[Fea] Support nvtx profiling

docs/zh/user_guide.md

@@ -795,3 +795,49 @@ solver = ppsci.solver.Solver(
     !!! info "影响说明"
 
         个别多任务学习方法（如weight based method）可能会改变**训练过程**中损失函数的计算方式，但仅限于影响训练过程，模型**评估过程**的损失计算方式保持不变。
+
+## 3. 使用 Nsight 进行性能分析
+
+Nsight是NVIDIA面相开发者提供的开发工具套件，能提供深入的跟踪、调试、评测和分析，以优化跨 NVIDIA GPU和CPU的复杂计算应用程序。详细文档可参考：[Nsight Systems Document](https://docs.nvidia.com/nsight-systems/index.html)
+
+PaddleScience 初步支持使用 Nsight 进行性能分析，以 linux 开发环境 + laplace2d 案例为例，按照如下步骤使用 nsight 工具生成性能分析报告并查看分析结果。
+
+1. 安装 nsight-system
+
+    开发机上下载 linux nsight-system 软件：nsight-systems/2023.4.1，并将 nsight 添加到环境变量 `PATH` 中：
+
+    执行：`PATH=/path/to/nsight-systems/2023.4.1/bin:$PATH`，同时在 windows 机器上安装**相同版本**的 nsight-system 软件。
+
+2. 用 nsys 命令运行程序，生成性能分析文件
+
+    ``` sh
+    {==NVTX=1 nsys profile -t cuda,nvtx --stats=true -o==} {++laplace2d++} python laplace2d.py
+    ```
+
+3. 查看分析结果
+
+    程序结束后，在终端内会打印出性能分析数据（如下所示），同时在上述 `-o` 参数指定的相对文件路径生成 `{++laplace2d++}.nsys-rep` 和 `{++laplace2d++}.sqlite` 两个文件。
+
+    在 windows 上使用 NVIDIA Nsight Systems 软件打开 `laplace2d.nsys-rep`，即可在图形化的界面上查看性能分析数据。
+
+    ``` log
+    ...
+    ...
+    Only run 25 steps when 'NVTX' is set in environment for nsight analysis. Exit now ......
+
+    Generating '/tmp/nsys-report-18e4.qdstrm'
+    [1/7] [========================100%] laplace2d.nsys-rep
+    [2/7] [========================100%] laplace2d.sqlite
+    [3/7] Executing 'nvtx_sum' stats report
+
+    Time (%)  Total Time (ns)  Instances    Avg (ns)       Med (ns)      Min (ns)     Max (ns)     StdDev (ns)    Style                  Range
+    --------  ---------------  ---------  -------------  -------------  -----------  -----------  -------------  -------  ------------------------------------
+        15.1      794,212,341         25   31,768,493.6    5,446,410.0    5,328,471  661,841,104  131,265,333.9  PushPop  Loss computation
+        14.5      766,452,142         25   30,658,085.7    4,369,873.0    4,281,927  659,795,434  131,070,475.4  PushPop  Constraint EQ
+        13.0      687,324,359      1,300      528,711.0       32,567.5       21,218  641,625,892   17,794,532.4  PushPop  matmul dygraph
+        12.9      678,475,194          1  678,475,194.0  678,475,194.0  678,475,194  678,475,194            0.0  PushPop  Training iteration 1
+        12.8      673,614,062      1,300      518,164.7       19,802.5       14,499  641,525,121   17,792,027.2  PushPop  matmul compute
+        3.9      203,945,648         25    8,157,825.9    8,029,819.0    7,797,185    9,119,496      359,173.3  PushPop  Loss backward
+        ...
+        ...
+    ```

ppsci/solver/printer.py

@@ -30,59 +30,59 @@
 
 
 def update_train_loss(
-    trainer: "solver.Solver", loss_dict: Dict[str, float], batch_size: int
+    solver: "solver.Solver", loss_dict: Dict[str, float], batch_size: int
 ):
     for key in loss_dict:
-        if key not in trainer.train_output_info:
-            trainer.train_output_info[key] = misc.AverageMeter(key, "7.5f")
-        trainer.train_output_info[key].update(float(loss_dict[key]), batch_size)
-        if key not in trainer.train_loss_info:
-            trainer.train_loss_info[key] = misc.AverageMeter(key, ".5f")
-        trainer.train_loss_info[key].update(float(loss_dict[key]))
+        if key not in solver.train_output_info:
+            solver.train_output_info[key] = misc.AverageMeter(key, "7.5f")
+        solver.train_output_info[key].update(float(loss_dict[key]), batch_size)
+        if key not in solver.train_loss_info:
+            solver.train_loss_info[key] = misc.AverageMeter(key, ".5f")
+        solver.train_loss_info[key].update(float(loss_dict[key]))
 
