Avoid Lines. Pre-ordering saves you the hassle of waiting in line to make and collect an order. Further, you needn't worry that your favorite item and size will be out of stock by the time you get to the front of that long line. Place an order in advance and it will be ready for you to collect at the event. No fuss, no muss.
Save money. Prices on pre-ordered items will be lower than those sold at the event. Take advantage of the lower prices offered during the pre-order period. Use the leftover cash to buy a pizza.
The pre-order process is as easy as placing any other online shopping order. Once pre-orders are open, navigate to your state's page and make your order selections.
Simply click the "Place Individual Order" button to add an item to the cart. You will customize the item prior to checkout.
Ordering seven or more items for the same team qualifies as a team order. Team orders are eligible for a discount.
Placing a team pre-order works very similarly to placing an individual pre-order. Select the number of players and then click the "Place Team Order" button. You will customize your items prior to checkout.
Please ensure all information is correct before finalizing your order. We will use this information to prepare your order and to stay in contact with you regarding the status of your order. Once an item is personalized there are no refunds.
We receive the team names one week before each tournament starts. We will learn the location of your first game and send you an email the Wednesday before game day to let you know where to collect your order. In the event of a tournament cancellation, we will ship your order to you at no extra cost.


High On Poems - A premium platform for poets and poetry.
Welcome to High On Poems!
It takes a brave heart to know poetry or be a poet. While the realists may proclaim that poetry is an escape, a refuge from the harsh truths of life, it cannot be so. A poet is a warrior and a magician who has the power to confront his worst demons and cast them out into the world dressed in naked pain, honesty and the cadence of words. And he is a dreamer and a wisher! Poetry is the answer to the silent whispering of your heart and all that can be felt, but not seen, finds its way into the world of Poets. From the golden blaze of the morning sun to the rosy hues of the evening and everything in between, that, which is called life, all of it is Poetry. One only needs to pause in the endless race of life, still the heart, tune into the world around and a poem will be created. A poet can paint pictures in words, out of the diaphanous, ephemeral clouds of thoughts, that the rest of the world struggles to concretize into comprehensible shape.
Thank you and here's wishing you a Wonderful World Poetry Day!


text:
Sponsored By: Department of Epidemiology
Onsite | Part-Time | 1-3 years
The timely detection, investigation, control, and prevention of outbreaks and major long-term public health problems require a well-trained and competent epidemiology workforce as a key component of a national public health infrastructure. The Epidemiology Workforce in State and Local Health Departments - United States, 2010 Weekly March 30, 2012/61(12);205-208
Epidemiology is an integral component of public health practice. The discipline aims to provide the basis to prevent disease and to promote the health of populations through the study of the occurrence and distribution of health-related states or events, including the study of determinants influencing such states. Professional epidemiologic methods, defined as the application of epidemiologic methods to public health practice, entail the combination of analytical methods and applied epidemiology oriented to problem solving in public health. The principal areas of professional epidemiology include epidemiologic assessment of public health data, health situation and trend analyses, public health surveillance and health program impact assessment. These areas are closely linked to the essential public health function and services. This certificate program is intended to provide the concepts, methods and tools needed for the assessment of health situations and trends of population groups.
Upon completion of the core courses in this certificate program, individuals will have gained specialized knowledge and skills on the application of epidemiologic concepts and methods to public health problems, as follows:
Understand the place of epidemiology in public health, specifically how epidemiology is used to identify causes of disease, identify populations at high risk for disease, develop preventative methods and evaluate public health strategies.
Calculate and interpret basic epidemiologic measures of disease frequency, identify sources of data for measuring health outcomes, and identify key aspects of measurement problems,
Identify distinguishing features of fundamental study designs, including randomized trials, cohort and case-control studies, birth cohort and ecologic studies, and pre-post and quasi-experimental studies. Students will be able to describe strengths and limitations of the different study designs, and key sources of confounding and bias in epidemiological studies and
Interpret and make inferences from results of epidemiologic studies.
With regard to the usual functions of public health agencies, students will be able to:
Identify the Problem Solving Framework for measuring the severity of priority health problems,
Recognize the role of routine and public health information systems in epidemiologic assessments,
Identify tools and measurements used to monitor the quality of performance of public health information systems,
Identify the main indicators for measuring the burden of diseases at global, national and local levels
Design health situation analyses: epidemiological profiles and community health status assessments,
Interpret measures of health burden, association and effectiveness,
Describe the framework of the public health surveillance cycle and sources of information,
Design, implement and evaluate disease surveillance systems,
Communicate epidemiological information and synthesis to policy makers, professionals, and lay audiences.
Start Terms & Application Due Dates
Please visit our Academic Catalogue to see the full certificate curriculum requirements. Please also review the certificate completion requirements.
The certificate program is open to Bloomberg School students, graduate students in other divisions of Johns Hopkins University, and non-students.
The certificate program can be completed entirely with a combination of online and on-campus courses.
Degree Students
The certificate program is designed for masters and doctoral students enrolled at the Bloomberg School of Public Health and in other university divisions, including the Schools of Medicine and Nursing and the Krieger School of Arts and Sciences. JHSPH MAS students are not eligible to apply until they have completed their primary degree program.
Applying to the certificate program as a JHU graduate student
Students already enrolled in a graduate program at JHU are not required to submit the School's electronic admissions application, but must email Certificate Program Contact to request application forms prior to starting coursework.
Email Certificate Program Contact
The certificate program is also designed for junior and mid-level public health professionals interested in expanding their knowledge of epidemiologic methods as related to public health issues and their ability to address public health problems.
Applying to the certificate program as a non-degree applicant
Students who are not currently enrolled in a graduate program at JHU are required to apply to certificate programs using SOPHAS Express.
English Language Proficiency Test Results
Results are required.
Prerequisites or special requirements
Application for Non-Degree Students
Information regarding the cost of tuition and fees can be found on the Bloomberg School's Certificate Programs Tuition page.
See Cost Breakdown
Questions about the program? We're happy to help.
Certificate Program Contact
Ayesha Khan, Department of Epidemiology
akhan6@jhu.edu
Faculty Sponsor
Carlos Castillo Salgado, MD, DrPH, JD
Faculty Co-Sponsor
Moyses Szklo, MD, MPH, DrPH
Request Info Receive more information about the Bloomberg School.
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text:
#include "tensorflow/cc/ops/const_op.h"
#include "tensorflow/cc/ops/sendrecv_ops.h"
#include "tensorflow/cc/ops/standard_ops.h"
#include "tensorflow/core/framework/tensor_testutil.h"
#include "tensorflow/core/lib/core/status_test_util.h"
#include "tensorflow/core/lib/io/path.h"
#include "tensorflow/core/lib/strings/strcat.h"
#include "tensorflow/core/platform/test.h"
#include "tensorflow/core/platform/test_benchmark.h"
#include "tensorflow/core/public/session.h"
#include "tensorflow/core/util/tensor_bundle/tensor_bundle.h"
#include "tensorflow/tools/graph_transforms/transform_utils.h"

