[PRIM][IR]fix comment for vjp (#56137)

* [prim][newir] add basic framework for primitive

* support desctensor in new ir

* add vjp interface

* support vjp in new ir

* support vjp in new ir

* polish vjp interface

* fix stop_gradients set

* fix vjp dispatch

* add comment

* add vjp test for new ir

* add test for tanh vjp

* [prim][newir] add basic framework for primitive

* support desctensor in new ir

* support vjp in new ir

* support vjp in new ir

* polish vjp interface

* fix stop_gradients set

* fix vjp dispatch

* add comment

* add vjp test for new ir

* add test for tanh vjp

* add eager and static backend for warp lower level api

* support call_vjp pybind

* polish code and add test for vjp

* remove useless code

* polish code

* remove useless code

* support mean vjp

* add test for mean vjp and support has_vjp function

* fix call_vjp

* polish code

* add primitive ops set for backend

* add vjp test for tanh_

* fix inference CI

* fix inference ci

* modify fluid cmake

* remove useless deps

* add cmake

* fix comment

* fix test

* polish code

* modify backward stop_gradients

* modify static_backend.cc

* remove useless code

---------
Co-authored-by: Ncxxly <chenxx_id@163.com>
Co-authored-by: Nzhangbo9674 <zhangbo54@baidu.com>

paddle/fluid/ir/dialect/op_generator/op_interface_gen.py

@@ -40,5 +40,5 @@ def gen_exclusive_interface_str(op_info):
             "  static void InferMeta( phi::InferMetaContext *infer_meta );"
         )
     if op_info.op_phi_name[0] in vjp_interface_gen_op_list:
-        exclusive_interface_str += "\n  static std::vector<std::vector<ir::OpResult>> Vjp(ir::Operation* op, const std::vector<std::vector<ir::OpResult>>& out_grads, const std::vector<std::vector<int>>& stop_gradients);"
+        exclusive_interface_str += "\n  static std::vector<std::vector<ir::OpResult>> Vjp(ir::Operation* op, const std::vector<std::vector<ir::OpResult>>& out_grads, const std::vector<std::vector<bool>>& stop_gradients);"
     return exclusive_interface_str

paddle/fluid/ir/dialect/pd_api.cc

@@ -72,7 +72,7 @@ ir::OpResult tanh_grad(ir::OpResult out, ir::OpResult grad_out) {
 
 ir::OpResult mean_grad(ir::OpResult x,
                        ir::OpResult out_grad,
-                       std::vector<int64_t> axis,
+                       const std::vector<int64_t>& axis,
                        bool keepdim,
                        bool reduce_all) {
   paddle::dialect::MeanGradOp mean_grad_op =

paddle/fluid/ir/dialect/pd_api.h

@@ -44,7 +44,7 @@ ir::OpResult tanh_grad(ir::OpResult out, ir::OpResult grad_out);
 
 ir::OpResult mean_grad(ir::OpResult x,
                        ir::OpResult out_grad,
-                       std::vector<int64_t> axis = {},
+                       const std::vector<int64_t>& axis = {},
                        bool keepdim = false,
                        bool reduce_all = false);
 }  // namespace dialect

paddle/fluid/ir/dialect/pd_dialect.h

@@ -17,6 +17,7 @@
 #include "paddle/fluid/framework/variable.h"
 #include "paddle/ir/core/dialect.h"
 #include "paddle/ir/core/enforce.h"
+#include "paddle/ir/core/macros.h"
 #include "paddle/ir/core/parameter.h"
 #include "paddle/ir/core/program.h"
 
@@ -92,7 +93,7 @@ class APIBuilder {
     ctx_->GetOrRegisterDialect<paddle::dialect::PaddleDialect>();
   }
 
-  APIBuilder(const APIBuilder&) = delete;
+  DISABLE_COPY_AND_ASSIGN(APIBuilder);
 
   ir::IrContext* ctx_;
   std::shared_ptr<ir::Builder> builder_;

paddle/fluid/ir/dialect/pd_op_vjp_manual.cc

@@ -17,16 +17,19 @@
 #include "paddle/fluid/primitive/rule/vjp/vjp.h"
 #include "paddle/fluid/primitive/type/desc_tensor.h"
 #include "paddle/ir/core/op_base.h"
+#include "paddle/phi/common/int_array.h"
 
