cherry-pick from the develop PR#26792, fix the argmin, argmax

cherry-pick from the develop PR#26792, fix the argmin, argmax

paddle/fluid/operators/arg_max_op.cc

@@ -12,6 +12,7 @@ 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. */
 
+#include "paddle/fluid/framework/op_version_registry.h"
 #include "paddle/fluid/operators/arg_min_max_op_base.h"
 
 REGISTER_OPERATOR(
@@ -31,3 +32,20 @@ REGISTER_OP_CPU_KERNEL(
                                     int16_t>,
     paddle::operators::ArgMaxKernel<paddle::platform::CPUDeviceContext,
                                     uint8_t>);
+REGISTER_OP_VERSION(arg_max)
+    .AddCheckpoint(
+        R"ROC(
+              Upgrade argmax add a new attribute [flatten] and modify the attribute of dtype)ROC",
+        paddle::framework::compatible::OpVersionDesc()
+            .NewAttr("flatten",
+                     "In order to compute the argmax over the flattened array "
+                     "when the "
+                     "argument `axis` in python API is None.",
+                     false)
+            .ModifyAttr(
+                "dtype",
+                "change the default value of dtype, the older version "
+                "is -1, means return the int64 indices."
+                "The new version is 3, return the int64 indices directly."
+                "And supporting the dtype of -1 in new version.",
+                3));

paddle/fluid/operators/arg_min_max_op_base.h

@@ -70,6 +70,8 @@ struct VisitDataArgMinMaxFunctor {
     auto axis = ctx.Attr<int64_t>("axis");
     auto keepdims = ctx.Attr<bool>("keepdims");
     const bool& flatten = ctx.Attr<bool>("flatten");
+    // paddle do not have the scalar tensor, just return the shape [1] tensor
+    if (flatten) keepdims = true;
 
     // if flatten, will construct the new dims for the cacluate
     framework::DDim x_dims;
@@ -164,15 +166,30 @@ class ArgMinMaxOp : public framework::OperatorWithKernel {
         platform::errors::InvalidArgument(
             "'axis'(%d) must be less than Rank(X)(%d).", axis, x_dims.size()));
 
+    auto x_rank = x_dims.size();
+    if (axis < 0) axis += x_rank;
+    if (ctx->IsRuntime()) {
+      const int& dtype = ctx->Attrs().Get<int>("dtype");
+      if (dtype == framework::proto::VarType::INT32) {
+        int64_t all_element_num = 0;
+        if (flatten) {
+          all_element_num = framework::product(x_dims);
+
+        } else {
+          all_element_num = x_dims[axis];
+        }
+        PADDLE_ENFORCE_LE(
+            all_element_num, INT_MAX,
+            "The element num of the argmin/argmax input at axis is "
+            "%d, is larger than int32 maximum value:%d, you must "
+            "set the dtype of argmin/argmax to 'int64'.",
+            all_element_num, INT_MAX);
+      }
+    }
     std::vector<int64_t> vec;
     if (flatten) {
-      // if is flatten, will return the only on element
-      if (keepdims) {
-        vec.emplace_back(static_cast<int64_t>(1));
-      }
+      vec.emplace_back(static_cast<int64_t>(1));
     } else {
-      auto x_rank = x_dims.size();
-      if (axis < 0) axis += x_rank;
       for (int64_t i = 0; i < axis; i++) vec.emplace_back(x_dims[i]);
       if (keepdims) {
         vec.emplace_back(static_cast<int64_t>(1));
@@ -194,10 +211,14 @@ class BaseArgMinMaxOpMaker : public framework::OpProtoAndCheckerMaker {
     AddOutput("Out", "Output tensor.");
     AddAttr<int64_t>("axis", "The axis in which to compute the arg indics.");
     AddAttr<bool>("keepdims", "Keep the dim that to reduce.").SetDefault(false);
-    AddAttr<int>("dtype", "Keep the dim that to reduce.").SetDefault(-1);
     AddAttr<bool>("flatten",
                   "Flatten the input value, and search the min or max indices")
         .SetDefault(false);
+    AddAttr<int>("dtype",
+                 "(int, 3), the dtype of indices, the indices dtype must be "
+                 "int32, int64."
+                 "default dtype is int64, and proto value is 3.")
+        .SetDefault(3);
     AddComment(string::Sprintf(R"DOC(
       %s Operator.
 

paddle/fluid/operators/arg_min_op.cc

@@ -12,6 +12,7 @@ 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. */
 
+#include "paddle/fluid/framework/op_version_registry.h"
 #include "paddle/fluid/operators/arg_min_max_op_base.h"
 