namespace tensorflow {
namespace graph_transforms {

// Declarations so we don't need a public header.
Status SparsifyGather(const GraphDef& input_graph_def,
                      const TransformFuncContext& context,
                      GraphDef* output_graph_def);
Status ReadTensorFromCheckpoint(
    const string& tensor_name, const std::unique_ptr<BundleReader>& ckpt_reader,
    const string& shape_and_slice, Tensor* tensor);

class SparsifyGatherTest : public ::testing::Test {
 protected:
  NodeDef* CreateNode(const StringPiece name, const StringPiece op,
                      const std::vector<NodeDef*>& inputs, GraphDef* graph_def,
                      bool control_dep = false) {
    NodeDef* node_def = graph_def->add_node();
    node_def->set_name(string(name));
    node_def->set_op(string(op));
    if (!control_dep) {
      std::for_each(inputs.begin(), inputs.end(), [&node_def](NodeDef* input) {
        node_def->add_input(input->name());
      });
    } else {
      std::for_each(inputs.begin(), inputs.end(), [&node_def](NodeDef* input) {
        node_def->add_input(strings::StrCat("^", input->name()));
      });
    }
    return node_def;
  }

  void MakeGather(StringPiece name, bool gather_v2, NodeDef* params,
                  NodeDef* indices, GraphDef* graph_def) {
    if (gather_v2) {
      NodeDef* axis_node =
          CreateNode(strings::StrCat(name, "_axis"), "Const", {}, graph_def);
      Tensor axis_t(DT_INT32, TensorShape({}));
      axis_t.scalar<int32>()() = 0;
      SetNodeTensorAttr<int32>("value", axis_t, axis_node);
      CreateNode(name, "GatherV2", {params, indices, axis_node}, graph_def);
    } else {
      CreateNode(name, "Gather", {params, indices}, graph_def);
    }
  }

  void TestSinglePartition(bool gather_v2, bool include_shared_init,
                           bool test_variable, bool test_kept_concat,
                           const string& shared_init_name = "group_deps") {
    GraphDef graph_def;

    const auto checkpoint_path =
        io::JoinPath(testing::TmpDir(), "checkpoint_single");
    // Build the graph.
    NodeDef* input_node = CreateNode("ids", "Const", {}, &graph_def);
    NodeDef* w_node;
    NodeDef* zeros_const;
    NodeDef* zeros_shape;
    NodeDef* zeros_node;
    NodeDef* assign_node;