 // TODO(wanghao107)
 // this file will be generated in pd_op.cc
 
 namespace paddle {
 namespace dialect {
+using IntArray = paddle::experimental::IntArray;
+
 std::vector<std::vector<ir::OpResult>> TanhOp::Vjp(
     ir::Operation* op,
     const std::vector<std::vector<ir::OpResult>>& out_grads,
-    const std::vector<std::vector<int>>& stop_gradients) {
+    const std::vector<std::vector<bool>>& stop_gradients) {
   TanhOp op_obj = op->dyn_cast<TanhOp>();
   Tensor out(
       std::make_shared<primitive::experimental::DescTensor>(op_obj.out()));
@@ -35,7 +38,7 @@ std::vector<std::vector<ir::OpResult>> TanhOp::Vjp(
   std::vector<std::vector<Tensor>> tensor_res =
       primitive::experimental::tanh_vjp(out, grad_out, stop_gradients);
   std::vector<std::vector<ir::OpResult>> res(1, std::vector<ir::OpResult>(1));
-  if (!stop_gradients[0][0]) {
+  if (tensor_res[0][0].defined()) {
     res[0][0] = std::static_pointer_cast<primitive::experimental::DescTensor>(
                     tensor_res[0][0].impl())
                     ->getValue()
@@ -47,7 +50,7 @@ std::vector<std::vector<ir::OpResult>> TanhOp::Vjp(
 std::vector<std::vector<ir::OpResult>> Tanh_Op::Vjp(
     ir::Operation* op,
     const std::vector<std::vector<ir::OpResult>>& out_grads,
-    const std::vector<std::vector<int>>& stop_gradients) {
+    const std::vector<std::vector<bool>>& stop_gradients) {
   // TODO(wanghao107)
   // we don't support inplace now,
   // so use the non-inplace version instead currently.
@@ -60,7 +63,7 @@ std::vector<std::vector<ir::OpResult>> Tanh_Op::Vjp(
   std::vector<std::vector<Tensor>> tensor_res =
       primitive::experimental::tanh_vjp(out, grad_out, stop_gradients);
   std::vector<std::vector<ir::OpResult>> res(1, std::vector<ir::OpResult>(1));
-  if (!stop_gradients[0][0]) {
+  if (tensor_res[0][0].defined()) {
     res[0][0] = std::static_pointer_cast<primitive::experimental::DescTensor>(
                     tensor_res[0][0].impl())
                     ->getValue()
@@ -72,24 +75,22 @@ std::vector<std::vector<ir::OpResult>> Tanh_Op::Vjp(
 std::vector<std::vector<ir::OpResult>> MeanOp::Vjp(
     ir::Operation* op,
     const std::vector<std::vector<ir::OpResult>>& out_grads,
-    const std::vector<std::vector<int>>& stop_gradients) {
+    const std::vector<std::vector<bool>>& stop_gradients) {
   MeanOp op_obj = op->dyn_cast<MeanOp>();
   Tensor x(std::make_shared<primitive::experimental::DescTensor>(op_obj.x()));
   Tensor out_grad(
       std::make_shared<primitive::experimental::DescTensor>(out_grads[0][0]));
 
-  std::vector<int64_t> axis =
-      op->attribute("axis")
-          .dyn_cast<paddle::dialect::IntArrayAttribute>()
-          .data()
-          .GetData();
+  IntArray axis = op->attribute("axis")
+                      .dyn_cast<paddle::dialect::IntArrayAttribute>()
+                      .data();
   bool keepdim = op->attribute("keepdim").dyn_cast<ir::BoolAttribute>().data();
   bool reduce_all = false;
   std::vector<std::vector<Tensor>> tensor_res =
       primitive::experimental::mean_vjp(
           x, out_grad, axis, keepdim, reduce_all, stop_gradients);
   std::vector<std::vector<ir::OpResult>> res(1, std::vector<ir::OpResult>(1));
-  if (!stop_gradients[0][0]) {
+  if (tensor_res[0][0].defined()) {
     res[0][0] = std::static_pointer_cast<primitive::experimental::DescTensor>(
                     tensor_res[0][0].impl())
                     ->getValue()

