Fix: TPU driver error

to_dlpack/from_dlpack was causing issues with tensor with new jax version

requirements_mtj.txt

@@ -2,9 +2,9 @@ torch >= 1.9, < 1.13
 numpy
 tqdm
 requests
-dm-haiku == 0.0.5
+dm-haiku==0.0.9
-jax == 0.2.21
+jax==0.3.25
-jaxlib >= 0.1.69, <= 0.3.7
+jaxlib==0.3.25
 transformers == 4.28.0
 chex == 0.1.5
 huggingface_hub==0.12.1

tpu_mtj_backend.py

@@ -1095,7 +1095,7 @@ def read_neox_checkpoint(state, path, config, checkpoint_shards=2):
 
     koboldai_vars.status_message = ""
 
-def load_model(path: str, driver_version="tpu_driver0.1_dev20210607", hf_checkpoint=False, socketio_queue=None, initial_load=False, logger=None, **kwargs) -> None:
+def load_model(path: str, driver_version="tpu_driver_nightly", hf_checkpoint=False, socketio_queue=None, initial_load=False, logger=None, **kwargs) -> None:
     global thread_resources_env, seq, tokenizer, network, params, pad_token_id
 
     if "pad_token_id" in kwargs:
@@ -1270,11 +1270,6 @@ def load_model(path: str, driver_version="tpu_driver0.1_dev20210607", hf_checkpo
         logger.message(f"KoboldAI has finished loading and is available at the following link for UI 1: {koboldai_vars.cloudflare_link}")
         logger.message(f"KoboldAI has finished loading and is available at the following link for UI 2: {koboldai_vars.cloudflare_link}/new_ui")
 
-
-    global shard_xmap, batch_xmap
-    shard_xmap = __shard_xmap()
-    batch_xmap = __batch_xmap(shard_dim=cores_per_replica)
-
     global badwords
     # These are the tokens that we don't want the AI to ever write
     badwords = jnp.array(koboldai_vars.badwordsids).squeeze()
@@ -1401,19 +1396,20 @@ def load_model(path: str, driver_version="tpu_driver0.1_dev20210607", hf_checkpo
                     #if "no_transpose" not in transforms and tensor.ndim == 2:
                     #    tensor = tensor.T
                     tensor.unsqueeze_(0)
-                    if tensor.dtype is torch.float16 or tensor.dtype is torch.float32:
+                    
-                        tensor = tensor.bfloat16()
 
                     # Shard the tensor so that parts of the tensor can be used
                     # on different TPU cores
+                    tensor = reshard_reverse(
+                        tensor,
+                        params["cores_per_replica"],
+                        network.state["params"][spec["module"]][spec["param"]].shape,
+                    )
+                    tensor = jnp.array(tensor.detach())
+                    if tensor.dtype is torch.float16 or tensor.dtype is torch.float32:
+                        tensor = tensor.bfloat16()
                     network.state["params"][spec["module"]][spec["param"]] = move_xmap(
-                        jax.dlpack.from_dlpack(torch.utils.dlpack.to_dlpack(
+                        tensor,
-                            reshard_reverse(
-                                tensor,
-                                params["cores_per_replica"],
-                                network.state["params"][spec["module"]][spec["param"]].shape,
-                            )
-                        )).copy(),
                         np.empty(params["cores_per_replica"]),
                     )
                     
@@ -1506,3 +1502,6 @@ def load_model(path: str, driver_version="tpu_driver0.1_dev20210607", hf_checkpo
                 model     = GPTNeoForCausalLM.from_pretrained(koboldai_vars.model, revision=koboldai_vars.revision, cache_dir="cache")
 
     #network.state = network.move_xmap(network.state, np.zeros(cores_per_replica))
+    global shard_xmap, batch_xmap
+    shard_xmap = __shard_xmap()
+    batch_xmap = __batch_xmap(shard_dim=cores_per_replica)