| import numpy as np |
| |
| from caffe2.python import workspace, memonger, core, model_helper, brew |
| from caffe2.proto import caffe2_pb2 |
| import caffe2.python.hypothesis_test_util as hu |
| from future.utils import viewvalues |
| import hypothesis.strategies as st |
| from hypothesis import given, settings |
| import unittest |
| |
| |
| 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)) |
| @settings(max_examples=5, deadline=None) |
| def test_simple_memonger(self, input_dim, output_dim, batch_size, do, algo): |
| m = model_helper.ModelHelper() |
| fc1 = brew.fc(m, "data", "fc1", dim_in=input_dim, dim_out=output_dim) |
| fc2 = brew.fc(m, fc1, "fc2", dim_in=output_dim, dim_out=output_dim) |
| fc3 = brew.fc(m, 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=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)) |
| @settings(max_examples=5, deadline=None) |
| def test_fast_memonger(self, input_dim, output_dim, batch_size, do): |
| m = model_helper.ModelHelper() |
| fc1 = brew.fc(m, "data", "fc1", dim_in=input_dim, dim_out=output_dim) |
| fc2 = brew.fc(m, fc1, "fc2", dim_in=output_dim, dim_out=output_dim) |
| fc3 = brew.fc(m, 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"]] |
| |
| optimized_net = memonger.optimize_inference_fast( |
| m.Proto(), static_blobs) |
| 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(optimized_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) |
| |
| self.assertLess(count_blobs(optimized_net), count_blobs(m.Proto())) |
| |
| def test_fast_memonger_unique_outputs(self): |
| m = model_helper.ModelHelper() |
| fc = [] |
| for i in range(2): |
| z = brew.fc( |
| m, "data{}".format(i), "fc".format(i), dim_in=2, dim_out=2) |
| fc.append(z) |
| r = [] |
| # Trick is here to have same input appear twice in a same Sum |
| for x in fc: |
| for y in fc: |
| r.append(brew.sum(m, [x, y], 1)) |
| concated = brew.concat(m, r, "concated") |
| brew.relu(m, concated, "merged") |
| |
| static_blobs = \ |
| [o for op in m.param_init_net.Proto().op for o in op.output] + \ |
| ["merged"] + ["data{}".format(i) for i in range(len(fc))] |
| |
| optimized_net = memonger.optimize_inference_fast( |
| m.Proto(), static_blobs) |
| for op in optimized_net.op: |
| self.assertEqual(len(op.output), len(set(op.output)), str(op)) |
| |
| @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 = model_helper.ModelHelper() |
| with core.NameScope("name_x"): |
| fc1 = brew.fc(m, "data", "fc1", dim_in=input_dim, dim_out=output_dim) |
| fc2 = brew.fc(m, fc1, "fc2", dim_in=output_dim, dim_out=output_dim) |
| fc3 = brew.fc(m, fc2, "fc3", dim_in=output_dim, dim_out=output_dim) |
| fc4 = brew.fc(m, fc3, "fc4", dim_in=output_dim, dim_out=output_dim) |
| fc5 = brew.fc(m, 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(viewvalues(m.param_to_grad)), |
| "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(viewvalues(m.param_to_grad)), |
| "name_x/", |
| share_activations=True, |
| dont_share_blobs=set([str(input_to_grad["name_x/fc1_w"])]), |
| ) |
| 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) |
| |
| workspace.FeedBlob(str(input_to_grad["name_x/fc1_w"]), np.array([0.0])) |
| |
| # 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) |
| |
| @unittest.skipIf(not workspace.has_gpu_support, "No gpu support.") |
| def test_memonger_mix_cpu_gpu(self): |
| ''' |
| Check that memonger does not make blobs cross CPU/GPU boundary |
| ''' |
