mirror of
https://github.com/ml-explore/mlx-examples.git
synced 2025-08-29 18:26:37 +08:00
258 lines
9.1 KiB
Python
258 lines
9.1 KiB
Python
import math
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from dataclasses import dataclass
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from typing import List, Optional, Tuple, Union
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import mlx.core as mx
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import mlx.nn as nn
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from .base import BaseModelArgs, KVCache, create_attention_mask
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from .su_rope import SuScaledRotaryEmbedding
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@dataclass
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class ModelArgs(BaseModelArgs):
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model_type: str = "phimoe"
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vocab_size: int = 30000
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hidden_size: int = 1024
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intermediate_size: int = 4096
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num_hidden_layers: int = 12
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num_attention_heads: int = 16
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num_key_value_heads: int = 16
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max_position_embeddings: int = 2048
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initializer_range: float = 0.02
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rms_norm_eps: float = 1e-6
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pad_token_id: Optional[int] = None
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rope_traditional: bool = False
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num_local_experts: int = 8
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num_experts_per_tok: int = 2
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attention_bias: bool = False
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rope_theta: float = 10000.0
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def __post_init__(self):
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if self.num_key_value_heads is None:
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self.num_key_value_heads = self.num_attention_heads
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if self.rope_scaling:
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required_keys = {"long_factor", "type"}
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if not all(key in self.rope_scaling for key in required_keys):
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raise ValueError(f"rope_scaling must contain keys {required_keys}")
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if self.rope_scaling["type"] not in ["longrope", "su", "linear"]:
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print(
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"[WARNING] rope_scaling 'type' currently only supports 'linear', 'su', and 'longrope'; setting rope scaling to false."
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)
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self.rope_scaling = None
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class Attention(nn.Module):
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def __init__(self, args: ModelArgs):
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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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assert args.num_key_value_heads is not None
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self.n_kv_heads = n_kv_heads = args.num_key_value_heads
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head_dim = args.hidden_size // n_heads
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self.scale = head_dim**-0.5
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self.q_proj = nn.Linear(dim, n_heads * head_dim, bias=True)
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self.k_proj = nn.Linear(dim, n_kv_heads * head_dim, bias=True)
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self.v_proj = nn.Linear(dim, n_kv_heads * head_dim, bias=True)
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self.o_proj = nn.Linear(n_heads * head_dim, dim, bias=False)
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rope_scale = 1.0
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if args.rope_scaling and args.rope_scaling["type"] in ["longrope", "su"]:
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self.rope = SuScaledRotaryEmbedding(
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head_dim,
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traditional=False,
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base=args.rope_theta,
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scale=rope_scale,
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max_position_embeddings=args.max_position_embeddings,
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original_max_position_embeddings=args.original_max_position_embeddings,
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short_factor=args.rope_scaling["short_factor"],
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long_factor=args.rope_scaling["long_factor"],
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)
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else:
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if args.rope_scaling and args.rope_scaling["type"] == "linear":
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assert isinstance(args.rope_scaling["factor"], float)
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rope_scale = 1 / args.rope_scaling["factor"]
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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=args.rope_theta,
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scale=rope_scale,
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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[KVCache] = 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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# Prepare the queries, keys and values for the attention computation
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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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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 = 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 PhiMoEBlockSparseTop2MLP(nn.Module):
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def __init__(self, args: ModelArgs):
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super().__init__()
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self.ffn_dim = args.intermediate_size
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self.hidden_dim = args.hidden_size
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self.w1 = nn.Linear(self.hidden_dim, self.ffn_dim, bias=False)
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self.w2 = nn.Linear(self.ffn_dim, self.hidden_dim, bias=False)
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self.w3 = nn.Linear(self.hidden_dim, self.ffn_dim, bias=False)
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self.act_fn = nn.GELU()
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def __call__(self, hidden_states):
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current_hidden_states = self.act_fn(self.w1(hidden_states)) * self.w3(
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hidden_states
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)
