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import torch
import torch.nn as nn
import torch.nn.functional as F
import math
from dataclasses import dataclass
from typing import Optional, Tuple
import tiktoken
import os
import numpy as np
torch.set_float32_matmul_precision('high')
def load_tokens(filename):
npt = np.load(filename)
npt = npt.astype(np.int32) # added after video
ptt = torch.tensor(npt, dtype=torch.long)
return ptt
class DataLoaderLite:
def __init__(self, B, T, split):
self.B = B
self.T = T
assert split in {'train', 'val'}
# get the shard filenames
data_root = "tinystories"
shards = os.listdir(data_root)
shards = [s for s in shards if split in s]
shards = sorted(shards)
shards = [os.path.join(data_root, s) for s in shards]
self.shards = shards
assert len(shards) > 0, f"no shards found for split {split}"
print(f"found {len(shards)} shards for split {split}")
self.reset()
def reset(self):
# state, init at shard zero
self.current_shard = 0
self.tokens = load_tokens(self.shards[self.current_shard])
self.current_position = self.B * self.T
def next_batch(self):
B, T = self.B, self.T
buf = self.tokens[self.current_position : self.current_position+B*T+1]
x = (buf[:-1]).view(B, T) # inputs
y = (buf[1:]).view(B, T) # targets
# advance the position in the tensor
self.current_position += B * T
# if loading the next batch would be out of bounds, advance to next shard
if self.current_position + (B * T ) > len(self.tokens):
self.current_shard = (self.current_shard + 1) % len(self.shards)
self.tokens = load_tokens(self.shards[self.current_shard])
self.current_position = B * T
return x, y
@dataclass # the hyperparameters
class ModelArgs:
dim: int = 768
n_layers: int = 16
n_heads: int = 16
n_kv_heads: Optional[int] = 4
vocab_size: int = 50304
multiple_of: int = 256
ffn_dim_multiplier: Optional[float] = None
norm_eps: float = 1e-5
rope_theta: float = 50000
max_batch_size: int = 4
max_seq_len: int = 1024
device: str = 'cuda' if torch.cuda.is_available() else 'cpu'
dropout_rate: float = 0.1
params = ModelArgs()
class RMSNorm(torch.nn.Module):
def __init__(self, dim: int, eps: float = 1e-6):
super().__init__()
self.eps = eps
self.weight = nn.Parameter(torch.ones(dim))
def _norm(self, x):
return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
def forward(self, x):
output = self._norm(x.float()).type_as(x)
return output * self.weight
def precompute_freqs_cis(dim: int, end: int, theta: float = 10000.0):
freqs = 1.0 / (theta ** (torch.arange(0, dim, 2)[: (dim // 2)].float() / dim))
t = torch.arange(end, device=freqs.device, dtype=torch.float32)
freqs = torch.outer(t, freqs)
freqs_cis = torch.polar(torch.ones_like(freqs), freqs) # complex64
return freqs_cis.to(params.device)
def reshape_for_broadcast(freqs_cis: torch.Tensor, x: torch.Tensor):
ndim = x.ndim
assert 0 <= 1 < ndim
assert freqs_cis.shape == (x.shape[1], x.shape[-1]), f'freqs_cis.shape {freqs_cis.shape} != (x.shape[1], x.shape[-1]) {(x.shape[1], x.shape[-1])}'
shape = [d if i == 1 or i == ndim - 1 else 1 for i, d in enumerate(x.shape)]
return freqs_cis.view(*shape)
def apply_rotary_emb(
xq: torch.Tensor,
xk: torch.Tensor,
freqs_cis: torch.Tensor,
) -> Tuple[torch.Tensor, torch.Tensor]:
xq_ = torch.view_as_complex(xq.float().reshape(*xq.shape[:-1], -1, 2))
xk_ = torch.view_as_complex(xk.float().reshape(*xk.shape[:-1], -1, 2))
freqs_cis = reshape_for_broadcast(freqs_cis, xq_)
xq_out = torch.view_as_real(xq_ * freqs_cis).flatten(3)
xk_out = torch.view_as_real(xk_ * freqs_cis).flatten(3)
return xq_out.type_as(xq), xk_out.type_as(xk)
def repeat_kv(x: torch.Tensor, n_rep: int) -> torch.Tensor:
"""torch.repeat_interleave(x, dim=2, repeats=n_rep)"""
bs, seqlen, n_kv_heads, head_dim = x.shape
if n_rep == 1:
return x
return (
x[:, :, :, None, :]
.expand(bs, seqlen, n_kv_heads, n_rep, head_dim)
