Implement aten::repeat_interleave operators

onnxscript/function_libs/torch_lib/ops/core.py

@@ -7280,12 +7280,166 @@ def aten_repeat(self: TTensor, repeats: Sequence[TInt]) -> TTensor:
     return op.Tile(self_expanded, repeats)
 
 
+@torch_op("aten::repeat_interleave.Tensor", trace_only=True)
 def aten_repeat_interleave(
     repeats: TensorType, output_size: Optional[int] = None
 ) -> TensorType:
     """repeat_interleave.Tensor(Tensor repeats, *, int? output_size=None) -> Tensor"""
 
-    raise NotImplementedError()
+    # Convert repeats to int64 for ONNX compatibility
+    repeats_int64 = op.Cast(repeats, to=INT64.dtype)
+
+    # Get cumulative sum of repeats to find the boundaries
+    cumsum = op.CumSum(repeats_int64, axis=0)
+    total_size = op.Gather(cumsum, op.Constant(value_ints=[-1]), axis=0)
+
+    # Create output tensor indices
+    output_range = op.Range(
+        op.Constant(value_ints=[0]), total_size, op.Constant(value_ints=[1])
+    )
+
+    # Find which original index each output position corresponds to
+    # We need to find the first cumsum position > each output position
+    # This is equivalent to a searchsorted operation
+
+    # Expand dimensions for broadcasting
+    cumsum_expanded = op.Unsqueeze(cumsum, [0])  # Shape: [1, len(repeats)]
+    output_range_expanded = op.Unsqueeze(output_range, [1])  # Shape: [total_size, 1]
+
+    # Find positions where output_range < cumsum
+    mask = op.Less(output_range_expanded, cumsum_expanded)  # Shape: [total_size, len(repeats)]
+
+    # For each row, find the first True position (argmax will do this since True=1, False=0)
+    result_indices = op.ArgMax(op.Cast(mask, to=INT64.dtype), axis=1, keepdims=False)
+
+    return result_indices
+
+
+@torch_op("aten::repeat_interleave.self_Tensor", trace_only=True)
+def aten_repeat_interleave_self_tensor(
+    self: TensorType,
+    repeats: TensorType,
+    dim: Optional[int] = None,
+    output_size: Optional[int] = None,
+) -> TensorType:
+    """repeat_interleave.self_Tensor(Tensor self, Tensor repeats, int? dim=None, *, SymInt? output_size=None) -> Tensor"""
+
+    if dim is None:
+        # Flatten the tensor first, then repeat elements
+        self_flat = op.Reshape(self, [-1])
+
+        # Convert repeats to int64 for ONNX compatibility
+        repeats_int64 = op.Cast(repeats, to=INT64.dtype)
+
+        # Get cumulative sum of repeats to find the boundaries
+        cumsum = op.CumSum(repeats_int64, axis=0)
+        total_size = op.Gather(cumsum, op.Constant(value_ints=[-1]), axis=0)
+
+        # Create output tensor indices
+        output_range = op.Range(
+            op.Constant(value_ints=[0]), total_size, op.Constant(value_ints=[1])
+        )
+
+        # Find which original index each output position corresponds to
+        cumsum_expanded = op.Unsqueeze(cumsum, [0])  # Shape: [1, len(repeats)]
+        output_range_expanded = op.Unsqueeze(output_range, [1])  # Shape: [total_size, 1]
+
+        # Find positions where output_range < cumsum
+        mask = op.Less(
+            output_range_expanded, cumsum_expanded
+        )  # Shape: [total_size, len(repeats)]
+
+        # For each row, find the first True position
+        indices = op.ArgMax(op.Cast(mask, to=INT64.dtype), axis=1, keepdims=False)
+
+        # Gather elements from the flattened tensor
+        result = op.Gather(self_flat, indices, axis=0)
+        return result
+
+    else:
+        # Repeat along specific dimension
+        # Convert repeats to int64 for ONNX compatibility
+        repeats_int64 = op.Cast(repeats, to=INT64.dtype)
+
+        # Get cumulative sum of repeats to find the boundaries
+        cumsum = op.CumSum(repeats_int64, axis=0)
+        total_size = op.Gather(cumsum, op.Constant(value_ints=[-1]), axis=0)
+
+        # Create output tensor indices for the specified dimension
