[feat] [assistant] [I54KH0] add new aicpu operator SmoothL1LossV2

docs/api/api_python/ops/mindspore.ops.func_smooth_l1_loss.rst

@@ -27,11 +27,8 @@ mindspore.ops.smooth_l1_loss
 
     其中， :math:`\text{beta}` 控制损失函数从二次元变为线性的point。默认值是1.0。 :math:`N` 为batch size。
 
-    .. note::
-        在Ascend上，目前不支持 `logits` 的数据类型是float64。
-
     参数：
-        - **logits** (Tensor) - shape： :math:`(N, *)` ，其中 :math:`*` 表示任意数量的附加维度。数据类型为float16或float32， CPU和GPU后端还支持float64。
+        - **logits** (Tensor) - shape： :math:`(N, *)` ，其中 :math:`*` 表示任意数量的附加维度。数据类型为float16，float32和float64。
         - **labels** (Tensor) - shape： :math:`(N, *)` ，与 `logits` 的shape和数据类型相同。
         - **beta** (float) - 控制损失函数在L1Loss和L2Loss间变换的阈值。默认值：1.0。
         - **reduction** (str) - 缩减输出的方法。默认值：'none'。其他选项：'mean'和'sum'。
@@ -45,4 +42,3 @@ mindspore.ops.smooth_l1_loss
         - **TypeError** - `logits` 或 `labels` 的数据类型不是float16，float32和float64中的任一者。
         - **ValueError** - `beta` 小于0。
         - **ValueError** - `logits` 与 `labels` 的shape不同。
-        - **TypeError** - Ascend后端不支持数据类型是float64的 `logits` 输入。

mindspore/ccsrc/plugin/device/ascend/kernel/aicpu/aicpu_util.h

@@ -188,6 +188,8 @@ constexpr auto kSliceGrad = "SliceGrad";
 constexpr auto kStatelessDropOutGenMask = "StatelessDropOutGenMask";
 constexpr auto kRaggedTensorToTensor = "RaggedTensorToTensor";
 constexpr auto kAdaptiveMaxPool3D = "AdaptiveMaxPool3D";
+constexpr auto kSmoothL1Loss = "SmoothL1Loss";
+constexpr auto kSmoothL1LossGrad = "SmoothL1LossGrad";
 
 const std::set<std::string> kCpuKernelOps{kIdentity,
                                           kMaskedSelect,
@@ -311,6 +313,8 @@ const std::map<std::string, std::string> kOpNameToAicpuOpNameMap{
   {kSampleDistortedBoundingBoxV2, "SampleDistortedBoundingBoxExt2"},
   {kSparseSoftmaxCrossEntropyWithLogitsV2, "SparseSoftmaxCrossEntropyWithLogits"},
   {kSparseToDenseV2, "SparseToDense"},
+  {kSmoothL1Loss, "SmoothL1LossV2"},
+  {kSmoothL1LossGrad, "SmoothL1LossGradV2"},
   {kAvgPoolV1, "AvgPool"},
   {kNonZero, "Where"},
   {kAvgPoolGradV1, "AvgPoolGrad"},

mindspore/core/ops/grad/smooth_l1_loss_grad.cc

@@ -50,10 +50,7 @@ std::string SmoothL1LossGrad::get_reduction() const {
 namespace {
 abstract::ShapePtr SmoothL1LossGradInferShape(const PrimitivePtr &primitive,
                                               const std::vector<AbstractBasePtr> &input_args) {
-  MS_EXCEPTION_IF_NULL(primitive);
   auto prim_name = primitive->name();
-  const int64_t input_num = 3;
-  CheckAndConvertUtils::CheckInputArgs(input_args, kEqual, input_num, prim_name);
   auto prediction = CheckAndConvertUtils::CheckArgs<abstract::AbstractTensor>(prim_name, input_args, kInputIndex0);
   auto target = CheckAndConvertUtils::CheckArgs<abstract::AbstractTensor>(prim_name, input_args, kInputIndex1);
   abstract::CheckShapeSame(prim_name, prediction, target);
@@ -76,17 +73,18 @@ TypePtr SmoothL1LossGradInferType(const PrimitivePtr &prim, const std::vector<Ab
   std::map<std::string, TypePtr> args;
   (void)args.emplace("prediction", input_args[kInputIndex0]->BuildType());
   (void)args.emplace("target", input_args[kInputIndex1]->BuildType());
-  auto dloss_type = CheckAndConvertUtils::CheckTensorTypeSame(args, valid_types, prim->name());
-  return dloss_type;
+  (void)CheckAndConvertUtils::CheckTensorTypeSame(args, valid_types, prim->name());
+  return input_args[kInputIndex0]->BuildType();
 }
 }  // namespace
 
