chore: fix mypy violations in post_training modules

Note: this patch touches all files but post_training.py that will be
significantly changed by #1437, hence leaving it out of the picture for
now.

running_loss is now always Tensor (on-device) and doesn't change its
type from int to Tensor (which made mypy unhappy).

Signed-off-by: Ihar Hrachyshka <ihar.hrachyshka@gmail.com>
This commit is contained in:
Ihar Hrachyshka 2025-03-11 11:19:45 -04:00
parent 3b35a39b8b
commit 8c01246344
9 changed files with 56 additions and 69 deletions

View file

@ -9830,23 +9830,6 @@
],
"title": "ScoreBatchResponse"
},
"AlgorithmConfig": {
"oneOf": [
{
"$ref": "#/components/schemas/LoraFinetuningConfig"
},
{
"$ref": "#/components/schemas/QATFinetuningConfig"
}
],
"discriminator": {
"propertyName": "type",
"mapping": {
"LoRA": "#/components/schemas/LoraFinetuningConfig",
"QAT": "#/components/schemas/QATFinetuningConfig"
}
}
},
"LoraFinetuningConfig": {
"type": "object",
"properties": {
@ -9982,7 +9965,14 @@
"type": "string"
},
"algorithm_config": {
"$ref": "#/components/schemas/AlgorithmConfig"
"oneOf": [
{
"$ref": "#/components/schemas/LoraFinetuningConfig"
},
{
"$ref": "#/components/schemas/QATFinetuningConfig"
}
]
}
},
"additionalProperties": false,

View file

@ -6615,15 +6615,6 @@ components:
required:
- results
title: ScoreBatchResponse
AlgorithmConfig:
oneOf:
- $ref: '#/components/schemas/LoraFinetuningConfig'
- $ref: '#/components/schemas/QATFinetuningConfig'
discriminator:
propertyName: type
mapping:
LoRA: '#/components/schemas/LoraFinetuningConfig'
QAT: '#/components/schemas/QATFinetuningConfig'
LoraFinetuningConfig:
type: object
properties:
@ -6707,7 +6698,9 @@ components:
checkpoint_dir:
type: string
algorithm_config:
$ref: '#/components/schemas/AlgorithmConfig'
oneOf:
- $ref: '#/components/schemas/LoraFinetuningConfig'
- $ref: '#/components/schemas/QATFinetuningConfig'
additionalProperties: false
required:
- job_uuid

View file

@ -6,7 +6,7 @@
from datetime import datetime
from enum import Enum
from typing import Any, Dict, List, Literal, Optional, Protocol, Union
from typing import Any, Dict, List, Literal, Optional, Protocol
from pydantic import BaseModel, Field
from typing_extensions import Annotated
@ -89,7 +89,7 @@ class QATFinetuningConfig(BaseModel):
AlgorithmConfig = register_schema(
Annotated[Union[LoraFinetuningConfig, QATFinetuningConfig], Field(discriminator="type")],
Annotated[LoraFinetuningConfig | QATFinetuningConfig, Field(discriminator="type")],
name="AlgorithmConfig",
)
@ -184,7 +184,7 @@ class PostTraining(Protocol):
description="Model descriptor from `llama model list`",
),
checkpoint_dir: Optional[str] = None,
algorithm_config: Optional[AlgorithmConfig] = None,
algorithm_config: Optional[LoraFinetuningConfig | QATFinetuningConfig] = None,
) -> PostTrainingJob: ...
@webmethod(route="/post-training/preference-optimize", method="POST")

View file

@ -9,6 +9,9 @@
#
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
from typing import Any
from llama_stack.apis.common.type_system import (
ChatCompletionInputType,
DialogType,
@ -20,7 +23,7 @@ from llama_stack.providers.utils.common.data_schema_validator import (
validate_dataset_schema,
)
EXPECTED_DATASET_SCHEMA = {
EXPECTED_DATASET_SCHEMA: dict[str, list[dict[str, Any]]] = {
"instruct": [
{
ColumnName.chat_completion_input.value: ChatCompletionInputType(),
@ -41,6 +44,9 @@ async def validate_input_dataset_schema(
dataset_type: str,
) -> None:
dataset_def = await datasets_api.get_dataset(dataset_id=dataset_id)
if not dataset_def:
raise ValueError(f"Dataset {dataset_id} does not exist.")
if not dataset_def.dataset_schema or len(dataset_def.dataset_schema) == 0:
raise ValueError(f"Dataset {dataset_id} does not have a schema defined.")

