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import math | |
import inspect | |
from dataclasses import dataclass | |
import torch | |
import torch.nn as nn | |
from torch.nn import functional as F | |
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 CausalSelfAttention(nn.Module): | |
def __init__(self, config): | |
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 not self.flash: | |
print("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): | |
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 | |
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) | |
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) | |
# 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, 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) | |
y = y.transpose(1, 2).contiguous().view(B, T, C) # re-assemble all head outputs side by side | |
# output projection | |
y = self.resid_dropout(self.c_proj(y)) | |
return y | |
class MLP(nn.Module): | |
def __init__(self, config): | |
super().__init__() | |
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.dropout = nn.Dropout(config.dropout) | |
def forward(self, x): | |
x = self.c_fc(x) | |
x = self.gelu(x) | |
x = self.c_proj(x) | |
x = self.dropout(x) | |
return x | |
class Block(nn.Module): | |
def __init__(self, config): | |
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 GPTConfig: | |
block_size: int = 1024 | |
vocab_size: int = 100265 # GPT-2 vocab_size of 50257, padded up to nearest multiple of 64 for efficiency | |
n_layer: int = 24 | |
n_head: int = 16 | |
n_embd: int = 1024 | |
dropout: float = 0.0 | |
bias: bool = True # True: bias in Linears and LayerNorms, like GPT-2. False: a bit better and faster | |
class GPT(nn.Module): | |
def __init__(self, config): | |
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 | |
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): | |
""" | |
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() | |
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 | |
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: | |
# inference-time mini-optimization: only forward the lm_head on the very last position | |
logits = self.lm_head(x[:, [-1], :]) # note: using list [-1] to preserve the time dim | |
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 = nn.Parameter(self.transformer.wpe.weight[:block_size]) | |
for block in self.transformer.h: | |
if hasattr(block.attn, 'bias'): | |
block.attn.bias = block.attn.bias[:,:,:block_size,:block_size] | |
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 | |
print("loading weights from pretrained gpt: %s" % model_type) | |
# n_layer, n_head and n_embd are determined from model_type | |
config_args = { | |
'gpt2': dict(n_layer=12, n_head=12, n_embd=768), # 124M params | |
'gpt2-medium': dict(n_layer=24, n_head=16, n_embd=1024), # 350M params | |
'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 | |
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 | |
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 | |
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 | |
mfu = flops_achieved / flops_promised | |
return mfu | |
def generate(self, idx, max_new_tokens, temperature=1.0, top_k=None, top_p=0.95, repetition_penalty=1.2, eor_token_id=None): | |
generated = idx | |
for _ in range(max_new_tokens): | |
idx_cond = generated if generated.size(1) <= self.config.block_size else generated[:, -self.config.block_size:] | |
logits, _ = self(idx_cond) | |
logits = logits[:, -1, :] / temperature | |
if top_k is not None: | |
v, _ = torch.topk(logits, min(top_k, logits.size(-1))) | |
logits[logits < v[:, [-1]]] = -float('Inf') | |
if top_p < 1.0: | |
sorted_logits, sorted_indices = torch.sort(logits, descending=True) | |
cumulative_probs = torch.cumsum(F.softmax(sorted_logits, dim=-1), dim=-1) | |
sorted_indices_to_remove = cumulative_probs > top_p | |
if sorted_indices_to_remove[:, 1:].sum().item() > 0: | |
sorted_indices_to_remove[:, 1:] = sorted_indices_to_remove[:, :-1].clone() | |
sorted_indices_to_remove[:, 0] = 0 | |
indices_to_remove = sorted_indices[sorted_indices_to_remove] | |
logits[:, indices_to_remove] = -float('Inf') | |
probs = F.softmax(logits, dim=-1) | |
if repetition_penalty != 1.0: | |
for i in range(generated.size(1)): | |
token_id = generated[0, i] | |
probs[0, token_id] /= repetition_penalty | |
idx_next = torch.multinomial(probs, num_samples=1) | |
generated = torch.cat((generated, idx_next), dim=1) | |
if eor_token_id is not None and idx_next.item() == eor_token_id: | |
break | |
return generated |