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GPT2RGAX.py
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GPT2RGAX.py
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#! /usr/bin/python3
r'''###############################################################################
###################################################################################
#
# GPT2RGAX.py
#
# GPT-2 with Relative Global Attention Python Module
# Experimental Version
#
# Version 1.0
#
# PLEASE NOTE THAT THIS IS A WORK IN PROGRESS
# CHECK BACK FOR UPDATES SOON
#
# Based upon a source-code of Sashmark97:
# https://github.com/Sashmark97/midigen
#
# Project Los Angeles
# Tegridy Code 2021
#
# https://github.com/Tegridy-Code/Project-Los-Angeles
#
#
###################################################################################
###################################################################################
# Copyright 2021 Project Los Angeles / Tegridy Code
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
###################################################################################
###################################################################################'''
########################################################
#
# Critical dependencies/requirements:
#
# pip install torch
# pip install tqdm
#
########################################################
print('Loading GPT2-RGA Experimental Module...')
########################################################
import glob
import os
import sys
import math
import time
import random
import pickle
import joblib
from tqdm import tqdm
import torch
from torch.utils.data import Dataset, DataLoader
from torch.optim import Adam
import torch.nn as nn
from torch.nn import functional as F
from torch.optim.lr_scheduler import LambdaLR
from torch.nn.modules.normalization import LayerNorm
from torch.nn.parameter import Parameter
from torch.nn.modules.linear import Linear
from torch.nn.modules.dropout import Dropout
from torch.nn.modules.normalization import LayerNorm
from torch.nn.init import *
from torch.nn.functional import linear, softmax, dropout
########################################################
# Constants
SEQUENCE_START = 0
RANGE_NOTE_ON = 128
RANGE_NOTE_OFF = 128
RANGE_VEL = 32
RANGE_TIME_SHIFT = 100
# Taken from the paper
ADAM_BETA_1 = 0.9
ADAM_BETA_2 = 0.98
ADAM_EPSILON = 10e-9
LR_DEFAULT_START = 1.0
SCHEDULER_WARMUP_STEPS = 4000
# LABEL_SMOOTHING_E = 0.1
# DROPOUT_P = 0.1
TOKEN_END = 256+512 # RANGE_NOTE_ON + RANGE_NOTE_OFF + RANGE_VEL + RANGE_TIME_SHIFT
TOKEN_PAD = TOKEN_END + 1
VOCAB_SIZE = TOKEN_PAD + 1
TORCH_FLOAT = torch.float32
TORCH_INT = torch.int32
TORCH_LABEL_TYPE = torch.long
PREPEND_ZEROS_WIDTH = 4
TORCH_CPU_DEVICE = torch.device("cpu")
USE_CUDA = 1
TORCH_CUDA_DEVICE = torch.device("cuda")
#====
weight_modulus = 1
print_modulus = 1
n_workers = 1
lr = None
ce_smoothing = None
batch_size = 4
random_seq = True
epochs = 5
rpr = False #'store_true'
enable_rpr = True
max_seq = 1024
n_layers = 6
num_heads = 8
d_model = 512
dim_feedforward = 512
dropout_prob = 0.1
########################################################
def cpu_device():
return TORCH_CPU_DEVICE
def get_device():
if((not USE_CUDA) or (TORCH_CUDA_DEVICE is None)):
return TORCH_CPU_DEVICE
else:
return TORCH_CUDA_DEVICE
def train(cur_epoch, model, dataloader, loss, opt, lr_scheduler=None, num_iters=-1, save_checkpoint_steps=1000):
best_eval_acc = 0.0
best_eval_acc_epoch = -1
best_eval_loss = float("inf")
best_eval_loss_epoch = -1
loss_hist = []
save_steps = 0
out = -1
model.train()
with tqdm(total=len(dataloader)) as bar_train:
for batch_num, batch in enumerate(dataloader):
time_before = time.time()
opt.zero_grad()
x = batch[0].to(get_device())
tgt = batch[1].to(get_device())
y, _ = model(x)
y = y.reshape(y.shape[0] * y.shape[1], -1)
tgt = tgt.flatten()
out = loss.forward(y, tgt)
out.backward()
opt.step()
if(lr_scheduler is not None):
lr_scheduler.step()
time_after = time.time()
time_took = time_after - time_before
lr = opt.param_groups[0]['lr']
bar_train.set_description(f'Epoch: {cur_epoch} Loss: {float(out):.4} LR: {float(lr):.8}')
bar_train.update(1)
loss_hist.append(out.item())
if save_steps % save_checkpoint_steps == 0:
print('Saving model progress. Please wait...')
