[gptj] support older pytorch version (#22325)

* [gptj] support older pytorch version

* contributor

* contributor

* make copies

---------
Co-authored-by: Michael Wyatt <michaelwyatt@microsoft.com>
Co-authored-by: Nick Hill <nickhill@us.ibm.com>

src/transformers/models/codegen/modeling_codegen.py

@@ -55,7 +55,7 @@ CODEGEN_PRETRAINED_MODEL_ARCHIVE_LIST = [
 def create_sinusoidal_positions(num_pos: int, dim: int) -> torch.Tensor:
     inv_freq = 1.0 / (10000 ** (torch.arange(0, dim, 2) / dim))
     sinusoid_inp = torch.einsum("i , j -> i j", torch.arange(num_pos, dtype=torch.float), inv_freq).float()
-    return torch.concat((torch.sin(sinusoid_inp), torch.cos(sinusoid_inp)), dim=1)
+    return torch.cat((torch.sin(sinusoid_inp), torch.cos(sinusoid_inp)), dim=1)
 
 
 # Copied from transformers.models.gptj.modeling_gptj.rotate_every_two

src/transformers/models/gptj/modeling_gptj.py

@@ -18,6 +18,7 @@ import warnings
 from typing import Optional, Tuple, Union
 
 import torch
+import torch.fx
 import torch.utils.checkpoint
 from torch import nn
 from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
@@ -57,7 +58,7 @@ GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST = [
 def create_sinusoidal_positions(num_pos: int, dim: int) -> torch.Tensor:
     inv_freq = 1.0 / (10000 ** (torch.arange(0, dim, 2) / dim))
     sinusoid_inp = torch.einsum("i , j -> i j", torch.arange(num_pos, dtype=torch.float), inv_freq).float()
-    return torch.concat((torch.sin(sinusoid_inp), torch.cos(sinusoid_inp)), dim=1)
+    return torch.cat((torch.sin(sinusoid_inp), torch.cos(sinusoid_inp)), dim=1)
 
 
 @torch.fx.wrap