    Tensor weights(DT_FLOAT, TensorShape({4, 1}));
    test::FillValues<float>(&weights, {0.2, 0.000001, 1.2, 0.001});

    if (!test_variable) {
      w_node = CreateNode("w/part_1", "Const", {}, &graph_def);
      SetNodeTensorAttr<float>("value", weights, w_node);
    } else {
      w_node = CreateNode("w/part_1", "VariableV2", {}, &graph_def);

      zeros_shape = CreateNode("w/part_1/Initializer/zeros/shape_as_tensor",
                               "Const", {}, &graph_def);
      zeros_const = CreateNode("w/part_1/Initializer/zeros/Const", "Const", {},
                               &graph_def);
      zeros_node = CreateNode("w/part_1/Initializer/zeros", "Fill",
                              {zeros_shape, zeros_const}, &graph_def);
      assign_node = CreateNode("w/part_1/Assign", "Assign",
                               {w_node, zeros_node}, &graph_def);

      NodeDef* save_const_node =
          CreateNode("save/Const", "Const", {}, &graph_def);

      Tensor tensor_names_values(DT_STRING, TensorShape({1}));
      test::FillValues<string>(&tensor_names_values, {"w"});
      NodeDef* tensor_names_node =
          CreateNode("save/RestoreV2/tensor_names", "Const", {}, &graph_def);
      SetNodeTensorAttr<string>("value", tensor_names_values,
                                tensor_names_node);

      NodeDef* tensor_shapes_slices_node = CreateNode(
          "save/RestoreV2/shape_and_slices", "Const", {}, &graph_def);
      Tensor shapes_slices_val(DT_STRING, TensorShape({1}));
      shapes_slices_val.flat<string>()(0) = "4 1 0,4:0,1";
      SetNodeTensorAttr<string>("value", shapes_slices_val,
                                tensor_shapes_slices_node);

      NodeDef* restore_node = CreateNode(
          "save/RestoreV2", "RestoreV2",
          {save_const_node, tensor_names_node, tensor_shapes_slices_node},
          &graph_def);
      CreateNode("save/Assign", "Assign", {w_node, restore_node}, &graph_def);

      BundleWriter writer(Env::Default(), checkpoint_path);
      TF_ASSERT_OK(writer.Add("w", weights));
      TF_ASSERT_OK(writer.Finish());
    }
    SetNodeAttr("dtype", DT_FLOAT, w_node);

    NodeDef* identity_node =
        CreateNode("w/read", "Identity", {w_node}, &graph_def);
    MakeGather("gather", gather_v2, identity_node, input_node, &graph_def);
    if (include_shared_init) {
      if (!test_variable) {
        CreateNode(shared_init_name, "NoOp", {}, &graph_def);
      } else {
        CreateNode(shared_init_name, "NoOp", {assign_node}, &graph_def, true);
      }
    }

    NodeDef* concat_axis_node =
        CreateNode("linear/concat/axis", "Const", {}, &graph_def);
    NodeDef* concat_input_node =
        CreateNode("concat/input/node", "Const", {}, &graph_def);
    NodeDef* concat_node = nullptr;
    if (!test_kept_concat) {
      concat_node = CreateNode(
          "concat/node", "ConcatV2",
          {identity_node, concat_input_node, concat_axis_node}, &graph_def);
      SetNodeAttr("N", 2, concat_node);
    } else {
      NodeDef* concat_input_node_2 =
          CreateNode("concat/input/node_2", "Const", {}, &graph_def);
      concat_node = CreateNode("concat/node", "ConcatV2",
                               {identity_node, concat_input_node,
                                concat_input_node_2, concat_axis_node},
                               &graph_def);
      SetNodeAttr("N", 3, concat_node);
    }

    // Run the op.
    GraphDef result;
    TransformFuncContext context;
    context.input_names = {"ids"};
    context.output_names = {"gather"};
    if (test_variable) {
      context.params["input_checkpoint"] = {checkpoint_path};
    }
    if (shared_init_name != "group_deps") {
      context.params["group_init_node"] = {shared_init_name};
    }
    TF_ASSERT_OK(SparsifyGather(graph_def, context, &result));

    // Validation begins.
    std::map<string, const NodeDef*> node_lookup;
    MapNamesToNodes(result, &node_lookup);

    // Check nodes.
    EXPECT_EQ(0,
              node_lookup.count("w/part_1/Initializer/zeros/shape_as_tensor"));
    EXPECT_EQ(0, node_lookup.count("w/part_1/Initializer/zeros/Const"));
    EXPECT_EQ(0, node_lookup.count("w/part_1/Initializer/zeros"));
    EXPECT_EQ(0, node_lookup.count("w/part_1/Assign"));

    EXPECT_EQ(1, node_lookup.count("ids"));
    EXPECT_EQ("Const", node_lookup.at("ids")->op());

