code: '''
      code by TaeHwan Jung(@graykode)
      Original Paper and repository here : https://github.com/openai/gpt-2
      GPT2 Pytorch Model : https://github.com/huggingface/pytorch-pretrained-BERT
  '''
  
  import copy
  import torch
  import math
  import torch.nn as nn
  from torch.nn.parameter import Parameter
  
  def gelu(x):
      return 0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3))))
  
  def load_weight(model, state_dict):
      old_keys = []
      new_keys = []
      for key in state_dict.keys():
          new_key = None
          if key.endswith(".g"):
              new_key = key[:-2] + ".weight"
          elif key.endswith(".b"):
              new_key = key[:-2] + ".bias"
          elif key.endswith(".w"):
              new_key = key[:-2] + ".weight"
          if new_key:
              old_keys.append(key)
              new_keys.append(new_key)
      for old_key, new_key in zip(old_keys, new_keys):
          state_dict[new_key] = state_dict.pop(old_key)
  
      missing_keys = []
      unexpected_keys = []
      error_msgs = []
      # copy state_dict so _load_from_state_dict can modify it
      metadata = getattr(state_dict, "_metadata", None)
      state_dict = state_dict.copy()
      if metadata is not None:
          state_dict._metadata = metadata
  
      def load(module, prefix=""):
          local_metadata = {} if metadata is None else metadata.get(prefix[:-1], {})
          module._load_from_state_dict(
              state_dict, prefix, local_metadata, True, missing_keys, unexpected_keys, error_msgs
          )
          for name, child in module._modules.items():
              if child is not None:
                  load(child, prefix + name + ".")
  
      start_model = model
      if hasattr(model, "transformer") and all(not s.startswith('transformer.') for s in state_dict.keys()):
          start_model = model.transformer
      load(start_model, prefix="")
  
      # Make sure we are still sharing the output and input embeddings after loading weights
      model.set_tied()
      return model
  
  class LayerNorm(nn.Module):
      def __init__(self, hidden_size, eps=1e-12):
          """Construct a layernorm module in the TF style (epsilon inside the square root).
          """
          super(LayerNorm, self).__init__()
          self.weight = nn.Parameter(torch.ones(hidden_size))
          self.bias = nn.Parameter(torch.zeros(hidden_size))
          self.variance_epsilon = eps
  
      def forward(self, x):
          u = x.mean(-1, keepdim=True)
          s = (x - u).pow(2).mean(-1, keepdim=True)
          x = (x - u) / torch.sqrt(s + self.variance_epsilon)
          return self.weight * x + self.bias
  
  
  class Conv1D(nn.Module):
      def __init__(self, nf, nx):
          super(Conv1D, self).__init__()
          self.nf = nf
          w = torch.empty(nx, nf)
          nn.init.normal_(w, std=0.02)
          self.weight = Parameter(w)
          self.bias = Parameter(torch.zeros(nf))
  
      def forward(self, x):
          size_out = x.size()[:-1] + (self.nf,)
          x = torch.addmm(self.bias, x.view(-1, x.size(-1)), self.weight)
          x = x.view(*size_out)
          return x
  
  
  class Attention(nn.Module):
      def __init__(self, nx, n_ctx, config, scale=False):
          super(Attention, self).__init__()
          n_state = nx  # in Attention: n_state=768 (nx=n_embd)
          # [switch nx => n_state from Block to Attention to keep identical to TF implem]
          assert n_state % config.n_head == 0
          self.register_buffer("bias", torch.tril(torch.ones(n_ctx, n_ctx)).view(1, 1, n_ctx, n_ctx))
          self.n_head = config.n_head
          self.split_size = n_state
          self.scale = scale
          self.c_attn = Conv1D(n_state * 3, nx)
          self.c_proj = Conv1D(n_state, nx)
  
      def _attn(self, q, k, v):
          w = torch.matmul(q, k)
          if self.scale:
              w = w / math.sqrt(v.size(-1))
          nd, ns = w.size(-2), w.size(-1)
          b = self.bias[:, :, ns - nd:ns, :ns]
          # Here the bias b also serves as the mask to remove future information
          w = w * b - 1e10 * (1 - b)
          w = nn.Softmax(dim=-1)(w)
          return torch.matmul(w, v)
  
      def merge_heads(self, x):
          x = x.permute(0, 2, 1, 3).contiguous()
          new_x_shape = x.size()[:-2] + (x.size(-2) * x.size(-1),)
          return x.view(*new_x_shape)  # in Tensorflow implem: fct merge_states
  
      def split_heads(self, x, k=False):
          new_x_shape = x.size()[:-1] + (self.n_head, x.size(-1) // self.n_head)
          x = x.view(*new_x_shape)  # in Tensorflow implem: fct split_states
          if k:
              return x.permute(0, 2, 3, 1)  # (batch, head, head_features, seq_length)
          else:
              return x.permute(0, 2, 1, 3)  # (batch, head, seq_length, head_features)
  
