mirror of
https://github.com/wassname/openai-transformer-lm-gutenberg-erotic.git
synced 2026-06-26 16:00:39 +08:00
wassname
280 lines
8.7 KiB
Python
280 lines
8.7 KiB
Python
import copy
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import json
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import math
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import re
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import collections
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import numpy as np
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from torch.nn.parameter import Parameter
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def gelu(x):
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return 0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3))))
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def swish(x):
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return x * torch.sigmoid(x)
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ACT_FNS = {
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'relu': nn.ReLU,
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'swish': swish,
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'gelu': gelu
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}
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class LayerNorm(nn.Module):
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"Construct a layernorm module in the OpenAI style (epsilon inside the square root)."
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def __init__(self, n_state, e=1e-5):
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super(LayerNorm, self).__init__()
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self.g = nn.Parameter(torch.ones(n_state))
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self.b = nn.Parameter(torch.zeros(n_state))
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self.e = e
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def forward(self, x):
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u = x.mean(-1, keepdim=True)
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s = (x - u).pow(2).mean(-1, keepdim=True)
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x = (x - u) / torch.sqrt(s + self.e)
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return self.g * x + self.b
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class Conv1D(nn.Module):
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def __init__(self, nf, rf, nx):
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super(Conv1D, self).__init__()
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self.rf = rf
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self.nf = nf
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if rf == 1: # faster 1x1 conv
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w = torch.empty(nx, nf)
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nn.init.normal_(w, std=0.02)
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self.w = Parameter(w)
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self.b = Parameter(torch.zeros(nf))
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else: # was used to train LM
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raise NotImplementedError
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def forward(self, x):
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if self.rf == 1:
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size_out = x.size()[:-1] + (self.nf,)
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x = torch.addmm(self.b, x.view(-1, x.size(-1)), self.w)
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x = x.view(*size_out)
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else:
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raise NotImplementedError
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return x
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class Attention(nn.Module):
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def __init__(self, nx, n_ctx, cfg, scale=False):
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super(Attention, self).__init__()
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n_state = nx # in Attention: n_state=768 (nx=n_embd)
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# [switch nx => n_state from Block to Attention to keep identical to TF implem]
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assert n_state % cfg.n_head == 0
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self.register_buffer('b', torch.tril(torch.ones(n_ctx, n_ctx)).view(1, 1, n_ctx, n_ctx))
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self.n_head = cfg.n_head
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self.split_size = n_state
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self.scale = scale
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self.c_attn = Conv1D(n_state * 3, 1, nx)
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self.c_proj = Conv1D(n_state, 1, nx)
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self.attn_dropout = nn.Dropout(cfg.attn_pdrop)
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self.resid_dropout = nn.Dropout(cfg.resid_pdrop)
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def _attn(self, q, k, v):
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w = torch.matmul(q, k)
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if self.scale:
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w = w / math.sqrt(v.size(-1))
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w = w * self.b + -1e9 * (1 - self.b) # TF implem method: mask_attn_weights
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w = nn.Softmax(dim=-1)(w)
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w = self.attn_dropout(w)
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return torch.matmul(w, v)
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def merge_heads(self, x):
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x = x.permute(0, 2, 1, 3).contiguous()
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new_x_shape = x.size()[:-2] + (x.size(-2) * x.size(-1),)
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return x.view(*new_x_shape) # in Tensorflow implem: fct merge_states
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def split_heads(self, x, k=False):
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new_x_shape = x.size()[:-1] + (self.n_head, x.size(-1) // self.n_head)
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x = x.view(*new_x_shape) # in Tensorflow implem: fct split_states
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if k:
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return x.permute(0, 2, 3, 1)
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else:
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return x.permute(0, 2, 1, 3)
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def forward(self, x):
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x = self.c_attn(x)
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query, key, value = x.split(self.split_size, dim=2)
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query = self.split_heads(query)
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key = self.split_heads(key, k=True)
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value = self.split_heads(value)
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a = self._attn(query, key, value)
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a = self.merge_heads(a)
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a = self.c_proj(a)
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a = self.resid_dropout(a)
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return a
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class MLP(nn.Module):
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def __init__(self, n_state, cfg): # in MLP: n_state=3072 (4 * n_embd)
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super(MLP, self).__init__()
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nx = cfg.n_embd
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self.c_fc = Conv1D(n_state, 1, nx)
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self.c_proj = Conv1D(nx, 1, n_state)
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self.act = ACT_FNS[cfg.afn]
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self.dropout = nn.Dropout(cfg.resid_pdrop)
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def forward(self, x):
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h = self.act(self.c_fc(x))
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h2 = self.c_proj(h)
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return self.dropout(h2)
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class Block(nn.Module):
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def __init__(self, n_ctx, cfg, scale=False):
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super(Block, self).__init__()
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nx = cfg.n_embd
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self.attn = Attention(nx, n_ctx, cfg, scale)
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self.ln_1 = LayerNorm(nx)
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self.mlp = MLP(4 * nx, cfg)
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self.ln_2 = LayerNorm(nx)
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def forward(self, x):
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a = self.attn(x)
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n = self.ln_1(x + a)