    EXPECT_EQ(1, node_lookup.count("concat/node"));

    if (!test_kept_concat) {
      EXPECT_EQ(0, node_lookup.count("linear/concat/axis"));
      EXPECT_EQ("Identity", node_lookup.at("concat/node")->op());
      EXPECT_EQ(1, node_lookup.at("concat/node")->input_size());
      EXPECT_EQ("concat/input/node", node_lookup.at("concat/node")->input(0));
    } else {
      EXPECT_EQ(1, node_lookup.count("linear/concat/axis"));
      EXPECT_EQ("ConcatV2", node_lookup.at("concat/node")->op());
      EXPECT_EQ(3, node_lookup.at("concat/node")->input_size());
      EXPECT_EQ("concat/input/node", node_lookup.at("concat/node")->input(0));
      EXPECT_EQ("concat/input/node_2", node_lookup.at("concat/node")->input(1));
      EXPECT_EQ("linear/concat/axis", node_lookup.at("concat/node")->input(2));
      EXPECT_EQ(2, node_lookup.at("concat/node")->attr().at("N").i());
    }

    EXPECT_EQ(1, node_lookup.count("w/part_1/indices"));
    EXPECT_EQ("Const", node_lookup.at("w/part_1/indices")->op());
    Tensor expected_indices_tensor(DT_INT64, TensorShape({3}));
    test::FillValues<int64>(&expected_indices_tensor, {0, 2, 3});
    test::ExpectTensorEqual<int64>(
        expected_indices_tensor,
        GetNodeTensorAttr(*(node_lookup.at("w/part_1/indices")), "value"));

    EXPECT_EQ(1, node_lookup.count("w/part_1/values"));
    EXPECT_EQ("Const", node_lookup.at("w/part_1/values")->op());
    Tensor expected_values_tensor(DT_FLOAT, TensorShape({3}));
    test::FillValues<float>(&expected_values_tensor, {0.2, 1.2, 0.001});
    test::ExpectTensorNear<float>(
        expected_values_tensor,
        GetNodeTensorAttr(*(node_lookup.at("w/part_1/values")), "value"), 1e-5);

    EXPECT_EQ(1, node_lookup.count("w/part_1/HashTable"));
    EXPECT_EQ("HashTable", node_lookup.at("w/part_1/HashTable")->op());

    EXPECT_EQ(1, node_lookup.count("w/part_1/InitializeTable"));
    EXPECT_EQ("InitializeTable",
              node_lookup.at("w/part_1/InitializeTable")->op());

    // Nodes in "gather" scope.
    EXPECT_EQ(1, node_lookup.count("gather/LookupTableFind"));
    EXPECT_EQ("LookupTableFind",
              node_lookup.at("gather/LookupTableFind")->op());

    EXPECT_EQ(1, node_lookup.count("gather/Const"));
    EXPECT_EQ("Const", node_lookup.at("gather/Const")->op());
    Tensor expected_gather_default_tensor(DT_FLOAT, TensorShape({}));
    test::FillValues<float>(&expected_gather_default_tensor, {0.0});
    test::ExpectTensorNear<float>(
        expected_gather_default_tensor,
        GetNodeTensorAttr(*(node_lookup.at("gather/Const")), "value"), 1e-5);

    EXPECT_EQ(1, node_lookup.count("gather/ExpandDims/Const"));
    EXPECT_EQ("Const", node_lookup.at("gather/ExpandDims/Const")->op());
    Tensor expected_expand_dims_tensor(DT_INT32, TensorShape({}));
    test::FillValues<int32>(&expected_expand_dims_tensor, {-1});
    test::ExpectTensorEqual<int32>(
        expected_expand_dims_tensor,
        GetNodeTensorAttr(*(node_lookup.at("gather/ExpandDims/Const")),
                          "value"));

    EXPECT_EQ(1, node_lookup.count("gather"));
    EXPECT_EQ("ExpandDims", node_lookup.at("gather")->op());

    EXPECT_EQ(1, node_lookup.count(shared_init_name));
    EXPECT_EQ("NoOp", node_lookup.at(shared_init_name)->op());

    // Check connections
    EXPECT_EQ("w/part_1/HashTable",
              node_lookup.at("w/part_1/InitializeTable")->input(0));
    EXPECT_EQ("w/part_1/indices",
              node_lookup.at("w/part_1/InitializeTable")->input(1));
    EXPECT_EQ("w/part_1/values",
              node_lookup.at("w/part_1/InitializeTable")->input(2));

    EXPECT_EQ("w/part_1/HashTable",
              node_lookup.at("gather/LookupTableFind")->input(0));
    EXPECT_EQ("ids", node_lookup.at("gather/LookupTableFind")->input(1));
    EXPECT_EQ("gather/Const",
              node_lookup.at("gather/LookupTableFind")->input(2));

