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| # -*- encoding: utf-8 -*- | |
| ''' | |
| @File : cogvideo_pipeline.py | |
| @Time : 2022/07/15 11:24:56 | |
| @Author : Wenyi Hong | |
| @Version : 1.0 | |
| @Contact : hwy22@mails.tsinghua.edu.cn | |
| ''' | |
| # here put the import lib | |
| import os | |
| import sys | |
| import torch | |
| import argparse | |
| import time | |
| from torchvision.utils import save_image | |
| import stat | |
| from icetk import icetk as tokenizer | |
| import logging, sys | |
| import torch.distributed as dist | |
| tokenizer.add_special_tokens(['<start_of_image>', '<start_of_english>', '<start_of_chinese>']) | |
| from SwissArmyTransformer import get_args | |
| from SwissArmyTransformer.data_utils import BinaryDataset, make_loaders | |
| from SwissArmyTransformer.generation.sampling_strategies import BaseStrategy | |
| from SwissArmyTransformer.generation.utils import timed_name, save_multiple_images, generate_continually | |
| from SwissArmyTransformer.resources import auto_create | |
| from models.cogvideo_cache_model import CogVideoCacheModel | |
| from coglm_strategy import CoglmStrategy | |
| def get_masks_and_position_ids_stage1(data, textlen, framelen): | |
| # Extract batch size and sequence length. | |
| tokens = data | |
| seq_length = len(data[0]) | |
| # Attention mask (lower triangular). | |
| attention_mask = torch.ones((1, textlen+framelen, textlen+framelen), device=data.device) | |
| attention_mask[:, :textlen, textlen:] = 0 | |
| attention_mask[:, textlen:, textlen:].tril_() | |
| attention_mask.unsqueeze_(1) | |
| # Unaligned version | |
| position_ids = torch.zeros(seq_length, dtype=torch.long, | |
| device=data.device) | |
| torch.arange(textlen, out=position_ids[:textlen], | |
| dtype=torch.long, device=data.device) | |
| torch.arange(512, 512+seq_length-textlen, out=position_ids[textlen:], | |
| dtype=torch.long, device=data.device) | |
| position_ids = position_ids.unsqueeze(0) | |
| return tokens, attention_mask, position_ids | |
| def get_masks_and_position_ids_stage2(data, textlen, framelen): | |
| # Extract batch size and sequence length. | |
| tokens = data | |
| seq_length = len(data[0]) | |
| # Attention mask (lower triangular). | |
| attention_mask = torch.ones((1, textlen+framelen, textlen+framelen), device=data.device) | |
| attention_mask[:, :textlen, textlen:] = 0 | |
| attention_mask[:, textlen:, textlen:].tril_() | |
| attention_mask.unsqueeze_(1) | |
| # Unaligned version | |
| position_ids = torch.zeros(seq_length, dtype=torch.long, | |
| device=data.device) | |
| torch.arange(textlen, out=position_ids[:textlen], | |
| dtype=torch.long, device=data.device) | |
| frame_num = (seq_length-textlen)//framelen | |
| assert frame_num == 5 | |
| torch.arange(512, 512+framelen, out=position_ids[textlen:textlen+framelen], | |
| dtype=torch.long, device=data.device) | |
| torch.arange(512+framelen*2, 512+framelen*3, out=position_ids[textlen+framelen:textlen+framelen*2], | |
| dtype=torch.long, device=data.device) | |
| torch.arange(512+framelen*(frame_num-1), 512+framelen*frame_num, out=position_ids[textlen+framelen*2:textlen+framelen*3], | |
| dtype=torch.long, device=data.device) | |
| torch.arange(512+framelen*1, 512+framelen*2, out=position_ids[textlen+framelen*3:textlen+framelen*4], | |
| dtype=torch.long, device=data.device) | |
| torch.arange(512+framelen*3, 512+framelen*4, out=position_ids[textlen+framelen*4:textlen+framelen*5], | |
| dtype=torch.long, device=data.device) | |
| position_ids = position_ids.unsqueeze(0) | |
| return tokens, attention_mask, position_ids | |
| def my_update_mems(hiddens, mems_buffers, mems_indexs, limited_spatial_channel_mem, text_len, frame_len): | |
| if hiddens is None: | |
| return None, mems_indexs | |
| mem_num = len(hiddens) | |
| ret_mem = [] | |
| with torch.no_grad(): | |
| for id in range(mem_num): | |
| if hiddens[id][0] is None: | |
| ret_mem.append(None) | |
| else: | |
| if id == 0 and limited_spatial_channel_mem and mems_indexs[id]+hiddens[0][0].shape[1] >= text_len+frame_len: | |
| if mems_indexs[id] == 0: | |
| for layer, hidden in enumerate(hiddens[id]): | |
| mems_buffers[id][layer, :, :text_len] = hidden.expand(mems_buffers[id].shape[1], -1, -1)[:, :text_len] | |
| new_mem_len_part2 = (mems_indexs[id]+hiddens[0][0].shape[1]-text_len)%frame_len | |
| if new_mem_len_part2 > 0: | |
| for layer, hidden in enumerate(hiddens[id]): | |
| mems_buffers[id][layer, :, text_len:text_len+new_mem_len_part2] = hidden.expand(mems_buffers[id].shape[1], -1, -1)[:, -new_mem_len_part2:] | |
| mems_indexs[id] = text_len+new_mem_len_part2 | |
| else: | |
| for layer, hidden in enumerate(hiddens[id]): | |
| mems_buffers[id][layer, :, mems_indexs[id]:mems_indexs[id]+hidden.shape[1]] = hidden.expand(mems_buffers[id].shape[1], -1, -1) | |
