Provided one global DeviceConnector instance. Moved typedefs from uti…

…ls to a new file.

tests/infra/base_tester.py

@@ -17,7 +17,7 @@
     compare_pcc,
 )
 from .device_runner import DeviceRunner
-from .utils import Tensor
+from .types import Tensor
 
 
 class BaseTester(ABC):

tests/infra/comparison.py

@@ -10,7 +10,7 @@
 import jax.numpy as jnp
 
 from .device_runner import run_on_cpu
-from .utils import Tensor
+from .types import Tensor
 
 
 @dataclass

tests/infra/device_connector.py

@@ -62,15 +62,6 @@ def __init__(self) -> None:
         self._plugin_path = plugin_path
         self._initialize_backend()
 
-    @staticmethod
-    def get_instance() -> DeviceConnector:
-        """
-        Factory method returning singleton connector instance.
-
-        Use this method instead of constructor.
-        """
-        return DeviceConnector()
-
     def is_initialized(self) -> bool:
         """Checks if connector is already initialized."""
         if hasattr(self, "_initialized") and self._initialized == True:
@@ -119,3 +110,8 @@ def _initialize_backend(self) -> None:
         jax.config.update("jax_platforms", self._supported_devices_str())
 
         self._initialized = True
+
+
+# `DeviceConnector._initialize_backend` must be executed before anything jax related is
+# called. By providing this global instance, that is secured.
+device_connector = DeviceConnector()

tests/infra/device_runner.py

@@ -6,8 +6,8 @@
 
 import jax
 
-from .device_connector import DeviceConnector, DeviceType
-from .utils import Tensor, Workload
+from .device_connector import DeviceType, device_connector
+from .types import Tensor, Workload
 
 
 class DeviceRunner:
@@ -64,27 +64,23 @@ def put_tensors_on_gpu(*tensors: Tensor) -> Sequence[Tensor]:
     def _run_on_device(device_type: DeviceType, workload: Workload) -> Tensor:
         """Runs `workload` on device identified by `device_type`."""
         device_workload = DeviceRunner._put_on_device(device_type, workload)
-
-        connector = DeviceConnector().get_instance()
-        device = connector.connect_device(device_type)
+        device = device_connector.connect_device(device_type)
 
         with jax.default_device(device):
             return device_workload.execute()
 
     @staticmethod
     def _put_on_device(device_type: DeviceType, workload: Workload) -> Workload:
         """Puts `workload` on device and returns it."""
-        connector = DeviceConnector().get_instance()
-        device = connector.connect_device(device_type)
+        device = device_connector.connect_device(device_type)
         return DeviceRunner._safely_put_workload_on_device(workload, device)
 
     @staticmethod
     def _put_tensors_on_device(
         device_type: DeviceType, tensors: Sequence[Tensor]
     ) -> Sequence[Tensor]:
         """Puts `tensors` on device identified by `device_type`."""
-        connector = DeviceConnector().get_instance()
-        device = connector.connect_device(device_type)
+        device = device_connector.connect_device(device_type)
         return [jax.device_put(t, device) for t in tensors]
 
     @staticmethod

tests/infra/graph_tester.py

@@ -8,7 +8,7 @@
 
 from .comparison import ComparisonConfig
 from .op_tester import OpTester
-from .utils import Tensor, Workload
+from .types import Tensor, Workload
 
 
 class GraphTester(OpTester):

tests/infra/model_tester.py

@@ -14,7 +14,7 @@
 from .base_tester import BaseTester
 from .comparison import ComparisonConfig
 from .device_runner import DeviceRunner
-from .utils import Model, Workload
+from .types import Model, Workload
 
 
 class RunMode(Enum):

tests/infra/op_tester.py

@@ -9,7 +9,8 @@
 from .base_tester import BaseTester
 from .comparison import ComparisonConfig
 from .device_runner import DeviceRunner
-from .utils import Tensor, Workload, random_tensor
+from .types import Tensor, Workload
+from .utils import random_tensor
 
 
 class OpTester(BaseTester):

tests/infra/types.py

@@ -0,0 +1,39 @@
+# SPDX-FileCopyrightText: (c) 2024 Tenstorrent AI ULC
+#
+# SPDX-License-Identifier: Apache-2.0
+
+from dataclasses import dataclass
+from enum import Enum
+from typing import Any, Callable, Mapping, Optional, Sequence, Union
+
+import jax
+from flax import linen, nnx
+
+# Convenience alias. Could be used to represent jax.Array, torch.Tensor, np.ndarray, etc.
+Tensor = Union[jax.Array]
+
+# Convenience alias. Could be used to represent nnx.Module, torch.nn.Module, etc.
+# NOTE nnx.Module is the newest API, linen.Module is legacy but it is used in some
+# huggingface models.
+Model = Union[nnx.Module, linen.Module]
+
+
+@dataclass
+class Workload:
+    executable: Callable
+    args: Sequence[Any]
+    kwargs: Optional[Mapping[str, Any]] = None
+
+    def __post_init__(self):
+        # If kwargs is None, initialize it to an empty dictionary.
+        if self.kwargs is None:
+            self.kwargs = {}
+
+    def execute(self) -> Any:
+        return self.executable(*self.args, **self.kwargs)
+
+
+class Framework(Enum):
+    JAX = "jax"
+    TORCH = "torch"
+    NUMPY = "numpy"

tests/infra/utils.py

@@ -2,44 +2,12 @@
 #
 # SPDX-License-Identifier: Apache-2.0
 
-from dataclasses import dataclass
-from enum import Enum
-from typing import Any, Callable, Mapping, Optional, Sequence, Union
-
 import jax
 import jax.numpy as jnp
-from flax import linen, nnx
 from jax import export
 
-
-@dataclass
-class Workload:
-    executable: Callable
-    args: Sequence[Any]
-    kwargs: Optional[Mapping[str, Any]] = None
-
-    def __post_init__(self):
-        # If kwargs is None, initialize it to an empty dictionary.
-        if self.kwargs is None:
-            self.kwargs = {}
-
-    def execute(self) -> Any:
-        return self.executable(*self.args, **self.kwargs)
-
-
-class Framework(Enum):
-    JAX = "jax"
-    TORCH = "torch"
-    NUMPY = "numpy"
-
-
-# Convenience alias. Could be used to represent jax.Array, torch.Tensor, np.ndarray, etc.
-Tensor = Union[jax.Array]
-
-# Convenience alias. Could be used to represent nnx.Module, torch.nn.Module, etc.
-# NOTE nnx.Module is the newest API, linen.Module is legacy but it is used in some
-# huggingface models.
-Model = Union[nnx.Module, linen.Module]
+from .device_runner import run_on_cpu
+from .types import Framework, Tensor, Workload
 
 
 def _str_to_dtype(dtype_str: str, framework: Framework = Framework.JAX):
@@ -50,6 +18,7 @@ def _str_to_dtype(dtype_str: str, framework: Framework = Framework.JAX):
         raise ValueError(f"Unsupported framework: {framework.value}.")
 
 
+@run_on_cpu
 def random_tensor(
     shape: tuple,
     dtype: str = "float32",