def kernel_conv_fn_forward(F, Y, R, norm_coef, Rs_in, Rs_out, selection_rule,
set_of_l_filters):
"""
:param F: tensor [batch, b, l_in * mul_in * m_in]
:param Y: tensor [l_filter * m_filter, batch, a, b]
:param R: tensor [batch, a, b, l_out * l_in * mul_out * mul_in * l_filter]
:param norm_coef: tensor [l_out, l_in]
:return: tensor [batch, a, l_out * mul_out * m_out, l_in * mul_in * m_in]
"""
batch, a, b, _ = Y.shape
n_out = rs.dim(Rs_out)
kernel_conv = Y.new_zeros(batch, a, n_out)
# note: for the normalization we assume that the variance of R[i] is one
begin_R = 0
begin_out = 0
for i, (mul_out, l_out, p_out) in enumerate(Rs_out):
s_out = slice(begin_out, begin_out + mul_out * (2 * l_out + 1))
begin_out += mul_out * (2 * l_out + 1)
begin_in = 0
for j, (mul_in, l_in, p_in) in enumerate(Rs_in):
s_in = slice(begin_in, begin_in + mul_in * (2 * l_in + 1))
begin_in += mul_in * (2 * l_in + 1)
l_filters = selection_rule(l_in, p_in, l_out, p_out)
if not l_filters:
continue
# extract the subset of the `R` that corresponds to the couple (l_out, l_in)
n = mul_out * mul_in * len(l_filters)
sub_R = R[:, :, :, begin_R:begin_R + n].reshape(
batch, a, b, mul_out, mul_in,
-1) # [batch, a, b, mul_out, mul_in, l_filter]
begin_R += n
K = 0
for k, l_filter in enumerate(l_filters):
offset = sum(2 * l + 1 for l in set_of_l_filters
if l < l_filter)
sub_Y = Y[...,
offset:offset + 2 * l_filter + 1] # [batch, a, b, m]
C = o3.wigner_3j(l_out,
l_in,
l_filter,
cached=True,
like=kernel_conv) # [m_out, m_in, m]
K += norm_coef[i, j] * torch.einsum(
"ijk,zabk,zabuv,zbvj->zaui", C,
sub_Y, sub_R[..., k], F[..., s_in].reshape(
batch, b, mul_in, -1)) # [batch, a, mul_out, m_out]
if not isinstance(K, int):
kernel_conv[:, :, s_out] += K.reshape(batch, a, -1)
return kernel_conv
def kernel_fn_forward(Y, R, norm_coef, Rs_in, Rs_out, selection_rule,
set_of_l_filters):
"""
:param Y: tensor [batch, l_filter * m_filter]
:param R: tensor [batch, l_out * l_in * mul_out * mul_in * l_filter]
:param norm_coef: tensor [l_out, l_in]
:return: tensor [batch, l_out * mul_out * m_out, l_in * mul_in * m_in]
"""
batch = Y.shape[0]
n_in = rs.dim(Rs_in)
n_out = rs.dim(Rs_out)
kernel = Y.new_zeros(batch, n_out, n_in)
# note: for the normalization we assume that the variance of R[i] is one
begin_R = 0
begin_out = 0
for i, (mul_out, l_out, p_out) in enumerate(Rs_out):
s_out = slice(begin_out, begin_out + mul_out * (2 * l_out + 1))
begin_out += mul_out * (2 * l_out + 1)
begin_in = 0
for j, (mul_in, l_in, p_in) in enumerate(Rs_in):
s_in = slice(begin_in, begin_in + mul_in * (2 * l_in + 1))
begin_in += mul_in * (2 * l_in + 1)
l_filters = selection_rule(l_in, p_in, l_out, p_out)
if not l_filters:
continue
# extract the subset of the `R` that corresponds to the couple (l_out, l_in)
n = mul_out * mul_in * len(l_filters)
sub_R = R[:, begin_R:begin_R + n].reshape(
batch, mul_out, mul_in,
len(l_filters)) # [batch, mul_out, mul_in, l_filter]
begin_R += n
# note: I don't know if we can vectorize this for loop because [l_filter * m_filter] cannot be put into [l_filter, m_filter]
K = 0
for k, l_filter in enumerate(l_filters):
tmp = sum(2 * l + 1 for l in set_of_l_filters if l < l_filter)
sub_Y = Y[:, tmp:tmp + 2 * l_filter + 1] # [batch, m]
C = o3.wigner_3j(l_out,
l_in,
l_filter,
cached=True,
like=kernel) # [m_out, m_in, m]
# note: The multiplication with `sub_R` could also be done outside of the for loop
K += norm_coef[i, j] * torch.einsum(
"ijk,zk,zuv->zuivj",
(C, sub_Y,
sub_R[..., k])) # [batch, mul_out, m_out, mul_in, m_in]
if not isinstance(K, int):
kernel[:, s_out, s_in] = K.reshape_as(kernel[:, s_out, s_in])
return kernel
def _generate_spherical_harmonics(lmax, device=None): # pragma: no cover
r"""code used to generate the code above
based on `wigner_3j`
"""
torch.set_default_dtype(torch.float64)
print("sh_0_0 = torch.ones(x.shape, dtype=x.dtype, device=x.device)")
print("if lmax == 0:")
print(" return sh_0_0")
print()
y, z, x = sympy.symbols('y z x')
polynomials = [y, z, x]
polynormz = [0, 1, 0]
for l in range(1, lmax+1):
names = sympy.symbols(" ".join(f'sh_{l}_{m}' for m in range(2 * l + 1)))
for n, p in zip(names, polynomials):
p = sympy.simplify(p)
p = sympy.N(p, n=20)
print(f"{n} = {pycode(p)}")
print(f"if lmax == {l}:")
u = ",\n ".join(", ".join(f"sh_{j}_{m}" for m in range(2 * j + 1)) for j in range(l + 1))
print(f" return torch.stack([\n {u}\n ], dim=-1)")
print()
if l == lmax:
break
polynomials = [
sum(
c.item() * v * p
for cj, v in zip(cij, [y, z, x])
for c, p in zip(cj, names)
)
for cij in o3.wigner_3j(l+1, 1, l, device=device)
]
def sub(p, names, polynormz):
p = p.subs(x, 0).subs(y, 0).subs(z, 1)
for n, c in zip(names, polynormz):
p = p.subs(n, c)
return p
polynormz = [
sub(p, names, polynormz)
for p in polynomials
]
norm = sum(p ** 2 for p in polynormz) ** 0.5
polynomials = [p / norm for p in polynomials]
polynormz = [p / norm for p in polynormz]
polynomials = [
sympy.nsimplify(p, full=True)
for p in polynomials
]
def test_wigner_3j_sh_norm():
with o3.torch_default_dtype(torch.float64):
for l_out in range(3 + 1):
for l_in in range(l_out, 4 + 1):
for l_f in range(abs(l_out - l_in), l_out + l_in + 1):
Q = o3.wigner_3j(l_out, l_in, l_f)
Y = rsh.spherical_harmonics_xyz([l_f], torch.randn(3))
QY = math.sqrt(4 * math.pi) * Q @ Y
assert abs(QY.norm() - 1) < 1e-10
def test_wigner_3j_orthogonal(self):
with o3.torch_default_dtype(torch.float64):
for l_out in range(3 + 1):
for l_in in range(l_out, 4 + 1):
for l_f in range(abs(l_out - l_in), l_out + l_in + 1):
Q = o3.wigner_3j(l_f, l_in, l_out).reshape(2 * l_f + 1, -1)
e = (2 * l_f + 1) * Q @ Q.t()
d = e - torch.eye(2 * l_f + 1)
self.assertLess(d.pow(2).mean().sqrt(), 1e-10)
def test_wigner_3j(float_tolerance):
abc = o3.rand_angles(10)
l1, l2, l3 = 1, 2, 3
C = o3.wigner_3j(l1, l2, l3)
D1 = o3.Irrep(l1, 1).D_from_angles(*abc)
D2 = o3.Irrep(l2, 1).D_from_angles(*abc)
D3 = o3.Irrep(l3, 1).D_from_angles(*abc)
C2 = torch.einsum("ijk,zil,zjm,zkn->zlmn", C, D1, D2, D3)
assert (C - C2).abs().max() < float_tolerance
def _wigner_nj(*irrepss, normalization='component', filter_ir_mid=None, dtype=None, device=None):
irrepss = [o3.Irreps(irreps) for irreps in irrepss]
if filter_ir_mid is not None:
filter_ir_mid = [o3.Irrep(ir) for ir in filter_ir_mid]
if len(irrepss) == 1:
irreps, = irrepss
ret = []
e = torch.eye(irreps.dim, dtype=dtype, device=device)
i = 0
for mul, ir in irreps:
for _ in range(mul):
sl = slice(i, i + ir.dim)
ret += [
(ir, _INPUT(0, sl.start, sl.stop), e[sl])
]
i += ir.dim
return ret
*irrepss_left, irreps_right = irrepss
ret = []
for ir_left, path_left, C_left in _wigner_nj(*irrepss_left, normalization=normalization, filter_ir_mid=filter_ir_mid, dtype=dtype, device=device):
i = 0
for mul, ir in irreps_right:
for ir_out in ir_left * ir:
if filter_ir_mid is not None and ir_out not in filter_ir_mid:
continue
C = o3.wigner_3j(ir_out.l, ir_left.l, ir.l, dtype=dtype, device=device)
if normalization == 'component':
C *= ir_out.dim**0.5
if normalization == 'norm':
C *= ir_left.dim**0.5 * ir.dim**0.5
C = torch.einsum('jk,ijl->ikl', C_left.flatten(1), C)
C = C.reshape(ir_out.dim, *(irreps.dim for irreps in irrepss_left), ir.dim)
for u in range(mul):
E = torch.zeros(ir_out.dim, *(irreps.dim for irreps in irrepss_left), irreps_right.dim, dtype=dtype, device=device)
sl = slice(i + u * ir.dim, i + (u+1) * ir.dim)
E[..., sl] = C
ret += [
(
ir_out,
_TP(
op=(ir_left, ir, ir_out),
args=(path_left, _INPUT(len(irrepss_left), sl.start, sl.stop))
),
E
)
]
i += mul * ir.dim
return sorted(ret, key=lambda x: x[0])
def backward(ctx, grad_kernel): # pragma: no cover
Y, R, norm_coef = ctx.saved_tensors
grad_Y = grad_R = None
if ctx.needs_input_grad[0]:
grad_Y = grad_kernel.new_zeros(*ctx.Y_shape) # [batch, l_filter * m_filter]
if ctx.needs_input_grad[1]:
grad_R = grad_kernel.new_zeros(*ctx.R_shape) # [batch, l_out * l_in * mul_out * mul_in * l_filter]
begin_R = 0
begin_out = 0
for i, (mul_out, l_out, p_out) in enumerate(ctx.Rs_out):
s_out = slice(begin_out, begin_out + mul_out * (2 * l_out + 1))
begin_out += mul_out * (2 * l_out + 1)
begin_in = 0
for j, (mul_in, l_in, p_in) in enumerate(ctx.Rs_in):
s_in = slice(begin_in, begin_in + mul_in * (2 * l_in + 1))
begin_in += mul_in * (2 * l_in + 1)
l_filters = ctx.selection_rule(l_in, p_in, l_out, p_out)
if not l_filters:
continue
n = mul_out * mul_in * len(l_filters)
if grad_Y is not None:
sub_R = R[:, begin_R: begin_R + n].reshape(
-1, mul_out, mul_in, len(l_filters)
) # [batch, mul_out, mul_in, l_filter]
if grad_R is not None:
sub_grad_R = grad_R[:, begin_R: begin_R + n].reshape(
-1, mul_out, mul_in, len(l_filters)
) # [batch, mul_out, mul_in, l_filter]
begin_R += n
grad_K = grad_kernel[:, s_out, s_in].reshape(-1, mul_out, 2 * l_out + 1, mul_in, 2 * l_in + 1)
for k, l_filter in enumerate(l_filters):
tmp = sum(2 * l + 1 for l in ctx.set_of_l_filters if l < l_filter)
C = o3.wigner_3j(l_out, l_in, l_filter, cached=True, like=grad_kernel) # [m_out, m_in, m]
if grad_Y is not None:
grad_Y[:, tmp: tmp + 2 * l_filter + 1] += norm_coef[i, j] * torch.einsum("zuivj,ijk,zuv->zk", grad_K, C, sub_R[..., k])
if grad_R is not None:
sub_Y = Y[:, tmp: tmp + 2 * l_filter + 1] # [batch, m]
sub_grad_R[..., k] = norm_coef[i, j] * torch.einsum("zuivj,ijk,zk->zuv", grad_K, C, sub_Y)
del ctx
return grad_Y, grad_R, None, None, None, None, None
def test_recurrence_relation(float_tolerance, l):
if torch.get_default_dtype() != torch.float64 and l > 6:
pytest.xfail('we expect this to fail for high l and single precision')
x = torch.randn(3, requires_grad=True)
a = o3.spherical_harmonics(l + 1, x, False)
b = torch.einsum('ijk,j,k->i', o3.wigner_3j(l + 1, l, 1),
o3.spherical_harmonics(l, x, False), x)
alpha = b.norm() / a.norm()
assert (a / a.norm() - b / b.norm()).abs().max() < 10 * float_tolerance
def f(x):
return o3.spherical_harmonics(l + 1, x, False)
a = torch.autograd.functional.jacobian(f, x)
b = (l + 1) / alpha * torch.einsum('ijk,j->ik', o3.wigner_3j(l + 1, l, 1),
o3.spherical_harmonics(l, x, False))
assert (a - b).abs().max() < 100 * float_tolerance
def test_irr_repr_wigner_3j(self):
"""Test irr_repr and wigner_3j equivariance."""
