repo_description: 🤗 Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both inference and training. 
repo_topics: audio deep-learning deepseek gemma glm hacktoberfest llm machine-learning model-hub natural-language-processing nlp pretrained-models python pytorch pytorch-transformers qwen speech-recognition transformer vlm
code: # Copyright 2020 The HuggingFace Team. All rights reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  
  import collections
  
  from .utils import ExplicitEnum, is_torch_available, logging
  
  
  if is_torch_available():
      import torch
  
  
  logger = logging.get_logger(__name__)
  
  
  class DebugUnderflowOverflow:
      """
      This debug class helps detect and understand where the model starts getting very large or very small, and more
      importantly `nan` or `inf` weight and activation elements.
  
      There are 2 working modes:
  
      1. Underflow/overflow detection (default)
      2. Specific batch absolute min/max tracing without detection
  
      Mode 1: Underflow/overflow detection
  
      To activate the underflow/overflow detection, initialize the object with the model :
  
      ```python
      debug_overflow = DebugUnderflowOverflow(model)
      ```
  
      then run the training as normal and if `nan` or `inf` gets detected in at least one of the weight, input or output
      elements this module will throw an exception and will print `max_frames_to_save` frames that lead to this event,
      each frame reporting
  
      1. the fully qualified module name plus the class name whose `forward` was run
      2. the absolute min and max value of all elements for each module weights, and the inputs and output
  
      For example, here is the header and the last few frames in detection report for `google/mt5-small` run in fp16
      mixed precision :
  
      ```
      Detected inf/nan during batch_number=0
      Last 21 forward frames:
      abs min  abs max  metadata
      [...]
                        encoder.block.2.layer.1.DenseReluDense.wi_0 Linear
      2.17e-07 4.50e+00 weight
      1.79e-06 4.65e+00 input[0]
      2.68e-06 3.70e+01 output
                        encoder.block.2.layer.1.DenseReluDense.wi_1 Linear
      8.08e-07 2.66e+01 weight
      1.79e-06 4.65e+00 input[0]
      1.27e-04 2.37e+02 output
                        encoder.block.2.layer.1.DenseReluDense.wo Linear
      1.01e-06 6.44e+00 weight
      0.00e+00 9.74e+03 input[0]
      3.18e-04 6.27e+04 output
                        encoder.block.2.layer.1.DenseReluDense T5DenseGatedGeluDense
      1.79e-06 4.65e+00 input[0]
      3.18e-04 6.27e+04 output
                        encoder.block.2.layer.1.dropout Dropout
      3.18e-04 6.27e+04 input[0]
      0.00e+00      inf output
      ```
  
      You can see here, that `T5DenseGatedGeluDense.forward` resulted in output activations, whose absolute max value was
      around 62.7K, which is very close to fp16's top limit of 64K. In the next frame we have `Dropout` which
      renormalizes the weights, after it zeroed some of the elements, which pushes the absolute max value to more than
      64K, and we get an overflow.
  
      As you can see it's the previous frames that we need to look into when the numbers start going into very large for
      fp16 numbers.
  
      The tracking is done in a forward hook, which gets invoked immediately after `forward` has completed.
  
      By default the last 21 frames are printed. You can change the default to adjust for your needs. For example :
  
      ```python
      debug_overflow = DebugUnderflowOverflow(model, max_frames_to_save=100)
      ```
  
          To validate that you have set up this debugging feature correctly, and you intend to use it in a training that
          may take hours to complete, first run it with normal tracing enabled for one of a few batches as explained in
          the next section.
  
  
          Mode 2. Specific batch absolute min/max tracing without detection
  
          The second work mode is per-batch tracing with the underflow/overflow detection feature turned off.
  
          Let's say you want to watch the absolute min and max values for all the ingredients of each `forward` call of a
      given batch, and only do that for batches 1 and 3. Then you instantiate this class as :
  
      ```python
      debug_overflow = DebugUnderflowOverflow(model, trace_batch_nums=[1, 3])
      ```
  
      And now full batches 1 and 3 will be traced using the same format as explained above. Batches are 0-indexed.
  
