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import math
import struct
import inspect
from .LMConfig import LMConfig
from typing import Any, Optional, Tuple
import numpy as np
import torch
import torch.nn.functional as F
from torch import nn
from transformers import PreTrainedModel
class RMSNorm(torch.nn.Module):
def __init__(self, dim: int, eps: float):
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) # type: ignore
freqs = torch.outer(t, freqs).float() # type: ignore
freqs_cos = torch.cos(freqs) # real part
freqs_sin = torch.sin(freqs) # imaginary part
return freqs_cos, freqs_sin
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])
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_cos: torch.Tensor,
freqs_sin: torch.Tensor
) -> Tuple[torch.Tensor, torch.Tensor]:
# reshape xq and xk to match the complex representation
xq_r, xq_i = xq.float().reshape(xq.shape[:-1] + (-1, 2)).unbind(-1)
xk_r, xk_i = xk.float().reshape(xk.shape[:-1] + (-1, 2)).unbind(-1)
# reshape freqs_cos and freqs_sin for broadcasting
freqs_cos = reshape_for_broadcast(freqs_cos, xq_r)
freqs_sin = reshape_for_broadcast(freqs_sin, xq_r)
# apply rotation using real numbers
xq_out_r = xq_r * freqs_cos - xq_i * freqs_sin
xq_out_i = xq_r * freqs_sin + xq_i * freqs_cos
xk_out_r = xk_r * freqs_cos - xk_i * freqs_sin
xk_out_i = xk_r * freqs_sin + xk_i * freqs_cos
# flatten last two dimensions
xq_out = torch.stack([xq_out_r, xq_out_i], dim=-1).flatten(3)
xk_out = torch.stack([xk_out_r, xk_out_i], dim=-1).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, slen, n_kv_heads, head_dim = x.shape
if n_rep == 1:
return x
return (
x[:, :, :, None, :]
.expand(bs, slen, n_kv_heads, n_rep, head_dim)
.reshape(bs, slen, n_kv_heads * n_rep, head_dim)
)
class Attention(nn.Module):
def __init__(self, args: LMConfig):
super().__init__()
self.n_kv_heads = args.n_heads if args.n_kv_heads is None else args.n_kv_heads
assert args.n_heads % self.n_kv_heads == 0
model_parallel_size = 1
self.n_local_heads = args.n_heads // model_parallel_size
self.n_local_kv_heads = self.n_kv_heads // model_parallel_size
self.n_rep = self.n_local_heads // self.n_local_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.attn_dropout = nn.Dropout(args.dropout)
self.resid_dropout = nn.Dropout(args.dropout)
self.dropout = args.dropout
# use flash attention or a manual implementation?
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")
mask = torch.full((1, 1, args.max_seq_len, args.max_seq_len), float("-inf"))
mask = torch.triu(mask, diagonal=1)
self.register_buffer("mask", mask)
def forward(
self,
x: torch.Tensor,
freqs_cos: torch.Tensor,
freqs_sin: torch.Tensor,
):
bsz, seqlen, _ = x.shape
# QKV
xq, xk, xv = self.wq(x), self.wk(x), self.wv(x)
xq = xq.view(bsz, seqlen, self.n_local_heads, self.head_dim)
xk = xk.view(bsz, seqlen, self.n_local_kv_heads, self.head_dim)
xv = xv.view(bsz, seqlen, self.n_local_kv_heads, self.head_dim)
# RoPE relative positional embeddings
xq, xk = apply_rotary_emb(xq, xk, freqs_cos, freqs_sin)
# grouped multiquery attention: expand out keys and values
xk = repeat_kv(xk, self.n_rep) # (bs, seqlen, n_local_heads, head_dim)
xv = repeat_kv(xv, self.n_rep) # (bs, seqlen, n_local_heads, head_dim)
