test(autogram): Revamp engine tests

tests/speed/autogram/grad_vs_jac_vs_gram.py

@@ -1,3 +1,4 @@
+import gc
 import time
 
 import torch
@@ -15,6 +16,7 @@
 )
 from utils.forward_backwards import (
     autograd_forward_backward,
+    autograd_gramian_forward_backward,
     autogram_forward_backward,
     autojac_forward_backward,
     make_mse_loss_fn,
@@ -31,8 +33,8 @@
     (AlexNet, 8),
     (InstanceNormResNet18, 16),
     (GroupNormMobileNetV3Small, 16),
-    (SqueezeNet, 16),
-    (InstanceNormMobileNetV2, 8),
+    (SqueezeNet, 4),
+    (InstanceNormMobileNetV2, 2),
 ]
 
 
@@ -58,20 +60,31 @@ def fn_autograd():
 
     def init_fn_autograd():
         torch.cuda.empty_cache()
+        gc.collect()
         fn_autograd()
 
+    def fn_autograd_gramian():
+        autograd_gramian_forward_backward(model, inputs, list(model.parameters()), loss_fn, W)
+
+    def init_fn_autograd_gramian():
+        torch.cuda.empty_cache()
+        gc.collect()
+        fn_autograd_gramian()
+
     def fn_autojac():
         autojac_forward_backward(model, inputs, loss_fn, A)
 
     def init_fn_autojac():
         torch.cuda.empty_cache()
+        gc.collect()
         fn_autojac()
 
     def fn_autogram():
         autogram_forward_backward(model, engine, W, inputs, loss_fn)
 
     def init_fn_autogram():
         torch.cuda.empty_cache()
+        gc.collect()
         fn_autogram()
 
     def optionally_cuda_sync():
@@ -91,6 +104,16 @@ def post_fn():
     print(autograd_times)
     print()
 
+    autograd_gramian_times = torch.tensor(
+        time_call(fn_autograd_gramian, init_fn_autograd_gramian, pre_fn, post_fn, n_runs)
+    )
+    print(
+        f"autograd gramian times (avg = {autograd_gramian_times.mean():.5f}, std = "
+        f"{autograd_gramian_times.std():.5f}"
+    )
+    print(autograd_gramian_times)
+    print()
+
     autojac_times = torch.tensor(time_call(fn_autojac, init_fn_autojac, pre_fn, post_fn, n_runs))
     print(f"autojac times (avg = {autojac_times.mean():.5f}, std = {autojac_times.std():.5f}")
     print(autojac_times)

tests/unit/autogram/test_engine.py

@@ -5,6 +5,7 @@
 from pytest import mark, param
 from torch import nn
 from torch.optim import SGD
+from torch.testing import assert_close
 from unit.conftest import DEVICE
 from utils.architectures import (
     AlexNet,
@@ -52,15 +53,13 @@
 from utils.forward_backwards import (
     autograd_forward_backward,
     autogram_forward_backward,
-    autojac_forward_backward,
+    compute_gramian_with_autograd,
     make_mse_loss_fn,
 )
 from utils.tensors import make_tensors
 
-from torchjd.aggregation import UPGrad, UPGradWeighting
+from torchjd.aggregation import UPGradWeighting
 from torchjd.autogram._engine import Engine
-from torchjd.autojac._transform import Diagonalize, Init, Jac, OrderedSet
-from torchjd.autojac._transform._aggregate import _Matrixify
 
 PARAMETRIZATIONS = [
     (OverlyNested, 32),
@@ -107,110 +106,34 @@
 
 
 @mark.parametrize(["architecture", "batch_size"], PARAMETRIZATIONS)
-def test_equivalence_autojac_autogram(
-    architecture: type[ShapedModule],
-    batch_size: int,
-):
-    """
-    Tests that the autogram engine gives the same results as the autojac engine on IWRM for several
-    JD steps.
-    """
-
-    n_iter = 3
+def test_compute_gramian(architecture: type[ShapedModule], batch_size: int):
+    """Tests that the autograd and the autogram engines compute the same gramian."""
 
     input_shapes = architecture.INPUT_SHAPES
     output_shapes = architecture.OUTPUT_SHAPES
 
