From 1b9b010ced1a0b642f5bd57868ded2134b14b562 Mon Sep 17 00:00:00 2001 From: "zhenglaiwen.zlw" Date: Wed, 29 May 2024 10:10:47 +0800 Subject: [PATCH] support Qwen2, change dashinfer model extensions - support Qwen2, add model_type Qwen_v20 - change dashinfer model extensions (asgraph, asparam -> dimodel, ditensors) - remove xxx_quantize.json config file, use command line arg instead --- README.md | 4 +- README_CN.md | 21 ++- csrc/common/as_engine.cpp | 65 +++---- csrc/common/engine_runtime.h | 1 + csrc/core/model/qwen/qwen.cpp | 1 + csrc/core/model/qwen/qwen.h | 6 + .../general/get_last_line/get_last_line.h | 2 +- .../operator/general/rotary/rotary_op.cpp | 45 +++-- csrc/core/operator/general/rotary/rotary_op.h | 3 + .../generate_opt/batch_mqa/batch_mqa_op.cpp | 31 ++-- csrc/interface/allspark.h | 104 +++++++++-- documents/CN/examples_python.md | 2 +- documents/EN/examples_python.md | 2 +- documents/resources/image/workflow-deps.jpg | Bin 165655 -> 165019 bytes examples/cpp/0_basic/example_qwen.cpp | 10 +- examples/cpp/1_apiserver/apiserver.cpp | 8 +- examples/cpp/utils/misc.hpp | 16 +- .../python/0_basic/basic_example_chatglm2.py | 8 +- .../python/0_basic/basic_example_chatglm3.py | 8 +- .../python/0_basic/basic_example_llama2.py | 8 +- .../python/0_basic/basic_example_qwen_v10.py | 8 +- .../0_basic/basic_example_qwen_v10_io.ipynb | 16 +- .../0_basic/basic_example_qwen_v10_io.py | 6 + .../python/0_basic/basic_example_qwen_v15.py | 8 +- .../0_basic/basic_example_qwen_v15_dimodel.py | 122 +++++++++++++ .../basic_example_qwen_v15_dimodel_simple.py | 38 ++++ .../python/0_basic/basic_example_qwen_v20.py | 172 ++++++++++++++++++ .../1_performance/performance_test_llama2.py | 4 +- .../performance_test_qwen_v15.py | 4 +- .../2_evaluation/evaluate_chat_ceval.py | 3 +- .../2_evaluation/evaluate_chat_gsm8k.py | 3 +- .../2_evaluation/evaluate_chat_humaneval.py | 3 +- .../python/2_evaluation/evaluate_chat_mmlu.py | 3 +- .../model_config/config_chatglm2_6b.json | 13 +- .../model_config/config_chatglm2_6b_32k.json | 13 +- .../config_chatglm2_6b_32k_quantize.json | 40 ---- .../config_chatglm2_6b_quantize.json | 40 ---- .../model_config/config_chatglm3_6b.json | 13 +- .../model_config/config_chatglm3_6b_32k.json | 13 +- .../config_chatglm3_6b_32k_quantize.json | 40 ---- .../config_chatglm3_6b_quantize.json | 40 ---- .../python/model_config/config_llama2_7b.json | 13 +- .../config_llama2_7b_quantize.json | 40 ---- .../model_config/config_qwen_v10_14b.json | 13 +- .../model_config/config_qwen_v10_1_8b.json | 13 +- .../config_qwen_v10_1_8b_quantize.json | 51 ------ .../model_config/config_qwen_v10_7b.json | 13 +- .../model_config/config_qwen_v15_0_5b.json | 13 +- .../model_config/config_qwen_v15_14b.json | 13 +- .../config_qwen_v15_14b_quantize.json | 51 ------ .../model_config/config_qwen_v15_1_8b.json | 13 +- .../config_qwen_v15_1_8b_quantize.json | 52 ------ .../model_config/config_qwen_v15_4b.json | 13 +- .../config_qwen_v15_4b_quantize.json | 51 ------ .../model_config/config_qwen_v15_7b.json | 13 +- .../config_qwen_v15_7b_quantize.json | 51 ------ ...uantize.json => config_qwen_v20_0_5b.json} | 10 +- ...uantize.json => config_qwen_v20_1_5b.json} | 12 +- ..._quantize.json => config_qwen_v20_7b.json} | 12 +- python/allspark_binding.cpp | 93 +++++++--- python/dashinfer/allspark/engine.py | 11 +- python/dashinfer/allspark/model/__init__.py | 2 + python/dashinfer/allspark/model/qwen_v15.py | 39 +++- python/dashinfer/allspark/model/qwen_v20.py | 26 +++ python/dashinfer/helper/helper.py | 10 +- scripts/release/python_manylinux_test.sh | 3 +- scripts/release/python_pypi_test.sh | 3 +- 67 files changed, 867 insertions(+), 714 deletions(-) create mode 100644 examples/python/0_basic/basic_example_qwen_v15_dimodel.py create mode 100644 examples/python/0_basic/basic_example_qwen_v15_dimodel_simple.py create mode 100644 examples/python/0_basic/basic_example_qwen_v20.py delete mode 100644 examples/python/model_config/config_chatglm2_6b_32k_quantize.json delete mode 100644 examples/python/model_config/config_chatglm2_6b_quantize.json delete mode 100644 examples/python/model_config/config_chatglm3_6b_32k_quantize.json delete mode 100644 examples/python/model_config/config_chatglm3_6b_quantize.json delete mode 100644 examples/python/model_config/config_llama2_7b_quantize.json delete mode 100644 examples/python/model_config/config_qwen_v10_1_8b_quantize.json delete mode 100644 examples/python/model_config/config_qwen_v15_14b_quantize.json delete mode 100644 examples/python/model_config/config_qwen_v15_1_8b_quantize.json delete mode 100644 examples/python/model_config/config_qwen_v15_4b_quantize.json delete mode 100644 examples/python/model_config/config_qwen_v15_7b_quantize.json rename examples/python/model_config/{config_qwen_v15_0_5b_quantize.json => config_qwen_v20_0_5b.json} (90%) rename examples/python/model_config/{config_qwen_v10_7b_quantize.json => config_qwen_v20_1_5b.json} (88%) rename examples/python/model_config/{config_qwen_v10_14b_quantize.json => config_qwen_v20_7b.json} (88%) create mode 100644 python/dashinfer/allspark/model/qwen_v20.py diff --git a/README.md b/README.md index 41eaef57..93b3cc4f 100644 --- a/README.md +++ b/README.md @@ -94,12 +94,12 @@ During inference, the quantized weight is recovered as bfloat16 for matrix multi ![Workflow and Dependency](documents/resources/image/workflow-deps.jpg?row=true) -1. **Model Loading and Serialization**: This procedure involves loading model weights, setting up transformation parameters, and quantization settings. Based on this information, the model is serialized and converted into the DashInfer format (.asparam, .asgraph). This functionality is accessible exclusively through a Python interface and relies on the PyTorch and transformers libraries to access the weights. The version requirements for PyTorch and transformers may vary from model to model. DashInfer itself does not impose any specific version constraints. +1. **Model Loading and Serialization**: This procedure involves loading model weights, setting up transformation parameters, and quantization settings. Based on this information, the model is serialized and converted into the DashInfer format (.dimodel, .ditensors). This functionality is accessible exclusively through a Python interface and relies on the PyTorch and transformers libraries to access the weights. The version requirements for PyTorch and transformers may vary from model to model. DashInfer itself does not impose any specific version constraints. 2. **Model Inference**: This step is responsible for executing the model inference using the serialized model with DashInfer, without depending on components like PyTorch. DashInfer employs [DLPack](https://github.com/dmlc/dlpack) format tensors to facilitate interaction with external frameworks, such as PyTorch. Tensors in DLPack format can be manually created or generated through tensor conversion functions provided by deep learning frameworks. Regarding the C++ interface, since most dependencies have been statically linked, it primarily relies on the OpenMP runtime library and C++ system libraries. We applied [control over symbol exports](https://anadoxin.org/blog/control-over-symbol-exports-in-gcc.html/) to ensure that only DashInfer's API interface symbols are visible, thereby preventing version conflicts with existing libraries in the user's system, such as protobuf. > Note: -> - .asparam, .asgraph is a special model format defined by DashInfer kernel (allspark). +> - .dimodel, .ditensors is a special model format defined by DashInfer kernel. > - When utilizing the Python interface, you can combine the code from steps 1 and 2. However, due to the lack of functionality for loading Huggingface models at the C++ level, the C++ interface is limited to conducting inferences with models in the DashInfer format. Therefore, it's essential to serialize the model first using the Python interface before proceeding with the C++ interface. ## Single-NUMA Architecture diff --git a/README_CN.md b/README_CN.md index fcae9a54..ed57ebc8 100644 --- a/README_CN.md +++ b/README_CN.md @@ -1,6 +1,19 @@ -# 简介 +
+ +[![PyPI](https://img.shields.io/pypi/v/dashinfer)](https://pypi.org/project/dashinfer/) + + +

+

+ English | + 中文 +

+

-DashInfer用于推理预训练大语言模型（LLM）的推理引擎。 + +
+ +# 简介 DashInfer采用C++ Runtime编写，提供C++和Python语言接口。DashInfer具有生产级别的高性能表现，适用于多种CPU架构，包括x86和ARMv9。DashInfer支持连续批处理（Continuous Batching）和多NUMA推理（NUMA-Aware），能够充分利用服务器级CPU的算力，为推理14B及以下的LLM模型提供更多的硬件选择。 @@ -82,12 +95,12 @@ $$ x_{u8} = x_{fp32} / scale + zeropoint $$ ![Workflow and Dependency](documents/resources/image/workflow-deps.jpg?row=true) -1. **模型加载与序列化**：此过程负责读取模型权重、配置模型转换参数及量化参数，并根据这些信息对模型进行序列化，并生成DashInfer格式（.asparam、.asgraph）的模型。此功能仅提供Python接口，并依赖于PyTorch和transformers库来访问权重。不同模型对PyTorch和transformers的版本要求可能有所不同，DashInfer本身并没有特殊的版本要求。 +1. **模型加载与序列化**：此过程负责读取模型权重、配置模型转换参数及量化参数，并根据这些信息对模型进行序列化，并生成DashInfer格式（.dimodel、.ditensors）的模型。此功能仅提供Python接口，并依赖于PyTorch和transformers库来访问权重。不同模型对PyTorch和transformers的版本要求可能有所不同，DashInfer本身并没有特殊的版本要求。 2. **模型推理**：此步骤负责执行模型推理，使用DashInfer推理序列化后的模型，不依赖PyTorch等组件。DashInfer采用[DLPack](https://github.com/dmlc/dlpack)格式的tensor来实现与外部框架（如PyTorch）的交互。DLPack格式的tensor，可以通过手动创建或由深度学习框架的tensor转换函数产生。对于C++接口，由于已经将几乎所有依赖静态编译，仅对openmp运行时库以及C++系统库的有依赖。我们进行了[链接符号处理](https://anadoxin.org/blog/control-over-symbol-exports-in-gcc.html/)，以确保只有DashInfer的API接口符号可见，避免与客户系统中已有的公共库（如protobuf等）发生版本冲突。 > 说明： -> - .asparam、.asgraph是由DashInfer内核（allspark）定义的一种特殊的模型格式。 +> - .dimodel、.ditensors是由DashInfer内核定义的一种特殊的模型格式。 > - 使用Python接口时，可以将步骤1和2的代码放在一起。由于缺少C++层面加载Huggingface模型的功能，C++接口只能进行DashInfer格式的模型推理，因此在使用C++接口前，必须先用Python接口先对模型进行序列化。 ## 单NUMA架构图 diff --git a/csrc/common/as_engine.cpp b/csrc/common/as_engine.cpp index f683b40d..adeeef30 100644 --- a/csrc/common/as_engine.cpp +++ b/csrc/common/as_engine.cpp @@ -245,10 +245,6 @@ AsStatus AsEngineImpl::SetNumThreads(int num_threads) { DLOG(INFO) << "AsEngineImpl::SetNumThreads()" << std::endl; AsStatus ret = AsStatus::ALLSPARK_SUCCESS; device_ctx_->SetNumThreads(num_threads); - if (nranks_ > threadpool_size_) { - threadpool_size_ = nranks_ * 2; - threadpool_ = std::make_unique(threadpool_size_); - } std::future result[nranks_]; for (int i = 0; i < workers_.size(); ++i) { result[i] = threadpool_->enqueue([this, i, &num_threads]() { @@ -561,10 +557,6 @@ AsStatus AsEngineImpl::UnloadModelFromDeviceMemory(const char* model_name) { DLOG(INFO) << "[" << model_name << "] " << "AsEngineImpl::UnloadModelFromDeviceMemory()" << std::endl; AsStatus ret = AsStatus::ALLSPARK_SUCCESS; - if (nranks_ > threadpool_size_) { - threadpool_size_ = nranks_ * 2; - threadpool_ = std::make_unique(threadpool_size_); - } std::future result[nranks_]; for (int i = 0; i < nranks_; ++i) { result[i] = threadpool_->enqueue( @@ -635,10 +627,6 @@ AsStatus AsEngineImpl::StartModel(const char* model_name, bool do_warmup) { int64_t min_bytes_available = std::numeric_limits::max(); int64_t rank_0_bytes_available{0}; if (use_adaptive_cache_) { - if (nranks_ > threadpool_size_) { - threadpool_size_ = nranks_ * 2; - threadpool_ = std::make_unique(threadpool_size_); - } std::future result[nranks_]; for (int i = 0; i < nranks_; ++i) { result[i] = threadpool_->enqueue([this, i]() -> int64_t { @@ -850,6 +838,7 @@ AsStatus AsEngineImpl::StopModel(const char* model_name) { model_state->cond_var->notify_all(); auto ret = reply_promise->get_future().get(); + model_state->model_stopping = true; if (ret != AsStatus::ALLSPARK_SUCCESS) { LOG(ERROR) << "[" << model_name << "] " @@ -885,10 +874,6 @@ AsStatus AsEngineImpl::ReloadModelFromDeviceMemory(const char* model_name) { } AsStatus ret = AsStatus::ALLSPARK_SUCCESS; - if (nranks_ > threadpool_size_) { - threadpool_size_ = nranks_ * 2; - threadpool_ = std::make_unique(threadpool_size_); - } std::future result[nranks_]; for (int i = 0; i < nranks_; ++i) { result[i] = threadpool_->enqueue([this, i, &model_ir]() { @@ -1159,10 +1144,6 @@ AsStatus AsEngineImpl::RunTextGenerationContinue(const char* model_name) { return AsStatus::ALLSPARK_INVALID_CALL_ERROR; } AsStatus ret = AsStatus::ALLSPARK_SUCCESS; - if (nranks_ > threadpool_size_) { - threadpool_size_ = nranks_ * 2; - threadpool_ = std::make_unique(threadpool_size_); - } std::future result[nranks_]; for (int i = 0; i < nranks_; ++i) { result[i] = threadpool_->enqueue([this, i]() { @@ -1236,10 +1217,6 @@ AsStatus AsEngineImpl::RunTextGenerationContext(const char* model_name) { return AsStatus::ALLSPARK_INVALID_CALL_ERROR; } AsStatus ret = AsStatus::ALLSPARK_SUCCESS; - if (nranks_ > threadpool_size_) { - threadpool_size_ = nranks_ * 2; - threadpool_ = std::make_unique(threadpool_size_); - } std::future result[nranks_]; for (int i = 0; i < nranks_; ++i) { result[i] = threadpool_->enqueue([this, i]() { @@ -1293,11 +1270,7 @@ AsStatus AsEngineImpl::StopRequestByRequestID(const char* model_name, LOG(ERROR) << "Invalid model name : " << model_name << std::endl; return AsStatus::ALLSPARK_PARAM_ERROR; } - AsStatus ret = AsStatus::ALLSPARK_SUCCESS; - if (nranks_ > threadpool_size_) { - threadpool_size_ = nranks_ * 2; - threadpool_ = std::make_unique(threadpool_size_); - } + std::future result[nranks_]; for (int i = 0; i < nranks_; ++i) { result[i] = threadpool_->enqueue([this, i, request_id]() { @@ -1305,6 +1278,7 @@ AsStatus AsEngineImpl::StopRequestByRequestID(const char* model_name, }); } + AsStatus ret = AsStatus::ALLSPARK_SUCCESS; // 即使失败、异常，也要让各子线程运行完毕，以保证原子性。在可恢复的情况下，确保下一次请求有干净的环境 AsStatus failed_ret = AsStatus::ALLSPARK_SUCCESS; for (int i = 0; i < nranks_; ++i) { @@ -1333,11 +1307,7 @@ AsStatus AsEngineImpl::ReleaseRequestByRequestID(const char* model_name, LOG(ERROR) << "Invalid model name : " << model_name << std::endl; return AsStatus::ALLSPARK_PARAM_ERROR; } - AsStatus ret = AsStatus::ALLSPARK_SUCCESS; - if (nranks_ > threadpool_size_) { - threadpool_size_ = nranks_ * 2; - threadpool_ = std::make_unique(threadpool_size_); - } + std::future result[nranks_]; for (int i = 0; i < nranks_; ++i) { result[i] = threadpool_->enqueue([this, i, request_id]() { @@ -1345,6 +1315,7 @@ AsStatus AsEngineImpl::ReleaseRequestByRequestID(const char* model_name, }); } + AsStatus ret = AsStatus::ALLSPARK_SUCCESS; // 即使失败、异常，也要让各子线程运行完毕，以保证原子性。在可恢复的情况下，确保下一次请求有干净的环境 AsStatus failed_ret = AsStatus::ALLSPARK_SUCCESS; for (int i = 0; i < nranks_; ++i) { @@ -1461,10 +1432,6 @@ AsStatus AsEngineImpl::StartRequestImpl( {out_name, std::make_shared(out_name, DeviceType::CPU, DataType::INT64, DataMode::DENSE, Shape{1, engine_max_length_})}); - if (nranks_ > threadpool_size_) { - threadpool_size_ = nranks_ * 2; - threadpool_ = std::make_unique(threadpool_size_); - } std::future result[nranks_]; for (int i = 0; i < nranks_; ++i) { result[i] = threadpool_->enqueue( @@ -1668,7 +1635,6 @@ void AsEngineImpl::ModelRunningThread( s.c_str()); // set the name (pthread_self() returns the // pthread_t of the current thread) bool looping = true; - long loop_cnt = 0; bool graceful_stop_phase = false; bool graceful_final_released = false; std::unique_ptr graceful_stop_msg = nullptr; @@ -1682,7 +1648,6 @@ void AsEngineImpl::ModelRunningThread( while (looping) { util::Timer time_outer; - loop_cnt++; UpdateAsEngineStat(); // print the engine state for easier service trace. // for multiple numa, only print this info on node 0. @@ -1957,6 +1922,7 @@ void AsEngineImpl::ModelRunningThread( if (graceful_final_released) { assert(graceful_stop_msg != nullptr); graceful_stop_msg->promise->set_value(AsStatus::ALLSPARK_SUCCESS); + model_state->model_stopped = true; DLOG(INFO) << "All done, gracefully stopped!"; break; } @@ -2144,4 +2110,23 @@ std::string AsEngineStat::ToString() const { return result; } +std::map AsEngineStat::ToMap() const { + std::map engine_stat_map; + engine_stat_map["free_token"] = std::to_string(free_token); + engine_stat_map["total_token"] = std::to_string(total_token); + engine_stat_map["pendding_request"] = std::to_string(pendding_request); + engine_stat_map["running_request"] = std::to_string(running_request); + engine_stat_map["total_device_memory_pool_size"] = + std::to_string(total_device_memory_pool_size); + engine_stat_map["used_device_memory_pool_size"] = + std::to_string(used_device_memory_pool_size); + engine_stat_map["total_generated_token"] = + std::to_string(total_generated_token); + engine_stat_map["total_prefill_token"] = std::to_string(total_prefill_token); + engine_stat_map["generate_token_persec"] = + std::to_string(generate_token_persec); + engine_stat_map["process_token_persec"] = + std::to_string(process_token_persec); + return engine_stat_map; +} } // namespace allspark diff --git a/csrc/common/engine_runtime.h b/csrc/common/engine_runtime.h index cf8f11a3..f0f39a38 100644 --- a/csrc/common/engine_runtime.h +++ b/csrc/common/engine_runtime.h @@ -35,6 +35,7 @@ class ModelControlState final { result_queue_map; bool model_stopping = false; // after GracefulStopModel called... + bool model_stopped = false; // after GracefulStopModel is done. ModelControlState(const std::string& name) : model_name(name) { lock = std::make_unique(); diff --git a/csrc/core/model/qwen/qwen.cpp b/csrc/core/model/qwen/qwen.cpp index 049ed113..483d1444 100644 --- a/csrc/core/model/qwen/qwen.cpp +++ b/csrc/core/model/qwen/qwen.cpp @@ -24,4 +24,5 @@ AsStatus QwenModel::Init(const TransformerProto& model_proto, REGISTER_MODEL("Qwen", QwenModel) REGISTER_MODEL("Qwen_v10", QwenModel_v10) REGISTER_MODEL("Qwen_v15", QwenModel_v15) +REGISTER_MODEL("Qwen_v20", QwenModel_v20) } // namespace allspark diff --git a/csrc/core/model/qwen/qwen.h b/csrc/core/model/qwen/qwen.h index de5616f5..95fda201 100644 --- a/csrc/core/model/qwen/qwen.h +++ b/csrc/core/model/qwen/qwen.h @@ -31,4 +31,10 @@ class QwenModel_v15 : public QwenModel { : QwenModel(model_type){}; }; +class QwenModel_v20 : public QwenModel { + public: + explicit QwenModel_v20(const std::string& model_type = "") + : QwenModel(model_type){}; +}; + } // namespace allspark diff --git a/csrc/core/operator/general/get_last_line/get_last_line.h b/csrc/core/operator/general/get_last_line/get_last_line.h index 40f9d5be..9615e6f9 100644 --- a/csrc/core/operator/general/get_last_line/get_last_line.h +++ b/csrc/core/operator/general/get_last_line/get_last_line.h @@ -14,7 +14,7 @@ namespace allspark { class GetLastLineOp : public AsOperator { public: explicit GetLastLineOp(const std::string& op_type = "") - : AsOperator(op_type), batch_(0), seq_(0), hid_(0) {} + : AsOperator(op_type) {} AsStatus Init(const OperatorProto& op_proto, const DeviceContext& ctx, const TensorMap& weights_map, TensorMap* tensor_map); AsStatus Reshape() override; diff --git a/csrc/core/operator/general/rotary/rotary_op.cpp b/csrc/core/operator/general/rotary/rotary_op.cpp index c79b4d7e..e7b2e6b0 100644 --- a/csrc/core/operator/general/rotary/rotary_op.cpp +++ b/csrc/core/operator/general/rotary/rotary_op.cpp @@ -98,10 +98,14 @@ AsStatus RotaryOp::Init(const OperatorProto& op_proto, const DeviceContext& ctx, if (attr_map.find("rotary_base") != attr_map.end()) { base_ = *(float*)(attr_map.at("rotary_base").c_str()); } - xlogn_ = -1; - if (attr_map.find("logn_model_embedding") != attr_map.end()) { - xlogn_ = *(int*)(attr_map.at("logn_model_embedding").c_str()); + + // attr.8 multi_query_group_num + if (attr_map.find("multi_query_group_num") != attr_map.end()) { + group_num_ = *(int*)(attr_map.at("multi_query_group_num").c_str()); + } else { + group_num_ = num_heads_; } + size_per_head_ = ctx.GetSizePerHead(); // backend switch DeviceType backend = ctx.GetDeviceType(); @@ -109,6 +113,9 @@ AsStatus RotaryOp::Init(const OperatorProto& op_proto, const DeviceContext& ctx, case DeviceType::CPU: { const CPUContext* cpu_ctx = static_cast(ctx_); num_heads_ /= cpu_ctx->GetNranks(); + if (group_num_ != 1) { + group_num_ /= cpu_ctx->GetNranks(); + } break; } default: @@ -116,6 +123,8 @@ AsStatus RotaryOp::Init(const OperatorProto& op_proto, const DeviceContext& ctx, << DeviceType_Name(backend) << " device type" << std::endl; return AsStatus::ALLSPARK_RUNTIME_ERROR; } + kv_stride_ = size_per_head_ * group_num_; + hidden_size_ = size_per_head_ * num_heads_; return AsStatus::ALLSPARK_SUCCESS; } AsStatus RotaryOp::Reshape(RuntimeContext* runtime_ctx) { @@ -124,42 +133,40 @@ AsStatus RotaryOp::Reshape(RuntimeContext* runtime_ctx) { Shape y_shape(x_shape); batch_size_ = y_shape[0]; seq_len_ = y_shape[1]; - hidden_size_ = y_shape[2] / 3; - tensor_map_->at(out_names_[0])->SetShape(std::move(y_shape)); - // set variable - if (hidden_size_ % num_heads_) { - LOG(ERROR) << "Invalid attribute in RotaryOp. hidden_size : " - << hidden_size_ << ", num_heads : " << num_heads_ << std::endl; + qkv_stride_ = y_shape[2]; + if (qkv_stride_ != hidden_size_ + 2 * kv_stride_) { + LOG(ERROR) << "Invalid qkv_stride_ in RotaryOp" + << ", qkv_strde = " << qkv_stride_ + << ", hidden_size = " << hidden_size_ + << ", kv_stride = " << kv_stride_ << std::endl; return AsStatus::ALLSPARK_RUNTIME_ERROR; } - size_per_head_ = hidden_size_ / num_heads_; - gemm_batch_ = batch_size_ * num_heads_; + tensor_map_->at(out_names_[0])->SetShape(std::move(y_shape)); return AsStatus::ALLSPARK_SUCCESS; } AsStatus RotaryOp::RunRotary(int run_batch_size, AsTensor* rotary_step, AsTensor* rotary_inv_freq) { int* run_step = (int*)rotary_step->GetDataPtr(); float* inv_freq = (float*)rotary_inv_freq->GetDataPtr(); - int qkv_stride = 3 * hidden_size_; + int qkv_stride = qkv_stride_; int* batch_offset = nullptr; - int offset = hidden_size_ * SizeofType(dtype_); void* q_buf = (char*)tensor_map_->at(in_names_[0])->GetDataPtr(); - void* k_buf = (char*)q_buf + offset; - void* v_buf = (char*)k_buf + offset; + void* k_buf = (char*)q_buf + hidden_size_ * SizeofType(dtype_); + void* v_buf = (char*)k_buf + kv_stride_ * SizeofType(dtype_); void* outq_buf = (char*)tensor_map_->at(out_names_[0])->GetDataPtr(); - void* outk_buf = (char*)outq_buf + offset; - void* outv_buf = (char*)outk_buf + offset; + void* outk_buf = (char*)outq_buf + hidden_size_ * SizeofType(dtype_); + void* outv_buf = (char*)outk_buf + kv_stride_ * SizeofType(dtype_); rotary_launcher(dtype_, outq_buf, q_buf, inv_freq, batch_offset, run_batch_size, seq_len_, run_step, hidden_size_, num_heads_, size_per_head_, 0, qkv_stride, rotary_type_, rotary_pct_, xlogn_, ctx_); rotary_launcher(dtype_, outk_buf, k_buf, inv_freq, batch_offset, - run_batch_size, seq_len_, run_step, hidden_size_, num_heads_, + run_batch_size, seq_len_, run_step, hidden_size_, group_num_, size_per_head_, 0, qkv_stride, rotary_type_, rotary_pct_, -1, ctx_); rotary_launcher(dtype_, outv_buf, v_buf, nullptr, batch_offset, - run_batch_size, seq_len_, run_step, hidden_size_, num_heads_, + run_batch_size, seq_len_, run_step, hidden_size_, group_num_, size_per_head_, 0, qkv_stride, rotary_type_, rotary_pct_, -1, ctx_); return AsStatus::ALLSPARK_SUCCESS; diff --git a/csrc/core/operator/general/rotary/rotary_op.h b/csrc/core/operator/general/rotary/rotary_op.h index 2d0d7a71..6175206a 100644 --- a/csrc/core/operator/general/rotary/rotary_op.h +++ b/csrc/core/operator/general/rotary/rotary_op.h @@ -82,6 +82,9 @@ class RotaryOp : public AsOperator { float rotary_pct_; float seqlen_extrapolation_; int ntk_model_embed_; + int group_num_ = 0; + int qkv_stride_ = 0; + int kv_stride_ = 0; }; } // namespace allspark diff --git a/csrc/core/operator/generate_opt/batch_mqa/batch_mqa_op.cpp b/csrc/core/operator/generate_opt/batch_mqa/batch_mqa_op.cpp index 3c4f7179..fa71ccbe 100644 --- a/csrc/core/operator/generate_opt/batch_mqa/batch_mqa_op.cpp +++ b/csrc/core/operator/generate_opt/batch_mqa/batch_mqa_op.cpp @@ -84,6 +84,7 @@ AsStatus BatchMQAOp::Init(const OperatorProto& op_proto, // type inference dtype_ = tensor_map_->at(in_names_[0])->GetDataType(); tensor_map_->at(out_names_[0])->SetDataType(dtype_); + // attr auto& attr_map = op_proto.attr(); if (attr_map.find("multinode") != attr_map.end()) { @@ -91,28 +92,19 @@ AsStatus BatchMQAOp::Init(const OperatorProto& op_proto, } else { multi_nodes_ = true; } + if (attr_map.find("num_heads") == attr_map.end()) { LOG(ERROR) << "BatchMQAOp : can't find num_heads attribute." << std::endl; return AsStatus::ALLSPARK_PARAM_ERROR; } num_heads_ = *(int*)(attr_map.at("num_heads").c_str()); - if (attr_map.find("hidden_size") == attr_map.end()) { - LOG(ERROR) << "BatchMQAOp : can't find hidden_size attribute." << std::endl; - return AsStatus::ALLSPARK_PARAM_ERROR; - } - hidden_size_ = *(int*)(attr_map.at("hidden_size").c_str()); - if (hidden_size_ % num_heads_) { - LOG(ERROR) << "Invalid attribute in BatchMQAOp. hidden_size : " - << hidden_size_ << ", num_heads : " << num_heads_ << std::endl; - return AsStatus::ALLSPARK_RUNTIME_ERROR; - } - size_per_head_ = hidden_size_ / num_heads_; - if (attr_map.find("multi_query_group_num") == attr_map.end()) { - LOG(ERROR) << "BatchMQAOp : can't find multi_query_group_num attribute." - << std::endl; - return AsStatus::ALLSPARK_PARAM_ERROR; + + size_per_head_ = ctx.GetSizePerHead(); + if (attr_map.find("multi_query_group_num") != attr_map.end()) { + group_num_ = *(int*)(attr_map.at("multi_query_group_num").c_str()); + } else { + group_num_ = num_heads_; } - group_num_ = *(int*)(attr_map.at("multi_query_group_num").c_str()); DeviceType backend = ctx.GetDeviceType(); switch (backend) { @@ -120,7 +112,6 @@ AsStatus BatchMQAOp::Init(const OperatorProto& op_proto, kernel_launcher = cpu_dec_single_mqa; if (multi_nodes_) { const CPUContext* cpu_ctx = static_cast(ctx_); - hidden_size_ /= cpu_ctx->GetNranks(); num_heads_ /= cpu_ctx->GetNranks(); if (group_num_ != 1) { group_num_ /= cpu_ctx->GetNranks(); @@ -134,6 +125,7 @@ AsStatus BatchMQAOp::Init(const OperatorProto& op_proto, return AsStatus::ALLSPARK_RUNTIME_ERROR; } kv_stride_ = size_per_head_ * group_num_; + hidden_size_ = size_per_head_ * num_heads_; return AsStatus::ALLSPARK_SUCCESS; } AsStatus BatchMQAOp::Reshape(RuntimeContext* runtime_ctx) { @@ -146,11 +138,12 @@ AsStatus BatchMQAOp::Reshape(RuntimeContext* runtime_ctx) { qkv_stride_ = y_shape[2]; if (qkv_stride_ != hidden_size_ + 2 * kv_stride_) { LOG(ERROR) << "Invalid qkv_stride_ in BatchMQAOp" - << "qkv_strde = " << qkv_stride_ - << ",hidden_size = " << hidden_size_ + << ", qkv_strde = " << qkv_stride_ + << ", hidden_size = " << hidden_size_ << ", kv_stride = " << kv_stride_ << std::endl; return AsStatus::ALLSPARK_RUNTIME_ERROR; } + // set variable gemm_batch_ = single_batch * num_heads_; if (alpha_ < 0) { diff --git a/csrc/interface/allspark.h b/csrc/interface/allspark.h index a45c03e5..8ed47e2a 100644 --- a/csrc/interface/allspark.h +++ b/csrc/interface/allspark.h @@ -36,7 +36,7 @@ enum class AsCacheMode { }; /** - * Converted asparam, asgraph file info class. + * Converted ditensors, dimodel file info class. */ class AsFileInfo { @@ -74,8 +74,8 @@ struct GenerateConfig { bad_words_ids; ///< IDs of words to avoid during generation, will supress ///< thoese ids's generation. float temperature = 1.0; ///< Sampling temperature. - int top_k = 1; ///< Top-K sampling hyperparameter. - float top_p = 0.0; ///< Top-P sampling hyperparameter. + int top_k = 50; ///< Top-K sampling hyperparameter. + float top_p = 1.0; ///< Top-P sampling hyperparameter. float repetition_penalty = 1.0; ///< Penalty for word repetition. float length_penalty = 1.0; ///< Penalty for the length of the sequence. float presence_penalty = 0; ///< Penalty for the presence of words. @@ -153,6 +153,7 @@ class AsEngineStat { AsEngineStat(std::string in_model_name) : model_name(std::move(in_model_name)) {} std::string ToString() const; + std::map ToMap() const; public: std::string model_name; @@ -213,19 +214,41 @@ class AsEngine final { GenerateConfig config; }; - // two type of elements, one is generated IDs, the other is for - // oridinary requests. + /** + * The GeneratedElements class represents generated elements and associated + * probability information. It consists of four main data members: + * 1. ids_from_generate - Stores generated IDs, which only keep new results + * and exclude historical ones. + * 2. log_probs_list - Stores a probability list for each token, along with + * the top_logprobs tokens and their probabilities when generated. + * 3. token_logprobs_list - Stores the probability value for each selected + * token. + * 4. tensors_from_model_inference - Contains tensors from model inference. + */ class GeneratedElements { public: - std::vector - ids_from_generate; /// this output is for GPT style generative - /// models, only keeps new results and will not - /// include history results. + /** + * Stores generated IDs used by GPT-style generative models, excluding + * historical results. + */ + std::vector ids_from_generate; + + /** + * A probability list for each token, including the top_logprobs tokens and + * their probabilities when generated. Dimension: [num_token][top_logprobs], + * where each token has a pair [token_id, prob]. + */ std::vector>> log_probs_list; + + /** + * Stores the probability value for each selected token. + */ std::vector token_logprobs_list; - DLTensorMap - tensors_from_model_inference; /// this output only has data when - /// infer mode is model inference. + + /** + * Tensor outputs from model inference. + */ + DLTensorMap tensors_from_model_inference; }; enum class GenerateRequestStatus { @@ -238,26 +261,78 @@ class AsEngine final { /// was unfortunedly stopped. }; + /** + * @class ResultQueue + * + * The ResultQueue class is designed to generate status, and retrieve results + * in a queue. It provides four main virtual methods: + * 1. A method for generating status; + * 2. A method to get the length of generated results; + * 3. A method to fetch a result from the queue; and + * 4. A method to fetch a result without waiting. + */ class ResultQueue { public: + /** + * Generates the status of a request. + * + * @return GenerateRequestStatus Returns the status of the generation + * request. + */ virtual GenerateRequestStatus GenerateStatus() { throw std::runtime_error("Function not implemented."); } + + /** + * Retrieves the length of generated results. + * + * @return size_t Returns the length of the generated results. + */ virtual size_t GeneratedLength() { throw std::runtime_error("Function not implemented."); } + + /** + * Fetches a result from the queue, will be block until new token generated + * + * @return std::shared_ptr Returns a smart pointer to the + * generated elements. + */ virtual std::shared_ptr Get() { throw std::runtime_error("Function not implemented."); } + + /** + * Retrieves a result from the queue without waiting. + * + * @return std::shared_ptr Returns a smart pointer to the + * generated elements. + */ virtual std::shared_ptr GetNoWait() { throw std::runtime_error("Function not implemented."); } }; + + /** + * @typedef ResultQueue_t + * + * ResultQueue_t is an alias for a pointer to a ResultQueue object, providing + * a clearer syntax for pointer usage. + */ typedef ResultQueue* ResultQueue_t; AsEngine(); ~AsEngine(); + /** + * Builds a model from the provided configuration structure. + * + * @param model_config A reference to the model configuration struct + * containing all necessary settings for building the model. + * @return Returns an AsStatus value indicating the success or failure of the + * model building operation. If successful, returns AS_SUCCESS; otherwise, + * returns an error code indicating the specific issue. + */ AsStatus BuildModelFromConfigStruct(AsModelConfig& model_config); /** @@ -341,6 +416,11 @@ class AsEngine final { */ AsStatus SyncRequest(const char* model_name, RequestHandle_t request_handle); + /** + * Retrieves the statistic information for specified model + * @param model_name the model name already install into engine. + * @return return the statistic information structure + */ AsEngineStat GetAsEngineStat(const char* model_name); /** diff --git a/documents/CN/examples_python.md b/documents/CN/examples_python.md index 70bc7275..a975fbc8 100644 --- a/documents/CN/examples_python.md +++ b/documents/CN/examples_python.md @@ -304,7 +304,7 @@ To create a public link, set `share=True` in `launch()`. 以下是对config中的参数说明： - `model_name`: DashInfer模型名称，自定义； -- `model_type`: DashInfer模型类型，可选项：LLaMA_v2、ChatGLM_v2、ChatGLM_v3、Qwen_v10、Qwen_v15； +- `model_type`: DashInfer模型类型，可选项：LLaMA_v2、ChatGLM_v2、ChatGLM_v3、Qwen_v10、Qwen_v15、Qwen_v20； - `model_path`: DashInfer模型导出路径； - `data_type`: 输出的数据类型，可选项：float32； - `device_type`: 推理硬件，可选项：CPU； diff --git a/documents/EN/examples_python.md b/documents/EN/examples_python.md index 5f9d994a..6351fcaa 100644 --- a/documents/EN/examples_python.md +++ b/documents/EN/examples_python.md @@ -304,7 +304,7 @@ The `/examples/python/model_config` directory provides severa Here is an explanation of the parameters within the config: - `model_name`: Custom name for the DashInfer model. -- `model_type`: The type of the DashInfer model. Options include: LLaMA_v2, ChatGLM_v2, ChatGLM_v3, Qwen_v10, Qwen_v15. +- `model_type`: The type of the DashInfer model. Options include: LLaMA_v2, ChatGLM_v2, ChatGLM_v3, Qwen_v10, Qwen_v15, Qwen_v20. - `model_path`: The export path for the DashInfer model. - `data_type`: The data type of the output. Options include: float32. - `device_type`: The inference hardware. 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uPj(1@TA5EP^XVj+Y!CdmtxU@(--B}t8r&uJ^nHduI$GN{r);$U@qYmehE3@J diff --git a/examples/cpp/0_basic/example_qwen.cpp b/examples/cpp/0_basic/example_qwen.cpp index f3ee916d..8606ea4f 100644 --- a/examples/cpp/0_basic/example_qwen.cpp +++ b/examples/cpp/0_basic/example_qwen.cpp @@ -72,8 +72,8 @@ int main(int argc, char** argv) { auto all_exists = check_model_file_exists(model_path, tiktoken_file); if (!all_exists) return 1; - std::string asgraph_file = model_path + ".asgraph"; - std::string asparam_file = model_path + ".asparam"; + std::string dimodel_file = model_path + ".dimodel"; + std::string ditensors_file = model_path + ".ditensors"; // create an inference engine instance. std::unique_ptr as_engine = std::make_unique(); @@ -88,15 +88,15 @@ int main(int argc, char** argv) { // the convert model part, please see python example, // it will generate a running model from // a huggleface type model to a allspark model, - // file name will be like "qwen_v15.asparam, qwen_v15.asgraph" + // file name will be like "qwen_v15.ditensors, qwen_v15.dimodel" // // use model config builder to build model config. AsModelConfigBuilder model_config_builder; auto model_config = model_config_builder.withModelName(model_name) - .withModelPath(asgraph_file) - .withWeightsPath(asparam_file) + .withModelPath(dimodel_file) + .withWeightsPath(ditensors_file) .withEngineMaxLength(default_engine_max_length) .withEngineMaxBatch(16) .withMatmulPrecision("medium") diff --git a/examples/cpp/1_apiserver/apiserver.cpp b/examples/cpp/1_apiserver/apiserver.cpp index ed68233e..ca00e4eb 100644 --- a/examples/cpp/1_apiserver/apiserver.cpp +++ b/examples/cpp/1_apiserver/apiserver.cpp @@ -383,8 +383,8 @@ int main(int argc, const char** argv) { auto all_exists = check_model_file_exists(model_path, tiktoken_file); if (!all_exists) return 1; - std::string asgraph_file = model_path + ".asgraph"; - std::string asparam_file = model_path + ".asparam"; + std::string dimodel_file = model_path + ".dimodel"; + std::string ditensors_file = model_path + ".ditensors"; // create an inference engine instance. setup_tiktoken_tokenizer(tiktoken_file, tokenizer); @@ -395,8 +395,8 @@ int main(int argc, const char** argv) { AsModelConfigBuilder model_config_builder; auto model_config = model_config_builder.withModelName(model_name) - .withModelPath(asgraph_file) - .withWeightsPath(asparam_file) + .withModelPath(dimodel_file) + .withWeightsPath(ditensors_file) .withEngineMaxLength(default_engine_max_length) .withEngineMaxBatch(16) .withMatmulPrecision("medium") diff --git a/examples/cpp/utils/misc.hpp b/examples/cpp/utils/misc.hpp index 5e179b9e..243252f4 100644 --- a/examples/cpp/utils/misc.hpp +++ b/examples/cpp/utils/misc.hpp @@ -20,22 +20,22 @@ static bool file_exists(const std::string& path) { static bool check_model_file_exists(const std::string& model_path, const std::string& tiktoken_file) { - std::string asgraph_file = model_path + ".asgraph"; - std::string asparam_file = model_path + ".asparam"; + std::string dimodel_file = model_path + ".dimodel"; + std::string ditensors_file = model_path + ".ditensors"; std::cout << "Model Path: " << model_path << std::endl; - std::cout << "ASGraph File: " << asgraph_file << std::endl; - std::cout << "ASParam File: " << asparam_file << std::endl; + std::cout << "Dash-Infer Model File: " << dimodel_file << std::endl; + std::cout << "Dash-Infer Tensor File: " << ditensors_file << std::endl; std::cout << "Token table File: " << tiktoken_file << std::endl; - if (!file_exists(asgraph_file)) { - std::cerr << "Error: ASGraph file does not exist at '" << asgraph_file + if (!file_exists(dimodel_file)) { + std::cerr << "Error: dimodel file does not exist at '" << dimodel_file << "'" << std::endl; return false; } - if (!file_exists(asparam_file)) { - std::cerr << "Error: ASParam file does not exist at '" << asparam_file + if (!file_exists(ditensors_file)) { + std::cerr << "Error: ditensors file does not exist at '" << ditensors_file << "'" << std::endl; return false; } diff --git a/examples/python/0_basic/basic_example_chatglm2.py b/examples/python/0_basic/basic_example_chatglm2.py index 9d90543c..fa6b731c 100644 --- a/examples/python/0_basic/basic_example_chatglm2.py +++ b/examples/python/0_basic/basic_example_chatglm2.py @@ -3,11 +3,10 @@ # @file basic_example_chatglm2.py # import os -import sys import copy import time -import queue import random +import argparse import subprocess from jinja2 import Template from concurrent.futures import ThreadPoolExecutor @@ -83,8 +82,13 @@ def done_callback(future): if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('--quantize', action='store_true') + args = parser.parse_args() + config_file = "../model_config/config_chatglm2_6b.json" config = ConfigManager.get_config_from_json(config_file) + config["convert_config"]["do_dynamic_quantize_convert"] = args.quantize cmd = f"pip show dashinfer | grep 'Location' | cut -d ' ' -f 2" package_location = subprocess.run(cmd, diff --git a/examples/python/0_basic/basic_example_chatglm3.py b/examples/python/0_basic/basic_example_chatglm3.py index 56273ca0..fb6f3279 100644 --- a/examples/python/0_basic/basic_example_chatglm3.py +++ b/examples/python/0_basic/basic_example_chatglm3.py @@ -3,11 +3,10 @@ # @file basic_example_chatglm3.py # import os -import sys import copy import time -import queue import random +import argparse import subprocess from jinja2 import Template from concurrent.futures import ThreadPoolExecutor @@ -86,8 +85,13 @@ def done_callback(future): if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('--quantize', action='store_true') + args = parser.parse_args() + config_file = "../model_config/config_chatglm3_6b.json" config = ConfigManager.get_config_from_json(config_file) + config["convert_config"]["do_dynamic_quantize_convert"] = args.quantize cmd = f"pip show dashinfer | grep 'Location' | cut -d ' ' -f 2" package_location = subprocess.run(cmd, diff --git a/examples/python/0_basic/basic_example_llama2.py b/examples/python/0_basic/basic_example_llama2.py index b0702488..5b8b7289 100644 --- a/examples/python/0_basic/basic_example_llama2.py +++ b/examples/python/0_basic/basic_example_llama2.py @@ -3,11 +3,10 @@ # @file basic_example_llama2.py # import os -import sys import copy import time -import queue import random +import argparse import subprocess from jinja2 import Template from concurrent.futures import ThreadPoolExecutor @@ -74,8 +73,13 @@ def done_callback(future): if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('--quantize', action='store_true') + args = parser.parse_args() + config_file = "../model_config/config_llama2_7b.json" config = ConfigManager.get_config_from_json(config_file) + config["convert_config"]["do_dynamic_quantize_convert"] = args.quantize cmd = f"pip show dashinfer | grep 'Location' | cut -d ' ' -f 2" package_location = subprocess.run(cmd, diff --git a/examples/python/0_basic/basic_example_qwen_v10.py b/examples/python/0_basic/basic_example_qwen_v10.py index 8b860a6d..4b2ca4f8 100644 --- a/examples/python/0_basic/basic_example_qwen_v10.py +++ b/examples/python/0_basic/basic_example_qwen_v10.py @@ -3,11 +3,10 @@ # @file basic_example_qwen_v10.py # import os -import sys import copy import time -import queue import random +import argparse import subprocess from jinja2 import Template from concurrent.futures import ThreadPoolExecutor @@ -92,8 +91,13 @@ def done_callback(future): if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('--quantize', action='store_true') + args = parser.parse_args() + config_file = "../model_config/config_qwen_v10_1_8b.json" config = ConfigManager.get_config_from_json(config_file) + config["convert_config"]["do_dynamic_quantize_convert"] = args.quantize cmd = f"pip show dashinfer | grep 'Location' | cut -d ' ' -f 2" package_location = subprocess.run(cmd, diff --git a/examples/python/0_basic/basic_example_qwen_v10_io.ipynb b/examples/python/0_basic/basic_example_qwen_v10_io.ipynb index 7e09606f..bf3368d2 100644 --- a/examples/python/0_basic/basic_example_qwen_v10_io.ipynb +++ b/examples/python/0_basic/basic_example_qwen_v10_io.ipynb @@ -226,7 +226,7 @@ "### convert_config: {'do_dynamic_quantize_convert': False}\n", "### engine_config: {'engine_max_length': 2048, 'engine_max_batch': 8, 'do_profiling': False, 'num_threads': 0, 'matmul_precision': 'medium'}\n", "\n", - "No such file or directory: ./dashinfer_models/Qwen-1_8B-Chat_cpu_single_float32.asgraph\n", + "No such file or directory: ./dashinfer_models/Qwen-1_8B-Chat_cpu_single_float32.dimodel\n", "\n" ] }, @@ -245,11 +245,11 @@ "trans model from huggingface model: /mnt/workspace/.cache/modelscope/qwen/Qwen-1_8B-Chat\n", "Dashinfer model will save to ./dashinfer_models/\n", "### model_config: {'vocab_size': 151936, 'hidden_size': 2048, 'intermediate_size': 11008, 'num_hidden_layers': 24, 'num_attention_heads': 16, 'emb_dropout_prob': 0.0, 'attn_dropout_prob': 0.0, 'layer_norm_epsilon': 1e-06, 'initializer_range': 0.02, 'scale_attn_weights': True, 'use_cache': True, 'max_position_embeddings': 8192, 'bf16': False, 'fp16': False, 'fp32': True, 'kv_channels': 128, 'rotary_pct': 1.0, 'rotary_emb_base': 10000, 'use_dynamic_ntk': True, 'use_logn_attn': True, 'use_flash_attn': False, 'no_bias': True, 'use_cache_quantization': False, 'use_cache_kernel': False, 'softmax_in_fp32': False, 'return_dict': True, 'output_hidden_states': False, 'output_attentions': False, 'torchscript': False, 'torch_dtype': None, 'use_bfloat16': False, 'tf_legacy_loss': False, 'pruned_heads': {}, 'tie_word_embeddings': False, 'chunk_size_feed_forward': 0, 'is_encoder_decoder': False, 'is_decoder': False, 'cross_attention_hidden_size': None, 'add_cross_attention': False, 'tie_encoder_decoder': False, 'max_length': 20, 'min_length': 0, 'do_sample': False, 'early_stopping': False, 'num_beams': 1, 'num_beam_groups': 1, 'diversity_penalty': 0.0, 'temperature': 1.0, 'top_k': 50, 'top_p': 1.0, 'typical_p': 1.0, 'repetition_penalty': 1.0, 'length_penalty': 1.0, 'no_repeat_ngram_size': 0, 'encoder_no_repeat_ngram_size': 0, 'bad_words_ids': None, 'num_return_sequences': 1, 'output_scores': False, 'return_dict_in_generate': False, 'forced_bos_token_id': None, 'forced_eos_token_id': None, 'remove_invalid_values': False, 'exponential_decay_length_penalty': None, 'suppress_tokens': None, 'begin_suppress_tokens': None, 'architectures': ['QWenLMHeadModel'], 'finetuning_task': None, 'id2label': {0: 'LABEL_0', 1: 'LABEL_1'}, 'label2id': {'LABEL_0': 0, 'LABEL_1': 1}, 'tokenizer_class': 'QWenTokenizer', 'prefix': None, 'bos_token_id': None, 'pad_token_id': None, 'eos_token_id': None, 'sep_token_id': None, 'decoder_start_token_id': None, 'task_specific_params': None, 'problem_type': None, '_name_or_path': '/mnt/workspace/.cache/modelscope/qwen/Qwen-1_8B-Chat', '_commit_hash': None, '_attn_implementation_internal': None, 'transformers_version': '4.32.0', 'auto_map': {'AutoConfig': 'configuration_qwen.QWenConfig', 'AutoModelForCausalLM': 'modeling_qwen.QWenLMHeadModel'}, 'model_type': 'qwen', 'onnx_safe': None, 'seq_length': 8192, 'size_per_head': 128}\n", - "save asgraph to ./dashinfer_models/Qwen-1_8B-Chat_cpu_single_float32.asgraph\n", - "save asparam to ./dashinfer_models/Qwen-1_8B-Chat_cpu_single_float32.asparam\n", + "save dimodel to ./dashinfer_models/Qwen-1_8B-Chat_cpu_single_float32.dimodel\n", + "save ditensors to ./dashinfer_models/Qwen-1_8B-Chat_cpu_single_float32.ditensors\n", "parse weight time: 8.782530069351196\n", "current allspark version major[ 1 ] minor[ 0 ] patch[ 4 ] commit = 0549ab25\n", - "calculate md5 of asgraph = 2d5d46b154e9f0c8bd51274775675eea\n", + "calculate md5 of dimodel = 2d5d46b154e9f0c8bd51274775675eea\n", "torch build meta: \t model_name \t: Qwen-1_8B-Chat_cpu_single_float32\n", "torch build meta: \t model_type \t: Qwen_v10\n", "torch build meta: \t save_dir \t: ./dashinfer_models/\n", @@ -272,8 +272,8 @@ "I20240513 10:46:39.666299 732 as_engine.cpp:384] Build model use following config:\n", "AsModelConfig :\n", "\tmodel_name: Qwen-1_8B-Chat_cpu_single_float32\n", - "\tmodel_path: ./dashinfer_models/Qwen-1_8B-Chat_cpu_single_float32.asgraph\n", - "\tweights_path: ./dashinfer_models/Qwen-1_8B-Chat_cpu_single_float32.asparam\n", + "\tmodel_path: ./dashinfer_models/Qwen-1_8B-Chat_cpu_single_float32.dimodel\n", + "\tweights_path: ./dashinfer_models/Qwen-1_8B-Chat_cpu_single_float32.ditensors\n", "\tcompute_unit: CPU:0\n", "\tnum_threads: 32\n", "\tmatmul_precision: medium\n", @@ -282,7 +282,7 @@ "\tengine_max_length = 2048\n", "\tengine_max_batch = 8\n", "\n", - "I20240513 10:46:39.666332 732 as_engine.cpp:388] Load model from : ./dashinfer_models/Qwen-1_8B-Chat_cpu_single_float32.asgraph\n", + "I20240513 10:46:39.666332 732 as_engine.cpp:388] Load model from : ./dashinfer_models/Qwen-1_8B-Chat_cpu_single_float32.dimodel\n", "I20240513 10:46:39.666359 732 as_engine.cpp:303] SetDeviceIds: DeviceIDs.size() 1\n", "I20240513 10:46:39.666363 732 as_engine.cpp:310] Start create 1 Device: CPU workers.\n", "I20240513 10:46:39.666476 988 cpu_context.cpp:114] CPUContext::InitMCCL() rank: 0 nRanks: 1\n", @@ -290,7 +290,7 @@ "I20240513 10:46:39.720805 732 as_engine.cpp:511] Start BuildModel\n", "I20240513 10:46:39.720876 990 as_engine.cpp:521] Start Build model for rank: 0\n", "I20240513 10:46:39.720901 990 weight_manager.cpp:131] Start Loading weight for model RankInfo[0/1]\n", - "I20240513 10:46:39.720912 990 weight_manager.cpp:52] Start open model file ./dashinfer_models/Qwen-1_8B-Chat_cpu_single_float32.asparam\n", + "I20240513 10:46:39.720912 990 weight_manager.cpp:52] Start open model file ./dashinfer_models/Qwen-1_8B-Chat_cpu_single_float32.ditensors\n", "I20240513 10:46:39.720919 990 weight_manager.cpp:59] Open model file " ] }, diff --git a/examples/python/0_basic/basic_example_qwen_v10_io.py b/examples/python/0_basic/basic_example_qwen_v10_io.py index cc611272..eabd10b8 100644 --- a/examples/python/0_basic/basic_example_qwen_v10_io.py +++ b/examples/python/0_basic/basic_example_qwen_v10_io.py @@ -7,6 +7,7 @@ import copy import time import random +import argparse import subprocess from jinja2 import Template from concurrent.futures import ThreadPoolExecutor @@ -71,8 +72,13 @@ def print_in_place(generator): if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('--quantize', action='store_true') + args = parser.parse_args() + config_file = "../model_config/config_qwen_v10_1_8b.json" config = ConfigManager.get_config_from_json(config_file) + config["convert_config"]["do_dynamic_quantize_convert"] = args.quantize cmd = f"pip show dashinfer | grep 'Location' | cut -d ' ' -f 2" package_location = subprocess.run(cmd, diff --git a/examples/python/0_basic/basic_example_qwen_v15.py b/examples/python/0_basic/basic_example_qwen_v15.py index 963836b7..0ff39a03 100644 --- a/examples/python/0_basic/basic_example_qwen_v15.py +++ b/examples/python/0_basic/basic_example_qwen_v15.py @@ -3,11 +3,10 @@ # @file basic_example_qwen_v15.py # import os -import sys import copy import time -import queue import random +import argparse import subprocess from jinja2 import Template from concurrent.futures import ThreadPoolExecutor @@ -106,8 +105,13 @@ def done_callback(future): if __name__ == '__main__': check_transformers_version() + parser = argparse.ArgumentParser() + parser.add_argument('--quantize', action='store_true') + args = parser.parse_args() + config_file = "../model_config/config_qwen_v15_1_8b.json" config = ConfigManager.get_config_from_json(config_file) + config["convert_config"]["do_dynamic_quantize_convert"] = args.quantize cmd = f"pip show dashinfer | grep 'Location' | cut -d ' ' -f 2" package_location = subprocess.run(cmd, diff --git a/examples/python/0_basic/basic_example_qwen_v15_dimodel.py b/examples/python/0_basic/basic_example_qwen_v15_dimodel.py new file mode 100644 index 00000000..31146f3c --- /dev/null +++ b/examples/python/0_basic/basic_example_qwen_v15_dimodel.py @@ -0,0 +1,122 @@ +# +# Copyright (c) Alibaba, Inc. and its affiliates. +# @file basic_example_qwen_v15_dimodel.py +# +import os +import copy +import time +import random +import argparse +import subprocess +from jinja2 import Template +from concurrent.futures import ThreadPoolExecutor + +from modelscope import snapshot_download +from dashinfer.helper import EngineHelper, ConfigManager + + +def create_test_prompt(default_gen_cfg=None): + input_list = [ + "浙江的省会在哪", + "Where is the capital of Zhejiang?", + "将“温故而知新”翻译成英文，并解释其含义", + ] + + start_text = "<|im_start|>" + end_text = "<|im_end|>" + system_msg = {"role": "system", "content": "You are a helpful assistant."} + user_msg = {"role": "user", "content": ""} + assistant_msg = {"role": "assistant", "content": ""} + + prompt_template = Template( + "{{start_text}}" + "{{system_role}}\n" + "{{system_content}}" + "{{end_text}}\n" + + "{{start_text}}" + "{{user_role}}\n" + "{{user_content}}" + "{{end_text}}\n" + + "{{start_text}}" + "{{assistant_role}}\n") + + gen_cfg_list = [] + prompt_list = [] + for i in range(len(input_list)): + user_msg["content"] = input_list[i] + prompt = prompt_template.render(start_text=start_text, end_text=end_text, + system_role=system_msg["role"], system_content=system_msg["content"], + user_role=user_msg["role"], user_content=user_msg["content"], + assistant_role=assistant_msg["role"]) + prompt_list.append(prompt) + if default_gen_cfg != None: + gen_cfg = copy.deepcopy(default_gen_cfg) + gen_cfg["seed"] = random.randint(0, 10000) + gen_cfg_list.append(gen_cfg) + + return prompt_list, gen_cfg_list + + +def process_request(request_list, engine_helper: EngineHelper): + + def done_callback(future): + request = future.argument + future.result() + engine_helper.print_inference_result(request) + + # create a threadpool + executor = ThreadPoolExecutor( + max_workers=engine_helper.engine_config["engine_max_batch"]) + + try: + # submit all tasks to the threadpool + futures = [] + for request in request_list: + future = executor.submit(engine_helper.process_one_request, request) + future.argument = request + future.add_done_callback(done_callback) + futures.append(future) + finally: + executor.shutdown(wait=True) + + return + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('--quantize', action='store_true') + args = parser.parse_args() + + model_path = snapshot_download("dash-infer/Qwen1.5-1.8B-Chat-DI") + + config_file = model_path + "/" + "di_config.json" + config = ConfigManager.get_config_from_json(config_file) + config["convert_config"]["do_dynamic_quantize_convert"] = args.quantize + config["model_path"] = model_path + + cmd = f"pip show dashinfer | grep 'Location' | cut -d ' ' -f 2" + package_location = subprocess.run(cmd, + stdout=subprocess.PIPE, + stderr=subprocess.PIPE, + shell=True, + text=True) + package_location = package_location.stdout.strip() + os.environ["AS_DAEMON_PATH"] = package_location + "/dashinfer/allspark/bin" + os.environ["AS_NUMA_NUM"] = str(len(config["device_ids"])) + os.environ["AS_NUMA_OFFSET"] = str(config["device_ids"][0]) + + ## init EngineHelper class + engine_helper = EngineHelper(config) + engine_helper.verbose = True + engine_helper.init_tokenizer(model_path) + + ## inference + engine_helper.init_engine() + + prompt_list, gen_cfg_list = create_test_prompt( + engine_helper.default_gen_cfg) + request_list = engine_helper.create_request(prompt_list, gen_cfg_list) + + global_start = time.time() + process_request(request_list, engine_helper) + global_end = time.time() + + total_timecost = global_end - global_start + # engine_helper.print_inference_result_all(request_list) + engine_helper.print_profiling_data(request_list, total_timecost) + print(f"total timecost: {total_timecost} s") + + engine_helper.uninit_engine() diff --git a/examples/python/0_basic/basic_example_qwen_v15_dimodel_simple.py b/examples/python/0_basic/basic_example_qwen_v15_dimodel_simple.py new file mode 100644 index 00000000..a175a825 --- /dev/null +++ b/examples/python/0_basic/basic_example_qwen_v15_dimodel_simple.py @@ -0,0 +1,38 @@ +# +# Copyright (c) Alibaba, Inc. and its affiliates. +# @file basic_example_qwen_v15_dimodel_simple.py +# +import copy +import random + +from modelscope import snapshot_download +from dashinfer.helper import EngineHelper, ConfigManager + +model_path = snapshot_download("dash-infer/Qwen1.5-1.8B-Chat-DI") + +config_file = model_path + "/" + "di_config.json" +config = ConfigManager.get_config_from_json(config_file) +config["model_path"] = model_path + +## init EngineHelper class +engine_helper = EngineHelper(config) +engine_helper.verbose = True +engine_helper.init_tokenizer(model_path) + +## init engine +engine_helper.init_engine() + +## prepare inputs and generation configs +user_input = "浙江的省会在哪" +prompt = "<|im_start|>" + "system\n" + "You are a helpful assistant." + "<|im_end|>\n" + \ + "<|im_start|>" + "user\n" + user_input + "<|im_end|>\n" + \ + "<|im_start|>" + "assistant\n" +gen_cfg = copy.deepcopy(engine_helper.default_gen_cfg) +gen_cfg["seed"] = random.randint(0, 10000) +request_list = engine_helper.create_request([prompt], [gen_cfg]) + +## inference +engine_helper.process_one_request(request_list[0]) +engine_helper.print_inference_result_all(request_list) + +engine_helper.uninit_engine() diff --git a/examples/python/0_basic/basic_example_qwen_v20.py b/examples/python/0_basic/basic_example_qwen_v20.py new file mode 100644 index 00000000..a2d2c0b0 --- /dev/null +++ b/examples/python/0_basic/basic_example_qwen_v20.py @@ -0,0 +1,172 @@ +# +# Copyright (c) Alibaba, Inc. and its affiliates. +# @file basic_example_qwen_v20.py +# +import os +import copy +import time +import random +import argparse +import subprocess +from jinja2 import Template +from concurrent.futures import ThreadPoolExecutor + +from dashinfer.helper import EngineHelper, ConfigManager + + +def check_transformers_version(): + import transformers + required_version = "4.37.0" + current_version = transformers.__version__ + + if current_version < required_version: + raise Exception( + f"Transformers version {current_version} is lower than required version {required_version}. Please upgrade transformers to version {required_version}." + ) + exit() + + +def download_model(model_id, revision, source="modelscope"): + print(f"Downloading model {model_id} (revision: {revision}) from {source}") + if source == "modelscope": + from modelscope import snapshot_download + model_dir = snapshot_download(model_id, revision=revision) + elif source == "huggingface": + from huggingface_hub import snapshot_download + model_dir = snapshot_download(repo_id=model_id) + else: + raise ValueError("Unknown source") + + print(f"Save model to path {model_dir}") + + return model_dir + + +def create_test_prompt(default_gen_cfg=None): + input_list = [ + "浙江的省会在哪", + "Where is the capital of Zhejiang?", + "将“温故而知新”翻译成英文，并解释其含义", + ] + + start_text = "<|im_start|>" + end_text = "<|im_end|>" + system_msg = {"role": "system", "content": "You are a helpful assistant."} + user_msg = {"role": "user", "content": ""} + assistant_msg = {"role": "assistant", "content": ""} + + prompt_template = Template( + "{{start_text}}" + "{{system_role}}\n" + "{{system_content}}" + "{{end_text}}\n" + + "{{start_text}}" + "{{user_role}}\n" + "{{user_content}}" + "{{end_text}}\n" + + "{{start_text}}" + "{{assistant_role}}\n") + + gen_cfg_list = [] + prompt_list = [] + for i in range(len(input_list)): + user_msg["content"] = input_list[i] + prompt = prompt_template.render(start_text=start_text, end_text=end_text, + system_role=system_msg["role"], system_content=system_msg["content"], + user_role=user_msg["role"], user_content=user_msg["content"], + assistant_role=assistant_msg["role"]) + prompt_list.append(prompt) + if default_gen_cfg != None: + gen_cfg = copy.deepcopy(default_gen_cfg) + gen_cfg["seed"] = random.randint(0, 10000) + gen_cfg_list.append(gen_cfg) + + return prompt_list, gen_cfg_list + + +def process_request(request_list, engine_helper: EngineHelper): + + def done_callback(future): + request = future.argument + future.result() + engine_helper.print_inference_result(request) + + # create a threadpool + executor = ThreadPoolExecutor( + max_workers=engine_helper.engine_config["engine_max_batch"]) + + try: + # submit all tasks to the threadpool + futures = [] + for request in request_list: + future = executor.submit(engine_helper.process_one_request, request) + future.argument = request + future.add_done_callback(done_callback) + futures.append(future) + finally: + executor.shutdown(wait=True) + + return + + +if __name__ == '__main__': + check_transformers_version() + + parser = argparse.ArgumentParser() + parser.add_argument('--quantize', action='store_true') + args = parser.parse_args() + + config_file = "../model_config/config_qwen_v20_1_5b.json" + config = ConfigManager.get_config_from_json(config_file) + config["convert_config"]["do_dynamic_quantize_convert"] = args.quantize + + cmd = f"pip show dashinfer | grep 'Location' | cut -d ' ' -f 2" + package_location = subprocess.run(cmd, + stdout=subprocess.PIPE, + stderr=subprocess.PIPE, + shell=True, + text=True) + package_location = package_location.stdout.strip() + os.environ["AS_DAEMON_PATH"] = package_location + "/dashinfer/allspark/bin" + os.environ["AS_NUMA_NUM"] = str(len(config["device_ids"])) + os.environ["AS_NUMA_OFFSET"] = str(config["device_ids"][0]) + + ## download original model + ## download model from huggingface + # original_model = { + # "source": "huggingface", + # "model_id": "Qwen/Qwen2-1.5B", + # "revision": "", + # "model_path": "" + # } + + ## download model from modelscope + original_model = { + "source": "modelscope", + "model_id": "qwen/Qwen2-1.5B", + "revision": "master", + "model_path": "" + } + original_model["model_path"] = download_model(original_model["model_id"], + original_model["revision"], + original_model["source"]) + + ## init EngineHelper class + engine_helper = EngineHelper(config) + engine_helper.verbose = True + engine_helper.init_tokenizer(original_model["model_path"]) + + ## convert huggingface model to dashinfer model + ## only one conversion is required + engine_helper.convert_model(original_model["model_path"]) + + ## inference + engine_helper.init_engine() + + prompt_list, gen_cfg_list = create_test_prompt( + engine_helper.default_gen_cfg) + request_list = engine_helper.create_request(prompt_list, gen_cfg_list) + + global_start = time.time() + process_request(request_list, engine_helper) + global_end = time.time() + + total_timecost = global_end - global_start + # engine_helper.print_inference_result_all(request_list) + engine_helper.print_profiling_data(request_list, total_timecost) + print(f"total timecost: {total_timecost} s") + + engine_helper.uninit_engine() diff --git a/examples/python/1_performance/performance_test_llama2.py b/examples/python/1_performance/performance_test_llama2.py index 3ebbe928..81f55944 100644 --- a/examples/python/1_performance/performance_test_llama2.py +++ b/examples/python/1_performance/performance_test_llama2.py @@ -8,10 +8,10 @@ import time import queue import random +import argparse import subprocess import numpy as np from concurrent.futures import ThreadPoolExecutor -import argparse from dashinfer.helper import EngineHelper, ConfigManager @@ -76,6 +76,7 @@ def done_callback(future): parser.add_argument('--config_file', type=str, default='config_llama2_7b.json') parser.add_argument('--device_ids', nargs='+', type=int, default=[0]) parser.add_argument('--multinode_mode', action='store_true') + parser.add_argument('--quantize', action='store_true') args = parser.parse_args() @@ -86,6 +87,7 @@ def done_callback(future): config["generation_config"]["stop_words_ids"] = [] config["device_ids"] = args.device_ids config["multinode_mode"] = args.multinode_mode + config["convert_config"]["do_dynamic_quantize_convert"] = args.quantize cmd = f"pip show dashinfer | grep 'Location' | cut -d ' ' -f 2" package_location = subprocess.run(cmd, diff --git a/examples/python/1_performance/performance_test_qwen_v15.py b/examples/python/1_performance/performance_test_qwen_v15.py index 88e72dc5..9bdc222d 100644 --- a/examples/python/1_performance/performance_test_qwen_v15.py +++ b/examples/python/1_performance/performance_test_qwen_v15.py @@ -8,10 +8,10 @@ import time import queue import random +import argparse import subprocess import numpy as np from concurrent.futures import ThreadPoolExecutor -import argparse from dashinfer.helper import EngineHelper, ConfigManager @@ -76,6 +76,7 @@ def done_callback(future): parser.add_argument('--config_file', type=str, default='config_qwen_v15_4b.json') parser.add_argument('--device_ids', nargs='+', type=int, default=[0]) parser.add_argument('--multinode_mode', action='store_true') + parser.add_argument('--quantize', action='store_true') args = parser.parse_args() @@ -86,6 +87,7 @@ def done_callback(future): config["generation_config"]["stop_words_ids"] = [] config["device_ids"] = args.device_ids config["multinode_mode"] = args.multinode_mode + config["convert_config"]["do_dynamic_quantize_convert"] = args.quantize cmd = f"pip show dashinfer | grep 'Location' | cut -d ' ' -f 2" package_location = subprocess.run(cmd, diff --git a/examples/python/2_evaluation/evaluate_chat_ceval.py b/examples/python/2_evaluation/evaluate_chat_ceval.py index f82bd41a..a9edf571 100644 --- a/examples/python/2_evaluation/evaluate_chat_ceval.py +++ b/examples/python/2_evaluation/evaluate_chat_ceval.py @@ -445,6 +445,7 @@ def prepare(args): os.environ["AS_NUMA_NUM"] = str(len(config["device_ids"])) os.environ["AS_NUMA_OFFSET"] = str(config["device_ids"][0]) config["model_path"] = args.model_path + config["convert_config"]["do_dynamic_quantize_convert"] = True ## download model from modelscope original_model = { @@ -476,7 +477,7 @@ def prepare(args): if __name__ == "__main__": parser = argparse.ArgumentParser(description="Test HF checkpoint.") parser.add_argument('--model_path', type=str, default='~/dashinfer_models/') - parser.add_argument('--config_file', type=str, default='../model_config/config_qwen_v10_7b_quantize.json') + parser.add_argument('--config_file', type=str, default='../model_config/config_qwen_v10_7b.json') # Provide extra arguments required for tasks group = parser.add_argument_group(title="Evaluation options") diff --git a/examples/python/2_evaluation/evaluate_chat_gsm8k.py b/examples/python/2_evaluation/evaluate_chat_gsm8k.py index d8c7c86d..b75d386b 100644 --- a/examples/python/2_evaluation/evaluate_chat_gsm8k.py +++ b/examples/python/2_evaluation/evaluate_chat_gsm8k.py @@ -114,6 +114,7 @@ def prepare(args): os.environ["AS_NUMA_NUM"] = str(len(config["device_ids"])) os.environ["AS_NUMA_OFFSET"] = str(config["device_ids"][0]) config["model_path"] = args.model_path + config["convert_config"]["do_dynamic_quantize_convert"] = True ## download model from modelscope original_model = { @@ -145,7 +146,7 @@ def prepare(args): if __name__ == "__main__": parser = argparse.ArgumentParser(description="Test HF checkpoint.") parser.add_argument('--model_path', type=str, default='~/dashinfer_models/') - parser.add_argument('--config_file', type=str, default='../model_config/config_qwen_v10_7b_quantize.json') + parser.add_argument('--config_file', type=str, default='../model_config/config_qwen_v10_7b.json') parser.add_argument("-f", "--sample-input-file", type=str, default=None) parser.add_argument( diff --git a/examples/python/2_evaluation/evaluate_chat_humaneval.py b/examples/python/2_evaluation/evaluate_chat_humaneval.py index 95b091e3..ad650d1f 100644 --- a/examples/python/2_evaluation/evaluate_chat_humaneval.py +++ b/examples/python/2_evaluation/evaluate_chat_humaneval.py @@ -94,6 +94,7 @@ def prepare(args): os.environ["AS_NUMA_NUM"] = str(len(config["device_ids"])) os.environ["AS_NUMA_OFFSET"] = str(config["device_ids"][0]) config["model_path"] = args.model_path + config["convert_config"]["do_dynamic_quantize_convert"] = True ## download model from modelscope original_model = { @@ -125,7 +126,7 @@ def prepare(args): if __name__ == "__main__": parser = argparse.ArgumentParser(description="Test HF checkpoint.") parser.add_argument('--model_path', type=str, default='~/dashinfer_models/') - parser.add_argument('--config_file', type=str, default='../model_config/config_qwen_v10_7b_quantize.json') + parser.add_argument('--config_file', type=str, default='../model_config/config_qwen_v10_7b.json') parser.add_argument( "-f", diff --git a/examples/python/2_evaluation/evaluate_chat_mmlu.py b/examples/python/2_evaluation/evaluate_chat_mmlu.py index 44498408..52329387 100644 --- a/examples/python/2_evaluation/evaluate_chat_mmlu.py +++ b/examples/python/2_evaluation/evaluate_chat_mmlu.py @@ -298,6 +298,7 @@ def prepare(args): os.environ["AS_NUMA_NUM"] = str(len(config["device_ids"])) os.environ["AS_NUMA_OFFSET"] = str(config["device_ids"][0]) config["model_path"] = args.model_path + config["convert_config"]["do_dynamic_quantize_convert"] = True ## download model from modelscope original_model = { @@ -329,7 +330,7 @@ def prepare(args): if __name__ == "__main__": parser = argparse.ArgumentParser(description="Test HF checkpoint.") parser.add_argument('--model_path', type=str, default='~/dashinfer_models/') - parser.add_argument('--config_file', type=str, default='../model_config/config_qwen_v10_7b_quantize.json') + parser.add_argument('--config_file', type=str, default='../model_config/config_qwen_v10_7b.json') # Provide extra arguments required for tasks group = parser.add_argument_group(title="Evaluation options") diff --git a/examples/python/model_config/config_chatglm2_6b.json b/examples/python/model_config/config_chatglm2_6b.json index 8d267d78..9f3b9b61 100644 --- a/examples/python/model_config/config_chatglm2_6b.json +++ b/examples/python/model_config/config_chatglm2_6b.json @@ -8,9 +8,6 @@ 0 ], "multinode_mode": false, - "convert_config": { - "do_dynamic_quantize_convert": false - }, "engine_config": { "engine_max_length": 2048, "engine_max_batch": 8, @@ -31,5 +28,13 @@ "eos_token_id": 2, "seed": 1234 }, - "quantization_config": null + "convert_config": { + "do_dynamic_quantize_convert": false + }, + "quantization_config": { + "activation_type": "bfloat16", + "weight_type": "uint8", + "SubChannel": true, + "GroupSize": 512 + } } \ No newline at end of file diff --git a/examples/python/model_config/config_chatglm2_6b_32k.json b/examples/python/model_config/config_chatglm2_6b_32k.json index 511f9afe..166730ff 100644 --- a/examples/python/model_config/config_chatglm2_6b_32k.json +++ b/examples/python/model_config/config_chatglm2_6b_32k.json @@ -8,9 +8,6 @@ 0 ], "multinode_mode": false, - "convert_config": { - "do_dynamic_quantize_convert": false - }, "engine_config": { "engine_max_length": 11000, "engine_max_batch": 8, @@ -31,5 +28,13 @@ "eos_token_id": 2, "seed": 1234 }, - "quantization_config": null + "convert_config": { + "do_dynamic_quantize_convert": false + }, + "quantization_config": { + "activation_type": "bfloat16", + "weight_type": "uint8", + "SubChannel": true, + "GroupSize": 512 + } } \ No newline at end of file diff --git a/examples/python/model_config/config_chatglm2_6b_32k_quantize.json b/examples/python/model_config/config_chatglm2_6b_32k_quantize.json deleted file mode 100644 index 6753730e..00000000 --- a/examples/python/model_config/config_chatglm2_6b_32k_quantize.json +++ /dev/null @@ -1,40 +0,0 @@ -{ - "model_name": "chatglm2-6b-32k", - "model_type": "ChatGLM_v2", - "model_path": "~/dashinfer_models/", - "data_type": "float32", - "device_type": "CPU", - "device_ids": [ - 0 - ], - "multinode_mode": false, - "convert_config": { - "do_dynamic_quantize_convert": true - }, - "engine_config": { - "engine_max_length": 11000, - "engine_max_batch": 8, - "do_profiling": false, - "num_threads": 0, - "matmul_precision": "medium" - }, - "generation_config": { - "temperature": 0.8, - "early_stopping": true, - "top_k": 1024, - "top_p": 0.8, - "repetition_penalty": 1.0, - "presence_penalty": 0.0, - "min_length": 0, - "max_length": 11000, - "no_repeat_ngram_size": 0, - "eos_token_id": 2, - "seed": 1234 - }, - "quantization_config": { - "activation_type": "bfloat16", - "weight_type": "uint8", - "SubChannel": true, - "GroupSize": 512 - } -} \ No newline at end of file diff --git a/examples/python/model_config/config_chatglm2_6b_quantize.json b/examples/python/model_config/config_chatglm2_6b_quantize.json deleted file mode 100644 index cb448420..00000000 --- a/examples/python/model_config/config_chatglm2_6b_quantize.json +++ /dev/null @@ -1,40 +0,0 @@ -{ - "model_name": "chatglm2-6b", - "model_type": "ChatGLM_v2", - "model_path": "~/dashinfer_models/", - "data_type": "float32", - "device_type": "CPU", - "device_ids": [ - 0 - ], - "multinode_mode": false, - "convert_config": { - "do_dynamic_quantize_convert": true - }, - "engine_config": { - "engine_max_length": 2048, - "engine_max_batch": 8, - "do_profiling": false, - "num_threads": 0, - "matmul_precision": "medium" - }, - "generation_config": { - "temperature": 0.8, - "early_stopping": true, - "top_k": 1024, - "top_p": 0.8, - "repetition_penalty": 1.0, - "presence_penalty": 0.0, - "min_length": 0, - "max_length": 2048, - "no_repeat_ngram_size": 0, - "eos_token_id": 2, - "seed": 1234 - }, - "quantization_config": { - "activation_type": "bfloat16", - "weight_type": "uint8", - "SubChannel": true, - "GroupSize": 512 - } -} \ No newline at end of file diff --git a/examples/python/model_config/config_chatglm3_6b.json b/examples/python/model_config/config_chatglm3_6b.json index 8af7c8fd..a33e4990 100644 --- a/examples/python/model_config/config_chatglm3_6b.json +++ b/examples/python/model_config/config_chatglm3_6b.json @@ -8,9 +8,6 @@ 0 ], "multinode_mode": false, - "convert_config": { - "do_dynamic_quantize_convert": false - }, "engine_config": { "engine_max_length": 2048, "engine_max_batch": 8, @@ -31,5 +28,13 @@ "eos_token_id": 2, "seed": 1234 }, - "quantization_config": null + "convert_config": { + "do_dynamic_quantize_convert": false + }, + "quantization_config": { + "activation_type": "bfloat16", + "weight_type": "uint8", + "SubChannel": true, + "GroupSize": 512 + } } \ No newline at end of file diff --git a/examples/python/model_config/config_chatglm3_6b_32k.json b/examples/python/model_config/config_chatglm3_6b_32k.json index 94fbbfc5..5a64b6ba 100644 --- a/examples/python/model_config/config_chatglm3_6b_32k.json +++ b/examples/python/model_config/config_chatglm3_6b_32k.json @@ -8,9 +8,6 @@ 0 ], "multinode_mode": false, - "convert_config": { - "do_dynamic_quantize_convert": false - }, "engine_config": { "engine_max_length": 11000, "engine_max_batch": 8, @@ -31,5 +28,13 @@ "eos_token_id": 2, "seed": 1234 }, - "quantization_config": null + "convert_config": { + "do_dynamic_quantize_convert": false + }, + "quantization_config": { + "activation_type": "bfloat16", + "weight_type": "uint8", + "SubChannel": true, + "GroupSize": 512 + } } \ No newline at end of file diff --git a/examples/python/model_config/config_chatglm3_6b_32k_quantize.json b/examples/python/model_config/config_chatglm3_6b_32k_quantize.json deleted file mode 100644 index 6173dbb7..00000000 --- a/examples/python/model_config/config_chatglm3_6b_32k_quantize.json +++ /dev/null @@ -1,40 +0,0 @@ -{ - "model_name": "chatglm3-6b-32k", - "model_type": "ChatGLM_v3", - "model_path": "~/dashinfer_models/", - "data_type": "float32", - "device_type": "CPU", - "device_ids": [ - 0 - ], - "multinode_mode": false, - "convert_config": { - "do_dynamic_quantize_convert": true - }, - "engine_config": { - "engine_max_length": 11000, - "engine_max_batch": 8, - "do_profiling": false, - "num_threads": 0, - "matmul_precision": "medium" - }, - "generation_config": { - "temperature": 0.8, - "early_stopping": true, - "top_k": 1024, - "top_p": 0.8, - "repetition_penalty": 1.0, - "presence_penalty": 0.0, - "min_length": 0, - "max_length": 11000, - "no_repeat_ngram_size": 0, - "eos_token_id": 2, - "seed": 1234 - }, - "quantization_config": { - "activation_type": "bfloat16", - "weight_type": "uint8", - "SubChannel": true, - "GroupSize": 512 - } -} \ No newline at end of file diff --git a/examples/python/model_config/config_chatglm3_6b_quantize.json b/examples/python/model_config/config_chatglm3_6b_quantize.json deleted file mode 100644 index 279d5e8d..00000000 --- a/examples/python/model_config/config_chatglm3_6b_quantize.json +++ /dev/null @@ -1,40 +0,0 @@ -{ - "model_name": "chatglm3-6b", - "model_type": "ChatGLM_v3", - "model_path": "~/dashinfer_models/", - "data_type": "float32", - "device_type": "CPU", - "device_ids": [ - 0 - ], - "multinode_mode": false, - "convert_config": { - "do_dynamic_quantize_convert": true - }, - "engine_config": { - "engine_max_length": 2048, - "engine_max_batch": 8, - "do_profiling": false, - "num_threads": 0, - "matmul_precision": "medium" - }, - "generation_config": { - "temperature": 0.8, - "early_stopping": true, - "top_k": 1024, - "top_p": 0.8, - "repetition_penalty": 1.0, - "presence_penalty": 0.0, - "min_length": 0, - "max_length": 2048, - "no_repeat_ngram_size": 0, - "eos_token_id": 2, - "seed": 1234 - }, - "quantization_config": { - "activation_type": "bfloat16", - "weight_type": "uint8", - "SubChannel": true, - "GroupSize": 512 - } -} \ No newline at end of file diff --git a/examples/python/model_config/config_llama2_7b.json b/examples/python/model_config/config_llama2_7b.json index b34cfadc..40c7fb4a 100644 --- a/examples/python/model_config/config_llama2_7b.json +++ b/examples/python/model_config/config_llama2_7b.json @@ -8,9 +8,6 @@ 0 ], "multinode_mode": false, - "convert_config": { - "do_dynamic_quantize_convert": false - }, "engine_config": { "engine_max_length": 2048, "engine_max_batch": 8, @@ -31,5 +28,13 @@ "eos_token_id": 2, "seed": 1234 }, - "quantization_config": null + "convert_config": { + "do_dynamic_quantize_convert": false + }, + "quantization_config": { + "activation_type": "bfloat16", + "weight_type": "uint8", + "SubChannel": true, + "GroupSize": 512 + } } \ No newline at end of file diff --git a/examples/python/model_config/config_llama2_7b_quantize.json b/examples/python/model_config/config_llama2_7b_quantize.json deleted file mode 100644 index 8f7b7a33..00000000 --- a/examples/python/model_config/config_llama2_7b_quantize.json +++ /dev/null @@ -1,40 +0,0 @@ -{ - "model_name": "Llama-2-7b-chat-ms", - "model_type": "LLaMA_v2", - "model_path": "~/dashinfer_models/", - "data_type": "float32", - "device_type": "CPU", - "device_ids": [ - 0 - ], - "multinode_mode": false, - "convert_config": { - "do_dynamic_quantize_convert": true - }, - "engine_config": { - "engine_max_length": 2048, - "engine_max_batch": 8, - "do_profiling": false, - "num_threads": 0, - "matmul_precision": "medium" - }, - "generation_config": { - "temperature": 0.6, - "early_stopping": true, - "top_k": 1024, - "top_p": 0.9, - "repetition_penalty": 1.0, - "presence_penalty": 0.0, - "min_length": 0, - "max_length": 2048, - "no_repeat_ngram_size": 0, - "eos_token_id": 2, - "seed": 1234 - }, - "quantization_config": { - "activation_type": "bfloat16", - "weight_type": "uint8", - "SubChannel": true, - "GroupSize": 512 - } -} \ No newline at end of file diff --git a/examples/python/model_config/config_qwen_v10_14b.json b/examples/python/model_config/config_qwen_v10_14b.json index 46a5b350..9f1798ac 100644 --- a/examples/python/model_config/config_qwen_v10_14b.json +++ b/examples/python/model_config/config_qwen_v10_14b.json @@ -8,9 +8,6 @@ 0 ], "multinode_mode": false, - "convert_config": { - "do_dynamic_quantize_convert": false - }, "engine_config": { "engine_max_length": 2048, "engine_max_batch": 8, @@ -42,5 +39,13 @@ ] ] }, - "quantization_config": null + "convert_config": { + "do_dynamic_quantize_convert": false + }, + "quantization_config": { + "activation_type": "bfloat16", + "weight_type": "uint8", + "SubChannel": true, + "GroupSize": 64 + } } \ No newline at end of file diff --git a/examples/python/model_config/config_qwen_v10_1_8b.json b/examples/python/model_config/config_qwen_v10_1_8b.json index 07753814..c72a70e9 100644 --- a/examples/python/model_config/config_qwen_v10_1_8b.json +++ b/examples/python/model_config/config_qwen_v10_1_8b.json @@ -8,9 +8,6 @@ 0 ], "multinode_mode": false, - "convert_config": { - "do_dynamic_quantize_convert": false - }, "engine_config": { "engine_max_length": 2048, "engine_max_batch": 8, @@ -42,5 +39,13 @@ ] ] }, - "quantization_config": null + "convert_config": { + "do_dynamic_quantize_convert": false + }, + "quantization_config": { + "activation_type": "bfloat16", + "weight_type": "uint8", + "SubChannel": true, + "GroupSize": 64 + } } \ No newline at end of file diff --git a/examples/python/model_config/config_qwen_v10_1_8b_quantize.json b/examples/python/model_config/config_qwen_v10_1_8b_quantize.json deleted file mode 100644 index 48be1dae..00000000 --- a/examples/python/model_config/config_qwen_v10_1_8b_quantize.json +++ /dev/null @@ -1,51 +0,0 @@ -{ - "model_name": "Qwen-1_8B-Chat", - "model_type": "Qwen_v10", - "model_path": "~/dashinfer_models/", - "data_type": "float32", - "device_type": "CPU", - "device_ids": [ - 0 - ], - "multinode_mode": false, - "convert_config": { - "do_dynamic_quantize_convert": true - }, - "engine_config": { - "engine_max_length": 2048, - "engine_max_batch": 8, - "do_profiling": false, - "num_threads": 0, - "matmul_precision": "medium" - }, - "generation_config": { - "temperature": 1.0, - "early_stopping": true, - "top_k": 1024, - "top_p": 0.8, - "repetition_penalty": 1.1, - "presence_penalty": 0.0, - "min_length": 0, - "max_length": 2048, - "no_repeat_ngram_size": 0, - "eos_token_id": 151643, - "seed": 1234, - "stop_words_ids": [ - [ - 151643 - ], - [ - 151644 - ], - [ - 151645 - ] - ] - }, - "quantization_config": { - "activation_type": "bfloat16", - "weight_type": "uint8", - "SubChannel": true, - "GroupSize": 64 - } -} \ No newline at end of file diff --git a/examples/python/model_config/config_qwen_v10_7b.json b/examples/python/model_config/config_qwen_v10_7b.json index 9c50db40..fe8383b2 100644 --- a/examples/python/model_config/config_qwen_v10_7b.json +++ b/examples/python/model_config/config_qwen_v10_7b.json @@ -8,9 +8,6 @@ 0 ], "multinode_mode": false, - "convert_config": { - "do_dynamic_quantize_convert": false - }, "engine_config": { "engine_max_length": 2048, "engine_max_batch": 8, @@ -42,5 +39,13 @@ ] ] }, - "quantization_config": null + "convert_config": { + "do_dynamic_quantize_convert": false + }, + "quantization_config": { + "activation_type": "bfloat16", + "weight_type": "uint8", + "SubChannel": true, + "GroupSize": 128 + } } \ No newline at end of file diff --git a/examples/python/model_config/config_qwen_v15_0_5b.json b/examples/python/model_config/config_qwen_v15_0_5b.json index 01d3f3b6..02a14ab5 100644 --- a/examples/python/model_config/config_qwen_v15_0_5b.json +++ b/examples/python/model_config/config_qwen_v15_0_5b.json @@ -8,9 +8,6 @@ 0 ], "multinode_mode": false, - "convert_config": { - "do_dynamic_quantize_convert": false - }, "engine_config": { "engine_max_length": 2048, "engine_max_batch": 8, @@ -42,5 +39,13 @@ ] ] }, - "quantization_config": null + "convert_config": { + "do_dynamic_quantize_convert": false + }, + "quantization_config": { + "activation_type": "bfloat16", + "weight_type": "uint8", + "SubChannel": true, + "GroupSize": 512 + } } \ No newline at end of file diff --git a/examples/python/model_config/config_qwen_v15_14b.json b/examples/python/model_config/config_qwen_v15_14b.json index fad80f48..27dd079d 100644 --- a/examples/python/model_config/config_qwen_v15_14b.json +++ b/examples/python/model_config/config_qwen_v15_14b.json @@ -8,9 +8,6 @@ 0 ], "multinode_mode": false, - "convert_config": { - "do_dynamic_quantize_convert": false - }, "engine_config": { "engine_max_length": 2048, "engine_max_batch": 8, @@ -42,5 +39,13 @@ ] ] }, - "quantization_config": null + "convert_config": { + "do_dynamic_quantize_convert": false + }, + "quantization_config": { + "activation_type": "bfloat16", + "weight_type": "uint8", + "SubChannel": true, + "GroupSize": 256 + } } \ No newline at end of file diff --git a/examples/python/model_config/config_qwen_v15_14b_quantize.json b/examples/python/model_config/config_qwen_v15_14b_quantize.json deleted file mode 100644 index 4edd5583..00000000 --- a/examples/python/model_config/config_qwen_v15_14b_quantize.json +++ /dev/null @@ -1,51 +0,0 @@ -{ - "model_name": "Qwen1.5-14B-Chat", - "model_type": "Qwen_v15", - "model_path": "~/dashinfer_models/", - "data_type": "float32", - "device_type": "CPU", - "device_ids": [ - 0 - ], - "multinode_mode": false, - "convert_config": { - "do_dynamic_quantize_convert": true - }, - "engine_config": { - "engine_max_length": 2048, - "engine_max_batch": 8, - "do_profiling": false, - "num_threads": 0, - "matmul_precision": "medium" - }, - "generation_config": { - "temperature": 0.7, - "early_stopping": true, - "top_k": 20, - "top_p": 0.8, - "repetition_penalty": 1.05, - "presence_penalty": 0.0, - "min_length": 0, - "max_length": 2048, - "no_repeat_ngram_size": 0, - "eos_token_id": 151643, - "seed": 1234, - "stop_words_ids": [ - [ - 151643 - ], - [ - 151644 - ], - [ - 151645 - ] - ] - }, - "quantization_config": { - "activation_type": "bfloat16", - "weight_type": "uint8", - "SubChannel": true, - "GroupSize": 256 - } -} \ No newline at end of file diff --git a/examples/python/model_config/config_qwen_v15_1_8b.json b/examples/python/model_config/config_qwen_v15_1_8b.json index 82910dd8..a72dd0fb 100644 --- a/examples/python/model_config/config_qwen_v15_1_8b.json +++ b/examples/python/model_config/config_qwen_v15_1_8b.json @@ -8,9 +8,6 @@ 0 ], "multinode_mode": false, - "convert_config": { - "do_dynamic_quantize_convert": false - }, "engine_config": { "engine_max_length": 2048, "engine_max_batch": 8, @@ -42,5 +39,13 @@ ] ] }, - "quantization_config": null + "convert_config": { + "do_dynamic_quantize_convert": false + }, + "quantization_config": { + "activation_type": "bfloat16", + "weight_type": "uint8", + "SubChannel": true, + "GroupSize": 256 + } } \ No newline at end of file diff --git a/examples/python/model_config/config_qwen_v15_1_8b_quantize.json b/examples/python/model_config/config_qwen_v15_1_8b_quantize.json deleted file mode 100644 index b56bcb9e..00000000 --- a/examples/python/model_config/config_qwen_v15_1_8b_quantize.json +++ /dev/null @@ -1,52 +0,0 @@ -{ - "model_name": "Qwen1.5-1.8B-Chat", - "model_type": "Qwen_v15", - "model_path": "~/dashinfer_models/", - "data_type": "float32", - "device_type": "CPU", - "device_ids": [ - 0 - ], - "multinode_mode": false, - "convert_config": { - "do_dynamic_quantize_convert": true - }, - "engine_config": { - "engine_max_length": 2048, - "engine_max_batch": 8, - "do_profiling": false, - "num_threads": 0, - "matmul_precision": "medium" - }, - "generation_config": { - "num_return_sequences": 1, - "temperature": 1.0, - "early_stopping": true, - "top_k": 1024, - "top_p": 0.8, - "repetition_penalty": 1.1, - "presence_penalty": 0.0, - "min_length": 0, - "max_length": 2048, - "no_repeat_ngram_size": 0, - "eos_token_id": 151643, - "seed": 1234, - "stop_words_ids": [ - [ - 151643 - ], - [ - 151644 - ], - [ - 151645 - ] - ] - }, - "quantization_config": { - "activation_type": "bfloat16", - "weight_type": "uint8", - "SubChannel": true, - "GroupSize": 256 - } -} \ No newline at end of file diff --git a/examples/python/model_config/config_qwen_v15_4b.json b/examples/python/model_config/config_qwen_v15_4b.json index d6ef01a5..4a24ad10 100644 --- a/examples/python/model_config/config_qwen_v15_4b.json +++ b/examples/python/model_config/config_qwen_v15_4b.json @@ -8,9 +8,6 @@ 0 ], "multinode_mode": false, - "convert_config": { - "do_dynamic_quantize_convert": false - }, "engine_config": { "engine_max_length": 2048, "engine_max_batch": 8, @@ -42,5 +39,13 @@ ] ] }, - "quantization_config": null + "convert_config": { + "do_dynamic_quantize_convert": false + }, + "quantization_config": { + "activation_type": "bfloat16", + "weight_type": "uint8", + "SubChannel": true, + "GroupSize": 128 + } } \ No newline at end of file diff --git a/examples/python/model_config/config_qwen_v15_4b_quantize.json b/examples/python/model_config/config_qwen_v15_4b_quantize.json deleted file mode 100644 index a28580c0..00000000 --- a/examples/python/model_config/config_qwen_v15_4b_quantize.json +++ /dev/null @@ -1,51 +0,0 @@ -{ - "model_name": "Qwen1.5-4B-Chat", - "model_type": "Qwen_v15", - "model_path": "~/dashinfer_models/", - "data_type": "float32", - "device_type": "CPU", - "device_ids": [ - 0 - ], - "multinode_mode": false, - "convert_config": { - "do_dynamic_quantize_convert": true - }, - "engine_config": { - "engine_max_length": 2048, - "engine_max_batch": 8, - "do_profiling": false, - "num_threads": 0, - "matmul_precision": "medium" - }, - "generation_config": { - "temperature": 1.0, - "early_stopping": true, - "top_k": 1024, - "top_p": 0.8, - "repetition_penalty": 1.1, - "presence_penalty": 0.0, - "min_length": 0, - "max_length": 2048, - "no_repeat_ngram_size": 0, - "eos_token_id": 151643, - "seed": 1234, - "stop_words_ids": [ - [ - 151643 - ], - [ - 151644 - ], - [ - 151645 - ] - ] - }, - "quantization_config": { - "activation_type": "bfloat16", - "weight_type": "uint8", - "SubChannel": true, - "GroupSize": 128 - } -} \ No newline at end of file diff --git a/examples/python/model_config/config_qwen_v15_7b.json b/examples/python/model_config/config_qwen_v15_7b.json index 1ea3cf52..e8dee605 100644 --- a/examples/python/model_config/config_qwen_v15_7b.json +++ b/examples/python/model_config/config_qwen_v15_7b.json @@ -8,9 +8,6 @@ 0 ], "multinode_mode": false, - "convert_config": { - "do_dynamic_quantize_convert": false - }, "engine_config": { "engine_max_length": 2048, "engine_max_batch": 8, @@ -42,5 +39,13 @@ ] ] }, - "quantization_config": null + "convert_config": { + "do_dynamic_quantize_convert": false + }, + "quantization_config": { + "activation_type": "bfloat16", + "weight_type": "uint8", + "SubChannel": true, + "GroupSize": 512 + } } \ No newline at end of file diff --git a/examples/python/model_config/config_qwen_v15_7b_quantize.json b/examples/python/model_config/config_qwen_v15_7b_quantize.json deleted file mode 100644 index c6f30603..00000000 --- a/examples/python/model_config/config_qwen_v15_7b_quantize.json +++ /dev/null @@ -1,51 +0,0 @@ -{ - "model_name": "Qwen1.5-7B-Chat", - "model_type": "Qwen_v15", - "model_path": "~/dashinfer_models/", - "data_type": "float32", - "device_type": "CPU", - "device_ids": [ - 0 - ], - "multinode_mode": false, - "convert_config": { - "do_dynamic_quantize_convert": true - }, - "engine_config": { - "engine_max_length": 2048, - "engine_max_batch": 8, - "do_profiling": false, - "num_threads": 0, - "matmul_precision": "medium" - }, - "generation_config": { - "temperature": 0.7, - "early_stopping": true, - "top_k": 20, - "top_p": 0.8, - "repetition_penalty": 1.05, - "presence_penalty": 0.0, - "min_length": 0, - "max_length": 2048, - "no_repeat_ngram_size": 0, - "eos_token_id": 151643, - "seed": 1234, - "stop_words_ids": [ - [ - 151643 - ], - [ - 151644 - ], - [ - 151645 - ] - ] - }, - "quantization_config": { - "activation_type": "bfloat16", - "weight_type": "uint8", - "SubChannel": true, - "GroupSize": 512 - } -} \ No newline at end of file diff --git a/examples/python/model_config/config_qwen_v15_0_5b_quantize.json b/examples/python/model_config/config_qwen_v20_0_5b.json similarity index 90% rename from examples/python/model_config/config_qwen_v15_0_5b_quantize.json rename to examples/python/model_config/config_qwen_v20_0_5b.json index cb3c19b4..6ab5cf5c 100644 --- a/examples/python/model_config/config_qwen_v15_0_5b_quantize.json +++ b/examples/python/model_config/config_qwen_v20_0_5b.json @@ -1,6 +1,6 @@ { - "model_name": "Qwen1.5-0.5B-Chat", - "model_type": "Qwen_v15", + "model_name": "Qwen2-0.5B", + "model_type": "Qwen_v20", "model_path": "~/dashinfer_models/", "data_type": "float32", "device_type": "CPU", @@ -8,9 +8,6 @@ 0 ], "multinode_mode": false, - "convert_config": { - "do_dynamic_quantize_convert": true - }, "engine_config": { "engine_max_length": 2048, "engine_max_batch": 8, @@ -42,6 +39,9 @@ ] ] }, + "convert_config": { + "do_dynamic_quantize_convert": false + }, "quantization_config": { "activation_type": "bfloat16", "weight_type": "uint8", diff --git a/examples/python/model_config/config_qwen_v10_7b_quantize.json b/examples/python/model_config/config_qwen_v20_1_5b.json similarity index 88% rename from examples/python/model_config/config_qwen_v10_7b_quantize.json rename to examples/python/model_config/config_qwen_v20_1_5b.json index 1b7822cd..b282b330 100644 --- a/examples/python/model_config/config_qwen_v10_7b_quantize.json +++ b/examples/python/model_config/config_qwen_v20_1_5b.json @@ -1,6 +1,6 @@ { - "model_name": "Qwen-7B-Chat", - "model_type": "Qwen_v10", + "model_name": "Qwen2-1.5B", + "model_type": "Qwen_v20", "model_path": "~/dashinfer_models/", "data_type": "float32", "device_type": "CPU", @@ -8,9 +8,6 @@ 0 ], "multinode_mode": false, - "convert_config": { - "do_dynamic_quantize_convert": true - }, "engine_config": { "engine_max_length": 2048, "engine_max_batch": 8, @@ -42,10 +39,13 @@ ] ] }, + "convert_config": { + "do_dynamic_quantize_convert": false + }, "quantization_config": { "activation_type": "bfloat16", "weight_type": "uint8", "SubChannel": true, - "GroupSize": 128 + "GroupSize": 512 } } \ No newline at end of file diff --git a/examples/python/model_config/config_qwen_v10_14b_quantize.json b/examples/python/model_config/config_qwen_v20_7b.json similarity index 88% rename from examples/python/model_config/config_qwen_v10_14b_quantize.json rename to examples/python/model_config/config_qwen_v20_7b.json index 4a6e1ac0..e734b735 100644 --- a/examples/python/model_config/config_qwen_v10_14b_quantize.json +++ b/examples/python/model_config/config_qwen_v20_7b.json @@ -1,6 +1,6 @@ { - "model_name": "Qwen-14B-Chat", - "model_type": "Qwen_v10", + "model_name": "Qwen2-7B", + "model_type": "Qwen_v20", "model_path": "~/dashinfer_models/", "data_type": "float32", "device_type": "CPU", @@ -8,9 +8,6 @@ 0 ], "multinode_mode": false, - "convert_config": { - "do_dynamic_quantize_convert": true - }, "engine_config": { "engine_max_length": 2048, "engine_max_batch": 8, @@ -42,10 +39,13 @@ ] ] }, + "convert_config": { + "do_dynamic_quantize_convert": false + }, "quantization_config": { "activation_type": "bfloat16", "weight_type": "uint8", "SubChannel": true, - "GroupSize": 64 + "GroupSize": 512 } } \ No newline at end of file diff --git a/python/allspark_binding.cpp b/python/allspark_binding.cpp index c5cafdf4..8e1b1884 100644 --- a/python/allspark_binding.cpp +++ b/python/allspark_binding.cpp @@ -186,9 +186,11 @@ PYBIND11_MODULE(_allspark, m) { .def(py::init<>( [](std::string model_name) { return new AsEngineStat(model_name); })) .def("ToString", [](AsEngineStat* self) { return self->ToString(); }) + .def("dict", [](AsEngineStat* self) { return self->ToMap(); }) // required field: model_name / model_path / weights_path .def_readwrite("model_name", &AsEngineStat::model_name) .def_readwrite("free_token", &AsEngineStat::free_token) + .def_readwrite("total_token", &AsEngineStat::total_token) .def_readwrite("pendding_request", &AsEngineStat::pendding_request) .def_readwrite("running_request", &AsEngineStat::running_request) .def_readwrite("total_generated_token", @@ -204,12 +206,24 @@ PYBIND11_MODULE(_allspark, m) { &AsEngineStat::used_device_memory_pool_size); py::class_>( - m, "GeneratedElements") + m, "GeneratedElements", py::module_local(), + "Generated Token class, contains token(s) and related information, it " + "may contains multiple tokens between last get.") .def(py::init<>()) // 默认构造函数 - .def_readwrite("ids_from_generate", &GeneratedElements::ids_from_generate) - .def_readwrite("log_probs_list", &GeneratedElements::log_probs_list) + .def_readwrite("ids_from_generate", &GeneratedElements::ids_from_generate, + "Token(s) from this generation") + .def_readwrite( + "log_probs_list", &GeneratedElements::log_probs_list, + "A probability list for each token, including the top_logprobs " + "tokens and their probabilities when generated.