with torch_default_dtype(torch.float64):
l_in = 3
l_out = 2
for l_f in range(abs(l_in - l_out), l_in + l_out + 1):
r = torch.randn(100, 3)
Q = o3.wigner_3j(l_out, l_in, l_f)
abc = torch.randn(3)
D_in = o3.irr_repr(l_in, *abc)
D_out = o3.irr_repr(l_out, *abc)
Y = rsh.spherical_harmonics_xyz([l_f], r @ o3.rot(*abc).t())
W = torch.einsum("ijk,zk->zij", (Q, Y))
W1 = torch.einsum("zij,jk->zik", (W, D_in))
Y = rsh.spherical_harmonics_xyz([l_f], r)
W = torch.einsum("ijk,zk->zij", (Q, Y))
W2 = torch.einsum("ij,zjk->zik", (D_out, W))
self.assertLess((W1 - W2).norm(), 1e-5 * W.norm(), l_f)
def elementwise_tensor_product(
Rs_in1: TY_RS_LOOSE,
Rs_in2: TY_RS_LOOSE,
selection_rule: o3.TY_SELECTION_RULE = o3.selection_rule,
normalization: str = 'component') -> Tuple[TY_RS_STRICT, SparseTensor]:
"""
:return: Rs_out, matrix
m_kij A_i B_j
"""
assert normalization in [
'norm', 'component'
], "normalization needs to be 'norm' or 'component'"
Rs_in1 = simplify(Rs_in1)
Rs_in2 = simplify(Rs_in2)
assert sum(mul for mul, _, _ in Rs_in1) == sum(mul for mul, _, _ in Rs_in2)
i = 0
while i < len(Rs_in1):
mul_1, l_1, p_1 = Rs_in1[i]
mul_2, l_2, p_2 = Rs_in2[i]
if mul_1 < mul_2:
Rs_in2[i] = (mul_1, l_2, p_2)
Rs_in2.insert(i + 1, (mul_2 - mul_1, l_2, p_2))
if mul_2 < mul_1:
Rs_in1[i] = (mul_2, l_1, p_1)
Rs_in1.insert(i + 1, (mul_1 - mul_2, l_1, p_1))
i += 1
Rs_out = []
for (mul, l_1, p_1), (mul_2, l_2, p_2) in zip(Rs_in1, Rs_in2):
assert mul == mul_2
for l in selection_rule(l_1, p_1, l_2, p_2):
Rs_out.append((mul, l, p_1 * p_2))
Rs_out = simplify(Rs_out)
dim_in2 = dim(Rs_in2)
row = []
col = []
val = []
index_out = 0
index_1 = 0
index_2 = 0
for (mul, l_1, p_1), (mul_2, l_2, p_2) in zip(Rs_in1, Rs_in2):
assert mul == mul_2
dim_1 = mul * (2 * l_1 + 1)
dim_2 = mul * (2 * l_2 + 1)
for l_out in selection_rule(l_1, p_1, l_2, p_2):
dim_out = mul * (2 * l_out + 1)
C = o3.wigner_3j(l_out, l_1, l_2, cached=True)
if normalization == 'component':
C *= (2 * l_out + 1)**0.5
if normalization == 'norm':
C *= (2 * l_1 + 1)**0.5 * (2 * l_2 + 1)**0.5
I = torch.einsum("uv,wu->wuv", torch.eye(mul), torch.eye(mul))
m = torch.einsum("wuv,kij->wkuivj", I,
C).reshape(dim_out, dim_1, dim_2)
i_out, i_1, i_2 = m.nonzero(as_tuple=False).T
i_out += index_out
i_1 += index_1
i_2 += index_2
row.append(i_out)
col.append(i_1 * dim_in2 + i_2)
val.append(m[m != 0])
index_out += dim_out
index_1 += dim_1
index_2 += dim_2
wigner_3j_tensor = SparseTensor(
row=torch.cat(row) if row else torch.zeros(0, dtype=torch.long),
col=torch.cat(col) if col else torch.zeros(0, dtype=torch.long),
value=torch.cat(val) if val else torch.zeros(0),
sparse_sizes=(dim(Rs_out), dim(Rs_in1) * dim(Rs_in2)))
return Rs_out, wigner_3j_tensor
def _tensor_product_in_out(Rs_in1, selection_rule, Rs_out, normalization,
sorted):
"""
Compute the matrix Q
from Rs_out to Rs_in1 tensor product with Rs_in2
where Rs_in2 is a direct sum of irreducible representations
For normalization='component',
The set of "lines" { Q[i] }_i is orthonormal
:return: Rs_in2, Q
example:
_, Q = tensor_product_in_out(Rs_in1, Rs_out)
torch.einsum('kij,i,j->k', Q, A, B)
"""
assert normalization in [
'norm', 'component'
], "normalization needs to be 'norm' or 'component'"
Rs_in1 = simplify(Rs_in1)
Rs_out = simplify(Rs_out)
Rs_in2 = []
for mul_out, l_out, p_out in Rs_out:
for mul_1, l_1, p_1 in Rs_in1:
for l_2 in selection_rule(l_1, p_1, l_out, p_out):
Rs_in2.append((mul_1 * mul_out, l_2, p_1 * p_out))
Rs_in2 = simplify(Rs_in2)
dim_in2 = dim(Rs_in2)
row = []
col = []
val = []
index_2 = 0
index_out = 0
for mul_out, l_out, p_out in Rs_out:
dim_out = mul_out * (2 * l_out + 1)
n_path = 0
for mul_1, l_1, p_1 in Rs_in1:
for l_2 in selection_rule(l_1, p_1, l_out, p_out):
n_path += mul_1
index_1 = 0
for mul_1, l_1, p_1 in Rs_in1:
dim_1 = mul_1 * (2 * l_1 + 1)
for l_2 in selection_rule(l_1, p_1, l_out, p_out):
if l_2 == 0:
assert l_out == l_1
l = l_1
dim_2 = mul_1 * mul_out
i_out = []
i_1 = []
i_2 = []
v = 0
for w in range(mul_out):
for u in range(mul_1):
i_out += [(2 * l + 1) * w + m
for m in range(2 * l + 1)]
i_1 += [(2 * l + 1) * u + m
for m in range(2 * l + 1)]
i_2 += (2 * l + 1) * [v]
v += 1
i_out = index_out + torch.tensor(i_out)
i_1 = index_1 + torch.tensor(i_1)
i_2 = index_2 + torch.tensor(i_2)
m = torch.ones((2 * l + 1) * dim_2) / n_path**0.5
else:
dim_2 = mul_1 * mul_out * (2 * l_2 + 1)
C = o3.wigner_3j(l_out, l_1, l_2, cached=True)
if normalization == 'component':
C *= (2 * l_out + 1)**0.5
if normalization == 'norm':
C *= (2 * l_1 + 1)**0.5 * (2 * l_2 + 1)**0.5
I = torch.eye(mul_out * mul_1).reshape(
mul_out, mul_1, mul_out * mul_1) / n_path**0.5
m = torch.einsum("wuv,kij->wkuivj", I,
C).reshape(dim_out, dim_1, dim_2)
i_out, i_1, i_2 = m.nonzero(as_tuple=True) # slow part
m = m[(i_out, i_1, i_2)]
i_out += index_out
i_1 += index_1
i_2 += index_2
row.append(i_out)
col.append(i_1 * dim_in2 + i_2)
val.append(m)
index_2 += dim_2
index_1 += dim_1
index_out += dim_out
wigner_3j_tensor = SparseTensor(
row=torch.cat(row) if row else torch.zeros(0, dtype=torch.long),
col=torch.cat(col) if col else torch.zeros(0, dtype=torch.long),
value=torch.cat(val) if val else torch.zeros(0),
sparse_sizes=(dim(Rs_out), dim(Rs_in1) * dim(Rs_in2)))
if sorted:
Rs_in2, perm_mat = sort(Rs_in2)
Rs_in2 = simplify(Rs_in2)
# sorted = perm_mat @ unsorted
wigner_3j_tensor = wigner_3j_tensor.sparse_reshape(-1, dim(Rs_in2))
wigner_3j_tensor = wigner_3j_tensor @ perm_mat.t() # slow part
wigner_3j_tensor = wigner_3j_tensor.sparse_reshape(
-1,
dim(Rs_in1) * dim(Rs_in2))
return Rs_in2, wigner_3j_tensor
def tensor_square(Rs_in: TY_RS_LOOSE,
selection_rule: o3.TY_SELECTION_RULE = o3.selection_rule,
normalization: str = 'component',
sorted: bool = False) -> Tuple[TY_RS_STRICT, SparseTensor]:
"""
Compute the matrix Q
from Rs_out to Rs_in tensor product with Rs_in
