mirror of
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gguf.py : fix vertical alignment
This commit is contained in:
Georgi Gerganov
parent
4dbce7d009
commit
e426b3cfc8
220
gguf.py
220
gguf.py
@ -11,55 +11,55 @@ from typing import Any, IO, List
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# constants
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#
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GGUF_MAGIC = 0x47475546
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GGUF_VERSION = 1
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GGUF_MAGIC = 0x47475546
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GGUF_VERSION = 1
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GGUF_DEFAULT_ALIGNMENT = 32
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# general
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KEY_GENERAL_ARCHITECTURE = "general.architecture"
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KEY_GENERAL_ARCHITECTURE = "general.architecture"
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KEY_GENERAL_QUANTIZATION_VERSION = "general.quantization_version"
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KEY_GENERAL_ALIGNMENT = "general.alignment"
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KEY_GENERAL_NAME = "general.name"
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KEY_GENERAL_AUTHOR = "general.author"
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KEY_GENERAL_URL = "general.url"
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KEY_GENERAL_DESCRIPTION = "general.description"
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KEY_GENERAL_LICENSE = "general.license"
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KEY_GENERAL_SOURCE_URL = "general.source.url"
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KEY_GENERAL_SOURCE_HF_REPO = "general.source.hugginface.repository"
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KEY_GENERAL_ALIGNMENT = "general.alignment"
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KEY_GENERAL_NAME = "general.name"
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KEY_GENERAL_AUTHOR = "general.author"
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KEY_GENERAL_URL = "general.url"
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KEY_GENERAL_DESCRIPTION = "general.description"
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KEY_GENERAL_LICENSE = "general.license"
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KEY_GENERAL_SOURCE_URL = "general.source.url"
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KEY_GENERAL_SOURCE_HF_REPO = "general.source.hugginface.repository"
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# LLM
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KEY_LLM_CONTEXT_LENGTH = "{arch}.context_length"
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KEY_LLM_EMBEDDING_LENGTH = "{arch}.embedding_length"
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KEY_LLM_BLOCK_COUNT = "{arch}.block_count"
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KEY_LLM_FEED_FORWARD_LENGTH = "{arch}.feed_forward_length"
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KEY_LLM_CONTEXT_LENGTH = "{arch}.context_length"
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KEY_LLM_EMBEDDING_LENGTH = "{arch}.embedding_length"
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KEY_LLM_BLOCK_COUNT = "{arch}.block_count"
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KEY_LLM_FEED_FORWARD_LENGTH = "{arch}.feed_forward_length"
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KEY_LLM_USE_PARALLEL_RESIDUAL = "{arch}.use_parallel_residual"
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KEY_LLM_TENSOR_DATA_LAYOUT = "{arch}.tensor_data_layout"
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KEY_LLM_TENSOR_DATA_LAYOUT = "{arch}.tensor_data_layout"
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# attention
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KEY_ATTENTION_HEAD_COUNT = "{arch}.attention.head_count"
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KEY_ATTENTION_HEAD_COUNT_KV = "{arch}.attention.head_count_kv"
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KEY_ATTENTION_MAX_ALIBI_BIAS = "{arch}.attention.max_alibi_bias"
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KEY_ATTENTION_CLAMP_KQV = "{arch}.attention.clamp_kqv"
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KEY_ATTENTION_LAYERNORM_EPS = "{arch}.attention.layer_norm_epsilon"
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KEY_ATTENTION_HEAD_COUNT = "{arch}.attention.head_count"
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KEY_ATTENTION_HEAD_COUNT_KV = "{arch}.attention.head_count_kv"
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KEY_ATTENTION_MAX_ALIBI_BIAS = "{arch}.attention.max_alibi_bias"
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KEY_ATTENTION_CLAMP_KQV = "{arch}.attention.clamp_kqv"
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KEY_ATTENTION_LAYERNORM_EPS = "{arch}.attention.layer_norm_epsilon"
