Improves implementation of aten_index_put

onnxscript/function_libs/torch_lib/ops/core.py

@@ -4233,6 +4233,30 @@ def aten_index_put(
     See implementation of `torch.onnx.symbolic_opset11.index_put
     <https://github.com/pytorch/pytorch/blob/main/torch/onnx/symbolic_opset11.py#L212>`_.
     """
+    if (
+        len(indices) > 1
+        and any(
+            isinstance(index, torch.onnx._internal.exporter._tensors.SymbolicTensor)  # pylint: disable=protected-access
+            for index in indices
+        )
+        and len(values.shape) == 1
+    ):
+        return _aten_index_put_dynamic(self, indices, values, accumulate=accumulate)
+
+    not_none = [i for i, ind in enumerate(indices) if ind is not None]
+    if (
+        len(not_none) == 1
+        and len(indices[not_none[0]].shape) == 1
+        and len(self.shape) == len(values.shape)
+    ):
+        return _aten_index_put_scatter_nd(self, indices, values, accumulate)
+
+    if len(indices) == 1 and set(indices[0].shape[:-1]) == {1} and indices[0].shape[0] == 1:
+        # shape(self) = (5,5), shape(indices[0]) = (1,2), shape(values) = (2,5)
+        # This case was only found in ops_data test.
+        return _aten_index_put_scatter_nd(
+            self, [op.Reshape(indices[0], [-1])], values, accumulate
+        )
 
     def _make_reshape_list_broadcastable(reshape_list, values_shape):
         # Remove ones until the rank of reshape_list matches values_shape.
@@ -4245,7 +4269,13 @@ def _make_reshape_list_broadcastable(reshape_list, values_shape):
         # the reshape list should be : [[2, 1], [1, 3], [2, 1]]
         for i, r in enumerate(reshape_list):
             if r not in (1, values_shape[i]):
-                value_index = values_shape.index(r)
+                try:
+                    value_index = values_shape.index(r)
+                except ValueError as e:
+                    raise RuntimeError(
+                        f"Unable to find element {r!r} in shape {values_shape}, "
+                        f"reshape_list={reshape_list}"
+                    ) from e
                 # Swap elements
                 # For the example above the current reshape list is [1, 2] for last dim,
                 # to make it broadcastable, we swap the elements
@@ -4269,15 +4299,22 @@ def _make_reshape_list_broadcastable(reshape_list, values_shape):
             reshape_update = self.shape[i]
         else:
             idx = indices[i]
-            reshape_update = math.prod(idx.shape)
-            # when Index is more than 1D, flatten it and also the values shape
-            # Example: self shape: (10, 3), indices[i] shape: (2, 4), values shape: (2, 4, 3)
-            # Indices -> (2*4,) and values shape (2*4, 32)
-            if len(idx.shape) > 1:
-                values_shape = (reshape_update, *values_shape[len(idx.shape) :])
-
-            # Flatten index (always working with 1D index in each dim)
-            idx = op.Reshape(idx, [-1])
+            if all(isinstance(s, int) for s in idx.shape):
+                reshape_update = math.prod(idx.shape)
+                # when Index is more than 1D, flatten it and also the values shape
+                # Example: self shape: (10, 3), indices[i] shape: (2, 4), values shape: (2, 4, 3)
+                # Indices -> (2*4,) and values shape (2*4, 32)
+                if len(idx.shape) > 1:
+                    values_shape = (reshape_update, *values_shape[len(idx.shape) :])
+
+                # Flatten index (always working with 1D index in each dim)
+                idx = op.Reshape(idx, [-1])
+            else:
+                raise RuntimeError(
+                    f"Unable to handle index {indices[i]} for axis={i} "
+                    f"because one of the dimension is not static as shape="
+                    f"{idx.shape}, indices={indices}"
+                )
 
         # Create a reshape pattern: one value per index dimension,
         # with the current dimension set to the update size.
@@ -4302,14 +4339,131 @@ def _make_reshape_list_broadcastable(reshape_list, values_shape):
     # Flatten values to match the indices
     flat_values = op.Reshape(values, [-1])
 
-    if accumulate:
-        result = op.ScatterND(self, new_index, flat_values, reduction="add")
-    else:
-        result = op.ScatterND(self, new_index, flat_values)
-
+    scatter_kwargs = dict(reduction="add") if accumulate else {}
+    result = op.ScatterND(self, new_index, flat_values, **scatter_kwargs)
     return result
 