print('gpt2_rpr_checkpoint_' + str(cur_epoch) + '_epoch_' + str(save_steps) + '_steps_' + str(round(float(out), 4)) + '_loss.pth')
torch.save(model.state_dict(), 'gpt2_rpr_checkpoint_' + str(cur_epoch) + '_epoch_' + str(save_steps) + '_steps_' + str(round(float(out), 4)) + '_loss.pth')
print('Done! Continuing training...')
save_steps +=1
if batch_num == num_iters:
break
return loss_hist
def compute_epiano_accuracy(out, tgt):
softmax = nn.Softmax(dim=-1)
out = torch.argmax(softmax(out), dim=-1)
out = out.flatten()
tgt = tgt.flatten()
mask = (tgt != TOKEN_PAD)
out = out[mask]
tgt = tgt[mask]
if(len(tgt) == 0):
return 1.0
num_right = (out == tgt)
num_right = torch.sum(num_right).type(TORCH_FLOAT)
acc = num_right / len(tgt)
return acc
def eval_model(model, dataloader, loss, num_iters=-1):
model.eval()
avg_acc = -1
avg_loss = -1
with torch.set_grad_enabled(False):
n_test = len(dataloader)
sum_loss = 0.0
sum_acc = 0.0
with tqdm(total=len(dataloader)) as bar_eval:
for batch in dataloader:
x = batch[0].to(get_device())
tgt = batch[1].to(get_device())
y, _ = model(x)
sum_acc += float(compute_epiano_accuracy(y, tgt))
y = y.reshape(y.shape[0] * y.shape[1], -1)
tgt = tgt.flatten()
out = loss.forward(y, tgt)
sum_loss += float(out)
bar_eval.set_description(f'Loss val: {float(out):.4} Acc: {float(sum_acc / (bar_eval.n + 1)):.4}')
bar_eval.update(1)
if bar_eval.n == num_iters:
break
avg_loss = sum_loss / n_test
avg_acc = sum_acc / n_test
return avg_loss, avg_acc
class LrStepTracker:
def __init__(self, model_dim=512, warmup_steps=4000, init_steps=0):
# Store Values
self.warmup_steps = warmup_steps
self.model_dim = model_dim
self.init_steps = init_steps
# Begin Calculations
self.invsqrt_dim = (1 / math.sqrt(model_dim))
self.invsqrt_warmup = (1 / (warmup_steps * math.sqrt(warmup_steps)))
# step
def step(self, step):
step += self.init_steps
if(step <= self.warmup_steps):
return self.invsqrt_dim * self.invsqrt_warmup * step
else:
invsqrt_step = (1 / math.sqrt(step))
return self.invsqrt_dim * invsqrt_step
# get_lr
def get_lr(optimizer):
for param_group in optimizer.param_groups:
return param_group['lr']
########################################################
#@title Functions
class EPianoDataset(Dataset):
"""
----------
Author: Damon Gwinn
----------
Pytorch Dataset for the Maestro e-piano dataset (https://magenta.tensorflow.org/datasets/maestro).
Recommended to use with Dataloader (https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader)
Uses all files found in the given root directory of pre-processed (preprocess_midi.py)
Maestro midi files.
----------
"""
def __init__(self, midi_list, max_seq=2048, random_seq=True):
self.max_seq = max_seq
self.random_seq = random_seq
self.data_files = midi_list
def __len__(self):
"""
----------
Author: Damon Gwinn
----------
How many data files exist in the given directory
----------
"""
return len(self.data_files)
def __getitem__(self, idx):
"""
----------
Author: Damon Gwinn
----------
Gets the indexed midi batch. Gets random sequence or from start depending on random_seq.
Returns the input and the target.
----------
"""
raw_mid = torch.tensor(self.data_files, dtype=TORCH_LABEL_TYPE, device=cpu_device())
x, tgt = process_midi(raw_mid, self.max_seq, self.random_seq)
return x, tgt
def process_midi(raw_mid, max_seq, random_seq):
"""
----------
Author: Damon Gwinn
----------
Takes in pre-processed raw midi and returns the input and target. Can use a random sequence or
go from the start based on random_seq.
