From aefdbaf6465b4c668e6ece899f2d22ae216de018 Mon Sep 17 00:00:00 2001 From: Jason Yik Date: Thu, 1 May 2025 22:35:19 -0400 Subject: [PATCH 1/9] add snntorch neuronhook bugfix #258 --- neurobench/hooks/neuron.py | 10 +++++++++- 1 file changed, 9 insertions(+), 1 deletion(-) diff --git a/neurobench/hooks/neuron.py b/neurobench/hooks/neuron.py index 9893b56f..cd8141ce 100644 --- a/neurobench/hooks/neuron.py +++ b/neurobench/hooks/neuron.py @@ -67,7 +67,15 @@ def hook_fn(self, layer, input, output): """ if self.spiking: - self.activation_outputs.append(output[0]) + if ( + self.layer.init_hidden + and not self.layer.output + and not isinstance(output, tuple) + ): + self.activation_outputs.append(output) + else: + self.activation_outputs.append(output[0]) + if hasattr(layer, "mem"): self.post_fire_mem_potential.append(layer.mem) From a7df52ce371d9cf0ef2fc24f63b5501e9a58e10a Mon Sep 17 00:00:00 2001 From: Younes Bouhadjar Date: Fri, 20 Jun 2025 12:39:15 +0200 Subject: [PATCH 2/9] Fix LSTM benchmark to evaluate on test set using warmed-up states at the end of training --- examples/mackey_glass/lstm_benchmark.py | 15 +++++++------- examples/mackey_glass/lstm_model.py | 26 +++++++++++++++---------- 2 files changed, 24 insertions(+), 17 deletions(-) diff --git a/examples/mackey_glass/lstm_benchmark.py b/examples/mackey_glass/lstm_benchmark.py index 37ff4c95..c0d080b6 100644 --- a/examples/mackey_glass/lstm_benchmark.py +++ b/examples/mackey_glass/lstm_benchmark.py @@ -165,9 +165,11 @@ plt.close() # Testing - test_set_loader = DataLoader(train_set, + test_set_loader = DataLoader(test_set, batch_size=mg.testtime_pts, shuffle=False) + + lstm.mode = "autonomous" lstm.device = torch.device("cpu") lstm.to(torch.device("cpu")) @@ -210,9 +212,8 @@ f"on tau {args.tau}") # With the default params, repeat 30, tau=17 -# sMAPE score = 15.156239883579927, -# connection_sparsity = 0.0, -# activation_sparsity = 0.45951777777777786, -# synop_macs = 14534.032622222225, -# synop_dense = 14552.413333333332, -# on time series id 0 +#sMAPE score = 14.331047058514564, +#connection_sparsity = 0.0, +#activation_sparsity = 0.4604411111111111, +#synop_macs = 14541.582355555556, +#synop_dense = 14560.0, diff --git a/examples/mackey_glass/lstm_model.py b/examples/mackey_glass/lstm_model.py index 2b043d5b..d1bb9255 100644 --- a/examples/mackey_glass/lstm_model.py +++ b/examples/mackey_glass/lstm_model.py @@ -111,16 +111,22 @@ def forward(self, batch): 3D tesnor: output data of shape [num_steps, output_dim] """ - # Reset register buffers - for i in range(self.n_layers): - getattr(self, f'h{i}')[:] = 0. - for i in range(self.n_layers): - getattr(self, f'c{i}')[:] = 0. - self.inp[:] = 0. - - self.h = [getattr(self, f'h{i}') for i in range(self.n_layers)] - self.c = [getattr(self, f'c{i}') for i in range(self.n_layers)] - + if self.mode != 'autonomous': + # Reset register buffers + for i in range(self.n_layers): + getattr(self, f'h{i}')[:] = 0. + for i in range(self.n_layers): + getattr(self, f'c{i}')[:] = 0. + + self.h = [getattr(self, f'h{i}') for i in range(self.n_layers)] + self.c = [getattr(self, f'c{i}') for i in range(self.n_layers)] + self.inp[:] = 0. + else: + # The warmed-up LSTM states and the buffer at the end of training are used during + # autonomous replay (for an explanation, see section 3.1 in https://doi.org/10.1016/j.mlwa.2022.100300) + self.h = [self.h[i].to(batch.device) for i in range(self.n_layers)] + self.c = [self.c[i].to(batch.device) for i in range(self.n_layers)] + predictions = [] for i, sample in enumerate(batch): From e4b3f836ecd0102d0d5a780f39349b81d841d4e4 Mon Sep 17 