Support broadcast tensor in phi system

paddle/phi/api/lib/api_gen_utils.cc

@@ -31,14 +31,14 @@ paddle::optional<phi::DenseTensor> TensorToDenseTensor(
   return nullptr;
 }
 
-std::unique_ptr<std::vector<phi::DenseTensor>> TensorToDenseTensor(
+std::unique_ptr<std::vector<phi::DenseTensor*>> TensorToDenseTensor(
     const std::vector<Tensor>& tensors) {
-  auto pt_tensors = std::make_unique<std::vector<phi::DenseTensor>>();
+  auto pt_tensors = std::make_unique<std::vector<phi::DenseTensor*>>();
   pt_tensors->reserve(tensors.size());
 
   for (const auto& t : tensors) {
     pt_tensors->push_back(
-        *std::dynamic_pointer_cast<phi::DenseTensor>(t.impl()));
+        std::dynamic_pointer_cast<phi::DenseTensor>(t.impl()).get());
   }
 
   return pt_tensors;

paddle/phi/api/lib/api_gen_utils.h

@@ -35,7 +35,7 @@ std::shared_ptr<phi::DenseTensor> TensorToDenseTensor(const Tensor& tensor);
 paddle::optional<phi::DenseTensor> TensorToDenseTensor(
     const paddle::optional<Tensor>& tensor);
 
-std::unique_ptr<std::vector<phi::DenseTensor>> TensorToDenseTensor(
+std::unique_ptr<std::vector<phi::DenseTensor*>> TensorToDenseTensor(
     const std::vector<Tensor>& tensors);
 
 std::shared_ptr<phi::SelectedRows> TensorToSelectedRows(const Tensor& tensor);

paddle/phi/api/yaml/generator/api_base.py

@@ -582,18 +582,18 @@ def get_kernel_args(self, kernel_tensor_type=None, code_indent=''):
                         trans_flag = "{false, true}"
                     if input_name in self.optional_vars:
                         input_tensor_code = input_tensor_code + f"""
-{code_indent}  auto {PREFIX_TENSOR_NAME}{input_name} = PrepareData({input_name}, kernel.InputAt({i}), {trans_flag});"""
+{code_indent}  auto {PREFIX_TENSOR_NAME}{input_name} = PrepareData({input_name}, kernel.InputAt({kernel_param.index(input_name)}), {trans_flag});"""
 
                     else:
                         if self.inputs['input_info'][
                                 input_name] == "const Tensor&":
                             input_tensor_code = input_tensor_code + f"""
-{code_indent}  auto {PREFIX_TENSOR_NAME}{input_name} = PrepareData({input_name}, kernel.InputAt({i}), {trans_flag});"""
+{code_indent}  auto {PREFIX_TENSOR_NAME}{input_name} = PrepareData({input_name}, kernel.InputAt({kernel_param.index(input_name)}), {trans_flag});"""
 
                         elif self.inputs['input_info'][
                                 input_name] == "const std::vector<Tensor>&":
                             input_tensor_code = input_tensor_code + f"""
-{code_indent}  auto {PREFIX_TENSOR_NAME}{input_name}_vec = PrepareData({input_name}, kernel.InputAt({i}), {trans_flag});
+{code_indent}  auto {PREFIX_TENSOR_NAME}{input_name}_vec = PrepareData({input_name}, kernel.InputAt({kernel_param.index(input_name)}), {trans_flag});
 {code_indent}  std::vector<const phi::DenseTensor*> {PREFIX_TENSOR_NAME}{input_name}({PREFIX_TENSOR_NAME}{input_name}_vec->size());
 {code_indent}  for (size_t i = 0; i < {PREFIX_TENSOR_NAME}{input_name}.size(); ++i) {{
 {code_indent}    {PREFIX_TENSOR_NAME}{input_name}[i] = &{PREFIX_TENSOR_NAME}{input_name}_vec->at(i);
@@ -612,7 +612,13 @@ def get_kernel_args(self, kernel_tensor_type=None, code_indent=''):
 {code_indent}  paddle::optional<phi::TensorBase> {PREFIX_TENSOR_NAME}{input_name} = {input_name} ? paddle::optional<phi::TensorBase>(*{input_name}->impl()) : paddle::none;"""
 
