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import torch
import torch.nn as nn
from torch.nn import functional as F
from .layers import layer_norm, mlp, QuantizedLinear
from .rope import apply_rotary_emb, precompute_freqs_cis
from .config import TextConfig
def text_encoder(input_ids: torch.Tensor, w: nn.Module):
return F.embedding(input_ids, w.wte)
def attn(
x: torch.Tensor,
w: nn.Module,
freqs_cis: torch.Tensor,
kv_cache: nn.Module,
attn_mask: torch.Tensor,
n_heads: int,
n_kv_heads: int,
position_ids: torch.Tensor,
):
bsz, q_len, d_model = x.shape
head_dim = d_model // n_heads
qkv_out = w.qkv(x) # shape: (bsz, q_len, (n_heads + 2*n_kv_heads)*head_dim)
q_dim = n_heads * head_dim
kv_dim = n_kv_heads * head_dim
q, k, v = qkv_out.split([q_dim, kv_dim, kv_dim], dim=-1)
del qkv_out
q = q.view(bsz, q_len, n_heads, head_dim).transpose(1, 2)
k = k.view(bsz, q_len, n_kv_heads, head_dim).transpose(1, 2)
v = v.view(bsz, q_len, n_kv_heads, head_dim).transpose(1, 2)
q = apply_rotary_emb(q, freqs_cis, position_ids, n_heads)
k = apply_rotary_emb(k, freqs_cis, position_ids, n_kv_heads)
if kv_cache is not None:
k, v = kv_cache.update(position_ids, k, v)
out = F.scaled_dot_product_attention(
q, k, v, attn_mask=attn_mask, enable_gqa=n_heads != n_kv_heads
)
out = out.transpose(1, 2).reshape(bsz, q_len, d_model)
out = w.proj(out)
return out
def _attn(
x: torch.Tensor,
w: torch.Tensor,
freqs_cis: torch.Tensor,
attn_mask: torch.Tensor,
n_heads: int,
n_kv_heads: int,
):
bsz, q_len, d_model = x.shape
head_dim = d_model // n_heads
pos = 0
qkv_out = w.qkv(x) # shape: (bsz, q_len, (n_heads + 2*n_kv_heads)*head_dim)
q_dim = n_heads * head_dim
kv_dim = n_kv_heads * head_dim
q = qkv_out[..., :q_dim].view(bsz, q_len, n_heads, head_dim).transpose(1, 2)
k = (
qkv_out[..., q_dim : q_dim + kv_dim]
.view(bsz, q_len, n_kv_heads, head_dim)
.transpose(1, 2)
)
v = (
qkv_out[..., q_dim + kv_dim :]
.view(bsz, q_len, n_kv_heads, head_dim)
.transpose(1, 2)
)
position_ids = torch.arange(pos, pos + q_len, dtype=torch.long)
q = apply_rotary_emb(q, freqs_cis, position_ids, n_heads)
k = apply_rotary_emb(k, freqs_cis, position_ids, n_kv_heads)
out = F.scaled_dot_product_attention(
q, k, v, attn_mask=attn_mask, enable_gqa=n_heads != n_kv_heads
)
out = out.transpose(1, 2).reshape(bsz, q_len, d_model)
out = w.proj(out)
return out
def _produce_hidden(inputs_embeds: torch.Tensor, w: nn.Module, config: TextConfig):
hidden_BTC = inputs_embeds
bsz, q_len, d_model = inputs_embeds.shape
attn_mask = torch.zeros(q_len, q_len)
attn_mask[:730, :730] = 1
for i in range(730, q_len):
attn_mask[i, : i + 1] = 1
attn_mask = attn_mask.to(dtype=torch.bool)
for i, block in enumerate(w.blocks):
l_in = layer_norm(hidden_BTC, block.ln)
l_attn = _attn(
x=l_in,
w=block.attn,
freqs_cis=w.freqs_cis,
attn_mask=attn_mask,
n_heads=config.n_heads,
n_kv_heads=config.n_kv_heads,
)
l_mlp = mlp(l_in, block.mlp)
hidden_BTC = hidden_BTC + l_attn + l_mlp
return hidden_BTC
def text_decoder(
x: torch.Tensor,
w: nn.Module,
attn_mask: torch.Tensor,
position_ids: torch.Tensor,
config: TextConfig,
):
for i, block in enumerate(w.blocks):
l_in = layer_norm(x, block.ln)
l_attn = attn(
l_in,
block.attn,
freqs_cis=w.freqs_cis,
kv_cache=block.kv_cache,
attn_mask=attn_mask,
n_heads=config.n_heads,
n_kv_heads=config.n_kv_heads,
position_ids=position_ids,
)
l_mlp = mlp(l_in, block.mlp)
x = x + l_attn + l_mlp
return x
def lm_head(hidden_BTC: torch.Tensor, w: nn.Module):
hidden_BC = hidden_BTC[:, -1, :]
hidden_BC = layer_norm(hidden_BC, w.post_ln)
logits = w.lm_head(hidden_BC)
return logits
def _lm_head(hidden_BTC: torch.Tensor, w: nn.Module):
hidden_BTC = layer_norm(hidden_BTC, w.post_ln)
logits = w.lm_head(hidden_BTC)
return logits
def build_text_model(config: TextConfig, dtype: torch.dtype) -> nn.Module:
qkv_dim = int(config.dim * (1 + 2 * config.n_kv_heads / config.n_heads))
linear_cls = QuantizedLinear if config.group_size is not None else nn.Linear
text = nn.ModuleDict(
{
"blocks": nn.ModuleList(
[
nn.ModuleDict(
{
"ln": nn.LayerNorm(config.dim, dtype=dtype),
"attn": nn.ModuleDict(
{
"qkv": linear_cls(config.dim, qkv_dim, dtype=dtype),
"proj": linear_cls(
config.dim, config.dim, dtype=dtype
),
}
),
"mlp": nn.ModuleDict(
{
"fc1": linear_cls(
config.dim, config.ff_dim, dtype=dtype
),
"fc2": linear_cls(
config.ff_dim, config.dim, dtype=dtype
),
}
),
}
)
for _ in range(config.n_layers)
]
),
"post_ln": nn.LayerNorm(config.dim, dtype=dtype),
"lm_head": nn.Linear(config.dim, config.vocab_size, dtype=dtype),
}
)
text.wte = nn.Parameter(torch.empty(config.vocab_size, config.dim, dtype=dtype))
text.register_buffer(
"freqs_cis",
precompute_freqs_cis(config.dim // (2 * config.n_heads), config.max_context),
persistent=False,
)
return text
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