Gökdeniz Gülmez
1974376d70
* Add GLM4 MoE DSA model implementation with configurable parameters * Update Acknowledgments to include GLM4 MoE DSA support * format * update ackn. * Fixes * Update acknowledgments to include contributions for GLM MoE DSA and additional architectures * use dsv32 for glm5 * fix * Fix rope theta --------- Co-authored-by: Tarjei Mandt <kernelpool@gmail.com> Co-authored-by: Awni Hannun <awni@apple.com>
658 lines
24 KiB
Python
658 lines
24 KiB
Python
# Copyright © 2025 Apple Inc.
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import math
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from dataclasses import dataclass
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from typing import Any, Dict, Optional
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import mlx.core as mx
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import mlx.nn as nn
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from mlx.nn.layers.distributed import shard_inplace, shard_linear, sum_gradients
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from .activations import swiglu
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from .base import BaseModelArgs, create_attention_mask, scaled_dot_product_attention
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from .cache import CacheList, KVCache
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from .mla import MultiLinear
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from .rope_utils import initialize_rope
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from .switch_layers import SwitchGLU
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@dataclass
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class ModelArgs(BaseModelArgs):
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model_type: str = "deepseek_v32"
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vocab_size: int = 102400
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hidden_size: int = 4096
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index_head_dim: int = 128
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index_n_heads: int = 64
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index_topk: int = 2048
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intermediate_size: int = 11008
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moe_intermediate_size: int = 1407
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num_hidden_layers: int = 30
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num_attention_heads: int = 32
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num_key_value_heads: int = 32
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n_shared_experts: Optional[int] = None
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n_routed_experts: Optional[int] = None
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routed_scaling_factor: float = 1.0
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kv_lora_rank: int = 512
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q_lora_rank: int = 1536
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qk_rope_head_dim: int = 64
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v_head_dim: int = 128
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qk_nope_head_dim: int = 128
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topk_method: str = "noaux_tc"
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scoring_func: str = "sigmoid"
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norm_topk_prob: bool = True
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n_group: int = 1
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topk_group: int = 1
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num_experts_per_tok: int = 1
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moe_layer_freq: int = 1
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first_k_dense_replace: int = 0
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max_position_embeddings: int = 2048
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rms_norm_eps: float = 1e-6
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rope_theta: float = 10000.0
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rope_scaling: Dict = None
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attention_bias: bool = False
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class Indexer(nn.Module):
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def __init__(self, args: ModelArgs):
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super().__init__()
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self.dim = args.hidden_size
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self.n_heads = args.index_n_heads
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self.head_dim = args.index_head_dim
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self.rope_head_dim = args.qk_rope_head_dim
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self.index_topk = args.index_topk
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self.q_lora_rank = args.q_lora_rank
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self.wq_b = nn.Linear(
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self.q_lora_rank, self.n_heads * self.head_dim, bias=False
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)
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self.wk = nn.Linear(self.dim, self.head_dim, bias=False)
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self.k_norm = nn.LayerNorm(self.head_dim)
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self.weights_proj = nn.Linear(self.dim, self.n_heads, bias=False)
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self.softmax_scale = self.head_dim**-0.5
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self.rope = initialize_rope(
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dims=args.qk_rope_head_dim,
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base=args.rope_theta,
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traditional=True,
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max_position_embeddings=args.max_position_embeddings,
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scaling_config=args.rope_scaling,
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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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qr: mx.array,
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mask: Optional[mx.array],
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cache: Optional[Any] = None,
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):
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# Computes top_k indices for attention
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b, s, _ = x.shape
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q = self.wq_b(qr)
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q = q.reshape(b, s, self.n_heads, self.head_dim).swapaxes(1, 2)
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q_pe, q_nope = mx.split(q, [self.rope_head_dim], axis=-1)
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offset = cache.offset if cache is not None else 0
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q_pe = self.rope(q_pe, offset=offset)
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q = mx.concatenate([q_pe, q_nope], axis=-1)
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k = self.wk(x)
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k = self.k_norm(k)
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k = mx.reshape(k, (b, 1, s, self.head_dim))
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k_pe, k_nope = mx.split(k, [self.rope_head_dim], axis=-1)
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k_pe = self.rope(k_pe, offset=offset)
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k = mx.concatenate([k_pe, k_nope], axis=-1)