    EXPECT_EQ("gather/LookupTableFind", node_lookup.at("gather")->input(0));

    // Check control dependency.
    EXPECT_NE(std::find(node_lookup.at(shared_init_name)->input().begin(),
                        node_lookup.at(shared_init_name)->input().end(),
                        "^w/part_1/InitializeTable"),
              node_lookup.at(shared_init_name)->input().end());
    EXPECT_EQ(1, node_lookup.at(shared_init_name)->input().size());
  }

  void TestMultiPartition(bool gather_v2, bool include_shared_init,
                          bool test_variable,
                          const string& shared_init_name = "group_deps") {
    // The 'ids' node is served input for two 'Gather's.
    GraphDef graph_def;

    const auto checkpoint_path =
        io::JoinPath(testing::TmpDir(), "checkpoint_multiple");
    // Build Graph:
    // Shared input node
    NodeDef* input_node = CreateNode("ids", "Const", {}, &graph_def);

    // Two partitions
    NodeDef* w_node1;
    NodeDef* w_node2;
    NodeDef* zeros_const1;
    NodeDef* zeros_shape1;
    NodeDef* zeros_node1;
    NodeDef* zeros_const2;
    NodeDef* zeros_shape2;
    NodeDef* zeros_node2;
    NodeDef* assign_node1;
    NodeDef* assign_node2;

    Tensor weights(DT_FLOAT, TensorShape({4, 1}));
    test::FillValues<float>(&weights, {0.2, 0.000001, 1.2, 0.001});
    if (!test_variable) {
      w_node1 = CreateNode("w1/part_1", "Const", {}, &graph_def);
      w_node2 = CreateNode("w2/part_1", "Const", {}, &graph_def);
      SetNodeTensorAttr<float>("value", weights, w_node1);
      SetNodeTensorAttr<float>("value", weights, w_node2);
    } else {
      NodeDef* save_const_node =
          CreateNode("save/Const", "Const", {}, &graph_def);

      NodeDef* tensor_names_node =
          CreateNode("save/RestoreV2/tensor_names", "Const", {}, &graph_def);
      Tensor tensor_names_values(DT_STRING, TensorShape({2}));
      test::FillValues<string>(&tensor_names_values, {"w1", "w2"});
      SetNodeTensorAttr<string>("value", tensor_names_values,
                                tensor_names_node);

      NodeDef* tensor_shapes_slices_node = CreateNode(
          "save/RestoreV2/shape_and_slices", "Const", {}, &graph_def);
      Tensor shapes_slices_val(DT_STRING, TensorShape({2}));
      shapes_slices_val.flat<string>()(0) = "4 1 0,4:0,1";
      shapes_slices_val.flat<string>()(1) = "4 1 0,4:0,1";
      SetNodeTensorAttr<string>("value", shapes_slices_val,
                                tensor_shapes_slices_node);

      NodeDef* restore_node = CreateNode(
          "save/RestoreV2", "RestoreV2",
          {save_const_node, tensor_names_node, tensor_shapes_slices_node},
          &graph_def);

      w_node1 = CreateNode("w1/part_1", "VariableV2", {}, &graph_def);

      zeros_shape1 = CreateNode("w1/part_1/Initializer/zeros/shape_as_tensor",
                                "Const", {}, &graph_def);
      zeros_const1 = CreateNode("w1/part_1/Initializer/zeros/Const", "Const",
                                {}, &graph_def);
      zeros_node1 = CreateNode("w1/part_1/Initializer/zeros", "Fill",
                               {zeros_shape1, zeros_const1}, &graph_def);
      assign_node1 = CreateNode("w1/part_1/Assign", "Assign",
                                {w_node1, zeros_node1}, &graph_def);

      CreateNode("save/Assign", "Assign", {w_node1, restore_node}, &graph_def);

      w_node2 = CreateNode("w2/part_1", "VariableV2", {}, &graph_def);
      zeros_shape2 = CreateNode("w2/part_1/Initializer/zeros/shape_as_tensor",
                                "Const", {}, &graph_def);
      zeros_const2 = CreateNode("w2/part_1/Initializer/zeros/Const", "Const",
                                {}, &graph_def);
      zeros_node2 = CreateNode("w2/part_1/Initializer/zeros", "Fill",
                               {zeros_shape2, zeros_const2}, &graph_def);
      assign_node2 = CreateNode("w2/part_1/Assign", "Assign",
                                {w_node2, zeros_node2}, &graph_def);

      CreateNode("save/Assign_1", "Assign", {w_node2, restore_node},
                 &graph_def);

      BundleWriter writer(Env::Default(), checkpoint_path);
      TF_ASSERT_OK(writer.Add("w1", weights));
      TF_ASSERT_OK(writer.Add("w2", weights));
      TF_ASSERT_OK(writer.Finish());
    }
    SetNodeAttr("dtype", DT_FLOAT, w_node1);
    SetNodeAttr("dtype", DT_FLOAT, w_node2);

