mirror of
https://github.com/ml-explore/mlx-examples.git
synced 2025-06-24 01:17:28 +08:00
fca087be49
* fix rotating kv cache for chat use case * reorg + fixes to caching, unify prompt caching across types and use cases for e.g. caching during a chat * nit in chat * fix tests * fix tests * fix tests * docs * chat command * comments + docs * Define meta_state on all Cache implementations * fixes + trim_prompt_cache api * fix default model --------- Co-authored-by: Angelos Katharopoulos <a_katharopoulos@apple.com>
252 lines
7.8 KiB
Python
252 lines
7.8 KiB
Python
# Copyright © 2023-2024 Apple Inc.
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from dataclasses import dataclass
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from typing import Any, Optional, Tuple
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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, create_attention_mask
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@dataclass
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class ModelArgs(BaseModelArgs):
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model_type: str
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vocab_size: int
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d_model: int
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ffn_config: dict
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attn_config: dict
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n_layers: int
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n_heads: int
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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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self.num_heads = args.n_heads
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self.d_model = args.d_model
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self.head_dim = args.d_model // args.n_heads
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self.num_key_value_heads = args.attn_config["kv_n_heads"]
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self.clip_qkv = args.attn_config["clip_qkv"]
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self.rope_theta = args.attn_config["rope_theta"]
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self.scale = self.head_dim**-0.5
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self.Wqkv = nn.Linear(
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args.d_model,
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(self.num_key_value_heads * 2 + self.num_heads) * self.head_dim,
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bias=False,
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)
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self.out_proj = nn.Linear(args.d_model, args.d_model, bias=False)
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self.rope = nn.RoPE(
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self.head_dim,
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traditional=False,
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base=self.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[Any] = None,
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) -> mx.array:
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qkv = self.Wqkv(x)
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qkv = mx.clip(qkv, a_min=-self.clip_qkv, a_max=self.clip_qkv)
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splits = [self.d_model, self.d_model + self.head_dim * self.num_key_value_heads]
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queries, keys, values = mx.split(qkv, splits, axis=-1)
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B, L, D = x.shape
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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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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.out_proj(output)
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class NormAttnNorm(nn.Module):
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def __init__(self, args: ModelArgs):
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super().__init__()
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self.norm_1 = nn.LayerNorm(args.d_model, bias=False)
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self.norm_2 = nn.LayerNorm(args.d_model, bias=False)
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self.attn = Attention(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[Any] = None,
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) -> mx.array:
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h = self.attn(self.norm_1(x), mask=mask, cache=cache)
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x = h + x
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return x, self.norm_2(x)
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class MLP(nn.Module):
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def __init__(self, d_model: int, ffn_dim: int):
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super().__init__()
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self.v1 = nn.Linear(d_model, ffn_dim, bias=False)
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self.w1 = nn.Linear(d_model, ffn_dim, bias=False)
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self.w2 = nn.Linear(ffn_dim, d_model, 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.v1(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 Router(nn.Module):
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def __init__(self, d_model: int, num_experts: int):
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super().__init__()
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self.layer = nn.Linear(d_model, num_experts, bias=False)
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def __call__(self, x: mx.array):
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return self.layer(x)
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class SparseMoeBlock(nn.Module):
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def __init__(self, args: ModelArgs):
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super().__init__()
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self.d_model = args.d_model
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self.ffn_dim = args.ffn_config["ffn_hidden_size"]
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self.num_experts = args.ffn_config["moe_num_experts"]
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self.num_experts_per_tok = args.ffn_config["moe_top_k"]
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self.router = Router(self.d_model, self.num_experts)
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self.experts = [
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MLP(self.d_model, self.ffn_dim) 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.router(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 - 1, axis=-1)[:, :ne])
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scores = mx.take_along_axis(gates, inds, axis=-1)
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scores = scores / mx.linalg.norm(scores, ord=1, axis=-1, keepdims=True)
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scores = scores.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((x.shape[0], ne, x.shape[-1]), 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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return y.reshape(orig_shape)
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class DecoderLayer(nn.Module):
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def __init__(self, args: ModelArgs):
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super().__init__()
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self.ffn = SparseMoeBlock(args)
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self.norm_attn_norm = NormAttnNorm(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[Any] = None,
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) -> mx.array:
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r, h = self.norm_attn_norm(x, mask, cache)
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out = self.ffn(h) + r
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return out
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class DBRX(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.wte = nn.Embedding(args.vocab_size, args.d_model)
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self.blocks = [DecoderLayer(args=args) for _ in range(args.n_layers)]
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self.norm_f = nn.LayerNorm(args.d_model, 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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h = self.wte(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.blocks)
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for layer, c in zip(self.blocks, cache):
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h = layer(h, mask, c)
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return self.norm_f(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.model_type = args.model_type
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self.transformer = DBRX(args)
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self.lm_head = nn.Linear(args.d_model, args.vocab_size, bias=False)
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self.args = args
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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.transformer(inputs, cache)
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return self.lm_head(out)
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@property
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def layers(self):
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return self.transformer.blocks
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def sanitize(self, weights):
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# Split experts into sub matrices
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num_experts = self.args.ffn_config["moe_num_experts"]
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dim = self.args.ffn_config["ffn_hidden_size"]
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pattern = "experts.mlp"
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new_weights = {k: v for k, v in weights.items() if pattern not in k}
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for k, v in weights.items():
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if pattern in k:
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experts = [
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(k.replace(".mlp", f".{e}") + ".weight", sv)
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for e, sv in enumerate(mx.split(v, num_experts, axis=0))
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]
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if k.endswith("w2"):
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experts = [(s, sv.T) for s, sv in experts]
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new_weights.update(experts)
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return new_weights
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