Update to solve jax deprecations

Benchmarks/qgt.py

@@ -57,7 +57,7 @@ def QGT(*args, **kwargs):
 
 def construct_and_solve(vstate, rhs):
     qgt_ = QGT(vstate=vstate, diag_shift=0.01)
-    return jax.tree_map(lambda x: x.block_until_ready(), qgt_.solve(cg, rhs))
+    return jax.tree.map(lambda x: x.block_until_ready(), qgt_.solve(cg, rhs))
 
 
 # Benchmark starts here
@@ -111,7 +111,7 @@ def _benchmark(n_nodes, n_samples, n_layers, width):
     qgt_ = QGT(vstate=vstate, diag_shift=0.01)
     Tsolve = (
         timeit_gc(
-            lambda: jax.tree_map(lambda x: x.block_until_ready(), qgt_.solve(cg, rhs2)),
+            lambda: jax.tree.map(lambda x: x.block_until_ready(), qgt_.solve(cg, rhs2)),
             number=5,
         )
         / 5

Benchmarks/qgt_gcnn.py

@@ -23,7 +23,7 @@ def QGT(*args, **kwargs):
 
 def construct_and_solve(vstate, rhs):
     qgt_ = QGT(vstate=vstate, diag_shift=0.01)
-    return jax.tree_map(lambda x: x.block_until_ready(), qgt_.solve(cg, rhs))
+    return jax.tree.map(lambda x: x.block_until_ready(), qgt_.solve(cg, rhs))
 
 
 # Benchmark starts here
@@ -82,7 +82,7 @@ def benchmark(side, n_samples, layers, features):
 
     Tsolve = (
         timeit_gc(
-            lambda: jax.tree_map(lambda x: x.block_until_ready(), qgt_.solve(cg, rhs2)),
+            lambda: jax.tree.map(lambda x: x.block_until_ready(), qgt_.solve(cg, rhs2)),
             number=5,
         )
         / 5

docs/advanced/custom_operators.ipynb

@@ -293,7 +293,7 @@
     "    # this is the expectation value, as before\n",
     "    expval = np.array(0, dtype=op.dtype)\n",
     "    # this is the gradient, which of course is zero.\n",
-    "    grad = jax.tree_map(jnp.zeros_like, vstate.parameters)\n",
+    "    grad = jax.tree.map(jnp.zeros_like, vstate.parameters)\n",
     "    return expval, grad"
    ]
   },

