mirror of
https://github.com/Vision-CAIR/MiniGPT-4.git
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182 lines
5.8 KiB
Python
182 lines
5.8 KiB
Python
"""
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Copyright (c) 2022, salesforce.com, inc.
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All rights reserved.
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SPDX-License-Identifier: BSD-3-Clause
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For full license text, see the LICENSE_Lavis file in the repo root or https://opensource.org/licenses/BSD-3-Clause
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"""
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import sys
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sys.path.append("/mnt/pfs-guan-ssai/nlu/wanghanzi/multimodal/PromptMoE")
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from minigpt4.models.QformerMoE import (
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BertConfig,
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BertMoELMHeadModel
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)
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vision_width = 1408
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cross_attention_freq = 2
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num_query_token = 32
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# init_QformerMoE
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moe_encoder_config = BertConfig.from_pretrained("/mnt/pfs-guan-ssai/nlu/wanghanzi/models/bert-base-uncased")
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moe_encoder_config.encoder_width = vision_width
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# insert cross-attention layer every other block
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moe_encoder_config.add_cross_attention = True
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moe_encoder_config.cross_attention_freq = cross_attention_freq
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moe_encoder_config.query_length = num_query_token
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moe_encoder_config.moebert_expert_num = 4
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moe_encoder_config.moebert_route_method = "gate-sentence"
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moe_encoder_config.moe_topk = 2
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moe_encoder_config.moebert_load_balance = 0.1
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moe_encoder_config.moebert_share_importance = 512 # TODO: meaning?
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MoEQformer = BertMoELMHeadModel.from_pretrained(
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"/mnt/pfs-guan-ssai/nlu/wanghanzi/models/bert-base-uncased", config=moe_encoder_config
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)
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"""
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Compare Qformer & QformerMoE
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"""
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# blip2_qformer
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# calculate parameters
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from minigpt4.models import load_model
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model = load_model("blip2", "pretrain")
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model.QformerMoE, model.query_tokens_moe = model.init_QformerMoE(
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num_query_token, model.visual_encoder.num_features, cross_attention_freq
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)
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model.Qformer, model.query_tokens = model.init_Qformer(
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num_query_token, model.visual_encoder.num_features, cross_attention_freq
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)
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state_dict = model.Qformer.state_dict()
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for name, param in model.Qformer.named_parameters():
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if "_query" in name:
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key_orig = name.replace("_query", "")
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param.data.copy_(state_dict[key_orig])
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if "10" in name:
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print(name)
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"""
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blip2_t5_qformer_moe
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Calculate Num Parameters
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"""
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import torch
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import sys
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sys.path.append("/mnt/pfs-guan-ssai/nlu/wanghanzi/multimodal/PromptMoE")
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from minigpt4.models import model_zoo
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from minigpt4.models import load_model
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print(model_zoo)
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device = torch.device("cuda:2" if torch.cuda.is_available() else "cpu")
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model = load_model("blip2_t5_qformer_moe", "flant5xxl", device=device)
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num_parameters=0
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for n, p in model.Qformer.named_parameters():
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if not p.requires_grad:
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continue # frozen weights
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if "11.experts.experts" in n:
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print(n)
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num_parameters += p.data.nelement()
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print(num_parameters) # 23,619,840
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# total trainable parameter: 415,631,104
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num_parameters=0
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for n, p in model.named_parameters():
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if not p.requires_grad:
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continue # frozen weights
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num_parameters += p.data.nelement()
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print(num_parameters) # 23,619,840
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# total trainable parameter: 415,631,104
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num_parameters=0
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for n, p in model.named_parameters():
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if not p.requires_grad:
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continue # frozen weights
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if 'Qformer.bert.encoder.layer.6.crossattention' in n:
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num_parameters += p.data.nelement()
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# if 'Qformer.bert.encoder.layer.11.output' in n:
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# num_parameters += p.data.nelement()
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print(num_parameters)
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"""
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forward
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"""
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import torch
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import sys
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sys.path.append("/mnt/pfs-guan-ssai/nlu/wanghanzi/multimodal/PromptMoE")
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from minigpt4.models import load_model
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device = torch.device("cuda:2" if torch.cuda.is_available() else "cpu")
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model = load_model("blip2", "pretrain", device=device)
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samples = {
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'q_input':["What is around the open window?", # n23181
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"Is the ground blue or brown?", # n168412
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"What color are the pants?", # n446242
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"What is the airplane flying above?"], # n414992
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'llm_input':["What is around the open window?", # n23181
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"Is the ground blue or brown?", # n168412
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"What color are the pants?", # n446242
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"What is the airplane flying above?"], # n414992
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'text_output':["drapes",
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"brown",
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"red",
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"ocean"
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],
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'image': torch.randn(4, 3, 224, 224).half().to(device)
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# 'image': torch.randn(4, 3, 336, 336).half().to(device)
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}
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Qformer, query_tokens = model.init_QformerMoE(
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num_query_token=32,
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vision_width=1408,
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moebert_expert_num=5,
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moebert_route_method="gate-sentence",
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moebert_load_balance=0.1,
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moe_topk=2,
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cross_attention_freq=2
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)
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Qformer = Qformer.to(device)
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def maybe_autocast(device, dtype=torch.float16):
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# if on cpu, don't use autocast
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# if on gpu, use autocast with dtype if provided, otherwise use torch.float16
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enable_autocast = device != torch.device("cpu")
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if enable_autocast:
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return torch.cuda.amp.autocast(dtype=dtype)
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else:
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return contextlib.nullcontext()
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image = samples["image"]
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with maybe_autocast(device):
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image_embeds = model.ln_vision(model.visual_encoder(image))
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image_atts = torch.ones(image_embeds.size()[:-1], dtype=torch.long).to(image.device)
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bs = image.size(0)
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query_tokens = query_tokens.expand(bs, -1, -1).to(device)
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# image = samples["image"]
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# image_atts = torch.ones(4, 257).to(device)
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# image_embeds = torch.randn(4, 257, 1408).to(device)
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# bz = image_embeds.shape[0]
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# query_tokens = query_tokens.expand(bz, -1, -1).to(device)
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text_Qformer = model.tokenizer(
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samples["q_input"],
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padding='longest',
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truncation=True,
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max_length=32,
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return_tensors="pt",
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).to(image.device)
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query_atts = torch.ones(query_tokens.size()[:-1], dtype=torch.long).to(image.device)
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Qformer_atts = torch.cat([query_atts,text_Qformer.attention_mask],dim=1).to(device)
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query_output = Qformer.bert(
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text_Qformer.input_ids,
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attention_mask=Qformer_atts,
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query_embeds=query_tokens,
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encoder_hidden_states=image_embeds,
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encoder_attention_mask=image_atts,
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return_dict=True,
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)
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