mirror of
https://github.com/ggerganov/llama.cpp.git
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llama : remove Persimmon (#7408)
* llama : remove Persimmon * requirements : remove
This commit is contained in:
Georgi Gerganov
GitHub
parent
20385cebcc
commit
fabf30b4c4
@ -107,7 +107,6 @@ Typically finetunes of the base models below are supported as well.
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- [X] [Aquila 1 & 2](https://huggingface.co/models?search=BAAI/Aquila)
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- [X] [Starcoder models](https://github.com/ggerganov/llama.cpp/pull/3187)
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- [X] [Refact](https://huggingface.co/smallcloudai/Refact-1_6B-fim)
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- [X] [Persimmon 8B](https://github.com/ggerganov/llama.cpp/pull/3410)
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- [X] [MPT](https://github.com/ggerganov/llama.cpp/pull/3417)
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- [X] [Bloom](https://github.com/ggerganov/llama.cpp/pull/3553)
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- [x] [Yi models](https://huggingface.co/models?search=01-ai/Yi)
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@ -1148,45 +1148,6 @@ class RefactModel(Model):
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return tensors
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@Model.register("PersimmonForCausalLM")
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class PersimmonModel(Model):
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model_arch = gguf.MODEL_ARCH.PERSIMMON
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def set_gguf_parameters(self):
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block_count = self.hparams.get("num_layers", self.hparams.get("num_hidden_layers"))
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head_count = self.hparams["num_attention_heads"]
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head_count_kv = head_count
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hidden_size = self.hparams["hidden_size"]
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self.gguf_writer.add_name('persimmon-8b-chat')
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self.gguf_writer.add_context_length(self.hparams["max_position_embeddings"])
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self.gguf_writer.add_embedding_length(hidden_size)
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self.gguf_writer.add_block_count(block_count)
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self.gguf_writer.add_feed_forward_length(self.hparams["intermediate_size"])
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# NOTE: not sure about this change - why does the model not have a rope dimension count when it is smaller
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# than the head size?
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# ref: https://github.com/ggerganov/llama.cpp/pull/4889
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# self.gguf_writer.add_rope_dimension_count(hidden_size // head_count)
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self.gguf_writer.add_rope_dimension_count(hidden_size // head_count // 2)
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self.gguf_writer.add_head_count(head_count)
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self.gguf_writer.add_head_count_kv(head_count_kv)
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self.gguf_writer.add_rope_freq_base(self.hparams["rope_theta"])
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self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_eps"])
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def set_vocab(self):
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self._set_vocab_sentencepiece()
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# self.gguf_writer.add_bos_token_id(71013)
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# self.gguf_writer.add_eos_token_id(71013)
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def extra_f32_tensors(self, name: str, new_name: str, bid: int | None, n_dims: int) -> bool:
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del name, new_name, bid, n_dims # unused
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# TODO: FP16 conversion produces garbage outputs. (Q8_0 does not, so..?)
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return True
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@Model.register("StableLmForCausalLM", "StableLMEpochForCausalLM", "LlavaStableLMEpochForCausalLM")
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class StableLMModel(Model):
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model_arch = gguf.MODEL_ARCH.STABLELM
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@ -1,143 +0,0 @@
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#!/usr/bin/env python3
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from __future__ import annotations
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import logging
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import argparse
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import os
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import sys
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from pathlib import Path
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from pprint import pprint
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import torch
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from sentencepiece import SentencePieceProcessor
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if 'NO_LOCAL_GGUF' not in os.environ:
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sys.path.insert(1, str(Path(__file__).parent / 'gguf-py'))
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import gguf
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logger = logging.getLogger("persimmon-to-gguf")
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def _flatten_dict(dct, tensors, prefix=None):
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assert isinstance(dct, dict)
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for key in dct.keys():
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new_prefix = prefix + '.' + key if prefix is not None else key
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if isinstance(dct[key], torch.Tensor):
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tensors[new_prefix] = dct[key]
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elif isinstance(dct[key], dict):
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_flatten_dict(dct[key], tensors, new_prefix)
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else:
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raise ValueError(type(dct[key]))
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return None
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def _get_sentencepiece_tokenizer_info(dir_model: Path):
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tokenizer_path = dir_model / 'adept_vocab.model'
