mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2024-12-27 22:59:24 +01:00
471 lines
23 KiB
Python
471 lines
23 KiB
Python
from __future__ import annotations
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from typing import Sequence
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from .constants import MODEL_ARCH, MODEL_TENSOR, MODEL_TENSORS, TENSOR_NAMES
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class TensorNameMap:
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mappings_cfg: dict[MODEL_TENSOR, tuple[str, ...]] = {
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# Token embeddings
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MODEL_TENSOR.TOKEN_EMBD: (
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"gpt_neox.embed_in", # gptneox
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"transformer.wte", # gpt2 gpt-j mpt refact qwen dbrx
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"transformer.word_embeddings", # falcon
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"word_embeddings", # bloom
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"model.embed_tokens", # llama-hf
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"tok_embeddings", # llama-pth
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"embeddings.word_embeddings", # bert nomic-bert
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"language_model.embedding.word_embeddings", # persimmon
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"wte", # gpt2
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"transformer.embd.wte", # phi2
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"model.tok_embeddings", # internlm2
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"model.embedding", # mamba-qbert
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"backbone.embedding", # mamba
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"backbone.embeddings", # mamba-hf
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"transformer.in_out_embed", # Grok
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),
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# Token type embeddings
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MODEL_TENSOR.TOKEN_TYPES: (
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"embeddings.token_type_embeddings", # bert nomic-bert
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),
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# Normalization of token embeddings
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MODEL_TENSOR.TOKEN_EMBD_NORM: (
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"word_embeddings_layernorm", # bloom
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"embeddings.LayerNorm", # bert
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"emb_ln", # nomic-bert
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),
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# Position embeddings
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MODEL_TENSOR.POS_EMBD: (
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"transformer.wpe", # gpt2
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"embeddings.position_embeddings", # bert
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"wpe", # gpt2
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),
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# Output
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MODEL_TENSOR.OUTPUT: (
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"embed_out", # gptneox
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"lm_head", # gpt2 mpt falcon llama-hf baichuan qwen mamba dbrx
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"output", # llama-pth bloom internlm2
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"word_embeddings_for_head", # persimmon
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"lm_head.linear", # phi2
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),
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# Output norm
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MODEL_TENSOR.OUTPUT_NORM: (
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"gpt_neox.final_layer_norm", # gptneox
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"transformer.ln_f", # gpt2 gpt-j falcon
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"model.norm", # llama-hf baichuan internlm2
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"norm", # llama-pth
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"transformer.norm_f", # mpt dbrx
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"ln_f", # refact bloom qwen gpt2
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"language_model.encoder.final_layernorm", # persimmon
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"model.final_layernorm", # persimmon
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"lm_head.ln", # phi2
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"model.norm_f", # mamba-qbert
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"backbone.norm_f", # mamba
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"transformer.rms_norm", # Grok
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),
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# Rope frequencies
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MODEL_TENSOR.ROPE_FREQS: (
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"rope.freqs", # llama-pth
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),
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}
