add LARS support (#10374)

python/paddle/fluid/layers/learning_rate_scheduler.py

@@ -25,10 +25,11 @@ import nn
 import ops
 import tensor
 from ..initializer import init_on_cpu
+from ..framework import default_main_program, Parameter
 
 __all__ = [
     'exponential_decay', 'natural_exp_decay', 'inverse_time_decay',
-    'polynomial_decay', 'piecewise_decay', 'noam_decay'
+    'polynomial_decay', 'piecewise_decay', 'noam_decay', 'append_LARS'
 ]
 
 
@@ -261,3 +262,41 @@ def piecewise_decay(boundaries, values):
                 tensor.assign(last_value_var, lr)
 
     return lr
+
+
+def append_LARS(params_grads, learning_rate, weight_decay):
+    """Applies LARS (LAYER-WISE ADAPTIVE RATE SCALING) to learning rate for
+       each layer.
+
+    ```python
+        learning_rate *= local_gw_ratio * sqrt(sumsq(param))
+                        / (sqrt(sumsq(gradient))+ weight_decay * sqrt(sumsq(param)))
+    ```
+
+    Args:
+        learning_rate: A learning rate Variable. This
+          is the global learning rate for LARS.
+        weight_decay: A Python `float` number.
+
+    Returns:
+        The decayed learning rate
+    """
+
+    def _balanced_weight(param_norm, grad_norm):
+        if weight_decay == 1.0:
+            return grad_norm + param_norm
+        else:
+            return grad_norm + weight_decay * param_norm
+
+    for param, grad in params_grads:
+        param_lr = param.optimize_attr['learning_rate']
+        param_norm = ops.sqrt(nn.reduce_sum(input=ops.square(param)))
+        grad_norm = ops.sqrt(nn.reduce_sum(input=ops.square(grad)))
+        if type(param_lr) == float and param_lr == 1.0:
+            decayed_lr = learning_rate * param_norm \
+                         / _balanced_weight(param_norm, grad_norm)
+        else:
+            decayed_lr = learning_rate * param_lr * param_norm \
+                         / _balanced_weight(param_norm, grad_norm)
+        # set back param local learning rate
+        param.optimize_attr['learning_rate'] = decayed_lr

python/paddle/fluid/optimizer.py

@@ -13,7 +13,7 @@
 # limitations under the License.
 import re
 from collections import defaultdict
-from paddle.fluid.framework import Program
+from paddle.fluid.framework import Program, Variable
 import framework
 import layers
 from backward import append_backward
@@ -41,7 +41,10 @@ class Optimizer(object):
     but need to use one of it's implementation.
     """
 
-    def __init__(self, learning_rate, regularization=None):
+    def __init__(self,
+                 learning_rate,
+                 regularization=None,
+                 LARS_weight_decay=0.0):
         if not isinstance(learning_rate, float) and \
                 not isinstance(learning_rate, framework.Variable):
             raise TypeError("learning rate should be float or Variable")
@@ -61,6 +64,7 @@ class Optimizer(object):
         # {accum_name : { paramter_name : accumulator_for_parameter, ...}, ...}
         self._accumulators = defaultdict(lambda: dict())
         self.helper = None
+        self._LARS_weight_decay = LARS_weight_decay
 
     def _create_global_learning_rate(self):
         lr = self.global_learning_rate()
@@ -100,10 +104,15 @@ class Optimizer(object):
         # create learning rate variable for every parameter
         param = param_and_grad[0]
         param_lr = param.optimize_attr['learning_rate']
-        if param_lr == 1.0:
-            return self.global_learning_rate()
+        if type(param_lr) == Variable:
+            # param learning rate has been updated (LARS)
+            print("returns updated param lr ", param_lr)
+            return param_lr
         else:
-            return self.global_learning_rate() * param_lr
+            if param_lr == 1.0:
+                return self.global_learning_rate()
+            else:
+                return self.global_learning_rate() * param_lr
 
     def _create_accumulators(self, block, parameters):
         """Create all accumulators needed by the parameters
@@ -210,6 +219,10 @@ class Optimizer(object):
             self._create_accumulators(loss.block,
                                       [p[0] for p in parameters_and_grads])
             self._create_global_learning_rate()
+            if self._LARS_weight_decay > 0.0:
+                layers.append_LARS(parameters_and_grads,
+                                   self.global_learning_rate(),
+                                   self._LARS_weight_decay)
 
             optimize_ops = []
             for param_and_grad in parameters_and_grads:

python/paddle/fluid/tests/book/test_recognize_digits.py

@@ -94,7 +94,7 @@ def train(nn_type,
 
     test_program = fluid.default_main_program().clone(for_test=True)
 
-    optimizer = fluid.optimizer.Adam(learning_rate=0.001)
+    optimizer = fluid.optimizer.Adam(learning_rate=0.001, LARS_weight_decay=0.3)
     optimizer.minimize(avg_loss)
 
     place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()