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<p class="MsoNormal">Hello,<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">One of the test cases in test_einsum causes integer overflow for i2 type. The test goes like this:<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">>>> import numpy as np<o:p></o:p></p>
<p class="MsoNormal">>>> dtype = 'i2'<o:p></o:p></p>
<p class="MsoNormal">>>> n = 15<o:p></o:p></p>
<p class="MsoNormal">>>> a = np.arange(4*n, dtype=dtype).reshape(4,n)<o:p></o:p></p>
<p class="MsoNormal">>>> b = np.arange(n*6, dtype=dtype).reshape(n,6)<o:p></o:p></p>
<p class="MsoNormal">>>> c = np.arange(24, dtype=dtype).reshape(4,6)<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">It then calculates AxB using einsum. The problem is that the values in the last row of the result do not fit into i2:<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">>>> np.einsum("ij,jk", a, b, dtype='f8', casting='unsafe')<o:p></o:p></p>
<p class="MsoNormal">array([[ 6090., 6195., 6300., 6405., 6510., 6615.],<o:p></o:p></p>
<p class="MsoNormal"> [ 15540., 15870., 16200., 16530., 16860., 17190.],<o:p></o:p></p>
<p class="MsoNormal"> [ 24990., 25545., 26100., 26655., 27210., 27765.],<o:p></o:p></p>
<p class="MsoNormal"> [ 34440., 35220., 36000., 36780., 37560., 38340.]])<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">In my build this produces different results depending on whether out or .astype is used:<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">>>> np.einsum("ij,jk", a, b, dtype='f8', casting='unsafe').astype(dtype)<o:p></o:p></p>
<p class="MsoNormal">array([[ 6090, 6195, 6300, 6405, 6510, 6615],<o:p></o:p></p>
<p class="MsoNormal"> [ 15540, 15870, 16200, 16530, 16860, 17190],<o:p></o:p></p>
<p class="MsoNormal"> [ 24990, 25545, 26100, 26655, 27210, 27765],<o:p></o:p></p>
<p class="MsoNormal"> [-31096, -30316, -29536, -28756, -27976, -27196]], dtype=int16)<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">>>> np.einsum("ij,jk", a, b, out=c, dtype='f8', casting='unsafe')<o:p></o:p></p>
<p class="MsoNormal">array([[ 6090, 6195, 6300, 6405, 6510, 6615],<o:p></o:p></p>
<p class="MsoNormal"> [ 15540, 15870, 16200, 16530, 16860, 17190],<o:p></o:p></p>
<p class="MsoNormal"> [ 24990, 25545, 26100, 26655, 27210, 27765],<o:p></o:p></p>
<p class="MsoNormal"> [-32768, -32768, -32768, -32768, -32768, -32768]], dtype=int16)<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">The test wants these (actually the same using numpy.dot) to be equal, so this difference causes it to fail. Both ways to handle overflow seem reasonable to me.
<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">Does numpy in general assign a defined behavior to integer overflow (e.g. two’s complement)?<o:p></o:p></p>
<p class="MsoNormal">Is this use of integer overflow in the test intentional and is expected to work, or is my build broken?<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">Best regards,<o:p></o:p></p>
<p class="MsoNormal">Eugene<o:p></o:p></p>
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