Merge f551d36 into 898b722

.dockerignore

@@ -3,6 +3,8 @@ output/
 download/
 logs/
 protos/
+docs/
+build/
 
 # Byte-compiled / optimized / DLL files
 __pycache__/

README.md

@@ -98,10 +98,10 @@ The consumer is configured using environment variables. Please find a table of a
 | `REDIS_TIMEOUT` | Timeout for each Redis request, in seconds. | `3` |
 | `EMPTY_QUEUE_TIMEOUT` | Time to wait after finding an empty queue, in seconds. | `5` |
 | `EXPIRE_TIME` | Expire Redis items this many seconds after completion. | `3600` |
+| `METADATA_EXPIRE_TIME` | Expire cached model metadata after this many seconds. | `30` |
 | `TF_HOST` | The IP address or hostname of TensorFlow Serving. | `"tf-serving"` |
 | `TF_PORT` | The port used to connect to TensorFlow Serving. | `8500` |
 | `TF_TENSOR_NAME` | Name of input tensor for the exported model. | `"image"` |
-| `TF_TENSOR_DTYPE` | The `dtype` used for the exported model. | `"DT_FLOAT"` |
 | `GRPC_TIMEOUT` | Timeout for gRPC API requests, in seconds. | `30` |
 | `GRPC_BACKOFF` | Time to wait before retrying a gRPC API request. | `3` |
 | `MAX_RETRY` | Maximum number of retries for a failed TensorFlow Serving request. | `5` |

protos/attr_value.proto

@@ -0,0 +1,62 @@
+syntax = "proto3";
+
+package tensorflow;
+option cc_enable_arenas = true;
+option java_outer_classname = "AttrValueProtos";
+option java_multiple_files = true;
+option java_package = "org.tensorflow.framework";
+option go_package = "github.com/tensorflow/tensorflow/tensorflow/go/core/framework";
+import "tensor.proto";
+import "tensor_shape.proto";
+import "types.proto";
+
+// Protocol buffer representing the value for an attr used to configure an Op.
+// Comment indicates the corresponding attr type.  Only the field matching the
+// attr type may be filled.
+message AttrValue {
+  // LINT.IfChange
+  message ListValue {
+    repeated bytes s = 2;                        // "list(string)"
+    repeated int64 i = 3 [packed = true];        // "list(int)"
+    repeated float f = 4 [packed = true];        // "list(float)"
+    repeated bool b = 5 [packed = true];         // "list(bool)"
+    repeated DataType type = 6 [packed = true];  // "list(type)"
+    repeated TensorShapeProto shape = 7;         // "list(shape)"
+    repeated TensorProto tensor = 8;             // "list(tensor)"
+    repeated NameAttrList func = 9;              // "list(attr)"
+  }
+  // LINT.ThenChange(https://www.tensorflow.org/code/tensorflow/c/c_api.cc)
+
+  oneof value {
+    bytes s = 2;                 // "string"
+    int64 i = 3;                 // "int"
+    float f = 4;                 // "float"
+    bool b = 5;                  // "bool"
+    DataType type = 6;           // "type"
+    TensorShapeProto shape = 7;  // "shape"
+    TensorProto tensor = 8;      // "tensor"
+    ListValue list = 1;          // any "list(...)"
+
+    // "func" represents a function. func.name is a function's name or
+    // a primitive op's name. func.attr.first is the name of an attr
+    // defined for that function. func.attr.second is the value for
+    // that attr in the instantiation.
+    NameAttrList func = 10;
+
+    // This is a placeholder only used in nodes defined inside a
+    // function.  It indicates the attr value will be supplied when
+    // the function is instantiated.  For example, let us suppose a
+    // node "N" in function "FN". "N" has an attr "A" with value
+    // placeholder = "foo". When FN is instantiated with attr "foo"
+    // set to "bar", the instantiated node N's attr A will have been
+    // given the value "bar".
+    string placeholder = 9;
+  }
+}
+
+// A list of attr names and their values. The whole list is attached
+// with a string name.  E.g., MatMul[T=float].
+message NameAttrList {
+  string name = 1;
+  map<string, AttrValue> attr = 2;
+}

protos/function.proto

@@ -1,12 +1,113 @@
 syntax = "proto3";
 