| m = model_helper.ModelHelper() |
| with core.DeviceScope(core.DeviceOption(workspace.GpuDeviceType, 0)): |
| fc1 = brew.fc(m, "data", "fc1", dim_in=2, dim_out=2) |
| fc2 = brew.fc(m, fc1, "fc2", dim_in=2, dim_out=2) |
| fc3 = brew.fc(m, fc2, "fc3", dim_in=2, dim_out=2) |
| fc4 = brew.fc(m, fc3, "fc4", dim_in=2, dim_out=2) |
| fc4_cpu = m.net.CopyGPUToCPU(fc4, "fc4_cpu") |
| with core.DeviceScope(core.DeviceOption(caffe2_pb2.CPU, 0)): |
| fc5_cpu = brew.fc(m, fc4_cpu, "fc5_cpu", dim_in=2, dim_out=2) |
| fc6_cpu = brew.fc(m, fc5_cpu, "fc6_cpu", dim_in=2, dim_out=2) |
| fc7_cpu = brew.fc(m, fc6_cpu, "fc7_cpu", dim_in=2, dim_out=2) |
| fc7_cpu.Relu([], fc7_cpu) \ |
| .Softmax([], "pred") \ |
| .LabelCrossEntropy(["label"], ["xent"]) \ |
| .AveragedLoss([], "loss") |
| m.AddGradientOperators(["loss"]) |
| |
| blobs_before = count_blobs(m.net.Proto()) |
| optim_proto = memonger.share_grad_blobs( |
| m.net, |
| ["loss"], |
| set(viewvalues(m.param_to_grad)), |
| "", |
| share_activations=True, |
| dont_share_blobs=set(), |
| ) |
| blobs_after = count_blobs(optim_proto) |
| self.assertLess(blobs_after, blobs_before) |
| |
| # Create set of blobs on CPU side and GPU side and check they don't |
| # overlap |
| device_blobs = {caffe2_pb2.CPU: set(), workspace.GpuDeviceType: set()} |
| for op in optim_proto.op: |
| if op.type not in ['CopyCPUToGPU', "CopyGPUToCPU"]: |
| dev = op.device_option.device_type |
| for b in list(op.input) + list(op.output): |
| device_blobs[dev].add(b) |
| |
| device_crossers = device_blobs[caffe2_pb2.CPU].intersection( |
| device_blobs[workspace.GpuDeviceType] |
| ) |
| self.assertEquals(device_crossers, set()) |
| |
| @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)) |
| @settings(deadline=1000) |
| def test_gradient_optim_tree(self, input_dim, output_dim, batch_size): |
| m = model_helper.ModelHelper() |
| with core.NameScope("name_x"): |
| fc1 = brew.fc(m, "data", "fc1", dim_in=input_dim, dim_out=output_dim) |
| fc2 = brew.fc(m, fc1, "fc2", dim_in=output_dim, dim_out=output_dim) |
| fc3 = brew.fc(m, fc2, "fc3", dim_in=output_dim, dim_out=output_dim) |
| fc4 = brew.fc(m, fc3, "fc4", dim_in=output_dim, dim_out=output_dim) |
| fc5 = brew.fc(m, fc4, "fc5", dim_in=output_dim, dim_out=output_dim) |
| fc5.Relu([], fc5) \ |
| .Softmax([], "pred1") \ |
| .LabelCrossEntropy(["label"], ["xent1"]) \ |
| .AveragedLoss([], "loss1") |
| fc6 = brew.fc(m, 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(viewvalues(m.param_to_grad)), |
| "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', |
| str(input_to_grad["name_x/fc1_w"])]), |
| ) |
| 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.FeedBlob(str(input_to_grad["name_x/fc1_w"]), np.array([0.0])) |
| |
| 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)) |
| @settings(deadline=1000) |
| def test_forward_optim_tree_daggy(self, input_dim, output_dim, batch_size): |
| m = model_helper.ModelHelper() |
| m.Proto().type = "dag" |
| m.Proto().num_workers = 4 |
| |
| with core.NameScope("name_x"): |
| fc1 = brew.fc(m, "data", "fc1", dim_in=input_dim, dim_out=output_dim) |
| fc2 = brew.fc(m, fc1, "fc2", dim_in=output_dim, dim_out=output_dim) |
| |
| fc3 = brew.fc(m, fc2, "fc3", dim_in=output_dim, dim_out=output_dim) |
| fc4 = brew.fc(m, fc3, "fc4", dim_in=output_dim, dim_out=output_dim) |
| fc5 = brew.fc(m, fc4, "fc5", dim_in=output_dim, dim_out=output_dim) |
| |
| # Branch |
