def test_div_list(self, device, dtype):
if dtype in torch.testing.integral_types_and(torch.bool):
if self.device_type == 'cpu':
with self.assertRaisesRegex(
RuntimeError,
"result type Float can't be cast to the desired output type"
):
self._test_bin_op_list(device, dtype, torch._foreach_div,
torch._foreach_div_, torch.div)
else:
self.skipTest(
"Skipped! See https://github.com/pytorch/pytorch/issues/44489"
)
return
for N in N_values:
tensors1 = self._get_test_data(device, dtype, N)
if dtype in [torch.bfloat16, torch.bool, torch.float16]:
tensors2 = [
torch.zeros(N, N, device=device, dtype=dtype).add(2)
for _ in range(N)
]
else:
tensors2 = self._get_test_data(device, dtype, N)
expected = [torch.div(tensors1[i], tensors2[i]) for i in range(N)]
res = torch._foreach_div(tensors1, tensors2)
torch._foreach_div_(tensors1, tensors2)
self.assertEqual(res, tensors1)
self.assertEqual(tensors1, res)
def adagrad(params: List[Tensor], grads: List[Tensor],
state_sums: List[Tensor], state_steps: List[int],
has_sparse_grad: bool, *, lr: float, weight_decay: float,
lr_decay: float, eps: float):
r"""Functional API that performs Adagrad algorithm computation.
See :class:`~torch.optim.Adagrad` for details.
"""
if weight_decay != 0:
if has_sparse_grad:
raise RuntimeError(
"weight_decay option is not compatible with sparse gradients")
torch._foreach_add_(grads, params, alpha=weight_decay)
minus_clr = [-lr / (1 + (step - 1) * lr_decay) for step in state_steps]
if has_sparse_grad:
# sparse is not supported by multi_tensor. Fall back to optim.adagrad
# implementation for sparse gradients
for i, (param, grad, state_sum,
step) in enumerate(zip(params, grads, state_sums,
state_steps)):
grad = grad.coalesce(
) # the update is non-linear so indices must be unique
grad_indices = grad._indices()
grad_values = grad._values()
size = grad.size()
state_sum.add_(_make_sparse(grad, grad_indices,
grad_values.pow(2)))
std_sparse = state_sum.sparse_mask(grad)
std_sparse_values = std_sparse._values().sqrt_().add_(eps)
param.add_(
_make_sparse(grad, grad_indices,
grad_values / std_sparse_values),
alpha=minus_clr[i],
)
else:
grads = [
torch.view_as_real(x) if torch.is_complex(x) else x for x in grads
]
state_sums = [
torch.view_as_real(x) if torch.is_complex(x) else x
for x in state_sums
]
torch._foreach_addcmul_(state_sums, grads, grads, value=1)
std = torch._foreach_add(torch._foreach_sqrt(state_sums), eps)
toAdd = torch._foreach_div(torch._foreach_mul(grads, minus_clr), std)
toAdd = [
torch.view_as_complex(x) if torch.is_complex(params[i]) else x
for i, x in enumerate(toAdd)
]
torch._foreach_add_(params, toAdd)
state_sums = [
torch.view_as_complex(x) if torch.is_complex(params[i]) else x
for i, x in enumerate(state_sums)
]
def _multi_tensor_radam(params: List[Tensor], grads: List[Tensor],
exp_avgs: List[Tensor], exp_avg_sqs: List[Tensor],
state_steps: List[Tensor], *, beta1: float,
beta2: float, lr: float, weight_decay: float,
eps: float):
if len(params) == 0:
return
# Update steps
torch._foreach_add_(state_steps, 1)
# maximum length of the approximated SMA
rho_inf = 2 / (1 - beta2) - 1
# compute the length of the approximated SMA
rho_t_list = [
rho_inf - 2 * step.item() * (beta2**step.item()) /
(1 - beta2**step.item()) for step in state_steps
]
bias_correction1 = [1 - beta1**step.item() for step in state_steps]
bias_correction2 = [1 - beta2**step.item() for step in state_steps]
if weight_decay != 0:
torch._foreach_add_(grads, params, alpha=weight_decay)
# Decay the first and second moment running average coefficient
torch._foreach_mul_(exp_avgs, beta1)
torch._foreach_add_(exp_avgs, grads, alpha=1 - beta1)
torch._foreach_mul_(exp_avg_sqs, beta2)
torch._foreach_addcmul_(exp_avg_sqs, grads, grads, 1 - beta2)
rect = [
math.sqrt((rho_t - 4) * (rho_t - 2) * rho_inf /
((rho_inf - 4) * (rho_inf - 2) * rho_t)) if rho_t > 5 else 0
for rho_t in rho_t_list
]
unrectified = [0 if rect > 0 else 1. for rect in rect]
exp_avg_sq_sqrt = torch._foreach_sqrt(exp_avg_sqs)
bias_correction_sqrt = [math.sqrt(bc) for bc in bias_correction2]
denom = torch._foreach_div(exp_avg_sq_sqrt, bias_correction_sqrt)
step_size = [(lr * rect / bc) * -1
for rect, bc in zip(rect, bias_correction1)]
torch._foreach_addcdiv_(params, exp_avgs, denom, step_size)
denom = [
torch.ones_like(exp_av, memory_format=torch.preserve_format)
for exp_av in exp_avgs
]
step_size = [(lr * rect / bc) * -1
for rect, bc in zip(unrectified, bias_correction1)]
torch._foreach_addcdiv_(params, exp_avgs, denom, step_size)
def _multi_tensor_adagrad(params: List[Tensor], grads: List[Tensor],
state_sums: List[Tensor], state_steps: List[Tensor],
*, lr: float, weight_decay: float, lr_decay: float,
eps: float, has_sparse_grad: bool):
# Foreach functions will throw errors if given empty lists
if len(params) == 0:
return
if has_sparse_grad is None:
has_sparse_grad = any([grad.is_sparse for grad in grads])
if has_sparse_grad:
return _single_tensor_adagrad(params,
grads,
state_sums,
state_steps,
lr=lr,
weight_decay=weight_decay,
lr_decay=lr_decay,
eps=eps,
has_sparse_grad=has_sparse_grad)
# Update steps
torch._foreach_add_(state_steps, 1)
if weight_decay != 0:
torch._foreach_add_(grads, params, alpha=weight_decay)
minus_clr = [-lr / (1 + (step - 1) * lr_decay) for step in state_steps]
grads = [
torch.view_as_real(x) if torch.is_complex(x) else x for x in grads
]
state_sums = [
torch.view_as_real(x) if torch.is_complex(x) else x for x in state_sums
]
torch._foreach_addcmul_(state_sums, grads, grads, value=1)
std = torch._foreach_add(torch._foreach_sqrt(state_sums), eps)
toAdd = torch._foreach_div(torch._foreach_mul(grads, minus_clr), std)
toAdd = [
torch.view_as_complex(x) if torch.is_complex(params[i]) else x
for i, x in enumerate(toAdd)
]
torch._foreach_add_(params, toAdd)
state_sums = [
torch.view_as_complex(x) if torch.is_complex(params[i]) else x
for i, x in enumerate(state_sums)
]
def radam(params: List[Tensor], grads: List[Tensor], exp_avg: List[Tensor],
exp_avg_sq: List[Tensor], states: List[Dict], *, beta1: float,
beta2: float, lr: float, weight_decay: float, eps: float):
r"""Functional API that performs RAdam algorithm computation.
See :class:`~torch.optim.RAdam` for details.
