update README instructions; fix linalg import

Author: dsikka

Diff for: setup.py

@@ -162,7 +162,11 @@ def _parse_requirements_file(file_path):
     "haystack_reqs.txt",
 )
 _haystack_integration_deps = _parse_requirements_file(_haystack_requirements_file_path)
-_clip_deps = ["open_clip_torch==2.20.0", "scipy==1.10.1"]
+_clip_deps = [
+    "open_clip_torch==2.20.0",
+    "scipy==1.10.1",
+    f"{'nm-transformers' if is_release else 'nm-transformers-nightly'}",
+]
 
 
 def _check_supported_system():

Diff for: src/deepsparse/clip/README.md

@@ -4,6 +4,7 @@ DeepSparse allows inference on [CLIP](https://github.com/mlfoundations/open_clip
 
 The CLIP integration currently supports the following task:
 - **Zero-shot Image Classification** - Classifying images given possible classes
+- **Caption Generation** - Generate a caption given an image
 
 ## Getting Started
 
@@ -13,20 +14,34 @@ Before you start your adventure with the DeepSparse Engine, make sure that your
 ```pip install deepsparse[clip]```
 
 ### Model Format
-By default, to deploy CLIP models using the DeepSparse Engine, it is required to supply the model in the ONNX format. This grants the engine the flexibility to serve any model in a framework-agnostic environment. To see examples of pulling CLIP models and exporting them to ONNX, please see the [sparseml documentation](https://github.com/neuralmagic/sparseml/tree/main/integrations/clip). For the Zero-shot image classification workflow, two ONNX models are required, a visual model for CLIP's visual branch, and a text model for CLIP's text branch. Both of these model should be produced through the sparseml integration linked above.
+By default, to deploy CLIP models using the DeepSparse Engine, it is required to supply the model in the ONNX format. This grants the engine the flexibility to serve any model in a framework-agnostic environment. To see examples of pulling CLIP models and exporting them to ONNX, please see the [sparseml documentation](https://github.com/neuralmagic/sparseml/tree/main/integrations/clip). 
+
+For the Zero-shot image classification workflow, two ONNX models are required, a visual model for CLIP's visual branch, and a text model for CLIP's text branch. Both of these models can be produced through the sparseml integration linked above. For caption generation, specific models called CoCa models are required and instructions on how to export CoCa models are also provided in the sparseml documentation above. The CoCa exporting pathway will generate one additional decoder model, along with the text and visual models.
 
 ### Deployment examples:
-The following example uses pipelines to run the CLIP models for inference. As input, the pipeline ingests a list of images and a list of possible classes. A class is returned for each of the provided images.
+The following example uses pipelines to run the CLIP models for inference. For Zero-shot prediction, the pipeline ingests a list of images and a list of possible classes. A class is returned for each of the provided images. For caption generation, only an image file is required.
 
 If you don't have images ready, pull down the sample images using the following commands:
 
 ```bash
 wget -O basilica.jpg https://raw.githubusercontent.com/neuralmagic/deepsparse/main/src/deepsparse/yolo/sample_images/basilica.jpg
+```
 
+```bash
 wget -O buddy.jpeg https://raw.githubusercontent.com/neuralmagic/deepsparse/main/tests/deepsparse/pipelines/sample_images/buddy.jpeg
 ```
 
-This will pull down two images, one with a happy dog and one with St.Peter's basilica. 
+```bash
+wget -O thailand.jpg https://raw.githubusercontent.com/neuralmagic/deepsparse/main/src/deepsparse/yolact/sample_images/thailand.jpg
+```
+
+<img width="333" src="https://raw.githubusercontent.com/neuralmagic/deepsparse/main/src/deepsparse/yolo/sample_images/basilica.jpg">
+
+<img width="333" src="https://raw.githubusercontent.com/neuralmagic/deepsparse/main/tests/deepsparse/pipelines/sample_images/buddy.jpeg">
+
+<img width="333" src="https://raw.githubusercontent.com/neuralmagic/deepsparse/main/src/deepsparse/yolact/sample_images/thailand.jpg">
+
+This will pull down 3 images, a happy dog, St.Peter's basilica, and two elephants.
 
 #### Zero-shot Prediction
 
@@ -43,7 +58,7 @@ from deepsparse.clip import (
 )
 
 possible_classes = ["ice cream", "an elephant", "a dog", "a building", "a church"]
-images = ["basilica.jpg", "buddy.jpeg"]
+images = ["basilica.jpg", "buddy.jpeg", "thailand.jpg"]
 
 model_path_text = "zeroshot_research/text/model.onnx"
 model_path_visual = "zeroshot_research/visual/model.onnx"
@@ -72,4 +87,36 @@ DeepSparse, Copyright 2021-present / Neuralmagic, Inc. version: 1.6.0.20230727 C
 
 Image basilica.jpg is a picture of a church
 Image buddy.jpeg is a picture of a dog
+Image thailand.jpg is a picture of an elephant
+```
+
+#### Caption Generation
+Let's try a caption generation example. We'll leverage the `thailand.jpg` file that was pulled down earlier.
+
+```python
+from deepsparse import BasePipeline
+from deepsparse.clip import CLIPCaptionInput, CLIPVisualInput
+
+root = "caption_models"
+model_path_visual = f"{root}/clip_visual.onnx"
+model_path_text = f"{root}/clip_text.onnx"
+model_path_decoder = f"{root}/clip_text_decoder.onnx"
+
+kwargs = {
+    "visual_model_path": model_path_visual,
+    "text_model_path": model_path_text,
+    "decoder_model_path": model_path_decoder,
+}
+pipeline = BasePipeline.create(task="clip_caption", **kwargs)
+
+pipeline_input = CLIPCaptionInput(image=CLIPVisualInput(images="thailand.jpg"))
+output = pipeline(pipeline_input)
+print(output[0])
+```
+Running the code above, we get the following caption:
+
+```
+DeepSparse, Copyright 2021-present / Neuralmagic, Inc. version: 1.6.0.20230727 COMMUNITY | (3cb4a3e5) (optimized) (system=avx2, binary=avx2)
+
+an adult elephant and a baby elephant .
 ```

Diff for: src/deepsparse/clip/zeroshot_pipeline.py

@@ -15,7 +15,7 @@
 from typing import Any, List, Type
 
 import numpy as np
-from numpy import linalg as la
+from numpy import linalg as lingalg
 from pydantic import BaseModel, Field
 
 from deepsparse.clip import CLIPTextInput, CLIPVisualInput
@@ -82,8 +82,8 @@ def __call__(self, *args, **kwargs):
         visual_output = self.visual(pipeline_inputs.image).image_embeddings[0]
         text_output = self.text(pipeline_inputs.text).text_embeddings[0]
 
-        visual_output /= la.norm(visual_output, axis=-1, keepdims=True)
-        text_output /= la.norm(text_output, axis=-1, keepdims=True)
+        visual_output /= lingalg.norm(visual_output, axis=-1, keepdims=True)
+        text_output /= lingalg.norm(text_output, axis=-1, keepdims=True)
 
         output_product = 100.0 * visual_output @ text_output.T
         text_probs = softmax(output_product, axis=-1)