| from __future__ import absolute_import |
| from __future__ import division |
| from __future__ import print_function |
| from __future__ import unicode_literals |
| |
| import numpy as np |
| |
| from caffe2.python import workspace, cnn, memonger, core |
| import caffe2.python.hypothesis_test_util as hu |
| import hypothesis.strategies as st |
| from hypothesis import given |
| |
| |
| def has_blob(proto, needle): |
| for op in proto.op: |
| for inp in op.input: |
| if inp == needle: |
| return True |
| for outp in op.output: |
| if outp == needle: |
| return True |
| return False |
| |
| |
| def count_blobs(proto): |
| blobs = set() |
| for op in proto.op: |
| blobs = blobs.union(set(op.input)).union(set(op.output)) |
| return len(blobs) |
| |
| |
| class MemongerTest(hu.HypothesisTestCase): |
| @given(input_dim=st.integers(min_value=1, max_value=10), |
| output_dim=st.integers(min_value=1, max_value=10), |
| batch_size=st.integers(min_value=1, max_value=10), |
| do=st.sampled_from(hu.device_options), |
| algo=st.sampled_from(memonger.AssignmentAlgorithm)) |
| def test_simple_memonger(self, input_dim, output_dim, batch_size, do, algo): |
| m = cnn.CNNModelHelper() |
| fc1 = m.FC("data", "fc1", dim_in=input_dim, dim_out=output_dim) |
| fc2 = m.FC(fc1, "fc2", dim_in=output_dim, dim_out=output_dim) |
| fc3 = m.FC(fc2, "fc3", dim_in=output_dim, dim_out=output_dim) |
| |
| fc3.Relu([], fc3)\ |
| .Softmax([], "pred") \ |
| .LabelCrossEntropy(["label"], ["xent"]) \ |
| .AveragedLoss([], "loss") |
| input_to_grad = m.AddGradientOperators(["loss"]) |
| m.net.Proto().device_option.CopyFrom(do) |
| m.param_init_net.Proto().device_option.CopyFrom(do) |
| static_blobs = \ |
| [o for op in m.param_init_net.Proto().op for o in op.output] + \ |
| ["data", "label", "loss", input_to_grad["fc1_w"]] |
| |
| optimization = memonger.optimize_interference( |
| m.Proto(), static_blobs, algo=algo) |
| data = np.random.randn(batch_size, input_dim).astype(np.float32) |
| label = np.random.randint( |
| low=0, high=output_dim, size=(batch_size,)).astype(np.int32) |
| workspace.RunNetOnce(m.param_init_net) |
| workspace.FeedBlob("data", data, device_option=do) |
| workspace.FeedBlob("label", label, device_option=do) |
| workspace.RunNetOnce(m.net) |
| loss = workspace.FetchBlob("loss") |
| grad = workspace.FetchBlob(str(input_to_grad["fc1_w"])) |
| workspace.RunNetOnce(optimization.net) |
| optimized_loss = workspace.FetchBlob("loss") |
| optimized_grad = workspace.FetchBlob(str(input_to_grad["fc1_w"])) |
| np.testing.assert_almost_equal(loss, optimized_loss) |
| np.testing.assert_almost_equal(grad, optimized_grad) |
| stats = memonger.compute_statistics(optimization.assignments) |
| self.assertLess(stats.optimized_nbytes, stats.baseline_nbytes) |
| |
| # run with blob sizes |
| blob_sizes = memonger.collect_blob_sizes(m.Proto()) |
| optimization1 = memonger.optimize_interference( |
| m.Proto(), static_blobs, blob_sizes=blob_sizes, algo=algo) |
| workspace.RunNetOnce(optimization1.net) |
| optimized_loss = workspace.FetchBlob("loss") |
| optimized_grad = workspace.FetchBlob(str(input_to_grad["fc1_w"])) |
| np.testing.assert_almost_equal(loss, optimized_loss) |
| np.testing.assert_almost_equal(grad, optimized_grad) |
| stats = memonger.compute_statistics(optimization1.assignments) |
| self.assertLessEqual(stats.optimized_nbytes, stats.baseline_nbytes) |
| |
| @given(input_dim=st.integers(min_value=1, max_value=4), |
| output_dim=st.integers(min_value=1, max_value=4), |
| batch_size=st.integers(min_value=1, max_value=4)) |
