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import json
from pathlib import Path
from typing import Any, Dict, Optional, Tuple
import mlx.core as mx
import mlx.nn as nn
WEIGHT_FILES = {
"bf16": "model-bf16.safetensors",
"16bit": "model-bf16.safetensors",
"8bit": "model-8bit.safetensors",
"4bit": "model-4bit.safetensors",
}
class EuroBertConfig:
def __init__(self, **kwargs: Any):
self.vocab_size = kwargs.get("vocab_size", 128257)
self.hidden_size = kwargs.get("hidden_size", 768)
self.intermediate_size = kwargs.get("intermediate_size", 3072)
self.num_hidden_layers = kwargs.get("num_hidden_layers", 12)
self.num_attention_heads = kwargs.get("num_attention_heads", 12)
self.num_key_value_heads = kwargs.get(
"num_key_value_heads", self.num_attention_heads
)
self.head_dim = kwargs.get(
"head_dim", self.hidden_size // self.num_attention_heads
)
self.hidden_act = kwargs.get("hidden_act", "silu")
self.rms_norm_eps = kwargs.get("rms_norm_eps", 1e-5)
self.rope_theta = kwargs.get("rope_theta", 250000.0)
self.attention_bias = kwargs.get("attention_bias", False)
self.mlp_bias = kwargs.get("mlp_bias", False)
self.pad_token_id = kwargs.get("pad_token_id", 128001)
self.max_position_embeddings = kwargs.get("max_position_embeddings", 8192)
self.num_labels = kwargs.get("num_labels", len(kwargs.get("id2label", {})) or 2)
self.raw = kwargs
@classmethod
def from_json(cls, path: str | Path) -> "EuroBertConfig":
with open(path, "r", encoding="utf-8") as f:
return cls(**json.load(f))
def _silu(x: mx.array) -> mx.array:
return x * mx.sigmoid(x)
def _rotate_half(x: mx.array) -> mx.array:
x1 = x[..., : x.shape[-1] // 2]
x2 = x[..., x.shape[-1] // 2 :]
return mx.concatenate([-x2, x1], axis=-1)
def _apply_rotary_pos_emb(
q: mx.array, k: mx.array, cos: mx.array, sin: mx.array
) -> Tuple[mx.array, mx.array]:
cos = cos[:, None, :, :]
sin = sin[:, None, :, :]
return (q * cos) + (_rotate_half(q) * sin), (k * cos) + (_rotate_half(k) * sin)
class EuroBertRotaryEmbedding(nn.Module):
def __init__(self, config: EuroBertConfig):
super().__init__()
self.head_dim = config.head_dim
self.rope_theta = config.rope_theta
def __call__(self, position_ids: mx.array, dtype: mx.Dtype) -> Tuple[mx.array, mx.array]:
steps = mx.arange(0, self.head_dim, 2).astype(mx.float32)
inv_freq = 1.0 / (self.rope_theta ** (steps / self.head_dim))
pos = position_ids.astype(mx.float32)
freqs = pos[..., None] * inv_freq[None, None, :]
emb = mx.concatenate([freqs, freqs], axis=-1)
return mx.cos(emb).astype(dtype), mx.sin(emb).astype(dtype)
class EuroBertAttention(nn.Module):
def __init__(self, config: EuroBertConfig):
super().__init__()
self.num_heads = config.num_attention_heads
self.num_key_value_heads = config.num_key_value_heads
self.num_key_value_groups = self.num_heads // self.num_key_value_heads
self.head_dim = config.head_dim
self.scaling = self.head_dim**-0.5
self.q_proj = nn.Linear(
config.hidden_size,
config.num_attention_heads * self.head_dim,
bias=config.attention_bias,
)
self.k_proj = nn.Linear(
config.hidden_size,
config.num_key_value_heads * self.head_dim,
bias=config.attention_bias,
)
self.v_proj = nn.Linear(
config.hidden_size,
config.num_key_value_heads * self.head_dim,
bias=config.attention_bias,
)
self.o_proj = nn.Linear(
config.num_attention_heads * self.head_dim,
config.hidden_size,
bias=config.attention_bias,
)
def _shape(self, x: mx.array, heads: int) -> mx.array:
batch, seq_len, _ = x.shape
x = x.reshape(batch, seq_len, heads, self.head_dim)
return mx.transpose(x, (0, 2, 1, 3))
def __call__(
self,
hidden_states: mx.array,
position_embeddings: Tuple[mx.array, mx.array],
attention_mask: Optional[mx.array],
) -> mx.array:
batch, seq_len, _ = hidden_states.shape
q = self._shape(self.q_proj(hidden_states), self.num_heads)
k = self._shape(self.k_proj(hidden_states), self.num_key_value_heads)
v = self._shape(self.v_proj(hidden_states), self.num_key_value_heads)
cos, sin = position_embeddings
q, k = _apply_rotary_pos_emb(q, k, cos, sin)
if self.num_key_value_groups != 1:
k = mx.repeat(k, self.num_key_value_groups, axis=1)
v = mx.repeat(v, self.num_key_value_groups, axis=1)
scores = (q @ mx.transpose(k, (0, 1, 3, 2))).astype(mx.float32)
scores = scores * self.scaling
if attention_mask is not None:
scores = scores + attention_mask
probs = mx.softmax(scores, axis=-1).astype(q.dtype)
out = probs @ v