where Rs_out is a direct sum of irreducible representations
For normalization='component',
The set of "lines" { Q[i] }_i is orthonormal
:return: Rs_out, Q
example:
_, Q = tensor_square(Rs_in)
torch.einsum('kij,i,j->k', Q, A, A)
"""
assert normalization in [
'norm', 'component'
], "normalization needs to be 'norm' or 'component'"
Rs_in = simplify(Rs_in)
Rs_out = []
for i, (mul_1, l_1, p_1) in enumerate(Rs_in):
for l_out in selection_rule(l_1, p_1, l_1, p_1):
if l_out % 2 == 0:
Rs_out.append((mul_1 * (mul_1 + 1) // 2, l_out, p_1**2))
else:
Rs_out.append((mul_1 * (mul_1 - 1) // 2, l_out, p_1**2))
for mul_2, l_2, p_2 in Rs_in[i + 1:]:
for l_out in selection_rule(l_1, p_1, l_2, p_2):
Rs_out.append((mul_1 * mul_2, l_out, p_1 * p_2))
Rs_out = simplify(Rs_out)
dim_in = dim(Rs_in)
row = []
col = []
val = []
index_out = 0
index_1 = 0
for i, (mul_1, l_1, p_1) in enumerate(Rs_in):
dim_1 = mul_1 * (2 * l_1 + 1)
for l_out in selection_rule(l_1, p_1, l_1, p_1):
I = torch.eye(mul_1**2).reshape(mul_1**2, mul_1, mul_1)
uv = I.nonzero(as_tuple=False)[:, 1:]
if l_out % 2 == 0:
I = I[uv[:, 0] <= uv[:, 1]]
else:
I = I[uv[:, 0] < uv[:, 1]]
if I.shape[0] == 0:
continue
C = o3.wigner_3j(l_out, l_1, l_1)
if normalization == 'component':
C *= (2 * l_out + 1)**0.5
if normalization == 'norm':
C *= (2 * l_1 + 1)**0.5 * (2 * l_1 + 1)**0.5
dim_out = I.shape[0] * (2 * l_out + 1)
m = torch.einsum("wuv,kij->wkuivj", I,
C).reshape(dim_out, dim_1, dim_1)
i_out, i_1, i_2 = m.nonzero(as_tuple=False).T
i_out += index_out
i_1 += index_1
i_2 += index_1
row.append(i_out)
col.append(i_1 * dim_in + i_2)
val.append(m[m != 0])
index_out += dim_out
index_2 = index_1 + dim_1
for mul_2, l_2, p_2 in Rs_in[i + 1:]:
dim_2 = mul_2 * (2 * l_2 + 1)
for l_out in selection_rule(l_1, p_1, l_2, p_2):
I = torch.eye(mul_1 * mul_2).reshape(mul_1 * mul_2, mul_1,
mul_2)
C = o3.wigner_3j(l_out, l_1, l_2)
if normalization == 'component':
C *= (2 * l_out + 1)**0.5
if normalization == 'norm':
C *= (2 * l_1 + 1)**0.5 * (2 * l_2 + 1)**0.5
dim_out = I.shape[0] * (2 * l_out + 1)
m = torch.einsum("wuv,kij->wkuivj", I,
C).reshape(dim_out, dim_1, dim_2)
i_out, i_1, i_2 = m.nonzero(as_tuple=False).T
i_out += index_out
i_1 += index_1
i_2 += index_2
row.append(i_out)
col.append(i_1 * dim_in + i_2)
val.append(m[m != 0])
index_out += dim_out
index_2 += dim_2
index_1 += dim_1
wigner_3j_tensor = SparseTensor(
row=torch.cat(row) if row else torch.zeros(0, dtype=torch.long),
col=torch.cat(col) if col else torch.zeros(0, dtype=torch.long),
value=torch.cat(val) if val else torch.zeros(0),
sparse_sizes=(dim(Rs_out), dim(Rs_in) * dim(Rs_in)))
if sorted:
Rs_out, perm_mat = sort(Rs_out)
Rs_out = simplify(Rs_out)
# sorted = perm_mat @ unsorted
wigner_3j_tensor = perm_mat @ wigner_3j_tensor
return Rs_out, wigner_3j_tensor
def test_wigner_3j_symmetry():
assert torch.allclose(o3.wigner_3j(1, 2, 3), o3.wigner_3j(1, 3, 2).transpose(1, 2))
assert torch.allclose(o3.wigner_3j(1, 2, 3), o3.wigner_3j(2, 1, 3).transpose(0, 1))
assert torch.allclose(o3.wigner_3j(1, 2, 3), o3.wigner_3j(3, 2, 1).transpose(0, 2))
assert torch.allclose(o3.wigner_3j(1, 2, 3), o3.wigner_3j(3, 1, 2).transpose(0, 1).transpose(1, 2))
assert torch.allclose(o3.wigner_3j(1, 2, 3), o3.wigner_3j(2, 3, 1).transpose(0, 2).transpose(1, 2))
def _tensor_product_in_in(Rs_in1, Rs_in2, selection_rule, normalization,
sorted):
"""
Compute the matrix Q
from Rs_out to Rs_in1 tensor product with Rs_in2
where Rs_out is a direct sum of irreducible representations
For normalization='component',
The set of "lines" { Q[i] }_i is orthonormal
:return: Rs_out, Q
example:
_, Q = tensor_product_in_in(Rs_in1, Rs_in2)
torch.einsum('kij,i,j->k', Q, A, B)
"""
assert normalization in [
'norm', 'component'
], "normalization needs to be 'norm' or 'component'"
Rs_in1 = simplify(Rs_in1)
Rs_in2 = simplify(Rs_in2)
Rs_out = []
for mul_1, l_1, p_1 in Rs_in1:
for mul_2, l_2, p_2 in Rs_in2:
for l_out in selection_rule(l_1, p_1, l_2, p_2):
Rs_out.append((mul_1 * mul_2, l_out, p_1 * p_2))
Rs_out = simplify(Rs_out)
dim_in2 = dim(Rs_in2)
row = []
col = []
val = []
index_out = 0
index_1 = 0
for mul_1, l_1, p_1 in Rs_in1:
dim_1 = mul_1 * (2 * l_1 + 1)
index_2 = 0
for mul_2, l_2, p_2 in Rs_in2:
dim_2 = mul_2 * (2 * l_2 + 1)
for l_out in selection_rule(l_1, p_1, l_2, p_2):
dim_out = mul_1 * mul_2 * (2 * l_out + 1)
C = o3.wigner_3j(l_out, l_1, l_2, cached=True)
if normalization == 'component':
C *= (2 * l_out + 1)**0.5
if normalization == 'norm':
C *= (2 * l_1 + 1)**0.5 * (2 * l_2 + 1)**0.5
I = torch.eye(mul_1 * mul_2).reshape(mul_1 * mul_2, mul_1,
mul_2)
m = torch.einsum("wuv,kij->wkuivj", I,
C).reshape(dim_out, dim_1, dim_2)
i_out, i_1, i_2 = m.nonzero(as_tuple=False).T
i_out += index_out
i_1 += index_1
i_2 += index_2
row.append(i_out)
col.append(i_1 * dim_in2 + i_2)
val.append(m[m != 0])
index_out += dim_out
index_2 += dim_2
index_1 += dim_1
wigner_3j_tensor = SparseTensor(
row=torch.cat(row) if row else torch.zeros(0, dtype=torch.long),
col=torch.cat(col) if col else torch.zeros(0, dtype=torch.long),
value=torch.cat(val) if val else torch.zeros(0),
sparse_sizes=(dim(Rs_out), dim(Rs_in1) * dim(Rs_in2)))
if sorted:
Rs_out, perm_mat = sort(Rs_out)
Rs_out = simplify(Rs_out)
wigner_3j_tensor = perm_mat @ wigner_3j_tensor
return Rs_out, wigner_3j_tensor
def __init__(self,
Rs_in1: rs.TY_RS_LOOSE,
Rs_in2: rs.TY_RS_LOOSE,
Rs_out: rs.TY_RS_LOOSE,
instr: List[Tuple[int, int, int, str]],
normalization: str = 'component',
own_weight: bool = True):
"""
Create a Tensor Product operation that has each of his path weighted by a parameter.
`instr` is a list of instructions.
An instruction if of the form (i_1, i_2, i_out, mode)
it means "Put `Rs_in1[i_1] otimes Rs_in2[i_2] into Rs_out[i_out]"
`mode` determines the way the multiplicities are treated.
The default mode should be 'uvw', meaning that all paths are created.
