mirror of
https://github.com/ggerganov/llama.cpp.git
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llama : add support for StarCoder model architectures (#3187)
* add placeholder of starcoder in gguf / llama.cpp * support convert starcoder weights to gguf * convert MQA to MHA * fix ffn_down name * add LLM_ARCH_STARCODER to llama.cpp * set head_count_kv = 1 * load starcoder weight * add max_position_embeddings * set n_positions to max_positioin_embeddings * properly load all starcoder params * fix head count kv * fix comments * fix vram calculation for starcoder * store mqa directly * add input embeddings handling * add TBD * working in cpu, metal buggy * cleanup useless code * metal : fix out-of-bounds access in soft_max kernels * llama : make starcoder graph build more consistent with others * refactor: cleanup comments a bit * add other starcoder models: 3B, 7B, 15B * support-mqa-directly * fix: remove max_position_embeddings, use n_train_ctx * Update llama.cpp Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * Update llama.cpp Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * Apply suggestions from code review Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * fix: switch to space from tab --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
This commit is contained in:
Meng Zhang
GitHub
parent
80291a1d02
commit
4fe09dfe66
248
convert-starcoder-hf-to-gguf.py
Executable file
248
convert-starcoder-hf-to-gguf.py
Executable file
@ -0,0 +1,248 @@
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#!/usr/bin/env python3
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# HF starcoder --> gguf conversion
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from __future__ import annotations
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import argparse
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import json
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import os
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import struct
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import sys
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from pathlib import Path
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from typing import Any
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import numpy as np
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import torch
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from transformers import AutoTokenizer # type: ignore[import]
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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' / 'gguf'))
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import gguf
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def bytes_to_unicode():
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# ref: https://github.com/openai/gpt-2/blob/master/src/encoder.py
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"""
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Returns list of utf-8 byte and a corresponding list of unicode strings.
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The reversible bpe codes work on unicode strings.
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This means you need a large # of unicode characters in your vocab if you want to avoid UNKs.
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When you're at something like a 10B token dataset you end up needing around 5K for decent coverage.
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This is a significant percentage of your normal, say, 32K bpe vocab.
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To avoid that, we want lookup tables between utf-8 bytes and unicode strings.
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And avoids mapping to whitespace/control characters the bpe code barfs on.
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"""
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bs = list(range(ord("!"), ord("~")+1))+list(range(ord("¡"), ord("¬")+1))+list(range(ord("®"), ord("ÿ")+1))
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cs = bs[:]
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n = 0
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for b in range(2**8):
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if b not in bs:
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bs.append(b)
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cs.append(2**8+n)
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n += 1
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return dict(zip(bs, (chr(n) for n in cs)))
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def count_model_parts(dir_model: Path) -> int:
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num_parts = 0
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for filename in os.listdir(dir_model):
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if filename.startswith("pytorch_model-"):
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num_parts += 1
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if num_parts > 0:
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print("gguf: found " + str(num_parts) + " model parts")
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return num_parts
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def parse_args() -> argparse.Namespace:
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parser = argparse.ArgumentParser(description="Convert a StarCoder model to a GGML compatible file")
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parser.add_argument("--vocab-only", action="store_true", help="extract only the vocab")
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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("model", type=Path, help="directory containing model file, or model file itself (*.bin)")
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parser.add_argument("ftype", type=int, help="output format - use 0 for float32, 1 for float16", choices=[0, 1], default = 1)
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return parser.parse_args()
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args = parse_args()
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dir_model = args.model
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ftype = args.ftype
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if not dir_model.is_dir():
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print(f'Error: {args.model} is not a directory', file = sys.stderr)
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sys.exit(1)
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# possible tensor data types
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# ftype == 0 -> float32
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# ftype == 1 -> float16
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# map from ftype to string
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ftype_str = ["f32", "f16"]
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if args.outfile is not None:
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fname_out = args.outfile
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else:
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# output in the same directory as the model by default
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fname_out = dir_model / f'ggml-model-{ftype_str[ftype]}.gguf'
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print("gguf: loading model "+dir_model.name)
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with open(dir_model / "config.json", "r", encoding="utf-8") as f:
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hparams = json.load(f)
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if hparams["architectures"][0] != "GPTBigCodeForCausalLM":
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print("Model architecture not supported: " + hparams["architectures"][0])
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sys.exit(1)
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# get number of model parts
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num_parts = count_model_parts(dir_model)
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ARCH=gguf.MODEL_ARCH.STARCODER
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gguf_writer = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[ARCH])
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print("gguf: get model metadata")
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block_count = hparams["n_layer"]
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gguf_writer.add_name("StarCoder")
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gguf_writer.add_context_length(hparams["n_positions"])
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gguf_writer.add_embedding_length(hparams["n_embd"])
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gguf_writer.add_feed_forward_length(4 * hparams["n_embd"])
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gguf_writer.add_block_count(block_count)
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gguf_writer.add_head_count(hparams["n_head"])
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gguf_writer.add_head_count_kv(1)
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gguf_writer.add_layer_norm_eps(hparams["layer_norm_epsilon"])
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gguf_writer.add_file_type(ftype)
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# TOKENIZATION
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print("gguf: get tokenizer metadata")
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tokens: list[bytearray] = []
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scores: list[float] = []
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toktypes: list[int] = []
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tokenizer_json_file = dir_model / 'tokenizer.json'
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if not tokenizer_json_file.is_file():
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print(f'Error: Missing {tokenizer_json_file}', file = sys.stderr)
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sys.exit(1)
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# gpt2 tokenizer
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gguf_writer.add_tokenizer_model("gpt2")
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with open(tokenizer_json_file, "r", encoding="utf-8") as f:
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tokenizer_json = json.load(f)
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print("gguf: get gpt2 tokenizer vocab")
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# The number of tokens in tokenizer.json can differ from the expected vocab size.
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# This causes downstream issues with mismatched tensor sizes when running the inference
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vocab_size = hparams["vocab_size"] if "vocab_size" in hparams else len(tokenizer_json["model"]["vocab"])
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# ref: https://github.com/cmp-nct/ggllm.cpp/blob/master/falcon_convert.py
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tokenizer = AutoTokenizer.from_pretrained(dir_model)
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reverse_vocab = {id: encoded_tok for encoded_tok, id in tokenizer.vocab.items()}
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byte_encoder = bytes_to_unicode()
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byte_decoder = {v: k for k, v in byte_encoder.items()}
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for i in range(vocab_size):
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if i in reverse_vocab:
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try:
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text = bytearray([byte_decoder[c] for c in reverse_vocab[i]])
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except KeyError:
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text = bytearray()
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for c in reverse_vocab[i]:
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if ord(c) < 256: # single byte character
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text.append(byte_decoder[ord(c)])
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else: # multibyte special token character
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text.extend(c.encode('utf-8'))
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else:
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print(f"Key {i} not in tokenizer vocabulary. Padding with an arbitrary token.")
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pad_token = f"[PAD{i}]".encode("utf8")
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text = bytearray(pad_token)
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tokens.append(text)
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scores.append(0.0) # dymmy
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toktypes.append(gguf.TokenType.NORMAL) # dummy
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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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special_vocab = gguf.SpecialVocab(dir_model, load_merges = True)
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special_vocab.add_to_gguf(gguf_writer)
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# TENSORS
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tensor_map = gguf.get_tensor_name_map(ARCH,block_count)
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# params for qkv transform
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n_head = hparams["n_head"]
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n_head_kv = hparams["n_head_kv"] if "n_head_kv" in hparams else 1
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head_dim = hparams["n_embd"] // n_head
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# tensor info
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print("gguf: get tensor metadata")
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if num_parts == 0:
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part_names = iter(("pytorch_model.bin",))
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else:
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part_names = (
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f"pytorch_model-{n:05}-of-{num_parts:05}.bin" for n in range(1, num_parts + 1)
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)
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for part_name in part_names:
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if args.vocab_only:
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break
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print("gguf: loading model part '" + part_name + "'")
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model_part = torch.load(dir_model / part_name, map_location="cpu")
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for name in model_part.keys():
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data = model_part[name]
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old_dtype = data.dtype
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# convert any unsupported data types to float32
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if data.dtype != torch.float16 and data.dtype != torch.float32:
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data = data.to(torch.float32)
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data = data.squeeze().numpy()
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# map tensor names
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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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print("Can not map tensor '" + name + "'")
