fix regressions and messages

axlearn/cloud/gcp/tpu_health_check.py

@@ -31,6 +31,7 @@
 from contextlib import contextmanager
 from datetime import datetime
 from typing import Literal, Optional, Union
+import pytest
 
 import tensorflow as tf
 from absl import flags, logging

axlearn/common/flash_attention/neuron_attention_test.py

@@ -11,6 +11,10 @@
 from axlearn.common.flash_attention.utils import mha_reference
 
 
+if jax.default_backend() != "neuron":
+    pytestmark = pytest.mark.skip(reason="Incompatible hardware, AWS Neuron only test.")
+
+
 @pytest.mark.parametrize(
     "batch_size,seq_len,num_heads,per_head_dim",
     [
@@ -25,7 +29,6 @@
 @pytest.mark.parametrize("use_fwd", [True, False])
 @pytest.mark.parametrize("causal", [True, False])
 @pytest.mark.parametrize("input_dtype", [jnp.float16, jnp.bfloat16, jnp.float32])
-@pytest.mark.skipif(jax.devices()[0].platform != "neuron", reason="Test only runs on Neuron.")
 def test_fwd_against_ref(
     batch_size: int,
     seq_len: int,
@@ -81,7 +84,6 @@ def impl(q, k, v, bias):
 )
 @pytest.mark.parametrize("causal", [True, False])
 @pytest.mark.parametrize("input_dtype", [jnp.bfloat16, jnp.float16, jnp.float32])
-@pytest.mark.skipif(jax.devices()[0].platform != "neuron", reason="Test only runs on Neuron.")
 def test_bwd_against_ref(
     batch_size: int,
     num_heads: int,

axlearn/common/host_array_test.py

@@ -12,6 +12,7 @@
 from absl.testing import absltest, parameterized
 from jax import numpy as jnp
 from jax.experimental import mesh_utils
+import pytest
 
 from axlearn.common.test_utils import TestCase, is_supported_mesh_shape, is_supported_platform
 from axlearn.common.utils import (

axlearn/common/inference_test.py

@@ -20,6 +20,7 @@
 from absl.testing import absltest, parameterized
 from jax.experimental import mesh_utils
 from jax.experimental.pjit import pjit
+import pytest
 
 from axlearn.common import layers, test_utils, utils
 from axlearn.common.base_model import BaseModel
@@ -168,13 +169,10 @@ def predict_batch(self, input_batch: NestedTensor) -> NestedTensor:
 def is_supported(
     platform: str,
     mesh_shape: tuple[int, int],
-    param_dtype: jnp.dtype,
     inference_dtype: Optional[jnp.dtype],
     global_batch_size: int,
     data_partition: DataPartitionType,
-    use_ema: bool = False,
 ):
-    del param_dtype, use_ema  # not used
     # TODO(xuan-zou): jax 0.4.25 breaks bfloat16 on CPU due to high variance on
     # the final result (up to 10% precision diff), will re-enable when fixed.
     # NOTE: bfloat16 test on GPU is added and verified.