from __future__ import annotations from typing import Sequence from .constants import MODEL_ARCH, MODEL_TENSOR, MODEL_TENSORS, TENSOR_NAMES class TensorNameMap: mappings_cfg: dict[MODEL_TENSOR, tuple[str, ...]] = { # Token embeddings MODEL_TENSOR.TOKEN_EMBD: ( "gpt_neox.embed_in", # gptneox "transformer.wte", # gpt2 gpt-j mpt refact qwen dbrx "transformer.word_embeddings", # falcon "word_embeddings", # bloom "model.embed_tokens", # llama-hf "tok_embeddings", # llama-pth "embeddings.word_embeddings", # bert nomic-bert "language_model.embedding.word_embeddings", # persimmon "wte", # gpt2 "transformer.embd.wte", # phi2 "model.tok_embeddings", # internlm2 "model.embedding", # mamba-qbert "backbone.embedding", # mamba "backbone.embeddings", # mamba-hf "transformer.in_out_embed", # Grok ), # Token type embeddings MODEL_TENSOR.TOKEN_TYPES: ( "embeddings.token_type_embeddings", # bert nomic-bert ), # Normalization of token embeddings MODEL_TENSOR.TOKEN_EMBD_NORM: ( "word_embeddings_layernorm", # bloom "embeddings.LayerNorm", # bert "emb_ln", # nomic-bert ), # Position embeddings MODEL_TENSOR.POS_EMBD: ( "transformer.wpe", # gpt2 "embeddings.position_embeddings", # bert "wpe", # gpt2 ), # Output MODEL_TENSOR.OUTPUT: ( "embed_out", # gptneox "lm_head", # gpt2 mpt falcon llama-hf baichuan qwen mamba dbrx "output", # llama-pth bloom internlm2 "word_embeddings_for_head", # persimmon "lm_head.linear", # phi2 ), # Output norm MODEL_TENSOR.OUTPUT_NORM: ( "gpt_neox.final_layer_norm", # gptneox "transformer.ln_f", # gpt2 gpt-j falcon "model.norm", # llama-hf baichuan internlm2 "norm", # llama-pth "transformer.norm_f", # mpt dbrx "ln_f", # refact bloom qwen gpt2 "language_model.encoder.final_layernorm", # persimmon "model.final_layernorm", # persimmon "lm_head.ln", # phi2 "model.norm_f", # mamba-qbert "backbone.norm_f", # mamba "transformer.rms_norm", # Grok ), # Rope frequencies MODEL_TENSOR.ROPE_FREQS: ( "rope.freqs", # llama-pth ), } block_mappings_cfg: dict[MODEL_TENSOR, tuple[str, ...]] = { # Attention norm MODEL_TENSOR.ATTN_NORM: ( "gpt_neox.layers.{bid}.input_layernorm", # gptneox "transformer.h.{bid}.ln_1", # gpt2 gpt-j refact qwen "transformer.blocks.{bid}.norm_1", # mpt "transformer.h.{bid}.input_layernorm", # falcon7b "h.{bid}.input_layernorm", # bloom "transformer.h.{bid}.ln_mlp", # falcon40b "model.layers.{bid}.input_layernorm", # llama-hf "layers.{bid}.attention_norm", # llama-pth "language_model.encoder.layers.{bid}.input_layernorm", # persimmon "model.layers.{bid}.ln1", # yi "h.{bid}.ln_1", # gpt2 "transformer.h.{bid}.ln", # phi2 "model.layers.layers.{bid}.norm", # plamo "model.layers.{bid}.attention_norm", # internlm2 "model.layers.{bid}.norm", # mamba-qbert "backbone.layers.{bid}.norm", # mamba "transformer.decoder_layer.{bid}.rms_norm", # Grok "transformer.blocks.