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Trying to shrink the copy+paste code and do more code sharing between backend model impl.

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This commit is contained in:
Adam Treat 2023-06-01 23:15:58 -04:00 committed by AT
parent 031d7149a7
commit a41bd6ac0a
9 changed files with 41 additions and 96 deletions
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31
gpt4all-backend/gptj.cpp
View File

@ -944,8 +944,7 @@ void GPTJ::prompt(const std::string &prompt,
assert(promptCtx.n_past + int32_t(batch.size()) <= promptCtx.n_ctx);
}
if (!gptj_eval(*d_ptr->model, d_ptr->n_threads, promptCtx.n_past, batch, promptCtx.logits,
d_ptr->mem_per_token)) {
if (!evalTokens(promptCtx, batch)) {
std::cerr << "GPT-J ERROR: Failed to process prompt\n";
return;
}
@ -995,8 +994,7 @@ void GPTJ::prompt(const std::string &prompt,
assert(promptCtx.n_past + 1 <= promptCtx.n_ctx);
}
if (!gptj_eval(*d_ptr->model, d_ptr->n_threads, promptCtx.n_past, { id }, promptCtx.logits,
d_ptr->mem_per_token)) {
if (!evalTokens(promptCtx, { id })) {
std::cerr << "GPT-J ERROR: Failed to predict next token\n";
return;
}
@ -1042,30 +1040,9 @@ void GPTJ::prompt(const std::string &prompt,
}
}
void GPTJ::recalculateContext(PromptContext &promptCtx, std::function<bool(bool)> recalculate)
bool GPTJ::evalTokens(PromptContext &ctx, const std::vector<int32_t> &tokens)
{
size_t i = 0;
promptCtx.n_past = 0;
while (i < promptCtx.tokens.size()) {
size_t batch_end = std::min(i + promptCtx.n_batch, promptCtx.tokens.size());
std::vector<gpt_vocab::id> batch(promptCtx.tokens.begin() + i, promptCtx.tokens.begin() + batch_end);
assert(promptCtx.n_past + int32_t(batch.size()) <= promptCtx.n_ctx);
if (!gptj_eval(*d_ptr->model, d_ptr->n_threads, promptCtx.n_past, batch, promptCtx.logits,
d_ptr->mem_per_token)) {
std::cerr << "GPTJ ERROR: Failed to process prompt\n";
goto stop_generating;
}
promptCtx.n_past += batch.size();
if (!recalculate(true))
goto stop_generating;
i = batch_end;
}
assert(promptCtx.n_past == int32_t(promptCtx.tokens.size()));
stop_generating:
recalculate(false);
return gptj_eval(*d_ptr->model, d_ptr->n_threads, ctx.n_past, tokens, ctx.logits, d_ptr->mem_per_token);
}
#if defined(_WIN32)

5
gpt4all-backend/gptj_impl.h
View File

@ -25,13 +25,10 @@ public:
std::function<bool(int32_t, const std::string&)> responseCallback,
std::function<bool(bool)> recalculateCallback,
PromptContext &ctx) override;
bool evalTokens(PromptContext &ctx, const std::vector<int32_t> &tokens) override;
void setThreadCount(int32_t n_threads) override;
int32_t threadCount() const override;
protected:
void recalculateContext(PromptContext &promptCtx,
std::function<bool(bool)> recalculate) override;
private:
GPTJPrivate *d_ptr;
};

