[mlir][NFC] Address filecheck_lint findings in tosa-to-linalg-named.mlir.

mlir/test/Conversion/TosaToLinalg/tosa-to-linalg-named.mlir

func.func @avg_pool_f32(%arg0: tensor<1x6x34x62xf32>) -> (tensor<1x5x33x62xf32>) {
 
   // Fill the pooling target:
   // CHECK: %[[F0:.+]] = arith.constant 0.000000e+00 : f32
   // CHECK: %[[EMPTY:.+]] = tensor.empty() : tensor<1x5x33x62xf32>
   // CHECK: %[[FILL:.+]] = linalg.fill ins(%[[F0]] : f32) outs(%[[EMPTY]] : tensor<1x5x33x62xf32>)
 
   // Compute the sum padding:
   // CHECK: %[[KERNEL:.+]] = tensor.empty() : tensor<4x4xf32>
-  // CHECK: %[[POOL:.+]] = linalg.pooling_nhwc_sum 
+  // CHECK: %[[POOL:.+]] = linalg.pooling_nhwc_sum
   // CHECK-SAME: dilations = dense<1> : vector<2xi64>, strides = dense<1> : vector<2xi64>}
   // CHECK-SAME: ins(%[[PAD]], %[[KERNEL]] : tensor<1x8x36x62xf32>, tensor<4x4xf32>)
   // CHECK-SAME: outs(%[[FILL]] : tensor<1x5x33x62xf32>)
 
   // Compute dimension based constants:
   // CHECK: %[[I1:.+]] = arith.constant 1 : index
   // CHECK: %[[DIM1:.+]] = tensor.dim %[[POOL]], %[[I1]]
   // CHECK: %[[I2:.+]] = arith.constant 2 : index
   // CHECK: %[[DIM2:.+]] = tensor.dim %[[POOL]], %[[I2]]
   // CHECK: %[[ONE:.+]] = arith.constant 1 : index
   // CHECK: %[[HEIGHT:.+]] = arith.subi %[[DIM1]], %[[ONE]] : index
   // CHECK: %[[WIDTH:.+]] = arith.subi %[[DIM2]], %[[ONE]] : index
 
   // Divide the sum pooling by the number of summed values.
   // CHECK: %[[EMPTY:.+]] = tensor.empty() : tensor<1x5x33x62xf32>
-  // CHECK: %[[GENERIC:.+]] = linalg.generic 
+  // CHECK: %[[GENERIC:.+]] = linalg.generic
   // CHECK-SAME: indexing_maps = [#map, #map], iterator_types = ["parallel", "parallel", "parallel", "parallel"]}
   // CHECK-SAME: ins(%[[POOL]] : tensor<1x5x33x62xf32>)
   // CHECK-SAME: outs(%[[EMPTY]] : tensor<1x5x33x62xf32>)
   // CHECK: ^bb0(%[[IN:.+]]: f32, %{{.+}}: f32)
   // CHECK:   %[[ZERO:.+]] = arith.constant 0
 
   // Compute how much of the height does not include padding:
   // CHECK:   %[[STRIDE:.+]] = arith.constant 1
func.func @avg_pool_f32(%arg0: tensor<1x6x34x62xf32>) -> (tensor<1x5x33x62xf32>) {
   // CHECK:   %[[DIV:.+]] = arith.divf %[[IN]], %[[FLT]]
   // CHECK:   linalg.yield %[[DIV]]
   %0 = "tosa.avg_pool2d"(%arg0) {pad = array<i64: 1, 1, 1, 1>, kernel = array<i64: 4, 4>, stride = array<i64: 1, 1>} : (tensor<1x6x34x62xf32>)  -> (tensor<1x5x33x62xf32>)
   return %0 : tensor<1x5x33x62xf32>
 }
 
