wehub-resource-sync
284 lines
9.5 KiB
Python
284 lines
9.5 KiB
Python
"""GPU cached feature for GraphBolt."""
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from typing import Dict, Union
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import torch
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from ..feature_store import (
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bytes_to_number_of_items,
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Feature,
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FeatureKey,
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wrap_with_cached_feature,
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)
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from .gpu_feature_cache import GPUFeatureCache
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__all__ = ["GPUCachedFeature", "gpu_cached_feature"]
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class GPUCachedFeature(Feature):
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r"""GPU cached feature wrapping a fallback feature. It uses the least
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recently used (LRU) algorithm as the cache eviction policy. Use
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`gpu_cached_feature` to construct an instance of this class.
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Places the GPU cache to torch.cuda.current_device().
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Parameters
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----------
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fallback_feature : Feature
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The fallback feature.
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cache : GPUFeatureCache
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A GPUFeatureCache instance to serve as the cache backend.
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offset : int, optional
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The offset value to add to the given ids before using the cache. This
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parameter is useful if multiple `GPUCachedFeature`s are sharing a single
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GPUFeatureCache object.
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Examples
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--------
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>>> import torch
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>>> from dgl import graphbolt as gb
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>>> torch_feat = torch.arange(10).reshape(2, -1).to("cuda")
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>>> cache_size = 5
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>>> fallback_feature = gb.TorchBasedFeature(torch_feat)
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>>> feature = gb.gpu_cached_feature(fallback_feature, cache_size)
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>>> feature.read()
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tensor([[0, 1, 2, 3, 4],
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[5, 6, 7, 8, 9]], device='cuda:0')
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>>> feature.read(torch.tensor([0]).to("cuda"))
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tensor([[0, 1, 2, 3, 4]], device='cuda:0')
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>>> feature.update(torch.tensor([[1 for _ in range(5)]]).to("cuda"),
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... torch.tensor([1]).to("cuda"))
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>>> feature.read(torch.tensor([0, 1]).to("cuda"))
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tensor([[0, 1, 2, 3, 4],
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[1, 1, 1, 1, 1]], device='cuda:0')
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>>> feature.size()
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torch.Size([5])
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"""
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_cache_type = GPUFeatureCache
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def __init__(
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self,
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fallback_feature: Feature,
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cache: GPUFeatureCache,
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offset: int = 0,
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):
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super(GPUCachedFeature, self).__init__()
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assert isinstance(fallback_feature, Feature), (
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f"The fallback_feature must be an instance of Feature, but got "
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f"{type(fallback_feature)}."
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)
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self._fallback_feature = fallback_feature
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self._feature = cache
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self._offset = offset
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def read(self, ids: torch.Tensor = None):
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"""Read the feature by index.
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The returned tensor is always in GPU memory, no matter whether the
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fallback feature is in memory or on disk.
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Parameters
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----------
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ids : torch.Tensor, optional
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The index of the feature. If specified, only the specified indices
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of the feature are read. If None, the entire feature is returned.
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Returns
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-------
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torch.Tensor
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The read feature.
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"""
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if ids is None:
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return self._fallback_feature.read()
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values, missing_index, missing_keys = self._feature.query(
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ids if self._offset == 0 else ids + self._offset
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)
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missing_values = self._fallback_feature.read(
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missing_keys if self._offset == 0 else missing_keys - self._offset
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)
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values[missing_index] = missing_values
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self._feature.replace(missing_keys, missing_values)
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return values
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def read_async(self, ids: torch.Tensor):
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r"""Read the feature by index asynchronously.
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Parameters
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----------
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ids : torch.Tensor
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The index of the feature. Only the specified indices of the
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feature are read.
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Returns
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-------
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A generator object.
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The returned generator object returns a future on
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``read_async_num_stages(ids.device)``\ th invocation. The return result
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can be accessed by calling ``.wait()``. on the returned future object.
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It is undefined behavior to call ``.wait()`` more than once.
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Examples
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--------
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>>> import dgl.graphbolt as gb
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>>> feature = gb.Feature(...)
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>>> ids = torch.tensor([0, 2])
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>>> for stage, future in enumerate(feature.read_async(ids)):
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... pass
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>>> assert stage + 1 == feature.read_async_num_stages(ids.device)
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>>> result = future.wait() # result contains the read values.
