From 79ca346f3424ccb9d70c03e4ba0abaf8b5f6021b Mon Sep 17 00:00:00 2001 From: Chang Liu Date: Fri, 8 Dec 2023 18:44:03 -0800 Subject: [PATCH 01/14] [Bugfix] Fix a typo in one of the unit test (#680) This is to fix: https://github.com/awslabs/graphstorm/issues/679. --- tests/unit-tests/test_model_save_load.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/unit-tests/test_model_save_load.py b/tests/unit-tests/test_model_save_load.py index 7887c2c304..6be8716999 100644 --- a/tests/unit-tests/test_model_save_load.py +++ b/tests/unit-tests/test_model_save_load.py @@ -146,7 +146,7 @@ def check_sparse_emb(mock_get_world_size, mock_get_rank): for i in range(infer_world_size): mock_get_rank.side_effect = [i] * 2 - mock_get_world_size.side_effect = [train_world_size] * 2 + mock_get_world_size.side_effect = [infer_world_size] * 2 load_sparse_embeds(model_path, embed_layer) load_sparse_embs = \ {ntype: sparse_emb._tensor[th.arange(embed_layer.g.number_of_nodes(ntype))] \ From ca0ed1ff48e865563974bc36c66301dbf667568e Mon Sep 17 00:00:00 2001 From: "xiang song(charlie.song)" Date: Mon, 11 Dec 2023 13:53:48 -0800 Subject: [PATCH 02/14] Update builtin train/inference entries to accept unknown arguments (#681) *Issue #, if available:* As we chain train/inference with node id remapping in the train and inference pipeline, both train/inference entries and remap entry should be more robust to unknown arguments. Related issue: #674 *Description of changes:* Update builtin train/inference entries to accept unknown arguments. By submitting this pull request, I confirm that you can use, modify, copy, and redistribute this contribution, under the terms of your choice. Co-authored-by: Xiang Song --- examples/customized_models/HGT/hgt_nc.py | 3 ++- examples/temporal_graph_learning/main_nc.py | 4 ++-- python/graphstorm/run/gsgnn_dt/distill_gnn.py | 6 +++--- python/graphstorm/run/gsgnn_emb/gsgnn_node_emb.py | 5 +++-- python/graphstorm/run/gsgnn_ep/ep_infer_gnn.py | 5 +++-- python/graphstorm/run/gsgnn_ep/ep_infer_lm.py | 5 +++-- python/graphstorm/run/gsgnn_ep/gsgnn_ep.py | 5 +++-- python/graphstorm/run/gsgnn_ep/gsgnn_lm_ep.py | 5 +++-- python/graphstorm/run/gsgnn_lp/gsgnn_lm_lp.py | 5 +++-- python/graphstorm/run/gsgnn_lp/gsgnn_lp.py | 5 +++-- python/graphstorm/run/gsgnn_lp/lp_infer_gnn.py | 5 +++-- python/graphstorm/run/gsgnn_lp/lp_infer_lm.py | 5 +++-- python/graphstorm/run/gsgnn_np/gsgnn_np.py | 5 +++-- python/graphstorm/run/gsgnn_np/np_infer_gnn.py | 5 +++-- sagemaker/launch/launch_infer.py | 1 + 15 files changed, 41 insertions(+), 28 deletions(-) diff --git a/examples/customized_models/HGT/hgt_nc.py b/examples/customized_models/HGT/hgt_nc.py index 7164266def..6da88e6870 100644 --- a/examples/customized_models/HGT/hgt_nc.py +++ b/examples/customized_models/HGT/hgt_nc.py @@ -393,7 +393,8 @@ def main(args): argparser.add_argument("--local_rank", type=int, help="The rank of the trainer. \ For customized models, MUST have this argument!!") - args = argparser.parse_args() + # Ignore unknown args to make script more robust to input arguments + args, _ = argparser.parse_known_args() print(args) main(args) diff --git a/examples/temporal_graph_learning/main_nc.py b/examples/temporal_graph_learning/main_nc.py index cf3f360874..20fe18889b 100644 --- a/examples/temporal_graph_learning/main_nc.py +++ b/examples/temporal_graph_learning/main_nc.py @@ -106,8 +106,8 @@ def generate_parser(): if __name__ == "__main__": arg_parser = generate_parser() - args = arg_parser.parse_args() - print(args) + # Ignore unknown args to make script more robust to input arguments + args, _ = arg_parser.parse_known_args() main(args) diff --git a/python/graphstorm/run/gsgnn_dt/distill_gnn.py b/python/graphstorm/run/gsgnn_dt/distill_gnn.py index 17c9b3becd..556ad9940e 100644 --- a/python/graphstorm/run/gsgnn_dt/distill_gnn.py +++ b/python/graphstorm/run/gsgnn_dt/distill_gnn.py @@ -92,6 +92,6 @@ def generate_parser(): if __name__ == '__main__': arg_parser=generate_parser() - args = arg_parser.parse_args() - print(args) - main(args) + # Ignore unknown args to make script more robust to input arguments + gs_args, _ = arg_parser.parse_known_args() + main(gs_args) diff --git a/python/graphstorm/run/gsgnn_emb/gsgnn_node_emb.py b/python/graphstorm/run/gsgnn_emb/gsgnn_node_emb.py index 4f402600b0..a4d6eee6a3 100644 --- a/python/graphstorm/run/gsgnn_emb/gsgnn_node_emb.py +++ b/python/graphstorm/run/gsgnn_emb/gsgnn_node_emb.py @@ -103,5 +103,6 @@ def generate_parser(): if __name__ == '__main__': arg_parser = generate_parser() - args = arg_parser.parse_args() - main(args) + # Ignore unknown args to make script more robust to input arguments + gs_args, _ = arg_parser.parse_known_args() + main(gs_args) diff --git a/python/graphstorm/run/gsgnn_ep/ep_infer_gnn.py b/python/graphstorm/run/gsgnn_ep/ep_infer_gnn.py index 7ae09cab29..8a6bd6b98b 100644 --- a/python/graphstorm/run/gsgnn_ep/ep_infer_gnn.py +++ b/python/graphstorm/run/gsgnn_ep/ep_infer_gnn.py @@ -99,5 +99,6 @@ def generate_parser(): if __name__ == '__main__': arg_parser=generate_parser() - args = arg_parser.parse_args() - main(args) + # Ignore unknown args to make script more robust to input arguments + gs_args, _ = arg_parser.parse_known_args() + main(gs_args) diff --git a/python/graphstorm/run/gsgnn_ep/ep_infer_lm.py b/python/graphstorm/run/gsgnn_ep/ep_infer_lm.py index 7e43ff18cb..e26d8a8118 100644 --- a/python/graphstorm/run/gsgnn_ep/ep_infer_lm.py +++ b/python/graphstorm/run/gsgnn_ep/ep_infer_lm.py @@ -89,5 +89,6 @@ def generate_parser(): if __name__ == '__main__': arg_parser=generate_parser() - args = arg_parser.parse_args() - main(args) + # Ignore unknown args to make script more robust to input arguments + gs_args, _ = arg_parser.parse_known_args() + main(gs_args) diff --git a/python/graphstorm/run/gsgnn_ep/gsgnn_ep.py b/python/graphstorm/run/gsgnn_ep/gsgnn_ep.py index aedfd5c83b..661c9e14d3 100644 --- a/python/graphstorm/run/gsgnn_ep/gsgnn_ep.py +++ b/python/graphstorm/run/gsgnn_ep/gsgnn_ep.py @@ -171,5 +171,6 @@ def generate_parser(): if __name__ == '__main__': arg_parser=generate_parser() - args = arg_parser.parse_args() - main(args) + # Ignore unknown args to make script more robust to input arguments + gs_args, _ = arg_parser.parse_known_args() + main(gs_args) diff --git a/python/graphstorm/run/gsgnn_ep/gsgnn_lm_ep.py b/python/graphstorm/run/gsgnn_ep/gsgnn_lm_ep.py index 2a85a49011..39c54c995c 100644 --- a/python/graphstorm/run/gsgnn_ep/gsgnn_lm_ep.py +++ b/python/graphstorm/run/gsgnn_ep/gsgnn_lm_ep.py @@ -148,5 +148,6 @@ def generate_parser(): if __name__ == '__main__': arg_parser=generate_parser() - args = arg_parser.parse_args() - main(args) + # Ignore unknown args to make script more robust to input arguments + gs_args, _ = arg_parser.parse_known_args() + main(gs_args) diff --git a/python/graphstorm/run/gsgnn_lp/gsgnn_lm_lp.py b/python/graphstorm/run/gsgnn_lp/gsgnn_lm_lp.py index 143fd58e06..cfd3d5fe3e 100644 --- a/python/graphstorm/run/gsgnn_lp/gsgnn_lm_lp.py +++ b/python/graphstorm/run/gsgnn_lp/gsgnn_lm_lp.py @@ -197,5 +197,6 @@ def generate_parser(): if __name__ == '__main__': arg_parser=generate_parser() - args = arg_parser.parse_args() - main(args) + # Ignore unknown args to make script more robust to input arguments + gs_args, _ = arg_parser.parse_known_args() + main(gs_args) diff --git a/python/graphstorm/run/gsgnn_lp/gsgnn_lp.py b/python/graphstorm/run/gsgnn_lp/gsgnn_lp.py index 92a40dc737..acc0c2fdbc 100644 --- a/python/graphstorm/run/gsgnn_lp/gsgnn_lp.py +++ b/python/graphstorm/run/gsgnn_lp/gsgnn_lp.py @@ -223,5 +223,6 @@ def generate_parser(): if __name__ == '__main__': arg_parser=generate_parser() - args = arg_parser.parse_args() - main(args) + # Ignore unknown args to make script more robust to input arguments + gs_args, _ = arg_parser.parse_known_args() + main(gs_args) diff --git a/python/graphstorm/run/gsgnn_lp/lp_infer_gnn.py b/python/graphstorm/run/gsgnn_lp/lp_infer_gnn.py index 50a2e97acc..0e95a89841 100644 --- a/python/graphstorm/run/gsgnn_lp/lp_infer_gnn.py +++ b/python/graphstorm/run/gsgnn_lp/lp_infer_gnn.py @@ -88,5 +88,6 @@ def generate_parser(): if __name__ == '__main__': arg_parser=generate_parser() - args = arg_parser.parse_args() - main(args) + # Ignore unknown args to make script more robust to input arguments + gs_args, _ = arg_parser.parse_known_args() + main(gs_args) diff --git a/python/graphstorm/run/gsgnn_lp/lp_infer_lm.py b/python/graphstorm/run/gsgnn_lp/lp_infer_lm.py index e196d3fd83..aa7c051b90 100644 --- a/python/graphstorm/run/gsgnn_lp/lp_infer_lm.py +++ b/python/graphstorm/run/gsgnn_lp/lp_infer_lm.py @@ -90,5 +90,6 @@ def generate_parser(): if __name__ == '__main__': arg_parser=generate_parser() - args = arg_parser.parse_args() - main(args) + # Ignore unknown args to make script more robust to input arguments + gs_args, _ = arg_parser.parse_known_args() + main(gs_args) diff --git a/python/graphstorm/run/gsgnn_np/gsgnn_np.py b/python/graphstorm/run/gsgnn_np/gsgnn_np.py index 4306dac1b0..2292f65955 100644 --- a/python/graphstorm/run/gsgnn_np/gsgnn_np.py +++ b/python/graphstorm/run/gsgnn_np/gsgnn_np.py @@ -182,5 +182,6 @@ def generate_parser(): if __name__ == '__main__': arg_parser=generate_parser() - args = arg_parser.parse_args() - main(args) + # Ignore unknown args to make script more robust to input arguments + gs_args, _ = arg_parser.parse_known_args() + main(gs_args) diff --git a/python/graphstorm/run/gsgnn_np/np_infer_gnn.py b/python/graphstorm/run/gsgnn_np/np_infer_gnn.py index 3c58f33a6b..10a84e9108 100644 --- a/python/graphstorm/run/gsgnn_np/np_infer_gnn.py +++ b/python/graphstorm/run/gsgnn_np/np_infer_gnn.py @@ -94,5 +94,6 @@ def generate_parser(): if __name__ == '__main__': arg_parser=generate_parser() - args = arg_parser.parse_args() - main(args) + # Ignore unknown args to make script more robust to input arguments + gs_args, _ = arg_parser.parse_known_args() + main(gs_args) diff --git a/sagemaker/launch/launch_infer.py b/sagemaker/launch/launch_infer.py index 725d23da57..d47e588e51 100644 --- a/sagemaker/launch/launch_infer.py +++ b/sagemaker/launch/launch_infer.py @@ -92,6 +92,7 @@ def run_job(input_args, image, unknownargs): # We must handle cases like # --target-etype query,clicks,asin query,search,asin # --feat-name ntype0:feat0 ntype1:feat1 + # --column-names nid,~id emb,embedding unknow_idx = 0 while unknow_idx < len(unknownargs): print(unknownargs[unknow_idx]) From 5e7cbb8b1e781fb90959f280c47bd84b4c429508 Mon Sep 17 00:00:00 2001 From: "xiang song(charlie.song)" Date: Mon, 11 Dec 2023 14:46:43 -0800 Subject: [PATCH 03/14] [Bug fix] Fix a bug when --column-names does not cover all builtin names (#682) *Issue #, if available:* When --column-names does not cover all builtin names, the remap will crash Related issue: #674 *Description of changes:* By submitting this pull request, I confirm that you can use, modify, copy, and redistribute this contribution, under the terms of your choice. --------- Co-authored-by: Xiang Song --- python/graphstorm/gconstruct/remap_result.py | 17 ++- .../gconstruct/test_remap_result.py | 109 ++++++++++++++++++ 2 files changed, 124 insertions(+), 2 deletions(-) diff --git a/python/graphstorm/gconstruct/remap_result.py b/python/graphstorm/gconstruct/remap_result.py index ec3f97da48..2fc0ea606f 100644 --- a/python/graphstorm/gconstruct/remap_result.py +++ b/python/graphstorm/gconstruct/remap_result.py @@ -79,7 +79,14 @@ def write_data_parquet_file(data, file_prefix, col_name_map=None): A mapping from builtin column name to user defined column name. """ if col_name_map is not None: - data = {col_name_map[key]: val for key, val in data.items()} + updated_data = {} + for key, val in data.items(): + if key in col_name_map: + updated_data[col_name_map[key]] = val + else: + updated_data[key] = val + data = updated_data + output_fname = f"{file_prefix}.parquet" write_data_parquet(data, output_fname) @@ -107,7 +114,13 @@ def write_data_csv_file(data, file_prefix, delimiter=",", col_name_map=None): A mapping from builtin column name to user defined column name. """ if col_name_map is not None: - data = {col_name_map[key]: val for key, val in data.items()} + updated_data = {} + for key, val in data.items(): + if key in col_name_map: + updated_data[col_name_map[key]] = val + else: + updated_data[key] = val + data = updated_data output_fname = f"{file_prefix}.csv" csv_data = {} diff --git a/tests/unit-tests/gconstruct/test_remap_result.py b/tests/unit-tests/gconstruct/test_remap_result.py index 1421c8f5b5..6d6c3f59af 100644 --- a/tests/unit-tests/gconstruct/test_remap_result.py +++ b/tests/unit-tests/gconstruct/test_remap_result.py @@ -278,7 +278,116 @@ def test__get_file_range(): assert start == 7 assert end == 10 +def test_write_data_parquet_file(): + data = {"emb": np.random.rand(10, 10), + "nid": np.arange(10), + "pred": np.random.rand(10, 10)} + + def check_write_content(fname, col_names): + # col_names should in order of emb, nid and pred + parq_data = read_data_parquet(fname, col_names) + assert_almost_equal(data["emb"], parq_data[col_names[0]]) + assert_equal(data["nid"], parq_data[col_names[1]]) + assert_almost_equal(data["pred"], parq_data[col_names[2]]) + + # without renaming columns + with tempfile.TemporaryDirectory() as tmpdirname: + file_prefix = os.path.join(tmpdirname, "test") + write_data_parquet_file(data, file_prefix, None) + output_fname = f"{file_prefix}.parquet" + + check_write_content(output_fname, ["emb", "nid", "pred"]) + + # rename all column names + with tempfile.TemporaryDirectory() as tmpdirname: + col_name_map = { + "emb": "new_emb", + "nid": "new_nid", + "pred": "new_pred" + } + file_prefix = os.path.join(tmpdirname, "test") + write_data_parquet_file(data, file_prefix, col_name_map) + output_fname = f"{file_prefix}.parquet" + + check_write_content(output_fname, ["new_emb", "new_nid", "new_pred"]) + + # rename part of column names + with tempfile.TemporaryDirectory() as tmpdirname: + col_name_map = { + "emb": "new_emb", + "nid": "new_nid", + } + file_prefix = os.path.join(tmpdirname, "test") + write_data_parquet_file(data, file_prefix, col_name_map) + output_fname = f"{file_prefix}.parquet" + + check_write_content(output_fname, ["new_emb", "new_nid", "pred"]) + +def test_write_data_csv_file(): + data = {"emb": np.random.rand(10, 10), + "nid": np.arange(10), + "pred": np.random.rand(10, 10)} + + def check_write_content(fname, col_names): + # col_names should in order of emb, nid and pred + csv_data = pd.read_csv(fname, delimiter=",") + # emb + assert col_names[0] in csv_data + csv_emb_data = csv_data[col_names[0]].values.tolist() + csv_emb_data = [d.split(";") for d in csv_emb_data] + csv_emb_data = np.array(csv_emb_data, dtype=np.float32) + assert_almost_equal(data["emb"], csv_emb_data) + + # nid + assert col_names[1] in csv_data + csv_nid_data = csv_data[col_names[1]].values.tolist() + csv_nid_data = np.array(csv_nid_data, dtype=np.int32) + assert_equal(data["nid"], csv_nid_data) + + # pred + assert col_names[2] in csv_data + csv_pred_data = csv_data[col_names[2]].values.tolist() + csv_pred_data = [d.split(";") for d in csv_pred_data] + csv_pred_data = np.array(csv_pred_data, dtype=np.float32) + assert_almost_equal(data["pred"], csv_pred_data) + + # without renaming columns + with tempfile.TemporaryDirectory() as tmpdirname: + file_prefix = os.path.join(tmpdirname, "test") + write_data_csv_file(data, file_prefix, col_name_map=None) + output_fname = f"{file_prefix}.csv" + + check_write_content(output_fname, ["emb", "nid", "pred"]) + + # rename all column names + with tempfile.TemporaryDirectory() as tmpdirname: + col_name_map = { + "emb": "new_emb", + "nid": "new_nid", + "pred": "new_pred" + } + file_prefix = os.path.join(tmpdirname, "test") + write_data_csv_file(data, file_prefix, col_name_map=col_name_map) + output_fname = f"{file_prefix}.csv" + + check_write_content(output_fname, ["new_emb", "new_nid", "new_pred"]) + + # rename part of column names + with tempfile.TemporaryDirectory() as tmpdirname: + col_name_map = { + "emb": "new_emb", + "nid": "new_nid", + } + file_prefix = os.path.join(tmpdirname, "test") + write_data_csv_file(data, file_prefix, col_name_map=col_name_map) + output_fname = f"{file_prefix}.csv" + + check_write_content(output_fname, ["new_emb", "new_nid", "pred"]) + + if __name__ == '__main__': + test_write_data_csv_file() + test_write_data_parquet_file() test__get_file_range() test_worker_remap_edge_pred() test_worker_remap_node_data("pred") From 1bbb300b1b18e07a125809996e0ea5bded8af17c Mon Sep 17 00:00:00 2001 From: Da Zheng Date: Tue, 19 Dec 2023 19:19:18 +0800 Subject: [PATCH 04/14] Add flush operation (#689) *Description of changes:* When writing data to distributed tensors, we need to flush data to ensure all data have been written to distributed tensors so that we can perform the next operation (e.g., read). By submitting this pull request, I confirm that you can use, modify, copy, and redistribute this contribution, under the terms of your choice. --------- Co-authored-by: Ubuntu Co-authored-by: Ubuntu --- .github/workflow_scripts/lint_check.sh | 1 + python/graphstorm/distributed/__init__.py | 17 ++++ python/graphstorm/distributed/primitives.py | 91 +++++++++++++++++++ python/graphstorm/model/gnn_encoder_base.py | 2 + python/graphstorm/model/utils.py | 3 +- .../end2end-tests/graphstorm-nc/mgpu_test.sh | 8 +- 6 files changed, 117 insertions(+), 5 deletions(-) create mode 100644 python/graphstorm/distributed/__init__.py create mode 100644 python/graphstorm/distributed/primitives.py diff --git a/.github/workflow_scripts/lint_check.sh b/.github/workflow_scripts/lint_check.sh index 75233c7677..109811aea3 100644 --- a/.github/workflow_scripts/lint_check.sh +++ b/.github/workflow_scripts/lint_check.sh @@ -7,6 +7,7 @@ python3 -m pip install --upgrade prospector pip yes | pip3 install astroid==v3.0.0 FORCE_CUDA=1 python3 -m pip install -e '.[test]' --no-build-isolation pylint --rcfile=./tests/lint/pylintrc ./python/graphstorm/data/*.py +pylint --rcfile=./tests/lint/pylintrc ./python/graphstorm/distributed/ pylint --rcfile=./tests/lint/pylintrc ./python/graphstorm/dataloading/ pylint --rcfile=./tests/lint/pylintrc ./python/graphstorm/gconstruct/ pylint --rcfile=./tests/lint/pylintrc ./python/graphstorm/config/ diff --git a/python/graphstorm/distributed/__init__.py b/python/graphstorm/distributed/__init__.py new file mode 100644 index 0000000000..6a824bee15 --- /dev/null +++ b/python/graphstorm/distributed/__init__.py @@ -0,0 +1,17 @@ +""" + Copyright 2023 Contributors + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. +""" + +from .primitives import flush_data diff --git a/python/graphstorm/distributed/primitives.py b/python/graphstorm/distributed/primitives.py new file mode 100644 index 0000000000..4923dda141 --- /dev/null +++ b/python/graphstorm/distributed/primitives.py @@ -0,0 +1,91 @@ +""" + Copyright 2023 Contributors + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. + + The primitives required for distributed computations. +""" + +from dgl.distributed import rpc + +from ..utils import barrier + +FLUSH_DATA = 1000001 + +class FlushRequest(rpc.Request): + """This request flushes data in DGL's distributed computation components. + + When DGL performs writing to distributed tensors, it returns without data + being fully written to the distributed tensors. This operation is to ensure + that all data has been written to the distributed tensors on the server + when the operation returns. In practice, we don't need to perform any operations + in the request, except just sending responses to the client. The reason is + that when servers receive requests from clients, they processes them in + the FIFO order. When a server gets the opportunities to process the Flush request, + it means that the server has processed all requests before it and has written + data to the distributed tensors. + """ + + def __init__(self): + pass + + def __getstate__(self): + return None + + def __setstate__(self, state): + pass + + def process_request(self, server_state): + """ Process the request. + + Here we don't need to do anything except returning the flush response. + """ + return FlushResponse() + +class FlushResponse(rpc.Response): + """Ack the flush request""" + + def __init__(self): + pass + + def __getstate__(self): + return None + + def __setstate__(self, state): + pass + +rpc.register_service(FLUSH_DATA, FlushRequest, FlushResponse) + +def flush_data(): + """ Flush data in distributed writes of DGL. + + All processes need to talk to all server processes and make sure + all server processes complete processing the write requests issued by + the trainer processes. The reason that we need to have all processes + to communicate with all servers is that there are N*M communication channels, + where N is the number of trainer processes and M is the number of servers. + We need to make sure we flush data in all communication channels. + This function is called after trainer processes have finished issuing write + requests to servers and have written data to shared memory. + We can guarantee that all data are written to distributed tensors when + this function returns. + """ + request = FlushRequest() + # send request to all the server nodes + server_count = rpc.get_num_server() + for server_id in range(server_count): + rpc.send_request(server_id, request) + # recv response from all the server nodes + for _ in range(server_count): + _ = rpc.recv_response() + barrier() diff --git a/python/graphstorm/model/gnn_encoder_base.py b/python/graphstorm/model/gnn_encoder_base.py index 759d558008..450711a7cc 100644 --- a/python/graphstorm/model/gnn_encoder_base.py +++ b/python/graphstorm/model/gnn_encoder_base.py @@ -26,6 +26,7 @@ from .gs_layer import GSLayer from ..utils import get_rank, barrier, is_distributed, create_dist_tensor, is_wholegraph +from ..distributed import flush_data class GraphConvEncoder(GSLayer): # pylint: disable=abstract-method r"""General encoder for graph data. @@ -336,6 +337,7 @@ def dist_inference_one_layer(layer_id, g, dataloader, target_ntypes, layer, get_ if k in output_nodes: assert k in y, "All mini-batch outputs should have the same tensor names." y[k][output_nodes[k]] = h[k].cpu() + flush_data() return y def dist_inference(g, gnn_encoder, get_input_embeds, batch_size, fanout, diff --git a/python/graphstorm/model/utils.py b/python/graphstorm/model/utils.py index aea73096f4..aab4cb5ca3 100644 --- a/python/graphstorm/model/utils.py +++ b/python/graphstorm/model/utils.py @@ -32,6 +32,7 @@ from ..gconstruct.file_io import stream_dist_tensors_to_hdf5 from ..utils import get_rank, barrier, get_world_size, create_dist_tensor from ..data.utils import alltoallv_cpu, alltoallv_nccl +from ..distributed import flush_data # placeholder of the ntype for homogeneous graphs NTYPE = dgl.NTYPE @@ -1039,7 +1040,7 @@ def _load_id_mapping(self, g, ntype, id_mappings): f"Expect {id_mapping.shape[0]}, but get {num_nodes}" # Save ID mapping into dist tensor id_mapping_info[th.arange(num_nodes)] = id_mapping - barrier() + flush_data() return id_mapping_info def shuffle_nids(self, ntype, nids): diff --git a/tests/end2end-tests/graphstorm-nc/mgpu_test.sh b/tests/end2end-tests/graphstorm-nc/mgpu_test.sh index 0662ec2c9b..97f511b190 100644 --- a/tests/end2end-tests/graphstorm-nc/mgpu_test.sh +++ b/tests/end2end-tests/graphstorm-nc/mgpu_test.sh @@ -152,7 +152,7 @@ python3 $GS_HOME/tests/end2end-tests/check_np_infer_emb.py --train-embout /data/ error_and_exit $? echo "**************dataset: Movielens, do inference on saved model, remap without shared file system" -python3 -m graphstorm.run.gs_node_classification --inference --workspace $GS_HOME/inference_scripts/np_infer/ --num-trainers $NUM_INFERs --num-servers 1 --num-samplers 0 --part-config /data/movielen_100k_train_val_1p_4t/movie-lens-100k.json --ip-config ip_list.txt --ssh-port 2222 --cf ml_nc_infer.yaml --use-mini-batch-infer false --save-embed-path /data/gsgnn_nc_ml/infer-emb-nosfs/ --restore-model-path /data/gsgnn_nc_ml/epoch-$best_epoch/ --save-prediction-path /data/gsgnn_nc_ml/prediction-nosfs/ --logging-file /tmp/log.txt --preserve-input True --with-shared-fs False +python3 -m graphstorm.run.gs_node_classification --inference --workspace $GS_HOME/inference_scripts/np_infer/ --num-trainers $NUM_INFERs --num-servers 2 --num-samplers 0 --part-config /data/movielen_100k_train_val_1p_4t/movie-lens-100k.json --ip-config ip_list.txt --ssh-port 2222 --cf ml_nc_infer.yaml --use-mini-batch-infer false --save-embed-path /data/gsgnn_nc_ml/infer-emb-nosfs/ --restore-model-path /data/gsgnn_nc_ml/epoch-$best_epoch/ --save-prediction-path /data/gsgnn_nc_ml/prediction-nosfs/ --logging-file /tmp/log.txt --preserve-input True --with-shared-fs False error_and_exit $? rm /tmp/log.txt @@ -160,7 +160,7 @@ rm /tmp/log.txt python3 $GS_HOME/tests/end2end-tests/check_np_infer_emb.py --train-embout /data/gsgnn_nc_ml/emb/ --infer-embout /data/gsgnn_nc_ml/infer-emb-nosfs/ echo "**************dataset: Movielens, use gen_embeddings to generate embeddings on node classification" -python3 -m graphstorm.run.gs_gen_node_embedding --workspace $GS_HOME/training_scripts/gsgnn_np/ --num-trainers $NUM_TRAINERS --num-servers 1 --num-samplers 0 --part-config /data/movielen_100k_train_val_1p_4t/movie-lens-100k.json --ip-config ip_list.txt --ssh-port 2222 --cf ml_nc.yaml --restore-model-path /data/gsgnn_nc_ml/epoch-$best_epoch/ --save-embed-path /data/gsgnn_nc_ml/save-emb/ --use-mini-batch-infer false --logging-file /tmp/train_log.txt --logging-level debug --preserve-input True +python3 -m graphstorm.run.gs_gen_node_embedding --workspace $GS_HOME/training_scripts/gsgnn_np/ --num-trainers $NUM_TRAINERS --num-servers 2 --num-samplers 0 --part-config /data/movielen_100k_train_val_1p_4t/movie-lens-100k.json --ip-config ip_list.txt --ssh-port 2222 --cf ml_nc.yaml --restore-model-path /data/gsgnn_nc_ml/epoch-$best_epoch/ --save-embed-path /data/gsgnn_nc_ml/save-emb/ --use-mini-batch-infer false --logging-file /tmp/train_log.txt --logging-level debug --preserve-input True error_and_exit $? @@ -250,7 +250,7 @@ error_and_exit $? rm /tmp/train_log.txt echo "**************dataset: Movielens, do inference on saved model, RGCN layer: 1, node feat: BERT nodes: movie, user" -python3 -m graphstorm.run.gs_node_classification --inference --workspace $GS_HOME/inference_scripts/np_infer/ --num-trainers $NUM_INFERs --num-servers 1 --num-samplers 0 --part-config /data/movielen_100k_lm_encoder_train_val_1p_4t/movie-lens-100k-text.json --ip-config ip_list.txt --ssh-port 2222 --cf ml_nc_text_infer.yaml --use-mini-batch-infer false --save-embed-path /data/gsgnn_nc_ml_text/infer-emb/ --restore-model-path /data/gsgnn_nc_ml_text/epoch-$best_epoch/ --save-prediction-path /data/gsgnn_nc_ml_text/prediction/ --logging-file /tmp/log.txt --logging-level debug --preserve-input True +python3 -m graphstorm.run.gs_node_classification --inference --workspace $GS_HOME/inference_scripts/np_infer/ --num-trainers $NUM_INFERs --num-servers 2 --num-samplers 0 --part-config /data/movielen_100k_lm_encoder_train_val_1p_4t/movie-lens-100k-text.json --ip-config ip_list.txt --ssh-port 2222 --cf ml_nc_text_infer.yaml --use-mini-batch-infer false --save-embed-path /data/gsgnn_nc_ml_text/infer-emb/ --restore-model-path /data/gsgnn_nc_ml_text/epoch-$best_epoch/ --save-prediction-path /data/gsgnn_nc_ml_text/prediction/ --logging-file /tmp/log.txt --logging-level debug --preserve-input True error_and_exit $? @@ -326,7 +326,7 @@ echo "The best model is saved in epoch $best_epoch" rm /tmp/train_log.txt echo "**************dataset: Movielens, do inference on saved model, node feat: BERT nodes: movie, user, with warmup" -python3 -m graphstorm.run.gs_node_classification --inference --workspace $GS_HOME/inference_scripts/np_infer/ --num-trainers $NUM_INFERs --num-servers 1 --num-samplers 0 --part-config /data/movielen_100k_lm_encoder_train_val_1p_4t/movie-lens-100k-text.json --ip-config ip_list.txt --ssh-port 2222 --cf ml_nc_text_infer.yaml --use-mini-batch-infer false --save-embed-path /data/gsgnn_nc_ml_text/infer-emb/ --restore-model-path /data/gsgnn_nc_ml_text/epoch-$best_epoch/ --save-prediction-path /data/gsgnn_nc_ml_text/prediction/ --logging-file /tmp/log.txt --logging-level debug --preserve-input True +python3 -m graphstorm.run.gs_node_classification --inference --workspace $GS_HOME/inference_scripts/np_infer/ --num-trainers $NUM_INFERs --num-servers 2 --num-samplers 0 --part-config /data/movielen_100k_lm_encoder_train_val_1p_4t/movie-lens-100k-text.json --ip-config ip_list.txt --ssh-port 2222 --cf ml_nc_text_infer.yaml --use-mini-batch-infer false --save-embed-path /data/gsgnn_nc_ml_text/infer-emb/ --restore-model-path /data/gsgnn_nc_ml_text/epoch-$best_epoch/ --save-prediction-path /data/gsgnn_nc_ml_text/prediction/ --logging-file /tmp/log.txt --logging-level debug --preserve-input True error_and_exit $? From 61aada69c336c288a571922d9f1d9d0d17d51b90 Mon Sep 17 00:00:00 2001 From: jalencato Date: Fri, 15 Dec 2023 13:40:53 -0800 Subject: [PATCH 05/14] [Homo Optmization] Add homogeneous graph optimization (#683) *Issue #, if available:* *Description of changes:* By submitting this pull request, I confirm that you can use, modify, copy, and redistribute this contribution, under the terms of your choice. --------- Co-authored-by: xiang song(charlie.song) --- .github/workflow_scripts/e2e_check.sh | 1 + .../graphstorm/gconstruct/construct_graph.py | 57 +++++++++++++++-- .../data_gen/movielens_homogeneous.json | 63 +++++++++++++++++++ .../data_gen/process_movielens.py | 5 ++ .../data_process/check_homogeneous.py | 60 ++++++++++++++++++ .../data_process/homogeneous_test.sh | 50 +++++++++++++++ .../gconstruct/test_construct_graph.py | 59 ++++++++++++++++- 7 files changed, 288 insertions(+), 7 deletions(-) create mode 100644 tests/end2end-tests/data_gen/movielens_homogeneous.json create mode 100644 tests/end2end-tests/data_process/check_homogeneous.py create mode 100644 tests/end2end-tests/data_process/homogeneous_test.sh diff --git a/.github/workflow_scripts/e2e_check.sh b/.github/workflow_scripts/e2e_check.sh index 9851a35529..8c122c9f9d 100644 --- a/.github/workflow_scripts/e2e_check.sh +++ b/.github/workflow_scripts/e2e_check.sh @@ -8,6 +8,7 @@ sh ./tests/end2end-tests/create_data.sh sh ./tests/end2end-tests/tools/test_mem_est.sh sh ./tests/end2end-tests/data_process/test.sh sh ./tests/end2end-tests/data_process/movielens_test.sh +sh ./tests/end2end-tests/data_process/homogeneous_test.sh sh ./tests/end2end-tests/custom-gnn/run_test.sh bash ./tests/end2end-tests/graphstorm-nc/test.sh bash ./tests/end2end-tests/graphstorm-lp/test.sh diff --git a/python/graphstorm/gconstruct/construct_graph.py b/python/graphstorm/gconstruct/construct_graph.py index 0065da5403..259399328b 100644 --- a/python/graphstorm/gconstruct/construct_graph.py +++ b/python/graphstorm/gconstruct/construct_graph.py @@ -28,6 +28,7 @@ import numpy as np import torch as th import dgl +from dgl.distributed.constants import DEFAULT_NTYPE, DEFAULT_ETYPE from ..utils import sys_tracker, get_log_level from .file_io import parse_node_file_format, parse_edge_file_format @@ -582,8 +583,23 @@ def process_edge_data(process_confs, node_id_map, arr_merger, return (edges, edge_data, label_stats) +def is_homogeneous(confs): + """ Verify if it is a homogeneous graph + Parameter + --------- + confs: dict + A dict containing all user input config + """ + ntypes = {conf['node_type'] for conf in confs["nodes"]} + etypes = set(tuple(conf['relation']) for conf in confs["edges"]) + return len(ntypes) == 1 and len(etypes) == 1 + def verify_confs(confs): """ Verify the configuration of the input data. + Parameter + --------- + confs: dict + A dict containing all user input config """ if "version" not in confs: # TODO: Make a requirement with v1.0 launch @@ -599,6 +615,14 @@ def verify_confs(confs): f"source node type {src_type} does not exist. Please check your input data." assert dst_type in ntypes, \ f"dest node type {dst_type} does not exist. Please check your input data." + # Adjust input to DGL homogeneous graph format if it is a homogeneous graph + if is_homogeneous(confs): + logging.warning("Generated Graph is a homogeneous graph, so the node type will be " + "changed to _N and edge type will be changed to [_N, _E, _N]") + for node in confs['nodes']: + node['node_type'] = DEFAULT_NTYPE + for edge in confs['edges']: + edge['relation'] = list(DEFAULT_ETYPE) def print_graph_info(g, node_data, edge_data, node_label_stats, edge_label_stats): """ Print graph information. @@ -698,12 +722,35 @@ def process_graph(args): if args.add_reverse_edges: edges1 = {} - for etype in edges: - e = edges[etype] + if is_homogeneous(process_confs): + logging.warning("For homogeneous graph, the generated reverse edge will " + "be the same edge type as the original graph. Instead for " + "heterogeneous graph, the generated reverse edge type will " + "add -rev as a suffix") + e = edges[DEFAULT_ETYPE] assert isinstance(e, tuple) and len(e) == 2 - assert isinstance(etype, tuple) and len(etype) == 3 - edges1[etype] = e - edges1[etype[2], etype[1] + "-rev", etype[0]] = (e[1], e[0]) + edges1[DEFAULT_ETYPE] = (np.concatenate([e[0], e[1]]), + np.concatenate([e[1], e[0]])) + # Double edge feature as it is necessary to match tensor size in generated graph + # Only generate mask on original graph + if edge_data: + data = edge_data[DEFAULT_ETYPE] + logging.warning("Reverse edge for homogeneous graph will have same feature as " + "what we have in the original edges") + for key, value in data.items(): + if key not in ["train_mask", "test_mask", "val_mask"]: + data[key] = np.concatenate([value, value]) + else: + data[key] = np.concatenate([value, np.zeros(value.shape, + dtype=value.dtype)]) + + else: + for etype in edges: + e = edges[etype] + assert isinstance(e, tuple) and len(e) == 2 + assert isinstance(etype, tuple) and len(etype) == 3 + edges1[etype] = e + edges1[etype[2], etype[1] + "-rev", etype[0]] = (e[1], e[0]) edges = edges1 sys_tracker.check('Add reverse edges') g = dgl.heterograph(edges, num_nodes_dict=num_nodes) diff --git a/tests/end2end-tests/data_gen/movielens_homogeneous.json b/tests/end2end-tests/data_gen/movielens_homogeneous.json new file mode 100644 index 0000000000..018776e82e --- /dev/null +++ b/tests/end2end-tests/data_gen/movielens_homogeneous.json @@ -0,0 +1,63 @@ +{ + "version": "gconstruct-v0.1", + "nodes": [ + { + "node_id_col": "id", + "node_type": "movie", + "format": {"name": "parquet"}, + "files": "/data/ml-100k/movie.parquet", + "features": [ + { + "feature_col": "title", + "transform": { + "name": "bert_hf", + "bert_model": "bert-base-uncased", + "max_seq_length": 16 + } + } + ], + "labels": [ + { + "label_col": "label", + "task_type": "classification", + "split_pct": [0.8, 0.1, 0.1] + } + ] + }, + { + "node_type": "movie", + "format": {"name": "parquet"}, + "files": "/data/ml-100k/movie.parquet", + "features": [ + { + "feature_col": "id" + } + ] + } + ], + "edges": [ + { + "source_id_col": "src_id", + "dest_id_col": "dst_id", + "relation": ["movie", "rating", "movie"], + "format": {"name": "parquet"}, + "files": "/data/ml-100k/edges_homogeneous.parquet", + "features": [ + { + "feature_col": "rate" + }], + "labels": [ + { + "label_col": "rate", + "task_type": "classification", + "split_pct": [0.1, 0.1, 0.1] + } + ] + }, + { + "relation": ["movie", "rating", "movie"], + "format": {"name": "parquet"}, + "files": "/data/ml-100k/edges_homogeneous.parquet" + } + ] +} \ No newline at end of file diff --git a/tests/end2end-tests/data_gen/process_movielens.py b/tests/end2end-tests/data_gen/process_movielens.py index 90fdcd1702..a9ca90873e 100644 --- a/tests/end2end-tests/data_gen/process_movielens.py +++ b/tests/end2end-tests/data_gen/process_movielens.py @@ -90,6 +90,11 @@ def write_data_parquet(data, data_file): edge_data = {'src_id': edges[0], 'dst_id': edges[1], 'rate': edges[2]} write_data_parquet(edge_data, '/data/ml-100k/edges.parquet') +# generate data for homogeneous optimization test +edges = pandas.read_csv('/data/ml-100k/u.data', delimiter='\t', header=None) +edge_data = {'src_id': edges[1], 'dst_id': edges[1], 'rate': edges[2]} +write_data_parquet(edge_data, '/data/ml-100k/edges_homogeneous.parquet') + # generate synthetic user data with label user_labels = np.random.randint(11, size=feat.shape[0]) user_data = {'id': user['id'].values, 'feat': feat, 'occupation': user['occupation'], 'label': user_labels} diff --git a/tests/end2end-tests/data_process/check_homogeneous.py b/tests/end2end-tests/data_process/check_homogeneous.py new file mode 100644 index 0000000000..daeb6f0ada --- /dev/null +++ b/tests/end2end-tests/data_process/check_homogeneous.py @@ -0,0 +1,60 @@ +""" + Copyright 2023 Contributors + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. + +""" +import os +import argparse +import dgl +from dgl.distributed.constants import DEFAULT_NTYPE, DEFAULT_ETYPE +from numpy.testing import assert_almost_equal + + +def check_reverse_edge(args): + + g_orig = dgl.load_graphs(os.path.join(args.orig_graph_path, "graph.dgl"))[0][0] + g_rev = dgl.load_graphs(os.path.join(args.rev_graph_path, "graph.dgl"))[0][0] + assert g_orig.ntypes == g_rev.ntypes + assert g_orig.etypes == g_rev.etypes + assert g_orig.number_of_nodes(DEFAULT_NTYPE) == g_rev.number_of_nodes(DEFAULT_NTYPE) + assert 2 * g_orig.number_of_edges(DEFAULT_ETYPE) == g_rev.number_of_edges(DEFAULT_ETYPE) + for ntype in g_orig.ntypes: + assert g_orig.number_of_nodes(ntype) == g_rev.number_of_nodes(ntype) + for name in g_orig.nodes[ntype].data: + # We should skip '*_mask' because data split is split randomly. + if 'mask' not in name: + assert_almost_equal(g_orig.nodes[ntype].data[name].numpy(), + g_rev.nodes[ntype].data[name].numpy()) + + # Check edge feature + g_orig_feat = dgl.data.load_tensors(os.path.join(args.orig_graph_path, "edge_feat.dgl")) + g_rev_feat = dgl.data.load_tensors(os.path.join(args.rev_graph_path, "edge_feat.dgl")) + for feat_type in g_orig_feat.keys(): + if "mask" not in feat_type: + assert_almost_equal(g_orig_feat[feat_type].numpy(), + g_rev_feat[feat_type].numpy()[:g_orig.number_of_edges(DEFAULT_ETYPE)]) + else: + assert_almost_equal(g_rev_feat[feat_type].numpy()[g_orig.number_of_edges(DEFAULT_ETYPE):], + [0] * g_orig.number_of_edges(DEFAULT_ETYPE)) + +if __name__ == '__main__': + argparser = argparse.ArgumentParser("Check edge prediction remapping") + argparser.add_argument("--orig-graph-path", type=str, default="/tmp/movielen_100k_train_val_1p_4t_homogeneous/part0/", + help="Path to save the generated data") + argparser.add_argument("--rev-graph-path", type=str, default="/tmp/movielen_100k_train_val_1p_4t_homogeneous_rev/part0/", + help="Path to save the generated data") + + args = argparser.parse_args() + + check_reverse_edge(args) \ No newline at end of file diff --git a/tests/end2end-tests/data_process/homogeneous_test.sh b/tests/end2end-tests/data_process/homogeneous_test.sh new file mode 100644 index 0000000000..dd3f55907d --- /dev/null +++ b/tests/end2end-tests/data_process/homogeneous_test.sh @@ -0,0 +1,50 @@ +#!/bin/bash + +service ssh restart + +GS_HOME=$(pwd) +NUM_TRAINERS=4 +export PYTHONPATH=$GS_HOME/python/ +cd $GS_HOME/training_scripts/gsgnn_np +echo "127.0.0.1" > ip_list.txt +cd $GS_HOME/training_scripts/gsgnn_ep +echo "127.0.0.1" > ip_list.txt + +error_and_exit () { + # check exec status of launch.py + status=$1 + echo $status + + if test $status -ne 0 + then + exit -1 + fi +} + + +echo "********* Test Homogeneous Graph Optimization ********" +python3 -m graphstorm.gconstruct.construct_graph --conf-file $GS_HOME/tests/end2end-tests/data_gen/movielens_homogeneous.json --num-processes 1 --output-dir /tmp/movielen_100k_train_val_1p_4t_homogeneous --graph-name movie-lens-100k +error_and_exit $? + +echo "********* Test Node Classification on GConstruct Homogeneous Graph ********" +python3 -m graphstorm.run.gs_node_classification --workspace $GS_HOME/training_scripts/gsgnn_np/ --num-trainers $NUM_TRAINERS --num-servers 1 --num-samplers 0 --part-config /tmp/movielen_100k_train_val_1p_4t_homogeneous/movie-lens-100k.json --ip-config ip_list.txt --ssh-port 2222 --cf ml_nc.yaml --target-ntype _N +error_and_exit $? + +echo "********* Test Edge Classification on GConstruct Homogeneous Graph ********" +python3 -m graphstorm.run.gs_edge_classification --workspace $GS_HOME/training_scripts/gsgnn_ep/ --num-trainers $NUM_TRAINERS --num-servers 1 --num-samplers 0 --part-config /tmp/movielen_100k_train_val_1p_4t_homogeneous/movie-lens-100k.json --ip-config ip_list.txt --ssh-port 2222 --cf ml_ec.yaml --target-etype _N,_E,_N +error_and_exit $? + +echo "********* Test Homogeneous Graph Optimization on reverse edge********" +python3 -m graphstorm.gconstruct.construct_graph --conf-file $GS_HOME/tests/end2end-tests/data_gen/movielens_homogeneous.json --num-processes 1 --output-dir /tmp/movielen_100k_train_val_1p_4t_homogeneous_rev --graph-name movie-lens-100k --add-reverse-edges +error_and_exit $? + +python3 $GS_HOME/tests/end2end-tests/data_process/check_homogeneous.py +error_and_exit $? + +echo "********* Test Node Classification on GConstruct Homogeneous Graph with reverse edge********" +python3 -m graphstorm.run.gs_node_classification --workspace $GS_HOME/training_scripts/gsgnn_np/ --num-trainers $NUM_TRAINERS --num-servers 1 --num-samplers 0 --part-config /tmp/movielen_100k_train_val_1p_4t_homogeneous_rev/movie-lens-100k.json --ip-config ip_list.txt --ssh-port 2222 --cf ml_nc.yaml --target-ntype _N +error_and_exit $? + +echo "********* Test Edge Classification on GConstruct Homogeneous Graph with reverse edge ********" +python3 -m graphstorm.run.gs_edge_classification --workspace $GS_HOME/training_scripts/gsgnn_ep/ --num-trainers $NUM_TRAINERS --num-servers 1 --num-samplers 0 --part-config /tmp/movielen_100k_train_val_1p_4t_homogeneous_rev/movie-lens-100k.json --ip-config ip_list.txt --ssh-port 2222 --cf ml_ec.yaml --target-etype _N,_E,_N +error_and_exit $? \ No newline at end of file diff --git a/tests/unit-tests/gconstruct/test_construct_graph.py b/tests/unit-tests/gconstruct/test_construct_graph.py index a03a7cbec7..d7c9ae6650 100644 --- a/tests/unit-tests/gconstruct/test_construct_graph.py +++ b/tests/unit-tests/gconstruct/test_construct_graph.py @@ -22,11 +22,12 @@ import numpy as np import dgl import torch as th +import copy from functools import partial from numpy.testing import assert_equal, assert_almost_equal -from graphstorm.gconstruct.construct_graph import parse_edge_data +from graphstorm.gconstruct.construct_graph import parse_edge_data, verify_confs, is_homogeneous from graphstorm.gconstruct.file_io import write_data_parquet, read_data_parquet from graphstorm.gconstruct.file_io import write_data_json, read_data_json from graphstorm.gconstruct.file_io import write_data_csv, read_data_csv @@ -1705,6 +1706,59 @@ def test_gc(): assert not os.path.isdir("/tmp_featurewrapper2"), \ "Directory /tmp_featurewrapper2 should not exist after gc" + +def test_homogeneous(): + # single node type and edge type input + conf = { + "version": "gconstruct-v0.1", "nodes": [ + {"node_id_col": "id", "node_type": "movie", "format": {"name": "parquet"}, + "files": "/data/ml-100k/movie.parquet", "features": [ + {"feature_col": "title", "transform": { + "name": "bert_hf", "bert_model": "bert-base-uncased", "max_seq_length": 16}}], + "labels": [{"label_col": "label", "task_type": "classification", "split_pct": [0.8, 0.1, 0.1]}]}], + "edges": [ + {"source_id_col": "src_id", "dest_id_col": "dst_id", "relation": ["movie", "rating", "movie"], + "format": {"name": "parquet"}, "files": "/data/ml-100k/edges_homo.parquet", "labels": [ + {"label_col": "rate", "task_type": "classification", "split_pct": [0.1, 0.1, 0.1]}]}] + } + assert is_homogeneous(conf) + verify_confs(conf) + assert conf['nodes'][0]["node_type"] == "_N" + assert conf['edges'][0]['relation'] == ["_N", "_E", "_N"] + conf["edges"][0]["relation"] = ["movie_fake", "rating", "movie"] + conf["nodes"].append(copy.deepcopy(conf["nodes"][0])) + conf["nodes"][0]["node_type"] = "movie" + conf["nodes"][1]["node_type"] = "movie_fake" + assert not is_homogeneous(conf) + + + # multiple node types and edge types input + conf = { + "version": "gconstruct-v0.1", "nodes": [ + {"node_id_col": "id", "node_type": "movie", "format": {"name": "parquet"}, + "files": "/data/ml-100k/movie.parquet", "features": [ + {"feature_col": "title", "transform": { + "name": "bert_hf", "bert_model": "bert-base-uncased", "max_seq_length": 16}}], + "labels": [{"label_col": "label", "task_type": "classification", "split_pct": [0.8, 0.1, 0.1]}]}, + {"node_type": "movie", "format": {"name": "parquet"}, "files": "/data/ml-100k/movie.parquet", + "features": [{"feature_col": "id"}]}], + "edges": [ + {"source_id_col": "src_id", "dest_id_col": "dst_id", "relation": ["movie", "rating", "movie"], + "format": {"name": "parquet"}, "files": "/data/ml-100k/edges_homo.parquet", "labels": [ + {"label_col": "rate", "task_type": "classification", "split_pct": [0.1, 0.1, 0.1]}]}, + {"relation": ["movie", "rating", "movie"], "format": {"name": "parquet"}, + "files": "/data/ml-100k/edges_homo.parquet"}] + } + assert is_homogeneous(conf) + verify_confs(conf) + assert conf['nodes'][0]["node_type"] == "_N" + assert conf['edges'][0]['relation'] == ["_N", "_E", "_N"] + conf["edges"][0]["relation"] = ["movie_fake", "rating", "movie"] + conf["nodes"].append(copy.deepcopy(conf["nodes"][0])) + conf["nodes"][0]["node_type"] = "movie" + conf["nodes"][1]["node_type"] = "movie_fake" + assert not is_homogeneous(conf) + if __name__ == '__main__': test_parse_edge_data() test_multiprocessing_checks() @@ -1723,4 +1777,5 @@ def test_gc(): test_label() test_multicolumn(None) test_multicolumn("/") - test_feature_wrapper() \ No newline at end of file + test_feature_wrapper() + test_homogeneous() From fa9643e330d6dbb8b379ee2eb6cf93b8f430eeee Mon Sep 17 00:00:00 2001 From: jalencato Date: Tue, 19 Dec 2023 17:01:11 -0800 Subject: [PATCH 06/14] [Homo Optimization] Optimization on GSF (#686) *Issue #, if available:* *Description of changes:* Allow users to start training/inferring job without specifying target_ntype/target_etype on homogeneous graph. By submitting this pull request, I confirm that you can use, modify, copy, and redistribute this contribution, under the terms of your choice. --------- Co-authored-by: xiang song(charlie.song) Co-authored-by: Xiang Song --- .../configuration/configuration-run.rst | 8 +- python/graphstorm/config/argument.py | 16 ++- python/graphstorm/dataloading/dataset.py | 36 +++++- .../data_process/homogeneous_test.sh | 