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if cache is not None:
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k, _ = cache.update_and_fetch(k, mx.zeros([b, 1, s, 0]))
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if k.shape[2] <= self.index_topk:
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return None
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scores = q @ k.swapaxes(-1, -2)
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scores = mx.maximum(scores, 0)
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weights = self.weights_proj(x) * (self.n_heads**-0.5 * self.softmax_scale)
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weights = weights.swapaxes(-1, -2)[..., None]
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scores = scores * weights
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scores = scores.sum(axis=1, keepdims=True)
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if mask is not None:
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scores = mx.where(mask, scores, -float("inf"))
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return mx.argpartition(scores, kth=-self.index_topk, axis=-1)[
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..., -self.index_topk :
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]
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class DeepseekV32Attention(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.hidden_size = config.hidden_size
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self.num_heads = config.num_attention_heads
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self.max_position_embeddings = config.max_position_embeddings
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self.rope_theta = config.rope_theta
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self.q_lora_rank = config.q_lora_rank
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self.qk_rope_head_dim = config.qk_rope_head_dim
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self.kv_lora_rank = config.kv_lora_rank
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self.v_head_dim = config.v_head_dim
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self.qk_nope_head_dim = config.qk_nope_head_dim
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self.q_head_dim = config.qk_nope_head_dim + config.qk_rope_head_dim
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self.scale = self.q_head_dim**-0.5
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self.q_a_proj = nn.Linear(
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self.hidden_size, self.q_lora_rank, bias=config.attention_bias
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)
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self.q_a_layernorm = nn.RMSNorm(self.q_lora_rank, eps=1e-6)
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self.q_b_proj = nn.Linear(
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self.q_lora_rank, self.num_heads * self.q_head_dim, bias=False
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)
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self.kv_a_proj_with_mqa = nn.Linear(
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self.hidden_size,
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self.kv_lora_rank + self.qk_rope_head_dim,
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bias=config.attention_bias,
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)
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self.kv_a_layernorm = nn.RMSNorm(self.kv_lora_rank, eps=1e-6)
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self.embed_q = MultiLinear(
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self.qk_nope_head_dim, self.kv_lora_rank, self.num_heads
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)
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self.unembed_out = MultiLinear(
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self.kv_lora_rank, self.v_head_dim, self.num_heads
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)
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self.o_proj = nn.Linear(
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self.num_heads * self.v_head_dim,
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self.hidden_size,
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bias=config.attention_bias,
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)
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if self.config.rope_scaling is not None:
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mscale_all_dim = self.config.rope_scaling.get("mscale_all_dim", 0)
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if mscale_all_dim:
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scaling_factor = self.config.rope_scaling["factor"]
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if scaling_factor > 1:
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s = 0.1 * mscale_all_dim * math.log(scaling_factor) + 1.0
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self.scale = self.scale * s * s
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self.indexer = Indexer(config)
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self.rope = initialize_rope(
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dims=self.qk_rope_head_dim,
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base=self.rope_theta,
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traditional=True,
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max_position_embeddings=self.max_position_embeddings,
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scaling_config=self.config.rope_scaling,
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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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qr = self.q_a_layernorm(self.q_a_proj(x))
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q = self.q_b_proj(qr)
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q = q.reshape(B, L, self.num_heads, self.q_head_dim).transpose(0, 2, 1, 3)
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q_nope, q_pe = mx.split(q, [self.qk_nope_head_dim], axis=-1)
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compressed_kv = self.kv_a_proj_with_mqa(x)
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compressed_kv, k_pe = mx.split(compressed_kv, [self.kv_lora_rank], axis=-1)
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k_pe = k_pe.reshape(B, L, 1, self.qk_rope_head_dim).transpose(0, 2, 1, 3)
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kv_latent = self.kv_a_layernorm(compressed_kv)
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offset = cache[0].offset if cache is not None else 0
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q_pe = self.rope(q_pe, offset)
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k_pe = self.rope(k_pe, offset)
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kv_latent = mx.expand_dims(kv_latent, axis=1)
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if cache is not None:
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kv_latent, k_pe = cache[0].update_and_fetch(kv_latent, k_pe)
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else:
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cache = [None] * 2
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topk_indices = self.indexer(x, qr, mask, cache=cache[1])
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if topk_indices is not None:
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if L == 1:
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idx = topk_indices[:, :, 0, :, None]
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kv_latent = mx.take_along_axis(
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kv_latent,
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mx.broadcast_to(idx, idx.shape[:-1] + (kv_latent.shape[-1],)),
