https://github.com/saforem2/llm-workshop-talk
Simple tutorial on creating Small(-ish) LLMs (pt. 2 🎉!!)
https://github.com/saforem2/llm-workshop-talk
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Simple tutorial on creating Small(-ish) LLMs (pt. 2 🎉!!)
- Host: GitHub
- URL: https://github.com/saforem2/llm-workshop-talk
- Owner: saforem2
- Created: 2024-02-12T20:58:36.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2024-09-10T14:51:56.000Z (almost 2 years ago)
- Last Synced: 2025-12-01T04:43:44.948Z (7 months ago)
- Language: Python
- Homepage: https://saforem2.github.io/llm-workshop-talk/
- Size: 5.04 MB
- Stars: 3
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: docs/README 2.html
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README
Creating Small(-ish) LLMs
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Creating Small(-ish) LLMs
Creating Small(-ish) LLMs
LLMs from Scratch
Emergent Abilities
Training LLMs
Life-Cycle of the LLM
Forward Pass
Generating Text
Life-Cycle of the LLM: Pre-training
Life-Cycle of the LLM: Fine-Tuning
Assistant Models
saforem2/wordplay 🎮💬
- Fork of Andrej Karpathy’s
nanoGPT
saforem2/wordplay 🎮💬
Install
python3 -m pip install "git+https://github.com/saforem2/wordplay.git"
python3 -c 'import wordplay; print(wordplay.__file__)'
# ./wordplay/src/wordplay/__init__.py
Dependencies
-
transformers for transformers (to load GPT-2 checkpoints)
-
datasets for datasets (if you want to use OpenWebText)
-
tiktoken for OpenAI’s fast BPE code
-
wandb for optional logging
-
tqdm for progress bars
Quick Start
-
We start with training a character-level GPT on the works of Shakespeare.
- Downloading the data (~ 1MB) file
- Convert raw text to one large stream of integers
This will create data/shakespeare_char/{train.bin, val.bin}.
Model model.py
class CausalSelfAttention(nn.Module):
def __init__(self, config: GPTModelConfig):
super().__init__()
assert config.n_embd % config.n_head == 0
# 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
)
# output projection
self.c_proj = nn.Linear(
config.n_embd,
config.n_embd,
bias=config.bias
)
# regularization
self.attn_dropout = nn.Dropout(config.dropout)
self.resid_dropout = nn.Dropout(config.dropout)
self.n_head = config.n_head
self.n_embd = config.n_embd
self.dropout = config.dropout
# flash attention make GPU go brrrrr but support is only in
# PyTorch >= 2.0
self.flash = hasattr(
torch.nn.functional,
'scaled_dot_product_attention'
)
# if self.flash and RANK == 0:
# log.warning(
# f'Using torch.nn.functional.scaled_dot_product_attention'
# '(Flash Attn)'
# )
if not self.flash:
log.warning(
"WARNING: using slow attention."
"Flash Attention requires PyTorch >= 2.0"
)
# 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
)
).view(1, 1, config.block_size, config.block_size)
)
def forward(self, x):
# batch size, sequence length, embedding dimensionality (n_embd)
B, T, C = x.size()
# 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)
# (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)
v = v.view(B, T, self.n_head, C // self.n_head).transpose(1, 2)
# causal self-attention; Self-attend:
# (B, nh, T, hs) x (B, nh, hs, T) -> (B, nh, T, T)
if self.flash:
# 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
)
else:
# manual implementation of attention
att = (q @ k.transpose(-2, -1)) * (1.0 / math.sqrt(k.size(-1)))
att = att.masked_fill(
self.bias[:, :, :T, :T] == 0, # type:ignore
float('-inf')
)
att = F.softmax(att, dim=-1)
att = self.attn_dropout(att)
y = att @ v # (B, nh, T, T) x (B, nh, T, hs) -> (B, nh, T, hs)
# re-assemble all head outputs side by side
y = y.transpose(1, 2).contiguous().view(B, T, C)
# output projection
y = self.resid_dropout(self.c_proj(y))
return y
class LayerNorm(nn.Module):
"""
LayerNorm but with an optional bias.
(PyTorch doesn't support simply bias=False)
"""
def __init__(self, ndim, bias):
super().__init__()
self.weight = nn.Parameter(torch.ones(ndim))
self.bias = nn.Parameter(torch.zeros(ndim)) if bias else None
def forward(self, input):
return F.layer_norm(
input,
self.weight.shape,
self.weight,
self.bias,
1e-5
)
class MLP(nn.Module):
def __init__(
self,
config: GPTModelConfig,
activation: str = 'gelu',
):
super().__init__()
self.c_fc = nn.Linear(
config.n_embd,
4 * config.n_embd,
bias=config.bias
)
if activation.lower() in ACTIVATIONS:
self.act_fn = ACTIVATIONS[activation.lower()]
else:
try:
act_fn = getattr(nn, activation)
assert callable(act_fn)
self.act_fn = act_fn()
except Exception as exc:
log.error(f'{activation} not yet supported!')
