From 638bd27ceb839a394576acba1d3ec11f0f76bb36 Mon Sep 17 00:00:00 2001 From: epens94 Date: Wed, 1 May 2024 20:43:15 +0200 Subject: [PATCH 01/13] add bernstein and physnet radial basis plus real spherical harmonics --- src/schnetpack/nn/ops/spherical.py | 158 +++++++++++++++++++++++++++++ src/schnetpack/nn/radial.py | 116 +++++++++++++++++++++ 2 files changed, 274 insertions(+) create mode 100644 src/schnetpack/nn/ops/spherical.py diff --git a/src/schnetpack/nn/ops/spherical.py b/src/schnetpack/nn/ops/spherical.py new file mode 100644 index 000000000..0f1495280 --- /dev/null +++ b/src/schnetpack/nn/ops/spherical.py @@ -0,0 +1,158 @@ +import numpy as np +import logging + +''' +calculation of real spherical harmonics +x,y,z are normalized unit vector (aka x/r, y/r, z/r, with r = sqrt(x^2 + y^2 + z^2) +''' + +# TODO: checken in so3Net ob beide Sachen kombiniert werden können + +# l = 0 +_Y00 = lambda x, y, z: 1/2 * np.sqrt(1/np.pi) # in shape: (...) / out shape: (...) + +# l = 1 +_Y1_1 = lambda x, y, z: np.sqrt(3/(4*np.pi)) * y # in shape: (...) / out shape: (...) +_Y10 = lambda x, y, z: np.sqrt(3/(4*np.pi)) * z # in shape: (...) / out_shape: (...) +_Y11 = lambda x, y, z: np.sqrt(3/(4*np.pi)) * x # in shape: (...) / out_shape: (...) + +# l = 2 +_Y2_2 = lambda x, y, z: 1/2 * np.sqrt(15/np.pi) * x * y +_Y2_1 = lambda x, y, z: 1/2 * np.sqrt(15/np.pi) * y * z +_Y20 = lambda x, y, z: 1/4 * np.sqrt(5/np.pi) * (3*z**2 - 1) +_Y21 = lambda x, y, z: 1/2 * np.sqrt(15/np.pi) * x * z +_Y22 = lambda x, y, z: 1/4 * np.sqrt(15/np.pi) * (x**2 - y**2) + +# l = 3 +_Y3_3 = lambda x, y, z: 1/4 * np.sqrt(35 / (2*np.pi)) * y * (3*x**2 - y**2) +_Y3_2 = lambda x, y, z: 1/2 * np.sqrt(105 / np.pi) * x * y * z +_Y3_1 = lambda x, y, z: 1/4 * np.sqrt(21 / (2*np.pi)) * y * (5*z**2 - 1) +_Y30 = lambda x, y, z: 1/4 * np.sqrt(7/np.pi) * (5*z**3 - 3*z) +_Y31 = lambda x, y, z: 1/4 * np.sqrt(21 / (2*np.pi)) * x * (5*z**2 - 1) +_Y32 = lambda x, y, z: 1/4 * np.sqrt(105 / np.pi) * (x**2 - y**2) * z +_Y33 = lambda x, y, z: 1/4 * np.sqrt(35 / (2*np.pi)) * x * (x**2 - 3*y**2) + +# l = 4 +_Y4_4 = lambda x, y, z: 3/4 * np.sqrt(35 / np.pi) * x * y * (x**2 - y**2) +_Y4_3 = lambda x, y, z: 3/4 * np.sqrt(35 / (2*np.pi)) * y * (3*x**2 - y**2) * z +_Y4_2 = lambda x, y, z: 3/4 * np.sqrt(5 / np.pi) * x * y * (7*z**2 - 1) +_Y4_1 = lambda x, y, z: 3/4 * np.sqrt(5 / (2*np.pi)) * y * (7*z**3 - 3*z) +_Y40 = lambda x, y, z: 3/16 * np.sqrt(1 / np.pi) * (35*z**4 - 30*z**2 + 3) +_Y41 = lambda x, y, z: 3/4 * np.sqrt(5 / (2*np.pi)) * x * (7*z**3 - 3*z) +_Y42 = lambda x, y, z: 3/8 * np.sqrt(5 / np.pi) * (x**2 - y**2) * (7*z**2 - 1) +_Y43 = lambda x, y, z: 3/4 * np.sqrt(35 / (2*np.pi)) * x * (x**2 - 3*y**2) * z +_Y44 = lambda x, y, z: 3/16 * np.sqrt(35 / np.pi) * (x**2 * (x**2 - 3*y**2) - y**2 * (3*x**2 - y**2)) + + + +def fn_Y0(x: np.ndarray, y: np.ndarray, z: np.ndarray) -> np.ndarray: + """ + Spherical harmonics expansion of order l=0. Distance vector is assumed to be normalized to 1. + + Args: + x (): X-coordinate, shape: (...,1) + y (): Y-coordinate, shape: (...,1) + z (): Z-coordinate, shape: (...,1) + + Returns: Expansion coefficients, shape: (...,2*l+1) = (...,1) + + """ + return np.ones_like(x)*_Y00(x, y, z) + + + +def fn_Y1(x: np.ndarray, y: np.ndarray, z: np.ndarray) -> np.ndarray: + """ + Spherical harmonics expansion of order l=1. Distance vector is assumed to be normalized to 1. + + Args: + x (): X-coordinate, shape: (...,1) + y (): Y-coordinate, shape: (...,1) + z (): Z-coordinate, shape: (...,1) + + Returns: Expansion coefficients, shape: (...,2*l + 1) = (...,3) + + """ + return np.concatenate([_Y1_1(x, y, z), _Y10(x, y, z), _Y11(x, y, z)], axis=-1) + + + +def fn_Y2(x: np.ndarray, y: np.ndarray, z: np.ndarray) -> np.ndarray: + """ + Spherical harmonics expansion of order l=2. Distance vector is assumed to be normalized to 1. + + Args: + x (): X-coordinate, shape: (...,1) + y (): Y-coordinate, shape: (...,1) + z (): Z-coordinate, shape: (...,1) + + Returns: Expansion coefficients, shape: (...,2*l + 1) = (...,5) + + """ + return np.concatenate([_Y2_2(x, y, z), + _Y2_1(x, y, z), + _Y20(x, y, z), + _Y21(x, y, z), + _Y22(x, y, z)], axis=-1) + + + +def fn_Y3(x: np.ndarray, y: np.ndarray, z: np.ndarray) -> np.ndarray: + """ + Spherical harmonics expansion of order l=3. Distance vector is assumed to be normalized to 1. + + Args: + x (): X-coordinate, shape: (...,1) + y (): Y-coordinate, shape: (...,1) + z (): Z-coordinate, shape: (...,1) + + Returns: Expansion coefficients, shape: (...,2*l + 1) = (...,7) + + """ + return np.concatenate([_Y3_3(x, y, z), + _Y3_2(x, y, z), + _Y3_1(x, y, z), + _Y30(x, y, z), + _Y31(x, y, z), + _Y32(x, y, z), + _Y33(x, y, z)], axis=-1) + + + +def fn_Y4(x: np.ndarray, y: np.ndarray, z: np.ndarray) -> np.ndarray: + """ + Spherical harmonics expansion of order l=4. Distance vector is assumed to be normalized to 1. + + Args: + x (): X-coordinate, shape: (...,1) + y (): Y-coordinate, shape: (...,1) + z (): Z-coordinate, shape: (...,1) + + Returns: Expansion coefficients, shape: (...,2*l + 1) = (...,9) + + """ + return np.concatenate([_Y4_4(x, y, z), + _Y4_3(x, y, z), + _Y4_2(x, y, z), + _Y4_1(x, y, z), + _Y40(x, y, z), + _Y41(x, y, z), + _Y42(x, y, z), + _Y43(x, y, z), + _Y44(x, y, z)], axis=-1) + + +def init_sph_fn(l: int): + if l == 0: + return lambda rij: fn_Y0(*np.split(rij, indices_or_sections=3, axis=-1)) + elif l == 1: + return lambda rij: fn_Y1(*np.split(rij, indices_or_sections=3, axis=-1)) + elif l == 2: + return lambda rij: fn_Y2(*np.split(rij, indices_or_sections=3, axis=-1)) + elif l == 3: + return lambda rij: fn_Y3(*np.split(rij, indices_or_sections=3, axis=-1)) + elif l == 4: + return lambda rij: fn_Y4(*np.split(rij, indices_or_sections=3, axis=-1)) + else: + logging.error('Spherical harmonics are only defined up to order l = 4.') + raise NotImplementedError \ No newline at end of file diff --git a/src/schnetpack/nn/radial.py b/src/schnetpack/nn/radial.py index 8c63c2a07..e37f840fe 100644 --- a/src/schnetpack/nn/radial.py +++ b/src/schnetpack/nn/radial.py @@ -108,3 +108,119 @@ def forward(self, inputs): norm = torch.where(inputs == 0, torch.tensor(1.0, device=inputs.device), inputs) y = sinax / norm[..., None] return y + + +class BernsteinRBF(torch.nn.Module): + + + r"""Bernstein radial basis functions. + + According to + B_{v,n}(x) = \binom{n}{v} x^v (1 - x)^{n - v} + with + B as the Bernstein polynomial of degree v + binom{k}{n} as the binomial coefficient n! / (k! * (n - k)!) + they become in logaritmic form log(n!) - log(k!) - log((n - k)!) + n as index running from 0 to degree k + + The logarithmic form of the k-th Bernstein polynominal of degree n is + + log(B_{k}_{n}) = logBinomCoeff + k * log(x) - (n-k) * log(1-x) + k_term is here k*log(x) + n_k_term is here (n-k)*log(1-x) + x is here the radial basis expansion : exp[-alpha*d] + + logBinomCoeff is a scalar + k_term is a vector + n_k_term is also a vector + + log to avoid numerical overflow errors, and ensure stability + """ + + def __init__( + self, n_rbf: int, cutoff:float, init_alpha:0.95): + """ + Args: + n_rbf: total number of Bernstein functions, :math:`N_g`. + cutoff: center of last Bernstein function, :math:`\mu_{N_g}` + """ + + super(BernsteinRBF, self).