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Update rope_scaling to rope_parameters in preparation for Transformers v5 (#28542)

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Signed-off-by: Harry Mellor <19981378+hmellor@users.noreply.github.com>
This commit is contained in:
Harry Mellor
2025-11-19 18:06:36 +01:00
committed by GitHub
parent d44e9df7d4
commit a8b70304d6
104 changed files with 542 additions and 910 deletions
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18
vllm/model_executor/models/phimoe.py
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@@ -86,7 +86,7 @@ class PhiMoEConfig(PretrainedConfig):
bos_token_id=1,
eos_token_id=2,
tie_word_embeddings=False,
rope_theta=1e6,
rope_parameters=None,
sliding_window=None,
attention_dropout=0.0,
num_experts_per_tok=2,
@@ -119,7 +119,9 @@ class PhiMoEConfig(PretrainedConfig):
self.initializer_range = initializer_range
self.rms_norm_eps = rms_norm_eps
self.use_cache = use_cache
self.rope_theta = rope_theta
if rope_parameters is None:
rope_theta = kwargs.pop("rope_theta", 1e6)
rope_parameters = {"rope_type": "default", "rope_theta": rope_theta}
self.attention_dropout = attention_dropout
self.num_experts_per_tok = num_experts_per_tok
@@ -302,12 +304,11 @@ class PhiMoEAttention(nn.Module):
hidden_size: int,
num_heads: int,
num_kv_heads: int,
rope_parameters: dict,
head_dim: int | None = None,
max_position: int = 4096 * 32,
rope_theta: float = 10000,
cache_config: CacheConfig | None = None,
quant_config: QuantizationConfig | None = None,
rope_scaling: dict | None = None,
prefix: str = "",
) -> None:
super().__init__()
@@ -332,8 +333,6 @@ class PhiMoEAttention(nn.Module):
self.q_size = self.num_heads * self.head_dim
self.kv_size = self.num_kv_heads * self.head_dim
self.scaling = self.head_dim**-0.5
self.rope_theta = rope_theta
self.rope_scaling = rope_scaling
self.qkv_proj = QKVParallelLinear(
hidden_size,
@@ -355,9 +354,8 @@ class PhiMoEAttention(nn.Module):
self.head_dim,
rotary_dim=self.head_dim,
max_position=max_position,
base=int(self.rope_theta),
rope_parameters=rope_parameters,
is_neox_style=True,
rope_scaling=self.rope_scaling,
)
self.attn = Attention(
self.num_heads,
@@ -393,7 +391,6 @@ class PhiMoEDecoderLayer(nn.Module):
super().__init__()
self.hidden_size = config.hidden_size
# Requires transformers > 4.32.0
rope_theta = getattr(config, "rope_theta", 10000)
self.self_attn = PhiMoEAttention(
hidden_size=self.hidden_size,
num_heads=config.num_attention_heads,
@@ -402,10 +399,9 @@ class PhiMoEDecoderLayer(nn.Module):
head_dim=getattr(
config, "head_dim", self.hidden_size // config.num_attention_heads
),
rope_theta=rope_theta,
cache_config=cache_config,
quant_config=quant_config,
rope_scaling=config.rope_scaling,
rope_parameters=config.rope_parameters,
prefix=f"{prefix}.self_attn",
)
self.block_sparse_moe = PhiMoE(