From d25eb2b3e52e399377aae746dbd49bae1d6cbafd Mon Sep 17 00:00:00 2001 From: John Mai Date: Fri, 2 May 2025 12:21:11 +0800 Subject: [PATCH] :sparkles: feat: Add support for Xiaomi MiMo model (#142) * :sparkles: feat: Add support for XiaomiMiMo/MiMo-7B-SFT model * :art: style: Format code * refactor: Remove MTP layer --- mlx_lm/models/mimo.py | 194 ++++++++++++++++++++++++++++++++++++++++++ mlx_lm/tuner/utils.py | 1 + 2 files changed, 195 insertions(+) create mode 100644 mlx_lm/models/mimo.py diff --git a/mlx_lm/models/mimo.py b/mlx_lm/models/mimo.py new file mode 100644 index 0000000..77da96e --- /dev/null +++ b/mlx_lm/models/mimo.py @@ -0,0 +1,194 @@ +from dataclasses import dataclass +from typing import Any, Dict, Optional, Union + +import mlx.core as mx +import mlx.nn as nn + +from .base import BaseModelArgs, create_attention_mask, scaled_dot_product_attention +from .rope_utils import initialize_rope + + +@dataclass +class ModelArgs(BaseModelArgs): + model_type: str + hidden_size: int + num_hidden_layers: int + intermediate_size: int + num_attention_heads: int + rms_norm_eps: float + vocab_size: int + num_key_value_heads: int + max_position_embeddings: int = 32768 + rope_theta: float = 10000.0 + rope_traditional: bool = False + rope_scaling: Optional[Dict[str, Union[float, str]]] = None + tie_word_embeddings: bool = False + num_nextn_predict_layers: int = 2 + + +class Attention(nn.Module): + def __init__(self, args: ModelArgs): + super().__init__() + + dim = args.hidden_size + self.n_heads = n_heads = args.num_attention_heads + assert args.num_key_value_heads is not None + self.n_kv_heads = n_kv_heads = args.num_key_value_heads + + head_dim = args.hidden_size // n_heads + self.scale = head_dim**-0.5 + + self.q_proj = nn.Linear(dim, n_heads * head_dim, bias=True) + self.k_proj = nn.Linear(dim, n_kv_heads * head_dim, bias=True) + self.v_proj = nn.Linear(dim, n_kv_heads * head_dim, bias=True) + self.o_proj = nn.Linear(n_heads * head_dim, dim, bias=False) + + self.rope = initialize_rope( + head_dim, + base=args.rope_theta, + traditional=args.rope_traditional, + scaling_config=args.rope_scaling, + max_position_embeddings=args.max_position_embeddings, + ) + + def __call__( + self, + x: mx.array, + mask: Optional[mx.array] = None, + cache: Optional[Any] = None, + ) -> mx.array: + B, L, D = x.shape + + queries, keys, values = self.q_proj(x), self.k_proj(x), self.v_proj(x) + + queries = queries.reshape(B, L, self.n_heads, -1).transpose(0, 2, 1, 3) + keys = keys.reshape(B, L, self.n_kv_heads, -1).transpose(0, 2, 1, 3) + values = values.reshape(B, L, self.n_kv_heads, -1).transpose(0, 2, 1, 3) + + if cache is not None: + queries = self.rope(queries, offset=cache.offset) + keys = self.rope(keys, offset=cache.offset) + keys, values = cache.update_and_fetch(keys, values) + else: + queries = self.rope(queries) + keys = self.rope(keys) + + output = scaled_dot_product_attention( + queries, keys, values, cache=cache, scale=self.scale, mask=mask + ) + output = output.transpose(0, 2, 1, 3).reshape(B, L, -1) + return self.o_proj(output) + + +class MLP(nn.Module): + def __init__(self, dim, hidden_dim): + super().__init__() + self.gate_proj = nn.Linear(dim, hidden_dim, bias=False) + self.down_proj = nn.Linear(hidden_dim, dim, bias=False) + self.up_proj = nn.Linear(dim, hidden_dim, bias=False) + + def __call__(self, x) -> mx.array: + return self.down_proj(nn.silu(self.gate_proj(x)) * self.up_proj(x)) + + +class TransformerBlock(nn.Module): + def __init__(self, args: ModelArgs): + super().__init__() + self.num_attention_heads = args.num_attention_heads + self.hidden_size = args.hidden_size + self.self_attn = Attention(args) + self.mlp = MLP(args.hidden_size, args.intermediate_size) + self.input_layernorm = nn.RMSNorm(args.hidden_size, eps=args.rms_norm_eps) + self.post_attention_layernorm = nn.RMSNorm( + args.hidden_size, eps=args.rms_norm_eps + ) + self.args = args + + def __call__( + self, + x: mx.array, + mask: Optional[mx.array] = None, + cache: Optional[Any] = None, + ) -> mx.array: + r = self.self_attn(self.input_layernorm(x), mask, cache) + h = x + r + r = self.mlp(self.post_attention_layernorm(h)) + out = h + r + return out + + +class MiMoModel(nn.Module): + def __init__(self, args: ModelArgs): + super().__init__() + self.args = args + self.vocab_size = args.vocab_size + self.num_hidden_layers = args.num_hidden_layers + self.num_nextn_predict_layers = args.num_nextn_predict_layers + + assert self.vocab_size > 0 + self.embed_tokens = nn.Embedding(args.vocab_size, args.hidden_size) + self.layers = [ + TransformerBlock(args=args) for _ in range(args.num_hidden_layers) + ] + self.norm = nn.RMSNorm(args.hidden_size, eps=args.rms_norm_eps) + + def __call__( + self, + inputs: mx.array, + mask: mx.array = None, + cache=None, + ): + h = self.embed_tokens(inputs) + + if mask is None: + mask = create_attention_mask(h, cache) + + if cache is None: + cache = [None] * len(self.layers) + + for layer, c in zip(self.layers, cache): + h = layer(h, mask, c) + + h = self.norm(h) + + return h + + +class Model(nn.Module): + def __init__(self, args: ModelArgs): + super().__init__() + self.args = args + self.model_type = args.model_type + self.model = MiMoModel(args) + if not args.tie_word_embeddings: + self.lm_head = nn.Linear(args.hidden_size, args.vocab_size, bias=False) + + def __call__( + self, + inputs: mx.array, + mask: mx.array = None, + cache=None, + ): + out = self.model(inputs, mask, cache) + + if self.args.tie_word_embeddings: + out = self.model.embed_tokens.as_linear(out) + else: + out = self.lm_head(out) + + return out + + def sanitize(self, weights): + if self.args.tie_word_embeddings: + weights.pop("lm_head.weight", None) + + return { + k: v + for k, v in weights.items() + if "self_attn.rotary_emb.inv_freq" not in k + and not k.startswith("model.mtp_layers.") + } + + @property + def layers(self): + return self.model.layers diff --git a/mlx_lm/tuner/utils.py b/mlx_lm/tuner/utils.py index 17f1a01..7f1525f 100644 --- a/mlx_lm/tuner/utils.py +++ b/mlx_lm/tuner/utils.py @@ -106,6 +106,7 @@ def linear_to_lora_layers( "olmoe", "internlm3", "glm4", + "mimo", ]: keys = set(["self_attn.q_proj", "self_attn.v_proj"]) if model.model_type in ["mixtral", "phimoe"]: