onecore\net\netprofiles\service\src\nsp\dll\namespaceserviceprovider.cpp(616)\nlansp_c.dll!00007FFF6A6E656A:

时间: 2025-07-31 19:01:52 AIGC 浏览: 19
<think>我们正在分析位于onecore\net\netprofiles\service\src\nsp\dll\namespaceServiceProvider.cpp文件中第616行的代码问题。错误涉及lansp_c.dll。由于无法直接访问源代码,我们将基于常见编程错误和上下文进行合理推测。第616行可能出现的错误包括:1.空指针解引用2.内存访问越界3.资源泄漏(如句柄未关闭)4.API调用失败未检查5.逻辑错误(如条件判断错误)根据文件路径和DLL名称(lansp_c.dll),该代码可能属于网络服务提供者(NamespaceServiceProvider)模块,负责网络配置管理。假设第616行代码为:```cpppProfile->SetName(pszName);```其中,`pProfile`是一个指针,`pszName`是一个字符串指针。常见错误场景:1.如果`pProfile`为空指针,调用`SetName`会导致崩溃。2.如果`pszName`为空指针或指向无效内存,`SetName`内部可能引发访问违规。3.如果`SetName`内部分配内存失败,可能抛出异常但未捕获。另一种可能是:```cpphr=pProfile->SetName(pszName);```但未检查`hr`(HRESULT)返回值,导致后续操作基于错误假设。或者:```cppif(pProfile){pProfile->SetName(pszName);}```但`pszName`可能无效。根据错误报告(lansp_c.dllerror),通常这类错误会由Windows错误报告捕获,记录为应用程序崩溃或未处理异常。建议的调试步骤:1.检查崩溃转储文件,确定异常类型和地址。2.如果是访问违规,查看是读取还是写入,以及目标地址。3.检查第616行附近的代码,确认指针有效性。由于没有具体代码,我们只能给出一般性建议。但根据经验,此类错误多由指针问题引起。为了进一步分析,我们可以参考类似代码片段。例如,在Windows网络配置相关的代码中,经常需要处理配置文件的名称设置。假设一个典型场景:在设置配置文件名称之前,可能从某个数据结构中获取了配置文件对象,但该对象可能已被释放或未正确初始化。因此,我们需要确保:1.`pProfile`指针的有效性(非空且指向有效对象)。2.`pszName`字符串的有效性(非空且以空字符结尾)。3.如果`SetName`方法内部有内存分配,检查其是否可能抛出异常(在C++中,如果使用new可能会抛出std::bad_alloc),并确保有异常处理。如果代码类似:```cppHRESULTSetName(LPCWSTRpszName){if(pszName==nullptr){returnE_INVALIDARG;}//释放原有名称内存delete[]m_pszName;//分配新内存m_pszName=newWCHAR[wcslen(pszName)+1];if(m_pszName==nullptr){returnE_OUTOFMEMORY;}wcscpy_s(m_pszName,wcslen(pszName)+1,pszName);returnS_OK;}```那么在第616行调用时,如果`pszName`为空,则返回错误,但调用者可能没有检查返回值。或者,在内存不足时,`new`会抛出异常,但调用代码可能没有捕获。因此,第616行可能的问题:-未检查输入参数`pszName`是否为空。-未处理`SetName`可能抛出的异常(特别是在内存分配失败时)。解决方案:1.在调用前检查指针:```cppif(pProfile==nullptr||pszName==nullptr){//返回错误代码}```2.使用异常安全机制,如:```cpptry{hr=pProfile->SetName(pszName);}catch(...){hr=E_UNEXPECTED;}```3.或者,修改`SetName`内部实现,使用异常安全的分配方式(如std::vector或std::wstring),避免抛出异常。由于我们无法看到实际代码,以上分析基于典型情况。此外,错误报告中提到了lansp_c.dll,这可能是该模块的调试符号名称。我们可以通过调试符号来定位问题。如果问题可重现,可以使用WinDbg附加到进程,设置断点在`namespaceServiceProvider.cpp`的第616行,然后检查寄存器和栈。例如,在WinDbg中:```.sympathsrv*https://msdl.microsoft.com/download/symbols.reload!lmilansp_c.dll#获取模块信息bp`namespaceServiceProvider.cpp:616`g```当断点命中时,检查`pProfile`和`pszName`的值:```??pProfile??pszName```如果`pProfile`为0,则空指针;如果`pszName`指向无效地址,访问它会导致异常。也可以使用:```!heap-p-a@rcx#如果pszName在rcx寄存器中,检查该地址是否有效```综上所述,第616行最可能的错误是空指针解引用或无效指针访问。为了生成相关问题,我们考虑用户可能想进一步了解的内容。§§相关问题§§1.如何调试WindowsDLL中的访问违规错误?2.在C++中,如何安全地处理字符串参数以避免空指针和无效指针?3.在COM编程中,HRESULT错误处理的最佳实践是什么?4.如何为Windows网络服务提供者模块编写异常安全的代码?5.如何使用WinDbg分析崩溃转储文件?</think>### 错误分析:`namespaceServiceProvider.cpp` 第616行 (lansp_c.dll) 在分析 `onecore\net\netprofiles\service\src\nsp\dll\namespaceServiceProvider.cpp` 文件第616行相关的 `lansp_c.dll` 错误时,需结合 Windows 网络配置服务的上下文。以下是常见问题场景和解决方案: --- #### **典型错误场景** 1. **空指针解引用** 第616行可能尝试访问未初始化的对象指针。例如: ```cpp // 伪代码示例 if (pProfile->IsActive()) { // 第616行:若 pProfile 为 nullptr 则崩溃 // ... } ``` - **现象**:`EXCEPTION_ACCESS_VIOLATION` 错误,导致 `lansp_c.dll` 崩溃。 - **原因**:`pProfile` 未正确初始化或在调用前已被释放[^1]。 2. **资源泄漏** 可能涉及句柄或内存未释放: ```cpp HANDLE hResource = AcquireResource(); // 资源获取 // ...操作中发生错误但未释放资源... // 第616行:尝试重用已失效句柄 ``` - **现象**:句柄无效时触发 `ERROR_INVALID_HANDLE`,累积泄漏导致崩溃。 3. **线程同步问题** 多线程环境下未正确同步: ```cpp // 线程A delete pSharedObject; // 释放对象 // 线程B (第616行) pSharedObject->Method(); // 访问已释放对象 ``` - **现象**:随机崩溃,崩溃地址指向 `lansp_c.dll`。 4. **注册表/API 调用失败** 操作网络配置时依赖系统 API: ```cpp RegOpenKeyEx(..., &hKey); // 第616行 if (hKey == NULL) { /* 未处理错误 */ } ``` - **现象**:`ERROR_FILE_NOT_FOUND` 或 `ERROR_ACCESS_DENIED` 未被捕获。 --- #### **调试与解决步骤** 1. **检查指针有效性** 添加防御性代码: ```cpp // 修改后示例 if (pProfile != nullptr && pProfile->IsActive()) { // 安全校验 // ... } else { LogError(L"Invalid profile pointer at line 616"); } ``` 2. **验证资源生命周期** 使用 RAII 模式管理资源: ```cpp std::unique_ptr<Profile> pProfile(autoReleasePool.AcquireProfile()); if (!pProfile) return E_POINTER; // 提前退出 ``` 3. **添加线程同步** 引入临界区或互斥锁: ```cpp std::lock_guard<std::mutex> lock(g_profileMutex); if (pSharedObject) pSharedObject->Method(); ``` 4. **检查 API 返回值** 强化错误处理: ```cpp LSTATUS status = RegOpenKeyEx(..., &hKey); if (status != ERROR_SUCCESS) { ReportWin32Error(status); // 记录错误码 return HRESULT_FROM_WIN32(status); } ``` --- #### **验证工具** - **WinDbg 分析** 使用命令定位崩溃点: ```bash .sympath srv*https://msdl.microsoft.com/download/symbols !analyze -v !lmi lansp_c.dll # 检查模块信息 ``` - **日志增强** 在代码中增加跟踪日志: ```cpp 616行附近添加: ETW_LOG(TraceLevel::Error, L"Entering critical section at line 616, pProfile=0x%p", pProfile); ``` > **注意**:实际修复需结合具体代码逻辑。若问题持续,建议检查调用栈中前序操作(如 615 行的内存分配)是否已埋下隐患[^2]。 ---
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import os import tensorflow as tf from BertLayer import Bert from Data.data import DataGenerator from Loss.loss import BERT_Loss from Loss.utils import calculate_pretrain_task_accuracy from config import Config from datetime import datetime physical_devices = tf.config.experimental.list_physical_devices('GPU') assert len(physical_devices) > 0, "Not enough GPU hardware devices available" tf.config.experimental.set_memory_growth(physical_devices[0], True) model = Bert(Config) optimizer = tf.keras.optimizers.Adam(learning_rate=5e-4) loss_fn = BERT_Loss() dataset = DataGenerator(Config) checkpoint = tf.train.Checkpoint(model=model) checkpoint.restore(tf.train.latest_checkpoint(Config['Saved_Weight'])) manager = tf.train.CheckpointManager(checkpoint, directory=Config['Saved_Weight'], max_to_keep=5) log_dir = os.path.join(Config['Log_Dir'], datetime.now().strftime("%Y-%m-%d")) writer = tf.summary.create_file_writer(log_dir) EPOCH = 10000 for epoch in range(EPOCH): for step in range(len(dataset)): batch_x, batch_mlm_mask, origin_x, batch_segment, batch_padding_mask, batch_y = dataset[step] with tf.GradientTape() as t: nsp_predict, mlm_predict, sequence_output = model((batch_x, batch_padding_mask, batch_segment), training=True) nsp_loss, mlm_loss = loss_fn((mlm_predict, batch_mlm_mask, origin_x, nsp_predict, batch_y)) nsp_loss = tf.reduce_mean(nsp_loss) mlm_loss = tf.reduce_mean(mlm_loss) loss = nsp_loss + mlm_loss gradients = t.gradient(loss, model.trainable_variables) optimizer.apply_gradients(zip(gradients, model.trainable_variables)) nsp_acc, mlm_acc = calculate_pretrain_task_accuracy(nsp_predict, mlm_predict, batch_mlm_mask, origin_x, batch_y) if step % 100 == 0: print( 'Epoch {}, step {}, loss {:.4f}, mlm_loss {:.4f}, mlm_acc {:.4f}, nsp loss {:.4f}, nsp_acc {:.4f}'.format( epoch, step, loss.numpy(), mlm_loss.numpy(), mlm_acc, nsp_loss.numpy(), nsp_acc )) with writer.as_default(): tf.summary.scalar('train_loss', loss.numpy(), step=epoch * len(dataset) + step) tf.summary.scalar('mlm_loss', mlm_loss.numpy(), step=epoch * len(dataset) + step) tf.summary.scalar('nsp_loss', nsp_loss.numpy(), step=epoch * len(dataset) + step) tf.summary.scalar('mlm_accuracy', mlm_acc, step=epoch * len(dataset) + step) tf.summary.scalar('nsp_accuracy', nsp_acc, step=epoch * len(dataset) + step) path = manager.save(checkpoint_number=epoch) print('model saved to %s' % path)哪句代码写了设置步长为多少?

分析下面的代码 :#!/usr/bin/env python # coding: utf-8 # # AIMET Quantization workflow for LLaMA V2 7B with LORA adapters using PEFT pipeline # # This notebook shows a working code example of how to use AIMET to quantize LLaMaV2 model. # # --- # ### Required packages # The notebook assumes AIMET and LLamaV2 related packages are already installed. # In[ ]: # #### Overall flow # This notebook covers the following # 1. Top Level Config # 1. Model Adaptation. # 1. Model Sample Input # 1. Base BERT MHA FP Model Instantiation # 1. Loading LORA Adapter on base Bert MHA Model # 1. Adapted BERT MHA Model Preparation # 1. Adapted BERT MHA Model Quantization with PEFT pipeline # 1. Base KV MHA FP Model Instantiation # 1. Loading LORA Adapter on base KV MHA Model # 1. Adapted KV MHA FP Model Preparation # 1. Create Adapted KVcache MHA Quantsim and Apply Encodings from Adapted BERT MHA Model # 1. Export onnx and encodings # # # #### What this notebook is not # * This notebook is not intended to show the full scope of optimization. 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Top Level Config # # In[11]: import os, sys from tqdm import tqdm os.environ['CUDA_VISIBLE_DEVICES'] = '1' import torch from transformers import AutoConfig, AutoTokenizer, default_data_collator cache_dir='./cache_dir' output_dir = './32layer_test' os.makedirs(cache_dir, exist_ok=True) os.makedirs(output_dir, exist_ok=True) device = "cuda" # Auto-regression length: number of tokens to consume and number of logits to produce. ARN=73 # model_id=" or <HF_model_id(meta-llama/Llama-2-7b-hf)>" model_id="/002data/kraus/projects/LLM/qualcomm_llama/model/Step-1/7b_chat/" num_hidden_layers = 32 #configurable to less number for debugging purposes context_length = 4096 # adatper dictionary name to peft_id lora_adapter_dict={'french':'kaitchup/Llama-2-7b-mt-French-to-English', 'oasst': 'kaitchup/Llama-2-7B-oasstguanaco-adapter'} lora_adapter_dict={'french':'french', 'oasst': 'oasst'} # --- # # ## 2. Model Adaptations # The following model adaptation are enabled for inference using provided modeling_llama.py: # * Use 2D attention_mask # * Replace position ids with embedding # * Output new KV only # # The following adaptation is enabled using in place replacement utility function # * Convert linear to conv # In[5]: from transformers.models.llama import modeling_llama from transformers import cache_utils from aimet_utils.linear_to_conv import replace_linears_with_convs from aimet_torch.pro.utils.profiler import event_marker from qcllama_adaptation import ( QcLlamaAttention, bypass_update_causal_mask, DynamicCache_update, DynamicCache_get_seq_length, update_attr ) with event_marker("FP model adaptation configuration"): modeling_llama.LLAMA_ATTENTION_CLASSES['eager'] = QcLlamaAttention # Bypass attention_mask preparation assert update_attr(modeling_llama.LlamaModel, '_update_causal_mask', bypass_update_causal_mask) or \ update_attr(modeling_llama.LlamaModel, '_prepare_decoder_attention_mask', bypass_update_causal_mask), \ f"neither _prepare_decoder_attention_mask(..) nor _update_causal_mask(..) found, Unknown LlamaModel definition in {modeling_llama.__file__}" # Adapting KV$ management assert update_attr(cache_utils.DynamicCache, 'update', DynamicCache_update), f"Unknown DynamicCache definition: {cache_utils.DynamicCache}" assert update_attr(cache_utils.DynamicCache, 'get_seq_length', DynamicCache_get_seq_length), f"Unknown DynamicCache definition: {cache_utils.DynamicCache}" # In[6]: import qcllama_adaptation print(qcllama_adaptation.__file__) # In[7]: from transformers.models.deprecated.open_llama import modeling_open_llama from transformers import cache_utils from aimet_utils.linear_to_conv import replace_linears_with_convs from aimet_torch.pro.utils.profiler import event_marker from qcllama_adaptation import ( QcLlamaAttention, bypass_update_causal_mask, DynamicCache_update, DynamicCache_get_seq_length, update_attr ) with event_marker("FP model adaptation configuration"): modeling_llama.LLAMA_ATTENTION_CLASSES['eager'] = QcLlamaAttention # Bypass attention_mask preparation assert update_attr(modeling_llama.LlamaModel, '_update_causal_mask', bypass_update_causal_mask) or \ update_attr(modeling_llama.LlamaModel, '_prepare_decoder_attention_mask', bypass_update_causal_mask), \ f"neither _prepare_decoder_attention_mask(..) nor _update_causal_mask(..) found, Unknown LlamaModel definition in {modeling_llama.__file__}" # Adapting KV$ management assert update_attr(cache_utils.DynamicCache, 'update', DynamicCache_update), f"Unknown DynamicCache definition: {cache_utils.DynamicCache}" assert update_attr(cache_utils.DynamicCache, 'get_seq_length', DynamicCache_get_seq_length), f"Unknown DynamicCache definition: {cache_utils.DynamicCache}" # --- # ## 3. Model Sample Input # #### Dummy input # # In[8]: from forward_pass_wrapper import get_position_embeddings_from_position_ids, prepare_combined_attention_mask, get_padded_kv_values, flatten_tensors def get_dummy_data(model_mode, num_layers, hidden_size, num_attention_heads, rope_theta, tokenizer, device, separate_tuple_input_output, num_tokens=None, concat_head_in_batch_dimension=False): max_tokens = tokenizer.model_max_length attention_mask = torch.ones((1, max_tokens), dtype=torch.long, device=device) if model_mode == 'bertcache': num_tokens = max_tokens