"""
super().__init__()
self.Rs_in1 = rs.convention(Rs_in1)
self.Rs_in2 = rs.convention(Rs_in2)
self.Rs_out = rs.convention(Rs_out)
code = ""
index_w = 0
wigners = set()
count = [0 for _ in range(rs.dim(self.Rs_out))]
instr = sorted(instr) # for optimization
last_s1, last_s2, last_ss = None, None, None
for i_1, i_2, i_out, mode in instr:
mul_1, l_1, p_1 = self.Rs_in1[i_1]
mul_2, l_2, p_2 = self.Rs_in2[i_2]
mul_out, l_out, p_out = self.Rs_out[i_out]
dim_1 = mul_1 * (2 * l_1 + 1)
dim_2 = mul_2 * (2 * l_2 + 1)
dim_out = mul_out * (2 * l_out + 1)
index_1 = rs.dim(self.Rs_in1[:i_1])
index_2 = rs.dim(self.Rs_in2[:i_2])
index_out = rs.dim(self.Rs_out[:i_out])
assert p_1 * p_2 == p_out
assert abs(l_1 - l_2) <= l_out <= l_1 + l_2
if dim_1 == 0 or dim_2 == 0 or dim_out == 0:
continue
if last_s1 != i_1:
code += f" s1 = x1[:, {index_1}:{index_1+dim_1}].reshape(batch, {mul_1}, {2 * l_1 + 1})\n"
last_s1 = i_1
if last_s2 != i_2:
code += f" s2 = x2[:, {index_2}:{index_2+dim_2}].reshape(batch, {mul_2}, {2 * l_2 + 1})\n"
last_s2 = i_2
assert mode in ['uvw', 'uvu', 'uvv', 'uuw', 'uuu', 'uvuv']
if last_ss != (i_1, i_2, mode[:2]):
if mode[:2] == 'uv':
code += f" ss = ein('zui,zvj->zuvij', s1, s2)\n"
if mode[:2] == 'uu':
code += f" ss = ein('zui,zuj->zuij', s1, s2)\n"
last_ss = (i_1, i_2, mode[:2])
wigners.add((l_1, l_2, l_out))
if mode == 'uvw':
dim_w = mul_1 * mul_2 * mul_out
code += f" sw = w[:, {index_w}:{index_w+dim_w}].reshape(batch, {mul_1}, {mul_2}, {mul_out})\n"
code += f" out[:, {index_out}:{index_out+dim_out}] += ein('zuvw,ijk,zuvij->zwk', sw, C{l_1}_{l_2}_{l_out}, ss).reshape(batch, {dim_out})\n"
for pos in range(index_out, index_out + dim_out):
count[pos] += mul_1 * mul_2
if mode == 'uvu':
assert mul_1 == mul_out
dim_w = mul_1 * mul_2
code += f" sw = w[:, {index_w}:{index_w+dim_w}].reshape(batch, {mul_1}, {mul_2})\n"
code += f" out[:, {index_out}:{index_out+dim_out}] += ein('zuv,ijk,zuvij->zuk', sw, C{l_1}_{l_2}_{l_out}, ss).reshape(batch, {dim_out})\n"
for pos in range(index_out, index_out + dim_out):
count[pos] += mul_2
if mode == 'uvv':
assert mul_2 == mul_out
dim_w = mul_1 * mul_2
code += f" sw = w[:, {index_w}:{index_w+dim_w}].reshape(batch, {mul_1}, {mul_2})\n"
code += f" out[:, {index_out}:{index_out+dim_out}] += ein('zuv,ijk,zuvij->zvk', sw, C{l_1}_{l_2}_{l_out}, ss).reshape(batch, {dim_out})\n"
for pos in range(index_out, index_out + dim_out):
count[pos] += mul_1
if mode == 'uuw':
assert mul_1 == mul_2
dim_w = mul_1 * mul_out
code += f" sw = w[:, {index_w}:{index_w+dim_w}].reshape(batch, {mul_1}, {mul_out})\n"
code += f" out[:, {index_out}:{index_out+dim_out}] += ein('zuw,ijk,zuij->zwk', sw, C{l_1}_{l_2}_{l_out}, ss).reshape(batch, {dim_out})\n"
for pos in range(index_out, index_out + dim_out):
count[pos] += mul_1
if mode == 'uuu':
assert mul_1 == mul_2 == mul_out
dim_w = mul_1
code += f" sw = w[:, {index_w}:{index_w+dim_w}].reshape(batch, {mul_1})\n"
code += f" out[:, {index_out}:{index_out+dim_out}] += ein('zu,ijk,zuij->zuk', sw, C{l_1}_{l_2}_{l_out}, ss).reshape(batch, {dim_out})\n"
for pos in range(index_out, index_out + dim_out):
count[pos] += 1
if mode == 'uvuv':
assert mul_1 * mul_2 == mul_out
dim_w = mul_1 * mul_2
code += f" sw = w[:, {index_w}:{index_w+dim_w}].reshape(batch, {mul_1}, {mul_2})\n"
code += f" out[:, {index_out}:{index_out+dim_out}] += ein('zuv,ijk,zuvij->zuvk', sw, C{l_1}_{l_2}_{l_out}, ss).reshape(batch, {dim_out})\n"
for pos in range(index_out, index_out + dim_out):
count[pos] += 1
index_w += dim_w
code += "\n"
ilast = 0
clast = count[0]
for i, c in enumerate(count):
if clast != c:
if clast > 1:
code += f" out[:, {ilast}:{i}].div_({clast ** 0.5})\n"
clast = c
ilast = i
if clast > 1:
code += f" out[:, {ilast}:].div_({clast ** 0.5})\n"
wigners = sorted(wigners)
self.wigners_names = [
f"C{l_1}_{l_2}_{l_3}" for l_1, l_2, l_3 in wigners
]
args = ", ".join(f"{arg}: torch.Tensor" for arg in self.wigners_names)
for arg, (l_1, l_2, l_out) in zip(self.wigners_names, wigners):
wig = o3.wigner_3j(l_1, l_2, l_out)
if normalization == 'component':
wig *= (2 * l_out + 1)**0.5
if normalization == 'norm':
wig *= (2 * l_1 + 1)**0.5 * (2 * l_2 + 1)**0.5
self.register_buffer(arg, wig)
x = _tensor_product_code
x = x.replace("DIM", f"{rs.dim(self.Rs_out)}")
x = x.replace("ARGS", args)
x = x.replace("CODE", code)
self.code = x
self.main = eval_code(x).main
self.nweight = index_w
if own_weight:
self.weight = torch.nn.Parameter(torch.randn(self.nweight))
def _tensor_product_in_out(Rs_in1, selection_rule, Rs_out, normalization, sorted):
"""
Compute the matrix Q
from Rs_out to Rs_in1 tensor product with Rs_in2
where Rs_in2 is a direct sum of irreducible representations
For normalization='component',
The set of "lines" { Q[i] }_i is orthonormal
:return: Rs_in2, Q
example:
_, Q = tensor_product_in_out(Rs_in1, Rs_out)
torch.einsum('kij,i,j->k', Q, A, B)
"""
assert normalization in ['norm', 'component'], "normalization needs to be 'norm' or 'component'"
Rs_in1 = simplify(Rs_in1)
Rs_out = simplify(Rs_out)
Rs_in2 = []
for mul_out, l_out, p_out in Rs_out:
for mul_1, l_1, p_1 in Rs_in1:
for l_2 in selection_rule(l_1, p_1, l_out, p_out):
Rs_in2.append((mul_1 * mul_out, l_2, p_1 * p_out))
Rs_in2 = simplify(Rs_in2)
wigner_3j_tensor = torch.zeros(dim(Rs_out), dim(Rs_in1), dim(Rs_in2))
index_2 = 0
index_out = 0
for mul_out, l_out, p_out in Rs_out:
dim_out = mul_out * (2 * l_out + 1)
n_path = 0
for mul_1, l_1, p_1 in Rs_in1:
for l_2 in selection_rule(l_1, p_1, l_out, p_out):
n_path += mul_1
index_1 = 0
for mul_1, l_1, p_1 in Rs_in1:
dim_1 = mul_1 * (2 * l_1 + 1)
for l_2 in selection_rule(l_1, p_1, l_out, p_out):
dim_2 = mul_1 * mul_out * (2 * l_2 + 1)
C = o3.wigner_3j(l_out, l_1, l_2, cached=True)
if normalization == 'component':
C *= (2 * l_out + 1) ** 0.5
if normalization == 'norm':
C *= (2 * l_1 + 1) ** 0.5 * (2 * l_2 + 1) ** 0.5
I = torch.eye(mul_out * mul_1).reshape(mul_out, mul_1, mul_out * mul_1) / n_path ** 0.5
m = torch.einsum("wuv,kij->wkuivj", I, C).reshape(dim_out, dim_1, dim_2)
wigner_3j_tensor[index_out:index_out + dim_out, index_1:index_1 + dim_1, index_2:index_2 + dim_2] = m
index_2 += dim_2
index_1 += dim_1
index_out += dim_out
if sorted:
Rs_in2, perm = sort(Rs_in2)
Rs_in2 = simplify(Rs_in2)
wigner_3j_tensor = torch.einsum('jl,kil->kij', perm, wigner_3j_tensor)
return Rs_in2, wigner_3j_tensor
def tensor_square(Rs_in, selection_rule=o3.selection_rule, normalization='component', sorted=False):
"""
Compute the matrix Q
from Rs_out to Rs_in tensor product with Rs_in
where Rs_out is a direct sum of irreducible representations
For normalization='component',
The set of "lines" { Q[i] }_i is orthonormal
:return: Rs_out, Q
example:
_, Q = tensor_square(Rs_in)
torch.einsum('kij,i,j->k', Q, A, A)
"""
assert normalization in ['norm', 'component'], "normalization needs to be 'norm' or 'component'"
Rs_in = simplify(Rs_in)
Rs_out = []
for i, (mul_1, l_1, p_1) in enumerate(Rs_in):
for l_out in selection_rule(l_1, p_1, l_1, p_1):
if l_out % 2 == 0:
Rs_out.append((mul_1 * (mul_1 + 1) // 2, l_out, p_1**2))
else:
Rs_out.append((mul_1 * (mul_1 - 1) // 2, l_out, p_1**2))
for mul_2, l_2, p_2 in Rs_in[i + 1:]:
for l_out in selection_rule(l_1, p_1, l_2, p_2):
Rs_out.append((mul_1 * mul_2, l_out, p_1 * p_2))
Rs_out = simplify(Rs_out)
wigner_3j_tensor = torch.zeros(dim(Rs_out), dim(Rs_in), dim(Rs_in))
index_out = 0
index_1 = 0
for i, (mul_1, l_1, p_1) in enumerate(Rs_in):
dim_1 = mul_1 * (2 * l_1 + 1)
for l_out in selection_rule(l_1, p_1, l_1, p_1):
I = torch.eye(mul_1**2).reshape(mul_1**2, mul_1, mul_1)
uv = I.nonzero()[:, 1:]
if l_out % 2 == 0:
I = I[uv[:, 0] <= uv[:, 1]]
else:
I = I[uv[:, 0] < uv[:, 1]]
if I.shape[0] == 0:
continue
C = o3.wigner_3j(l_out, l_1, l_1)
if normalization == 'component':
C *= (2 * l_out + 1) ** 0.5
if normalization == 'norm':
C *= (2 * l_1 + 1) ** 0.5 * (2 * l_1 + 1) ** 0.5
dim_out = I.shape[0] * (2 * l_out + 1)
m = torch.einsum("wuv,kij->wkuivj", I, C).reshape(dim_out, dim_1, dim_1)
wigner_3j_tensor[index_out:index_out + dim_out, index_1:index_1 + dim_1, index_1:index_1 + dim_1] = m
index_out += dim_out
index_2 = index_1 + dim_1
for mul_2, l_2, p_2 in Rs_in[i + 1:]:
dim_2 = mul_2 * (2 * l_2 + 1)
for l_out in selection_rule(l_1, p_1, l_2, p_2):
I = torch.eye(mul_1 * mul_2).reshape(mul_1 * mul_2, mul_1, mul_2)
C = o3.wigner_3j(l_out, l_1, l_2)
if normalization == 'component':
C *= (2 * l_out + 1) ** 0.5
if normalization == 'norm':
C *= (2 * l_1 + 1) ** 0.5 * (2 * l_2 + 1) ** 0.5
dim_out = I.shape[0] * (2 * l_out + 1)
m = torch.einsum("wuv,kij->wkuivj", I, C).reshape(dim_out, dim_1, dim_2)
wigner_3j_tensor[index_out:index_out + dim_out, index_1:index_1 + dim_1, index_2:index_2 + dim_2] = m
index_out += dim_out
index_2 += dim_2
index_1 += dim_1
if sorted:
Rs_out, perm = sort(Rs_out)
Rs_out = simplify(Rs_out)
wigner_3j_tensor = torch.einsum('ij,jkl->ikl', perm, wigner_3j_tensor)
return Rs_out, wigner_3j_tensor
def codegen_tensor_product(
irreps_in1: o3.Irreps,
in1_var: List[float],
irreps_in2: o3.Irreps,
in2_var: List[float],
irreps_out: o3.Irreps,
out_var: List[float],
instructions: List[Instruction],
normalization: str = 'component',
shared_weights: bool = False,
specialized_code: bool = True,
optimize_einsums: bool = True,
) -> Tuple[fx.GraphModule, fx.GraphModule]:
graph_out = fx.Graph()
graph_right = fx.Graph()
# = Function definitions =
x1s_out = fx.Proxy(graph_out.placeholder('x1', torch.Tensor))
x2s_out = fx.Proxy(graph_out.placeholder('x2', torch.Tensor))
ws_out = fx.Proxy(graph_out.placeholder('w', torch.Tensor))
x2s_right = fx.Proxy(graph_right.placeholder('x2', torch.Tensor))
ws_right = fx.Proxy(graph_right.placeholder('w', torch.Tensor))
empty_out = fx.Proxy(
graph_out.call_function(torch.empty, ((), ), dict(device='cpu')))
empty_right = fx.Proxy(
graph_right.call_function(torch.empty, ((), ), dict(device='cpu')))
if shared_weights:
size_out = torch.broadcast_tensors(
empty_out.expand(x1s_out.shape[:-1]),
empty_out.expand(x2s_out.shape[:-1]))[0].shape
size_right = x2s_right.shape[:-1]
else:
size_out = torch.broadcast_tensors(
empty_out.expand(x1s_out.shape[:-1]),
empty_out.expand(x2s_out.shape[:-1]),
empty_out.expand(ws_out.shape[:-1]))[0].shape
size_right = torch.broadcast_tensors(
empty_right.expand(x2s_right.shape[:-1]),
empty_right.expand(ws_right.shape[:-1]))[0].shape
# = Short-circut for zero dimensional =
# We produce no code for empty instructions
instructions = [ins for ins in instructions if 0 not in ins.path_shape]
if len(instructions) == 0:
out_out = x1s_out.new_zeros(size_out + (irreps_out.dim, ))
out_right = x2s_right.new_zeros(size_right + (
irreps_in1.dim,
irreps_out.dim,
))
graph_out.output(out_out.node, torch.Tensor)
graph_right.output(out_right.node, torch.Tensor)
# Short circut
return (fx.GraphModule({}, graph_out, "tp_forward"),
fx.GraphModule({}, graph_right, "tp_right"))
# = Broadcast inputs =
if shared_weights:
x1s_out, x2s_out = x1s_out.broadcast_to(
size_out + (-1, )), x2s_out.broadcast_to(size_out + (-1, ))
else:
x1s_out, x2s_out, ws_out = x1s_out.broadcast_to(
size_out + (-1, )), x2s_out.broadcast_to(
size_out + (-1, )), ws_out.broadcast_to(size_out + (-1, ))
x2s_right, ws_right = x2s_right.broadcast_to(
size_right + (-1, )), ws_right.broadcast_to(size_right + (-1, ))
outsize_out = size_out + (irreps_out.dim, )
outsize_right = size_right + (
irreps_in1.dim,
irreps_out.dim,
)
x1s_out = x1s_out.reshape(-1, irreps_in1.dim)
x2s_out = x2s_out.reshape(-1, irreps_in2.dim)
x2s_right = x2s_right.reshape(-1, irreps_in2.dim)
batch_out = x1s_out.shape[0]
batch_right = x2s_right.shape[0]
# = Determine number of weights and reshape weights ==
weight_numel = sum(
prod(ins.path_shape) for ins in instructions if ins.has_weight)
if weight_numel > 0:
ws_out = ws_out.reshape(-1, weight_numel)
ws_right = ws_right.reshape(-1, weight_numel)
del weight_numel
# = book-keeping for wigners =
w3j = []
w3j_dict_out = dict()
w3j_dict_right = dict()
# = extract individual input irreps =
# If only one input irrep, can avoid creating a view
if len(irreps_in1) == 1:
x1_list_out = [
x1s_out.reshape(batch_out, irreps_in1[0].mul, irreps_in1[0].ir.dim)
]
else:
x1_list_out = [
x1s_out[:, i].reshape(batch_out, mul_ir.mul, mul_ir.ir.dim)
for i, mul_ir in zip(irreps_in1.slices(), irreps_in1)
]
x2_list_out = []
x2_list_right = []
# If only one input irrep, can avoid creating a view
if len(irreps_in2) == 1:
x2_list_out.append(
x2s_out.reshape(batch_out, irreps_in2[0].mul,
irreps_in2[0].ir.dim))
x2_list_right.append(
x2s_right.reshape(batch_right, irreps_in2[0].mul,
irreps_in2[0].ir.dim))
else:
for i, mul_ir in zip(irreps_in2.slices(), irreps_in2):
x2_list_out.append(x2s_out[:, i].reshape(batch_out, mul_ir.mul,
mul_ir.ir.dim))
x2_list_right.append(x2s_right[:,
i].reshape(batch_right, mul_ir.mul,
mul_ir.ir.dim))
# The einsum string index to prepend to the weights if the weights are not shared and have a batch dimension
z = '' if shared_weights else 'z'
# Cache of input irrep pairs whose outer products (xx) have already been computed
xx_dict = dict()
# Current index in the flat weight tensor
flat_weight_index = 0
out_list_out = []
out_list_right = []
for ins in instructions:
mul_ir_in1 = irreps_in1[ins.i_in1]
mul_ir_in2 = irreps_in2[ins.i_in2]
mul_ir_out = irreps_out[ins.i_out]
assert mul_ir_in1.ir.p * mul_ir_in2.ir.p == mul_ir_out.ir.p
assert abs(mul_ir_in1.ir.l - mul_ir_in2.ir.l
) <= mul_ir_out.ir.l <= mul_ir_in1.ir.l + mul_ir_in2.ir.l
if mul_ir_in1.dim == 0 or mul_ir_in2.dim == 0 or mul_ir_out.dim == 0:
continue
alpha = ins.path_weight * out_var[ins.i_out] / sum(
in1_var[i.i_in1] * in2_var[i.i_in2]
for i in instructions if i.i_out == ins.i_out)
# Open the profiler block
name = f"{mul_ir_in1} x {mul_ir_in2} = {mul_ir_out} {ins.connection_mode} {ins.has_weight}"
handle_out = graph_out.call_function(
torch.ops.profiler._record_function_enter, (name, ))
handle_right = graph_right.call_function(
torch.ops.profiler._record_function_enter, (name, ))
x1_out = x1_list_out[ins.i_in1]
x2_out = x2_list_out[ins.i_in2]
x2_right = x2_list_right[ins.i_in2]
e1_right = fx.Proxy(
graph_right.call_function(
torch.eye, (mul_ir_in1.mul, ),
dict(dtype=x2s_right.dtype.node,
device=x2s_right.device.node)))
e2_right = fx.Proxy(
graph_right.call_function(
torch.eye, (mul_ir_in2.mul, ),
dict(dtype=x2s_right.dtype.node,
device=x2s_right.device.node)))
i1_right = fx.Proxy(
graph_right.call_function(
torch.eye, (mul_ir_in1.ir.dim, ),
dict(dtype=x2s_right.dtype.node,
device=x2s_right.device.node)))
assert ins.connection_mode in [
'uvw', 'uvu', 'uvv', 'uuw', 'uuu', 'uvuv'
]
alpha = sqrt(
alpha / {
'uvw': (mul_ir_in1.mul * mul_ir_in2.mul),
'uvu': mul_ir_in2.mul,
'uvv': mul_ir_in1.mul,
'uuw': mul_ir_in1.mul,
'uuu': 1,
'uvuv': 1,
}[ins.connection_mode])
if ins.has_weight:
# Extract the weight from the flattened weight tensor
w_out = ws_out[:, flat_weight_index:flat_weight_index +
prod(ins.path_shape)].reshape((
() if shared_weights else (-1, )) +
tuple(ins.path_shape))
w_right = ws_right[:, flat_weight_index:flat_weight_index +
prod(ins.path_shape)].reshape(
(() if shared_weights else (-1, )) +
tuple(ins.path_shape))
flat_weight_index += prod(ins.path_shape)
# Construct the general xx in case this instruction isn't specialized
# If this isn't used, the dead code will get removed
key = (ins.i_in1, ins.i_in2, ins.connection_mode[:2])
if key not in xx_dict:
if ins.connection_mode[:2] == 'uv':
xx_dict[key] = torch.einsum('zui,zvj->zuvij', x1_out, x2_out)
if ins.connection_mode[:2] == 'uu':
xx_dict[key] = torch.einsum('zui,zuj->zuij', x1_out, x2_out)
xx = xx_dict[key]