| def test_gradient_optim(self, input_dim, output_dim, batch_size): |
| m = cnn.CNNModelHelper() |
| with core.NameScope("name_x"): |
| fc1 = m.FC("data", "fc1", dim_in=input_dim, dim_out=output_dim) |
| fc2 = m.FC(fc1, "fc2", dim_in=output_dim, dim_out=output_dim) |
| fc3 = m.FC(fc2, "fc3", dim_in=output_dim, dim_out=output_dim) |
| fc4 = m.FC(fc3, "fc4", dim_in=output_dim, dim_out=output_dim) |
| fc5 = m.FC(fc4, "fc5", dim_in=output_dim, dim_out=output_dim) |
| fc5.Relu([], fc5)\ |
| .Softmax([], "pred") \ |
| .LabelCrossEntropy(["label"], ["xent"]) \ |
| .AveragedLoss([], "loss") |
| input_to_grad = m.AddGradientOperators(["name_x/loss"]) |
| |
| blobs_before = count_blobs(m.net.Proto()) |
| optim_proto = memonger.share_grad_blobs( |
| m.net, |
| ["name_x/loss"], |
| set(m.param_to_grad.values()), |
| "name_x/", |
| share_activations=False, |
| ) |
| blobs_after = count_blobs(optim_proto) |
| self.assertLess(blobs_after, blobs_before) |
| |
| optim_proto_wacts = memonger.share_grad_blobs( |
| m.net, |
| ["name_x/loss"], |
| set(m.param_to_grad.values()), |
| "name_x/", |
| share_activations=True, |
| ) |
| blobs_wact_optim = count_blobs(optim_proto_wacts) |
| self.assertLessEqual(blobs_wact_optim, blobs_after) |
| |
| # Check that the last activations are not shared |
| self.assertTrue(has_blob(optim_proto, "name_x/fc5")) |
| self.assertTrue( |
| has_blob(optim_proto_wacts, "name_x/fc5"), |
| "Dont remap final activation", |
| ) |
| |
| # Test networks produce exactly same gradients |
| data = np.random.randn(batch_size, input_dim).astype(np.float32) |
| label = np.random.randint( |
| low=0, high=output_dim, size=(batch_size,)).astype(np.int32) |
| workspace.RunNetOnce(m.param_init_net) |
| workspace.FeedBlob("name_x/data", data) |
| workspace.FeedBlob("name_x/label", label) |
| workspace.RunNetOnce(m.net) |
| loss = workspace.FetchBlob("name_x/loss") |
| grad = workspace.FetchBlob(str(input_to_grad["name_x/fc1_w"])) |
| workspace.RunNetOnce(optim_proto) |
| optimized_loss = workspace.FetchBlob("name_x/loss") |
| optimized_grad = workspace.FetchBlob(str(input_to_grad["name_x/fc1_w"])) |
| np.testing.assert_almost_equal(loss, optimized_loss) |
| np.testing.assert_almost_equal(grad, optimized_grad) |
| |
| # Run with the forward optimization |
| workspace.RunNetOnce(optim_proto_wacts) |
| optimized_loss = workspace.FetchBlob("name_x/loss") |
| optimized_grad = workspace.FetchBlob(str(input_to_grad["name_x/fc1_w"])) |
| np.testing.assert_almost_equal(loss, optimized_loss) |
| np.testing.assert_almost_equal(grad, optimized_grad) |
| |
| @given(input_dim=st.integers(min_value=4, max_value=4), |
| output_dim=st.integers(min_value=4, max_value=4), |
| batch_size=st.integers(min_value=4, max_value=4)) |
| def test_gradient_optim_tree(self, input_dim, output_dim, batch_size): |
| m = cnn.CNNModelHelper() |
| with core.NameScope("name_x"): |
| fc1 = m.FC("data", "fc1", dim_in=input_dim, dim_out=output_dim) |
| fc2 = m.FC(fc1, "fc2", dim_in=output_dim, dim_out=output_dim) |
| fc3 = m.FC(fc2, "fc3", dim_in=output_dim, dim_out=output_dim) |
| fc4 = m.FC(fc3, "fc4", dim_in=output_dim, dim_out=output_dim) |
| fc5 = m.FC(fc4, "fc5", dim_in=output_dim, dim_out=output_dim) |
| fc5.Relu([], fc5) \ |
| .Softmax([], "pred1") \ |
| .LabelCrossEntropy(["label"], ["xent1"]) \ |
| .AveragedLoss([], "loss1") |
| fc6 = m.FC(fc5, "fc6", dim_in=output_dim, dim_out=output_dim) |
| fc6.Relu([], fc6) \ |
| .Softmax([], "pred2") \ |
| .LabelCrossEntropy(["label"], ["xent2"]) \ |