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current_hidden_states = self.w2(current_hidden_states)
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return current_hidden_states
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class PhiMoESparseMoeBlock(nn.Module):
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def __init__(self, args: ModelArgs):
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super().__init__()
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self.hidden_dim = args.hidden_size
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self.ffn_dim = args.intermediate_size
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self.num_experts = args.num_local_experts
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self.top_k = args.num_experts_per_tok
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self.gate = nn.Linear(self.hidden_dim, self.num_experts, bias=False)
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self.experts = [PhiMoEBlockSparseTop2MLP(args) for _ in range(self.num_experts)]
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def __call__(self, hidden_states):
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batch_size, sequence_length, hidden_dim = hidden_states.shape
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hidden_states = hidden_states.reshape(-1, hidden_dim)
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router_logits = self.gate(hidden_states)
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routing_weights = mx.softmax(router_logits, axis=-1)
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expert_indices = mx.argmax(routing_weights, axis=-1)
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final_hidden_states = mx.zeros((batch_size * sequence_length, hidden_dim))
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for expert_idx in range(self.num_experts):
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expert_layer = self.experts[expert_idx]
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expert_mask = expert_indices == expert_idx
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if mx.sum(expert_mask) > 0:
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expert_input = hidden_states[expert_mask]
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expert_output = expert_layer(expert_input)
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final_hidden_states = mx.where(
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expert_mask[:, None], expert_output, final_hidden_states
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)
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final_hidden_states = final_hidden_states.reshape(
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batch_size, sequence_length, hidden_dim
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)
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return final_hidden_states, router_logits
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class PhiMoEDecoderLayer(nn.Module):
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def __init__(self, args: ModelArgs):
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super().__init__()
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self.hidden_size = args.hidden_size
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self.self_attn = Attention(args)
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self.block_sparse_moe = PhiMoESparseMoeBlock(args)
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self.input_layernorm = nn.LayerNorm(args.hidden_size, eps=args.rms_norm_eps)
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self.post_attention_layernorm = nn.LayerNorm(
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args.hidden_size, eps=args.rms_norm_eps
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)
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def __call__(self, hidden_states, attention_mask=None, position_ids=None):
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residual = hidden_states
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hidden_states = self.input_layernorm(hidden_states)
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hidden_states = self.self_attn(
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hidden_states=hidden_states,
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attention_mask=attention_mask,
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position_ids=position_ids,
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)
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hidden_states = residual + hidden_states
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residual = hidden_states
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hidden_states = self.post_attention_layernorm(hidden_states)
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hidden_states, router_logits = self.block_sparse_moe(hidden_states)
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hidden_states = residual + hidden_states
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return hidden_states, router_logits
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class PhiMoEModel(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.padding_idx = args.pad_token_id
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self.vocab_size = args.vocab_size
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self.embed_tokens = nn.Embedding(
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args.vocab_size, args.hidden_size, self.padding_idx
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)
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self.layers = [PhiMoEDecoderLayer(args) for _ in range(args.num_hidden_layers)]
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self.norm = nn.LayerNorm(args.hidden_size, eps=args.rms_norm_eps)
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def __call__(self, input_ids, attention_mask=None, position_ids=None):
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hidden_states = self.embed_tokens(input_ids)
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for layer in self.layers:
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hidden_states, _ = layer(hidden_states, attention_mask, position_ids)
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hidden_states = self.norm(hidden_states)
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return hidden_states
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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 = PhiMoEModel(args)
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self.lm_head = nn.Linear(args.hidden_size, args.vocab_size, bias=False)
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def __call__(self, input_ids, attention_mask=None, position_ids=None):
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hidden_states = self.model(input_ids, attention_mask, position_ids)
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logits = self.lm_head(hidden_states)
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return logits
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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.hidden_size // self.args.num_attention_heads
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def sanitize(self, weights):
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# Remove unused precomputed rotary freqs
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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 n_kv_heads(self):
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return self.args.num_key_value_heads
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