# Create a proxy & request for the relevant wigner w3j
# If not used (because of specialized code), will get removed later.
key = (mul_ir_in1.ir.l, mul_ir_in2.ir.l, mul_ir_out.ir.l)
if key not in w3j:
w3j_dict_out[key] = fx.Proxy(
graph_out.get_attr(f"_w3j_{key[0]}_{key[1]}_{key[2]}"))
w3j_dict_right[key] = fx.Proxy(
graph_right.get_attr(f"_w3j_{key[0]}_{key[1]}_{key[2]}"))
w3j.append(key)
w3j_out = w3j_dict_out[key]
w3j_right = w3j_dict_right[key]
exp = {'component': 1, 'norm': -1}[normalization]
if ins.connection_mode == 'uvw':
assert ins.has_weight
if specialized_code and key == (0, 0, 0):
ein_out = torch.einsum(
f"{z}uvw,zu,zv->zw", w_out,
x1_out.reshape(batch_out, mul_ir_in1.dim),
x2_out.reshape(batch_out, mul_ir_in2.dim))
ein_right = torch.einsum(
f"{z}uvw,zv->zuw", w_right,
x2_right.reshape(batch_right, mul_ir_in2.dim))
elif specialized_code and mul_ir_in1.ir.l == 0:
ein_out = torch.einsum(
f"{z}uvw,zu,zvj->zwj", w_out,
x1_out.reshape(batch_out, mul_ir_in1.dim), x2_out)
ein_right = torch.einsum(f"{z}uvw,zvi->zuwi", w_right,
x2_right)
elif specialized_code and mul_ir_in2.ir.l == 0:
ein_out = torch.einsum(
f"{z}uvw,zui,zv->zwi", w_out, x1_out,
x2_out.reshape(batch_out, mul_ir_in2.dim))
ein_right = torch.einsum(
f"{z}uvw,ij,zv->zuiwj", w_right, i1_right,
x2_right.reshape(batch_right, mul_ir_in2.dim))
elif specialized_code and mul_ir_out.ir.l == 0:
ein_out = torch.einsum(f"{z}uvw,zui,zvi->zw", w_out, x1_out,
x2_out) / sqrt(mul_ir_in1.ir.dim)**exp
ein_right = torch.einsum(f"{z}uvw,zvi->zuiw", w_right,
x2_right) / sqrt(
mul_ir_in1.ir.dim)**exp
else:
ein_out = torch.einsum(f"{z}uvw,ijk,zuvij->zwk", w_out,
w3j_out, xx)
ein_right = torch.einsum(f"{z}uvw,ijk,zvj->zuiwk", w_right,
w3j_right, x2_right)
if ins.connection_mode == 'uvu':
assert mul_ir_in1.mul == mul_ir_out.mul
if ins.has_weight:
if specialized_code and key == (0, 0, 0):
ein_out = torch.einsum(
f"{z}uv,zu,zv->zu", w_out,
x1_out.reshape(batch_out, mul_ir_in1.dim),
x2_out.reshape(batch_out, mul_ir_in2.dim))
ein_right = torch.einsum(
f"{z}uv,uw,zv->zuw", w_right, e1_right,
x2_right.reshape(batch_right, mul_ir_in2.dim))
elif specialized_code and mul_ir_in1.ir.l == 0:
ein_out = torch.einsum(
f"{z}uv,zu,zvj->zuj", w_out,
x1_out.reshape(batch_out, mul_ir_in1.dim), x2_out)
ein_right = torch.einsum(f"{z}uv,uw,zvi->zuwi", w_right,
e1_right, x2_right)
elif specialized_code and mul_ir_in2.ir.l == 0:
ein_out = torch.einsum(
f"{z}uv,zui,zv->zui", w_out, x1_out,
x2_out.reshape(batch_out, mul_ir_in2.dim))
ein_right = torch.einsum(
f"{z}uv,ij,uw,zv->zuiwj", w_right, i1_right, e1_right,
x2_right.reshape(batch_right, mul_ir_in2.dim))
elif specialized_code and mul_ir_out.ir.l == 0:
ein_out = torch.einsum(f"{z}uv,zui,zvi->zu", w_out, x1_out,
x2_out) / sqrt(
mul_ir_in1.ir.dim)**exp
ein_right = torch.einsum(f"{z}uv,uw,zvi->zuiw", w_right,
e1_right, x2_right) / sqrt(
mul_ir_in1.ir.dim)**exp
else:
ein_out = torch.einsum(f"{z}uv,ijk,zuvij->zuk", w_out,
w3j_out, xx)
ein_right = torch.einsum(f"{z}uv,ijk,uw,zvj->zuiwk",
w_right, w3j_right, e1_right,
x2_right)
else:
# not so useful operation because v is summed
ein_out = torch.einsum("ijk,zuvij->zuk", w3j_out, xx)
ein_right = torch.einsum("ijk,uw,zvj->zuiwk", w3j_right,
e1_right, x2_right)
if ins.connection_mode == 'uvv':
assert mul_ir_in2.mul == mul_ir_out.mul
if ins.has_weight:
if specialized_code and key == (0, 0, 0):
ein_out = torch.einsum(
f"{z}uv,zu,zv->zv", w_out,
x1_out.reshape(batch_out, mul_ir_in1.dim),
x2_out.reshape(batch_out, mul_ir_in2.dim))
ein_right = torch.einsum(
f"{z}uv,vw,zv->zuw", w_right, e2_right,
x2_right.reshape(batch_right, mul_ir_in2.dim))
elif specialized_code and mul_ir_in1.ir.l == 0:
ein_out = torch.einsum(
f"{z}uv,zu,zvj->zvj", w_out,
x1_out.reshape(batch_out, mul_ir_in1.dim), x2_out)
ein_right = torch.einsum(f"{z}uv,vw,zvi->zuwi", w_right,
e2_right, x2_right)
elif specialized_code and mul_ir_in2.ir.l == 0:
ein_out = torch.einsum(
f"{z}uv,zui,zv->zvi", w_out, x1_out,
x2_out.reshape(batch_out, mul_ir_in2.dim))
ein_right = torch.einsum(
f"{z}uv,ij,vw,zv->zuiwj", w_right, i1_right, e2_right,
x2_right.reshape(batch_right, mul_ir_in2.dim))
elif specialized_code and mul_ir_out.ir.l == 0:
ein_out = torch.einsum(f"{z}uv,zui,zvi->zv", w_out, x1_out,
x2_out) / sqrt(
mul_ir_in1.ir.dim)**exp
ein_right = torch.einsum(f"{z}uv,vw,zvi->zuiw", w_right,
e2_right, x2_right) / sqrt(
mul_ir_in1.ir.dim)**exp
else:
ein_out = torch.einsum(f"{z}uv,ijk,zuvij->zvk", w_out,
w3j_out, xx)
ein_right = torch.einsum(f"{z}uv,ijk,zvj->zuivk", w_right,
w3j_right, x2_right)
else:
# not so useful operation because u is summed
# only specialize out for this path
if specialized_code and key == (0, 0, 0):
ein_out = torch.einsum(
"zu,zv->zv", x1_out.reshape(batch_out, mul_ir_in1.dim),
x2_out.reshape(batch_out, mul_ir_in2.dim))
elif specialized_code and mul_ir_in1.ir.l == 0:
ein_out = torch.einsum(
"zu,zvj->zvj", x1_out.reshape(batch_out,
mul_ir_in1.dim), x2_out)
elif specialized_code and mul_ir_in2.ir.l == 0:
ein_out = torch.einsum(
"zui,zv->zvi", x1_out,
x2_out.reshape(batch_out, mul_ir_in2.dim))
elif specialized_code and mul_ir_out.ir.l == 0:
ein_out = torch.einsum("zui,zvi->zv", x1_out,
x2_out) / sqrt(
mul_ir_in1.ir.dim)**exp
else:
ein_out = torch.einsum("ijk,zuvij->zvk", w3j_out, xx)
s2ones = fx.Proxy(
graph_right.call_function(
torch.ones, (mul_ir_in1.mul, ),
dict(device=x2_right.device.node,
dtype=x2_right.dtype.node)))
ein_right = torch.einsum("u,ijk,zvj->zuivk", s2ones, w3j_right,
x2_right)
if ins.connection_mode == 'uuw':
assert mul_ir_in1.mul == mul_ir_in2.mul
if ins.has_weight:
if specialized_code and key == (0, 0, 0):
ein_out = torch.einsum(
f"{z}uw,zu,zu->zw", w_out,
x1_out.reshape(batch_out, mul_ir_in1.dim),
x2_out.reshape(batch_out, mul_ir_in2.dim))
elif specialized_code and mul_ir_in1.ir.l == 0:
ein_out = torch.einsum(
f"{z}uw,zu,zuj->zwj", w_out,
x1_out.reshape(batch_out, mul_ir_in1.dim), x2_out)
elif specialized_code and mul_ir_in2.ir.l == 0:
ein_out = torch.einsum(
f"{z}uw,zui,zu->zwi", w_out, x1_out,
x2_out.reshape(batch_out, mul_ir_in2.dim))
elif specialized_code and mul_ir_out.ir.l == 0:
ein_out = torch.einsum(f"{z}uw,zui,zui->zw", w_out, x1_out,
x2_out) / sqrt(
mul_ir_in1.ir.dim)**exp
else:
ein_out = torch.einsum(f"{z}uw,ijk,zuij->zwk", w_out,
w3j_out, xx)
# TODO: specialize right()
ein_right = torch.einsum(f"{z}uw,ijk,zuj->zuiwk", w_right,
w3j_right, x2_right)
else:
# equivalent to tp(x, y, 'uuu').sum('u')
assert mul_ir_out.mul == 1
ein_out = torch.einsum("ijk,zuij->zk", w3j_out, xx)
ein_right = torch.einsum("ijk,zuj->zuik", w3j_right, x2_right)
if ins.connection_mode == 'uuu':
assert mul_ir_in1.mul == mul_ir_in2.mul == mul_ir_out.mul
if ins.has_weight:
if specialized_code and key == (0, 0, 0):
ein_out = torch.einsum(
f"{z}u,zu,zu->zu", w_out,
x1_out.reshape(batch_out, mul_ir_in1.dim),
x2_out.reshape(batch_out, mul_ir_in2.dim))
ein_right = torch.einsum(
f"{z}u,uw,zu->zuw", w_right, e2_right,
x2_right.reshape(batch_right, mul_ir_in2.dim))
elif specialized_code and key == (
1, 1, 1) and normalization == "component":
ein_out = torch.einsum(f"{z}u,zui->zui", w_out,
torch.cross(x1_out, x2_out,
dim=2)) / sqrt(2)
# For cross product, use the general case right()
ein_right = torch.einsum(f"{z}u,ijk,uw,zuj->zuiwk",
w_right, w3j_right, e1_right,
x2_right)
elif specialized_code and mul_ir_in1.ir.l == 0:
ein_out = torch.einsum(
f"{z}u,zu,zuj->zuj", w_out,
x1_out.reshape(batch_out, mul_ir_in1.dim), x2_out)
ein_right = torch.einsum(f"{z}u,uw,zui->zuwi", w_right,
e2_right, x2_right)
elif specialized_code and mul_ir_in2.ir.l == 0:
ein_out = torch.einsum(
f"{z}u,zui,zu->zui", w_out, x1_out,
x2_out.reshape(batch_out, mul_ir_in2.dim))
ein_right = torch.einsum(
f"{z}u,ij,uw,zu->zuiwj", w_right, i1_right, e2_right,
x2_right.reshape(batch_right, mul_ir_in2.dim))
elif specialized_code and mul_ir_out.ir.l == 0:
ein_out = torch.einsum(f"{z}u,zui,zui->zu", w_out, x1_out,
x2_out) / sqrt(
mul_ir_in1.ir.dim)**exp
ein_right = torch.einsum(f"{z}u,uw,zui->zuiw", w_right,
e2_right, x2_right) / sqrt(
mul_ir_in1.ir.dim)**exp
else:
ein_out = torch.einsum(f"{z}u,ijk,zuij->zuk", w_out,
w3j_out, xx)
ein_right = torch.einsum(f"{z}u,ijk,uw,zuj->zuiwk",
w_right, w3j_right, e1_right,
x2_right)
else:
if specialized_code and key == (0, 0, 0):
ein_out = torch.einsum(
"zu,zu->zu", x1_out.reshape(batch_out, mul_ir_in1.dim),
x2_out.reshape(batch_out, mul_ir_in2.dim))
ein_right = torch.einsum(
"uw,zu->zuw", e2_right,
x2_right.reshape(batch_right, mul_ir_in2.dim))
elif specialized_code and key == (
1, 1, 1) and normalization == "component":
ein_out = torch.cross(x1_out, x2_out,
dim=2) * (1.0 / sqrt(2))
# For cross product, use the general case right()
ein_right = torch.einsum("ijk,uw,zuj->zuiwk", w3j_right,
e1_right, x2_right)
elif specialized_code and mul_ir_in1.ir.l == 0:
ein_out = torch.einsum(
"zu,zuj->zuj", x1_out.reshape(batch_out,
mul_ir_in1.dim), x2_out)
ein_right = torch.einsum("uw,zui->zuwi", e2_right,
x2_right)
elif specialized_code and mul_ir_in2.ir.l == 0:
ein_out = torch.einsum(
"zui,zu->zui", x1_out,
x2_out.reshape(batch_out, mul_ir_in2.dim))
ein_right = torch.einsum(
"ij,uw,zu->zuiwj", i1_right, e2_right,
x2_right.reshape(batch_right, mul_ir_in2.dim))
elif specialized_code and mul_ir_out.ir.l == 0:
ein_out = torch.einsum("zui,zui->zu", x1_out,
x2_out) / sqrt(
mul_ir_in1.ir.dim)**exp
ein_right = torch.einsum("uw,zui->zuiw", e2_right,
x2_right) / sqrt(
mul_ir_in1.ir.dim)**exp
else:
ein_out = torch.einsum("ijk,zuij->zuk", w3j_out, xx)
ein_right = torch.einsum("ijk,uw,zuj->zuiwk", w3j_right,
e1_right, x2_right)
if ins.connection_mode == 'uvuv':
assert mul_ir_in1.mul * mul_ir_in2.mul == mul_ir_out.mul
if ins.has_weight:
# TODO implement specialized code
ein_out = torch.einsum(f"{z}uv,ijk,zuvij->zuvk", w_out,
w3j_out, xx)
ein_right = torch.einsum(f"{z}uv,ijk,uw,zvj->zuiwvk", w_right,
w3j_right, e1_right, x2_right)
else:
# TODO implement specialized code
ein_out = torch.einsum("ijk,zuvij->zuvk", w3j_out, xx)
ein_right = torch.einsum("ijk,uw,zvj->zuiwvk", w3j_right,
e1_right, x2_right)
ein_out = alpha * ein_out
ein_right = alpha * ein_right
out_list_out += [ein_out.reshape(batch_out, mul_ir_out.dim)]
out_list_right += [
ein_right.reshape(batch_right, mul_ir_in1.dim, mul_ir_out.dim)
]
# Close the profiler block
graph_out.call_function(torch.ops.profiler._record_function_exit,
(handle_out, ))
graph_right.call_function(torch.ops.profiler._record_function_exit,
(handle_right, ))
# Remove unused w3js:
if len(w3j_out.node.users) == 0 and len(w3j_right.node.users) == 0:
del w3j[-1]
