Update sgl-kernel UTs for activation/topk/norm/rope kernels (#6452)

test/srt/cpu/test_activation.py

+import itertools
+import unittest
+
+import sgl_kernel
+import torch
+import torch.nn.functional as F
+from utils import SiluAndMul, precision
+
+from sglang.test.test_utils import CustomTestCase
+
+
+class TestActivation(CustomTestCase):
+    M = [128, 129, 257]
+    N = [22016, 22018]
+    dtype = [torch.float16, torch.bfloat16]
+
+    def _activation_test(self, m, n, dtype):
+        x = torch.randn([m, n], dtype=dtype)
+
+        out = torch.ops.sgl_kernel.silu_and_mul_cpu(x)
+        ref_out = SiluAndMul(x)
+
+        atol = rtol = precision[ref_out.dtype]
+        self.assertTrue(torch.allclose(ref_out, out, atol=atol, rtol=rtol))
+
+    def test_activation(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._activation_test(*params)
+
+
+if __name__ == "__main__":
+    unittest.main()

test/srt/cpu/test_norm.py

+import itertools
+import unittest
+from typing import Optional, Tuple, Union
+
+import sgl_kernel
+import torch
+from utils import precision
+
+from sglang.test.test_utils import CustomTestCase
+
+
+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_test(self, m, n, dtype):
+
+        x = torch.randn([m, n], dtype=dtype)
+        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]
+        self.assertTrue(torch.allclose(ref_out, out, atol=atol, rtol=rtol))
+
+        ref_x = x.clone()
+        residual = torch.randn([m, n], 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
+        )
+
+        self.assertTrue(torch.allclose(x, ref_x, atol=atol, rtol=rtol))
+        self.assertTrue(torch.allclose(residual, ref_residual, 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)
+
+
+if __name__ == "__main__":
+    unittest.main()

test/srt/cpu/test_rope.py

+import unittest
+
+import sgl_kernel
+import torch
+from utils import precision
+
+from sglang.srt.layers.rotary_embedding import DeepseekScalingRotaryEmbedding
+from sglang.test.test_utils import CustomTestCase
+
+
+class TestROPE(CustomTestCase):
+    def test_deepseek_v2_rope(self):
+        num_head = 16
+        seq_len = 1024
+        q_head_dim = 192
+        qk_nope_head_dim = 128
+        qk_rope_head_dim = 64
+        max_pos = 256
+        k_dim = 576
+        rotary_dim = 64
+        is_neox_style = False
+
+        # Create cos_sin_cache
+        freqs = torch.rand(max_pos, qk_rope_head_dim // 2)
+        cos = freqs.cos() * 0.7
+        sin = freqs.sin() * 0.7
+        cos_sin_cache = torch.cat((cos, sin), dim=-1).to(torch.bfloat16)
+        positions = torch.randint(0, max_pos, (seq_len,))
+
+        rope = DeepseekScalingRotaryEmbedding(
+            qk_rope_head_dim,
+            rotary_dim,
+            max_pos,
+            16,  # not used since cos_sin_cache is provided
+            is_neox_style,
+            1.0,
+            torch.bfloat16,
+            device="cpu",
+        )
+        rope.register_buffer("cos_sin_cache", cos_sin_cache)
+
+        for dtype in [torch.bfloat16]:
+            enable_autocast = True
+
+            with torch.no_grad(), torch.amp.autocast("cpu", enabled=enable_autocast):
+                q = torch.randn(seq_len, num_head, q_head_dim, dtype=dtype)
+                q_clone = q.clone()
+                k = torch.randn(seq_len, 1, k_dim, dtype=dtype)
+                k_clone = k.clone()
+                _, q_pe = q.split([qk_nope_head_dim, qk_rope_head_dim], dim=-1)
+                _, q_pe_clone = q_clone.split(
+                    [qk_nope_head_dim, qk_rope_head_dim], dim=-1
+                )
+                k_pe = k[:, :, k_dim - qk_rope_head_dim :]
+                k_pe_clone = k_clone[:, :, k_dim - qk_rope_head_dim :]
+
+                # ref kernel
+                q_pe, k_pe = rope.forward_native(
+                    query=q_pe,
+                    key=k_pe,
+                    positions=positions,
+                )
+
+                # fused rope kernel
+                q_pe_clone, k_pe_clone = (
+                    torch.ops.sgl_kernel.rotary_position_embedding_cpu(
+                        positions, q_pe_clone, k_pe_clone, cos_sin_cache
+                    )
+                )
+
+                atol = rtol = precision[q_pe.dtype]
+                self.assertTrue(torch.allclose(q_pe, q_pe_clone, atol=atol, rtol=rtol))
+                self.assertTrue(torch.allclose(k_pe, k_pe_clone, atol=atol, rtol=rtol))
+                torch.testing.assert_close(k_pe, k_pe_clone)
+
+
+if __name__ == "__main__":
+    unittest.main()