{bid}.norm_attn_norm.norm_1", # dbrx ), # Attention norm 2 MODEL_TENSOR.ATTN_NORM_2: ( "transformer.h.{bid}.ln_attn", # falcon40b "encoder.layer.{bid}.layer_norm_1", # jina-v2-code ), # Attention query-key-value MODEL_TENSOR.ATTN_QKV: ( "gpt_neox.layers.{bid}.attention.query_key_value", # gptneox "transformer.h.{bid}.attn.c_attn", # gpt2 qwen "transformer.blocks.{bid}.attn.Wqkv", # mpt "transformer.blocks.{bid}.norm_attn_norm.attn.Wqkv", # dbrx "transformer.h.{bid}.self_attention.query_key_value", # falcon "h.{bid}.self_attention.query_key_value", # bloom "language_model.encoder.layers.{bid}.self_attention.query_key_value", # persimmon "model.layers.{bid}.self_attn.query_key_value", # persimmon "h.{bid}.attn.c_attn", # gpt2 "transformer.h.{bid}.mixer.Wqkv", # phi2 "encoder.layers.{bid}.attn.Wqkv", # nomic-bert "model.layers.{bid}.self_attn.qkv_proj" # phi3 ), # Attention query MODEL_TENSOR.ATTN_Q: ( "model.layers.{bid}.self_attn.q_proj", # llama-hf "layers.{bid}.attention.wq", # llama-pth "encoder.layer.{bid}.attention.self.query", # bert "transformer.h.{bid}.attn.q_proj", # gpt-j "model.layers.layers.{bid}.self_attn.q_proj", # plamo "model.layers.{bid}.attention.wq", # internlm2 "transformer.decoder_layer.{bid}.multi_head_attention.query" # Grok ), # Attention key MODEL_TENSOR.ATTN_K: ( "model.layers.{bid}.self_attn.k_proj", # llama-hf "layers.{bid}.attention.wk", # llama-pth "encoder.layer.{bid}.attention.self.key", # bert "transformer.h.{bid}.attn.k_proj", # gpt-j "transformer.h.{bid}.attn.k", # refact "model.layers.layers.{bid}.self_attn.k_proj", # plamo "model.layers.{bid}.attention.wk", # internlm2 "transformer.decoder_layer.{bid}.multi_head_attention.key" # Grok ), # Attention value MODEL_TENSOR.ATTN_V: ( "model.layers.{bid}.self_attn.v_proj", # llama-hf "layers.{bid}.attention.wv", # llama-pth "encoder.layer.{bid}.attention.self.value", # bert "transformer.h.{bid}.attn.v_proj", # gpt-j "transformer.h.{bid}.attn.v", # refact "model.layers.layers.{bid}.self_attn.v_proj", # plamo "model.layers.{bid}.attention.wv", # internlm2 "transformer.decoder_layer.{bid}.multi_head_attention.value" # Grok ), # Attention output MODEL_TENSOR.ATTN_OUT: ( "gpt_neox.layers.{bid}.attention.dense", # gptneox "transformer.h.{bid}.attn.c_proj", # gpt2 refact qwen "transformer.blocks.{bid}.attn.out_proj", # mpt "transformer.h.{bid}.self_attention.dense", # falcon "h.{bid}.self_attention.dense", # bloom "model.layers.{bid}.self_attn.o_proj", # llama-hf "layers.{bid}.attention.wo", # llama-pth "encoder.layer.{bid}.attention.output.dense", # bert "transformer.h.{bid}.attn.out_proj", # gpt-j "language_model.encoder.layers.{bid}.self_attention.dense", # persimmon "model.layers.{bid}.self_attn.dense", # persimmon "h.{bid}.attn.c_proj", # gpt2 "transformer.h.{bid}.mixer.out_proj", # phi2 "model.layers.layers.