paddle/fluid/ir/interface/vjp.h

@@ -23,12 +23,12 @@ class VjpInterface : public ir::OpInterfaceBase<VjpInterface> {
     explicit Concept(std::vector<std::vector<ir::OpResult>> (*vjp)(
         ir::Operation* op,
         const std::vector<std::vector<ir::OpResult>>& out_grads,
-        const std::vector<std::vector<int>>& stop_gradients))
+        const std::vector<std::vector<bool>>& stop_gradients))
         : vjp_(vjp) {}
     std::vector<std::vector<ir::OpResult>> (*vjp_)(
         ir::Operation* op,
         const std::vector<std::vector<ir::OpResult>>& out_grads,
-        const std::vector<std::vector<int>>& stop_gradients);
+        const std::vector<std::vector<bool>>& stop_gradients);
   };
 
   template <class ConcreteOp>
@@ -36,7 +36,7 @@ class VjpInterface : public ir::OpInterfaceBase<VjpInterface> {
     static std::vector<std::vector<ir::OpResult>> Vjp(
         ir::Operation* op,
         const std::vector<std::vector<ir::OpResult>>& out_grads,
-        const std::vector<std::vector<int>>& stop_gradients) {
+        const std::vector<std::vector<bool>>& stop_gradients) {
       return ConcreteOp::Vjp(op, out_grads, stop_gradients);
     }
 
@@ -49,7 +49,7 @@ class VjpInterface : public ir::OpInterfaceBase<VjpInterface> {
   std::vector<std::vector<ir::OpResult>> Vjp(
       ir::Operation* op,
       const std::vector<std::vector<ir::OpResult>>& out_grads,
-      const std::vector<std::vector<int>>& stop_gradients) {
+      const std::vector<std::vector<bool>>& stop_gradients) {
     return impl_->vjp_(op, out_grads, stop_gradients);
   }
 

paddle/fluid/primitive/backend/CMakeLists.txt

-if(NOT (NOT WITH_PYTHON AND ON_INFER))
+if(WITH_PYTHON OR NOT ON_INFER)
   cc_library(
     primitive_backend_eager_experimental
     SRCS eager_backend.cc

paddle/fluid/primitive/backend/static_backend.cc

@@ -42,7 +42,7 @@ Tensor tanh_grad<DescTensor>(const Tensor& out, const Tensor& grad_out) {
 template <>
 Tensor mean_grad<DescTensor>(const Tensor& x,
                              const Tensor& out_grad,
-                             std::vector<int64_t> axis,
+                             const IntArray& axis,
                              bool keepdim,
                              bool reduce_all) {
   ir::OpResult x_res = std::static_pointer_cast<DescTensor>(x.impl())
@@ -54,7 +54,7 @@ Tensor mean_grad<DescTensor>(const Tensor& x,
           .dyn_cast<ir::OpResult>();
 
   ir::OpResult op_res = paddle::dialect::mean_grad(
-      x_res, out_grad_res, axis, keepdim, reduce_all);
+      x_res, out_grad_res, axis.GetData(), keepdim, reduce_all);
 
   return Tensor(std::make_shared<primitive::experimental::DescTensor>(op_res));
 }

paddle/fluid/primitive/backend/static_backend.h

@@ -18,6 +18,7 @@
 #include <vector>
 
 #include "paddle/phi/api/include/tensor.h"
+#include "paddle/phi/common/int_array.h"
 
 namespace paddle {
 namespace primitive {
@@ -25,6 +26,7 @@ namespace backend {
 namespace experimental {
 
 using Tensor = paddle::Tensor;
+using IntArray = paddle::experimental::IntArray;
 
 template <typename T>
 Tensor tanh_grad(const Tensor& out, const Tensor& grad_out);
@@ -32,7 +34,7 @@ Tensor tanh_grad(const Tensor& out, const Tensor& grad_out);
 template <typename T>
 Tensor mean_grad(const Tensor& x,
                  const Tensor& out_grad,
-                 std::vector<int64_t> axis = {},
+                 const IntArray& axis = {},
                  bool keepdim = false,
                  bool reduce_all = false);
 }  // namespace experimental