00:00:00 2001 From: Jason Yik Date: Fri, 20 Jun 2025 17:31:48 -0400 Subject: [PATCH 3/9] update re-run of training set to init hidden states --- examples/mackey_glass/lstm_benchmark.py | 14 +++++++++----- 1 file changed, 9 insertions(+), 5 deletions(-) diff --git a/examples/mackey_glass/lstm_benchmark.py b/examples/mackey_glass/lstm_benchmark.py index c0d080b6..5c00d09e 100644 --- a/examples/mackey_glass/lstm_benchmark.py +++ b/examples/mackey_glass/lstm_benchmark.py @@ -164,6 +164,10 @@ wandb.log({'fig_train': wandb.Image(fig)}) plt.close() + # Initialize hidden states with the training data, + # now that weights are trained + lstm(train_data.to(device)) + # Testing test_set_loader = DataLoader(test_set, batch_size=mg.testtime_pts, @@ -212,8 +216,8 @@ f"on tau {args.tau}") # With the default params, repeat 30, tau=17 -#sMAPE score = 14.331047058514564, -#connection_sparsity = 0.0, -#activation_sparsity = 0.4604411111111111, -#synop_macs = 14541.582355555556, -#synop_dense = 14560.0, +# sMAPE score = 14.347067906362048, +# connection_sparsity = 0.0, +# activation_sparsity = 0.46036666666666665, +# synop_macs = 14541.585333333329, +# synop_dense = 14560.0, From 58ee9f03ceb65b5c81201ce60d35b9df7c2fbc47 Mon Sep 17 00:00:00 2001 From: benedettoleto Date: Mon, 14 Jul 2025 18:00:07 +0200 Subject: [PATCH 4/9] feat: MACs neuron calculation --- examples/nehar/benchmark.py | 14 ++- neurobench/metrics/workload/__init__.py | 9 +- .../metrics/workload/neuron_operations.py | 103 ++++++++++++++++++ 3 files changed, 121 insertions(+), 5 deletions(-) create mode 100644 neurobench/metrics/workload/neuron_operations.py diff --git a/examples/nehar/benchmark.py b/examples/nehar/benchmark.py index b32cb274..c4e27a26 100644 --- a/examples/nehar/benchmark.py +++ b/examples/nehar/benchmark.py @@ -1,4 +1,11 @@ import os +import sys +import os + +SCRIPT_DIR = os.path.dirname(os.path.abspath("./")) +sys.path.append(os.path.dirname(SCRIPT_DIR)) +print("Adding path to sys.path:", os.path.dirname(SCRIPT_DIR)) + from neurobench.datasets import WISDM from training import SpikingNetwork from neurobench.processors.postprocessors import ChooseMaxCount @@ -11,6 +18,7 @@ SynapticOperations, ClassificationAccuracy, ActivationSparsityByLayer, + NeuronOperations ) from neurobench.metrics.static import ( ParameterCount, @@ -34,9 +42,7 @@ num_outputs = data_module.num_outputs num_steps = data_module.num_steps - spiking_network = SpikingNetwork.load_from_checkpoint( - model_path, map_location="cpu" - ) + spiking_network = SpikingNetwork(lr=1) model = SNNTorchModel(spiking_network.model, custom_forward=True) test_set_loader = data_module.test_dataloader() @@ -48,7 +54,7 @@ # # static_metrics = [ParameterCount, Footprint, ConnectionSparsity] - workload_metrics = [ActivationSparsity, ActivationSparsityByLayer,MembraneUpdates, SynapticOperations, ClassificationAccuracy] + workload_metrics = [ActivationSparsity, ActivationSparsityByLayer,MembraneUpdates, SynapticOperations, ClassificationAccuracy, NeuronOperations] # # benchmark = Benchmark( model, test_set_loader, [], postprocessors, [static_metrics, workload_metrics] diff --git a/neurobench/metrics/workload/__init__.py b/neurobench/metrics/workload/__init__.py index 15ebd3aa..f18464f0 100644 --- a/neurobench/metrics/workload/__init__.py +++ b/neurobench/metrics/workload/__init__.py @@ -7,6 +7,7 @@ from .smape import SMAPE from .r2 import R2 from .coco_map import CocoMap +from .neuron_operations import NeuronOperations __stateless__ = [ "ClassificationAccuracy", @@ -16,6 +17,12 @@ "ActivationSparsityByLayer", ] -__stateful__ = ["MembraneUpdates", "SynapticOperations", "R2", "CocoMap"] +__stateful__ = [ + "MembraneUpdates", + "SynapticOperations", + "R2", + "CocoMap", + "NeuronOperations", +] __all__ = __stateful__ + __stateless__ diff --git a/neurobench/metrics/workload/neuron_operations.py b/neurobench/metrics/workload/neuron_operations.py new file mode 100644 index 00000000..14005961 --- /dev/null +++ b/neurobench/metrics/workload/neuron_operations.py @@ -0,0 +1,103 @@ +import torch +from neurobench.metrics.abstract.workload_metric import AccumulatedMetric +from collections import defaultdict + + +neuron_macs_reset_operations = { + "Leaky": { + "subtract": 2, + "zero": 2, + }, + "Synaptic": { + "subtract": 3, + "zero": 2, + }, + "Lapicque": { + "subtract": 3, + "zero": 2, + }, + "Alpha": { + "subtract": 3, + "zero": 3, + }, +} + + +class NeuronOperations(AccumulatedMetric): + """ + Neuron operations metric. + + This metric computes the number of operations performed by neurons during the + forward pass of the model. The operations are tracked per neuron, per layer. + + """ + + def __init__(self): + """Initialize the NeuronOperations metric.""" + super().__init__(requires_hooks=True) + self.total_samples = 0 + self.dense = defaultdict(int) + self.macs = defaultdict(int) + + def reset(self): + """Reset the metric state for a new evaluation.""" + self.total_samples = 0 + self.dense = defaultdict(int) + self.macs = defaultdict(int) + + def __call__(self, model, preds, data): + """ + Accumulate the neuron operations. + + Args: + model: A NeuroBenchModel. + preds: A tensor of model predictions. + data: A tuple of data and labels. + Returns: + float: Number of membrane potential updates. + + """ + for hook in model.activation_hooks: + layer_type = hook.layer.__class__.__name__ + reset_mechanism = hook.layer._reset_mechanism + updates = 0 + + if len(hook.pre_fire_mem_potential) > 1: + pre_fire_mem = torch.stack(hook.pre_fire_mem_potential[1:]) + post_fire_mem = torch.stack(hook.post_fire_mem_potential[1:]) + updates += torch.count_nonzero(pre_fire_mem - post_fire_mem).item() + if hook.post_fire_mem_potential: + updates += hook.post_fire_mem_potential[0].numel() + + self.macs[layer_type] += ( + updates * neuron_macs_reset_operations[layer_type][reset_mechanism] + ) + self.dense[layer_type] += ( + hook.post_fire_mem_potential[0].numel() + * len(hook.post_fire_mem_potential) + * neuron_macs_reset_operations[layer_type][reset_mechanism] + ) + + self.total_samples += data[0].size(0) + + return self.compute() + + def compute(self): + """ + Compute the total membrane updates normalized by the number of samples. + + Returns: + float: Compute the total updates to each neuron's membrane potential within the model, + aggregated across all neurons and normalized by the number of samples processed. + + """ + if self.total_samples == 0: + return {"Neuron MACs": 0, "Neuron Dense": 0} + + macs = sum(self.macs.values()) + dense = sum(self.dense.values()) + + return { + "Neuron MACs": macs / self.total_samples, + "Neuron Dense": dense / self.total_samples, + } From 6bb85171d61b309ca61453e2cd8fb379f404200c Mon Sep 17 00:00:00 2001 From: benedettoleto Date: Wed, 30 Jul 2025 16:51:10 +0200 Subject: [PATCH 5/9] change name for neuron MACs --- neurobench/metrics/workload/neuron_operations.