Implemented evaluator for link-prediction in retrieval setting

python/graphstorm/config/argument.py

@@ -313,6 +313,7 @@ def verify_arguments(self, is_train):
         _ = self.restore_model_layers
         _ = self.restore_model_path
         _ = self.restore_optimizer_path
+        _ = self.restore_embed_path
         _ = self.save_embed_path
         _ = self.save_embed_format
 
@@ -1003,6 +1004,15 @@ def restore_optimizer_path(self):
             return self._restore_optimizer_path
         return None
 
+    @property
+    def restore_embed_path(self):
+        """ Path to the saved GNN embeddings.
+        """
+        # pylint: disable=no-member
+        if hasattr(self, "_restore_embed_path"):
+            return self._restore_embed_path
+        return None
+
     ### Save model ###
     @property
     def save_embed_path(self):
@@ -2209,6 +2219,8 @@ def _add_input_args(parser):
             help='Restore the model weights saved in the specified directory.')
     group.add_argument('--restore-optimizer-path', type=str, default=argparse.SUPPRESS,
             help='Restore the optimizer snapshot saved in the specified directory.')
+    group.add_argument('--restore-embed-path', type=str, default=argparse.SUPPRESS,
+            help='Restore the GNN embeddings saved in the specified directory.')
     return parser
 
 def _add_output_args(parser):

python/graphstorm/dataloading/__init__.py

@@ -26,6 +26,7 @@
 from .dataloading import GSgnnNodeDataLoader, GSgnnNodeSemiSupDataLoader
 from .dataloading import GSgnnLinkPredictionTestDataLoader
 from .dataloading import GSgnnLinkPredictionJointTestDataLoader
+from .dataloading import GSgnnLinkPredictionRetrievalDataLoader
 from .dataloading import (FastGSgnnLinkPredictionDataLoader,
                           FastGSgnnLPLocalJointNegDataLoader,
                           FastGSgnnLPJointNegDataLoader,
@@ -41,6 +42,7 @@
 
 from .dataloading import (BUILTIN_LP_UNIFORM_NEG_SAMPLER,
                           BUILTIN_LP_JOINT_NEG_SAMPLER,
+                          BUILTIN_LP_RETRIEVAL_NEG_SAMPLER,
                           BUILTIN_LP_INBATCH_JOINT_NEG_SAMPLER,
                           BUILTIN_LP_LOCALUNIFORM_NEG_SAMPLER,
                           BUILTIN_LP_LOCALJOINT_NEG_SAMPLER)

python/graphstorm/dataloading/dataloading.py

@@ -359,6 +359,7 @@ def fanout(self):
 
 BUILTIN_LP_UNIFORM_NEG_SAMPLER = 'uniform'
 BUILTIN_LP_JOINT_NEG_SAMPLER = 'joint'
+BUILTIN_LP_RETRIEVAL_NEG_SAMPLER = 'full'
 BUILTIN_LP_INBATCH_JOINT_NEG_SAMPLER = 'inbatch_joint'
 BUILTIN_LP_LOCALUNIFORM_NEG_SAMPLER = 'localuniform'
 BUILTIN_LP_LOCALJOINT_NEG_SAMPLER = 'localjoint'
@@ -1059,6 +1060,33 @@ def _prepare_negative_sampler(self, num_negative_edges):
         self._neg_sample_type = BUILTIN_LP_JOINT_NEG_SAMPLER
         return negative_sampler
 
