Lint whole repo

megatron/deprecated_data_utils/tokenization_gpt2.py

@@ -27,7 +27,8 @@ try:
     from functools import lru_cache
 except ImportError:
     # Just a dummy decorator to get the checks to run on python2
-    # because honestly I don't want to support a byte-level unicode BPE tokenizer on python 2 right now.
+    # because honestly I don't want to support a byte-level unicode BPE
+    # tokenizer on python 2 right now.
     def lru_cache():
         return lambda func: func
 
@@ -48,6 +49,7 @@ VOCAB_NAME = 'vocab.json'
 MERGES_NAME = 'merges.txt'
 SPECIAL_TOKENS_NAME = 'special_tokens.txt'
 
+
 @lru_cache()
 def bytes_to_unicode():
     """
@@ -60,17 +62,19 @@ def bytes_to_unicode():
     And avoids mapping to whitespace/control characters the bpe code barfs on.
     """
     _chr = unichr if sys.version_info[0] == 2 else chr
-    bs = list(range(ord("!"), ord("~")+1))+list(range(ord("¡"), ord("¬")+1))+list(range(ord("®"), ord("ÿ")+1))
+    bs = list(range(ord("!"), ord("~") + 1)) + list(range(ord("¡"), ord("¬") + 1)) + \
+        list(range(ord("®"), ord("ÿ") + 1))
     cs = bs[:]
     n = 0
     for b in range(2**8):
         if b not in bs:
             bs.append(b)
-            cs.append(2**8+n)
+            cs.append(2**8 + n)
             n += 1
     cs = [_chr(n) for n in cs]
     return dict(zip(bs, cs))
 
+
 def get_pairs(word):
     """Return set of symbol pairs in a word.
 
@@ -83,6 +87,7 @@ def get_pairs(word):
         prev_char = char
     return pairs
 
+
 class GPT2Tokenizer(object):
     """
     GPT-2 BPE tokenizer. Peculiarities:
@@ -138,23 +143,31 @@ class GPT2Tokenizer(object):
             special_tokens = open(special_tokens_file, encoding='utf-8').read().split('\n')[:-1]
         else:
             special_tokens = kwargs.pop('special_tokens', [])
-        tokenizer = cls(resolved_vocab_file, resolved_merges_file, special_tokens=special_tokens, *inputs, **kwargs)
+        tokenizer = cls(
+            resolved_vocab_file,
+            resolved_merges_file,
+            special_tokens=special_tokens,
+            *inputs,
+            **kwargs)
         return tokenizer
 
-    def __init__(self, vocab_file, merges_file, errors='replace', special_tokens=None, max_len=None):
+    def __init__(self, vocab_file, merges_file, errors='replace',
+                 special_tokens=None, max_len=None):
         self.max_len = max_len if max_len is not None else int(1e12)
         self.encoder = json.load(open(vocab_file))
-        self.decoder = {v:k for k,v in self.encoder.items()}
-        self.errors = errors # how to handle errors in decoding
+        self.decoder = {v: k for k, v in self.encoder.items()}
+        self.errors = errors  # how to handle errors in decoding
         self.byte_encoder = bytes_to_unicode()
-        self.byte_decoder = {v:k for k, v in self.byte_encoder.items()}
+        self.byte_decoder = {v: k for k, v in self.byte_encoder.items()}
         bpe_data = open(merges_file, encoding='utf-8').read().split('\n')[1:-1]
         bpe_merges = [tuple(merge.split()) for merge in bpe_data]
         self.bpe_ranks = dict(zip(bpe_merges, range(len(bpe_merges))))
         self.cache = {}
 
-        # Should haved added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
-        self.pat = re.compile(r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+""")
+        # Should haved added re.IGNORECASE so BPE merges can happen for
+        # capitalized versions of contractions
+        self.pat = re.compile(
+            r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+""")
 
         self.special_tokens = {}
         self.special_tokens_decoder = {}
@@ -172,8 +185,9 @@ class GPT2Tokenizer(object):
             self.special_tokens = {}
             self.special_tokens_decoder = {}
             return
-        self.special_tokens = dict((tok, len(self.encoder) + i) for i, tok in enumerate(special_tokens))
-        self.special_tokens_decoder = {v:k for k, v in self.special_tokens.items()}
+        self.special_tokens = dict((tok, len(self.encoder) + i)
+                                   for i, tok in enumerate(special_tokens))
+        self.special_tokens_decoder = {v: k for k, v in self.special_tokens.items()}
         logger.info("Special tokens {}".format(self.special_tokens))
 
     def bpe(self, token):
@@ -186,7 +200,7 @@ class GPT2Tokenizer(object):
             return token
 
         while True:
-            bigram = min(pairs, key = lambda pair: self.bpe_ranks.get(pair, float('inf')))
+            bigram = min(pairs, key=lambda pair: self.bpe_ranks.get(pair, float('inf')))
             if bigram not in self.bpe_ranks:
                 break
             first, second = bigram
@@ -197,12 +211,12 @@ class GPT2Tokenizer(object):
                     j = word.index(first, i)
                     new_word.extend(word[i:j])
                     i = j
-                except:
+                except BaseException:
                     new_word.extend(word[i:])
                     break
 
-                if word[i] == first and i < len(word)-1 and word[i+1] == second:
-                    new_word.append(first+second)
+                if word[i] == first and i < len(word) - 1 and word[i + 1] == second:
+                    new_word.append(first + second)
                     i += 2
                 else:
                     new_word.append(word[i])
@@ -245,7 +259,8 @@ class GPT2Tokenizer(object):
             logger.warning(
                 "Token indices sequence length is longer than the specified maximum "
                 " sequence length for this OpenAI GPT model ({} > {}). Running this"
-                " sequence through the model will result in indexing errors".format(len(ids), self.max_len)
+                " sequence through the model will result in indexing errors".format(
+                    len(ids), self.max_len)
             )
         return ids
 

megatron/deprecated_data_utils/wordpiece.py

@@ -99,19 +99,19 @@ class BertTokenizer(object):
             [(ids, tok) for tok, ids in self.vocab.items()])
         self.do_basic_tokenize = do_basic_tokenize
         if do_basic_tokenize:
-          self.basic_tokenizer = BasicTokenizer(do_lower_case=do_lower_case,
-                                                never_split=never_split)
+            self.basic_tokenizer = BasicTokenizer(do_lower_case=do_lower_case,
+                                                  never_split=never_split)
         self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab)
         self.max_len = max_len if max_len is not None else int(1e12)
 
     def tokenize(self, text):
         if self.do_basic_tokenize:
-          split_tokens = []
-          for token in self.basic_tokenizer.tokenize(text):
-              for sub_token in self.wordpiece_tokenizer.tokenize(token):
-                  split_tokens.append(sub_token)
+            split_tokens = []
+            for token in self.basic_tokenizer.tokenize(text):
+                for sub_token in self.wordpiece_tokenizer.tokenize(token):
+                    split_tokens.append(sub_token)
         else:
-          split_tokens = self.wordpiece_tokenizer.tokenize(text)
+            split_tokens = self.wordpiece_tokenizer.tokenize(text)
         return split_tokens
 
     def convert_tokens_to_ids(self, tokens):
@@ -123,7 +123,8 @@ class BertTokenizer(object):
             logger.warning(
                 "Token indices sequence length is longer than the specified maximum "
                 " sequence length for this BERT model ({} > {}). Running this"
-                " sequence through BERT will result in indexing errors".format(len(ids), self.max_len)
+                " sequence through BERT will result in indexing errors".format(
+                    len(ids), self.max_len)
             )
         return ids
 

megatron/fp16/fp16.py

@@ -28,15 +28,17 @@ from megatron.module import MegatronModule
 FLOAT_TYPES = (torch.FloatTensor, torch.cuda.FloatTensor)
 HALF_TYPES = (torch.HalfTensor, torch.cuda.HalfTensor)
 
