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# Conflicts: # .github/workflows/build.yml # .github/workflows/docker.yml # CMakeLists.txt # Makefile # README.md # flake.nix # tests/CMakeLists.txt
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,22 @@ | ||
| node('x86_runner1'){ // Running on x86 runner containing latest vector qemu, latest vector gcc and all the necessary libraries | ||
| stage('Cleanup'){ | ||
| cleanWs() // Cleaning previous CI build in workspace | ||
| } | ||
| stage('checkout repo'){ | ||
| retry(5){ // Retry if the cloning fails due to some reason | ||
| checkout scm // Clone the repo on Runner | ||
| } | ||
| } | ||
| stage('Compiling llama.cpp'){ | ||
| sh'''#!/bin/bash | ||
| make RISCV=1 RISCV_CROSS_COMPILE=1 # Compiling llama for RISC-V | ||
| ''' | ||
| } | ||
| stage('Running llama.cpp'){ | ||
| sh'''#!/bin/bash | ||
| module load gnu-bin2/0.1 # loading latest versions of vector qemu and vector gcc | ||
| qemu-riscv64 -L /softwares/gnu-bin2/sysroot -cpu rv64,v=true,vlen=256,elen=64,vext_spec=v1.0 ./main -m /home/alitariq/codellama-7b.Q4_K_M.gguf -p "Anything" -n 9 > llama_log.txt # Running llama.cpp on vector qemu-riscv64 | ||
| cat llama_log.txt # Printing results | ||
| ''' | ||
| } | ||
| } |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,292 @@ | ||
| #!/usr/bin/env python3 | ||
| # HF baichuan --> gguf conversion | ||
|
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| from __future__ import annotations | ||
|
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| import argparse | ||
| import json | ||
| import os | ||
| import struct | ||
| import sys | ||
| from pathlib import Path | ||
| from typing import TYPE_CHECKING, Any | ||
| import itertools | ||
| import gguf | ||
| import numpy as np | ||
| import torch | ||
| from sentencepiece import SentencePieceProcessor # type: ignore[import] | ||
|
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| if TYPE_CHECKING: | ||
| from typing import TypeAlias | ||
|
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| NDArray: TypeAlias = 'np.ndarray[Any, Any]' | ||
|
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| # reverse HF permute back to original pth layout | ||
|
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| def reverse_hf_permute(weights: NDArray, n_head: int, n_kv_head: int | None = None) -> NDArray: | ||
| if n_kv_head is not None and n_head != n_kv_head: | ||
| n_head //= n_kv_head | ||
|
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| return (weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:]) | ||
| .swapaxes(1, 2) | ||
| .reshape(weights.shape)) | ||
|
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| def reverse_hf_permute_part(weights: NDArray, n_part: int, n_head: int, n_head_kv: int| None = None) -> NDArray: | ||
| r = weights.shape[0] // 3 | ||
| return (reverse_hf_permute(weights[r * n_part : r * n_part + r, ...], n_head, n_head_kv)) | ||
|
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| def reverse_hf_part(weights: NDArray, n_part: int) -> NDArray: | ||
| r = weights.shape[0] // 3 | ||
| return weights[r * n_part : r * n_part + r, ...] | ||
|
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| def count_model_parts(dir_model: str) -> int: | ||
| num_parts = 0 | ||
|
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| for filename in os.listdir(dir_model): | ||
| if filename.startswith("pytorch_model-"): | ||
| num_parts += 1 | ||
|
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| if num_parts > 0: | ||
| print("gguf: found " + str(num_parts) + " model parts") | ||
|
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| return num_parts | ||
|
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|
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|
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| def parse_args() -> argparse.Namespace: | ||
| parser = argparse.ArgumentParser(description="Convert a HuggingFace LLaMA model to a GGML compatible file") | ||
| parser.add_argument("--vocab-only", action="store_true", help="extract only the vocab") | ||
| parser.add_argument("--outfile", type=Path, help="path to write to; default: based on input") | ||
| parser.add_argument("model", type=Path, help="directory containing model file, or model file itself (*.bin)") | ||
| parser.add_argument("ftype", type=int, choices=[0, 1], help="output format - use 0 for float32, 1 for float16", default = 1) | ||
| return parser.parse_args() | ||
|
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| args = parse_args() | ||
|
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| dir_model = args.model | ||
| ftype = args.ftype | ||
| if not dir_model.is_dir(): | ||
