[Regression] Fix regression tests (#2639)

* add bench jenkins

* instance type

* fix

* fix

* fix

* 111

* test

* 111

* 111

* fix

* test

* run

* fix

* fix

* fix

* fix

* fix

* publish results

* 111

* regression

* launch ec2 script

* fix

* add

* run on master

* change

* rrr

* run gpu

* fix

* fix

* try fix

* fix

* ff

* fix

* fix

* fix

* refactor

* fix

* fix

* update

* fix

* fix

* fix

* fix

* remove import torchtext

* add shm size

* update

* fix

* fix

* fix

* fix

* fix this!!!!

* 111

* fix

* remove verbose

* fix

* fix

* fix

* fix

* fix

* fix

* fix

* fix

* update readme

* fix

* fix

* fix

* change asv default to head

* commit sage and rgcn

* fix

* update

* add benchmarks

* add

* fix

* update

* remove RandomState

* tmp remove

* new batch

* fix

* fix

* fix

* address comment

* fix warning

* fix

* fix

* fix

* fix

* add multiupdate all

* address comment

* fix

* add benchmarks

* add

* fix timing

* fix

* push

* add -v

* [Example] NGCF (#2564)

* ngcf

* ngcf

* update
Co-authored-by: zhjwy9343 <6593865@qq.com>

* Revert "[Example] NGCF (#2564)" (#2611)

This reverts commit a75e04f408c719289f478ca129784e05655d8def.

* fix

* change task

* fix

* fix

* fix2

* enable tensoradapter when benchmark

* minor fix

* trigger ci

* fix

* fix

* fix

* fix

* fix

* fix

* lint

* fix
Co-authored-by: Minjie Wang <wmjlyjemaine@gmail.com>
Co-authored-by: KounianhuaDu <51199171+KounianhuaDu@users.noreply.github.com>
Co-authored-by: zhjwy9343 <6593865@qq.com>

benchmarks/benchmarks/api/bench_builtin_apply_edges.py

@@ -8,7 +8,7 @@ from .. import utils
 
 
 @utils.benchmark('time', timeout=600)
-@utils.parametrize('graph_name', ['cora', 'reddit'])
+@utils.parametrize('graph_name', ['cora', 'ogbn-arxiv'])
 @utils.parametrize('format', ['coo', 'csr'])
 @utils.parametrize('feat_size', [8, 128, 512])
 @utils.parametrize('reduce_type', ['u->e', 'u+v'])

benchmarks/benchmarks/api/bench_builtin_update_all.py → benchmarks/benchmarks/api/bench_builtin_update_all_coo.py

@@ -7,11 +7,10 @@ import dgl.function as fn
 from .. import utils
 
 
-@utils.benchmark('time', timeout=7200)
-@utils.parametrize('graph_name', ['cora', 'reddit'])
-@utils.parametrize('format', ['coo', 'csr'])
-@utils.parametrize_cpu('feat_size', [8, 128, 512])
-@utils.parametrize_gpu('feat_size', [8, 32, 256])
+@utils.benchmark('time', timeout=600)
+@utils.parametrize('graph_name', ['ogbn-arxiv'])
+@utils.parametrize('format', ['coo'])
+@utils.parametrize('feat_size', [4, 32, 256])
 @utils.parametrize('msg_type', ['copy_u', 'u_mul_e'])
 @utils.parametrize('reduce_type', ['sum', 'mean', 'max'])
 def track_time(graph_name, format, feat_size, msg_type, reduce_type):
@@ -25,7 +24,7 @@ def track_time(graph_name, format, feat_size, msg_type, reduce_type):
 
     msg_builtin_dict = {
         'copy_u': fn.copy_u('h', 'x'),
-        'u_mul_e': fn.u_mul_e('h', 'e','x'),
+        'u_mul_e': fn.u_mul_e('h', 'e', 'x'),
     }
 
     reduce_builtin_dict = {
@@ -35,13 +34,14 @@ def track_time(graph_name, format, feat_size, msg_type, reduce_type):
     }
 
     # dry run
-    graph.update_all(msg_builtin_dict[msg_type], reduce_builtin_dict[reduce_type])
+    graph.update_all(msg_builtin_dict[msg_type],
+                     reduce_builtin_dict[reduce_type])
 
     # timing
 
     with utils.Timer() as t:
         for i in range(3):
-            graph.update_all(msg_builtin_dict[msg_type], reduce_builtin_dict[reduce_type])
+            graph.update_all(
+                msg_builtin_dict[msg_type], reduce_builtin_dict[reduce_type])
 