+
 def conversion_helper(val, conversion):
     """Apply conversion to val. Recursively apply conversion if `val` is a nested tuple/list structure."""
     if not isinstance(val, (tuple, list)):
         return conversion(val)
-    rtn =  [conversion_helper(v, conversion) for v in val]
+    rtn = [conversion_helper(v, conversion) for v in val]
     if isinstance(val, tuple):
         rtn = tuple(rtn)
     return rtn
 
+
 def fp32_to_fp16(val):
     """Convert fp32 `val` to fp16"""
     def half_conversion(val):
@@ -48,6 +50,7 @@ def fp32_to_fp16(val):
         return val
     return conversion_helper(val, half_conversion)
 
+
 def fp16_to_fp32(val):
     """Convert fp16 `val` to fp32"""
     def float_conversion(val):
@@ -59,6 +62,7 @@ def fp16_to_fp32(val):
         return val
     return conversion_helper(val, float_conversion)
 
+
 class FP16_Module(MegatronModule):
     def __init__(self, module):
         super(FP16_Module, self).__init__()
@@ -79,9 +83,11 @@ class FP16_Module(MegatronModule):
         self.module.load_state_dict(state_dict, strict=strict)
 
 # TODO:  Update overflow check + downscale to use Carl's fused kernel.
+
+
 class FP16_Optimizer(object):
     """
-    :class:`FP16_Optimizer` is designed to wrap an existing PyTorch optimizer, 
+    :class:`FP16_Optimizer` is designed to wrap an existing PyTorch optimizer,
     and manage static or dynamic loss scaling and master weights in a manner transparent to the user.
     For standard use, only two lines must be changed:  creating the :class:`FP16_Optimizer` instance,
     and changing the call to ``backward``.
@@ -104,45 +110,45 @@ class FP16_Optimizer(object):
         optimizer = FP16_Optimizer(optimizer, dynamic_loss_scale=True)
                                    # optional arg to control dynamic loss scaling behavior
                                    # dynamic_loss_args={'scale_window' : 500})
-                                   # Usually, dynamic_loss_args is not necessary. 
+                                   # Usually, dynamic_loss_args is not necessary.
 
     Args:
-        init_optimizer (torch.optim.optimizer):  Existing optimizer created with the parameters to optimize.  Internally, :class:`FP16_Optimizer` replaces the passed optimizer's fp16 parameters, if any, with fp32 master parameters copied from the original ones.  :class:`FP16_Optimizer` also stores references to the original fp16 parameters, and updates these fp16 parameters from the master fp32 copy at the end of each :attr:`step`.  
+        init_optimizer (torch.optim.optimizer):  Existing optimizer created with the parameters to optimize.  Internally, :class:`FP16_Optimizer` replaces the passed optimizer's fp16 parameters, if any, with fp32 master parameters copied from the original ones.  :class:`FP16_Optimizer` also stores references to the original fp16 parameters, and updates these fp16 parameters from the master fp32 copy at the end of each :attr:`step`.
         static_loss_scale (float, optional, default=1.0):  Loss scale used internally to scale gradients computed by the model.  Any fp16 gradients will be copied to fp32, then downscaled before being applied to the fp32 master params, so ``static_loss_scale`` should not affect learning rate.
         dynamic_loss_scale (bool, optional, default=False):  Use dynamic loss scaling.  If True, this will override any ``static_loss_scale`` option.
         dynamic_loss_args (dict, optional, default=None):  Dict of kwargs that will be forwarded to the internal :class:`DynamicLossScaler` instance's constructor.  Keys of this dict must match kwargs accepted by :class:`DynamicLossScaler`'s constructor.  If ``dynamic_loss_args`` is unspecified, :class:`DynamicLossScaler`'s defaults will be used.
         verbose (bool, optional, default=True):  By default, FP16_Optimizer's constructor prints out the parameters and parameter groups it is ingesting, as a sanity check.  If this becomes annoying (e.g. for large models), it can be disabled by passing ``verbose=False``.  ``verbose=False`` will not disable printing when the loss scale is readjusted during dynamic loss scaling.
 
-    ``init_optimizer`` is expected to have been constructed in the ordinary way.  
-    It is recommended (although not required) that the newly constructed :class:`FP16_Optimizer` instance be 
-    named to replace ``init_optimizer``, for two reasons:  
+    ``init_optimizer`` is expected to have been constructed in the ordinary way.
+    It is recommended (although not required) that the newly constructed :class:`FP16_Optimizer` instance be
+    named to replace ``init_optimizer``, for two reasons:
     First, it means that references to the same name
-    later in the file will not have to change.  
-    Second, :class:`FP16_Optimizer` reserves the right (as an implementation detail) to 
+    later in the file will not have to change.
+    Second, :class:`FP16_Optimizer` reserves the right (as an implementation detail) to
     modify ``init_optimizer``.  If you do choose a unique name for the new
     :class:`FP16_Optimizer` instance, you should only work with this new instance,
     because the preexisting optimizer might no longer behave as expected.
 
-    ``init_optimizer`` may be any Pytorch optimizer. 
-    It may contain a mixture of fp16 and fp32 parameters organized into any number of 
-    ``param_groups`` with different hyperparameters.  The :class:`FP16_Optimizer` constructor will 
-    ingest these ``param_groups`` and remember them. 
+    ``init_optimizer`` may be any Pytorch optimizer.
+    It may contain a mixture of fp16 and fp32 parameters organized into any number of
+    ``param_groups`` with different hyperparameters.  The :class:`FP16_Optimizer` constructor will
+    ingest these ``param_groups`` and remember them.
 
     Calls to ::
 
-        loss.backward() 
+        loss.backward()
 
     must be replaced with ::
 
-        optimizer.backward(loss)  
+        optimizer.backward(loss)
 
-    because :class:`FP16_Optimizer` requires ownership of the backward pass to implement 
+    because :class:`FP16_Optimizer` requires ownership of the backward pass to implement
     loss scaling and copies to master gradients.
 
     .. note::
         Loss scaling, either static or dynamic, is orthogonal to learning rate, because gradients
         are downscaled before being applied.  This means that adjusting the loss scale, or using
-        dynamic loss scaling, should not require retuning the learning rate or any other 
+        dynamic loss scaling, should not require retuning the learning rate or any other
         hyperparameters.
 
 
@@ -152,7 +158,7 @@ class FP16_Optimizer(object):
     See docstring for :attr:`step`.
 
     **Gradient clipping**:  Use :attr:`clip_master_grads`.
-    
+
     **Multiple losses**:  If your model accumulates gradients from multiple losses,
     this can be made more efficient by supplying ``update_master_grads=False``
     to :attr:`backward`.  See docstring for :attr:`backward`.
@@ -163,19 +169,19 @@ class FP16_Optimizer(object):
         optimizer.loss_scale = new_loss_scale
 
     For static loss scaling, manually adjusting the loss scale over time is a reasonable
-    thing to do.  During later epochs, gradients may become smaller, and a 
+    thing to do.  During later epochs, gradients may become smaller, and a
     higher loss scale may be required, analogous to scheduling the learning rate.  Dynamic loss
-    scaling is more subtle (see :class:`DynamicLossScaler`) and in this case, manually adjusting 
+    scaling is more subtle (see :class:`DynamicLossScaler`) and in this case, manually adjusting
     the loss scale is not recommended.
 