 
 def update_eval_loss(
-    trainer: "solver.Solver", loss_dict: Dict[str, float], batch_size: int
+    solver: "solver.Solver", loss_dict: Dict[str, float], batch_size: int
 ):
     for key in loss_dict:
-        if key not in trainer.eval_output_info:
-            trainer.eval_output_info[key] = misc.AverageMeter(key, "7.5f")
-        trainer.eval_output_info[key].update(float(loss_dict[key]), batch_size)
+        if key not in solver.eval_output_info:
+            solver.eval_output_info[key] = misc.AverageMeter(key, "7.5f")
+        solver.eval_output_info[key].update(float(loss_dict[key]), batch_size)
 
 
 def log_train_info(
-    trainer: "solver.Solver", batch_size: int, epoch_id: int, iter_id: int
+    solver: "solver.Solver", batch_size: int, epoch_id: int, iter_id: int
 ):
-    lr_msg = f"lr: {trainer.optimizer.get_lr():.5f}"
+    lr_msg = f"lr: {solver.optimizer.get_lr():.5f}"
 
     metric_msg = ", ".join(
         [
-            f"{key}: {trainer.train_output_info[key].avg:.5f}"
-            for key in trainer.train_output_info
+            f"{key}: {solver.train_output_info[key].avg:.5f}"
+            for key in solver.train_output_info
         ]
     )
 
     time_msg = ", ".join(
-        [trainer.train_time_info[key].mean for key in trainer.train_time_info]
+        [solver.train_time_info[key].mean for key in solver.train_time_info]
     )
 
-    ips_msg = f"ips: {batch_size / trainer.train_time_info['batch_cost'].avg:.2f}"
-    if trainer.benchmark_flag:
+    ips_msg = f"ips: {batch_size / solver.train_time_info['batch_cost'].avg:.2f}"
+    if solver.benchmark_flag:
         ips_msg += " samples/s"
 
     eta_sec = (
-        (trainer.epochs - epoch_id + 1) * trainer.iters_per_epoch - iter_id
-    ) * trainer.train_time_info["batch_cost"].avg
+        (solver.epochs - epoch_id + 1) * solver.iters_per_epoch - iter_id
+    ) * solver.train_time_info["batch_cost"].avg
     eta_msg = f"eta: {str(datetime.timedelta(seconds=int(eta_sec)))}"
 
-    epoch_width = len(str(trainer.epochs))
-    iters_width = len(str(trainer.iters_per_epoch))
+    epoch_width = len(str(solver.epochs))
+    iters_width = len(str(solver.iters_per_epoch))
     log_str = (
-        f"[Train][Epoch {epoch_id:>{epoch_width}}/{trainer.epochs}]"
-        f"[Iter {iter_id:>{iters_width}}/{trainer.iters_per_epoch}] {lr_msg}, "
+        f"[Train][Epoch {epoch_id:>{epoch_width}}/{solver.epochs}]"
+        f"[Iter {iter_id:>{iters_width}}/{solver.iters_per_epoch}] {lr_msg}, "
         f"{metric_msg}, {time_msg}, {ips_msg}, {eta_msg}"
     )
-    if trainer.benchmark_flag:
+    if solver.benchmark_flag:
         max_mem_reserved_msg = (
             f"max_mem_reserved: {device.cuda.max_memory_reserved() // (1 << 20)} MB"
         )
@@ -94,57 +94,57 @@ def log_train_info(
 
     logger.scalar(
         {
-            "train/lr": trainer.optimizer.get_lr(),
+            "train/lr": solver.optimizer.get_lr(),
             **{
-                f"train/{key}": trainer.train_output_info[key].avg
-                for key in trainer.train_output_info
+                f"train/{key}": solver.train_output_info[key].avg
+                for key in solver.train_output_info
             },
         },
-        step=trainer.global_step,
-        vdl_writer=trainer.vdl_writer,
-        wandb_writer=trainer.wandb_writer,
-        tbd_writer=trainer.tbd_writer,
+        step=solver.global_step,
+        vdl_writer=solver.vdl_writer,
+        wandb_writer=solver.wandb_writer,
+        tbd_writer=solver.tbd_writer,
     )
 
 
 def log_eval_info(
-    trainer: "solver.Solver",
+    solver: "solver.Solver",
     batch_size: int,
     epoch_id: int,
     iters_per_epoch: int,
     iter_id: int,
 ):
     metric_msg = ", ".join(
         [
-            f"{key}: {trainer.eval_output_info[key].avg:.5f}"
-            for key in trainer.eval_output_info
+            f"{key}: {solver.eval_output_info[key].avg:.5f}"
+            for key in solver.eval_output_info
         ]
     )
 
     time_msg = ", ".join(
-        [trainer.eval_time_info[key].mean for key in trainer.eval_time_info]
+        [solver.eval_time_info[key].mean for key in solver.eval_time_info]
     )
 
