Transformer: nanoGPT.model vs Bark.model
218 removals
330 lines
94 additions
218 lines
"""
"""
Full definition of a GPT Language Model, all of it in this single file.
Much of this code is adapted from Andrej Karpathy's NanoGPT
References:
(https://github.com/karpathy/nanoGPT)
1) the official GPT-2 TensorFlow implementation released by OpenAI:
https://github.com/openai/gpt-2/blob/master/src/model.py
2) huggingface/transformers PyTorch implementation:
https://github.com/huggingface/transformers/blob/main/src/transformers/models/gpt2/modeling_gpt2.py
"""
"""
import math
import math
import inspect
from dataclasses import dataclass
from dataclasses import dataclass
import torch
import torch
import torch.nn as nn
import torch.nn as nn
from torch.nn import functional as F
from torch.nn import functional as F
class LayerNorm(nn.Module):
class LayerNorm(nn.Module):
""" LayerNorm but with an optional bias. PyTorch doesn't support simply bias=False """
""" LayerNorm but with an optional bias. PyTorch doesn't support simply bias=False """
def __init__(self, ndim, bias):
def __init__(self, ndim, bias):
super().__init__()
super().__init__()
self.weight = nn.Parameter(torch.ones(ndim))
self.weight = nn.Parameter(torch.ones(ndim))
self.bias = nn.Parameter(torch.zeros(ndim)) if bias else None
self.bias = nn.Parameter(torch.zeros(ndim)) if bias else None
def forward(self, input):
def forward(self, input):
return F.layer_norm(input, self.weight.shape, self.weight, self.bias, 1e-5)
return F.layer_norm(input, self.weight.shape, self.weight, self.bias, 1e-5)
class CausalSelfAttention(nn.Module):
class CausalSelfAttention(nn.Module):
def __init__(self, config):
def __init__(self, config):
super().__init__()
super().__init__()
assert config.n_embd % config.n_head == 0
assert config.n_embd % config.n_head == 0
# key, query, value projections for all heads, but in a batch
# key, query, value projections for all heads, but in a batch
self.c_attn = nn.Linear(config.n_embd, 3 * config.n_embd, bias=config.bias)
self.c_attn = nn.Linear(config.n_embd, 3 * config.n_embd, bias=config.bias)
# output projection
# output projection
self.c_proj = nn.Linear(config.n_embd, config.n_embd, bias=config.bias)
self.c_proj = nn.Linear(config.n_embd, config.n_embd, bias=config.bias)
# regularization
# regularization
self.attn_dropout = nn.Dropout(config.dropout)
self.attn_dropout = nn.Dropout(config.dropout)
self.resid_dropout = nn.Dropout(config.dropout)
self.resid_dropout = nn.Dropout(config.dropout)
self.n_head = config.n_head
self.n_head = config.n_head
self.n_embd = config.n_embd
self.n_embd = config.n_embd
self.dropout = config.dropout
self.dropout = config.dropout
# flash attention make GPU go brrrrr but support is only in PyTorch >= 2.0
# flash attention make GPU go brrrrr but support is only in PyTorch nightly and still a bit scary
self.flash = hasattr(torch.nn.functional, 'scaled_dot_product_attention')
self.flash = hasattr(torch.nn.functional, 'scaled_dot_product_attention')
if not self.flash:
if not self.flash:
print("WARNING: using slow attention. Flash Attention requires PyTorch >= 2.0")
# print("WARNING: using slow attention. Flash Attention atm needs PyTorch nightly and dropout=0.0")
# causal mask to ensure that attention is only applied to the left in the input sequence
# causal mask to ensure that attention is only applied to the left in the input sequence
self.register_buffer("bias", torch.tril(torch.ones(config.block_size, config.block_size))
self.register_buffer("bias", torch.tril(torch.ones(config.block_size, config.block_size))
.view(1, 1, config.block_size, config.block_size))
.view(1, 1, config.block_size, config.block_size))
def forward(self, x):
def forward(self, x, past_kv=None, use_cache=False):
B, T, C = x.size() # batch size, sequence length, embedding dimensionality (n_embd)
B, T, C = x.size() # batch size, sequence length, embedding dimensionality (n_embd)
# calculate query, key, values for all heads in batch and move head forward to be the batch dim
# calculate query, key, values for all heads in batch and move head forward to be the batch dim
q, k, v = self.c_attn(x).split(self.n_embd, dim=2)
q, k ,v = self.c_attn(x).split(self.n_embd, dim=2)
k = k.view(B, T, self.n_head, C // self.n_head).transpose(1, 2) # (B, nh, T, hs)
k = k.view(B, T, self.n_head, C // self.n_head).transpose(1, 2) # (B, nh, T, hs)
q = q.view(B, T, self.n_head, C // self.n_head).transpose(1, 2) # (B, nh, T, hs)
q = q.view(B, T, self.n_head, C // self.n_head).transpose(1, 2) # (B, nh, T, hs)
v = v.view(B, T, self.n_head, C // self.n_head).transpose(1, 2) # (B, nh, T, hs)
v = v.view(B, T, self.n_head, C // self.n_head).transpose(1, 2) # (B, nh, T, hs)
if past_kv is not None:
past_key = past_kv[0]
past_value = past_kv[1]
k = torch.cat((past_key, k), dim=-2)
v = torch.cat((past_value, v), dim=-2)
FULL_T = k.shape[-2]
if use_cache is True:
present = (k, v)
else:
present = None
# causal self-attention; Self-attend: (B, nh, T, hs) x (B, nh, hs, T) -> (B, nh, T, T)
# causal self-attention; Self-attend: (B, nh, T, hs) x (B, nh, hs, T) -> (B, nh, T, T)
if self.flash:
if self.flash:
# efficient attention using Flash Attention CUDA kernels
# efficient attention using Flash Attention CUDA kernels
y = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=None, dropout_p=self.dropout if self.training else 0, is_causal=True)
if past_kv is not None:
# When `past_kv` is provided, we're doing incremental decoding and `q.shape[2] == 1`: q only contains
# the query for the last token. scaled_dot_product_attention interprets this as the first token in the
# sequence, so if is_causal=True it will mask out all attention from it. This is not what we want, so
# to work around this we set is_causal=False.
is_causal = False
else:
is_causal = True
y = torch.nn.functional.scaled_dot_product_attention(q, k, v, dropout_p=self.dropout, is_causal=is_causal)
else:
else:
# manual implementation of attention
# manual implementation of attention
att = (q @ k.transpose(-2, -1)) * (1.0 / math.sqrt(k.size(-1)))
att = (q @ k.transpose(-2, -1)) * (1.0 / math.sqrt(k.size(-1)))
att = att.masked_fill(self.bias[:,:,:T,:T] == 0, float('-inf'))
att = att.masked_fill(self.bias[:,:,FULL_T-T:FULL_T,:FULL_T] == 0, float('-inf'))
att = F.softmax(att, dim=-1)
att = F.softmax(att, dim=-1)
att = self.attn_dropout(att)
att = self.attn_dropout(att)
y = att @ v # (B, nh, T, T) x (B, nh, T, hs) -> (B, nh, T, hs)
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
y = y.transpose(1, 2).contiguous().view(B, T, C) # re-assemble all head outputs side by side
# output projection
# output projection
y = self.resid_dropout(self.c_proj(y))
y = self.resid_dropout(self.c_proj(y))
return y
return (y, present)
