eric-haibin-lin commented on a change in pull request #10104: [WIP] Fused RNN
implementation for CPU
URL: https://github.com/apache/incubator-mxnet/pull/10104#discussion_r174622363
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File path: tests/python/unittest/test_operator.py
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@@ -27,6 +27,83 @@
from common import setup_module, with_seed
import unittest
+def check_lstm_with_type(xpu, type1, type2, atol):
+ X = mx.sym.Variable('x')
+ Params = mx.sym.Variable('params')
+ HX = mx.sym.Variable('state')
+ CX = mx.sym.Variable('state_cell')
+ T, N, I, H, nd, nl = 4, 16, 800, 800, 1, 1
+ size = (I + H + 2) * H * 4 * nd; # first layer
+ x1 = mx.random.uniform(-1, 1, (T, N, I), ctx=xpu, dtype=type1)
+ wx = mx.random.uniform(-1, 1, (4 * H, I), ctx=xpu,dtype=type1)
+ wh = mx.random.uniform(-1, 1, (4 * H, H), ctx=xpu,dtype=type1)
+ bx = mx.nd.zeros((4 * H,), ctx=xpu, dtype=type1)
+ bh = mx.nd.zeros((4 * H,), ctx=xpu, dtype=type1)
+ x1.attach_grad()
Review comment:
why do you need to manually attach grad??
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