.reshape(bs, seqlen, n_kv_heads * n_rep, head_dim)
)
class Attention(nn.Module):
def __init__(self, args: ModelArgs):
super().__init__()
self.n_heads = args.n_heads
self.n_kv_heads = args.n_heads if args.n_kv_heads is None else args.n_kv_heads
self.n_rep = args.n_heads // self.n_kv_heads
self.head_dim = args.dim // args.n_heads
self.wq = nn.Linear(args.dim, args.n_heads * self.head_dim, bias=False)
self.wk = nn.Linear(args.dim, self.n_kv_heads * self.head_dim, bias=False)
self.wv = nn.Linear(args.dim, self.n_kv_heads * self.head_dim, bias=False)
self.wo = nn.Linear(args.n_heads * self.head_dim, args.dim, bias=False)
self.cache_k = torch.zeros(
(args.max_batch_size, args.max_seq_len, self.n_kv_heads, self.head_dim),
requires_grad = False
).to(args.device)
self.cache_v = torch.zeros(
(args.max_batch_size, args.max_seq_len, self.n_kv_heads, self.head_dim),
requires_grad = False
).to(args.device)
def forward(
self,
x: torch.Tensor,
freqs_cis: torch.Tensor,
mask: Optional[torch.Tensor],
start_pos: int = None,
):
bsz, seqlen, _ = x.shape
xq, xk, xv = self.wq(x), self.wk(x), self.wv(x)
xq = xq.view(bsz, seqlen, self.n_heads, self.head_dim)
xk = xk.view(bsz, seqlen, self.n_kv_heads, self.head_dim)
xv = xv.view(bsz, seqlen, self.n_kv_heads, self.head_dim)
xq, xk = apply_rotary_emb(xq, xk, freqs_cis=freqs_cis)
if start_pos is not None: # if we're performing inference, use kv caching
self.cache_k = self.cache_k.to(xq)
self.cache_v = self.cache_v.to(xq)
# set the values in our cache according to the current input
self.cache_k[:bsz, start_pos : start_pos + seqlen] = xk
self.cache_v[:bsz, start_pos : start_pos + seqlen] = xv
# grab our key and value matrixes which have a longer sequence length than our queries
keys = self.cache_k[:bsz, : start_pos + seqlen]
values = self.cache_v[:bsz, : start_pos + seqlen]
else:
# if we're training, do full sequence length
keys, values = xk, xv
keys = repeat_kv(keys, self.n_rep)
values = repeat_kv(values, self.n_rep)
xq = xq.transpose(1, 2)
keys = keys.transpose(1, 2)
values = values.transpose(1, 2)
scores = torch.matmul(xq, keys.transpose(2, 3)) / math.sqrt(self.head_dim)
if mask is not None:
scores = scores + mask
scores = F.softmax(scores.float(), dim=-1).type_as(xq)
output = torch.matmul(scores, values)
output = output.transpose(1, 2).contiguous().view(bsz, seqlen, -1)
return self.wo(output)
class FeedForward(nn.Module):
def __init__(
self,
dim: int,
hidden_dim: int,
multiple_of: int,
ffn_dim_multiplier: Optional[float],
):
super().__init__()
# custom dim factor multiplier that ensures we're using a multiple of "multiple_of" for hardware efficiency reasons
hidden_dim = int(2 * hidden_dim / 3)
if ffn_dim_multiplier is not None:
hidden_dim = int(ffn_dim_multiplier * hidden_dim)
hidden_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of)
self.w1 = nn.Linear(dim, hidden_dim, bias=False)
self.w2 = nn.Linear(hidden_dim, dim, bias=False)
self.w3 = nn.Linear(dim, hidden_dim, bias=False)
def forward(self, x):
return self.w2(F.silu(self.w1(x)) * self.w3(x))
class TransformerBlock(nn.Module):
def __init__(self, args: ModelArgs):
super().__init__()
self.n_heads = args.n_heads
self.dim = args.dim
self.head_dim = args.dim // args.n_heads
self.attention = Attention(args)
self.feed_forward = FeedForward(
dim=args.dim,
hidden_dim=4 * args.dim,
multiple_of=args.multiple_of,
ffn_dim_multiplier=args.ffn_dim_multiplier,
)
self.attention_norm = RMSNorm(args.dim, eps=args.norm_eps)
self.ffn_norm = RMSNorm(args.dim, eps=args.norm_eps)
self.dropout_rate = args.dropout_rate
def forward(
self,
x: torch.Tensor,
freqs_cis: torch.Tensor,
mask: Optional[torch.Tensor],
start_pos: int = None,
training = False,
):
h = x + F.dropout(self.attention(self.attention_norm(x), freqs_cis, mask, start_pos), p=self.dropout_rate, training=training)
