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# Copyright (c) NoteDance, Inc. and affiliates.
# This software may be used and distributed in accordance with the terms of the Llama 3 Community License Agreement.
import tensorflow as tf
from tensorflow.keras.layers import Embedding,Dense
from tensorflow.keras import Model
import math
from dataclasses import dataclass
from typing import Optional
@dataclass
class ModelArgs:
dim: int = 4096
n_layers: int = 32
n_heads: int = 32
n_kv_heads: Optional[int] = None
vocab_size: int = -1
multiple_of: int = 256 # make SwiGLU hidden layer size multiple of large power of 2
ffn_dim_multiplier: Optional[float] = None
norm_eps: float = 1e-5
rope_theta: float = 500000
max_batch_size: int = 32
max_seq_len: int = 2048
class RMSNorm(tf.keras.layers.Layer):
def __init__(self, dim: int, eps: float = 1e-6):
self.eps = eps
self.weight = self.add_weight(
name='weight',
shape=(self.dim,),
initializer=tf.keras.initializers.Ones(),
trainable=True
)
def _norm(self, x):
return x * tf.math.rsqrt(tf.reduce_mean(tf.pow(x, 2), -1, keepdims=True) + self.eps)
def __call__(self, x):
output = tf.cast(self._norm(tf.cast(x, 'float32')), x.dtype)
return output * self.weight
def precompute_freqs_cis(dim: int, end: int, theta: float = 10000.0):
freqs = 1.0 / (theta ** (tf.cast(tf.range(0, dim, 2)[: (dim // 2)], 'float32') / dim))
t = tf.range(end, dtype='float32')
freqs = tf.experimental.numpy.outer(t, freqs)
freqs_cis = tf.complex(tf.ones_like(freqs), freqs)
real_part = tf.math.cos(freqs)
imag_part = tf.math.sin(freqs)
freqs_cis = tf.complex(real_part, imag_part) # complex64
return freqs_cis
def reshape_for_broadcast(freqs_cis, x):
ndim = x.ndim
assert 0 <= 1 < ndim
assert freqs_cis.shape == (x.shape[1], x.shape[-1])
shape = [d if i == 1 or i == ndim - 1 else 1 for i, d in enumerate(x.shape)]
return tf.reshape(freqs_cis, shape)
def apply_rotary_emb(
xq,
xk,
freqs_cis,
):
xq = tf.reshape(tf.cast(xq, 'float32'), (xq.shape[:-1] + (xq.shape[-1] // 2, 2)))
real_part = xq[..., 0]
imag_part = xq[..., 1]
xq_ = tf.complex(real_part, imag_part)
xk = tf.reshape(tf.cast(xk, 'float32'), (xk.shape[:-1] + (xk.shape[-1] // 2, 2)))
real_part = xk[..., 0]
imag_part = xk[..., 1]
xk_ = tf.complex(real_part, imag_part)
freqs_cis = reshape_for_broadcast(freqs_cis, xq_)
xq_freqs_cis = xq_ * freqs_cis
xq_out = tf.stack([tf.math.real(xq_freqs_cis), tf.math.imag(xq_freqs_cis)], axis=-1)
shape = xq_out.shape
xq_out = tf.reshape(xq_out, [-1, shape[1], shape[2], shape[3] * shape[4]])
xk_freqs_cis = xk_ * freqs_cis
xk_out = tf.stack([tf.math.real(xk_freqs_cis), tf.math.imag(xk_freqs_cis)], axis=-1)
shape = xk_out.shape
xk_out = tf.reshape(xk_out, [-1, shape[1], shape[2], shape[3] * shape[4]])
return tf.cast(xq_out, xq.dtype), tf.cast(xk_out, xk.dtype)
def repeat_kv(x, n_rep: int):
bs, slen, n_kv_heads, head_dim = x.shape
if n_rep == 1:
return x
return tf.reshape(tf.tile(x[:, :, :, None, :], [1, 1, 1, n_rep, 1]), (bs, slen, n_kv_heads * n_rep, head_dim))
class Attention(tf.keras.layers.Layer):
def __init__(self, args: ModelArgs):
self.n_kv_heads = args.n_heads if args.n_kv_heads is None else args.n_kv_heads
model_parallel_size = 1
self.n_local_heads = args.n_heads // model_parallel_size
self.n_local_kv_heads = self.n_kv_heads // model_parallel_size
self.n_rep = self.n_local_heads // self.n_local_kv_heads
self.head_dim = args.dim // args.n_heads
self.wq = Dense(
args.n_heads * self.head_dim,
use_bias=False,
)
self.wk = Dense(
self.n_kv_heads * self.head_dim,
use_bias=False,
)
self.wv = Dense(
self.n_kv_heads * self.head_dim,
use_bias=False,
)
self.wo = Dense(
args.dim,
use_bias=False,
)
self.cache_k = self.add_weight(
name='cache_k',
shape=(
args.max_batch_size,
args.max_seq_len,
self.n_local_kv_heads,
self.head_dim,
),
initializer=tf.keras.initializers.Zeros(),
trainable=False
)
self.cache_v = self.add_weight(
name='cache_v',
shape=(
args.max_batch_size,
args.max_seq_len,
self.n_local_kv_heads,
self.head_dim,
),
initializer=tf.keras.initializers.Zeros(),
trainable=False
)
def __call__(
self,
x,
start_pos: int,
freqs_cis,
mask,
):
bsz, seqlen, _ = x.shape
xq, xk, xv = self.wq(x), self.wk(x), self.wv(x)