    NodeDef* identity_node1 =
        CreateNode("w1/part_1/read", "Identity", {w_node1}, &graph_def);
    NodeDef* identity_node2 =
        CreateNode("w2/part_1/read", "Identity", {w_node2}, &graph_def);
    MakeGather("gather1", gather_v2, identity_node1, input_node, &graph_def);
    MakeGather("gather2", gather_v2, identity_node2, input_node, &graph_def);

    NodeDef* concat_axis_node =
        CreateNode("linear/concat/axis", "Const", {}, &graph_def);
    NodeDef* concat_node = CreateNode(
        "concat/node", "ConcatV2",
        {identity_node1, identity_node2, concat_axis_node}, &graph_def);
    SetNodeAttr("N", 2, concat_node);

    // Shared init node
    if (include_shared_init) {
      if (!test_variable) {
        CreateNode(shared_init_name, "NoOp", {}, &graph_def);
      } else {
        CreateNode(shared_init_name, "NoOp", {assign_node1, assign_node2},
                   &graph_def, true);
      }
    }

    // Run the op.
    GraphDef result;
    TransformFuncContext context;
    context.input_names = {"ids"};
    context.output_names = {"gather1", "gather2"};
    if (test_variable) {
      context.params["input_checkpoint"] = {checkpoint_path};
    }
    if (shared_init_name != "group_deps") {
      context.params["group_init_node"] = {shared_init_name};
    }
    TF_ASSERT_OK(SparsifyGather(graph_def, context, &result));

    // Validation begins.
    std::map<string, const NodeDef*> node_lookup;
    MapNamesToNodes(result, &node_lookup);

    // Check nodes.
    EXPECT_EQ(0,
              node_lookup.count("w1/part_1/Initializer/zeros/shape_as_tensor"));
    EXPECT_EQ(0, node_lookup.count("w1/part_1/Initializer/zeros/Const"));
    EXPECT_EQ(0, node_lookup.count("w1/part_1/Initializer/zeros"));
    EXPECT_EQ(0, node_lookup.count("w1/part_1/Assign"));
    EXPECT_EQ(0,
              node_lookup.count("w2/part_1/Initializer/zeros/shape_as_tensor"));
    EXPECT_EQ(0, node_lookup.count("w2/part_1/Initializer/zeros/Const"));
    EXPECT_EQ(0, node_lookup.count("w2/part_1/Initializer/zeros"));
    EXPECT_EQ(0, node_lookup.count("w2/part_1/Assign"));
    EXPECT_EQ(1, node_lookup.count("ids"));
    EXPECT_EQ("Const", node_lookup.at("ids")->op());

    EXPECT_EQ(1, node_lookup.count(shared_init_name));
    EXPECT_EQ("NoOp", node_lookup.at(shared_init_name)->op());

    EXPECT_EQ(1, node_lookup.count("w1/part_1/indices"));
    EXPECT_EQ("Const", node_lookup.at("w1/part_1/indices")->op());
    Tensor expected_indices_tensor1(DT_INT64, TensorShape({3}));
    test::FillValues<int64>(&expected_indices_tensor1, {0, 2, 3});
    test::ExpectTensorEqual<int64>(
        expected_indices_tensor1,
        GetNodeTensorAttr(*(node_lookup.at("w1/part_1/indices")), "value"));

    EXPECT_EQ(1, node_lookup.count("w1/part_1/values"));
    EXPECT_EQ("Const", node_lookup.at("w1/part_1/values")->op());
    Tensor expected_values_tensor1(DT_FLOAT, TensorShape({3}));
    test::FillValues<float>(&expected_values_tensor1, {0.2, 1.2, 0.001});
    test::ExpectTensorNear<float>(
        expected_values_tensor1,
        GetNodeTensorAttr(*(node_lookup.at("w1/part_1/values")), "value"),
        1e-5);

    EXPECT_EQ(1, node_lookup.count("w1/part_1/HashTable"));
    EXPECT_EQ("HashTable", node_lookup.at("w1/part_1/HashTable")->op());

    EXPECT_EQ(1, node_lookup.count("w1/part_1/InitializeTable"));
    EXPECT_EQ("InitializeTable",
              node_lookup.at("w1/part_1/InitializeTable")->op());

    // Nodes in "gather1" scope.
    EXPECT_EQ(1, node_lookup.count("gather1/LookupTableFind"));
    EXPECT_EQ("LookupTableFind",
              node_lookup.at("gather1/LookupTableFind")->op());

