wehub-resource-sync
500 lines
18 KiB
Python
500 lines
18 KiB
Python
"""CPU cached feature for GraphBolt."""
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from typing import Dict, Optional, Union
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import torch
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from ..base import get_device_to_host_uva_stream, get_host_to_device_uva_stream
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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 .cpu_feature_cache import CPUFeatureCache
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__all__ = ["CPUCachedFeature", "cpu_cached_feature"]
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class CPUCachedFeature(Feature):
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r"""CPU cached feature wrapping a fallback feature. Use `cpu_cached_feature`
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to construct an instance of this class.
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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 : CPUFeatureCache
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A CPUFeatureCache 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 `CPUCachedFeature`s are sharing a single
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CPUFeatureCache object.
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"""
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_cache_type = CPUFeatureCache
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def __init__(
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self,
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fallback_feature: Feature,
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cache: CPUFeatureCache,
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offset: int = 0,
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):
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super(CPUCachedFeature, 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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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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return self._feature.query_and_replace(
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ids.cpu(), self._fallback_feature.read, self._offset
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).to(ids.device)
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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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policy = self._feature._policy
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cache = self._feature._cache
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if ids.is_cuda and self.is_pinned():
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ids_device = ids.device
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current_stream = torch.cuda.current_stream()
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device_to_host_stream = get_device_to_host_uva_stream()
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device_to_host_stream.wait_stream(current_stream)
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with torch.cuda.stream(device_to_host_stream):
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ids.record_stream(torch.cuda.current_stream())
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ids = ids.to("cpu", non_blocking=True)
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ids_copy_event = torch.cuda.Event()
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ids_copy_event.record()
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yield # first stage is done.
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ids_copy_event.synchronize()
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policy_future = policy.query_and_replace_async(ids, self._offset)
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yield
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(
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positions,
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index,
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pointers,
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missing_keys,
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found_offsets,
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missing_offsets,
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) = policy_future.wait()
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self._feature.total_queries += ids.shape[0]
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self._feature.total_miss += missing_keys.shape[0]
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found_cnt = ids.size(0) - missing_keys.size(0)
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found_positions = positions[:found_cnt]
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missing_positions = positions[found_cnt:]
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found_pointers = pointers[:found_cnt]
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missing_pointers = pointers[found_cnt:]
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host_to_device_stream = get_host_to_device_uva_stream()
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with torch.cuda.stream(host_to_device_stream):
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found_positions = found_positions.to(
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ids_device, non_blocking=True
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)
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values_from_cpu = cache.index_select(found_positions)
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values_from_cpu.record_stream(current_stream)
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values_from_cpu_copy_event = torch.cuda.Event()
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values_from_cpu_copy_event.record()
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fallback_reader = self._fallback_feature.read_async(missing_keys)
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for _ in range(
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self._fallback_feature.read_async_num_stages(
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missing_keys.device
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)
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):
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missing_values_future = next(fallback_reader, None)
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yield # fallback feature stages.
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values_from_cpu_copy_event.synchronize()
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reading_completed = policy.reading_completed_async(
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found_pointers, found_offsets
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)
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missing_values = missing_values_future.wait()
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replace_future = cache.replace_async(
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missing_positions, missing_values
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)
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host_to_device_stream = get_host_to_device_uva_stream()
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with torch.cuda.stream(host_to_device_stream):
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index = index.to(ids_device, non_blocking=True)
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missing_values = missing_values.to(
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ids_device, non_blocking=True
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)
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index.record_stream(current_stream)
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missing_values.record_stream(current_stream)
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missing_values_copy_event = torch.cuda.Event()
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missing_values_copy_event.record()
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yield
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reading_completed.wait()
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replace_future.wait()
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writing_completed = policy.writing_completed_async(
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missing_pointers, missing_offsets
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)
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class _Waiter:
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def __init__(self, events, existing, missing, index):
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self.events = events
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self.existing = existing
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self.missing = missing
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self.index = index
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def wait(self):
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"""Returns the stored value when invoked."""
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for event in self.events:
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event.wait()
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values = torch.empty(
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(self.index.shape[0],) + self.missing.shape[1:],
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dtype=self.missing.dtype,
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device=ids_device,
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)
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num_found = self.existing.size(0)
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found_index = self.index[:num_found]
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missing_index = self.index[num_found:]
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values[found_index] = self.existing
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values[missing_index] = self.missing
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# Ensure there is no memory leak.
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self.events = self.existing = None
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self.missing = self.index = None
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return values
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yield _Waiter(
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[
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writing_completed,
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values_from_cpu_copy_event,
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missing_values_copy_event,
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],
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values_from_cpu,
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missing_values,
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index,
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)
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elif ids.is_cuda:
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ids_device = ids.device
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current_stream = torch.cuda.current_stream()
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device_to_host_stream = get_device_to_host_uva_stream()
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device_to_host_stream.wait_stream(current_stream)
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with torch.cuda.stream(device_to_host_stream):
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ids.record_stream(torch.cuda.current_stream())
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ids = ids.to("cpu", non_blocking=True)
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ids_copy_event = torch.cuda.Event()
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ids_copy_event.record()
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yield # first stage is done.