 
+def _aten_index_put_scatter_nd(
+    x: TReal,
+    indices: Sequence[INT64],
+    values: TReal,
+    accumulate: bool = False,
+) -> TReal:
+    def _1dint(i: int):
+        return op.Constant(value_ints=ir.AttrInt64s("value_ints", [i]))
+
+    not_none = [i for i, ind in enumerate(indices) if ind is not None]
+    assert len(not_none) == 1, f"Unable to handle that case: not_none={not_none}"
+    unsq = op.Unsqueeze(indices[not_none[0]], _1dint(1))
+    if not_none[0] == 0:
+        return op.ScatterND(x, unsq, values, reduction="add" if accumulate else "none")
+
+    perm = list(range(len(x.shape)))
+    perm[not_none[0]], perm[0] = perm[0], perm[not_none[0]]
+    return op.Transpose(
+        op.ScatterND(
+            op.Transpose(x, perm=perm),
+            unsq,
+            op.Transpose(values, perm=perm),
+            reduction="add" if accumulate else "none",
+        ),
+        perm=perm,
+    )
+
+
+def _aten_index_put_dynamic(
+    x: TReal,
+    indices: Sequence[INT64],
+    values: TReal,
+    accumulate: bool = False,
+) -> TReal:
+    def _1dint(i: int):
+        return op.Constant(value_ints=ir.AttrInt64s("value_ints", [i]))
+
+    def _0dint(i: int):
+        return op.Constant(value_int=ir.AttrInt64("value_int", i))
+
+    def _make_range_or_cast(ind, shape_x, static_shape: bool, dim: int):
+        if ind is not None:
+            return op.Cast(ind, to=INT64.dtype), False
+        return (
+            op.Cast(
+                op.Range(  # Range does not return a typed result
+                    _0dint(0),
+                    op.Squeeze(op.Shape(x, start=dim, end=dim + 1)),
+                    _0dint(1),
+                ),
+                to=INT64.dtype,
+            ),
+            True,
+        )
+
+    shape_x = op.Shape(x)
+    exped = []
+    fixed = []
+    reshape_value_shape2 = []
+    expand_value_shape = []
+    for i, ind in enumerate(indices):
+        if isinstance(ind, torch.onnx._internal.exporter._tensors.SymbolicTensor):  # pylint: disable=protected-access
+            ind.dtype = ir.DataType.INT64
+        ind, expanded = _make_range_or_cast(ind, shape_x, False, i)
+        if expanded:
+            exped.append((i, ind))
+            expand_value_shape.append(op.Shape(x, start=i, end=i + 1))
+            reshape_value_shape2.append(_1dint(1))
+        else:
+            expand_value_shape.append(_1dint(1))
+            reshape_value_shape2.append(op.Shape(ind))
+            fixed.append((i, ind))
+
+    reshape_value_shape1 = [_1dint(1)] * len(indices)
+    if len(fixed) <= 1:
+        reshape_value_shape1 = None
+    elif fixed:
+        reshape_value_shape1[fixed[-1][0]] = _1dint(-1)
+
+    def _mkstride(x, i):
+        if i >= len(x.shape) - 1:
+            return _1dint(1)
+        if i == len(x.shape) - 2:
+            return op.Shape(x, start=i + 1)
+        return op.ReduceProd(op.Shape(x, start=i + 1), keepdims=1)
+
+    shape = [1] * (len(x.shape) + 1)
+    reshaped_fixed = []
+    if fixed:
+        new_shape = shape.copy()
+        new_shape[-1] = -1
+        reshaped_fixed = [op.Reshape(op.Mul(_mkstride(x, i), f), new_shape) for i, f in fixed]
+
+    reshaped_exped = []
+    for i, e in exped:
+        new_shape = shape.copy()
+        new_shape[i] = -1
+        reshaped_exped.append(op.Reshape(op.Mul(_mkstride(x, i), e), new_shape))
+
+    # final sum
+    unflat = None
+    for a in [*reshaped_fixed, *reshaped_exped]:
+        if unflat is None:
+            unflat = a
+            continue
+        unflat = op.Add(unflat, a)
+
+    # value_shape
+    expanded_values = values
+    if reshape_value_shape1 is not None:
+        expanded_values = op.Reshape(expanded_values, op.Concat(*reshape_value_shape1, axis=0))
+    expanded_values = op.Expand(expanded_values, op.Concat(*expand_value_shape, axis=0))
+    flat_ind = op.Reshape(unflat, _1dint(-1))
+    expanded_values = op.Reshape(expanded_values, _1dint(-1))
+    flat_x = op.Reshape(x, _1dint(-1))
+    scat_kwargs = {"reduction": "add"} if accumulate else {}
+    flat_up_x = op.ScatterElements(flat_x, flat_ind, expanded_values, **scat_kwargs)
+    return op.Reshape(flat_up_x, op.Shape(x))
+
+
 @torch_op("aten::index_put", trace_only=True)
 def aten_index_put_bool(
     self: TReal,

tests/function_libs/torch_lib/e2e_ops_tests.py

@@ -5,6 +5,7 @@
 
 import unittest
 
+import numpy as np
 import torch
 from torch.onnx._internal.exporter import _testing
 
