Add test that shards, exports and runs with IREE a Conv2DLayer

sharktank/conftest.py

@@ -5,6 +5,8 @@
 # SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
 
 from pathlib import Path
+import pytest
+from typing import Optional
 
 
 # Tests under each top-level directory will get a mark.
@@ -24,3 +26,50 @@ def pytest_collection_modifyitems(items, config):
         mark = TLD_MARKS.get(tld)
         if mark:
             item.add_marker(mark)
+
+
+def pytest_addoption(parser):
+    parser.addoption(
+        "--mlir",
+        type=Path,
+        default=None,
+        help="Path to exported MLIR program. If not specified a temporary file will be used.",
+    )
+    parser.addoption(
+        "--module",
+        type=Path,
+        default=None,
+        help="Path to exported IREE module. If not specified a temporary file will be used.",
+    )
+    parser.addoption(
+        "--parameters",
+        type=Path,
+        default=None,
+        help="Exported model parameters. If not specified a temporary file will be used.",
+    )
+    parser.addoption(
+        "--caching",
+        action="store_true",
+        default=False,
+        help="Load cached results if present instead of recomputing.",
+    )
+
+
+@pytest.fixture(scope="session")
+def mlir_path(pytestconfig: pytest.Config) -> Optional[Path]:
+    return pytestconfig.getoption("mlir")
+
+
+@pytest.fixture(scope="session")
+def module_path(pytestconfig: pytest.Config) -> Optional[Path]:
+    return pytestconfig.getoption("module")
+
+
+@pytest.fixture(scope="session")
+def parameters_path(pytestconfig: pytest.Config) -> Optional[Path]:
+    return pytestconfig.getoption("parameters")
+
+
+@pytest.fixture(scope="session")
+def caching(pytestconfig: pytest.Config) -> Optional[Path]:
+    return pytestconfig.getoption("caching")

sharktank/sharktank/ops/sharded_impls.py

@@ -88,7 +88,7 @@ def conv2d_all_split(
         input.is_replicated or input.shard_dim == 1
     ), "Only sharding of input channel dimension is supported"
     assert (
-        weight.shard_dim == 0 and bias.shard_dim == 0
+        bias is None or weight.shard_dim == 0 and bias.shard_dim == 0
     ), "Only sharding of output channel dimension is supported"
 
     # TODO: allow for implementation where we don't all-gather, but gather
@@ -146,7 +146,7 @@ def conv2d_replicated_input_split_weight_and_bias(
     assert input.shard_count == weight.shard_count
     assert bias is None or weight.shard_count == bias.shard_count
     assert (
-        weight.shard_dim == 0 and bias.shard_dim == 0
+        bias is None or weight.shard_dim == 0 and bias.shard_dim == 0
     ), "Only sharding of output channel dimension is supported"
     assert groups == 1
 
@@ -189,7 +189,8 @@ def conv2d_split_weight_and_bias(
     accum_dtype,
 ) -> SplitPrimitiveTensor:
     assert accum_dtype is None, "accum_dtype not supported"
-    assert weight.shard_count == bias.shard_count
+    if bias is not None:
+        assert weight.shard_count == bias.shard_count
 
     # Output channels dimension is split.
     if weight.shard_dim == 0 and groups == 1:

sharktank/tests/layers/sharded_conv2d_with_iree_test.py

@@ -0,0 +1,212 @@
+import unittest
+
+# Copyright 2024 Advanced Micro Devices, Inc.
+#
+# Licensed under the Apache License v2.0 with LLVM Exceptions.
+# See https://llvm.org/LICENSE.txt for license information.
+# SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
+
+from pathlib import Path
+import tempfile
+import torch
+from shark_turbine import aot
+from sharktank.models.punet.layers import Conv2DLayer
+from sharktank import ops
+from sharktank.types import (
+    Dataset,
+    DefaultPrimitiveTensor,
+    Theta,
+    ShardedTensor,
+    SplitPrimitiveTensor,
+    unbox_tensor,
+)
+from sharktank.types.sharding import Conv2DSplitOutputChannelSharding
+import iree.runtime
+from typing import List, Optional
+import os
+
+vm_context: iree.runtime.VmContext = None
+
+
+def get_compiler_args(target_device_kind: str, shard_count: int) -> List[str]:
+    result = [
+        f"--iree-hal-target-device={target_device_kind}[{i}]"
+        for i in range(shard_count)
+    ]
+    return result
+
+
+def compile_iree_module(
+    export_output: aot.ExportOutput, module_path: str, shard_count: int
+):
+    export_output.session.set_flags(
+        *get_compiler_args(target_device_kind="llvm-cpu", shard_count=shard_count)
+    )
+    export_output.compile(save_to=module_path, target_backends=None)
+
+
+# TODO: improve IREE's Python API to be more concise in a multi-device context.
+# This run function should be way shorter.
+def run_iree_module(
+    sharded_input_image: ShardedTensor,
+    module_path: str,
+    parameters_path: str,
+) -> ShardedTensor:
+    shard_count = sharded_input_image.shard_count
+    hal_driver = iree.runtime.get_driver("local-task")
+    vm_instance = iree.runtime.VmInstance()
+    available_devices = hal_driver.query_available_devices()
+    # Use the same actual device for all devices.
+    devices = [
+        hal_driver.create_device(available_devices[0]) for _ in range(shard_count)
+    ]
+    hal_module = iree.runtime.create_hal_module(instance=vm_instance, devices=devices)
+    params_path = Path(parameters_path)
+    # TODO: make IREE able to load the parameters from the top parameter file
+    # without having to specify the parameter file for each shard separately.
+    parameter_index = iree.runtime.ParameterIndex()
+    for i in range(shard_count):
+        parameter_index.load(
+            file_path=str(
+                Path(params_path).with_suffix(f".rank{i}{params_path.suffix}")
+            )
+        )
+    parameter_provider = parameter_index.create_provider(scope="model")
+    parameters_module = iree.runtime.create_io_parameters_module(
+        vm_instance, parameter_provider
+    )
+
+    vm_module = iree.runtime.VmModule.mmap(vm_instance, str(module_path))
+
+    # The context needs to be destroyed after the buffers, although
+    # it is not associate with them on the API level.
+    global vm_context
+    vm_context = iree.runtime.VmContext(
+        instance=vm_instance, modules=(hal_module, parameters_module, vm_module)
+    )
+    module_input_args = [
+        iree.runtime.asdevicearray(
+            devices[i], sharded_input_image.shards[i].as_torch().to("cpu").numpy()
+        )
+        for i in range(shard_count)
+    ]
+
+    vm_function = vm_module.lookup_function("main")
+    invoker = iree.runtime.FunctionInvoker(
+        vm_context=vm_context,
+        # TODO: rework iree.runtime.FunctionInvoker interface for multiple devices.
+        # This works, but does not look right.
+        device=devices[0],
+        vm_function=vm_function,
+    )
+    results = invoker(*module_input_args)
+    shards = [torch.tensor(tensor.to_host()) for tensor in results]
+    return SplitPrimitiveTensor(ts=shards, shard_dim=1)
+
+
+def run_test_sharded_conv2d_with_iree(
+    mlir_path: Path, module_path: Path, parameters_path: Path, caching: bool
+):
+    torch.set_default_dtype(torch.float32)
+    torch.manual_seed(123456)
+    batches = 2
+    in_channels = 6
+    out_channels = 8
+    height = 11
+    width = 13
+    kernel_height = 5
+    kernel_width = 5
+    shard_count = 2