| print(f'Error: {args.model} is not a directory', file = sys.stderr) | ||
| sys.exit(1) | ||
|
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| # possible tensor data types | ||
| # ftype == 0 -> float32 | ||
| # ftype == 1 -> float16 | ||
|
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| # map from ftype to string | ||
| ftype_str = ["f32", "f16"] | ||
|
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| if args.outfile is not None: | ||
| fname_out = args.outfile | ||
| else: | ||
| # output in the same directory as the model by default | ||
| 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: | ||
| hparams = json.load(f) | ||
| print("hello print: ",hparams["architectures"][0]) | ||
| if hparams["architectures"][0] != "BaichuanForCausalLM": | ||
| print("Model architecture not supported: " + hparams["architectures"][0]) | ||
|
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| sys.exit() | ||
|
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| # get number of model parts | ||
| num_parts = count_model_parts(dir_model) | ||
| print(f"num_parts:{num_parts}\n") | ||
| ARCH=gguf.MODEL_ARCH.BAICHUAN | ||
| 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["num_hidden_layers"] | ||
| head_count = hparams["num_attention_heads"] | ||
|
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| if "num_key_value_heads" in hparams: | ||
| head_count_kv = hparams["num_key_value_heads"] | ||
| else: | ||
| head_count_kv = head_count | ||
|
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| if "_name_or_path" in hparams: | ||
| hf_repo = hparams["_name_or_path"] | ||
| else: | ||
| hf_repo = "" | ||
|
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| if "max_sequence_length" in hparams: | ||
| ctx_length = hparams["max_sequence_length"] | ||
| elif "max_position_embeddings" in hparams: | ||
| ctx_length = hparams["max_position_embeddings"] | ||
| elif "model_max_length" in hparams: | ||
| ctx_length = hparams["model_max_length"] | ||
| else: | ||
| print("gguf: can not find ctx length parameter.") | ||
|
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| sys.exit() | ||
|
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| gguf_writer.add_name(dir_model.name) | ||
| gguf_writer.add_source_hf_repo(hf_repo) | ||
| gguf_writer.add_tensor_data_layout("Meta AI original pth") | ||
| gguf_writer.add_context_length(ctx_length) | ||
| gguf_writer.add_embedding_length(hparams["hidden_size"]) | ||
| gguf_writer.add_block_count(block_count) | ||
| gguf_writer.add_feed_forward_length(hparams["intermediate_size"]) | ||
| gguf_writer.add_rope_dimension_count(hparams["hidden_size"] // hparams["num_attention_heads"]) | ||
| gguf_writer.add_head_count(head_count) | ||
| gguf_writer.add_head_count_kv(head_count_kv) | ||
| gguf_writer.add_layer_norm_rms_eps(hparams["rms_norm_eps"]) | ||
|
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| if "rope_scaling" in hparams and hparams["rope_scaling"] != None and "factor" in hparams["rope_scaling"]: | ||
| if "type" in hparams["rope_scaling"]: | ||
| if hparams["rope_scaling"]["type"] == "linear": | ||
| gguf_writer.add_rope_scale_linear(hparams["rope_scaling"]["factor"]) | ||
|
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| # TOKENIZATION | ||
|
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| print("gguf: get tokenizer metadata") | ||
|
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| tokens: list[bytes] = [] | ||
| scores: list[float] = [] | ||
| toktypes: list[int] = [] | ||
|
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| tokenizer_model_file = dir_model / 'tokenizer.model' | ||
| if not tokenizer_model_file.is_file(): | ||
| print(f'Error: Missing {tokenizer_model_file}', file = sys.stderr) | ||
| sys.exit(1) | ||
|
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| # vocab type sentencepiece | ||
| print("gguf: get sentencepiece tokenizer vocab, scores and token types") | ||
|
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| tokenizer = SentencePieceProcessor(str(tokenizer_model_file)) | ||
|
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| for i in range(tokenizer.vocab_size()): | ||
| text: bytes | ||
| score: float | ||
|
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| piece = tokenizer.id_to_piece(i) | ||
| text = piece.encode("utf-8") | ||
| score = tokenizer.get_score(i) | ||
|
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| toktype = 1 # defualt to normal token type | ||
| if tokenizer.is_unknown(i): | ||
| toktype = 2 | ||
| if tokenizer.is_control(i): | ||
| toktype = 3 | ||
|
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| # toktype = 4 is user-defined = tokens from added_tokens.json | ||
