fix: training speed might be incorrect

deepmd/pt/train/training.py

@@ -935,12 +935,19 @@
                             eta=eta,
                         )
                     )
-                # the first training time is not accurate
                 if (
-                    (_step_id + 1 - self.start_step) > self.disp_freq
-                    or self.num_steps - self.start_step < 2 * self.disp_freq
+                    (self.num_steps - self.start_step)
+                    <= 2 * self.disp_freq  # not enough steps
+                    or (_step_id - self.start_step)
+                    >= self.disp_freq  # skip first disp_freq steps
                 ):
                     self.total_train_time += train_time
+                    if display_step_id == 1:
+                        self.timed_steps += 1
+                    else:
+                        self.timed_steps += min(
+                            self.disp_freq, _step_id - self.start_step
+                        )
 
                 if fout:
                     if self.lcurve_should_print_header:
@@ -951,11 +958,14 @@
                     )
 
             if (
-                ((_step_id + 1) % self.save_freq == 0 and _step_id != self.start_step)
-                or (_step_id + 1) == self.num_steps
+                (
+                    (display_step_id) % self.save_freq == 0
+                    and _step_id != self.start_step
+                )
+                or (display_step_id) == self.num_steps
             ) and (self.rank == 0 or dist.get_rank() == 0):
                 # Handle the case if rank 0 aborted and re-assigned
-                self.latest_model = Path(self.save_ckpt + f"-{_step_id + 1}.pt")
+                self.latest_model = Path(self.save_ckpt + f"-{display_step_id}.pt")
 
                 module = (
                     self.wrapper.module
@@ -982,6 +992,7 @@
         self.wrapper.train()
         self.t0 = time.time()
         self.total_train_time = 0.0
+        self.timed_steps = 0
         for step_id in range(self.start_step, self.num_steps):
             step(step_id)
             if JIT:
@@ -1021,24 +1032,12 @@
                 with open("checkpoint", "w") as f:
                     f.write(str(self.latest_model))
 
-            elapsed_batch = self.num_steps - self.start_step
-            if self.timing_in_training and elapsed_batch // self.disp_freq > 0:
-                if self.start_step >= 2 * self.disp_freq:
-                    log.info(
-                        "average training time: %.4f s/batch (exclude first %d batches)",
-                        self.total_train_time
-                        / (
-                            elapsed_batch // self.disp_freq * self.disp_freq
-                            - self.disp_freq
-                        ),
-                        self.disp_freq,
-                    )
-                else:
-                    log.info(
-                        "average training time: %.4f s/batch",
-                        self.total_train_time
-                        / (elapsed_batch // self.disp_freq * self.disp_freq),
-                    )
+            if self.timing_in_training and self.timed_steps:
+                msg = f"average training time: {self.total_train_time / self.timed_steps:.4f} s/batch"
+                excluded_steps = self.num_steps - self.start_step - self.timed_steps
+                if excluded_steps > 0:
+                    msg += f" ({excluded_steps} batches excluded)"
+                log.info(msg)
 
             if JIT:
                 pth_model_path = (