def onthefly_mat_treevec(
S: QGTOnTheFly, vec: Union[PyTree,
jnp.ndarray]) -> Union[PyTree, jnp.ndarray]:
"""
Perform the lazy mat-vec product, where vec is either a tree with the same structure as
params or a ravelled vector
"""
# if hasa ndim it's an array and not a pytree
if hasattr(vec, "ndim"):
if not vec.ndim == 1:
raise ValueError("Unsupported mat-vec for chunks of vectors")
# If the input is a vector
if not nkjax.tree_size(S._params) == vec.size:
raise ValueError(
"""Size mismatch between number of parameters ({nkjax.tree_size(S.params)})
and vector size {vec.size}.
""")
_, unravel = nkjax.tree_ravel(S._params)
vec = unravel(vec)
ravel_result = True
else:
ravel_result = False
check_valid_vector_type(S._params, vec)
vec = nkjax.tree_cast(vec, S._params)
res = S._mat_vec(vec, S.diag_shift)
if ravel_result:
res, _ = nkjax.tree_ravel(res)
return res
def _solve(self: QGTOnTheFlyT, solve_fun, y: PyTree, *, x0: Optional[PyTree],
**kwargs) -> PyTree:
y = nkjax.tree_cast(y, self.params)
# we could cache this...
if x0 is None:
x0 = jax.tree_map(jnp.zeros_like, y)
out, info = solve_fun(self, y, x0=x0)
return out, info
def OH_w(forward_fn, params, samples, w):
r"""
compute O^H w
(where ^H is the hermitian transpose)
"""
# O^H w = (w^H O)^H
# The transposition of the 1D arrays is omitted in the implementation:
# (w^H O)^H -> (w* O)*
# TODO The allreduce in O_vjp could be deferred until after the tree_cast
# where the amount of data to be transferred would potentially be smaller
res = tree_conj(O_vjp(forward_fn, params, samples, w.conjugate()))
return tree_cast(res, params)
def onthefly_mat_treevec(
S: QGTOnTheFly, vec: Union[PyTree,
jnp.ndarray]) -> Union[PyTree, jnp.ndarray]:
"""
Perform the lazy mat-vec product, where vec is either a tree with the same structure as
params or a ravelled vector
"""
# if hasa ndim it's an array and not a pytree
if hasattr(vec, "ndim"):
if not vec.ndim == 1:
raise ValueError("Unsupported mat-vec for batches of vectors")
# If the input is a vector
if not nkjax.tree_size(S.params) == vec.size:
raise ValueError(
"""Size mismatch between number of parameters ({nkjax.tree_size(S.params)})
and vector size {vec.size}.
""")
_, unravel = nkjax.tree_ravel(S.params)
vec = unravel(vec)
ravel_result = True
else:
ravel_result = False
vec = nkjax.tree_cast(vec, S.params)
def fun(W, σ):
return S.apply_fun({"params": W, **S.model_state}, σ)
mat_vec = partial(
mat_vec_onthefly,
forward_fn=fun,
params=S.params,
samples=S.samples,
diag_shift=S.diag_shift,
)
res = mat_vec(vec)
if ravel_result:
res, _ = nkjax.tree_ravel(res)
return res
def reassemble_complex(x, target, fun=tree_toreal_flat):
# target: a tree with the expected shape and types of the result
(res,) = jax.linear_transpose(fun, target)(x)
res = qgt_onthefly_logic.tree_conj(res)
# fix the dtypes:
return nkjax.tree_cast(res, target)
def _mat_vec(v: PyTree, oks: PyTree) -> PyTree:
"""
Compute ⟨O† O⟩v = ∑ₗ ⟨Oₖᴴ Oₗ⟩ vₗ
"""
res = tree_conj(_vjp(oks, _jvp(oks, v).conjugate()))
return tree_cast(res, v)
