memonger_test.py

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()