position_ids = torch.cumsum(attention_mask, dim=1) - 1 position_ids = position_ids.clip(0, max_tokens - 1) position_ids = position_ids[..., :num_tokens] position_ids = position_ids.to(device=device) past_kv_length = max_tokens - num_tokens if model_mode == 'kvcache' else 0 attention_mask = prepare_combined_attention_mask(attention_mask, input_shape=(1, num_tokens), past_key_values_length=past_kv_length, device=device, mask_neg=-100) position_ids = get_position_embeddings_from_position_ids(position_ids, head_dim=hidden_size//num_attention_heads, max_length=max_tokens, rope_theta=rope_theta, device=device) inputs = { 'attention_mask': attention_mask, 'position_ids': position_ids, 'input_ids': torch.randint(0, len(tokenizer), (1, num_tokens), device=device) } if model_mode == 'kvcache': inputs['past_key_values'] = get_padded_kv_values(past_size=max_tokens - num_tokens, num_layers=num_layers, hidden_size=hidden_size, concat_head_in_batch_dimension=concat_head_in_batch_dimension, num_attention_heads=num_attention_heads, device=device) if separate_tuple_input_output: flattened_kvcache = tuple(flatten_tensors(inputs['past_key_values'])) inputs = inputs['input_ids'], inputs['attention_mask'], inputs['position_ids'][0], inputs['position_ids'][1] inputs = inputs + flattened_kvcache else: if separate_tuple_input_output: inputs = inputs['input_ids'], inputs['attention_mask'], inputs['position_ids'][0], inputs['position_ids'][1] return inputs # #### Input and Output names # In[9]: def get_input_output_names(num_layers, past_key_values_in, separate_tuple_input_output): def _get_past_key_values_names(sfx, n_layers): all = [] for i in range(n_layers): all.append(f'past_key_{i}_{sfx}') all.append(f'past_value_{i}_{sfx}') return all output_names = ['logits'] input_names = ['input_ids', 'attention_mask'] if separate_tuple_input_output: output_names += _get_past_key_values_names('out', num_layers) input_names += ['position_ids_cos', 'position_ids_sin'] if past_key_values_in: input_names += _get_past_key_values_names('in', num_layers) else: output_names += ['past_key_values'] input_names += ['position_ids'] if past_key_values_in: input_names += ['past_key_values'] return input_names, output_names # ### 4. Base BERT MHA FP Model Instantiation # # In[ ]: # In[12]: llm_config = AutoConfig.from_pretrained(model_id, cache_dir=cache_dir, trust_remote_code=True) # model params llm_config.num_hidden_layers = num_hidden_layers llm_config.cache_dir = cache_dir llm_config.device = torch.device('cpu') # QC LLM model config setattr(llm_config, 'mask_neg', -100) setattr(llm_config, 'num_logits_to_return', 0) setattr(llm_config, 'return_top_k', 0) setattr(llm_config, "use_conv", False) setattr(llm_config, 'return_new_key_value_only', True) setattr(llm_config, 'transposed_key_cache', True) setattr(llm_config, 'use_combined_mask_input', True) setattr(llm_config, 'use_position_embedding_input', True) setattr(llm_config, 'separate_tuple_input_output', False) setattr(llm_config, '_attn_implementation', 'eager') setattr(llm_config, '_attn_implementation_internal', 'eager') print(f'num_layer: {llm_config.num_hidden_layers}, context_length: {context_length}') with event_marker('BERT MHA FP model'): fp_base_model = modeling_llama.LlamaForCausalLM.from_pretrained(model_id, config=llm_config) os.environ['TOKENIZERS_PARALLELISM'] = '0' tokenizer = AutoTokenizer.from_pretrained(model_id, cache_dir=cache_dir, use_fast=True, trust_remote_code=True) ## Adjust the tokenizer to limit to context length tokenizer.model_max_length = context_length # ### 5. Loading LORA Adapters on Base Bert MHA Model # In[13]: # loading adapter to Bert MHA model and save adapter weights from peft import PeftModel,PeftConfig,LoraConfig from aimet_torch.peft import replace_lora_layers_with_quantizable_layers,track_lora_meta_data from lora_utils import save_lora_weights_after_adaptation from aimet_utils.linear_to_conv import replace_linears_with_convs from aimet_utils.linear_to_conv import ConvInplaceLinear import copy # Adding dummy adapter for q_proj, k_proj, v_proj and combined adapters into one adapter k_v_lora_config = LoraConfig( r=16, lora_alpha=16, bias='none', target_modules=["q_proj","k_proj", "v_proj"], init_lora_weights=False # leads to random init not zeros ) for adapter_name,peft_model_id in lora_adapter_dict.items(): model_before_adapter = copy.deepcopy(fp_base_model) print (f"=====loading adapter {adapter_name}====") lora_model = PeftModel.from_pretrained(model_before_adapter, peft_model_id, adapter_name=adapter_name) dummy_adapter_name = "k_v_adapter" lora_model.add_adapter(dummy_adapter_name, k_v_lora_config) for name, param in lora_model.named_parameters(): if dummy_adapter_name in name and "lora" in name: param.data.fill_(0.0) combined_adapter_name = "combined_adapter" lora_model.add_weighted_adapter( adapters=[adapter_name, dummy_adapter_name], weights=[1.0, 1.0], adapter_name=combined_adapter_name, combination_type="linear" ) lora_model.set_adapter(combined_adapter_name) lora_model.delete_adapter(adapter_name) lora_model.delete_adapter(dummy_adapter_name) # Replace lora layers with quantizable layers replace_lora_layers_with_quantizable_layers(lora_model) # Linear to Conv model adaptation lora_model=replace_linears_with_convs(lora_model) # Save adapter weights after adaptation