def _multi_tensor_adadelta(params: List[Tensor],
grads: List[Tensor],
square_avgs: List[Tensor],
acc_deltas: List[Tensor],
*,
lr: float,
weight_decay: float,
rho: float,
eps: float,
maximize: bool):
if len(params) == 0:
return
if maximize:
grads = torch._foreach_neg(grads)
if weight_decay != 0:
torch._foreach_add_(grads, params, alpha=weight_decay)
torch._foreach_mul_(square_avgs, rho)
torch._foreach_addcmul_(square_avgs, grads, grads, value=1 - rho)
std = torch._foreach_add(square_avgs, eps)
torch._foreach_sqrt_(std)
deltas = torch._foreach_add(acc_deltas, eps)
torch._foreach_sqrt_(deltas)
torch._foreach_div_(deltas, std)
torch._foreach_mul_(deltas, grads)
torch._foreach_add_(params, deltas, alpha=-lr)
torch._foreach_mul_(acc_deltas, rho)
torch._foreach_addcmul_(acc_deltas, deltas, deltas, value=1 - rho)
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 adadelta(params: List[Tensor], grads: List[Tensor],
square_avgs: List[Tensor], acc_deltas: List[Tensor], *, lr: float,
weight_decay: float, rho: float, eps: float):
r"""Functional API that performs Adadelta algorithm computation.
See :class:`~torch.optim.Adadelta` for details.
"""
if weight_decay != 0:
torch._foreach_add_(grads, params, alpha=weight_decay)
torch._foreach_mul_(square_avgs, rho)
torch._foreach_addcmul_(square_avgs, grads, grads, value=1 - rho)
std = torch._foreach_add(square_avgs, eps)
torch._foreach_sqrt_(std)
deltas = torch._foreach_add(acc_deltas, eps)
torch._foreach_sqrt_(deltas)
torch._foreach_div_(deltas, std)
torch._foreach_mul_(deltas, grads)
torch._foreach_add_(params, deltas, alpha=-lr)
torch._foreach_mul_(acc_deltas, rho)
torch._foreach_addcmul_(acc_deltas, deltas, deltas, value=1 - rho)
def _multi_tensor_adam(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):
if len(params) == 0:
return
# update steps
torch._foreach_add_(state_steps, 1)
if maximize:
grads = torch._foreach_neg(tuple(grads)) # type: ignore[assignment]
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)
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 amsgrad:
# Maintains the maximum of all 2nd moment running avg. till now
max_exp_avg_sqs = torch._foreach_maximum(max_exp_avg_sqs, exp_avg_sqs) # type: ignore[assignment]
# Use the max. for normalizing running avg. of gradient
max_exp_avg_sq_sqrt = torch._foreach_sqrt(max_exp_avg_sqs)
bias_correction_sqrt = [math.sqrt(bc) for bc in bias_correction2]
torch._foreach_div_(max_exp_avg_sq_sqrt, bias_correction_sqrt)
denom = torch._foreach_add(max_exp_avg_sq_sqrt, eps)
else:
exp_avg_sq_sqrt = torch._foreach_sqrt(exp_avg_sqs)
bias_correction_sqrt = [math.sqrt(bc) for bc in bias_correction2]
torch._foreach_div_(exp_avg_sq_sqrt, bias_correction_sqrt)
denom = torch._foreach_add(exp_avg_sq_sqrt, eps)
step_size = [(lr / bc) * -1 for bc in bias_correction1]
torch._foreach_addcdiv_(params, exp_avgs, denom, step_size)
def _multi_tensor_nadam(params: List[Tensor], grads: List[Tensor],
exp_avgs: List[Tensor], exp_avg_sqs: List[Tensor],
mu_products: List[Tensor], state_steps: List[Tensor],
*, beta1: float, beta2: float, lr: float,
weight_decay: float, momentum_decay: float,
eps: float):
if len(params) == 0:
return
# update steps
torch._foreach_add_(state_steps, 1)
bias_correction1 = [1 - beta1**step.item() for step in state_steps]
bias_correction2 = [1 - beta2**step.item() for step in state_steps]
mus = [
beta1 * (1. - 0.5 * (0.96**(step.item() * momentum_decay)))
