From e428bb5a9a4964785a078342e81de0d6546db815 Mon Sep 17 00:00:00 2001 From: oreomaker Date: Fri, 8 Nov 2024 16:03:42 +0800 Subject: [PATCH 01/34] dev: qnn multi input inference developing --- examples/demo_phonelm_npu.cpp | 23 +++++++++++++++++---- examples/demo_qwen_npu.cpp | 4 ++-- examples/main_phonelm_npu.cpp | 12 +++++------ examples/main_qwen_npu.cpp | 12 +++++------ include/Types.hpp | 2 +- src/backends/cpu/CPUBackend.hpp | 15 ++++++++++---- src/backends/cpu/CPUIRoPE.cpp | 8 +++++++ src/backends/cpu/CPUKVCacheNPU.cpp | 12 ++++++++--- src/backends/cpu/CPURoPE.cpp | 7 +++++++ src/backends/cpu/CPUSoftMax.cpp | 2 +- src/backends/qnn/QNNExecutor.hpp | 2 +- src/models/phonelm/modeling_phonelm_npu.hpp | 6 +++++- 12 files changed, 76 insertions(+), 29 deletions(-) diff --git a/examples/demo_phonelm_npu.cpp b/examples/demo_phonelm_npu.cpp index 6d774230..d4ce1484 100644 --- a/examples/demo_phonelm_npu.cpp +++ b/examples/demo_phonelm_npu.cpp @@ -1,3 +1,4 @@ +#include "Types.hpp" #ifdef USE_QNN #include "backends/cpu/CPUBackend.hpp" #include "cmdline.h" @@ -31,6 +32,8 @@ int main(int argc, char **argv) { vector in_strs = { "Give me a short introduction to large language model.", + "What is the Beijing University of Posts and Telecommunications.", + "What is the meaning of life?", }; for (int i = 0; i < in_strs.size(); ++i) { @@ -50,7 +53,13 @@ int main(int argc, char **argv) { .is_padding = true, .seq_before_padding = real_seq_length, }; + bool isSwitched = false; model.generate(input_tensor, opt, [&](unsigned int out_token) -> bool { + if (i != 0 && !isSwitched) { + // turn off switching + static_cast(Backend::global_backends[MLLM_CPU])->toggleSwitching(); + isSwitched = true; + } auto out_string = tokenizer.detokenize({out_token}); auto [not_end, output_string] = tokenizer.postprocess(out_string); if (!not_end) { return false; } @@ -58,8 +67,10 @@ int main(int argc, char **argv) { return true; }); + // turn on switching, set sequence length and execution type static_cast(Backend::global_backends[MLLM_CPU])->setSequenceLength(real_seq_length); - static_cast(Backend::global_backends[MLLM_CPU])->switchDecodeTag(); + static_cast(Backend::global_backends[MLLM_CPU])->setExecutionType(AUTOREGRESSIVE); + static_cast(Backend::global_backends[MLLM_CPU])->toggleSwitching(); LlmTextGeneratorOpts decoding_opt{ .max_new_tokens = 100, @@ -69,11 +80,11 @@ int main(int argc, char **argv) { .top_p = 0.f, .is_padding = false, }; - bool isSwitched = false; + isSwitched = false; decoding_model.generate(input_tensor, decoding_opt, [&](unsigned int out_token) -> bool { - // call only once of switchDecodeTag if (!isSwitched) { - static_cast(Backend::global_backends[MLLM_CPU])->switchDecodeTag(); + // turn off switching + static_cast(Backend::global_backends[MLLM_CPU])->toggleSwitching(); isSwitched = true; } auto out_string = tokenizer.detokenize({out_token}); @@ -86,6 +97,10 @@ int main(int argc, char **argv) { return true; }); std::cout << "\n---------------" << std::endl; + // turn on switching, set sequence length and execution type + static_cast(Backend::global_backends[MLLM_CPU])->setSequenceLength(0); + static_cast(Backend::global_backends[MLLM_CPU])->setExecutionType(PROMPT); + static_cast(Backend::global_backends[MLLM_CPU])->toggleSwitching(); } } #endif \ No newline at end of file diff --git a/examples/demo_qwen_npu.cpp b/examples/demo_qwen_npu.cpp index 421e7d0e..2ab916c7 100644 --- a/examples/demo_qwen_npu.cpp +++ b/examples/demo_qwen_npu.cpp @@ -61,7 +61,7 @@ int main(int argc, char **argv) { }); static_cast(Backend::global_backends[MLLM_CPU])->setSequenceLength(real_seq_length); - static_cast(Backend::global_backends[MLLM_CPU])->switchDecodeTag(); + static_cast(Backend::global_backends[MLLM_CPU])->toggleSwitching(); LlmTextGeneratorOpts decoding_opt{ .max_new_tokens = 100, @@ -75,7 +75,7 @@ int main(int argc, char **argv) { decoding_model.generate(input_tensor, decoding_opt, [&](unsigned int out_token) -> bool { // call only once of switchDecodeTag if (!isSwitched) { - static_cast(Backend::global_backends[MLLM_CPU])->switchDecodeTag(); + static_cast(Backend::global_backends[MLLM_CPU])->toggleSwitching(); isSwitched = true; } auto out_string = tokenizer.detokenize({out_token}); diff --git a/examples/main_phonelm_npu.cpp b/examples/main_phonelm_npu.cpp index 0cff3898..af0b6186 100644 --- a/examples/main_phonelm_npu.cpp +++ b/examples/main_phonelm_npu.cpp @@ -190,11 +190,11 @@ int main(int argc, char **argv) { auto inter_cpu_backend = dynamic_cast(interNet.backends()[MLLM_CPU].get()); auto decode_cpu_backend = dynamic_cast(cpuNet.backends()[MLLM_CPU].get()); prefill_cpu_backend->setSequenceLength(real_seq_length); - prefill_cpu_backend->switchDecodeTag(); + prefill_cpu_backend->toggleSwitching(); inter_cpu_backend->setSequenceLength(real_seq_length); - inter_cpu_backend->switchDecodeTag(); + inter_cpu_backend->toggleSwitching(); decode_cpu_backend->setSequenceLength(real_seq_length); - decode_cpu_backend->switchDecodeTag(); + decode_cpu_backend->toggleSwitching(); // // 2: Decoding stage using CPU execute for (int step = real_seq_length; step < real_seq_length + 100; step++) { @@ -210,9 +210,9 @@ int main(int argc, char **argv) { std::cout << out_token << std::flush; if (step == real_seq_length) { - prefill_cpu_backend->switchDecodeTag(); - inter_cpu_backend->switchDecodeTag(); - decode_cpu_backend->switchDecodeTag(); + prefill_cpu_backend->toggleSwitching(); + inter_cpu_backend->toggleSwitching(); + decode_cpu_backend->toggleSwitching(); } } } while (false); diff --git a/examples/main_qwen_npu.cpp b/examples/main_qwen_npu.cpp index b096100b..4adc7fa7 100644 --- a/examples/main_qwen_npu.cpp +++ b/examples/main_qwen_npu.cpp @@ -187,11 +187,11 @@ int main(int argc, char **argv) { auto inter_cpu_backend = dynamic_cast(interNet.backends()[MLLM_CPU].get()); auto decode_cpu_backend = dynamic_cast(cpuNet.backends()[MLLM_CPU].get()); prefill_cpu_backend->setSequenceLength(real_seq_length); - prefill_cpu_backend->switchDecodeTag(); + prefill_cpu_backend->toggleSwitching(); inter_cpu_backend->setSequenceLength(real_seq_length); - inter_cpu_backend->switchDecodeTag(); + inter_cpu_backend->toggleSwitching(); decode_cpu_backend->setSequenceLength(real_seq_length); - decode_cpu_backend->switchDecodeTag(); + decode_cpu_backend->toggleSwitching(); // // 2: Decoding stage using CPU execute for (int step = real_seq_length; step < real_seq_length + 100; step++) { @@ -207,9 +207,9 @@ int main(int argc, char **argv) { std::cout << out_token << std::flush; if (step == real_seq_length) { - prefill_cpu_backend->switchDecodeTag(); - inter_cpu_backend->switchDecodeTag(); - decode_cpu_backend->switchDecodeTag(); + prefill_cpu_backend->toggleSwitching(); + inter_cpu_backend->toggleSwitching(); + decode_cpu_backend->toggleSwitching(); } } } while (false); diff --git a/include/Types.hpp b/include/Types.hpp index 70cd9ae4..3ce92ff4 100644 --- a/include/Types.hpp +++ b/include/Types.hpp @@ -140,7 +140,7 @@ enum RoPEType { MLAROPE = 5, }; -enum QNNExecutionType { +enum ExecutionType { PROMPT = 0, AUTOREGRESSIVE = 1, }; diff --git a/src/backends/cpu/CPUBackend.hpp b/src/backends/cpu/CPUBackend.hpp index 816935d9..75f96412 100644 --- a/src/backends/cpu/CPUBackend.hpp +++ b/src/backends/cpu/CPUBackend.hpp @@ -39,11 +39,17 @@ class CPUBackend final : public Backend { int getSequenceLength() { return sequence_length_; } - void switchDecodeTag() { - isPrefillToDecode = !isPrefillToDecode; + void toggleSwitching() { + isSwitchingStage = !isSwitchingStage; } bool isStageSwitching() { - return isPrefillToDecode; + return isSwitchingStage; + } + void setExecutionType(ExecutionType type) { + execution_type = type; + } + ExecutionType getExecutionType() { + return execution_type; } #endif private: @@ -51,7 +57,8 @@ class CPUBackend final : public Backend { std::map map_function_; #ifdef USE_QNN int sequence_length_ = 0; - bool isPrefillToDecode = false; + bool isSwitchingStage = false; + ExecutionType execution_type = PROMPT; #endif }; diff --git a/src/backends/cpu/CPUIRoPE.cpp b/src/backends/cpu/CPUIRoPE.cpp index 46562df2..12b4ba39 100644 --- a/src/backends/cpu/CPUIRoPE.cpp +++ b/src/backends/cpu/CPUIRoPE.cpp @@ -362,6 +362,14 @@ void CPUIRoPE::rope_mla(shared_ptr input, shared_ptr output){ } ErrorCode CPUIRoPE::execute(vector> inputs, vector> outputs) { + // if use QNN, when a new prompt input, the seq should be reset to 0 here as the setUp is not called +#ifdef USE_QNN + auto cpuBackend = dynamic_cast(backend_); + if (cpuBackend->isStageSwitching() && cpuBackend->getExecutionType() == PROMPT) { + h_cnt_ = 0; + } +#endif + auto &input = inputs[0]; auto &output = outputs[0]; auto out_dtype = output->dtype(); diff --git a/src/backends/cpu/CPUKVCacheNPU.cpp b/src/backends/cpu/CPUKVCacheNPU.cpp index 582a52e8..ae2c24bd 100644 --- a/src/backends/cpu/CPUKVCacheNPU.cpp +++ b/src/backends/cpu/CPUKVCacheNPU.cpp @@ -34,7 +34,7 @@ ErrorCode CPUKVCacheNPU::reshape(vector> inputs, vector(backend_); - if (cpuBackend->isStageSwitching()) { + if (cpuBackend->isStageSwitching() && cpuBackend->getExecutionType() == AUTOREGRESSIVE) { cache_seq_len_ = cpuBackend->getSequenceLength(); isDecoding = true; } @@ -57,9 +57,16 @@ ErrorCode CPUKVCacheNPU::load(AbstructLoader &loader) { } ErrorCode CPUKVCacheNPU::execute(vector> inputs, vector> outputs) { + // if a new prompt is given, the cache should be updated + auto cpuBackend = dynamic_cast(backend_); + if (cpuBackend->isStageSwitching() && cpuBackend->getExecutionType() == PROMPT) { + isDecoding = false; + cache_seq_len_ = 0; + outputs[0]->deepCopyFrom(cache_, false, {0, 0, cache_seq_len_ / cache_limit_, 0}); + } + // when decoding, the input will deepCopy from cache, no need to execute if (isDecoding) { - int cache_seq_len_old = cache_seq_len_; cache_seq_len_ += inputs[0]->sequence(); return MLLM_NO_ERROR; } @@ -125,7 +132,6 @@ ErrorCode CPUKVCacheNPU::execute(vector> inputs, vectorsequence(); return Op::execute(inputs, outputs); diff --git a/src/backends/cpu/CPURoPE.cpp b/src/backends/cpu/CPURoPE.cpp index 493b2847..b00ab8aa 100644 --- a/src/backends/cpu/CPURoPE.cpp +++ b/src/backends/cpu/CPURoPE.cpp @@ -331,6 +331,13 @@ void CPURoPE::rope_mla(shared_ptr input, shared_ptr output) { } ErrorCode CPURoPE::execute(vector> inputs, vector> outputs) { + // if use QNN, when a new prompt input, the seq should be reset to 0 here as the setUp is not called +#ifdef USE_QNN + auto cpuBackend = dynamic_cast(backend_); + if (cpuBackend->isStageSwitching() && cpuBackend->getExecutionType() == PROMPT) { + h_cnt_ = 0; + } +#endif auto &input = inputs[0]; auto &output = outputs[0]; auto out_dtype = output->dtype(); diff --git a/src/backends/cpu/CPUSoftMax.cpp b/src/backends/cpu/CPUSoftMax.cpp index 45545eaa..d9c5c372 100644 --- a/src/backends/cpu/CPUSoftMax.cpp +++ b/src/backends/cpu/CPUSoftMax.cpp @@ -37,7 +37,7 @@ ErrorCode CPUSoftMax::execute(vector> inputs, vectordimension() - input->sequence(); -#elif defined(USE_QNN) // TODO: 就前端才用到，一定要记得删除 +#elif defined(USE_QNN) // TODO: 旧前端才用到，一定要记得删除 old_dim = input->dimension() - input->sequence(); #endif } diff --git a/src/backends/qnn/QNNExecutor.hpp b/src/backends/qnn/QNNExecutor.hpp index 6d335c08..5f29d15e 100644 --- a/src/backends/qnn/QNNExecutor.hpp +++ b/src/backends/qnn/QNNExecutor.hpp @@ -63,7 +63,7 @@ class QNNExecutor : public Executor { protected: bool isSetup_ = false; - QNNExecutionType executionType_ = PROMPT; + ExecutionType executionType_ = PROMPT; }; class QNNPipelineExecutor : public QNNExecutor { diff --git a/src/models/phonelm/modeling_phonelm_npu.hpp b/src/models/phonelm/modeling_phonelm_npu.hpp index 29554c32..0b03cb1d 100644 --- a/src/models/phonelm/modeling_phonelm_npu.hpp +++ b/src/models/phonelm/modeling_phonelm_npu.hpp @@ -124,7 +124,11 @@ class PhoneLMQKVmm final : public Module { v = v_cache(v); auto qk = Tensor::mm(q, k.transpose(Chl::SEQUENCE, Chl::DIMENSION)); - qk = softmax(qk, k_cache.getCacheSeqLen()); + if (k_cache.ready() && v_cache.ready()) { + qk = softmax(qk, k_cache.getCacheSeqLen()); + } else { + qk = softmax(qk); + } auto o = Tensor::mm(qk, v); o = o_quantize(o); From f94937d6cdfe6256292bc0d44fab7389c99915d0 Mon Sep 17 00:00:00 2001 From: oreomaker Date: Wed, 13 Nov 2024 17:03:30 +0800 Subject: [PATCH 02/34] feat: qnn multi chunk prefilling in new frontend refactor: qnn module setup skip in following chunks todo: multi input --- examples/demo_phonelm_npu.cpp | 66 ++++++++++++++++------- include/Types.hpp | 1 + src/Generate.hpp | 13 +++-- src/Module.cpp | 3 ++ src/Module.hpp | 15 ++++++ src/backends/cpu/CPUKVCacheNPU.cpp | 12 ++--- src/backends/cpu/CPURoPE.cpp | 7 --- src/backends/xnnpack/third_party/fmt | 1 + src/models/smollm/tokenization_smollm.hpp | 52 ++++++++++++++++++ 9 files changed, 133 insertions(+), 37 deletions(-) create mode 160000 src/backends/xnnpack/third_party/fmt diff --git a/examples/demo_phonelm_npu.cpp b/examples/demo_phonelm_npu.cpp index d4ce1484..7f568f58 100644 --- a/examples/demo_phonelm_npu.cpp +++ b/examples/demo_phonelm_npu.cpp @@ -1,4 +1,6 @@ +#include "Module.hpp" #include "Types.hpp" +#include #ifdef USE_QNN #include "backends/cpu/CPUBackend.hpp" #include "cmdline.h" @@ -15,12 +17,14 @@ int main(int argc, char **argv) { cmdParser.add("model", 'm', "specify mllm model path", false, "../models/PhoneLM-1.5B-Instruct-128.mllm"); cmdParser.add("limits", 'l', "max KV cache size", false, 400); cmdParser.add("thread", 't', "num of threads", false, 4); + cmdParser.add("chunk", 'c', "chunk size", false, 64); cmdParser.parse_check(argc, argv); string vocab_path = cmdParser.get("vocab"); string merge_path = cmdParser.get("merge"); string model_path = cmdParser.get("model"); int tokens_limit = cmdParser.get("limits"); + int chunk_size = cmdParser.get("chunk"); CPUBackend::cpu_threads = cmdParser.get("thread"); auto tokenizer = SmolLMTokenizer(vocab_path, merge_path); @@ -36,36 +40,57 @@ int main(int argc, char **argv) { "What is the meaning of life?", }; + // turn on the multi-chunk prefilling + Module::isMultiChunkPrefilling = true; + for (int i = 0; i < in_strs.size(); ++i) { auto input_str = tokenizer.apply_chat_template(in_strs[i]); - auto [real_seq_length, input_tensor] = tokenizer.tokenizeWithPadding(input_str, 64, config.vocab_size); - std::cout << real_seq_length << endl; - std::cout << input_str << std::endl; + auto [real_seq_length, input_tensor] = tokenizer.tokenizePaddingByChunk(input_str, chunk_size, config.vocab_size); + + const int seq_length_padding = (chunk_size - real_seq_length % chunk_size) + real_seq_length; + const int chunk_num = seq_length_padding / chunk_size; + bool isSwitched = false; + + std::cout << "real seq length: " << real_seq_length << " padding to: " << seq_length_padding << " chunk num: " << chunk_num << std::endl; + std::cout << "[Q] " << in_strs[i] << std::endl; std::cout << "[A] " << std::flush; + // tensor vectors to save the chunked tensors of the QNN prefilling input + vector chunked_tensors(chunk_num); LlmTextGeneratorOpts opt{ .max_new_tokens = 1, .do_sample = false, - .temperature = 0.3f, - .top_k = 50, - .top_p = 0.f, .is_padding = true, .seq_before_padding = real_seq_length, + .chunk_size = chunk_size, }; - bool isSwitched = false; - model.generate(input_tensor, opt, [&](unsigned int out_token) -> bool { - if (i != 0 && !isSwitched) { - // turn off switching - static_cast(Backend::global_backends[MLLM_CPU])->toggleSwitching(); - isSwitched = true; - } - auto out_string = tokenizer.detokenize({out_token}); - auto [not_end, output_string] = tokenizer.postprocess(out_string); - if (!not_end) { return false; } - std::cout << output_string << std::flush; - return true; - }); + + for (int chunk_id = 0; chunk_id < chunk_num; ++chunk_id) { + chunked_tensors[chunk_id].setBackend(Backend::global_backends[MLLM_CPU]); + chunked_tensors[chunk_id].setTtype(INPUT_TENSOR); + chunked_tensors[chunk_id].reshape(1, 1, chunk_size, 1); + chunked_tensors[chunk_id].setName("input-chunk-" + to_string(chunk_id)); + chunked_tensors[chunk_id].deepCopyFrom(&input_tensor, false, {0, 0, chunk_id * chunk_size, 0}); + + model.generate(chunked_tensors[chunk_id], opt, [&](unsigned int out_token) -> bool { + if (i != 0 && !isSwitched && chunk_id == 0) { + // turn off switching at the first chunk of following inputs + static_cast(Backend::global_backends[MLLM_CPU])->toggleSwitching(); + isSwitched = true; + } + auto out_string = tokenizer.detokenize({out_token}); + auto [not_end, output_string] = tokenizer.postprocess(out_string); + if (!not_end) { return false; } + + if (chunk_id == chunk_num - 1) { // print the output of the last chunk + std::cout << output_string << std::flush; + } + return true; + }); + + Module::isFirstChunk = false; + } // turn on switching, set sequence length and execution type static_cast(Backend::global_backends[MLLM_CPU])->setSequenceLength(real_seq_length); @@ -80,8 +105,9 @@ int main(int argc, char **argv) { .top_p = 0.f, .is_padding = false, }; + isSwitched = false; - decoding_model.generate(input_tensor, decoding_opt, [&](unsigned int out_token) -> bool { + decoding_model.generate(chunked_tensors.back(), decoding_opt, [&](unsigned int out_token) -> bool { if (!isSwitched) { // turn off switching static_cast(Backend::global_backends[MLLM_CPU])->toggleSwitching(); diff --git a/include/Types.hpp b/include/Types.hpp index 9b634493..040e6712 100644 --- a/include/Types.hpp +++ b/include/Types.hpp @@ -36,6 +36,7 @@ enum TensorStatus { // TENSOR_DYNAMIC, TENSOR_STATIC_INIT, TENSOR_STATIC_READY, + TENSOR_UNDEFINED, }; enum ErrorCode { diff --git a/src/Generate.hpp b/src/Generate.hpp index 7c5b11b9..4ae9a863 100644 --- a/src/Generate.hpp +++ b/src/Generate.hpp @@ -29,6 +29,7 @@ struct LlmTextGeneratorOpts { float top_p = 0.92; bool is_padding = false; int seq_before_padding = 0; + int chunk_size = -1; }; template @@ -51,12 +52,14 @@ enum class LLmTextGeneratorType : int32_t { class _LlmTextGenerateMethod { bool is_padding = false; int seq_before_padding = 0; + int chunk_size = -1; public: virtual ~_LlmTextGenerateMethod() = default; virtual unsigned int generate(Tensor &t) = 0; - inline void setPadding(bool is_padding, int seq_before_padding) { + inline void setPadding(bool is_padding, int seq_before_padding, int chunk_size) { this->is_padding = is_padding; this->seq_before_padding = seq_before_padding; + this->chunk_size = chunk_size; } inline void _tensor_to_vec(Tensor &t, std::vector &scores) { assert(t.batch() == 1 && "Batch size of result is not 1. Which is not supported for now."); @@ -65,7 +68,11 @@ class _LlmTextGenerateMethod { int _seq = t.sequence() - 1; // padding prefill for QNN if (is_padding) { - _seq = seq_before_padding - 1; + if (chunk_size > 0) { + _seq = (seq_before_padding - 1) % chunk_size; + } else { + _seq = seq_before_padding - 1; + } } for (int i = 0; i < _dims; ++i) { auto value = t.dataAt(0, 0, _seq, i); @@ -159,7 +166,7 @@ class LlmTextGenerator { // padding prefill for QNN if (opt.is_padding) { - m_method_class->setPadding(opt.is_padding, opt.seq_before_padding); + m_method_class->setPadding(opt.is_padding, opt.seq_before_padding, opt.chunk_size); } } diff --git a/src/Module.cpp b/src/Module.cpp index 942e9941..95479179 100644 --- a/src/Module.cpp +++ b/src/Module.cpp @@ -13,6 +13,9 @@ namespace mllm { // The llm_model_ptr is a pointer to the outmost module Module *Module::llm_model_ptr; +bool Module::isMultiChunkPrefilling = false; +bool Module::isFirstChunk = true; + int Module::listIdx; int Module::runlistIdx; // TensorStatus Tensor::tensor_status; diff --git a/src/Module.hpp b/src/Module.hpp index 92f5e48e..69256561 100644 --- a/src/Module.hpp +++ b/src/Module.hpp @@ -39,6 +39,10 @@ class Module { bool doLoad = false; static Module *llm_model_ptr; + // tag to indicate the multi-chunk prefilling + static bool isMultiChunkPrefilling; + // tag to indicate the first chunk + static bool isFirstChunk; static int listIdx; static int runlistIdx; @@ -188,6 +192,11 @@ class Module { if (inputs[0].sequence() != 1 && !last_shape_bshd_.empty()) { // if LLM/VLLM model, the `need_setup` should be `true` if (input.batch() == last_shape_bshd_[i][0] & input.sequence() == last_shape_bshd_[i][1] & input.head() == last_shape_bshd_[i][2] & input.dimension() == last_shape_bshd_[i][3]) { + // if it is the QNN multi-chunk prefilling, the `need_setup` should be `true` to reshape & setUp CPU Ops + if (Module::isMultiChunkPrefilling) { + need_setup = true; + break; + } need_setup = false; } } @@ -215,6 +224,12 @@ class Module { } else { // inner Modules // offload according to the backends' info inited during loading if (Tensor::tensor_status == TENSOR_STATIC_INIT && device_ != MLLM_CPU) { // backend specific module reshape & setup + if (Module::isMultiChunkPrefilling && !Module::isFirstChunk) { // set to TENSOR_UNDEFINED and SKIP executing qnn layers + Tensor::tensor_status = TENSOR_UNDEFINED; + auto outputs = Forward(inputs, anyArgs); + Tensor::tensor_status = TENSOR_STATIC_INIT; + return outputs; + } auto inputs_vec = vector>(); auto outputs_vec = vector>(); for (auto &i : inputs) { diff --git a/src/backends/cpu/CPUKVCacheNPU.cpp b/src/backends/cpu/CPUKVCacheNPU.cpp index 06d32ef8..c7b24236 100644 --- a/src/backends/cpu/CPUKVCacheNPU.cpp +++ b/src/backends/cpu/CPUKVCacheNPU.cpp @@ -38,6 +38,11 @@ ErrorCode CPUKVCacheNPU::reshape(vector> inputs, vectorgetSequenceLength(); isDecoding = true; } + // if a new prompt is given, the cache should be updated + if (cpuBackend->isStageSwitching() && cpuBackend->getExecutionType() == PROMPT) { + cache_seq_len_ = cpuBackend->getSequenceLength(); + isDecoding = false; + } #endif outputs[0]->reshape(inputs[0]->batch(), inputs[0]->head(), inputs[0]->sequence() + cache_seq_len_, inputs[0]->dimension()); @@ -56,13 +61,6 @@ ErrorCode CPUKVCacheNPU::load(AbstructLoader &loader) { } ErrorCode CPUKVCacheNPU::execute(vector> inputs, vector> outputs) { - // if a new prompt is given, the cache should be updated - auto cpuBackend = dynamic_cast(backend_); - if (cpuBackend->isStageSwitching() && cpuBackend->getExecutionType() == PROMPT) { - isDecoding = false; - cache_seq_len_ = 0; - outputs[0]->deepCopyFrom(cache_, false, {0, 0, cache_seq_len_ / cache_limit_, 0}); - } // when decoding, the input will deepCopy from cache, no need to execute if (isDecoding) { diff --git a/src/backends/cpu/CPURoPE.cpp b/src/backends/cpu/CPURoPE.cpp index 981c484f..2235dad6 100644 --- a/src/backends/cpu/CPURoPE.cpp +++ b/src/backends/cpu/CPURoPE.cpp @@ -331,13 +331,6 @@ void CPURoPE::rope_mla(shared_ptr input, shared_ptr output) { } ErrorCode CPURoPE::execute(vector> inputs, vector> outputs) { - // if use QNN, when a new prompt input, the seq should be reset to 0 here as the setUp is not called -#ifdef USE_QNN - auto cpuBackend = dynamic_cast(backend_); - if (cpuBackend->isStageSwitching() && cpuBackend->getExecutionType() == PROMPT) { - h_cnt_ = 0; - } -#endif auto &input = inputs[0]; auto &output = outputs[0]; auto out_dtype = output->dtype(); diff --git a/src/backends/xnnpack/third_party/fmt b/src/backends/xnnpack/third_party/fmt new file mode 160000 index 00000000..0c9fce2f --- /dev/null +++ b/src/backends/xnnpack/third_party/fmt @@ -0,0 +1 @@ +Subproject commit 0c9fce2ffefecfdce794e1859584e25877b7b592 diff --git a/src/models/smollm/tokenization_smollm.hpp b/src/models/smollm/tokenization_smollm.hpp index 23a7af98..44ed7251 100644 --- a/src/models/smollm/tokenization_smollm.hpp +++ b/src/models/smollm/tokenization_smollm.hpp @@ -229,6 +229,58 @@ class SmolLMTokenizer final : public BPETokenizer { return std::make_pair(realLength, Tokenizer::tokens2Input(ret)); } + // padding the input by neareast multiplication of chunk_size + std::pair tokenizePaddingByChunk(std::string &text, int chunk_size, int vocab_size) { + std::vector ret; + + if (split_special_tokens_) { + const auto word_collection = unicode_regex_split(text, regex_exprs); + for (auto &piece : word_collection) { + // look up table + // std::string token; + // for (auto b : UTF8(piece)) token += byte_encoder_[b]; + + // using bpe + std::vector tmp; + BPETokenizer::tokenize(piece, tmp, false, true, ""); + ret.insert(ret.end(), tmp.begin(), tmp.end() - 1); + } + } else { + auto parts = _splitWithDelimiters(text, special_tokens); + // for (auto p : parts) { + // std::cout << "\"" << p << "\"" << std::endl; + // } + for (auto &p : parts) { + if (std::find(special_tokens.begin(), special_tokens.end(), p) != special_tokens.end()) { + std::string token; + for (auto b : UTF8(p)) token += byte_encoder_[b]; + + std::vector tmp; + BPETokenizer::tokenize(token, tmp, false, special_tokens, true); + ret.insert(ret.end(), tmp.begin(), tmp.end() - 1); + } else { + const auto word_collection = unicode_regex_split(p, regex_exprs); + for (auto &piece : word_collection) { + // look up table + // std::string token; + // for (auto b : UTF8(piece)) token += byte_encoder_[b]; + + // using bpe + std::vector tmp; + BPETokenizer::tokenize(piece, tmp, false, true, ""); + assert(!tmp.empty()); + ret.insert(ret.end(), tmp.begin(), tmp.end() - 1); + } + } + } + } + + auto realLength = ret.size(); + int paddingLength = (chunk_size - realLength % chunk_size) % chunk_size; + ret.resize(realLength + paddingLength, vocab_size); + return std::make_pair(realLength, Tokenizer::tokens2Input(ret)); + } + std::string _byte_decode_(const std::string &text) { std::string ret; auto _ = ORD(text); From 5ff02cdb6a4c6eae939da9b0fd1b724282b56119 Mon Sep 17 00:00:00 2001 From: yirongjie Date: Thu, 14 Nov 2024 02:27:50 +0000 Subject: [PATCH 03/34] fix: clearCache in RoPE --- examples/demo_phonelm_npu.cpp | 2 + examples/demo_qwen2.5_npu.cpp | 4 +- scripts/build_android_app.sh | 10 +-- scripts/build_qnn_android.sh | 2 +- src/Layer.hpp | 8 +++ src/Op.hpp | 2 +- src/backends/cpu/CPUIRoPE.cpp | 72 +++++++++---------- src/backends/cpu/CPUIRoPE.hpp | 4 +- src/backends/cpu/CPURoPE.cpp | 2 +- src/backends/cpu/CPURoPE.hpp | 4 +- src/backends/xnnpack/third_party/fmt | 1 - src/models/dclm/modeling_dclm.hpp | 4 +- src/models/fuyu/modeling_fuyu.hpp | 6 +- src/models/llama/modeling_elastic_llama.hpp | 15 ++-- src/models/llama/modeling_llama.hpp | 8 +-- src/models/minicpm/modeling_minicpm.hpp | 8 +-- src/models/openelm/modeling_openelm.hpp | 4 +- src/models/opt/modeling_opt_qnn.hpp | 8 +-- src/models/opt/tokenization_opt.hpp | 2 +- src/models/phi3/modeling_phi3.hpp | 8 +-- src/models/phonelm/modeling_phonelm.hpp | 16 +++-- src/models/phonelm/modeling_phonelm_npu.hpp | 31 +++++++- src/models/qwen/modeling_qwen.hpp | 13 ++-- src/models/qwen/modeling_qwen_npu.hpp | 4 +- src/models/qwen/modeling_qwen_xp_sdpa.hpp | 4 +- src/models/smollm/modeling_smollm.hpp | 8 +-- src/models/stablelm/modeling_stablelm.hpp | 11 ++- .../transformer/modeling_transformer.hpp | 7 +- test/xnnpack/XpLlamaMHATest.cpp | 4 +- tools/jni/LibHelper.cpp | 8 +-- 30 files changed, 167 insertions(+), 113 deletions(-) delete mode 160000 src/backends/xnnpack/third_party/fmt diff --git a/examples/demo_phonelm_npu.cpp b/examples/demo_phonelm_npu.cpp index 42324cb4..6607585f 100644 --- a/examples/demo_phonelm_npu.cpp +++ b/examples/demo_phonelm_npu.cpp @@ -129,6 +129,8 @@ int main(int argc, char **argv) { static_cast(Backend::global_backends[MLLM_CPU])->setSequenceLength(0); static_cast(Backend::global_backends[MLLM_CPU])->setExecutionType(PROMPT); static_cast(Backend::global_backends[MLLM_CPU])->toggleSwitching(); + model.clear_kvcache(); + decoding_model.clear_kvcache(); } } #endif \ No newline at end of file diff --git a/examples/demo_qwen2.5_npu.cpp b/examples/demo_qwen2.5_npu.cpp index f49fb33e..77c0356a 100644 --- a/examples/demo_qwen2.5_npu.cpp +++ b/examples/demo_qwen2.5_npu.cpp @@ -61,7 +61,7 @@ int main(int argc, char **argv) { }); static_cast(Backend::global_backends[MLLM_CPU])->setSequenceLength(real_seq_length); - static_cast(Backend::global_backends[MLLM_CPU])->switchDecodeTag(); + static_cast(Backend::global_backends[MLLM_CPU])->toggleSwitching(); LlmTextGeneratorOpts decoding_opt{ .max_new_tokens = 100, @@ -75,7 +75,7 @@ int main(int argc, char **argv) { decoding_model.generate(input_tensor, decoding_opt, [&](unsigned int out_token) -> bool { // call only