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axis=2,
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)
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k_pe = mx.take_along_axis(
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k_pe,
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mx.broadcast_to(idx, idx.shape[:-1] + (k_pe.shape[-1],)),
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axis=2,
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)
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mask = None
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else:
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shape = list(topk_indices.shape)
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shape[-1] = kv_latent.shape[2]
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sparse_mask = mx.zeros(shape, dtype=mx.bool_)
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sparse_mask = mx.put_along_axis(
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sparse_mask, topk_indices, mx.array(True), axis=-1
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)
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if mask is not None:
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sparse_mask = sparse_mask & mask
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mask = sparse_mask
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# Ensure the indexer cache is evaluated even if the topk_indices are unused
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# to keep the graph from getting too large
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if cache is not None and cache[0] is not None:
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cache[0].keys = mx.depends(cache[0].keys, (cache[1].keys, cache[1].values))
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pe_scores = (q_pe * self.scale) @ k_pe.swapaxes(-1, -2)
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if mask is not None:
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pe_scores = mx.where(
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mask,
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pe_scores,
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mx.array(mx.finfo(pe_scores.dtype).min, pe_scores.dtype),
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)
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if L == 1:
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q_nope = self.embed_q(q_nope)
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k = v = kv_latent
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else:
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k = self.embed_q(kv_latent, transpose=False)
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v = self.unembed_out(kv_latent)
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output = scaled_dot_product_attention(
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q_nope, k, v, cache=cache, scale=self.scale, mask=pe_scores
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)
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if L == 1:
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output = self.unembed_out(output)
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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 DeepseekV32MLP(nn.Module):
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def __init__(
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self, config: ModelArgs, hidden_size: int = None, intermediate_size: int = None
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):
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super().__init__()
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self.config = config
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self.hidden_size = config.hidden_size if hidden_size is None else hidden_size
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self.intermediate_size = (
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config.intermediate_size if intermediate_size is None else intermediate_size
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)
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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):
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down_proj = self.down_proj(swiglu(self.gate_proj(x), self.up_proj(x)))
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return down_proj
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@mx.compile
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def group_expert_select(
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gates,
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e_score_correction_bias,
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top_k,
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n_group,
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topk_group,
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routed_scaling_factor,
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norm_topk_prob,
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):
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scores = mx.sigmoid(gates.astype(mx.float32))
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orig_scores = scores
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scores = scores + e_score_correction_bias
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if n_group > 1:
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scores = mx.unflatten(scores, axis=-1, shape=(n_group, -1))
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group_scores = mx.topk(scores, 2, axis=-1).sum(axis=-1, keepdims=True)
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k = n_group - topk_group
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group_idx = mx.argpartition(group_scores, kth=k - 1, axis=-2)[..., :k, :]
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scores = mx.put_along_axis(
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scores, mx.stop_gradient(group_idx), mx.array(0.0), axis=-2
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)
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scores = mx.flatten(scores, -2, -1)
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k = top_k
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inds = mx.argpartition(-scores, kth=k - 1, axis=-1)[..., :k]
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scores = mx.take_along_axis(orig_scores, inds, axis=-1)
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if top_k > 1 and norm_topk_prob:
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denominator = scores.sum(axis=-1, keepdims=True)
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scores = scores / denominator
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scores = scores * routed_scaling_factor
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return inds, scores
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class MoEGate(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.top_k = config.num_experts_per_tok
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self.norm_topk_prob = config.norm_topk_prob
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self.n_routed_experts = config.n_routed_experts
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self.routed_scaling_factor = config.routed_scaling_factor
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self.n_group = config.n_group
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self.topk_group = config.topk_group
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self.weight = mx.zeros((self.n_routed_experts, config.hidden_size))
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self.e_score_correction_bias = mx.zeros((self.n_routed_experts,))
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assert config.topk_method == "noaux_tc", "Unsupported topk method."
