[scripts] implement max-change within customized SGD optimizer

Author: aadps

egs/aishell/s10/chain/sgd_mc.py

@@ -0,0 +1,127 @@
+import torch
+from torch.optim.optimizer import Optimizer, required
+
+
+class SGD_MC(Optimizer):
+    r"""Implements stochastic gradient descent (optionally with momentum).
+    Nesterov momentum is based on the formula from
+    `On the importance of initialization and momentum in deep learning`__.
+    Args:
+        params (iterable): iterable of parameters to optimize or dicts defining
+            parameter groups
+        lr (float): learning rate
+        momentum (float, optional): momentum factor (default: 0)
+        weight_decay (float, optional): weight decay (L2 penalty) (default: 0)
+        dampening (float, optional): dampening for momentum (default: 0)
+        nesterov (bool, optional): enables Nesterov momentum (default: False)
+    Example:
+        >>> optimizer = torch.optim.SGD(model.parameters(), lr=0.1, momentum=0.9)
+        >>> optimizer.zero_grad()
+        >>> loss_fn(model(input), target).backward()
+        >>> optimizer.step()
+    __ http://www.cs.toronto.edu/%7Ehinton/absps/momentum.pdf
+    .. note::
+        The implementation of SGD with Momentum/Nesterov subtly differs from
+        Sutskever et. al. and implementations in some other frameworks.
+        Considering the specific case of Momentum, the update can be written as
+        .. math::
+            \begin{aligned}
+                v_{t+1} & = \mu * v_{t} + g_{t+1}, \\
+                p_{t+1} & = p_{t} - \text{lr} * v_{t+1},
+            \end{aligned}
+        where :math:`p`, :math:`g`, :math:`v` and :math:`\mu` denote the 
+        parameters, gradient, velocity, and momentum respectively.
+        This is in contrast to Sutskever et. al. and
+        other frameworks which employ an update of the form
+        .. math::
+            \begin{aligned}
+                v_{t+1} & = \mu * v_{t} + \text{lr} * g_{t+1}, \\
+                p_{t+1} & = p_{t} - v_{t+1}.
+            \end{aligned}
+        The Nesterov version is analogously modified.
+    """
+
+    def __init__(self, params, lr=required, momentum=0, dampening=0,
+                 weight_decay=0, nesterov=False, max_change_per_layer=0.75, max_change=1.5):
+        if lr is not required and lr < 0.0:
+            raise ValueError("Invalid learning rate: {}".format(lr))
+        if momentum < 0.0:
+            raise ValueError("Invalid momentum value: {}".format(momentum))
+        if weight_decay < 0.0:
+            raise ValueError("Invalid weight_decay value: {}".format(weight_decay))
+
+        defaults = dict(lr=lr, momentum=momentum, dampening=dampening,
+                        weight_decay=weight_decay, nesterov=nesterov, 
+                        max_change_per_layer=max_change_per_layer, 
+                        max_change=max_change)
+        if nesterov and (momentum <= 0 or dampening != 0):
+            raise ValueError("Nesterov momentum requires a momentum and zero dampening")
+        super(SGD_MC, self).__init__(params, defaults)
+
+    def __setstate__(self, state):
+        super(SGD_MC, self).__setstate__(state)
+        for group in self.param_groups:
+            group.setdefault('nesterov', False)
+
+    @torch.no_grad()
+    def step(self, closure=None):
+        """Performs a single optimization step.
+        Arguments:
+            closure (callable, optional): A closure that reevaluates the model
+                and returns the loss.
+        """
+        loss = None
+        if closure is not None:
+            with torch.enable_grad():
+                loss = closure()
+        change = 0
+
+        for group in self.param_groups:
+            weight_decay = group['weight_decay']
+            momentum = group['momentum']
+            dampening = group['dampening']
+            nesterov = group['nesterov']
+            max_change_per_layer = group['max_change_per_layer']
+            max_change = group['max_change']
+            
+            delta = []
+            total_norm = 0
+
+            for i in range(len(group['params'])):
+                p = group['params'][i]
+                if p.grad is None:
+                    continue
+                d_p = p.grad
+                if weight_decay != 0:
+                    d_p = d_p.add(p, alpha=weight_decay)
+                if momentum != 0:
+                    param_state = self.state[p]
+                    if 'momentum_buffer' not in param_state:
+                        buf = param_state['momentum_buffer'] = torch.clone(d_p).detach()
+                    else:
+                        buf = param_state['momentum_buffer']
+                        buf.mul_(momentum).add_(d_p, alpha=1 - dampening)
+                    if nesterov:
+                        d_p = d_p.add(buf, alpha=momentum)
+                    else:
+                        d_p = buf
+                norm = d_p.norm(2).item()
+                if norm * group['lr'] > max_change_per_layer:
+                    d_p.mul_(max_change_per_layer / norm)
+                delta.append(d_p)
+                total_norm += d_p.norm(2).item() ** 2.
+
+            total_norm = total_norm ** 0.5
+
+            for i in range(len(group['params'])):
+                p = group['params'][i]
+                if p.grad is None:
+                    continue
+                if total_norm * group['lr'] > max_change:
+                    p.add_(delta[i], alpha=-group['lr'] * max_change / total_norm)
+                else:
+                    p.add_(delta[i], alpha=-group['lr'])
+
+            change += total_norm * group['lr']
+        
+        return loss, change