"""
# maximum length of the approximated SMA
rho_inf = 2 / (1 - beta2) - 1
# compute the length of the approximated SMA
rho_t_list = [
rho_inf - 2 * state['step'] * (beta2**state['step']) /
(1 - beta2**state['step']) for state in states
]
bias_correction1 = [1 - beta1**state['step'] for state in states]
bias_correction2 = [1 - beta2**state['step'] for state in states]
if weight_decay != 0:
torch._foreach_add_(grads, params, alpha=weight_decay)
# Decay the first and second moment running average coefficient
torch._foreach_mul_(exp_avg, beta1)
torch._foreach_add_(exp_avg, grads, alpha=1 - beta1)
torch._foreach_mul_(exp_avg_sq, beta2)
torch._foreach_addcmul_(exp_avg_sq, grads, grads, 1 - beta2)
rect = [
math.sqrt((rho_t - 4) * (rho_t - 2) * rho_inf /
((rho_inf - 4) * (rho_inf - 2) * rho_t)) if rho_t > 5 else 0
for rho_t in rho_t_list
]
unrectified = [0 if rect > 0 else 1. for rect in rect]
exp_avg_sq_sqrt = torch._foreach_sqrt(exp_avg_sq)
bias_correction_sqrt = [math.sqrt(bc) for bc in bias_correction2]
denom = torch._foreach_div(exp_avg_sq_sqrt, bias_correction_sqrt)
step_size = [(lr * rect / bc) * -1
for rect, bc in zip(rect, bias_correction1)]
torch._foreach_addcdiv_(params, exp_avg, denom, step_size)
denom = [
torch.ones_like(exp_av, memory_format=torch.preserve_format)
for exp_av in exp_avg
]
step_size = [(lr * rect / bc) * -1
for rect, bc in zip(unrectified, bias_correction1)]
torch._foreach_addcdiv_(params, exp_avg, denom, step_size)
def _multi_tensor_adamw(params: List[Tensor], grads: List[Tensor],
exp_avgs: List[Tensor], exp_avg_sqs: List[Tensor],
max_exp_avg_sqs: List[Tensor],
state_steps: List[Tensor], *, amsgrad: bool,
beta1: float, beta2: float, lr: float,
weight_decay: float, eps: float, maximize: bool,
capturable: bool):
if len(params) == 0:
return
if capturable:
assert all(p.is_cuda and step.is_cuda for p, step in zip(params, state_steps)), \
"If capturable=True, params and state_steps must be CUDA tensors."
if maximize:
grads = torch._foreach_neg(tuple(grads)) # type: ignore[assignment]
grads = [
torch.view_as_real(x) if torch.is_complex(x) else x for x in grads
]
exp_avgs = [
torch.view_as_real(x) if torch.is_complex(x) else x for x in exp_avgs
]
exp_avg_sqs = [
torch.view_as_real(x) if torch.is_complex(x) else x
for x in exp_avg_sqs
]
params = [
torch.view_as_real(x) if torch.is_complex(x) else x for x in params
]
# update steps
torch._foreach_add_(state_steps, 1)
# Perform stepweight decay
torch._foreach_mul_(params, 1 - lr * weight_decay)
# Decay the first and second moment running average coefficient
torch._foreach_mul_(exp_avgs, beta1)
torch._foreach_add_(exp_avgs, grads, alpha=1 - beta1)
torch._foreach_mul_(exp_avg_sqs, beta2)
torch._foreach_addcmul_(exp_avg_sqs, grads, grads, 1 - beta2)
if capturable:
# TODO: use foreach_pow if/when foreach_pow is added
bias_correction1 = [torch.pow(beta1, step) for step in state_steps]
bias_correction2 = [torch.pow(beta2, step) for step in state_steps]
# foreach_sub doesn't allow a scalar as the first arg
torch._foreach_sub_(bias_correction1, 1)
torch._foreach_sub_(bias_correction2, 1)
torch._foreach_neg_(bias_correction1)
torch._foreach_neg_(bias_correction2)
# foreach_div doesn't allow a scalar as the first arg
step_size = torch._foreach_div(bias_correction1, lr)
torch._foreach_reciprocal_(step_size)
torch._foreach_neg_(step_size)
bias_correction2_sqrt = torch._foreach_sqrt(bias_correction2)
if amsgrad:
# Maintains the maximum of all 2nd moment running avg. till now
torch._foreach_maximum_(max_exp_avg_sqs, exp_avg_sqs)
# Use the max. for normalizing running avg. of gradient
max_exp_avg_sq_sqrt = torch._foreach_sqrt(max_exp_avg_sqs)
# Folds in (admittedly ugly) 1-elem step_size math here to avoid extra param-set-sized read+write
# (can't fold it into addcdiv_ below because addcdiv_ requires value is a Number, not a Tensor)
torch._foreach_div_(
max_exp_avg_sq_sqrt,
torch._foreach_mul(bias_correction2_sqrt, step_size))
eps_over_step_size = torch._foreach_div(step_size, eps)
torch._foreach_reciprocal_(eps_over_step_size)
denom = torch._foreach_add(max_exp_avg_sq_sqrt, eps_over_step_size)
else:
exp_avg_sq_sqrt = torch._foreach_sqrt(exp_avg_sqs)
torch._foreach_div_(
exp_avg_sq_sqrt,
torch._foreach_mul(bias_correction2_sqrt, step_size))
eps_over_step_size = torch._foreach_div(step_size, eps)
torch._foreach_reciprocal_(eps_over_step_size)
denom = torch._foreach_add(exp_avg_sq_sqrt, eps_over_step_size)
torch._foreach_addcdiv_(params, exp_avgs, denom)
else:
bias_correction1 = [1 - beta1**step.item() for step in state_steps]
bias_correction2 = [1 - beta2**step.item() for step in state_steps]
step_size = [(lr / bc) * -1 for bc in bias_correction1]
bias_correction2_sqrt = [math.sqrt(bc) for bc in bias_correction2]
if amsgrad:
# Maintains the maximum of all 2nd moment running avg. till now
torch._foreach_maximum_(max_exp_avg_sqs, exp_avg_sqs)
# Use the max. for normalizing running avg. of gradient
max_exp_avg_sq_sqrt = torch._foreach_sqrt(max_exp_avg_sqs)
torch._foreach_div_(max_exp_avg_sq_sqrt, bias_correction2_sqrt)
denom = torch._foreach_add(max_exp_avg_sq_sqrt, eps)
else:
exp_avg_sq_sqrt = torch._foreach_sqrt(exp_avg_sqs)
torch._foreach_div_(exp_avg_sq_sqrt, bias_correction2_sqrt)
denom = torch._foreach_add(exp_avg_sq_sqrt, eps)
torch._foreach_addcdiv_(params, exp_avgs, denom, step_size)
def step(self, closure=None):
"""Performs a single optimization step.
