 989])
@pytest.mark.parametrize("hidden_size", [111, 500, 1024, 3072, 3584, 4096, 8192, 16384])
@pytest.mark.parametrize("dtype", [torch.float16])
@pytest.mark.parametrize("specify_out", [True, False])
def test_norm(batch_size, hidden_size, dtype, specify_out):
x = torch.randn(batch_size, hidden_size).to(0).to(dtype)
w = torch.randn(hidden_size).to(0).to(dtype)
y_ref = llama_rms_norm(x, w)
enable_pdl = is_arch_support_pdl()
if specify_out:
y = torch.empty_like(x)
sgl_kernel.rmsnorm(x, w, out=y, enable_pdl=enable_pdl)
else:
y = sgl_kernel.rmsnorm(x, w, enable_pdl=enable_pdl)
torch.testing.assert_close(y_ref, y, rtol=1e-3, atol=1e-3)
@pytest.mark.parametrize("batch_size", [1, 19, 99, 989])
@pytest.mark.parametrize("hidden_size", [111, 500, 1024, 3072, 3584, 4096, 8192, 16384])
@pytest.mark.parametrize("dtype", [torch.float16, torch.float32])
def test_fused_add_rmsnorm(batch_size, hidden_size, dtype):
eps = 1e-6
x = torch.randn(batch_size, hidden_size, dtype=dtype, device="cuda")
residual = torch.randn_like(x)
weight = torch.randn(hidden_size, dtype=dtype, device="cuda")
x_native, residual_native = fused_add_rms_norm(
x.clone(), residual.clone(), weight, eps
)
x_fused = x.clone()
residual_fused = residual.clone()
enable_pdl = is_arch_support_pdl()
sgl_kernel.fused_add_rmsnorm(
x_fused, residual_fused, weight, eps, enable_pdl=enable_pdl
)
torch.testing.assert_close(x_fused, x_native, rtol=1e-3, atol=1e-3)
torch.testing.assert_close(residual_fused, residual_native, rtol=1e-3, atol=1e-3)
@pytest.mark.parametrize("batch_size", [1, 19, 99, 989])
@pytest.mark.parametrize("hidden_size", [111, 500, 1024, 3072, 3584, 4096, 8192, 16384])
@pytest.mark.parametrize("dtype", [torch.float16])
@pytest.mark.parametrize("specify_out", [True, False])
def test_gemma_norm(batch_size, hidden_size, dtype, specify_out):
x = torch.randn(batch_size, hidden_size).to(0).to(dtype)
w = torch.randn(hidden_size).to(0).to(dtype)
y_ref = gemma_rms_norm(x, w)
enable_pdl = is_arch_support_pdl()
if specify_out:
y = torch.empty_like(x)
sgl_kernel.gemma_rmsnorm(x, w, out=y, enable_pdl=enable_pdl)
else:
y = sgl_kernel.gemma_rmsnorm(x, w, enable_pdl=enable_pdl)
torch.testing.assert_close(y_ref, y, rtol=1e-3, atol=1e-3)
@pytest.mark.parametrize("batch_size", [1, 19, 99, 989])
@pytest.mark.parametrize("hidden_size", [111, 500, 1024, 3072, 3584, 4096, 8192, 16384])
@pytest.mark.parametrize("dtype", [torch.float16])
def test_gemma_fused_add_rmsnorm(batch_size, hidden_size, dtype):
eps = 1e-6
x = torch.randn(batch_size, hidden_size, dtype=dtype, device="cuda")
residual = torch.randn_like(x)
weight = torch.randn(hidden_size, dtype=dtype, device="cuda")
x_native, residual_native = gemma_fused_add_rms_norm(
x.clone(), residual.clone(), weight, eps
)
x_fused = x.clone()
residual_fused = residual.clone()
enable_pdl = is_arch_support_pdl()
sgl_kernel.gemma_fused_add_rmsnorm(
x_fused, residual_fused, weight, eps, enable_pdl=enable_pdl
)
torch.testing.assert_close(x_fused, x_native, rtol=1e-3, atol=1e-3)
torch.testing.assert_close(residual_fused, residual_native, rtol=1e-3, atol=1e-3)
if __name__ == "__main__":
pytest.main([__file__])
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