 // -----
 
-// CHECK-LABLE: @avg_pool_i8
+// CHECK-LABEL: @avg_pool_i8
 func.func @avg_pool_i8(%arg0: tensor<1x6x34x62xi8>) -> (tensor<1x5x33x62xi8>) {
-  // CHECK: %[[GENERIC:.+]] = linalg.generic 
+  // CHECK: %[[GENERIC:.+]] = linalg.generic
   // CHECK-SAME: indexing_maps = [#map, #map], iterator_types = ["parallel", "parallel", "parallel", "parallel"]}
-  // CHECK-SAME: ins(%[[POOL]] : tensor<1x5x33x62xi32>)
-  // CHECK-SAME: outs(%[[EMPTY]] : tensor<1x5x33x62xi8>)
+  // CHECK-SAME: ins(%[[POOL:.+]] : tensor<1x5x33x62xi32>)
+  // CHECK-SAME: outs(%[[EMPTY:.+]] : tensor<1x5x33x62xi8>)
   // CHECK: ^bb0(%[[IN:.+]]: i32, %{{.+}}: i8)
 
   // Only different behavior is how the division is performed.
   // First we compute the mul and shift values for average pool:
   // CHECK: %[[COUNT:.+]] = arith.muli %21, %35
   // CHECK: %[[ICAST:.+]] = arith.index_cast %[[COUNT]]
   // CHECK: %[[C1:.+]] = arith.constant 1
   // CHECK: %[[C32:.+]] = arith.constant 32
func.func @avg_pool_dyn(%arg0: tensor<?x6x34x62xf32>) -> (tensor<?x5x33x62xf32>) {
   // CHECK: %[[BATCH:.+]] = tensor.dim %arg0, %[[C0]]
   // CHECK: %[[F0:.+]] = arith.constant 0.000000e+00 : f32
   // CHECK: %[[PADDED:.+]] = tensor.pad %arg0 low[0, 1, 1, 0] high[0, 1, 1, 0]
   // CHECK:   tensor.yield %[[F0]]
   // CHECK: %[[F0:.+]] = arith.constant 0.000000e+00 : f32
   // CHECK: %[[EMPTY:.+]] = tensor.empty(%[[BATCH]]) : tensor<?x5x33x62xf32>
   // CHECK: %[[FILL:.+]] = linalg.fill ins(%[[F0]] : f32) outs(%[[EMPTY]] : tensor<?x5x33x62xf32>)
   // CHECK: %[[KERNEL:.+]] = tensor.empty() : tensor<4x4xf32>
-  // CHECK: %[[POOL:.+]] = linalg.pooling_nhwc_sum 
+  // CHECK: %[[POOL:.+]] = linalg.pooling_nhwc_sum
   // CHECK-SAME: dilations = dense<1> : vector<2xi64>, strides = dense<1> : vector<2xi64>
   // CHECK-SAME: ins(%[[PADDED]], %[[KERNEL]] : tensor<?x8x36x62xf32>, tensor<4x4xf32>)
   // CHECK-SAME: outs(%[[FILL]] : tensor<?x5x33x62xf32>) -> tensor<?x5x33x62xf32>
   // CHECK: %[[EMPTY:.+]] = tensor.empty(%[[BATCH]]) : tensor<?x5x33x62xf32>
   // CHECK: %[[GENERIC:.+]] = linalg.generic
   %0 = "tosa.avg_pool2d"(%arg0) {pad = array<i64: 1, 1, 1, 1>, kernel = array<i64: 4, 4>, stride = array<i64: 1, 1>} : (tensor<?x6x34x62xf32>)  -> (tensor<?x5x33x62xf32>)
   return %0 : tensor<?x5x33x62xf32>
 }
func.func @conv3d_f32(%input: tensor<1x49x48x47x27xf32>, %weights: tensor<28x3x4x5x27xf32>, %bias: tensor<28xf32>) -> () {
   // CHECK-DAG: %[[EMPTY:.+]] = tensor.empty()
   // CHECK-DAG: %[[CONV3D:.+]] = linalg.conv_3d_ndhwc_dhwcf
   // CHECK-SAME: {dilations = dense<1> : tensor<3xi64>, strides = dense<1> : tensor<3xi64>}
   // CHECK-SAME: ins(%arg0, %[[TRANSPOSE]] : tensor<1x49x48x47x27xf32>, tensor<3x4x5x27x28xf32>)
   // CHECK-SAME: outs(%[[FILL]] : tensor<1x47x45x43x28xf32>) -> tensor<1x47x45x43x28xf32>
   // CHECK: %[[GENERIC:.+]] = linalg.generic
   // CHECK-SAME: {indexing_maps = [#map, #map1, #map1], iterator_types = ["parallel", "parallel", "parallel", "parallel", "parallel"]}
   // CHECK-SAME: ins(%arg2, %[[CONV3D]] : tensor<28xf32>, tensor<1x47x45x43x28xf32>)
-  // CHECK--SAME: outs(%[[EMPTY]] : tensor<1x47x45x43x28xf32>) {
+  // CHECK-SAME: outs(%[[EMPTY]] : tensor<1x47x45x43x28xf32>) {
   // CHECK: ^bb0(%[[A1:.+]]: f32, %[[A2:.+]]: f32, %{{.+}}: f32):
   // CHECK: %[[ADD:.+]] = arith.addf %[[A1]], %[[A2]] : f32
   // CHECK: linalg.yield %[[ADD]]
   %0 = "tosa.conv3d"(%input, %weights, %bias) {pad = array<i64: 0, 0, 0, 0, 0, 0>, stride = array<i64: 1, 1, 1>, dilation = array<i64: 1, 1, 1>} : (tensor<1x49x48x47x27xf32>, tensor<28x3x4x5x27xf32>, tensor<28xf32>)  -> tensor<1x47x45x43x28xf32>
   return
 }
 