 REGISTER_OPERATOR(
@@ -31,3 +32,20 @@ REGISTER_OP_CPU_KERNEL(
                                     int16_t>,
     paddle::operators::ArgMinKernel<paddle::platform::CPUDeviceContext,
                                     uint8_t>);
+REGISTER_OP_VERSION(arg_min)
+    .AddCheckpoint(
+        R"ROC(
+              Upgrade argmin add a new attribute [flatten] and modify the attribute of dtype)ROC",
+        paddle::framework::compatible::OpVersionDesc()
+            .NewAttr("flatten",
+                     "In order to compute the argmin over the flattened array "
+                     "when the "
+                     "argument `axis` in python API is None.",
+                     false)
+            .ModifyAttr(
+                "dtype",
+                "change the default value of dtype, the older version "
+                "is -1, means return the int64 indices."
+                "The new version is 3, return the int64 indices directly."
+                "And supporting the dtype of -1 in new version.",
+                3));

python/paddle/fluid/tests/unittests/test_arg_min_max_v2_op.py

@@ -218,7 +218,7 @@ def create_test_case(op_type):
                 self.assertTrue("test_arg_api" in result.name)
 
         def run_dygraph(self, place):
-            paddle.disable_static()
+            paddle.disable_static(place)
             op = eval("paddle.%s" % (op_type))
             data_tensor = paddle.to_tensor(self.input_data)
 
@@ -240,7 +240,7 @@ def create_test_case(op_type):
             #case 4 
             result_data = op(data_tensor, axis=-1, keepdim=True)
             excepted_data = self.numpy_op(self.input_data, axis=-1)
-            excepted_data = excepted_data.reshape((10))
+            excepted_data = excepted_data.reshape((10, 1))
             self.assertTrue((result_data.numpy() == excepted_data).all(), True)
 
             #case 5 
@@ -299,14 +299,28 @@ class TestArgMinMaxOpError(unittest.TestCase):
                     name="test_argmax", shape=[10], dtype="float32")
                 output = paddle.argmax(x=data, dtype="float32")
 
-            self.assertRaises(ValueError, test_argmax_attr_type)
+            self.assertRaises(TypeError, test_argmax_attr_type)
 
             def test_argmin_attr_type():
                 data = paddle.static.data(
                     name="test_argmax", shape=[10], dtype="float32")
                 output = paddle.argmin(x=data, dtype="float32")
 
-            self.assertRaises(ValueError, test_argmin_attr_type)
+            self.assertRaises(TypeError, test_argmin_attr_type)
+
+            def test_argmax_axis_type():
+                data = paddle.static.data(
+                    name="test_argmax", shape=[10], dtype="float32")
+                output = paddle.argmax(x=data, axis=1.2)
+
+            self.assertRaises(TypeError, test_argmax_axis_type)
+
+            def test_argmin_axis_type():
+                data = paddle.static.data(
+                    name="test_argmin", shape=[10], dtype="float32")
+                output = paddle.argmin(x=data, axis=1.2)
+
+            self.assertRaises(TypeError, test_argmin_axis_type)
 
 
 if __name__ == '__main__':

python/paddle/tensor/search.py

@@ -18,7 +18,6 @@ from ..fluid.data_feeder import check_variable_and_dtype, check_type, check_dtyp
 from ..fluid import core, layers
 
 # TODO: define searching & indexing functions of a tensor  
-from ..fluid.layers import argmin  #DEFINE_ALIAS
 from ..fluid.layers import has_inf  #DEFINE_ALIAS
 from ..fluid.layers import has_nan  #DEFINE_ALIAS
 
@@ -123,7 +122,7 @@ def argsort(x, axis=-1, descending=False, name=None):
     return ids
 
 
-def argmax(x, axis=None, dtype=None, keepdim=False, name=None):
+def argmax(x, axis=None, keepdim=False, dtype="int64", name=None):
     """
     This OP computes the indices of the max elements of the input tensor's
     element along the provided axis.
@@ -134,10 +133,10 @@ def argmax(x, axis=None, dtype=None, keepdim=False, name=None):
         axis(int, optional): Axis to compute indices along. The effective range
             is [-R, R), where R is x.ndim. when axis < 0, it works the same way
             as axis + R. Default is None, the input `x` will be into the flatten tensor, and selecting the min value index.
-        dtype(str): Data type of the output tensor which can
-                    be int32, int64. The default value is None, and it will
-                    return the int64 indices.
         keepdim(bool, optional): Keep the axis that selecting max. The defalut value is False.
+        dtype(str|np.dtype, optional): Data type of the output tensor which can
+                    be int32, int64. The default value is 'int64', and it will
+                    return the int64 indices.
         name(str, optional): The default value is None. Normally there is no
             need for user to set this property. For more information, please
             refer to :ref:`api_guide_Name`.
@@ -163,48 +162,39 @@ def argmax(x, axis=None, dtype=None, keepdim=False, name=None):
             print(out3.numpy()) 
             # [2 3 1]
     """
+    if axis is not None and not isinstance(axis, int):
+        raise TypeError(
+            "The type of 'axis'  must be int or None in argmax, but received %s."
+            % (type(axis)))
+    var_dtype = convert_np_dtype_to_dtype_(dtype)
+    check_dtype(var_dtype, 'dtype', ['int32', 'int64'], 'argmin')
     flatten = False
     if axis is None:
         flatten = True
         axis = 0
 