{bid}.self_attn.o_proj", # plamo "model.layers.{bid}.attention.wo", # internlm2 "encoder.layers.{bid}.attn.out_proj", # nomic-bert "transformer.decoder_layer.{bid}.multi_head_attention.linear", # Grok "transformer.blocks.{bid}.norm_attn_norm.attn.out_proj", # dbrx ), # Attention output norm MODEL_TENSOR.ATTN_OUT_NORM: ( "encoder.layer.{bid}.attention.output.LayerNorm", # bert "encoder.layers.{bid}.norm1", # nomic-bert "transformer.decoder_layer.{bid}.rms_norm_1", # Grok "transformer.blocks.{bid}.norm_attn_norm.norm_2", # dbrx ), # Rotary embeddings MODEL_TENSOR.ATTN_ROT_EMBD: ( "model.layers.{bid}.self_attn.rotary_emb.inv_freq", # llama-hf "layers.{bid}.attention.inner_attention.rope.freqs", # llama-pth "model.layers.layers.{bid}.self_attn.rotary_emb.inv_freq", # plamo "transformer.h.{bid}.attn.rotary_emb.inv_freq", # codeshell ), # Feed-forward norm MODEL_TENSOR.FFN_NORM: ( "gpt_neox.layers.{bid}.post_attention_layernorm", # gptneox "transformer.h.{bid}.ln_2", # gpt2 refact qwen "h.{bid}.post_attention_layernorm", # bloom "transformer.blocks.{bid}.norm_2", # mpt "model.layers.{bid}.post_attention_layernorm", # llama-hf "layers.{bid}.ffn_norm", # llama-pth "language_model.encoder.layers.{bid}.post_attention_layernorm", # persimmon "model.layers.{bid}.ln2", # yi "h.{bid}.ln_2", # gpt2 "model.layers.{bid}.ffn_norm", # internlm2 "transformer.decoder_layer.{bid}.rms_norm_2", # Grok ), MODEL_TENSOR.FFN_GATE_INP: ( "layers.{bid}.feed_forward.gate", # mixtral "model.layers.{bid}.block_sparse_moe.gate", # mixtral "model.layers.{bid}.mlp.gate", # qwen2moe "transformer.decoder_layer.{bid}.router", # Grok "transformer.blocks.{bid}.ffn.router.layer", # dbrx ), MODEL_TENSOR.FFN_GATE_INP_SHEXP: ( "model.layers.{bid}.mlp.shared_expert_gate", # qwen2moe ), # Feed-forward up MODEL_TENSOR.FFN_UP: ( "gpt_neox.layers.{bid}.mlp.dense_h_to_4h", # gptneox "transformer.h.{bid}.mlp.c_fc", # gpt2 "transformer.blocks.{bid}.ffn.up_proj", # mpt "transformer.h.{bid}.mlp.dense_h_to_4h", # falcon "h.{bid}.mlp.dense_h_to_4h", # bloom "model.layers.{bid}.mlp.up_proj", # llama-hf refact "layers.{bid}.feed_forward.w3", # llama-pth "encoder.layer.{bid}.intermediate.dense", # bert "transformer.h.{bid}.mlp.fc_in", # gpt-j "transformer.h.{bid}.mlp.linear_3", # refact "language_model.encoder.layers.{bid}.mlp.dense_h_to_4h", # persimmon "model.layers.{bid}.mlp.dense_h_to_4h", # persimmon "transformer.h.{bid}.mlp.w1", # qwen "h.{bid}.mlp.c_fc", # gpt2 "transformer.h.{bid}.mlp.fc1", # phi2 "model.layers.{bid}.mlp.fc1", # phi2 "model.layers.{bid}.mlp.gate_up_proj", # phi3 "model.layers.layers.{bid}.mlp.up_proj", # plamo "model.layers.{bid}.feed_forward.w3", # internlm2 "encoder.layers.{bid}.mlp.fc11", # nomic-bert "model.layers.{bid}.mlp.c_fc", # starcoder2 "encoder.layer.{bid}.mlp.gated_layers_v", # jina-bert-v2 "model.layers.{bid}.residual_mlp.w3", # arctic ), MODEL_TENSOR.FFN_UP_EXP: ( "layers.{bid}.feed_forward.experts.w3", # mixtral (merged) "transformer.decoder_layer.{bid}.moe.linear_v", # Grok (merged) "transformer.blocks.{bid}.ffn.experts.mlp.v1", # dbrx "model.layers.{bid}.mlp.experts.up_proj", # qwen2moe (merged) ), MODEL_TENSOR.FFN_UP_SHEXP: ( "model.layers.{bid}.mlp.shared_expert.up_proj", # qwen2moe "model.layers.{bid}.mlp.shared_experts.up_proj", # deepseek2 ), # AWQ-activation gate MODEL_TENSOR.FFN_ACT: ( "transformer.blocks.{bid}.ffn.act", # mpt ), # Feed-forward gate MODEL_TENSOR.FFN_GATE: ( "model.layers.{bid}.mlp.gate_proj", # llama-hf refact "layers.{bid}.feed_forward.w1", # llama-pth "transformer.h.{bid}.mlp.w2", # qwen "model.layers.layers.{bid}.mlp.gate_proj", # plamo "model.layers.{bid}.feed_forward.w1", # internlm2 "encoder.layers.{bid}.mlp.fc12", # nomic-bert "encoder.layer.{bid}.mlp.gated_layers_w", # jina-bert-v2 "transformer.h.{bid}.mlp.linear_1", # refact "model.layers.{bid}.residual_mlp.w1", # arctic ), MODEL_TENSOR.FFN_GATE_EXP: ( "layers.{bid}.feed_forward.experts.w1", # mixtral (merged) "transformer.decoder_layer.{bid}.moe.linear", # Grok (merged) "transformer.blocks.{bid}.ffn.experts.mlp.w1", # dbrx "model.layers.{bid}.mlp.experts.gate_proj", # qwen2moe (merged) ), MODEL_TENSOR.FFN_GATE_SHEXP: ( "model.layers.{bid}.mlp.shared_expert.gate_proj", # qwen2moe "model.layers.{bid}.mlp.shared_experts.gate_proj", # deepseek2 ), # Feed-forward down MODEL_TENSOR.FFN_DOWN: ( "gpt_neox.layers.{bid}.mlp.dense_4h_to_h", # gptneox "transformer.h.{bid}.mlp.c_proj", # gpt2 refact qwen "transformer.blocks.{bid}.ffn.down_proj", # mpt "transformer.h.{bid}.mlp.dense_4h_to_h", # falcon "h.{bid}.mlp.dense_4h_to_h", # bloom "model.layers.{bid}.mlp.down_proj", # llama-hf "layers.{bid}.feed_forward.w2", # llama-pth "encoder.layer.{bid}.output.dense", # bert "transformer.h.{bid}.mlp.fc_out", # gpt-j "language_model.encoder.layers.{bid}.mlp.dense_4h_to_h", # persimmon "model.layers.{bid}.mlp.dense_4h_to_h", # persimmon "h.{bid}.mlp.c_proj", # gpt2 "transformer.h.{bid}.mlp.fc2", # phi2 "model.layers.{bid}.mlp.fc2", # phi2 "model.layers.layers.{bid}.mlp.down_proj", # plamo "model.layers.{bid}.feed_forward.w2", # internlm2 "encoder.layers.{bid}.mlp.fc2", # nomic-bert "model.layers.{bid}.mlp.c_proj", # starcoder2 "encoder.layer.{bid}.mlp.wo", # jina-bert-v2 "model.layers.{bid}.residual_mlp.w2", # arctic "encoder.layer.{bid}.mlp.down_layer", # jina-bert-v2 ), MODEL_TENSOR.FFN_DOWN_EXP: ( "layers.{bid}.feed_forward.experts.w2", # mixtral (merged) "transformer.decoder_layer.{bid}.moe.linear_1", # Grok (merged) "transformer.blocks.{bid}.ffn.experts.mlp.w2", # dbrx "model.layers.{bid}.mlp.experts.down_proj", # qwen2moe (merged) ), MODEL_TENSOR.FFN_DOWN_SHEXP: ( "model.layers.{bid}.mlp.shared_expert.down_proj", # qwen2moe "model.layers.