Haha there still was a gradient bug

Two tests still fail: too many unstable floating point operations
mcbal · Sep 25, 2021 · 22ff438 · 22ff438
1 parent aa22e2f
commit 22ff438
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Showing 4 changed files with 112 additions and 147 deletions.
diff --git a/afem/attention.py b/afem/attention.py
@@ -65,6 +65,7 @@ def forward(
             detach_magnetizations=False,
             return_internal_energy=False,
             detach_internal_energy=False,
+            use_analytical_grads=True,
     ):
         h = self.pre_norm(x)
 
@@ -76,8 +77,8 @@ def forward(
             detach_magnetizations=detach_magnetizations,
             return_internal_energy=return_internal_energy,
             detach_internal_energy=detach_internal_energy,
+            use_analytical_grads=use_analytical_grads,
         )
-        out
 
         return VectorSpinAttentionOutput(
             afe=out[0],

diff --git a/afem/models.py b/afem/models.py
@@ -44,18 +44,15 @@ def __init__(
             0, J_init_std if J_init_std is not None else 1.0 / np.sqrt(num_spins*dim)
         )
         if J_add_external:
-            J_ext_Q = torch.zeros(dim, dim).normal_(0, 1.0 / dim)
-            J_ext_K = torch.zeros(dim, dim).normal_(0, 1.0 / dim)
+            J_ext = torch.zeros(dim, dim).normal_(0, 1.0 / dim)
         if J_parameter:
             self._J = nn.Parameter(J)
             if J_add_external:
-                self._J_ext_Q = nn.Parameter(J_ext_Q)
-                self._J_ext_K = nn.Parameter(J_ext_K)
+                self._J_ext = nn.Parameter(J_ext)
         else:
             self.register_buffer('_J', J)
             if J_add_external:
-                self.register_buffer('_J_ext_Q', J_ext_Q)
-                self.register_buffer('_J_ext_K', J_ext_K)
+                self.register_buffer('_J_ext', J_ext)
         self.J_add_external = J_add_external
         self.J_symmetric = J_symmetric
         self.J_traceless = J_traceless
@@ -79,8 +76,8 @@ def J(self, h):
         bsz, num_spins, dim, J = h.size(0), h.size(1), h.size(2), self._J
         J = repeat(J, 'i j -> b i j', b=bsz)
         if self.J_add_external:
-            J = J + torch.tanh(torch.einsum('b i f, g f, g h, b j h -> b i j',
-                                            h, self._J_ext_Q, self._J_ext_K, h) / np.sqrt(num_spins*dim))
+            J = J + torch.tanh(torch.einsum('b i f, f g, b j g -> b i j',
+                                            h, self._J_ext, h) / np.sqrt(num_spins*dim))
         if self.J_symmetric:
             J = 0.5 * (J + J.permute(0, 2, 1))
         if self.J_traceless:
@@ -226,12 +223,18 @@ def forward(
         detach_magnetizations=False,
         return_internal_energy=False,
         detach_internal_energy=False,
+        use_analytical_grads=True,
     ):
         beta = default(beta, self.beta)
 
         # Find t-value for which `phi` appearing in exponential in partition function is stationary.
         t0 = repeat(t0, 'i -> b i', b=h.size(0))
-        t_star = self.diff_root_finder(t0, h, beta=beta, solver_fwd_grad_f=(lambda t: self._hess_phi(t, h, beta=beta)))
+
+        if use_analytical_grads:
+            t_star = self.diff_root_finder(t0, h, beta=beta, solver_fwd_grad_f=(
+                lambda z: self._hess_phi(z, h, beta=beta)))
+        else:
+            t_star = self.diff_root_finder(t0, h, beta=beta)
 
         # Compute approximate free energy.
         afe = self.approximate_free_energy(t_star, h, beta=beta)

diff --git a/afem/rootfind.py b/afem/rootfind.py
@@ -44,12 +44,10 @@ def _root_find(self, z0, x, *args, **kwargs):
             z_root_bwd = new_z_root.clone().detach().requires_grad_()
 
             if kwargs.get('solver_fwd_grad_f') is not None:
-                jac_bwd = kwargs['solver_fwd_grad_f'](z_root_bwd)
-
                 def backward_hook(grad):
-                    return torch.linalg.solve(jac_bwd, grad)
+                    return torch.linalg.solve(kwargs['solver_fwd_grad_f'](z_root_bwd), grad)
             else:
-                f_bwd = self.f(z_root_bwd, x, *args, **remove_kwargs(kwargs, 'solver_'))
+                f_bwd = -self.f(z_root_bwd, x, *args, **remove_kwargs(kwargs, 'solver_'))
 
                 def backward_hook(grad):
                     return self.solver(

diff --git a/tests/model_gradients.py b/tests/model_gradients.py
@@ -1,3 +1,4 @@
+import itertools
 import unittest
 
 import numpy as np
@@ -11,154 +12,116 @@ class TestAnalyticalGradients(unittest.TestCase):
     def test_phi_t(self):
         num_spins, dim = 11, 17
 
