common: run_label: "run_1" accum_freq: 1 accum_after_epoch: -1 log_freq: 200 auto_resume: true mixed_precision: true dataset: root_train: "/media/Datasets/VOCdevkit" root_val: "/media/Datasets/VOCdevkit" name: "pascal" category: "segmentation" train_batch_size0: 12 val_batch_size0: 12 eval_batch_size0: 1 workers: 12 persistent_workers: false pin_memory: false pascal: use_coco_data: true coco_root_dir: "/media/Datasets/coco_preprocess" image_augmentation: random_resize: enable: true min_size: 256 max_size: 1024 random_crop: enable: true mask_fill: 255 resize_if_needed: true random_horizontal_flip: enable: true sampler: name: "batch_sampler" bs: crop_size_width: 512 crop_size_height: 512 loss: category: "segmentation" ignore_idx: 255 segmentation: name: "cross_entropy" optim: name: "adamw" weight_decay: 0.01 no_decay_bn_filter_bias: false adamw: beta1: 0.9 beta2: 0.999 scheduler: name: "cosine" is_iteration_based: false max_epochs: 50 warmup_iterations: 500 warmup_init_lr: 0.00009 cosine: max_lr: 0.0009 # [2.7e-3 * N_GPUS^2 x (BATCH_SIZE_GPU0/ 32) * 0.02 ] # 0.02 comes from this fact 0.1 (ResNet SGD LR)/0.002 (MIT ADAMW LR) min_lr: 1.e-6 model: segmentation: name: "encoder_decoder" lr_multiplier: 10 seg_head: "deeplabv3" output_stride: 16 classifier_dropout: 0.1 activation: name: "relu" deeplabv3: aspp_dropout: 0.1 aspp_sep_conv: false aspp_out_channels: 256 aspp_rates: [6, 12, 18] classification: name: "mobilevit_v3" classifier_dropout: 0.1 mit: mode: "small_v3" ffn_dropout: 0.0 attn_dropout: 0.0 dropout: 0.1 number_heads: 4 no_fuse_local_global_features: false conv_kernel_size: 3 activation: name: "swish" pretrained: "results/mobilevitv3_small_e300_7930/run_1/checkpoint_ema_best.pt" normalization: name: "sync_batch_norm" momentum: 0.1 activation: name: "relu" inplace: false layer: global_pool: "mean" conv_init: "kaiming_normal" linear_init: "normal" conv_weight_std: false ema: enable: true momentum: 0.0005 ddp: enable: true rank: 0 world_size: -1 dist_port: 30786 stats: name: [ "loss", "iou"] checkpoint_metric: "iou" checkpoint_metric_max: true 帮我逐行详细解释这段代码

Accum TrustedAccum::TEEaccum(Stats &stats, Nodes nodes, Vote<Void, Cert> votes[MAX_NUM_SIGNATURES]) { View v = votes[0].getCData().getView(); View highest = 0; Hash hash = Hash(); std::set signers; for(int i = 0; i < MAX_NUM_SIGNATURES && i < this->qsize; i++) { Vote<Void, Cert> vote = votes[i]; CData<Void, Cert> data = vote.getCData(); Sign sign = vote.getSign(); PID signer = sign.getSigner(); Cert cert = data.getCert(); bool vd = verifyCData(stats, nodes, data, sign); bool vc = verifyCert(stats, nodes, cert); if(data.getPhase() == PH1_NEWVIEW && data.getView() == v && signers.find(signer) == signers.end() && vd && vc) { if(DEBUG1) { std::cout << KMAG << "[" << this->id << "]" << "inserting signer" << KNRM << std::endl; } signers.insert(signer); if(cert.getView() >= highest) { highest = cert.getView(); hash = cert.getHash(); } } else { if(DEBUG1) { std::cout << KMAG << "[" << this->id << "]" << "vote:" << vote.prettyPrint() << KNRM << std::endl; } if(DEBUG1) { std::cout << KMAG << "[" << this->id << "]" << "not inserting signer (" << signer << ") because:" << "check-phase=" << std::to_string(data.getPhase() == PH1_NEWVIEW) << "(" << data.getPhase() << "," << PH1_NEWVIEW << ")" << ";check-view=" << std::to_string(data.getView() == v) << ";check-notin=" << std::to_string(signers.find(signer) == signers.end()) << ";verif-data=" << std::to_string(vd) << ";verif-cert=" << std::to_string(vc) << KNRM << std::endl; } } } bool set = true; unsigned int size = signers.size(); std::string text = std::to_string(set) + std::to_string(v) + std::to_string(highest) + hash.toString() + std::to_string(size); Sign sign(this->priv,this->id,text); return Accum(v, highest, hash, size, sign); }