      This is helpful if you know that the program starts misbehaving after a certain batch number, so you can
      fast-forward right to that area.
  
  
      Early stopping:
  
      You can also specify the batch number after which to stop the training, with :
  
      ```python
      debug_overflow = DebugUnderflowOverflow(model, trace_batch_nums=[1, 3], abort_after_batch_num=3)
      ```
  
      This feature is mainly useful in the tracing mode, but you can use it for any mode.
  
  
      **Performance**:
  
      As this module measures absolute `min`/``max` of each weight of the model on every forward it'll slow the training
      down. Therefore remember to turn it off once the debugging needs have been met.
  
      Args:
          model (`nn.Module`):
              The model to debug.
          max_frames_to_save (`int`, *optional*, defaults to 21):
              How many frames back to record
          trace_batch_nums(`list[int]`, *optional*, defaults to `[]`):
              Which batch numbers to trace (turns detection off)
          abort_after_batch_num  (`int``, *optional*):
              Whether to abort after a certain batch number has finished
      """
  
      def __init__(self, model, max_frames_to_save=21, trace_batch_nums=None, abort_after_batch_num=None):
          if trace_batch_nums is None:
              trace_batch_nums = []
          self.model = model
          self.trace_batch_nums = trace_batch_nums
          self.abort_after_batch_num = abort_after_batch_num
  
          # keep a LIFO buffer of frames to dump as soon as inf/nan is encountered to give context to the problem emergence
          self.frames = collections.deque([], max_frames_to_save)
          self.frame = []
          self.batch_number = 0
          self.total_calls = 0
          self.detected_overflow = False
          self.prefix = "                 "
  
          self.analyse_model()
  
          self.register_forward_hook()
  
      def save_frame(self, frame=None):
          if frame is not None:
              self.expand_frame(frame)
          self.frames.append("\n".join(self.frame))
          self.frame = []  # start a new frame
  
      def expand_frame(self, line):
          self.frame.append(line)
  
      def trace_frames(self):
          print("\n".join(self.frames))
          self.frames = []
  
      def reset_saved_frames(self):
          self.frames = []
  
      def dump_saved_frames(self):
          print(f"\nDetected inf/nan during batch_number={self.batch_number}")
          print(f"Last {len(self.frames)} forward frames:")
          print(f"{'abs min':8} {'abs max':8} metadata")
          print("\n".join(self.frames))
          print("\n\n")
          self.frames = []
  
      def analyse_model(self):
          # extract the fully qualified module names, to be able to report at run time. e.g.:
          # encoder.block.2.layer.0.SelfAttention.o
          #
          # for shared weights only the first shared module name will be registered
          self.module_names = {m: name for name, m in self.model.named_modules()}
          # self.longest_module_name = max(len(v) for v in self.module_names.values())
  
      def analyse_variable(self, var, ctx):
          if torch.is_tensor(var):
              self.expand_frame(get_abs_min_max(var, ctx))
              if detect_overflow(var, ctx):
                  self.detected_overflow = True
          elif var is None:
              self.expand_frame(f"{'None':>17} {ctx}")
          else:
              self.expand_frame(f"{'not a tensor':>17} {ctx}")
  
      def batch_start_frame(self):
          self.expand_frame(f"\n\n{self.prefix} *** Starting batch number={self.batch_number} ***")
          self.expand_frame(f"{'abs min':8} {'abs max':8} metadata")
  
      def batch_end_frame(self):
          self.expand_frame(f"{self.prefix} *** Finished batch number={self.batch_number - 1} ***\n\n")
  
      def create_frame(self, module, input, output):
          self.expand_frame(f"{self.prefix} {self.module_names[module]} {module.__class__.__name__}")
  
          # params
          for name, p in module.named_parameters(recurse=False):
              self.analyse_variable(p, name)
  
          # inputs
          if isinstance(input, tuple):
              for i, x in enumerate(input):
                  self.analyse_variable(x, f"input[{i}]")
          else:
              self.analyse_variable(input, "input")
  
