addconv2d mlir files

benchmarks/DeepLearning/Ops/Conv2DNchwFchwOp/Conv2DNchwFchw.mlir

+// generate from iree processed alexnet or lenet mlir file
+func @conv_2d_input_nchw_filter_fchw(%input: memref<?x?x?x?xf32>, %filter: memref<?x?x?x?xf32>, %output: memref<?x?x?x?xf32>)
+{
+    linalg.conv_2d_nchw_fchw 
+    {
+        dilations = dense<1> : tensor<2xi64>, 
+        strides = dense<1> : tensor<2xi64>
+    } 
+    ins(%input, %filter : memref<?x?x?x?xf32>, memref<?x?x?x?xf32>) 
+    outs(%output : memref<?x?x?x?xf32>) -> memref<?x?x?x?xf32>
+    return
+}
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benchmarks/DeepLearning/Ops/Conv2DNhwcHwcfOp/Conv2DNhwcHwcf.mlir

+// generate from iree processed mobilenet mlir file
+func @conv_2d_input_nhwc_filter_hwcf(%input: memref<?x?x?x?xf32>, %filter: memref<?x?x?x?xf32>, %output: memref<?x?x?x?xf32>) 
+{
+    linalg.conv_2d_nhwc_hwcf 
+    {
+        dilations = dense<1> : tensor<2xi64>, 
+        strides = dense<1> : tensor<2xi64>
+    } 
+    ins(%input, %filter : memref<?x?x?x?xf32>, memref<?x?x?x?xf32>) 
+    outs(%output : memref<?x?x?x?xf32>) -> memref<?x?x?x?xf32>
+    return
+}
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