chore: vendor sglang v0.5.10 snapshot

third_party/sglang/sgl-kernel/tests/test_moe_topk_sigmoid.py

@@ -0,0 +1,184 @@
+import itertools
+import sys
+
+import pytest
+import torch
+from sgl_kernel import topk_sigmoid
+
+
+@pytest.mark.parametrize(
+    "num_tokens, num_experts, topk",
+    list(
+        itertools.product(
+            [1, 16, 128, 512, 1024, 2048],  # num_tokens
+            [4, 8, 16, 32, 64, 128, 256],  # num_experts
+            [1, 2, 4],  # topk
+        )
+    ),
+)
+def test_topk_sigmoid(num_tokens, num_experts, topk):
+    gating_output = torch.randn(
+        (num_tokens, num_experts), dtype=torch.float32, device="cuda"
+    )
+
+    topk_weights = torch.empty((num_tokens, topk), dtype=torch.float32, device="cuda")
+    topk_indices = torch.empty((num_tokens, topk), dtype=torch.int32, device="cuda")
+
+    topk_sigmoid(
+        topk_weights,
+        topk_indices,
+        gating_output,
+    )
+
+    # Native torch implementation
+    sigmoid_output = torch.sigmoid(gating_output)
+    topk_weights_ref, topk_indices_ref = torch.topk(sigmoid_output, topk, dim=-1)
+
+    # Verify the top-k weights and indices match the torch native ones
+    assert torch.allclose(
+        topk_weights_ref, topk_weights, atol=1e-3, rtol=1e-3
+    ), f"Weights mismatch: torch={topk_weights_ref} vs SGLang={topk_weights}"
+
+    assert torch.allclose(
+        topk_indices_ref.int(), topk_indices, atol=0, rtol=0
+    ), f"Indices mismatch: torch={topk_indices_ref}, SGLang={topk_indices}"
+
+
+@pytest.mark.parametrize(
+    "num_tokens, num_experts, topk, dtype",
+    list(
+        itertools.product(
+            [1, 16, 128, 512, 1024, 2048],  # num_tokens
+            [4, 8, 16, 32, 64, 128, 256],  # num_experts
+            [1, 2, 4],  # topk
+            [torch.float16, torch.bfloat16, torch.float32],  # dtype
+        )
+    ),
+)
+def test_topk_sigmoid_dtype_regression(num_tokens, num_experts, topk, dtype):
+    gating_output = torch.randn((num_tokens, num_experts), dtype=dtype, device="cuda")
+
+    topk_weights = torch.empty((num_tokens, topk), dtype=torch.float32, device="cuda")
+    topk_indices = torch.empty((num_tokens, topk), dtype=torch.int32, device="cuda")
+
+    topk_sigmoid(
+        topk_weights,
+        topk_indices,
+        gating_output,
+    )
+
+    topk_weights_ref = torch.empty(
+        (num_tokens, topk), dtype=torch.float32, device="cuda"
+    )
+    topk_indices_ref = torch.empty((num_tokens, topk), dtype=torch.int32, device="cuda")
+
+    topk_sigmoid(
+        topk_weights_ref,
+        topk_indices_ref,
+        gating_output.float(),
+    )
+
+    assert torch.allclose(
+        topk_weights_ref, topk_weights, atol=1e-3, rtol=1e-3
+    ), f"Weights mismatch: SGLang old interface={topk_weights_ref} vs SGLang new interface={topk_weights}"
+
+    assert torch.allclose(
+        topk_indices_ref.int(), topk_indices, atol=0, rtol=0
+    ), f"Indices mismatch: SGLang old interface={topk_indices_ref}, SGLang new interface={topk_indices}"
+
+
+@pytest.mark.parametrize(
+    "num_tokens, num_experts, topk",
+    list(
+        itertools.product(
+            [1, 16, 128, 512, 1024, 2048],  # num_tokens
+            [4, 8, 16, 32, 64, 128, 256],  # num_experts