          # outputs
          if isinstance(output, tuple):
              for i, x in enumerate(output):
                  # possibly a tuple of tuples
                  if isinstance(x, tuple):
                      for j, y in enumerate(x):
                          self.analyse_variable(y, f"output[{i}][{j}]")
                  else:
                      self.analyse_variable(x, f"output[{i}]")
          else:
              self.analyse_variable(output, "output")
  
          self.save_frame()
  
      def register_forward_hook(self):
          self.model.apply(self._register_forward_hook)
  
      def _register_forward_hook(self, module):
          module.register_forward_hook(self.forward_hook)
  
      def forward_hook(self, module, input, output):
          # - input is a tuple of packed inputs (could be non-Tensors)
          # - output could be a Tensor or a tuple of Tensors and non-Tensors
  
          last_frame_of_batch = False
  
          trace_mode = self.batch_number in self.trace_batch_nums
          if trace_mode:
              self.reset_saved_frames()
  
          if self.total_calls == 0:
              self.batch_start_frame()
          self.total_calls += 1
  
          # count batch numbers - the very first forward hook of the batch will be called when the
          # batch completes - i.e. it gets called very last - we know this batch has finished
          if module == self.model:
              self.batch_number += 1
              last_frame_of_batch = True
  
          self.create_frame(module, input, output)
  
          # if last_frame_of_batch:
          #     self.batch_end_frame()
  
          if trace_mode:
              self.trace_frames()
  
          if last_frame_of_batch:
              self.batch_start_frame()
  
          if self.detected_overflow and not trace_mode:
              self.dump_saved_frames()
  
              # now we can abort, as it's pointless to continue running
              raise ValueError(
                  "DebugUnderflowOverflow: inf/nan detected, aborting as there is no point running further. "
                  "Please scroll up above this traceback to see the activation values prior to this event."
              )
  
          # abort after certain batch if requested to do so
          if self.abort_after_batch_num is not None and self.batch_number > self.abort_after_batch_num:
              raise ValueError(
                  f"DebugUnderflowOverflow: aborting after {self.batch_number} batches due to"
                  f" `abort_after_batch_num={self.abort_after_batch_num}` arg"
              )
  
  
  def get_abs_min_max(var, ctx):
      abs_var = var.abs()
      return f"{abs_var.min():8.2e} {abs_var.max():8.2e} {ctx}"
  
  
  def detect_overflow(var, ctx):
      """
      Report whether the tensor contains any `nan` or `inf` entries.
  
      This is useful for detecting overflows/underflows and best to call right after the function that did some math that
      modified the tensor in question.
  
      This function contains a few other helper features that you can enable and tweak directly if you want to track
      various other things.
  
      Args:
          var: the tensor variable to check
          ctx: the message to print as a context
  
      Return:
          `True` if `inf` or `nan` was detected, `False` otherwise
      """
      detected = False
      if torch.isnan(var).any().item():
          detected = True
          print(f"{ctx} has nans")
      if torch.isinf(var).any().item():
          detected = True
          print(f"{ctx} has infs")
  
      # if needed to monitor large elements can enable the following
      if 0:  # and detected:
          n100 = var[torch.ge(var.abs(), 100)]
          if n100.numel() > 0:
              print(f"{ctx}:  n100={n100.numel()}")
          n1000 = var[torch.ge(var.abs(), 1000)]
          if n1000.numel() > 0:
              print(f"{ctx}: n1000={n1000.numel()}")
          n10000 = var[torch.ge(var.abs(), 10000)]
          if n10000.numel() > 0:
              print(f"{ctx}: n10000={n10000.numel()}")
  
      if 0:
          print(f"min={var.min():9.2e} max={var.max():9.2e}")
  
      if 0:
          print(f"min={var.min():9.2e} max={var.max():9.2e} var={var.var():9.2e} mean={var.mean():9.2e} ({ctx})")
  
      return detected
  
  
  class DebugOption(ExplicitEnum):
      UNDERFLOW_OVERFLOW = "underflow_overflow"
      TPU_METRICS_DEBUG = "tpu_metrics_debug"
  