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def __call__(self, x):
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return group_expert_select(
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x @ self.weight.T,
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self.e_score_correction_bias,
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self.top_k,
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self.n_group,
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self.topk_group,
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self.routed_scaling_factor,
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self.norm_topk_prob,
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)
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class DeepseekV32MoE(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.num_experts_per_tok = config.num_experts_per_tok
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self.switch_mlp = SwitchGLU(
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config.hidden_size,
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config.moe_intermediate_size,
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config.n_routed_experts,
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)
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self.gate = MoEGate(config)
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if config.n_shared_experts is not None:
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intermediate_size = config.moe_intermediate_size * config.n_shared_experts
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self.shared_experts = DeepseekV32MLP(
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config=config, intermediate_size=intermediate_size
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)
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self.sharding_group = None
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def __call__(self, x):
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if self.sharding_group is not None:
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x = sum_gradients(self.sharding_group)(x)
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inds, scores = self.gate(x)
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y = self.switch_mlp(x, inds)
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y = (y * scores[..., None]).sum(axis=-2).astype(y.dtype)
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if self.config.n_shared_experts is not None:
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y = y + self.shared_experts(x)
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if self.sharding_group is not None:
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y = mx.distributed.all_sum(y, group=self.sharding_group)
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return y
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class DeepseekV32DecoderLayer(nn.Module):
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def __init__(self, config: ModelArgs, layer_idx: int):
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super().__init__()
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self.self_attn = DeepseekV32Attention(config)
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self.mlp = (
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DeepseekV32MoE(config)
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if (
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config.n_routed_experts is not None
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and layer_idx >= config.first_k_dense_replace
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and layer_idx % config.moe_layer_freq == 0
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)
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else DeepseekV32MLP(config)
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)
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self.input_layernorm = nn.RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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self.post_attention_layernorm = nn.RMSNorm(
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config.hidden_size, eps=config.rms_norm_eps
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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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r = self.self_attn(self.input_layernorm(x), mask, cache)
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h = x + r
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r = self.mlp(self.post_attention_layernorm(h))
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return h + r
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class DeepseekV32Model(nn.Module):
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def __init__(self, config: ModelArgs):
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super().__init__()
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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 = [
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DeepseekV32DecoderLayer(config, idx)
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for idx in range(config.num_hidden_layers)
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]
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self.start_idx = 0
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self.end_idx = len(self.layers)
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self.num_layers = self.end_idx
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self.norm = nn.RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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self.pipeline_rank = 0
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self.pipeline_size = 1
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def pipeline(self, group):
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# Split layers in reverse so rank=0 gets the last layers and
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# rank=pipeline_size-1 gets the first
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self.pipeline_rank = group.rank()
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self.pipeline_size = group.size()
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layers_per_rank = len(self.layers) // self.pipeline_size
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extra = len(self.layers) - layers_per_rank * self.pipeline_size
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if self.pipeline_rank < extra:
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layers_per_rank += 1
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self.start_idx = (self.pipeline_size - self.pipeline_rank - 1) * layers_per_rank
|
|
self.end_idx = self.start_idx + layers_per_rank
|
|
self.layers = self.layers[: self.end_idx]
|
|
self.layers[: self.start_idx] = [None] * self.start_idx
|
|
self.num_layers = len(self.layers) - self.start_idx
|
|
|
|
def __call__(
|
|
self,
|
|
x: mx.array,
|
|
cache: Optional[Any] = None,
|
|
) -> mx.array:
|
|
h = self.embed_tokens(x)
|
|
|
|
pipeline_rank = self.pipeline_rank
|
|
pipeline_size = self.pipeline_size
|
|
|
|
if cache is None:
|
|
cache = [None] * self.num_layers
|
|
mask = create_attention_mask(
|
|
h, cache[0][0] if cache[0] else None, return_array=True
|
|
)
|
|
|
|
# Receive from the previous process in the pipeline
|
|
|
|
if pipeline_rank < pipeline_size - 1:
|
|
h = mx.distributed.recv_like(h, (pipeline_rank + 1))
|
|
|
|
for i in range(self.num_layers):
|
|
h = self.layers[self.start_idx + i](h, mask, cache[i])
|
|
|
|
# Send to the next process in the pipeline
|
|
if pipeline_rank != 0:
|
|
h = mx.distributed.send(h, (pipeline_rank - 1) % pipeline_size)
|
|
if cache[-1] is not None:
|
|
cache[-1][0].keys = mx.depends(cache[-1][0].keys, h)
|
|
|
|
# Broadcast h while keeping it in the graph
|
|
if pipeline_size > 1:
|
|
h = mx.distributed.all_gather(h)[: h.shape[0]]
|
|
|
|
return self.norm(h)
|
|
|
|
|
|
class Model(nn.Module):
|
|
def __init__(self, config: ModelArgs):
|
|
super().__init__()
|
|
self.args = config
|
|
self.model_type = config.model_type
|
|
self.model = DeepseekV32Model(config)
|
|
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
|
|
|
|
def __call__(
|
|
self,
|
|
inputs: mx.array,
|
|
cache: Optional[Any] = None,
|
|
):
|
|
out = self.model(inputs, cache)
|
|
return self.lm_head(out)
|
|
|
|
def sanitize(self, weights):
|
|
# Remove multi-token prediction layers
|
|
mpt_layer = self.args.num_hidden_layers
|
|
new_weights = {}
|
|
for k, v in weights.items():
|
|
parts = k.split(".")