     **Multi_GPU training**:  If the wrapped ``init_optimizer`` was created from a model wrapped in
-    Pytorch DistributedDataParallel or Apex DistributedDataParallel, :class:`FP16_Optimizer` 
+    Pytorch DistributedDataParallel or Apex DistributedDataParallel, :class:`FP16_Optimizer`
     should still work as intended.
     """
 
-    def __init__(self, 
-                 init_optimizer, 
-                 static_loss_scale=1.0, 
+    def __init__(self,
+                 init_optimizer,
+                 static_loss_scale=1.0,
                  dynamic_loss_scale=False,
                  dynamic_loss_args=None,
                  verbose=False):
@@ -212,7 +218,7 @@ class FP16_Optimizer(object):
                         # Reset existing state dict key to the new master param.
                         # We still need to recast per-param state tensors, if any, to FP32.
                         if param in self.optimizer.state:
-                           self.optimizer.state[master_param] = self.optimizer.state.pop(param) 
+                            self.optimizer.state[master_param] = self.optimizer.state.pop(param)
                     elif param.type() == 'torch.cuda.FloatTensor':
                         self.maybe_print("FP16_Optimizer received torch.cuda.FloatTensor with {}"
                                          .format(param.size()))
@@ -220,9 +226,9 @@ class FP16_Optimizer(object):
                         param_group['params'][i] = param
                     else:
                         raise TypeError("Wrapped parameters must be either "
-                                        "torch.cuda.FloatTensor or torch.cuda.HalfTensor. "  
+                                        "torch.cuda.FloatTensor or torch.cuda.HalfTensor. "
                                         "Received {}".format(param.type()))
-            
+
             self.fp16_groups.append(fp16_params_this_group)
             self.fp32_from_fp16_groups.append(fp32_from_fp16_params_this_group)
             self.fp32_from_fp32_groups.append(fp32_params_this_group)
@@ -250,7 +256,7 @@ class FP16_Optimizer(object):
     def maybe_print(self, msg):
         if self.verbose:
             print(msg)
-            
+
     def __getstate__(self):
         raise RuntimeError("FP16_Optimizer should be serialized using state_dict().")
 
@@ -265,13 +271,13 @@ class FP16_Optimizer(object):
         # because gradients are copied into the FP32 master params.  However, we zero
         # all gradients owned by the optimizer, just to be safe:
         for group in self.optimizer.param_groups:
-             for p in group['params']:
-                 if set_grads_to_None:
-                     p.grad = None
-                 else:
-                     if p.grad is not None:
-                         p.grad.detach_()
-                         p.grad.zero_()
+            for p in group['params']:
+                if set_grads_to_None:
+                    p.grad = None
+                else:
+                    if p.grad is not None:
+                        p.grad.detach_()
+                        p.grad.zero_()
 
         # Zero fp16 gradients owned by the model:
         for fp16_group in self.fp16_groups:
@@ -280,11 +286,11 @@ class FP16_Optimizer(object):
                     param.grad = None
                 else:
                     if param.grad is not None:
-                        param.grad.detach_() # as in torch.optim.optimizer.zero_grad()
+                        param.grad.detach_()  # as in torch.optim.optimizer.zero_grad()
                         param.grad.zero_()
 
     def _check_overflow(self):
-        params = [] 
+        params = []
         for group in self.fp16_groups:
             for param in group:
                 params.append(param)
@@ -304,8 +310,9 @@ class FP16_Optimizer(object):
         for fp16_group, fp32_from_fp16_group in zip(self.fp16_groups, self.fp32_from_fp16_groups):
             master_params_to_model_params(fp32_from_fp16_group, fp16_group)
 
-    # To consider:  Integrate distributed with this wrapper by registering a hook on each variable 
-    # that does the overflow check, gradient copy + downscale, and fp32 allreduce in a different stream.
+    # To consider:  Integrate distributed with this wrapper by registering a hook on each variable
+    # that does the overflow check, gradient copy + downscale, and fp32
+    # allreduce in a different stream.
     def _model_grads_to_master_grads(self):
         for fp16_group, fp32_from_fp16_group in zip(self.fp16_groups, self.fp32_from_fp16_groups):
             model_grads_to_master_grads(fp16_group, fp32_from_fp16_group)
@@ -315,7 +322,7 @@ class FP16_Optimizer(object):
             for group in self.optimizer.param_groups:
                 for param in group['params']:
                     if param.grad is not None:
-                        param.grad.data.mul_(1./self.loss_scale)
+                        param.grad.data.mul_(1. / self.loss_scale)
 
     def clip_master_grads(self, max_norm, norm_type=2):
         """
@@ -364,9 +371,9 @@ class FP16_Optimizer(object):
 
     def load_state_dict(self, state_dict):
         """
-        Loads a state_dict created by an earlier call to state_dict(). 
-        If ``fp16_optimizer_instance`` was constructed from some ``init_optimizer``, 
-        whose parameters in turn came from ``model``, it is expected that the user 
+        Loads a state_dict created by an earlier call to state_dict().
+        If ``fp16_optimizer_instance`` was constructed from some ``init_optimizer``,
+        whose parameters in turn came from ``model``, it is expected that the user
         will call ``model.load_state_dict()`` before
         ``fp16_optimizer_instance.load_state_dict()`` is called.
 
@@ -387,33 +394,34 @@ class FP16_Optimizer(object):
         self.first_closure_call_this_step = state_dict['first_closure_call_this_step']
         self.optimizer.load_state_dict(state_dict['optimizer_state_dict'])
         # At this point, the optimizer's references to the model's fp32 parameters are up to date.
-        # The optimizer's hyperparameters and internal buffers are also up to date.  
+        # The optimizer's hyperparameters and internal buffers are also up to date.
         # However, the fp32 master copies of the model's fp16 params stored by the optimizer are still
-        # out of date.  There are two options.  
-        # 1:  Refresh the master params from the model's fp16 params.  
+        # out of date.  There are two options.
+        # 1:  Refresh the master params from the model's fp16 params.
         # This requires less storage but incurs precision loss.
         # 2:  Save and restore the fp32 master copies separately.
         # We choose option 2.
-        # 
-        # Pytorch Optimizer.load_state_dict casts saved buffers (e.g. momentum) to the type and device 
-        # of their associated parameters, because it's possible those buffers might not exist yet in 
-        # the current optimizer instance.  In our case, as long as the current FP16_Optimizer has been 
+        #
+        # Pytorch Optimizer.load_state_dict casts saved buffers (e.g. momentum) to the type and device
+        # of their associated parameters, because it's possible those buffers might not exist yet in
+        # the current optimizer instance.  In our case, as long as the current FP16_Optimizer has been
         # constructed in the same way as the one whose state_dict we are loading, the same master params
         # are guaranteed to exist, so we can just copy_() from the saved master params.
-        for current_group, saved_group in zip(self.fp32_from_fp16_groups, state_dict['fp32_from_fp16']):
+        for current_group, saved_group in zip(
+                self.fp32_from_fp16_groups, state_dict['fp32_from_fp16']):
             for current, saved in zip(current_group, saved_group):
                 current.data.copy_(saved.data)
 
-    def step(self, closure=None): # could add clip option.
+    def step(self, closure=None):  # could add clip option.
         """
-        If no closure is supplied, :attr:`step` should be called after 
+        If no closure is supplied, :attr:`step` should be called after
         ``fp16_optimizer_obj.backward(loss)``.
         :attr:`step` updates the fp32 master copy of parameters using the optimizer supplied to
         :class:`FP16_Optimizer`'s constructor, then copies the updated fp32 params into the fp16 params
         originally referenced by :class:`FP16_Optimizer`'s constructor, so the user may immediately run
         another forward pass using their model.
 