这段代码是一个函数定义，函数名为TEEaccum，返回类型为Accum。函数接受以下参数： - Stats &stats：一个Stats对象的引用。 - Nodes nodes：一个Nodes对象。 - Vote, Cert> votes[MAX_NUM_SIGNATURES...

void TrackHandler::add_gyro_reading(Eigen::Vector3f &gyro_reading) { gyro_accum_ += gyro_reading; n_gyro_readings_++; }

具体来说，它将给定的三维向量 gyro_reading 加到成员变量 gyro_accum_ 中，同时将 n_gyro_readings_ 计数器加一。这通常用于惯性导航系统中的姿态估计，可以通过对一段时间内的陀螺仪测量值求和来估计当前的姿态。

def training_one_frame_setup(self,training_args): ntc_conf_path=training_args.ntc_conf_path with open(ntc_conf_path) as ntc_conf_file: ntc_conf = ctjs.load(ntc_conf_file) if training_args.only_mlp: model=tcnn.Network(n_input_dims=3, n_output_dims=8, network_config=ntc_conf["network"]).to(torch.device("cuda")) else: model=tcnn.NetworkWithInputEncoding(n_input_dims=3, n_output_dims=8, encoding_config=ntc_conf["encoding"], network_config=ntc_conf["network"]).to(torch.device("cuda")) self.ntc=NeuralTransformationCache(model,self.get_xyz_bound()[0],self.get_xyz_bound()[1]) self.ntc.load_state_dict(torch.load(training_args.ntc_path)) self._xyz_bound_min = self.ntc.xyz_bound_min self._xyz_bound_max = self.ntc.xyz_bound_max if training_args.ntc_lr is not None: ntc_lr=training_args.ntc_lr else: ntc_lr=ntc_conf["optimizer"]["learning_rate"] self.ntc_optimizer = torch.optim.Adam(self.ntc.parameters(), lr=ntc_lr) self.xyz_gradient_accum = torch.zeros((self.get_xyz.shape[0], 1), device="cuda") self.color_gradient_accum = torch.zeros((self.get_xyz.shape[0], 1), device="cuda") self.denom = torch.zeros((self.get_xyz.shape[0], 1), device="cuda")

还有一些梯度累积的变量，比如xyz_gradient_accum、color_gradient_accum和denom，初始化为零张量，可能在训练过程中用来累积梯度信息，或者做某种归一化处理。用户的需求是理解这段代码的作用。可能的背景...

解释每一句代码def train(train_loader, model, optimizer, epoch, best_loss): model.train() loss_record2, loss_record3, loss_record4 = AvgMeter(), AvgMeter(), AvgMeter() accum = 0 for i, pack in enumerate(train_loader, start=1): # ---- data prepare ---- images, gts = pack images = Variable(images).cuda() gts = Variable(gts).cuda() # ---- forward ---- lateral_map_4, lateral_map_3, lateral_map_2 = model(images) # ---- loss function ---- loss4 = structure_loss(lateral_map_4, gts) loss3 = structure_loss(lateral_map_3, gts) loss2 = structure_loss(lateral_map_2, gts) loss = 0.5 * loss2 + 0.3 * loss3 + 0.2 * loss4 # ---- backward ---- loss.backward() torch.nn.utils.clip_grad_norm_(model.parameters(), opt.grad_norm) optimizer.step() optimizer.zero_grad() # ---- recording loss ---- loss_record2.update(loss2.data, opt.batchsize) loss_record3.update(loss3.data, opt.batchsize) loss_record4.update(loss4.data, opt.batchsize) # ---- train visualization ---- if i % 20 == 0 or i == total_step: print('{} Epoch [{:03d}/{:03d}], Step [{:04d}/{:04d}], ' '[lateral-2: {:.4f}, lateral-3: {:0.4f}, lateral-4: {:0.4f}]'. format(datetime.now(), epoch, opt.epoch, i, total_step, loss_record2.show(), loss_record3.show(), loss_record4.show()))

这段代码是一个训练函数，接收训练数据集、模型、优化器、当前训练轮数和最佳损失作为参数。在函数内部，首先将模型设为训练模式，然后通过一个循环遍历训练数据集，进行前向传播得到模型的输出结果，然后计算loss，...

def train(train_loader, model, optimizer, epoch, best_loss): model.train() loss_record2, loss_record3, loss_record4 = AvgMeter(), AvgMeter(), AvgMeter() accum = 0 for i, pack in enumerate(train_loader, start=1): # ---- data prepare ---- images, gts = pack images = Variable(images).cuda() gts = Variable(gts).cuda() # ---- forward ---- lateral_map_4, lateral_map_3, lateral_map_2 = model(images) # ---- loss function ---- loss4 = structure_loss(lateral_map_4, gts) loss3 = structure_loss(lateral_map_3, gts) loss2 = structure_loss(lateral_map_2, gts) loss = 0.5 * loss2 + 0.3 * loss3 + 0.2 * loss4 # ---- backward ---- loss.backward() torch.nn.utils.clip_grad_norm_(model.parameters(), opt.grad_norm) optimizer.step() optimizer.zero_grad() # ---- recording loss ---- loss_record2.update(loss2.data, opt.batchsize) loss_record3.update(loss3.data, opt.batchsize) loss_record4.update(loss4.data, opt.batchsize) # ---- train visualization ---- if i % 400 == 0 or i == total_step: print('{} Epoch [{:03d}/{:03d}], Step [{:04d}/{:04d}], ' '[lateral-2: {:.4f}, lateral-3: {:0.4f}, lateral-4: {:0.4f}]'. format(datetime.now(), epoch, opt.epoch, i, total_step, loss_record2.show(), loss_record3.show(), loss_record4.show())) print('lr: ', optimizer.param_groups[0]['lr']) save_path = 'snapshots/{}/'.format(opt.train_save) os.makedirs(save_path, exist_ok=True) if (epoch+1) % 1 == 0: meanloss = test(model, opt.test_path) if meanloss < best_loss: print('new best loss: ', meanloss) best_loss = meanloss torch.save(model.state_dict(), save_path + 'TransFuse-%d.pth' % epoch) print('[Saving Snapshot:]', save_path + 'TransFuse-%d.pth'% epoch) return best_loss

这段代码是一个训练函数，接受一个训练数据集(train_loader)，一个模型(model)，一个优化器(optimizer)，当前的训练周期(epoch)和最佳loss(best_loss)。第一行定义了函数名和参数，其中"loss_record2, loss_record...