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block_mappings_cfg: dict[MODEL_TENSOR, tuple[str, ...]] = {
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# Attention norm
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MODEL_TENSOR.ATTN_NORM: (
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"gpt_neox.layers.{bid}.input_layernorm", # gptneox
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"transformer.h.{bid}.ln_1", # gpt2 gpt-j refact qwen
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"transformer.blocks.{bid}.norm_1", # mpt
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"transformer.h.{bid}.input_layernorm", # falcon7b
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"h.{bid}.input_layernorm", # bloom
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"transformer.h.{bid}.ln_mlp", # falcon40b
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"model.layers.{bid}.input_layernorm", # llama-hf
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"layers.{bid}.attention_norm", # llama-pth
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"language_model.encoder.layers.{bid}.input_layernorm", # persimmon
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"model.layers.{bid}.ln1", # yi
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"h.{bid}.ln_1", # gpt2
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"transformer.h.{bid}.ln", # phi2
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"model.layers.layers.{bid}.norm", # plamo
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"model.layers.{bid}.attention_norm", # internlm2
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"model.layers.{bid}.norm", # mamba-qbert
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"backbone.layers.{bid}.norm", # mamba
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"transformer.decoder_layer.{bid}.rms_norm", # Grok
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"transformer.blocks.{bid}.norm_attn_norm.norm_1", # dbrx
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),
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# Attention norm 2
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MODEL_TENSOR.ATTN_NORM_2: (
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"transformer.h.{bid}.ln_attn", # falcon40b
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),
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# Attention query-key-value
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MODEL_TENSOR.ATTN_QKV: (
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"gpt_neox.layers.{bid}.attention.query_key_value", # gptneox
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"transformer.h.{bid}.attn.c_attn", # gpt2 qwen
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"transformer.blocks.{bid}.attn.Wqkv", # mpt
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"transformer.blocks.{bid}.norm_attn_norm.attn.Wqkv", # dbrx
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"transformer.h.{bid}.self_attention.query_key_value", # falcon
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"h.{bid}.self_attention.query_key_value", # bloom
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"language_model.encoder.layers.{bid}.self_attention.query_key_value", # persimmon
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"model.layers.{bid}.self_attn.query_key_value", # persimmon
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"h.{bid}.attn.c_attn", # gpt2
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"transformer.h.{bid}.mixer.Wqkv", # phi2
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"encoder.layers.{bid}.attn.Wqkv", # nomic-bert
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"model.layers.{bid}.self_attn.qkv_proj" # phi3
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),
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# Attention query
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MODEL_TENSOR.ATTN_Q: (
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"model.layers.{bid}.self_attn.q_proj", # llama-hf
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"layers.{bid}.attention.wq", # llama-pth
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"encoder.layer.{bid}.attention.self.query", # bert
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"transformer.h.{bid}.attn.q_proj", # gpt-j
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"model.layers.layers.{bid}.self_attn.q_proj", # plamo
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"model.layers.{bid}.attention.wq", # internlm2
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"transformer.decoder_layer.{bid}.multi_head_attention.query" # Grok
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),
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# Attention key
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MODEL_TENSOR.ATTN_K: (
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"model.layers.{bid}.self_attn.k_proj", # llama-hf
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"layers.{bid}.attention.wk", # llama-pth
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"encoder.layer.{bid}.attention.self.key", # bert
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"transformer.h.{bid}.attn.k_proj", # gpt-j
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"transformer.h.{bid}.attn.k", # refact
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"model.layers.layers.{bid}.self_attn.k_proj", # plamo
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"model.layers.{bid}.attention.wk", # internlm2
