On Fri, Nov 9, 2012 at 3:32 AM, Nathaniel Smith <[email protected]> wrote:
> On Fri, Nov 9, 2012 at 6:18 AM, Nicolas SCHEFFER > <[email protected]> wrote: > > Fred, > > > > Thanks for the advice. > > The code will only affect the part in _dotblas.c where gemm is called. > > There's tons of check before that make sure both matrices are of ndim 2. > > We should check though if we can do these tricks in other parts of the > function. > > > > Otherwise: > > - I've built against ATLAS 3.10 > > - I'm happy to add a couple more test for C and F-contiguous. I'm not > > sure how to get the third type (strided), would you have an example? > > def with_memory_order(a, order): > assert order in ("C", "F", "discontig") > assert a.ndim == 2 > if order in ("C", "F"): > return np.asarray(a, order=order) > else: > buf = np.empty((a.shape[0] * 2, a.shape[1] * 2), dtype=a.dtype) > buf[::2, ::2] = a > # This returns a view onto every other element of 'buf': > result = buf[::2, ::2] > assert not result.flags.c_contiguous and not > result.flags.f_contiguous > return result > > > The following test for instance checks integrity against > > multiarray.dot, which I believe is default when not compiled with > > BLAS. > > Dot is a hard function to test imho, so if anybody has ideas on what > > kind of test they'd like to see, please let me know. > > > > If that's ok I might now be able to: > > - Check for more bugs, I need to dig a bit more in the gemm call, make > > sure everything is ok. > > - Create an issue on github and link to this discussion > > - Make a commit in a seperate branch > > - Move forward like that. > > > > == > > import numpy as np > > from time import time > > from numpy.testing import assert_almost_equal > > > > def test_dot_regression(): > > """ Test numpy dot by comparing with multiarray dot > > """ > > np.random.seed(7) > > a = np.random.randn(3, 3) > > b = np.random.randn(3, 2) > > c = np.random.randn(2, 3) > > > > _dot = np.core.multiarray.dot > > > > assert_almost_equal(np.dot(a, a), _dot(a, a)) > > assert_almost_equal(np.dot(b, c), _dot(b, c)) > > assert_almost_equal(np.dot(b.T, c.T), _dot(b.T, c.T)) > > > > assert_almost_equal(np.dot(a.T, a), _dot(a.T, a)) > > assert_almost_equal(np.dot(a, a.T), _dot(a, a.T)) > > assert_almost_equal(np.dot(a.T, a.T), _dot(a.T, a.T)) > > You should check that the result is C-contiguous in all cases too. > > for a_order in ("C", "F", "discontig"): > for b_order in ("C", "F", "discontig"): > this_a = with_memory_order(a, a_order) > this_b = with_memory_order(b, b_order) > result = np.dot(this_a, this_b) > assert_almost_equal(result, expected) > assert result.flags.c_contiguous > > You could also wrap the above in yet another loop to try a few > different combinations of a and b matrices (perhaps after sticking the > code into a utility function, like run_dot_tests(a, b, expected), so > the indentation doesn't get out of hand ;-)). Then you can easily test > some of the edge cases, like Nx1 matrices. > I agree that tests are needed the for Nx1 and variant cases. I saw blas error being raised with some blas version. You also need to test with the output provided, so there is 3 loops: for a_order in ("C", "F", "discontig", "neg"): for b_order in ("C", "F", "discontig", "neg"): for c_order in ("C", "F", "discontig", "neg"): I also added the stride type "neg", I'm not sure if it is needed, but that is other corner cases. neg => result = buf[::-1, ::-1] I just looked again at our code and there is another constrain: that the strides are multiple of the elemsize. Theano do not support not aligned array, but numpy does, so there is a need for test for this. You can make an unaligned array like this: dtype = "b1,f4" a = numpy.empty(1e4, dtype=dtype)['f1'] I just saw the strides problems that affect only some. I think that the best explaination is our code: /* create appropriate strides for malformed matrices that are row or column * vectors, or empty matrices. * In that case, the value of the stride does not really matter, but * some versions of BLAS insist that: * - they are not smaller than the number of elements in the array, * - they are not 0. */ sx_0 = (Nx[0] > 1) ? Sx[0]/type_size : (Nx[1] + 1); sx_1 = (Nx[1] > 1) ? Sx[1]/type_size : (Nx[0] + 1); sy_0 = (Ny[0] > 1) ? Sy[0]/type_size : (Ny[1] + 1); sy_1 = (Ny[1] > 1) ? Sy[1]/type_size : (Ny[0] + 1); sz_0 = (Nz[0] > 1) ? Sz[0]/type_size : (Nz[1] + 1); sz_1 = (Nz[1] > 1) ? Sz[1]/type_size : (Nz[0] + 1); So this ask for test with empty matrices too. HTH Fred
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