mirror of
https://github.com/ggml-org/llama.cpp.git
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fd621880f3
* feat: Add python-side constants and conversion for adapter.lora.invocation_string Branch: gabe-l-hart/alora-support Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: Add c++ side constants for adapter.lora.invocation_string Branch: gabe-l-hart/alora-support Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: Parse invocation string for adapters from GGUF Branch: gabe-l-hart/alora-support Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix(python): Update conversion to alora_invocation_tokens This is the preferred method in PEFT which is the source of ground truth https://github.com/huggingface/peft/pull/2609/files#diff-13380145401d203d5935c5189dd09879f990b81aa63e8e3aaff8ce9110333f0e Branch: gabe-l-hart/alora-support Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix(cpp): Update to alora_invocation_tokens on c++ side Branch: gabe-l-hart/alora-support Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: Add C APIs to get alora invocation token array from lora Branch: gabe-l-hart/alora-support Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: Initial implementation of alora cache logic in server This does not yet do the part to identify the invocation tokens and only apply the lora adapter afterwards, but it does seem to produce correct results if the invocation tokens are the beginning of the uncached input. Branch: gabe-l-hart/alora-support Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: Identify alora invocation sequences This currently limits to a single enabled alora per slot. Multiple aloras with different invocation sequences would be possible, but it would require a more complex integration of the adapter toggling and is not really a well studied case for alora since it's unclear if one alora can reuse cache from previous prefill computed with a different alora. Branch: gabe-l-hart/alora-support Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: Only reuse cache for tokens before the alora invocation start This is a bit of an edge case, but theoretically a user could try the same query with the alora disabled (just using the base model), then retry with the alora. The cached tokens from the first pass should be invalid. Branch: gabe-l-hart/alora-support Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: Handle un-cached tokens that come before the alora activation The solution is to only fill up to the token before the invocation start in the batch if there are any tokens to be prefilled between those pulled from cache and the invocation start. When this is detected, the alora is temporarily disabled with a scale of 0.0, then immediately re-enabled after it has been initialized for the internal graph. Since the batch does not complete the prompt tokens, the remaining prompt tokens are handled in the next task, pulling all of the non-alora tokens from cache and proceeding with prefill for the alora tokens. Branch: gabe-l-hart/alora-support Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Use || instead of 'or' Too much python 🤦 Branch: gabe-l-hart/alora-support Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Fix off-by-one for limiting cached tokens to before alora start This was the cause of the inconsistent results from the dummy test script with and without the turn that runs the prompt without the adapter before running it with the adapter. Branch: gabe-l-hart/alora-support Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Support backwards-compatibility for "invocation_string" in adapter_config.json While this has been replaced in the PEFT PR in favor of alora_invocation_tokens, the existing adapters in the ibm-granite org on HF use "invocation_string," so this will enable backwards compatibility and enable testing now (before PEFT PR changes have percolated everywhere). Branch: gabe-l-hart/alora-support Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Remove duplicate logging Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com> * feat: Report alora_invocation_string and alora_invocation_tokens from /lora-adapters Branch: gabe-l-hart/alora-support Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> --------- Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
506 lines
14 KiB
C++
506 lines
14 KiB
C++
#pragma once
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#include "ggml.h" // ggml_op
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#include <string>
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//
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// gguf constants (sync with gguf.py)
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//
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enum llm_arch {
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LLM_ARCH_LLAMA,