-    ips_msg = f"ips: {batch_size / trainer.eval_time_info['batch_cost'].avg:.2f}"
+    ips_msg = f"ips: {batch_size / solver.eval_time_info['batch_cost'].avg:.2f}"
 
-    eta_sec = (iters_per_epoch - iter_id) * trainer.eval_time_info["batch_cost"].avg
+    eta_sec = (iters_per_epoch - iter_id) * solver.eval_time_info["batch_cost"].avg
     eta_msg = f"eta: {str(datetime.timedelta(seconds=int(eta_sec)))}"
 
-    epoch_width = len(str(trainer.epochs))
+    epoch_width = len(str(solver.epochs))
     iters_width = len(str(iters_per_epoch))
     logger.info(
-        f"[Eval][Epoch {epoch_id:>{epoch_width}}/{trainer.epochs}]"
+        f"[Eval][Epoch {epoch_id:>{epoch_width}}/{solver.epochs}]"
         f"[Iter {iter_id:>{iters_width}}/{iters_per_epoch}] "
         f"{metric_msg}, {time_msg}, {ips_msg}, {eta_msg}"
     )
 
     logger.scalar(
         {
-            f"eval/{key}": trainer.eval_output_info[key].avg
-            for key in trainer.eval_output_info
+            f"eval/{key}": solver.eval_output_info[key].avg
+            for key in solver.eval_output_info
         },
-        step=trainer.global_step,
-        vdl_writer=trainer.vdl_writer,
-        wandb_writer=trainer.wandb_writer,
-        tbd_writer=trainer.tbd_writer,
+        step=solver.global_step,
+        vdl_writer=solver.vdl_writer,
+        wandb_writer=solver.wandb_writer,
+        tbd_writer=solver.tbd_writer,
     )

ppsci/solver/solver.py

@@ -15,6 +15,7 @@
 from __future__ import annotations
 
 import contextlib
+import functools
 import importlib
 import itertools
 import os
@@ -39,6 +40,7 @@
 from paddle import nn
 from paddle import optimizer as optim
 from paddle.distributed import fleet
+from paddle.framework import core
 from paddle.static import InputSpec
 from typing_extensions import Literal
 
@@ -444,11 +446,19 @@ def convert_expr(
         # set up benchmark flag, will print memory stat if enabled
         self.benchmark_flag: bool = os.getenv("BENCHMARK_ROOT", None) is not None
 
+        # set up nvtx flag for nsight analysis
+        self.nvtx_flag: bool = os.getenv("NVTX", None) is not None
+        self.forward_helper.nvtx_flag = self.nvtx_flag
+
     def train(self) -> None:
         """Training."""
         self.global_step = self.best_metric["epoch"] * self.iters_per_epoch
         start_epoch = self.best_metric["epoch"] + 1
 
+        if self.nvtx_flag:
+            core.nvprof_start()
+            core.nvprof_enable_record_event()
+
         for epoch_id in range(start_epoch, self.epochs + 1):
             self.train_epoch_func(self, epoch_id, self.log_freq)
             self.train_output_info.clear()
@@ -764,6 +774,7 @@ def export(
             )
             logger.message(f"ONNX model has been exported to: {export_path}.onnx")
 
+    @functools.lru_cache()
     def autocast_context_manager(
         self, enable: bool, level: Literal["O0", "O1", "O2", "OD"] = "O1"
     ) -> contextlib.AbstractContextManager:
@@ -786,6 +797,7 @@ def autocast_context_manager(
             )
         return ctx_manager
 
+    @functools.lru_cache()
     def no_grad_context_manager(
         self, enable: bool
     ) -> contextlib.AbstractContextManager:
@@ -807,6 +819,7 @@ def no_grad_context_manager(
             )
         return ctx_manager
 
+    @functools.lru_cache()
     def no_sync_context_manager(
         self,
         enable: bool,

ppsci/solver/train.py

@@ -14,11 +14,13 @@
 
 from __future__ import annotations
 
+import sys
 import time
 from typing import TYPE_CHECKING
 
 import paddle
 from paddle.distributed.fleet.utils import hybrid_parallel_util as hpu
+from paddle.framework import core
 
 from ppsci.solver import printer
 from ppsci.utils import misc
@@ -38,6 +40,11 @@ def train_epoch_func(solver: "solver.Solver", epoch_id: int, log_freq: int):
     batch_tic = time.perf_counter()
 
     for iter_id in range(1, solver.iters_per_epoch + 1):
+        if solver.nvtx_flag:  # only for nsight analysis
+            core.nvprof_nvtx_push(
+                f"Training iteration {solver.global_step + 1}"
+            )  # Training iteration
+
         total_loss = 0.0
         total_batch_size = 0
         reader_cost = 0.0
@@ -77,6 +84,9 @@ def train_epoch_func(solver: "solver.Solver", epoch_id: int, log_freq: int):
         # forward for every constraint, including model and equation expression
         with solver.no_sync_context_manager(solver.world_size > 1, solver.model):
             with solver.autocast_context_manager(solver.use_amp, solver.amp_level):
+                if solver.nvtx_flag:  # only for nsight analysis
+                    core.nvprof_nvtx_push("Loss computation")
+
                 constraint_losses = solver.forward_helper.train_forward(
                     tuple(
                         _constraint.output_expr
@@ -88,17 +98,31 @@ def train_epoch_func(solver: "solver.Solver", epoch_id: int, log_freq: int):
                     label_dicts,
                     weight_dicts,
                 )
+
+                if solver.nvtx_flag:  # only for nsight analysis
+                    core.nvprof_nvtx_pop()  # Loss computation
+
                 # accumulate all losses
+                if solver.nvtx_flag:  # only for nsight analysis
+                    core.nvprof_nvtx_push("Loss aggregator")
+
                 for i, _constraint in enumerate(solver.constraint.values()):
                     total_loss += constraint_losses[i]
                     loss_dict[_constraint.name] += (
                         float(constraint_losses[i]) / solver.update_freq
                     )
                 if solver.update_freq > 1:
                     total_loss = total_loss / solver.update_freq
+
+                if solver.nvtx_flag:  # only for nsight analysis
+                    core.nvprof_nvtx_pop()  # Loss aggregator
+
                 loss_dict["loss"] = float(total_loss)
 
             # backward
+            if solver.nvtx_flag:  # only for nsight analysis
+                core.nvprof_nvtx_push("Loss backward")
+
             if solver.loss_aggregator is None:
                 if solver.use_amp:
                     total_loss_scaled = solver.scaler.scale(total_loss)
@@ -108,8 +132,14 @@ def train_epoch_func(solver: "solver.Solver", epoch_id: int, log_freq: int):
             else:
                 solver.loss_aggregator(constraint_losses, solver.global_step).backward()
 
+            if solver.nvtx_flag:  # only for nsight analysis
+                core.nvprof_nvtx_pop()  # Loss backward
+
         # update parameters
         if iter_id % solver.update_freq == 0 or iter_id == solver.iters_per_epoch:
+            if solver.nvtx_flag:  # only for nsight analysis
+                core.nvprof_nvtx_push("Optimizer update")
+
             if solver.world_size > 1:
                 # fuse + allreduce manually before optimization if use DDP + no_sync
                 # details in https://github.com/PaddlePaddle/Paddle/issues/48898#issuecomment-1343838622
@@ -118,6 +148,10 @@ def train_epoch_func(solver: "solver.Solver", epoch_id: int, log_freq: int):
                 solver.scaler.minimize(solver.optimizer, total_loss_scaled)
             else:
                 solver.optimizer.step()
+
+            if solver.nvtx_flag:  # only for nsight analysis
+                core.nvprof_nvtx_pop()  # Optimizer update
+
             solver.optimizer.clear_grad()
 
         # update learning rate by step
@@ -138,6 +172,17 @@ def train_epoch_func(solver: "solver.Solver", epoch_id: int, log_freq: int):
 
         batch_tic = time.perf_counter()
 
+        if solver.nvtx_flag:  # only for nsight analysis
+            core.nvprof_nvtx_pop()  # Training iteration
+            NVTX_STOP_ITER = 25
+            if solver.global_step >= NVTX_STOP_ITER:
+                print(
+                    f"Only run {NVTX_STOP_ITER} steps when 'NVTX' is set in environment"
+                    " for nsight analysis. Exit now ......\n"
+                )
+                core.nvprof_stop()
+                sys.exit(0)
+
 
 def train_LBFGS_epoch_func(solver: "solver.Solver", epoch_id: int, log_freq: int):
     """Train function for one epoch with L-BFGS optimizer.