From e040dd9badbc32bc43dde54e477ef22ba8f47cf4 Mon Sep 17 00:00:00 2001 From: Jiahao Li Date: Thu, 25 Jul 2024 10:03:28 +0800 Subject: [PATCH] Support GLM4V --- .gitmodules | 3 + CMakeLists.txt | 4 +- README.md | 67 +++- chatglm.cpp | 539 +++++++++++++++++++++------- chatglm.h | 549 +++++++++++++++++++++++------ chatglm_cpp/_C.pyi | 63 +++- chatglm_cpp/__init__.py | 41 ++- chatglm_cpp/convert.py | 252 +++++++++---- chatglm_cpp/openai_api.py | 46 ++- chatglm_pybind.cpp | 70 +++- chatglm_test.cpp | 472 ++++++++++++++++--------- examples/03-Confusing-Pictures.jpg | Bin 0 -> 46296 bytes examples/cli_demo.py | 17 +- examples/hmmm.jpg | Bin 0 -> 144082 bytes examples/openai_client.py | 18 +- main.cpp | 23 +- tests/data/baichuan13b_model.data | Bin 54932 -> 0 bytes tests/data/baichuan7b_model.data | Bin 54932 -> 0 bytes tests/data/glm4v_model.data | Bin 0 -> 116224 bytes tests/data/glm4v_model_text.data | Bin 0 -> 30932 bytes tests/data/internlm_model.data | Bin 55444 -> 0 bytes tests/perplexity.cpp | 2 +- tests/test_chatglm_cpp.py | 96 ++++- tests/test_convert.py | 331 +++++++++-------- third_party/stb | 1 + 25 files changed, 1929 insertions(+), 665 deletions(-) create mode 100644 examples/03-Confusing-Pictures.jpg create mode 100644 examples/hmmm.jpg delete mode 100644 tests/data/baichuan13b_model.data delete mode 100644 tests/data/baichuan7b_model.data create mode 100644 tests/data/glm4v_model.data create mode 100644 tests/data/glm4v_model_text.data delete mode 100644 tests/data/internlm_model.data create mode 160000 third_party/stb diff --git a/.gitmodules b/.gitmodules index 7ac75854..9033490c 100644 --- a/.gitmodules +++ b/.gitmodules @@ -14,3 +14,6 @@ [submodule "third_party/abseil-cpp"] path = third_party/abseil-cpp url = https://github.com/abseil/abseil-cpp.git +[submodule "third_party/stb"] + path = third_party/stb + url = https://github.com/nothings/stb.git diff --git a/CMakeLists.txt b/CMakeLists.txt index 3a9fe5ed..18b547bb 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -58,6 +58,8 @@ add_subdirectory(third_party/abseil-cpp) add_subdirectory(third_party/re2) +include_directories(third_party/stb) + if (GGML_METAL) add_compile_definitions(GGML_USE_METAL) configure_file(third_party/ggml/src/ggml-metal.metal ${CMAKE_LIBRARY_OUTPUT_DIRECTORY}/ggml-metal.metal COPYONLY) @@ -135,7 +137,7 @@ add_custom_target(check-all COMMAND cmake --build build -j COMMAND ./build/bin/chatglm_test COMMAND python3 setup.py develop - COMMAND python3 -m pytest tests/test_chatglm_cpp.py + COMMAND python3 -m pytest --forked tests/test_chatglm_cpp.py WORKING_DIRECTORY ${PROJECT_SOURCE_DIR} ) diff --git a/README.md b/README.md index d65c70fa..9c8cd0bc 100644 --- a/README.md +++ b/README.md @@ -6,7 +6,7 @@ ![Python](https://img.shields.io/pypi/pyversions/chatglm-cpp) [![License: MIT](https://img.shields.io/badge/license-MIT-blue)](LICENSE) -C++ implementation of [ChatGLM-6B](https://github.com/THUDM/ChatGLM-6B), [ChatGLM2-6B](https://github.com/THUDM/ChatGLM2-6B), [ChatGLM3](https://github.com/THUDM/ChatGLM3) and [GLM-4](https://github.com/THUDM/GLM-4) for real-time chatting on your MacBook. +C++ implementation of [ChatGLM-6B](https://github.com/THUDM/ChatGLM-6B), [ChatGLM2-6B](https://github.com/THUDM/ChatGLM2-6B), [ChatGLM3](https://github.com/THUDM/ChatGLM3) and [GLM-4](https://github.com/THUDM/GLM-4)(V) for real-time chatting on your MacBook. ![demo](docs/demo.gif) @@ -22,7 +22,7 @@ Highlights: Support Matrix: * Hardwares: x86/arm CPU, NVIDIA GPU, Apple Silicon GPU * Platforms: Linux, MacOS, Windows -* Models: [ChatGLM-6B](https://github.com/THUDM/ChatGLM-6B), [ChatGLM2-6B](https://github.com/THUDM/ChatGLM2-6B), [ChatGLM3](https://github.com/THUDM/ChatGLM3), [GLM-4](https://github.com/THUDM/GLM-4), [CodeGeeX2](https://github.com/THUDM/CodeGeeX2) +* Models: [ChatGLM-6B](https://github.com/THUDM/ChatGLM-6B), [ChatGLM2-6B](https://github.com/THUDM/ChatGLM2-6B), [ChatGLM3](https://github.com/THUDM/ChatGLM3), [GLM-4](https://github.com/THUDM/GLM-4)(V), [CodeGeeX2](https://github.com/THUDM/CodeGeeX2) ## Getting Started @@ -53,9 +53,9 @@ python3 chatglm_cpp/convert.py -i THUDM/chatglm-6b -t q4_0 -o models/chatglm-ggm The original model (`-i `) can be a Hugging Face model name or a local path to your pre-downloaded model. Currently supported models are: * ChatGLM-6B: `THUDM/chatglm-6b`, `THUDM/chatglm-6b-int8`, `THUDM/chatglm-6b-int4` -* ChatGLM2-6B: `THUDM/chatglm2-6b`, `THUDM/chatglm2-6b-int4` -* ChatGLM3-6B: `THUDM/chatglm3-6b` -* ChatGLM4-9B: `THUDM/glm-4-9b-chat` +* ChatGLM2-6B: `THUDM/chatglm2-6b`, `THUDM/chatglm2-6b-int4`, `THUDM/chatglm2-6b-32k`, `THUDM/chatglm2-6b-32k-int4` +* ChatGLM3-6B: `THUDM/chatglm3-6b`, `THUDM/chatglm3-6b-32k`, `THUDM/chatglm3-6b-128k`, `THUDM/chatglm3-6b-base` +* ChatGLM4(V)-9B: `THUDM/glm-4-9b-chat`, `THUDM/glm-4-9b-chat-1m`, `THUDM/glm-4-9b`, `THUDM/glm-4v-9b` * CodeGeeX2: `THUDM/codegeex2-6b`, `THUDM/codegeex2-6b-int4` You are free to try any of the below quantization types by specifying `-t `: @@ -188,6 +188,42 @@ python3 chatglm_cpp/convert.py -i THUDM/glm-4-9b-chat -t q4_0 -o models/chatglm4 +
+ChatGLM4V-9B + +You may use `-vt ` to set quantization type for the vision encoder. It is recommended to run GLM4V on GPU since vision encoder runs slowly on CPU even with 4-bit quantization. +``` +python3 chatglm_cpp/convert.py -i THUDM/glm-4v-9b -t q4_0 -vt q4_0 -o models/chatglm4v-ggml.bin +``` + + + + + + + + + + + +
+ +```sh +./build/bin/main -m models/chatglm4v-ggml.bin --image examples/03-Confusing-Pictures.jpg -p "这张图片有什么不寻常之处" --temp 0 +# 这张图片中不寻常的是，一个男人站在一辆黄色SUV的后备箱上，正在使用一个铁板熨烫衣物。通常情况下，熨衣是在室内进行的，使用的是家用电熨斗，而不是在户外使用汽车后备箱作为工作台。此外，他似乎是在一个繁忙的城市街道上，周围有行驶的车辆和建筑物，这增加了场景的荒谬性。 +``` + + + +```sh +./build/bin/main -m models/chatglm4v-ggml.bin --image examples/hmmm.jpg -p "这幅图像有什么好笑的？" --temp 0 +# 这幅图像的幽默之处在于它将现代技术与过时的技术并置。智能手机是现代技术的象征，但在这里，它却被连接到一个看起来像是旧式电脑串行端口的蓝色适配器上。这种组合出人意料，颇具幽默感，因为它将两个截然不同的技术时代混合在一起。 +``` + +
+ +
+
CodeGeeX2 @@ -361,6 +397,15 @@ python3 cli_demo.py -m ../models/chatglm4-ggml.bin -p 你好 --temp 0.8 --top_p ```
+
+ChatGLM4V-9B + +Chat mode: +```sh +python3 cli_demo.py -m ../models/chatglm4v-ggml.bin --image 03-Confusing-Pictures.jpg -p "这张图片有什么不寻常之处" --temp 0 +``` +
+
CodeGeeX2 @@ -450,12 +495,20 @@ Use the OpenAI client to chat with your model: For stream response, check out the example client script: ```sh -OPENAI_BASE_URL=http://127.0.0.1:8000/v1 python3 examples/openai_client.py --stream --prompt 你好 +python3 examples/openai_client.py --base_url http://127.0.0.1:8000/v1 --stream --prompt 你好 ``` Tool calling is also supported: ```sh -OPENAI_BASE_URL=http://127.0.0.1:8000/v1 python3 examples/openai_client.py --tool_call --prompt 上海天气怎么样 +python3 examples/openai_client.py --base_url http://127.0.0.1:8000/v1 --tool_call --prompt 上海天气怎么样 +``` + +Request GLM4V with image inputs: +```sh +# request with local image file +python3 examples/openai_client.py --base_url http://127.0.0.1:8000/v1 --tool_call --prompt 描述这张图片 --image examples/03-Confusing-Pictures.jpg +# request with image url +python3 examples/openai_client.py --base_url http://127.0.0.1:8000/v1 --tool_call --prompt 描述这张图片 --image https://www.barnorama.com/wp-content/uploads/2016/12/03-Confusing-Pictures.jpg ``` With this API server as backend, ChatGLM.cpp models can be seamlessly integrated into any frontend that uses OpenAI-style API, including [mckaywrigley/chatbot-ui](https://github.com/mckaywrigley/chatbot-ui), [fuergaosi233/wechat-chatgpt](https://github.com/fuergaosi233/wechat-chatgpt), [Yidadaa/ChatGPT-Next-Web](https://github.com/Yidadaa/ChatGPT-Next-Web), and more. diff --git a/chatglm.cpp b/chatglm.cpp index a7fc3369..c9f943bb 100644 --- a/chatglm.cpp +++ b/chatglm.cpp @@ -17,6 +17,11 @@ #include #include +#define STB_IMAGE_IMPLEMENTATION +#define STB_IMAGE_RESIZE2_IMPLEMENTATION +#include +#include + #ifdef __has_include #if __has_include() #include @@ -390,11 +395,27 @@ StateDict ModelLoader::read_state_dict() { return sd; } -ModelContext::ModelContext(ggml_type dtype) - : dtype(dtype), compute_meta(ggml_tensor_overhead() * GGML_DEFAULT_GRAPH_SIZE + ggml_graph_overhead()), +Image Image::open(const std::string &path) { + int width, height, channels; + uint8_t *buffer = stbi_load(path.c_str(), &width, &height, &channels, 3); + CHATGLM_CHECK(channels == 3); + Image image(width, height, channels, buffer); + stbi_image_free(buffer); + return image; +} + +Image Image::resize(size_t new_width, size_t new_height) const { + Image output(new_width, new_height, channels); + stbir_resize_uint8_srgb(pixels.data(), width, height, 0, output.pixels.data(), new_width, new_height, 0, STBIR_RGB); + return output; +} + +ModelContext::ModelContext() + : compute_meta(ggml_tensor_overhead() * (GGML_DEFAULT_GRAPH_SIZE * 4) + ggml_graph_overhead()), ctx_w(make_unique_ggml_context(ggml_tensor_overhead() * GGML_DEFAULT_GRAPH_SIZE, nullptr, true)), ctx_kv(make_unique_ggml_context(ggml_tensor_overhead() * GGML_DEFAULT_GRAPH_SIZE, nullptr, true)), - ctx_b(make_unique_ggml_context(compute_meta.size(), compute_meta.data(), true)), gf(ggml_new_graph(ctx_b.get())) { + ctx_b(make_unique_ggml_context(compute_meta.size(), compute_meta.data(), true)), + gf(ggml_new_graph_custom(ctx_b.get(), GGML_DEFAULT_GRAPH_SIZE * 4, false)) { #if defined(GGML_USE_CUDA) backend = unique_ggml_backend_t(ggml_backend_cuda_init(0)); @@ -428,8 +449,7 @@ ggml_tensor *Linear::forward(ModelContext *mctx, ggml_tensor *input) const { ggml_tensor *LayerNorm::forward(ModelContext *mctx, ggml_tensor *input) const { // input: [seqlen, normalized_shape] ggml_context *ctx = mctx->ctx_b.get(); - auto ggml_norm_fn = inplace ? ggml_norm_inplace : ggml_norm; - ggml_tensor *output = ggml_norm_fn(ctx, input, eps); + ggml_tensor *output = ggml_norm(ctx, input, eps); output = ggml_mul_inplace(ctx, output, weight); output = ggml_add_inplace(ctx, output, bias); return output; @@ -437,8 +457,7 @@ ggml_tensor *LayerNorm::forward(ModelContext *mctx, ggml_tensor *input) const { ggml_tensor *RMSNorm::forward(ModelContext *mctx, ggml_tensor *input) const { ggml_context *ctx = mctx->ctx_b.get(); - auto ggml_rms_norm_fn = inplace ? ggml_rms_norm_inplace : ggml_rms_norm; - ggml_tensor *output = ggml_rms_norm_fn(ctx, input, eps); + ggml_tensor *output = ggml_rms_norm(ctx, input, eps); output = ggml_mul_inplace(ctx, output, weight); return output; } @@ -506,18 +525,12 @@ static void set_default_num_threads(ggml_backend_t backend, int num_tokens) { } std::string to_string(ModelType model_type) { - switch (model_type) { - case ModelType::CHATGLM: - return "ChatGLM"; - case ModelType::CHATGLM2: - return "ChatGLM2"; - case ModelType::CHATGLM3: - return "ChatGLM3"; - case ModelType::CHATGLM4: - return "ChatGLM4"; - default: - CHATGLM_THROW << "unknown model type " << (int)model_type; - } + static const std::unordered_map m{{ModelType::CHATGLM, "ChatGLM"}, + {ModelType::CHATGLM2, "ChatGLM2"}, + {ModelType::CHATGLM3, "ChatGLM3"}, + {ModelType::CHATGLM4, "ChatGLM4"}, + {ModelType::CHATGLM4V, "ChatGLM4V"}}; + return m.at(model_type); } static ggml_tensor *apply_rotary_emb_basic(ModelContext *mctx, ggml_tensor *layer, ggml_tensor *position_ids, @@ -664,23 +677,28 @@ ggml_tensor *BasicAttention::forward(ModelContext *mctx, ggml_tensor *hidden_sta key_layer = ggml_permute(ctx, key_layer, 0, 2, 1, 3); // [#kvh, s, d] value_layer = ggml_permute(ctx, value_layer, 1, 2, 0, 3); // [#kvh, d, s] - // store key & value to cache - ggml_tensor *k_cache_view = - ggml_view_3d(ctx, k_cache, head_size, qlen, num_key_value_heads, k_cache->nb[1], k_cache->nb[2], - (num_virtual_tokens + n_past) * head_size * ggml_element_size(k_cache)); // [#kvh, s, d] - ggml_build_forward_expand(mctx->gf, ggml_cpy(ctx, key_layer, k_cache_view)); - ggml_tensor *v_cache_view = - ggml_view_3d(ctx, v_cache, qlen, head_size, num_key_value_heads, v_cache->nb[1], v_cache->nb[2], - (num_virtual_tokens + n_past) * ggml_element_size(v_cache)); // [#kvh, d, s] - ggml_build_forward_expand(mctx->gf, ggml_cpy(ctx, value_layer, v_cache_view)); - - // concat key & value with past kv - key_layer = ggml_view_3d(ctx, k_cache, head_size, num_virtual_tokens + n_past + qlen, num_key_value_heads, - k_cache->nb[1], k_cache->nb[2], - 0); // [#kvh, kvs, d] - value_layer = ggml_view_3d(ctx, v_cache, num_virtual_tokens + n_past + qlen, head_size, num_key_value_heads, - v_cache->nb[1], v_cache->nb[2], - 0); // [#kvh, d, kvs] + if (k_cache && v_cache) { + // store key & value to cache + ggml_tensor *k_cache_view = + ggml_view_3d(ctx, k_cache, head_size, qlen, num_key_value_heads, k_cache->nb[1], k_cache->nb[2], + (num_virtual_tokens + n_past) * k_cache->nb[1]); // [#kvh, s, d] + ggml_tensor *v_cache_view = + ggml_view_3d(ctx, v_cache, qlen, head_size, num_key_value_heads, v_cache->nb[1], v_cache->nb[2], + (num_virtual_tokens + n_past) * v_cache->nb[0]); // [#kvh, d, s] + ggml_build_forward_expand(mctx->gf, ggml_cpy(ctx, key_layer, k_cache_view)); + ggml_build_forward_expand(mctx->gf, ggml_cpy(ctx, value_layer, v_cache_view)); + + // concat key & value with past kv + key_layer = ggml_view_3d(ctx, k_cache, head_size, num_virtual_tokens + n_past + qlen, num_key_value_heads, + k_cache->nb[1], k_cache->nb[2], + 0); // [#kvh, kvs, d] + value_layer = ggml_view_3d(ctx, v_cache, num_virtual_tokens + n_past + qlen, head_size, num_key_value_heads, + v_cache->nb[1], v_cache->nb[2], + 0); // [#kvh, d, kvs] + } else { + key_layer = ggml_cont(ctx, key_layer); + value_layer = ggml_cont(ctx, value_layer); + } // attention query_layer = ggml_scale_inplace(ctx, query_layer, 1.f / std::sqrt(head_size)); @@ -693,7 +711,9 @@ ggml_tensor *BasicAttention::forward(ModelContext *mctx, ggml_tensor *hidden_sta num_attention_heads); // [#h, s, kvs] } - if (attn_mask_type == AttentionMaskType::CAUSAL) { + if (attn_mask_type == AttentionMaskType::BIDIRECTIONAL) { + // pass + } else if (attn_mask_type == AttentionMaskType::CAUSAL) { attn_scores = ggml_diag_mask_inf_inplace(ctx, attn_scores, num_virtual_tokens + n_past); } else { attn_scores = ggml_add_inplace(ctx, attn_scores, attention_mask); @@ -721,28 +741,40 @@ ggml_tensor *BasicAttention::forward(ModelContext *mctx, ggml_tensor *hidden_sta } BaseModelForCausalLM::BaseModelForCausalLM(ModelConfig config) - : config(config), mctx_(std::make_unique(config.dtype)) {} + : config(config), mctx_(std::make_unique()) {} -ggml_tensor *BaseModelForCausalLM::forward_graph_compute(const std::vector &input_ids, int n_past, int n_ctx, +ggml_tensor *BaseModelForCausalLM::forward_graph_compute(const std::vector &input_ids, + const std::optional &image, int n_past, int n_ctx, bool is_decoding) { mctx_->ctx_b = make_unique_ggml_context(mctx_->compute_meta.size(), mctx_->compute_meta.data(), true); - mctx_->gf = ggml_new_graph(mctx_->ctx_b.get()); + mctx_->gf = ggml_new_graph_custom(mctx_->ctx_b.get(), GGML_DEFAULT_GRAPH_SIZE * 4, false); - const int qlen = input_ids.size() - n_past; + const int qlen = (n_past == 0) ? input_ids.size() : 1; ggml_tensor *curr_input_ids = ggml_new_tensor_1d(mctx_->ctx_b.get(), GGML_TYPE_I32, qlen); ggml_set_name(curr_input_ids, "input_ids"); ggml_set_input(curr_input_ids); - ggml_tensor *lm_logits = forward(mctx_.get(), curr_input_ids, n_past, n_ctx, is_decoding); + ggml_tensor *curr_image = nullptr; + if (n_past == 0 && image) { + curr_image = ggml_new_tensor_3d(mctx_->ctx_b.get(), GGML_TYPE_F32, config.vision.image_size, + config.vision.image_size, config.vision.in_channels); + ggml_set_name(curr_image, "image"); + ggml_set_input(curr_image); + } + + ggml_tensor *lm_logits = forward(mctx_.get(), curr_input_ids, curr_image, input_ids, n_past, is_decoding); ggml_set_output(lm_logits); ggml_build_forward_expand(mctx_->gf, lm_logits); CHATGLM_CHECK(ggml_gallocr_alloc_graph(mctx_->allocr.get(), mctx_->gf)); - ggml_backend_tensor_set(curr_input_ids, input_ids.data() + n_past, 0, qlen * sizeof(int)); - - set_graph_inputs(qlen, n_past, n_ctx); + // TODO: move into set_graph_inputs + curr_input_ids = ggml_graph_get_tensor(mctx_->gf, "input_ids"); + if (curr_input_ids) { + ggml_backend_tensor_set(curr_input_ids, input_ids.data() + input_ids.size() - qlen, 0, qlen * sizeof(int)); + } + set_graph_inputs(input_ids, image, n_past, n_ctx); set_default_num_threads(mctx_->backend.get(), qlen); CHATGLM_CHECK(ggml_backend_graph_compute(mctx_->backend.get(), mctx_->gf) == GGML_STATUS_SUCCESS); @@ -754,9 +786,9 @@ ggml_tensor *BaseModelForCausalLM::forward_graph_compute(const std::vector return lm_logits; } -int BaseModelForCausalLM::generate_next_token(const std::vector &input_ids, const GenerationConfig &gen_config, - int n_past, int n_ctx) { - ggml_tensor *lm_logits = forward_graph_compute(input_ids, n_past, n_ctx, true); +int BaseModelForCausalLM::generate_next_token(const std::vector &input_ids, const std::optional &image, + const GenerationConfig &gen_config, int n_past, int n_ctx) { + ggml_tensor *lm_logits = forward_graph_compute(input_ids, image, n_past, n_ctx, true); CHATGLM_CHECK(ggml_n_dims(lm_logits) == 1); int vocab_size = lm_logits->ne[0]; @@ -883,8 +915,8 @@ void BaseModelForCausalLM::sampling_softmax_inplace(TokenIdScore *first, TokenId } } -std::vector BaseModelForCausalLM::generate(const std::vector &input_ids, const GenerationConfig &gen_config, - BaseStreamer *streamer) { +std::vector BaseModelForCausalLM::generate(const std::vector &input_ids, const std::optional &image, + const GenerationConfig &gen_config, BaseStreamer *streamer) { CHATGLM_CHECK(gen_config.max_length <= config.max_length) << "Requested max_length (" << gen_config.max_length << ") exceeds pre-configured model max_length (" << config.max_length << ")"; @@ -901,9 +933,9 @@ std::vector BaseModelForCausalLM::generate(const std::vector &input_id const int max_new_tokens = (gen_config.max_new_tokens > 0) ? gen_config.max_new_tokens : gen_config.max_length; while ((int)output_ids.size() < std::min(gen_config.max_length, n_ctx + max_new_tokens)) { - int next_token_id = generate_next_token(output_ids, gen_config, n_past, n_ctx); + int next_token_id = generate_next_token(output_ids, image, gen_config, n_past, n_ctx); - n_past = output_ids.size(); + n_past = count_tokens(output_ids, image); output_ids.emplace_back(next_token_id); if (streamer) { @@ -1086,25 +1118,8 @@ ggml_tensor *GLMBlock::forward(ModelContext *mctx, ggml_tensor *hidden_states, g return output; } -ChatGLMForCausalLM::ChatGLMForCausalLM(const ModelConfig &config) : BasicModelForCausalLM(config) {} - void ChatGLMForCausalLM::load_state_dict(const StateDict &sd) { - void *sd_buf_base = ggml_backend_buffer_get_base(sd.buf.get()); - const size_t sd_buf_size = ggml_backend_buffer_get_size(sd.buf.get()); - if (ggml_backend_is_cpu(mctx_->backend.get())) { - mctx_->buf_w = unique_ggml_backend_buffer_t(ggml_backend_cpu_buffer_from_ptr(sd_buf_base, sd_buf_size)); - } -#ifdef GGML_USE_METAL - else if (ggml_backend_is_metal(mctx_->backend.get())) { - const size_t max_size = ggml_get_max_tensor_size(mctx_->ctx_w.get()); - mctx_->buf_w = - unique_ggml_backend_buffer_t(ggml_backend_metal_buffer_from_ptr(sd_buf_base, sd_buf_size, max_size)); - } -#endif - else { - mctx_->buf_w = - unique_ggml_backend_buffer_t(ggml_backend_alloc_ctx_tensors(mctx_->ctx_w.get(), mctx_->backend.get())); - } + alloc_weight_context(sd.buf.get()); StateDict self_sd = state_dict(); for (auto &item : self_sd.kv) { @@ -1120,13 +1135,11 @@ void ChatGLMForCausalLM::load_state_dict(const StateDict &sd) { } } -void ChatGLMForCausalLM::set_graph_inputs(int qlen, int n_past, int n_ctx) const { - set_graph_inputs(mctx_->gf, qlen, n_past, n_ctx); -} - -void ChatGLMForCausalLM::set_graph_inputs(ggml_cgraph *gf, int qlen, int n_past, int n_ctx) { +void ChatGLMModel::set_graph_inputs(ggml_cgraph *gf, const std::vector &input_ids, + const std::optional &image, int n_past, int n_ctx) const { // attention_mask: [s, kvs] auto broadcast to [#h, s, kvs] // semantic: attn_scores[:, :-1, -1] = -inf + const int qlen = input_ids.size() - n_past; if (n_past == 0) { ggml_tensor *attention_mask = ggml_graph_get_tensor(gf, "attention_mask"); const int kvlen = attention_mask->ne[0]; @@ -1245,25 +1258,8 @@ bool ChatGLM2Tokenizer::is_special_id(int id) const { id == eop_token_id; } -ChatGLM2ForCausalLM::ChatGLM2ForCausalLM(const ModelConfig &config) : BasicModelForCausalLM(config) {} - void ChatGLM2ForCausalLM::load_state_dict(const StateDict &sd) { - void *sd_buf_base = ggml_backend_buffer_get_base(sd.buf.get()); - const size_t sd_buf_size = ggml_backend_buffer_get_size(sd.buf.get()); - if (ggml_backend_is_cpu(mctx_->backend.get())) { - mctx_->buf_w = unique_ggml_backend_buffer_t(ggml_backend_cpu_buffer_from_ptr(sd_buf_base, sd_buf_size)); - } -#ifdef GGML_USE_METAL - else if (ggml_backend_is_metal(mctx_->backend.get())) { - const size_t max_size = ggml_get_max_tensor_size(mctx_->ctx_w.get()); - mctx_->buf_w = - unique_ggml_backend_buffer_t(ggml_backend_metal_buffer_from_ptr(sd_buf_base, sd_buf_size, max_size)); - } -#endif - else { - mctx_->buf_w = - unique_ggml_backend_buffer_t(ggml_backend_alloc_ctx_tensors(mctx_->ctx_w.get(), mctx_->backend.get())); - } + alloc_weight_context(sd.buf.get()); if (config.num_virtual_tokens > 0) { ggml_tensor *past_key_values = sd.kv.at("past_key_values"); @@ -1271,7 +1267,11 @@ void ChatGLM2ForCausalLM::load_state_dict(const StateDict &sd) { } auto self_sd = state_dict(); - for (auto it = sd.kv.begin(); it != sd.kv.end(); it++) { + load_state_dict(mctx_.get(), self_sd, sd); +} + +void ChatGLM2ForCausalLM::load_state_dict(ModelContext *mctx, StateDict &dst, const StateDict &src) { + for (auto it = src.kv.begin(); it != src.kv.end(); it++) { const std::string &name = it->first; ggml_tensor *ckpt_weight = it->second; @@ -1283,16 +1283,16 @@ void ChatGLM2ForCausalLM::load_state_dict(const StateDict &sd) { if (pos != std::string::npos) { // split dense_h_to_4h to gate & up std::string gate_name = name.substr(0, pos) + "mlp.gate_proj.weight"; - ggml_tensor *gate_proj = self_sd.kv.at(gate_name); + ggml_tensor *gate_proj = dst.kv.at(gate_name); std::string up_name = name.substr(0, pos) + "mlp.up_proj.weight"; - ggml_tensor *up_proj = self_sd.kv.at(up_name); + ggml_tensor *up_proj = dst.kv.at(up_name); - CHATGLM_CHECK(ggml_nbytes(ckpt_weight) == ggml_nbytes(gate_proj) + ggml_nbytes(up_proj)); + CHATGLM_CHECK(ggml_nbytes(ckpt_weight) == ggml_nbytes(gate_proj) + ggml_nbytes(up_proj)) << name; - if (ggml_backend_is_cpu(mctx_->backend.get()) || ggml_cpu_has_metal()) { - ggml_backend_tensor_alloc(mctx_->buf_w.get(), gate_proj, ckpt_weight->data); - ggml_backend_tensor_alloc(mctx_->buf_w.get(), up_proj, + if (ggml_backend_is_cpu(mctx->backend.get()) || ggml_cpu_has_metal()) { + ggml_backend_tensor_alloc(mctx->buf_w.get(), gate_proj, ckpt_weight->data); + ggml_backend_tensor_alloc(mctx->buf_w.get(), up_proj, (char *)ckpt_weight->data + ggml_nbytes(gate_proj)); } else { ggml_backend_tensor_set(gate_proj, ckpt_weight->data, 0, ggml_nbytes(gate_proj)); @@ -1301,10 +1301,10 @@ void ChatGLM2ForCausalLM::load_state_dict(const StateDict &sd) { } } else { // normal weight - ggml_tensor *self_weight = self_sd.kv.at(name); - CHATGLM_CHECK(ggml_nbytes(self_weight) == ggml_nbytes(ckpt_weight)); - if (ggml_backend_is_cpu(mctx_->backend.get()) || ggml_cpu_has_metal()) { - ggml_backend_tensor_alloc(mctx_->buf_w.get(), self_weight, ckpt_weight->data); + ggml_tensor *self_weight = dst.kv.at(name); + CHATGLM_CHECK(ggml_nbytes(self_weight) == ggml_nbytes(ckpt_weight)) << name; + if (ggml_backend_is_cpu(mctx->backend.get()) || ggml_cpu_has_metal()) { + ggml_backend_tensor_alloc(mctx->buf_w.get(), self_weight, ckpt_weight->data); } else { ggml_backend_tensor_set(self_weight, ckpt_weight->data, 0, ggml_nbytes(self_weight)); } @@ -1312,12 +1312,10 @@ void ChatGLM2ForCausalLM::load_state_dict(const StateDict &sd) { } } -void ChatGLM2ForCausalLM::set_graph_inputs(int qlen, int n_past, int n_ctx) const { - set_graph_inputs(mctx_->gf, qlen, n_past, n_ctx); -} - -void ChatGLM2ForCausalLM::set_graph_inputs(ggml_cgraph *gf, int qlen, int n_past, int n_ctx) { +void ChatGLM2Model::set_graph_inputs(ggml_cgraph *gf, const std::vector &input_ids, + const std::optional &image, int n_past, int n_ctx) const { ggml_tensor *position_ids = ggml_graph_get_tensor(gf, "position_ids"); + const int qlen = input_ids.size() - n_past; CHATGLM_CHECK(ggml_n_dims(position_ids) == 1 && position_ids->ne[0] == qlen) << "invalid position ids size " << position_ids->ne[0]; @@ -1470,7 +1468,7 @@ ChatMessage ChatGLM3Tokenizer::decode_message(const std::vector &ids) const std::string code_output = output.substr(ci_pos + ci_delim.size()); code_output = remove_special_tokens(code_output); trim(code_output); - message = ChatMessage(ChatMessage::ROLE_ASSISTANT, std::move(chat_output), + message = ChatMessage(ChatMessage::ROLE_ASSISTANT, std::move(chat_output), std::nullopt, {ToolCallMessage(CodeMessage(std::move(code_output)))}); } else { // tool call @@ -1498,7 +1496,7 @@ ChatMessage ChatGLM3Tokenizer::decode_message(const std::vector &ids) const tool_args = sm[1]; } - message = ChatMessage(ChatMessage::ROLE_ASSISTANT, std::move(output), + message = ChatMessage(ChatMessage::ROLE_ASSISTANT, std::move(output), std::nullopt, {ToolCallMessage(FunctionMessage(std::move(tool_name), std::move(tool_args)))}); } } else { @@ -1691,11 +1689,14 @@ ChatGLM4Tokenizer::ChatGLM4Tokenizer(const std::string &vocab_text) { special_tokens_encoder.emplace(token, vocab_size++); } // common special token ids + eos_token_id = special_tokens_encoder.at("<|endoftext|>"); gmask_token_id = special_tokens_encoder.at("[gMASK]"); sop_token_id = special_tokens_encoder.at(""); user_token_id = special_tokens_encoder.at("<|user|>"); assistant_token_id = special_tokens_encoder.at("<|assistant|>"); observation_token_id = special_tokens_encoder.at("<|observation|>"); + boi_token_id = special_tokens_encoder.at("<|begin_of_image|>"); + eoi_token_id = special_tokens_encoder.at("<|end_of_image|>"); const std::string pattern = R"((?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\r\n\p{L}\p{N}]?\p{L}+|\p{N}{1,3}| ?[^\s\p{L}\p{N}]+[\r\n]*|\s*[\r\n]+|(\s+)(?:\s)|\s+)"; @@ -1733,6 +1734,9 @@ std::vector ChatGLM4Tokenizer::apply_chat_template(const std::vector")); input_ids.insert(input_ids.end(), newline_ids.begin(), newline_ids.end()); + if (msg.image.has_value()) { + input_ids.insert(input_ids.end(), {boi_token_id, eos_token_id, eoi_token_id}); + } std::vector content_ids = core_bpe.encode_ordinary(msg.content); input_ids.insert(input_ids.end(), content_ids.begin(), content_ids.end()); } @@ -1749,6 +1753,282 @@ void ChatGLM4Tokenizer::truncate(std::vector &ids, int max_length) { } } +// ===== GLM4V-9B ===== + +ggml_tensor *Conv2d::forward(ModelContext *mctx, ggml_tensor *input) const { + // input: [b, c, h, w] + ggml_context *ctx = mctx->ctx_b.get(); + ggml_tensor *output = ggml_conv_2d(ctx, weight, input, stride, stride, 0, 0, 1, 1); // [b, oc, oh, ow] + output = ggml_add_inplace(ctx, output, bias); + return output; +} + +ggml_tensor *PatchEmbedding::forward(ModelContext *mctx, ggml_tensor *input) const { + // input: [c, h, w] + ggml_context *ctx = mctx->ctx_b.get(); + input = proj.forward(mctx, input); // [oc, oh, ow] + input = ggml_reshape_2d(ctx, input, input->ne[0] * input->ne[1], input->ne[2]); // [oc, oh * ow] + input = ggml_permute(ctx, input, 1, 0, 2, 3); // [oh * ow, oc] view as [s, h] + + ggml_tensor *embeddings = ggml_new_tensor_2d(ctx, input->type, hidden_size(), num_positions()); + ggml_set_name(embeddings, "embeddings"); + ggml_set_input(embeddings); + + // concat (cls, x) + ggml_tensor *cls_embedding_view = + ggml_cpy(ctx, cls_embedding, ggml_view_1d(ctx, embeddings, cls_embedding->ne[0], 0)); + ggml_tensor *img_embedding_view = ggml_cpy( + ctx, input, ggml_view_2d(ctx, embeddings, input->ne[0], input->ne[1], embeddings->nb[1], embeddings->nb[1])); + ggml_build_forward_expand(mctx->gf, cls_embedding_view); + ggml_build_forward_expand(mctx->gf, img_embedding_view); + + embeddings = ggml_add_inplace(ctx, embeddings, position_embedding.weight); // [s, h] + return embeddings; +} + +ggml_tensor *EVA2CLIPBlock::forward(ModelContext *mctx, ggml_tensor *hidden_states, ggml_tensor *attention_mask, + ggml_tensor *position_ids, int n_past) const { + ggml_context *ctx = mctx->ctx_b.get(); + + ggml_tensor *residual = hidden_states; + hidden_states = attention.forward(mctx, hidden_states, attention_mask, position_ids, n_past); + hidden_states = input_layernorm.forward(mctx, hidden_states); + hidden_states = ggml_add_inplace(ctx, hidden_states, residual); + + residual = hidden_states; + hidden_states = mlp.forward(mctx, hidden_states); + hidden_states = post_attention_layernorm.forward(mctx, hidden_states); + hidden_states = ggml_add_inplace(ctx, hidden_states, residual); + + return hidden_states; +} + +EVA2CLIPTransformer::EVA2CLIPTransformer(ModelContext *mctx, const VisionModelConfig &config) { + layers.reserve(config.num_hidden_layers); + for (int layer_id = 0; layer_id < config.num_hidden_layers; layer_id++) { + layers.emplace_back(mctx, config.dtype, config.hidden_size, config.num_attention_heads, + config.num_attention_heads, config.intermediate_size, config.num_positions, config.norm_eps, + config.hidden_act, true, true, false, RopeType::DISABLED, -1, + AttentionMaskType::BIDIRECTIONAL, 0, false); + } +} + +ggml_tensor *EVA2CLIPTransformer::forward(ModelContext *mctx, ggml_tensor *hidden_states) const { + for (const auto &layer : layers) { + hidden_states = layer.forward(mctx, hidden_states, nullptr, nullptr, 0); + } + return hidden_states; +} + +ggml_tensor *EVA2CLIPModel::forward(ModelContext *mctx, ggml_tensor *input) const { + ggml_context *ctx = mctx->ctx_b.get(); + + ggml_tensor *hidden_states = patch_embedding.forward(mctx, input); + hidden_states = transformer.forward(mctx, hidden_states); // [s, hd] + + const int grid_size = std::round(std::sqrt(hidden_states->ne[1] - 1)); + hidden_states = ggml_view_3d(ctx, hidden_states, hidden_states->ne[0], grid_size, grid_size, hidden_states->nb[1], + grid_size * hidden_states->nb[1], hidden_states->nb[1]); // [g, g, hd] + // TODO: must use this cont? + hidden_states = ggml_cont(ctx, ggml_permute(ctx, hidden_states, 2, 0, 1, 3)); // [hd, g, g] + hidden_states = conv.forward(mctx, hidden_states); // [hd, g/2, g/2] + hidden_states = ggml_reshape_2d(ctx, hidden_states, hidden_states->ne[0] * hidden_states->ne[1], + hidden_states->ne[2]); // [hd, s] + // TODO: this cont? + hidden_states = ggml_cont(ctx, ggml_permute(ctx, hidden_states, 1, 0, 2, 3)); // [s, hd] + + hidden_states = linear_proj.forward(mctx, hidden_states); + hidden_states = norm1.forward(mctx, hidden_states); + hidden_states = ggml_gelu_inplace(ctx, hidden_states); + hidden_states = glu.forward(mctx, hidden_states); + + ggml_tensor *output = ggml_new_tensor_2d(ctx, hidden_states->type, hidden_states->ne[0], hidden_states->ne[1] + 2); + + // concat (boi, x, eoi) + ggml_tensor *boi_view = ggml_cpy(ctx, boi, ggml_view_1d(ctx, output, hidden_states->ne[0], 0)); + ggml_tensor *hidden_states_view = + ggml_cpy(ctx, hidden_states, + ggml_view_2d(ctx, output, hidden_states->ne[0], hidden_states->ne[1], output->nb[1], output->nb[1])); + ggml_tensor *eoi_view = + ggml_cpy(ctx, eoi, ggml_view_1d(ctx, output, hidden_states->ne[0], (hidden_states->ne[1] + 1) * output->nb[1])); + ggml_build_forward_expand(mctx->gf, boi_view); + ggml_build_forward_expand(mctx->gf, hidden_states_view); + ggml_build_forward_expand(mctx->gf, eoi_view); + + output = ggml_scale_inplace(ctx, output, 1.f / scaling_factor); + + return output; +} + +ggml_tensor *ChatGLM4VModel::forward_embeddings(ModelContext *mctx, ggml_tensor *input_ids, ggml_tensor *images, + const std::vector &input_ids_vec, int n_past) const { + ggml_context *ctx = mctx->ctx_b.get(); + + ggml_tensor *text_emb = word_embeddings.forward(mctx, input_ids); + + if (!(n_past == 0 && images)) { + return text_emb; + } + + CHATGLM_CHECK(images->ne[0] == images->ne[1] && images->ne[2] == 3); + + const int vision_begin = + std::find(input_ids_vec.begin(), input_ids_vec.end(), config.boi_token_id) - input_ids_vec.begin(); + const int vision_end = + std::find(input_ids_vec.begin(), input_ids_vec.end(), config.eoi_token_id) - input_ids_vec.begin() + 1; + CHATGLM_CHECK(vision_begin < (int)input_ids_vec.size() && vision_end <= (int)input_ids_vec.size() && + vision_begin + 3 == vision_end); + + ggml_tensor *vision_emb = vision.forward(mctx, images); + + const int text_tokens = input_ids->ne[0]; + const int total_tokens = text_tokens - 1 + num_vision_tokens(); + + ggml_tensor *embeddings = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, config.hidden_size, total_tokens); + // before boi + ggml_tensor *text_emb0_view = + ggml_cpy(ctx, ggml_view_2d(ctx, text_emb, config.hidden_size, vision_begin, text_emb->nb[1], 0), + ggml_view_2d(ctx, embeddings, config.hidden_size, vision_begin, embeddings->nb[1], 0)); + // vision tokens + ggml_tensor *vision_emb_view = ggml_cpy(ctx, vision_emb, + ggml_view_2d(ctx, embeddings, config.hidden_size, vision_emb->ne[1], + embeddings->nb[1], vision_begin * embeddings->nb[1])); + // after eoi + ggml_tensor *text_emb1_view = + ggml_cpy(ctx, + ggml_view_2d(ctx, text_emb, config.hidden_size, text_tokens - vision_end, text_emb->nb[1], + vision_end * text_emb->nb[1]), + ggml_view_2d(ctx, embeddings, config.hidden_size, text_tokens - vision_end, embeddings->nb[1], + (total_tokens - (text_tokens - vision_end)) * embeddings->nb[1])); + + ggml_build_forward_expand(mctx->gf, text_emb0_view); + ggml_build_forward_expand(mctx->gf, vision_emb_view); + ggml_build_forward_expand(mctx->gf, text_emb1_view); + + return embeddings; +} + +void ChatGLM4VModel::set_graph_inputs(ggml_cgraph *gf, const std::vector &input_ids, + const std::optional &image, int n_past, int n_ctx) const { + // set position_ids + ggml_tensor *position_ids = ggml_graph_get_tensor(gf, "position_ids"); + if (position_ids) { + std::vector position_ids_buf(position_ids->ne[0]); + const auto vision_idx = + std::find(input_ids.begin(), input_ids.end(), config.boi_token_id) - input_ids.begin() + 1; + if (n_past == 0 && vision_idx < (int)input_ids.size()) { + std::iota(position_ids_buf.begin(), position_ids_buf.begin() + vision_idx, 0); + std::fill(position_ids_buf.begin() + vision_idx, + position_ids_buf.begin() + vision_idx + num_vision_tokens(), vision_idx); + std::iota(position_ids_buf.begin() + vision_idx + num_vision_tokens(), position_ids_buf.end(), + vision_idx + 1); + } else { + const int n_past_text = (n_past > 0) ? input_ids.size() - 1 : 0; + std::iota(position_ids_buf.begin(), position_ids_buf.end(), n_past_text); + } + ggml_backend_tensor_set(position_ids, position_ids_buf.data(), 0, position_ids_buf.size() * sizeof(int)); + } + + // set image + ggml_tensor *image_tensor = ggml_graph_get_tensor(gf, "image"); + if (image_tensor) { + // resize + Image src_resized = image->resize(config.vision.image_size, config.vision.image_size); + // to float, normalize, channel first + std::vector pixels_f32(src_resized.pixels.size()); + const size_t num_pixels = src_resized.height * src_resized.width; + for (size_t i = 0; i < num_pixels; i++) { + pixels_f32[0 * num_pixels + i] = + (src_resized.pixels[i * src_resized.channels + 0] / 255.f - 0.48145466f) / 0.26862954f; + pixels_f32[1 * num_pixels + i] = + (src_resized.pixels[i * src_resized.channels + 1] / 255.f - 0.45782750f) / 0.26130258f; + pixels_f32[2 * num_pixels + i] = + (src_resized.pixels[i * src_resized.channels + 2] / 255.f - 0.40821073f) / 0.27577711f; + } + // copy to tensor + ggml_backend_tensor_set(image_tensor, pixels_f32.data(), 0, ggml_nbytes(image_tensor)); + } +} + +int ChatGLM4VForCausalLM::count_tokens(const std::vector &input_ids, const std::optional &image) const { + int token_cnt = input_ids.size(); + if (image) { + token_cnt += transformer.num_vision_tokens() - 1; + } + return token_cnt; +} + +void ChatGLM4VForCausalLM::load_state_dict(const StateDict &sd) { + alloc_weight_context(sd.buf.get()); + + auto self_sd = state_dict(); + ChatGLM2ForCausalLM::load_state_dict(mctx_.get(), self_sd, sd); +} + +StateDict ChatGLM4VForCausalLM::state_dict() const { + StateDict sd; + + // vision + sd.kv.emplace("transformer.vision.patch_embedding.cls_embedding", transformer.vision.patch_embedding.cls_embedding); + sd.kv.emplace("transformer.vision.patch_embedding.proj.weight", transformer.vision.patch_embedding.proj.weight); + sd.kv.emplace("transformer.vision.patch_embedding.proj.bias", transformer.vision.patch_embedding.proj.bias); + sd.kv.emplace("transformer.vision.patch_embedding.position_embedding.weight", + transformer.vision.patch_embedding.position_embedding.weight); + + for (size_t i = 0; i < transformer.vision.transformer.layers.size(); i++) { + const std::string prefix = "transformer.vision.transformer.layers." + std::to_string(i) + '.'; + sd.kv.emplace(prefix + "input_layernorm.weight", + transformer.vision.transformer.layers[i].input_layernorm.weight); + sd.kv.emplace(prefix + "input_layernorm.bias", transformer.vision.transformer.layers[i].input_layernorm.bias); + sd.kv.emplace(prefix + "attention.query_key_value.weight", + transformer.vision.transformer.layers[i].attention.query_key_value.weight); + sd.kv.emplace(prefix + "attention.query_key_value.bias", + transformer.vision.transformer.layers[i].attention.query_key_value.bias); + sd.kv.emplace(prefix + "attention.dense.weight", + transformer.vision.transformer.layers[i].attention.dense.weight); + sd.kv.emplace(prefix + "attention.dense.bias", transformer.vision.transformer.layers[i].attention.dense.bias); + sd.kv.emplace(prefix + "mlp.fc1.weight", transformer.vision.transformer.layers[i].mlp.dense_h_to_4h.weight); + sd.kv.emplace(prefix + "mlp.fc1.bias", transformer.vision.transformer.layers[i].mlp.dense_h_to_4h.bias); + sd.kv.emplace(prefix + "mlp.fc2.weight", transformer.vision.transformer.layers[i].mlp.dense_4h_to_h.weight); + sd.kv.emplace(prefix + "mlp.fc2.bias", transformer.vision.transformer.layers[i].mlp.dense_4h_to_h.bias); + sd.kv.emplace(prefix + "post_attention_layernorm.weight", + transformer.vision.transformer.layers[i].post_attention_layernorm.weight); + sd.kv.emplace(prefix + "post_attention_layernorm.bias", + transformer.vision.transformer.layers[i].post_attention_layernorm.bias); + } + sd.kv.emplace("transformer.vision.conv.weight", transformer.vision.conv.weight); + sd.kv.emplace("transformer.vision.conv.bias", transformer.vision.conv.bias); + sd.kv.emplace("transformer.vision.linear_proj.linear_proj.weight", transformer.vision.linear_proj.weight); + sd.kv.emplace("transformer.vision.linear_proj.norm1.weight", transformer.vision.norm1.weight); + sd.kv.emplace("transformer.vision.linear_proj.norm1.bias", transformer.vision.norm1.bias); + sd.kv.emplace("transformer.vision.linear_proj.gate_proj.weight", transformer.vision.glu.gate_proj.weight); + sd.kv.emplace("transformer.vision.linear_proj.dense_h_to_4h.weight", transformer.vision.glu.up_proj.weight); + sd.kv.emplace("transformer.vision.linear_proj.dense_4h_to_h.weight", transformer.vision.glu.down_proj.weight); + sd.kv.emplace("transformer.vision.boi", transformer.vision.boi); + sd.kv.emplace("transformer.vision.eoi", transformer.vision.eoi); + + // text + sd.kv.emplace("transformer.embedding.word_embeddings.weight", transformer.word_embeddings.weight); + for (int i = 0; i < config.num_hidden_layers; i++) { + const std::string layer_prefix = "transformer.encoder.layers." + std::to_string(i) + '.'; + sd.kv.emplace(layer_prefix + "input_layernorm.weight", transformer.layers[i].input_layernorm.weight); + sd.kv.emplace(layer_prefix + "self_attention.query_key_value.weight", + transformer.layers[i].attention.query_key_value.weight); + sd.kv.emplace(layer_prefix + "self_attention.query_key_value.bias", + transformer.layers[i].attention.query_key_value.bias); + sd.kv.emplace(layer_prefix + "self_attention.dense.weight", transformer.layers[i].attention.dense.weight); + sd.kv.emplace(layer_prefix + "post_attention_layernorm.weight", + transformer.layers[i].post_attention_layernorm.weight); + sd.kv.emplace(layer_prefix + "mlp.gate_proj.weight", transformer.layers[i].mlp.gate_proj.weight); + sd.kv.emplace(layer_prefix + "mlp.up_proj.weight", transformer.layers[i].mlp.up_proj.weight); + // for compatibility + sd.kv.emplace(layer_prefix + "mlp.dense_4h_to_h.weight", transformer.layers[i].mlp.down_proj.weight); + } + sd.kv.emplace("transformer.encoder.final_layernorm.weight", transformer.final_layernorm.weight); + sd.kv.emplace("transformer.output_layer.weight", lm_head.weight); + return sd; +} + // ===== pipeline ===== Pipeline::Pipeline(const std::string &path, int max_length) { @@ -1776,9 +2056,9 @@ Pipeline::Pipeline(const std::string &path, int max_length) { // load config ModelConfig config; if (version == 1) { - config = ModelConfig(model_type, loader.read_basic(), 1e-5f, 10000.f, 0); + config = ModelConfig(model_type, loader.read_basic(), 1e-5f, 10000.f, 0); } else if (version == 2) { - config = ModelConfig(model_type, loader.read_basic()); + config = ModelConfig(model_type, loader.read_basic()); } else { CHATGLM_THROW << "only support version 1 or 2 for now but got " << version; } @@ -1798,9 +2078,9 @@ Pipeline::Pipeline(const std::string &path, int max_length) { // load config ModelConfig config; if (version == 1) { - config = ModelConfig(model_type, loader.read_basic(), 1e-5f, 10000.f, 0); + config = ModelConfig(model_type, loader.read_basic(), 1e-5f, 10000.f, 0); } else if (version == 2) { - config = ModelConfig(model_type, loader.read_basic()); + config = ModelConfig(model_type, loader.read_basic()); } else { CHATGLM_THROW << "only support version 1 or 2 for now but got " << version; } @@ -1825,21 +2105,31 @@ Pipeline::Pipeline(const std::string &path, int max_length) { // load model StateDict sd = loader.read_state_dict(); model->load_state_dict(sd); - } else if (model_type == ModelType::CHATGLM4) { + } else if (model_type == ModelType::CHATGLM4 || model_type == ModelType::CHATGLM4V) { // load config CHATGLM_CHECK(version == 2) << "only support version 2 for now but got " << version; - ModelConfig config(model_type, loader.read_basic()); + ModelConfig config(model_type, loader.read_basic()); _update_config_max_length(config, max_length); + if (model_type == ModelType::CHATGLM4V) { + config.vision = VisionModelConfig(loader.read_basic()); + } + // load tokenizer int vocab_text_size = loader.read_basic(); std::string vocab_text = loader.read_string(vocab_text_size); auto chatglm4_tokenizer = std::make_unique(vocab_text); config.extra_eos_token_ids = {chatglm4_tokenizer->observation_token_id, chatglm4_tokenizer->user_token_id}; + config.boi_token_id = chatglm4_tokenizer->boi_token_id; + config.eoi_token_id = chatglm4_tokenizer->eoi_token_id; tokenizer = std::move(chatglm4_tokenizer); // load model - model = std::make_unique(config); + if (model_type == ModelType::CHATGLM4V) { + model = std::make_unique(config); + } else { + model = std::make_unique(config); + } StateDict sd = loader.read_state_dict(); model->load_state_dict(sd); } else { @@ -1847,9 +2137,9 @@ Pipeline::Pipeline(const std::string &path, int max_length) { } } -std::vector Pipeline::generate(const std::vector &input_ids, const GenerationConfig &gen_config, - BaseStreamer *streamer) const { - std::vector output_ids = model->generate(input_ids, gen_config, streamer); +std::vector Pipeline::generate(const std::vector &input_ids, const std::optional &image, + const GenerationConfig &gen_config, BaseStreamer *streamer) const { + std::vector output_ids = model->generate(input_ids, image, gen_config, streamer); std::vector new_output_ids(output_ids.begin() + input_ids.size(), output_ids.end()); return new_output_ids; } @@ -1857,15 +2147,18 @@ std::vector Pipeline::generate(const std::vector &input_ids, const Gen std::string Pipeline::generate(const std::string &prompt, const GenerationConfig &gen_config, BaseStreamer *streamer) const { std::vector input_ids = tokenizer->encode(prompt, gen_config.max_context_length); - std::vector new_output_ids = generate(input_ids, gen_config, streamer); + std::vector new_output_ids = generate(input_ids, std::nullopt, gen_config, streamer); std::string output = tokenizer->decode(new_output_ids); return output; } ChatMessage Pipeline::chat(const std::vector &messages, const GenerationConfig &gen_config, BaseStreamer *streamer) const { + auto it = + std::find_if(messages.begin(), messages.end(), [](const ChatMessage &msg) { return msg.image.has_value(); }); + const std::optional image = (it != messages.end()) ? it->image : std::nullopt; std::vector input_ids = tokenizer->apply_chat_template(messages, gen_config.max_context_length); - std::vector new_output_ids = generate(input_ids, gen_config, streamer); + std::vector new_output_ids = generate(input_ids, image, gen_config, streamer); ChatMessage output = tokenizer->decode_message(new_output_ids); return output; } diff --git a/chatglm.h b/chatglm.h index f42be536..6f0d5e37 100644 --- a/chatglm.h +++ b/chatglm.h @@ -41,12 +41,13 @@ enum class ModelType { CHATGLM2 = 2, CHATGLM3 = 3, CHATGLM4 = 4, + CHATGLM4V = 1004, }; std::string to_string(ModelType model_type); // For compatibility -struct ConfigRecordV1 { +struct ModelConfigRecordV1 { // common attributes ggml_type dtype; int vocab_size; @@ -64,12 +65,25 @@ struct ConfigRecordV1 { }; // For compatibility -struct ConfigRecordV1GQA : public ConfigRecordV1 { +struct ModelConfigRecordV1GQA { + // ModelConfigRecordV1 + ggml_type dtype; + int vocab_size; + int hidden_size; + int num_attention_heads; + int num_hidden_layers; + int intermediate_size; + int max_length; + int bos_token_id; + int eos_token_id; + int pad_token_id; + int sep_token_id; + // GQA int num_key_value_heads; }; // TODO: use json to serialize config -struct ConfigRecordV2 { +struct ModelConfigRecordV2 { ggml_type dtype; int vocab_size; int hidden_size; @@ -99,10 +113,64 @@ enum class RopeType { }; enum class AttentionMaskType { + BIDIRECTIONAL, CAUSAL, CHATGLM, }; +struct VisionModelConfigRecord { + ggml_type dtype; + int hidden_size; + int image_size; + int in_channels; + int intermediate_size; + float norm_eps; + int num_attention_heads; + int num_hidden_layers; + int num_positions; + int patch_size; + float scaling_factor; +}; + +struct VisionModelConfig { + ggml_type dtype; + ActivationType hidden_act; + int hidden_size; + int image_size; + int in_channels; + int intermediate_size; + float norm_eps; + int num_attention_heads; + int num_hidden_layers; + int num_positions; + int patch_size; + float scaling_factor; + + VisionModelConfig() = default; + + VisionModelConfig(ggml_type dtype, ActivationType hidden_act, int hidden_size, int image_size, int in_channels, + int intermediate_size, float norm_eps, int num_attention_heads, int num_hidden_layers, + int num_positions, int patch_size, float scaling_factor) + : dtype(dtype), hidden_act(hidden_act), hidden_size(hidden_size), image_size(image_size), + in_channels(in_channels), intermediate_size(intermediate_size), norm_eps(norm_eps), + num_attention_heads(num_attention_heads), num_hidden_layers(num_hidden_layers), num_positions(num_positions), + patch_size(patch_size), scaling_factor(scaling_factor) {} + + VisionModelConfig(const VisionModelConfigRecord &rec) + : VisionModelConfig(rec.dtype, ActivationType::GELU, rec.hidden_size, rec.image_size, rec.in_channels, + rec.intermediate_size, rec.norm_eps, rec.num_attention_heads, rec.num_hidden_layers, + rec.num_positions, rec.patch_size, rec.scaling_factor) {} + + friend std::ostream &operator<<(std::ostream &os, const VisionModelConfig &self) { + return os << "VisionModelConfig(dtype=" << self.dtype << ", hidden_act=" << (int)self.hidden_act + << ", hidden_size=" << self.hidden_size << ", image_size=" << self.image_size + << ", in_channels=" << self.in_channels << ", intermediate_size=" << self.intermediate_size + << ", norm_eps=" << self.norm_eps << ", num_attention_heads=" << self.num_attention_heads + << ", num_hidden_layers=" << self.num_hidden_layers << ", num_positions=" + << ", patch_size=" << self.patch_size << ", scaling_factor=" << self.scaling_factor << ")"; + } +}; + // Should save kv record of ModelConfig in the future class ModelConfig { public: @@ -111,13 +179,15 @@ class ModelConfig { ModelConfig(ModelType model_type, ggml_type dtype, int vocab_size, int hidden_size, int num_attention_heads, int num_key_value_heads, int num_hidden_layers, int intermediate_size, float norm_eps, float rope_theta, int num_virtual_tokens, int max_length, int bos_token_id, int eos_token_id, int pad_token_id, - int sep_token_id, std::vector extra_eos_token_ids) + int sep_token_id, int boi_token_id, int eoi_token_id, std::vector extra_eos_token_ids, + const VisionModelConfig &vision) : model_type(model_type), dtype(dtype), vocab_size(vocab_size), hidden_size(hidden_size), num_attention_heads(num_attention_heads), num_key_value_heads(num_key_value_heads), num_hidden_layers(num_hidden_layers), intermediate_size(intermediate_size), norm_eps(norm_eps), rope_theta(rope_theta), num_virtual_tokens(num_virtual_tokens), max_length(max_length), bos_token_id(bos_token_id), eos_token_id(eos_token_id), pad_token_id(pad_token_id), - sep_token_id(sep_token_id), extra_eos_token_ids(std::move(extra_eos_token_ids)) { + sep_token_id(sep_token_id), boi_token_id(boi_token_id), eoi_token_id(eoi_token_id), + extra_eos_token_ids(std::move(extra_eos_token_ids)), vision(vision) { if (model_type == ModelType::CHATGLM) { hidden_act = ActivationType::GELU; use_qkv_bias = true; @@ -137,25 +207,25 @@ class ModelConfig { } } - ModelConfig(ModelType model_type, const ConfigRecordV1 &rec, float norm_eps, float rope_theta, + ModelConfig(ModelType model_type, const ModelConfigRecordV1 &rec, float norm_eps, float rope_theta, int num_virtual_tokens) : ModelConfig(model_type, rec.dtype, rec.vocab_size, rec.hidden_size, rec.num_attention_heads, rec.num_attention_heads, rec.num_hidden_layers, rec.intermediate_size, norm_eps, rope_theta, num_virtual_tokens, rec.max_length, rec.bos_token_id, rec.eos_token_id, rec.pad_token_id, - rec.sep_token_id, {}) {} + rec.sep_token_id, -1, -1, {}, {}) {} - ModelConfig(ModelType model_type, const ConfigRecordV1GQA &rec, float norm_eps, float rope_theta, + ModelConfig(ModelType model_type, const ModelConfigRecordV1GQA &rec, float norm_eps, float rope_theta, int num_virtual_tokens) : ModelConfig(model_type, rec.dtype, rec.vocab_size, rec.hidden_size, rec.num_attention_heads, rec.num_key_value_heads, rec.num_hidden_layers, rec.intermediate_size, norm_eps, rope_theta, num_virtual_tokens, rec.max_length, rec.bos_token_id, rec.eos_token_id, rec.pad_token_id, - rec.sep_token_id, {}) {} + rec.sep_token_id, -1, -1, {}, {}) {} - ModelConfig(ModelType model_type, const ConfigRecordV2 &rec) + ModelConfig(ModelType model_type, const ModelConfigRecordV2 &rec) : ModelConfig(model_type, rec.dtype, rec.vocab_size, rec.hidden_size, rec.num_attention_heads, rec.num_key_value_heads, rec.num_hidden_layers, rec.intermediate_size, rec.norm_eps, rec.rope_theta, rec.num_virtual_tokens, rec.max_length, -1, rec.eos_token_id, rec.pad_token_id, - -1, {}) {} + -1, -1, -1, {}, {}) {} std::string model_type_name() const { return to_string(model_type); } @@ -176,7 +246,7 @@ class ModelConfig { for (size_t i = 0; i < self.extra_eos_token_ids.size(); i++) { os << (i > 0 ? ", " : "") << self.extra_eos_token_ids[i]; } - return os << "})"; + return os << "}, vision=" << self.vision << ")"; } public: @@ -203,7 +273,10 @@ class ModelConfig { int eos_token_id; int pad_token_id; int sep_token_id; + int boi_token_id; + int eoi_token_id; std::vector extra_eos_token_ids; + VisionModelConfig vision; }; struct FunctionMessage { @@ -248,9 +321,55 @@ struct ToolCallMessage { } }; +struct Image { + size_t width = 0; + size_t height = 0; + size_t channels = 0; + std::vector pixels; + + Image() = default; + + Image(size_t width, size_t height, size_t channels) + : width(width), height(height), channels(channels), pixels(width * height * channels) {} + + Image(size_t width, size_t height, size_t channels, uint8_t *data) + : width(width), height(height), channels(channels), pixels(data, data + width * height * channels) {} + + Image(const Image &other) = default; + + Image(Image &&other) { *this = std::move(other); } + + Image &operator=(const Image &other) = default; + + Image &operator=(Image &&other) { + width = other.width; + height = other.height; + channels = other.channels; + pixels = std::move(other.pixels); + other.clear(); + return *this; + } + + static Image open(const std::string &path); + + Image resize(size_t new_width, size_t new_height) const; + + bool empty() const { return pixels.empty(); } + + void clear() { + width = height = channels = 0; + pixels.clear(); + } + + friend std::ostream &operator<<(std::ostream &os, const Image &self) { + return os << "Image(mode=RGB, size=" << self.width << "x" << self.height << ")"; + } +}; + struct ChatMessage { std::string role; std::string content; + std::optional image; std::vector tool_calls; static const std::string ROLE_USER; @@ -259,12 +378,18 @@ struct ChatMessage { static const std::string ROLE_OBSERVATION; ChatMessage() = default; - ChatMessage(std::string role, std::string content, std::vector tool_calls = {}) - : role(std::move(role)), content(std::move(content)), tool_calls(std::move(tool_calls)) {} + + ChatMessage(std::string role, std::string content, std::optional image = std::nullopt, + std::vector tool_calls = {}) + : role(std::move(role)), content(std::move(content)), image(std::move(image)), + tool_calls(std::move(tool_calls)) {} friend std::ostream &operator<<(std::ostream &os, const ChatMessage &self) { - os << "ChatMessage(role=" << std::quoted(self.role) << ", content=" << std::quoted(self.content) - << ", tool_calls=["; + os << "ChatMessage(role=" << std::quoted(self.role) << ", content=" << std::quoted(self.content); + if (self.image.has_value()) { + os << ", image=" << *self.image; + } + os << ", tool_calls=["; for (size_t i = 0; i < self.tool_calls.size(); i++) { os << (i > 0 ? ", " : "") << self.tool_calls[i]; } @@ -329,8 +454,6 @@ struct no_init { }; struct ModelContext { - ggml_type dtype; - std::vector> compute_meta; unique_ggml_context_t ctx_w; // weight @@ -344,26 +467,32 @@ struct ModelContext { unique_ggml_backend_buffer_t buf_w; unique_ggml_backend_buffer_t buf_kv; - ModelContext(ggml_type dtype); + ModelContext(); }; class Embedding { public: - Embedding() : weight(nullptr) {} - Embedding(ModelContext *mctx, int num_embeddings, int embedding_dim) - : weight(ggml_new_tensor_2d(mctx->ctx_w.get(), mctx->dtype, embedding_dim, num_embeddings)) {} + Embedding() = default; + + Embedding(ModelContext *mctx, ggml_type dtype, int num_embeddings, int embedding_dim) + : weight(ggml_new_tensor_2d(mctx->ctx_w.get(), dtype, embedding_dim, num_embeddings)) {} + + int num_embeddings() const { return weight->ne[1]; } + + int embedding_dim() const { return weight->ne[0]; } ggml_tensor *forward(ModelContext *mctx, ggml_tensor *input) const; public: - ggml_tensor *weight; + ggml_tensor *weight = nullptr; }; class Linear { public: - Linear() : weight(nullptr), bias(nullptr) {} - Linear(ModelContext *mctx, int in_features, int out_features, bool use_bias = true) - : weight(ggml_new_tensor_2d(mctx->ctx_w.get(), mctx->dtype, in_features, out_features)), + Linear() = default; + + Linear(ModelContext *mctx, ggml_type dtype, int in_features, int out_features, bool use_bias = true) + : weight(ggml_new_tensor_2d(mctx->ctx_w.get(), dtype, in_features, out_features)), bias(use_bias ? ggml_new_tensor_1d(mctx->ctx_w.get(), GGML_TYPE_F32, out_features) : nullptr) {} int in_features() const { return weight->ne[0]; } @@ -372,46 +501,47 @@ class Linear { ggml_tensor *forward(ModelContext *mctx, ggml_tensor *input) const; public: - ggml_tensor *weight; // [out_features, in_features] - ggml_tensor *bias; // [out_features] + ggml_tensor *weight = nullptr; // [out_features, in_features] + ggml_tensor *bias = nullptr; // [out_features] }; class LayerNorm { public: LayerNorm() = default; - LayerNorm(ModelContext *mctx, int normalized_shape, bool inplace = true, float eps = 1e-5f) + + LayerNorm(ModelContext *mctx, int normalized_shape, float eps = 1e-5f) : weight(ggml_new_tensor_1d(mctx->ctx_w.get(), GGML_TYPE_F32, normalized_shape)), - bias(ggml_new_tensor_1d(mctx->ctx_w.get(), GGML_TYPE_F32, normalized_shape)), inplace(inplace), eps(eps) {} + bias(ggml_new_tensor_1d(mctx->ctx_w.get(), GGML_TYPE_F32, normalized_shape)), eps(eps) {} ggml_tensor *forward(ModelContext *mctx, ggml_tensor *input) const; public: - ggml_tensor *weight; // [normalized_shape] - ggml_tensor *bias; // [normalized_shape] - bool inplace; - float eps; + ggml_tensor *weight = nullptr; // [normalized_shape] + ggml_tensor *bias = nullptr; // [normalized_shape] + float eps = 0.f; }; class RMSNorm { public: RMSNorm() = default; - RMSNorm(ModelContext *mctx, int normalized_shape, bool inplace = true, float eps = 1e-5f) - : weight(ggml_new_tensor_1d(mctx->ctx_w.get(), GGML_TYPE_F32, normalized_shape)), inplace(inplace), eps(eps) {} + + RMSNorm(ModelContext *mctx, int normalized_shape, float eps = 1e-5f) + : weight(ggml_new_tensor_1d(mctx->ctx_w.get(), GGML_TYPE_F32, normalized_shape)), eps(eps) {} ggml_tensor *forward(ModelContext *mctx, ggml_tensor *input) const; public: - ggml_tensor *weight; // [normalized_shape] - bool inplace; - float eps; + ggml_tensor *weight = nullptr; // [normalized_shape] + float eps = 0.f; }; class BasicMLP { public: BasicMLP() = default; - BasicMLP(ModelContext *mctx, int hidden_size, int intermediate_size, ActivationType hidden_act) - : dense_h_to_4h(mctx, hidden_size, intermediate_size), dense_4h_to_h(mctx, intermediate_size, hidden_size), - hidden_act(hidden_act) {} + + BasicMLP(ModelContext *mctx, ggml_type dtype, int hidden_size, int intermediate_size, ActivationType hidden_act) + : dense_h_to_4h(mctx, dtype, hidden_size, intermediate_size), + dense_4h_to_h(mctx, dtype, intermediate_size, hidden_size), hidden_act(hidden_act) {} ggml_tensor *forward(ModelContext *mctx, ggml_tensor *hidden_states) const; @@ -424,9 +554,11 @@ class BasicMLP { class BasicGLU { public: BasicGLU() = default; - BasicGLU(ModelContext *mctx, int hidden_size, int intermediate_size, ActivationType hidden_act) - : gate_proj(mctx, hidden_size, intermediate_size, false), up_proj(mctx, hidden_size, intermediate_size, false), - down_proj(mctx, intermediate_size, hidden_size, false), hidden_act(hidden_act) {} + + BasicGLU(ModelContext *mctx, ggml_type dtype, int hidden_size, int intermediate_size, ActivationType hidden_act) + : gate_proj(mctx, dtype, hidden_size, intermediate_size, false), + up_proj(mctx, dtype, hidden_size, intermediate_size, false), + down_proj(mctx, dtype, intermediate_size, hidden_size, false), hidden_act(hidden_act) {} ggml_tensor *forward(ModelContext *mctx, ggml_tensor *hidden_states) const; @@ -440,19 +572,23 @@ class BasicGLU { class BasicAttention { public: BasicAttention() = default; - BasicAttention(ModelContext *mctx, int hidden_size, int num_attention_heads, int num_key_value_heads, - int max_length, bool use_qkv_bias, bool use_dense_bias, bool interleaved_qkv, RopeType rope_type, - float rope_theta, AttentionMaskType attn_mask_type, int num_virtual_tokens) + + BasicAttention(ModelContext *mctx, ggml_type dtype, int hidden_size, int num_attention_heads, + int num_key_value_heads, int max_length, bool use_qkv_bias, bool use_dense_bias, + bool interleaved_qkv, RopeType rope_type, float rope_theta, AttentionMaskType attn_mask_type, + int num_virtual_tokens, bool use_cache) : num_attention_heads(num_attention_heads), num_key_value_heads(num_key_value_heads), interleaved_qkv(interleaved_qkv), rope_type(rope_type), rope_theta(rope_theta), attn_mask_type(attn_mask_type), num_virtual_tokens(num_virtual_tokens), - query_key_value(mctx, hidden_size, + query_key_value(mctx, dtype, hidden_size, hidden_size + 2 * (hidden_size / num_attention_heads) * num_key_value_heads, use_qkv_bias), - dense(mctx, hidden_size, hidden_size, use_dense_bias), - k_cache(ggml_new_tensor_3d(mctx->ctx_kv.get(), GGML_TYPE_F16, hidden_size / num_attention_heads, - max_length + num_virtual_tokens, num_key_value_heads)), - v_cache(ggml_new_tensor_3d(mctx->ctx_kv.get(), GGML_TYPE_F16, max_length + num_virtual_tokens, - hidden_size / num_attention_heads, num_key_value_heads)) {} + dense(mctx, dtype, hidden_size, hidden_size, use_dense_bias), + k_cache(use_cache ? ggml_new_tensor_3d(mctx->ctx_kv.get(), GGML_TYPE_F32, hidden_size / num_attention_heads, + max_length + num_virtual_tokens, num_key_value_heads) + : nullptr), + v_cache(use_cache ? ggml_new_tensor_3d(mctx->ctx_kv.get(), GGML_TYPE_F32, max_length + num_virtual_tokens, + hidden_size / num_attention_heads, num_key_value_heads) + : nullptr) {} ggml_tensor *forward(ModelContext *mctx, ggml_tensor *hidden_states, ggml_tensor *attention_mask, ggml_tensor *position_ids, int n_past) const; @@ -467,23 +603,24 @@ class BasicAttention { int num_virtual_tokens; Linear query_key_value; Linear dense; - ggml_tensor *k_cache; // [#kvh, s, d] - ggml_tensor *v_cache; // [#kvh, d, s] + ggml_tensor *k_cache = nullptr; // [#kvh, s, d] + ggml_tensor *v_cache = nullptr; // [#kvh, d, s] }; template class BasicBlock { public: BasicBlock() = default; - BasicBlock(ModelContext *mctx, int hidden_size, int num_attention_heads, int num_key_value_heads, + BasicBlock(ModelContext *mctx, ggml_type dtype, int hidden_size, int num_attention_heads, int num_key_value_heads, int intermediate_size, int max_length, float norm_eps, ActivationType hidden_act, bool use_qkv_bias, bool use_dense_bias, bool interleaved_qkv, RopeType rope_type, float rope_theta, - AttentionMaskType attn_mask_type, int num_virtual_tokens) - : input_layernorm(mctx, hidden_size, false, norm_eps), - attention(mctx, hidden_size, num_attention_heads, num_key_value_heads, max_length, use_qkv_bias, - use_dense_bias, interleaved_qkv, rope_type, rope_theta, attn_mask_type, num_virtual_tokens), - post_attention_layernorm(mctx, hidden_size, false, norm_eps), - mlp(mctx, hidden_size, intermediate_size, hidden_act) {} + AttentionMaskType attn_mask_type, int num_virtual_tokens, bool use_cache) + : input_layernorm(mctx, hidden_size, norm_eps), + attention(mctx, dtype, hidden_size, num_attention_heads, num_key_value_heads, max_length, use_qkv_bias, + use_dense_bias, interleaved_qkv, rope_type, rope_theta, attn_mask_type, num_virtual_tokens, + use_cache), + post_attention_layernorm(mctx, hidden_size, norm_eps), + mlp(mctx, dtype, hidden_size, intermediate_size, hidden_act) {} ggml_tensor *forward(ModelContext *mctx, ggml_tensor *hidden_states, ggml_tensor *attention_mask, ggml_tensor *position_ids, int n_past) const { @@ -547,13 +684,16 @@ class BasicModel { : word_embeddings(word_embeddings), layers(std::move(layers)), final_layernorm(final_layernorm) {} BasicModel(ModelContext *mctx, const ModelConfig &config) - : word_embeddings(mctx, config.vocab_size, config.hidden_size), layers(build_layers(mctx, config)), - final_layernorm(mctx, config.hidden_size) {} + : word_embeddings(mctx, config.dtype, config.vocab_size, config.hidden_size), + layers(build_layers(mctx, config)), final_layernorm(mctx, config.hidden_size) {} - ggml_tensor *forward(ModelContext *mctx, ggml_tensor *input_ids, int n_past) const { + ggml_tensor *forward(ModelContext *mctx, ggml_tensor *input_ids, ggml_tensor *images, + const std::vector &input_ids_vec, int n_past) const { ggml_context *ctx = mctx->ctx_b.get(); - const int qlen = input_ids->ne[0]; + ggml_tensor *hidden_states = forward_embeddings(mctx, input_ids, images, input_ids_vec, n_past); + + const int qlen = hidden_states->ne[1]; const int kvlen = layers.front().attention.num_virtual_tokens + n_past + qlen; ggml_tensor *position_ids = pos_ids_alloc_(ctx, qlen); @@ -568,7 +708,6 @@ class BasicModel { ggml_set_input(attention_mask); } - ggml_tensor *hidden_states = word_embeddings.forward(mctx, input_ids); for (const auto &layer : layers) { hidden_states = layer.forward(mctx, hidden_states, attention_mask, position_ids, n_past); } @@ -577,9 +716,15 @@ class BasicModel { return hidden_states; } + virtual ggml_tensor *forward_embeddings(ModelContext *mctx, ggml_tensor *input_ids, ggml_tensor *images, + const std::vector &input_ids_vec, int n_past) const { + CHATGLM_CHECK(images == nullptr) << "unimplemented"; + return word_embeddings.forward(mctx, input_ids); + } + void load_prefix_cache(const ModelConfig &config, ggml_tensor *past_key_values) { // past_key_values: [l * 2, #h, v, d] - ModelContext mctx(config.dtype); + ModelContext mctx; ggml_tensor *backend_past_key_values = ggml_new_tensor(mctx.ctx_kv.get(), past_key_values->type, ggml_n_dims(past_key_values), past_key_values->ne); @@ -619,10 +764,11 @@ class BasicModel { std::vector layers; layers.reserve(config.num_hidden_layers); for (int layer_id = 0; layer_id < config.num_hidden_layers; layer_id++) { - layers.emplace_back(mctx, config.hidden_size, config.num_attention_heads, config.num_key_value_heads, - config.intermediate_size, config.max_length, config.norm_eps, config.hidden_act, - config.use_qkv_bias, config.use_dense_bias, config.interleaved_qkv, config.rope_type, - config.rope_theta, config.attn_mask_type, config.num_virtual_tokens); + layers.emplace_back(mctx, config.dtype, config.hidden_size, config.num_attention_heads, + config.num_key_value_heads, config.intermediate_size, config.max_length, + config.norm_eps, config.hidden_act, config.use_qkv_bias, config.use_dense_bias, + config.interleaved_qkv, config.rope_type, config.rope_theta, config.attn_mask_type, + config.num_virtual_tokens, true); } mctx->buf_kv = unique_ggml_backend_buffer_t(ggml_backend_alloc_ctx_tensors(mctx->ctx_kv.get(), mctx->backend.get())); @@ -785,18 +931,22 @@ class BaseModelForCausalLM { virtual void load_state_dict(const StateDict &sd) = 0; - virtual ggml_tensor *forward(ModelContext *mctx, ggml_tensor *input_ids, int n_past, int n_ctx, - bool is_decoding) const = 0; + virtual ggml_tensor *forward(ModelContext *mctx, ggml_tensor *input_ids, ggml_tensor *images, + const std::vector &input_ids_vec, int n_past, bool is_decoding) const = 0; + + virtual void set_graph_inputs(const std::vector &input_ids, const std::optional &image, int n_past, + int n_ctx) const = 0; - virtual void set_graph_inputs(int qlen, int n_past, int n_ctx) const = 0; + virtual int count_tokens(const std::vector &input_ids, const std::optional &image) const = 0; - ggml_tensor *forward_graph_compute(const std::vector &input_ids, int n_past, int n_ctx, bool is_decoding); + ggml_tensor *forward_graph_compute(const std::vector &input_ids, const std::optional &image, int n_past, + int n_ctx, bool is_decoding); - std::vector generate(const std::vector &input_ids, const GenerationConfig &gen_config, - BaseStreamer *streamer = nullptr); + std::vector generate(const std::vector &input_ids, const std::optional &image, + const GenerationConfig &gen_config, BaseStreamer *streamer = nullptr); - int generate_next_token(const std::vector &input_ids, const GenerationConfig &gen_config, int n_past, - int n_ctx); + int generate_next_token(const std::vector &input_ids, const std::optional &image, + const GenerationConfig &gen_config, int n_past, int n_ctx); // logits processor static void sampling_repetition_penalty(float *first, float *last, const std::vector &input_ids, @@ -820,28 +970,57 @@ class BasicModelForCausalLM : public BaseModelForCausalLM { protected: BasicModelForCausalLM(const ModelConfig &config) : BaseModelForCausalLM(config), transformer(mctx_.get(), config), - lm_head(mctx_.get(), config.hidden_size, config.vocab_size, false) { + lm_head(mctx_.get(), config.dtype, config.hidden_size, config.vocab_size, false) { if (config.tie_word_embeddings) { lm_head.weight = transformer.word_embeddings.weight; } } public: - ggml_tensor *forward(ModelContext *mctx, ggml_tensor *input_ids, int n_past, int n_ctx, - bool is_decoding) const override { - ggml_tensor *transformer_outputs = transformer.forward(mctx, input_ids, n_past); + ggml_tensor *forward(ModelContext *mctx, ggml_tensor *input_ids, ggml_tensor *images, + const std::vector &input_ids_vec, int n_past, bool is_decoding) const override { + ggml_tensor *transformer_outputs = transformer.forward(mctx, input_ids, images, input_ids_vec, n_past); // NOTE: only compute next token logits for decoding - if (is_decoding && input_ids->ne[0] > 1) { - transformer_outputs = - ggml_view_1d(mctx->ctx_b.get(), transformer_outputs, config.hidden_size, - (input_ids->ne[0] - 1) * config.hidden_size * ggml_element_size(transformer_outputs)); + if (is_decoding && transformer_outputs->ne[1] > 1) { + transformer_outputs = ggml_view_1d(mctx->ctx_b.get(), transformer_outputs, transformer_outputs->ne[0], + (transformer_outputs->ne[1] - 1) * transformer_outputs->nb[1]); } ggml_tensor *lm_logits = lm_head.forward(mctx, transformer_outputs); return lm_logits; } + void set_graph_inputs(const std::vector &input_ids, const std::optional &image, int n_past, + int n_ctx) const override { + transformer.set_graph_inputs(mctx_->gf, input_ids, image, n_past, n_ctx); + } + + int count_tokens(const std::vector &input_ids, const std::optional &image) const override { + CHATGLM_CHECK(!image) << "unimplemented"; + return input_ids.size(); + } + void load_prefix_cache(ggml_tensor *past_key_values) { transformer.load_prefix_cache(config, past_key_values); } + protected: + void alloc_weight_context(const ggml_backend_buffer_t sd_buf) const { + void *sd_buf_base = ggml_backend_buffer_get_base(sd_buf); + const size_t sd_buf_size = ggml_backend_buffer_get_size(sd_buf); + if (ggml_backend_is_cpu(mctx_->backend.get())) { + mctx_->buf_w = unique_ggml_backend_buffer_t(ggml_backend_cpu_buffer_from_ptr(sd_buf_base, sd_buf_size)); + } +#ifdef GGML_USE_METAL + else if (ggml_backend_is_metal(mctx_->backend.get())) { + const size_t max_size = ggml_get_max_tensor_size(mctx_->ctx_w.get()); + mctx_->buf_w = + unique_ggml_backend_buffer_t(ggml_backend_metal_buffer_from_ptr(sd_buf_base, sd_buf_size, max_size)); + } +#endif + else { + mctx_->buf_w = + unique_ggml_backend_buffer_t(ggml_backend_alloc_ctx_tensors(mctx_->ctx_w.get(), mctx_->backend.get())); + } + } + public: Model transformer; Linear lm_head; @@ -879,16 +1058,17 @@ class ChatGLMTokenizer : public BaseTokenizer { class GLMBlock : public BasicBlock { public: GLMBlock() = default; - GLMBlock(ModelContext *mctx, int hidden_size, int num_attention_heads, int num_key_value_heads, + + GLMBlock(ModelContext *mctx, ggml_type dtype, int hidden_size, int num_attention_heads, int num_key_value_heads, int intermediate_size, int max_length, float norm_eps, ActivationType hidden_act, bool use_qkv_bias, bool use_dense_bias, bool interleaved_qkv, RopeType rope_type, float rope_theta, - AttentionMaskType attn_mask_type, int num_virtual_tokens) - : BasicBlock(LayerNorm(mctx, hidden_size, false, norm_eps), - BasicAttention(mctx, hidden_size, num_attention_heads, num_attention_heads, max_length, + AttentionMaskType attn_mask_type, int num_virtual_tokens, bool use_cache) + : BasicBlock(LayerNorm(mctx, hidden_size, norm_eps), + BasicAttention(mctx, dtype, hidden_size, num_attention_heads, num_attention_heads, max_length, use_qkv_bias, use_dense_bias, interleaved_qkv, rope_type, rope_theta, - attn_mask_type, num_virtual_tokens), - LayerNorm(mctx, hidden_size, false, norm_eps), - BasicMLP(mctx, hidden_size, intermediate_size, hidden_act)), + attn_mask_type, num_virtual_tokens, use_cache), + LayerNorm(mctx, hidden_size, norm_eps), + BasicMLP(mctx, dtype, hidden_size, intermediate_size, hidden_act)), alpha(std::sqrt(2.f * 28)) {} ggml_tensor *forward(ModelContext *mctx, ggml_tensor *hidden_states, ggml_tensor *attention_mask, @@ -898,18 +1078,22 @@ class GLMBlock : public BasicBlock { float alpha; }; -using ChatGLMModel = BasicModel; +class ChatGLMModel : public BasicModel { + public: + ChatGLMModel() = default; + + ChatGLMModel(ModelContext *mctx, const ModelConfig &config) : BasicModel(mctx, config) {} + + void set_graph_inputs(ggml_cgraph *gf, const std::vector &input_ids, const std::optional &image, + int n_past, int n_ctx) const; +}; class ChatGLMForCausalLM : public BasicModelForCausalLM { public: - ChatGLMForCausalLM(const ModelConfig &config); + ChatGLMForCausalLM(const ModelConfig &config) : BasicModelForCausalLM(config) {} void load_state_dict(const StateDict &sd) override; - void set_graph_inputs(int qlen, int n_past, int n_ctx) const override; - - static void set_graph_inputs(ggml_cgraph *gf, int qlen, int n_past, int n_ctx); - private: StateDict state_dict() const; }; @@ -942,17 +1126,23 @@ class ChatGLM2Tokenizer : public BaseTokenizer { using GLM2Block = BasicBlock; -using ChatGLM2Model = BasicModel; +class ChatGLM2Model : public BasicModel { + public: + ChatGLM2Model() = default; + + ChatGLM2Model(ModelContext *mctx, const ModelConfig &config) : BasicModel(mctx, config) {} + + void set_graph_inputs(ggml_cgraph *gf, const std::vector &input_ids, const std::optional &image, + int n_past, int n_ctx) const; +}; class ChatGLM2ForCausalLM : public BasicModelForCausalLM { public: - ChatGLM2ForCausalLM(const ModelConfig &config); + ChatGLM2ForCausalLM(const ModelConfig &config) : BasicModelForCausalLM(config) {} void load_state_dict(const StateDict &sd) override; - void set_graph_inputs(int qlen, int n_past, int n_ctx) const override; - - static void set_graph_inputs(ggml_cgraph *gf, int qlen, int n_past, int n_ctx); + static void load_state_dict(ModelContext *mctx, StateDict &dst, const StateDict &src); private: StateDict state_dict() const; @@ -1052,7 +1242,7 @@ class ChatGLM4Tokenizer : public BaseTokenizer { public: TiktokenCoreBPE core_bpe; - // int eos_token_id; + int eos_token_id; // int mask_token_id; int gmask_token_id; // int smask_token_id; @@ -1062,20 +1252,159 @@ class ChatGLM4Tokenizer : public BaseTokenizer { int user_token_id; int assistant_token_id; int observation_token_id; + int boi_token_id; + int eoi_token_id; }; using ChatGLM4Model = ChatGLM2Model; using ChatGLM4ForCausalLM = ChatGLM2ForCausalLM; +// ===== GLM4V-9B ===== + +class Conv2d { + public: + Conv2d() : weight(nullptr), bias(nullptr), stride(0) {} + + Conv2d(ModelContext *mctx, int in_channels, int out_channels, int kernel_size, int stride) + : weight(ggml_new_tensor_4d(mctx->ctx_w.get(), GGML_TYPE_F16, kernel_size, kernel_size, in_channels, + out_channels)), + bias(ggml_new_tensor_3d(mctx->ctx_w.get(), GGML_TYPE_F32, 1, 1, out_channels)), stride(stride) {} + + int in_channels() const { return weight->ne[2]; } + + int out_channels() const { return weight->ne[3]; } + + ggml_tensor *forward(ModelContext *mctx, ggml_tensor *input) const; + + public: + ggml_tensor *weight; + ggml_tensor *bias; + int stride; +}; + +class PatchEmbedding { + public: + PatchEmbedding() = default; + + PatchEmbedding(ModelContext *mctx, int in_channels, int hidden_size, int patch_size, int num_positions) + : proj(mctx, in_channels, hidden_size, patch_size, patch_size), + position_embedding(mctx, GGML_TYPE_F32, num_positions, hidden_size), + cls_embedding(ggml_new_tensor_1d(mctx->ctx_w.get(), GGML_TYPE_F16, hidden_size)) {} + + int hidden_size() const { return proj.out_channels(); } + + int num_positions() const { return position_embedding.num_embeddings(); } + + ggml_tensor *forward(ModelContext *mctx, ggml_tensor *input) const; + + public: + Conv2d proj; + Embedding position_embedding; + ggml_tensor *cls_embedding = nullptr; // [H] +}; + +class EVA2CLIPBlock : public BasicBlock { + public: + EVA2CLIPBlock() = default; + + EVA2CLIPBlock(ModelContext *mctx, ggml_type dtype, int hidden_size, int num_attention_heads, + int num_key_value_heads, int intermediate_size, int max_length, float norm_eps, + ActivationType hidden_act, bool use_qkv_bias, bool use_dense_bias, bool interleaved_qkv, + RopeType rope_type, float rope_theta, AttentionMaskType attn_mask_type, int num_virtual_tokens, + bool use_cache) + : BasicBlock(mctx, dtype, hidden_size, num_attention_heads, num_key_value_heads, intermediate_size, max_length, + norm_eps, hidden_act, use_qkv_bias, use_dense_bias, interleaved_qkv, rope_type, rope_theta, + attn_mask_type, num_virtual_tokens, use_cache) {} + + ggml_tensor *forward(ModelContext *mctx, ggml_tensor *hidden_states, ggml_tensor *attention_mask, + ggml_tensor *position_ids, int n_past) const; +}; + +class EVA2CLIPTransformer { + public: + EVA2CLIPTransformer() = default; + + EVA2CLIPTransformer(ModelContext *mctx, const VisionModelConfig &config); + + ggml_tensor *forward(ModelContext *mctx, ggml_tensor *hidden_states) const; + + public: + std::vector layers; +}; + +class EVA2CLIPModel { + public: + EVA2CLIPModel() = default; + + EVA2CLIPModel(ModelContext *mctx, const ModelConfig &config) + : patch_embedding(mctx, config.vision.in_channels, config.vision.hidden_size, config.vision.patch_size, + config.vision.num_positions), + transformer(mctx, config.vision), conv(mctx, config.vision.hidden_size, config.hidden_size, 2, 2), + linear_proj(mctx, config.vision.dtype, config.hidden_size, config.hidden_size, false), + norm1(mctx, config.hidden_size), + glu(mctx, config.vision.dtype, config.hidden_size, config.intermediate_size, ActivationType::SILU), + boi(ggml_new_tensor_1d(mctx->ctx_w.get(), GGML_TYPE_F16, config.hidden_size)), + eoi(ggml_new_tensor_1d(mctx->ctx_w.get(), GGML_TYPE_F16, config.hidden_size)), + scaling_factor(config.vision.scaling_factor) {} + + ggml_tensor *forward(ModelContext *mctx, ggml_tensor *input) const; + + public: + PatchEmbedding patch_embedding; + EVA2CLIPTransformer transformer; + Conv2d conv; + Linear linear_proj; + LayerNorm norm1; + BasicGLU glu; + ggml_tensor *boi = nullptr; + ggml_tensor *eoi = nullptr; + float scaling_factor = 0.f; +}; + +class ChatGLM4VModel : public ChatGLM4Model { + public: + ChatGLM4VModel() = default; + + ChatGLM4VModel(ModelContext *mctx, const ModelConfig &config) + : ChatGLM4Model(mctx, config), config(config), vision(mctx, config) {} + + ggml_tensor *forward_embeddings(ModelContext *mctx, ggml_tensor *input_ids, ggml_tensor *images, + const std::vector &input_ids_vec, int n_past) const override; + + int num_vision_tokens() const { + auto square = [](int x) { return x * x; }; + return square(config.vision.image_size / config.vision.patch_size / 2); + } + + void set_graph_inputs(ggml_cgraph *gf, const std::vector &input_ids, const std::optional &image, + int n_past, int n_ctx) const; + + public: + ModelConfig config; + EVA2CLIPModel vision; +}; + +class ChatGLM4VForCausalLM : public BasicModelForCausalLM { + public: + ChatGLM4VForCausalLM(const ModelConfig &config) : BasicModelForCausalLM(config) {} + + int count_tokens(const std::vector &input_ids, const std::optional &image) const override; + + void load_state_dict(const StateDict &sd) override; + + private: + StateDict state_dict() const; +}; + // ===== pipeline ===== class Pipeline { public: Pipeline(const std::string &path, int max_length = -1); - std::vector generate(const std::vector &input_ids, const GenerationConfig &gen_config, - BaseStreamer *streamer = nullptr) const; + std::vector generate(const std::vector &input_ids, const std::optional &image, + const GenerationConfig &gen_config, BaseStreamer *streamer = nullptr) const; std::string generate(const std::string &prompt, const GenerationConfig &gen_config, BaseStreamer *streamer = nullptr) const; diff --git a/chatglm_cpp/_C.pyi b/chatglm_cpp/_C.pyi index cae0f2f1..18191bda 100644 --- a/chatglm_cpp/_C.pyi +++ b/chatglm_cpp/_C.pyi @@ -3,9 +3,12 @@ ChatGLM.cpp python binding """ from __future__ import annotations import typing -__all__ = ['BaseModelForCausalLM', 'BaseTokenizer', 'ChatGLM2ForCausalLM', 'ChatGLM2Tokenizer', 'ChatGLM3Tokenizer', 'ChatGLM4Tokenizer', 'ChatGLMForCausalLM', 'ChatGLMTokenizer', 'ChatMessage', 'CodeMessage', 'FunctionMessage', 'GenerationConfig', 'ModelConfig', 'ModelType', 'Pipeline', 'ToolCallMessage'] +import typing_extensions +__all__ = ['BaseModelForCausalLM', 'BaseTokenizer', 'ChatGLM2ForCausalLM', 'ChatGLM2Tokenizer', 'ChatGLM3Tokenizer', 'ChatGLM4Tokenizer', 'ChatGLMForCausalLM', 'ChatGLMTokenizer', 'ChatMessage', 'CodeMessage', 'FunctionMessage', 'GenerationConfig', 'Image', 'ModelConfig', 'ModelType', 'Pipeline', 'ToolCallMessage', 'VisionModelConfig'] class BaseModelForCausalLM: - def generate_next_token(self, input_ids: list[int], gen_config: GenerationConfig, n_past: int, n_ctx: int) -> int: + def count_tokens(self, input_ids: list[int], image: Image | None) -> int: + ... + def generate_next_token(self, input_ids: list[int], image: Image | None, gen_config: GenerationConfig, n_past: int, n_ctx: int) -> int: ... @property def config(self) -> ModelConfig: @@ -37,9 +40,10 @@ class ChatMessage: ROLE_SYSTEM: typing.ClassVar[str] = 'system' ROLE_USER: typing.ClassVar[str] = 'user' content: str + image: Image | None role: str tool_calls: list[ToolCallMessage] - def __init__(self, role: str, content: str, tool_calls: list[ToolCallMessage] = []) -> None: + def __init__(self, role: str, content: str, image: Image | None = None, tool_calls: list[ToolCallMessage] = []) -> None: ... def __repr__(self) -> str: ... @@ -69,6 +73,25 @@ class GenerationConfig: top_p: float def __init__(self, max_length: int = 2048, max_new_tokens: int = -1, max_context_length: int = 512, do_sample: bool = True, top_k: int = 0, top_p: float = 0.7, temperature: float = 0.95, repetition_penalty: float = 1.0) -> None: ... +class Image: + def __init__(self, arg0: typing_extensions.Buffer) -> None: + ... + def __repr__(self) -> str: + ... + def __str__(self) -> str: + ... + @property + def channels(self) -> int: + ... + @property + def height(self) -> int: + ... + @property + def pixels(self) -> list[int]: + ... + @property + def width(self) -> int: + ... class ModelConfig: @property def bos_token_id(self) -> int: @@ -113,6 +136,9 @@ class ModelConfig: def sep_token_id(self) -> int: ... @property + def vision(self) -> VisionModelConfig: + ... + @property def vocab_size(self) -> int: ... class ModelType: @@ -175,3 +201,34 @@ class ToolCallMessage: ... def __str__(self) -> str: ... +class VisionModelConfig: + @property + def hidden_size(self) -> int: + ... + @property + def image_size(self) -> int: + ... + @property + def in_channels(self) -> int: + ... + @property + def intermediate_size(self) -> int: + ... + @property + def norm_eps(self) -> float: + ... + @property + def num_attention_heads(self) -> int: + ... + @property + def num_hidden_layers(self) -> int: + ... + @property + def num_positions(self) -> int: + ... + @property + def patch_size(self) -> int: + ... + @property + def scaling_factor(self) -> float: + ... diff --git a/chatglm_cpp/__init__.py b/chatglm_cpp/__init__.py index 48da0b5a..209bf241 100644 --- a/chatglm_cpp/__init__.py +++ b/chatglm_cpp/__init__.py @@ -4,7 +4,7 @@ from typing import Any, Dict, Iterator, List, Optional, Union import chatglm_cpp._C as _C -from chatglm_cpp._C import ChatMessage +from chatglm_cpp._C import ChatMessage, Image __version__ = "0.4.1" @@ -72,9 +72,16 @@ def chat( temperature=temperature, repetition_penalty=repetition_penalty, ) + + image = None + for msg in messages: + if msg.image is not None: + image = msg.image + break + if stream: - return self._stream_chat(input_ids=input_ids, gen_config=gen_config) - return self._sync_chat(input_ids=input_ids, gen_config=gen_config) + return self._stream_chat(input_ids=input_ids, image=image, gen_config=gen_config) + return self._sync_chat(input_ids=input_ids, image=image, gen_config=gen_config) def generate( self, @@ -105,26 +112,30 @@ def generate( return self._stream_generate(input_ids=input_ids, gen_config=gen_config) return self._sync_generate(input_ids=input_ids, gen_config=gen_config) - def _stream_generate_ids(self, input_ids: List[int], gen_config: _C.GenerationConfig) -> Iterator[int]: + def _stream_generate_ids( + self, input_ids: List[int], image: Optional[Image], gen_config: _C.GenerationConfig + ) -> Iterator[int]: input_ids = input_ids.copy() n_past = 0 n_ctx = len(input_ids) max_new_tokens = gen_config.max_new_tokens if gen_config.max_new_tokens > 0 else gen_config.max_length while len(input_ids) < min(gen_config.max_length, n_ctx + max_new_tokens): - next_token_id = self.model.generate_next_token(input_ids, gen_config, n_past, n_ctx) + next_token_id = self.model.generate_next_token(input_ids, image, gen_config, n_past, n_ctx) yield next_token_id - n_past = len(input_ids) + n_past = self.model.count_tokens(input_ids, image) input_ids.append(next_token_id) if next_token_id in [self.model.config.eos_token_id, *self.model.config.extra_eos_token_ids]: break - def _stream_chat(self, input_ids: List[int], gen_config: _C.GenerationConfig) -> Iterator[DeltaMessage]: + def _stream_chat( + self, input_ids: List[int], image: Optional[Image], gen_config: _C.GenerationConfig + ) -> Iterator[DeltaMessage]: token_cache = [] print_len = 0 print_token_len = 0 - for next_token_id in self._stream_generate_ids(input_ids=input_ids, gen_config=gen_config): + for next_token_id in self._stream_generate_ids(input_ids=input_ids, image=image, gen_config=gen_config): token_cache.append(next_token_id) try: @@ -157,15 +168,17 @@ def _stream_generate(self, input_ids: List[int], gen_config: _C.GenerationConfig for msg in self._stream_chat(input_ids=input_ids, gen_config=gen_config): yield msg.content - def _sync_generate_ids(self, input_ids: List[int], gen_config: _C.GenerationConfig) -> List[int]: - return list(self._stream_generate_ids(input_ids=input_ids, gen_config=gen_config)) + def _sync_generate_ids( + self, input_ids: List[int], image: Optional[Image], gen_config: _C.GenerationConfig + ) -> List[int]: + return list(self._stream_generate_ids(input_ids=input_ids, image=image, gen_config=gen_config)) - def _sync_generate(self, input_ids: List[int], gen_config: _C.GenerationConfig) -> str: - output_ids = self._sync_generate_ids(input_ids=input_ids, gen_config=gen_config) + def _sync_generate(self, input_ids: List[int], image: Optional[Image], gen_config: _C.GenerationConfig) -> str: + output_ids = self._sync_generate_ids(input_ids=input_ids, image=image, gen_config=gen_config) return self.tokenizer.decode(output_ids) - def _sync_chat(self, input_ids: List[int], gen_config: _C.GenerationConfig) -> ChatMessage: - output_ids = self._sync_generate_ids(input_ids=input_ids, gen_config=gen_config) + def _sync_chat(self, input_ids: List[int], image: Optional[Image], gen_config: _C.GenerationConfig) -> ChatMessage: + output_ids = self._sync_generate_ids(input_ids=input_ids, image=image, gen_config=gen_config) return self.tokenizer.decode_message(output_ids) def merge_streaming_messages(self, chunks: List[DeltaMessage]) -> ChatMessage: diff --git a/chatglm_cpp/convert.py b/chatglm_cpp/convert.py index f24a8133..ccc25691 100644 --- a/chatglm_cpp/convert.py +++ b/chatglm_cpp/convert.py @@ -8,7 +8,7 @@ import sys from enum import Enum from pathlib import Path -from typing import BinaryIO, Optional +from typing import BinaryIO, NamedTuple, Optional import torch import torch.nn.functional as F @@ -49,6 +49,12 @@ class ModelType(Enum): CHATGLM2 = 2 CHATGLM3 = 3 CHATGLM4 = 4 + CHATGLM4V = 1004 + + +class WeightMeta(NamedTuple): + name: str + dtype: ModelType def quantize_q8_0(tensor: torch.Tensor) -> torch.Tensor: @@ -162,51 +168,36 @@ def dump_tensor(f, name: str, tensor: torch.Tensor, ggml_type: GGMLType): tensor.numpy().tofile(f) -def dump_state_dict(f, weight_names, state_dict, quantization_bit, ggml_type): +def dump_state_dict(f, weight_meta, state_dict, quantization_bit): tensor_info = [] - for name in tqdm(weight_names, desc="Processing model states"): - tensor = state_dict[name] - if name == "past_key_values": - tensor_ggml_type = GGMLType.F16 - elif tensor.ndim == 2: - # 2d weight: should quantize it if needed - - # step 1: de-quantize it back to float32 - if tensor.dtype == torch.int8: - assert quantization_bit in [4, 8] - scale = state_dict[f"{name}_scale"].float() # channel-wise scale - - if quantization_bit == 4: - # convert int4 weight to int8 - low_bits = ((tensor << 4) & 0xF0) >> 4 - high_bits = (tensor & 0xF0) >> 4 - tensor = torch.stack((high_bits, low_bits), dim=-1).view(tensor.shape[0], -1) - tensor = tensor * scale[:, None] - else: - tensor = tensor.float() - - # step 2: quantize it into ggml format - tensor_ggml_type = ggml_type - else: - # 1d weight: convert it to float32 - assert tensor.ndim == 1 - tensor = tensor.float() - tensor_ggml_type = GGMLType.F32 - - dump_tensor(f, name, tensor, tensor_ggml_type) - tensor_info.append((name, tuple(tensor.shape), tensor_ggml_type.name)) + for meta in tqdm(weight_meta, desc="Processing model states"): + tensor = state_dict[meta.name] + if tensor.ndim == 2 and tensor.dtype == torch.int8: + # de-quantize gemm weight back to float32 + assert quantization_bit in [4, 8] + scale = state_dict[f"{meta.name}_scale"].float() # channel-wise scale + + if quantization_bit == 4: + # convert int4 weight to int8 + low_bits = ((tensor << 4) & 0xF0) >> 4 + high_bits = (tensor & 0xF0) >> 4 + tensor = torch.stack((high_bits, low_bits), dim=-1).view(tensor.shape[0], -1) + tensor = tensor * scale[:, None] + + dump_tensor(f, meta.name, tensor, meta.dtype) + tensor_info.append((meta.name, tuple(tensor.shape), meta.dtype.name)) print(tabulate(tensor_info, headers=["name", "shape", "dtype"], tablefmt="psql")) class BaseConverter: @classmethod - def convert(cls, f, model, tokenizer, ggml_type): + def convert(cls, f, model, tokenizer, ggml_type, vision_type=None): f.write(b"ggml") # magic f.write(struct.pack("i", cls.MODEL_TYPE.value)) # model type - cls.dump_config(f, model.config, ggml_type) + cls.dump_config(f, model.config, ggml_type, vision_type) cls.dump_tokenizer(f, tokenizer) - cls.dump_model(f, model, ggml_type) + cls.dump_model(f, model, ggml_type, vision_type) def get_prefix_cache(prefix_encoder, pre_seq_len, num_layers, num_key_value_heads, head_size): @@ -226,7 +217,7 @@ class ChatGLMConverter(BaseConverter): MODEL_TYPE = ModelType.CHATGLM @staticmethod - def dump_config(f, config, ggml_type): + def dump_config(f, config, ggml_type, vision_type): assert config.position_encoding_2d, "unimplemented: position_encoding_2d should be True" assert ( config.inner_hidden_size == 4 * config.hidden_size @@ -257,39 +248,39 @@ def dump_tokenizer(f, tokenizer): f.write(serialized_model_proto) @staticmethod - def dump_model(f, model, ggml_type): + def dump_model(f, model, ggml_type, vision_type): assert torch.allclose( model.state_dict()["transformer.word_embeddings.weight"], model.state_dict()["lm_head.weight"] ), "unimplemented: lm_head weight must be tied to input embedding" - weight_names = ["transformer.word_embeddings.weight"] + weight_meta = [WeightMeta("transformer.word_embeddings.weight", ggml_type)] for i in range(model.config.num_layers): - weight_names += [ - f"transformer.layers.{i}.input_layernorm.weight", - f"transformer.layers.{i}.input_layernorm.bias", - f"transformer.layers.{i}.attention.query_key_value.weight", - f"transformer.layers.{i}.attention.query_key_value.bias", - f"transformer.layers.{i}.attention.dense.weight", - f"transformer.layers.{i}.attention.dense.bias", - f"transformer.layers.{i}.post_attention_layernorm.weight", - f"transformer.layers.{i}.post_attention_layernorm.bias", - f"transformer.layers.{i}.mlp.dense_h_to_4h.weight", - f"transformer.layers.{i}.mlp.dense_h_to_4h.bias", - f"transformer.layers.{i}.mlp.dense_4h_to_h.weight", - f"transformer.layers.{i}.mlp.dense_4h_to_h.bias", + weight_meta += [ + WeightMeta(f"transformer.layers.{i}.input_layernorm.weight", GGMLType.F32), + WeightMeta(f"transformer.layers.{i}.input_layernorm.bias", GGMLType.F32), + WeightMeta(f"transformer.layers.{i}.attention.query_key_value.weight", ggml_type), + WeightMeta(f"transformer.layers.{i}.attention.query_key_value.bias", GGMLType.F32), + WeightMeta(f"transformer.layers.{i}.attention.dense.weight", ggml_type), + WeightMeta(f"transformer.layers.{i}.attention.dense.bias", GGMLType.F32), + WeightMeta(f"transformer.layers.{i}.post_attention_layernorm.weight", GGMLType.F32), + WeightMeta(f"transformer.layers.{i}.post_attention_layernorm.bias", GGMLType.F32), + WeightMeta(f"transformer.layers.{i}.mlp.dense_h_to_4h.weight", ggml_type), + WeightMeta(f"transformer.layers.{i}.mlp.dense_h_to_4h.bias", GGMLType.F32), + WeightMeta(f"transformer.layers.{i}.mlp.dense_4h_to_h.weight", ggml_type), + WeightMeta(f"transformer.layers.{i}.mlp.dense_4h_to_h.bias", GGMLType.F32), ] - weight_names += [ - "transformer.final_layernorm.weight", - "transformer.final_layernorm.bias", + weight_meta += [ + WeightMeta("transformer.final_layernorm.weight", GGMLType.F32), + WeightMeta("transformer.final_layernorm.bias", GGMLType.F32), ] - dump_state_dict(f, weight_names, model.state_dict(), model.config.quantization_bit, ggml_type) + dump_state_dict(f, weight_meta, model.state_dict(), model.config.quantization_bit) class ChatGLM2Converter(BaseConverter): MODEL_TYPE = ModelType.CHATGLM2 @classmethod - def dump_config(cls, f, config, ggml_type): + def dump_config(cls, f, config, ggml_type, vision_type): assert config.add_bias_linear is False, "unimplemented: add_bias_linear must be false" assert config.add_qkv_bias is True, "unimplemented: add_qkv_bias must be true" assert ( @@ -329,12 +320,12 @@ def dump_tokenizer(f, tokenizer): f.write(serialized_model_proto) @staticmethod - def dump_model(f, model, ggml_type): + def dump_model(f, model, ggml_type, vision_type): config = model.config state_dict = model.state_dict() - weight_names = [] + weight_meta = [] if getattr(config, "pre_seq_len", None) is not None and config.pre_seq_len > 0: past_key_values = get_prefix_cache( model.transformer.prefix_encoder, @@ -344,29 +335,28 @@ def dump_model(f, model, ggml_type): config.kv_channels, ) state_dict["past_key_values"] = past_key_values - weight_names.append("past_key_values") + weight_meta.append(WeightMeta("past_key_values", GGMLType.F16)) - weight_names.append("transformer.embedding.word_embeddings.weight") + weight_meta.append(WeightMeta("transformer.embedding.word_embeddings.weight", ggml_type)) for i in range(config.num_layers): - weight_names += [ - f"transformer.encoder.layers.{i}.input_layernorm.weight", - f"transformer.encoder.layers.{i}.self_attention.query_key_value.weight", - f"transformer.encoder.layers.{i}.self_attention.query_key_value.bias", - f"transformer.encoder.layers.{i}.self_attention.dense.weight", - f"transformer.encoder.layers.{i}.post_attention_layernorm.weight", - f"transformer.encoder.layers.{i}.mlp.dense_h_to_4h.weight", - f"transformer.encoder.layers.{i}.mlp.dense_4h_to_h.weight", + weight_meta += [ + WeightMeta(f"transformer.encoder.layers.{i}.input_layernorm.weight", GGMLType.F32), + WeightMeta(f"transformer.encoder.layers.{i}.self_attention.query_key_value.weight", ggml_type), + WeightMeta(f"transformer.encoder.layers.{i}.self_attention.query_key_value.bias", GGMLType.F32), + WeightMeta(f"transformer.encoder.layers.{i}.self_attention.dense.weight", ggml_type), + WeightMeta(f"transformer.encoder.layers.{i}.post_attention_layernorm.weight", GGMLType.F32), + WeightMeta(f"transformer.encoder.layers.{i}.mlp.dense_h_to_4h.weight", ggml_type), + WeightMeta(f"transformer.encoder.layers.{i}.mlp.dense_4h_to_h.weight", ggml_type), ] - weight_names += [ - "transformer.encoder.final_layernorm.weight", - "transformer.output_layer.weight", + weight_meta += [ + WeightMeta("transformer.encoder.final_layernorm.weight", GGMLType.F32), + WeightMeta("transformer.output_layer.weight", ggml_type), ] dump_state_dict( f, - weight_names=weight_names, + weight_meta=weight_meta, state_dict=state_dict, quantization_bit=getattr(config, "quantization_bit", None), - ggml_type=ggml_type, ) @@ -384,8 +374,103 @@ def dump_tokenizer(f, tokenizer): f.write(vocab_text) -def convert(f: BinaryIO, model_name_or_path: str, lora_model_name_or_path: Optional[str] = None, dtype: str = "q4_0"): +class ChatGLM4VConverter(ChatGLM4Converter): + MODEL_TYPE = ModelType.CHATGLM4V + + @classmethod + def dump_config(cls, f, config, ggml_type, vision_type): + ChatGLM4Converter.dump_config(f, config, ggml_type, vision_type) + + config_values = [ + vision_type.value, + config.vision_config["hidden_size"], + config.vision_config["image_size"], + config.vision_config["in_channels"], + config.vision_config["intermediate_size"], + config.vision_config["layer_norm_eps"], + config.vision_config["num_heads"], + config.vision_config["num_hidden_layers"], + config.vision_config["num_positions"], + config.vision_config["patch_size"], + config.vision_config["scaling_factor"], + ] + + f.write(struct.pack("iiiiifiiiif", *config_values)) + + @staticmethod + def dump_model(f, model, ggml_type, vision_type): + config = model.config + + state_dict = model.state_dict() + + # vision + weight_meta = [ + WeightMeta("transformer.vision.patch_embedding.cls_embedding", GGMLType.F16), + WeightMeta("transformer.vision.patch_embedding.proj.weight", GGMLType.F16), + WeightMeta("transformer.vision.patch_embedding.proj.bias", GGMLType.F32), + WeightMeta("transformer.vision.patch_embedding.position_embedding.weight", GGMLType.F32), + ] + for i in range(config.vision_config["num_hidden_layers"]): + weight_meta += [ + WeightMeta(f"transformer.vision.transformer.layers.{i}.input_layernorm.weight", GGMLType.F32), + WeightMeta(f"transformer.vision.transformer.layers.{i}.input_layernorm.bias", GGMLType.F32), + WeightMeta(f"transformer.vision.transformer.layers.{i}.attention.query_key_value.weight", vision_type), + WeightMeta(f"transformer.vision.transformer.layers.{i}.attention.query_key_value.bias", GGMLType.F32), + WeightMeta(f"transformer.vision.transformer.layers.{i}.attention.dense.weight", vision_type), + WeightMeta(f"transformer.vision.transformer.layers.{i}.attention.dense.bias", GGMLType.F32), + WeightMeta(f"transformer.vision.transformer.layers.{i}.mlp.fc1.weight", vision_type), + WeightMeta(f"transformer.vision.transformer.layers.{i}.mlp.fc1.bias", GGMLType.F32), + WeightMeta(f"transformer.vision.transformer.layers.{i}.mlp.fc2.weight", vision_type), + WeightMeta(f"transformer.vision.transformer.layers.{i}.mlp.fc2.bias", GGMLType.F32), + WeightMeta(f"transformer.vision.transformer.layers.{i}.post_attention_layernorm.weight", GGMLType.F32), + WeightMeta(f"transformer.vision.transformer.layers.{i}.post_attention_layernorm.bias", GGMLType.F32), + ] + weight_meta += [ + WeightMeta("transformer.vision.conv.weight", GGMLType.F16), + WeightMeta("transformer.vision.conv.bias", GGMLType.F32), + WeightMeta("transformer.vision.linear_proj.linear_proj.weight", vision_type), + WeightMeta("transformer.vision.linear_proj.norm1.weight", GGMLType.F32), + WeightMeta("transformer.vision.linear_proj.norm1.bias", GGMLType.F32), + WeightMeta("transformer.vision.linear_proj.gate_proj.weight", vision_type), + WeightMeta("transformer.vision.linear_proj.dense_h_to_4h.weight", vision_type), + WeightMeta("transformer.vision.linear_proj.dense_4h_to_h.weight", vision_type), + WeightMeta("transformer.vision.boi", GGMLType.F16), + WeightMeta("transformer.vision.eoi", GGMLType.F16), + ] + + # text + weight_meta.append(WeightMeta("transformer.embedding.word_embeddings.weight", ggml_type)) + for i in range(config.num_layers): + weight_meta += [ + WeightMeta(f"transformer.encoder.layers.{i}.input_layernorm.weight", GGMLType.F32), + WeightMeta(f"transformer.encoder.layers.{i}.self_attention.query_key_value.weight", ggml_type), + WeightMeta(f"transformer.encoder.layers.{i}.self_attention.query_key_value.bias", GGMLType.F32), + WeightMeta(f"transformer.encoder.layers.{i}.self_attention.dense.weight", ggml_type), + WeightMeta(f"transformer.encoder.layers.{i}.post_attention_layernorm.weight", GGMLType.F32), + WeightMeta(f"transformer.encoder.layers.{i}.mlp.dense_h_to_4h.weight", ggml_type), + WeightMeta(f"transformer.encoder.layers.