fde9ad2531
Co-authored-by: sglang-bot <232288953+sglang-bot@users.noreply.github.com> Co-authored-by: Brayden Zhong <brayden.zhong@radixark.ai> Co-authored-by: Jimmy Shong <69131491+Jiminator@users.noreply.github.com> Co-authored-by: hnyls2002 <lsyincs@gmail.com> Co-authored-by: Alex Nails <alex.nails@radixark.ai> Co-authored-by: Liangsheng Yin <hnyls2002@gmail.com>
73 lines
2.8 KiB
Python
73 lines
2.8 KiB
Python
# Copyright 2023-2026 SGLang Team
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Per-architecture GGUF -> HF tensor name maps.
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``GGUFModelLoader`` normally derives this map from ``gguf.get_tensor_name_map``,
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which only covers architectures upstream gguf-py knows, and from a meta-device
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``AutoModelForCausalLM.from_config`` to enumerate the HF parameter names. Neither
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works for an architecture that lives outside transformers, so those are supplied
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here instead.
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A builder returns the complete ``{gguf_tensor_name: hf_param_name}`` map. Any
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GGUF tensor left out of the map is skipped by ``gguf_quant_weights_iterator``,
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which is how dummy tensors are dropped.
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"""
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from typing import Callable, Dict
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from transformers import PretrainedConfig
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# Sandwich naming: ffn_norm is the pre-FFN norm.
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_MUSE_GLIMMER_LAYER_TENSORS = {
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"attn_norm": "input_layernorm",
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"post_attention_norm": "post_attn_norm",
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"ffn_norm": "post_attention_layernorm",
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"post_ffw_norm": "post_ffn_norm",
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"attn_q": "self_attn.q_proj",
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"attn_k": "self_attn.k_proj",
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"attn_v": "self_attn.v_proj",
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"attn_output": "self_attn.o_proj",
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"attn_gate": "self_attn.output_gate_proj",
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"ffn_gate": "mlp.gate_proj",
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"ffn_up": "mlp.up_proj",
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"ffn_down": "mlp.down_proj",
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}
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_MUSE_GLIMMER_GLOBAL_TENSORS = {
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"token_embd": "model.embed_tokens",
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"output_norm": "model.norm",
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"output": "lm_head",
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}
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# attn_q_norm/attn_k_norm omitted: Muse Glimmer's QK-norm is non-parametric.
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def build_muse_glimmer_name_map(config: PretrainedConfig) -> Dict[str, str]:
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name_map = {
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f"{gguf}.weight": f"{hf}.weight"
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for gguf, hf in _MUSE_GLIMMER_GLOBAL_TENSORS.items()
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}
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for layer in range(config.num_hidden_layers):
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for gguf, hf in _MUSE_GLIMMER_LAYER_TENSORS.items():
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name_map[f"blk.{layer}.{gguf}.weight"] = f"model.layers.{layer}.{hf}.weight"
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return name_map
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# Keyed by HF ``config.model_type`` (loader.py looks it up with that), which is
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# not the GGUF ``general.architecture`` that GGUF_NATIVE_CONFIG_BUILDERS uses:
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# llama.cpp spells the arch "muse-glimmer" while the HF config says "muse_glimmer".
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GGUF_HF_NAME_MAP_BUILDERS: Dict[str, Callable[[PretrainedConfig], Dict[str, str]]] = {
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"muse_glimmer": build_muse_glimmer_name_map,
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}
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