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sys.exit()
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n_dims = len(data.shape)
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data_dtype = data.dtype
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# if f32 desired, convert any float16 to float32
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if ftype == 0 and data_dtype == np.float16:
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data = data.astype(np.float32)
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# TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32
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if ftype == 1 and data_dtype == np.float16 and n_dims == 1:
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data = data.astype(np.float32)
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# if f16 desired, convert any float32 2-dim weight tensors to float16
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if ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2:
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data = data.astype(np.float16)
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print(name, "=>", new_name + ", shape = " + str(data.shape) + ", " + str(old_dtype) + " --> " + str(data.dtype))
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gguf_writer.add_tensor(new_name, data)
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print("gguf: write header")
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gguf_writer.write_header_to_file()
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print("gguf: write metadata")
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gguf_writer.write_kv_data_to_file()
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if not args.vocab_only:
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print("gguf: write tensors")
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gguf_writer.write_tensors_to_file()
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gguf_writer.close()
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print(f"gguf: model successfully exported to '{fname_out}'")
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print("")
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@ -77,13 +77,14 @@ KEY_TOKENIZER_RWKV = "tokenizer.rwkv.world"
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class MODEL_ARCH(IntEnum):
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class MODEL_ARCH(IntEnum):
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LLAMA : int = auto()
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LLAMA : int = auto()
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FALCON : int = auto()
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FALCON : int = auto()
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BAICHUAN:int = auto()
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BAICHUAN : int = auto()
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GPT2 : int = auto()
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GPT2 : int = auto()
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GPTJ : int = auto()
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GPTJ : int = auto()
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GPTNEOX: int = auto()
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GPTNEOX : int = auto()
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MPT : int = auto()
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MPT : int = auto()
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STARCODER : int = auto()
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class MODEL_TENSOR(IntEnum):
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class MODEL_TENSOR(IntEnum):
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@ -107,13 +108,14 @@ class MODEL_TENSOR(IntEnum):
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MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
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MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
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MODEL_ARCH.LLAMA: "llama",
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MODEL_ARCH.LLAMA: "llama",
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MODEL_ARCH.FALCON: "falcon",
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MODEL_ARCH.FALCON: "falcon",
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MODEL_ARCH.BAICHUAN:"baichuan",
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MODEL_ARCH.BAICHUAN: "baichuan",
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MODEL_ARCH.GPT2: "gpt2",
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MODEL_ARCH.GPT2: "gpt2",
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MODEL_ARCH.GPTJ: "gptj",
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MODEL_ARCH.GPTJ: "gptj",
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MODEL_ARCH.GPTNEOX: "gptneox",