| .AveragedLoss([], "loss2") |
| input_to_grad = m.AddGradientOperators(["name_x/loss1", "name_x/loss2"]) |
| |
| blobs_before = count_blobs(m.net.Proto()) |
| optim_proto = memonger.share_grad_blobs( |
| m.net, |
| ["name_x/loss1", "name_x/loss2"], |
| set(m.param_to_grad.values()), |
| "name_x", # "name_x//shared_gradinp_0_shared" if using "name_x/" |
| share_activations=True, |
| dont_share_blobs=set(['name_x/fc6', 'name_x/fc5']), |
| ) |
| blobs_after = count_blobs(optim_proto) |
| self.assertLess(blobs_after, blobs_before) |
| self.assertTrue(has_blob(optim_proto, "name_x/fc6")) |
| |
| # Test networks produce exactly same gradients |
| data = np.random.randn(batch_size, input_dim).astype(np.float32) |
| label = np.random.randint( |
| low=0, high=output_dim, size=(batch_size,)).astype(np.int32) |
| workspace.RunNetOnce(m.param_init_net) |
| workspace.FeedBlob("name_x/data", data) |
| workspace.FeedBlob("name_x/label", label) |
| workspace.RunNetOnce(m.net) |
| loss1 = workspace.FetchBlob("name_x/loss1") |
| loss2 = workspace.FetchBlob("name_x/loss2") |
| grad = workspace.FetchBlob(str(input_to_grad["name_x/fc1_w"])) |
| workspace.RunNetOnce(optim_proto) |
| optimized_loss1 = workspace.FetchBlob("name_x/loss1") |
| optimized_loss2 = workspace.FetchBlob("name_x/loss2") |
| optimized_grad = workspace.FetchBlob(str(input_to_grad["name_x/fc1_w"])) |
| np.testing.assert_almost_equal(loss1, optimized_loss1) |
| np.testing.assert_almost_equal(loss2, optimized_loss2) |
| np.testing.assert_almost_equal(grad, optimized_grad) |
| |
| @given(input_dim=st.integers(min_value=4, max_value=4), |
| output_dim=st.integers(min_value=4, max_value=4), |
| batch_size=st.integers(min_value=4, max_value=4)) |
| def test_forward_optim_tree_daggy(self, input_dim, output_dim, batch_size): |
| m = cnn.CNNModelHelper() |
| m.Proto().type = "dag" |
| m.Proto().num_workers = 4 |
| |
| with core.NameScope("name_x"): |
| fc1 = m.FC("data", "fc1", dim_in=input_dim, dim_out=output_dim) |
| fc2 = m.FC(fc1, "fc2", dim_in=output_dim, dim_out=output_dim) |
| |
| fc3 = m.FC(fc2, "fc3", dim_in=output_dim, dim_out=output_dim) |
| fc4 = m.FC(fc3, "fc4", dim_in=output_dim, dim_out=output_dim) |
| fc5 = m.FC(fc4, "fc5", dim_in=output_dim, dim_out=output_dim) |
| |
| # Branch |
| fc3b = m.FC(fc2, "fc3b", dim_in=output_dim, dim_out=output_dim) |
| fc4b = m.FC(fc3b, "fc4b", dim_in=output_dim, dim_out=output_dim) |
| fc5b = m.FC(fc4b, "fc5b", dim_in=output_dim, dim_out=output_dim) |
| |
| fc5sum = m.Sum([fc5, fc5b], "fc5sum") |
| |
| fc5.Relu([], fc5sum) \ |
| .Softmax([], "pred1") \ |
| .LabelCrossEntropy(["label"], ["xent1"]) \ |
| .AveragedLoss([], "loss1") |
| fc6 = m.FC(fc5, "fc6", dim_in=output_dim, dim_out=output_dim) |
| fc6.Relu([], fc6) \ |
| .Softmax([], "pred2") \ |
| .LabelCrossEntropy(["label"], ["xent2"]) \ |
| .AveragedLoss([], "loss2") |
| |
| blobs_before = count_blobs(m.net.Proto()) |
| optim_proto = memonger.optimize_inference_for_dag( |
| m.net, ["name_x/data"], "name_x" |
| ) |
| blobs_after = count_blobs(optim_proto) |
| self.assertLess(blobs_after, blobs_before) |
| |
| # Test networks produce exactly same results |
| data = np.random.randn(batch_size, input_dim).astype(np.float32) |
| label = np.random.randint( |
| low=0, high=output_dim, size=(batch_size,)).astype(np.int32) |
| workspace.RunNetOnce(m.param_init_net) |
| workspace.FeedBlob("name_x/data", data) |
| workspace.FeedBlob("name_x/label", label) |
| workspace.RunNetOnce(m.net) |
| loss1 = workspace.FetchBlob("name_x/loss1") |