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LLM_ARCH_LLAMA4,
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LLM_ARCH_DECI,
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LLM_ARCH_FALCON,
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LLM_ARCH_BAICHUAN,
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LLM_ARCH_GROK,
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LLM_ARCH_GPT2,
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LLM_ARCH_GPTJ,
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LLM_ARCH_GPTNEOX,
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LLM_ARCH_MPT,
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LLM_ARCH_STARCODER,
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LLM_ARCH_REFACT,
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LLM_ARCH_BERT,
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LLM_ARCH_NOMIC_BERT,
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LLM_ARCH_NOMIC_BERT_MOE,
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LLM_ARCH_NEO_BERT,
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LLM_ARCH_JINA_BERT_V2,
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LLM_ARCH_JINA_BERT_V3,
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LLM_ARCH_BLOOM,
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LLM_ARCH_STABLELM,
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LLM_ARCH_QWEN,
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LLM_ARCH_QWEN2,
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LLM_ARCH_QWEN2MOE,
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LLM_ARCH_QWEN2VL,
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LLM_ARCH_QWEN3,
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LLM_ARCH_QWEN3MOE,
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LLM_ARCH_PHI2,
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LLM_ARCH_PHI3,
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LLM_ARCH_PHIMOE,
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LLM_ARCH_PLAMO,
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LLM_ARCH_PLAMO2,
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LLM_ARCH_CODESHELL,
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LLM_ARCH_ORION,
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LLM_ARCH_INTERNLM2,
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LLM_ARCH_MINICPM,
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LLM_ARCH_MINICPM3,
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LLM_ARCH_GEMMA,
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LLM_ARCH_GEMMA2,
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LLM_ARCH_GEMMA3,
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LLM_ARCH_GEMMA3N,
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LLM_ARCH_GEMMA_EMBEDDING,
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LLM_ARCH_STARCODER2,
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LLM_ARCH_MAMBA,
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LLM_ARCH_MAMBA2,
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LLM_ARCH_JAMBA,
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LLM_ARCH_FALCON_H1,
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LLM_ARCH_XVERSE,
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LLM_ARCH_COMMAND_R,
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LLM_ARCH_COHERE2,
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LLM_ARCH_DBRX,
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LLM_ARCH_OLMO,
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LLM_ARCH_OLMO2,
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LLM_ARCH_OLMOE,
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LLM_ARCH_OPENELM,
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LLM_ARCH_ARCTIC,
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LLM_ARCH_DEEPSEEK,
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LLM_ARCH_DEEPSEEK2,
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LLM_ARCH_CHATGLM,
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LLM_ARCH_GLM4,
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LLM_ARCH_GLM4_MOE,
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LLM_ARCH_BITNET,
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LLM_ARCH_T5,
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LLM_ARCH_T5ENCODER,
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LLM_ARCH_JAIS,
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LLM_ARCH_NEMOTRON,
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LLM_ARCH_NEMOTRON_H,
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LLM_ARCH_EXAONE,
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LLM_ARCH_EXAONE4,
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LLM_ARCH_RWKV6,
