mirror of
https://github.com/meta-llama/llama-stack.git
synced 2026-01-02 21:52:15 +00:00
fix all iterrows callsites
This commit is contained in:
Xi Yan
parent
f117407af6
commit
b561cfd902
6 changed files with 56 additions and 163 deletions
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@ -17,7 +17,8 @@ import torch
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from torch import nn
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from torch.optim import Optimizer
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from torch.utils.data import DataLoader, DistributedSampler
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from torchtune import modules, training, utils as torchtune_utils
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from torchtune import modules, training
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from torchtune import utils as torchtune_utils
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from torchtune.data import padded_collate_sft
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from torchtune.modules.loss import CEWithChunkedOutputLoss
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from torchtune.modules.peft import (
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@ -88,9 +89,7 @@ class LoraFinetuningSingleDevice:
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self.job_uuid = job_uuid
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self.training_config = training_config
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if not isinstance(algorithm_config, LoraFinetuningConfig):
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raise ValueError(
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"You need to speicifc LoraFinetuningConfig for LoRA finetuning"
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)
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raise ValueError("You need to speicifc LoraFinetuningConfig for LoRA finetuning")
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self.algorithm_config = algorithm_config
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self._device = torchtune_utils.get_device()
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self._dtype = training.get_dtype(training_config.dtype, device=self._device)
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@ -99,10 +98,7 @@ class LoraFinetuningSingleDevice:
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def model_checkpoint_dir(model) -> str:
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checkpoint_dir = Path(model_local_dir(model.descriptor()))
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paths = [
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Path(checkpoint_dir / f"consolidated.{ext}")
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for ext in ["pth", "00.pth"]
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]
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paths = [Path(checkpoint_dir / f"consolidated.{ext}") for ext in ["pth", "00.pth"]]
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if not any(p.exists() for p in paths):
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checkpoint_dir = checkpoint_dir / "original"
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@ -117,9 +113,7 @@ class LoraFinetuningSingleDevice:
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else:
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model = resolve_model(self.model_id)
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if model is None:
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raise ValueError(
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f"{self.model_id} not found. Your model id should be in the llama models SKU list"
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)
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raise ValueError(f"{self.model_id} not found. Your model id should be in the llama models SKU list")
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self.checkpoint_dir = model_checkpoint_dir(model)
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self._output_dir = str(DEFAULT_CHECKPOINT_DIR)
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@ -191,9 +185,7 @@ class LoraFinetuningSingleDevice:
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self._tokenizer = await self._setup_tokenizer()
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log.info("Tokenizer is initialized.")
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self._optimizer = await self._setup_optimizer(
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optimizer_config=self.training_config.optimizer_config
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)
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self._optimizer = await self._setup_optimizer(optimizer_config=self.training_config.optimizer_config)
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log.info("Optimizer is initialized.")
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self._loss_fn = CEWithChunkedOutputLoss()
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@ -221,13 +213,8 @@ class LoraFinetuningSingleDevice:
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# by the dataloader and the max_steps_per_epoch param set by the user and is used
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# for logging and tracking training state. This should be computed after the dataloader
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# has been setup
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self._steps_per_epoch = (
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len(self._training_dataloader) // self._gradient_accumulation_steps
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)
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if (
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self.max_steps_per_epoch is not None
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and self.max_steps_per_epoch < self._steps_per_epoch
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):
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self._steps_per_epoch = len(self._training_dataloader) // self._gradient_accumulation_steps
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if self.max_steps_per_epoch is not None and self.max_steps_per_epoch < self._steps_per_epoch:
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self._steps_per_epoch = self.max_steps_per_epoch
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self.global_step = self.epochs_run * self._steps_per_epoch
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@ -241,9 +228,7 @@ class LoraFinetuningSingleDevice:
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log.info("Learning rate scheduler is initialized.")
