mirror of
https://github.com/nomic-ai/gpt4all.git
synced 2024-10-01 01:06:10 -04:00
Revert "New tokenizer implementation for MPT and GPT-J"
This reverts commit bbcee1ced5.
This commit is contained in:
Adam Treat
parent
cdc7d6ccc4
commit
7f9f91ad94
@ -1,4 +1,4 @@
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[codespell]
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skip = .git,*.pdf,*.svg,*_tokenizer_config.h
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skip = .git,*.pdf,*.svg
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#
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# ignore-words-list =
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@ -23,7 +23,6 @@ set(LLMODEL_VERSION "${LLMODEL_VERSION_MAJOR}.${LLMODEL_VERSION_MINOR}.${LLMODEL
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project(llmodel VERSION ${LLMODEL_VERSION} LANGUAGES CXX C)
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set(CMAKE_CXX_STANDARD_REQUIRED ON)
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set(CMAKE_CXX_STANDARD 17)
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set(LLAMA_BUILD_EXAMPLES ON CACHE BOOL "llama: build examples" FORCE)
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set(BUILD_SHARED_LIBS ON FORCE)
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@ -35,7 +34,6 @@ if (GPT4ALL_AVX_ONLY)
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set(LLAMA_FMA OFF CACHE BOOL "llama: enable FMA" FORCE)
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endif()
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find_package(ICU REQUIRED COMPONENTS uc i18n)
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add_subdirectory(llama.cpp)
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add_library(llmodel
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@ -43,14 +41,12 @@ add_library(llmodel
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llamamodel.h llamamodel.cpp
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llama.cpp/examples/common.cpp
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llmodel.h llmodel_c.h llmodel_c.cpp
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mpt.h mpt.cpp tokenizer/bpe.cpp tokenizer/bpe.h
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tokenizer/mpt_tokenizer_config.h tokenizer/gptj_tokenizer_config.h
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mpt.h mpt.cpp
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utils.h utils.cpp
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)
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target_link_libraries(llmodel
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PRIVATE llama
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PUBLIC ICU::uc ICU::i18n)
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PRIVATE llama)
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set_target_properties(llmodel PROPERTIES
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VERSION ${PROJECT_VERSION}
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@ -7,7 +7,6 @@
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#include <cmath>
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#include <cstdio>
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#include <cstring>
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#include <filesystem>
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#include <fstream>
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#include <map>
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#include <string>
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@ -861,8 +860,6 @@ bool GPTJ::loadModel(const std::string &modelPath) {
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d_ptr->n_threads = std::min(4, (int32_t) std::thread::hardware_concurrency());
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d_ptr->modelLoaded = true;
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fflush(stdout);
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get_bpecpp_tokenizer(TokenizerType::GPTJ, m_bpe, m_tokav);
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return true;
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}
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@ -918,7 +915,7 @@ void GPTJ::prompt(const std::string &prompt,
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int64_t t_prompt_us = 0;
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// tokenize the prompt
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std::vector<uint32_t> embd_inp = m_tokav->encode(prompt, *m_bpe);
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std::vector<gpt_vocab::id> embd_inp = ::gpt_tokenize(d_ptr->vocab, prompt);
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// save the context size
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promptCtx.n_ctx = d_ptr->model->hparams.n_ctx;
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@ -1035,7 +1032,7 @@ void GPTJ::prompt(const std::string &prompt,
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if (id == 50256 /*end of text*/)
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goto stop_generating;
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const std::string str = m_tokav->decode({(uint32_t) id}, *m_bpe, true, false);
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const std::string str = d_ptr->vocab.id_to_token[id];
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// Check if the provided str is part of our reverse prompts
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bool foundPartialReversePrompt = false;
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@ -1065,8 +1062,7 @@ void GPTJ::prompt(const std::string &prompt,
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if (promptCtx.tokens.size() == promptCtx.n_ctx)
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promptCtx.tokens.erase(promptCtx.tokens.begin());
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promptCtx.tokens.push_back(t);
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const std::string decoded = m_tokav->decode({(uint32_t) t}, *m_bpe, true, false);
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if (!responseCallback(t, decoded))
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if (!responseCallback(t, d_ptr->vocab.id_to_token[t]))
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goto stop_generating;
