OSS Hash MC Modules (pytorch#2797)

Summary:
Pull Request resolved: pytorch#2797
This diff moves the Hash MC module to OSS directory

Reviewed By: zlzhao1104

Differential Revision: D70970107

fbshipit-source-id: bba24ab79654f2a1f2c6c27aef3d6159df314d4b

Author: kausv

torchrec/modules/hash_mc_evictions.py

@@ -0,0 +1,253 @@
+#!/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
+
+#!/usr/bin/env python3
+
+import logging
+import time
+from dataclasses import dataclass
+from enum import Enum, unique
+from typing import List, Optional, Tuple
+
+import torch
+from pyre_extensions import none_throws
+
+from torchrec.sparse.jagged_tensor import JaggedTensor
+
+logger: logging.Logger = logging.getLogger(__name__)
+
+
+@unique
+class HashZchEvictionPolicyName(Enum):
+    # eviction based on the time the ID is last seen during training,
+    # and a single TTL
+    SINGLE_TTL_EVICTION = "SINGLE_TTL_EVICTION"
+    # eviction based on the time the ID is last seen during training,
+    # and per-feature TTLs
+    PER_FEATURE_TTL_EVICTION = "PER_FEATURE_TTL_EVICTION"
+    # eviction based on least recently seen ID within the probe range
+    LRU_EVICTION = "LRU_EVICTION"
+
+
+@torch.jit.script
+@dataclass
+class HashZchEvictionConfig:
+    features: List[str]
+    single_ttl: Optional[int] = None
+    per_feature_ttl: Optional[List[int]] = None
+
+
+@torch.fx.wrap
+def get_kernel_from_policy(
+    policy_name: Optional[HashZchEvictionPolicyName],
+) -> int:
+    return (
+        1
+        if policy_name is not None
+        and policy_name == HashZchEvictionPolicyName.LRU_EVICTION
+        else 0
+    )
+
+
+class HashZchEvictionScorer:
+    def __init__(self, config: HashZchEvictionConfig) -> None:
+        self._config: HashZchEvictionConfig = config
+
+    def gen_score(self, feature: JaggedTensor) -> torch.Tensor:
+        return torch.empty(0, device="cuda")
+
+    def gen_threshold(self) -> int:
+        return -1
+
+
+class HashZchSingleTtlScorer(HashZchEvictionScorer):
+    def gen_score(self, feature: JaggedTensor) -> torch.Tensor:
+        assert (
+            self._config.single_ttl is not None
+        ), "To use scorer HashZchSingleTtlScorer, single_ttl is required."
+
+        return torch.full_like(
+            feature.values(),
+            # pyre-ignore [58]
+            self._config.single_ttl + int(time.time() / 3600),
+            dtype=torch.int32,
+            device="cuda",
+        )
+
+    def gen_threshold(self) -> int:
+        return int(time.time() / 3600)
+
+
+class HashZchPerFeatureTtlScorer(HashZchEvictionScorer):
+    def __init__(self, config: HashZchEvictionConfig) -> None:
+        super().__init__(config)
+
+        assert self._config.per_feature_ttl is not None and len(
+            self._config.features
+        ) == len(
+            # pyre-ignore [6]
+            self._config.per_feature_ttl
+        ), "To use scorer HashZchPerFeatureTtlScorer, a 1:1 mapping between features and per_feature_ttl is required."
+
+        self._per_feature_ttl = torch.IntTensor(self._config.per_feature_ttl)
+
+    def gen_score(self, feature: JaggedTensor) -> torch.Tensor:
+        feature_split = feature.weights()
+        assert feature_split.size(0) == self._per_feature_ttl.size(0)
+
+        scores = self._per_feature_ttl.repeat_interleave(feature_split) + int(
+            time.time() / 3600
+        )
+
+        return scores.to(device="cuda")
+
+    def gen_threshold(self) -> int:
+        return int(time.time() / 3600)
+
+
+@torch.fx.wrap
+def get_eviction_scorer(
+    policy_name: str, config: HashZchEvictionConfig
+) -> HashZchEvictionScorer:
+    if policy_name == HashZchEvictionPolicyName.SINGLE_TTL_EVICTION:
+        return HashZchSingleTtlScorer(config)
+    elif policy_name == HashZchEvictionPolicyName.PER_FEATURE_TTL_EVICTION:
+        return HashZchPerFeatureTtlScorer(config)
+    elif policy_name == HashZchEvictionPolicyName.LRU_EVICTION:
+        return HashZchSingleTtlScorer(config)
+    else:
+        return HashZchEvictionScorer(config)
+
+
+class HashZchThresholdEvictionModule(torch.nn.Module):
+    """
+    This module manages the computation of eviction score for input IDs. Based on the selected
