chore: vendor sglang v0.5.10 snapshot

2026-04-24 12:29:36 +00:00
parent 78f0d15221
commit bded08301f
4308 changed files with 1200894 additions and 2 deletions
--- a/third_party/sglang/test/srt/cpu/test_norm.py
+++ b/third_party/sglang/test/srt/cpu/test_norm.py
@@ -0,0 +1,339 @@
+import itertools
+import unittest
+from typing import Optional, Tuple, Union
+
+import torch
+from utils import make_non_contiguous, parametrize, precision
+
+from sglang.test.test_utils import CustomTestCase
+
+torch.manual_seed(1234)
+
+
+class TestNorm(CustomTestCase):
+    M = [4096, 1024]
+    N = [4096, 4096 + 13]
+    dtype = [torch.float16, torch.bfloat16]
+
+    def _forward_native(
+        self,
+        x: torch.Tensor,
+        weight: torch.Tensor,
+        variance_epsilon: float = 1e-6,
+        residual: Optional[torch.Tensor] = None,
+    ) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
+        orig_dtype = x.dtype
+        x = x.to(torch.float32)
+        if residual is not None:
+            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 + variance_epsilon)
+        x = x.to(orig_dtype) * weight
+        if residual is None:
+            return x
+        else:
+            return x, residual
+
+    def _norm(self, x, eps):
+        return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + eps)
+
+    def _gemma3_rmsnorm_native(
+        self, x: torch.Tensor, weight: torch.Tensor, variance_epsilon: float = 1e-6
+    ):
+        output = self._norm(x.float(), variance_epsilon)
+        output = output * (1.0 + weight.float())
+        return output.type_as(x)
+
+    def _gemma_rmsnorm_native(
+        self,
+        x: torch.Tensor,
+        weight: torch.Tensor,
+        variance_epsilon: float = 1e-6,
+        residual: Optional[torch.Tensor] = None,
+    ) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
+        orig_dtype = x.dtype
+        if residual is not None:
+            x = x + residual
+            residual = x
+
+        x = x.float()
+        variance = x.pow(2).mean(dim=-1, keepdim=True)
+        x = x * torch.rsqrt(variance + variance_epsilon)
+        x = x * (1.0 + weight.float())
+        x = x.to(orig_dtype)
+        return x if residual is None else (x, residual)
+
+    def _norm_test(self, m, n, dtype):
+
+        x = torch.randn([m, n], dtype=dtype)
+        x = make_non_contiguous(x)
+        hidden_size = x.size(-1)
+        weight = torch.randn(hidden_size, dtype=dtype)
+        variance_epsilon = 1e-6
+
+        out = torch.ops.sgl_kernel.rmsnorm_cpu(x, weight, variance_epsilon)
+        ref_out = self._forward_native(x, weight, variance_epsilon)
+
+        atol = rtol = precision[ref_out.dtype]
+        torch.testing.assert_close(ref_out, out, atol=atol, rtol=rtol)
+
+        ref_x = x.clone()
+        residual = torch.randn([m, hidden_size], dtype=dtype)
+        ref_residual = residual.clone()
+
+        torch.ops.sgl_kernel.fused_add_rmsnorm_cpu(
+            x, residual, weight, variance_epsilon
+        )
+
+        ref_x, ref_residual = self._forward_native(
+            ref_x, weight, variance_epsilon, ref_residual
+        )
+
+        torch.testing.assert_close(x, ref_x, atol=atol, rtol=rtol)
+        torch.testing.assert_close(residual, ref_residual, atol=atol, rtol=rtol)
+
+    def _l2norm_test(self, m, n, dtype):
+
+        x = torch.randn([m, n], dtype=dtype)
+        hidden_size = x.size(-1)
+        fake_ones_weight = torch.ones(hidden_size, dtype=dtype)
+        variance_epsilon = 1e-6
+
+        out = torch.ops.sgl_kernel.l2norm_cpu(x, variance_epsilon)
+        ref_out = self._forward_native(x, fake_ones_weight, variance_epsilon)
+
+        atol = rtol = precision[ref_out.dtype]
+        torch.testing.assert_close(ref_out, out, atol=atol, rtol=rtol)
+
+    def _gemma_rmsnorm_test(self, m, n, dtype):
+
+        x = torch.randn([m, n], dtype=dtype)
+        x = make_non_contiguous(x)
+        hidden_size = x.size(-1)
+        weight = torch.randn(hidden_size, dtype=dtype)
+        variance_epsilon = 1e-6
+
+        out = torch.ops.sgl_kernel.gemma_rmsnorm_cpu(x, weight, variance_epsilon)
+        ref_out = self._gemma_rmsnorm_native(x, weight, variance_epsilon)
+
+        atol = rtol = precision[ref_out.dtype]
+        torch.testing.assert_close(ref_out, out, atol=atol, rtol=rtol)