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LLM_ARCH_RWKV6QWEN2,
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LLM_ARCH_RWKV7,
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LLM_ARCH_ARWKV7,
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LLM_ARCH_GRANITE,
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LLM_ARCH_GRANITE_MOE,
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LLM_ARCH_GRANITE_HYBRID,
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LLM_ARCH_CHAMELEON,
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LLM_ARCH_WAVTOKENIZER_DEC,
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LLM_ARCH_PLM,
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LLM_ARCH_BAILINGMOE,
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LLM_ARCH_DOTS1,
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LLM_ARCH_ARCEE,
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LLM_ARCH_ERNIE4_5,
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LLM_ARCH_ERNIE4_5_MOE,
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LLM_ARCH_HUNYUAN_MOE,
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LLM_ARCH_HUNYUAN_DENSE,
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LLM_ARCH_SMOLLM3,
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LLM_ARCH_OPENAI_MOE,
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LLM_ARCH_LFM2,
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LLM_ARCH_DREAM,
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LLM_ARCH_SMALLTHINKER,
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LLM_ARCH_LLADA,
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LLM_ARCH_SEED_OSS,
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LLM_ARCH_UNKNOWN,
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};
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enum llm_kv {
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LLM_KV_GENERAL_TYPE,
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LLM_KV_GENERAL_ARCHITECTURE,
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LLM_KV_GENERAL_QUANTIZATION_VERSION,
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LLM_KV_GENERAL_ALIGNMENT,
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LLM_KV_GENERAL_FILE_TYPE,
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LLM_KV_GENERAL_NAME,
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LLM_KV_GENERAL_AUTHOR,
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LLM_KV_GENERAL_VERSION,
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LLM_KV_GENERAL_URL,
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LLM_KV_GENERAL_DESCRIPTION,
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LLM_KV_GENERAL_LICENSE,
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LLM_KV_GENERAL_SOURCE_URL,
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LLM_KV_GENERAL_SOURCE_HF_REPO,
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LLM_KV_VOCAB_SIZE,
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LLM_KV_CONTEXT_LENGTH,
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LLM_KV_EMBEDDING_LENGTH,
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LLM_KV_FEATURES_LENGTH,
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LLM_KV_BLOCK_COUNT,
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LLM_KV_LEADING_DENSE_BLOCK_COUNT,
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LLM_KV_FEED_FORWARD_LENGTH,
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LLM_KV_EXPERT_FEED_FORWARD_LENGTH,
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LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH,
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LLM_KV_USE_PARALLEL_RESIDUAL,
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LLM_KV_TENSOR_DATA_LAYOUT,
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LLM_KV_EXPERT_COUNT,
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LLM_KV_EXPERT_USED_COUNT,
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LLM_KV_EXPERT_SHARED_COUNT,
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LLM_KV_EXPERT_WEIGHTS_SCALE,
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LLM_KV_EXPERT_WEIGHTS_NORM,
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LLM_KV_EXPERT_GATING_FUNC,
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LLM_KV_MOE_EVERY_N_LAYERS,
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LLM_KV_NEXTN_PREDICT_LAYERS,
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LLM_KV_POOLING_TYPE,
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LLM_KV_LOGIT_SCALE,
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LLM_KV_DECODER_START_TOKEN_ID,
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LLM_KV_ATTN_LOGIT_SOFTCAPPING,
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LLM_KV_FINAL_LOGIT_SOFTCAPPING,
