Adding first test

tests/infra/__init__.py

@@ -8,3 +8,4 @@
 from .model_tester import ModelTester, RunMode
 from .op_tester import run_op_test, run_op_test_with_random_inputs
 from .utils import random_tensor, supported_dtypes
+from .multichip_tester import run_multichip_test_with_random_inputs

tests/infra/device_runner.py

@@ -10,6 +10,8 @@
 from .types import Tensor
 from .workload import Workload
 
+from jax.sharding import Mesh, PartitionSpec, NamedSharding
+
 import inspect
 
 
@@ -18,6 +20,11 @@ class DeviceRunner:
     Class providing methods to put and run workload on any supported device.
     """
 
+    @staticmethod
+    def run_manual(workload: Workload) -> Tensor:
+        """Runs `workload` on TT device."""
+        return DeviceRunner._run_manual(workload)
+
     @staticmethod
     def run_on_tt_device(workload: Workload, device_num: int = 0) -> Tensor:
         """Runs `workload` on TT device."""
@@ -63,6 +70,43 @@ def put_tensors_on_gpu(*tensors: Tensor) -> Sequence[Tensor]:
         """Puts `tensors` on GPU."""
         raise NotImplementedError("Support for GPUs not implemented")
 
+    @staticmethod
+    def put_with_none_sharding(
+        workload: Workload,
+        mesh: jax.sharding.Mesh,
+        in_specs: Sequence[jax.sharding.PartitionSpec],
+    ) -> Tensor:
+        """Gives inputs shardings for multichip workloads"""
+        args_on_device = []
+        spec_index = 0
+        for arg in workload.args:
+            if not isinstance(arg, Tensor):
+                args_on_device.append(arg)
+            else:
+                args_on_device.append(
+                    DeviceRunner._put_tensor_none_sharding(
+                        arg, mesh, in_specs[spec_index]
+                    )
+                )
+                spec_index += 1
+
+        kwargs_on_device = {}
+        for key, value in workload.kwargs.items():
+            if not isinstance(value, Tensor):
+                kwargs_on_device[key] = value
+            else:
+                kwargs_on_device[key] = DeviceRunner._put_tensor_none_sharding(
+                    value, mesh, in_specs[spec_index]
+                )
+                spec_index += 1
+
+        return Workload(workload.executable, args_on_device, kwargs_on_device)
+
+    @staticmethod
+    def _run_manual(workload: Workload) -> Tensor:
+        """Runs `workload` on a device."""
+        return workload.execute().block_until_ready()
+
     @staticmethod
     def _run_on_device(
         workload: Workload, device_type: DeviceType, device_num: int = 0
@@ -74,6 +118,15 @@ def _run_on_device(
         with jax.default_device(device):
             return device_workload.execute()
 
+    @staticmethod
+    def _put_tensor_none_sharding(
+        tensor: Tensor, mesh: jax.sharding.Mesh, in_spec: jax.sharding.PartitionSpec
+    ) -> Tensor:
+        """Needed for multichip: Uses put_device to give inputs shardings."""
+        none_tuple = (None,) * len(in_spec)
+        none_spec = PartitionSpec(*none_tuple)
+        return jax.device_put(tensor, NamedSharding(mesh, none_spec), may_alias=True)
+
     @staticmethod
     def _put_on_device(
         workload: Workload, device_type: DeviceType, device_num: int = 0