{bid}.mlp.shared_experts.down_proj", # deepseek2 ), MODEL_TENSOR.ATTN_Q_NORM: ( "language_model.encoder.layers.{bid}.self_attention.q_layernorm", "model.layers.{bid}.self_attn.q_layernorm", # persimmon "model.layers.{bid}.self_attn.q_norm", # cohere "transformer.blocks.{bid}.attn.q_ln", # sea-lion "encoder.layer.{bid}.attention.self.layer_norm_q" # jina-bert-v2 ), MODEL_TENSOR.ATTN_K_NORM: ( "language_model.encoder.layers.{bid}.self_attention.k_layernorm", "model.layers.{bid}.self_attn.k_layernorm", # persimmon "model.layers.{bid}.self_attn.k_norm", # cohere "transformer.blocks.{bid}.attn.k_ln", # sea-lion "encoder.layer.{bid}.attention.self.layer_norm_k" # jina-bert-v2 ), MODEL_TENSOR.ROPE_FREQS: ( "language_model.encoder.layers.{bid}.self_attention.rotary_emb.inv_freq", # persimmon ), MODEL_TENSOR.LAYER_OUT_NORM: ( "encoder.layer.{bid}.output.LayerNorm", # bert "encoder.layers.{bid}.norm2", # nomic-bert "transformer.decoder_layer.{bid}.rms_norm_3", # Grok "encoder.layer.{bid}.mlp.layernorm", # jina-bert-v2 "encoder.layer.{bid}.layer_norm_2" # jina-v2-code ), MODEL_TENSOR.SSM_IN: ( "model.layers.{bid}.in_proj", "backbone.layers.{bid}.mixer.in_proj", ), MODEL_TENSOR.SSM_CONV1D: ( "model.layers.{bid}.conv1d", "backbone.layers.{bid}.mixer.conv1d", ), MODEL_TENSOR.SSM_X: ( "model.layers.{bid}.x_proj", "backbone.layers.{bid}.mixer.x_proj", ), MODEL_TENSOR.SSM_DT: ( "model.layers.{bid}.dt_proj", "backbone.layers.{bid}.mixer.dt_proj", ), MODEL_TENSOR.SSM_A: ( "model.layers.{bid}.A_log", "backbone.layers.{bid}.mixer.A_log", ), MODEL_TENSOR.SSM_D: ( "model.layers.{bid}.D", "backbone.layers.{bid}.mixer.D", ), MODEL_TENSOR.SSM_OUT: ( "model.layers.{bid}.out_proj", "backbone.layers.{bid}.mixer.out_proj", ), MODEL_TENSOR.ATTN_Q_A: ( "model.layers.{bid}.self_attn.q_a_proj", # deepseek2 ), MODEL_TENSOR.ATTN_Q_B: ( "model.layers.{bid}.self_attn.q_b_proj", # deepseek2 ), MODEL_TENSOR.ATTN_KV_A_MQA: ( "model.layers.{bid}.self_attn.kv_a_proj_with_mqa", # deepseek2 ), MODEL_TENSOR.ATTN_KV_B: ( "model.layers.{bid}.self_attn.kv_b_proj", # deepseek2 ), MODEL_TENSOR.ATTN_Q_A_NORM: ( "model.layers.{bid}.self_attn.q_a_layernorm", # deepseek2 ), MODEL_TENSOR.ATTN_KV_A_NORM: ( "model.layers.{bid}.self_attn.kv_a_layernorm", # deepseek2 ), } # architecture-specific block mappings arch_block_mappings_cfg: dict[MODEL_ARCH, dict[MODEL_TENSOR, tuple[str, ...]]] = { MODEL_ARCH.ARCTIC: { MODEL_TENSOR.FFN_NORM: ( "model.layers.{bid}.residual_layernorm", ), MODEL_TENSOR.FFN_NORM_EXP: ( "model.layers.{bid}.post_attention_layernorm", ), }, } 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) if arch in self.arch_block_mappings_cfg: self.block_mappings_cfg.update(self.arch_block_mappings_cfg[arch]) 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 = 160 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)