【PIR api adaptor No.48、49】 Migrate cummax/min into pir (#58629)

DrRyanHuang · web-flow · commit ee3655446c5f · 2023-11-06T15:02:52.000+08:00
diff --git a/paddle/phi/api/yaml/backward.yaml b/paddle/phi/api/yaml/backward.yaml
@@ -495,24 +495,26 @@
     data_type : out_grad
 
 - backward_op : cummax_grad
-  forward : cummax(Tensor x, int axis=-1, int dtype=3) -> Tensor(out), Tensor(indices)
-  args : (Tensor x, Tensor indices, Tensor out_grad, int axis, int dtype)
+  forward : cummax(Tensor x, int axis=-1, DataType dtype = DataType::INT64) -> Tensor(out), Tensor(indices)
+  args : (Tensor x, Tensor indices, Tensor out_grad, int axis, DataType dtype)
   output : Tensor(x_grad)
   infer_meta :
     func : UnchangedInferMeta
     param: [x]
   kernel :
     func : cummax_grad
+    data_type : out_grad
 
 - backward_op : cummin_grad
-  forward : cummin(Tensor x, int axis=-1, int dtype=3) -> Tensor(out), Tensor(indices)
-  args : (Tensor x, Tensor indices, Tensor out_grad, int axis, int dtype)
+  forward : cummin(Tensor x, int axis=-1, DataType dtype = DataType::INT64) -> Tensor(out), Tensor(indices)
+  args : (Tensor x, Tensor indices, Tensor out_grad, int axis, DataType dtype)
   output : Tensor(x_grad)
   infer_meta :
     func : UnchangedInferMeta
     param: [x]
   kernel :
     func : cummin_grad
+    data_type : out_grad
 
 - backward_op : cumprod_grad
   forward : cumprod (Tensor x, int dim) -> Tensor(out)
diff --git a/paddle/phi/api/yaml/ops.yaml b/paddle/phi/api/yaml/ops.yaml
@@ -602,21 +602,23 @@
   backward : cross_entropy_with_softmax_grad
 
 - op : cummax
-  args : (Tensor x, int axis=-1, int dtype=3)
+  args : (Tensor x, int axis=-1, DataType dtype = DataType::INT64)
   output : Tensor(out), Tensor(indices)
   infer_meta :
     func : CumWithIndicesInferMeta
   kernel :
     func : cummax
+    data_type : x
   backward : cummax_grad
 
 - op : cummin
-  args : (Tensor x, int axis=-1, int dtype=3)
+  args : (Tensor x, int axis=-1, DataType dtype = DataType::INT64)
   output : Tensor(out), Tensor(indices)
   infer_meta :
     func : CumWithIndicesInferMeta
   kernel :
     func : cummin
+    data_type : x
   backward : cummin_grad
 
 - op : cumprod
diff --git a/paddle/phi/infermeta/unary.cc b/paddle/phi/infermeta/unary.cc
@@ -549,17 +549,17 @@ void CumScalarAxisInferMeta(const MetaTensor& x,
 
 void CumWithIndicesInferMeta(const MetaTensor& x,
                              int axis,
-                             int dtype,
+                             DataType dtype,
                              MetaTensor* out,
                              MetaTensor* indices) {
   auto x_dims = x.dims();
-  auto indices_type = phi::TransToPhiDataType(dtype);
   PADDLE_ENFORCE_EQ(
-      (indices_type == DataType::INT32 || indices_type == DataType::INT64),
+      (dtype == DataType::INT32 || dtype == DataType::INT64),
       true,
-      phi::errors::InvalidArgument("dtype of indices must be int32 or int64"));
+      phi::errors::InvalidArgument(
+          "dtype of indices must be DataType::INT32 or DataType::INT64"));
 
-  if (indices_type == DataType::INT32) {
+  if (dtype == DataType::INT32) {
     int _axis = 0;
     if (axis < 0) {
       _axis = axis + x_dims.size();
@@ -606,7 +606,7 @@ void CumWithIndicesInferMeta(const MetaTensor& x,
   out->set_dtype(x.dtype());
   out->share_lod(x);
   indices->set_dims(x_dims);
-  indices->set_dtype(indices_type);
+  indices->set_dtype(dtype);
   indices->share_lod(x);
 }
 
diff --git a/paddle/phi/infermeta/unary.h b/paddle/phi/infermeta/unary.h
@@ -137,7 +137,7 @@ void CumScalarAxisInferMeta(const MetaTensor& x,
 
