freeze all requirements and document the project requirements (#1236)

montanalow · Montana Low · web-flow · commit 316a1c61f324 · 2023-12-11T21:28:39.000-06:00
Co-authored-by: Montana Low &lt;montanalow@gmail.com&gt;
diff --git a/packages/postgresml-python/build.sh b/packages/postgresml-python/build.sh
@@ -29,8 +29,6 @@ rm "$deb_dir/release.sh"
 (cat ${SCRIPT_DIR}/DEBIAN/postrm | envsubst '${PGVERSION}') > "$deb_dir/DEBIAN/postrm"
 
 cp ${SCRIPT_DIR}/../../pgml-extension/requirements.txt "$deb_dir/etc/postgresml-python/requirements.txt"
-cp ${SCRIPT_DIR}/../../pgml-extension/requirements-autogptq.txt "$deb_dir/etc/postgresml-python/requirements-autogptq.txt"
-cp ${SCRIPT_DIR}/../../pgml-extension/requirements-xformers.txt "$deb_dir/etc/postgresml-python/requirements-xformers.txt"
 
 virtualenv --python="python$PYTHON_VERSION" "$deb_dir/var/lib/postgresml-python/pgml-venv"
 source "$deb_dir/var/lib/postgresml-python/pgml-venv/bin/activate"
diff --git a/pgml-cms/docs/resources/developer-docs/installation.md b/pgml-cms/docs/resources/developer-docs/installation.md
@@ -63,8 +63,7 @@ To install the necessary Python packages into a virtual environment, use the `vi
 ```bash
 virtualenv pgml-venv && \
 source pgml-venv/bin/activate && \
-pip install -r requirements.txt && \
-pip install -r requirements-xformers.txt --no-dependencies
+pip install -r requirements.txt
 ```
 {% endtab %}
 
diff --git a/pgml-extension/examples/multi_classification.sql b/pgml-extension/examples/multi_classification.sql
@@ -31,9 +31,9 @@ LIMIT 10;
 
 -- linear models
 SELECT * FROM pgml.train('Iris Flower Types', algorithm => 'ridge');
-SELECT * FROM pgml.train('Iris Flower Types', algorithm => 'stochastic_gradient_descent');
-SELECT * FROM pgml.train('Iris Flower Types', algorithm => 'perceptron');
-SELECT * FROM pgml.train('Iris Flower Types', algorithm => 'passive_aggressive');
+--SELECT * FROM pgml.train('Iris Flower Types', algorithm => 'stochastic_gradient_descent');
+--SELECT * FROM pgml.train('Iris Flower Types', algorithm => 'perceptron');
+--SELECT * FROM pgml.train('Iris Flower Types', algorithm => 'passive_aggressive');
 
 -- support vector machines
 SELECT * FROM pgml.train('Iris Flower Types', algorithm => 'svm');
diff --git a/pgml-extension/examples/transformers.sql b/pgml-extension/examples/transformers.sql
@@ -8,6 +8,19 @@ SELECT pgml.embed('intfloat/e5-small', 'hi mom', '{"device": "cpu"}');
 
 SELECT pgml.embed('hkunlp/instructor-xl', 'hi mom', '{"instruction": "Encode it with love"}');
 