xq = tf.reshape(xq, (bsz, seqlen, self.n_local_heads, self.head_dim))
xk = tf.reshape(xk, (bsz, seqlen, self.n_local_kv_heads, self.head_dim))
xv = tf.reshape(xv, (bsz, seqlen, self.n_local_kv_heads, self.head_dim))
xq, xk = apply_rotary_emb(xq, xk, freqs_cis=freqs_cis)
self.cache_k = tf.cast(self.cache_k, xq.dtype)
self.cache_v = tf.cast(self.cache_v, xq.dtype)
self.cache_k[:bsz, start_pos : start_pos + seqlen].assign(xk)
self.cache_v[:bsz, start_pos : start_pos + seqlen].assign(xv)
keys = self.cache_k[:bsz, : start_pos + seqlen]
values = self.cache_v[:bsz, : start_pos + seqlen]
# repeat k/v heads if n_kv_heads < n_heads
keys = repeat_kv(
keys, self.n_rep
) # (bs, cache_len + seqlen, n_local_heads, head_dim)
values = repeat_kv(
values, self.n_rep
) # (bs, cache_len + seqlen, n_local_heads, head_dim)
xq = tf.transpose(xq, (0, 2, 1, 3)) # (bs, n_local_heads, seqlen, head_dim)
keys = tf.transpose(keys, (0, 2, 1, 3)) # (bs, n_local_heads, cache_len + seqlen, head_dim)
values = tf.transpose(values,
(0, 2, 1, 3)
) # (bs, n_local_heads, cache_len + seqlen, head_dim)
scores = tf.matmul(xq, tf.transpose(keys, (0, 1, 3, 2))) / math.sqrt(self.head_dim)
if mask is not None:
scores = scores + mask # (bs, n_local_heads, seqlen, cache_len + seqlen)
scores = tf.cast(tf.nn.softmax(tf.cast(scores, 'float32')), xq.dtype)
output = tf.matmul(scores, values) # (bs, n_local_heads, seqlen, head_dim)
output = tf.reshape(tf.transpose(output, (0, 2, 1, 3)), (bsz, seqlen, -1))
return self.wo(output)
class FeedForward:
def __init__(
self,
dim: int,
hidden_dim: int,
multiple_of: int,
ffn_dim_multiplier: Optional[float],
):
hidden_dim = int(2 * hidden_dim / 3)
# custom dim factor multiplier
if ffn_dim_multiplier is not None:
hidden_dim = int(ffn_dim_multiplier * hidden_dim)
hidden_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of)
self.w1 = Dense(
hidden_dim, use_bias=False
)
self.w2 = Dense(
dim, use_bias=False
)
self.w3 = Dense(
hidden_dim, use_bias=False
)
def __call__(self, x):
return self.w2(tf.nn.silu(self.w1(x)) * self.w3(x))
class TransformerBlock:
def __init__(self, layer_id: int, args: ModelArgs):
self.n_heads = args.n_heads
self.dim = args.dim
self.head_dim = args.dim // args.n_heads
self.attention = Attention(args)
self.feed_forward = FeedForward(
dim=args.dim,
hidden_dim=4 * args.dim,
multiple_of=args.multiple_of,
ffn_dim_multiplier=args.ffn_dim_multiplier,
)
self.layer_id = layer_id
self.attention_norm = RMSNorm(args.dim, eps=args.norm_eps)
self.ffn_norm = RMSNorm(args.dim, eps=args.norm_eps)
def __call__(
self,
x,
start_pos,
freqs_cis,
mask,
):
h = x + self.attention(self.attention_norm(x), start_pos, freqs_cis, mask)
out = h + self.feed_forward(self.ffn_norm(h))
return out
class Llama3(Model):
def __init__(self, params: ModelArgs):
self.params = params
self.vocab_size = params.vocab_size
self.n_layers = params.n_layers
self.tok_embeddings = Embedding(
params.vocab_size, params.dim
)
self.layers_ = []
for layer_id in range(params.n_layers):
self.layers_.append(TransformerBlock(layer_id, params))
self.norm = RMSNorm(params.dim, eps=params.norm_eps)
self.output_ = Dense(
params.vocab_size, use_bias=False
)
self.freqs_cis = precompute_freqs_cis(
params.dim // params.n_heads,
params.max_seq_len * 2,
params.rope_theta,
)
def __call__(self, tokens, start_pos: int):
_bsz, seqlen = tokens.shape
h = self.tok_embeddings(tokens)
self.freqs_cis = self.freqs_cis
freqs_cis = self.freqs_cis[start_pos : start_pos + seqlen]
mask = None
if seqlen > 1:
mask = tf.fill([seqlen, seqlen], float("-inf"))
mask = tf.linalg.band_part(mask, 0, -1)
mask = mask - tf.linalg.band_part(mask, 0, 0)
# When performing key-value caching, we compute the attention scores
# only for the new sequence. Thus, the matrix of scores is of size
# (seqlen, cache_len + seqlen), and the only masked entries are (i, j) for
# j > cache_len + i, since row i corresponds to token cache_len + i.
mask = tf.linalg.set_diag(mask, tf.zeros(seqlen))
mask = tf.cast(mask, h.dtype)
for layer in self.layers_:
h = layer(h, start_pos, freqs_cis, mask)
h = self.norm(h)
output = tf.cast(self.output_(h), 'float32')
return output |