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import json
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers import PreTrainedModel, PretrainedConfig
from .configuration_minitransformer import MiniTransformerConfig
from transformers.modeling_outputs import CausalLMOutput
try:
from flash_attn import flash_attn_func
except ImportError as e:
print(
f"Unable to import Triton-based flash attention: {e}. No alternative currently available."
)
def precompute_freqs_cis(head_dim: int, max_seq_len: int, theta: float = 10000.0):
# For half the dimensions, build the scale factor:
freq_seq = torch.arange(0, head_dim, 2).float() / head_dim
freqs = 1.0 / (theta ** freq_seq)
# Outer product with positions
t = torch.arange(max_seq_len, dtype=torch.float32)
angles = torch.outer(t, freqs)
# Build a complex exponential e^{i * theta}
freqs_cis = torch.polar(
torch.ones_like(angles),
angles
)
return freqs_cis
def reshape_for_broadcast(freqs_cis: torch.Tensor, x: torch.Tensor):
"""
x is [B, n_heads, seq_len, head_dim_as_complex],
so we want to broadcast freqs_cis from [max_seq_len, half_dim]
to [1, 1, seq_len, half_dim].
"""
seq_len = x.shape[2]
freqs_cis = freqs_cis[:seq_len] # slice down to current seq_len
return freqs_cis.view(1, 1, seq_len, -1)
def apply_rotary_emb(
xq: torch.Tensor,
xk: torch.Tensor,
freqs_cis: torch.Tensor,
) -> tuple[torch.Tensor, torch.Tensor]:
# Convert real -> complex by grouping last dim in pairs
# shape => [B, n_heads, seq_len, head_dim//2, 2] => complex => [B, n_heads, seq_len, head_dim//2]
xq_complex = torch.view_as_complex(xq.float().reshape(*xq.shape[:-1], -1, 2))
xk_complex = torch.view_as_complex(xk.float().reshape(*xk.shape[:-1], -1, 2))
# Broadcast the frequencies to match [B, n_heads, seq_len, head_dim//2]
freqs_cis = reshape_for_broadcast(freqs_cis, xq_complex)
# Multiply => apply rotation
xq_complex = xq_complex * freqs_cis
xk_complex = xk_complex * freqs_cis
# Convert back to real => shape [B, n_heads, seq_len, head_dim]
xq_out = torch.view_as_real(xq_complex).reshape(*xq.shape)
xk_out = torch.view_as_real(xk_complex).reshape(*xk.shape)
return xq_out.type_as(xq), xk_out.type_as(xk)
def nearest_power_of_two(x: int, round_up: bool = False) -> int:
return (
1 << math.floor(math.log2(x)) if not round_up else 1 << math.ceil(math.log2(x))
)
class Attention(nn.Module):
def __init__(self, config):
super(Attention, self).__init__()
self.dim, self.num_heads = config.dim, config.num_heads
assert config.dim % config.num_heads == 0, f"dim ({self.dim}) must be divisible num_heads ({self.num_heads})"
self.head_dim = config.dim // config.num_heads
self.c_attn = nn.Linear(self.dim, 3*self.dim, bias=config.bias)
self.c_proj = nn.Linear(config.dim, config.dim, bias=config.bias)
self.c_proj.SCALE_INIT = 1
self.alibi_slopes = self._get_alibi_slopes(self.num_heads) if config.use_alibi else None
self.window_size = config.window_size
self.softcap = config.softcap
self.dropout = config.dropout
self.resid_dropout = nn.Dropout(self.dropout)
def _generate_slopes(self, n: int):
start = 2 ** (-(2 ** -(math.log2(n) - 3)))
return [start * (start**i) for i in range(n)]
def _get_alibi_slopes(self, num_heads: int, interpolation_factor: float = 0.25):
# If n_heads is a power of 2, generate slopes directly
if math.log2(num_heads).is_integer():
slopes = self._generate_slopes(num_heads)
else:
# Get slopes for the nearest power of two
n = nearest_power_of_two(num_heads, round_up=False)
slopes_power_of_two = self._generate_slopes(n)
# Generate extra slopes
extra_slopes = self._generate_slopes(2 * n)
extra_slopes_trunc = extra_slopes[0::2][: num_heads - n]
slopes = slopes_power_of_two + extra_slopes_trunc
slopes = torch.tensor(slopes, device=torch.device("cuda"))
slopes = slopes * interpolation_factor # https://arxiv.org/pdf/2310.13017
return slopes
def forward(
self,
x: torch.Tensor = None,
q: torch.Tensor = None,
k: torch.Tensor = None,
v: torch.Tensor = None,
freqs_cis: torch.Tensor = None,
) -> torch.Tensor:
if x is not None:
q = k = v = x
if any(t is None for t in [q, k, v]):
raise ValueError("Must provide either x for self-attention or q/k/v for cross-attention.")
