Fix Torchax backend on Pathways

tpu_inference/kernels/ragged_paged_attention/v3/util.py

@@ -43,7 +43,7 @@ def get_tpu_version() -> int:
         return -1
     if kind.endswith(' lite'):
         kind = kind[:-len(' lite')]
-    if kind.endswith('p'):
+    if kind.endswith('p') or kind.endswith('e'):
         kind = kind[:-1]
     if kind == 'TPU7x':
         return 7

tpu_inference/layers/vllm/sharding.py

@@ -1,11 +1,11 @@
 import jax
+import jax.numpy as jnp
 import torch
 import torchax
 from jax.sharding import Mesh, NamedSharding, PartitionSpec
 from torch.nn import Parameter
 from torch.utils import _pytree as pytree
-from torchax.interop import jax_view, torch_view
-from torchax.ops.mappings import t2j
+from torchax.interop import torch_view
 from vllm.lora.layers import (MergedColumnParallelLinearWithLoRA,
                               MergedQKVParallelLinearWithLoRA,
                               RowParallelLinearWithLoRA)
@@ -19,6 +19,12 @@
 
 logger = init_logger(__name__)
 
+TORCH_TO_JAX_DTYPE_MAP = {
+    torch.float32: jnp.float32,
+    torch.float16: jnp.float16,
+    torch.bfloat16: jnp.bfloat16,
+}
+
 
 def shard_model_to_tpu(model: torch.nn.Module,
                        mesh: Mesh) -> dict[str, torchax.torch.Tensor]:
@@ -75,11 +81,9 @@ def _tensor_is_in_cpu(tensor: torch.tensor) -> bool:
 
 def _convert_to_torchax_and_shard(tensor: torch.Tensor,
                                   sharding: NamedSharding) -> torch.Tensor:
-    if isinstance(tensor, torchax.tensor.Tensor):
-        tensor = jax_view(tensor)
-    else:
-        tensor = t2j(tensor)
-    return torch_view(_sharded_device_put(tensor, sharding))
+    np_tensor = tensor.detach().cpu().to(torch.float32).numpy()
+    dtype = TORCH_TO_JAX_DTYPE_MAP.get(tensor.dtype, jnp.float32)
+    return torch_view(jax.device_put(np_tensor, sharding).astype(dtype))
 
 
 def _shard_tensor_to_tpu_replicated(tensor: torch.Tensor,

tpu_inference/models/vllm/vllm_model_wrapper.py

@@ -105,7 +105,7 @@ def load_weights(self):
 
         # Load the vLLM model and wrap it into a new model whose forward
         # function can calculate the hidden_state and logits.
-        with load_context, jax.default_device(jax.devices('cpu')[0]):
+        with load_context:
             vllm_model = vllm_get_model(vllm_config=vllm_config_for_load)
         lora_manager = None
         if vllm_config_for_load.lora_config is not None: