{ "cells": [ { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Defaulting to user installation because normal site-packages is not writeable\n", "Collecting torchtext\n", " Downloading torchtext-0.15.2-cp310-cp310-manylinux1_x86_64.whl (2.0 MB)\n", "\u001b[2K \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.0/2.0 MB\u001b[0m \u001b[31m1.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m[36m0:00:01\u001b[0m[36m0:00:01\u001b[0m:01\u001b[0m\n", "\u001b[?25hCollecting tqdm\n", " Using cached tqdm-4.65.0-py3-none-any.whl (77 kB)\n", "Requirement already satisfied: numpy in /home/gedin/.local/lib/python3.10/site-packages (from torchtext) (1.24.3)\n", "Collecting torchdata==0.6.1\n", " Downloading torchdata-0.6.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (4.6 MB)\n", "\u001b[2K 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sympy, nvidia-nvtx-cu11, nvidia-nccl-cu11, nvidia-cusparse-cu11, nvidia-curand-cu11, nvidia-cufft-cu11, nvidia-cuda-runtime-cu11, nvidia-cuda-nvrtc-cu11, nvidia-cuda-cupti-cu11, nvidia-cublas-cu11, networkx, filelock, nvidia-cusolver-cu11, nvidia-cudnn-cu11, triton, torch, torchdata, torchtext\n", "Successfully installed cmake-3.26.3 filelock-3.12.0 lit-16.0.5 mpmath-1.3.0 networkx-3.1 nvidia-cublas-cu11-11.10.3.66 nvidia-cuda-cupti-cu11-11.7.101 nvidia-cuda-nvrtc-cu11-11.7.99 nvidia-cuda-runtime-cu11-11.7.99 nvidia-cudnn-cu11-8.5.0.96 nvidia-cufft-cu11-10.9.0.58 nvidia-curand-cu11-10.2.10.91 nvidia-cusolver-cu11-11.4.0.1 nvidia-cusparse-cu11-11.7.4.91 nvidia-nccl-cu11-2.14.3 nvidia-nvtx-cu11-11.7.91 sympy-1.12 torch-2.0.1 torchdata-0.6.1 torchtext-0.15.2 tqdm-4.65.0 triton-2.0.0 typing-extensions-4.6.3\n" ] } ], "source": [ "!pip install torchtext" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [], "source": [ "train_file ='train/in.tsv.xz'\n", "test_file = 'dev-0/in.tsv.xz'\n", "out_file = 'dev-0/out.tsv'" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "from itertools import islice\n", "import regex as re\n", "import sys\n", "from torchtext.vocab import build_vocab_from_iterator\n", "import lzma\n", "import pickle\n", "import re\n", "import torch\n", "from torch import nn\n", "from torch.utils.data import IterableDataset\n", "import itertools\n", "from torch.utils.data import DataLoader\n", "import gc" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "embed_size = 300\n", "device = 'cuda'\n", "vocab_size = 25000\n", "batch_s = 3200\n", "learning_rate = 0.0001\n", "epochs = 4\n", "k = 20 #top k words\n", "wildcard_minweight = 0.1" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "scrolled": true }, "outputs": [], "source": [ "###preprocessing\n", "def preprocess(line):\n", " line = get_rid_of_header(line)\n", " line = replace_endline(line)\n", " return line\n", "\n", "def get_rid_of_header(line):\n", " line = line.split('\\t')[6:]\n", " return \"\".join(line)\n", " \n", "def replace_endline(line):\n", " line = line.replace(\"\\\\n\", \" \")\n", " return line\n", "\n", "\n", "def get_last_word(text):\n", " \"\"\"Return the last word of a string.\"\"\"\n", " last_word = \"\"\n", " for i in range(len(text)-1, -1, -1):\n", " if text[i] == ' ':\n", " return last_word[::-1].rstrip()\n", " else:\n", " last_word += text[i]\n", " return last_word[::-1].rstrip()\n", "\n", "def get_first_word(text):\n", " \"\"\"Return the first word of a string.\"\"\"\n", " word = \"\"\n", " for i in range(len(text)-1):\n", " if text[i] == ' ':\n", " return word\n", " else:\n", " word += text[i]\n", " return word\n", "\n", "\n", "def get_words_from_line(line):\n", " line = line.rstrip()\n", " yield ''\n", " line = preprocess(line)\n", " for t in line.split(' '):\n", " yield t\n", " yield ''\n", "\n", "\n", "def get_word_lines_from_file(file_name):\n", " n = 0\n", " with lzma.open(file_name, 'r') as fh:\n", " for line in fh:\n", " n+=1\n", " if n%1000==0:\n", " print(n)\n", " yield get_words_from_line(line.decode('utf-8'))" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1000\n", "2000\n", "3000\n", "4000\n", "5000\n", "6000\n", "7000\n", "8000\n", "9000\n", "10000\n", "11000\n", "12000\n", "13000\n", "14000\n", "15000\n", "16000\n", "17000\n", "18000\n", "19000\n", "20000\n", "21000\n", "22000\n", "23000\n", "24000\n", "25000\n", "26000\n", "27000\n", "28000\n", "29000\n", "30000\n", "31000\n", "32000\n", "33000\n", "34000\n", "35000\n", "36000\n", "37000\n", "38000\n", "39000\n", "40000\n", "41000\n", "42000\n", "43000\n", "44000\n", "45000\n", "46000\n", "47000\n", "48000\n", "49000\n", "50000\n", "51000\n", "52000\n", "53000\n", "54000\n", "55000\n", "56000\n", "57000\n", "58000\n", "59000\n", 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2, 10, 2000])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Definicja sieci\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Naszą prostą sieć neuronową zaimplementujemy używając frameworku PyTorch.\n", "\n" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "class SimpleBigramNeuralLanguageModel(nn.Module):\n", " def __init__(self, vocabulary_size, embedding_size):\n", " super(SimpleBigramNeuralLanguageModel, self).__init__()\n", " self.model = nn.Sequential(\n", " nn.Embedding(vocabulary_size, embedding_size),\n", " nn.Linear(embedding_size, vocabulary_size),\n", " nn.Softmax()\n", " )\n", " \n", " def forward(self, x):\n", " return self.model(x)\n", "\n", "with open('filename.pickle','rb') as handle:\n", " vocab = pickle.load(handle)\n", "\n", "vocab.set_default_index(vocab[''])" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Help on Vocab in module torchtext.vocab.vocab object:\n", "\n", "class Vocab(torch.nn.modules.module.Module)\n", " | Vocab(vocab) -> None\n", " | \n", " | Base class for all neural network modules.\n", " | \n", " | Your models should also subclass this class.\n", " | \n", " | Modules can also contain other Modules, allowing to nest them in\n", " | a tree structure. You can assign the submodules as regular attributes::\n", " | \n", " | import torch.nn as nn\n", " | import torch.nn.functional as F\n", " | \n", " | class Model(nn.Module):\n", " | def __init__(self):\n", " | super().__init__()\n", " | self.conv1 = nn.Conv2d(1, 20, 5)\n", " | self.conv2 = nn.Conv2d(20, 20, 5)\n", " | \n", " | def forward(self, x):\n", " | x = F.relu(self.conv1(x))\n", " | return F.relu(self.conv2(x))\n", " | \n", " | Submodules assigned in this way will be registered, and will have their\n", " | parameters converted too when you call :meth:`to`, etc.\n", " | \n", " | .. note::\n", " | As per the example above, an ``__init__()`` call to the parent class\n", " | must be made before assignment on the child.\n", " | \n", " | :ivar training: Boolean represents whether this module is in training or\n", " | evaluation mode.\n", " | :vartype training: bool\n", " | \n", " | Method resolution order:\n", " | Vocab\n", " | torch.nn.modules.module.Module\n", " | builtins.object\n", " | \n", " | Methods defined here:\n", " | \n", " | __contains__(self, token: str) -> bool\n", " | Args:\n", " | token: The token for which to check the membership.\n", " | \n", " | Returns:\n", " | Whether the token is member of vocab or not.\n", " | \n", " | __getitem__(self, token: str) -> int\n", " | Args:\n", " | token: The token used to lookup the corresponding index.\n", " | \n", " | Returns:\n", " | The index corresponding to the associated token.\n", " | \n", " | __init__(self, vocab) -> None\n", " | Initializes internal Module state, shared by both nn.Module and ScriptModule.\n", " | \n", " | __len__(self) -> int\n", " | Returns:\n", " | The length of the vocab.\n", " | \n", " | __prepare_scriptable__(self)\n", " | Return a JITable Vocab.\n", " | \n", " | append_token(self, token: str) -> None\n", " | Args:\n", " | token: The token used to lookup the corresponding index.\n", " | \n", " | Raises:\n", " | RuntimeError: If `token` already exists in the vocab\n", " | \n", " | forward(self, tokens: List[str]) -> List[int]\n", " | Calls the `lookup_indices` method\n", " | \n", " | Args:\n", " | tokens: a list of tokens used to lookup their corresponding `indices`.\n", " | \n", " | Returns:\n", " | The indices associated with a list of `tokens`.\n", " | \n", " | get_default_index(self) -> Union[int, NoneType]\n", " | Returns:\n", " | Value of default index if it is set.\n", " | \n", " | get_itos(self) -> List[str]\n", " | Returns:\n", " | List mapping indices to tokens.\n", " | \n", " | get_stoi(self) -> Dict[str, int]\n", " | Returns:\n", " | Dictionary mapping tokens to indices.\n", " | \n", " | insert_token(self, token: str, index: int) -> None\n", " | Args:\n", " | token: The token used to lookup the corresponding index.\n", " | index: The index corresponding to the associated token.\n", " | Raises:\n", " | RuntimeError: If `index` is not in range [0, Vocab.size()] or if `token` already exists in the vocab.\n", " | \n", " | lookup_indices(self, tokens: List[str]) -> List[int]\n", " | Args:\n", " | tokens: the tokens used to lookup their corresponding `indices`.\n", " | \n", " | Returns:\n", " | The 'indices` associated with `tokens`.\n", " | \n", " | lookup_token(self, index: int) -> str\n", " | Args:\n", " | index: The index corresponding to the associated token.\n", " | \n", " | Returns:\n", " | token: The token used to lookup the corresponding index.\n", " | \n", " | Raises:\n", " | RuntimeError: If `index` not in range [0, itos.size()).\n", " | \n", " | lookup_tokens(self, indices: List[int]) -> List[str]\n", " | Args:\n", " | indices: The `indices` used to lookup their corresponding`tokens`.\n", " | \n", " | Returns:\n", " | The `tokens` associated with `indices`.\n", " | \n", " | Raises:\n", " | RuntimeError: If an index within `indices` is not int range [0, itos.size()).\n", " | \n", " | set_default_index(self, index: Union[int, NoneType]) -> None\n", " | Args:\n", " | index: Value of default index. This index will be returned when OOV token is queried.\n", " | \n", " | ----------------------------------------------------------------------\n", " | Readonly properties defined here:\n", " | \n", " | is_jitable\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data and other attributes defined here:\n", " | \n", " | __jit_unused_properties__ = ['is_jitable']\n", " | \n", " | ----------------------------------------------------------------------\n", " | Methods inherited from torch.nn.modules.module.Module:\n", " | \n", " | __call__ = _call_impl(self, *args, **kwargs)\n", " | \n", " | __delattr__(self, name)\n", " | Implement delattr(self, name).\n", " | \n", " | __dir__(self)\n", " | Default dir() implementation.\n", " | \n", " | __getattr__(self, name: str) -> Union[torch.Tensor, ForwardRef('Module')]\n", " | \n", " | __repr__(self)\n", " | Return repr(self).\n", " | \n", " | __setattr__(self, name: str, value: Union[torch.Tensor, ForwardRef('Module')]) -> None\n", " | Implement setattr(self, name, value).\n", " | \n", " | __setstate__(self, state)\n", " | \n", " | add_module(self, name: str, module: Union[ForwardRef('Module'), NoneType]) -> None\n", " | Adds a child module to the current module.\n", " | \n", " | The module can be accessed as an attribute using the given name.\n", " | \n", " | Args:\n", " | name (str): name of the child module. The child module can be\n", " | accessed from this module using the given name\n", " | module (Module): child module to be added to the module.\n", " | \n", " | apply(self: ~T, fn: Callable[[ForwardRef('Module')], NoneType]) -> ~T\n", " | Applies ``fn`` recursively to every submodule (as returned by ``.children()``)\n", " | as well as self. Typical use includes initializing the parameters of a model\n", " | (see also :ref:`nn-init-doc`).\n", " | \n", " | Args:\n", " | fn (:class:`Module` -> None): function to be applied to each submodule\n", " | \n", " | Returns:\n", " | Module: self\n", " | \n", " | Example::\n", " | \n", " | >>> @torch.no_grad()\n", " | >>> def init_weights(m):\n", " | >>> print(m)\n", " | >>> if type(m) == nn.Linear:\n", " | >>> m.weight.fill_(1.0)\n", " | >>> print(m.weight)\n", " | >>> net = nn.Sequential(nn.Linear(2, 2), nn.Linear(2, 2))\n", " | >>> net.apply(init_weights)\n", " | Linear(in_features=2, out_features=2, bias=True)\n", " | Parameter containing:\n", " | tensor([[1., 1.],\n", " | [1., 1.]], requires_grad=True)\n", " | Linear(in_features=2, out_features=2, bias=True)\n", " | Parameter containing:\n", " | tensor([[1., 1.],\n", " | [1., 1.]], requires_grad=True)\n", " | Sequential(\n", " | (0): Linear(in_features=2, out_features=2, bias=True)\n", " | (1): Linear(in_features=2, out_features=2, bias=True)\n", " | )\n", " | \n", " | bfloat16(self: ~T) -> ~T\n", " | Casts all floating point parameters and buffers to ``bfloat16`` datatype.\n", " | \n", " | .. note::\n", " | This method modifies the module in-place.\n", " | \n", " | Returns:\n", " | Module: self\n", " | \n", " | buffers(self, recurse: bool = True) -> Iterator[torch.Tensor]\n", " | Returns an iterator over module buffers.\n", " | \n", " | Args:\n", " | recurse (bool): if True, then yields buffers of this module\n", " | and all submodules. Otherwise, yields only buffers that\n", " | are direct members of this module.\n", " | \n", " | Yields:\n", " | torch.Tensor: module buffer\n", " | \n", " | Example::\n", " | \n", " | >>> # xdoctest: +SKIP(\"undefined vars\")\n", " | >>> for buf in model.buffers():\n", " | >>> print(type(buf), buf.size())\n", " | (20L,)\n", " | (20L, 1L, 5L, 5L)\n", " | \n", " | children(self) -> Iterator[ForwardRef('Module')]\n", " | Returns an iterator over immediate children modules.\n", " | \n", " | Yields:\n", " | Module: a child module\n", " | \n", " | cpu(self: ~T) -> ~T\n", " | Moves all model parameters and buffers to the CPU.\n", " | \n", " | .. note::\n", " | This method modifies the module in-place.\n", " | \n", " | Returns:\n", " | Module: self\n", " | \n", " | cuda(self: ~T, device: Union[int, torch.device, NoneType] = None) -> ~T\n", " | Moves all model parameters and buffers to the GPU.\n", " | \n", " | This also makes associated parameters and buffers different objects. So\n", " | it should be called before constructing optimizer if the module will\n", " | live on GPU while being optimized.\n", " | \n", " | .. note::\n", " | This method modifies the module in-place.\n", " | \n", " | Args:\n", " | device (int, optional): if specified, all parameters will be\n", " | copied to that device\n", " | \n", " | Returns:\n", " | Module: self\n", " | \n", " | double(self: ~T) -> ~T\n", " | Casts all floating point parameters and buffers to ``double`` datatype.\n", " | \n", " | .. note::\n", " | This method modifies the module in-place.\n", " | \n", " | Returns:\n", " | Module: self\n", " | \n", " | eval(self: ~T) -> ~T\n", " | Sets the module in evaluation mode.\n", " | \n", " | This has any effect only on certain modules. See documentations of\n", " | particular modules for details of their behaviors in training/evaluation\n", " | mode, if they are affected, e.g. :class:`Dropout`, :class:`BatchNorm`,\n", " | etc.\n", " | \n", " | This is equivalent with :meth:`self.train(False) `.\n", " | \n", " | See :ref:`locally-disable-grad-doc` for a comparison between\n", " | `.eval()` and several similar mechanisms that may be confused with it.\n", " | \n", " | Returns:\n", " | Module: self\n", " | \n", " | extra_repr(self) -> str\n", " | Set the extra representation of the module\n", " | \n", " | To print customized extra information, you should re-implement\n", " | this method in your own modules. Both single-line and multi-line\n", " | strings are acceptable.\n", " | \n", " | float(self: ~T) -> ~T\n", " | Casts all floating point parameters and buffers to ``float`` datatype.\n", " | \n", " | .. note::\n", " | This method modifies the module in-place.\n", " | \n", " | Returns:\n", " | Module: self\n", " | \n", " | get_buffer(self, target: str) -> 'Tensor'\n", " | Returns the buffer given by ``target`` if it exists,\n", " | otherwise throws an error.\n", " | \n", " | See the docstring for ``get_submodule`` for a more detailed\n", " | explanation of this method's functionality as well as how to\n", " | correctly specify ``target``.\n", " | \n", " | Args:\n", " | target: The fully-qualified string name of the buffer\n", " | to look for. (See ``get_submodule`` for how to specify a\n", " | fully-qualified string.)\n", " | \n", " | Returns:\n", " | torch.Tensor: The buffer referenced by ``target``\n", " | \n", " | Raises:\n", " | AttributeError: If the target string references an invalid\n", " | path or resolves to something that is not a\n", " | buffer\n", " | \n", " | get_extra_state(self) -> Any\n", " | Returns any extra state to include in the module's state_dict.\n", " | Implement this and a corresponding :func:`set_extra_state` for your module\n", " | if you need to store extra state. This function is called when building the\n", " | module's `state_dict()`.\n", " | \n", " | Note that extra state should be picklable to ensure working serialization\n", " | of the state_dict. We only provide provide backwards compatibility guarantees\n", " | for serializing Tensors; other objects may break backwards compatibility if\n", " | their serialized pickled form changes.\n", " | \n", " | Returns:\n", " | object: Any extra state to store in the module's state_dict\n", " | \n", " | get_parameter(self, target: str) -> 'Parameter'\n", " | Returns the parameter given by ``target`` if it exists,\n", " | otherwise throws an error.\n", " | \n", " | See the docstring for ``get_submodule`` for a more detailed\n", " | explanation of this method's functionality as well as how to\n", " | correctly specify ``target``.\n", " | \n", " | Args:\n", " | target: The fully-qualified string name of the Parameter\n", " | to look for. (See ``get_submodule`` for how to specify a\n", " | fully-qualified string.)\n", " | \n", " | Returns:\n", " | torch.nn.Parameter: The Parameter referenced by ``target``\n", " | \n", " | Raises:\n", " | AttributeError: If the target string references an invalid\n", " | path or resolves to something that is not an\n", " | ``nn.Parameter``\n", " | \n", " | get_submodule(self, target: str) -> 'Module'\n", " | Returns the submodule given by ``target`` if it exists,\n", " | otherwise throws an error.\n", " | \n", " | For example, let's say you have an ``nn.Module`` ``A`` that\n", " | looks like this:\n", " | \n", " | .. code-block:: text\n", " | \n", " | A(\n", " | (net_b): Module(\n", " | (net_c): Module(\n", " | (conv): Conv2d(16, 33, kernel_size=(3, 3), stride=(2, 2))\n", " | )\n", " | (linear): Linear(in_features=100, out_features=200, bias=True)\n", " | )\n", " | )\n", " | \n", " | (The diagram shows an ``nn.Module`` ``A``. ``A`` has a nested\n", " | submodule ``net_b``, which itself has two submodules ``net_c``\n", " | and ``linear``. ``net_c`` then has a submodule ``conv``.)\n", " | \n", " | To check whether or not we have the ``linear`` submodule, we\n", " | would call ``get_submodule(\"net_b.linear\")``. To check whether\n", " | we have the ``conv`` submodule, we would call\n", " | ``get_submodule(\"net_b.net_c.conv\")``.\n", " | \n", " | The runtime of ``get_submodule`` is bounded by the degree\n", " | of module nesting in ``target``. A query against\n", " | ``named_modules`` achieves the same result, but it is O(N) in\n", " | the number of transitive modules. So, for a simple check to see\n", " | if some submodule exists, ``get_submodule`` should always be\n", " | used.\n", " | \n", " | Args:\n", " | target: The fully-qualified string name of the submodule\n", " | to look for. (See above example for how to specify a\n", " | fully-qualified string.)\n", " | \n", " | Returns:\n", " | torch.nn.Module: The submodule referenced by ``target``\n", " | \n", " | Raises:\n", " | AttributeError: If the target string references an invalid\n", " | path or resolves to something that is not an\n", " | ``nn.Module``\n", " | \n", " | half(self: ~T) -> ~T\n", " | Casts all floating point parameters and buffers to ``half`` datatype.\n", " | \n", " | .. note::\n", " | This method modifies the module in-place.\n", " | \n", " | Returns:\n", " | Module: self\n", " | \n", " | ipu(self: ~T, device: Union[int, torch.device, NoneType] = None) -> ~T\n", " | Moves all model parameters and buffers to the IPU.\n", " | \n", " | This also makes associated parameters and buffers different objects. So\n", " | it should be called before constructing optimizer if the module will\n", " | live on IPU while being optimized.\n", " | \n", " | .. note::\n", " | This method modifies the module in-place.\n", " | \n", " | Arguments:\n", " | device (int, optional): if specified, all parameters will be\n", " | copied to that device\n", " | \n", " | Returns:\n", " | Module: self\n", " | \n", " | load_state_dict(self, state_dict: Mapping[str, Any], strict: bool = True)\n", " | Copies parameters and buffers from :attr:`state_dict` into\n", " | this module and its descendants. If :attr:`strict` is ``True``, then\n", " | the keys of :attr:`state_dict` must exactly match the keys returned\n", " | by this module's :meth:`~torch.nn.Module.state_dict` function.\n", " | \n", " | Args:\n", " | state_dict (dict): a dict containing parameters and\n", " | persistent buffers.\n", " | strict (bool, optional): whether to strictly enforce that the keys\n", " | in :attr:`state_dict` match the keys returned by this module's\n", " | :meth:`~torch.nn.Module.state_dict` function. Default: ``True``\n", " | \n", " | Returns:\n", " | ``NamedTuple`` with ``missing_keys`` and ``unexpected_keys`` fields:\n", " | * **missing_keys** is a list of str containing the missing keys\n", " | * **unexpected_keys** is a list of str containing the unexpected keys\n", " | \n", " | Note:\n", " | If a parameter or buffer is registered as ``None`` and its corresponding key\n", " | exists in :attr:`state_dict`, :meth:`load_state_dict` will raise a\n", " | ``RuntimeError``.\n", " | \n", " | modules(self) -> Iterator[ForwardRef('Module')]\n", " | Returns an iterator over all modules in the network.\n", " | \n", " | Yields:\n", " | Module: a module in the network\n", " | \n", " | Note:\n", " | Duplicate modules are returned only once. In the following\n", " | example, ``l`` will be returned only once.\n", " | \n", " | Example::\n", " | \n", " | >>> l = nn.Linear(2, 2)\n", " | >>> net = nn.Sequential(l, l)\n", " | >>> for idx, m in enumerate(net.modules()):\n", " | ... print(idx, '->', m)\n", " | \n", " | 0 -> Sequential(\n", " | (0): Linear(in_features=2, out_features=2, bias=True)\n", " | (1): Linear(in_features=2, out_features=2, bias=True)\n", " | )\n", " | 1 -> Linear(in_features=2, out_features=2, bias=True)\n", " | \n", " | named_buffers(self, prefix: str = '', recurse: bool = True, remove_duplicate: bool = True) -> Iterator[Tuple[str, torch.Tensor]]\n", " | Returns an iterator over module buffers, yielding both the\n", " | name of the buffer as well as the buffer itself.\n", " | \n", " | Args:\n", " | prefix (str): prefix to prepend to all buffer names.\n", " | recurse (bool, optional): if True, then yields buffers of this module\n", " | and all submodules. Otherwise, yields only buffers that\n", " | are direct members of this module. Defaults to True.\n", " | remove_duplicate (bool, optional): whether to remove the duplicated buffers in the result. Defaults to True.\n", " | \n", " | Yields:\n", " | (str, torch.Tensor): Tuple containing the name and buffer\n", " | \n", " | Example::\n", " | \n", " | >>> # xdoctest: +SKIP(\"undefined vars\")\n", " | >>> for name, buf in self.named_buffers():\n", " | >>> if name in ['running_var']:\n", " | >>> print(buf.size())\n", " | \n", " | named_children(self) -> Iterator[Tuple[str, ForwardRef('Module')]]\n", " | Returns an iterator over immediate children modules, yielding both\n", " | the name of the module as well as the module itself.\n", " | \n", " | Yields:\n", " | (str, Module): Tuple containing a name and child module\n", " | \n", " | Example::\n", " | \n", " | >>> # xdoctest: +SKIP(\"undefined vars\")\n", " | >>> for name, module in model.named_children():\n", " | >>> if name in ['conv4', 'conv5']:\n", " | >>> print(module)\n", " | \n", " | named_modules(self, memo: Union[Set[ForwardRef('Module')], NoneType] = None, prefix: str = '', remove_duplicate: bool = True)\n", " | Returns an iterator over all modules in the network, yielding\n", " | both the name of the module as well as the module itself.\n", " | \n", " | Args:\n", " | memo: a memo to store the set of modules already added to the result\n", " | prefix: a prefix that will be added to the name of the module\n", " | remove_duplicate: whether to remove the duplicated module instances in the result\n", " | or not\n", " | \n", " | Yields:\n", " | (str, Module): Tuple of name and module\n", " | \n", " | Note:\n", " | Duplicate modules are returned only once. In the following\n", " | example, ``l`` will be returned only once.\n", " | \n", " | Example::\n", " | \n", " | >>> l = nn.Linear(2, 2)\n", " | >>> net = nn.Sequential(l, l)\n", " | >>> for idx, m in enumerate(net.named_modules()):\n", " | ... print(idx, '->', m)\n", " | \n", " | 0 -> ('', Sequential(\n", " | (0): Linear(in_features=2, out_features=2, bias=True)\n", " | (1): Linear(in_features=2, out_features=2, bias=True)\n", " | ))\n", " | 1 -> ('0', Linear(in_features=2, out_features=2, bias=True))\n", " | \n", " | named_parameters(self, prefix: str = '', recurse: bool = True, remove_duplicate: bool = True) -> Iterator[Tuple[str, torch.nn.parameter.Parameter]]\n", " | Returns an iterator over module parameters, yielding both the\n", " | name of the parameter as well as the parameter itself.\n", " | \n", " | Args:\n", " | prefix (str): prefix to prepend to all parameter names.\n", " | recurse (bool): if True, then yields parameters of this module\n", " | and all submodules. Otherwise, yields only parameters that\n", " | are direct members of this module.\n", " | remove_duplicate (bool, optional): whether to remove the duplicated\n", " | parameters in the result. Defaults to True.\n", " | \n", " | Yields:\n", " | (str, Parameter): Tuple containing the name and parameter\n", " | \n", " | Example::\n", " | \n", " | >>> # xdoctest: +SKIP(\"undefined vars\")\n", " | >>> for name, param in self.named_parameters():\n", " | >>> if name in ['bias']:\n", " | >>> print(param.size())\n", " | \n", " | parameters(self, recurse: bool = True) -> Iterator[torch.nn.parameter.Parameter]\n", " | Returns an iterator over module parameters.\n", " | \n", " | This is typically passed to an optimizer.\n", " | \n", " | Args:\n", " | recurse (bool): if True, then yields parameters of this module\n", " | and all submodules. Otherwise, yields only parameters that\n", " | are direct members of this module.\n", " | \n", " | Yields:\n", " | Parameter: module parameter\n", " | \n", " | Example::\n", " | \n", " | >>> # xdoctest: +SKIP(\"undefined vars\")\n", " | >>> for param in model.parameters():\n", " | >>> print(type(param), param.size())\n", " | (20L,)\n", " | (20L, 1L, 5L, 5L)\n", " | \n", " | register_backward_hook(self, hook: Callable[[ForwardRef('Module'), Union[Tuple[torch.Tensor, ...], torch.Tensor], Union[Tuple[torch.Tensor, ...], torch.Tensor]], Union[NoneType, Tuple[torch.Tensor, ...], torch.Tensor]]) -> torch.utils.hooks.RemovableHandle\n", " | Registers a backward hook on the module.\n", " | \n", " | This function is deprecated in favor of :meth:`~torch.nn.Module.register_full_backward_hook` and\n", " | the behavior of this function will change in future versions.\n", " | \n", " | Returns:\n", " | :class:`torch.utils.hooks.RemovableHandle`:\n", " | a handle that can be used to remove the added hook by calling\n", " | ``handle.remove()``\n", " | \n", " | register_buffer(self, name: str, tensor: Union[torch.Tensor, NoneType], persistent: bool = True) -> None\n", " | Adds a buffer to the module.\n", " | \n", " | This is typically used to register a buffer that should not to be\n", " | considered a model parameter. For example, BatchNorm's ``running_mean``\n", " | is not a parameter, but is part of the module's state. Buffers, by\n", " | default, are persistent and will be saved alongside parameters. This\n", " | behavior can be changed by setting :attr:`persistent` to ``False``. The\n", " | only difference between a persistent buffer and a non-persistent buffer\n", " | is that the latter will not be a part of this module's\n", " | :attr:`state_dict`.\n", " | \n", " | Buffers can be accessed as attributes using given names.\n", " | \n", " | Args:\n", " | name (str): name of the buffer. The buffer can be accessed\n", " | from this module using the given name\n", " | tensor (Tensor or None): buffer to be registered. If ``None``, then operations\n", " | that run on buffers, such as :attr:`cuda`, are ignored. If ``None``,\n", " | the buffer is **not** included in the module's :attr:`state_dict`.\n", " | persistent (bool): whether the buffer is part of this module's\n", " | :attr:`state_dict`.\n", " | \n", " | Example::\n", " | \n", " | >>> # xdoctest: +SKIP(\"undefined vars\")\n", " | >>> self.register_buffer('running_mean', torch.zeros(num_features))\n", " | \n", " | register_forward_hook(self, hook: Union[Callable[[~T, Tuple[Any, ...], Any], Union[Any, NoneType]], Callable[[~T, Tuple[Any, ...], Dict[str, Any], Any], Union[Any, NoneType]]], *, prepend: bool = False, with_kwargs: bool = False) -> torch.utils.hooks.RemovableHandle\n", " | Registers a forward hook on the module.\n", " | \n", " | The hook will be called every time after :func:`forward` has computed an output.\n", " | \n", " | If ``with_kwargs`` is ``False`` or not specified, the input contains only\n", " | the positional arguments given to the module. Keyword arguments won't be\n", " | passed to the hooks and only to the ``forward``. The hook can modify the\n", " | output. It can modify the input inplace but it will not have effect on\n", " | forward since this is called after :func:`forward` is called. The hook\n", " | should have the following signature::\n", " | \n", " | hook(module, args, output) -> None or modified output\n", " | \n", " | If ``with_kwargs`` is ``True``, the forward hook will be passed the\n", " | ``kwargs`` given to the forward function and be expected to return the\n", " | output possibly modified. The hook should have the following signature::\n", " | \n", " | hook(module, args, kwargs, output) -> None or modified output\n", " | \n", " | Args:\n", " | hook (Callable): The user defined hook to be registered.\n", " | prepend (bool): If ``True``, the provided ``hook`` will be fired\n", " | before all existing ``forward`` hooks on this\n", " | :class:`torch.nn.modules.Module`. Otherwise, the provided\n", " | ``hook`` will be fired after all existing ``forward`` hooks on\n", " | this :class:`torch.nn.modules.Module`. Note that global\n", " | ``forward`` hooks registered with\n", " | :func:`register_module_forward_hook` will fire before all hooks\n", " | registered by this method.\n", " | Default: ``False``\n", " | with_kwargs (bool): If ``True``, the ``hook`` will be passed the\n", " | kwargs given to the forward function.\n", " | Default: ``False``\n", " | \n", " | Returns:\n", " | :class:`torch.utils.hooks.RemovableHandle`:\n", " | a handle that can be used to remove the added hook by calling\n", " | ``handle.remove()``\n", " | \n", " | register_forward_pre_hook(self, hook: Union[Callable[[~T, Tuple[Any, ...]], Union[Any, NoneType]], Callable[[~T, Tuple[Any, ...], Dict[str, Any]], Union[Tuple[Any, Dict[str, Any]], NoneType]]], *, prepend: bool = False, with_kwargs: bool = False) -> torch.utils.hooks.RemovableHandle\n", " | Registers a forward pre-hook on the module.\n", " | \n", " | The hook will be called every time before :func:`forward` is invoked.\n", " | \n", " | \n", " | If ``with_kwargs`` is false or not specified, the input contains only\n", " | the positional arguments given to the module. Keyword arguments won't be\n", " | passed to the hooks and only to the ``forward``. The hook can modify the\n", " | input. User can either return a tuple or a single modified value in the\n", " | hook. We will wrap the value into a tuple if a single value is returned\n", " | (unless that value is already a tuple). The hook should have the\n", " | following signature::\n", " | \n", " | hook(module, args) -> None or modified input\n", " | \n", " | If ``with_kwargs`` is true, the forward pre-hook will be passed the\n", " | kwargs given to the forward function. And if the hook modifies the\n", " | input, both the args and kwargs should be returned. The hook should have\n", " | the following signature::\n", " | \n", " | hook(module, args, kwargs) -> None or a tuple of modified input and kwargs\n", " | \n", " | Args:\n", " | hook (Callable): The user defined hook to be registered.\n", " | prepend (bool): If true, the provided ``hook`` will be fired before\n", " | all existing ``forward_pre`` hooks on this\n", " | :class:`torch.nn.modules.Module`. Otherwise, the provided\n", " | ``hook`` will be fired after all existing ``forward_pre`` hooks\n", " | on this :class:`torch.nn.modules.Module`. Note that global\n", " | ``forward_pre`` hooks registered with\n", " | :func:`register_module_forward_pre_hook` will fire before all\n", " | hooks registered by this method.\n", " | Default: ``False``\n", " | with_kwargs (bool): If true, the ``hook`` will be passed the kwargs\n", " | given to the forward function.\n", " | Default: ``False``\n", " | \n", " | Returns:\n", " | :class:`torch.utils.hooks.RemovableHandle`:\n", " | a handle that can be used to remove the added hook by calling\n", " | ``handle.remove()``\n", " | \n", " | register_full_backward_hook(self, hook: Callable[[ForwardRef('Module'), Union[Tuple[torch.Tensor, ...], torch.Tensor], Union[Tuple[torch.Tensor, ...], torch.Tensor]], Union[NoneType, Tuple[torch.Tensor, ...], torch.Tensor]], prepend: bool = False) -> torch.utils.hooks.RemovableHandle\n", " | Registers a backward hook on the module.\n", " | \n", " | The hook will be called every time the gradients with respect to a module\n", " | are computed, i.e. the hook will execute if and only if the gradients with\n", " | respect to module outputs are computed. The hook should have the following\n", " | signature::\n", " | \n", " | hook(module, grad_input, grad_output) -> tuple(Tensor) or None\n", " | \n", " | The :attr:`grad_input` and :attr:`grad_output` are tuples that contain the gradients\n", " | with respect to the inputs and outputs respectively. The hook should\n", " | not modify its arguments, but it can optionally return a new gradient with\n", " | respect to the input that will be used in place of :attr:`grad_input` in\n", " | subsequent computations. :attr:`grad_input` will only correspond to the inputs given\n", " | as positional arguments and all kwarg arguments are ignored. Entries\n", " | in :attr:`grad_input` and :attr:`grad_output` will be ``None`` for all non-Tensor\n", " | arguments.\n", " | \n", " | For technical reasons, when this hook is applied to a Module, its forward function will\n", " | receive a view of each Tensor passed to the Module. Similarly the caller will receive a view\n", " | of each Tensor returned by the Module's forward function.\n", " | \n", " | .. warning ::\n", " | Modifying inputs or outputs inplace is not allowed when using backward hooks and\n", " | will raise an error.\n", " | \n", " | Args:\n", " | hook (Callable): The user-defined hook to be registered.\n", " | prepend (bool): If true, the provided ``hook`` will be fired before\n", " | all existing ``backward`` hooks on this\n", " | :class:`torch.nn.modules.Module`. Otherwise, the provided\n", " | ``hook`` will be fired after all existing ``backward`` hooks on\n", " | this :class:`torch.nn.modules.Module`. Note that global\n", " | ``backward`` hooks registered with\n", " | :func:`register_module_full_backward_hook` will fire before\n", " | all hooks registered by this method.\n", " | \n", " | Returns:\n", " | :class:`torch.utils.hooks.RemovableHandle`:\n", " | a handle that can be used to remove the added hook by calling\n", " | ``handle.remove()``\n", " | \n", " | register_full_backward_pre_hook(self, hook: Callable[[ForwardRef('Module'), Union[Tuple[torch.Tensor, ...], torch.Tensor]], Union[NoneType, Tuple[torch.Tensor, ...], torch.Tensor]], prepend: bool = False) -> torch.utils.hooks.RemovableHandle\n", " | Registers a backward pre-hook on the module.\n", " | \n", " | The hook will be called every time the gradients for the module are computed.\n", " | The hook should have the following signature::\n", " | \n", " | hook(module, grad_output) -> Tensor or None\n", " | \n", " | The :attr:`grad_output` is a tuple. The hook should\n", " | not modify its arguments, but it can optionally return a new gradient with\n", " | respect to the output that will be used in place of :attr:`grad_output` in\n", " | subsequent computations. Entries in :attr:`grad_output` will be ``None`` for\n", " | all non-Tensor arguments.\n", " | \n", " | For technical reasons, when this hook is applied to a Module, its forward function will\n", " | receive a view of each Tensor passed to the Module. Similarly the caller will receive a view\n", " | of each Tensor returned by the Module's forward function.\n", " | \n", " | .. warning ::\n", " | Modifying inputs inplace is not allowed when using backward hooks and\n", " | will raise an error.\n", " | \n", " | Args:\n", " | hook (Callable): The user-defined hook to be registered.