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# ------------------------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License (MIT). See LICENSE in the repo root for license information.
# ------------------------------------------------------------------------------------------
import logging
import math
import os
from collections import OrderedDict
import copy
import math
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
import torch.nn.functional as F
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
from torch.nn.parameter import Parameter
import loralib as lora
def gelu(x):
return 0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3))))
def gelu_fast(x):
return 0.5 * x * (1.0 + torch.tanh(x * 0.7978845608 * (1.0 + 0.044715 * x * x)))
def gelu_new(x):
""" Implementation of the gelu activation function currently in Google Bert repo (identical to OpenAI GPT).
Also see https://arxiv.org/abs/1606.08415
"""
return 0.5 * x * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi) * (x + 0.044715 * torch.pow(x, 3.0))))
def swish(x):
return x * torch.sigmoid(x)
def _gelu_python(x):
""" Original Implementation of the gelu activation function in Google Bert repo when initially created.
For information: OpenAI GPT's gelu is slightly different (and gives slightly different results):
0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3))))
This is now written in C in torch.nn.functional
Also see https://arxiv.org/abs/1606.08415
"""
return x * 0.5 * (1.0 + torch.erf(x / math.sqrt(2.0)))
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_attn = lora.MergedLinear(
nx, n_state * 3,
r=config.lora_attn_dim,
lora_alpha=config.lora_attn_alpha,
lora_dropout=config.lora_dropout,
enable_lora=[True, False, True],
fan_in_fan_out=True,
merge_weights=False
)
# self.c_attn = lora.Linear(
# nx, n_state * 3,
# r=config.lora_attn_dim,
# lora_alpha=config.lora_attn_alpha,
# lora_dropout=config.lora_dropout,
# fan_in_fan_out=True,
# merge_weights=False
# )
print(f"scaling = {config.lora_attn_alpha / config.lora_attn_dim}")
self.c_proj = Conv1D(n_state, nx)
self.config = config
def _attn(self, q, k, v, len_kv=None):
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]
w = w * b - 1e10 * (1 - b)
# q : (batch, head, q_seq_length, head_features)
# k : (batch, head, head_features, kv_seq_length)
# w : (batch, head, q_seq_length, kv_seq_length)
# v : (batch, head, kv_seq_length, head_features)
if len_kv is not None:
_len = torch.arange(k.size(-1), device=k.device)
_input_msk = _len[None, :] >= (len_kv)[:, None]
w = w.masked_fill(_input_msk.unsqueeze(1).unsqueeze(2), -1.0e10)
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).contiguous() # (batch, head, head_features, seq_length)
else:
return x.permute(0, 2, 1, 3).contiguous() # (batch, head, seq_length, head_features)
def forward(self, x, history=None, layer_past=None, len_past=None):
hidden_states = x
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)
#_input_msk = None
len_kv = None
if layer_past is not None:
# key : (batch, head, head_features, seq_length)
# value : (batch, head, seq_length, head_features)
# layer_past, key : (batch, head, seq_length, head_features)
if len_past is 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)
else:
key_seq = key.shape[-1]
assert key_seq == 1
_batch = torch.arange(0, key.shape[0], dtype=torch.long, device=key.device)
past_key, past_value = layer_past[0], layer_past[1]
past_key[_batch,:,len_past,:] = key.squeeze(-1)
past_value[_batch,:,len_past,:] = value.squeeze(-2)
key = past_key.transpose(-2, -1)
value = past_value
len_kv = len_past + 1
present = torch.stack((key.transpose(-2, -1), value)) # transpose to have same shapes for stacking
a = self._attn(query, key, value, len_kv = len_kv)
a = self.merge_heads(a)
a = self.c_proj(a)
# logging.info(f"attention forward: {a[0,0,:100]}, present: {present[0,0,0,:]}")
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, len_past=None):
a, present = self.attn(self.ln_1(x), layer_past=layer_past, len_past=len_past)
x = x + a
m = self.mlp(self.ln_2(x))
x = x + m
return x, present
class GPT2Model(nn.Module):
def __init__(self, config):
super(GPT2Model, self).__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)
self.config = config
def forward(
self,
input_ids,
position_ids=None,
token_type_ids=None,
past=None,
len_past=None
):