|
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| if tokenizer.is_unused(i): | ||
| toktype = 5 | ||
| if tokenizer.is_byte(i): | ||
| toktype = 6 | ||
|
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| tokens.append(text) | ||
| scores.append(score) | ||
| toktypes.append(toktype) | ||
|
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| added_tokens_file = dir_model / 'added_tokens.json' | ||
| if added_tokens_file.is_file(): | ||
| with open(added_tokens_file, "r", encoding="utf-8") as f: | ||
| addtokens_json = json.load(f) | ||
|
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| print("gguf: get added tokens") | ||
|
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| for key in addtokens_json: | ||
| tokens.append( key.encode("utf-8") ) | ||
| scores.append(-1000.0) | ||
| toktypes.append(4) # user-defined token type | ||
|
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| gguf_writer.add_tokenizer_model("llama") | ||
| gguf_writer.add_token_list(tokens) | ||
| gguf_writer.add_token_scores(scores) | ||
| gguf_writer.add_token_types(toktypes) | ||
|
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| special_vocab = gguf.SpecialVocab(dir_model) | ||
| 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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| # tensor info | ||
| print("gguf: get tensor metadata") | ||
|
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| if num_parts == 0: | ||
| part_names = iter(("pytorch_model.bin",)) | ||
| else: | ||
| part_names = ( | ||
| 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: | ||
| if args.vocab_only: | ||
| break | ||
| print("gguf: loading model part '" + part_name + "'") | ||
| model_part = torch.load(f"{dir_model}/{part_name}", map_location="cpu") | ||
|
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| tmp=model_part | ||
| for i in range(block_count): | ||
| if f"model.layers.{i}.self_attn.W_pack.weight" in model_part: | ||
| print(f"Unpacking and permuting layer {i}") | ||
| tmp[f"model.layers.{i}.self_attn.q_proj.weight"]=reverse_hf_permute_part(model_part[f"model.layers.{i}.self_attn.W_pack.weight"],0,head_count,head_count) | ||
| tmp[f"model.layers.{i}.self_attn.k_proj.weight"]=reverse_hf_permute_part(model_part[f"model.layers.{i}.self_attn.W_pack.weight"],1,head_count,head_count_kv) | ||
| tmp[f"model.layers.{i}.self_attn.v_proj.weight"]=reverse_hf_part(model_part[f"model.layers.{i}.self_attn.W_pack.weight"],2) | ||
| del tmp[f"model.layers.{i}.self_attn.W_pack.weight"] | ||
|
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| for name in model_part.keys(): | ||
| data = model_part[name] | ||
| # we don't need these | ||
| if name.endswith(".rotary_emb.inv_freq"): | ||
| continue | ||
|
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| old_dtype = data.dtype | ||
|
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| # convert any unsupported data types to float32 | ||
| if data.dtype != torch.float16 and data.dtype != torch.float32: | ||
| data = data.to(torch.float32) | ||
|
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| data = data.squeeze().numpy() | ||
|
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| # map tensor names | ||
| new_name = tensor_map.get_name(name, try_suffixes = (".weight", ".bias")) | ||
| if new_name is None: | ||
| print("Can not map tensor '" + name + "'") | ||
| sys.exit() | ||
|
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| n_dims = len(data.shape) | ||
| data_dtype = data.dtype | ||
|
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| # if f32 desired, convert any float16 to float32 | ||
| if ftype == 0 and data_dtype == np.float16: | ||
| 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 | ||
| if ftype == 1 and data_dtype == np.float16 and n_dims == 1: | ||
| data = data.astype(np.float32) | ||
|
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| # if f16 desired, convert any float32 2-dim weight tensors to float16 | ||
| if ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2: | ||
| data = data.astype(np.float16) | ||
|
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| print(name + " -> " + new_name + ", n_dims = " + str(n_dims) + ", " + str(old_dtype) + " --> " + str(data.dtype)) | ||
| gguf_writer.add_tensor(new_name, data) | ||
|
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| print("gguf: write header") | ||
| gguf_writer.write_header_to_file() | ||
| print("gguf: write metadata") | ||
| gguf_writer.write_kv_data_to_file() | ||
| if not args.vocab_only: | ||
| print("gguf: write tensors") | ||
| 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}'") | ||
| print("") |
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