| mems_indexs[id] += hidden.shape[1] | |
| ret_mem.append(mems_buffers[id][:, :, :mems_indexs[id]]) | |
| return ret_mem, mems_indexs | |
| def my_save_multiple_images(imgs, path, subdir, debug=True): | |
| # imgs: list of tensor images | |
| if debug: | |
| imgs = torch.cat(imgs, dim=0) | |
| print("\nSave to: ", path, flush=True) | |
| save_image(imgs, path, normalize=True) | |
| else: | |
| print("\nSave to: ", path, flush=True) | |
| single_frame_path = os.path.join(path, subdir) | |
| os.makedirs(single_frame_path, exist_ok=True) | |
| for i in range(len(imgs)): | |
| save_image(imgs[i], os.path.join(single_frame_path, f'{str(i).rjust(4,"0")}.jpg'), normalize=True) | |
| os.chmod(os.path.join(single_frame_path,f'{str(i).rjust(4,"0")}.jpg'), stat.S_IRWXO+stat.S_IRWXG+stat.S_IRWXU) | |
| save_image(torch.cat(imgs, dim=0), os.path.join(single_frame_path,f'frame_concat.jpg'), normalize=True) | |
| os.chmod(os.path.join(single_frame_path,f'frame_concat.jpg'), stat.S_IRWXO+stat.S_IRWXG+stat.S_IRWXU) | |
| def calc_next_tokens_frame_begin_id(text_len, frame_len, total_len): | |
| # The fisrt token's position id of the frame that the next token belongs to; | |
| if total_len < text_len: | |
| return None | |
| return (total_len-text_len)//frame_len * frame_len + text_len | |
| def my_filling_sequence( | |
| model, | |
| args, | |
| seq, | |
| batch_size, | |
| get_masks_and_position_ids, | |
| text_len, | |
| frame_len, | |
| strategy=BaseStrategy(), | |
| strategy2=BaseStrategy(), | |
| mems=None, | |
| log_text_attention_weights=0, # default to 0: no artificial change | |
| mode_stage1=True, | |
| enforce_no_swin=False, | |
| guider_seq=None, | |
| guider_text_len=0, | |
| guidance_alpha=1, | |
| limited_spatial_channel_mem=False, # 空间通道的存储限制在本帧内 | |
| **kw_args | |
| ): | |
| ''' | |
| seq: [2, 3, 5, ..., -1(to be generated), -1, ...] | |
| mems: [num_layers, batch_size, len_mems(index), mem_hidden_size] | |
| cache, should be first mems.shape[1] parts of context_tokens. | |
| mems are the first-level citizens here, but we don't assume what is memorized. | |
| input mems are used when multi-phase generation. | |
| ''' | |
| if guider_seq is not None: | |
| logging.debug("Using Guidance In Inference") | |
| if limited_spatial_channel_mem: | |
| logging.debug("Limit spatial-channel's mem to current frame") | |
| assert len(seq.shape) == 2 | |
| # building the initial tokens, attention_mask, and position_ids | |
| actual_context_length = 0 | |
| while seq[-1][actual_context_length] >= 0: # the last seq has least given tokens | |
| actual_context_length += 1 # [0, context_length-1] are given | |
| assert actual_context_length > 0 | |
| current_frame_num = (actual_context_length-text_len) // frame_len | |
| assert current_frame_num >= 0 | |
| context_length = text_len + current_frame_num * frame_len | |
| tokens, attention_mask, position_ids = get_masks_and_position_ids(seq, text_len, frame_len) | |
| tokens = tokens[..., :context_length] | |
| input_tokens = tokens.clone() | |
| if guider_seq is not None: | |
| guider_index_delta = text_len - guider_text_len | |
| guider_tokens, guider_attention_mask, guider_position_ids = get_masks_and_position_ids(guider_seq, guider_text_len, frame_len) | |
| guider_tokens = guider_tokens[..., :context_length-guider_index_delta] | |
| guider_input_tokens = guider_tokens.clone() | |
| for fid in range(current_frame_num): | |
| input_tokens[:, text_len+400*fid] = tokenizer['<start_of_image>'] | |
| if guider_seq is not None: | |
| guider_input_tokens[:, guider_text_len+400*fid] = tokenizer['<start_of_image>'] | |
| attention_mask = attention_mask.type_as(next(model.parameters())) # if fp16 | |
| # initialize generation | |
| counter = context_length - 1 # Last fixed index is ``counter'' | |
| index = 0 # Next forward starting index, also the length of cache. | |
| mems_buffers_on_GPU = False | |
| mems_indexs = [0, 0] | |
| mems_len = [(400+74) if limited_spatial_channel_mem else 5*400+74, 5*400+74] | |
| mems_buffers = [torch.zeros(args.num_layers, batch_size, mem_len, args.hidden_size*2, dtype=next(model.parameters()).dtype) | |
| for mem_len in mems_len] | |
| if guider_seq is not None: | |
| guider_attention_mask = guider_attention_mask.type_as(next(model.parameters())) # if fp16 | |
| guider_mems_buffers = [torch.zeros(args.num_layers, batch_size, mem_len, args.hidden_size*2, dtype=next(model.parameters()).dtype) | |
| for mem_len in mems_len] | |
| guider_mems_indexs = [0, 0] | |
| guider_mems = None | |
| torch.cuda.empty_cache() | |
| # step-by-step generation | |
| while counter < len(seq[0]) - 1: | |
| # we have generated counter+1 tokens | |
| # Now, we want to generate seq[counter + 1], | |
| # token[:, index: counter+1] needs forwarding. | |