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"transformer.decoder_layer.{bid}.multi_head_attention.key" # Grok
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),
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# Attention value
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MODEL_TENSOR.ATTN_V: (
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"model.layers.{bid}.self_attn.v_proj", # llama-hf
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"layers.{bid}.attention.wv", # llama-pth
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"encoder.layer.{bid}.attention.self.value", # bert
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"transformer.h.{bid}.attn.v_proj", # gpt-j
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"transformer.h.{bid}.attn.v", # refact
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"model.layers.layers.{bid}.self_attn.v_proj", # plamo
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"model.layers.{bid}.attention.wv", # internlm2
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"transformer.decoder_layer.{bid}.multi_head_attention.value" # Grok
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),
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# Attention output
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MODEL_TENSOR.ATTN_OUT: (
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"gpt_neox.layers.{bid}.attention.dense", # gptneox
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"transformer.h.{bid}.attn.c_proj", # gpt2 refact qwen
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"transformer.blocks.{bid}.attn.out_proj", # mpt
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"transformer.h.{bid}.self_attention.dense", # falcon
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"h.{bid}.self_attention.dense", # bloom
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"model.layers.{bid}.self_attn.o_proj", # llama-hf
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"layers.{bid}.attention.wo", # llama-pth
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"encoder.layer.{bid}.attention.output.dense", # bert
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"transformer.h.{bid}.attn.out_proj", # gpt-j
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"language_model.encoder.layers.{bid}.self_attention.dense", # persimmon
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"model.layers.{bid}.self_attn.dense", # persimmon
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"h.{bid}.attn.c_proj", # gpt2
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"transformer.h.{bid}.mixer.out_proj", # phi2
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"model.layers.layers.{bid}.self_attn.o_proj", # plamo
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"model.layers.{bid}.attention.wo", # internlm2
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"encoder.layers.{bid}.attn.out_proj", # nomic-bert
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"transformer.decoder_layer.{bid}.multi_head_attention.linear", # Grok
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"transformer.blocks.{bid}.norm_attn_norm.attn.out_proj", # dbrx
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),
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# Attention output norm
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MODEL_TENSOR.ATTN_OUT_NORM: (
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"encoder.layer.{bid}.attention.output.LayerNorm", # bert
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"encoder.layers.{bid}.norm1", # nomic-bert
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"transformer.decoder_layer.{bid}.rms_norm_1", # Grok
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"transformer.blocks.{bid}.norm_attn_norm.norm_2", # dbrx
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),
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# Rotary embeddings
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MODEL_TENSOR.ATTN_ROT_EMBD: (
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"model.layers.{bid}.self_attn.rotary_emb.inv_freq", # llama-hf
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"layers.{bid}.attention.inner_attention.rope.freqs", # llama-pth
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"model.layers.layers.{bid}.self_attn.rotary_emb.inv_freq", # plamo
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"transformer.h.{bid}.attn.rotary_emb.inv_freq", # codeshell
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),
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# Feed-forward norm
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MODEL_TENSOR.FFN_NORM: (
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"gpt_neox.layers.{bid}.post_attention_layernorm", # gptneox
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"transformer.h.{bid}.ln_2", # gpt2 refact qwen
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"h.{bid}.post_attention_layernorm", # bloom
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"transformer.blocks.{bid}.norm_2", # mpt
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"model.layers.{bid}.post_attention_layernorm", # llama-hf
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"layers.{bid}.ffn_norm", # llama-pth
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"language_model.encoder.layers.{bid}.post_attention_layernorm", # persimmon
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"model.layers.{bid}.ln2", # yi
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"h.{bid}.ln_2", # gpt2
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"model.layers.{bid}.ffn_norm", # internlm2
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"transformer.decoder_layer.{bid}.rms_norm_2", # Grok
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"model.layers.{bid}.pre_ff_layernorm", # jamba
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),
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MODEL_TENSOR.FFN_GATE_INP: (
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"layers.{bid}.feed_forward.gate", # mixtral
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"model.layers.{bid}.block_sparse_moe.gate", # mixtral
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"model.layers.{bid}.mlp.gate", # qwen2moe
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"transformer.decoder_layer.{bid}.router", # Grok
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"transformer.blocks.{bid}.ffn.router.layer", # dbrx
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"model.layers.{bid}.feed_forward.router", # jamba
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),
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MODEL_TENSOR.FFN_GATE_INP_SHEXP: (
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"model.layers.{bid}.mlp.shared_expert_gate", # qwen2moe
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),
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# Feed-forward up
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MODEL_TENSOR.FFN_UP: (
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"gpt_neox.layers.{bid}.mlp.dense_h_to_4h", # gptneox
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"transformer.h.{bid}.mlp.c_fc", # gpt2
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"transformer.blocks.{bid}.ffn.up_proj", # mpt
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"transformer.h.{bid}.mlp.dense_h_to_4h", # falcon
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"h.{bid}.mlp.dense_h_to_4h", # bloom
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"model.layers.{bid}.mlp.up_proj", # llama-hf refact
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"layers.{bid}.feed_forward.w3", # llama-pth
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"encoder.layer.{bid}.intermediate.dense", # bert
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"transformer.h.{bid}.mlp.fc_in", # gpt-j
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"transformer.h.{bid}.mlp.linear_3", # refact
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"language_model.encoder.layers.{bid}.mlp.dense_h_to_4h", # persimmon
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"model.layers.{bid}.mlp.dense_h_to_4h", # persimmon
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"transformer.h.{bid}.mlp.w1", # qwen
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"h.{bid}.mlp.c_fc", # gpt2
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"transformer.h.{bid}.mlp.fc1", # phi2
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"model.layers.{bid}.mlp.fc1", # phi2
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"model.layers.{bid}.mlp.gate_up_proj", # phi3
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"model.layers.layers.{bid}.mlp.up_proj", # plamo
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"model.layers.{bid}.feed_forward.w3", # internlm2
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"encoder.layers.{bid}.mlp.fc11", # nomic-bert
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"model.layers.{bid}.mlp.c_fc", # starcoder2
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"encoder.layer.{bid}.mlp.gated_layers_v", # jina-bert-v2
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"model.layers.{bid}.feed_forward.up_proj", # jamba
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),
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MODEL_TENSOR.FFN_UP_EXP: (
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"layers.{bid}.feed_forward.experts.w3", # mixtral (merged)
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"transformer.decoder_layer.{bid}.moe.linear_v", # Grok (merged)
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"transformer.blocks.{bid}.ffn.experts.mlp.v1", # dbrx
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"model.layers.{bid}.mlp.experts.up_proj", # qwen2moe (merged)
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),
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MODEL_TENSOR.FFN_UP_SHEXP: (
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"model.layers.{bid}.mlp.shared_expert.up_proj", # qwen2moe
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),
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# AWQ-activation gate
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MODEL_TENSOR.FFN_ACT: (
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"transformer.blocks.{bid}.ffn.act", # mpt
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),
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# Feed-forward gate
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MODEL_TENSOR.FFN_GATE: (
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"model.layers.{bid}.mlp.gate_proj", # llama-hf refact
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"layers.{bid}.feed_forward.w1", # llama-pth
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"transformer.h.{bid}.mlp.w2", # qwen
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"model.layers.layers.{bid}.mlp.gate_proj", # plamo
