NCCL + XLA fails for multi-GPU training.
Hi @cyanic-selkie ,

Could you please provide complete code snippet required for replication of this problem. Thanks! Sorry for the delay, @SuryanarayanaY, I updated my issue with the full code snippet. Hi @cyanic-selkie ,

As you are using TF2.15 version with tf.keras,first you need to import `tf-keras` package using `pip install tf-keras`
and then set environment variable `os.environ["TF_USE_LEGACY_KERAS"]="1"`.

Then you can import keras from tensorflow or simply use tf.keras.

Can you try this and comeback with outcome. Thanks! @SuryanarayanaY I did as you said, there is no difference. Hi @cyanic-selkie ,

Could you please provide minimal code snippet for testing? Thanks! @SuryanarayanaY I already provided the minimum reproducible example in the initial post. Is there something else you need? same bug 'xla.gpu.all_reduce' failed, i found that "tensorflow.keras.applications.inception_resnet_v2.InceptionResNetV2" was called before all_reduce, the problem occur, but if i use dense layer as replacement, it work well.... sb. help me plz i guess the former batchnormalize layer called cause the later all_reduce crash as the code above
### Issue type

Bug

### Have you reproduced the bug with TensorFlow Nightly?

Yes

### Source

binary

### TensorFlow version

tf 2.15

### Custom code

Yes

### OS platform and distribution

Linux Ubuntu 22.04

### Mobile device

_No response_

### Python version

3.11

### Bazel version

_No response_

### GCC/compiler version

_No response_

### CUDA/cuDNN version

12.2/8.9.4

### GPU model and memory

A100 40GB (20GB MiG)

### Current behavior?

I am trying to run multi-GPU training with an XLA compiled model (simple CNN with a classification head). Without XLA, everything runs fine. With XLA enabled, I get one of two errors in the log, depending on whether I am using 4 GPUs or 2 GPUs. The GPUs are split into 2 MiGs.

I also tried on previous TF/CUDA versions and I get the same result.

### Standalone code to reproduce the issue

```python
import argparse
import json
import os
from typing import Any

import numpy as np
import tensorflow as tf

def get_replica_hostnames():
    ...


def get_replica_id():
    ...


def set_multiworker_env_config():
    hostnames = get_replica_hostnames()
    replica_index = get_replica_id()

    os.environ["TF_CONFIG"] = json.dumps(
        {
            "cluster": {
                "worker": hostnames,
            },
            "task": {"type": "worker", "index": replica_index},
        }
    )


class Model(tf.keras.models.Model):
    def __init__(self, *args: Any, **kwargs: Any) -> None:
        super().__init__(*args, **kwargs)

        self._embedder = tf.keras.Sequential(
            [
                tf.keras.layers.Conv2D(
                    filters=8,
                    kernel_size=3,
                    padding="same",
                    activation=tf.keras.activations.relu,
                    use_bias=False,
                ),
                tf.keras.layers.BatchNormalization(),
                tf.keras.layers.Conv2D(
                    filters=8,
                    kernel_size=3,
                    padding="same",
                    activation=tf.keras.activations.relu,
                    use_bias=False,
                ),
                tf.keras.layers.BatchNormalization(),
                tf.keras.layers.MaxPool2D(),
                tf.keras.layers.GlobalAveragePooling2D(),
            ]
        )

        self._classifier = tf.keras.layers.Dense(550)

    def call(self, x: tf.Tensor) -> tf.Tensor:
        x = self._embedder(x)
        x = self._classifier(x)
        x = tf.keras.layers.Activation("linear", dtype="float32")(x)
        return x


def create_dummy_dataset(batch_size: int) -> tf.data.Dataset:
    X = np.random.rand(batch_size, 384, 640, 1)
    y = np.random.randint(550, size=batch_size)
    return tf.data.Dataset.from_tensor_slices((X, y)).batch(batch_size, True).repeat()


def train():
    set_multiworker_env_config()
    strategy = tf.distribute.MultiWorkerMirroredStrategy()
    num_replicas = strategy.num_replicas_in_sync

    batch_size = 16 * num_replicas
    dataset = create_dummy_dataset(batch_size)
    dataset = strategy.experimental_distribute_dataset(dataset)

    with strategy.scope():
        model = Model()
        model.compile(
            loss=tf.keras.losses.SparseCategoricalCrossentropy(
                from_logits=True,
            ),
            optimizer=tf.keras.optimizers.Adam(
                learning_rate=1e-3,
                weight_decay=1e-5,
            ),
            metrics=[
                "accuracy",
            ],
            jit_compile=True,
        )

    model.fit(
        dataset,
        epochs=10,
        steps_per_epoch=100,
    )


if __name__ == "__main__":
    train()
```