class MLP(nn.Module):
class MLP(nn.Module):
def __init__(self, config):
def __init__(self, config):
super().__init__()
super().__init__()
self.c_fc = nn.Linear(config.n_embd, 4 * config.n_embd, bias=config.bias)
self.c_fc = nn.Linear(config.n_embd, 4 * config.n_embd, bias=config.bias)
self.gelu = nn.GELU()
self.c_proj = nn.Linear(4 * config.n_embd, config.n_embd, bias=config.bias)
self.c_proj = nn.Linear(4 * config.n_embd, config.n_embd, bias=config.bias)
self.dropout = nn.Dropout(config.dropout)
self.dropout = nn.Dropout(config.dropout)
self.gelu = nn.GELU()
def forward(self, x):
def forward(self, x):
x = self.c_fc(x)
x = self.c_fc(x)
x = self.gelu(x)
x = self.gelu(x)
x = self.c_proj(x)
x = self.c_proj(x)
x = self.dropout(x)
x = self.dropout(x)
return x
return x
class Block(nn.Module):
class Block(nn.Module):
def __init__(self, config):
def __init__(self, config, layer_idx):
super().__init__()
super().__init__()
self.ln_1 = LayerNorm(config.n_embd, bias=config.bias)
self.ln_1 = LayerNorm(config.n_embd, bias=config.bias)
self.attn = CausalSelfAttention(config)
self.attn = CausalSelfAttention(config)
self.ln_2 = LayerNorm(config.n_embd, bias=config.bias)
self.ln_2 = LayerNorm(config.n_embd, bias=config.bias)
self.mlp = MLP(config)
self.mlp = MLP(config)
self.layer_idx = layer_idx
def forward(self, x):
def forward(self, x, past_kv=None, use_cache=False):
x = x + self.attn(self.ln_1(x))
attn_output, prev_kvs = self.attn(self.ln_1(x), past_kv=past_kv, use_cache=use_cache)
x = x + attn_output
x = x + self.mlp(self.ln_2(x))
x = x + self.mlp(self.ln_2(x))
return x
return (x, prev_kvs)
@dataclass
@dataclass
class GPTConfig:
class GPTConfig:
block_size: int = 1024
block_size: int = 1024
vocab_size: int = 50304 # GPT-2 vocab_size of 50257, padded up to nearest multiple of 64 for efficiency
input_vocab_size: int = 10_048
output_vocab_size: int = 10_048
n_layer: int = 12
n_layer: int = 12
n_head: int = 12
n_head: int = 12
n_embd: int = 768
n_embd: int = 768
dropout: float = 0.0
dropout: float = 0.0
bias: bool = True # True: bias in Linears and LayerNorms, like GPT-2. False: a bit better and faster
bias: bool = True # True: bias in Linears and LayerNorms, like GPT-2. False: a bit better and faster
class GPT(nn.Module):
class GPT(nn.Module):
def __init__(self, config):
def __init__(self, config):
super().__init__()
super().__init__()
assert config.vocab_size is not None
assert config.input_vocab_size is not None
assert config.output_vocab_size is not None
assert config.block_size is not None
assert config.block_size is not None
self.config = config
self.config = config
self.transformer = nn.ModuleDict(dict(
self.transformer = nn.ModuleDict(dict(
wte = nn.Embedding(config.vocab_size, config.n_embd),
wte = nn.Embedding(config.input_vocab_size, config.n_embd),
wpe = nn.Embedding(config.block_size, config.n_embd),
wpe = nn.Embedding(config.block_size, config.n_embd),
drop = nn.Dropout(config.dropout),
drop = nn.Dropout(config.dropout),
h = nn.ModuleList([Block(config) for _ in range(config.n_layer)]),
h = nn.ModuleList([Block(config, idx) for idx in range(config.n_layer)]),
ln_f = LayerNorm(config.n_embd, bias=config.bias),
ln_f = LayerNorm(config.n_embd, bias=config.bias),
))
))
self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
self.lm_head = nn.Linear(config.n_embd, config.output_vocab_size, bias=False)