+
+        ref_x = x.clone()
+        residual = torch.randn([m, hidden_size], dtype=dtype)
+        ref_residual = residual.clone()
+
+        torch.ops.sgl_kernel.gemma_fused_add_rmsnorm_cpu(
+            x, residual, weight, variance_epsilon
+        )
+
+        ref_x, ref_residual = self._gemma_rmsnorm_native(
+            ref_x, weight, variance_epsilon, ref_residual
+        )
+
+        torch.testing.assert_close(x, ref_x, atol=atol, rtol=rtol)
+        torch.testing.assert_close(residual, ref_residual, atol=atol, rtol=rtol)
+
+    def _gemma3_rmsnorm_test(self, m, n, dtype):
+        x_list = [
+            torch.randn([m, n], dtype=dtype),
+            torch.randn([1, m, 2, n], dtype=dtype),
+        ]
+        for x in x_list:
+            x = make_non_contiguous(x)
+            hidden_size = x.size(-1)
+            weight = torch.randn(hidden_size, dtype=dtype)
+            variance_epsilon = 1e-6
+            out = torch.ops.sgl_kernel.gemma3_rmsnorm_cpu(x, weight, variance_epsilon)
+            ref_out = self._gemma3_rmsnorm_native(x, weight, variance_epsilon)
+
+            atol = rtol = precision[ref_out.dtype]
+            torch.testing.assert_close(ref_out, out, atol=atol, rtol=rtol)
+
+    def test_norm(self):
+        for params in itertools.product(self.M, self.N, self.dtype):
+            with self.subTest(m=params[0], n=params[1], dtype=params[2]):
+                self._norm_test(*params)
+                self._l2norm_test(*params)
+                self._gemma_rmsnorm_test(*params)
+                self._gemma3_rmsnorm_test(*params)
+
+
+class TestFusedRMSNormGated(CustomTestCase):
+    M = [4096, 1024]
+    N = [4096, 4096 + 13]
+    dtype = [torch.float16, torch.bfloat16]
+
+    def _forward_native(
+        self,
+        hidden_states: torch.Tensor,
+        weight: torch.Tensor,
+        variance_epsilon: float = 1e-6,
+        gate: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        input_dtype = hidden_states.dtype
+        hidden_states = hidden_states.to(torch.float32)
+        variance = hidden_states.pow(2).mean(-1, keepdim=True)
+        # Norm before gate
+        hidden_states = hidden_states * torch.rsqrt(variance + variance_epsilon)
+        hidden_states = weight * hidden_states.to(input_dtype)
+        hidden_states = hidden_states * torch.nn.functional.silu(gate.to(torch.float32))
+
+        return hidden_states.to(input_dtype)
+
+    def _norm_test(self, m, n, dtype):
+
+        x = torch.randn([m, n], dtype=dtype)
+        x = make_non_contiguous(x)
+        batch_size = x.size(0)
+        hidden_size = x.size(-1)
+        weight = torch.randn(hidden_size, dtype=dtype)
+        variance_epsilon = 1e-6
+        gate = torch.randn([batch_size, hidden_size], dtype=dtype)
+
+        out = torch.ops.sgl_kernel.fused_rmsnorm_gated_cpu(
+            x, weight, gate, variance_epsilon
+        )
+        ref_out = self._forward_native(x, weight, variance_epsilon, gate)
+
+        atol = rtol = precision[ref_out.dtype] * 2
+        torch.testing.assert_close(ref_out, out, atol=atol, rtol=rtol)
+
+    def test_norm(self):
+        for params in itertools.product(self.M, self.N, self.dtype):
+            with self.subTest(m=params[0], n=params[1], dtype=params[2]):
+                self._norm_test(*params)
+
+
+class TestLayerNorm(CustomTestCase):
+
+    def _forward_native(
+        self,
+        x: torch.Tensor,
+        weight: torch.Tensor,
+        variance_epsilon: float,
+        residual: Optional[torch.Tensor] = None,
+        bias: Optional[torch.Tensor] = None,
+    ) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
+        orig_dtype = x.dtype
+        x = x.to(torch.float32)
+        if residual is not None:
+            x = x + residual.to(torch.float32)
+            residual = x.to(orig_dtype)
+
+        variance, mean = torch.var_mean(x, dim=-1, keepdim=True, correction=0)
+        x = (x - mean) * torch.rsqrt(variance + variance_epsilon)
+        x = x * weight.to(torch.float32)
+        if bias is not None:
+            x = x + bias.to(torch.float32)
+        x = x.to(orig_dtype)
+        return x if residual is None else (x, residual)
+
+    @parametrize(
+        m=[4096, 1024],
+        n=[4096, 4109],
+        dtype=[torch.float16, torch.bfloat16],