### Relevant log output

```shell
Log output for 4 GPUs:

2024-01-08 12:48:24.103746: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
2024-01-08 12:48:24.103807: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
2024-01-08 12:48:24.104896: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
2024-01-08 12:48:24.111875: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2024-01-08 12:48:24.978525: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
2024-01-08 12:48:27.492019: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1929] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 18370 MB memory:  -> device: 0, name: NVIDIA A100-SXM4-40GB MIG 3g.20gb, pci bus id: 0000:01:00.0, compute capability: 8.0
2024-01-08 12:48:27.503592: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1929] Created device /job:worker/replica:0/task:0/device:GPU:0 with 18370 MB memory:  -> device: 0, name: NVIDIA A100-SXM4-40GB MIG 3g.20gb, pci bus id: 0000:01:00.0, compute capability: 8.0
2024-01-08 12:48:27.528772: I tensorflow/core/distributed_runtime/rpc/grpc_server_lib.cc:457] Started server with target: grpc://gen-svc-4e03d749-0565-47d7-b7ff-63437f0ab5b3:80
2024-01-08 12:48:27.535501: I external/local_tsl/tsl/distributed_runtime/coordination/coordination_service.cc:553] /job:worker/replica:0/task:0 has connected to coordination service. Incarnation: 9493928235207696637
2024-01-08 12:48:27.535846: I external/local_tsl/tsl/distributed_runtime/coordination/coordination_service_agent.cc:304] Coordination agent has successfully connected.
2024-01-08 12:48:28.425710: I external/local_tsl/tsl/distributed_runtime/coordination/coordination_service.cc:553] /job:worker/replica:0/task:1 has connected to coordination service. Incarnation: 17537869834892823189
2024-01-08 12:48:29.509519: I external/local_tsl/tsl/distributed_runtime/coordination/coordination_service.cc:553] /job:worker/replica:0/task:2 has connected to coordination service. Incarnation: 11924625857490357420
2024-01-08 12:48:29.766944: I external/local_tsl/tsl/distributed_runtime/coordination/coordination_service.cc:553] /job:worker/replica:0/task:3 has connected to coordination service. Incarnation: 8010175117178506894
WARNING:absl:You use TensorFlow DType <dtype: 'string'> in tfds.features This will soon be deprecated in favor of NumPy DTypes. In the meantime it was converted to object.
WARNING:absl:You use TensorFlow DType <dtype: 'int64'> in tfds.features This will soon be deprecated in favor of NumPy DTypes. In the meantime it was converted to int64.
ml-wf-receipt-ext-logo-classifier-pipelinenxxxs-train-template:37:563 [0] NCCL INFO Bootstrap : Using eth0:10.233.118.112<0>
ml-wf-receipt-ext-logo-classifier-pipelinenxxxs-train-template:37:563 [0] NCCL INFO NET/Plugin : No plugin found (libnccl-net.so), using internal implementation
2024-01-08 12:48:32.756935: I external/local_tsl/tsl/profiler/lib/profiler_session.cc:104] Profiler session initializing.
2024-01-08 12:48:32.756993: I external/local_tsl/tsl/profiler/lib/profiler_session.cc:119] Profiler session started.
2024-01-08 12:48:32.757173: I external/local_xla/xla/backends/profiler/gpu/cupti_tracer.cc:1883] Profiler found 1 GPUs
2024-01-08 12:48:32.790801: I external/local_tsl/tsl/profiler/lib/profiler_session.cc:131] Profiler session tear down.
2024-01-08 12:48:32.790934: I external/local_xla/xla/backends/profiler/gpu/cupti_tracer.cc:2017] CUPTI activity buffer flushed
Epoch 1/10