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}
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cachedTokens.clear();
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@ -5,7 +5,6 @@
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#include <functional>
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#include <vector>
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#include "llmodel.h"
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#include "tokenizer/bpe.h"
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class GPTJPrivate;
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class GPTJ : public LLModel {
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@ -32,8 +31,6 @@ protected:
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private:
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GPTJPrivate *d_ptr;
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std::unique_ptr<bpecpp::AdditionalVocabAdapter> m_tokav;
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std::unique_ptr<bpecpp::BPE> m_bpe;
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};
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#endif // GPTJ_H
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@ -7,7 +7,6 @@
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#include <cmath>
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#include <cstdio>
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#include <cstring>
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#include <filesystem>
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#include <fstream>
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#include <map>
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#include <random>
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@ -786,12 +785,6 @@ bool MPT::loadModel(const std::string &modelPath) {
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d_ptr->modelLoaded = true;
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d_ptr->has_im_end = d_ptr->vocab.token_to_id.find("<|im_end|>") != d_ptr->vocab.token_to_id.end();
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fflush(stdout);
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if (modelPath.find("-chat") != std::string::npos) {
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get_bpecpp_tokenizer(TokenizerType::MPT_CHAT, m_bpe, m_tokav);
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} else {
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get_bpecpp_tokenizer(TokenizerType::MPT, m_bpe, m_tokav);
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}
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return true;
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}
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@ -847,7 +840,7 @@ void MPT::prompt(const std::string &prompt,
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int64_t t_prompt_us = 0;
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// tokenize the prompt
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std::vector<uint32_t> embd_inp = m_tokav->encode(prompt, *m_bpe);
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std::vector<int> embd_inp = gpt_tokenize(d_ptr->vocab, prompt);
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// save the context size
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promptCtx.n_ctx = d_ptr->model->hparams.n_ctx;
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@ -913,7 +906,6 @@ void MPT::prompt(const std::string &prompt,
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int r_instructFound = 0;
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std::string cachedResponse;
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std::string decodeBuffer;
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std::vector<int> cachedTokens;
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std::unordered_set<std::string> reversePrompts
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= { "### Instruction", "### Prompt", "### Response", "### Human", "### Assistant", "### Context" };
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@ -969,7 +961,7 @@ void MPT::prompt(const std::string &prompt,
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if (id == 0 /*end of text*/)
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goto stop_generating;
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const std::string str = m_tokav->decode({(uint32_t) id}, *m_bpe, true, false);
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const std::string str = d_ptr->vocab.id_to_token[id];
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// Check if the provided str is part of our reverse prompts
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bool foundPartialReversePrompt = false;
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@ -999,8 +991,7 @@ void MPT::prompt(const std::string &prompt,
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if (promptCtx.tokens.size() == promptCtx.n_ctx)
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promptCtx.tokens.erase(promptCtx.tokens.begin());
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promptCtx.tokens.push_back(t);
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const std::string decoded = m_tokav->decode({(uint32_t) t}, *m_bpe, true, false);
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if (!responseCallback(t, decoded))
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if (!responseCallback(t, d_ptr->vocab.id_to_token[t]))
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goto stop_generating;
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}
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cachedTokens.clear();
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@ -5,7 +5,6 @@
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#include <functional>
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#include <vector>
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#include "llmodel.h"
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#include "tokenizer/bpe.h"
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class MPTPrivate;
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class MPT : public LLModel {
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@ -32,8 +31,6 @@ protected:
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private:
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MPTPrivate *d_ptr;
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std::unique_ptr<bpecpp::AdditionalVocabAdapter> m_tokav;
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std::unique_ptr<bpecpp::BPE> m_bpe;
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};
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#endif // MPT_H
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@ -1,136 +0,0 @@
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import sys
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import json
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from dataclasses import dataclass
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def iter_with_last(lst):
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llen = len(lst)
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for i, entry in enumerate(lst):
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last = i == (llen - 1)
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yield last, entry
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@dataclass
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class BufSlice:
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offset: int
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length: int
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def __repr__(self):
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return '{'f'0x{self.offset:x},{self.length}''}'
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def c_str_dump(bs):
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s = bytearray()
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s += b'"'
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llen = 0
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lasthex = False
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for byte in bs:
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if byte in (b' 01234567890abcdefghijklmnopqrstuvwxyz_-=/;:<>'
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b'ABCDEFGHIJKLMNOPQRSTUVWXYZ!@#$%^&*(),.[]{}`~|'):
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# need to avoid hex characters not part of a hex escape
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# appearing directly after a hex scape
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if lasthex and byte in b'0123456789abcdefABCDEF':
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s += b'""'
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llen += 2
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s += bytes([byte])
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llen += 1
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lasthex = False
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else:
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s += f'\\x{byte:02x}'.encode('utf8')
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llen += 4
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lasthex = True
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if llen >= 80:
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llen = 0
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s += b"\"\n\""
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s += b'"'
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return s.decode('utf8')
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class Buf:
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def __init__(self):
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self.buf = b''
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self.cache = {}
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def get(self, s):
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if s in self.cache:
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return self.cache[s]
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offset = len(self.buf)
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bs = s.encode('utf8')
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exoffs = self.buf.find(bs)
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if exoffs != -1:
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slc = BufSlice(offset=exoffs, length=len(bs))
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self.cache[s] = slc
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return slc
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return None
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def insert(self, s):
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slc = self.get(s)
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if slc is None:
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bs = s.encode('utf8')
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offset = len(self.buf)
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self.buf += bs
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slc = BufSlice(offset=offset, length=len(bs))
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return slc
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class BreakEvery:
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def __init__(self, n):
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self.counter = 0
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self.n = n
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def __repr__(self):
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self.counter += 1
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self.counter %= self.n
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if self.counter == 0:
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return '\n'
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return ''
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def do_convert(tkfilename, prefix):
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with open(tkfilename, 'rb') as tkf:
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tokconfig = json.load(tkf)
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# every string in the vocab also appears in the merges list so we can store
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# much less data in the binary by deduplicating these references, sorting by
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# length descending makes it more likely prefixes of longer strings get