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logger.info('getting sentencepiece tokenizer from', tokenizer_path)
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tokenizer = SentencePieceProcessor(str(tokenizer_path))
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logger.info('adding tokens')
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tokens: list[bytes] = []
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scores: list[float] = []
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toktypes: list[int] = []
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for i in range(tokenizer.vocab_size()):
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text: bytes
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score: float
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piece = tokenizer.id_to_piece(i)
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text = piece.encode("utf-8")
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score = tokenizer.get_score(i)
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toktype = 1
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if tokenizer.is_unknown(i):
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toktype = 2
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if tokenizer.is_control(i):
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toktype = 3
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if tokenizer.is_unused(i):
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toktype = 5
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if tokenizer.is_byte(i):
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toktype = 6
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tokens.append(text)
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scores.append(score)
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toktypes.append(toktype)
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pass
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return tokens, scores, toktypes
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def main():
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parser = argparse.ArgumentParser(description="Convert a Persimmon model from Adept (e.g. Persimmon 8b chat) to a GGML compatible file")
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parser.add_argument("--outfile", type=Path, help="path to write to; default: based on input")
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parser.add_argument("--ckpt-path", type=Path, help="path to persimmon checkpoint .pt file")
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parser.add_argument("--model-dir", type=Path, help="directory containing model e.g. 8b_chat_model_release")
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parser.add_argument("--adept-inference-dir", type=str, help="path to adept-inference code directory")
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parser.add_argument("--verbose", action="store_true", help="increase output verbosity")
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args = parser.parse_args()
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logging.basicConfig(level=logging.DEBUG if args.verbose else logging.INFO)
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sys.path.append(str(args.adept_inference_dir))
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persimmon_model = torch.load(args.ckpt_path)
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hparams = persimmon_model['args']
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pprint(hparams)
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tensors: dict[str, torch.Tensor] = {}
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_flatten_dict(persimmon_model['model'], tensors, None)
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arch = gguf.MODEL_ARCH.PERSIMMON
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gguf_writer = gguf.GGUFWriter(args.outfile, gguf.MODEL_ARCH_NAMES[arch])
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block_count = hparams.num_layers
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head_count = hparams.num_attention_heads
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head_count_kv = head_count
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ctx_length = hparams.seq_length
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hidden_size = hparams.hidden_size
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gguf_writer.add_name('persimmon-8b-chat')
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gguf_writer.add_context_length(ctx_length)
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gguf_writer.add_embedding_length(hidden_size)
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gguf_writer.add_block_count(block_count)
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gguf_writer.add_feed_forward_length(hparams.ffn_hidden_size)
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# ref: https://github.com/ggerganov/llama.cpp/pull/4889/commits/eea19039fc52ea2dbd1aab45b59ab4e3e29a3443
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gguf_writer.add_rope_dimension_count(hidden_size // head_count // 2)
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gguf_writer.add_head_count(head_count)
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gguf_writer.add_head_count_kv(head_count_kv)
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gguf_writer.add_rope_freq_base(hparams.rotary_emb_base)
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gguf_writer.add_layer_norm_eps(hparams.layernorm_epsilon)
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tokens, scores, toktypes = _get_sentencepiece_tokenizer_info(args.model_dir)
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gguf_writer.add_tokenizer_model('llama')
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gguf_writer.add_tokenizer_pre('default')
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gguf_writer.add_token_list(tokens)
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gguf_writer.add_token_scores(scores)
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gguf_writer.add_token_types(toktypes)
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gguf_writer.add_bos_token_id(71013)
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gguf_writer.add_eos_token_id(71013)
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tensor_map = gguf.get_tensor_name_map(arch, block_count)
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logger.info(tensor_map)
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for name in tensors.keys():
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data_torch = tensors[name]
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if name.endswith(".self_attention.rotary_emb.inv_freq"):
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continue
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old_dtype = data_torch.dtype
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# TODO: FP16 conversion produces garbage outputs. (Q8_0 does not, so..?)