----------
"""
x = torch.full((max_seq, ), TOKEN_PAD, dtype=TORCH_LABEL_TYPE, device=cpu_device())
tgt = torch.full((max_seq, ), TOKEN_PAD, dtype=TORCH_LABEL_TYPE, device=cpu_device())
raw_len = len(raw_mid)
full_seq = max_seq + 1 # Performing seq2seq
if(raw_len == 0):
return x, tgt
start = 0
end = 0
# Randomly selecting a range
if (random_seq):
end_range = raw_len - full_seq
start = random.randint(abs(SEQUENCE_START), abs(end_range))
# Always taking from the start to as far as we can
else:
start = SEQUENCE_START
end = start + full_seq
data = raw_mid[start:end]
x = data[:max_seq]
tgt = data[1:full_seq]
return x, tgt
########################################################
class CausalSelfAttention(nn.Module):
"""
A vanilla multi-head masked self-attention layer with a projection at the end.
It is possible to use torch.nn.MultiheadAttention here but I am including an
explicit implementation here to show that there is nothing too scary here.
"""
def __init__(self, config):
super().__init__()
assert config.n_embd % config.n_head == 0
# key, query, value projections for all heads
self.key = nn.Linear(config.n_embd, config.n_embd)
self.query = nn.Linear(config.n_embd, config.n_embd)
self.value = nn.Linear(config.n_embd, config.n_embd)
# regularization
self.attn_drop = nn.Dropout(config.attn_pdrop)
self.resid_drop = nn.Dropout(config.resid_pdrop)
# output projection
self.proj = nn.Linear(config.n_embd, config.n_embd)
# causal mask to ensure that attention is only applied to the left in the input sequence
self.register_buffer("mask", torch.tril(torch.ones(config.block_size, config.block_size))
.view(1, 1, config.block_size, config.block_size))
self.n_head = config.n_head
def forward(self, x, layer_past=None):
B, T, C = x.size()
# calculate query, key, values for all heads in batch and move head forward to be the batch dim
k = self.key(x).view(B, T, self.n_head, C // self.n_head).transpose(1, 2) # (B, nh, T, hs)
q = self.query(x).view(B, T, self.n_head, C // self.n_head).transpose(1, 2) # (B, nh, T, hs)
v = self.value(x).view(B, T, self.n_head, C // self.n_head).transpose(1, 2) # (B, nh, T, hs)
# causal self-attention; Self-attend: (B, nh, T, hs) x (B, nh, hs, T) -> (B, nh, T, T)
att = (q @ k.transpose(-2, -1)) * (1.0 / math.sqrt(k.size(-1)))
att = att.masked_fill(self.mask[:,:,:T,:T] == 0, float('-inf'))
att = F.softmax(att, dim=-1)
att = self.attn_drop(att)
y = att @ v # (B, nh, T, T) x (B, nh, T, hs) -> (B, nh, T, hs)
y = y.transpose(1, 2).contiguous().view(B, T, C) # re-assemble all head outputs side by side
# output projection
y = self.resid_drop(self.proj(y))
return y
class Block(nn.Module):
""" an unassuming Transformer block """
def __init__(self, config):
super().__init__()
self.ln1 = nn.LayerNorm(config.n_embd)
self.ln2 = nn.LayerNorm(config.n_embd)
self.enable_rpr = config.enable_rpr
if config.enable_rpr:
self.attn = MultiheadAttentionRPR(config.n_embd, config.n_head, config.attn_pdrop, er_len=config.er_len)
else:
self.attn = CausalSelfAttention(config)
self.mlp = nn.Sequential(
nn.Linear(config.n_embd, config.dim_feedforward),
nn.GELU(),
nn.Linear(config.dim_feedforward, config.n_embd),
nn.Dropout(config.resid_pdrop),
)
def forward(self, x, mask=None):
if self.enable_rpr:
x = x + self.attn(self.ln1(x), self.ln1(x), self.ln1(x), attn_mask=mask)[0]