if past is None:
past_length = 0
past = [None] * len(self.h)
elif len_past is None:
# equal size for past. []
past_length = past[0][0].size(-2)
if position_ids is None and len_past 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)
elif len_past is not None:
position_ids = (len_past).unsqueeze(1) #.long()
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 = layer_past, len_past=len_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 GPT2LMHead(nn.Module):
def __init__(self, model_embeddings_weights, config):
super(GPT2LMHead, self).__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 GPT2Config(object):
def __init__(
self,
vocab_size_or_config_json_file=50257,
n_positions=1024,
n_ctx=1024,
n_embd=768,
n_layer=12,
n_head=12,
layer_norm_epsilon=1e-5,
initializer_range=0.02,
lora_attn_dim=0,
lora_attn_alpha=128,
lora_dropout=0.0,
lora_r_dropout=0.0,
fix_dropout=0.0,
):
self.vocab_size = vocab_size_or_config_json_file
self.n_ctx = n_ctx
self.n_positions = n_positions
self.n_embd = n_embd
self.n_layer = n_layer
self.n_head = n_head
self.layer_norm_epsilon = layer_norm_epsilon
self.initializer_range = initializer_range
self.lora_attn_dim = lora_attn_dim
self.lora_attn_alpha = lora_attn_alpha
self.lora_dropout = lora_dropout
self.lora_r_dropout = lora_r_dropout
self.fix_dropout = fix_dropout
class GPT2LMModel(nn.Module):
def __init__(self, config):
super(GPT2LMModel, self).__init__()
self.transformer = GPT2Model(config)
self.lm_head = GPT2LMHead(self.transformer.wte.weight, config)
self.apply(self._init_weights)
def set_tied(self):
""" Make sure we are sharing the embeddings"""
self.lm_head.set_embeddings_weights(self.transformer.wte.weight)
def forward(
self,
input_ids,
lm_labels=None,
lm_mask=None,
past=None,
len_past=None,
label_smooth=0.0,
is_report_accuracy=False
):
_batch, _len = input_ids.shape
hidden_states, presents = self.transformer(input_ids, past=past, len_past=len_past)
# batch, seq, vocab
lm_logits = self.lm_head(hidden_states)
if lm_labels is not None:
if is_report_accuracy:
_pred_token = torch.argmax(lm_logits, dim=-1)
_hit = (_pred_token == lm_labels) * lm_mask
_t1_acc = torch.zeros(_batch, dtype=torch.float, device=input_ids.device)
_all_acc = torch.zeros(_batch, dtype=torch.float, device=input_ids.device)
for _b in range(0, _batch):
for _i in range(0, _len):
if lm_mask[_b, _i] >= 1.0:
if _hit[_b, _i] > 0:
_t1_acc[_b] = 1.0
break
_is_succ = True
for _i in range(0, _len):
if lm_mask[_b, _i] >= 1.0:
if _hit[_b, _i] <= 0:
_is_succ = False
break
if _is_succ:
_all_acc[_b] = 1.0
#_t1_acc = _t1_acc * 1.0 / _batch
#_all_acc = _all_acc * 1.0 / _batch
if label_smooth > 0.0001:
logprobs = torch.nn.functional.log_softmax(lm_logits.view(-1, lm_logits.size(-1)), dim=-1)
nll_loss = -logprobs.gather(dim=-1, index=lm_labels.view(-1).unsqueeze(1))
nll_loss = nll_loss.squeeze(1)
smooth_loss = -logprobs.mean(dim=-1)
loss = (1.0 - label_smooth) * nll_loss + label_smooth * smooth_loss
loss = loss.view(_batch, _len)
else:
loss_fct = nn.CrossEntropyLoss(ignore_index=-1, reduce=False)
loss = loss_fct(lm_logits.view(-1, lm_logits.size(-1)), lm_labels.view(-1)).view(_batch, _len)
if lm_mask is None:
lm_mask = torch.ones(loss.shape, dtype=loss.dtype, device=loss.device)
loss = loss * lm_mask
loss = loss.sum() / (lm_mask.sum() + 0.0001)
if is_report_accuracy:
return lm_logits, loss, _t1_acc, _all_acc
else:
return lm_logits, loss
return lm_logits, presents
def _init_weights(self, module):
if isinstance(module, (nn.Linear, nn.Embedding)):
module.weight.data.normal_(mean=0.0, std=0.02)
elif isinstance(module, nn.LayerNorm):
module.bias.data.zero_()
module.weight.data.fill_(1.0)
if isinstance(module, nn.Linear) and module.bias is not None:
module.bias.data.zero_()
def load_weight(self, state_dict):
if 'model_state_dict' in state_dict:
state_dict = state_dict['model_state_dict']
state_dict_tmp = copy.deepcopy(state_dict)
old_keys = []
new_keys = []
for key in state_dict_tmp:
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 key.startswith("module.transformer."):
new_key = key[len("module.transformer."):]
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)
for n, p in self.transformer.named_parameters():
if n not in state_dict:
state_dict[n] = p
self.transformer.load_state_dict(state_dict, strict=False)
self.set_tied()
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