[mlir][tosa] Add acc_type to Tosa Conv Ops

- Adjust to TileOp multiples as input

Signed-off-by: Won Jeon <[email protected]>
Change-Id: Idf1701ec4d92c1aba852ff11c3330d87e5bf5f29

Author: wonjeon

tensorflow/compiler/mlir/tosa/tests/tf-to-tosa-pipeline.mlir

@@ -1,7 +1,7 @@
 // RUN: tf-opt --tf-to-tosa-pipeline  --verify-each %s | FileCheck %s
-// REQUIRES: tf_tosa
+
 // RUN: tf-opt --tf-tfl-to-tosa-pipeline  --verify-each %s | FileCheck %s
-// REQUIRES: tf_tosa
+
 
 // Operations for testing tf-to-tosa-pipeline
 // TODO: These tests are fairly minimal. Expand the checks to be more robust.
@@ -12,7 +12,7 @@
 // CHECK-DAG: %[[VAR0:.*]] = "tosa.const"() <{value = dense<0.000000e+00> : tensor<16xf32>}>
 // CHECK-DAG: %[[VAR1:.*]] = "tosa.const"() <{value = dense<[3, 0, 1, 2]> : tensor<4xi32>}>
 // CHECK-DAG: %[[VAR2:.*]] = tosa.transpose %arg1, %[[VAR1]]
-// CHECK: %[[VAR3:.*]] = tosa.conv2d %arg0, %[[VAR2]], %[[VAR0]] {dilation = array<i64: 1, 1>, pad = array<i64: 0, 1, 0, 1>, stride = array<i64: 1, 1>}
+// CHECK: %[[VAR3:.*]] = tosa.conv2d %arg0, %[[VAR2]], %[[VAR0]] {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 1, 0, 1>, stride = array<i64: 1, 1>}
 func.func @test_conv2d(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<2x2x8x16xf32>) -> tensor<1x32x32x16xf32> {
   %3 = "tf.Conv2D"(%arg0, %arg1)  {data_format = "NHWC", dilations = [1, 1, 1, 1], explicit_paddings = [], padding = "SAME", strides = [1, 1, 1, 1], use_cudnn_on_gpu = true}  : (tensor<1x32x32x8xf32>, tensor<2x2x8x16xf32>) -> tensor<1x32x32x16xf32>
   func.return %3 : tensor<1x32x32x16xf32>
@@ -22,7 +22,7 @@ func.func @test_conv2d(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<2x2x8x16xf32>
 
