sampling : refactor init to use llama_sampling_params (#3696)

* sampling : refactor init to use llama_sampling_params

* llama : combine repetition, frequency and presence penalties in 1 call

* examples : remove embd-input and gptneox-wip

* sampling : rename penalty params + reduce size of "prev" vector

* sampling : add llama_sampling_print helper

* sampling : hide prev behind API and apply #3661

ggml-ci

1 files changed, 0 insertions, 71 deletions

diff --git a/examples/embd-input/llava.py b/examples/embd-input/llava.py
deleted file mode 100755
index 06fad55f..00000000
--- a/examples/embd-input/llava.py
+++ /dev/null
@@ -1,71 +0,0 @@
-#!/usr/bin/env python3
-import sys
-import os
-sys.path.insert(0, os.path.dirname(__file__))
-from embd_input import MyModel
-import numpy as np
-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, 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", *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.model.generate_with_print()
-
-    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 = 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()[0]
-        self.model.eval_string("user: ")
-        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: ")
-        return self.model.generate_with_print()
-
-
-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_projection.pth"))
-    respose = a.chat_with_image(
-        Image.open("./media/llama1-logo.png").convert('RGB'),
-        "what is the text in the picture?")
-    respose
-    a.chat("what is the color of it?")
-
-
-