else:
x = x + self.attn(self.ln1(x))
x = x + self.mlp(self.ln2(x))
return x
class MultiheadAttentionRPR(nn.Module):
"""
----------
Author: Pytorch
Modified: Damon Gwinn
----------
For Relative Position Representation support (https://arxiv.org/abs/1803.02155)
https://pytorch.org/docs/1.2.0/_modules/torch/nn/modules/activation.html#MultiheadAttention
Modification to add RPR embedding Er and call custom multi_head_attention_forward_rpr
----------
"""
def __init__(self, embed_dim, num_heads, dropout=0., bias=True, add_bias_kv=False,
add_zero_attn=False, kdim=None, vdim=None, er_len=None):
super(MultiheadAttentionRPR, self).__init__()
self.embed_dim = embed_dim
self.kdim = kdim if kdim is not None else embed_dim
self.vdim = vdim if vdim is not None else embed_dim
self._qkv_same_embed_dim = self.kdim == embed_dim and self.vdim == embed_dim
self.num_heads = num_heads
self.dropout = dropout
self.head_dim = embed_dim // num_heads
assert self.head_dim * num_heads == self.embed_dim, "embed_dim must be divisible by num_heads"
self.in_proj_weight = Parameter(torch.empty(3 * embed_dim, embed_dim))
if self._qkv_same_embed_dim is False:
self.q_proj_weight = Parameter(torch.Tensor(embed_dim, embed_dim))
self.k_proj_weight = Parameter(torch.Tensor(embed_dim, self.kdim))
self.v_proj_weight = Parameter(torch.Tensor(embed_dim, self.vdim))
if bias:
self.in_proj_bias = Parameter(torch.empty(3 * embed_dim))
else:
self.register_parameter('in_proj_bias', None)
self.out_proj = Linear(embed_dim, embed_dim, bias=bias)
if add_bias_kv:
self.bias_k = Parameter(torch.empty(1, 1, embed_dim))
self.bias_v = Parameter(torch.empty(1, 1, embed_dim))
else:
self.bias_k = self.bias_v = None
self.add_zero_attn = add_zero_attn
# Adding RPR embedding matrix
if(er_len is not None):
self.Er = Parameter(torch.rand((er_len, self.head_dim), dtype=torch.float32))
else:
self.Er = None
self._reset_parameters()
def _reset_parameters(self):
if self._qkv_same_embed_dim:
xavier_uniform_(self.in_proj_weight)
else:
xavier_uniform_(self.q_proj_weight)
xavier_uniform_(self.k_proj_weight)
xavier_uniform_(self.v_proj_weight)
if self.in_proj_bias is not None:
constant_(self.in_proj_bias, 0.)
constant_(self.out_proj.bias, 0.)
if self.bias_k is not None:
xavier_normal_(self.bias_k)
if self.bias_v is not None:
xavier_normal_(self.bias_v)
def forward(self, query, key, value, key_padding_mask=None,
need_weights=True, attn_mask=None):
if hasattr(self, '_qkv_same_embed_dim') and self._qkv_same_embed_dim is False:
# return F.multi_head_attention_forward(
# query, key, value, self.embed_dim, self.num_heads,
# self.in_proj_weight, self.in_proj_bias,
# self.bias_k, self.bias_v, self.add_zero_attn,
# self.dropout, self.out_proj.weight, self.out_proj.bias,
# training=self.training,
# key_padding_mask=key_padding_mask, need_weights=need_weights,
# attn_mask=attn_mask, use_separate_proj_weight=True,
# q_proj_weight=self.q_proj_weight, k_proj_weight=self.k_proj_weight,
# v_proj_weight=self.v_proj_weight)
return multi_head_attention_forward_rpr(
query, key, value, self.embed_dim, self.num_heads,
self.in_proj_weight, self.in_proj_bias,
self.bias_k, self.bias_v, self.add_zero_attn,
self.dropout, self.out_proj.weight, self.out_proj.bias,
training=self.training,
key_padding_mask=key_padding_mask, need_weights=need_weights,