once of switchDecodeTag if (!isSwitched) { - static_cast(Backend::global_backends[MLLM_CPU])->switchDecodeTag(); + static_cast(Backend::global_backends[MLLM_CPU])->toggleSwitching(); isSwitched = true; } auto out_string = tokenizer.detokenize({out_token}); diff --git a/scripts/build_android_app.sh b/scripts/build_android_app.sh index f11bab92..f2211c6f 100755 --- a/scripts/build_android_app.sh +++ b/scripts/build_android_app.sh @@ -21,9 +21,9 @@ cmake .. \ make mllm_lib -j16 -# 2. copy libs -cp ./libmllm_lib.a ../android/app/src/main/cpp/libs/ +# # 2. copy libs +# cp ./libmllm_lib.a ../android/app/src/main/cpp/libs/ -# 3. build android apk -cd ../android || exit -./gradlew assembleDebug \ No newline at end of file +# # 3. build android apk +# cd ../android || exit +# ./gradlew assembleDebug \ No newline at end of file diff --git a/scripts/build_qnn_android.sh b/scripts/build_qnn_android.sh index f772de80..4daff80d 100755 --- a/scripts/build_qnn_android.sh +++ b/scripts/build_qnn_android.sh @@ -16,4 +16,4 @@ cmake .. \ -DQNN_VALIDATE_NODE=ON \ -DMLLM_BUILD_XNNPACK_BACKEND=OFF -make -j16 +make -j40 diff --git a/src/Layer.hpp b/src/Layer.hpp index 7d729b84..5babf22f 100644 --- a/src/Layer.hpp +++ b/src/Layer.hpp @@ -484,6 +484,7 @@ class SlidingWindowMask final : public Layer { class RoPE final : public Layer { public: + RoPE() = default; explicit RoPE(int pose_type, std::string name) { param_["pose_type"] = pose_type; init(std::move(name), OpType::ROPE); @@ -505,10 +506,14 @@ class RoPE final : public Layer { auto ts = run({input}, 1); return ts[0].get(); } + void clearCache() { + return op_->clearCache(); + } }; class IRoPE final : public Layer { public: + IRoPE() = default; explicit IRoPE(int pose_type, std::string name) { param_["pose_type"] = pose_type; init(std::move(name), OpType::IROPE); @@ -530,6 +535,9 @@ class IRoPE final : public Layer { auto ts = run({input}, 1); return ts[0].get(); } + void clearCache() { + return op_->clearCache(); + } }; class KVCache final : public Layer { diff --git a/src/Op.hpp b/src/Op.hpp index 43787971..be04de18 100644 --- a/src/Op.hpp +++ b/src/Op.hpp @@ -121,7 +121,7 @@ class Op { return -1; } virtual void clearCache() { - assert(type_ == OpType::KVCACHE); + assert(type_ == OpType::KVCACHE || type_ == OpType::IROPE || type_ == OpType::ROPE); std::cout << "only for KVCache" << std::endl; } diff --git a/src/backends/cpu/CPUIRoPE.cpp b/src/backends/cpu/CPUIRoPE.cpp index 330b9a6f..fe2a5798 100644 --- a/src/backends/cpu/CPUIRoPE.cpp +++ b/src/backends/cpu/CPUIRoPE.cpp @@ -124,7 +124,7 @@ ErrorCode CPUIRoPE::reshape(vector> inputs, vectorreshape(inputs[0]->batch(), inputs[0]->head(), inputs[0]->sequence(), inputs[0]->dimension()); ishape = inputs[0]->dimension() * partial_rotary_factor_; // pos_max_ = 16384; - if (sin_.empty() || ishape_old < ishape || global_pose_type_ != pose_type_ ) { + if (sin_.empty() || ishape_old < ishape || global_pose_type_ != pose_type_) { global_pose_type_ = pose_type_; ishape_old = ishape; if (pose_type_ == LLAMAROPE) { @@ -145,39 +145,38 @@ ErrorCode CPUIRoPE::reshape(vector> inputs, vector input, shared_ptr output){ +void CPUIRoPE::rope_llama(shared_ptr input, shared_ptr output) { auto out_dtype = output->dtype(); int partial_dimension = (input->dimension()) * partial_rotary_factor_; #pragma omp parallel for collapse(4) num_threads(thread_count) for (int n = 0; n < input->batch(); ++n) { for (int h = 0; h < input->head(); ++h) { for (int s = 0; s < input->sequence(); ++s) { // sequance - for (int d = 0; d < partial_dimension; d+=2) { + for (int d = 0; d < partial_dimension; d += 2) { float in_value = input->dataAt(n, h, s, d); float in_value_2 = input->dataAt(n, h, s, d + 1); - float sin_value = static_cast(sin_[s + h_cnt_][d])/127 * sin_max; - float cos_value = static_cast(cos_[s + h_cnt_][d])/127 * cos_max; + float sin_value = static_cast(sin_[s + h_cnt_][d]) / 127 * sin_max; + float cos_value = static_cast(cos_[s + h_cnt_][d]) / 127 * cos_max; auto value = in_value * cos_value - in_value_2 * sin_value; auto value2 = in_value * sin_value + in_value_2 * cos_value; if (out_dtype == MLLM_TYPE_F32) { output->setDataAt(n, h, s, d, value); - output->setDataAt(n, h, s, d+1, value2); + output->setDataAt(n, h, s, d + 1, value2); } else if (out_dtype == MLLM_TYPE_F16) { output->setDataAt(n, h, s, d, MLLM_FP32_TO_FP16(value)); - output->setDataAt(n, h, s, d+1, MLLM_FP32_TO_FP16(value2)); + output->setDataAt(n, h, s, d + 1, MLLM_FP32_TO_FP16(value2)); } } } } } } -void CPUIRoPE::rope_hf(shared_ptr input, shared_ptr output){ +void CPUIRoPE::rope_hf(shared_ptr input, shared_ptr output) { auto out_dtype = output->dtype(); int partial_dimension = (input->dimension()) * partial_rotary_factor_; int half = (int)(partial_dimension / 2); - assert(partial_dimension%2==0); - if(output->ctype() == BSHD){ + assert(partial_dimension % 2 == 0); + if (output->ctype() == BSHD) { if (input->dtype() == MLLM_TYPE_F16) { #pragma omp parallel for collapse(4) num_threads(thread_count) for (int n = 0; n < input->batch(); ++n) { @@ -188,9 +187,9 @@ void CPUIRoPE::rope_hf(shared_ptr input, shared_ptr output){ auto o = output->ptrAt(n, h, s, d); float in_value = static_cast(v[0]); float in_value_2 = static_cast(v[half]); - float sin_value = static_cast(sin_[s + h_cnt_][d])/127 * sin_max; + float sin_value = static_cast(sin_[s + h_cnt_][d]) / 127 * sin_max; auto c = static_cast(cos_[s + h_cnt_][d]); - float cos_value = c/127 * cos_max; + float cos_value = c / 127 * cos_max; auto value = in_value * cos_value - in_value_2 * sin_value; auto value2 = in_value * sin_value + in_value_2 * cos_value; o[0] = MLLM_FP32_TO_FP16(value); @@ -200,20 +199,19 @@ void CPUIRoPE::rope_hf(shared_ptr input, shared_ptr output){ } } - } else - if (out_dtype == MLLM_TYPE_F32){ + } else if (out_dtype == MLLM_TYPE_F32) { #pragma omp parallel for collapse(4) num_threads(thread_count) for (int n = 0; n < input->batch(); ++n) { for (int h = 0; h < input->head(); ++h) { for (int s = 0; s < input->sequence(); ++s) { // sequance - for (int d = 0; d < partial_dimension/2; ++d) { + for (int d = 0; d < partial_dimension / 2; ++d) { auto v = input->ptrAt(n, h, s, d); auto o = output->ptrAt(n, h, s, d); float in_value = v[0]; float in_value_2 = v[half]; - float sin_value = static_cast(sin_[s + h_cnt_][d])/127 * sin_max; + float sin_value = static_cast(sin_[s + h_cnt_][d]) / 127 * sin_max; auto c = static_cast(cos_[s + h_cnt_][d]); - float cos_value = c/127 * cos_max; + float cos_value = c / 127 * cos_max; auto value = in_value * cos_value - in_value_2 * sin_value; auto value2 = in_value * sin_value + in_value_2 * cos_value; o[0] = value; @@ -222,18 +220,18 @@ void CPUIRoPE::rope_hf(shared_ptr input, shared_ptr output){ } } } - }else if(out_dtype == MLLM_TYPE_F16){ + } else if (out_dtype == MLLM_TYPE_F16) { #pragma omp parallel for collapse(4) num_threads(thread_count) for (int n = 0; n < input->batch(); ++n) { for (int h = 0; h < input->head(); ++h) { for (int s = 0; s < input->sequence(); ++s) { // sequance - for (int d = 0; d < partial_dimension/2; ++d) { + for (int d = 0; d < partial_dimension / 2; ++d) { auto v = input->ptrAt(n, h, s, d); auto o = output->ptrAt(n, h, s, d); float in_value = v[0]; float in_value_2 = v[half]; float sin_value = static_cast(sin_[s + h_cnt_][d]) / 127 * sin_max; - float cos_value = static_cast(cos_[s + h_cnt_][d])/127 * cos_max; + float cos_value = static_cast(cos_[s + h_cnt_][d]) / 127 * cos_max; auto value = in_value * cos_value - in_value_2 * sin_value; auto value2 = in_value * sin_value + in_value_2 * cos_value; o[0] = MLLM_FP32_TO_FP16(value); @@ -249,7 +247,7 @@ void CPUIRoPE::rope_hf(shared_ptr input, shared_ptr output){ for (int n = 0; n < input->batch(); ++n) { for (int h = 0; h < input->head(); ++h) { for (int s = 0; s < input->sequence(); ++s) { // sequance - for (int d = 0; d < partial_dimension/2; ++d) { + for (int d = 0; d < partial_dimension / 2; ++d) { if (input->dtype() == MLLM_TYPE_F16) { float in_value = static_cast(input->dataAt(n, h, s, d)); float in_value_2 = static_cast(input->dataAt(n, h, s, d + partial_dimension / 2)); @@ -268,16 +266,16 @@ void CPUIRoPE::rope_hf(shared_ptr input, shared_ptr output){ } else { float in_value = input->dataAt(n, h, s, d); float in_value_2 = input->dataAt(n, h, s, d + partial_dimension / 2); - float sin_value = static_cast(sin_[s + h_cnt_][d])/127 * sin_max; - float cos_value = static_cast(cos_[s + h_cnt_][d])/127 * cos_max; + float sin_value = static_cast(sin_[s + h_cnt_][d]) / 127 * sin_max; + float cos_value = static_cast(cos_[s + h_cnt_][d]) / 127 * cos_max; auto value = in_value * cos_value - in_value_2 * sin_value; auto value2 = in_value * sin_value + in_value_2 * cos_value; if (out_dtype == MLLM_TYPE_F32) { output->setDataAt(n, h, s, d, value); - output->setDataAt(n, h, s, d+ partial_dimension / 2, value2); + output->setDataAt(n, h, s, d + partial_dimension / 2, value2); } else if (out_dtype == MLLM_TYPE_F16) { output->setDataAt(n, h, s, d, MLLM_FP32_TO_FP16(value)); - output->setDataAt(n, h, s, d+ partial_dimension / 2, MLLM_FP32_TO_FP16(value2)); + output->setDataAt(n, h, s, d + partial_dimension / 2, MLLM_FP32_TO_FP16(value2)); } } } @@ -285,7 +283,7 @@ void CPUIRoPE::rope_hf(shared_ptr input, shared_ptr output){ } } } -void CPUIRoPE::rope_permission(shared_ptr input, shared_ptr output){ +void CPUIRoPE::rope_permission(shared_ptr input, shared_ptr output) { auto out_dtype = output->dtype(); int partial_dimension = (input->dimension()) * partial_rotary_factor_; #pragma omp parallel for collapse(4) num_threads(thread_count) @@ -293,10 +291,10 @@ void CPUIRoPE::rope_permission(shared_ptr input, shared_ptr outp for (int h = 0; h < input->head(); ++h) { for (int s = 0; s < input->sequence(); ++s) { // sequance for (int d = 0; d < partial_dimension; ++d) { - float in_value = input->dataAt(n, h, s, d); + float in_value = input->dataAt(n, h, s, d); float in_value_2; - float sin_value = static_cast(sin_[s + h_cnt_][d])/127 * sin_max; - float cos_value = static_cast(cos_[s + h_cnt_][d])/127 * cos_max; + float sin_value = static_cast(sin_[s + h_cnt_][d]) / 127 * sin_max; + float cos_value = static_cast(cos_[s + h_cnt_][d]) / 127 * cos_max; if (d < partial_dimension / 4) { in_value_2 = -input->dataAt(n, h, s, d + partial_dimension / 4); auto value = in_value * cos_value + in_value_2 * sin_value; @@ -325,7 +323,7 @@ void CPUIRoPE::rope_permission(shared_ptr input, shared_ptr outp } } } -void CPUIRoPE::rope_mla(shared_ptr input, shared_ptr output){ +void CPUIRoPE::rope_mla(shared_ptr input, shared_ptr output) { auto out_dtype = output->dtype(); int partial_dimension = (input->dimension()) * partial_rotary_factor_; #pragma omp parallel for collapse(4) num_threads(thread_count) @@ -338,17 +336,17 @@ void CPUIRoPE::rope_mla(shared_ptr input, shared_ptr output){ if (d < half_dim) { in_value = input->dataAt(n, h, s, d * 2); } else { - in_value = input->dataAt(n, h, s, 2 *(d - half_dim)+1); + in_value = input->dataAt(n, h, s, 2 * (d - half_dim) + 1); } float in_value_2; if (d < half_dim) { - in_value_2 = -input->dataAt(n, h, s, 2 *d+1); + in_value_2 = -input->dataAt(n, h, s, 2 * d + 1); } else { - in_value_2 = input->dataAt(n, h, s, 2 *(d - half_dim)); + in_value_2 = input->dataAt(n, h, s, 2 * (d - half_dim)); } // no change - float sin_value = static_cast(sin_[s + h_cnt_][d])/127 * sin_max; - float cos_value = static_cast(cos_[s + h_cnt_][d])/127 * cos_max; + float sin_value = static_cast(sin_[s + h_cnt_][d]) / 127 * sin_max; + float cos_value = static_cast(cos_[s + h_cnt_][d]) / 127 * cos_max; auto value = in_value * cos_value + in_value_2 * sin_value; if (out_dtype == MLLM_TYPE_F32) { output->setDataAt(n, h, s, d, value); @@ -486,7 +484,7 @@ ErrorCode CPUIRoPE::execute(vector> inputs, vectorsequence(); - if (h_cnt_ > pos_max_) { + if (h_cnt_ >= pos_max_) { h_cnt_ = 0; } diff --git a/src/backends/cpu/CPUIRoPE.hpp b/src/backends/cpu/CPUIRoPE.hpp index 2e8aee56..bd872fec 100644 --- a/src/backends/cpu/CPUIRoPE.hpp +++ b/src/backends/cpu/CPUIRoPE.hpp @@ -35,11 +35,13 @@ class CPUIRoPE final : public Op { int thread_count = 4; float partial_rotary_factor_ = 1; - void rope_llama(shared_ptr input, shared_ptr output); void rope_hf(shared_ptr input, shared_ptr output); void rope_permission(shared_ptr input, shared_ptr output); void rope_mla(shared_ptr input, shared_ptr output); + void clearCache() override { + h_cnt_ = 0; + } }; class CPUIRoPECreator : public CPUBackend::Creator { diff --git a/src/backends/cpu/CPURoPE.cpp b/src/backends/cpu/CPURoPE.cpp index 2235dad6..919df5a7 100644 --- a/src/backends/cpu/CPURoPE.cpp +++ b/src/backends/cpu/CPURoPE.cpp @@ -447,7 +447,7 @@ ErrorCode CPURoPE::execute(vector> inputs, vectorsequence(); - if (h_cnt_ > pos_max_) { + if (h_cnt_ >= pos_max_) { h_cnt_ = 0; } diff --git a/src/backends/cpu/CPURoPE.hpp b/src/backends/cpu/CPURoPE.hpp index 058a2746..57ee958d 100644 --- a/src/backends/cpu/CPURoPE.hpp +++ b/src/backends/cpu/CPURoPE.hpp @@ -33,11 +33,13 @@ class CPURoPE final : public Op { int thread_count = 4; float partial_rotary_factor_ = 1; - void rope_llama(shared_ptr input, shared_ptr output); void rope_hf(shared_ptr input, shared_ptr output); void rope_permission(shared_ptr input, shared_ptr output); void rope_mla(shared_ptr input, shared_ptr output); + void clearCache() override { + h_cnt_ = 0; + } }; class CPURoPECreator : public CPUBackend::Creator { diff --git a/src/backends/xnnpack/third_party/fmt b/src/backends/xnnpack/third_party/fmt deleted file mode 160000 index 0c9fce2f..00000000 --- a/src/backends/xnnpack/third_party/fmt +++ /dev/null @@ -1 +0,0 @@ -Subproject commit 0c9fce2ffefecfdce794e1859584e25877b7b592 diff --git a/src/models/dclm/modeling_dclm.hpp b/src/models/dclm/modeling_dclm.hpp index 82508199..266e3871 100644 --- a/src/models/dclm/modeling_dclm.hpp +++ b/src/models/dclm/modeling_dclm.hpp @@ -53,8 +53,8 @@ class DCLMAttention final : public Module { Layer out_proj; Layer q_norm; Layer k_norm; - Layer q_rope; - Layer k_rope; + RoPE q_rope; + RoPE k_rope; KVCache k_cache; KVCache v_cache; Layer softmax; diff --git a/src/models/fuyu/modeling_fuyu.hpp b/src/models/fuyu/modeling_fuyu.hpp index 5bfe6575..14065bde 100644 --- a/src/models/fuyu/modeling_fuyu.hpp +++ b/src/models/fuyu/modeling_fuyu.hpp @@ -67,8 +67,10 @@ class Persimmon final : public Module { } void clear_kvcache() override { for (auto &block : blocks) { - auto kvcahce = block.get_attention().get_cache(); - for (auto &cache : kvcahce) { cache->clearCache(); } + auto kvcache = block.get_attention().get_cache(); + for (auto &cache : kvcache) { cache->clearCache(); } + auto ropes = block.get_attention().get_rope(); + for (auto &rope : ropes) { rope->clearCache(); } } } }; diff --git a/src/models/llama/modeling_elastic_llama.hpp b/src/models/llama/modeling_elastic_llama.hpp index 84abda6f..a43af045 100644 --- a/src/models/llama/modeling_elastic_llama.hpp +++ b/src/models/llama/modeling_elastic_llama.hpp @@ -17,8 +17,8 @@ class ElasticMultiHeadAttention final : public Module { ElasticLinear q_proj; ElasticLinear k_proj; ElasticLinear v_proj; - Layer q_rope; - Layer k_rope; + RoPE q_rope; + RoPE k_rope; KVCache k_cache; KVCache v_cache; Softmax softmax; @@ -86,6 +86,9 @@ class ElasticMultiHeadAttention final : public Module { vector get_cache() { return {&k_cache, &v_cache}; } + vector get_rope() { + return {&q_rope, &k_rope}; + } }; class ElasticLLaMAMLP final : public Module { @@ -180,10 +183,10 @@ class ElasticLLaMAModel final : public Module { void clear_kvcache() override { for (auto &block : blocks) { - auto kvcahce = block.get_attention().get_cache(); - for (auto &cache : kvcahce) { - cache->clearCache(); - } + auto kvcache = block.get_attention().get_cache(); + for (auto &cache : kvcache) { cache->clearCache(); } + auto ropes = block.get_attention().get_rope(); + for (auto &rope : ropes) { rope->clearCache(); } } } }; diff --git a/src/models/llama/modeling_llama.hpp b/src/models/llama/modeling_llama.hpp index 31d917e0..5fccd18e 100644 --- a/src/models/llama/modeling_llama.hpp +++ b/src/models/llama/modeling_llama.hpp @@ -97,10 +97,10 @@ class LLaMAModel final : public Module { void clear_kvcache() override { for (auto &block : blocks) { - auto kvcahce = block.get_attention().get_cache(); - for (auto &cache : kvcahce) { - cache->clearCache(); - } + auto kvcache = block.get_attention().get_cache(); + for (auto &cache : kvcache) { cache->clearCache(); } + auto ropes = block.get_attention().get_rope(); + for (auto &rope : ropes) { rope->clearCache(); } } } }; diff --git a/src/models/minicpm/modeling_minicpm.hpp b/src/models/minicpm/modeling_minicpm.hpp index 42145856..163bc0b4 100644 --- a/src/models/minicpm/modeling_minicpm.hpp +++ b/src/models/minicpm/modeling_minicpm.hpp @@ -94,10 +94,10 @@ class MiniCPMModel final : public Module { void clear_kvcache() override { for (auto &block : blocks) { - auto kvcahce = block.get_attention().get_cache(); - for (auto &cache : kvcahce) { - cache->clearCache(); - } + auto kvcache = block.get_attention().get_cache(); + for (auto &cache : kvcache) { cache->clearCache(); } + auto ropes = block.get_attention().get_rope(); + for (auto &rope : ropes) { rope->clearCache(); } } } diff --git a/src/models/openelm/modeling_openelm.hpp b/src/models/openelm/modeling_openelm.hpp index 1d798b8e..2cc1a476 100644 --- a/src/models/openelm/modeling_openelm.hpp +++ b/src/models/openelm/modeling_openelm.hpp @@ -39,8 +39,8 @@ class OpenELMMultiHeadCausalAttention final : public Module { int v_heads_; Layer qkv_proj; - Layer q_rope; - Layer k_rope; + RoPE q_rope; + RoPE k_rope; Layer q_norm; Layer k_norm; Layer out_proj; diff --git a/src/models/opt/modeling_opt_qnn.hpp b/src/models/opt/modeling_opt_qnn.hpp index 101ddac7..e35e3093 100644 --- a/src/models/opt/modeling_opt_qnn.hpp +++ b/src/models/opt/modeling_opt_qnn.hpp @@ -16,8 +16,8 @@ class OPTEncoderBlockPart1 final : public Module { Layer q_proj; Layer k_proj; Layer v_proj; - Layer q_rope; - Layer k_rope; + RoPE q_rope; + RoPE k_rope; Layer norm1; int head_size_{}; int kv_head_size_{}; @@ -74,11 +74,11 @@ class OPTQKVmm final : public Module { const OPTNameConfig &names, const string &base_name) { attn_hidden_dim_ = attn_hidden_dim; head_size_ = head_size; - if(cache_limit>0){ + if (cache_limit > 0) { k_cache = KVCache(head_size / kv_head_size, cache_limit, false, base_name + names._attn_base_name + "k_cache"); v_cache = KVCache(head_size / kv_head_size, cache_limit, false, base_name + names._attn_base_name + "v_cache"); } - + if (do_mask) { mask = Causalmask(base_name + "mask"); } diff --git a/src/models/opt/tokenization_opt.hpp b/src/models/opt/tokenization_opt.hpp index 363906e2..1900d899 100644 --- a/src/models/opt/tokenization_opt.hpp +++ b/src/models/opt/tokenization_opt.hpp @@ -26,7 +26,7 @@ class OPTTokenizer final : public BPETokenizer { } Tensor tokenize(const std::string &text, string name = "input", BackendType type = MLLM_CPU) override { - string new_text; + string new_text = text; if (text[0] != ' ') { new_text = ' ' + text; } diff --git a/src/models/phi3/modeling_phi3.hpp b/src/models/phi3/modeling_phi3.hpp index 3016c084..f5f9ea9b 100644 --- a/src/models/phi3/modeling_phi3.hpp +++ b/src/models/phi3/modeling_phi3.hpp @@ -97,10 +97,10 @@ class Phi3Model final : public Module { void clear_kvcache() override { for (auto &block : blocks) { - auto kvcahce = block.get_attention().get_cache(); - for (auto &cache : kvcahce) { - cache->clearCache(); - } + auto kvcache = block.get_attention().get_cache(); + for (auto &cache : kvcache) { cache->clearCache(); } + auto ropes = block.get_attention().get_rope(); + for (auto &rope : ropes) { rope->clearCache(); } } } }; diff --git a/src/models/phonelm/modeling_phonelm.hpp b/src/models/phonelm/modeling_phonelm.hpp index 69bbecda..151ba193 100644 --- a/src/models/phonelm/modeling_phonelm.hpp +++ b/src/models/phonelm/modeling_phonelm.hpp @@ -98,10 +98,12 @@ class PhoneLMAttention final : public Module { return {o}; } - vector - get_cache() { + vector get_cache() { return {&k_cache, &v_cache}; } + vector get_rope() { + return {&q_rope, &k_rope}; + } private: int hidden_size; @@ -113,8 +115,8 @@ class PhoneLMAttention final : public Module { Layer k_proj; Layer v_proj; Layer o_proj; - Layer q_rope; - Layer k_rope; + IRoPE q_rope; + IRoPE k_rope; KVCache k_cache; KVCache v_cache; Softmax softmax; @@ -170,8 +172,10 @@ class PhoneLMModel final : public Module { void clear_kvcache() override { for (auto &block : blocks) { - auto kvcahce = block.get_attention().get_cache(); - for (auto &cache : kvcahce) { cache->clearCache(); } + auto kvcache = block.get_attention().get_cache(); + for (auto &cache : kvcache) { cache->clearCache(); } + auto ropes = block.get_attention().get_rope(); + for (auto &rope : ropes) { rope->clearCache(); } } } diff --git a/src/models/phonelm/modeling_phonelm_npu.hpp b/src/models/phonelm/modeling_phonelm_npu.hpp index 0b03cb1d..27f44fb6 100644 --- a/src/models/phonelm/modeling_phonelm_npu.hpp +++ b/src/models/phonelm/modeling_phonelm_npu.hpp @@ -82,8 +82,8 @@ class PhoneLMDecoderNPUPart1 final : public Module { // CPU QKV MM part class PhoneLMQKVmm final : public Module { - Layer q_rope; - Layer k_rope; + IRoPE q_rope; + IRoPE k_rope; KVCache k_cache; KVCache v_cache; Softmax softmax; @@ -135,6 +135,12 @@ class PhoneLMQKVmm final : public Module { return {o}; } + vector get_cache() { + return {&k_cache, &v_cache}; + } + vector get_rope() { + return {&q_rope, &k_rope}; + } }; // QNN mlp part @@ -415,6 +421,9 @@ class PhoneLMNPU_CPUDecoder final : public Module { return {x}; } + PhoneLMQKVmm &get_mm() { + return qkv_mm; + } }; class PhoneLMNPU_CPUDecoderWithShadow final : public Module { @@ -483,6 +492,9 @@ class PhoneLMNPU_CPUDecoderWithShadow final : public Module { return {x}; } + PhoneLMQKVmm &get_mm() { + return qkv_mm; + } }; // Copied from GemmaModel with Gemma->PhoneLM and set RmsNorm(without add_unit_offset) @@ -525,6 +537,18 @@ class PhoneLMModel_NPU final : public Module { return {x}; } + void clear_kvcache() override { + for (auto &block : blocks) { + auto decoder = dynamic_cast(block.get()); + if (decoder) { + auto kvcache = decoder->get_mm().get_cache(); + for (auto &cache : kvcache) { cache->clearCache(); } + auto ropes = decoder->get_mm().get_rope(); + for (auto &rope : ropes) { rope->clearCache(); } + } + } + } + private: std::vector> blocks; Layer norm; @@ -588,6 +612,9 @@ class PhoneLMForCausalLM_NPU final : public Module { return; } } + void clear_kvcache() override { + model.clear_kvcache(); + } private: int hidden_size; diff --git a/src/models/qwen/modeling_qwen.hpp b/src/models/qwen/modeling_qwen.hpp index f2b61aaa..f6ca72e9 100644 --- a/src/models/qwen/modeling_qwen.hpp +++ b/src/models/qwen/modeling_qwen.hpp @@ -115,6 +115,9 @@ class QWenAttention final : public Module { vector get_cache() { return {&k_cache, &v_cache}; } + vector get_rope() { + return {&q_rope, &k_rope}; + } private: int hidden_size; @@ -126,8 +129,8 @@ class QWenAttention final : public Module { Layer k_proj; Layer v_proj; Layer o_proj; - Layer q_rope; - Layer k_rope; + RoPE q_rope; + RoPE k_rope; KVCache k_cache; KVCache v_cache; Causalmask mask; @@ -187,8 +190,10 @@ class QWenModel final : public Module { void clear_kvcache() override { for (auto &block : blocks) { - auto kvcahce = block.get_attention().get_cache(); - for (auto &cache : kvcahce) { cache->clearCache(); } + auto kvcache = block.get_attention().get_cache(); + for (auto &cache : kvcache) { cache->clearCache(); } + auto ropes = block.get_attention().get_rope(); + for (auto &rope : ropes) { rope->clearCache(); } } } diff --git a/src/models/qwen/modeling_qwen_npu.hpp b/src/models/qwen/modeling_qwen_npu.hpp index c96a765b..c051170a 100644 --- a/src/models/qwen/modeling_qwen_npu.hpp +++ b/src/models/qwen/modeling_qwen_npu.hpp @@ -82,8 +82,8 @@ class QwenDecoderNPUPart1 final : public Module { // CPU QKV MM part class QwenQKVmm final : public Module { - Layer q_rope; - Layer k_rope; + RoPE q_rope; + RoPE k_rope; KVCache k_cache; KVCache v_cache; Softmax softmax; diff --git a/src/models/qwen/modeling_qwen_xp_sdpa.hpp b/src/models/qwen/modeling_qwen_xp_sdpa.hpp index e9225d3a..dfb28151 100644 --- a/src/models/qwen/modeling_qwen_xp_sdpa.hpp +++ b/src/models/qwen/modeling_qwen_xp_sdpa.hpp @@ -21,8 +21,8 @@ class XpDecoderSeperatedPart_1_Xnn : public Module { Layer q_proj; Layer k_proj; Layer v_proj; - Layer q_rope; - Layer k_rope; + RoPE q_rope; + RoPE k_rope; Layer input_layernorm; int hidden_size = 0; diff --git a/src/models/smollm/modeling_smollm.hpp b/src/models/smollm/modeling_smollm.hpp index 99febfe5..86d8d6ca 100644 --- a/src/models/smollm/modeling_smollm.hpp +++ b/src/models/smollm/modeling_smollm.hpp @@ -104,10 +104,10 @@ class SmolLMModel final : public Module { void clear_kvcache() override { for (auto &block : blocks) { - auto kvcahce = block.get_attention().get_cache(); - for (auto &cache : kvcahce) { - cache->clearCache(); - } + auto kvcache = block.get_attention().get_cache(); + for (auto &cache : kvcache) { cache->clearCache(); } + auto ropes = block.get_attention().get_rope(); + for (auto &rope : ropes) { rope->clearCache(); } } } }; diff --git a/src/models/stablelm/modeling_stablelm.hpp b/src/models/stablelm/modeling_stablelm.hpp index 50737a2a..9e7c0703 100644 --- a/src/models/stablelm/modeling_stablelm.hpp +++ b/src/models/stablelm/modeling_stablelm.hpp @@ -12,8 +12,8 @@ class StableLMMultiHeadAttention final : public Module { Layer q_proj; Layer k_proj; Layer v_proj; - Layer q_rope; - Layer k_rope; + RoPE q_rope; + RoPE k_rope; KVCache k_cache; KVCache v_cache; Softmax softmax; @@ -26,8 +26,8 @@ class StableLMMultiHeadAttention final : public Module { public: StableLMMultiHeadAttention() = default; StableLMMultiHeadAttention(int hidden_dim, int head_size, int kv_head_size, int attn_hidden_dim, - RoPEType RoPE_type, int cache_limit, bool do_mask, bool bias, - const TransformerNameConfig &names, const string &base_name) { + RoPEType RoPE_type, int cache_limit, bool do_mask, bool bias, + const TransformerNameConfig &names, const string &base_name) { attn_hidden_dim_ = attn_hidden_dim; head_size_ = head_size; kv_head_size_ = kv_head_size; @@ -44,7 +44,6 @@ class StableLMMultiHeadAttention final : public Module { } softmax = Softmax(DIMENSION, do_mask, base_name + "softmax"); o_proj = Linear(head_size * attn_hidden_dim, hidden_dim, false, base_name + names._o_proj_name); - } vector Forward(vector inputs, vector args) override { Tensor q, k, v; @@ -107,7 +106,7 @@ class StableLMBlock final : public Module { StableLMBlock() = default; StableLMBlock(int hidden_dim, int head_size, int ffn_hidden, RoPEType RoPE_type, int cache_limit, const stablelmNameConfig &names, const string &base_name) { attention = StableLMMultiHeadAttention(hidden_dim, head_size, head_size, hidden_dim / head_size, - RoPE_type, cache_limit, true, true, names, base_name + names._attn_base_name); + RoPE_type, cache_limit, true, true, names, base_name + names._attn_base_name); mlp = StableLMMLP(hidden_dim, ffn_hidden, names, base_name + names._ffn_base_name); norm1 = LayerNorm(hidden_dim, true, 1e-5, base_name + names._attn_norm_name); norm2 = LayerNorm(hidden_dim, true, 1e-5, base_name + names._ffn_norm_name); diff --git a/src/models/transformer/modeling_transformer.hpp b/src/models/transformer/modeling_transformer.hpp index a4a05040..61244611 100644 --- a/src/models/transformer/modeling_transformer.hpp +++ b/src/models/transformer/modeling_transformer.hpp @@ -17,8 +17,8 @@ class MultiHeadAttention final : public Module { Layer q_proj; Layer k_proj; Layer v_proj; - Layer q_rope; - Layer k_rope; + RoPE q_rope; + RoPE k_rope; Layer q_norm; Layer k_norm; KVCache k_cache; @@ -117,6 +117,9 @@ class MultiHeadAttention final : public Module { vector get_cache() { return {&k_cache, &v_cache}; } + vector get_rope() { + return {&q_rope, &k_rope}; + } }; class FeedForward final : public Module { diff --git a/test/xnnpack/XpLlamaMHATest.cpp b/test/xnnpack/XpLlamaMHATest.cpp index e056203e..540f441a 100644 --- a/test/xnnpack/XpLlamaMHATest.cpp +++ b/test/xnnpack/XpLlamaMHATest.cpp @@ -19,8 +19,8 @@ class