bugfix* fix bug in output tuple of tuple.* check kRWWrite input no-variable* input x of ScatterNdUpdate should be a parameter node

example/resnet101_imagenet2012/train.py

@@ -14,7 +14,6 @@
 # ============================================================================
 """train_imagenet."""
 import os
-import math
 import argparse
 import random
 import numpy as np

mindspore/ccsrc/operator/composite/do_signature.cc

@@ -195,6 +195,8 @@ AnfNodePtr BuildNewCNode(const FuncGraphPtr &func_graph, const std::string &func
         param = func_graph->NewCNode({NewValueNode(prim::kPrimGetRefKey), param});
       }
       // If sig is SignatureEnumRW::kRWRef, not do anything.
+    } else if (sig == SignatureEnumRW::kRWWrite && type->type_id() != kObjectTypeRefKey) {
+      MS_EXCEPTION(TypeError) << "Function " << func_name << "'s input " << i << " should be a Parameter.";
     }
     // add cast op here
     if (assign_source != nullptr && sig != SignatureEnumRW::kRWWrite) {

mindspore/common/api.py

@@ -70,12 +70,11 @@ def _wrap_func(fn):
         def _convert_data(data):
             if isinstance(data, Tensor) and not isinstance(data, MsTensor):
                 return MsTensor(data)
+            if isinstance(data, tuple):
+                return tuple(_convert_data(x) for x in data)
+            if isinstance(data, list):
+                return list(_convert_data(x) for x in data)
             return data
-
-        if isinstance(results, tuple):
-            return tuple(_convert_data(x) for x in results)
-        if isinstance(results, list):
-            return list(_convert_data(x) for x in results)
         return _convert_data(results)
 
     return wrapper

mindspore/nn/optim/adam.py

@@ -57,21 +57,22 @@ def _update_run_op(beta1, beta2, eps, lr, weight_decay_tensor, param, m, v, grad
     op_reshape = P.Reshape()
     op_shape = P.Shape()
 
-    param = op_cast(param, mstype.float32)
-    m = op_cast(m, mstype.float32)
-    v = op_cast(v, mstype.float32)
-    gradient = op_cast(gradient, mstype.float32)
+    param_fp32 = op_cast(param, mstype.float32)
+    m_fp32 = op_cast(m, mstype.float32)
+    v_fp32 = op_cast(v, mstype.float32)
+    gradient_fp32 = op_cast(gradient, mstype.float32)
 
-    next_m = op_mul(beta1, m) + op_mul(op_cast(F.tuple_to_array((1.0,)), mstype.float32) - beta1, gradient)
+    next_m = op_mul(beta1, m_fp32) + op_mul(op_cast(F.tuple_to_array((1.0,)), mstype.float32) - beta1, gradient_fp32)
 
-    next_v = op_mul(beta2, v) + op_mul(op_cast(F.tuple_to_array((1.0,)), mstype.float32) - beta2, op_square(gradient))
+    next_v = op_mul(beta2, v_fp32) + op_mul(op_cast(F.tuple_to_array((1.0,)), mstype.float32)
+                                            - beta2, op_square(gradient_fp32))
 
     update = next_m / (op_sqrt(next_v) + eps)
     if decay_flag:
-        update = update + op_mul(weight_decay_tensor, param)
+        update = update + op_mul(weight_decay_tensor, param_fp32)
 
     update_with_lr = op_mul(lr, update)
-    next_param = param - op_reshape(update_with_lr, op_shape(param))
+    next_param = param_fp32 - op_reshape(update_with_lr, op_shape(param_fp32))
 
     next_v = F.depend(next_v, F.assign(param, next_param))
     next_v = F.depend(next_v, F.assign(m, next_m))

mindspore/nn/optim/lamb.py

@@ -67,23 +67,23 @@ def _update_run_op(beta1, beta2, eps, lr, weight_decay_tensor, global_step, para
     op_fill = P.Fill()
     op_dtype = P.DType()
 