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KEY_ATTENTION_LAYERNORM_RMS_EPS = "{arch}.attention.layer_norm_rms_epsilon"
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# RoPE
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KEY_ROPE_DIMENSION_COUNT = "{arch}.rope.dimension_count"
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KEY_ROPE_SCALE = "{arch}.rope.scale"
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KEY_ROPE_SCALE = "{arch}.rope.scale"
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# tokenization
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KEY_TOKENIZER_MODEL = "tokenizer.ggml.model"
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KEY_TOKENIZER_LIST = "tokenizer.ggml.tokens"
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KEY_TOKENIZER_MODEL = "tokenizer.ggml.model"
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KEY_TOKENIZER_LIST = "tokenizer.ggml.tokens"
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KEY_TOKENIZER_TOKEN_TYPE = "tokenizer.ggml.token_type"
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KEY_TOKENIZER_SCORES = "tokenizer.ggml.scores"
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KEY_TOKENIZER_MERGES = "tokenizer.ggml.merges"
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KEY_TOKENIZER_BOS_ID = "tokenizer.ggml.bos_token_id"
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KEY_TOKENIZER_EOS_ID = "tokenizer.ggml.eos_token_id"
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KEY_TOKENIZER_UNK_ID = "tokenizer.ggml.unknown_token_id"
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KEY_TOKENIZER_SEP_ID = "tokenizer.ggml.seperator_token_id"
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KEY_TOKENIZER_PAD_ID = "tokenizer.ggml.padding_token_id"
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KEY_TOKENIZER_HF_JSON = "tokenizer.huggingface.json"
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KEY_TOKENIZER_RWKV = "tokenizer.rwkv.world"
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KEY_TOKENIZER_SCORES = "tokenizer.ggml.scores"
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KEY_TOKENIZER_MERGES = "tokenizer.ggml.merges"
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KEY_TOKENIZER_BOS_ID = "tokenizer.ggml.bos_token_id"
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KEY_TOKENIZER_EOS_ID = "tokenizer.ggml.eos_token_id"
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KEY_TOKENIZER_UNK_ID = "tokenizer.ggml.unknown_token_id"
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KEY_TOKENIZER_SEP_ID = "tokenizer.ggml.seperator_token_id"
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KEY_TOKENIZER_PAD_ID = "tokenizer.ggml.padding_token_id"
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KEY_TOKENIZER_HF_JSON = "tokenizer.huggingface.json"
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KEY_TOKENIZER_RWKV = "tokenizer.rwkv.world"
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#
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# recommended mapping of model tensor names for storage in gguf
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@ -96,41 +96,41 @@ class MODEL_TENSOR(IntEnum):
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MODEL_ARCH_NAMES = {
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MODEL_ARCH.LLAMA: "llama",
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MODEL_ARCH.FALCON: "falcon",
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MODEL_ARCH.GPT2: "gpt2",
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MODEL_ARCH.GPTJ: "gptj",
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MODEL_ARCH.LLAMA: "llama",
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MODEL_ARCH.FALCON: "falcon",
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MODEL_ARCH.GPT2: "gpt2",
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MODEL_ARCH.GPTJ: "gptj",
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MODEL_ARCH.GPTNEOX: "gptneox",
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MODEL_ARCH.MPT: "mpt",
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MODEL_ARCH.MPT: "mpt",
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}
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MODEL_TENSOR_NAMES = {
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MODEL_ARCH.LLAMA: {
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MODEL_TENSOR.TOKEN_EMBD: "token_embd",
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MODEL_TENSOR.OUTPUT_NORM: "output_norm",
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MODEL_TENSOR.OUTPUT: "output",
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MODEL_TENSOR.ROPE_FREQS: "rope_freqs",
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MODEL_TENSOR.ATTN_NORM: "blk.{bid}.attn_norm",
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MODEL_TENSOR.ATTN_Q: "blk.{bid}.attn_q",
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MODEL_TENSOR.ATTN_K: "blk.{bid}.attn_k",
