added '''estimating''' iterations until milestones (lr=[1, 0.5, 0.1] and final lr, very, very inaccurate because it uses instantaneous delta lr, I'll need to do a riemann sum later

src/utils.py

@@ -550,8 +550,11 @@ class TrainingState():
 		self.eta_hhmmss = "?"
 
 		self.last_info_check_at = 0
+		self.statistics = []
 		self.losses = []
 
+		self.loss_milestones = [ 1.0, 0.15, 0.05 ]
+
 		self.load_losses()
 		if keep_x_past_datasets > 0:
 			self.cleanup_old(keep=keep_x_past_datasets)
@@ -578,7 +581,7 @@ class TrainingState():
 		highest_step = self.last_info_check_at
 
 		if not update:
-			self.losses = []
+			self.statistics = []
 
 		if use_tensorboard:
 			logs = sorted([f'{self.dataset_dir}/tb_logger/{d}' for d in os.listdir(f'{self.dataset_dir}/tb_logger/') if d[:6] == "events" ])
@@ -596,9 +599,12 @@ class TrainingState():
 							if update and s.step <= self.last_info_check_at:
 								continue
 							highest_step = max( highest_step, s.step )
+							self.statistics.append( { "step": s.step, "value": s.value, "type": key } )
+
+							if key == 'loss_gpt_total':
 								self.losses.append( { "step": s.step, "value": s.value, "type": key } )
+
 				except Exception as e:
-					print("Failed to parse event log:", log)
 					pass
 
 		else:
@@ -630,6 +636,9 @@ class TrainingState():
 						if update and int(k) <= self.last_info_check_at:
 							continue
 						highest_step = max( highest_step, s.step )
+						self.statistics.append({ "step": int(k), "value": infos[k][key], "type": key })
+
+						if key == "loss_gpt_total":
 							self.losses.append({ "step": int(k), "value": infos[k][key], "type": key })
 
 		self.last_info_check_at = highest_step
@@ -739,20 +748,36 @@ class TrainingState():
 						metric_loss.append(f'Loss: {"{:3f}".format(self.losses[-1]["value"])}')
 
 					if len(self.losses) >= 2:
-						delta_loss = self.losses[-2]["value"] - self.losses[-1]["value"]
+						# i can probably do a """riemann sum""" to get a better derivative, but the instantaneous one works fine
-						delta_step = self.losses[-2]["step"] - self.losses[-1]["step"]
+						d1_loss = self.losses[-1]["value"]
+						d2_loss = self.losses[-2]["value"]
+						dloss = d2_loss - d1_loss
 						
-						inst_deriv = delta_loss / delta_step
+						d1_step = self.losses[-1]["step"]
-						est_loss = delta_loss + (self.its - self.it) * inst_deriv
+						d2_step = self.losses[-2]["step"]
-						metric_loss.append(f'Est. Final Loss: {"{:3f}".format(est_loss)}')
+						dstep = d2_step - d1_step
 
-						print(delta_loss, delta_step, inst_deriv, est_loss)
+						# don't bother if the loss went up
+						if dloss < 0:
+							its_remain = self.its - self.it
+							inst_deriv = dloss / dstep
 
+							next_milestone = None
+							for milestone in self.loss_milestones:
+								if d1_loss > milestone:
+									next_milestone = milestone
+									break
+
+							if next_milestone:
+								# tfw can do simple calculus but not basic algebra in my head
+								est_its = (next_milestone - d1_loss) * (dstep / dloss)
+								metric_loss.append(f'Est. milestone {next_milestone} in: {int(est_its)}its')
+							else:
+								est_loss = inst_deriv * its_remain + d1_loss
+								metric_loss.append(f'Est. final loss: {"{:3f}".format(est_loss)}')
 
 					metric_loss = ", ".join(metric_loss)
 
-
-
 					message = f'[{metric_step}] [{metric_rate}] [ETA: {eta_hhmmss}] [{metric_loss}]'
 
 			if lapsed:
@@ -859,9 +884,9 @@ def run_training(config_path, verbose=False, gpus=1, keep_x_past_datasets=0, pro
 
 def get_training_losses():
 	global training_state
-	if not training_state or not training_state.losses:
+	if not training_state or not training_state.statistics:
 		return
-	return pd.DataFrame(training_state.losses)
+	return pd.DataFrame(training_state.statistics)
 
 def update_training_dataplot(config_path=None):
 	global training_state
@@ -870,13 +895,13 @@ def update_training_dataplot(config_path=None):
 	if not training_state:
 		if config_path:
 			training_state = TrainingState(config_path=config_path, start=False)
-			if training_state.losses:
+			if training_state.statistics:
-				update = gr.LinePlot.update(value=pd.DataFrame(training_state.losses))
+				update = gr.LinePlot.update(value=pd.DataFrame(training_state.statistics))
 			del training_state
 			training_state = None
-	elif training_state.losses:
+	elif training_state.statistics:
 		training_state.load_losses()
-		update = gr.LinePlot.update(value=pd.DataFrame(training_state.losses))
+		update = gr.LinePlot.update(value=pd.DataFrame(training_state.statistics))
 
 	return update