mirror of
https://github.com/ml-explore/mlx-examples.git
synced 2025-06-24 17:31:18 +08:00
229 lines
7.1 KiB
Python
229 lines
7.1 KiB
Python
|
from dataclasses import dataclass
|
||
|
|
from typing import Dict, List, Optional, Tuple, Union
|
||
|
|
|
||
|
|
import mlx.core as mx
|
||
|
|
import mlx.nn as nn
|
||
|
|
|
||
|
|
from .base import BaseModelArgs
|
||
|
|
|
||
|
|
|
||
|
|
@dataclass
|
||
|
|
class ModelArgs(BaseModelArgs):
|
||
|
|
model_type: str
|
||
|
|
head_dim: int
|
||
|
|
num_transformer_layers: int
|
||
|
|
model_dim: int
|
||
|
|
vocab_size: int
|
||
|
|
ffn_dim_divisor: int
|
||
|
|
num_query_heads: List
|
||
|
|
num_kv_heads: List
|
||
|
|
ffn_multipliers: List
|
||
|
|
ffn_with_glu: bool = True
|
||
|
|
normalize_qk_projections: bool = True
|
||
|
|
share_input_output_layers: bool = True
|
||
|
|
rms_norm_eps: float = 1e-6
|
||
|
|
rope_theta: float = 10000
|
||
|
|
rope_traditional: bool = False
|
||
|
|
|
||
|
|
|
||
|
|
def make_divisible(
|
||
|
|
v: Union[float, int],
|
||
|
|
divisor: Optional[int] = 8,
|
||
|
|
min_value: Optional[Union[float, int]] = None,
|
||
|
|
) -> Union[float, int]:
|
||
|
|
"""
|
||
|
|
This function is taken from the original tf repo.
|
||
|
|
It ensures that all layers have a channel number that is divisible by the divisor
|
||
|
|
It can be seen at:
|
||
|
|
https://github.com/tensorflow/models/blob/2cfc99eff5e5eb729c6793d2f3d03aa1c9be2b15/research/slim/nets/mobilenet/mobilenet.py#L62
|
||
|
|
Args:
|
||
|
|
v: input value
|
||
|
|
divisor: default to 8
|
||
|
|
min_value: minimum divisor value
|
||
|
|
Returns:
|
||
|
|
new_v: new divisible value
|
||
|
|
"""
|
||
|
|
if min_value is None:
|
||
|
|
min_value = divisor
|
||
|
|
new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
|
||
|
|
# Make sure that round down does not go down by more than 10%.
|
||
|
|
if new_v < 0.9 * v:
|
||
|
|
new_v += divisor
|
||
|
|
return new_v
|
||
|
|
|
||
|
|
|
||
|
|
class Attention(nn.Module):
|
||
|
|
def __init__(self, args: ModelArgs, layer_id: int):
|
||
|
|
super().__init__()
|
||
|
|
self.head_dim = head_dim = args.head_dim
|
||
|
|
self.layer_id = layer_id
|
||
|
|
self.model_dim = model_dim = args.model_dim
|
||
|
|
|
||
|
|
self.n_heads = n_heads = args.num_query_heads[layer_id]
|
||
|
|
self.n_kv_heads = n_kv_heads = args.num_kv_heads[layer_id]
|
||
|
|
self.scale = head_dim**-0.5
|
||
|
|
|
||
|
|
op_size = (n_heads + (n_kv_heads * 2)) * head_dim
|
||
|
|
self.qkv_proj = nn.Linear(model_dim, op_size, bias=False)
|
||
|
|
self.out_proj = nn.Linear(n_heads * head_dim, model_dim, bias=False)
|
||
|
|
|
||
|
|
self.normalize_qk_projections = args.normalize_qk_projections
|
||
|
|
|
||
|
|
