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Multi-GPU fine-tuning works correctly

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nottombrown
2018-06-26 20:23:56 -07:00
parent 3906aa6801
commit 09d28722a7
2 changed files with 66 additions and 34 deletions
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model_pytorch.py
+62 -33
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@@ -11,10 +11,12 @@ 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))))
return 0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3))))
def swish(x):
return x*torch.sigmoid(x)
return x * torch.sigmoid(x)
ACT_FNS = {
'relu': nn.ReLU,
@@ -25,6 +27,7 @@ ACT_FNS = {
class LayerNorm(nn.Module):
"Construct a layernorm module in the OpenAI style (epsilon inside the square root)."
def __init__(self, n_state, e=1e-5):
super(LayerNorm, self).__init__()
self.g = nn.Parameter(torch.ones(n_state))
@@ -43,12 +46,12 @@ class Conv1D(nn.Module):
super(Conv1D, self).__init__()
self.rf = rf
self.nf = nf
if rf == 1: #faster 1x1 conv
if rf == 1: # faster 1x1 conv
w = torch.empty(nx, nf)
nn.init.normal_(w, std=0.02)
self.w = Parameter(w)
self.b = Parameter(torch.zeros(nf))
else: #was used to train LM
else: # was used to train LM
raise NotImplementedError
def forward(self, x):
@@ -64,9 +67,9 @@ class Conv1D(nn.Module):
class Attention(nn.Module):
def __init__(self, nx, n_ctx, cfg, 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 % cfg.n_head==0
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 % cfg.n_head == 0
self.register_buffer('b', torch.tril(torch.ones(n_ctx, n_ctx)).view(1, 1, n_ctx, n_ctx))
self.n_head = cfg.n_head
self.split_size = n_state
@@ -80,7 +83,7 @@ class Attention(nn.Module):
w = torch.matmul(q, k)
if self.scale:
w = w / math.sqrt(v.size(-1))
w = w * self.b + -1e9*(1-self.b) # TF implem method: mask_attn_weights
w = w * self.b + -1e9 * (1 - self.b) # TF implem method: mask_attn_weights
w = nn.Softmax(dim=-1)(w)
w = self.attn_dropout(w)
return torch.matmul(w, v)
@@ -88,11 +91,11 @@ class Attention(nn.Module):
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
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
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)
else:
@@ -112,7 +115,7 @@ class Attention(nn.Module):
class MLP(nn.Module):
def __init__(self, n_state, cfg): # in MLP: n_state=3072 (4 * n_embd)
def __init__(self, n_state, cfg): # in MLP: n_state=3072 (4 * n_embd)
super(MLP, self).__init__()
nx = cfg.n_embd
self.c_fc = Conv1D(n_state, 1, nx)
@@ -132,19 +135,20 @@ class Block(nn.Module):
nx = cfg.n_embd
self.attn = Attention(nx, n_ctx, cfg, scale)
self.ln_1 = LayerNorm(nx)
self.mlp = MLP(4*nx, cfg)
self.mlp = MLP(4 * nx, cfg)
self.ln_2 = LayerNorm(nx)
def forward(self, x):
a = self.attn(x)
n = self.ln_1(x+a)
n = self.ln_1(x + a)
m = self.mlp(n)
h = self.ln_2(n+m)
h = self.ln_2(n + m)
return h
class Model(nn.Module):
""" Transformer model """
def __init__(self, cfg, vocab=40990, n_ctx=512):
super(Model, self).__init__()
self.vocab = vocab
@@ -153,8 +157,8 @@ class Model(nn.Module):
block = Block(n_ctx, cfg, scale=True)
self.h = nn.ModuleList([copy.deepcopy(block) for _ in range(cfg.n_layer)])
self.decoder = nn.Linear(cfg.n_embd, vocab, bias=False)
self.decoder.weight = self.embed.weight # Tied weights
self.clf_dropout = nn.Dropout2d(cfg.clf_pdrop) # To reproduce the noise_shape parameter of TF implementation
self.decoder.weight = self.embed.weight # Tied weights
self.clf_dropout = nn.Dropout2d(cfg.clf_pdrop) # To reproduce the noise_shape parameter of TF implementation
nn.init.normal_(self.embed.weight, std=0.02)
@@ -169,25 +173,27 @@ class Model(nn.Module):
class LMHead(nn.Module):
""" Language Model Head for the transformer """
def __init__(self, model, cfg):
super(LMHead, self).__init__()
self.n_embd = cfg.n_embd
self.decoder = lambda x: F.linear(x, model.embed.weight) # Tied weights
self.decoder = lambda x: F.linear(x, model.embed.weight) # Tied weights
def forward(self, h):
# Truncated Language modeling logits (we remove the last token)
h_trunc = h[:, :-1].contiguous().view(-1, self.n_embd) # Shape: 252, 768
h_trunc = h[:, :-1].contiguous().view(-1, self.n_embd) # Shape: 252, 768
lm_logits = self.decoder(h_trunc)
return lm_logits
class ClfHead(nn.Module):
""" Classifier Head for the transformer """
def __init__(self, clf_token, cfg):
super(ClfHead, self).__init__()
self.n_embd = cfg.n_embd
self.clf_token = clf_token