@@ -225,6 +226,109 @@ def forward(self, q, k, v):
         )
         _testing.assert_onnx_program(onnx_program)
 
+    def test_index_put_dynamic(self):
+        for dimension in [3, 4, 2]:
+            with self.subTest(dimension=dimension):
+
+                class Model(torch.nn.Module):
+                    def __init__(self, dimension):
+                        super().__init__()
+                        self.params = torch.zeros(
+                            (4, 5)
+                            if dimension == 2
+                            else ((2, 4, 5) if dimension == 3 else (1, 1, 4, 5))
+                        )
+                        self.dimension = dimension
+
+                    def forward(self, update, index1, index2):
+                        copy = self.params.clone()
+                        if self.dimension == 2:
+                            copy[index1, index2] = update
+                        elif self.dimension == 3:
+                            copy[:, index1, index2] = update
+                        else:
+                            copy[:, :, index1, index2] = update
+                        return copy
+
+                update = (torch.arange(2) + 10).reshape((2,)).to(torch.float32)
+                index1 = torch.tensor([1, 2], dtype=torch.int64)
+                index2 = torch.tensor([3, 4], dtype=torch.int64)
+                feeds = dict(zip(["update", "index1", "index2"], (update, index1, index2)))
+                onnx_program = torch.onnx.export(
+                    Model(dimension),
+                    tuple(feeds.values()),
+                    input_names=["update", "index1", "index2"],
+                    output_names=["output"],
+                    opset_version=18,
+                    dynamo=True,
+                    dynamic_shapes={
+                        "update": {0: "dn"},
+                        "index1": {0: "dn"},
+                        "index2": {0: "dn"},
+                    },
+                )
+                _testing.assert_onnx_program(onnx_program)
+
+    def test_index_put_55_12_25(self):
+        class Model(torch.nn.Module):
+            def forward(self, x, index, update):
+                return torch.ops.aten.index_put(x, [index], update)
+
+        x = torch.zeros((6, 5), dtype=torch.float32)
+        index = torch.tensor([[2, 1]], dtype=torch.int64)
+        update = (torch.arange(10) + 10).reshape((2, -1)).to(torch.float32)
+        onnx_program = torch.onnx.export(
+            Model(),
+            (x, index, update),
+            input_names=["x", "index", "update"],
+            output_names=["output"],
+            opset_version=18,
+            dynamo=True,
+        )
+        _testing.assert_onnx_program(onnx_program)
+
+    def test_index_put_55_2_25(self):
+        class Model(torch.nn.Module):
+            def forward(self, x, index, update):
+                return torch.ops.aten.index_put(x, [index], update, accumulate=True)
+
+        x = torch.ones((6, 5), dtype=torch.float32)
+        index = torch.tensor([4, 3], dtype=torch.int64)
+        update = (torch.arange(10) + 10).reshape((2, -1)).to(torch.float32)
+        onnx_program = torch.onnx.export(
+            Model(),
+            (x, index, update),
+            input_names=["x", "index", "update"],
+            output_names=["output"],
+            opset_version=18,
+            dynamo=True,
+        )
+        _testing.assert_onnx_program(onnx_program)
+
+    def test_index_put_scatter_nd(self):
+        class Model(torch.nn.Module):
+            def forward(self, x, index, update):
+                x = x.clone()
+                return torch.ops.aten.index_put(x, [None, index, None], update)
+
+        shape = (2, 3, 2)
+        N = int(np.prod(shape))
+        x = torch.arange(N, dtype=torch.float32).reshape(shape)
+        update = (torch.arange(N, dtype=torch.float32).reshape(shape) + 1) * 100
+        index = ((torch.arange(shape[-2])).to(torch.int64) + 1) % shape[-2]
+
+        feeds = dict(zip(["x", "index", "update"], (x, index, update)))
+        onnx_program = torch.onnx.export(
+            Model(),
+            tuple(feeds.values()),
+            input_names=["x", "index", "update"],
+            output_names=["output"],
+            opset_version=18,
+            dynamo=True,
+            dynamic_shapes=({0: "a", 1: "b", 2: "c"}, {0: "d"}, {0: "e", 1: "f", 2: "g"}),
+        )
+        _testing.assert_onnx_program(onnx_program)
+
     def test_bitwise_and_scalar(self):
         class Model(torch.nn.Module):
             def forward(self, x):

tests/function_libs/torch_lib/ops_test_data.py

@@ -313,7 +313,10 @@ def _im2col_input_wrangler(
 def _index_put_input_wrangler(
     args: list[Any], kwargs: dict[str, Any]
 ) -> tuple[list[Any], dict[str, Any]]:
-    args[1] = [np.array(elem) for elem in args[1]]
+    args[1] = [
+        (elem.detach().cpu().numpy() if hasattr(elem, "detach") else np.array(elem))
+        for elem in args[1]
+    ]
     return args, kwargs