def test_linear_zeros(backend_default, basic_linargs): # basic sanity check with 0 weights random inputs nin, nout, batch_size = basic_linargs NervanaObject.be.bsz = batch_size dtypeu = np.float32 init_unif = Uniform(low=0.0, high=0.0) layer = Linear(nout=nout, init=init_unif) inp = layer.be.array(dtypeu(np.random.random((nin, batch_size)))) layer.configure(nin) layer.prev_layer = True # Hack to force delta buffer allocation layer.allocate() layer.set_deltas([layer.be.iobuf(nin)]) out = layer.fprop(inp).get() assert np.min(out) == 0.0 and np.max(out) == 0.0 err = dtypeu(np.zeros((nout, batch_size))) deltas = layer.bprop(layer.be.array(err)).get() assert np.min(deltas) == 0.0 and np.max(deltas) == 0.0 dw = layer.dW.get() assert np.min(dw) == 0.0 and np.max(dw) == 0.0 return
def test_linear_zeros(backend_default, basic_linargs): # basic sanity check with 0 weights random inputs nin, nout, batch_size = basic_linargs NervanaObject.be.bsz = batch_size dtypeu = np.float32 init_unif = Uniform(low=0.0, high=0.0) layer = Linear(nout=nout, init=init_unif) inp = layer.be.array(dtypeu(np.random.random((nin, batch_size)))) layer.configure(nin) layer.prev_layer = True # Hack to force delta buffer allocation layer.allocate() layer.set_deltas([layer.be.iobuf(nin)]) out = layer.fprop(inp).get() assert np.min(out) == 0.0 and np.max(out) == 0.0 err = dtypeu(np.zeros((nout, batch_size))) deltas = layer.bprop(layer.be.array(err)).asnumpyarray() assert np.min(deltas) == 0.0 and np.max(deltas) == 0.0 dw = layer.dW.asnumpyarray() assert np.min(dw) == 0.0 and np.max(dw) == 0.0 return
def test_all_rand(backend_default, allrand_args, deltas_buffer): # test with random weights and random inputs dtypeu = np.float32 w_rng, rngmax = allrand_args inp_rng = [0.0, rngmax] nin = 1024 nout = 2048 batch_size = 16 NervanaObject.be.bsz = batch_size init_unif = Uniform(low=w_rng[0], high=w_rng[1]) layer = Linear(nout=nout, init=init_unif) inp = np.random.random((nin, batch_size)) inp *= inp_rng[1] - inp_rng[0] inp += inp_rng[0] inp = inp.astype(dtypeu) layer.configure(nin) layer.prev_layer = True # Hack to force delta buffer allocation layer.allocate() layer.allocate_deltas(deltas_buffer) deltas_buffer.allocate_buffers() layer.set_deltas(deltas_buffer) out = layer.fprop(layer.be.array(inp)).get() w = layer.W.get() # the expected output using numpy out_exp = np.dot(w, inp) # for larger layers need to estimate numerical precision atol = 2 * est_mm_prec(w, inp, ntrials=1) assert np.allclose(out_exp, out, atol=atol, rtol=0.0), \ '%e %e' % (np.max(np.abs(out - out_exp)), atol) err = np.random.random((nout, batch_size)) err = err * (inp_rng[1] - inp_rng[0]) + inp_rng[0] err = err.astype(dtypeu) deltas = layer.bprop(layer.be.array(err)).get() dw = layer.dW.get() deltas_exp = np.dot(w.T, err) atol = 2 * est_mm_prec(w.T, err, ntrials=1) assert np.allclose(deltas_exp, deltas, atol=atol, rtol=0.0), \ '%e %e' % (np.max(np.abs(deltas_exp - deltas)), atol) dw_exp = np.dot(err, inp.T) atol = 2 * est_mm_prec(err, inp.T, ntrials=1) assert np.allclose(dw_exp, dw, atol=atol, rtol=0.0), \ '%e %e' % (np.max(np.abs(dw_exp - dw)), atol) return
def test_linear_zeros(backend, basic_linargs): # basic sanity check with 0 weights random inputs nin, nout, batch_size = basic_linargs NervanaObject.be.bsz = NervanaObject.be.bs = batch_size dtypeu = np.float32 init_unif = Uniform(low=0.0, high=0.0) layer = Linear(nout=nout, init=init_unif) inp = layer.be.array(dtypeu(np.random.random((nin, batch_size)))) out = layer.fprop(inp).get() assert np.min(out) == 0.0 and np.max(out) == 0.0 err = dtypeu(np.zeros((nout, batch_size))) deltas = layer.bprop(layer.be.array(err)).asnumpyarray() assert np.min(deltas) == 0.0 and np.max(deltas) == 0.0 dw = layer.dW.asnumpyarray() assert np.min(dw) == 0.0 and np.max(dw) == 0.0 return