XpLLaMAMHA final : public Module { Layer q_proj; Layer k_proj; Layer v_proj; - Layer q_rope; - Layer k_rope; + RoPE q_rope; + RoPE k_rope; Layer k_cache; Layer v_cache; Layer o_proj; diff --git a/tools/jni/LibHelper.cpp b/tools/jni/LibHelper.cpp index 16d0d67e..0964ed50 100644 --- a/tools/jni/LibHelper.cpp +++ b/tools/jni/LibHelper.cpp @@ -137,7 +137,7 @@ void LibHelper::run(std::string &input_str, uint8_t *image, unsigned max_step, u return true; }); static_cast(Backend::global_backends[MLLM_CPU])->setSequenceLength(real_seq_length); - static_cast(Backend::global_backends[MLLM_CPU])->switchDecodeTag(); + static_cast(Backend::global_backends[MLLM_CPU])->toggleSwitching(); opt = LlmTextGeneratorOpts{ .max_new_tokens = max_new_tokens, .do_sample = false, @@ -150,7 +150,7 @@ void LibHelper::run(std::string &input_str, uint8_t *image, unsigned max_step, u bool isSwitched = false; module_->generate(input_tensor, opt, [&](unsigned int out_token) -> bool { if (!isSwitched && backend_ == MLLMBackendType::QNN) { - static_cast(Backend::global_backends[MLLM_CPU])->switchDecodeTag(); + static_cast(Backend::global_backends[MLLM_CPU])->toggleSwitching(); isSwitched = true; } auto out_token_string = tokenizer_->detokenize({out_token}); @@ -206,7 +206,7 @@ void LibHelper::run(std::string &input_str, uint8_t *image, unsigned max_step, u return true; }); static_cast(Backend::global_backends[MLLM_CPU])->setSequenceLength(real_seq_length); - static_cast(Backend::global_backends[MLLM_CPU])->switchDecodeTag(); + static_cast(Backend::global_backends[MLLM_CPU])->toggleSwitching(); opt = LlmTextGeneratorOpts{ .max_new_tokens = max_new_tokens, @@ -220,7 +220,7 @@ void LibHelper::run(std::string &input_str, uint8_t *image, unsigned max_step, u bool isSwitched = false; module_->generate(input_tensor, opt, [&](unsigned int out_token) -> bool { if (!isSwitched && backend_ == MLLMBackendType::QNN) { - static_cast(Backend::global_backends[MLLM_CPU])->switchDecodeTag(); + static_cast(Backend::global_backends[MLLM_CPU])->toggleSwitching(); isSwitched = true; } auto out_token_string = tokenizer_->detokenize({out_token}); From 68ddb997d4a6e7893302ea14702631e31260d772 Mon Sep 17 00:00:00 2001 From: yirongjie Date: Thu, 14 Nov 2024 10:57:26 +0000 Subject: [PATCH 04/34] fix: genarate with Padding --- examples/demo_phonelm_npu.cpp | 34 ++++++---------- examples/demo_qwen2.5_npu.cpp | 4 +- examples/demo_qwen_npu.cpp | 4 +- scripts/build_android_app.sh | 10 ++--- scripts/run_phonelm_qnn.sh | 44 +++++++++++++++++++++ src/Generate.hpp | 8 ++++ src/Op.hpp | 4 +- src/backends/cpu/CPUBackend.hpp | 8 ++-- src/models/phonelm/modeling_phonelm_npu.hpp | 29 +------------- src/models/qwen/modeling_qwen_npu.hpp | 2 +- 10 files changed, 79 insertions(+), 68 deletions(-) create mode 100755 scripts/run_phonelm_qnn.sh diff --git a/examples/demo_phonelm_npu.cpp b/examples/demo_phonelm_npu.cpp index 6607585f..e4112bec 100644 --- a/examples/demo_phonelm_npu.cpp +++ b/examples/demo_phonelm_npu.cpp @@ -1,7 +1,6 @@ #include "Module.hpp" #include "Types.hpp" #include -#ifdef USE_QNN #include "backends/cpu/CPUBackend.hpp" #include "cmdline.h" #include "models/phonelm/modeling_phonelm.hpp" @@ -15,7 +14,7 @@ int main(int argc, char **argv) { cmdParser.add("vocab", 'v', "specify mllm tokenizer model path", false, "../vocab/phonelm_vocab.mllm"); cmdParser.add("merge", 'e', "specify mllm merge file path", false, "../vocab/phonelm_merges.txt"); cmdParser.add("model", 'm', "specify mllm model path", false, "../models/PhoneLM-1.5B-Instruct-128.mllm"); - cmdParser.add("decoding", 'd', "specify mllm decoding model path", false, "../models/phonelm-1.5b-droidcall-q4_0_4_4.mllm"); + cmdParser.add("decoding", 'd', "specify mllm decoding model path", false, "../models/phonelm-1.5b-instruct-q4_0_4_4.mllm"); cmdParser.add("limits", 'l', "max KV cache size", false, 400); cmdParser.add("thread", 't', "num of threads", false, 4); cmdParser.add("chunk", 'c', "chunk size", false, 64); @@ -40,6 +39,9 @@ int main(int argc, char **argv) { "Give me a short introduction to large language model.", "What is the Beijing University of Posts and Telecommunications.", "What is the meaning of life?", + "Hello, who are you?", + "What can you do?", + "Please introduce Beijing University of Posts and Telecommunications.", }; // turn on the multi-chunk prefilling @@ -48,13 +50,10 @@ int main(int argc, char **argv) { for (int i = 0; i < in_strs.size(); ++i) { auto input_str = tokenizer.apply_chat_template(in_strs[i]); auto [real_seq_length, input_tensor] = tokenizer.tokenizePaddingByChunk(input_str, chunk_size, config.vocab_size); - const int seq_length_padding = (chunk_size - real_seq_length % chunk_size) + real_seq_length; const int chunk_num = seq_length_padding / chunk_size; bool isSwitched = false; - - std::cout << "real seq length: " << real_seq_length << " padding to: " << seq_length_padding << " chunk num: " << chunk_num << std::endl; - + // std::cout << "real seq length: " << real_seq_length << " padding to: " << seq_length_padding << " chunk num: " << chunk_num << std::endl; std::cout << "[Q] " << in_strs[i] << std::endl; std::cout << "[A] " << std::flush; @@ -84,13 +83,11 @@ int main(int argc, char **argv) { auto out_string = tokenizer.detokenize({out_token}); auto [not_end, output_string] = tokenizer.postprocess(out_string); if (!not_end) { return false; } - if (chunk_id == chunk_num - 1) { // print the output of the last chunk std::cout << output_string << std::flush; } return true; }); - Module::isFirstChunk = false; } @@ -107,30 +104,23 @@ int main(int argc, char **argv) { .top_p = 0.f, .is_padding = false, }; - isSwitched = false; decoding_model.generate(chunked_tensors.back(), decoding_opt, [&](unsigned int out_token) -> bool { - if (!isSwitched) { - // turn off switching + if (!isSwitched) { // turn off switching static_cast(Backend::global_backends[MLLM_CPU])->toggleSwitching(); isSwitched = true; } auto out_string = tokenizer.detokenize({out_token}); - auto [isOk, print_string] = tokenizer.postprocess(out_string); - if (isOk) { - std::cout << print_string << std::flush; - } else { - return false; - } + auto [not_end, output_string] = tokenizer.postprocess(out_string); + if (!not_end) { return false; } + std::cout << output_string << std::flush; return true; }); - std::cout << "\n---------------" << std::endl; + // turn on switching, set sequence length and execution type static_cast(Backend::global_backends[MLLM_CPU])->setSequenceLength(0); static_cast(Backend::global_backends[MLLM_CPU])->setExecutionType(PROMPT); static_cast(Backend::global_backends[MLLM_CPU])->toggleSwitching(); - model.clear_kvcache(); - decoding_model.clear_kvcache(); + std::cout << "\n"; } -} -#endif \ No newline at end of file +} \ No newline at end of file diff --git a/examples/demo_qwen2.5_npu.cpp b/examples/demo_qwen2.5_npu.cpp index 77c0356a..af6b2e34 100644 --- a/examples/demo_qwen2.5_npu.cpp +++ b/examples/demo_qwen2.5_npu.cpp @@ -1,4 +1,3 @@ -#ifdef USE_QNN #include "backends/cpu/CPUBackend.hpp" #include "cmdline.h" #include "models/qwen/configuration_qwen.hpp" @@ -89,5 +88,4 @@ int main(int argc, char **argv) { }); std::cout << "\n---------------" << std::endl; } -} -#endif \ No newline at end of file +} \ No newline at end of file diff --git a/examples/demo_qwen_npu.cpp b/examples/demo_qwen_npu.cpp index 2ab916c7..04875c30 100644 --- a/examples/demo_qwen_npu.cpp +++ b/examples/demo_qwen_npu.cpp @@ -1,4 +1,3 @@ -#ifdef USE_QNN #include "backends/cpu/CPUBackend.hpp" #include "cmdline.h" #include "models/qwen/configuration_qwen.hpp" @@ -89,5 +88,4 @@ int main(int argc, char **argv) { }); std::cout << "\n---------------" << std::endl; } -} -#endif \ No newline at end of file +} \ No newline at end of file diff --git a/scripts/build_android_app.sh b/scripts/build_android_app.sh index f2211c6f..f11bab92 100755 --- a/scripts/build_android_app.sh +++ b/scripts/build_android_app.sh @@ -21,9 +21,9 @@ cmake .. \ make mllm_lib -j16 -# # 2. copy libs -# cp ./libmllm_lib.a ../android/app/src/main/cpp/libs/ +# 2. copy libs +cp ./libmllm_lib.a ../android/app/src/main/cpp/libs/ -# # 3. build android apk -# cd ../android || exit -# ./gradlew assembleDebug \ No newline at end of file +# 3. build android apk +cd ../android || exit +./gradlew assembleDebug \ No newline at end of file diff --git a/scripts/run_phonelm_qnn.sh b/scripts/run_phonelm_qnn.sh new file mode 100755 index 00000000..a3f814de --- /dev/null +++ b/scripts/run_phonelm_qnn.sh @@ -0,0 +1,44 @@ +!/bin/bash + +adb shell mkdir -p /data/local/tmp/mllm/vocab +adb shell mkdir -p /data/local/tmp/mllm/qnn-lib + +adb push ../vocab/phonelm_vocab.mllm /data/local/tmp/mllm/vocab/ + + +if ! adb shell [ -f "/data/local/tmp/mllm/models/PhoneLM-1.5B-Instruct-128.mllm" ]; then + adb push ../models/PhoneLM-1.5B-Instruct-128.mllm "/data/local/tmp/mllm/models/PhoneLM-1.5B-Instruct-128.mllm" +else + echo "PhoneLM-1.5B-Instruct-128 file already exists" +fi + + +if ! adb shell [ -f "/data/local/tmp/mllm/models//phonelm-1.5b-instruct-q4_0_4_4.mllm" ]; then + adb push ../models//phonelm-1.5b-instruct-q4_0_4_4.mllm "/data/local/tmp/mllm/models//phonelm-1.5b-instruct-q4_0_4_4.mllm" +else + echo "/phonelm-1.5b-instruct-q4_0_4_4.mllm file already exists" +fi + +LIBPATH=../src/backends/qnn/qualcomm_ai_engine_direct_220/ +ANDR_LIB=$LIBPATH/lib/aarch64-android +OP_PATH=../src/backends/qnn/LLaMAOpPackageHtp/LLaMAPackage/build +DEST=/data/local/tmp/mllm/qnn-lib + +adb push $ANDR_LIB/libQnnHtp.so $DEST +adb push $ANDR_LIB/libQnnHtpV75Stub.so $DEST +adb push $ANDR_LIB/libQnnHtpPrepare.so $DEST +adb push $ANDR_LIB/libQnnHtpProfilingReader.so $DEST +adb push $ANDR_LIB/libQnnHtpOptraceProfilingReader.so $DEST +adb push $ANDR_LIB/libQnnHtpV75CalculatorStub.so $DEST +adb push $LIBPATH/lib/hexagon-v75/unsigned/libQnnHtpV75Skel.so $DEST +adb push $OP_PATH/aarch64-android/libQnnLLaMAPackage.so $DEST/libQnnLLaMAPackage_CPU.so +adb push $OP_PATH/hexagon-v75/libQnnLLaMAPackage.so $DEST/libQnnLLaMAPackage_HTP.so + + +if [ $? -ne 0 ]; then + echo "adb push failed" + exit 1 +fi + +adb push ../bin-arm/demo_phonelm_npu /data/local/tmp/mllm/bin/ +adb shell "cd /data/local/tmp/mllm/bin && export LD_LIBRARY_PATH=/data/local/tmp/mllm/qnn-lib && export ADSP_LIBRARY_PATH=/data/local/tmp/mllm/qnn-lib && ./demo_phonelm_npu" \ No newline at end of file diff --git a/src/Generate.hpp b/src/Generate.hpp index 4ae9a863..4eb669d8 100644 --- a/src/Generate.hpp +++ b/src/Generate.hpp @@ -53,6 +53,7 @@ class _LlmTextGenerateMethod { bool is_padding = false; int seq_before_padding = 0; int chunk_size = -1; + public: virtual ~_LlmTextGenerateMethod() = default; virtual unsigned int generate(Tensor &t) = 0; @@ -174,6 +175,13 @@ class LlmTextGenerator { return m_method_class->generate(t); } + inline unsigned int generate(Tensor &t, const LlmTextGeneratorOpts &opt) { + if (opt.is_padding) { + m_method_class->setPadding(opt.is_padding, opt.seq_before_padding, opt.chunk_size); + } + return m_method_class->generate(t); + } + inline LLmTextGeneratorType type() { return m_type; } diff --git a/src/Op.hpp b/src/Op.hpp index be04de18..47a4b721 100644 --- a/src/Op.hpp +++ b/src/Op.hpp @@ -116,12 +116,12 @@ class Op { } virtual int getCacheSeqLen() { - assert(type_ == OpType::KVCACHE); + assert(type_ == OpType::KVCACHE || type_ == OpType::KVCACHENPU); std::cout << "only for KVCache" << std::endl; return -1; } virtual void clearCache() { - assert(type_ == OpType::KVCACHE || type_ == OpType::IROPE || type_ == OpType::ROPE); + assert(type_ == OpType::KVCACHE || type_ == OpType::KVCACHENPU || type_ == OpType::IROPE || type_ == OpType::ROPE); std::cout << "only for KVCache" << std::endl; } diff --git a/src/backends/cpu/CPUBackend.hpp b/src/backends/cpu/CPUBackend.hpp index 75f96412..c808db80 100644 --- a/src/backends/cpu/CPUBackend.hpp +++ b/src/backends/cpu/CPUBackend.hpp @@ -32,7 +32,7 @@ class CPUBackend final : public Backend { static int cpu_threads; -#ifdef USE_QNN + // #ifdef USE_QNN void setSequenceLength(int sequence_length) { sequence_length_ = sequence_length; } @@ -51,15 +51,15 @@ class CPUBackend final : public Backend { ExecutionType getExecutionType() { return execution_type; } -#endif + // #endif private: std::map map_creator_; std::map map_function_; -#ifdef USE_QNN + // #ifdef USE_QNN int sequence_length_ = 0; bool isSwitchingStage = false; ExecutionType execution_type = PROMPT; -#endif + // #endif }; } // namespace mllm diff --git a/src/models/phonelm/modeling_phonelm_npu.hpp b/src/models/phonelm/modeling_phonelm_npu.hpp index 27f44fb6..295bdab7 100644 --- a/src/models/phonelm/modeling_phonelm_npu.hpp +++ b/src/models/phonelm/modeling_phonelm_npu.hpp @@ -135,12 +135,6 @@ class PhoneLMQKVmm final : public Module { return {o}; } - vector get_cache() { - return {&k_cache, &v_cache}; - } - vector get_rope() { - return {&q_rope, &k_rope}; - } }; // QNN mlp part @@ -421,9 +415,6 @@ class PhoneLMNPU_CPUDecoder final : public Module { return {x}; } - PhoneLMQKVmm &get_mm() { - return qkv_mm; - } }; class PhoneLMNPU_CPUDecoderWithShadow final : public Module { @@ -492,9 +483,6 @@ class PhoneLMNPU_CPUDecoderWithShadow final : public Module { return {x}; } - PhoneLMQKVmm &get_mm() { - return qkv_mm; - } }; // Copied from GemmaModel with Gemma->PhoneLM and set RmsNorm(without add_unit_offset) @@ -537,18 +525,6 @@ class PhoneLMModel_NPU final : public Module { return {x}; } - void clear_kvcache() override { - for (auto &block : blocks) { - auto decoder = dynamic_cast(block.get()); - if (decoder) { - auto kvcache = decoder->get_mm().get_cache(); - for (auto &cache : kvcache) { cache->clearCache(); } - auto ropes = decoder->get_mm().get_rope(); - for (auto &rope : ropes) { rope->clearCache(); } - } - } - } - private: std::vector> blocks; Layer norm; @@ -606,15 +582,12 @@ class PhoneLMForCausalLM_NPU final : public Module { for (int step = 0; step < opt.max_new_tokens; ++step) { auto _out = (*this)({input_ids}); - auto out_token = text_generator_->generate(_out[0]); + auto out_token = text_generator_->generate(_out[0], opt); if (!call_back(out_token)) break; chatPostProcessing(out_token, input_ids, {}); return; } } - void clear_kvcache() override { - model.clear_kvcache(); - } private: int hidden_size; diff --git a/src/models/qwen/modeling_qwen_npu.hpp b/src/models/qwen/modeling_qwen_npu.hpp index c051170a..acc4d7e3 100644 --- a/src/models/qwen/modeling_qwen_npu.hpp +++ b/src/models/qwen/modeling_qwen_npu.hpp @@ -586,7 +586,7 @@ class QWenForCausalLM_NPU final : public Module { for (int step = 0; step < opt.max_new_tokens; ++step) { auto _out = (*this)({input_ids}); - auto out_token = text_generator_->generate(_out[0]); + auto out_token = text_generator_->generate(_out[0], opt); if (!call_back(out_token)) break; chatPostProcessing(out_token, input_ids, {}); return; From 28811fefc0ed8b4f515f66c8fa05c84a20942730 Mon Sep 17 00:00:00 2001 From: oreomaker Date: Thu, 14 Nov 2024 22:41:58 +0800 Subject: [PATCH 05/34] feat: make chunk_size configurable in demo_phonelm_npu --- examples/demo_phonelm_npu.cpp | 2 +- src/models/phonelm/modeling_phonelm_npu.hpp | 66 +++++++++++++-------- 2 files changed, 43 insertions(+), 25 deletions(-) diff --git a/examples/demo_phonelm_npu.cpp b/examples/demo_phonelm_npu.cpp index e4112bec..804fa70e 100644 --- a/examples/demo_phonelm_npu.cpp +++ b/examples/demo_phonelm_npu.cpp @@ -30,7 +30,7 @@ int main(int argc, char **argv) { auto tokenizer = SmolLMTokenizer(vocab_path, merge_path); PhoneLMConfig config(tokens_limit, "1.5B"); - auto model = PhoneLMForCausalLM_NPU(config); + auto model = PhoneLMForCausalLM_NPU(config, chunk_size); model.load(model_path); auto decoding_model = PhoneLMForCausalLM(config); decoding_model.load(decoding_path); diff --git a/src/models/phonelm/modeling_phonelm_npu.hpp b/src/models/phonelm/modeling_phonelm_npu.hpp index 295bdab7..26cbd071 100644 --- a/src/models/phonelm/modeling_phonelm_npu.hpp +++ b/src/models/phonelm/modeling_phonelm_npu.hpp @@ -10,6 +10,17 @@ using namespace mllm; +// get the closest factors of a number, used in NPU part2 view to speed up the QNN linear +inline pair closestFactors(int n) { + int root = static_cast(sqrt(n)); + for (int i = root; i > 0; --i) { + if (n % i == 0) { + return {i, n / i}; + } + } + return {1, n}; +} + // NPU QKV part class PhoneLMDecoderNPUPart1 final : public Module { int hidden_size; @@ -36,7 +47,8 @@ class PhoneLMDecoderNPUPart1 final : public Module { public: PhoneLMDecoderNPUPart1() = default; - PhoneLMDecoderNPUPart1(const PhoneLMConfig &config, const PhoneLMNameConfig &names, const string &base_name) { + + PhoneLMDecoderNPUPart1(const PhoneLMConfig &config, const PhoneLMNameConfig &names, int chunk_size, const string &base_name) { hidden_size = config.hidden_size; num_heads = config.num_attention_heads; head_dim = config.hidden_size / num_heads; @@ -97,7 +109,8 @@ class PhoneLMQKVmm final : public Module { public: PhoneLMQKVmm() = default; - PhoneLMQKVmm(const PhoneLMConfig &config, const PhoneLMNameConfig &names, const string &base_name) { + + PhoneLMQKVmm(const PhoneLMConfig &config, const PhoneLMNameConfig &names, int chunk_size, const string &base_name) { hidden_size = config.hidden_size; num_heads = config.num_attention_heads * config.hidden_size / config.num_attention_heads; @@ -173,7 +186,8 @@ class PhoneLMDecoderNPUPart2 final : public Module { public: PhoneLMDecoderNPUPart2() = default; - PhoneLMDecoderNPUPart2(const PhoneLMConfig &config, const PhoneLMNameConfig &names, const string &base_name) { + + PhoneLMDecoderNPUPart2(const PhoneLMConfig &config, const PhoneLMNameConfig &names, int chunk_size, const string &base_name) { hidden_size = config.hidden_size; num_heads = config.num_attention_heads; head_dim = config.hidden_size / num_heads; @@ -182,10 +196,10 @@ class PhoneLMDecoderNPUPart2 final : public Module { num_key_value_groups = num_heads / num_key_value_heads; // for QNN linear speed up - pre_oproj_view = View(1, 2, 32, head_dim * num_heads, base_name + names._attn_base_name + "or_split-00_view_"); + pre_oproj_view = View(1, closestFactors(chunk_size).first, closestFactors(chunk_size).second, head_dim * num_heads, base_name + names._attn_base_name + "or_split-00_view_"); out_proj = Linear(hidden_size, hidden_size, false, base_name + names._attn_base_name + names._o_proj_name); post_oproj_dequantize = Dequantize(true, base_name + names._attn_base_name + names._o_proj_name + ".dequantize"); - post_oproj_view = View(1, 1, 64, hidden_size, base_name + names._attn_base_name + names._o_proj_name + ".dequantize-00_view_"); + post_oproj_view = View(1, 1, chunk_size, hidden_size, base_name + names._attn_base_name + names._o_proj_name + ".dequantize-00_view_"); post_atten_res_add = Add(base_name + names._attn_base_name + "post_atten_add"); post_attn_layernorm = @@ -193,7 +207,7 @@ class PhoneLMDecoderNPUPart2 final : public Module { auto mlp_base_name = base_name + names._ffn_base_name; pre_mlp_quantize = Quantize(true, mlp_base_name + names._up_proj_name + ".quantize"); - pre_mlp_view = View(1, 2, 32, hidden_size, mlp_base_name + names._up_proj_name + ".quantize-00_view_"); + pre_mlp_view = View(1, closestFactors(chunk_size).first, closestFactors(chunk_size).second, hidden_size, mlp_base_name + names._up_proj_name + ".quantize-00_view_"); gate_proj = Linear(hidden_size, intermediate_size, false, mlp_base_name + names._gate_proj_name); relu = ReLU(mlp_base_name + names._gate_proj_name + ".relu"); up_proj = Linear(hidden_size, intermediate_size, false, mlp_base_name + names._up_proj_name); @@ -203,7 +217,7 @@ class PhoneLMDecoderNPUPart2 final : public Module { down_proj = Linear(intermediate_size, hidden_size, false, mlp_base_name + names._down_proj_name); pre_down_proj_quantize = Quantize(true, mlp_base_name + names._down_proj_name + ".quantize"); post_down_proj_dequantize = Dequantize(true, mlp_base_name + names._down_proj_name + ".dequantize"); - post_mlp_view = View(1, 1, 64, hidden_size, mlp_base_name + names._down_proj_name + ".dequantize-00_view_"); + post_mlp_view = View(1, 1, chunk_size, hidden_size, mlp_base_name + names._down_proj_name + ".dequantize-00_view_"); mlp_mul = Mul(mlp_base_name + names._gate_proj_name + ".relu-00_mul_"); post_mlp_res_add = Add(mlp_base_name + "res_add"); @@ -282,7 +296,8 @@ class PhoneLMDecoderNPUPart2WithShadow final : public Module { public: PhoneLMDecoderNPUPart2WithShadow() = default; - PhoneLMDecoderNPUPart2WithShadow(const PhoneLMConfig &config, const PhoneLMNameConfig &names, const string &base_name) { + + PhoneLMDecoderNPUPart2WithShadow(const PhoneLMConfig &config, const PhoneLMNameConfig &names, int chunk_size, const string &base_name) { hidden_size = config.hidden_size; num_heads = config.num_attention_heads; head_dim = config.hidden_size / num_heads; @@ -291,10 +306,10 @@ class PhoneLMDecoderNPUPart2WithShadow final : public Module { num_key_value_groups = num_heads / num_key_value_heads; // for QNN linear speed up - pre_oproj_view = View(1, 2, 32, head_dim * num_heads, base_name + names._attn_base_name + "or_split-00_view_"); + pre_oproj_view = View(1, closestFactors(chunk_size).first, closestFactors(chunk_size).second, head_dim * num_heads, base_name + names._attn_base_name + "or_split-00_view_"); out_proj = Linear(hidden_size, hidden_size, false, base_name + names._attn_base_name + names._o_proj_name); post_oproj_dequantize = Dequantize(true, base_name + names._attn_base_name + names._o_proj_name + ".dequantize"); - post_oproj_view = View(1, 1, 64, hidden_size, base_name + names._attn_base_name + names._o_proj_name + ".dequantize-00_view_"); + post_oproj_view = View(1, 1, chunk_size, hidden_size, base_name + names._attn_base_name + names._o_proj_name + ".dequantize-00_view_"); post_atten_res_add = Add(base_name + names._attn_base_name + "post_atten_add"); post_attn_layernorm = @@ -302,7 +317,7 @@ class PhoneLMDecoderNPUPart2WithShadow final : public Module { auto mlp_base_name = base_name + names._ffn_base_name; pre_mlp_quantize = Quantize(true, mlp_base_name + names._up_proj_name + ".quantize"); - pre_mlp_view = View(1, 2, 32, hidden_size, mlp_base_name + names._up_proj_name + ".quantize-00_view_"); + pre_mlp_view = View(1, closestFactors(chunk_size).first, closestFactors(chunk_size).second, hidden_size, mlp_base_name + names._up_proj_name + ".quantize-00_view_"); gate_proj = Linear(hidden_size, intermediate_size, false, mlp_base_name + names._gate_proj_name); relu = ReLU(mlp_base_name + names._gate_proj_name + ".relu"); up_proj = Linear(hidden_size, intermediate_size, false, mlp_base_name + names._up_proj_name); @@ -312,7 +327,7 @@ class PhoneLMDecoderNPUPart2WithShadow final : public Module { down_proj = Linear(intermediate_size, hidden_size, false, mlp_base_name + names._down_proj_name); pre_down_proj_quantize = Quantize(true, mlp_base_name + names._down_proj_name + ".quantize"); post_down_proj_dequantize = Dequantize(true, mlp_base_name + names._down_proj_name + ".dequantize"); - post_mlp_view = View(1, 1, 64, hidden_size, mlp_base_name + names._down_proj_name + ".dequantize-00_view_"); + post_mlp_view = View(1, 1, chunk_size, hidden_size, mlp_base_name + names._down_proj_name + ".dequantize-00_view_"); mlp_mul = Mul(mlp_base_name + "mul"); post_mlp_res_add = Add(mlp_base_name + "res_add"); @@ -374,7 +389,8 @@ class PhoneLMNPU_CPUDecoder final : public Module { public: PhoneLMNPU_CPUDecoder() = default; - PhoneLMNPU_CPUDecoder(const PhoneLMConfig &config, const PhoneLMNameConfig &names, const string &base_name) { + + PhoneLMNPU_CPUDecoder(const PhoneLMConfig &config, const PhoneLMNameConfig &names, int chunk_size, const string &base_name) { hidden_size = config.hidden_size; num_heads = config.num_attention_heads; head_dim = config.hidden_size / num_heads; @@ -384,13 +400,13 @@ class PhoneLMNPU_CPUDecoder final : public Module { input_layernorm = RMSNorm(config.hidden_size, config.rms_norm_eps, base_name + names._attn_norm_name); pre_attn_quantize = Quantize(true, base_name + names._attn_base_name + names._q_proj_name + ".quantize"); - part1 = PhoneLMDecoderNPUPart1(config, names, base_name + names._attn_base_name); + part1 = PhoneLMDecoderNPUPart1(config, names, chunk_size, base_name + names._attn_base_name); part1.to(MLLM_QNN); - qkv_mm = PhoneLMQKVmm(config, names, base_name + names._attn_base_name); + qkv_mm = PhoneLMQKVmm(config, names, chunk_size, base_name + names._attn_base_name); qkv_mm.to(MLLM_CPU); - part2 = PhoneLMDecoderNPUPart2(config, names, base_name); + part2 = PhoneLMDecoderNPUPart2(config, names, chunk_size, base_name); part2.to(MLLM_QNN); } @@ -433,7 +449,8 @@ class PhoneLMNPU_CPUDecoderWithShadow final : public Module { public: PhoneLMNPU_CPUDecoderWithShadow() = default; - PhoneLMNPU_CPUDecoderWithShadow(const PhoneLMConfig &config, const PhoneLMNameConfig &names, const string &base_name) { + + PhoneLMNPU_CPUDecoderWithShadow(const PhoneLMConfig &config, const PhoneLMNameConfig &names, int chunk_size, const string &base_name) { hidden_size = config.hidden_size; num_heads = config.num_attention_heads; head_dim = config.hidden_size / num_heads; @@ -443,13 +460,13 @@ class PhoneLMNPU_CPUDecoderWithShadow final : public Module { input_layernorm = RMSNorm(config.hidden_size, config.rms_norm_eps, base_name + names._attn_norm_name); pre_attn_quantize = Quantize(true, base_name + names._attn_base_name + names._q_proj_name + ".quantize"); - part1 = PhoneLMDecoderNPUPart1(config, names, base_name + names._attn_base_name); + part1 = PhoneLMDecoderNPUPart1(config, names, chunk_size, base_name + names._attn_base_name); part1.to(MLLM_QNN); - qkv_mm = PhoneLMQKVmm(config, names, base_name + names._attn_base_name); + qkv_mm = PhoneLMQKVmm(config, names, chunk_size, base_name + names._attn_base_name); qkv_mm.to(MLLM_CPU); - part2 = PhoneLMDecoderNPUPart2WithShadow(config, names, base_name); + part2 = PhoneLMDecoderNPUPart2WithShadow(config, names, chunk_size, base_name); part2.to(MLLM_QNN); shadow_linear = ShadowLinear(config.intermediate_size, hidden_size, 1024, false, base_name + names._ffn_base_name + names._down_proj_name + ".shadow"); @@ -510,9 +527,10 @@ class PhoneLMModel_NPU final : public Module { public: PhoneLMModel_NPU() = default; - PhoneLMModel_NPU(const PhoneLMConfig &config, const PhoneLMNameConfig &names, const string &base_name) { + + PhoneLMModel_NPU(const PhoneLMConfig &config, const PhoneLMNameConfig &names, const string &base_name, int chunk_size) { // blocks = List(1, config, names, base_name); - blocks = ListWithShadow(config.num_hidden_layers, config, names, base_name); + blocks = ListWithShadow(config.num_hidden_layers, config, names, chunk_size, base_name); norm = RMSNorm(config.hidden_size, config.rms_norm_eps, names.post_norm_name); } @@ -532,12 +550,12 @@ class PhoneLMModel_NPU final : public Module { class PhoneLMForCausalLM_NPU final : public Module { public: - PhoneLMForCausalLM_NPU(PhoneLMConfig &config) { + PhoneLMForCausalLM_NPU(PhoneLMConfig &config, int chunk_size = 64) { auto names = config.names_config; hidden_size = config.hidden_size; tie_embedding_words = config.tie_embedding_words; embedding = Embedding(config.vocab_size, config.hidden_size, names.token_embd_name); - model = PhoneLMModel_NPU(config, names, names.blk_name); + model = PhoneLMModel_NPU(config, names, names.blk_name, chunk_size); lm_head_layer = Linear(config.hidden_size, config.vocab_size, false, names.lm_head_name); } From 9406c026a2457935d2289935d4369989365b383d Mon Sep 17 00:00:00 2001 From: oreomaker Date: Sat, 16 Nov 2024 21:23:26 +0800 Subject: [PATCH 06/34] chore: andoid submodule diff --- android | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/android b/android index ad54a1a2..fe27fefa 160000 --- a/android +++ b/android @@ -1 +1 @@ -Subproject commit ad54a1a2d8febf901389af27ce09077ea54664d8 +Subproject commit fe27fefaa1fac4670a135cf8104719913d8ff1b2 From caf4f9ca254bf76a25bc59174ad398ee48464609 Mon Sep 17 00:00:00 2001 From: oreomaker Date: Mon, 18 Nov 2024 22:05:58 +0800 Subject: [PATCH 07/34] fix: qwen npu demos execution type set --- examples/demo_qwen2.5_npu.cpp | 8 +++++++- examples/demo_qwen_npu.cpp | 8 +++++++- 2 files changed, 14 insertions(+), 2 deletions(-) diff --git