{i}.mlp.dense_4h_to_h.weight", ggml_type), + ] + weight_meta += [ + WeightMeta("transformer.encoder.final_layernorm.weight", GGMLType.F32), + WeightMeta("transformer.output_layer.weight", ggml_type), + ] + dump_state_dict( + f, + weight_meta=weight_meta, + state_dict=state_dict, + quantization_bit=getattr(config, "quantization_bit", None), + ) + + +def convert( + f: BinaryIO, + model_name_or_path: str, + lora_model_name_or_path: Optional[str] = None, + dtype: str = "q4_0", + vision_dtype: str = "f16", +): ggml_type = GGMLType[dtype.upper()] + vision_type = GGMLType[vision_dtype.upper()] tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, trust_remote_code=True) @@ -415,7 +500,10 @@ def convert(f: BinaryIO, model_name_or_path: str, lora_model_name_or_path: Optio if tiktoken is not None and isinstance(tokenizer.tokenizer, tiktoken.Encoding): # TODO: store all eos token ids model.config.eos_token_id = tokenizer.eos_token_id - ChatGLM4Converter.convert(f, model, tokenizer, ggml_type) + if getattr(model.config, "vision_config", None) is not None: + ChatGLM4VConverter.convert(f, model, tokenizer, ggml_type, vision_type) + else: + ChatGLM4Converter.convert(f, model, tokenizer, ggml_type) elif "<|system|>" in tokenizer.tokenizer.special_tokens: ChatGLM3Converter.convert(f, model, tokenizer, ggml_type) else: @@ -453,10 +541,20 @@ def main(): choices=["f32", "f16", "q8_0", "q4_0", "q4_1", "q5_0", "q5_1"], help="GGML model quantization type", ) + parser.add_argument( + "-vt", + "--vision_type", + default="f16", + type=str, + choices=["f32", "f16", "q8_0", "q4_0", "q4_1", "q5_0", "q5_1"], + help="Vision model quantization type", + ) args = parser.parse_args() with open(args.save_path, "wb") as f: - convert(f, args.model_name_or_path, args.lora_model_name_or_path, dtype=args.type) + convert( + f, args.model_name_or_path, args.lora_model_name_or_path, dtype=args.type, vision_dtype=args.vision_type + ) print(f"GGML model saved to {args.save_path}") diff --git a/chatglm_cpp/openai_api.py b/chatglm_cpp/openai_api.py index 6d37844d..9fad4263 100644 --- a/chatglm_cpp/openai_api.py +++ b/chatglm_cpp/openai_api.py @@ -1,4 +1,6 @@ import asyncio +import base64 +import io import json import logging import time @@ -30,9 +32,24 @@ class ToolCall(BaseModel): type: Literal["function"] +class ContentText(BaseModel): + type: Literal["text"] = "text" + text: str + + +class ContentImageUrlData(BaseModel): + url: str + detail: str = "high" + + +class ContentImageUrl(BaseModel): + type: Literal["image_url"] = "image_url" + image_url: ContentImageUrlData + + class ChatMessage(BaseModel): role: Literal["system", "user", "assistant"] - content: str + content: Union[str, List[Union[ContentText, ContentImageUrl]]] tool_calls: Optional[List[ToolCall]] = None @@ -186,7 +203,32 @@ def tool_call(**kwargs): if not body.messages: raise HTTPException(status.HTTP_400_BAD_REQUEST, "empty messages") - messages = [chatglm_cpp.ChatMessage(role=msg.role, content=msg.content) for msg in body.messages] + messages = [] + for msg in body.messages: + if isinstance(msg.content, str): + messages.append(chatglm_cpp.ChatMessage(role=msg.role, content=msg.content)) + else: + if not (len(msg.content) == 2 and msg.content[0].type == "text" and msg.content[1].type == "image_url"): + raise HTTPException( + status.HTTP_400_BAD_REQUEST, + "multimodal content must have a text item followed by an image_url item", + ) + + import numpy as np + from PIL import Image + + text = msg.content[0].text + image_url = msg.content[1].image_url.url + if image_url.startswith("data:"): + image_bytes = base64.b64decode(image_url.split(",")[1]) + else: + import requests + + image_bytes = requests.get(image_url).content + image = chatglm_cpp.Image(np.asarray(Image.open(io.BytesIO(image_bytes)))) + + messages.append(chatglm_cpp.ChatMessage(role=msg.role, content=text, image=image)) + if body.tools: system_content = ( "Answer the following questions as best as you can. You have access to the following tools:\n" diff --git a/chatglm_pybind.cpp b/chatglm_pybind.cpp index 5143e04a..1cf08c1c 100644 --- a/chatglm_pybind.cpp +++ b/chatglm_pybind.cpp @@ -30,14 +30,19 @@ class PyBaseModelForCausalLM : public BaseModelForCausalLM { PYBIND11_OVERLOAD_PURE(void, PyBaseModelForCausalLM, load_state_dict, sd); } - ggml_tensor *forward(ModelContext *ctx, ggml_tensor *input_ids, int n_past, int n_ctx, - bool is_decoding) const override { - PYBIND11_OVERLOAD_PURE(ggml_tensor *, PyBaseModelForCausalLM, forward, ctx, input_ids, n_past, n_ctx, - is_decoding) + ggml_tensor *forward(ModelContext *mctx, ggml_tensor *input_ids, ggml_tensor *images, + const std::vector &input_ids_vec, int n_past, bool is_decoding) const override { + PYBIND11_OVERLOAD_PURE(ggml_tensor *, PyBaseModelForCausalLM, forward, mctx, input_ids, images, input_ids_vec, + n_past, is_decoding); } - void set_graph_inputs(int qlen, int n_past, int n_ctx) const override { - PYBIND11_OVERLOAD_PURE(void, PyBaseModelForCausalLM, set_graph_inputs, qlen, n_past, n_ctx); + void set_graph_inputs(const std::vector &input_ids, const std::optional &image, int n_past, + int n_ctx) const override { + PYBIND11_OVERLOAD_PURE(void, PyBaseModelForCausalLM, set_graph_inputs, input_ids, image, n_past, n_ctx); + } + + int count_tokens(const std::vector &input_ids, const std::optional &image) const override { + PYBIND11_OVERLOAD_PURE(int, PyBaseModelForCausalLM, count_tokens, input_ids, image); } }; @@ -57,6 +62,20 @@ PYBIND11_MODULE(_C, m) { .value("CHATGLM3", ModelType::CHATGLM3) .value("CHATGLM4", ModelType::CHATGLM4); + py::class_(m, "VisionModelConfig") + // .def_readonly("dtype", &VisionModelConfig::dtype) + // .def_readonly("hidden_act", &VisionModelConfig::hidden_act) + .def_readonly("hidden_size", &VisionModelConfig::hidden_size) + .def_readonly("image_size", &VisionModelConfig::image_size) + .def_readonly("in_channels", &VisionModelConfig::in_channels) + .def_readonly("intermediate_size", &VisionModelConfig::intermediate_size) + .def_readonly("norm_eps", &VisionModelConfig::norm_eps) + .def_readonly("num_attention_heads", &VisionModelConfig::num_attention_heads) + .def_readonly("num_hidden_layers", &VisionModelConfig::num_hidden_layers) + .def_readonly("num_positions", &VisionModelConfig::num_positions) + .def_readonly("patch_size", &VisionModelConfig::patch_size) + .def_readonly("scaling_factor", &VisionModelConfig::scaling_factor); + py::class_(m, "ModelConfig") .def_readonly("model_type", &ModelConfig::model_type) // .def_readonly("dtype", &ModelConfig::dtype) @@ -73,6 +92,7 @@ PYBIND11_MODULE(_C, m) { .def_readonly("pad_token_id", &ModelConfig::pad_token_id) .def_readonly("sep_token_id", &ModelConfig::sep_token_id) .def_readonly("extra_eos_token_ids", &ModelConfig::extra_eos_token_ids) + .def_readonly("vision", &ModelConfig::vision) .def_property_readonly("model_type_name", &ModelConfig::model_type_name); py::class_(m, "GenerationConfig") @@ -106,9 +126,37 @@ PYBIND11_MODULE(_C, m) { .def_readwrite("function", &ToolCallMessage::function) .def_readwrite("code", &ToolCallMessage::code); + py::class_(m, "Image", py::buffer_protocol()) + .def(py::init([](py::buffer b) { + py::buffer_info info = b.request(); + + CHATGLM_CHECK(info.format == py::format_descriptor::format()) + << "Incompatible format: expect a byte array!"; + CHATGLM_CHECK(info.ndim == 3 && info.shape[2] == 3) << "Only support RGB image for now"; + + for (int i = 1; i < info.ndim; i++) { + CHATGLM_CHECK(info.strides[i] * info.shape[i] == info.strides[i - 1]) + << "Only support contiguous array"; + } + + return Image(info.shape[1], info.shape[0], info.shape[2], (uint8_t *)info.ptr); + })) + .def_buffer([](Image &self) { + return py::buffer_info( + self.pixels.data(), sizeof(uint8_t), py::format_descriptor::format(), 3, + {self.height, self.width, self.channels}, + {self.width * self.channels * sizeof(uint8_t), self.channels * sizeof(uint8_t), sizeof(uint8_t)}); + }) + .def("__repr__", &to_string) + .def("__str__", &to_string) + .def_readonly("width", &Image::width) + .def_readonly("height", &Image::height) + .def_readonly("channels", &Image::channels) + .def_readonly("pixels", &Image::pixels); + py::class_(m, "ChatMessage") - .def(py::init>(), "role"_a, "content"_a, - "tool_calls"_a = std::vector{}) + .def(py::init, std::vector>(), "role"_a, + "content"_a, "image"_a = std::nullopt, "tool_calls"_a = std::vector{}) .def("__repr__", &to_string) .def("__str__", &to_string) .def_readonly_static("ROLE_SYSTEM", &ChatMessage::ROLE_SYSTEM) @@ -117,6 +165,7 @@ PYBIND11_MODULE(_C, m) { .def_readonly_static("ROLE_OBSERVATION", &ChatMessage::ROLE_OBSERVATION) .def_readwrite("role", &ChatMessage::role) .def_readwrite("content", &ChatMessage::content) + .def_readwrite("image", &ChatMessage::image) .def_readwrite("tool_calls", &ChatMessage::tool_calls); py::class_(m, "BaseTokenizer") @@ -126,8 +175,9 @@ PYBIND11_MODULE(_C, m) { .def("decode_message", &BaseTokenizer::decode_message, "ids"_a); py::class_(m, "BaseModelForCausalLM") - .def("generate_next_token", &BaseModelForCausalLM::generate_next_token, "input_ids"_a, "gen_config"_a, - "n_past"_a, "n_ctx"_a) + .def("generate_next_token", &BaseModelForCausalLM::generate_next_token, "input_ids"_a, "image"_a, + "gen_config"_a, "n_past"_a, "n_ctx"_a) + .def("count_tokens", &BaseModelForCausalLM::count_tokens, "input_ids"_a, "image"_a) .def_readonly("config", &BaseModelForCausalLM::config); // ===== ChatGLM ===== diff --git a/chatglm_test.cpp b/chatglm_test.cpp index 96c66fdf..554faa66 100644 --- a/chatglm_test.cpp +++ b/chatglm_test.cpp @@ -52,7 +52,7 @@ static inline void expect_all_close(ggml_tensor *a, ggml_tensor *b, float atol = static inline void read_backend_tensor_data(std::istream &is, ggml_tensor *tensor) { std::vector> buf(ggml_nbytes(tensor)); - is.read((char *)buf.data(), buf.size()); + CHATGLM_CHECK(is.read((char *)buf.data(), buf.size())); ggml_backend_tensor_set(tensor, buf.data(), 0, buf.size()); } @@ -261,7 +261,7 @@ class ChatGLMTest : public ::testing::Test { protected: std::unique_ptr mctx_; - void SetUp() override { mctx_ = std::make_unique(GGML_TYPE_F32); } + void SetUp() override { mctx_ = std::make_unique(); } float perf_graph_compute() { auto fn = [this] { @@ -274,17 +274,6 @@ class ChatGLMTest : public ::testing::Test { } } - template - static void set_graph_inputs(ggml_cgraph *gf, int qlen, int n_past, int n_ctx) { - static_assert(std::is_same_v || std::is_same_v, - "invalid model type"); - if (std::is_same_v) { - ChatGLMForCausalLM::set_graph_inputs(gf, qlen, n_past, n_ctx); - } else { - ChatGLM2ForCausalLM::set_graph_inputs(gf, qlen, n_past, n_ctx); - } - } - template void test_model(Model &model, const ModelConfig &config, const fs::path &data_path, int seq_len, const std::vector &all_weights) { @@ -326,35 +315,48 @@ class ChatGLMTest : public ::testing::Test { } ASSERT_TRUE(ifs.peek() == EOF); - // self attention + auto input_ids_to_vec = [](ggml_tensor *input_ids) { + std::vector input_ids_vec(ggml_nelements(input_ids)); + ggml_backend_tensor_get(input_ids, input_ids_vec.data(), 0, ggml_nbytes(input_ids)); + return input_ids_vec; + }; + + std::vector input_ids; + + // prefill { + std::vector x1_vec = input_ids_to_vec(x1); + input_ids.insert(input_ids.end(), x1_vec.begin(), x1_vec.end()); ggml_graph_clear(mctx_->gf); - ggml_tensor *out_y1 = model.forward(mctx_.get(), x1, 0); + ggml_tensor *out_y1 = model.forward(mctx_.get(), x1, nullptr, input_ids, 0); ggml_build_forward_expand(mctx_->gf, out_y1); CHATGLM_CHECK(ggml_gallocr_alloc_graph(mctx_->allocr.get(), mctx_->gf)); - set_graph_inputs(mctx_->gf, seq_len, 0, seq_len); + model.set_graph_inputs(mctx_->gf, input_ids, std::nullopt, 0, seq_len); CHATGLM_CHECK(ggml_backend_graph_compute(mctx_->backend.get(), mctx_->gf) == GGML_STATUS_SUCCESS); expect_all_close(ref_y1, out_y1, 5e-4); } - - // cross attention + // decode { + std::vector x2_vec = input_ids_to_vec(x2); + input_ids.insert(input_ids.end(), x2_vec.begin(), x2_vec.end()); ggml_graph_clear(mctx_->gf); - ggml_tensor *out_y2 = model.forward(mctx_.get(), x2, seq_len); + ggml_tensor *out_y2 = model.forward(mctx_.get(), x2, nullptr, input_ids, seq_len); ggml_build_forward_expand(mctx_->gf, out_y2); CHATGLM_CHECK(ggml_gallocr_alloc_graph(mctx_->allocr.get(), mctx_->gf)); - set_graph_inputs(mctx_->gf, 1, seq_len, seq_len); + model.set_graph_inputs(mctx_->gf, input_ids, std::nullopt, seq_len, seq_len); CHATGLM_CHECK(ggml_backend_graph_compute(mctx_->backend.get(), mctx_->gf) == GGML_STATUS_SUCCESS); expect_all_close(ref_y2, out_y2, 5e-4); } { + std::vector x3_vec = input_ids_to_vec(x3); + input_ids.insert(input_ids.end(), x3_vec.begin(), x3_vec.end()); ggml_graph_clear(mctx_->gf); - ggml_tensor *out_y3 = model.forward(mctx_.get(), x3, seq_len + 1); + ggml_tensor *out_y3 = model.forward(mctx_.get(), x3, nullptr, input_ids, seq_len + 1); ggml_build_forward_expand(mctx_->gf, out_y3); CHATGLM_CHECK(ggml_gallocr_alloc_graph(mctx_->allocr.get(), mctx_->gf)); - set_graph_inputs(mctx_->gf, 1, seq_len + 1, seq_len); + model.set_graph_inputs(mctx_->gf, input_ids, std::nullopt, seq_len + 1, seq_len); CHATGLM_CHECK(ggml_backend_graph_compute(mctx_->backend.get(), mctx_->gf) == GGML_STATUS_SUCCESS); expect_all_close(ref_y3, out_y3, 5e-4); @@ -370,7 +372,7 @@ TEST_F(ChatGLMTest, Embedding) { -2.1788, 0.4033, 0.8380, -0.7193, -0.4033, -0.5966, 0.1820}; ggml_tensor *x = ggml_new_tensor_1d(mctx_->ctx_b.get(), GGML_TYPE_I32, 5); - Embedding model(mctx_.get(), 4, 3); + Embedding model(mctx_.get(), GGML_TYPE_F32, 4, 3); ggml_tensor *ref = ggml_new_tensor_2d(mctx_->ctx_b.get(), GGML_TYPE_F32, 3, 5); auto buf_b = unique_ggml_backend_buffer_t(ggml_backend_alloc_ctx_tensors(mctx_->ctx_b.get(), mctx_->backend.get())); @@ -432,8 +434,7 @@ TEST_F(ChatGLMTest, Linear) { }; for (const auto &config : test_configs) { - mctx_->dtype = config.dtype; - Linear model(mctx_.get(), 64, 32); + Linear model(mctx_.get(), config.dtype, 64, 32); auto buf_w = unique_ggml_backend_buffer_t(ggml_backend_alloc_ctx_tensors(mctx_->ctx_w.get(), mctx_->backend.get())); @@ -471,9 +472,9 @@ TEST_F(ChatGLMTest, BenchmarkLinear) { std::vector dtypes{GGML_TYPE_F32, GGML_TYPE_F16, GGML_TYPE_Q8_0, GGML_TYPE_Q5_1, GGML_TYPE_Q5_0, GGML_TYPE_Q4_1, GGML_TYPE_Q4_0}; for (ggml_type dtype : dtypes) { - mctx_ = std::make_unique(dtype); + mctx_ = std::make_unique(); - Linear m(mctx_.get(), K, N); + Linear m(mctx_.get(), dtype, K, N); ggml_tensor *x = ggml_new_tensor_2d(mctx_->ctx_b.get(), GGML_TYPE_F32, K, M); ggml_tensor *y = m.forward(mctx_.get(), x); @@ -485,8 +486,7 @@ TEST_F(ChatGLMTest, BenchmarkLinear) { randn_(tensor); } - std::cout << "[Benchmark] Linear " << ggml_type_name(mctx_->dtype) << " time: " << perf_graph_compute() - << " ms\n"; + std::cout << "[Benchmark] Linear " << ggml_type_name(dtype) << " time: " << perf_graph_compute() << " ms\n"; } } @@ -537,7 +537,7 @@ TEST_F(ChatGLMTest, BenchmarkLayerNorm) { ggml_tensor *y = m.forward(mctx_.get(), x); ggml_build_forward_expand(mctx_->gf, y); CHATGLM_CHECK(ggml_gallocr_alloc_graph(mctx_->allocr.get(), mctx_->gf)); - std::cout << "[Benchmark] LayerNorm " << ggml_type_name(mctx_->dtype) << " time: " << perf_graph_compute() + std::cout << "[Benchmark] LayerNorm " << ggml_type_name(GGML_TYPE_F32) << " time: " << perf_graph_compute() << " ms\n"; } @@ -588,19 +588,19 @@ TEST_F(ChatGLMTest, BenchmarkRMSNorm) { ggml_tensor *y = m.forward(mctx_.get(), x); ggml_build_forward_expand(mctx_->gf, y); CHATGLM_CHECK(ggml_gallocr_alloc_graph(mctx_->allocr.get(), mctx_->gf)); - std::cout << "[Benchmark] RMSNorm " << ggml_type_name(mctx_->dtype) << " time: " << perf_graph_compute() << " ms\n"; + std::cout << "[Benchmark] RMSNorm " << ggml_type_name(GGML_TYPE_F32) << " time: " << perf_graph_compute() + << " ms\n"; } TEST_F(ChatGLMTest, GLMModel) { fs::path data_path = fs::path(__FILE__).parent_path() / "tests/data/glm_model.data"; - ModelConfig config( - ModelType::CHATGLM, GGML_TYPE_F32, /*vocab_size=*/5, /*hidden_size=*/32, /*num_attention_heads=*/8, - /*num_key_value_heads=*/8, /*num_hidden_layers=*/1, /*intermediate_size=*/128, /*norm_eps=*/1e-5f, - /*rope_theta=*/10000.f, - /*num_virtual_tokens=*/0, - /*max_length=*/8, /*bos_token_id=*/-1, /*eos_token_id=*/-1, /*pad_token_id=*/-1, /*sep_token_id=*/-1, - /*extra_eos_token_ids=*/{}); + ModelConfig config(ModelType::CHATGLM, GGML_TYPE_F32, /*vocab_size=*/5, /*hidden_size=*/32, + /*num_attention_heads=*/8, /*num_key_value_heads=*/8, /*num_hidden_layers=*/1, + /*intermediate_size=*/128, /*norm_eps=*/1e-5f, /*rope_theta=*/10000.f, /*num_virtual_tokens=*/0, + /*max_length=*/8, /*bos_token_id=*/-1, /*eos_token_id=*/-1, /*pad_token_id=*/-1, + /*sep_token_id=*/-1, /*boi_token_id=*/-1, /*eoi_token_id=*/-1, /*extra_eos_token_ids=*/{}, + /*vision=*/{}); constexpr int seq_len = 3; @@ -628,13 +628,12 @@ TEST_F(ChatGLMTest, GLMModel) { TEST_F(ChatGLMTest, GLMPTuningV2Model) { fs::path data_path = fs::path(__FILE__).parent_path() / "tests/data/glm_ptuning_v2_model.data"; - ModelConfig config( - ModelType::CHATGLM, GGML_TYPE_F32, /*vocab_size=*/5, /*hidden_size=*/32, /*num_attention_heads=*/8, - /*num_key_value_heads=*/8, /*num_hidden_layers=*/1, /*intermediate_size=*/128, /*norm_eps=*/1e-5f, - /*rope_theta=*/10000.f, - /*num_virtual_tokens=*/5, - /*max_length=*/8, /*bos_token_id=*/-1, /*eos_token_id=*/-1, /*pad_token_id=*/-1, /*sep_token_id=*/-1, - /*extra_eos_token_ids=*/{}); + ModelConfig config(ModelType::CHATGLM, GGML_TYPE_F32, /*vocab_size=*/5, /*hidden_size=*/32, + /*num_attention_heads=*/8, /*num_key_value_heads=*/8, /*num_hidden_layers=*/1, + /*intermediate_size=*/128, /*norm_eps=*/1e-5f, /*rope_theta=*/10000.f, /*num_virtual_tokens=*/5, + /*max_length=*/8, /*bos_token_id=*/-1, /*eos_token_id=*/-1, /*pad_token_id=*/-1, + /*sep_token_id=*/-1, /*boi_token_id=*/-1, /*eoi_token_id=*/-1, /*extra_eos_token_ids=*/{}, + /*vision=*/{}); constexpr int seq_len = 3; @@ -662,13 +661,12 @@ TEST_F(ChatGLMTest, GLMPTuningV2Model) { TEST_F(ChatGLMTest, GLM2Model) { fs::path data_path = fs::path(__FILE__).parent_path() / "tests/data/glm2_model.data"; - ModelConfig config( - ModelType::CHATGLM2, GGML_TYPE_F32, /*vocab_size=*/5, /*hidden_size=*/32, /*num_attention_heads=*/8, - /*num_key_value_heads=*/2, /*num_hidden_layers=*/1, /*intermediate_size=*/48, /*norm_eps=*/1e-5f, - /*rope_theta=*/10000.f, - /*num_virtual_tokens=*/0, - /*max_length=*/8, /*bos_token_id=*/-1, /*eos_token_id=*/-1, /*pad_token_id=*/-1, /*sep_token_id=*/-1, - /*extra_eos_token_ids=*/{}); + ModelConfig config(ModelType::CHATGLM2, GGML_TYPE_F32, /*vocab_size=*/5, /*hidden_size=*/32, + /*num_attention_heads=*/8, /*num_key_value_heads=*/2, /*num_hidden_layers=*/1, + /*intermediate_size=*/48, /*norm_eps=*/1e-5f, /*rope_theta=*/10000.f, /*num_virtual_tokens=*/0, + /*max_length=*/8, /*bos_token_id=*/-1, /*eos_token_id=*/-1, /*pad_token_id=*/-1, + /*sep_token_id=*/-1, /*boi_token_id=*/-1, /*eoi_token_id=*/-1, /*extra_eos_token_ids=*/{}, + /*vision=*/{}); constexpr int seq_len = 3; @@ -691,13 +689,12 @@ TEST_F(ChatGLMTest, GLM2Model) { TEST_F(ChatGLMTest, GLM3Model) { fs::path data_path = fs::path(__FILE__).parent_path() / "tests/data/glm3_model.data"; - ModelConfig config( - ModelType::CHATGLM3, GGML_TYPE_F32, /*vocab_size=*/5, /*hidden_size=*/32, /*num_attention_heads=*/8, - /*num_key_value_heads=*/2, /*num_hidden_layers=*/1, /*intermediate_size=*/48, /*norm_eps=*/1e-5f, - /*rope_theta=*/10000.f, - /*num_virtual_tokens=*/0, - /*max_length=*/8, /*bos_token_id=*/-1, /*eos_token_id=*/-1, /*pad_token_id=*/-1, /*sep_token_id=*/-1, - /*extra_eos_token_ids=*/{}); + ModelConfig config(ModelType::CHATGLM3, GGML_TYPE_F32, /*vocab_size=*/5, /*hidden_size=*/32, + /*num_attention_heads=*/8, /*num_key_value_heads=*/2, /*num_hidden_layers=*/1, + /*intermediate_size=*/48, /*norm_eps=*/1e-5f, /*rope_theta=*/10000.f, /*num_virtual_tokens=*/0, + /*max_length=*/8, /*bos_token_id=*/-1, /*eos_token_id=*/-1, /*pad_token_id=*/-1, + /*sep_token_id=*/-1, /*boi_token_id=*/-1, /*eoi_token_id=*/-1, /*extra_eos_token_ids=*/{}, + /*vision=*/{}); constexpr int seq_len = 3; @@ -720,13 +717,12 @@ TEST_F(ChatGLMTest, GLM3Model) { TEST_F(ChatGLMTest, GLM3PTuningV2Model) { fs::path data_path = fs::path(__FILE__).parent_path() / "tests/data/glm3_ptuning_v2_model.data"; - ModelConfig config( - ModelType::CHATGLM3, GGML_TYPE_F32, /*vocab_size=*/5, /*hidden_size=*/32, /*num_attention_heads=*/8, - /*num_key_value_heads=*/2, /*num_hidden_layers=*/1, /*intermediate_size=*/48, /*norm_eps=*/1e-5f, - /*rope_theta=*/10000.f, - /*num_virtual_tokens=*/5, - /*max_length=*/8, /*bos_token_id=*/-1, /*eos_token_id=*/-1, /*pad_token_id=*/-1, /*sep_token_id=*/-1, - /*extra_eos_token_ids=*/{}); + ModelConfig config(ModelType::CHATGLM3, GGML_TYPE_F32, /*vocab_size=*/5, /*hidden_size=*/32, + /*num_attention_heads=*/8, /*num_key_value_heads=*/2, /*num_hidden_layers=*/1, + /*intermediate_size=*/48, /*norm_eps=*/1e-5f, /*rope_theta=*/10000.f, /*num_virtual_tokens=*/5, + /*max_length=*/8, /*bos_token_id=*/-1, /*eos_token_id=*/-1, /*pad_token_id=*/-1, + /*sep_token_id=*/-1, /*boi_token_id=*/-1, /*eoi_token_id=*/-1, /*extra_eos_token_ids=*/{}, + /*vision=*/{}); constexpr int seq_len = 3; @@ -749,13 +745,12 @@ TEST_F(ChatGLMTest, GLM3PTuningV2Model) { TEST_F(ChatGLMTest, GLM4Model) { fs::path data_path = fs::path(__FILE__).parent_path() / "tests/data/glm4_model.data"; - ModelConfig config( - ModelType::CHATGLM4, GGML_TYPE_F32, /*vocab_size=*/5, /*hidden_size=*/32, /*num_attention_heads=*/8, - /*num_key_value_heads=*/2, /*num_hidden_layers=*/1, /*intermediate_size=*/48, /*norm_eps=*/1e-5f, - /*rope_theta=*/10000.f, - /*num_virtual_tokens=*/0, - /*max_length=*/8, /*bos_token_id=*/-1, /*eos_token_id=*/-1, /*pad_token_id=*/-1, /*sep_token_id=*/-1, - /*extra_eos_token_ids=*/{}); + ModelConfig config(ModelType::CHATGLM4, GGML_TYPE_F32, /*vocab_size=*/5, /*hidden_size=*/32, + /*num_attention_heads=*/8, /*num_key_value_heads=*/2, /*num_hidden_layers=*/1, + /*intermediate_size=*/48, /*norm_eps=*/1e-5f, /*rope_theta=*/10000.f, /*num_virtual_tokens=*/0, + /*max_length=*/8, /*bos_token_id=*/-1, /*eos_token_id=*/-1, /*pad_token_id=*/-1, + /*sep_token_id=*/-1, /*boi_token_id=*/-1, /*eoi_token_id=*/-1, /*extra_eos_token_ids=*/{}, + /*vision=*/{}); constexpr int seq_len = 3; @@ -775,6 +770,175 @@ TEST_F(ChatGLMTest, GLM4Model) { test_model(model, config, data_path, seq_len, all_weights); } +TEST_F(ChatGLMTest, GLM4VModelText) { + fs::path