+        output_range = op.Range(
+            op.Constant(value_ints=[0]), total_size, op.Constant(value_ints=[1])
+        )
+
+        # Find which original index each output position corresponds to
+        cumsum_expanded = op.Unsqueeze(cumsum, [0])  # Shape: [1, len(repeats)]
+        output_range_expanded = op.Unsqueeze(output_range, [1])  # Shape: [total_size, 1]
+
+        # Find positions where output_range < cumsum
+        mask = op.Less(
+            output_range_expanded, cumsum_expanded
+        )  # Shape: [total_size, len(repeats)]
+
+        # For each row, find the first True position
+        indices = op.ArgMax(op.Cast(mask, to=INT64.dtype), axis=1, keepdims=False)
+
+        # Gather elements along the specified dimension
+        result = op.Gather(self, indices, axis=dim)
+        return result
+
+
+@torch_op("aten::repeat_interleave.self_int", trace_only=True)
+def aten_repeat_interleave_self_int(
+    self: TensorType,
+    repeats: int,
+    dim: Optional[int] = None,
+    output_size: Optional[int] = None,
+) -> TensorType:
+    """repeat_interleave.self_int(Tensor self, SymInt repeats, int? dim=None, *, SymInt? output_size=None) -> Tensor"""
+
+    if dim is None:
+        # Flatten the tensor first, then repeat each element 'repeats' times
+        self_flat = op.Reshape(self, [-1])
+        num_elements = op.Shape(self_flat, start=0, end=1)
+
+        # Create indices that repeat each original index 'repeats' times
+        # For input [a, b, c] with repeats=2, we want indices [0, 0, 1, 1, 2, 2]
+        original_indices = op.Range(
+            op.Constant(value_ints=[0]), num_elements, op.Constant(value_ints=[1])
+        )
+
+        # Repeat each index 'repeats' times
+        # We can use Tile with appropriate reshaping
+        indices_reshaped = op.Unsqueeze(original_indices, [1])  # Shape: [num_elements, 1]
+        repeat_pattern = op.Constant(value_ints=[1, repeats])
+        repeated_indices = op.Tile(
+            indices_reshaped, repeat_pattern
+        )  # Shape: [num_elements, repeats]
+        final_indices = op.Reshape(repeated_indices, [-1])  # Shape: [num_elements * repeats]
+
+        # Gather elements from the flattened tensor
+        result = op.Gather(self_flat, final_indices, axis=0)
+        return result
+
+    else:
+        # Repeat along specific dimension
+        dim_size = op.Shape(self, start=dim, end=dim + 1)
+
+        # Create indices that repeat each original index 'repeats' times
+        original_indices = op.Range(
+            op.Constant(value_ints=[0]), dim_size, op.Constant(value_ints=[1])
+        )
+
+        # Repeat each index 'repeats' times
+        indices_reshaped = op.Unsqueeze(original_indices, [1])  # Shape: [dim_size, 1]
+        repeat_pattern = op.Constant(value_ints=[1, repeats])
+        repeated_indices = op.Tile(
+            indices_reshaped, repeat_pattern
+        )  # Shape: [dim_size, repeats]
+        final_indices = op.Reshape(repeated_indices, [-1])  # Shape: [dim_size * repeats]
+
+        # Gather elements along the specified dimension
+        result = op.Gather(self, final_indices, axis=dim)
+        return result
 
 
 @torch_op("aten::reshape")

tests/function_libs/torch_lib/ops_test_data.py

@@ -1249,6 +1249,9 @@ def _where_input_wrangler(
         core_ops.aten_remainder,
     ),
     TorchLibOpInfo("repeat", core_ops.aten_repeat),
+    TorchLibOpInfo("repeat_interleave", core_ops.aten_repeat_interleave),
+    TorchLibOpInfo("repeat_interleave.self_Tensor", core_ops.aten_repeat_interleave_self_tensor),
+    TorchLibOpInfo("repeat_interleave.self_int", core_ops.aten_repeat_interleave_self_int),
     TorchLibOpInfo("reshape", core_ops.aten_reshape),
     TorchLibOpInfo("resolve_conj", core_ops.aten_resolve_conj),
     TorchLibOpInfo("resolve_neg", core_ops.aten_resolve_neg),