From 031aa7f14d3cb1244271fb61e2837ab12997f316 Mon Sep 17 00:00:00 2001 From: EnricoTrizio Date: Fri, 6 Dec 2024 18:32:19 +0100 Subject: [PATCH] First tests committor --- mlcolvar/core/loss/committor_loss.py | 141 +- mlcolvar/cvs/committor/committor.py | 111 +- test_graphs/data/biased.dcd | Bin 0 -> 31076 bytes test_graphs/data/biased.trajectory.h5 | Bin 0 -> 5026672 bytes test_graphs/data/colvar | 50001 ++++++++++++++++++++++++ test_graphs/test_committor.ipynb | 307 + 6 files changed, 50482 insertions(+), 78 deletions(-) create mode 100644 test_graphs/data/biased.dcd create mode 100644 test_graphs/data/biased.trajectory.h5 create mode 100644 test_graphs/data/colvar create mode 100644 test_graphs/test_committor.ipynb diff --git a/mlcolvar/core/loss/committor_loss.py b/mlcolvar/core/loss/committor_loss.py index 991971c8..00c467d1 100644 --- a/mlcolvar/core/loss/committor_loss.py +++ b/mlcolvar/core/loss/committor_loss.py @@ -15,7 +15,9 @@ # ============================================================================= import torch -from torch_scatter import scatter +from torch_scatter import scatter, scatter_sum +from typing import Union +import torch_geometric # ============================================================================= # LOSS FUNCTIONS @@ -26,19 +28,19 @@ class CommittorLoss(torch.nn.Module): """Compute a loss function based on Kolmogorov's variational principle for the determination of the committor function""" def __init__(self, - mass: torch.Tensor, + atomic_masses: torch.Tensor, alpha: float, - cell: float = None, - gamma: float = 10000, - delta_f: float = 0, + gamma: float = 10000.0, + delta_f: float = 0.0, separate_boundary_dataset : bool = True, - descriptors_derivatives : torch.nn.Module = None + descriptors_derivatives : torch.nn.Module = None, + log_var: bool = True ): """Compute Kolmogorov's variational principle loss and impose boundary conditions on the metastable states Parameters ---------- - mass : torch.Tensor + atomic_masses : torch.Tensor Atomic masses of the atoms in the system alpha : float Hyperparamer that scales the boundary conditions contribution to loss, i.e. alpha*(loss_bound_A + loss_bound_B) @@ -57,29 +59,33 @@ def __init__(self, """ super().__init__() - self.register_buffer("mass", mass) + self.register_buffer("atomic_masses", atomic_masses) self.alpha = alpha - self.cell = cell self.gamma = gamma self.delta_f = delta_f - self.descriptors_derivatives=descriptors_derivatives + self.descriptors_derivatives = descriptors_derivatives self.separate_boundary_dataset = separate_boundary_dataset - - def forward( - self, x: torch.Tensor, q: torch.Tensor, labels: torch.Tensor, w: torch.Tensor, create_graph: bool = True + self.log_var = log_var + + def forward(self, + x: Union[torch.Tensor, torch_geometric.data.Batch], + q: torch.Tensor, + labels: torch.Tensor, + w: torch.Tensor, + create_graph: bool = True ) -> torch.Tensor: return committor_loss(x=x, q=q, labels=labels, w=w, - mass=self.mass, + atomic_masses=self.atomic_masses, alpha=self.alpha, gamma=self.gamma, delta_f=self.delta_f, create_graph=create_graph, - cell=self.cell, separate_boundary_dataset=self.separate_boundary_dataset, - descriptors_derivatives=self.descriptors_derivatives + descriptors_derivatives=self.descriptors_derivatives, + log_var = self.log_var ) @@ -87,14 +93,14 @@ def committor_loss(x: torch.Tensor, q: torch.Tensor, labels: torch.Tensor, w: torch.Tensor, - mass: torch.Tensor, + atomic_masses: torch.Tensor, alpha: float, gamma: float = 10000, delta_f: float = 0, create_graph: bool = True, - cell: float = None, - separate_boundary_dataset : bool = True, - descriptors_derivatives : torch.nn.Module = None + separate_boundary_dataset: bool = True, + descriptors_derivatives: torch.nn.Module = None, + log_var: bool = True ): """Compute variational loss for committor optimization with boundary conditions @@ -108,7 +114,7 @@ def committor_loss(x: torch.Tensor, Labels for states, A and B states for boundary conditions w : torch.Tensor Reweighing factors to Boltzmann distribution. This should depend on the simulation in which the data were collected. - mass : torch.Tensor + atomic_masses : torch.Tensor List of masses of all the atoms we are using, for each atom we need to repeat three times for x,y,z. Can be created using `committor.utils.initialize_committor_masses` alpha : float @@ -138,63 +144,94 @@ def committor_loss(x: torch.Tensor, The boundary loss term on basin A gamma*alpha*loss_B : torch.Tensor The boundary loss term on basin B - """ + """ + # ============================== SETUP ============================== + # check if input is graph + _is_graph_data = False + if isinstance(x, torch_geometric.data.batch.Batch): + batch = torch.clone(x['batch']) + node_types = torch.where(x['node_attrs'])[1] + x = x['positions'] + _is_graph_data = True + + # inherit right device device = x.device + dtype = x.dtype - mass = mass.to(device) # Create masks to access different states data - mask_A = torch.nonzero(labels.squeeze() == 0, as_tuple=True) - mask_B = torch.nonzero(labels.squeeze() == 1, as_tuple=True) + mask_A = torch.nonzero(labels.squeeze() == 0) + mask_B = torch.nonzero(labels.squeeze() == 1) if separate_boundary_dataset: - mask_var = torch.nonzero(labels.squeeze() > 1, as_tuple=True) + if _is_graph_data: + # this needs to be on the batch index, not only the labels + mask_var = torch.nonzero(labels.squeeze() > 1) + aux = torch.where(mask_var)[0] + mask_var_batches = torch.isin(batch, aux) + mask_var_batches = (batch[mask_var_batches]) + else: + mask_var = torch.nonzero(labels.squeeze() > 1) + mask_var_batches = mask_var else: - mask_var = torch.ones(len(x), dtype=torch.bool) - - # Update weights of basin B using the information on the delta_f + mask_var = torch.ones(len(x), dtype=torch.bool) + mask_var_batches = mask_var + + # setup atomic masses + atomic_masses = atomic_masses.to(dtype).to(device) + # mass should have size [1, n_atoms*spatial_dims] + if _is_graph_data: + + atomic_masses = atomic_masses[node_types[mask_var_batches]].unsqueeze(-1) + + else: + atomic_masses = atomic_masses.unsqueeze(0) + + # Update weights for bc confs using the information on the delta_f delta_f = torch.Tensor([delta_f]) - if delta_f < 0: # B higher in energy --> A-B < 0 + # B higher in energy --> A-B < 0 + if delta_f < 0: w[mask_B] = w[mask_B] * torch.exp(delta_f.to(device)) - elif delta_f > 0: # A higher in energy --> A-B > 0 + # A higher in energy --> A-B > 0 + elif delta_f > 0: w[mask_A] = w[mask_A] * torch.exp(-delta_f.to(device)) - ###### VARIATIONAL PRINICIPLE LOSS ###### + # weights should have size [n_batch, 1] + w = w.unsqueeze(-1) + # ============================== LOSS ============================== # Each loss contribution is scaled by the number of samples - # We need the gradient of q(x) + # 1. VARIATIONAL LOSS + # Compute gradients of q(x) wrt x grad_outputs = torch.ones_like(q[mask_var]) grad = torch.autograd.grad(q[mask_var], x, grad_outputs=grad_outputs, retain_graph=True, create_graph=create_graph)[0] - grad = grad[mask_var] - - # TODO this fixes cell size issue - if cell is not None: - grad = grad / cell - + grad = grad[mask_var_batches] if descriptors_derivatives is not None: + # we use the precomputed derivatives from descriptors to pos grad_square = descriptors_derivatives(grad) else: - # we get the square of grad(q) and we multiply by the weight grad_square = torch.pow(grad, 2) - - # we sanitize the shapes of mass and weights tensors - # mass should have size [1, n_atoms*spatial_dims] - mass = mass.unsqueeze(0) - # weights should have size [n_batch, 1] - w = w.unsqueeze(-1) - grad_square = torch.sum((grad_square * (1/mass)), axis=1, keepdim=True) + grad_square = torch.sum((grad_square * (1/atomic_masses)), axis=1, keepdim=True) + + if _is_graph_data: + # we need to sum on the right batch first + grad_square = scatter_sum(grad_square, mask_var_batches, dim=0) + grad_square = grad_square * w[mask_var] # variational contribution to loss: we sum