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# deduped, and secondarily sorting lexicographically them makes the buffer
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# data more compressible (they are not compressed in the binary itself, but
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# the binary will be more compressible)
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split_merges = [s.split(' ') for s in tokconfig['model']['merges']]
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len_then = lambda m: (len(m),m)
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avwords = sorted((av['content'] for av in tokconfig['added_tokens']), key=len_then, reverse=True)
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all_strs = avwords + sorted(list(tokconfig['model']['vocab'].keys()), key=len_then, reverse=True)
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buf = Buf()
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for s in all_strs:
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buf.insert(s)
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print('// @generated GENERATED BY scripts/gen_tokenizer_include.py DO NOT MODIFY')
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print(f'#ifndef {prefix.upper()}_TOKENIZER_CONFIG_H_')
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print(f'#define {prefix.upper()}_TOKENIZER_CONFIG_H_')
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print('#include "bpe.h"')
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print(f"// buflen {len(buf.buf)}")
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print(f"constexpr const char {prefix}_buffer[] =\n{c_str_dump(buf.buf)};")
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avilen = len(tokconfig['added_tokens'])
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print(f'constexpr std::array<bpecpp::additional_vocab_item_embedded, {avilen}> {prefix}_additional_vocab = ''{{')
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for last, avi in iter_with_last(tokconfig['added_tokens']):
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comma = ',' if not last else ''
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print(' {'f'.id = {avi["id"]}, .content={buf.get(avi["content"])}, .special={json.dumps(avi["special"])}''}' + comma)
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print('}};')
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print()
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mergeslen = len(tokconfig['model']['merges'])
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print(f'constexpr std::array<std::pair<bpecpp::buf_ref, bpecpp::buf_ref>, {mergeslen}> {prefix}_merges = ''{{')
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breaker = BreakEvery(4)
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for last, (ma, mb) in iter_with_last(split_merges):
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comma = ',' if not last else ''
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print(' {'f'{buf.get(ma)},{buf.get(mb)}''}' + comma + repr(breaker), end='')
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print('\n}};')
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vocablen = len(tokconfig['model']['vocab'])
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print(f'constexpr std::array<bpecpp::buf_ref, {vocablen}> {prefix}_vocab = ''{{')
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breaker = BreakEvery(8)
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for last, vi in iter_with_last(tokconfig['model']['vocab']):
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comma = ',' if not last else ''
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print(f' {buf.get(vi)}' + comma + repr(breaker), end='')
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print('\n}};')
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print(f'#endif // {prefix.upper()}_TOKENIZER_CONFIG_H_')
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def main():
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if len(sys.argv) < 3:
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print(f'Usage: {sys.argv[0]} <hf tokenizer json> <symbol prefix>')
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sys.exit(1)
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do_convert(sys.argv[1], sys.argv[2])
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if __name__ == '__main__':
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main()
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@ -1,257 +0,0 @@
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#include "bpe.h"
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#include <unicode/normalizer2.h>
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#include <unicode/regex.h>
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#include <unicode/schriter.h>
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#include <unicode/unistr.h>
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#include <regex>
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#include <stdexcept>
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#include <iostream>
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namespace bpecpp {
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const std::string_view BPE_PRETOK_REGEX =
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R"('s|'t|'re|'ve|'m|'ll|'d| ?[[:alpha:]]+| ?[[:digit:]]+| ?[^\s[:alpha:][:digit:]]+|\s+(?!\S)|\s+)";
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static void get_bigrams(const std::vector<icu::UnicodeString>& input,
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std::unordered_set<UnicodeBigram, bigram_hash>& pairs) {
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pairs.clear();
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auto i = input.begin();
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auto prev = *i++;
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for (; i != input.end(); ++i) {
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pairs.insert({prev, *i});
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prev = *i;
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}
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}
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BPE::BPE(const std::unordered_map<std::string_view, uint32_t>& vocab,
