Add llava model bringup

forge/test/models/pytorch/multimodal/llava/test_llava.py

@@ -0,0 +1,65 @@
+# SPDX-FileCopyrightText: © 2024 Tenstorrent AI ULC
+
+# SPDX-License-Identifier: Apache-2.0
+
+
+import pytest
+import torch
+from transformers import AutoProcessor, LlavaForConditionalGeneration
+
+import forge
+from forge.verify.verify import verify
+
+from .utils import load_inputs
+from test.models.utils import Framework, Source, Task, build_module_name
+
+
+class Wrapper(torch.nn.Module):
+    def __init__(self, model):
+        super().__init__()
+        self.model = model
+
+    def forward(self, input_ids, attention_mask, pixel_values):
+        inputs = {"input_ids": input_ids, "attention_mask": attention_mask, "pixel_values": pixel_values}
+        output = self.model(**inputs)
+        return output.logits
+
+
+def load_model(variant):
+    processor = AutoProcessor.from_pretrained(variant)
+    model = LlavaForConditionalGeneration.from_pretrained(variant)
+    model = Wrapper(model)
+    return model, processor
+
+
+variants = ["llava-hf/llava-1.5-7b-hf"]
+
+
+@pytest.mark.nightly
+@pytest.mark.parametrize("variant", variants, ids=variants)
+def test_llava(record_forge_property, variant):
+    # Build Module Name
+    module_name = build_module_name(
+        framework=Framework.PYTORCH,
+        model="llava",
+        variant=variant,
+        task=Task.CONDITIONAL_GENERATION,
+        source=Source.HUGGINGFACE,
+    )
+
+    # Record Forge Property
+    record_forge_property("model_name", module_name)
+
+    framework_model, processor = load_model(variant)
+    image = "https://www.ilankelman.org/stopsigns/australia.jpg"
+    text = "What’s shown in this image?"
+
+    # Input sample
+    input_ids, attn_mask, pixel_values = load_inputs(image, text, processor)
+    inputs = [input_ids, attn_mask, pixel_values]
+
+    # Forge compile framework model
+    compiled_model = forge.compile(framework_model, sample_inputs=inputs, module_name=module_name)
+
+    # Model Verification
+    verify(inputs, framework_model, compiled_model)

forge/test/models/pytorch/multimodal/llava/utils/__init__.py

@@ -0,0 +1,4 @@
+# SPDX-FileCopyrightText: (c) 2024 Tenstorrent AI ULC
+#
+# SPDX-License-Identifier: Apache-2.0
+from .utils import load_inputs, load_model

forge/test/models/pytorch/multimodal/llava/utils/utils.py

@@ -0,0 +1,39 @@
+# SPDX-FileCopyrightText: (c) 2024 Tenstorrent AI ULC
+#
+# SPDX-License-Identifier: Apache-2.0
+import re
+
+import requests
+from PIL import Image
+
+
+def is_url(url):
+    regex = r"^(https?)://[^\s/$.?#].[^\s]*$"
+    return bool(re.match(regex, url))
+
+
+def load_inputs(inp_image, text, processor):
+    conversation = [
+        {
+            "role": "user",
+            "content": [
+                {"type": "image"},
+                {"type": "text", "text": text},
+            ],
+        }
+    ]
+    text_prompt = processor.apply_chat_template(conversation, padding=True, add_generation_prompt=True)
+    if is_url(inp_image):
+        image = Image.open(requests.get(inp_image, stream=True).raw)
+    else:
+        if os.path.isfile(inp_image):
+            image = Image.open(inp_image)
+        else:
+            raise ValueError("Input is neither a valid URL nor a valid file path.")
+
+    inputs = processor(images=image, text=text_prompt, return_tensors="pt")
+    input_ids = inputs["input_ids"]
+    attn_mask = inputs["attention_mask"]
+    pixel_values = inputs["pixel_values"]
+
+    return input_ids, attn_mask, pixel_values

forge/test/models/utils.py

@@ -33,6 +33,7 @@ class Task(StrEnum):
     OBJECT_DETECTION = "obj_det"
     SEMANTIC_SEGMENTATION = "sem_seg"
     MASKED_IMAGE_MODELLING = "masked_img"
+    CONDITIONAL_GENERATION = "cond_gen"
     IMAGE_ENCODING = "img_enc"
     VISUAL_BACKBONE = "visual_bb"