def test_pybind_pr_with_return(self):
module = graphit.compile_and_load(self.root_test_input_dir +
"export_pr_with_return.gt")
graph = csr_matrix(([0, 0, 0, 0, 0, 0], [1, 2, 3, 0, 0,
0], [0, 3, 4, 5, 6]))
ranks = module.export_func(graph)
self.assertEqual(np.sum(ranks), 1.0)
def test_pybind_cf(self):
module = graphit.compile_and_load(
self.root_test_input_dir + "export_cf_vector_input_with_return.gt")
graph = csr_matrix(([0, 0, 0, 0, 0, 0], [1, 2, 3, 0, 0,
0], [0, 3, 4, 5, 6]))
cf_result = module.export_func(graph)
self.assertEqual(cf_result.shape, (4, 1))
def test_pybind_constant_size_vector_of_vector_return(self):
module = graphit.compile_and_load(
self.root_test_input_dir +
"export_constant_size_vector_of_vector_return.gt")
vector_return = module.export_func()
self.assertEqual(vector_return.shape, (10, 10))
self.assertEqual(np.sum(vector_return), 550)
def test_pybind_vector_of_constant_size_arg(self):
module = graphit.compile_and_load(
self.root_test_input_dir + "export_vector_of_constant_size_arg.gt")
graph = csr_matrix(([0, 0, 0, 0, 0, 0], [1, 2, 3, 0, 0,
0], [0, 3, 4, 5, 6]))
vector_of_constant_size = np.ones(100)
module.export_func(graph, vector_of_constant_size)
def test_pybind_pr_load_file(self):
module = graphit.compile_and_load(self.root_test_input_dir +
"export_pr_with_return.gt")
graph = csr_matrix(scipy.io.mmread(self.root_test_graph_dir + "4.mtx"))
ranks = module.export_func(graph)
self.assertEqual(len(ranks), graph.shape[0])
self.assertTrue(abs(np.sum(ranks) - 1.0) < 0.1)
def test_pybind_vector_of_vector_arg(self):
module = graphit.compile_and_load(self.root_test_input_dir +
"export_vector_of_vector.gt")
graph = csr_matrix(([0, 0, 0, 0, 0, 0], [1, 2, 3, 0, 0,
0], [0, 3, 4, 5, 6]))
vector_of_vector = np.ones((4, 100))
output_data = module.export_func(graph, vector_of_vector)
self.assertEqual(output_data.sum(), 404)
def test_pybind_pr_with_vector_input(self):
module = graphit.compile_and_load(
self.root_test_input_dir + "export_pagerank_with_vector_input.gt")
graph = csr_matrix(([0, 0, 0, 0, 0, 0], [1, 2, 3, 0, 0,
0], [0, 3, 4, 5, 6]))
new_rank_array = np.array([0, 0, 0, 0, 0], dtype=np.int32)
ranks = module.export_func(graph, new_rank_array)
self.assertEqual(np.sum(ranks), 1.0)
def test_pybind_pr_delta(self):
module = graphit.compile_and_load(self.root_test_input_dir +
"export_pr_delta.gt")
graph = csr_matrix(([4, 5, 6, 4, 5, 6], [1, 2, 3, 0, 0,
0], [0, 3, 4, 5, 6]))
ranks = module.export_func(graph)
self.assertEqual(len(ranks), 4)
self.assertTrue(abs(np.sum(ranks) - 1.0) < 0.001)
def test_pybind_extern_apply_src_add_one(self):
module = graphit.compile_and_load(
self.root_test_input_dir +
"export_extern_simple_edgeset_appply.gt",
[self.root_test_input_dir + "extern_src_add_one.cpp"])
graph = csr_matrix(([4, 5, 6, 4, 5, 6], [1, 2, 3, 0, 0,
0], [0, 3, 4, 5, 6]))
sum_returned = module.export_func(graph)
self.assertEqual(sum_returned, 6)
def test_pybind_various_type_vector_args(self):
module = graphit.compile_and_load(self.root_test_input_dir +
"export_various_types_vector_arg.gt")
graph = csr_matrix(([0, 0, 0, 0, 0, 0], [1, 2, 3, 0, 0,
0], [0, 3, 4, 5, 6]))
vector_of_vector = np.ones((4, 100))
vector_of_constant_size = np.ones(100)
output = module.export_func(graph, vector_of_vector,
vector_of_constant_size)
self.assertEqual(output.sum(), 800)
def test_pybind_sssp_UW(self):
module = graphit.compile_and_load(self.root_test_input_dir +
"export_sssp_UW.gt")
graph = csr_matrix(([4, 5, 6, 4, 5, 6], [1, 2, 3, 0, 0,
0], [0, 3, 4, 5, 6]))
distances = module.do_sssp(graph, 0)
self.assertEqual(len(distances), 4)
self.assertEqual(distances[0], 0)
self.assertEqual(distances[1], 4)
self.assertEqual(distances[2], 5)
self.assertEqual(distances[3], 6)
import graphit
from scipy.sparse import csr_matrix
import torch
# In GraphIt, `edgeset`s define graphs. Use SciPy's `csr_matrix` to
# create these values, which we'll pass into a GraphIt function.
graph = csr_matrix((
[4, 5, 6, 4, 5, 6],
[1, 2, 3, 0, 0, 0],
[0, 3, 4, 5, 6],
))
# Compile a GraphIt source file and load it so we can call its function.
sssp_module = graphit.compile_and_load("/workspace/py-graphit-example/sssp.gt")
# Invoke the `do_sssp` GraphIt function from the loaded module.
distances = sssp_module.do_sssp(graph, 0)
# A vector{Vertex} value is represented as a NumPy array. Use
# `torch.tensor(...)` to convert it to a tensor.
print(torch.tensor(distances))
import graphit
import scipy.io
from scipy.sparse import csr_matrix
import sys
import time
module = graphit.compile_and_load("pagerank_delta_export.gt")
graph = csr_matrix(scipy.io.mmread(sys.argv[1]))
module.set_graph(graph)
start_time = time.perf_counter()
ranks = module.do_pagerank_delta()
end_time = time.perf_counter()
print("Time elapsed = " + str(end_time - start_time) + " seconds")
def benchmark_paperank(self):
module = graphit.compile_and_load(GRAPHIT_SOURCE_DIRECTORY + "pagerank.gt",
parallelization_type=PARALLEL_OPENMP)
for dataset in datasets:
graph = scipy.sparse.load_npz(PATH + dataset)
module.export_func(graph)
# This file is copied from a repo - (https://github.com/bespoke-silicon-group/hb_starlite/tree/master/py-graphit-example)
# author: mrutt92
import graphit
from scipy.sparse import csr_matrix
# In GraphIt, `edgeset`s define graphs. Use SciPy's `csr_matrix` to
# create these values, which we'll pass into a GraphIt function.
graph = csr_matrix((
[4, 5, 6, 4, 5, 6],
[1, 2, 3, 0, 0, 0],
[0, 3, 4, 5, 6],
))
# Compile a GraphIt source file and load it so we can call its function.
sssp_module = graphit.compile_and_load("./sssp.gt")
# Invoke the `do_sssp` GraphIt function from the loaded module.
distances = sssp_module.do_sssp(graph, 0)
# A vector{Vertex} value is represented as a NumPy array. Use
# `torch.tensor(...)` to convert it to a tensor.
print(distances)
