def calcRepulsiveTdist(graph, embed, nV): D = _gsddmm(graph._graph, "sub", embed[:nV], embed[:nV], lhs_target='u', rhs_target='v') if torch.isnan(D).any(): print("D got problem") d = _gsddmm(graph._graph, 'dot', D, D, lhs_target='e', rhs_target='e') if torch.isnan(d).any(): print("d got problem") E = 2.0 / ((d) * (1.0 + d)) if torch.isnan(E).any(): print("First E got problem") E = _gsddmm(graph._graph, "mul", D, E, lhs_target='e', rhs_target='e') if torch.isnan(E).any(): print("Second E got problem") E = allTdistScale(E) E = 0.02 * E if torch.isnan(E).any(): print("Scaling E got problem") outputr = _gspmm(graph._graph.reverse(), "copy_rhs", "sum", E, E)[0] if torch.isnan(outputr).any(): print("outputr in recals got problem") return outputr
def dglusingtdistribution(batchgraphs, embed, iterations=1, lrate=1.0): it = 0 totalktime = 0 kerneltime = [] purektime = 0 while it < iterations: for [graph, s, e] in batchgraphs: #just to make sure: dim(sparse graph) == dim(embedding) pV = len(graph.nodes()) start = time.time() #SDDMM D = _gsddmm(graph._graph, "sub", embed[:pV], embed[:pV], lhs_target='u', rhs_target='v') d = _gsddmm(graph._graph, 'dot', D, D, lhs_target='e', rhs_target='e') end1 = time.time() #NonlinearTransformation #E = - 2.0 / (1.0 + d) # for t-distribution E = 1.0 + 1.0 / d # for FR-model start1 = time.time() #SDDMM E = _gsddmm(graph._graph, "mul", D, E, lhs_target='e', rhs_target='e') end2 = time.time() #scaling #E = allscale(E) # for t-distribution start2 = time.time() #SPMM outputa = _gspmm(graph._graph.reverse(), "copy_rhs", "sum", E, E)[0] end = time.time() #embed[:pV] = embed[:pV] + lrate * outputa[:pV] #purektime += end1 - start + end2 - start1 + end - start2 kerneltime.append(end1 - start + start1 - end1 + end2 - start1 + end - start2) totalktime += end - start it += 1 print("Total GDL Kernel Time:", totalktime, "s", ", Kernel T:", kerneltime, "Avg. Kernel T:", sum(kerneltime) / len(kerneltime)) print("dglTime:", len(embed), ":DIM", len(embed[0]), ":avgtime:", sum(kerneltime) / len(kerneltime)) return embed
def forward(ctx, gidx: HeteroGraphIndex, scores: Tensor, eids: Tensor, end_n_ids: Tensor, norm_by: str): if not is_all(eids): gidx = gidx.edge_subgraph([eids], True).graph if norm_by == "src": gidx = gidx.reverse() # use feat - max(feat) for numerical stability. scores = scores.float() scores_max = _gspmm(gidx, "copy_rhs", "max", None, scores)[0] scores = _gsddmm(gidx, "sub", scores, scores_max, "e", "v") # find threshold for each node and perform ReLU(u-t(u)) operation. tau, supp_size = _threshold_and_support_graph(gidx, scores, end_n_ids) out = torch.clamp(_gsddmm(gidx, "sub", scores, tau, "e", "v"), min=0) ctx.backward_cache = gidx ctx.save_for_backward(supp_size, out) torch.cuda.empty_cache() return out
def calcRepulsive(graph, embed, sm_table, V): X = _gsddmm(graph._graph, 'dot', embed[:V], embed[:V], lhs_target='u', rhs_target='v') Y = allFastSigmoid(X, sm_table, 0) output = _gspmm(graph._graph.reverse(), "mul", "sum", embed[:V], Y)[0] return output
def backward(ctx, grad_out): gidx = ctx.backward_cache supp_size, out = ctx.saved_tensors grad_in = grad_out.clone() # grad for ReLU grad_in[out == 0] = 0 # dL/dv_i = dL/do_i - 1/k \sum_{j=1}^k dL/do_j v_hat = _gspmm(gidx, "copy_rhs", "sum", None, grad_in)[0] / supp_size.to(out.dtype) grad_in_modify = _gsddmm(gidx, "sub", grad_in, v_hat, "e", "v") grad_in = torch.where(out != 0, grad_in_modify, grad_in) del gidx torch.cuda.empty_cache() return None, grad_in, None, None, None
def dglusingsigmoid(batchgraphs, embed, iterations=1, lrate=1.0): it = 0 totaltime = [] kerneltime = [] purekerneltime = 0 #sm_table = init_SM_TABLE() #print(sm_table) while it < iterations: start = time.time() for [graph, s, e] in batchgraphs: ngraph = negativeSamples(graph, s, e) nV = len(ngraph.nodes()) #just to make sure: dim(sparse graph) == dim(embedding) pV = len(graph.nodes()) #SDDMM operation kstart = time.time() X = _gsddmm(graph._graph, 'dot', embed[:pV], embed[:pV], lhs_target='u', rhs_target='v') kend1 = time.time() #non-linear transformation Y = 1.0 * X #- 1.0 / (1 + torch.exp(-X)) #Y = allFastSigmoid(X, sm_table) #SPMM operation kstart1 = time.time() outputa = _gspmm(graph._graph.reverse(), "mul", "sum", embed[:pV], Y)[0] kend = time.time() #outputr = calcRepulsive(ngraph, embed, sm_table, nV) #embed[s:e] = lrate * outputa[s:e] #embed[s:e] = embed[s:e] + lrate * outputr[s:e] purekerneltime += (kend1 - kstart) + (kend - kstart1) kerneltime.append((kend1 - kstart) + (kend - kstart1) + (kstart1 - kend1)) end = time.time() totaltime.append(end - start) it += 1 print("GDL Total Time:", sum(totaltime), "s", kerneltime, ", Kernel Time:", sum(kerneltime), 'Avg. Kernel T:', sum(kerneltime) / len(kerneltime)) print("dglTime:", len(embed), ":DIM", len(embed[0]), ":avgtime:", sum(kerneltime) / len(kerneltime)) return embed
def dglusingGCN(batchgraphs, embed, iterations=1, lrate=1.0): it = 0 totaltime = [] kerneltime = [] while it < iterations: for [graph, s, e] in batchgraphs: ngraph = negativeSamples(graph, s, e) nV = len(ngraph.nodes()) pV = len(graph.nodes()) kstart = time.time() E = _gsddmm(graph._graph, "copy_rhs", embed[:pV], embed[:pV], lhs_target='u', rhs_target='v') output = _gspmm(graph._graph.reverse(), "copy_rhs", "sum", E, E)[0] kend = time.time() kerneltime.append(kend - kstart) it += 1 print("dglusingGCN") print("dglTime:", len(embed), ":DIM", len(embed[0]), ":avgtime:", sum(kerneltime) / len(kerneltime)) return output