View file

@ -37,7 +37,7 @@ class TorchtuneCheckpointer:
checkpoint_files: List[str],
output_dir: str,
model_type: str,
) -> None:
):
# Fail fast if ``checkpoint_files`` is invalid
# TODO: support loading more than one file
if len(checkpoint_files) != 1:
@ -58,7 +58,7 @@ class TorchtuneCheckpointer:
"""
Load Meta checkpoint from file. Currently only loading from a single file is supported.
"""
state_dict: Dict[str:Any] = {}
state_dict: Dict[str, Any] = {}
model_state_dict = safe_torch_load(self._checkpoint_path)
if self._model_type == ModelType.LLAMA3_VISION:
from torchtune.models.llama3_2_vision._convert_weights import (
@ -85,10 +85,10 @@ class TorchtuneCheckpointer:
state_dict: Dict[str, Any],
epoch: int,
adapter_only: bool = False,
checkpoint_format: str = "meta",
checkpoint_format: str | None = None,
) -> str:
model_file_path = Path(self._output_dir) / f"{self._model_id}-{self._training_algorithm}-{epoch}"
if checkpoint_format == "meta":
if checkpoint_format == "meta" or checkpoint_format is None:
self._save_meta_format_checkpoint(model_file_path, state_dict, adapter_only)
elif checkpoint_format == "huggingface":
# Note: for saving hugging face format checkpoints, we only suppport saving adapter weights now

View file

@ -10,7 +10,7 @@
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
from typing import Any, Callable, Dict
from typing import Callable, Dict
import torch
from pydantic import BaseModel
@ -25,10 +25,13 @@ from llama_stack.apis.post_training import DatasetFormat
from llama_stack.models.llama.datatypes import Model
from llama_stack.models.llama.sku_list import resolve_model
BuildLoraModelCallable = Callable[..., torch.nn.Module]
BuildTokenizerCallable = Callable[..., Llama3Tokenizer]
class ModelConfig(BaseModel):
model_definition: Any
tokenizer_type: Any
model_definition: BuildLoraModelCallable
tokenizer_type: BuildTokenizerCallable
checkpoint_type: str
@ -51,10 +54,6 @@ DATA_FORMATS: Dict[str, Transform] = {
}
BuildLoraModelCallable = Callable[..., torch.nn.Module]
BuildTokenizerCallable = Callable[..., Llama3Tokenizer]
def _validate_model_id(model_id: str) -> Model:
model = resolve_model(model_id)
if model is None or model.core_model_id.value not in MODEL_CONFIGS:

View file

@ -55,7 +55,7 @@ class SFTDataset(Dataset):
if "messages" in transformed_sample:
validate_messages(transformed_sample["messages"])
tokenized_dict = self._model_transform(transformed_sample)
tokenized_dict: dict[str, Any] = self._model_transform(transformed_sample)
if not ("tokens" in tokenized_dict and "mask" in tokenized_dict):
keys_str = ", ".join(tokenized_dict.keys())