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data = data_torch.to(torch.float32).squeeze().numpy()
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new_name = tensor_map.get_name(name, try_suffixes = (".weight", ".bias"))
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if new_name is None:
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raise ValueError(f"Can not map tensor '{name}'")
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n_dims = len(data.shape)
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logger.debug(f"{new_name}, n_dims = {str(n_dims)}, {str(old_dtype)} --> {str(data.dtype)}")
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gguf_writer.add_tensor(new_name, data)
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logger.info("gguf: write header")
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gguf_writer.write_header_to_file()
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logger.info("gguf: write metadata")
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gguf_writer.write_kv_data_to_file()
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logger.info("gguf: write tensors")
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gguf_writer.write_tensors_to_file()
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gguf_writer.close()
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logger.info(f"gguf: model successfully exported to '{args.outfile}'")
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if __name__ == '__main__':
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main()
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@ -115,7 +115,6 @@ class MODEL_ARCH(IntEnum):
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GPTNEOX = auto()
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MPT = auto()
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STARCODER = auto()
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PERSIMMON = auto()
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REFACT = auto()
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BERT = auto()
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NOMIC_BERT = auto()
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@ -193,7 +192,6 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
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MODEL_ARCH.GPTNEOX: "gptneox",
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MODEL_ARCH.MPT: "mpt",
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MODEL_ARCH.STARCODER: "starcoder",
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MODEL_ARCH.PERSIMMON: "persimmon",
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MODEL_ARCH.REFACT: "refact",
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MODEL_ARCH.BERT: "bert",
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MODEL_ARCH.NOMIC_BERT: "nomic-bert",
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@ -426,20 +424,6 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
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MODEL_TENSOR.FFN_DOWN,
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MODEL_TENSOR.FFN_UP,
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],
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MODEL_ARCH.PERSIMMON: [
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MODEL_TENSOR.TOKEN_EMBD,
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MODEL_TENSOR.OUTPUT,
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MODEL_TENSOR.OUTPUT_NORM,
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MODEL_TENSOR.ATTN_NORM,
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MODEL_TENSOR.ATTN_QKV,
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MODEL_TENSOR.ATTN_OUT,
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MODEL_TENSOR.FFN_NORM,
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MODEL_TENSOR.FFN_DOWN,
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MODEL_TENSOR.FFN_UP,
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MODEL_TENSOR.ATTN_Q_NORM,
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MODEL_TENSOR.ATTN_K_NORM,
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MODEL_TENSOR.ATTN_ROT_EMBD,
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],
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MODEL_ARCH.REFACT: [
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MODEL_TENSOR.TOKEN_EMBD,
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MODEL_TENSOR.OUTPUT_NORM,
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@ -756,9 +740,6 @@ MODEL_TENSOR_SKIP: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
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MODEL_TENSOR.ROPE_FREQS,
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MODEL_TENSOR.ATTN_ROT_EMBD,
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],
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MODEL_ARCH.PERSIMMON: [
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MODEL_TENSOR.ROPE_FREQS,
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],
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MODEL_ARCH.QWEN: [
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MODEL_TENSOR.ROPE_FREQS,
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MODEL_TENSOR.ATTN_ROT_EMBD,
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280
llama.cpp
280
llama.cpp
@ -202,7 +202,6 @@ enum llm_arch {
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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_PERSIMMON,
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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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@ -239,7 +238,6 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
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{ LLM_ARCH_MPT, "mpt" },
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{ LLM_ARCH_BAICHUAN, "baichuan" },
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{ LLM_ARCH_STARCODER, "starcoder" },
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{ LLM_ARCH_PERSIMMON, "persimmon" },
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{ LLM_ARCH_REFACT, "refact" },
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{ LLM_ARCH_BERT, "bert" },
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{ LLM_ARCH_NOMIC_BERT, "nomic-bert" },
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@ -595,23 +593,6 @@ static const std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NA
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{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
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},
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},
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{
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LLM_ARCH_PERSIMMON,
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{
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{ LLM_TENSOR_TOKEN_EMBD, "token_embd"},
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{ LLM_TENSOR_OUTPUT_NORM, "output_norm"},
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{ LLM_TENSOR_OUTPUT, "output"},
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{ LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm"},
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{ LLM_TENSOR_ATTN_QKV, "blk.%d.attn_qkv"},
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{ LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output"},
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{ LLM_TENSOR_ATTN_Q_NORM, "blk.%d.attn_q_norm"},
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{ LLM_TENSOR_ATTN_K_NORM, "blk.%d.attn_k_norm"},
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{ LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm"},
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{ LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down"},
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{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up"},
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{ LLM_TENSOR_ATTN_ROT_EMBD, "blk.%d.attn_rot_embd"},
