TSAI_S22 / tsai_gpt /model.py
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"""Full definition of a GPT NeoX Language Model, all of it in this single file.
Based on the nanoGPT implementation: https://github.com/karpathy/nanoGPT and
https://github.com/EleutherAI/gpt-neox/tree/main/megatron/model.
"""
import math
from typing import Any, Optional, Tuple
import torch
import torch.nn as nn
from typing_extensions import Self
from tsai_gpt.config import Config
from torch.nn import functional as F
class GPT(nn.Module):
def __init__(self, config: Config) -> None:
super().__init__()
assert config.padded_vocab_size is not None
self.config = config
self.lm_head = nn.Linear(config.n_embd, config.padded_vocab_size, bias=config.lm_head_bias)
self.transformer = nn.ModuleDict(
dict(
wte=nn.Embedding(config.padded_vocab_size, config.n_embd),
h=nn.ModuleList(Block(config) for _ in range(config.n_layer)),
ln_f=config.norm_class(config.n_embd, eps=config.norm_eps),
)
)
self.max_seq_length = self.config.block_size
self.mask_cache: Optional[torch.Tensor] = None
@property
def max_seq_length(self) -> int:
return self._max_seq_length
@max_seq_length.setter
def max_seq_length(self, value: int) -> None:
"""
When doing inference, the sequences used might be shorter than the model's context length.
This allows setting a smaller number to avoid allocating unused memory
"""
if value > self.config.block_size:
raise ValueError(f"Cannot attend to {value}, block size is only {self.config.block_size}")
self._max_seq_length = value
if not hasattr(self, "cos"):
# first call
cos, sin = self.rope_cache()
self.register_buffer("cos", cos, persistent=False)
self.register_buffer("sin", sin, persistent=False)
elif value != self.cos.size(0):
# override
self.cos, self.sin = self.rope_cache(device=self.cos.device)
# the mask and kv cache size will get updated on `set_kv_cache`. we cannot update it here because we don't know
# if the kv cache is expected
def reset_parameters(self) -> None:
# Trigger resetting the rope-cache
self.max_seq_length = self.config.block_size
def _init_weights(self, module: nn.Module) -> None:
"""Meant to be used with `gpt.apply(gpt._init_weights)`."""
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: torch.Tensor, input_pos: Optional[torch.Tensor] = None) -> torch.Tensor:
T = idx.size(1)
if self.max_seq_length < T:
raise ValueError(f"Cannot forward sequence of length {T}, max seq length is only {self.max_seq_length}.")
if input_pos is not None: # use the kv cache
cos = self.cos.index_select(0, input_pos)
sin = self.sin.index_select(0, input_pos)
if self.mask_cache is None:
raise TypeError("You need to call `gpt.set_kv_cache()`")
mask = self.mask_cache.index_select(2, input_pos)
else:
cos = self.cos[:T]
sin = self.sin[:T]
mask = None
x = self.transformer.wte(idx) # token embeddings of shape (b, t, n_embd)
for block in self.transformer.h:
x = block(x, cos, sin, mask, input_pos)
x = self.transformer.ln_f(x)
return self.lm_head(x) # (b, t, vocab_size)
@classmethod
def from_name(cls, name: str, **kwargs: Any) -> Self:
return cls(Config.from_name(name, **kwargs))
def rope_cache(self, device: Optional[torch.device] = None) -> Tuple[torch.Tensor, torch.Tensor]:
return build_rope_cache(
seq_len=self.max_seq_length,
n_elem=self.config.rope_n_elem,
device=device,
condense_ratio=self.config.rope_condense_ratio,
base=self.config.rope_base,
)
def set_kv_cache(
self,
batch_size: int,
rope_cache_length: Optional[int] = None,
device: Optional[torch.device] = None,
dtype: Optional[torch.dtype] = None,
) -> None:
if rope_cache_length is None:
rope_cache_length = self.cos.size(-1)
max_seq_length = self.max_seq_length
# initialize the kv cache for all blocks
for block in self.transformer.h:
block.attn.kv_cache = block.attn.build_kv_cache(
batch_size, max_seq_length, rope_cache_length, device, dtype
)
if self.mask_cache is None or self.mask_cache.size(3) != max_seq_length:
# passing `attn_mask` to SDPA downgrades it to use the inefficient implementation. since we only need the mask
# for the kv-cache support (only during inference), we only create it in that situation
# this will be resolved by https://github.com/pytorch/pytorch/issues/96099
ones = torch.ones((max_seq_length, max_seq_length), device=device, dtype=torch.bool)
