build: format codebase imports using ruff linter (#1028)

# What does this PR do?

- Configured ruff linter to automatically fix import sorting issues.
- Set --exit-non-zero-on-fix to ensure non-zero exit code when fixes are
applied.
- Enabled the 'I' selection to focus on import-related linting rules.
- Ran the linter, and formatted all codebase imports accordingly.
- Removed the black dep from the "dev" group since we use ruff

Signed-off-by: Sébastien Han <seb@redhat.com>

[//]: # (If resolving an issue, uncomment and update the line below)
[//]: # (Closes #[issue-number])

## Test Plan
[Describe the tests you ran to verify your changes with result
summaries. *Provide clear instructions so the plan can be easily
re-executed.*]

[//]: # (## Documentation)
[//]: # (- [ ] Added a Changelog entry if the change is significant)

Signed-off-by: Sébastien Han <seb@redhat.com>

llama_stack/providers/inline/inference/meta_reference/quantization/fp8_impls.py

@@ -8,7 +8,6 @@
 # This software may be used and distributed in accordance with the terms of the Llama 3 Community License Agreement.
 
 import collections
-
 import logging
 from typing import Optional, Type
 
@@ -23,7 +22,7 @@ except ImportError:
     raise
 
 import torch
-from torch import nn, Tensor
+from torch import Tensor, nn
 
 
 class Fp8ScaledWeights:

llama_stack/providers/inline/inference/meta_reference/quantization/fp8_txest_disabled.py

@@ -10,9 +10,9 @@
 import unittest
 
 import torch
-
-from fp8_impls import ffn_swiglu_fp8_dynamic, FfnQuantizeMode, quantize_fp8
-from hypothesis import given, settings, strategies as st
+from fp8_impls import FfnQuantizeMode, ffn_swiglu_fp8_dynamic, quantize_fp8
+from hypothesis import given, settings
+from hypothesis import strategies as st
 from torch import Tensor
 
 

llama_stack/providers/inline/inference/meta_reference/quantization/loader.py

@@ -12,18 +12,13 @@ import os
 from typing import Any, Dict, List, Optional
 
 import torch
-
 from fairscale.nn.model_parallel.layers import ColumnParallelLinear, RowParallelLinear
 from fairscale.nn.model_parallel.mappings import reduce_from_model_parallel_region
-
 from llama_models.datatypes import CheckpointQuantizationFormat
-
 from llama_models.llama3.api.args import ModelArgs
 from llama_models.llama3.reference_impl.model import Transformer, TransformerBlock
 from llama_models.sku_list import resolve_model
-
-from torch import nn, Tensor
-
+from torch import Tensor, nn
 from torchao.quantization.GPTQ import Int8DynActInt4WeightLinear
 
 from llama_stack.apis.inference import QuantizationType

llama_stack/providers/inline/inference/meta_reference/quantization/scripts/quantize_checkpoint.py

@@ -16,14 +16,12 @@ from pathlib import Path
 from typing import Optional
 
 import fire
-
 import torch
 from fairscale.nn.model_parallel.initialize import (
     get_model_parallel_rank,
     initialize_model_parallel,
     model_parallel_is_initialized,
 )
-
 from llama_models.llama3.api.args import ModelArgs
 from llama_models.llama3.api.tokenizer import Tokenizer
 from llama_models.llama3.reference_impl.model import Transformer, TransformerBlock