test/srt/cpu/test_topk.py

+import itertools
+import unittest
+
+import sgl_kernel
+import torch
+from utils import precision
+
+from sglang.srt.layers.moe.topk import (
+    biased_grouped_topk_impl as native_biased_grouped_topk,
+)
+from sglang.srt.layers.moe.topk import grouped_topk as native_grouped_topk
+from sglang.test.test_utils import CustomTestCase
+
+
+# This is used by the Deepseek-V2 model
+class TestGroupedTopK(CustomTestCase):
+    def _run_single_test(self, M, E, G, topk, topk_group, renormalize, dtype):
+        torch.manual_seed(1234)
+
+        # expand gating_output by M, otherwise bfloat16 fall into same value aftering truncating
+        hidden_states = torch.randn(M, 100, dtype=dtype)
+        gating_output = torch.randn(M, E, dtype=dtype) * 2 * M
+
+        ref_topk_weights, ref_topk_ids = native_grouped_topk(
+            hidden_states.float(),
+            gating_output.float(),
+            topk,
+            renormalize,
+            G,
+            topk_group,
+        )
+
+        # fused version
+        topk_weights, topk_ids = torch.ops.sgl_kernel.grouped_topk_cpu(
+            hidden_states, gating_output, topk, renormalize, G, topk_group
+        )
+
+        res = torch.zeros(M, E, dtype=torch.float)
+        ref = torch.zeros(M, E, dtype=torch.float)
+        res.scatter_(1, topk_ids.long(), topk_weights)
+        ref.scatter_(1, ref_topk_ids.long(), ref_topk_weights)
+        torch.testing.assert_close(res, ref)
+
+    def test_grouped_topk(self):
+        for renormalize in [True, False]:
+            self._run_single_test(123, 8, 2, 2, 1, renormalize, torch.bfloat16)
+            self._run_single_test(123, 16, 4, 3, 2, renormalize, torch.bfloat16)
+            self._run_single_test(123, 32, 4, 3, 2, renormalize, torch.bfloat16)
+            self._run_single_test(1123, 32, 4, 3, 2, renormalize, torch.bfloat16)
+            self._run_single_test(123, 64, 1, 6, 1, renormalize, torch.bfloat16)
+            self._run_single_test(123, 256, 8, 4, 8, renormalize, torch.bfloat16)
+            self._run_single_test(123, 160, 8, 6, 2, renormalize, torch.bfloat16)
+
+
+# DeepSeek V2/V3/R1 uses biased_grouped_top
+class TestBiasedGroupedTopK(CustomTestCase):
+    def _run_single_test(self, M, E, G, topk, topk_group, renormalize, dtype):
+        torch.manual_seed(1234)
+
+        # expand gating_output by M, otherwise bfloat16 fall into same value aftering truncating
+        hidden_states = torch.randn(M, 100, dtype=dtype)
+        gating_output = torch.randn(M, E, dtype=dtype) * 2 * M
+        correction_bias = torch.randn(E, dtype=dtype)
+
+        ref_topk_weights, ref_topk_ids = native_biased_grouped_topk(
+            hidden_states.float(),
+            gating_output.float(),
+            correction_bias.float(),
+            topk,
+            renormalize,
+            G,
+            topk_group,
+        )
+
+        # fused version
+        topk_weights, topk_ids = torch.ops.sgl_kernel.biased_grouped_topk_cpu(
+            hidden_states,
+            gating_output,
+            correction_bias,
+            topk,
+            renormalize,
+            G,
+            topk_group,
+        )
+
+        res = torch.zeros(M, E, dtype=torch.float)
+        ref = torch.zeros(M, E, dtype=torch.float)
+        res.scatter_(1, topk_ids.long(), topk_weights)
+        ref.scatter_(1, ref_topk_ids.long(), ref_topk_weights)
+        torch.testing.assert_close(res, ref)
+
+    def test_biased_grouped_topk(self):
+        for renormalize in [True, False]:
+            self._run_single_test(122, 256, 8, 8, 2, renormalize, torch.bfloat16)
+
+
+if __name__ == "__main__":
+    unittest.main()