一、混合精度训练(AMP)深度优化
1.1 AMP配置全景图
# amp_configuration.py import mindspore as ms import mindspore.nn as nn import mindspore.ops as ops from mindspore.amp import DynamicLossScaler, StaticLossScaler, all_finite import numpy as np from enum import Enum class AMPLevel(Enum): """AMP级别定义""" O0 = "O0" # 全精度训练 O1 = "O1" # 自动混合精度（白名单算子FP16） O2 = "O2" # 大部分FP16，部分保持FP32 O3 = "O3" # 全FP16（实验性） class AscendAMPConfig: """昇腾专用AMP配置优化""" def __init__(self, amp_level: str = "O2", loss_scale: float = 1024.0, dynamic_loss_scale: bool = True, dynamic_loss_scale_window: int = 2000, initial_scale: float = 2**16, enable_fp16_compute_type: bool = True): self.amp_level = AMPLevel(amp_level) self.dynamic_loss_scale = dynamic_loss_scale # 昇腾特定优化配置 self.ascend_config = { "precision_mode": self._get_precision_mode(), "jit_level": "O1", # 与AMP协同优化 "graph_kernel_flags": "--enable_cluster_ops=MatMul,Conv2D,BatchMatMul", "enable_auto_mixed_precision": True, "keep_fp32_ops": self._get_fp32_ops_list(), "cast_before_mixed_precision_ops": True } # 损失缩放器配置 if dynamic_loss_scale: self.loss_scaler = DynamicLossScaler( scale_value=initial_scale, scale_factor=2, scale_window=dynamic_loss_scale_window ) else: self.loss_scaler = StaticLossScaler(loss_scale) # 白名单和黑名单配置 self.white_list = self._get_white_list() self.black_list = self._get_black_list() # 性能监控 self.fp16_ratio_history = [] self.gradient_overflow_count = 0 def _get_precision_mode(self) -> str: """获取昇腾精度模式""" mapping = { AMPLevel.O0: "allow_fp32", AMPLevel.O1: "allow_mix_precision", AMPLevel.O2: "force_fp16", AMPLevel.O3: "must_keep_origin_dtype" } return mapping.get(self.amp_level, "allow_mix_precision") def _get_white_list(self) -> list: """FP16白名单 - 这些算子使用FP16""" white_list = [ nn.Conv2d, nn.Dense, nn.Conv1d, nn.Conv3d, nn.Conv2dTranspose, ops.MatMul, ops.BatchMatMul, nn.ReLU, nn.GELU, nn.Sigmoid, nn.Tanh, nn.MaxPool2d, nn.AvgPool2d, nn.AdaptiveAvgPool2d, nn.LayerNorm, # 昇腾上LayerNorm FP16表现良好 nn.BatchNorm2d, ] # 昇腾特定白名单 ascend_white_list = [ "FusedBatchNorm", "FusedGeLU", "FusedAddRelu", "DepthwiseConv2dNative", "FusedMatMul" ] return white_list + ascend_white_list def _get_black_list(self) -> list: """FP32黑名单 - 这些算子保持FP32""" black_list = [ nn.Softmax, # 需要高数值稳定性 nn.LogSoftmax, # 需要高数值稳定性 nn.CrossEntropyLoss, nn.Embedding, # 索引操作不需要FP16 nn.Dropout, # 随机操作 nn.PReLU, # 参数化ReLU ] # 昇腾建议保持FP32的算子 ascend_black_list = [ "CumSum", # 累积求和易溢出 "CumProd", # 累积乘积易溢出 "Exp", # 指数运算易溢出 "Log", # 对数运算 "Pow", # 幂运算 "Reciprocal", # 倒数 ] return black_list + ascend_black_list def _get_fp32_ops_list(self) -> list: """昇腾特定需要保持FP32的算子列表""" return [ "Softmax", "LayerNorm", "Attention", # 注意力机制需要高精度 "LSTM", # RNN类网络 "GRU", "Adam", # 优化器 "Momentum", "SGD" ] def configure_context(self): """配置MindSpore上下文""" ms.set_context( device_target="Ascend", mode=ms.GRAPH_MODE, # AMP在图模式下效果最好 ascend_config=self.ascend_config ) # 设置自动混合精度 ms.amp.auto_mixed_precision( level=self.amp_level.value, white_list=self.white_list, black_list=self.black_list, dtype=ms.float16 ) print(f"AMP配置完成: level={self.amp_level.value}") print(f"昇腾精度模式: {self.ascend_config['precision_mode']}") def