-    param = op_cast(param, mstype.float32)
-    m = op_cast(m, mstype.float32)
-    v = op_cast(v, mstype.float32)
-    gradient = op_cast(gradient, mstype.float32)
+    param_fp32 = op_cast(param, mstype.float32)
+    m_fp32 = op_cast(m, mstype.float32)
+    v_fp32 = op_cast(v, mstype.float32)
+    gradient_fp32 = op_cast(gradient, mstype.float32)
 
-    next_m = op_mul(beta1, m) + op_mul(op_cast(num_one, mstype.float32) - beta1, gradient)
+    next_m = op_mul(beta1, m_fp32) + op_mul(op_cast(num_one, mstype.float32) - beta1, gradient_fp32)
 
-    next_v = op_mul(beta2, v) + op_mul(op_cast(num_one, mstype.float32) - beta2, op_square(gradient))
+    next_v = op_mul(beta2, v_fp32) + op_mul(op_cast(num_one, mstype.float32) - beta2, op_square(gradient_fp32))
 
     next_mm = next_m / (op_cast(num_one, mstype.float32)
                         - op_pow(beta1, op_cast(global_step + num_one, mstype.float32)))
     next_vv = next_v / (op_cast(num_one, mstype.float32) -
                         op_pow(beta2, op_cast(global_step + num_one, mstype.float32)))
-    w_norm = op_norm(param)
-    g_norm = op_norm(gradient)
+    w_norm = op_norm(param_fp32)
+    g_norm = op_norm(gradient_fp32)
 
-    g_norm_hat = op_norm(op_mul(next_mm, op_rsqrt(next_vv + eps)) + weight_decay_tensor * param)
+    g_norm_hat = op_norm(op_mul(next_mm, op_rsqrt(next_vv + eps)) + weight_decay_tensor * param_fp32)
     zeros = F.zeros_like_tensor(w_norm)
     ones = op_fill(op_dtype(w_norm), op_shape(w_norm), 1.0)
     trust_ratio = op_select(
@@ -95,11 +95,11 @@ def _update_run_op(beta1, beta2, eps, lr, weight_decay_tensor, global_step, para
     update = next_mm / (op_sqrt(next_vv) + eps)
 
     if decay_flag:
-        update = update + op_mul(weight_decay_tensor, param)
+        update = update + op_mul(weight_decay_tensor, param_fp32)
 
     update_with_lr = op_mul(op_mul(trust_ratio, lr), update)
 
-    next_param = param - op_reshape(update_with_lr, op_shape(param))
+    next_param = param_fp32 - op_reshape(update_with_lr, op_shape(param_fp32))
 
     next_v = F.depend(next_v, F.assign(param, next_param))
     next_v = F.depend(next_v, F.assign(m, next_m))

mindspore/ops/operations/array_ops.py

@@ -24,6 +24,8 @@ import itertools
 import numbers
 import numpy as np
 
+from ..._c_expression import signature_rw as sig_rw
+from ..._c_expression import signature_kind as sig_kind
 from ..._checkparam import Validator as validator
 from ..._checkparam import Rel
 from ...common import dtype as mstype
@@ -1965,6 +1967,11 @@ class ScatterNdUpdate(PrimitiveWithInfer):
         >>> op = P.ScatterNdUpdate()
         >>> output = op(input_x, indices, update)
     """
+    __mindspore_signature__ = (
+        ('input_x', sig_rw.RW_WRITE, sig_kind.KIND_POSITIONAL_KEYWORD),
+        ('indices', sig_rw.RW_READ, sig_kind.KIND_POSITIONAL_KEYWORD),
+        ('value', sig_rw.RW_READ, sig_kind.KIND_POSITIONAL_KEYWORD)
+    )
 
     @prim_attr_register
     def __init__(self, use_locking=True):

tests/st/ops/gpu/test_assign_add_op.py

@@ -14,19 +14,20 @@
 # ============================================================================
 
 import pytest
-from mindspore import Tensor
+from mindspore import Tensor, Parameter
 from mindspore.ops import operations as P
 import mindspore.nn as nn
 import numpy as np
 import mindspore.context as context
 
 class AssignAdd(nn.Cell):
-    def __init__( self):
+    def __init__(self, value):
         super(AssignAdd, self).__init__()
+        self.var = Parameter(value, name="var")
         self.add = P.AssignAdd()
 