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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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MODEL_ARCH.STARCODER: "starcoder",
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}
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}
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MODEL_TENSOR_NAMES: dict[MODEL_ARCH, dict[MODEL_TENSOR, str]] = {
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MODEL_TENSOR_NAMES: dict[MODEL_ARCH, dict[MODEL_TENSOR, str]] = {
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@ -171,6 +173,18 @@ MODEL_TENSOR_NAMES: dict[MODEL_ARCH, dict[MODEL_TENSOR, str]] = {
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MODEL_TENSOR.FFN_DOWN: "blk.{bid}.ffn_down",
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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_UP: "blk.{bid}.ffn_up",
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},
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},
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MODEL_ARCH.STARCODER: {
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MODEL_TENSOR.TOKEN_EMBD: "token_embd",
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MODEL_TENSOR.POS_EMBD: "position_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.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_NORM: "blk.{bid}.ffn_norm",
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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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MODEL_ARCH.GPT2: {
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# TODO
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# TODO
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},
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},
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368
llama.cpp
368
llama.cpp
@ -160,17 +160,19 @@ enum llm_arch {
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LLM_ARCH_GPTJ,
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LLM_ARCH_GPTJ,
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LLM_ARCH_GPTNEOX,
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LLM_ARCH_GPTNEOX,
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LLM_ARCH_MPT,
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LLM_ARCH_MPT,
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LLM_ARCH_STARCODER,
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LLM_ARCH_UNKNOWN,
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LLM_ARCH_UNKNOWN,
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};
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};
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static std::map<llm_arch, std::string> LLM_ARCH_NAMES = {
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static std::map<llm_arch, std::string> LLM_ARCH_NAMES = {
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{ LLM_ARCH_LLAMA, "llama" },
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{ LLM_ARCH_LLAMA, "llama" },
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{ LLM_ARCH_FALCON, "falcon" },
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{ LLM_ARCH_FALCON, "falcon" },
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{ LLM_ARCH_GPT2, "gpt2" },
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{ LLM_ARCH_GPT2, "gpt2" },
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{ LLM_ARCH_GPTJ, "gptj" },
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{ LLM_ARCH_GPTJ, "gptj" },
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{ LLM_ARCH_GPTNEOX, "gptneox" },
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{ LLM_ARCH_GPTNEOX, "gptneox" },
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{ LLM_ARCH_MPT, "mpt" },
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{ LLM_ARCH_MPT, "mpt" },
|
||||||
{ LLM_ARCH_BAICHUAN,"baichuan" },
|
{ LLM_ARCH_BAICHUAN, "baichuan" },
|
||||||
|
{ LLM_ARCH_STARCODER, "starcoder" },
|
||||||
};
|
};
|
||||||
|
|
||||||
enum llm_kv {
|
enum llm_kv {
|
||||||
@ -376,6 +378,21 @@ static std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NAMES =
|
|||||||
{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
|
{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
|
||||||
},
|
},
|
||||||
},
|
},
|
||||||
|
{
|
||||||
|
LLM_ARCH_STARCODER,
|
||||||
|
{
|
||||||
|
{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
|
||||||
|
{ LLM_TENSOR_POS_EMBD, "position_embd" },
|
||||||
|
{ LLM_TENSOR_OUTPUT_NORM, "output_norm" },
|
||||||
|
{ LLM_TENSOR_OUTPUT, "output" },
|
||||||
|
{ LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" },
|
||||||
|
{ LLM_TENSOR_ATTN_QKV, "blk.%d.attn_qkv" },
|
||||||
|
{ LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
|
||||||
|
{ LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" },
|
||||||
|
{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
|
||||||
|
{ LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
|
||||||
|
},
|
||||||
|
},
|
||||||
{
|
{
|
||||||
LLM_ARCH_UNKNOWN,
|
LLM_ARCH_UNKNOWN,
|
||||||
{
|
{
|
||||||
@ -895,9 +912,11 @@ static llama_state g_state;
|
|||||||
// available llama models
|
// available llama models
|
||||||
