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https://github.com/huggingface/pytorch-image-models.git
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Remove an indent level in init_group for adopt, update optim tests, adopt failing rosenbrock
This commit is contained in:
Ross Wightman
parent
6db271015d
commit
d73e8e7531
@ -175,7 +175,7 @@ def _test_basic_cases(constructor, scheduler_constructors=None):
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)
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def _test_model(optimizer, params, device=torch.device('cpu')):
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def _test_model(optimizer, params, device=torch.device('cpu'), after_step=0):
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weight = torch.tensor(
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[[-0.2109, -0.4976], [-0.1413, -0.3420], [-0.2524, 0.6976]],
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device=device, requires_grad=True)
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@ -206,7 +206,8 @@ def _test_model(optimizer, params, device=torch.device('cpu')):
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loss = output.sum()
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loss.backward()
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loss = loss.item()
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assert loss < prev_loss
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if i > after_step:
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assert loss < prev_loss
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prev_loss = loss
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optimizer.step()
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@ -235,31 +236,44 @@ def _test_rosenbrock(constructor, scheduler_constructors=None):
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solution = torch.tensor([1, 1])
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initial_dist = params.clone().detach().dist(solution)
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def eval(params, w):
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def get_grad(_param, _sparse_grad, _w):
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grad = drosenbrock(params.clone().detach())
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# Depending on w, provide only the x or y gradient
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if _sparse_grad:
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if _w:
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i = torch.tensor([[0, 0]], dtype=torch.int64)
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x = grad[0]
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v = torch.tensor([x / 4.0, x - x / 4.0])
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else:
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i = torch.tensor([[1, 1]], dtype=torch.int64)
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y = grad[1]
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v = torch.tensor([y - y / 4.0, y / 4.0])
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grad_out = torch.sparse_coo_tensor(i, v, (2,), dtype=v.dtype)
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else:
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if _w:
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grad_out = torch.tensor([grad[0], 0], dtype=_param.dtype)
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else:
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grad_out = torch.tensor([0, grad[1]], dtype=_param.dtype)
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return grad_out
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def eval(_param, _sparse_grad, _w):
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# Depending on w, provide only the x or y gradient
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optimizer.zero_grad()
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loss = rosenbrock(params)
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loss = rosenbrock(_param)
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loss.backward()
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grad = drosenbrock(params.clone().detach())
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# NB: We torture test the optimizer by returning an
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# uncoalesced sparse tensor
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if w:
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i = torch.LongTensor([[0, 0]])
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x = grad[0]
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v = torch.tensor([x / 4., x - x / 4.])
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else:
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i = torch.LongTensor([[1, 1]])
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y = grad[1]
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v = torch.tensor([y - y / 4., y / 4.])
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x = torch.sparse.DoubleTensor(i, v, torch.Size([2])).to(dtype=v.dtype)
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grad_out = get_grad(_param, _sparse_grad, _w)
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with torch.no_grad():
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params.grad = x.to_dense()
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_param.grad = grad_out.to_dense()
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return loss
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for i in range(2000):
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# Do cyclic coordinate descent
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w = i % 2
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optimizer.step(functools.partial(eval, params, w))
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optimizer.step(functools.partial(eval, params, True, w))
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for scheduler in schedulers:
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if isinstance(scheduler, PlateauLRScheduler):
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scheduler.step(rosenbrock(params))
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@ -340,7 +354,7 @@ def test_sgd(optimizer):
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_test_model(optimizer, dict(lr=1e-3))
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@pytest.mark.parametrize('optimizer', ['adamw', 'adam', 'nadam', 'adamax'])
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@pytest.mark.parametrize('optimizer', ['adamw', 'adam', 'nadam', 'adamax', 'nadamw'])
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def test_adam(optimizer):
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_test_basic_cases(
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lambda weight, bias: create_optimizer_v2([weight, bias], optimizer, lr=1e-3)
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@ -363,6 +377,30 @@ def test_adam(optimizer):
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_test_model(optimizer, dict(lr=5e-2))
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@pytest.mark.parametrize('optimizer', ['adopt', 'adoptw'])
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def test_adopt(optimizer):
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_test_basic_cases(
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lambda weight, bias: create_optimizer_v2([weight, bias], optimizer, lr=1e-3)
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)
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_test_basic_cases(
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lambda weight, bias: create_optimizer_v2(
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_build_params_dict(weight, bias, lr=3e-3),
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optimizer,
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lr=1e-3)
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)
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_test_basic_cases(
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lambda weight, bias: create_optimizer_v2(
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_build_params_dict_single(weight, bias, lr=3e-3),
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optimizer,
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lr=1e-3)
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)
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# FIXME rosenbrock is not passing for ADOPT
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# _test_rosenbrock(
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# lambda params: create_optimizer_v2(params, optimizer, lr=1e-3)
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# )
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_test_model(optimizer, dict(lr=5e-2), after_step=1) # note no convergence in first step for ADOPT
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@pytest.mark.parametrize('optimizer', ['adabelief'])
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def test_adabelief(optimizer):