\n", " | prepend (bool): If true, the provided ``hook`` will be fired before\n", " | all existing ``backward_pre`` hooks on this\n", " | :class:`torch.nn.modules.Module`. Otherwise, the provided\n", " | ``hook`` will be fired after all existing ``backward_pre`` hooks\n", " | on this :class:`torch.nn.modules.Module`. Note that global\n", " | ``backward_pre`` hooks registered with\n", " | :func:`register_module_full_backward_pre_hook` will fire before\n", " | all hooks registered by this method.\n", " | \n", " | Returns:\n", " | :class:`torch.utils.hooks.RemovableHandle`:\n", " | a handle that can be used to remove the added hook by calling\n", " | ``handle.remove()``\n", " | \n", " | register_load_state_dict_post_hook(self, hook)\n", " | Registers a post hook to be run after module's ``load_state_dict``\n", " | is called.\n", " | \n", " | It should have the following signature::\n", " | hook(module, incompatible_keys) -> None\n", " | \n", " | The ``module`` argument is the current module that this hook is registered\n", " | on, and the ``incompatible_keys`` argument is a ``NamedTuple`` consisting\n", " | of attributes ``missing_keys`` and ``unexpected_keys``. ``missing_keys``\n", " | is a ``list`` of ``str`` containing the missing keys and\n", " | ``unexpected_keys`` is a ``list`` of ``str`` containing the unexpected keys.\n", " | \n", " | The given incompatible_keys can be modified inplace if needed.\n", " | \n", " | Note that the checks performed when calling :func:`load_state_dict` with\n", " | ``strict=True`` are affected by modifications the hook makes to\n", " | ``missing_keys`` or ``unexpected_keys``, as expected. Additions to either\n", " | set of keys will result in an error being thrown when ``strict=True``, and\n", " | clearing out both missing and unexpected keys will avoid an error.\n", " | \n", " | Returns:\n", " | :class:`torch.utils.hooks.RemovableHandle`:\n", " | a handle that can be used to remove the added hook by calling\n", " | ``handle.remove()``\n", " | \n", " | register_module(self, name: str, module: Union[ForwardRef('Module'), NoneType]) -> None\n", " | Alias for :func:`add_module`.\n", " | \n", " | register_parameter(self, name: str, param: Union[torch.nn.parameter.Parameter, NoneType]) -> None\n", " | Adds a parameter to the module.\n", " | \n", " | The parameter can be accessed as an attribute using given name.\n", " | \n", " | Args:\n", " | name (str): name of the parameter. The parameter can be accessed\n", " | from this module using the given name\n", " | param (Parameter or None): parameter to be added to the module. If\n", " | ``None``, then operations that run on parameters, such as :attr:`cuda`,\n", " | are ignored. If ``None``, the parameter is **not** included in the\n", " | module's :attr:`state_dict`.\n", " | \n", " | register_state_dict_pre_hook(self, hook)\n", " | These hooks will be called with arguments: ``self``, ``prefix``,\n", " | and ``keep_vars`` before calling ``state_dict`` on ``self``. The registered\n", " | hooks can be used to perform pre-processing before the ``state_dict``\n", " | call is made.\n", " | \n", " | requires_grad_(self: ~T, requires_grad: bool = True) -> ~T\n", " | Change if autograd should record operations on parameters in this\n", " | module.\n", " | \n", " | This method sets the parameters' :attr:`requires_grad` attributes\n", " | in-place.\n", " | \n", " | This method is helpful for freezing part of the module for finetuning\n", " | or training parts of a model individually (e.g., GAN training).\n", " | \n", " | See :ref:`locally-disable-grad-doc` for a comparison between\n", " | `.requires_grad_()` and several similar mechanisms that may be confused with it.\n", " | \n", " | Args:\n", " | requires_grad (bool): whether autograd should record operations on\n", " | parameters in this module. Default: ``True``.\n", " | \n", " | Returns:\n", " | Module: self\n", " | \n", " | set_extra_state(self, state: Any)\n", " | This function is called from :func:`load_state_dict` to handle any extra state\n", " | found within the `state_dict`. Implement this function and a corresponding\n", " | :func:`get_extra_state` for your module if you need to store extra state within its\n", " | `state_dict`.\n", " | \n", " | Args:\n", " | state (dict): Extra state from the `state_dict`\n", " | \n", " | share_memory(self: ~T) -> ~T\n", " | See :meth:`torch.Tensor.share_memory_`\n", " | \n", " | state_dict(self, *args, destination=None, prefix='', keep_vars=False)\n", " | Returns a dictionary containing references to the whole state of the module.\n", " | \n", " | Both parameters and persistent buffers (e.g. running averages) are\n", " | included. Keys are corresponding parameter and buffer names.\n", " | Parameters and buffers set to ``None`` are not included.\n", " | \n", " | .. note::\n", " | The returned object is a shallow copy. It contains references\n", " | to the module's parameters and buffers.\n", " | \n", " | .. warning::\n", " | Currently ``state_dict()`` also accepts positional arguments for\n", " | ``destination``, ``prefix`` and ``keep_vars`` in order. However,\n", " | this is being deprecated and keyword arguments will be enforced in\n", " | future releases.\n", " | \n", " | .. warning::\n", " | Please avoid the use of argument ``destination`` as it is not\n", " | designed for end-users.\n", " | \n", " | Args:\n", " | destination (dict, optional): If provided, the state of module will\n", " | be updated into the dict and the same object is returned.\n", " | Otherwise, an ``OrderedDict`` will be created and returned.\n", " | Default: ``None``.\n", " | prefix (str, optional): a prefix added to parameter and buffer\n", " | names to compose the keys in state_dict. Default: ``''``.\n", " | keep_vars (bool, optional): by default the :class:`~torch.Tensor` s\n", " | returned in the state dict are detached from autograd. If it's\n", " | set to ``True``, detaching will not be performed.\n", " | Default: ``False``.\n", " | \n", " | Returns:\n", " | dict:\n", " | a dictionary containing a whole state of the module\n", " | \n", " | Example::\n", " | \n", " | >>> # xdoctest: +SKIP(\"undefined vars\")\n", " | >>> module.state_dict().keys()\n", " | ['bias', 'weight']\n", " | \n", " | to(self, *args, **kwargs)\n", " | Moves and/or casts the parameters and buffers.\n", " | \n", " | This can be called as\n", " | \n", " | .. function:: to(device=None, dtype=None, non_blocking=False)\n", " | :noindex:\n", " | \n", " | .. function:: to(dtype, non_blocking=False)\n", " | :noindex:\n", " | \n", " | .. function:: to(tensor, non_blocking=False)\n", " | :noindex:\n", " | \n", " | .. function:: to(memory_format=torch.channels_last)\n", " | :noindex:\n", " | \n", " | Its signature is similar to :meth:`torch.Tensor.to`, but only accepts\n", " | floating point or complex :attr:`dtype`\\ s. In addition, this method will\n", " | only cast the floating point or complex parameters and buffers to :attr:`dtype`\n", " | (if given). The integral parameters and buffers will be moved\n", " | :attr:`device`, if that is given, but with dtypes unchanged. When\n", " | :attr:`non_blocking` is set, it tries to convert/move asynchronously\n", " | with respect to the host if possible, e.g., moving CPU Tensors with\n", " | pinned memory to CUDA devices.\n", " | \n", " | See below for examples.\n", " | \n", " | .. note::\n", " | This method modifies the module in-place.