From d8e129c0e2cbcb205741fe3c27c09137554d56dc Mon Sep 17 00:00:00 2001 From: benedettoleto Date: Sun, 2 Jun 2024 14:57:18 +0200 Subject: [PATCH 01/19] refactor unittests --- tests/models/__init__.py | 0 tests/models/model_list.py | 140 +++ tests/test_accumulators.py | 62 +- tests/test_frameworks.py | 77 +- tests/test_metrics.py | 898 +++++++----------- .../{ => test_data}/sample_audio.wav | Bin tests/test_preprocessors/test_mfcc.py | 23 +- .../test_preprocessors/test_speech2spikes.py | 80 +- 8 files changed, 677 insertions(+), 603 deletions(-) create mode 100644 tests/models/__init__.py create mode 100644 tests/models/model_list.py rename tests/test_preprocessors/{ => test_data}/sample_audio.wav (100%) diff --git a/tests/models/__init__.py b/tests/models/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/tests/models/model_list.py b/tests/models/model_list.py new file mode 100644 index 00000000..5f526e25 --- /dev/null +++ b/tests/models/model_list.py @@ -0,0 +1,140 @@ +import torch.nn as nn +import snntorch as snn +import snntorch.surrogate as surrogate + +beta = 0.9 +spike_grad = surrogate.fast_sigmoid() + +net = nn.Sequential( + nn.Flatten(), + nn.Linear(20, 256), + snn.Leaky(beta=beta, spike_grad=spike_grad, init_hidden=True), + nn.Linear(256, 256), + snn.Leaky(beta=beta, spike_grad=spike_grad, init_hidden=True), + nn.Linear(256, 256), + snn.Leaky(beta=beta, spike_grad=spike_grad, init_hidden=True), + nn.Linear(256, 35), + snn.Leaky(beta=beta, spike_grad=spike_grad, init_hidden=True, output=True), +) + +net_rnn = nn.Sequential( + nn.Flatten(), + nn.RNN(input_size=20, hidden_size=20, num_layers=2, bidirectional=True), + snn.Leaky(beta=beta, spike_grad=spike_grad, init_hidden=True), + nn.Linear(20, 256), + snn.Leaky(beta=beta, spike_grad=spike_grad, init_hidden=True), + nn.Linear(256, 256), + snn.Leaky(beta=beta, spike_grad=spike_grad, init_hidden=True), + nn.Linear(256, 35), + snn.Leaky(beta=beta, spike_grad=spike_grad, init_hidden=True, output=True), +) + +net_relu_0 = nn.Sequential( + nn.Identity(), + nn.ReLU(), +) + +net_relu_0_2 = nn.Sequential( + nn.Linear(20, 25, bias=False), + nn.Sigmoid(), + nn.Linear(25, 25, bias=False), + nn.ReLU(), +) + +net_relu_50 = nn.Sequential( + # nn.Flatten(), + nn.Identity(), + nn.ReLU(), +) + +net_relu_50_2 = nn.Sequential( + # nn.Flatten(), + nn.Linear(20, 20, bias=False), # 400 ops + nn.Sigmoid(), + nn.Linear(20, 25, bias=False), # 500 ops + nn.Sigmoid(), + nn.Linear(25, 25, bias=False), # 625 ops + nn.Sigmoid(), + nn.Linear(25, 25, bias=False), # 625 ops + nn.Sigmoid(), +) + +act = nn.ReLU() +net_torch_relu_0 = nn.Sequential( + # nn.Flatten(), + nn.Identity(), + act, + nn.Identity(), + act, + nn.Identity(), + act, + nn.Identity(), + act, +) + +net_sigm = nn.Sequential( + nn.Identity(), + nn.Sigmoid(), +) + +net_conv_2d = nn.Sequential( + nn.Conv2d(1, 1, 3, bias=False), + nn.ReLU(), +) + +net_conv_1d = nn.Sequential( + nn.Conv1d(5, 1, 5, bias=False), + nn.ReLU(), +) + +net_snn = nn.Sequential( + nn.Linear(20, 5, bias=False), + snn.Leaky( + beta=0.9, spike_grad=surrogate.fast_sigmoid(), init_hidden=True, output=True + ), +) + + +class simple_LSTM(nn.Module): + """Nonsense LSTM for operations testing Should be 615 MACs.""" + + def __init__(self): + super(simple_LSTM, self).__init__() + self.lstm = nn.LSTMCell(input_size=25, hidden_size=5, bias=True) + self.rel = nn.ReLU() + + def forward(self, inp): + x, states = inp[0], inp[1] + x, _ = self.lstm(x, states) + # x = self.rel(x) + return x + + +class simple_RNN(nn.Module): + """Nonsense RNN for operations testing Should be 150 MACs.""" + + def __init__(self): + super(simple_RNN, self).__init__() + self.RNN = nn.RNNCell(input_size=25, hidden_size=5, bias=True) + self.rel = nn.ReLU() + + def forward(self, inp): + x, states = inp[0], inp[1] + x = self.RNN(x, states) + x = self.rel(x) + return x + + +class simple_GRU(nn.Module): + """Nonsense GRU/RNN for operations testing Should be 465 MACs.""" + + def __init__(self): + super(simple_GRU, self).__init__() + self.GRU = nn.GRUCell(input_size=25, hidden_size=5, bias=True) + self.rel = nn.ReLU() + + def forward(self, inp): + x, states = inp[0], inp[1] + x = self.GRU(x, states) + x = self.rel(x) + return x diff --git a/tests/test_accumulators.py b/tests/test_accumulators.py index fb0b8d1f..d376b54d 100644 --- a/tests/test_accumulators.py +++ b/tests/test_accumulators.py @@ -1,11 +1,59 @@ +from neurobench.postprocessing import choose_max_count, aggregate +import unittest import torch -from neurobench.postprocessing import choose_max_count +class TestChooseMaxCount(unittest.TestCase): + """Tests choose_max_count functions.""" -def test_choose_max_count(): - # Create a tensor of all 0's except for one class - a = torch.zeros((256, 1000, 35)) - a[:, :, 5] = 1 - assert choose_max_count(a).shape == (256,) - assert torch.equal(choose_max_count(a), torch.tensor([5] * 256)) + def setUp(self): + """Set up the test data.""" + + self.bach_size = 256 + self.timesteps = 1000 + self.classes = 35 + + self.target_class = 5 + + # Create a tensor of all 0's except for one class + self.input_tensor = torch.zeros( + (self.bach_size, self.target_class, self.classes) + ) + self.input_tensor[:, :, self.target_class] = 1 + + def test_choose_max_count_shape(self): + """Test that choose_max_count returns the correct shape.""" + result = choose_max_count(self.input_tensor) + expected_shape = (self.bach_size,) + self.assertEqual(result.shape, expected_shape) + + def test_chose_max_count_value(self): + """Test that choose_max_count returns the correct class indices.""" + result = choose_max_count(self.input_tensor) + expected_result = torch.tensor([self.target_class] * self.bach_size) + self.assertTrue(torch.equal(result, expected_result)) + + +class TestAggregate(unittest.TestCase): + """Tests aggregate functions.""" + + def setUp(self): + """Set up the test data.""" + + self.bach_size = 256 + self.timesteps = 100 + self.classes = 5 + + # Create a tensor of all 1's except for one class + self.input_tensor = torch.ones((self.bach_size, self.timesteps, self.classes)) + + def test_aggregate_shape(self): + """Test that aggregate returns the correct shape.""" + result = aggregate(self.input_tensor) + self.assertEqual(result.shape, (self.bach_size, self.classes)) + + def test_aggregate_value(self): + """Test that aggregate returns the correct spike aggregation.""" + result = aggregate(self.input_tensor) + expected_result = torch.ones((self.bach_size, self.classes)) * self.timesteps + self.assertTrue(torch.equal(result, expected_result)) diff --git a/tests/test_frameworks.py b/tests/test_frameworks.py index d68298ba..999bbf76 100644 --- a/tests/test_frameworks.py +++ b/tests/test_frameworks.py @@ -1,41 +1,44 @@ -import pytest - -import torch +from neurobench.models import SNNTorchModel +import tests.models.model_list as models +from snntorch import surrogate import snntorch as snn - from torch import nn -from snntorch import surrogate - -from neurobench.models import SNNTorchModel +import unittest +import torch -def test_snntorch_framework(): - beta = 0.9 - spike_grad = surrogate.fast_sigmoid() - net = nn.Sequential( - nn.Flatten(), - nn.Linear(20, 256), - snn.Leaky(beta=beta, spike_grad=spike_grad, init_hidden=True), - nn.Linear(256, 256), - snn.Leaky(beta=beta, spike_grad=spike_grad, init_hidden=True), - nn.Linear(256, 256), - snn.Leaky(beta=beta, spike_grad=spike_grad, init_hidden=True), - nn.Linear(256, 35), - snn.Leaky(beta=beta, spike_grad=spike_grad, init_hidden=True, output=True), - ) - model = SNNTorchModel(net) - - # Random data of shape (batch=256, ts=1000, chan=20) - data = torch.rand((256, 1000, 20)) - spikes = model(data) - assert data.shape == (256, 1000, 20) - assert spikes.shape == (256, 1000, 35) - - data = torch.rand((256, 1000, 2, 2, 5)) - spikes = model(data) - assert data.shape == (256, 1000, 2, 2, 5) - assert spikes.shape == (256, 1000, 35) - - data = torch.rand((256, 1000, 10, 5)) - with pytest.raises(RuntimeError, match="mat1 and mat2 shapes cannot be multiplied"): - spikes = model(data) +class TestSNNTorchModel(unittest.TestCase): + """Tests SNNTorchModel class.""" + + def setUp(self): + """Set up the test data.""" + self.input_size = 20 + self.output_size = 35 + self.batch_size = 256 + self.timesteps = 1000 + + net = models.net + self.model = SNNTorchModel(net) + + def test_snntorch_framework_model_runtime(self): + """Test that the SNNTorchModel returns the correct shape.""" + + data = torch.rand((self.batch_size, self.timesteps, self.input_size)) + spikes = self.model(data) + self.assertEqual(data.shape, (self.batch_size, self.timesteps, self.input_size)) + self.assertEqual( + spikes.shape, (self.batch_size, self.timesteps, self.output_size) + ) + + data = torch.rand((self.batch_size, self.timesteps, 2, 2, 5)) + spikes = self.model(data) + self.assertEqual(data.shape, (self.batch_size, self.timesteps, 2, 2, 5)) + self.assertEqual( + spikes.shape, (self.batch_size, self.timesteps, self.output_size) + ) + + def test_snntorch_framework_model_runtime_error(self): + """Test that the SNNTorchModel raises an error if the input shape is + incorrect.""" + data = torch.rand((self.batch_size, self.timesteps, 10, 5)) + self.assertRaises(RuntimeError, self.model, data) diff --git a/tests/test_metrics.py b/tests/test_metrics.py index 2b6e221c..99646946 100644 --- a/tests/test_metrics.py +++ b/tests/test_metrics.py @@ -1,17 +1,4 @@ -import torch -import torch.nn as nn -import snntorch as snn -import snntorch.surrogate as surrogate from neurobench.models import SNNTorchModel, TorchModel - -# from neurobench.benchmarks.static_metrics import footprint, parameter_count, connection_sparsity, -# from neurobench.benchmarks.workload_metrics import activation_sparsity, classification_accuracy, MSE, sMAPE, r2, - -from neurobench.benchmarks.static_metrics import ( - footprint, - parameter_count, - connection_sparsity, -) from neurobench.benchmarks.workload_metrics import ( classification_accuracy, MSE, @@ -23,588 +10,425 @@ number_neuron_updates, membrane_updates, ) -from torch.profiler import profile, record_function, ProfilerActivity +from neurobench.benchmarks.static_metrics import ( + footprint, + parameter_count, + connection_sparsity, +) +import tests.models.model_list as models +import torch.nn as nn +import unittest +import torch # Pytest for footprint from benchmarks/static_metrics -def test_footprint(): - beta = 0.9 - spike_grad = surrogate.fast_sigmoid() - net = nn.Sequential( - nn.Flatten(), - nn.Linear(20, 256), - snn.Leaky(beta=beta, spike_grad=spike_grad, init_hidden=True), - nn.Linear(256, 256), - snn.Leaky(beta=beta, spike_grad=spike_grad, init_hidden=True), - nn.Linear(256, 256), - snn.Leaky(beta=beta, spike_grad=spike_grad, init_hidden=True), - nn.Linear(256, 35), - snn.Leaky(beta=beta, spike_grad=spike_grad, init_hidden=True, output=True), - ) - model = SNNTorchModel(net) - assert footprint(model) == 583900 - - -# Pytest for parameter_count from benchmarks/static_metrics -def test_parameter_count(): - beta = 0.9 - spike_grad = surrogate.fast_sigmoid() - net = nn.Sequential( - nn.Flatten(), - nn.Linear(20, 256), - snn.Leaky(beta=beta, spike_grad=spike_grad, init_hidden=True), - nn.Linear(256, 256), - snn.Leaky(beta=beta, spike_grad=spike_grad, init_hidden=True), - nn.Linear(256, 256), - snn.Leaky(beta=beta, spike_grad=spike_grad, init_hidden=True), - nn.Linear(256, 35), - snn.Leaky(beta=beta, spike_grad=spike_grad, init_hidden=True, output=True), - ) - model = SNNTorchModel(net) - assert parameter_count(model) == 145955 - - -# Pytest for connection_sparsity from benchmarks/static_metrics -def test_connection_sparsity(): - beta = 0.9 - spike_grad = surrogate.fast_sigmoid() - net = nn.Sequential( - nn.Flatten(), - nn.Linear(20, 256), - snn.Leaky(beta=beta, spike_grad=spike_grad, init_hidden=True), - nn.Linear(256, 256), - snn.Leaky(beta=beta, spike_grad=spike_grad, init_hidden=True), - nn.Linear(256, 256), - snn.Leaky(beta=beta, spike_grad=spike_grad, init_hidden=True), - nn.Linear(256, 35), - snn.Leaky(beta=beta, spike_grad=spike_grad, init_hidden=True, output=True), - ) - # Set all the weights to zero - for param in net.parameters(): - param.data = torch.zeros_like(param.data) - model = SNNTorchModel(net) - assert connection_sparsity(model) == 1.0 - - # Set all the weights to one - for param in net.parameters(): - param.data = torch.ones_like(param.data) - model = SNNTorchModel(net) - assert connection_sparsity(model) == 0.0 - - # Set all the weights to a random value - for param in net.parameters(): - param.data = torch.rand_like(param.data) - model = SNNTorchModel(net) - assert connection_sparsity(model) == 0.0 - - # Set half the weights to zero and half the weights to one - for param in net.parameters(): - param.data[: param.data.shape[0] // 2] = torch.zeros_like( - param.data[: param.data.shape[0] // 2] - ) - param.data[param.data.shape[0] // 2 :] = torch.ones_like( - param.data[param.data.shape[0] // 2 :] + + +class TestStaticMetrics(unittest.TestCase): + + def setUp(self): + + self.dummy_net = nn.Module() + + self.net = models.net + + self.net_rnn = models.net_rnn + + def test_footprint(self): + model = SNNTorchModel(self.net) + self.assertEqual(footprint(model), 583900) + + def test_parameter_count(self): + model = SNNTorchModel(self.net) + self.assertEqual(parameter_count(model), 145955) + + def test_connection_sparsity(self): + # Set all the weights to zero + for param in self.net.parameters(): + param.data = torch.zeros_like(param.data) + model = SNNTorchModel(self.net) + self.assertEqual(connection_sparsity(model), 1.0) + + # Set all the weights to one + for param in self.net.parameters(): + param.data = torch.ones_like(param.data) + model = SNNTorchModel(self.net) + self.assertEqual(connection_sparsity(model), 0.0) + + # Set all the weights to a random value + for param in self.net.parameters(): + param.data = torch.rand_like(param.data) + model = SNNTorchModel(self.net) + self.assertEqual(connection_sparsity(model), 0.0) + + # Set half the weights to zero and half the weights to one + for param in self.net.parameters(): + param.data[: param.data.shape[0] // 2] = torch.zeros_like( + param.data[: param.data.shape[0] // 2] + ) + param.data[param.data.shape[0] // 2 :] = torch.ones_like( + param.data[param.data.shape[0] // 2 :] + ) + model = SNNTorchModel(self.net) + # Assert the connection sparsity is within 0.001 of 0.5 + self.assertLess(abs(connection_sparsity(model) - 0.5), 0.001) + + model = SNNTorchModel(self.net_rnn) + self.assertLess(connection_sparsity(model), 0.001) + + +class TestWorkloadMetrics(unittest.TestCase): + def setUp(self): + self.dummy_net = nn.Module() + self.net = models.net + self.net_relu_0 = models.net_relu_0 + self.net_relu_0_2 = models.net_relu_0_2 + self.net_relu_50 = models.net_relu_50 + self.net_relu_50_2 = models.net_relu_50_2 + self.net_torch_relu_0 = models.net_torch_relu_0 + self.net_sigm = models.net_sigm + self.net_conv_2d = models.net_conv_2d + self.net_conv_1d = models.net_conv_1d + self.net_snn = models.net_snn + self.net_RNN = models.simple_RNN() + self.net_GRU = models.simple_GRU() + self.net_lstm = models.simple_LSTM() + + def test_classification_accuracy(self): + model = SNNTorchModel(self.dummy_net) + + batch_size = 10 + data = ( + torch.randn(batch_size), + torch.arange(1, batch_size + 1), ) - model = SNNTorchModel(net) - # Assert the connection sparsity is within 0.001 of 0.5 - assert abs(connection_sparsity(model) - 0.5) < 0.001 - net_rnn = nn.Sequential( - nn.Flatten(), - nn.RNN(input_size=20, hidden_size=20, num_layers=2, bidirectional=True), - snn.Leaky(beta=beta, spike_grad=spike_grad, init_hidden=True), - nn.Linear(20, 