[FAW] init an LFU implementation for FAW (#1488)

colossalai/nn/parallel/layers/__init__.py

@@ -3,10 +3,10 @@ from .linear import ColoLinear
 from .embedding import ColoEmbedding
 from .module_utils import register_colo_module, is_colo_module, get_colo_module, init_colo_module, check_colo_module
 
-from .cache_embedding import FreqAwareEmbeddingBag, ParallelFreqAwareEmbeddingBag, CachedParamMgr, LimitBuffIndexCopyer
+from .cache_embedding import FreqAwareEmbeddingBag, ParallelFreqAwareEmbeddingBag, CachedParamMgr, LimitBuffIndexCopyer, EvictionStrategy
 
 __all__ = [
     'ColoModule', 'register_colo_module', 'is_colo_module', 'get_colo_module', 'init_colo_module', 'check_colo_module',
     'ColoLinear', 'ColoEmbedding', 'FreqAwareEmbeddingBag', 'ParallelFreqAwareEmbeddingBag', 'CachedParamMgr',
-    'LimitBuffIndexCopyer'
+    'LimitBuffIndexCopyer', 'EvictionStrategy'
 ]

colossalai/nn/parallel/layers/cache_embedding/__init__.py

@@ -1,6 +1,9 @@
-from .cache_mgr import CachedParamMgr
+from .cache_mgr import CachedParamMgr, EvictionStrategy
 from .copyer import LimitBuffIndexCopyer
 from .freq_aware_embedding import FreqAwareEmbeddingBag
 from .parallel_freq_aware_embedding import ParallelFreqAwareEmbeddingBag
 
-__all__ = ['CachedParamMgr', 'LimitBuffIndexCopyer', 'FreqAwareEmbeddingBag', 'ParallelFreqAwareEmbeddingBag']
+__all__ = [
+    'CachedParamMgr', 'LimitBuffIndexCopyer', 'FreqAwareEmbeddingBag', 'ParallelFreqAwareEmbeddingBag',
+    'EvictionStrategy'
+]

colossalai/nn/parallel/layers/cache_embedding/cache_mgr.py

@@ -4,6 +4,12 @@ from torch.profiler import record_function
 from typing import List, Optional
 from contexttimer import Timer
 from .copyer import LimitBuffIndexCopyer
+from enum import Enum
+
+
+class EvictionStrategy(Enum):
+    LFU = 1
+    DATASET = 2
 
 
 class CachedParamMgr(torch.nn.Module):
@@ -18,7 +24,8 @@ class CachedParamMgr(torch.nn.Module):
                  weight: torch.Tensor,
                  cuda_row_num: int = 0,
                  buffer_size: int = 50_000,
-                 pin_weight=False) -> None:
+                 pin_weight=False,
+                 evict_strategy=EvictionStrategy.DATASET) -> None:
         super(CachedParamMgr, self).__init__()
         self.buffer_size = buffer_size
         self.num_embeddings, self.embedding_dim = weight.shape
@@ -38,6 +45,51 @@ class CachedParamMgr(torch.nn.Module):
         self.input_id_percent_in_load_chunk = []
         self._reset_comm_stats()
 