+class GSgnnLinkPredictionRetrievalDataLoader(GSgnnLinkPredictionTestDataLoader):
+    """ Link prediction minibatch dataloader for validation and test
+        with the full train graph as the negative sampler.
+        This is intended for retrieval setting, where a model should compute negative scores
+        from all the nodes in the training graph 
+    """
+    def _prepare_negative_sampler(self, num_negative_edges):
+        # set `_negative_sampler` to None
+        negative_sampler = None
+        self._neg_sample_type = BUILTIN_LP_RETRIEVAL_NEG_SAMPLER
+        return negative_sampler
+
+    def _next_data(self, etype):
+        """ Get postive edges for the next iteration for a specific edge type
+        """
+        g = self._data.g
+        current_pos = self._current_pos[etype]
+        end_of_etype = current_pos + self._batch_size >= self._fixed_test_size[etype]
+
+        pos_eids = self._target_idx[etype][current_pos:self._fixed_test_size[etype]] \
+            if end_of_etype \
+            else self._target_idx[etype][current_pos:current_pos+self._batch_size]
+        pos_pairs = g.find_edges(pos_eids, etype=etype)
+        self._current_pos[etype] += self._batch_size
+        return {etype: pos_pairs}, end_of_etype
+
+
 ################ Minibatch DataLoader (Node classification) #######################
 
 class GSgnnNodeDataLoaderBase():

python/graphstorm/inference/lp_infer.py

@@ -19,7 +19,7 @@
 
 from .graphstorm_infer import GSInferrer
 from ..model.utils import save_full_node_embeddings as save_gsgnn_embeddings
-from ..model.utils import save_relation_embeddings
+from ..model.utils import save_relation_embeddings, load_gsgnn_embeddings
 from ..model.edge_decoder import LinkPredictDistMultDecoder
 from ..model import do_full_graph_inference, do_mini_batch_inference
 from ..model.lp_gnn import lp_mini_batch_predict
@@ -43,7 +43,9 @@ def infer(self, data, loader, save_embed_path,
             edge_mask_for_gnn_embeddings='train_mask',
             use_mini_batch_infer=False,
             node_id_mapping_file=None,
-            save_embed_format="pytorch"):
+            save_embed_format="pytorch",
+            load_embed_path=None
+            ):
         """ Do inference
 
         The inference can do two things:
@@ -70,20 +72,25 @@ def infer(self, data, loader, save_embed_path,
             graph partition algorithm.
         save_embed_format : str
             Specify the format of saved embeddings.
+        load_embed_path : str
+            If provided, load the embedding from disk instead of computing them.
         """
         sys_tracker.check('start inferencing')
         self._model.eval()
-        if use_mini_batch_infer:
-            embs = do_mini_batch_inference(self._model, data, fanout=loader.fanout,
-                                           edge_mask=edge_mask_for_gnn_embeddings,
-                                           task_tracker=self.task_tracker)
+        g = data.g
+        if load_embed_path is None:
+            if use_mini_batch_infer:
+                embs = do_mini_batch_inference(self._model, data, fanout=loader.fanout,
+                                            edge_mask=edge_mask_for_gnn_embeddings,
+                                            task_tracker=self.task_tracker)
+            else:
+                embs = do_full_graph_inference(self._model, data, fanout=loader.fanout,
+                                            edge_mask=edge_mask_for_gnn_embeddings,
+                                            task_tracker=self.task_tracker)
+            sys_tracker.check('compute embeddings')
         else:
-            embs = do_full_graph_inference(self._model, data, fanout=loader.fanout,
-                                           edge_mask=edge_mask_for_gnn_embeddings,
-                                           task_tracker=self.task_tracker)
-        sys_tracker.check('compute embeddings')
+            embs = load_gsgnn_embeddings(load_embed_path, g)
         device = self.device
-        g = data.g
         if save_embed_path is not None:
             save_gsgnn_embeddings(g,
                                   save_embed_path,

python/graphstorm/model/edge_decoder.py

@@ -694,6 +694,48 @@ def calc_test_scores(self, emb, pos_neg_tuple, neg_sample_type, device):
         scores[canonical_etype] = (pos_scores, neg_scores)
         return scores
 