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"""
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future = self._feature.query(
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ids if self._offset == 0 else ids + self._offset, async_op=True
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)
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yield
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values, missing_index, missing_keys = future.wait()
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fallback_reader = self._fallback_feature.read_async(
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missing_keys if self._offset == 0 else missing_keys - self._offset
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)
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fallback_num_stages = self._fallback_feature.read_async_num_stages(
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missing_keys.device
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)
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for i in range(fallback_num_stages):
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missing_values_future = next(fallback_reader, None)
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if i < fallback_num_stages - 1:
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yield # fallback feature stages.
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class _Waiter:
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def __init__(
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self,
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feature,
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values,
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missing_index,
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missing_keys,
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missing_values_future,
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):
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self.feature = feature
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self.values = values
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self.missing_index = missing_index
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self.missing_keys = missing_keys
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self.missing_values_future = missing_values_future
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def wait(self):
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"""Returns the stored value when invoked."""
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missing_values = self.missing_values_future.wait()
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self.feature.replace(self.missing_keys, missing_values)
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self.values[self.missing_index] = missing_values
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values = self.values
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# Ensure there is no memory leak.
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self.feature = self.values = self.missing_index = None
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self.missing_keys = self.missing_values_future = None
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return values
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yield _Waiter(
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self._feature,
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values,
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missing_index,
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missing_keys,
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missing_values_future,
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)
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def read_async_num_stages(self, ids_device: torch.device):
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"""The number of stages of the read_async operation. See read_async
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function for directions on its use. This function is required to return
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the number of yield operations when read_async is used with a tensor
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residing on ids_device.
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Parameters
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----------
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ids_device : torch.device
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The device of the ids parameter passed into read_async.
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Returns
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-------
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int
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The number of stages of the read_async operation.
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"""
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assert ids_device.type == "cuda"
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return 1 + self._fallback_feature.read_async_num_stages(ids_device)
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def size(self):
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"""Get the size of the feature.
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Returns
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-------
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torch.Size
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The size of the feature.
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"""
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return self._fallback_feature.size()
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def count(self):
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"""Get the count of the feature.
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Returns
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-------
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int
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The count of the feature.
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"""
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return self._fallback_feature.count()
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def update(self, value: torch.Tensor, ids: torch.Tensor = None):
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"""Update the feature.
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Parameters
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----------
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value : torch.Tensor
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The updated value of the feature.
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ids : torch.Tensor, optional
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The indices of the feature to update. If specified, only the
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specified indices of the feature will be updated. For the feature,
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the `ids[i]` row is updated to `value[i]`. So the indices and value
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must have the same length. If None, the entire feature will be
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updated.
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"""
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if ids is None:
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feat0 = value[:1]
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self._fallback_feature.update(value)
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cache_size = min(
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bytes_to_number_of_items(self.cache_size_in_bytes, feat0),
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value.shape[0],
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)
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self._feature = None # Destroy the existing cache first.
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self._feature = self._cache_type(
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(cache_size,) + feat0.shape[1:], feat0.dtype
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)
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else:
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self._fallback_feature.update(value, ids)
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self._feature.replace(ids, value)
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@property
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def cache_size_in_bytes(self):
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"""Return the size taken by the cache in bytes."""
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return self._feature.max_size_in_bytes
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@property
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def miss_rate(self):
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"""Returns the cache miss rate since creation."""
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return self._feature.miss_rate
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def gpu_cached_feature(
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fallback_features: Union[Feature, Dict[FeatureKey, Feature]],
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max_cache_size_in_bytes: int,
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) -> Union[GPUCachedFeature, Dict[FeatureKey, GPUCachedFeature]]:
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r"""GPU cached feature wrapping a fallback feature. It uses the least
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recently used (LRU) algorithm as the cache eviction policy.
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Places the GPU cache to torch.cuda.current_device().
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Parameters
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----------
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fallback_features : Union[Feature, Dict[FeatureKey, Feature]]
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The fallback feature(s).
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max_cache_size_in_bytes : int
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The capacity of the GPU cache in bytes.
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Returns
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-------
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Union[GPUCachedFeature, Dict[FeatureKey, GPUCachedFeature]]
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The feature(s) wrapped with GPUCachedFeature.
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"""
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return wrap_with_cached_feature(
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GPUCachedFeature, fallback_features, max_cache_size_in_bytes
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)
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