mirror of
https://github.com/ml-explore/mlx-examples.git
synced 2025-06-24 09:21:18 +08:00
250 lines
7.9 KiB
Python
250 lines
7.9 KiB
Python
import inspect
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from dataclasses import dataclass
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from typing import Any, Dict, List, Optional, Union
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import mlx.core as mx
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import mlx.nn as nn
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from .cache import KVCache, RotatingKVCache
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from .base import BaseModelArgs, create_attention_mask
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@dataclass
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class ModelArgs(BaseModelArgs):
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model_type: str
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hidden_size: int
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num_hidden_layers: int
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intermediate_size: int
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num_attention_heads: int = 8
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head_dim: int = 256
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rms_norm_eps: float = 1.0e-6
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vocab_size: int = 262208
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num_key_value_heads: int = 4
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rope_global_base_freq: float = 1_000_000.0
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rope_local_base_freq: float = 10_000.0
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rope_traditional: bool = False
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query_pre_attn_scalar: float = 0.0625
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sliding_window: int = 1024
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rope_scaling: Optional[Dict[str, Union[float, List[float]]]] = None
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mm_tokens_per_image: int = 256
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sliding_window_pattern: int = 6
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@classmethod
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def from_dict(cls, params):
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return cls(
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**{
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k: v
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for k, v in params.items()
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if k in inspect.signature(cls).parameters
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}
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)
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class Attention(nn.Module):
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def __init__(self, args: ModelArgs, layer_idx: int):
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super().__init__()
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dim = args.hidden_size
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self.n_heads = n_heads = args.num_attention_heads
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self.n_kv_heads = n_kv_heads = args.num_key_value_heads
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self.repeats = n_heads // n_kv_heads
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self.head_dim = head_dim = args.head_dim
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self.layer_idx = layer_idx
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self.scale = args.query_pre_attn_scalar**-0.5
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self.q_proj = nn.Linear(dim, n_heads * head_dim, bias=False)
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self.k_proj = nn.Linear(dim, n_kv_heads * head_dim, bias=False)
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self.v_proj = nn.Linear(dim, n_kv_heads * head_dim, bias=False)
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self.o_proj = nn.Linear(n_heads * head_dim, dim, bias=False)
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self.q_norm = nn.RMSNorm(dims=head_dim, eps=args.rms_norm_eps)
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self.k_norm = nn.RMSNorm(dims=head_dim, eps=args.rms_norm_eps)
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self.is_sliding = (layer_idx + 1) % args.sliding_window_pattern != 0
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self.rope = nn.RoPE(
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head_dim,
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traditional=args.rope_traditional,
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base=(
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args.rope_local_base_freq
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if self.is_sliding
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else args.rope_global_base_freq
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),
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)
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def __call__(
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self,
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x: mx.array,
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mask: Optional[mx.array] = None,
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cache: Optional[Any] = None,
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) -> mx.array:
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B, L, _ = x.shape
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queries, keys, values = self.q_proj(x), self.k_proj(x), self.v_proj(x)
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queries = queries.reshape(B, L, self.n_heads, -1).transpose(0, 2, 1, 3)
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keys = keys.reshape(B, L, self.n_kv_heads, -1).transpose(0, 2, 1, 3)
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values = values.reshape(B, L, self.n_kv_heads, -1).transpose(0, 2, 1, 3)
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queries = self.q_norm(queries)
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keys = self.k_norm(keys)
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if cache is not None:
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queries = self.rope(queries, offset=cache.offset)
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keys = self.rope(keys, offset=cache.offset)
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keys, values = cache.update_and_fetch(keys, values)
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else:
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queries = self.rope(queries)
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keys = self.rope(keys)
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# Sliding window
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if self.is_sliding and mask is not None:
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key_len = keys.shape[-2]
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if mask.shape[-1] != key_len:
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mask = mask[..., :key_len]
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output = mx.fast.scaled_dot_product_attention(
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queries, keys, values, scale=self.scale, mask=mask
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)
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output = output.transpose(0, 2, 1, 3).reshape(B, L, -1)
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return self.o_proj(output)
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class MLP(nn.Module):
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def __init__(self, dim, hidden_dim):
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super().__init__()
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self.gate_proj = nn.Linear(dim, hidden_dim, bias=False)
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self.down_proj = nn.Linear(hidden_dim, dim, bias=False)
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self.up_proj = nn.Linear(dim, hidden_dim, bias=False)
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def __call__(self, x) -> mx.array:
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# This should not be GELU approx, jax.nn.gelu
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return self.down_proj(nn.gelu_approx(self.gate_proj(x)) * self.up_proj(x))
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class TransformerBlock(nn.Module):
