From 4fee3e7f1ea6c2aefe7594e325ede1e530e54d3d Mon Sep 17 00:00:00 2001 From: Guo Liyong Date: Mon, 28 Nov 2022 16:55:18 +0800 Subject: [PATCH] impove comment --- .../ASR/pruned_transducer_stateless7/optim.py | 63 +++++++++++++------ .../ASR/pruned_transducer_stateless7/train.py | 12 +++- 2 files changed, 54 insertions(+), 21 deletions(-) diff --git a/egs/librispeech/ASR/pruned_transducer_stateless7/optim.py b/egs/librispeech/ASR/pruned_transducer_stateless7/optim.py index 790752fe1..ff8fbb32c 100644 --- a/egs/librispeech/ASR/pruned_transducer_stateless7/optim.py +++ b/egs/librispeech/ASR/pruned_transducer_stateless7/optim.py @@ -64,13 +64,15 @@ class BatchedOptimizer(Optimizer): you can do: with self.batched_params(group["params"]) as batches: - for p, state in batches: + for p, state, p_names in batches: ... Args: group: a parameter group, which is a list of parameters; should be - one of self.groups. + one of self.param_groups. + group_params_names: name for each parameter in group, + which is List[str]. """ batches = defaultdict( list @@ -79,6 +81,7 @@ class BatchedOptimizer(Optimizer): list ) # `batches` maps from tuple (dtype_as_str,*shape) to list of str + assert len(param_group) == len(group_params_names) for p, named_p in zip(param_group, group_params_names): key = (str(p.dtype), *p.shape) batches[key].append(p) @@ -94,9 +97,9 @@ class BatchedOptimizer(Optimizer): stacked_params_dict = dict() # turn batches into a list, in deterministic order. - # pairs will contain pairs of (stacked_param, state), one for each batch - # in `batches`. - pairs = [] + # tuples will contain tuples of (stacked_param, state, stacked_params_names), + # one for each batch in `batches`. + tuples = [] for batch, batch_names in zip(batches, batches_names): p = batch[0] @@ -110,11 +113,11 @@ class BatchedOptimizer(Optimizer): ) p_stacked.grad = grad stacked_params_dict[key] = p_stacked - pairs.append((p_stacked, state, batch_names)) + tuples.append((p_stacked, state, batch_names)) - yield pairs # <-- calling code will do the actual optimization here! + yield tuples # <-- calling code will do the actual optimization here! - for ((stacked_params, _state, _names), batch) in zip(pairs, batches): + for ((stacked_params, _state, _names), batch) in zip(tuples, batches): for i, p in enumerate(batch): # batch is list of Parameter p.copy_(stacked_params[i]) @@ -179,6 +182,11 @@ class ScaledAdam(BatchedOptimizer): show_dominant_parameters=True, ): + assert parameters_names is not None, ( + "Please prepare parameters_names," + "which is a List[List[str]]. Each List[str] is for a group" + "and each str is for a parameter" + ) defaults = dict( lr=lr, clipping_scale=clipping_scale, @@ -193,6 +201,7 @@ class ScaledAdam(BatchedOptimizer): ) super(ScaledAdam, self).__init__(params, defaults) + assert len(self.param_groups) == len(parameters_names) self.parameters_names = parameters_names self.show_dominant_parameters = show_dominant_parameters @@ -213,7 +222,6 @@ class ScaledAdam(BatchedOptimizer): loss = closure() batch = True - assert len(self.param_groups) == len(self.parameters_names) for group, group_params_names in zip(self.param_groups, self.parameters_names): @@ -292,7 +300,7 @@ class ScaledAdam(BatchedOptimizer): state["exp_avg_sq"] = torch.zeros_like(p, memory_format=torch.preserve_format) def _get_clipping_scale( - self, group: dict, pairs: List[Tuple[Tensor, dict, List[str]]] + self, group: dict, tuples: List[Tuple[Tensor, dict, List[str]]] ) -> float: """ Returns a scalar factor <= 1.0 that dictates gradient clipping, i.e. we will scale the gradients @@ -300,12 +308,16 @@ class ScaledAdam(BatchedOptimizer): Args: group: the parameter group, an item in self.param_groups - pairs: a list of pairs of (param, state) where param is a batched set of parameters, with a .grad - (1st dim is batch dim) and state is the state-dict where optimization parameters are kept. + tuples: a list of tuples of (param, state, param_names) + where param is a batched set of parameters, + with a .grad (1st dim is batch dim) + and state is the state-dict where optimization parameters are kept. + param_names is a List[str] while each str is name for a parameter + in batched set of parameters "param". """ - assert len(pairs) >= 1 + assert len(tuples) >= 1 clipping_scale = group["clipping_scale"] - (first_p, first_state, _) = pairs[0] + (first_p, first_state, _) = tuples[0] step = first_state["step"] if clipping_scale is None or step == 0: # no clipping. return early on step == 0 because the other @@ -314,7 +326,7 @@ class ScaledAdam(BatchedOptimizer): clipping_update_period = group["clipping_update_period"] tot_sumsq = torch.tensor(0.0, device=first_p.device) - for (p, state, param_names) in pairs: + for (p, state, param_names) in tuples: grad = p.grad if grad.is_sparse: raise RuntimeError( @@ -379,12 +391,27 @@ class ScaledAdam(BatchedOptimizer): ) if self.show_dominant_parameters: assert p.shape[0] == len(param_names) - self._show_gradient_dominating_parameter(pairs, tot_sumsq) + self._show_gradient_dominating_parameter(tuples, tot_sumsq) return ans - def _show_gradient_dominating_parameter(self, pairs, tot_sumsq): + def _show_gradient_dominating_parameter( + self, tuples: List[Tuple[Tensor, dict, List[str]]], tot_sumsq: Tensor + ): + """ + Show information of parameter wihch dominanting tot_sumsq. + + Args: + tuples: a list of tuples of (param, state, param_names) + where param is a batched set of parameters, + with a .grad (1st dim is batch dim) + and state is the state-dict where optimization parameters are kept. + param_names is a List[str] while each str is name for a parameter + in batched set of parameters "param". + tot_sumsq: sumsq of all parameters. Though it's could be calculated + from tuples, we still pass it to save some time. + """ all_sumsq_orig = {} - for (p, state, batch_param_names) in pairs: + for (p, state, batch_param_names) in tuples: # p is a stacked batch parameters. batch_grad = p.grad if p.numel() == p.shape[0]: # a batch of scalars diff --git a/egs/librispeech/ASR/pruned_transducer_stateless7/train.py b/egs/librispeech/ASR/pruned_transducer_stateless7/train.py index e5a3e68df..31a3a0505 100755 --- a/egs/librispeech/ASR/pruned_transducer_stateless7/train.py +++ b/egs/librispeech/ASR/pruned_transducer_stateless7/train.py @@ -989,9 +989,15 @@ def run(rank, world_size, args): model = DDP(model, device_ids=[rank], find_unused_parameters=True) parameters_names = [] - parameters_names.append([name_param_pair[0] for name_param_pair in model.named_parameters()]) - optimizer = ScaledAdam(model.parameters(), lr=params.base_lr, - clipping_scale=2.0, parameters_names=parameters_names) + parameters_names.append( + [name_param_pair[0] for name_param_pair in model.named_parameters()] + ) + optimizer = ScaledAdam( + model.parameters(), + lr=params.base_lr, + clipping_scale=2.0, + parameters_names=parameters_names, + ) scheduler = Eden(optimizer, params.lr_batches, params.lr_epochs)