attn_mask=attn_mask, use_separate_proj_weight=True,
q_proj_weight=self.q_proj_weight, k_proj_weight=self.k_proj_weight,
v_proj_weight=self.v_proj_weight, rpr_mat=self.Er)
else:
if not hasattr(self, '_qkv_same_embed_dim'):
warnings.warn('A new version of MultiheadAttention module has been implemented. \
Please re-train your model with the new module',
UserWarning)
# return F.multi_head_attention_forward(
# query, key, value, self.embed_dim, self.num_heads,
# self.in_proj_weight, self.in_proj_bias,
# self.bias_k, self.bias_v, self.add_zero_attn,
# self.dropout, self.out_proj.weight, self.out_proj.bias,
# training=self.training,
# key_padding_mask=key_padding_mask, need_weights=need_weights,
# attn_mask=attn_mask)
return multi_head_attention_forward_rpr(
query, key, value, self.embed_dim, self.num_heads,
self.in_proj_weight, self.in_proj_bias,
self.bias_k, self.bias_v, self.add_zero_attn,
self.dropout, self.out_proj.weight, self.out_proj.bias,
training=self.training,
key_padding_mask=key_padding_mask, need_weights=need_weights,
attn_mask=attn_mask, rpr_mat=self.Er)
# multi_head_attention_forward_rpr
def multi_head_attention_forward_rpr(query, # type: Tensor
key, # type: Tensor
value, # type: Tensor
embed_dim_to_check, # type: int
num_heads, # type: int
in_proj_weight, # type: Tensor
in_proj_bias, # type: Tensor
bias_k, # type: Optional[Tensor]
bias_v, # type: Optional[Tensor]
add_zero_attn, # type: bool
dropout_p, # type: float
out_proj_weight, # type: Tensor
out_proj_bias, # type: Tensor
training=True, # type: bool
key_padding_mask=None, # type: Optional[Tensor]
need_weights=True, # type: bool
attn_mask=None, # type: Optional[Tensor]
use_separate_proj_weight=False, # type: bool
q_proj_weight=None, # type: Optional[Tensor]
k_proj_weight=None, # type: Optional[Tensor]
v_proj_weight=None, # type: Optional[Tensor]
static_k=None, # type: Optional[Tensor]
static_v=None, # type: Optional[Tensor]
rpr_mat=None
):
'''
print('Query: ', query.shape, 'Key: ', key.shape, 'Value: ', value.shape)
print('Equal: ', torch.equal(query, key) and torch.equal(key, value))
print('embed_dim_to_check: ', embed_dim_to_check)
print('num_heads:', num_heads)
print('in_proj_weight: ', in_proj_weight.shape)
print('in_proj_bias: ', in_proj_bias.shape)
print('bias_k:', bias_k, 'bias_v', bias_v)
print('add_zero_attn:', add_zero_attn)
print('dropout_p: ', dropout_p)
print('out_proj_weight: ', out_proj_weight.shape)
print('out_proj_bias:', out_proj_bias.shape)
print('training:', training)
print('need_weights:', need_weights)
print('use_separate_proj_weight:', use_separate_proj_weight)
print('key_padding_mask:', key_padding_mask)
print('attn_mask:', attn_mask.shape)
print('q_proj_weight:', q_proj_weight)
print('k_proj_weight:', k_proj_weight)
print('v_proj_weight:', v_proj_weight)
print('static_k:', static_k)
print('static_v:', static_v)
print('rpr_mat:', rpr_mat.shape)
'''
"""
----------
Author: Pytorch
Modified: Damon Gwinn
----------
For Relative Position Representation support (https://arxiv.org/abs/1803.02155)
https://pytorch.org/docs/1.2.0/_modules/torch/nn/functional.html
Modification to take RPR embedding matrix and perform skew optimized RPR (https://arxiv.org/abs/1809.04281)
----------
"""
# type: (...) -> Tuple[Tensor, Optional[Tensor]]
qkv_same = torch.equal(query, key) and torch.equal(key, value)
kv_same = torch.equal(key, value)
tgt_len, bsz, embed_dim = query.size()
assert embed_dim == embed_dim_to_check
assert list(query.size()) == [tgt_len, bsz, embed_dim]
assert key.size() == value.size()
head_dim = embed_dim // num_heads
assert head_dim * num_heads == embed_dim, "embed_dim must be divisible by num_heads"
scaling = float(head_dim) ** -0.5
if use_separate_proj_weight is not True:
if qkv_same:
# self-attention
q, k, v = linear(query, in_proj_weight, in_proj_bias).chunk(3, dim=-1)
elif kv_same:
# encoder-decoder attention
# This is inline in_proj function with in_proj_weight and in_proj_bias
_b = in_proj_bias
_start = 0
_end = embed_dim
_w = in_proj_weight[_start:_end, :]
if _b is not None:
_b = _b[_start:_end]
q = linear(query, _w, _b)
if key is None:
assert value is None
k = None
v = None
else:
# This is inline in_proj function with in_proj_weight and in_proj_bias
_b = in_proj_bias
_start = embed_dim
_end = None
_w = in_proj_weight[_start:, :]
if _b is not None:
_b = _b[_start:]
k, v = linear(key, _w, _b).chunk(2, dim=-1)
else:
# This is inline in_proj function with in_proj_weight and in_proj_bias
_b = in_proj_bias
_start = 0
_end = embed_dim
_w = in_proj_weight[_start:_end, :]
if _b is not None:
_b = _b[_start:_end]
q = linear(query, _w, _b)
# This is inline in_proj function with in_proj_weight and in_proj_bias
_b = in_proj_bias
_start = embed_dim
_end = embed_dim * 2
_w = in_proj_weight[_start:_end, :]
if _b is not None:
_b = _b[_start:_end]
k = linear(key, _w, _b)
# This is inline in_proj function with in_proj_weight and in_proj_bias
_b = in_proj_bias
_start = embed_dim * 2
_end = None
_w = in_proj_weight[_start:, :]
if _b is not None:
_b = _b[_start:]
v = linear(value, _w, _b)
else:
q_proj_weight_non_opt = torch.jit._unwrap_optional(q_proj_weight)
len1, len2 = q_proj_weight_non_opt.size()
assert len1 == embed_dim and len2 == query.size(-1)
k_proj_weight_non_opt = torch.jit._unwrap_optional(k_proj_weight)
len1, len2 = k_proj_weight_non_opt.size()
assert len1 == embed_dim and len2 == key.size(-1)
v_proj_weight_non_opt = torch.jit._unwrap_optional(v_proj_weight)
len1, len2 = v_proj_weight_non_opt.size()
assert len1 == embed_dim and len2 == value.size(-1)
if in_proj_bias is not None:
q = linear(query, q_proj_weight_non_opt, in_proj_bias[0:embed_dim])
k = linear(key, k_proj_weight_non_opt, in_proj_bias[embed_dim:(embed_dim * 2)])
v = linear(value, v_proj_weight_non_opt, in_proj_bias[(embed_dim * 2):])
else:
q = linear(query, q_proj_weight_non_opt, in_proj_bias)
k = linear(key, k_proj_weight_non_opt, in_proj_bias)
v = linear(value, v_proj_weight_non_opt, in_proj_bias)
q = q * scaling
if bias_k is not None and bias_v is not None:
if static_k is None and static_v is None:
k = torch.cat([k, bias_k.repeat(1, bsz, 1)])
v = torch.cat([v, bias_v.repeat(1, bsz, 1)])
if attn_mask is not None:
attn_mask = torch.cat([attn_mask,
torch.zeros((attn_mask.size(0), 1),
dtype=attn_mask.dtype,
device=attn_mask.device)], dim=1)
if key_padding_mask is not None:
key_padding_mask = torch.cat(
[key_padding_mask, torch.zeros((key_padding_mask.size(0), 1),
dtype=key_padding_mask.dtype,
device=key_padding_mask.device)], dim=1)
else:
assert static_k is None, "bias cannot be added to static key."
assert static_v is None, "bias cannot be added to static value."