a/examples/demo_qwen2.5_npu.cpp b/examples/demo_qwen2.5_npu.cpp index af6b2e34..2d970e9a 100644 --- a/examples/demo_qwen2.5_npu.cpp +++ b/examples/demo_qwen2.5_npu.cpp @@ -60,6 +60,7 @@ int main(int argc, char **argv) { }); static_cast(Backend::global_backends[MLLM_CPU])->setSequenceLength(real_seq_length); + static_cast(Backend::global_backends[MLLM_CPU])->setExecutionType(AUTOREGRESSIVE); static_cast(Backend::global_backends[MLLM_CPU])->toggleSwitching(); LlmTextGeneratorOpts decoding_opt{ @@ -86,6 +87,11 @@ int main(int argc, char **argv) { } return true; }); - std::cout << "\n---------------" << std::endl; + + // turn on switching, set sequence length and execution type + static_cast(Backend::global_backends[MLLM_CPU])->setSequenceLength(0); + static_cast(Backend::global_backends[MLLM_CPU])->setExecutionType(PROMPT); + static_cast(Backend::global_backends[MLLM_CPU])->toggleSwitching(); + std::cout << "\n"; } } \ No newline at end of file diff --git a/examples/demo_qwen_npu.cpp b/examples/demo_qwen_npu.cpp index 04875c30..8ce4d656 100644 --- a/examples/demo_qwen_npu.cpp +++ b/examples/demo_qwen_npu.cpp @@ -60,6 +60,7 @@ int main(int argc, char **argv) { }); static_cast(Backend::global_backends[MLLM_CPU])->setSequenceLength(real_seq_length); + static_cast(Backend::global_backends[MLLM_CPU])->setExecutionType(AUTOREGRESSIVE); static_cast(Backend::global_backends[MLLM_CPU])->toggleSwitching(); LlmTextGeneratorOpts decoding_opt{ @@ -86,6 +87,11 @@ int main(int argc, char **argv) { } return true; }); - std::cout << "\n---------------" << std::endl; + + // turn on switching, set sequence length and execution type + static_cast(Backend::global_backends[MLLM_CPU])->setSequenceLength(0); + static_cast(Backend::global_backends[MLLM_CPU])->setExecutionType(PROMPT); + static_cast(Backend::global_backends[MLLM_CPU])->toggleSwitching(); + std::cout << "\n"; } } \ No newline at end of file From dfac9d5c4b06a88a04d49468838fe05d138829e7 Mon Sep 17 00:00:00 2001 From: oreomaker Date: Mon, 18 Nov 2024 22:34:49 +0800 Subject: [PATCH 08/34] feat: KVCacheNPU GQA --- src/backends/cpu/CPUKVCacheNPU.cpp | 133 ++++++++++++++++++++++++----- 1 file changed, 114 insertions(+), 19 deletions(-) diff --git a/src/backends/cpu/CPUKVCacheNPU.cpp b/src/backends/cpu/CPUKVCacheNPU.cpp index c7b24236..7bd2ebdb 100644 --- a/src/backends/cpu/CPUKVCacheNPU.cpp +++ b/src/backends/cpu/CPUKVCacheNPU.cpp @@ -14,13 +14,14 @@ CPUKVCacheNPU::CPUKVCacheNPU(Backend *bn, string opName, int n_rep, int cache_ma // TODO: Chaning it to FP16 cache_.setDtype(MLLM_TYPE_F16); cache_limit_ = cache_max; + n_rep_ = n_rep; } ErrorCode CPUKVCacheNPU::reshape(vector> inputs, vector> outputs) { assert(inputs.size() == 1); assert(outputs.size() == 1); if (cache_seq_len_ < 0) { - cache_.reshape(inputs[0]->batch(), inputs[0]->head(), cache_limit_, inputs[0]->dimension()); + cache_.reshape(inputs[0]->batch(), inputs[0]->head() * n_rep_, cache_limit_, inputs[0]->dimension()); cache_.setName(name() + ".Cache"); cache_.alloc(); cache_seq_len_ = 0; @@ -45,7 +46,7 @@ ErrorCode CPUKVCacheNPU::reshape(vector> inputs, vectorreshape(inputs[0]->batch(), inputs[0]->head(), inputs[0]->sequence() + cache_seq_len_, inputs[0]->dimension()); + outputs[0]->reshape(inputs[0]->batch(), inputs[0]->head() * n_rep_, inputs[0]->sequence() + cache_seq_len_, inputs[0]->dimension()); if (inputs[0]->sequence() + cache_seq_len_ > cache_limit_) { MLLM_LOG_ERROR_STREAM << "\n[ERROR]: Current tokens exceed cache limit: " << inputs[0]->sequence() + cache_seq_len_ << ">" << cache_limit_ << ";" << "\n Please set args `--limits` >" << cache_limit_ << std::endl; @@ -61,9 +62,118 @@ ErrorCode CPUKVCacheNPU::load(AbstructLoader &loader) { } ErrorCode CPUKVCacheNPU::execute(vector> inputs, vector> outputs) { + // Group Query Attention + if (n_rep_ > 1 && isDecoding) { + int cache_seq_len_old = cache_seq_len_; + cache_seq_len_ += inputs[0]->sequence(); + if (cache_.ctype() == BSHD) { + for (int b = 0; b < cache_.batch(); ++b) { + for (int h = inputs[0]->head() - 1; h >= 0; --h) { +#pragma omp parallel for collapse(2) num_threads(thread_count) + for (int seq = cache_seq_len_old; seq < cache_seq_len_; ++seq) { + for (int i_rep = 0; i_rep < n_rep_; ++i_rep) { + auto cache_head = h * n_rep_ + i_rep; + if (cache_.dtype() == MLLM_TYPE_F32) { + auto src_ptr = + inputs[0]->ptrAt(b, h, seq - cache_seq_len_old, 0); + auto dest_ptr = cache_.ptrAt(b, cache_head, seq, 0); + int copy_size = cache_.dimension(); + memcpy(dest_ptr, src_ptr, copy_size * sizeof(float)); + } else if (cache_.dtype() == MLLM_TYPE_F16) { + auto src_ptr = + inputs[0]->ptrAt(b, h, seq - cache_seq_len_old, 0); + auto dest_ptr = cache_.ptrAt(b, cache_head, seq, 0); + int copy_size = cache_.dimension(); + memcpy(dest_ptr, src_ptr, copy_size * sizeof(mllm_fp16_t)); + } + } + } + } + } + } else if (cache_.ctype() == BHDS) { + for (int b = 0; b < cache_.batch(); ++b) { + for (int h = inputs[0]->head() - 1; h >= 0; --h) { +#pragma omp parallel for collapse(2) num_threads(thread_count) + for (int d = 0; d < inputs[0]->dimension(); ++d) { + for (int i_rep = 0; i_rep < n_rep_; ++i_rep) { + auto cache_head = h * n_rep_ + i_rep; + if (cache_.dtype() == MLLM_TYPE_F32) { + auto src_ptr = inputs[0]->ptrAt(b, h, 0, d); + auto dest_ptr = + cache_.ptrAt(b, cache_head, cache_seq_len_old, d); + int copy_size = cache_seq_len_ - cache_seq_len_old; + memcpy(dest_ptr, src_ptr, copy_size * sizeof(float)); + } else if (cache_.dtype() == MLLM_TYPE_F16) { + auto src_ptr = inputs[0]->ptrAt(b, h, 0, d); + auto dest_ptr = + cache_.ptrAt(b, cache_head, cache_seq_len_old, d); + int copy_size = cache_seq_len_ - cache_seq_len_old; + memcpy(dest_ptr, src_ptr, copy_size * sizeof(mllm_fp16_t)); + } + } + } + } + } + } else { + std::cout << "ERROR Ctype in KVCcache;" << std::endl; + } + return MLLM_NO_ERROR; + } + if (n_rep_ > 1 && !isDecoding) { + if (cache_.ctype() == BSHD) { + for (int b = 0; b < cache_.batch(); ++b) { + for (int h = 0; h < inputs[0]->head(); ++h) { +#pragma omp parallel for collapse(2) num_threads(thread_count) + for (int seq = 0; seq < inputs[0]->sequence(); ++seq) { + for (int i_rep = 0; i_rep < n_rep_; ++i_rep) { + auto cache_head = h * n_rep_ + i_rep; + if (cache_.dtype() == MLLM_TYPE_F32) { + auto src_ptr = + inputs[0]->ptrAt(b, h, seq, 0); + auto dest_ptr = cache_.ptrAt(b, cache_head, cache_seq_len_ + seq, 0); + memcpy(dest_ptr, src_ptr, inputs[0]->dimension() * sizeof(float)); + } else if (cache_.dtype() == MLLM_TYPE_F16) { + auto src_ptr = + inputs[0]->ptrAt(b, h, seq, 0); + auto dest_ptr = cache_.ptrAt(b, cache_head, cache_seq_len_ + seq, 0); + memcpy(dest_ptr, src_ptr, inputs[0]->dimension() * sizeof(mllm_fp16_t)); + } + } + } + } + } + } else if (cache_.ctype() == BHDS) { + for (int b = 0; b < cache_.batch(); ++b) { + for (int h = 0; h < inputs[0]->head(); ++h) { +#pragma omp parallel for collapse(2) num_threads(thread_count) + for (int d = 0; d < inputs[0]->dimension(); ++d) { + for (int i_rep = 0; i_rep < n_rep_; ++i_rep) { + auto cache_head = h * n_rep_ + i_rep; + if (cache_.dtype() == MLLM_TYPE_F32) { + auto src_ptr = inputs[0]->ptrAt(b, h, 0, d); + auto dest_ptr = + cache_.ptrAt(b, cache_head, cache_seq_len_, d); + memcpy(dest_ptr, src_ptr, inputs[0]->sequence() * sizeof(float)); + } else if (cache_.dtype() == MLLM_TYPE_F16) { + auto src_ptr = inputs[0]->ptrAt(b, h, 0, d); + auto dest_ptr = + cache_.ptrAt(b, cache_head, cache_seq_len_, d); + memcpy(dest_ptr, src_ptr, inputs[0]->sequence() * sizeof(mllm_fp16_t)); + } + } + } + } + } + } else { + std::cout << "ERROR Ctype in KVCcache;" << std::endl; + } + cache_seq_len_ += inputs[0]->sequence(); + return MLLM_NO_ERROR; + } + // q head == kv cache head // when decoding, the input will deepCopy from cache, no need to execute - if (isDecoding) { + if (n_rep_ <= 1 && isDecoding) { cache_seq_len_ += inputs[0]->sequence(); return MLLM_NO_ERROR; } @@ -111,22 +221,7 @@ ErrorCode CPUKVCacheNPU::execute(vector> inputs, vectorhead(); ++h) { - for (int s = 0; s < inputs[0]->sequence(); ++s) { - for (int d = 0; d < inputs[0]->dimension(); ++d) { - if (cache_.dtype() == MLLM_TYPE_F32) { - cache_.setDataAt(b, h, cache_seq_len_ + s, d, inputs[0]->dataAt(b, h, s, d)); - } else if (cache_.dtype() == MLLM_TYPE_F16) { - auto src_ptr = inputs[0]->ptrAt(b, h, s, d); - auto dest_ptr = cache_.ptrAt(b, h, cache_seq_len_ + s, d); - memcpy(dest_ptr, src_ptr, 1 * sizeof(mllm_fp16_t)); - } - } - } - } - } + std::cout << "ERROR Ctype in KVCcache;" << std::endl; } cache_seq_len_ += inputs[0]->sequence(); From 2b204723b88a4e09756ea3879130c2b5ea27ce93 Mon Sep 17 00:00:00 2001 From: oreomaker Date: Thu, 21 Nov 2024 16:00:48 +0800 Subject: [PATCH 09/34] refactor: simplify kvcache npu --- src/backends/cpu/CPUKVCacheNPU.cpp | 210 +++++++++++------------------ 1 file changed, 78 insertions(+), 132 deletions(-) diff --git a/src/backends/cpu/CPUKVCacheNPU.cpp b/src/backends/cpu/CPUKVCacheNPU.cpp index 7bd2ebdb..c9fef4da 100644 --- a/src/backends/cpu/CPUKVCacheNPU.cpp +++ b/src/backends/cpu/CPUKVCacheNPU.cpp @@ -62,170 +62,116 @@ ErrorCode CPUKVCacheNPU::load(AbstructLoader &loader) { } ErrorCode CPUKVCacheNPU::execute(vector> inputs, vector> outputs) { - // Group Query Attention - if (n_rep_ > 1 && isDecoding) { - int cache_seq_len_old = cache_seq_len_; - cache_seq_len_ += inputs[0]->sequence(); - if (cache_.ctype() == BSHD) { - for (int b = 0; b < cache_.batch(); ++b) { - for (int h = inputs[0]->head() - 1; h >= 0; --h) { -#pragma omp parallel for collapse(2) num_threads(thread_count) - for (int seq = cache_seq_len_old; seq < cache_seq_len_; ++seq) { - for (int i_rep = 0; i_rep < n_rep_; ++i_rep) { - auto cache_head = h * n_rep_ + i_rep; - if (cache_.dtype() == MLLM_TYPE_F32) { - auto src_ptr = - inputs[0]->ptrAt(b, h, seq - cache_seq_len_old, 0); - auto dest_ptr = cache_.ptrAt(b, cache_head, seq, 0); - int copy_size = cache_.dimension(); - memcpy(dest_ptr, src_ptr, copy_size * sizeof(float)); - } else if (cache_.dtype() == MLLM_TYPE_F16) { - auto src_ptr = - inputs[0]->ptrAt(b, h, seq - cache_seq_len_old, 0); - auto dest_ptr = cache_.ptrAt(b, cache_head, seq, 0); - int copy_size = cache_.dimension(); - memcpy(dest_ptr, src_ptr, copy_size * sizeof(mllm_fp16_t)); - } - } - } - } - } - } else if (cache_.ctype() == BHDS) { - for (int b = 0; b < cache_.batch(); ++b) { - for (int h = inputs[0]->head() - 1; h >= 0; --h) { -#pragma omp parallel for collapse(2) num_threads(thread_count) - for (int d = 0; d < inputs[0]->dimension(); ++d) { - for (int i_rep = 0; i_rep < n_rep_; ++i_rep) { - auto cache_head = h * n_rep_ + i_rep; - if (cache_.dtype() == MLLM_TYPE_F32) { - auto src_ptr = inputs[0]->ptrAt(b, h, 0, d); - auto dest_ptr = - cache_.ptrAt(b, cache_head, cache_seq_len_old, d); - int copy_size = cache_seq_len_ - cache_seq_len_old; - memcpy(dest_ptr, src_ptr, copy_size * sizeof(float)); - } else if (cache_.dtype() == MLLM_TYPE_F16) { - auto src_ptr = inputs[0]->ptrAt(b, h, 0, d); - auto dest_ptr = - cache_.ptrAt(b, cache_head, cache_seq_len_old, d); - int copy_size = cache_seq_len_ - cache_seq_len_old; - memcpy(dest_ptr, src_ptr, copy_size * sizeof(mllm_fp16_t)); - } - } - } - } - } - } else { - std::cout << "ERROR Ctype in KVCcache;" << std::endl; - } - return MLLM_NO_ERROR; - } - if (n_rep_ > 1 && !isDecoding) { - if (cache_.ctype() == BSHD) { - for (int b = 0; b < cache_.batch(); ++b) { - for (int h = 0; h < inputs[0]->head(); ++h) { + int input_seq = inputs[0]->sequence(); + int cache_seq_len_old = cache_seq_len_; + cache_seq_len_ += inputs[0]->sequence(); + + if (isDecoding) { + // Group Query Attention + if (n_rep_ > 1) { + if (cache_.ctype() == BSHD) { + for (int b = 0; b < cache_.batch(); ++b) { + for (int h = inputs[0]->head() - 1; h >= 0; --h) { #pragma omp parallel for collapse(2) num_threads(thread_count) - for (int seq = 0; seq < inputs[0]->sequence(); ++seq) { - for (int i_rep = 0; i_rep < n_rep_; ++i_rep) { - auto cache_head = h * n_rep_ + i_rep; - if (cache_.dtype() == MLLM_TYPE_F32) { - auto src_ptr = - inputs[0]->ptrAt(b, h, seq, 0); - auto dest_ptr = cache_.ptrAt(b, cache_head, cache_seq_len_ + seq, 0); - memcpy(dest_ptr, src_ptr, inputs[0]->dimension() * sizeof(float)); - } else if (cache_.dtype() == MLLM_TYPE_F16) { - auto src_ptr = - inputs[0]->ptrAt(b, h, seq, 0); - auto dest_ptr = cache_.ptrAt(b, cache_head, cache_seq_len_ + seq, 0); - memcpy(dest_ptr, src_ptr, inputs[0]->dimension() * sizeof(mllm_fp16_t)); + for (int seq = cache_seq_len_old; seq < cache_seq_len_; ++seq) { + for (int i_rep = 0; i_rep < n_rep_; ++i_rep) { + auto cache_head = h * n_rep_ + i_rep; + if (cache_.dtype() == MLLM_TYPE_F32) { + auto src_ptr = + inputs[0]->ptrAt(b, h, input_seq, 0); + auto dest_ptr = cache_.ptrAt(b, cache_head, seq, 0); + int copy_size = cache_.dimension(); + memcpy(dest_ptr, src_ptr, copy_size * sizeof(float)); + } else if (cache_.dtype() == MLLM_TYPE_F16) { + auto src_ptr = + inputs[0]->ptrAt(b, h, input_seq, 0); + auto dest_ptr = cache_.ptrAt(b, cache_head, seq, 0); + int copy_size = cache_.dimension(); + memcpy(dest_ptr, src_ptr, copy_size * sizeof(mllm_fp16_t)); + } } } } } - } - } else if (cache_.ctype() == BHDS) { - for (int b = 0; b < cache_.batch(); ++b) { - for (int h = 0; h < inputs[0]->head(); ++h) { + } else if (cache_.ctype() == BHDS) { + for (int b = 0; b < cache_.batch(); ++b) { + for (int h = inputs[0]->head() - 1; h >= 0; --h) { #pragma omp parallel for collapse(2) num_threads(thread_count) - for (int d = 0; d < inputs[0]->dimension(); ++d) { - for (int i_rep = 0; i_rep < n_rep_; ++i_rep) { - auto cache_head = h * n_rep_ + i_rep; - if (cache_.dtype() == MLLM_TYPE_F32) { - auto src_ptr = inputs[0]->ptrAt(b, h, 0, d); - auto dest_ptr = - cache_.ptrAt(b, cache_head, cache_seq_len_, d); - memcpy(dest_ptr, src_ptr, inputs[0]->sequence() * sizeof(float)); - } else if (cache_.dtype() == MLLM_TYPE_F16) { - auto src_ptr = inputs[0]->ptrAt(b, h, 0, d); - auto dest_ptr = - cache_.ptrAt(b, cache_head, cache_seq_len_, d); - memcpy(dest_ptr, src_ptr, inputs[0]->sequence() * sizeof(mllm_fp16_t)); + for (int d = 0; d < inputs[0]->dimension(); ++d) { + for (int i_rep = 0; i_rep < n_rep_; ++i_rep) { + auto cache_head = h * n_rep_ + i_rep; + if (cache_.dtype() == MLLM_TYPE_F32) { + auto src_ptr = inputs[0]->ptrAt(b, h, 0, d); + auto dest_ptr = + cache_.ptrAt(b, cache_head, cache_seq_len_old, d); + memcpy(dest_ptr, src_ptr, input_seq * sizeof(float)); + } else if (cache_.dtype() == MLLM_TYPE_F16) { + auto src_ptr = inputs[0]->ptrAt(b, h, 0, d); + auto dest_ptr = + cache_.ptrAt(b, cache_head, cache_seq_len_old, d); + memcpy(dest_ptr, src_ptr, input_seq * sizeof(mllm_fp16_t)); + } } } } } + } else { + std::cout << "ERROR Ctype in KVCcache;" << std::endl; } - } else { - std::cout << "ERROR Ctype in KVCcache;" << std::endl; } - cache_seq_len_ += inputs[0]->sequence(); - return MLLM_NO_ERROR; - } - - // q head == kv cache head - // when decoding, the input will deepCopy from cache, no need to execute - if (n_rep_ <= 1 && isDecoding) { - cache_seq_len_ += inputs[0]->sequence(); + // normal attention decoding doesn't need to copy the input to the cache return MLLM_NO_ERROR; } - if (cache_.ctype() == BSHD && inputs[0]->ctype() == BSHD) { // 'K' -#pragma omp parallel for collapse(3) num_threads(thread_count) + // prefilling execution, copy the input to the cache + if (cache_.ctype() == BSHD) { for (int b = 0; b < cache_.batch(); ++b) { for (int h = 0; h < inputs[0]->head(); ++h) { +#pragma omp parallel for collapse(2) num_threads(thread_count) for (int seq = 0; seq < inputs[0]->sequence(); ++seq) { - if (cache_.dtype() == MLLM_TYPE_F32) { - auto src_ptr = inputs[0]->ptrAt(b, h, seq, 0); - auto dest_ptr = cache_.ptrAt(b, h, cache_seq_len_ + seq, 0); - memcpy(dest_ptr, src_ptr, cache_.dimension() * sizeof(float)); - } else if (cache_.dtype() == MLLM_TYPE_F16) { - auto src_ptr = inputs[0]->ptrAt(b, h, seq, 0); - auto dest_ptr = cache_.ptrAt(b, h, cache_seq_len_ + seq, 0); - memcpy(dest_ptr, src_ptr, cache_.dimension() * sizeof(mllm_fp16_t)); - } else if (cache_.dtype() == MLLM_TYPE_I8) { - auto src_ptr = inputs[0]->ptrAt(b, h, seq, 0); - auto dest_ptr = cache_.ptrAt(b, h, cache_seq_len_ + seq, 0); - memcpy(dest_ptr, src_ptr, cache_.dimension() * sizeof(int8_t)); + for (int i_rep = 0; i_rep < n_rep_; ++i_rep) { + auto cache_head = h * n_rep_ + i_rep; + if (cache_.dtype() == MLLM_TYPE_F32) { + auto src_ptr = + inputs[0]->ptrAt(b, h, seq, 0); + auto dest_ptr = cache_.ptrAt(b, cache_head, cache_seq_len_old + seq, 0); + memcpy(dest_ptr, src_ptr, inputs[0]->dimension() * sizeof(float)); + } else if (cache_.dtype() == MLLM_TYPE_F16) { + auto src_ptr = + inputs[0]->ptrAt(b, h, seq, 0); + auto dest_ptr = cache_.ptrAt(b, cache_head, cache_seq_len_old + seq, 0); + memcpy(dest_ptr, src_ptr, inputs[0]->dimension() * sizeof(mllm_fp16_t)); + } } } } } - } else if (cache_.ctype() == BHDS && inputs[0]->ctype() == BHDS) { // 'V' -#pragma omp parallel for collapse(3) num_threads(thread_count) + } else if (cache_.ctype() == BHDS) { for (int b = 0; b < cache_.batch(); ++b) { for (int h = 0; h < inputs[0]->head(); ++h) { +#pragma omp parallel for collapse(2) num_threads(thread_count) for (int d = 0; d < inputs[0]->dimension(); ++d) { - if (cache_.dtype() == MLLM_TYPE_F32) { - auto src_ptr = inputs[0]->ptrAt(b, h, 0, d); - auto dest_ptr = cache_.ptrAt(b, h, cache_seq_len_, d); - memcpy(dest_ptr, src_ptr, inputs[0]->sequence() * sizeof(float)); - } else if (cache_.dtype() == MLLM_TYPE_F16) { - auto src_ptr = inputs[0]->ptrAt(b, h, 0, d); - auto dest_ptr = cache_.ptrAt(b, h, cache_seq_len_, d); - memcpy(dest_ptr, src_ptr, inputs[0]->sequence() * sizeof(mllm_fp16_t)); - } else if (cache_.dtype() == MLLM_TYPE_I8) { - auto src_ptr = inputs[0]->ptrAt(b, h, 0, d); - auto dest_ptr = cache_.ptrAt(b, h, cache_seq_len_, d); - memcpy(dest_ptr, src_ptr, inputs[0]->sequence() * sizeof(int8_t)); + for (int i_rep = 0; i_rep < n_rep_; ++i_rep) { + auto cache_head = h * n_rep_ + i_rep; + if (cache_.dtype() == MLLM_TYPE_F32) { + auto src_ptr = inputs[0]->ptrAt(b, h, 0, d); + auto dest_ptr = + cache_.ptrAt(b, cache_head, cache_seq_len_old, d); + memcpy(dest_ptr, src_ptr, inputs[0]->sequence() * sizeof(float)); + } else if (cache_.dtype() == MLLM_TYPE_F16) { + auto src_ptr = inputs[0]->ptrAt(b, h, 0, d); + auto dest_ptr = + cache_.ptrAt(b, cache_head, cache_seq_len_old, d); + memcpy(dest_ptr, src_ptr, inputs[0]->sequence() * sizeof(mllm_fp16_t)); + } } } } } - } else { // naive case + } else { std::cout << "ERROR Ctype in KVCcache;" << std::endl; } - cache_seq_len_ += inputs[0]->sequence(); - return Op::execute(inputs, outputs); } From fb95bcdcf0b11fd228088f5203ff866b90fda99c Mon Sep 17 00:00:00 2001 From: oreomaker Date: Thu, 21 Nov 2024 16:18:06 +0800 Subject: [PATCH 10/34] fix: kvcache npu seq --- src/backends/cpu/CPUKVCacheNPU.cpp | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/backends/cpu/CPUKVCacheNPU.cpp b/src/backends/cpu/CPUKVCacheNPU.cpp index c9fef4da..42db3b56 100644 --- a/src/backends/cpu/CPUKVCacheNPU.cpp +++ b/src/backends/cpu/CPUKVCacheNPU.cpp @@ -78,13 +78,13 @@ ErrorCode CPUKVCacheNPU::execute(vector> inputs, vectorptrAt(b, h, input_seq, 0); + inputs[0]->ptrAt(b, h, seq - cache_seq_len_old, 0); auto dest_ptr = cache_.ptrAt(b, cache_head, seq, 0); int copy_size = cache_.dimension(); memcpy(dest_ptr, src_ptr, copy_size * sizeof(float)); } else if (cache_.dtype() == MLLM_TYPE_F16) { auto src_ptr = - inputs[0]->ptrAt(b, h, input_seq, 0); + inputs[0]->ptrAt(b, h, seq - cache_seq_len_old, 0); auto dest_ptr = cache_.ptrAt(b, cache_head, seq, 0); int copy_size = cache_.dimension(); memcpy(dest_ptr, src_ptr, copy_size * sizeof(mllm_fp16_t)); From 3ec8d77a1fa65a243b2d23b3f9ca133a0f648f52 Mon Sep 17 00:00:00 2001 From: oreomaker Date: Wed, 27 Nov 2024 20:42:30 +0800 Subject: [PATCH 11/34] chore: android diff --- android | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/android b/android index e8dff3a9..fe27fefa 160000 --- a/android +++ b/android @@ -1 +1 @@ -Subproject commit e8dff3a90c2f969c927f60c6dd748ea37ab1460d +Subproject commit fe27fefaa1fac4670a135cf8104719913d8ff1b2 From 3b0042d84251ac55461e78ecdf7fda52667fa03e Mon Sep 17 00:00:00 2001 From: oreomaker Date: Wed, 4 Dec 2024 16:17:32 +0800 Subject: [PATCH 12/34] dev: pipeline class init --- CMakeLists.txt | 2 +- examples/CMakeLists.txt | 51 ++++++------- examples/demo_qwen_pipeline.cpp | 122 ++++++++++++++++++++++++++++++++ src/Parallel.cpp | 6 ++ src/Parallel.hpp | 54 ++++++++++++++ 5 files changed, 209 insertions(+), 26 deletions(-) create mode 100644 examples/demo_qwen_pipeline.cpp create mode 100644 src/Parallel.cpp create mode 100644 src/Parallel.hpp diff --git a/CMakeLists.txt b/CMakeLists.txt index 5c6dc067..4d296244 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -68,7 +68,7 @@ if(DEBUG) endif() # backend options -option(QNN "Enable QNN" OFF) +option(QNN "Enable QNN" ON) option(QNN_OLD_FRONTEND "Enable Old QNN" OFF) if(QNN) add_definitions(-DUSE_QNN) # the USE_QNN should come before cpu subdirectory diff --git a/examples/CMakeLists.txt b/examples/CMakeLists.txt index af9c7530..59fde426 100644 --- a/examples/CMakeLists.txt +++ b/examples/CMakeLists.txt @@ -54,33 +54,33 @@ macro(func_vlm_add_executable target) func_link_libaries(${target}) endmacro() -func_llm_add_executable(mllm_benchmark) -func_llm_add_executable(demo_llama) -func_llm_add_executable(demo_tinyllama) -func_llm_add_executable(demo_stablelm) -func_llm_add_executable(demo_sparse_llama) -func_llm_add_executable(demo_elastic_llama) -func_llm_add_executable(demo_gemma) -func_llm_add_executable(demo_qwen) -func_llm_add_executable(demo_mistral) -func_llm_add_executable(demo_yi) -func_llm_add_executable(demo_opt) -func_llm_add_executable(demo_phi3) -func_llm_add_executable(demo_minicpm) -func_llm_add_executable(demo_smollm) -func_llm_add_executable(demo_openelm) -func_llm_add_executable(demo_dclm) -func_llm_add_executable(demo_bert) -func_llm_add_executable(demo_phonelm) +# func_llm_add_executable(mllm_benchmark) +# func_llm_add_executable(demo_llama) +# func_llm_add_executable(demo_tinyllama) +# func_llm_add_executable(demo_stablelm) +# func_llm_add_executable(demo_sparse_llama) +# func_llm_add_executable(demo_elastic_llama) +# func_llm_add_executable(demo_gemma) +# func_llm_add_executable(demo_qwen) +# func_llm_add_executable(demo_mistral) +# func_llm_add_executable(demo_yi) +# func_llm_add_executable(demo_opt) +# func_llm_add_executable(demo_phi3) +# func_llm_add_executable(demo_minicpm) +# func_llm_add_executable(demo_smollm) +# func_llm_add_executable(demo_openelm) +# func_llm_add_executable(demo_dclm) +# func_llm_add_executable(demo_bert) +# func_llm_add_executable(demo_phonelm) -func_vlm_add_executable(demo_llava) -func_vlm_add_executable(demo_fuyu) -func_vlm_add_executable(demo_vit) -func_vlm_add_executable(demo_clip) -func_vlm_add_executable(demo_imagebind) -func_vlm_add_executable(demo_imagebind_1mod) -func_vlm_add_executable(demo_phi3v) +# func_vlm_add_executable(demo_llava) +# func_vlm_add_executable(demo_fuyu) +# func_vlm_add_executable(demo_vit) +# func_vlm_add_executable(demo_clip) +# func_vlm_add_executable(demo_imagebind) +# func_vlm_add_executable(demo_imagebind_1mod) +# func_vlm_add_executable(demo_phi3v) # func_vlm_add_executable(demo) # QNN demo @@ -91,6 +91,7 @@ if(QNN) func_llm_add_executable(demo_phonelm_npu) func_llm_add_executable(main_phonelm_npu) func_llm_add_executable(demo_qwen2.5_npu) + func_llm_add_executable(demo_qwen_pipeline) endif() diff --git a/examples/demo_qwen_pipeline.cpp b/examples/demo_qwen_pipeline.cpp new file mode 100644 index 00000000..c5ae5ea8 --- /dev/null +++ b/examples/demo_qwen_pipeline.cpp @@ -0,0 +1,122 @@ +#include "backends/cpu/CPUBackend.hpp" +#include "cmdline.h" +#include "models/qwen/configuration_qwen.hpp" +#include "models/qwen/modeling_qwen_npu.hpp" +#include "models/qwen/modeling_qwen.hpp" +#include "models/qwen/tokenization_qwen.hpp" +#include "processor/PostProcess.hpp" +#include "Parallel.hpp" + +using namespace mllm; + +int main(int argc, char **argv) { + cmdline::parser cmdParser; + cmdParser.add("vocab", 'v', "specify mllm tokenizer model path", false, "../vocab/qwen_vocab.mllm"); + cmdParser.add("merge", 'e', "specify mllm merge file path", false, "../vocab/qwen_merges.txt"); + cmdParser.add("model", 'm', "specify mllm model path", false, "../models/qwen-1.5-1.8b-chat-int8.mllm"); + cmdParser.add("billion", 'b', "[0.5B | 1.8B]", false, "1.8B"); + cmdParser.add("limits", 'l', "max KV cache size", false, 400); + cmdParser.add("thread", 't', "num of threads", false, 4); + cmdParser.parse_check(argc, argv); + + string vocab_path = cmdParser.get("vocab"); + string merge_path = cmdParser.get("merge"); + string model_path = cmdParser.get("model"); + string model_billion = cmdParser.get("billion"); + int tokens_limit = cmdParser.get("limits"); + const int chunk_size = 64; + CPUBackend::cpu_threads = cmdParser.get("thread"); + + auto tokenizer = QWenTokenizer(vocab_path, merge_path); + QWenConfig config(tokens_limit, model_billion, RoPEType::HFHUBROPE); + auto model = QWenForCausalLM_NPU(config); + model.load(model_path); + auto decoding_model = QWenForCausalLM(config); + decoding_model.load("../models/qwen-1.5-1.8b-chat-q4k.mllm"); + + // warmup START + std::string input_str = " "; + auto [real_seq_length, input_tensor] = tokenizer.tokenizePaddingByChunk(input_str, chunk_size, config.vocab_size); + LlmTextGeneratorOpts opt{ + .max_new_tokens = 1, + .do_sample = false, + .is_padding = true, + .seq_before_padding = real_seq_length, + .chunk_size = chunk_size, + }; + model.generate(input_tensor, opt, [&](unsigned int out_token) -> bool { + auto out_string = tokenizer.detokenize({out_token}); + auto [not_end, output_string] = tokenizer.postprocess(out_string); + if (!not_end) { return false; } + return true; + }); + Module::isFirstChunk = false; + static_cast(Backend::global_backends[MLLM_CPU])->setSequenceLength(0); + static_cast(Backend::global_backends[MLLM_CPU])->setExecutionType(PROMPT); + static_cast(Backend::global_backends[MLLM_CPU])->toggleSwitching(); + // turn on the multi-chunk prefilling + Module::isMultiChunkPrefilling = true; + // warmup END + std::cout << "Warmup finished." << std::endl; + + vector in_strs = { + " Give me a short introduction to large language model.", + "\"Large Language Models (LLMs) are advanced artificial intelligence systems designed to understand and generate human-like text. These models are trained on vast amounts of data, enabling them to perform a wide range of tasks, from answering questions and summarizing text to generating creative content and engaging in conversational dialogue. LLMs like GPT-3 and GPT-4, developed by OpenAI, have set new benchmarks in natural language processing by leveraging deep learning architectures, particularly transformer models, which excel at capturing context and relationships within text. The scalability and versatility of LLMs make them invaluable tools for applications in education, customer service, content creation, and more. However, their deployment also raises ethical considerations, including issues of bias, misinformation, and the potential for misuse. As the field continues to evolve, ongoing research and responsible deployment strategies are essential to harnessing the full potential of these powerful AI systems while mitigating their risks.\"\nGenerate a title based on the above text."