diff --git a/docs/assets/create_openapi_with_server_hook.sh b/docs/assets/create_openapi_with_server_hook.sh index b1ca5d95..9dcdfdd0 100755 --- a/docs/assets/create_openapi_with_server_hook.sh +++ b/docs/assets/create_openapi_with_server_hook.sh @@ -16,13 +16,13 @@ cleanup() { trap cleanup EXIT # Start infinity_emb in the background -infinity_emb v2 --log-level error --engine debugengine & +infinity_emb v2 --log-level error --engine debugengine --port 7996 & INFINITY_PID=$! echo "infinity_emb started with PID $INFINITY_PID" # Wait for infinity_emb to be ready for i in {1..10}; do - if wget -q --spider http://0.0.0.0:7997/openapi.json; then + if wget -q --spider http://0.0.0.0:7996/openapi.json; then echo "infinity_emb is ready." break else @@ -32,4 +32,4 @@ for i in {1..10}; do done # Download the openapi.json -wget http://0.0.0.0:7997/openapi.json -O "$SCRIPT_DIR/openapi.json" \ No newline at end of file +wget http://0.0.0.0:7996/openapi.json -O "$SCRIPT_DIR/openapi.json" \ No newline at end of file diff --git a/docs/assets/openapi.json b/docs/assets/openapi.json index 521de4cd..085930bb 100644 --- a/docs/assets/openapi.json +++ b/docs/assets/openapi.json @@ -1 +1 @@ -{"openapi":"3.1.0","info":{"title":"♾️ Infinity - Embedding Inference Server","summary":"Infinity is a high-throughput, low-latency REST API for serving text-embeddings, reranking models and clip. Infinity is developed under MIT License at https://github.com/michaelfeil/infinity.","contact":{"name":"Michael Feil"},"license":{"name":"MIT License","identifier":"MIT"},"version":"0.0.63"},"paths":{"/health":{"get":{"summary":" Health","description":"health check endpoint\n\nReturns:\n dict(unix=float): dict with unix time stamp","operationId":"health","responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"additionalProperties":{"type":"number"},"type":"object","title":"Response Health"}}}}}}},"/":{"get":{"summary":"Redirect","operationId":"redirect__get","responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{}}}}}}},"/models":{"get":{"summary":" Models","description":"get models endpoint","operationId":"models","responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/OpenAIModelInfo"}}}}}}},"/embeddings":{"post":{"summary":" Embeddings","description":"Encode Embeddings. Supports with multimodal inputs. Aligned with OpenAI Embeddings API.\n\n## Running Text Embeddings\n```python\nimport requests, base64\nrequests.post(\"http://..:7997/embeddings\",\n json={\"model\":\"openai/clip-vit-base-patch32\",\"input\":[\"Two cute cats.\"]})\n```\n\n## Running Image Embeddings\n```python\nrequests.post(\"http://..:7997/embeddings\",\n json={\n \"model\": \"openai/clip-vit-base-patch32\",\n \"encoding_format\": \"base64\",\n \"input\": [\n http://images.cocodataset.org/val2017/000000039769.jpg\",\n # can also be base64 encoded\n ],\n # set extra modality to image to process as image\n \"modality\": \"image\"\n)\n```\n\n## Running Audio Embeddings\n```python\nimport requests, base64\nurl = \"https://github.com/michaelfeil/infinity/raw/3b72eb7c14bae06e68ddd07c1f23fe0bf403f220/libs/infinity_emb/tests/data/audio/beep.wav\"\n\ndef url_to_base64(url, modality = \"image\"):\n '''small helper to convert url to base64 without server requiring access to the url'''\n response = requests.get(url)\n response.raise_for_status()\n base64_encoded = base64.b64encode(response.content).decode('utf-8')\n mimetype = f\"{modality}/{url.split('.')[-1]}\"\n return f\"data:{mimetype};base64,{base64_encoded}\"\n\nrequests.post(\"http://localhost:7997/embeddings\",\n json={\n \"model\": \"laion/larger_clap_general\",\n \"encoding_format\": \"float\",\n \"input\": [\n url, url_to_base64(url, \"audio\")\n ],\n # set extra modality to audio to process as audio\n \"modality\": \"audio\"\n }\n)\n```\n\n## Running via OpenAI Client\n```python\nfrom openai import OpenAI # pip install openai==1.51.0\nclient = OpenAI(base_url=\"http://localhost:7997/\")\nclient.embeddings.create(\n model=\"laion/larger_clap_general\",\n input=[url_to_base64(url, \"audio\")],\n encoding_format= \"base64\",\n extra_body={\n \"modality\": \"audio\"\n }\n)\n\nclient.embeddings.create(\n model=\"laion/larger_clap_general\",\n input=[\"the sound of a beep\", \"the sound of a cat\"],\n encoding_format= \"base64\",\n extra_body={\n \"modality\": \"text\"\n }\n)\n```\n\n### Hint: Run all the above models on one server:\n```bash\ninfinity_emb v2 --model-id BAAI/bge-small-en-v1.5 --model-id openai/clip-vit-base-patch32 --model-id laion/larger_clap_general\n```","operationId":"embeddings","requestBody":{"content":{"application/json":{"schema":{"$ref":"#/components/schemas/MultiModalOpenAIEmbedding"}}},"required":true},"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/OpenAIEmbeddingResult"}}}},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/rerank":{"post":{"summary":" Rerank","description":"Rerank documents. Aligned with Cohere API (https://docs.cohere.com/reference/rerank)\n\n```python\nimport requests\nrequests.post(\"http://..:7997/rerank\",\n json={\n \"model\":\"mixedbread-ai/mxbai-rerank-xsmall-v1\",\n \"query\":\"Where is Munich?\",\n \"documents\":[\"Munich is in Germany.\", \"The sky is blue.\"]\n })\n```","operationId":"rerank","requestBody":{"content":{"application/json":{"schema":{"$ref":"#/components/schemas/RerankInput"}}},"required":true},"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/ReRankResult"}}}},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/classify":{"post":{"summary":" Classify","description":"Score or Classify Sentiments\n\n```python\nimport requests\nrequests.post(\"http://..:7997/classify\",\n json={\"model\":\"SamLowe/roberta-base-go_emotions\",\"input\":[\"I am not having a great day.\"]})\n```","operationId":"classify","requestBody":{"content":{"application/json":{"schema":{"$ref":"#/components/schemas/ClassifyInput"}}},"required":true},"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/ClassifyResult"}}}},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/embeddings_image":{"post":{"summary":"Deprecated: Use `embeddings` with `modality` set to `image`","description":"Encode Embeddings from Image files\n\nSupports URLs of Images and Base64-encoded Images\n\n```python\nimport requests\nrequests.post(\"http://..:7997/embeddings_image\",\n json={\n \"model\":\"openai/clip-vit-base-patch32\",\n \"input\": [\n \"http://images.cocodataset.org/val2017/000000039769.jpg\",\n \"data:image/png;base64,iVBORw0KGgoDEMOoSAMPLEoENCODEDIMAGE\"\n ]\n })\n```","operationId":"embeddings_image","requestBody":{"content":{"application/json":{"schema":{"$ref":"#/components/schemas/ImageEmbeddingInput"}}},"required":true},"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/OpenAIEmbeddingResult"}}}},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}},"deprecated":true}},"/embeddings_audio":{"post":{"summary":"Deprecated: Use `embeddings` with `modality` set to `audio`","description":"Encode Embeddings from Audio files\n\nSupports URLs of Audios and Base64-encoded Audios\n\n```python\nimport requests\nrequests.post(\"http://..:7997/embeddings_audio\",\n json={\n \"model\":\"laion/larger_clap_general\",\n \"input\": [\n \"https://github.com/michaelfeil/infinity/raw/3b72eb7c14bae06e68ddd07c1f23fe0bf403f220/libs/infinity_emb/tests/data/audio/beep.wav\",\n \"data:audio/wav;base64,iVBORw0KGgoDEMOoSAMPLEoENCODEDAUDIO\"\n ]\n })\n```","operationId":"embeddings_audio","requestBody":{"content":{"application/json":{"schema":{"$ref":"#/components/schemas/AudioEmbeddingInput"}}},"required":true},"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/OpenAIEmbeddingResult"}}}},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}},"deprecated":true}},"/metrics":{"get":{"summary":"Metrics","description":"Endpoint that serves Prometheus metrics.","operationId":"metrics_metrics_get","responses":{"200":{"description":"Successful 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the Cohere protocol for Rerankers."},"RerankInput":{"properties":{"query":{"type":"string","maxLength":122880,"title":"Query"},"documents":{"items":{"type":"string","maxLength":122880},"type":"array","maxItems":2048,"minItems":1,"title":"Documents"},"return_documents":{"type":"boolean","title":"Return Documents","default":false},"raw_scores":{"type":"boolean","title":"Raw Scores","default":false},"model":{"type":"string","title":"Model","default":"default/not-specified"},"top_n":{"anyOf":[{"type":"integer","exclusiveMinimum":0.0},{"type":"null"}],"title":"Top N"}},"type":"object","required":["query","documents"],"title":"RerankInput","description":"Input for reranking"},"ValidationError":{"properties":{"loc":{"items":{"anyOf":[{"type":"string"},{"type":"integer"}]},"type":"array","title":"Location"},"msg":{"type":"string","title":"Message"},"type":{"type":"string","title":"Error Type"}},"type":"object","required":["loc","msg","type"],"title":"ValidationError"},"_ClassifyObject":{"properties":{"score":{"type":"number","title":"Score"},"label":{"type":"string","title":"Label"}},"type":"object","required":["score","label"],"title":"_ClassifyObject"},"_EmbeddingObject":{"properties":{"object":{"type":"string","enum":["embedding"],"const":"embedding","title":"Object","default":"embedding"},"embedding":{"anyOf":[{"items":{"type":"number"},"type":"array"},{"type":"string","format":"binary"}],"title":"Embedding"},"index":{"type":"integer","title":"Index"}},"type":"object","required":["embedding","index"],"title":"_EmbeddingObject"},"_OpenAIEmbeddingInput_Text":{"properties":{"model":{"type":"string","title":"Model","default":"default/not-specified"},"encoding_format":{"$ref":"#/components/schemas/EmbeddingEncodingFormat","default":"float"},"user":{"anyOf":[{"type":"string"},{"type":"null"}],"title":"User"},"input":{"anyOf":[{"items":{"type":"string","maxLength":122880},"type":"array","maxItems":2048,"minItems":1},{"type":"string","maxLength":122880}],"title":"Input"},"modality":{"type":"string","enum":["text"],"const":"text","title":"Modality","default":"text"}},"type":"object","required":["input"],"title":"_OpenAIEmbeddingInput_Text","description":"helper"},"_ReRankObject":{"properties":{"relevance_score":{"type":"number","title":"Relevance Score"},"index":{"type":"integer","title":"Index"},"document":{"anyOf":[{"type":"string"},{"type":"null"}],"title":"Document"}},"type":"object","required":["relevance_score","index"],"title":"_ReRankObject"},"_Usage":{"properties":{"prompt_tokens":{"type":"integer","title":"Prompt Tokens"},"total_tokens":{"type":"integer","title":"Total Tokens"}},"type":"object","required":["prompt_tokens","total_tokens"],"title":"_Usage"}}}} \ No newline at end of file +{"openapi":"3.1.0","info":{"title":"♾️ Infinity - Embedding Inference Server","summary":"Infinity is a high-throughput, low-latency REST API for serving text-embeddings, reranking models and clip. Infinity is developed under MIT License at https://github.com/michaelfeil/infinity.","contact":{"name":"Michael Feil"},"license":{"name":"MIT License","identifier":"MIT"},"version":"0.0.63"},"paths":{"/health":{"get":{"summary":" Health","description":"health check endpoint\n\nReturns:\n dict(unix=float): dict with unix time stamp","operationId":"health","responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"additionalProperties":{"type":"number"},"type":"object","title":"Response Health"}}}}}}},"/":{"get":{"summary":"Redirect","operationId":"redirect__get","responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{}}}}}}},"/models":{"get":{"summary":" Models","description":"get models endpoint","operationId":"models","responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/OpenAIModelInfo"}}}}}}},"/embeddings":{"post":{"summary":" Embeddings","description":"Encode Embeddings. Supports with multimodal inputs. Aligned with OpenAI Embeddings API.\n\n## Running Text Embeddings\n```python\nimport requests, base64\nrequests.post(\"http://..:7997/embeddings\",\n json={\"model\":\"openai/clip-vit-base-patch32\",\"input\":[\"Two cute cats.\"]})\n```\n\n## Running Image Embeddings\n```python\nrequests.post(\"http://..:7997/embeddings\",\n json={\n \"model\": \"openai/clip-vit-base-patch32\",\n \"encoding_format\": \"base64\",\n \"input\": [\n \"http://images.cocodataset.org/val2017/000000039769.jpg\",\n # can also be base64 encoded\n ],\n # set extra modality to image to process as image\n \"modality\": \"image\"\n)\n```\n\n## Running Audio Embeddings\n```python\nimport requests, base64\nurl = \"https://github.com/michaelfeil/infinity/raw/3b72eb7c14bae06e68ddd07c1f23fe0bf403f220/libs/infinity_emb/tests/data/audio/beep.wav\"\n\ndef url_to_base64(url, modality = \"image\"):\n '''small helper to convert url to base64 without server requiring access to the url'''\n response = requests.get(url)\n response.raise_for_status()\n base64_encoded = base64.b64encode(response.content).decode('utf-8')\n mimetype = f\"{modality}/{url.split('.')[-1]}\"\n return f\"data:{mimetype};base64,{base64_encoded}\"\n\nrequests.post(\"http://localhost:7997/embeddings\",\n json={\n \"model\": \"laion/larger_clap_general\",\n \"encoding_format\": \"float\",\n \"input\": [\n url, url_to_base64(url, \"audio\")\n ],\n # set extra modality to audio to process as audio\n \"modality\": \"audio\"\n }\n)\n```\n\n## Running via OpenAI Client\n```python\nfrom openai import OpenAI # pip install openai==1.51.0\nclient = OpenAI(base_url=\"http://localhost:7997/\")\nclient.embeddings.create(\n model=\"laion/larger_clap_general\",\n input=[url_to_base64(url, \"audio\")],\n encoding_format=\"float\",\n extra_body={\n \"modality\": \"audio\"\n }\n)\n\nclient.embeddings.create(\n model=\"laion/larger_clap_general\",\n input=[\"the sound of a beep\", \"the sound of a cat\"],\n encoding_format=\"base64\", # base64: optional high performance setting\n extra_body={\n \"modality\": \"text\"\n }\n)\n```\n\n### Hint: Run all the above models on one server:\n```bash\ninfinity_emb v2 --model-id BAAI/bge-small-en-v1.5 --model-id openai/clip-vit-base-patch32 --model-id laion/larger_clap_general\n```","operationId":"embeddings","requestBody":{"content":{"application/json":{"schema":{"$ref":"#/components/schemas/MultiModalOpenAIEmbedding"}}},"required":true},"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/OpenAIEmbeddingResult"}}}},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/rerank":{"post":{"summary":" Rerank","description":"Rerank documents. Aligned with Cohere API (https://docs.cohere.com/reference/rerank)\n\n```python\nimport requests\nrequests.post(\"http://..:7997/rerank\",\n json={\n \"model\":\"mixedbread-ai/mxbai-rerank-xsmall-v1\",\n \"query\":\"Where is Munich?\",\n \"documents\":[\"Munich is in Germany.\", \"The sky is blue.\"]\n })\n```","operationId":"rerank","requestBody":{"content":{"application/json":{"schema":{"$ref":"#/components/schemas/RerankInput"}}},"required":true},"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/ReRankResult"}}}},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/classify":{"post":{"summary":" Classify","description":"Score or Classify Sentiments\n\n```python\nimport requests\nrequests.post(\"http://..:7997/classify\",\n json={\"model\":\"SamLowe/roberta-base-go_emotions\",\"input\":[\"I am not having a great day.\"]})\n```","operationId":"classify","requestBody":{"content":{"application/json":{"schema":{"$ref":"#/components/schemas/ClassifyInput"}}},"required":true},"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/ClassifyResult"}}}},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/embeddings_image":{"post":{"summary":"Deprecated: Use `embeddings` with `modality` set to `image`","description":"Encode Embeddings from Image files\n\nSupports URLs of Images and Base64-encoded Images\n\n```python\nimport requests\nrequests.post(\"http://..:7997/embeddings_image\",\n json={\n \"model\":\"openai/clip-vit-base-patch32\",\n \"input\": [\n \"http://images.cocodataset.org/val2017/000000039769.jpg\",\n \"data:image/png;base64,iVBORw0KGgoDEMOoSAMPLEoENCODEDIMAGE\"\n ]\n 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Score"},"index":{"type":"integer","title":"Index"},"document":{"anyOf":[{"type":"string"},{"type":"null"}],"title":"Document"}},"type":"object","required":["relevance_score","index"],"title":"_ReRankObject"},"_Usage":{"properties":{"prompt_tokens":{"type":"integer","title":"Prompt Tokens"},"total_tokens":{"type":"integer","title":"Total Tokens"}},"type":"object","required":["prompt_tokens","total_tokens"],"title":"_Usage"}}}} \ No newline at end of file diff --git a/libs/client_infinity/run_generate_with_hook.sh b/libs/client_infinity/run_generate_with_hook.sh index 6c316293..1722362d 100755 --- a/libs/client_infinity/run_generate_with_hook.sh +++ b/libs/client_infinity/run_generate_with_hook.sh @@ -14,13 +14,13 @@ cleanup() { trap cleanup EXIT # Start infinity_emb in the background -infinity_emb v2 --log-level error --engine debugengine & +infinity_emb v2 --log-level error --engine debugengine --no-model-warmup --port 7994 & INFINITY_PID=$! echo "infinity_emb started with PID $INFINITY_PID" # Wait for infinity_emb to be ready for i in {1..10}; do - if wget -q --spider http://0.0.0.0:7997/openapi.json; then + if wget -q --spider http://0.0.0.0:7994/openapi.json; then echo "infinity_emb is ready." break else diff --git a/libs/client_infinity/run_tests_with_hook.sh b/libs/client_infinity/run_tests_with_hook.sh index 5ea1d773..eba6b858 100755 --- a/libs/client_infinity/run_tests_with_hook.sh +++ b/libs/client_infinity/run_tests_with_hook.sh @@ -14,13 +14,13 @@ cleanup() { trap cleanup EXIT # Start infinity_emb in the background -infinity_emb v2 --log-level error --engine debugengine & +infinity_emb v2 --log-level error --engine debugengine --port 7993 & INFINITY_PID=$! echo "infinity_emb started with PID $INFINITY_PID" # Wait for infinity_emb to be ready for i in {1..10}; do - if wget -q --spider http://0.0.0.0:7997/openapi.json; then + if wget -q --spider http://0.0.0.0:7993/openapi.json; then