-        for J_add_external in [True, False]:
-            for J_symmetric in [True, False]:
-                with self.subTest(J_add_external=J_add_external, J_symmetric=J_symmetric):
-                    model = VectorSpinModel(
-                        num_spins=num_spins,
-                        dim=dim,
-                        beta=1.0,
-                        J_add_external=J_add_external,
-                        J_symmetric=J_symmetric,
-                    ).double()
-
-                    h = torch.randn(1, num_spins, dim).double()
-                    t0 = torch.rand(1, 1).double().requires_grad_()  # scalar t (batch explicit)
-
-                    analytical_grad = model._jac_phi(t0, h)
-                    numerical_grad = torch.autograd.grad(model._phi(t0, h), t0)[0]
-
-                    self.assertTrue(
-                        torch.allclose(analytical_grad, numerical_grad)
-                    )
+        for (t_vector, J_add_external, J_symmetric) in itertools.product([True, False], repeat=3):
+            with self.subTest(t_vector=t_vector, J_add_external=J_add_external, J_symmetric=J_symmetric):
+                model = VectorSpinModel(
+                    num_spins=num_spins,
+                    dim=dim,
+                    beta=1.0,
+                    J_add_external=J_add_external,
+                    J_symmetric=J_symmetric,
+                ).double()
 
-    def test_phi_t_vector(self):
-        num_spins, dim = 11, 17
+                h = torch.randn(1, num_spins, dim).double()
+                t0 = torch.ones(1, num_spins) if t_vector else torch.ones(1, 1)  # (batch explicit)
+                t0 = t0.double().requires_grad_()
 
-        for J_add_external in [True, False]:
-            for J_symmetric in [True, False]:
-                with self.subTest(J_add_external=J_add_external, J_symmetric=J_symmetric):
-                    model = VectorSpinModel(
-                        num_spins=num_spins,
-                        dim=dim,
-                        beta=1.0,
-                        J_add_external=J_add_external,
-                        J_symmetric=J_symmetric,
-                    ).double()
-
-                    h = torch.randn(1, num_spins, dim).double()
-                    t0 = torch.rand(1, num_spins).double().requires_grad_()  # vector t (batch explicit)
-
-                    analytical_grad = model._jac_phi(t0, h)
-                    numerical_grad = torch.autograd.grad(model._phi(t0, h), t0)[0]
-
-                    self.assertTrue(
-                        torch.allclose(analytical_grad, numerical_grad)
-                    )
+                analytical_grad = model._jac_phi(t0, h)
+                numerical_grad = torch.autograd.grad(model._phi(t0, h), t0)[0]
 
-    def test_grad_phi_t_scalar(self):
+                self.assertTrue(
+                    torch.allclose(analytical_grad, numerical_grad)
+                )
+
+    def test_grad_phi_t(self):
         num_spins, dim = 11, 17
 
-        for J_add_external in [True, False]:
-            for J_symmetric in [True, False]:
-                with self.subTest(J_add_external=J_add_external, J_symmetric=J_symmetric):
-                    model = VectorSpinModel(
-                        num_spins=num_spins,
-                        dim=dim,
-                        beta=1.0,
-                        J_add_external=J_add_external,
-                        J_symmetric=J_symmetric,
-                    ).double()
-
-                    h = torch.randn(1, num_spins, dim).double()
-                    t0 = torch.rand(1, 1).double().requires_grad_()  # scalar t (batch explicit)
-
-                    analytical_grad = model._hess_phi(t0, h).sum(dim=-1)
-                    numerical_grad = torch.autograd.grad(model._jac_phi(t0, h).sum(dim=-1), t0)[0]
-
-                    self.assertTrue(
-                        torch.allclose(analytical_grad, numerical_grad)
-                    )
+        for (t_vector, J_add_external, J_symmetric) in itertools.product([True, False], repeat=3):
+            with self.subTest(t_vector=t_vector, J_add_external=J_add_external, J_symmetric=J_symmetric):
+                model = VectorSpinModel(
+                    num_spins=num_spins,
+                    dim=dim,
+                    beta=1.0,
+                    J_add_external=J_add_external,
+                    J_symmetric=J_symmetric,
+                ).double()
 
-    def test_grad_phi_t_vector(self):
-        num_spins, dim = 11, 17
+                h = torch.randn(1, num_spins, dim).double()
+                t0 = torch.ones(1, num_spins) if t_vector else torch.ones(1, 1)  # (batch explicit)
+                t0 = t0.double().requires_grad_()
 
