Update app.py

app.py

@@ -1,129 +1,3 @@
-import gradio as gr
-import os, gc, copy, torch
-from datetime import datetime
-from huggingface_hub import hf_hub_download
-from transformers import CLIPVisionModel
-import torch.nn as nn
-import torch.nn.functional as F
-
-ctx_limit = 3500
-title = "rwkv1b5-vitl336p14-577token_mix665k_rwkv"
-
-os.environ["RWKV_JIT_ON"] = '1'
-os.environ["RWKV_CUDA_ON"] = '0' # if '1' then use CUDA kernel for seq mode (much faster)
-
-from rwkv.model import RWKV
-model_path = hf_hub_download(repo_id="howard-hou/visualrwkv-5", filename=f"{title}.pth")
-model = RWKV(model=model_path, strategy='cpu fp32')
-from rwkv.utils import PIPELINE, PIPELINE_ARGS
-pipeline = PIPELINE(model, "rwkv_vocab_v20230424")
-
-
-class VisualRWKV(nn.Module):
- def __init__(self, args):
- super().__init__()
- self.args = args
- self.vit = CLIPVisionModel.from_pretrained(args.vision_tower_name)
- self.proj = nn.Linear(self.vit.config.hidden_size, args.n_embd, bias=False)
-
- def encode_images(self, images):
- B, N, C, H, W = images.shape
- images = images.view(B*N, C, H, W)
- image_features = self.vit(images).last_hidden_state
- L, D = image_features.shape[1], image_features.shape[2]
- # rerange [B*N, L, D] -> [B, N, L, D]
- image_features = image_features.view(B, N, L, D)[:, 0, :, :]
- image_features = self.grid_pooling(image_features)
- return self.proj(image_features)
- 
- def grid_pooling(self, image_features):
- if self.args.grid_size == -1: # no grid pooling
- return image_features
- if self.args.grid_size == 0: # take cls token
- return image_features[:, 0:1, :]
- if self.args.grid_size == 1: # global avg pooling
- return image_features.mean(dim=1, keepdim=True)
- cls_features = image_features[:, 0:1, :]
- image_features = image_features[:, 1:, :] #drop cls token
- B, L, D = image_features.shape
- H_or_W = int(L**0.5)
- image_features = image_features.view(B, H_or_W, H_or_W, D)
- grid_stride = H_or_W // self.args.grid_size
- image_features = F.avg_pool2d(image_features.permute(0, 3, 1, 2), 
- padding=0,
- kernel_size=grid_stride, 
- stride=grid_stride)
- image_features = image_features.permute(0, 2, 3, 1).view(B, -1, D)
- return torch.cat((cls_features, image_features), dim=1)
-
-
-##########################################################################
-
-
-def generate_prompt(instruction, input=""):
- instruction = instruction.strip().replace('\r\n','\n').replace('\n\n','\n')
- input = input.strip().replace('\r\n','\n').replace('\n\n','\n')
- if input:
- return f"""Instruction: {instruction}
-
-Input: {input}
-
-Response:"""
- else:
- return f"""User: hi
-
-Assistant: Hi. I am your assistant and I will provide expert full response in full details. Please feel free to ask any question and I will always answer it.
-
-User: {instruction}
-
-Assistant:"""
-
-def evaluate(
- ctx,
- token_count=200,
- temperature=1.0,
- top_p=0.7,
- presencePenalty = 0.1,
- countPenalty = 0.1,
-):
- args = PIPELINE_ARGS(temperature = max(0.2, float(temperature)), top_p = float(top_p),
- alpha_frequency = countPenalty,
- alpha_presence = presencePenalty,
- token_ban = [], # ban the generation of some tokens
- token_stop = [0]) # stop generation whenever you see any token here
- ctx = ctx.strip()
- all_tokens = []
- out_last = 0
- out_str = ''
- occurrence = {}
- state = None
- for i in range(int(token_count)):
- out, state = model.forward(pipeline.encode(ctx)[-ctx_limit:] if i == 0 else [token], state)
- for n in occurrence:
- out[n] -= (args.alpha_presence + occurrence[n] * args.alpha_frequency)
-
- token = pipeline.sample_logits(out, temperature=args.temperature, top_p=args.top_p)
- if token in args.token_stop:
- break
- all_tokens += [token]
- for xxx in occurrence:
- occurrence[xxx] *= 0.996 
- if token not in occurrence:
- occurrence[token] = 1
- else:
- occurrence[token] += 1
- 
- tmp = pipeline.decode(all_tokens[out_last:])
- if '\ufffd' not in tmp:
- out_str += tmp
- yield out_str.strip()
- out_last = i + 1
-
- del out
- del state
- gc.collect()
- yield out_str.strip()
-
 import gradio as gr
 import os, gc
 from datetime import datetime
@@ -142,7 +16,7 @@ from rwkv.utils import PIPELINE, PIPELINE_ARGS
 pipeline = PIPELINE(model, "rwkv_vocab_v20230424")
 
 ##########################################################################
-from model import VisualEncoder, EmbeddingMixer, VisualEncoderConfig
+from .model import VisualEncoder, EmbeddingMixer, VisualEncoderConfig
 emb_mixer = EmbeddingMixer(model.w["emb.weight"], num_image_embeddings=4096)
 config = VisualEncoderConfig(n_embd=model.args.n_embd, 
  vision_tower_name='openai/clip-vit-large-patch14-336',