Getting index out of self error
I am trying to do a forward pass on the LEDForConditionalGeneration with the PRIMER checkpoint, but I am getting an error. How do I fix this?Error Message:IndexError                                Traceback (most recent call last)in <cell line: 12>()38     global_attention_mask[input_ids == DOCSEP_TOKEN_ID] = 139—> 40     outputs = MODEL(input_ids) # <---------------------------------------------------------------------------------------------- causing a bug41     # outputs = MODEL(input_ids=input_ids_all, global_attention_mask=global_attention_mask)4213 frames/usr/local/lib/python3.10/dist-packages/torch/nn/functional.py in embedding(input, weight, padding_idx, max_norm, norm_type, scale_grad_by_freq, sparse)2231         # remove once script supports set_grad_enabled2232no_grad_embedding_renorm(weight, input, max_norm, norm_type)→ 2233     return torch.embedding(weight, input, padding_idx, scale_grad_by_freq, sparse)22342235IndexError: index out of range in selfModel Architecture:LEDForConditionalGeneration((led): LEDModel((shared): Embedding(50266, 1024, padding_idx=1)(encoder): LEDEncoder((embed_tokens): Embedding(50266, 1024, padding_idx=1)(embed_positions): LEDLearnedPositionalEmbedding(4096, 1024)(layers): ModuleList((0-11): 12 x LEDEncoderLayer((self_attn): LEDEncoderAttention((longformer_self_attn): LEDEncoderSelfAttention((query): Linear(in_features=1024, out_features=1024, bias=True)(key): Linear(in_features=1024, out_features=1024, bias=True)(value): Linear(in_features=1024, out_features=1024, bias=True)(query_global): Linear(in_features=1024, out_features=1024, bias=True)(key_global): Linear(in_features=1024, out_features=1024, bias=True)(value_global): Linear(in_features=1024, out_features=1024, bias=True))(output): Linear(in_features=1024, out_features=1024, bias=True))(self_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)(activation_fn): GELUActivation()(fc1): Linear(in_features=1024, out_features=4096, bias=True)(fc2): Linear(in_features=4096, out_features=1024, bias=True)(final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)))(layernorm_embedding): LayerNorm((1024,), eps=1e-05, elementwise_affine=True))(decoder): LEDDecoder((embed_tokens): Embedding(50266, 1024, padding_idx=1)(embed_positions): LEDLearnedPositionalEmbedding(1024, 1024)(layers): ModuleList((0-11): 12 x LEDDecoderLayer((self_attn): LEDDecoderAttention((k_proj): Linear(in_features=1024, out_features=1024, bias=True)(v_proj): Linear(in_features=1024, out_features=1024, bias=True)(q_proj): Linear(in_features=1024, out_features=1024, bias=True)(out_proj): Linear(in_features=1024, out_features=1024, bias=True))(activation_fn): GELUActivation()(self_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)(encoder_attn): LEDDecoderAttention((k_proj): Linear(in_features=1024, out_features=1024, bias=True)(v_proj): Linear(in_features=1024, out_features=1024, bias=True)(q_proj): Linear(in_features=1024, out_features=1024, bias=True)(out_proj): Linear(in_features=1024, out_features=1024, bias=True))(encoder_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)(fc1): Linear(in_features=1024, out_features=4096, bias=True)(fc2): Linear(in_features=4096, out_features=1024, bias=True)(final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)))(layernorm_embedding): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)))(lm_head): Linear(in_features=1024, out_features=50266, bias=False))Input Shape:tensor([[    0, 43070, 26945,  …,     4, 50265,     2],[    0, 34543,    35,  …,     1,     1,     1]])torch.Size([2, 1047])