__init__() + self.n_rbf = n_rbf + + # log binomal coefficient vector + b = self.calculate_log_binomial_coefficients(n_rbf) + n_idx = torch.arange(0, n_rbf) + n_k_idx = n_rbf - 1 - n_idx + + # register buffers and parameters + self.register_buffer("cutoff",torch.tensor(cutoff)) + self.register_buffer("b", b) + self.register_buffer("n", n_idx) + self.register_buffer("n_k", n_k_idx) + self.register_buffer("init_alpha",torch.tensor(init_alpha)) + + # log of factorial (n! or k! or n-k!) + def log_factorial(self,n): + # log of factorial degree n + return torch.sum(torch.log(torch.arange(1, n + 1))) + + # calculate log binominal coefficient + def log_binomial_coefficient(self,n, k): + # n_factorial - k_factorial - n_k_factorial + return self.log_factorial(n) - (self.log_factorial(k) + self.log_factorial(n - k)) + + # vector of log binominal coefficients + def calculate_log_binomial_coefficients(self,n_rbf): + # store the log binomial coefficients + # Loop through each value from 0 to n_rbf-1 + log_binomial_coeffs = [ + self.log_binomial_coefficient(n_rbf - 1, x) for x in range(n_rbf) + ] + return torch.tensor(log_binomial_coeffs) + + def forward(self, inputs): + exp_x = -self.init_alpha * inputs[...,None] + x = torch.exp(exp_x) + k_term = self.n * torch.where(self.n != 0, torch.log(x), torch.zeros_like(x)) + n_k_term = self.n_k * torch.where(self.n_k != 0, torch.log(1 - x), torch.zeros_like(x)) + y = torch.exp(self.b + k_term + n_k_term) + return y + + +class PhysNetBasisRBF(torch.nn.Module): + + """ + Expand distances in the basis used in PhysNet (see https://arxiv.org/abs/1902.08408) + + width (beta_k) = (2K^⁻1 * (1 - exp(-cutoff)))^-2) + center (mu_k) = equally spaced between exp(-cutoff) and 1 + + """ + + def __init__(self, n_rbf: int, cutoff:float, trainable:bool): + + """ + Args: + n_rbf: total number of basis functions. + cutoff: cutoff basis functions + """ + + super(PhysNetBasisRBF, self).__init__() + self.n_rbf = n_rbf + + # compute offset and width of Gaussian functions + widths = ((2 / self.n_rbf) * (1 - torch.exp(torch.Tensor([-cutoff])))) ** (-2) + r_0 = torch.exp(torch.Tensor([-cutoff])).item() + centers = torch.linspace(r_0,1,self.n_rbf) + + if trainable: + self.widths = torch.nn.Parameter(widths) + self.centers = torch.nn.Parameter(centers) + else: + self.register_buffer("widths", widths) + self.register_buffer("centers", centers) + + + def forward(self, inputs: torch.Tensor): + return torch.exp(-abs(self.widths) * (torch.exp(-inputs[...,None]) - self.centers) ** 2) \ No newline at end of file From 603deb2645db7fe0d5d4bf9858cbf2207d076f4c Mon Sep 17 00:00:00 2001 From: epens94 Date: Mon, 22 Jul 2024 00:44:35 +0200 Subject: [PATCH 02/13] initial commit for so3krates port from jax to pytorch --- .gitignore | 3 +- src/schnetpack/nn/__init__.py | 2 + src/schnetpack/nn/attention.py | 348 +++++++++++ src/schnetpack/nn/cutoff.py | 25 + src/schnetpack/nn/embedding.py | 88 ++- src/schnetpack/nn/filter.py | 128 ++++ src/schnetpack/nn/ops/cgmatrix.npz | Bin 0 -> 768325 bytes src/schnetpack/nn/ops/spherical.py | 215 +++++-- src/schnetpack/nn/utils.py | 27 +- src/schnetpack/representation/__init__.py | 1 + src/schnetpack/representation/so3krates.py | 655 +++++++++++++++++++++ src/schnetpack/train/callbacks.py | 46 +- 12 files changed, 1486 insertions(+), 52 deletions(-) create mode 100644 src/schnetpack/nn/attention.py create mode 100644 src/schnetpack/nn/filter.py create mode 100644 src/schnetpack/nn/ops/cgmatrix.npz create mode 100644 src/schnetpack/representation/so3krates.py diff --git a/.gitignore b/.gitignore index 554dc8b09..4d22a21b1 100644 --- a/.gitignore +++ b/.gitignore @@ -125,4 +125,5 @@ interfaces/lammps/examples/*/*.dat interfaces/lammps/examples/*/deployed_model # batchwise optimizer examples -examples/howtos/howto_batchwise_relaxations_outputs/* \ No newline at end of file +examples/howtos/howto_batchwise_relaxations_outputs/* +.vscode/launch.json diff --git a/src/schnetpack/nn/__init__.py b/src/schnetpack/nn/__init__.py index 71ece42f6..9d930f480 100644 --- a/src/schnetpack/nn/__init__.py +++ b/src/schnetpack/nn/__init__.py @@ -13,3 +13,5 @@ from schnetpack.nn.radial import * from schnetpack.nn.utils import * from schnetpack.nn.embedding import * +from schnetpack.nn.attention import * +from schnetpack.nn.filter import * \ No newline at end of file diff --git a/src/schnetpack/nn/attention.py b/src/schnetpack/nn/attention.py new file