|
|
if len(parts) >= 3 and parts[1] == "layers" and int(parts[2]) >= mpt_layer:
|
|
continue
|
|
new_weights[k] = v
|
|
weights = new_weights
|
|
|
|
def dequant(weight, scale_inv):
|
|
dtype = mx.bfloat16
|
|
weight = mx.from_fp8(weight, dtype=mx.bfloat16)
|
|
bs = 128 # block size
|
|
m, n = weight.shape
|
|
pad_bottom = (-m) % bs
|
|
pad_side = (-n) % bs
|
|
weight = mx.pad(weight, ((0, pad_bottom), (0, pad_side)))
|
|
weight = weight.reshape(
|
|
((m + pad_bottom) // bs, bs, (n + pad_side) // bs, bs)
|
|
)
|
|
weight = (weight * scale_inv[:, None, :, None]).reshape(
|
|
m + pad_bottom, n + pad_side
|
|
)
|
|
return weight[:m, :n].astype(dtype)
|
|
|
|
# Dequantize
|
|
new_weights = {}
|
|
for k, v in weights.items():
|
|
if "weight_scale_inv" in k:
|
|
scale_inv = v
|
|
wk = k.replace("_scale_inv", "")
|
|
weight = weights[wk]
|
|
weight = dequant(weight, scale_inv)
|
|
new_weights[wk] = weight
|
|
elif k not in new_weights:
|
|
new_weights[k] = v
|
|
weights = new_weights
|
|
|
|
# Stack experts
|
|
for l in range(self.args.num_hidden_layers):
|
|
prefix = f"model.layers.{l}"
|
|
for n, m in [("w1", "gate_proj"), ("w2", "down_proj"), ("w3", "up_proj")]:
|
|
for k in ["weight", "scales", "biases"]:
|
|
if f"{prefix}.mlp.experts.0.{m}.{k}" in weights:
|
|
to_join = [
|
|
weights.pop(f"{prefix}.mlp.experts.{e}.{m}.{k}")
|
|
for e in range(self.args.n_routed_experts)
|
|
]
|
|
weights[f"{prefix}.mlp.switch_mlp.{m}.{k}"] = mx.stack(to_join)
|
|
prefix = f"model.layers.{l}.self_attn"
|
|
if f"{prefix}.kv_b_proj.weight" in weights:
|
|
layer = self.model.layers[l].self_attn.embed_q
|
|
quantized = f"{prefix}.kv_b_proj.scales" in weights
|
|
v = weights.pop(f"{prefix}.kv_b_proj.weight")
|
|
head_dim = self.args.qk_nope_head_dim + self.args.v_head_dim
|
|
|
|
if quantized:
|
|
dims = self.args.kv_lora_rank
|
|
scales = weights.pop(f"{prefix}.kv_b_proj.scales")
|
|
biases = weights.pop(f"{prefix}.kv_b_proj.biases")
|
|
# Try to infer bits and group size
|
|
bits = (v.shape[-1] * 32) // dims
|
|
group_size = dims // scales.shape[-1]
|
|
v = mx.dequantize(
|
|
v, scales, biases, bits=bits, group_size=group_size
|
|
)
|
|
num_heads = self.args.num_attention_heads
|
|
v = v.reshape(num_heads, head_dim, -1)
|
|
wk = mx.contiguous(
|
|
v[:, : self.args.qk_nope_head_dim, :].swapaxes(-1, -2)
|
|
)
|
|
wv = mx.contiguous(v[:, self.args.qk_nope_head_dim :, :])
|
|
if quantized:
|
|
wk, wk_scales, wk_biases = mx.quantize(
|
|
wk, bits=bits, group_size=group_size
|
|
)
|
|
wv, wv_scales, wv_biases = mx.quantize(
