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hardware_consistency_test.py
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# Copyright (c) 2023 Khaleelulla Khan Nazeer
# This file incorporates work covered by the following copyright:
# Copyright 2020 LMNT, Inc. All Rights Reserved.
#
# 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 unittest
from unittest import mock
import torch
import evnn_pytorch as evnn
import numpy as np
RNN_MAP = {
'egru': evnn.EGRU,
}
batch_size = 10
time_steps = 8
input_size = 4
hidden_size = 8
class EGRUCPUForwardTest(unittest.TestCase):
def setUp(self):
rnn = RNN_MAP['egru']
self.seed = 5526
torch.manual_seed(self.seed)
torch.cuda.manual_seed(self.seed)
np.random.seed(self.seed)
x = torch.rand(batch_size, time_steps, input_size, dtype=torch.float32)
self.egru = rnn(input_size, hidden_size, zoneout=0.0, batch_first=True)
self.x_cpu = x.clone()
self.x_cpu_torch = self.x_cpu.detach().clone()
def test_forward_y(self):
with torch.no_grad():
y1, _ = self.egru.forward(self.x_cpu)
torch.manual_seed(self.seed)
with mock.patch.object(self.egru, "use_custom_cuda", False):
y2, _ = self.egru.forward(self.x_cpu_torch)
assert torch.allclose(y1, y2)
def test_forward_h(self):
with torch.no_grad():
_, (h1, _, _) = self.egru.forward(self.x_cpu)
torch.manual_seed(self.seed)
with mock.patch.object(self.egru, "use_custom_cuda", False):
_, (h2, _, _) = self.egru.forward(self.x_cpu_torch)
assert torch.allclose(h1, h2)
def test_forward_o(self):
with torch.no_grad():
_, (_, o1, _) = self.egru.forward(self.x_cpu)
torch.manual_seed(self.seed)
with mock.patch.object(self.egru, "use_custom_cuda", False):
_, (_, o2, _) = self.egru.forward(self.x_cpu_torch)
assert torch.allclose(o1, o2)
def test_forward_trace(self):
with torch.no_grad():
_, (_, _, t1) = self.egru.forward(self.x_cpu)
torch.manual_seed(self.seed)
with mock.patch.object(self.egru, "use_custom_cuda", False):
_, (_, _, t2) = self.egru.forward(self.x_cpu_torch)
assert torch.allclose(t1, t2)
class EGRUCUDABackwardTest(unittest.TestCase):
def setUp(self):
rnn = RNN_MAP['egru']
self.seed = 5526
torch.manual_seed(self.seed)
torch.cuda.manual_seed(self.seed)
np.random.seed(self.seed)
x = torch.rand(batch_size, time_steps, input_size, dtype=torch.float32)
self.egru = rnn(input_size, hidden_size, zoneout=0.0, batch_first=True)
self.x_cpu = x.clone()
self.x_cpu_torch = self.x_cpu.detach().clone()
self.x_cpu.requires_grad_(True)
self.x_cpu_torch.requires_grad_(True)
def test_backward_y(self):
y1, _ = self.egru.forward(self.x_cpu)
y1.backward(torch.ones_like(y1), retain_graph=True)
torch.manual_seed(self.seed)
y2, _ = self.egru.forward(self.x_cpu_torch)
y2.backward(torch.ones_like(y2), retain_graph=True)
assert torch.allclose(self.x_cpu.grad.data,
self.x_cpu_torch.grad.data, atol=1e-06)
def test_backward_h(self):
_, (h1, _, _) = self.egru.forward(self.x_cpu)
h1.backward(torch.ones_like(h1), retain_graph=True)
torch.manual_seed(self.seed)
_, (h2, _, _) = self.egru.forward(self.x_cpu_torch)
h2.backward(torch.ones_like(h2), retain_graph=True)
assert torch.allclose(self.x_cpu.grad.data,
self.x_cpu_torch.grad.data, atol=1e-06)
def test_backward_o(self):
_, (_, o1, _) = self.egru.forward(self.x_cpu)
o1.backward(torch.ones_like(o1), retain_graph=True)
torch.manual_seed(self.seed)
_, (_, o2, _) = self.egru.forward(self.x_cpu_torch)
o2.backward(torch.ones_like(o2), retain_graph=True)
assert torch.allclose(self.x_cpu.grad.data,
self.x_cpu_torch.grad.data, atol=1e-06)
def test_backward_trace(self):
_, (_, _, t1) = self.egru.forward(self.x_cpu)
t1.backward(torch.ones_like(t1), retain_graph=True)
torch.manual_seed(self.seed)
_, (_, _, t2) = self.egru.forward(self.x_cpu_torch)
t2.backward(torch.ones_like(t2), retain_graph=True)
assert torch.allclose(self.x_cpu.grad.data,
self.x_cpu_torch.grad.data, atol=1e-06)
if __name__ == '__main__':
unittest.main()