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},
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},
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{
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LLM_ARCH_MPT,
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{
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@ -3967,14 +3948,6 @@ static void llm_load_hparams(
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default: model.type = e_model::MODEL_UNKNOWN;
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}
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} break;
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case LLM_ARCH_PERSIMMON:
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{
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ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
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switch (hparams.n_layer) {
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case 36: model.type = e_model::MODEL_8B; break;
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default: model.type = e_model::MODEL_UNKNOWN;
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}
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} break;
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case LLM_ARCH_REFACT:
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{
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ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
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@ -5221,47 +5194,6 @@ static bool llm_load_tensors(
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layer.ffn_up_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff});
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}
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} break;
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case LLM_ARCH_PERSIMMON:
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{
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model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
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{
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model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
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model.output_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd});
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model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab});
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}
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for (int i = 0; i < n_layer; ++i) {
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ggml_context * ctx_layer = ctx_for_layer(i);
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ggml_context * ctx_split = ctx_for_layer_split(i);
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auto & layer = model.layers[i];
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layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
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layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd});
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layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa});
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layer.bqkv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa});
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layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
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layer.bo = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd});
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layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd});
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layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd});
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layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff});
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layer.ffn_up_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff});
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layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
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layer.ffn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd});
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layer.attn_q_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {64});
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layer.attn_q_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "bias", i), {64});
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layer.attn_k_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {64});
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layer.attn_k_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "bias", i), {64});
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}
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} break;
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case LLM_ARCH_BERT:
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case LLM_ARCH_NOMIC_BERT:
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{
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@ -7923,213 +7855,6 @@ struct llm_build_context {
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return gf;
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}
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struct ggml_cgraph * build_persimmon() {
|
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struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
|
||||
|
||||
const int64_t n_embd_head = hparams.n_embd_head_v;
|
||||
GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
|
||||
GGML_ASSERT(n_embd_head/2 == hparams.n_rot);
|
||||
|
||||
struct ggml_tensor * cur;
|
||||
struct ggml_tensor * inpL;
|
||||
|
||||
inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
|
||||
|
||||
// inp_pos - contains the positions
|
||||
struct ggml_tensor * inp_pos = build_inp_pos();
|
||||
|
||||
// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
|
||||
struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
|
||||
|
||||
for (int il = 0; il < n_layer; ++il) {
|
||||
struct ggml_tensor * residual = inpL;
|
||||
|
||||
cur = llm_build_norm(ctx0, inpL, hparams,
|
||||
model.layers[il].attn_norm,
|
||||
model.layers[il].attn_norm_b,
|
||||
LLM_NORM, cb, il);
|
||||
cb(cur, "attn_norm", il);
|
||||
|
||||
// self attention
|
||||
{
|
||||
cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur);
|
||||
cb(cur, "wqkv", il);
|
||||
|
||||
cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
|
||||
cb(cur, "bqkv", il);
|
||||
|
||||
// split qkv
|
||||
GGML_ASSERT(n_head_kv == n_head);
|
||||
|
||||
struct ggml_tensor * tmpqkv = ggml_reshape_4d(ctx0, cur, n_embd_head, 3, n_head, n_tokens);
|
||||
cb(tmpqkv, "tmpqkv", il);
|
||||
|
||||
struct ggml_tensor * tmpqkv_perm = ggml_cont(ctx0, ggml_permute(ctx0, tmpqkv, 0, 3, 1, 2));
|
||||
cb(tmpqkv_perm, "tmpqkv", il);
|
||||
|
||||
struct ggml_tensor * tmpq = ggml_view_3d(
|
||||
ctx0, tmpqkv_perm, n_embd_head, n_head, n_tokens,
|
||||
ggml_element_size(tmpqkv_perm) * n_embd_head,
|
||||
ggml_element_size(tmpqkv_perm) * n_embd_head * n_head,
|
||||
0
|
||||
);
|
||||
cb(tmpq, "tmpq", il);
|
||||
|
||||
struct ggml_tensor * tmpk = ggml_view_3d(
|
||||
ctx0, tmpqkv_perm, n_embd_head, n_head, n_tokens,
|
||||
ggml_element_size(tmpqkv_perm) * n_embd_head,
|
||||
ggml_element_size(tmpqkv_perm) * n_embd_head * n_head,
|
||||
ggml_element_size(tmpqkv_perm) * n_embd_head * n_head * n_tokens
|
||||
);
|
||||
cb(tmpk, "tmpk", il);
|
||||
|
||||
// Q/K Layernorm
|
||||
tmpq = llm_build_norm(ctx0, tmpq, hparams,
|
||||
model.layers[il].attn_q_norm,
|
||||
model.layers[il].attn_q_norm_b,
|
||||
LLM_NORM, cb, il);
|
||||
cb(tmpq, "tmpq", il);
|
||||
|
||||
tmpk = llm_build_norm(ctx0, tmpk, hparams,
|
||||
model.layers[il].attn_k_norm,
|
||||
model.layers[il].attn_k_norm_b,
|
||||
LLM_NORM, cb, il);
|
||||
cb(tmpk, "tmpk", il);
|
||||
|
||||
// RoPE the first n_rot of q/k, pass the other half, and concat.