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# Used to ignore labels for loss computation
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self.ignore_labels_cache = torch.full(
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(self._batch_size, 1), self._loss_fn.ignore_index, device=self._device
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)
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self.ignore_labels_cache = torch.full((self._batch_size, 1), self._loss_fn.ignore_index, device=self._device)
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def _log_memory_stats(self):
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# torchtune raises: "Logging memory stats is not supported on CPU devices"; do nothing
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@ -284,13 +269,9 @@ class LoraFinetuningSingleDevice:
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set_trainable_params(model, self.adapter_params)
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if enable_activation_checkpointing:
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training.set_activation_checkpointing(
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model, auto_wrap_policy={modules.TransformerSelfAttentionLayer}
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)
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training.set_activation_checkpointing(model, auto_wrap_policy={modules.TransformerSelfAttentionLayer})
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base_missing, base_unexpected = model.load_state_dict(
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base_model_state_dict, strict=False
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)
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base_missing, base_unexpected = model.load_state_dict(base_model_state_dict, strict=False)
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# This is for any adapters that need to be initialized after base weights
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# have been loaded (e.g. DoRA).
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@ -299,9 +280,7 @@ class LoraFinetuningSingleDevice:
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if hasattr(m, "initialize_dora_magnitude"):
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m.initialize_dora_magnitude()
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if lora_weights_state_dict:
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lora_missing, lora_unexpected = model.load_state_dict(
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lora_weights_state_dict, strict=False
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)
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lora_missing, lora_unexpected = model.load_state_dict(lora_weights_state_dict, strict=False)
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else:
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lora_missing, lora_unexpected = None, None
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validate_missing_and_unexpected_for_lora(
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@ -315,14 +294,10 @@ class LoraFinetuningSingleDevice:
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)
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# Validate model adapter params were loaded in with the expected dtype
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training.validate_expected_param_dtype(
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self.adapter_params.items(), dtype=self._dtype
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)
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training.validate_expected_param_dtype(self.adapter_params.items(), dtype=self._dtype)
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# activation offloading
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self.activations_handling_ctx = training.get_act_offloading_ctx_manager(
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model, enable_activation_offloading
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)
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self.activations_handling_ctx = training.get_act_offloading_ctx_manager(model, enable_activation_offloading)
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self._log_memory_stats()
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@ -458,9 +433,7 @@ class LoraFinetuningSingleDevice:
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# Shift labels to compute loss
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# equivalent to doing labels[..., 1:] and logits[..., :-1, :]
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# But this way we dont need to slice the logits. We just add an ignore index to labels.
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labels = torch.hstack(
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(labels[..., 1:], self.ignore_labels_cache[: labels.shape[0]])
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)
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labels = torch.hstack((labels[..., 1:], self.ignore_labels_cache[: labels.shape[0]]))
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if not isinstance(logits, list):
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labels = labels.reshape(-1)
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logits = logits.reshape(-1, logits.size(-1))
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@ -489,9 +462,7 @@ class LoraFinetuningSingleDevice:
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for curr_epoch in range(self.epochs_run, self.total_epochs):
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# Update the sampler to ensure data is correctly shuffled across epochs
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# in case shuffle is True
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metric_logger = DiskLogger(
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log_dir=self._output_dir + f"/{self.model_id}-sft-{curr_epoch}/log"
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)
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metric_logger = DiskLogger(log_dir=self._output_dir + f"/{self.model_id}-sft-{curr_epoch}/log")
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self._training_sampler.set_epoch(curr_epoch)
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loss_to_log = 0.0
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@ -499,8 +470,7 @@ class LoraFinetuningSingleDevice:
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for idx, batch in enumerate(self._training_dataloader):
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if (
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self.max_steps_per_epoch is not None
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and (idx // self._gradient_accumulation_steps)
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== self.max_steps_per_epoch
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and (idx // self._gradient_accumulation_steps) == self.max_steps_per_epoch
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):
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break
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@ -508,9 +478,7 @@ class LoraFinetuningSingleDevice:
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# Calculate the number of unmasked tokens in the current batch
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# and increment the total number of tokens seen in the step
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current_num_tokens = (
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batch["labels"] != self._loss_fn.ignore_index
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).sum()
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current_num_tokens = (batch["labels"] != self._loss_fn.ignore_index).sum()
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num_tokens += current_num_tokens
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# Loss is normalized by default so we multiply by the number of tokens
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@ -535,9 +503,7 @@ class LoraFinetuningSingleDevice:
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loss_to_log = running_loss.item() / num_tokens
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pbar.update(1)
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pbar.set_description(
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f"{curr_epoch + 1}|{self.global_step}|Loss: {loss_to_log}"
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)
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pbar.set_description(f"{curr_epoch + 1}|{self.global_step}|Loss: {loss_to_log}")
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time_per_step = time.perf_counter() - t0
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log_dict = {
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