if self.normalize_qk_projections:
|
||
|
|
self.q_norm = nn.RMSNorm(head_dim, eps=args.rms_norm_eps)
|
||
|
|
self.k_norm = nn.RMSNorm(head_dim, eps=args.rms_norm_eps)
|
||
|
|
|
||
|
|
self.rope = nn.RoPE(
|
||
|
|
head_dim, traditional=args.rope_traditional, base=args.rope_theta
|
||
|
|
)
|
||
|
|
|
||
|
|
def __call__(
|
||
|
|
self,
|
||
|
|
x: mx.array,
|
||
|
|
mask: Optional[mx.array] = None,
|
||
|
|
cache: Optional[Tuple[mx.array, mx.array]] = None,
|
||
|
|
) -> mx.array:
|
||
|
|
B, L, D = x.shape
|
||
|
|
|
||
|
|
qkv = self.qkv_proj(x)
|
||
|
|
|
||
|
|
# [B, S, (q_h + k_h + v_h) * h] --> [B, S, (q_h + k_h + v_h), h] -> [B, (q_h + k_h + v_h), S, h]
|
||
|
|
qkv = qkv.reshape(
|
||
|
|
B, L, self.n_heads + (self.n_kv_heads * 2), self.head_dim
|
||
|
|
).transpose(0, 2, 1, 3)
|
||
|
|
|
||
|
|
# [B, (q_h + k_h + v_h), S, h] --> [B, q_h, S h], [B, k_h, S, h], [B, v_h, S, h]
|
||
|
|
queries, keys, values = mx.split(
|
||
|
|
qkv, [self.n_heads, self.n_heads + self.n_kv_heads], axis=1
|
||
|
|
)
|
||
|
|
|
||
|
|
# Prepare the queries, keys and values for the attention computation
|
||
|
|
if self.normalize_qk_projections:
|
||
|
|
queries = self.q_norm(queries)
|
||
|
|
keys = self.k_norm(keys)
|
||
|
|
|
||
|
|
if cache is not None:
|
||
|
|
key_cache, value_cache = cache
|
||
|
|
queries = self.rope(queries, offset=key_cache.shape[2])
|
||
|
|
keys = self.rope(keys, offset=key_cache.shape[2])
|
||
|
|
keys = mx.concatenate([key_cache, keys], axis=2)
|
||
|
|
values = mx.concatenate([value_cache, values], axis=2)
|
||
|
|
else:
|
||
|
|
queries = self.rope(queries)
|
||
|
|
keys = self.rope(keys)
|
||
|
|
|
||
|
|
output = mx.fast.scaled_dot_product_attention(
|
||
|
|
queries, keys, values, scale=self.scale, mask=mask
|
||
|
|
)
|
||
|
|
|
||
|
|
output = output.transpose(0, 2, 1, 3).reshape(B, L, -1)
|
||
|
|
|
||
|
|
return self.out_proj(output), (keys, values)
|
||
|
|
|
||
|
|
|
||
|
|
class MLP(nn.Module):
|
||
|
|
def __init__(self, args: ModelArgs, layer_id: int):
|
||
|
|
super().__init__()
|
||
|
|
self.args = args
|
||
|
|
dim = args.model_dim
|
||
|
|
ffn_multiplier = args.ffn_multipliers[layer_id]
|
||
|
|
|
||
|
|
intermediate_dim = int(
|
||
|
|
make_divisible(
|
||
|
|
ffn_multiplier * args.model_dim,
|
||
|
|
divisor=args.ffn_dim_divisor,
|
||
|
|
)
|
||
|
|
)
|
||
|
|
|
||
|
|
self.proj_1 = nn.Linear(dim, 2 * intermediate_dim, bias=False)
|
||
|
|
self.proj_2 = nn.Linear(intermediate_dim, dim, bias=False)
|
||
|
|
|
||
|
|
def __call__(self, x) -> mx.array:
|
||
|
|
x = self.proj_1(x)
|
||
|
|
gate, x = mx.split(x, 2, axis=-1)
|
||
|
|
return self.proj_2(nn.silu(gate) * x)
|
||
|
|
|
||
|
|
|
||
|
|
class TransformerBlock(nn.Module):