28
gpt4all-backend/llamamodel.cpp
View File

@ -216,7 +216,7 @@ void LLamaModel::prompt(const std::string &prompt,
assert(promptCtx.n_past + int32_t(batch.size()) <= promptCtx.n_ctx);
}
if (llama_eval(d_ptr->ctx, batch.data(), batch.size(), promptCtx.n_past, d_ptr->n_threads)) {
if (!evalTokens(promptCtx, batch)) {
std::cerr << "LLAMA ERROR: Failed to process prompt\n";
return;
}
@ -258,7 +258,7 @@ void LLamaModel::prompt(const std::string &prompt,
assert(promptCtx.n_past + 1 <= promptCtx.n_ctx);
}
if (llama_eval(d_ptr->ctx, &id, 1, promptCtx.n_past, d_ptr->n_threads)) {
if (!evalTokens(promptCtx, { id })) {
std::cerr << "LLAMA ERROR: Failed to predict next token\n";
return;
}
@ -305,29 +305,9 @@ void LLamaModel::prompt(const std::string &prompt,
}
}
void LLamaModel::recalculateContext(PromptContext &promptCtx, std::function<bool(bool)> recalculate)
bool LLamaModel::evalTokens(PromptContext &ctx, const std::vector<int32_t> &tokens)
{
size_t i = 0;
promptCtx.n_past = 0;
while (i < promptCtx.tokens.size()) {
size_t batch_end = std::min(i + promptCtx.n_batch, promptCtx.tokens.size());
std::vector<llama_token> batch(promptCtx.tokens.begin() + i, promptCtx.tokens.begin() + batch_end);
assert(promptCtx.n_past + int32_t(batch.size()) <= promptCtx.n_ctx);
if (llama_eval(d_ptr->ctx, batch.data(), batch.size(), promptCtx.n_past, d_ptr->n_threads)) {
std::cerr << "LLAMA ERROR: Failed to process prompt\n";
goto stop_generating;
}
promptCtx.n_past += batch.size();
if (!recalculate(true))
goto stop_generating;
i = batch_end;
}
assert(promptCtx.n_past == int32_t(promptCtx.tokens.size()));
stop_generating:
recalculate(false);
return llama_eval(d_ptr->ctx, tokens.data(), tokens.size(), ctx.n_past, d_ptr->n_threads) == 0;
}
#if defined(_WIN32)

5
gpt4all-backend/llamamodel_impl.h
View File

@ -25,13 +25,10 @@ public:
std::function<bool(int32_t, const std::string&)> responseCallback,
std::function<bool(bool)> recalculateCallback,
PromptContext &ctx) override;
bool evalTokens(PromptContext &ctx, const std::vector<int32_t> &tokens) override;
void setThreadCount(int32_t n_threads) override;
int32_t threadCount() const override;
protected:
void recalculateContext(PromptContext &promptCtx,
std::function<bool(bool)> recalculate) override;
private:
LLamaPrivate *d_ptr;
};

23
gpt4all-backend/llmodel.cpp
View File

@ -1,6 +1,7 @@
#include "llmodel.h"
#include "dlhandle.h"
#include <iostream>
#include <string>
#include <vector>
#include <fstream>
@ -95,6 +96,28 @@ const LLModel::Implementation* LLModel::implementation(std::ifstream& f, const s
return nullptr;
}
void LLModel::recalculateContext(PromptContext &promptCtx, std::function<bool(bool)> recalculate) {
size_t i = 0;
promptCtx.n_past = 0;
while (i < promptCtx.tokens.size()) {
size_t batch_end = std::min(i + promptCtx.n_batch, promptCtx.tokens.size());
std::vector<int32_t> batch(promptCtx.tokens.begin() + i, promptCtx.tokens.begin() + batch_end);
assert(promptCtx.n_past + int32_t(batch.size()) <= promptCtx.n_ctx);
if (!evalTokens(promptCtx, batch)) {
std::cerr << "LLModel ERROR: Failed to process prompt\n";
goto stop_generating;
}
promptCtx.n_past += batch.size();
if (!recalculate(true))
goto stop_generating;
i = batch_end;
}
assert(promptCtx.n_past == int32_t(promptCtx.tokens.size()));
stop_generating:
recalculate(false);
}
LLModel *LLModel::construct(const std::string &modelPath, std::string buildVariant) {
//TODO: Auto-detect CUDA/OpenCL
if (buildVariant == "auto") {

4
gpt4all-backend/llmodel.h
View File

@ -64,6 +64,7 @@ public:
std::function<bool(int32_t, const std::string&)> responseCallback,
std::function<bool(bool)> recalculateCallback,
PromptContext &ctx) = 0;
virtual bool evalTokens(PromptContext &ctx, const std::vector<int32_t> &tokens) = 0;
virtual void setThreadCount(int32_t /*n_threads*/) {}
virtual int32_t threadCount() const { return 1; }
@ -78,7 +79,6 @@ public:
protected:
const Implementation *m_implementation = nullptr;
virtual void recalculateContext(PromptContext &promptCtx,
std::function<bool(bool)> recalculate) = 0;
void recalculateContext(PromptContext &promptCtx, std::function<bool(bool)> recalculate);
};
#endif // LLMODEL_H

