Format llm-lora project

llm-lora-finetuning/lit_gpt/lora.py

@@ -373,7 +373,9 @@ def zero_pad(self, x: torch.Tensor) -> torch.Tensor:
         )  # (4096, 256)
         return result.view(
             (*x.shape[:-1], self.linear.out_features)
-        ).transpose(0, 1)  # (64, 64, 384)
+        ).transpose(
+            0, 1
+        )  # (64, 64, 384)
 
     def conv1d(
         self, input: torch.Tensor, weight: torch.Tensor

llm-lora-finetuning/lit_gpt/model.py

@@ -217,7 +217,9 @@ def forward(
             B,
             T,
             C,
-        ) = x.size()  # batch size, sequence length, embedding dimensionality (n_embd)
+        ) = (
+            x.size()
+        )  # batch size, sequence length, embedding dimensionality (n_embd)
 
         qkv = self.attn(x)
 
@@ -394,7 +396,9 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
             B,
             T,
             C,
-        ) = x.size()  # batch size, sequence length, embedding dimensionality (n_embd)
+        ) = (
+            x.size()
+        )  # batch size, sequence length, embedding dimensionality (n_embd)
         x = x.view(-1, C)  # (B*T, C)
         router = self.gate(x)  # (B*T, n_expert)
         probs, indices = torch.topk(

llm-lora-finetuning/pipelines/evaluate.py

@@ -15,11 +15,11 @@
 # limitations under the License.
 #
 
-from steps import evaluate
-
 from zenml import pipeline
 from zenml.config import DockerSettings
 
+from steps import evaluate
+
 
 @pipeline(
     settings={

llm-lora-finetuning/pipelines/feature_engineering.py

@@ -15,11 +15,11 @@
 # limitations under the License.
 #
 
-from steps import feature_engineering
-
 from zenml import pipeline
 from zenml.config import DockerSettings
 
+from steps import feature_engineering
+
 
 @pipeline(
     settings={

llm-lora-finetuning/pipelines/finetuning.py

@@ -17,11 +17,11 @@
 
 from typing import Optional
 
-from steps import finetune
-
 from zenml import get_pipeline_context, pipeline
 from zenml.config import DockerSettings
 
+from steps import finetune
+
 
 @pipeline(
     settings={

llm-lora-finetuning/pipelines/merge.py

@@ -15,11 +15,11 @@
 # limitations under the License.
 #
 
-from steps import merge
-
 from zenml import pipeline
 from zenml.config import DockerSettings
 
+from steps import merge
+
 
 @pipeline(
     settings={

llm-lora-finetuning/run.py

@@ -19,15 +19,15 @@
 from typing import Optional
 
 import click
+from zenml.logger import get_logger
+
 from pipelines import (
     llm_lora_evaluation,
     llm_lora_feature_engineering,
     llm_lora_finetuning,
     llm_lora_merging,
 )
 
-from zenml.logger import get_logger
-
 logger = get_logger(__name__)
 
 

llm-lora-finetuning/steps/evaluate.py

@@ -21,20 +21,20 @@
 from typing import Any, Dict, List, Literal, Optional
 
 import torch
-from evaluate.lm_eval_harness import run_eval_harness
 from huggingface_hub import snapshot_download
 from pydantic import BaseModel
-from scripts.download import download_from_hub
-from scripts.merge_lora import merge_lora
 from typing_extensions import Annotated
+from zenml import step
+from zenml.logger import get_logger
 
+from evaluate.lm_eval_harness import run_eval_harness
+from scripts.download import download_from_hub
+from scripts.merge_lora import merge_lora
 from steps.params import LoraParameters
 from steps.utils import (
     convert_to_lit_checkpoint_if_necessary,
     get_huggingface_access_token,
 )
-from zenml import step
-from zenml.logger import get_logger
 
 logger = get_logger(__file__)
 

llm-lora-finetuning/steps/feature_engineering.py

@@ -21,14 +21,14 @@
 from pathlib import Path
 from typing import Any, Dict
 
-from lit_gpt import Config
-from materializers.directory_materializer import DirectoryMaterializer
 from pydantic import BaseModel
-from scripts.download import download_from_hub
 from typing_extensions import Annotated
+from zenml import log_artifact_metadata, step
 
+from lit_gpt import Config
+from materializers.directory_materializer import DirectoryMaterializer
+from scripts.download import download_from_hub
 from steps.utils import get_huggingface_access_token
-from zenml import log_artifact_metadata, step
 
 
 class FeatureEngineeringParameters(BaseModel):

llm-lora-finetuning/steps/finetune.py

@@ -20,25 +20,25 @@
 from typing import Literal, Optional
 
 import torch
-from finetune.lora import setup
 from huggingface_hub import upload_folder
+from pydantic import BaseModel
+from typing_extensions import Annotated
+from zenml import get_step_context, log_model_metadata, step
+from zenml.logger import get_logger
+from zenml.materializers import BuiltInMaterializer
+
+from finetune.lora import setup
 from lit_gpt.args import EvalArgs, IOArgs, TrainArgs
 from materializers.directory_materializer import DirectoryMaterializer
-from pydantic import BaseModel
 from scripts.convert_lit_checkpoint import convert_lit_checkpoint
 from scripts.download import download_from_hub
 from scripts.merge_lora import merge_lora
 from scripts.prepare_alpaca import prepare
-from typing_extensions import Annotated
-
 from steps.params import LoraParameters
 from steps.utils import (
     convert_to_lit_checkpoint_if_necessary,
     get_huggingface_access_token,
 )
-from zenml import get_step_context, log_model_metadata, step
-from zenml.logger import get_logger
-from zenml.materializers import BuiltInMaterializer
 
 logger = get_logger(__file__)
 

llm-lora-finetuning/steps/merge.py

@@ -21,17 +21,17 @@
 
 from huggingface_hub import snapshot_download, upload_folder
 from pydantic import BaseModel
+from zenml import log_model_metadata, step
+from zenml.logger import get_logger
+
 from scripts.convert_lit_checkpoint import convert_lit_checkpoint
 from scripts.download import download_from_hub
 from scripts.merge_lora import merge_lora
-
 from steps.params import LoraParameters
 from steps.utils import (
     convert_to_lit_checkpoint_if_necessary,
     get_huggingface_access_token,
 )
-from zenml import log_model_metadata, step
-from zenml.logger import get_logger
 
 logger = get_logger(__file__)
 

llm-lora-finetuning/steps/utils.py

@@ -19,10 +19,10 @@
 from pathlib import Path
 from typing import Optional
 
-from scripts.convert_hf_checkpoint import convert_hf_checkpoint
-
 from zenml.client import Client
 
+from scripts.convert_hf_checkpoint import convert_hf_checkpoint
+
 
 def get_huggingface_access_token() -> Optional[str]:
     """Get access token for huggingface.