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LLM_KV_SWIN_NORM,
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LLM_KV_RESCALE_EVERY_N_LAYERS,
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LLM_KV_TIME_MIX_EXTRA_DIM,
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LLM_KV_TIME_DECAY_EXTRA_DIM,
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LLM_KV_RESIDUAL_SCALE,
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LLM_KV_EMBEDDING_SCALE,
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LLM_KV_TOKEN_SHIFT_COUNT,
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LLM_KV_INTERLEAVE_MOE_LAYER_STEP,
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LLM_KV_ATTENTION_HEAD_COUNT,
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LLM_KV_ATTENTION_HEAD_COUNT_KV,
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LLM_KV_ATTENTION_MAX_ALIBI_BIAS,
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LLM_KV_ATTENTION_CLAMP_KQV,
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LLM_KV_ATTENTION_KEY_LENGTH,
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LLM_KV_ATTENTION_VALUE_LENGTH,
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LLM_KV_ATTENTION_LAYERNORM_EPS,
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LLM_KV_ATTENTION_LAYERNORM_RMS_EPS,
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LLM_KV_ATTENTION_GROUPNORM_EPS,
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LLM_KV_ATTENTION_GROUPNORM_GROUPS,
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LLM_KV_ATTENTION_CAUSAL,
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LLM_KV_ATTENTION_Q_LORA_RANK,
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LLM_KV_ATTENTION_KV_LORA_RANK,
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LLM_KV_ATTENTION_DECAY_LORA_RANK,
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LLM_KV_ATTENTION_ICLR_LORA_RANK,
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LLM_KV_ATTENTION_VALUE_RESIDUAL_MIX_LORA_RANK,
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LLM_KV_ATTENTION_GATE_LORA_RANK,
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LLM_KV_ATTENTION_RELATIVE_BUCKETS_COUNT,
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LLM_KV_ATTENTION_SLIDING_WINDOW,
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LLM_KV_ATTENTION_SCALE,
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LLM_KV_ATTENTION_KEY_LENGTH_MLA,
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LLM_KV_ATTENTION_VALUE_LENGTH_MLA,
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LLM_KV_ROPE_DIMENSION_COUNT,
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LLM_KV_ROPE_DIMENSION_SECTIONS,
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LLM_KV_ROPE_FREQ_BASE,
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LLM_KV_ROPE_SCALE_LINEAR,
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LLM_KV_ROPE_SCALING_TYPE,
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LLM_KV_ROPE_SCALING_FACTOR,
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LLM_KV_ROPE_SCALING_ATTN_FACTOR,
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LLM_KV_ROPE_SCALING_ORIG_CTX_LEN,
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LLM_KV_ROPE_SCALING_FINETUNED,
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LLM_KV_ROPE_SCALING_YARN_LOG_MUL,
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LLM_KV_SPLIT_NO,
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LLM_KV_SPLIT_COUNT,
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LLM_KV_SPLIT_TENSORS_COUNT,
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LLM_KV_SSM_INNER_SIZE,
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LLM_KV_SSM_CONV_KERNEL,
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LLM_KV_SSM_STATE_SIZE,
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LLM_KV_SSM_TIME_STEP_RANK,
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LLM_KV_SSM_GROUP_COUNT,
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LLM_KV_SSM_DT_B_C_RMS,
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LLM_KV_WKV_HEAD_SIZE,
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LLM_KV_TOKENIZER_MODEL,
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LLM_KV_TOKENIZER_PRE,
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LLM_KV_TOKENIZER_LIST,
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LLM_KV_TOKENIZER_TOKEN_TYPE,
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LLM_KV_TOKENIZER_TOKEN_TYPE_COUNT,
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LLM_KV_TOKENIZER_SCORES,
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LLM_KV_TOKENIZER_MERGES,
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LLM_KV_TOKENIZER_BOS_ID,
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LLM_KV_TOKENIZER_EOS_ID,