 // CHECK-LABEL: test_depthwise_conv2d
 // CHECK-DAG: %[[VAR0:.*]] = "tosa.const"() <{value = dense<0.000000e+00> : tensor<16xf32>}>
-// CHECK: %[[VAR1:.*]] = tosa.depthwise_conv2d %arg0, %arg1, %0 {dilation = array<i64: 1, 1>, pad = array<i64: 0, 1, 0, 1>, stride = array<i64: 1, 1>}
+// CHECK: %[[VAR1:.*]] = tosa.depthwise_conv2d %arg0, %arg1, %0 {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 1, 0, 1>, stride = array<i64: 1, 1>}
 func.func @test_depthwise_conv2d(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<2x2x8x2xf32>) -> tensor<1x32x32x16xf32> {
   %5 = "tf.DepthwiseConv2dNative"(%arg0, %arg1)  {data_format = "NHWC", dilations = [1, 1, 1, 1], explicit_paddings = [], padding = "SAME", strides = [1, 1, 1, 1]}  : (tensor<1x32x32x8xf32>, tensor<2x2x8x2xf32>) -> tensor<1x32x32x16xf32>
   %6 = "tf.Identity"(%5)   : (tensor<1x32x32x16xf32>) -> tensor<1x32x32x16xf32>
@@ -35,7 +35,7 @@ func.func @test_depthwise_conv2d(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<2x2
 // CHECK-SAME:      %[[ARG0:.*]]: tensor<1x32x32x8xf32>, %[[ARG1:.*]]: tensor<1x1x16x8xf32>
 // CHECK:         %[[CONST:.*]] = "tosa.const"() <{value = dense<0.000000e+00> : tensor<16xf32>}>
 // CHECK:         %[[RESHAPE:.*]] = tosa.reshape %[[ARG1]] {new_shape = array<i64: 16, 1, 1, 8>}
-// CHECK:         %[[TRANSPOSE:.*]] = tosa.transpose_conv2d %[[ARG0]], %[[RESHAPE]], %[[CONST]] {out_pad = array<i64: 0, 0, 0, 0>, out_shape = array<i64: 1, 32, 32, 16>, stride = array<i64: 1, 1>}
+// CHECK:         %[[TRANSPOSE:.*]] = tosa.transpose_conv2d %[[ARG0]], %[[RESHAPE]], %[[CONST]] {acc_type = f32, out_pad = array<i64: 0, 0, 0, 0>, out_shape = array<i64: 1, 32, 32, 16>, stride = array<i64: 1, 1>}
 // CHECK:         return %[[TRANSPOSE]]
 func.func @test_transpose_conv2d(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<1x1x16x8xf32>) -> tensor<1x32x32x16xf32> {
   %3 = "tf.Const"()  {value = dense<[1, 32, 32, 16]> : tensor<4xi32>}  : () -> tensor<4xi32>
@@ -51,7 +51,7 @@ func.func @test_transpose_conv2d(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<1x1
 // CHECK-DAG: %[[VAL_2:.*]] = "tosa.const"() <{value = dense<0.000000e+00> : tensor<4xf32>}>
 // CHECK-DAG: %[[VAL_3:.*]] = "tosa.const"() <{value = dense<[4, 0, 1, 2, 3]> : tensor<5xi32>}>
 // CHECK: %[[VAL_4:.*]] = tosa.transpose %[[VAL_1]], %[[VAL_3]]
-// CHECK: %[[VAL_5:.*]] = tosa.conv3d %[[VAL_0]], %[[VAL_4]], %[[VAL_2]] {dilation = array<i64: 1, 1, 1>, pad = array<i64: 0, 1, 0, 1, 0, 1>, stride = array<i64: 1, 2, 2>}
+// CHECK: %[[VAL_5:.*]] = tosa.conv3d %[[VAL_0]], %[[VAL_4]], %[[VAL_2]] {acc_type = f32, dilation = array<i64: 1, 1, 1>, pad = array<i64: 0, 1, 0, 1, 0, 1>, stride = array<i64: 1, 2, 2>}
 func.func @test_conv3d(%arg0: tensor<2x4x128x128x8xf32>, %arg1: tensor<2x3x3x2x4xf32>) -> tensor<2x4x64x64x4xf32> {
   %0 = "tf.Conv3D"(%arg0, %arg1) {data_format = "NDHWC", device = "", dilations = [1, 1, 1, 1, 1], padding = "SAME", strides = [1, 1, 2, 2, 1]} : (tensor<2x4x128x128x8xf32>, tensor<2x3x3x2x4xf32>) -> tensor<2x4x64x64x4xf32>
   return %0 : tensor<2x4x64x64x4xf32>
@@ -65,7 +65,7 @@ func.func @test_conv3d(%arg0: tensor<2x4x128x128x8xf32>, %arg1: tensor<2x3x3x2x4
 // CHECK-SAME: %[[VAL_2:.*]]: tensor<10xf32>) -> tensor<3x32x16x16x10xf32>
 // CHECK-DAG: %[[VAL_3:.*]] = "tosa.const"() <{value = dense<[4, 0, 1, 2, 3]> : tensor<5xi32>}>
 // CHECK: %[[VAL_4:.*]] = tosa.transpose %[[VAL_1]], %[[VAL_3]]
-// CHECK: %[[VAL_5:.*]] = tosa.conv3d %[[VAL_0]], %[[VAL_4]], %[[VAL_2]] {dilation = array<i64: 1, 1, 1>, pad = array<i64: 0, 1, 1, 1, 1, 1>, stride = array<i64: 1, 1, 1>}
+// CHECK: %[[VAL_5:.*]] = tosa.conv3d %[[VAL_0]], %[[VAL_4]], %[[VAL_2]] {acc_type = f32, dilation = array<i64: 1, 1, 1>, pad = array<i64: 0, 1, 1, 1, 1, 1>, stride = array<i64: 1, 1, 1>}
 func.func @test_conv3d_bias(%arg0: tensor<3x32x16x16x5xf32>, %arg1: tensor<2x3x3x5x10xf32>, %bias: tensor<10xf32>) -> tensor<3x32x16x16x10xf32> {
   %0 = "tf.Conv3D"(%arg0, %arg1) {data_format = "NDHWC", device = "", dilations = [1, 1, 1, 1, 1], padding = "SAME", strides = [1, 1, 1, 1, 1]} : (tensor<3x32x16x16x5xf32>, tensor<2x3x3x5x10xf32>) -> tensor<3x32x16x16x10xf32>
   %1 = "tf.BiasAdd"(%0, %bias) {data_format = "NHWC", device = ""} : (tensor<3x32x16x16x10xf32>, tensor<10xf32>) -> tensor<3x32x16x16x10xf32>
@@ -906,10 +906,12 @@ func.func @test_right_shift(%arg0: tensor<4x4xi32>, %arg1: tensor<1x1xi32>) -> t
 
