Merge branch 'main' into draft-demo

LICENSE

@@ -1,8 +1,7 @@
 Copyright 2020-2022 Jina AI Limited.  All rights reserved.
 
-The following three files are licensed under MIT License via https://github.com/openai/CLIP Copyright (c) 2021 OpenAI
+The following two files are licensed under MIT License via https://github.com/openai/CLIP Copyright (c) 2021 OpenAI
     server/clip_server/model/clip.py
-    server/clip_server/model/model.py
     server/clip_server/model/simple_tokenizer.py
 
 

server/clip_server/executors/clip_onnx.py

@@ -12,6 +12,7 @@
 )
 from clip_server.model import clip
 from clip_server.model.clip_onnx import CLIPOnnxModel
+from clip_server.model.tokenization import Tokenizer
 from jina import Executor, requests, DocumentArray
 
 
@@ -31,10 +32,12 @@ def __init__(
         self._minibatch_size = minibatch_size
         self._traversal_paths = traversal_paths
 
-        self._preprocess_tensor = clip._transform_ndarray(clip.MODEL_SIZE[name])
         self._pool = ThreadPool(processes=num_worker_preprocess)
 
         self._model = CLIPOnnxModel(name, model_path)
+        self._tokenizer = Tokenizer(name)
+
+        self._image_transform = clip._transform_ndarray(clip.MODEL_SIZE[name])
 
         import torch
 
@@ -84,15 +87,15 @@ def _preproc_images(self, docs: 'DocumentArray'):
             documentation='images preprocess time in seconds',
         ):
             return preproc_image(
-                docs, preprocess_fn=self._preprocess_tensor, return_np=True
+                docs, preprocess_fn=self._image_transform, return_np=True
             )
 
     def _preproc_texts(self, docs: 'DocumentArray'):
         with self.monitor(
             name='preprocess_texts_seconds',
             documentation='texts preprocess time in seconds',
         ):
-            return preproc_text(docs, return_np=True)
+            return preproc_text(docs, tokenizer=self._tokenizer, return_np=True)
 
     @requests(on='/rank')
     async def rank(self, docs: 'DocumentArray', parameters: Dict, **kwargs):

server/clip_server/executors/clip_tensorrt.py

@@ -9,6 +9,7 @@
     set_rank,
 )
 from clip_server.model import clip
+from clip_server.model.tokenization import Tokenizer
 from clip_server.model.clip_trt import CLIPTensorRTModel
 from jina import Executor, requests, DocumentArray
 
@@ -25,7 +26,6 @@ def __init__(
     ):
         super().__init__(**kwargs)
 
-        self._preprocess_tensor = clip._transform_ndarray(clip.MODEL_SIZE[name])
         self._pool = ThreadPool(processes=num_worker_preprocess)
 
         self._minibatch_size = minibatch_size
@@ -48,14 +48,17 @@ def __init__(
 
         self._model.start_engines()
 
+        self._tokenizer = Tokenizer(name)
+        self._image_transform = clip._transform_ndarray(clip.MODEL_SIZE[name])
+
     def _preproc_images(self, docs: 'DocumentArray'):
         with self.monitor(
             name='preprocess_images_seconds',
             documentation='images preprocess time in seconds',
         ):
             return preproc_image(
                 docs,
-                preprocess_fn=self._preprocess_tensor,
+                preprocess_fn=self._image_transform,
                 device=self._device,
                 return_np=False,
             )
@@ -65,7 +68,9 @@ def _preproc_texts(self, docs: 'DocumentArray'):
             name='preprocess_texts_seconds',
             documentation='texts preprocess time in seconds',
         ):
-            return preproc_text(docs, device=self._device, return_np=False)
+            return preproc_text(
+                docs, tokenizer=self._tokenizer, device=self._device, return_np=False
+            )
 
