"""Neighbor subgraph samplers for GraphBolt.""" from functools import partial import torch import torch.distributed as thd from torch.utils.data import functional_datapipe from torch.utils.data.datapipes.iter import Mapper from ..base import ( etype_str_to_tuple, get_host_to_device_uva_stream, index_select, ORIGINAL_EDGE_ID, ) from ..internal import ( compact_csc_format, unique_and_compact, unique_and_compact_csc_formats, ) from ..minibatch_transformer import MiniBatchTransformer from ..subgraph_sampler import all_to_all, revert_to_homo, SubgraphSampler from .fused_csc_sampling_graph import fused_csc_sampling_graph from .sampled_subgraph_impl import SampledSubgraphImpl __all__ = [ "NeighborSampler", "LayerNeighborSampler", "SamplePerLayer", "FetchInsubgraphData", "CombineCachedAndFetchedInSubgraph", ] @functional_datapipe("fetch_cached_insubgraph_data") class FetchCachedInsubgraphData(Mapper): """Queries the GPUGraphCache and returns the missing seeds and a generator handle that can be called with the fetched graph structure. """ def __init__(self, datapipe, gpu_graph_cache): datapipe = datapipe.transform(self._fetch_per_layer).buffer() super().__init__(datapipe, self._wait_query_future) self.cache = gpu_graph_cache def _fetch_per_layer(self, minibatch): minibatch._async_handle = self.cache.query_async(minibatch._seeds) # Start first stage next(minibatch._async_handle) return minibatch @staticmethod def _wait_query_future(minibatch): minibatch._seeds = next(minibatch._async_handle) return minibatch @functional_datapipe("combine_cached_and_fetched_insubgraph") class CombineCachedAndFetchedInSubgraph(Mapper): """Combined the fetched graph structure with the graph structure already found inside the GPUGraphCache. """ def __init__(self, datapipe, prob_name): datapipe = datapipe.transform(self._combine_per_layer).buffer() super().__init__(datapipe, self._wait_replace_future) self.prob_name = prob_name def _combine_per_layer(self, minibatch): subgraph = minibatch._sliced_sampling_graph edge_tensors = [subgraph.indices] if subgraph.type_per_edge is not None: edge_tensors.append(subgraph.type_per_edge) probs_or_mask = subgraph.edge_attribute(self.prob_name) if probs_or_mask is not None: edge_tensors.append(probs_or_mask) edge_tensors.append(subgraph.edge_attribute(ORIGINAL_EDGE_ID)) minibatch._future = minibatch._async_handle.send( (subgraph.csc_indptr, edge_tensors) ) delattr(minibatch, "_async_handle") return minibatch def _wait_replace_future(self, minibatch): subgraph = minibatch._sliced_sampling_graph subgraph.csc_indptr, edge_tensors = minibatch._future.wait() delattr(minibatch, "_future") subgraph.indices = edge_tensors[0] edge_tensors = edge_tensors[1:] if subgraph.type_per_edge is not None: subgraph.type_per_edge = edge_tensors[0] edge_tensors = edge_tensors[1:] probs_or_mask = subgraph.edge_attribute(self.prob_name) if probs_or_mask is not None: subgraph.add_edge_attribute(self.prob_name, edge_tensors[0]) edge_tensors = edge_tensors[1:] subgraph.add_edge_attribute(ORIGINAL_EDGE_ID, edge_tensors[0]) edge_tensors = edge_tensors[1:] assert len(edge_tensors) == 0 return minibatch @functional_datapipe("fetch_insubgraph_data") class FetchInsubgraphData(MiniBatchTransformer): """Fetches the insubgraph and wraps it in a FusedCSCSamplingGraph object. If the provided sample_per_layer_obj has a valid prob_name, then it reads the probabilies of all the fetched edges. Furthermore, if type_per_array tensor exists in the underlying graph, then the types of all the fetched edges are read as well.""" def __init__( self, datapipe, graph, prob_name, ): datapipe = datapipe.transform(self._concat_hetero_seeds) if graph._gpu_graph_cache is not None: datapipe = datapipe.fetch_cached_insubgraph_data( graph._gpu_graph_cache ) datapipe = datapipe.transform(self._fetch_per_layer_stage_1) datapipe = datapipe.buffer() datapipe = datapipe.transform(self._fetch_per_layer_stage_2) if graph._gpu_graph_cache is not None: datapipe = datapipe.combine_cached_and_fetched_insubgraph(prob_name) super().