lint

onnxruntime/contrib_ops/cuda/cutlass_extensions/ft_gemm_configs.h

@@ -54,4 +54,3 @@ struct CutlassGemmConfig {
   int stages = -1;
 };
 }  // namespace ort_fastertransformer
-

onnxruntime/contrib_ops/cuda/cutlass_extensions/gemm/kernel/default_fpA_intB_traits.h

@@ -141,4 +141,3 @@ struct MixedGemmArchTraits<
 }  // namespace kernel
 }  // namespace gemm
 }  // namespace cutlass
-

onnxruntime/contrib_ops/cuda/cutlass_extensions/gemm/kernel/gemm_moe_problem_visitor.h

@@ -76,4 +76,3 @@ struct GemmMoeProblemVisitor
 }  // namespace kernel
 }  // namespace gemm
 }  // namespace cutlass
-

onnxruntime/contrib_ops/cuda/cutlass_extensions/gemm/kernel/moe_cutlass_kernel.h

@@ -505,4 +505,3 @@ struct MoeFCGemm {
 }  // namespace cutlass
 
 /////////////////////////////////////////////////////////////////////////////////////////////////
-

onnxruntime/contrib_ops/cuda/moe/moe_base.h

@@ -48,7 +48,7 @@ class MoEBase {
     int64_t hidden_size = input_dims[input_dims.size() - 1];
     int64_t local_num_experts = fc1_experts_weights_dims[0];
     int64_t num_experts = router_probs_dims[1];
-    int64_t inter_size = fc1_experts_weights_dims[2] * (is_uint8_t?2:1);
+    int64_t inter_size = fc1_experts_weights_dims[2] * (is_uint8_t ? 2 : 1);
 
     if (fc1_experts_weights_dims.size() != 3) {
       return ORT_MAKE_STATUS(ONNXRUNTIME, INVALID_ARGUMENT, "fc1_experts_weights_dims must be 3D, got ",
@@ -69,17 +69,17 @@ class MoEBase {
                              fc2_experts_weights_dims[1],
                              " and ", inter_size);
     }
-    //if (fc1_experts_weights_dims[2] != inter_size) {
-    //  return ORT_MAKE_STATUS(ONNXRUNTIME, INVALID_ARGUMENT,
-    //                         "fc1_experts_weights_dims[2] must be equal to inter_size, got ",
-    //                         fc1_experts_weights_dims[2],
-    //                         " and ", inter_size);
-    //}
-    //if (fc2_experts_weights_dims[2] != hidden_size) {
-    //  return ORT_MAKE_STATUS(ONNXRUNTIME, INVALID_ARGUMENT,
-    //                         "fc2_experts_weights_dims[2] must be equal to hidden_size, got ",
-    //                         fc2_experts_weights_dims[2], " and ", hidden_size);
-    //}
+    // if (fc1_experts_weights_dims[2] != inter_size) {
+    //   return ORT_MAKE_STATUS(ONNXRUNTIME, INVALID_ARGUMENT,
+    //                          "fc1_experts_weights_dims[2] must be equal to inter_size, got ",
+    //                          fc1_experts_weights_dims[2],
+    //                          " and ", inter_size);
+    // }
+    // if (fc2_experts_weights_dims[2] != hidden_size) {
+    //   return ORT_MAKE_STATUS(ONNXRUNTIME, INVALID_ARGUMENT,
+    //                          "fc2_experts_weights_dims[2] must be equal to hidden_size, got ",
+    //                          fc2_experts_weights_dims[2], " and ", hidden_size);
+    // }
     if (router_probs_dims.size() != 2) {
       return ORT_MAKE_STATUS(ONNXRUNTIME, INVALID_ARGUMENT, "router_probs_dims must be 2D, got ",
                              router_probs_dims.size());

onnxruntime/test/python/transformers/test_parity_mixtral_moe_int4.py

@@ -45,22 +45,24 @@ def print_tensor(name, numpy_array):
     print(f"const std::vector<float> {name} = {value_string_of(numpy_array)};")
 
 
-def quant_dequant(weights, quant_mode:bool=True):
+def quant_dequant(weights, quant_mode: bool = True):
     # use the test version `_symmetric_...` to get the non-interleaved weights
     type = torch.quint4x2 if quant_mode else torch.int8
-    quant_weights, processed_q_weight, torch_weight_scales = torch.ops.fastertransformer._symmetric_quantize_last_axis_of_batched_matrix(
-        weights.T.cpu().contiguous(), type)
+    (
+        quant_weights,
+        processed_q_weight,
+        torch_weight_scales,
+    ) = torch.ops.fastertransformer._symmetric_quantize_last_axis_of_batched_matrix(weights.T.cpu().contiguous(), type)
 
     # Unpack the int4s int int8s
     if quant_mode:
-        upper = (quant_weights >> 4)
+        upper = quant_weights >> 4
         lower = (quant_weights << 4) >> 4  # Arithmetic right shift sign extends
         dq_quant_weights = torch.stack((lower, upper), dim=2).view(weights.T.shape)
 
     dq_quant_weights = dq_quant_weights.to(dtype=weights.dtype)
-    result = torch.multiply(dq_quant_weights,
-                            torch_weight_scales.unsqueeze(0)).T.contiguous()
-    return torch_weight_scales,processed_q_weight, result.to(device=weights.device)
+    result = torch.multiply(dq_quant_weights, torch_weight_scales.unsqueeze(0)).T.contiguous()
+    return torch_weight_scales, processed_q_weight, result.to(device=weights.device)
 
 
 def create_moe_onnx_graph(
@@ -102,9 +104,9 @@ def create_moe_onnx_graph(
         ),
     ]
 
-    fc1_shape = [num_experts, hidden_size, inter_size//2]
-    fc2_shape = [num_experts, inter_size, hidden_size//2]
-    fc3_shape = [num_experts, hidden_size, inter_size//2]
+    fc1_shape = [num_experts, hidden_size, inter_size // 2]
+    fc2_shape = [num_experts, inter_size, hidden_size // 2]
+    fc3_shape = [num_experts, hidden_size, inter_size // 2]
 
     torch_type = torch.float16 if ORT_DTYPE == TensorProto.FLOAT16 else torch.float32
     np_type = numpy.uint8