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const std::vector<std::pair<std::string_view, std::string_view>>& merges) {
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for (auto pair : vocab) {
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icu::UnicodeString encd = icu::UnicodeString::fromUTF8(pair.first);
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m_vocab[encd] = pair.second;
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m_reverse_vocab[pair.second] = encd;
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}
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size_t n = 0;
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for (auto merge : merges) {
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auto left = icu::UnicodeString::fromUTF8(merge.first);
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auto right = icu::UnicodeString::fromUTF8(merge.second);
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m_merges[{left, right}] = n++;
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}
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}
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std::vector<uint32_t> BPE::encode(const std::string& input) {
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auto normalized = normalize_nfc(input);
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auto pretokenized = pretokenize(normalized);
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std::vector<icu::UnicodeString> tokens_merged;
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for (auto &ptok : pretokenized) {
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bpe(ptok, tokens_merged);
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}
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std::vector<uint32_t> final_tokens;
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for (auto &mtok : tokens_merged) {
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final_tokens.push_back(m_vocab[mtok]);
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}
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return final_tokens;
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}
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std::string BPE::decode(const std::vector<uint32_t>& tokens, bool valid_utf8) {
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std::string out;
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for (uint32_t t : tokens) {
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icu::UnicodeString benc = m_reverse_vocab[t];
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icu::StringCharacterIterator schriter(benc);
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for (UChar32 c = schriter.first32(); schriter.hasNext();
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c = schriter.next32()) {
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out.push_back(m_bs_table.codepoint_to_byte((uint32_t)c));
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}
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}
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// roundtrip through ICU to replace invalid utf8 with U+FFFD
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if (valid_utf8) {
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auto tmp = icu::UnicodeString::fromUTF8(out);
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out.clear();
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tmp.toUTF8String(out);
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}
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return out;
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}
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// https://github.com/karpathy/minGPT/blob/37baab71b9abea1b76ab957409a1cc2fbfba8a26/mingpt/bpe.py#L95
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void BPE::bpe(icu::UnicodeString token_pretoked,
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std::vector<icu::UnicodeString>& output) {
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if (token_pretoked.length() < 2) {
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output.push_back(token_pretoked);
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return;
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}
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std::vector<icu::UnicodeString> words;
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std::vector<icu::UnicodeString> words_update;
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icu::StringCharacterIterator schriter(token_pretoked);
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UChar32 c;
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for (schriter.setToStart(); schriter.hasNext();) {
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c = schriter.next32PostInc();
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icu::UnicodeString w;
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w.append(c);
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words.push_back(w);
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}
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std::unordered_set<UnicodeBigram, bigram_hash> pairs;
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get_bigrams(words, pairs);
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while (true) {
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size_t min_rank = SIZE_MAX;
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UnicodeBigram to_merge;
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for (auto &bigram : pairs) {
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auto loc = m_merges.find(bigram);
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if (loc != m_merges.end() && loc->second < min_rank) {
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min_rank = loc->second;
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to_merge = loc->first;
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}
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}
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if (min_rank == SIZE_MAX) {
|
||||
break;
|
||||
} else {
|
||||
auto i = words.begin();
|
||||
while (i < words.end()) {
|
||||
if (*i == to_merge.first) {
|
||||
auto inext = i;
|
||||
inext++;
|
||||
if (inext != words.end() && *inext == to_merge.second) {
|
||||
words_update.push_back(*i + *inext);