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LLM_KV_TOKENIZER_EOT_ID,
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LLM_KV_TOKENIZER_EOM_ID,
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LLM_KV_TOKENIZER_UNK_ID,
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LLM_KV_TOKENIZER_SEP_ID,
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LLM_KV_TOKENIZER_PAD_ID,
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LLM_KV_TOKENIZER_CLS_ID,
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LLM_KV_TOKENIZER_MASK_ID,
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LLM_KV_TOKENIZER_ADD_BOS,
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LLM_KV_TOKENIZER_ADD_EOS,
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LLM_KV_TOKENIZER_ADD_SEP,
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LLM_KV_TOKENIZER_ADD_PREFIX,
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LLM_KV_TOKENIZER_REMOVE_EXTRA_WS,
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LLM_KV_TOKENIZER_PRECOMPILED_CHARSMAP,
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LLM_KV_TOKENIZER_HF_JSON,
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LLM_KV_TOKENIZER_RWKV,
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LLM_KV_TOKENIZER_CHAT_TEMPLATE,
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LLM_KV_TOKENIZER_FIM_PRE_ID,
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LLM_KV_TOKENIZER_FIM_SUF_ID,
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LLM_KV_TOKENIZER_FIM_MID_ID,
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LLM_KV_TOKENIZER_FIM_PAD_ID,
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LLM_KV_TOKENIZER_FIM_REP_ID,
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LLM_KV_TOKENIZER_FIM_SEP_ID,
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LLM_KV_ADAPTER_TYPE,
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LLM_KV_ADAPTER_LORA_ALPHA,
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LLM_KV_ADAPTER_LORA_TASK_NAME,
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LLM_KV_ADAPTER_LORA_PROMPT_PREFIX,
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LLM_KV_ADAPTER_ALORA_INVOCATION_TOKENS,
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LLM_KV_POSNET_EMBEDDING_LENGTH,
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LLM_KV_POSNET_BLOCK_COUNT,
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LLM_KV_CONVNEXT_EMBEDDING_LENGTH,
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LLM_KV_CONVNEXT_BLOCK_COUNT,
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LLM_KV_CLASSIFIER_OUTPUT_LABELS,
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LLM_KV_SHORTCONV_L_CACHE,
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// deprecated:
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LLM_KV_TOKENIZER_PREFIX_ID,
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LLM_KV_TOKENIZER_SUFFIX_ID,
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LLM_KV_TOKENIZER_MIDDLE_ID,
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};
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enum llm_tensor {
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LLM_TENSOR_TOKEN_EMBD,
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LLM_TENSOR_TOKEN_EMBD_NORM,
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LLM_TENSOR_TOKEN_TYPES,
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LLM_TENSOR_POS_EMBD,
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LLM_TENSOR_OUTPUT,
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LLM_TENSOR_OUTPUT_NORM,
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LLM_TENSOR_ROPE_FREQS,
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LLM_TENSOR_ROPE_FACTORS_LONG,
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LLM_TENSOR_ROPE_FACTORS_SHORT,
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LLM_TENSOR_ATTN_Q,
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LLM_TENSOR_ATTN_K,
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LLM_TENSOR_ATTN_V,
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LLM_TENSOR_ATTN_QKV,
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LLM_TENSOR_ATTN_OUT,
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LLM_TENSOR_ATTN_NORM,
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LLM_TENSOR_ATTN_NORM_2,
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LLM_TENSOR_ATTN_OUT_NORM,
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LLM_TENSOR_ATTN_POST_NORM,
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LLM_TENSOR_ATTN_ROT_EMBD,
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LLM_TENSOR_ATTN_SINKS,
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LLM_TENSOR_FFN_GATE_INP,
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LLM_TENSOR_FFN_GATE_INP_SHEXP,