\n" + "Dimension: [num_token][top_logprobs], where each token has a " + "pair [token_id, prob].") + .def_readwrite("token_logprobs_list", + &GeneratedElements::token_logprobs_list, + "Stores the probability value for each selected token.") .def_readwrite("tensors_from_model_inference", - &GeneratedElements::tensors_from_model_inference); + &GeneratedElements::tensors_from_model_inference, + "Tensor outputs from model inference."); // Allspark API // We should make sure interfaces are the same with AsClientEngine @@ -279,7 +293,10 @@ PYBIND11_MODULE(_allspark, m) { return emptyVec; } }, - py::return_value_policy::copy) + py::return_value_policy::copy, + "get output token non-block api, return None if no new token " + "available" + "[deprecated function] please use ResultQueue function.") .def( "_get_wait", [](AsEngine* self, const char* model_name, void* result_queue) { @@ -296,7 +313,10 @@ PYBIND11_MODULE(_allspark, m) { return emptyVec; } }, - py::return_value_policy::copy) + py::return_value_policy::copy, + "get output token block api, will block until new token or status " + "change" + ". [deprecated function] please use ResultQueue function.") .def("_get_request_status", [](AsEngine* self, const char* model_name, void* result_queue) { return ((AsEngine::ResultQueue_t)result_queue)->GenerateStatus(); @@ -524,29 +544,46 @@ PYBIND11_MODULE(_allspark, m) { util::set_global_header(weights_path); }); - py::class_>(m, "ResultQueue") + py::class_>( + m, "ResultQueue", py::module_local(), + "The ResultQueue class is designed to generate status and retrieve " + "results") .def(py::init<>()) // Assuming there's a default constructor - .def("GenerateStatus", &ResultQueue::GenerateStatus) - .def("GeneratedLength", &ResultQueue::GeneratedLength) - .def("Get", - [](ResultQueue* self) -> py::object { - py::gil_scoped_release release; - auto ele = self->Get(); - py::gil_scoped_acquire acquire; - if (ele == nullptr) { - return py::none(); - } else { - return py::cast(ele); - } - }) - .def("GetNoWait", [](ResultQueue* self) -> py::object { - auto ele = self->GetNoWait(); - if (ele == nullptr) { - return py::none(); - } else { - return py::cast(ele); - } - }); + + .def("GenerateStatus", &ResultQueue::GenerateStatus, + "Get generation status, this api will not block.") + + .def("GeneratedLength", &ResultQueue::GeneratedLength, + "Get current generated length, it's accumulated generate token " + "number") + + .def( + "Get", + [](ResultQueue* self) -> py::object { + py::gil_scoped_release release; + auto ele = self->Get(); + py::gil_scoped_acquire acquire; + if (ele == nullptr) { + return py::none(); + } else { + return py::cast(ele); + } + }, + "Fetches new tokens(s) from the queue, will be block until new token " + "generated.") + + .def( + "GetNoWait", + [](ResultQueue* self) -> py::object { + auto ele = self->GetNoWait(); + if (ele == nullptr) { + return py::none(); + } else { + return py::cast(ele); + } + }, + "Fetches new token(s) from the queue, will return None if no new " + "tokens, non block api."); } #undef CHECK_CONFIG diff --git a/python/dashinfer/allspark/engine.py b/python/dashinfer/allspark/engine.py index a80d7b07..a8189c6f 100644 --- a/python/dashinfer/allspark/engine.py +++ b/python/dashinfer/allspark/engine.py @@ -22,6 +22,7 @@ def __init__(self): "ChatGLM_v3": ChatGLM_v3, "Qwen_v10": Qwen_v10, "Qwen_v15": Qwen_v15, + "Qwen_v20": Qwen_v20, } self.version = "" @@ -65,12 +66,12 @@ def serialize_model_from_torch(self, if not path.isdir(save_dir): makedirs(save_dir) assert model_name, 'model_name should not be empty!' - model_path = path.join(save_dir, model_name + ".asgraph") - weights_path = path.join(save_dir, model_name + ".asparam") + model_path = path.join(save_dir, model_name + ".dimodel") + weights_path = path.join(save_dir, model_name + ".ditensors") start_time = time.time() - print("save asgraph to ", model_path) - print("save asparam to ", weights_path) + print("save dimodel to ", model_path) + print("save ditensors to ", weights_path) # 清空原本的权重文件 with open(weights_path, "w") as f: f.truncate(0) @@ -128,7 +129,7 @@ def dump_build_meta_to_proto(self, model_proto, weights_path, hash_length = len(weight_file_content) hash.hash_length.extend([hash_length]) hash_md5 = hashlib.md5(weight_file_content).hexdigest() - print("calculate md5 of asgraph = ", hash_md5) + print("calculate md5 of dimodel = ", hash_md5) hash.hash.extend([hash_md5]) # config dict def valid_torch_kvpair(key, value) -> bool: diff --git a/python/dashinfer/allspark/model/__init__.py b/python/dashinfer/allspark/model/__init__.py index efc58be3..bef28fba 100644 --- a/python/dashinfer/allspark/model/__init__.py +++ b/python/dashinfer/allspark/model/__init__.py @@ -7,6 +7,7 @@ from .chatglm_v3 import * from .qwen_v10 import * from .qwen_v15 import * +from .qwen_v20 import * __all__ = [ "LLaMA_v2", @@ -14,4 +15,5 @@ "ChatGLM_v3", "Qwen_v10", "Qwen_v15", + "Qwen_v20", ] diff --git a/python/dashinfer/allspark/model/qwen_v15.py b/python/dashinfer/allspark/model/qwen_v15.py index a538290e..c5880bd9 100644 --- a/python/dashinfer/allspark/model/qwen_v15.py +++ b/python/dashinfer/allspark/model/qwen_v15.py @@ -34,6 +34,8 @@ def _build_graph(self, torch_cfg, derive_type): cfg.ln_eps = torch_cfg.get('rms_norm_eps', 1e-5) cfg.num_heads = torch_cfg.get('num_attention_heads', 12) cfg.multi_query_group_num = torch_cfg.get('num_key_value_heads', 0) + if (cfg.multi_query_group_num == 0): + cfg.multi_query_group_num = cfg.num_heads cfg.dec_layer = torch_cfg.get('num_hidden_layers', 12) hidden_size_ = torch_cfg.get('hidden_size', 4096) cfg.kv_channels = int(hidden_size_ / cfg.num_heads) @@ -113,7 +115,7 @@ def _build_graph(self, torch_cfg, derive_type): self.split_map["embedding.word_embeddings"] = VSPLIT for i in range(cfg.dec_layer): prefix = "decoder.layer.{}.".format(i) - if cfg.multi_query_group_num == 0 or cfg.multi_query_group_num == cfg.num_heads: + if cfg.multi_query_group_num == cfg.num_heads: self.split_map[prefix + "attention.self.weight"] = QKVSPLIT self.split_map[prefix + "attention.self.bias"] = QKVSPLIT else: @@ -203,16 +205,32 @@ def _build_graph(self, torch_cfg, derive_type): if self.use_logn_attn and hasattr(self, "model_sequence_length"): rotary_attributes["logn_model_embedding"] = int( self.model_sequence_length) + if cfg.multi_query_group_num != cfg.num_heads: + rotary_attributes["multi_query_group_num"] = int( + cfg.multi_query_group_num) rotary_embedding = Rotary( prefix + "rotary", [attn_self_gemm.outputs[0], mask.outputs[1]], rotary_attributes, )() - attn = MultiHeadAttention( - prefix + "attention", - [rotary_embedding.outputs[0], mask.outputs[0]], - {"num_heads": cfg.num_heads}, - )() + if cfg.multi_query_group_num == cfg.num_heads: + attn = MultiHeadAttention( + prefix + "attention", + [rotary_embedding.outputs[0], mask.outputs[0]], + {"num_heads": cfg.num_heads}, + )() + else: + attn = MultiQueryAttention( + prefix + "attention", + [rotary_embedding.outputs[0], mask.outputs[0]], + { + "num_heads": cfg.num_heads, + "size_per_head": cfg.size_per_head, + "multi_query_group_num": cfg.multi_query_group_num, + "multinode": 1, + "hidden_size": hidden_size_ + }, + )() attn_op_list = [] ffn_op_list = [] ffn_ln = None @@ -360,6 +378,8 @@ def _build_graph(self, torch_cfg, derive_type): op.inputs[0].CopyFrom(preprocess_ids.outputs[0]) elif op.op_type == "MultiHeadAttention": op.op_type = "DecOptMHA" + elif op.op_type == "MultiQueryAttention": + op.op_type = "DecOptMQA" gen_op = GenerateOp( "generate", [graph.ops[-1].outputs[0], preprocess_ids.outputs[1]], @@ -388,6 +408,7 @@ def _trans_weight(self, torch_weight): k for k, v in Model.dtype_dict.items() if v == self.dtype ][0] for key, torch_name in weight_name_map.items(): + # print("trans_tensor: {}, {}".format(key, torch_name)) if isinstance(torch_name, list): # attention qkv weights tensor = (torch.concat( [torch_weight[name] for name in torch_name]).cpu()) @@ -403,11 +424,11 @@ def _trans_weight(self, torch_weight): else: group_list = [ self.model.model_conf.num_heads * - self.model.model_conf.kv_channels, + self.model.model_conf.size_per_head, self.model.model_conf.multi_query_group_num * - self.model.model_conf.kv_channels, + self.model.model_conf.size_per_head, self.model.model_conf.multi_query_group_num * - self.model.model_conf.kv_channels + self.model.model_conf.size_per_head ] quantize_mode = False if key not in quantize_map else quantize_map[ key] diff --git a/python/dashinfer/allspark/model/qwen_v20.py b/python/dashinfer/allspark/model/qwen_v20.py new file mode 100644 index 00000000..688876e3 --- /dev/null +++ b/python/dashinfer/allspark/model/qwen_v20.py @@ -0,0 +1,26 @@ +# +# Copyright (c) Alibaba, Inc. and its affiliates. +# @file qwen_v20.py +# +from .qwen_v15 import * + + +class Qwen_v20(Qwen_v15): + + def __init__(self, torch_model, data_type, derive_type, **kwargs): + super(Qwen_v15, self).__init__("Qwen_v20", data_type, **kwargs) + self.model.inputs.append( + make_tensor("input_ids", np.empty(shape=(0, 0), dtype=np.int64))) + self.model.inputs.append( + make_tensor("attention_mask", np.empty(shape=(0, 0), + dtype=np.int64))) + self.model.outputs.append(make_tensor("last_hidden_state")) + self.is_generate = kwargs.get('is_generate', True) + self.weight_real_names = set() + for v in torch_model: + self.weight_real_names.add(v) + self._build_graph(self.model_config, derive_type) + start_time = time.time() + self._trans_weight(torch_model) + print("parse weight time: ", time.time() - start_time) + diff --git a/python/dashinfer/helper/helper.py b/python/dashinfer/helper/helper.py index 6c0ccda0..3fae1f66 100644 --- a/python/dashinfer/helper/helper.py +++ b/python/dashinfer/helper/helper.py @@ -231,12 +231,12 @@ def _uninit_torch_model(self): def check_model_exist(self): model_path = os.path.join(self.model_path, - self.model_name + ".asgraph") + self.model_name + ".dimodel") if not os.path.exists(model_path): print(f"\nNo such file or directory: {model_path}\n") return False model_path = os.path.join(self.model_path, - self.model_name + ".asparam") + self.model_name + ".ditensors") if not os.path.exists(model_path): print(f"\nNo such file or directory: {model_path}\n") return False @@ -279,9 +279,9 @@ def get_physical_cores_per_numa_node(): as_model_config = allspark.AsModelConfig( model_name=self.model_name, model_path=os.path.join(self.model_path, - self.model_name + ".asgraph"), + self.model_name + ".dimodel"), weights_path=os.path.join(self.model_path, - self.model_name + ".asparam"), + self.model_name + ".ditensors"), engine_max_length=self.engine_config["engine_max_length"], engine_max_batch=self.engine_config["engine_max_batch"], ) @@ -620,7 +620,7 @@ def check_config(config): raise ValueError("config['{}']: key not exists or unsupported value".format(key)) key = "model_type" - model_types = ["LLaMA_v2", "ChatGLM_v2", "ChatGLM_v3", "Qwen_v10", "Qwen_v15"] + model_types = ["LLaMA_v2", "ChatGLM_v2", "ChatGLM_v3", "Qwen_v10", "Qwen_v15", "Qwen_v20"] if key in config and isinstance(config[key], str) and config[key] in model_types: pass else: diff --git a/scripts/release/python_manylinux_test.sh b/scripts/release/python_manylinux_test.sh index 96483b68..8f7ee3cb 100755 --- a/scripts/release/python_manylinux_test.sh +++ b/scripts/release/python_manylinux_test.sh @@ -34,8 +34,9 @@ run_python_example() { # python basic_example_chatglm2.py # python basic_example_chatglm3.py # python basic_example_llama2.py - python basic_example_qwen_v15.py python basic_example_qwen_v10.py + python basic_example_qwen_v15.py + # python basic_example_qwen_v20.py cd ${root_path} cd ${root_path}/examples/python/1_performance diff --git a/scripts/release/python_pypi_test.sh b/scripts/release/python_pypi_test.sh index 1dc63a94..93e17f0f 100755 --- a/scripts/release/python_pypi_test.sh +++ b/scripts/release/python_pypi_test.sh @@ -27,8 +27,9 @@ run_python_example() { # python basic_example_chatglm2.py # python basic_example_chatglm3.py # python basic_example_llama2.py - python basic_example_qwen_v15.py python basic_example_qwen_v10.py + python basic_example_qwen_v15.py + # python basic_example_qwen_v20.py cd ${root_path} cd ${root_path}/examples/python/1_performance