|
||||
struct ggml_tensor * qrot = ggml_view_3d(
|
||||
ctx0, tmpq, n_rot, n_head, n_tokens,
|
||||
ggml_element_size(tmpq) * n_embd_head,
|
||||
ggml_element_size(tmpq) * n_embd_head * n_head,
|
||||
0
|
||||
);
|
||||
cb(qrot, "qrot", il);
|
||||
|
||||
struct ggml_tensor * krot = ggml_view_3d(
|
||||
ctx0, tmpk, n_rot, n_head, n_tokens,
|
||||
ggml_element_size(tmpk) * n_embd_head,
|
||||
ggml_element_size(tmpk) * n_embd_head * n_head,
|
||||
0
|
||||
);
|
||||
cb(krot, "krot", il);
|
||||
|
||||
// get the second half of tmpq, e.g tmpq[n_rot:, :, :]
|
||||
struct ggml_tensor * qpass = ggml_view_3d(
|
||||
ctx0, tmpq, n_rot, n_head, n_tokens,
|
||||
ggml_element_size(tmpq) * n_embd_head,
|
||||
ggml_element_size(tmpq) * n_embd_head * n_head,
|
||||
ggml_element_size(tmpq) * n_rot
|
||||
);
|
||||
cb(qpass, "qpass", il);
|
||||
|
||||
struct ggml_tensor * kpass = ggml_view_3d(
|
||||
ctx0, tmpk, n_rot, n_head, n_tokens,
|
||||
ggml_element_size(tmpk) * n_embd_head,
|
||||
ggml_element_size(tmpk) * n_embd_head * n_head,
|
||||
ggml_element_size(tmpk) * n_rot
|
||||
);
|
||||
cb(kpass, "kpass", il);
|
||||
|
||||
struct ggml_tensor * qrotated = ggml_rope_custom(
|
||||
ctx0, qrot, inp_pos, n_rot, rope_type, 0, n_orig_ctx,
|
||||
freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
|
||||
);
|
||||
cb(qrotated, "qrotated", il);
|
||||
|
||||
struct ggml_tensor * krotated = ggml_rope_custom(
|
||||
ctx0, krot, inp_pos, n_rot, rope_type, 0, n_orig_ctx,
|
||||
freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
|
||||
);
|
||||
cb(krotated, "krotated", il);
|
||||
|
||||
// ggml currently only supports concatenation on dim=2
|
||||
// so we need to permute qrot, qpass, concat, then permute back.
|
||||
qrotated = ggml_cont(ctx0, ggml_permute(ctx0, qrotated, 2, 1, 0, 3));
|
||||
cb(qrotated, "qrotated", il);
|
||||
|
||||
krotated = ggml_cont(ctx0, ggml_permute(ctx0, krotated, 2, 1, 0, 3));
|
||||
cb(krotated, "krotated", il);
|
||||
|
||||
qpass = ggml_cont(ctx0, ggml_permute(ctx0, qpass, 2, 1, 0, 3));
|
||||
cb(qpass, "qpass", il);
|
||||
|
||||
kpass = ggml_cont(ctx0, ggml_permute(ctx0, kpass, 2, 1, 0, 3));
|
||||
cb(kpass, "kpass", il);
|
||||
|
||||
struct ggml_tensor * Qcur = ggml_concat(ctx0, qrotated, qpass);
|
||||
cb(Qcur, "Qcur", il);
|
||||
|
||||
struct ggml_tensor * Kcur = ggml_concat(ctx0, krotated, kpass);
|
||||
cb(Kcur, "Kcur", il);
|
||||
|
||||
struct ggml_tensor * Q = ggml_cont(ctx0, ggml_permute(ctx0, Qcur, 2, 1, 0, 3));
|
||||
cb(Q, "Q", il);
|
||||
|
||||
Kcur = ggml_cont(ctx0, ggml_permute(ctx0, Kcur, 2, 1, 0, 3));
|
||||
cb(Kcur, "Kcur", il);
|
||||
|
||||
struct ggml_tensor * Vcur = ggml_view_3d(
|
||||
ctx0, tmpqkv_perm, n_embd_head, n_head, n_tokens,
|
||||
ggml_element_size(tmpqkv_perm) * n_embd_head,
|
||||
ggml_element_size(tmpqkv_perm) * n_embd_head * n_head,
|
||||
