!83 Adapt ApplyAdam remove outputs m and v

Merge pull request !83 from zhaozhenlong/adapt-apply-adam

graphengine

@@ -1 +1 @@
-Subproject commit d345a800a4f7c32eb768ea48667d1ce00b841748
+Subproject commit 976d1e31b777d65f87333c3a125093946e682a6e

mindspore/ccsrc/transform/op_declare.cc

@@ -466,11 +466,7 @@ INPUT_MAP(ApplyAdam) = {{1, INPUT_DESC(var)},         {2, INPUT_DESC(m)},
                         {10, INPUT_DESC(grad)}};
 ATTR_MAP(ApplyAdam) = {{"use_locking", ATTR_DESC(use_locking, AnyTraits<bool>())},
                        {"use_nesterov", ATTR_DESC(use_nesterov, AnyTraits<bool>())}};
-#ifdef ENABLE_GE
-OUTPUT_MAP(ApplyAdam) = {{0, OUTPUT_DESC(var)}, {1, OUTPUT_DESC(m)}, {2, OUTPUT_DESC(v)}};
-#else
 OUTPUT_MAP(ApplyAdam) = {{0, OUTPUT_DESC(var)}};
-#endif
 
 // Relu6
 INPUT_MAP(Relu6) = {{1, INPUT_DESC(x)}};

mindspore/ops/operations/nn_ops.py

@@ -2323,11 +2323,7 @@ class Adam(PrimitiveWithInfer):
         - **gradient** (Tensor) - Gradients.
 
     Outputs:
-        Tuple of 3 Tensor, the updated parameters.
-
         - **var** (Tensor) - The same shape and data type as `var`.
-        - **m** (Tensor) - The same shape and data type as `m`.
-        - **v** (Tensor) - The same shape and data type as `v`.
     """
 
     @prim_attr_register
@@ -2340,7 +2336,7 @@ class Adam(PrimitiveWithInfer):
         validator.check("var_shape", var_shape, "m_shape", m_shape, Rel.EQ, self.name)
         validator.check("var_shape", var_shape, "v_shape", v_shape, Rel.EQ, self.name)
         validator.check("var_shape", var_shape, "grad_shape", grad_shape, Rel.EQ, self.name)
-        return var_shape, m_shape, v_shape
+        return var_shape
 
     def infer_dtype(self, var_dtype, m_dtype, v_dtype, beta1_power_dtype, beta2_power_dtype, lr_dtype,
                     beta1_dtype, beta2_dtype, epsilon_dtype, grad_dtype):
@@ -2350,7 +2346,7 @@ class Adam(PrimitiveWithInfer):
         args = {"beta1_power": beta1_power_dtype, "beta2_power": beta2_power_dtype, 'lr': lr_dtype,
                 "beta1": beta1_dtype, "beta2": beta2_dtype, "epsilon": epsilon_dtype}
         validator.check_scalar_or_tensor_type_same(args, [mstype.float16, mstype.float32], self.name, True)
-        return var_dtype, m_dtype, v_dtype
+        return var_dtype
 
 
 class BinaryCrossEntropy(PrimitiveWithInfer):