-        If a closure is supplied, :attr:`step` may be called without a prior call to 
+        If a closure is supplied, :attr:`step` may be called without a prior call to
         :attr:`backward(loss)`.
         This control flow is identical to `ordinary Pytorch optimizer use`_ with closures.
         However, the user should take care that any ``loss.backward()`` call within the closure
@@ -424,7 +432,7 @@ class FP16_Optimizer(object):
 
         Example with closure::
 
-            # optimizer is assumed to be an FP16_Optimizer object, previously constructed from an 
+            # optimizer is assumed to be an FP16_Optimizer object, previously constructed from an
             # existing pytorch optimizer.
             for input, target in dataset:
                 def closure():
@@ -448,9 +456,9 @@ class FP16_Optimizer(object):
 
         if self.overflow:
             self.maybe_print("OVERFLOW! Skipping step. Attempted loss scale: {}, reducing to {}"
-                .format(scale, self.loss_scale))
+                             .format(scale, self.loss_scale))
             return
-        
+
         if closure is not None:
             retval = self._step_with_closure(closure)
         else:
@@ -472,7 +480,7 @@ class FP16_Optimizer(object):
                 self.first_closure_call_this_step = False
             else:
                 # If self.optimizer.step() internally calls wrapped_closure more than once,
-                # it may update the fp32 params after each call.  However, self.optimizer 
+                # it may update the fp32 params after each call.  However, self.optimizer
                 # doesn't know about the fp16 params at all.  If the fp32 params get updated,
                 # we can't rely on self.optimizer to refresh the fp16 params.  We need
                 # to handle that manually:
@@ -480,16 +488,16 @@ class FP16_Optimizer(object):
             # Our API expects the user to give us ownership of the backward() call by
             # replacing all calls to loss.backward() with optimizer.backward(loss).
             # This requirement holds whether or not the call to backward() is made within a closure.
-            # If the user is properly calling optimizer.backward(loss) within "closure," 
+            # If the user is properly calling optimizer.backward(loss) within "closure,"
             # calling closure() here will give the fp32 master params fresh gradients
-            # for the optimizer to play with, so all wrapped_closure needs to do is call 
+            # for the optimizer to play with, so all wrapped_closure needs to do is call
             # closure() and return the loss.
-            temp_loss = closure() 
+            temp_loss = closure()
             while(self.overflow):
                 scale = self.loss_scaler.loss_scale
                 self._update_scale(self.overflow)
                 self.maybe_print("OVERFLOW within closure! Skipping step. Attempted loss scale: {}, "
-                      "reducing to {}".format(scale, self.loss_scale))
+                                 "reducing to {}".format(scale, self.loss_scale))
                 temp_loss = closure()
             return temp_loss
 
@@ -500,7 +508,7 @@ class FP16_Optimizer(object):
         return retval
 
     def backward(self, loss, update_master_grads=True, retain_graph=False):
-        """ 
+        """
         :attr:`backward` performs the following conceptual steps:
 
         1. fp32_loss = loss.float() (see first Note below)
@@ -514,19 +522,19 @@ class FP16_Optimizer(object):
 
         .. note::
             :attr:`backward` internally converts the loss to fp32 before applying the loss scale.
-            This provides some additional safety against overflow if the user has supplied an 
-            fp16 loss value.  
+            This provides some additional safety against overflow if the user has supplied an
+            fp16 loss value.
             However, for maximum overflow safety, the user should
-            compute the loss criterion (MSE, cross entropy, etc) in fp32 before supplying it to 
+            compute the loss criterion (MSE, cross entropy, etc) in fp32 before supplying it to
             :attr:`backward`.
 
         .. warning::
-            The gradients found in a model's leaves after the call to 
-            :attr:`backward` should not be regarded as valid in general, 
-            because it's possible 
-            they have been scaled (and in the case of dynamic loss scaling, 
-            the scale factor may change over time).  
-            If the user wants to inspect gradients after a call to :attr:`backward`,  
+            The gradients found in a model's leaves after the call to
+            :attr:`backward` should not be regarded as valid in general,
+            because it's possible
+            they have been scaled (and in the case of dynamic loss scaling,
+            the scale factor may change over time).
+            If the user wants to inspect gradients after a call to :attr:`backward`,
             only the master gradients should be regarded as valid.  These can be retrieved via
             :attr:`inspect_master_grad_data()`.
 
@@ -541,54 +549,55 @@ class FP16_Optimizer(object):
             optimizer.backward(loss)
 
             # Naive operation with multiple losses (technically valid, but less efficient):
-            # fp32 grads will be correct after the second call,  but 
+            # fp32 grads will be correct after the second call,  but
             # the first call incurs an unnecessary fp16->fp32 grad copy.
             optimizer.backward(loss1)
             optimizer.backward(loss2)
 
             # More efficient way to handle multiple losses:
-            # The fp16->fp32 grad copy is delayed until fp16 grads from all 
+            # The fp16->fp32 grad copy is delayed until fp16 grads from all
             # losses have been accumulated.
             optimizer.backward(loss1, update_master_grads=False)
             optimizer.backward(loss2, update_master_grads=False)
             optimizer.update_master_grads()
-        """ 
-        # To consider:  try multiple backward passes using retain_grad=True to find 
+        """
+        # To consider:  try multiple backward passes using retain_grad=True to find
         # a loss scale that works.  After you find a loss scale that works, do a final dummy
-        # backward pass with retain_graph=False to tear down the graph.  Doing this would avoid 
-        # discarding the iteration,  but probably wouldn't improve overall efficiency.  
+        # backward pass with retain_graph=False to tear down the graph.  Doing this would avoid
+        # discarding the iteration,  but probably wouldn't improve overall efficiency.
         self.loss_scaler.backward(loss.float(), retain_graph=retain_graph)
         if update_master_grads:
             self.update_master_grads()
 
     def update_master_grads(self):
         """
-        Copy the ``.grad`` attribute from stored references to fp16 parameters to 
-        the ``.grad`` attribute of the fp32 master parameters that are directly 
+        Copy the ``.grad`` attribute from stored references to fp16 parameters to
+        the ``.grad`` attribute of the fp32 master parameters that are directly
         updated by the optimizer.  :attr:`update_master_grads` only needs to be called if
         ``fp16_optimizer_obj.backward`` was called with ``update_master_grads=False``.
         """
         if self.dynamic_loss_scale:
             self._check_overflow()
-            if self.overflow: return
+            if self.overflow:
+                return
         self._model_grads_to_master_grads()
         self._downscale_master()
 
     def inspect_master_grad_data(self):
         """
-        When running with :class:`FP16_Optimizer`, 
+        When running with :class:`FP16_Optimizer`,
         ``.grad`` attributes of a model's fp16 leaves should not be
-        regarded as truthful, because they might be scaled.  
+        regarded as truthful, because they might be scaled.
         After a call to :attr:`fp16_optimizer_obj.backward(loss)`, if no overflow was encountered,
         the fp32 master params' ``.grad``
-        attributes will contain valid gradients properly divided by the loss scale.  However, 
-        because :class:`FP16_Optimizer` flattens some parameters, accessing them may be 
+        attributes will contain valid gradients properly divided by the loss scale.  However,
+        because :class:`FP16_Optimizer` flattens some parameters, accessing them may be
         nonintuitive.  :attr:`inspect_master_grad_data`
         allows those gradients to be viewed with shapes corresponding to their associated model leaves.
 