tests/infra/multichip_tester.py

@@ -0,0 +1,116 @@
+# SPDX-FileCopyrightText: (c) 2024 Tenstorrent AI ULC
+#
+# SPDX-License-Identifier: Apache-2.0
+
+from __future__ import annotations
+
+import jax
+from jax.experimental.shard_map import shard_map
+from jax.sharding import NamedSharding
+from typing import Callable, Sequence
+
+from .base_tester import BaseTester
+from .comparison import ComparisonConfig
+from .device_runner import DeviceRunner
+from .workload import Workload
+
+
+class MultichipTester(BaseTester):
+    """Specific tester for ops."""
+
+    def __init__(
+        self,
+        mesh: jax.Mesh,
+        in_specs: tuple,
+        out_specs: jax.sharding.PartitionSpec,
+        comparison_config: ComparisonConfig = ComparisonConfig(),
+    ) -> None:
+        self.mesh = mesh
+        self.in_specs = in_specs
+        self.out_specs = out_specs
+        super().__init__(comparison_config)
+
+    def _compile_cpu(
+        self, executable: Callable, static_argnames: Sequence[str] = None
+    ) -> Callable:
+        """Sets up `executable` for just-in-time compile - specifically for CPU."""
+        return jax.jit(executable, static_argnames=static_argnames)
+
+    def _compile(
+        self, executable: Callable, static_argnames: Sequence[str] = None
+    ) -> Callable:
+        """Sets up `executable` for just-in-time compile."""
+        module_sharded = shard_map(
+            executable, mesh=self.mesh, in_specs=self.in_specs, out_specs=self.out_specs
+        )
+        output_sharding = NamedSharding(self.mesh, self.out_specs)
+        return jax.jit(
+            module_sharded,
+            out_shardings=output_sharding,
+            static_argnames=static_argnames,
+        )
+
+    def test(self, workload: Workload, cpu_workload: Workload) -> None:
+        """
+        Runs test by running `workload` on TT device and CPU and comparing the results.
+        """
+        compiled_executable = self._compile(workload.executable)
+        cpu_compiled_executable = self._compile_cpu(cpu_workload.executable)
+
+        cpu_compiled_workload = Workload(
+            cpu_compiled_executable, cpu_workload.args, cpu_workload.kwargs
+        )
+
+        compiled_workload = Workload(
+            compiled_executable, workload.args, workload.kwargs
+        )
+
+        non_sharded_workload = DeviceRunner.put_with_none_sharding(
+            compiled_workload, self.mesh, in_specs=self.in_specs
+        )
+
+        tt_res = DeviceRunner.run_manual(non_sharded_workload)
+        cpu_res = DeviceRunner.run_on_cpu(cpu_compiled_workload)
+
+        self._compare(tt_res, cpu_res)
+
+    def test_with_random_inputs(
+        self,
+        f: Callable,
+        golden_f: Callable,
+        input_shapes: Sequence[tuple],
+        minval: float = 0.0,
+        maxval: float = 1.0,
+    ) -> None:
+        """
+        Tests `f` by running it with random inputs in range [`minval`, `maxval`) on
+        TT device and CPU and comparing the results.
+        """
+        inputs = [
+            jax.random.uniform(
+                key=jax.random.key(0), shape=shape, minval=minval, maxval=maxval
+            )
+            for shape in input_shapes
+        ]
+        workload = Workload(f, inputs)
+        cpu_workload = Workload(golden_f, inputs)
+        self.test(workload, cpu_workload)
+
+
+def run_multichip_test_with_random_inputs(
+    mesh_test: Callable,
+    golden_test: Callable,
+    input_shapes: Sequence[tuple],
+    mesh: jax.Mesh,
+    in_specs: tuple,
+    out_specs: jax.sharding.PartitionSpec,
+    minval: float = 0.0,
+    maxval: float = 1.0,
+    comparison_config: ComparisonConfig = ComparisonConfig(),
+) -> None:
+    """
+    Tests `mesh_test` with random inputs in range [`minval`, `maxval`) by running it on
+    TT device and CPU and comparing the results based on `comparison_config`.
+    """
+    tester = MultichipTester(mesh, in_specs, out_specs, comparison_config)
+    tester.test_with_random_inputs(mesh_test, golden_test, input_shapes, minval, maxval)

tests/jax/multichip/manual/all_gather.py

@@ -0,0 +1,33 @@
+# SPDX-FileCopyrightText: (c) 2025 Tenstorrent AI ULC
+#
+# SPDX-License-Identifier: Apache-2.0
+
+import jax
+import jax.numpy as jnp
+from jax import jit
+from jax.experimental.shard_map import shard_map
+from jax.sharding import PartitionSpec
+from functools import partial
+from infra import run_multichip_test_with_random_inputs
+import pytest
+
+
+@pytest.mark.parametrize("x_shape", [(8192, 784)])
+@pytest.mark.skip(reason="Compilation fails")
+def all_gather_test(x_shape: tuple):
+    def fwd(batch):
+        act = jax.lax.all_gather(batch, "batch", axis=0, tiled=True)
+        return act
+
+    def golden_fwd(batch):
+        return jnp.tile(batch, (2, 1))
+
+    devices = jax.devices("tt")
+    mesh = jax.make_mesh((2,), ("batch"), devices=devices)
+
+    in_specs = (PartitionSpec("batch"),)
+    out_specs = PartitionSpec("batch")
+
+    run_multichip_test_with_random_inputs(
+        fwd, golden_fwd, [x_shape], mesh, in_specs, out_specs
+    )

tests/jax/multichip/manual/unary_eltwise.py

@@ -0,0 +1,38 @@
+# SPDX-FileCopyrightText: (c) 2025 Tenstorrent AI ULC
+#
+# SPDX-License-Identifier: Apache-2.0
+
+import jax
+import jax.numpy as jnp
+from jax import jit
+from jax.experimental.shard_map import shard_map
+from jax.sharding import PartitionSpec
+from functools import partial
+from infra import run_multichip_test_with_random_inputs
+import pytest
+
+
+@pytest.mark.parametrize("x_shape", [(256, 256)])
+@pytest.mark.skip(reason="Multichip still in development")
+def unary_eltwise_test(x_shape: tuple):
+    def fwd(a_block):
+        b_block = jnp.negative(a_block)
+        stitched_result = jax.lax.psum(b_block, ("x", "y"))
+        return stitched_result
+
+    def fwd_single_device(a_block):
+        a1, a2 = jnp.split(a_block, 2, axis=1)
+
+        b1, b2 = jnp.negative(a1), jnp.negative(a2)
+
+        stitched_result = b1 + b2
+        return stitched_result
+
+    devices = jax.devices("tt")
+    mesh = jax.make_mesh((1, 2), ("x", "y"), devices=devices)
+    in_specs = (PartitionSpec("x", "y"),)
+    out_specs = PartitionSpec(None, None)
+
+    run_multichip_test_with_random_inputs(
+        fwd, fwd_single_device, [x_shape], mesh, in_specs, out_specs
+    )