for step in state_steps
]
mu_nexts = [
beta1 * (1. - 0.5 * (0.96**((step.item() + 1) * momentum_decay)))
for step in state_steps
]
# update mu_products
torch._foreach_mul_(mu_products, mus)
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)
exp_avg_sq_sqrt = torch._foreach_sqrt(exp_avg_sqs)
bias_correction_sqrt = [math.sqrt(bc) for bc in bias_correction2]
torch._foreach_div_(exp_avg_sq_sqrt, bias_correction_sqrt)
denom = torch._foreach_add(exp_avg_sq_sqrt, eps)
step_size_grads = [(lr * (1. - mu) / (1. - mu_product.item())) * -1
for mu_product, mu in zip(mu_products, mus)]
step_size_expavg = [
(lr * mu_next / (1. - mu_product.item() * mu_next)) * -1
for mu_product, mu_next in zip(mu_products, mu_nexts)
]
torch._foreach_addcdiv_(params, grads, denom, step_size_grads)
torch._foreach_addcdiv_(params, exp_avgs, denom, step_size_expavg)
def nadam(params: List[Tensor],
grads: List[Tensor],
exp_avg: List[Tensor],
exp_avg_sq: List[Tensor],
mu_products: List[Tensor],
states: List[Dict],
*,
beta1: float,
beta2: float,
lr: float,
weight_decay: float,
momentum_decay: float,
eps: float):
r"""Functional API that performs NAdam algorithm computation.
See :class:`~torch.optim.NAdam` for details.
"""
bias_correction1 = [1 - beta1 ** state['step'] for state in states]
bias_correction2 = [1 - beta2 ** state['step'] for state in states]
mus = [beta1 * (1. - 0.5 * (0.96 ** (state['step'] * momentum_decay))) for state in states]
mu_nexts = [beta1 * (1. - 0.5 * (0.96 ** ((state['step'] + 1) * momentum_decay)))
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)
exp_avg_sq_sqrt = torch._foreach_sqrt(exp_avg_sq)
bias_correction_sqrt = [math.sqrt(bc) for bc in bias_correction2]
torch._foreach_div_(exp_avg_sq_sqrt, bias_correction_sqrt)
denom = torch._foreach_add(exp_avg_sq_sqrt, eps)
step_size_grads = [(lr * (1. - mu) / (1. - mu_product)) * -1
for mu_product, mu in zip(mu_products, mus)]
step_size_expavg = [(lr * mu_next / (1. - mu_product * mu_next)) * -1
for mu_product, mu_next in zip(mu_products, mu_nexts)]
torch._foreach_addcdiv_(params, grads, denom, step_size_grads)
torch._foreach_addcdiv_(params, exp_avg, denom, step_size_expavg)
def step(self, closure=None):
"""Performs a single optimization step.
Arguments:
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(
'Adam does not support sparse gradients, please consider SparseAdam instead'
)
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.zeros_like(
p, memory_format=torch.preserve_format)
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]
if group['weight_decay'] != 0:
grads = torch._foreach_add(grads,
params_with_grad,
alpha=group['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)
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(max_exp_avg_sq)
bias_correction_sqrt = [
math.sqrt(bc) for bc in bias_correction2
]
torch._foreach_div_(max_exp_avg_sq_sqrt, bias_correction_sqrt)
denom = torch._foreach_add(max_exp_avg_sq_sqrt, group['eps'])
else:
exp_avg_sq_sqrt = torch._foreach_sqrt(exp_avg_sq)
bias_correction_sqrt = [
math.sqrt(bc) for bc in bias_correction2
]
torch._foreach_div_(exp_avg_sq_sqrt, bias_correction_sqrt)
denom = torch._foreach_add(exp_avg_sq_sqrt, group['eps'])
step_size = [(group['lr'] / bc) * -1 for bc in bias_correction1]
torch._foreach_addcdiv_(params_with_grad, exp_avg, denom,
step_size)
return loss
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.