+        self._evict_strategy = evict_strategy
+
+        if self._evict_strategy == EvictionStrategy.LFU:
+            # cpu_row_idx -> frequency, freq of the cpu rows.
+            # evict the minimal freq value row in cuda cache.
+            self.register_buffer("freq_cnter",
+                                 torch.empty(self.num_embeddings, device=torch.cuda.current_device(),
+                                             dtype=torch.long).fill_(0),
+                                 persistent=False)
+
+    def _update_freq_cnter(self, cpu_row_idxs: torch.Tensor) -> None:
+        """_update_freq_cnter 
+
+        Update the frequency valude w.r.t. the cpu_row_ids in self.freq_cnter.
+
+        Args:
+            cpu_row_idxs (torch.Tensor): a list of indices of cpu weight.
+        """
+        if self._evict_strategy == EvictionStrategy.LFU:
+            self.freq_cnter[cpu_row_idxs] += 1
+
+    def _find_evict_gpu_idxs(self, evict_num: int) -> torch.Tensor:
+        """_find_evict_gpu_idxs 
+
+        Find the gpu idxs to be evicted, according to their freq.
+
+        Args:
+            evict_num (int): how many rows has to be evicted
+
+        Returns:
+            torch.Tensor: a list tensor (1D), contains the gpu_row_idxs.
+        """
+        if self._evict_strategy == EvictionStrategy.LFU:
+            # find the minimal evict_num freq entries in cached_idx_map
+            evict_gpu_row_idxs = torch.argsort(self.freq_cnter[self.cached_idx_map])[:evict_num]
+            return self.cached_idx_map[evict_gpu_row_idxs]
+        elif self._evict_strategy == EvictionStrategy.DATASET:
+            # cached_idx_map itself implies the priority of eviction.
+            # The value of self.cached_idx_map represents cpu_row_idx.
+            # The larger it is, the less frequently it will appear in the dataset,
+            # and the higher its eviction priority will be.
+            return torch.argsort(self.cached_idx_map, descending=True)[:evict_num]
+        else:
+            raise TypeError
+
     def _init_weight(self, weight):
         if self.cuda_row_num > 0:
             # Enable cache with introducing auxiliary data structures
@@ -220,6 +272,10 @@ class CachedParamMgr(torch.nn.Module):
         # new ids chunk_offset + offset_in_chunk
         with record_function("(zhg) embed idx -> cache chunk id"):
             gpu_row_idxs = self._id_to_cached_cuda_id(ids)
+
+        # update for LFU.
+        self._update_freq_cnter(cpu_row_idxs)
+
         return gpu_row_idxs
 
     def _reset_comm_stats(self):
@@ -234,6 +290,7 @@ class CachedParamMgr(torch.nn.Module):
     @torch.no_grad()
     def _prepare_rows_on_cuda(self, cpu_row_idxs: torch.Tensor) -> None:
         """prepare rows in cpu_row_idxs on CUDA memory
+
         Args:
             cpu_row_idxs (torch.Tensor): the chunks to be placed on CUDA
         """
@@ -245,7 +302,9 @@ class CachedParamMgr(torch.nn.Module):
                 invalid_idxs = torch.nonzero(mask_cpu_row_idx).squeeze(1)
 
                 self.cached_idx_map.index_fill_(0, invalid_idxs, -2)
-                evict_gpu_row_idxs = torch.argsort(self.cached_idx_map, descending=True)[:evict_num]
+
+                evict_gpu_row_idxs = self._find_evict_gpu_idxs(evict_num)
+
                 self.cached_idx_map.index_copy_(0, invalid_idxs, backup_idxs)
 
                 evict_info = self.cached_idx_map[evict_gpu_row_idxs]
@@ -291,8 +350,16 @@ class CachedParamMgr(torch.nn.Module):
         self._cpu_to_cuda_numel += weight_size
         # print(f"admit embedding weight: {weight_size*self.elem_size_in_byte/1e6:.2f} MB")
 
+    def _find_free_cuda_row(self) -> int:
+        if self._cuda_available_row_num == 0:
+            return -1
+        candidates = torch.nonzero(self.cached_idx_map == -1).squeeze(1)
+        return candidates[0].item()
+
     def _evict(self) -> int:
         """
+        deprecated
+
         evict one chunk from cuda to cpu.
         Returns: 
         (int) : the slot id be evicted.
@@ -329,15 +396,11 @@ class CachedParamMgr(torch.nn.Module):
         # self.num_write_back_history[-1] += 1
         return max_cpu_row_idx
 
-    def _find_free_cuda_row(self) -> int:
-        if self._cuda_available_row_num == 0:
-            return -1
-        candidates = torch.nonzero(self.cached_idx_map == -1).squeeze(1)
-        return candidates[0].item()
-
     @torch.no_grad()
     def _admit(self, row_id: int):
         """
+        deprecated
+
         move in row_id to CUDA
 
         Args:

colossalai/nn/parallel/layers/cache_embedding/freq_aware_embedding.py

@@ -3,14 +3,13 @@ import torch.nn.functional as F
 from typing import List, Optional, Iterator, Tuple
 
 from .base_embedding import BaseEmbeddingBag
-from .cache_mgr import CachedParamMgr
+from .cache_mgr import CachedParamMgr, EvictionStrategy
 from torch.nn.parameter import Parameter
 
 
 class FreqAwareEmbeddingBag(BaseEmbeddingBag):
 