data_path = fs::path(__FILE__).parent_path() / "tests/data/glm4v_model_text.data"; + + VisionModelConfig vision(GGML_TYPE_F32, ActivationType::GELU, 32, 28, 3, 56, 1e-6, 2, 1, 17, 7, 8); + + ModelConfig config(ModelType::CHATGLM4V, GGML_TYPE_F32, /*vocab_size=*/8, /*hidden_size=*/32, + /*num_attention_heads=*/8, /*num_key_value_heads=*/2, /*num_hidden_layers=*/1, + /*intermediate_size=*/48, /*norm_eps=*/0.00000015625f, /*rope_theta=*/10000.f, + /*num_virtual_tokens=*/0, + /*max_length=*/16, /*bos_token_id=*/-1, /*eos_token_id=*/4, /*pad_token_id=*/-1, + /*sep_token_id=*/-1, /*boi_token_id=*/2, /*eoi_token_id=*/4, /*extra_eos_token_ids=*/{}, + /*vision=*/vision); + + constexpr int seq_len = 3; + + ChatGLM4VModel model(mctx_.get(), config); + + std::vector all_weights{model.word_embeddings.weight, + model.layers[0].input_layernorm.weight, + model.layers[0].attention.query_key_value.weight, + model.layers[0].attention.query_key_value.bias, + model.layers[0].attention.dense.weight, + model.layers[0].post_attention_layernorm.weight, + model.layers[0].mlp.gate_proj.weight, + model.layers[0].mlp.up_proj.weight, + model.layers[0].mlp.down_proj.weight, + model.final_layernorm.weight}; + + test_model(model, config, data_path, seq_len, all_weights); +} + +TEST_F(ChatGLMTest, GLM4VModel) { + fs::path data_path = fs::path(__FILE__).parent_path() / "tests/data/glm4v_model.data"; + + VisionModelConfig vision(GGML_TYPE_F32, ActivationType::GELU, 32, 28, 3, 56, 1e-6, 2, 1, 17, 7, 8); + + ModelConfig config(ModelType::CHATGLM4V, GGML_TYPE_F32, /*vocab_size=*/8, /*hidden_size=*/32, + /*num_attention_heads=*/8, /*num_key_value_heads=*/2, /*num_hidden_layers=*/1, + /*intermediate_size=*/48, /*norm_eps=*/0.00000015625f, /*rope_theta=*/10000.f, + /*num_virtual_tokens=*/0, + /*max_length=*/16, /*bos_token_id=*/-1, /*eos_token_id=*/4, /*pad_token_id=*/-1, + /*sep_token_id=*/-1, /*boi_token_id=*/2, /*eoi_token_id=*/4, /*extra_eos_token_ids=*/{}, + /*vision=*/vision); + + std::ifstream fin(data_path, std::ios::binary); + ASSERT_TRUE(fin) << "cannot open file " << data_path; + + ChatGLM4VModel model(mctx_.get(), config); + + ggml_tensor *images = ggml_new_tensor_3d(mctx_->ctx_b.get(), GGML_TYPE_F32, config.vision.image_size, + config.vision.image_size, config.vision.in_channels); + + const int vision_tokens = model.num_vision_tokens(); + + const int seq_len = 6; + ggml_tensor *x1 = ggml_new_tensor_1d(mctx_->ctx_b.get(), GGML_TYPE_I32, seq_len); + ggml_tensor *ref_y1 = + ggml_new_tensor_2d(mctx_->ctx_b.get(), GGML_TYPE_F32, config.hidden_size, seq_len - 1 + vision_tokens); + ggml_tensor *x2 = ggml_new_tensor_1d(mctx_->ctx_b.get(), GGML_TYPE_I32, 1); + ggml_tensor *ref_y2 = ggml_new_tensor_1d(mctx_->ctx_b.get(), GGML_TYPE_F32, config.hidden_size); + ggml_tensor *x3 = ggml_new_tensor_1d(mctx_->ctx_b.get(), GGML_TYPE_I32, 1); + ggml_tensor *ref_y3 = ggml_new_tensor_1d(mctx_->ctx_b.get(), GGML_TYPE_F32, config.hidden_size); + + auto buf_b = unique_ggml_backend_buffer_t(ggml_backend_alloc_ctx_tensors(mctx_->ctx_b.get(), mctx_->backend.get())); + auto buf_w = unique_ggml_backend_buffer_t(ggml_backend_alloc_ctx_tensors(mctx_->ctx_w.get(), mctx_->backend.get())); + + std::vector all_tensors{ + // vision + model.vision.patch_embedding.cls_embedding, + model.vision.patch_embedding.proj.weight, + model.vision.patch_embedding.proj.bias, + model.vision.patch_embedding.position_embedding.weight, + model.vision.transformer.layers[0].input_layernorm.weight, + model.vision.transformer.layers[0].input_layernorm.bias, + model.vision.transformer.layers[0].attention.query_key_value.weight, + model.vision.transformer.layers[0].attention.query_key_value.bias, + model.vision.transformer.layers[0].attention.dense.weight, + model.vision.transformer.layers[0].attention.dense.bias, + model.vision.transformer.layers[0].mlp.dense_h_to_4h.weight, + model.vision.transformer.layers[0].mlp.dense_h_to_4h.bias, + model.vision.transformer.layers[0].mlp.dense_4h_to_h.weight, + model.vision.transformer.layers[0].mlp.dense_4h_to_h.bias, + model.vision.transformer.layers[0].post_attention_layernorm.weight, + model.vision.transformer.layers[0].post_attention_layernorm.bias, + model.vision.conv.weight, + model.vision.conv.bias, + model.vision.linear_proj.weight, + model.vision.norm1.weight, + model.vision.norm1.bias, + model.vision.glu.gate_proj.weight, + model.vision.glu.up_proj.weight, + model.vision.glu.down_proj.weight, + model.vision.boi, + model.vision.eoi, + // text + model.word_embeddings.weight, + model.layers[0].input_layernorm.weight, + model.layers[0].attention.query_key_value.weight, + model.layers[0].attention.query_key_value.bias, + model.layers[0].attention.dense.weight, + model.layers[0].post_attention_layernorm.weight, + model.layers[0].mlp.gate_proj.weight, + model.layers[0].mlp.up_proj.weight, + model.layers[0].mlp.down_proj.weight, + model.final_layernorm.weight, + // inputs & outputs + images, + x1, + ref_y1, + x2, + ref_y2, + x3, + ref_y3, + }; + + for (ggml_tensor *tensor : all_tensors) { + read_backend_tensor_data(fin, tensor); + } + ASSERT_TRUE(fin.peek() == EOF); + + auto input_ids_to_vec = [](ggml_tensor *input_ids) { + std::vector input_ids_vec(ggml_nelements(input_ids)); + ggml_backend_tensor_get(input_ids, input_ids_vec.data(), 0, ggml_nbytes(input_ids)); + return input_ids_vec; + }; + + std::vector input_ids; + + // prefill + { + std::vector x1_vec = input_ids_to_vec(x1); + input_ids.insert(input_ids.end(), x1_vec.begin(), x1_vec.end()); + ggml_graph_clear(mctx_->gf); + ggml_tensor *out_y1 = model.forward(mctx_.get(), x1, images, input_ids, 0); + ggml_build_forward_expand(mctx_->gf, out_y1); + CHATGLM_CHECK(ggml_gallocr_alloc_graph(mctx_->allocr.get(), mctx_->gf)); + model.set_graph_inputs(mctx_->gf, input_ids, std::nullopt, 0, ref_y1->ne[1]); + CHATGLM_CHECK(ggml_backend_graph_compute(mctx_->backend.get(), mctx_->gf) == GGML_STATUS_SUCCESS); + + expect_all_close(ref_y1, out_y1, 1e-2); + } + // decode + { + std::vector x2_vec = input_ids_to_vec(x2); + input_ids.insert(input_ids.end(), x2_vec.begin(), x2_vec.end()); + ggml_graph_clear(mctx_->gf); + ggml_tensor *out_y2 = + model.forward(mctx_.get(), x2, images, input_ids, seq_len - 1 + model.num_vision_tokens()); + ggml_build_forward_expand(mctx_->gf, out_y2); + CHATGLM_CHECK(ggml_gallocr_alloc_graph(mctx_->allocr.get(), mctx_->gf)); + model.set_graph_inputs(mctx_->gf, input_ids, std::nullopt, seq_len, seq_len); + CHATGLM_CHECK(ggml_backend_graph_compute(mctx_->backend.get(), mctx_->gf) == GGML_STATUS_SUCCESS); + + expect_all_close(ref_y2, out_y2, 1e-2); + } + { + std::vector x3_vec = input_ids_to_vec(x3); + input_ids.insert(input_ids.end(), x3_vec.begin(), x3_vec.end()); + ggml_graph_clear(mctx_->gf); + ggml_tensor *out_y3 = model.forward(mctx_.get(), x3, images, input_ids, seq_len + model.num_vision_tokens()); + ggml_build_forward_expand(mctx_->gf, out_y3); + CHATGLM_CHECK(ggml_gallocr_alloc_graph(mctx_->allocr.get(), mctx_->gf)); + model.set_graph_inputs(mctx_->gf, input_ids, std::nullopt, seq_len + 1, seq_len); + CHATGLM_CHECK(ggml_backend_graph_compute(mctx_->backend.get(), mctx_->gf) == GGML_STATUS_SUCCESS); + + expect_all_close(ref_y3, out_y3, 1e-2); + } +} + TEST_F(ChatGLMTest, quantize) { const float src_data[]{ -1.1258e+00, -1.1524e+00, -2.5058e-01, -4.3388e-01, 8.4871e-01, 6.9201e-01, -3.1601e-01, -2.1152e+00, @@ -996,21 +1160,6 @@ TEST(Pipeline, ChatGLM) { "ChatGLM-6B，很高兴见到你，欢迎问我任何问题。\n[Round 1]\n问：晚上睡不着应该怎么办\n答："); } - // memory test - { - GenerationConfig gen_config; - gen_config.max_length = 2048; - gen_config.max_context_length = gen_config.max_length - 1; - gen_config.do_sample = false; - - std::ostringstream oss; - for (int i = 0; i < gen_config.max_context_length; i++) { - oss << "你好"; - } - std::vector messages{{ChatMessage::ROLE_USER, oss.str()}}; - pipeline.chat(messages, gen_config); - } - // chat { check_chat_format(pipeline); @@ -1063,21 +1212,6 @@ TEST(Pipeline, ChatGLM2) { "ChatGLM2-6B，很高兴见到你，欢迎问我任何问题。\n\n[Round 2]\n\n问：晚上睡不着应该怎么办\n\n答："); } - // memory test - { - GenerationConfig gen_config; - gen_config.max_length = 2048; - gen_config.max_context_length = gen_config.max_length - 1; - gen_config.do_sample = false; - - std::ostringstream oss; - for (int i = 0; i < gen_config.max_context_length; i++) { - oss << "你好"; - } - std::vector messages{{ChatMessage::ROLE_USER, oss.str()}}; - pipeline.chat(messages, gen_config); - } - // chat { check_chat_format(pipeline); @@ -1163,21 +1297,6 @@ TEST(Pipeline, ChatGLM3) { EXPECT_EQ(input_ids, target_ids); } - // memory test - { - GenerationConfig gen_config; - gen_config.max_length = 2048; - gen_config.max_context_length = gen_config.max_length - 1; - gen_config.do_sample = false; - - std::ostringstream oss; - for (int i = 0; i < gen_config.max_context_length; i++) { - oss << "你好"; - } - std::vector messages{{ChatMessage::ROLE_USER, oss.str()}}; - pipeline.chat(messages, gen_config); - } - // chat { // check_chat_format(pipeline); @@ -1218,31 +1337,41 @@ TEST(Pipeline, ChatGLM3) { gen_config.do_sample = false; std::vector messages{ {ChatMessage::ROLE_SYSTEM, system_ci}, - {ChatMessage::ROLE_USER, "列出100以内的所有质数"}, + {ChatMessage::ROLE_USER, "找出100以内的所有质数"}, }; { ChatMessage output = pipeline.chat(messages, gen_config); EXPECT_EQ(output.role, ChatMessage::ROLE_ASSISTANT); - EXPECT_EQ(output.content, R"(好的，我会为您列出100以内的所有质数。 - -(Note: 质数是指只能被1和它本身整除的正整数。))"); - EXPECT_EQ(output.tool_calls.front().code.input, R"(```python -def is_prime(n): - """Check if a number is prime.""" - if n <= 1: - return False - if n <= 3: - return True - if n % 2 == 0 or n % 3 == 0: - return False - i = 5 - while i * i <= n: - if n % i == 0 or n % (i + 2) == 0: - return False - i += 6 - return True - -primes_upto_100 = [i for i in range(2, 101) if is_prime(i)] + EXPECT_EQ(output.content, + R"(质数是只能被1和它本身整除的正整数。我们可以通过简单的算法来找出100以内的所有质数。 + +这里我们将使用一个简单的线性筛法来找出100以内的所有质数。 + +线性筛法的基本思想是： +1. 创建一个列表，其中包含1到n的所有整数。 +2. 从列表中删除所有可以被2整除的数。 +3. 然后从剩余的数中删除所有可以被3整除的数。 +4. 重复上述步骤，直到列表中的数少于100为止。 + +让我们开始计算。)"); + EXPECT_EQ(output.tool_calls.at(0).code.input, R"(```python +def sieve_of_eratosthenes(n): + # Create a boolean array "prime[0..n]" and initialize all entries as true. + # A value in prime[i] will finally be false if i is Not a prime, else true bool val. + prime = [True for _ in range(n+1)] + p = 2 + while p**2 <= n: + # If prime[p] is not changed, then it is a prime + if prime[p]: + # Update all multiples of p + for i in range(p**2, n+1, p): + prime[i] = False + p += 1 + + # Return the list of prime numbers + return [p for p in range(2, n+1) if prime[p]] + +primes_upto_100 = sieve_of_eratosthenes(100) primes_upto_100 ```)"); messages.emplace_back(std::move(output)); @@ -1253,9 +1382,9 @@ primes_upto_100 { ChatMessage output = pipeline.chat(messages, gen_config); EXPECT_EQ(output.role, ChatMessage::ROLE_ASSISTANT); - EXPECT_EQ(output.content, R"(100以内的所有质数为： - -$$2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41, 43, 47, 53, 59, 61, 67, 71, 73, 79, 83, 89, 97$$)"); + EXPECT_EQ( + output.content, + R"(100以内的所有质数是：2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41, 43, 47, 53, 59, 61, 67, 71, 73, 79, 83, 89, 97。)"); } } } @@ -1378,21 +1507,6 @@ if __name__ == '__main__': // EXPECT_EQ(input_ids, target_ids); // } - // memory test - { - GenerationConfig gen_config; - gen_config.max_length = 2048; - gen_config.max_context_length = gen_config.max_length - 1; - gen_config.do_sample = false; - - std::ostringstream oss; - for (int i = 0; i < gen_config.max_context_length; i++) { - oss << "你好 "; - } - std::vector messages{{ChatMessage::ROLE_USER, oss.str()}}; - pipeline.chat(messages, gen_config); - } - // chat { // check_chat_format(pipeline); @@ -1400,7 +1514,7 @@ if __name__ == '__main__': gen_config.do_sample = false; std::vector messages{{ChatMessage::ROLE_USER, "你好"}}; ChatMessage output = pipeline.chat(messages, gen_config); - EXPECT_EQ(output.content, "你好👋！我是人工智能助手，很高兴见到你，有什么可以帮助你的吗？"); + EXPECT_EQ(output.content, "你好👋！很高兴能帮助你，有什么问题或者需要帮助的地方吗？"); } } @@ -1445,6 +1559,48 @@ print(bubble_sort([5, 4, 3, 2, 1])))"; } } +TEST(Pipeline, ChatGLM4V) { + fs::path model_path = fs::path(__FILE__).parent_path() / "models/chatglm4v-ggml.bin"; + if (!fs::exists(model_path)) { + GTEST_SKIP() << "Skipping ChatGLM4V e2e test (ggml model not found)"; + } + Pipeline pipeline(model_path.string()); + ASSERT_TRUE(dynamic_cast(pipeline.tokenizer.get())); + ASSERT_TRUE(dynamic_cast(pipeline.model.get())); + + // tokenizer + { + Image image = Image::open(fs::path(__FILE__).parent_path() / "examples/03-Confusing-Pictures.jpg"); + std::vector messages{{ChatMessage::ROLE_USER, "描述这张图片", image}}; + std::vector target_ids{151331, 151333, 151336, 198, 151339, 151329, + 151340, 100395, 108627, 100736, 151337}; + std::vector input_ids = pipeline.tokenizer->apply_chat_template(messages, 2048); + EXPECT_EQ(input_ids, target_ids); + } + // chat + { + // check_chat_format(pipeline); + GenerationConfig gen_config; + gen_config.do_sample = false; + std::vector messages{{ChatMessage::ROLE_USER, "你好"}}; + ChatMessage output = pipeline.chat(messages, gen_config); + EXPECT_EQ(output.content, "你好👋！很高兴见到你，欢迎问我任何问题。"); + } + // chat with image + { + GenerationConfig gen_config; + gen_config.do_sample = false; + fs::path image_path = fs::path(__FILE__).parent_path() / "examples/03-Confusing-Pictures.jpg"; + Image image = Image::open(image_path.string()); + std::vector messages{{ChatMessage::ROLE_USER, "描述这张图片", std::move(image)}}; + ChatMessage output = pipeline.chat(messages, gen_config); + EXPECT_EQ(output.content, + "这张图片的幽默之处在于场景的荒谬性。看到一个人在出租车后面熨衣服是非常不寻常的，因为这不是出租车通常" + "被用来做的事情。熨衣板和衣服悬挂在车尾，男子站在上面，似乎在专心熨烫衣物。出租车在行驶中，而男子却稳" + "稳地站在上面熨衣服，这样的场景给人一种不稳定的感觉，增加了喜剧效果。"); + } +} + static void run_benchmark(const fs::path &model_path) { if (!fs::exists(model_path)) { GTEST_SKIP() << "Skipping benchmark test (model " << model_path << " not found)"; diff --git a/examples/03-Confusing-Pictures.jpg b/examples/03-Confusing-Pictures.jpg new file mode 100644 index 0000000000000000000000000000000000000000..f3bb120c6a7b60646c4b5c46da881eb22e879798 GIT binary patch literal 46296 zcmbSyWl$VZ*XH0Z!QEXFBshcs!QGwUHn_V7%OC>*g1bAx-3OP!HMj?pxirZuQfr``qW8e)?_kZ3BQUCnYNdfO$7I7&ZXlZ4)3T>1AUH04OQ~SO5S3 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not None: + import numpy as np + from PIL import Image + + image = chatglm_cpp.Image(np.asarray(Image.open(args.image).convert("RGB"))) + if not args.interactive: if args.mode == "chat": - messages.append(chatglm_cpp.ChatMessage(role="user", content=prompt)) + messages.append(chatglm_cpp.ChatMessage(role="user", content=prompt, image=image)) for chunk in pipeline.chat(messages, **generation_kwargs): print(chunk.content, sep="", end="", flush=True) else: @@ -107,6 +115,7 @@ def main() -> None: if system: print(f"{'System':{prompt_width}} > {system}") + prompt_image = image while True: if messages and messages[-1].tool_calls: (tool_call,) = messages[-1].tool_calls @@ -135,10 +144,12 @@ def main() -> None: if prompt == "stop": break if prompt == "clear": - messages = system_messages + messages = system_messages.copy() + prompt_image = image continue - messages.append(chatglm_cpp.ChatMessage(role=role, content=prompt)) + messages.append(chatglm_cpp.ChatMessage(role=role, content=prompt, image=prompt_image)) + prompt_image = None print(f"{pipeline.model.config.model_type_name} > ", sep="", end="") chunks = [] for chunk in pipeline.chat(messages, **generation_kwargs): diff --git a/examples/hmmm.jpg b/examples/hmmm.jpg new file mode 100644 index 0000000000000000000000000000000000000000..069cb5e7e17abecbb27560de96d56fa7a0f37a9a GIT binary patch literal 144082 zcmcG#cT`i+vM?S*1r$L*ngo!p5a}&cl@g^&6OhnLL_i2dYCr@9q)YEgZ-RjID!nGs zrI!GqNC}|^0>60gd+)yY$9>=WeQSLu>txM2v(MhMXZFnAduI0K@5@ENZ8g#_|%Lw*%} zRf?2^6L5uwgp`KlvJ(I#(s_;KFS-ADT?LR_A-#5;jGTxyxJ?4Ma)pGH_~OdVD-<`b z+$7=vG*_?Da$Fao<9tkZN5>K_TFJ=uMAyJQFgAB&nf@z-n52@P{^yFH+~UesHm>jE zvX^+Q-GbUBRC3U~cEKLRYVQ&o{Kxg5Z4!yNLVESubs~8$JcbkE~f#MMC=tBQW}6FU?m`^+#oq^J|%d8a}{$;*S|oNcUK(!;=<3#;@0fePJ;_S^M8TI z=~}{GLVF&Yt?T7j`hYC<6PZK91M?GN6R6DH&+-*PKmSF<|D@;tF)FL-pKK^UC-$A# z)?c<<0{+F9|A0?-Uv`yR?3piajRy>yFY{jl6omd8>^o86z$C}=k6sy`d}>97{|n5- zh5sK=vWMFL!HdwdzkDIq@Ru(;=;WV^mwc)#?*$vqQGWe`s@Z@*f5BOfTmBb_X+q0(KXv$d3HYxu^gqPMi?v>#{9lIC|BkhK 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+parser.add_argument("--api_key", default="Bearer chatglm-cpp-example", type=str) +parser.add_argument("--base_url", default=None, type=str) parser.add_argument("--stream", action="store_true") parser.add_argument("--prompt", default="你好", type=str) parser.add_argument("--tool_call", action="store_true") +parser.add_argument("--image", default=None, type=str) args = parser.parse_args() -client = OpenAI() +client = OpenAI(api_key=args.api_key, base_url=args.base_url) tools = None if args.tool_call: @@ -33,7 +38,16 @@ } ] -messages = [{"role": "user", "content": args.prompt}] +if args.image is not None: + image_url = args.image + if not image_url.startswith(("http://", "https://")): + base64_image = base64.b64encode(Path(image_url).read_bytes()).decode() + image_url = f"data:image/jpeg;base64,{base64_image}" + user_content = [{"type": "text", "text": args.prompt}, {"type": "image_url", "image_url": {"url": image_url}}] +else: + user_content = args.prompt + +messages = [{"role": "user", "content": user_content}] if args.stream: response = client.chat.completions.create(model="default-model", messages=messages, stream=True, tools=tools) for chunk in response: diff --git a/main.cpp b/main.cpp index 037f8340..bff31e5a 100644 --- a/main.cpp +++ b/main.cpp @@ -27,6 +27,7 @@ struct Args { bool sync = false; std::string prompt = "你好"; std::string system = ""; + std::string image_path = ""; int max_length = 2048; int max_new_tokens = -1; int max_context_length = 512; @@ -50,6 +51,7 @@ static void usage(const std::string &prog) { --pp, --prompt_path path to the plain text file that stores the prompt -s, --system SYSTEM system message to set the behavior of the assistant --sp, --system_path path to the plain text file that stores the system message + --image path to the input image for visual language models -i, --interactive run in interactive mode -l, --max_length N max total length including prompt and output (default: 2048) --max_new_tokens N max number of tokens to generate, ignoring the number of prompt tokens @@ -94,6 +96,8 @@ static Args parse_args(const std::vector &argv) { args.system = argv.at(++i); } else if (arg == "--sp" || arg == "--system_path") { args.system = read_text(argv.at(++i)); + } else if (arg == "--image") { + args.image_path = argv.at(++i); } else if (arg == "-i" || arg == "--interactive") { args.interactive = true; } else if (arg == "-l" || arg == "--max_length") { @@ -219,6 +223,16 @@ static void chat(Args &args) { args.interactive = false; } + if (!args.image_path.empty() && pipeline.model->config.model_type != chatglm::ModelType::CHATGLM4V) { + std::cerr << "image is specified for model without visual ability, falling back to language mode\n"; + args.image_path.clear(); + } + + std::optional image; + if (!args.image_path.empty()) { + image = chatglm::Image::open(args.image_path); + } + std::vector system_messages; if (!args.system.empty()) { system_messages.emplace_back(chatglm::ChatMessage::ROLE_SYSTEM, args.system); @@ -242,7 +256,8 @@ static void chat(Args &args) { std::cout << std::setw(model_name.size()) << std::left << "System" << " > " << args.system << std::endl; } - while (1) { + auto prompt_image = image; + while (true) { std::string role; if (!messages.empty() && !messages.back().tool_calls.empty()) { const auto &tool_call = messages.back().tool_calls.front(); @@ -273,9 +288,11 @@ static void chat(Args &args) { } if (prompt == "clear") { messages = system_messages; + prompt_image = image; continue; } - messages.emplace_back(std::move(role), std::move(prompt)); + messages.emplace_back(std::move(role), std::move(prompt), std::move(prompt_image)); + prompt_image.reset(); std::cout << model_name << " > "; chatglm::ChatMessage output = pipeline.chat(messages, gen_config, streamer.get()); if (args.sync) { @@ -291,7 +308,7 @@ static void chat(Args &args) { } else { if (args.mode == INFERENCE_MODE_CHAT) { std::vector messages = system_messages; - 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zqW3D?