Feature/kimi linear support #17592
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Feature/kimi linear support #17592
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- Implement KDA layer (linear attention with gates and decay) - Implement MLA layer (multi-head latent attention with KV compression) - Support MoE FFN with shared experts - Add TikToken tokenizer support for Kimi models - Fix vocab loading for large vocabularies - Model loads and runs inference (27 layers, 603 tensors)
- Add missing MoE metadata to GGUF conversion: - moe_intermediate_size (1024) - num_shared_experts (1) - first_k_dense_replace (1) - routed_scaling_factor (2.446) - expert_gating_func (sigmoid) - Fix MoE gating function default to SIGMOID (was SOFTMAX) - Add expert_weights_scale loading with default 2.446 - Enable moe_renormalize (norm_w=true) in build_moe_ffn - Add fallback for exp_probs_b tensor suffix compatibility
- Add KDA (Kimi Delta Attention) CUDA kernel (kda-scan.cu) - Fix recurrence order: decay first, then retrieval - Verify CPU/CUDA implementation consistency - Support head_dim=128, L2 normalization for Q/K
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Nov 29, 2025
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| # KimiLinearModel is defined later in this file (line ~5140) as a TextModel subclass | ||
| # This old definition has been removed to avoid conflicts | ||
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| @ModelBase.register( |
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| # KimiLinearModel is defined later in this file (line ~5140) as a TextModel subclass | |
| # This old definition has been removed to avoid conflicts | |
| @ModelBase.register( | |
| @ModelBase.register( |
| (self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_Q, bid), q), | ||
| (self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_K, bid), k), | ||
| (self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_V, bid), v), | ||
| ] |
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| ] | |
| ] | |
| else: | |
| return [(self.map_tensor_name(name), data_torch)] |
convert_hf_to_gguf.py
Outdated
| @ModelBase.register("KimiLinearModel", "KimiLinearForCausalLM") | ||
| class KimiLinearModel(TextModel): | ||
| """Kimi-Linear model with hybrid MLA+KDA architecture""" | ||
| model_arch = gguf.MODEL_ARCH.KIMI |
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| model_arch = gguf.MODEL_ARCH.KIMI | |
| model_arch = gguf.MODEL_ARCH.KIMI_LINEAR |
| _experts: list[dict[str, Tensor]] | None = None | ||
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| def set_gguf_parameters(self): | ||
| self.gguf_writer.add_vocab_size(self.hparams["vocab_size"]) |
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| self.gguf_writer.add_vocab_size(self.hparams["vocab_size"]) | |
| super().set_gguf_parameters() | |
| self.gguf_writer.add_vocab_size(self.hparams["vocab_size"]) |
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| # Use find_hparam for context length | ||
| # Kimi uses model_max_length | ||
| n_ctx = self.find_hparam(["max_position_embeddings", "model_max_length", "n_ctx", "n_positions"], optional=True) | ||
| if n_ctx is not None: | ||
| self.gguf_writer.add_context_length(n_ctx) | ||
| else: | ||
| return [(self.map_tensor_name(name), data_torch)] | ||
| # Default to 4096 if not found | ||
| logger.warning("No context length found in config, defaulting to 4096") | ||
| self.gguf_writer.add_context_length(4096) |
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Add model_max_length to TextModel.set_gguf_parameters instead, the fallback is not necessary.
Author
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I have fixed these errors in the commit at cacaview@780dd78 |
Collaborator
Please address the remaining unresolved ones as well. |
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This is the current work progress：
#16930 (comment)