+    unsharded_theta = Theta(
+        {
+            "weight": DefaultPrimitiveTensor(
+                data=torch.rand(
+                    out_channels,
+                    in_channels,
+                    kernel_height,
+                    kernel_width,
+                )
+            ),
+        }
+    )
+    unsharded_theta.rename_tensors_to_paths()
+
+    if not caching or not os.path.exists(parameters_path):
+        sharding_spec = Conv2DSplitOutputChannelSharding(shard_count=shard_count)
+        sharded_theta = ops.reshard(unsharded_theta, sharding_spec)
+
+        # Roundtrip the dataset, which anchors the tensors as parameters to be loaded
+        # vs constants to be frozen (TODO: This is a bit wonky).
+        sharded_dataset = Dataset({}, sharded_theta)
+        sharded_dataset.save(parameters_path)
+
+    sharded_dataset = Dataset.load(parameters_path)
+
+    input_image = torch.rand(
+        batches,
+        in_channels,
+        height,
+        width,
+    )
+
+    sharded_torch_module = Conv2DLayer(sharded_dataset.root_theta, padding=(0, 0))
+    sharded_input_image = ops.reshard_split(input_image, dim=1, count=shard_count)
+    expected_result = sharded_torch_module(sharded_input_image)
+
+    if not caching or not os.path.exists(module_path):
+        exported_module = aot.export(
+            sharded_torch_module,
+            args=(sharded_input_image,),
+        )
+        exported_module.save_mlir(mlir_path)
+
+        compile_iree_module(
+            export_output=exported_module,
+            module_path=module_path,
+            shard_count=shard_count,
+        )
+
+    actual_result = run_iree_module(
+        sharded_input_image=sharded_input_image,
+        module_path=module_path,
+        parameters_path=parameters_path,
+    )
+    assert len(actual_result.shards) == len(expected_result.shards)
+    assert actual_result.shard_dim == expected_result.shard_dim
+    # TODO: reenable this check once numerical issues are resolved.
+    # See https://github.com/iree-org/iree/issues/18283
+    # for actual_shard, expected_shard in zip(
+    #     actual_result.shards, expected_result.shards
+    # ):
+    #     torch.testing.assert_close(
+    #         unbox_tensor(actual_shard), unbox_tensor(expected_shard)
+    #     )
+
+
+def test_sharded_conv2d_with_iree(
+    mlir_path: Optional[Path],
+    module_path: Optional[Path],
+    parameters_path: Optional[Path],
+    caching: bool,
+):
+    """Test sharding, exporting and running with IREE a 2D convolution layer."""
+
+    with tempfile.TemporaryDirectory(
+        # TODO: verify hypothesis and remove ignore_cleanup_errors=True after a fix.
+        # torch.export.export is spawning some processes that don't exit when the
+        # function returns, this causes some objects to not get destroyed, which
+        # in turn holds files params.rank0.irpa and params.rank1.irpa open.
+        ignore_cleanup_errors=True
+    ) as tmp_dir:
+        mlir_path = Path(tmp_dir) / "model.mlir" if mlir_path is None else mlir_path
+        module_path = (
+            Path(tmp_dir) / "module.vmfb" if module_path is None else module_path
+        )
+        parameters_path = (
+            Path(tmp_dir) / "params.irpa"
+            if parameters_path is None
+            else parameters_path
+        )
+        run_test_sharded_conv2d_with_iree(
+            mlir_path, module_path, parameters_path, caching
+        )

sharktank/tests/models/punet/sharded_resnet_block_with_iree_test.py

@@ -40,6 +40,8 @@ def compile_iree_module(
     export_output.compile(save_to=module_path, target_backends=None)
 
 
+# TODO: improve IREE's Python API to be more concise in a multi-device context.
+# This run function should be way shorter.
 def run_iree_module(
     sharded_input_image: ShardedTensor,
     sharded_input_time_emb: ShardedTensor,
@@ -206,6 +208,7 @@ def run_test_sharded_resnet_block_with_iree(
     )
     assert len(actual_result.shards) == len(expected_result.shards)
     # TODO: reenable this check once numerical issues are resolved.
+    # See https://github.com/iree-org/iree/issues/18283
     # for actual_shard, expected_shard in zip(
     #     actual_result.shards, expected_result.shards
     # ):