# with weight tying when using torch.compile() some warnings get generated:
# "UserWarning: functional_call was passed multiple values for tied weights.
# This behavior is deprecated and will be an error in future versions"
# not 100% sure what this is, so far seems to be harmless. TODO investigate
self.transformer.wte.weight = self.lm_head.weight # https://paperswithcode.com/method/weight-tying
# init all weights
self.apply(self._init_weights)
# apply special scaled init to the residual projections, per GPT-2 paper
for pn, p in self.named_parameters():
if pn.endswith('c_proj.weight'):
torch.nn.init.normal_(p, mean=0.0, std=0.02/math.sqrt(2 * config.n_layer))
# report number of parameters
print("number of parameters: %.2fM" % (self.get_num_params()/1e6,))
def get_num_params(self, non_embedding=True):
def get_num_params(self, non_embedding=True):
"""
"""
Return the number of parameters in the model.
Return the number of parameters in the model.
For non-embedding count (default), the position embeddings get subtracted.
For non-embedding count (default), the position embeddings get subtracted.
The token embeddings would too, except due to the parameter sharing these
The token embeddings would too, except due to the parameter sharing these
params are actually used as weights in the final layer, so we include them.
params are actually used as weights in the final layer, so we include them.
"""
"""
n_params = sum(p.numel() for p in self.parameters())
n_params = sum(p.numel() for p in self.parameters())
if non_embedding:
if non_embedding:
n_params -= self.transformer.wte.weight.numel()
n_params -= self.transformer.wpe.weight.numel()
n_params -= self.transformer.wpe.weight.numel()
return n_params
return n_params
def _init_weights(self, module):
def forward(self, idx, merge_context=False, past_kv=None, position_ids=None, use_cache=False):
if isinstance(module, nn.Linear):
torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
if module.bias is not None:
torch.nn.init.zeros_(module.bias)
elif isinstance(module, nn.Embedding):
torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
def forward(self, idx, targets=None):
device = idx.device
device = idx.device
b, t = idx.size()
b, t = idx.size()
assert t <= self.config.block_size, f"Cannot forward sequence of length {t}, block size is only {self.config.block_size}"
if past_kv is not None:
pos = torch.arange(0, t, dtype=torch.long, device=device) # shape (t)
assert t == 1
tok_emb = self.transformer.wte(idx) # token embeddings of shape (b, t, n_embd)
# forward the GPT model itself
tok_emb = self.transformer.wte(idx) # token embeddings of shape (b, t, n_embd)
pos_emb = self.transformer.wpe(pos) # position embeddings of shape (t, n_embd)
x = self.transformer.drop(tok_emb + pos_emb)
for block in self.transformer.h:
x = block(x)
x = self.transformer.ln_f(x)
if targets is not None:
# if we are given some desired targets also calculate the loss
logits = self.lm_head(x)
loss = F.cross_entropy(logits.view(-1, logits.size(-1)), targets.view(-1), ignore_index=-1)
else:
else:
# inference-time mini-optimization: only forward the lm_head on the very last position
if merge_context:
logits = self.lm_head(x[:, [-1], :]) # note: using list [-1] to preserve the time dim
assert(idx.shape[1] >= 256+256+1)
loss = None
t = idx.shape[1] - 256
else:
return logits, loss
assert t <= self.config.block_size, f"Cannot forward sequence of length {t}, block size is only {self.config.block_size}"
def crop_block_size(self, block_size):
# forward the GPT model itself
# model surgery to decrease the block size if necessary
if merge_context:
# e.g. we may load the GPT2 pretrained model checkpoint (block size 1024)
tok_emb = torch.cat([
# but want to use a smaller block size for some smaller, simpler model
self.transformer.wte(idx[:,:256]) + self.transformer.wte(idx[:,256:256+256]),
