[sgl-kernel][3/N]Support Expert Specialization Grouped GEMM (#11674)

sgl-kernel/tests/test_es_fp8_blockwise_moe.py

@@ -168,7 +168,7 @@ def test_fp8_blockwise_scaled_grouped_mm(num_experts, out_dtype):
         ].t()  # b_scale_stack[g] -- (k, n):(n, 1), we need transpose & contiguous later
     b_stack = b_stack.transpose(1, 2)  # Transpose Matrix B to Column-Major.
     b_scale_stack = b_scale_stack.transpose(1, 2)
-
+    workspace = torch.empty((1024 * 1024 * 1024), device=device, dtype=torch.uint8)
     c_out = torch.empty((expert_offsets[-1], n_g), device=device, dtype=out_dtype)
     a_strides = torch.full(
         (num_experts,), a_stack.stride(0), device=device, dtype=torch.int64
@@ -188,6 +188,7 @@ def test_fp8_blockwise_scaled_grouped_mm(num_experts, out_dtype):
         d_strides,
         problem_sizes,
         expert_offsets[:-1],
+        workspace,
     )
 
     for g in range(num_experts):