Paddle

Commit Graph

Author	SHA1	Message	Date
QI JUN	61ec0b9516	Refine device context (#6433 ) There are mainly following fixes: - take `DeviceContext` as the template parameter of math functors and OpKernel instead of `Place` - remove `eigen_device` interface in base class `DeviceContext` - remove `GetEigenDevice` interface in `ExecutionContext` and base class `DeviceContext` - remove unused `platform::EigenDeviceConverter` - rename `REGISTER_OP_GPU_KERNEL` to `REGISTER_OP_CUDA_KERNEL` - rename `USE_GPU_ONLY_OP` to `USE_CUDA_ONLY_OP`	7 years ago
Yu Yang	a5e73f9eaf	Support many data types of several operators (#5731 ) * Support many data types of several operators * SeqConv only support float/double * Revert adagrad	7 years ago
Abhinav Arora	116800378a	Adding the Adam Optimizer operator (#4733 ) * add adam op moment1_out = beta1 * moment1 + (1 − beta1) * grad moment2_out = beta2 * moment2 + (1 − beta2) * grad * grad moment1_hat = moment1_out / (1 - beta1^t) moment2_hat = moment2_out / (1 - beta2^t) param_out = param - learning_rate * moment1_hat / (sqrt(moment2_hat) + epsilon) * fix moment 2 * Adding the Adam optimization operator * Adding more tests for Adam op	7 years ago

Author

SHA1

Message

Date

QI JUN

61ec0b9516

Refine device context (#6433 )

There are mainly following fixes:

- take `DeviceContext` as the template parameter of math functors and OpKernel instead of `Place`
- remove `eigen_device` interface in base class  `DeviceContext`
- remove `GetEigenDevice` interface in `ExecutionContext` and base class `DeviceContext`
- remove unused `platform::EigenDeviceConverter`
- rename `REGISTER_OP_GPU_KERNEL` to `REGISTER_OP_CUDA_KERNEL`
- rename `USE_GPU_ONLY_OP` to `USE_CUDA_ONLY_OP`

Yu Yang

a5e73f9eaf

Support many data types of several operators (#5731 )

* Support many data types of several operators

* SeqConv only support float/double

* Revert adagrad

Abhinav Arora

116800378a

Adding the Adam Optimizer operator (#4733 )

* add adam op

moment1_out = beta1 * moment1 + (1 − beta1) * grad
moment2_out = beta2 * moment2 + (1 − beta2) * grad * grad
moment1_hat =  moment1_out / (1 - beta1^t)
moment2_hat =  moment2_out / (1 - beta2^t)
param_out = param - learning_rate * moment1_hat / (sqrt(moment2_hat) +
epsilon)

* fix moment 2

* Adding the Adam optimization operator

* Adding more tests for Adam op

3 Commits (0f1c685c60161ae2d60ef073df076185356da54a)