2024-01-08 12:48:37.520453: I external/local_xla/xla/service/service.cc:168] XLA service 0x7fe284006a40 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
2024-01-08 12:48:37.520583: I external/local_xla/xla/service/service.cc:176]   StreamExecutor device (0): NVIDIA A100-SXM4-40GB MIG 3g.20gb, Compute Capability 8.0
2024-01-08 12:48:37.680983: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:269] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.
2024-01-08 12:48:38.693930: I external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:454] Loaded cuDNN version 8904
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1704718137.764316     430 device_compiler.h:186] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.
ml-wf-receipt-ext-logo-classifier-pipelinenxxxs-train-template:37:1288 [0] NCCL INFO cudaDriverVersion 12020
NCCL version 2.16.5+cudaCUDA_MAJOR.CUDA_MINOR
ml-wf-receipt-ext-logo-classifier-pipelinenxxxs-train-template:37:1288 [0] NCCL INFO Failed to open libibverbs.so[.1]
ml-wf-receipt-ext-logo-classifier-pipelinenxxxs-train-template:37:1288 [0] NCCL INFO NET/Socket : Using [0]eth0:10.233.118.112<0>
ml-wf-receipt-ext-logo-classifier-pipelinenxxxs-train-template:37:1288 [0] NCCL INFO Using network Socket

ml-wf-receipt-ext-logo-classifier-pipelinenxxxs-train-template:37:1288 [0] external/nccl_archive/src/init.cc:642 NCCL WARN Duplicate GPU detected : rank 0 and rank 2 both on CUDA device 1000
ml-wf-receipt-ext-logo-classifier-pipelinenxxxs-train-template:37:1288 [0] NCCL INFO external/nccl_archive/src/init.cc:1100 -> 5
ml-wf-receipt-ext-logo-classifier-pipelinenxxxs-train-template:37:1288 [0] NCCL INFO external/nccl_archive/src/init.cc:1173 -> 5
ml-wf-receipt-ext-logo-classifier-pipelinenxxxs-train-template:37:1288 [0] NCCL INFO external/nccl_archive/src/init.cc:1209 -> 5
2024-01-08 12:48:58.577448: W external/local_xla/xla/service/gpu/runtime/support.cc:58] Intercepted XLA runtime error:
INTERNAL: external/local_xla/xla/service/gpu/nccl_utils.cc:297: NCCL operation ncclCommInitRank(&comm, nranks, id, rank) failed: invalid usage
2024-01-08 12:48:58.577775: I tensorflow/core/framework/local_rendezvous.cc:421] Local rendezvous recv item cancelled. Key hash: 16898057275935290807
2024-01-08 12:48:58.577798: I tensorflow/core/framework/local_rendezvous.cc:421] Local rendezvous recv item cancelled. Key hash: 7386102362502530449
2024-01-08 12:48:58.577844: I tensorflow/core/framework/local_rendezvous.cc:421] Local rendezvous recv item cancelled. Key hash: 11220456033729140565
Traceback (most recent call last):
  File "/kirax_source/train.py", line 346, in <module>
    train_tf(args=args, jit_compile=XLA)
  File "/kirax_source/train.py", line 246, in train_tf
    model.fit(
  File "/usr/local/lib/python3.11/dist-packages/keras/src/utils/traceback_utils.py", line 70, in error_handler
    raise e.with_traceback(filtered_tb) from None
  File "/usr/local/lib/python3.11/dist-packages/tensorflow/python/eager/execute.py", line 53, in quick_execute
    tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
tensorflow.python.framework.errors_impl.InternalError: Graph execution error:

Detected at node StatefulPartitionedCall defined at (most recent call last):
  File "/usr/lib/python3.11/threading.py", line 1002, in _bootstrap

  File "/usr/lib/python3.11/threading.py", line 1045, in _bootstrap_inner

Detected at node StatefulPartitionedCall defined at (most recent call last):
  File "/usr/lib/python3.11/threading.py", line 1002, in _bootstrap