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"model.layers.{bid}.feed_forward.w1", # internlm2
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"encoder.layers.{bid}.mlp.fc12", # nomic-bert
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"encoder.layer.{bid}.mlp.gated_layers_w", # jina-bert-v2
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"transformer.h.{bid}.mlp.linear_1", # refact
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"model.layers.{bid}.feed_forward.gate_proj", # jamba
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),
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MODEL_TENSOR.FFN_GATE_EXP: (
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"layers.{bid}.feed_forward.experts.w1", # mixtral (merged)
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"transformer.decoder_layer.{bid}.moe.linear", # Grok (merged)
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"transformer.blocks.{bid}.ffn.experts.mlp.w1", # dbrx
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"model.layers.{bid}.mlp.experts.gate_proj", # qwen2moe (merged)
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),
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MODEL_TENSOR.FFN_GATE_SHEXP: (
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"model.layers.{bid}.mlp.shared_expert.gate_proj", # qwen2moe
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),
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# Feed-forward down
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MODEL_TENSOR.FFN_DOWN: (
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"gpt_neox.layers.{bid}.mlp.dense_4h_to_h", # gptneox
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"transformer.h.{bid}.mlp.c_proj", # gpt2 refact qwen
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"transformer.blocks.{bid}.ffn.down_proj", # mpt
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"transformer.h.{bid}.mlp.dense_4h_to_h", # falcon
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"h.{bid}.mlp.dense_4h_to_h", # bloom
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"model.layers.{bid}.mlp.down_proj", # llama-hf
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"layers.{bid}.feed_forward.w2", # llama-pth
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"encoder.layer.{bid}.output.dense", # bert
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"transformer.h.{bid}.mlp.fc_out", # gpt-j
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"language_model.encoder.layers.{bid}.mlp.dense_4h_to_h", # persimmon
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"model.layers.{bid}.mlp.dense_4h_to_h", # persimmon
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"h.{bid}.mlp.c_proj", # gpt2
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"transformer.h.{bid}.mlp.fc2", # phi2
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"model.layers.{bid}.mlp.fc2", # phi2
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"model.layers.layers.{bid}.mlp.down_proj", # plamo
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"model.layers.{bid}.feed_forward.w2", # internlm2
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"encoder.layers.{bid}.mlp.fc2", # nomic-bert
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"model.layers.{bid}.mlp.c_proj", # starcoder2
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"encoder.layer.{bid}.mlp.wo", # jina-bert-v2
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"model.layers.{bid}.feed_forward.down_proj", # jamba
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),
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MODEL_TENSOR.FFN_DOWN_EXP: (
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"layers.{bid}.feed_forward.experts.w2", # mixtral (merged)
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"transformer.decoder_layer.{bid}.moe.linear_1", # Grok (merged)
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"transformer.blocks.{bid}.ffn.experts.mlp.w2", # dbrx
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"model.layers.{bid}.mlp.experts.down_proj", # qwen2moe (merged)
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),
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MODEL_TENSOR.FFN_DOWN_SHEXP: (
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"model.layers.{bid}.mlp.shared_expert.down_proj", # qwen2moe
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),
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MODEL_TENSOR.ATTN_Q_NORM: (
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"language_model.encoder.layers.{bid}.self_attention.q_layernorm",
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"model.layers.{bid}.self_attn.q_layernorm", # persimmon
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"model.layers.{bid}.self_attn.q_norm", # cohere
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"transformer.blocks.{bid}.attn.q_ln", # sea-lion
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"encoder.layer.{bid}.attention.self.layer_norm_q" # jina-bert-v2
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),
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MODEL_TENSOR.ATTN_K_NORM: (
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"language_model.encoder.layers.{bid}.self_attention.k_layernorm",
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"model.layers.{bid}.self_attn.k_layernorm", # persimmon
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"model.layers.{bid}.self_attn.k_norm", # cohere
