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>
221 lines
6.6 KiB
Python
221 lines
6.6 KiB
Python
# Copyright © 2023-2024 Apple Inc.
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from dataclasses import dataclass
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from typing import Any, Dict, 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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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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head_dim: int
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num_transformer_layers: int
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model_dim: int
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vocab_size: int
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ffn_dim_divisor: int
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num_query_heads: List
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num_kv_heads: List
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ffn_multipliers: List
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ffn_with_glu: bool = True
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normalize_qk_projections: bool = True
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share_input_output_layers: bool = True
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rms_norm_eps: float = 1e-6
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rope_freq_constant: float = 10000
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def make_divisible(
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v: Union[float, int],
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divisor: Optional[int] = 8,
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min_value: Optional[Union[float, int]] = None,
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) -> Union[float, int]:
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"""
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This function is taken from the original tf repo.
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It ensures that all layers have a channel number that is divisible by the divisor
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It can be seen at:
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https://github.com/tensorflow/models/blob/2cfc99eff5e5eb729c6793d2f3d03aa1c9be2b15/research/slim/nets/mobilenet/mobilenet.py#L62
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Args:
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v: input value
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divisor: default to 8
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min_value: minimum divisor value
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Returns:
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new_v: new divisible value
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"""
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if min_value is None:
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min_value = divisor
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new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
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# Make sure that round down does not go down by more than 10%.
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if new_v < 0.9 * v:
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new_v += divisor
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return new_v
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class Attention(nn.Module):
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def __init__(self, args: ModelArgs, layer_id: int):
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super().__init__()
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self.head_dim = head_dim = args.head_dim
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self.layer_id = layer_id
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self.model_dim = model_dim = args.model_dim
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self.n_heads = n_heads = args.num_query_heads[layer_id]
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self.n_kv_heads = n_kv_heads = args.num_kv_heads[layer_id]
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self.scale = head_dim**-0.5
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op_size = (n_heads + (n_kv_heads * 2)) * head_dim
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self.qkv_proj = nn.Linear(model_dim, op_size, bias=False)
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self.out_proj = nn.Linear(n_heads * head_dim, model_dim, bias=False)
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self.normalize_qk_projections = args.normalize_qk_projections
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if self.normalize_qk_projections:
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self.q_norm = nn.RMSNorm(head_dim, eps=args.rms_norm_eps)
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self.k_norm = nn.RMSNorm(head_dim, eps=args.rms_norm_eps)
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self.rope = nn.RoPE(head_dim, traditional=False, base=args.rope_freq_constant)
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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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B, L, D = x.shape
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qkv = self.qkv_proj(x)
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qkv = qkv.reshape(
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B, L, self.n_heads + (self.n_kv_heads * 2), self.head_dim
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).transpose(0, 2, 1, 3)
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queries, keys, values = mx.split(
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qkv, [self.n_heads, self.n_heads + self.n_kv_heads], axis=1
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)
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# Prepare the queries, keys and values for the attention computation
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if self.normalize_qk_projections:
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queries = self.q_norm(queries)
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keys = self.k_norm(keys)
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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 MLP(nn.Module):
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def __init__(self, args: ModelArgs, layer_id: int):
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super().__init__()
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self.args = args
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dim = args.model_dim
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ffn_multiplier = args.ffn_multipliers[layer_id]
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intermediate_dim = int(
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make_divisible(
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ffn_multiplier * args.model_dim,
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divisor=args.ffn_dim_divisor,
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)
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)
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self.proj_1 = nn.Linear(dim, 2 * intermediate_dim, bias=False)
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self.proj_2 = nn.Linear(intermediate_dim, dim, bias=False)
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def __call__(self, x) -> mx.array:
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x = self.proj_1(x)
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gate, x = mx.split(x, 2, axis=-1)
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return self.proj_2(nn.silu(gate) * x)
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class TransformerBlock(nn.Module):
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def __init__(self, args: ModelArgs, layer_id: int):
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super().__init__()
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dim = args.model_dim
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self.attn = Attention(args, layer_id=layer_id)
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self.ffn = MLP(args, layer_id=layer_id)
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self.ffn_norm = nn.RMSNorm(dim, eps=args.rms_norm_eps)
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self.attn_norm = nn.RMSNorm(dim, eps=args.rms_norm_eps)
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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 = self.attn(self.attn_norm(x), mask, cache)
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h = x + r
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r = self.ffn(self.ffn_norm(h))
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out = h + r
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return out
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class OpenELMModel(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_transformer_layers = args.num_transformer_layers
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assert self.vocab_size > 0
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self.token_embeddings = nn.Embedding(args.vocab_size, args.model_dim)
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self.layers = [
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TransformerBlock(args, layer_id=layer_id)
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for layer_id in range(self.num_transformer_layers)
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]
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self.norm = nn.RMSNorm(args.model_dim, 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.token_embeddings(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.layers)
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for layer, c in zip(self.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):
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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.transformer = OpenELMModel(args)
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if not args.share_input_output_layers:
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self.lm_head = nn.Linear(args.model_dim, 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 = self.transformer(inputs, cache)
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if self.args.share_input_output_layers:
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out = self.transformer.token_embeddings.as_linear(out)
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else:
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out = self.lm_head(out)
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return out
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@property
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def layers(self):
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return self.transformer.layers
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