else:
assert bias_k is None
assert bias_v is None
q = q.contiguous().view(tgt_len, bsz * num_heads, head_dim).transpose(0, 1)
if k is not None:
k = k.contiguous().view(-1, bsz * num_heads, head_dim).transpose(0, 1)
if v is not None:
v = v.contiguous().view(-1, bsz * num_heads, head_dim).transpose(0, 1)
if static_k is not None:
assert static_k.size(0) == bsz * num_heads
assert static_k.size(2) == head_dim
k = static_k
if static_v is not None:
assert static_v.size(0) == bsz * num_heads
assert static_v.size(2) == head_dim
v = static_v
src_len = k.size(1)
if key_padding_mask is not None:
assert key_padding_mask.size(0) == bsz
assert key_padding_mask.size(1) == src_len
if add_zero_attn:
src_len += 1
k = torch.cat([k, torch.zeros((k.size(0), 1) + k.size()[2:], dtype=k.dtype, device=k.device)], dim=1)
v = torch.cat([v, torch.zeros((v.size(0), 1) + v.size()[2:], dtype=v.dtype, device=v.device)], dim=1)
if attn_mask is not None:
attn_mask = torch.cat([attn_mask, torch.zeros((attn_mask.size(0), 1),
dtype=attn_mask.dtype,
device=attn_mask.device)], dim=1)
if key_padding_mask is not None:
key_padding_mask = torch.cat(
[key_padding_mask, torch.zeros((key_padding_mask.size(0), 1),
dtype=key_padding_mask.dtype,
device=key_padding_mask.device)], dim=1)
attn_output_weights = torch.bmm(q, k.transpose(1, 2))
assert list(attn_output_weights.size()) == [bsz * num_heads, tgt_len, src_len]
######### ADDITION OF RPR ###########
if(rpr_mat is not None):
rpr_mat = _get_valid_embedding(rpr_mat, q.shape[1], k.shape[1])
qe = torch.einsum("hld,md->hlm", q, rpr_mat)
srel = _skew(qe)
attn_output_weights += srel
if attn_mask is not None:
attn_mask = attn_mask.unsqueeze(0)
attn_output_weights += attn_mask
if key_padding_mask is not None:
attn_output_weights = attn_output_weights.view(bsz, num_heads, tgt_len, src_len)
attn_output_weights = attn_output_weights.masked_fill(
key_padding_mask.unsqueeze(1).unsqueeze(2),
float('-inf'),
)
attn_output_weights = attn_output_weights.view(bsz * num_heads, tgt_len, src_len)
attn_output_weights = softmax(
attn_output_weights, dim=-1)
attn_output_weights = dropout(attn_output_weights, p=dropout_p, training=training)
attn_output = torch.bmm(attn_output_weights, v)
assert list(attn_output.size()) == [bsz * num_heads, tgt_len, head_dim]
attn_output = attn_output.transpose(0, 1).contiguous().view(tgt_len, bsz, embed_dim)
attn_output = linear(attn_output, out_proj_weight, out_proj_bias)
if need_weights:
# average attention weights over heads
attn_output_weights = attn_output_weights.view(bsz, num_heads, tgt_len, src_len)
return attn_output, attn_output_weights.sum(dim=1) / num_heads
else:
return attn_output, None
def _get_valid_embedding(Er, len_q, len_k):
"""
----------
Author: Damon Gwinn
----------
Gets valid embeddings based on max length of RPR attention
----------
"""
len_e = Er.shape[0]
start = max(0, len_e - len_q)
return Er[start:, :]
def _skew(qe):
"""
----------
Author: Damon Gwinn
----------
Performs the skew optimized RPR computation (https://arxiv.org/abs/1809.04281)
----------
"""
sz = qe.shape[1]
mask = (torch.triu(torch.ones(sz, sz).to(qe.device)) == 1).float().flip(0)
qe = mask * qe
qe = F.pad(qe, (1,0, 0,0, 0,0))
qe = torch.reshape(qe, (qe.shape[0], qe.shape[2], qe.shape[1]))
srel = qe[:, 1:, :]
return srel
class GPT(nn.Module):
""" the full GPT language model, with a context size of block_size """
def __init__(self, config):
super().__init__()
# input embedding stem
self.tok_emb = nn.Embedding(config.vocab_size, config.n_embd)
self.pos_emb = nn.Parameter(torch.zeros(1, config.block_size, config.n_embd))
self.drop = nn.Dropout(config.embd_pdrop)
# transformer
self.blocks = nn.Sequential(*[Block(config) for _ in range(config.n_layer)])
# decoder head
self.ln_f = nn.LayerNorm(config.n_embd)
self.head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
self.softmax = nn.Softmax(dim=-1)
self.enable_rpr = config.enable_rpr
self.block_size = config.block_size
self.apply(self._init_weights)
logger.info("number of parameters: %e", sum(p.numel() for p in self.parameters()))
def get_block_size(self):
return self.block_size
def _init_weights(self, module):
if isinstance(module, (nn.Linear, nn.Embedding)):
module.weight.data.normal_(mean=0.0, std=0.02)
if isinstance(module, nn.Linear) and module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.LayerNorm):
module.bias.data.zero_()
module.weight.data.fill_(1.0)
def configure_optimizers(self, train_config):
"""
This long function is unfortunately doing something very simple and is being very defensive:
We are separating out all parameters of the model into two buckets: those that will experience
weight decay for regularization and those that won't (biases, and layernorm/embedding weights).
We are then returning the PyTorch optimizer object.
"""
# separate out all parameters to those that will and won't experience regularizing weight decay
decay = set()
no_decay = set()
whitelist_weight_modules = (torch.nn.Linear, )
blacklist_weight_modules = (torch.nn.LayerNorm, torch.nn.Embedding)
for mn, m in self.named_modules():
for pn, p in m.named_parameters():
fpn = '%s.%s' % (mn, pn) if mn else pn # full param name
if pn.endswith('bias'):
# all biases will not be decayed
no_decay.add(fpn)
elif pn.endswith('weight') and isinstance(m, whitelist_weight_modules):
# weights of whitelist modules will be weight decayed
decay.add(fpn)
elif pn.endswith('weight') and isinstance(m, blacklist_weight_modules):
# weights of blacklist modules will NOT be weight decayed
no_decay.add(fpn)
# special case the position embedding parameter in the root GPT module as not decayed
no_decay.add('pos_emb')
# validate that we considered every parameter
param_dict = {pn: p for pn, p in self.named_parameters()}
inter_params = decay & no_decay
union_params = decay | no_decay
assert len(inter_params) == 0, "parameters %s made it into both decay/no_decay sets!" % (str(inter_params), )
assert len(param_dict.keys() - union_params) == 0, "parameters %s were not separated into either decay/no_decay set!" \
% (str(param_dict.keys() - union_params), )
# create the pytorch optimizer object
optim_groups = [
{"params": [param_dict[pn] for pn in sorted(list(decay))], "weight_decay": train_config.weight_decay},
{"params": [param_dict[pn] for pn in sorted(list(no_decay))], "weight_decay": 0.0},
]
optimizer = torch.optim.AdamW(optim_groups, lr=train_config.learning_rate, betas=train_config.betas)
return optimizer
def forward(self, idx, targets=None):
b, t = idx.size()
if self.enable_rpr:
mask = generate_square_subsequent_mask(t).to(get_device())
else:
mask = None
assert t <= self.block_size, "Cannot forward, model block size is exhausted."
# forward the GPT model
token_embeddings = self.tok_emb(idx) # each index maps to a (learnable) vector
position_embeddings = self.pos_emb[:, :t, :] # each position maps to a (learnable) vector
x = self.drop(token_embeddings + position_embeddings)
if self.enable_rpr:
x = x.permute(1,0,2)
for module in self.blocks:
x = module(x, mask=mask)
x = x.permute(1,0,2)
else:
x = self.blocks(x)
x = self.ln_f(x)
logits = self.head(x)
# if we are given some desired targets also calculate the loss
loss = None
if targets is not None:
loss = F.cross_entropy(logits.view(-1, logits.size(-1)), targets.view(-1))
if self.enable_rpr:
del mask
return logits, loss
def generate(self, primer=None, target_seq_length=1024, beam=0, beam_chance=1.0, temperature=0,
stop_token=TOKEN_END, verbose=True):
assert (not self.training), "Cannot generate while in training mode"
if verbose: print("Generating sequence of max length:", target_seq_length)
gen_seq = torch.full((1,target_seq_length), TOKEN_PAD, dtype=TORCH_LABEL_TYPE, device=get_device())
num_primer = len(primer)
gen_seq[..., :num_primer] = primer.type(TORCH_LABEL_TYPE).to(get_device())
cur_i = num_primer
while(cur_i < target_seq_length):
logits, _ = self.forward(gen_seq[..., :cur_i])
y = self.softmax(logits)[..., :stop_token+1]
token_probs = y[:, cur_i-1, :] / (temperature if temperature > 0 else 1.)
if(beam == 0):
beam_ran = 2.0
else:
beam_ran = random.uniform(0,1)
if(beam_ran <= beam_chance):
token_probs = token_probs.flatten()
top_res, top_i = torch.topk(token_probs, beam)
beam_rows = top_i // VOCAB_SIZE
beam_cols = top_i % VOCAB_SIZE
gen_seq = gen_seq[beam_rows, :]
gen_seq[..., cur_i] = beam_cols
else:
distrib = torch.distributions.categorical.Categorical(probs=token_probs)