Update sgl-kernel UTs for activation/topk/norm/rope kernels

test/srt/cpu/test_activation.py

@@ -0,0 +1,33 @@
+import itertools
+import unittest
+
+import torch
+import torch.nn.functional as F
+from sgl_kernel.common_ops import silu_and_mul_cpu as silu_and_mul
+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 = silu_and_mul(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

@@ -0,0 +1,72 @@
+import itertools
+import unittest
+from typing import Optional, Tuple, Union
+
+import torch
+from sgl_kernel.common_ops import fused_add_rmsnorm_cpu as fused_add_rmsnorm
+from sgl_kernel.common_ops import rmsnorm_cpu as rmsnorm
+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 = rmsnorm(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()
+
+        fused_add_rmsnorm(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

@@ -0,0 +1,103 @@
+import unittest
+
+import torch
+from sgl_kernel.common_ops import (
+    rotary_position_embedding_cpu as rotary_position_embedding,
+)
+from utils import _rotate_gptj, _rotate_neox, precision
+
+from sglang.test.test_utils import CustomTestCase
+
+
+class TestROPE(CustomTestCase):
+
+    def _forward_ref(
+        self,
+        positions,
+        query,
+        key,
+        cos_sin_cache,
+        rotary_dim,
+        head_size,
+        is_neox_style,
+        offsets=None,
+    ):
+        query_rot = query[..., :rotary_dim]
+        key_rot = key[..., :rotary_dim]
+        if rotary_dim < head_size:
+            query_pass = query[..., rotary_dim:]
+            key_pass = key[..., rotary_dim:]
+
+        cos_sin = cos_sin_cache[
+            torch.add(positions, offsets) if offsets is not None else positions
+        ]
+        cos, sin = cos_sin.chunk(2, dim=-1)
+        if is_neox_style:
+            # shape [batch_size, seq_len].
+            cos = cos.repeat(1, 1, 2).unsqueeze(-2)
+            sin = sin.repeat(1, 1, 2).unsqueeze(-2)
+        else:
+            cos = cos.repeat_interleave(2, dim=-1).unsqueeze(-2)
+            sin = sin.repeat_interleave(2, dim=-1).unsqueeze(-2)
+
+        rotate_fn = _rotate_neox if is_neox_style else _rotate_gptj
+        query_rot = query_rot * cos + rotate_fn(query_rot) * sin
+        key_rot = key_rot * cos + rotate_fn(key_rot) * sin
+
+        if rotary_dim < head_size:
+            query = torch.cat((query_rot, query_pass), dim=-1)
+            key = torch.cat((key_rot, key_pass), dim=-1)
+        else:
+            query = query_rot
+            key = key_rot
+        return query, key
+
+    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
+
+        # 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,))
+
+        for dtype in [torch.bfloat16]:
+            enable_autocast = True
+
+            with torch.no_grad(), torch.cpu.amp.autocast(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 = self._forward_ref(
+                    positions, q_pe, k_pe, cos_sin_cache, 64, 64, False
+                )
+
+                # fused rope kernel
+                q_pe_clone, k_pe_clone = rotary_position_embedding(
+                    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

@@ -0,0 +1,97 @@
+import itertools
+import unittest
+
+import torch
+from sgl_kernel.common_ops import biased_grouped_topk_cpu as biased_grouped_topk
+from sgl_kernel.common_ops import grouped_topk_cpu as grouped_topk
+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):
+
+        # 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 = grouped_topk(
+            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):
+
+        # 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 = biased_grouped_topk(
+            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()

test/srt/cpu/utils.py

@@ -148,3 +148,16 @@ def scaled_weight(weight, scales):
         .contiguous()
         .view(E, N, K)
     )
+
+
+def _rotate_neox(x: torch.Tensor) -> torch.Tensor:
+    x1 = x[..., : x.shape[-1] // 2]
+    x2 = x[..., x.shape[-1] // 2 :]
+    return torch.cat((-x2, x1), dim=-1)
+
+
+def _rotate_gptj(x: torch.Tensor) -> torch.Tensor:
+    x1 = x[..., ::2]
+    x2 = x[..., 1::2]
+    x = torch.stack((-x2, x1), dim=-1)
+    return x.flatten(-2)