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import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers import PreTrainedModel
from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
from .configuration_tiny_gpt import TinyGPTConfig
_FLASH2_KERNEL = None
_FLASH3_KERNEL = None
def _get_flash2_kernel():
global _FLASH2_KERNEL
if _FLASH2_KERNEL is None:
kernels = importlib.import_module("kernels")
_FLASH2_KERNEL = kernels.get_kernel("kernels-community/flash-attn2", version=1)
return _FLASH2_KERNEL
def _get_flash3_kernel():
global _FLASH3_KERNEL
if _FLASH3_KERNEL is None:
kernels = importlib.import_module("kernels")
_FLASH3_KERNEL = kernels.get_kernel("kernels-community/flash-attn3", version=1)
return _FLASH3_KERNEL
def _get_sageattn():
module = importlib.import_module("sageattention")
return module.sageattn
def rotate_half(x):
x_even = x[..., ::2]
x_odd = x[..., 1::2]
x_rot = torch.stack((-x_odd, x_even), dim=-1)
return x_rot.flatten(start_dim=-2)
def apply_rope(x, cos, sin):
return (x * cos) + (rotate_half(x) * sin)
class RotaryEmbedding(nn.Module):
def __init__(self, dim, max_position_embeddings, base=10000.0):
super().__init__()
if dim % 2 != 0:
raise ValueError(f"RoPE dim must be even, got {dim}")
self.dim = int(dim)
self.max_position_embeddings = int(max_position_embeddings)
self.base = float(base)
inv_freq = 1.0 / (
self.base
** (
torch.arange(
0,
self.dim,
2,
dtype=torch.float32,
)
/ self.dim
)
)
self.register_buffer(
"inv_freq",
inv_freq,
persistent=False,
)
self._cos_cached = None
self._sin_cached = None
self._seq_len_cached = 0
self._device_cached = None
self._dtype_cached = None
def _build_cache(self, seq_len, device, dtype):
t = torch.arange(
seq_len,
device=device,
dtype=torch.float32,
)
freqs = torch.einsum(
"i,j->ij",
t,
self.inv_freq.to(device=device, dtype=torch.float32),
)
emb = torch.repeat_interleave(freqs, repeats=2, dim=-1)
cos = emb.cos().to(dtype=dtype).view(1, 1, seq_len, self.dim)
sin = emb.sin().to(dtype=dtype).view(1, 1, seq_len, self.dim)
self._cos_cached = cos
self._sin_cached = sin
self._seq_len_cached = int(seq_len)
self._device_cached = device
self._dtype_cached = dtype
def forward(self, seq_len, device, dtype):
if (
self._cos_cached is None
or self._sin_cached is None
or self._seq_len_cached < seq_len
or self._device_cached != device
or self._dtype_cached != dtype
):
self._build_cache(
seq_len=seq_len,
device=device,
dtype=dtype,
)
return (
self._cos_cached[:, :, :seq_len, :],
self._sin_cached[:, :, :seq_len, :],
)
class CausalSelfAttention(nn.Module):
def __init__(self, config: TinyGPTConfig):
super().__init__()
if config.n_embd % config.n_head != 0:
raise ValueError("n_embd must be divisible by n_head")
self.n_head = int(config.n_head)
self.head_dim = int(config.n_embd // config.n_head)
self.attention_backend = str(getattr(config, "attention_backend", "torch"))
self.torch_fallback = bool(getattr(config, "torch_fallback", False))
self.dropout_p = float(config.dropout)
if self.head_dim % 2 != 0:
raise ValueError(f"RoPE requires even head_dim, got {self.head_dim}")
if self.attention_backend not in ("sage", "torch", "flash2", "flash3"):
raise ValueError("attention_backend must be sage, torch, flash2 or flash3")
if self.attention_backend == "sage" and self.head_dim not in (64, 96, 128):
raise ValueError(f"SageAttention requires head_dim in [64, 96, 128], got {self.head_dim}")
if self.attention_backend == "sage" and self.dropout_p != 0.0:
raise ValueError("SageAttention requires dropout=0.0")
if self.attention_backend == "flash3" and self.dropout_p != 0.0:
raise ValueError("FlashAttention3 requires dropout=0.0")
if self.attention_backend in ("flash2", "flash3") and self.head_dim % 8 != 0:
raise ValueError(f"FlashAttention requires head_dim multiple of 8, got {self.head_dim}")
self.qkv = nn.Linear(config.n_embd, 3 * config.n_embd, bias=False)
self.proj = nn.Linear(config.n_embd, config.n_embd, bias=False)
self.dropout = nn.Dropout(config.dropout)
self.rope = RotaryEmbedding(
dim=self.head_dim,
max_position_embeddings=config.ctx_len,
base=float(getattr(config, "rope_base", 10000.0)),
)
mask = torch.tril(torch.ones(config.ctx_len, config.ctx_len, dtype=torch.bool))
self.register_buffer(
"mask",
mask.view(1, 1, config.ctx_len, config.ctx_len),
persistent=False,
)
self.sageattn = None
self.flash_kernel = None
if self.attention_backend == "sage":
try:
self.sageattn = _get_sageattn()
except Exception:
if self.torch_fallback:
self.attention_backend = "torch"
else:
raise
if self.attention_backend == "flash2":
try:
self.flash_kernel = _get_flash2_kernel()
except Exception:
if self.torch_fallback:
self.attention_backend = "torch"
else:
raise
if self.attention_backend == "flash3":
try:
self.flash_kernel = _get_flash3_kernel()
except Exception:
if self.torch_fallback:
self.attention_backend = "torch"
else:
raise
def _torch_attention(self, q, k, v, t):
scores = (q @ k.transpose(-2, -1)) / math.sqrt(self.head_dim)
scores = scores.masked_fill(self.mask[:, :, :t, :t] == 0, float("-inf"))
att = F.softmax(scores.float(), dim=-1).to(q.dtype)
att = self.dropout(att)
return att @ v
def _sage_attention(self, q, k, v):
if self.sageattn is None or not q.is_cuda:
if self.torch_fallback:
return None
raise RuntimeError("SageAttention requires CUDA + sageattention")
return self.sageattn(
q.contiguous(),
k.contiguous(),
v.contiguous(),
tensor_layout="HND",
is_causal=True,
)
def _flash2_attention(self, q, k, v):
if self.flash_kernel is None or not q.is_cuda:
if self.torch_fallback:
return None
raise RuntimeError("FlashAttention2 requires CUDA + kernels")
q = q.transpose(1, 2).contiguous()
k = k.transpose(1, 2).contiguous()
v = v.transpose(1, 2).contiguous()
dropout_p = self.dropout_p if self.training else 0.0
y = self.flash_kernel.flash_attn_func(
q,
k,
v,
dropout_p=dropout_p,
causal=True,
)
return y.transpose(1, 2).contiguous()
def _flash3_attention(self, q, k, v):
if self.flash_kernel is None or not q.is_cuda:
if self.torch_fallback:
return None
raise RuntimeError("FlashAttention3 requires CUDA + kernels")
q = q.transpose(1, 2).contiguous()
k = k.transpose(1, 2).contiguous()
v = v.transpose(1, 2).contiguous()
y = self.flash_kernel.flash_attn_func(
q,
k,
v,
causal=True,
)
return y.transpose(1, 2).contiguous()
def forward(self, x):
b, t, c = x.shape
qkv = self.qkv(x)
q, k, v = qkv.chunk(3, dim=-1)
q = q.view(b, t, self.n_head, self.head_dim).transpose(1, 2).contiguous()
k = k.view(b, t, self.n_head, self.head_dim).transpose(1, 2).contiguous()
v = v.view(b, t, self.n_head, self.head_dim).transpose(1, 2).contiguous()
cos, sin = self.rope(
seq_len=t,
device=q.device,
dtype=q.dtype,
)
q = apply_rope(q, cos, sin)
k = apply_rope(k, cos, sin)
if self.attention_backend == "sage":
y = self._sage_attention(q, k, v)
if y is None:
y = self._torch_attention(q, k, v, t)
elif self.attention_backend == "flash2":
y = self._flash2_attention(q, k, v)
if y is None:
y = self._torch_attention(q, k, v, t)
elif self.attention_backend == "flash3":
y = self._flash3_attention(q, k, v)
if y is None:
y = self._torch_attention(q, k, v, t)
else:
y = self._torch_attention(q, k, v, t)
y = y.transpose(1, 2).contiguous().view(b, t, c)
return self.proj(y)
class MLP(nn.Module):
def __init__(self, config: TinyGPTConfig):
super().__init__()
self.fc = nn.Linear(config.n_embd, 4 * config.n_embd, bias=False)
self.proj = nn.Linear(4 * config.n_embd, config.n_embd, bias=False)
self.dropout = nn.Dropout(config.dropout)