  File "/usr/lib/python3.11/threading.py", line 1045, in _bootstrap_inner

2 root error(s) found.
  (0) INTERNAL:  Failed to execute XLA Runtime executable: run time error: custom call 'xla.gpu.all_reduce' failed: external/local_xla/xla/service/gpu/nccl_utils.cc:297: NCCL operation ncclCommInitRank(&comm, nranks, id, rank) failed: invalid usage; current tracing scope: all-reduce-start.4; current profiling annotation: XlaModule:#hlo_module=a_inference_run_step_7562__.4006,program_id=447#.
         [[{{node StatefulPartitionedCall}}]]
         [[Reshape_3/_22]]
  (1) INTERNAL:  Failed to execute XLA Runtime executable: run time error: custom call 'xla.gpu.all_reduce' failed: external/local_xla/xla/service/gpu/nccl_utils.cc:297: NCCL operation ncclCommInitRank(&comm, nranks, id, rank) failed: invalid usage; current tracing scope: all-reduce-start.4; current profiling annotation: XlaModule:#hlo_module=a_inference_run_step_7562__.4006,program_id=447#.
         [[{{node StatefulPartitionedCall}}]]
0 successful operations.
0 derived errors ignored. [Op:__inference_train_function_7900]

Log output for 2 GPUs:

2024-01-08 13:32:56.555585: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
2024-01-08 13:32:56.555666: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
2024-01-08 13:32:56.557055: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
2024-01-08 13:32:56.563733: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2024-01-08 13:32:57.475551: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
2024-01-08 13:32:59.868467: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1929] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 18370 MB memory:  -> device: 0, name: NVIDIA A100-SXM4-40GB MIG 3g.20gb, pci bus id: 0000:41:00.0, compute capability: 8.0
2024-01-08 13:32:59.880625: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1929] Created device /job:worker/replica:0/task:0/device:GPU:0 with 18370 MB memory:  -> device: 0, name: NVIDIA A100-SXM4-40GB MIG 3g.20gb, pci bus id: 0000:41:00.0, compute capability: 8.0
2024-01-08 13:32:59.903354: I tensorflow/core/distributed_runtime/rpc/grpc_server_lib.cc:457] Started server with target: grpc://gen-svc-d871b43e-6f9e-4d8f-9faf-d98a734319f3:80
2024-01-08 13:32:59.912608: I external/local_tsl/tsl/distributed_runtime/coordination/coordination_service.cc:553] /job:worker/replica:0/task:0 has connected to coordination service. Incarnation: 12924650074147221766
2024-01-08 13:32:59.913045: I external/local_tsl/tsl/distributed_runtime/coordination/coordination_service_agent.cc:304] Coordination agent has successfully connected.
2024-01-08 13:33:00.820050: I external/local_tsl/tsl/distributed_runtime/coordination/coordination_service.cc:553] /job:worker/replica:0/task:1 has connected to coordination service. Incarnation: 4023732460757352804
WARNING:absl:You use TensorFlow DType <dtype: 'string'> in tfds.features This will soon be deprecated in favor of NumPy DTypes. In the meantime it was converted to object.
WARNING:absl:You use TensorFlow DType <dtype: 'int64'> in tfds.features This will soon be deprecated in favor of NumPy DTypes. In the meantime it was converted to int64.
ml-wf-receipt-ext-logo-classifier-pipelinegl897-train-template:31:784 [0] NCCL INFO Bootstrap : Using eth0:10.233.118.63<0>
ml-wf-receipt-ext-logo-classifier-pipelinegl897-train-template:31:784 [0] NCCL INFO NET/Plugin : No plugin found (libnccl-net.so), using internal implementation
2024-01-08 13:33:03.552619: I external/local_tsl/tsl/profiler/lib/profiler_session.cc:104] Profiler session initializing.
2024-01-08 13:33:03.552667: I external/local_tsl/tsl/profiler/lib/profiler_session.cc:119] Profiler session started.
2024-01-08 13:33:03.552814: I external/local_xla/xla/backends/profiler/gpu/cupti_tracer.cc:1883] Profiler found 1 GPUs
2024-01-08 13:33:03.587433: I external/local_tsl/tsl/profiler/lib/profiler_session.cc:131] Profiler session tear down.
2024-01-08 13:33:03.587611: I external/local_xla/xla/backends/profiler/gpu/cupti_tracer.cc:2017] CUPTI activity buffer flushed
Epoch 1/10
2024-01-08 13:33:08.149597: I external/local_xla/xla/service/service.cc:168] XLA service 0x7ff338008540 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
2024-01-08 13:33:08.149730: I external/local_xla/xla/service/service.cc:176]   StreamExecutor device (0): NVIDIA A100-SXM4-40GB MIG 3g.20gb, Compute Capability 8.0
2024-01-08 13:33:08.732338: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:269] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.
2024-01-08 13:33:09.871101: I external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:454] Loaded cuDNN version 8904
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1704720809.106117     425 device_compiler.h:186] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.
ml-wf-receipt-ext-logo-classifier-pipelinegl897-train-template:31:1280 [0] NCCL INFO cudaDriverVersion 12020
NCCL version 2.16.5+cudaCUDA_MAJOR.CUDA_MINOR
ml-wf-receipt-ext-logo-classifier-pipelinegl897-train-template:31:1280 [0] NCCL INFO Failed to open libibverbs.so[.1]
ml-wf-receipt-ext-logo-classifier-pipelinegl897-train-template:31:1280 [0] NCCL INFO NET/Socket : Using [0]eth0:10.233.118.63<0>
ml-wf-receipt-ext-logo-classifier-pipelinegl897-train-template:31:1280 [0] NCCL INFO Using network Socket

ml-wf-receipt-ext-logo-classifier-pipelinegl897-train-template:31:1280 [0] external/nccl_archive/src/misc/nvmlwrap.cc:183 NCCL WARN nvmlDeviceGetHandleByPciBusId() failed: Not Found
ml-wf-receipt-ext-logo-classifier-pipelinegl897-train-template:31:1280 [0] NCCL INFO Setting affinity for GPU 0 to ffffffff,00000000,ffffffff
ml-wf-receipt-ext-logo-classifier-pipelinegl897-train-template:31:1280 [0] NCCL INFO Channel 00/02 :    0   1
ml-wf-receipt-ext-logo-classifier-pipelinegl897-train-template:31:1280 [0] NCCL INFO Channel 01/02 :    0   1
ml-wf-receipt-ext-logo-classifier-pipelinegl897-train-template:31:1280 [0] NCCL INFO Trees [0] 1/-1/-1->0->-1 [1] -1/-1/-1->0->1
ml-wf-receipt-ext-logo-classifier-pipelinegl897-train-template:31:1280 [0] NCCL INFO P2P Chunksize set to 131072
ml-wf-receipt-ext-logo-classifier-pipelinegl897-train-template:31:1284 [0] NCCL INFO NCCL_SOCKET_NTHREADS set by environment to 8.
ml-wf-receipt-ext-logo-classifier-pipelinegl897-train-template:31:1284 [0] NCCL INFO NET/Socket: Using 8 threads and 1 sockets per thread
ml-wf-receipt-ext-logo-classifier-pipelinegl897-train-template:31:1280 [0] NCCL INFO Channel 00/0 : 1[1000] -> 0[41000] [receive] via NET/Socket/0
ml-wf-receipt-ext-logo-classifier-pipelinegl897-train-template:31:1284 [0] NCCL INFO NET/Socket: Using 8 threads and 1 sockets per thread
ml-wf-receipt-ext-logo-classifier-pipelinegl897-train-template:31:1280 [0] NCCL INFO Channel 01/0 : 1[1000] -> 0[41000] [receive] via NET/Socket/0
ml-wf-receipt-ext-logo-classifier-pipelinegl897-train-template:31:1280 [0] NCCL INFO Channel 00/0 : 0[41000] -> 1[1000] [send] via NET/Socket/0
