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llama.cpp
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replaced all hparams.get with find_hparams

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Yee Man Chan 2026-01-31 08:46:19 +08:00
parent 6216273ede
commit aaf05bddc7
1 changed files with 12 additions and 11 deletions
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23
convert_hf_to_gguf.py
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@ -5096,7 +5096,7 @@ class KimiLinearModel(TextModel):
# KDA & MLA params # KDA & MLA params
# Get ssm_d_conv from linear_attn_config.short_conv_kernel_size or ssm_d_conv # Get ssm_d_conv from linear_attn_config.short_conv_kernel_size or ssm_d_conv
linear_attn_config = self.hparams.get("linear_attn_config", {}) linear_attn_config = self.find_hparam(["linear_attn_config"], optional=False)
# n_head == 0 for KDA layers, n_head > 0 for MLA layers # n_head == 0 for KDA layers, n_head > 0 for MLA layers
# full_attention_layers list will be used to distingush layer type # full_attention_layers list will be used to distingush layer type
_num_kv_heads = list() _num_kv_heads = list()
@ -5123,23 +5123,24 @@ class KimiLinearModel(TextModel):
# MLA head dimensions # MLA head dimensions
# Support HuggingFace naming: qk_nope_head_dim, qk_rope_head_dim, v_head_dim # Support HuggingFace naming: qk_nope_head_dim, qk_rope_head_dim, v_head_dim
qk_nope_head_dim = self.hparams.get("qk_nope_head_dim") qk_nope_head_dim = self.find_hparam(["qk_nope_head_dim"], optional=False)
qk_rope_head_dim = self.hparams.get("qk_rope_head_dim") qk_rope_head_dim = self.find_hparam(["qk_rope_head_dim"], optional=False)
v_head_dim = self.hparams.get("v_head_dim") v_head_dim = self.find_hparam(["v_head_dim"], optional=False)
kv_lora_rank = self.find_hparam(["kv_lora_rank"], optional=False)
# To enable MLA KV cache, MLA needs to be converted into MQA with larger heads, then decompresses to MHA # To enable MLA KV cache, MLA needs to be converted into MQA with larger heads, then decompresses to MHA
self.gguf_writer.add_key_length(self.hparams["kv_lora_rank"] + self.hparams["qk_rope_head_dim"]) self.gguf_writer.add_key_length(kv_lora_rank + qk_rope_head_dim)
self.gguf_writer.add_value_length(self.hparams["kv_lora_rank"]) self.gguf_writer.add_value_length(kv_lora_rank)
# Calculate n_embd_head_k_mla = qk_nope_head_dim + qk_rope_head_dim # Calculate n_embd_head_k_mla = qk_nope_head_dim + qk_rope_head_dim
if "n_embd_head_k_mla" in self.hparams: if (n_embd_head_k_mla := self.find_hparam(["n_embd_head_k_mla"], optional=True)) is not None:
self.gguf_writer.add_key_length_mla(self.hparams["n_embd_head_k_mla"]) self.gguf_writer.add_key_length_mla(n_embd_head_k_mla)
elif qk_nope_head_dim is not None and qk_rope_head_dim is not None: elif qk_nope_head_dim is not None and qk_rope_head_dim is not None:
n_embd_head_k_mla = qk_nope_head_dim + qk_rope_head_dim n_embd_head_k_mla = qk_nope_head_dim + qk_rope_head_dim
self.gguf_writer.add_key_length_mla(n_embd_head_k_mla) self.gguf_writer.add_key_length_mla(n_embd_head_k_mla)
# n_embd_head_v_mla = v_head_dim # n_embd_head_v_mla = v_head_dim
if "n_embd_head_v_mla" in self.hparams: if (n_embd_head_v_mla := self.find_hparam(["n_embd_head_v_mla"], optional=True)) is not None:
self.gguf_writer.add_value_length_mla(self.hparams["n_embd_head_v_mla"]) self.gguf_writer.add_value_length_mla(n_embd_head_v_mla)
elif v_head_dim is not None: elif v_head_dim is not None:
self.gguf_writer.add_value_length_mla(v_head_dim) self.gguf_writer.add_value_length_mla(v_head_dim)
@ -5216,7 +5217,7 @@ class KimiLinearModel(TextModel):
# process the experts separately # process the experts separately
if name.find("block_sparse_moe.experts") != -1: if name.find("block_sparse_moe.experts") != -1:
n_experts = self.hparams.get("num_local_experts", self.hparams.get("num_experts")) n_experts = self.find_hparam(["num_experts"], optional=False)
assert bid is not None assert bid is not None
if self._experts is None: if self._experts is None: