diff --git a/pgml-extension/requirements.txt b/pgml-extension/requirements.txt
index ca21fc0fb..382be4155 100644
--- a/pgml-extension/requirements.txt
+++ b/pgml-extension/requirements.txt
@@ -1,6 +1,7 @@
accelerate==0.19.0
-datasets==2.10.1
+datasets==2.12.0
deepspeed==0.8.1
+huggingface-hub==0.14.1
InstructorEmbedding
lightgbm
pandas==1.5.3
@@ -14,6 +15,6 @@ sentence-transformers==2.2.2
torch==1.13.1
torchaudio==0.13.1
torchvision==0.14.1
-tqdm==4.64.1
-transformers==4.28.1
+tqdm==4.65.0
+transformers==4.29.1
xgboost
diff --git a/pgml-extension/src/bindings/transformers.py b/pgml-extension/src/bindings/transformers.py
index ad97171f1..8334532d1 100644
--- a/pgml-extension/src/bindings/transformers.py
+++ b/pgml-extension/src/bindings/transformers.py
@@ -43,6 +43,44 @@
__cache_transform_pipeline_by_task = {}
+DTYPE_MAP = {
+ "uint8": torch.uint8,
+ "int8": torch.int8,
+ "int16": torch.int16,
+ "int32": torch.int32,
+ "int64": torch.int64,
+ "bfloat16": torch.bfloat16,
+ "float16": torch.float16,
+ "float32": torch.float32,
+ "float64": torch.float64,
+ "complex64": torch.complex64,
+ "complex128": torch.complex128,
+ "bool": torch.bool,
+}
+
+
+def convert_dtype(kwargs):
+ if "torch_dtype" in kwargs:
+ kwargs["torch_dtype"] = DTYPE_MAP[kwargs["torch_dtype"]]
+
+
+def convert_eos_token(tokenizer, args):
+ if "eos_token" in args:
+ args["eos_token_id"] = tokenizer.convert_tokens_to_ids(args.pop("eos_token"))
+ else:
+ args["eos_token_id"] = tokenizer.eos_token_id
+
+
+def ensure_device(kwargs):
+ device = kwargs.get("device")
+ device_map = kwargs.get("device_map")
+ if device is None and device_map is None:
+ if torch.cuda.is_available():
+ kwargs["device"] = "cuda:" + str(os.getpid() % torch.cuda.device_count())
+ else:
+ kwargs["device"] = "cpu"
+
+
class NumpyJSONEncoder(json.JSONEncoder):
def default(self, obj):
if isinstance(obj, np.float32):
@@ -55,9 +93,10 @@ def transform(task, args, inputs):
args = json.loads(args)
inputs = json.loads(inputs)
+ key = ",".join([f"{key}:{val}" for (key, val) in sorted(task.items())])
ensure_device(task)
+ convert_dtype(task)
- key = ",".join([f"{key}:{val}" for (key, val) in sorted(task.items())])
if key not in __cache_transform_pipeline_by_task:
__cache_transform_pipeline_by_task[key] = transformers.pipeline(**task)
pipe = __cache_transform_pipeline_by_task[key]
@@ -65,6 +104,8 @@ def transform(task, args, inputs):
if pipe.task == "question-answering":
inputs = [json.loads(input) for input in inputs]
+ convert_eos_token(pipe.tokenizer, args)
+
return json.dumps(pipe(inputs, **args), cls=NumpyJSONEncoder)
@@ -540,12 +581,3 @@ def generate(model_id, data, config):
return all_preds
-def ensure_device(kwargs):
- device = kwargs.get("device")
- device_map = kwargs.get("device_map")
- if device is None and device_map is None:
- if torch.cuda.is_available():
- kwargs["device"] = "cuda:" + str(os.getpid() % torch.cuda.device_count())
- else:
- kwargs["device"] = "cpu"
-
pFad - Phonifier reborn
Pfad - The Proxy pFad of © 2024 Garber Painting. All rights reserved.
Note: This service is not intended for secure transactions such as banking, social media, email, or purchasing. Use at your own risk. We assume no liability whatsoever for broken pages.
Alternative Proxies:
Alternative Proxy
pFad Proxy
pFad v3 Proxy
pFad v4 Proxy