[CINN] Fixed gather_nd incorrect logic for negative inputs.

paddle/cinn/hlir/op/contrib/gather_nd.cc

@@ -42,46 +42,21 @@ namespace op {
 using cinn::common::CINNValue;
 using cinn::common::CINNValuePack;
 
-ir::Tensor GatherNd(const ir::Tensor &x,
- const ir::Tensor &index,
- const std::string &name) {
- std::vector<Expr> x_shape = x->shape;
- std::vector<Expr> index_shape = index->shape;
- size_t x_shape_size = x_shape.size();
- size_t index_shape_size = index_shape.size();
- std::vector<Expr> out_shape;
- out_shape.insert(out_shape.end(), index_shape.begin(), index_shape.end() - 1);
- out_shape.insert(out_shape.end(),
- x_shape.begin() + index_shape.back().as_int32(),
- x_shape.end());
- auto res = Compute(
- out_shape,
- [=](const std::vector<Expr> &indices) {
- std::vector<Expr> indices_position;
- for (size_t i = 0; i < index_shape_size - 1; ++i) {
- indices_position.push_back(
- ir::Cast::Make(cinn::common::Int(32), indices[i]));
- }
- indices_position.push_back(
- ir::Cast::Make(cinn::common::Int(32), Expr(0)));
- size_t indices_position_size = indices_position.size();
- std::vector<Expr> real_indices;
- for (size_t i = 0; i < index_shape.back().as_int32(); ++i) {
- indices_position[indices_position_size - 1] =
- ir::Cast::Make(cinn::common::Int(32), Expr(i));
- real_indices.push_back(
- ir::Cast::Make(cinn::common::Int(32), index(indices_position)));
- }
- if (real_indices.size() == x_shape_size) {
- return x(real_indices);
- }
- for (size_t i = index_shape_size - 1; i < indices.size(); ++i) {
- real_indices.push_back(indices[i]);
- }
- return x(real_indices);
- },
- name);
- return res;
+Expr CastShapeElementType(Expr shape_element) {
+ // constant values should already been cast to int64_t for shape calc
+ // make sure shape expr does not contain non-matched int type for min and max
+ if (shape_element.is_constant()) return shape_element;
+ if (auto max_elem = shape_element.As<ir::Max>()) {
+ max_elem->a() = CastShapeElementType(max_elem->a());
+ max_elem->b() = CastShapeElementType(max_elem->b());
+ } else if (auto min_elem = shape_element.As<ir::Min>()) {
+ min_elem->a() = CastShapeElementType(min_elem->a());
+ min_elem->b() = CastShapeElementType(min_elem->b());
+ } else {
+ shape_element = ir::Call::Make(
+ ir::Int(64), "int64_t", {shape_element}, {}, ir::CallType::Intrinsic);
+ }
+ return shape_element;
 }
 
 ir::Tensor GatherNdSymbolic(const ir::Tensor &x,
@@ -111,8 +86,15 @@ ir::Tensor GatherNdSymbolic(const ir::Tensor &x,
  for (size_t i = 0; i < index_shape.back().as_int64(); ++i) {
  indices_position[indices_position_size - 1] =
  ir::Cast::Make(cinn::common::Int(64), Expr(i));
+ // support negative indices
+ auto idx_expr =
+ ir::Cast::Make(cinn::common::Int(64), index(indices_position));
+ auto real_expr =
+ ir::Select::Make(ir::GE::Make(idx_expr, Expr(0)),
+ idx_expr,
+ CastShapeElementType(x_shape[i]) + idx_expr);
  real_indices.push_back(
- ir::Cast::Make(cinn::common::Int(64), index(indices_position)));
+ ir::Cast::Make(cinn::common::Int(64), real_expr));
  }
  if (real_indices.size() == x_shape_size) {
  return x(real_indices);
@@ -126,67 +108,6 @@ ir::Tensor GatherNdSymbolic(const ir::Tensor &x,
  return res;
 }
 
-std::shared_ptr<framework::OpStrategy> StrategyForGatherNd(
- const framework::NodeAttr &attrs,
- const std::vector<ir::Tensor> &inputs,
- const std::vector<Type> &out_type,
- const std::vector<std::vector<int>> &output_shapes,
- const Target &target) {
- std::string op_name("gather_nd");
-
- framework::CINNCompute gather_nd_compute([=](lang::Args args,
- lang::RetValue *ret) {
- PADDLE_ENFORCE_NE(
- args.empty(),
- true,
- ::common::errors::InvalidArgument(
- "The input argument of %s compute is empty! Please check.",
- op_name));
- CINNValuePack pack_args = args[0];
- PADDLE_ENFORCE_GE(
- pack_args.size(),
- 2U,
- ::common::errors::InvalidArgument("2 input tensors for compute\n"));
- Expr x = pack_args[0];
- Expr index = pack_args[1];
- PADDLE_ENFORCE_NOT_NULL(x.as_tensor(),
- ::common::errors::InvalidArgument(
- "Required x must be a tensor. Please check."));
- PADDLE_ENFORCE_NOT_NULL(
- index.as_tensor(),
- ::common::errors::InvalidArgument(
- "Required index must be a tensor. Please check."));
- PADDLE_ENFORCE_NE(
- output_shapes.empty(),
- true,
- ::common::errors::InvalidArgument(
- "The output shape of gather_nd is empty! Please check."));
- auto tensor_x = x.as_tensor_ref();
- auto tensor_index = index.as_tensor_ref();
- VLOG(3) << "x shape: " << utils::Join(tensor_x->shape, ", ")
- << ", index shape: " << utils::Join(tensor_index->shape, ", ")
- << ", output_shapes: " << utils::Join(output_shapes[0], ", ");
- PADDLE_ENFORCE_EQ(pack_args.size(),
- 3U,
- ::common::errors::InvalidArgument(
- "The size of pack_args should be 3\n"));
- std::string tensor_name = pack_args[2].operator std::string();
- ir::Tensor out = GatherNd(tensor_x, tensor_index, tensor_name);
- std::vector<CINNValue> res;
- res.push_back(CINNValue(out));
- PADDLE_ENFORCE_NE(
- out_type.empty(),
- true,
- ::common::errors::InvalidArgument(
- "The output type of gather_nd is empty! Please check."));
- *ret = CINNValuePack{res};
- });
-
- auto strategy = std::make_shared<framework::OpStrategy>();
- strategy->AddImpl(gather_nd_compute, "strategy.gather_nd.x86", 1);
- return strategy;
-}
-
 std::shared_ptr<framework::OpStrategy> StrategyForGatherNdSymbolic(
  const framework::NodeAttr &attrs,
  const std::vector<ir::Tensor> &inputs,
@@ -259,8 +180,6 @@ CINN_REGISTER_HELPER(gather_nd_ops) {
  .set_num_outputs(1)
  .set_attr<cinn::hlir::framework::StrategyFunctionSymbolic>(
  "CINNStrategySymbolic", cinn::hlir::op::StrategyForGatherNdSymbolic)
- .set_attr<cinn::hlir::framework::StrategyFunction>(
- "CINNStrategy", cinn::hlir::op::StrategyForGatherNd)
  .set_attr<cinn::hlir::framework::OpPatternKind>(
  "OpPattern", cinn::hlir::framework::OpPatternKind::kInjective)
  .set_support_level(4);

