seq_length=input_shape[1] to_mask=tf.cast( tf.reshape(to_mask, [batch_size,1,seq_length]),tf.float32) # broadcast_ones = [batch_size, seq_length, 1] broadcast_ones=tf.ones( shape=[batch_size,seq_length,1],dtype=tf.float32) # mask = [batch_size, seq_length, seq_length] mas...
torch.Size([4, 2262, 6144]) InternLMBlockPipeLayer.29 cuda:2 torch.Size([4, 2361, 6144]) InternLMBlockPipeLayer.25 cuda:3 tensor([712564646, 1668], device='cuda:2') tensor([757955384, 1668], device='cuda:3') torch.Size([4, 2262, 6144]) InternLMBlockPipeLayer.30 cuda:2 torch.Si...
(0) Internal: Failed to call ThenRnnForward with model config: [rnn_mode, rnn_input_mode, rnn_direction_mode]: 2, 0, 0 , [num_layers, input_size, num_units, dir_count, max_seq_length, batch_size, cell_num_units]: [1, 2048, 2048, 1, 63, 12, 2048] [[{{node tower_0/cud...
a series of captures can be designed in an iterative manner, in which the initial capture returns the first batch of detected genes, and subsequent captures use probe sets that include only genes that failed to return isoforms in the first round. Alternatively, multiple...
from open_clip import get_input_dtype from training.distributed import is_master from training.utils import get_autocast @@ -237,7 +238,7 @@ def __init__( self._batch_size = batch_size self._max_seq_length = max_seq_length self._device = device print(clip_model) if isinstance(clip...
model.set_kv_cache(batch_size=1, max_seq_length=max_seq_length_setting, device=self.fabric.device) ValueError: Cannot generate a response with 56 tokens. This model has a maximum context length of 10 tokens. The prompt contains 6 tokens, leaving 4 for the response, which is not enough....