}; + + for (int i = 0; i < in_strs.size(); ++i) { + auto input_str = tokenizer.apply_chat_template(in_strs[i]); + auto [real_seq_length, input_tensor] = tokenizer.tokenizePaddingByChunk(input_str, chunk_size, config.vocab_size); + + std::cout << "[Q] " << in_strs[i] << std::endl; + std::cout << "[A] " << std::flush; + + LlmTextGeneratorOpts opt{ + .max_new_tokens = 1, + .do_sample = false, + .is_padding = true, + .seq_before_padding = real_seq_length, + .chunk_size = chunk_size, + }; + + // tensor vectors to save the chunked tensors of the QNN prefilling input + bool isSwitched = false; + + ChunkPipeline pipeline(real_seq_length, chunk_size); + auto prefill_result = pipeline.run(input_tensor, opt, tokenizer, model, isSwitched); + + static_cast(Backend::global_backends[MLLM_CPU])->setSequenceLength(real_seq_length); + static_cast(Backend::global_backends[MLLM_CPU])->setExecutionType(AUTOREGRESSIVE); + static_cast(Backend::global_backends[MLLM_CPU])->toggleSwitching(); + + LlmTextGeneratorOpts decoding_opt{ + .max_new_tokens = 100, + .do_sample = false, + .temperature = 0.3f, + .top_k = 50, + .top_p = 0.f, + .is_padding = false, + }; + isSwitched = false; + decoding_model.generate(prefill_result, decoding_opt, [&](unsigned int out_token) -> bool { + // call only once of switchDecodeTag + if (!isSwitched) { + static_cast(Backend::global_backends[MLLM_CPU])->toggleSwitching(); + isSwitched = true; + } + auto out_string = tokenizer.detokenize({out_token}); + auto [isOk, print_string] = tokenizer.postprocess(out_string); + if (isOk) { + std::cout << print_string << std::flush; + } else { + return false; + } + return true; + }); + + // turn on switching, set sequence length and execution type + static_cast(Backend::global_backends[MLLM_CPU])->setSequenceLength(0); + static_cast(Backend::global_backends[MLLM_CPU])->setExecutionType(PROMPT); + static_cast(Backend::global_backends[MLLM_CPU])->toggleSwitching(); + std::cout << "\n"; + } +} \ No newline at end of file diff --git a/src/Parallel.cpp b/src/Parallel.cpp new file mode 100644 index 00000000..e05a339e --- /dev/null +++ b/src/Parallel.cpp @@ -0,0 +1,6 @@ +#include "Parallel.hpp" + +namespace mllm { + + +} diff --git a/src/Parallel.hpp b/src/Parallel.hpp new file mode 100644 index 00000000..96b5ede8 --- /dev/null +++ b/src/Parallel.hpp @@ -0,0 +1,54 @@ +#ifndef PARALLEL_HPP +#define PARALLEL_HPP + +#include "Module.hpp" +#include "Tensor.hpp" +#include "Types.hpp" +#include "tokenizers/Tokenizer.hpp" +#include + +namespace mllm { + +class ChunkPipeline { + int real_seq_length, chunk_size, chunk_num; + vector chunked_tensors; + +public: + ChunkPipeline(int real_seq_length = 4, int chunk_size = 64) : + real_seq_length(real_seq_length), chunk_size(chunk_size) { + const int seq_length_padding = (chunk_size - real_seq_length % chunk_size) + real_seq_length; + chunk_num = seq_length_padding / chunk_size; + chunked_tensors.resize(chunk_num); + } + + Tensor run(Tensor &input_tensor, LlmTextGeneratorOpts &opt, Tokenizer &tokenizer, Module &model, bool &isSwitched) { + for (int chunk_id = 0; chunk_id < chunk_num; ++chunk_id) { + chunked_tensors[chunk_id].setBackend(Backend::global_backends[MLLM_CPU]); + chunked_tensors[chunk_id].setTtype(INPUT_TENSOR); + chunked_tensors[chunk_id].reshape(1, 1, chunk_size, 1); + chunked_tensors[chunk_id].setName("input-chunk-" + std::to_string(chunk_id)); + chunked_tensors[chunk_id].deepCopyFrom(&input_tensor, false, {0, 0, chunk_id * chunk_size, 0}); + + model.generate(chunked_tensors[chunk_id], opt, [&](unsigned int out_token) -> bool { + if (!isSwitched && chunk_id == 0 && static_cast(Backend::global_backends[MLLM_CPU])->isStageSwitching()) { + // turn off switching at the first chunk of following inputs + static_cast(Backend::global_backends[MLLM_CPU])->toggleSwitching(); + isSwitched = true; + } + auto out_string = tokenizer.detokenize({out_token}); + auto [not_end, output_string] = tokenizer.postprocess(out_string); + if (!not_end) { return false; } + if (chunk_id == chunk_num - 1) { // print the output of the last chunk + std::cout << output_string << std::flush; + } + return true; + }); + Module::isFirstChunk = false; + } + return chunked_tensors.back(); + } +}; + +} // namespace mllm + +#endif // PARALLEL_HPP \ No newline at end of file From 05bcd778b3d5dac18ae10e06553ca4539b668b2a Mon Sep 17 00:00:00 2001 From: oreomaker Date: Wed, 4 Dec 2024 20:27:31 +0800 Subject: [PATCH 13/34] refactor: configurable chunk_size in qwen --- examples/demo_qwen2.5_npu.cpp | 2 +- examples/demo_qwen_npu.cpp | 2 +- src/models/qwen/modeling_qwen_npu.hpp | 99 ++++++++++++++------------- 3 files changed, 53 insertions(+), 50 deletions(-) diff --git a/examples/demo_qwen2.5_npu.cpp b/examples/demo_qwen2.5_npu.cpp index 2d970e9a..f9d0b9d6 100644 --- a/examples/demo_qwen2.5_npu.cpp +++ b/examples/demo_qwen2.5_npu.cpp @@ -27,7 +27,7 @@ int main(int argc, char **argv) { auto tokenizer = QWenTokenizer(vocab_path, merge_path); QWenConfig config(tokens_limit, "1.5B", RoPEType::HFHUBROPE); - auto model = QWenForCausalLM_NPU(config); + auto model = QWenForCausalLM_NPU(config, 64); model.load(model_path); auto decoding_model = QWenForCausalLM(config); decoding_model.load("../models/qwen-2.5-1.5b-instruct-q4_0_4_4.mllm"); diff --git a/examples/demo_qwen_npu.cpp b/examples/demo_qwen_npu.cpp index cbd24d35..0ac27711 100644 --- a/examples/demo_qwen_npu.cpp +++ b/examples/demo_qwen_npu.cpp @@ -28,7 +28,7 @@ int main(int argc, char **argv) { auto tokenizer = QWenTokenizer(vocab_path, merge_path); QWenConfig config(tokens_limit, model_billion, RoPEType::HFHUBROPE); - auto model = QWenForCausalLM_NPU(config); + auto model = QWenForCausalLM_NPU(config, chunk_size); model.load(model_path); auto decoding_model = QWenForCausalLM(config); decoding_model.load("../models/qwen-1.5-1.8b-chat-q4k.mllm"); diff --git a/src/models/qwen/modeling_qwen_npu.hpp b/src/models/qwen/modeling_qwen_npu.hpp index acc4d7e3..8d4ecf00 100644 --- a/src/models/qwen/modeling_qwen_npu.hpp +++ b/src/models/qwen/modeling_qwen_npu.hpp @@ -10,6 +10,17 @@ using namespace mllm; +// get the closest factors of a number, used in NPU part2 view to speed up the QNN linear +inline pair closestFactors(int n) { + int root = static_cast(sqrt(n)); + for (int i = root; i > 0; --i) { + if (n % i == 0) { + return {i, n / i}; + } + } + return {1, n}; +} + // NPU QKV part class QwenDecoderNPUPart1 final : public Module { int hidden_size; @@ -36,7 +47,7 @@ class QwenDecoderNPUPart1 final : public Module { public: QwenDecoderNPUPart1() = default; - QwenDecoderNPUPart1(const QWenConfig &config, const QWenNameConfig &names, const string &base_name) { + QwenDecoderNPUPart1(const QWenConfig &config, const QWenNameConfig &names, int chunk_size, const string &base_name) { hidden_size = config.hidden_size; num_heads = config.num_attention_heads; head_dim = config.hidden_size / num_heads; @@ -97,7 +108,7 @@ class QwenQKVmm final : public Module { public: QwenQKVmm() = default; - QwenQKVmm(const QWenConfig &config, const QWenNameConfig &names, const string &base_name) { + QwenQKVmm(const QWenConfig &config, const QWenNameConfig &names, int chunk_size, const string &base_name) { hidden_size = config.hidden_size; num_heads = config.num_attention_heads * config.hidden_size / config.num_attention_heads; @@ -169,7 +180,7 @@ class QwenDecoderNPUPart2 final : public Module { public: QwenDecoderNPUPart2() = default; - QwenDecoderNPUPart2(const QWenConfig &config, const QWenNameConfig &names, const string &base_name) { + QwenDecoderNPUPart2(const QWenConfig &config, const QWenNameConfig &names, int chunk_size, const string &base_name) { hidden_size = config.hidden_size; num_heads = config.num_attention_heads; head_dim = config.hidden_size / num_heads; @@ -178,10 +189,10 @@ class QwenDecoderNPUPart2 final : public Module { num_key_value_groups = num_heads / num_key_value_heads; // for QNN linear speed up - pre_oproj_view = View(1, 2, 32, head_dim * num_heads, base_name + names._attn_base_name + "or_split-00_view_"); + pre_oproj_view = View(1, closestFactors(chunk_size).first, closestFactors(chunk_size).second, head_dim * num_heads, base_name + names._attn_base_name + "or_split-00_view_"); out_proj = Linear(hidden_size, hidden_size, false, base_name + names._attn_base_name + names._o_proj_name); post_oproj_dequantize = Dequantize(true, base_name + names._attn_base_name + names._o_proj_name + ".dequantize"); - post_oproj_view = View(1, 1, 64, hidden_size, base_name + names._attn_base_name + names._o_proj_name + ".dequantize-00_view_"); + post_oproj_view = View(1, 1, chunk_size, hidden_size, base_name + names._attn_base_name + names._o_proj_name + ".dequantize-00_view_"); post_atten_res_add = Add(base_name + names._attn_base_name + "post_atten_add"); post_attn_layernorm = @@ -189,7 +200,7 @@ class QwenDecoderNPUPart2 final : public Module { auto mlp_base_name = base_name + names._ffn_base_name; pre_mlp_quantize = Quantize(true, mlp_base_name + names._up_proj_name + ".quantize"); - pre_mlp_view = View(1, 2, 32, hidden_size, mlp_base_name + names._up_proj_name + ".quantize-00_view_"); + pre_mlp_view = View(1, closestFactors(chunk_size).first, closestFactors(chunk_size).second, hidden_size, mlp_base_name + names._up_proj_name + ".quantize-00_view_"); gate_proj = Linear(hidden_size, intermediate_size, false, mlp_base_name + names._gate_proj_name); silu = SiLU(mlp_base_name + "act"); up_proj = Linear(hidden_size, intermediate_size, false, mlp_base_name + names._up_proj_name); @@ -199,7 +210,7 @@ class QwenDecoderNPUPart2 final : public Module { down_proj = Linear(intermediate_size, hidden_size, false, mlp_base_name + names._down_proj_name); pre_down_proj_quantize = Quantize(true, mlp_base_name + names._down_proj_name + ".quantize"); post_down_proj_dequantize = Dequantize(true, mlp_base_name + names._down_proj_name + ".dequantize"); - post_mlp_view = View(1, 1, 64, hidden_size, mlp_base_name + names._down_proj_name + ".dequantize-00_view_"); + post_mlp_view = View(1, 1, chunk_size, hidden_size, mlp_base_name + names._down_proj_name + ".dequantize-00_view_"); mlp_mul = Mul(mlp_base_name + "mul"); post_mlp_res_add = Add(mlp_base_name + "res_add"); @@ -278,7 +289,7 @@ class QwenDecoderNPUPart2WithShadow final : public Module { public: QwenDecoderNPUPart2WithShadow() = default; - QwenDecoderNPUPart2WithShadow(const QWenConfig &config, const QWenNameConfig &names, const string &base_name) { + QwenDecoderNPUPart2WithShadow(const QWenConfig &config, const QWenNameConfig &names, int chunk_size, const string &base_name) { hidden_size = config.hidden_size; num_heads = config.num_attention_heads; head_dim = config.hidden_size / num_heads; @@ -287,10 +298,10 @@ class QwenDecoderNPUPart2WithShadow final : public Module { num_key_value_groups = num_heads / num_key_value_heads; // for QNN linear speed up - pre_oproj_view = View(1, 2, 32, head_dim * num_heads, base_name + names._attn_base_name + "or_split-00_view_"); + pre_oproj_view = View(1, closestFactors(chunk_size).first, closestFactors(chunk_size).second, head_dim * num_heads, base_name + names._attn_base_name + "or_split-00_view_"); out_proj = Linear(hidden_size, hidden_size, false, base_name + names._attn_base_name + names._o_proj_name); post_oproj_dequantize = Dequantize(true, base_name + names._attn_base_name + names._o_proj_name + ".dequantize"); - post_oproj_view = View(1, 1, 64, hidden_size, base_name + names._attn_base_name + names._o_proj_name + ".dequantize-00_view_"); + post_oproj_view = View(1, 1, chunk_size, hidden_size, base_name + names._attn_base_name + names._o_proj_name + ".dequantize-00_view_"); post_atten_res_add = Add(base_name + names._attn_base_name + "post_atten_add"); post_attn_layernorm = @@ -298,7 +309,7 @@ class QwenDecoderNPUPart2WithShadow final : public Module { auto mlp_base_name = base_name + names._ffn_base_name; pre_mlp_quantize = Quantize(true, mlp_base_name + names._up_proj_name + ".quantize"); - pre_mlp_view = View(1, 2, 32, hidden_size, mlp_base_name + names._up_proj_name + ".quantize-00_view_"); + pre_mlp_view = View(1, closestFactors(chunk_size).first, closestFactors(chunk_size).second, hidden_size, mlp_base_name + names._up_proj_name + ".quantize-00_view_"); gate_proj = Linear(hidden_size, intermediate_size, false, mlp_base_name + names._gate_proj_name); silu = SiLU(mlp_base_name + "act"); up_proj = Linear(hidden_size, intermediate_size, false, mlp_base_name + names._up_proj_name); @@ -308,7 +319,7 @@ class QwenDecoderNPUPart2WithShadow final : public Module { down_proj = Linear(intermediate_size, hidden_size, false, mlp_base_name + names._down_proj_name); pre_down_proj_quantize = Quantize(true, mlp_base_name + names._down_proj_name + ".quantize"); post_down_proj_dequantize = Dequantize(true, mlp_base_name + names._down_proj_name + ".dequantize"); - post_mlp_view = View(1, 1, 64, hidden_size, mlp_base_name + names._down_proj_name + ".dequantize-00_view_"); + post_mlp_view = View(1, 1, chunk_size, hidden_size, mlp_base_name + names._down_proj_name + ".dequantize-00_view_"); mlp_mul = Mul(mlp_base_name + "mul"); post_mlp_res_add = Add(mlp_base_name + "res_add"); @@ -370,7 +381,7 @@ class QwenNPU_CPUDecoder final : public Module { public: QwenNPU_CPUDecoder() = default; - QwenNPU_CPUDecoder(const QWenConfig &config, const QWenNameConfig &names, const string &base_name) { + QwenNPU_CPUDecoder(const QWenConfig &config, const QWenNameConfig &names, int chunk_size, const string &base_name) { hidden_size = config.hidden_size; num_heads = config.num_attention_heads; head_dim = config.hidden_size / num_heads; @@ -380,13 +391,13 @@ class QwenNPU_CPUDecoder final : public Module { input_layernorm = RMSNorm(config.hidden_size, config.rms_norm_eps, base_name + names._attn_norm_name); pre_attn_quantize = Quantize(true, base_name + names._attn_base_name + names._q_proj_name + ".quantize"); - part1 = QwenDecoderNPUPart1(config, names, base_name + names._attn_base_name); + part1 = QwenDecoderNPUPart1(config, names, chunk_size, base_name + names._attn_base_name); part1.to(MLLM_QNN); - qkv_mm = QwenQKVmm(config, names, base_name + names._attn_base_name); + qkv_mm = QwenQKVmm(config, names, chunk_size, base_name + names._attn_base_name); qkv_mm.to(MLLM_CPU); - part2 = QwenDecoderNPUPart2(config, names, base_name); + part2 = QwenDecoderNPUPart2(config, names, chunk_size, base_name); part2.to(MLLM_QNN); } @@ -394,20 +405,17 @@ class QwenNPU_CPUDecoder final : public Module { auto x = input_layernorm(inputs[0]); x = pre_attn_quantize(x); - if (x.device() != MLLM_QNN) { - x = Tensor::toQNN({x})[0]; - } - + x = Tensor::toQNN({x})[0]; auto q_k_v = part1({x}); // q,k,v + q_k_v = Tensor::toCPU(q_k_v); + auto o_x = qkv_mm(q_k_v)[0]; - if (o_x.device() != MLLM_QNN) { - o_x = Tensor::toQNN({o_x})[0]; - } - if (inputs[0].device() != MLLM_QNN) { - inputs[0] = Tensor::toQNN({inputs[0]})[0]; - } + auto qnn_tensor = Tensor::toQNN({o_x, inputs[0]}); + o_x = qnn_tensor[0]; + inputs[0] = qnn_tensor[1]; x = part2({o_x, inputs[0]})[0]; + x = Tensor::toCPU({x})[0]; return {x}; } @@ -429,7 +437,7 @@ class QwenNPU_CPUDecoderWithShadow final : public Module { public: QwenNPU_CPUDecoderWithShadow() = default; - QwenNPU_CPUDecoderWithShadow(const QWenConfig &config, const QWenNameConfig &names, const string &base_name) { + QwenNPU_CPUDecoderWithShadow(const QWenConfig &config, const QWenNameConfig &names, int chunk_size, const string &base_name) { hidden_size = config.hidden_size; num_heads = config.num_attention_heads; head_dim = config.hidden_size / num_heads; @@ -439,13 +447,13 @@ class QwenNPU_CPUDecoderWithShadow final : public Module { input_layernorm = RMSNorm(config.hidden_size, config.rms_norm_eps, base_name + names._attn_norm_name); pre_attn_quantize = Quantize(true, base_name + names._attn_base_name + names._q_proj_name + ".quantize"); - part1 = QwenDecoderNPUPart1(config, names, base_name + names._attn_base_name); + part1 = QwenDecoderNPUPart1(config, names, chunk_size, base_name + names._attn_base_name); part1.to(MLLM_QNN); - qkv_mm = QwenQKVmm(config, names, base_name + names._attn_base_name); + qkv_mm = QwenQKVmm(config, names, chunk_size, base_name + names._attn_base_name); qkv_mm.to(MLLM_CPU); - part2 = QwenDecoderNPUPart2WithShadow(config, names, base_name); + part2 = QwenDecoderNPUPart2WithShadow(config, names, chunk_size, base_name); part2.to(MLLM_QNN); shadow_linear = ShadowLinear(config.intermediate_size, hidden_size, 1024, false, base_name + names._ffn_base_name + names._down_proj_name + ".shadow"); @@ -455,23 +463,18 @@ class QwenNPU_CPUDecoderWithShadow final : public Module { auto x = input_layernorm(inputs[0]); x = pre_attn_quantize(x); - if (x.device() != MLLM_QNN) { - x = Tensor::toQNN({x})[0]; - } - + x = Tensor::toQNN({x})[0]; auto q_k_v = part1({x}); // q,k,v + q_k_v = Tensor::toCPU(q_k_v); + auto o_x = qkv_mm(q_k_v)[0]; - if (o_x.device() != MLLM_QNN) { - o_x = Tensor::toQNN({o_x})[0]; - } - if (inputs[0].device() != MLLM_QNN) { - inputs[0] = Tensor::toQNN({inputs[0]})[0]; - } + auto qnn_tensor = Tensor::toQNN({o_x, inputs[0]}); + o_x = qnn_tensor[0]; + inputs[0] = qnn_tensor[1]; auto decoder_out = part2({o_x, inputs[0]}); - if (decoder_out[0].device() != MLLM_CPU) { - decoder_out = Tensor::toCPU(decoder_out); - } + decoder_out = Tensor::toCPU(decoder_out); + auto shadow_input_1 = decoder_out[0]; auto shadow_input_2 = decoder_out[1]; x = decoder_out[2]; @@ -506,9 +509,9 @@ class QWenModel_NPU final : public Module { public: QWenModel_NPU() = default; - QWenModel_NPU(const QWenConfig &config, const QWenNameConfig &names, const string &base_name) { + QWenModel_NPU(const QWenConfig &config, const QWenNameConfig &names, int chunk_size, const string &base_name) { // blocks = List(1, config, names, base_name); - blocks = ListWithShadow(24, config, names, base_name); + blocks = ListWithShadow(24, config, names, chunk_size, base_name); norm = RMSNorm(config.hidden_size, config.rms_norm_eps, names.post_norm_name); } @@ -528,12 +531,12 @@ class QWenModel_NPU final : public Module { class QWenForCausalLM_NPU final : public Module { public: - QWenForCausalLM_NPU(QWenConfig &config) { + QWenForCausalLM_NPU(QWenConfig &config, int chunk_size) { auto names = config.names_config; hidden_size = config.hidden_size; tie_embedding_words = config.tie_embedding_words; embedding = Embedding(config.vocab_size, config.hidden_size, names.token_embd_name); - model = QWenModel_NPU(config, names, names.blk_name); + model = QWenModel_NPU(config, names, chunk_size, names.blk_name); // Qwen-0.5 use tied embedding // Others use nn.Linear() @@ -602,4 +605,4 @@ class QWenForCausalLM_NPU final : public Module { QWenModel_NPU model; }; -#endif //! MODELING_QWENNPU_HPP \ No newline at end of file +#endif //! MODELING_QWENNPU_HPP chunk_size, \ No newline at end of file From c3a0c4efdbf6bb3b63880d4db96110906bb849d2 Mon Sep 17 00:00:00 2001 From: oreomaker Date: Sat, 7 Dec 2024 15:01:40 +0800 Subject: [PATCH 14/34] chore: remove qnn getBuildId --- src/backends/qnn/QNNBackend.cpp | 1 - src/backends/qnn/Utils/BuildId.hpp | 17 ----------------- 2 files changed, 18 deletions(-) delete mode 100644 src/backends/qnn/Utils/BuildId.hpp diff --git a/src/backends/qnn/QNNBackend.cpp b/src/backends/qnn/QNNBackend.cpp index 1fb6f727..ebc8b65e 100755 --- a/src/backends/qnn/QNNBackend.cpp +++ b/src/backends/qnn/QNNBackend.cpp @@ -145,7 +145,6 @@ QNNBackend::QNNBackend(shared_ptr mm) : // init qnn resources { - MLLM_LOG_INFO_LEGACY("qnn-backend build version: %s", getBuildId().c_str()); MLLM_LOG_INFO_LEGACY("Backend build version: %s", getBackendBuildId().c_str()); // initialize logging in the backend diff --git a/src/backends/qnn/Utils/BuildId.hpp b/src/backends/qnn/Utils/BuildId.hpp deleted file mode 100644 index d8187e8b..00000000 --- a/src/backends/qnn/Utils/BuildId.hpp +++ /dev/null @@ -1,17 +0,0 @@ -//============================================================================== -// -// Copyright (c) 2020 Qualcomm Technologies, Inc. -// All Rights Reserved. -// Confidential and Proprietary - Qualcomm Technologies, Inc. -// -//============================================================================== - -#pragma once - -namespace qnn { -namespace tools { - -inline std::string getBuildId() { return std::string("v2.16.4.231110151339_60331"); } - -} // namespace tools -} // namespace qnn From 9a56c4c9211312bb4a6d3126b6c9972a3f6db745 Mon Sep 17 00:00:00 2001 From: oreomaker Date: Sat, 7 Dec 2024 15:02:15 +0800 Subject: [PATCH 15/34] chore: clean qnn backend include --- src/backends/qnn/QNNBackend.cpp | 1 - 1 file changed, 1 deletion(-) diff --git a/src/backends/qnn/QNNBackend.cpp b/src/backends/qnn/QNNBackend.cpp index ebc8b65e..ed314f87 100755 --- a/src/backends/qnn/QNNBackend.cpp +++ b/src/backends/qnn/QNNBackend.cpp @@ -11,7 +11,6 @@ #include "QNNBackend.hpp" #include "ParamLoader.hpp" #include "QnnModel.hpp" -#include "Utils/BuildId.hpp" #include "Utils/QnnSampleAppUtils.hpp" #include "Utils/IOTensor.hpp" #include "Utils/DynamicLoadUtil.hpp" From f627381daa5b716df61895c0965546b76af82b0d Mon Sep 17 00:00:00 2001 From: oreomaker Date: Sat, 7 Dec 2024 15:21:22 +0800 Subject: [PATCH 16/34] dev: qnn new frontend pipeline(wrap implement) --- examples/demo_qwen_pipeline.cpp | 52 +++++++-------- include/Types.hpp | 6 ++ src/Layer.hpp | 13 +++- src/Module.hpp | 72 ++++++++++++++++++++ src/Op.hpp | 47 +++++++++++++ src/Parallel.hpp | 95 ++++++++++++++++++++------- src/Tensor.cpp | 12 ++++ src/Tensor.hpp | 4 +- src/Trace.cpp | 60 +++++++++++++++++ src/Trace.hpp | 31 +++++++++ src/models/qwen/modeling_qwen_npu.hpp | 2 +- 11 files changed, 339 insertions(+), 55 deletions(-) create mode 100644 src/Trace.cpp create mode 100644 src/Trace.hpp diff --git a/examples/demo_qwen_pipeline.cpp b/examples/demo_qwen_pipeline.cpp index c5ae5ea8..88fe0d8d 100644 --- a/examples/demo_qwen_pipeline.cpp +++ b/examples/demo_qwen_pipeline.cpp @@ -1,3 +1,6 @@ +#include "Backend.hpp" +#include "Trace.hpp" +#include "Types.hpp" #include "backends/cpu/CPUBackend.hpp" #include "cmdline.h" #include "models/qwen/configuration_qwen.hpp" @@ -24,43 +27,30 @@ int main(int argc, char **argv) { string model_path = cmdParser.get("model"); string model_billion = cmdParser.get("billion"); int tokens_limit = cmdParser.get("limits"); - const int chunk_size = 64; + const int chunk_size = 128; CPUBackend::cpu_threads = cmdParser.get("thread"); auto tokenizer = QWenTokenizer(vocab_path, merge_path); QWenConfig config(tokens_limit, model_billion, RoPEType::HFHUBROPE); - auto model = QWenForCausalLM_NPU(config); + auto model = QWenForCausalLM_NPU(config, chunk_size); model.load(model_path); auto decoding_model = QWenForCausalLM(config); decoding_model.load("../models/qwen-1.5-1.8b-chat-q4k.mllm"); - // warmup START - std::string input_str = " "; - auto [real_seq_length, input_tensor] = tokenizer.tokenizePaddingByChunk(input_str, chunk_size, config.vocab_size); - LlmTextGeneratorOpts opt{ - .max_new_tokens = 1, - .do_sample = false, - .is_padding = true, - .seq_before_padding = real_seq_length, - .chunk_size = chunk_size, - }; - model.generate(input_tensor, opt, [&](unsigned int out_token) -> bool { - auto out_string = tokenizer.detokenize({out_token}); - auto [not_end, output_string] = tokenizer.postprocess(out_string); - if (!not_end) { return false; } - return true; - }); - Module::isFirstChunk = false; - static_cast(Backend::global_backends[MLLM_CPU])->setSequenceLength(0); - static_cast(Backend::global_backends[MLLM_CPU])->setExecutionType(PROMPT); - static_cast(Backend::global_backends[MLLM_CPU])->toggleSwitching(); - // turn on the multi-chunk prefilling + vector trace_inputs(1); + trace_inputs[0].setBackend(Backend::global_backends[MLLM_CPU]); + trace_inputs[0].setTtype(INPUT_TENSOR); + trace_inputs[0].reshape(1, 1, chunk_size, 1); + trace_inputs[0].setName("input0"); + trace_inputs[0].alloc(); + Tracer::trace(&model, trace_inputs); + std::cout << "Trace and Warmup finished" << std::endl; + Module::isMultiChunkPrefilling = true; - // warmup END - std::cout << "Warmup finished." << std::endl; + Module::isFirstChunk = false; vector in_strs = { - " Give me a short introduction to large language model.", + // " Give me a short introduction to large language model.", "\"Large Language Models (LLMs) are advanced artificial intelligence systems designed to understand and generate human-like text. These models are trained on vast amounts of data, enabling them to perform a wide range of tasks, from answering questions and summarizing text to generating creative content and engaging in conversational dialogue. LLMs like GPT-3 and GPT-4, developed by OpenAI, have set new benchmarks in natural language processing by leveraging deep learning architectures, particularly transformer models, which excel at capturing context and relationships within text. The scalability and versatility of LLMs make them invaluable tools for applications in education, customer service, content creation, and more. However, their deployment also raises ethical considerations, including issues of bias, misinformation, and the potential for misuse. As the field continues to evolve, ongoing research and responsible deployment strategies are essential to harnessing the full potential of these powerful AI systems while mitigating their risks.\"\nGenerate a title based on the above text."