add READMD for llava.py

examples/embd_input/README.md

@@ -0,0 +1,20 @@
+### Examples for input embedding directly
+
+## LLAVA example  (llava.py)
+
+1. obtian llava model (following https://github.com/haotian-liu/LLaVA/ , use https://huggingface.co/liuhaotian/LLaVA-13b-delta-v1-1/)
+2. convert it to ggml format
+3. llava_projection.pth is [pytorch_model-00003-of-00003.bin](https://huggingface.co/liuhaotian/LLaVA-13b-delta-v1-1/blob/main/pytorch_model-00003-of-00003.bin)
+
+```
+import torch
+
+bin_path = "../LLaVA-13b-delta-v1-1/pytorch_model-00003-of-00003.bin"
+pth_path = "./examples/embd_input/llava_projection.pth"
+
+dic = torch.load(bin_path)
+used_key = ["model.mm_projector.weight","model.mm_projector.bias"]
+torch.save({k: dic[k] for k in used_key}, pth_path)
+```
+
+

examples/embd_input/embd_input_lib.cpp

@@ -272,7 +272,10 @@ llama_token sampling_id(struct MyModel* mymodel) {
 const char* sampling(struct MyModel* mymodel) {
     llama_context* ctx = mymodel->ctx;
     int id = sampling_id(mymodel);
-    std::string ret = llama_token_to_str(ctx, id);
+
+    std::string ret;
+    if (id == llama_token_eos()) ret = "</s>";
+    else ret = llama_token_to_str(ctx, id);
     eval_id(mymodel, id);
     return ret.c_str();
 }

examples/embd_input/embd_input_test.cpp

@@ -25,7 +25,7 @@ int main(int argc, char** argv) {
     for (int i=0;i < 500; i++) {
         // int id = sampling_id(mymodel);
         tmp = sampling(mymodel);
-        if (strlen(tmp) == 0) break;
+        if (strcmp(tmp, "</s>")==0) break;
         printf("%s", tmp); // llama_token_to_str(mymodel->ctx, id));
         fflush(stdout);
         // eval_id(mymodel, id);

examples/embd_input/llava.py

@@ -7,40 +7,72 @@ from torch import nn
 import torch
 from transformers import CLIPVisionModel,  CLIPImageProcessor
 from PIL import Image
+
+# model parameters from 'liuhaotian/LLaVA-13b-delta-v1-1'
 vision_tower = "openai/clip-vit-large-patch14"
+select_hidden_state_layer = -2
+# (vision_config.image_size // vision_config.patch_size) ** 2
+image_token_len = (224//14)**2
 
 class Llava:
-    def __init__(self):
+    def __init__(self, args):
         self.image_processor = CLIPImageProcessor.from_pretrained(vision_tower)
         self.vision_tower = CLIPVisionModel.from_pretrained(vision_tower)
         self.mm_projector = nn.Linear(1024, 5120)
-        self.model = MyModel(["main", "--model", "../llama.cpp/models/ggml-vic13b-q4_1.bin", "-c", "2048"])
+        self.model = MyModel(["main", *args])
+
+    def load_projection(self, path):
+        state = torch.load(path)
+        self.mm_projector.load_state_dict({
+            "weight": state["model.mm_projector.weight"],
+            "bias": state["model.mm_projector.bias"]})
+
+    def chat(self, question):
+        self.model.eval_string("user: ")
+        self.model.eval_string(question)
+        self.model.eval_string("\nassistant: ")
+        return self.sampling()
 
     def chat_with_image(self, image, question):
         with torch.no_grad():
             embd_image = self.image_processor.preprocess(image, return_tensors='pt')['pixel_values'][0]
             image_forward_out = self.vision_tower(embd_image.unsqueeze(0), output_hidden_states=True)
-            select_hidden_state_layer = -2
             select_hidden_state = image_forward_out.hidden_states[select_hidden_state_layer]
             image_feature = select_hidden_state[:, 1:]
             embd_image = self.mm_projector(image_feature)
-            embd_image = embd_image.cpu().numpy()
+            embd_image = embd_image.cpu().numpy()[0]
         self.model.eval_string("user: ")
-        # print(embd_image.shape)
+        self.model.eval_token(32003-2) # im_start
         self.model.eval_float(embd_image.T)
+        for i in range(image_token_len-embd_image.shape[0]):
+            self.model.eval_token(32003-3) # im_patch
+        self.model.eval_token(32003-1) # im_end
         self.model.eval_string(question)
         self.model.eval_string("\nassistant: ")
-        ret = ""
+        return self.sampling()
+
+    def sampling(self):
+        ret = b""
         for _ in range(500):
-            tmp = self.model.sampling().decode()
-            if tmp == "":
+            tmp = self.model.sampling() # .decode()
+            if tmp == b"</s>":
                 break
             ret += tmp
-        return ret
+        return ret.decode()
+
+if __name__=="__main__":
+    # model form liuhaotian/LLaVA-13b-delta-v1-1
+    a = Llava(["--model", "./models/ggml-llava-13b-v1.1.bin", "-c", "2048"])
+    # Extract from https://huggingface.co/liuhaotian/LLaVA-13b-delta-v1-1/blob/main/pytorch_model-00003-of-00003.bin.
+    # Also here can use pytorch_model-00003-of-00003.bin directly.
+    a.load_projection(os.path.join(
+        os.path.dirname(__file__) ,
+        "llava_projetion.pth"))
+    respose = a.chat_with_image(
+        Image.open("./media/llama1-logo.png").convert('RGB'),
+        "what is the text in the picture?")
+    print(respose)
+    print(a.chat("what is the color of it?"))
 
-a = Llava()
-state = torch.load(os.path.dirname(__file__) + "/a.pth")
-a.mm_projector.load_state_dict({"weight": state["model.mm_projector.weight"], "bias": state["model.mm_projector.bias"]})
-print(a.chat_with_image(Image.open("./media/llama1-logo.png").convert('RGB'), "what is the text in the picture?"))