Arguments:
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:
grads = []
params_with_grad = []
states = []
square_avgs = []
acc_deltas = []
rho, eps = group['rho'], group['eps']
for p in group['params']:
if p.grad is not None:
if p.grad.is_sparse:
raise RuntimeError(
'Adadelta does not support sparse gradients')
grads.append(p.grad)
params_with_grad.append(p)
state = self.state[p]
# State initialization
if len(state) == 0:
state['step'] = 0
state['square_avg'] = torch.zeros_like(
p, memory_format=torch.preserve_format)
state['acc_delta'] = torch.zeros_like(
p, memory_format=torch.preserve_format)
square_avgs.append(state['square_avg'])
acc_deltas.append(state['acc_delta'])
state['step'] += 1
states.append(state)
if group['weight_decay'] != 0:
torch._foreach_add_(grads,
params_with_grad,
alpha=group['weight_decay'])
torch._foreach_mul_(square_avgs, rho)
torch._foreach_addcmul_(square_avgs, grads, grads, value=1 - rho)
std = torch._foreach_add(square_avgs, eps)
torch._foreach_sqrt_(std)
deltas = torch._foreach_add(acc_deltas, eps)
torch._foreach_sqrt_(deltas)
torch._foreach_div_(deltas, std)
torch._foreach_mul_(deltas, grads)
torch._foreach_add_(params_with_grad, deltas, alpha=-group['lr'])
torch._foreach_mul_(acc_deltas, rho)
torch._foreach_addcmul_(acc_deltas, deltas, deltas, value=1 - rho)
return loss
def test_tensorlist_int_scalar_op(self, device, dtype):
for N in N_values:
for foreach_bin_op, foreach_bin_op_, torch_bin_op in self.bin_ops:
tensors = self._get_test_data(device, dtype, N)
scalar = 3
if dtype == torch.bool:
if foreach_bin_op == torch._foreach_sub:
with self.assertRaisesRegex(
RuntimeError,
"Subtraction, the `-` operator,"):
res = foreach_bin_op(tensors, scalar)
with self.assertRaisesRegex(
RuntimeError,
"Subtraction, the `-` operator,"):
expected = [
torch_bin_op(t, scalar) for t in tensors
]
# Test In-place
with self.assertRaisesRegex(
RuntimeError,
"Subtraction, the `-` operator,"):
foreach_bin_op_(tensors, scalar)
with self.assertRaisesRegex(
RuntimeError,
"Subtraction, the `-` operator,"):
[t.sub_(scalar) for t in tensors]
continue
res = foreach_bin_op(tensors, scalar)
expected = [torch_bin_op(t, scalar) for t in tensors]
self.assertEqual(res, expected)
# Test In-place
with self.assertRaisesRegex(
RuntimeError,
"can't be cast to the desired output type"):
foreach_bin_op_(tensors, scalar)
with self.assertRaisesRegex(
RuntimeError,
"can't be cast to the desired output type"):
[t.div_(scalar) for t in tensors]
with self.assertRaisesRegex(
RuntimeError,
"can't be cast to the desired output type"):
[t.mul_(scalar) for t in tensors]
with self.assertRaisesRegex(
RuntimeError,
"can't be cast to the desired output type"):
[t.add_(scalar) for t in tensors]
with self.assertRaisesRegex(
RuntimeError,
"Subtraction, the `-` operator, with a bool"):
[t.sub_(scalar) for t in tensors]
continue
expected = [torch_bin_op(t, scalar) for t in tensors]
res = foreach_bin_op(tensors, scalar)
# In case of In-place division with integers, we can't change the dtype
if foreach_bin_op_ == torch._foreach_div_ and dtype in torch.testing.integral_types(
):
with self.assertRaisesRegex(
RuntimeError,
"can't be cast to the desired output type"):
[t.div_(scalar) for t in tensors]
with self.assertRaisesRegex(
RuntimeError,
"can't be cast to the desired output type"):
torch._foreach_div_(tensors, scalar)
continue
self.assertEqual(res, expected)
# In case of In-place op, we can't change the dtype
foreach_bin_op_(tensors, scalar)
self.assertEqual(tensors, expected)