+    def calc_retrieval_scores(self, emb, pos_pairs, device):
+        """ Compute scores for positive edges among all possible edges for retrieval setting
+
+        Parameters
+        ----------
+        emb: dict of Tensor
+            Node embeddings.
+        pos_pairs: dict of tuple
+            Positive edges stored in a tuple:
+            tuple(positive source, postive destination).
+        device: th.device
+            Device used to compute scores
+
+        Return
+        ------
+        Dict of (Tensor, Tensor)
+            Return a dictionary of edge type to
+            (positive scores, negative scores)
+        """
+        assert isinstance(pos_pairs, dict) and len(pos_pairs) == 1, \
+            "DotDecoder is only applicable to link prediction task with " \
+            "single target training edge type"
+        canonical_etype = list(pos_pairs.keys())[0]
+        pos_src, pos_dst = pos_pairs[canonical_etype]
+        utype, _, vtype = canonical_etype
+        pos_src_emb = emb[utype][pos_src].to(device)
+        pos_dst_emb = emb[vtype][pos_dst].to(device)
+        scores = {}
+        pos_scores = calc_dot_pos_score(pos_src_emb, pos_dst_emb)
+        neg_dst_emb = emb[vtype][np.arange(emb[vtype].shape[0])].to(device)
+        neg_scores = th.mm(pos_src_emb, neg_dst_emb.transpose(0, 1)) # [n_pos, n_embs]
+        # gloo with cpu will consume less GPU memory
+        neg_scores = neg_scores.cpu() \
+            if is_distributed() and get_backend() == "gloo" \
+            else neg_scores
+        pos_scores = pos_scores.detach()
+        pos_scores = pos_scores.cpu() \
+            if is_distributed() and get_backend() == "gloo" \
+            else pos_scores
+        scores[canonical_etype] = (pos_scores, neg_scores)
+        return scores
+
     @property
     def in_dims(self):
         """ The number of input dimensions.

python/graphstorm/model/lp_gnn.py

@@ -157,9 +157,14 @@ def lp_mini_batch_predict(model, emb, loader, device):
     with th.no_grad():
         ranking = {}
         for pos_neg_tuple, neg_sample_type in loader:
-            score = \
-                decoder.calc_test_scores(
-                    emb, pos_neg_tuple, neg_sample_type, device)
+            if neg_sample_type == 'full':
+                score = \
+                    decoder.calc_retrieval_scores(emb, pos_neg_tuple, device)
+            else:
+                score = \
+                    decoder.calc_test_scores(
+                        emb, pos_neg_tuple, neg_sample_type, device)
+
             for canonical_etype, s in score.items():
                 # We do not concatenate rankings into a single
                 # ranking tensor to avoid unnecessary data copy.

python/graphstorm/model/utils.py

@@ -907,6 +907,35 @@ def save_full_node_embeddings(g, save_embed_path,
 
     save_shuffled_node_embeddings(shuffled_embs, save_embed_path, save_embed_format)
 
+def load_gsgnn_embeddings(emb_path, g):
+    '''Load from `save_full_node_embeddings` to a dict of DistTensor's
+    '''
+    with open(os.path.join(emb_path, "emb_info.json"), 'r', encoding='utf-8') as f:
+        emb_info = json.load(f)
+    embs = {}
+    for ntype in emb_info["emb_name"]:
+        dist_emb = None
+
+        ntype_emb_path = os.path.join(emb_path, ntype)
+        emb_files = os.listdir(ntype_emb_path)
+        ntype_emb_files = [file for file in emb_files if file.endswith(".pt") and
+                           file.startswith("emb")]
+        ntype_nid_files = [file for file in emb_files if file.endswith(".pt") and
+                           file.startswith("nids")]
+        ntype_emb_files = sorted(ntype_emb_files)
+        ntype_nid_files = sorted(ntype_nid_files)
+        part_policy = g.get_node_partition_policy(ntype)
+        for emb_file, nid_file in zip(ntype_emb_files, ntype_nid_files):
+            # Only work with torch 1.13+
+            emb = th.load(os.path.join(ntype_emb_path, emb_file),weights_only=True)
+            nids = th.load(os.path.join(ntype_emb_path, nid_file),weights_only=True)
+            if dist_emb is None:
+                dist_emb = create_dist_tensor((part_policy.get_size(), emb.shape[1]), emb.dtype,
+                        name=ntype, part_policy=part_policy)
+            dist_emb[nids] = emb
+            barrier()
+        embs[ntype] = dist_emb
+    return embs
 