View file

@ -37,10 +37,10 @@ from llama_stack.apis.common.training_types import PostTrainingMetric
from llama_stack.apis.datasetio import DatasetIO
from llama_stack.apis.datasets import Datasets
from llama_stack.apis.post_training import (
AlgorithmConfig,
Checkpoint,
LoraFinetuningConfig,
OptimizerConfig,
QATFinetuningConfig,
TrainingConfig,
)
from llama_stack.distribution.utils.config_dirs import DEFAULT_CHECKPOINT_DIR
@ -73,6 +73,9 @@ class LoraFinetuningSingleDevice:
# Currently logging only logs limited training metrics to local disk
# will figure out more loggings and how it works with telemetry in future PRs
_checkpointer: TorchtuneCheckpointer
def __init__(
self,
config: TorchtunePostTrainingConfig,
@ -82,7 +85,7 @@ class LoraFinetuningSingleDevice:
logger_config: Dict[str, Any],
model: str,
checkpoint_dir: Optional[str],
algorithm_config: Optional[AlgorithmConfig],
algorithm_config: LoraFinetuningConfig | QATFinetuningConfig | None,
datasetio_api: DatasetIO,
datasets_api: Datasets,
) -> None:
@ -109,12 +112,12 @@ class LoraFinetuningSingleDevice:
return str(checkpoint_dir)
if checkpoint_dir and checkpoint_dir != "null":
self.checkpoint_dir = config.checkpoint_dir
self.checkpoint_dir = checkpoint_dir
else:
model = resolve_model(self.model_id)
if model is None:
model_obj = resolve_model(self.model_id)
if model_obj is None:
raise ValueError(f"{self.model_id} not found. Your model id should be in the llama models SKU list")
self.checkpoint_dir = model_checkpoint_dir(model)
self.checkpoint_dir = model_checkpoint_dir(model_obj)
self._output_dir = str(DEFAULT_CHECKPOINT_DIR)
self._checkpoint_format = config.checkpoint_format
@ -135,16 +138,16 @@ class LoraFinetuningSingleDevice:
self.max_validation_steps = training_config.max_validation_steps
self._clip_grad_norm = 1.0
self._enable_activation_checkpointing = False
self._enable_activation_offloading = False
if training_config.efficiency_config:
if training_config.efficiency_config.enable_activation_checkpointing:
self._enable_activation_checkpointing = (
(training_config.efficiency_config.enable_activation_checkpointing)
if training_config.efficiency_config
else False
)
self._enable_activation_offloading = (
(training_config.efficiency_config.enable_activation_offloading)
if training_config.efficiency_config
else False
training_config.efficiency_config.enable_activation_checkpointing
)
if training_config.efficiency_config.enable_activation_offloading:
self._enable_activation_offloading = training_config.efficiency_config.enable_activation_offloading
self.datasetio_api = datasetio_api
self.datasets_api = datasets_api
@ -451,12 +454,12 @@ class LoraFinetuningSingleDevice:
"""
# Initialize tokens count and running loss (for grad accumulation)
t0 = time.perf_counter()
running_loss = 0
running_loss: float = 0.0
num_tokens = 0
# training artifacts
checkpoints = []
memory_stats = {}
memory_stats: Dict[str, Any] = {}
# self.epochs_run should be non-zero when we're resuming from a checkpoint
for curr_epoch in range(self.epochs_run, self.total_epochs):
@ -484,7 +487,7 @@ class LoraFinetuningSingleDevice:
# Loss is normalized by default so we multiply by the number of tokens
# This way we can normalize by the total number of tokens if we're accumulating gradients
current_loss = await self._loss_step(batch) * current_num_tokens
running_loss += current_loss
running_loss += current_loss.detach().item()
current_loss.backward()
# Step with optimizer
@ -500,7 +503,7 @@ class LoraFinetuningSingleDevice:
# Update the number of steps when the weights are updated
self.global_step += 1
loss_to_log = running_loss.item() / num_tokens
loss_to_log = running_loss / num_tokens
pbar.update(1)
pbar.set_description(f"{curr_epoch + 1}|{self.global_step}|Loss: {loss_to_log}")
@ -523,7 +526,7 @@ class LoraFinetuningSingleDevice:
)
# Reset running stats for the next step
running_loss = 0
running_loss = 0.0
num_tokens = 0
t0 = time.perf_counter()

View file

@ -229,10 +229,6 @@ exclude = [
"^llama_stack/providers/inline/inference/sentence_transformers/sentence_transformers\\.py$",
"^llama_stack/providers/inline/inference/vllm/",
"^llama_stack/providers/inline/post_training/common/validator\\.py$",
"^llama_stack/providers/inline/post_training/torchtune/common/checkpointer\\.py$",
"^llama_stack/providers/inline/post_training/torchtune/common/utils\\.py$",
"^llama_stack/providers/inline/post_training/torchtune/datasets/sft\\.py$",
"^llama_stack/providers/inline/post_training/torchtune/recipes/lora_finetuning_single_device\\.py$",
"^llama_stack/providers/inline/post_training/torchtune/post_training\\.py$",
"^llama_stack/providers/inline/safety/code_scanner/",
"^llama_stack/providers/inline/safety/llama_guard/",