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m = self.mlp(n)
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h = self.ln_2(n + m)
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return h
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class TransformerModel(nn.Module):
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""" Transformer model """
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def __init__(self, cfg, vocab=40990, n_ctx=512):
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super(TransformerModel, self).__init__()
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self.vocab = vocab
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self.embed = nn.Embedding(vocab, cfg.n_embd)
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self.drop = nn.Dropout(cfg.embd_pdrop)
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block = Block(n_ctx, cfg, scale=True)
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self.h = nn.ModuleList([copy.deepcopy(block) for _ in range(cfg.n_layer)])
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nn.init.normal_(self.embed.weight, std=0.02)
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def forward(self, x):
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x = x.view(-1, x.size(-2), x.size(-1))
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e = self.embed(x)
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# Add the position information to the input embeddings
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h = e.sum(dim=2)
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for block in self.h:
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h = block(h)
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return h
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class LMHead(nn.Module):
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""" Language Model Head for the transformer """
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def __init__(self, model, cfg):
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super(LMHead, self).__init__()
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self.n_embd = cfg.n_embd
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embed_shape = model.embed.weight.shape
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self.decoder = nn.Linear(embed_shape[1], embed_shape[0], bias=False)
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self.decoder.weight = model.embed.weight # Tied weights
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def forward(self, h):
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# Truncated Language modeling logits (we remove the last token)
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h_trunc = h[:, :-1].contiguous().view(-1, self.n_embd)
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lm_logits = self.decoder(h_trunc)
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return lm_logits
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class LanguageModel(nn.Module):
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""" Transformer with language model """
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def __init__(self, cfg, vocab=40990, n_ctx=512):
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super(LanguageModel, self).__init__()
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self.transformer = TransformerModel(cfg, vocab=vocab, n_ctx=n_ctx)
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self.lm_head = LMHead(self.transformer, cfg)
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def forward(self, x):
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h = self.transformer(x)
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lm_logits = self.lm_head(h)
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return lm_logits
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def load_openai_pretrained_model(model, n_ctx=-1, n_special=-1, n_transfer=12, n_embd=768, path='./model/',
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path_names='./'):
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# Load weights from TF model
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print("Loading weights...")
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names = json.load(open(path_names + 'parameters_names.json'))
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shapes = json.load(open(path + 'params_shapes.json'))
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offsets = np.cumsum([np.prod(shape) for shape in shapes])
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init_params = [np.load(path + 'params_{}.npy'.format(n)) for n in range(10)]
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init_params = np.split(np.concatenate(init_params, 0), offsets)[:-1]
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init_params = [param.reshape(shape) for param, shape in zip(init_params, shapes)]
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if n_ctx > 0:
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init_params[0] = init_params[0][:n_ctx]
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if n_special > 0:
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init_params[0] = np.concatenate(
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[init_params[1],
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(np.random.randn(n_special, n_embd) * 0.02).astype(np.float32),
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init_params[0]
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], 0)
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else:
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init_params[0] = np.concatenate(
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[init_params[1],
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init_params[0]
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], 0)
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del init_params[1]
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if n_transfer == -1:
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n_transfer = 0
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else:
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n_transfer = 1 + n_transfer * 12
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init_params = [arr.squeeze() for arr in init_params]
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try:
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assert model.embed.weight.shape == init_params[0].shape
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except AssertionError as e:
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e.args += (model.embed.weight.shape, init_params[0].shape)
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raise
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model.embed.weight.data = torch.from_numpy(init_params[0])
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for name, ip in zip(names[1:n_transfer], init_params[1:n_transfer]):
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name = name[6:] # skip "model/"
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assert name[-2:] == ":0"
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name = name[:-2]
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name = name.split('/')
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pointer = model
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for m_name in name:
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if re.fullmatch(r'[A-Za-z]+\d+', m_name):
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l = re.split(r'(\d+)', m_name)
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else:
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l = [m_name]
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pointer = getattr(pointer, l[0])
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if len(l) >= 2:
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num = int(l[1])
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pointer = pointer[num]
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try:
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assert pointer.shape == ip.shape
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except AssertionError as e:
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e.args += (pointer.shape, ip.shape)
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raise
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pointer.data = torch.from_numpy(ip)
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class dotdict(dict):
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"""dot.notation access to dictionary attributes"""
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__getattr__ = dict.get
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__setattr__ = dict.__setitem__
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__delattr__ = dict.__delitem__
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DEFAULT_CONFIG = dotdict({
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'n_embd': 768,
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'n_head': 12,
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'n_layer': 12,
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'embd_pdrop': 0.1,
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'attn_pdrop': 0.1,
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'resid_pdrop': 0.1,
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'afn': 'gelu',
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'clf_pdrop': 0.1})
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