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model.py
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model.py
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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import math
from dataclasses import dataclass
from typing import Optional
import torch
import torch.nn as nn
from torch import Tensor
from torch.nn import functional as F
def find_multiple(n: int, k: int) -> int:
if n % k == 0:
return n
return n + k - (n % k)
@dataclass
class ModelArgs:
block_size: int = 2048
vocab_size: int = 32000
n_layer: int = 32
n_head: int = 32
dim: int = 4096
intermediate_size: int = None
n_local_heads: int = -1
head_dim: int = 64
rope_base: float = 10000
norm_eps: float = 1e-5
rope_scaling: Optional[dict] = None
def __post_init__(self):
if self.n_local_heads == -1:
self.n_local_heads = self.n_head
if self.intermediate_size is None:
hidden_dim = 4 * self.dim
n_hidden = int(2 * hidden_dim / 3)
self.intermediate_size = find_multiple(n_hidden, 256)
self.head_dim = self.dim // self.n_head
@classmethod
def from_name(cls, name: str):
if name in transformer_configs:
return cls(**transformer_configs[name])
# fuzzy search
config = [config for config in transformer_configs if config.lower() in str(name).lower()]
# We may have two or more configs matched (e.g. "7B" and "Mistral-7B"). Find the best config match,
# take longer name (as it have more symbols matched)
if len(config) > 1:
config.sort(key=len, reverse=True)
assert len(config[0]) != len(config[1]), name # make sure only one 'best' match
return cls(**transformer_configs[config[0]])
transformer_configs = {
"CodeLlama-7b-Python-hf": dict(block_size=16384, vocab_size=32000, n_layer=32, dim = 4096, rope_base=1000000),
"7B": dict(n_layer=32, n_head=32, dim=4096),
"13B": dict(n_layer=40, n_head=40, dim=5120),
"30B": dict(n_layer=60, n_head=52, dim=6656),
"34B": dict(n_layer=48, n_head=64, dim=8192, vocab_size=32000, n_local_heads=8, intermediate_size=22016, rope_base=1000000), # CodeLlama-34B-Python-hf
"70B": dict(n_layer=80, n_head=64, dim=8192, n_local_heads=8, intermediate_size=28672),
"Mistral-7B": dict(n_layer=32, n_head=32, n_local_heads=8, dim=4096, intermediate_size=14336, vocab_size=32000),
"stories15M": dict(n_layer=6, n_head=6, dim=288),
"stories110M": dict(n_layer=12, n_head=12, dim=768),
"llama-3-8b": dict(block_size=8192, n_layer=32, n_head=32, n_local_heads=8, dim=4096, intermediate_size=14336, vocab_size=128256, rope_base=500000),
"llama-3-70b": dict(block_size=8192, n_layer=80, n_head=64, n_local_heads=8, dim=8192, intermediate_size=28672, vocab_size=128256, rope_base=500000),
"llama-3.1-8b": dict(block_size=131072, n_layer=32, n_head=32, n_local_heads=8, dim=4096, intermediate_size=14336, vocab_size=128256, rope_base=500000,
rope_scaling=dict(factor=8.0, low_freq_factor=1.0, high_freq_factor=4.0, original_max_position_embeddings=8192),
),
"llama-3.1-70b": dict(block_size=131072, n_layer=80, n_head=64, n_local_heads=8, dim=8192, intermediate_size=28672, vocab_size=128256, rope_base=500000,
rope_scaling=dict(factor=8.0, low_freq_factor=1.0, high_freq_factor=4.0, original_max_position_embeddings=8192),
),
"llama-3.1-405b": dict(block_size=131072, n_layer=126, n_head=128, n_local_heads=8, dim=16384, intermediate_size=53248, vocab_size=128256, rope_base=500000,
rope_scaling=dict(factor=8.0, low_freq_factor=1.0, high_freq_factor=4.0, original_max_position_embeddings=8192),
),
}
class KVCache(nn.Module):
def __init__(self, max_batch_size, max_seq_length, n_heads, head_dim, dtype=torch.bfloat16):
super().__init__()