     if in_dygraph_mode():
-        if dtype != None:
-            var_dtype = convert_np_dtype_to_dtype_(dtype)
-            out = core.ops.arg_max(x, 'axis', axis, 'dtype', var_dtype,
-                                   'keepdim', keepdim, 'flatten', flatten)
-        else:
-            out = core.ops.arg_max(x, 'axis', axis, 'keepdim', keepdim,
-                                   'flatten', flatten)
+        out = core.ops.arg_max(x, 'axis', axis, 'dtype', var_dtype, 'keepdims',
+                               keepdim, 'flatten', flatten)
         return out
 
     helper = LayerHelper("argmax", **locals())
     check_variable_and_dtype(
         x, 'x', ['float32', 'float64', 'int16', 'int32', 'int64', 'uint8'],
         'paddle.argmax')
-    var_dtype = None
     attrs = {}
-    if dtype is not None:
-        if dtype not in ['int32', 'int64']:
-            raise ValueError(
-                "The value of 'dtype' in argmax op must be int32, int64, but received of {}".
-                format(dtype))
-        var_dtype = convert_np_dtype_to_dtype_(dtype)
-        attrs["dtype"] = var_dtype
-    else:
-        var_dtype = VarDesc.VarType.INT64
-
     out = helper.create_variable_for_type_inference(var_dtype)
     attrs['keepdims'] = keepdim
     attrs['axis'] = axis
     attrs['flatten'] = flatten
+    attrs['dtype'] = var_dtype
     helper.append_op(
         type='arg_max', inputs={'X': x}, outputs={'Out': [out]}, attrs=attrs)
     out.stop_gradient = True
     return out
 
 
-def argmin(x, axis=None, dtype=None, keepdim=False, name=None):
+def argmin(x, axis=None, keepdim=False, dtype="int64", name=None):
     """
     This OP computes the indices of the min elements of the input tensor's
     element along the provided axis.
@@ -215,10 +205,10 @@ def argmin(x, axis=None, dtype=None, keepdim=False, name=None):
         axis(int, optional): Axis to compute indices along. The effective range
             is [-R, R), where R is x.ndim. when axis < 0, it works the same way
             as axis + R. Default is None, the input `x` will be into the flatten tensor, and selecting the min value index.
+        keepdim(bool, optional): Keep the axis that selecting min. The defalut value is False.
         dtype(str): Data type of the output tensor which can
-                    be int32, int64. The default value is None, and it will
+                    be int32, int64. The default value is 'int64', and it will
                     return the int64 indices.
-        keepdim(bool, optional): Keep the axis that selecting min. The defalut value is False.
         name(str, optional): The default value is None. Normally there is no
             need for user to set this property. For more information, please
             refer to :ref:`api_guide_Name`.
@@ -244,41 +234,32 @@ def argmin(x, axis=None, dtype=None, keepdim=False, name=None):
             print(out3.numpy()) 
             # [0 0 2]
     """
+    if axis is not None and not isinstance(axis, int):
+        raise TypeError(
+            "The type of 'axis'  must be int or None in argmin, but received %s."
+            % (type(axis)))
+    var_dtype = convert_np_dtype_to_dtype_(dtype)
+    check_dtype(var_dtype, 'dtype', ['int32', 'int64'], 'argmin')
     flatten = False
     if axis is None:
         flatten = True
         axis = 0
 
     if in_dygraph_mode():
-        if dtype != None:
-            var_dtype = convert_np_dtype_to_dtype_(dtype)
-            out = core.ops.arg_min(x, 'axis', axis, 'dtype', var_dtype,
-                                   'keepdim', keepdim, 'flatten', flatten)
-        else:
-            out = core.ops.arg_min(x, 'axis', axis, 'keepdim', keepdim,
-                                   'flatten', flatten)
+        out = core.ops.arg_min(x, 'axis', axis, 'dtype', var_dtype, 'keepdims',
+                               keepdim, 'flatten', flatten)
         return out
 
     helper = LayerHelper("argmin", **locals())
     check_variable_and_dtype(
         x, 'x', ['float32', 'float64', 'int16', 'int32', 'int64', 'uint8'],
         'paddle.argmin')
-    var_dtype = None
-    attrs = {}
-    if dtype is not None:
-        if dtype not in ['int32', 'int64']:
-            raise ValueError(
-                "The value of 'dtype' in argmin op must be int32, int64, but received of {}".
-                format(dtype))
-        var_dtype = convert_np_dtype_to_dtype_(dtype)
-        attrs["dtype"] = var_dtype
-    else:
-        var_dtype = VarDesc.VarType.INT64
-
     out = helper.create_variable_for_type_inference(var_dtype)
+    attrs = {}
     attrs['keepdims'] = keepdim
     attrs['axis'] = axis
     attrs['flatten'] = flatten
+    attrs['dtype'] = var_dtype
     helper.append_op(
         type='arg_min', inputs={'X': x}, outputs={'Out': [out]}, attrs=attrs)
     out.stop_gradient = True