|
||||
i = inext;
|
||||
} else {
|
||||
words_update.push_back(*i);
|
||||
}
|
||||
} else {
|
||||
words_update.push_back(*i);
|
||||
}
|
||||
++i;
|
||||
}
|
||||
words.swap(words_update);
|
||||
words_update.clear();
|
||||
get_bigrams(words, pairs);
|
||||
}
|
||||
}
|
||||
output.insert(output.end(), words.begin(), words.end());
|
||||
}
|
||||
|
||||
std::string BPE::normalize_nfc(const std::string& input) {
|
||||
UErrorCode uerror = U_ZERO_ERROR;
|
||||
auto nfcnorm = icu::Normalizer2::getNFCInstance(uerror);
|
||||
if (!U_SUCCESS(uerror))
|
||||
throw std::runtime_error("could not get ICU NFC normalizer");
|
||||
auto icu_ti = icu::UnicodeString::fromUTF8(input);
|
||||
std::string out;
|
||||
nfcnorm->normalize(icu_ti, uerror).toUTF8String(out);
|
||||
if (!U_SUCCESS(uerror))
|
||||
throw std::runtime_error("ICU string normalization failed");
|
||||
return out;
|
||||
}
|
||||
|
||||
std::vector<icu::UnicodeString> BPE::pretokenize(const std::string& input) {
|
||||
UParseError pe;
|
||||
UErrorCode uerror = U_ZERO_ERROR;
|
||||
auto bpe_re_icustr = icu::UnicodeString::fromUTF8(BPE_PRETOK_REGEX);
|
||||
if (m_pretok_re == nullptr) {
|
||||
m_pretok_re = std::unique_ptr<icu::RegexPattern>(
|
||||
icu::RegexPattern::compile(bpe_re_icustr, pe, uerror));
|
||||
if (!U_SUCCESS(uerror))
|
||||
throw std::runtime_error("Compiling BPE pretokenizer regex failed");
|
||||
}
|
||||
auto uinput = icu::UnicodeString::fromUTF8(input);
|
||||
std::unique_ptr<icu::RegexMatcher> pretok_matcher(
|
||||
m_pretok_re->matcher(uinput, uerror));
|
||||
std::vector<icu::UnicodeString> pretoks;
|
||||
if (!U_SUCCESS(uerror))
|
||||
throw std::runtime_error("Creating BPE pretokenizer matcher failed");
|
||||
while (pretok_matcher->find()) {
|
||||
auto match = pretok_matcher->group(uerror);
|
||||
if (!U_SUCCESS(uerror))
|
||||
throw std::runtime_error(
|
||||
"Getting BPE pretokenizer regex match failed");
|
||||
std::string s;
|
||||
icu::UnicodeString out;
|
||||
match.toUTF8String(s);
|
||||
for (char c : s) {
|
||||
uint32_t codepoint = m_bs_table.byte_to_codepoint((uint8_t)c);
|
||||
out.append((UChar32)codepoint);
|
||||
}
|
||||
pretoks.push_back(out);
|
||||
}
|
||||
return pretoks;
|
||||
}
|
||||
|
||||
static std::string regex_escape(const std::string_view inp) {
|
||||
std::string s(inp);
|
||||
static const std::regex metacharacters(R"([\.\^\$\-\+\(\)\[\]\{\}\|\?\*])");
|
||||
return std::regex_replace(s, metacharacters, "\\$&");
|
||||
}
|
||||
|
||||
AdditionalVocabAdapter::AdditionalVocabAdapter(
|
||||
const std::vector<additional_vocab_item>& vocab) {
|
||||
std::string addedtoken_regex;
|
||||
for (const additional_vocab_item& item : vocab) {
|
||||
if (!addedtoken_regex.empty()) {
|
||||
addedtoken_regex += "|";
|
||||
}
|
||||
addedtoken_regex += regex_escape(item.content);
|
||||
m_token_to_id[item.content] = item.id;
|
||||
m_id_to_token[item.id] = item.content;
|
||||
if (item.special) {
|
||||
m_special_ids.insert(item.id);
|
||||
}
|
||||
}
|
||||
m_addedtoken_re = std::regex(addedtoken_regex);
|
||||
}
|
||||
|
||||
std::vector<uint32_t> AdditionalVocabAdapter::encode(
|
||||
const std::string& input,
|
||||
BPE& bpemodel,
|
||||
bool encode_special_tokens) {
|
||||
if (m_token_to_id.empty()) {
|
||||
return bpemodel.encode(input);
|
||||
}
|
||||
std::vector<uint32_t> out;
|
||||
std::string work = input;
|
||||
std::smatch m;
|
||||
while (std::regex_search(work, m, m_addedtoken_re)) {
|
||||
auto tokloc = m_token_to_id.find(m.str());
|
||||
if (tokloc != m_token_to_id.end()) {
|
||||
auto tokid = tokloc->second;
|
||||
auto prefix_decoded = bpemodel.encode(m.prefix());
|
||||
out.insert(out.end(), prefix_decoded.begin(), prefix_decoded.end());
|
||||
bool special = m_special_ids.find(tokid) != m_special_ids.end();
|
||||
if (!special || encode_special_tokens) {
|
||||
out.push_back(tokid);
|
||||
}
|
||||
work = m.suffix();
|
||||
}
|
||||
}
|
||||
if (!work.empty()) {
|
||||
auto rest_decoded = bpemodel.encode(work);
|
||||
out.insert(out.end(), rest_decoded.begin(), rest_decoded.end());
|
||||
}
|
||||
return out;
|
||||
}
|
||||
|
||||
std::string AdditionalVocabAdapter::decode(const std::vector<uint32_t>& tokens,
|
||||
BPE& bpemodel,
|
||||
bool decode_special_tokens,
|
||||
bool valid_utf8) {
|
||||
std::string out;
|
||||
std::vector<uint32_t> to_decode;
|
||||
for (auto tokid : tokens) {
|
||||
auto tokloc = m_id_to_token.find(tokid);
|
||||
if (tokloc != m_id_to_token.end()) { // is an added token
|
||||
if (!to_decode.empty()) {
|
||||
out += bpemodel.decode(to_decode, valid_utf8);
|
||||
to_decode.clear();
|
||||
}
|
||||
bool special = m_special_ids.find(tokid) != m_special_ids.end();
|
||||
// only include non-special tokens unless decode_special_tokens
|
||||
if (!special || decode_special_tokens) {
|
||||
out += tokloc->second;
|
||||
}
|
||||
} else {
|
||||
// non-added, regular token.
|
||||
to_decode.push_back(tokid);
|
||||
}
|
||||
}
|
||||
if (!to_decode.empty()) {
|
||||
out += bpemodel.decode(to_decode, valid_utf8);
|
||||
}
|
||||
return out;
|
||||
}
|
||||
} // namespace bpecpp
|
||||
@ -1,123 +0,0 @@
|
||||
#pragma once
|
||||
#include <unicode/regex.h>
|
||||
#include <unicode/unistr.h>
|
||||
|
||||
#include <cstdint>
|
||||
#include <regex>
|
||||
#include <unordered_map>
|
||||
#include <unordered_set>
|
||||
#include <vector>
|
||||
#include <string_view>
|
||||
|
||||
namespace bpecpp {
|
||||
typedef std::pair<icu::UnicodeString, icu::UnicodeString> UnicodeBigram;
|
||||
|
||||
class bpe_char_byte_table {
|
||||
public:
|
||||
bpe_char_byte_table() {
|
||||
int n = 0;
|
||||
for (uint8_t byte = 0; m_codepoint_to_byte.size() < 256; byte++) {
|
||||
bool keep = (byte >= '!' && byte <= '~') ||
|
||||
(byte >= 0xa1 && byte <= 0xac) ||
|
||||
(byte >= 0xae && byte <= 0xff);
|
||||
uint32_t codepoint = byte;
|
||||
if (!keep) {
|
||||
codepoint = 256 + n;
|
||||
n++;
|
||||
}
|
||||
m_byte_to_codepoint[byte] = codepoint;
|
||||
m_codepoint_to_byte[codepoint] = byte;
|
||||
};
|
||||
}
|
||||
uint32_t byte_to_codepoint(uint8_t byte) {
|
||||
return m_byte_to_codepoint[byte];
|
||||
}
|
||||
|
||||
uint8_t codepoint_to_byte(uint32_t codepoint) {
|
||||
return m_codepoint_to_byte.at(codepoint);
|
||||
}
|
||||
|
||||
private:
|
||||
std::array<uint32_t, 256> m_byte_to_codepoint;
|
||||
std::unordered_map<uint32_t, uint8_t> m_codepoint_to_byte;
|
||||
};
|
||||
|
||||