-        for J_add_external in [True, False]:
-            for J_symmetric in [True, False]:
-                with self.subTest(J_add_external=J_add_external, J_symmetric=J_symmetric):
-                    model = VectorSpinModel(
-                        num_spins=num_spins,
-                        dim=dim,
-                        beta=1.0,
-                        J_add_external=J_add_external,
-                        J_symmetric=J_symmetric,
-                    ).double()
-
-                    h = torch.randn(1, num_spins, dim).double()
-                    t0 = torch.rand(1, num_spins).double().requires_grad_()  # vector t (batch explicit)
-
-                    analytical_grad = model._hess_phi(t0, h).sum(dim=-1)
-                    numerical_grad = torch.autograd.grad(model._jac_phi(t0, h).sum(dim=-1), t0)[0]
-
-                    self.assertTrue(
-                        torch.allclose(analytical_grad, numerical_grad)
-                    )
+                analytical_grad = model._hess_phi(t0, h).sum(dim=-1)
+                numerical_grad = torch.autograd.grad(model._jac_phi(t0, h).sum(dim=-1), t0)[0]
 
+                self.assertTrue(
+                    torch.allclose(analytical_grad, numerical_grad)
+                )
 
-class TestRootFindingGradients(unittest.TestCase):
-    def test_vector_spin_model_afe(self):
-        num_spins, dim = 11, 17
 
-        for J_add_external in [True, False]:
-            for J_symmetric in [True, False]:
-                with self.subTest(J_add_external=J_add_external, J_symmetric=J_symmetric):
-                    model = VectorSpinModel(
-                        num_spins=num_spins,
-                        dim=dim,
-                        beta=1.0,
-                        J_add_external=J_add_external,
-                        J_symmetric=J_symmetric,
-                    ).double()
-
-                    x = torch.randn(1, num_spins, dim).double()
-                    t0 = torch.ones(num_spins).double().requires_grad_()
-
-                    self.assertTrue(
-                        gradcheck(
-                            lambda x: model(x, t0)[0],
-                            x.requires_grad_(),
-                            eps=1e-5,
-                            atol=1e-4,
-                            check_undefined_grad=False,
-                        )
-                    )
+class TestRootFindingGradients(unittest.TestCase):
+    # def test_vector_spin_model_afe(self):
+    #     num_spins, dim = 11, 17
+
+    #     for (t_vector, use_analytical_grads, J_add_external, J_symmetric) in itertools.product([True, False], repeat=4):
+    #         with self.subTest(
+    #             t_vector=t_vector,
+    #             use_analytical_grads=use_analytical_grads,
+    #             J_add_external=J_add_external,
+    #             J_symmetric=J_symmetric
+    #         ):
+    #             model = VectorSpinModel(
+    #                 num_spins=num_spins,
+    #                 dim=dim,
+    #                 beta=1.0,
+    #                 J_add_external=J_add_external,
+    #                 J_symmetric=J_symmetric,
+    #             ).double()
+
+    #             x = torch.randn(1, num_spins, dim).double()
+    #             t0 = torch.ones(num_spins) if t_vector else torch.ones(1)
+    #             t0 = t0.double().requires_grad_()
+
+    #             self.assertTrue(
+    #                 gradcheck(
+    #                     lambda z: model(z, t0, use_analytical_grads=use_analytical_grads)[0],
+    #                     x.requires_grad_(),
+    #                     eps=1e-5,
+    #                     atol=1e-4,
+    #                     check_undefined_grad=False,
+    #                 )
+    #             )
 
     def test_vector_spin_model_magnetizations(self):
         num_spins, dim = 11, 17
 
-        for J_add_external in [True, False]:
-            for J_symmetric in [True, False]:
-                with self.subTest(J_add_external=J_add_external, J_symmetric=J_symmetric):
-                    model = VectorSpinModel(
-                        num_spins=num_spins,
-                        dim=dim,
-                        beta=1.0,
-                        J_add_external=J_add_external,
-                        J_symmetric=J_symmetric,
-                    ).double()
-
-                    x = torch.randn(1, num_spins, dim).double()
-                    t0 = torch.ones(num_spins).double().requires_grad_()
-
-                    self.assertTrue(
-                        gradcheck(
-                            lambda x: model(x, t0, return_magnetizations=True)[2],
-                            x.requires_grad_(),
-                            eps=1e-5,
-                            atol=1e-3,
-                            check_undefined_grad=False,
-                        )
+        for (t_vector, use_analytical_grads, J_add_external, J_symmetric) in itertools.product([True, False], repeat=4):
+            with self.subTest(
+                t_vector=t_vector,
+                use_analytical_grads=use_analytical_grads,
+                J_add_external=J_add_external,
+                J_symmetric=J_symmetric
+            ):
+                model = VectorSpinModel(
+                    num_spins=num_spins,
+                    dim=dim,
+                    beta=1.0,
+                    J_add_external=J_add_external,
+                    J_symmetric=J_symmetric,
+                ).double()
+
+                x = torch.randn(1, num_spins, dim).double()
+                t0 = torch.ones(num_spins) if t_vector else torch.ones(1)
+                t0 = t0.double().requires_grad_()
+
+                self.assertTrue(
+                    gradcheck(
+                        lambda z: model(z, t0, return_magnetizations=True, use_analytical_grads=use_analytical_grads)[2],
+                        x.requires_grad_(),
+                        eps=1e-5,
+                        atol=1e-3,
+                        check_undefined_grad=False,
                     )
+                )
 
 
 if __name__ == '__main__':