31
gpt4all-backend/mpt.cpp
View File

@ -869,8 +869,7 @@ void MPT::prompt(const std::string &prompt,
assert(promptCtx.n_past + int32_t(batch.size()) <= promptCtx.n_ctx);
}
if (!mpt_eval(*d_ptr->model, d_ptr->n_threads, promptCtx.n_past, batch, promptCtx.logits,
d_ptr->mem_per_token)) {
if (!evalTokens(promptCtx, batch)) {
std::cerr << "GPT-J ERROR: Failed to process prompt\n";
return;
}
@ -920,8 +919,7 @@ void MPT::prompt(const std::string &prompt,
assert(promptCtx.n_past + 1 <= promptCtx.n_ctx);
}
if (!mpt_eval(*d_ptr->model, d_ptr->n_threads, promptCtx.n_past, { id }, promptCtx.logits,
d_ptr->mem_per_token)) {
if (!evalTokens(promptCtx, { id })) {
std::cerr << "GPT-J ERROR: Failed to predict next token\n";
return;
}
@ -971,30 +969,9 @@ void MPT::prompt(const std::string &prompt,
}
}
void MPT::recalculateContext(PromptContext &promptCtx, std::function<bool(bool)> recalculate)
bool MPT::evalTokens(PromptContext &ctx, const std::vector<int32_t> &tokens)
{
size_t i = 0;
promptCtx.n_past = 0;
while (i < promptCtx.tokens.size()) {
size_t batch_end = std::min(i + promptCtx.n_batch, promptCtx.tokens.size());
std::vector<int> batch(promptCtx.tokens.begin() + i, promptCtx.tokens.begin() + batch_end);
assert(promptCtx.n_past + int32_t(batch.size()) <= promptCtx.n_ctx);
if (!mpt_eval(*d_ptr->model, d_ptr->n_threads, promptCtx.n_past, batch, promptCtx.logits,
d_ptr->mem_per_token)) {
std::cerr << "MPT ERROR: Failed to process prompt\n";
goto stop_generating;
}
promptCtx.n_past += batch.size();
if (!recalculate(true))
goto stop_generating;
i = batch_end;
}
assert(promptCtx.n_past == int32_t(promptCtx.tokens.size()));
stop_generating:
recalculate(false);
return mpt_eval(*d_ptr->model, d_ptr->n_threads, ctx.n_past, tokens, ctx.logits, d_ptr->mem_per_token);
}
#if defined(_WIN32)

5
gpt4all-backend/mpt_impl.h
View File

@ -25,13 +25,10 @@ public:
std::function<bool(int32_t, const std::string&)> responseCallback,
std::function<bool(bool)> recalculateCallback,
PromptContext &ctx) override;
bool evalTokens(PromptContext &ctx, const std::vector<int32_t> &tokens) override;
void setThreadCount(int32_t n_threads) override;
int32_t threadCount() const override;
protected:
void recalculateContext(PromptContext &promptCtx,
std::function<bool(bool)> recalculate) override;
private:
MPTPrivate *d_ptr;
};

5
gpt4all-chat/chatgpt.h
View File

@ -24,6 +24,7 @@ public:
std::function<bool(int32_t, const std::string&)> responseCallback,
std::function<bool(bool)> recalculateCallback,
PromptContext &ctx) override;
bool evalTokens(PromptContext &ctx, const std::vector<int32_t> &tokens) override { return true; }
void setThreadCount(int32_t n_threads) override;
int32_t threadCount() const override;
@ -33,10 +34,6 @@ public:
QList<QString> context() const { return m_context; }
void setContext(const QList<QString> &context) { m_context = context; }
protected:
void recalculateContext(PromptContext &promptCtx,
std::function<bool(bool)> recalculate) override {}
private Q_SLOTS:
void handleFinished();
void handleReadyRead();