def forward(self, x):
x = self.fc(x)
x = F.gelu(x)
x = self.proj(x)
x = self.dropout(x)
return x
class Block(nn.Module):
def __init__(self, config: TinyGPTConfig):
super().__init__()
self.ln1 = nn.LayerNorm(config.n_embd)
self.attn = CausalSelfAttention(config)
self.ln2 = nn.LayerNorm(config.n_embd)
self.mlp = MLP(config)
def forward(self, x):
x = x + self.attn(self.ln1(x))
x = x + self.mlp(self.ln2(x))
return x
class TinyGPTPreTrainedModel(PreTrainedModel):
config_class = TinyGPTConfig
base_model_prefix = "tiny_gpt"
supports_gradient_checkpointing = False
def _init_weights(self, module):
if isinstance(module, nn.Linear):
nn.init.normal_(module.weight, mean=0.0, std=0.02)
if module.bias is not None:
nn.init.zeros_(module.bias)
elif isinstance(module, nn.Embedding):
nn.init.normal_(module.weight, mean=0.0, std=0.02)
class TinyGPTModel(TinyGPTPreTrainedModel):
_tied_weights_keys = ["head.weight"]
def __init__(self, config: TinyGPTConfig):
super().__init__(config)
self.tok_emb = nn.Embedding(config.vocab_size, config.n_embd)
self.drop = nn.Dropout(config.dropout)
self.blocks = nn.ModuleList(
[Block(config) for _ in range(config.n_layer)]
)
self.ln_f = nn.LayerNorm(config.n_embd)
self.head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
self.post_init()
self.tie_weights()
def get_input_embeddings(self):
return self.tok_emb
def set_input_embeddings(self, value):
self.tok_emb = value
self.tie_weights()
def get_output_embeddings(self):
return self.head
def set_output_embeddings(self, new_embeddings):
self.head = new_embeddings
def tie_weights(self):
self._tie_or_clone_weights(self.head, self.tok_emb)
def forward(
self,
input_ids,
attention_mask=None,
return_dict=True,
return_logits=False,
**kwargs,
):
b, t = input_ids.shape
if t > self.config.ctx_len:
raise ValueError(
f"Input length {t} > ctx_len {self.config.ctx_len}. "
"Truncate before calling the model."
)
x = self.tok_emb(input_ids)
x = self.drop(x)
for block in self.blocks:
x = block(x)
hidden = self.ln_f(x)
logits = self.head(hidden) if return_logits else None
if not return_dict:
return (hidden, logits) if return_logits else (hidden,)
if return_logits:
return hidden, logits
return BaseModelOutputWithPast(
last_hidden_state=hidden,
past_key_values=None,
hidden_states=None,
attentions=None,
)
class TinyGPTForCausalLM(TinyGPTPreTrainedModel):
_tied_weights_keys = ["tiny_gpt.head.weight"]
def __init__(self, config: TinyGPTConfig):
super().__init__(config)
self.tiny_gpt = TinyGPTModel(config)
self.post_init()
self.tie_weights()
def get_input_embeddings(self):
return self.tiny_gpt.tok_emb
def set_input_embeddings(self, value):
self.tiny_gpt.tok_emb = value
self.tie_weights()
def get_output_embeddings(self):
return self.tiny_gpt.head
def set_output_embeddings(self, new_embeddings):
self.tiny_gpt.head = new_embeddings
def tie_weights(self):
self._tie_or_clone_weights(
self.tiny_gpt.head,
self.tiny_gpt.tok_emb,
)
def prepare_inputs_for_generation(self, input_ids, **kwargs):
return {"input_ids": input_ids}
def forward(
self,
input_ids,
attention_mask=None,
labels=None,
return_dict=True,
**kwargs,
):
hidden, logits = self.tiny_gpt(
input_ids=input_ids,
attention_mask=attention_mask,
return_dict=True,
return_logits=True,
)
loss = None
if labels is not None:
shift_logits = logits[:, :-1, :].contiguous()
shift_labels = labels[:, 1:].contiguous()
loss = F.cross_entropy(
shift_logits.view(-1, shift_logits.size(-1)).float(),
shift_labels.view(-1),
)
if not return_dict:
return ((loss, logits) if loss is not None else (logits,))
return CausalLMOutputWithPast(
loss=loss,
logits=logits,
past_key_values=None,
hidden_states=None,
attentions=None,
)
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