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Update main.py
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main.py
CHANGED
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import torch
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import torch.nn as nn
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from model import (
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SwitchTransformer,
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SwitchTransformerLayer,
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MultiHeadAttention,
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SwitchFeedForward,
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FeedForward,
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)
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from transformers import AutoTokenizer
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device = 'cpu'
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ff = FeedForward(768, 768*4)
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attn = MultiHeadAttention(8, 768, 0.2)
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st_ff = SwitchFeedForward(
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capacity_factor=1.25,
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drop_tokens=False,
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n_experts=4,
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expert=ff,
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d_model=768,
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is_scale_prob=True,
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)
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st_layer = SwitchTransformerLayer(
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d_model=768,
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attn=attn,
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feed_forward=st_ff,
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dropout_prob=0.2
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)
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model = SwitchTransformer(
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layer=st_layer,
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n_layers=4,
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n_experts=4,
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device=device,
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load_balancing_loss_ceof=0.05,
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).to(device)
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model.load_state_dict(torch.load("switch_transformer.pt"))
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tokenizer = AutoTokenizer.from_pretrained("Kyrmasch/kaz-roberta-squad2-kaz")
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import torch
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import torch.nn as nn
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from model import (
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SwitchTransformer,
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SwitchTransformerLayer,
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MultiHeadAttention,
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SwitchFeedForward,
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FeedForward,
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)
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from transformers import AutoTokenizer
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device = 'cpu'
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ff = FeedForward(768, 768*4)
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attn = MultiHeadAttention(8, 768, 0.2)
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st_ff = SwitchFeedForward(
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capacity_factor=1.25,
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drop_tokens=False,
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n_experts=4,
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expert=ff,
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d_model=768,
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is_scale_prob=True,
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)
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st_layer = SwitchTransformerLayer(
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d_model=768,
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attn=attn,
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feed_forward=st_ff,
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dropout_prob=0.2
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)
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model = SwitchTransformer(
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layer=st_layer,
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n_layers=4,
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n_experts=4,
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device=device,
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load_balancing_loss_ceof=0.05,
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).to(device)
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model.load_state_dict(torch.load("switch_transformer.pt", map_location=torch.device('cpu')))
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tokenizer = AutoTokenizer.from_pretrained("Kyrmasch/kaz-roberta-squad2-kaz")
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