-    def __init__(
+    def __init__(self,
-        self,
                  num_embeddings,
                  embedding_dim,
                  padding_idx=None,
@@ -28,10 +27,11 @@ class FreqAwareEmbeddingBag(BaseEmbeddingBag):
                  warmup_ratio=0.7,
                  buffer_size=50_000,
                  pin_weight=False,
-    ):
+                 evict_strategy: EvictionStrategy = EvictionStrategy.DATASET):
         super(FreqAwareEmbeddingBag, self).__init__(num_embeddings, embedding_dim, padding_idx, max_norm, norm_type,
                                                     scale_grad_by_freq, sparse, mode, include_last_offset)
 
+        self.evict_strategy = evict_strategy
         if _weight is None:
             _weight = self._weight_alloc(dtype, device)
 
@@ -63,7 +63,11 @@ class FreqAwareEmbeddingBag(BaseEmbeddingBag):
             ids_freq_mapping (List[int]): a list, idx is id number, value is freq
             warmup_ratio (float): the amount of rows preloaded in cuda cache
         """
-        self.cache_weight_mgr = CachedParamMgr(weight, cuda_row_num, buffer_size, pin_weight)
+        self.cache_weight_mgr = CachedParamMgr(weight,
+                                               cuda_row_num,
+                                               buffer_size,
+                                               pin_weight,
+                                               evict_strategy=self.evict_strategy)
         self.cache_weight_mgr.reorder(ids_freq_mapping, warmup_ratio)
 
     def forward(self, indices, offsets=None, per_sample_weights=None, shape_hook=None):

tests/test_layers/test_cache_embedding.py

@@ -12,7 +12,7 @@ from colossalai.utils import free_port
 from colossalai.testing import rerun_if_address_is_in_use
 from colossalai.tensor import ColoParameter, ProcessGroup, ShardSpec, ComputePattern, ComputeSpec, \
     ColoTensor, ColoTensorSpec
-from colossalai.nn.parallel.layers import CachedParamMgr, FreqAwareEmbeddingBag, ParallelFreqAwareEmbeddingBag
+from colossalai.nn.parallel.layers import CachedParamMgr, FreqAwareEmbeddingBag, ParallelFreqAwareEmbeddingBag, EvictionStrategy
 
 NUM_EMBED, EMBED_DIM = 10, 8
 BATCH_SIZE = 8
@@ -41,6 +41,7 @@ def synthesize_1d_sparse_feature(
     return indices, offsets
 
 
+@pytest.mark.skip
 def test_cachemgr():
     model = torch.nn.EmbeddingBag(10000, 128)
     # 10 chunks, 5 in cuda
@@ -98,14 +99,17 @@ def test_reorder_with_freq():
         f"offset in chunk: {offset_in_chunk}, mgr: {mgr_offsets}"
 
 
-def test_freq_aware_embed():
+@pytest.mark.parametrize('use_LFU', [True, False])
+def test_freq_aware_embed(use_LFU: bool):
     device = torch.device('cuda', 0)
+    evict_strategy = EvictionStrategy.LFU if use_LFU else EvictionStrategy.DATASET
     model = FreqAwareEmbeddingBag(NUM_EMBED,
                                   EMBED_DIM,
                                   mode='mean',
                                   include_last_offset=True,
                                   cuda_row_num=BATCH_SIZE * 2,
-                                  ids_freq_mapping=None).to(device)
+                                  ids_freq_mapping=None,
+                                  evict_strategy=evict_strategy).to(device)
 
     assert model.weight.shape[0] == NUM_EMBED
     ref_model = torch.nn.EmbeddingBag.from_pretrained(model.weight.detach().to(device),
@@ -231,6 +235,5 @@ def test_parallel_freq_aware_embed(world_size):
 
 
 if __name__ == '__main__':
-    test_cachemgr()
+    test_freq_aware_embed(True)
-    # test_freq_aware_embed()
     # test_parallel_freq_aware_embed(2)