Args:
closure (callable, optional): A closure that reevaluates the model
and returns the loss.
"""
loss = None
if closure is not None:
with torch.enable_grad():
loss = closure()
for group in self.param_groups:
amsgrad = group["amsgrad"]
grads = []
states = []
exp_avg = []
exp_avg_sq = []
max_exp_avg_sq = []
params_with_grad = []
for p in group["params"]:
if p.grad is not None:
if p.grad.is_sparse:
raise RuntimeError(
"AdamW does not support sparse gradients")
# Perform stepweight decay
p.mul_(1 - group["lr"] * group["weight_decay"])
params_with_grad.append(p)
grads.append(p.grad)
for p in params_with_grad:
state = self.state[p]
# State initialization
if len(state) == 0:
state["step"] = 0
# Exponential moving average of gradient values
state["exp_avg"] = torch.zeros_like(
p, memory_format=torch.preserve_format)
# Exponential moving average of squared gradient values
state["exp_avg_sq"] = torch.ones_like(
p, memory_format=torch.preserve_format
) # torch init to zeros
if amsgrad:
# Maintains max of all exp. moving avg. of sq. grad. values
state["max_exp_avg_sq"] = torch.zeros_like(
p, memory_format=torch.preserve_format)
exp_avg.append(state["exp_avg"])
exp_avg_sq.append(state["exp_avg_sq"])
if amsgrad:
max_exp_avg_sq.append(state["max_exp_avg_sq"])
state["step"] += 1
states.append(state)
beta1, beta2 = group["betas"]
bias_correction1 = [1 - beta1**state["step"] for state in states]
bias_correction2 = [1 - beta2**state["step"] for state in states]
#
# Decay the first and second moment running average coefficient
#
torch._foreach_mul_(exp_avg, beta1)
torch._foreach_add_(exp_avg, grads, alpha=1 - beta1)
torch._foreach_mul_(exp_avg_sq, beta2)
torch._foreach_addcmul_(exp_avg_sq, grads, grads, 1 - beta2)
if amsgrad:
# Maintains the maximum of all 2nd moment running avg. till now
max_exp_avg_sq = torch._foreach_maximum(
max_exp_avg_sq, exp_avg_sq)
# Use the max. for normalizing running avg. of gradient
max_exp_avg_sq_sqrt = torch._foreach_sqrt(
torch._foreach_add(max_exp_avg_sq, group["eps"]))
bias_correction_sqrt = [
math.sqrt(bc) for bc in bias_correction2
]
denom = torch._foreach_div(max_exp_avg_sq_sqrt,
bias_correction_sqrt)
else:
exp_avg_sq_sqrt = torch._foreach_sqrt(
torch._foreach_add(exp_avg_sq, group["eps"]))
bias_correction_sqrt = [
math.sqrt(bc) for bc in bias_correction2
]
denom = torch._foreach_div(exp_avg_sq_sqrt,
bias_correction_sqrt)
step_size = [-1 * (group["lr"] / bc) for bc in bias_correction1]
torch._foreach_addcdiv_(params_with_grad, exp_avg, denom,
step_size)
return loss