cache_shape = (max_batch_size, n_heads, max_seq_length, head_dim)
self.register_buffer('k_cache', torch.zeros(cache_shape, dtype=dtype))
self.register_buffer('v_cache', torch.zeros(cache_shape, dtype=dtype))
def update(self, input_pos, k_val, v_val):
# input_pos: [S], k_val: [B, H, S, D]
assert input_pos.shape[0] == k_val.shape[2]
k_out = self.k_cache
v_out = self.v_cache
k_out[:, :, input_pos] = k_val
v_out[:, :, input_pos] = v_val
return k_out, v_out
class Transformer(nn.Module):
def __init__(self, config: ModelArgs) -> None:
super().__init__()
self.config = config
self.tok_embeddings = nn.Embedding(config.vocab_size, config.dim)
self.layers = nn.ModuleList(TransformerBlock(config) for _ in range(config.n_layer))
self.norm = RMSNorm(config.dim, eps=config.norm_eps)
self.output = nn.Linear(config.dim, config.vocab_size, bias=False)
self.freqs_cis: Optional[Tensor] = None
self.mask_cache: Optional[Tensor] = None
self.max_batch_size = -1
self.max_seq_length = -1
def setup_caches(self, max_batch_size, max_seq_length):
if self.max_seq_length >= max_seq_length and self.max_batch_size >= max_batch_size:
return
head_dim = self.config.dim // self.config.n_head
max_seq_length = find_multiple(max_seq_length, 8)
self.max_seq_length = max_seq_length
self.max_batch_size = max_batch_size
dtype = self.output.weight.dtype
# For quantized layers, dtype is encoded in scales
if hasattr(self.output, "scales"):
dtype = self.output.scales.dtype
elif hasattr(self.output, "scales_and_zeros"):
dtype = self.output.scales_and_zeros.dtype
for b in self.layers:
b.attention.kv_cache = KVCache(max_batch_size, max_seq_length, self.config.n_local_heads, head_dim, dtype)
self.freqs_cis = precompute_freqs_cis(self.config.block_size, self.config.dim // self.config.n_head, self.config.rope_base, dtype, self.config.rope_scaling)
self.causal_mask = torch.tril(torch.ones(self.max_seq_length, self.max_seq_length, dtype=torch.bool))
def forward(self, idx: Tensor, input_pos: Optional[Tensor] = None) -> Tensor:
assert self.freqs_cis is not None, "Caches must be initialized first"
mask = self.causal_mask[None, None, input_pos]
freqs_cis = self.freqs_cis[input_pos]
x = self.tok_embeddings(idx)
for i, layer in enumerate(self.layers):
x = layer(x, input_pos, freqs_cis, mask)
x = self.norm(x)
logits = self.output(x)
return logits
@classmethod
def from_name(cls, name: str):
return cls(ModelArgs.from_name(name))
class TransformerBlock(nn.Module):
def __init__(self, config: ModelArgs) -> None:
super().__init__()
self.attention = Attention(config)
self.feed_forward = FeedForward(config)
self.ffn_norm = RMSNorm(config.dim, config.norm_eps)
self.attention_norm = RMSNorm(config.dim, config.norm_eps)
def forward(self, x: Tensor, input_pos: Tensor, freqs_cis: Tensor, mask: Tensor) -> Tensor:
h = x + self.attention(self.attention_norm(x), freqs_cis, mask, input_pos)
out = h + self.feed_forward(self.ffn_norm(h))
return out
class Attention(nn.Module):
def __init__(self, config: ModelArgs):
super().__init__()
assert config.dim % config.n_head == 0
total_head_dim = (config.n_head + 2 * config.n_local_heads) * config.head_dim
# key, query, value projections for all heads, but in a batch
self.wqkv = nn.Linear(config.dim, total_head_dim, bias=False)
self.wo = nn.Linear(config.dim, config.dim, bias=False)
self.kv_cache = None
self.n_head = config.n_head
self.head_dim = config.head_dim
self.n_local_heads = config.n_local_heads
self.dim = config.dim
self._register_load_state_dict_pre_hook(self.load_hook)
def load_hook(self, state_dict, prefix, *args):
if prefix + "wq.weight" in state_dict:
wq = state_dict.pop(prefix + "wq.weight")
wk = state_dict.pop(prefix + "wk.weight")
wv = state_dict.pop(prefix + "wv.weight")
state_dict[prefix + "wqkv.weight"] = torch.cat([wq, wk, wv])
def forward(self, x: Tensor, freqs_cis: Tensor, mask: Tensor, input_pos: Optional[Tensor] = None) -> Tensor:
bsz, seqlen, _ = x.shape
kv_size = self.n_local_heads * self.head_dim
q, k, v = self.wqkv(x).split([self.dim, kv_size, kv_size], dim=-1)
q = q.view(bsz, seqlen, self.n_head, self.head_dim)
k = k.view(bsz, seqlen, self.n_local_heads, self.head_dim)
v = v.view(bsz, seqlen, self.n_local_heads, self.head_dim)
q = apply_rotary_emb(q, freqs_cis)
k = apply_rotary_emb(k, freqs_cis)
q, k, v = map(lambda x: x.transpose(1, 2), (q, k, v))
if self.kv_cache is not None:
k, v = self.kv_cache.update(input_pos, k, v)
k = k.repeat_interleave(self.n_head // self.n_local_heads, dim=1)
v = v.repeat_interleave(self.n_head // self.n_local_heads, dim=1)
y = F.scaled_dot_product_attention(q, k, v, attn_mask=mask, dropout_p=0.0)
y = y.transpose(1, 2).contiguous().view(bsz, seqlen, self.dim)
y = self.wo(y)
return y
class FeedForward(nn.Module):
def __init__(self, config: ModelArgs) -> None:
super().__init__()
self.w1 = nn.Linear(config.dim, config.intermediate_size, bias=False)
self.w3 = nn.Linear(config.dim, config.intermediate_size, bias=False)
self.w2 = nn.Linear(config.intermediate_size, config.dim, bias=False)
def forward(self, x: Tensor) -> Tensor:
return self.w2(F.silu(self.w1(x)) * self.w3(x))
class RMSNorm(nn.Module):
def __init__(self, dim: int, eps: float = 1e-5):
super().__init__()
self.eps = eps
self.weight = nn.Parameter(torch.ones(dim))
def _norm(self, x):
return x * torch.rsqrt(torch.mean(x * x, dim=-1, keepdim=True) + self.eps)
def forward(self, x: Tensor) -> Tensor:
output = self._norm(x.float()).type_as(x)
return output * self.weight
def apply_rope_scaling(freqs: torch.Tensor, rope_scaling: Optional[dict] = None):
factor = rope_scaling["factor"]
low_freq_factor = rope_scaling["low_freq_factor"]
high_freq_factor = rope_scaling["high_freq_factor"]
old_context_len = rope_scaling["original_max_position_embeddings"]
low_freq_wavelen = old_context_len / low_freq_factor
high_freq_wavelen = old_context_len / high_freq_factor
new_freqs = []
for freq in freqs:
wavelen = 2 * math.pi / freq
if wavelen < high_freq_wavelen:
new_freqs.append(freq)
elif wavelen > low_freq_wavelen:
new_freqs.append(freq / factor)
else:
assert low_freq_wavelen != high_freq_wavelen
smooth = (old_context_len / wavelen - low_freq_factor) / (high_freq_factor - low_freq_factor)
new_freqs.append((1 - smooth) * freq / factor + smooth * freq)
return torch.tensor(new_freqs, dtype=freqs.dtype, device=freqs.device)
def precompute_freqs_cis(
seq_len: int, n_elem: int, base: int = 10000,
dtype: torch.dtype = torch.bfloat16,
rope_scaling: Optional[dict] = None,
) -> Tensor:
freqs = 1.0 / (base ** (torch.arange(0, n_elem, 2)[: (n_elem // 2)].float() / n_elem))
if rope_scaling is not None:
freqs = apply_rope_scaling(freqs, rope_scaling)
t = torch.arange(seq_len, device=freqs.device)
freqs = torch.outer(t, freqs)
freqs_cis = torch.polar(torch.ones_like(freqs), freqs)
cache = torch.stack([freqs_cis.real, freqs_cis.imag], dim=-1)
return cache.to(dtype=dtype)
def apply_rotary_emb(x: Tensor, freqs_cis: Tensor) -> Tensor:
xshaped = x.float().reshape(*x.shape[:-1], -1, 2)
freqs_cis = freqs_cis.view(1, xshaped.size(1), 1, xshaped.size(3), 2)
x_out2 = torch.stack(
[
xshaped[..., 0] * freqs_cis[..., 0] - xshaped[..., 1] * freqs_cis[..., 1],
xshaped[..., 1] * freqs_cis[..., 0] + xshaped[..., 0] * freqs_cis[..., 1],
],
-1,
)
x_out2 = x_out2.flatten(3)
return x_out2.type_as(x)