__init__(datapipe) self.graph = graph self.prob_name = prob_name def _concat_hetero_seeds(self, minibatch): """Concatenates the seeds into a single tensor in the hetero case.""" seeds = minibatch._seed_nodes if isinstance(seeds, dict): ( seeds, seed_offsets, ) = self.graph._convert_to_homogeneous_nodes(seeds) else: seed_offsets = None minibatch._seeds = seeds minibatch._seed_offsets = seed_offsets return minibatch def _fetch_per_layer_stage_1(self, minibatch): minibatch._async_handle_fetch = self._fetch_per_layer_async(minibatch) next(minibatch._async_handle_fetch) return minibatch def _fetch_per_layer_stage_2(self, minibatch): minibatch = next(minibatch._async_handle_fetch) delattr(minibatch, "_async_handle_fetch") return minibatch def _fetch_per_layer_async(self, minibatch): stream = torch.cuda.current_stream() uva_stream = get_host_to_device_uva_stream() uva_stream.wait_stream(stream) with torch.cuda.stream(uva_stream): seeds = minibatch._seeds seed_offsets = minibatch._seed_offsets delattr(minibatch, "_seeds") delattr(minibatch, "_seed_offsets") seeds.record_stream(torch.cuda.current_stream()) # Packs tensors for batch slicing. tensors_to_be_sliced = [self.graph.indices] has_type_per_edge = False if self.graph.type_per_edge is not None: tensors_to_be_sliced.append(self.graph.type_per_edge) has_type_per_edge = True has_probs_or_mask = False has_original_edge_ids = False if self.graph.edge_attributes is not None: probs_or_mask = self.graph.edge_attributes.get( self.prob_name, None ) if probs_or_mask is not None: tensors_to_be_sliced.append(probs_or_mask) has_probs_or_mask = True original_edge_ids = self.graph.edge_attributes.get( ORIGINAL_EDGE_ID, None ) if original_edge_ids is not None: tensors_to_be_sliced.append(original_edge_ids) has_original_edge_ids = True # Slices the batched tensors. future = torch.ops.graphbolt.index_select_csc_batched_async( self.graph.csc_indptr, tensors_to_be_sliced, seeds, # When there are no edge ids, we assume it is arange(num_edges). not has_original_edge_ids, None, ) yield # graphbolt::async has already recorded a CUDAEvent for us and # called CUDAStreamWaitEvent for us on the current stream. indptr, sliced_tensors = future.wait() for tensor in [indptr] + sliced_tensors: tensor.record_stream(stream) # Unpacks the sliced tensors. indices = sliced_tensors[0] sliced_tensors = sliced_tensors[1:] type_per_edge = None if has_type_per_edge: type_per_edge = sliced_tensors[0] sliced_tensors = sliced_tensors[1:] probs_or_mask = None if has_probs_or_mask: probs_or_mask = sliced_tensors[0] sliced_tensors = sliced_tensors[1:] edge_ids = sliced_tensors[0] sliced_tensors = sliced_tensors[1:] assert len(sliced_tensors) == 0 subgraph = fused_csc_sampling_graph( indptr, indices, node_type_offset=self.graph.node_type_offset, type_per_edge=type_per_edge, node_type_to_id=self.graph.node_type_to_id, edge_type_to_id=self.graph.edge_type_to_id, ) if self.prob_name is not None and probs_or_mask is not None: subgraph.add_edge_attribute(self.prob_name, probs_or_mask) subgraph.add_edge_attribute(ORIGINAL_EDGE_ID, edge_ids) subgraph._indptr_node_type_offset_list = seed_offsets minibatch._sliced_sampling_graph = subgraph yield minibatch @functional_datapipe("sample_per_layer") class SamplePerLayer(MiniBatchTransformer): """Sample neighbor edges from a graph for a single layer.""" def __init__( self, datapipe, sampler, fanout, replace, prob_name, overlap_fetch, asynchronous=False, ): graph = sampler.__self__ self.returning_indices_and_original_edge_ids_are_optional = False original_edge_ids = ( None if graph.edge_attributes is None else graph.edge_attributes.get(ORIGINAL_EDGE_ID, None) ) if ( overlap_fetch and sampler.