 // -----
func.func @conv3d_i8(%input: tensor<1x49x48x47x27xi8>, %weights: tensor<28x3x4x5x27xi8>, %bias: tensor<28xi32>) -> () {
   // CHECK-DAG: %[[FZP:.+]] = arith.constant 42 : i32
   // CHECK-DAG: %[[CONV3D:.+]] = linalg.conv_3d_ndhwc_dhwcf_q
   // CHECK-SAME: {dilations = dense<1> : tensor<3xi64>, strides = dense<1> : tensor<3xi64>}
   // CHECK-SAME: ins(%arg0, %[[TRANSPOSE]], %[[IZP]], %[[FZP]] : tensor<1x49x48x47x27xi8>, tensor<3x4x5x27x28xi8>, i32, i32)
   // CHECK-SAME: outs(%[[FILL]] : tensor<1x47x45x43x28xi32>) -> tensor<1x47x45x43x28xi32>
   // CHECK: %[[GENERIC:.+]] = linalg.generic
   // CHECK-SAME: {indexing_maps = [#map, #map1, #map1], iterator_types = ["parallel", "parallel", "parallel", "parallel", "parallel"]}
   // CHECK-SAME: ins(%arg2, %[[CONV3D]] : tensor<28xi32>, tensor<1x47x45x43x28xi32>)
-  // CHECK--SAME: outs(%[[EMPTY]] : tensor<1x47x45x43x28xi32>) {
+  // CHECK-SAME: outs(%[[EMPTY]] : tensor<1x47x45x43x28xi32>) {
   // CHECK: ^bb0(%[[A1:.+]]: i32, %[[A2:.+]]: i32, %{{.+}}: i32):
   // CHECK: %[[ADD:.+]] = arith.addi %[[A1]], %[[A2]] : i32
   // CHECK: linalg.yield %[[ADD]]
   %0 = "tosa.conv3d"(%input, %weights, %bias) {pad = array<i64: 0, 0, 0, 0, 0, 0>, quantization_info = #tosa.conv_quant<input_zp = -128, weight_zp = 42>, stride = array<i64: 1, 1, 1>, dilation = array<i64: 1, 1, 1>} : (tensor<1x49x48x47x27xi8>, tensor<28x3x4x5x27xi8>, tensor<28xi32>)  -> tensor<1x47x45x43x28xi32>
   return
 }