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FusedAdam.cpp
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FusedAdam.cpp
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#define TORCH_ASSERT_ONLY_METHOD_OPERATORS
#include <ATen/core/Tensor.h>
#include <ATen/native/DispatchStub.h>
#include <ATen/native/FusedAdam.h>
#ifndef AT_PER_OPERATOR_HEADERS
#include <ATen/Functions.h>
#include <ATen/NativeFunctions.h>
#else
#include <ATen/ops/_fused_adam.h>
#include <ATen/ops/_fused_adam_native.h>
#include <ATen/ops/_fused_adamw.h>
#include <ATen/ops/_fused_adamw_native.h>
#endif
namespace at {
namespace native {
void _fused_adam_kernel_cpu_(
at::TensorList params,
at::TensorList grads,
at::TensorList exp_avgs,
at::TensorList exp_avg_sqs,
at::TensorList max_exp_avg_sqs,
at::TensorList state_steps,
const double lr,
const double beta1,
const double beta2,
const double weight_decay,
const double eps,
const bool amsgrad,
const bool maximize,
const c10::optional<at::Tensor>& grad_scale,
const c10::optional<at::Tensor>& found_inf) {
const float* grad_scale_ptr =
grad_scale.has_value() ? grad_scale->data_ptr<float>() : nullptr;
const float* found_inf_ptr =
found_inf.has_value() ? found_inf->data_ptr<float>() : nullptr;
if (found_inf_ptr && *found_inf_ptr == 1.0) {
return;
}
size_t n_tensors = params.size();
TORCH_CHECK(grads.size() == n_tensors);
TORCH_CHECK(exp_avgs.size() == n_tensors);
TORCH_CHECK(exp_avg_sqs.size() == n_tensors);
if (amsgrad) {
TORCH_CHECK(max_exp_avg_sqs.size() == n_tensors);
} else {
TORCH_CHECK(max_exp_avg_sqs.size() == 0);
}
TORCH_CHECK(state_steps.size() == n_tensors);
at::Tensor max_exp_avg_sq = at::Tensor();
for (size_t i = 0; i < n_tensors; i++){
if (amsgrad) max_exp_avg_sq = max_exp_avg_sqs[i];
fused_adam_stub(
kCPU,
params[i],
grads[i],
exp_avgs[i],
exp_avg_sqs[i],
max_exp_avg_sq,
state_steps[i],
lr,
beta1,
beta2,
weight_decay,
eps,
amsgrad,
maximize,
grad_scale_ptr,
ADAM_MODE::ORIGINAL);
}
}
// The following overload simply has a Tensor lr
void _fused_adam_kernel_cpu_(
at::TensorList params,
at::TensorList grads,
at::TensorList exp_avgs,
at::TensorList exp_avg_sqs,
at::TensorList max_exp_avg_sqs,
at::TensorList state_steps,
const at::Tensor& lr,
const double beta1,
const double beta2,
const double weight_decay,
const double eps,
const bool amsgrad,
const bool maximize,
const c10::optional<at::Tensor>& grad_scale,
const c10::optional<at::Tensor>& found_inf) {
_fused_adam_kernel_cpu_(params, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps, lr.item<double>(), beta1, beta2, weight_decay, eps, amsgrad, maximize, grad_scale, found_inf);
}
void _fused_adamw_kernel_cpu_(
at::TensorList params,
at::TensorList grads,
at::TensorList exp_avgs,
at::TensorList exp_avg_sqs,
at::TensorList max_exp_avg_sqs,
at::TensorList state_steps,
const double lr,
const double beta1,
const double beta2,
const double weight_decay,
const double eps,
const bool amsgrad,
const bool maximize,
const c10::optional<at::Tensor>& grad_scale,
const c10::optional<at::Tensor>& found_inf) {
const float* grad_scale_ptr =
grad_scale.has_value() ? grad_scale->data_ptr<float>() : nullptr;
const float* found_inf_ptr =
found_inf.has_value() ? found_inf->data_ptr<float>() : nullptr;
if (found_inf_ptr && *found_inf_ptr == 1.0) {
return;
}
size_t n_tensors = params.size();
TORCH_CHECK(grads.size() == n_tensors);
TORCH_CHECK(exp_avgs.size() == n_tensors);
TORCH_CHECK(exp_avg_sqs.size() == n_tensors);
if (amsgrad) {
TORCH_CHECK(max_exp_avg_sqs.size() == n_tensors);
} else {
TORCH_CHECK(max_exp_avg_sqs.size() == 0);
}
TORCH_CHECK(state_steps.size() == n_tensors);
at::Tensor max_exp_avg_sq = at::Tensor();
for (size_t i = 0; i < n_tensors; i++){
if (amsgrad) max_exp_avg_sq = max_exp_avg_sqs[i];
fused_adam_stub(
kCPU,
params[i],
grads[i],
exp_avgs[i],
exp_avg_sqs[i],
max_exp_avg_sq,
state_steps[i],
lr,
beta1,
beta2,
weight_decay,
eps,
amsgrad,
maximize,
grad_scale_ptr,
ADAM_MODE::ADAMW);
}
}
// The following overload simply has a Tensor lr
void _fused_adamw_kernel_cpu_(
at::TensorList params,
at::TensorList grads,
at::TensorList exp_avgs,
at::TensorList exp_avg_sqs,
at::TensorList max_exp_avg_sqs,
at::TensorList state_steps,
const at::Tensor& lr,
const double beta1,
const double beta2,
const double weight_decay,
const double eps,
const bool amsgrad,
const bool maximize,
const c10::optional<at::Tensor>& grad_scale,
const c10::optional<at::Tensor>& found_inf) {
_fused_adamw_kernel_cpu_(params, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps, lr.item<double>(), beta1, beta2, weight_decay, eps, amsgrad, maximize, grad_scale, found_inf);
}
DEFINE_DISPATCH(fused_adam_stub);
}
}