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MODEL_TENSOR.ATTN_V: "blk.{bid}.attn_v",
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MODEL_TENSOR.ATTN_OUT: "blk.{bid}.attn_output",
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MODEL_TENSOR.TOKEN_EMBD: "token_embd",
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MODEL_TENSOR.OUTPUT_NORM: "output_norm",
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MODEL_TENSOR.OUTPUT: "output",
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MODEL_TENSOR.ROPE_FREQS: "rope_freqs",
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MODEL_TENSOR.ATTN_NORM: "blk.{bid}.attn_norm",
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MODEL_TENSOR.ATTN_Q: "blk.{bid}.attn_q",
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MODEL_TENSOR.ATTN_K: "blk.{bid}.attn_k",
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MODEL_TENSOR.ATTN_V: "blk.{bid}.attn_v",
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MODEL_TENSOR.ATTN_OUT: "blk.{bid}.attn_output",
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MODEL_TENSOR.ATTN_ROT_EMBD: "blk.{bid}.attn_rot_embd",
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MODEL_TENSOR.FFN_NORM: "blk.{bid}.ffn_norm",
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MODEL_TENSOR.FFN_GATE: "blk.{bid}.ffn_gate",
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MODEL_TENSOR.FFN_DOWN: "blk.{bid}.ffn_down",
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MODEL_TENSOR.FFN_UP: "blk.{bid}.ffn_up",
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MODEL_TENSOR.FFN_NORM: "blk.{bid}.ffn_norm",
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MODEL_TENSOR.FFN_GATE: "blk.{bid}.ffn_gate",
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MODEL_TENSOR.FFN_DOWN: "blk.{bid}.ffn_down",
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MODEL_TENSOR.FFN_UP: "blk.{bid}.ffn_up",
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},
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MODEL_ARCH.FALCON: {
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MODEL_TENSOR.TOKEN_EMBD: "token_embd",
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MODEL_TENSOR.TOKEN_EMBD: "token_embd",
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MODEL_TENSOR.OUTPUT_NORM: "output_norm",
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MODEL_TENSOR.OUTPUT: "output",
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MODEL_TENSOR.ATTN_NORM: "blk.{bid}.attn_norm",
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MODEL_TENSOR.OUTPUT: "output",
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MODEL_TENSOR.ATTN_NORM: "blk.{bid}.attn_norm",
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MODEL_TENSOR.ATTN_NORM_2: "blk.{bid}.attn_norm_2",
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MODEL_TENSOR.ATTN_QKV: "blk.{bid}.attn_qkv",
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MODEL_TENSOR.ATTN_OUT: "blk.{bid}.attn_output",
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MODEL_TENSOR.FFN_DOWN: "blk.{bid}.ffn_down",
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MODEL_TENSOR.FFN_UP: "blk.{bid}.ffn_up",
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MODEL_TENSOR.ATTN_QKV: "blk.{bid}.attn_qkv",
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MODEL_TENSOR.ATTN_OUT: "blk.{bid}.attn_output",
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MODEL_TENSOR.FFN_DOWN: "blk.{bid}.ffn_down",
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MODEL_TENSOR.FFN_UP: "blk.{bid}.ffn_up",
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},
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MODEL_ARCH.GPT2: {
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# TODO
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@ -162,11 +162,11 @@ def get_tensor_name_map(arch: MODEL_ARCH, n_blocks: int) -> dict:
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# Token embeddings
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mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.TOKEN_EMBD, None)
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tensor_map["gpt_neox.embed_in"] = mapped_to # gptneox
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tensor_map["transformer.wte"] = mapped_to # gpt2 mpt
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tensor_map["gpt_neox.embed_in"] = mapped_to # gptneox
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tensor_map["transformer.wte"] = mapped_to # gpt2 mpt
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tensor_map["transformer.word_embeddings"] = mapped_to # falcon
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tensor_map["model.embed_tokens"] = mapped_to # llama-hf
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tensor_map["tok_embeddings"] = mapped_to # llama-pth
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tensor_map["model.embed_tokens"] = mapped_to # llama-hf
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tensor_map["tok_embeddings"] = mapped_to # llama-pth
