optimize clip_norm

mindspore/ccsrc/session/session_basic.cc

@@ -588,7 +588,7 @@ std::shared_ptr<KernelGraph> SessionBasic::ConstructKernelGraph(const FuncGraphP
   graph->set_output_null(is_trace_back);
   AddParameterToGraphInputs(func_graph->parameters(), graph.get());
   MS_EXCEPTION_IF_NULL(context_);
-  FuncGraphManagerPtr manager = context_->manager();
+  FuncGraphManagerPtr manager = MakeManager({graph});
   if (manager) {
     manager->AddFuncGraph(graph);
     graph->set_manager(manager);

mindspore/nn/layer/basic.py

@@ -22,6 +22,7 @@ from mindspore.ops import operations as P
 from mindspore.ops import functional as F
 from mindspore.ops.functional import identity
 from mindspore.ops.operations import _inner_ops as inner
+from mindspore.ops.primitive import constexpr
 from mindspore.common.parameter import Parameter
 from mindspore._extends import cell_attr_register
 from mindspore.common.api import ms_function
@@ -236,6 +237,13 @@ class Dense(Cell):
         return str_info
 
 
+@constexpr
+def _is_equal_one(x):
+    if x is None:
+        return False
+    return bool(x.asnumpy().mean() == 1.0)
+
+
 class ClipByNorm(Cell):
     r"""
     Clips tensor values to a maximum :math:`L_2`-norm.
@@ -290,7 +298,10 @@ class ClipByNorm(Cell):
         l2sum_safe = self.select_(cond, l2sum, self.cast(ones_, self.dtype(l2sum)))
         l2norm = self.select_(cond, self.sqrt(l2sum_safe), l2sum)
 
-        intermediate = x * clip_norm
+        if _is_equal_one(clip_norm):
+            intermediate = x
+        else:
+            intermediate = x * clip_norm
         max_norm = self.max_op(l2norm, clip_norm)
         values_clip = self.cast(intermediate, mstype.float32) / self.expand_dims(max_norm, -1)
         values_clip = self.reshape(values_clip, self.shape(x))

model_zoo/bert/src/bert_for_pre_training.py

@@ -32,7 +32,6 @@ from .bert_model import BertModel
 GRADIENT_CLIP_TYPE = 1
 GRADIENT_CLIP_VALUE = 1.0
 
-_nn_clip_by_norm = nn.ClipByNorm()
 clip_grad = C.MultitypeFuncGraph("clip_grad")
 
 
@@ -57,7 +56,7 @@ def _clip_grad(clip_type, clip_value, grad):
         new_grad = C.clip_by_value(grad, F.cast(F.tuple_to_array((-clip_value,)), dt),
                                    F.cast(F.tuple_to_array((clip_value,)), dt))
     else:
-        new_grad = _nn_clip_by_norm(grad, F.cast(F.tuple_to_array((clip_value,)), dt))
+        new_grad = nn.ClipByNorm()(grad, F.cast(F.tuple_to_array((clip_value,)), dt))
     return new_grad
 
 

model_zoo/bert/src/config.py

@@ -56,7 +56,7 @@ if cfg.bert_network == 'base':
     bert_net_cfg = BertConfig(
         batch_size=32,
         seq_length=128,
-        vocab_size=21136,
+        vocab_size=21128,
         hidden_size=768,
         num_hidden_layers=12,
         num_attention_heads=12,
@@ -77,7 +77,7 @@ if cfg.bert_network == 'nezha':
     bert_net_cfg = BertConfig(
         batch_size=32,
         seq_length=128,
-        vocab_size=21136,
+        vocab_size=21128,
         hidden_size=1024,
         num_hidden_layers=24,
         num_attention_heads=16,
@@ -98,7 +98,7 @@ if cfg.bert_network == 'large':
     bert_net_cfg = BertConfig(
         batch_size=16,
         seq_length=512,
-        vocab_size=30528,
+        vocab_size=30522,
         hidden_size=1024,
         num_hidden_layers=24,
         num_attention_heads=16,

tests/ut/python/nn/test_clip_by_norm.py

@@ -26,3 +26,19 @@ def test_clip_by_norm():
     x = Tensor(np.array([[-2, 0, 0], [0, 3, 4]]).astype(np.float32))
     clip_norm = Tensor(np.array([1]).astype(np.float32))
     clip_by_norm(x, clip_norm)
+
+
+@non_graph_engine
+def test_clip_by_norm_const():
+    class Network(nn.Cell):
+        def __init__(self):
+            super(Network, self).__init__()
+            self.norm_value = Tensor(np.array([1]).astype(np.float32))
+            self.clip = nn.ClipByNorm()
+        
+        def construct(self, x):
+            return self.clip(x, self.norm_value)
+
+    net = Network()
+    x = Tensor(np.array([[-2, 0, 0], [0, 3, 4]]).astype(np.float32))
+    output = net(x)