23 +++- tests/unit-tests/data_utils.py | 108 ++++++++++++++++++ tests/unit-tests/test_config.py | 7 +- tests/unit-tests/test_dataloading.py | 103 +++++++++++++++++ .../gsgnn_ep/ml_ec_homogeneous.yaml | 38 ++++++ .../gsgnn_np/ml_nc_homogeneous.yaml | 35 ++++++ 9 files changed, 359 insertions(+), 15 deletions(-) create mode 100644 training_scripts/gsgnn_ep/ml_ec_homogeneous.yaml create mode 100644 training_scripts/gsgnn_np/ml_nc_homogeneous.yaml diff --git a/docs/source/configuration/configuration-run.rst b/docs/source/configuration/configuration-run.rst index 1f4b71d12c..f0c1afa854 100644 --- a/docs/source/configuration/configuration-run.rst +++ b/docs/source/configuration/configuration-run.rst @@ -381,20 +381,20 @@ Classification and Regression Task Node Classification/Regression Specific ......................................... -- **target_ntype**: (**Required**) The node type for prediction. +- **target_ntype**: The node type for prediction. - Yaml: ``target_ntype: movie`` - Argument: ``--target-ntype movie`` - - Default value: This parameter must be provided by user. + - Default value: For heterogeneous input graph, this parameter must be provided by the user. If not provided, GraphStorm will assume the input graph is a homogeneous graph and set ``target_ntype`` to "_N". Edge Classification/Regression Specific .......................................... -- **target_etype**: (**Required**) The list of canonical edge types that will be added as a training target in edge classification/regression tasks, for example ``--train-etype query,clicks,asin`` or ``--train-etype query,clicks,asin query,search,asin``. A canonical edge type should be formatted as `src_node_type,relation_type,dst_node_type`. Currently, GraphStorm only supports single task edge classification/regression, i.e., it only accepts one canonical edge type. +- **target_etype**: The list of canonical edge types that will be added as training targets in edge classification/regression tasks, for example ``--train-etype query,clicks,asin`` or ``--train-etype query,clicks,asin query,search,asin``. A canonical edge type should be formatted as `src_node_type,relation_type,dst_node_type`. Currently, GraphStorm only supports single task edge classification/regression, i.e., it only accepts one canonical edge type. - Yaml: ``target_etype:`` | ``- query,clicks,asin`` - Argument: ``--target-etype query,clicks,asin`` - - Default value: This parameter must be provided by user. + - Default value: For heterogeneous input graph, this parameter must be provided by the user. If not provided, GraphStorm will assume the input graph is a homogeneous graph and set ``target_etype`` to ("_N", "_E", "_N"). - **remove_target_edge_type**: When set to true, GraphStorm removes target_etype in message passing, i.e., any edge with target_etype will not be sampled during training and inference. - Yaml: ``remove_target_edge_type: false`` diff --git a/python/graphstorm/config/argument.py b/python/graphstorm/config/argument.py index a5d58c88ce..bbada03934 100644 --- a/python/graphstorm/config/argument.py +++ b/python/graphstorm/config/argument.py @@ -25,6 +25,7 @@ import yaml import torch as th import torch.nn.functional as F +from dgl.distributed.constants import DEFAULT_NTYPE, DEFAULT_ETYPE from .config import BUILTIN_GNN_ENCODER from .config import BUILTIN_ENCODER @@ -1573,9 +1574,12 @@ def target_ntype(self): """ The node type for prediction """ # pylint: disable=no-member - assert hasattr(self, "_target_ntype"), \ - "Must provide the target ntype through target_ntype" - return self._target_ntype + if hasattr(self, "_target_ntype"): + return self._target_ntype + else: + logging.warning("There is not target ntype provided, " + "will treat the input graph as a homogeneous graph") + return DEFAULT_NTYPE @property def eval_target_ntype(self): @@ -1648,8 +1652,10 @@ def target_etype(self): classification/regression. Support multiple tasks when needed. """ # pylint: disable=no-member - assert hasattr(self, "_target_etype"), \ - "Edge classification task needs a target etype" + if not hasattr(self, "_target_etype"): + logging.warning("There is not target etype provided, " + "will treat the input graph as a homogeneous graph") + return [DEFAULT_ETYPE] assert isinstance(self._target_etype, list), \ "target_etype must be a list in format: " \ "[\"query,clicks,asin\", \"query,search,asin\"]." diff --git a/python/graphstorm/dataloading/dataset.py b/python/graphstorm/dataloading/dataset.py index 90fd4ee67b..227ceff8bc 100644 --- a/python/graphstorm/dataloading/dataset.py +++ b/python/graphstorm/dataloading/dataset.py @@ -23,6 +23,7 @@ import torch as th import dgl +from dgl.distributed.constants import DEFAULT_NTYPE, DEFAULT_ETYPE from torch.utils.data import Dataset import pandas as pd @@ -554,6 +555,15 @@ def __init__(self, graph_name, part_config, train_etypes, eval_etypes=None, lm_feat_ntypes=lm_feat_ntypes, lm_feat_etypes=lm_feat_etypes) + if self._train_etypes == [DEFAULT_ETYPE]: + # DGL Graph edge type is not canonical. It is just list[str]. + assert self._g.ntypes == [DEFAULT_NTYPE] and \ + self._g.etypes == [DEFAULT_ETYPE[1]], \ + f"It is required to be a homogeneous graph when target_etype is not provided " \ + f"or is set to {DEFAULT_ETYPE} on edge tasks, expect node type " \ + f"to be {[DEFAULT_NTYPE]} and edge type to be {[DEFAULT_ETYPE[1]]}, " \ + f"but get {self._g.ntypes} and {self._g.etypes}" + def prepare_data(self, g): """ Prepare the training, validation and testing edge set. @@ -731,6 +741,14 @@ def __init__(self, graph_name, part_config, eval_etypes, decoder_edge_feat, lm_feat_ntypes=lm_feat_ntypes, lm_feat_etypes=lm_feat_etypes) + if self._eval_etypes == [DEFAULT_ETYPE]: + # DGL Graph edge type is not canonical. It is just list[str]. + assert self._g.ntypes == [DEFAULT_NTYPE] and \ + self._g.etypes == [DEFAULT_ETYPE[1]], \ + f"It is required to be a homogeneous graph when target_etype is not provided " \ + f"or is set to {DEFAULT_ETYPE} on edge tasks, expect node type " \ + f"to be {[DEFAULT_NTYPE]} and edge type to be {[DEFAULT_ETYPE[1]]}, " \ + f"but get {self._g.ntypes} and {self._g.etypes}" def prepare_data(self, g): """ Prepare the testing edge set if any @@ -916,7 +934,6 @@ def __init__(self, graph_name, part_config, train_ntypes, eval_ntypes=None, assert isinstance(train_ntypes, list), \ "prediction ntypes for training has to be a string or a list of strings." self._train_ntypes = train_ntypes - if eval_ntypes is not None: if isinstance(eval_ntypes, str): eval_ntypes = [eval_ntypes] @@ -932,6 +949,14 @@ def __init__(self, graph_name, part_config, train_ntypes, eval_ntypes=None, edge_feat_field=edge_feat_field, lm_feat_ntypes=lm_feat_ntypes, lm_feat_etypes=lm_feat_etypes) + if self._train_ntypes == [DEFAULT_NTYPE]: + # DGL Graph edge type is not canonical. It is just list[str]. + assert self._g.ntypes == [DEFAULT_NTYPE] and \ + self._g.etypes == [DEFAULT_ETYPE[1]], \ + f"It is required to be a homogeneous graph when target_ntype is not provided " \ + f"or is set to {DEFAULT_NTYPE} on node tasks, expect node type " \ + f"to be {[DEFAULT_NTYPE]} and edge type to be {[DEFAULT_ETYPE[1]]}, " \ + f"but get {self._g.ntypes} and {self._g.etypes}" def prepare_data(self, g): pb = g.get_partition_book() @@ -1072,6 +1097,15 @@ def __init__(self, graph_name, part_config, eval_ntypes, lm_feat_ntypes=lm_feat_ntypes, lm_feat_etypes=lm_feat_etypes) + if self._eval_ntypes == [DEFAULT_NTYPE]: + # DGL Graph edge type is not canonical. It is just list[str]. + assert self._g.ntypes == [DEFAULT_NTYPE] and \ + self._g.etypes == [DEFAULT_ETYPE[1]], \ + f"It is required to be a homogeneous graph when target_ntype is not provided " \ + f"or is set to {DEFAULT_NTYPE} on node tasks, expect node type " \ + f"to be {[DEFAULT_NTYPE]} and edge type to be {[DEFAULT_ETYPE[1]]}, " \ + f"but get {self._g.ntypes} and {self._g.etypes}" + def prepare_data(self, g): """ Prepare the testing node set if any diff --git a/tests/end2end-tests/data_process/homogeneous_test.sh b/tests/end2end-tests/data_process/homogeneous_test.sh index dd3f55907d..ea2cc197e3 100644 --- a/tests/end2end-tests/data_process/homogeneous_test.sh +++ b/tests/end2end-tests/data_process/homogeneous_test.sh @@ -45,6 +45,25 @@ echo "********* Test Node Classification on GConstruct Homogeneous Graph with re python3 -m graphstorm.run.gs_node_classification --workspace $GS_HOME/training_scripts/gsgnn_np/ --num-trainers $NUM_TRAINERS --num-servers 1 --num-samplers 0 --part-config /tmp/movielen_100k_train_val_1p_4t_homogeneous_rev/movie-lens-100k.json --ip-config ip_list.txt --ssh-port 2222 --cf ml_nc.yaml --target-ntype _N error_and_exit $? -echo "********* Test Edge Classification on GConstruct Homogeneous Graph with reverse edge ********" +echo "********* Test Edge Classification on GConstruct Homogeneous Graph with reverse edge********" python3 -m graphstorm.run.gs_edge_classification --workspace $GS_HOME/training_scripts/gsgnn_ep/ --num-trainers $NUM_TRAINERS --num-servers 1 --num-samplers 0 --part-config /tmp/movielen_100k_train_val_1p_4t_homogeneous_rev/movie-lens-100k.json --ip-config ip_list.txt --ssh-port 2222 --cf ml_ec.yaml --target-etype _N,_E,_N -error_and_exit $? \ No newline at end of file +error_and_exit $? + +echo "********* Test Node Classification with homogeneous graph optimization********" +python3 -m graphstorm.run.gs_node_classification --workspace $GS_HOME/training_scripts/gsgnn_np/ --num-trainers $NUM_TRAINERS --num-servers 1 --num-samplers 0 --part-config /tmp/movielen_100k_train_val_1p_4t_homogeneous_rev/movie-lens-100k.json --ip-config ip_list.txt --ssh-port 2222 --cf ml_nc_homogeneous.yaml --save-model-path /tmp/homogeneous_node_model +error_and_exit $? + +echo "********* Test Node Classification with homogeneous graph optimization doing inference********" +python3 -m graphstorm.run.gs_node_classification --inference --workspace $GS_HOME/training_scripts/gsgnn_np/ --num-trainers $NUM_TRAINERS --num-servers 1 --num-samplers 0 --part-config /tmp/movielen_100k_train_val_1p_4t_homogeneous_rev/movie-lens-100k.json --ip-config ip_list.txt --ssh-port 2222 --cf ml_nc_homogeneous.yaml --restore-model-path /tmp/homogeneous_node_model/epoch-2 +error_and_exit $? + +echo "********* Test Edge Classification with homogeneous graph optimization********" +python3 -m graphstorm.run.gs_edge_classification --workspace $GS_HOME/training_scripts/gsgnn_ep/ --num-trainers $NUM_TRAINERS --num-servers 1 --num-samplers 0 --part-config /tmp/movielen_100k_train_val_1p_4t_homogeneous_rev/movie-lens-100k.json --ip-config ip_list.txt --ssh-port 2222 --cf ml_ec_homogeneous.yaml --save-model-path /tmp/homogeneous_edge_model +error_and_exit $? + +echo "********* Test Edge Classification with homogeneous graph optimization doing inference********" +python3 -m graphstorm.run.gs_edge_classification --inference --workspace $GS_HOME/training_scripts/gsgnn_ep/ --num-trainers $NUM_TRAINERS --num-servers 1 --num-samplers 0 --part-config /tmp/movielen_100k_train_val_1p_4t_homogeneous_rev/movie-lens-100k.json --ip-config ip_list.txt --ssh-port 2222 --cf ml_ec_homogeneous.yaml --restore-model-path /tmp/homogeneous_edge_model/epoch-2 +error_and_exit $? + +rm -rf /tmp/homogeneous_node_model +rm -rf /tmp/homogeneous_edge_model diff --git a/tests/unit-tests/data_utils.py b/tests/unit-tests/data_utils.py index e6e9f094b8..8ce83c7779 100644 --- a/tests/unit-tests/data_utils.py +++ b/tests/unit-tests/data_utils.py @@ -343,6 +343,92 @@ def generate_dummy_homo_graph(size='tiny', gen_mask=True): return hetero_graph +def generate_dummy_homogeneous_failure_graph(size='tiny', gen_mask=True, type='node'): + """ + generate a dummy homogeneous graph for failure case. + + In a homogeneous graph, the correct node type is defined as ["_N"], and the correct edge type is [("_N", "_E", "_N")]. + Any deviation from this specification implies an invalid input for a homogeneous graph. This function is designed + to create test cases that intentionally fail for homogeneous graph inputs. For type="node", it will produce a graph + with the correct node type ["_N"] but with an altered edge type set as [("_N", "_E", "_N"), ("_N", "fake_E", "_N")]. + Conversely, for type="edge", the function generates a graph with an incorrect node type ["_N", "fake_N"] while + maintaining the correct edge type [("_N", "_E", "_N")]. The unit test is expected to identify and flag errors + in both these scenarios. + + Parameters + ---------- + size: the size of dummy graph data, could be one of tiny, small, medium, large, and largest + type: task type to generate failure case + + :return: + hg: a homogeneous graph in one node type and one edge type. + """ + size_dict = { + 'tiny': 1e+2, + 'small': 1e+4, + 'medium': 1e+6, + 'large': 1e+8, + 'largest': 1e+10 + } + + data_size = int(size_dict[size]) + + if type == 'node': + ntype = "_N" + etype = ("_N", "fake_E", "_N") + + num_nodes_dict = { + ntype: data_size, + } + else: + ntype = "_N" + etype = ("_N", "_E", "_N") + num_nodes_dict = { + ntype: data_size, + "fake_N": data_size + } + + edges = { + etype: (th.randint(data_size, (2 * data_size,)), + th.randint(data_size, (2 * data_size,))) + } + + hetero_graph = dgl.heterograph(edges, num_nodes_dict=num_nodes_dict) + + # set node and edge features + node_feat = {ntype: th.randn(data_size, 2)} + + edge_feat = {etype: th.randn(2 * data_size, 2)} + + hetero_graph.nodes[ntype].data['feat'] = node_feat[ntype] + hetero_graph.nodes[ntype].data['label'] = th.randint(10, (hetero_graph.number_of_nodes(ntype), )) + + hetero_graph.edges[etype].data['feat'] = edge_feat[etype] + hetero_graph.edges[etype].data['label'] = th.randint(10, (hetero_graph.number_of_edges(etype), )) + + # set train/val/test masks for nodes and edges + if gen_mask: + target_ntype = [ntype] + target_etype = [etype] + + node_train_mask = generate_mask([0,1], data_size) + node_val_mask = generate_mask([2,3], data_size) + node_test_mask = generate_mask([4,5], data_size) + + edge_train_mask = generate_mask([0,1], 2 * data_size) + edge_val_mask = generate_mask([2,3], 2 * data_size) + edge_test_mask = generate_mask([4,5], 2 * data_size) + + hetero_graph.nodes[target_ntype[0]].data['train_mask'] = node_train_mask + hetero_graph.nodes[target_ntype[0]].data['val_mask'] = node_val_mask + hetero_graph.nodes[target_ntype[0]].data['test_mask'] = node_test_mask + + hetero_graph.edges[target_etype[0]].data['train_mask'] = edge_train_mask + hetero_graph.edges[target_etype[0]].data['val_mask'] = edge_val_mask + hetero_graph.edges[target_etype[0]].data['test_mask'] = edge_test_mask + + return hetero_graph + def partion_and_load_distributed_graph(hetero_graph, dirname, graph_name='dummy'): """ @@ -431,6 +517,28 @@ def generate_dummy_dist_graph_multi_target_ntypes(dirname, size='tiny', graph_na return partion_and_load_distributed_graph(hetero_graph=hetero_graph, dirname=dirname, graph_name=graph_name) + +def generate_dummy_dist_graph_homogeneous_failure_graph(dirname, size='tiny', graph_name='dummy', + gen_mask=True, type='node'): + """ + Generate a dummy DGL distributed graph with the given size + Parameters + ---------- + dirname : the directory where the graph will be partitioned and stored. + size: the size of dummy graph data, could be one of tiny, small, medium, large, and largest + graph_name: string as a name + type: task type to generate failure case + + Returns + ------- + dist_graph: a DGL distributed graph + part_config : the path of the partition configuration file. + type: + """ + hetero_graph = generate_dummy_homogeneous_failure_graph(size=size, gen_mask=gen_mask, type=type) + return partion_and_load_distributed_graph(hetero_graph=hetero_graph, dirname=dirname, + graph_name=graph_name) + def load_lm_graph(part_config): with open(part_config) as f: part_metadata = json.load(f) diff --git a/tests/unit-tests/test_config.py b/tests/unit-tests/test_config.py index 48cc4c2146..0b97aa4dc0 100644 --- a/tests/unit-tests/test_config.py +++ b/tests/unit-tests/test_config.py @@ -21,6 +21,7 @@ import math import tempfile from argparse import Namespace +from dgl.distributed.constants import DEFAULT_NTYPE, DEFAULT_ETYPE import dgl import torch as th @@ -613,7 +614,7 @@ def test_node_class_info(): create_node_class_config(Path(tmpdirname), 'node_class_test') args = Namespace(yaml_config_file=os.path.join(Path(tmpdirname), 'node_class_test_default.yaml'), local_rank=0) config = GSConfig(args) - check_failure(config, "target_ntype") + assert config.target_ntype == DEFAULT_NTYPE check_failure(config, "label_field") assert config.multilabel == False assert config.multilabel_weights == None @@ -748,7 +749,7 @@ def test_node_regress_info(): create_node_regress_config(Path(tmpdirname), 'node_regress_test') args = Namespace(yaml_config_file=os.path.join(Path(tmpdirname), 'node_regress_test_default.yaml'), local_rank=0) config = GSConfig(args) - check_failure(config, "target_ntype") + assert config.target_ntype == DEFAULT_NTYPE check_failure(config, "label_field") assert len(config.eval_metric) == 1 assert config.eval_metric[0] == "rmse" @@ -840,7 +841,7 @@ def test_edge_class_info(): create_edge_class_config(Path(tmpdirname), 'edge_class_test') args = Namespace(yaml_config_file=os.path.join(Path(tmpdirname), 'edge_class_test_default.yaml'), local_rank=0) config = GSConfig(args) - check_failure(config, "target_etype") + assert config.target_etype == [DEFAULT_ETYPE] assert config.decoder_type == "DenseBiDecoder" assert config.num_decoder_basis == 2 assert config.remove_target_edge_type == True diff --git a/tests/unit-tests/test_dataloading.py b/tests/unit-tests/test_dataloading.py index 18613e2314..0c688d586e 100644 --- a/tests/unit-tests/test_dataloading.py +++ b/tests/unit-tests/test_dataloading.py @@ -29,6 +29,7 @@ from data_utils import ( generate_dummy_dist_graph, generate_dummy_dist_graph_reconstruct, + generate_dummy_dist_graph_homogeneous_failure_graph, create_distill_data, ) @@ -1354,6 +1355,106 @@ def test_inbatch_joint_neg_sampler(num_pos, num_neg): assert_equal(in_batch_dst[i*(num_pos-1):(i+1)*(num_pos-1)].numpy(), np.arange(num_pos)[tmp_idx]) +def test_GSgnnTrainData_homogeneous(): + # initialize the torch distributed environment + th.distributed.init_process_group(backend='gloo', + init_method='tcp://127.0.0.1:23456', + rank=0, + world_size=1) + tr_ntypes = ["_N"] + va_ntypes = ["_N"] + + with tempfile.TemporaryDirectory() as tmpdirname: + # generate the test dummy homogeneous distributed graph and + # test if it is possible to create GSgnnNodeTrainData on homogeneous graph + dist_graph, part_config = generate_dummy_dist_graph(graph_name='dummy', + dirname=tmpdirname, + is_homo=True) + _ = GSgnnNodeTrainData(graph_name='dummy', part_config=part_config, + train_ntypes=tr_ntypes, eval_ntypes=va_ntypes, + label_field='label') + + # generate the test dummy distributed graph with "_N" node type. As it is expected to be + # a homogeneous graph with "_N" as node type and ("_N", "_E", "_N") as edge type. + # It should throw an error to clarify that. + dist_graph, part_config = generate_dummy_dist_graph_homogeneous_failure_graph(graph_name='dummy', + dirname=tmpdirname) + try: + _ = GSgnnNodeTrainData(graph_name='dummy', part_config=part_config, + train_ntypes=tr_ntypes, eval_ntypes=va_ntypes, + label_field='label') + assert False, "expected Error raised for non-homogeneous graph input" + except AssertionError as _: + pass + + # generate the test dummy homogeneous distributed graph and + # test if it is possible to create GSgnnNodeInferData on homogeneous graph + dist_graph, part_config = generate_dummy_dist_graph(graph_name='dummy', + dirname=tmpdirname, + is_homo=True) + _ = GSgnnNodeInferData(graph_name='dummy', part_config=part_config, + eval_ntypes=va_ntypes) + + # generate the test dummy distributed graph with "_N" node type. As it is expected to be + # a homogeneous graph with "_N" as node type and ("_N", "_E", "_N") as edge type. + # It should throw an error to clarify that. + dist_graph, part_config = generate_dummy_dist_graph_homogeneous_failure_graph(graph_name='dummy', + dirname=tmpdirname) + try: + _ = GSgnnNodeInferData(graph_name='dummy', part_config=part_config, + eval_ntypes=va_ntypes) + assert False, "expected Error raised for non-homogeneous graph input" + except AssertionError as _: + pass + + tr_etypes = [("_N", "_E", "_N")] + va_etypes = [("_N", "_E", "_N")] + + with tempfile.TemporaryDirectory() as tmpdirname: + # generate the test dummy homogeneous distributed graph and + # test if it is possible to create GSgnnEdgeTrainData on homogeneous graph + dist_graph, part_config = generate_dummy_dist_graph(graph_name='dummy', + dirname=os.path.join(tmpdirname, 'dummy'), + is_homo=True) + _ = GSgnnEdgeTrainData(graph_name='dummy', part_config=part_config, + train_etypes=tr_etypes, eval_etypes=va_etypes, + label_field='label') + + # generate the test dummy distributed graph with "_N" node type. As it is expected to be + # a homogeneous graph with "_N" as node type and ("_N", "_E", "_N") as edge type. + # It should throw an error to clarify that. + dist_graph, part_config = generate_dummy_dist_graph_homogeneous_failure_graph(graph_name='dummy', + dirname=os.path.join(tmpdirname, 'dummy')) + try: + _ = GSgnnEdgeTrainData(graph_name='dummy', part_config=part_config, + train_etypes=tr_etypes, eval_etypes=va_etypes, + label_field='label') + assert False, "expected Error raised for non-homogeneous graph input" + except AssertionError as _: + pass + + # generate the test dummy homogeneous distributed graph and + # test if it is possible to create GSgnnEdgeInferData on homogeneous graph + dist_graph, part_config = generate_dummy_dist_graph(graph_name='dummy', + dirname=os.path.join(tmpdirname, 'dummy'), + is_homo=True) + _ = GSgnnEdgeInferData(graph_name='dummy', part_config=part_config, + eval_etypes=va_etypes) + + # generate the test dummy distributed graph with "_N" node type. As it is expected to be + # a homogeneous graph with "_N" as node type and ("_N", "_E", "_N") as edge type. + # It should throw an error to clarify that. + dist_graph, part_config = generate_dummy_dist_graph_homogeneous_failure_graph(graph_name='dummy', + dirname=os.path.join(tmpdirname, 'dummy')) + try: + _ = GSgnnEdgeInferData(graph_name='dummy', part_config=part_config, + eval_etypes=va_etypes) + assert False, "expected Error raised for non-homogeneous graph input" + except AssertionError as _: + pass + + # after test pass, destroy all process group + th.distributed.destroy_process_group() if __name__ == '__main__': test_inbatch_joint_neg_sampler(10, 20) @@ -1387,3 +1488,5 @@ def test_inbatch_joint_neg_sampler(num_pos, num_neg): test_DistillDistributedFileSampler(num_files=7, is_train=True, \ infinite=False, shuffle=True) test_DistillDataloaderGenerator("gloo", 7, True) + + test_GSgnnTrainData_homogeneous() diff --git a/training_scripts/gsgnn_ep/ml_ec_homogeneous.yaml b/training_scripts/gsgnn_ep/ml_ec_homogeneous.yaml new file mode 100644 index 0000000000..7e519f9276 --- /dev/null +++ b/training_scripts/gsgnn_ep/ml_ec_homogeneous.yaml @@ -0,0 +1,38 @@ +--- +version: 1.0 +gsf: + basic: + backend: gloo + verbose: false + save_perf_results_path: null + gnn: + model_encoder_type: rgcn + fanout: "4" + num_layers: 1 + hidden_size: 128 + use_mini_batch_infer: true + input: + restore_model_path: null + output: + save_model_path: null + save_embed_path: null + hyperparam: + dropout: 0. + lr: 0.001 + lm_tune_lr: 0.0001 + num_epochs: 3 + batch_size: 64 + wd_l2norm: 0 + no_validation: false + eval_frequency: 1000 + rgcn: + num_bases: -1 + use_self_loop: true + sparse_optimizer_lr: 1e-2 + use_node_embeddings: false + edge_classification: + label_field: "rate" + multilabel: false + num_classes: 6 + num_decoder_basis: 32 + exclude_training_targets: false diff --git a/training_scripts/gsgnn_np/ml_nc_homogeneous.yaml b/training_scripts/gsgnn_np/ml_nc_homogeneous.yaml new file mode 100644 index 0000000000..7f12b9e011 --- /dev/null +++ b/training_scripts/gsgnn_np/ml_nc_homogeneous.yaml @@ -0,0 +1,35 @@ +--- +version: 1.0 +gsf: + basic: + backend: gloo + verbose: false + save_perf_results_path: null + gnn: + model_encoder_type: rgcn + fanout: "4" + num_layers: 1 + hidden_size: 128 + use_mini_batch_infer: true + input: + restore_model_path: null + output: + save_model_path: null + save_embed_path: null + hyperparam: + dropout: 0. + lr: 0.001 + lm_tune_lr: 0.0001 + num_epochs: 3 + batch_size: 128 + wd_l2norm: 0 + no_validation: false + rgcn: + num_bases: -1 + use_self_loop: true + sparse_optimizer_lr: 1e-2 + use_node_embeddings: false + node_classification: + label_field: "label" + multilabel: false + num_classes: 19 From 4a0b462e0ff584d6eec269e78c2f573d297c278d Mon Sep 17 00:00:00 2001 From: Da Zheng Date: Tue, 26 Dec 2023 16:14:38 +0800 Subject: [PATCH 07/14] Fix a bug in gconstruct when saving intermediate data on disks (#694) *Description of changes:* Numpy has a bug when storing a list of objects in numpy memmap array. We should avoid saving such a list to a memmap array. By submitting this pull request, I confirm that you can use, modify, copy, and redistribute this contribution, under the terms of your choice. --- python/graphstorm/gconstruct/utils.py | 10 +++++++++- tests/unit-tests/gconstruct/test_gconstruct_utils.py | 4 +++- 2 files changed, 12 insertions(+), 2 deletions(-) diff --git a/python/graphstorm/gconstruct/utils.py b/python/graphstorm/gconstruct/utils.py index 7ed99dcc03..4b84e96ff6 100644 --- a/python/graphstorm/gconstruct/utils.py +++ b/python/graphstorm/gconstruct/utils.py @@ -38,11 +38,19 @@ PICKLE_CROSS_PROCESS_STORAGE = "pickle" EXT_MEMORY_STORAGE = "ext_memory" +def _is_numeric(arr): + """ Check if the input array has the numeric data type. + """ + return np.issubdtype(arr.dtype, np.number) or arr.dtype == bool + def _to_ext_memory(name, data, path): if isinstance(data, np.ndarray): assert name is not None path = os.path.join(path, f"{name}.npy") - if len(data) > 0: + # We only save a data array with numeric or boolean values to the disk. + # This avoids the problem of saving an array of objects to disks. + # Note: There is a bug in Numpy when saving an array of objects to disks. + if len(data) > 0 and _is_numeric(data): logging.debug("save data %s in %s.", name, path) data = convert_to_ext_mem_numpy(path, data) # We need to pass the array to another process. We don't want it diff --git a/tests/unit-tests/gconstruct/test_gconstruct_utils.py b/tests/unit-tests/gconstruct/test_gconstruct_utils.py index b2472058b8..a2e3011f6a 100644 --- a/tests/unit-tests/gconstruct/test_gconstruct_utils.py +++ b/tests/unit-tests/gconstruct/test_gconstruct_utils.py @@ -182,9 +182,10 @@ def test_ext_mem_array(): data2 = np.random.uniform(size=(1000,)).astype(np.float32) data3 = np.random.uniform(size=(1000, 10)).astype(np.float32) data4 = np.random.uniform(size=(1000,)).astype(np.float32) + data5 = np.random.uniform(size=(1000,)).astype(str) arr_dict = { "test1": (data1, data2), - "test2": [data3, data4], + "test2": [data3, data4, data5], } arr_dict1 = _to_ext_memory(None, arr_dict, tmpdirname) assert isinstance(arr_dict1, dict) @@ -196,6 +197,7 @@ def test_ext_mem_array(): assert isinstance(arr_dict1["test1"][1], ExtNumpyWrapper) assert isinstance(arr_dict1["test2"][0], ExtNumpyWrapper) assert isinstance(arr_dict1["test2"][1], ExtNumpyWrapper) + assert isinstance(arr_dict1["test2"][2], np.ndarray) assert np.all(arr_dict1["test1"][0].to_numpy() == data1) assert np.all(arr_dict1["test1"][1].to_numpy() == data2) assert np.all(arr_dict1["test2"][0].to_numpy() == data3) From 2ff241c666d11e92a647bc2aae01607c9b4eca10 Mon Sep 17 00:00:00 2001 From: Bran Zhu Date: Wed, 27 Dec 2023 13:47:04 +0800 Subject: [PATCH 08/14] PEFT LLM training example (#673) *Issue #, if available:* *Description of changes:* Added PEFT LLM training using customized model interface By submitting this pull request, I confirm that you can use, modify, copy, and redistribute this contribution, under the terms of your choice. --------- Co-authored-by: Da Zheng --- examples/peft_llm_gnn/README.md | 51 ++++++ examples/peft_llm_gnn/llm_gnn_model.py | 163 ++++++++++++++++++ examples/peft_llm_gnn/main_nc.py | 136 +++++++++++++++ .../peft_llm_gnn/nc_config_Video_Games.yaml | 45 +++++ .../peft_llm_gnn/preprocess_amazon_review.py | 149 ++++++++++++++++ python/graphstorm/eval/utils.py | 2 +- 6 files changed, 545 insertions(+), 1 deletion(-) create mode 100644 examples/peft_llm_gnn/README.md create mode 100644 examples/peft_llm_gnn/llm_gnn_model.py create mode 100644 examples/peft_llm_gnn/main_nc.py create mode 100644 examples/peft_llm_gnn/nc_config_Video_Games.yaml create mode 100644 examples/peft_llm_gnn/preprocess_amazon_review.py diff --git a/examples/peft_llm_gnn/README.md b/examples/peft_llm_gnn/README.md new file mode 100644 index 0000000000..fce61111b5 --- /dev/null +++ b/examples/peft_llm_gnn/README.md @@ -0,0 +1,51 @@ +This folder contains the data processing script to process the raw Amazon Review dataset +downloaded from https://cseweb.ucsd.edu/~jmcauley/datasets/amazon_v2/. We use domain Video +Games https://datarepo.eng.ucsd.edu/mcauley_group/data/amazon_v2/metaFiles2/meta_Video_Games.json.gz +and put it under raw_data as an example. +``` +wget https://datarepo.eng.ucsd.edu/mcauley_group/data/amazon_v2/metaFiles2/meta_Video_Games.json.gz \ +-P raw_data/ +``` + +To construct a graph for GNN training in GraphStorm, we need to preprocess the data +and convert them to the format supported by GraphStorm on a CPU machine. +``` +python preprocess_amazon_review.py +``` + +Once the data are processed, run the following command to construct a graph +for PEFT LLM-GNNs in GraphStorm for node classification on level-3 product type. +The command takes `AR_Video_Games.json` that specifies the input data for graph +construction, constructs the graph, and saves the parition to `amazon_review`. + +``` +python -m graphstorm.gconstruct.construct_graph \ + --conf-file AR_Video_Games.json \ + --output-dir datasets/amazon_review_nc_Video_Games/ \ + --graph-name amazon_review \ + --num-processes 16 --num-parts 1 \ + --skip-nonexist-edges --add-reverse-edges + +``` + +## Train LLM-GNN model to predict product type of items +The command below runs parameter-efficient fine-tuning of LLM-GNNs on node +classification via `main_nc.py`. +``` +WORKSPACE=$PWD +dataset=amazon_review +domain=Video_Games + +python3 -m graphstorm.run.launch \ + --workspace "$WORKSPACE" \ + --part-config datasets/amazon_review_nc_"$domain"/amazon_review.json \ + --ip-config ./ip_list.txt \ + --num-trainers 8 \ + --num-servers 1 \ + --num-samplers 0 \ + --ssh-port 22 \ + main_nc.py \ + --cf ./nc_config_"$domain".yaml \ + --save-model-path "$WORKSPACE"/model/nc/"$domain"/ \ + --save-prediction-path "$WORKSPACE"/results/nc/"$domain"/ +``` \ No newline at end of file diff --git a/examples/peft_llm_gnn/llm_gnn_model.py b/examples/peft_llm_gnn/llm_gnn_model.py new file mode 100644 index 0000000000..87ffe027e7 --- /dev/null +++ b/examples/peft_llm_gnn/llm_gnn_model.py @@ -0,0 +1,163 @@ +""" + Copyright 2023 Contributors + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. + + LLM-GNNs implementation. +""" +import dgl +import graphstorm as gs +import torch +import torch.nn as nn +import torch.nn.functional as F +import os +import logging + +from graphstorm import model as gsmodel +from graphstorm.model.lm_model import TOKEN_IDX, ATT_MASK_IDX + +from peft import LoraConfig, get_peft_model +from transformers import ( + AutoConfig, + AutoModel, +) + +def get_lm_node_feats(g, lm_feat_names, lm_ntypes): + """ Collect language model related node features + + Parameters + ---------- + g: graph + Graph data. + lm_feat_names: list of str + A list of llm features + lm_ntypes: list of str + A list of node types. + + Return + ------ + A dict of dict of distributed tensor. + {Node type: {Feature name: Feature stored as distributed tensor}} + """ + lm_feats = {} + for ntype in lm_ntypes: + lm_feats[ntype] = {} + for lm_fname in lm_feat_names: + if lm_fname in g.nodes[ntype].data: + lm_feats[ntype][lm_fname] = g.nodes[ntype].data[lm_fname] + + return lm_feats + + +class LLMGraphModel(gsmodel.GSgnnNodeModelBase): + """ LLMGNN Model Class + + Parameters + ---------- + g : dgl DistGraph + Input DistDGL graph + node_lm_configs : list + language model config for each node type + h_dim : int + hidden dimension of GNN encoder + out_dim : int + Output dimension of the model, e.g. number of classes. + num_layers : int + Number of GNN encoder layers. + target_ntype : str + The node type for prediction. + use_norm: boolean, optional + If use layer normalization or not. Default: True + alpha_l2norm: float, optional + The alpha for L2 normalization. Default: 0 + lr : float, optional + Normalization Method. Default: 0.001 + """ + def __init__(self, g, node_lm_configs, h_dim, out_dim, num_layers, + target_ntype, # the node type to be predict + use_norm = True, # use normalization or not, default is True + alpha_l2norm = 0, + lr = 0.001): + super(LLMGraphModel, self).__init__() + self.num_layers = num_layers + self.target_ntype=target_ntype + self.alpha_l2norm = alpha_l2norm + self.lr = lr + # assume only one LLM is used + model_id = node_lm_configs[0]["model_name"] + self.config = AutoConfig.from_pretrained(model_id) + base_model = AutoModel.from_pretrained( + model_id,config=self.config + ) + peft_config = LoraConfig(inference_mode=False, r=8, lora_alpha=16, lora_dropout=0.1) + self.llm = get_peft_model(base_model, peft_config) + lm_feat_names = [TOKEN_IDX, ATT_MASK_IDX] + self._lm_node_feats = {} + for lm_config in node_lm_configs: + # A list of node types sharing the same lm model + lm_ntypes = lm_config["node_types"] + if lm_config["model_name"] != model_id: + logging.warning("Multiple LLM model names are found in the config. However, the example only supports one LLM.") + lm_node_feats = get_lm_node_feats(g, lm_feat_names, lm_ntypes) + for ntype, feats in lm_node_feats.items(): + assert ntype not in self._lm_node_feats, \ + f"More than one BERT model runs on Node {ntype}." + self._lm_node_feats[ntype] = feats + self.out = nn.Linear(self.config.hidden_size, out_dim) + self._loss_fn = gsmodel.ClassifyLossFunc(multilabel=False) + #TODO (@qzhuamzn): add initialization for gnn encoding + + def forward(self, blocks, node_feats, edge_feats, labels, input_nodes): + # TODO (qzhuamzn): use GNNs to generate graph tokens + h = {} + output_nodes = blocks[-1].dstdata[dgl.NID] + + input_ids = self._lm_node_feats[self.target_ntype][TOKEN_IDX][output_nodes].to(self.llm.device) + attention_mask = self._lm_node_feats[self.target_ntype][ATT_MASK_IDX][output_nodes].to(self.llm.device) + # TODO (qzhuamzn): modify input_ids into input_embeds=[graph_tokens, input_embeds] to support GPEFT + model_output = self.llm(input_ids=input_ids, attention_mask=attention_mask) + # We use the last token in order to do the classification, as other causal models + h = self.out(model_output.last_hidden_state[:,-1,:]) + loss = self._loss_fn(h, labels[self.target_ntype]) + + return loss + + + def predict(self, blocks, node_feats, _, input_nodes, return_proba): + # TODO (qzhuamzn): use h as gnn token embeddings + h = {} + output_nodes = blocks[-1].dstdata[dgl.NID] + input_ids = self._lm_node_feats[self.target_ntype][TOKEN_IDX][output_nodes].to(self.llm.device) + attention_mask = self._lm_node_feats[self.target_ntype][ATT_MASK_IDX][output_nodes].to(self.llm.device) + model_output = self.llm(input_ids=input_ids, attention_mask=attention_mask) + logits = self.out(model_output.last_hidden_state[:,-1,:]) + if return_proba: + return logits.argmax(dim=-1), torch.softmax(logits, 1) + else: + return logits.argmax(dim=-1), logits + + def create_optimizer(self): + # Here we assume there are no sparse embeddings. + pytorch_total_params = sum(p.numel() for p in self.parameters() if p.requires_grad) + logging.debug(f"Num of trainable params: {pytorch_total_params}") + + return torch.optim.Adam(self.parameters(), lr=self.lr) + + def restore_model(self, restore_model_path): + self.llm = AutoModel.from_pretrained(restore_model_path, config=self.config) + + def save_model(self, model_path): + os.makedirs(model_path, exist_ok=True) + os.chmod(model_path, 0o767) + if gs.get_rank() == 0: + self.llm.save_pretrained(model_path) diff --git a/examples/peft_llm_gnn/main_nc.py b/examples/peft_llm_gnn/main_nc.py new file mode 100644 index 0000000000..ae45ffeb1f --- /dev/null +++ b/examples/peft_llm_gnn/main_nc.py @@ -0,0 +1,136 @@ +import torch as th +import graphstorm as gs +from graphstorm.config import get_argument_parser +from graphstorm.config import GSConfig +from graphstorm.dataloading import GSgnnNodeDataLoader +from graphstorm.eval import GSgnnAccEvaluator +from graphstorm.dataloading import GSgnnNodeTrainData +from graphstorm.utils import setup_device +from graphstorm.inference import GSgnnNodePredictionInferrer +from graphstorm.trainer import GSgnnNodePredictionTrainer +from graphstorm.tracker import GSSageMakerTaskTracker +from llm_gnn_model import LLMGraphModel + +def main(config_args): + """ main function + """ + config = GSConfig(config_args) + gs.initialize(ip_config=config.ip_config, backend=config.backend) + device = setup_device(config.local_rank) + # Define the training dataset + train_data = GSgnnNodeTrainData( + config.graph_name, + config.part_config, + train_ntypes=config.target_ntype, + eval_ntypes=config.eval_target_ntype, + label_field=config.label_field, + node_feat_field=config.node_feat_name, + ) + + model = LLMGraphModel( + g=train_data.g, + node_lm_configs=config.node_lm_configs, + h_dim=config.hidden_size, + out_dim=config.num_classes, + num_layers=config.num_layers, + target_ntype=config.target_ntype, + use_norm=True, + alpha_l2norm=config.alpha_l2norm, + lr=config.lr) + + # Create a trainer for NC tasks. + trainer = GSgnnNodePredictionTrainer( + model, + ) + + if config.restore_model_path is not None: + trainer.restore_model( + model_path=config.restore_model_path, + model_layer_to_load=["gnn", "embed"], + ) + + trainer.setup_device(device=device) + + # set evaluator + evaluator = GSgnnAccEvaluator( + config.eval_frequency, + config.eval_metric, + config.multilabel, + config.use_early_stop, + config.early_stop_burnin_rounds, + config.early_stop_rounds, + config.early_stop_strategy, + ) + trainer.setup_evaluator(evaluator) + tracker = GSSageMakerTaskTracker(config) + trainer.setup_task_tracker(tracker) + + # create train loader + dataloader = GSgnnNodeDataLoader( + train_data, + train_data.train_idxs, + fanout=config.fanout, + batch_size=config.batch_size, + device=device, + train_task=True, + ) + + # create val loader + val_dataloader = GSgnnNodeDataLoader( + train_data, + train_data.val_idxs, + fanout=config.fanout, + batch_size=config.eval_batch_size, + device=device, + train_task=False, + ) + + # Start the training process. + model.prepare_input_encoder(train_data) + trainer.fit( + train_loader=dataloader, + num_epochs=config.num_epochs, + val_loader=val_dataloader, + # disable testing during training + test_loader=None, + save_model_path=config.save_model_path, + save_model_frequency=config.save_model_frequency, + use_mini_batch_infer=True + ) + + # Load the best checkpoint + best_model_path = trainer.get_best_model_path() + model.restore_model(best_model_path) + + # Create an inference for a node task. + infer = GSgnnNodePredictionInferrer(model) + infer.setup_device(device=device) + infer.setup_evaluator(evaluator) + infer.setup_task_tracker(tracker) + # Create test loader + test_dataloader = GSgnnNodeDataLoader( + train_data, + train_data.test_idxs, + fanout=config.fanout, + batch_size=config.eval_batch_size, + device=device, + train_task=False, + ) + # Run inference on the inference dataset and save the GNN embeddings in the specified path. + infer.infer(test_dataloader, save_embed_path=None, + save_prediction_path=config.save_prediction_path, + use_mini_batch_infer=True) + + +def generate_parser(): + """Generate an argument parser""" + parser = get_argument_parser() + return parser + +if __name__ == "__main__": + arg_parser = generate_parser() + + args = arg_parser.parse_args() + print(args) + main(args) + diff --git a/examples/peft_llm_gnn/nc_config_Video_Games.yaml b/examples/peft_llm_gnn/nc_config_Video_Games.yaml new file mode 100644 index 0000000000..6a2d0129dd --- /dev/null +++ b/examples/peft_llm_gnn/nc_config_Video_Games.yaml @@ -0,0 +1,45 @@ +lm_model: + node_lm_models: + - + lm_type: opt + model_name: facebook/opt-2.7b + node_types: + - item +gsf: + basic: + backend: nccl + verbose: false + gnn: + model_encoder_type: rgcn + fanout: 10, 5 + hidden_size: 768 + num_layers: 2 + use_mini_batch_infer: true + hyperparam: + batch_size: 4 + dropout: 0.1 + eval_batch_size: 4 + # eval_frequency: 100 + #log_report_frequency: 50 + lr: 0.0001 + num_epochs: 10 + # save_model_frequency: 300 + wd_l2norm: 1.0e-06 + input: + restore_model_path: null + output: + save_model_path: null + save_embed_path: null + topk_model_to_save: 3 + node_classification: + eval_metric: + - accuracy + label_field: label + multilabel: false + node_feat_name: + - item:h + num_classes: 23 + target_ntype: item + rgcn: + num_bases: -1 + use_self_loop: true \ No newline at end of file diff --git a/examples/peft_llm_gnn/preprocess_amazon_review.py b/examples/peft_llm_gnn/preprocess_amazon_review.py new file mode 100644 index 0000000000..2ce3ac2fab --- /dev/null +++ b/examples/peft_llm_gnn/preprocess_amazon_review.py @@ -0,0 +1,149 @@ +import os +import random +import json +import pickle +from copy import deepcopy +from tqdm import tqdm +from collections import defaultdict +import gzip +import dgl +from dgl.data.utils import save_graphs +from IPython import embed +import torch +import pandas as pd +import pyarrow as pa +import pyarrow.parquet as pq +import numpy as np +import math + +# text processing function +def text_process(text): + p_text = ' '.join(text.split('\r\n')) + p_text = ' '.join(p_text.split('\n\r')) + p_text = ' '.join(p_text.split('\n')) + p_text = ' '.join(p_text.split('\t')) + p_text = ' '.join(p_text.split('\rm')) + p_text = ' '.join(p_text.split('\r')) + p_text = ''.join(p_text.split('$')) + p_text = ''.join(p_text.split('*')) + + return p_text + +def tag_clean(cate): + cate = cate.replace('','') + return cate + +def encode_parquet(sub_g, edge_dict, idx2asin, asin_data, field_name): + item, item_text, pt_lvl3 = [], [], [] + for idx, label in zip(sub_g.ndata['_ID'].tolist(), sub_g.ndata['label'].tolist()): + item.append(idx) + item_text.append(asin_data[idx2asin[idx]]) + if label > 0: + pt_lvl3.append(label) + else: + pt_lvl3.append(math.nan) + df = pd.DataFrame({'item': item, 'text': item_text, 'pt_lvl3': np.array(pt_lvl3)}) + table = pa.Table.from_pandas(df) + pq.write_table(table, f'data/amazon_review/{field_name}/item.parquet') + os.makedirs(f'data/amazon_review/{field_name}/', exist_ok=True) + for etype in edge_dict: + u,v = edge_dict[etype] + edge_mask = u < v + df = pd.DataFrame({'src_item': u[edge_mask].tolist(), 'dst_item': v[edge_mask].tolist()}) + table = pa.Table.from_pandas(df) + pq.write_table(table, f'data/amazon_review/{field_name}/{etype}.parquet') + return + +def construct_one_domain(full_file_path, field_name, asin_dict, asin_data, edge_dict, dup_asin): + # read raw data + print(f"Processing {field_name}") + with gzip.open(full_file_path) as f: + raw_data = {} + readin = f.readlines() + for line in tqdm(readin): + tmp = eval(line.strip()) + raw_data[tmp['asin']] = tmp + data = {} + category_dict_list = [{'':0}, {'':0}, {'':0}] + label_freq = defaultdict(int) + label_id = defaultdict(int) + for idd in raw_data: + if 'title' in raw_data[idd]: + data[idd] = raw_data[idd] + if idd not in asin_dict: + asin_dict[idd] = len(asin_dict) + else: + dup_asin.add(idd) + if 'category' in data[idd] and len(data[idd]['category']) > 0: + # breakpoint() + cate = data[idd]['category'] + cate = cate + [''] * (3-len(cate)) + if f'{cate[0]}/{cate[1]}/{cate[2]}' not in category_dict_list[2]: + category_dict_list[2][f'{cate[0]}/{cate[1]}/{cate[2]}'] = len(category_dict_list[2]) + label_freq[f'{cate[0]}/{cate[1]}/{cate[2]}'] += 1 + for k,v in label_freq.items(): + if v > len(raw_data) * 0.01: + label_id[k] = len(label_id) + item_label = [] + for idd in data: + if 'description' in data[idd]: + q_text = text_process(data[idd]['title'] + ' ' + ' '.join(data[idd]['description'])) + else: + q_text = text_process(data[idd]['title']) + asin_data[idd] = q_text + + if 'also_buy' in data[idd]: + for dst_id in data[idd]['also_buy']: + if dst_id in asin_dict: + edge_dict[('item', 'also_buy', 'item')].append([asin_dict[idd], asin_dict[dst_id]]) + + if 'also_view' in data[idd]: + for dst_id in data[idd]['also_view']: + if dst_id in asin_dict: + edge_dict[('item', 'also_view', 'item')].append([asin_dict[idd], asin_dict[dst_id]]) + + if 'category' in data[idd] and len(data[idd]['category']) > 0: + cate = data[idd]['category'] + cate = cate + [''] * (3-len(cate)) + if f'{cate[0]}/{cate[1]}/{cate[2]}' in label_id: + item_label.append(label_id[f'{cate[0]}/{cate[1]}/{cate[2]}']) + else: + item_label.append(-1) + else: + item_label.append(-1) + return item_label, label_id + + + +def construct_graph(directory_path, ood_fields = ['Video_Games, Automotive']): + random.seed(2023) + + cnt = 0 + for dirpath, dirnames, filenames in os.walk(directory_path): + for filename in filenames: + full_file_path = os.path.join(dirpath, filename) + if filename.endswith('.json.gz'): + cnt += 1 + field_name = filename.replace('.json.gz', '').replace('meta_', '') + if field_name in ood_fields: + asin_dict = dict() + dup_asin = set() + asin_data = dict() + edge_dict = { + ('item', 'also_buy', 'item'): [], + ('item', 'also_view', 'item'): [], + } + labels, label_id = construct_one_domain(full_file_path, field_name, asin_dict, asin_data, edge_dict, dup_asin) + labels = torch.LongTensor(labels) + print(f"number of asins:{len(asin_dict)}, number of edges:{[(k,len(v)) for k,v in edge_dict.items()]}") + g = dgl.heterograph(edge_dict, num_nodes_dict={'item': len(asin_dict)} ) + g.nodes['item'].data['label'] = labels + # + sub_g = dgl.edge_subgraph(g, {_etype: g.edges(form='eid', etype=_etype) for _etype in g.etypes}, relabel_nodes=True, store_ids=True) + #sub_g = g + idx2asin = {v:k for k,v in asin_dict.items()} + encode_parquet(sub_g, {_etype: g.edges(form='uv', etype=_etype) for _etype in g.etypes}, idx2asin, asin_data, field_name) + +if __name__ == '__main__': + directory_path = 'raw_data/' + construct_graph(directory_path, ['Video_Games']) \ No newline at end of file diff --git a/python/graphstorm/eval/utils.py