 // CHECK-LABEL: @test_one_hot
 // CHECK-SAME:      %[[ARG0_0:.*]]: tensor<4x4xi32>, %[[ARG1_0:.*]]: tensor<f32>, %[[ARG2:.*]]: tensor<f32>
+// CHECK:         %[[CST1:.*]] = tosa.const_shape {value = dense<[16, 1, 1]> : tensor<3xindex>} : () -> !tosa.shape<3>
+// CHECK:         %[[CST2:.*]] = tosa.const_shape {value = dense<[16, 2, 1]> : tensor<3xindex>} : () -> !tosa.shape<3>
 // CHECK:         %[[RESHAPE_0:.*]] = tosa.reshape %[[ARG1_0]] {new_shape = array<i64: 1, 1, 1>}
-// CHECK:         %[[TILE:.*]] = tosa.tile %[[RESHAPE_0]] {multiples = array<i64: 16, 1, 1>}
+// CHECK:         %[[TILE:.*]] = tosa.tile %[[RESHAPE_0]], %[[CST1]]
 // CHECK:         %[[RESHAPE_1:.*]] = tosa.reshape %[[ARG2]] {new_shape = array<i64: 1, 1, 1>}
-// CHECK:         %[[TILE_0:.*]] = tosa.tile %[[RESHAPE_1]] {multiples = array<i64: 16, 2, 1>}
+// CHECK:         %[[TILE_0:.*]] = tosa.tile %[[RESHAPE_1]], %[[CST2]]
 // CHECK:         %[[RESHAPE_2:.*]] = tosa.reshape %[[ARG0_0]] {new_shape = array<i64: 16, 1>}
 // CHECK:         %[[SCATTER:.*]] = tosa.scatter %[[TILE_0]], %[[RESHAPE_2]], %[[TILE]]
 // CHECK:         %[[RESHAPE_3:.*]] = tosa.reshape %[[SCATTER]] {new_shape = array<i64: 4, 4, 2>}

tensorflow/compiler/mlir/tosa/tests/tfl-to-tosa-pipeline-filtered.mlir

@@ -31,7 +31,7 @@ func.func @test_softmax(%arg0: tensor<13x21x3xf32>) -> tensor<13x21x3xf32> {
 // CHECK: %[[VAR1:.*]] = tosa.transpose %arg1, %[[CST]]
 // CHECK-DAG: %[[VAR2:.*]] = tosa.reshape %arg0 {new_shape = array<i64: 14, 1, 1, 19>}
 // CHECK-DAG: %[[VAR3:.*]] = tosa.reshape %[[VAR1]] {new_shape = array<i64: 28, 1, 1, 19>}
-// CHECK-DAG: %[[VAR4:.*]] = tosa.conv2d %[[VAR2]], %[[VAR3]], %[[VAR0]] {dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>}
+// CHECK-DAG: %[[VAR4:.*]] = tosa.conv2d %[[VAR2]], %[[VAR3]], %[[VAR0]] {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>}
 // CHECK: %[[VAR5:.*]] = tosa.reshape %[[VAR4]] {new_shape = array<i64: 14, 28>}
 func.func @test_matmul(%arg0: tensor<14x19xf32>, %arg1: tensor<19x28xf32>) -> tensor<*xf32> {
   %cst = arith.constant dense<[1, 0]> : tensor<2xi32>