ml-wf-receipt-ext-logo-classifier-pipelinegl897-train-template:31:1280 [0] NCCL INFO Channel 01/0 : 0[41000] -> 1[1000] [send] via NET/Socket/0
ml-wf-receipt-ext-logo-classifier-pipelinegl897-train-template:31:1280 [0] NCCL INFO Connected all rings
ml-wf-receipt-ext-logo-classifier-pipelinegl897-train-template:31:1280 [0] NCCL INFO Connected all trees
ml-wf-receipt-ext-logo-classifier-pipelinegl897-train-template:31:1280 [0] NCCL INFO threadThresholds 8/8/64 | 16/8/64 | 512 | 512
ml-wf-receipt-ext-logo-classifier-pipelinegl897-train-template:31:1280 [0] NCCL INFO 2 coll channels, 2 p2p channels, 2 p2p channels per peer
ml-wf-receipt-ext-logo-classifier-pipelinegl897-train-template:31:1280 [0] NCCL INFO comm 0x7fe61cffcb20 rank 0 nranks 2 cudaDev 0 busId 41000 commId 0xe105507e5746b5a2 - Init COMPLETE
2024-01-08 13:33:29.780117: W external/local_xla/xla/service/gpu/runtime/support.cc:58] Intercepted XLA runtime error:
INTERNAL: There was an error before calling cuModuleGetFunction (101): cudaErrorInvalidDevice : invalid device ordinal
2024-01-08 13:33:29.780331: I tensorflow/core/framework/local_rendezvous.cc:421] Local rendezvous recv item cancelled. Key hash: 14229809023376429067
2024-01-08 13:33:29.780349: I tensorflow/core/framework/local_rendezvous.cc:421] Local rendezvous recv item cancelled. Key hash: 10883816187180422187
2024-01-08 13:33:29.780389: I tensorflow/core/framework/local_rendezvous.cc:421] Local rendezvous recv item cancelled. Key hash: 5243788738829094043
Traceback (most recent call last):
  File "/kirax_source/train.py", line 346, in <module>
    train_tf(args=args, jit_compile=XLA)
  File "/kirax_source/train.py", line 246, in train_tf
    model.fit(
  File "/usr/local/lib/python3.11/dist-packages/keras/src/utils/traceback_utils.py", line 70, in error_handler
    raise e.with_traceback(filtered_tb) from None
  File "/usr/local/lib/python3.11/dist-packages/tensorflow/python/eager/execute.py", line 53, in quick_execute
    tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
tensorflow.python.framework.errors_impl.InternalError: Graph execution error:

Detected at node StatefulPartitionedCall defined at (most recent call last):
  File "/usr/lib/python3.11/threading.py", line 1002, in _bootstrap

  File "/usr/lib/python3.11/threading.py", line 1045, in _bootstrap_inner

Detected at node StatefulPartitionedCall defined at (most recent call last):
  File "/usr/lib/python3.11/threading.py", line 1002, in _bootstrap

  File "/usr/lib/python3.11/threading.py", line 1045, in _bootstrap_inner

2 root error(s) found.
  (0) INTERNAL:  Failed to execute XLA Runtime executable: run time error: custom call 'xla.gpu.func.launch' failed: There was an error before calling cuModuleGetFunction (101): cudaErrorInvalidDevice : invalid device ordinal; current tracing scope: fusion.274; current profiling annotation: XlaModule:#hlo_module=a_inference_run_step_7562__.4006,program_id=447#.
         [[{{node StatefulPartitionedCall}}]]
         [[CollectiveReduceV2_1/_17]]
  (1) INTERNAL:  Failed to execute XLA Runtime executable: run time error: custom call 'xla.gpu.func.launch' failed: There was an error before calling cuModuleGetFunction (101): cudaErrorInvalidDevice : invalid device ordinal; current tracing scope: fusion.274; current profiling annotation: XlaModule:#hlo_module=a_inference_run_step_7562__.4006,program_id=447#.
         [[{{node StatefulPartitionedCall}}]]
0 successful operations.
0 derived errors ignored. [Op:__inference_train_function_7900]
```