}; for (int i = 0; i < in_strs.size(); ++i) { @@ -97,7 +87,15 @@ int main(int argc, char **argv) { .is_padding = false, }; isSwitched = false; - decoding_model.generate(prefill_result, decoding_opt, [&](unsigned int out_token) -> bool { + + Tensor decoding_input; + decoding_input.setBackend(Backend::global_backends[MLLM_CPU]); + decoding_input.setTtype(INPUT_TENSOR); + decoding_input.reshape(1, 1, 1, 1); + decoding_input.setName("input0"); + decoding_input.alloc(); + decoding_input.setDataAt(0, 0, 0, 0, prefill_result->dataAt(0, 0, 0, 0)); + decoding_model.generate(decoding_input, decoding_opt, [&](unsigned int out_token) -> bool { // call only once of switchDecodeTag if (!isSwitched) { static_cast(Backend::global_backends[MLLM_CPU])->toggleSwitching(); diff --git a/include/Types.hpp b/include/Types.hpp index 3c99ea0a..af2cfa09 100644 --- a/include/Types.hpp +++ b/include/Types.hpp @@ -35,9 +35,15 @@ enum TensorStatus { // TENSOR_DYNAMIC, TENSOR_STATIC_INIT, TENSOR_STATIC_READY, + TENSOR_STATIC_TRACE, TENSOR_UNDEFINED, }; +enum CallableType { + OP, + TENSOR_FUNC +}; + enum ErrorCode { MLLM_NO_ERROR = 0, OUT_OF_MEMORY = 1, diff --git a/src/Layer.hpp b/src/Layer.hpp index 8792c432..96f4428c 100644 --- a/src/Layer.hpp +++ b/src/Layer.hpp @@ -15,6 +15,7 @@ #include "Op.hpp" #include "ParamLoader.hpp" #include "Backend.hpp" +#include "Trace.hpp" #include @@ -120,9 +121,11 @@ class Layer { auto &activation_tensors_num = module->activation_tensors_num; Module::runlistIdx = saved_list_idx; bool do_init = false; - // set backend to current module device and try to create op - backend_ = Backend::global_backends[Module::tmp_device]; + if (module->doLoad || !inited_loaded) { + // set backend to current module device and try to create op + // use Module::tmp_device only when creating the op as the recersive module backend only handled in load and init stage + backend_ = Backend::global_backends[Module::tmp_device]; do_init = !inited_loaded; if (op_ == nullptr) { #ifdef USE_QNN @@ -235,6 +238,12 @@ class Layer { op_->execute(input_tensors, output_tensors); break; } + case TENSOR_STATIC_TRACE : { + if (backend_->type() == BackendType::MLLM_CPU) { + Tracer::addOp(op_, input_tensors, output_tensors); + } + break; + } default: { break; } diff --git a/src/Module.hpp b/src/Module.hpp index 7b628655..3bbdcd15 100644 --- a/src/Module.hpp +++ b/src/Module.hpp @@ -10,6 +10,7 @@ #include "ParamLoader.hpp" #include "Backend.hpp" #include "Timing.hpp" +#include "Trace.hpp" #include "Types.hpp" #include "backends/cpu/CPUBackend.hpp" #include @@ -77,6 +78,10 @@ class Module { Module() = default; virtual ~Module() = default; + BackendType device() const { + return device_; + } + static void initBackend(BackendType type = BackendType::MLLM_CPU) { if (Backend::global_backends.find(type) == Backend::global_backends.end() || Backend::global_backends[type] == nullptr) { switch (type) { @@ -278,6 +283,18 @@ class Module { } return outputs; + } else if (Tensor::tensor_status == TENSOR_STATIC_TRACE && device_ != MLLM_CPU) { + auto inputs_vec = vector>(); + for (auto &i : inputs) { + inputs_vec.push_back(inputs[0].module()->activation_tensors[i.name()]); + } + auto getUinqueName = [this]() -> string { + std::ostringstream oss; + oss << "Module@" << this; + return oss.str(); + }; + Tracer::addModule(inputs_vec, {}, getUinqueName()); + return Forward(inputs, anyArgs); } return Forward(inputs, anyArgs); } @@ -315,6 +332,61 @@ class Module { vector generate(Tensor &input_ids, const LlmTextGeneratorOpts &opt, int end_token = -1); }; +class CPUModuleWrapper : public Module { +public: + vector> traces_; + + void addOp(Op *op, + vector> inputs, + vector> outputs) { + auto callable = std::make_shared(CallableType::OP); + callable->opInputs = inputs; + callable->opOutputs = outputs; + callable->op = op; + traces_.push_back(callable); + } + + void addTensorFunction(TensorFunction *func, + vector inputs, vector outputs, vector args) { + auto callable = std::make_shared(CallableType::TENSOR_FUNC); + callable->tensorFunc = func; + callable->tensorInputs = inputs; + callable->tensorOutputs = outputs; + for (auto arg : args) { + callable->args.push_back(arg); + } + traces_.push_back(callable); + } + + virtual vector Forward(vector inputs, vector args) override { + for (auto &callable : traces_) { + callable->setUp(); + } + + for (int i = 0; i < traces_.size(); i++) { + traces_[i]->execute(); + } + return {}; + } + + vector> result() { + return traces_.back()->outputs(); + } +}; + +class QNNModuleWrapper : public Module { +public: + string name_; + vector> inputs_; + vector> outputs_; + + virtual vector Forward(vector inputs, vector args) override { + Backend::global_backends[MLLM_QNN]->onExecuteStart(inputs_, outputs_, name_); + Backend::global_backends[MLLM_QNN]->onExecuteEnd(outputs_, name_); + return {}; + } +}; + } // namespace mllm #endif // MODULE_HPP diff --git a/src/Op.hpp b/src/Op.hpp index 47a4b721..de382db7 100644 --- a/src/Op.hpp +++ b/src/Op.hpp @@ -1,11 +1,13 @@ #ifndef MLLM_OP_H #define MLLM_OP_H // #define DEBUGPRINT +#include "Backend.hpp" #include "Tensor.hpp" #include "Types.hpp" #include #include #include +#include #include "ParamLoader.hpp" #include "Timing.hpp" using std::function; @@ -139,6 +141,51 @@ class Op { static DataType no_load_weights_dtype_; }; +class Callable { +public: + CallableType type_; + vector> opInputs; + vector> opOutputs; + Op *op; + vector tensorInputs; + vector tensorOutputs; + vector args; + TensorFunction *tensorFunc; + + Callable(CallableType type) : + type_(type) { + } + + void setUp() { + if (type_ == CallableType::OP) { + op->reshape(opInputs, opOutputs); + op->setUp(opInputs, opOutputs); + } else { + tensorFunc->setup(tensorOutputs, tensorInputs, args); + } + } + + void execute() { + if (type_ == CallableType::OP) { + op->execute(opInputs, opOutputs); + } else { + tensorFunc->execute(tensorOutputs, tensorInputs, args); + } + } + + vector> outputs() { + if (type_ == CallableType::OP) { + return opOutputs; + } else { + vector> outputs; + for (auto tensor : tensorOutputs) { + outputs.push_back(std::shared_ptr(tensor, [](Tensor *) {})); + } + return outputs; + } + } +}; + } // namespace mllm #endif // MLLM_OP_H \ No newline at end of file diff --git a/src/Parallel.hpp b/src/Parallel.hpp index 96b5ede8..e3653403 100644 --- a/src/Parallel.hpp +++ b/src/Parallel.hpp @@ -1,50 +1,97 @@ #ifndef PARALLEL_HPP #define PARALLEL_HPP +#include "Backend.hpp" #include "Module.hpp" #include "Tensor.hpp" +#include "Trace.hpp" #include "Types.hpp" #include "tokenizers/Tokenizer.hpp" -#include +#include namespace mllm { class ChunkPipeline { int real_seq_length, chunk_size, chunk_num; - vector chunked_tensors; + vector> chunked_tensors; public: ChunkPipeline(int real_seq_length = 4, int chunk_size = 64) : real_seq_length(real_seq_length), chunk_size(chunk_size) { const int seq_length_padding = (chunk_size - real_seq_length % chunk_size) + real_seq_length; chunk_num = seq_length_padding / chunk_size; - chunked_tensors.resize(chunk_num); } - Tensor run(Tensor &input_tensor, LlmTextGeneratorOpts &opt, Tokenizer &tokenizer, Module &model, bool &isSwitched) { + shared_ptr run(Tensor &input_tensor, LlmTextGeneratorOpts &opt, Tokenizer &tokenizer, Module &model, bool &isSwitched) { + const int num_graph = Tracer::model_.size(); + std::cout << "num_graph: " << num_graph << std::endl; + for (int chunk_id = 0; chunk_id < chunk_num; ++chunk_id) { - chunked_tensors[chunk_id].setBackend(Backend::global_backends[MLLM_CPU]); - chunked_tensors[chunk_id].setTtype(INPUT_TENSOR); - chunked_tensors[chunk_id].reshape(1, 1, chunk_size, 1); - chunked_tensors[chunk_id].setName("input-chunk-" + std::to_string(chunk_id)); - chunked_tensors[chunk_id].deepCopyFrom(&input_tensor, false, {0, 0, chunk_id * chunk_size, 0}); - - model.generate(chunked_tensors[chunk_id], opt, [&](unsigned int out_token) -> bool { - if (!isSwitched && chunk_id == 0 && static_cast(Backend::global_backends[MLLM_CPU])->isStageSwitching()) { - // turn off switching at the first chunk of following inputs - static_cast(Backend::global_backends[MLLM_CPU])->toggleSwitching(); - isSwitched = true; - } - auto out_string = tokenizer.detokenize({out_token}); - auto [not_end, output_string] = tokenizer.postprocess(out_string); - if (!not_end) { return false; } - if (chunk_id == chunk_num - 1) { // print the output of the last chunk - std::cout << output_string << std::flush; + chunked_tensors.push_back(std::make_shared(Backend::global_backends[MLLM_CPU])); + chunked_tensors[chunk_id]->setTtype(INPUT_TENSOR); + chunked_tensors[chunk_id]->setName("input0"); + chunked_tensors[chunk_id]->reshape(1, 1, chunk_size, 1); + chunked_tensors[chunk_id]->deepCopyFrom(&input_tensor, false, {0, 0, chunk_id * chunk_size, 0}); + } + + std::function executeFunc = [&](int chunk_id, int graphIdx) { + int i = graphIdx - chunk_id; + // out of range + if (i < 0 || i >= num_graph) { + return; + } + // chunk 0 don't need to execute the last graph + if(i == num_graph - 2 && chunk_id == 0) { + return; + } + // before the first graph, need to refresh the input tensor + if (i == 0) { + Tracer::refleshInputTensor({chunked_tensors[chunk_id]}); + } + + std::cout << "chunk_id: " << chunk_id << ", graphIdx: " << i << std::endl << std::flush; + + auto &graph = Tracer::model_[i]; + graph->Forward({}, {chunk_id}); + }; + + omp_set_max_active_levels(3); + for (int chunk_id = 0; chunk_id < chunk_num / 2; ++chunk_id) { + // for every two chunk, start at chunk_id * 2 to avoid no execute for + for (int i = chunk_id * 2; i < num_graph + chunk_id * 2 + 5; ++i) { +#pragma omp parallel for num_threads(2) + for (int pair_idx = 0; pair_idx < 2; ++pair_idx) { + executeFunc(chunk_id * 2 + pair_idx, i - pair_idx * 4); } - return true; - }); - Module::isFirstChunk = false; +#pragma omp barrier + std::cout << "---------------------------" << std::endl; + } } + + auto postProcessing = [&](shared_ptr result, shared_ptr &out_result, int real_seq_length) -> unsigned int { + assert(result->batch() == 1); + assert(result->head() == 1); + out_result->reshape(1, 1, 1, 1); + out_result->alloc(); + vector scores; + for (int i = 0; i < result->dimension(); ++i) { + auto value = result->dataAt(0, 0, real_seq_length % chunk_size - 1, i); + scores.push_back(value); + } + auto arg_max = [&]() -> unsigned int { + return std::max_element(scores.begin(), scores.end()) - scores.begin(); + }; + auto token_idx = arg_max(); + out_result->setDataAt(0, 0, 0, 0, token_idx); + return token_idx; + }; + + auto cpuModulePtr = std::dynamic_pointer_cast(Tracer::model_.back()); + auto result = cpuModulePtr->result(); + result[0]->printShape(); + auto token_idx = postProcessing(result[0], chunked_tensors.back(), real_seq_length); + auto out_string = tokenizer.detokenize({token_idx}); + std::cout << out_string << std::flush; return chunked_tensors.back(); } }; diff --git a/src/Tensor.cpp b/src/Tensor.cpp index db6b26ae..f6e77454 100644 --- a/src/Tensor.cpp +++ b/src/Tensor.cpp @@ -199,6 +199,12 @@ Tensor &Tensor::getFunc(const std::string &suffix, const TensorFuncType type, func->execute({module_tensors[next_name].get()}, tensorPtrs, float_args); break; } + case TENSOR_STATIC_TRACE: { + if (backend_->type() == BackendType::MLLM_CPU) { + Tracer::addTensorFunction(func, tensorPtrs, {module_tensors[next_name].get()}, float_args); + } + break; + } default: { } } @@ -289,6 +295,12 @@ std::vector> Tensor::getStaticFunc(vectorexecute(outPtrs, input_tensors, float_args); break; } + case TENSOR_STATIC_TRACE: { + if (backend_h->type() == BackendType::MLLM_CPU) { + Tracer::addTensorFunction(func, input_tensors, outPtrs, float_args); + } + break; + } default: { } } diff --git a/src/Tensor.hpp b/src/Tensor.hpp index 15277904..d4126e69 100644 --- a/src/Tensor.hpp +++ b/src/Tensor.hpp @@ -1127,7 +1127,9 @@ class Tensor { Tensor &to(BackendType backend_type); static vector toDevice(vector inputs, BackendType backend_type) { for (auto &input : inputs) { - input.to(backend_type); + if (input.device() != backend_type) { + input.to(backend_type); + } } return inputs; }; diff --git a/src/Trace.cpp b/src/Trace.cpp new file mode 100644 index 00000000..fcd2c070 --- /dev/null +++ b/src/Trace.cpp @@ -0,0 +1,60 @@ +#include "Trace.hpp" +#include "Module.hpp" +#include "Tensor.hpp" +#include "Types.hpp" +#include + +namespace mllm { + +vector> Tracer::model_; +bool Tracer::isToCreateNewModule = true; + +void Tracer::addModule(vector> inputs, vector> outputs, string name) { + model_.push_back(std::make_shared()); + auto *wrapperPtr = static_cast(model_.back().get()); + wrapperPtr->name_ = name; + wrapperPtr->inputs_ = inputs; + wrapperPtr->outputs_ = outputs; + isToCreateNewModule = true; +} + +void Tracer::addOp(Op *op, vector> inputs, vector> outputs) { + if (isToCreateNewModule) { + model_.push_back(std::make_shared()); + isToCreateNewModule = false; + } + auto wrapper_module = std::dynamic_pointer_cast(model_.back()); + assert(wrapper_module != nullptr); + wrapper_module->addOp(op, inputs, outputs); +} + +void Tracer::addTensorFunction(TensorFunction *func, + vector inputs, vector outputs, vector args) { + if (isToCreateNewModule) { + model_.push_back(std::make_shared()); + isToCreateNewModule = false; + } + // cast model_.back() to wrapper module + auto wrapper_module = std::dynamic_pointer_cast(model_.back()); + assert(wrapper_module != nullptr); + wrapper_module->addTensorFunction(func, inputs, outputs, args); +} + +void Tracer::trace(Module *model, vector inputs) { + inputs[0].setTtype(TensorType::NORMAL_TENSOR); + model->activation_tensors[inputs[0].name()] = std::shared_ptr(&inputs[0], [](Tensor *) {}); + model->activation_tensors[inputs[0].name()]->setName(inputs[0].name()); + model->activation_tensors[inputs[0].name()]->setModule(model); + Tensor::tensor_status = TENSOR_STATIC_INIT; + model->Forward(inputs, {}); + Tensor::tensor_status = TENSOR_STATIC_TRACE; + model->Forward(inputs, {}); +} + +void Tracer::refleshInputTensor(vector> inputs) { + auto cpuPtr = std::dynamic_pointer_cast(model_[0]); + assert(cpuPtr != nullptr); + cpuPtr->traces_[0]->opInputs = inputs; +} + +} \ No newline at end of file diff --git a/src/Trace.hpp b/src/Trace.hpp new file mode 100644 index 00000000..d31f360d --- /dev/null +++ b/src/Trace.hpp @@ -0,0 +1,31 @@ +#ifndef TRACE_HPP +#define TRACE_HPP + +#include "Tensor.hpp" +#include "Types.hpp" +#include + +namespace mllm { + +class Tracer { +public: + static vector> model_; + + static bool isToCreateNewModule; + + static void addModule(vector> inputs, vector> outputs, string name); + + static void addOp(Op *op, vector> inputs, vector> outputs); + + static void addTensorFunction(TensorFunction *func, + vector inputs, vector outputs, vector args); + + static void trace(Module *model, vector inputs); + + static void refleshInputTensor(vector> inputs); + +}; + +} // namespace mllm + +#endif // TRACE_HPP \ No newline at end of file diff --git a/src/models/qwen/modeling_qwen_npu.hpp b/src/models/qwen/modeling_qwen_npu.hpp index 8d4ecf00..c637c740 100644 --- a/src/models/qwen/modeling_qwen_npu.hpp +++ b/src/models/qwen/modeling_qwen_npu.hpp @@ -135,7 +135,7 @@ class QwenQKVmm final : public Module { v = v_cache(v); auto qk = Tensor::mm(q, k.transpose(Chl::SEQUENCE, Chl::DIMENSION)); - qk = softmax(qk, k_cache.getCacheSeqLen()); + qk = softmax(qk); auto o = Tensor::mm(qk, v); o = o_quantize(o); From bec0a63506bf1ca478a89143c4b6e62602275e97 Mon Sep 17 00:00:00 2001 From: oreomaker Date: Wed, 11 Dec 2024 16:12:30 +0800 Subject: [PATCH 17/34] fix: kvcache nrep and stage switching bug in old frontend --- examples/main_phonelm_npu.cpp | 3 +++ examples/main_qwen_npu.cpp | 3 +++ src/backends/cpu/op/CPUKVCacheNPU.hpp | 2 +- 3 files changed, 7 insertions(+), 1 deletion(-) diff --git a/examples/main_phonelm_npu.cpp b/examples/main_phonelm_npu.cpp index c56d0195..8caf57c6 100644 --- a/examples/main_phonelm_npu.cpp +++ b/examples/main_phonelm_npu.cpp @@ -190,10 +190,13 @@ int main(int argc, char **argv) { auto inter_cpu_backend = dynamic_cast(interNet.backends()[MLLM_CPU].get()); auto decode_cpu_backend = dynamic_cast(cpuNet.backends()[MLLM_CPU].get()); prefill_cpu_backend->setSequenceLength(real_seq_length); + prefill_cpu_backend->setExecutionType(AUTOREGRESSIVE); prefill_cpu_backend->toggleSwitching(); inter_cpu_backend->setSequenceLength(real_seq_length); + inter_cpu_backend->setExecutionType(AUTOREGRESSIVE); inter_cpu_backend->toggleSwitching(); decode_cpu_backend->setSequenceLength(real_seq_length); + decode_cpu_backend->setExecutionType(AUTOREGRESSIVE); decode_cpu_backend->toggleSwitching(); // // 2: Decoding stage using CPU execute diff --git a/examples/main_qwen_npu.cpp b/examples/main_qwen_npu.cpp index 4adc7fa7..91b439b6 100644 --- a/examples/main_qwen_npu.cpp +++ b/examples/main_qwen_npu.cpp @@ -187,10 +187,13 @@ int main(int argc, char **argv) { auto inter_cpu_backend = dynamic_cast(interNet.backends()[MLLM_CPU].get()); auto decode_cpu_backend = dynamic_cast(cpuNet.backends()[MLLM_CPU].get()); prefill_cpu_backend->setSequenceLength(real_seq_length); + prefill_cpu_backend->setExecutionType(AUTOREGRESSIVE); prefill_cpu_backend->toggleSwitching(); inter_cpu_backend->setSequenceLength(real_seq_length); + inter_cpu_backend->setExecutionType(AUTOREGRESSIVE); inter_cpu_backend->toggleSwitching(); decode_cpu_backend->setSequenceLength(real_seq_length); + decode_cpu_backend->setExecutionType(AUTOREGRESSIVE); decode_cpu_backend->toggleSwitching(); // // 2: Decoding stage using CPU execute diff --git a/src/backends/cpu/op/CPUKVCacheNPU.hpp b/src/backends/cpu/op/CPUKVCacheNPU.hpp index f444e048..54cf0a3a 100644 --- a/src/backends/cpu/op/CPUKVCacheNPU.hpp +++ b/src/backends/cpu/op/CPUKVCacheNPU.hpp @@ -46,7 +46,7 @@ class CPUKVCacheNPU final : public Op { class CPUKVCacheNPUCreator : public CPUBackend::Creator { public: virtual Op *create(OpParam op_param, Backend *bn, string name, int threadCount) const { - int n_rep = (int)op_param["n_rep"]; + int n_rep = (int)op_param["n_rep"] == 0 ? 1 : (int)op_param["n_rep"]; int cache_max = (int)op_param["cache_max"]; return new CPUKVCacheNPU(bn, name, n_rep, cache_max, threadCount); } From 38fff581b942d7841544547703f7f2bcf06bde05 Mon Sep 17 00:00:00 2001 From: oreomaker Date: Mon, 6 Jan 2025 17:25:30 +0800 Subject: [PATCH 18/34] feat: new frontend pipeline --- examples/demo_qwen_npu.cpp | 3 +- examples/demo_qwen_pipeline.cpp | 8 +++-- examples/main_qwen_npu.cpp | 9 ++--- src/Module.cpp | 2 ++ src/Module.hpp | 53 +++++++++++++++++------------- src/Op.hpp | 7 +++- src/Parallel.hpp | 17 ++++++---- src/Trace.cpp | 7 ++-- src/backends/cpu/op/CPUSoftMax.cpp | 2 +- src/backends/qnn/QNNExecutor.cpp | 4 +-- 10 files changed, 69 insertions(+), 43 deletions(-) diff --git a/examples/demo_qwen_npu.cpp b/examples/demo_qwen_npu.cpp index 0ac27711..72049521 100644 --- a/examples/demo_qwen_npu.cpp +++ b/examples/demo_qwen_npu.cpp @@ -59,7 +59,7 @@ int main(int argc, char **argv) { std::cout << "Warmup finished." << std::endl; vector in_strs = { - " Give me a short introduction to large language model.", + // " Give me a short introduction to large language model.", "\"Large Language Models (LLMs) are advanced artificial intelligence systems designed to understand and generate human-like text. These models are trained on vast amounts of data, enabling them to perform a wide range of tasks, from answering questions and summarizing text to generating creative content and engaging in conversational dialogue. LLMs like GPT-3 and GPT-4, developed by OpenAI, have set new benchmarks in natural language processing by leveraging deep learning architectures, particularly transformer models, which excel at capturing context and relationships within text. The scalability and versatility of LLMs make them invaluable tools for applications in education, customer service, content creation, and more. However, their deployment also raises ethical considerations, including issues of bias, misinformation, and the potential for misuse. As the field continues to evolve, ongoing research and responsible deployment strategies are essential to harnessing the full potential of these powerful AI systems while mitigating their risks.\"\nGenerate a title based on the above text."}; for (int i = 0; i < in_strs.size(); ++i) { @@ -109,6 +109,7 @@ int main(int argc, char **argv) { static_cast(Backend::global_backends[MLLM_CPU])->setSequenceLength(real_seq_length); static_cast(Backend::global_backends[MLLM_CPU])->setExecutionType(AUTOREGRESSIVE); static_cast(Backend::global_backends[MLLM_CPU])->toggleSwitching(); + exit(0); LlmTextGeneratorOpts decoding_opt{ .max_new_tokens = 100, diff --git a/examples/demo_qwen_pipeline.cpp b/examples/demo_qwen_pipeline.cpp index 88fe0d8d..283e8d40 100644 --- a/examples/demo_qwen_pipeline.cpp +++ b/examples/demo_qwen_pipeline.cpp @@ -46,9 +46,6 @@ int main(int argc, char **argv) { Tracer::trace(&model, trace_inputs); std::cout << "Trace and Warmup finished" << std::endl; - Module::isMultiChunkPrefilling = true; - Module::isFirstChunk = false; - vector in_strs = { // " Give me a short introduction to large language model.", "\"Large Language Models (LLMs) are advanced artificial intelligence systems designed to understand and generate human-like text. These models are trained on vast amounts of data, enabling them to perform a wide range of tasks, from answering questions and summarizing text to generating creative content and engaging in conversational dialogue. LLMs like GPT-3 and GPT-4, developed by OpenAI, have set new benchmarks in natural language processing by leveraging deep learning architectures, particularly transformer models, which excel at capturing context and relationships within text. The scalability and versatility of LLMs make them invaluable tools for applications in education, customer service, content creation, and more. However, their deployment also raises ethical considerations, including issues of bias, misinformation, and the potential for misuse. As the field continues to evolve, ongoing research and responsible deployment strategies are essential to harnessing the full potential of these powerful AI systems while mitigating their risks.\"\nGenerate a title based on the above text."}; @@ -74,10 +71,15 @@ int main(int argc, char **argv) { ChunkPipeline pipeline(real_seq_length, chunk_size); auto prefill_result = pipeline.run(input_tensor, opt, tokenizer, model, isSwitched); + Module::isMultiChunkPrefilling = true; + Module::isFirstChunk = false; + static_cast(Backend::global_backends[MLLM_CPU])->setSequenceLength(real_seq_length); static_cast(Backend::global_backends[MLLM_CPU])->setExecutionType(AUTOREGRESSIVE); static_cast(Backend::global_backends[MLLM_CPU])->toggleSwitching(); + exit(0); + LlmTextGeneratorOpts decoding_opt{ .max_new_tokens = 100, .do_sample = false, diff --git a/examples/main_qwen_npu.cpp b/examples/main_qwen_npu.cpp index 91b439b6..365b0f32 100644 --- a/examples/main_qwen_npu.cpp +++ b/examples/main_qwen_npu.cpp @@ -82,14 +82,14 @@ int main(int argc, char **argv) { int chunk = 1; if (isChunkExecute) - chunk = seqLength / 256; + chunk = seqLength / 128; int vocab_size = 151936; int hidden_dim = cmdParser.get("hds"); int ffn_hidden_dim = cmdParser.get("ffn"); vector in_strs = { - "<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n<|im_start|>user\nGive me a short introduction to large language model.<|im_end|>\n<|im_start|>assistant\n", + "\"Large Language Models (LLMs) are advanced artificial intelligence systems designed to understand and generate human-like text. These models are trained on vast amounts of data, enabling them to perform a wide range of tasks, from answering questions and summarizing text to generating creative content and engaging in conversational dialogue. LLMs like GPT-3 and GPT-4, developed by OpenAI, have set new benchmarks in natural language processing by leveraging deep learning architectures, particularly transformer models, which excel at capturing context and relationships within text. The scalability and versatility of LLMs make them invaluable tools for applications in education, customer service, content creation, and more. However, their deployment also raises ethical considerations, including issues of bias, misinformation, and the potential for misuse. As the field continues to evolve, ongoing research and responsible deployment strategies are essential to harnessing the full potential of these powerful AI systems while mitigating their risks.\"\nGenerate a title based on the above text." // " What can you do?", // "Please introduce Beijing University of Posts and Telecommunications."}; }; @@ -152,7 +152,8 @@ int main(int argc, char **argv) { for (int str_i = 0; str_i < in_strs.size(); ++str_i) { // auto in_str = in_strs[str_i]; - auto [real_seq_length, input_tensor] = tokenizer.tokenizeWithPadding(input_string, seqLength, vocab_size); + auto input_str = tokenizer.apply_chat_template(input_string); + auto [real_seq_length, input_tensor] = tokenizer.tokenizeWithPadding(input_str, seqLength, vocab_size); auto input = std::make_shared(input_tensor); if (chunk != 1) @@ -178,7 +179,7 @@ int main(int argc, char **argv) { if (token_idx == 2) { // "" break; } - // exit(0); + auto out_token = tokenizer.detokenize({token_idx}); std::cout << out_token << std::flush; diff --git a/src/Module.cpp b/src/Module.cpp index 95479179..508071ec 100644 --- a/src/Module.cpp +++ b/src/Module.cpp @@ -22,6 +22,8 @@ int Module::runlistIdx; BackendType Module::tmp_device = MLLM_CPU; std::unordered_map> Module::tensor_func_ops; +int Module::graphIdx = 0; + vector Module::profiling(string name) { vector output; // printf("\n"); diff --git a/src/Module.hpp b/src/Module.hpp index 3bbdcd15..0bb51924 100644 --- a/src/Module.hpp +++ b/src/Module.hpp @@ -73,9 +73,12 @@ class Module { auto tail_tuple = change_last(tail...); return std::tuple_cat(std::make_tuple(head), tail_tuple); } - + int idx; public: - Module() = default; + Module() { + idx = Module::graphIdx; + Module::graphIdx++; + } virtual ~Module() = default; BackendType device() const { @@ -164,6 +167,14 @@ class Module { virtual vector Forward(vector inputs, vector args) = 0; + static int graphIdx; + string getUinqueName(){ + std::ostringstream oss; + oss << "Module@" << idx; + graphIdx++; + return oss.str(); + }; + template vector operator()(vector inputs, Args... args) { vector anyArgs = convertArgsToAnyVector(args...); @@ -231,11 +242,7 @@ class Module { for (auto &i : inputs) { inputs_vec.push_back(inputs[0].module()->activation_tensors[i.name()]); } - auto getUinqueName = [this]() -> string { - std::ostringstream oss; - oss << "Module@" << this; - return oss.str(); - }; + Backend::global_backends[device_]->onSetUpStart(inputs_vec, outputs_vec, getUinqueName()); // for xnnpack currently @@ -261,18 +268,13 @@ class Module { for (auto &i : inputs) { inputs_vec.push_back(inputs[0].module()->activation_tensors[i.name()]); } - auto getUinqueName = [this]() -> string { - std::ostringstream oss; - oss << "Module@" << this; - return oss.str(); - }; - Backend::global_backends[device_]->onExecuteStart(inputs_vec, outputs_vec, getUinqueName()); auto outputs = Forward(inputs, anyArgs); for (auto &output : outputs) { outputs_vec.push_back(inputs[0].module()->activation_tensors[output.name()]); } + Backend::global_backends[device_]->onExecuteStart(inputs_vec, outputs_vec, getUinqueName()); Backend::global_backends[device_]->onExecuteEnd(outputs_vec, getUinqueName()); @@ -285,16 +287,18 @@ class Module { return outputs; } else if (Tensor::tensor_status == TENSOR_STATIC_TRACE && device_ != MLLM_CPU) { auto inputs_vec = vector>(); + auto outputs_vec = vector>(); for (auto &i : inputs) { inputs_vec.push_back(inputs[0].module()->activation_tensors[i.name()]); } - auto getUinqueName = [this]() -> string { - std::ostringstream oss; - oss << "Module@" << this; - return oss.str(); - }; - Tracer::addModule(inputs_vec, {}, getUinqueName()); - return Forward(inputs, anyArgs); + + auto outputs = Forward(inputs, anyArgs); + + for (auto &output : outputs) { + outputs_vec.push_back(inputs[0].module()->activation_tensors[output.name()]); + } + Tracer::addModule(inputs_vec, outputs_vec, getUinqueName()); + return outputs; } return Forward(inputs, anyArgs); } @@ -359,8 +363,13 @@ class CPUModuleWrapper : public Module { } virtual vector Forward(vector inputs, vector args) override { - for (auto &callable : traces_) { - callable->setUp(); + // get chunk_id from args + int chunk_id = std::any_cast(args[0]); + if (chunk_id != 0) { + for (auto &callable : traces_) { + callable->reshape(); + callable->setUp(); + } } for (int i = 0; i < traces_.size(); i++) { diff --git a/src/Op.hpp b/src/Op.hpp index de382db7..0530c37b 100644 --- a/src/Op.hpp +++ b/src/Op.hpp @@ -156,9 +156,14 @@ class Callable { type_(type) { } - void setUp() { + void reshape(){ if (type_ == CallableType::OP) { op->reshape(opInputs, opOutputs); + } + } + + void setUp() { + if (type_ == CallableType::OP) { op->setUp(opInputs, opOutputs); } else { tensorFunc->setup(tensorOutputs, tensorInputs, args); diff --git a/src/Parallel.hpp b/src/Parallel.hpp index e3653403..3715284f 100644 --- a/src/Parallel.hpp +++ b/src/Parallel.hpp @@ -24,12 +24,13 @@ class ChunkPipeline { shared_ptr run(Tensor &input_tensor, LlmTextGeneratorOpts &opt, Tokenizer &tokenizer, Module &model, bool &isSwitched) { const int num_graph = Tracer::model_.size(); + Tensor::tensor_status = TENSOR_STATIC_READY; std::cout << "num_graph: " << num_graph << std::endl; for (int chunk_id = 0; chunk_id < chunk_num; ++chunk_id) { chunked_tensors.push_back(std::make_shared(Backend::global_backends[MLLM_CPU])); chunked_tensors[chunk_id]->setTtype(INPUT_TENSOR); - chunked_tensors[chunk_id]->setName("input0"); + chunked_tensors[chunk_id]->setName(input_tensor.name()); chunked_tensors[chunk_id]->reshape(1, 1, chunk_size, 1); chunked_tensors[chunk_id]->deepCopyFrom(&input_tensor, false, {0, 0, chunk_id * chunk_size, 0}); } @@ -40,8 +41,8 @@ class ChunkPipeline { if (i < 0 || i >= num_graph) { return; } - // chunk 0 don't need to execute the last graph - if(i == num_graph - 2 && chunk_id == 0) { + // only the last chunk need to execute the last graph + if(i == num_graph - 1 && chunk_id != chunk_num - 1) { return; } // before the first graph, need to refresh the input tensor @@ -49,12 +50,13 @@ class ChunkPipeline { Tracer::refleshInputTensor({chunked_tensors[chunk_id]}); } - std::cout << "chunk_id: " << chunk_id << ", graphIdx: " << i << std::endl << std::flush; - + auto graph_start = mllm_time_us(); auto &graph = Tracer::model_[i]; graph->Forward({}, {chunk_id}); + auto graph_end = mllm_time_us(); + std::cout << "chunk_id: " << chunk_id << ", graphIdx: " << i << ", graph time: " << (graph_end - graph_start) / 1000.0F << "ms" << std::endl; }; - + auto start_t = mllm_time_us(); omp_set_max_active_levels(3); for (int chunk_id = 0; chunk_id < chunk_num / 2; ++chunk_id) { // for every two chunk, start at chunk_id * 2 to avoid no execute for @@ -67,6 +69,8 @@ class ChunkPipeline { std::cout << "---------------------------" << std::endl; } } + auto end_t = mllm_time_us(); + std::cout << "time: " << (end_t - start_t) / 1000.0F << "ms" << std::endl; auto postProcessing = [&](shared_ptr result, shared_ptr &out_result, int real_seq_length) -> unsigned int { assert(result->batch() == 1); @@ -88,7 +92,6 @@ class ChunkPipeline { auto cpuModulePtr = std::dynamic_pointer_cast(Tracer::model_.back()); auto result = cpuModulePtr->result(); - result[0]->printShape(); auto token_idx = postProcessing(result[0], chunked_tensors.back(), real_seq_length); auto out_string = tokenizer.detokenize({token_idx}); std::cout << out_string << std::flush; diff --git a/src/Trace.cpp b/src/Trace.cpp index fcd2c070..da6fd5f0 100644 --- a/src/Trace.cpp +++ b/src/Trace.cpp @@ -29,7 +29,7 @@ void Tracer::addOp(Op *op, vector> inputs, vector inputs, vector outputs, vector args) { + vector inputs, vector outputs, vector args) { if (isToCreateNewModule) { model_.push_back(std::make_shared()); isToCreateNewModule = false; @@ -45,6 +45,9 @@ void Tracer::trace(Module *model, vector inputs) { model->activation_tensors[inputs[0].name()] = std::shared_ptr(&inputs[0], [](Tensor *) {}); model->activation_tensors[inputs[0].name()]->setName(inputs[0].name()); model->activation_tensors[inputs[0].name()]->setModule(model); + + Module::llm_model_ptr = model; + Tensor::tensor_status = TENSOR_STATIC_INIT; model->Forward(inputs, {}); Tensor::tensor_status = TENSOR_STATIC_TRACE; @@ -57,4 +60,4 @@ void Tracer::refleshInputTensor(vector> inputs) { cpuPtr->traces_[0]->opInputs = inputs; } -} \ No newline at end of file +} // namespace mllm diff --git a/src/backends/cpu/op/CPUSoftMax.cpp b/src/backends/cpu/op/CPUSoftMax.cpp index 04e8f3a6..9566da22 100644 --- a/src/backends/cpu/op/CPUSoftMax.cpp +++ b/src/backends/cpu/op/CPUSoftMax.cpp @@ -37,7 +37,7 @@ ErrorCode CPUSoftMax::execute(vector> inputs, vectordimension() - input->sequence(); -#elif defined(USE_QNN) // TODO: 旧前端才用到，一定要记得删除 +#elif defined(USE_QNN) // used for single param softmax in QNN old_dim = input->dimension() - input->sequence(); #endif } diff --git a/src/backends/qnn/QNNExecutor.cpp b/src/backends/qnn/QNNExecutor.cpp index 39cad4e8..18e79cf1 100644 --- a/src/backends/qnn/QNNExecutor.cpp +++ b/src/backends/qnn/QNNExecutor.cpp @@ -410,7 +410,7 @@ void QNNPipelineExecutor::run(Context *ctx, Net *net, vector> void QNNPipelineExecutor::warmup(Context *ctx, Net *net, vector> input_tensors) { auto ex_time_start = mllm_time_us(); // input will be split into chunks and execute in pipeline - const int chunk_size = 256; + const int chunk_size = 128; int chunk_num = (input_tensors[0]->sequence() + chunk_size - 1) / chunk_size; // we suppose the tensor(s) of input_tensors is the only one or all have the same seq length for (int i = 0; i < input_tensors.size(); ++i) { @@ -492,7 +492,7 @@ void QNNPipelineExecutor::runExp(Context *ctx, Net *net, vectorsequence() + chunk_size - 1) / chunk_size; // we suppose the tensor(s) of input_tensors is the only one or all have the same seq length for (int i = 0; i < input_tensors.size(); ++i) { From 8d0758895da5dca4ff84a649e4eb831482bfa1a5 Mon Sep 17 00:00:00 2001 From: oreomaker Date: Mon, 6 Jan 2025 21:32:06 +0800 Subject: [PATCH 19/34] chore: qnn qwen executable change --- examples/demo_qwen_npu.cpp | 1 - examples/demo_qwen_pipeline.cpp | 12 +++--------- src/backends/cpu/op/CPUKVCacheNPU.cpp | 1 - 3 files changed, 3 insertions(+), 11 deletions(-) diff --git a/examples/demo_qwen_npu.cpp b/examples/demo_qwen_npu.cpp index 72049521..45a63b1f 100644 --- a/examples/demo_qwen_npu.cpp +++ b/examples/demo_qwen_npu.cpp @@ -109,7 +109,6 @@ int main(int argc, char **argv) { static_cast(Backend::global_backends[MLLM_CPU])->setSequenceLength(real_seq_length); static_cast(Backend::global_backends[MLLM_CPU])->setExecutionType(AUTOREGRESSIVE); static_cast(Backend::global_backends[MLLM_CPU])->toggleSwitching(); - exit(0); LlmTextGeneratorOpts decoding_opt{ .max_new_tokens = 100, diff --git a/examples/demo_qwen_pipeline.cpp b/examples/demo_qwen_pipeline.cpp index 283e8d40..8a793808 100644 --- a/examples/demo_qwen_pipeline.cpp +++ b/examples/demo_qwen_pipeline.cpp @@ -37,13 +37,9 @@ int main(int argc, char **argv) { auto decoding_model = QWenForCausalLM(config); decoding_model.load("../models/qwen-1.5-1.8b-chat-q4k.mllm"); - vector trace_inputs(1); - trace_inputs[0].setBackend(Backend::global_backends[MLLM_CPU]); - trace_inputs[0].setTtype(INPUT_TENSOR); - trace_inputs[0].reshape(1, 1, chunk_size, 1); - trace_inputs[0].setName("input0"); - trace_inputs[0].alloc(); - Tracer::trace(&model, trace_inputs); + string trace_string = " "; + auto [_, input_tensor] = tokenizer.tokenizePaddingByChunk(trace_string, chunk_size, config.vocab_size); + Tracer::trace(&model, {input_tensor}); std::cout << "Trace and Warmup finished" << std::endl; vector in_strs = { @@ -78,8 +74,6 @@ int main(int argc, char **argv) { static_cast(Backend::global_backends[MLLM_CPU])->setExecutionType(AUTOREGRESSIVE); static_cast(Backend::global_backends[MLLM_CPU])->toggleSwitching(); - exit(0); - LlmTextGeneratorOpts decoding_opt{ .max_new_tokens = 100, .do_sample = false, diff --git a/src/backends/cpu/op/CPUKVCacheNPU.cpp b/src/backends/cpu/op/CPUKVCacheNPU.cpp index 42db3b56..123ad133 100644 --- a/src/backends/cpu/op/CPUKVCacheNPU.cpp +++ b/src/backends/cpu/op/CPUKVCacheNPU.cpp @@ -3,7 +3,6 @@ #include "CPUKVCacheNPU.hpp" #include "ParamLoader.hpp" #include "Types.hpp" -#include namespace mllm { CPUKVCacheNPU::CPUKVCacheNPU(Backend *bn, string opName, int n_rep, int cache_max, int threadCount) : From 693700b184e9483d335955c8b69cfc186e95ae35 Mon Sep 17 00:00:00 2001 From: oreomaker Date: Tue, 7 Jan 2025 19:57:41 +0800 Subject: [PATCH 20/34] feat: qnn prefill optimization, only do 1 seq lm_head --- examples/demo_qwen_pipeline.cpp | 6 +- include/OpDefined.hpp | 2 + src/Layer.hpp | 14 +++ src/backends/cpu/CPUBackend.cpp | 2 + src/backends/cpu/op/CPUHeadLinear.cpp | 168 ++++++++++++++++++++++++++ src/backends/cpu/op/CPUHeadLinear.hpp | 48 ++++++++ src/models/qwen/modeling_qwen_npu.hpp | 2 +- 7 files changed, 239 insertions(+), 3 deletions(-) create mode 100644 src/backends/cpu/op/CPUHeadLinear.cpp create mode 100644 src/backends/cpu/op/CPUHeadLinear.hpp diff --git a/examples/demo_qwen_pipeline.cpp b/examples/demo_qwen_pipeline.cpp index 8a793808..f8bbf5b0 100644 --- a/examples/demo_qwen_pipeline.cpp +++ b/examples/demo_qwen_pipeline.cpp @@ -38,7 +38,7 @@ int main(int argc, char **argv) { decoding_model.load("../models/qwen-1.5-1.8b-chat-q4k.mllm"); string trace_string = " "; - auto [_, input_tensor] = tokenizer.tokenizePaddingByChunk(trace_string, chunk_size, config.vocab_size); + auto [_, input_tensor] = tokenizer.tokenizePaddingByChunk(trace_string, chunk_size, config.vocab_size); Tracer::trace(&model, {input_tensor}); std::cout << "Trace and Warmup finished" << std::endl; @@ -50,8 +50,11 @@ int main(int argc, char **argv) { auto input_str = tokenizer.apply_chat_template(in_strs[i]); auto [real_seq_length, input_tensor] = tokenizer.tokenizePaddingByChunk(input_str, chunk_size, config.vocab_size); + static_cast(Backend::global_backends[MLLM_CPU])->setSequenceLength(real_seq_length); + std::cout << "[Q] " << in_strs[i] << std::endl; std::cout << "[A] " << std::flush; + std::cout << "real_seq_length: " << real_seq_length << std::endl; LlmTextGeneratorOpts opt{ .max_new_tokens = 1, @@ -70,7 +73,6 @@ int main(int argc, char **argv) { Module::isMultiChunkPrefilling = true; Module::isFirstChunk = false; - static_cast(Backend::global_backends[MLLM_CPU])->setSequenceLength(real_seq_length); static_cast(Backend::global_backends[MLLM_CPU])->setExecutionType(AUTOREGRESSIVE); static_cast(Backend::global_backends[MLLM_CPU])->toggleSwitching(); diff --git a/include/OpDefined.hpp b/include/OpDefined.hpp index fda4792d..e924cbfb 100644 --- a/include/OpDefined.hpp +++ b/include/OpDefined.hpp @@ -73,6 +73,7 @@ enum OpType { // new front-end SUPERSILU, + HEADLINEAR, }; static const vector OpNames = { @@ -139,6 +140,7 @@ static const vector OpNames = { "XP_KVCACHE", "SDPA", "SuperSiLU", + "HeadLinear", }; enum TensorFuncType { diff --git a/src/Layer.hpp b/src/Layer.hpp index 96f4428c..1cc2419b 100644 --- a/src/Layer.hpp +++ b/src/Layer.hpp @@ -309,6 +309,20 @@ class Linear final : public Layer { } }; +class HeadLinear final : public Layer { +public: + explicit HeadLinear(int in_features, int out_features, bool bias, std::string name) { + param_["in_features"] = in_features; + param_["out_features"] = out_features; + param_["bias"] = (float)bias; + init(std::move(name), OpType::HEADLINEAR); + } + Tensor &operator()(Tensor &input) { + auto ts = run({input}, 1); + return ts[0].get(); + } +}; + class SparseIdLinear final : public Layer { public: SparseIdLinear(int in_dim, int out_dim, std::string name) { diff --git a/src/backends/cpu/CPUBackend.cpp b/src/backends/cpu/CPUBackend.cpp index 76271aa6..abdefea2 100644 --- a/src/backends/cpu/CPUBackend.cpp +++ b/src/backends/cpu/CPUBackend.cpp @@ -7,6 +7,7 @@ #include "Types.hpp" #include "memory/SystemMemoryManager.hpp" +#include "op/CPUHeadLinear.hpp" #include "op/CPULinearInt8.hpp" #include "op/CPUPoEmbedding.hpp" #include "op/CPUSplitInput.hpp" @@ -164,6 +165,7 @@ void CPUBackend::registerOps() { addCreator(LINEARINT8SHADOW, (CPUBackend::Creator *)(new CPULinearINT8ShadowCreator())); addCreator(IROPE, (CPUBackend::Creator *)(new CPUIRoPECreator())); addCreator(XP_KVCACHE, (CPUBackend::Creator *)(new CPUKVCacheXpCreator())); + addCreator(HEADLINEAR, (CPUBackend::Creator *)(new CPUHeadLinearCreator())); } TensorFunction *CPUBackend::funcCreate(const TensorFuncType type) { auto iter = map_function_.find(type); diff --git a/src/backends/cpu/op/CPUHeadLinear.cpp b/src/backends/cpu/op/CPUHeadLinear.cpp new file mode 100644 index 00000000..9ea15733 --- /dev/null +++ b/src/backends/cpu/op/CPUHeadLinear.cpp @@ -0,0 +1,168 @@ + +#include "CPUHeadLinear.hpp" +#include "Types.hpp" +#include +#include + +namespace mllm { + +CPUHeadLinear::CPUHeadLinear(Backend *bn, string opName, int in_features, int out_features, bool bias, int threadCount) : + thread_count(threadCount), + Op(bn, opName) { + in_features_ = in_features; + out_features_ = out_features; + support_bias_ = bias; + thread_count = threadCount; + weight_.setBackend(bn); + bias_.setBackend(bn); +} + +ErrorCode CPUHeadLinear::reshape(vector> inputs, vector> outputs) { + // std::cout << name() << " CPUHeadLinear reshape" << std::endl; + assert(inputs.size() == 1); + assert(outputs.size() == 1); + if (inputs[0]->count() == 0) { + outputs[0]->reshape(0, 0, 0, 0); + return Op::reshape(inputs, outputs); + } + // N | C | H | W + // ----------------------------------------------- + // 1 |out_channel | in_channel | 1 + // |out_features| in_features | + // ----------------------------------------------- + // batch |in_channel | seq_len | 1 + // |in_features | inputs[0]->sequence() | + // ----------------------------------------------- + // batch |out_channel | seq_len | 1 + // |out_features| inputs[0]->sequence() | + assert(inputs[0]->head() == 1); + assert(in_features_ == inputs[0]->dimension()); + outputs[0]->reshape(inputs[0]->batch(), inputs[0]->head(), inputs[0]->sequence(), out_features_); + // outputs[0]->setDtype(activationDtype()); + return Op::reshape(inputs, outputs); +} + +ErrorCode CPUHeadLinear::load(AbstructLoader &loader) { + // std::cout << name() << " CPUHeadLinear load" << std::endl; + weight_.setName(name() + ".weight"); + weight_.reshape(1, 1, out_features_, in_features_); + if (loader.getDataType(weight_.name()) != MLLM_TYPE_COUNT) { + weight_.setDtype(loader.getDataType(weight_.name())); + weight_.alloc(); + loader.load(&weight_); + } else { + if (weight_.name().find('v') != std::string::npos && Op::noLoadWeightsDtype() == MLLM_TYPE_Q4_0_4_4) { + weight_.setDtype(MLLM_TYPE_Q4_0); + } else { + weight_.setDtype(Op::noLoadWeightsDtype()); + } + weight_.alloc(); + } + if (support_bias_) { + bias_.setName(name() + ".bias"); + bias_.reshape(1, 1, 1, out_features_); + if (loader.getDataType(bias_.name()) != MLLM_TYPE_COUNT) { + bias_.setDtype(loader.getDataType(bias_.name())); + bias_.alloc(); + loader.load(&bias_); + } else { + bias_.setDtype(MLLM_TYPE_F32); + bias_.alloc(); + } + } + return Op::load(loader); +} + +ErrorCode CPUHeadLinear::execute(vector> inputs, vector> outputs) { + auto cpuBackend = dynamic_cast(backend_); + int seqLength = cpuBackend->getSequenceLength(); + shared_ptr tmp_in = std::make_shared(backend_); + tmp_in->reshape(1, 1, 1, inputs[0]->dimension()); + tmp_in->deepCopyFrom(inputs[0].get(), false, {0, 0, seqLength % 128 - 1, 0}); + shared_ptr tmp_out = std::make_shared(backend_); + tmp_out->reshape(1, 1, 1, out_features_); + tmp_out->deepCopyFrom(outputs[0].get(), false, {0, 0, seqLength % 128 - 1, 0}); + + // auto start = mllm::mllm_time_us(); + if (inputs[0]->count() == 0) { + return Op::execute(inputs, outputs); + } + // TODO: Q8_0 KVCache can not use!! + if (outputs[0]->dtype() == MLLM_TYPE_Q8_0) { + auto tmp_out = std::make_shared(outputs[0]->backend()); + // tmp_out->setBackend(outputs[0]->backend()); + auto b = outputs[0]->batch(); + auto h = outputs[0]->head(); + auto d = outputs[0]->dimension(); + auto s = outputs[0]->sequence(); + tmp_out->chls() = outputs[0]->chls(); + tmp_out->setCtype(outputs[0]->ctype()); + tmp_out->reshape(b, h, s, d); + tmp_out->setDtype(MLLM_TYPE_F32); + tmp_out->alloc(); + mat_mul(inputs[0].get(), &weight_, tmp_out.get(), support_bias_, &bias_, false, true, thread_count); + if (tmp_out->ctype() == BSHD) { +#pragma omp parallel for collapse(3) num_threads(thread_count) + for (int b = 0; b < tmp_out->batch(); b++) { + for (int h = 0; h < tmp_out->head(); h++) { + for (int s = 0; s < tmp_out->sequence(); s++) { + quantize_row_q8_0(tmp_out->hostPtr() + tmp_out->offset(b, h, s, 0), + (char *)outputs[0]->rawHostPtr() + + outputs[0]->offset(b, h, s, 0) * sizeof(block_q8_0) / QK8_0, + tmp_out->dimension()); + } + } + } + } else { // BHDS +#pragma omp parallel for collapse(3) num_threads(thread_count) + for (int b = 0; b < tmp_out->batch(); b++) { + for (int h = 0; h < tmp_out->head(); h++) { + for (int d = 0; d < tmp_out->dimension(); d++) { + quantize_row_q8_0(tmp_out->hostPtr() + tmp_out->offset(b, h, 0, d), + (char *)outputs[0]->rawHostPtr() + + outputs[0]->offset(b, h, 0, d) * sizeof(block_q8_0) / QK8_0, + outputs[0]->sequence()); + } + } + } + } + } else { + mat_mul(tmp_in.get(), &weight_, tmp_out.get(), support_bias_, &bias_, false, true, thread_count); + } + // std::cout << name() << " CPUHeadLinear()" << std::endl; + /* + switch (weight_.dtype()) { + case MLLM_TYPE_F32: { + mat_mul_fp32(inputs[0].get(), &weight_, outputs[0].get(), support_bias_, &bias_, false, true, thread_count); + break; + } + case MLLM_TYPE_F16: break; + case MLLM_TYPE_Q4_0: { + mat_mul_fp32_q4_0(inputs[0].get(), &weight_, outputs[0].get(), support_bias_, &bias_, thread_count); + break; + } + case MLLM_TYPE_Q4_K: { + mat_mul_fp32_q4_K(inputs[0].get(), &weight_, outputs[0].get(), support_bias_, &bias_, thread_count); + break; + } + case MLLM_TYPE_Q6_K: { + mat_mul_fp32_q6_K(inputs[0].get(), &weight_, outputs[0].get(), support_bias_, &bias_, thread_count); + break; + } + default: + break; + } + */ + // auto end = mllm::mllm_time_us(); + // printf("exec time: %ld us\n", end - start); + return Op::execute(inputs, outputs); +} +ErrorCode CPUHeadLinear::free(vector> inputs, vector> outputs) { + weight_.free(); + if (support_bias_) { + bias_.free(); + } + return Op::free(inputs, outputs); +} + +} // namespace mllm diff --git a/src/backends/cpu/op/CPUHeadLinear.hpp b/src/backends/cpu/op/CPUHeadLinear.hpp new file mode 100644 index 00000000..e9f1a90b --- /dev/null +++ b/src/backends/cpu/op/CPUHeadLinear.hpp @@ -0,0 +1,48 @@ +#ifndef MLLM_CPUHEADLINEAR_H +#define MLLM_CPUHEADLINEAR_H + +#include "Op.hpp" +#include "../CPUBackend.hpp" +#include "../compute/Matmul.hpp" + +namespace mllm { + +class Tensor; +class CPUHeadLinear final : public Op { +public: + CPUHeadLinear(Backend *bn, string opName, int in_features, int out_features, bool bias, int threadCount); + virtual ~CPUHeadLinear() = default; + virtual ErrorCode reshape(vector> inputs, vector> outputs) override; + virtual ErrorCode load(AbstructLoader &loader) override; + virtual ErrorCode execute(vector> inputs, vector> outputs) override; + virtual ErrorCode free(vector> inputs, vector> outputs) override; + + Tensor &weight() { + return weight_; + } + Tensor &bias() { + return bias_; + } + +private: + int in_features_; + int out_features_; + bool support_bias_; + int thread_count = 4; + Tensor weight_; + Tensor bias_; +}; + +class CPUHeadLinearCreator : public CPUBackend::Creator { +public: + virtual Op *create(OpParam op_param, Backend *bn, string name, int threadCount) const { + int in_features = op_param["in_features"]; + int out_features = op_param["out_features"]; + int bias = op_param["bias"]; + return new CPUHeadLinear(bn, name, in_features, out_features, (bool)bias, threadCount); + } +}; + +} // namespace mllm + +#endif // MLLM_CPUHEADLINEAR_H \ No newline at end of file diff --git a/src/models/qwen/modeling_qwen_npu.hpp b/src/models/qwen/modeling_qwen_npu.hpp index c637c740..0305f1b9 100644 --- a/src/models/qwen/modeling_qwen_npu.hpp +++ b/src/models/qwen/modeling_qwen_npu.hpp @@ -543,7 +543,7 @@ class QWenForCausalLM_NPU final : public Module { if (tie_embedding_words) { lm_head = Parameter(1, config.vocab_size, 1, config.hidden_size, names.token_embd_name + ".weight"); } else { - lm_head_layer = Linear(config.hidden_size, config.vocab_size, false, names.lm_head_name); + lm_head_layer = HeadLinear(config.hidden_size, config.vocab_size, false, names.lm_head_name); } } From be8258f31670a953033f1420986976bd69bb655c Mon Sep 17 00:00:00 2001 From: oreomaker Date: Tue, 7 Jan 2025 20:40:53 +0800 Subject: [PATCH 21/34] refactor: main qwen npu token post process --- examples/main_qwen_npu.cpp | 11 ++++++----- 1 file changed, 6 insertions(+), 5 deletions(-) diff --git a/examples/main_qwen_npu.cpp b/examples/main_qwen_npu.cpp index 365b0f32..00f29479 100644 --- a/examples/main_qwen_npu.cpp +++ b/examples/main_qwen_npu.cpp @@ -180,7 +180,6 @@ int main(int argc, char **argv) { break; } - auto out_token = tokenizer.detokenize({token_idx}); std::cout << out_token << std::flush; @@ -203,13 +202,15 @@ int main(int argc, char **argv) { auto result = cpuExe.result(); auto token_idx = postProcessing(result[0], input); - if (token_idx == 2) { // "" + auto out_token = tokenizer.detokenize({token_idx}); + + auto [isOk, print_string] = tokenizer.postprocess(out_token); + if (isOk) { + std::cout << print_string << std::flush; + } else { break; } - auto out_token = tokenizer.detokenize({token_idx}); - std::cout << out_token << std::flush; - if (step == real_seq_length) { prefill_cpu_backend->toggleSwitching(); inter_cpu_backend->toggleSwitching(); From 7e20e8b892f116edfd9658b773c0a705d468e06d Mon Sep 17 00:00:00 2001 From: oreomaker Date: Tue, 7 Jan 2025 21:37:54 +0800 Subject: [PATCH 22/34] fix: qnn old frontend modeling backend assign --- examples/main_phonelm_npu.hpp | 4 ++-- examples/main_qwen_npu.hpp | 4 ++-- src/backends/qnn/QNNExecutor.cpp | 11 ++--------- 3 files changed, 6 insertions(+), 13 deletions(-) diff --git a/examples/main_phonelm_npu.hpp b/examples/main_phonelm_npu.hpp index 07b0b409..63db0beb 100644 --- a/examples/main_phonelm_npu.hpp +++ b/examples/main_phonelm_npu.hpp @@ -79,7 +79,7 @@ std::vector PhoneLM_CPUNPUAttention(Context *c, NetTensor *x, NetTe m = _MergeOutput({o, res}, name + ".or_merge"); // -------------------- - _SubgraphBegin(c); + _SubgraphBegin(c, MLLM_QNN); // -------------------- s = _SplitInput({m}, true, 2, name + ".or_split"); @@ -165,7 +165,7 @@ void phonelm_npu(Context *c, int vocab_size = 32000, int hidden_dim = 4096, int if (layer == 0 || phonelm_shadow_layers.find(layer - 1) != phonelm_shadow_layers.end()) { i = i->view(-1, mutil_head_size, -1, hidden_dim / mutil_head_size); auto m = _MergeOutput({i}, "model.layers." + std::to_string(layer) + ".ires_merge"); - _SubgraphBegin(c); + _SubgraphBegin(c, MLLM_QNN); auto s = _SplitInput(m, true, 2, "model.layers." + std::to_string(layer) + ".self_attn.ires_split"); i = s[0]; } diff --git a/examples/main_qwen_npu.hpp b/examples/main_qwen_npu.hpp index 6f9c426e..b1093c16 100644 --- a/examples/main_qwen_npu.hpp +++ b/examples/main_qwen_npu.hpp @@ -77,7 +77,7 @@ std::vector Qwen_CPUNPUAttention(Context *c, NetTensor *x, NetTenso auto merge = _MergeOutput({o, res}, name + ".or_merge"); // -------------------- - _SubgraphBegin(c); + _SubgraphBegin(c, MLLM_QNN); // -------------------- s = _SplitInput(merge, true, 2, name + ".or_split"); @@ -162,7 +162,7 @@ void qwen_npu(Context *c, int vocab_size = 32000, int hidden_dim = 4096, int ffn if (layer == 0 || qwen_shadow_layers.find(layer - 1) != qwen_shadow_layers.end()) { i = i->view(-1, mutil_head_size, -1, hidden_dim / mutil_head_size); auto m = _MergeOutput({i}, "model.layers." + std::to_string(layer) + ".ires_merge"); - _SubgraphBegin(c); + _SubgraphBegin(c, MLLM_QNN); auto s = _SplitInput(m, true, 2, "model.layers." + std::to_string(layer) + ".self_attn.ires_split"); i = s[0]; } diff --git a/src/backends/qnn/QNNExecutor.cpp b/src/backends/qnn/QNNExecutor.cpp index d6f6883a..23b4e0fd 100644 --- a/src/backends/qnn/QNNExecutor.cpp +++ b/src/backends/qnn/QNNExecutor.cpp @@ -23,11 +23,7 @@ BackendType QNNExecutor::graphOffloadRule(BackendType expectedBackend, int graph if (expectedBackend != MLLM_CPU && expectedBackend != MLLM_QNN) { return MLLM_CPU; } else { - if (graphIndex == 0) { // use CPU graph and CPU backend for embedding, based on specific subgraph split - return MLLM_CPU; - } else { - return MLLM_QNN; - } + return expectedBackend; } } @@ -644,13 +640,10 @@ void QNNPipelineExecutor::runExp(Context *ctx, Net *net, vectorsubGraph().size(); ++i) { auto expectedBackend = ctx->sub_backend_[i]; - if (expectedBackend != MLLM_CPU && expectedBackend != MLLM_QNN) { - continue; - } else if (i == 0) { // use CPU graph and CPU backend for embedding, based on specific subgraph split + if (expectedBackend == MLLM_CPU || i == 0) { // use CPU graph and CPU backend for embedding, based on specific subgraph split continue; } From 1d526c142ab0a919b39eada33b7856a6444b9e44 Mon Sep 17 00:00:00 2001 From: oreomaker Date: Tue, 7 Jan 2025 23:22:50 +0800 Subject: [PATCH 23/34] fix: qnn total length and cur length conflict --- examples/demo_phonelm_npu.cpp | 9 ++++++--- examples/demo_qwen2.5_npu.cpp | 7 +++++-- examples/demo_qwen_npu.cpp | 11 +++++++---- examples/demo_qwen_pipeline.cpp | 6 ++++-- examples/main_phonelm_npu.cpp | 6 +++--- examples/main_qwen_npu.cpp | 6 +++--- src/backends/cpu/CPUBackend.hpp | 19 ++++++++++++++----- src/backends/cpu/op/CPUHeadLinear.cpp | 2 +- src/backends/cpu/op/CPUIRoPE.cpp | 2 +- src/backends/cpu/op/CPUKVCacheNPU.cpp | 4 ++-- src/backends/cpu/op/CPURoPE.cpp | 2 +- 11 files changed, 47 insertions(+), 27 deletions(-) diff --git a/examples/demo_phonelm_npu.cpp b/examples/demo_phonelm_npu.cpp index 47256f69..e76fee79 100644 --- a/examples/demo_phonelm_npu.cpp +++ b/examples/demo_phonelm_npu.cpp @@ -52,7 +52,7 @@ int main(int argc, char **argv) { return true; }); Module::isFirstChunk = false; - static_cast(Backend::global_backends[MLLM_CPU])->setSequenceLength(0); + static_cast(Backend::global_backends[MLLM_CPU])->setCurSequenceLength(0); static_cast(Backend::global_backends[MLLM_CPU])->setExecutionType(PROMPT); static_cast(Backend::global_backends[MLLM_CPU])->toggleSwitching(); // turn on the multi-chunk prefilling @@ -79,6 +79,9 @@ int main(int argc, char **argv) { std::cout << "[Q] " << in_strs[i] << std::endl; std::cout << "[A] " << std::flush; + // set total seq length for HeadLinear execute, which can not get the real seq length from Opts + static_cast(Backend::global_backends[MLLM_CPU])->setTotalSequenceLength(real_seq_length); + // tensor vectors to save the chunked tensors of the QNN prefilling input vector chunked_tensors(chunk_num); LlmTextGeneratorOpts opt{ @@ -115,7 +118,7 @@ int main(int argc, char **argv) { } // turn on switching, set sequence length and execution type - static_cast(Backend::global_backends[MLLM_CPU])->setSequenceLength(real_seq_length); + static_cast(Backend::global_backends[MLLM_CPU])->setCurSequenceLength(real_seq_length); static_cast(Backend::global_backends[MLLM_CPU])->setExecutionType(AUTOREGRESSIVE); static_cast(Backend::global_backends[MLLM_CPU])->toggleSwitching(); @@ -141,7 +144,7 @@ int main(int argc, char **argv) { }); // turn on switching, set sequence length and execution type - static_cast(Backend::global_backends[MLLM_CPU])->setSequenceLength(0); + static_cast(Backend::global_backends[MLLM_CPU])->setCurSequenceLength(0); static_cast(Backend::global_backends[MLLM_CPU])->setExecutionType(PROMPT); static_cast(Backend::global_backends[MLLM_CPU])->toggleSwitching(); std::cout << "\n"; diff --git a/examples/demo_qwen2.5_npu.cpp b/examples/demo_qwen2.5_npu.cpp index f9d0b9d6..761a3492 100644 --- a/examples/demo_qwen2.5_npu.cpp +++ b/examples/demo_qwen2.5_npu.cpp @@ -42,6 +42,9 @@ int main(int argc, char **argv) { std::cout << "[Q] " << in_strs[i] << std::endl; std::cout << "[A] " << std::flush; + // set total seq length for HeadLinear execute, which can not get the real seq length from Opts + static_cast(Backend::global_backends[MLLM_CPU])->setTotalSequenceLength(real_seq_length); + LlmTextGeneratorOpts opt{ .max_new_tokens = 1, .do_sample = false, @@ -59,7 +62,7 @@ int main(int argc, char **argv) { return true; }); - static_cast(Backend::global_backends[MLLM_CPU])->setSequenceLength(real_seq_length); + static_cast(Backend::global_backends[MLLM_CPU])->setCurSequenceLength(real_seq_length); static_cast(Backend::global_backends[MLLM_CPU])->setExecutionType(AUTOREGRESSIVE); static_cast(Backend::global_backends[MLLM_CPU])->toggleSwitching(); @@ -89,7 +92,7 @@ int main(int argc, char **argv) { }); // turn on switching, set sequence length and execution type - static_cast(Backend::global_backends[MLLM_CPU])->setSequenceLength(0); + static_cast(Backend::global_backends[MLLM_CPU])->setCurSequenceLength(0); static_cast(Backend::global_backends[MLLM_CPU])->setExecutionType(PROMPT); static_cast(Backend::global_backends[MLLM_CPU])->toggleSwitching(); std::cout << "\n"; diff --git a/examples/demo_qwen_npu.cpp b/examples/demo_qwen_npu.cpp index 45a63b1f..ba9a58db 100644 --- a/examples/demo_qwen_npu.cpp +++ b/examples/demo_qwen_npu.cpp @@ -23,7 +23,7 @@ int main(int argc, char **argv) { string model_path = cmdParser.get("model"); string model_billion = cmdParser.get("billion"); int tokens_limit = cmdParser.get("limits"); - const int chunk_size = 64; + const int chunk_size = 128; CPUBackend::cpu_threads = cmdParser.get("thread"); auto tokenizer = QWenTokenizer(vocab_path, merge_path); @@ -50,7 +50,7 @@ int main(int argc, char **argv) { return true; }); Module::isFirstChunk = false; - static_cast(Backend::global_backends[MLLM_CPU])->setSequenceLength(0); + static_cast(Backend::global_backends[MLLM_CPU])->setCurSequenceLength(0); static_cast(Backend::global_backends[MLLM_CPU])->setExecutionType(PROMPT); static_cast(Backend::global_backends[MLLM_CPU])->toggleSwitching(); // turn on the multi-chunk prefilling @@ -71,6 +71,9 @@ int main(int argc, char **argv) { std::cout << "[Q] " << in_strs[i] << std::endl; std::cout << "[A] " << std::flush; + // set total seq length for HeadLinear execute, which can not get the real seq length from Opts + static_cast(Backend::global_backends[MLLM_CPU])->setTotalSequenceLength(real_seq_length); + LlmTextGeneratorOpts opt{ .max_new_tokens = 1, .do_sample = false, @@ -106,7 +109,7 @@ int main(int argc, char **argv) { Module::isFirstChunk = false; } - static_cast(Backend::global_backends[MLLM_CPU])->setSequenceLength(real_seq_length); + static_cast(Backend::global_backends[MLLM_CPU])->setCurSequenceLength(real_seq_length); static_cast(Backend::global_backends[MLLM_CPU])->setExecutionType(AUTOREGRESSIVE); static_cast(Backend::global_backends[MLLM_CPU])->toggleSwitching(); @@ -136,7 +139,7 @@ int main(int argc, char **argv) { }); // turn on switching, set sequence length and execution type - static_cast(Backend::global_backends[MLLM_CPU])->setSequenceLength(0); + static_cast(Backend::global_backends[MLLM_CPU])->setCurSequenceLength(0); static_cast(Backend::global_backends[MLLM_CPU])->setExecutionType(PROMPT); static_cast(Backend::global_backends[MLLM_CPU])->toggleSwitching(); std::cout << "\n"; diff --git a/examples/demo_qwen_pipeline.cpp b/examples/demo_qwen_pipeline.cpp index f8bbf5b0..f0c91fc5 100644 --- a/examples/demo_qwen_pipeline.cpp +++ b/examples/demo_qwen_pipeline.cpp @@ -50,7 +50,8 @@ int main(int argc, char **argv) { auto input_str = tokenizer.apply_chat_template(in_strs[i]); auto [real_seq_length, input_tensor] = tokenizer.tokenizePaddingByChunk(input_str, chunk_size, config.vocab_size); - static_cast(Backend::global_backends[MLLM_CPU])->setSequenceLength(real_seq_length); + // set total seq length for HeadLinear execute, which can not get the real seq length from Opts + static_cast(Backend::global_backends[MLLM_CPU])->setTotalSequenceLength(real_seq_length); std::cout << "[Q] " << in_strs[i] << std::endl; std::cout << "[A] " << std::flush; @@ -73,6 +74,7 @@ int main(int argc, char **argv) { Module::isMultiChunkPrefilling = true; Module::isFirstChunk = false; + static_cast(Backend::global_backends[MLLM_CPU])->setCurSequenceLength(real_seq_length); static_cast(Backend::global_backends[MLLM_CPU])->setExecutionType(AUTOREGRESSIVE); static_cast(Backend::global_backends[MLLM_CPU])->toggleSwitching(); @@ -110,7 +112,7 @@ int main(int argc, char **argv) { }); // turn on switching, set sequence length and execution type - static_cast(Backend::global_backends[MLLM_CPU])->setSequenceLength(0); + static_cast(Backend::global_backends[MLLM_CPU])->setCurSequenceLength(0); static_cast(Backend::global_backends[MLLM_CPU])->setExecutionType(PROMPT); static_cast(Backend::global_backends[MLLM_CPU])->toggleSwitching(); std::cout << "\n"; diff --git a/examples/main_phonelm_npu.cpp b/examples/main_phonelm_npu.cpp index 8caf57c6..0f754bdd 100644 --- a/examples/main_phonelm_npu.cpp +++ b/examples/main_phonelm_npu.cpp @@ -189,13 +189,13 @@ int main(int argc, char **argv) { auto prefill_cpu_backend = dynamic_cast(npuNet.backends()[MLLM_CPU].get()); auto inter_cpu_backend = dynamic_cast(interNet.backends()[MLLM_CPU].get()); auto decode_cpu_backend = dynamic_cast(cpuNet.backends()[MLLM_CPU].get()); - prefill_cpu_backend->setSequenceLength(real_seq_length); + prefill_cpu_backend->setCurSequenceLength(real_seq_length); prefill_cpu_backend->setExecutionType(AUTOREGRESSIVE); prefill_cpu_backend->toggleSwitching(); - inter_cpu_backend->setSequenceLength(real_seq_length); + inter_cpu_backend->setCurSequenceLength(real_seq_length); inter_cpu_backend->setExecutionType(AUTOREGRESSIVE); inter_cpu_backend->toggleSwitching(); - decode_cpu_backend->setSequenceLength(real_seq_length); + decode_cpu_backend->setCurSequenceLength(real_seq_length); decode_cpu_backend->setExecutionType(AUTOREGRESSIVE); decode_cpu_backend->toggleSwitching(); diff --git a/examples/main_qwen_npu.cpp b/examples/main_qwen_npu.cpp index 00f29479..564b2839 100644 --- a/examples/main_qwen_npu.cpp +++ b/examples/main_qwen_npu.cpp @@ -186,13 +186,13 @@ int main(int argc, char **argv) { auto prefill_cpu_backend = dynamic_cast(npuNet.backends()[MLLM_CPU].get()); auto inter_cpu_backend = dynamic_cast(interNet.backends()[MLLM_CPU].get()); auto decode_cpu_backend = dynamic_cast(cpuNet.backends()[MLLM_CPU].get()); - prefill_cpu_backend->setSequenceLength(real_seq_length); + prefill_cpu_backend->setCurSequenceLength(real_seq_length); prefill_cpu_backend->setExecutionType(AUTOREGRESSIVE); prefill_cpu_backend->toggleSwitching(); - inter_cpu_backend->setSequenceLength(real_seq_length); + inter_cpu_backend->setCurSequenceLength(real_seq_length); inter_cpu_backend->setExecutionType(AUTOREGRESSIVE); inter_cpu_backend->toggleSwitching(); - decode_cpu_backend->setSequenceLength(real_seq_length); + decode_cpu_backend->setCurSequenceLength(real_seq_length); decode_cpu_backend->setExecutionType(AUTOREGRESSIVE); decode_cpu_backend->toggleSwitching(); diff --git a/src/backends/cpu/CPUBackend.hpp b/src/backends/cpu/CPUBackend.hpp index c808db80..9c695e28 100644 --- a/src/backends/cpu/CPUBackend.hpp +++ b/src/backends/cpu/CPUBackend.hpp @@ -33,11 +33,17 @@ class CPUBackend final : public Backend { static int cpu_threads; // #ifdef USE_QNN - void setSequenceLength(int sequence_length) { - sequence_length_ = sequence_length; + void setCurSequenceLength(int sequence_length) { + cur_sequence_length_ = sequence_length; } - int getSequenceLength() { - return sequence_length_; + int getCurSequenceLength() { + return cur_sequence_length_; + } + void setTotalSequenceLength(int sequence_length) { + total_sequence_length_ = sequence_length; + } + int getTotalSequenceLength() { + return total_sequence_length_; } void toggleSwitching() { isSwitchingStage = !isSwitchingStage; @@ -56,7 +62,10 @@ class CPUBackend final : public Backend { std::map map_creator_; std::map map_function_; // #ifdef USE_QNN - int sequence_length_ = 0; + // auto regression seq state + int cur_sequence_length_ = 0; + // total real seq length used for chunk&padding input + int total_sequence_length_ = 0; bool isSwitchingStage = false; ExecutionType execution_type = PROMPT; // #endif diff --git a/src/backends/cpu/op/CPUHeadLinear.cpp b/src/backends/cpu/op/CPUHeadLinear.cpp index 9ea15733..f05e96b8 100644 --- a/src/backends/cpu/op/CPUHeadLinear.cpp +++ b/src/backends/cpu/op/CPUHeadLinear.cpp @@ -75,7 +75,7 @@ ErrorCode CPUHeadLinear::load(AbstructLoader &loader) { ErrorCode CPUHeadLinear::execute(vector> inputs, vector> outputs) { auto cpuBackend = dynamic_cast(backend_); - int seqLength = cpuBackend->getSequenceLength(); + int seqLength = cpuBackend->getTotalSequenceLength(); shared_ptr tmp_in = std::make_shared(backend_); tmp_in->reshape(1, 1, 1, inputs[0]->dimension()); tmp_in->deepCopyFrom(inputs[0].get(), false, {0, 0, seqLength % 128 - 1, 0}); diff --git a/src/backends/cpu/op/CPUIRoPE.cpp b/src/backends/cpu/op/CPUIRoPE.cpp index 39e5b77f..95c9cd36 100644 --- a/src/backends/cpu/op/CPUIRoPE.cpp +++ b/src/backends/cpu/op/CPUIRoPE.cpp @@ -140,7 +140,7 @@ ErrorCode CPUIRoPE::reshape(vector> inputs, vector(backend_); if (cpuBackend->isStageSwitching()) { - h_cnt_ = cpuBackend->getSequenceLength(); + h_cnt_ = cpuBackend->getCurSequenceLength(); } #endif return Op::reshape(inputs, outputs); diff --git a/src/backends/cpu/op/CPUKVCacheNPU.cpp b/src/backends/cpu/op/CPUKVCacheNPU.cpp index 123ad133..74c2fcee 100644 --- a/src/backends/cpu/op/CPUKVCacheNPU.cpp +++ b/src/backends/cpu/op/CPUKVCacheNPU.cpp @@ -35,12 +35,12 @@ ErrorCode CPUKVCacheNPU::reshape(vector> inputs, vector(backend_); if (cpuBackend->isStageSwitching() && cpuBackend->getExecutionType() == AUTOREGRESSIVE) { - cache_seq_len_ = cpuBackend->getSequenceLength(); + cache_seq_len_ = cpuBackend->getCurSequenceLength(); isDecoding = true; } // if a new prompt is given, the cache should be updated if (cpuBackend->isStageSwitching() && cpuBackend->getExecutionType() == PROMPT) { - cache_seq_len_ = cpuBackend->getSequenceLength(); + cache_seq_len_ = cpuBackend->getCurSequenceLength(); isDecoding = false; } #endif diff --git a/src/backends/cpu/op/CPURoPE.cpp b/src/backends/cpu/op/CPURoPE.cpp index e71c7aa8..de0ac555 100644 --- a/src/backends/cpu/op/CPURoPE.cpp +++ b/src/backends/cpu/op/CPURoPE.cpp @@ -111,7 +111,7 @@ ErrorCode CPURoPE::reshape(vector> inputs, vector(backend_); if (cpuBackend->isStageSwitching()) { - h_cnt_ = cpuBackend->getSequenceLength(); + h_cnt_ = cpuBackend->getCurSequenceLength(); } #endif return Op::reshape(inputs, outputs); From 6070a4a904127f5db60e3b4ef0b0aa883df47a97 Mon Sep 17 00:00:00 2001 From: oreomaker Date: Tue, 7 Jan 2025 23:27:52 +0800 Subject: [PATCH 24/34] feat: configurable chunk size for HeadLinear --- examples/demo_phonelm_npu.cpp | 2 ++ examples/demo_qwen_npu.cpp | 2 ++ examples/demo_qwen_pipeline.cpp | 2 ++ src/backends/cpu/CPUBackend.hpp | 8 ++++++++ src/backends/cpu/op/CPUHeadLinear.cpp | 6 ++++-- 5 files changed, 18 insertions(+), 2 deletions(-) diff --git a/examples/demo_phonelm_npu.cpp b/examples/demo_phonelm_npu.cpp index e76fee79..203b79e3 100644 --- a/examples/demo_phonelm_npu.cpp +++ b/examples/demo_phonelm_npu.cpp @@ -81,6 +81,8 @@ int main(int argc, char **argv) { // set total seq length for HeadLinear execute, which can not get the real seq length from Opts static_cast(Backend::global_backends[MLLM_CPU])->setTotalSequenceLength(real_seq_length); + // set chunk size for the HeadLinear execute, which can not get the chunk size from Opts + static_cast(Backend::global_backends[MLLM_CPU])->setChunkSize(chunk_size); // tensor vectors to save the chunked tensors of the QNN prefilling input vector chunked_tensors(chunk_num); diff --git a/examples/demo_qwen_npu.cpp b/examples/demo_qwen_npu.cpp index ba9a58db..a049e315 100644 --- a/examples/demo_qwen_npu.cpp +++ b/examples/demo_qwen_npu.cpp @@ -73,6 +73,8 @@ int main(int argc, char **argv) { // set total seq length for HeadLinear execute, which can not get the real seq length from Opts static_cast(Backend::global_backends[MLLM_CPU])->setTotalSequenceLength(real_seq_length); + // set chunk size for the HeadLinear execute, which can not get the chunk size from Opts + static_cast(Backend::global_backends[MLLM_CPU])->setChunkSize(chunk_size); LlmTextGeneratorOpts opt{ .max_new_tokens = 1, diff --git a/examples/demo_qwen_pipeline.cpp b/examples/demo_qwen_pipeline.cpp index f0c91fc5..f2f8bb8d 100644 --- a/examples/demo_qwen_pipeline.cpp +++ b/examples/demo_qwen_pipeline.cpp @@ -52,6 +52,8 @@ int main(int argc, char **argv) { // set total seq length for HeadLinear execute, which can not get the real seq length from Opts static_cast(Backend::global_backends[MLLM_CPU])->setTotalSequenceLength(real_seq_length); + // set chunk size for the HeadLinear execute, which can not get the chunk size from Opts + static_cast(Backend::global_backends[MLLM_CPU])->setChunkSize(chunk_size); std::cout << "[Q] " << in_strs[i] << std::endl; std::cout << "[A] " << std::flush; diff --git a/src/backends/cpu/CPUBackend.hpp b/src/backends/cpu/CPUBackend.hpp index 9c695e28..3a4ea5b1 100644 --- a/src/backends/cpu/CPUBackend.hpp +++ b/src/backends/cpu/CPUBackend.hpp @@ -48,6 +48,12 @@ class CPUBackend final : public Backend { void toggleSwitching() { isSwitchingStage = !isSwitchingStage; } + void setChunkSize(int chunk_size) { + chunk_size_ = chunk_size; + } + int getChunkSize() { + return chunk_size_; + } bool isStageSwitching() { return isSwitchingStage; } @@ -66,6 +72,8 @@ class CPUBackend final : public Backend { int cur_sequence_length_ = 0; // total real seq length used for chunk&padding input int total_sequence_length_ = 0; + // chunk size used in HeadLinear + int chunk_size_ = 0; bool isSwitchingStage = false; ExecutionType execution_type = PROMPT; // #endif diff --git a/src/backends/cpu/op/CPUHeadLinear.cpp b/src/backends/cpu/op/CPUHeadLinear.cpp index f05e96b8..3c4aaa25 100644 --- a/src/backends/cpu/op/CPUHeadLinear.cpp +++ b/src/backends/cpu/op/CPUHeadLinear.cpp @@ -76,12 +76,14 @@ ErrorCode CPUHeadLinear::load(AbstructLoader &loader) { ErrorCode CPUHeadLinear::execute(vector> inputs, vector> outputs) { auto cpuBackend = dynamic_cast(backend_); int seqLength = cpuBackend->getTotalSequenceLength(); + int chunk_size = cpuBackend->getChunkSize(); + shared_ptr tmp_in = std::make_shared(backend_); tmp_in->reshape(1, 1, 1, inputs[0]->dimension()); - tmp_in->deepCopyFrom(inputs[0].get(), false, {0, 0, seqLength % 128 - 1, 0}); + tmp_in->deepCopyFrom(inputs[0].get(), false, {0, 0, seqLength % chunk_size - 1, 0}); shared_ptr tmp_out = std::make_shared(backend_); tmp_out->reshape(1, 1, 1, out_features_); - tmp_out->deepCopyFrom(outputs[0].get(), false, {0, 0, seqLength % 128 - 1, 0}); + tmp_out->deepCopyFrom(outputs[0].get(), false, {0, 0, seqLength % chunk_size - 1, 0}); // auto start = mllm::mllm_time_us(); if (inputs[0]->count() == 0) { From a459c74bb0d851239b05196ef59b953780d9aa65 Mon Sep 17 00:00:00 2001 From: Rongjie Yi <41737961+yirongjie@users.noreply.github.com> Date: Wed, 8 Jan 2025 09:15:51 +0800 Subject: [PATCH 25/34] Update CMakeLists.txt --- CMakeLists.txt | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index 4d296244..170815e1 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -68,7 +68,7 @@ if(DEBUG) endif() # backend options -option(QNN "Enable QNN" ON) +option(QNN "Enable QNN" OFF) option(QNN_OLD_FRONTEND "Enable Old QNN" OFF) if(QNN) add_definitions(-DUSE_QNN) # the USE_QNN should come before cpu subdirectory @@ -293,4 +293,4 @@ if(MLLM_ENABLE_PYTHON) TARGETS _C LIBRARY DESTINATION mllm/ ) -endif() \ No newline at end of file +endif() From 0f03af22b7f3bd3d863a997869e3bed158ad50e7 Mon Sep 17 00:00:00 2001 From: yirongjie Date: Wed, 8 Jan 2025 01:26:27 +0000 Subject: [PATCH 26/34] fix: --- android | 2 +- examples/CMakeLists.txt | 16 ++++++++-------- 2 files changed, 9 insertions(+), 9 deletions(-) diff --git a/android b/android index fe27fefa..e8dff3a9 160000 --- a/android +++ b/android @@ -1 +1 @@ -Subproject commit fe27fefaa1fac4670a135cf8104719913d8ff1b2 +Subproject commit e8dff3a90c2f969c927f60c6dd748ea37ab1460d diff --git a/examples/CMakeLists.txt b/examples/CMakeLists.txt index 69a2a9b3..c86642d1 100644 --- a/examples/CMakeLists.txt +++ b/examples/CMakeLists.txt @@ -76,14 +76,14 @@ func_llm_add_executable(demo_phonelm) func_llm_add_executable(demo_minicpm_moe_mbm) -# func_vlm_add_executable(demo_llava) -# func_vlm_add_executable(demo_fuyu) -# func_vlm_add_executable(demo_vit) -# func_vlm_add_executable(demo_clip) -# func_vlm_add_executable(demo_imagebind) -# func_vlm_add_executable(demo_imagebind_1mod) -# func_vlm_add_executable(demo_phi3v) -# func_vlm_add_executable(demo) +func_vlm_add_executable(demo_llava) +func_vlm_add_executable(demo_fuyu) +func_vlm_add_executable(demo_vit) +func_vlm_add_executable(demo_clip) +func_vlm_add_executable(demo_imagebind) +func_vlm_add_executable(demo_imagebind_1mod) +func_vlm_add_executable(demo_phi3v) +func_vlm_add_executable(demo) # QNN demo From 79151f2c0209f35e7f6e5454f06b9557329d939b Mon Sep 17 00:00:00 2001 From: yirongjie Date: Wed, 8 Jan 2025 01:45:16 +0000 Subject: [PATCH 27/34] fix --- examples/CMakeLists.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/CMakeLists.txt b/examples/CMakeLists.txt index c86642d1..24d076b4 100644 --- a/examples/CMakeLists.txt +++ b/examples/CMakeLists.txt @@ -83,7 +83,7 @@ func_vlm_add_executable(demo_clip) func_vlm_add_executable(demo_imagebind) func_vlm_add_executable(demo_imagebind_1mod) func_vlm_add_executable(demo_phi3v) -func_vlm_add_executable(demo) +# func_vlm_add_executable(demo) # QNN demo From c5973f11baca2a83d9c5081336c1dc48e182c825 Mon Sep 17 00:00:00 2001 From: oreomaker Date: Fri, 10 Jan 2025 16:33:12 +0800 Subject: [PATCH 28/34] feat: add gemma 2 model --- examples/CMakeLists.txt | 1 + examples/demo_gemma2.cpp | 53 +++++ src/models/gemma2/configuration_gemma2.hpp | 79 +++++++ src/models/gemma2/modeling_gemma2.hpp | 228 +++++++++++++++++++++ 4 files changed, 361 insertions(+) create mode 100644 examples/demo_gemma2.cpp create mode 100644 src/models/gemma2/configuration_gemma2.hpp create mode 100644 src/models/gemma2/modeling_gemma2.hpp diff --git a/examples/CMakeLists.txt b/examples/CMakeLists.txt index 24d076b4..6ad9609b 100644 --- a/examples/CMakeLists.txt +++ b/examples/CMakeLists.txt @@ -61,6 +61,7 @@ func_llm_add_executable(demo_stablelm) func_llm_add_executable(demo_sparse_llama) func_llm_add_executable(demo_elastic_llama) func_llm_add_executable(demo_gemma) +func_llm_add_executable(demo_gemma2) func_llm_add_executable(demo_qwen) func_llm_add_executable(demo_mistral) func_llm_add_executable(demo_yi) diff --git a/examples/demo_gemma2.cpp b/examples/demo_gemma2.cpp new file mode 100644 index 00000000..a02b33d3 --- /dev/null +++ b/examples/demo_gemma2.cpp @@ -0,0 +1,53 @@ +#include "cmdline.h" +#include "models/gemma2/configuration_gemma2.hpp" +#include "models/gemma2/modeling_gemma2.hpp" +#include "models/gemma/tokenization_gemma.hpp" +#include "processor/PostProcess.hpp" + +using namespace mllm; + +int main(int argc, char **argv) { + cmdline::parser cmdParser; + cmdParser.add("vocab", 'v', "specify mllm tokenizer model path", false, "../vocab/vocab_gemma2.mllm"); + cmdParser.add("model", 'm', "specify mllm model path", false, "../models/gemma-2-2b-q4_k.mllm"); + cmdParser.add("limits", 'l', "max KV cache size", false, 400); + cmdParser.add("thread", 't', "num of threads", false, 4); + cmdParser.parse_check(argc, argv); + + string vocab_path = cmdParser.get("vocab"); + string model_path = cmdParser.get("model"); + int tokens_limit = cmdParser.get("limits"); + CPUBackend::cpu_threads = cmdParser.get("thread"); + + // gemma2 uses the same tokenizer as gemma + auto tokenizer = GemmaTokenizer(vocab_path); + + Gemma2Config config(tokens_limit, "2B", RoPEType::HFHUBROPE); + auto model = Gemma2ForCausalLM(config); + model.load(model_path); + + vector in_strs = { + "Hello, who are you?", + "What can you do?", + "Please introduce Beijing University of Posts and Telecommunications."}; + + for (int i = 0; i < in_strs.size(); ++i) { + auto in_str = in_strs[i]; + auto input_tensor = tokenizer.tokenize(in_str); + + std::cout << "[Q] " << in_str << std::endl; + std::cout << "[A] " << std::flush; + for (int step = 0; step < 200; step++) { + auto result = model({input_tensor}); + auto [out_string, out_token] = tokenizer.detokenize(result[0]); + auto [not_end, output_string] = tokenizer.postprocess(out_string); + if (!not_end) { break; } + std::cout << output_string << std::flush; + chatPostProcessing(out_token, input_tensor, {}); + } + printf("\n"); + model.clear_kvcache(); + } + + return 0; +} diff --git a/src/models/gemma2/configuration_gemma2.hpp b/src/models/gemma2/configuration_gemma2.hpp new file mode 100644 index 00000000..0d62acc2 --- /dev/null +++ b/src/models/gemma2/configuration_gemma2.hpp @@ -0,0 +1,79 @@ +#ifndef CONFIG_GEMMA2_HPP +#define CONFIG_GEMMA2_HPP +#include "Types.hpp" +#include "models/transformer/configuration_transformer.hpp" + +using namespace mllm; + +class Gemma2NameConfig : public TransformerNameConfig { +public: + /** + * @brief Gemma following the hugging face naming method + * + * @param type RoPEType + */ + void init(RoPEType type = RoPEType::HFHUBROPE) { + switch (type) { + case RoPEType::HFHUBROPE: { + blk_name = "model.layers."; + _attn_base_name = "self_attn."; + _ffn_base_name = "mlp."; + _q_proj_name = "q_proj"; + _k_proj_name = "k_proj"; + _v_proj_name = "v_proj"; + _o_proj_name = "o_proj"; + _gate_proj_name = "gate_proj"; + _up_proj_name = "up_proj"; + _down_proj_name = "down_proj"; + _attn_norm_name = "input_layernorm"; + _ffn_norm_name = "post_attention_layernorm"; + _pre_feedforward_layernorm = "pre_feedforward_layernorm"; + _post_feedforward_layernorm = "post_feedforward_layernorm"; + token_embd_name = "model.embed_tokens"; + post_norm_name = "model.norm"; + lm_head_name = "model.embed_tokens"; + break; + } + default: { + throw std::runtime_error("Unsupported gemma RoPE type"); + } + } + } + + std::string blk_name; + std::string token_embd_name; + std::string post_norm_name; + std::string lm_head_name; + std::string _gate_proj_name; + std::string _pre_feedforward_layernorm; + std::string _post_feedforward_layernorm; +}; + +struct Gemma2Config : public TransformerConfig { + explicit Gemma2Config(int token_limit, const string billions = "2B", RoPEType type = RoPEType::HFHUBROPE) : + cache_limit(token_limit) { + names_config.init(type); + if (!(billions == "2B" || billions == "2b")) { + throw std::runtime_error("Unsupported model size"); + } + RoPE_type = type; + }; + + int vocab_size = 256000; + int max_position_embeddings = 8192; + int num_hidden_layers = 26; + int num_attention_heads = 8; + int num_key_value_heads = 4; + int hidden_size = 2304; + int sliding_window = 4096; + int intermediate_size = 9216; + int head_dim = 256; + float rms_norm_eps = 1e-6; + float rope_theta = 10000; + + int cache_limit; + RoPEType RoPE_type = RoPEType::HFHUBROPE; + Gemma2NameConfig names_config; +}; + +#endif //! CONFIG_GEMMA2_HPP diff --git a/src/models/gemma2/modeling_gemma2.hpp b/src/models/gemma2/modeling_gemma2.hpp new file mode 100644 index 00000000..2e1845c0 --- /dev/null +++ b/src/models/gemma2/modeling_gemma2.hpp @@ -0,0 +1,228 @@ +#ifndef MODELING_GEMMA2_HPP +#define MODELING_GEMMA2_HPP + +#include "Backend.hpp" +#include "Layer.hpp" +#include "Module.hpp" +#include "Tensor.hpp" +#include "configuration_gemma2.hpp" +#include +using namespace mllm; + +class Gemma2Attention final : public Module { +public: + Gemma2Attention() {} + Gemma2Attention(const Gemma2Config &config, const Gemma2NameConfig &names, const string &base_name) { + hidden_size = config.hidden_size; + num_heads = config.num_attention_heads; + // in gemma2, the head_dim is fixed to 2048 / num_heads rather than hidden_size(2304) / num_heads + head_dim = 2048 / num_heads; + num_key_value_heads = config.num_key_value_heads; + num_key_value_groups = num_heads / num_key_value_heads; + + // init layers + q_proj = Linear(hidden_size, head_dim * num_heads, false, base_name + names._q_proj_name); + k_proj = Linear(hidden_size, head_dim * num_key_value_heads, false, + base_name + names._k_proj_name); + v_proj = Linear(hidden_size, head_dim * num_key_value_heads, false, + base_name + names._v_proj_name); + o_proj = Linear(head_dim * num_heads, hidden_size, false, base_name + names._o_proj_name); + q_rope = RoPE(config.RoPE_type, config.rope_theta, config.max_position_embeddings, + base_name + "q_rope"); + k_rope = RoPE(config.RoPE_type, config.rope_theta, config.max_position_embeddings, + base_name + "k_rope"); + k_cache = KVCache(num_key_value_groups, config.cache_limit, base_name + "k_cache"); + v_cache = KVCache(num_key_value_groups, config.cache_limit, base_name + "v_cache"); + + softmax = Softmax(DIMENSION, true, base_name + "softmax"); + } + + std::vector Forward(std::vector inputs, std::vector args) override { + auto query_states = q_proj(inputs[0]); + auto key_states = k_proj(inputs[1]); + auto value_states = v_proj(inputs[2]); + + // [batch, heads, sequence, dims] + query_states = query_states.view(-1, num_heads, -1, head_dim); + key_states = key_states.view(-1, num_key_value_heads, -1, head_dim); + value_states = value_states.view(-1, num_key_value_heads, -1, head_dim); + + // embedding + query_states = q_rope(query_states); + key_states = k_rope(key_states); + + // kv cache + key_states = k_cache(key_states); + value_states = v_cache(value_states); + + // attention weight + auto atten_weight = + Tensor::mm(query_states, key_states.transpose(Chl::SEQUENCE, Chl::DIMENSION)) + / std::sqrt(head_dim); + + atten_weight = softmax(atten_weight, k_cache.getCacheSeqLen()); + + // attention output + auto atten_output = Tensor::mm(atten_weight, value_states); + atten_output = atten_output.view(-1, 1, -1, head_dim * num_heads); + atten_output = o_proj(atten_output); + return {atten_output}; + } + + vector get_cache() { + return {&k_cache, &v_cache}; + } + vector get_rope() { + return {&q_rope, &k_rope}; + } + +private: + int hidden_size; + int num_heads; + int head_dim; + int num_key_value_heads; + int num_key_value_groups; + int layer_num = 0; + Layer q_proj; + Layer k_proj; + Layer v_proj; + Layer o_proj; + RoPE q_rope; + RoPE k_rope; + KVCache k_cache; + KVCache v_cache; + Softmax softmax; +}; + +class Gemma2MLP final : public Module { +public: + Gemma2MLP() = default; + Gemma2MLP(int hidden_size, int intermediate_size, const Gemma2NameConfig &names, const std::string &base_name) { + gate_proj = Linear(hidden_size, intermediate_size, false, base_name + names._gate_proj_name); + gelu = GELU(base_name + "act"); + up_proj = Linear(hidden_size, intermediate_size, false, base_name + names._up_proj_name); + down_proj = Linear(intermediate_size, hidden_size, false, base_name + names._down_proj_name); + } + + std::vector Forward(std::vector inputs, std::vector args) override { + auto x = gate_proj(inputs[0]); + x = gelu(x); + auto y = up_proj(inputs[0]); + x = x * y; + x = down_proj(x); + return {x}; + } + +private: + Layer gate_proj; + Layer up_proj; + Layer down_proj; + + Layer gelu; ///< F.gelu(gate, approximate="tanh") +}; + +class Gemma2Decoder final : public Module { +public: + Gemma2Decoder() = default; + Gemma2Decoder(const Gemma2Config &config, const Gemma2NameConfig &names, const string &base_name) { + self_attn = Gemma2Attention(config, names, base_name + names._attn_base_name); + mlp = Gemma2MLP(config.hidden_size, config.intermediate_size, names, base_name + names._ffn_base_name); + input_layernorm = RMSNorm(config.hidden_size, config.rms_norm_eps, true, base_name + names._attn_norm_name); + post_attention_layernorm = RMSNorm(config.hidden_size, config.rms_norm_eps, true, base_name + names._ffn_norm_name); + pre_feedforward_layernorm = RMSNorm(config.hidden_size, config.rms_norm_eps, true, base_name + names._pre_feedforward_layernorm); + post_feedforward_layernorm = RMSNorm(config.hidden_size, config.rms_norm_eps, true, base_name + names._post_feedforward_layernorm); + } + + std::vector Forward(std::vector inputs, std::vector args) override { + auto x = input_layernorm(inputs[0]); + x = self_attn({x, x, x})[0]; + x = post_attention_layernorm(x); + auto tmp = x + inputs[0]; + x = pre_feedforward_layernorm(tmp); + x = mlp({x})[0]; + x = post_feedforward_layernorm(x); + x = x + tmp; + return {x}; + } + + Gemma2Attention &get_attention() { + return self_attn; + } + +private: + // MultiHeadAttention self_attn; + Gemma2Attention self_attn; + Gemma2MLP mlp; + Layer input_layernorm; + Layer post_attention_layernorm; + Layer pre_feedforward_layernorm; + Layer post_feedforward_layernorm; +}; + +class Gemma2Model final : public Module { +public: + Gemma2Model() = default; + Gemma2Model(const Gemma2Config &config, const Gemma2NameConfig &names, const string &base_name) { + blocks = List(config.num_hidden_layers, config, names, base_name); + norm = RMSNorm(config.hidden_size, config.rms_norm_eps, true, names.post_norm_name); + } + + std::vector Forward(std::vector inputs, std::vector args) override { + auto x = inputs[0]; + for (auto &block : blocks) { + x = block({x})[0]; + } + x = norm(x); + return {x}; + } + + void clear_kvcache() override { + for (auto &block : blocks) { + auto kvcache = block.get_attention().get_cache(); + for (auto &cache : kvcache) { cache->clearCache(); } + auto ropes = block.get_attention().get_rope(); + for (auto &rope : ropes) { rope->clearCache(); } + } + } + +private: + std::vector blocks; + Layer norm; +}; + +class Gemma2ForCausalLM final : public Module { +public: + Gemma2ForCausalLM(Gemma2Config &config) { + auto names = config.names_config; + hidden_size = config.hidden_size; + embedding = Embedding(config.vocab_size, config.hidden_size, names.token_embd_name); + model = Gemma2Model(config, names, names.blk_name); + + // gemma's lm_head and tok_embedding is tied together. + // They share same parameters. Use a Transpose to do the lm_head instead. + lm_head = Parameter(1, config.vocab_size, 1, config.hidden_size, names.lm_head_name + ".weight"); + } + + std::vector Forward(std::vector inputs, std::vector args) override { + auto x = embedding(inputs[0]); + + // do nomalize + x = x * std::sqrt(hidden_size); + + // go through model + auto outputs = model({x})[0]; + outputs = Tensor::mm(outputs, lm_head().transpose(Chl::SEQUENCE, Chl::DIMENSION)); + return {outputs}; + } + void clear_kvcache() override { + model.clear_kvcache(); + } + +private: + int hidden_size; + Layer embedding; + Parameter lm_head; + Gemma2Model model; +}; + +#endif //! MODELING_GEMMA2_HPP From c19a0adebcf39fff030185e64218bdcdf84fdd25 Mon Sep 17 00:00:00 2001 From: oreomaker Date: Fri, 10 Jan 2025 16:38:06 +0800 Subject: [PATCH 29/34] chore: update gemma2 vocab --- examples/demo_gemma2.cpp | 2 +- vocab/gemma2_vocab.mllm | Bin 0 -> 5010718 bytes 2 files changed, 1 insertion(+), 1 deletion(-) create mode 100644 vocab/gemma2_vocab.mllm diff --git a/examples/demo_gemma2.cpp b/examples/demo_gemma2.cpp index a02b33d3..7903c663 100644 --- a/examples/demo_gemma2.cpp +++ b/examples/demo_gemma2.cpp @@ -8,7 +8,7 @@ using namespace mllm; int main(int argc, char **argv) { cmdline::parser cmdParser; - cmdParser.add("vocab", 'v', "specify mllm tokenizer model path", false, "../vocab/vocab_gemma2.mllm"); + cmdParser.add("vocab", 'v', "specify mllm tokenizer model path", false, "../vocab/gemma2_vocab.mllm"); cmdParser.add("model", 'm', "specify mllm model path", false, "../models/gemma-2-2b-q4_k.mllm"); cmdParser.add("limits", 'l', "max KV cache size", false, 400); 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