set USE_JBLAS on as default. fix some lint

cmake/CMakeLists.txt

@@ -87,7 +87,7 @@ option(onnxruntime_USE_QNN "Build with QNN support" OFF)
 option(onnxruntime_USE_SNPE "Build with SNPE support" OFF)
 option(onnxruntime_USE_RKNPU "Build with RKNPU support" OFF)
 option(onnxruntime_USE_DNNL "Build with DNNL support" OFF)
-option(onnxruntime_USE_JBLAS "Build MLAS with JBLAS support" OFF)
+option(onnxruntime_USE_JBLAS "Build MLAS with JBLAS support" ON)
 option(onnxruntime_USE_JSEP "Build with JavaScript implemented kernels support" OFF)
 option(onnxruntime_BUILD_UNIT_TESTS "Build ONNXRuntime unit tests" ON)
 option(onnxruntime_BUILD_CSHARP "Build C# library" OFF)

docs/ContribOperators.md

@@ -2828,6 +2828,8 @@ This version of the operator has been available since version 1 of the 'com.micr
 <dd>number of bits used for weight quantization (default 4)</dd>
 <dt><tt>block_size</tt> : int (required)</dt>
 <dd>number of groupsize used for weight quantization,(default 128). It needs to be a power of 2 and not smaller than 16.</dd>
+<dt><tt>accuracy_level</tt> : int</dt>
+<dd>The minimum accuracy level of input A, can be: 0(unset), 1(fp32), 2(fp16), 3(bf16), or 4(int8) (default unset). It is used to control how input A is quantized or downcast internally while doing computation, for example: 0 means input A will not be quantized or downcast while doing computation. 4 means input A can be quantized with the same block_size to int8 internally from type T1.</dd>
 </dl>
 
 #### Inputs (3 - 4)

onnxruntime/contrib_ops/cpu/quantization/matmul_nbits.cc

@@ -162,7 +162,8 @@ Status MatMulNBits::Compute(OpKernelContext* ctx) const {
       gemm_params[i].C = y_data + helper.OutputOffsets()[i];
       gemm_params[i].ldc = N;
     }
-    MlasNBitsGemmBatchPackedB(M, N, K, max_len, gemm_params.data(), reinterpret_cast<int8_t*>(ws_ptr.get()), thread_pool);
+    MlasNBitsGemmBatchPackedB(M, N, K, max_len, gemm_params.data(), reinterpret_cast<int8_t*>(ws_ptr.get()),
+                              thread_pool);
     return Status::OK();
   }
 

onnxruntime/core/graph/contrib_ops/contrib_defs.cc

@@ -3364,12 +3364,12 @@ Input zero_points is stored as uint8_t. If bits <= 4, two zero points are stored
       .Attr("bits", "number of bits used for weight quantization (default 4)", AttributeProto::INT)
       .Attr("block_size", "number of groupsize used for weight quantization,(default 128). It needs to be a power of 2 and not smaller than 16.", AttributeProto::INT)
       .Attr("accuracy_level",
-            "minimum accuracy level of input A, can be: 0(unset), 1(fp32), 2(fp16), 3(bf16), or 4(int8) (default unset)."
-            "It is used to control how input A is quantized or downcast internally while doing computation, for example:"
-            "0 means input A will not be quantized or downcast while doing computation."
-            "4 means input A can be quantized with the same block_size to int8 internally from type T1.",
-            AttributeProto::INT,
-            static_cast<int64_t>(0))
+            "The minimum accuracy level of input A, can be: 0(unset), 1(fp32), 2(fp16), 3(bf16), or 4(int8) "
+            "(default unset). It is used to control how input A is quantized or downcast internally while "
+            "doing computation, for example: 0 means input A will not be quantized or downcast while doing "
+            "computation. 4 means input A can be quantized with the same block_size to int8 internally from "
+            "type T1.",
+            AttributeProto::INT, static_cast<int64_t>(0))
       .Input(0, "A", "The input tensor, not quantized", "T1")
       .Input(1, "B", "1-dimensional data blob", "T2")
       .Input(2, "scales", "quantization scale", "T1")