self.mask_cache = torch.tril(ones).unsqueeze(0).unsqueeze(0)
def clear_kv_cache(self) -> None:
self.mask_cache = None
for block in self.transformer.h:
block.attn.kv_cache = None
def generate(self, idx, max_new_tokens):
# idx is (B, T) array of indices in the current context
for _ in range(max_new_tokens):
# crop idx to the last block_size tokens
idx_cond = idx[:, -self.config.block_size:]
# get the predictions
logits = self(idx_cond)
# focus only on the last time step
logits = logits[:, -1, :] # becomes (B, C)
# apply softmax to get probabilities
probs = F.softmax(logits, dim=-1) # (B, C)
# sample from the distribution
idx_next = torch.multinomial(probs, num_samples=1) # (B, 1)
# append sampled index to the running sequence
idx = torch.cat((idx, idx_next), dim=1) # (B, T+1)
return idx
class Block(nn.Module):
def __init__(self, config: Config) -> None:
super().__init__()
self.norm_1 = config.norm_class(config.n_embd, eps=config.norm_eps)
self.attn = CausalSelfAttention(config)
self.norm_2 = None if config.shared_attention_norm else config.norm_class(config.n_embd, eps=config.norm_eps)
self.mlp = config.mlp_class(config)
self.config = config
def forward(
self,
x: torch.Tensor,
cos: torch.Tensor,
sin: torch.Tensor,
mask: Optional[torch.Tensor] = None,
input_pos: Optional[torch.Tensor] = None,
) -> torch.Tensor:
n_1 = self.norm_1(x)
h = self.attn(n_1, cos, sin, mask, input_pos)
if self.config.parallel_residual:
n_2 = n_1 if self.config.shared_attention_norm else self.norm_2(x)
x = self.mlp(n_2) + h + x
else:
if self.config.shared_attention_norm:
raise NotImplementedError(
"No checkpoint amongst the ones we support uses this configuration"
" (non-parallel residual and shared attention norm)."
)
x = h + x
x = self.mlp(self.norm_2(x)) + x
return x
class CausalSelfAttention(nn.Module):
def __init__(self, config: Config) -> None:
super().__init__()
shape = (config.n_head + 2 * config.n_query_groups) * config.head_size
# key, query, value projections for all heads, but in a batch
self.attn = nn.Linear(config.n_embd, shape, bias=config.bias)
# output projection
self.proj = nn.Linear(config.n_embd, config.n_embd, bias=config.bias)
# disabled by default
self.kv_cache: Optional[KVCache] = None
self.config = config
def forward(
self,
x: torch.Tensor,
cos: torch.Tensor,
sin: torch.Tensor,
mask: Optional[torch.Tensor] = None,
input_pos: Optional[torch.Tensor] = None,
) -> torch.Tensor:
B, T, C = x.size() # batch size, sequence length, embedding dimensionality (n_embd)
qkv = self.attn(x)
# assemble into a number of query groups to support MHA, MQA and GQA together (see `config.n_query_groups`)
q_per_kv = self.config.n_head // self.config.n_query_groups
total_qkv = q_per_kv + 2 # each group has 1+ queries, 1 key, and 1 value
qkv = qkv.view(B, T, self.config.n_query_groups, total_qkv, self.config.head_size)
qkv = qkv.permute(0, 2, 3, 1, 4) # (B, n_query_groups, total_qkv, T, hs)
# split batched computation into three
q, k, v = qkv.split((q_per_kv, 1, 1), dim=2)
# maybe repeat k and v if for the non multi-head attention cases
# training: flash attention requires it
# inference: multi-query would require a full kv cache so avoid it to limit its memory usage
if self.config.n_query_groups != self.config.n_head and (input_pos is None or self.config.n_query_groups != 1):
k = k.expand(B, self.config.n_query_groups, q_per_kv, T, self.config.head_size)
v = v.expand(B, self.config.n_query_groups, q_per_kv, T, self.config.head_size)
q = q.reshape(B, -1, T, self.config.head_size) # (B, nh_q, T, hs)
k = k.reshape(B, -1, T, self.config.head_size) # (B, nh_k, T, hs)
v = v.reshape(B, -1, T, self.config.head_size) # (B, nh_v, T, hs)
q_roped = apply_rope(q[..., : self.config.rope_n_elem], cos, sin)
k_roped = apply_rope(k[..., : self.config.rope_n_elem], cos, sin)
q = torch.cat((q_roped, q[..., self.config.rope_n_elem :]), dim=-1)
k = torch.cat((k_roped, k[..., self.config.rope_n_elem :]), dim=-1)
if input_pos is not None:
if not isinstance(self.kv_cache, KVCache):