-    def construct(self, x, y):
-        res = self.add(x, y)
+    def construct(self, y):
+        res = self.add(self.var, y)
         return res
 
 @pytest.mark.level0
@@ -58,15 +59,17 @@ def test_assign_add():
     y2 = Tensor(np.arange(1 * 3 * 3 * 3).reshape(1, 3, 3, 3).astype(np.float32))
 
     context.set_context(mode=context.PYNATIVE_MODE, device_target='GPU')
-    add = AssignAdd()
-    output1 = add(x1, y1)
+    add = AssignAdd(x1)
+    output1 = add(y1)
     assert (output1.asnumpy() == expect1).all()
-    output2 = add(output1, y1)
+    add = AssignAdd(output1)
+    output2 = add(y1)
     assert (output2.asnumpy() == expect2).all()
 
     context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
-    add = AssignAdd()
-    output1 = add(x2, y2)
+    add = AssignAdd(x2)
+    output1 = add(y2)
     assert (output1.asnumpy() == expect1).all()
-    output2 = add(output1, y2)
+    add = AssignAdd(output1)
+    output2 = add(y2)
     assert (output2.asnumpy() == expect2).all()

tests/st/ops/gpu/test_assign_op.py

@@ -14,7 +14,7 @@
 # ============================================================================
 
 import pytest
-from mindspore import Tensor
+from mindspore import Tensor, Parameter
 from mindspore.ops import operations as P
 import mindspore.nn as nn
 import numpy as np
@@ -22,12 +22,13 @@ import mindspore.context as context
 
 
 class Net(nn.Cell):
-    def __init__(self):
+    def __init__(self, value):
         super(Net, self).__init__()
+        self.var = Parameter(value, name="var")
         self.assign = P.Assign()
 
-    def construct(self, var, value):
-        return self.assign(var, value)
+    def construct(self, value):
+        return self.assign(self.var, value)
 
 x = np.array([[1.2, 1], [1, 0]]).astype(np.float32)
 value = np.array([[1, 2], [3, 4.0]]).astype(np.float32)
@@ -37,13 +38,13 @@ value = np.array([[1, 2], [3, 4.0]]).astype(np.float32)
 @pytest.mark.env_onecard
 def test_assign():
     context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
-    assign = Net()
     var = Tensor(x)
-    output = assign(var, Tensor(value))
+    assign = Net(var)
+    output = assign(Tensor(value))
 
     error = np.ones(shape=[2, 2]) * 1.0e-6
     diff1 = output.asnumpy() - value
-    diff2 = var.asnumpy() - value
+    diff2 = assign.var.default_input.asnumpy() - value
     assert np.all(diff1 < error)
     assert np.all(-diff1 < error)
     assert np.all(diff2 < error)

tests/ut/python/ops/test_math_ops.py

@@ -341,6 +341,15 @@ class SignNet(nn.Cell):
     def construct(self, x):
         return self.sign(x)
 
+class AssignAdd(nn.Cell):
+    def __init__(self):
+        super().__init__()
+        self.op = P.AssignAdd()
+        self.inputdata = Parameter(initializer(1, [1], ms.float32), name="global_step")
+
+    def construct(self, input_):
+        self.inputdata = input_
+        return self.op(self.inputdata, input_)
 
 test_case_math_ops = [
     ('MatMulGrad', {
@@ -413,6 +422,9 @@ raise_set = [
     ('StridedSlice_4_Error', {
         'block': (lambda x: P.StridedSlice(new_axis_mask="1.1"), {'exception': TypeError}),
         'desc_inputs': [0]}),
+    ('AssignAdd_Error', {
+        'block': (P.AssignAdd(), {'exception': TypeError}),
+        'desc_inputs': [[1]]}),
 ]
 