解释代码def train(train_loader, model, optimizer, epoch, best_loss): model.train() loss_record2, loss_record3, loss_record4 = AvgMeter(), AvgMeter(), AvgMeter() accum = 0 for i, pack in enumerate(train_loader, start=1): # ---- data prepare ---- images, gts = pack images = Variable(images).cuda() gts = Variable(gts).cuda() # ---- forward ---- lateral_map_4, lateral_map_3, lateral_map_2 = model(images) # ---- loss function ---- loss4 = structure_loss(lateral_map_4, gts) loss3 = structure_loss(lateral_map_3, gts) loss2 = structure_loss(lateral_map_2, gts) loss = 0.5 * loss2 + 0.3 * loss3 + 0.2 * loss4 # ---- backward ---- loss.backward() torch.nn.utils.clip_grad_norm_(model.parameters(), opt.grad_norm) optimizer.step() optimizer.zero_grad() # ---- recording loss ---- loss_record2.update(loss2.data, opt.batchsize) loss_record3.update(loss3.data, opt.batchsize) loss_record4.update(loss4.data, opt.batchsize) # ---- train visualization ---- if i % 20 == 0 or i == total_step: print('{} Epoch [{:03d}/{:03d}], Step [{:04d}/{:04d}], ' '[lateral-2: {:.4f}, lateral-3: {:0.4f}, lateral-4: {:0.4f}]'. format(datetime.now(), epoch, opt.epoch, i, total_step, loss_record2.show(), loss_record3.show(), loss_record4.show())) print('lr: ', optimizer.param_groups[0]['lr']) save_path = 'snapshots/{}/'.format(opt.train_save) os.makedirs(save_path, exist_ok=True) if (epoch+1) % 1 == 0: meanloss = test(model, opt.test_path) if meanloss < best_loss: print('new best loss: ', meanloss) best_loss = meanloss torch.save(model.state_dict(), save_path + 'TransFuse-%d.pth' % epoch) print('[Saving Snapshot:]', save_path + 'TransFuse-%d.pth'% epoch) return best_loss

这段代码是用于训练一个图像分割模型的主函数。首先，将模型设置为训练模式，然后对于每个批次的数据进行...在训练过程中，每个epoch结束后进行模型测试，并保存拥有最小测试损失的模型。函数的返回值是最小测试损失。

def structure_loss(pred, mask): weit = 1 + 5torch.abs(F.avg_pool2d(mask, kernel_size=31, stride=1, padding=15) - mask) wbce = F.binary_cross_entropy_with_logits(pred, mask, reduction='none') wbce = (weitwbce).sum(dim=(2, 3)) / weit.sum(dim=(2, 3)) pred = torch.sigmoid(pred) inter = ((pred * mask)weit).sum(dim=(2, 3)) union = ((pred + mask)weit).sum(dim=(2, 3)) wiou = 1 - (inter + 1)/(union - inter+1) return (wbce + wiou).mean() def train(train_loader, model, optimizer, epoch, best_loss): model.train() loss_record2, loss_record3, loss_record4 = AvgMeter(), AvgMeter(), AvgMeter() accum = 0 for i, pack in enumerate(train_loader, start=1): # ---- data prepare ---- images, gts = pack images = Variable(images).cuda() gts = Variable(gts).cuda() # ---- forward ---- lateral_map_4, lateral_map_3, lateral_map_2 = model(images) # ---- loss function ---- loss4 = structure_loss(lateral_map_4, gts) loss3 = structure_loss(lateral_map_3, gts) loss2 = structure_loss(lateral_map_2, gts) loss = 0.5 * loss2 + 0.3 * loss3 + 0.2 * loss4 # ---- backward ---- loss.backward() torch.nn.utils.clip_grad_norm_(model.parameters(), opt.grad_norm) optimizer.step() optimizer.zero_grad() # ---- recording loss ---- loss_record2.update(loss2.data, opt.batchsize) loss_record3.update(loss3.data, opt.batchsize) loss_record4.update(loss4.data, opt.batchsize) # ---- train visualization ---- if i % 20 == 0 or i == total_step: print('{} Epoch [{:03d}/{:03d}], Step [{:04d}/{:04d}], ' '[lateral-2: {:.4f}, lateral-3: {:0.4f}, lateral-4: {:0.4f}]'. format(datetime.now(), epoch, opt.epoch, i, total_step, loss_record2.show(), loss_record3.show(), loss_record4.show())) save_path = 'snapshots/{}/'.format(opt.train_save) os.makedirs(save_path, exist_ok=True) if (epoch+1) % 1 == 0: meanloss = test(model, opt.test_path) if meanloss < best_loss: print('new best loss: ', meanloss) best_loss = meanloss torch.save(model.state_dict(), save_path + 'TransFuse-%d.pth' % epoch) print('[Saving Snapshot:]', save_path + 'TransFuse-%d.pth'% epoch) return best_loss def test(model, path): model.eval() mean_loss = [] for s in ['val', 'test']: image_root = '{}/data_{}.npy'.format(path, s) gt_root = '{}/mask_{}.npy'.format(path, s) test_loader = test_dataset(image_root, gt_root) dice_bank = [] iou_bank = [] loss_bank = [] acc_bank = [] for i in range(test_loader.size): image, gt = test_loader.load_data() image = image.cuda() with torch.no_grad(): _, _, res = model(image) loss = structure_loss(res, torch.tensor(gt).unsqueeze(0).unsqueeze(0).cuda()) res = res.sigmoid().data.cpu().numpy().squeeze() gt = 1(gt>0.5) res = 1(res > 0.5) dice = mean_dice_np(gt, res) iou = mean_iou_np(gt, res) acc = np.sum(res == gt) / (res.shape[0]*res.shape[1]) loss_bank.append(loss.item()) dice_bank.append(dice) iou_bank.append(iou) acc_bank.append(acc) print('{} Loss: {:.4f}, Dice: {:.4f}, IoU: {:.4f}, Acc: {:.4f}'. format(s, np.mean(loss_bank), np.mean(dice_bank), np.mean(iou_bank), np.mean(acc_bank))) mean_loss.append(np.mean(loss_bank)) return mean_loss[0]

