Created by: Sand3r-

I've managed to correct the code of AnalysisPredictor (analysis_predictor.cc) as to actually have it run an MKL-DNN engine (previously the MKL-DNN layers weren't called at all) in the analyzer_vis_tester.cc. The corrected code can be found at https://github.com/Sand3r-/Paddle at branch mgallus/fix_mkldnn_at_vis_test.

However, after running the test with MKL-DNN enabled it crashed.

To assess whether that was caused by the fuses, I have disabled all the relevant fuses. The program remained crashing anyway.

I have then debugged the program deep enough to find out that the crash was caused by MKLDNN's check for pooling output size consistency (the relevant line can be found here). It says that the determined output size of (1, 16, 24, 385) is incorrect, and that the correct output shape should be of (1, 16, 24, 384), given that: the input to the pooling has shape of (1, 16, 48, 769), kernel size is (2, 2), padding of (0,0), and strides of (2, 2).

Which according to formula found at http://cs231n.github.io/convolutional-networks/?utm_source=top.caibaojian.com/48879 Section Pooling Layer provides the following:

width = (old_width - filter_size) / stride + 1

width = ((769 - 2)/2 + 1) = 384, hence the MKL-DNN check is correct.

That would imply, that model downloaded for the purposes of test_analyzer_ocr is flawed, as it contains incorrectly computed output pooling size(s?).

After an investigation into how does the reference CPU implementation of Paddle computes the output shape, I have noticed that it must've had used the ceil_mode attribute, which uses the formula (input_size - filter_size + 2 * padding + stride - 1) / stride + 1 here for output shape computation. That indeed results with computation of output width to be 385 and not 384. Now the question is, is that ceil_mode formula necessary, and if so, why? Cannot the model be trained without it? I haven't found any usages in the code that would alter the behaviour of pooling when this parameter is enabled. Moreover, is the formula even correct if it outputs different results?