enum e_model {
|
enum e_model {
|
||||||
MODEL_UNKNOWN,
|
MODEL_UNKNOWN,
|
||||||
|
MODEL_1B,
|
||||||
MODEL_3B,
|
MODEL_3B,
|
||||||
MODEL_7B,
|
MODEL_7B,
|
||||||
MODEL_13B,
|
MODEL_13B,
|
||||||
|
MODEL_15B,
|
||||||
MODEL_30B,
|
MODEL_30B,
|
||||||
MODEL_34B,
|
MODEL_34B,
|
||||||
MODEL_40B,
|
MODEL_40B,
|
||||||
@ -966,13 +985,22 @@ struct llama_layer {
|
|||||||
struct ggml_tensor * wo;
|
struct ggml_tensor * wo;
|
||||||
struct ggml_tensor * wqkv;
|
struct ggml_tensor * wqkv;
|
||||||
|
|
||||||
|
// attention bias
|
||||||
|
struct ggml_tensor * bo;
|
||||||
|
struct ggml_tensor * bqkv;
|
||||||
|
|
||||||
// normalization
|
// normalization
|
||||||
struct ggml_tensor * ffn_norm;
|
struct ggml_tensor * ffn_norm;
|
||||||
|
struct ggml_tensor * ffn_norm_b;
|
||||||
|
|
||||||
// ff
|
// ff
|
||||||
struct ggml_tensor * w1; // ffn_gate
|
struct ggml_tensor * w1; // ffn_gate
|
||||||
struct ggml_tensor * w2; // ffn_down
|
struct ggml_tensor * w2; // ffn_down
|
||||||
struct ggml_tensor * w3; // ffn_up
|
struct ggml_tensor * w3; // ffn_up
|
||||||
|
|
||||||
|
// ff bias
|
||||||
|
struct ggml_tensor * b2; // ffn_down
|
||||||
|
struct ggml_tensor * b3; // ffn_up
|
||||||
};
|
};
|
||||||
|
|
||||||
struct llama_kv_cache {
|
struct llama_kv_cache {
|
||||||
@ -1050,6 +1078,7 @@ struct llama_model {
|
|||||||
llama_vocab vocab;
|
llama_vocab vocab;
|
||||||
|
|
||||||
struct ggml_tensor * tok_embeddings;
|
struct ggml_tensor * tok_embeddings;
|
||||||
|
struct ggml_tensor * pos_embeddings;
|
||||||
|
|
||||||
struct ggml_tensor * output_norm;
|
struct ggml_tensor * output_norm;
|
||||||
struct ggml_tensor * output_norm_b;
|
struct ggml_tensor * output_norm_b;
|
||||||
@ -1593,9 +1622,11 @@ std::string llama_model_ftype_name(enum llama_ftype ftype) {
|
|||||||
|
|
||||||
static const char * llama_model_type_name(e_model type) {
|
static const char * llama_model_type_name(e_model type) {
|
||||||
switch (type) {
|
switch (type) {
|
||||||
|
case MODEL_1B: return "1B";
|
||||||
case MODEL_3B: return "3B";
|
case MODEL_3B: return "3B";
|
||||||
case MODEL_7B: return "7B";
|
case MODEL_7B: return "7B";
|
||||||
case MODEL_13B: return "13B";
|
case MODEL_13B: return "13B";
|
||||||
|
case MODEL_15B: return "15B";
|
||||||
case MODEL_30B: return "30B";
|
case MODEL_30B: return "30B";
|
||||||
case MODEL_34B: return "34B";
|
case MODEL_34B: return "34B";
|
||||||
case MODEL_40B: return "40B";
|
case MODEL_40B: return "40B";
|
||||||
@ -1713,6 +1744,17 @@ static void llm_load_hparams(
|
|||||||
default: model.type = e_model::MODEL_UNKNOWN;
|
default: model.type = e_model::MODEL_UNKNOWN;
|
||||||
}
|
}
|
||||||
} break;
|
} break;
|
||||||
|
case LLM_ARCH_STARCODER:
|
||||||
|
{
|
||||||
|
GGUF_GET_KEY(ctx, hparams.f_norm_eps, gguf_get_val_f32, GGUF_TYPE_FLOAT32, true, kv(LLM_KV_ATTENTION_LAYERNORM_EPS));
|
||||||
|
switch (hparams.n_layer) {
|
||||||
|
case 24: model.type = e_model::MODEL_1B; break;
|
||||||
|
case 36: model.type = e_model::MODEL_3B; break;
|
||||||
|
case 42: model.type = e_model::MODEL_7B; break;
|
||||||
|
case 40: model.type = e_model::MODEL_15B; break;
|
||||||
|
default: model.type = e_model::MODEL_UNKNOWN;
|
||||||
|
}
|
||||||
|
} break;
|
||||||
default: (void)0;
|
default: (void)0;
|
||||||
};
|
};
|
||||||
|
|
||||||
@ -2166,6 +2208,85 @@ static void llm_load_tensors(
|
|||||||
}
|
}
|
||||||
}
|
}
|
||||||
} break;
|
} break;
|
||||||
|
case LLM_ARCH_STARCODER:
|
||||||
|
{
|
||||||
|
model.tok_embeddings = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, GGML_BACKEND_CPU);
|
||||||
|
model.pos_embeddings = ml.create_tensor(ctx, tn(LLM_TENSOR_POS_EMBD, "weight"), {n_embd, hparams.n_ctx_train}, GGML_BACKEND_CPU);
|
||||||
|
|
||||||
|
// output
|
||||||
|
{
|
||||||
|
ggml_backend backend_norm;
|
||||||
|
ggml_backend backend_output;
|
||||||
|
|
||||||
|
if (n_gpu_layers > int(n_layer)) {
|
||||||
|
// norm is not performance relevant on its own but keeping it in VRAM reduces data copying
|
||||||
|
// on Windows however this is detrimental unless everything is on the GPU
|
||||||
|
#ifndef _WIN32
|
||||||
|
backend_norm = low_vram ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD;
|
||||||
|
#else
|
||||||
|
backend_norm = low_vram || n_gpu_layers <= (int) n_layer + 2 ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD;
|
||||||
|
#endif // _WIN32
|
||||||
|
|
||||||
|
backend_output = LLAMA_BACKEND_OFFLOAD_SPLIT;
|
||||||
|
} else {
|
||||||
|
backend_norm = GGML_BACKEND_CPU;
|
||||||
|
backend_output = GGML_BACKEND_CPU;
|
||||||
|
}
|
||||||
|
|
||||||
|
model.output_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, backend_norm);
|
||||||
|
model.output_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}, backend_norm);
|
||||||
|
model.output = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, backend_output);
|
||||||
|
|
||||||
|
if (backend_norm == GGML_BACKEND_GPU) {
|
||||||
|
vram_weights += ggml_nbytes(model.output_norm);
|
||||||
|
vram_weights += ggml_nbytes(model.output_norm_b);
|
||||||
|
}
|
||||||
|
if (backend_output == GGML_BACKEND_GPU_SPLIT) {
|
||||||
|
vram_weights += ggml_nbytes(model.output);