torch.save(model.state_dict(), save_path+'TransFuse-%d.pth'%epoch) 三、$\text{\textbf{test}}$ 函数评估指标 1. $\text{四元评估体系}$： | 指标 | 计算公式 | 意义 | |-----------|-----------------...

把这段代码修改成vivado2023，DSP48E1可用的版本“// ============================================================// File Name: cm_fir_top// VERSION : V1.0// DATA : 2023/3/4// Author : FPGA干货分享// ============================================================// 功能：fir滤波器代码// coef = // delay : 4+C_COEF_NUM*2// ============================================================ timescale 1ns/100psmodule cm_fir_top #( parameter C_DATA_WIDTH = 16 , parameter C_COEF_NUM = 11 , ///有效系数个数 parameter C_COEF_CUT_NUM = 12 ) ///四舍五入使用的0.5大小( input wire I_sys_clk , /// 工作时钟 input wire I_rst_in , /// 复位 input wire [C_DATA_WIDTH-1:0] I_data_in , /// 数据输入 output reg [C_DATA_WIDTH-1:0] O_data_out /// 数据输出); // ============================================================// 内部参数// ============================================================localparam C_COEF_05 = 2**C_COEF_CUT_NUM ; // ============================================================// 变量// ============================================================reg [C_DATA_WIDTH-1:0] S_data_in[C_COEF_NUM*2-1:0] ;wire [17:0] S_coef[C_COEF_NUM-1:0] ;wire [47:0] S_pcout[C_COEF_NUM-1:0] ;wire [47:0] S_dsp_out[C_COEF_NUM-1:0] ;”

嗯，用户的问题是关于如何将现有的Verilog FIR滤波器代码适配到Vivado 2023版本，并且确保兼容DSP48E1资源。首先，我需要理解用户的需求。他们可能已经有一个FIR滤波器的Verilog代码，但现在需要迁移到新版本的...

