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>
201 lines
6.3 KiB
Python
201 lines
6.3 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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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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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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head_dim: int
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rms_norm_eps: float
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vocab_size: int
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num_key_value_heads: int
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rope_theta: float = 10000
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rope_traditional: bool = False
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attn_logit_softcapping: float = 50.0
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final_logit_softcapping: float = 30.0
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query_pre_attn_scalar: float = 144.0
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class RMSNorm(nn.Module):
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def __init__(self, dims: int, eps: float = 1e-5):
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super().__init__()
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self.weight = mx.ones((dims,))
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self.eps = eps
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def __call__(self, x):
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return mx.fast.rms_norm(x, 1.0 + self.weight, self.eps)
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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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dim = args.hidden_size
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self.n_heads = n_heads = args.num_attention_heads
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self.n_kv_heads = n_kv_heads = args.num_key_value_heads
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self.repeats = n_heads // n_kv_heads
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self.head_dim = head_dim = args.head_dim
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self.scale = 1.0 / (args.query_pre_attn_scalar**0.5)
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self.q_proj = nn.Linear(dim, n_heads * head_dim, bias=False)
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self.k_proj = nn.Linear(dim, n_kv_heads * head_dim, bias=False)
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self.v_proj = nn.Linear(dim, n_kv_heads * head_dim, bias=False)
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self.o_proj = nn.Linear(n_heads * head_dim, dim, bias=False)
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self.attn_logit_softcapping = args.attn_logit_softcapping
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self.rope = nn.RoPE(
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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[Any] = 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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queries = queries.reshape(B, L, self.n_heads, -1).transpose(0, 2, 1, 3)
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keys = keys.reshape(B, L, self.n_kv_heads, -1).transpose(0, 2, 1, 3)
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values = values.reshape(B, L, self.n_kv_heads, -1).transpose(0, 2, 1, 3)
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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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queries = queries * self.scale
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if self.repeats > 1:
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queries = queries.reshape(
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B, self.n_kv_heads, self.repeats, L, self.head_dim
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)
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keys = mx.expand_dims(keys, 2)
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values = mx.expand_dims(values, 2)
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scores = queries @ keys.swapaxes(-1, -2)
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scores = mx.tanh(scores / self.attn_logit_softcapping)
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scores *= self.attn_logit_softcapping
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if mask is not None:
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scores = scores + mask
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scores = mx.softmax(scores, precise=True, axis=-1)
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output = scores @ values
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if self.repeats > 1:
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output = output.reshape(B, self.n_heads, L, self.head_dim)
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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 MLP(nn.Module):
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def __init__(self, dim, hidden_dim):
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super().__init__()
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self.gate_proj = nn.Linear(dim, hidden_dim, bias=False)
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self.down_proj = nn.Linear(hidden_dim, dim, bias=False)
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self.up_proj = nn.Linear(dim, hidden_dim, bias=False)
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def __call__(self, x) -> mx.array:
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return self.down_proj(nn.gelu(self.gate_proj(x)) * self.up_proj(x))
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class TransformerBlock(nn.Module):
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def __init__(self, args: ModelArgs):
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super().__init__()
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self.num_attention_heads = args.num_attention_heads
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self.hidden_size = args.hidden_size
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self.self_attn = Attention(args)
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self.mlp = MLP(args.hidden_size, args.intermediate_size)
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self.input_layernorm = RMSNorm(args.hidden_size, eps=args.rms_norm_eps)
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self.pre_feedforward_layernorm = RMSNorm(
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args.hidden_size, eps=args.rms_norm_eps
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)
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self.post_feedforward_layernorm = RMSNorm(
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args.hidden_size, eps=args.rms_norm_eps
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)
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self.post_attention_layernorm = RMSNorm(args.hidden_size, eps=args.rms_norm_eps)
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self.args = 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 = self.self_attn(self.input_layernorm(x), mask, cache)
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h = x + self.post_attention_layernorm(r)
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r = self.mlp(self.pre_feedforward_layernorm(h))
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out = h + self.post_feedforward_layernorm(r)
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return out
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class GemmaModel(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_hidden_layers = args.num_hidden_layers
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assert self.vocab_size > 0
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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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TransformerBlock(args=args) for _ in range(args.num_hidden_layers)
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]
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self.norm = 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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h = h * (self.args.hidden_size**0.5)
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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, 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.final_logit_softcapping = args.final_logit_softcapping
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self.model = GemmaModel(args)
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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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out = self.model.embed_tokens.as_linear(out)
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out = mx.tanh(out / self.final_logit_softcapping)
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out = out * self.final_logit_softcapping
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return 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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