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[REVIEW]Optimize cugraph-DGL csc codepath (#3977)
This PR optimizes `cugraph-DGL` csc codepath and adds an end to end benchmark using cugraph-dgl.
```python3
sampled_dir = "/raid/vjawa/nov_1_bulksampling_benchmarks/ogbn_papers100M[2]_b512_f[10, 10, 10]"
dataset = HomogenousBulkSamplerDataset(meta_json_d["total_num_nodes"], edge_dir=edge_dir, sparse_format="csc", return_type="cugraph_dgl.nn.SparseGraph")
dataset.set_input_files(input_directory=sampled_dir+"/samples")
dataloader = torch.utils.data.DataLoader(dataset, collate_fn=lambda x:x, shuffle=False, num_workers=0, batch_size=None)
def run(dataloader):
for input_nodes, output_nodes, blocks in dataloader:
pass
```
```python3
%%timeit
run(dataloader)
```
With PR :
```python3
2.48 s ± 14 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
```
MAIN:
```python3
%%timeit
9.52 s ± 151 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
```
### E2E Benchmarks:
```
python3 cugraph_dgl_benchmark.py
```
PR:
```python3
...
Epoch time = 70.41 seconds
Time to create MFG = 3.37 seconds
Time analysis for fanout = [10, 10, 10], batch_size = 512
mfg_creation_time_per_epoch = 3.37 seconds
feature_time_per_epoch = 44.09 seconds
m_fwd_time_per_epoch = 7.31 seconds
m_bkwd_time_per_epoch = 15.60 seconds
```
MAIN:
```python3
....
Epoch time = 84.72 seconds
Time to create MFG = 10.79 seconds
Time analysis for fanout = [10, 10, 10], batch_size = 512
mfg_creation_time_per_epoch = 10.79 seconds
feature_time_per_epoch = 47.09 seconds
m_fwd_time_per_epoch = 8.24 seconds
m_bkwd_time_per_epoch = 18.58 seconds
```
Authors:
- Vibhu Jawa (https://github.com/VibhuJawa)
Approvers:
- Alex Barghi (https://github.com/alexbarghi-nv)
- Tingyu Wang (https://github.com/tingyu66)
URL: #3977- Loading branch information
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benchmarks/cugraph-dgl/scale-benchmarks/cugraph_dgl_benchmark.py
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,152 @@ | ||
| # Copyright (c) 2018-2023, NVIDIA CORPORATION. | ||
| # 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 | ||
|
|
||
| os.environ["LIBCUDF_CUFILE_POLICY"] = "KVIKIO" | ||
| os.environ["KVIKIO_NTHREADS"] = "64" | ||
| os.environ["RAPIDS_NO_INITIALIZE"] = "1" | ||
| import json | ||
| import pandas as pd | ||
| import os | ||
| import time | ||
| from rmm.allocators.torch import rmm_torch_allocator | ||
| import rmm | ||
| import torch | ||
| from cugraph_dgl.dataloading import HomogenousBulkSamplerDataset | ||
| from model import run_1_epoch | ||
| from argparse import ArgumentParser | ||
| from load_graph_feats import load_node_labels, load_node_features | ||
|
|
||
|
|
||
| def create_dataloader(sampled_dir, total_num_nodes, sparse_format, return_type): | ||
| print("Creating dataloader", flush=True) | ||
| st = time.time() | ||
| dataset = HomogenousBulkSamplerDataset( | ||
| total_num_nodes, | ||
| edge_dir="in", | ||
| sparse_format=sparse_format, | ||
| return_type=return_type, | ||
| ) | ||
|
|
||
| dataset.set_input_files(sampled_dir) | ||
| dataloader = torch.utils.data.DataLoader( | ||
| dataset, collate_fn=lambda x: x, shuffle=False, num_workers=0, batch_size=None | ||
| ) | ||
| et = time.time() | ||
| print(f"Time to create dataloader = {et - st:.2f} seconds", flush=True) | ||
| return dataloader | ||
|
|
||
|
|
||
| def setup_common_pool(): | ||
| rmm.reinitialize(initial_pool_size=5e9, pool_allocator=True) | ||