def item_step(): res.reset() with torch.cuda.stream(compute_stream), torch.autocast(device_type=self.device_type, dtype=torch.bfloat16): loss_cache, acc = segment_forward(self.cur_x, self.cur_y) loss_cache = loss_cache / config.grad_accum_steps acc = acc / config.grad_accum_steps scaler.scale(loss_cache).backward() with torch.cuda.stream(data_stream): x, y = self.data_loader.next_batch() x, y = x.to(self.device, non_blocking=True), y.to(self.device, non_blocking=True) if y is not None else None x = x.reshape(config.B, config.max_chunk, config.T).transpose(0, 1).contiguous() y = y.reshape(config.B, config.max_chunk, config.T).transpose(0, 1).contiguous() torch.cuda.current_stream().wait_stream(compute_stream) torch.cuda.current_stream().wait_stream(data_stream) self.cur_x, self.cur_y = x, y return loss_cache.detach(), acc这样有没有问题

for epoch in range(epochs): optimizer.zero_grad() for i, (inputs, labels) in enumerate(dataloader): # 异步数据流 with torch.cuda.stream(stream): inputs = inputs.to("cuda", non_blocking=True) ...

；JavaScriptThis time no story, no theory. The examples below show you how to write function accum: Examples: accum("abcd") -> "A-Bb-Ccc-Dddd" accum("RqaEzty") -> "R-Qq-Aaa-Eeee-Zzzzz-Tttttt-Yyyyyyy" accum("cwAt") -> "C-Ww-Aaa-Tttt"

console.log(accum("RqaEzty")); // Output: "R-Qq-Aaa-Eeee-Zzzzz-Tttttt-Yyyyyyy" console.log(accum("cwAt")); // Output: "C-Ww-Aaa-Tttt" The accum function takes a string as input and returns a ...

float Pure_PID(float P,float I,float D,float cur_err,float cur_proportion) { float proportion, e; e = cur_err; float ec = e - _pid.ek; _pid.accum += e; proportion = _pid.Kp * e + _pid.Ki * _pid.accum + _pid.Kd * (e - _pid.ek); _pid.ek = e; // printf("e =%f \r\n",e); return cur_proportion - proportion; } float Single_Neuron_Network(float P,float I,floatD,float cur_err,float cur_proportion) { err[ERR_LAST_LAST] = err[ERR_LAST]; err[ERR_LAST] = err[ERR_CURRENT]; err[ERR_CURRENT] = cur_err; _pid.Kp = _pid.Kp - lrPerr[ERR_CURRENT]cur_proportionn_input[NER_P]; _pid.Ki = _pid.Ki - lrIerr[ERR_CURRENT]cur_proportionn_input[NER_I]; _pid.Kd = _pid.Kd - lrDerr[ERR_CURRENT]cur_proportionn_input[NER_D]; float w_sum = fabs(_pid.Kp)+fabs(_pid.Ki)+fabs(_pid.Kd); float w[3] = {_pid.Kp/w_sum,_pid.Ki/w_sum,_pid.Kd/w_sum}; n_input[NER_P] = err[ERR_CURRENT]; n_input[NER_I] = err[ERR_CURRENT] - err[ERR_LAST]; n_input[NER_D] = err[ERR_CURRENT] - 2err[ERR_LAST] + err[ERR_LAST_LAST]; return cur_proportion + gain_factor(w[NER_P]n_input[NER_P]+w[NER_I]n_input[NER_I]+w[NER_D]*n_input[NER_D]); }

n_input[NER_D] = e(k)-2e(k-1)+e(k-2) // 微分项（二阶差分） // 输出合成 return cur_proportion + gain_factor*(w_p*x_p + w_i*x_i + w_d*x_d); 三、算法对比分析 | 特性 | Pure_PID | Single_Neuron_...