echo "infinity_emb is ready." break else @@ -32,7 +32,7 @@ done # Run the tests pip install openapi-python-client==0.21.1 openapi-python-client generate \ - --url http://0.0.0.0:7997/openapi.json \ + --url http://0.0.0.0:7993/openapi.json \ --config client_config.yaml \ --overwrite \ --custom-template-path=./template diff --git a/libs/client_infinity/tests/conftest.py b/libs/client_infinity/tests/conftest.py index 47edbbe1..c435be1f 100644 --- a/libs/client_infinity/tests/conftest.py +++ b/libs/client_infinity/tests/conftest.py @@ -2,7 +2,7 @@ import time import pytest -pytest.URL = "http://0.0.0.0:7997" +pytest.URL = "http://0.0.0.0:7994" @pytest.fixture diff --git a/libs/infinity_emb/infinity_emb/__init__.py b/libs/infinity_emb/infinity_emb/__init__.py index 7a0b6d23..ac3887d8 100644 --- a/libs/infinity_emb/infinity_emb/__init__.py +++ b/libs/infinity_emb/infinity_emb/__init__.py @@ -4,8 +4,6 @@ import importlib.metadata import os -import huggingface_hub.constants # type: ignore - ### Check if HF_HUB_ENABLE_HF_TRANSFER is set, if not try to enable it if "HF_HUB_ENABLE_HF_TRANSFER" not in os.environ: try: @@ -13,9 +11,15 @@ import hf_transfer # type: ignore # noqa # Needs to be at the top of the file / before other + os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "1" + os.environ["HF_HUB_DISABLE_PROGRESS_BARS"] = "1" + import huggingface_hub.constants # type: ignore + huggingface_hub.constants.HF_HUB_ENABLE_HF_TRANSFER = True except ImportError: pass +import huggingface_hub.constants # type: ignore + huggingface_hub.constants.HF_HUB_DISABLE_PROGRESS_BARS = True diff --git a/libs/infinity_emb/infinity_emb/_optional_imports.py b/libs/infinity_emb/infinity_emb/_optional_imports.py index a7fb17a2..64d45bab 100644 --- a/libs/infinity_emb/infinity_emb/_optional_imports.py +++ b/libs/infinity_emb/infinity_emb/_optional_imports.py @@ -57,20 +57,20 @@ def _raise_error(self) -> None: raise ImportError(msg) -CHECK_DISKCACHE = OptionalImports("diskcache", "cache") +CHECK_AIOHTTP = OptionalImports("aiohttp", "server") +CHECK_COLPALI_ENGINE = OptionalImports("colpali_engine", "vision") CHECK_CTRANSLATE2 = OptionalImports("ctranslate2", "ctranslate2") +CHECK_DISKCACHE = OptionalImports("diskcache", "cache") CHECK_FASTAPI = OptionalImports("fastapi", "server") CHECK_ONNXRUNTIME = OptionalImports("optimum.onnxruntime", "optimum") CHECK_OPTIMUM = OptionalImports("optimum", "optimum") CHECK_OPTIMUM_NEURON = OptionalImports("optimum.neuron", "neuronx") -CHECK_SENTENCE_TRANSFORMERS = OptionalImports("sentence_transformers", "torch") -CHECK_TRANSFORMERS = OptionalImports("transformers", "torch") -CHECK_TORCH = OptionalImports("torch.nn", "torch") -# CHECK_REQUESTS = OptionalImports("requests", "server") -CHECK_POSTHOG = OptionalImports("posthog", "server") -CHECK_AIOHTTP = OptionalImports("aiohttp", "server") CHECK_PIL = OptionalImports("PIL", "vision") -CHECK_SOUNDFILE = OptionalImports("soundfile", "audio") +CHECK_POSTHOG = OptionalImports("posthog", "server") CHECK_PYDANTIC = OptionalImports("pydantic", "server") +CHECK_SENTENCE_TRANSFORMERS = OptionalImports("sentence_transformers", "torch") +CHECK_SOUNDFILE = OptionalImports("soundfile", "audio") +CHECK_TORCH = OptionalImports("torch.nn", "torch") +CHECK_TRANSFORMERS = OptionalImports("transformers", "torch") CHECK_TYPER = OptionalImports("typer", "server") CHECK_UVICORN = OptionalImports("uvicorn", "server") diff --git a/libs/infinity_emb/infinity_emb/fastapi_schemas/pymodels.py b/libs/infinity_emb/infinity_emb/fastapi_schemas/pymodels.py index f40bae29..cd031bf1 100644 --- a/libs/infinity_emb/infinity_emb/fastapi_schemas/pymodels.py +++ b/libs/infinity_emb/infinity_emb/fastapi_schemas/pymodels.py @@ -173,7 +173,7 @@ class AudioEmbeddingInput(ImageEmbeddingInput): class _EmbeddingObject(BaseModel): object: Literal["embedding"] = "embedding" - embedding: Union[list[float], bytes] + embedding: Union[list[float], bytes, list[list[float]]] index: int @@ -187,7 +187,7 @@ class OpenAIEmbeddingResult(BaseModel): @staticmethod def to_embeddings_response( - embeddings: Iterable["EmbeddingReturnType"], + embeddings: Union[Iterable["EmbeddingReturnType"], np.ndarray], engine_args: "EngineArgs", usage: int, encoding_format: EmbeddingEncodingFormat = EmbeddingEncodingFormat.float, @@ -198,7 +198,10 @@ def to_embeddings_response( f"model {engine_args.served_model_name} does not support base64 encoding, as it uses uint8-bitpacking with {engine_args.embedding_dtype}" ) embeddings = [base64.b64encode(np.frombuffer(emb.astype(np.float32), dtype=np.float32)) for emb in embeddings] # type: ignore - + elif isinstance(embeddings, np.ndarray): + embeddings = embeddings.tolist() + else: + embeddings = [e.tolist() for e in embeddings] return dict( model=engine_args.served_model_name, data=[ diff --git a/libs/infinity_emb/infinity_emb/inference/select_model.py b/libs/infinity_emb/infinity_emb/inference/select_model.py index 3dad3b74..ea901f8c 100644 --- a/libs/infinity_emb/infinity_emb/inference/select_model.py +++ b/libs/infinity_emb/infinity_emb/inference/select_model.py @@ -11,9 +11,9 @@ from infinity_emb.log_handler import logger from infinity_emb.transformer.abstract import BaseCrossEncoder, BaseEmbedder from infinity_emb.transformer.utils import ( - ClapLikeEngine, - ClipLikeEngine, + AudioEmbedEngine, EmbedderEngine, + ImageEmbedEngine, InferenceEngine, PredictEngine, RerankEngine, @@ -22,7 +22,9 @@ def get_engine_type_from_config( engine_args: EngineArgs, -) -> Union[EmbedderEngine, RerankEngine, PredictEngine, ClipLikeEngine, ClapLikeEngine]: +) -> Union[ + EmbedderEngine, RerankEngine, PredictEngine, ImageEmbedEngine, AudioEmbedEngine +]: """resolved the class of inference engine path from config.json of the repo.""" if engine_args.engine in [InferenceEngine.debugengine]: return EmbedderEngine.from_inference_engine(engine_args.engine) @@ -50,10 +52,10 @@ def get_engine_type_from_config( return RerankEngine.from_inference_engine(engine_args.engine) else: return