Fix dgc clip & rampup step, test=release/1.6 (#21519)

paddle/fluid/operators/dgc_op.h

@@ -28,7 +28,7 @@ inline float get_period_sparcity(const std::vector<float>& sparsity,
 
   size_t idx = static_cast<int>(cur_step * sparsity.size() / rampup_steps);
   if (idx >= sparsity.size()) {
-    return 0.999;
+    idx = sparsity.size() - 1;
   }
 
   PADDLE_ENFORCE_LT(idx, sparsity.size());
@@ -102,8 +102,9 @@ class DGCOpKernel : public framework::OpKernel<T> {
     }
 
     float ratio =
-        1 - get_period_sparcity(sparsity, static_cast<float>(*current_step),
-                                rampup_step);
+        1 - get_period_sparcity(
+                sparsity, static_cast<float>(*current_step - rampup_begin_step),
+                rampup_step);
     PADDLE_ENFORCE_GE(ratio, 0.0);
     PADDLE_ENFORCE_LT(ratio, 1.0);
     int k = static_cast<int>(g->numel() * ratio);

python/paddle/fluid/optimizer.py

@@ -947,6 +947,7 @@ class DGCMomentumOptimizer(Optimizer):
         self._momentum = momentum
         self._use_nesterov = bool(use_nesterov)
 
+        assert rampup_begin_step >= 0, "rampup_begin_step must >= 0"
         self._rampup_begin_step = rampup_begin_step
         self._rampup_step = rampup_step
         self._sparsity = sparsity
@@ -963,8 +964,7 @@ class DGCMomentumOptimizer(Optimizer):
 
             self._local_grad_clip_norm = local_grad_clip_norm
             self._num_trainers = num_trainers
-            self._clip_norm = local_grad_clip_norm / (num_trainers *
-                                                      num_trainers)
+            self._clip_norm = local_grad_clip_norm * (num_trainers**-0.5)
 
         self._get_dgc_regularization_param()
 

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

@@ -67,6 +67,8 @@ class TestDGCMomentumOptimizer(unittest.TestCase):
             learning_rate=learning_rate,
             momentum=0.2,
             rampup_begin_step=0,
+            local_grad_clip_norm=1.0,
+            num_trainers=2,
             regularization=regularization)
         mean_out = block.create_var(
             dtype="float32", shape=[1], lod_level=0, name="mean.out")