struct bigram_hash {
|
||||
std::size_t operator()(const UnicodeBigram& pair) const {
|
||||
return pair.first.hashCode() + pair.second.hashCode();
|
||||
}
|
||||
};
|
||||
|
||||
struct icu_hash {
|
||||
std::size_t operator()(const icu::UnicodeString& us) const {
|
||||
return us.hashCode();
|
||||
}
|
||||
};
|
||||
|
||||
class BPE {
|
||||
public:
|
||||
BPE(const std::unordered_map<std::string_view, uint32_t> &vocab,
|
||||
const std::vector<std::pair<std::string_view, std::string_view>> &merges);
|
||||
|
||||
std::vector<uint32_t> encode(const std::string& input);
|
||||
|
||||
std::string decode(const std::vector<uint32_t>& tokens,
|
||||
bool valid_utf8 = true);
|
||||
|
||||
private:
|
||||
std::unordered_map<icu::UnicodeString, uint32_t, icu_hash> m_vocab;
|
||||
std::unordered_map<uint32_t, icu::UnicodeString> m_reverse_vocab;
|
||||
std::unordered_map<UnicodeBigram, size_t, bigram_hash> m_merges;
|
||||
bpe_char_byte_table m_bs_table;
|
||||
|
||||
void bpe(icu::UnicodeString token_pretoked,
|
||||
std::vector<icu::UnicodeString>& output);
|
||||
std::unique_ptr<icu::RegexPattern> m_pretok_re;
|
||||
std::string normalize_nfc(const std::string& input);
|
||||
std::vector<icu::UnicodeString> pretokenize(const std::string& input);
|
||||
};
|
||||
|
||||
// for embedding tokenizer configs in the library - had initially constructed
|
||||
// `string_view`s in the generated headers, *but* generating thousands actual
|
||||
// references into the buffer generates thousands of *relocations* and makes
|
||||
// compilation rather slow, delaying resolving the real address into a
|
||||
// string_view until runtime fixes that
|
||||
struct buf_ref {
|
||||
// packing these into a single u32 reduces the size of the embedded
|
||||
// configs significantly (5.0MB->1.6MB)
|
||||
uint32_t offset : 20;
|
||||
uint32_t length : 12;
|
||||
|
||||
std::string_view into(const char* buf) {
|
||||
return std::string_view(&buf[offset], length);
|
||||
}
|
||||
};
|
||||
struct additional_vocab_item_embedded {
|
||||
uint32_t id;
|
||||
buf_ref content;
|
||||
bool special;
|
||||
};
|
||||
struct additional_vocab_item {
|
||||
uint32_t id;
|
||||
std::string_view content;
|
||||
bool special = false;
|
||||
};
|
||||
class AdditionalVocabAdapter {
|
||||
public:
|
||||
AdditionalVocabAdapter(const std::vector<additional_vocab_item> &vocab);
|
||||
std::vector<uint32_t> encode(const std::string& input,
|
||||
BPE& bpemodel,
|
||||
bool encode_special_tokens = true);
|
||||
std::string decode(const std::vector<uint32_t>& tokens,
|
||||
BPE& bpemodel,
|
||||
bool decode_special_tokens = true,
|
||||
bool valid_utf8 = true);
|
||||
|
||||
private:
|
||||
std::unordered_map<std::string_view, uint32_t> m_token_to_id;
|
||||
std::unordered_map<uint32_t, std::string_view> m_id_to_token;
|
||||
std::unordered_set<uint32_t> m_special_ids;
|
||||
std::regex m_addedtoken_re;
|
||||
};
|
||||
|
||||
} // namespace bpecpp
|
||||
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
@ -1,49 +1,220 @@
|
||||
#include "utils.h"
|
||||
#include "tokenizer/bpe.h"
|
||||
#include "tokenizer/mpt_tokenizer_config.h"
|
||||
#include "tokenizer/gptj_tokenizer_config.h"
|
||||
|
||||
#include <fstream>
|
||||
#include <regex>
|
||||
#include <stdexcept>
|
||||
|
||||
void get_bpecpp_tokenizer(const TokenizerType ttype, std::unique_ptr<bpecpp::BPE>& bpe, std::unique_ptr<bpecpp::AdditionalVocabAdapter>& av) {
|
||||
std::vector<bpecpp::additional_vocab_item> avis;
|
||||
std::unordered_map<std::string_view, uint32_t> vocab;
|
||||
std::vector<std::pair<std::string_view, std::string_view>> merges;
|
||||
|
||||
uint32_t tok_id = 0;
|
||||
switch (ttype) {
|
||||
case TokenizerType::MPT_CHAT:
|
||||
avis.push_back({ .id = 50277, .content = std::string_view("<|im_start|>"), .special = true });
|
||||
avis.push_back({ .id = 50278, .content = std::string_view("<|im_end|>"), .special = true });
|
||||
case TokenizerType::MPT:
|
||||
for (auto avi_e: mpt_additional_vocab) {
|
||||
avis.push_back({avi_e.id, avi_e.content.into(mpt_buffer), avi_e.special});
|
||||
}
|
||||
for (auto merge: mpt_merges) {
|
||||
merges.push_back({merge.first.into(mpt_buffer), merge.second.into(mpt_buffer)});
|
||||
}
|
||||
for (auto bufref: mpt_vocab) {
|
||||
vocab.insert({bufref.into(mpt_buffer), tok_id++});
|
||||
}
|
||||
break;
|
||||
case TokenizerType::GPTJ:
|
||||
for (auto avi_e: gptj_additional_vocab) {
|
||||
avis.push_back({avi_e.id, avi_e.content.into(gptj_buffer), avi_e.special});
|
||||
}
|
||||
for (auto merge: gptj_merges) {
|
||||
merges.push_back({merge.first.into(gptj_buffer), merge.second.into(gptj_buffer)});
|
||||
}
|
||||
for (auto bufref: gptj_vocab) {
|
||||
vocab.insert({bufref.into(gptj_buffer), tok_id++});
|
||||
}
|
||||
break;
|
||||
default:
|
||||
throw std::invalid_argument("invalid tokenizer type");
|
||||
void replace(std::string & str, const std::string & needle, const std::string & replacement) {
|
||||
size_t pos = 0;
|
||||
while ((pos = str.find(needle, pos)) != std::string::npos) {
|
||||
str.replace(pos, needle.length(), replacement);
|
||||
pos += replacement.length();
|
||||
}
|
||||
av = std::make_unique<bpecpp::AdditionalVocabAdapter>(avis);
|
||||
bpe = std::make_unique<bpecpp::BPE>(vocab, merges);
|
||||
}
|
||||
|
||||
std::map<std::string, int32_t> json_parse(const std::string & fname) {
|
||||
std::map<std::string, int32_t> result;
|
||||
|
||||
// read file into string
|
||||
std::string json;
|
||||
{
|
||||
std::ifstream ifs(fname);
|
||||
if (!ifs) {
|
||||
fprintf(stderr, "Failed to open %s\n", fname.c_str());
|
||||
exit(1);
|
||||
}
|
||||
|
||||
json = std::string((std::istreambuf_iterator<char>(ifs)),
|
||||
(std::istreambuf_iterator<char>()));
|
||||
}
|
||||
|
||||
if (json[0] != '{') {
|
||||
return result;
|
||||
}
|
||||
|
||||
// parse json
|
||||
{
|
||||
bool has_key = false;
|
||||
bool in_token = false;
|
||||
|
||||
std::string str_key = "";
|
||||
std::string str_val = "";
|
||||
|
||||
int n = json.size();
|
||||
for (int i = 1; i < n; ++i) {
|
||||
if (!in_token) {
|
||||
if (json[i] == ' ') continue;
|
||||
if (json[i] == '"') {
|
||||
in_token = true;
|
||||
continue;
|
||||
}
|
||||
} else {
|
||||
if (json[i] == '\\' && i+1 < n) {
|
||||
if (has_key == false) {
|
||||
str_key += json[i];
|
||||
} else {
|
||||
str_val += json[i];
|
||||
}
|
||||
++i;
|
||||
} else if (json[i] == '"') {
|
||||