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
const uint32_t n_ff = hparams.n_ff;
|
||||||
|
|
||||||
|
const int i_gpu_start = n_layer - n_gpu_layers;
|
||||||
|
|
||||||
|
model.layers.resize(n_layer);
|
||||||
|
|
||||||
|
for (uint32_t i = 0; i < n_layer; ++i) {
|
||||||
|
const ggml_backend backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD; // NOLINT
|
||||||
|
const ggml_backend backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD_SPLIT; // NOLINT
|
||||||
|
|
||||||
|
auto & layer = model.layers[i];
|
||||||
|
|
||||||
|
layer.attn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, backend);
|
||||||
|
layer.attn_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}, backend);
|
||||||
|
|
||||||
|
layer.wqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, backend_split);
|
||||||
|
layer.bqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}, backend_split);
|
||||||
|
|
||||||
|
layer.wo = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, backend_split);
|
||||||
|
layer.bo = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, backend_split);
|
||||||
|
|
||||||
|
layer.ffn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, backend);
|
||||||
|
layer.ffn_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}, backend);
|
||||||
|
|
||||||
|
layer.w2 = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, backend_split);
|
||||||
|
layer.b2 = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, backend_split);
|
||||||
|
|
||||||
|
layer.w3 = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, backend_split);
|
||||||
|
layer.b3 = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}, backend_split);
|
||||||
|
|
||||||
|
if (backend == GGML_BACKEND_GPU) {
|
||||||
|
vram_weights +=
|
||||||
|
ggml_nbytes(layer.attn_norm) + ggml_nbytes(layer.attn_norm_b) +
|
||||||
|
ggml_nbytes(layer.wqkv) + ggml_nbytes(layer.bqkv) +
|
||||||
|
ggml_nbytes(layer.wo) + ggml_nbytes(layer.bo) +
|
||||||
|
ggml_nbytes(layer.ffn_norm) + ggml_nbytes(layer.ffn_norm_b) +
|
||||||
|
ggml_nbytes(layer.w2) + ggml_nbytes(layer.b2) +
|
||||||
|
ggml_nbytes(layer.w3) + ggml_nbytes(layer.b3);
|
||||||
|
}
|
||||||
|
}
|
||||||
|
} break;
|
||||||
default:
|
default:
|
||||||
throw std::runtime_error("unknown architecture");
|
throw std::runtime_error("unknown architecture");
|
||||||
};
|
};
|
||||||
@ -3305,6 +3426,235 @@ static struct ggml_cgraph * llm_build_falcon(
|
|||||||
return gf;
|
return gf;
|
||||||
}
|
}
|
||||||
|
|
||||||
|
static struct ggml_cgraph * llm_build_starcoder(
|
||||||
|
llama_context & lctx,
|
||||||
|
const llama_token * tokens,
|
||||||
|
const float * embd,
|
||||||
|
int n_tokens,
|
||||||
|
int n_past) {
|
||||||
|
|
||||||
|
GGML_ASSERT((!tokens && embd) || (tokens && !embd)); // NOLINT
|
||||||
|
|
||||||
|
const int N = n_tokens;
|
||||||
|
|
||||||
|
const auto & model = lctx.model;
|
||||||
|
const auto & hparams = model.hparams;
|
||||||
|
|
||||||
|
const auto & kv_self = lctx.kv_self;
|
||||||
|
|
||||||
|
GGML_ASSERT(!!kv_self.ctx);
|
||||||
|
|
||||||
|
const int64_t n_embd = hparams.n_embd;
|
||||||
|
const int64_t n_layer = hparams.n_layer;
|
||||||
|
const int64_t n_ctx = hparams.n_ctx;
|
||||||
|
const int64_t n_head = hparams.n_head;
|
||||||
|
const int64_t n_head_kv = hparams.n_head_kv;
|
||||||
|
const int64_t n_embd_head = hparams.n_embd_head();
|
||||||
|
const int64_t n_embd_gqa = hparams.n_embd_gqa();
|
||||||
|
|
||||||
|
GGML_ASSERT(n_embd_head == hparams.n_rot);
|
||||||
|
|
||||||
|
const float norm_eps = hparams.f_norm_eps;
|
||||||
|
|
||||||
|
auto & buf_compute = lctx.buf_compute;
|
||||||
|
|
||||||
|
struct ggml_init_params params = {
|
||||||
|
/*.mem_size =*/ buf_compute.size,
|
||||||
|
/*.mem_buffer =*/ buf_compute.data,
|
||||||
|
/*.no_alloc =*/ false,
|
||||||
|
};
|
||||||
|
|
||||||
|
params.no_alloc = true;
|
||||||
|
|
||||||
|
struct ggml_context * ctx0 = ggml_init(params);
|
||||||
|
|
||||||
|
ggml_cgraph * gf = ggml_new_graph(ctx0);
|
||||||
|
|
||||||
|
struct ggml_tensor * cur;
|
||||||
|
struct ggml_tensor * token;
|
||||||
|
struct ggml_tensor * position;
|
||||||
|
struct ggml_tensor * inpL;
|
||||||
|
|
||||||
|
if (tokens) {
|
||||||
|
struct ggml_tensor * inp_tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N);
|
||||||
|
|
||||||
|
ggml_allocr_alloc(lctx.alloc, inp_tokens);
|
||||||
|
if (!ggml_allocr_is_measure(lctx.alloc)) {
|
||||||
|
memcpy(inp_tokens->data, tokens, N*ggml_element_size(inp_tokens));
|
||||||
|
}
|
||||||
|
ggml_set_name(inp_tokens, "inp_tokens");
|
||||||
|
|
||||||
|
token = ggml_get_rows(ctx0, model.tok_embeddings, inp_tokens);
|
||||||
|
} else {
|
||||||
|
#ifdef GGML_USE_MPI
|
||||||
|
GGML_ASSERT(false && "not implemented");
|
||||||
|
#endif
|
||||||
|
|
||||||
|
token = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, N);
|
||||||
|
|
||||||
|
ggml_allocr_alloc(lctx.alloc, token);
|
||||||
|
if (!ggml_allocr_is_measure(lctx.alloc)) {
|
||||||
|
memcpy(token->data, embd, N * n_embd * ggml_element_size(inpL));
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
{
|
||||||
|
// Compute position embeddings.