\n", " | \n", " | Args:\n", " | device (:class:`torch.device`): the desired device of the parameters\n", " | and buffers in this module\n", " | dtype (:class:`torch.dtype`): the desired floating point or complex dtype of\n", " | the parameters and buffers in this module\n", " | tensor (torch.Tensor): Tensor whose dtype and device are the desired\n", " | dtype and device for all parameters and buffers in this module\n", " | memory_format (:class:`torch.memory_format`): the desired memory\n", " | format for 4D parameters and buffers in this module (keyword\n", " | only argument)\n", " | \n", " | Returns:\n", " | Module: self\n", " | \n", " | Examples::\n", " | \n", " | >>> # xdoctest: +IGNORE_WANT(\"non-deterministic\")\n", " | >>> linear = nn.Linear(2, 2)\n", " | >>> linear.weight\n", " | Parameter containing:\n", " | tensor([[ 0.1913, -0.3420],\n", " | [-0.5113, -0.2325]])\n", " | >>> linear.to(torch.double)\n", " | Linear(in_features=2, out_features=2, bias=True)\n", " | >>> linear.weight\n", " | Parameter containing:\n", " | tensor([[ 0.1913, -0.3420],\n", " | [-0.5113, -0.2325]], dtype=torch.float64)\n", " | >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA1)\n", " | >>> gpu1 = torch.device(\"cuda:1\")\n", " | >>> linear.to(gpu1, dtype=torch.half, non_blocking=True)\n", " | Linear(in_features=2, out_features=2, bias=True)\n", " | >>> linear.weight\n", " | Parameter containing:\n", " | tensor([[ 0.1914, -0.3420],\n", " | [-0.5112, -0.2324]], dtype=torch.float16, device='cuda:1')\n", " | >>> cpu = torch.device(\"cpu\")\n", " | >>> linear.to(cpu)\n", " | Linear(in_features=2, out_features=2, bias=True)\n", " | >>> linear.weight\n", " | Parameter containing:\n", " | tensor([[ 0.1914, -0.3420],\n", " | [-0.5112, -0.2324]], dtype=torch.float16)\n", " | \n", " | >>> linear = nn.Linear(2, 2, bias=None).to(torch.cdouble)\n", " | >>> linear.weight\n", " | Parameter containing:\n", " | tensor([[ 0.3741+0.j, 0.2382+0.j],\n", " | [ 0.5593+0.j, -0.4443+0.j]], dtype=torch.complex128)\n", " | >>> linear(torch.ones(3, 2, dtype=torch.cdouble))\n", " | tensor([[0.6122+0.j, 0.1150+0.j],\n", " | [0.6122+0.j, 0.1150+0.j],\n", " | [0.6122+0.j, 0.1150+0.j]], dtype=torch.complex128)\n", " | \n", " | to_empty(self: ~T, *, device: Union[str, torch.device]) -> ~T\n", " | Moves the parameters and buffers to the specified device without copying storage.\n", " | \n", " | Args:\n", " | device (:class:`torch.device`): The desired device of the parameters\n", " | and buffers in this module.\n", " | \n", " | Returns:\n", " | Module: self\n", " | \n", " | train(self: ~T, mode: bool = True) -> ~T\n", " | Sets the module in training mode.\n", " | \n", " | This has any effect only on certain modules. See documentations of\n", " | particular modules for details of their behaviors in training/evaluation\n", " | mode, if they are affected, e.g. :class:`Dropout`, :class:`BatchNorm`,\n", " | etc.\n", " | \n", " | Args:\n", " | mode (bool): whether to set training mode (``True``) or evaluation\n", " | mode (``False``). Default: ``True``.\n", " | \n", " | Returns:\n", " | Module: self\n", " | \n", " | type(self: ~T, dst_type: Union[torch.dtype, str]) -> ~T\n", " | Casts all parameters and buffers to :attr:`dst_type`.\n", " | \n", " | .. note::\n", " | This method modifies the module in-place.\n", " | \n", " | Args:\n", " | dst_type (type or string): the desired type\n", " | \n", " | Returns:\n", " | Module: self\n", " | \n", " | xpu(self: ~T, device: Union[int, torch.device, NoneType] = None) -> ~T\n", " | Moves all model parameters and buffers to the XPU.\n", " | \n", " | This also makes associated parameters and buffers different objects. So\n", " | it should be called before constructing optimizer if the module will\n", " | live on XPU while being optimized.\n", " | \n", " | .. note::\n", " | This method modifies the module in-place.\n", " | \n", " | Arguments:\n", " | device (int, optional): if specified, all parameters will be\n", " | copied to that device\n", " | \n", " | Returns:\n", " | Module: self\n", " | \n", " | zero_grad(self, set_to_none: bool = True) -> None\n", " | Sets gradients of all model parameters to zero. See similar function\n", " | under :class:`torch.optim.Optimizer` for more context.\n", " | \n", " | Args:\n", " | set_to_none (bool): instead of setting to zero, set the grads to None.\n", " | See :meth:`torch.optim.Optimizer.zero_grad` for details.\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data descriptors inherited from torch.nn.modules.module.Module:\n", " | \n", " | __dict__\n", " | dictionary for instance variables (if defined)\n", " | \n", " | __weakref__\n", " | list of weak references to the object (if defined)\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data and other attributes inherited from torch.nn.modules.module.Module:\n", " | \n", " | T_destination = ~T_destination\n", " | \n", " | __annotations__ = {'__call__': typing.Callable[..., typing.Any], '_bac...\n", " | \n", " | call_super_init = False\n", " | \n", " | dump_patches = False\n", "\n" ] } ], "source": [ 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vocab_size)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<__main__.Bigrams object at 0x7fdd26d23940>\n" ] } ], "source": [ "print(train_dataset)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": true }, "outputs": [ { "data": { "text/plain": [ "'|===========================================================================|\\n| PyTorch CUDA memory summary, device ID 0 |\\n|---------------------------------------------------------------------------|\\n| CUDA OOMs: 1 | cudaMalloc retries: 1 |\\n|===========================================================================|\\n| Metric | Cur Usage | Peak Usage | Tot Alloc | Tot Freed |\\n|---------------------------------------------------------------------------|\\n| Allocated memory | 699613 KiB | 1903 MiB | 3735 MiB | 3052 MiB |\\n| from large pool | 699414 KiB | 1903 MiB | 3734 MiB | 3051 MiB |\\n| from small pool | 199 KiB | 1 MiB | 1 MiB | 1 MiB |\\n|---------------------------------------------------------------------------|\\n| Active memory | 699613 KiB | 1903 MiB | 3735 MiB | 3052 MiB |\\n| from large pool | 699414 KiB | 1903 MiB | 3734 MiB | 3051 MiB |\\n| from small pool | 199 KiB | 1 MiB | 1 MiB | 1 MiB |\\n|---------------------------------------------------------------------------|\\n| Requested memory | 699611 KiB | 1903 MiB | 3735 MiB | 3052 MiB |\\n| from large pool | 699413 KiB | 1903 MiB | 3734 MiB | 3051 MiB |\\n| from small pool | 197 KiB | 1 MiB | 1 MiB | 1 MiB |\\n|---------------------------------------------------------------------------|\\n| GPU reserved memory | 710656 KiB | 1918 MiB | 1918 MiB | 1224 MiB |\\n| from large pool | 708608 KiB | 1916 MiB | 1916 MiB | 1224 MiB |\\n| from small pool | 2048 KiB | 2 MiB | 2 MiB | 0 MiB |\\n|---------------------------------------------------------------------------|\\n| Non-releasable memory | 11043 KiB | 19364 KiB | 28939 KiB | 17896 KiB |\\n| from large pool | 9194 KiB | 17514 KiB | 25954 KiB | 16760 KiB |\\n| from small pool | 1849 KiB | 1950 KiB | 2985 KiB | 1136 KiB |\\n|---------------------------------------------------------------------------|\\n| Allocations | 10 | 17 | 38 | 28 |\\n| from large pool | 5 | 7 | 10 | 5 |\\n| from small pool | 5 | 11 | 28 | 23 |\\n|---------------------------------------------------------------------------|\\n| Active allocs | 10 | 17 | 38 | 28 |\\n| from large pool | 5 | 7 | 10 | 5 |\\n| from small pool | 5 | 11 | 28 | 23 |\\n|---------------------------------------------------------------------------|\\n| GPU reserved segments | 5 | 7 | 7 | 2 |\\n| from large pool | 4 | 6 | 6 | 2 |\\n| from small pool | 1 | 1 | 1 | 0 |\\n|---------------------------------------------------------------------------|\\n| Non-releasable allocs | 6 | 8 | 20 | 14 |\\n| from large pool | 4 | 6 | 9 | 5 |\\n| from small pool | 2 | 3 | 11 | 9 |\\n|---------------------------------------------------------------------------|\\n| Oversize allocations | 0 | 0 | 0 | 0 |\\n|---------------------------------------------------------------------------|\\n| Oversize GPU segments | 0 | 0 | 0 | 0 |\\n|===========================================================================|\\n'" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "torch.cuda.memory_summary(device=None, abbreviated=False)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "import os\n", "os.environ[\"PYTORCH_CUDA_ALLOC_CONF\"] = \"max_split_size_mb:256\"" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "device = 'cuda'\n", "model = SimpleBigramNeuralLanguageModel(vocab_size, embed_size).to(device)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "epoch: = 1\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/gedin/.local/lib/python3.8/site-packages/torch/nn/modules/container.py:217: UserWarning: Implicit dimension choice for softmax has been deprecated. 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grad_fn=)\n", "430000\n", "43500 tensor(5.1078, device='cuda:0', grad_fn=)\n", "431000\n", "43600 tensor(5.3045, device='cuda:0', grad_fn=)\n", "432000\n" ] } ], "source": [ "data = DataLoader(train_dataset, batch_size=batch_s)\n", "optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)\n", "criterion = torch.nn.NLLLoss()\n", "torch.cuda.empty_cache()\n", "gc.collect()\n", "\n", "model.load_state_dict(torch.load('model-bigram_final.bin'))\n", "for i in range(1, epochs+1):\n", " print('epoch: =', i)\n", " model.train()\n", " step = 0\n", " for x, y in data: # prev, predicting, following words\n", " x = x.to(device)\n", " y = y.to(device)\n", " optimizer.zero_grad()\n", " ypredicted = model(x) #previous, following word\n", " loss = criterion(torch.log(ypredicted), y)\n", " if step % 100 == 0:\n", " print(step, loss)\n", " step += 1\n", " loss.backward()\n", " optimizer.step()\n", " torch.save(model.state_dict(), f'model-bigram_2nd-run{i}.bin') \n", "torch.save(model.state_dict(), f'model-bigram_final.bin') " ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": true }, "outputs": [ { "data": { "text/plain": [ "[('be', 11, 0.2570849657058716),\n", " ('', 0, 0.07411641627550125),\n", " ('not', 22, 0.05940083786845207),\n", " ('have', 28, 0.02751326560974121),\n", " ('bo', 167, 0.014936885796487331),\n", " ('make', 116, 0.013943656347692013),\n", " ('give', 193, 0.011286991648375988),\n", " ('take', 153, 0.011171611957252026),\n", " ('do', 86, 0.010088067501783371),\n", " ('he', 20, 0.009703895077109337)]" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "device = 'cuda'\n", "torch.cuda.empty_cache()\n", "model = SimpleBigramNeuralLanguageModel(vocab_size, embed_size).to(device)\n", "model.load_state_dict(torch.load(f'model-bigram_final.bin'))\n", "model.eval()\n", "\n", "ixs = torch.tensor(vocab.forward(['will'])).to(device)\n", "\n", "out = model(ixs)\n", "top = torch.topk(out[0], 10)\n", "top_indices = top.indices.tolist()\n", "top_probs = top.values.tolist()\n", "top_words = vocab.lookup_tokens(top_indices)\n", "list(zip(top_words, top_indices, top_probs))" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": true }, "outputs": [ { "data": { "text/plain": [ "[('', 0, 0.19996878504753113),\n", " ('and', 3, 0.05288130044937134),\n", " ('of', 2, 0.042051784694194794),\n", " ('the', 1, 0.026572922244668007),\n", " ('to', 4, 0.022689413279294968),\n", " ('in', 6, 0.015904497355222702),\n", " ('The', 17, 0.012827681377530098),\n", " ('a', 5, 0.00961760152131319),\n", " ('for', 8, 0.008938422426581383),\n", " ('', 32, 0.00840282253921032)]" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "vocab = train_dataset.vocab\n", "ixs = torch.tensor(vocab.forward(['cerned.'])).to(device)\n", "\n", "out = model(ixs)\n", "top = torch.topk(out[0], 10)\n", "top_indices = top.indices.tolist()\n", "top_probs = top.values.tolist()\n", "top_words = vocab.lookup_tokens(top_indices)\n", "list(zip(top_words, top_indices, top_probs))" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[('', 0, 1.0),\n", " ('particular,', 14538, 0.24527804553508759),\n", " ('revolution.', 20446, 0.23776617646217346),\n", " ('Territory.', 14189, 0.23417341709136963),\n", " ('or-', 2261, 0.22888363897800446),\n", " ('3', 479, 0.2288265973329544),\n", " ('speak.', 13722, 0.2252315878868103),\n", " ('attend.', 19397, 0.22110989689826965),\n", " ('say,', 1455, 0.22106117010116577),\n", " ('Lee.', 15326, 0.21764159202575684)]" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cos = nn.CosineSimilarity(dim=1, eps=1e-6)\n", "\n", "embeddings = model.model[0].weight\n", "\n", "vec = embeddings[vocab['cerned.']]\n", "\n", "similarities = cos(vec, embeddings)\n", "\n", "top = torch.topk(similarities, 10)\n", "\n", "top_indices = top.indices.tolist()\n", "top_probs = top.values.tolist()\n", "top_words = vocab.lookup_tokens(top_indices)\n", "list(zip(top_words, top_indices, top_probs))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [], "source": [ "def get_values_from_model(presc_word, model, vocab, k):\n", " ixs = torch.tensor(vocab.forward([presc_word])).to(device)\n", " out = model(ixs)\n", " top = torch.topk(out[0], k)\n", " top_indices = top.indices.tolist()\n", " top_probs = top.values.tolist()\n", " top_words = vocab.lookup_tokens(top_indices)\n", " return list(zip(top_words, top_probs))\n", "\n", "def gonito_format(dic):\n", " tab = summarize_probs_unk(dic)\n", " result = ''\n", " for element in tab[:-1]:\n", " result+=str(element[0])+':'+str(element[1])+'\\t'\n", " result+=':'+ str(tab[-1][1])+'\\n'\n", " return result\n", "\n", "def summarize_probs_unk(dic):\n", " if '' in dic.keys():\n", " probsum = sum(float(val) for key, val in dic.items())\n", " for key in dic:\n", " dic[key] = dic[key]/probsum ###leave some space for wildcard\n", " wildcard = dic['']\n", " del dic['']\n", " tab = [(key, val) for key, val in dic.items()]\n", " tab.append(('', wildcard))\n", " else:\n", " probsum = sum(float(val) for key, val in dic.items())\n", " for key in dic:\n", " dic[key] = dic[key]/(probsum*(1+wildcard_minweight)) #plus, becouse it's denominator\n", " tab = [(key, val) for key, val in dic.items()]\n", " tab.append(('', 1-sum([val for val in dic.values()])))\n", " return tab\n", "\n" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.load_state_dict(torch.load('model-bigram_final.bin'))" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [], "source": [ "\n", "with lzma.open(test_file, 'rt') as file:\n", " predict_words = []\n", " results = []\n", " for line in file:\n", "# print(line)\n", " line = preprocess(line) #get only relevant\n", " split = line.split('\\t')\n", " predict_words.append(get_last_word(split[0])) #get_first_word(split[1])\n", " vocab = train_dataset.vocab\n", " for presc_word in predict_words:\n", " results.append(dict(get_values_from_model(presc_word, model, vocab, k=k)))\n", " with open(out_file, 'w') as outfile:\n", " for elem in results:\n", " outfile.write(gonito_format(elem))\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.6" }, "org": null }, "nbformat": 4, "nbformat_minor": 1 }