b/python/graphstorm/eval/utils.py index 847919171b..5582f1ae63 100644 --- a/python/graphstorm/eval/utils.py +++ b/python/graphstorm/eval/utils.py @@ -304,7 +304,7 @@ def broadcast_data(rank, world_size, data_tensor): if get_backend() == "gloo": device = "cpu" elif get_backend() == "nccl": - assert data_tensor.is_cuda + data_tensor = data_tensor.cuda() device = data_tensor.device else: assert False, f"backend {get_backend()} not supported." From ab51d9a76c22f8e708ae3968a0a17a1106730149 Mon Sep 17 00:00:00 2001 From: Da Zheng Date: Thu, 28 Dec 2023 16:05:54 +0800 Subject: [PATCH 09/14] Fix a bug in loading cached LM embeddings. (#695) *Description of changes:* The current code cannot load cached LM embeddings correctly. By submitting this pull request, I confirm that you can use, modify, copy, and redistribute this contribution, under the terms of your choice. --- python/graphstorm/model/lm_embed.py | 41 ++++++++++++++++++++++------- tests/unit-tests/test_embed.py | 11 ++++++-- 2 files changed, 40 insertions(+), 12 deletions(-) diff --git a/python/graphstorm/model/lm_embed.py b/python/graphstorm/model/lm_embed.py index 3693a20f85..7adcb29c1d 100644 --- a/python/graphstorm/model/lm_embed.py +++ b/python/graphstorm/model/lm_embed.py @@ -267,9 +267,10 @@ def _get_model_name(self, ntype): return model_name + "-" + model_hash[0:10] def _load_embeddings(self): - """ Load LM embeddings from files. + """ Load the cached LM embeddings from files. """ embed_ndata_names = self.embed_ndata_name + self._lm_emb_cache = {} for ntype in self._lm_models.ntypes: embed_path = os.path.join(os.path.join( os.path.join(self._embed_path, "lm_cache"), ntype), @@ -281,9 +282,23 @@ def _load_embeddings(self): embed_name = embed_ndata_names[ntype] self._lm_emb_cache[ntype] = load_pytorch_embedding(embed_path, self._g.get_node_partition_policy(ntype), embed_name) + if set(self._lm_emb_cache.keys()) == set(self._lm_models.ntypes): + logging.debug("Successfully load all embeddings from the cache.") + # The model name contains the hash code. If we can load the embeddings + # correctly, we should have loaded the cached embeddings of the LM models. + # We can save the hash code of all LM models. + self._lm_hash = self._lm_models.get_all_lm_hashes() + elif len(self._lm_emb_cache) > 0: + # In this case, some of the cached LM embeddings have been loaded. + # We need to clear up the cache. + if get_rank() == 0: + logging.warning("Fail to load all embeddings from the cache.") + logging.warning("Not use the cached data.") + self._lm_emb_cache = {} + self._lm_hash = '' def _save_embeddings(self): - """ Save LM embeddings. + """ Save LM embeddings to files. """ for ntype in self._lm_models.ntypes: embed_path = os.path.join(os.path.join( @@ -327,25 +342,28 @@ def update_cache(self, lm_infer_batch_size, use_fp16=True): lm_infer_batch_size: int Language model inference batch size use_fp16 : bool - Use float16 to store BERT embeddings. + Use float16 to store LM embeddings. + + Returns + ------- + bool : return True if new LM embeddings are computed else return False. """ - # If the embeddings have been cached, we just load them instead of + # If the embeddings have been cached in files, we should load them instead of # computing them from scratch. - if self._embed_path is not None: + if self._embed_path is not None and len(self._lm_emb_cache) == 0: self._load_embeddings() - # If all embeddings are cached: + # If all cached embeddings has been loaded to memory: if np.all([ntype in self._lm_emb_cache for ntype in self._lm_models.ntypes]): - # check if lm is updated, if lm is updated, clear the current cache + # check if lm is updated. if it is updated, clear the current cache if self._lm_hash != self._lm_models.get_all_lm_hashes(): self._clear_cache() else: # if lm models was not updated, don't compute the embeddings again - return + return False + # We need to compute the LM embeddings from scratch. embed_ndata_names = self.embed_ndata_name - # store/update the lm model hash used for the cache - self._lm_hash = self._lm_models.get_all_lm_hashes() for ntype in self._lm_models.ntypes: if get_rank() == 0: logging.debug("compute embedding for node type %s", ntype) @@ -388,9 +406,12 @@ def update_cache(self, lm_infer_batch_size, use_fp16=True): logging.info('Computing bert embedding on node %s takes %.3f seconds', ntype, time.time() - start) lm_model.train() + # store/update the lm model hash used for the cache + self._lm_hash = self._lm_models.get_all_lm_hashes() if self._embed_path is not None: self._save_embeddings() + return True def _clear_cache(self): """ Delete the current LM embed cache. diff --git a/tests/unit-tests/test_embed.py b/tests/unit-tests/test_embed.py index 062e6f7c51..5c9069b869 100644 --- a/tests/unit-tests/test_embed.py +++ b/tests/unit-tests/test_embed.py @@ -220,7 +220,8 @@ def test_lm_cache(): lm_models = LMModels(g, lm_config, 0, 10) lm_cache = LMCache(g, lm_models, tmpdirname) - lm_cache.update_cache(100) + ret = lm_cache.update_cache(100) + assert ret == True # This is the first time we need to compute the BERT embeddings. assert len(lm_cache) == 1 assert len(lm_cache.ntypes) == 1 assert lm_cache.ntypes[0] == 'n0' @@ -229,11 +230,17 @@ def test_lm_cache(): # Create the second cache. It should loads the embeddings from # the first cache. lm_cache2 = LMCache(g, lm_models, tmpdirname) - lm_cache2.update_cache(100) + ret = lm_cache2.update_cache(100) + assert ret == False # It loads LM embeddings directly. assert len(lm_cache2) == 1 emb1 = lm_cache["n0"] emb2 = lm_cache2["n0"] assert np.all(emb1[0:len(emb1)].numpy() == emb2[0:len(emb2)].numpy()) + ret = lm_cache2.update_cache(100) + assert ret == False # It uses the loaded embeddings. + lm_cache2.clear_cache() + ret = lm_cache2.update_cache(100) + assert ret == False # Load LM embeddings from the disk. # If the model is changed, the model name should also be changed. model_name = lm_cache._get_model_name("n0") From e82cb8ed95402994a0a82a89117038ff6f65559c Mon Sep 17 00:00:00 2001 From: Da Zheng Date: Sat, 30 Dec 2023 15:13:21 +0800 Subject: [PATCH 10/14] Fix a bug in inference code (#696) *Description of changes:* This PR reorganizes the code of performing inference of GNN and LM models. Specifically, we split the nodes for inference based on locality. In this case, the embeddings are saved to local partitions via shared memory and we only need to run barrier before returning fron the inference function. However, when computing LM embeddings, we split the nodes evenly to ensure all processes take roughly the same amount of time to compute LM embeddings. Otherwise, we will see a timeout in barrier in some processes. Because now the nodes are split evenly, we need to write data to remote memory. Before returning from the inference function, we need to call flush_data to ensure all data written to distributed memory can be read correctly. By submitting this pull request, I confirm that you can use, modify, copy, and redistribute this contribution, under the terms of your choice. --------- Co-authored-by: Ubuntu Co-authored-by: Ubuntu --- python/graphstorm/model/embed.py | 4 ++++ python/graphstorm/model/gnn_encoder_base.py | 7 ++++++- python/graphstorm/model/lm_embed.py | 22 +++++++++++++++------ 3 files changed, 26 insertions(+), 7 deletions(-) diff --git a/python/graphstorm/model/embed.py b/python/graphstorm/model/embed.py index eed50977c7..62c5aa66e6 100644 --- a/python/graphstorm/model/embed.py +++ b/python/graphstorm/model/embed.py @@ -450,6 +450,10 @@ def compute_node_input_embeddings(g, batch_size, embed_layer, assert g.nodes[ntype].data['input_emb'].shape[1] == embed_size input_emb = g.nodes[ntype].data['input_emb'] # TODO(zhengda) this is not a memory efficient way of implementing this. + # Here `force_even` is set to False, this means that we compute the input node + # embeddings for the nodes in the local partition and save the embeddings to + # the local partition with shared memory. Therefore, we don't need to call + # flush at the end of inference. infer_nodes = node_split(th.ones((g.number_of_nodes(ntype),), dtype=th.bool), partition_book=g.get_partition_book(), ntype=ntype, force_even=False) diff --git a/python/graphstorm/model/gnn_encoder_base.py b/python/graphstorm/model/gnn_encoder_base.py index 450711a7cc..a820fa3ea0 100644 --- a/python/graphstorm/model/gnn_encoder_base.py +++ b/python/graphstorm/model/gnn_encoder_base.py @@ -227,6 +227,12 @@ def dist_minibatch_inference(g, gnn_encoder, get_input_embeds, batch_size, fanou for ntype, out_nodes in output_nodes.items(): out_embs[ntype][out_nodes] = output[ntype].cpu() + # The nodes are split in such a way that all processes only need to compute + # the embeddings of the nodes in the local partition. Therefore, a barrier + # is enough to ensure that all data have been written to memory for distributed + # read after this function is returned. + # Note: there is a risk here. If the nodes for inference on each partition + # are very skewed, some of the processes may timeout in the barrier. barrier() return out_embs @@ -392,5 +398,4 @@ def get_input_embeds1(input_nodes, node_feats): list(infer_nodes.keys()), layer, get_input_embeds, device, task_tracker) - barrier() return next_layer_input diff --git a/python/graphstorm/model/lm_embed.py b/python/graphstorm/model/lm_embed.py index 7adcb29c1d..5c13ad167d 100644 --- a/python/graphstorm/model/lm_embed.py +++ b/python/graphstorm/model/lm_embed.py @@ -32,7 +32,8 @@ from .lm_model import init_lm_model from .lm_model import get_lm_node_feats from .utils import load_pytorch_embedding, save_pytorch_embedding -from ..utils import get_rank, get_world_size, barrier, create_dist_tensor +from ..utils import get_rank, get_world_size, create_dist_tensor +from ..distributed import flush_data class LMModels(nn.Module): """ LM model collection @@ -381,17 +382,20 @@ def update_cache(self, lm_infer_batch_size, use_fp16=True): part_policy=self._g.get_node_partition_policy(ntype), persistent=True) emb = self._lm_emb_cache[ntype] + # LM computations are very computationally expensive. It's better to force + # an even split to ensure all processes have roughly the same number of nodes + # for LM inference. infer_nodes = dgl.distributed.node_split( th.ones((self._g.number_of_nodes(ntype),), dtype=th.bool), partition_book=self._g.get_partition_book(), - ntype=ntype, force_even=False) - logging.debug("node %s, local infer set: %d, batch size: %d", - ntype, len(infer_nodes), lm_infer_batch_size) + ntype=ntype, force_even=True) + logging.debug("Rank %d: node %s, local infer set: %d, batch size: %d", + get_rank(), ntype, len(infer_nodes), lm_infer_batch_size) node_list = th.split(infer_nodes, lm_infer_batch_size) input_ntypes = [ntype] with th.no_grad(): - for input_nodes in node_list: + for i, input_nodes in enumerate(node_list): input_lm_feats = {} input_lm_feats[ntype] = { fname: feat[input_nodes] for fname, feat in lm_node_feat.items() @@ -401,7 +405,13 @@ def update_cache(self, lm_infer_batch_size, use_fp16=True): emb[input_nodes] = text_embs[ntype].half().to('cpu') else: emb[input_nodes] = text_embs[ntype].to('cpu') - barrier() + if i % 1000 == 0 and get_rank() == 0: + logging.debug("Compute LM embeddings on %d batches out of %d", + i, len(node_list)) + # Because we split the nodes evenly, we need to write data to remote machines. + # Therefore, we need to flush data here to ensure that we can load data + # correctly afterwards. + flush_data() if get_rank() == 0: logging.info('Computing bert embedding on node %s takes %.3f seconds', ntype, time.time() - start) From b57fbe1aed8ceb0ff2528dbafed5ef025d8c43ef Mon Sep 17 00:00:00 2001 From: "Jian Zhang (James)" <6593865@qq.com> Date: Wed, 10 Jan 2024 14:25:58 -0800 Subject: [PATCH 11/14] [Feature] New ACM data and a notebook for data prepare (#691) *Issue #, if available:* *Description of changes:* This PR include two changes: 1. Modify the ACM generation python code to include more real-like setting; 2. Add a new Notebook to introduce how to prepare the ACM data for other notebooks. By submitting this pull request, I confirm that you can use, modify, copy, and redistribute this contribution, under the terms of your choice. --------- Co-authored-by: Ubuntu Co-authored-by: Ubuntu --- docs/requirements.txt | 2 + docs/source/conf.py | 1 + docs/source/index.rst | 8 + .../notebooks/Notebook_0_Data_Prepare.ipynb | 1399 +++++++++++++++++ docs/source/tutorials/own-data.rst | 4 + examples/acm_data.py | 27 +- tutorial/ACM_raw_parquet.png | Bin 78518 -> 331471 bytes tutorial/ACM_w_text.png | Bin 358690 -> 411118 bytes 8 files changed, 1435 insertions(+), 6 deletions(-) create mode 100644 docs/source/notebooks/Notebook_0_Data_Prepare.ipynb diff --git a/docs/requirements.txt b/docs/requirements.txt index b34aa1be8c..a58aa1a417 100644 --- a/docs/requirements.txt +++ b/docs/requirements.txt @@ -1,5 +1,7 @@ sphinx==7.1.2 sphinx-rtd-theme==1.3.0 +nbsphinx +pandoc --extra-index-url https://download.pytorch.org/whl/cpu torch==1.13.1+cpu -f https://data.dgl.ai/wheels-internal/repo.html diff --git a/docs/source/conf.py b/docs/source/conf.py index beaaf1a5a0..3e57f059f8 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -35,6 +35,7 @@ "sphinx.ext.autosummary", "sphinx.ext.coverage", "sphinx.ext.mathjax", + "nbsphinx", ] templates_path = ['_templates'] exclude_patterns = [] diff --git a/docs/source/index.rst b/docs/source/index.rst index 24eac883c1..9c54eb966f 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -35,6 +35,14 @@ Welcome to the GraphStorm Documentation and Tutorials scale/distributed scale/sagemaker +.. toctree:: + :maxdepth: 1 + :caption: Programming User Guide + :hidden: + :glob: + + notebooks/Notebook_0_Data_Prepare + .. toctree:: :maxdepth: 1 :caption: Advanced Topics diff --git a/docs/source/notebooks/Notebook_0_Data_Prepare.ipynb b/docs/source/notebooks/Notebook_0_Data_Prepare.ipynb new file mode 100644 index 0000000000..7b618e682b --- /dev/null +++ b/docs/source/notebooks/Notebook_0_Data_Prepare.ipynb @@ -0,0 +1,1399 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "c4392287-0c04-44e6-a6b2-e34f5d7157ad", + "metadata": {}, + "source": [ + "# Notebook 0: Data Prepare\n", + "\n", + "This notebook will create an example graph data to be used in the other notebooks to demonstrate how to program using GraphStorm APIs.\n", + "The example graph data comes from [DGL's ACM publication dataset](https://data.dgl.ai/dataset/ACM.mat), which is the same as the data explainedin the [Use Your Own Data tutorial](https://graphstorm.readthedocs.io/en/latest/tutorials/own-data.html).\n", + "\n", + "-----\n", + "\n", + "## Prerequisites\n", + "This notebook assumes the following:\n", + "- Python 3;\n", + "- Linux OS, Ubuntu or Amazon Linux;\n", + "- GraphStorm and its dependencies (following the [Setup GraphStorm with pip packages tutorial](https://graphstorm.readthedocs.io/en/latest/install/env-setup.html#setup-graphstorm-with-pip-packages))\n", + "- [Jupyter web interactive server](https://jupyter.org/).\n", + "\n", + "Users can use the following command to check if the above prerequisites are met." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "ef5d9b4a-2efa-4158-be55-53b22226d3d2", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.2.1\n" + ] + } + ], + "source": [ + "import graphstorm as gs\n", + "print(gs.__version__)" + ] + }, + { + "cell_type": "markdown", + "id": "edeaa1cd-283a-4c5e-8f20-bbd3dd186928", + "metadata": {}, + "source": [ + "## Download Data Generation Script\n", + "GraphStorm provides a Python script that can download and convert the DGL ACM publication data for GraphStorm usage. Therefore, first let's download the script file from the [GraphStorm Github repository](https://github.com/awslabs/graphstorm)." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "609b0869-b4eb-4467-8353-31e2acc92203", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--2023-12-11 18:58:35-- https://github.com/awslabs/graphstorm/raw/main/examples/acm_data.py\n", + "Resolving github.com (github.com)... 192.30.255.113\n", + "Connecting to github.com (github.com)|192.30.255.113|:443... connected.\n", + "HTTP request sent, awaiting response... 302 Found\n", + "Location: https://raw.githubusercontent.com/awslabs/graphstorm/main/examples/acm_data.py [following]\n", + "--2023-12-11 18:58:35-- https://raw.githubusercontent.com/awslabs/graphstorm/main/examples/acm_data.py\n", + "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.110.133, 185.199.111.133, 185.199.108.133, ...\n", + "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.110.133|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 18296 (18K) [text/plain]\n", + "Saving to: ‘./acm_data.py’\n", + "\n", + "./acm_data.py 100%[===================>] 17.87K --.-KB/s in 0.001s \n", + "\n", + "2023-12-11 18:58:35 (29.6 MB/s) - ‘./acm_data.py’ saved [18296/18296]\n", + "\n" + ] + } + ], + "source": [ + "!wget -O ./acm_data.py https://github.com/awslabs/graphstorm/raw/main/examples/acm_data.py" + ] + }, + { + "cell_type": "markdown", + "id": "a78c88ef-91f0-4737-9506-985dc7d03c7f", + "metadata": {}, + "source": [ + "## Generate ACM Raw Table Data\n", + "Then we can use the command below to build the raw table data, which is the standard input data for GraphStorm's gconstruct module." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "4e3e6c4e-9d6d-4a6d-9c51-cf856eef06fc", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Namespace(download_path='/tmp/ACM.mat', dataset_name='acm', output_type='raw_w_text', output_path='./acm_raw')\n", + "Graph(num_nodes={'author': 17431, 'paper': 12499, 'subject': 73},\n", + " num_edges={('author', 'writing', 'paper'): 37055, ('paper', 'cited', 'paper'): 30789, ('paper', 'citing', 'paper'): 30789, ('paper', 'is-about', 'subject'): 12499, ('paper', 'written-by', 'author'): 37055, ('subject', 'has', 'paper'): 12499},\n", + " metagraph=[('author', 'paper', 'writing'), ('paper', 'paper', 'cited'), ('paper', 'paper', 'citing'), ('paper', 'subject', 'is-about'), ('paper', 'author', 'written-by'), ('subject', 'paper', 'has')])\n", + "\n", + " Number of classes: 14\n", + "\n", + " Paper node labels: torch.Size([12499])\n", + "\n", + " ('paper', 'citing', 'paper') edge labels:30789\n", + "Saving ACM data to /tmp/acm.dgl ......\n", + "/tmp/acm.dgl saved.\n", + "Saving ACM node text to /tmp/acm_text.pkl ......\n", + "/tmp/acm_text.pkl saved.\n", + "author nodes have: Index(['node_id', 'feat', 'text'], dtype='object') columns ......\n", + "paper nodes have: Index(['node_id', 'label', 'feat', 'text'], dtype='object') columns ......\n", + "subject nodes have: Index(['node_id', 'feat', 'text'], dtype='object') columns ......\n", + "Saved author node data to ./acm_raw/nodes/author.parquet.\n", + "Saved paper node data to ./acm_raw/nodes/paper.parquet.\n", + "Saved subject node data to ./acm_raw/nodes/subject.parquet.\n", + "Saved ('author', 'writing', 'paper') edge data to ./acm_raw/edges/author_writing_paper.parquet\n", + "Saved ('paper', 'cited', 'paper') edge data to ./acm_raw/edges/paper_cited_paper.parquet\n", + "Saved ('paper', 'citing', 'paper') edge data to ./acm_raw/edges/paper_citing_paper.parquet\n", + "Saved ('paper', 'is-about', 'subject') edge data to ./acm_raw/edges/paper_is-about_subject.parquet\n", + "Saved ('paper', 'written-by', 'author') edge data to ./acm_raw/edges/paper_written-by_author.parquet\n", + "Saved ('subject', 'has', 'paper') edge data to ./acm_raw/edges/subject_has_paper.parquet\n" + ] + } + ], + "source": [ + "!python ./acm_data.py --output-path ./acm_raw --output-type raw_w_text" + ] + }, + { + "cell_type": "markdown", + "id": "02183d5a-f574-4938-956a-69eeec1fd1ee", + "metadata": {}, + "source": [ + "## Construct GraphStorm Input Graph Data\n", + "With the raw ACM tables we then can use GraphStorm's graph construction method to prepare the ACM graph for other notebooks. The graph construction module perform:\n", + "- read in the raw data, and convert it to DGL graph;\n", + "- split the DGL graph into multiple partitions as the distributed DGL graphs;\n", + "- produce node id mapping files and other supporting files.\n", + "\n", + "For the GraphStorm Standalone mode, we only need one partition. Therefore, in the command below we set the `--num-parts` to be `1`. For other arguments, users can refer to [GraphStorm Graph Construction arguments](https://graphstorm.readthedocs.io/en/latest/configuration/configuration-gconstruction.html)." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "2b8c752c-b6e8-4562-9637-4c56a4b09875", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:root:The graph has 3 node types and 6 edge types.\n", + "INFO:root:Node type author has 17431 nodes\n", + "INFO:root:Node type paper has 12499 nodes\n", + "INFO:root:Node type subject has 73 nodes\n", + "INFO:root:Edge type ('author', 'writing', 'paper') has 37055 edges\n", + "INFO:root:Edge type ('paper', 'cited', 'paper') has 30789 edges\n", + "INFO:root:Edge type ('paper', 'citing', 'paper') has 30789 edges\n", + "INFO:root:Edge type ('paper', 'is-about', 'subject') has 12499 edges\n", + "INFO:root:Edge type ('paper', 'written-by', 'author') has 37055 edges\n", + "INFO:root:Edge type ('subject', 'has', 'paper') has 12499 edges\n", + "INFO:root:Node type author has features: ['feat', 'input_ids', 'attention_mask', 'token_type_ids'].\n", + "INFO:root:Node type paper has features: ['feat', 'input_ids', 'attention_mask', 'token_type_ids', 'train_mask', 'val_mask', 'test_mask', 'label'].\n", + "INFO:root:Train/val/test on paper: 9999, 1249, 1249\n", + "INFO:root:Node type subject has features: ['feat', 'input_ids', 'attention_mask', 'token_type_ids'].\n", + "INFO:root:Edge type ('paper', 'citing', 'paper') has features: ['train_mask', 'val_mask', 'test_mask'].\n", + "INFO:root:Train/val/test on ('paper', 'citing', 'paper'): 24631, 3078, 3078\n", + "Converting to homogeneous graph takes 0.004s, peak mem: 3.161 GB\n", + "Save partitions: 0.020 seconds, peak memory: 3.349 GB\n", + "There are 160686 edges in the graph and 0 edge cuts for 1 partitions.\n", + "INFO:root:Graph construction generates new node IDs for 'author'. The ID map is saved in ./acm_gs_1p/author_id_remap.parquet.\n", + "INFO:root:Graph construction generates new node IDs for 'paper'. The ID map is saved in ./acm_gs_1p/paper_id_remap.parquet.\n", + "INFO:root:Graph construction generates new node IDs for 'subject'. The ID map is saved in ./acm_gs_1p/subject_id_remap.parquet.\n" + ] + } + ], + "source": [ + "!python -m graphstorm.gconstruct.construct_graph \\\n", + " --conf-file ./acm_raw/config.json \\\n", + " --output-dir ./acm_gs_1p \\\n", + " --num-parts 1 \\\n", + " --graph-name acm" + ] + }, + { + "cell_type": "markdown", + "id": "314eed68-b10b-4f59-94c5-fcac538cc202", + "metadata": {}, + "source": [ + "#### 3-Partition Input Data\n", + "To better illustrate GraphStorm required input data structure, we can use the following command to create a 3-partition input data." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "fc5eb53c-50ed-4b51-8b94-57094ee4dc15", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:root:The graph has 3 node types and 6 edge types.