| if index == 0: | |
| group_size = 2 if (input_tokens.shape[0] == batch_size and not mode_stage1) else batch_size | |
| logits_all = None | |
| for batch_idx in range(0, input_tokens.shape[0], group_size): | |
| logits, *output_per_layers = model( | |
| input_tokens[batch_idx:batch_idx+group_size, index:], | |
| position_ids[..., index: counter+1], | |
| attention_mask, # TODO memlen | |
| mems=mems, | |
| text_len=text_len, | |
| frame_len=frame_len, | |
| counter=counter, | |
| log_text_attention_weights=log_text_attention_weights, | |
| enforce_no_swin=enforce_no_swin, | |
| **kw_args | |
| ) | |
| logits_all = torch.cat((logits_all, logits), dim=0) if logits_all is not None else logits | |
| mem_kv01 = [[o['mem_kv'][0] for o in output_per_layers], [o['mem_kv'][1] for o in output_per_layers]] | |
| next_tokens_frame_begin_id = calc_next_tokens_frame_begin_id(text_len, frame_len, mem_kv01[0][0].shape[1]) | |
| for id, mem_kv in enumerate(mem_kv01): | |
| for layer, mem_kv_perlayer in enumerate(mem_kv): | |
| if limited_spatial_channel_mem and id == 0: | |
| mems_buffers[id][layer, batch_idx:batch_idx+group_size, :text_len] = mem_kv_perlayer.expand(min(group_size, input_tokens.shape[0]-batch_idx), -1, -1)[:, :text_len] | |
| mems_buffers[id][layer, batch_idx:batch_idx+group_size, text_len:text_len+mem_kv_perlayer.shape[1]-next_tokens_frame_begin_id] =\ | |
| mem_kv_perlayer.expand(min(group_size, input_tokens.shape[0]-batch_idx), -1, -1)[:, next_tokens_frame_begin_id:] | |
| else: | |
| mems_buffers[id][layer, batch_idx:batch_idx+group_size, :mem_kv_perlayer.shape[1]] = mem_kv_perlayer.expand(min(group_size, input_tokens.shape[0]-batch_idx), -1, -1) | |
| mems_indexs[0], mems_indexs[1] = mem_kv01[0][0].shape[1], mem_kv01[1][0].shape[1] | |
| if limited_spatial_channel_mem: | |
| mems_indexs[0] -= (next_tokens_frame_begin_id - text_len) | |
| mems = [mems_buffers[id][:, :, :mems_indexs[id]] for id in range(2)] | |
| logits = logits_all | |
| # Guider | |
| if guider_seq is not None: | |
| guider_logits_all = None | |
| for batch_idx in range(0, guider_input_tokens.shape[0], group_size): | |
| guider_logits, *guider_output_per_layers = model( | |
| guider_input_tokens[batch_idx:batch_idx+group_size, max(index-guider_index_delta, 0):], | |
| guider_position_ids[..., max(index-guider_index_delta, 0): counter+1-guider_index_delta], | |
| guider_attention_mask, | |
| mems=guider_mems, | |
| text_len=guider_text_len, | |
| frame_len=frame_len, | |
| counter=counter-guider_index_delta, | |
| log_text_attention_weights=log_text_attention_weights, | |
| enforce_no_swin=enforce_no_swin, | |
| **kw_args | |
| ) | |
| guider_logits_all = torch.cat((guider_logits_all, guider_logits), dim=0) if guider_logits_all is not None else guider_logits | |
| guider_mem_kv01 = [[o['mem_kv'][0] for o in guider_output_per_layers], [o['mem_kv'][1] for o in guider_output_per_layers]] | |
| for id, guider_mem_kv in enumerate(guider_mem_kv01): | |
| for layer, guider_mem_kv_perlayer in enumerate(guider_mem_kv): | |
| if limited_spatial_channel_mem and id == 0: | |
| guider_mems_buffers[id][layer, batch_idx:batch_idx+group_size, :guider_text_len] = guider_mem_kv_perlayer.expand(min(group_size, input_tokens.shape[0]-batch_idx), -1, -1)[:, :guider_text_len] | |
| guider_next_tokens_frame_begin_id = calc_next_tokens_frame_begin_id(guider_text_len, frame_len, guider_mem_kv_perlayer.shape[1]) | |
| guider_mems_buffers[id][layer, batch_idx:batch_idx+group_size, guider_text_len:guider_text_len+guider_mem_kv_perlayer.shape[1]-guider_next_tokens_frame_begin_id] =\ | |
| guider_mem_kv_perlayer.expand(min(group_size, input_tokens.shape[0]-batch_idx), -1, -1)[:, guider_next_tokens_frame_begin_id:] | |
| else: | |
| guider_mems_buffers[id][layer, batch_idx:batch_idx+group_size, :guider_mem_kv_perlayer.shape[1]] = guider_mem_kv_perlayer.expand(min(group_size, input_tokens.shape[0]-batch_idx), -1, -1) | |
| guider_mems_indexs[0], guider_mems_indexs[1] = guider_mem_kv01[0][0].shape[1], guider_mem_kv01[1][0].shape[1] | |
| if limited_spatial_channel_mem: | |
| guider_mems_indexs[0] -= (guider_next_tokens_frame_begin_id-guider_text_len) | |
| guider_mems = [guider_mems_buffers[id][:, :, :guider_mems_indexs[id]] for id in range(2)] | |
| guider_logits = guider_logits_all | |
| else: | |
| if not mems_buffers_on_GPU: | |
| if not mode_stage1: | |
| torch.cuda.empty_cache() | |
| for idx, mem in enumerate(mems): | |
| mems[idx] = mem.to(next(model.parameters()).device) | |
| if guider_seq is not None: | |
| for idx, mem in enumerate(guider_mems): | |
| guider_mems[idx] = mem.to(next(model.parameters()).device) | |
| else: | |
| torch.cuda.empty_cache() | |
| for idx, mem_buffer in enumerate(mems_buffers): | |