-    weighting = UPGradWeighting()
-    aggregator = UPGrad()
-
     torch.manual_seed(0)
-    model_autojac = architecture().to(device=DEVICE)
+    model_autograd = architecture().to(device=DEVICE)
     torch.manual_seed(0)
     model_autogram = architecture().to(device=DEVICE)
 
     engine = Engine(model_autogram.modules())
-    optimizer_autojac = SGD(model_autojac.parameters(), lr=1e-7)
-    optimizer_autogram = SGD(model_autogram.parameters(), lr=1e-7)
-
-    for i in range(n_iter):
-        inputs = make_tensors(batch_size, input_shapes)
-        targets = make_tensors(batch_size, output_shapes)
-        loss_fn = make_mse_loss_fn(targets)
-
-        torch.random.manual_seed(0)  # Fix randomness for random aggregators and random models
-        autojac_forward_backward(model_autojac, inputs, loss_fn, aggregator)
-        expected_grads = {
-            name: p.grad for name, p in model_autojac.named_parameters() if p.grad is not None
-        }
-
-        torch.random.manual_seed(0)  # Fix randomness for random weightings and random models
-        autogram_forward_backward(model_autogram, engine, weighting, inputs, loss_fn)
-        grads = {
-            name: p.grad for name, p in model_autogram.named_parameters() if p.grad is not None
-        }
-
-        assert_tensor_dicts_are_close(grads, expected_grads)
-
-        optimizer_autojac.step()
-        model_autojac.zero_grad()
-
-        optimizer_autogram.step()
-        model_autogram.zero_grad()
-
-
-@mark.parametrize(["architecture", "batch_size"], PARAMETRIZATIONS)
-def test_autograd_while_modules_are_hooked(architecture: type[ShapedModule], batch_size: int):
-    """
-    Tests that the hooks added when constructing the engine do not interfere with a simple autograd
-    call.
-    """
-
-    input_shapes = architecture.INPUT_SHAPES
-    output_shapes = architecture.OUTPUT_SHAPES
 
-    W = UPGradWeighting()
-    A = UPGrad()
-    input = make_tensors(batch_size, input_shapes)
+    inputs = make_tensors(batch_size, input_shapes)
     targets = make_tensors(batch_size, output_shapes)
     loss_fn = make_mse_loss_fn(targets)
 
-    torch.manual_seed(0)
-    model = architecture().to(device=DEVICE)
-
-    torch.manual_seed(0)  # Fix randomness for random models
-    autojac_forward_backward(model, input, loss_fn, A)
-    autojac_grads = {
-        name: p.grad.clone() for name, p in model.named_parameters() if p.grad is not None
-    }
-    model.zero_grad()
-
-    torch.manual_seed(0)  # Fix randomness for random models
-    autograd_forward_backward(model, input, loss_fn)
-    autograd_grads = {
-        name: p.grad.clone() for name, p in model.named_parameters() if p.grad is not None
-    }
-
-    torch.manual_seed(0)
-    model_autogram = architecture().to(device=DEVICE)
+    torch.random.manual_seed(0)  # Fix randomness for random models
+    output = model_autograd(inputs)
+    losses = loss_fn(output)
+    autograd_gramian = compute_gramian_with_autograd(losses, list(model_autograd.parameters()))
 
-    # Hook modules and verify that we're equivalent to autojac when using the engine
-    engine = Engine(model_autogram.modules())
-    torch.manual_seed(0)  # Fix randomness for random models
-    autogram_forward_backward(model_autogram, engine, W, input, loss_fn)
-    grads = {name: p.grad for name, p in model_autogram.named_parameters() if p.grad is not None}
-    assert_tensor_dicts_are_close(grads, autojac_grads)
-    model_autogram.zero_grad()
+    torch.random.manual_seed(0)  # Fix randomness for random models
+    output = model_autogram(inputs)
+    losses = loss_fn(output)
+    autogram_gramian = engine.compute_gramian(losses)
 
-    # Verify that even with the hooked modules, autograd works normally when not using the engine.
-    # Results should be the same as a normal call to autograd, and no time should be spent computing
-    # the gramian at all.
-    torch.manual_seed(0)  # Fix randomness for random models
-    autograd_forward_backward(model_autogram, input, loss_fn)
-    assert engine._gramian_accumulator.gramian is None
-    grads = {name: p.grad for name, p in model_autogram.named_parameters() if p.grad is not None}
-    assert_tensor_dicts_are_close(grads, autograd_grads)
-    model_autogram.zero_grad()
+    assert_close(autogram_gramian, autograd_gramian, rtol=1e-4, atol=1e-5)
 
 
 def _non_empty_subsets(elements: set) -> list[set]:
@@ -221,20 +144,15 @@ def _non_empty_subsets(elements: set) -> list[set]:
 
 
 @mark.parametrize("gramian_module_names", _non_empty_subsets({"fc0", "fc1", "fc2", "fc3", "fc4"}))
-def test_partial_autogram(gramian_module_names: set[str]):
+def test_compute_partial_gramian(gramian_module_names: set[str]):
     """
-    Tests that partial JD via the autogram engine works similarly as if the gramian was computed via
-    the autojac engine.
-
-    Note that this test is a bit redundant now that we have the Engine interface, because it now
-    just compares two ways of computing the Gramian, which is independant of the idea of partial JD.
+    Tests that the autograd and the autogram engines compute the same gramian when only a subset of
+    the model parameters is specified.
     """
 
     architecture = SimpleBranched
     batch_size = 64
 
-    weighting = UPGradWeighting()
-
     input_shapes = architecture.INPUT_SHAPES
     output_shapes = architecture.OUTPUT_SHAPES
 