assert block_size <= self.config.block_size
self.transformer.wte(idx[:,256+256:])
self.config.block_size = block_size
], dim=1)
self.transformer.wpe.weight = nn.Parameter(self.transformer.wpe.weight[:block_size])
else:
for block in self.transformer.h:
tok_emb = self.transformer.wte(idx) # token embeddings of shape (b, t, n_embd)
if hasattr(block.attn, 'bias'):
block.attn.bias = block.attn.bias[:,:,:block_size,:block_size]
@classmethod
if past_kv is None:
def from_pretrained(cls, model_type, override_args=None):
past_length = 0
assert model_type in {'gpt2', 'gpt2-medium', 'gpt2-large', 'gpt2-xl'}
past_kv = tuple([None] * len(self.transformer.h))
override_args = override_args or {} # default to empty dict
else:
# only dropout can be overridden see more notes below
past_length = past_kv[0][0].size(-2)
assert all(k == 'dropout' for k in override_args)
from transformers import GPT2LMHeadModel
print("loading weights from pretrained gpt: %s" % model_type)
# n_layer, n_head and n_embd are determined from model_type
if position_ids is None:
config_args = {
position_ids = torch.arange(past_length, t + past_length, dtype=torch.long, device=device)
'gpt2': dict(n_layer=12, n_head=12, n_embd=768), # 124M params
position_ids = position_ids.unsqueeze(0) # shape (1, t)
'gpt2-medium': dict(n_layer=24, n_head=16, n_embd=1024), # 350M params
assert position_ids.shape == (1, t)
'gpt2-large': dict(n_layer=36, n_head=20, n_embd=1280), # 774M params
'gpt2-xl': dict(n_layer=48, n_head=25, n_embd=1600), # 1558M params
}[model_type]
print("forcing vocab_size=50257, block_size=1024, bias=True")
config_args['vocab_size'] = 50257 # always 50257 for GPT model checkpoints
config_args['block_size'] = 1024 # always 1024 for GPT model checkpoints
config_args['bias'] = True # always True for GPT model checkpoints
# we can override the dropout rate, if desired
if 'dropout' in override_args:
print(f"overriding dropout rate to {override_args['dropout']}")
config_args['dropout'] = override_args['dropout']
# create a from-scratch initialized minGPT model
config = GPTConfig(**config_args)
model = GPT(config)
sd = model.state_dict()
sd_keys = sd.keys()
sd_keys = [k for k in sd_keys if not k.endswith('.attn.bias')] # discard this mask / buffer, not a param
# init a huggingface/transformers model
pos_emb = self.transformer.wpe(position_ids) # position embeddings of shape (1, t, n_embd)
model_hf = GPT2LMHeadModel.from_pretrained(model_type)
sd_hf = model_hf.state_dict()
# copy while ensuring all of the parameters are aligned and match in names and shapes
x = self.transformer.drop(tok_emb + pos_emb)
sd_keys_hf = sd_hf.keys()
sd_keys_hf = [k for k in sd_keys_hf if not k.endswith('.attn.masked_bias')] # ignore these, just a buffer
sd_keys_hf = [k for k in sd_keys_hf if not k.endswith('.attn.bias')] # same, just the mask (buffer)
transposed = ['attn.c_attn.weight', 'attn.c_proj.weight', 'mlp.c_fc.weight', 'mlp.c_proj.weight']
# basically the openai checkpoints use a "Conv1D" module, but we only want to use a vanilla Linear
# this means that we have to transpose these weights when we import them
assert len(sd_keys_hf) == len(sd_keys), f"mismatched keys: {len(sd_keys_hf)} != {len(sd_keys)}"
for k in sd_keys_hf:
if any(k.endswith(w) for w in transposed):
# special treatment for the Conv1D weights we need to transpose
assert sd_hf[k].shape[::-1] == sd[k].shape
with torch.no_grad():
sd[k].copy_(sd_hf[k].t())
else:
# vanilla copy over the other parameters
assert sd_hf[k].shape == sd[k].shape
with torch.no_grad():
sd[k].copy_(sd_hf[k])
return model
new_kv = () if use_cache else None
def configure_optimizers(self, weight_decay, learning_rate, betas, device_type):
for i, (block, past_layer_kv) in enumerate(zip(self.transformer.h, past_kv)):
# start with all of the candidate parameters
x, kv = block(x, past_kv=past_layer_kv, use_cache=use_cache)