-package tensorflow.serving;
+package tensorflow;
 option cc_enable_arenas = true;
+option java_outer_classname = "FunctionProtos";
+option java_multiple_files = true;
+option java_package = "org.tensorflow.framework";
+option go_package = "github.com/tensorflow/tensorflow/tensorflow/go/core/framework";
+import "attr_value.proto";
+import "node_def.proto";
+import "op_def.proto";
 
-// Metadata for an inference request such as the processing name and type
-message FunctionSpec {
-  // Required function name.
-  string name = 1;
-  // Required function type.
-  string type = 2;
+// A library is a set of named functions.
+message FunctionDefLibrary {
+  repeated FunctionDef function = 1;
+  repeated GradientDef gradient = 2;
+}
+
+// A function can be instantiated when the runtime can bind every attr
+// with a value. When a GraphDef has a call to a function, it must
+// have binding for every attr defined in the signature.
+//
+// TODO(zhifengc):
+//   * device spec, etc.
+message FunctionDef {
+  // The definition of the function's name, arguments, return values,
+  // attrs etc.
+  OpDef signature = 1;
+
+  // Attributes specific to this function definition.
+  map<string, AttrValue> attr = 5;
+
+  // Attributes for function arguments. These attributes are the same set of
+  // valid attributes as to _Arg nodes.
+  message ArgAttrs {
+    map<string, AttrValue> attr = 1;
+  }
+  map<uint32, ArgAttrs> arg_attr = 7;
+
+  // NOTE: field id 2 deleted on Jan 11, 2017, GraphDef version 21.
+  reserved 2;
+
+  // In both of the following fields, there is the need to specify an
+  // output that is used as either the input to another node (in
+  // `node_def`) or as a return value of the function (in `ret`).
+  // Unlike the NodeDefs in GraphDef, we need to be able to specify a
+  // list in some cases (instead of just single outputs).  Also, we
+  // need to be able to deal with lists of unknown length (so the
+  // output index may not be known at function definition time).  So
+  // we use the following format instead:
+  // * "fun_in" where "fun_in" is the name of a function input arg in
+  //   the `signature` field above.  This represents that input, whether
+  //   it is a single tensor or a list.
+  // * "fun_in:0" gives the first element of a function input arg (a
+  //   non-list input is considered a list of length 1 for these
+  //   purposes).
+  // * "node:out" where "node" is the name of a node in `node_def` and
+  //   "out" is the name one of its op's output arguments (the name
+  //   comes from the OpDef of the node's op). This represents that
+  //   node's output, whether it is a single tensor or a list.
+  //   Note: We enforce that an op's output arguments are never
+  //   renamed in the backwards-compatibility test.
+  // * "node:out:0" gives the first element of a node output arg (a
+  //   non-list output is considered a list of length 1 for these
+  //   purposes).
+  //
+  // NOT CURRENTLY SUPPORTED (but may be in the future):
+  // * "node:out:-1" gives last element in a node output list
+  // * "node:out:1:" gives a list with all but the first element in a
+  //   node output list
+  // * "node:out::-1" gives a list with all but the last element in a
+  //   node output list
+
+  // The body of the function.  Unlike the NodeDefs in a GraphDef, attrs
+  // may have values of type `placeholder` and the `input` field uses
+  // the "output" format above.
+
+  // By convention, "op" in node_def is resolved by consulting with a
+  // user-defined library first. If not resolved, "func" is assumed to
+  // be a builtin op.
+  repeated NodeDef node_def = 3;
+
+  // A mapping from the output arg names from `signature` to the
+  // outputs from `node_def` that should be returned by the function.
+  map<string, string> ret = 4;
+
+  // A mapping from control output names from `signature` to node names in
+  // `node_def` which should be control outputs of this function.
+  map<string, string> control_ret = 6;
+}
+
+// GradientDef defines the gradient function of a function defined in
+// a function library.
+//
+// A gradient function g (specified by gradient_func) for a function f
+// (specified by function_name) must follow the following:
+//
+// The function 'f' must be a numerical function which takes N inputs
+// and produces M outputs. Its gradient function 'g', which is a
+// function taking N + M inputs and produces N outputs.
+//
+// I.e. if we have
+//    (y1, y2, ..., y_M) = f(x1, x2, ..., x_N),
+// then, g is
+//    (dL/dx1, dL/dx2, ..., dL/dx_N) = g(x1, x2, ..., x_N,
+//                                      dL/dy1, dL/dy2, ..., dL/dy_M),
+// where L is a scalar-value function of (x1, x2, ..., xN) (e.g., the
+// loss function). dL/dx_i is the partial derivative of L with respect
+// to x_i.
+message GradientDef {
+  string function_name = 1;  // The function name.
+  string gradient_func = 2;  // The gradient function's name.
 }

