Trainableparams:99,360 Non-trainableparams:0 --- Inputsize(MB):3.00 Forward/backwardpasssize(MB):
[00:08<00:00, 2.21s/it] model.dtype= torch.bfloat16 trainable params: 4,718,592 || all params: 8,034,979,840 || trainable%: 0.0587 C:\ProgramData\miniconda3\envs\llama\lib\site-packages\accelerate\accelerator.py:446: FutureWarning: Passing the following arguments to `Accelerator` is ...
re: batchnorm; each replica might have the same params, but is getting a different batch, so would collect different batch norm stats after the stateless_call. these aren't being aggregated? re: dropout; the RNGKeys for dropout are in the non_trainable, but never change. shouldn't these ...
The attention weights SS for E outE out can be represented as s=softmax(ωTtanh(V⋅ETout)),(10)(10)s=softmax(ωTtanh(V⋅EoutT)), where ω ∈ Rh×1ω ∈ Rh×1 and V ∈ Rh× DV ∈ Rh× D are trainable parameters for KCA, hh is the size of hidden dimension for ωω...
params: 393,562 Trainable params: 392,538 Non-trainable params: 1,024 --- Input size (MB): 0.01 Forward/backward pass size (MB): 3.94 Params size (MB): 1.50 Estimated Total Size (MB): 5.45 --- {'total_params': 393562, 'trainable_params': 392538} 训练 In [...
network_opt = nn.SGD(network.trainable_params(), lr, momentum=0.9, weight_decay=0.0001) # Define loss function. network_loss = SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean") # set checkpoint for the network ckpt_config = CheckpointConfig( ...
label_float = tf.cast(self.train_label,tf.float32)# label_matrix = tf.Variable(tf.diag(tf.ones(self.label_size)),trainable=False)label_matrix = tf.diag(tf.ones(self.label_size)) embed_label = tf.nn.embedding_lookup(label_matrix,self.train_label) ...
ModelTrainable paramsAccuracy on glue-sst2 Bert-base109M93.37 Hybrid94M93.23 HybridNT94M92.20 KEN80M93.80 Hybrid66M91.97 HybridNT66M90.71 Sajjad66M90.30 Gordon66M90.80 Flop66M83.20 KEN63M92.90 KEN aims to reduce the size of transformer models, including their file sizes. It uses a subnetwork...
When tf.gather is used (such as when selecting embeddings for words in a sentence), backprop into a dense trainable variable (such as an embedding matrix) is nondeterministic, but not because the backprop of tf.gather is itself nondeterministic (as previously suggested). tf.gather produces ...