 void CumWithIndicesInferMeta(const MetaTensor& x,
                              int axis,
-                             int dtype,
+                             DataType dtype,
                              MetaTensor* out,
                              MetaTensor* indices);
 
diff --git a/paddle/phi/kernels/cpu/cum_maxmin_grad_kernel.cc b/paddle/phi/kernels/cpu/cum_maxmin_grad_kernel.cc
@@ -28,19 +28,18 @@ void CummaxGradKernel(const Context& dev_ctx,
                       const DenseTensor& indices,
                       const DenseTensor& out_grad,
                       int axis,
-                      int dtype,
+                      DataType dtype,
                       DenseTensor* x_grad) {
   dev_ctx.template Alloc<T>(x_grad);
   phi::funcs::SetConstant<Context, T> functor;
   functor(dev_ctx, x_grad, static_cast<T>(0));
   if (axis < 0) {
     axis = axis + x.dims().size();
   }
-  auto indices_type = phi::TransToPhiDataType(dtype);
-  if (indices_type == DataType::INT32) {
+  if (dtype == DataType::INT32) {
     phi::funcs::cpu_scatter_add_kernel<T, int32_t>(
         *x_grad, axis, indices, out_grad, dev_ctx);
-  } else if (indices_type == DataType::INT64) {
+  } else if (dtype == DataType::INT64) {
     phi::funcs::cpu_scatter_add_kernel<T, int64_t>(
         *x_grad, axis, indices, out_grad, dev_ctx);
   }
@@ -52,19 +51,18 @@ void CumminGradKernel(const Context& dev_ctx,
                       const DenseTensor& indices,
                       const DenseTensor& out_grad,
                       int axis,
-                      int dtype,
+                      DataType dtype,
                       DenseTensor* x_grad) {
   dev_ctx.template Alloc<T>(x_grad);
   phi::funcs::SetConstant<Context, T> functor;
   functor(dev_ctx, x_grad, static_cast<T>(0));
   if (axis < 0) {
     axis = axis + x.dims().size();
   }
-  auto indices_type = phi::TransToPhiDataType(dtype);
-  if (indices_type == DataType::INT32) {
+  if (dtype == DataType::INT32) {
     phi::funcs::cpu_scatter_add_kernel<T, int32_t>(
         *x_grad, axis, indices, out_grad, dev_ctx);
-  } else if (indices_type == DataType::INT64) {
+  } else if (dtype == DataType::INT64) {
     phi::funcs::cpu_scatter_add_kernel<T, int64_t>(
         *x_grad, axis, indices, out_grad, dev_ctx);
   }
diff --git a/paddle/phi/kernels/cpu/cum_maxmin_kernel.cc b/paddle/phi/kernels/cpu/cum_maxmin_kernel.cc
@@ -149,14 +149,13 @@ template <typename T, typename Context>
 void CummaxKernel(const Context& dev_ctx,
                   const DenseTensor& x,
                   int axis,
-                  int dtype,
+                  DataType dtype,
                   DenseTensor* out,
                   DenseTensor* indices) {
-  auto indices_type = phi::TransToPhiDataType(dtype);
-  if (indices_type == DataType::INT32) {
+  if (dtype == DataType::INT32) {
     ScanWithIndicesKernel<T, int32_t, std::greater_equal<T>, Context>(
         dev_ctx, x, axis, out, indices);
-  } else if (indices_type == DataType::INT64) {
+  } else if (dtype == DataType::INT64) {
     ScanWithIndicesKernel<T, int64_t, std::greater_equal<T>, Context>(
         dev_ctx, x, axis, out, indices);
   }
@@ -166,14 +165,13 @@ template <typename T, typename Context>
 void CumminKernel(const Context& dev_ctx,
                   const DenseTensor& x,
                   int axis,
-                  int dtype,
+                  DataType dtype,
                   DenseTensor* out,
                   DenseTensor* indices) {
-  auto indices_type = phi::TransToPhiDataType(dtype);
-  if (indices_type == DataType::INT32) {
+  if (dtype == DataType::INT32) {
     ScanWithIndicesKernel<T, int32_t, std::less_equal<T>, Context>(
         dev_ctx, x, axis, out, indices);
-  } else if (indices_type == DataType::INT64) {
+  } else if (dtype == DataType::INT64) {
     ScanWithIndicesKernel<T, int64_t, std::less_equal<T>, Context>(
         dev_ctx, x, axis, out, indices);
   }
diff --git a/paddle/phi/kernels/cum_maxmin_grad_kernel.h b/paddle/phi/kernels/cum_maxmin_grad_kernel.h
@@ -24,7 +24,7 @@ void CummaxGradKernel(const Context& dev_ctx,
                       const DenseTensor& indices,
                       const DenseTensor& out_grad,
                       int axis,
-                      int dtype,
+                      DataType dtype,
                       DenseTensor* x_grad);
 
 template <typename T, typename Context>
@@ -33,7 +33,7 @@ void CumminGradKernel(const Context& dev_ctx,
                       const DenseTensor& indices,
                       const DenseTensor& out_grad,
                       int axis,
-                      int dtype,
+                      DataType dtype,
                       DenseTensor* x_grad);
 