mode 100644 index 000000000..1bc91c955 --- /dev/null +++ b/src/schnetpack/nn/attention.py @@ -0,0 +1,348 @@ +from typing import Callable, Dict, Union, Tuple, Sequence, Optional, Any +from functools import partial +import math + +import torch +from torch import nn +from torch.nn import functional as F +import functorch +from functorch import combine_state_for_ensemble +import schnetpack.nn as snn +from schnetpack.nn.utils import equal_head_split + +__all__ = [ + "DummyIdentity", + "ConvAttention", + "SphConvAttention", + "AttentionAggregation", + "ConvAttentionCoefficients", + "VmapConvAttention", + "VmapSphConvAttention" + ] + + + +class DummyIdentity(nn.Module): + r"""A placeholder identity operator + for attention aggregation in spherical convolution attention layer. + """ + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__() + + def forward( + self, + x: torch.Tensor, + alpha_ij:torch.Tensor, + idx_i: torch.Tensor, + idx_j: torch.Tensor) -> torch.Tensor: + return alpha_ij + +class AttentionAggregation(nn.Module): + + def __init__(self,num_features): + super().__init__() + self.v_j_linear = nn.Linear(num_features,num_features,bias=False) + + def forward( + self, + x: torch.Tensor, + alpha_ij: torch.Tensor, + idx_i: torch.Tensor, + idx_j: torch.Tensor) -> torch.Tensor: + """ + + Args: + x: atomic embeddings, shape: (n,F) + alpha_ij: attention coefficients, shape: (n_pairs) + idx_i: index centering atom, shape: (n_pairs) + idx_j: index neighboring atom, shape: (n_pairs) + + """ + v_j = self.v_j_linear(x)[idx_j] # shape: (n_pairs,F) + y = snn.scatter_add(alpha_ij[:,None] * v_j, idx_i, dim_size=x.shape[0]) # shape: (n,F) + return y + +class ConvAttentionCoefficients(nn.Module): + + def __init__(self,num_features): + super().__init__() + + self.q_i_layer = nn.Linear(num_features,num_features,bias=False) + self.k_j_layer = nn.Linear(num_features,num_features,bias=False) + + def forward( + self, + x: torch.Tensor, + w_ij: torch.Tensor, + idx_i: torch.Tensor, + idx_j: torch.Tensor) -> torch.Tensor: + + """ + Args: + x (Array): atomic embeddings, shape: (n,F) + w_ij (Array): filter, shape: (n_pairs,F) + idx_i (Array): index centering atom, shape: (n_pairs) + idx_j (Array): index neighboring atom, shape: (n_pairs) + + Returns: Geometric attention coefficients, shape: (n_pairs) + + """ + + q_i = self.q_i_layer(x)[idx_i]#[:,idx_i,:] + k_j = self.k_j_layer(x)[idx_j] # shape: (n_pairs,F) + out = (q_i * w_ij * k_j).sum(axis=-1) / math.sqrt(x.shape[-1]) + return out + + + +class VmapConvAttention(nn.Module): + + def __init__(self,nheads,num_features): + super().__init__() + + self.nheads = nheads + self.num_features = num_features // nheads + self.coeff_fn = nn.ModuleList( + [ConvAttentionCoefficients(self.num_features) for _ in range(self.nheads)]) + + self.aggregate_fn = nn.ModuleList( + [AttentionAggregation(self.num_features) for _ in range(self.nheads)] + ) + + def vec_calc(self, nn, x_heads, w_heads, idx_i, idx_j): + '''to vectorize the head calculations, resulting in speed up''' + fmodel, params, buffers = combine_state_for_ensemble(nn) + value = functorch.vmap( + fmodel, (0,0,0,0,None, None),out_dims=1)( + params, + buffers, + x_heads, + w_heads, + idx_i,idx_j) + return value + + def forward(self,x,w_ij,idx_i,idx_j,phi_r_cut): + + # first view in block structure and then permutating it + x_heads = x.view(x.shape[0], self.nheads, self.num_features).permute(1, 0, 2).contiguous() + w_heads = w_ij.view(w_ij.shape[0], self.nheads, self.num_features).permute(1, 0, 2).contiguous() + + # calculate alpha and aggregate for every head + alpha = self.vec_calc(self.coeff_fn, x_heads, w_heads, idx_i, idx_j) * phi_r_cut#[:,None] + # permutation necessary for using the aggregation function + alpha = alpha.permute(1,0).contiguous() + out = self.vec_calc(self.aggregate_fn, x_heads, alpha, idx_i, idx_j) + x_ = out.permute(1,0,2).contiguous().view(x.shape[0], -1) + + return x_ + + +class VmapSphConvAttention(nn.Module): + + r"""the number of heads in the SPHC update equals the + number of degrees in the SPHC vector + the vecorized version of the SphConvAttention layer""" + def __init__( + self, + num_features: int, + degrees: Sequence[int], # degrees + aggregate_fn: Union[Callable, nn.Module] = None): + + super().