chore: vendor sglang v0.5.10 snapshot

third_party/sglang/sgl-kernel/python/sgl_kernel/expert_specialization.py

@@ -0,0 +1,50 @@
+import torch
+
+
+def es_fp8_blockwise_scaled_grouped_mm(
+    output,
+    a,
+    b,
+    scales_a,
+    scales_b,
+    stride_a,
+    stride_b,
+    stride_d,
+    problem_sizes,
+    expert_offsets,
+    workspace,
+):
+    torch.ops.sgl_kernel.es_fp8_blockwise_scaled_grouped_mm.default(
+        output,
+        a,
+        b,
+        scales_a,
+        scales_b,
+        stride_a,
+        stride_b,
+        stride_d,
+        problem_sizes,
+        expert_offsets,
+        workspace,
+    )
+
+
+def es_sm100_mxfp8_blockscaled_grouped_mm(
+    output, a, b, sfa, sfb, problem_sizes, expert_offsets, blockscale_offsets
+):
+    torch.ops.sgl_kernel.es_sm100_mxfp8_blockscaled_grouped_mm.default(
+        a, b, sfa, sfb, output, problem_sizes, expert_offsets, blockscale_offsets
+    )
+
+
+def es_sm100_mxfp8_blockscaled_grouped_quant(
+    input, problem_sizes, expert_offsets, blockscale_offsets, quant_output, scale_factor
+):
+    torch.ops.sgl_kernel.es_sm100_mxfp8_blockscaled_grouped_quant.default(
+        input,
+        problem_sizes,
+        expert_offsets,
+        blockscale_offsets,
+        quant_output,
+        scale_factor,
+    )