mirror of
https://github.com/ml-explore/mlx-examples.git
synced 2025-06-24 09:21:18 +08:00
d661440dbb
* add sparsemoe block and update decoder logic * update file name to match HF * update name * Code formatting * update gates calculation * add support for Qwen2MoE. * fix pytest * code formatting and fix missing comma in utils * Remove decoder sparse step. Co-authored-by: bozheng-hit <dsoul0621@gmail.com> * remove gate layer anti-quantisation * remove unused argument --------- Co-authored-by: bozheng-hit <dsoul0621@gmail.com>
258 lines
8.3 KiB
Python
258 lines
8.3 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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import numpy as np
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from .base import BaseModelArgs
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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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num_experts_per_tok: int
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num_experts: int
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moe_intermediate_size: int
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shared_expert_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 = None
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rope_theta: float = 1000000
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rope_traditional: bool = False
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rope_scaling: Optional[Dict[str, Union[float, str]]] = None
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tie_word_embeddings: bool = False
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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 = {"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"] != "linear":
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raise ValueError("rope_scaling 'type' currently only supports 'linear'")
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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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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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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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)
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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.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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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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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), (keys, values)
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class Qwen2MoeMLP(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(nn.silu(self.gate_proj(x)) * self.up_proj(x))
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class Qwen2MoeSparseMoeBlock(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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intermediate_size = args.moe_intermediate_size
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shared_expert_intermediate_size = args.shared_expert_intermediate_size
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self.num_experts = num_experts = args.num_experts
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self.top_k = args.num_experts_per_tok
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# gating
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self.gate = nn.Linear(dim, num_experts, bias=False)
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self.experts = [
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Qwen2MoeMLP(dim, intermediate_size) for _ in range(self.num_experts)
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]
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self.shared_expert = Qwen2MoeMLP(dim, shared_expert_intermediate_size)
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self.shared_expert_gate = nn.Linear(dim, 1, bias=False)
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def __call__(
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self,
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x: mx.array,
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):
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ne = self.top_k
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B, L, D = x.shape
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x = x.reshape(-1, D)
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# router_logits: (batch * sequence_length, n_experts)
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gates = self.gate(x)
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gates = mx.softmax(gates.astype(mx.float32), axis=-1)
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inds = mx.stop_gradient(mx.argpartition(-gates, kth=ne, axis=-1)[:, :ne])
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scores = mx.take_along_axis(gates, inds, axis=-1).astype(x.dtype)
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if self.training:
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inds = np.array(inds)
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y = mx.zeros((B, ne, D), x.dtype)
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for e, expert in enumerate(self.experts):
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idx1, idx2 = map(mx.array, np.where(inds == e))
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if idx1.size == 0:
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continue
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y[idx1, idx2] = expert(x[idx1])
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y = (y * scores[:, :, None]).sum(axis=1)
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else:
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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.stack([self.experts[e](xt) for e in it], axis=-1)
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yt = (yt * st).sum(axis=-1)
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y.append(yt)
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y = mx.stack(y, axis=0)
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shared_expert_output = self.shared_expert(x)
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shared_expert_output = (
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mx.sigmoid(self.shared_expert_gate(x)) * shared_expert_output
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)
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y += shared_expert_output
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return y.reshape(B, L, -1)
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class Qwen2MoeDecoderLayer(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.mlp = Qwen2MoeSparseMoeBlock(args)
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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.args = args
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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.mlp(self.post_attention_layernorm(h))
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out = h + r
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return out, cache
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class Qwen2MoeModel(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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Qwen2MoeDecoderLayer(args=args) for _ 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=None,
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):
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h = self.embed_tokens(inputs)
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mask = None
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if h.shape[1] > 1:
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mask = nn.MultiHeadAttention.create_additive_causal_mask(h.shape[1])
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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), 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.args = args
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self.model_type = args.model_type
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self.model = Qwen2MoeModel(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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def sanitize(self, weights):
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if self.args.tie_word_embeddings and "lm_head.weight" not in weights:
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weights["lm_head.weight"] = weights["model.embed_tokens.weight"]
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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 layers(self):
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return self.model.layers
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