| fc3b = brew.fc(m, fc2, "fc3b", dim_in=output_dim, dim_out=output_dim) |
| fc4b = brew.fc(m, fc3b, "fc4b", dim_in=output_dim, dim_out=output_dim) |
| fc5b = brew.fc(m, fc4b, "fc5b", dim_in=output_dim, dim_out=output_dim) |
| |
| fc5sum = brew.sum(m, [fc5, fc5b], "fc5sum") |
| |
| fc5.Relu([], fc5sum) \ |
| .Softmax([], "pred1") \ |
| .LabelCrossEntropy(["label"], ["xent1"]) \ |
| .AveragedLoss([], "loss1") |
| fc6 = brew.fc(m, 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) |
| |
| @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)) |
| @settings(deadline=10000) |
| def test_forward_optim_tree_harder(self, input_dim, output_dim, batch_size): |
| m = model_helper.ModelHelper() |
| m.net.Proto().type = "dag" |
| m.net.Proto().num_workers = 4 |
| m.net.AddExternalInput("label") |
| m.net.AddExternalInput("data") |
| |
| with core.NameScope("name_x"): |
| fc1 = brew.fc(m, "data", "fc1", dim_in=input_dim, dim_out=output_dim) |
| fc2 = brew.fc(m, fc1, "fc2", dim_in=output_dim, dim_out=output_dim) |
| |
| fc3 = brew.fc(m, fc2, "fc3", dim_in=output_dim, dim_out=output_dim) |
| fc4 = brew.fc(m, fc3, "fc4", dim_in=output_dim, dim_out=output_dim) |
| fc5 = brew.fc(m, fc4, "fc5", dim_in=output_dim, dim_out=output_dim) |
| |
| # Branch |
| fc3b = brew.fc(m, fc2, "fc3b", dim_in=output_dim, dim_out=output_dim) |
| fc4b = brew.fc(m, fc3b, "fc4b", dim_in=output_dim, dim_out=output_dim) |
| fc5b = brew.fc(m, fc4b, "fc5b", dim_in=output_dim, dim_out=output_dim) |
| |
| fc5sum = brew.sum(m, [fc5, fc5b], "fc5sum") |
| fc5sum.Relu([], "relu1") \ |
| .Softmax([], "pred1") \ |
| .LabelCrossEntropy(["label"], ["xent1"]) \ |
| .AveragedLoss([], "loss1") |
| fc6 = brew.fc(m, 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) |
| |
| # Extra test with when one of the parameters is also an input. |
| # This caused a bug before. |
| optim_proto_extra_input = memonger.optimize_inference_for_dag( |
| m.net, ["name_x/data", "name_x/fc1_w"], "name_x/" |
| ) |
| blobs_after_extra_input = count_blobs(optim_proto_extra_input) |
| self.assertEqual(blobs_after, blobs_after_extra_input) |
| ### |
| |
| print(str(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) |
| |
| # This test reproduces scenario where dag traversal for finding |
| # shared blobs was not always starting from ops with in degree of 0 |
| @settings(deadline=10000) |
| def test_forward_optim_tree_dag_traversal(self): |
| input_dim = 4 |
| output_dim = 4 |
| batch_size = 4 |
| |
| m = model_helper.ModelHelper() |
| m.Proto().type = "dag" |
| m.Proto().num_workers = 4 |
| |
| with core.NameScope("name_x"): |
| fc1 = brew.fc(m, "data", "fc1", dim_in=input_dim, dim_out=output_dim) |
| fc2 = brew.fc(m, fc1, "fc2", dim_in=output_dim, dim_out=output_dim) |
| |
| fc3 = brew.fc(m, fc2, "fc3", dim_in=output_dim, dim_out=output_dim) |
| fc4 = brew.fc(m, fc3, "fc4", dim_in=output_dim, dim_out=output_dim) |
| fc5 = brew.fc(m, fc4, "fc5", dim_in=output_dim, dim_out=output_dim) |
| |
| # Branch |
| fc3b = brew.fc(m, fc2, "fc3b", dim_in=output_dim, dim_out=output_dim) |
| fc4b = brew.fc(m, fc3b, "fc4b", dim_in=output_dim, dim_out=output_dim) |
| fc5b = brew.fc(m, fc4b, "fc5b", dim_in=output_dim, dim_out=output_dim) |
| |
| fc5sum = brew.sum(m, [fc5, fc5b], "fc5sum") |
| |
| fc5.Relu([], fc5sum) \ |
| .Softmax([], "pred1") \ |
| .LabelCrossEntropy(["label"], ["xent1"]) \ |
| .AveragedLoss([], "loss1") |
| fc6 = brew.fc(m, 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()) |