 }  // namespace phi
diff --git a/paddle/phi/kernels/cum_maxmin_kernel.h b/paddle/phi/kernels/cum_maxmin_kernel.h
@@ -22,15 +22,15 @@ template <typename T, typename Context>
 void CummaxKernel(const Context& dev_ctx,
                   const DenseTensor& x,
                   int axis,
-                  int dtype,
+                  DataType dtype,
                   DenseTensor* out,
                   DenseTensor* indices);
 
 template <typename T, typename Context>
 void CumminKernel(const Context& dev_ctx,
                   const DenseTensor& x,
                   int axis,
-                  int dtype,
+                  DataType dtype,
                   DenseTensor* out,
                   DenseTensor* indices);
 
diff --git a/paddle/phi/kernels/gpu/cum_maxmin_grad_kernel.cu b/paddle/phi/kernels/gpu/cum_maxmin_grad_kernel.cu
@@ -28,19 +28,19 @@ void CummaxGradKernel(const Context& dev_ctx,
                       const DenseTensor& indices,
                       const DenseTensor& out_grad,
                       int axis,
-                      int dtype,
+                      DataType dtype,
                       DenseTensor* x_grad) {
   dev_ctx.template Alloc<T>(x_grad);
   phi::funcs::SetConstant<Context, T> functor;
   functor(dev_ctx, x_grad, static_cast<T>(0));
   if (axis < 0) {
     axis = axis + x.dims().size();
   }
-  auto indices_type = phi::TransToPhiDataType(dtype);
-  if (indices_type == DataType::INT32) {
+
+  if (dtype == DataType::INT32) {
     phi::funcs::gpu_scatter_add_kernel<T, int32_t>(
         *x_grad, axis, indices, out_grad, dev_ctx);
-  } else if (indices_type == DataType::INT64) {
+  } else if (dtype == DataType::INT64) {
     phi::funcs::gpu_scatter_add_kernel<T, int64_t>(
         *x_grad, axis, indices, out_grad, dev_ctx);
   }
@@ -52,19 +52,19 @@ void CumminGradKernel(const Context& dev_ctx,
                       const DenseTensor& indices,
                       const DenseTensor& out_grad,
                       int axis,
-                      int dtype,
+                      DataType dtype,
                       DenseTensor* x_grad) {
   dev_ctx.template Alloc<T>(x_grad);
   phi::funcs::SetConstant<Context, T> functor;
   functor(dev_ctx, x_grad, static_cast<T>(0));
   if (axis < 0) {
     axis = axis + x.dims().size();
   }
-  auto indices_type = phi::TransToPhiDataType(dtype);
-  if (indices_type == DataType::INT32) {
+
+  if (dtype == DataType::INT32) {
     phi::funcs::gpu_scatter_add_kernel<T, int32_t>(
         *x_grad, axis, indices, out_grad, dev_ctx);
-  } else if (indices_type == DataType::INT64) {
+  } else if (dtype == DataType::INT64) {
     phi::funcs::gpu_scatter_add_kernel<T, int64_t>(
         *x_grad, axis, indices, out_grad, dev_ctx);
   }
diff --git a/paddle/phi/kernels/gpu/cum_maxmin_kernel.cu b/paddle/phi/kernels/gpu/cum_maxmin_kernel.cu
@@ -312,17 +312,16 @@ template <typename T, typename Context>
 void CummaxKernel(const Context& dev_ctx,
                   const DenseTensor& x,
                   int axis,
-                  int dtype,
+                  DataType dtype,
                   DenseTensor* out,
                   DenseTensor* indices) {
-  auto indices_type = phi::TransToPhiDataType(dtype);
   T init = std::is_floating_point<T>::value
                ? (-1 * std::numeric_limits<T>::infinity())
                : std::numeric_limits<T>::lowest();
-  if (indices_type == DataType::INT32) {
+  if (dtype == DataType::INT32) {
     ScanWithIndicesKernel<T, int32_t, std::greater_equal<T>, Context>(
         dev_ctx, x, axis, init, out, indices);
-  } else if (indices_type == DataType::INT64) {
+  } else if (dtype == DataType::INT64) {
     ScanWithIndicesKernel<T, int64_t, std::greater_equal<T>, Context>(
         dev_ctx, x, axis, init, out, indices);
   }
@@ -332,16 +331,15 @@ template <typename T, typename Context>
 void CumminKernel(const Context& dev_ctx,
                   const DenseTensor& x,
                   int axis,
-                  int dtype,
+                  DataType dtype,
                   DenseTensor* out,
                   DenseTensor* indices) {
-  auto indices_type = phi::TransToPhiDataType(dtype);
   T init = std::is_floating_point<T>::value ? std::numeric_limits<T>::infinity()
                                             : std::numeric_limits<T>::max();
-  if (indices_type == DataType::INT32) {
+  if (dtype == DataType::INT32) {
     ScanWithIndicesKernel<T, int32_t, std::less_equal<T>, Context>(
         dev_ctx, x, axis, init, out, indices);
-  } else if (indices_type == DataType::INT64) {
+  } else if (dtype == DataType::INT64) {
     ScanWithIndicesKernel<T, int64_t, std::less_equal<T>, Context>(
         dev_ctx, x, axis, init, out, indices);
   }
diff --git a/python/paddle/tensor/math.py b/python/paddle/tensor/math.py
@@ -4035,7 +4035,7 @@ def cummax(x, axis=None, dtype='int64', name=None):
     check_dtype(dtype, 'dtype', ['int32', 'int64'], 'cummax')
     dtype = convert_np_dtype_to_dtype_(dtype)
 