      def forward(self, x, layer_past=None):
          x = self.c_attn(x)
          query, key, value = x.split(self.split_size, dim=2)
          query = self.split_heads(query)
          key = self.split_heads(key, k=True)
          value = self.split_heads(value)
          if layer_past is not None:
              past_key, past_value = layer_past[0].transpose(-2, -1), layer_past[1]  # transpose back cf below
              key = torch.cat((past_key, key), dim=-1)
              value = torch.cat((past_value, value), dim=-2)
          present = torch.stack((key.transpose(-2, -1), value))  # transpose to have same shapes for stacking
          a = self._attn(query, key, value)
          a = self.merge_heads(a)
          a = self.c_proj(a)
          return a, present
  
  
  class MLP(nn.Module):
      def __init__(self, n_state, config):  # in MLP: n_state=3072 (4 * n_embd)
          super(MLP, self).__init__()
          nx = config.n_embd
          self.c_fc = Conv1D(n_state, nx)
          self.c_proj = Conv1D(nx, n_state)
          self.act = gelu
  
      def forward(self, x):
          h = self.act(self.c_fc(x))
          h2 = self.c_proj(h)
          return h2
  
  
  class Block(nn.Module):
      def __init__(self, n_ctx, config, scale=False):
          super(Block, self).__init__()
          nx = config.n_embd
          self.ln_1 = LayerNorm(nx, eps=config.layer_norm_epsilon)
          self.attn = Attention(nx, n_ctx, config, scale)
          self.ln_2 = LayerNorm(nx, eps=config.layer_norm_epsilon)
          self.mlp = MLP(4 * nx, config)
  
      def forward(self, x, layer_past=None):
          a, present = self.attn(self.ln_1(x), layer_past=layer_past)
          x = x + a
          m = self.mlp(self.ln_2(x))
          x = x + m
          return x, present
  
  
  class Transformer(nn.Module):
      def __init__(self, config):
          super().__init__()
          self.n_layer = config.n_layer
          self.n_embd = config.n_embd
          self.n_vocab = config.vocab_size
  
          self.wte = nn.Embedding(config.vocab_size, config.n_embd)
          self.wpe = nn.Embedding(config.n_positions, config.n_embd)
          block = Block(config.n_ctx, config, scale=True)
          self.h = nn.ModuleList([copy.deepcopy(block) for _ in range(config.n_layer)])
          self.ln_f = LayerNorm(config.n_embd, eps=config.layer_norm_epsilon)
  
      def set_embeddings_weights(self, model_embeddings_weights):
          embed_shape = model_embeddings_weights.shape
          self.decoder = nn.Linear(embed_shape[1], embed_shape[0], bias=False)
          self.decoder.weight = model_embeddings_weights  # Tied weights
  
      def forward(self, input_ids, position_ids=None, token_type_ids=None, past=None):
          if past is None:
              past_length = 0
              past = [None] * len(self.h)
          else:
              past_length = past[0][0].size(-2)
          if position_ids is None:
              position_ids = torch.arange(past_length, input_ids.size(-1) + past_length, dtype=torch.long,
                                          device=input_ids.device)
              position_ids = position_ids.unsqueeze(0).expand_as(input_ids)
  
          input_shape = input_ids.size()
          input_ids = input_ids.view(-1, input_ids.size(-1))
          position_ids = position_ids.view(-1, position_ids.size(-1))
  
          inputs_embeds = self.wte(input_ids)
          position_embeds = self.wpe(position_ids)
          if token_type_ids is not None:
              token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1))
              token_type_embeds = self.wte(token_type_ids)
          else:
              token_type_embeds = 0
          hidden_states = inputs_embeds + position_embeds + token_type_embeds
          presents = []
          for block, layer_past in zip(self.h, past):
              hidden_states, present = block(hidden_states, layer_past)
              presents.append(present)
          hidden_states = self.ln_f(hidden_states)
          output_shape = input_shape + (hidden_states.size(-1),)
          return hidden_states.view(*output_shape), presents
  
  
  class LinearReadoutHead(nn.Module):
      def __init__(self, model_embeddings_weights, config):
          super().__init__()
          self.n_embd = config.n_embd
          self.set_embeddings_weights(model_embeddings_weights)
  
      def set_embeddings_weights(self, model_embeddings_weights):
          embed_shape = model_embeddings_weights.shape
          self.decoder = nn.Linear(embed_shape[1], embed_shape[0], bias=False)
          self.decoder.weight = model_embeddings_weights  # Tied weights
  
      def forward(self, hidden_state):
          # Truncated Language modeling logits (we remove the last token)
          # h_trunc = h[:, :-1].contiguous().view(-1, self.n_embd)
          lm_logits = self.decoder(hidden_state)
          return lm_logits
  
  
  class GPT2(nn.Module):
      def __init__(self, config):
          super().__init__()
          self.transformer = Transformer(config)
          self.readout_head = LinearReadoutHead(self.transformer.wte.weight, config)
  
      def set_tied(self):
          """ Make sure we are sharing the embeddings
          """
          self.readout_head.set_embeddings_weights(self.transformer.wte.weight)
  
      def forward(self, input_ids, position_ids=None, token_type_ids=None, past=None):
          hidden_states, presents = self.transformer(input_ids, position_ids, token_type_ids, past)
          return hidden_states
apis: torch.empty torch.ones torch.nn.Linear torch.pow torch.nn.Softmax torch.sqrt torch.nn.Embedding torch.nn.init.normal_ torch.zeros torch.cat torch.nn.parameter.Parameter torch.matmul