 def save_embeddings(emb_path, embeddings, rank, world_size,
     device=th.device('cpu'), node_id_mapping_file=None,

python/graphstorm/run/gsgnn_lp/lp_infer_gnn.py

@@ -24,8 +24,10 @@
 from graphstorm.dataloading import GSgnnEdgeInferData
 from graphstorm.dataloading import GSgnnLinkPredictionTestDataLoader
 from graphstorm.dataloading import GSgnnLinkPredictionJointTestDataLoader
+from graphstorm.dataloading import GSgnnLinkPredictionRetrievalDataLoader
 from graphstorm.dataloading import BUILTIN_LP_UNIFORM_NEG_SAMPLER
 from graphstorm.dataloading import BUILTIN_LP_JOINT_NEG_SAMPLER
+from graphstorm.dataloading import BUILTIN_LP_RETRIEVAL_NEG_SAMPLER
 from graphstorm.utils import setup_device, get_lm_ntypes
 
 def main(config_args):
@@ -62,6 +64,8 @@ def main(config_args):
         test_dataloader_cls = GSgnnLinkPredictionTestDataLoader
     elif config.eval_negative_sampler == BUILTIN_LP_JOINT_NEG_SAMPLER:
         test_dataloader_cls = GSgnnLinkPredictionJointTestDataLoader
+    elif config.eval_negative_sampler == BUILTIN_LP_RETRIEVAL_NEG_SAMPLER:
+        test_dataloader_cls = GSgnnLinkPredictionRetrievalDataLoader
     else:
         raise ValueError('Unknown test negative sampler.'
             'Supported test negative samplers include '
@@ -71,13 +75,16 @@ def main(config_args):
                                      batch_size=config.eval_batch_size,
                                      num_negative_edges=config.num_negative_edges_eval,
                                      fanout=config.eval_fanout)
+    # the line below produce gnn embeddings for all nodes on the graph
     infer.infer(infer_data, dataloader,
                 save_embed_path=config.save_embed_path,
                 edge_mask_for_gnn_embeddings=None if config.no_validation else \
                     'train_mask', # if no validation,any edge can be used in message passing.
                 use_mini_batch_infer=config.use_mini_batch_infer,
                 node_id_mapping_file=config.node_id_mapping_file,
-                save_embed_format=config.save_embed_format)
+                save_embed_format=config.save_embed_format,
+                load_embed_path=config.restore_embed_path
+                )
 
