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Implement BatchNormalization functions from cuDNN in juice #146

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Implement BatchNormalization functions from cuDNN in juice #146

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30 changes: 30 additions & 0 deletions rcudnn/cudnn/src/api/normalization.rs
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Original file line number Diff line number Diff line change
Expand Up @@ -277,4 +277,34 @@ impl API {

}
}

#[allow(clippy::clippy::too_many_arguments)]
unsafe fn ffi_batch_normalization_forward_training(
handle: cudnnHandle_t,
mode: cudnnBatchNormMode_t,
alpha: *const ::libc::c_void,
beta: *const ::libc::c_void,
x_desc: cudnnTensorDescriptor_t,
x: *const ::libc::c_void,
y_desc: cudnnTensorDescriptor_t,
y: *mut ::libc::c_void,
bn_scale_bias_mean_var_desc: cudnnTensorDescriptor_t,
bn_scale: *const ::libc::c_void,
bn_bias: *const ::libc::c_void,
exponential_average_factor: f64,
result_running_mean: *mut ::libc::c_void,
result_running_variance: *mut ::libc::c_void,
epsilon: f64,
result_save_mean: *mut ::libc::c_void,
result_save_inv_variance: *mut ::libc::c_void,
) -> Result<(), Error> {
match cudnnBatchNormalizationForwardTraining(handle, mode, alpha, beta, x_desc, x, y_desc, y, bn_scale_bias_mean_var_desc, bn_scale, bn_bias, exponential_average_factor, result_running_mean, result_running_variance, epsilon, result_save_mean, result_save_inv_variance) {
cudnnStatus_t::CUDNN_STATUS_SUCCESS => Ok(()),
cudnnStatus_t::CUDNN_STATUS_BAD_PARAM => Err(Error::BadParam("At least one of the following conditions are met: One of the pointers `alpha`, `beta`, `x`, `y`, `bnScale`, `bnBias` is NULL. The number of `xDesc` or `yDesc` tensor descriptor dimensions is not within the range of [4,5] (only 4D and 5D tensors are supported). `bnScaleBiasMeanVarDesc` dimensions are not `1xCx1x1` for 4D and `1xCx1x1x1` for 5D for spatial, and are not `1xCxHxW` for 4D and `1xCxDxHxW` for 5D for per-activation mode. Exactly one of `resultSaveMean`, `resultSaveInvVariance` pointers are NULL. Exactly one of `resultRunningMean`, `resultRunningInvVariance` pointers are NULL. `epsilon` value is less than `CUDNN_BN_MIN_EPSILON`. Dimensions or data types mismatch for `xDesc`, `yDesc`.")),
cudnnStatus_t::CUDNN_STATUS_NOT_SUPPORTED => Err(Error::NotSupported("`mode` is invalid or dimensions of input and output tensor differ or `data_type` or strides of the tensors differ.")),
cudnnStatus_t::CUDNN_STATUS_EXECUTION_FAILED => Err(Error::ExecutionFailed("Execution failed to launch on GPU.")),
status => Err(Error::Unknown("Unable to compute batch normalization forward training.", status as i32 as u64)),

}
}
}