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test_utils.py
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test_utils.py
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# Standard Library
import json
import os
# First Party
from smdebug.tensorflow.utils import (
TFDistributionStrategy,
get_chief_worker_from_tf_config,
get_num_workers_from_tf_config,
get_worker_id_from_tf_config,
is_parameter_server_strategy,
tensor_can_be_saved,
)
def test_read_tf_config(monkeypatch):
# Case 1: No TF_CONFIG
assert is_parameter_server_strategy(os.getenv("TF_CONFIG")) is False
# Case 2: TF_CONFIG present but empty
monkeypatch.setenv("TF_CONFIG", json.dumps({}))
assert is_parameter_server_strategy(json.loads(os.getenv("TF_CONFIG"))) is False
# Case 3: TF_CONFIG present but invalid because of missing ps field
monkeypatch.setenv(
"TF_CONFIG",
json.dumps(
{
"cluster": {"worker": ["host1:port", "host2:port", "host3:port"]},
"task": {"type": "worker", "index": 1},
}
),
)
assert is_parameter_server_strategy(json.loads(os.getenv("TF_CONFIG"))) is False
# Case 4: TF_CONFIG present and valid
monkeypatch.setenv(
"TF_CONFIG",
json.dumps(
{
"cluster": {
"worker": ["host1:port", "host2:port", "host3:port"],
"ps": ["host4:port", "host5:port"],
},
"task": {"type": "worker", "index": 1},
}
),
)
assert is_parameter_server_strategy(json.loads(os.getenv("TF_CONFIG"))) is True
def test_get_worker_id_from_tf_config(monkeypatch):
monkeypatch.setenv(
"TF_CONFIG",
json.dumps(
{
"cluster": {
"worker": ["host1:port", "host2:port", "host3:port"],
"ps": ["host4:port", "host5:port"],
},
"task": {"type": "worker", "index": 1},
}
),
)
worker_id = get_worker_id_from_tf_config(json.loads(os.getenv("TF_CONFIG")))
assert worker_id == "worker_1"
monkeypatch.setenv(
"TF_CONFIG",
json.dumps(
{
"cluster": {
"worker": ["host1:port", "host2:port", "host3:port"],
"ps": ["host4:port", "host5:port"],
},
"task": {"type": "ps", "index": 0},
}
),
)
worker_id = get_worker_id_from_tf_config(json.loads(os.getenv("TF_CONFIG")))
assert worker_id == "ps_0"
monkeypatch.setenv(
"TF_CONFIG",
json.dumps(
{
"cluster": {
"chief": ["host0:port"],
"worker": ["host1:port", "host2:port", "host3:port"],
"ps": ["host4:port", "host5:port"],
},
"task": {"type": "chief", "index": 0},
}
),
)
worker_id = get_worker_id_from_tf_config(json.loads(os.getenv("TF_CONFIG")))
assert worker_id == "chief_0"
monkeypatch.setenv(
"TF_CONFIG",
json.dumps(
{
"cluster": {
"chief": ["host0:port"],
"worker": ["host1:port", "host2:port", "host3:port"],
"ps": ["host4:port", "host5:port"],
},
"task": {"type": "evaluator", "index": 0},
}
),
)
worker_id = get_worker_id_from_tf_config(json.loads(os.getenv("TF_CONFIG")))
assert worker_id == "evaluator_0"
def test_get_num_workers_from_tf_config(monkeypatch):
monkeypatch.setenv(
"TF_CONFIG",
json.dumps(
{
"cluster": {
"worker": ["host1:port", "host2:port", "host3:port"],
"ps": ["host4:port", "host5:port"],
},
"task": {"type": "worker", "index": 1},
}
),
)
num_workers = get_num_workers_from_tf_config(json.loads(os.getenv("TF_CONFIG")))
assert num_workers == 3
monkeypatch.setenv(
"TF_CONFIG",
json.dumps(
{
"cluster": {
"chief": ["host0:port"],
"worker": ["host1:port", "host2:port", "host3:port"],
"ps": ["host4:port", "host5:port"],
},
"task": {"type": "worker", "index": 1},
}
),
)
num_workers = get_num_workers_from_tf_config(json.loads(os.getenv("TF_CONFIG")))
assert num_workers == 4
def test_get_chief():
conf = json.dumps(
{
"cluster": {
"chief": ["host0:port"],
"worker": ["host1:port", "host2:port", "host3:port"],
"ps": ["host4:port", "host5:port"],
},
"task": {"type": "worker", "index": 1},
}
)
assert get_chief_worker_from_tf_config(json.loads(conf)) == "chief_0"
class TFOpMock:
def __init__(self, name, inputs):
self.inputs = inputs
self.name = name
self.op = self
class TFTensorMock:
def __init__(self, op):
self.op = op
def test_path_to_subgraph_1():
"""
out1
in1 out2
in2 in3
"""
in1 = TFOpMock("in1", [])
in2 = TFOpMock("in2", [])
in3 = TFOpMock("in3", [])
out2 = TFOpMock("out2", [in2, in3])
out1 = TFOpMock("out1", [in1, out2])
subgraph_nodes = {in1.name, in2.name, in3.name}
assert tensor_can_be_saved(out1, subgraph_nodes, set())
def test_path_to_subgraph_2():
"""
out1
in1 out2
in2 out3
"""
in1 = TFOpMock("in1", [])
in2 = TFOpMock("in2", [])
out3 = TFOpMock("out3", [])
out2 = TFOpMock("out2", [in2, out3])
out1 = TFOpMock("out1", [in1, out2])
subgraph_nodes = {in1.name, in2.name}
assert tensor_can_be_saved(out1, subgraph_nodes, {out3}) is False
assert tensor_can_be_saved(out1, subgraph_nodes, {})
def test_path_to_subgraph_3():
"""
out1
out2 out3
out4 out5
"""
out4 = TFOpMock("out4", [])
out5 = TFOpMock("out5", [])
out3 = TFOpMock("out3", [out5])
out2 = TFOpMock("out2", [out4])
out1 = TFOpMock("out1", [out2, out3])
tf_tensor = TFTensorMock(out1)
subgraph_nodes = {}
assert tensor_can_be_saved(out1, subgraph_nodes, set())
assert tensor_can_be_saved(out1, subgraph_nodes, {out5}) is False
def test_path_to_subgraph_4():
"""
out1
"""
out1 = TFOpMock("out1", [])
subgraph_nodes = {}
assert tensor_can_be_saved(out1, subgraph_nodes, {out1}) is False
assert tensor_can_be_saved(out1, subgraph_nodes, {})
def test_path_to_subgraph_5():
"""
in1
"""
in1 = TFOpMock("in1", [])
subgraph_nodes = {in1.name}
assert tensor_can_be_saved(in1, subgraph_nodes, {in1})
assert tensor_can_be_saved(in1, subgraph_nodes, {})
def test_path_to_subgraph_6():
"""
in1
in2 in3
in5 in4 in6
"""
in5 = TFOpMock("in5", [])
in4 = TFOpMock("in4", [])
in6 = TFOpMock("in6", [])
in2 = TFOpMock("in2", [in5, in4])
in3 = TFOpMock("in3", [in6])
in1 = TFOpMock("in1", [in2, in3])
subgraph_nodes = {in1.name, in2.name, in3.name, in4.name, in5.name, in6.name}
assert tensor_can_be_saved(in1, subgraph_nodes, {in2, in3})
assert tensor_can_be_saved(in1, subgraph_nodes, {in1})