mirror of
https://github.com/ml-explore/mlx-examples.git
synced 2025-09-02 13:40:48 +08:00
249 lines
8.5 KiB
Python
249 lines
8.5 KiB
Python
import math
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from dataclasses import dataclass
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from typing import Dict, 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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from .switch_layers import SwitchGLU
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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 = 32064
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hidden_size: int = 4096
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intermediate_size: int = 6400
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num_hidden_layers: int = 32
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num_attention_heads: int = 32
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num_key_value_heads: int = 8
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max_position_embeddings: int = 131072
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original_max_position_embeddings: int = 4096
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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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rope_scaling: Optional[Dict[str, Union[float, List[float]]]] = None
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num_local_experts: int = 16
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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=True)
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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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base=args.rope_theta,
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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 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.switch_mlp = SwitchGLU(self.hidden_dim, self.ffn_dim, self.num_experts)
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def __call__(self, x: mx.array) -> mx.array:
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gates = self.gate(x)
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k = self.top_k
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inds = mx.stop_gradient(mx.argpartition(-gates, kth=k - 1, axis=-1)[..., :k])
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scores = mx.take_along_axis(gates, inds, axis=-1)
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scores = mx.softmax(scores, axis=-1, precise=True)
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y = self.switch_mlp(x, inds)
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y = (y * scores[..., None]).sum(axis=-2)
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return y
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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__(
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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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residual = x
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hidden_states = self.input_layernorm(x)
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hidden_states = self.self_attn(x=hidden_states, mask=mask, cache=cache)
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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 = self.block_sparse_moe(hidden_states)
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hidden_states = residual + hidden_states
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return hidden_states
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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.vocab_size = args.vocab_size
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self.embed_tokens = nn.Embedding(args.vocab_size, args.hidden_size)
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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__(
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self,
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inputs: mx.array,
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cache=None,
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) -> mx.array:
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h = self.embed_tokens(inputs)
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mask = create_attention_mask(h, cache)
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if cache is None:
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cache = [None] * len(self.layers)
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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 = PhiMoEModel(args)
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self.lm_head = nn.Linear(args.hidden_size, args.vocab_size, bias=True)
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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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):
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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.block_sparse_moe.experts.0.w1.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}"
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for n, m in [("w1", "gate_proj"), ("w2", "down_proj"), ("w3", "up_proj")]:
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for k in ["weight", "scales", "biases"]:
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if f"{prefix}.block_sparse_moe.experts.0.{n}.{k}" in weights:
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to_join = [
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weights.pop(
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f"{prefix}.block_sparse_moe.experts.{e}.{n}.{k}"
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)
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for e in range(self.args.num_local_experts)
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]
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weights[f"{prefix}.block_sparse_moe.switch_mlp.{m}.{k}"] = (
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mx.stack(to_join)
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)
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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 head_dim(self):
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return self.args.hidden_size // self.args.num_attention_heads
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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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