raise TypeError("You need to call `gpt.set_kv_cache()`")
k, v = self.kv_cache(input_pos, k, v)
y = self.scaled_dot_product_attention(q, k, v, mask)
y = y.reshape(B, T, C) # re-assemble all head outputs side by side
# output projection
return self.proj(y)
def scaled_dot_product_attention(
self, q: torch.Tensor, k: torch.Tensor, v: torch.Tensor, mask: Optional[torch.Tensor] = None
) -> torch.Tensor:
scale = 1.0 / math.sqrt(self.config.head_size)
y = torch.nn.functional.scaled_dot_product_attention(
q, k, v, attn_mask=mask, dropout_p=0.0, is_causal=mask is None
) #scale=scale,
return y.transpose(1, 2)
def build_kv_cache(
self,
batch_size: int,
max_seq_length: int,
rope_cache_length: Optional[int] = None,
device: Optional[torch.device] = None,
dtype: Optional[torch.dtype] = None,
) -> "KVCache":
heads = 1 if self.config.n_query_groups == 1 else self.config.n_head
v_shape = (batch_size, heads, max_seq_length, self.config.head_size)
if rope_cache_length is None:
if self.config.rotary_percentage != 1.0:
raise TypeError("Please pass the `rope_cache_length=gpt.cos.size(-1)` value")
k_shape = v_shape
else:
k_shape = (
batch_size,
heads,
max_seq_length,
rope_cache_length + self.config.head_size - self.config.rope_n_elem,
)
return KVCache(k_shape, v_shape, device=device, dtype=dtype)
class GptNeoxMLP(nn.Module):
def __init__(self, config: Config) -> None:
super().__init__()
self.fc = nn.Linear(config.n_embd, config.intermediate_size, bias=config.bias)
self.proj = nn.Linear(config.intermediate_size, config.n_embd, bias=config.bias)
self.config = config
def forward(self, x: torch.Tensor) -> torch.Tensor:
x = self.fc(x)
x = torch.nn.functional.gelu(x, approximate=self.config.gelu_approximate)
return self.proj(x)
class LLaMAMLP(nn.Module):
def __init__(self, config: Config) -> None:
super().__init__()
self.fc_1 = nn.Linear(config.n_embd, config.intermediate_size, bias=config.bias)
self.fc_2 = nn.Linear(config.n_embd, config.intermediate_size, bias=config.bias)
self.proj = nn.Linear(config.intermediate_size, config.n_embd, bias=config.bias)
def forward(self, x: torch.Tensor) -> torch.Tensor:
x_fc_1 = self.fc_1(x)
x_fc_2 = self.fc_2(x)
x = torch.nn.functional.silu(x_fc_1) * x_fc_2
return self.proj(x)
def build_rope_cache(
seq_len: int, n_elem: int, device: Optional[torch.device] = None, base: int = 10000, condense_ratio: int = 1
) -> Tuple[torch.Tensor, torch.Tensor]:
"""Enhanced Transformer with Rotary Position Embedding.
Derived from: https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/labml_nn/
transformers/rope/__init__.py. MIT License:
https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/license.
"""
# $\Theta = {\theta_i = 10000^{\frac{2(i-1)}{d}}, i \in [1, 2, ..., \frac{d}{2}]}$
theta = 1.0 / (base ** (torch.arange(0, n_elem, 2, device=device).float() / n_elem))
# Create position indexes `[0, 1, ..., seq_len - 1]`
seq_idx = torch.arange(seq_len, device=device) / condense_ratio
# Calculate the product of position index and $\theta_i$
idx_theta = torch.outer(seq_idx, theta).repeat(1, 2)
return torch.cos(idx_theta), torch.sin(idx_theta)
def apply_rope(x: torch.Tensor, cos: torch.Tensor, sin: torch.Tensor) -> torch.Tensor:
head_size = x.size(-1)
x1 = x[..., : head_size // 2] # (B, nh, T, hs/2)
x2 = x[..., head_size // 2 :] # (B, nh, T, hs/2)
rotated = torch.cat((-x2, x1), dim=-1) # (B, nh, T, hs)
roped = (x * cos) + (rotated * sin)
return roped.type_as(x)
class KVCache(nn.Module):
def __init__(
self,
k_shape: Tuple[int, int, int, int],
v_shape: Tuple[int, int, int, int],
device: Optional[torch.device] = None,
dtype: Optional[torch.dtype] = None,
) -> None:
super().__init__()
self.register_buffer("k", torch.zeros(k_shape, device=device, dtype=dtype), persistent=False)
self.register_buffer("v", torch.zeros(v_shape, device=device, dtype=dtype), persistent=False)
def forward(self, input_pos: torch.Tensor, k: torch.Tensor, v: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
# move the buffer to the activation dtype for when AMP is used
self.k = self.k.to(k.dtype)
self.v = self.v.to(v.dtype)
# update the cache
k = self.k.index_copy_(2, input_pos, k)
v = self.v.index_copy_(2, input_pos, v)
return k, v