paddle/fluid/primitive/rule/vjp/vjp.cc

@@ -12,12 +12,9 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
-#include <math.h>
-#include <vector>
-
+#include "paddle/fluid/primitive/rule/vjp/vjp.h"
 #include "paddle/fluid/ir/dialect/pd_api.h"
 #include "paddle/fluid/primitive/backend/static_backend.h"
-#include "paddle/fluid/primitive/rule/vjp/vjp.h"
 #include "paddle/fluid/primitive/type/desc_tensor.h"
 #include "paddle/ir/core/operation.h"
 // TODO(wanghao107):
@@ -26,10 +23,11 @@
 namespace paddle {
 namespace primitive {
 namespace experimental {
+
 std::vector<std::vector<paddle::Tensor>> tanh_vjp(
     const Tensor& out,
     const Tensor& grad_out,
-    const std::vector<std::vector<int>>& stop_gradients) {
+    const std::vector<std::vector<bool>>& stop_gradients) {
   std::vector<std::vector<paddle::Tensor>> vjp_res(
       1, std::vector<paddle::Tensor>(1));
   // get tanh_grad res.
@@ -71,10 +69,10 @@ std::vector<std::vector<paddle::Tensor>> tanh_vjp(
 std::vector<std::vector<paddle::Tensor>> mean_vjp(
     const Tensor& x,
     const Tensor& out_grad,
-    std::vector<int64_t> axis,
+    const IntArray& axis,
     bool keepdim,
     bool reduce_all,
-    const std::vector<std::vector<int>>& stop_gradients) {
+    const std::vector<std::vector<bool>>& stop_gradients) {
   std::vector<std::vector<paddle::Tensor>> vjp_res(
       1, std::vector<paddle::Tensor>(1));
   // get mean_grad res.

paddle/fluid/primitive/rule/vjp/vjp.h

@@ -24,24 +24,27 @@
 #include "paddle/fluid/primitive/primitive/primitive.h"
 #include "paddle/ir/core/value.h"
 #include "paddle/phi/api/include/tensor.h"
+#include "paddle/phi/common/int_array.h"
 
 namespace paddle {
 namespace primitive {
 namespace experimental {
+
+using IntArray = paddle::experimental::IntArray;
 // TODO(wanghao107):
 //  op's vjp will be auto generated.
 std::vector<std::vector<paddle::Tensor>> tanh_vjp(
     const Tensor& out,
     const Tensor& grad_out,
-    const std::vector<std::vector<int>>& stop_gradients);
+    const std::vector<std::vector<bool>>& stop_gradients);
 
 std::vector<std::vector<paddle::Tensor>> mean_vjp(
     const Tensor& x,
     const Tensor& out_grad,
-    std::vector<int64_t> axis,
+    const IntArray& axis,
     bool keepdim,
     bool reduce_all,
-    const std::vector<std::vector<int>>& stop_gradients);
+    const std::vector<std::vector<bool>>& stop_gradients);
 
 namespace details {
 // NOTE: this namespace will store

paddle/fluid/primitive/type/desc_tensor.h

@@ -43,14 +43,11 @@ class DescTensor : public phi::ExtendedTensor,
 
   ir::Value getValue() const { return value_; }
 
-  const phi::Place& place() const override { return place_; }
-
   bool initialized() const override { return value_.impl() != nullptr; }
 
  private:
   ir::Value value_;
   mutable phi::DDim dims_;
-  phi::Place place_;
 };
 
 }  // namespace experimental

paddle/fluid/pybind/pybind.cc

@@ -693,7 +693,7 @@ void BindVjp(pybind11::module *m) {
       "call_vjp",
       [](ir::Operation &fwd_op,
          const std::vector<std::vector<ir::OpResult>> &out_grads,
-         const std::vector<std::vector<int>> &stop_gradients) {
+         const std::vector<std::vector<bool>> &stop_gradients) {
         py::list res;
         ir::IrContext *ctx = ir::IrContext::Instance();
         ir::OpInfo fwd_op_info = ctx->GetRegisteredOpInfo(fwd_op.name());
@@ -731,7 +731,7 @@ void BindVjp(pybind11::module *m) {
                                 vjp_res[i].size()));
           py::list sub_res;
           for (size_t j = 0; j < vjp_res[i].size(); ++j) {
-            if (stop_gradients[i][j]) {
+            if (!vjp_res[i][j]) {
               sub_res.append(nullptr);
             } else {
               sub_res.append(vjp_res[i][j]);

python/paddle/autograd/backward.py

@@ -377,9 +377,9 @@ def append_backward_ops(
             input_grad_stopgradient_list = []
             for input in op.operands_source():
                 if input in no_grad_set:
-                    input_grad_stopgradient_list.append([1])
+                    input_grad_stopgradient_list.append([True])
                 else:
-                    input_grad_stopgradient_list.append([0])
+                    input_grad_stopgradient_list.append([False])
 