param_dict = {pn: p for pn, p in self.named_parameters()}
# filter out those that do not require grad
param_dict = {pn: p for pn, p in param_dict.items() if p.requires_grad}
# create optim groups. Any parameters that is 2D will be weight decayed, otherwise no.
# i.e. all weight tensors in matmuls + embeddings decay, all biases and layernorms don't.
decay_params = [p for n, p in param_dict.items() if p.dim() >= 2]
nodecay_params = [p for n, p in param_dict.items() if p.dim() < 2]
optim_groups = [
{'params': decay_params, 'weight_decay': weight_decay},
{'params': nodecay_params, 'weight_decay': 0.0}
]
num_decay_params = sum(p.numel() for p in decay_params)
num_nodecay_params = sum(p.numel() for p in nodecay_params)
print(f"num decayed parameter tensors: {len(decay_params)}, with {num_decay_params:,} parameters")
print(f"num non-decayed parameter tensors: {len(nodecay_params)}, with {num_nodecay_params:,} parameters")
# Create AdamW optimizer and use the fused version if it is available
fused_available = 'fused' in inspect.signature(torch.optim.AdamW).parameters
use_fused = fused_available and device_type == 'cuda'
extra_args = dict(fused=True) if use_fused else dict()
optimizer = torch.optim.AdamW(optim_groups, lr=learning_rate, betas=betas, **extra_args)
print(f"using fused AdamW: {use_fused}")
return optimizer
if use_cache:
new_kv = new_kv + (kv,)
def estimate_mfu(self, fwdbwd_per_iter, dt):
x = self.transformer.ln_f(x)
""" estimate model flops utilization (MFU) in units of A100 bfloat16 peak FLOPS """
# first estimate the number of flops we do per iteration.
# see PaLM paper Appendix B as ref: https://arxiv.org/abs/2204.02311
N = self.get_num_params()
cfg = self.config
L, H, Q, T = cfg.n_layer, cfg.n_head, cfg.n_embd//cfg.n_head, cfg.block_size
flops_per_token = 6*N + 12*L*H*Q*T
flops_per_fwdbwd = flops_per_token * T
flops_per_iter = flops_per_fwdbwd * fwdbwd_per_iter
# express our flops throughput as ratio of A100 bfloat16 peak flops
flops_achieved = flops_per_iter * (1.0/dt) # per second
flops_promised = 312e12 # A100 GPU bfloat16 peak flops is 312 TFLOPS
mfu = flops_achieved / flops_promised
return mfu
@torch.no_grad()
# inference-time mini-optimization: only forward the lm_head on the very last position
def generate(self, idx, max_new_tokens, temperature=1.0, top_k=None):
logits = self.lm_head(x[:, [-1], :]) # note: using list [-1] to preserve the time dim
"""
Take a conditioning sequence of indices idx (LongTensor of shape (b,t)) and complete
the sequence max_new_tokens times, feeding the predictions back into the model each time.
Most likely you'll want to make sure to be in model.eval() mode of operation for this.
"""
for _ in range(max_new_tokens):
# if the sequence context is growing too long we must crop it at block_size
idx_cond = idx if idx.size(1) <= self.config.block_size else idx[:, -self.config.block_size:]
# forward the model to get the logits for the index in the sequence
logits, _ = self(idx_cond)
# pluck the logits at the final step and scale by desired temperature
logits = logits[:, -1, :] / temperature
# optionally crop the logits to only the top k options
if top_k is not None:
v, _ = torch.topk(logits, min(top_k, logits.size(-1)))
logits[logits < v[:, [-1]]] = -float('Inf')
# apply softmax to convert logits to (normalized) probabilities
probs = F.softmax(logits, dim=-1)
# sample from the distribution
idx_next = torch.multinomial(probs, num_samples=1)
# append sampled index to the running sequence and continue
idx = torch.cat((idx, idx_next), dim=1)
return idx
return (logits, new_kv)