264 lines
8.2 KiB
Python
264 lines
8.2 KiB
Python
# Copyright © 2025 Apple Inc.
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from dataclasses import dataclass
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from typing import Any, List, Optional
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import mlx.core as mx
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import mlx.nn as nn
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from .activations import swiglu
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from .base import BaseModelArgs, create_attention_mask, scaled_dot_product_attention
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from .switch_layers import SwitchGLU
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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
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attention_bias: bool
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mlp_only_layers: List[int]
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num_experts: int
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num_experts_per_tok: int
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decoder_sparse_step: int
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n_shared_experts: int
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moe_intermediate_size: int
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rms_norm_eps: float
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vocab_size: int
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num_key_value_heads: int
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rope_theta: float
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max_position_embeddings: int
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norm_topk_prob: bool
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class KlearAttention(nn.Module):
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def __init__(self, args: ModelArgs):
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super().__init__()
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self.num_attention_heads = args.num_attention_heads
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self.num_key_value_heads = args.num_key_value_heads
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self.head_dim = args.hidden_size // args.num_attention_heads
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self.scale = self.head_dim**-0.5
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self.q_proj = nn.Linear(
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args.hidden_size,
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self.num_attention_heads * self.head_dim,
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bias=args.attention_bias,
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)
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self.k_proj = nn.Linear(
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args.hidden_size,
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self.num_key_value_heads * self.head_dim,
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bias=args.attention_bias,
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)
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self.v_proj = nn.Linear(
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args.hidden_size,
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self.num_key_value_heads * self.head_dim,
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bias=args.attention_bias,
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)
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self.o_proj = nn.Linear(
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self.num_attention_heads * self.head_dim,
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args.hidden_size,
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bias=args.attention_bias,
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)
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self.q_norm = nn.RMSNorm(self.head_dim, eps=args.rms_norm_eps)
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self.k_norm = nn.RMSNorm(self.head_dim, eps=args.rms_norm_eps)
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self.rope = nn.RoPE(
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self.head_dim,
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traditional=False,
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base=args.rope_theta,
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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, D = 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 = self.q_norm(
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queries.reshape(B, L, self.num_attention_heads, -1)
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).transpose(0, 2, 1, 3)
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keys = self.k_norm(keys.reshape(B, L, self.num_key_value_heads, -1)).transpose(
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0, 2, 1, 3
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)
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values = values.reshape(B, L, self.num_key_value_heads, -1).transpose(
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0, 2, 1, 3
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)
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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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output = scaled_dot_product_attention(
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queries, keys, values, cache=cache, 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 KlearMLP(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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return self.down_proj(swiglu(self.gate_proj(x), self.up_proj(x)))
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class KlearSparseMoeBlock(nn.Module):
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def __init__(self, args: ModelArgs):
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super().__init__()
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self.norm_topk_prob = args.norm_topk_prob
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self.num_experts = args.num_experts
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self.top_k = args.num_experts_per_tok
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self.gate = nn.Linear(args.hidden_size, args.num_experts, bias=False)
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self.experts = SwitchGLU(
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args.hidden_size, args.moe_intermediate_size, args.num_experts
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)
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self.shared_experts = KlearMLP(
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args.hidden_size,
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hidden_dim=args.moe_intermediate_size * args.n_shared_experts,
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)
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self.coefficient = nn.Linear(args.hidden_size, 2)
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self.expert_bias = mx.zeros((self.num_experts,), dtype=mx.float32)
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def __call__(self, x: mx.array) -> mx.array:
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routing_weights = mx.sigmoid(self.gate(x).astype(mx.float32))
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biased_weights = routing_weights + self.expert_bias.reshape((1, 1, -1))
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k = self.top_k
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inds = mx.argpartition(-biased_weights, kth=k - 1, axis=-1)[..., :k]
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scores = mx.take_along_axis(routing_weights, inds, axis=-1)
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if self.norm_topk_prob:
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scores = scores / mx.sum(scores, axis=-1, keepdims=True)
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scores = scores.astype(x.dtype)
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expert_out = self.experts(x, inds)
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y_experts = (expert_out * scores[..., None]).sum(axis=-2)
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coef = mx.softmax(self.coefficient(x), axis=-1, precise=True)
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shared = self.shared_experts(x)
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y = y_experts * coef[..., :1] + shared * coef[..., 1:]
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return y
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class KlearDecoderLayer(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.self_attn = KlearAttention(args)
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if (layer_idx not in args.mlp_only_layers) and (
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args.num_experts > 0 and (layer_idx + 1) % args.decoder_sparse_step == 0
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):
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self.mlp = KlearSparseMoeBlock(args)
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else:
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self.mlp = KlearMLP(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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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 + r
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r = self.mlp(self.post_attention_layernorm(h))
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out = h + r
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return out
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class KlearModel(nn.Module):
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def __init__(self, args: ModelArgs):
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super().__init__()
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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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KlearDecoderLayer(args=args, layer_idx=i)
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for i 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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cache: Optional[Any] = None,
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) -> mx.array:
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h = self.embed_tokens(inputs)
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if cache is None:
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cache = [None] * len(self.layers)
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mask = create_attention_mask(h, cache[0])
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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, args: ModelArgs):
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super().__init__()
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self.args = args
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self.model_type = args.model_type
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self.model = KlearModel(args)
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self.lm_head = nn.Linear(args.hidden_size, args.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: Optional[Any] = None,
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):
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out = self.model(inputs, cache)
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return self.lm_head(out)
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def sanitize(self, weights):
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if "model.layers.0.mlp.experts.0.gate_proj.weight" not in weights:
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return weights
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for l in range(self.args.num_hidden_layers):
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prefix = f"model.layers.{l}.mlp.experts"
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for name in ["gate_proj", "up_proj", "down_proj"]:
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stacked = [
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weights.pop(f"{prefix}.{e}.{name}.weight")
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for e in range(self.args.num_experts)
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]
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weights[f"{prefix}.{name}.weight"] = mx.stack(stacked)
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return weights
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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 quant_predicate(self):
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def predicate(path, _):
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if path.endswith("mlp.gate"):
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return {"group_size": 64, "bits": 8}
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return True
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return predicate
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@property
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def cast_predicate(self):
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def predicate(k):
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return "expert_bias" not in k
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return predicate
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