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"transformer.blocks.{bid}.attn.k_ln", # sea-lion
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"encoder.layer.{bid}.attention.self.layer_norm_k" # jina-bert-v2
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),
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MODEL_TENSOR.ROPE_FREQS: (
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"language_model.encoder.layers.{bid}.self_attention.rotary_emb.inv_freq", # persimmon
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),
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MODEL_TENSOR.LAYER_OUT_NORM: (
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"encoder.layer.{bid}.output.LayerNorm", # bert
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"encoder.layers.{bid}.norm2", # nomic-bert
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"transformer.decoder_layer.{bid}.rms_norm_3", # Grok
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"encoder.layer.{bid}.mlp.layernorm", # jina-bert-v2
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),
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MODEL_TENSOR.SSM_IN: (
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"model.layers.{bid}.in_proj", # mamba-hf
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"backbone.layers.{bid}.mixer.in_proj", # mamba
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"model.layers.{bid}.mamba.in_proj", # jamba
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),
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MODEL_TENSOR.SSM_CONV1D: (
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"model.layers.{bid}.conv1d", # mamba-hf
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"backbone.layers.{bid}.mixer.conv1d", # mamba
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"model.layers.{bid}.mamba.conv1d", # jamba
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),
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MODEL_TENSOR.SSM_X: (
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"model.layers.{bid}.x_proj", # mamba-hf
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"backbone.layers.{bid}.mixer.x_proj", # mamba
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"model.layers.{bid}.mamba.x_proj", # jamba
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),
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MODEL_TENSOR.SSM_DT: (
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"model.layers.{bid}.dt_proj", # mamba-hf
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"backbone.layers.{bid}.mixer.dt_proj", # mamba
|
|
"model.layers.{bid}.mamba.dt_proj", # jamba
|
|
),
|
|
|
|
MODEL_TENSOR.SSM_DT_NORM: (
|
|
"model.layers.{bid}.mamba.dt_layernorm", # jamba
|
|
),
|
|
|
|
MODEL_TENSOR.SSM_A: (
|
|
"model.layers.{bid}.A_log", # mamba-hf
|
|
"backbone.layers.{bid}.mixer.A_log", # mamba
|
|
"model.layers.{bid}.mamba.A_log", # jamba
|
|
),
|
|
|
|
MODEL_TENSOR.SSM_B_NORM: (
|
|
"model.layers.{bid}.mamba.b_layernorm", # jamba
|
|
),
|
|
|
|
MODEL_TENSOR.SSM_C_NORM: (
|
|
"model.layers.{bid}.mamba.c_layernorm", # jamba
|
|
),
|
|
|
|
MODEL_TENSOR.SSM_D: (
|
|
"model.layers.{bid}.D", # mamba-hf
|
|
"backbone.layers.{bid}.mixer.D", # mamba
|
|
"model.layers.{bid}.mamba.D", # jamba
|
|
),
|
|
|
|
MODEL_TENSOR.SSM_OUT: (
|
|
"model.layers.{bid}.out_proj", # mamba-hf
|
|
"backbone.layers.{bid}.mixer.out_proj", # mamba
|
|
"model.layers.{bid}.mamba.out_proj", # jamba
|
|
),
|
|
}
|
|
|
|
mapping: dict[str, tuple[MODEL_TENSOR, str]]
|
|
|
|
def __init__(self, arch: MODEL_ARCH, n_blocks: int):
|
|
self.mapping = {}
|
|
for tensor, keys in self.mappings_cfg.items():
|
|
if tensor not in MODEL_TENSORS[arch]:
|
|
continue
|
|
tensor_name = TENSOR_NAMES[tensor]
|
|
self.mapping[tensor_name] = (tensor, tensor_name)
|
|
for key in keys:
|
|
self.mapping[key] = (tensor, tensor_name)
|
|
for bid in range(n_blocks):
|
|
for tensor, keys in self.block_mappings_cfg.items():
|
|
if tensor not in MODEL_TENSORS[arch]:
|
|
continue
|
|
# TODO: make this configurable
|
|
n_experts = 60
|
|
for xid in range(n_experts):
|
|
tensor_name = TENSOR_NAMES[tensor].format(bid = bid, xid = xid)
|
|
self.mapping[tensor_name] = (tensor, tensor_name)
|
|
for key in keys:
|
|
key = key.format(bid = bid, xid = xid)
|
|
self.mapping[key] = (tensor, tensor_name)
|
|
|
|
def get_type_and_name(self, key: str, try_suffixes: Sequence[str] = ()) -> tuple[MODEL_TENSOR, str] | None:
|
|
result = self.mapping.get(key)
|
|
if result is not None:
|
|
return result
|
|
for suffix in try_suffixes:
|
|
if key.endswith(suffix):
|
|
result = self.mapping.get(key[:-len(suffix)])
|
|
if result is not None:
|
|
return result[0], result[1] + suffix
|
|
return None
|
|
|
|
def get_name(self, key: str, try_suffixes: Sequence[str] = ()) -> str | None:
|
|
result = self.get_type_and_name(key, try_suffixes = try_suffixes)
|
|
if result is None:
|
|
return None
|
|
return result[1]
|
|
|
|
def get_type(self, key: str, try_suffixes: Sequence[str] = ()) -> MODEL_TENSOR | None:
|
|
result = self.get_type_and_name(key, try_suffixes = try_suffixes)
|
|
if result is None:
|
|
return None
|
|
return result[0]
|
|
|
|
def __getitem__(self, key: str) -> str:
|
|
try:
|
|
return self.mapping[key][1]
|
|
except KeyError:
|
|
raise KeyError(key)
|
|
|
|
def __contains__(self, key: str) -> bool:
|
|
return key in self.mapping
|
|
|
|
def __repr__(self) -> str:
|
|
return repr(self.mapping)
|
|
|
|
|
|
def get_tensor_name_map(arch: MODEL_ARCH, n_blocks: int) -> TensorNameMap:
|
|
return TensorNameMap(arch, n_blocks)
|