if (has_key == false) {
|
||||
has_key = true;
|
||||
++i;
|
||||
while (json[i] == ' ') ++i;
|
||||
++i; // :
|
||||
while (json[i] == ' ') ++i;
|
||||
if (json[i] != '\"') {
|
||||
while (json[i] != ',' && json[i] != '}') {
|
||||
str_val += json[i++];
|
||||
}
|
||||
has_key = false;
|
||||
} else {
|
||||
in_token = true;
|
||||
continue;
|
||||
}
|
||||
} else {
|
||||
has_key = false;
|
||||
}
|
||||
|
||||
::replace(str_key, "\\u0120", " " ); // \u0120 -> space
|
||||
::replace(str_key, "\\u010a", "\n"); // \u010a -> new line
|
||||
::replace(str_key, "\\\"", "\""); // \\\" -> "
|
||||
|
||||
try {
|
||||
result[str_key] = std::stoi(str_val);
|
||||
} catch (...) {
|
||||
//fprintf(stderr, "%s: ignoring key '%s' with value '%s'\n", fname.c_str(), str_key.c_str(), str_val.c_str());
|
||||
|
||||
}
|
||||
str_key = "";
|
||||
str_val = "";
|
||||
in_token = false;
|
||||
continue;
|
||||
}
|
||||
if (has_key == false) {
|
||||
str_key += json[i];
|
||||
} else {
|
||||
str_val += json[i];
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
return result;
|
||||
}
|
||||
|
||||
std::vector<gpt_vocab::id> gpt_tokenize_inner(const gpt_vocab & vocab, const std::string & text) {
|
||||
std::vector<std::string> words;
|
||||
|
||||
// first split the text into words
|
||||
{
|
||||
std::string str = text;
|
||||
std::string pat = R"('s|'t|'re|'ve|'m|'ll|'d| ?[[:alpha:]]+| ?[[:digit:]]+| ?[^\s[:alpha:][:digit:]]+|\s+(?!\S)|\s+)";
|
||||
|
||||
std::regex re(pat);
|
||||
std::smatch m;
|
||||
|
||||
while (std::regex_search(str, m, re)) {
|
||||
for (auto x : m) {
|
||||
words.push_back(x);
|
||||
}
|
||||
str = m.suffix();
|
||||
}
|
||||
}
|
||||
|
||||
// find the longest tokens that form the words:
|
||||
std::vector<gpt_vocab::id> tokens;
|
||||
for (const auto & word : words) {
|
||||
if (word.size() == 0) continue;
|
||||
|
||||
int i = 0;
|
||||
int n = word.size();
|
||||
while (i < n) {
|
||||
int j = n;
|
||||
while (j > i) {
|
||||
auto it = vocab.token_to_id.find(word.substr(i, j-i));
|
||||
if (it != vocab.token_to_id.end()) {
|
||||
tokens.push_back(it->second);
|
||||
i = j;
|
||||
break;
|
||||
}
|
||||
--j;
|
||||
}
|
||||
if (i == n) {
|
||||
break;
|
||||
}
|
||||
if (j == i) {
|
||||
auto sub = word.substr(i, 1);
|
||||
if (vocab.token_to_id.find(sub) != vocab.token_to_id.end()) {
|
||||
tokens.push_back(vocab.token_to_id.at(sub));
|
||||
} else {
|
||||
fprintf(stderr, "%s: unknown token '%s'\n", __func__, sub.data());
|
||||
}
|
||||
++i;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
return tokens;
|
||||
}
|
||||
|
||||
std::string regex_escape(const std::string &s) {
|
||||
static const std::regex metacharacters(R"([\.\^\$\-\+\(\)\[\]\{\}\|\?\*])");
|
||||
return std::regex_replace(s, metacharacters, "\\$&");
|
||||
}
|
||||
|
||||
std::vector<gpt_vocab::id> gpt_tokenize(const gpt_vocab & vocab, const std::string & text) {
|
||||
// Generate the subpattern from the special_tokens vector if it's not empty
|
||||
if (!vocab.special_tokens.empty()) {
|
||||
std::vector<gpt_vocab::id> out;
|
||||
std::vector<std::string> chunks;
|
||||
std::string str = text;
|
||||
std::string special_tokens_subpattern;
|
||||
for (const auto &token : vocab.special_tokens) {
|
||||
if (!special_tokens_subpattern.empty()) {
|
||||
special_tokens_subpattern += "|";
|
||||
}
|
||||
special_tokens_subpattern += regex_escape(token);
|
||||
}
|
||||
std::regex re(special_tokens_subpattern);
|
||||
std::smatch m;
|
||||
while (std::regex_search(str, m, re)) {
|
||||
auto tok = vocab.token_to_id.find(m.str());
|
||||
if (tok != vocab.token_to_id.end()) {
|
||||
auto tokid = tok->second;
|
||||
auto pfxtoks = gpt_tokenize_inner(vocab, m.prefix());
|
||||
out.insert(out.end(), pfxtoks.begin(), pfxtoks.end());
|
||||
out.push_back(tokid);
|
||||
str = m.suffix();
|
||||
}
|
||||
}
|
||||
if (!str.empty()) {
|
||||
auto tokrest = gpt_tokenize_inner(vocab, str);
|
||||
out.insert(out.end(), tokrest.begin(), tokrest.end());
|
||||
}
|
||||
return out;
|
||||
} else {
|
||||
return gpt_tokenize_inner(vocab, text);
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
bool gpt_vocab_init(const std::string & fname, gpt_vocab & vocab) {
|
||||
printf("%s: loading vocab from '%s'\n", __func__, fname.c_str());
|
||||
|
||||
vocab.token_to_id = ::json_parse(fname);
|
||||
|
||||
for (const auto & kv : vocab.token_to_id) {
|
||||
vocab.id_to_token[kv.second] = kv.first;
|
||||
}
|
||||
|
||||
printf("%s: vocab size = %d\n", __func__, (int) vocab.token_to_id.size());
|
||||
|
||||
// print the vocabulary
|
||||
//for (auto kv : vocab.token_to_id) {
|
||||
// printf("'%s' -> %d\n", kv.first.data(), kv.second);
|
||||
//}
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
gpt_vocab::id gpt_sample_top_k_top_p(
|
||||
|
||||
@ -7,7 +7,6 @@
|
||||
#include <vector>
|
||||
#include <random>
|
||||
#include <thread>
|
||||
#include "tokenizer/bpe.h"
|
||||
|
||||
//
|
||||
// CLI argument parsing
|
||||
@ -52,6 +51,26 @@ struct gpt_vocab {
|
||||
}
|
||||
};
|
||||
|
||||
void replace(std::string & str, const std::string & needle, const std::string & replacement);
|
||||
|
||||
// poor-man's JSON parsing
|
||||
std::map<std::string, int32_t> json_parse(const std::string & fname);
|
||||
|
||||
// split text into tokens
|
||||
//
|
||||
// ref: https://github.com/openai/gpt-2/blob/a74da5d99abaaba920de8131d64da2862a8f213b/src/encoder.py#L53
|
||||
//
|
||||
// Regex (Python):
|
||||
// r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"""
|
||||
//
|
||||
// Regex (C++):
|
||||
// R"('s|'t|'re|'ve|'m|'ll|'d| ?[[:alpha:]]+| ?[[:digit:]]+| ?[^\s[:alpha:][:digit:]]+|\s+(?!\S)|\s+)"
|
||||
//
|
||||
std::vector<gpt_vocab::id> gpt_tokenize(const gpt_vocab & vocab, const std::string & text);
|
||||
|
||||
// load the tokens from encoder.json
|
||||
bool gpt_vocab_init(const std::string & fname, gpt_vocab & vocab);
|
||||
|
||||
// sample next token given probabilities for each embedding
|
||||
//
|
||||
// - consider only the top K tokens
|
||||
@ -70,9 +89,3 @@ gpt_vocab::id gpt_sample_top_k_top_p(
|
||||
double temp,
|
||||
float repeat_penalty,
|
||||
std::mt19937 & rng);
|
||||
|
||||
enum TokenizerType {
|
||||
MPT, MPT_CHAT, GPTJ
|
||||
};
|
||||
|
||||
void get_bpecpp_tokenizer(const TokenizerType ttype, std::unique_ptr<bpecpp::BPE>& bpe, std::unique_ptr<bpecpp::AdditionalVocabAdapter>& av);
|
||||
|
||||
Loading…
Reference in New Issue
Block a user