over the batch loss_var = torch.mean(grad_square) + if False: + loss_var = loss_var.log() + + + # 2. BOUNDARY LOSS + loss_A = torch.mean( torch.pow(q[mask_A], 2)) + loss_B = torch.mean( torch.pow( (q[mask_B] - 1) , 2)) - # boundary conditions - q_A = q[mask_A] - q_B = q[mask_B] - loss_A = torch.mean( torch.pow(q_A, 2)) - loss_B = torch.mean( torch.pow( (q_B - 1) , 2)) + # 3. TOTAL LOSS loss = gamma*( loss_var + alpha*(loss_A + loss_B) ) # TODO maybe there is no need to detach them for logging diff --git a/mlcolvar/cvs/committor/committor.py b/mlcolvar/cvs/committor/committor.py index 819a5a3b..692ff657 100644 --- a/mlcolvar/cvs/committor/committor.py +++ b/mlcolvar/cvs/committor/committor.py @@ -1,9 +1,10 @@ import torch import lightning from mlcolvar.cvs import BaseCV -from mlcolvar.core import FeedForward +from mlcolvar.core import FeedForward, BaseGNN from mlcolvar.core.loss import CommittorLoss from mlcolvar.core.nn.utils import Custom_Sigmoid +from typing import Union, List __all__ = ["Committor"] @@ -31,18 +32,20 @@ class Committor(BaseCV, lightning.LightningModule): Utils to initialize the masses tensor for the training """ - BLOCKS = ["nn", "sigmoid"] + DEFAULT_BLOCKS = ["nn", "sigmoid"] + MODEL_BLOCKS = ["nn", "sigmoid"] def __init__( self, - layers: list, + model: Union[List[int], FeedForward, BaseGNN], mass: torch.Tensor, alpha: float, gamma: float = 10000, delta_f: float = 0, cell: float = None, - separate_boundary_dataset : bool = True, - descriptors_derivatives : torch.nn.Module = None, + separate_boundary_dataset: bool = True, + descriptors_derivatives: torch.nn.Module = None, + log_var: bool = True, options: dict = None, **kwargs, ): @@ -73,16 +76,16 @@ def __init__( Options for the building blocks of the model, by default {}. Available blocks: ['nn'] . """ - super().__init__(in_features=layers[0], out_features=layers[-1], **kwargs) + super().__init__(model, **kwargs) # ======= LOSS ======= - self.loss_fn = CommittorLoss(mass=mass, + self.loss_fn = CommittorLoss(atomic_masses=mass, alpha=alpha, gamma=gamma, delta_f=delta_f, - cell=cell, separate_boundary_dataset=separate_boundary_dataset, - descriptors_derivatives=descriptors_derivatives + descriptors_derivatives=descriptors_derivatives, + log_var=True ) # ======= OPTIONS ======= @@ -90,12 +93,18 @@ def __init__( options = self.parse_options(options) # ======= BLOCKS ======= - # initialize NN turning - o = "nn" - # set default activation to tanh - if "activation" not in options[o]: - options[o]["activation"] = "tanh" - self.nn = FeedForward(layers, **options[o]) + if not self._override_model: + # initialize NN + o = "nn" + # set default activation to tanh + if "activation" not in options[o]: + options[o]["activation"] = "tanh" + self.nn = FeedForward(self.layers, **options[o]) + elif self._override_model: + self.nn = model + + if self.nn.out_features != 1: + raise ValueError('Output of the model must be of dimension 1') # separately add sigmoid activation on last layer, this way it can be deactived o = "sigmoid" @@ -106,13 +115,18 @@ def __init__( def training_step(self, train_batch, batch_idx): """Compute and return the training loss and record metrics.""" # =================get data=================== - x = train_batch["data"] - # check data are have shape (n_data, -1) - x = x.reshape((x.shape[0], -1)) - x.requires_grad = True - - labels = train_batch["labels"] - weights = train_batch["weights"] + if isinstance(self.nn, FeedForward): + x = train_batch["data"] + # check data are have shape (n_data, -1) + x = x.reshape((x.shape[0], -1)) + x.requires_grad = True + + labels = train_batch["labels"] + weights = train_batch["weights"] + elif isinstance(self.nn, BaseGNN): + x = self._setup_graph_data(train_batch) + labels = x['graph_labels'] + weights = x['weight'].clone() # =================forward==================== # we use forward and not forward_cv to also apply the preprocessing (if present) @@ -158,20 +172,65 @@ def test_committor(): datamodule = DictModule(dataset, lengths=[1]) # train