 

tests/ut/python/ops/test_momentum.py

@@ -38,8 +38,7 @@ def tensor_run_opt(opt, iters, learning_rate, momentum,
                    gradient, variable, moment):
     """ tensor_run_opt """
     success = True
-    new_weight = opt(gradient, moment, variable,
-                     learning_rate, momentum)
+    new_weight = opt(variable, moment, learning_rate, gradient, momentum)
     success = F.depend(success, F.assign(variable, new_weight))
     return success
 

tests/ut/python/ops/test_nn_ops.py

@@ -446,12 +446,6 @@ test_cases = [
         'desc_inputs': [[128, 32, 32, 64]],
         'desc_bprop': [[128, 32, 32, 64]],
     }),
-    ('ApplyMomentum', {
-        'block': P.ApplyMomentum(),
-        'desc_inputs': [[2], [128, 32, 32, 64], [128, 32, 32, 64], [128, 32, 32, 64], [128, 32, 32, 64]],
-        'desc_bprop': [[128, 32, 32, 64]],
-        'skip': ['backward']
-    }),
     ('ScalarSummary', {
         'block': ScalarSummaryNet(),
         'desc_inputs': [2.2],
@@ -515,6 +509,12 @@ test_cases = [
 ]
 
 test_cases_for_verify_exception = [
+    ('ApplyMomentum_Error', {
+        'block': (P.ApplyMomentum(), {'exception': TypeError}),
+        'desc_inputs': [[2], [128, 32, 32, 64], [128, 32, 32, 64], [128, 32, 32, 64], [128, 32, 32, 64]],
+        'desc_bprop': [[128, 32, 32, 64]],
+        'skip': ['backward']
+    }),
     ('Conv2d_ValueError_1', {
         'block': (lambda _: P.Conv2D(3, 4, mode=-2.0), {'exception': TypeError}),
         'desc_inputs': [0],

tests/ut/python/ops/test_ops.py

@@ -674,12 +674,6 @@ test_case_nn_ops = [
         'desc_inputs': [[128, 64, 32, 32], [128, 64, 32, 32], [64], [64], [64], [64]],
         'desc_bprop': [[128, 64, 32, 32], [64], [64], [64], [64]],
         'skip': ['backward']}),
-    ('ApplyMomentum', {
-        'block': P.ApplyMomentum(),
-        'desc_inputs': [[128, 32, 32, 64], [128, 32, 32, 64],
-                        [32, 32, 64], [32, 32, 64], [32, 32, 64]],
-        'desc_bprop': [[128, 32, 32, 64]],
-        'skip': ['backward']}),
     ('TopK', {
         'block': P.TopK(),
         'desc_const': [5],
@@ -1113,12 +1107,6 @@ test_case_other_ops = [
         'desc_inputs': (Tensor(np.ones((1, 3, 6, 6), np.float32)),
                         Tensor(np.ones((2, 4), np.int32))),
         'desc_bprop': [[2]]}),
-    ('ScatterNdUpdate', {
-        'block': P.ScatterNdUpdate(),
-        'desc_inputs': (Tensor(np.ones((2, 3), np.float32)),
-                        Tensor(np.ones((2, 2), np.int32)),
-                        Tensor(np.ones((2,), np.float32))),
-        'desc_bprop': [[2, 3]]}),
     ('ScatterNd', {
         'block': P.ScatterNd(),
         'desc_const': [(3, 3)],
@@ -1178,7 +1166,7 @@ import mindspore.context as context
 @non_graph_engine
 @mindspore_test(pipeline_for_compile_forward_ge_graph_for_case_by_case_config)
 def test_exec():
-    context.set_context(mode=context.GRAPH_MODE)
+    context.set_context(mode=context.GRAPH_MODE, save_graphs=True)
     return test_exec_case
 
 
@@ -1207,6 +1195,12 @@ raise_set = [
         'block': (NetForFlatten0D(), {'exception': ValueError}),
         'desc_inputs': [Tensor(np.array(0).astype(np.int32))],
         'desc_bprop': [Tensor(np.array(0).astype(np.int32))]}),
+    ('ScatterNdUpdate', {
+        'block': (P.ScatterNdUpdate(), {'exception': TypeError}),
+        'desc_inputs': (Tensor(np.ones((2, 3), np.float32)),
+                        Tensor(np.ones((2, 2), np.int32)),
+                        Tensor(np.ones((2,), np.float32))),
+        'desc_bprop': [[2, 3]]}),
 ]