# The w3j nodes are reshapes, so we have to remove them from the graph
# Although they are dead code, they try to reshape to dimensions that don't exist
# (since the corresponding w3js are not in w3j)
# so they screw up the shape propagation, even though they would be removed later as dead code by TorchScript.
graph_out.erase_node(w3j_dict_out.pop(key).node)
graph_right.erase_node(w3j_dict_right.pop(key).node)
# = Return the result =
out_out = [
_sum_tensors([
out for ins, out in zip(instructions, out_list_out)
if ins.i_out == i_out
],
shape=(batch_out, mul_ir_out.dim),
like=x1s_out)
for i_out, mul_ir_out in enumerate(irreps_out) if mul_ir_out.mul > 0
]
if len(out_out) > 1:
out_out = torch.cat(out_out, dim=1)
else:
# Avoid an unnecessary copy in a size one torch.cat
out_out = out_out[0]
out_right = [
torch.cat([
_sum_tensors([
out for ins, out in zip(instructions, out_list_right)
if (ins.i_in1, ins.i_out) == (i_in1, i_out)
],
shape=(batch_right, mul_ir_in1.dim, mul_ir_out.dim),
like=x2s_right)
for i_out, mul_ir_out in enumerate(irreps_out)
if mul_ir_out.mul > 0
],
dim=2) for i_in1, mul_ir_in1 in enumerate(irreps_in1)
if mul_ir_in1.mul > 0
]
if len(out_right) > 1:
out_right = torch.cat(out_right, dim=1)
else:
out_right = out_right[0]
out_out = out_out.reshape(outsize_out)
out_right = out_right.reshape(outsize_right)
graph_out.output(out_out.node, torch.Tensor)
graph_right.output(out_right.node, torch.Tensor)
# check graphs
graph_out.lint()
graph_right.lint()
# Make GraphModules
wigner_mats = {}
for l_1, l_2, l_out in w3j:
wig = o3.wigner_3j(l_1, l_2, l_out)
if normalization == 'component':
wig *= (2 * l_out + 1)**0.5
if normalization == 'norm':
wig *= (2 * l_1 + 1)**0.5 * (2 * l_2 + 1)**0.5
wigner_mats[f"_w3j_{l_1}_{l_2}_{l_out}"] = wig
# By putting the constants in a Module rather than a dict,
# we force FX to copy them as buffers instead of as attributes.
#
# FX seems to have resolved this issue for dicts in 1.9, but we support all the way back to 1.8.0.
constants_root = torch.nn.Module()
for wkey, wmat in wigner_mats.items():
constants_root.register_buffer(wkey, wmat)
graphmod_out = fx.GraphModule(constants_root,
graph_out,
class_name="tp_forward")
graphmod_right = fx.GraphModule(constants_root,
graph_right,
class_name="tp_right")
# == Optimize ==
# TODO: when eliminate_dead_code() is in PyTorch stable, use that
if optimize_einsums:
# Note that for our einsums, we can optimize _once_ for _any_ batch dimension
# and still get the right path for _all_ batch dimensions.
# This is because our einsums are essentially of the form:
# zuvw,ijk,zuvij->zwk OR uvw,ijk,zuvij->zwk
# In the first case, all but one operands have the batch dimension
# => The first contraction gains the batch dimension
# => All following contractions have batch dimension
# => All possible contraction paths have cost that scales linearly in batch size
# => The optimal path is the same for all batch sizes
# For the second case, this logic follows as long as the first contraction is not between the first two operands. Since those two operands do not share any indexes, contracting them first is a rare pathological case. See
# https://github.com/dgasmith/opt_einsum/issues/158
# for more details.
#
# TODO: consider the impact maximum intermediate result size on this logic
# \- this is the `memory_limit` option in opt_einsum
# TODO: allow user to choose opt_einsum parameters?
#
# We use float32 and zeros to save memory and time, since opt_einsum_fx looks only at traced shapes, not values or dtypes.
batchdim = 4
example_inputs = (
torch.zeros((batchdim, irreps_in1.dim)),
torch.zeros((batchdim, irreps_in2.dim)),
torch.zeros(
1 if shared_weights else batchdim,
flat_weight_index,
),
)
graphmod_out = jitable(
optimize_einsums_full(graphmod_out, example_inputs))
graphmod_right = jitable(
optimize_einsums_full(graphmod_right, example_inputs[1:]))
return graphmod_out, graphmod_right
def elementwise_tensor_product(Rs_1, Rs_2, selection_rule=o3.selection_rule, normalization='component'):
"""
:return: Rs_out, matrix
m_kij A_i B_j
"""
assert normalization in ['norm', 'component'], "normalization needs to be 'norm' or 'component'"
Rs_1 = simplify(Rs_1)
Rs_2 = simplify(Rs_2)
assert sum(mul for mul, _, _ in Rs_1) == sum(mul for mul, _, _ in Rs_2)
i = 0
while i < len(Rs_1):
mul_1, l_1, p_1 = Rs_1[i]
mul_2, l_2, p_2 = Rs_2[i]
if mul_1 < mul_2:
Rs_2[i] = (mul_1, l_2, p_2)
Rs_2.insert(i + 1, (mul_2 - mul_1, l_2, p_2))
if mul_2 < mul_1:
Rs_1[i] = (mul_2, l_1, p_1)
Rs_1.insert(i + 1, (mul_1 - mul_2, l_1, p_1))
i += 1
Rs_out = []
for (mul, l_1, p_1), (mul_2, l_2, p_2) in zip(Rs_1, Rs_2):
assert mul == mul_2
for l in selection_rule(l_1, p_1, l_2, p_2):
Rs_out.append((mul, l, p_1 * p_2))
Rs_out = simplify(Rs_out)
wigner_3j_tensor = torch.zeros(dim(Rs_out), dim(Rs_1), dim(Rs_2))
index_out = 0
index_1 = 0
index_2 = 0
for (mul, l_1, p_1), (mul_2, l_2, p_2) in zip(Rs_1, Rs_2):
assert mul == mul_2
dim_1 = mul * (2 * l_1 + 1)
dim_2 = mul * (2 * l_2 + 1)
for l_out in selection_rule(l_1, p_1, l_2, p_2):
dim_out = mul * (2 * l_out + 1)
C = o3.wigner_3j(l_out, l_1, l_2, cached=True)
if normalization == 'component':
C *= (2 * l_out + 1) ** 0.5
if normalization == 'norm':
C *= (2 * l_1 + 1) ** 0.5 * (2 * l_2 + 1) ** 0.5
I = torch.einsum("uv,wu->wuv", torch.eye(mul), torch.eye(mul))
m = torch.einsum("wuv,kij->wkuivj", I, C).reshape(dim_out, dim_1, dim_2)
wigner_3j_tensor[index_out:index_out + dim_out, index_1:index_1 + dim_1, index_2:index_2 + dim_2] = m
index_out += dim_out
index_1 += dim_1
index_2 += dim_2
return Rs_out, wigner_3j_tensor
"""
Generate the .cache files
"""
from e3nn import o3
lmax = 10
for l1 in range(lmax + 1):
for l2 in range(lmax + 1):
for l3 in range(abs(l1 - l2), min(l1 + l2, lmax) + 1):
print(l1, l2, l3)
o3.wigner_3j(l1, l2, l3)
def get_clebsch_gordon(J: int, d_in: int, d_out: int, device) -> Tensor:
""" Get the (cached) Q^{d_out,d_in}_J matrices from equation (8) """
return o3.wigner_3j(J, d_in, d_out, dtype=torch.float64,
device=device).permute(2, 1, 0)
def backward(ctx, grad_kernel):
F, Y, R, norm_coef = ctx.saved_tensors
batch, a, b = ctx.batch, ctx.a, ctx.b
grad_F = grad_Y = grad_R = None
if ctx.needs_input_grad[0]:
grad_F = grad_kernel.new_zeros(
*ctx.F_shape) # [batch, b, l_in * mul_in * m_in]
if ctx.needs_input_grad[1]:
grad_Y = grad_kernel.new_zeros(
*ctx.Y_shape) # [l_filter * m_filter, batch, a, b]
if ctx.needs_input_grad[2]:
grad_R = grad_kernel.new_zeros(
*ctx.R_shape
) # [batch, a, b, l_out * l_in * mul_out * mul_in * l_filter]
begin_R = 0
begin_out = 0
for i, (mul_out, l_out, p_out) in enumerate(ctx.Rs_out):
s_out = slice(begin_out, begin_out + mul_out * (2 * l_out + 1))
begin_out += mul_out * (2 * l_out + 1)
begin_in = 0
for j, (mul_in, l_in, p_in) in enumerate(ctx.Rs_in):
s_in = slice(begin_in, begin_in + mul_in * (2 * l_in + 1))
begin_in += mul_in * (2 * l_in + 1)
l_filters = ctx.selection_rule(l_in, p_in, l_out, p_out)
if not l_filters:
continue
n = mul_out * mul_in * len(l_filters)
if (grad_Y is not None) or (grad_F is not None):
sub_R = R[:, :, :, begin_R:begin_R + n].reshape(
batch, a, b, mul_out, mul_in,
-1) # [batch, a, b, mul_out, mul_in, l_filter]
if grad_R is not None:
sub_grad_R = grad_R[:, :, :, begin_R:begin_R + n].clone(
).reshape(batch, a, b, mul_out, mul_in,
-1) # [batch, a, b, mul_out, mul_in, l_filter]
if grad_F is not None:
sub_grad_F = grad_F[:, :, s_in].clone().reshape(
batch, b, mul_in,
2 * l_in + 1) # [batch, b, mul_in, 2 * l_in + 1]
if (grad_Y is not None) or (grad_R is not None):
sub_F = F[..., s_in].reshape(batch, b, mul_in,