         Returns:
             List of lists (one list for each parameter group).  The list for each parameter group
-            is a list of the ``.grad.data`` attributes of the fp32 master params belonging to that group.                 
+            is a list of the ``.grad.data`` attributes of the fp32 master params belonging to that group.
         """
         if self.overflow:
             print("Warning:  calling FP16_Optimizer.inspect_master_grad_data while in an overflow state.  "
@@ -607,8 +616,8 @@ class FP16_Optimizer(object):
                 master_grads_data.append(master_grads_this_group)
             return master_grads_data
 
-
     # Promote loss scale so it can be retrieved or set via "fp16_optimizer_instance.loss_scale"
+
     def _get_loss_scale(self):
         return self.loss_scaler.loss_scale
 

megatron/fp16/fp16util.py

@@ -102,6 +102,7 @@ class FP16Model(nn.Module):
 def backwards_debug_hook(grad):
     raise RuntimeError("master_params recieved a gradient in the backward pass!")
 
+
 def prep_param_lists(model, flat_master=False):
     """
     Creates a list of FP32 master parameters for a given model, as in
@@ -131,9 +132,9 @@ def prep_param_lists(model, flat_master=False):
             # flatten_dense_tensors returns a contiguous flat array.
             # http://pytorch.org/docs/master/_modules/torch/_utils.html
             master_params = _flatten_dense_tensors([param.data for param in model_params]).float()
-        except:
+        except BaseException:
             print("Error in prep_param_lists:  model may contain a mixture of parameters "
-                      "of different types.  Use flat_master=False, or use F16_Optimizer.")
+                  "of different types.  Use flat_master=False, or use F16_Optimizer.")
             raise
         master_params = torch.nn.Parameter(master_params)
         master_params.requires_grad = True
@@ -150,7 +151,7 @@ def prep_param_lists(model, flat_master=False):
 
 def model_grads_to_master_grads(model_params, master_params, flat_master=False):
     """
-    Copy model gradients to master gradients.  
+    Copy model gradients to master gradients.
 
     Args:
         model_params:  List of model parameters created by :func:`prep_param_lists`.
@@ -179,7 +180,7 @@ def master_params_to_model_params(model_params, master_params, flat_master=False
         master_params:  List of FP32 master parameters created by :func:`prep_param_lists`.  If ``master_params`` was created with ``flat_master=True``, ``flat_master=True`` should also be supplied to :func:`master_params_to_model_params`.
     """
     if flat_master:
-        for model, master in zip(model_params, 
+        for model, master in zip(model_params,
                                  _unflatten_dense_tensors(master_params[0].data, model_params)):
             model.data.copy_(master)
     else:
@@ -188,17 +189,19 @@ def master_params_to_model_params(model_params, master_params, flat_master=False
 
 # Backward compatibility fixes
 
+
 def to_python_float(t):
     if hasattr(t, 'item'):
         return t.item()
     else:
         return t[0]
 
+
 TORCH_MAJOR = int(torch.__version__.split('.')[0])
 TORCH_MINOR = int(torch.__version__.split('.')[1])
 
 clip_grad_norm = mpu.clip_grad_norm
-#elif TORCH_MAJOR == 0 and TORCH_MINOR <= 4:
+# elif TORCH_MAJOR == 0 and TORCH_MINOR <= 4:
 #    clip_grad_norm = torch.nn.utils.clip_grad_norm
-#else:
+# else:
 #    clip_grad_norm = torch.nn.utils.clip_grad_norm_

megatron/fp16/loss_scaler.py

@@ -17,18 +17,21 @@ import torch
 from megatron import mpu
 
 # item() is a recent addition, so this helps with backward compatibility.
+
+
 def to_python_float(t):
     if hasattr(t, 'item'):
         return t.item()
     else:
         return t[0]
 
+
 class LossScaler:
     """
     Class that manages a static loss scale.  This class is intended to interact with
     :class:`FP16_Optimizer`, and should not be directly manipulated by the user.
 
-    Use of :class:`LossScaler` is enabled via the ``static_loss_scale`` argument to 
+    Use of :class:`LossScaler` is enabled via the ``static_loss_scale`` argument to
     :class:`FP16_Optimizer`'s constructor.
 
     Args:
@@ -57,13 +60,14 @@ class LossScaler:
         return tuple(self.loss_scale * g for g in grad_in)
 
     def backward(self, loss, retain_graph=False):
-        scaled_loss = loss*self.loss_scale
+        scaled_loss = loss * self.loss_scale
         scaled_loss.backward(retain_graph=retain_graph)
 
+
 class DynamicLossScaler:
     """
     Class that manages dynamic loss scaling.  It is recommended to use :class:`DynamicLossScaler`
-    indirectly, by supplying ``dynamic_loss_scale=True`` to the constructor of 
+    indirectly, by supplying ``dynamic_loss_scale=True`` to the constructor of
     :class:`FP16_Optimizer`.  However, it's important to understand how :class:`DynamicLossScaler`
     operates, because the default options can be changed using the
     the ``dynamic_loss_args`` argument to :class:`FP16_Optimizer`'s constructor.
@@ -71,18 +75,18 @@ class DynamicLossScaler:
     Loss scaling is designed to combat the problem of underflowing gradients encountered at long
     times when training fp16 networks.  Dynamic loss scaling begins by attempting a very high loss
     scale.  Ironically, this may result in OVERflowing gradients.  If overflowing gradients are
-    encountered, :class:`DynamicLossScaler` informs :class:`FP16_Optimizer` that an overflow has 
+    encountered, :class:`DynamicLossScaler` informs :class:`FP16_Optimizer` that an overflow has
     occurred.
     :class:`FP16_Optimizer` then skips the update step for this particular iteration/minibatch,
-    and :class:`DynamicLossScaler` adjusts the loss scale to a lower value.  
+    and :class:`DynamicLossScaler` adjusts the loss scale to a lower value.
     If a certain number of iterations occur without overflowing gradients detected,
     :class:`DynamicLossScaler` increases the loss scale once more.
-    In this way :class:`DynamicLossScaler` attempts to "ride the edge" of 
+    In this way :class:`DynamicLossScaler` attempts to "ride the edge" of
     always using the highest loss scale possible without incurring overflow.
 
     Args:
         init_scale (float, optional, default=2**32):  Initial loss scale attempted by :class:`DynamicLossScaler.`
-        scale_factor (float, optional, default=2.0):  Factor used when adjusting the loss scale. If an overflow is encountered, the loss scale is readjusted to loss scale/``scale_factor``.  If ``scale_window`` consecutive iterations take place without an overflow, the loss scale is readjusted to loss_scale*``scale_factor``. 
+        scale_factor (float, optional, default=2.0):  Factor used when adjusting the loss scale. If an overflow is encountered, the loss scale is readjusted to loss scale/``scale_factor``.  If ``scale_window`` consecutive iterations take place without an overflow, the loss scale is readjusted to loss_scale*``scale_factor``.
         scale_window (int, optional, default=1000):  Number of consecutive iterations without an overflow to wait before increasing the loss scale.
     """
 
@@ -122,12 +126,12 @@ class DynamicLossScaler:
         overflow = overflow_gpu[0].item()
         return bool(overflow)
 
