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chore: enable pyupgrade fixes (#1806)

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# What does this PR do?

The goal of this PR is code base modernization.

Schema reflection code needed a minor adjustment to handle UnionTypes
and collections.abc.AsyncIterator. (Both are preferred for latest Python
releases.)

Note to reviewers: almost all changes here are automatically generated
by pyupgrade. Some additional unused imports were cleaned up. The only
change worth of note can be found under `docs/openapi_generator` and
`llama_stack/strong_typing/schema.py` where reflection code was updated
to deal with "newer" types.

Signed-off-by: Ihar Hrachyshka <ihar.hrachyshka@gmail.com>
This commit is contained in:
Ihar Hrachyshka 2025-05-01 17:23:50 -04:00 • committed by GitHub
parent ffe3d0b2cd
commit 9e6561a1ec
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319 changed files with 2843 additions and 3033 deletions
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24
llama_stack/providers/remote/post_training/nvidia/config.py
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@ -5,7 +5,7 @@
# the root directory of this source tree.
import os
from typing import Any, Dict, Optional
from typing import Any
from pydantic import BaseModel, Field
@ -15,23 +15,23 @@ from pydantic import BaseModel, Field
class NvidiaPostTrainingConfig(BaseModel):
"""Configuration for NVIDIA Post Training implementation."""
api_key: Optional[str] = Field(
api_key: str | None = Field(
default_factory=lambda: os.getenv("NVIDIA_API_KEY"),
description="The NVIDIA API key.",
)
dataset_namespace: Optional[str] = Field(
dataset_namespace: str | None = Field(
default_factory=lambda: os.getenv("NVIDIA_DATASET_NAMESPACE", "default"),
description="The NVIDIA dataset namespace.",
)
project_id: Optional[str] = Field(
project_id: str | None = Field(
default_factory=lambda: os.getenv("NVIDIA_PROJECT_ID", "test-example-model@v1"),
description="The NVIDIA project ID.",
)
# ToDO: validate this, add default value
customizer_url: Optional[str] = Field(
customizer_url: str | None = Field(
default_factory=lambda: os.getenv("NVIDIA_CUSTOMIZER_URL"),
description="Base URL for the NeMo Customizer API",
)
@ -53,7 +53,7 @@ class NvidiaPostTrainingConfig(BaseModel):
)
@classmethod
def sample_run_config(cls, **kwargs) -> Dict[str, Any]:
def sample_run_config(cls, **kwargs) -> dict[str, Any]:
return {
"api_key": "${env.NVIDIA_API_KEY:}",
"dataset_namespace": "${env.NVIDIA_DATASET_NAMESPACE:default}",
@ -71,27 +71,27 @@ class SFTLoRADefaultConfig(BaseModel):
n_epochs: int = 50
# NeMo customizer specific parameters
log_every_n_steps: Optional[int] = None
log_every_n_steps: int | None = None
val_check_interval: float = 0.25
sequence_packing_enabled: bool = False
weight_decay: float = 0.01
lr: float = 0.0001
# SFT specific parameters
hidden_dropout: Optional[float] = None
attention_dropout: Optional[float] = None
ffn_dropout: Optional[float] = None
hidden_dropout: float | None = None
attention_dropout: float | None = None
ffn_dropout: float | None = None
# LoRA default parameters
lora_adapter_dim: int = 8
lora_adapter_dropout: Optional[float] = None
lora_adapter_dropout: float | None = None
lora_alpha: int = 16
# Data config
batch_size: int = 8
@classmethod
def sample_config(cls) -> Dict[str, Any]:
def sample_config(cls) -> dict[str, Any]:
"""Return a sample configuration for NVIDIA training."""
return {
"n_epochs": 50,