[Prim] support batch_norm vjp #51283
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| paddle::Tensor saved_mean = this->GetSingleForwardOutput("SavedMean"); | ||
| paddle::Tensor saved_variance = | ||
| this->GetSingleForwardOutput("SavedVariance"); | ||
| paddle::optional<paddle::Tensor> reserve_space = |
| auto use_global_stats = this->Attr<bool>("use_global_stats"); | ||
| auto trainable_statistics = this->Attr<bool>("trainable_statistics"); | ||
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| VLOG(0) << "Runing batch_norm composite func"; |
| @@ -130,6 +130,7 @@ | |||
| func : batch_norm_grad | |||
| data_type : out_grad | |||
| optional : mean_out, variance_out, reserve_space | |||
| composite: batch_norm_grad(x, scale, bias, mean_out, variance_out, saved_mean, saved_variance, reserve_space, out_grad, momentum, epsilon, data_layout, is_test, use_global_stats, trainable_statistics) | |||
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Describe
Pcard-66969
Prim batch_norm backward op.
Note
composite rule:
inv_var = 1 / sqrt(var + eps)
reduce_axis = [0, 2, 3] (NCHW) [0, 1, 2] (NHWC)
d_bias = np.sum(d_y, reduce_axis)
d_scale = np.sum((X - mean) / inv_var * dy, reduce_axis)
train mode
d_x = (1. / nhw) * scale * inv_var
*(nhw * d_y - np.sum(d_y, reduce_axis) - (X - mean) * inv_var * inv_var *
np.sum(d_y * (X - mean), reduce_axis))
test mode
d_x = d_y * scale * inv_var