protos/get_model_metadata.proto

@@ -0,0 +1,30 @@
+syntax = "proto3";
+
+package tensorflow.serving;
+option cc_enable_arenas = true;
+
+import "google/protobuf/any.proto";
+import "meta_graph.proto";
+import "model.proto";
+
+// Message returned for "signature_def" field.
+message SignatureDefMap {
+  map<string, SignatureDef> signature_def = 1;
+};
+
+message GetModelMetadataRequest {
+  // Model Specification indicating which model we are querying for metadata.
+  // If version is not specified, will use the latest (numerical) version.
+  ModelSpec model_spec = 1;
+  // Metadata fields to get. Currently supported: "signature_def".
+  repeated string metadata_field = 2;
+}
+
+message GetModelMetadataResponse {
+  // Model Specification indicating which model this metadata belongs to.
+  ModelSpec model_spec = 1;
+  // Map of metadata field name to metadata field. The options for metadata
+  // field name are listed in GetModelMetadataRequest. Currently supported:
+  // "signature_def".
+  map<string, google.protobuf.Any> metadata = 2;
+}

protos/graph.proto

@@ -0,0 +1,56 @@
+syntax = "proto3";
+
+package tensorflow;
+option cc_enable_arenas = true;
+option java_outer_classname = "GraphProtos";
+option java_multiple_files = true;
+option java_package = "org.tensorflow.framework";
+option go_package = "github.com/tensorflow/tensorflow/tensorflow/go/core/framework";
+import "node_def.proto";
+import "function.proto";
+import "versions.proto";
+
+// Represents the graph of operations
+message GraphDef {
+  repeated NodeDef node = 1;
+
+  // Compatibility versions of the graph.  See core/public/version.h for version
+  // history.  The GraphDef version is distinct from the TensorFlow version, and
+  // each release of TensorFlow will support a range of GraphDef versions.
+  VersionDef versions = 4;
+
+  // Deprecated single version field; use versions above instead.  Since all
+  // GraphDef changes before "versions" was introduced were forward
+  // compatible, this field is entirely ignored.
+  int32 version = 3 [deprecated = true];
+
+  // EXPERIMENTAL. DO NOT USE OR DEPEND ON THIS YET.
+  //
+  // "library" provides user-defined functions.
+  //
+  // Naming:
+  //   * library.function.name are in a flat namespace.
+  //     NOTE: We may need to change it to be hierarchical to support
+  //     different orgs. E.g.,
+  //     { "/google/nn", { ... }},
+  //     { "/google/vision", { ... }}
+  //     { "/org_foo/module_bar", { ... }}
+  //     map<string, FunctionDefLib> named_lib;
+  //   * If node[i].op is the name of one function in "library",
+  //     node[i] is deemed as a function call. Otherwise, node[i].op
+  //     must be a primitive operation supported by the runtime.
+  //
+  //
+  // Function call semantics:
+  //
+  //   * The callee may start execution as soon as some of its inputs
+  //     are ready. The caller may want to use Tuple() mechanism to
+  //     ensure all inputs are ready in the same time.
+  //
+  //   * The consumer of return values may start executing as soon as
+  //     the return values the consumer depends on are ready.  The
+  //     consumer may want to use Tuple() mechanism to ensure the
+  //     consumer does not start until all return values of the callee
+  //     function are ready.
+  FunctionDefLibrary library = 2;
+};