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ids_copy_event.synchronize()
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policy_future = policy.query_and_replace_async(ids, self._offset)
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yield
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(
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positions,
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index,
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pointers,
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missing_keys,
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found_offsets,
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missing_offsets,
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) = policy_future.wait()
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self._feature.total_queries += ids.shape[0]
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self._feature.total_miss += missing_keys.shape[0]
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found_cnt = ids.size(0) - missing_keys.size(0)
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found_positions = positions[:found_cnt]
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missing_positions = positions[found_cnt:]
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found_pointers = pointers[:found_cnt]
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missing_pointers = pointers[found_cnt:]
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values_future = cache.query_async(
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found_positions, index, ids.shape[0]
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)
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fallback_reader = self._fallback_feature.read_async(missing_keys)
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for _ in range(
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self._fallback_feature.read_async_num_stages(
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missing_keys.device
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)
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):
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missing_values_future = next(fallback_reader, None)
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yield # fallback feature stages.
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values = values_future.wait()
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reading_completed = policy.reading_completed_async(
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found_pointers, found_offsets
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)
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missing_index = index[found_cnt:]
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missing_values = missing_values_future.wait()
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replace_future = cache.replace_async(
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missing_positions, missing_values
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)
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values = torch.ops.graphbolt.scatter_async(
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values, missing_index, missing_values
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)
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yield
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host_to_device_stream = get_host_to_device_uva_stream()
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with torch.cuda.stream(host_to_device_stream):
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values = values.wait().to(ids_device, non_blocking=True)
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values.record_stream(current_stream)
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values_copy_event = torch.cuda.Event()
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values_copy_event.record()
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reading_completed.wait()
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replace_future.wait()
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writing_completed = policy.writing_completed_async(
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missing_pointers, missing_offsets
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)
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class _Waiter:
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def __init__(self, events, values):
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self.events = events
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self.values = values
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def wait(self):
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"""Returns the stored value when invoked."""
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for event in self.events:
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event.wait()
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values = self.values
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# Ensure there is no memory leak.
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self.events = self.values = None
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return values
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yield _Waiter([values_copy_event, writing_completed], values)
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else:
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policy_future = policy.query_and_replace_async(ids, self._offset)
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yield
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(
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positions,
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index,
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pointers,
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missing_keys,
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found_offsets,
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missing_offsets,
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) = policy_future.wait()
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self._feature.total_queries += ids.shape[0]
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self._feature.total_miss += missing_keys.shape[0]
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found_cnt = ids.size(0) - missing_keys.size(0)
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found_positions = positions[:found_cnt]
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missing_positions = positions[found_cnt:]
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found_pointers = pointers[:found_cnt]
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missing_pointers = pointers[found_cnt:]
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values_future = cache.query_async(
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found_positions, index, ids.shape[0]
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)
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fallback_reader = self._fallback_feature.read_async(missing_keys)
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for _ in range(
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self._fallback_feature.read_async_num_stages(
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missing_keys.device
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)
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):
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missing_values_future = next(fallback_reader, None)
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yield # fallback feature stages.
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values = values_future.wait()
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reading_completed = policy.reading_completed_async(
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found_pointers, found_offsets
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)
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missing_index = index[found_cnt:]
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missing_values = missing_values_future.wait()
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replace_future = cache.replace_async(
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missing_positions, missing_values
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)
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values = torch.ops.graphbolt.scatter_async(
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values, missing_index, missing_values
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)
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yield
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reading_completed.wait()
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replace_future.wait()
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writing_completed = policy.writing_completed_async(
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missing_pointers, missing_offsets
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)
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class _Waiter:
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def __init__(self, event, values):
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self.event = event
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self.values = values
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def wait(self):
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"""Returns the stored value when invoked."""
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self.event.wait()
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values = self.values.wait()
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# Ensure there is no memory leak.
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self.event = self.values = None
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return values
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yield _Waiter(writing_completed, values)
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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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if ids_device.type == "cuda":
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return 4 + self._fallback_feature.read_async_num_stages(
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torch.device("cpu")
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)
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else:
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return 3 + 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, None, self._offset)
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def is_pinned(self):
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"""Returns True if the cache storage is pinned."""
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return self._feature.is_pinned()
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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 cpu_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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policy: Optional[str] = None,
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pin_memory: bool = False,
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) -> Union[CPUCachedFeature, Dict[FeatureKey, CPUCachedFeature]]:
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r"""CPU cached feature wrapping a fallback feature.
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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 cache in bytes. The size should be a few factors
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larger than the size of each read request. Otherwise, the caching policy
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will hang due to all cache entries being read and/or write locked,
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resulting in a deadlock.
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policy : str, optional
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The cache eviction policy algorithm name. The available policies are
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["s3-fifo", "sieve", "lru", "clock"]. Default is "sieve".
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pin_memory : bool, optional
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Whether the cache storage should be allocated on system pinned memory.
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Default is False.
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Returns
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-------
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Union[CPUCachedFeature, Dict[FeatureKey, CPUCachedFeature]]
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New feature(s) wrapped with CPUCachedFeature.
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"""
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return wrap_with_cached_feature(
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CPUCachedFeature,
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fallback_features,
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max_cache_size_in_bytes,
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policy=policy,
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pin_memory=pin_memory,
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)
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