| torch.cuda.memory.change_current_allocator(rmm_torch_allocator) | ||
|
|
||
|
|
||
| def main(args): | ||
| print( | ||
| f"Running cugraph-dgl dataloading benchmark with the following parameters:\n" | ||
| f"Dataset path = {args.dataset_path}\n" | ||
| f"Sampling path = {args.sampling_path}\n" | ||
| ) | ||
| with open(os.path.join(args.dataset_path, "meta.json"), "r") as f: | ||
| input_meta = json.load(f) | ||
|
|
||
| sampled_dirs = [ | ||
| os.path.join(args.sampling_path, f) for f in os.listdir(args.sampling_path) | ||
| ] | ||
|
|
||
| time_ls = [] | ||
| for sampled_dir in sampled_dirs: | ||
| with open(os.path.join(sampled_dir, "output_meta.json"), "r") as f: | ||
| sampled_meta_d = json.load(f) | ||
|
|
||
| replication_factor = sampled_meta_d["replication_factor"] | ||
| feat_load_st = time.time() | ||
| label_data = load_node_labels( | ||
| args.dataset_path, replication_factor, input_meta | ||
| )["paper"]["y"] | ||
| feat_data = feat_data = load_node_features( | ||
| args.dataset_path, replication_factor, node_type="paper" | ||
| ) | ||
| print( | ||
| f"Feature and label data loading took = {time.time()-feat_load_st}", | ||
| flush=True, | ||
| ) | ||
|
|
||
| r_time_ls = e2e_benchmark(sampled_dir, feat_data, label_data, sampled_meta_d) | ||
| [x.update({"replication_factor": replication_factor}) for x in r_time_ls] | ||
| [x.update({"num_edges": sampled_meta_d["total_num_edges"]}) for x in r_time_ls] | ||
| time_ls.extend(r_time_ls) | ||
|
|
||
| print( | ||
| f"Benchmark completed for replication factor = {replication_factor}\n{'=' * 30}", | ||
| flush=True, | ||
| ) | ||
|
|
||
| df = pd.DataFrame(time_ls) | ||
| df.to_csv("cugraph_dgl_e2e_benchmark.csv", index=False) | ||
| print(f"Benchmark completed for all replication factors\n{'=' * 30}", flush=True) | ||
|
|
||
|
|
||
| def e2e_benchmark( | ||
| sampled_dir: str, feat: torch.Tensor, y: torch.Tensor, sampled_meta_d: dict | ||
| ): | ||
| """ | ||
| Run the e2e_benchmark | ||
| Args: | ||
| sampled_dir: directory containing the sampled graph | ||
| feat: node features | ||
| y: node labels | ||
| sampled_meta_d: dictionary containing the sampled graph metadata | ||
| """ | ||
| time_ls = [] | ||
|
|
||
| # TODO: Make this a parameter in bulk sampling script | ||
| sampled_meta_d["sparse_format"] = "csc" | ||
| sampled_dir = os.path.join(sampled_dir, "samples") | ||
| dataloader = create_dataloader( | ||
| sampled_dir, | ||
| sampled_meta_d["total_num_nodes"], | ||
| sampled_meta_d["sparse_format"], | ||
| return_type="cugraph_dgl.nn.SparseGraph", | ||
| ) | ||
| time_d = run_1_epoch( | ||
| dataloader, | ||
| feat, | ||
| y, | ||
| fanout=sampled_meta_d["fanout"], | ||
| batch_size=sampled_meta_d["batch_size"], | ||
| model_backend="cugraph_dgl", | ||
| ) | ||
| time_ls.append(time_d) | ||
| print("=" * 30) | ||
| return time_ls | ||
|
|
||
|
|
||
| def parse_arguments(): | ||
| parser = ArgumentParser() | ||
| parser.add_argument( | ||
| "--dataset_path", type=str, default="/raid/vjawa/ogbn_papers100M/" | ||
| ) | ||
| parser.add_argument( | ||
| "--sampling_path", | ||
| type=str, | ||
| default="/raid/vjawa/nov_1_bulksampling_benchmarks/", | ||
| ) | ||
| return parser.parse_args() | ||
|
|
||
|
|
||
| if __name__ == "__main__": | ||
| setup_common_pool() | ||
| arguments = parse_arguments() | ||
| main(arguments) |
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