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LLM_TENSOR_FFN_NORM,
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LLM_TENSOR_FFN_POST_NORM,
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LLM_TENSOR_FFN_GATE,
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LLM_TENSOR_FFN_DOWN,
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LLM_TENSOR_FFN_UP,
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LLM_TENSOR_FFN_ACT,
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LLM_TENSOR_FFN_DOWN_EXP, // split experts for backward compatibility
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LLM_TENSOR_FFN_GATE_EXP,
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LLM_TENSOR_FFN_UP_EXP,
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LLM_TENSOR_FFN_NORM_EXPS,
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LLM_TENSOR_FFN_DOWN_EXPS, // merged experts
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LLM_TENSOR_FFN_GATE_EXPS,
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LLM_TENSOR_FFN_UP_EXPS,
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LLM_TENSOR_FFN_DOWN_SHEXP,
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LLM_TENSOR_FFN_GATE_SHEXP,
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LLM_TENSOR_FFN_UP_SHEXP,
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LLM_TENSOR_FFN_EXP_PROBS_B,
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LLM_TENSOR_ATTN_Q_NORM,
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LLM_TENSOR_ATTN_K_NORM,
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LLM_TENSOR_LAYER_OUT_NORM,
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LLM_TENSOR_POST_ATTN_NORM,
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LLM_TENSOR_POST_MLP_NORM,
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LLM_TENSOR_PER_LAYER_TOKEN_EMBD, // gemma3n
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LLM_TENSOR_PER_LAYER_MODEL_PROJ, // gemma3n
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LLM_TENSOR_PER_LAYER_INP_GATE, // gemma3n
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LLM_TENSOR_PER_LAYER_PROJ, // gemma3n
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LLM_TENSOR_PER_LAYER_PROJ_NORM, // gemma3n
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LLM_TENSOR_PER_LAYER_POST_NORM, // gemma3n
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LLM_TENSOR_ALTUP_PROJ, // gemma3n
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LLM_TENSOR_ALTUP_UNEMBD_PROJ, // gemma3n
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LLM_TENSOR_ALTUP_CORRECT_COEF, // gemma3n
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LLM_TENSOR_ALTUP_CORRECT_SCALE, // gemma3n
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LLM_TENSOR_ALTUP_PREDICT_COEF, // gemma3n
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LLM_TENSOR_ALTUP_ROUTER, // gemma3n
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LLM_TENSOR_ALTUP_ROUTER_NORM, // gemma3n
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LLM_TENSOR_LAUREL_L, // gemma3n
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LLM_TENSOR_LAUREL_R, // gemma3n
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LLM_TENSOR_LAUREL_POST_NORM, // gemma3n
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LLM_TENSOR_SSM_IN,
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LLM_TENSOR_SSM_CONV1D,
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LLM_TENSOR_SSM_X,
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LLM_TENSOR_SSM_DT,
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LLM_TENSOR_SSM_DT_NORM,
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LLM_TENSOR_SSM_A,
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LLM_TENSOR_SSM_B_NORM,
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LLM_TENSOR_SSM_C_NORM,
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LLM_TENSOR_SSM_D,
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LLM_TENSOR_SSM_NORM,
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LLM_TENSOR_SSM_OUT,
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LLM_TENSOR_TIME_MIX_W0,
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LLM_TENSOR_TIME_MIX_W1,
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LLM_TENSOR_TIME_MIX_W2,
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LLM_TENSOR_TIME_MIX_A0,
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LLM_TENSOR_TIME_MIX_A1,
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|
LLM_TENSOR_TIME_MIX_A2,
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|
LLM_TENSOR_TIME_MIX_V0,
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|
LLM_TENSOR_TIME_MIX_V1,
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|
LLM_TENSOR_TIME_MIX_V2,
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|
LLM_TENSOR_TIME_MIX_G1,