|
|
wv, bits=bits, group_size=group_size
|
|
)
|
|
weights[f"{prefix}.embed_q.scales"] = wk_scales
|
|
weights[f"{prefix}.unembed_out.scales"] = wv_scales
|
|
weights[f"{prefix}.embed_q.biases"] = wk_biases
|
|
weights[f"{prefix}.unembed_out.biases"] = wv_biases
|
|
weights[f"{prefix}.embed_q.weight"] = wk
|
|
weights[f"{prefix}.unembed_out.weight"] = wv
|
|
|
|
return weights
|
|
|
|
def shard(self, group: Optional[mx.distributed.Group] = None):
|
|
group = group or mx.distributed.init()
|
|
N = group.size()
|
|
rank = group.rank()
|
|
for layer in self.model.layers:
|
|
layer.self_attn.q_b_proj = shard_linear(
|
|
layer.self_attn.q_b_proj, "all-to-sharded", group=group
|
|
)
|
|
|
|
layer.self_attn.o_proj = shard_linear(
|
|
layer.self_attn.o_proj, "sharded-to-all", group=group
|
|
)
|
|
layer.self_attn.num_heads //= N
|
|
num_heads = layer.self_attn.num_heads
|
|
sh = rank * num_heads
|
|
eh = sh + num_heads
|
|
|
|
def shard_heads(w):
|
|
return w[sh:eh]
|
|
|
|
layer.self_attn.embed_q.apply(shard_heads)
|
|
layer.self_attn.unembed_out.apply(shard_heads)
|
|
|
|
# Shard the MLP
|
|
if isinstance(layer.mlp, DeepseekV32MLP):
|
|
layer.mlp.gate_proj = shard_linear(
|
|
layer.mlp.gate_proj, "all-to-sharded", group=group
|
|
)
|
|
layer.mlp.down_proj = shard_linear(
|
|
layer.mlp.down_proj, "sharded-to-all", group=group
|
|
)
|
|
layer.mlp.up_proj = shard_linear(
|
|
layer.mlp.up_proj, "all-to-sharded", group=group
|
|
)
|
|
|
|
# Shard the MoE. Shard in place since the MoE should be responsible
|
|
# for aggregating the results.
|
|
else:
|
|
layer.mlp.sharding_group = group = group
|
|
shard_inplace(
|
|
layer.mlp.shared_experts.gate_proj, "all-to-sharded", group=group
|
|
)
|
|
shard_inplace(
|
|
layer.mlp.shared_experts.down_proj, "sharded-to-all", group=group
|
|
)
|
|
shard_inplace(
|
|
layer.mlp.shared_experts.up_proj, "all-to-sharded", group=group
|
|
)
|
|
shard_inplace(
|
|
layer.mlp.switch_mlp.gate_proj, "all-to-sharded", group=group
|
|
)
|
|
shard_inplace(
|
|
layer.mlp.switch_mlp.down_proj, "sharded-to-all", group=group
|
|
)
|
|
shard_inplace(
|
|
layer.mlp.switch_mlp.up_proj, "all-to-sharded", group=group
|
|
)
|
|
|
|
@property
|
|
def layers(self):
|
|
return self.model.layers[self.model.start_idx : self.model.end_idx]
|
|
|
|
@property
|
|
def cast_predicate(self):
|
|
def predicate(k):
|
|
return "e_score_correction_bias" not in k
|
|
|
|
return predicate
|
|
|
|
def make_cache(self):
|
|
return [CacheList(KVCache(), KVCache()) for _ in self.layers]
|