forgot to add 'bs / gradient accum < 2 clamp validation logic
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parent
df24827b9a
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1a9d159b2a
91
src/utils.py
91
src/utils.py
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@ -506,6 +506,8 @@ class TrainingState():
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with open(config_path, 'r') as file:
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self.config = yaml.safe_load(file)
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self.killed = False
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self.dataset_dir = f"./training/{self.config['name']}/"
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self.batch_size = self.config['datasets']['train']['batch_size']
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self.dataset_path = self.config['datasets']['train']['path']
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@ -527,7 +529,6 @@ class TrainingState():
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self.training_started = False
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self.info = {}
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self.status = "..."
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self.epoch_rate = ""
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self.epoch_time_start = 0
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@ -651,10 +652,12 @@ class TrainingState():
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print("Removing", path)
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os.remove(path)
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def parse(self, line, verbose=False, buffer_size=8, keep_x_past_datasets=0, progress=None ):
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def parse(self, line, verbose=False, keep_x_past_datasets=0, buffer_size=8, progress=None ):
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self.buffer.append(f'{line}')
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should_return = False
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percent = 0
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message = None
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# rip out iteration info
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if not self.training_started:
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@ -679,7 +682,7 @@ class TrainingState():
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match = re.findall(r'(\d+)%\|(.+?)\| (\d+|\?)\/(\d+|\?) \[(.+?)<(.+?), +(.+?)\]', line)
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if match and len(match) > 0:
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match = match[0]
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percent = int(match[0])/100.0
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per_cent = int(match[0])/100.0
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progressbar = match[1]
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step = int(match[2])
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steps = int(match[3])
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@ -698,15 +701,40 @@ class TrainingState():
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self.it_time_end = time.time()
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self.it_time_delta = self.it_time_end-self.it_time_start
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self.it_time_start = time.time()
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self.it_taken = self.it_taken + 1
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try:
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rate = f'{"{:.3f}".format(self.it_time_delta)}s/it' if self.it_time_delta >= 1 else f'{"{:.3f}".format(1/self.it_time_delta)}it/s'
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self.it_rate = rate
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except Exception as e:
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pass
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last_loss = ""
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metric_step = [f"{self.epoch}/{self.epochs}", f"{self.it}/{self.its}", f"{step}/{steps}"]
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metric_step = ", ".join(metric_step)
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metric_rate = []
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if self.epoch_rate:
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metric_rate.append(self.epoch_rate)
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if self.it_rate:
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metric_rate.append(self.it_rate)
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metric_rate = ", ".join(metric_rate)
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eta_hhmmss = "?"
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if self.eta_hhmmss:
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eta_hhmmss = self.eta_hhmmss
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else:
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try:
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eta = (self.its - self.it) * (self.it_time_deltas / self.it_taken)
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eta = str(timedelta(seconds=int(eta)))
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eta_hhmmss = eta
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except Exception as e:
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pass
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metric_loss = []
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if len(self.losses) > 0:
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last_loss = f'[Loss @ it. {self.losses[-1]["step"]}: {self.losses[-1]["value"]}]'
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message = f'[{self.epoch}/{self.epochs}, {self.it}/{self.its}, {step}/{steps}] [{self.epoch_rate}, {self.it_rate}] {last_loss} [ETA: {self.eta_hhmmss}]'
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metric_loss.append(f'Loss: {"{:3f}".format(self.losses[-1]["value"])}')
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metric_loss = ", ".join(metric_loss)
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message = f'[{metric_step}] [{metric_rate}] [{metric_loss}] [ETA: {eta_hhmmss}]'
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if lapsed:
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self.epoch = self.epoch + 1
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@ -741,16 +769,8 @@ class TrainingState():
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for k, v in match:
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self.info[k] = float(v.replace(",", ""))
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if 'loss_gpt_total' in self.info:
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self.status = f"Total loss at epoch {self.epoch}: {self.info['loss_gpt_total']}"
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"""
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self.losses.append({ "step": self.it, "value": self.info['loss_text_ce'], "type": "loss_text_ce" })
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self.losses.append({ "step": self.it, "value": self.info['loss_mel_ce'], "type": "loss_mel_ce" })
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self.losses.append({ "step": self.it, "value": self.info['loss_gpt_total'], "type": "loss_gpt_total" })
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"""
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should_return = True
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self.load_losses(update=True)
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should_return = True
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elif line.find('Saving models and training states') >= 0:
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self.checkpoint = self.checkpoint + 1
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@ -769,10 +789,18 @@ class TrainingState():
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should_return = True
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self.buffer = self.buffer[-buffer_size:]
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if should_return:
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return "".join(self.buffer)
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def run_training(config_path, verbose=False, gpus=1, buffer_size=8, keep_x_past_datasets=0, progress=gr.Progress(track_tqdm=True)):
