mirror of
https://github.com/ml-explore/mlx-examples.git
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Adds EXAONE architecture. (#1145)
* Adds EXAONE architecture. * nits + format * format * clean up and fix rope * clean up and fix rope --------- Co-authored-by: Awni Hannun <awni@apple.com>
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n8programs
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163
llms/mlx_lm/models/exaone.py
Normal file
163
llms/mlx_lm/models/exaone.py
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@ -0,0 +1,163 @@
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# Copyright © 2024 Apple Inc.
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from dataclasses import dataclass
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from typing import Any, Dict, Optional, 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, scaled_dot_product_attention
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from .rope_utils import initialize_rope
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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_layers: int
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intermediate_size: int
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num_attention_heads: int
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vocab_size: int
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rope_theta: float
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layer_norm_epsilon: float
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num_key_value_heads: int
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head_dim: Optional[int] = None
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max_position_embeddings: Optional[int] = None
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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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tie_word_embeddings: bool = True
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attention_bias: bool = False
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mlp_bias: bool = False
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class AttentionModule(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.head_dim = head_dim = args.head_dim or (dim // n_heads)
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self.scale = head_dim**-0.5
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self.q_proj = nn.Linear(dim, n_heads * head_dim, bias=args.attention_bias)
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self.k_proj = nn.Linear(dim, n_kv_heads * head_dim, bias=args.attention_bias)
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self.v_proj = nn.Linear(dim, n_kv_heads * head_dim, bias=args.attention_bias)
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self.out_proj = nn.Linear(n_heads * head_dim, dim, bias=args.attention_bias)
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self.rope = initialize_rope(
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self.head_dim,
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args.rope_theta,
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args.rope_traditional,
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args.rope_scaling,
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args.max_position_embeddings,
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)
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def __call__(
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self, x: mx.array, mask: Optional[mx.array] = None, cache: Optional[Any] = None
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) -> mx.array:
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B, L, D = x.shape
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q = self.q_proj(x).reshape(B, L, self.n_heads, -1).transpose(0, 2, 1, 3)
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k = self.k_proj(x).reshape(B, L, self.n_kv_heads, -1).transpose(0, 2, 1, 3)
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v = self.v_proj(x).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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q = self.rope(q, offset=cache.offset)
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k = self.rope(k, offset=cache.offset)
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k, v = cache.update_and_fetch(k, v)
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else:
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q = self.rope(q)
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k = self.rope(k)
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out = scaled_dot_product_attention(
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q, k, v, cache=cache, scale=self.scale, mask=mask
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)
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out = out.transpose(0, 2, 1, 3).reshape(B, L, D)
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return self.out_proj(out)
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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.attention = AttentionModule(args)
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class MLP(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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hidden_dim = args.intermediate_size
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self.c_fc_0 = nn.Linear(dim, hidden_dim, bias=args.mlp_bias)
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self.c_fc_1 = nn.Linear(dim, hidden_dim, bias=args.mlp_bias)
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self.c_proj = nn.Linear(hidden_dim, dim, bias=args.mlp_bias)
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def __call__(self, x: mx.array) -> mx.array:
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return self.c_proj(nn.silu(self.c_fc_0(x)) * self.c_fc_1(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.ln_1 = nn.RMSNorm(args.hidden_size, eps=args.layer_norm_epsilon)
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self.attn = Attention(args)
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self.ln_2 = nn.RMSNorm(args.hidden_size, eps=args.layer_norm_epsilon)
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self.mlp = MLP(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 = x + self.attn.attention(self.ln_1(x), mask, cache)
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out = h + self.mlp(self.ln_2(h))
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return out
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class ExaoneModel(nn.Module):
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def __init__(self, args: ModelArgs):
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super().__init__()
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self.wte = nn.Embedding(args.vocab_size, args.hidden_size)
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self.h = [TransformerBlock(args) for _ in range(args.num_layers)]
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self.ln_f = nn.RMSNorm(args.hidden_size, eps=args.layer_norm_epsilon)
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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.h)
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for layer, c in zip(self.h, cache):
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h = layer(h, mask, cache=c)
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return self.ln_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.args = args
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self.model_type = args.model_type
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self.transformer = ExaoneModel(args)
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if not args.tie_word_embeddings:
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self.lm_head = nn.Linear(args.hidden_size, 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.tie_word_embeddings:
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out = self.transformer.wte.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.h
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@ -7,6 +7,7 @@ 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, scaled_dot_product_attention
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from .rope_utils import initialize_rope
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@dataclass
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@ -32,117 +33,6 @@ class ModelArgs(BaseModelArgs):
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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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if self.rope_scaling:
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if not "factor" in self.rope_scaling:
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raise ValueError(f"rope_scaling must contain 'factor'")
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rope_type = self.rope_scaling.get("type") or self.rope_scaling.get(
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"rope_type"
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)
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if rope_type is None:
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raise ValueError(
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f"rope_scaling must contain either 'type' or 'rope_type'"
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)
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if rope_type not in ["linear", "dynamic", "llama3"]:
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raise ValueError(
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"rope_scaling 'type' currently only supports 'linear', 'dynamic' or 'llama3'"
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)
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class DynamicNTKScalingRoPE(nn.Module):
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"""Implements the rotary positional encoding with Dynamic NTK scaling and Llama 3 RoPE."""
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def __init__(
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self,
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dims: int,
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max_position_embeddings: int = 2048,
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traditional: bool = False,
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base: float = 10000,
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scale: float = 1.0,
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rope_type: str = "default",
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rope_scaling: dict = None,
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):
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super().__init__()
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self.dims = dims
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self.max_position_embeddings = max_position_embeddings
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self.traditional = traditional
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self.scale = scale
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self.rope_type = rope_type
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self.rope_scaling = rope_scaling
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self.base = base
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self.compute_freqs()
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def compute_freqs(self):
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if self.rope_type != "llama3":
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self._freqs = None
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return
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factor = self.rope_scaling["factor"]
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low_freq_factor = self.rope_scaling.get("low_freq_factor", 1.0)
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high_freq_factor = self.rope_scaling.get("high_freq_factor", 4.0)
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old_context_len = self.rope_scaling.get(
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"original_max_position_embeddings",
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8192,
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)
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low_freq_wavelen = old_context_len / low_freq_factor
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high_freq_wavelen = old_context_len / high_freq_factor
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freqs = self.base ** (mx.arange(0, self.dims, 2) / self.dims)
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wavelens = 2 * mx.pi * freqs
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freqs = mx.where(wavelens > low_freq_wavelen, freqs * factor, freqs)
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is_medium_freq = (wavelens > high_freq_wavelen) & (wavelens < low_freq_wavelen)
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smooth_factors = (old_context_len / wavelens - low_freq_factor) / (
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high_freq_factor - low_freq_factor
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)
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smooth_freqs = freqs / ((1 - smooth_factors) / factor + smooth_factors)
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self._freqs = mx.where(is_medium_freq, smooth_freqs, freqs)
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self.base = None
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def extra_repr(self):
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return (
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f"{self.dims}, traditional={self.traditional}, "
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f"max_position_embeddings={self.max_position_embeddings}, "
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f"scaling_factor={self.scale}, rope_type={self.rope_type}"
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)
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def __call__(self, x, offset: int = 0):
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return mx.fast.rope(
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x,
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self.dims,
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traditional=self.traditional,
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base=self.base,
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scale=self.scale,
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offset=offset,
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freqs=self._freqs,
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)
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def initialize_rope(args: ModelArgs):
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head_dim = args.head_dim or args.hidden_size // args.num_attention_heads
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rope_scaling = args.rope_scaling
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rope_type = "default"
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rope_scale = 1.0
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if rope_scaling is not None:
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rope_type = (
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rope_scaling.get("type") or rope_scaling.get("rope_type") or "default"
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)
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if rope_type == "linear":
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rope_scale = 1 / rope_scaling["factor"]