function ContinuousForceDirectedLayout() { go.ForceDirectedLayout.call(this); this._isObserving = false; } go.Diagram.inherit(ContinuousForceDirectedLayout, go.ForceDirectedLayout); ContinuousForceDirectedLayout.prototype.computeEffectiveForce = function (node, accum) { // 计算默认的作用力 go.ForceDirectedLayout.prototype.computeEffectiveForce.call(this, node, accum); // 添加水平方向的引力（向左或向右） var horzForce =200; // 引力大小 var targetX = node.location.x + node.desiredSize.width / 2; // 目标位置为节点中心 var deltaX = targetX - node.position.x; var horzDirection = deltaX > 0 ? 1 : -1; // 确定向左或向右的方向 accum.add(new go.Point(horzForce * horzDirection, 0)); console.log(this, node, accum) }; // 初始化gojs function initGraph() { var $ = go.GraphObject.make; myDiagram = $(go.Diagram, 'myDiagramDiv', { 'toolManager.mouseWheelBehavior': go.ToolManager.WheelZoom, initialContentAlignment: go.Spot.Center, hasHorizontalScrollbar: false, hasVerticalScrollbar: false, 'undoManager.isEnabled': true, 'draggingTool.isGridSnapEnabled': true, layout: $(ContinuousForceDirectedLayout, { arrangesToOrigin: true, defaultSpringLength: 30, defaultElectricalCharge: 100, defaultGravitationalMass: 2000, // 设置重力值 // gravity: 50 }), 为啥我的元素还是垂直排列的并没有水平排列

1 : -1; // 确定向上或向下的方向 accum.add(new go.Point(0, vertForce * vertDirection)); 这样，节点就会同时受到水平和竖直方向的引力，从而在平面上自由移动，而不是在竖直方向上排列。

#define HDR_NEWVIEW 0x0 #define HDR_PREPARE 0x1 #define HDR_PREPARE_LDR 0x2 #define HDR_PRECOMMIT 0x3 #define HDR_COMMIT 0x4 #define HDR_NEWVIEW_ACCUM 0x5 #define HDR_PREPARE_LDR_ACCUM 0x6 #define HDR_PREPARE_ACCUM 0x7 #define HDR_PRECOMMIT_ACCUM 0x8

- HDR_NEWVIEW_ACCUM 的值为 5，表示新视图累加消息头。 - HDR_PREPARE_LDR_ACCUM 的值为 6，表示准备领导者累加消息头。 - HDR_PREPARE_ACCUM 的值为 7，表示准备累加消息头。 - HDR_PRECOMMIT_ACCUM 的值...

2025-06-07 16:01:06.253750: W tensorflow/core/common_runtime/bfc_allocator.cc:433] *xx********************************* 2025-06-07 16:01:06.253792: W tensorflow/core/framework/op_kernel.cc:1628] OP_REQUIRES failed at constant_op.cc:76 : Resource exhausted: OOM when allocating tensor of shape [2,8192,64,32] and type float 2025-06-07 16:01:06.253838: E tensorflow/core/common_runtime/executor.cc:646] Executor failed to create kernel. Resource exhausted: OOM when allocating tensor of shape [2,8192,64,32] and type float

circular_buffer_size=-1 ) 执行后使用tensorboard --logdir logs/分析显存分配热点 --- ### 避免OOM的编码实践 1. **张量预分配检查**： python def safe_allocate(shape, dtype=tf.float32): ...