model - trainer = lightning.Trainer(max_epochs=5, logger=None, enable_checkpointing=False, limit_val_batches=0, num_sanity_val_steps=0) + trainer = lightning.Trainer(max_epochs=5, logger=False, enable_checkpointing=False, limit_val_batches=0, num_sanity_val_steps=0) + print() + print('NORMAL') + print() + # dataset separation + model = Committor(model=[6, 4, 2, 1], mass=atomic_masses, alpha=1e-1, delta_f=0) + trainer.fit(model, datamodule) + model(X).sum().backward() + bias_model = KolmogorovBias(input_model=model, beta=1, epsilon=1e-6, lambd=1) + bias_model(X) + + # naive whole dataset + trainer = lightning.Trainer(max_epochs=5, logger=False, enable_checkpointing=False, limit_val_batches=0, num_sanity_val_steps=0) + model = Committor(model=[6, 4, 2, 1], mass=atomic_masses, alpha=1e-1, delta_f=0, separate_boundary_dataset=False) + trainer.fit(model, datamodule) + model(X).sum().backward() + + + print() + print('EXTERNAL FEEDFORWARD') + print() # dataset separation - model = Committor(layers=[6, 4, 2, 1], mass=atomic_masses, alpha=1e-1, delta_f=0) + ff_model = FeedForward([6, 4, 2, 1]) + model = Committor(model=ff_model, mass=atomic_masses, alpha=1e-1, delta_f=0) trainer.fit(model, datamodule) model(X).sum().backward() bias_model = KolmogorovBias(input_model=model, beta=1, epsilon=1e-6, lambd=1) bias_model(X) # naive whole dataset - trainer = lightning.Trainer(max_epochs=5, logger=None, enable_checkpointing=False, limit_val_batches=0, num_sanity_val_steps=0) - model = Committor(layers=[6, 4, 2, 1], mass=atomic_masses, alpha=1e-1, delta_f=0, separate_boundary_dataset=False) + trainer = lightning.Trainer(max_epochs=5, logger=False, enable_checkpointing=False, limit_val_batches=0, num_sanity_val_steps=0) + model = Committor(model=ff_model, mass=atomic_masses, alpha=1e-1, delta_f=0, separate_boundary_dataset=False) trainer.fit(model, datamodule) model(X).sum().backward() + + print() + print('EXTERNAL GNN') + print() + from mlcolvar.core.nn.graph import SchNetModel + from mlcolvar.data.graph.utils import create_test_graph_input + gnn_model = SchNetModel(1, 0.1, [1, 8]) + + model = Committor(model=gnn_model, + mass=atomic_masses, + alpha=1e-1, + delta_f=0) + + datamodule = create_test_graph_input(output_type='datamodule', n_samples=100, n_states=3, n_atoms=3) + trainer = lightning.Trainer(max_epochs=5, logger=False, enable_checkpointing=False, limit_val_batches=0, num_sanity_val_steps=0, enable_model_summary=False) + trainer.fit(model, datamodule) + + example_input_graph_test = create_test_graph_input(output_type='example', n_atoms=4, n_samples=3, n_states=2) + + model(example_input_graph_test).sum().backward() + # bias_model = KolmogorovBias(input_model=model, beta=1, epsilon=1e-6, lambd=1) + # 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0.000000 0.000000 0.000000 diff --git a/test_graphs/test_committor.ipynb b/test_graphs/test_committor.ipynb new file mode 100644 index 00000000..5f47389e --- /dev/null +++ b/test_graphs/test_committor.ipynb @@ -0,0 +1,307 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "DictModule(dataset -> DictDataset( \"data_list\": 1700, \"z_table\": [6, 9], \"cutoff\": 8.0, \"data_type\": graphs ),\n", + "\t\t train_loader -> DictLoader(length=1, batch_size=1700, shuffle=False))\n" + ] + } + ], + "source": [ + "from mlcolvar.data import DictModule\n", + "from mlcolvar.utils.io import create_dataset_from_trajectories\n", + "from mlcolvar.utils.io import create_dataset_from_files\n", + "\n", + "\n", + "dataset_graph = create_dataset_from_trajectories(\n", + " trajectories=[\n", + " 'data/r.dcd',\n", + " 'data/p.dcd',\n", + " # 'data/biased.trajectory.h5',\n", + " 'data/biased.dcd',\n", + " #'data/biased.trajectory.h5',\n", + " #'data/r.dcd'\n", + " ],\n", + " top=['data/r.pdb', \n", + " 'data/p.pdb',\n", + " 'data/r.pdb',\n", + " #'data/r.pdb'\n", + " ],\n", + " cutoff=8.0, # Ang\n", + " create_labels=True,\n", + " system_selection='all and not type