| mems_buffers[idx] = mem_buffer.to(next(model.parameters()).device) | |
| mems = [mems_buffers[id][:, :, :mems_indexs[id]] for id in range(2)] | |
| if guider_seq is not None: | |
| for idx, guider_mem_buffer in enumerate(guider_mems_buffers): | |
| guider_mems_buffers[idx] = guider_mem_buffer.to(next(model.parameters()).device) | |
| guider_mems = [guider_mems_buffers[id][:, :, :guider_mems_indexs[id]] for id in range(2)] | |
| mems_buffers_on_GPU = True | |
| logits, *output_per_layers = model( | |
| input_tokens[:, index:], | |
| position_ids[..., index: counter+1], | |
| attention_mask, # TODO memlen | |
| mems=mems, | |
| text_len=text_len, | |
| frame_len=frame_len, | |
| counter=counter, | |
| log_text_attention_weights=log_text_attention_weights, | |
| enforce_no_swin=enforce_no_swin, | |
| limited_spatial_channel_mem=limited_spatial_channel_mem, | |
| **kw_args | |
| ) | |
| mem_kv0, mem_kv1 = [o['mem_kv'][0] for o in output_per_layers], [o['mem_kv'][1] for o in output_per_layers] | |
| if guider_seq is not None: | |
| guider_logits, *guider_output_per_layers = model( | |
| guider_input_tokens[:, max(index-guider_index_delta, 0):], | |
| guider_position_ids[..., max(index-guider_index_delta, 0): counter+1-guider_index_delta], | |
| guider_attention_mask, | |
| mems=guider_mems, | |
| text_len=guider_text_len, | |
| frame_len=frame_len, | |
| counter=counter-guider_index_delta, | |
| log_text_attention_weights=0, | |
| enforce_no_swin=enforce_no_swin, | |
| limited_spatial_channel_mem=limited_spatial_channel_mem, | |
| **kw_args | |
| ) | |
| guider_mem_kv0, guider_mem_kv1 = [o['mem_kv'][0] for o in guider_output_per_layers], [o['mem_kv'][1] for o in guider_output_per_layers] | |
| if not mems_buffers_on_GPU: | |
| torch.cuda.empty_cache() | |
| for idx, mem_buffer in enumerate(mems_buffers): | |
| mems_buffers[idx] = mem_buffer.to(next(model.parameters()).device) | |
| if guider_seq is not None: | |
| for idx, guider_mem_buffer in enumerate(guider_mems_buffers): | |
| guider_mems_buffers[idx] = guider_mem_buffer.to(next(model.parameters()).device) | |
| mems_buffers_on_GPU = True | |
| mems, mems_indexs = my_update_mems([mem_kv0, mem_kv1], mems_buffers, mems_indexs, limited_spatial_channel_mem, text_len, frame_len) | |
| if guider_seq is not None: | |
| guider_mems, guider_mems_indexs = my_update_mems([guider_mem_kv0, guider_mem_kv1], guider_mems_buffers, guider_mems_indexs, limited_spatial_channel_mem, guider_text_len, frame_len) | |
| counter += 1 | |
| index = counter | |
| logits = logits[:, -1].expand(batch_size, -1) # [batch size, vocab size] | |
| tokens = tokens.expand(batch_size, -1) | |
| if guider_seq is not None: | |
| guider_logits = guider_logits[:, -1].expand(batch_size, -1) | |
| guider_tokens = guider_tokens.expand(batch_size, -1) | |
| if seq[-1][counter].item() < 0: | |
| # sampling | |
| guided_logits = guider_logits+(logits-guider_logits)*guidance_alpha if guider_seq is not None else logits | |
| if mode_stage1 and counter < text_len + 400: | |
| tokens, mems = strategy.forward(guided_logits, tokens, mems) | |
| else: | |
| tokens, mems = strategy2.forward(guided_logits, tokens, mems) | |
| if guider_seq is not None: | |
| guider_tokens = torch.cat((guider_tokens, tokens[:, -1:]), dim=1) | |
| if seq[0][counter].item() >= 0: | |
| for si in range(seq.shape[0]): | |
| if seq[si][counter].item() >= 0: | |
| tokens[si, -1] = seq[si, counter] | |
| if guider_seq is not None: | |
| guider_tokens[si, -1] = guider_seq[si, counter-guider_index_delta] | |
| else: | |
| tokens = torch.cat((tokens, seq[:, counter:counter+1].clone().expand(tokens.shape[0], 1).to(device=tokens.device, dtype=tokens.dtype)), dim=1) | |
| if guider_seq is not None: | |
| guider_tokens = torch.cat((guider_tokens, | |
| guider_seq[:, counter-guider_index_delta:counter+1-guider_index_delta] | |
| .clone().expand(guider_tokens.shape[0], 1).to(device=guider_tokens.device, dtype=guider_tokens.dtype)), dim=1) | |
| input_tokens = tokens.clone() | |
| if guider_seq is not None: | |
| guider_input_tokens = guider_tokens.clone() | |
| if (index-text_len-1)//400 < (input_tokens.shape[-1]-text_len-1)//400: | |
| boi_idx = ((index-text_len-1)//400 +1)*400+text_len | |
| while boi_idx < input_tokens.shape[-1]: | |
| input_tokens[:, boi_idx] = tokenizer['<start_of_image>'] | |
| if guider_seq is not None: | |
| guider_input_tokens[:, boi_idx-guider_index_delta] = tokenizer['<start_of_image>'] | |
| boi_idx += 400 | |
| if strategy.is_done: | |
| break | |
| return strategy.finalize(tokens, mems) | |
| class InferenceModel_Sequential(CogVideoCacheModel): | |
| def __init__(self, args, transformer=None, parallel_output=True): | |
| super().__init__(args, transformer=transformer, parallel_output=parallel_output, window_size=-1, cogvideo_stage=1) | |