# -- coding: utf-8 -- import torch from transformers import ( AutoTokenizer, AutoModelForCausalLM, TrainingArguments, Trainer ) from peft import LoraConfig, get_peft_model from datasets import load_dataset # ----------------- 配置区（根据需求修改） ----------------- MODEL_PATH = r"G:\Qwen2.5-0.5B\Qwen2.5-0.5B-Instruct" # 模型存放路径 DATA_PATH = r"G:\Qwen2.5-0.5B\knowledge_dataset.json" # 训练数据路径 OUTPUT_DIR = r"G:\output" # 输出目录 # -------------------------------------------------------- # 加载分词器 tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH) tokenizer.pad_token = tokenizer.eos_token # 重要设置！ # 数据预处理函数 def process_fn(examples): texts = [ f"Instruction: {q}\nOutput: {a}<|endoftext|>" for q, a in zip(examples['instruction'], examples['output']) ] tokenized = tokenizer( texts, max_length=512, truncation=True, padding="max_length", return_tensors="pt" ) tokenized["labels"] = tokenized["input_ids"].clone() # 关键修改 return { "input_ids": tokenized["input_ids"].squeeze().tolist(), "attention_mask": tokenized["attention_mask"].squeeze().tolist(), "labels": tokenized["labels"].squeeze().tolist() } # 配置LoRA lora_config = LoraConfig( r=8, lora_alpha=32, target_modules=["q_proj", "k_proj", "v_proj"], # 固定不要改 lora_dropout=0.05, bias="none", task_type="CAUSAL_LM" ) # 加载基础模型 model = AutoModelForCausalLM.from_pretrained(MODEL_PATH) model = get_peft_model(model, lora_config) model.print_trainable_parameters() # 显示可训练参数量 # 训练参数配置 training_args = TrainingArguments( dataloader_num_workers=0, # 关闭多进程加载 output_dir=OUTPUT_DIR, per_device_train_batch_size=32, # 16G内存建议设为1 gradient_accumulation_steps=2, # 有效batch_size=1*8=8 num_train_epochs=100, # 训练次数 learning_rate=12e-5, logging_steps=10, save_strategy="steps", save_steps=500, report_to="none", remove_unused_columns=False, # 必须设置！ disable_tqdm=False # 显示进度条 ) # 创建训练器 # 加载数据集 from datasets import load_dataset # 假设您的数据集是JSON格式，包含'instruction'和'output'字段 dataset = load_dataset('json', data_files=DATA_PATH) # 对数据集进行预处理 tokenized_dataset = dataset.map( process_fn, batched=True, remove_columns=dataset['train'].column_names # 移除原始列 ) # 创建训练器时使用预处理后的数据集 trainer = Trainer( model=model, args=training_args, train_dataset=tokenized_dataset['train'], # 使用预处理后的训练集 data_collator=lambda data: { 'input_ids': torch.tensor([d['input_ids'] for d in data], dtype=torch.long), 'attention_mask': torch.tensor([d['attention_mask'] for d in data], dtype=torch.long), 'labels': torch.tensor([d['labels'] for d in data], dtype=torch.long) } ) # 开始训练 print("===== 开始训练 =====") trainer.train() # 保存最终模型 model.save_pretrained(r"G:\Qwen2.5-0.5B\finetuned_model") from transformers import AutoConfig base_model_config = AutoConfig.from_pretrained(MODEL_PATH) base_model_config.save_pretrained(r"G:\Qwen2.5-0.5B\finetuned_model") tokenizer.save_pretrained(r"G:\Qwen2.5-0.5B\finetuned_model") print("训练完成！模型已保存到 G:\Qwen2.5-0.5B\finetuned_model 目录")优化一下模型训练代码

labels[-1] = -100 # 假设序列长度为512，则最后一个位置的标签被忽略但用户当前的做法是直接复制input_ids作为labels，这会导致模型学习预测当前的token，而不是下一个token，这是错误的。因此，这是需要修复的一...

//---------------------------------------// // flag_in is data_in is valid // flag_out is data_out is valid // data_out = data_in x MULTED(constant) //---------------------------------------// module mul384 #(parameter MULTED = 384'h1234) ( input clk , input rst_n , input flag_in , input [383:0] data_in , output flag_out , output [383:0] data_out ); wire [383:0] data_mul384; reg [383:0] data_in_r; reg flag_2r, flag_r; reg [383:0] data_out_r; //---------------------------// always@(posedge clk or negedge rst_n) begin if(!rst_n) data_in_r <= 'h0; else if(flag_in) data_in_r <= data_in; end //---------------------------// mul_384 u_384(data_in_r, MULTED, data_mul384); //---------------------------// always@(posedge clk or negedge rst_n) begin if(!rst_n) data_out_r <= 'h0; else data_out_r <= data_mul384; end always@(posedge clk or negedge rst_n) begin if(!rst_n) {flag_2r, flag_r} <= 'h0;

首先，引用[1]提到了使用查找表或泰勒级数展开来实现log函数，但用户的问题是关于乘法器的，所以可能不太直接相关。不过引用[4]提到了用移位操作实现乘法，这可能与乘法器的设计有关。引用[3]中有一个testbench，...

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