access2rohit commented on a change in pull request #16885: Multi Precision Lamb
Update operator
URL: https://github.com/apache/incubator-mxnet/pull/16885#discussion_r355630163
##########
File path: tests/python/gpu/test_operator_gpu.py
##########
@@ -422,6 +422,65 @@ def test_preloaded_multi_sgd():
shapes = [np.random.randint(1, maxdim + 1, size=maxndim) for i
in range(nparam)]
check_preloaded_multi_sgd(dtype, shapes, momentum,
use_master_weights)
+
+def check_multi_lamb(dtype, shapes, use_master_weights):
+ def _flatten_list(nested_list):
+ return [item for sublist in nested_list for item in sublist]
+ weights_arr = [np.random.rand(*shape).astype(dtype) * 100. for shape in
shapes]
+ grads_arr = [np.random.rand(*shape).astype(dtype) * 100. for shape in
shapes]
+ means_arr = [np.random.rand(*shape).astype(dtype) * 100. for shape in
shapes]
+ variences_arr = [np.random.rand(*shape).astype(dtype) * 100. for shape in
shapes]
+ rescale_grad = (np.random.random() + 1.0)
+ mx_w = _make_ndarrays(weights_arr)
+ mx_g = _make_ndarrays(grads_arr)
+ mx_m = _make_ndarrays(means_arr)
+ mx_v = _make_ndarrays(variences_arr)
+ mx_p_w = _make_ndarrays(weights_arr)
+ mx_p_g = _make_ndarrays(grads_arr)
+ lrs = list((np.random.random(size=len(shapes)).astype('float32') + 0.1) /
100.)
+ mx_lrs = mx.nd.array(lrs, dtype='float32', ctx=mx.gpu(0))
+ wds = list((np.random.random(size=len(shapes)).astype('float32') + 0.1) /
1000.)
+ mx_wds = mx.nd.array(wds, dtype='float32', ctx=mx.gpu(0))
+
+ if use_master_weights:
+ weights32_arr = [arr.astype('float32') for arr in weights_arr]
+ mx_w32 = _make_ndarrays(weights32_arr)
+ mx_p_w32 = _make_ndarrays(weights32_arr)
+
+ mx.nd.multi_mp_lamb_update(
Review comment:
forgot to remove old code :P
just removed
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