out = h + F.dropout(self.feed_forward(self.ffn_norm(h)), p=self.dropout_rate, training=training)
return out
class Moose(nn.Module):
def __init__(self, params: ModelArgs):
super().__init__()
self.params = params
self.vocab_size = params.vocab_size
self.n_layers = params.n_layers
self.max_seq_len = params.max_seq_len
self.tok_embeddings = nn.Embedding(params.vocab_size, params.dim)
self.layers = torch.nn.ModuleList()
for _ in range(params.n_layers):
self.layers.append(TransformerBlock(params))
self.norm = RMSNorm(params.dim, eps=params.norm_eps)
self.output = nn.Linear(
params.dim,
params.vocab_size,
bias=False)
self.freqs_cis = precompute_freqs_cis(
params.dim // params.n_heads,
params.max_seq_len * 2,
params.rope_theta,)
# precompute the causal attention mask
mask = torch.full((params.max_seq_len, params.max_seq_len),
float("-inf"),
device=params.device)
mask = torch.triu(mask, diagonal=1)
self.register_buffer('mask', mask)
self.criterion = nn.CrossEntropyLoss()
def forward(self,
tokens: torch.Tensor,
targets: torch.Tensor):
bsz, seqlen = tokens.shape
assert tokens.shape == targets.shape
assert seqlen == self.max_seq_len
h = self.tok_embeddings(tokens)
freqs_cis = self.freqs_cis.to(h.device)
freqs_cis = self.freqs_cis[:seqlen]
for layer in self.layers:
h = layer(
h,
freqs_cis,
self.mask,
start_pos = None,
training = True
)
h = self.norm(h)
logits = self.output(h).float()
loss = self.criterion(
logits.view(bsz * seqlen, self.vocab_size),
targets.reshape(bsz * seqlen))
return logits, loss
@torch.inference_mode()
def forward_inference(self,
tokens: torch.Tensor,
start_pos: int,
max_context_window: int,
):
_bsz, seqlen = tokens.shape
h = self.tok_embeddings(tokens)
self.freqs_cis = self.freqs_cis.to(h.device)
freqs_cis = self.freqs_cis[start_pos : start_pos + seqlen]
mask = self.mask[:seqlen, :seqlen]
mask = torch.hstack(
[torch.zeros((seqlen, start_pos), device=tokens.device), mask]
).type_as(h)
for layer in self.layers:
h = layer(
h,
freqs_cis,
mask,
start_pos = start_pos
)
h = self.norm(h)
logits = self.output(h).float()
return logits
@torch.inference_mode()
def Sampler(
self,
logits: torch.Tensor,
temperature: float,
top_p: float
) -> torch.Tensor:
logits = logits[:,-1,:]
logits.div_(temperature)
probs = torch.softmax(logits, dim=-1, dtype=torch.float)
probs_sort, probs_idx = torch.sort(probs, dim=-1, descending=True)
probs_sum = torch.cumsum(probs_sort, dim=-1)
top_ps_mask = (probs_sum - probs_sort) > top_p
probs_sort = torch.where(top_ps_mask, 0, probs_sort)
probs_sort.div_(probs_sort.sum(dim=-1, keepdim=True))
probs = torch.gather(probs_sort, dim=-1, index=torch.argsort(probs_idx, dim=-1))
next_token_id = torch.multinomial(probs, num_samples=1)
return next_token_id
@torch.inference_mode()
def generate(
self,
prompt: str,
max_gen_len: int = 100,
memory_saver_div: int = 1,
temperature: float = 0.6,
top_p: float = 0.9,
) -> str:
assert ((memory_saver_div & (memory_saver_div-1)) == 0) & (memory_saver_div > 0), f'memory_saver_div {memory_saver_div} must be power of 2'
max_context_window = self.max_seq_len // memory_saver_div
enc = tiktoken.get_encoding('gpt2')
tokens = enc.encode(prompt)
tokens = torch.tensor(tokens, device=self.params.device)
tokens = tokens.unsqueeze(0) if len(tokens.shape)==1 else tokens
start_pos = max(tokens.shape[1] - max_context_window, 0)
eot = enc._special_tokens['<|endoftext|>'] # end of text token
while True:
logits = self.forward_inference(
tokens[:,-max_context_window:],
start_pos = start_pos,
max_context_window = max_context_window
)
next_token = self.Sampler(
logits = logits,
temperature = temperature,
top_p = top_p,
)
tokens = torch.cat((tokens, next_token), dim=1)
if next_token.item() == eot:
break
if tokens.shape[1] >= max_context_window:
start_pos += 1
output = enc.decode(tokens.squeeze(0).tolist())
return output[:-13] |