__name__ == "sample_neighbors" and ( graph.indices.is_pinned() or ( original_edge_ids is not None and original_edge_ids.is_pinned() ) ) and graph._gpu_graph_cache is None ): datapipe = datapipe.transform(self._sample_per_layer) if asynchronous: datapipe = datapipe.buffer() datapipe = datapipe.transform(self._wait_subgraph_future) fetch_indices_and_original_edge_ids_fn = partial( self._fetch_indices_and_original_edge_ids, graph.indices, original_edge_ids, ) datapipe = ( datapipe.transform(fetch_indices_and_original_edge_ids_fn) .buffer() .wait() ) if graph.type_per_edge is not None: # Hetero case. datapipe = datapipe.transform( partial( self._subtract_hetero_indices_offset, graph._node_type_offset_list, graph.node_type_to_id, ) ) self.returning_indices_and_original_edge_ids_are_optional = True elif overlap_fetch: datapipe = datapipe.fetch_insubgraph_data(graph, prob_name) datapipe = datapipe.transform( self._sample_per_layer_from_fetched_subgraph ) if asynchronous: datapipe = datapipe.buffer() datapipe = datapipe.transform(self._wait_subgraph_future) else: datapipe = datapipe.transform(self._sample_per_layer) if asynchronous: datapipe = datapipe.buffer() datapipe = datapipe.transform(self._wait_subgraph_future) super().__init__(datapipe) self.sampler = sampler self.fanout = fanout self.replace = replace self.prob_name = prob_name self.overlap_fetch = overlap_fetch self.asynchronous = asynchronous def _sample_per_layer(self, minibatch): kwargs = { key[1:]: getattr(minibatch, key) for key in ["_random_seed", "_seed2_contribution"] if hasattr(minibatch, key) } subgraph = self.sampler( minibatch._seed_nodes, self.fanout, self.replace, self.prob_name, self.returning_indices_and_original_edge_ids_are_optional, async_op=self.asynchronous, **kwargs, ) minibatch.sampled_subgraphs.insert(0, subgraph) return minibatch def _sample_per_layer_from_fetched_subgraph(self, minibatch): subgraph = minibatch._sliced_sampling_graph delattr(minibatch, "_sliced_sampling_graph") kwargs = { key[1:]: getattr(minibatch, key) for key in ["_random_seed", "_seed2_contribution"] if hasattr(minibatch, key) } sampled_subgraph = getattr(subgraph, self.sampler.__name__)( None, self.fanout, self.replace, self.prob_name, async_op=self.asynchronous, **kwargs, ) minibatch.sampled_subgraphs.insert(0, sampled_subgraph) return minibatch @staticmethod def _wait_subgraph_future(minibatch): minibatch.sampled_subgraphs[0] = minibatch.sampled_subgraphs[0].wait() return minibatch @staticmethod def _fetch_indices_and_original_edge_ids(indices, orig_edge_ids, minibatch): stream = torch.cuda.current_stream() host_to_device_stream = get_host_to_device_uva_stream() host_to_device_stream.wait_stream(stream) def record_stream(tensor): tensor.record_stream(stream) return tensor with torch.cuda.stream(host_to_device_stream): minibatch._indices_needs_offset_subtraction = False subgraph = minibatch.sampled_subgraphs[0] if isinstance(subgraph.sampled_csc, dict): for etype, pair in subgraph.sampled_csc.items(): if pair.indices is None: edge_ids = ( subgraph._edge_ids_in_fused_csc_sampling_graph[ etype ] ) edge_ids.record_stream(torch.cuda.current_stream()) pair.indices = record_stream( index_select(indices, edge_ids) ) minibatch._indices_needs_offset_subtraction = True if ( orig_edge_ids is not None and subgraph.original_edge_ids[etype] is None ): edge_ids = ( subgraph._edge_ids_in_fused_csc_sampling_graph[ etype ] ) edge_ids.record_stream(torch.cuda.current_stream()) subgraph.original_edge_ids[etype] = record_stream( index_select(orig_edge_ids, edge_ids) ) else: if subgraph.sampled_csc.indices is None: subgraph._edge_ids_in_fused_csc_sampling_graph.record_stream( torch.cuda.current_stream() ) subgraph.sampled_csc.indices = record_stream( index_select( indices, subgraph._edge_ids_in_fused_csc_sampling_graph, ) ) if ( orig_edge_ids is not None and subgraph.original_edge_ids is None ): subgraph._edge_ids_in_fused_csc_sampling_graph.record_stream( torch.cuda.current_stream() ) subgraph.original_edge_ids = record_stream( index_select( orig_edge_ids, subgraph._edge_ids_in_fused_csc_sampling_graph, ) ) subgraph._edge_ids_in_fused_csc_sampling_graph = None minibatch.wait = torch.cuda.current_stream().record_event().wait return minibatch @staticmethod def _subtract_hetero_indices_offset( node_type_offset, node_type_to_id, minibatch ): if minibatch._indices_needs_offset_subtraction: subgraph = minibatch.sampled_subgraphs[0] for etype, pair in subgraph.sampled_csc.items(): src_ntype = etype_str_to_tuple(etype)[0] src_ntype_id = node_type_to_id[src_ntype] pair.indices -= node_type_offset[src_ntype_id] delattr(minibatch, "_indices_needs_offset_subtraction") return minibatch @functional_datapipe("compact_per_layer") class CompactPerLayer(MiniBatchTransformer): """Compact the sampled edges for a single layer.""" def __init__( self, datapipe, deduplicate, cooperative=False, asynchronous=False ): self.deduplicate = deduplicate self.cooperative = cooperative if asynchronous and deduplicate: datapipe = datapipe.transform(self._compact_per_layer_async) datapipe = datapipe.buffer() datapipe = datapipe.transform(self._compact_per_layer_wait_future) if cooperative: datapipe = datapipe.transform( self._seeds_cooperative_exchange_1 ) datapipe = datapipe.buffer() datapipe = datapipe.transform( self._seeds_cooperative_exchange_2 ) datapipe = datapipe.buffer() datapipe = datapipe.transform( self._seeds_cooperative_exchange_3 ) datapipe = datapipe.buffer() datapipe = datapipe.transform( self._seeds_cooperative_exchange_4 ) super().__init__(datapipe) else: super().__init__(datapipe, self._compact_per_layer) def _compact_per_layer(self, minibatch): subgraph = minibatch.sampled_subgraphs[0] seeds = minibatch._seed_nodes if self.deduplicate: ( original_row_node_ids, compacted_csc_format, _, ) = unique_and_compact_csc_formats(subgraph.sampled_csc, seeds) subgraph = SampledSubgraphImpl( sampled_csc=compacted_csc_format, original_column_node_ids=seeds, original_row_node_ids=original_row_node_ids, original_edge_ids=subgraph.original_edge_ids, ) else: ( original_row_node_ids, compacted_csc_format, ) = compact_csc_format(subgraph.sampled_csc, seeds) subgraph = SampledSubgraphImpl( sampled_csc=compacted_csc_format, original_column_node_ids=seeds, original_row_node_ids=original_row_node_ids, original_edge_ids=subgraph.original_edge_ids, ) minibatch._seed_nodes = original_row_node_ids minibatch.sampled_subgraphs[0] = subgraph return minibatch def _compact_per_layer_async(self, minibatch): subgraph = minibatch.sampled_subgraphs[0] seeds = minibatch._seed_nodes assert self.deduplicate rank = thd.get_rank() if self.cooperative else 0 world_size = thd.get_world_size() if self.cooperative else 1 minibatch._future = unique_and_compact_csc_formats( subgraph.sampled_csc, seeds, rank, world_size, async_op=True ) return minibatch def _compact_per_layer_wait_future(self, minibatch): subgraph = minibatch.sampled_subgraphs[0] seeds = minibatch._seed_nodes ( original_row_node_ids, compacted_csc_format, seeds_offsets, ) = minibatch._future.wait() delattr(minibatch, "_future") subgraph = SampledSubgraphImpl( sampled_csc=compacted_csc_format, original_column_node_ids=seeds, original_row_node_ids=original_row_node_ids, original_edge_ids=subgraph.original_edge_ids, ) minibatch._seed_nodes = original_row_node_ids minibatch.sampled_subgraphs[0] = subgraph if self.cooperative: subgraph._seeds_offsets = seeds_offsets return minibatch @staticmethod def _seeds_cooperative_exchange_1(minibatch): world_size = thd.get_world_size() subgraph = minibatch.sampled_subgraphs[0] seeds_offsets = subgraph._seeds_offsets is_homogeneous = not isinstance(seeds_offsets, dict) if is_homogeneous: seeds_offsets = {"_N": seeds_offsets} num_ntypes = len(seeds_offsets) counts_sent = torch.empty(world_size * num_ntypes, dtype=torch.int64) for i, offsets in enumerate(seeds_offsets.values()): counts_sent[ torch.arange(i, world_size * num_ntypes, num_ntypes) ] = offsets.diff() counts_received = torch.empty_like(counts_sent) subgraph._counts_future = all_to_all( counts_received.split(num_ntypes), counts_sent.split(num_ntypes), async_op=True, ) subgraph._counts_sent = counts_sent subgraph._counts_received = counts_received return minibatch @staticmethod def _seeds_cooperative_exchange_2(minibatch): world_size = thd.get_world_size() seeds = minibatch._seed_nodes is_homogenous = not isinstance(seeds, dict) if is_homogenous: seeds = {"_N": seeds} subgraph = minibatch.sampled_subgraphs[0] subgraph._counts_future.wait() delattr(subgraph, "_counts_future") num_ntypes = len(seeds.keys()) seeds_received = {} counts_sent = {} counts_received = {} for i, (ntype, typed_seeds) in enumerate(seeds.items()): idx = torch.arange(i, world_size * num_ntypes, num_ntypes) typed_counts_sent = subgraph._counts_sent[idx].tolist() typed_counts_received = subgraph._counts_received[idx].tolist() typed_seeds_received = typed_seeds.new_empty( sum(typed_counts_received) ) all_to_all( typed_seeds_received.split(typed_counts_received), typed_seeds.split(typed_counts_sent), ) seeds_received[ntype] = typed_seeds_received counts_sent[ntype] = typed_counts_sent counts_received[ntype] = typed_counts_received minibatch._seed_nodes = seeds_received subgraph._counts_sent = revert_to_homo(counts_sent) subgraph._counts_received = revert_to_homo(counts_received) return minibatch @staticmethod def _seeds_cooperative_exchange_3(minibatch): nodes = { ntype: [typed_seeds] for ntype, typed_seeds in minibatch._seed_nodes.items() } minibatch._unique_future = unique_and_compact( nodes, 0, 1, async_op=True ) return minibatch @staticmethod def _seeds_cooperative_exchange_4(minibatch): unique_seeds, inverse_seeds, _ = minibatch._unique_future.wait() delattr(minibatch, "_unique_future") inverse_seeds = { ntype: typed_inv[0] for ntype, typed_inv in inverse_seeds.items() } minibatch._seed_nodes = revert_to_homo(unique_seeds) subgraph = minibatch.sampled_subgraphs[0] sizes = { ntype: typed_seeds.size(0) for ntype, typed_seeds in unique_seeds.items() } subgraph._seed_sizes = revert_to_homo(sizes) subgraph._seed_inverse_ids = revert_to_homo(inverse_seeds) return minibatch class NeighborSamplerImpl(SubgraphSampler): # pylint: disable=abstract-method """Base class for NeighborSamplers.""" # pylint: disable=useless-super-delegation def __init__( self, datapipe, graph, fanouts, replace, prob_name, deduplicate, sampler, overlap_fetch, num_gpu_cached_edges, gpu_cache_threshold, cooperative, asynchronous, layer_dependency=None, batch_dependency=None, ): if overlap_fetch and num_gpu_cached_edges > 0: if graph._gpu_graph_cache is None: graph._initialize_gpu_graph_cache( num_gpu_cached_edges, gpu_cache_threshold, prob_name ) if sampler.__name__ == "sample_layer_neighbors": self._init_seed(batch_dependency) super().__init__( datapipe, graph, fanouts, replace, prob_name, deduplicate, sampler, overlap_fetch, cooperative=cooperative, asynchronous=asynchronous, layer_dependency=layer_dependency, ) def _init_seed(self, batch_dependency): self.rng = torch.random.manual_seed( torch.randint(0, int(1e18), size=tuple()) ) self.cnt = [-1, int(batch_dependency)] self.random_seed = torch.empty( 2 if self.cnt[1] > 1 else 1, dtype=torch.int64 ) self.random_seed.random_(generator=self.rng) def _set_seed(self, minibatch): self.cnt[0] += 1 if self.cnt[1] > 0 and self.cnt[0] % self.cnt[1] == 0: self.random_seed[0] = self.random_seed[-1] self.random_seed[-1:].random_(generator=self.rng) minibatch._random_seed = self.random_seed.clone() minibatch._seed2_contribution = ( 0.0 if self.cnt[1] <= 1 else (self.cnt[0] % self.cnt[1]) / self.cnt[1] ) minibatch._iter = self.cnt[0] return minibatch @staticmethod def _increment_seed(minibatch): minibatch._random_seed = 1 + minibatch._random_seed return minibatch @staticmethod def _delattr_dependency(minibatch): delattr(minibatch, "_random_seed") delattr(minibatch, "_seed2_contribution") return minibatch @staticmethod def _prepare(node_type_to_id, minibatch): seeds = minibatch._seed_nodes # Enrich seeds with all node types. if isinstance(seeds, dict): ntypes = list(node_type_to_id.keys()) # Loop over different seeds to extract the device they are on. device = None dtype = None for _, seed in seeds.items(): device = seed.device dtype = seed.dtype break default_tensor = torch.tensor([], dtype=dtype, device=device) seeds = { ntype: seeds.get(ntype, default_tensor) for ntype in ntypes } minibatch._seed_nodes = seeds minibatch.sampled_subgraphs = [] return minibatch @staticmethod def _set_input_nodes(minibatch): minibatch.input_nodes = minibatch._seed_nodes return minibatch # pylint: disable=arguments-differ def sampling_stages( self, datapipe, graph, fanouts, replace, prob_name, deduplicate, sampler, overlap_fetch, cooperative, asynchronous, layer_dependency, ): datapipe = datapipe.transform( partial(self._prepare, graph.node_type_to_id) ) is_labor = sampler.