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# Position embeddings
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mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.POS_EMBD, None)
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@ -177,17 +177,17 @@ def get_tensor_name_map(arch: MODEL_ARCH, n_blocks: int) -> dict:
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mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.OUTPUT, None)
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tensor_map["embed_out"] = mapped_to # gptneox
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tensor_map["lm_head"] = mapped_to # gpt2 mpt falcon llama-hf
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tensor_map["output"] = mapped_to # llama-pth
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tensor_map["lm_head"] = mapped_to # gpt2 mpt falcon llama-hf
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tensor_map["output"] = mapped_to # llama-pth
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# Output norm
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mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.OUTPUT_NORM, None)
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tensor_map["gpt_neox.final_layer_norm"] = mapped_to # gptneox
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tensor_map["transformer.ln_f"] = mapped_to # gpt2 falcon
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tensor_map["transformer.norm_f"] = mapped_to # mpt
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tensor_map["model.norm"] = mapped_to # llama-hf
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tensor_map["norm"] = mapped_to # llama-pth
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tensor_map["transformer.ln_f"] = mapped_to # gpt2 falcon
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tensor_map["transformer.norm_f"] = mapped_to # mpt
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tensor_map["model.norm"] = mapped_to # llama-hf
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tensor_map["norm"] = mapped_to # llama-pth
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# Rope frequencies
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mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.ROPE_FREQS, None)
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@ -202,12 +202,12 @@ def get_tensor_name_map(arch: MODEL_ARCH, n_blocks: int) -> dict:
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mapped_to = mapped_to.format(bid=i) if mapped_to else None
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tensor_map["gpt_neox.layers."+str(i)+".input_layernorm"] = mapped_to # gptneox
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tensor_map["transformer.h."+str(i)+".ln_1"] = mapped_to # gpt2
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tensor_map["transformer.blocks."+str(i)+".norm_1"] = mapped_to # mpt
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tensor_map["transformer.h."+str(i)+".input_layernorm"] = mapped_to # falcon7b
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tensor_map["transformer.h."+str(i)+".ln_attn"] = mapped_to # falcon40b
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tensor_map["model.layers."+str(i)+".input_layernorm"] = mapped_to # llama-hf
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tensor_map["layers."+str(i)+".attention_norm"] = mapped_to # llama-pth
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tensor_map["transformer.h."+str(i)+".ln_1"] = mapped_to # gpt2
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tensor_map["transformer.blocks."+str(i)+".norm_1"] = mapped_to # mpt
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tensor_map["transformer.h."+str(i)+".input_layernorm"] = mapped_to # falcon7b
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tensor_map["transformer.h."+str(i)+".ln_attn"] = mapped_to # falcon40b
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tensor_map["model.layers."+str(i)+".input_layernorm"] = mapped_to # llama-hf
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tensor_map["layers."+str(i)+".attention_norm"] = mapped_to # llama-pth
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# Attention norm 2
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mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.ATTN_NORM_2, None)
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@ -219,9 +219,9 @@ def get_tensor_name_map(arch: MODEL_ARCH, n_blocks: int) -> dict:
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mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.ATTN_QKV, None)
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mapped_to = mapped_to.format(bid=i) if mapped_to is not None else None
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tensor_map["gpt_neox.layers."+str(i)+".attention.query_key_value"] = mapped_to # gptneox
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tensor_map["transformer.h."+str(i)+".attn.c_attn"] = mapped_to # gpt2