| # adding name_x/fc5_w as heads (which belongs to non-root op) |
| # to make sure that dag traversal always starts from root ops |
| optim_proto = memonger.optimize_inference_for_dag( |
| m.net, ["name_x/fc5_w", "name_x/data"], "name_x" |
| ) |
| blobs_after = count_blobs(optim_proto) |
| self.assertLess(blobs_after, blobs_before) |
| |
| # This is specifically to verify the op schema check being done in memonger |
| def test_forward_optim_tree_enforce_inplace_op_invalid(self): |
| m = model_helper.ModelHelper() |
| m.Proto().type = "dag" |
| m.Proto().num_workers = 4 |
| |
| net = m.net |
| net.IndexFreeze("A", "B") # enforce inplace op |
| net.Sum(["B", "B"], "C") |
| net.Relu("C", "D") |
| net.Sum(["D", "D"], "E") |
| |
| with self.assertRaises(RuntimeError): |
| memonger.optimize_inference_for_dag(net, ["A"], "") |
| |
| # Here inplace op is specifically a root op to repro the scenario where dag |
| # memonger could treat all the output blobs as shareable blobs and fails |
| # assertion of input blob with the same name not allowed to share |
| def test_forward_optim_tree_enforce_inplace_op_valid_and_as_head(self): |
| m = model_helper.ModelHelper() |
| m.Proto().type = "dag" |
| m.Proto().num_workers = 4 |
| |
| net = m.net |
| net.IndexFreeze("A", "A") # enforce inplace op |
| net.Sum(["A", "A"], "B") |
| net.Relu("B", "C") |
| net.Relu("C", "D") |
| net.Sum(["D", "D"], "E") |
| |
| blobs_before = count_blobs(m.net.Proto()) |
| optim_proto = memonger.optimize_inference_for_dag( |
| net, ["A"], "" |
| ) |
| blobs_after = count_blobs(optim_proto) |
| self.assertLess(blobs_after, blobs_before) |
| |
| def test_rnn(self): |
| from caffe2.python import rnn_cell |
| T = 5 |
| model = model_helper.ModelHelper() |
| seq_lengths, labels = \ |
| model.net.AddExternalInputs( |
| 'seq_lengths', 'labels', |
| ) |
| init_blobs = [] |
| for i in range(2): |
| hidden_init, cell_init = model.net.AddExternalInputs( |
| "hidden_init_{}".format(i), |
| "cell_init_{}".format(i) |
| ) |
| init_blobs.extend([hidden_init, cell_init]) |
| model.param_init_net.ConstantFill([], ["input"], shape=[T, 4, 10]) |
| output, last_hidden, _, last_state = rnn_cell.LSTM( |
| model=model, |
| input_blob="input", |
| seq_lengths=seq_lengths, |
| initial_states=init_blobs, |
| dim_in=10, |
| dim_out=[10, 10], |
| scope="lstm1", |
| forward_only=False, |
| drop_states=True, |
| return_last_layer_only=True, |
| ) |
| softmax, loss = model.net.SoftmaxWithLoss( |
| [model.Flatten(output), "labels"], |
| ['softmax', 'loss'], |
| ) |
| |
| model.AddGradientOperators([loss]) |
| blobs_before = count_blobs(model.net.Proto()) |
| optim_proto = memonger.share_grad_blobs( |
| model.net, |
| ["loss"], |
| set(viewvalues(model.param_to_grad)), |
| "", |
| share_activations=True, |
| dont_share_blobs=set(), |
| ) |
| blobs_after = count_blobs(optim_proto) |
| self.assertLess(blobs_after, blobs_before) |
| |
| # Run once to see all blobs are set up correctly |
| for init_blob in init_blobs: |
| workspace.FeedBlob(init_blob, np.zeros( |
| [1, 4, 10], dtype=np.float32 |
| )) |
| workspace.FeedBlob("seq_lengths", np.array([T] * 4, dtype=np.int32)) |
| workspace.FeedBlob("labels", np.random.rand(T).astype(np.int32)) |
| |
| workspace.RunNetOnce(model.param_init_net) |
| workspace.RunNetOnce(model.net) |
| |
| def test_compute_interference_graph_inplace_ops(self): |
| m = model_helper.ModelHelper() |
| m.Copy("b1", "b1") |