| # TODO: check it | |
| def final_forward(self, logits, **kwargs): | |
| logits_parallel = logits | |
| logits_parallel = torch.nn.functional.linear(logits_parallel.float(), self.transformer.word_embeddings.weight[:20000].float()) | |
| return logits_parallel | |
| class InferenceModel_Interpolate(CogVideoCacheModel): | |
| def __init__(self, args, transformer=None, parallel_output=True): | |
| super().__init__(args, transformer=transformer, parallel_output=parallel_output, window_size=10, cogvideo_stage=2) | |
| # TODO: check it | |
| def final_forward(self, logits, **kwargs): | |
| logits_parallel = logits | |
| logits_parallel = torch.nn.functional.linear(logits_parallel.float(), self.transformer.word_embeddings.weight[:20000].float()) | |
| return logits_parallel | |
| def main(args): | |
| assert int(args.stage_1) + int(args.stage_2) + int(args.both_stages) == 1 | |
| rank_id = args.device % args.parallel_size | |
| generate_frame_num = args.generate_frame_num | |
| if args.stage_1 or args.both_stages: | |
| model_stage1, args = InferenceModel_Sequential.from_pretrained(args, 'cogvideo-stage1') | |
| model_stage1.eval() | |
| if args.both_stages: | |
| model_stage1 = model_stage1.cpu() | |
| if args.stage_2 or args.both_stages: | |
| model_stage2, args = InferenceModel_Interpolate.from_pretrained(args, 'cogvideo-stage2') | |
| model_stage2.eval() | |
| if args.both_stages: | |
| model_stage2 = model_stage2.cpu() | |
| invalid_slices = [slice(tokenizer.num_image_tokens, None)] | |
| strategy_cogview2 = CoglmStrategy(invalid_slices, | |
| temperature=1.0, top_k=16) | |
| strategy_cogvideo = CoglmStrategy(invalid_slices, | |
| temperature=args.temperature, top_k=args.top_k, | |
| temperature2=args.coglm_temperature2) | |
| if not args.stage_1: | |
| from sr_pipeline import DirectSuperResolution | |
| dsr_path = auto_create('cogview2-dsr', path=None) # path=os.getenv('SAT_HOME', '~/.sat_models') | |
| dsr = DirectSuperResolution(args, dsr_path, | |
| max_bz=12, onCUDA=False) | |
| def process_stage2(model, seq_text, duration, video_raw_text=None, video_guidance_text="视频", parent_given_tokens=None, conddir=None, outputdir=None, gpu_rank=0, gpu_parallel_size=1): | |
| stage2_starttime = time.time() | |
| use_guidance = args.use_guidance_stage2 | |
| if args.both_stages: | |
| move_start_time = time.time() | |
| logging.debug("moving stage-2 model to cuda") | |
| model = model.cuda() | |
| logging.debug("moving in stage-2 model takes time: {:.2f}".format(time.time()-move_start_time)) | |
| try: | |
| if parent_given_tokens is None: | |
| assert conddir is not None | |
| parent_given_tokens = torch.load(os.path.join(conddir, 'frame_tokens.pt'), map_location='cpu') | |
| sample_num_allgpu = parent_given_tokens.shape[0] | |
| sample_num = sample_num_allgpu // gpu_parallel_size | |
| assert sample_num * gpu_parallel_size == sample_num_allgpu | |
| parent_given_tokens = parent_given_tokens[gpu_rank*sample_num:(gpu_rank+1)*sample_num] | |
| except: | |
| logging.critical("No frame_tokens found in interpolation, skip") | |
| return False | |
| # CogVideo Stage2 Generation | |
| while duration >= 0.5: # TODO: You can change the boundary to change the frame rate | |
| parent_given_tokens_num = parent_given_tokens.shape[1] | |
| generate_batchsize_persample = (parent_given_tokens_num-1)//2 | |
| generate_batchsize_total = generate_batchsize_persample * sample_num | |
| total_frames = generate_frame_num | |
| frame_len = 400 | |
| enc_text = tokenizer.encode(seq_text) | |
| enc_duration = tokenizer.encode(str(float(duration))+"秒") | |
| seq = enc_duration + [tokenizer['<n>']] + enc_text + [tokenizer['<start_of_image>']] + [-1]*400*generate_frame_num | |
| text_len = len(seq) - frame_len*generate_frame_num - 1 | |
| logging.info("[Stage2: Generating Frames, Frame Rate {:d}]\nraw text: {:s}".format(int(4/duration), tokenizer.decode(enc_text))) | |
| # generation | |
| seq = torch.cuda.LongTensor(seq, device=args.device).unsqueeze(0).repeat(generate_batchsize_total, 1) | |
| for sample_i in range(sample_num): | |
| for i in range(generate_batchsize_persample): | |
| seq[sample_i*generate_batchsize_persample+i][text_len+1:text_len+1+400] = parent_given_tokens[sample_i][2*i] | |
| seq[sample_i*generate_batchsize_persample+i][text_len+1+400:text_len+1+800] = parent_given_tokens[sample_i][2*i+1] | |
| seq[sample_i*generate_batchsize_persample+i][text_len+1+800:text_len+1+1200] = parent_given_tokens[sample_i][2*i+2] | |
| if use_guidance: | |
| guider_seq = enc_duration + [tokenizer['<n>']] + tokenizer.encode(video_guidance_text) + [tokenizer['<start_of_image>']] + [-1]*400*generate_frame_num | |
| guider_text_len = len(guider_seq) - frame_len*generate_frame_num - 1 | |
| guider_seq = torch.cuda.LongTensor(guider_seq, device=args.device).unsqueeze(0).repeat(generate_batchsize_total, 1) | |
| for sample_i in range(sample_num): | |
| for i in range(generate_batchsize_persample): | |
| guider_seq[sample_i*generate_batchsize_persample+i][text_len+1:text_len+1+400] = parent_given_tokens[sample_i][2*i] | |
| guider_seq[sample_i*generate_batchsize_persample+i][text_len+1+400:text_len+1+800] = parent_given_tokens[sample_i][2*i+1] | |
| guider_seq[sample_i*generate_batchsize_persample+i][text_len+1+800:text_len+1+1200] = parent_given_tokens[sample_i][2*i+2] | |
| video_log_text_attention_weights = 0 | |
| else: | |
| guider_seq=None | |
| guider_text_len=0 | |
| video_log_text_attention_weights = 1.4 | |
| mbz = args.max_inference_batch_size | |
| assert generate_batchsize_total < mbz or generate_batchsize_total % mbz == 0 | |
| output_list = [] | |
| start_time = time.time() | |
| for tim in range(max(generate_batchsize_total // mbz, 1)): | |
| input_seq = seq[:min(generate_batchsize_total, mbz)].clone() if tim == 0 else seq[mbz*tim:mbz*(tim+1)].clone() | |
| guider_seq2 = (guider_seq[:min(generate_batchsize_total, mbz)].clone() if tim == 0 else guider_seq[mbz*tim:mbz*(tim+1)].clone()) if guider_seq is not None else None | |
| output_list.append( | |
| my_filling_sequence(model, args, input_seq, | |
| batch_size=min(generate_batchsize_total, mbz), | |
| get_masks_and_position_ids=get_masks_and_position_ids_stage2, | |
| text_len=text_len, frame_len=frame_len, | |
| strategy=strategy_cogview2, | |
| strategy2=strategy_cogvideo, | |
| log_text_attention_weights=video_log_text_attention_weights, | |
| mode_stage1=False, | |
| guider_seq=guider_seq2, | |
| guider_text_len=guider_text_len, | |
| guidance_alpha=args.guidance_alpha, | |
| limited_spatial_channel_mem=True, | |
| )[0] | |
| ) | |
| logging.info("Duration {:.2f}, Taken time {:.2f}\n".format(duration, time.time() - start_time)) | |
| output_tokens = torch.cat(output_list, dim=0) | |
| output_tokens = output_tokens[:, text_len+1:text_len+1+(total_frames)*400].reshape(sample_num, -1, 400*total_frames) | |
| output_tokens_merge = torch.cat((output_tokens[:, :, :1*400], | |
| output_tokens[:, :, 400*3:4*400], | |
| output_tokens[:, :, 400*1:2*400], | |
| output_tokens[:, :, 400*4:(total_frames)*400]), dim=2).reshape(sample_num, -1, 400) | |
| output_tokens_merge = torch.cat((output_tokens_merge, output_tokens[:, -1:, 400*2:3*400]), dim=1) | |
| duration /= 2 | |
| parent_given_tokens = output_tokens_merge | |
| if args.both_stages: | |
| move_start_time = time.time() | |
| logging.debug("moving stage 2 model to cpu") | |
| model = model.cpu() | |
| torch.cuda.empty_cache() | |
| logging.debug("moving out model2 takes time: {:.2f}".format(time.time()-move_start_time)) | |
| logging.info("CogVideo Stage2 completed. Taken time {:.2f}\n".format(time.time() - stage2_starttime)) | |
| # decoding | |
| # imgs = [torch.nn.functional.interpolate(tokenizer.decode(image_ids=seq.tolist()), size=(480, 480)) for seq in output_tokens_merge] | |
| # os.makedirs(output_dir_full_path, exist_ok=True) | |
| # my_save_multiple_images(imgs, output_dir_full_path,subdir="frames", debug=False) | |
| # torch.save(output_tokens_merge.cpu(), os.path.join(output_dir_full_path, 'frame_token.pt')) | |
| # os.system(f"gifmaker -i '{output_dir_full_path}'/frames/0*.jpg -o '{output_dir_full_path}/{str(float(duration))}_concat.gif' -d 0.2") | |
| # direct super-resolution by CogView2 | |
| logging.info("[Direct super-resolution]") | |
| dsr_starttime = time.time() | |
| enc_text = tokenizer.encode(seq_text) | |
| frame_num_per_sample = parent_given_tokens.shape[1] | |
| parent_given_tokens_2d = parent_given_tokens.reshape(-1, 400) | |
| text_seq = torch.cuda.LongTensor(enc_text, device=args.device).unsqueeze(0).repeat(parent_given_tokens_2d.shape[0], 1) | |
| sred_tokens = dsr(text_seq, parent_given_tokens_2d) | |
| decoded_sr_videos = [] | |
| for sample_i in range(sample_num): | |
| decoded_sr_imgs = [] | |
| for frame_i in range(frame_num_per_sample): | |
| decoded_sr_img = tokenizer.decode(image_ids=sred_tokens[frame_i+sample_i*frame_num_per_sample][-3600:]) | |
| decoded_sr_imgs.append(torch.nn.functional.interpolate(decoded_sr_img, size=(480, 480))) | |
| decoded_sr_videos.append(decoded_sr_imgs) | |
| for sample_i in range(sample_num): | |
| my_save_multiple_images(decoded_sr_videos[sample_i], outputdir,subdir=f"frames/{sample_i+sample_num*gpu_rank}", debug=False) | |
| os.system(f"gifmaker -i '{outputdir}'/frames/'{sample_i+sample_num*gpu_rank}'/0*.jpg -o '{outputdir}/{sample_i+sample_num*gpu_rank}.gif' -d 0.125") | |
| logging.info("Direct super-resolution completed. Taken time {:.2f}\n".format(time.time() - dsr_starttime)) | |
| return True | |
