dyn col dim proposer (#1794)

Summary:
Pull Request resolved: #1794

Refactored version of D48339456.

design doc (wip): https://docs.google.com/document/d/1cM8jlJJJYwkXDfeRKGT9jo_S4IDnVZ0X2DWF2XwHTvE/

The idea is to decrease CW dim for certain tables that are causing max perf or max hbm usage.

Reviewed By: ge0405

Differential Revision: D54824506

fbshipit-source-id: 8fb18c00c89b059f00f63de62f0b435e60f0fe7b

torchrec/distributed/planner/perf_models.py

@@ -9,10 +9,21 @@
 
 from typing import cast, List
 
-from torchrec.distributed.planner.types import Perf, PerfModel, ShardingOption, Topology
+from torchrec.distributed.planner.types import (
+    Perf,
+    PerfModel,
+    ShardingOption,
+    Storage,
+    Topology,
+)
 
 
 class NoopPerfModel(PerfModel):
+    """
+    A no-op model that returns the maximum perf among all shards. Here, no-op
+    means we estimate the performance of a model without actually running it.
+    """
+
     def __init__(self, topology: Topology) -> None:
         self._topology = topology
 
@@ -24,3 +35,22 @@ def rate(self, plan: List[ShardingOption]) -> float:
                 perfs[shard.rank] += cast(Perf, shard.perf).total
 
         return max(perfs)
+
+
+class NoopStorageModel(PerfModel):
+    """
+    A no-op model that returns the maximum hbm usage among all shards. Here, no-op
+    means we estimate the performance of a model without actually running it.
+    """
+
+    def __init__(self, topology: Topology) -> None:
+        self._topology = topology
+
+    def rate(self, plan: List[ShardingOption]) -> float:
+        hbms = [0] * self._topology.world_size
+        for sharding_option in plan:
+            for shard in sharding_option.shards:
+                # pyre-ignore [6]: Expected `typing_extensions.SupportsIndex`
+                hbms[shard.rank] += cast(Storage, shard.storage).hbm
+
+        return max(hbms)

torchrec/distributed/planner/tests/test_perf_models.py

@@ -0,0 +1,62 @@
+#!/usr/bin/env python3
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+# pyre-strict
+
+import unittest
+from unittest.mock import MagicMock
+
+from torchrec.distributed.planner.perf_models import NoopPerfModel, NoopStorageModel
+from torchrec.distributed.planner.types import (
+    Perf,
+    Shard,
+    ShardingOption,
+    Storage,
+    Topology,
+)
+
+
+class TestPerfModels(unittest.TestCase):
+    def setUp(self) -> None:
+        self.topology = Topology(world_size=2, compute_device="cuda")
+        self.tables = [
+            ShardingOption(
+                name=MagicMock(),
+                tensor=MagicMock(),
+                module=MagicMock(),
+                input_lengths=MagicMock(),
+                batch_size=MagicMock(),
+                sharding_type=MagicMock(),
+                partition_by=MagicMock(),
+                compute_kernel=MagicMock(),
+                shards=[
+                    Shard(
+                        size=MagicMock(),
+                        offset=MagicMock(),
+                        rank=rank,
+                        perf=Perf(
+                            fwd_compute=2 - rank,
+                            fwd_comms=0,
+                            bwd_compute=0,
+                            bwd_comms=0,
+                        ),
+                        storage=Storage(hbm=100 * (rank + 1), ddr=0),
+                    ),
+                ],
+            )
+            for rank in range(2)
+        ]
+
+    def test_noop_perf_model(self) -> None:
+        perf_model = NoopPerfModel(self.topology)
+        perf_rating = perf_model.rate(self.tables)
+        self.assertEqual(perf_rating, 2)
+
+    def test_noop_storage_model(self) -> None:
+        perf_model = NoopStorageModel(self.topology)
+        perf_rating = perf_model.rate(self.tables)
+        self.assertEqual(perf_rating, 200)