@@ -247,39 +165,91 @@ def test_partial_autogram(gramian_module_names: set[str]):
 
     output = model(input)
     losses = loss_fn(output)
-    losses_ = OrderedSet(losses)
-
-    init = Init(losses_)
-    diag = Diagonalize(losses_)
 
     gramian_modules = [model.get_submodule(name) for name in gramian_module_names]
-    gramian_params = OrderedSet({})
+    gramian_params = []
     for m in gramian_modules:
-        gramian_params += OrderedSet(m.parameters())
+        gramian_params += list(m.parameters())
 
-    jac = Jac(losses_, OrderedSet(gramian_params), None, True)
-    mat = _Matrixify()
-    transform = mat << jac << diag << init
-
-    jacobian_matrices = transform({})
-    jacobian_matrix = torch.cat(list(jacobian_matrices.values()), dim=1)
-    gramian = jacobian_matrix @ jacobian_matrix.T
+    autograd_gramian = compute_gramian_with_autograd(losses, gramian_params, retain_graph=True)
     torch.manual_seed(0)
-    losses.backward(weighting(gramian))
-
-    expected_grads = {name: p.grad for name, p in model.named_parameters() if p.grad is not None}
-    model.zero_grad()
 
     engine = Engine(gramian_modules)
 
     output = model(input)
     losses = loss_fn(output)
     gramian = engine.compute_gramian(losses)
+
+    assert_close(gramian, autograd_gramian)
+
+
+@mark.parametrize(["architecture", "batch_size"], PARAMETRIZATIONS)
+def test_iwrm_steps_with_autogram(architecture: type[ShapedModule], batch_size: int):
+    """Tests that the autogram engine doesn't raise any error during several IWRM iterations."""
+
+    n_iter = 3
+
+    input_shapes = architecture.INPUT_SHAPES
+    output_shapes = architecture.OUTPUT_SHAPES
+
+    weighting = UPGradWeighting()
+
+    model = architecture().to(device=DEVICE)
+
+    engine = Engine(model.modules())
+    optimizer = SGD(model.parameters(), lr=1e-7)
+
+    for i in range(n_iter):
+        inputs = make_tensors(batch_size, input_shapes)
+        targets = make_tensors(batch_size, output_shapes)
+        loss_fn = make_mse_loss_fn(targets)
+
+        autogram_forward_backward(model, engine, weighting, inputs, loss_fn)
+
+        optimizer.step()
+        model.zero_grad()
+
+
+@mark.parametrize(["architecture", "batch_size"], PARAMETRIZATIONS)
+@mark.parametrize("compute_gramian", [False, True])
+def test_autograd_while_modules_are_hooked(
+    architecture: type[ShapedModule], batch_size: int, compute_gramian: bool
+):
+    """
+    Tests that the hooks added when constructing the engine do not interfere with a simple autograd
+    call.
+    """
+
+    input = make_tensors(batch_size, architecture.INPUT_SHAPES)
+    targets = make_tensors(batch_size, architecture.OUTPUT_SHAPES)
+    loss_fn = make_mse_loss_fn(targets)
+
+    torch.manual_seed(0)
+    model = architecture().to(device=DEVICE)
     torch.manual_seed(0)
-    losses.backward(weighting(gramian))
+    model_autogram = architecture().to(device=DEVICE)
+
+    torch.manual_seed(0)  # Fix randomness for random models
+    autograd_forward_backward(model, input, loss_fn)
+    autograd_grads = {name: p.grad for name, p in model.named_parameters() if p.grad is not None}
+
+    # Hook modules and optionally compute the Gramian
+    engine = Engine(model_autogram.modules())
+    if compute_gramian:
+        torch.manual_seed(0)  # Fix randomness for random models
+        output = model_autogram(input)
+        losses = loss_fn(output)
+        _ = engine.compute_gramian(losses)
 
-    grads = {name: p.grad for name, p in model.named_parameters() if p.grad is not None}
-    assert_tensor_dicts_are_close(grads, expected_grads)
+    # Verify that even with the hooked modules, autograd works normally when not using the engine.
+    # Results should be the same as a normal call to autograd, and no time should be spent computing
+    # the gramian at all.
+    torch.manual_seed(0)  # Fix randomness for random models
+    autograd_forward_backward(model_autogram, input, loss_fn)
+    grads = {name: p.grad for name, p in model_autogram.named_parameters() if p.grad is not None}
+
+    assert_tensor_dicts_are_close(grads, autograd_grads)
+    assert engine._gramian_accumulator.gramian is None
 
 
 @mark.parametrize("architecture", [WithRNN, WithModuleTrackingRunningStats])