    EXPECT_EQ(1, node_lookup.count("gather1/Const"));
    EXPECT_EQ("Const", node_lookup.at("gather1/Const")->op());
    Tensor expected_gather_default_tensor1(DT_FLOAT, TensorShape({}));
    test::FillValues<float>(&expected_gather_default_tensor1, {0.0});
    test::ExpectTensorNear<float>(
        expected_gather_default_tensor1,
        GetNodeTensorAttr(*(node_lookup.at("gather1/Const")), "value"), 1e-5);

    EXPECT_EQ(1, node_lookup.count("gather1/ExpandDims/Const"));
    EXPECT_EQ("Const", node_lookup.at("gather1/ExpandDims/Const")->op());
    Tensor expected_expand_dims_tensor1(DT_INT32, TensorShape({}));
    test::FillValues<int32>(&expected_expand_dims_tensor1, {-1});
    test::ExpectTensorEqual<int32>(
        expected_expand_dims_tensor1,
        GetNodeTensorAttr(*(node_lookup.at("gather1/ExpandDims/Const")),
                          "value"));

    EXPECT_EQ(1, node_lookup.count("gather1"));
    EXPECT_EQ("ExpandDims", node_lookup.at("gather1")->op());

    EXPECT_EQ(1, node_lookup.count("w2/part_1/indices"));
    EXPECT_EQ("Const", node_lookup.at("w2/part_1/indices")->op());
    Tensor expected_indices_tensor2(DT_INT64, TensorShape({3}));
    test::FillValues<int64>(&expected_indices_tensor2, {0, 2, 3});
    test::ExpectTensorEqual<int64>(
        expected_indices_tensor2,
        GetNodeTensorAttr(*(node_lookup.at("w2/part_1/indices")), "value"));

    EXPECT_EQ(1, node_lookup.count("w2/part_1/values"));
    EXPECT_EQ("Const", node_lookup.at("w2/part_1/values")->op());
    Tensor expected_values_tensor2(DT_FLOAT, TensorShape({3}));
    test::FillValues<float>(&expected_values_tensor2, {0.2, 1.2, 0.001});
    test::ExpectTensorNear<float>(
        expected_values_tensor2,
        GetNodeTensorAttr(*(node_lookup.at("w2/part_1/values")), "value"),
        1e-5);

    EXPECT_EQ(1, node_lookup.count("w2/part_1/HashTable"));
    EXPECT_EQ("HashTable", node_lookup.at("w2/part_1/HashTable")->op());

    EXPECT_EQ(1, node_lookup.count("w2/part_1/InitializeTable"));
    EXPECT_EQ("InitializeTable",
              node_lookup.at("w2/part_1/InitializeTable")->op());

    // Nodes in "gather2" scope.
    EXPECT_EQ(1, node_lookup.count("gather2/LookupTableFind"));
    EXPECT_EQ("LookupTableFind",
              node_lookup.at("gather2/LookupTableFind")->op());

    EXPECT_EQ(1, node_lookup.count("gather2/Const"));
    EXPECT_EQ("Const", node_lookup.at("gather2/Const")->op());
    Tensor expected_gather_default_tensor2(DT_FLOAT, TensorShape({}));
    test::FillValues<float>(&expected_gather_default_tensor2, {0.0});
    test::ExpectTensorNear<float>(
        expected_gather_default_tensor2,
        GetNodeTensorAttr(*(node_lookup.at("gather2/Const")), "value"), 1e-5);

    EXPECT_EQ(1, node_lookup.count("gather2/ExpandDims/Const"));
    EXPECT_EQ("Const", node_lookup.at("gather2/ExpandDims/Const")->op());
    Tensor expected_expand_dims_tensor2(DT_INT32, TensorShape({}));
    test::FillValues<int32>(&expected_expand_dims_tensor2, {-1});
    test::ExpectTensorEqual<int32>(
        expected_expand_dims_tensor2,
        GetNodeTensorAttr(*(node_lookup.at("gather2/ExpandDims/Const")),
                          "value"));

    EXPECT_EQ(1, node_lookup.count("gather2"));
    EXPECT_EQ("ExpandDims", node_lookup.at("gather2")->op());

    // Check connections
    EXPECT_EQ("w1/part_1/HashTable",
              node_lookup.at("w1/part_1/InitializeTable")->input(0));
    EXPECT_EQ("w1/part_1/indices",
              node_lookup.at("w1/part_1/InitializeTable")->input(1));
    EXPECT_EQ("w1/part_1/values",
              node_lookup.at("w1/part_1/InitializeTable")->input(2));

    EXPECT_EQ("w2/part_1/HashTable",
              node_lookup.at("w2/part_1/InitializeTable")->input(0));
    EXPECT_EQ("w2/part_1/indices",
              node_lookup.at("w2/part_1/InitializeTable")->input(1));
    EXPECT_EQ("w2/part_1/values",
              node_lookup.at("w2/part_1/InitializeTable")->input(2));