2 * l_in + 1)
grad_K = grad_kernel[:, :,
s_out].reshape(batch, a, mul_out,
2 * l_out + 1)
for k, l_filter in enumerate(l_filters):
tmp = sum(2 * l + 1 for l in ctx.set_of_l_filters
if l < l_filter)
C = o3.wigner_3j(l_out,
l_in,
l_filter,
cached=True,
like=grad_kernel) # [m_out, m_in, m]
if (grad_F is not None) or (grad_R is not None):
sub_Y = Y[:, :, :, tmp:tmp + 2 * l_filter +
1] # [batch, a, b, m]
if grad_F is not None:
sub_grad_F += norm_coef[i, j] * torch.einsum(
"zaui,ijk,zabk,zabuv->zbvj", grad_K, C, sub_Y,
sub_R[..., k]) # [batch, b, mul_in, 2 * l_in + 1
if grad_Y is not None:
grad_Y[..., tmp:tmp + 2 * l_filter +
1] += norm_coef[i, j] * torch.einsum(
"zaui,ijk,zabuv,zbvj->zabk", grad_K, C,
sub_R[..., k], sub_F) # [m, batch, a, b]
if grad_R is not None:
sub_grad_R[..., k] = norm_coef[i, j] * torch.einsum(
"zaui,ijk,zabk,zbvj->zabuv", grad_K, C, sub_Y,
sub_F) # [batch, a, b, mul_out, mul_in]
if grad_F is not None:
grad_F[:, :,
s_in] = sub_grad_F.reshape(batch, b,
mul_in * (2 * l_in + 1))
if grad_R is not None:
grad_R[..., begin_R:begin_R + n] += sub_grad_R.reshape(
batch, a, b, -1)
begin_R += n
return grad_F, grad_Y, grad_R, None, None, None, None, None
def __init__(self,
Rs_in1,
Rs_in2,
Rs_out,
selection_rule=o3.selection_rule,
normalization='component',
groups=1):
super().__init__()
self.Rs_in1 = rs.convention(Rs_in1)
self.Rs_in2 = rs.convention(Rs_in2)
self.Rs_out = rs.convention(Rs_out)
code = ""
index_w = 0
wigners = set()
count = [0 for _ in range(rs.dim(self.Rs_out))]
index_1 = 0
for mul_1, l_1, p_1 in self.Rs_in1:
dim_1 = mul_1 * (2 * l_1 + 1)
index_2 = 0
for mul_2, l_2, p_2 in self.Rs_in2:
dim_2 = mul_2 * (2 * l_2 + 1)
gmul_1s = [
mul_1 // groups + (g < mul_1 % groups)
for g in range(groups)
]
gmul_2s = [
mul_2 // groups + (g < mul_2 % groups)
for g in range(groups)
]
for g in range(groups):
if gmul_1s[g] * gmul_2s[g] == 0:
continue
code += f" s1 = x1[:, {index_1+sum(gmul_1s[:g])*(2*l_1+1)}:{index_1+sum(gmul_1s[:g+1])*(2*l_1+1)}].reshape(batch, {gmul_1s[g]}, {2 * l_1 + 1})\n"
code += f" s2 = x2[:, {index_2+sum(gmul_2s[:g])*(2*l_2+1)}:{index_2+sum(gmul_2s[:g+1])*(2*l_2+1)}].reshape(batch, {gmul_2s[g]}, {2 * l_2 + 1})\n"
code += f" ss = ein('zui,zvj->zuvij', s1, s2)\n"
index_out = 0
for mul_out, l_out, p_out in self.Rs_out:
dim_out = mul_out * (2 * l_out + 1)
if l_out in selection_rule(l_1, p_1, l_2,
p_2) and p_out == p_1 * p_2:
wigners.add((l_out, l_1, l_2))
gmul_outs = [
mul_out // groups + (g < mul_out % groups)
for g in range(groups)
]
dim_w = gmul_outs[g] * gmul_1s[g] * gmul_2s[g]
if gmul_outs[g] == 0:
continue
code += f" sw = w[:, {index_w}:{index_w+dim_w}].reshape(batch, {gmul_outs[g]}, {gmul_1s[g]}, {gmul_2s[g]})\n"
i = index_out + sum(
gmul_outs[:g]) * (2 * l_out + 1)
j = index_out + sum(
gmul_outs[:g + 1]) * (2 * l_out + 1)
code += f" out[:, {i}:{j}] += ein('zwuv,kij,zuvij->zwk', sw, C{l_out}_{l_1}_{l_2}, ss).reshape(batch, {gmul_outs[g]*(2*l_out+1)})\n"
code += "\n"
for k in range(i, j):
count[k] += gmul_1s[g] * gmul_2s[g]
index_w += dim_w
index_out += dim_out
index_2 += dim_2
index_1 += dim_1
ilast = 0
clast = count[0]
for i, c in enumerate(count):
if clast != c:
if clast > 1:
code += f" out[:, {ilast}:{i}].div_({clast ** 0.5})\n"
clast = c
ilast = i
if clast > 1:
code += f" out[:, {ilast}:].div_({clast ** 0.5})\n"
wigners = sorted(wigners)
self.wigners_names = [
f"C{l_out}_{l_1}_{l_2}" for l_out, l_1, l_2 in wigners
]
args = ", ".join(f"{arg}: torch.Tensor" for arg in self.wigners_names)
for arg, (l_out, l_1, l_2) in zip(self.wigners_names, wigners):
C = o3.wigner_3j(l_out, l_1, l_2)
if normalization == 'component':
C *= (2 * l_out + 1)**0.5
if normalization == 'norm':
C *= (2 * l_1 + 1)**0.5 * (2 * l_2 + 1)**0.5
self.register_buffer(arg, C)
x = _tensor_product_code
x = x.replace("DIM", f"{rs.dim(self.Rs_out)}")
x = x.replace("ARGS", args)
x = x.replace("CODE", code)
self.main = eval_code(x).main
self.nweight = index_w
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--l_in", type=int, required=True)
parser.add_argument("--l_out", type=int, required=True)
parser.add_argument("--n",
type=int,
default=30,
help="size of the SOFT grid")
parser.add_argument("--dpi", type=float, default=100)
parser.add_argument("--sep",
type=float,
default=0.5,
help="space between matrices")
args = parser.parse_args()
torch.set_default_dtype(torch.float64)
x, y, z, alpha, beta = spherical_surface(args.n)
out = []
for l in range(abs(args.l_out - args.l_in), args.l_out + args.l_in + 1):
C = o3.wigner_3j(args.l_out, args.l_in, l)
Y = rsh.spherical_harmonics(l, alpha, beta)
out.append(torch.einsum("ijk,k...->ij...", (C, Y)))
f = torch.stack(out)
nf, dim_out, dim_in, *_ = f.size()
f = 0.5 + 0.5 * f / f.abs().max()
fig = plt.figure(figsize=(nf * dim_in + (nf - 1) * args.sep, dim_out),
dpi=args.dpi)
for index in range(nf):
for i in range(dim_out):
for j in range(dim_in):
width = 1 / (nf * dim_in + (nf - 1) * args.sep)
height = 1 / dim_out
rect = [(index * (dim_in + args.sep) + j) * width,
(dim_out - i - 1) * height, width, height]
ax = fig.add_axes(rect, projection='3d')
fc = plt.get_cmap("bwr")(f[index, i, j].detach().cpu().numpy())
ax.plot_surface(x.numpy(),
y.numpy(),
z.numpy(),
rstride=1,
cstride=1,
facecolors=fc)
ax.set_axis_off()
a = 0.6
ax.set_xlim3d(-a, a)
ax.set_ylim3d(-a, a)
ax.set_zlim3d(-a, a)
ax.view_init(90, 0)
plt.savefig("kernels{}to{}.png".format(args.l_in, args.l_out),
transparent=True)
def kernel_conv_fn_forward(F, edge_index, Y, R, norm_coef, Rs_in, Rs_out, selection_rule, set_of_l_filters):
"""
:param F: tensor [b, l_in * mul_in * m_in]
:param Y: tensor [n_edges, l_filter * m_filter]
:param R: tensor [n_edges, l_out * l_in * mul_out * mul_in * l_filter]
:param norm_coef: tensor [l_out, l_in]
:return: tensor [a, l_out * mul_out * m_out, l_in * mul_in * m_in]
"""
n_edges = Y.shape[-2]
n_atoms = F.shape[-2]
n_out = rs.dim(Rs_out)
kernel_conv = Y.new_zeros(n_atoms, n_out)
# note: for the normalization we assume that the variance of R[i] is one
begin_R = 0
begin_out = 0
for i, (mul_out, l_out, p_out) in enumerate(Rs_out):
s_out = slice(begin_out, begin_out + mul_out * (2 * l_out + 1))
begin_out += mul_out * (2 * l_out + 1)
begin_in = 0
for j, (mul_in, l_in, p_in) in enumerate(Rs_in):
s_in = slice(begin_in, begin_in + mul_in * (2 * l_in + 1))
begin_in += mul_in * (2 * l_in + 1)
l_filters = selection_rule(l_in, p_in, l_out, p_out)
if not l_filters:
continue
# extract the subset of the `R` that corresponds to the couple (l_out, l_in)
n = mul_out * mul_in * len(l_filters)
sub_R = R[..., begin_R: begin_R + n].reshape(
n_edges, mul_out, mul_in, -1
) # [n_edges, mul_out, mul_in, l_filter]
begin_R += n
K = 0
for k, l_filter in enumerate(l_filters):
offset = sum(2 * l + 1 for l in set_of_l_filters if l < l_filter)
sub_Y = Y[..., offset: offset + 2 * l_filter + 1] # [n_edges, m]
C = o3.wigner_3j(l_out, l_in, l_filter, cached=True, like=kernel_conv) # [m_out, m_in, m]
# i - tensor product index for output
# j - tensor product index for feature (SUMMED)
# k - tensor product index for edge spherical harmonic Y (SUMMED)
# u - multiplicity output index
# v - multiplicity input index (SUMMED)
# a - atom ~ edge[0]
# b - atom ~ edge[1] (SUMMED SPARSELY)
EF = F[edge_index[1], s_in].reshape(n_edges, mul_in, -1) # [num_edges, mul_in, J]
D = norm_coef[i, j] * torch.einsum("ijk,ek,euv,evj->eui",
C, sub_Y, sub_R[..., k], EF) # [num_edges, mul_out, I]
K += scatter_add(D, edge_index[0], 0, n_atoms) # [n_atoms, mul_out, I]
if not isinstance(K, int):
kernel_conv[..., s_out] += K.reshape(n_atoms, -1)
return kernel_conv
