[relay][pass] Annotation for heterogeneous compilation

docs/langref/relay_op.rst

@@ -148,6 +148,8 @@ This level support backpropagation of broadcast operators. It is temporary.
    tvm.relay.broadcast_to_like
    tvm.relay.collapse_sum_like
    tvm.relay.slice_like
+   tvm.relay.device_copy
+   tvm.relay.annotation.on_device
 
 
 Level 1 Definitions

include/tvm/relay/attrs/annotation.h

@@ -0,0 +1,31 @@
+/*!
+ *  Copyright (c) 2018 by Contributors
+ * \file tvm/relay/attrs/annotation.h
+ * \brief Attribute for annotation operators.
+ */
+#ifndef TVM_RELAY_ATTRS_ANNOTATION_H_
+#define TVM_RELAY_ATTRS_ANNOTATION_H_
+
+#include <tvm/attrs.h>
+#include <string>
+
+namespace tvm {
+namespace relay {
+
+/*!
+ * \brief Options for the device annotation operators.
+ */
+struct OnDeviceAttrs : public tvm::AttrsNode<OnDeviceAttrs> {
+  int device_type;
+
+  TVM_DECLARE_ATTRS(OnDeviceAttrs, "relay.attrs.OnDeviceAttrs") {
+    TVM_ATTR_FIELD(device_type)
+      .describe(
+         "The virutal device/context type that an expression is annotated with.")
+      .set_default(0);
+  }
+};
+
+}  // namespace relay
+}  // namespace tvm
+#endif  // TVM_RELAY_ATTRS_ANNOTATION_H_

include/tvm/relay/attrs/device_copy.h

@@ -0,0 +1,36 @@
+/*!
+ *  Copyright (c) 2018 by Contributors
+ * \file tvm/relay/attrs/device_copy.h
+ * \brief Attribute for the device copy operator.
+ */
+#ifndef TVM_RELAY_ATTRS_DEVICE_COPY_H_
+#define TVM_RELAY_ATTRS_DEVICE_COPY_H_
+
+#include <tvm/attrs.h>
+#include <string>
+
+namespace tvm {
+namespace relay {
+
+/*!
+ * \brief Options for the device copy operators.
+ */
+struct DeviceCopyAttrs : public tvm::AttrsNode<DeviceCopyAttrs> {
+  int dst_dev_type;
+  int src_dev_type;
+
+  TVM_DECLARE_ATTRS(DeviceCopyAttrs, "relay.attrs.DeviceCopyAttrs") {
+    TVM_ATTR_FIELD(src_dev_type)
+      .describe(
+         "The virutal device/context type where the op copies data from.")
+      .set_default(0);
+    TVM_ATTR_FIELD(dst_dev_type)
+      .describe(
+         "The virutal device/context type where the op copies data to.")
+      .set_default(0);
+  }
+};
+
+}  // namespace relay
+}  // namespace tvm
+#endif  // TVM_RELAY_ATTRS_DEVICE_COPY_H_

include/tvm/relay/attrs/transform.h

@@ -164,7 +164,6 @@ struct ClipAttrs : public tvm::AttrsNode<ClipAttrs> {
   }
 };
 
-
 struct LayoutTransformAttrs : public tvm::AttrsNode<LayoutTransformAttrs> {
   std::string src_layout;
   std::string dst_layout;

include/tvm/relay/pass.h

@@ -188,6 +188,21 @@ Expr ForwardRewrite(const Expr& expr,
                     std::function<NodeRef(const Call&)> fcontext = nullptr,
                     std::function<Expr(const Expr&)> fmulti_ref_trigger = nullptr);
 
+/*!
+ * \brief Rewrite the annotated program.
+ * \param expr The expression.
+ * \param fallback_device The fallback device which is the default device for
+ *                        operators without annotation.
+ * \return The updated program.
+ */
+Expr RewriteAnnotatedOps(const Expr& expr, int fallback_device);
+
+/*!
+ * \brief Collect the device mapping information of each expression.
+ * \param expr The expression.
+ * \return The device mapping.
+ */
+Map<Expr, Integer> CollectDeviceInfo(const Expr& expr);
 
 /*! \brief A hashing structure in the style of std::hash. */
 struct StructuralHash {

python/tvm/__init__.py

@@ -25,6 +25,7 @@
 from .ndarray import vpi, rocm, opengl, ext_dev
 
 from ._ffi.runtime_ctypes import TypeCode
+from ._ffi.ndarray import TVMContext
 from ._ffi.function import Function
 from ._ffi.base import TVMError, __version__
 from .api import *

python/tvm/relay/__init__.py

@@ -18,6 +18,7 @@
 from .op.tensor import *
 from .op.transform import *
 from . import nn
+from . import annotation
 from . import vision
 from . import image
 from . import frontend

python/tvm/relay/annotation.py

@@ -0,0 +1,4 @@
+# pylint: disable=wildcard-import, unused-import, unused-wildcard-import
+"""Annotation related operators."""
+# Re-export in a specific file name so that autodoc can pick it up
+from .op.annotation import *

python/tvm/relay/backend/_backend.py

@@ -52,20 +52,21 @@ def build(funcs, target, target_host=None):
 
     Parameters
     ----------
-    funcs : List[tvm.LoweredFunc]
-         The list of lowered functions.
+    funcs : List[tvm.LoweredFunc] or Dict[str, List[tvm.LoweredFunc]]
+        A list of lowered functions or dictionary mapping from targets to
+        lowered functions.
 
 
     target : tvm.Target
-         The target to run the code on.
+        The target to run the code on.
 
     target_host : tvm.Target
-         The host target.
+        The host target.
 