\n", + "INFO:root:Node type author has 17431 nodes\n", + "INFO:root:Node type paper has 12499 nodes\n", + "INFO:root:Node type subject has 73 nodes\n", + "INFO:root:Edge type ('author', 'writing', 'paper') has 37055 edges\n", + "INFO:root:Edge type ('paper', 'cited', 'paper') has 30789 edges\n", + "INFO:root:Edge type ('paper', 'citing', 'paper') has 30789 edges\n", + "INFO:root:Edge type ('paper', 'is-about', 'subject') has 12499 edges\n", + "INFO:root:Edge type ('paper', 'written-by', 'author') has 37055 edges\n", + "INFO:root:Edge type ('subject', 'has', 'paper') has 12499 edges\n", + "INFO:root:Node type author has features: ['feat', 'input_ids', 'attention_mask', 'token_type_ids'].\n", + "INFO:root:Node type paper has features: ['feat', 'input_ids', 'attention_mask', 'token_type_ids', 'train_mask', 'val_mask', 'test_mask', 'label'].\n", + "INFO:root:Train/val/test on paper: 9999, 1249, 1249\n", + "INFO:root:Node type subject has features: ['feat', 'input_ids', 'attention_mask', 'token_type_ids'].\n", + "INFO:root:Edge type ('paper', 'citing', 'paper') has features: ['train_mask', 'val_mask', 'test_mask'].\n", + "INFO:root:Train/val/test on ('paper', 'citing', 'paper'): 24631, 3078, 3078\n", + "Converting to homogeneous graph takes 0.004s, peak mem: 3.121 GB\n", + "Convert a graph into a bidirected graph: 0.004 seconds, peak memory: 3.308 GB\n", + "Construct multi-constraint weights: 0.010 seconds, peak memory: 3.308 GB\n", + "[21:29:03] /opt/dgl/src/graph/transform/metis_partition_hetero.cc:89: Partition a graph with 30003 nodes and 160987 edges into 3 parts and get 9798 edge cuts\n", + "Metis partitioning: 0.043 seconds, peak memory: 3.308 GB\n", + "Assigning nodes to METIS partitions takes 0.059s, peak mem: 3.308 GB\n", + "Reshuffle nodes and edges: 0.022 seconds\n", + "Split the graph: 0.014 seconds\n", + "Construct subgraphs: 0.015 seconds\n", + "Splitting the graph into partitions takes 0.050s, peak mem: 3.308 GB\n", + "part 0 has 6672 nodes of type author and 6015 are inside the partition\n", + "part 0 has 6656 nodes of type paper and 4152 are inside the partition\n", + "part 0 has 58 nodes of type subject and 25 are inside the partition\n", + "part 0 has 13733 edges of type ('author', 'writing', 'paper') and 12901 are inside the partition\n", + "part 0 has 11889 edges of type ('paper', 'cited', 'paper') and 10774 are inside the partition\n", + "part 0 has 11889 edges of type ('paper', 'citing', 'paper') and 10550 are inside the partition\n", + "part 0 has 5329 edges of type ('paper', 'is-about', 'subject') and 4028 are inside the partition\n", + "part 0 has 13733 edges of type ('paper', 'written-by', 'author') and 12732 are inside the partition\n", + "part 0 has 5329 edges of type ('subject', 'has', 'paper') and 4152 are inside the partition\n", + "part 1 has 6133 nodes of type author and 5664 are inside the partition\n", + "part 1 has 6161 nodes of type paper and 4031 are inside the partition\n", + "part 1 has 59 nodes of type subject and 23 are inside the partition\n", + "part 1 has 13067 edges of type ('author', 'writing', 'paper') and 12137 are inside the partition\n", + "part 1 has 12508 edges of type ('paper', 'cited', 'paper') and 11280 are inside the partition\n", + "part 1 has 12508 edges of type ('paper', 'citing', 'paper') and 10927 are inside the partition\n", + "part 1 has 5216 edges of type ('paper', 'is-about', 'subject') and 4264 are inside the partition\n", + "part 1 has 13067 edges of type ('paper', 'written-by', 'author') and 12372 are inside the partition\n", + "part 1 has 5216 edges of type ('subject', 'has', 'paper') and 4031 are inside the partition\n", + "part 2 has 6271 nodes of type author and 5752 are inside the partition\n", + "part 2 has 6977 nodes of type paper and 4316 are inside the partition\n", + "part 2 has 66 nodes of type subject and 25 are inside the partition\n", + "part 2 has 12772 edges of type ('author', 'writing', 'paper') and 12017 are inside the partition\n", + "part 2 has 10404 edges of type ('paper', 'cited', 'paper') and 8735 are inside the partition\n", + "part 2 has 10404 edges of type ('paper', 'citing', 'paper') and 9312 are inside the partition\n", + "part 2 has 5222 edges of type ('paper', 'is-about', 'subject') and 4207 are inside the partition\n", + "part 2 has 12772 edges of type ('paper', 'written-by', 'author') and 11951 are inside the partition\n", + "part 2 has 5222 edges of type ('subject', 'has', 'paper') and 4316 are inside the partition\n", + "Save partitions: 0.020 seconds, peak memory: 3.308 GB\n", + "There are 160686 edges in the graph and 0 edge cuts for 3 partitions.\n", + "INFO:root:Graph construction generates new node IDs for 'author'. The ID map is saved in ./acm_gs_3p/author_id_remap.parquet.\n", + "INFO:root:Graph construction generates new node IDs for 'paper'. The ID map is saved in ./acm_gs_3p/paper_id_remap.parquet.\n", + "INFO:root:Graph construction generates new node IDs for 'subject'. The ID map is saved in ./acm_gs_3p/subject_id_remap.parquet.\n" + ] + } + ], + "source": [ + "!python -m graphstorm.gconstruct.construct_graph \\\n", + " --conf-file ./acm_raw/config.json \\\n", + " --output-dir ./acm_gs_3p \\\n", + " --num-parts 3 \\\n", + " --graph-name acm" + ] + }, + { + "cell_type": "markdown", + "id": "e79e2712-4a3c-41a2-8c1b-819c7e8364d2", + "metadata": {}, + "source": [ + "## Data Exploration and Explanation\n", + "The above commands created two sets of ACM data, i.e., the raw ACM data tables, and ACM GraphStorm input graphs. Below we explore these datasets, and explain their format so that users can prepare their own graph data easily." + ] + }, + { + "cell_type": "markdown", + "id": "148db3bc-ca76-4a75-b3b8-e2ed55784b03", + "metadata": {}, + "source": [ + "### Raw ACM Table Data in the `./acm_raw` Folder\n", + "We can explore the `acm_raw` folder with the `ls -al` command. " + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "c0a6cc31-2230-498e-9a19-33466ed0f916", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "total 24\n", + "drwxrwxr-x 4 ubuntu ubuntu 4096 Dec 19 21:27 .\n", + "drwxrwxr-x 6 ubuntu ubuntu 4096 Dec 19 21:29 ..\n", + "-rw-rw-r-- 1 ubuntu ubuntu 5306 Dec 19 21:27 config.json\n", + "drwxrwxr-x 2 ubuntu ubuntu 4096 Dec 19 21:27 edges\n", + "drwxrwxr-x 2 ubuntu ubuntu 4096 Dec 19 21:27 nodes\n" + ] + } + ], + "source": [ + "!ls -al ./acm_raw" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "2cc6e2db-b5fd-4a03-9192-11a577f36988", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "total 38744\n", + "drwxrwxr-x 2 ubuntu ubuntu 4096 Dec 19 21:27 .\n", + "drwxrwxr-x 4 ubuntu ubuntu 4096 Dec 19 21:27 ..\n", + "-rw-rw-r-- 1 ubuntu ubuntu 18843566 Dec 19 21:27 author.parquet\n", + "-rw-rw-r-- 1 ubuntu ubuntu 20704514 Dec 19 21:27 paper.parquet\n", + "-rw-rw-r-- 1 ubuntu ubuntu 113462 Dec 19 21:27 subject.parquet\n" + ] + } + ], + "source": [ + "!ls -al ./acm_raw/nodes" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "998dc284-8f0c-4f2a-89ca-f63c5d2fc65d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "total 1016\n", + "drwxrwxr-x 2 ubuntu ubuntu 4096 Dec 19 21:27 .\n", + "drwxrwxr-x 4 ubuntu ubuntu 4096 Dec 19 21:27 ..\n", + "-rw-rw-r-- 1 ubuntu ubuntu 263138 Dec 19 21:27 author_writing_paper.parquet\n", + "-rw-rw-r-- 1 ubuntu ubuntu 156358 Dec 19 21:27 paper_cited_paper.parquet\n", + "-rw-rw-r-- 1 ubuntu ubuntu 162714 Dec 19 21:27 paper_citing_paper.parquet\n", + "-rw-rw-r-- 1 ubuntu ubuntu 87792 Dec 19 21:27 paper_is-about_subject.parquet\n", + "-rw-rw-r-- 1 ubuntu ubuntu 265948 Dec 19 21:27 paper_written-by_author.parquet\n", + "-rw-rw-r-- 1 ubuntu ubuntu 84005 Dec 19 21:27 subject_has_paper.parquet\n" + ] + } + ], + "source": [ + "!ls -al ./acm_raw/edges" + ] + }, + { + "cell_type": "markdown", + "id": "74be0c90-64f8-4d83-a741-35a6f58b3a9b", + "metadata": {}, + "source": [ + "#### Graph Description JSON File `config.json`" + ] + }, + { + "cell_type": "markdown", + "id": "2346f42f-b8bb-4e5f-8485-cc9c508384f2", + "metadata": {}, + "source": [ + "The `acm_raw` folder includes one `config.json` file that describes the table-based raw graph data. Except for a **version** object, the JSON file contains a **nodes** object and an **edges** object.\n", + "\n", + "The **nodes** object contains a list of *node* objects, each of which includes a set of properties to describe one node type in a graph data. For example, in the `config.json` file, there is a node type, called \"papers\". For each node type, GraphStorm defines a few other properties, such as **format**, **files**, and **features**.\n", + "\n", + "Similarly, the **edges** object contains a list of *edge* objects. Most of *edge* properties are same as *node*'s except that *edge* object has the **relation** property that define an edge type in a canonical format, i.e., *source node type*, *relation type*, and *destination node type*.\n", + "\n", + "For a full list of the JSON configuration properties, users can refer to the [GraphStorm Graph Construction JSON Explanations](https://graphstorm.readthedocs.io/en/latest/configuration/configuration-gconstruction.html#configuration-json-explanations).\n", + "\n", + "To use your own graph, users need to prepare their own JSON file." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "ddfef233-7fff-41ab-84f7-30d96885c472", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{\n", + " \"version\": \"gconstruct-v0.1\",\n", + " \"nodes\": [\n", + " {\n", + " \"node_type\": \"author\",\n", + " \"format\": {\n", + " \"name\": \"parquet\"\n", + " },\n", + " \"files\": [\n", + " \"./acm_raw/nodes/author.parquet\"\n", + " ],\n", + " \"node_id_col\": \"node_id\",\n", + " \"features\": [\n", + " {\n", + " \"feature_col\": \"feat\",\n", + " \"feature_name\": \"feat\"\n", + " },\n", + " {\n", + " \"feature_col\": \"text\",\n", + " \"feature_name\": \"text\",\n", + " \"transform\": {\n", + " \"name\": \"tokenize_hf\",\n", + " \"bert_model\": \"bert-base-uncased\",\n", + " \"max_seq_length\": 16\n", + " }\n", + " }\n", + " ]\n", + " },\n", + " {\n", + " \"node_type\": \"paper\",\n", + " \"format\": {\n", + " \"name\": \"parquet\"\n", + " },\n", + " \"files\": [\n", + " \"./acm_raw/nodes/paper.parquet\"\n", + " ],\n", + " \"node_id_col\": \"node_id\",\n", + " \"features\": [\n", + " {\n", + " \"feature_col\": \"feat\",\n", + " \"feature_name\": \"feat\"\n", + " },\n", + " {\n", + " \"feature_col\": \"text\",\n", + " \"feature_name\": \"text\",\n", + " \"transform\": {\n", + " \"name\": \"tokenize_hf\",\n", + " \"bert_model\": \"bert-base-uncased\",\n", + " \"max_seq_length\": 16\n", + " }\n", + " }\n", + " ],\n", + " \"labels\": [\n", + " {\n", + " \"label_col\": \"label\",\n", + " \"task_type\": \"classification\",\n", + " \"split_pct\": [\n", + " 0.8,\n", + " 0.1,\n", + " 0.1\n", + " ],\n", + " \"label_stats_type\": \"frequency_cnt\"\n", + " }\n", + " ]\n", + " },\n", + " {\n", + " \"node_type\": \"subject\",\n", + " \"format\": {\n", + " \"name\": \"parquet\"\n", + " },\n", + " \"files\": [\n", + " \"./acm_raw/nodes/subject.parquet\"\n", + " ],\n", + " \"node_id_col\": \"node_id\",\n", + " \"features\": [\n", + " {\n", + " \"feature_col\": \"feat\",\n", + " \"feature_name\": \"feat\"\n", + " },\n", + " {\n", + " \"feature_col\": \"text\",\n", + " \"feature_name\": \"text\",\n", + " \"transform\": {\n", + " \"name\": \"tokenize_hf\",\n", + " \"bert_model\": \"bert-base-uncased\",\n", + " \"max_seq_length\": 16\n", + " }\n", + " }\n", + " ]\n", + " }\n", + " ],\n", + " \"edges\": [\n", + " {\n", + " \"relation\": [\n", + " \"author\",\n", + " \"writing\",\n", + " \"paper\"\n", + " ],\n", + " \"format\": {\n", + " \"name\": \"parquet\"\n", + " },\n", + " \"files\": [\n", + " \"./acm_raw/edges/author_writing_paper.parquet\"\n", + " ],\n", + " \"source_id_col\": \"source_id\",\n", + " \"dest_id_col\": \"dest_id\"\n", + " },\n", + " {\n", + " \"relation\": [\n", + " \"paper\",\n", + " \"cited\",\n", + " \"paper\"\n", + " ],\n", + " \"format\": {\n", + " \"name\": \"parquet\"\n", + " },\n", + " \"files\": [\n", + " \"./acm_raw/edges/paper_cited_paper.parquet\"\n", + " ],\n", + " \"source_id_col\": \"source_id\",\n", + " \"dest_id_col\": \"dest_id\"\n", + " },\n", + " {\n", + " \"relation\": [\n", + " \"paper\",\n", + " \"citing\",\n", + " \"paper\"\n", + " ],\n", + " \"format\": {\n", + " \"name\": \"parquet\"\n", + " },\n", + " \"files\": [\n", + " \"./acm_raw/edges/paper_citing_paper.parquet\"\n", + " ],\n", + " \"source_id_col\": \"source_id\",\n", + " \"dest_id_col\": \"dest_id\",\n", + " \"labels\": [\n", + " {\n", + " \"task_type\": \"link_prediction\",\n", + " \"split_pct\": [\n", + " 0.8,\n", + " 0.1,\n", + " 0.1\n", + " ]\n", + " }\n", + " ]\n", + " },\n", + " {\n", + " \"relation\": [\n", + " \"paper\",\n", + " \"is-about\",\n", + " \"subject\"\n", + " ],\n", + " \"format\": {\n", + " \"name\": \"parquet\"\n", + " },\n", + " \"files\": [\n", + " \"./acm_raw/edges/paper_is-about_subject.parquet\"\n", + " ],\n", + " \"source_id_col\": \"source_id\",\n", + " \"dest_id_col\": \"dest_id\"\n", + " },\n", + " {\n", + " \"relation\": [\n", + " \"paper\",\n", + " \"written-by\",\n", + " \"author\"\n", + " ],\n", + " \"format\": {\n", + " \"name\": \"parquet\"\n", + " },\n", + " \"files\": [\n", + " \"./acm_raw/edges/paper_written-by_author.parquet\"\n", + " ],\n", + " \"source_id_col\": \"source_id\",\n", + " \"dest_id_col\": \"dest_id\"\n", + " },\n", + " {\n", + " \"relation\": [\n", + " \"subject\",\n", + " \"has\",\n", + " \"paper\"\n", + " ],\n", + " \"format\": {\n", + " \"name\": \"parquet\"\n", + " },\n", + " \"files\": [\n", + " \"./acm_raw/edges/subject_has_paper.parquet\"\n", + " ],\n", + " \"source_id_col\": \"source_id\",\n", + " \"dest_id_col\": \"dest_id\"\n", + " }\n", + " ]\n", + "}" + ] + } + ], + "source": [ + "!cat ./acm_raw/config.json" + ] + }, + { + "cell_type": "markdown", + "id": "6babcfd3-b570-4045-9cb6-541bf7ceaf4d", + "metadata": {}, + "source": [ + "#### Raw ACM Tables in the `nodes/` and `edges/` folder.\n", + "As defined in the `./acm_raw/config.json` file, the node data files are stored at the `./acm_raw/nodes/` folder, and edge data files are stored at the `./acm_raw/edges/` folder. General description of these files can be found at the [Input raw node/edge data files](https://graphstorm.readthedocs.io/en/latest/tutorials/own-data.html#input-raw-node-edge-data-files). Here, we can read some node (\"paper\") and edge (\\[\"paper\", \"citing\", \"paper\"\\]) tables to learn more about them." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "475be4f2-405a-4809-b51e-7a439566f00d", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "\n", + "paper_node_path = './acm_raw/nodes/paper.parquet'\n", + "paper_citing_paper_edge_path = './acm_raw/edges/paper_citing_paper.parquet'" + ] + }, + { + "cell_type": "markdown", + "id": "7237ea83-fc92-4a36-87b8-adecae9bdf99", + "metadata": {}, + "source": [ + "**The \"paper\" node table**\n", + "\n", + "The paper node table could be read in as a Pandas DataFrame. The table has a few columns, whose names are used in the `config.json`. For the \"paper\" nodes, there is a `node_id` column, including a unique identifier for each node, a `feat` column, including a 256D numerical tensor for each node, a `text` column, including free text feature for each node, and a `label` column, including an integer to indicate the class that each node is assigned.\n", + "\n", + "The other two node types, \"author\" and \"subject\", have similar data tables. Users can explore them with the similar code below." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "930314ae-330c-4159-9c3b-d18ed8621c3d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(12499, 4)\n" + ] + }, + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " source_id dest_id label\n", + "28779 p11255 p12232 1.0\n", + "2791 p704 p6747 1.0\n", + "429 p119 p16 1.0\n", + "7301 p2354 p8747 1.0" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pcp_edge_df = pd.read_parquet(paper_citing_paper_edge_path)\n", + "\n", + "print(pcp_edge_df.shape)\n", + "pcp_edge_df.sample(4)" + ] + }, + { + "cell_type": "markdown", + "id": "8cd709f6-9089-4084-b218-71d8cf0a8924", + "metadata": {}, + "source": [ + "### GraphStorm Input Graph Data in the `./acm_gs_*p/` Folder\n", + "\n", + "In the above cells, we created a 1-partition graph in the `acm_gs_1p` folder and a 3-partition graph in the `acm_gs_3p` folder. The contents of the two folders are nearly the same, including \n", + "\n", + "1. a GraphStorm partitioned configuration JSON file;\n", + "2. original node id space to GraphStorm node id space mapping files, created during graph processing;\n", + "3. GraphStorm node id space to shuffle node id space mapping, created during graph patitioning;\n", + "4. label statitic files." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "86ef095b-5997-46e4-a8a1-99f1db61de57", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "total 1884\n", + "drwxrwxr-x 3 ubuntu ubuntu 4096 Dec 19 21:28 .\n", + "drwxrwxr-x 6 ubuntu ubuntu 4096 Dec 19 21:29 ..\n", + "-rw-rw-r-- 1 ubuntu ubuntu 1673 Dec 19 21:28 acm.json\n", + "-rw-rw-r-- 1 ubuntu ubuntu 213402 Dec 19 21:28 author_id_remap.parquet\n", + "-rw-rw-r-- 1 ubuntu ubuntu 191 Dec 19 21:28 edge_label_stats.json\n", + "-rw-rw-r-- 1 ubuntu ubuntu 1287802 Dec 19 21:28 edge_mapping.pt\n", + "-rw-rw-r-- 1 ubuntu ubuntu 515 Dec 19 21:28 node_label_stats.json\n", + "-rw-rw-r-- 1 ubuntu ubuntu 241655 Dec 19 21:28 node_mapping.pt\n", + "-rw-rw-r-- 1 ubuntu ubuntu 150409 Dec 19 21:28 paper_id_remap.parquet\n", + "drwxrwxr-x 2 ubuntu ubuntu 4096 Dec 19 21:28 part0\n", + "-rw-rw-r-- 1 ubuntu ubuntu 2934 Dec 19 21:28 subject_id_remap.parquet\n" + ] + } + ], + "source": [ + "!ls -al ./acm_gs_1p" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "d5a11133-75ef-4299-b167-81498c4f1dc4", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "total 1892\n", + "drwxrwxr-x 5 ubuntu ubuntu 4096 Dec 19 21:29 .\n", + "drwxrwxr-x 6 ubuntu ubuntu 4096 Dec 19 21:29 ..\n", + "-rw-rw-r-- 1 ubuntu ubuntu 3319 Dec 19 21:29 acm.json\n", + "-rw-rw-r-- 1 ubuntu ubuntu 213402 Dec 19 21:29 author_id_remap.parquet\n", + "-rw-rw-r-- 1 ubuntu ubuntu 191 Dec 19 21:29 edge_label_stats.json\n", + "-rw-rw-r-- 1 ubuntu ubuntu 1287802 Dec 19 21:29 edge_mapping.pt\n", + "-rw-rw-r-- 1 ubuntu ubuntu 515 Dec 19 21:29 node_label_stats.json\n", + "-rw-rw-r-- 1 ubuntu ubuntu 241655 Dec 19 21:29 node_mapping.pt\n", + "-rw-rw-r-- 1 ubuntu ubuntu 150409 Dec 19 21:29 paper_id_remap.parquet\n", + "drwxrwxr-x 2 ubuntu ubuntu 4096 Dec 19 21:29 part0\n", + "drwxrwxr-x 2 ubuntu ubuntu 4096 Dec 19 21:29 part1\n", + "drwxrwxr-x 2 ubuntu ubuntu 4096 Dec 19 21:29 part2\n", + "-rw-rw-r-- 1 ubuntu ubuntu 2934 Dec 19 21:29 subject_id_remap.parquet\n" + ] + } + ], + "source": [ + "!ls -al ./acm_gs_3p" + ] + }, + { + "cell_type": "markdown", + "id": "7cbd682e-36e8-4410-ae77-b0d6931f3052", + "metadata": {}, + "source": [ + "Because the choice of the different number of partitions, the two folders have different partition data sub-folders, named after \"part0\" to \"part***N***\", where ***N*** is the number of partitions specified with the `--num-parts` argument of construct_graph command.\n", + "\n", + "
\n", + "Tip: In the next sections, we use the 3-partition graph to explore these four sets of files and sub-folders one by one. But we will use the 1-partition graph in the other notebooks for GraphStorm standalone mode programming tutorials.
\n" + ] + }, + { + "cell_type": "markdown", + "id": "e675a0de-0324-4f5b-86b0-45e1909bbd68", + "metadata": {}, + "source": [ + "#### The GraphStorm Partition Configuration File `acm.json`\n", + "The `acm.json` file describe the partitioned graph that GraphStorm uses for model training and inference. \n", + "\n", + "It includes basic information about the partitioned graph, such as node and edge types, the number of each node and edge type, and the number of partitions along with the other partition mapping information." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "5f63bcba-fe24-4890-b232-4f9208886f02", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{\n", + " \"graph_name\": \"acm\",\n", + " \"num_nodes\": 30003,\n", + " \"num_edges\": 160686,\n", + " \"part_method\": \"metis\",\n", + " \"num_parts\": 3,\n", + " \"halo_hops\": 1,\n", + " \"node_map\": {\n", + " \"author\": [\n", + " [\n", + " 0,\n", + " 6015\n", + " ],\n", + " [\n", + " 10192,\n", + " 15856\n", + " ],\n", + " [\n", + " 19910,\n", + " 25662\n", + " ]\n", + " ],\n", + " \"paper\": [\n", + " [\n", + " 6015,\n", + " 10167\n", + " ],\n", + " [\n", + " 15856,\n", + " 19887\n", + " ],\n", + " [\n", + " 25662,\n", + " 29978\n", + " ]\n", + " ],\n", + " \"subject\": [\n", + " [\n", + " 10167,\n", + " 10192\n", + " ],\n", + " [\n", + " 19887,\n", + " 19910\n", + " ],\n", + " [\n", + " 29978,\n", + " 30003\n", + " ]\n", + " ]\n", + " },\n", + " \"edge_map\": {\n", + " \"author:writing:paper\": [\n", + " [\n", + " 0,\n", + " 12901\n", + " ],\n", + " [\n", + " 55137,\n", + " 67274\n", + " ],\n", + " [\n", + " 110148,\n", + " 122165\n", + " ]\n", + " ],\n", + " \"paper:cited:paper\": [\n", + " [\n", + " 12901,\n", + " 23675\n", + " ],\n", + " [\n", + " 67274,\n", + " 78554\n", + " ],\n", + " [\n", + " 122165,\n", + " 130900\n", + " ]\n", + " ],\n", + " \"paper:citing:paper\": [\n", + " [\n", + " 23675,\n", + " 34225\n", + " ],\n", + " [\n", + " 78554,\n", + " 89481\n", + " ],\n", + " [\n", + " 130900,\n", + " 140212\n", + " ]\n", + " ],\n", + " \"paper:is-about:subject\": [\n", + " [\n", + " 34225,\n", + " 38253\n", + " ],\n", + " [\n", + " 89481,\n", + " 93745\n", + " ],\n", + " [\n", + " 140212,\n", + " 144419\n", + " ]\n", + " ],\n", + " \"paper:written-by:author\": [\n", + " [\n", + " 38253,\n", + " 50985\n", + " ],\n", + " [\n", + " 93745,\n", + " 106117\n", + " ],\n", + " [\n", + " 144419,\n", + " 156370\n", + " ]\n", + " ],\n", + " \"subject:has:paper\": [\n", + " [\n", + " 50985,\n", + " 55137\n", + " ],\n", + " [\n", + " 106117,\n", + " 110148\n", + " ],\n", + " [\n", + " 156370,\n", + " 160686\n", + " ]\n", + " ]\n", + " },\n", + " \"ntypes\": {\n", + " \"author\": 0,\n", + " \"paper\": 1,\n", + " \"subject\": 2\n", + " },\n", + " \"etypes\": {\n", + " \"author:writing:paper\": 0,\n", + " \"paper:cited:paper\": 1,\n", + " \"paper:citing:paper\": 2,\n", + " \"paper:is-about:subject\": 3,\n", + " \"paper:written-by:author\": 4,\n", + " \"subject:has:paper\": 5\n", + " },\n", + " \"part-0\": {\n", + " \"node_feats\": \"part0/node_feat.dgl\",\n", + " \"edge_feats\": \"part0/edge_feat.dgl\",\n", + " \"part_graph\": \"part0/graph.dgl\"\n", + " },\n", + " \"part-1\": {\n", + " \"node_feats\": \"part1/node_feat.dgl\",\n", + " \"edge_feats\": \"part1/edge_feat.dgl\",\n", + " \"part_graph\": \"part1/graph.dgl\"\n", + " },\n", + " \"part-2\": {\n", + " \"node_feats\": \"part2/node_feat.dgl\",\n", + " \"edge_feats\": \"part2/edge_feat.dgl\",\n", + " \"part_graph\": \"part2/graph.dgl\"\n", + " }\n", + "}" + ] + } + ], + "source": [ + "!cat ./acm_gs_3p/acm.json" + ] + }, + { + "cell_type": "markdown", + "id": "c7c84137-0f41-4287-984f-a7358372fde6", + "metadata": {}, + "source": [ + "#### Raw Node ID Mapping Files `****_id_remap.parquet`\n", + "Because the original node ids could be any types, e.g., strings, integers, or even floats, during graph processing GraphStorm conducts an ID mapping, which map the original node ID space given by users into the interger type node ID space, starting from 0. This mapping information is stored in those `****_id_remap.parquet` files." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "bf47dee7-aabd-4d18-860b-218665a488a9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(17431, 2)\n" + ] + }, + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " orig new\n", + "765 a765 765\n", + "8438 a8438 8438\n", + "14914 a14914 14914\n", + "5227 a5227 5227" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "author_nid_mapping_df = pd.read_parquet('./acm_gs_3p/author_id_remap.parquet')\n", + "\n", + "print(author_nid_mapping_df.shape)\n", + "author_nid_mapping_df.sample(4)" + ] + }, + { + "cell_type": "markdown", + "id": "249a6a09-489a-4e45-a101-5ba459ffa50c", + "metadata": {}, + "source": [ + "As shown above, the `author_id_remap.parquet` file has two columns. The `orig` column contains the original string type node IDs in the raw node table data, while the `new` column contains the new integer node IDs in the Graph Node ID space." + ] + }, + { + "cell_type": "markdown", + "id": "05de822c-5eb6-4656-bbe6-b3bd0ba034b4", + "metadata": {}, + "source": [ + "#### GraphStorm Partition Node/Edge ID Mapping Files `****_mapping.pt`\n", + "GraphStorm relies on the distributed DGL graph as its input graph data. The distributed DGL graph has its own node ID space, thus creating another node id mapping during graph partition.\n", + "\n", + "These node id mappings, in the form of a python dictionary, are stored in those `****_mapping.pt` files, which can be loaded using Pytorch.\n", + "\n", + "