 def generate_parser():
     """ Generate an argument parser

tests/end2end-tests/graphstorm-lp/lp_retrieval_eval.sh

@@ -0,0 +1,76 @@
+DGL_HOME=/root/dgl
+GS_HOME=$(pwd)
+NUM_TRAINERS=4
+NUM_INFO_TRAINERS=2
+export PYTHONPATH=$GS_HOME/python/
+cd $GS_HOME/training_scripts/gsgnn_lp
+echo "127.0.0.1" > ip_list.txt
+cd $GS_HOME/inference_scripts/lp_infer
+echo "127.0.0.1" > ip_list.txt
+
+# train a model, save model and embeddings
+python3 -m graphstorm.run.gs_link_prediction --workspace $GS_HOME/training_scripts/gsgnn_lp --num-trainers $NUM_TRAINERS --num-servers 1 --num-samplers 0 --part-config /data/movielen_100k_lp_train_val_1p_4t/movie-lens-100k.json --ip-config ip_list.txt --ssh-port 2222 --cf ml_lp.yaml --fanout '10,15' --num-layers 2 --use-mini-batch-infer false --eval-batch-size 1024 --exclude-training-targets True --reverse-edge-types-map user,rating,rating-rev,movie  --save-model-path /data/gsgnn_lp_ml_dot/ --topk-model-to-save 1 --save-model-frequency 1000 --save-embed-path /data/gsgnn_lp_ml_dot/emb/ --logging-file /tmp/train_log.txt --logging-level debug --preserve-input True
+
+
+best_epoch_dot=$(grep "successfully save the model to" /tmp/train_log.txt | tail -1 | tr -d '\n' | tail -c 1)
+echo "The best model is saved in epoch $best_epoch_dot"
+
+echo "**************dataset: Movielens, do inference on saved model, decoder: dot"
+python3 -m graphstorm.run.gs_link_prediction --inference --workspace $GS_HOME/inference_scripts/lp_infer --num-trainers $NUM_INFO_TRAINERS --num-servers 1 --num-samplers 0 --part-config /data/movielen_100k_lp_train_val_1p_4t/movie-lens-100k.json --ip-config ip_list.txt --ssh-port 2222 --cf ml_lp_infer.yaml --fanout '10,15' --num-layers 2 --use-mini-batch-infer false --eval-batch-size 1024 --save-embed-path /data/gsgnn_lp_ml_dot/infer-emb/ --restore-model-path /data/gsgnn_lp_ml_dot/epoch-$best_epoch_dot/ --preserve-input True
+
+# inference for retrieval setting
+echo "**************dataset: Movielens, do inference on saved model, decoder: dot, retrieval setting:"
+python3 -m graphstorm.run.gs_link_prediction --inference --workspace $GS_HOME/inference_scripts/lp_infer --num-trainers $NUM_INFO_TRAINERS --num-servers 1 --num-samplers 0 --part-config /data/movielen_100k_lp_train_val_1p_4t/movie-lens-100k.json --ip-config ip_list.txt --ssh-port 2222 --cf ml_lp_infer.yaml --fanout '10,15' --num-layers 2 --use-mini-batch-infer false --eval-batch-size 1024 --restore-embed-path /data/gsgnn_lp_ml_dot/infer-emb/ --restore-model-path /data/gsgnn_lp_ml_dot/epoch-$best_epoch_dot/ --preserve-input True --eval-negative-sampler full --save-embed-path none
+
+# inferece for retrieval setting: ppi
+WORKSPACE=/industry-gml-benchmarks/primekg
+cd $WORKSPACE
+# 1. generate GNN embeddings
+python3 -m graphstorm.run.gs_link_prediction --inference --num-trainers 8 --num-servers 4 \
+--num-samplers 0 \
+--ssh-port 2222 \
+--part-config $WORKSPACE/4p/primekg_graph_tasks/1_ppi/primekg.json \
+--ip-config /data/ip_list_p4_zw.txt \
+--cf 1_ppi/frozen_lm_rgcn_lp.yaml \
+--batch-size 1024 \
+--hidden-size 256 \
+--restore-model-path /industry-gml-benchmarks/primekg/4p/primekg_graph_tasks/1_ppi/frozen_lm_rgcn_lp_model-sm/epoch-6 \
+--save-embed-path /industry-gml-benchmarks/primekg/4p/primekg_graph_tasks/1_ppi/frozen_lm_rgcn_lp_model-sm/epoch-6/embs
+
+# 2. calculate MRR in retrieval setting:
+python3 -m graphstorm.run.gs_link_prediction --inference --num-trainers 8 --num-servers 4 \
+--num-samplers 0 \
+--ssh-port 2222 \
+--part-config $WORKSPACE/4p/primekg_graph_tasks/1_ppi/primekg.json \
+--ip-config /data/ip_list_p4_zw.txt \
+--cf 1_ppi/frozen_lm_rgcn_lp.yaml \
+--batch-size 1024 \
+--hidden-size 256 \
+--restore-model-path /industry-gml-benchmarks/primekg/4p/primekg_graph_tasks/1_ppi/frozen_lm_rgcn_lp_model-sm/epoch-6 \
+--restore-embed-path /industry-gml-benchmarks/primekg/4p/primekg_graph_tasks/1_ppi/frozen_lm_rgcn_lp_model-sm/epoch-6/embs \
+--eval-negative-sampler full --save-embed-path none
+
+# 1. generate GNN embeddings
+python3 -m graphstorm.run.gs_link_prediction --inference --num-trainers 8 --num-servers 4 \
+--num-samplers 0 \
+--ssh-port 2222 \
+--part-config $WORKSPACE/4p/primekg_graph_tasks/2_protein_function_prediction/primekg.json \
+--ip-config /data/ip_list_p4_zw.txt \
+--cf 2_protein_function_prediction/frozen_lm_rgcn_lp.yaml \
+--batch-size 256 \
+--hidden-size 256 \
+--restore-model-path /industry-gml-benchmarks/primekg/4p/primekg_graph_tasks/2_protein_function_prediction/frozen_lm_rgcn_lp_model-sm/epoch-49 \
+--save-embed-path /industry-gml-benchmarks/primekg/4p/primekg_graph_tasks/2_protein_function_prediction/frozen_lm_rgcn_lp_model-sm/epoch-49/embs
+
+# 2. calculate MRR in retrieval setting:
+python3 -m graphstorm.run.gs_link_prediction --inference --num-trainers 8 --num-servers 4 \
+--num-samplers 0 \
+--ssh-port 2222 \
+--part-config $WORKSPACE/4p/primekg_graph_tasks/2_protein_function_prediction/primekg.json \
+--ip-config /data/ip_list_p4_zw.txt \
+--cf 2_protein_function_prediction/frozen_lm_rgcn_lp.yaml \
+--batch-size 256 \
+--hidden-size 256 \
+--restore-model-path /industry-gml-benchmarks/primekg/4p/primekg_graph_tasks/2_protein_function_prediction/frozen_lm_rgcn_lp_model-sm/epoch-49 \
+--restore-embed-path /industry-gml-benchmarks/primekg/4p/primekg_graph_tasks/2_protein_function_prediction/frozen_lm_rgcn_lp_model-sm/epoch-49/embs \
+--eval-negative-sampler full --save-embed-path none