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/neurobench/metrics/workload/neuron_operations.py b/neurobench/metrics/workload/neuron_operations.py index 14005961..d6d8175c 100644 --- a/neurobench/metrics/workload/neuron_operations.py +++ b/neurobench/metrics/workload/neuron_operations.py @@ -98,6 +98,6 @@ def compute(self): dense = sum(self.dense.values()) return { - "Neuron MACs": macs / self.total_samples, + "Effective Neuron MACs": macs / self.total_samples, "Neuron Dense": dense / self.total_samples, } From 29c623739b3ee75b0494e39d8804e2ad913e0f29 Mon Sep 17 00:00:00 2001 From: benedettoleto Date: Tue, 23 Dec 2025 11:23:22 +0100 Subject: [PATCH 6/9] refactor neuron operations and relative docs --- docs/metrics/workload_metrics/index.rst | 1 + .../workload_metrics/neuron_operations.rst | 85 +++++++++++++++++++ .../metrics/workload/neuron_operations.py | 66 +++++++++++--- 3 files changed, 138 insertions(+), 14 deletions(-) create mode 100644 docs/metrics/workload_metrics/neuron_operations.rst diff --git a/docs/metrics/workload_metrics/index.rst b/docs/metrics/workload_metrics/index.rst index 44405576..3fdbf421 100644 --- a/docs/metrics/workload_metrics/index.rst +++ b/docs/metrics/workload_metrics/index.rst @@ -13,4 +13,5 @@ Workload Metrics smape r2 mse + neuron_operations \ No newline at end of file diff --git a/docs/metrics/workload_metrics/neuron_operations.rst b/docs/metrics/workload_metrics/neuron_operations.rst new file mode 100644 index 00000000..87af3e24 --- /dev/null +++ b/docs/metrics/workload_metrics/neuron_operations.rst @@ -0,0 +1,85 @@ +Neuron Operations Metric +======================== + +The `NeuronOperations` metric is designed to measure the computational workload associated with neuron activity in spiking neural networks (SNNs). It tracks the number of operations required to update the membrane potential of neurons during the forward pass of a model. These operations include the reset mechanisms defined in the `neuron_ops_reset_operations` dictionary, such as "subtract" and "zero". + +Purpose +------- +The metric provides insights into the computational cost of neuron updates, which is critical for analyzing and optimizing the efficiency of spiking neural networks. By understanding the workload associated with different neuron types and reset mechanisms, researchers can identify bottlenecks and improve model performance. + +Reset Mechanisms +---------------- +Each neuron type has specific reset mechanisms that determine how the membrane potential is updated after a spike. The two main reset mechanisms are: + +1. **Subtract**: The membrane potential is reduced by a certain value after a spike. +2. **Zero**: The membrane potential is reset to zero after a spike. + +The computational cost of these mechanisms is defined in the `neuron_ops_reset_operations` dictionary. For example, the "Leaky" neuron type has the following costs: +- Subtract mechanism: 4 operations +- Zero mechanism: 4 operations + +Why 4 Operations for "Leaky" Neurons? +------------------------------------- +The computational cost of 4 operations for both the "subtract" and "zero" mechanisms in "Leaky" neurons is an abstraction that represents the number of basic mathematical operations required to perform the reset. These operations include the steps involved in updating the membrane potential, checking conditions, and writing the updated state back to memory. + +**Subtract Mechanism** +If the `reset_mechanism` is set to "subtract", the membrane potential :math:`U[t+1]` will have the `threshold` subtracted from it whenever the neuron emits a spike. The update equation is: + +.. math:: + + U[t+1] = \beta U[t] + I_{\rm in}[t+1] - R U_{\rm thr} + +Here’s the breakdown of the 4 operations: + +1. **Decay Term**: Multiply the previous membrane potential :math:`U[t]` by the decay factor :math:`\beta` (1 operation). +2. **Input Current**: Add the input current :math:`I_{\rm in}[t+1]` to the decayed potential (1 