    EXPECT_EQ("w1/part_1/HashTable",
              node_lookup.at("gather1/LookupTableFind")->input(0));
    EXPECT_EQ("ids", node_lookup.at("gather1/LookupTableFind")->input(1));
    EXPECT_EQ("gather1/Const",
              node_lookup.at("gather1/LookupTableFind")->input(2));
    EXPECT_EQ("gather1/LookupTableFind", node_lookup.at("gather1")->input(0));

    EXPECT_EQ("w2/part_1/HashTable",
              node_lookup.at("gather2/LookupTableFind")->input(0));
    EXPECT_EQ("ids", node_lookup.at("gather2/LookupTableFind")->input(1));
    EXPECT_EQ("gather2/Const",
              node_lookup.at("gather2/LookupTableFind")->input(2));
    EXPECT_EQ("gather2/LookupTableFind", node_lookup.at("gather2")->input(0));

    EXPECT_EQ(0, node_lookup.count("linear/concat/axis"));
    EXPECT_EQ(0, node_lookup.count("concat/node"));

    // Check control deps.
    EXPECT_EQ(2, node_lookup.at(shared_init_name)->input_size());
    EXPECT_NE(std::find(node_lookup.at(shared_init_name)->input().begin(),
                        node_lookup.at(shared_init_name)->input().end(),
                        "^w1/part_1/InitializeTable"),
              node_lookup.at(shared_init_name)->input().end());

    EXPECT_NE(std::find(node_lookup.at(shared_init_name)->input().begin(),
                        node_lookup.at(shared_init_name)->input().end(),
                        "^w2/part_1/InitializeTable"),
              node_lookup.at(shared_init_name)->input().end());
  }
  void TestReadTensorSlice() {
    const auto checkpoint_path =
        io::JoinPath(testing::TmpDir(), "checkpoint_slice");

    Tensor weights(DT_FLOAT, TensorShape({2, 1}));
    test::FillValues<float>(&weights, {0.2, 0.000001});
    BundleWriter writer(Env::Default(), checkpoint_path);
    TF_ASSERT_OK(writer.AddSlice("w", TensorShape({4, 1}),
                                 TensorSlice::ParseOrDie("0,2:0,1"), weights));
    TF_ASSERT_OK(writer.Finish());

    std::unique_ptr<BundleReader> reader(
        new BundleReader(Env::Default(), checkpoint_path));

    Tensor results;
    TF_ASSERT_OK(
        ReadTensorFromCheckpoint("w/part_0", reader, "4 1 0,2:0,1", &results));

    test::ExpectTensorEqual<float>(weights, results);
  }
};

TEST_F(SparsifyGatherTest, TestSinglePartition) {
  TestSinglePartition(false, false, false, false);
  TestSinglePartition(false, true, false, false);
  TestSinglePartition(true, false, false, false);
  TestSinglePartition(true, true, false, false);
  TestSinglePartition(false, false, true, false);
  TestSinglePartition(false, true, true, false);
  TestSinglePartition(true, false, true, false);
  TestSinglePartition(true, true, true, false);
  TestSinglePartition(false, true, false, false, "shared_inits");
  TestSinglePartition(true, true, false, false, "shared_inits");
  TestSinglePartition(false, true, true, false, "shared_inits");
  TestSinglePartition(true, true, true, false, "shared_inits");

  TestSinglePartition(false, false, false, true);
  TestSinglePartition(false, true, false, true);
  TestSinglePartition(true, false, false, true);
  TestSinglePartition(true, true, false, true);
  TestSinglePartition(false, false, true, true);
  TestSinglePartition(false, true, true, true);
  TestSinglePartition(true, false, true, true);
  TestSinglePartition(true, true, true, true);
  TestSinglePartition(false, true, false, true, "shared_inits");
  TestSinglePartition(true, true, false, true, "shared_inits");
  TestSinglePartition(false, true, true, true, "shared_inits");
  TestSinglePartition(true, true, true, true, "shared_inits");
}

TEST_F(SparsifyGatherTest, TestMultiPartition) {
  TestMultiPartition(false, false, false);
  TestMultiPartition(false, true, false);
  TestMultiPartition(true, false, false);
  TestMultiPartition(true, true, false);
  TestMultiPartition(false, false, true);
  TestMultiPartition(false, true, true);
  TestMultiPartition(true, false, true);
  TestMultiPartition(true, true, true);
  TestMultiPartition(false, true, false, "shared_inits");
  TestMultiPartition(true, true, false, "shared_inits");
  TestMultiPartition(false, true, true, "shared_inits");
  TestMultiPartition(true, true, true, "shared_inits");
}

TEST_F(SparsifyGatherTest, TestTensorSlice) { TestReadTensorSlice(); }

}  // namespace graph_transforms
}  // namespace tensorflow

redpajama_set_name: RedPajamaGithub


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