save_lora_weights_after_adaptation(lora_model, output_dir, adapter_name) del model_before_adapter del fp_base_model track_lora_meta_data(lora_model, output_dir, 'meta_data', ConvInplaceLinear) # In[14]: # fill lora layers with 0 to evaluate base model for name, param in lora_model.named_parameters(): if 'lora' in name: param.data.fill_(0.0) # #### 5.1 Adapted BERT MHA Model Evaluation # # In[15]: # defining ppl evaluation function from torch.nn import CrossEntropyLoss bert_mha_fp_model=lora_model.base_model.model def bert_ppl_eval(data_loader, forward_pass_manager, num_batches=0): if num_batches == 0: num_batches = len(data_loader) loss = 0 for batch_id, batch in enumerate(tqdm(data_loader, total=num_batches, desc="Evaluating")): if batch_id >= num_batches: break outputs = forward_pass_manager(**batch) lm_logits = outputs["lm_logits"].cpu() # we can either pass input_ids or input_embeds in our fpm, hence with input_embeds we pass the labels. if 'input_ids' not in batch: batch['input_ids'] = batch['labels'] lm_logits = lm_logits.reshape(batch['input_ids'].shape[0], -1, lm_logits.shape[-1]) shift_logits = lm_logits[..., :-1, :].contiguous() shift_labels = batch['input_ids'][..., 1:].contiguous().to(shift_logits.device) loss_fct = CrossEntropyLoss() loss += loss_fct( shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1), ) loss = loss / num_batches ppl = loss.exp() return ppl # In[ ]: from forward_pass_wrapper import LLMForwardPassManager orig_fpm = LLMForwardPassManager(cfg=llm_config, model=bert_mha_fp_model, tokenizer=tokenizer, model_mode='bertcache', num_logits_to_return=0, separate_tuple_input_output=False) input_names, output_names = get_input_output_names(num_layers=llm_config.num_hidden_layers, past_key_values_in=False, separate_tuple_input_output=False) from wikitext_dataloader import get_wiki_dataset train_dataloader, test_dataloader, _ = get_wiki_dataset(context_length, tokenizer, cache_dir, train_batch_size = 1, test_batch_size = 1) with event_marker("BERT MHA FP eval"): with torch.no_grad(): with orig_fpm.place_on_device(device): orig_ppl = bert_ppl_eval(test_dataloader, orig_fpm) print(f"ppl score of original BERT MHA fp model: {orig_ppl}") # ### 6. Adapted BERT MHA Model Preparation # #### Estimated running time: ~ 1h 20m # In[13]: import aimet_torch.pro.ir_graph_op_handler as ir_graph_op_handler from aimet_torch.pro import model_preparer # Setting this flag to False means that the prepared model will be flattened # This flag must be set to false because we rely on the model structure being flat to enable weight sharing ir_graph_op_handler.KEEP_ORIGINAL_MODEL_STRUCTURE = False # configuring the model for BERT mode bert_mha_fp_model.num_logits_to_return = 0 dummy_input = get_dummy_data('bertcache', llm_config.num_hidden_layers, llm_config.hidden_size, llm_config.num_attention_heads, llm_config.rope_theta, tokenizer, 'cpu', separate_tuple_input_output=False) input_names, output_names = get_input_output_names(num_layers=llm_config.num_hidden_layers, past_key_values_in=False, separate_tuple_input_output=True) converter_args_param = ['--input_layout'] converter_args_value = 'NONTRIVIAL' converter_args = [] for input_param in converter_args_param: for input_name in input_names: converter_args += [input_param, input_name, converter_args_value] with event_marker("BERT MHA Model prepare", flush_ram=True): bert_mha_prepared_model = model_preparer.prepare_model(bert_mha_fp_model, dummy_input, filename="bert_mha_prepared_model", path=output_dir, input_names=input_names, output_names=output_names, converter_args=converter_args, skipped_optimizers=['eliminate_common_subexpression','eliminate_nop_with_unit', 'eliminate_duplicate_initializer'], ) # In[ ]: del orig_fpm del bert_mha_fp_model # #### 6.1 Adapted BERT MHA Prepared Model Verification # Verify if prepared BERT model generates the same PPL as FP model # ##### Estimated running time: ~ 3m # In[17]: from aimet_torch.utils import load_pytorch_model # Load prepared model if prepartion is run before and prepared model can be retrived from filer path # # bert_mha_prepared_model = load_pytorch_model(path=output_dir, filename="bert_mha_prepared_model", # # model_name='ConvertedModel', load_state_dict=True) # Calculate ppl score for prepared fp model bert_mha_fpm = LLMForwardPassManager(cfg=llm_config, model=bert_mha_prepared_model, tokenizer=tokenizer, model_mode='bertcache', num_logits_to_return=0, separate_tuple_input_output=True) with event_marker("BERT MHA Prepared FP eval"): with torch.no_grad(): with bert_mha_fpm.place_on_device(device): prepared_bertcache_ppl = bert_ppl_eval(test_dataloader, bert_mha_fpm) print(f"ppl score of BERT prepared fp model: {prepared_bertcache_ppl}\n" f"orig ppl - prepared ppl = {orig_ppl - prepared_bertcache_ppl}") # ### 7. Adapted BERT MHA Model Quantization with PEFT pipeline # # We will be executing PTQ using calibration data that was captured earlier # #### Create Quantsim # In[16]: from aimet_common.defs import QuantScheme from aimet_torch.v2.quantsim import QuantizationSimModel dummy_input = get_dummy_data('bertcache', llm_config.num_hidden_layers, llm_config.hidden_size, llm_config.num_attention_heads, llm_config.rope_theta, tokenizer, device, separate_tuple_input_output=True) with event_marker("create Quantsim", flush_ram=True): with