-    if in_dynamic_mode():
+    if in_dynamic_or_pir_mode():
         return _C_ops.cummax(x, axis, dtype)
     else:
         check_variable_and_dtype(
@@ -4120,7 +4120,7 @@ def cummin(x, axis=None, dtype='int64', name=None):
     check_dtype(dtype, 'dtype', ['int32', 'int64'], 'cummin')
     dtype = convert_np_dtype_to_dtype_(dtype)
 
-    if in_dynamic_mode():
+    if in_dynamic_or_pir_mode():
         return _C_ops.cummin(x, axis, dtype)
     else:
         check_variable_and_dtype(
diff --git a/test/legacy_test/test_cummax_op.py b/test/legacy_test/test_cummax_op.py
@@ -21,6 +21,7 @@
 import paddle
 from paddle import base
 from paddle.base import core
+from paddle.pir_utils import test_with_pir_api
 
 
 def cummax_dim2(arr, axis=None):
@@ -91,11 +92,11 @@ def set_attrs(self):
 
     def test_check_output(self):
         paddle.enable_static()
-        self.check_output()
+        self.check_output(check_pir=True)
 
     def test_check_grad(self):
         paddle.enable_static()
-        self.check_grad(['x'], 'out')
+        self.check_grad(['x'], 'out', check_pir=True)
 
 
 class TestCummaxOpAxis1(TestCummaxOp):
@@ -151,6 +152,7 @@ def run_cases(self):
         np.testing.assert_array_equal(z, y.numpy())
         np.testing.assert_array_equal(ind, indices.numpy())
 
+    @test_with_pir_api
     def run_static(self, use_gpu=False):
         with base.program_guard(base.Program()):
             data_np = np.random.random((100, 100)).astype(np.float32)
@@ -163,20 +165,19 @@ def run_static(self, use_gpu=False):
 
             place = base.CUDAPlace(0) if use_gpu else base.CPUPlace()
             exe = base.Executor(place)
-            exe.run(base.default_startup_program())
             out = exe.run(
                 feed={'x': data_np},
                 fetch_list=[
-                    y1.name,
-                    indices1.name,
-                    y2.name,
-                    indices2.name,
-                    y3.name,
-                    indices3.name,
-                    y4.name,
-                    indices4.name,
-                    y5.name,
-                    indices5.name,
+                    y1,
+                    indices1,
+                    y2,
+                    indices2,
+                    y3,
+                    indices3,
+                    y4,
+                    indices4,
+                    y5,
+                    indices5,
                 ],
             )
 
@@ -218,6 +219,7 @@ def test_errors(self):
         paddle.enable_static()
         with base.program_guard(base.Program()):
 
+            @test_with_pir_api
             def test_x_type():
                 data = [1, 2, 3]
                 y, indices = paddle.cummax(data, axis=0)
diff --git a/test/legacy_test/test_cummin_op.py b/test/legacy_test/test_cummin_op.py