+SELECT pgml.transform_stream(
+  task   => '{
+    "task": "text-generation",
+    "model": "TheBloke/zephyr-7B-beta-GPTQ",
+    "model_type": "mistral",
+    "revision": "main",
+    "device_map": "auto"
+  }'::JSONB,
+  input => 'AI is going to',
+  args   => '{
+    "max_new_tokens": 100
+  }'::JSONB
+);
 -- BitsAndBytes support
 SELECT pgml.transform(
     task => '{
diff --git a/pgml-extension/requirements-autogptq.txt b/pgml-extension/requirements-autogptq.txt
diff --git a/pgml-extension/requirements-xformers.txt b/pgml-extension/requirements-xformers.txt
diff --git a/pgml-extension/requirements.base.txt b/pgml-extension/requirements.base.txt
@@ -0,0 +1,41 @@
+# The immediate dependencies of PostgresML are maintained here.
+
+# Locked because newer versions have bugs
+transformers-stream-generator==0.0.4
+optimum==1.13.2
+peft==0.6.2
+pyarrow==11.0.0
+torch==2.0.1 # 2.1.1 breaks sentence-transformers==2.2.2
+
+# ML
+catboost
+lightgbm
+torchaudio
+torchvision
+xgboost
+
+# Transformers
+accelerate
+auto-gptq; sys_platform == 'linux'
+bitsandbytes
+ctransformers
+huggingface-hub
+deepspeed
+einops
+tokenizers
+transformers
+xformers; sys_platform == 'linux'
+
+# Embeddings
+InstructorEmbedding
+sentence-transformers
+
+# Ratings
+rouge
+sacrebleu
+sacremoses
+
+# Utils
+datasets
+orjson
+langchain
diff --git a/pgml-extension/requirements.txt b/pgml-extension/requirements.txt
@@ -1,32 +1,101 @@
-accelerate==0.22.0
-bitsandbytes==0.41.1
-catboost==1.2
+accelerate==0.25.0
+aiohttp==3.9.1
+aiosignal==1.3.1
+annotated-types==0.6.0
+anyio==4.1.0
+attrs==23.1.0
+bitsandbytes==0.41.3.post2
+catboost==1.2.2
+certifi==2023.11.17
+charset-normalizer==3.3.2
+click==8.1.7
+colorama==0.4.6
+contourpy==1.2.0
 ctransformers==0.2.27
-datasets==2.14.5
-deepspeed==0.10.3
-huggingface-hub==0.17.1
+cycler==0.12.1
+dataclasses-json==0.6.3
+datasets==2.15.0
+deepspeed==0.12.4
+dill==0.3.7
+einops==0.7.0
+filelock==3.13.1
+fonttools==4.46.0
+frozenlist==1.4.0
+fsspec==2023.10.0
+graphviz==0.20.1
+hjson==3.1.0
+huggingface-hub==0.19.4
+idna==3.6
 InstructorEmbedding==1.0.1
+Jinja2==3.1.2
+joblib==1.3.2
+jsonpatch==1.33
+jsonpointer==2.4
+kiwisolver==1.4.5
+langchain==0.0.349
+langchain-community==0.0.1
+langchain-core==0.0.13
+langsmith==0.0.69
 lightgbm==4.1.0
-orjson==3.9.7
-pandas==2.1.0
-rich==13.5.2
+lxml==4.9.3
+MarkupSafe==2.1.3
+marshmallow==3.20.1
+matplotlib==3.8.2
+mpmath==1.3.0
+multidict==6.0.4
+multiprocess==0.70.15
+mypy-extensions==1.0.0
+networkx==3.2.1
+ninja==1.11.1.1
+nltk==3.8.1
+numpy==1.26.2
+optimum==1.13.2
+orjson==3.9.10
+packaging==23.2
+pandas==2.1.4
+peft==0.6.2
+Pillow==10.1.0
+plotly==5.18.0
+portalocker==2.8.2
+psutil==5.9.6
+py-cpuinfo==9.0.0
+pyarrow==14.0.1
+pyarrow-hotfix==0.6
+pydantic==2.5.2
+pydantic_core==2.14.5
+pynvml==11.5.0
+pyparsing==3.1.1
+python-dateutil==2.8.2
+pytz==2023.3.post1
+PyYAML==6.0.1
+regex==2023.10.3
+requests==2.31.0
 rouge==1.0.1
-sacrebleu==2.3.1
-sacremoses==0.0.53
-scikit-learn==1.3.0
-sentencepiece==0.1.99
+sacrebleu==2.3.3
+sacremoses==0.1.1
+safetensors==0.4.1
+scikit-learn==1.3.2
+scipy==1.11.4
 sentence-transformers==2.2.2
-tokenizers==0.14.1 
+sentencepiece==0.1.99
+six==1.16.0
+sniffio==1.3.0
+SQLAlchemy==2.0.23
+sympy==1.12
+tabulate==0.9.0
+tenacity==8.2.3
+threadpoolctl==3.2.0
+tokenizers==0.15.0
 torch==2.0.1
 torchaudio==2.0.2
 torchvision==0.15.2
 tqdm==4.66.1
-transformers==4.34.1
-xgboost==2.0.0
-langchain==0.0.287
-einops==0.6.1
-pynvml==11.5.0
+transformers==4.36.0
 transformers-stream-generator==0.0.4
-optimum==1.13.2
-peft==0.6.2
-pyarrow==11.0.0
+typing-inspect==0.9.0
+typing_extensions==4.9.0
+tzdata==2023.3
+urllib3==2.1.0
+xgboost==2.0.2
+xxhash==3.4.1
+yarl==1.9.4
diff --git a/pgml-extension/src/bindings/transformers/transformers.py b/pgml-extension/src/bindings/transformers/transformers.py
@@ -3,7 +3,6 @@
 import shutil
 import time
 import queue
-import sys
 
 import datasets
 from InstructorEmbedding import INSTRUCTOR
@@ -42,7 +41,6 @@
     Trainer,
 )
 from threading import Thread
-from typing import Optional
 
 __cache_transformer_by_model_id = {}
 __cache_sentence_transformer_by_name = {}
@@ -393,42 +391,28 @@ def transform(task, args, inputs, stream=False):
     return orjson.dumps(pipe(inputs, **args), default=orjson_default).decode()
 
 
-def create_embedding(transformer):
+def embed(transformer, inputs, kwargs):
+    kwargs = orjson.loads(kwargs)
+    ensure_device(kwargs)
     instructor = transformer.startswith("hkunlp/instructor")
-    klass = INSTRUCTOR if instructor else SentenceTransformer
-    return klass(transformer)
 
+    # Cache the model
+    if transformer not in __cache_sentence_transformer_by_name:
+        klass = INSTRUCTOR if instructor else SentenceTransformer
+        __cache_sentence_transformer_by_name[transformer] = klass(transformer)
+    model = __cache_sentence_transformer_by_name[transformer]
 
-def embed_using(model, transformer, inputs, kwargs):
-    if isinstance(kwargs, str):
-        kwargs = orjson.loads(kwargs)
-
-    instructor = transformer.startswith("hkunlp/instructor")
+    # Handle instruction encoding
     if instructor:
         texts_with_instructions = []
         instruction = kwargs.pop("instruction")
         for text in inputs:
             texts_with_instructions.append([instruction, text])
-
         inputs = texts_with_instructions
 
     return model.encode(inputs, **kwargs)
 
 
-def embed(transformer, inputs, kwargs):
-    kwargs = orjson.loads(kwargs)
-
-    ensure_device(kwargs)
-
-    if transformer not in __cache_sentence_transformer_by_name:
-        __cache_sentence_transformer_by_name[transformer] = create_embedding(
-            transformer
-        )
-    model = __cache_sentence_transformer_by_name[transformer]
-
-    return embed_using(model, transformer, inputs, kwargs)
-
-
 def clear_gpu_cache(memory_usage: None):
     if not torch.cuda.is_available():
         raise PgMLException(f"No GPU available")
