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 to_dense(self) -> jnp.ndarray:
"""
Convert the lazy matrix representation to a dense matrix representation.s
Returns:
A dense matrix representation of this S matrix.
"""
Npars = nkjax.tree_size(self.params)
I = jax.numpy.eye(Npars)
return jax.vmap(lambda S, x: self @ x, in_axes=(None, 0))(self, I)
def _to_dense(self: QGTOnTheFlyT) -> jnp.ndarray:
"""
Convert the lazy matrix representation to a dense matrix representation.s
Returns:
A dense matrix representation of this S matrix.
"""
Npars = nkjax.tree_size(self.params)
I = jax.numpy.eye(Npars)
out = jax.vmap(lambda x: self @ x, in_axes=0)(I)
if nkjax.is_complex(out):
out = out.T
return out
def lazysmatrix_mat_treevec(
S: LazySMatrix, 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
"""
def fun(W, σ):
return S.apply_fun({"params": W, **S.model_state}, σ)
# 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
samples = S.samples
if jnp.ndim(samples) != 2:
samples = samples.reshape((-1, samples.shape[-1]))
vec = tree_cast(vec, S.params)
mat_vec = partial(
mat_vec_onthefly,
forward_fn=fun,
params=S.params,
samples=samples,
diag_shift=S.sr.diag_shift,
centered=S.sr.centered,
)
res = mat_vec(vec)
if ravel_result:
res, _ = nkjax.tree_ravel(res)
return res
def _to_dense(self: QGTOnTheFlyT) -> jnp.ndarray:
"""
Convert the lazy matrix representation to a dense matrix representation
Returns:
A dense matrix representation of this S matrix.
"""
Npars = nkjax.tree_size(self._params)
I = jax.numpy.eye(Npars)
if self._chunking:
# the linear_call in mat_vec_chunked does currently not have a jax batching rule,
# so it cannot be vmapped but we can use scan
# which is better for reducing the memory consumption anyway
_, out = jax.lax.scan(lambda _, x: (None, self @ x), None, I)
else:
out = jax.vmap(lambda x: self @ x, in_axes=0)(I)
if nkjax.is_complex(out):
out = out.T
return out
def n_parameters(self) -> int:
r"""The total number of parameters in the model."""
return nkjax.tree_size(self.parameters)