| m.Copy("b1", "b1") |
| m.Copy("b1", "b1") |
| g = memonger.compute_interference_graph(m.net.Proto().op) |
| self.assertEqual(list(g.edges()), [(0, 1), (0, 2), (1, 2)]) |
| |
| def test_topological_sort_longest_path(self): |
| m = model_helper.ModelHelper() |
| # 0 |
| m.Copy("conv0_w_comp", "conv0_w") |
| # 1 |
| conv0 = brew.conv(m, "data", "conv0", 32, 32, 4) |
| # 2 |
| m.Copy("conv2_w", "conv2_w") |
| # 3 |
| brew.conv(m, 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, list(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, list(orders)) |
| |
| def test_topological_sort_longest_path_multi_target(self): |
| # two outputs: conv2 and data4 |
| m = model_helper.ModelHelper() |
| # 0 |
| m.Copy("conv0_w_comp", "conv0_w") |
| # 1 |
| conv0 = brew.conv(m, "data", "conv0", 32, 32, 4) |
| # 2 |
| m.Copy("conv2_w", "conv2_w") |
| # 3 |
| brew.conv(m, 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, list(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, list(orders)) |
| |
| def test_topological_sort_longest_path_single_node(self): |
| # single node |
| m = model_helper.ModelHelper() |
| # 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, list(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, list(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) |
| |
| @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_verify_graph_equality(self, input_dim, output_dim, batch_size): |
| m = model_helper.ModelHelper() |
| m.Proto().type = "dag" |
| m.Proto().num_workers = 4 |
| with core.NameScope("name_x"): |
| fc1 = brew.fc(m, "data", "x", dim_in=input_dim, dim_out=output_dim) |
| fc2 = brew.fc(m, fc1, "y", dim_in=output_dim, dim_out=output_dim) |
| fc3 = brew.fc(m, fc1, "z", dim_in=output_dim, dim_out=output_dim) |
| brew.sum(m, [fc2, fc3], "out") |
| |
| m2 = model_helper.ModelHelper() |
| m2.Proto().type = "dag" |
| m2.Proto().num_workers = 4 |
| with core.NameScope("name_x"): |
| fc1 = brew.fc(m2, "data", "other_x", dim_in=input_dim, dim_out=output_dim) |
| fc2 = brew.fc(m2, fc1, "other_y", dim_in=output_dim, dim_out=output_dim) |
| fc3 = brew.fc(m2, fc1, "other_z", dim_in=output_dim, dim_out=output_dim) |
| brew.sum(m2, [fc2, fc3], "out") |
| |
| self.assertTrue(memonger.verify_graph_equality(m.net.Proto(), m2.net.Proto())) |
| |
| @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_verify_graph_equality_harder(self, input_dim, output_dim, batch_size): |
| m = model_helper.ModelHelper() |
| m.Proto().type = "dag" |
| m.Proto().num_workers = 4 |
| with core.NameScope("name_x"): |
| fc1 = brew.fc(m, "data", "x", dim_in=input_dim, dim_out=output_dim) |
| fc2a = brew.fc(m, fc1, "y", dim_in=output_dim, dim_out=output_dim) |
| fc2b = brew.fc(m, fc1, "z", dim_in=output_dim, dim_out=output_dim) |
| fc3a = brew.fc(m, fc2a, "u", dim_in=output_dim, dim_out=output_dim) |
| fc3b = brew.fc(m, fc2b, "v", dim_in=output_dim, dim_out=output_dim) |
| brew.sum(m, [fc3a, fc3b], "out") |
| |
| m2 = model_helper.ModelHelper() |
| m2.Proto().type = "dag" |
| m2.Proto().num_workers = 4 |
| with core.NameScope("name_x"): |
| fc1 = brew.fc(m2, "data", "x", dim_in=input_dim, dim_out=output_dim) |
| fc2a = brew.fc(m2, fc1, "y", dim_in=output_dim, dim_out=output_dim) |
| fc2b = brew.fc(m2, fc1, "z", dim_in=output_dim, dim_out=output_dim) |
| fc3a = brew.fc(m2, fc2a, "y", dim_in=output_dim, dim_out=output_dim) |
| fc3b = brew.fc(m2, fc2b, "z", dim_in=output_dim, dim_out=output_dim) |