+            [1, 2, 4],  # topk
+        )
+    ),
+)
+def test_topk_sigmoid_renormalize(num_tokens, num_experts, topk):
+    gating_output = torch.randn(
+        (num_tokens, num_experts), dtype=torch.bfloat16, device="cuda"
+    )
+
+    topk_weights = torch.empty((num_tokens, topk), dtype=torch.float32, device="cuda")
+    topk_indices = torch.empty((num_tokens, topk), dtype=torch.int32, device="cuda")
+
+    topk_sigmoid(
+        topk_weights,
+        topk_indices,
+        gating_output,
+        renormalize=True,
+    )
+
+    topk_weights_ref = torch.empty(
+        (num_tokens, topk), dtype=torch.float32, device="cuda"
+    )
+    topk_indices_ref = torch.empty((num_tokens, topk), dtype=torch.int32, device="cuda")
+    token_expert_indices_ref = torch.empty(
+        (num_tokens, topk), dtype=torch.int32, device="cuda"
+    )
+
+    topk_sigmoid(
+        topk_weights_ref,
+        topk_indices_ref,
+        gating_output,
+    )
+    topk_weights_ref = topk_weights_ref / topk_weights_ref.sum(dim=-1, keepdim=True)
+
+    assert torch.allclose(
+        topk_weights_ref, topk_weights, atol=1e-3, rtol=1e-3
+    ), f"Weights mismatch: SGLang w/o fused renormalize={topk_weights_ref} vs SGLang w/ fused renormalize={topk_weights}"
+
+    assert torch.allclose(
+        topk_indices_ref.int(), topk_indices, atol=0, rtol=0
+    ), f"Indices mismatch: SGLang w/o fused renormalize={topk_indices_ref}, SGLang w/ fused renormalize={topk_indices}"
+
+
+@pytest.mark.parametrize(
+    "num_tokens, num_experts, topk",
+    list(
+        itertools.product(
+            [1, 16, 128, 512, 1024, 2048],  # num_tokens
+            [4, 8, 16, 32, 48, 64, 128, 256],  # num_experts
+            [1, 2, 4],  # topk
+        )
+    ),
+)
+def test_topk_sigmoid_renormalize_correction_bias(num_tokens, num_experts, topk):
+    gating_output = torch.randn(
+        (num_tokens, num_experts), dtype=torch.float32, device="cuda"
+    )
+    correction_bias = torch.randn((num_experts), dtype=torch.float32, device="cuda")
+
+    topk_weights = torch.empty((num_tokens, topk), dtype=torch.float32, device="cuda")
+    topk_indices = torch.empty((num_tokens, topk), dtype=torch.int32, device="cuda")
+
+    topk_sigmoid(
+        topk_weights,
+        topk_indices,
+        gating_output,
+        renormalize=True,
+        correction_bias=correction_bias,
+    )
+
+    # Native torch implementation
+    sigmoid_output = torch.sigmoid(gating_output)
+    sigmoid_scores = sigmoid_output.view(-1, num_experts) + correction_bias.unsqueeze(0)
+    _, topk_indices_ref = torch.topk(sigmoid_scores, k=topk, dim=-1)
+    topk_weights_ref = sigmoid_output.gather(1, topk_indices_ref)
+    topk_weights_ref = topk_weights_ref / topk_weights_ref.sum(dim=-1, keepdim=True)
+
+    # Verify the top-k weights and indices match the torch native ones
+    assert torch.allclose(
+        topk_weights_ref, topk_weights, atol=1e-3, rtol=1e-3
+    ), f"Weights mismatch: torch={topk_weights_ref} vs SGLang={topk_weights}"
+
+    assert torch.allclose(
+        topk_indices_ref.int(), topk_indices, atol=0, rtol=0
+    ), f"Indices mismatch: torch={topk_indices_ref}, SGLang={topk_indices}"
+
+
+if __name__ == "__main__":
+    sys.exit(pytest.main([__file__]))