ggml_element_size(tmpqkv_perm) * n_embd_head * n_head * n_tokens * 2
|
||||
);
|
||||
cb(Vcur, "Vcur", il);
|
||||
|
||||
cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf,
|
||||
model.layers[il].wo, model.layers[il].bo,
|
||||
Kcur, Vcur, Q, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
|
||||
}
|
||||
|
||||
if (il == n_layer - 1) {
|
||||
// skip computing output for unused tokens
|
||||
struct ggml_tensor * inp_out_ids = build_inp_out_ids();
|
||||
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
|
||||
residual = ggml_get_rows(ctx0, residual, inp_out_ids);
|
||||
}
|
||||
|
||||
struct ggml_tensor * ffn_inp = ggml_add(ctx0, residual, cur);
|
||||
cb(ffn_inp, "ffn_inp", il);
|
||||
|
||||
// feed-forward network
|
||||
{
|
||||
cur = llm_build_norm(ctx0, ffn_inp, hparams,
|
||||
model.layers[il].ffn_norm,
|
||||
model.layers[il].ffn_norm_b,
|
||||
LLM_NORM, cb, il);
|
||||
cb(cur, "ffn_norm", il);
|
||||
|
||||
cur = llm_build_ffn(ctx0, cur,
|
||||
model.layers[il].ffn_up, model.layers[il].ffn_up_b,
|
||||
NULL, NULL,
|
||||
model.layers[il].ffn_down, model.layers[il].ffn_down_b,
|
||||
NULL,
|
||||
LLM_FFN_RELU_SQR, LLM_FFN_SEQ, cb, il);
|
||||
cb(cur, "ffn_out", il);
|
||||
}
|
||||
|
||||
cur = ggml_add(ctx0, cur, ffn_inp);
|
||||
cb(cur, "l_out", il);
|
||||
|
||||
inpL = cur;
|
||||
}
|
||||
|
||||
cur = inpL;
|
||||
|
||||
cur = llm_build_norm(ctx0, cur, hparams,
|
||||
model.output_norm,
|
||||
model.output_norm_b,
|
||||
LLM_NORM, cb, -1);
|
||||
cb(cur, "result_norm", -1);
|
||||
|
||||
cur = ggml_mul_mat(ctx0, model.output, cur);
|
||||
cb(cur, "result_output", -1);
|
||||
|
||||
ggml_build_forward_expand(gf, cur);
|
||||
|
||||
return gf;
|
||||
}
|
||||
|
||||
struct ggml_cgraph * build_refact() {
|
||||
struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
|
||||
|
||||
@ -10898,10 +10623,6 @@ static struct ggml_cgraph * llama_build_graph(
|
||||
{
|
||||
result = llm.build_starcoder();
|
||||
} break;
|
||||
case LLM_ARCH_PERSIMMON:
|
||||
{
|
||||
result = llm.build_persimmon();
|
||||
} break;
|
||||
case LLM_ARCH_REFACT:
|
||||
{
|
||||
result = llm.build_refact();
|
||||
@ -15992,7 +15713,6 @@ enum llama_rope_type llama_rope_type(const struct llama_model * model) {
|
||||
case LLM_ARCH_FALCON:
|
||||
case LLM_ARCH_GROK:
|
||||
case LLM_ARCH_DBRX:
|
||||
case LLM_ARCH_PERSIMMON:
|
||||
case LLM_ARCH_BERT:
|
||||
case LLM_ARCH_NOMIC_BERT:
|
||||
case LLM_ARCH_STABLELM:
|
||||
|
||||
@ -9,4 +9,3 @@
|
||||
-r ./requirements/requirements-convert-hf-to-gguf.txt
|
||||
-r ./requirements/requirements-convert-hf-to-gguf-update.txt
|
||||
-r ./requirements/requirements-convert-llama-ggml-to-gguf.txt
|
||||
-r ./requirements/requirements-convert-persimmon-to-gguf.txt
|
||||
|
||||
@ -1,2 +0,0 @@
|
||||
-r ./requirements-convert.txt
|
||||
torch~=2.1.1
|
||||
Loading…
Reference in New Issue
Block a user