mirror of
https://github.com/ml-explore/mlx-examples.git
synced 2025-06-24 09:21:18 +08:00
ee60e2a9d5
* in place kv_cache * fix * fix kv cache size * partially fix kv cache dtype * step kv cache * multiple of step size * more teests + kv cache * more kv cache * udpate all models to use kv cache
249 lines
7.6 KiB
Python
249 lines
7.6 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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vocab_size: int = 32000
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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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rope_theta: float = 1e6
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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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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 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.rope_theta = args.rope_theta
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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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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 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.stop_gradient(mx.argpartition(-gates, kth=ne - 1, 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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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[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 = 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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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 = 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
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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 = 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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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 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.model_type = args.model_type
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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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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.model(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.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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