+    eviction policy, a scorer is initiated to generate a score for each ID. The kernel
+    will use this score to make eviction decisions.
+
+    Args:
+        policy_name: an enum value that indicates the eviction policy to use.
+        config: a config that contains information needed to run the eviction policy.
+
+    Example::
+        module = HashZchThresholdEvictionModule(...)
+        score = module(feature)
+    """
+
+    _eviction_scorer: HashZchEvictionScorer
+
+    def __init__(
+        self,
+        policy_name: HashZchEvictionPolicyName,
+        config: HashZchEvictionConfig,
+    ) -> None:
+        super().__init__()
+
+        self._policy_name: HashZchEvictionPolicyName = policy_name
+        self._config: HashZchEvictionConfig = config
+        self._eviction_scorer = get_eviction_scorer(
+            policy_name=self._policy_name,
+            config=self._config,
+        )
+
+        logger.info(
+            f"HashZchThresholdEvictionModule: {self._policy_name=}, {self._config=}"
+        )
+
+    def forward(self, feature: JaggedTensor) -> Tuple[torch.Tensor, int]:
+        """
+        Args:
+            feature: a jagged tensor that contains the input IDs, and their lengths and
+            weights (feature split).
+
+        Returns:
+            a tensor that contains the eviction score for each ID, plus an eviction threshold.
+        """
+        return (
+            self._eviction_scorer.gen_score(feature),
+            self._eviction_scorer.gen_threshold(),
+        )
+
+
+class HashZchOptEvictionModule(torch.nn.Module):
+    """
+    This module manages the eviction of IDs from the ZCH table based on the selected eviction policy.
+    Args:
+        policy_name: an enum value that indicates the eviction policy to use.
+    Example:
+        module = HashZchOptEvictionModule(policy_name=HashZchEvictionPolicyName.LRU_EVICTION)
+    """
+
+    def __init__(
+        self,
+        policy_name: HashZchEvictionPolicyName,
+    ) -> None:
+        super().__init__()
+
+        self._policy_name: HashZchEvictionPolicyName = policy_name
+
+    def forward(self, feature: JaggedTensor) -> Tuple[None, int]:
+        """
+        Does not apply to this Eviction Policy. Returns None and -1.
+        Args:
+            feature: No op
+        Returns:
+            None, -1
+        """
+        return None, -1
+
+
+@torch.fx.wrap
+def get_eviction_module(
+    policy_name: HashZchEvictionPolicyName, config: Optional[HashZchEvictionConfig]
+) -> torch.nn.Module:
+    if policy_name in (
+        HashZchEvictionPolicyName.SINGLE_TTL_EVICTION,
+        HashZchEvictionPolicyName.PER_FEATURE_TTL_EVICTION,
+        HashZchEvictionPolicyName.LRU_EVICTION,
+    ):
+        return HashZchThresholdEvictionModule(policy_name, none_throws(config))
+    else:
+        return HashZchOptEvictionModule(policy_name)
+
+
+class HashZchEvictionModule(torch.nn.Module):
+    """
+    This module manages the eviction of IDs from the ZCH table based on the selected eviction policy.
+    Args:
+        policy_name: an enum value that indicates the eviction policy to use.
+        config: an optional config required if threshold based eviction is selected.
+    Example:
+        module = HashZchEvictionModule(policy_name=HashZchEvictionPolicyName.LRU_EVICTION)
+    """
+
+    def __init__(
+        self,
+        policy_name: HashZchEvictionPolicyName,
+        config: Optional[HashZchEvictionConfig],
+    ) -> None:
+        super().__init__()
+
+        self._policy_name: HashZchEvictionPolicyName = policy_name
+        self._eviction_module: torch.nn.Module = get_eviction_module(
+            self._policy_name, config
+        )
+
+        logger.info(f"HashZchEvictionModule: {self._policy_name=}")
+
+    def forward(self, feature: JaggedTensor) -> Tuple[Optional[torch.Tensor], int]:
+        """
+        Args:
+            feature: a jagged tensor that contains the input IDs, and their lengths and
+            weights (feature split).
+
+        Returns:
+            For threshold eviction, a tensor that contains the eviction score for each ID, plus an eviction threshold. Otherwise None and -1.
+        """
+        return self._eviction_module(feature)