256), - snn.Leaky(beta=beta, spike_grad=spike_grad, init_hidden=True), - nn.Linear(256, 256), - snn.Leaky(beta=beta, spike_grad=spike_grad, init_hidden=True), - nn.Linear(256, 35), - snn.Leaky(beta=beta, spike_grad=spike_grad, init_hidden=True, output=True), - ) - model = SNNTorchModel(net_rnn) - assert connection_sparsity(model) < 0.001 - print("Passed connection sparsity") + # all wrong + preds = torch.zeros(batch_size) + self.assertEqual(round(classification_accuracy(model, preds, data), 1), 0.0) + + # one correct + preds = torch.ones(batch_size) + self.assertEqual(round(classification_accuracy(model, preds, data), 1), 0.1) + + # half correct + preds = torch.tensor([0 if i % 2 == 0 else i + 1 for i in range(batch_size)]) + self.assertEqual(round(classification_accuracy(model, preds, data), 1), 0.5) + + # all correct + preds = torch.arange(1, batch_size + 1) + self.assertEqual(round(classification_accuracy(model, preds, data), 1), 1.0) + + def test_MSE(self): + # dummy model + model = SNNTorchModel(self.dummy_net) + + batch_size = 10 + data = ( + torch.randn(batch_size), + torch.arange(0, batch_size), + ) # input and targets + + preds = torch.tensor([1.1 * i for i in range(batch_size)]) + + diff = [0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9] + diff = [i**2 for i in diff] + correct = sum(diff) / batch_size + + self.assertEqual(round(MSE(model, preds, data), 3), round(correct, 3)) + + def test_sMAPE(self): + # dummy model + model = SNNTorchModel(self.dummy_net) + + batch_size = 10 + data = ( + torch.randn(batch_size), + torch.arange(1, batch_size + 1), + ) # input and targets + + preds = torch.tensor([1.1 * (i + 1) for i in range(batch_size)]) + + diff = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0] + added = [i + 1.1 * i for i in range(1, batch_size + 1)] + + diff = [i / j for i, j in zip(diff, added)] + correct = sum(diff) * 200 / batch_size + + self.assertEqual(round(sMAPE(model, preds, data), 3), round(correct, 3)) + + def test_r2(self): + # dummy model + model = SNNTorchModel(self.dummy_net) + + batch_size = 10 + targets = [[i for i in range(batch_size)] for i in range(2)] + + data = ( + torch.randn(2, batch_size), + torch.tensor(targets, dtype=torch.float).transpose(0, 1), + ) # input and targets + + preds = [ + [1.1 * i for i in range(batch_size)], + [1.1 * (i + 1) for i in range(batch_size)], + ] + + preds = torch.tensor(preds).transpose(0, 1) + + x_diff = [0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9] + y_diff = [1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2.0] + x_num = sum([i**2 for i in x_diff]) + y_num = sum([i**2 for i in y_diff]) + + x_den = 8.25 * batch_size + y_den = 8.25 * batch_size + + x_r2 = 1 - x_num / x_den + y_r2 = 1 - y_num / y_den + correct = (x_r2 + y_r2) / 2 + + R2 = r2() + self.assertEqual(round(R2(model, preds, data), 3), round(correct, 3)) + + def test_activation_sparsity(self): + # test spiking model + model = SNNTorchModel(self.net) + detect_activations_connections(model) + self.assertEqual(len(model.activation_hooks), 4) + + # test ReLU model + + model_relu_0 = TorchModel(self.net_relu_0) + detect_activations_connections(model_relu_0) + inp = torch.ones(20) + + out_relu = model_relu_0(inp) + act_sp_relu_0 = activation_sparsity(model_relu_0, out_relu, inp) + + self.assertEqual(act_sp_relu_0, 0.0) + + # test ReLU model with half-negative inputs + inp = torch.ones(20) + inp[0:10] = -1 + + model_relu_50 = TorchModel(self.net_relu_50) + detect_activations_connections(model_relu_50) + out_relu_50 = model_relu_50(inp) + + act_sp_relu_50 = activation_sparsity(model_relu_50, out_relu_50, inp) + + self.assertEqual(act_sp_relu_50, 0.5) + + # test duplicating activation layers in the model + + model_torch_relu_0 = TorchModel(self.net_torch_relu_0) + detect_activations_connections(model_torch_relu_0) + inp = torch.ones(20) + + out_relu = model_torch_relu_0(inp) + act_sp_torch_relu_0 = activation_sparsity(model_torch_relu_0, out_relu, inp) + + self.assertEqual(act_sp_torch_relu_0, 0.0) + + model_torch_relu_0.reset_hooks() + inp = torch.ones(20) + inp[0:10] = -1 + out_relu = model_torch_relu_0(inp) + act_sp_relu_50 = activation_sparsity(model_torch_relu_0, out_relu, inp) + self.assertEqual(act_sp_relu_50, 0.5) -# Pytest for classification_accuracy from benchmarks/workload_metrics -def test_classification_accuracy(): - # dummy model - model = SNNTorchModel(net=nn.Module()) + # test Sigmoid model - batch_size = 10 - data = ( - torch.randn(batch_size), - torch.arange(1, batch_size + 1), - ) # input and targets + model_sigm = TorchModel(self.net_sigm) + detect_activations_connections(model_sigm) - # all wrong - preds = torch.zeros(batch_size) - assert round(classification_accuracy(model, preds, data), 1) == 0.0 + inp = torch.ones(20) + out_sigm = model_sigm(inp) + act_sp_sigm = activation_sparsity(model_sigm, out_sigm, inp) + self.assertEqual(act_sp_sigm, 0.0) - # one correct - preds = torch.ones(batch_size) - assert round(classification_accuracy(model, preds, data), 1) == 0.1 - - # half correct - preds = torch.tensor([0 if i % 2 == 0 else i + 1 for i in range(batch_size)]) - assert round(classification_accuracy(model, preds, data), 1) == 0.5 - - # all correct - preds = torch.arange(1, batch_size + 1) - assert round(classification_accuracy(model, preds, data), 1) == 1.0 - - -# Pytest for MSE from benchmarks/workload_metrics -def test_MSE(): - # dummy model - model = SNNTorchModel(net=nn.Module()) - - batch_size = 10 - data = (torch.randn(batch_size), torch.arange(0, batch_size)) # input and targets - - preds = torch.tensor([1.1 * i for i in range(batch_size)]) - - diff = [0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9] - diff = [i**2 for i in diff] - correct = sum(diff) / batch_size - - assert round(MSE(model, preds, data), 3) == round(correct, 3) - - -# Pytest for sMAPE from benchmarks/workload_metrics -def test_sMAPE(): - # dummy model - model = SNNTorchModel(net=nn.Module()) - - batch_size = 10 - data = ( - torch.randn(batch_size), - torch.arange(1, batch_size + 1), - ) # input and targets - - preds = torch.tensor([1.1 * (i + 1) for i in range(batch_size)]) - - diff = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0] - added = [i + 1.1 * i for i in range(1, batch_size + 1)] - - diff = [i / j for i, j in zip(diff, added)] - correct = sum(diff) * 200 / batch_size - - assert round(sMAPE(model, preds, data), 3) == round(correct, 3) - - -# Pytest for r2 from benchmarks/workload_metrics -def test_r2(): - # dummy model - model = SNNTorchModel(net=nn.Module()) - - batch_size = 10 - targets = [[i for i in range(batch_size)] for i in range(2)] - - data = ( - torch.randn(2, batch_size), - torch.tensor(targets, dtype=torch.float).transpose(0, 1), - ) # input and targets - - preds = [ - [1.1 * i for i in range(batch_size)], - [1.1 * (i + 1) for i in range(batch_size)], - ] - - preds = torch.tensor(preds).transpose(0, 1) - - x_diff = [0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9] - y_diff = [1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2.0] - x_num = sum([i**2 for i in x_diff]) - y_num = sum([i**2 for i in y_diff]) - - x_den = 8.25 * batch_size - y_den = 8.25 * batch_size - - x_r2 = 1 - x_num / x_den - y_r2 = 1 - y_num / y_den - correct = (x_r2 + y_r2) / 2 - - R2 = r2() - assert round(R2(model, preds, data), 3) == round(correct, 3) - - -def test_activation_sparsity(): - # test spiking model - beta = 0.9 - spike_grad = surrogate.fast_sigmoid() - net = nn.Sequential( - nn.Flatten(), - nn.Linear(20, 256), - snn.Leaky(beta=beta, spike_grad=spike_grad, init_hidden=True), - nn.Linear(256, 256), - snn.Leaky(beta=beta, spike_grad=spike_grad, init_hidden=True), - nn.Linear(256, 