tests/end2end-tests/graphstorm-lp/mgpu_test.sh

@@ -190,6 +190,12 @@ then
     echo "Dot product inference does not output edge embedding"
     exit -1
 fi
+
+echo "**************dataset: Movielens, do inference on saved model, decoder: dot, retrieval setting:"
+python3 -m graphstorm.run.gs_link_prediction --inference --workspace $GS_HOME/inference_scripts/lp_infer --num-trainers $NUM_INFO_TRAINERS --num-servers 1 --num-samplers 0 --part-config /data/movielen_100k_lp_train_val_1p_4t/movie-lens-100k.json --ip-config ip_list.txt --ssh-port 2222 --cf ml_lp_infer.yaml --fanout '10,15' --num-layers 2 --use-mini-batch-infer false --eval-batch-size 1024 --restore-embed-path /data/gsgnn_lp_ml_dot/infer-emb/ --restore-model-path /data/gsgnn_lp_ml_dot/epoch-$best_epoch_dot/ --preserve-input True --eval-negative-sampler full --save-embed-path none
+
+error_and_exit $?
+
 rm -fr /data/gsgnn_lp_ml_dot/infer-emb/
 
 echo "**************dataset: Movielens, do inference on saved model, decoder: dot, remap without shared file system"

tools/partition_graph_lp.py

@@ -49,7 +49,7 @@
                            help='split links for inductive settings: no overlapping nodes across '
                            + 'splits.')
     argparser.add_argument('--seed', type=int, default=42,
-                           help='random seed for splitting links')    
+                           help='random seed for splitting links')
     # graph modification arguments
     argparser.add_argument('--add-reverse-edges', action='store_true',
                            help='turn the graph into an undirected graph.')