test/ir/pir/cinn/test_cinn_gather_nd.py

@@ -0,0 +1,126 @@
+# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import unittest
+
+import numpy
+import utils
+
+import paddle
+
+
+class TestGatherNd(unittest.TestCase):
+ # Note that GatherNd is also used in index_put, so we can test it by using index_put.
+ def eval(self, dy_compute, inputs, input_spec=None):
+ dy_out = dy_compute(*inputs)
+
+ static_compute = utils.apply_to_static(
+ dy_compute, use_cinn=True, input_spec=None
+ )
+ st_out = static_compute(*inputs)
+
+ for a, b in zip(
+ paddle.utils.flatten(dy_out), paddle.utils.flatten(st_out)
+ ):
+ numpy.testing.assert_allclose(a, b, atol=1e-6, rtol=1e-6)
+
+ @staticmethod
+ def get_input(x_shape, indices_shape, value_shape, has_negative_index=True):
+ n_indices = indices_shape[0]
+ index_dim_size = indices_shape[1] if len(indices_shape) > 1 else 1
+
+ x_pd = paddle.randn(x_shape)
+ x_pd.stop_gradient = False
+
+ indices_pd = tuple(
+ [
+ paddle.randint(
+ -x_shape[i] if has_negative_index else 0,
+ x_shape[i],
+ [n_indices],
+ )
+ for i in range(max(index_dim_size, 1))
+ ]
+ )
+ value_pd = paddle.randn(value_shape)
+ value_pd.stop_gradient = False
+
+ dout_pd = paddle.randn(x_shape)
+ dout_pd.stop_gradient = False
+ return x_pd, indices_pd, value_pd, dout_pd
+
+ @staticmethod
+ def get_input_spec(indice_dim):
+ return [
+ paddle.static.InputSpec(shape=[-1, -1], dtype="float32"),
+ tuple(
+ paddle.static.InputSpec(shape=[-1], dtype="int64")
+ for _ in range(indice_dim)
+ ),
+ paddle.static.InputSpec(shape=[-1, -1], dtype="float32"),
+ paddle.static.InputSpec(shape=[-1, -1], dtype="float32"),
+ ]
+
+ @staticmethod
+ def index_put_grad(x, indices, v, dy):
+ y = paddle.index_put(x, indices, v, True)
+ return paddle.grad(y, [x, v], dy)
+
+ def test_index_put_grad_non_negative_index(self):
+ x_pd, indices_pd, value_pd, dout_pd = self.get_input(
+ [12, 13, 14], [88, 2], [88, 14], False
+ )
+
+ self.eval(
+ TestGatherNd.index_put_grad,
+ [x_pd, indices_pd, value_pd, dout_pd],
+ input_spec=self.get_input_spec(2),
+ )
+
+ def test_index_put_grad_negative_index_1(self):
+ x_pd, indices_pd, value_pd, dout_pd = self.get_input(
+ [12, 13, 14], [88, 1], [88, 13, 14]
+ )
+
+ self.eval(
+ TestGatherNd.index_put_grad,
+ [x_pd, indices_pd, value_pd, dout_pd],
+ input_spec=self.get_input_spec(1),
+ )
+
+ def test_index_put_grad_negative_index_2(self):
+ x_pd, indices_pd, value_pd, dout_pd = self.get_input(
+ [16, 16], [20, 2], [20]
+ )
+
+ self.eval(
+ TestGatherNd.index_put_grad,
+ [x_pd, indices_pd, value_pd, dout_pd],
+ input_spec=self.get_input_spec(2),
+ )
+
+ def test_gather_nd_fusion(self):
+ x_pd = paddle.randn([256, 128])
+ y_pd = paddle.randn_like(x_pd)
+ z_pd = paddle.randn([100])
+ indices_pd = paddle.randint(-128, 128, [100, 2])
+
+ def func(x, y, z, indices):
+ return paddle.gather_nd(x * y, indices) + z
+
+ self.eval(func, [x_pd, y_pd, z_pd, indices_pd])
+
+
+if __name__ == "__main__":
+ unittest.main()