operation). +3. **Reset Multiplication**: Multiply the reset factor :math:`R` by the threshold :math:`U_{\rm thr}` (1 operation). +4. **Threshold Subtraction**: Subtract the result of the reset multiplication from the decayed potential and input current (1 operation). + +**Zero Mechanism** +If the `reset_mechanism` is set to "zero", the membrane potential :math:`U[t+1]` will be reset to zero whenever the neuron emits a spike. The update equation is: + +.. math:: + + U[t+1] = \beta U[t] + I_{\rm syn}[t+1] - R(\beta U[t] + I_{\rm in}[t+1]) + +Here’s the breakdown of the 4 operations: + +1. **Decay Term**: Multiply the previous membrane potential :math:`U[t]` by the decay factor :math:`\beta` (1 operation). +2. **Input Current**: Add the synaptic input current :math:`I_{\rm syn}[t+1]` to the decayed potential (1 operation). +3. **Reset Multiplication**: Multiply the reset factor :math:`R` by the sum of the decayed potential and input current :math:`(\beta U[t] + I_{\rm in}[t+1])` (1 operation). +4. **Reset Subtraction**: Subtract the result of the reset multiplication from the decayed potential and input current (1 operation). + +**Why Abstract the Cost to 4 Operations?** + +The value of 4 operations is an abstraction that simplifies the computational workload into a consistent metric. While the actual number of operations may vary slightly depending on the implementation, this abstraction provides a way to compare the computational cost of different neuron types and reset mechanisms. It is particularly useful for analyzing and optimizing spiking neural networks across various implementations. + +Example: Leaky Neuron with snnTorch +----------------------------------- +Let’s consider an example using the "Leaky" neuron type with `snntorch`. Assume we have a layer of "Leaky" neurons, and we want to compute the workload for the "subtract" and "zero" reset mechanisms. + +1. **Subtract Mechanism**: + + - After a spike, the membrane potential is reduced by a fixed value. + - If there are 100 neurons in the layer and each neuron spikes once, the total computational cost is: + .. math:: + \text{Total Cost} = \text{Number of Neurons} \times \text{Cost per Subtract} + = 100 \times 4 = 400 \text{ operations.} + +2. **Zero Mechanism**: + + - After a spike, the membrane potential is reset to zero. + - If the same 100 neurons spike once, the total computational cost is: + .. math:: + \text{Total Cost} = \text{Number of Neurons} \times \text{Cost per Zero} + = 100 \times 4 = 400 \text{ operations.} + +Outputs +------- +The `NeuronOperations` metric provides two key outputs: + +1. **Effective Neuron Ops**: The total number of operations actually performed by neurons, normalized by the number of samples. This value accounts for the actual activity of the neurons during the forward pass, considering only the updates that occur when neurons spike. + +2. **Neuron Dense Ops**: The total number of operations that would be computed if all neurons were updated at every time step, regardless of whether they spiked or not. This represents the theoretical maximum workload for the network under full activity. + +These outputs help quantify the computational workload of the network, both in terms of actual activity and theoretical maximum activity, and can be used to optimize the model's efficiency. diff --git a/neurobench/metrics/workload/neuron_operations.py b/neurobench/metrics/workload/neuron_operations.py index d6d8175c..fe025f56 100644 --- a/neurobench/metrics/workload/neuron_operations.py +++ b/neurobench/metrics/workload/neuron_operations.py @@ -3,24 +3,56 @@ from collections import