__name__ == "sample_layer_neighbors" if is_labor: datapipe = datapipe.transform(self._set_seed) for fanout in reversed(fanouts): # Convert fanout to tensor. if not isinstance(fanout, torch.Tensor): fanout = torch.LongTensor([int(fanout)]) datapipe = datapipe.sample_per_layer( sampler, fanout, replace, prob_name, overlap_fetch, asynchronous ) datapipe = datapipe.compact_per_layer( deduplicate, cooperative, asynchronous ) if is_labor and not layer_dependency: datapipe = datapipe.transform(self._increment_seed) if is_labor: datapipe = datapipe.transform(self._delattr_dependency) return datapipe.transform(self._set_input_nodes) @functional_datapipe("sample_neighbor") class NeighborSampler(NeighborSamplerImpl): # pylint: disable=abstract-method """Sample neighbor edges from a graph and return a subgraph. Functional name: :obj:`sample_neighbor`. Neighbor sampler is responsible for sampling a subgraph from given data. It returns an induced subgraph along with compacted information. In the context of a node classification task, the neighbor sampler directly utilizes the nodes provided as seed nodes. However, in scenarios involving link prediction, the process needs another pre-peocess operation. That is, gathering unique nodes from the given node pairs, encompassing both positive and negative node pairs, and employs these nodes as the seed nodes for subsequent steps. When the graph is hetero, sampled subgraphs in minibatch will contain every edge type even though it is empty after sampling. Parameters ---------- datapipe : DataPipe The datapipe. graph : FusedCSCSamplingGraph The graph on which to perform subgraph sampling. fanouts: list[torch.Tensor] or list[int] The number of edges to be sampled for each node with or without considering edge types. The length of this parameter implicitly signifies the layer of sampling being conducted. Note: The fanout order is from the outermost layer to innermost layer. For example, the fanout '[15, 10, 5]' means that 15 to the outermost layer, 10 to the intermediate layer and 5 corresponds to the innermost layer. replace: bool Boolean indicating whether the sample is preformed with or without replacement. If True, a value can be selected multiple times. Otherwise, each value can be selected only once. prob_name: str, optional The name of an edge attribute used as the weights of sampling for each node. This attribute tensor should contain (unnormalized) probabilities corresponding to each neighboring edge of a node. It must be a 1D floating-point or boolean tensor, with the number of elements equalling the total number of edges. deduplicate: bool Boolean indicating whether seeds between hops will be deduplicated. If True, the same elements in seeds will be deleted to only one. Otherwise, the same elements will be remained. overlap_fetch : bool, optional If True, the data loader will overlap the UVA graph fetching operations with the rest of operations by using an alternative CUDA stream. This option should be enabled if you have moved your graph to the pinned memory for optimal performance. Default is False. num_gpu_cached_edges : int, optional If positive and overlap_graph_fetch is True, then the GPU will cache frequently accessed vertex neighborhoods to reduce the PCI-e bandwidth demand due to pinned graph accesses. gpu_cache_threshold : int, optional Determines how many times a vertex needs to be accessed before its neighborhood ends up being cached on the GPU. cooperative: bool, optional Boolean indicating whether Cooperative Minibatching, which was initially proposed in `Deep Graph Library PR#4337`__ and was later first fully described in `Cooperative Minibatching in Graph Neural Networks `__. Cooperation between the GPUs eliminates duplicate work performed across the GPUs due to the overlapping sampled k-hop neighborhoods of seed nodes when performing GNN minibatching. asynchronous: bool Boolean indicating whether sampling and compaction stages should run in background threads to hide the latency of CPU GPU synchronization. Should be enabled only when sampling on the GPU. Examples ------- >>> import torch >>> import dgl.graphbolt as gb >>> indptr = torch.LongTensor([0, 2, 4, 5, 6, 7 ,8]) >>> indices = torch.LongTensor([1, 2, 0, 3, 5, 4, 