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result = None
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if should_return:
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result = "".join(self.buffer) if not self.training_started else message
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return (
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result,
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percent,
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message,
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)
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def run_training(config_path, verbose=False, gpus=1, keep_x_past_datasets=0, progress=gr.Progress(track_tqdm=True)):
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global training_state
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if training_state and training_state.process:
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return "Training already in progress"
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@ -787,11 +815,10 @@ def run_training(config_path, verbose=False, gpus=1, buffer_size=8, keep_x_past_
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training_state = TrainingState(config_path=config_path, keep_x_past_datasets=keep_x_past_datasets, gpus=gpus)
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for line in iter(training_state.process.stdout.readline, ""):
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res = training_state.parse( line=line, verbose=verbose, buffer_size=buffer_size, keep_x_past_datasets=keep_x_past_datasets, progress=progress )
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result, percent, message = training_state.parse( line=line, verbose=verbose, keep_x_past_datasets=keep_x_past_datasets, progress=progress )
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print(f"[Training] [{datetime.now().isoformat()}] {line[:-1]}")
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if res:
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yield res
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if result:
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yield result
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if training_state:
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training_state.process.stdout.close()
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@ -824,15 +851,16 @@ def update_training_dataplot(config_path=None):
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return update
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def reconnect_training(verbose=False, buffer_size=8, progress=gr.Progress(track_tqdm=True)):
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def reconnect_training(verbose=False, progress=gr.Progress(track_tqdm=True)):
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global training_state
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if not training_state or not training_state.process:
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return "Training not in progress"
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for line in iter(training_state.process.stdout.readline, ""):
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res = training_state.parse( line=line, verbose=verbose, buffer_size=buffer_size, progress=progress )
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if res:
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yield res
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result, percent, message = training_state.parse( line=line, verbose=verbose, keep_x_past_datasets=keep_x_past_datasets, progress=progress )
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print(f"[Training] [{datetime.now().isoformat()}] {line[:-1]}")
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if result:
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yield result
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def stop_training():
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global training_state
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@ -845,6 +873,7 @@ def stop_training():
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training_state.process.send_signal(signal.SIGINT)
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return_code = training_state.process.wait()
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training_state = None
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print("Killed training process.")
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return f"Training cancelled: {return_code}"
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def get_halfp_model_path():
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@ -966,8 +995,18 @@ def optimize_training_settings( epochs, learning_rate, text_ce_lr_weight, learni
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if gradient_accumulation_size == 0:
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gradient_accumulation_size = 1
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if batch_size / gradient_accumulation_size < 2:
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gradient_accumulation_size = int(batch_size / 2)
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if gradient_accumulation_size == 0:
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gradient_accumulation_size = 1
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messages.append(f"Gradient accumulation size is too large for a given batch size, clamping gradient accumulation size to: {gradient_accumulation_size}")
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elif batch_size % gradient_accumulation_size != 0:
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gradient_accumulation_size = int(batch_size / gradient_accumulation_size)
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if gradient_accumulation_size == 0:
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gradient_accumulation_size = 1
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messages.append(f"Batch size is not evenly divisible by the gradient accumulation size, adjusting gradient accumulation size to: {gradient_accumulation_size}")
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iterations = calc_iterations(epochs=epochs, lines=lines, batch_size=batch_size)
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@ -535,9 +535,8 @@ def setup_gradio():
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verbose_training = gr.Checkbox(label="Verbose Console Output", value=True)
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with gr.Row():
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training_buffer_size = gr.Slider(label="Console Buffer Size", minimum=4, maximum=32, value=8)
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training_keep_x_past_datasets = gr.Slider(label="Keep X Previous States", minimum=0, maximum=8, value=0, step=1)
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training_gpu_count = gr.Number(label="GPUs", value=1)
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training_gpu_count = gr.Number(label="GPUs", value=1)
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with gr.Row():
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start_training_button = gr.Button(value="Train")
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stop_training_button = gr.Button(value="Stop")
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@ -746,7 +745,6 @@ def setup_gradio():
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training_configs,
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verbose_training,
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training_gpu_count,
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training_buffer_size,
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training_keep_x_past_datasets,
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],
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outputs=[
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@ -779,7 +777,6 @@ def setup_gradio():
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reconnect_training_button.click(reconnect_training,
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inputs=[
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verbose_training,
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training_buffer_size,
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],
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outputs=training_output #console_output
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)
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