-
     # `x` is a torch.Tensor
+
     def _has_inf_or_nan(x):
         try:
-            # if x is half, the .float() incurs an additional deep copy, but it's necessary if 
-            # Pytorch's .sum() creates a one-element tensor of the same type as x 
+            # if x is half, the .float() incurs an additional deep copy, but it's necessary if
+            # Pytorch's .sum() creates a one-element tensor of the same type as x
             # (which is true for some recent version of pytorch).
             cpu_sum = float(x.float().sum())
             # More efficient version that can be used if .sum() returns a Python scalar
@@ -158,7 +162,7 @@ class DynamicLossScaler:
         if overflow:
             # self.cur_scale /= self.scale_factor
             if self.delayed_shift == 1 or self.cur_hysteresis == 1:
-                self.cur_scale = max(self.cur_scale/self.scale_factor, self.min_scale)
+                self.cur_scale = max(self.cur_scale / self.scale_factor, self.min_scale)
             else:
                 self.cur_hysteresis -= 1
             self.last_overflow_iter = self.cur_iter
@@ -179,10 +183,11 @@ class DynamicLossScaler:
         return tuple(self.loss_scale * g for g in grad_in)
 
     def backward(self, loss, retain_graph=False):
-        scaled_loss = loss*self.loss_scale
+        scaled_loss = loss * self.loss_scale
         scaled_loss.backward(retain_graph=retain_graph)
-        
-##############################################################        
+
+
+##############################################################
 # Example usage below here -- assuming it's in a separate file
 ##############################################################
 """
@@ -218,10 +223,10 @@ if __name__ == "__main__":
         # Run backprop
         optimizer.zero_grad()
         loss.backward()
-        
+
         # Check for overflow
         has_overflow = DynamicLossScaler.has_overflow(parameters)
-        
+
         # If no overflow, unscale grad and update as usual
         if not has_overflow:
             for param in parameters:

megatron/global_vars.py

@@ -124,7 +124,7 @@ def _set_adlr_autoresume(args):
         sys.path.append(os.environ.get('SUBMIT_SCRIPTS', '.'))
         try:
             from userlib.auto_resume import AutoResume
-        except:
+        except BaseException:
             print('ADLR autoresume is not available, exiting ...')
             sys.exit()
 

megatron/learning_rates.py

@@ -48,7 +48,6 @@ class AnnealingLR(object):
 
         print_rank_0('> learning rate decay style: {}'.format(self.decay_style))
 
-
     def get_lr(self):
         """Learning rate decay functions from:
               https://openreview.net/pdf?id=BJYwwY9ll pg. 4"""
@@ -71,7 +70,6 @@ class AnnealingLR(object):
             lr = self.start_lr
         return max(lr, self.min_lr)
 
-
     def step(self, step_num=None):
         """Set lr for all parameters groups."""
         if step_num is None:
@@ -81,7 +79,6 @@ class AnnealingLR(object):
         for group in self.optimizer.param_groups:
             group['lr'] = new_lr
 
-
     def state_dict(self):
         state_dict = {
             'start_lr': self.start_lr,
@@ -93,7 +90,6 @@ class AnnealingLR(object):
         }
         return state_dict
 
-
     def _check_and_set(self, cls_value, sd_value, name):
         """Auxiliary function for checking the values in the checkpoint and
         setting them."""
@@ -108,7 +104,6 @@ class AnnealingLR(object):
                                                                   name))
         return sd_value
 
-
     def load_state_dict(self, sd):
 
         self.start_lr = self._check_and_set(self.start_lr, sd['start_lr'],

megatron/model/bert_model.py

@@ -66,7 +66,6 @@ def bert_position_ids(token_ids):
     return position_ids
 
 
-
 class BertLMHead(MegatronModule):
     """Masked LM head for Bert
 
@@ -77,6 +76,7 @@ class BertLMHead(MegatronModule):
         layernorm_epsilon: tolerance for layer norm divisions
         parallel_output: wether output logits being distributed or not.
     """
+
     def __init__(self, mpu_vocab_size, hidden_size, init_method,
                  layernorm_epsilon, parallel_output):
 
@@ -91,7 +91,6 @@ class BertLMHead(MegatronModule):
         self.dense = get_linear_layer(hidden_size, hidden_size, init_method)
         self.layernorm = LayerNorm(hidden_size, eps=layernorm_epsilon)
 
-
     def forward(self, hidden_states, word_embeddings_weight):
         hidden_states = self.dense(hidden_states)
         hidden_states = gelu(hidden_states)
@@ -103,7 +102,6 @@ class BertLMHead(MegatronModule):
         return output
 
 
-
 class BertModel(MegatronModule):
     """Bert Language model."""
 
@@ -136,7 +134,6 @@ class BertModel(MegatronModule):
                                                 init_method)
             self._binary_head_key = 'binary_head'
 
-
     def forward(self, input_ids, attention_mask, tokentype_ids=None):
 
         extended_attention_mask = bert_extended_attention_mask(
@@ -166,7 +163,6 @@ class BertModel(MegatronModule):
 
         return lm_logits, None
 
-
     def state_dict_for_save_checkpoint(self, destination=None, prefix='',
                                        keep_vars=False):
         """For easy load when model is combined with other heads,
@@ -184,7 +180,6 @@ class BertModel(MegatronModule):
                 = self.binary_head.state_dict(destination, prefix, keep_vars)
         return state_dict_
 
-
     def load_state_dict(self, state_dict, strict=True):
         """Customized load."""
 

megatron/model/classification.py

@@ -53,7 +53,6 @@ class Classification(MegatronModule):
                                                     init_method)
         self._classification_head_key = 'classification_head'
 
-
     def forward(self, input_ids, attention_mask, tokentype_ids):
 
         extended_attention_mask = bert_extended_attention_mask(
@@ -74,7 +73,6 @@ class Classification(MegatronModule):
 
         return classification_logits
 
-
     def state_dict_for_save_checkpoint(self, destination=None, prefix='',
                                        keep_vars=False):
         """For easy load when model is combined with other heads,
@@ -89,7 +87,6 @@ class Classification(MegatronModule):
                 destination, prefix, keep_vars)
         return state_dict_
 
-
     def load_state_dict(self, state_dict, strict=True):
         """Customized load."""
 

megatron/model/distributed.py

@@ -71,8 +71,8 @@ class DistributedDataParallel(MegatronModule):
             def allreduce_hook(*unused):
                 Variable._execution_engine.queue_callback(allreduce_params)
         #    handle = param.register_hook(allreduce_hook)
-            #self.hooks.append(allreduce_hook)
-            #self.hook_handles.append(handle)
+            # self.hooks.append(allreduce_hook)
+            # self.hook_handles.append(handle)
         self.allreduce_params = allreduce_params
 
     def forward(self, *inputs, **kwargs):
@@ -114,4 +114,3 @@ class DistributedDataParallel(MegatronModule):
         super(DistributedDataParallel, self).train(mode)
         self.module.train(mode)
     '''
-

megatron/model/gpt2_model.py

@@ -28,7 +28,7 @@ from .utils import scaled_init_method_normal
 
 def gpt2_attention_mask_func(attention_scores, ltor_mask):
     attention_scores = torch.mul(attention_scores, ltor_mask) - \
-                       10000.0 * (1.0 - ltor_mask)
+        10000.0 * (1.0 - ltor_mask)
     return attention_scores
 
 
@@ -49,7 +49,6 @@ class GPT2Model(MegatronModule):
             scaled_init_method=scaled_init_method_normal(args.init_method_std,
                                                          args.num_layers))
 
-
     def forward(self, input_ids, position_ids, attention_mask,
                 tokentype_ids=None, layer_past=None, get_key_value=False,
                 forward_method_parallel_output=None):
@@ -79,7 +78,6 @@ class GPT2Model(MegatronModule):
 
         return output
 
-
     def state_dict_for_save_checkpoint(self, destination=None, prefix='',
                                        keep_vars=False):
 
@@ -89,7 +87,6 @@ class GPT2Model(MegatronModule):
                 destination, prefix, keep_vars)
         return state_dict_
 
-
     def load_state_dict(self, state_dict, strict=True):
         """Customized load."""
 

megatron/model/language_model.py

@@ -62,7 +62,6 @@ def get_language_model(attention_mask_func, num_tokentypes, add_pooler,
     return language_model, language_model_key
 
 
-
 class Pooler(MegatronModule):
     """Pooler layer.
 