                     else:
-                        input_tensor_code = input_tensor_code + f"""
+                        if self.inputs['input_info'][
+                                input_name] == "const std::vector<Tensor>&":
+                            input_tensor_code = input_tensor_code + f"""
+{code_indent}  auto {PREFIX_TENSOR_NAME}{input_name}_uq_ptr = TensorToDenseTensor({input_name});
+{code_indent}  const auto& {PREFIX_TENSOR_NAME}{input_name} = *{PREFIX_TENSOR_NAME}{input_name}_uq_ptr;"""
+                        else:
+                            input_tensor_code = input_tensor_code + f"""
 {code_indent}  auto {PREFIX_TENSOR_NAME}{input_name} = {input_name}.impl();"""
 
         kernel_args = ["*dev_ctx"]

paddle/phi/api/yaml/legacy_api.yaml

@@ -2360,6 +2360,15 @@
   output : Tensor
   invoke : full_like(x, 0, dtype, place)
 
+- api: broadcast_tensors
+  args: (Tensor[] x)
+  output: Tensor[]{x.size()}
+  infer_meta:
+    func: BroadcastTensorsInferMeta
+  kernel:
+    func: broadcast_tensors
+  backward: broadcast_tensors_grad
+
 # eig
 - api: eig
   args: (Tensor x)

paddle/phi/api/yaml/legacy_backward.yaml

@@ -271,6 +271,18 @@
     func : brelu_grad
   inplace : (out_grad -> x_grad)
 
+- backward_api : broadcast_tensors_grad
+  forward : broadcast_tensors (Tensor[] x) -> Tensor[](out)
+  args : (Tensor[] x, Tensor[] out_grad)
+  output : Tensor[](x_grad)
+  infer_meta :
+    func : UnchangedMultiInferMeta
+    param : [x]
+  kernel :
+    func : broadcast_tensors_grad
+    param : [out_grad]
+  no_need_buffer : x
+
 - backward_api : cast_grad
   forward : cast (Tensor x, DataType out_dtype) -> Tensor(out)
   args : (Tensor x, Tensor out_grad)

python/paddle/fluid/tests/unittests/test_broadcast_tensors_op.py

@@ -99,26 +99,49 @@ def setUp(self):
         ]
         self.set_place()
         self.set_dtypes()
+        self.python_api = paddle.broadcast_tensors
 
-    def run_test(self, test_func, args):
+    def run_dual_test(self, test_func, args):
         for dtype in self.dtypes:
             for gen_func in self.test_gen_func_list:
                 self.inputs, self.outputs = gen_func(dtype)
-                test_func(**args)
+                if len(self.outputs["Out"]) < 3:
+                    self.python_out_sig = [
+                        f"out{i}" for i in range(len(self.outputs["Out"]))
+                    ]
+                    test_func(**args)
+
+    def run_triple_in_test(self, test_func, args):
+        for dtype in self.dtypes:
+            self.inputs, self.outputs = self.test_gen_func_list[2](dtype)
+            self.python_out_sig = [
+                f"out{i}" for i in range(len(self.outputs["Out"]))
+            ]
+            test_func(**args)
 
     def test_check_output(self):
-        self.run_test(self.check_output_with_place, {
+        self.run_dual_test(self.check_output_with_place, {
             "place": self.place,
-            "atol": 1e-1
+            "atol": 1e-1,
+            "check_eager": True
         })
 
     def test_check_grad_normal(self):
-        self.run_test(
+        self.run_dual_test(
             self.check_grad_with_place, {
                 "place": self.place,
                 "inputs_to_check": ['x0', 'x1'],
                 "output_names": ['out0', 'out1'],
                 "max_relative_error": 0.05,
+                "check_eager": True
+            })
+        self.run_triple_in_test(
+            self.check_grad_with_place, {
+                "place": self.place,
+                "inputs_to_check": ['x0', 'x1', 'x2'],
+                "output_names": ['out0', 'out1', "out2"],
+                "max_relative_error": 0.05,
+                "check_eager": True
             })
 
 

python/paddle/tensor/manipulation.py

@@ -1128,7 +1128,9 @@ def broadcast_tensors(input, name=None):
     """
 
     num_inputs = len(input)
-    if paddle.in_dynamic_mode():
+    if paddle.framework.in_dygraph_mode():
+        return _C_ops.final_state_broadcast_tensors(input)
+    if paddle.framework._non_static_mode():
         return _C_ops.broadcast_tensors(input, num_inputs)
 
     check_type(input, 'input', (list, tuple), 'broadcast_tensors')