%Ce#+;fpCpz@#dUz6uOvJZv-U6g8sMU_LBY$jSn{(`jAOb+U8@(tsHrGy-R# z`YQpp|Dj#Jf6Q1Ca`*< input_ids(corpus_ids.begin() + begin, corpus_ids.begin() + end); - ggml_tensor *lm_logits = pipeline.model->forward_graph_compute(input_ids, 0, 0, false); + ggml_tensor *lm_logits = pipeline.model->forward_graph_compute(input_ids, std::nullopt, 0, 0, false); const auto clk_fwd = std::chrono::system_clock::now(); diff --git a/tests/test_chatglm_cpp.py b/tests/test_chatglm_cpp.py index a490740d..3749daa8 100644 --- a/tests/test_chatglm_cpp.py +++ b/tests/test_chatglm_cpp.py @@ -1,7 +1,10 @@ +import base64 from pathlib import Path import chatglm_cpp +import numpy as np import pytest +from PIL import Image PROJECT_ROOT = Path(__file__).resolve().parent.parent @@ -9,6 +12,7 @@ CHATGLM2_MODEL_PATH = PROJECT_ROOT / "models/chatglm2-ggml.bin" CHATGLM3_MODEL_PATH = PROJECT_ROOT / "models/chatglm3-ggml.bin" CHATGLM4_MODEL_PATH = PROJECT_ROOT / "models/chatglm4-ggml.bin" +CHATGLM4V_MODEL_PATH = PROJECT_ROOT / "models/chatglm4v-ggml.bin" CODEGEEX2_MODEL_PATH = PROJECT_ROOT / "models/codegeex2-ggml.bin" @@ -16,8 +20,8 @@ def test_chatglm_version(): print(chatglm_cpp.__version__) -def check_pipeline(model_path, prompt, target, gen_kwargs={}): - messages = [chatglm_cpp.ChatMessage(role="user", content=prompt)] +def check_pipeline(model_path, prompt, target, gen_kwargs={}, image=None): + messages = [chatglm_cpp.ChatMessage(role="user", content=prompt, image=image)] pipeline = chatglm_cpp.Pipeline(model_path) output = pipeline.chat(messages, do_sample=False, **gen_kwargs).content @@ -76,7 +80,26 @@ def test_chatglm4_pipeline(): check_pipeline( model_path=CHATGLM4_MODEL_PATH, prompt="你好", - target="你好👋！我是人工智能助手，很高兴见到你，有什么可以帮助你的吗？", + target="你好👋！很高兴能帮助你，有什么问题或者需要帮助的地方吗？", + ) + + +@pytest.mark.skipif(not CHATGLM4V_MODEL_PATH.exists(), reason="model file not found") +def test_chatglm4v_pipeline(): + check_pipeline( + model_path=CHATGLM4V_MODEL_PATH, + prompt="你好", + target="你好👋！很高兴见到你，欢迎问我任何问题。", + ) + + image = chatglm_cpp.Image( + np.asarray(Image.open(PROJECT_ROOT / "examples/03-Confusing-Pictures.jpg").convert("RGB")) + ) + check_pipeline( + model_path=CHATGLM4V_MODEL_PATH, + prompt="这张图片有什么不寻常之处", + image=image, + target="这张图片中不寻常的是，一个男人站在一辆黄色SUV的后备箱上，正在使用一个铁板熨烫衣物。通常情况下，熨衣是在室内进行的，使用的是家用电熨斗，而不是在户外使用汽车后备箱作为工作台。此外，他似乎是在一个繁忙的城市街道上，周围有行驶的车辆和建筑物，这增加了场景的异想天开性。", ) @@ -117,7 +140,7 @@ def test_langchain_api(): client = TestClient(app) response = client.post("/", json={"prompt": "你好", "temperature": 0}) assert response.status_code == 200 - assert response.json()["response"] == "你好👋！我是人工智能助手，很高兴见到你，有什么可以帮助你的吗？" + assert response.json()["response"] == "你好👋！很高兴能帮助你，有什么问题或者需要帮助的地方吗？" @pytest.mark.skipif(not CHATGLM4_MODEL_PATH.exists(), reason="model file not found") @@ -137,4 +160,67 @@ def test_openai_api(): assert response.status_code == 200 response_message = response.json()["choices"][0]["message"] assert response_message["role"] == "assistant" - assert response_message["content"] == "你好👋！我是人工智能助手，很高兴见到你，有什么可以帮助你的吗？" + assert response_message["content"] == "你好👋！很高兴能帮助你，有什么问题或者需要帮助的地方吗？" + + +@pytest.mark.skipif(not CHATGLM4V_MODEL_PATH.exists(), reason="model file not found") +def test_openai_api_vision(): + import os + from unittest.mock import patch + + from fastapi.testclient import TestClient + + with patch.dict(os.environ, {"MODEL": str(CHATGLM4V_MODEL_PATH)}): + from chatglm_cpp.openai_api import app + + client = TestClient(app) + + # request with image url + payload = { + "messages": [ + { + "role": "user", + "content": [ + {"type": "text", "text": "这张图片有什么不寻常之处"}, + { + "type": "image_url", + "image_url": { + "url": "https://www.barnorama.com/wp-content/uploads/2016/12/03-Confusing-Pictures.jpg" + }, + }, + ], + } + ], + "temperature": 0, + } + response = client.post("/v1/chat/completions", json=payload) + assert response.status_code == 200 + response_message = response.json()["choices"][0]["message"] + assert response_message["role"] == "assistant" + assert ( + response_message["content"] + == "这张图片中不寻常的是，一个男人站在一辆黄色SUV的后备箱上，正在使用一个铁板熨烫衣物。通常情况下，熨衣是在室内进行的，使用的是家用电熨斗，而不是在户外使用汽车后备箱作为工作台。此外，他似乎是在一个繁忙的城市街道上，周围有行驶的车辆和建筑物，这增加了场景的异想天开性。" + ) + + # request with base64 image + base64_image = base64.b64encode(Path(PROJECT_ROOT / "examples/03-Confusing-Pictures.jpg").read_bytes()).decode() + payload = { + "messages": [ + { + "role": "user", + "content": [ + {"type": "text", "text": "这张图片有什么不寻常之处"}, + {"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{base64_image}"}}, + ], + } + ], + "temperature": 0, + } + response = client.post("/v1/chat/completions", json=payload) + assert response.status_code == 200 + response_message = response.json()["choices"][0]["message"] + assert response_message["role"] == "assistant" + assert ( + response_message["content"] + == "这张图片中不寻常的是，一个男人站在一辆黄色SUV的后备箱上，正在使用一个铁板熨烫衣物。通常情况下，熨衣是在室内进行的，使用的是家用电熨斗，而不是在户外使用汽车后备箱作为工作台。此外，他似乎是在一个繁忙的城市街道上，周围有行驶的车辆和建筑物，这增加了场景的异想天开性。" + ) diff --git a/tests/test_convert.py b/tests/test_convert.py index 25955999..b74a495e 100644 --- a/tests/test_convert.py +++ b/tests/test_convert.py @@ -1,4 +1,6 @@ +import re import sys +from contextlib import contextmanager from pathlib import Path import torch @@ -175,8 +177,19 @@ def test_quantize_q5_1(): assert (q_tensor == ggml_q_tensor).all() +@contextmanager +def relative_to_absolute_import(path): + code = Path(path).read_text() + fix_code = re.sub(r"(from )\.(.+ import)", r"\1\2", code) + Path(path).write_text(fix_code) + try: + yield + finally: + Path(path).write_text(code) + + CHATGLM2_MODEL_PATH = Path( - "~/.cache/huggingface/hub/models--THUDM--chatglm2-6b/snapshots/b1502f4f75c71499a3d566b14463edd62620ce9f" + "~/.cache/huggingface/hub/models--THUDM--chatglm2-6b/snapshots/7fabe56db91e085c9c027f56f1c654d137bdba40" ).expanduser() @@ -277,7 +290,8 @@ def _forward_steps(model, seq_len): ).expanduser() sys.path.append(str(CHATGLM_MODEL_PATH)) - from modeling_chatglm import ChatGLMModel + with relative_to_absolute_import(CHATGLM_MODEL_PATH / "modeling_chatglm.py"): + from modeling_chatglm import ChatGLMModel from transformers import AutoConfig config = AutoConfig.from_pretrained(CHATGLM_MODEL_PATH, trust_remote_code=True) @@ -366,33 +380,13 @@ def _forward_steps(model, seq_len): y3.data.numpy().tofile(f) -def make_data_glm2_model(): - sys.path.append(str(CHATGLM2_MODEL_PATH)) - from modeling_chatglm import ChatGLMModel - from transformers import AutoConfig - - config = AutoConfig.from_pretrained(CHATGLM2_MODEL_PATH, trust_remote_code=True) - config.hidden_size = 32 - config.num_attention_heads = 8 - config.num_layers = 1 - config.padded_vocab_size = 5 - config.multi_query_group_num = 2 - config.ffn_hidden_size = 48 - config.kv_channels = config.hidden_size // config.num_attention_heads - config.torch_dtype = torch.float32 - - m = ChatGLMModel(config).float().eval() - for param in m.parameters(): - param.data.uniform_(-0.5, 0.5) - - seq_len = 3 - +def _forward_steps(model, seq_len, **kwargs): # self attention x1 = torch.arange(seq_len, dtype=torch.int64)[None, :] position_ids = torch.arange(seq_len, dtype=torch.int64)[None, :] attn_mask = torch.ones(1, seq_len, dtype=torch.int64) with torch.no_grad(): - out = m(x1, position_ids=position_ids, attention_mask=attn_mask, use_cache=True) + out = model(x1, position_ids=position_ids, attention_mask=attn_mask, use_cache=True, **kwargs) y1 = out.last_hidden_state kv_cache = out.past_key_values @@ -401,7 +395,9 @@ def make_data_glm2_model(): position_ids = torch.tensor([[seq_len]], dtype=torch.int64) attn_mask = torch.ones(1, seq_len + 1, dtype=torch.int64) with torch.no_grad(): - out = m(x2, position_ids=position_ids, attention_mask=attn_mask, past_key_values=kv_cache, use_cache=True) + out = model( + x2, position_ids=position_ids, attention_mask=attn_mask, past_key_values=kv_cache, use_cache=True, **kwargs + ) y2 = out.last_hidden_state kv_cache = out.past_key_values @@ -410,10 +406,37 @@ def make_data_glm2_model(): position_ids = torch.tensor([[seq_len + 1]], dtype=torch.int64) attn_mask = torch.ones(1, seq_len + 2, dtype=torch.int64) with torch.no_grad(): - out = m(x3, position_ids=position_ids, attention_mask=attn_mask, past_key_values=kv_cache, use_cache=True) + out = model( + x3, position_ids=position_ids, attention_mask=attn_mask, past_key_values=kv_cache, use_cache=True, **kwargs + ) y3 = out.last_hidden_state kv_cache = out.past_key_values + return x1, y1, x2, y2, x3, y3 + + +def make_data_glm2_model(): + sys.path.append(str(CHATGLM2_MODEL_PATH)) + with relative_to_absolute_import(CHATGLM2_MODEL_PATH / "modeling_chatglm.py"): + from modeling_chatglm import ChatGLMModel + from transformers import AutoConfig + + config = AutoConfig.from_pretrained(CHATGLM2_MODEL_PATH, trust_remote_code=True) + config.hidden_size = 32 + config.num_attention_heads = 8 + config.num_layers = 1 + config.padded_vocab_size = 5 + config.multi_query_group_num = 2 + config.ffn_hidden_size = 48 + config.kv_channels = config.hidden_size // config.num_attention_heads + config.torch_dtype = torch.float32 + + m = ChatGLMModel(config).float().eval() + for param in m.parameters(): + param.data.uniform_(-0.5, 0.5) + + x1, y1, x2, y2, x3, y3 = _forward_steps(m, seq_len=3) + print(m) with open(HERE / "data/glm2_model.data", "wb") as f: @@ -437,46 +460,13 @@ def make_data_glm2_model(): def make_data_glm3_model(): - def _forward_steps(model, seq_len): - # self attention - x1 = torch.arange(seq_len, dtype=torch.int64)[None, :] - position_ids = torch.arange(seq_len, dtype=torch.int64)[None, :] - attn_mask = torch.ones(1, seq_len, dtype=torch.int64) - with torch.no_grad(): - out = model(x1, position_ids=position_ids, attention_mask=attn_mask, use_cache=True) - y1 = out.last_hidden_state - kv_cache = out.past_key_values - - # cross attention - x2 = torch.tensor([[seq_len]], dtype=torch.int64) - position_ids = torch.tensor([[seq_len]], dtype=torch.int64) - attn_mask = torch.ones(1, seq_len + 1, dtype=torch.int64) - with torch.no_grad(): - out = model( - x2, position_ids=position_ids, attention_mask=attn_mask, past_key_values=kv_cache, use_cache=True - ) - y2 = out.last_hidden_state - kv_cache = out.past_key_values - - # cross attention - x3 = torch.tensor([[seq_len + 1]], dtype=torch.int64) - position_ids = torch.tensor([[seq_len + 1]], dtype=torch.int64) - attn_mask = torch.ones(1, seq_len + 2, dtype=torch.int64) - with torch.no_grad(): - out = model( - x3, position_ids=position_ids, attention_mask=attn_mask, past_key_values=kv_cache, use_cache=True - ) - y3 = out.last_hidden_state - kv_cache = out.past_key_values - - return x1, y1, x2, y2, x3, y3 - CHATGLM3_MODEL_PATH = Path( - "~/.cache/huggingface/hub/models--THUDM--chatglm3-6b/snapshots/a5ba5501eb873d40d48bd0983bd2a8dd006bb838" + "~/.cache/huggingface/hub/models--THUDM--chatglm3-6b/snapshots/6f3b58ec10f088978ae174398f9d20b6dfc71552" ).expanduser() sys.path.append(str(CHATGLM3_MODEL_PATH)) - from modeling_chatglm import ChatGLMModel + with relative_to_absolute_import(CHATGLM3_MODEL_PATH / "modeling_chatglm.py"): + from modeling_chatglm import ChatGLMModel from transformers import AutoConfig config = AutoConfig.from_pretrained(CHATGLM3_MODEL_PATH, trust_remote_code=True) @@ -552,46 +542,13 @@ def _forward_steps(model, seq_len): def make_data_glm4_model(): - def _forward_steps(model, seq_len): - # self attention - x1 = torch.arange(seq_len, dtype=torch.int64)[None, :] - position_ids = torch.arange(seq_len, dtype=torch.int64)[None, :] - attn_mask = torch.ones(1, seq_len, dtype=torch.int64) - with torch.no_grad(): - out = model(x1, position_ids=position_ids, attention_mask=attn_mask, use_cache=True) - y1 = out.last_hidden_state - kv_cache = out.past_key_values - - # cross attention - x2 = torch.tensor([[seq_len]], dtype=torch.int64) - position_ids = torch.tensor([[seq_len]], dtype=torch.int64) - attn_mask = torch.ones(1, seq_len + 1, dtype=torch.int64) - with torch.no_grad(): - out = model( - x2, position_ids=position_ids, attention_mask=attn_mask, past_key_values=kv_cache, use_cache=True - ) - y2 = out.last_hidden_state - kv_cache = out.past_key_values - - # cross attention - x3 = torch.tensor([[seq_len + 1]], dtype=torch.int64) - position_ids = torch.tensor([[seq_len + 1]], dtype=torch.int64) - attn_mask = torch.ones(1, seq_len + 2, dtype=torch.int64) - with torch.no_grad(): - out = model( - x3, position_ids=position_ids, attention_mask=attn_mask, past_key_values=kv_cache, use_cache=True - ) - y3 = out.last_hidden_state - kv_cache = out.past_key_values - - return x1, y1, x2, y2, x3, y3 - CHATGLM4_MODEL_PATH = Path( - "~/.cache/huggingface/hub/models--THUDM--glm-4-9b-chat/snapshots/6f060dcb542d48b6bc743628634924b271545428" + "~/.cache/huggingface/hub/models--THUDM--glm-4-9b-chat/snapshots/04419001bc63e05e70991ade6da1f91c4aeec278" ).expanduser() sys.path.append(str(CHATGLM4_MODEL_PATH)) - from modeling_chatglm import ChatGLMModel + with relative_to_absolute_import(CHATGLM4_MODEL_PATH / "modeling_chatglm.py"): + from modeling_chatglm import ChatGLMModel from transformers import AutoConfig config = AutoConfig.from_pretrained(CHATGLM4_MODEL_PATH, trust_remote_code=True) @@ -633,59 +590,124 @@ def _forward_steps(model, seq_len): y3.numpy().tofile(f) -def make_glm4_pipeline_data(): - from transformers import AutoModelForCausalLM, AutoTokenizer - - tokenizer = AutoTokenizer.from_pretrained("THUDM/glm-4-9b-chat", trust_remote_code=True) - - # tiktoken - chktxt = "\n \n\n \n\n\n \t \t\t \t\n \n \n \n \n🚀 (normal) 😶\u200d🌫️ (multiple emojis concatenated) ✅ 🦙🦙 3 33 333 3333 33333 333333 3333333 33333333 3.3 3..3 3...3 កាន់តែពិសេសអាច😁 ?我想在apple工作1314151天～ ------======= нещо на Български ''''''```````\"\"\"\"......!!!!!!?????? I've been 'told he's there, 'RE you sure? 'M not sure I'll make it, 'D you like some tea? We'Ve a'lL" - print("tiktoken:", tokenizer.tokenizer.encode(chktxt, disallowed_special=())) +def make_data_glm4v_model(): + CHATGLM4V_MODEL_PATH = Path( + "~/.cache/huggingface/hub/models--THUDM--glm-4v-9b/snapshots/6c2e4732db8443f64a48d5af04b74425a7d169c4" + ).expanduser() - # tokenizer - inputs = tokenizer("你好") - print(f"encode: {inputs=}") + sys.path.append(str(CHATGLM4V_MODEL_PATH)) + with relative_to_absolute_import(CHATGLM4V_MODEL_PATH / "modeling_chatglm.py"): + from modeling_chatglm import ChatGLMModel + from transformers import AutoConfig - conversation = [{"role": "user", "content": "你好"}] - inputs = tokenizer.apply_chat_template( - conversation, - add_generation_prompt=True, - tokenize=True, - return_tensors="pt", - return_dict=True, + config = AutoConfig.from_pretrained(CHATGLM4V_MODEL_PATH, trust_remote_code=True) + config.hidden_size = 32 + config.num_attention_heads = 8 + config.num_layers = 1 + config.padded_vocab_size = 8 + config.multi_query_group_num = 2 + config.ffn_hidden_size = 48 + config.kv_channels = config.hidden_size // config.num_attention_heads + config.torch_dtype = torch.float32 + config.vision_config.update( + num_hidden_layers=1, + hidden_size=32, + patch_size=7, + num_heads=2, + intermediate_size=56, + num_positions=17, + image_size=28, ) - print(f"apply_chat_template: {conversation=} {inputs=}") - # round 1 - inputs = inputs.to("cuda") - model = AutoModelForCausalLM.from_pretrained( - "THUDM/glm-4-9b-chat", - torch_dtype=torch.bfloat16, - low_cpu_mem_usage=True, - trust_remote_code=True, - device_map="auto", - ).eval() - - outputs = model.generate(**inputs, do_sample=False) - outputs = outputs[:, inputs["input_ids"].shape[1] :] - response = tokenizer.decode(outputs[0], skip_special_tokens=True).strip() - print(f"generate: {response=}") + m = ChatGLMModel(config).float().eval() + for param in m.parameters(): + param.data.uniform_(-0.5, 0.5) - conversation += [{"role": "assistant", "content": response}, {"role": "user", "content": "晚上睡不着应该怎么办"}] - inputs = tokenizer.apply_chat_template( - conversation, - add_generation_prompt=True, - tokenize=True, - return_tensors="pt", - return_dict=True, - ) - print(f"apply_chat_template: {conversation=} {inputs=}") + print(m) - # round 2 - outputs = model.generate(**inputs, do_sample=False) - outputs = outputs[:, inputs["input_ids"].shape[1] :] - response = tokenizer.decode(outputs[0], skip_special_tokens=True).strip() - print(f"generate: {response=}") + x1, y1, x2, y2, x3, y3 = _forward_steps(m, seq_len=3) + + # text + with open(HERE / "data/glm4v_model_text.data", "wb") as f: + tensors = [ + # text model + m.embedding.word_embeddings.weight, + m.encoder.layers[0].input_layernorm.weight, + m.encoder.layers[0].self_attention.query_key_value.weight, + m.encoder.layers[0].self_attention.query_key_value.bias, + m.encoder.layers[0].self_attention.dense.weight, + m.encoder.layers[0].post_attention_layernorm.weight, + m.encoder.layers[0].mlp.dense_h_to_4h.weight, + m.encoder.layers[0].mlp.dense_4h_to_h.weight, + m.encoder.final_layernorm.weight, + # inputs & outputs + x1.int(), + y1, + x2.int(), + y2, + x3.int(), + y3, + ] + for tensor in tensors: + tensor.data.numpy().tofile(f) + + m.config.boi_token_id = 2 + m.config.eoi_token_id = 4 + + images = torch.randn(1, 3, config.vision_config["image_size"], config.vision_config["image_size"]) + x1, y1, x2, y2, x3, y3 = _forward_steps(m, seq_len=6, images=images) + + # vision & text + with open(HERE / "data/glm4v_model.data", "wb") as f: + tensors = [ + # vision model + m.vision.patch_embedding.cls_embedding.half(), + m.vision.patch_embedding.proj.weight.half(), + m.vision.patch_embedding.proj.bias, + m.vision.patch_embedding.position_embedding.weight, + m.vision.transformer.layers[0].input_layernorm.weight, + m.vision.transformer.layers[0].input_layernorm.bias, + m.vision.transformer.layers[0].attention.query_key_value.weight, + m.vision.transformer.layers[0].attention.query_key_value.bias, + m.vision.transformer.layers[0].attention.dense.weight, + m.vision.transformer.layers[0].attention.dense.bias, + m.vision.transformer.layers[0].mlp.fc1.weight, + m.vision.transformer.layers[0].mlp.fc1.bias, + m.vision.transformer.layers[0].mlp.fc2.weight, + m.vision.transformer.layers[0].mlp.fc2.bias, + m.vision.transformer.layers[0].post_attention_layernorm.weight, + m.vision.transformer.layers[0].post_attention_layernorm.bias, + m.vision.conv.weight.half(), + m.vision.conv.bias, + m.vision.linear_proj.linear_proj.weight, + m.vision.linear_proj.norm1.weight, + m.vision.linear_proj.norm1.bias, + m.vision.linear_proj.gate_proj.weight, + m.vision.linear_proj.dense_h_to_4h.weight, + m.vision.linear_proj.dense_4h_to_h.weight, + m.vision.boi.half(), + m.vision.eoi.half(), + # text model + m.embedding.word_embeddings.weight, + m.encoder.layers[0].input_layernorm.weight, + m.encoder.layers[0].self_attention.query_key_value.weight, + m.encoder.layers[0].self_attention.query_key_value.bias, + m.encoder.layers[0].self_attention.dense.weight, + m.encoder.layers[0].post_attention_layernorm.weight, + m.encoder.layers[0].mlp.dense_h_to_4h.weight, + m.encoder.layers[0].mlp.dense_4h_to_h.weight, + m.encoder.final_layernorm.weight, + # inputs & outputs + images, + x1.int(), + y1, + x2.int(), + y2, + x3.int(), + y3, + ] + for tensor in tensors: + tensor.data.numpy().tofile(f) def make_glm4_pipeline_data(): @@ -753,11 +775,27 @@ def make_glm4_pipeline_data(): print(f"apply_chat_template: {conversation=} {inputs=}") # round 2 + inputs = inputs.to("cuda") outputs = model.generate(**inputs, do_sample=False) outputs = outputs[:, inputs["input_ids"].shape[1] :] response = tokenizer.decode(outputs[0], skip_special_tokens=True).strip() print(f"generate: {response=}") + # with image + from PIL import Image + + tokenizer = AutoTokenizer.from_pretrained("THUDM/glm-4v-9b", trust_remote_code=True) + image = Image.open(HERE / "../examples/03-Confusing-Pictures.jpg") + conversation = [{"role": "user", "content": "描述这张图片", "image": image}] + inputs = tokenizer.apply_chat_template( + conversation, + add_generation_prompt=True, + tokenize=True, + return_tensors="pt", + return_dict=True, + ) + print(f"apply_chat_template: {conversation=} {inputs=}") + def main(): torch.manual_seed(0) @@ -769,7 +807,8 @@ def main(): # make_data_glm2_model() # make_data_glm3_model() # make_data_glm4_model() - make_glm4_pipeline_data() + make_data_glm4v_model() + # make_glm4_pipeline_data() if __name__ == "__main__": diff --git a/third_party/stb b/third_party/stb new file mode 160000 index 00000000..013ac3be --- /dev/null +++ b/third_party/stb @@ -0,0 +1 @@ +Subproject commit 013ac3beddff3dbffafd5177e7972067cd2b5083