defaultdict -neuron_macs_reset_operations = { +neuron_ops_reset_operations = { "Leaky": { - "subtract": 2, - "zero": 2, + "subtract": 4, + "zero": 4, }, "Synaptic": { - "subtract": 3, - "zero": 2, + "subtract": 6, + "zero": 8, }, "Lapicque": { - "subtract": 3, - "zero": 2, + "subtract": 11, + "zero": 11, }, "Alpha": { - "subtract": 3, - "zero": 3, + "subtract": 18, + "zero": 24, }, } +""" +The `neuron_ops_reset_operations` dictionary defines the computational cost associated +with resetting the membrane potential of neurons for different neuron types. The reset +mechanisms are categorized into two types: + +1. **Subtract**: Represents a reset mechanism where the membrane potential is reduced + by a certain value. The value associated with this mechanism indicates the computational + cost (in terms of basic operations) required to perform this type of reset. + +2. **Zero**: Represents a reset mechanism where the membrane potential is reset to zero. + The value associated with this mechanism indicates the computational cost (in terms of + basic operations) required to perform this type of reset. + +### Neuron Types and Their Computational Costs: +- **Leaky**: + - Subtract mechanism: 4 operations + - Zero mechanism: 4 operations +- **Synaptic**: + - Subtract mechanism: 6 operations + - Zero mechanism: 8 operations +- **Lapicque**: + - Subtract mechanism: 11 operations + - Zero mechanism: 11 operations +- **Alpha**: + - Subtract mechanism: 18 operations + - Zero mechanism: 24 operations + +### Purpose: +The values in this dictionary represent the computational cost (measured in terms of +basic operations like addition, subtraction, etc.) required for each neuron type to +reset its membrane potential using a specific reset mechanism. +""" class NeuronOperations(AccumulatedMetric): @@ -30,6 +62,12 @@ class NeuronOperations(AccumulatedMetric): This metric computes the number of operations performed by neurons during the forward pass of the model. The operations are tracked per neuron, per layer. + The `NeuronOperations` metric is designed to measure the computational workload + associated with neuron activity in spiking neural networks. Specifically, it tracks + the number of operations required to update the membrane potential of neurons during + the forward pass. These operations include the reset mechanisms defined in the + `neuron_ops_reset_operations` dictionary, such as "subtract" and "zero". + """ def __init__(self): @@ -70,12 +108,12 @@ def __call__(self, model, preds, data): updates += hook.post_fire_mem_potential[0].numel() self.macs[layer_type] += ( - updates * neuron_macs_reset_operations[layer_type][reset_mechanism] + updates * neuron_ops_reset_operations[layer_type][reset_mechanism] ) self.dense[layer_type] += ( hook.post_fire_mem_potential[0].numel() * len(hook.post_fire_mem_potential) - * neuron_macs_reset_operations[layer_type][reset_mechanism] + * neuron_ops_reset_operations[layer_type][reset_mechanism] ) self.total_samples += data[0].size(0) @@ -92,12 +130,12 @@ def compute(self): """ if self.total_samples == 0: - return {"Neuron MACs": 0, "Neuron Dense": 0} + return {"Effective Neuron Ops": 0, "Neuron Dense Ops": 0} macs = sum(self.macs.values()) dense = sum(self.dense.values()) return { - "Effective Neuron MACs": macs / self.total_samples, - "Neuron Dense": dense / self.total_samples, + "Effective Neuron Ops": macs / self.total_samples, + "Neuron Dense Ops": dense / self.total_samples, } From a162aa7c39df0388b1088f5aec1504af7f5a83bf Mon Sep 17 00:00:00 2001 From: benedettoleto Date: Tue, 23 Dec 2025 11:23:35 +0100 Subject: [PATCH 7/9] update lock file --- poetry.lock | 3509 ++++++++++++++++++++++++++++++--------------------- 1 file changed, 2073 insertions(+), 1436 deletions(-) diff --git a/poetry.lock b/poetry.lock index 20e71ca4..8ed76547 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,109 +1,150 @@ -# This file is automatically @generated by Poetry 1.8.4 and should not be changed by hand. +# This file is automatically @generated by Poetry 2.0.1 and should not be changed by hand. 