Disable kernels with issue

Signed-off-by: Dongfeng Yu <[email protected]>

Author: dongfengy

cpp/tensorrt_llm/thop/fp4BlockScaleMoe.cpp

@@ -45,78 +45,6 @@ std::vector<torch::Tensor> run_fp4_block_scale_moe_runner(torch::optional<torch:
     bool const do_finalize, btg::Dtype const dtype, MoeRunnerType& moe_runner, int64_t const moeConfigIndex,
     torch::optional<torch::Tensor> const& topk_weights, torch::optional<torch::Tensor> const& topk_ids)
 {
-    std::cout << "Function: run_fp4_block_scale_moe_runner" << std::endl;
-
-    auto print_tensor = [](std::string name, torch::Tensor const& t)
-    {
-        std::cout << name << ": shape=[";
-        for (auto s : t.sizes())
-        {
-            std::cout << s << ",";
-        }
-        std::cout << "], dtype=" << t.scalar_type() << std::endl;
-    };
-
-    auto print_opt_tensor = [&](std::string name, auto const& t)
-    {
-        if (t.has_value())
-        {
-            print_tensor(name, t.value());
-        }
-        else
-        {
-            std::cout << name << ": None" << std::endl;
-        }
-    };
-
-    auto print_val = [](std::string name, auto const& v) { std::cout << name << ": " << v << std::endl; };
-
-    auto print_opt_val = [&](std::string name, auto const& v)
-    {
-        if (v.has_value())
-        {
-            std::cout << name << ": " << v.value() << std::endl;
-        }
-        else
-        {
-            std::cout << name << ": None" << std::endl;
-        }
-    };
-
-    print_opt_tensor("routing_logits", routing_logits);
-    print_opt_tensor("routing_bias", routing_bias);
-    print_tensor("hidden_states", hidden_states);
-    print_opt_tensor("hidden_states_scale", hidden_states_scale);
-    print_tensor("gemm1_weights", gemm1_weights);
-    print_tensor("gemm1_weights_scale", gemm1_weights_scale);
-    print_opt_tensor("gemm1_bias", gemm1_bias);
-    print_opt_tensor("gemm1_alpha", gemm1_alpha);
-    print_opt_tensor("gemm1_beta", gemm1_beta);
-    print_opt_tensor("gemm1_clamp_limit", gemm1_clamp_limit);
-    print_tensor("gemm2_weights", gemm2_weights);
-    print_tensor("gemm2_weights_scale", gemm2_weights_scale);
-    print_opt_tensor("gemm2_bias", gemm2_bias);
-    print_tensor("output1_scales_scalar", output1_scales_scalar);
-    print_tensor("output1_scales_gate_scalar", output1_scales_gate_scalar);
-    print_tensor("output2_scales_scalar", output2_scales_scalar);
-
-    print_val("num_experts", num_experts);
-    print_val("top_k", top_k);
-    print_opt_val("n_group", n_group);
-    print_opt_val("topk_group", topk_group);
-    print_val("intermediate_size", intermediate_size);
-    print_val("local_expert_offset", local_expert_offset);
-    print_val("local_num_experts", local_num_experts);
-    print_opt_val("routed_scaling_factor", routed_scaling_factor);
-    print_val("tile_tokens_dim", tile_tokens_dim);
-    print_val("routing_method_type", routing_method_type);
-    print_val("do_finalize", do_finalize);
-    print_val("dtype", static_cast<int>(dtype));
-    print_val("moeConfigIndex", moeConfigIndex);
-    print_opt_tensor("topk_weights", topk_weights);
-    print_opt_tensor("topk_ids", topk_ids);
-    std::cout << "--------------------------------" << std::endl;
-
     TORCH_CHECK(dtype == btg::Dtype::E4m3 || dtype == btg::Dtype::E2m1, "dtype can only be e4m3 or e2m1.");
     TORCH_CHECK(tensorrt_llm::common::isSM100Family(), "Only SM100f is supported by FP4 block scale MOE");
     TORCH_CHECK(tile_tokens_dim == 8 || tile_tokens_dim == 16 || tile_tokens_dim == 32 || tile_tokens_dim == 64
@@ -526,7 +454,7 @@ class FP4BlockScaleMoeRunner : public torch::CustomClassHolder
 public:
     explicit FP4BlockScaleMoeRunner()
         // Update this as new cubins come in
-        : mSupportedTileN{8, 16, 32, 64, 128, 256}
+        : mSupportedTileN{8, 16, 32, 64, 128}
     {
         for (int tileN : mSupportedTileN)
         {