self.dropout = nn.Dropout2d(cfg.clf_pdrop) # To reproduce the noise_shape parameter of TF implementation
self.dropout = nn.Dropout2d(cfg.clf_pdrop) # To reproduce the noise_shape parameter of TF implementation
self.linear = nn.Linear(cfg.n_embd, 1)
nn.init.normal_(self.linear.weight, std=0.02)
nn.init.normal_(self.linear.bias, 0)
@@ -196,8 +202,8 @@ class ClfHead(nn.Module):
# Classification logits
clf_h = h.view(-1, self.n_embd)
flat = x[:, :, :, 0].contiguous().view(-1)
#pool_idx = torch.eq(x[:, :, 0].contiguous().view(-1), self.clf_token)
clf_h = clf_h[flat == self.clf_token, :] #.index_select(0, pool_idx)
# pool_idx = torch.eq(x[:, :, 0].contiguous().view(-1), self.clf_token)
clf_h = clf_h[flat == self.clf_token, :] # .index_select(0, pool_idx)
clf_h = clf_h.view(-1, 2, self.n_embd, 1)
clf_h = self.dropout(clf_h)
clf_h = clf_h.view(-1, self.n_embd)
@@ -205,8 +211,21 @@ class ClfHead(nn.Module):
return clf_logits.view(-1, 2)
def load_openai_pretrained_model(model, n_ctx=-1, n_special=-1, n_transfer=12, n_embd=768, path='./model/', path_names='./'):
class DataParallelWithEmbed(torch.nn.DataParallel):
"""DataParallel that proxies the embed property to the wrapped module"""
def __init__(self, model):
super(DataParallelWithEmbed, self).__init__(model)
@property
def embed(self):
return self.module.embed
def load_openai_pretrained_model(model, n_ctx=-1, n_special=-1, n_transfer=12, n_embd=768, path='./model/',
path_names='./'):
# Load weights from TF model
print("Loading weights...")
names = json.load(open(path_names + 'parameters_names.json'))
shapes = json.load(open(path + 'params_shapes.json'))
offsets = np.cumsum([np.prod(shape) for shape in shapes])
@@ -216,32 +235,40 @@ def load_openai_pretrained_model(model, n_ctx=-1, n_special=-1, n_transfer=12, n
if n_ctx > 0:
init_params[0] = init_params[0][:n_ctx]
if n_special > 0:
init_params[0] = np.concatenate([init_params[1],
(np.random.randn(n_special, n_embd)*0.02).astype(np.float32),
init_params[0]
], 0)
init_params[0] = np.concatenate(
[init_params[1],
(np.random.randn(n_special, n_embd) * 0.02).astype(np.float32),
init_params[0]
], 0)
else:
init_params[0] = np.concatenate([init_params[1],
init_params[0]
], 0)
init_params[0] = np.concatenate(
[init_params[1],
init_params[0]
], 0)
del init_params[1]
if n_transfer == -1:
n_transfer = 0
else:
n_transfer = 1+n_transfer*12
n_transfer = 1 + n_transfer * 12
init_params = [arr.squeeze() for arr in init_params]
try:
assert model.embed.weight.shape == init_params[0].shape
except AssertionError as e:
e.args += (model.embed.weight.shape, init_params[0].shape)
raise
model.embed.weight.data = torch.from_numpy(init_params[0])
# Load the weights into our torch module
module = model.module
for name, ip in zip(names[1:n_transfer], init_params[1:n_transfer]):
name = name[6:] # skip "model/"
name = name[6:] # skip "model/"
assert name[-2:] == ":0"
name = name[:-2]
name = name.split('/')
pointer = model
pointer = module
for m_name in name:
if re.fullmatch(r'[A-Za-z]+\d+', m_name):
l = re.split(r'(\d+)', m_name)
@@ -258,12 +285,14 @@ def load_openai_pretrained_model(model, n_ctx=-1, n_special=-1, n_transfer=12, n
raise
pointer.data = torch.from_numpy(ip)
class dotdict(dict):
"""dot.notation access to dictionary attributes"""
__getattr__ = dict.get
__setattr__ = dict.__setitem__
__delattr__ = dict.__delitem__
DEFAULT_CONFIG = dotdict({
'n_embd': 768,
'n_head': 12,
train.py
+4 -1
View File
@@ -10,7 +10,7 @@ from sklearn.utils import shuffle
from analysis import rocstories as rocstories_analysis
from datasets import rocstories
from model_pytorch import Model, LMHead, ClfHead, load_openai_pretrained_model
from model_pytorch import Model, LMHead, ClfHead, load_openai_pretrained_model, DataParallelWithEmbed
from opt import OpenAIAdam
from text_utils import TextEncoder
from utils import (encode_dataset, iter_data,
@@ -237,6 +237,7 @@ if __name__ == '__main__':
encoder = text_encoder.encoder
n_vocab = len(text_encoder.encoder)
print("Encoding dataset...")
(trX1, trX2, trX3, trY), (vaX1, vaX2, vaX3, vaY), (teX1, teX2, teX3) = encode_dataset(
rocstories(data_dir, n_valid=args.n_valid), encoder=text_encoder)
n_y = 2
@@ -266,6 +267,8 @@ if __name__ == '__main__':
n_updates_total = (n_train // n_batch_train) * args.n_iter
model = Model(args, vocab, n_ctx)
model = DataParallelWithEmbed(model).cuda()
lm_head = LMHead(model, args)
clf_head = ClfHead(clf_token, args)