out = mx.transpose(out, (0, 2, 1, 3)).reshape(batch, seq_len, -1)
return self.o_proj(out)
class EuroBertMLP(nn.Module):
def __init__(self, config: EuroBertConfig):
super().__init__()
self.gate_proj = nn.Linear(
config.hidden_size, config.intermediate_size, bias=config.mlp_bias
)
self.up_proj = nn.Linear(
config.hidden_size, config.intermediate_size, bias=config.mlp_bias
)
self.down_proj = nn.Linear(
config.intermediate_size, config.hidden_size, bias=config.mlp_bias
)
def __call__(self, x: mx.array) -> mx.array:
return self.down_proj(_silu(self.gate_proj(x)) * self.up_proj(x))
class EuroBertDecoderLayer(nn.Module):
def __init__(self, config: EuroBertConfig):
super().__init__()
self.self_attn = EuroBertAttention(config)
self.mlp = EuroBertMLP(config)
self.input_layernorm = nn.RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.post_attention_layernorm = nn.RMSNorm(
config.hidden_size, eps=config.rms_norm_eps
)
def __call__(
self,
hidden_states: mx.array,
attention_mask: Optional[mx.array],
position_embeddings: Tuple[mx.array, mx.array],
) -> mx.array:
residual = hidden_states
hidden_states = self.input_layernorm(hidden_states)
hidden_states = self.self_attn(
hidden_states, position_embeddings, attention_mask
)
hidden_states = residual + hidden_states
residual = hidden_states
hidden_states = self.post_attention_layernorm(hidden_states)
hidden_states = self.mlp(hidden_states)
return residual + hidden_states
class EuroBertModel(nn.Module):
def __init__(self, config: EuroBertConfig):
super().__init__()
self.config = config
self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size)
self.layers = [
EuroBertDecoderLayer(config) for _ in range(config.num_hidden_layers)
]
self.norm = nn.RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.rotary_emb = EuroBertRotaryEmbedding(config)
def _attention_mask(self, attention_mask: Optional[mx.array]) -> Optional[mx.array]:
if attention_mask is None:
return None
keep = attention_mask.astype(mx.bool_)
mask = mx.where(keep[:, None, None, :], 0.0, mx.finfo(mx.float32).min)
return mask.astype(mx.float32)
def __call__(
self,
input_ids: mx.array,
attention_mask: Optional[mx.array] = None,
position_ids: Optional[mx.array] = None,
) -> mx.array:
hidden_states = self.embed_tokens(input_ids)
batch, seq_len = input_ids.shape
if position_ids is None:
position_ids = mx.broadcast_to(mx.arange(seq_len)[None, :], (batch, seq_len))
mask = self._attention_mask(attention_mask)
position_embeddings = self.rotary_emb(position_ids, hidden_states.dtype)
for layer in self.layers:
hidden_states = layer(hidden_states, mask, position_embeddings)
return self.norm(hidden_states)
class EuroBertForTokenClassification(nn.Module):
def __init__(self, config: EuroBertConfig):
super().__init__()
self.config = config
self.model = EuroBertModel(config)
self.classifier = nn.Linear(config.hidden_size, config.num_labels)
def __call__(
self,
input_ids: mx.array,
attention_mask: Optional[mx.array] = None,
position_ids: Optional[mx.array] = None,
) -> mx.array:
hidden_states = self.model(input_ids, attention_mask, position_ids)
return self.classifier(hidden_states)
def build_model(
config: EuroBertConfig | Dict[str, Any],
quantization: Optional[Dict[str, Any]] = None,
) -> EuroBertForTokenClassification:
if isinstance(config, dict):
config = EuroBertConfig(**config)
model = EuroBertForTokenClassification(config)
if quantization:
nn.quantize(
model,
group_size=quantization.get("group_size", 64),
bits=quantization["bits"],
mode=quantization.get("mode", "affine"),
)
return model
def load_model(
model_path: str | Path,
variant: str = "bf16",
) -> EuroBertForTokenClassification:
model_path = Path(model_path)
with open(model_path / "config.json", "r", encoding="utf-8") as f:
raw_config = json.load(f)
with open(model_path / "mlx_config.json", "r", encoding="utf-8") as f:
mlx_config = json.load(f)
variant = variant.lower()
if variant not in WEIGHT_FILES:
raise ValueError(f"Unknown variant {variant!r}; expected one of {sorted(WEIGHT_FILES)}")
weight_name = WEIGHT_FILES[variant]
quantization = mlx_config["variants"].get(weight_name, {}).get("quantization")
model = build_model(raw_config, quantization=quantization)
model.load_weights(str(model_path / weight_name))
mx.eval(model.parameters())
return model
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