H',\n", + " show_progress=False\n", + ")\n", + "\n", + "\n", + "datamodule_graph = DictModule(dataset_graph, lengths=[1], shuffle=False)\n", + "print(datamodule_graph)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "import torch\n", + "from mlcolvar.utils.io import load_dataframe\n", + "\n", + "df = load_dataframe('data/colvar', stride=100)\n", + "weights = torch.exp(1*torch.tensor(df['opes.bias'].values))\n", + "weights = weights / weights.sum()\n", + "weights\n", + "\n", + "aux = torch.zeros(len(dataset_graph))\n", + "for i in range(len(weights)):\n", + " aux[-(len(weights)-i)] = weights[i]\n", + "aux\n", + "\n", + "for i in range(len(dataset_graph)):\n", + " dataset_graph['data_list'][i]['weight'] = aux[i]" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1700" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(dataset_graph)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/etrizio@iit.local/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/lightning/pytorch/utilities/parsing.py:198: Attribute 'model' is an instance of `nn.Module` and is already saved during checkpointing. It is recommended to ignore them using `self.save_hyperparameters(ignore=['model'])`.\n" + ] + }, + { + "data": { + "text/plain": [ + "Committor(\n", + " (loss_fn): CommittorLoss()\n", + " (nn): SchNetModel(\n", + " (_radial_embedding): RadialEmbeddingBlock(\n", + " (bessel_fn): GAUSSIANBASIS [ \u001b[32m16\u001b[0m\u001b[36m 󰯰 \u001b[0m| \u001b[32m8.000000\u001b[0m\u001b[36m 󰳁 \u001b[0m]\n", + " )\n", + " (W_v): Linear(in_features=2, out_features=32, bias=False)\n", + " (layers): ModuleList(\n", + " (0-1): 2 x InteractionBlock(\n", + " (mlp): Sequential(\n", + " (0): Linear(in_features=16, out_features=32, bias=True)\n", + " (1): ShiftedSoftplus()\n", + " (2): Linear(in_features=32, out_features=32, bias=True)\n", + " )\n", + " (conv): CFConv()\n", + " (act): ShiftedSoftplus()\n", + " (lin): Linear(in_features=32, out_features=32, bias=True)\n", + " )\n", + " )\n", + " (W_out): ModuleList(\n", + " (0): Linear(in_features=32, out_features=16, bias=True)\n", + " (1): ShiftedSoftplus()\n", + " (2): Linear(in_features=16, out_features=1, bias=True)\n", + " )\n", + " )\n", + " (sigmoid): Custom_Sigmoid()\n", + ")" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from mlcolvar.cvs.committor import Committor\n", + "from mlcolvar.core.nn.graph.schnet import SchNetModel\n", + "import torch\n", + "\n", + "gnn_model = SchNetModel(n_out=1,\n", + " cutoff=dataset_graph.metadata['cutoff'],\n", + " atomic_numbers=dataset_graph.metadata['z_table'],\n", + " n_bases=16,\n", + " n_layers=2,\n", + " n_filters=32,\n", + " n_hidden_channels=32,\n", + " )\n", + "\n", + "model = Committor(model=gnn_model,\n", + " mass=torch.Tensor([12, 19]),\n", + " alpha=1)\n", + "\n", + "model" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "GPU available: True (cuda), used: True\n", + "TPU available: False, using: 0 TPU cores\n", + "IPU available: False, using: 0 IPUs\n", + "HPU available: False, using: 0 HPUs\n", + "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n", + "/home/etrizio@iit.local/Bin/miniconda3/envs/graph_mlcolvar_test/lib/python3.9/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:441: The 'train_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=63` in the `DataLoader` to improve performance.\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "020e80012d2d46809f41270cdb5692e2", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Training: | | 0/? [00:00)" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "out_graph" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
      " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "# plt.hist(out_graph.detach().squeeze())\n", + "plt.plot(out_graph.detach().squeeze(), '.')\n", + "plt.ylim(0,1)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "graph_mlcolvar_test", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.18" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}

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