mirror of https://github.com/google/gemma.cpp.git
Improve `GemmaChatTemplate` to handle vision prompt wrapping
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c39295f497
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cc2e14e654
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@ -121,6 +121,11 @@ void GemmaChatTemplate::Init(const GemmaTokenizer& tokenizer) {
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HWY_ASSERT(tokenizer.Encode("<start_of_turn>model\n", &sot_model_));
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eot_.reserve(2);
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HWY_ASSERT(tokenizer.Encode("<end_of_turn>\n", &eot_));
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HWY_ASSERT(tokenizer.Encode("\n", &pali_sep_));
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vlm_soi_.reserve(2);
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HWY_ASSERT(tokenizer.Encode("\n\n<start_of_image>", &vlm_soi_));
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vlm_eoi_.reserve(2);
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HWY_ASSERT(tokenizer.Encode("<end_of_image>\n\n", &vlm_eoi_));
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}
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std::vector<int> GemmaChatTemplate::Apply(size_t pos,
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@ -145,6 +150,33 @@ std::vector<int> GemmaChatTemplate::Apply(size_t pos,
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return out;
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}
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std::vector<int> GemmaChatTemplate::WrapPali(const std::vector<int>& text_part,
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size_t image_batch_size) const {
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HWY_ASSERT_M(!pali_sep_.empty(),
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"GemmaChatTemplate has not been initialized.");
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std::vector<int> out;
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out.reserve(image_batch_size + 1 + text_part.size() + pali_sep_.size());
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out.resize(image_batch_size, 0);
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out.push_back(BOS_ID);
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out.insert(out.cend(), text_part.cbegin(), text_part.cend());
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out.insert(out.cend(), pali_sep_.cbegin(), pali_sep_.cend());
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return out;
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}
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std::vector<int> GemmaChatTemplate::WrapVLM(const std::vector<int>& text_part,
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size_t image_batch_size) const {
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HWY_ASSERT_M(!vlm_soi_.empty() && !vlm_eoi_.empty(),
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"GemmaChatTemplate has not been initialized.");
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std::vector<int> out;
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out.reserve(
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text_part.size() + vlm_soi_.size() + image_batch_size + vlm_eoi_.size());
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out.insert(out.cend(), text_part.cbegin(), text_part.cend());
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out.insert(out.cend(), vlm_soi_.cbegin(), vlm_soi_.cend());
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out.insert(out.cend(), image_batch_size, -2);
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out.insert(out.cend(), vlm_eoi_.cbegin(), vlm_eoi_.cend());
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return out;
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}
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std::vector<int> WrapAndTokenize(const GemmaTokenizer& tokenizer,
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const GemmaChatTemplate& chat_template,
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const ModelInfo& info, size_t pos,
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@ -170,33 +202,13 @@ std::vector<int> WrapAndTokenize(const GemmaTokenizer& tokenizer,
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size_t image_batch_size) {
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std::vector<int> text_part;
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HWY_ASSERT(tokenizer.Encode(prompt, &text_part));
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std::vector<int> tokens;
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switch (info.wrapping) {
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case PromptWrapping::PALIGEMMA: {
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std::vector<int> sep;
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HWY_ASSERT(tokenizer.Encode("\n", &sep));
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tokens.reserve(image_batch_size + 1 + text_part.size() + sep.size());
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tokens.resize(image_batch_size, 0);
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case PromptWrapping::PALIGEMMA:
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HWY_ASSERT(pos == 0);
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tokens.push_back(BOS_ID);
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tokens.insert(tokens.cend(), text_part.cbegin(), text_part.cend());
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tokens.insert(tokens.cend(), sep.cbegin(), sep.cend());
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return tokens;
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}
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case PromptWrapping::GEMMA_VLM: {
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std::vector<int> soi;
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soi.reserve(2);
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HWY_ASSERT(tokenizer.Encode("\n\n<start_of_image>", &soi));
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std::vector<int> eoi;
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eoi.reserve(2);
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HWY_ASSERT(tokenizer.Encode("<end_of_image>\n\n", &eoi));
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tokens.reserve(text_part.size() + soi.size() + image_batch_size + eoi.size());
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tokens.insert(tokens.cend(), text_part.cbegin(), text_part.cend());
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tokens.insert(tokens.cend(), soi.cbegin(), soi.cend());
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tokens.insert(tokens.cend(), image_batch_size, -2);
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tokens.insert(tokens.cend(), eoi.cbegin(), eoi.cend());
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return chat_template.Apply(pos, tokens);
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}
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return chat_template.WrapPali(text_part, image_batch_size);
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case PromptWrapping::GEMMA_VLM:
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return chat_template.Apply(pos, chat_template.WrapVLM(text_part,
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image_batch_size));
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default:
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HWY_ASSERT_M(false, "Current variant does not support vision prompt.");
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}
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@ -63,11 +63,18 @@ class GemmaChatTemplate {
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void Init(const GemmaTokenizer& tokenizer);
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std::vector<int> Apply(size_t pos, const std::vector<int>& ids) const;
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std::vector<int> WrapPali(const std::vector<int>& text_part,
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size_t image_batch_size) const;
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std::vector<int> WrapVLM(const std::vector<int>& text_part,
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size_t image_batch_size) const;
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private:
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std::vector<int> sot_user_;
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std::vector<int> sot_model_;
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std::vector<int> eot_;
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std::vector<int> pali_sep_;
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std::vector<int> vlm_soi_;
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std::vector<int> vlm_eoi_;
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};
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std::vector<int> WrapAndTokenize(const GemmaTokenizer& tokenizer,
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