256), - snn.Leaky(beta=beta, spike_grad=spike_grad, init_hidden=True), - nn.Linear(256, 35), - snn.Leaky(beta=beta, spike_grad=spike_grad, init_hidden=True, output=True), - ) - model = SNNTorchModel(net) - detect_activations_connections(model) - assert len(model.activation_hooks) == 4 - - # test ReLU model - net_relu_0 = nn.Sequential( - # nn.Flatten(), - nn.Identity(), - nn.ReLU(), - ) - net_relu_50 = nn.Sequential( - # nn.Flatten(), - nn.Identity(), - nn.ReLU(), - ) - model_relu_0 = TorchModel(net_relu_0) - detect_activations_connections(model_relu_0) - inp = torch.ones(20) - - out_relu = model_relu_0(inp) - act_sp_relu_0 = activation_sparsity(model_relu_0, out_relu, inp) - - assert act_sp_relu_0 == 0.0 - - # test ReLU model with half negative inputs - inp = torch.ones(20) - inp[0:10] = -1 - - model_relu_50 = TorchModel(net_relu_50) - detect_activations_connections(model_relu_50) - out_relu_50 = model_relu_50(inp) - - act_sp_relu_50 = activation_sparsity(model_relu_50, out_relu_50, inp) - - assert act_sp_relu_50 == 0.5 - - # test duplicating activation layers in model - act = nn.ReLU() - net_torch_relu_0 = nn.Sequential( - # nn.Flatten(), - nn.Identity(), - act, - nn.Identity(), - act, - nn.Identity(), - act, - nn.Identity(), - act, - ) - model_torch_relu_0 = TorchModel(net_torch_relu_0) - detect_activations_connections(model_torch_relu_0) - inp = torch.ones(20) - - out_relu = model_torch_relu_0(inp) - act_sp_torch_relu_0 = activation_sparsity(model_torch_relu_0, out_relu, inp) - - assert act_sp_torch_relu_0 == 0.0 - - model_torch_relu_0.reset_hooks() - inp = torch.ones(20) - inp[0:10] = -1 - out_relu = model_torch_relu_0(inp) - act_sp_relu_50 = activation_sparsity(model_torch_relu_0, out_relu, inp) - - assert act_sp_relu_50 == 0.5 - - # test Sigmoid model - net_sigm = nn.Sequential( - nn.Identity(), - nn.Sigmoid(), - ) - model_sigm = TorchModel(net_sigm) - detect_activations_connections(model_sigm) - - inp = torch.ones(20) - out_sigm = model_sigm(inp) - act_sp_sigm = activation_sparsity(model_sigm, out_sigm, inp) - assert act_sp_sigm == 0.0 - print("Passed activation sparsity") - - -def test_synaptic_ops(): - # test ReLU model - net_relu_0 = nn.Sequential( - # nn.Flatten(), - nn.Linear(20, 25, bias=False), - nn.Sigmoid(), - nn.Linear(25, 25, bias=False), - nn.ReLU(), - ) - - model_relu_0 = TorchModel(net_relu_0) - detect_activations_connections(model_relu_0) - inp = torch.ones(1, 20) - inp[:, 0:10] = 5 - - out_relu = model_relu_0(inp) - syn = synaptic_operations() - syn_ops = syn(model_relu_0, out_relu, (inp, 0)) - - print(syn_ops) - - assert syn_ops["Effective_MACs"] == 1125 - assert syn_ops["Effective_ACs"] == 0 - - # test model with Identity layer as first layer - net_relu_50 = nn.Sequential( - # nn.Flatten(), - nn.Linear(20, 20, bias=False), # 400 ops - nn.Sigmoid(), - nn.Linear(20, 25, bias=False), # 500 ops - nn.Sigmoid(), - nn.Linear(25, 25, bias=False), # 625 ops - nn.Sigmoid(), - nn.Linear(25, 25, bias=False), # 625 ops - nn.Sigmoid(), - ) - inp = torch.ones(1, 20) - inp[:, 0:10] = 5 - - model_relu_50 = TorchModel(net_relu_50) - detect_activations_connections(model_relu_50) - out_relu_50 = model_relu_50(inp) - - syn = synaptic_operations() - syn_ops = syn(model_relu_50, out_relu_50, (inp, 0)) - - assert syn_ops["Effective_MACs"] == (2 * 625 + 400 + 500) - assert syn_ops["Effective_ACs"] == 0 - - # test conv2d layers - net_conv = nn.Sequential( - nn.Conv2d(1, 1, 3, bias=False), - nn.ReLU(), - ) - - inp = torch.ones(1, 1, 3, 3) # 9 syn ops - inp[0, 0, 0, 0] = 4 # avoid getting classified as snn - - model = TorchModel(net_conv) - detect_activations_connections(model) - - out = model(inp) - syn = synaptic_operations() - syn_ops = syn(model, out, (inp, 0)) - - assert syn_ops["Effective_MACs"] == 9 - assert syn_ops["Effective_ACs"] == 0 - - model.reset_hooks() - inp = torch.ones( - 1, 1, 12, 12 - ) # (12-(kernelsize-1))**2 * 9 synops per kernel ops= 100*9 syn ops = 900 - inp[0, 0, 0, 0] = 4 # avoid getting classified as snn + def test_synaptic_ops(self): + # test ReLU model - out = model(inp) - syn = synaptic_operations() - syn_ops = syn(model, out, (inp, 0)) + model_relu_0 = TorchModel(self.net_relu_0_2) + detect_activations_connections(model_relu_0) + inp = torch.ones(1, 20) + inp[:, 0:10] = 5 - assert syn_ops["Effective_MACs"] == 900 - assert syn_ops["Effective_ACs"] == 0 + out_relu = model_relu_0(inp) + syn = synaptic_operations() + syn_ops = syn(model_relu_0, out_relu, (inp, 0)) - # test conv1d layers - net_conv = nn.Sequential( - # nn.Flatten(), - nn.Conv1d(5, 1, 5, bias=False), - nn.ReLU(), - ) - inp = torch.ones(1, 5, 10) # 5*5*(10-(5-1)) = 150 syn ops - inp[0, 0, 0] = 4 # avoid getting classified as snn + self.assertEqual(syn_ops["Effective_MACs"], 1125) + self.assertEqual(syn_ops["Effective_ACs"], 0) - model = TorchModel(net_conv) + # test model with Identity layer as first layer - detect_activations_connections(model) + inp = torch.ones(1, 20) + inp[:, 0:10] = 5 - out = model(inp) - syn = synaptic_operations() - syn_ops = syn(model, out, (inp, 0)) + model_relu_50 = TorchModel(self.net_relu_50_2) + detect_activations_connections(model_relu_50) + out_relu_50 = model_relu_50(inp) - assert syn_ops["Effective_MACs"] == 150 - assert syn_ops["Effective_ACs"] == 0 - - # test snn layers - net_snn = nn.Sequential( - # nn.Flatten(), - nn.Linear(20, 5, bias=False), - snn.Leaky( - beta=0.9, spike_grad=surrogate.fast_sigmoid(), init_hidden=True, output=True - ), - ) - # should be 20*5 = 100 syn ops per call, for timesteps (1 sample per batch): 10*100 = 1000 + syn = synaptic_operations() + syn_ops = syn(model_relu_50, out_relu_50, (inp, 0)) - # simulate spiking input with only ones - inp = torch.ones(5, 10, 20) # batch size, time steps, input size + self.assertEqual(syn_ops["Effective_MACs"], (2 * 625 + 400 + 500)) + self.assertEqual(syn_ops["Effective_ACs"], 0) - model = SNNTorchModel(net_snn) - - detect_activations_connections(model) - - out = model(inp) - syn = synaptic_operations() - syn_ops = syn(model, out, (inp, 0)) - - assert syn_ops["Effective_MACs"] == 0 - assert syn_ops["Effective_ACs"] == 1000 - - # test lstm network - net_lstm = simple_LSTM() + # test conv2d layers + inp = torch.ones(1, 1, 3, 3) # 9 syn ops + inp[0, 0, 0, 0] = 4 # avoid getting classified as snn - inp = [ - torch.ones(1, 25), - (torch.ones(1, 5), torch.ones(1, 5)), - ] # input, (hidden, cell) - inp[0][0, 0] = 4 # avoid getting classified as snn - model = TorchModel(net_lstm) + model = TorchModel(self.net_conv_2d) + detect_activations_connections(model) - detect_activations_connections(model) + out = model(inp) + syn = synaptic_operations() + syn_ops = syn(model, out, (inp, 0)) - out = model(inp) + self.assertEqual(syn_ops["Effective_MACs"], 9) + self.assertEqual(syn_ops["Effective_ACs"], 0) - syn = synaptic_operations() - syn_ops = syn(model, out, inp) + model.reset_hooks() + inp = torch.ones( + 1, 1, 12, 12 + ) # (12-(kernelsize-1))**2 * 9 synops per kernel ops= 100*9 syn ops = 900 + inp[0, 0, 0, 0] = 4 # avoid getting classified as snn - assert syn_ops["Effective_MACs"] == 615 - assert syn_ops["Effective_ACs"] == 0 + out = model(inp) + syn = synaptic_operations() + syn_ops = syn(model, out, (inp, 0)) - # test RNN network - net_RNN = simple_RNN() + self.assertEqual(syn_ops["Effective_MACs"], 900) + self.assertEqual(syn_ops["Effective_ACs"], 0) - inp = [torch.ones(1, 25), torch.ones(1, 5)] # input, (hidden, cell) - inp[0][0, 0] = 4 # avoid getting classified as snn - model = TorchModel(net_RNN) + inp = torch.ones(1, 5, 10) # 5*5*(10-(5-1)) = 150 syn ops + inp[0, 0, 0] = 4 # avoid getting classified as snn - detect_activations_connections(model) + model = TorchModel(self.net_conv_1d) - out = model(inp) + detect_activations_connections(model) - syn = synaptic_operations() - syn_ops = syn(model, out, inp) + out = model(inp) + syn = synaptic_operations() + syn_ops = syn(model, out, (inp, 0)) - assert syn_ops["Effective_MACs"] == 150 - assert syn_ops["Effective_ACs"] == 0 + self.assertEqual(syn_ops["Effective_MACs"], 150) + self.assertEqual(syn_ops["Effective_ACs"], 0) - # test GRU network - net_GRU = simple_GRU() + # test snn layers - inp = [torch.ones(1, 25), torch.ones(1, 5)] # input, (hidden, cell) - inp[0][0, 0] = 4 # avoid getting classified as snn - model = TorchModel(net_GRU) + # should be 20*5 = 100 syn ops per call, for timesteps (1 sample per batch): 10*100 = 1000 - detect_activations_connections(model) + # simulate spiking input with only ones + inp = torch.ones(5, 10, 20) # batch size, time steps, input size - out = model(inp) + model = SNNTorchModel(self.net_snn) - syn = synaptic_operations() - syn_ops = syn(model, out, inp) + detect_activations_connections(model) - assert syn_ops["Effective_MACs"] == 465 - assert syn_ops["Effective_ACs"] == 0 + out = model(inp) + syn = synaptic_operations() + syn_ops = syn(model, out, (inp, 0)) - print("Passed synaptic ops") + self.assertEqual(syn_ops["Effective_MACs"], 0) + self.assertEqual(syn_ops["Effective_ACs"], 1000) + # test lstm network -def test_neuron_update_metric(): - net_relu_0 = nn.Sequential( - # nn.Flatten(), - nn.Linear(20, 25, bias=False), - nn.Sigmoid(), - nn.Linear(25, 25, bias=False), - nn.ReLU(), - nn.Linear(25, 25, bias=False), - nn.Sigmoid(), - nn.Linear(25, 25, bias=False), - nn.ReLU(), - nn.Linear(25, 25, bias=False), - snn.Lapicque( - beta=0.9, spike_grad=surrogate.fast_sigmoid(), init_hidden=True, output=True - ), - ) - model_relu_0 = SNNTorchModel(net_relu_0) - detect_activations_connections(model_relu_0) - inp = torch.ones(1, 1, 20) - out_relu = model_relu_0(inp) - neuron_updates = number_neuron_updates(model_relu_0, out_relu, (inp, 0)) - print(neuron_updates) - print("Manual check!") - print("Passed neuron update metric") + inp = [ + torch.ones(1, 25), + (torch.ones(1, 5), torch.ones(1, 5)), + ] # input, (hidden, cell) + inp[0][0, 0] = 4 # avoid getting classified as snn + model = TorchModel(self.net_lstm) + detect_activations_connections(model) -def test_membrane_potential_updates(): + out = model(inp) - # test snn layers - net_snn = nn.Sequential( - # nn.Flatten(), - nn.Linear(20, 5, bias=False), - snn.Leaky( - beta=0.9, spike_grad=surrogate.fast_sigmoid(), init_hidden=True, output=True - ), - ) + syn = synaptic_operations() + syn_ops = syn(model, out, inp) - # simulate spiking input with only ones - inp = torch.ones(5, 10, 20) # batch size, time steps, input size + self.assertEqual(syn_ops["Effective_MACs"], 615) + self.assertEqual(syn_ops["Effective_ACs"], 0) - model = SNNTorchModel(net_snn) + # test RNN network - detect_activations_connections(model) + inp = [torch.ones(1, 25), torch.ones(1, 5)] # input, (hidden, cell) + inp[0][0, 0] = 4 # avoid getting classified as snn + model = TorchModel(self.net_RNN) - out = model(inp) - mem_updates = membrane_updates() - tot_mem_updates = mem_updates(model, out, (inp, 0)) + detect_activations_connections(model) - assert tot_mem_updates == 50 + out = model(inp) - print("Passed membrane updates") + syn = synaptic_operations() + syn_ops = syn(model, out, inp) + self.assertEqual(syn_ops["Effective_MACs"], 150) + self.assertEqual(syn_ops["Effective_ACs"], 0) -class simple_LSTM(nn.Module): - """Nonsense LSTM for operations testing Should be 615 MACs.""" + # test GRU network + inp = [torch.ones(1, 25), torch.ones(1, 5)] # input, (hidden, cell) + inp[0][0, 0] = 4 # avoid getting classified as snn + model = TorchModel(self.net_GRU) - def __init__(self): - super(simple_LSTM, self).__init__() - self.lstm = nn.LSTMCell(input_size=25, hidden_size=5, bias=True) - self.rel = nn.ReLU() + detect_activations_connections(model) - def forward(self, inp): - x, states = inp[0], inp[1] - x, _ = self.lstm(x, states) - # x = self.rel(x) - return x + out = model(inp) + syn = synaptic_operations() + syn_ops = syn(model, out, inp) -class simple_RNN(nn.Module): - """Nonsense RNN for operations testing Should be 150 MACs.""" + self.assertEqual(syn_ops["Effective_MACs"], 465) + self.assertEqual(syn_ops["Effective_ACs"], 0) - def __init__(self): - super(simple_RNN, self).__init__() - self.RNN = nn.RNNCell(input_size=25, hidden_size=5, bias=True) - self.rel = nn.ReLU() + def test_membrane_potential_updates(self): + # simulate spiking input with only ones + inp = torch.ones(5, 10, 20) # batch size, time steps, input size - def forward(self, inp): - x, states = inp[0], inp[1] - x = self.RNN(x, states) - x = self.rel(x) - return x + model = SNNTorchModel(self.net_snn) + detect_activations_connections(model) -class simple_GRU(nn.Module): - """Nonsense GRU/RNN for operations testing Should be 465 MACs.""" + out = model(inp) + mem_updates = membrane_updates() + tot_mem_updates = mem_updates(model, out, (inp, 0)) - def __init__(self): - super(simple_GRU, self).__init__() - self.GRU = nn.GRUCell(input_size=25, hidden_size=5, bias=True) - self.rel = nn.ReLU() + self.assertEqual(tot_mem_updates, 50) - def forward(self, inp): - x, states = inp[0], inp[1] - x = self.GRU(x, states) - x = self.rel(x) - return x - -test_connection_sparsity() -test_activation_sparsity() -test_synaptic_ops() -test_neuron_update_metric() +# TODO: refactor this metric if needed +# def test_neuron_update_metric(): +# net_relu_0 = nn.Sequential( +# # nn.Flatten(), +# nn.Linear(20, 25, bias=False), +# nn.Sigmoid(), +# nn.Linear(25, 25, bias=False), +# nn.ReLU(), +# nn.Linear(25, 25, bias=False), +# nn.Sigmoid(), +# nn.Linear(25, 25, bias=False), +# nn.ReLU(), +# nn.Linear(25, 25, bias=False), +# snn.Lapicque( +# beta=0.9, spike_grad=surrogate.fast_sigmoid(), init_hidden=True, output=True +# ), +# ) +# model_relu_0 = SNNTorchModel(net_relu_0) +# detect_activations_connections(model_relu_0) +# inp = torch.ones(1, 1, 20) +# out_relu = model_relu_0(inp) +# neuron_updates = number_neuron_updates(model_relu_0, out_relu, (inp, 0)) +# print(neuron_updates) +# print("Manual check!") +# print("Passed neuron update metric") +# +# +# +# test_neuron_update_metric() diff --git a/tests/test_preprocessors/sample_audio.wav b/tests/test_preprocessors/test_data/sample_audio.wav similarity index 100% rename from tests/test_preprocessors/sample_audio.wav rename to tests/test_preprocessors/test_data/sample_audio.wav diff --git a/tests/test_preprocessors/test_mfcc.py b/tests/test_preprocessors/test_mfcc.py index a3ba1227..e757f4c3 100644 --- a/tests/test_preprocessors/test_mfcc.py +++ b/tests/test_preprocessors/test_mfcc.py @@ -1,14 +1,15 @@ -import unittest -from pathlib import Path from neurobench.preprocessing.mfcc import MFCCPreProcessor -import torch +from pathlib import Path import torchaudio -import numpy as np +import unittest class TestMFCCPreProcessor(unittest.TestCase): + """Tests MFCCPreProcessor class.""" + def setUp(self): - sample_file = Path(__file__).parent.joinpath("sample_audio.wav") + """Set up the test data.""" + sample_file = Path(__file__).parent.joinpath("test_data/sample_audio.wav") self.sample_audio, self.sampling_rate = torchaudio.load(sample_file) self.init_args = { @@ -29,18 +30,22 @@ def setUp(self): self.sample_audio = self.sample_audio.permute(0, 1) def test_mfcc_non_tuple(self): + """Test that the MFCCPreProcessor raises an error if the input is not a + tuple.""" mfcc = MFCCPreProcessor(**self.init_args) audio = [1, 2, 3] - with self.assertRaises(TypeError): - mfcc(audio) + self.assertRaises(TypeError, mfcc, audio) def test_mfcc_tuple_wrong_shape(self): + """Test that the MFCCPreProcessor raises an error if the input tuple has the + wrong shape.""" mfcc = MFCCPreProcessor(**self.init_args) audio = (self.sample_audio, 2, 3, 4) - with self.assertRaises(ValueError): - mfcc(audio) + self.assertRaises(ValueError, mfcc, audio) def