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elif rope_type == "llama3":
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rope_scale = 1.0 # The scaling is handled internally for llama3
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return DynamicNTKScalingRoPE(
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dims=head_dim,
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max_position_embeddings=args.max_position_embeddings,
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traditional=args.rope_traditional,
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base=args.rope_theta,
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scale=rope_scale,
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rope_type=rope_type,
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rope_scaling=rope_scaling,
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)
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class Attention(nn.Module):
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def __init__(self, args: ModelArgs):
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@ -165,7 +55,13 @@ class Attention(nn.Module):
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self.v_proj = nn.Linear(dim, n_kv_heads * head_dim, bias=attention_bias)
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self.o_proj = nn.Linear(n_heads * head_dim, dim, bias=attention_bias)
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self.rope = initialize_rope(args)
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self.rope = initialize_rope(
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self.head_dim,
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args.rope_theta,
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args.rope_traditional,
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args.rope_scaling,
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args.max_position_embeddings,
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)
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def __call__(
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self,
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@ -7,6 +7,7 @@ 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, scaled_dot_product_attention
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from .rope_utils import initialize_rope
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@dataclass
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@ -32,117 +33,6 @@ class ModelArgs(BaseModelArgs):
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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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if self.rope_scaling:
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if not "factor" in self.rope_scaling:
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raise ValueError(f"rope_scaling must contain 'factor'")
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rope_type = self.rope_scaling.get("type") or self.rope_scaling.get(
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"rope_type"
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)
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if rope_type is None:
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raise ValueError(
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f"rope_scaling must contain either 'type' or 'rope_type'"
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)
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if rope_type not in ["linear", "dynamic", "llama3"]:
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raise ValueError(
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"rope_scaling 'type' currently only supports 'linear', 'dynamic' or 'llama3'"
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)
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class DynamicNTKScalingRoPE(nn.Module):
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"""Implements the rotary positional encoding with Dynamic NTK scaling and Llama 3 RoPE."""
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def __init__(
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self,
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dims: int,
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max_position_embeddings: int = 2048,
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traditional: bool = False,
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base: float = 10000,
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scale: float = 1.0,
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rope_type: str = "default",
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rope_scaling: dict = None,
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):
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super().__init__()
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self.dims = dims
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self.max_position_embeddings = max_position_embeddings
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self.traditional = traditional
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self.scale = scale
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self.rope_type = rope_type
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self.rope_scaling = rope_scaling
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self.base = base
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self.compute_freqs()
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def compute_freqs(self):
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if self.rope_type != "llama3":
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self._freqs = None
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return
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factor = self.rope_scaling["factor"]
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low_freq_factor = self.rope_scaling.get("low_freq_factor", 1.0)
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high_freq_factor = self.rope_scaling.get("high_freq_factor", 4.0)
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old_context_len = self.rope_scaling.get(
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"original_max_position_embeddings",
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8192,
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)
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low_freq_wavelen = old_context_len / low_freq_factor
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high_freq_wavelen = old_context_len / high_freq_factor
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freqs = self.base ** (mx.arange(0, self.dims, 2) / self.dims)
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wavelens = 2 * mx.pi * freqs
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freqs = mx.where(wavelens > low_freq_wavelen, freqs * factor, freqs)
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is_medium_freq = (wavelens > high_freq_wavelen) & (wavelens < low_freq_wavelen)
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smooth_factors = (old_context_len / wavelens - low_freq_factor) / (
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high_freq_factor - low_freq_factor
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)
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smooth_freqs = freqs / ((1 - smooth_factors) / factor + smooth_factors)
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self._freqs = mx.where(is_medium_freq, smooth_freqs, freqs)
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self.base = None
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def extra_repr(self):
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return (
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f"{self.dims}, traditional={self.traditional}, "
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f"max_position_embeddings={self.max_position_embeddings}, "
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f"scaling_factor={self.scale}, rope_type={self.rope_type}"
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)
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def __call__(self, x, offset: int = 0):
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return mx.fast.rope(
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x,
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self.dims,
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traditional=self.traditional,
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base=self.base,
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scale=self.scale,
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offset=offset,
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freqs=self._freqs,
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)
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def initialize_rope(args: ModelArgs):
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head_dim = args.head_dim or args.hidden_size // args.num_attention_heads
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rope_scaling = args.rope_scaling
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rope_type = "default"
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rope_scale = 1.0
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if rope_scaling is not None:
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rope_type = (
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rope_scaling.get("type") or rope_scaling.get("rope_type") or "default"
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)
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if rope_type == "linear":
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rope_scale = 1 / rope_scaling["factor"]
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elif rope_type == "llama3":
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rope_scale = 1.0 # The scaling is handled internally for llama3
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return DynamicNTKScalingRoPE(
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dims=head_dim,
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max_position_embeddings=args.max_position_embeddings,
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traditional=args.rope_traditional,
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base=args.rope_theta,
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scale=rope_scale,
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rope_type=rope_type,
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rope_scaling=rope_scaling,
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)
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class Attention(nn.Module):
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def __init__(self, args: ModelArgs):
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@ -165,7 +55,14 @@ class Attention(nn.Module):
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self.v_proj = nn.Linear(dim, n_kv_heads * head_dim, bias=attention_bias)
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self.o_proj = nn.Linear(n_heads * head_dim, dim, bias=attention_bias)
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self.rope = initialize_rope(args)
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self.rope = initialize_rope(
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self.head_dim,
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args.rope_theta,
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args.rope_traditional,
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args.rope_scaling,
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args.max_position_embeddings,
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)
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self.q_norm = nn.RMSNorm(n_heads * head_dim, args.rms_norm_eps)
|
||||
self.k_norm = nn.RMSNorm(n_kv_heads * head_dim, args.rms_norm_eps)
|
||||
|
||||
|
||||
91
llms/mlx_lm/models/rope_utils.py
Normal file
91
llms/mlx_lm/models/rope_utils.py
Normal file
@ -0,0 +1,91 @@
|
||||
# Copyright © 2023-2024 Apple Inc.
|
||||
|
||||
from typing import Optional
|
||||
|
||||
import mlx.core as mx
|
||||
import mlx.nn as nn
|
||||
|
||||
|
||||
class Llama3RoPE(nn.Module):
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
dims: int,
|
||||
max_position_embeddings: int = 2048,
|
||||
traditional: bool = False,
|
||||
base: float = 10000,
|
||||
scaling_config: dict = None,
|
||||
):
|
||||
super().__init__()
|
||||
self.dims = dims
|
||||
self.max_position_embeddings = max_position_embeddings
|
||||
self.traditional = traditional
|
||||
|
||||
factor = scaling_config["factor"]
|
||||
low_freq_factor = scaling_config.get("low_freq_factor", 1.0)
|
||||
high_freq_factor = scaling_config.get("high_freq_factor", 4.0)
|
||||
old_context_len = scaling_config.get(
|
||||
"original_max_position_embeddings",
|
||||
8192,
|
||||
)
|
||||
|
||||
low_freq_wavelen = old_context_len / low_freq_factor
|
||||
high_freq_wavelen = old_context_len / high_freq_factor
|
||||
|
||||
freqs = base ** (mx.arange(0, dims, 2) / dims)
|
||||
wavelens = 2 * mx.pi * freqs
|
||||
|
||||
freqs = mx.where(wavelens > low_freq_wavelen, freqs * factor, freqs)
|
||||
is_medium_freq = (wavelens > high_freq_wavelen) & (wavelens < low_freq_wavelen)
|
||||
smooth_factors = (old_context_len / wavelens - low_freq_factor) / (
|
||||
high_freq_factor - low_freq_factor
|
||||
)
|
||||
smooth_freqs = freqs / ((1 - smooth_factors) / factor + smooth_factors)
|
||||
self._freqs = mx.where(is_medium_freq, smooth_freqs, freqs)
|
||||
|
||||
def extra_repr(self):
|
||||
return (
|
||||
f"{self.dims}, traditional={self.traditional}, "
|
||||
f"max_position_embeddings={self.max_position_embeddings}"
|
||||
)
|
||||
|
||||
def __call__(self, x, offset: int = 0):
|
||||
return mx.fast.rope(
|
||||
x,
|
||||
self.dims,
|
||||
traditional=self.traditional,
|
||||
base=None,
|
||||
scale=1.0,
|
||||
offset=offset,
|
||||
freqs=self._freqs,
|
||||
)
|
||||
|
||||
|
||||
def initialize_rope(
|
||||
dims,
|
||||
base,
|
||||
traditional,
|
||||
scaling_config: Optional[dict] = None,
|
||||
max_position_embeddings: Optional[int] = None,
|
||||
):
|
||||
if scaling_config is not None:
|
||||
rope_type = scaling_config.get("type") or scaling_config.get(
|
||||
"rope_type", "default"
|
||||
)
|
||||
else:
|
||||
rope_type = "default"
|
||||
|
||||
if rope_type in ["default", "linear"]:
|
||||
scale = 1 / scaling_config["factor"] if rope_type == "linear" else 1.0
|
||||
return nn.RoPE(dims, traditional=traditional, base=base, scale=scale)
|
||||
|
||||
elif rope_type == "llama3":
|
||||
return Llama3RoPE(
|
||||
dims=dims,
|
||||
max_position_embeddings=max_position_embeddings,
|
||||
traditional=traditional,
|
||||
base=base,
|
||||
scaling_config=scaling_config,
|
||||
)
|
||||
else:
|
||||
raise ValueError(f"Unsupported RoPE type {rope_type}")
|
||||
@ -144,6 +144,8 @@ def linear_to_lora_layers(
|
||||
"mixer.out_proj",
|
||||
]
|
||||
)
|
||||
elif model.model_type == "exaone":
|
||||
keys = set(["attn.attention.q_proj", "attn.attention.v_proj"])
|
||||
else:
|
||||
raise ValueError(f"Lora does not support {model.model_type}")
|
||||
|
||||
|
||||
@ -2,7 +2,9 @@
|
||||
import unittest
|
||||
|
||||
import mlx.core as mx
|
||||
import mlx.nn as nn
|
||||
from mlx.utils import tree_map
|
||||
from mlx_lm.models import rope_utils
|
||||
from mlx_lm.models.cache import KVCache, RotatingKVCache, make_prompt_cache
|
||||
|
||||
|
||||
@ -126,6 +128,26 @@ class TestModels(unittest.TestCase):
|
||||
self.assertEqual(cache.offset, 22)
|
||||
self.assertTrue(mx.allclose(x, k[..., -2:, :]))
|
||||
|
||||
def test_rope(self):
|
||||
rope = rope_utils.initialize_rope(32, base=100, traditional=False)
|
||||
self.assertTrue(isinstance(rope, nn.RoPE))
|
||||
|
||||
rope = rope_utils.initialize_rope(
|
||||
32,
|
||||
base=100,
|
||||
traditional=False,
|
||||
scaling_config={"rope_type": "linear", "factor": 10.0},
|
||||
)
|
||||
self.assertTrue(isinstance(rope, nn.RoPE))
|
||||
|
||||
rope = rope_utils.initialize_rope(
|
||||
32,
|
||||
base=100,
|
||||
traditional=False,
|
||||
scaling_config={"rope_type": "llama3", "factor": 2.0},
|
||||
)
|
||||
self.assertTrue(isinstance(rope, rope_utils.Llama3RoPE))
|
||||
|
||||
def model_test_runner(self, model, model_type, vocab_size, num_layers):
|
||||
|
||||
self.assertEqual(len(model.layers), num_layers)
|
||||
@ -812,6 +834,23 @@ class TestModels(unittest.TestCase):
|
||||
model, args.model_type, args.vocab_size, args.num_hidden_layers
|
||||
)
|
||||
|
||||
def test_exaone(self):
|
||||
from mlx_lm.models import exaone
|
||||
|
||||
args = exaone.ModelArgs(
|
||||
model_type="exaone",
|
||||
hidden_size=128,
|
||||
num_layers=4,
|
||||
intermediate_size=256,
|
||||
num_attention_heads=8,
|
||||
num_key_value_heads=2,
|
||||
vocab_size=1000,
|
||||
layer_norm_epsilon=1e-4,
|
||||
rope_theta=10000,
|
||||
)
|
||||
model = exaone.Model(args)
|
||||
self.model_test_runner(model, args.model_type, args.vocab_size, args.num_layers)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
unittest.main()
|
||||
|
||||
Loading…
Reference in New Issue
Block a user