|
||
|
|
def __init__(self, args: ModelArgs, layer_id: int):
|
||
|
|
super().__init__()
|
||
|
|
dim = args.model_dim
|
||
|
|
self.attn = Attention(args, layer_id=layer_id)
|
||
|
|
self.ffn = MLP(args, layer_id=layer_id)
|
||
|
|
self.ffn_norm = nn.RMSNorm(dim, eps=args.rms_norm_eps)
|
||
|
|
self.attn_norm = nn.RMSNorm(dim, eps=args.rms_norm_eps)
|
||
|
|
|
||
|
|
def __call__(
|
||
|
|
self,
|
||
|
|
x: mx.array,
|
||
|
|
mask: Optional[mx.array] = None,
|
||
|
|
cache: Optional[Tuple[mx.array, mx.array]] = None,
|
||
|
|
) -> mx.array:
|
||
|
|
r, cache = self.attn(self.attn_norm(x), mask, cache)
|
||
|
|
h = x + r
|
||
|
|
r = self.ffn(self.ffn_norm(h))
|
||
|
|
out = h + r
|
||
|
|
return out, cache
|
||
|
|
|
||
|
|
|
||
|
|
class OpenELMModel(nn.Module):
|
||
|
|
def __init__(self, args: ModelArgs):
|
||
|
|
super().__init__()
|
||
|
|
self.args = args
|
||
|
|
self.vocab_size = args.vocab_size
|
||
|
|
self.num_transformer_layers = args.num_transformer_layers
|
||
|
|
assert self.vocab_size > 0
|
||
|
|
self.token_embeddings = nn.Embedding(args.vocab_size, args.model_dim)
|
||
|
|
self.layers = [
|
||
|
|
TransformerBlock(args, layer_id=layer_id)
|
||
|
|
for layer_id in range(self.num_transformer_layers)
|
||
|
|
]
|
||
|
|
self.norm = nn.RMSNorm(args.model_dim, eps=args.rms_norm_eps)
|
||
|
|
|
||
|
|
def __call__(
|
||
|
|
self,
|
||
|
|
inputs: mx.array,
|
||
|
|
cache=None,
|
||
|
|
):
|
||
|
|
h = self.token_embeddings(inputs)
|
||
|
|
|
||
|
|
mask = None
|
||
|
|
if h.shape[1] > 1:
|
||
|
|
mask = nn.MultiHeadAttention.create_additive_causal_mask(h.shape[1])
|
||
|
|
mask = mask.astype(h.dtype)
|
||
|
|
|
||
|
|
if cache is None:
|
||
|
|
cache = [None] * len(self.layers)
|
||
|
|
|
||
|
|
for e, layer in enumerate(self.layers):
|
||
|
|
h, cache[e] = layer(h, mask, cache[e])
|
||
|
|
|
||
|
|
return self.norm(h), cache
|
||
|
|
|
||
|
|
|
||
|
|
class Model(nn.Module):
|
||
|
|
def __init__(self, args: ModelArgs):
|
||
|
|
super().__init__()
|
||
|
|
self.args = args
|
||
|
|
self.model_type = args.model_type
|
||
|
|
self.transformer = OpenELMModel(args)
|
||
|
|
if not args.share_input_output_layers:
|
||
|
|
self.lm_head = nn.Linear(args.model_dim, args.vocab_size, bias=False)
|
||
|
|
|
||
|
|
def __call__(
|
||
|
|
self,
|
||
|
|
inputs: mx.array,
|
||
|
|
cache=None,
|
||
|
|
):
|
||
|
|
out, cache = self.transformer(inputs, cache)
|
||
|
|
if self.args.share_input_output_layers:
|
||
|
|
out = self.transformer.token_embeddings.as_linear(out)
|
||
|
|
else:
|
||
|
|
out = self.lm_head(out)
|
||
|
|
|
||
|
|
return out, cache
|
||
|
|
|
||
|
|
@property
|
||
|
|
def layers(self):
|
||
|
|
return self.transformer.layers
|