Remove DynamicReshape and DynamicBroadcastInDim ops

These operations are being moved to a separate PR to allow the Shape
and shape inference changes to be merged independently.

Author: ajroetker

internal/optypes/gen_optype_enumer.go

@@ -102,12 +102,10 @@ const (
 	CollectivePermute
 	Composite
 	CustomCall
-	DynamicBroadcastInDim
 	DynamicConv
 	DynamicGather
 	DynamicIota
 	DynamicPad
-	DynamicReshape
 	GetDimensionSize
 	GetTupleElement
 	Infeed

internal/optypes/optypes.go

@@ -1728,142 +1728,6 @@ func GetDimensionSize(operand *Value, dimension int) (*Value, error) {
 	return stmt.Outputs[0], nil
 }
 
-// DynamicReshape reshapes the operand to a shape specified by a 1D tensor,
-// with caller-provided bounds for the output dimensions.
-//
-// This is the dynamic version of Reshape where the output shape is determined at runtime.
-// The total number of elements must remain the same.
-//
-// Parameters:
-//   - operand: the tensor to reshape.
-//   - outputShape: a 1D tensor of i32 or i64 values specifying the target shape dimensions.
-//   - bounds: dimension bounds for the output shape. Must have length equal to output rank.
-//     Use the actual dimension value for known dimensions, or an upper bound for dynamic ones.
-//
-// XLA requires bounded dynamic dimensions, so bounds must be provided. The caller should
-// determine these bounds before generating StableHLO (e.g., from model configuration or
-// by analyzing the computation graph).
-func DynamicReshape(operand *Value, outputShape *Value, bounds []int) (*Value, error) {
-	op := optypes.DynamicReshape
-	fn := operand.fn
-	if fn.Returned {
-		return nil, errors.Errorf("cannot add operation %s after returning, in function %q",
-			op, fn.Name)
-	}
-	if outputShape.fn != fn {
-		return nil, errors.Errorf("cannot add operation %s to function %q, because operand and outputShape are from different functions",
-			op, fn.Name)
-	}
-
-	// Validate outputShape is 1D tensor of integer type
-	if outputShape.shape.Rank() != 1 {
-		return nil, errors.Errorf("outputShape must be a 1D tensor, got rank %d",
-			outputShape.shape.Rank())
-	}
-	if !outputShape.shape.DType.IsInt() {
-		return nil, errors.Errorf("outputShape must be integer type, got %s",
-			outputShape.shape.DType)
-	}
-
-	// Get output rank from the shape tensor
-	outputRank := outputShape.shape.Dimensions[0]
-	if outputRank < 0 {
-		return nil, errors.Errorf("outputShape tensor has dynamic size, cannot determine output rank")
-	}
-
-	// Validate bounds length
-	if len(bounds) != outputRank {
-		return nil, errors.Errorf("bounds length (%d) must match output rank (%d)",
-			len(bounds), outputRank)
-	}
-
-	// Create output shape with dynamic dimensions and caller-provided bounds
-	resultShape := operand.shape.Clone()
-	resultShape.Dimensions = make([]int, outputRank)
-	resultShape.DimensionBounds = make([]int, outputRank)
-	for i := 0; i < outputRank; i++ {
-		resultShape.Dimensions[i] = shapes.DimUnknown // Dynamic
-		resultShape.DimensionBounds[i] = bounds[i]
-	}
-	// dynamic_reshape requires bounded output in XLA, so we must encode bounds
-	resultShape.EncodeBounds = true
-
-	stmt := fn.addOp(op, resultShape, operand, outputShape)
-	return stmt.Outputs[0], nil
-}
-
-// DynamicBroadcastInDim broadcasts the operand to a shape specified by a 1D tensor,
-// with caller-provided bounds for the output dimensions.
-//
-// This is the dynamic version of BroadcastInDim where the output shape is determined at runtime.
-//
-// Parameters:
-//   - operand: the tensor to broadcast.
-//   - outputDimensions: a 1D tensor of i32 or i64 values specifying the target shape.
-//   - broadcastDimensions: maps operand axes to output axes (like BroadcastInDim).
-//   - bounds: dimension bounds for the output shape. Must have length equal to output rank.
-//
-// XLA requires bounded dynamic dimensions, so bounds must be provided. The caller should
-// determine these bounds before generating StableHLO (e.g., from model configuration or
-// by analyzing the computation graph).
-func DynamicBroadcastInDim(operand *Value, outputDimensions *Value, broadcastDimensions []int, bounds []int) (*Value, error) {
-	op := optypes.DynamicBroadcastInDim
-	fn := operand.fn
-	if fn.Returned {
-		return nil, errors.Errorf("cannot add operation %s after returning, in function %q",
-			op, fn.Name)
-	}
-	if outputDimensions.fn != fn {
-		return nil, errors.Errorf("cannot add operation %s to function %q, because operand and outputDimensions are from different functions",
-			op, fn.Name)
-	}
-
-	// Validate outputDimensions is 1D tensor of integer type
-	if outputDimensions.shape.Rank() != 1 {
-		return nil, errors.Errorf("outputDimensions must be a 1D tensor, got rank %d",
-			outputDimensions.shape.Rank())
-	}
-	if !outputDimensions.shape.DType.IsInt() {
-		return nil, errors.Errorf("outputDimensions must be integer type, got %s",
-			outputDimensions.shape.DType)
-	}
-
-	// Validate broadcastDimensions length matches operand rank
-	if len(broadcastDimensions) != operand.shape.Rank() {
-		return nil, errors.Errorf("broadcastDimensions length (%d) must match operand rank (%d)",
-			len(broadcastDimensions), operand.shape.Rank())
-	}
-
-	// Get output rank from the dimensions tensor
-	outputRank := outputDimensions.shape.Dimensions[0]
-	if outputRank < 0 {
-		return nil, errors.Errorf("outputDimensions tensor has dynamic size, cannot determine output rank")
-	}
-
-	// Validate bounds length
-	if len(bounds) != outputRank {
-		return nil, errors.Errorf("bounds length (%d) must match output rank (%d)",
-			len(bounds), outputRank)
-	}
-
-	// Create output shape with dynamic dimensions and caller-provided bounds
-	outputShape := operand.shape.Clone()
-	outputShape.Dimensions = make([]int, outputRank)
-	outputShape.DimensionBounds = make([]int, outputRank)
-	for i := 0; i < outputRank; i++ {
-		outputShape.Dimensions[i] = shapes.DimUnknown // Dynamic
-		outputShape.DimensionBounds[i] = bounds[i]
-	}
-	// dynamic_broadcast_in_dim requires bounded output in XLA, so we must encode bounds
-	outputShape.EncodeBounds = true
-
-	stmt := fn.addOp(op, outputShape, operand, outputDimensions)
-	stmt.Attributes = map[string]any{
-		"broadcast_dimensions": intSliceToArrayI64StableHLO(broadcastDimensions),
-	}
-	return stmt.Outputs[0], nil
-}
-
 // While executes body repeatedly while condition returns true.
 //
 // The While operation implements a loop that continues executing the body function