__init__() + self.nheads = len(list(degrees)) + self.harmonic_orders = degrees + self.record = {} + self.aggregate_fn = aggregate_fn + self.num_features = num_features // self.nheads + self.register_buffer("degrees", torch.LongTensor(degrees)) + self.register_buffer("repeats", torch.LongTensor([2 * y + 1 for y in list(self.degrees)])) + + # more concise structure and allows to use aggregation function as used in feature block if needed + if self.aggregate_fn is None: + self.aggregate_fn = nn.ModuleList([DummyIdentity(self.num_features) for _ in range(self.nheads)]) + + self.coeff_fn = nn.ModuleList( + [ConvAttentionCoefficients(self.num_features) for _ in range(self.nheads)] + ) + + def vec_calc(self, nn, x_heads, w_heads, idx_i, idx_j): + + fmodel, params, buffers = combine_state_for_ensemble(nn) + value = functorch.vmap( + fmodel, (0,0,0,0,None, None),out_dims=1)( + params, + buffers, + x_heads, + w_heads, + idx_i,idx_j) + + return value + + def forward( + self, + chi:torch.Tensor, + sph_ij:torch.Tensor, + x: torch.Tensor, + w_ij: torch.Tensor, + idx_i: torch.Tensor, + phi_r_cut: torch.Tensor, + phi_chi_cut: torch.Tensor, + idx_j: torch.Tensor) -> torch.Tensor: + + + # first view in block structure and then permutating it + x_heads = x.view(x.shape[0], self.nheads, self.num_features).permute(1, 0, 2) # shape: (n_heads,n,F_head) + w_heads = w_ij.view(w_ij.shape[0], self.nheads, self.num_features).permute(1, 0, 2) # shape (n_heads,n_pairs,F_head) + + alpha_ij = self.vec_calc(self.coeff_fn, x_heads, w_heads, idx_i, idx_j) + alpha_r_ij = (alpha_ij * phi_r_cut)#.permute(1,0).contiguous() + alpha_s_ij = (alpha_ij * phi_chi_cut[:,None])#.permute(1,0).contiguous() + + alpha_ij = alpha_r_ij + alpha_s_ij + self.record["alpha"] = alpha_ij + + alpha_ij = torch.repeat_interleave(alpha_ij,self.repeats,dim=-1) # shape: (n_pairs,m_tot) + chi_ = snn.scatter_add(alpha_ij * sph_ij, idx_i, dim_size=x.shape[0]) # shape: (n,m_tot) + + return chi_ + + +class ConvAttention(nn.Module): + + """slow implementation of ConvAttention since not vectorized""" + def __init__( + self, + num_features:int, + nheads:int = 2, + debug_tag: str = None): + + super().__init__() + self.nheads = nheads + self.num_features = num_features // self.nheads + self.record = {} + + # every layer consists of n heads + self.coeff_fn = nn.ModuleList( + [ConvAttentionCoefficients(self.num_features) for _ in range(self.nheads)] + ) + + self.aggregate_fn = nn.ModuleList( + [AttentionAggregation(self.num_features) for _ in range(self.nheads)] + ) + + def forward( + self, + x: torch.Tensor, + w_ij: torch.Tensor, + phi_r_cut: torch.Tensor, + idx_i: torch.Tensor, + idx_j: torch.Tensor) -> torch.Tensor: + + """ + + Args: + x:atomic embeddings, shape: (n,F) + w_ij: filter, shape: (n_pairs,F) + phi_r_cut: cutoff that scales attention coefficients, shape: (n_pairs) + + Returns: + + """ + + # split input into heads is equal to ref, because head_so3krates - head_here is zero + original_shape = x.shape + inv_x_head_split, x_heads = equal_head_split(x, n_heads=self.nheads) # shape: (n,n_heads,F_head) + _, w_heads = equal_head_split(w_ij, n_heads=self.nheads) # shape: (n_pairs,n_heads,F_head) + + results = [] + alphas = [] + for head in range(self.nheads): + # TODO make phi_r_cut shape consistent should only be (n_pairs) + alpha = self.coeff_fn[head](x_heads[:,head,:], w_heads[:,head,:], idx_i, idx_j) * phi_r_cut[:,0] + alphas.append(alpha) + alpha_compare = torch.stack(alphas,dim=1) + + # self.record["alpha"] = alpha_compare + for head in range(self.nheads): + res = self.aggregate_fn[head](x_heads[:,head,:], alpha_compare[:,head], idx_i, idx_j) + results.append(res) + # # saving the attention weights for later analysis + + results_compare = torch.stack(results,dim=1) + self.record["alpha"] = alpha_compare + x_ = inv_x_head_split(results_compare) # shape: (n_heads,n_pairs) + return x_ # shape: (n_heads, n_pairs) + + + +class SphConvAttention(nn.Module): + + r"""the number of heads in the SPHC update equals the + number of degrees in the SPHC vector """ + def __init__( + self, + num_features: int, + degrees: Sequence[int], # degrees + aggregate_fn: Union[Callable, nn.Module] = None, + debug_tag: str = None): + + super().__init__() + self.nheads = len(list(degrees)) + self.harmonic_orders = degrees + self.record = {} + self.aggregate_fn = aggregate_fn + self.num_features = num_features // self.nheads + self.register_buffer("degrees", torch.LongTensor(degrees)) + self.register_buffer("repeats", torch.LongTensor([2 * y + 1 for y in list(self.degrees)])) + # more concise structure and allows to use aggregation function as used in feature block if needed + if self.aggregate_fn is None: + self.aggregate_fn = nn.ModuleList([DummyIdentity(self.num_features) for _ in range(self.nheads)]) + + + self.coeff_fn = nn.ModuleList( + [ConvAttentionCoefficients(self.num_features) for _ in range(self.nheads)] + ) + + + def forward( + self, + chi:torch.Tensor, + sph_ij:torch.Tensor, + x: torch.Tensor, + w_ij: torch.Tensor, + idx_i: torch.Tensor, + phi_r_cut: torch.Tensor, + phi_chi_cut: torch.Tensor, + idx_j: torch.Tensor) -> torch.Tensor: + + + + inv_x_head_split, x_heads = equal_head_split(x, n_heads=self.nheads) # shape: (n,n_heads,F_head) + _, w_ij_heads = equal_head_split(w_ij, n_heads=self.nheads) # shape: (n_pairs,n_heads,F_head) + + # Apply each head's ConvAttentionCoefficients and stack results + # which is super slow + results = [] + all_alphas_r_ij,all_alphas_s_ij,all_alphas = ([],[],[]) + + for head in range(self.nheads): + # TODO make phi_r_cut shape consistent should only be (n_pairs) + alpha = self.coeff_fn[head](x_heads[:,head,:], w_ij_heads[:,head,:], idx_i, idx_j) + alpha_r_ij = alpha * phi_r_cut[:,0] + alpha_s_ij = alpha * phi_chi_cut # make consistent shape ist shape (n_pairs,) soll shape (n_pairs,) + all_alphas_r_ij.append(alpha_r_ij) + all_alphas_s_ij.append(alpha_s_ij) + all_alphas.append(alpha_r_ij + alpha_s_ij) + + # alphas sind correct to ref + #alpha_compare_r_ij = torch.stack(all_alphas_r_ij,dim=1) + alpha_ij = torch.stack(all_alphas,dim=1) + self.record["alpha"] = alpha_ij + + # gleich wie ref methode + alpha_ij = torch.repeat_interleave(alpha_ij,self.repeats,dim=-1) # shape: (n_pairs,m_tot) + # gleich wie ref methode + chi_ = snn.scatter_add(alpha_ij * sph_ij, idx_i, dim_size=x.shape[0]) # shape: (n,m_tot) + + return chi_ + \ No newline at end of file diff --git a/src/schnetpack/nn/cutoff.py b/src/schnetpack/nn/cutoff.py index 8ff7c3562..dcf53931f 100644 --- a/src/schnetpack/nn/cutoff.py +++ b/src/schnetpack/nn/cutoff.py @@ -8,6 +8,8 @@ "mollifier_cutoff", "cosine_cutoff", "SwitchFunction", + "zero_cutoff", + "ZeroCutoff" ] @@ -156,3 +158,26 @@ def forward(self, x: torch.Tensor) -> torch.Tensor: f_switch = torch.where(x <= 0, ones, torch.where(x >= 1, zeros, fm / (fp + fm))) return f_switch + + +def zero_cutoff(input: torch.Tensor): + """ dummy zero cutoff function for sphc in so3krates layer + if none is requested to ensure consistent behavior.""" + + # Compute values of cutoff function + input_cut = torch.zeros(input.shape[0], device=input.device) + return input_cut + +class ZeroCutoff(nn.Module): + r""" Dummy cutoff function for sphc to only return zeros. + if none is requested to ensure consistent behavior""" + + def __init__(self): + """ + Args: + cutoff (float, optional): cutoff radius. + """ + super().