bsz, q_len, dim = q.shape
_, k_len, _ = k.shape
_, v_len, _ = v.shape
qkv = self.c_attn(x)
q, k, v = torch.chunk(qkv, 3, dim=2)
q = q.view(bsz, q_len, self.num_heads, self.head_dim)
k = k.view(bsz, k_len, self.num_heads, self.head_dim)
v = v.view(bsz, v_len, self.num_heads, self.head_dim)
if self.alibi_slopes is None: # Use either ALiBi or RoPE
q, k = apply_rotary_emb(q, k, freqs_cis=freqs_cis)
y = flash_attn_func( # https://arxiv.org/pdf/2307.08691
q=q, k=k, v=v,
dropout_p=self.dropout if self.training else 0.0,
causal=True,
window_size=(self.window_size, 0), # Set to config.seq_len if full attention
alibi_slopes=self.alibi_slopes, # https://arxiv.org/pdf/2108.12409
softcap=self.softcap, # https://arxiv.org/pdf/2408.00118
)
y = y.contiguous().view(bsz, q_len, -1)
y = self.resid_dropout(self.c_proj(y))
return y
class AttentionLayer(nn.Module):
def __init__(self, config) -> None:
super(AttentionLayer, self).__init__()
self.attn_norm = nn.RMSNorm(config.dim)
self.attn = Attention(config=config)
self.mlp_norm = nn.RMSNorm(config.dim)
self.mlp = MLP(config)
def forward(self, x: torch.Tensor, freqs_cis: torch.Tensor=None) -> torch.Tensor:
x = x + self.attn(x=self.attn_norm(x), freqs_cis=freqs_cis)
x = x + self.mlp(self.mlp_norm(x))
return x
class MLP(nn.Module):
def __init__(self, config):
# https://arxiv.org/pdf/2002.05202
super().__init__()
self.hidden_size = config.dim
self.intermediate_size = config.dim * config.mlp_scale
self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=config.bias)
self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=config.bias)
self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=config.bias)
self.dropout = nn.Dropout(config.dropout)
def forward(self, x):
gate = self.gate_proj(x)
gate = F.gelu(gate, approximate="tanh")
up = self.up_proj(x)
fuse = gate * up
outputs = self.down_proj(fuse)
outputs = self.dropout(outputs)
return outputs
class MiniTransformer(PreTrainedModel):
config_class = MiniTransformerConfig
def __init__(self, config) -> None:
super(MiniTransformer, self).__init__(config)
self.num_layers = config.num_layers
assert config.dim % config.num_heads == 0, f"dim ({self.dim}) must be divisible num_heads ({self.num_heads})"
self.head_dim = config.dim // config.num_heads
# From pytorch/pytorch#123411, we set persistent=True for torch.compile and PP compatibility
self.register_buffer("freqs_cis", precompute_freqs_cis(
head_dim=self.head_dim,
max_seq_len=config.seq_len,
theta=config.theta,
), persistent=True)
self.tok_emb = nn.Embedding(config.vocab_size, config.dim)
self.dropout = nn.Dropout(config.dropout)
self.layers = nn.ModuleList()
for _ in range(self.num_layers):
layer = AttentionLayer(config=config)
self.layers.append(layer)
self.norm = nn.RMSNorm(config.dim)
self.lm_head = nn.Linear(config.dim, config.vocab_size, bias=config.bias)
if config.weight_tying:
self.tok_emb.weight = self.lm_head.weight
self.std = config.dim ** -0.5
self.apply(self._init_weights)
print("Model Parameter Count: %.2fM\n" % (self._get_num_params() / 1e6,))
def forward(
self,
input_ids: torch.Tensor,
labels: torch.Tensor = None,
**kwargs
) -> CausalLMOutput:
# Compute embeddings
tok_emb = self.tok_emb(input_ids)
for layer in self.layers:
tok_emb = layer(tok_emb, self.freqs_cis)
# Normalize and project to vocabulary
tok_emb = self.norm(tok_emb)
logits = self.lm_head(tok_emb)
loss = None
if labels is not None:
# Shift so that tokens predict the next token
shift_logits = logits[..., :-1, :].contiguous()
shift_labels = labels[..., 1:].contiguous()
loss_fct = nn.CrossEntropyLoss()
loss = loss_fct(
shift_logits.view(-1, shift_logits.size(-1)),
shift_labels.view(-1)
)
return CausalLMOutput(
loss=loss,
logits=logits,
)
def _get_num_params(self):
n_params = sum(p.numel() for p in self.parameters())
if hasattr(self, "pos_emb") and self.pos_emb is not None:
n_params -= self.pos_emb.weight.numel()
return n_params
def _init_weights(self, module):
if isinstance(module, nn.Linear):
if hasattr(module, "SCALE_INIT"):
self.std *= (2 * self.num_layers) ** -0.5
torch.nn.init.normal_(module.weight, mean=0.0, std=self.std)
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=self.std)
elif isinstance(module, Attention):
torch.nn.init.xavier_normal_(module.c_attn.weight)
torch.nn.init.xavier_normal_(module.c_proj.weight)
if module.c_attn.bias is not None:
torch.nn.init.zeros_(module.c_attn.bias)
if module.c_proj.bias is not None:
torch.nn.init.zeros_(module.c_proj.bias)
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