docs/docs/hilbert.md

@@ -260,7 +260,7 @@ If you then want to sample this space, you'll encounter the following error:
 
 ```python
 >>> import jax
->>> hi.random_state(jax.random.PRNGKey(3), 3)
+>>> hi.random_state(jax.random.key(3), 3)
 Traceback (most recent call last):
   File "<stdin>", line 1, in <module>
   File ".../netket/hilbert/abstract_hilbert.py", line 84, in random_state

docs/docs/sampler.ipynb

@@ -187,7 +187,7 @@
     "log_pdf = nk.models.RBM(param_dtype=float)\n",
     "\n",
     "# and we initialize it's parameters\n",
-    "param_seed = jax.random.PRNGKey(0)\n",
+    "param_seed = jax.random.key(0)\n",
     "\n",
     "pars = log_pdf.init(param_seed, hilbert.random_state(param_seed, 3))"
    ]
@@ -268,7 +268,7 @@
     "sampler = nk.sampler.ExactSampler(hilbert, dtype=jnp.int8)\n",
     "\n",
     "# We create the state of the sampler\n",
-    "sampler_state = sampler.init_state(log_pdf, pars, jax.random.PRNGKey(1))\n",
+    "sampler_state = sampler.init_state(log_pdf, pars, jax.random.key(1))\n",
     "\n",
     "# We call reset (this will pre-compute the log_pdf on the whole hilbert space)\n",
     "sampler_state = sampler.reset(log_pdf, pars, sampler_state)\n",
@@ -415,7 +415,7 @@
    ],
    "source": [
     "# We create the state of the sampler\n",
-    "sampler_state = sampler.init_state(log_pdf, pars, jax.random.PRNGKey(1))\n",
+    "sampler_state = sampler.init_state(log_pdf, pars, jax.random.key(1))\n",
     "\n",
     "# We call reset (this will pre-compute the log_pdf on the whole hilbert space)\n",
     "sampler_state = sampler.reset(log_pdf, pars, sampler_state)\n",
@@ -520,7 +520,7 @@
    "outputs": [],
    "source": [
     "# We create the state of the sampler\n",
-    "sampler_state = sampler.init_state(log_pdf, pars, jax.random.PRNGKey(1))\n",
+    "sampler_state = sampler.init_state(log_pdf, pars, jax.random.key(1))\n",
     "\n",
     "# We call reset (this will pre-compute the log_pdf on the whole hilbert space)\n",
     "sampler_state = sampler.reset(log_pdf, pars, sampler_state)\n",

docs/docs/varstate.md

@@ -139,7 +139,7 @@ vstate.parameters['visible_bias']
               0.06278384,  0.00275547,  0.05843748,  0.07516951,
               0.21897993, -0.01632223], dtype=float64)
 
-vstate.parameters = jax.tree_map(lambda x: x+0.1, vstate.parameters)
+vstate.parameters = jax.tree.map(lambda x: x+0.1, vstate.parameters)
 
 # Look at the new values
 vstate.parameters['visible_bias']
@@ -219,7 +219,7 @@ the {py:attr}`~netket.vqs.VariationalState.model_state` attribute.
 
 Parameters are stored as a set of nested dictionaries.
 In Jax jargon, Parameters are a PyTree (see [PyTree documentation](https://jax.readthedocs.io/en/latest/pytrees.html)) and they
-can be operated upon with functions like [jax.tree_map](https://jax.readthedocs.io/en/latest/jax.tree_util.html?highlight=tree_map#jax.tree_util.tree_map).
+can be operated upon with functions like [jax.tree.map](https://jax.readthedocs.io/en/latest/_autosummary/jax.tree.map.html).
 
 Before modifying the parameters or what is stored inside of a dictionary of parameters, it is a good idea to make a copy using {func}`flax.core.copy`, which calls recursively `{}.copy()` in all nested dictionaries. 
 If you do not do this, you will only copy the outermost dictionary but the inner ones will be referenced, and so modifying them will modify the original parameters as well.

docs/tutorials/gs-continuous-space.ipynb

@@ -172,7 +172,7 @@
    ],
    "source": [
     "import jax\n",
-    "states = hi.random_state(jax.random.PRNGKey(0), 1)\n",
+    "states = hi.random_state(jax.random.key(0), 1)\n",
     "\n",
     "# You can always reshape those configurations to NxD matrix\n",
     "states.reshape(N, 3)"

docs/tutorials/gs-ising.ipynb

@@ -127,7 +127,7 @@
     "NetKet's Hilbert spaces define the computational basis of the calculation, and are used to label and generate elements from it. \n",
     "The standard Spin-basis implicitly selects the `z` basis and elements of that basis will be elements $ v\\in\\{\\pm 1\\}^N $.\n",
     "\n",
-    "It is possible to generate random basis elements through the function `random_state(rng, shape, dtype)`, where the first argument must be a jax RNG state (usually built with `jax.random.PRNGKey(seed)`, second is an integer or a tuple giving the shape of the samples and the last is the dtype of the generated states."
+    "It is possible to generate random basis elements through the function `random_state(rng, shape, dtype)`, where the first argument must be a jax RNG state (usually built with `jax.random.key(seed)`, second is an integer or a tuple giving the shape of the samples and the last is the dtype of the generated states."
    ]
   },
   {
@@ -154,7 +154,7 @@
    ],
    "source": [
     "import jax\n",
-    "hi.random_state(jax.random.PRNGKey(0), 3)"
+    "hi.random_state(jax.random.key(0), 3)"
    ]
   },
   {
@@ -592,7 +592,7 @@
     "    energy_history.append(E.mean.real)\n",
     "    # equivalent to vstate.parameters - 0.05*E_grad , but it performs this\n",
     "    # function on every leaf of the dictionaries containing the set of parameters\n",
-    "    new_pars = jax.tree_map(lambda x,y: x-0.05*y, vstate.parameters, E_grad)\n",
+    "    new_pars = jax.tree.map(lambda x,y: x-0.05*y, vstate.parameters, E_grad)\n",
     "    # actually update the parameters\n",
     "    vstate.parameters = new_pars"
    ]

docs/tutorials/vmc-from-scratch.ipynb

@@ -666,7 +666,7 @@
     "model = MF()\n",
     "\n",
     "# pick a RNG key to initialise the random parameters\n",
-    "key = jax.random.PRNGKey(0)\n",
+    "key = jax.random.key(0)\n",
     "\n",
     "# initialise the weights\n",
     "parameters = model.init(key, np.random.rand(hi.size))"
@@ -709,7 +709,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Conceptually, parameters are vectors, even though they are stored in memory differently. You can apply mathematical operations to those vectors using `jax.tree_map(function, trees...)`, which calls the function on every element of the pytrees."
+    "Conceptually, parameters are vectors, even though they are stored in memory differently. You can apply mathematical operations to those vectors using `jax.tree.map(function, trees...)`, which calls the function on every element of the pytrees."
    ]
   },
   {
@@ -738,19 +738,19 @@
     "def multiply_by_10(x):\n",
     "    return 10*x\n",
     "\n",
-    "print(\"multiply_by_10:             \", jax.tree_map(multiply_by_10,  dict1))\n",
+    "print(\"multiply_by_10:             \", jax.tree.map(multiply_by_10,  dict1))\n",
     "# this can also be done by defining the function as a lambda function, which is more compact\n",
-    "print(\"multiply_by_10, with lambda:\", jax.tree_map(lambda x: 10*x,  dict1))\n",
+    "print(\"multiply_by_10, with lambda:\", jax.tree.map(lambda x: 10*x,  dict1))\n",
     "\n",
     "def add(x,y):\n",
     "    return x+y\n",
-    "print(\"add dict1 and 2          :\", jax.tree_map(add, dict1, dict2))\n",
+    "print(\"add dict1 and 2          :\", jax.tree.map(add, dict1, dict2))\n",
     "\n",
     "\n",
     "def sub(x,y):\n",
     "    return x-y\n",
-    "print(\"subtract dict1 and 2        :\", jax.tree_map(sub, dict1, dict2))\n",
-    "print(\"subtract dict1 and 2, lambda:\", jax.tree_map(lambda x,y:x-y, dict1, dict2))\n"
+    "print(\"subtract dict1 and 2        :\", jax.tree.map(sub, dict1, dict2))\n",
+    "print(\"subtract dict1 and 2, lambda:\", jax.tree.map(lambda x,y:x-y, dict1, dict2))\n"
    ]
   },
   {
@@ -777,7 +777,7 @@
    ],
    "source": [
     "# generate 4 random inputs\n",
-    "inputs = hi.random_state(jax.random.PRNGKey(1), (4,))\n",
+    "inputs = hi.random_state(jax.random.key(1), (4,))\n",
     "\n",
     "log_psi = model.apply(parameters, inputs)\n",
     "# notice that logpsi has shape (4,) because we fed it 4 random configurations.\n",
@@ -1115,7 +1115,7 @@
     "\n",
     "# initialise \n",
     "model = MF()\n",
-    "parameters = model.init(jax.random.PRNGKey(0), np.ones((hi.size, )))\n",
+    "parameters = model.init(jax.random.key(0), np.ones((hi.size, )))\n",
     "\n",
     "# logging: you can (if you want) use netket loggers to avoid writing a lot of boilerplate...\n",
     "# they accumulate data you throw at them\n",
@@ -1127,7 +1127,7 @@
     "    \n",
     "    # update parameters. Try using a learning rate of 0.01\n",
     "    # to update the parameters, which are stored as a dictionary (or pytree)\n",
-    "    # you can use jax.tree_map as shown above.\n",
+    "    # you can use jax.tree.map as shown above.\n",
     "    #...\n",
     "    \n",
     "    # log energy: the logger takes a step argument and a dictionary of variables to be logged\n",
@@ -1144,7 +1144,7 @@
     "```python\n",
     "# initialise \n",