__init__() + + def forward(self, input: torch.Tensor): + return zero_cutoff(input) \ No newline at end of file diff --git a/src/schnetpack/nn/embedding.py b/src/schnetpack/nn/embedding.py index d72485f9d..b94021ea0 100644 --- a/src/schnetpack/nn/embedding.py +++ b/src/schnetpack/nn/embedding.py @@ -4,7 +4,11 @@ import torch.nn as nn import torch.nn.functional as F from schnetpack.nn.blocks import ResidualMLP -from typing import Callable, Union +from typing import Callable, Union, Sequence, Dict, Optional +from schnetpack.nn.activations import shifted_softplus +from schnetpack.nn.ops.spherical import init_sph_fn +import schnetpack.nn as snn +import schnetpack.properties as properties ''' The usage of the electron configuration is to provide a shorthand descriptor. This descriptor encode @@ -149,6 +153,88 @@ electron_config = electron_config / np.max(electron_config, axis=0) + +class SPHC_Embedding(nn.Module): + + """ + Embedding of the spherical harmonic coordinates used in So3krates (source!) + The degrees has to be in consective monotically increasing order [1,2,3] is ok but [1,3] is not + rij is number of pairs from pairwise distances (len(idx_j or idx_i)) + This is the initial embedding, the sphc are contracted per order in further layers + """ + + def __init__( + self, + degrees: Sequence[int], + sphc_normalization: Optional[float] = None, + solid_harmonic: bool = False): + + # init spherical harmonics distances for each degree + super().__init__() + #register_buffer("tmp_normalizat", torch.tensor(degrees)) + self.degrees = degrees + self.sph_fns = [init_sph_fn(y) for y in self.degrees] + self.sphc_normalization = sphc_normalization + self.solid_harmonic = solid_harmonic + """ + Args: + degrees: List of degrees of the spherical harmonics to be used (e.g [1,2,3]). + unit_r_ij: Normalized distance vectors from the + radial basis expansion functions of the distances of shape (rij,3) + phi_r_cut: cutoff function for the radial basis expansion of distances shape (rij) + sphc_normalization: to decide if neighborhood dependent embedding or not + this is the scatter_add sum of cutoff function + """ + + def forward(self,unit_r_ij:torch.Tensor,phi_r_cut:torch.Tensor,r_ij:torch.Tensor,Z:torch.Tensor, idx_m:torch.Tensor): + + # Spherical harmonics + sph_harms_ij = [] + g_ij = None + for sph_fn in self.sph_fns: + sph_ij = sph_fn(unit_r_ij) # shape: (n_pairs,2l+1) + sph_harms_ij += [sph_ij] # len: |L| / shape: (n_pairs,2l+1) + + # direct sum of degree dimension space (LD) shape: (n_pairs,m_tot) + sph_harms_ij = torch.concatenate(sph_harms_ij, axis=-1) if len(self.degrees) > 0 else None + + # Initialize SPHCs to zero and keep it that way if no normalization is used + # althought the normalization corresponds much more a to weighted average + # of the spherical harmonics by the cutoff function (as equation 11 in appendix of so3krates) + chi = torch.zeros((Z.shape[-1],sph_harms_ij.shape[-1]),device=Z.device) + if not self.sphc_normalization == None: + chi += self._init_sphc(idx_m,sph_harms_ij,phi_r_cut,self.sphc_normalization) + + # if real spherical harmonics (with radial part) are requested + if self.solid_harmonic: + g_ij = self._init_solid_harmonic_sphc(sph_harms_ij,r_ij,phi_r_cut,self.sphc_normalization) + + return chi, g_ij,sph_harms_ij + + def _init_sphc(self,idx_m, sph_ij, phi_r_cut,sphc_normalization): + + # Initialize SPHCs with a neighborhood dependent embedding if mp normalization is used + _sph_harms_ij = torch.where( + phi_r_cut != 0, + sph_ij * phi_r_cut,0) # shape: (n_pairs,m_tot) phi_r_cut shape (n_pairs,1) + + maxm = int(idx_m[-1]) + 1 + #sphc_normalization = snn.scatter_add(phi_r_cut,idx_m,dim_size=maxm)[:,None] # shape(n,) + chi = snn.scatter_add(_sph_harms_ij, idx_m, dim_size=maxm) # shape: (n,m_tot) + # TODO check nochmal warum diese art gewählt wurde + #chi = snn.scatter_add(_sph_harms_ij, idx_m, dim_size=maxm) / sphc_normalization # shape: (n,m_tot) + + return chi / sphc_normalization + + def _init_solid_harmonic_sphc(self,sph_harms_ij,r_ij,phi_r_cut): + + rbf_ij = torch.where( + phi_r_cut[:,None] != 0, + r_ij * phi_r_cut[:,None],0) # shape: (n_pairs,n_rbfs) + + g_ij = sph_harms_ij[:, :, None] * rbf_ij[:, None, :] # shape: (n_pair,m_tot,K) + return g_ij + class NuclearEmbedding(nn.Module): """ Embedding which maps scalar nuclear charges Z to vectors in a diff --git a/src/schnetpack/nn/filter.py b/src/schnetpack/nn/filter.py new file mode 100644 index 000000000..de8f50860 --- /dev/null +++ b/src/schnetpack/nn/filter.py @@ -0,0 +1,128 @@ +from typing import Callable, Sequence + +import torch +from torch import nn +from torch.nn import functional as F + +from schnetpack.nn import Dense + +__all__ = [ + "RadialFilter", + "SphericalFilter", + "RadialSphericalFilter", + "build_so3krates_mlp" + ] + + + +def build_so3krates_mlp(num_features: Sequence[int], activation: Callable) -> nn.Module: + + # TODO change to more general case + # activation not for the last layer + + layers = [] + for n in range(len(num_features)-1): + + if n != len(num_features)-1: + layer = Dense( + in_features=num_features[n], + out_features=num_features[n+1], + activation=activation) + else: + layer = Dense( + in_features=num_features[n], + out_features=num_features[n+1], + activation=None) + layers.append(layer) + + return nn.ModuleList(layers) + + +class RadialFilter(nn.Module): + + def __init__( + self, + num_features: Sequence[int], + activation: Callable = F.silu): + + super().__init__() + self.num_features = num_features # [32,128] + + # filter network for the radial part + # TODO should be adapted to work with arbitrary number of layers + l1 = Dense(in_features=self.num_features[0],activation=activation,out_features=self.num_features[1]) + l2 = Dense(in_features=self.num_features[1],activation=None,out_features=self.num_features[1]) + + self.rad_filter_fn = nn.ModuleList([l1,l2]) + + + def forward(self,x:torch.Tensor) -> torch.Tensor: + + for layer in self.rad_filter_fn: + x = layer(x) + return x + + +class SphericalFilter(nn.Module): + + def __init__( + self, + degrees: Sequence[int], + num_features: int, + activation: Callable = F.silu): + + super().__init__() + self.register_buffer("degrees", torch.LongTensor(degrees)) + self.num_features = [len(self.degrees)] + list(num_features) + + # TODO should be adapted to work with arbitrary number of layers + l1 = Dense(in_features=self.degrees,activation=activation,out_features=self.num_features[0]) + l2 = Dense(in_features=self.num_features[0],activation=None,out_features=self.num_features[1]) + # TODO check ob equivalent to sequential (sollte sein, aber sicherheitshalber checken) + self.sph_filter_fn = nn.ModuleList([l1,l2]) + + + def forward(self,x:torch.Tensor) -> torch.Tensor: + + for layer in self.sph_filter_fn: + x = layer(x) + return x + + +class RadialSphericalFilter(nn.Module): + + def __init__( + self, + num_features: Sequence[int], + degrees: Sequence[int], + activation: Callable = F.silu, + debug_tag: str = None): + + super().__init__() + self.register_buffer("degrees", torch.LongTensor(degrees)) + self.num_features = list(num_features) + # filter network for the radial part + self.rad_filter_fn = RadialFilter(self.num_features,activation) + # filter network for the spherical coordinates + self.sph_filter_fn = SphericalFilter(self.degrees,num_features,activation) + + + def forward(self,rbf:torch.Tensor,d_gamma:torch.Tensor) -> torch.Tensor: + + """ + Filter build from invariant geometric features, + with K number of radial basis functions + n_l number of degrees + n_pairs number of pairwise distance pairs + + Args: + rbf (Array): pairwise, radial basis expansion, shape: (n_pairs,K) + d_gamma (Array): pairwise distance of spherical 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