\n", + "Tip:In general, uses do not need to do the id mapping back operations. If use GraphStorm's command line interface to train models and do inference, GraphStorm will automatically remapping the partitioned ID space to the original node ID space.
" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "96b2fa7e-cf62-44f0-8f14-714b472e1c17", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Node id mapping:\n", + "Node mapping keys: ['author', 'paper', 'subject']\n", + "Node type 'author' first 10 mapping ids: tensor([16442, 7664, 7665, 7667, 16448, 7669, 7670, 16443, 7674, 16453])\n", + "\n", + "Edge id mapping:\n", + "Edge mapping keys: [('author', 'writing', 'paper'), ('paper', 'cited', 'paper'), ('paper', 'citing', 'paper'), ('paper', 'is-about', 'subject'), ('paper', 'written-by', 'author'), ('subject', 'has', 'paper')]\n", + "Edge type '('author', 'writing', 'paper')' first 10 mapping ids: tensor([ 8198, 15018, 3479, 253, 21728, 20622, 15980, 13148, 11788, 9858])\n", + "\n" + ] + } + ], + "source": [ + "import torch as th\n", + "\n", + "node_mapping_dict = th.load('./acm_gs_3p/node_mapping.pt')\n", + "print('Node id mapping:')\n", + "print(f'Node mapping keys: {list(node_mapping_dict.keys())}')\n", + "ntype0 = list(node_mapping_dict.keys())[0]\n", + "print(f'Node type \\'{ntype0}\\' first 10 mapping ids: {node_mapping_dict[ntype0][:10]}\\n')\n", + "\n", + "edge_mapping_dict = th.load('./acm_gs_3p/edge_mapping.pt')\n", + "print('Edge id mapping:')\n", + "print(f'Edge mapping keys: {list(edge_mapping_dict.keys())}')\n", + "etype0 = list(edge_mapping_dict.keys())[0]\n", + "print(f'Edge type \\'{etype0}\\' first 10 mapping ids: {edge_mapping_dict[etype0][:10]}\\n')" + ] + }, + { + "cell_type": "markdown", + "id": "80ff4d2a", + "metadata": {}, + "source": [ + "The ID mapping logic in those tensors is that GraphStorm graph ID is stored in these tensors, and their position indexes are the new partitioned node IDs. For example, for \"author\" nodes, the GraphStorm graph ID `16442` has a new partitioned node ID `0` because the number `16642` is in the first position (index=`0`) of the mapping tensor." + ] + }, + { + "cell_type": "markdown", + "id": "431462ea-401b-497b-a8b0-73a78f92a34e", + "metadata": {}, + "source": [ + "#### Label Statistic Files `****_label_stats.json`\n", + "\n", + "If users specify the label statistc property in the `config.json` file, e.g., for the \"paper\" node's `label` object setting `\"label_stats_type\": \"frequency_cnt\"`, GraphStorm will collect labels' statistics and stored in the `****_label_stats.json` files." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "144790b0-dcca-4907-ab5b-f68d522a985d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{\n", + " \"author\": {},\n", + " \"paper\": {\n", + " \"label\": {\n", + " \"stats_type\": \"frequency_cnt\",\n", + " \"info\": {\n", + " \"0\": 838,\n", + " \"1\": 1118,\n", + " \"2\": 1349,\n", + " \"3\": 1277,\n", + " \"4\": 1381,\n", + " \"5\": 521,\n", + " \"7\": 270,\n", + " \"8\": 370,\n", + " \"9\": 509,\n", + " \"10\": 258,\n", + " \"11\": 374,\n", + " \"12\": 1270,\n", + " \"13\": 464\n", + " }\n", + " }\n", + " },\n", + " \"subject\": {}\n", + "}" + ] + } + ], + "source": [ + "!cat ./acm_gs_3p/node_label_stats.json" + ] + }, + { + "cell_type": "markdown", + "id": "2ae5ed8d-72e5-4fbb-b31b-0291dfb43ab3", + "metadata": {}, + "source": [ + "#### Partitioned Graph Data `partN/***.dgl`\n", + "\n", + "The distributed DGL graph datasets are saved in these `partN` subfolders, each of which contains three DGL formated files:\n", + "1. `edge_feat.dgl`: edge features of one partition if have.\n", + "2. `graph.dgl`: graph structure of one partition.\n", + "3. `node_feat.dgl`: node features of one partition if have." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "4de36699-68f3-4c2f-a7aa-ccc8640ea3e1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "total 13892\n", + "drwxrwxr-x 2 ubuntu ubuntu 4096 Dec 19 21:29 .\n", + "drwxrwxr-x 6 ubuntu ubuntu 4096 Dec 19 21:29 ..\n", + "-rw-rw-r-- 1 ubuntu ubuntu 31926 Dec 19 21:29 edge_feat.dgl\n", + "-rw-rw-r-- 1 ubuntu ubuntu 2081555 Dec 19 21:29 graph.dgl\n", + "-rw-rw-r-- 1 ubuntu ubuntu 12097671 Dec 19 21:29 node_feat.dgl\n" + ] + } + ], + "source": [ + "!ls -al ./acm_gs_3p/part0" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "466a4da8-85be-490f-9da6-b9f5c3f92029", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "gsf", + "language": "python", + "name": "gsf" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.18" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/source/tutorials/own-data.rst b/docs/source/tutorials/own-data.rst index 0c4226ff09..69c1cf2161 100644 --- a/docs/source/tutorials/own-data.rst +++ b/docs/source/tutorials/own-data.rst @@ -217,14 +217,18 @@ The above command reads in the JSON file, and matchs its contents with the node /tmp/acm_gs acm.json + author_id_remap.parquet + edge_label_stats.json edge_label_stats.json edge_mapping.pt node_label_stats.json node_mapping.pt + paper_id_remap.parquet |- part0 edge_feat.dgl graph.dgl node_feat.dgl + subject_id_remap.parquet Because the above command specifies the ``--num-parts`` to be ``1``, there is only one partition created, which is saved in the ``part0`` folder. These files become the inputs of GraphStorm's launch scripts. diff --git a/examples/acm_data.py b/examples/acm_data.py index 5fda275df4..3573753fdf 100644 --- a/examples/acm_data.py +++ b/examples/acm_data.py @@ -67,6 +67,10 @@ def create_acm_raw_data(graph, This graph is based on the DGL graph created by the create_acm_dgl_graph() function. Because we only use three relationships in the original ACM data, the number of graph nodes could be less than the papers, authors, and subjects in the original node lists. + + In addition, to demonstrate the use of string type node ids in the raw graph data, we add the + first letter of each node type name to the original numerical ids, i.e., "author" -> "a", + "paper" -> "p", and "subject" -> "s". Parameters ---------- @@ -88,12 +92,19 @@ def create_acm_raw_data(graph, # generate node dataframe: we use the graph node ids and node name as node_type node_list = [] + # extract the first letter of each node type name as the prefix + node_prefix_dict = {} + for ntype in graph.ntypes: + node_prefix_dict[ntype] = ntype[0] + for ntype in graph.ntypes: node_dict = {} # generate the id column node_ids = graph.nodes(ntype) - # convert tensor to list of arrays for saving in parquet format - node_dict['node_id'] = convert_tensor_to_list_arrays(node_ids) + # pad a prefix before each node id + str_node_ids = np.array([f'{node_prefix_dict[ntype]}{i}' for i in node_ids.numpy()]) + + node_dict['node_id'] = str_node_ids # generate the feature columns and label column if graph.nodes[ntype].data: @@ -124,9 +135,11 @@ def create_acm_raw_data(graph, edge_dict = {} # generate the ids columns for both source nodes and destination nodes src_ids, dst_ids = graph.edges(etype=(src_ntype, etype, dst_ntype)) - # convert tensor to list of arrays for saving in parquet format - edge_dict['source_id'] = convert_tensor_to_list_arrays(src_ids) - edge_dict['dest_id'] = convert_tensor_to_list_arrays(dst_ids) + # pad a prefix before each node id + str_src_ids = np.array([f'{node_prefix_dict[src_ntype]}{i}' for i in src_ids.numpy()]) + str_dst_ids = np.array([f'{node_prefix_dict[dst_ntype]}{i}' for i in dst_ids.numpy()]) + edge_dict['source_id'] = str_src_ids + edge_dict['dest_id'] = str_dst_ids # generate feature columns and label col if graph.edges[(src_ntype, etype, dst_ntype)].data: @@ -190,6 +203,7 @@ def create_acm_raw_data(graph, label_dict['label_col'] = col label_dict['task_type'] = 'classification' label_dict['split_pct'] = [0.8, 0.1, 0.1] + label_dict['label_stats_type'] = 'frequency_cnt' labels_list.append(label_dict) elif col == 'text': feat_dict['feature_col'] = col @@ -231,7 +245,7 @@ def create_acm_raw_data(graph, elif col == 'dest_id': edge_dict['dest_id_col'] = col elif col == 'label': - label_dict['task_type'] = 'link_prediction' # In ACM data, we do not have this + label_dict['task_type'] = 'link_prediction' # In ACM data, we do not have this # edge task. 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zFbsVd0^-c0_TYyF=E?JIz_2N>hs0JnNY5AeTY<&pWMY*$2uHpZ|JdB+yr9>MHiEtj zN&ik_`Q^Vm`|5dM!iSw}WdD_4nOo+D7>r5EI~Mx&{#R9mvVz$z`$Sjj2dev#W>(px z0(Qh*XFfVU&Z2slDydP0zvDmtW*3%IGVEQ$7EJzs_s zte%o4l;=c}E>J1Q-(`V!cpCqi|APw(EjFCedtk0yBD=`^EB!D2b6EwobK*)S-{QH? zt?aHzjgbkxQ@k1UA^TpmCOFNFWE;K)oB#R>mVit?LZ@U9+ng}CUz$1CqOzwwS~=hS zQln(11dhAVO=XvcQ^HbP3iWh2$!A6Xu@+yN=u~S%8hd?YhDSo>&e Date: Thu, 11 Jan 2024 08:39:05 -0800 Subject: [PATCH 12/14] [Enhancement] change the input argument of GSTaskTrackerAbc to be an integer (#699) *Issue #, if available:* *Description of changes:* - This PR changes the input argument of `GSTaskTrackerAbc` from `GSConfig` object into an integer because the `GSTaskTrackerAbc` only needs an integer to set the `log_report_frequency` attribute. - Using the `GSConfig` object will prevent users from using task tracker to monitor running process because creating a GSConfig is NOT publicly open, and is very complex. - Decouple the `GSTaskTracker` from using `GSConfig` could help users to construct task trackers and use them in the GraphStorm programming APIs. By submitting this pull request, I confirm that you can use, modify, copy, and redistribute this contribution, under the terms of your choice. --------- Co-authored-by: Ubuntu Co-authored-by: xiang song(charlie.song) --- docs/source/advanced/own-models.rst | 2 +- docs/source/configuration/configuration-run.rst | 7 +------ examples/customized_models/HGT/hgt_nc.py | 2 +- examples/peft_llm_gnn/main_nc.py | 2 +- examples/peft_llm_gnn/nc_config_Video_Games.yaml | 3 --- python/graphstorm/gsf.py | 2 +- python/graphstorm/tracker/graphstorm_tracker.py | 10 ++++++---- python/graphstorm/tracker/sagemaker_tracker.py | 7 +++++-- 8 files changed, 16 insertions(+), 19 deletions(-) diff --git a/docs/source/advanced/own-models.rst b/docs/source/advanced/own-models.rst index 3b9d9eb8c5..bde3b66d46 100644 --- a/docs/source/advanced/own-models.rst +++ b/docs/source/advanced/own-models.rst @@ -272,7 +272,7 @@ The GraphStorm trainers can have evaluators and task trackers associated. The fo config.early_stop_strategy) trainer.setup_evaluator(evaluator) # Optional: set up a task tracker to show the progress of training. - tracker = GSSageMakerTaskTracker(config) + tracker = GSSageMakerTaskTracker(config.eval_frequency) trainer.setup_task_tracker(tracker) GraphStorm's `evaluators `_ could help to compute the required evaluation metrics, such as ``accuracy``, ``f1``, ``mrr``, and etc. Users can select the proper evaluator and use the trainer's ``setup_evaluator()`` method to attach them. GraphStorm's `task trackers `_ serve as log collectors, which are used to show the process information. diff --git a/docs/source/configuration/configuration-run.rst b/docs/source/configuration/configuration-run.rst index f0c1afa854..276e97d08d 100644 --- a/docs/source/configuration/configuration-run.rst +++ b/docs/source/configuration/configuration-run.rst @@ -126,11 +126,6 @@ GraphStorm provides a set of parameters to control how and where to save and res - Yaml: ``task_tracker: sagemaker_task_tracker`` - Argument: ``--task_tracker sagemaker_task_tracker`` - Default value: ``sagemaker_task_tracker`` -- **log_report_frequency**: The frequency of reporting model performance metrics through task_tracker. The frequency is defined by using number of iterations, i.e., every N iterations the evaluation metrics will be reported. (Please note the evaluation metrics should be generated at the reporting iteration. See "eval_frequency" for how evaluation frequency is controlled.) - - - Yaml: ``log_report_frequency: 1000`` - - Argument: ``--log-report-frequency 1000`` - - Default value: ``1000`` - **restore_model_path**: A path where GraphStorm model parameters were saved. For training, if restore_model_path is set, GraphStom will retrieve the model parameters from restore_model_path instead of initializing the parameters. For inference, restore_model_path must be provided. - Yaml: ``restore_model_path: /model/checkpoint/`` @@ -278,7 +273,7 @@ GraphStorm provides a set of parameters to control model evaluation. - Yaml: ``use_mini_batch_infer: false`` - Argument: ``--use-mini-batch-infer false`` - Default value: ``true`` -- **eval_frequency**: The frequency of doing evaluation. GraphStorm trainers do evaluation at the end of each epoch. However, for large-scale graphs, training one epoch may take hundreds of thousands of iterations. One may want to do evaluations in the middle of an epoch. When eval_frequency is set, every **eval_frequency** iterations, the trainer will do evaluation once. The evaluation results can be printed and reported. See **log_report_frequency** for more details. +- **eval_frequency**: The frequency of doing evaluation. GraphStorm trainers do evaluation at the end of each epoch. However, for large-scale graphs, training one epoch may take hundreds of thousands of iterations. One may want to do evaluations in the middle of an epoch. When eval_frequency is set, every **eval_frequency** iterations, the trainer will do evaluation once. The evaluation results can be printed and reported. - Yaml: ``eval_frequency: 10000`` - Argument: ``--eval-frequency 10000`` diff --git a/examples/customized_models/HGT/hgt_nc.py b/examples/customized_models/HGT/hgt_nc.py index 6da88e6870..debb185858 100644 --- a/examples/customized_models/HGT/hgt_nc.py +++ b/examples/customized_models/HGT/hgt_nc.py @@ -335,7 +335,7 @@ def main(args): config.early_stop_strategy) trainer.setup_evaluator(evaluator) # Optional: set up a task tracker to show the progress of training. - tracker = GSSageMakerTaskTracker(config) + tracker = GSSageMakerTaskTracker(config.eval_frequency) trainer.setup_task_tracker(tracker) # Start the training process. diff --git a/examples/peft_llm_gnn/main_nc.py b/examples/peft_llm_gnn/main_nc.py index ae45ffeb1f..6fcd7237f9 100644 --- a/examples/peft_llm_gnn/main_nc.py +++ b/examples/peft_llm_gnn/main_nc.py @@ -62,7 +62,7 @@ def main(config_args): config.early_stop_strategy, ) trainer.setup_evaluator(evaluator) - tracker = GSSageMakerTaskTracker(config) + tracker = GSSageMakerTaskTracker(config.eval_frequency) trainer.setup_task_tracker(tracker) # create train loader diff --git a/examples/peft_llm_gnn/nc_config_Video_Games.yaml b/examples/peft_llm_gnn/nc_config_Video_Games.yaml index 6a2d0129dd..626553d8c9 100644 --- a/examples/peft_llm_gnn/nc_config_Video_Games.yaml +++ b/examples/peft_llm_gnn/nc_config_Video_Games.yaml @@ -19,11 +19,8 @@ gsf: batch_size: 4 dropout: 0.1 eval_batch_size: 4 - # eval_frequency: 100 - #log_report_frequency: 50 lr: 0.0001 num_epochs: 10 - # save_model_frequency: 300 wd_l2norm: 1.0e-06 input: restore_model_path: null diff --git a/python/graphstorm/gsf.py b/python/graphstorm/gsf.py index 124ed576a6..97dabd1164 100644 --- a/python/graphstorm/gsf.py +++ b/python/graphstorm/gsf.py @@ -656,4 +656,4 @@ def check_homo(g): def create_builtin_task_tracker(config): tracker_class = get_task_tracker_class(config.task_tracker) - return tracker_class(config) + return tracker_class(config.eval_frequency) diff --git a/python/graphstorm/tracker/graphstorm_tracker.py b/python/graphstorm/tracker/graphstorm_tracker.py index ece0955e84..a9e0c6055c 100644 --- a/python/graphstorm/tracker/graphstorm_tracker.py +++ b/python/graphstorm/tracker/graphstorm_tracker.py @@ -22,11 +22,13 @@ class GSTaskTrackerAbc(): Parameters ---------- - config: GSConfig - Configurations. Users can add their own configures in the yaml config file. + log_report_frequency: int + The frequency of reporting model performance metrics through task_tracker. + The frequency is defined by using number of iterations, i.e., every N iterations + the evaluation metrics will be reported. """ - def __init__(self, config): - self._report_frequency = config.log_report_frequency # Can be None if not provided + def __init__(self, log_report_frequency): + self._report_frequency = log_report_frequency # Can be None if not provided @abc.abstractmethod def log_metric(self, metric_name, metric_value, step, force_report=False): diff --git a/python/graphstorm/tracker/sagemaker_tracker.py b/python/graphstorm/tracker/sagemaker_tracker.py index 06276943bd..3ae5fd5a8a 100644 --- a/python/graphstorm/tracker/sagemaker_tracker.py +++ b/python/graphstorm/tracker/sagemaker_tracker.py @@ -25,8 +25,11 @@ class GSSageMakerTaskTracker(GSTaskTrackerAbc): Parameters ---------- - config: GSConfig - Configurations. Users can add their own configures in the yaml config file. + log_report_frequency: int + The frequency of reporting model performance metrics through task_tracker. + The frequency is defined by using number of iterations, i.e., every N iterations + the evaluation metrics will be reported. + """ def _do_report(self, step): From f8f493703c27d2222485f78561ffc90da65e6092 Mon Sep 17 00:00:00 2001 From: jalencato Date: Thu, 11 Jan 2024 18:25:10 -0800 Subject: [PATCH 13/14] [SageMaker] Upgrade DGL Tool Version (#698) *Issue #, if available:* *Description of changes:* By submitting this pull request, I confirm that you can use, modify, copy, and redistribute this contribution, under the terms of your choice. --- docker/sagemaker/Dockerfile.sm | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docker/sagemaker/Dockerfile.sm b/docker/sagemaker/Dockerfile.sm index bad7dd4841..93c2c6092f 100644 --- a/docker/sagemaker/Dockerfile.sm +++ b/docker/sagemaker/Dockerfile.sm @@ -46,7 +46,7 @@ ENV PYTHONPATH="/opt/ml/code/graphstorm/python/:${PYTHONPATH}" RUN cp /opt/ml/code/graphstorm/sagemaker/run/* /opt/ml/code/ # Download DGL source code -RUN cd /root; git clone https://github.com/dmlc/dgl.git; cd dgl; git checkout -b 1.1.0 1.1.0 +RUN cd /root; git clone https://github.com/dmlc/dgl.git # Un-comment if we prefer a local DGL distribution # COPY dgl /root/dgl ENV PYTHONPATH="/root/dgl/tools/:${PYTHONPATH}" From aacf5204c60036cd3782d48d4dc532fc95977000 Mon Sep 17 00:00:00 2001 From: Chang Liu Date: Thu, 11 Jan 2024 21:34:37 -0800 Subject: [PATCH 14/14] [WholeGraph] Refactor code to separate WholeGraph-related functions (#697) This pull request refactors the code to separate WholeGraph-related functions into a separate module. This improves code organization and makes it easier to work with/extend WholeGraph functionality. --- python/graphstorm/dataloading/dataset.py | 81 ++---- python/graphstorm/dataloading/utils.py | 10 - python/graphstorm/gsf.py | 25 +- python/graphstorm/wholegraph/__init__.py | 19 ++ python/graphstorm/wholegraph/utils.py | 28 ++ python/graphstorm/wholegraph/wholegraph.py | 285 +++++++++++++++++++++ tools/convert_feat_to_wholegraph.py | 184 +------------ 7 files changed, 353 insertions(+), 279 deletions(-) create mode 100644 python/graphstorm/wholegraph/__init__.py create mode 100644 python/graphstorm/wholegraph/utils.py create mode 100644 python/graphstorm/wholegraph/wholegraph.py diff --git a/python/graphstorm/dataloading/dataset.py b/python/graphstorm/dataloading/dataset.py index 227ceff8bc..14e25eaf10 100644 --- a/python/graphstorm/dataloading/dataset.py +++ b/python/graphstorm/dataloading/dataset.py @@ -17,7 +17,6 @@ """ import os import abc -import json import logging import re @@ -27,9 +26,11 @@ from torch.utils.data import Dataset import pandas as pd -from ..utils import get_rank, get_world_size, is_distributed, barrier -from ..utils import sys_tracker, is_wholegraph -from .utils import dist_sum, flip_node_mask, is_wholegraph_embedding +from ..utils import get_rank, get_world_size, is_distributed, barrier, is_wholegraph +from ..utils import sys_tracker +from .utils import dist_sum, flip_node_mask + +from ..wholegraph import is_wholegraph_embedding def split_full_edge_list(g, etype, rank): ''' Split the full edge list of a graph. @@ -168,6 +169,7 @@ def __init__(self, graph_name, part_config, node_feat_field, edge_feat_field, # Use wholegraph for feature transfer if is_distributed() and is_wholegraph(): + from ..wholegraph import load_wg_feat logging.info("Allocate features with Wholegraph") num_parts = self._g.get_partition_book().num_partitions() @@ -184,7 +186,7 @@ def __init__(self, graph_name, part_config, node_feat_field, edge_feat_field, f"Feature '{name}' of '{ntype}' is not in WholeGraph format. " \ f"Please convert all the available features to WholeGraph " \ f"format to utilize WholeGraph." - data[name] = self.load_wg_feat(part_config, num_parts, ntype, name) + data[name] = load_wg_feat(part_config, num_parts, ntype, name) if len(self._g.ntypes) == 1: self._g._ndata_store.update(data) else: @@ -206,7 +208,7 @@ def __init__(self, graph_name, part_config, node_feat_field, edge_feat_field, f"Feature '{name}' of '{etype}' is not in WholeGraph format. " \ f"Please convert all the available features to WholeGraph " \ f"format to utilize WholeGraph." - data[name] = self.load_wg_feat(part_config, num_parts, etype_wg, name) + data[name] = load_wg_feat(part_config, num_parts, etype_wg, name) if len(self._g.canonical_etypes) == 1: self._g._edata_store.update(data) else: @@ -241,53 +243,6 @@ def edge_feat_field(self): """the field of edge feature""" return self._edge_feat_field - def load_wg_feat(self, part_config_path, num_parts, type_name, name): - """Load features from wholegraph memory - - Parameters - ---------- - part_config_path : str - The path of the partition configuration file. - num_parts : int - The number of partitions of the dataset - type_name: str - The type of node or edge for which to fetch features or labels for. - name: str - The name of the features to load - """ - import pylibwholegraph.torch as wgth - - global_comm = wgth.comm.get_global_communicator() - feature_comm = global_comm - embedding_wholememory_type = 'distributed' - embedding_wholememory_location = 'cpu' - cache_policy = wgth.create_builtin_cache_policy( - "none", # cache type - embedding_wholememory_type, - embedding_wholememory_location, - "readonly", # access type - 0.0, # cache ratio - ) - metadata_file = os.path.join(os.path.dirname(part_config_path), - 'wholegraph/metadata.json') - with open(metadata_file, encoding="utf8") as f: - wg_metadata = json.load(f) - data_shape = wg_metadata[type_name + '/' + name]['shape'] - feat_wm_embedding = wgth.create_embedding( - feature_comm, - embedding_wholememory_type, - embedding_wholememory_location, - getattr(th, wg_metadata[type_name + '/' + name]['dtype'].split('.')