| def process_stage1(model, seq_text, duration, video_raw_text=None, video_guidance_text="视频", image_text_suffix="", outputdir=None, batch_size=1): | |
| process_start_time = time.time() | |
| use_guide = args.use_guidance_stage1 | |
| if args.both_stages: | |
| move_start_time = time.time() | |
| logging.debug("moving stage 1 model to cuda") | |
| model = model.cuda() | |
| logging.debug("moving in model1 takes time: {:.2f}".format(time.time()-move_start_time)) | |
| if video_raw_text is None: | |
| video_raw_text = seq_text | |
| mbz = args.stage1_max_inference_batch_size if args.stage1_max_inference_batch_size > 0 else args.max_inference_batch_size | |
| assert batch_size < mbz or batch_size % mbz == 0 | |
| frame_len = 400 | |
| # generate the first frame: | |
| enc_text = tokenizer.encode(seq_text+image_text_suffix) | |
| seq_1st = enc_text + [tokenizer['<start_of_image>']] + [-1]*400 # IV!! # test local!!! # test randboi!!! | |
| logging.info("[Generating First Frame with CogView2]Raw text: {:s}".format(tokenizer.decode(enc_text))) | |
| text_len_1st = len(seq_1st) - frame_len*1 - 1 | |
| seq_1st = torch.cuda.LongTensor(seq_1st, device=args.device).unsqueeze(0) | |
| output_list_1st = [] | |
| for tim in range(max(batch_size // mbz, 1)): | |
| start_time = time.time() | |
| output_list_1st.append( | |
| my_filling_sequence(model, args,seq_1st.clone(), | |
| batch_size=min(batch_size, mbz), | |
| get_masks_and_position_ids=get_masks_and_position_ids_stage1, | |
| text_len=text_len_1st, | |
| frame_len=frame_len, | |
| strategy=strategy_cogview2, | |
| strategy2=strategy_cogvideo, | |
| log_text_attention_weights=1.4, | |
| enforce_no_swin=True, | |
| mode_stage1=True, | |
| )[0] | |
| ) | |
| logging.info("[First Frame]Taken time {:.2f}\n".format(time.time() - start_time)) | |
| output_tokens_1st = torch.cat(output_list_1st, dim=0) | |
| given_tokens = output_tokens_1st[:, text_len_1st+1:text_len_1st+401].unsqueeze(1) # given_tokens.shape: [bs, frame_num, 400] | |
| # generate subsequent frames: | |
| total_frames = generate_frame_num | |
| enc_duration = tokenizer.encode(str(float(duration))+"秒") | |
| if use_guide: | |
| video_raw_text = video_raw_text + " 视频" | |
| enc_text_video = tokenizer.encode(video_raw_text) | |
| seq = enc_duration + [tokenizer['<n>']] + enc_text_video + [tokenizer['<start_of_image>']] + [-1]*400*generate_frame_num | |
| guider_seq = enc_duration + [tokenizer['<n>']] + tokenizer.encode(video_guidance_text) + [tokenizer['<start_of_image>']] + [-1]*400*generate_frame_num | |
| logging.info("[Stage1: Generating Subsequent Frames, Frame Rate {:.1f}]\nraw text: {:s}".format(4/duration, tokenizer.decode(enc_text_video))) | |
| text_len = len(seq) - frame_len*generate_frame_num - 1 | |
| guider_text_len = len(guider_seq) - frame_len*generate_frame_num - 1 | |
| seq = torch.cuda.LongTensor(seq, device=args.device).unsqueeze(0).repeat(batch_size, 1) | |
| guider_seq = torch.cuda.LongTensor(guider_seq, device=args.device).unsqueeze(0).repeat(batch_size, 1) | |
| for given_frame_id in range(given_tokens.shape[1]): | |
| seq[:, text_len+1+given_frame_id*400: text_len+1+(given_frame_id+1)*400] = given_tokens[:, given_frame_id] | |
| guider_seq[:, guider_text_len+1+given_frame_id*400:guider_text_len+1+(given_frame_id+1)*400] = given_tokens[:, given_frame_id] | |
| output_list = [] | |
| if use_guide: | |
| video_log_text_attention_weights = 0 | |
| else: | |
| guider_seq = None | |
| video_log_text_attention_weights = 1.4 | |
| for tim in range(max(batch_size // mbz, 1)): | |
| start_time = time.time() | |
| input_seq = seq[:min(batch_size, mbz)].clone() if tim == 0 else seq[mbz*tim:mbz*(tim+1)].clone() | |
| guider_seq2 = (guider_seq[:min(batch_size, mbz)].clone() if tim == 0 else guider_seq[mbz*tim:mbz*(tim+1)].clone()) if guider_seq is not None else None | |
| output_list.append( | |
| my_filling_sequence(model, args,input_seq, | |
| batch_size=min(batch_size, mbz), | |
| get_masks_and_position_ids=get_masks_and_position_ids_stage1, | |
| text_len=text_len, frame_len=frame_len, | |
| strategy=strategy_cogview2, | |
| strategy2=strategy_cogvideo, | |
| log_text_attention_weights=video_log_text_attention_weights, | |
| guider_seq=guider_seq2, | |
| guider_text_len=guider_text_len, | |
| guidance_alpha=args.guidance_alpha, | |
| limited_spatial_channel_mem=True, | |
| mode_stage1=True, | |
| )[0] | |
| ) | |
| output_tokens = torch.cat(output_list, dim=0)[:, 1+text_len:] | |
| if args.both_stages: | |
| move_start_time = time.time() | |
| logging.debug("moving stage 1 model to cpu") | |
| model = model.cpu() | |
| torch.cuda.empty_cache() | |
| logging.debug("moving in model1 takes time: {:.2f}".format(time.time()-move_start_time)) | |
| # decoding | |
| imgs, sred_imgs, txts = [], [], [] | |
| for seq in output_tokens: | |