egs/aishell/s10/chain/train.py

@@ -34,6 +34,7 @@
 from libs.nnet3.train.dropout_schedule import _get_dropout_proportions
 from model import get_chain_model
 from options import get_args
+from sgd_mc import SGD_MC
 
 def get_objf(batch, model, device, criterion, opts, den_graph, training, optimizer=None, dropout=0.):
     feature, supervision = batch
@@ -67,20 +68,20 @@ def get_objf(batch, model, device, criterion, opts, den_graph, training, optimiz
                                 supervision, nnet_output,
                                 xent_output)
     objf = objf_l2_term_weight[0]
+    change = 0
     if training:
         optimizer.zero_grad()
         objf.backward()
-        clip_grad_value_(model.parameters(), 5.0)
-        optimizer.step()
+        # clip_grad_value_(model.parameters(), 5.0)
+        _, change = optimizer.step()
 
     objf_l2_term_weight = objf_l2_term_weight.detach().cpu()
 
     total_objf = objf_l2_term_weight[0].item()
     total_weight = objf_l2_term_weight[2].item()
     total_frames = nnet_output.shape[0]
 
-    return total_objf, total_weight, total_frames
-
+    return total_objf, total_weight, total_frames, change
 
 def get_validation_objf(dataloader, model, device, criterion, opts, den_graph):
     total_objf = 0.
@@ -90,7 +91,7 @@ def get_validation_objf(dataloader, model, device, criterion, opts, den_graph):
     model.eval()
 
     for batch_idx, (pseudo_epoch, batch) in enumerate(dataloader):
-        objf, weight, frames = get_objf(
+        objf, weight, frames, _ = get_objf(
             batch, model, device, criterion, opts, den_graph, False) 
         total_objf += objf
         total_weight += weight
@@ -116,7 +117,7 @@ def train_one_epoch(dataloader, valid_dataloader, model, device, optimizer, crit
                          len(dataloader)) / (len(dataloader) * num_epochs)
         _, dropout = _get_dropout_proportions(
             dropout_schedule, data_fraction)[0]
-        curr_batch_objf, curr_batch_weight, curr_batch_frames = get_objf(
+        curr_batch_objf, curr_batch_weight, curr_batch_frames, curr_batch_change = get_objf(
             batch, model, device, criterion, opts, den_graph, True, optimizer, dropout=dropout)
 
         total_objf += curr_batch_objf
@@ -127,13 +128,13 @@ def train_one_epoch(dataloader, valid_dataloader, model, device, optimizer, crit
             logging.info(
                 'Device ({}) processing batch {}, current pseudo-epoch is {}/{}({:.6f}%), '
                 'global average objf: {:.6f} over {} '
-                'frames, current batch average objf: {:.6f} over {} frames, epoch {}'
+                'frames, current batch average objf: {:.6f} over {} frames, minibatch change: {:.6f}, epoch {}'
                 .format(
                     device.index, batch_idx, pseudo_epoch, len(dataloader),
                     float(pseudo_epoch) / len(dataloader) * 100,
                     total_objf / total_weight, total_frames,
                     curr_batch_objf / curr_batch_weight, 
-                    curr_batch_frames, current_epoch))
+                    curr_batch_frames, curr_batch_change, current_epoch))
 
         if valid_dataloader and batch_idx % 1000 == 0:
             total_valid_objf, total_valid_weight, total_valid_frames = get_validation_objf(
@@ -301,7 +302,7 @@ def process_job(learning_rate, device_id=None, local_rank=None):
     else:
         valid_dataloader = None
 
-    optimizer = optim.Adam(model.parameters(),
+    optimizer = SGD_MC(model.parameters(),
                            lr=learning_rate,
                            weight_decay=5e-4)