     return t.elapsed_secs / 3
-

benchmarks/benchmarks/api/bench_builtin_update_all_csc.py

+import time
+import dgl
+import torch
+import numpy as np
+import dgl.function as fn
+
+from .. import utils
+
+
+@utils.benchmark('time', timeout=600)
+@utils.parametrize('graph_name', ['ogbn-arxiv', 'reddit', 'ogbn-proteins'])
+@utils.parametrize('format', ['csc'])
+@utils.parametrize('feat_size', [4, 32, 256])
+@utils.parametrize('msg_type', ['copy_u', 'u_mul_e'])
+@utils.parametrize('reduce_type', ['sum', 'mean', 'max'])
+def track_time(graph_name, format, feat_size, msg_type, reduce_type):
+    device = utils.get_bench_device()
+    graph = utils.get_graph(graph_name, format)
+    graph = graph.to(device)
+    graph.ndata['h'] = torch.randn(
+        (graph.num_nodes(), feat_size), device=device)
+    graph.edata['e'] = torch.randn(
+        (graph.num_edges(), 1), device=device)
+
+    msg_builtin_dict = {
+        'copy_u': fn.copy_u('h', 'x'),
+        'u_mul_e': fn.u_mul_e('h', 'e', 'x'),
+    }
+
+    reduce_builtin_dict = {
+        'sum': fn.sum('x', 'h_new'),
+        'mean': fn.mean('x', 'h_new'),
+        'max': fn.max('x', 'h_new'),
+    }
+
+    # dry run
+    graph.update_all(msg_builtin_dict[msg_type],
+                     reduce_builtin_dict[reduce_type])
+
+    # timing
+
+    with utils.Timer() as t:
+        for i in range(3):
+            graph.update_all(
+                msg_builtin_dict[msg_type], reduce_builtin_dict[reduce_type])
+
+    return t.elapsed_secs / 3

benchmarks/benchmarks/api/bench_find_edges.py

@@ -6,7 +6,7 @@ import numpy as np
 from .. import utils
 
 
-@utils.benchmark('time', timeout=1200)
+@utils.benchmark('time', timeout=600)
 @utils.parametrize_cpu('graph_name', ['cora', 'livejournal', 'friendster'])
 @utils.parametrize_gpu('graph_name', ['cora', 'livejournal'])
 @utils.parametrize('format', ['coo'])  # csc is not supported

benchmarks/benchmarks/api/bench_format_conversion.py

@@ -6,7 +6,7 @@ import numpy as np
 from .. import utils
 
 
-@utils.benchmark('time', timeout=1200)
+@utils.benchmark('time', timeout=600)
 @utils.parametrize_cpu('graph_name', ['cora', 'livejournal', 'friendster'])
 @utils.parametrize_gpu('graph_name', ['cora', 'livejournal'])
 @utils.parametrize('format',

benchmarks/benchmarks/api/bench_knn_graph.py

@@ -8,8 +8,8 @@ from .. import utils
 
 @utils.benchmark('time', timeout=60)
 @utils.parametrize('k', [3, 5, 10])
-@utils.parametrize('size', [50, 200, ])
-@utils.parametrize('dim', [16, 64, 128])
+@utils.parametrize('size', [50, 200, 1000])
+@utils.parametrize('dim', [16, 128, 512])
 def track_time(size, dim, k):
     device = utils.get_bench_device()
     features = np.random.randn(size, dim)

benchmarks/benchmarks/api/bench_metis_partition.py

@@ -15,8 +15,7 @@ def track_time(graph_name, k):
     data = utils.process_data(graph_name)
     graph = data[0]
     # dry run
-    dry_run_data = utils.process_data('pubmed')
-    gg = dgl.transform.metis_partition(dry_run_data[0], k)
+    gg = dgl.transform.metis_partition(graph, k)
 
     # timing
     with utils.Timer() as t:

benchmarks/benchmarks/api/bench_udf_apply_edges.py

@@ -8,7 +8,7 @@ from .. import utils
 
 
 @utils.benchmark('time', timeout=7200)
-@utils.parametrize('graph_name', ['cora', 'pubmed'])
+@utils.parametrize('graph_name', ['ogbn-arxiv', 'pubmed'])
 @utils.parametrize('format', ['coo'])  # only coo supports udf
 @utils.parametrize('feat_size', [8, 32, 128, 512])
 @utils.parametrize('reduce_type', ['u->e', 'u+v'])

benchmarks/benchmarks/api/bench_udf_update_all.py

@@ -7,10 +7,10 @@ import dgl.function as fn
 from .. import utils
 
 
-@utils.benchmark('time', timeout=7200)
-@utils.parametrize('graph_name', ['cora', 'pubmed'])
+@utils.benchmark('time', timeout=600)
+@utils.parametrize('graph_name', ['pubmed', 'ogbn-arxiv'])
 @utils.parametrize('format', ['coo'])  # only coo supports udf
-@utils.parametrize('feat_size', [8, 32, 128, 512])
+@utils.parametrize('feat_size', [8, 64, 512])
 @utils.parametrize('msg_type', ['copy_u', 'u_mul_e'])
 @utils.parametrize('reduce_type', ['sum', 'mean', 'max'])
 def track_time(graph_name, format, feat_size, msg_type, reduce_type):
@@ -39,7 +39,7 @@ def track_time(graph_name, format, feat_size, msg_type, reduce_type):
     # timing
     with utils.Timer() as t:
         for i in range(3):
-            graph.update_all(msg_udf_dict[msg_type], reduct_udf_dict[reduce_type])
+            graph.update_all(msg_udf_dict[msg_type],
+                             reduct_udf_dict[reduce_type])
 
     return t.elapsed_secs / 3
-