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
217 lines
6.7 KiB
Python
217 lines
6.7 KiB
Python
from dataclasses import dataclass
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from typing import Any, List, 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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rms_norm_eps: float
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vocab_size: int
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n_shared_head: int = 8
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rope_theta: float = 10000
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rope_traditional: bool = False
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class Attention(nn.Module):
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def __init__(self, config: ModelArgs) -> None:
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super().__init__()
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self.config = config
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self.hidden_size = config.hidden_size
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head_dim = self.hidden_size // config.num_attention_heads
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self.q_num_heads = config.num_attention_heads
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self.qk_dim = self.v_dim = head_dim
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self.k_num_heads = self.v_num_heads = int(
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np.ceil(self.q_num_heads / config.n_shared_head)
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)
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self.scale = head_dim**-0.5
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self.q_proj = nn.Linear(
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self.hidden_size, self.q_num_heads * self.qk_dim, bias=False
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)
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self.k_proj = nn.Linear(
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self.hidden_size, self.k_num_heads * self.qk_dim, bias=False
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)
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self.v_proj = nn.Linear(
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self.hidden_size, self.v_num_heads * self.v_dim, bias=False
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)
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self.o_proj = nn.Linear(
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self.q_num_heads * self.v_dim, self.hidden_size, bias=False
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)
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self.rotary_emb = nn.RoPE(
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head_dim,
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traditional=config.rope_traditional,
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base=config.rope_theta,
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scale=1.0,
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)
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def __call__(
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self,
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hidden_states: mx.array,
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attention_mask: Optional[mx.array] = None,
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cache: Optional[Tuple[mx.array, mx.array]] = None,
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) -> Tuple[mx.array, Tuple[mx.array, mx.array]]:
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bsz, q_len, _ = hidden_states.shape
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queries = self.q_proj(hidden_states)
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keys = self.k_proj(hidden_states)
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values = self.v_proj(hidden_states)
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# Prepare the queries, keys and values for the attention computation
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queries = queries.reshape(bsz, q_len, self.q_num_heads, self.qk_dim).transpose(
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0, 2, 1, 3
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)
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keys = keys.reshape(bsz, q_len, self.k_num_heads, self.qk_dim).transpose(
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0, 2, 1, 3
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)
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values = values.reshape(bsz, q_len, self.v_num_heads, self.v_dim).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.rotary_emb(queries, offset=cache.offset)
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keys = self.rotary_emb(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.rotary_emb(queries)
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keys = self.rotary_emb(keys)
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output = mx.fast.scaled_dot_product_attention(
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queries,
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keys,
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values,
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scale=self.scale,
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mask=attention_mask,
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)
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output = output.transpose(0, 2, 1, 3).reshape(bsz, q_len, -1)
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return self.o_proj(output)
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class MLP(nn.Module):
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def __init__(self, config: ModelArgs) -> None:
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super().__init__()
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self.config = config
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self.hidden_size = config.hidden_size
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self.intermediate_size = config.intermediate_size
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self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
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self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
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self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
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def __call__(self, x: mx.array) -> mx.array:
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return self.down_proj(nn.silu(self.gate_proj(x)) * self.up_proj(x)) # type: ignore
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class PlamoDecoderLayer(nn.Module):
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def __init__(self, config: ModelArgs) -> None:
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super().__init__()
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self.config = config
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self.hidden_size = config.hidden_size
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self.self_attn = Attention(config)
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self.mlp = MLP(config)
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self.norm = nn.RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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def __call__(
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self,
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hidden_states: mx.array,
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attention_mask: Optional[mx.array] = None,
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cache: Optional[Tuple[mx.array, mx.array]] = None,
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) -> Tuple[Any, ...]:
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# from LlamaDecoder
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residual = hidden_states
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hidden_states = self.norm(hidden_states)
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# Self Attention
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hidden_states_sa = self.self_attn(
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hidden_states=hidden_states,
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attention_mask=attention_mask,
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cache=cache,
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)
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# Fully Connected
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hidden_states_mlp = self.mlp(hidden_states)
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hidden_states = residual + hidden_states_sa + hidden_states_mlp
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return hidden_states
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class PlamoDecoder(nn.Module):
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def __init__(self, config: ModelArgs) -> None:
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super().__init__()
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self.layers = [
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PlamoDecoderLayer(config) for _ in range(config.num_hidden_layers)
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]
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class PlamoModel(nn.Module):
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def __init__(self, config: ModelArgs):
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super().__init__()
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self.config = config
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self.vocab_size = config.vocab_size
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self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size)
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self.layers = PlamoDecoder(config) # type: ignore
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self.norm = nn.RMSNorm(config.hidden_size, eps=config.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: Optional[List[Union[Tuple[mx.array, mx.array], None]]] = None,
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) -> Tuple[mx.array, Optional[List[Union[Tuple[mx.array, mx.array], None]]]]:
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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(self.embed_tokens.weight.dtype)
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if cache is None:
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cache = [None for _ in range(len(self.layers.layers))]
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for layer, c in zip(self.layers.layers, cache):
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h = layer(h, mask, cache=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) -> None:
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super().__init__()
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self.model_type = args.model_type
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self.model = PlamoModel(args)
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self.lm_head: nn.Module = nn.Linear(
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args.hidden_size, args.vocab_size, bias=False
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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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inputs: mx.array,
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cache: Optional[List[Tuple[mx.array, mx.array]]] = None,
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) -> Tuple[mx.array, mx.array]:
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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.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_attention_heads // self.args.n_shared_head
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