create_amp_network(self, network, optimizer, loss_fn): """创建AMP优化网络""" # 使用build_train_network包装 net_with_amp = ms.amp.build_train_network( network, optimizer, loss_fn, level=self.amp_level.value, loss_scale_manager=self.loss_scaler, cast_model_type=ms.float16 if self.amp_level != AMPLevel.O0 else ms.float32 ) return net_with_amp class DynamicPrecisionScheduler: """动态精度调度器 - 训练过程中调整精度""" def __init__(self, initial_level="O0", warmup_steps=1000, schedule_type="cosine"): self.current_level = AMPLevel(initial_level) self.warmup_steps = warmup_steps self.schedule_type = schedule_type self.step_count = 0 # 精度级别映射 self.level_mapping = { 0: AMPLevel.O0, 1: AMPLevel.O1, 2: AMPLevel.O2, 3: AMPLevel.O3 } def step(self): """每一步更新""" self.step_count += 1 # 动态调整策略 if self.schedule_type == "cosine": level = self._cosine_schedule() elif self.schedule_type == "step": level = self._step_schedule() elif self.schedule_type == "adaptive": level = self._adaptive_schedule() else: level = self.current_level if level != self.current_level: print(f"AMP级别变化: {self.current_level.value} -> {level.value}") self.current_level = level return level def _cosine_schedule(self): """余弦退火精度调度""" if self.step_count < self.warmup_steps: # 热身阶段逐渐增加精度 progress = self.step_count / self.warmup_steps target_level = min(3, int(progress * 4)) return self.level_mapping.get(target_level, AMPLevel.O1) else: # 训练稳定后保持O2 return AMPLevel.O2 def _adaptive_schedule(self, gradient_stats=None): """自适应精度调度""" if gradient_stats is None: return self.current_level # 基于梯度统计调整精度 overflow_ratio = gradient_stats.get("overflow_ratio", 0) gradient_norm = gradient_stats.get("gradient_norm", 0) if overflow_ratio > 0.1: # 梯度溢出超过10% # 降低精度以提高稳定性 current_idx = list(self.level_mapping.values()).index(self.current_level) new_idx = max(0, current_idx - 1) return self.level_mapping[new_idx] elif overflow_ratio < 0.01 and gradient_norm > 1.0: # 梯度稳定，可提高精度 current_idx = list(self.level_mapping.values()).index(self.current_level) new_idx = min(3, current_idx + 1) return self.level_mapping[new_idx] return self.current_level class GradientStatisticsMonitor: """梯度统计监控器""" def __init__(self, window_size=100): self.window_size = window_size self.gradient_history = [] self.overflow_history = [] def record(self, gradients, overflow_status): """记录梯度统计""" # 计算梯度范数 total_norm = 0.0 for grad in gradients: if grad is not None: norm = ops.norm(grad) total_norm += norm.asnumpy() ** 2 gradient_norm = np.sqrt(total_norm) # 记录历史 self.gradient_history.append(gradient_norm) self.overflow_history.append(overflow_status) # 保持窗口大小 if len(self.gradient_history) > self.window_size: self.gradient_history.pop(0) self.overflow_history.pop(0) def get_statistics(self): """获取统计信息""" if not self.gradient_history: return {} return { "avg_gradient_norm": np.mean(self.gradient_history), "std_gradient_norm": np.std(self.gradient_history), "overflow_ratio": np.mean(self.overflow_history), "max_gradient": np.max(self.gradient_history), "min_gradient": np.min(self.gradient_history) }
1.2 AMP实战训练循环