PredictEngine.from_inference_engine(engine_args.engine) - if config.get("vision_config") and "clip" in config.get("model_type", "").lower(): - return ClipLikeEngine.from_inference_engine(engine_args.engine) + if config.get("vision_config"): + return ImageEmbedEngine.from_inference_engine(engine_args.engine) if config.get("audio_config") and "clap" in config.get("model_type", "").lower(): - return ClapLikeEngine.from_inference_engine(engine_args.engine) + return AudioEmbedEngine.from_inference_engine(engine_args.engine) else: return EmbedderEngine.from_inference_engine(engine_args.engine) diff --git a/libs/infinity_emb/infinity_emb/infinity_server.py b/libs/infinity_emb/infinity_emb/infinity_server.py index b00a2cb4..6b3727bc 100644 --- a/libs/infinity_emb/infinity_emb/infinity_server.py +++ b/libs/infinity_emb/infinity_emb/infinity_server.py @@ -269,7 +269,7 @@ async def _embeddings(data: MultiModalOpenAIEmbedding): "model": "openai/clip-vit-base-patch32", "encoding_format": "base64", "input": [ - http://images.cocodataset.org/val2017/000000039769.jpg", + "http://images.cocodataset.org/val2017/000000039769.jpg", # can also be base64 encoded ], # set extra modality to image to process as image @@ -310,7 +310,7 @@ def url_to_base64(url, modality = "image"): client.embeddings.create( model="laion/larger_clap_general", input=[url_to_base64(url, "audio")], - encoding_format= "base64", + encoding_format="float", extra_body={ "modality": "audio" } @@ -319,7 +319,7 @@ def url_to_base64(url, modality = "image"): client.embeddings.create( model="laion/larger_clap_general", input=["the sound of a beep", "the sound of a cat"], - encoding_format= "base64", + encoding_format="base64", # base64: optional high performance setting extra_body={ "modality": "text" } diff --git a/libs/infinity_emb/infinity_emb/primitives.py b/libs/infinity_emb/infinity_emb/primitives.py index f19ab48c..8cc5f85a 100644 --- a/libs/infinity_emb/infinity_emb/primitives.py +++ b/libs/infinity_emb/infinity_emb/primitives.py @@ -117,6 +117,7 @@ def default_value(): return Device.auto.value def resolve(self) -> Optional[str]: + """gets the torch device string""" if self == Device.auto: return None return self.value @@ -134,6 +135,12 @@ class Dtype(EnumType): def default_value(): return Dtype.auto.value + def resolve(self) -> Optional[str]: + """gets the torch dtype string""" + if self == Dtype.auto: + return None + return self.value + class EmbeddingDtype(EnumType): float32: str = "float32" diff --git a/libs/infinity_emb/infinity_emb/transformer/abstract.py b/libs/infinity_emb/infinity_emb/transformer/abstract.py index d9d43604..3e962796 100644 --- a/libs/infinity_emb/infinity_emb/transformer/abstract.py +++ b/libs/infinity_emb/infinity_emb/transformer/abstract.py @@ -95,7 +95,7 @@ def warmup(self, *, batch_size: int = 64, n_tokens=1) -> tuple[float, float, str return run_warmup(self, inp) -class BaseClipVisionModel(BaseEmbedder): # Inherit from ABC(Abstract base class) +class BaseTIMM(BaseEmbedder): # Inherit from ABC(Abstract base class) capabilities = {"embed", "image_embed"} @property @@ -136,7 +136,7 @@ def warmup(self, *, batch_size: int = 64, n_tokens=1) -> tuple[float, float, str return run_warmup(self, inp) -class BaseClapAudioModel(BaseEmbedder): # Inherit from ABC(Abstract base class) +class BaseAudioEmbedModel(BaseEmbedder): # Inherit from ABC(Abstract base class) capabilities = {"embed", "audio_embed"} @property diff --git a/libs/infinity_emb/infinity_emb/transformer/audio/torch.py b/libs/infinity_emb/infinity_emb/transformer/audio/torch.py index 6ace4540..41ce443d 100644 --- a/libs/infinity_emb/infinity_emb/transformer/audio/torch.py +++ b/libs/infinity_emb/infinity_emb/transformer/audio/torch.py @@ -8,7 +8,7 @@ from infinity_emb._optional_imports import CHECK_TORCH, CHECK_TRANSFORMERS from infinity_emb.args import EngineArgs from infinity_emb.primitives import AudioInputType -from infinity_emb.transformer.abstract import BaseClapAudioModel +from infinity_emb.transformer.abstract import BaseAudioEmbedModel from infinity_emb.transformer.quantization.interface import quant_embedding_decorator if TYPE_CHECKING: @@ -20,7 +20,7 @@ from transformers import AutoModel, AutoProcessor # type: ignore -class ClapLikeModel(BaseClapAudioModel): +class TorchAudioModel(BaseAudioEmbedModel): """Audio model for CLAP models""" def __init__(self, *, engine_args: EngineArgs): diff --git a/libs/infinity_emb/infinity_emb/transformer/embedder/dummytransformer.py b/libs/infinity_emb/infinity_emb/transformer/embedder/dummytransformer.py index 7501679c..54c4b20a 100644 --- a/libs/infinity_emb/infinity_emb/transformer/embedder/dummytransformer.py +++ b/libs/infinity_emb/infinity_emb/transformer/embedder/dummytransformer.py @@ -26,7 +26,7 @@ def encode_core(self, features: np.ndarray) -> EmbeddingReturnType: @quant_embedding_decorator() def encode_post(self, embedding: EmbeddingReturnType): - return embedding + return [e for e in embedding] def tokenize_lengths(self, sentences: list[str]) -> list[int]: return [len(s) for s in sentences] diff --git a/libs/infinity_emb/infinity_emb/transformer/utils.py b/libs/infinity_emb/infinity_emb/transformer/utils.py index aad7af35..5ed56786 100644 --- a/libs/infinity_emb/infinity_emb/transformer/utils.py +++ b/libs/infinity_emb/infinity_emb/transformer/utils.py @@ -5,7 +5,7 @@ from typing import Callable from infinity_emb.primitives import InferenceEngine -from infinity_emb.transformer.audio.torch import ClapLikeModel +from infinity_emb.transformer.audio.torch import TorchAudioModel from infinity_emb.transformer.classifier.torch import SentenceClassifier from infinity_emb.transformer.crossencoder.optimum import OptimumCrossEncoder from infinity_emb.transformer.crossencoder.torch import ( @@ -18,7 +18,7 @@ from infinity_emb.transformer.embedder.sentence_transformer import ( SentenceTransformerPatched, ) -from infinity_emb.transformer.vision.torch_vision import ClipLikeModel +from infinity_emb.transformer.vision.torch_vision import TIMM __all__ = [ "length_tokenizer", @@ -63,26 +63,26 @@ def from_inference_engine(engine: InferenceEngine): raise NotImplementedError(f"RerankEngine for {engine} not implemented") -class ClipLikeEngine(Enum): - torch = ClipLikeModel +class ImageEmbedEngine(Enum): + torch = TIMM @staticmethod def from_inference_engine(engine: InferenceEngine): if engine == InferenceEngine.torch: - return ClipLikeEngine.torch + return ImageEmbedEngine.torch else: - raise NotImplementedError(f"ClipLikeEngine for {engine} not implemented") + raise NotImplementedError(f"ImageEmbedEngine for {engine} not implemented") -class ClapLikeEngine(Enum): - torch = ClapLikeModel +class AudioEmbedEngine(Enum): + torch = TorchAudioModel @staticmethod def from_inference_engine(engine: InferenceEngine): if engine == InferenceEngine.torch: - return ClapLikeEngine.torch + return AudioEmbedEngine.torch else: - raise NotImplementedError(f"ClapLikeEngine for {engine} not implemented") + raise NotImplementedError(f"AudioEmbedEngine for {engine} not implemented") class PredictEngine(Enum): diff --git a/libs/infinity_emb/infinity_emb/transformer/vision/__init__.py b/libs/infinity_emb/infinity_emb/transformer/vision/__init__.py index 10d9165b..c634b229 100644 --- a/libs/infinity_emb/infinity_emb/transformer/vision/__init__.py +++ b/libs/infinity_emb/infinity_emb/transformer/vision/__init__.py @@ -1,2 +1,10 @@ # SPDX-License-Identifier: MIT # Copyright (c) 2023-now michaelfeil + +IMAGE_COL_MODELS = [ + "ColPali", + "ColQwen2", + "ColIdefics2", +] + +IMAGE_MODELS = ["Clip"] + IMAGE_COL_MODELS diff --git a/libs/infinity_emb/infinity_emb/transformer/vision/torch_vision.py b/libs/infinity_emb/infinity_emb/transformer/vision/torch_vision.py index f76cafce..363f3bd1 100644 --- a/libs/infinity_emb/infinity_emb/transformer/vision/torch_vision.py +++ b/libs/infinity_emb/infinity_emb/transformer/vision/torch_vision.py @@ -5,11 +5,20 @@ from typing import TYPE_CHECKING, Any, Iterable, Optional, Union -from infinity_emb._optional_imports import CHECK_TORCH, CHECK_TRANSFORMERS +from infinity_emb._optional_imports import ( + CHECK_COLPALI_ENGINE, + CHECK_PIL, + CHECK_TORCH, + CHECK_TRANSFORMERS, +) from infinity_emb.args import EngineArgs -from infinity_emb.primitives import Dtype -from infinity_emb.transformer.abstract import BaseClipVisionModel -from infinity_emb.transformer.quantization.interface import quant_embedding_decorator +from infinity_emb.primitives import Device, Dtype +from infinity_emb.transformer.abstract import BaseTIMM +from infinity_emb.transformer.quantization.interface import ( + quant_embedding_decorator, + quant_interface, +) +from infinity_emb.transformer.vision import IMAGE_COL_MODELS if TYPE_CHECKING: from PIL.Image import Image as ImageClass @@ -18,49 +27,106 @@ if CHECK_TORCH.is_available: import torch if CHECK_TRANSFORMERS.is_available: - from transformers import AutoModel, AutoProcessor # type: ignore + from transformers import AutoConfig, AutoModel, AutoProcessor # type: ignore +if CHECK_PIL.is_available: + from PIL import Image -class ClipLikeModel(BaseClipVisionModel): +class TIMM(BaseTIMM): """CrossEncoder with .encode_core() and no microbatching""" - def __init__(self, *, engine_args: EngineArgs): + def __init__(self, *, engine_args: "EngineArgs"): CHECK_TORCH.mark_required() CHECK_TRANSFORMERS.mark_required() - self.model = AutoModel.from_pretrained( + config = AutoConfig.from_pretrained( engine_args.model_name_or_path, revision=engine_args.revision, - trust_remote_code=engine_args.trust_remote_code, - # attn_implementation="eager" if engine_args.bettertransformer else None, ) + self.is_colipali = config.architectures[0] in IMAGE_COL_MODELS + self.mock_image = Image.new("RGB", (16, 16), color="black") + if self.is_colipali: + CHECK_COLPALI_ENGINE.mark_required() + from colpali_engine.models import ( # type: ignore + ColIdefics2, + ColIdefics2Processor, + ColPali, + ColPaliProcessor, + ColQwen2, + ColQwen2Processor, + ) + extra_model_args = {} + device = engine_args.device + if device == Device.auto and torch.cuda.is_available(): + device = Device.cuda + if device == "cuda" and engine_args.dtype in (Dtype.float16, Dtype.bfloat16): + extra_model_args["torch_dtype"] = engine_args.dtype.value + elif device == "cuda" and engine_args.dtype in (Dtype.auto): + extra_model_args["torch_dtype"] = "float16" + + if self.is_colipali: + model_cls = { + "ColPali": ColPali, + "ColQwen2": ColQwen2, + "ColIdefics2": ColIdefics2, + }[config.architectures[0]] + processor_cls = { + "ColPali": ColPaliProcessor, + "ColQwen2": ColQwen2Processor, + "ColIdefics2": ColIdefics2Processor, + }[config.architectures[0]] + + self.model = model_cls.from_pretrained( + engine_args.model_name_or_path, + revision=engine_args.revision, + trust_remote_code=engine_args.trust_remote_code, + **extra_model_args, + ) + + self.processor = processor_cls.from_pretrained( + engine_args.model_name_or_path, + revision=engine_args.revision, + trust_remote_code=engine_args.trust_remote_code, + ) + else: + self.model = AutoModel.from_pretrained( + engine_args.model_name_or_path, + revision=engine_args.revision, + trust_remote_code=engine_args.trust_remote_code, + **extra_model_args + # attn_implementation="eager" if engine_args.bettertransformer else None, + ) + + self.processor = AutoProcessor.from_pretrained( + engine_args.model_name_or_path, + revision=engine_args.revision, + trust_remote_code=engine_args.trust_remote_code, + ) + assert hasattr( + self.model, "get_text_features" + ), f"AutoModel of {engine_args.model_name_or_path} does not have get_text_features method" + assert hasattr( + self.model, "get_image_features" + ), f"AutoModel of {engine_args.model_name_or_path} does not have get_image_features method" if torch.cuda.is_available(): self.model = self.model.cuda() if engine_args.dtype in (Dtype.float16, Dtype.auto): self.model = self.model.half() - # self.model = to_bettertransformer( - # self.model, - # engine_args, - # logger, - # ) - self.processor = AutoProcessor.from_pretrained( - engine_args.model_name_or_path, - revision=engine_args.revision, - trust_remote_code=engine_args.trust_remote_code, - ) + + if engine_args.dtype in (Dtype.int8, Dtype.fp8): + self.model = quant_interface(self.model, engine_args.dtype, device=device) self.engine_args = engine_args if engine_args.compile: - self.model.vision_model = torch.compile( - self.model.vision_model, dynamic=True - ) - self.model.text_model = torch.compile(self.model.text_model, dynamic=True) - - assert hasattr( - self.model, "get_text_features" - ), f"AutoModel of {engine_args.model_name_or_path} does not have get_text_features method" - assert hasattr( - self.model, "get_image_features" - ), f"AutoModel of {engine_args.model_name_or_path} does not have get_image_features method" + if self.is_colipali: + self.model = torch.compile(self.model, dynamic=True) + else: + self.model.vision_model = torch.compile( + self.model.vision_model, dynamic=True + ) + self.model.text_model = torch.compile( + self.model.text_model, dynamic=True + ) + self.max_length = None if hasattr(self.model.config, "max_length"): self.max_length = self.model.config.max_length @@ -84,23 +150,44 @@ def encode_pre(self, sentences_or_images: list[Union[str, "ImageClass"]]): else: image_list.append(im_or_text) type_is_img.append(True) - - preprocessed = self.processor( - images=image_list if image_list else None, - text=text_list if text_list else None, - return_tensors="pt", - padding=True, - truncation=True, - max_length=self.max_length, - ) - preprocessed = {k: v.to(self.model.device) for k, v in preprocessed.items()} + if self.is_colipali: + preprocessed_q = {} # type: ignore + if text_list: + preprocessed_q = { + k: v.to(self.model.device) + for k, v in self.processor.process_queries(text_list).items() + } + + preprocessed_i = {} # type: ignore + if image_list: + preprocessed_i = { + k: v.to(self.model.device) + for k, v in self.processor.process_images(image_list).items() + } + preprocessed = (preprocessed_q, preprocessed_i) # type: ignore + else: + preprocessed = self.processor( + images=image_list if image_list else None, + text=text_list if text_list else None, + return_tensors="pt", + padding=True, + truncation=True, + max_length=self.max_length, + ) + preprocessed = {k: v.to(self.model.device) for k, v in preprocessed.items()} # type: ignore return (preprocessed, type_is_img) - def _normalize_cpu(self, tensor: Optional["Tensor"]) -> Iterable["Tensor"]: + def _normalize_cpu( + self, tensor: Optional["Tensor"], normalize: bool + ) -> Iterable["Tensor"]: if tensor is None: return iter([]) - return iter((tensor / tensor.norm(p=2, dim=-1, keepdim=True)).cpu().numpy()) + tensor = tensor.to(torch.float32) + if normalize: + return iter((tensor / tensor.norm(p=2, dim=-1, keepdim=True)).cpu().numpy()) + else: + return iter(tensor.cpu().numpy()) def encode_core( self, features_and_types: tuple[dict[str, "Tensor"], list[bool]] @@ -109,29 +196,37 @@ def encode_core( Computes sentence embeddings """ features, type_is_img = features_and_types + text_embeds, image_embeds = None, None # type: ignore with torch.no_grad(): # TODO: torch.cuda.stream() - if "input_ids" in features: - text_embeds = self.model.get_text_features( - input_ids=features.get("input_ids"), - attention_mask=features.get("attention_mask"), - ) - else: - text_embeds = None # type: ignore - if "pixel_values" in features: - image_embeds = self.model.get_image_features( - pixel_values=features.get("pixel_values"), - ) + if self.is_colipali: + text, image = features + if text: + text_embeds: "Tensor" = self.model.forward( # type: ignore + **text, + ) + if image: + image_embeds: "Tensor" = self.model.forward( # type: ignore + **image, + ) else: - image_embeds = None - - return text_embeds, image_embeds, type_is_img + if "input_ids" in features: + text_embeds: "Tensor" = self.model.get_text_features( # type: ignore + input_ids=features.get("input_ids"), + attention_mask=features.get("attention_mask"), + ) + if "pixel_values" in features: + image_embeds: "Tensor" = self.model.get_image_features( # type: ignore + pixel_values=features.get("pixel_values"), + ) + return text_embeds, image_embeds, type_is_img # type: ignore @quant_embedding_decorator() def encode_post(self, out_features) -> list[float]: text_embeds, image_embeds, type_is_img = out_features - text_embeds = self._normalize_cpu(text_embeds) - image_embeds = self._normalize_cpu(image_embeds) + text_embeds = self._normalize_cpu(text_embeds, normalize=not self.is_colipali) + image_embeds = self._normalize_cpu(image_embeds, normalize=not self.is_colipali) + embeddings = list( next(image_embeds if is_img else text_embeds) for is_img in type_is_img ) @@ -139,6 +234,9 @@ def encode_post(self, out_features) -> list[float]: def tokenize_lengths(self, text_list: list[str]) -> list[int]: preprocessed = 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["big-O", "importlib-resources", "jaraco.functools", "jaraco.itertools", type = ["pytest-mypy"] [extras] -all = ["ctranslate2", "diskcache", "einops", "fastapi", "optimum", "orjson", "pillow", "posthog", "prometheus-fastapi-instrumentator", "pydantic", "rich", "sentence-transformers", "soundfile", "timm", "torch", "typer", "uvicorn"] +all = ["colpali-engine", "ctranslate2", "diskcache", "einops", "fastapi", "optimum", "orjson", "pillow", "posthog", "prometheus-fastapi-instrumentator", "pydantic", "rich", "sentence-transformers", "soundfile", "timm", "torch", "typer", "uvicorn"] audio = ["soundfile"] cache = ["diskcache"] ct2 = ["ctranslate2", "sentence-transformers", "torch", "transformers"] @@ -4808,9 +5065,9 @@ optimum = ["optimum"] server = ["fastapi", "orjson", "posthog", "prometheus-fastapi-instrumentator", "pydantic", "rich", "typer", "uvicorn"] tensorrt = ["tensorrt"] torch = ["sentence-transformers", "torch"] -vision = ["pillow", "timm"] +vision = ["colpali-engine", "pillow", "timm"] [metadata] lock-version = "2.0" python-versions = ">=3.9,<4" -content-hash = "0c763ca18d0acb2628b5f25516cf7702214d22425fc8c6761ff7054c6285e9dd" +content-hash = "ddb2234dae1ebb22503ff767029fcaa8a7f15b394ace3f15864d4547fd866966" diff --git a/libs/infinity_emb/pyproject.toml b/libs/infinity_emb/pyproject.toml index 4e83ebca..acab8757 100644 --- a/libs/infinity_emb/pyproject.toml +++ b/libs/infinity_emb/pyproject.toml @@ -36,6 +36,7 @@ einops = {version = "*", optional=true} # vision pillow = {version = "*", optional=true} timm = {version = "*", optional=true} +colpali-engine = {version="^0.3.1", optional=true} # cache diskcache = {version = "*", optional=true} # gpu @@ -43,6 +44,7 @@ onnxruntime-gpu = {version = "*", optional=true} tensorrt = {version = "^8.6.1", optional=true} soundfile = {version="^0.12.1", optional=true} + [tool.poetry.scripts] infinity_emb = "infinity_emb.infinity_server:cli" @@ -88,7 +90,7 @@ torch=["sentence-transformers","torch"] einops=["einops"] logging=["rich"] cache=["diskcache"] -vision=["pillow","timm"] +vision=["colpali-engine","pillow","timm"] audio=["soundfile"] server=[ "fastapi", @@ -101,6 +103,7 @@ server=[ "uvicorn", ] all=[ + "colpali-engine", "ctranslate2", "diskcache", "einops", diff --git a/libs/infinity_emb/tests/conftest.py b/libs/infinity_emb/tests/conftest.py index e33ced4b..32166ecc 100644 --- a/libs/infinity_emb/tests/conftest.py +++ b/libs/infinity_emb/tests/conftest.py @@ -12,6 +12,7 @@ pytest.DEFAULT_CLASSIFIER_MODEL = "SamLowe/roberta-base-go_emotions" pytest.DEFAULT_AUDIO_MODEL = "laion/clap-htsat-unfused" pytest.DEFAULT_IMAGE_MODEL = "wkcn/TinyCLIP-ViT-8M-16-Text-3M-YFCC15M" +pytest.DEFAULT_IMAGE_COLPALI_MODEL = "michaelfeil/colpali-v12-random-testing" pytest.IMAGE_SAMPLE_URL = "https://github.com/michaelfeil/infinity/raw/06fd1f4d8f0a869f4482fc1c78b62a75ccbb66a1/docs/assets/cats_coco_sample.jpg" pytest.AUDIO_SAMPLE_URL = "https://github.com/michaelfeil/infinity/raw/3b72eb7c14bae06e68ddd07c1f23fe0bf403f220/libs/infinity_emb/tests/data/audio/beep.wav" diff --git a/libs/infinity_emb/tests/unit_test/transformer/audio/test_audio.py b/libs/infinity_emb/tests/unit_test/transformer/audio/test_audio.py index 75b5fdfb..b3099815 100644 --- a/libs/infinity_emb/tests/unit_test/transformer/audio/test_audio.py +++ b/libs/infinity_emb/tests/unit_test/transformer/audio/test_audio.py @@ -7,12 +7,12 @@ from transformers import ClapModel, ClapProcessor # type: ignore from infinity_emb.args import EngineArgs -from infinity_emb.transformer.audio.torch import ClapLikeModel +from infinity_emb.transformer.audio.torch import TorchAudioModel def test_clap_like_model(audio_sample): model_name = pytest.DEFAULT_AUDIO_MODEL - model = ClapLikeModel(engine_args=EngineArgs(model_name_or_path=model_name)) + model = TorchAudioModel(engine_args=EngineArgs(model_name_or_path=model_name)) raw_bytes = audio_sample[0].content data, samplerate = sf.read(io.BytesIO(raw_bytes)) diff --git a/libs/infinity_emb/tests/unit_test/transformer/vision/test_torch_vision.py b/libs/infinity_emb/tests/unit_test/transformer/vision/test_torch_vision.py index c84c1b0c..cea10c0f 100644 --- a/libs/infinity_emb/tests/unit_test/transformer/vision/test_torch_vision.py +++ b/libs/infinity_emb/tests/unit_test/transformer/vision/test_torch_vision.py @@ -1,18 +1,17 @@ import numpy as np import pytest import torch +from colpali_engine.models import ColPali, ColPaliProcessor # type: ignore from PIL import Image # type: ignore from transformers import CLIPModel, CLIPProcessor # type: ignore from infinity_emb.args import EngineArgs -from infinity_emb.transformer.vision.torch_vision import ClipLikeModel +from infinity_emb.transformer.vision.torch_vision import TIMM def test_clip_like_model(image_sample): model_name = pytest.DEFAULT_IMAGE_MODEL - model = ClipLikeModel( - engine_args=EngineArgs(model_name_or_path=model_name, dtype="auto") - ) + model = TIMM(engine_args=EngineArgs(model_name_or_path=model_name, dtype="auto")) image = Image.open(image_sample[0].raw) inputs = [ @@ -43,3 +42,76 @@ def test_clip_like_model(image_sample): outputs.image_embeds[0], embeddings[3], check_dtype=False, rtol=0, atol=1e-3 ) torch.testing.assert_close(embeddings[1], embeddings[3]) + + +@pytest.mark.parametrize("dtype", ["auto"]) +def test_colpali(dtype, image_sample): + model_name = pytest.DEFAULT_IMAGE_COLPALI_MODEL + revision = "main" + + model = TIMM( + engine_args=EngineArgs( + model_name_or_path=model_name, dtype=dtype, revision=revision, device="cpu" + ) + ) + image = Image.open(image_sample[0].raw) + + inputs = [ + "a photo of a cat", + image, + "a photo of a dog", + image, + ] + embeddings = model.encode_post(model.encode_core(model.encode_pre(inputs))) + + assert isinstance(embeddings, list) + assert isinstance(embeddings[0], np.ndarray) + assert len(embeddings) == len(inputs) + embeddings = [torch.tensor(t) for t in embeddings] + + model = None + torch.testing.assert_close(embeddings[1], embeddings[3]) + if dtype == "auto": + model = ColPali.from_pretrained( + model_name, + ).eval() + + processor = ColPaliProcessor.from_pretrained(model_name, revision=revision) + + # Your inputs + images = [ + image, + image, + Image.new("RGB", (32, 32), color="white"), + Image.new("RGB", (16, 16), color="black"), + ] + queries = [ + "a photo of a cat", + "a photo of a dog", + ] + + # Process the inputs + batch_images = processor.process_images(images).to(model.device) + batch_queries = processor.process_queries(queries).to(model.device) + + # Forward pass + with torch.no_grad(): + image_embeddings = model(**batch_images).to("cpu") + query_embeddings = model(**batch_queries).to("cpu") + torch.testing.assert_close( + query_embeddings[0], embeddings[0], check_dtype=False, rtol=0, atol=5e-3 + ) + torch.testing.assert_close( + image_embeddings[0].mean(0), + embeddings[3].mean(0), + check_dtype=False, + rtol=0, + atol=5e-3, + ) + + +if __name__ == "__main__": + import requests + + test_colpali("int8", requests.get(pytest.DEFAULT_IMAGE_SAMPLE, stream=True)) # type: ignore + test_colpali("auto", requests.get(pytest.DEFAULT_IMAGE_SAMPLE, stream=True)) # type: ignore