|
||||||
|
struct ggml_tensor * inp_positions = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N);
|
||||||
|
ggml_allocr_alloc(lctx.alloc, inp_positions);
|
||||||
|
if (!ggml_allocr_is_measure(lctx.alloc)) {
|
||||||
|
for (int i = 0; i < N; ++i) {
|
||||||
|
((int32_t *) inp_positions->data)[i] = n_past + i;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
ggml_set_name(inp_positions, "inp_positions");
|
||||||
|
|
||||||
|
position = ggml_get_rows(ctx0, model.pos_embeddings, inp_positions);
|
||||||
|
}
|
||||||
|
|
||||||
|
struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1);
|
||||||
|
ggml_allocr_alloc(lctx.alloc, KQ_scale);
|
||||||
|
if (!ggml_allocr_is_measure(lctx.alloc)) {
|
||||||
|
ggml_set_f32(KQ_scale, 1.0f/sqrtf(float(n_embd)/n_head));
|
||||||
|
}
|
||||||
|
ggml_set_name(KQ_scale, "1/sqrt(n_embd_head)");
|
||||||
|
|
||||||
|
inpL = ggml_add(ctx0, token, position);
|
||||||
|
ggml_set_name(inpL, "inpL");
|
||||||
|
|
||||||
|
for (int il = 0; il < n_layer; ++il) {
|
||||||
|
{
|
||||||
|
// Norm
|
||||||
|
cur = ggml_norm(ctx0, inpL, norm_eps);
|
||||||
|
cur = ggml_add(ctx0, ggml_mul(ctx0, cur, model.layers[il].attn_norm), model.layers[il].attn_norm_b);
|
||||||
|
}
|
||||||
|
|
||||||
|
{
|
||||||
|
// Self Attention
|
||||||
|
cur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].wqkv, cur), model.layers[il].bqkv);
|
||||||
|
|
||||||
|
struct ggml_tensor * tmpq = ggml_view_2d(ctx0, cur, n_embd, N, cur->nb[1], 0*sizeof(float)*n_embd);
|
||||||
|
struct ggml_tensor * tmpk = ggml_view_2d(ctx0, cur, n_embd_gqa, N, cur->nb[1], sizeof(float)*n_embd);
|
||||||
|
struct ggml_tensor * tmpv = ggml_view_2d(ctx0, cur, n_embd_gqa, N, cur->nb[1], sizeof(float)*(n_embd + n_embd_gqa));
|
||||||
|
|
||||||
|
struct ggml_tensor * Qcur = tmpq;
|
||||||
|
struct ggml_tensor * Kcur = tmpk;
|
||||||
|
|
||||||
|
{
|
||||||
|
struct ggml_tensor * Vcur = ggml_transpose(ctx0, ggml_reshape_2d(ctx0, ggml_cont(ctx0, tmpv), n_embd_gqa, N));
|
||||||
|
ggml_set_name(Vcur, "Vcur");
|
||||||
|
|
||||||
|
struct ggml_tensor * k = ggml_view_1d(ctx0, kv_self.k, N*n_embd_gqa, (ggml_element_size(kv_self.k)*n_embd_gqa)*(il*n_ctx + n_past));
|
||||||
|
ggml_set_name(k, "k");
|
||||||
|
|
||||||
|
struct ggml_tensor * v = ggml_view_2d(ctx0, kv_self.v, N, n_embd_gqa,
|
||||||
|
( n_ctx)*ggml_element_size(kv_self.v),
|
||||||
|
(il*n_ctx)*ggml_element_size(kv_self.v)*n_embd_gqa + n_past*ggml_element_size(kv_self.v));
|
||||||
|
|
||||||
|
ggml_build_forward_expand(gf, ggml_cpy(ctx0, Kcur, k));
|
||||||
|
ggml_build_forward_expand(gf, ggml_cpy(ctx0, Vcur, v));
|
||||||
|
}
|
||||||
|
|
||||||
|
struct ggml_tensor * Q =
|
||||||
|
ggml_permute(ctx0,
|
||||||
|
ggml_cpy(ctx0,
|
||||||
|
Qcur,
|
||||||
|
ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_embd_head, n_head, N)),
|
||||||
|
0, 2, 1, 3);
|
||||||
|
ggml_set_name(Q, "Q");
|
||||||
|
|
||||||
|
struct ggml_tensor * K =
|
||||||
|
ggml_view_3d(ctx0, kv_self.k,
|
||||||
|
n_embd_head, n_past + N, n_head_kv,
|
||||||
|
ggml_element_size(kv_self.k)*n_embd_gqa,
|
||||||
|
ggml_element_size(kv_self.k)*n_embd_head,
|
||||||
|
ggml_element_size(kv_self.k)*n_embd_gqa*n_ctx*il);
|
||||||
|
ggml_set_name(K, "K");
|
||||||
|
|
||||||
|
// K * Q
|
||||||
|
struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q);
|
||||||
|
ggml_set_name(KQ, "KQ");
|
||||||
|
|
||||||
|
// KQ_scaled = KQ / sqrt(n_embd_head)
|
||||||
|
// KQ_scaled shape [n_past + N, N, n_head, 1]
|
||||||
|
struct ggml_tensor * KQ_scaled = ggml_scale_inplace(ctx0, KQ, KQ_scale);
|
||||||
|
ggml_set_name(KQ_scaled, "KQ_scaled");
|