# amp_training_loop.py class AMPTrainingLoop: """AMP优化训练循环""" def __init__(self, model, optimizer, loss_fn, amp_config: AscendAMPConfig): self.model = model self.optimizer = optimizer self.loss_fn = loss_fn self.amp_config = amp_config # 创建AMP网络 self.net_with_amp = amp_config.create_amp_network( model, optimizer, loss_fn ) # 梯度统计 self.gradient_monitor = GradientStatisticsMonitor() # 动态精度调度 self.precision_scheduler = DynamicPrecisionScheduler( initial_level="O1", warmup_steps=500 ) # 性能计数器 self.fp16_time = 0 self.fp32_time = 0 self.total_steps = 0 def train_step(self, data, label): """单个训练步骤""" start_time = time.time() # 前向传播（自动混合精度） loss = self.net_with_amp(data, label) # 获取梯度溢出状态 overflow_status = not all_finite(loss) if overflow_status: self.amp_config.gradient_overflow_count += 1 # 梯度溢出处理 if self.amp_config.dynamic_loss_scale: # 动态调整损失缩放 scale = self.amp_config.loss_scaler.get_loss_scale() new_scale = scale / 2.0 self.amp_config.loss_scaler.adjust(new_scale) print(f"梯度溢出！调整损失缩放: {scale:.2e} -> {new_scale:.2e}") # 获取梯度进行统计 gradients = self.optimizer.parameters self.gradient_monitor.record(gradients, overflow_status) # 动态调整精度 if self.total_steps % 100 == 0: stats = self.gradient_monitor.get_statistics() new_level = self.precision_scheduler._adaptive_schedule(stats) if new_level != self.amp_config.amp_level: # 重新配置AMP self.amp_config.amp_level = new_level self.amp_config.configure_context() self.net_with_amp = self.amp_config.create_amp_network( self.model, self.optimizer, self.loss_fn ) # 记录时间 step_time = time.time() - start_time # 统计FP16/FP32比例 if self.total_steps % 50 == 0: self._log_precision_ratio() self.total_steps += 1 return loss def _log_precision_ratio(self): """记录精度使用比例""" # 获取模型中各张量的精度 fp16_count = 0 fp32_count = 0 for param in self.model.get_parameters(): if param.dtype == ms.float16: fp16_count += 1 elif param.dtype == ms.float32: fp32_count += 1 total = fp16_count + fp32_count if total > 0: fp16_ratio = fp16_count / total self.amp_config.fp16_ratio_history.append(fp16_ratio) print(f"FP16参数比例: {fp16_ratio:.2%} " f"({fp16_count}/{total} tensors)") def train_epoch(self, dataloader, epoch): """训练一个epoch""" total_loss = 0 batch_count = 0 print(f"\nEpoch {epoch}: AMP训练开始") print(f"当前AMP级别: {self.amp_config.amp_level.value}") print(f"损失缩放: {self.amp_config.loss_scaler.get_loss_scale():.2e}") for batch_idx, (data, label) in enumerate(dataloader): # 训练步骤 loss = self.train_step(data, label) # 统计 total_loss += loss.asnumpy() batch_count += 1 # 每50步输出进度 if batch_idx % 50 == 0: avg_loss = total_loss / batch_count overflow_count = self.amp_config.gradient_overflow_count print(f" Batch {batch_idx}: loss={avg_loss:.4f}, " f"overflow={overflow_count}") # 输出梯度统计 stats = self.gradient_monitor.get_statistics() if stats: print(f" 梯度范数: {stats['avg_gradient_norm']:.4f}±{stats['std_gradient_norm']:.4f}") avg_loss = total_loss / batch_count if batch_count > 0 else 0 # epoch结束报告 self._print_epoch_summary(epoch, avg_loss) return avg_loss def _print_epoch_summary(self, epoch, avg_loss): """输出epoch总结""" print(f"\nEpoch {epoch} 完成:") print(f" 平均损失: {avg_loss:.4f}") print(f" 总步数: {self.total_steps}") print(f" 梯度溢出次数: {self.amp_config.gradient_overflow_count}") # 