     "model = MF()\n",
-    "parameters = model.init(jax.random.PRNGKey(0), np.ones((hi.size, )))\n",
+    "parameters = model.init(jax.random.key(0), np.ones((hi.size, )))\n",
     "\n",
     "# logging: you can (if you want) use netket loggers to avoid writing a lot of boilerplate...\n",
     "# they accumulate data you throw at them\n",
@@ -1155,7 +1155,7 @@
     "    energy, gradient = compute_energy_and_gradient(model, parameters, hamiltonian_jax_sparse)\n",
     "    \n",
     "    # update parameters\n",
-    "    parameters = jax.tree_map(lambda x,y:x-0.01*y, parameters, gradient)\n",
+    "    parameters = jax.tree.map(lambda x,y:x-0.01*y, parameters, gradient)\n",
     "    \n",
     "    # log energy: the logger takes a step argument and a dictionary of variables to be logged\n",
     "    logger(step=i, item={'Energy':energy})\n",
@@ -1291,7 +1291,10 @@
     "            \"J\", nn.initializers.normal(), (n_sites,n_sites), float\n",
     "        )\n",
     "        # ensure same data types\n",
-    "        J, input_x = nn.dtypes.kernel, x_in = promote_dtype(J, input_x, dtype=None)\n",
+    "        dtype = jax.numpy.promote_types(J.dtype, input_x.dtype)\n",
+    "        J = J.astype(dtype)\n",
+    "        input_x = input_x.astype(dtype)\n",
+    "        \n",
     "        # note that J_ij is not symmetric. So we symmetrize it by hand\n",
     "        J_symm = J.T + J\n",
     "        \n",
@@ -1334,11 +1337,11 @@
     "# if the code above is correct, this should run\n",
     "model_jastrow = Jastrow()\n",
     "\n",
-    "one_sample = hi.random_state(jax.random.PRNGKey(0))\n",
-    "batch_samples = hi.random_state(jax.random.PRNGKey(0), (5,))\n",
-    "multibatch_samples = hi.random_state(jax.random.PRNGKey(0), (5,4,))\n",
+    "one_sample = hi.random_state(jax.random.key(0))\n",
+    "batch_samples = hi.random_state(jax.random.key(0), (5,))\n",
+    "multibatch_samples = hi.random_state(jax.random.key(0), (5,4,))\n",
     "\n",
-    "parameters_jastrow = model_jastrow.init(jax.random.PRNGKey(0), one_sample)\n",
+    "parameters_jastrow = model_jastrow.init(jax.random.key(0), one_sample)\n",
     "assert parameters_jastrow['params']['J'].shape == (hi.size, hi.size)\n",
     "assert model_jastrow.apply(parameters_jastrow, one_sample).shape == ()\n",
     "assert model_jastrow.apply(parameters_jastrow, batch_samples).shape == batch_samples.shape[:-1]\n",
@@ -1484,7 +1487,7 @@
    "outputs": [],
    "source": [
     "# given sigma\n",
-    "sigma = hi.random_state(jax.random.PRNGKey(1))\n",
+    "sigma = hi.random_state(jax.random.key(1))\n",
     "\n",
     "eta, H_sigmaeta = hamiltonian_jax.get_conn_padded(sigma)"
    ]
@@ -1542,7 +1545,7 @@
    ],
    "source": [
     "# given sigma\n",
-    "sigma = hi.random_state(jax.random.PRNGKey(1), (4,5))\n",
+    "sigma = hi.random_state(jax.random.key(1), (4,5))\n",
     "\n",
     "eta, H_sigmaeta = hamiltonian_jax.get_conn_padded(sigma)\n",
     "\n",
@@ -1844,16 +1847,16 @@
     "jacobian = jax.jacrev(logpsi_sigma_fun)(parameters_jastrow)\n",
     "\n",
     "#\n",
-    "print(\"The parameters of jastrow have shape:\\n\" , jax.tree_map(lambda x: x.shape, parameters_jastrow))\n",
+    "print(\"The parameters of jastrow have shape:\\n\" , jax.tree.map(lambda x: x.shape, parameters_jastrow))\n",
     "\n",
-    "print(\"The jacobian of jastrow have shape:\\n\" , jax.tree_map(lambda x: x.shape, jacobian))"
+    "print(\"The jacobian of jastrow have shape:\\n\" , jax.tree.map(lambda x: x.shape, jacobian))"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Now implement a function that computes the jacobian-vector product in order to estimate the gradient of the energy. You can either do this vector-Jacobian-transpose product manually by using `jax.jacrev` and `jax.tree_map`, but you can also have a look at `jax.vjp` which does it automatically for you."
+    "Now implement a function that computes the jacobian-vector product in order to estimate the gradient of the energy. You can either do this vector-Jacobian-transpose product manually by using `jax.jacrev` and `jax.tree.map`, but you can also have a look at `jax.vjp` which does it automatically for you."
    ]
   },
   {
@@ -1875,7 +1878,7 @@
     "    # first define the function to be differentiated\n",
     "    logpsi_sigma_fun = lambda pars : model_jastrow.apply(parameters_jastrow, sigma_vector)\n",
     "    ...\n",