||||||
|
|
||||||
|
// KQ_masked = mask_past(KQ_scaled)
|
||||||
|
struct ggml_tensor * KQ_masked = ggml_diag_mask_inf_inplace(ctx0, KQ_scaled, n_past);
|
||||||
|
ggml_set_name(KQ_masked, "KQ_masked");
|
||||||
|
|
||||||
|
// KQ = soft_max(KQ_masked)
|
||||||
|
struct ggml_tensor * KQ_soft_max = ggml_soft_max_inplace(ctx0, KQ_masked);
|
||||||
|
ggml_set_name(KQ_soft_max, "KQ_soft_max");
|
||||||
|
|
||||||
|
// split cached V into n_head heads
|
||||||
|
struct ggml_tensor * V =
|
||||||
|
ggml_view_3d(ctx0, kv_self.v,
|
||||||
|
n_past + N, n_embd_head, n_head_kv,
|
||||||
|
ggml_element_size(kv_self.v)*n_ctx,
|
||||||
|
ggml_element_size(kv_self.v)*n_ctx*n_embd_head,
|
||||||
|
ggml_element_size(kv_self.v)*n_ctx*n_embd_gqa*il);
|
||||||
|
ggml_set_name(V, "V");
|
||||||
|
|
||||||
|
struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V, KQ_soft_max);
|
||||||
|
ggml_set_name(KQV, "KQV");
|
||||||
|
|
||||||
|
// KQV_merged = KQV.permute(0, 2, 1, 3)
|
||||||
|
struct ggml_tensor * KQV_merged = ggml_permute(ctx0, KQV, 0, 2, 1, 3);
|
||||||
|
ggml_set_name(KQV_merged, "KQV_merged");
|
||||||
|
|
||||||
|
// cur = KQV_merged.contiguous().view(n_embd, N)
|
||||||
|
cur = ggml_cpy(ctx0,
|
||||||
|
KQV_merged,
|
||||||
|
ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, N));
|
||||||
|
ggml_set_name(cur, "KQV_merged_contiguous");
|
||||||
|
}
|
||||||
|
|
||||||
|
// Projection
|
||||||
|
cur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].wo, cur), model.layers[il].bo);
|
||||||
|
|
||||||
|
// Add the input
|
||||||
|
cur = ggml_add(ctx0, cur, inpL);
|
||||||
|
|
||||||
|
struct ggml_tensor * inpFF = cur;
|
||||||
|
|
||||||
|
// FF
|
||||||
|
{
|
||||||
|
// Norm
|
||||||
|
{
|
||||||
|
cur = ggml_norm(ctx0, inpFF, norm_eps);
|
||||||
|
cur = ggml_add(ctx0, ggml_mul(ctx0, cur, model.layers[il].ffn_norm), model.layers[il].ffn_norm_b);
|
||||||
|
}
|
||||||
|
|
||||||
|
cur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].w3, cur), model.layers[il].b3);
|
||||||
|
|
||||||
|
// GELU activation
|
||||||
|
cur = ggml_gelu(ctx0, cur);
|
||||||
|
|
||||||
|
// Projection
|
||||||
|
cur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].w2, cur), model.layers[il].b2);
|
||||||
|
}
|
||||||
|
|
||||||
|
inpL = ggml_add(ctx0, cur, inpFF);
|
||||||
|
}
|
||||||
|
|
||||||
|
// Output Norm
|
||||||
|
{
|
||||||
|
cur = ggml_norm(ctx0, inpL, norm_eps);
|
||||||
|
cur = ggml_add(ctx0, ggml_mul(ctx0, cur, model.output_norm), model.output_norm_b);
|
||||||
|
}
|
||||||
|
ggml_set_name(cur, "result_norm");
|
||||||
|
|
||||||
|
cur = ggml_mul_mat(ctx0, model.output, cur);
|
||||||
|
ggml_set_name(cur, "result_output");
|
||||||
|
|
||||||
|
ggml_build_forward_expand(gf, cur);
|
||||||
|
ggml_free(ctx0);
|
||||||
|
|
||||||
|
return gf;
|
||||||
|
}
|
||||||
|
|
||||||
static struct ggml_cgraph * llama_build_graph(
|
static struct ggml_cgraph * llama_build_graph(
|
||||||
llama_context & lctx,
|
llama_context & lctx,
|
||||||
const llama_token * tokens,
|
const llama_token * tokens,
|
||||||
@ -3328,6 +3678,10 @@ static struct ggml_cgraph * llama_build_graph(
|
|||||||
{
|
{
|
||||||
result = llm_build_falcon(lctx, tokens, embd, n_tokens, n_past);
|
result = llm_build_falcon(lctx, tokens, embd, n_tokens, n_past);
|
||||||
} break;
|
} break;
|
||||||
|
case LLM_ARCH_STARCODER:
|
||||||
|
{
|
||||||
|
result = llm_build_starcoder(lctx, tokens, embd, n_tokens, n_past);
|
||||||
|
} break;
|
||||||
default:
|
default:
|
||||||
GGML_ASSERT(false);
|
GGML_ASSERT(false);
|
||||||
};
|
};
|
||||||
|
|||||||
Loading…
Reference in New Issue
Block a user