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_test_basic_cases(
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@ -446,7 +484,7 @@ def test_adaother(optimizer):
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_test_model(optimizer, dict(lr=5e-2))
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@pytest.mark.parametrize('optimizer', ['adafactor'])
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@pytest.mark.parametrize('optimizer', ['adafactor', 'adafactorbv'])
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def test_adafactor(optimizer):
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_test_basic_cases(
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lambda weight, bias: create_optimizer_v2([weight, bias], optimizer, lr=1e-3)
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@ -129,58 +129,55 @@ class Adopt(Optimizer):
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):
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has_complex = False
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for p in group["params"]:
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if p.grad is not None:
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has_complex |= torch.is_complex(p)
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params_with_grad.append(p)
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if p.grad.is_sparse:
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raise RuntimeError(
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"ADOPT does not support sparse gradients"
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)
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grads.append(p.grad)
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if p.grad is None:
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continue
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has_complex |= torch.is_complex(p)
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params_with_grad.append(p)
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if p.grad.is_sparse:
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raise RuntimeError(
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"ADOPT does not support sparse gradients"
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)
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grads.append(p.grad)
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state = self.state[p]
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# Lazy state initialization
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if len(state) == 0:
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# note(crcrpar): [special device hosting for step]
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# Deliberately host `step` on CPU if both capturable and fused are off.
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# This is because kernel launches are costly on CUDA and XLA.
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state["step"] = (
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torch.zeros(
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(),
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dtype=_get_scalar_dtype(),
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device=p.device,
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)
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if group["capturable"]
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else torch.tensor(0.0, dtype=_get_scalar_dtype())
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)
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# Exponential moving average of gradient values
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state["exp_avg"] = torch.zeros_like(
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p, memory_format=torch.preserve_format
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)
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# Exponential moving average of squared gradient values
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state["exp_avg_sq"] = torch.zeros_like(
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p, memory_format=torch.preserve_format
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state = self.state[p]
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# Lazy state initialization
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if len(state) == 0:
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# note(crcrpar): [special device hosting for step]
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# Deliberately host `step` on CPU if both capturable and fused are off.
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# This is because kernel launches are costly on CUDA and XLA.
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state["step"] = (
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torch.zeros(
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(),
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dtype=_get_scalar_dtype(),
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device=p.grad.device,
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)
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if group["capturable"]
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else torch.tensor(0.0, dtype=_get_scalar_dtype())
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)
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# Exponential moving average of gradient values
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state["exp_avg"] = torch.zeros_like(
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p.grad, memory_format=torch.preserve_format
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)
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# Exponential moving average of squared gradient values
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state["exp_avg_sq"] = torch.zeros_like(
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p.grad, memory_format=torch.preserve_format
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)
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exp_avgs.append(state["exp_avg"])
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exp_avg_sqs.append(state["exp_avg_sq"])
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exp_avgs.append(state["exp_avg"])
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exp_avg_sqs.append(state["exp_avg_sq"])
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if group["differentiable"] and state["step"].requires_grad:
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raise RuntimeError(
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"`requires_grad` is not supported for `step` in differentiable mode"
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)
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if group["differentiable"] and state["step"].requires_grad:
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raise RuntimeError(
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"`requires_grad` is not supported for `step` in differentiable mode"
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)
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# Foreach without capturable does not support a tensor lr
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if (
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group["foreach"]
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and torch.is_tensor(group["lr"])
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and not group["capturable"]
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):
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raise RuntimeError(
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"lr as a Tensor is not supported for capturable=False and foreach=True"
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)
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# Foreach without capturable does not support a tensor lr
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if group["foreach"] and torch.is_tensor(group["lr"]) and not group["capturable"]:
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raise RuntimeError(
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"lr as a Tensor is not supported for capturable=False and foreach=True"
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)
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state_steps.append(state["step"])
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state_steps.append(state["step"])
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return has_complex
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#@_use_grad_for_differentiable # FIXME internal context mgr, can't use
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@ -312,6 +309,7 @@ def _single_tensor_adopt(
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exp_avg.mul_(beta1).addcdiv_(grad, denom, value=1 - beta1)
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param.add_(exp_avg, alpha=-lr)
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exp_avg_sq.mul_(beta2).addcmul_(grad, grad.conj(), value=1 - beta2)
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