Source code for transformers.debug_utils

# 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
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
# limitations under the License.

import collections

from .file_utils import ExplicitEnum, is_torch_available
from .utils import logging

if is_torch_available():
    import torch

logger = logging.get_logger(__name__)

[docs]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 :: 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 overlow. 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 :: 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 :: 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 :: 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 (:obj:`nn.Module`): The model to debug. max_frames_to_save (:obj:`int`, `optional`, defaults to 21): How many frames back to record trace_batch_nums(:obj:`List[int]`, `optional`, defaults to ``[]``): Which batch numbers to trace (turns detection off) abort_after_batch_num (:obj:`int`, `optional`): Whether to abort after a certain batch number has finished """ def __init__(self, model, max_frames_to_save=21, trace_batch_nums=[], abort_after_batch_num=None): 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 = True if self.batch_number in self.trace_batch_nums else False 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 `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: :obj:`True` if ``inf`` or ``nan`` was detected, :obj:`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[, 100)] if n100.numel() > 0: print(f"{ctx}: n100={n100.numel()}") n1000 = var[, 1000)] if n1000.numel() > 0: print(f"{ctx}: n1000={n1000.numel()}") n10000 = var[, 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"