| brew.sum(m2, [fc3a, fc3b], "out") |
| |
| self.assertTrue(memonger.verify_graph_equality(m.net.Proto(), m2.net.Proto())) |
| |
| @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_verify_graph_inequality(self, input_dim, output_dim, batch_size): |
| m = model_helper.ModelHelper() |
| m.Proto().type = "dag" |
| m.Proto().num_workers = 4 |
| with core.NameScope("name_x"): |
| fc1 = brew.fc(m, "data", "x", dim_in=input_dim, dim_out=output_dim) |
| fc2 = brew.fc(m, fc1, "y", dim_in=output_dim, dim_out=output_dim) |
| fc3 = brew.fc(m, fc1, "z", dim_in=output_dim, dim_out=output_dim) |
| brew.sum(m, [fc2, fc3], "out") |
| |
| m2 = model_helper.ModelHelper() |
| m2.Proto().type = "dag" |
| m2.Proto().num_workers = 4 |
| with core.NameScope("name_x"): |
| fc1 = brew.fc(m2, "data", "x", dim_in=input_dim, dim_out=output_dim) |
| fc2 = brew.fc(m2, fc1, "y", dim_in=output_dim, dim_out=output_dim) |
| fc3 = brew.fc(m2, fc1, "y", dim_in=output_dim, dim_out=output_dim) |
| brew.sum(m2, [fc2, fc3], "out") |
| |
| self.assertFalse(memonger.verify_graph_equality(m.net.Proto(), m2.net.Proto())) |
| |
| @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_verify_graph_inequality_harder(self, input_dim, output_dim, batch_size): |
| m = model_helper.ModelHelper() |
| m.Proto().type = "dag" |
| m.Proto().num_workers = 4 |
| with core.NameScope("name_x"): |
| fc1 = brew.fc(m, "data", "x", dim_in=input_dim, dim_out=output_dim) |
| fc2a = brew.fc(m, fc1, "y", dim_in=output_dim, dim_out=output_dim) |
| fc2b = brew.fc(m, fc1, "z", dim_in=output_dim, dim_out=output_dim) |
| fc3a = brew.fc(m, fc2a, "u", dim_in=output_dim, dim_out=output_dim) |
| fc3b = brew.fc(m, fc2b, "v", dim_in=output_dim, dim_out=output_dim) |
| brew.sum(m, [fc3a, fc3b], "out") |
| |
| m2 = model_helper.ModelHelper() |
| m2.Proto().type = "dag" |
| m2.Proto().num_workers = 4 |
| with core.NameScope("name_x"): |
| fc1 = brew.fc(m2, "data", "x", dim_in=input_dim, dim_out=output_dim) |
| fc2a = brew.fc(m2, fc1, "y", dim_in=output_dim, dim_out=output_dim) |
| fc2b = brew.fc(m2, fc1, "y", dim_in=output_dim, dim_out=output_dim) |
| fc3a = brew.fc(m2, fc2a, "u", dim_in=output_dim, dim_out=output_dim) |
| fc3b = brew.fc(m2, fc2b, "v", dim_in=output_dim, dim_out=output_dim) |
| brew.sum(m2, [fc3a, fc3b], "out") |
| |
| self.assertFalse(memonger.verify_graph_equality(m.net.Proto(), m2.net.Proto())) |
| |
| def test_release_blobs_when_used(self): |
| m = model_helper.ModelHelper() |
| fc1 = brew.fc(m, "data", "x", dim_in=2, dim_out=2) |
| fc2 = brew.fc(m, fc1, "y", dim_in=2, dim_out=2) |
| fc3 = brew.fc(m, fc1, "z", dim_in=2, dim_out=2) |
| fc4 = brew.fc(m, fc2, "u", dim_in=2, dim_out=2) |
| m.net.Alias(["u"], ["u_alias"]) |
| |
| brew.sum(m, [fc3, fc4], "out") |
| |
| with_frees = memonger.release_blobs_when_used(m.net.Proto(), set("data")) |
| |
| expect_frees = {"x", "y", "z"} # out is external output |
| # and u is aliased so cannot be freed |
| found_frees = set() |
| for op in with_frees.op: |
| if op.type == "Free": |
| self.assertFalse(op.input[0] in found_frees) # no double frees |
| found_frees.add(op.input[0]) |
| else: |
| # Check a freed blob is not used anymore |
| for inp in op.input: |
| self.assertFalse(inp in found_frees) |
| for outp in op.output: |
| self.assertFalse(outp in found_frees) |
| |
| self.assertEqual(expect_frees, found_frees) |
| |
| |
| if __name__ == '__main__': |
| unittest.main() |