[1]), - [data_shape[0],1] if len(data_shape) == 1 else data_shape, - optimizer=None, - cache_policy=cache_policy, - ) - feat_path = os.path.join(os.path.dirname(part_config_path), 'wholegraph', \ - type_name + '~' + name) - feat_wm_embedding.get_embedding_tensor().from_file_prefix(feat_path, - part_count=num_parts) - return feat_wm_embedding - def has_node_feats(self, ntype): """ Test if the specified node type has features. @@ -514,7 +469,7 @@ class GSgnnEdgeTrainData(GSgnnEdgeData): different feature names. decoder_edge_feat: str or dict of list of str Edge features used by decoder - + Examples ---------- @@ -525,7 +480,7 @@ class GSgnnEdgeTrainData(GSgnnEdgeData): ep_data = GSgnnEdgeTrainData(graph_name='dummy', part_config=part_config, train_etypes=[('n1', 'e1', 'n2')], label_field='label', node_feat_field='node_feat', edge_feat_field='edge_feat') - ep_dataloader = GSgnnEdgeDataLoader(ep_data, target_idx={"e1":[0]}, + ep_dataloader = GSgnnEdgeDataLoader(ep_data, target_idx={"e1":[0]}, fanout=[15, 10], batch_size=128) """ def __init__(self, graph_name, part_config, train_etypes, eval_etypes=None, @@ -710,7 +665,7 @@ class GSgnnEdgeInferData(GSgnnEdgeData): The node types that contains text features. lm_feat_etypes : list of tuples The edge types that contains text features. - + Examples ---------- @@ -721,7 +676,7 @@ class GSgnnEdgeInferData(GSgnnEdgeData): ep_data = GSgnnEdgeInferData(graph_name='dummy', part_config=part_config, eval_etypes=[('n1', 'e1', 'n2')], label_field='label', node_feat_field='node_feat', edge_feat_field='edge_feat') - ep_dataloader = GSgnnEdgeDataLoader(ep_data, target_idx={"e1":[0]}, + ep_dataloader = GSgnnEdgeDataLoader(ep_data, target_idx={"e1":[0]}, fanout=[15, 10], batch_size=128) """ def __init__(self, graph_name, part_config, eval_etypes, @@ -911,7 +866,7 @@ class GSgnnNodeTrainData(GSgnnNodeData): The node types that contains text features. lm_feat_etypes : list of tuples The edge types that contains text features. - + Examples ---------- @@ -923,7 +878,7 @@ class GSgnnNodeTrainData(GSgnnNodeData): np_data = GSgnnNodeTrainData(graph_name='dummy', part_config=part_config, train_ntypes=['n1'], label_field='label', node_feat_field='feat') - np_dataloader = GSgnnNodeDataLoader(np_data, target_idx={'n1':[0]}, + np_dataloader = GSgnnNodeDataLoader(np_data, target_idx={'n1':[0]}, fanout=[15, 10], batch_size=128) """ def __init__(self, graph_name, part_config, train_ntypes, eval_ntypes=None, @@ -1066,19 +1021,19 @@ class GSgnnNodeInferData(GSgnnNodeData): The node types that contains text features. lm_feat_etypes : list of tuples The edge types that contains text features. - + Examples ---------- - + .. code:: python from graphstorm.dataloading import GSgnnNodeInferData - from graphstorm.dataloading import + from graphstorm.dataloading import np_data = GSgnnNodeInferData(graph_name='dummy', part_config=part_config, eval_ntypes=['n1'], label_field='label', node_feat_field='feat') - np_dataloader = GSgnnNodeDataLoader(np_data, target_idx={'n1':[0]}, + np_dataloader = GSgnnNodeDataLoader(np_data, target_idx={'n1':[0]}, fanout=[15, 10], batch_size=128) """ def __init__(self, graph_name, part_config, eval_ntypes, diff --git a/python/graphstorm/dataloading/utils.py b/python/graphstorm/dataloading/utils.py index 78ac1cc84d..122ddeab34 100644 --- a/python/graphstorm/dataloading/utils.py +++ b/python/graphstorm/dataloading/utils.py @@ -137,13 +137,3 @@ def flip_node_mask(dist_tensor, indices): part_policy=dist_tensor.part_policy) flipped_dist_tensor[indices] = 1 - dist_tensor[indices] return flipped_dist_tensor - -def is_wholegraph_embedding(data): - """ Check if the data is in WholeMemory emedding format which - is required to use wholegraph framework. - """ - try: - import pylibwholegraph - return isinstance(data, pylibwholegraph.torch.WholeMemoryEmbedding) - except: # pylint: disable=bare-except - return False diff --git a/python/graphstorm/gsf.py b/python/graphstorm/gsf.py index 97dabd1164..03afdb8603 100644 --- a/python/graphstorm/gsf.py +++ b/python/graphstorm/gsf.py @@ -22,7 +22,6 @@ import dgl import torch as th import torch.nn.functional as F -from dataclasses import dataclass from dgl.distributed import role from dgl.distributed.constants import DEFAULT_NTYPE from dgl.distributed.constants import DEFAULT_ETYPE @@ -65,29 +64,6 @@ LinkPredictWeightedDistMultDecoder) from .tracker import get_task_tracker_class -def init_wholegraph(): - """ Initialize Wholegraph""" - import pylibwholegraph.torch as wgth - import pylibwholegraph.binding.wholememory_binding as wmb - - @dataclass - class Options: # pylint: disable=missing-class-docstring - pass - Options.launch_agent = 'pytorch' - Options.launch_env_name_world_rank = 'RANK' - Options.launch_env_name_world_size = 'WORLD_SIZE' - Options.launch_env_name_local_rank = 'LOCAL_RANK' - Options.launch_env_name_local_size = 'LOCAL_WORLD_SIZE' - Options.launch_env_name_master_addr = 'MASTER_ADDR' - Options.launch_env_name_master_port = 'MASTER_PORT' - Options.local_rank = get_rank() % role.get_num_trainers() - Options.local_size = role.get_num_trainers() - - wgth.distributed_launch(Options, lambda: None) - wmb.init(0) - wgth.comm.set_world_info(get_rank(), get_world_size(), Options.local_rank, - Options.local_size) - def initialize(ip_config, backend, use_wholegraph=False): """ Initialize distributed training and inference context. @@ -108,6 +84,7 @@ def initialize(ip_config, backend, use_wholegraph=False): th.distributed.init_process_group(backend=backend) # Use wholegraph for feature and label fetching if use_wholegraph: + from .wholegraph import init_wholegraph init_wholegraph() sys_tracker.check("load DistDGL") diff --git a/python/graphstorm/wholegraph/__init__.py b/python/graphstorm/wholegraph/__init__.py new file mode 100644 index 0000000000..705b2f7a81 --- /dev/null +++ b/python/graphstorm/wholegraph/__init__.py @@ -0,0 +1,19 @@ +""" + Copyright 2023 Contributors + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. + + Initial to import WholeGraph-related function and classes +""" +from .wholegraph import (init_wholegraph, convert_feat_to_wholegraph, load_wg_feat) +from .utils import is_wholegraph_embedding diff --git a/python/graphstorm/wholegraph/utils.py b/python/graphstorm/wholegraph/utils.py new file mode 100644 index 0000000000..b0cbb7ec59 --- /dev/null +++ b/python/graphstorm/wholegraph/utils.py @@ -0,0 +1,28 @@ +""" + Copyright 2023 Contributors + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. + + Utils for integrating WholeGraph into GraphStorm +""" + + +def is_wholegraph_embedding(data): + """ Check if the data is in WholeMemory emedding format which + is required to use wholegraph framework. + """ + try: + import pylibwholegraph + return isinstance(data, pylibwholegraph.torch.WholeMemoryEmbedding) + except ImportError: + return False diff --git a/python/graphstorm/wholegraph/wholegraph.py b/python/graphstorm/wholegraph/wholegraph.py new file mode 100644 index 0000000000..6f212fec4d --- /dev/null +++ b/python/graphstorm/wholegraph/wholegraph.py @@ -0,0 +1,285 @@ +""" + Copyright 2023 Contributors + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. + + Functions/classes for integrating WholeGraph into GraphStorm +""" + +import os + +import json +import gc +import logging +import re + +import torch as th +import dgl +from typing import Optional +from dataclasses import dataclass + +from ..utils import get_rank, get_world_size + +try: + import pylibwholegraph.torch as wgth +except ImportError: + wgth = None + + +def init_wholegraph(): + """ Initialize Wholegraph""" + if wgth is None: + raise ImportError("WholeGraph is not installed") + from dgl.distributed import role + import pylibwholegraph.binding.wholememory_binding as wmb + + @dataclass + class Options: # pylint: disable=missing-class-docstring + pass + Options.launch_agent = 'pytorch' + Options.launch_env_name_world_rank = 'RANK' + Options.launch_env_name_world_size = 'WORLD_SIZE' + Options.launch_env_name_local_rank = 'LOCAL_RANK' + Options.launch_env_name_local_size = 'LOCAL_WORLD_SIZE' + Options.launch_env_name_master_addr = 'MASTER_ADDR' + Options.launch_env_name_master_port = 'MASTER_PORT' + Options.local_rank = get_rank() % role.get_num_trainers() + Options.local_size = role.get_num_trainers() + + wgth.distributed_launch(Options, lambda: None) + wmb.init(0) + wgth.comm.set_world_info(get_rank(), get_world_size(), Options.local_rank, + Options.local_size) + + +def wholegraph_processing( + whole_feat_tensor, metadata, feat, wg_folder, num_parts +): + """Convert DGL tensors to wholememory tensor + + Parameters + ---------- + whole_feat_tensor : Tensor + The concatenated feature tensor of different partitions + metadata : Tensor + Metadata of the feature tensor + feat : str + Name of the feature to be converted + wg_folder : str + Name of the folder to store the converted files + num_parts : int + Number of partitions of the input features + """ + metadata[feat] = { + "shape": list(whole_feat_tensor.shape), + "dtype": str(whole_feat_tensor.dtype), + } + local_comm = wgth.comm.get_local_device_communicator() + # Round up the integer division to match WholeGraph partitioning scheme + subpart_size = -(whole_feat_tensor.shape[0] // -num_parts) + + for part_num in range(num_parts): + st = part_num * subpart_size + end = (part_num + 1) * subpart_size \ + if part_num != (num_parts - 1) \ + else whole_feat_tensor.shape[0] + + wg_tensor = wgth.create_wholememory_tensor( + local_comm, + "continuous", + "cpu", + (end - st, *whole_feat_tensor.shape[1:]), + whole_feat_tensor.dtype, + None, + ) + local_tensor, _ = wg_tensor.get_local_tensor(host_view=True) + local_tensor.copy_(whole_feat_tensor[st:end]) + filename = wgth.utils.get_part_file_name( + feat.replace("/", "~"), part_num, num_parts + ) + wg_tensor.local_to_file(os.path.join(wg_folder, filename)) + wgth.destroy_wholememory_tensor(wg_tensor) + + +def trim_feat_files(trimmed_feats, folder, file_name, part): + """Save new truncated distDGL tensors + Parameters + ---------- + trimmed_feats : list of tensors + distDGL tensors after trimming out the processed features + folder : str + Name of the folder of the input feature files + file_name : str + Name of the feature file, either node_feat.dgl or edge_feat.dgl + part : int + Partition number of the input feature files + + """ + dgl.data.utils.save_tensors( + os.path.join(folder, f"part{part}", "new_" + file_name), trimmed_feats[part] + ) + os.rename( + os.path.join(folder, f"part{part}", file_name), + os.path.join(folder, f"part{part}", file_name + ".bak"), + ) + os.rename( + os.path.join(folder, f"part{part}", "new_" + file_name), + os.path.join(folder, f"part{part}", file_name), + ) + + +def convert_feat_to_wholegraph(fname_dict, file_name, metadata, folder, use_low_mem): + """Convert features from distDGL tensor format to WholeGraph format + + Parameters + ---------- + fname_dict: dict of list + Dict of names of the edge features of different edge types + file_name: + Name of the feature file, either node_feat.dgl or edge_feat.dgl + metadata : Tensor + Metadata of the feature tensor + folder: str + Name of the folder of the input feature files + use_low_mem: bool + Whether to use low memory version for conversion + """ + wg_folder = os.path.join(folder, "wholegraph") + folder_pattern = re.compile(r"^part[0-9]+$") + part_files = [ + f + for f in os.listdir(folder) + if os.path.isdir(os.path.join(folder, f)) and folder_pattern.match(f) + ] + part_files = sorted(part_files, key=lambda x: int(x.split("part")[1])) + feats_data = [] + + # When 'use_low_mem' is not enabled, this code loads and appends features from individual + # partitions. Then features are concatenated and converted into the WholeGraph format one + # by one. The minimum memory requirement for this approach is 2X the size of the input + # nodes or edges features in the graph. + if not use_low_mem: + # Read features from file + for path in (os.path.join(folder, name) for name in part_files): + feats_data.append(dgl.data.utils.load_tensors(f"{path}/{file_name}")) + num_parts = len(feats_data) + for type_name, feats in fname_dict.items(): + for feat in feats: + feat = type_name + "/" + feat + if feat not in feats_data[0]: + raise RuntimeError( + f"Error: Unknown feature '{feat}'. Files contain \ + the following features: {feats_data[0].keys()}." + ) + logging.info("Processing %s features...", feat) + whole_feat_tensor = th.concat( + tuple(t[feat] for t in feats_data), dim=0 + ) + # Delete processed feature from memory + for t in feats_data: + del t[feat] + wholegraph_processing( + whole_feat_tensor, metadata, feat, wg_folder, num_parts + ) + # Trim the original distDGL tensors + for part in range(num_parts): + trim_feat_files(feats_data, folder, file_name, part) + + # This low-memory version loads one partition at a time. It processes features one by one, + # iterating through all the partitions and appending only the current feature, converting + # it to a WholeGraph. The minimum memory requirement for this approach is 2X the size of + # the largest node or edge feature in the graph. + else: # low-mem + for ntype, feats in fname_dict.items(): + for feat in feats: + feat = ntype + "/" + feat + node_feats_data = None + num_parts = 0 + # Read features from file + for path in (os.path.join(folder, name) for name in part_files): + nfeat = dgl.data.utils.load_tensors(f"{path}/{file_name}") + if feat not in nfeat: + raise RuntimeError( + f"Error: Unknown feature '{feat}'. Files contain \ + the following features: {nfeat.keys()}." + ) + if node_feats_data is None: + node_feats_data = nfeat[feat] + else: + node_feats_data = th.concat((node_feats_data, nfeat[feat]), dim=0) + num_parts += 1 + del nfeat + gc.collect() + wholegraph_processing( + node_feats_data, + metadata, + feat, + wg_folder, + num_parts, + ) + num_parts = 0 + for path in (os.path.join(folder, name) for name in part_files): + feats_data = dgl.data.utils.load_tensors(f"{path}/{file_name}") + for type_name, feats in fname_dict.items(): + for feat in feats: + feat = type_name + "/" + feat + # Delete processed feature from memory + del feats_data[feat] + num_parts += 1 + trim_feat_files(feats_data, folder, file_name, num_parts) + + +def load_wg_feat(part_config_path, num_parts, type_name, name): + """Load features from wholegraph memory + + Parameters + ---------- + part_config_path : str + The path of the partition configuration file. + num_parts : int + The number of partitions of the dataset + type_name: str + The type of node or edge for which to fetch features or labels for. + name: str + The name of the features to load + """ + global_comm = wgth.comm.get_global_communicator() + feature_comm = global_comm + embedding_wholememory_type = 'distributed' + embedding_wholememory_location = 'cpu' + cache_policy = wgth.create_builtin_cache_policy( + "none", # cache type + embedding_wholememory_type, + embedding_wholememory_location, + "readonly", # access type + 0.0, # cache ratio + ) + metadata_file = os.path.join(os.path.dirname(part_config_path), + 'wholegraph/metadata.json') + with open(metadata_file, encoding="utf8") as f: + wg_metadata = json.load(f) + data_shape = wg_metadata[type_name + '/' + name]['shape'] + feat_wm_embedding = wgth.create_embedding( + feature_comm, + embedding_wholememory_type, + embedding_wholememory_location, + getattr(th, wg_metadata[type_name + '/' + name]['dtype'].split('.')[1]), + [data_shape[0], 1] if len(data_shape) == 1 else data_shape, + optimizer=None, + cache_policy=cache_policy, + ) + feat_path = os.path.join(os.path.dirname(part_config_path), 'wholegraph', \ + type_name + '~' + name) + feat_wm_embedding.get_embedding_tensor().from_file_prefix(feat_path, + part_count=num_parts) + return feat_wm_embedding diff --git a/tools/convert_feat_to_wholegraph.py b/tools/convert_feat_to_wholegraph.py index 969ca73c1d..9d2152b1c5 100755 --- a/tools/convert_feat_to_wholegraph.py +++ b/tools/convert_feat_to_wholegraph.py @@ -16,16 +16,14 @@ Tool to convert node features from distDGL format to WholeGraph format. """ import argparse -import gc + import json import logging import os -import re -import dgl -import pylibwholegraph.torch as wgth import torch +from graphstorm.wholegraph import convert_feat_to_wholegraph def get_node_feat_info(node_feat_names): """Process node feature names @@ -93,184 +91,6 @@ def get_edge_feat_info(edge_feat_names): fname_dict[etype] = feat_info[1].split(",") return fname_dict - -def wholegraph_processing( - whole_feat_tensor, metadata, feat, wg_folder, num_parts -): - """Convert DGL tensors to wholememory tensor - - Parameters - ---------- - whole_feat_tensor : Tensor - The concatenated feature tensor of different partitions - metadata : Tensor - Metadata of the feature tensor - feat : str - Name of the feature to be converted - wg_folder : str - Name of the folder to store the converted files - num_parts : int - Number of partitions of the input features - """ - metadata[feat] = { - "shape": list(whole_feat_tensor.shape), - "dtype": str(whole_feat_tensor.dtype), - } - local_comm = wgth.comm.get_local_device_communicator() - # Round up the integer division to match WholeGraph partitioning scheme - subpart_size = -(whole_feat_tensor.shape[0] // -num_parts) - - for part_num in range(num_parts): - st = part_num * subpart_size - end = (part_num + 1) * subpart_size \ - if part_num != (num_parts - 1) \ - else whole_feat_tensor.shape[0] - - wg_tensor = wgth.create_wholememory_tensor( - local_comm, - "continuous", - "cpu", - (end - st, *whole_feat_tensor.shape[1:]), - whole_feat_tensor.dtype, - None, - ) - local_tensor, _ = wg_tensor.get_local_tensor(host_view=True) - local_tensor.copy_(whole_feat_tensor[st:end]) - filename = wgth.utils.get_part_file_name( - feat.replace("/", "~"), part_num, num_parts - ) - wg_tensor.local_to_file(os.path.join(wg_folder, filename)) - wgth.destroy_wholememory_tensor(wg_tensor) - - -def convert_feat_to_wholegraph(fname_dict, file_name, metadata, folder, use_low_mem): - """Convert features from distDGL tensor format to WholeGraph format - - Parameters - ---------- - fname_dict: dict of list - Dict of names of the edge features of different edge types - file_name: - Name of the feature file, either node_feat.dgl or edge_feat.dgl - metadata : Tensor - Metadata of the feature tensor - folder: str - Name of the folder of the input feature files - use_low_mem: bool - Whether to use low memory version for conversion - """ - wg_folder = os.path.join(folder, "wholegraph") - folder_pattern = re.compile(r"^part[0-9]+$") - part_files = [ - f - for f in os.listdir(folder) - if os.path.isdir(os.path.join(folder, f)) and folder_pattern.match(f) - ] - part_files = sorted(part_files, key=lambda x: int(x.split("part")[1])) - feats_data = [] - - # When 'use_low_mem' is not enabled, this code loads and appends features from individual - # partitions. Then features are concatenated and converted into the WholeGraph format one - # by one. The minimum memory requirement for this approach is 2X the size of the input - # nodes or edges features in the graph. - if not use_low_mem: - # Read features from file - for path in (os.path.join(folder, name) for name in part_files): - feats_data.append(dgl.data.utils.load_tensors(f"{path}/{file_name}")) - num_parts = len(feats_data) - for type_name, feats in fname_dict.items(): - for feat in feats: - feat = type_name + "/" + feat - if feat not in feats_data[0]: - raise RuntimeError( - f"Error: Unknown feature '{feat}'. Files contain \ - the following features: {feats_data[0].keys()}." - ) - logging.info("Processing %s features...", feat) - whole_feat_tensor = torch.concat( - tuple(t[feat] for t in feats_data), dim=0 - ) - # Delete processed feature from memory - for t in feats_data: - del t[feat] - wholegraph_processing( - whole_feat_tensor, metadata, feat, wg_folder, num_parts - ) - # Trim the original distDGL tensors - for part in range(num_parts): - trim_feat_files(feats_data, folder, file_name, part) - - # This low-memory version loads one partition at a time. It processes features one by one, - # iterating through all the partitions and appending only the current feature, converting - # it to a WholeGraph. The minimum memory requirement for this approach is 2X the size of - # the largest node or edge feature in the graph. - else: # low-mem - for ntype, feats in fname_dict.items(): - for feat in feats: - feat = ntype + "/" + feat - node_feats_data = None - num_parts = 0 - # Read features from file - for path in (os.path.join(folder, name) for name in part_files): - nfeat = dgl.data.utils.load_tensors(f"{path}/{file_name}") - if feat not in nfeat: - raise RuntimeError( - f"Error: Unknown feature '{feat}'. Files contain \ - the following features: {nfeat.keys()}." - ) - if node_feats_data is None: - node_feats_data = nfeat[feat] - else: - node_feats_data = torch.concat((node_feats_data, nfeat[feat]), dim=0) - num_parts += 1 - del nfeat - gc.collect() - wholegraph_processing( - node_feats_data, - metadata, - feat, - wg_folder, - num_parts, - ) - num_parts = 0 - for path in (os.path.join(folder, name) for name in part_files): - feats_data = dgl.data.utils.load_tensors(f"{path}/{file_name}") - for type_name, feats in fname_dict.items(): - for feat in feats: - feat = type_name + "/" + feat - # Delete processed feature from memory - del feats_data[feat] - num_parts += 1 - trim_feat_files(feats_data, folder, file_name, num_parts) - - -def trim_feat_files(trimmed_feats, folder, file_name, part): - """Save new truncated distDGL tensors - Parameters - ---------- - trimmed_feats : list of tensors - distDGL tensors after trimming out the processed features - folder : str - Name of the folder of the input feature files - file_name : str - Name of the feature file, either node_feat.dgl or edge_feat.dgl - part : int - Partition number of the input feature files - - """ - dgl.data.utils.save_tensors( - os.path.join(folder, f"part{part}", "new_" + file_name), trimmed_feats[part] - ) - os.rename( - os.path.join(folder, f"part{part}", file_name), - os.path.join(folder, f"part{part}", file_name + ".bak"), - ) - os.rename( - os.path.join(folder, f"part{part}", "new_" + file_name), - os.path.join(folder, f"part{part}", file_name), - ) - - def main(folder, node_feat_names, edge_feat_names, use_low_mem=False): """Convert features from distDGL tensor format to WholeGraph format""" os.environ["MASTER_ADDR"] = "127.0.0.1"

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