add mip module

Signed-off-by: Daniel Korzekwa <[email protected]>

Author: danielkorzekwa

modelopt/torch/_compress/mip/mip_and_realize_models.py

@@ -0,0 +1,87 @@
+# SPDX-FileCopyrightText: Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# mypy: ignore-errors
+from pathlib import Path
+from typing import List
+
+import hydra
+import torch
+import torch.distributed as dist
+from omegaconf import DictConfig
+from utils.dist_utils import is_distributed
+
+from modelopt.torch._compress.mip.run_puzzle import run_puzzle
+from modelopt.torch._compress.tools.hydra_utils import register_hydra_resolvers
+from modelopt.torch._compress.tools.logger import mprint
+from modelopt.torch._compress.tools.runtime import BaseRuntime, IRuntime, NativeDdpRuntime
+from modelopt.torch._compress.tools.validate_puzzle_with_multi_replacements import (
+    validate_puzzle_solutions,
+)
+
+
+def launch_mip(cfg: DictConfig) -> List[str]:
+    solution_paths = run_puzzle(args=cfg.mip)
+    return solution_paths
+
+
+def launch_realize_model(cfg: DictConfig, runtime: IRuntime):
+    validate_puzzle_solutions(args=cfg.realize_model, runtime=runtime)
+
+
+def launch_mip_and_realize_model(cfg: DictConfig, runtime: IRuntime):
+    if runtime.is_main_process:
+        solution_paths = launch_mip(cfg)
+        length_tensor = torch.tensor([len(solution_paths)], dtype=torch.long)
+    else:
+        solution_paths = None
+        length_tensor = torch.tensor([0], dtype=torch.long)
+
+    if not cfg.skip_realize_model:
+        if runtime.world_size > 1:
+            dist.broadcast(length_tensor, src=0)
+
+        list_length = length_tensor.item()
+
+        if runtime.global_rank != 0:
+            solution_paths = [None] * list_length
+
+        if runtime.world_size > 1:
+            dist.broadcast_object_list(solution_paths, src=0)
+
+        for solution_path in solution_paths:
+            mprint(f"Realize model for the solution: {solution_path}")
+            cfg.realize_model.solutions_path = Path(solution_path)
+            launch_realize_model(cfg, runtime=runtime)
+            runtime.wait_for_everyone()
+
+
+@hydra.main("", version_base="1.3")
+def main(cfg: DictConfig) -> None:
+    cfg = hydra.utils.instantiate(cfg)
+
+    _runtime = (
+        NativeDDP_Runtime(
+            dtype=torch.bfloat16, torch_distributed_timeout=getattr(cfg, "nccl_timeout_minutes")
+        )
+        if is_distributed()
+        else BaseRuntime(dtype=torch.bfloat16)
+    )
+    with _runtime as runtime:
+        launch_mip_and_realize_model(cfg, runtime)
+
+
+if __name__ == "__main__":
+    register_hydra_resolvers()
+    main()