@@ -74,11 +73,11 @@ class Pooler(MegatronModule):
         init_method: weight initialization method for the linear layer.
             bias is set to zero.
     """
+
     def __init__(self, hidden_size, init_method):
         super(Pooler, self).__init__()
         self.dense = get_linear_layer(hidden_size, hidden_size, init_method)
 
-
     def forward(self, hidden_states, sequence_index=0):
         # hidden_states: [b, s, h]
         # sequence_index: index of the token to pool.
@@ -101,6 +100,7 @@ class Embedding(MegatronModule):
         num_tokentypes: size of the token-type embeddings. 0 value
                         will ignore this embedding
     """
+
     def __init__(self,
                  hidden_size,
                  vocab_size,
@@ -142,7 +142,6 @@ class Embedding(MegatronModule):
         # Embeddings dropout
         self.embedding_dropout = torch.nn.Dropout(embedding_dropout_prob)
 
-
     def add_tokentype_embeddings(self, num_tokentypes):
         """Add token-type embedding. This function is provided so we can add
         token-type embeddings in case the pretrained model does not have it.
@@ -159,7 +158,6 @@ class Embedding(MegatronModule):
         # Initialize the token-type embeddings.
         self.init_method(self.tokentype_embeddings.weight)
 
-
     def forward(self, input_ids, position_ids, tokentype_ids=None):
         # Embeddings.
         words_embeddings = self.word_embeddings(input_ids)
@@ -176,7 +174,6 @@ class Embedding(MegatronModule):
 
         return embeddings
 
-
     def state_dict_for_save_checkpoint(self, destination=None, prefix='',
                                        keep_vars=False):
         """For easy load."""
@@ -194,7 +191,6 @@ class Embedding(MegatronModule):
 
         return state_dict_
 
-
     def load_state_dict(self, state_dict, strict=True):
         """Customized load."""
 
@@ -223,7 +219,7 @@ class Embedding(MegatronModule):
         self.position_embeddings.load_state_dict(state_dict_, strict=strict)
 
         # Tokentype embedding.
-        if  self.num_tokentypes > 0:
+        if self.num_tokentypes > 0:
             state_dict_ = {}
             if self._tokentype_embeddings_key in state_dict:
                 state_dict_ = state_dict[self._tokentype_embeddings_key]
@@ -241,7 +237,6 @@ class Embedding(MegatronModule):
                       'checkpoint but could not find it', flush=True)
 
 
-
 class TransformerLanguageModel(MegatronModule):
     """Transformer language model.
 
@@ -260,6 +255,7 @@ class TransformerLanguageModel(MegatronModule):
         num_tokentypes: size of the token-type embeddings. 0 value
                         will ignore this embedding
     """
+
     def __init__(self,
                  attention_mask_func,
                  mlp_activation_func,
@@ -295,7 +291,6 @@ class TransformerLanguageModel(MegatronModule):
             self.pooler = Pooler(self.hidden_size, self.init_method)
             self._pooler_key = 'pooler'
 
-
     def forward(self, input_ids, position_ids, attention_mask,
                 tokentype_ids=None, layer_past=None, get_key_value=False,
                 pooling_sequence_index=0):
@@ -317,7 +312,6 @@ class TransformerLanguageModel(MegatronModule):
 
         return transformer_output
 
-
     def state_dict_for_save_checkpoint(self, destination=None, prefix='',
                                        keep_vars=False):
         """For easy load."""
@@ -336,7 +330,6 @@ class TransformerLanguageModel(MegatronModule):
 
         return state_dict_
 
-
     def load_state_dict(self, state_dict, strict=True):
         """Customized load."""
 

megatron/model/multiple_choice.py

@@ -51,7 +51,6 @@ class MultipleChoice(MegatronModule):
                                                  init_method)
         self._multichoice_head_key = 'multichoice_head'
 
-
     def forward(self, input_ids, attention_mask, tokentype_ids):
 
         # [batch, choices, sequence] --> [batch * choices, sequence] -->
@@ -86,7 +85,6 @@ class MultipleChoice(MegatronModule):
 
         return multichoice_logits
 
-
     def state_dict_for_save_checkpoint(self, destination=None, prefix='',
                                        keep_vars=False):
         """For easy load when model is combined with other heads,
@@ -101,7 +99,6 @@ class MultipleChoice(MegatronModule):
                 destination, prefix, keep_vars)
         return state_dict_
 
-
     def load_state_dict(self, state_dict, strict=True):
         """Customized load."""
 

megatron/model/transformer.py

@@ -46,6 +46,7 @@ from megatron.module import MegatronModule
                                      unmaksed-attention-scores, attention-mask)
 """
 
+
 class ParallelMLP(MegatronModule):
     """MLP.
 
@@ -63,7 +64,7 @@ class ParallelMLP(MegatronModule):
         # Project to 4h.
         self.dense_h_to_4h = mpu.ColumnParallelLinear(
             args.hidden_size,
-            4*args.hidden_size,
+            4 * args.hidden_size,
             gather_output=False,
             init_method=init_method)
 
@@ -71,14 +72,13 @@ class ParallelMLP(MegatronModule):
 
         # Project back to h.
         self.dense_4h_to_h = mpu.RowParallelLinear(
-            4*args.hidden_size,
+            4 * args.hidden_size,
             args.hidden_size,
             input_is_parallel=True,
             init_method=output_layer_init_method)
 
         self.dropout = torch.nn.Dropout(args.hidden_dropout)
 
-
     def forward(self, hidden_states):
 
         # [b, s, 4hp]
@@ -91,13 +91,13 @@ class ParallelMLP(MegatronModule):
         return output
 
 
-
 class ParallelSelfAttention(MegatronModule):
     """Parallel self-attention layer abstract class.
 
     Self-attention layer takes input with size [b, s, h]
     and returns output of the same size.
     """
+
     def __init__(self, attention_mask_func, init_method,
                  output_layer_init_method, layer_number):
         super(ParallelSelfAttention, self).__init__()
@@ -123,7 +123,7 @@ class ParallelSelfAttention(MegatronModule):
         # Strided linear layer.
         self.query_key_value = mpu.ColumnParallelLinear(
             args.hidden_size,
-            3*args.hidden_size,
+            3 * args.hidden_size,
             stride=3,
             gather_output=False,
             init_method=init_method)
@@ -141,18 +141,16 @@ class ParallelSelfAttention(MegatronModule):
             init_method=output_layer_init_method)
         self.output_dropout = torch.nn.Dropout(args.hidden_dropout)
 
-
     def _transpose_for_scores(self, tensor):
         """Transpose a 3D tensor [b, s, np*hn] into a 4D tensor with
         size [b, np, s, hn].
         """
         new_tensor_shape = tensor.size()[:-1] + \
-                           (self.num_attention_heads_per_partition,
-                            self.hidden_size_per_attention_head)
+            (self.num_attention_heads_per_partition,
+             self.hidden_size_per_attention_head)
         tensor = tensor.view(*new_tensor_shape)
         return tensor.permute(0, 2, 1, 3)
 
-
     def _get_query_key_value(self, hidden_states):
         """Get query, key, and value and transpose to
         get size [b, np, s, hn].
@@ -170,7 +168,6 @@ class ParallelSelfAttention(MegatronModule):
 
         return query_layer, key_layer, value_layer
 
-
     def _get_unmasked_attention_scores(self, query_layer, key_layer):
         """Unmasked attention scores with size [b, np, s, s]."""
         coeff = 1
@@ -179,9 +176,8 @@ class ParallelSelfAttention(MegatronModule):
         norm_factor = math.sqrt(coeff *
                                 math.sqrt(self.hidden_size_per_attention_head))
         # Raw attention scores. [b, np, s, s]
-        return torch.matmul(query_layer/norm_factor,
-                            key_layer.transpose(-1, -2)/norm_factor)
-
+        return torch.matmul(query_layer / norm_factor,
+                            key_layer.transpose(-1, -2) / norm_factor)
 
     def _get_attention_probs(self, attention_scores):
         """Attention probabilies with dropout. The output has
@@ -198,7 +194,6 @@ class ParallelSelfAttention(MegatronModule):
 
         return attention_probs
 
-
     def _get_attended_context(self, attention_probs, value_layer):
         """Final attended tesnor and transposed back to [b, s, hp]."""
         # Context layer.
@@ -207,13 +202,12 @@ class ParallelSelfAttention(MegatronModule):
         # [b, s, np, hn]
         context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
         new_context_layer_shape = context_layer.size()[:-2] + \
-                                  (self.hidden_size_per_partition,)
+            (self.hidden_size_per_partition,)
         # [b, s, hp]
         context_layer = context_layer.view(*new_context_layer_shape)
 
         return context_layer
 
-
     def _get_output(self, context_layer):
         """Output layer with dropout."""
         # Output. [b, s, h]
@@ -222,7 +216,6 @@ class ParallelSelfAttention(MegatronModule):
 
         return output
 
-
     def forward(self, hidden_states, attention_mask, layer_past=None,
                 get_key_value=False):
         # hidden_states: [b, s, h]
@@ -254,7 +247,7 @@ class ParallelSelfAttention(MegatronModule):
                 if layer_past is not None:
                     attention_mask = attention_mask[
                         ...,
-                        attention_scores.size(3)-1,
+                        attention_scores.size(3) - 1,
                         :attention_scores.size(3)].unsqueeze(2)
                 else:
                     attention_mask = attention_mask[
@@ -283,13 +276,13 @@ class ParallelSelfAttention(MegatronModule):
         return output
 
 
-
 class ParallelTransformerLayer(MegatronModule):
     """A single transformer layer.
 
     Transformore layer takes input with size [b, s, h] and returns an
     output of the same size.
     """
+
     def __init__(self, attention_mask_func, mlp_activation_func,
                  init_method, output_layer_init_method, layer_number):
         args = get_args()
@@ -319,7 +312,6 @@ class ParallelTransformerLayer(MegatronModule):
         self.mlp = ParallelMLP(mlp_activation_func, init_method,
                                output_layer_init_method)
 
-
     def forward(self, hidden_states, attention_mask, layer_past=None,
                 get_key_value=False):
         # hidden_states: [b, s, h]
@@ -375,14 +367,13 @@ class ParallelTransformer(MegatronModule):
 
         # Transformer layers.
         self.layers = torch.nn.ModuleList(
-            [get_layer(i+1) for i in range(args.num_layers)])
+            [get_layer(i + 1) for i in range(args.num_layers)])
 
         # Final layer norm before output.
         self.final_layernorm = LayerNorm(
             args.hidden_size,
             eps=args.layernorm_epsilon)
 
-
     def _checkpointed_forward(self, hidden_states, attention_mask):
         """Forward method with activation checkpointing."""
         def custom(start, end):
@@ -398,13 +389,12 @@ class ParallelTransformer(MegatronModule):
         num_layers = len(self.layers)
         while l < num_layers:
             hidden_states = mpu.checkpoint(
-                custom(l, l+self.checkpoint_num_layers),
+                custom(l, l + self.checkpoint_num_layers),
                 hidden_states, attention_mask)
             l += self.checkpoint_num_layers
 
         return hidden_states
 
-
     def forward(self, hidden_states, attention_mask, layer_past=None,
                 get_key_value=False):
 

megatron/model/utils.py

@@ -33,6 +33,7 @@ def init_method_normal(sigma):
 def scaled_init_method_normal(sigma, num_layers):
     """Init method based on N(0, sigma/sqrt(2*num_layers)."""
     std = sigma / math.sqrt(2.0 * num_layers)
+
     def init_(tensor):
         return torch.nn.init.normal_(tensor, mean=0.0, std=std)
 
@@ -54,6 +55,7 @@ def gelu_impl(x):
     return 0.5 * x * (1.0 + torch.tanh(0.7978845608028654 * x *
                                        (1.0 + 0.044715 * x * x)))
 
+
 def gelu(x):
     return gelu_impl(x)
 

megatron/module.py

@@ -21,11 +21,9 @@ import torch
 class MegatronModule(torch.nn.Module):
     """Megatron specific extentions of torch Module."""
 
-
     def __init__(self):
         super(MegatronModule, self).__init__()
 
-
     def state_dict_for_save_checkpoint(self, destination=None, prefix='',
                                        keep_vars=False):
         """Use this function to override the state dict for

megatron/mpu/cross_entropy.py

@@ -72,7 +72,6 @@ class _VocabParallelCrossEntropy(torch.autograd.Function):
                                      op=torch.distributed.ReduceOp.SUM,
                                      group=get_model_parallel_group())
 
-
         # Loss = log(sum(exp(logits))) - predicted-logit.
         loss = torch.log(sum_exp_logits) - predicted_logits
 

megatron/mpu/layers.py

@@ -89,6 +89,7 @@ class VocabParallelEmbedding(torch.nn.Module):
         embedding_dim: size of hidden state.
         init_method: method to initialize weights.
     """
+
     def __init__(self, num_embeddings, embedding_dim,
                  init_method=init.xavier_normal_):
         super(VocabParallelEmbedding, self).__init__()
@@ -108,7 +109,7 @@ class VocabParallelEmbedding(torch.nn.Module):
                 self.num_embeddings, get_model_parallel_rank(),
                 get_model_parallel_world_size())
         self.num_embeddings_per_partition = self.vocab_end_index - \
-                                            self.vocab_start_index
+            self.vocab_start_index
 
         # Allocate weights.
         self.weight = Parameter(torch.Tensor(self.num_embeddings_per_partition,
@@ -147,6 +148,7 @@ class ParallelEmbedding(torch.nn.Module):
         embedding_dim: size of hidden state.
         init_method: method to initialize weights.
     """
+
     def __init__(self, num_embeddings, embedding_dim,
                  init_method=init.xavier_normal_,
                  keep_master_weight_for_test=False):
@@ -205,6 +207,7 @@ class ColumnParallelLinear(torch.nn.Module):
                                      set to False. It returns the master weights
                                      used for initialization.
     """
+
     def __init__(self, input_size, output_size, bias=True, gather_output=True,
                  init_method=init.xavier_normal_, stride=1,
                  keep_master_weight_for_test=False):
@@ -279,6 +282,7 @@ class RowParallelLinear(torch.nn.Module):
                                      set to False. It returns the master weights
                                      used for initialization.
     """
+
     def __init__(self, input_size, output_size, bias=True,
                  input_is_parallel=False,
                  init_method=init.xavier_normal_, stride=1,
@@ -327,4 +331,3 @@ class RowParallelLinear(torch.nn.Module):
         else:
             output = output_
         return output
-

megatron/mpu/mappings.py

@@ -131,11 +131,14 @@ class _GatherFromModelParallelRegion(torch.autograd.Function):
 def copy_to_model_parallel_region(input_):
     return _CopyToModelParallelRegion.apply(input_)
 
+
 def reduce_from_model_parallel_region(input_):
     return _ReduceFromModelParallelRegion.apply(input_)
 
+
 def scatter_to_model_parallel_region(input_):
     return _ScatterToModelParallelRegion.apply(input_)
 
+
 def gather_from_model_parallel_region(input_):
     return _GatherFromModelParallelRegion.apply(input_)

megatron/mpu/random.py

@@ -73,6 +73,7 @@ class CudaRNGStatesTracker:
     rng state, we can perform operations and return to our starting
     cuda state.
     """
+
     def __init__(self):
         # Map from a string name to the cuda rng state.
         self.states_ = {}