 MIND_API_OPERATOR_IMPL(SmoothL1LossGrad, BaseOperator);
 AbstractBasePtr SmoothL1LossGradInfer(const abstract::AnalysisEnginePtr &, const PrimitivePtr &primitive,
                                       const std::vector<AbstractBasePtr> &input_args) {
-  for (auto item : input_args) {
-    MS_EXCEPTION_IF_NULL(item);
-  }
+  MS_EXCEPTION_IF_NULL(primitive);
+  auto prim_name = primitive->name();
+  const int64_t input_num = 3;
+  CheckAndConvertUtils::CheckInputArgs(input_args, kEqual, input_num, prim_name);
   auto infer_type = SmoothL1LossGradInferType(primitive, input_args);
   auto infer_shape = SmoothL1LossGradInferShape(primitive, input_args);
   return abstract::MakeAbstract(infer_shape, infer_type);

mindspore/core/ops/smooth_l1_loss.cc

@@ -50,10 +50,7 @@ std::string SmoothL1Loss::get_reduction() const {
 namespace {
 abstract::ShapePtr SmoothL1LossInferShape(const PrimitivePtr &primitive,
                                           const std::vector<AbstractBasePtr> &input_args) {
-  MS_EXCEPTION_IF_NULL(primitive);
   auto prim_name = primitive->name();
-  const int64_t input_num = 2;
-  CheckAndConvertUtils::CheckInputArgs(input_args, kEqual, input_num, prim_name);
   auto prediction = CheckAndConvertUtils::CheckArgs<abstract::AbstractTensor>(prim_name, input_args, kInputIndex0);
   auto target = CheckAndConvertUtils::CheckArgs<abstract::AbstractTensor>(prim_name, input_args, kInputIndex1);
   auto prediction_shape = prediction->shape();
@@ -69,33 +66,29 @@ abstract::ShapePtr SmoothL1LossInferShape(const PrimitivePtr &primitive,
   if (reduction == kNone) {
     return prediction_shape;
   } else {
-    ShapeVector shape_out{1};
+    ShapeVector shape_out{};
     return std::make_shared<abstract::Shape>(shape_out);
   }
 }
 
 TypePtr SmoothL1LossInferType(const PrimitivePtr &prim, const std::vector<AbstractBasePtr> &input_args) {
   // Infer type
-  std::set<TypePtr> valid_types{};
-  auto context = MsContext::GetInstance();
-  MS_EXCEPTION_IF_NULL(context);
-  bool is_ascend = (context->get_param<std::string>(MS_CTX_DEVICE_TARGET) == kAscendDevice);
-  if (is_ascend) {
-    valid_types = {kFloat16, kFloat32};
-  } else {
-    valid_types = {kFloat16, kFloat32, kFloat64};
-  }
+  const std::set<TypePtr> valid_types = {kFloat16, kFloat32, kFloat64};
 
   std::map<std::string, TypePtr> args;
   (void)args.emplace("scale", input_args[kInputIndex0]->BuildType());
   (void)args.emplace("bias", input_args[kInputIndex1]->BuildType());
-  auto prediction_type = CheckAndConvertUtils::CheckTensorTypeSame(args, valid_types, prim->name());
-  return prediction_type;
+  (void)CheckAndConvertUtils::CheckTensorTypeSame(args, valid_types, prim->name());
+  return input_args[kInputIndex0]->BuildType();
 }
 }  // namespace
 
