mirror of
https://github.com/ml-explore/mlx-examples.git
synced 2025-11-04 21:48:09 +08:00
Awni Hannun
312
llms/mlx_lm/models/olmo2.py
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312
llms/mlx_lm/models/olmo2.py
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# Copyright © 2023-2024 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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@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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head_dim: Optional[int] = None
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max_position_embeddings: Optional[int] = None
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num_key_value_heads: Optional[int] = None
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attention_bias: bool = False
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mlp_bias: bool = False
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rope_theta: float = 10000
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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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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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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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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 args.hidden_size // n_heads
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self.scale = head_dim**-0.5
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if hasattr(args, "attention_bias"):
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attention_bias = args.attention_bias
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else:
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attention_bias = False
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self.q_proj = nn.Linear(dim, n_heads * head_dim, bias=attention_bias)
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self.k_proj = nn.Linear(dim, n_kv_heads * head_dim, bias=attention_bias)
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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.q_norm = nn.RMSNorm(n_heads * head_dim, args.rms_norm_eps)
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self.k_norm = nn.RMSNorm(n_kv_heads * head_dim, 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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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 = self.q_norm(queries)
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keys = self.k_norm(keys)
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# Prepare the queries, keys and values for the attention computation
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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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output = scaled_dot_product_attention(
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queries, keys, values, cache=cache, 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 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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if hasattr(args, "mlp_bias"):
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mlp_bias = args.mlp_bias
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else:
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mlp_bias = False
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self.gate_proj = nn.Linear(dim, hidden_dim, bias=mlp_bias)
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self.down_proj = nn.Linear(hidden_dim, dim, bias=mlp_bias)
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self.up_proj = nn.Linear(dim, hidden_dim, bias=mlp_bias)
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def __call__(self, x) -> mx.array:
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return self.down_proj(nn.silu(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)
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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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self.post_feedforward_layernorm = nn.RMSNorm(
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args.hidden_size, eps=args.rms_norm_eps
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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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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.post_attention_layernorm(self.self_attn(x, mask, cache))
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h = x + r
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r = self.post_feedforward_layernorm(self.mlp(h))
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out = h + r
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return out
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class LlamaModel(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 = 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 = 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.model = LlamaModel(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.model(inputs, cache)
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if self.args.tie_word_embeddings:
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out = self.model.embed_tokens.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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def sanitize(self, weights):
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# Remove unused precomputed rotary freqs
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return {
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k: v for k, v in weights.items() if "self_attn.rotary_emb.inv_freq" not in k
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}
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@property
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def layers(self):
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return self.model.layers
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