# RUN: tf-mlir-translate -graphdef-to-mlir -tf-enable-shape-inference-on-import=false %s -o - | FileCheck %s

node {
  name: "input0"
  op: "Placeholder"
  attr {
    key: "dtype"
    value {
      type: DT_INT32
    }
  }
}
versions {
  producer: 29
  min_consumer: 12
}

# Verify that functions from the library are properly imported.

# CHECK-LABEL:  func @main() {
# CHECK:    "tf.Placeholder"

  // CHECK-DAG:  %[[CSTR_LHS_SHAPE:.+]] = shape.shape_of %arg0
  // CHECK-DAG:  %[[CSTR_RHS_SHAPE:.+]] = shape.shape_of %arg1
  // CHECK-NEXT: %[[WITNESS:.+]] = shape.cstr_broadcastable %[[CSTR_LHS_SHAPE]], %[[CSTR_RHS_SHAPE]]
  // CHECK-NEXT: shape.assuming %[[WITNESS:.+]]
  // CHECK-DAG:    %[[LHS_SHAPE:.+]] = shape.shape_of %arg0
  // CHECK-DAG:    %[[RHS_SHAPE:.+]] = shape.shape_of %arg1

// RUN: tf-opt -split-input-file -verify-diagnostics --tf-shape-inference %s | FileCheck %s

module attributes {tf.versions = {producer = 888 : i32}} {
// CHECK-LABEL: testConv2dShapeValidPadding
func.func @testConv2dShapeValidPadding(%arg0: tensor<1x112x80x128xf32>, %arg1: tensor<128x3x3x128xf32>, %arg2: tensor<128xf32>) -> tensor<1x?x?x128xf32> {

// RUN: tf-mlir-translate -mlir-tf-to-hlo-text %s -tf-input-shapes=10,17:17,19 -tf-xla-emit-use-tuple-args -tf-xla-emit-return-tuple | FileCheck %s
// RUN: tf-mlir-translate -mlir-tf-to-hlo-text %s -tf-input-shapes=10,17:17,19 | FileCheck -check-prefix=NO_TUPLES %s
// RUN: tf-mlir-translate -mlir-tf-to-hlo-text-via-builder %s -tf-input-shapes=10,17:17,19 | FileCheck -check-prefix=NO_TUPLES %s

module attributes {tf.versions = {producer = 179 : i32}} {

  op: "InfeedDequeueTuple"
  attr {
    key: "shapes"
    value {
      list {
        shape {
          dim {
            size: -1
          }
          dim {
            size: -1
          }
          dim {
            size: -1
          }
        }
        shape {
          unknown_rank: true
        }
        shape {
          dim {
            size: 7
          }

class TensorSpec {
 public:
  // Constructs a scalar, DT_FLOAT TensorSpec
  TensorSpec();

  TensorSpec(PartialTensorShape shape, DataType dtype);

  explicit TensorSpec(const TensorSpecProto& proto);

  const PartialTensorShape& shape() const;
  DataType dtype() const;

 private:
  PartialTensorShape shape_;
  DataType dtype_;
};

}  // namespace tensorflow

  auto element_type = x.getElementType();
  auto shape_x = x.getShape();
  auto shape_y = y.getShape();

  if (shape_x.size() == shape_y.size()) {
    llvm::SmallVector<int64_t, 4> out_shape(shape_x.size());
    for (int i = 0; i < shape_x.size(); i++) {
      auto x_val = shape_x[i];
      auto y_val = shape_y[i];
      out_shape[i] = std::max(x_val, y_val);
    }

      rewriter.replaceOp(mul_op, {new_conv});
    } else {
      // For dynamic case, create new shape_of op and replace uses.
      shape_of_op =
          dyn_cast_or_null<mhlo::DynamicBroadcastInDimOp>(bcast_or_const_op)
              .getOutputDimensions()
              .getDefiningOp<shape::ShapeOfOp>();
      // Check if the shape come from the original conv op.
      if (!shape_of_op ||

// CHECK-NEXT:     value {
// CHECK-NEXT:       type: DT_INT32
// CHECK-NEXT:     }
// CHECK:          key: "_output_shapes"
// CHECK-NEXT:     value {
// CHECK-NEXT:       list {
// CHECK-NEXT:         shape {
// CHECK-NEXT:           dim {
// CHECK-NEXT:             size: 10
// CHECK:        name: "output0"
// CHECK-NEXT:   op: "_Retval"
// CHECK-NEXT:   input: "input0"
// CHECK-NEXT:   attr {
// CHECK-NEXT:     key: "T"

Hongmin Fan <******@****.***> 1614638143 -0800