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tensor_map["transformer.blocks."+str(i)+".attn.Wqkv"] = mapped_to # mpt
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tensor_map["gpt_neox.layers."+str(i)+".attention.query_key_value"] = mapped_to # gptneox
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tensor_map["transformer.h."+str(i)+".attn.c_attn"] = mapped_to # gpt2
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tensor_map["transformer.blocks."+str(i)+".attn.Wqkv"] = mapped_to # mpt
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tensor_map["transformer.h."+str(i)+".self_attention.query_key_value"] = mapped_to # falcon
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# Attention query
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@ -229,38 +229,38 @@ def get_tensor_name_map(arch: MODEL_ARCH, n_blocks: int) -> dict:
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mapped_to = mapped_to.format(bid=i) if mapped_to is not None else None
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tensor_map["model.layers."+str(i)+".self_attn.q_proj"] = mapped_to # llama-hf
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tensor_map["layers."+str(i)+".attention.wq"] = mapped_to # llama-pth
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tensor_map["layers."+str(i)+".attention.wq"] = mapped_to # llama-pth
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# Attention key
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mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.ATTN_K, None)
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mapped_to = mapped_to.format(bid=i) if mapped_to is not None else None
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tensor_map["model.layers."+str(i)+".self_attn.k_proj"] = mapped_to # llama-hf
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tensor_map["layers."+str(i)+".attention.wk"] = mapped_to # llama-pth
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tensor_map["layers."+str(i)+".attention.wk"] = mapped_to # llama-pth
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# Attention value
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mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.ATTN_V, None)
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mapped_to = mapped_to.format(bid=i) if mapped_to is not None else None
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tensor_map["model.layers."+str(i)+".self_attn.v_proj"] = mapped_to # llama-hf
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tensor_map["layers."+str(i)+".attention.wv"] = mapped_to # llama-pth
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tensor_map["layers."+str(i)+".attention.wv"] = mapped_to # llama-pth
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# Attention output
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mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.ATTN_OUT, None)
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mapped_to = mapped_to.format(bid=i) if mapped_to is not None else None
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tensor_map["gpt_neox.layers."+str(i)+".attention.dense"] = mapped_to # gptneox
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tensor_map["transformer.h."+str(i)+".attn.c_proj"] = mapped_to # gpt2
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tensor_map["transformer.blocks."+str(i)+".attn.out_proj"] = mapped_to # mpt
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tensor_map["gpt_neox.layers."+str(i)+".attention.dense"] = mapped_to # gptneox
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tensor_map["transformer.h."+str(i)+".attn.c_proj"] = mapped_to # gpt2
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tensor_map["transformer.blocks."+str(i)+".attn.out_proj"] = mapped_to # mpt
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tensor_map["transformer.h."+str(i)+".self_attention.dense"] = mapped_to # falcon
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tensor_map["model.layers."+str(i)+".self_attn.o_proj"] = mapped_to # llama-hf
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tensor_map["layers."+str(i)+".attention.wo"] = mapped_to # llama-pth
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tensor_map["model.layers."+str(i)+".self_attn.o_proj"] = mapped_to # llama-hf
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tensor_map["layers."+str(i)+".attention.wo"] = mapped_to # llama-pth
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# Rotary embeddings
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mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.ATTN_ROT_EMBD, None)
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mapped_to = mapped_to.format(bid=i) if mapped_to is not None else None
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tensor_map["model.layers."+str(i)+".self_attn.rotary_emb.inv_freq"] = mapped_to # llama-hf
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tensor_map["model.layers."+str(i)+".self_attn.rotary_emb.inv_freq"] = mapped_to # llama-hf