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def __init__(self, args: ModelArgs, layer_idx: int):
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super().__init__()
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self.num_attention_heads = args.num_attention_heads
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self.hidden_size = args.hidden_size
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self.self_attn = Attention(args, layer_idx)
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self.mlp = MLP(args.hidden_size, args.intermediate_size)
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self.input_layernorm = nn.RMSNorm(args.hidden_size, eps=args.rms_norm_eps)
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self.post_attention_layernorm = nn.RMSNorm(
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args.hidden_size, eps=args.rms_norm_eps
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)
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self.pre_feedforward_layernorm = nn.RMSNorm(
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args.hidden_size, eps=args.rms_norm_eps
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)
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self.post_feedforward_layernorm = nn.RMSNorm(
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args.hidden_size, eps=args.rms_norm_eps
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)
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def __call__(
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self,
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x: mx.array,
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mask: Optional[mx.array] = None,
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cache: Optional[Any] = None,
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) -> mx.array:
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r = self.self_attn(self.input_layernorm(x), mask, cache)
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h = x + self.post_attention_layernorm(r)
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r = self.mlp(self.pre_feedforward_layernorm(h))
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out = h + self.post_feedforward_layernorm(r)
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return out
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class Gemma3Model(nn.Module):
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def __init__(self, args: ModelArgs):
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super().__init__()
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self.args = args
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self.vocab_size = args.vocab_size
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self.num_hidden_layers = args.num_hidden_layers
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assert self.vocab_size > 0
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self.embed_tokens = nn.Embedding(args.vocab_size, args.hidden_size)
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self.layers = [
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TransformerBlock(args=args, layer_idx=layer_idx)
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for layer_idx in range(args.num_hidden_layers)
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]
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self.norm = nn.RMSNorm(args.hidden_size, eps=args.rms_norm_eps)
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def __call__(
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self,
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inputs: mx.array,
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inputs_embeds: mx.array = None,
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mask: mx.array = None,
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cache=None,
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):
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if inputs_embeds is None:
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h = self.embed_tokens(inputs)
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else:
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h = inputs_embeds
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h *= self.args.hidden_size**0.5 # persistent precision issue in scaling
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if cache is None:
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cache = [None] * len(self.layers)
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# Sliding window
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j = self.args.sliding_window_pattern
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mask = create_attention_mask(h, cache[j - 1 : j])
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for layer, c in zip(self.layers, cache):
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h = layer(h, mask, c)
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return self.norm(h)
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class Model(nn.Module):
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def __init__(self, config: ModelArgs):
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super().__init__()
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self.config = config
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self.model_type = config.model_type
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self.model = Gemma3Model(config)
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self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
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def __call__(
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self,
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inputs: mx.array,
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cache=None,
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inputs_embeds=None,
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mask: Optional[mx.array] = None,
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):
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out = self.model(inputs, inputs_embeds, mask, cache)
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out = self.lm_head(out)
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return out
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def sanitize(self, weights):
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if "lm_head.weight" not in weights:
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weights["language_model.lm_head.weight"] = weights[
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"language_model.model.embed_tokens.weight"
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]
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return {
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k: v for k, v in weights.items() if "self_attn.rotary_emb.inv_freq" not in k
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}
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@property
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def layers(self):
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return self.model.layers
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@property
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def head_dim(self):
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return self.args.head_dim
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@property
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def n_kv_heads(self):
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return self.args.num_key_value_heads
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def make_cache(self):
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caches = []
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for i in range(self.args.num_hidden_layers):
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if (
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i % self.args.sliding_window_pattern
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== self.args.sliding_window_pattern - 1
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):
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caches.append(
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KVCache()
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)
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else:
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caches.append(
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RotatingKVCache()
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)
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return caches |