test_mfcc_tuple_correct_shape(self): + """Test that the MFCCPreProcessor runs correctly with the correct input + shape.""" mfcc = MFCCPreProcessor(**self.init_args) audio = (self.sample_audio, 2) mfcc(audio) diff --git a/tests/test_preprocessors/test_speech2spikes.py b/tests/test_preprocessors/test_speech2spikes.py index b067e8b6..f363a166 100644 --- a/tests/test_preprocessors/test_speech2spikes.py +++ b/tests/test_preprocessors/test_speech2spikes.py @@ -1,20 +1,74 @@ +from neurobench.preprocessing.speech2spikes import S2SPreProcessor from pathlib import Path - -import torch import torchaudio +import unittest +import torch -from neurobench.preprocessing.speech2spikes import S2SPreProcessor +class TestS2SPreProcessor(unittest.TestCase): + """Tests S2SPreProcessor class.""" + + def setUp(self): + """Set up the test data.""" + sample_file = Path(__file__).parent.joinpath("test_data/sample_audio.wav") + self.sample_audio, _ = torchaudio.load(sample_file) + self.s2s = S2SPreProcessor() + + def test_initialization(self): + """Test the initialization of S2SPreProcessor.""" + self.assertEqual(self.s2s.device, None) + self.assertTrue(self.s2s.transpose) + self.assertEqual(self.s2s.log_offset, 1e-6) + self.assertEqual(self.s2s.spec_kwargs["sample_rate"], 16000) + self.assertEqual(self.s2s.threshold, 1) + + def test_configure(self): + """Test the configure method of S2SPreProcessor.""" + self.s2s.configure(threshold=2, n_mels=40, hop_length=160) + self.assertEqual(self.s2s.threshold, 2) + self.assertEqual(self.s2s.spec_kwargs["n_mels"], 40) + self.assertEqual(self.s2s.spec_kwargs["hop_length"], 160) + + def test_s2s_shape(self): + """Test that the S2SPreProcessor returns the correct shape.""" + # Get sample into shape (batch, timestep, features*) + sample_audio = torch.unsqueeze(self.sample_audio.T, 0) + sample_audio = torch.tile(sample_audio, (100, 1, 1)) + + tensors, targets = self.s2s((sample_audio, torch.Tensor([1] * 100))) + self.assertEqual(tensors.shape, (100, 60, 20)) + self.assertEqual(targets.shape, (100,)) + + def test_s2s_different_batch_sizes(self): + """Test that the S2SPreProcessor handles different batch sizes correctly.""" + batch_sizes = [10, 20, 256] + + for batch_size in batch_sizes: + with self.subTest(batch_size=batch_size): + sample_audio = torch.unsqueeze(self.sample_audio.T, 0) + sample_audio = torch.tile(sample_audio, (batch_size, 1, 1)) + + tensors, targets = self.s2s( + (sample_audio, torch.Tensor([1] * batch_size)) + ) + self.assertEqual(tensors.shape, (batch_size, 60, 20)) + self.assertEqual(targets.shape, (batch_size,)) + + def test_s2s_with_kwargs(self): + """Test that the S2SPreProcessor handles kwargs correctly.""" + sample_audio = torch.unsqueeze(self.sample_audio.T, 0) + sample_audio = torch.tile(sample_audio, (100, 1, 1)) -def test_s2s(): - sample_file = Path(__file__).parent.joinpath("sample_audio.wav") - sample_audio, sampling_rate = torchaudio.load(sample_file) + kwargs = {"extra_info": "test"} + tensors, targets, returned_kwargs = self.s2s( + (sample_audio, torch.Tensor([1] * 100), kwargs) + ) - # Get sample into shape (batch, timestep, features*) - sample_audio = torch.unsqueeze(sample_audio.T, 0) - sample_audio = torch.tile(sample_audio, (100, 1, 1)) + self.assertEqual(tensors.shape, (100, 60, 20)) + self.assertEqual(targets.shape, (100,)) + self.assertEqual(returned_kwargs, kwargs) - s2s = S2SPreProcessor() - tensors, targets = s2s((sample_audio, torch.Tensor([1] * 100))) - assert tensors.shape == (100, 60, 20) - assert targets.shape == (100,) + def test_s2s_invalid_input(self): + """Test that the S2SPreProcessor raises an error for invalid input shapes.""" + invalid_audio = torch.rand((10, 5)) # Invalid shape + self.assertRaises(IndexError, self.s2s, (invalid_audio, torch.Tensor([1] * 10))) From a6f5d11285b512a8ab2fdef5afedb841db3cfaa0 Mon Sep 17 00:00:00 2001 From: benedettoleto Date: Sun, 2 Jun 2024 14:58:59 +0200 Subject: [PATCH 02/19] refactor workflow merging code style check and test code --- .github/workflows/python-style-check.yaml | 35 -------------- .github/workflows/python-tests.yaml | 59 +++++++++++++++++++++++ 2 files changed, 59 insertions(+), 35 deletions(-) delete mode 100644 .github/workflows/python-style-check.yaml create mode 100644 .github/workflows/python-tests.yaml diff --git a/.github/workflows/python-style-check.yaml b/.github/workflows/python-style-check.yaml deleted file mode 100644 index e4c13dde..00000000 --- a/.github/workflows/python-style-check.yaml +++ /dev/null @@ -1,35 +0,0 @@ -name: Python Style Check - -on: - pull_request: - branches: - - dev - -jobs: - lint: - runs-on: ubuntu-latest - - steps: - - uses: actions/checkout@v2 - - - name: Set up Python - uses: actions/setup-python@v2 - with: - python-version: '3.10' - - - name: Install dependencies - run: | - python -m pip install --upgrade pip - pip install flake8==7.0.0 black==24.2.0 docformatter==1.7.5 - - - name: Check with Black - run: | - black --check -v --line-length 88 --exclude='/neurobench/examples/' . - - - name: Check with docformatter - run: | - docformatter --wrap-summaries 88 --wrap-descriptions 88 --pre-summary-newline --exclude /neurobench/examples/ --recursive --blank --black --check . - - - name: Run Flake8 - run: | - flake8 --count --ignore=E501,E203,W291,W503,F403,F401,F821 --max-line-length=88 --statistics --show-source --exclude=neurobench/examples/ . \ No newline at end of file diff --git a/.github/workflows/python-tests.yaml b/.github/workflows/python-tests.yaml new file mode 100644 index 00000000..58213354 --- /dev/null +++ b/.github/workflows/python-tests.yaml @@ -0,0 +1,59 @@ +name: Test code and documentation + +on: + push: + branches: + - dev + pull_request: + branches: + - dev + + +jobs: + test-code-style: + runs-on: ubuntu-latest + + steps: + - uses: actions/checkout@v4 + + - name: Set up Python + uses: actions/setup-python@v5 + with: + python-version: '3.11' + + - name: Install dependencies + run: | + python -m pip install --upgrade pip + poetry install --with code-quality + + - name: Check with Black + run: | + poetry run black . + + - name: Check with docformatter + run: | + poetry run docformatter . + + - name: Run Flake8 + run: | + poetry run pflake8 . + + test-code: + runs-on: ubuntu-latest + + steps: + - uses: actions/checkout@v4 + + - name: Set up Python + uses: actions/setup-python@v5 + with: + python-version: '3.11' + + - name: Install dependencies + run: | + python -m pip install --upgrade pip + poetry install --with dev + + - name: Run tests + run: | + poetry run black pytest \ No newline at end of file From 44fbe8eaaf4668d10aa652c66b3144037c9f1087 Mon Sep 17 00:00:00 2001 From: benedettoleto Date: Sun, 2 Jun 2024 14:59:43 +0200 Subject: [PATCH 03/19] refactor pyproject.toml to be compatible with the new workflow --- pyproject.toml | 42 +++++++++++++++++++++++++++++++++++++++++- 1 file changed, 41 insertions(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index 5b27ff47..9e959fa3 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -34,12 +34,52 @@ sphinx = "^7.2.5" sphinx-rtd-theme = "^1.3.0" pre-commit = "*" +[tool.poetry.group.code-quality] +optional = true + +[tool.poetry.group.code-quality.dependencies] +pyproject-flake8 = '7.0.0' +black = '24.2.0' +docformatter = '1.7.5' + + [tool.poetry.extras] mackey-glass = ["jitcdde"] nehar = ["pytorch-lightning", "gdown"] [tool.black] -exclude = '/examples/' +exclude = '/neurobench/examples/' +line-length = 88 +check = true + +[tool.docformatter] +exclude = '/neurobench/examples/' +check = true +recursive = true +wrap-summaries = 88 +wrap-descriptions = 88 +pre-summary-newline = true +blank = true +blck = true + +[tool.flake8] +exclude = './neurobench/examples/' +count = true +statistics = true +show-source = true +max-line-length = 88 +extend-ignore = ["E501", "E203", "W291", "W503", "F403", "F401", "F821"] + + +[tool.pytest.ini_options] +addopts = "-ra" +testpaths = [ + "tests/test_preprocessors", + "tests/test_accumulators.py", + "tests/test_benchmarks.py", + "tests/test_frameworks.py", + "tests/test_metrics.py", +] [build-system] requires = ["poetry-core"] From 493634cb7868487df0cbd6d2172bcdf32dc5c059 Mon Sep 17 00:00:00 2001 From: benedettoleto Date: Sun, 2 Jun 2024 15:03:01 +0200 Subject: [PATCH 04/19] fix workflow error --- .github/workflows/python-tests.yaml | 11 ++++++++++- 1 file changed, 10 insertions(+), 1 deletion(-) diff --git a/.github/workflows/python-tests.yaml b/.github/workflows/python-tests.yaml index 58213354..9516f2b5 100644 --- a/.github/workflows/python-tests.yaml +++ b/.github/workflows/python-tests.yaml @@ -21,9 +21,13 @@ jobs: with: python-version: '3.11' - - name: Install dependencies + - name: Install Poetry run: | python -m pip install --upgrade pip + pip install poetry + + - name: Install dependencies + run: | poetry install --with code-quality - name: Check with Black @@ -49,6 +53,11 @@ jobs: with: python-version: '3.11' + - name: Install Poetry + run: | + python -m pip install --upgrade pip + pip install poetry + - name: Install dependencies run: | python -m pip install --upgrade pip From 4e01845f3ffee223af2eb51b1bcad131aa97bd53 Mon Sep 17 00:00:00 2001 From: benedettoleto Date: Sun, 2 Jun 2024 15:05:14 +0200 Subject: [PATCH 05/19] delete poetry.lock to avoid installing dependencies from lockfile during workflow execution --- poetry.lock | 3315 --------------------------------------------------- 1 file changed, 3315 deletions(-) delete mode 100644 poetry.lock diff --git a/poetry.lock b/poetry.lock deleted file mode 100644 index be3c7960..00000000 --- a/poetry.lock +++ /dev/null @@ -1,3315 +0,0 @@ -[[package]] -name = "aiohttp" -version = "3.9.4" -description = "Async http client/server framework (asyncio)" -category = "main" -optional = false -python-versions = ">=3.8" - 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insertions(+) diff --git a/README.rst b/README.rst index ba65229b..170c3044 100644 --- a/README.rst +++ b/README.rst @@ -1,3 +1,10 @@ +.. raw:: html + + + + NeuroBench Banner + + ============ Introduction ============ From c3caadad6dd57b2707c573a39480a6ae2d2c9158 Mon Sep 17 00:00:00 2001 From: Jason Yik Date: Wed, 10 Jul 2024 15:10:55 -0600 Subject: [PATCH 09/19] Adjust logo size --- README.rst | 9 ++++----- 1 file changed, 4 insertions(+), 5 deletions(-) diff --git a/README.rst b/README.rst index 170c3044..15196526 100644 --- a/README.rst +++ b/README.rst @@ -1,13 +1,12 @@ .. raw:: html - - NeuroBench Banner + + NeuroBench Banner +
+
-============ -Introduction -============ A harness for running evaluations on `NeuroBench `__ algorithm benchmarks. From d980ed52144243a70e03fabd9c4c1a16bd3e23a4 Mon Sep 17 00:00:00 2001 From: Jason Yik Date: Wed, 10 Jul 2024 15:25:05 -0600 Subject: [PATCH 10/19] update logo for docs --- README.rst | 9 +++++++-- docs/index.rst | 3 --- 2 files changed, 7 insertions(+), 5 deletions(-) diff --git a/README.rst b/README.rst index 15196526..9860b562 100644 --- a/README.rst +++ b/README.rst @@ -1,13 +1,18 @@ .. raw:: html - - NeuroBench Banner + + NeuroBench Banner

+============ +Introduction +============ + + A harness for running evaluations on `NeuroBench `__ algorithm benchmarks. diff --git a/docs/index.rst b/docs/index.rst index 6388d612..f6c55162 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -1,6 +1,3 @@ -NeuroBench Documentation -====================================== - .. include:: ../README.rst .. toctree:: From f835982505ba89ac31f488a449760045596ef1c6 Mon Sep 17 00:00:00 2001 From: Jason Yik Date: Wed, 10 Jul 2024 15:26:01 -0600 Subject: [PATCH 11/19] Update README.rst --- README.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.rst b/README.rst index 9860b562..621f122d 100644 --- a/README.rst +++ b/README.rst @@ -1,7 +1,7 @@ .. raw:: html - + NeuroBench Banner
From 9c43aea6d535d61f0d8b3b2c9129c59ed208951c Mon Sep 17 00:00:00 2001 From: Jason Yik Date: Wed, 10 Jul 2024 15:35:58 -0600 Subject: [PATCH 12/19] Revert logo to jpeg --- README.rst | 12 +++--------- 1 file changed, 3 insertions(+), 9 deletions(-) diff --git a/README.rst b/README.rst index 621f122d..b64f8c90 100644 --- a/README.rst +++ b/README.rst @@ -1,12 +1,6 @@ -.. raw:: html - - - - NeuroBench Banner - -
-
- +.. image:: https://github.com/NeuroBench/neurobench/blob/main/docs/_static/neurobench_banner_light.jpeg?raw=true + :align: center + :width: 800 ============ Introduction From 195bec9e0a201d97dd7bbb874f70519067d75eca Mon Sep 17 00:00:00 2001 From: Jason Yik Date: Wed, 17 Jul 2024 13:21:19 -0600 Subject: [PATCH 13/19] update rnncell synops calculation --- neurobench/utils.py | 37 +++++++++++++++++++++++-------------- 1 file changed, 23 insertions(+), 14 deletions(-) diff --git a/neurobench/utils.py b/neurobench/utils.py index 82fb635c..0b7f7499 100644 --- a/neurobench/utils.py +++ b/neurobench/utils.py @@ -249,12 +249,13 @@ def single_layer_MACs(inputs, layer, total=False): # NOTE: these activation functions are currently NOT included in NeuroBench # if no explicit states are passed to recurrent layers, then h and c are initialized to zero (pytorch convention) layer_bin = make_binary_copy(layer, all_ones=total) - # transpose from batches, timesteps, features to features, batches - # print(layer_bin.weight_ih.shape) + # layer_weight_ih is [4*hidden_size, input_size] + # inputs[0].transpose(0, -1) is [input_size, batch_size] out_ih = torch.matmul( layer_bin.weight_ih, inputs[0].transpose(0, -1) ) # accounts for i,f,g,o out_hh = torch.zeros_like(out_ih) + # out shape is 4*h, batch, for hidden feature dim h biases = 0 bias_ih = 0 @@ -290,10 +291,16 @@ def single_layer_MACs(inputs, layer, total=False): out += biases # biases are added here for computation of c and h which depend on correct computation of ifgo out[out != 0] = 1 - # out is vector with i,f,g,o - ifgo = out.reshape(4, -1) # each row is one of i,f,g,o + + # out is vector with i,f,g,o, shape is 4*h, batch + hidden = out.shape[0] // 4 + ifgo = out.reshape(4, hidden, -1) # 4, h, B if in_states: - c_1 = ifgo[1, :] * inputs[1][1] + ifgo[0, :] * ifgo[2, :] + # inputs[1][1] shape is [B, h] + # element-wise multiply (vector products f*c + i*g) + c_1 = ( + ifgo[1, :] * inputs[1][1].transpose(0, -1) + ifgo[0, :] * ifgo[2, :] + ) else: c_1 = ifgo[0, :] * ifgo[2, :] @@ -323,9 +330,13 @@ def single_layer_MACs(inputs, layer, total=False): # Wir*x, Whr*h, Wiz*x, Whz*h, Win*x, Whn*h macs += rzn.sum() # multiplications of all weights and inputs/hidden states rzn += biases # add biases - rzn = rzn.reshape(3, -1) # each row is one of r,z,n - out_hh_n = out_hh.reshape(3, -1)[2, :] + bias_hh.reshape(3, -1)[2, :] + hidden = rzn.shape[0] // 3 + rzn = rzn.reshape(3, hidden, -1) # 3, h, B + out_hh = out_hh.reshape(3, hidden, -1) + bias_hh = bias_hh.reshape(3, hidden, -1) + + out_hh_n = out_hh[2, :] + bias_hh[2, :] r = rzn[0, :] # get r z = rzn[1, :] @@ -334,23 +345,21 @@ def single_layer_MACs(inputs, layer, total=False): n_hh_term_macs = ( r * out_hh_n - ) # elementwise_multiplication to find macs ofr*(Whn*h + bhn) specifically + ) # elementwise_multiplication to find macs of r*(Whn*h + bhn) specifically n_hh_term_macs[n_hh_term_macs != 0] = 1 macs += n_hh_term_macs.sum() # note hh part of n is already binarized, does not influence calculation of macs for n - n = ( - out_hh.reshape(3, -1)[2, :] - + bias_ih.reshape(3, -1)[2, :] - + n_hh_term_macs - ) + n = out_hh[2, :] + bias_ih[2, :] + n_hh_term_macs n[n != 0] = 1 z_a = 1 - z # only do this now because affects z_a z[z != 0] = 1 z_a[z_a != 0] = 1 t_1 = z_a * n - t_2 = z * inputs[1] + t_2 = z * inputs[1].transpose( + 0, -1 + ) # inputs are shape [B, h], all else is [h, B] t_1[t_1 != 0] = 1 t_2[t_2 != 0] = 1 From 4d3e0c46a23c602de37dd233d9ae471bd074fa01 Mon Sep 17 00:00:00 2001 From: benedettoleto Date: Thu, 18 Jul 2024 19:23:18 +0200 Subject: [PATCH 14/19] fix black configuration: checks are done only on the workflow action and not on the precommit stage --- .github/workflows/python-tests.yaml | 2 +- pyproject.toml | 1 - 2 files changed, 1 insertion(+), 2 deletions(-) diff --git a/.github/workflows/python-tests.yaml b/.github/workflows/python-tests.yaml index 7db9dff8..5359a8df 100644 --- a/.github/workflows/python-tests.yaml +++ b/.github/workflows/python-tests.yaml @@ -32,7 +32,7 @@ jobs: - name: Check with Black run: | - poetry run black . + poetry run black --check . - name: Check with docformatter run: | diff --git a/pyproject.toml b/pyproject.toml index 781bb9af..a98ad057 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -50,7 +50,6 @@ nehar = ["pytorch-lightning", "gdown"] [tool.black] exclude = '/neurobench/examples/' line-length = 88 -check = true [tool.docformatter] exclude = '/neurobench/examples/' From 82107d3767339f33b4d203126c6a8e8c2812a21a Mon Sep 17 00:00:00 2001 From: korneelf1 Date: Wed, 24 Jul 2024 16:36:16 +0200 Subject: [PATCH 15/19] update test with 2 as batch size for LSTMcell --- tests/test_metrics.py | 14 ++++++++------ 1 file changed, 8 insertions(+), 6 deletions(-) diff --git a/tests/test_metrics.py b/tests/test_metrics.py index 99646946..33940a52 100644 --- a/tests/test_metrics.py +++ b/tests/test_metrics.py @@ -339,11 +339,11 @@ def test_synaptic_ops(self): self.assertEqual(syn_ops["Effective_ACs"], 1000) # test lstm network - + batch_size = 2 inp = [ - torch.ones(1, 25), - (torch.ones(1, 5), torch.ones(1, 5)), - ] # input, (hidden, cell) + torch.ones(batch_size, 25), + (torch.ones(batch_size, 5), torch.ones(batch_size, 5)), + ] # input (batch_size, inp_size), (hidden, cell) inp[0][0, 0] = 4 # avoid getting classified as snn model = TorchModel(self.net_lstm) @@ -353,7 +353,7 @@ def test_synaptic_ops(self): syn = synaptic_operations() syn_ops = syn(model, out, inp) - + self.assertEqual(syn_ops["Effective_MACs"], 615) self.assertEqual(syn_ops["Effective_ACs"], 0) @@ -402,7 +402,9 @@ def test_membrane_potential_updates(self): self.assertEqual(tot_mem_updates, 50) - +test = TestWorkloadMetrics() +test.setUp() +test.test_synaptic_ops() # TODO: refactor this metric if needed # def test_neuron_update_metric(): # net_relu_0 = nn.Sequential( From 1b654b6e8f79d6e7950395db7c5e5cd84023565c Mon Sep 17 00:00:00 2001 From: korneelf1 Date: Wed, 24 Jul 2024 16:37:34 +0200 Subject: [PATCH 16/19] Batch size 2 for GRU and RNN tests --- tests/test_metrics.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/tests/test_metrics.py b/tests/test_metrics.py index 33940a52..1f83494a 100644 --- a/tests/test_metrics.py +++ b/tests/test_metrics.py @@ -358,8 +358,8 @@ def test_synaptic_ops(self): self.assertEqual(syn_ops["Effective_ACs"], 0) # test RNN network - - inp = [torch.ones(1, 25), torch.ones(1, 5)] # input, (hidden, cell) + batch_size = 2 + inp = [torch.ones(batch_size, 25), torch.ones(batch_size, 5)] # input, (hidden, cell) inp[0][0, 0] = 4 # avoid getting classified as snn model = TorchModel(self.net_RNN) @@ -374,7 +374,8 @@ def test_synaptic_ops(self): self.assertEqual(syn_ops["Effective_ACs"], 0) # test GRU network - inp = [torch.ones(1, 25), torch.ones(1, 5)] # input, (hidden, cell) + batch_size=2 + inp = [torch.ones(batch_size, 25), torch.ones(batch_size, 5)] # input, (hidden, cell) inp[0][0, 0] = 4 # avoid getting classified as snn model = TorchModel(self.net_GRU) From 0d2f25b687b97b32e62c33844ebbefa2eae7288e Mon Sep 17 00:00:00 2001 From: korneelf1 Date: Wed, 24 Jul 2024 16:40:20 +0200 Subject: [PATCH 17/19] remove local testing statement --- tests/test_metrics.py | 5 +---- 1 file changed, 1 insertion(+), 4 deletions(-) diff --git a/tests/test_metrics.py b/tests/test_metrics.py index 1f83494a..021b9b44 100644 --- a/tests/test_metrics.py +++ b/tests/test_metrics.py @@ -374,7 +374,7 @@ def test_synaptic_ops(self): self.assertEqual(syn_ops["Effective_ACs"], 0) # test GRU network - batch_size=2 + batch_size = 2 inp = [torch.ones(batch_size, 25), torch.ones(batch_size, 5)] # input, (hidden, cell) inp[0][0, 0] = 4 # avoid getting classified as snn model = TorchModel(self.net_GRU) @@ -403,9 +403,6 @@ def test_membrane_potential_updates(self): self.assertEqual(tot_mem_updates, 50) -test = TestWorkloadMetrics() -test.setUp() -test.test_synaptic_ops() # TODO: refactor this metric if needed # def test_neuron_update_metric(): # net_relu_0 = nn.Sequential( From 61eee43e6dacc86f8438375a8bdd011fb50165fb Mon Sep 17 00:00:00 2001 From: korneelf1 Date: Wed, 24 Jul 2024 16:54:10 +0200 Subject: [PATCH 18/19] refactoring --- tests/test_metrics.py | 15 ++++++++++----- 1 file changed, 10 insertions(+), 5 deletions(-) diff --git a/tests/test_metrics.py b/tests/test_metrics.py index 021b9b44..b0c2a21f 100644 --- a/tests/test_metrics.py +++ b/tests/test_metrics.py @@ -25,9 +25,7 @@ class TestStaticMetrics(unittest.TestCase): - def setUp(self): - self.dummy_net = nn.Module() self.net = models.net @@ -353,13 +351,16 @@ def test_synaptic_ops(self): syn = synaptic_operations() syn_ops = syn(model, out, inp) - + self.assertEqual(syn_ops["Effective_MACs"], 615) self.assertEqual(syn_ops["Effective_ACs"], 0) # test RNN network batch_size = 2 - inp = [torch.ones(batch_size, 25), torch.ones(batch_size, 5)] # input, (hidden, cell) + inp = [ + torch.ones(batch_size, 25), + torch.ones(batch_size, 5), + ] # input, (hidden, cell) inp[0][0, 0] = 4 # avoid getting classified as snn model = TorchModel(self.net_RNN) @@ -375,7 +376,10 @@ def test_synaptic_ops(self): # test GRU network batch_size = 2 - inp = [torch.ones(batch_size, 25), torch.ones(batch_size, 5)] # input, (hidden, cell) + inp = [ + torch.ones(batch_size, 25), + torch.ones(batch_size, 5), + ] # input, (hidden, cell) inp[0][0, 0] = 4 # avoid getting classified as snn model = TorchModel(self.net_GRU) @@ -403,6 +407,7 @@ def test_membrane_potential_updates(self): self.assertEqual(tot_mem_updates, 50) + # TODO: refactor this metric if needed # def test_neuron_update_metric(): # net_relu_0 = nn.Sequential( From 71e43673a43fce4f20405f5956a02bfc5b45d80e Mon Sep 17 00:00:00 2001 From: Jason Yik Date: Wed, 24 Jul 2024 12:48:13 -0400 Subject: [PATCH 19/19] =?UTF-8?q?Bump=20version:=201.0.5=20=E2=86=92=201.0?= =?UTF-8?q?.6?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .bumpversion.toml | 2 +- docs/conf.py | 2 +- pyproject.toml | 2 +- 3 files changed, 3 insertions(+), 3 deletions(-) diff --git a/.bumpversion.toml b/.bumpversion.toml index 05092ea5..35234989 100644 --- a/.bumpversion.toml +++ b/.bumpversion.toml @@ -3,7 +3,7 @@ parse = "(?P\\d+)\\.(?P\\d+)\\.(?P\\d+)" serialize = ["{major}.{minor}.{patch}"] regex = false -current_version = "1.0.5" +current_version = "1.0.6" ignore_missing_version = false search = "{current_version}" replace = "{new_version}" diff --git a/docs/conf.py b/docs/conf.py index 6c491cb0..35b41a6f 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -14,7 +14,7 @@ project = "NeuroBench" copyright = "2024, Jason Yik, Noah Pacik-Nelson, Korneel Van Den Berghe" author = "Jason Yik, Noah Pacik-Nelson, Korneel Van Den Berghe" -release = "1.0.5" +release = "1.0.6" # -- General configuration --------------------------------------------------- # https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration diff --git a/pyproject.toml b/pyproject.toml index a98ad057..7e707e60 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "neurobench" -version = "1.0.5" +version = "1.0.6" description = "Collaborative, Fair, and Representative Benchmarks for Neuromorphic Computing" authors = ["NeuroBench Team "] readme = "README.rst"