| loss2 = workspace.FetchBlob("name_x/loss2") |
| workspace.RunNetOnce(optim_proto) |
| optimized_loss1 = workspace.FetchBlob("name_x/loss1") |
| optimized_loss2 = workspace.FetchBlob("name_x/loss2") |
| np.testing.assert_almost_equal(loss1, optimized_loss1) |
| np.testing.assert_almost_equal(loss2, optimized_loss2) |
| |
| def test_topological_sort_longest_path(self): |
| m = cnn.CNNModelHelper() |
| # 0 |
| m.Copy("conv0_w_comp", "conv0_w") |
| # 1 |
| conv0 = m.Conv("data", "conv0", 32, 32, 4) |
| # 2 |
| m.Copy("conv2_w", "conv2_w") |
| # 3 |
| m.Conv(conv0, "conv2", 16, 32, 4) |
| |
| g = memonger.compute_interference_graph(m.net.Proto().op) |
| |
| orders_org = memonger.topological_sort_traversal(g) |
| orders_gt_org = [2, 0, 1, 3] |
| self.assertEqual(orders_gt_org, orders_org) |
| |
| orders = memonger.topological_sort_traversal_longest_path(g) |
| # longer path is in front of the shorter one |
| orders_gt = [0, 1, 2, 3] |
| self.assertEqual(orders_gt, orders) |
| |
| def test_topological_sort_longest_path_multi_target(self): |
| # two outputs: conv2 and data4 |
| m = cnn.CNNModelHelper() |
| # 0 |
| m.Copy("conv0_w_comp", "conv0_w") |
| # 1 |
| conv0 = m.Conv("data", "conv0", 32, 32, 4) |
| # 2 |
| m.Copy("conv2_w", "conv2_w") |
| # 3 |
| m.Conv(conv0, "conv2", 16, 32, 4) |
| # 4 |
| m.Copy("data1", "data2") |
| # 5 |
| m.Copy("data2", "data3") |
| |
| g = memonger.compute_interference_graph(m.net.Proto().op) |
| |
| orders_org = memonger.topological_sort_traversal(g) |
| orders_gt_org = [4, 5, 2, 0, 1, 3] |
| self.assertEqual(orders_gt_org, orders_org) |
| |
| orders = memonger.topological_sort_traversal_longest_path(g) |
| # longer path is in front of the shorter one |
| orders_gt = [0, 1, 2, 3, 4, 5] |
| self.assertEqual(orders_gt, orders) |
| |
| def test_topological_sort_longest_path_single_node(self): |
| # single node |
| m = cnn.CNNModelHelper() |
| # 0 |
| m.Copy("conv0_w_comp", "conv0_w") |
| |
| g = memonger.compute_interference_graph(m.net.Proto().op) |
| |
| orders_org = memonger.topological_sort_traversal(g) |
| orders_gt_org = [0] |
| self.assertEqual(orders_gt_org, orders_org) |
| |
| orders = memonger.topological_sort_traversal_longest_path(g) |
| # longer path is in front of the shorter one |
| orders_gt = [0] |
| self.assertEqual(orders_gt, orders) |
| |
| def test_compute_assignments_greedy(self): |
| LiveRange = memonger.LiveRange |
| ranges_sorted = [ |
| ('b1', LiveRange(1, 3, 10)), |
| ('b2', LiveRange(3, 4, 1)), |
| ('b3', LiveRange(5, 6, 1)), |
| ('b4', LiveRange(5, 7, 10)), |
| ] |
| assignment_gt = [ |
| [ranges_sorted[0], ranges_sorted[3]], |
| [ranges_sorted[1], ranges_sorted[2]], |
| ] |
| |
| best = memonger.compute_assignments_greedy(ranges_sorted, None) |
| self.assertEqual(memonger.get_memory_usage(best), 11) |
| self.assertEqual(best, assignment_gt) |
| |
| def test_compute_assignments_dp(self): |
| LiveRange = memonger.LiveRange |
| ranges_sorted = [ |
| ('b1', LiveRange(1, 3, 10)), |
| ('b2', LiveRange(3, 4, 1)), |
| ('b3', LiveRange(5, 6, 1)), |
| ('b4', LiveRange(5, 7, 10)), |
| ] |
| |
| best = memonger.compute_assignments_dp(ranges_sorted, None) |
| self.assertEqual(memonger.get_memory_usage(best), 11) |
| |
| def test_compute_assignments_dp1(self): |
| LiveRange = memonger.LiveRange |
| ranges_sorted = [ |
| ('b1', LiveRange(1, 2, 10)), |
| ('b2', LiveRange(4, 6, 1)), |
| ('b3', LiveRange(5, 6, 10)), |
| ] |
| |
| best = memonger.compute_assignments_dp(ranges_sorted, []) |
| self.assertEqual(memonger.get_memory_usage(best), 11) |
