yzhliu commented on a change in pull request #15855: [Numpy] cumprod
URL: https://github.com/apache/incubator-mxnet/pull/15855#discussion_r313238460
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File path: contrib/tvmop/basic/ufunc.py
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@@ -48,3 +51,186 @@ def vadd_gpu(dtype, ndim):
s[C].bind(bx, tvm.thread_axis("blockIdx.x"))
s[C].bind(tx, tvm.thread_axis("threadIdx.x"))
return s, [A, B, C]
+
+
+def cumprod(X, ishape, dtype, ndim, axis):
+ def swapaxis(idx, axis1, axis2):
+ ret = list(idx)
+ if axis1 != axis2:
+ ret[axis1], ret[axis2] = ret[axis2], ret[axis1]
+ return ret
+
+ sshape = swapaxis(ishape, 0, axis)
+ s_state = tvm.placeholder(sshape, dtype=dtype)
+ s_init = tvm.compute([1] + sshape[1:], lambda *idx: X[tuple(swapaxis(idx,
0, axis))])
+ s_update = tvm.compute(sshape, lambda *idx: s_state[(idx[0] - 1, ) +
idx[1:]] * X[tuple(swapaxis(idx, 0, axis))])
+ s_scan = tvm.scan(s_init, s_update, s_state)
+ ret = tvm.compute(ishape, lambda *idx: s_scan[tuple(swapaxis(idx, 0,
axis))])
+ return ret, [ret], [s_init, s_update]
+
+
+def compute_cumprod(dtype, ndim, axis):
+ ishape = [tvm.var() for _ in range(ndim)]
+ X = tvm.placeholder(ishape, name='X', dtype=dtype)
+ if ndim == 0:
+ tshape = [tvm.var()]
+ ret = tvm.compute(tshape, lambda *idx:X[()])
+ c_list, s_list = [ret], []
+ elif axis is None:
+ tshape = [tvm.var()]
+ Y = topi.reshape(X, tshape)
+ ret, c_list, s_list = cumprod(Y, tshape, dtype, 1, 0)
+ c_list.append(Y)
+ else:
+ ret, c_list, s_list = cumprod(X, ishape, dtype, ndim, axis)
+ s = tvm.create_schedule(ret.op)
+ return s, X, ret, c_list, s_list
+
+
+@defop(name="vcumprod", target="cpu", auto_broadcast=False,
+ dtype=AllTypesButHalf, ndim=list(range(0, 6)), axis=list(range(0, 5)) +
[None],
+ attrs=["axis"], attrs_valid=lambda **kwargs: kwargs['axis'] is None or
kwargs['axis'] < kwargs['ndim'])
+def vcumprod(dtype, ndim, axis):
Review comment:
should be just `cumprod`
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