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tensor_map["layers."+str(i)+".attention.inner_attention.rope.freqs"] = mapped_to # llama-pth
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# Feed-forward norm
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@ -268,39 +268,39 @@ def get_tensor_name_map(arch: MODEL_ARCH, n_blocks: int) -> dict:
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mapped_to = mapped_to.format(bid=i) if mapped_to is not None else None
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tensor_map["gpt_neox.layers."+str(i)+".post_attention_layernorm"] = mapped_to # gptneox
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tensor_map["transformer.h."+str(i)+".ln_2"] = mapped_to # gpt2
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tensor_map["transformer.blocks."+str(i)+".norm_2"] = mapped_to # mpt
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tensor_map["model.layers."+str(i)+".post_attention_layernorm"] = mapped_to # llama-hf
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tensor_map["layers."+str(i)+".ffn_norm"] = mapped_to # llama-pth
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tensor_map["transformer.h."+str(i)+".ln_2"] = mapped_to # gpt2
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tensor_map["transformer.blocks."+str(i)+".norm_2"] = mapped_to # mpt
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tensor_map["model.layers."+str(i)+".post_attention_layernorm"] = mapped_to # llama-hf
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tensor_map["layers."+str(i)+".ffn_norm"] = mapped_to # llama-pth
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# Feed-forward up
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mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.FFN_UP, None)
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mapped_to = mapped_to.format(bid=i) if mapped_to is not None else None
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tensor_map["gpt_neox.layers."+str(i)+".mlp.dense_h_to_4h"] = mapped_to # gptneox
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tensor_map["transformer.h."+str(i)+".mlp.c_fc"] = mapped_to # gpt2
|
||||
tensor_map["transformer.blocks."+str(i)+".ffn.up_proj"] = mapped_to # mpt
|
||||
tensor_map["transformer.h."+str(i)+".mlp.dense_h_to_4h"] = mapped_to # falcon
|
||||
tensor_map["model.layers."+str(i)+".mlp.up_proj"] = mapped_to # llama-hf
|
||||
tensor_map["layers."+str(i)+".feed_forward.w3"] = mapped_to # llama-pth
|
||||
tensor_map["transformer.h."+str(i)+".mlp.c_fc"] = mapped_to # gpt2
|
||||
tensor_map["transformer.blocks."+str(i)+".ffn.up_proj"] = mapped_to # mpt
|
||||
tensor_map["transformer.h."+str(i)+".mlp.dense_h_to_4h"] = mapped_to # falcon
|
||||
tensor_map["model.layers."+str(i)+".mlp.up_proj"] = mapped_to # llama-hf
|
||||
tensor_map["layers."+str(i)+".feed_forward.w3"] = mapped_to # llama-pth
|
||||
|
||||
# Feed-forward gate
|
||||
mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.FFN_GATE, None)
|
||||
mapped_to = mapped_to.format(bid=i) if mapped_to is not None else None
|
||||
|
||||
tensor_map["model.layers."+str(i)+".mlp.gate_proj"] = mapped_to # llama-hf
|
||||
tensor_map["layers."+str(i)+".feed_forward.w1"] = mapped_to # llama-pth
|
||||
tensor_map["layers."+str(i)+".feed_forward.w1"] = mapped_to # llama-pth
|
||||
|
||||
# Feed-forward down
|
||||
mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.FFN_DOWN, None)
|
||||
mapped_to = mapped_to.format(bid=i) if mapped_to is not None else None
|
||||
|
||||
tensor_map["gpt_neox.layers."+str(i)+".mlp.dense_4h_to_h"] = mapped_to # gptneox
|
||||
tensor_map["transformer.h."+str(i)+".mlp.c_proj"] = mapped_to # gpt2
|
||||
tensor_map["transformer.blocks."+str(i)+".ffn.down_proj"] = mapped_to # mpt
|
||||
tensor_map["transformer.h."+str(i)+".mlp.dense_4h_to_h"] = mapped_to # falcon
|
||||
tensor_map["model.layers."+str(i)+".mlp.down_proj"] = mapped_to # llama-hf
|
||||
tensor_map["layers."+str(i)+".feed_forward.w2"] = mapped_to # llama-pth
|
||||
tensor_map["transformer.h."+str(i)+".mlp.c_proj"] = mapped_to # gpt2
|
||||
tensor_map["transformer.blocks."+str(i)+".ffn.down_proj"] = mapped_to # mpt
|
||||
tensor_map["transformer.h."+str(i)+".mlp.dense_4h_to_h"] = mapped_to # falcon
|
||||
tensor_map["model.layers."+str(i)+".mlp.down_proj"] = mapped_to # llama-hf
|
||||
tensor_map["layers."+str(i)+".feed_forward.w2"] = mapped_to # llama-pth
|
||||
|
||||
return tensor_map
|
||||
|
||||
@ -315,16 +315,16 @@ class GGMLQuantizationType(IntEnum):
|
||||
|
||||
|
||||
class GGUFValueType(IntEnum):
|
||||
UINT8 = 0
|
||||
INT8 = 1
|
||||
UINT16 = 2
|
||||
INT16 = 3
|
||||
UINT32 = 4
|
||||
INT32 = 5
|
||||
UINT8 = 0
|
||||
INT8 = 1
|
||||
UINT16 = 2
|
||||
INT16 = 3
|
||||
UINT32 = 4
|
||||
INT32 = 5
|
||||
FLOAT32 = 6
|
||||
BOOL = 7
|
||||
STRING = 8
|
||||
ARRAY = 9
|
||||
BOOL = 7
|
||||
STRING = 8
|
||||
ARRAY = 9
|
||||
|
||||
@staticmethod
|
||||
def get_type(val):
|
||||
|
||||
Loading…
Reference in New Issue
Block a user