import os import subprocess from collections import OrderedDict from subprocess import PIPE from typing import Tuple, Dict, Union, List, Any import numpy as np import torch import torch.nn as nn from torch.utils.hooks import RemovableHandle from pytorch_lightning.utilities import AMPType PARAMETER_NUM_UNITS = [" ", "K", "M", "B", "T"] UNKNOWN_SIZE = "?" class LayerSummary(object): """ Summary class for a single layer in a :class:`~pytorch_lightning.core.lightning.LightningModule`. It collects the following information: - Type of the layer (e.g. Linear, BatchNorm1d, ...) - Input shape - Output shape - Number of parameters The input and output shapes are only known after the example input array was passed through the model. Example:: >>> model = torch.nn.Conv2d(3, 8, 3) >>> summary = LayerSummary(model) >>> summary.num_parameters 224 >>> summary.layer_type 'Conv2d' >>> output = model(torch.rand(1, 3, 5, 5)) >>> summary.in_size [1, 3, 5, 5] >>> summary.out_size [1, 8, 3, 3] Args: module: A module to summarize """ def __init__(self, module: nn.Module): super().__init__() self._module = module self._hook_handle = self._register_hook() self._in_size = None self._out_size = None def __del__(self): self.detach_hook() def _register_hook(self) -> RemovableHandle: """ Registers a hook on the module that computes the input- and output size(s) on the first forward pass. If the hook is called, it will remove itself from the from the module, meaning that recursive models will only record their input- and output shapes once. Return: A handle for the installed hook. """ def hook(module, inp, out): if len(inp) == 1: inp = inp[0] self._in_size = parse_batch_shape(inp) self._out_size = parse_batch_shape(out) self._hook_handle.remove() return self._module.register_forward_hook(hook) def detach_hook(self): """ Removes the forward hook if it was not already removed in the forward pass. Will be called after the summary is created. """ if self._hook_handle is not None: self._hook_handle.remove() @property def in_size(self) -> Union[str, List]: return self._in_size or UNKNOWN_SIZE @property def out_size(self) -> Union[str, List]: return self._out_size or UNKNOWN_SIZE @property def layer_type(self) -> str: """ Returns the class name of the module. """ return str(self._module.__class__.__name__) @property def num_parameters(self) -> int: """ Returns the number of parameters in this module. """ return sum(np.prod(p.shape) for p in self._module.parameters()) class ModelSummary(object): """ Generates a summary of all layers in a :class:`~pytorch_lightning.core.lightning.LightningModule`. Args: model: The model to summarize (also referred to as the root module) mode: Can be one of - `top` (default): only the top-level modules will be recorded (the children of the root module) - `full`: summarizes all layers and their submodules in the root module The string representation of this summary prints a table with columns containing the name, type and number of parameters for each layer. The root module may also have an attribute ``example_input_array`` as shown in the example below. If present, the root module will be called with it as input to determine the intermediate input- and output shapes of all layers. Supported are tensors and nested lists and tuples of tensors. All other types of inputs will be skipped and show as `?` in the summary table. The summary will also display `?` for layers not used in the forward pass. Example:: >>> import pytorch_lightning as pl >>> class LitModel(pl.LightningModule): ... ... def __init__(self): ... super().__init__() ... self.net = nn.Sequential(nn.Linear(256, 512), nn.BatchNorm1d(512)) ... self.example_input_array = torch.zeros(10, 256) # optional ... ... def forward(self, x): ... return self.net(x) ... >>> model = LitModel() >>> ModelSummary(model, mode='top') # doctest: +NORMALIZE_WHITESPACE | Name | Type | Params | In sizes | Out sizes ------------------------------------------------------------ 0 | net | Sequential | 132 K | [10, 256] | [10, 512] >>> ModelSummary(model, mode='full') # doctest: +NORMALIZE_WHITESPACE | Name | Type | Params | In sizes | Out sizes -------------------------------------------------------------- 0 | net | Sequential | 132 K | [10, 256] | [10, 512] 1 | net.0 | Linear | 131 K | [10, 256] | [10, 512] 2 | net.1 | BatchNorm1d | 1 K | [10, 512] | [10, 512] """ MODE_TOP = "top" MODE_FULL = "full" MODE_DEFAULT = MODE_TOP MODES = [MODE_FULL, MODE_TOP] def __init__(self, model, mode: str = MODE_DEFAULT): self._model = model self._mode = mode self._layer_summary = self.summarize() @property def named_modules(self) -> List[Tuple[str, nn.Module]]: if self._mode == ModelSummary.MODE_FULL: mods = self._model.named_modules() mods = list(mods)[1:] # do not include root module (LightningModule) elif self._mode == ModelSummary.MODE_TOP: # the children are the top-level modules mods = self._model.named_children() else: mods = [] return list(mods) @property def layer_names(self) -> List[str]: return list(self._layer_summary.keys()) @property def layer_types(self) -> List[str]: return [layer.layer_type for layer in self._layer_summary.values()] @property def in_sizes(self) -> List: return [layer.in_size for layer in self._layer_summary.values()] @property def out_sizes(self) -> List: return [layer.out_size for layer in self._layer_summary.values()] @property def param_nums(self) -> List[int]: return [layer.num_parameters for layer in self._layer_summary.values()] def summarize(self) -> Dict[str, LayerSummary]: summary = OrderedDict((name, LayerSummary(module)) for name, module in self.named_modules) if self._model.example_input_array is not None: self._forward_example_input() for layer in summary.values(): layer.detach_hook() return summary def _forward_example_input(self) -> None: """ Run the example input through each layer to get input- and output sizes. """ model = self._model trainer = self._model.trainer input_ = model.example_input_array input_ = model.transfer_batch_to_device(input_, model.device) if trainer is not None and trainer.amp_type == AMPType.NATIVE and not trainer.use_tpu: model.forward = torch.cuda.amp.autocast()(model.forward) mode = model.training model.eval() with torch.no_grad(): # let the model hooks collect the input- and output shapes if isinstance(input_, (list, tuple)): model(*input_) elif isinstance(input_, dict): model(**input_) else: model(input_) model.train(mode) # restore mode of module def __str__(self): """ Makes a summary listing with: Layer Name, Layer Type, Number of Parameters, Input Sizes, Output Sizes """ arrays = [ [" ", list(map(str, range(len(self._layer_summary))))], ["Name", self.layer_names], ["Type", self.layer_types], ["Params", list(map(get_human_readable_count, self.param_nums))], ] if self._model.example_input_array is not None: arrays.append(["In sizes", self.in_sizes]) arrays.append(["Out sizes", self.out_sizes]) return _format_summary_table(*arrays) def __repr__(self): return str(self) def parse_batch_shape(batch: Any) -> Union[str, List]: if hasattr(batch, "shape"): return list(batch.shape) if isinstance(batch, (list, tuple)): shape = [parse_batch_shape(el) for el in batch] return shape return UNKNOWN_SIZE def _format_summary_table(*cols) -> str: """ Takes in a number of arrays, each specifying a column in the summary table, and combines them all into one big string defining the summary table that are nicely formatted. """ n_rows = len(cols[0][1]) n_cols = 1 + len(cols) # Get formatting width of each column col_widths = [] for c in cols: col_width = max(len(str(a)) for a in c[1]) if n_rows else 0 col_width = max(col_width, len(c[0])) # minimum length is header length col_widths.append(col_width) # Formatting s = "{:<{}}" total_width = sum(col_widths) + 3 * n_cols header = [s.format(c[0], l) for c, l in zip(cols, col_widths)] # Summary = header + divider + Rest of table summary = " | ".join(header) + "\n" + "-" * total_width for i in range(n_rows): line = [] for c, l in zip(cols, col_widths): line.append(s.format(str(c[1][i]), l)) summary += "\n" + " | ".join(line) return summary def get_memory_profile(mode: str) -> Union[Dict[str, int], Dict[int, int]]: """ Get a profile of the current memory usage. Args: mode: There are two modes: - 'all' means return memory for all gpus - 'min_max' means return memory for max and min Return: A dictionary in which the keys are device ids as integers and values are memory usage as integers in MB. If mode is 'min_max', the dictionary will also contain two additional keys: - 'min_gpu_mem': the minimum memory usage in MB - 'max_gpu_mem': the maximum memory usage in MB """ memory_map = get_gpu_memory_map() if mode == "min_max": min_index, min_memory = min(memory_map.items(), key=lambda item: item[1]) max_index, max_memory = max(memory_map.items(), key=lambda item: item[1]) memory_map = {"min_gpu_mem": min_memory, "max_gpu_mem": max_memory} return memory_map def get_gpu_memory_map() -> Dict[str, int]: """Get the current gpu usage. Return: A dictionary in which the keys are device ids as integers and values are memory usage as integers in MB. """ result = subprocess.run( ["nvidia-smi", "--query-gpu=memory.used", "--format=csv,nounits,noheader",], encoding="utf-8", # capture_output=True, # valid for python version >=3.7 stdout=PIPE, stderr=PIPE, # for backward compatibility with python version 3.6 check=True, ) # Convert lines into a dictionary gpu_memory = [int(x) for x in result.stdout.strip().split(os.linesep)] gpu_memory_map = {f"gpu_{index}": memory for index, memory in enumerate(gpu_memory)} return gpu_memory_map def get_human_readable_count(number: int) -> str: """ Abbreviates an integer number with K, M, B, T for thousands, millions, billions and trillions, respectively. Examples: >>> get_human_readable_count(123) '123 ' >>> get_human_readable_count(1234) # (one thousand) '1 K' >>> get_human_readable_count(2e6) # (two million) '2 M' >>> get_human_readable_count(3e9) # (three billion) '3 B' >>> get_human_readable_count(4e12) # (four trillion) '4 T' >>> get_human_readable_count(5e15) # (more than trillion) '5,000 T' Args: number: a positive integer number Return: A string formatted according to the pattern described above. """ assert number >= 0 labels = PARAMETER_NUM_UNITS num_digits = int(np.floor(np.log10(number)) + 1 if number > 0 else 1) num_groups = int(np.ceil(num_digits / 3)) num_groups = min(num_groups, len(labels)) # don't abbreviate beyond trillions shift = -3 * (num_groups - 1) number = number * (10 ** shift) index = num_groups - 1 return f"{int(number):,d} {labels[index]}"