# make heads into a batch dimension
xq = xq.transpose(1, 2) # (bs, n_local_heads, seqlen, head_dim)
xk = xk.transpose(1, 2)
xv = xv.transpose(1, 2)
# flash implementation
if self.flash:
output = torch.nn.functional.scaled_dot_product_attention(xq, xk, xv, attn_mask=None,
dropout_p=self.dropout if self.training else 0.0,
is_causal=True)
else:
# manual implementation
scores = torch.matmul(xq, xk.transpose(2, 3)) / math.sqrt(self.head_dim)
assert hasattr(self, 'mask')
scores = scores + self.mask[:, :, :seqlen, :seqlen] # (bs, n_local_heads, seqlen, cache_len + seqlen)
scores = F.softmax(scores.float(), dim=-1).type_as(xq)
scores = self.attn_dropout(scores)
output = torch.matmul(scores, xv) # (bs, n_local_heads, seqlen, head_dim)
# restore time as batch dimension and concat heads
output = output.transpose(1, 2).contiguous().view(bsz, seqlen, -1)
# final projection into the residual stream
output = self.wo(output)
output = self.resid_dropout(output)
return output
class FeedForward(nn.Module):
def __init__(self, dim: int, hidden_dim: int, multiple_of: int, dropout: float):
super().__init__()
if hidden_dim is None:
hidden_dim = 4 * dim
hidden_dim = int(2 * hidden_dim / 3)
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)
self.dropout = nn.Dropout(dropout)
def forward(self, x):
return self.dropout(self.w2(F.silu(self.w1(x)) * self.w3(x)))
class TransformerBlock(nn.Module):
def __init__(self, layer_id: int, args: LMConfig):
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=args.hidden_dim,
multiple_of=args.multiple_of,
dropout=args.dropout,
)
self.layer_id = layer_id
self.attention_norm = RMSNorm(args.dim, eps=args.norm_eps)
self.ffn_norm = RMSNorm(args.dim, eps=args.norm_eps)
def forward(self, x, freqs_cos, freqs_sin):
h = x + self.attention.forward(self.attention_norm(x), freqs_cos, freqs_sin)
out = h + self.feed_forward.forward(self.ffn_norm(h))
return out
class Transformer(PreTrainedModel):
config_class = LMConfig
last_loss: Optional[torch.Tensor]
def __init__(self, params: LMConfig = None):
super().__init__(params)
if not params:
params = LMConfig()
self.params = params
self.vocab_size = params.vocab_size
self.n_layers = params.n_layers
self.tok_embeddings = nn.Embedding(params.vocab_size, params.dim)
self.dropout = nn.Dropout(params.dropout)
self.layers = torch.nn.ModuleList()
for layer_id in range(params.n_layers):
self.layers.append(TransformerBlock(layer_id, params))
self.norm = RMSNorm(params.dim, eps=params.norm_eps)
self.output = nn.Linear(params.dim, params.vocab_size, bias=False)
# share the unembedding parameters with the embedding parameters
self.tok_embeddings.weight = self.output.weight # https://paperswithcode.com/method/weight-tying
# some useful precompute for the RoPE relative positional embeddings
freqs_cos, freqs_sin = precompute_freqs_cis(self.params.dim // self.params.n_heads, self.params.max_seq_len)
self.register_buffer("freqs_cos", freqs_cos, persistent=False)
self.register_buffer("freqs_sin", freqs_sin, persistent=False)
# 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('w3.weight') or pn.endswith('wo.weight'):
torch.nn.init.normal_(p, mean=0.0, std=0.02 / math.sqrt(2 * params.n_layers))
# Initialize attribute for the loss of the last forward call. This will be set if the forward is called with a targets tensor.