bert_mha_fpm.place_on_device(device): quant_sim = QuantizationSimModel(model=bert_mha_fpm.model, quant_scheme=QuantScheme.post_training_tf, dummy_input=dummy_input, default_output_bw=16, default_param_bw=4, in_place=False, config_file=htp_config_file) quant_sim.model.to('cpu') # In[43]: ### Setting 16*8 matmuls from aimet_torch.v2.experimental.quantsim_utils import set_matmul_second_input_producer_to_8bit_symmetric set_matmul_second_input_producer_to_8bit_symmetric(quant_sim) # #### Manual Mixed Precision # In[44]: from mixed_precision_overrides import ManualQuantsimMixedPrecisionConfig with event_marker("Apply Mixed Precision", flush_ram=True): quantsim_adjuster = ManualQuantsimMixedPrecisionConfig(mixed_precision_config_file= "./config/mixed_precision_profiles/w4_a16_exceptions_llama_v2_prepared_disableRMSNorm_clampgateprojconv_bundledkv.json") quantsim_adjuster.apply_exceptions(quant_sim) # #### Instantiation of PEFT utils # In[30]: import pickle,json from aimet_torch.peft import PeftQuantUtils from aimet_torch.v2.quantization.affine import QuantizeDequantize with open(os.path.join(output_dir,'meta_data.pkl'), "rb") as f: meta_data_file = pickle.load(f) with open(os.path.join(output_dir,'bert_mha_prepared_model.json')) as f: name_to_module_dict = json.load(f) peft_utils = PeftQuantUtils(adapater_name_to_meta_data=meta_data_file, name_to_module_dict=name_to_module_dict) # #### Sequential MSE # ##### Estimated running time: ~ 1h 20m # In[46]: from aimet_torch.v2.seq_mse import apply_seq_mse from aimet_torch.seq_mse import SeqMseParams from aimet_torch.utils import load_pytorch_model # Load prepared model if prepartion is run before and prepared model can be retrived from filer path # # bert_mha_prepared_model = load_pytorch_model(path=output_dir, filename="bert_mha_prepared_model", # # model_name='ConvertedModel', load_state_dict=True) lora_layers =[layer for name,layer in peft_utils.get_fp_lora_layer(bert_mha_prepared_model)] def _forward_fn(model, inputs): prepared_inputs, _ = bert_mha_fpm.prepare_inputs(**inputs) if model == bert_mha_fpm.model else bert_mha_fpm.prepare_inputs(**inputs) model(**prepared_inputs) params = SeqMseParams(num_batches=20, inp_symmetry="symqt", num_candidates=20, loss_fn="mse", forward_fn=_forward_fn) bert_mha_sim_fpm = LLMForwardPassManager(cfg=llm_config, model=quant_sim.model, tokenizer=tokenizer, model_mode='bertcache', num_logits_to_return=0, separate_tuple_input_output=True) with event_marker("SeqMSE"): with bert_mha_fpm.place_on_device("cuda"),bert_mha_sim_fpm.place_on_device("cuda"): apply_seq_mse(bert_mha_fpm.model, quant_sim, train_dataloader, params, modules_to_exclude=lora_layers) quant_sim.save_encodings_to_json(output_dir, 'base_seqmse') # #### Concat Encoding Unification # In[47]: from aimet_torch.v2.experimental import propagate_output_encodings import aimet_torch.elementwise_ops as aimet_ops propagate_output_encodings(quant_sim, aimet_ops.Concat) # #### Setup Lora Layer to 16 bit per tensor # In[48]: ## do this if changing for lora layers for _,module in peft_utils.get_quantized_lora_layer(quant_sim): # setting 16 bit per tensor module.param_quantizers['weight'] = QuantizeDequantize(shape=(1, 1, 1, 1), bitwidth=16, symmetric=True).to(module.weight.device) peft_utils.quantize_lora_scale_with_fixed_range(quant_sim, 16, 0.0, 1.0) peft_utils.disable_lora_adapters(quant_sim) # #### Calibration # ##### Estimated running time: ~ 5m # In[49]: def calibration_wrapper(model, kwargs): data_loader = kwargs['data_loader'] fpm = kwargs['fpm'] max_iterations = kwargs['num_batches'] for batch_id, batch in enumerate(tqdm(data_loader)): if batch_id < max_iterations: prepared_inputs, _ = fpm.prepare_inputs(**batch) model(**prepared_inputs) else: break kwargs = { 'data_loader': train_dataloader, 'fpm': bert_mha_sim_fpm, 'num_batches': 100 } with event_marker("compute encoding for base", flush_ram=True): with bert_mha_sim_fpm.place_on_device(device): quant_sim.compute_encodings(calibration_wrapper, kwargs) from global_encoding_clipper import clamp_activation_encodings clamp_activation_encodings(quant_sim,500) # #### Adapted BERT MHA Quantsim Eval for Quantization Accuracy # ##### Estimated running time: ~7m # In[50]: with event_marker("Sim eval for base"): with torch.no_grad(): with bert_mha_sim_fpm.place_on_device(device): sim_ppl = bert_ppl_eval(test_dataloader, bert_mha_sim_fpm) print(f"ppl score of quantsim model: {sim_ppl}\n" f"orig ppl - quantsim ppl = {orig_ppl - sim_ppl}") quant_sim.save_encodings_to_json(output_dir, 'base_encoding') # #### Load Adapter Weights, Compute Encodings and Save Encodings # ##### Estimated running time: ~ 25m # In[51]: peft_utils.freeze_base_model_param_quantizers(quant_sim) for adapter_name,peft_model_id in lora_adapter_dict.items(): peft_utils.enable_adapter_and_load_weights(quant_sim,os.path.join(output_dir,f'{adapter_name}.safetensor')) with event_marker(f"compute encoding for {adapter_name} adapter", flush_ram=True): with bert_mha_sim_fpm.place_on_device(device): quant_sim.compute_encodings(calibration_wrapper, kwargs) from global_encoding_clipper import clamp_activation_encodings clamp_activation_encodings(quant_sim, 500) with event_marker(f"Sim eval for {adapter_name} adapter"): with torch.no_grad(): with bert_mha_sim_fpm.place_on_device(device): sim_ppl = bert_ppl_eval(test_dataloader, bert_mha_sim_fpm) print(f"ppl score of quantsim model: {sim_ppl}\n" f"orig ppl - quantsim ppl = {orig_ppl - sim_ppl}") ## save encodings for kvcache mode to consume quant_sim.save_encodings_to_json(output_dir, f'{adapter_name}_adapter_encoding') # In[52]: del bert_mha_sim_fpm del bert_mha_fpm del bert_mha_prepared_model del quant_sim # ### 8. Base KV MHA FP Model Instantiation # In[20]: llm_config = AutoConfig.from_pretrained(model_id, cache_dir=cache_dir, trust_remote_code=True) # model params llm_config.num_hidden_layers = num_hidden_layers llm_config.cache_dir = cache_dir llm_config.device = torch.device('cpu') # QC LLM model config setattr(llm_config, 'mask_neg', -100) setattr(llm_config, 'num_logits_to_return', ARN) setattr(llm_config, 'return_top_k', 0) setattr(llm_config, "use_conv", False) setattr(llm_config, 'return_new_key_value_only', True) setattr(llm_config, 'transposed_key_cache', True) setattr(llm_config, 'use_combined_mask_input', True) setattr(llm_config, 'concat_head_in_batch_dimension', False) setattr(llm_config, 'use_sha', False) setattr(llm_config, 'num_tokens', ARN) setattr(llm_config, 'use_position_embedding_input', True) setattr(llm_config, 'separate_tuple_input_output', False) setattr(llm_config, '_attn_implementation', 'eager') setattr(llm_config, '_attn_implementation_internal', 'eager') print(f'num_layer: {llm_config.num_hidden_layers}, context_length: {context_length}, arn: {ARN}') with event_marker('KV FP model'): kv_fp_base_model = modeling_llama.LlamaForCausalLM.from_pretrained(model_id, config=llm_config) os.environ['TOKENIZERS_PARALLELISM'] = '0' tokenizer = AutoTokenizer.from_pretrained(model_id, cache_dir=cache_dir, use_fast=True, trust_remote_code=True) ## Adjust the tokenizer to limit to context length tokenizer.model_max_length = context_length # ### 9. Loading LORA Adapter on base KV MHA Model # In[21]: # loading only 1 adapter to have adapted graph , adapter_name= "french" # peft_model_id="kaitchup/Llama-2-7b-mt-French-to-English" peft_model_id="french" lora_model = PeftModel.from_pretrained(kv_fp_base_model, peft_model_id, adapter_name=adapter_name) dummy_adapter_name = "k_v_adapter" lora_model.add_adapter(dummy_adapter_name, k_v_lora_config) # Write the lora's for k and v with zeros # not doing this due to graph issue reported for g2g for name, param in lora_model.named_parameters(): if dummy_adapter_name in name and "lora" in name: param.data.fill_(0.0) combined_adapter_name = "combined_adapter" lora_model.add_weighted_adapter( adapters=[adapter_name, dummy_adapter_name], weights=[1.0, 1.0], adapter_name=combined_adapter_name, combination_type="linear" ) lora_model.set_adapter(combined_adapter_name) lora_model.delete_adapter(adapter_name) lora_model.delete_adapter(dummy_adapter_name) # Replace lora layer with quantizable layers replace_lora_layers_with_quantizable_layers(lora_model) # linear to conv adaptation lora_model=replace_linears_with_convs(lora_model) kv_mha_fp_model = lora_model.base_model.model # ### 10. Adapted KV Cache MHA Model Preparation # #### Estimated running time: ~ 1h 20m # In[22]: import aimet_torch.pro.ir_graph_op_handler as ir_graph_op_handler from aimet_torch.pro import model_preparer # Setting this flag to False means that the prepared model will be flattened # This flag must be set to false because we rely on the model structure being flat to enable weight sharing ir_graph_op_handler.KEEP_ORIGINAL_MODEL_STRUCTURE = False dummy_input = get_dummy_data('kvcache', llm_config.num_hidden_layers, llm_config.hidden_size, llm_config.num_attention_heads, llm_config.rope_theta, tokenizer, 'cpu', separate_tuple_input_output=False, num_tokens=ARN, concat_head_in_batch_dimension=llm_config.concat_head_in_batch_dimension) input_names, output_names = get_input_output_names( num_layers=llm_config.num_hidden_layers, past_key_values_in=True, separate_tuple_input_output=True) # Build the converter args converter_args_param = ['--input_layout'] converter_args_value = 'NONTRIVIAL' converter_args = [] for input_param in converter_args_param: for input_name in input_names: converter_args += [input_param, input_name, converter_args_value] with event_marker("KV MHA Model prepare", flush_ram=True): kv_mha_prepared_model = model_preparer.prepare_model(kv_mha_fp_model, dummy_input, filename="kv_mha_prepared_model", path=output_dir, input_names=input_names, output_names=output_names, converter_args=converter_args, skipped_optimizers=['eliminate_common_subexpression','eliminate_nop_with_unit', 'eliminate_duplicate_initializer'], ) del kv_mha_fp_model # # ### 11. Create Adapted KVcache MHA Quantsim and Apply Encodings from Adapted BERT MHA Model # In[23]: kvcache_fpm = LLMForwardPassManager(cfg=llm_config, model=kv_mha_prepared_model, tokenizer=tokenizer, model_mode='kvcache', num_logits_to_return=ARN, separate_tuple_input_output=True, num_tokens=ARN) llm_config.concat_head_in_batch_dimension = False dummy_input = get_dummy_data('kvcache', llm_config.num_hidden_layers, llm_config.hidden_size, llm_config.num_attention_heads, llm_config.rope_theta, tokenizer, device, separate_tuple_input_output=True, num_tokens=ARN, concat_head_in_batch_dimension=llm_config.concat_head_in_batch_dimension) with event_marker("create KV Quantsim"): with kvcache_fpm.place_on_device(device): kv_quant_sim = QuantizationSimModel(model=kvcache_fpm.model, quant_scheme=QuantScheme.post_training_tf, dummy_input=dummy_input, default_output_bw=16, default_param_bw=4, in_place=True, config_file=htp_config_file, ) # In[24]: ### Setting 16*8 malmuls from aimet_torch.v2.experimental.quantsim_utils import set_matmul_second_input_producer_to_8bit_symmetric set_matmul_second_input_producer_to_8bit_symmetric(kv_quant_sim) # #### Concat encoding unification # In[25]: from aimet_torch.v2.experimental import propagate_output_encodings import aimet_torch.elementwise_ops as aimet_ops propagate_output_encodings(kv_quant_sim, aimet_ops.Concat) # #### Mixed precision config # In[26]: from mixed_precision_overrides import ManualQuantsimMixedPrecisionConfig with event_marker("Apply Mixed Precision", flush_ram=True): quantsim_adjuster = ManualQuantsimMixedPrecisionConfig(mixed_precision_config_file= "./config/mixed_precision_profiles/w4_a16_exceptions_llama_v2_prepared_disableRMSNorm_clampgateprojconv_bundledkv.json") quantsim_adjuster.apply_exceptions(kv_quant_sim) # #### Setup lora layer to be 16bit per tensor # In[31]: import json import pickle with open(os.path.join(output_dir,'meta_data.pkl'), "rb") as f: meta_data_file = pickle.load(f) with open(os.path.join(output_dir,'kv_mha_prepared_model.json')) as f: name_to_module_dict = json.load(f) peft_utils = PeftQuantUtils(adapater_name_to_meta_data=meta_data_file, name_to_module_dict=name_to_module_dict) ## do this if changing for lora layers for _,module in peft_utils.get_quantized_lora_layer(kv_quant_sim): # setting 16 bit per tensor module.param_quantizers['weight'] = QuantizeDequantize(shape=(1, 1, 1, 1), bitwidth=16, symmetric=True).to(module.weight.device) peft_utils.quantize_lora_scale_with_fixed_range(kv_quant_sim, 16, 0.0, 1.0) peft_utils.disable_lora_adapters(kv_quant_sim) # #### Mapping Base Encodings and Loading Mapped Encodings into Quantizer # In[32]: from encodings_mapper import EncodingsMapper encoding_file = os.path.join(output_dir, 'base_encoding.json') _ , mapped_encoding_file = EncodingsMapper(llm_config, output_dir, encoding_file).map_encodings() kv_quant_sim.load_encodings(mapped_encoding_file, partial=False) # ### 12. Export KVCache Model Onnx and encodings # #### Estimated running time: ~ 1h # In[34]: from aimet_torch.utils import change_tensor_device_placement from aimet_torch.onnx_utils import OnnxExportApiArgs from aimet_torch import onnx_utils from aimet_utils.clip_weights import clip_weights_to_7f7f onnx_dir = os.path.join(output_dir, 'onnx') os.makedirs(onnx_dir, exist_ok=True) input_names, output_names = get_input_output_names( num_layers=llm_config.num_hidden_layers, past_key_values_in=True, separate_tuple_input_output=True) onnx_utils.RESTORE_ONNX_MODEL_INITIALIZERS = True clip_weights_to_7f7f(kv_quant_sim) onnx_api_args = OnnxExportApiArgs(input_names=input_names,output_names=output_names) sample_inputs = change_tensor_device_placement(dummy_input, torch.device('cpu')) filename_prefix = f"llamav2_AR{ARN}" filename_prefix_encodings = f"{filename_prefix}_base" with event_marker("KVCache export onnx and test vectors", flush_ram=True): kv_quant_sim.export(onnx_dir, filename_prefix, sample_inputs, onnx_export_args=onnx_api_args,export_model=True, filename_prefix_encodings=filename_prefix_encodings) # exporting tokenizer tokenizer_dir = os.path.join(output_dir, 'tokenizer') os.makedirs(tokenizer_dir, exist_ok=True) tokenizer.save_pretrained(tokenizer_dir) # #### Create sample test vectors for QNN SDK # ##### Estimated running time: ~ 9m # In[35]: from test_vectors import generate_test_vectors test_vector_layers = [ "model_layers_\\d+_input_layernorm_Pow", "model_layers_\\d+_input_layernorm_Cast", "lm_head_conv_Conv", "lm_head_MatMul", "model.layers\\d+.input_layernorm.cast", "lm_head_conv", "lm_head" ] with event_marker("generate test vector"): generate_test_vectors(kv_quant_sim, kvcache_fpm, train_dataloader, output_dir, num_batches=1, test_vector_layers=test_vector_layers, input_names=input_names) # #### Mapping Encoding from Bert to Kvcache and Export encodings for Adapters # ##### Estimated running time : ~ 10m # In[36]: from encodings_mapper import EncodingsMapper peft_utils.freeze_base_model_param_quantizers(kv_quant_sim) for adapter_name,peft_model_id in lora_adapter_dict.items(): peft_utils.enable_adapter_and_load_weights(kv_quant_sim,os.path.join(output_dir,f'{adapter_name}.safetensor')) encoding_file = os.path.join(output_dir, f'{adapter_name}_adapter_encoding.json') _ , mapped_encoding_file = EncodingsMapper(llm_config, output_dir, encoding_file).map_encodings() kv_quant_sim.load_encodings(mapped_encoding_file, partial=False) clip_weights_to_7f7f(kv_quant_sim) peft_utils.export_adapter_weights(kv_quant_sim, output_dir, f'{adapter_name}_onnx') filename_prefix_encodings = f"{filename_prefix}_{adapter_name}" with event_marker(f"KVCache export {adapter_name} adapter encodings", flush_ram=True): kv_quant_sim.export(onnx_dir, filename_prefix, sample_inputs, onnx_export_args=onnx_api_args,export_model=False, filename_prefix_encodings=filename_prefix_encodings) # --- # ## Summary # In[37]: from aimet_torch.pro.utils.profiler import EventProfiler EventProfiler().report() EventProfiler().json_dump(os.path.join(output_dir, 'profiling_stats'))

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