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SynapticOperations, MembraneUpdates, ActivationSparsityByLayer, + NeuronOperations, ) from neurobench.metrics.static import ( Footprint, @@ -106,6 +107,7 @@ def setUp(self): self.synaptic_operations = SynapticOperations() self.mem_updates = MembraneUpdates() self.activation_sparsity_by_layer = ActivationSparsityByLayer() + self.neuron_operations = NeuronOperations() def test_classification_accuracy(self): model = SNNTorchModel(self.dummy_net) @@ -446,6 +448,36 @@ def test_activation_sparsity_by_layer(self): self.assertEqual(act_sparsity_by_layer["1"], 0.96) + def test_neuron_operations(self): + # simulate spiking input with only ones + inp = torch.ones(5, 10, 20) # batch size, time steps, input size + + model = SNNTorchModel(self.net_snn) + + model.register_hooks() + + out = model(inp) + neuron_ops = self.neuron_operations(model, out, (inp, 0)) + self.neuron_operations.reset() + + self.assertEqual(neuron_ops["Effective Neuron Ops"], 200) + self.assertEqual(neuron_ops["Neuron Dense Ops"], 200) + + def test_neuron_operations_no_spikes(self): + # simulate spiking input with no spikes + inp = torch.zeros(5, 10, 20) # batch size, time steps, input size + + model = SNNTorchModel(self.net_snn) + + model.register_hooks() + + out = model(inp) + neuron_ops = self.neuron_operations(model, out, (inp, 0)) + self.neuron_operations.reset() + + self.assertEqual(neuron_ops["Effective Neuron Ops"], 20) + self.assertEqual(neuron_ops["Neuron Dense Ops"], 200) + # TODO: refactor this metric if needed # def test_neuron_update_metric(): From 10fb433ed6199760abbdaa954e8d9134b5eabac8 Mon Sep 17 00:00:00 2001 From: benedettoleto Date: Tue, 30 Dec 2025 17:27:08 +0100 Subject: [PATCH 9/9] =?UTF-8?q?Bump=20version:=202.1.0=20=20=E2=86=92=202.?= =?UTF-8?q?2.0?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .bumpversion.toml | 2 +- docs/conf.py | 2 +- neurobench/version.py | 2 +- pyproject.toml | 2 +- 4 files changed, 4 insertions(+), 4 deletions(-) diff --git a/.bumpversion.toml b/.bumpversion.toml index 6f1e76c4..9a353a37 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 = "2.1.0" +current_version = "2.2.0" ignore_missing_version = false search = "{current_version}" replace = "{new_version}" diff --git a/docs/conf.py b/docs/conf.py index 93e8d310..fc663d15 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, Benedetto Leto" author = "Jason Yik, Noah Pacik-Nelson, Korneel Van Den Berghe" -release = "2.1.0" +release = "2.2.0" # -- General configuration --------------------------------------------------- # https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration diff --git a/neurobench/version.py b/neurobench/version.py index 9aa3f903..8a124bf6 100644 --- a/neurobench/version.py +++ b/neurobench/version.py @@ -1 +1 @@ -__version__ = "2.1.0" +__version__ = "2.2.0" diff --git a/pyproject.toml b/pyproject.toml index 00ce1420..0c834002 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "neurobench" -version = "2.1.0" +version = "2.2.0" description = "Collaborative, Fair, and Representative Benchmarks for Neuromorphic Computing" authors = ["NeuroBench Team "] readme = "README.rst"