+    )
+    def test_norm_input_2d(self, m: int, n: int, dtype: torch.dtype) -> None:
+        x = torch.randn([m, n], dtype=dtype)
+        x = make_non_contiguous(x)
+        hidden_size = x.size(-1)
+        weight = torch.randn(hidden_size, dtype=dtype)
+        bias = torch.randn(hidden_size, dtype=dtype)
+        variance_epsilon = 1e-6
+
+        ln_out = torch.ops.sgl_kernel.layernorm_cpu(x, weight, None, variance_epsilon)
+        ref_ln_out = self._forward_native(x, weight, variance_epsilon)
+
+        atol = rtol = precision[ref_ln_out.dtype]
+        torch.testing.assert_close(ln_out, ref_ln_out, atol=atol, rtol=rtol)
+
+        ln_out = torch.ops.sgl_kernel.layernorm_cpu(x, weight, bias, variance_epsilon)
+        ref_ln_out = self._forward_native(
+            x, weight, variance_epsilon, residual=None, bias=bias
+        )
+        torch.testing.assert_close(ln_out, ref_ln_out, atol=atol, rtol=rtol)
+
+        residual = torch.randn([m, hidden_size], dtype=dtype)
+        ref_residual = residual.clone()
+
+        add_ln_out = torch.ops.sgl_kernel.fused_add_layernorm_cpu(
+            x, residual, weight, None, variance_epsilon
+        )
+        ref_add_ln_out, ref_residual = self._forward_native(
+            x, weight, variance_epsilon, residual=ref_residual
+        )
+
+        torch.testing.assert_close(add_ln_out, ref_add_ln_out, atol=atol, rtol=rtol)
+        torch.testing.assert_close(residual, ref_residual, atol=atol, rtol=rtol)
+
+        residual = torch.randn([m, hidden_size], dtype=dtype)
+        ref_residual = residual.clone()
+
+        add_ln_out = torch.ops.sgl_kernel.fused_add_layernorm_cpu(
+            x, residual, weight, bias, variance_epsilon
+        )
+        ref_add_ln_out, ref_residual = self._forward_native(
+            x, weight, variance_epsilon, residual=ref_residual, bias=bias
+        )
+
+        torch.testing.assert_close(add_ln_out, ref_add_ln_out, atol=atol, rtol=rtol)
+        torch.testing.assert_close(residual, ref_residual, atol=atol, rtol=rtol)
+
+    @parametrize(
+        l=[4096, 1024],
+        m=[1, 4],
+        n=[4096, 4109, 2304],
+        dtype=[torch.float16, torch.bfloat16],
+    )
+    def test_norm_input_3d(self, l: int, m: int, n: int, dtype: torch.dtype) -> None:
+        x = torch.randn([l, m, n], dtype=dtype)
+        x = make_non_contiguous(x)
+        hidden_size = x.size(-1)
+        weight = torch.randn(hidden_size, dtype=dtype)
+        bias = torch.randn(hidden_size, dtype=dtype)
+        variance_epsilon = 1e-6
+
+        ln_out = torch.ops.sgl_kernel.layernorm_cpu(x, weight, None, variance_epsilon)
+        ref_ln_out = self._forward_native(x, weight, variance_epsilon)
+
+        atol = rtol = precision[ref_ln_out.dtype]
+        torch.testing.assert_close(ln_out, ref_ln_out, atol=atol, rtol=rtol)
+
+        ln_out = torch.ops.sgl_kernel.layernorm_cpu(x, weight, bias, variance_epsilon)
+        ref_ln_out = self._forward_native(
+            x, weight, variance_epsilon, residual=None, bias=bias
+        )
+        torch.testing.assert_close(ln_out, ref_ln_out, atol=atol, rtol=rtol)
+
+        residual = torch.randn([l, m, hidden_size], dtype=dtype)
+        ref_residual = residual.clone()
+
+        add_ln_out = torch.ops.sgl_kernel.fused_add_layernorm_cpu(
+            x, residual, weight, None, variance_epsilon
+        )
+        ref_add_ln_out, ref_residual = self._forward_native(
+            x, weight, variance_epsilon, ref_residual
+        )
+
+        torch.testing.assert_close(add_ln_out, ref_add_ln_out, atol=atol, rtol=rtol)
+        torch.testing.assert_close(residual, ref_residual, atol=atol, rtol=rtol)
+
+        residual = torch.randn([l, m, hidden_size], dtype=dtype)
+        ref_residual = residual.clone()
+
+        add_ln_out = torch.ops.sgl_kernel.fused_add_layernorm_cpu(
+            x, residual, weight, bias, variance_epsilon
+        )
+        ref_add_ln_out, ref_residual = self._forward_native(
+            x, weight, variance_epsilon, residual=ref_residual, bias=bias
+        )
+
+        torch.testing.assert_close(add_ln_out, ref_add_ln_out, atol=atol, rtol=rtol)
+        torch.testing.assert_close(residual, ref_residual, atol=atol, rtol=rtol)
+
+
+if __name__ == "__main__":
+    unittest.main()