3, 5]) >>> graph = gb.fused_csc_sampling_graph(indptr, indices) >>> seeds = torch.LongTensor([[0, 1], [1, 2]]) >>> item_set = gb.ItemSet(seeds, names="seeds") >>> datapipe = gb.ItemSampler(item_set, batch_size=1) >>> datapipe = datapipe.sample_uniform_negative(graph, 2) >>> datapipe = datapipe.sample_neighbor(graph, [5, 10, 15]) >>> next(iter(datapipe)).sampled_subgraphs [SampledSubgraphImpl(sampled_csc=CSCFormatBase( indptr=tensor([0, 2, 4, 5, 6, 7, 8]), indices=tensor([1, 4, 0, 5, 5, 3, 3, 2]), ), original_row_node_ids=tensor([0, 1, 4, 5, 2, 3]), original_edge_ids=None, original_column_node_ids=tensor([0, 1, 4, 5, 2, 3]), ), SampledSubgraphImpl(sampled_csc=CSCFormatBase( indptr=tensor([0, 2, 4, 5, 6, 7, 8]), indices=tensor([1, 4, 0, 5, 5, 3, 3, 2]), ), original_row_node_ids=tensor([0, 1, 4, 5, 2, 3]), original_edge_ids=None, original_column_node_ids=tensor([0, 1, 4, 5, 2, 3]), ), SampledSubgraphImpl(sampled_csc=CSCFormatBase( indptr=tensor([0, 2, 4, 5, 6]), indices=tensor([1, 4, 0, 5, 5, 3]), ), original_row_node_ids=tensor([0, 1, 4, 5, 2, 3]), original_edge_ids=None, original_column_node_ids=tensor([0, 1, 4, 5]), )] """ # pylint: disable=useless-super-delegation def __init__( self, datapipe, graph, fanouts, replace=False, prob_name=None, deduplicate=True, overlap_fetch=False, num_gpu_cached_edges=0, gpu_cache_threshold=1, cooperative=False, asynchronous=False, ): super().__init__( datapipe, graph, fanouts, replace, prob_name, deduplicate, graph.sample_neighbors, overlap_fetch, num_gpu_cached_edges, gpu_cache_threshold, cooperative, asynchronous, ) @functional_datapipe("sample_layer_neighbor") class LayerNeighborSampler(NeighborSamplerImpl): # pylint: disable=abstract-method """Sample layer neighbor edges from a graph and return a subgraph. Functional name: :obj:`sample_layer_neighbor`. Sampler that builds computational dependency of node representations via labor sampling for multilayer GNN from the NeurIPS 2023 paper `Layer-Neighbor Sampling -- Defusing Neighborhood Explosion in GNNs `__ Layer-Neighbor sampler is responsible for sampling a subgraph from given data. It returns an induced subgraph along with compacted information. In the context of a node classification task, the neighbor sampler directly utilizes the nodes provided as seed nodes. However, in scenarios involving link prediction, the process needs another pre-process operation. That is, gathering unique nodes from the given node pairs, encompassing both positive and negative node pairs, and employs these nodes as the seed nodes for subsequent steps. When the graph is hetero, sampled subgraphs in minibatch will contain every edge type even though it is empty after sampling. Implements the approach described in Appendix A.3 of the paper. Similar to dgl.dataloading.LaborSampler but this uses sequential poisson sampling instead of poisson sampling to keep the count of sampled edges per vertex deterministic like NeighborSampler. Thus, it is a drop-in replacement for NeighborSampler. However, unlike NeighborSampler, it samples fewer vertices and edges for multilayer GNN scenario without harming convergence speed with respect to training iterations. Parameters ---------- datapipe : DataPipe The datapipe. graph : FusedCSCSamplingGraph The graph on which to perform subgraph sampling. fanouts: list[torch.Tensor] The number of edges to be sampled for each node with or without considering edge types. The length of this parameter implicitly signifies the layer of sampling being conducted. replace: bool Boolean indicating whether the sample is preformed with or without replacement. If True, a value can be selected multiple times. Otherwise, each value can be selected only once. prob_name: str, optional The name of an edge attribute used as the weights of sampling for each node. This attribute tensor should contain (unnormalized) probabilities corresponding to each neighboring edge of a node. It must be a 1D floating-point or boolean tensor, with the number of elements equalling the total number of edges. deduplicate: bool Boolean indicating whether seeds between hops will be deduplicated. If True, the same elements