raise exc
# self.gelu = nn.GELU()
self.c_proj = nn.Linear(
4 * config.n_embd,
config.n_embd,
bias=config.bias
)
self.dropout = nn.Dropout(config.dropout)
def forward(self, x):
x = self.c_fc(x)
# x = self.gelu(x)
x = self.act_fn(x)
x = self.c_proj(x)
x = self.dropout(x)
return x
class Block(nn.Module):
def __init__(self, config: GPTModelConfig):
super().__init__()
self.ln_1 = LayerNorm(config.n_embd, bias=config.bias)
self.attn = CausalSelfAttention(config)
self.ln_2 = LayerNorm(config.n_embd, bias=config.bias)
self.mlp = MLP(config)
def forward(self, x):
x = x + self.attn(self.ln_1(x))
x = x + self.mlp(self.ln_2(x))
return x
class GPT(nn.Module):
def __init__(self, config: GPTModelConfig):
super().__init__()
assert config.vocab_size is not None
assert config.block_size is not None
self.config = config
self.transformer = nn.ModuleDict(dict(
wte=nn.Embedding(config.vocab_size, config.n_embd),
wpe=nn.Embedding(config.block_size, config.n_embd),
drop=nn.Dropout(config.dropout),
h=nn.ModuleList([Block(config) for _ in range(config.n_layer)]),
ln_f=LayerNorm(config.n_embd, bias=config.bias),
))
self.lm_head = nn.Linear(config.n_embd, config.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
# https://paperswithcode.com/method/weight-tying
self.transformer.wte.weight = self.lm_head.weight # type:ignore
# init all weights
self.apply(self._init_weights)
# apply special scaled init to the residual projections, per GPT-2
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
log.info("number of parameters: %.2fM" % (self.get_num_params()/1e6,))
def get_num_params(self, non_embedding=True):
"""
Return the number of parameters in the model.
For non-embedding count (default), the position embeddings get
subtracted.
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.
"""
n_params = sum(p.numel() for p in self.parameters())
if non_embedding:
n_params -= self.transformer.wpe.weight.numel() # type:ignore
return n_params
def _init_weights(self, module):
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
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}"
)
pos = torch.arange(
0,
t,
dtype=torch.long,
device=device
) # shape (t)
# forward the GPT model itself
# token embeddings of shape (b, t, n_embd)
tok_emb = self.transformer.wte(idx) # type:ignore
# position embeddings of shape (t, n_embd)
pos_emb = self.transformer.wpe(pos) # type:ignore
x = self.transformer.drop(tok_emb + pos_emb) # type:ignore
for block in self.transformer.h: # type:ignore
x = block(x)
x = self.transformer.ln_f(x) # type:ignore
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:
# inference-time mini-optimization: only forward the lm_head on the
# very last position
# note: using list [-1] to preserve the time dim
logits = self.lm_head(x[:, [-1], :])
loss = None
return logits, loss
def crop_block_size(self, block_size):
# model surgery to decrease the block size if necessary e.g. we may
# load the GPT2 pretrained model checkpoint (block size 1024) but want
# to use a smaller block size for some smaller, simpler model
assert block_size <= self.config.block_size
self.config.block_size = block_size
self.transformer.wpe.weight = ( # type:ignore
nn.Parameter(
self.transformer.wpe.weight[:block_size] # type:ignore
)
)
for block in self.transformer.h: # type:ignore
if hasattr(block.attn, 'bias'):
block.attn.bias = (
block.attn.bias[:, :, :block_size, :block_size]
)
@classmethod
def from_pretrained(cls, model_type, override_args=None):
assert model_type in {'gpt2', 'gpt2-medium', 'gpt2-large', 'gpt2-xl'}
override_args = override_args or {} # default to empty dict
# only dropout can be overridden see more notes below
assert all(k == 'dropout' for k in override_args)
from transformers import GPT2LMHeadModel
log.info(f"loading weights from pretrained gpt: {model_type=}")
# n_layer, n_head and n_embd are determined from model_type
# gpt2: 124M params
# gpt2-medium: 350M params
# gpt2-large: 774M params
# gpt2-xl: 1558M params
config_args = {
# 'baby-llama2': dict(n_layer=16, n_head=16, n_embed=1024),
# 'llama2-7b': dict(n_layer=32, n_head=32, n_embd=4096),
'gpt2': dict(n_layer=12, n_head=12, n_embd=768),
'gpt2-medium': dict(n_layer=24, n_head=16, n_embd=1024),
'gpt2-large': dict(n_layer=36, n_head=20, n_embd=1280),
'gpt2-xl': dict(n_layer=48, n_head=25, n_embd=1600),
}[model_type]
# we can override the dropout rate, if desired
if 'dropout' in override_args:
log.info(f"overriding dropout rate to {override_args['dropout']}")
config_args['dropout'] = override_args['dropout']
# create a from-scratch initialized minGPT model
log.info("forcing vocab_size=50257, block_size=1024, bias=True")
config = GPTModelConfig(
**config_args,
block_size=1024, # always 1024 for GPT model checkpoints
vocab_size=50257, # always 50257 for GPT model checkpoints
bias=True, # always True for GPT model checkpoints
)
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
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
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
def configure_optimizers(
self,
weight_decay,
learning_rate,
betas,
device_type
):
# start with all of the candidate parameters
# filter out those that do not require grad
# param_dict = {
# pn: p for pn, p in param_dict.items() if p.requires_grad
# }
param_dict = {
pn: p for pn, p in self.named_parameters() 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 _, p in param_dict.items() if p.dim() >= 2]
nodecay_params = [p for _, 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)
log.info(
f"num decayed parameter tensors: {len(decay_params)}, "
f"with {num_decay_params:,} parameters"
)
log.info(
f"num non-decayed parameter tensors: {len(nodecay_params)}, "
f"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 {}
optimizer = torch.optim.AdamW(
optim_groups,
lr=learning_rate,
betas=betas,
**extra_args
)
log.info(f"using fused AdamW: {use_fused}")
return optimizer
def estimate_mfu(self, fwdbwd_per_iter, dt):
"""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
return flops_achieved / flops_promised
@torch.no_grad()
def generate(self, idx, max_new_tokens, temperature=1.0, top_k=None):
"""
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.