self.last_loss = None
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, tokens: Optional[torch.Tensor] = None,
targets: Optional[torch.Tensor] = None, **keyargs) -> torch.Tensor:
if 'input_ids' in keyargs:
tokens = keyargs['input_ids']
if 'attention_mask' in keyargs:
targets = keyargs['attention_mask']
_bsz, seqlen = tokens.shape
h = self.tok_embeddings(tokens)
h = self.dropout(h)
freqs_cos = self.freqs_cos[:seqlen]
freqs_sin = self.freqs_sin[:seqlen]
for layer in self.layers:
h = layer(h, freqs_cos, freqs_sin)
h = self.norm(h)
if targets is not None:
# if we are given some desired targets also calculate the loss
logits = self.output(h)
self.last_loss = F.cross_entropy(logits.view(-1, logits.size(-1)), targets.view(-1), ignore_index=-1)
else:
# inference-time mini-optimization: only forward the output on the very last position
logits = self.output(h[:, [-1], :]) # note: using list [-1] to preserve the time dim
self.last_loss = None
return logits
def configure_optimizers(self, weight_decay, learning_rate, 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=(0.9, 0.999), **extra_args)
print(f"using fused AdamW: {use_fused}")
return optimizer
@torch.inference_mode()
def generate(self, idx, max_new_tokens=512, 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.
Also note this is a super inefficient version of sampling with no key/value cache.
"""
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.params.max_seq_len else idx[:, -self.params.max_seq_len:]
# forward the model to get the logits for the index in the sequence
logits = self(idx_cond)
logits = logits[:, -1, :] # crop to just the final time step
if temperature == 0.0:
# "sample" the single most likely index
_, idx_next = torch.topk(logits, k=1, dim=-1)
else:
# pluck the logits at the final step and scale by desired temperature
logits = logits / 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)
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
# @torch.inference_mode()
@torch.no_grad()
def stream_generate(self, idx, eos, 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.
Also note this is a super inefficient version of sampling with no key/value cache.
"""
idx_ = idx.shape[1]
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.params.max_seq_len else idx[:, -self.params.max_seq_len:]
# forward the model to get the logits for the index in the sequence
logits = self(idx_cond)
logits = logits[:, -1, :] # crop to just the final time step
if temperature == 0.0:
# "sample" the single most likely index
_, idx_next = torch.topk(logits, k=1, dim=-1)
else:
# pluck the logits at the final step and scale by desired temperature
logits = logits / 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)
idx_next = torch.multinomial(probs, num_samples=1, generator=None)
# append sampled index to the running sequence and continue
idx = torch.cat((idx, idx_next), dim=1)
yield idx[:, idx_:]
if idx_next == eos:
break
def export(self, filepath='model.bin'):
"""export the model weights in fp32 into .bin file to be read from C"""
f = open(filepath, 'wb')
def serialize(t):
d = t.detach().cpu().view(-1).numpy().astype(np.float32)
b = struct.pack(f'{len(d)}f', *d)
f.write(b)
# first write out the header
hidden_dim = self.layers[0].feed_forward.w1.weight.shape[0]
p = self.params
n_kv_heads = p.n_heads if p.n_kv_heads is None else p.n_kv_heads
header = struct.pack('iiiiiii', p.dim, hidden_dim, p.n_layers, p.n_heads,
n_kv_heads, p.vocab_size, p.max_seq_len)
f.write(header)
# next write out the embedding weights
serialize(self.tok_embeddings.weight)
# now all the layers
# attention weights
for layer in self.layers:
serialize(layer.attention_norm.weight)
for layer in self.layers:
serialize(layer.attention.wq.weight)
for layer in self.layers:
serialize(layer.attention.wk.weight)
for layer in self.layers:
serialize(layer.attention.wv.weight)
for layer in self.layers:
serialize(layer.attention.wo.weight)
# ffn weights
for layer in self.layers:
serialize(layer.ffn_norm.weight)
for layer in self.layers:
serialize(layer.feed_forward.w1.weight)
for layer in self.layers:
serialize(layer.feed_forward.w2.weight)
for layer in self.layers:
serialize(layer.feed_forward.w3.weight)
# final rmsnorm
serialize(self.norm.weight)
# note: no need to write final classifier weights due to weight sharing
# freqs_cis
serialize(self.freqs_cos[:p.max_seq_len])
serialize(self.freqs_sin[:p.max_seq_len])
# write to binary file
f.close()
print(f"wrote {filepath}")
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