-    "    # use jacrev with jax.tree_map, or even better, jax.vjp\n",
+    "    # use jacrev with jax.tree.map, or even better, jax.vjp\n",
     "    E_grad = ...\n",
     "    \n",
     "    # compute the energy as well\n",
@@ -1912,7 +1915,7 @@
     "    # first define the function to be differentiated\n",
     "    logpsi_sigma_fun = lambda pars : model.apply(pars, sigma)\n",
     "\n",
-    "    # use jacrev with jax.tree_map, or even better, jax.vjp\n",
+    "    # use jacrev with jax.tree.map, or even better, jax.vjp\n",
     "    _, vjpfun = jax.vjp(logpsi_sigma_fun, parameters)\n",
     "    E_grad = vjpfun((E_loc - E_average)/E_loc.size)\n",
     "\n",
@@ -1974,7 +1977,7 @@
     "chain_length = 1000//sampler.n_chains\n",
     "\n",
     "# initialise\n",
-    "parameters = model.init(jax.random.PRNGKey(0), np.ones((hi.size, )))\n",
+    "parameters = model.init(jax.random.key(0), np.ones((hi.size, )))\n",
     "sampler_state = sampler.init_state(model, parameters, seed=1)\n",
     "\n",
     "# logging: you can (if you want) use netket loggers to avoid writing a lot of boilerplate...\n",
@@ -2016,7 +2019,7 @@
     "chain_length = 1000//sampler.n_chains\n",
     "\n",
     "# initialise\n",
-    "parameters = model.init(jax.random.PRNGKey(0), np.ones((hi.size, )))\n",
+    "parameters = model.init(jax.random.key(0), np.ones((hi.size, )))\n",
     "sampler_state = sampler.init_state(model, parameters, seed=1)\n",
     "\n",
     "# logging: you can (if you want) use netket loggers to avoid writing a lot of boilerplate...\n",
@@ -2032,7 +2035,7 @@
     "    E, E_grad = estimate_energy_and_gradient(model, parameters, hamiltonian_jax, samples)\n",
     "    \n",
     "    # update parameters. Try using a learning rate of 0.01\n",
-    "    parameters = jax.tree_map(lambda x,y: x-0.005*y, parameters, E_grad)\n",
+    "    parameters = jax.tree.map(lambda x,y: x-0.005*y, parameters, E_grad)\n",
     "    \n",
     "    # log energy: the logger takes a step argument and a dictionary of variables to be logged\n",
     "    logger(step=i, item={'Energy':E})\n",

netket/driver/abstract_variational_driver.py

@@ -389,7 +389,7 @@ def estimate(self, observables):
 
         # Do not unpack operators, even if they are pytrees!
         # this is necessary to support jax operators.
-        return jax.tree_map(
+        return jax.tree_util.tree_map(
             self._estimate_stats,
             observables,
             is_leaf=lambda x: isinstance(x, AbstractObservable),

netket/errors.py

@@ -587,7 +587,7 @@ class RealQGTComplexDomainError(NetketError):
        >>> _, vec = vstate.expect_and_grad(nk.operator.spin.sigmax(vstate.hilbert, 1))
        >>> G = nk.optimizer.qgt.QGTOnTheFly(vstate, holomorphic=False)
        >>>
-       >>> vec_real = jax.tree_map(lambda x: x.real, vec)
+       >>> vec_real = jax.tree.map(lambda x: x.real, vec)
        >>> sol = G@vec_real
 
    Or, if you used the QGT in a linear solver, try using:
@@ -601,7 +601,7 @@ class RealQGTComplexDomainError(NetketError):
        >>> _, vec = vstate.expect_and_grad(nk.operator.spin.sigmax(vstate.hilbert, 1))
        >>>
        >>> G = nk.optimizer.qgt.QGTOnTheFly(vstate, holomorphic=False)
-       >>> vec_real = jax.tree_map(lambda x: x.real, vec)
+       >>> vec_real = jax.tree.map(lambda x: x.real, vec)
        >>>
        >>> linear_solver = jax.scipy.sparse.linalg.cg
        >>> solution, info = G.solve(linear_solver, vec_real)
@@ -624,14 +624,14 @@ def __init__(self):
 
             .. code:: python
 
-               >>> vec_real = jax.tree_map(lambda x: x.real, vec)
+               >>> vec_real = jax.tree_util.tree_map(lambda x: x.real, vec)
                >>> G@vec_real
 
             If you used the QGT in a linear solver, try using:
 
             .. code:: python
 
-               >>> vec_real = jax.tree_map(lambda x: x.real, vec)
+               >>> vec_real = jax.tree_util.tree_map(lambda x: x.real, vec)
                >>> G.solve(linear_solver, vec_real)
 
             to fix this error.

netket/experimental/driver/tdvp.py

@@ -162,7 +162,7 @@ def odefun_tdvp(  # noqa: F811
 
 @partial(jax.jit, static_argnums=(3, 4))
 def _map_parameters(forces, parameters, loss_grad_factor, propagation_type, state_T):
-    forces = jax.tree_map(
+    forces = jax.tree_util.tree_map(
         lambda x, target: loss_grad_factor * x,
         forces,
         parameters,