|
|
LLM_TENSOR_TIME_MIX_G2,
|
|
LLM_TENSOR_TIME_MIX_K_K,
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|
LLM_TENSOR_TIME_MIX_K_A,
|
|
LLM_TENSOR_TIME_MIX_R_K,
|
|
LLM_TENSOR_TIME_MIX_LERP_X,
|
|
LLM_TENSOR_TIME_MIX_LERP_W,
|
|
LLM_TENSOR_TIME_MIX_LERP_K,
|
|
LLM_TENSOR_TIME_MIX_LERP_V,
|
|
LLM_TENSOR_TIME_MIX_LERP_R,
|
|
LLM_TENSOR_TIME_MIX_LERP_G,
|
|
LLM_TENSOR_TIME_MIX_LERP_FUSED,
|
|
LLM_TENSOR_TIME_MIX_FIRST,
|
|
LLM_TENSOR_TIME_MIX_DECAY,
|
|
LLM_TENSOR_TIME_MIX_DECAY_W1,
|
|
LLM_TENSOR_TIME_MIX_DECAY_W2,
|
|
LLM_TENSOR_TIME_MIX_KEY,
|
|
LLM_TENSOR_TIME_MIX_VALUE,
|
|
LLM_TENSOR_TIME_MIX_RECEPTANCE,
|
|
LLM_TENSOR_TIME_MIX_GATE,
|
|
LLM_TENSOR_TIME_MIX_LN,
|
|
LLM_TENSOR_TIME_MIX_OUTPUT,
|
|
LLM_TENSOR_CHANNEL_MIX_LERP_K,
|
|
LLM_TENSOR_CHANNEL_MIX_LERP_R,
|
|
LLM_TENSOR_CHANNEL_MIX_KEY,
|
|
LLM_TENSOR_CHANNEL_MIX_RECEPTANCE,
|
|
LLM_TENSOR_CHANNEL_MIX_VALUE,
|
|
LLM_TENSOR_ATTN_Q_A,
|
|
LLM_TENSOR_ATTN_Q_B,
|
|
LLM_TENSOR_ATTN_KV_A_MQA,
|
|
LLM_TENSOR_ATTN_KV_B,
|
|
LLM_TENSOR_ATTN_K_B,
|
|
LLM_TENSOR_ATTN_V_B,
|
|
LLM_TENSOR_ATTN_Q_A_NORM,
|
|
LLM_TENSOR_ATTN_KV_A_NORM,
|
|
LLM_TENSOR_ATTN_SUB_NORM,
|
|
LLM_TENSOR_FFN_SUB_NORM,
|
|
LLM_TENSOR_DEC_ATTN_NORM,
|
|
LLM_TENSOR_DEC_ATTN_Q,
|
|
LLM_TENSOR_DEC_ATTN_K,
|
|
LLM_TENSOR_DEC_ATTN_V,
|
|
LLM_TENSOR_DEC_ATTN_OUT,
|
|
LLM_TENSOR_DEC_ATTN_REL_B,
|
|
LLM_TENSOR_DEC_CROSS_ATTN_NORM,
|
|
LLM_TENSOR_DEC_CROSS_ATTN_Q,
|
|
LLM_TENSOR_DEC_CROSS_ATTN_K,
|
|
LLM_TENSOR_DEC_CROSS_ATTN_V,
|
|
LLM_TENSOR_DEC_CROSS_ATTN_OUT,
|
|
LLM_TENSOR_DEC_CROSS_ATTN_REL_B,
|
|
LLM_TENSOR_DEC_FFN_NORM,
|
|
LLM_TENSOR_DEC_FFN_GATE,
|
|
LLM_TENSOR_DEC_FFN_DOWN,
|
|
LLM_TENSOR_DEC_FFN_UP,
|
|
LLM_TENSOR_DEC_OUTPUT_NORM,
|
|
LLM_TENSOR_ENC_ATTN_NORM,
|
|
LLM_TENSOR_ENC_ATTN_Q,
|
|
LLM_TENSOR_ENC_ATTN_K,
|
|
LLM_TENSOR_ENC_ATTN_V,
|
|
LLM_TENSOR_ENC_ATTN_OUT,
|
|
LLM_TENSOR_ENC_ATTN_REL_B,
|
|
LLM_TENSOR_ENC_FFN_NORM,
|
|
LLM_TENSOR_ENC_FFN_GATE,
|
|
LLM_TENSOR_ENC_FFN_DOWN,
|
|
LLM_TENSOR_ENC_FFN_UP,
|
|
LLM_TENSOR_ENC_OUTPUT_NORM,
|
|
LLM_TENSOR_CLS,
|
|
LLM_TENSOR_CLS_OUT,
|
|
LLM_TENSOR_CONV1D,
|
|
LLM_TENSOR_CONVNEXT_DW,
|
|
LLM_TENSOR_CONVNEXT_NORM,
|
|
LLM_TENSOR_CONVNEXT_PW1,
|
|
LLM_TENSOR_CONVNEXT_PW2,
|
|
LLM_TENSOR_CONVNEXT_GAMMA,
|
|
LLM_TENSOR_POS_NET_CONV1,
|
|
LLM_TENSOR_POS_NET_CONV2,
|
|
LLM_TENSOR_POS_NET_NORM,
|
|
LLM_TENSOR_POS_NET_NORM1,
|
|
LLM_TENSOR_POS_NET_NORM2,
|
|
LLM_TENSOR_POS_NET_ATTN_NORM,
|
|
LLM_TENSOR_POS_NET_ATTN_Q,
|
|
LLM_TENSOR_POS_NET_ATTN_K,
|
|
LLM_TENSOR_POS_NET_ATTN_V,
|
|
LLM_TENSOR_POS_NET_ATTN_OUT,
|
|
LLM_TENSOR_SHORTCONV_CONV,
|
|
LLM_TENSOR_SHORTCONV_INPROJ,
|
|
LLM_TENSOR_SHORTCONV_OUTPROJ,
|
|
LLM_TENSOR_NEXTN_EH_PROJ,
|
|
LLM_TENSOR_NEXTN_EMBED_TOKENS,
|
|
LLM_TENSOR_NEXTN_ENORM,
|
|
LLM_TENSOR_NEXTN_HNORM,
|
|
LLM_TENSOR_NEXTN_SHARED_HEAD_HEAD,
|
|
LLM_TENSOR_NEXTN_SHARED_HEAD_NORM,
|
|
};
|
|
|
|
enum llm_tensor_layer {
|
|
LLM_TENSOR_LAYER_INPUT,
|
|
LLM_TENSOR_LAYER_REPEATING,
|
|
LLM_TENSOR_LAYER_OUTPUT,
|
|
};
|
|
|
|
struct LLM_KV {
|
|
LLM_KV(llm_arch arch, const char * suffix = nullptr);
|
|
|
|
llm_arch arch;
|
|
const char * suffix;
|
|
|
|
std::string operator()(llm_kv kv) const;
|
|
};
|
|
|
|
// helper to handle gguf constants
|
|
// usage:
|
|
//
|
|
// const auto tn = LLM_TN(LLM_ARCH_LLAMA);
|
|
//
|
|
// std::string name = tn(LLM_TENSOR_OUTPUT); -> "output"
|
|
// std::string name = tn(LLM_TENSOR_TOKEN_EMBD, "bias"); -> "token_embd.bias"
|
|
// std::string name = tn(LLM_TENSOR_ATTN_NORM, "weight", 3); -> "blk.3.attn_norm.weight"
|
|
//
|
|
struct LLM_TN_IMPL {
|
|
const llm_arch arch;
|
|
const llm_tensor tensor;
|
|
const char * const suffix;
|
|
const int bid;
|
|
const int xid;
|
|
|
|
std::string str() const;
|
|
|
|
operator std::string() const {
|
|
return str();
|
|
}
|
|
|
|
friend bool operator==(const std::string & str, const LLM_TN_IMPL & tn) {
|
|
return str == tn.str();
|
|
}
|
|
|
|
friend bool operator!=(const std::string & str, const LLM_TN_IMPL & tn) {
|
|
return str != tn.str();
|
|
}
|
|
};
|
|
|
|
struct LLM_TN {
|
|
LLM_TN(llm_arch arch) : arch(arch) {}
|
|
|
|
llm_arch arch;
|
|
|
|
LLM_TN_IMPL operator()(llm_tensor tensor, const char * suffix, int bid = -1, int xid = -1) const {
|
|
return { arch, tensor, suffix, bid, xid };
|
|
}
|
|
|
|
LLM_TN_IMPL operator()(llm_tensor tensor, int bid = -1, int xid = -1) const {
|
|
return { arch, tensor, nullptr, bid, xid };
|
|
}
|
|
};
|
|
|
|
|
|
struct llm_tensor_info {
|
|
llm_tensor_layer layer;
|
|
ggml_op op;
|
|
};
|
|
|
|
const char * llm_arch_name(llm_arch arch);
|
|
|
|
llm_arch llm_arch_from_string(const std::string & name);
|
|
|
|
const llm_tensor_info & llm_tensor_info_for(llm_tensor tensor);
|
|
|
|
bool llm_arch_is_recurrent(const llm_arch & arch);
|
|
bool llm_arch_is_hybrid (const llm_arch & arch);
|
|
bool llm_arch_is_diffusion(const llm_arch & arch);
|