精度统计 if self.amp_config.fp16_ratio_history: avg_fp16_ratio = np.mean(self.amp_config.fp16_ratio_history[-100:]) print(f" FP16参数平均比例: {avg_fp16_ratio:.2%}") # 梯度统计 stats = self.gradient_monitor.get_statistics() if stats: print(f" 平均梯度范数: {stats['avg_gradient_norm']:.4f}") print(f" 梯度溢出比例: {stats['overflow_ratio']:.2%}") # 损失缩放信息 if self.amp_config.dynamic_loss_scale: scale = self.amp_config.loss_scaler.get_loss_scale() print(f" 当前损失缩放: {scale:.2e}")
1.3 自定义AMP包装器
# custom_amp_wrapper.py class CustomAMPWrapper: """自定义AMP包装器 - 更细粒度的控制""" def __init__(self, model, optimizer, loss_fn): self.model = model self.optimizer = optimizer self.loss_fn = loss_fn # 分离的参数组 self.fp16_params = [] self.fp32_params = [] self.fp32_master_params = {} # 用于权重更新的FP32副本 # 初始化参数分组 self._setup_parameter_groups() # 梯度缩放器 self.loss_scaler = DynamicLossScaler(2**16) # 创建梯度函数 self.grad_fn = ms.value_and_grad( self._forward_function, None, optimizer.parameters, has_aux=True ) def _setup_parameter_groups(self): """设置参数组 - 智能分组""" for name, param in self.model.parameters_and_names(): # 根据参数类型和层决定精度 should_be_fp16 = self._should_be_fp16(name, param) if should_be_fp16: # 创建FP32主副本用于优化器 fp32_master = ms.Parameter( param.data.clone().astype(ms.float32), name=f"{name}_fp32_master", requires_grad=True ) self.fp16_params.append(param) self.fp32_master_params[name] = fp32_master self.fp32_params.append(fp32_master) # 将参数替换为FP16版本 param.set_data(param.data.astype(ms.float16)) else: self.fp32_params.append(param) print(f"参数分组: FP16={len(self.fp16_params)}, FP32={len(self.fp32_params)}") def _should_be_fp16(self, name, param): """判断参数是否应使用FP16""" # 规则1: 嵌入层保持FP32 if "embedding" in name.lower(): return False # 规则2: 偏置参数保持FP32 if "bias" in name.lower(): return False # 规则3: 小参数保持FP32（避免下溢） if param.numel() < 100: return False # 规则4: 特定层保持FP32 sensitive_layers = ["norm", "ln", "bn", "ln_final"] for layer in sensitive_layers: if layer in name.lower(): return False return True def _forward_function(self, data, label): """前向函数 - 处理精度转换""" # 将FP32主参数复制到FP16参数 for name, fp32_param in self.fp32_master_params.items(): # 找到对应的FP16参数 for fp16_param in self.fp16_params: if name.replace("_fp32_master", "") in fp16_param.name: fp16_param.set_data(fp32_param.data.astype(ms.float16)) break # 前向传播 output = self.model(data) loss = self.loss_fn(output, label) return loss, output def _backward_and_update(self, loss): """反向传播和更新 - 自定义实现""" # 计算梯度 (loss_value, _), gradients = self.grad_fn(loss) # 检查梯度溢出 overflow = not all_finite(gradients) if overflow: # 调整损失缩放 self.loss_scaler.adjust(self.loss_scaler.get_loss_scale() / 2) print("梯度溢出，跳过更新") return loss_value, True # 梯度缩放 scaled_gradients = [] for grad in gradients: if grad is not None: scaled_grad = grad * self.loss_scaler.get_loss_scale() scaled_gradients.append(scaled_grad) else: scaled_gradients.append(None) # 更新FP32主参数 for i, (fp32_param, grad) in enumerate(zip(self.fp32_params, scaled_gradients)): if grad is not None: # 梯度更新（在FP32上） self.optimizer([fp32_param], [grad]) # 更新损失缩放器 self.loss_scaler.update() return loss_value / self.loss_scaler.get_loss_scale(), False def train_step(self, data, label): """训练步骤""" loss_value, overflow = self._backward_and_update(data, label) return loss_value # 使用示例 def setup_custom_amp(): # 模型定义 model = YourModel() # 优化器 - 注意要使用FP32主参数 optimizer = nn.Adam(model.trainable_params(), learning_rate=1e-3) # 损失函数 loss_fn = nn.CrossEntropyLoss() # 自定义AMP包装器 amp_wrapper = CustomAMPWrapper(model, optimizer, loss_fn) # 配置上下文 ms.set_context( device_target="Ascend", ascend_config={ "precision_mode": "allow_mix_precision", "keep_fp32_ops": ["Softmax", "LayerNorm"] } ) return amp_wrapper
二、图编译优化(JIT)深度实战
2.1 JIT配置全景图
# jit_optimization.py class JITOptimizationConfig: """图编译优化配置""" def __init__(self, jit_level: str = "O1", enable_graph_kernel: bool = True, enable_auto_mixed_precision: bool = True, enable_profiling: bool = False): self.jit_level = jit_level self.enable_graph_kernel = enable_graph_kernel # JIT级别详细配置 self.jit_configs = { "O0": { # 调试模式 "optimization_level": 0, "enable_inline": False, "enable_common_subexpr_elimination": False, "enable_constant_folding": False, "enable_dead_code_elimination": False, "compile_cache_capacity": 0, }, "O1": { # 推荐级别 "optimization_level": 1, "enable_inline": True, "enable_common_subexpr_elimination": True, "enable_constant_folding": True, "enable_dead_code_elimination": True, "enable_operator_fusion": True, "compile_cache_capacity": 10, "jit_syntax_level": "LAX", }, "O2": { # 激进优化 "optimization_level": 2, "enable_inline": True, "enable_common_subexpr_elimination": True, "enable_constant_folding": True, "enable_dead_code_elimination": True, "enable_operator_fusion": True, "enable_auto_parallel": True, "enable_memory_optimization": True, "compile_cache_capacity": 50, "jit_syntax_level": "STRICT", } } # 图算融合配置 self.graph_kernel_flags = self._get_graph_kernel_flags() # 昇腾特定配置 self.ascend_config = self._get_ascend_config() # 编译缓存目录 self.compile_cache_dir = "./jit_compile_cache" # 性能分析器 self.profiler = None if enable_profiling: self.profiler = JITProfiler() def _get_graph_kernel_flags(self) -> str: """获取图算融合标志""" flags = [] if self.enable_graph_kernel: flags.extend([ "--enable_cluster_ops=MatMul,Conv2D,BatchMatMul,LayerNorm", "--enable_fusion_ops=True", "--enable_parallel_fusion=True", "--enable_stitch_fusion=True", # 拼接融合 "--enable_recompute_fusion=True", # 重计算融合 f"--opt_level={self.jit_level}", "--dump_as_text=False", "--enable_auto_tiling=True", # 自动分块 ]) # 昇腾特定优化 flags.extend([ "--enable_cube_conv=True", # 使用Cube单元优化卷积 "--enable_cube_matmul=True", # 使用Cube单元优化矩阵乘法 "--enable_vector_core=True", # 使用向量单元 ]) return ",".join(flags) def _get_ascend_config(self) -> dict: """获取昇腾特定配置""" return { "jit_level": self.jit_level, "graph_kernel_flags": self.graph_kernel_flags, "precision_mode": "allow_mix_precision", "enable_small_channel": True, # 小通道优化 "fusion_switch_file": "./fusion_switch.cfg", # 融合开关配置 "op_select_implmode": "high_performance", # 高性能算子实现 "op_precision_mode": "op_precision.ini", # 算子精度配置 "optypelist_for_implmode": "./optype_list.ini", # 算子类型列表 "buffer_optimize": "l2_optimize", # 缓冲区优化 "enable_exception_dump": False, } def configure_context(self): """配置MindSpore上下文""" jit_config = self.jit_configs.get(self.jit_level, self.jit_configs["O1"]) # 设置上下文 ms.set_context( mode=ms.GRAPH_MODE, # JIT需要图模式 device_target="Ascend", jit_level=jit_config["jit_syntax_level"], compile_cache_dir=self.compile_cache_dir, compile_cache_capacity=jit_config["compile_cache_capacity"], enable_graph_kernel=self.enable_graph_kernel, graph_kernel_flags=self.graph_kernel_flags, ascend_config=self.ascend_config ) # 设置优化选项 ms.set_context( enable_sparse=self.jit_level == "O2", enable_reduce_precision=self.jit_level == "O2", enable_debugger=False, save_graphs=self.jit_level == "O0", # 调试时保存计算图 save_graphs_path="./graph_dump" if self.jit_level == "O0" else "" ) print(f"JIT配置完成: level={self.jit_level}") print(f"图算融合: {self.enable_graph_kernel}") if self.enable_graph_kernel: print(f"融合标志: {self.graph_kernel_flags[:100]}...") def optimize_model(self, model, sample_input): """编译优化模型""" print("开始模型编译优化...") start_time = time.time() # 编译模型 compiled_model = ms.jit(model) # 首次编译（可能较慢） print("首次编译...") warmup_output = compiled_model(*sample_input) # 性能分析 if self.profiler: self.profiler.profile_compilation(model, sample_input) compile_time = time.time() - start_time print(f"编译完成，耗时: {compile_time:.2f}s") return compiled_model class JITProfiler: """JIT编译性能分析器""" def __init__(self, output_dir="./jit_profiling"): self.output_dir = output_dir os.makedirs(output_dir, exist_ok=True) def profile_compilation(self, model, sample_input): """分析编译性能""" print("\nJIT编译性能分析:") print("-" * 40) # 获取计算图信息 try: # 编译并获取图信息 origin_graph = model.__mindspore_func__.graph # 分析算子数量 total_ops = len(origin_graph.nodes()) fused_ops = len([n for n in origin_graph.nodes() if hasattr(n, 'fusion_type')]) print(f"原始算子数量: {total_ops}") print(f"融合后算子数量: {total_ops - fused_ops}") print(f"融合算子数量: {fused_ops}") print(f"融合比例: {fused_ops/total_ops:.1%}") # 保存计算图 self._save_computation_graph(origin_graph, "original") except Exception as e: print(f"获取图信息失败: {e}") def _save_computation_graph(self, graph, name): """保存计算图""" try: import json # 提取图信息 graph_info = { "nodes": [], "edges": [], "metadata": { "total_nodes": len(graph.nodes()), "fusion_count": 0 } } for node in graph.nodes(): node_info = { "name": str(node), "type": str(node.type), "shape": str(node.shape) if hasattr(node, 'shape') else "", "dtype": str(node.dtype) if hasattr(node, 'dtype') else "", } if hasattr(node, 'fusion_type'): node_info["fusion_type"] = str(node.fusion_type) graph_info["metadata"]["fusion_count"] += 1 graph_info["nodes"].append(node_info) # 保存为JSON output_file = f"{self.output_dir}/{name}_graph.json" with open(output_file, "w") as f: json.dump(graph_info, f, indent=2) print(f"计算图已保存: {output_file}") except Exception as e: print(f"保存计算图失败: {e}") class DynamicJITManager: """动态JIT管理器 - 运行时调整优化级别""" def __init__(self, initial_level="O1", adaptation_window=100, enable_adaptive=True): self.current_level = initial_level self.adaptation_window = adaptation_window self.enable_adaptive = enable_adaptive # 性能历史 self.iteration_times = [] self.memory_usage = [] self.accuracy_history = [] # 配置管理器 self.config_manager = JITOptimizationConfig(initial_level) def