mirror of
https://github.com/ml-explore/mlx-examples.git
synced 2025-06-24 09:21:18 +08:00
248 lines
7.9 KiB
Python
248 lines
7.9 KiB
Python
from dataclasses import dataclass
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from typing import Dict, 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
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@dataclass
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class ModelArgs(BaseModelArgs):
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vocab_size: int = 32000
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max_position_embeddings: int = 4096 * 32
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hidden_size: int = 4096
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intermediate_size: int = 14336
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num_hidden_layers: int = 32
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num_attention_heads: int = 32
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num_experts_per_tok: int = 2
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num_key_value_heads: int = 8
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num_local_experts: int = 8
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rms_norm_eps: float = 1e-5
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vocab_size: int
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rope_theta: float = 1e6
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rope_traditional: bool = False
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model_type: str = None
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rope_scaling: Optional[Dict[str, Union[float, str]]] = None
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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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class RMSNorm(nn.Module):
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def __init__(self, dims: int, eps: float = 1e-5):
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super().__init__()
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self.weight = mx.ones((dims,))
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self.eps = eps
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def _norm(self, x):
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return x * mx.rsqrt(x.square().mean(-1, keepdims=True) + self.eps)
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def __call__(self, x):
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output = self._norm(x.astype(mx.float32)).astype(x.dtype)
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return self.weight * output
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class MixtralAttention(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.num_heads = args.num_attention_heads
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self.head_dim = self.hidden_size // self.num_heads
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self.num_key_value_heads = args.num_key_value_heads
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self.max_position_embeddings = args.max_position_embeddings
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self.rope_theta = args.rope_theta
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self.repeats = self.num_heads // self.num_key_value_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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self.hidden_size, self.num_heads * self.head_dim, bias=False
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)
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self.k_proj = nn.Linear(
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self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False
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)
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self.v_proj = nn.Linear(
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self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False
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)
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self.o_proj = nn.Linear(
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self.num_heads * self.head_dim, self.hidden_size, bias=False
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)
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self.rope = nn.RoPE(
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self.head_dim,
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traditional=args.rope_traditional,
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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[Tuple[mx.array, mx.array]] = 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.num_heads, -1).transpose(0, 2, 1, 3)
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keys = keys.reshape(B, L, self.num_key_value_heads, -1).transpose(0, 2, 1, 3)
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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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def repeat(a):
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a = mx.concatenate([mx.expand_dims(a, 2)] * self.repeats, axis=2)
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return a.reshape([B, self.num_heads, L, -1])
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if self.repeats > 1:
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keys, values = map(repeat, (keys, values))
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if cache is not None:
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key_cache, value_cache = cache
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queries = self.rope(queries, offset=key_cache.shape[2])
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keys = self.rope(keys, offset=key_cache.shape[2])
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keys = mx.concatenate([key_cache, keys], axis=2)
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values = mx.concatenate([value_cache, values], axis=2)
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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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scores = (queries * self.scale) @ keys.transpose(0, 1, 3, 2)
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if mask is not None:
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scores += mask
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scores = mx.softmax(scores.astype(mx.float32), axis=-1).astype(scores.dtype)
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output = (scores @ values).transpose(0, 2, 1, 3).reshape(B, L, -1)
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return self.o_proj(output), (keys, values)
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class MixtralBLockSparseTop2MLP(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.silu
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def __call__(self, x: mx.array) -> mx.array:
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current_hidden_states = self.act_fn(self.w1(x)) * self.w3(x)
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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 MixtralSparseMoeBlock(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.num_experts_per_tok = args.num_experts_per_tok
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# gating
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self.gate = nn.Linear(self.hidden_dim, self.num_experts, bias=False)
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self.experts = [
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MixtralBLockSparseTop2MLP(args=args) for _ in range(self.num_experts)
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]
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def __call__(self, x: mx.array) -> mx.array:
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ne = self.num_experts_per_tok
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orig_shape = x.shape
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x = x.reshape(-1, x.shape[-1])
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gates = self.gate(x)
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inds = mx.argpartition(-gates, kth=ne, axis=-1)[:, :ne]
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scores = mx.softmax(
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mx.take_along_axis(gates, inds, axis=-1).astype(mx.float32),
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axis=-1,
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).astype(gates.dtype)
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y = []
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for xt, st, it in zip(x, scores, inds.tolist()):
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yt = mx.concatenate([self.experts[e](xt)[:, None] for e in it], axis=-1)
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yt = (yt * st).sum(axis=-1)
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y.append(yt[None, :])
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y = mx.concatenate(y)
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return y.reshape(orig_shape)
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class MixtralDecoderLayer(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 = MixtralAttention(args)
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self.block_sparse_moe = MixtralSparseMoeBlock(args)
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self.input_layernorm = RMSNorm(args.hidden_size, eps=args.rms_norm_eps)
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self.post_attention_layernorm = RMSNorm(args.hidden_size, eps=args.rms_norm_eps)
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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[Tuple[mx.array, mx.array]] = None,
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) -> mx.array:
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r, cache = self.self_attn(self.input_layernorm(x), mask, cache)
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h = x + r
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r = self.block_sparse_moe(self.post_attention_layernorm(h))
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out = h + r
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return out, cache
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class MixtralModel(nn.Module):
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def __init__(self, args: ModelArgs):
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super().__init__()
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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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self.embed_tokens = nn.Embedding(args.vocab_size, args.hidden_size)
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self.layers = [
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MixtralDecoderLayer(args=args) for _ in range(args.num_hidden_layers)
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]
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self.norm = 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=None,
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):
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h = self.embed_tokens(inputs)
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mask = None
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T = h.shape[1]
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if T > 1:
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mask = nn.MultiHeadAttention.create_additive_causal_mask(T)
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mask = mask.astype(h.dtype)
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if cache is None:
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cache = [None] * len(self.layers)
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for e, layer in enumerate(self.layers):
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h, cache[e] = layer(h, mask, cache[e])
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return self.norm(h[:, T - 1 : T, :]), cache
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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.model = MixtralModel(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=None,
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):
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out, cache = self.model(inputs, cache)
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return self.lm_head(out), cache
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