in seeds will be deleted to only one. Otherwise, the same elements will be remained. layer_dependency: bool Boolean indicating whether different layers should use the same random variates. Results in a reduction in the number of nodes sampled and turns LayerNeighborSampler into a subgraph sampling method. Later layers will be guaranteed to sample overlapping neighbors as the previous layers. batch_dependency: int Specifies whether consecutive minibatches should use similar random variates. Results in a higher temporal access locality of sampled nodes and edges. Setting it to :math:`\\kappa` slows down the change in the random variates proportional to :math:`\\frac{1}{\\kappa}`. Implements the dependent minibatching approach in `arXiv:2310.12403 `__. overlap_fetch : bool, optional If True, the data loader will overlap the UVA graph fetching operations with the rest of operations by using an alternative CUDA stream. This option should be enabled if you have moved your graph to the pinned memory for optimal performance. Default is False. num_gpu_cached_edges : int, optional If positive and overlap_graph_fetch is True, then the GPU will cache frequently accessed vertex neighborhoods to reduce the PCI-e bandwidth demand due to pinned graph accesses. gpu_cache_threshold : int, optional Determines how many times a vertex needs to be accessed before its neighborhood ends up being cached on the GPU. cooperative: bool, optional Boolean indicating whether Cooperative Minibatching, which was initially proposed in `Deep Graph Library PR#4337`__ and was later first fully described in `Cooperative Minibatching in Graph Neural Networks `__. Cooperation between the GPUs eliminates duplicate work performed across the GPUs due to the overlapping sampled k-hop neighborhoods of seed nodes when performing GNN minibatching. asynchronous: bool Boolean indicating whether sampling and compaction stages should run in background threads to hide the latency of CPU GPU synchronization. Should be enabled only when sampling on the GPU. Examples ------- >>> import dgl.graphbolt as gb >>> import torch >>> indptr = torch.LongTensor([0, 2, 4, 5, 6, 7 ,8]) >>> indices = torch.LongTensor([1, 2, 0, 3, 5, 4, 3, 5]) >>> graph = gb.fused_csc_sampling_graph(indptr, indices) >>> seeds = torch.LongTensor([[0, 1], [1, 2]]) >>> item_set = gb.ItemSet(seeds, names="seeds") >>> item_sampler = gb.ItemSampler(item_set, batch_size=1,) >>> neg_sampler = gb.UniformNegativeSampler(item_sampler, graph, 2) >>> fanouts = [torch.LongTensor([5]), ... torch.LongTensor([10]),torch.LongTensor([15])] >>> subgraph_sampler = gb.LayerNeighborSampler(neg_sampler, graph, fanouts) >>> next(iter(subgraph_sampler)).sampled_subgraphs [SampledSubgraphImpl(sampled_csc=CSCFormatBase( indptr=tensor([0, 2, 4, 5, 6, 7, 8]), indices=tensor([1, 3, 0, 4, 2, 2, 5, 4]), ), original_row_node_ids=tensor([0, 1, 5, 2, 3, 4]), original_edge_ids=None, original_column_node_ids=tensor([0, 1, 5, 2, 3, 4]), ), SampledSubgraphImpl(sampled_csc=CSCFormatBase( indptr=tensor([0, 2, 4, 5, 6, 7]), indices=tensor([1, 3, 0, 4, 2, 2, 5]), ), original_row_node_ids=tensor([0, 1, 5, 2, 3, 4]), original_edge_ids=None, original_column_node_ids=tensor([0, 1, 5, 2, 3]), ), SampledSubgraphImpl(sampled_csc=CSCFormatBase( indptr=tensor([0, 2, 4, 5, 6]), indices=tensor([1, 3, 0, 4, 2, 2]), ), original_row_node_ids=tensor([0, 1, 5, 2, 3]), original_edge_ids=None, original_column_node_ids=tensor([0, 1, 5, 2]), )] >>> next(iter(subgraph_sampler)).compacted_seeds tensor([[0, 1], [0, 2], [0, 3]]) >>> next(iter(subgraph_sampler)).labels tensor([1., 0., 0.]) >>> next(iter(subgraph_sampler)).indexes tensor([0, 0, 0]) """ def __init__( self, datapipe, graph, fanouts, replace=False, prob_name=None, deduplicate=True, layer_dependency=False, batch_dependency=1, overlap_fetch=False, num_gpu_cached_edges=0, gpu_cache_threshold=1, cooperative=False, asynchronous=False, ): super().__init__( datapipe, graph, fanouts, replace, prob_name, deduplicate, graph.sample_layer_neighbors, overlap_fetch, num_gpu_cached_edges, gpu_cache_threshold, cooperative, asynchronous, layer_dependency, batch_dependency, )