     @requests(on='/rank')
     async def rank(self, docs: 'DocumentArray', parameters: Dict, **kwargs):

server/clip_server/executors/clip_torch.py

@@ -12,13 +12,15 @@
     set_rank,
 )
 from clip_server.model import clip
+from clip_server.model.clip_model import CLIPModel
+from clip_server.model.tokenization import Tokenizer
 from jina import Executor, requests, DocumentArray
 
 
 class CLIPEncoder(Executor):
     def __init__(
         self,
-        name: str = 'ViT-B/32',
+        name: str = 'ViT-B-32-quickgelu::openai',
         device: Optional[str] = None,
         jit: bool = False,
         num_worker_preprocess: int = 4,
@@ -53,12 +55,12 @@ def __init__(
             # For more details, please see https://pytorch.org/docs/stable/generated/torch.set_num_threads.html
             torch.set_num_threads(max(num_threads, 1))
             torch.set_num_interop_threads(1)
+        self._pool = ThreadPool(processes=num_worker_preprocess)
 
-        self._model, self._preprocess_tensor = clip.load(
-            name, device=self._device, jit=jit
-        )
+        self._model = CLIPModel(name, device=self._device, jit=jit, **kwargs)
+        self._tokenizer = Tokenizer(name)
 
-        self._pool = ThreadPool(processes=num_worker_preprocess)
+        self._image_transform = clip._transform_ndarray(self._model.image_size)
 
     def _preproc_images(self, docs: 'DocumentArray'):
         with self.monitor(
@@ -67,7 +69,7 @@ def _preproc_images(self, docs: 'DocumentArray'):
         ):
             return preproc_image(
                 docs,
-                preprocess_fn=self._preprocess_tensor,
+                preprocess_fn=self._image_transform,
                 device=self._device,
                 return_np=False,
             )
@@ -77,7 +79,9 @@ def _preproc_texts(self, docs: 'DocumentArray'):
             name='preprocess_texts_seconds',
             documentation='texts preprocess time in seconds',
         ):
-            return preproc_text(docs, device=self._device, return_np=False)
+            return preproc_text(
+                docs, tokenizer=self._tokenizer, device=self._device, return_np=False
+            )
 
     @requests(on='/rank')
     async def rank(self, docs: 'DocumentArray', parameters: Dict, **kwargs):
@@ -108,7 +112,7 @@ async def encode(self, docs: 'DocumentArray', parameters: Dict = {}, **kwargs):
                         documentation='images encode time in seconds',
                     ):
                         minibatch.embeddings = (
-                            self._model.encode_image(batch_data['pixel_values'])
+                            self._model.encode_image(**batch_data)
                             .cpu()
                             .numpy()
                             .astype(np.float32)
@@ -126,7 +130,7 @@ async def encode(self, docs: 'DocumentArray', parameters: Dict = {}, **kwargs):
                         documentation='texts encode time in seconds',
                     ):
                         minibatch.embeddings = (
-                            self._model.encode_text(batch_data['input_ids'])
+                            self._model.encode_text(**batch_data)
                             .cpu()
                             .numpy()
                             .astype(np.float32)

server/clip_server/executors/helper.py

@@ -4,7 +4,8 @@
 from docarray import Document, DocumentArray
 from docarray.math.distance.numpy import cosine
 
-from clip_server.model import clip
+
+from clip_server.model.tokenization import Tokenizer
 
 
 def numpy_softmax(x: 'np.ndarray', axis: int = -1) -> 'np.ndarray':
@@ -49,10 +50,13 @@ def preproc_image(
 
 
 def preproc_text(
-    da: 'DocumentArray', device: str = 'cpu', return_np: bool = False
+    da: 'DocumentArray',
+    tokenizer: 'Tokenizer',
+    device: str = 'cpu',
+    return_np: bool = False,
 ) -> Tuple['DocumentArray', Dict]:
 
-    inputs = clip.tokenize(da.texts)
+    inputs = tokenizer(da.texts)
     inputs['input_ids'] = inputs['input_ids'].detach()
 
     if return_np:
@@ -113,3 +117,9 @@ def set_rank(docs, _logit_scale=np.exp(4.60517)):
         )
 
         q.matches = final
+
+
+def get_image_size(name: str):
+    from clip_server.model.pretrained_models import _VISUAL_MODEL_IMAGE_SIZE
+
+    return _VISUAL_MODEL_IMAGE_SIZE[name]