| decoded_imgs = [torch.nn.functional.interpolate(tokenizer.decode(image_ids=seq.tolist()[i*400: (i+1)*400]), size=(480, 480)) for i in range(total_frames)] | |
| imgs.append(decoded_imgs) # only the last image (target) | |
| assert len(imgs) == batch_size | |
| save_tokens = output_tokens[:, :+total_frames*400].reshape(-1, total_frames, 400).cpu() | |
| if outputdir is not None: | |
| for clip_i in range(len(imgs)): | |
| # os.makedirs(output_dir_full_paths[clip_i], exist_ok=True) | |
| my_save_multiple_images(imgs[clip_i], outputdir, subdir=f"frames/{clip_i}", debug=False) | |
| os.system(f"gifmaker -i '{outputdir}'/frames/'{clip_i}'/0*.jpg -o '{outputdir}/{clip_i}.gif' -d 0.25") | |
| torch.save(save_tokens, os.path.join(outputdir, 'frame_tokens.pt')) | |
| logging.info("CogVideo Stage1 completed. Taken time {:.2f}\n".format(time.time() - process_start_time)) | |
| return save_tokens | |
| # ====================================================================================================== | |
| if args.stage_1 or args.both_stages: | |
| if args.input_source != "interactive": | |
| with open(args.input_source, 'r') as fin: | |
| promptlist = fin.readlines() | |
| promptlist = [p.strip() for p in promptlist] | |
| else: | |
| promptlist = None | |
| now_qi = -1 | |
| while True: | |
| now_qi += 1 | |
| if promptlist is not None: # with input-source | |
| if args.multi_gpu: | |
| if now_qi % dist.get_world_size() != dist.get_rank(): | |
| continue | |
| rk = dist.get_rank() | |
| else: | |
| rk = 0 | |
| raw_text = promptlist[now_qi] | |
| raw_text = raw_text.strip() | |
| print(f'Working on Line No. {now_qi} on {rk}... [{raw_text}]') | |
| else: # interactive | |
| raw_text = input("\nPlease Input Query (stop to exit) >>> ") | |
| raw_text = raw_text.strip() | |
| if not raw_text: | |
| print('Query should not be empty!') | |
| continue | |
| if raw_text == "stop": | |
| return | |
| try: | |
| path = os.path.join(args.output_path, f"{now_qi}_{raw_text}") | |
| parent_given_tokens = process_stage1(model_stage1, raw_text, duration=4.0, video_raw_text=raw_text, video_guidance_text="视频", | |
| image_text_suffix=" 高清摄影", | |
| outputdir=path if args.stage_1 else None, batch_size=args.batch_size) | |
| if args.both_stages: | |
| process_stage2(model_stage2, raw_text, duration=2.0, video_raw_text=raw_text+" 视频", | |
| video_guidance_text="视频", parent_given_tokens=parent_given_tokens, | |
| outputdir=path, | |
| gpu_rank=0, gpu_parallel_size=1) # TODO: 修改 | |
| except (ValueError, FileNotFoundError) as e: | |
| print(e) | |
| continue | |
| elif args.stage_2: | |
| sample_dirs = os.listdir(args.output_path) | |
| for sample in sample_dirs: | |
| raw_text = sample.split('_')[-1] | |
| path = os.path.join(args.output_path, sample, 'Interp') | |
| parent_given_tokens = torch.load(os.path.join(args.output_path, sample, "frame_tokens.pt")) | |
| process_stage2(raw_text, duration=2.0, video_raw_text=raw_text+" 视频", | |
| video_guidance_text="视频", parent_given_tokens=parent_given_tokens, | |
| outputdir=path, | |
| gpu_rank=0, gpu_parallel_size=1) # TODO: 修改 | |
| else: | |
| assert False | |
| if __name__ == "__main__": | |
| logging.basicConfig(stream=sys.stderr, level=logging.DEBUG) | |
| py_parser = argparse.ArgumentParser(add_help=False) | |
| py_parser.add_argument('--generate-frame-num', type=int, default=5) | |
| py_parser.add_argument('--coglm-temperature2', type=float, default=0.89) | |
| # py_parser.add_argument("--interp-duration", type=float, default=-1) # -1是顺序生成，0是超分，0.5/1/2是插帧 | |
| # py_parser.add_argument("--total-duration", type=float, default=4.0) # 整个的时间 | |
| py_parser.add_argument('--use-guidance-stage1', action='store_true') | |
| py_parser.add_argument('--use-guidance-stage2', action='store_false') | |
| py_parser.add_argument('--guidance-alpha', type=float, default=3.0) | |
| py_parser.add_argument('--stage-1', action='store_true') # stage 1: sequential generation | |
| py_parser.add_argument('--stage-2', action='store_false') # stage 2: interp + dsr | |
| py_parser.add_argument('--both-stages', action='store_false') # stage 1&2: sequential generation; interp + dsr | |
| py_parser.add_argument('--parallel-size', type=int, default=1) | |
| py_parser.add_argument('--stage1-max-inference-batch-size', type=int, default=1) # -1: use max-inference-batch-size | |
| py_parser.add_argument('--multi-gpu', action='store_false') | |
| CogVideoCacheModel.add_model_specific_args(py_parser) | |
| known, args_list = py_parser.parse_known_args() | |
| args = get_args(args_list) | |
| args = argparse.Namespace(**vars(args), **vars(known)) | |
| args.layout = [int(x) for x in args.layout.split(',')] | |
| args.do_train = False | |
| torch.cuda.set_device(args.device) | |
| with torch.no_grad(): | |
| main(args) |