monitor_performance(self, iteration_time, memory_used, accuracy=None): """监控性能""" self.iteration_times.append(iteration_time) self.memory_usage.append(memory_used) if accuracy is not None: self.accuracy_history.append(accuracy) # 保持窗口大小 if len(self.iteration_times) > self.adaptation_window: self.iteration_times.pop(0) self.memory_usage.pop(0) if self.accuracy_history: self.accuracy_history.pop(0) # 自适应调整 if self.enable_adaptive and len(self.iteration_times) >= 50: self._adaptive_adjust() def _adaptive_adjust(self): """自适应调整JIT级别""" avg_time = np.mean(self.iteration_times[-50:]) avg_memory = np.mean(self.memory_usage[-50:]) # 检查是否应该调整级别 should_upgrade = False should_downgrade = False # 规则1: 如果迭代时间稳定且内存充足，可升级优化 if (avg_time > 0.5 and # 迭代时间较长 avg_memory < 0.7 and # 内存使用率小于70% self.current_level in ["O0", "O1"]): should_upgrade = True # 规则2: 如果内存使用过高，降低优化级别 elif (avg_memory > 0.9 and # 内存使用率大于90% self.current_level in ["O2"]): should_downgrade = True # 规则3: 如果迭代时间波动大，降低优化级别 time_std = np.std(self.iteration_times[-50:]) if (time_std / avg_time > 0.3 and # 波动超过30% self.current_level in ["O2"]): should_downgrade = True # 执行调整 if should_upgrade: new_level = self._upgrade_level() if new_level != self.current_level: print(f"JIT级别升级: {self.current_level} -> {new_level}") self.current_level = new_level self._reconfigure() elif should_downgrade: new_level = self._downgrade_level() if new_level != self.current_level: print(f"JIT级别降级: {self.current_level} -> {new_level}") self.current_level = new_level self._reconfigure() def _upgrade_level(self): """升级JIT级别""" levels = ["O0", "O1", "O2"] current_idx = levels.index(self.current_level) if current_idx < len(levels) - 1: return levels[current_idx + 1] return self.current_level def _downgrade_level(self): """降级JIT级别""" levels = ["O0", "O1", "O2"] current_idx = levels.index(self.current_level) if current_idx > 0: return levels[current_idx - 1] return self.current_level def _reconfigure(self): """重新配置JIT""" self.config_manager = JITOptimizationConfig(self.current_level) self.config_manager.configure_context()
2.2 图算融合实战示例
# graph_kernel_fusion_examples.py class GraphKernelOptimizedModel(nn.Cell): """图算融合优化模型示例""" def __init__(self, in_channels=3, hidden_dims=[64, 128, 256, 512], num_classes=1000): super().__init__() # 卷积块 - 使用图算融合优化 self.conv_layers = nn.SequentialCell([ self._create_fused_conv_block(in_channels, hidden_dims[0]), self._create_fused_conv_block(hidden_dims[0], hidden_dims[1]), self._create_fused_conv_block(hidden_dims[1], hidden_dims[2]), self._create_fused_conv_block(hidden_dims[2], hidden_dims[3]), ]) # 自适应池化 self.adaptive_pool = nn.AdaptiveAvgPool2d((1, 1)) # 分类头 - 使用融合全连接层 self.classifier = self._create_fused_classifier( hidden_dims[-1], num_classes ) # 启用图算融合标记 self._enable_graph_kernel_fusion() def _create_fused_conv_block(self, in_ch, out_ch): """创建融合卷积块""" return nn.SequentialCell([ # 卷积+BN+ReLU融合 nn.Conv2d(in_ch, out_ch, kernel_size=3, stride=1, padding=1, has_bias=False, pad_mode='pad', weight_init='HeUniform'), nn.BatchNorm2d(out_ch), nn.ReLU(), nn.MaxPool2d(kernel
与图编译优化(JIT)昇腾实战指南)
)