             before_ops_num = len(block.ops)
             # prim should be a globel flag, it will make create_grad_op choose diffrient func

test/cpp/prim/test_vjp.cc

@@ -55,7 +55,7 @@ TEST(VJP, TanhBackwardTest) {
   paddle::dialect::FullOp op3 = builder->Build<paddle::dialect::FullOp>(
       std::vector<int64_t>{1}, 2.0, phi::DataType::FLOAT32, phi::CPUPlace());
 
-  std::vector<std::vector<int>> stop_gradients{{0}};
+  std::vector<std::vector<bool>> stop_gradients{{false}};
   std::vector<std::vector<ir::OpResult>> out_grads{{op3.out()}};
 
   ir::OpInfo op2_info = ctx->GetRegisteredOpInfo("pd.tanh");
@@ -109,7 +109,7 @@ TEST(VJP, Tanh_BackwardTest) {
   paddle::dialect::FullOp op3 = builder->Build<paddle::dialect::FullOp>(
       std::vector<int64_t>{1}, 2.0, phi::DataType::FLOAT32, phi::CPUPlace());
 
-  std::vector<std::vector<int>> stop_gradients{{0}};
+  std::vector<std::vector<bool>> stop_gradients{{false}};
   std::vector<std::vector<ir::OpResult>> out_grads{{op3.out()}};
 
   ir::OpInfo op2_info = ctx->GetRegisteredOpInfo("pd.tanh_");
@@ -163,7 +163,7 @@ TEST(VJP, MeanBackwardTest) {
   paddle::dialect::FullOp op3 = builder->Build<paddle::dialect::FullOp>(
       std::vector<int64_t>{}, 1.0, phi::DataType::FLOAT32, phi::CPUPlace());
 
-  std::vector<std::vector<int>> stop_gradients{{0}};
+  std::vector<std::vector<bool>> stop_gradients{{false}};
   std::vector<std::vector<ir::OpResult>> out_grads{{op3.out()}};
 
   ir::OpInfo op2_info = ctx->GetRegisteredOpInfo("pd.mean");

test/ir/new_ir/test_ir_vjp.py

@@ -41,7 +41,7 @@ class TestTanhVjp(unittest.TestCase):
         tanh_op = newir_program.block().ops[-2]
         fill_constant_op = newir_program.block().ops[-1]
         out_grads = [[fill_constant_op.result(0)]]
-        stop_gradients = [[0]]
+        stop_gradients = [[False]]
         with paddle.ir.core.program_guard(newir_program):
             grad_outs = call_vjp(tanh_op, out_grads, stop_gradients)
         self.assertEqual(
@@ -72,7 +72,7 @@ class TestTanhVjp(unittest.TestCase):
         tanh_op = newir_program.block().ops[-2]
         fill_constant_op = newir_program.block().ops[-1]
         out_grads = [[fill_constant_op.result(0)]]
-        stop_gradients = [[1]]
+        stop_gradients = [[True]]
         with paddle.ir.core.program_guard(newir_program):
             grad_outs = call_vjp(tanh_op, out_grads, stop_gradients)
         self.assertEqual(grad_outs[0][0], None)
@@ -93,7 +93,7 @@ class TestMeanVjp(unittest.TestCase):
         fill_constant_op = newir_program.block().ops[-1]
         mean_op = newir_program.block().ops[-2]
         out_grads = [[fill_constant_op.result(0)]]
-        stop_gradients = [[0]]
+        stop_gradients = [[False]]
         with paddle.ir.core.program_guard(newir_program):
             grad_outs = call_vjp(mean_op, out_grads, stop_gradients)
             self.assertEqual(
@@ -133,7 +133,7 @@ class TestMeanVjp(unittest.TestCase):
         fill_constant_op = newir_program.block().ops[-1]
         mean_op = newir_program.block().ops[-2]
         out_grads = [[fill_constant_op.result(0)]]
-        stop_gradients = [[1]]
+        stop_gradients = [[True]]
         with paddle.ir.core.program_guard(newir_program):
             grad_outs = call_vjp(mean_op, out_grads, stop_gradients)
             self.assertEqual(grad_outs[0][0], None)