mirror of
https://github.com/wassname/openai-transformer-lm-gutenberg-erotic.git
synced 2026-06-27 16:10:19 +08:00
thomwolf
252 lines
8.5 KiB
Python
252 lines
8.5 KiB
Python
import re
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import math
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import json
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import copy
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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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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 (See citation for details)."
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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, 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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mask_size = n_state // cfg.n_head
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self.register_buffer('b', torch.tril(torch.ones(mask_size, mask_size)).view(1, 1, mask_size, mask_size))
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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, 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, 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 Model(nn.Module):
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""" Transformer model """
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def __init__(self, vocab, cfg):
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super(Model, 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(cfg, scale=True)
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self.h = nn.ModuleList([copy.deepcopy(block) for _ in range(cfg.n_layer)])
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self.decoder = nn.Linear(cfg.n_embd, vocab, bias=False)
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self.decoder.weight = self.embed.weight # Tied weights
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self.clf_dropout = nn.Dropout2d(cfg.clf_pdrop) # To reproduce the noise_shape parameter of TF implementation
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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(3))
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e = self.embed(x)
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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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self.decoder = nn.Linear(cfg.n_embd, model.vocab, 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
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h_trunc = h[:, :-1].contiguous().view(-1, self.n_embd) # Shape: 252, 768
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lm_logits = self.decoder(h_trunc)
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return lm_logits
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class ClfHead(nn.Module):
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""" Classifier Head for the transformer """
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def __init__(self, clf_token, cfg):
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super(ClfHead, self).__init__()
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self.n_embd = cfg.n_embd
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self.clf_token = clf_token
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self.dropout = nn.Dropout2d(cfg.clf_pdrop) # To reproduce the noise_shape parameter of TF implementation
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self.linear = nn.Linear(cfg.n_embd, 1)
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nn.init.normal_(self.linear.weight, std=0.02)
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nn.init.normal_(self.linear.bias, 0)
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def forward(self, h, x):
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# Classification logits
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clf_h = h.view(-1, self.n_embd)
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flat = x[:, :, :, 0].contiguous().view(-1)
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#pool_idx = torch.eq(x[:, :, 0].contiguous().view(-1), self.clf_token)
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clf_h = clf_h[flat == self.clf_token, :] #.index_select(0, pool_idx)
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clf_h = clf_h.view(-1, 2, self.n_embd, 1)
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clf_h = self.dropout(clf_h)
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clf_h = clf_h.view(-1, self.n_embd)
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clf_logits = self.linear(clf_h)
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return clf_logits.view(-1, 2)
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def load_openai_pretrained_model(model, n_ctx, n_special, cfg, path='model'):
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# Load weights from TF model
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n_transfer = cfg.n_transfer
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shapes = json.load(open(path + '/params_shapes.json'))
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names = json.load(open(path + '/parameters_names.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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init_params[0] = init_params[0][:n_ctx]
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init_params[0] = np.concatenate([init_params[1], (np.random.randn(n_special, cfg.n_embd)*0.02).astype(np.float32), init_params[0]], 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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