 AbstractBasePtr SmoothL1LossInfer(const abstract::AnalysisEnginePtr &, const PrimitivePtr &primitive,
                                   const std::vector<AbstractBasePtr> &input_args) {
+  MS_EXCEPTION_IF_NULL(primitive);
+  auto prim_name = primitive->name();
+  const int64_t input_num = 2;
+  CheckAndConvertUtils::CheckInputArgs(input_args, kEqual, input_num, prim_name);
   auto infer_type = SmoothL1LossInferType(primitive, input_args);
   auto infer_shape = SmoothL1LossInferShape(primitive, input_args);
   return abstract::MakeAbstract(infer_shape, infer_type);

mindspore/python/mindspore/ops/_op_impl/aicpu/smooth_l1_loss.py

@@ -0,0 +1,35 @@
+# Copyright 2022 Huawei Technologies Co., Ltd
+#
+# 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.
+# ============================================================================
+
+"""SmoothL1Loss op"""
+from mindspore.ops.op_info_register import op_info_register, AiCPURegOp, DataType
+
+smooth_l1_loss_op_info = AiCPURegOp("SmoothL1Loss") \
+    .fusion_type("OPAQUE") \
+    .attr("sigma", "float") \
+    .attr("reduction", "str") \
+    .input(0, "prediction", "required") \
+    .input(1, "target", "required") \
+    .output(0, "output", "required") \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F64_Default, DataType.F64_Default, DataType.F64_Default) \
+    .get_op_info()
+
+
+@op_info_register(smooth_l1_loss_op_info)
+def _smooth_l1_loss_aicpu():
+    """SmoothL1Loss AiCPU register"""
+    return

mindspore/python/mindspore/ops/_op_impl/aicpu/smooth_l1_loss_grad.py

@@ -0,0 +1,37 @@
+# Copyright 2022 Huawei Technologies Co., Ltd
+#
+# 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.
+# ============================================================================
+
+"""SmoothL1LossGrad op"""
+from mindspore.ops.op_info_register import op_info_register, AiCPURegOp, DataType
+
+smooth_l1_loss_grad_op_info = AiCPURegOp("SmoothL1LossGrad") \
+    .fusion_type("OPAQUE") \
+    .attr("sigma", "float") \
+    .attr("reduction", "str") \
+    .input(0, "prediction", "required") \
+    .input(1, "target", "required") \
+    .input(2, "dout", "required") \
+    .output(0, "output", "required") \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F64_Default, DataType.F64_Default, DataType.F64_Default, DataType.F64_Default) \
+    .get_op_info()
+
+
+@op_info_register(smooth_l1_loss_grad_op_info)
+def _smooth_l1_loss_grad_aicpu():
+    """SmoothL1LossGrad AiCPU register"""
+    return
+    

mindspore/python/mindspore/ops/function/nn_func.py

@@ -2946,9 +2946,6 @@ def smooth_l1_loss(logits, labels, beta=1.0, reduction='none'):
     Here :math:`\text{beta}` controls the point where the loss function changes from quadratic to linear.
     Its default value is 1.0. :math:`N` is the batch size.
 
-    Note:
-        For Ascend platform, the float64 data type of `logits` is not support now.
-
     Args:
         logits (Tensor): Tensor of shape :math:`(N, *)` where :math:`*` means, any number of additional dimensions.
         labels (Tensor): Ground truth data, tensor of shape :math:`(N, *)`, same shape and dtype as the `logits`.
@@ -2963,10 +2960,9 @@ def smooth_l1_loss(logits, labels, beta=1.0, reduction='none'):
     Raises:
         TypeError: If `beta` is not a float.
         ValueError: If `reduction` is not one of 'none', 'mean', 'sum'.
-        TypeError: If dtype of `logits` or `labels` is neither float16 nor float32.
+        TypeError: If dtype of `logits` or `labels` is not one of float16, float32, float64.
         ValueError: If `beta` is less than or equal to 0.
         ValueError: If shape of `logits` is not the same as `labels`.
-        TypeError: The float64 data type of `logits` is support on Ascend platform.
 
     Supported Platforms:
         ``Ascend`` ``GPU`` ``CPU``

mindspore/python/mindspore/ops/operations/_grad_ops.py

@@ -2129,6 +2129,7 @@ class SmoothL1LossGrad(Primitive):
 
     @prim_attr_register
     def __init__(self, beta=1.0, reduction='none'):
+        self.add_prim_attr('sigma', self.beta)
         self.reduction = validator.check_string(
             reduction, ['none', 'sum', 'mean'], 'reduction', self.name)
 

mindspore/python/mindspore/ops/operations/nn_ops.py

@@ -2993,6 +2993,7 @@ class SmoothL1Loss(Primitive):
         validator.check('beta', beta, '', 0, Rel.GT, self.name)
         validator.check_string(
             reduction, ['none', 'sum', 'mean'], 'reduction', self.name)
+        self.add_prim_attr('sigma', self.beta)
         self.init_prim_io_names(inputs=['prediction', 'target'], outputs=['output'])