     Returns
     -------
     module : tvm.Module
-         The runtime module.
+        The runtime module.
     """
     if target_host == "":
         target_host = None

python/tvm/relay/backend/graph_runtime_codegen.py

@@ -20,13 +20,15 @@
 
 from __future__ import absolute_import
 import json
+from collections import defaultdict
 import attr
 from . import _backend
 from . import compile_engine
 from ..op import Op
 from ..expr import Function, GlobalVar
 from ..expr_functor import ExprFunctor
 from ..ty import TupleType, TensorType
+from ... import target as _target
 
 
 @attr.s
@@ -105,9 +107,9 @@ def __init__(self, mod, target):
         self.nodes = []
         self.var_map = {}
         self.params = {}
-        self.storage_map = None
+        self.storage_device_map = None
         self.compile_engine = compile_engine.get()
-        self.lowered_funcs = set()
+        self.lowered_funcs = defaultdict(set)
         self._name_map = {}
 
     def add_node(self, node, expr):
@@ -129,10 +131,20 @@ def add_node(self, node, expr):
         """
         checked_type = expr.checked_type
         # setup storage ids
-        assert expr in self.storage_map
-        node.attrs["storage_id"] = [
-            x.value for x in self.storage_map[expr]
-        ]
+        assert expr in self.storage_device_map
+        storage_device_info = self.storage_device_map[expr]
+        assert len(storage_device_info) == 2
+        node.attrs["storage_id"] = [x.value for x in storage_device_info[0]]
+        device_types = [x.value for x in storage_device_info[1]]
+        num_unknown_devices = device_types.count(0)
+        if num_unknown_devices != 0 and num_unknown_devices != len(device_types):
+            raise RuntimeError("The graph contains not annotated nodes for "
+                               "heterogeneous execution. All nodes must be "
+                               "annotated.")
+
+        # Add the `device_index` attribute when the graph is annotated.
+        if num_unknown_devices == 0:
+            node.attrs["device_index"] = device_types
 
         node_id = len(self.nodes)
         self.nodes.append(node)
@@ -232,9 +244,25 @@ def visit_call(self, call):
                 "TVM only support calls to primitive functions " +
                 "(i.e functions composed of fusable operator invocations)")
 
-        cached_func = self.compile_engine.lower(func, self.target)
+        assert call in self.storage_device_map
+        device_types = self.storage_device_map[call][1]
+        call_dev_type = device_types[0].value
+        if isinstance(self.target, (str, _target.Target)):
+            # homogeneous execution.
+            cached_func = self.compile_engine.lower(func, self.target)
+            self.target = {0: str(self.target)}
+        elif isinstance(self.target, dict):
+            # heterogeneous execution.
+            if call_dev_type not in self.target:
+                raise Exception("No target is provided for device " +
+                                "{0}".format(call_dev_type))
+            cached_func = self.compile_engine.lower(func,
+                                                    self.target[call_dev_type])
+        else:
+            raise ValueError("self.target must be the type of str," +
+                             "tvm.target.Target, or dict of int to str")
         for loweredf in cached_func.funcs:
-            self.lowered_funcs.add(loweredf)
+            self.lowered_funcs[self.target[call_dev_type]].add(loweredf)
 
         inputs = []
         # flatten tuple in the call.
@@ -284,20 +312,25 @@ def _get_json(self):
         num_entry = 0
         shapes = []
         storage_ids = []
+        device_types = []
         dltypes = []
         node_row_ptr = [0]
         for node in self.nodes:
             assert node.num_outputs == len(node.attrs["shape"])
             shapes += node.attrs["shape"]
             dltypes += node.attrs["dtype"]
             storage_ids += node.attrs["storage_id"]
+            if "device_index" in node.attrs:
+                device_types += node.attrs["device_index"]
             num_entry += node.num_outputs
             node_row_ptr.append(num_entry)
 
         # Compute "attrs" field.
         attrs = {}
         attrs["shape"] = ["list_shape", shapes]
         attrs["storage_id"] = ["list_int", storage_ids]
+        if device_types:
+            attrs["device_index"] = ["list_int", device_types]
         attrs["dltype"] = ["list_str", dltypes]
 
         json_dict = {
@@ -313,11 +346,24 @@ def _get_json(self):
     def debug_dump_memory_plan(self, func):
         """Debug function to dump memory plan."""
         def _annotate(expr):
-            if expr in self.storage_map:
-                return str(self.storage_map[expr])
+            if expr in self.storage_device_map:
+                storage_device_info = self.storage_device_map[expr]
+                assert len(storage_device_info) == 2
+                return str(storage_device_info[0])
+            return ""
+        return func.astext(show_meta_data=False, annotate=_annotate)
+
+    def debug_dump_device_annotation(self, func):
+        """Debug function to dump device annotation result."""
+        def _annotate(expr):
+            if expr in self.storage_device_map:
+                storage_device_info = self.storage_device_map[expr]
+                assert len(storage_device_info) == 2
+                return str(storage_device_info[1])
             return ""
         return func.astext(show_meta_data=False, annotate=_annotate)
 
+
     def codegen(self, func):
         """Compile a single function into a graph.
 
@@ -331,24 +377,31 @@ def codegen(self, func):
         graph_json : str
             The graph json that can be consumed by runtime.
 
-        lowered_funcs : List[tvm.LoweredFunc]
+        lowered_funcs : List[tvm.LoweredFunc] or Dict[str, List[tvm.LoweredFunc]]
             The lowered functions.
 
         params : Dict[str, tvm.nd.NDArray]
             Additional constant parameters.
         """
-        self.storage_map = _backend.GraphPlanMemory(func)
+        self.storage_device_map = _backend.GraphPlanMemory(func)
         # First we convert all the parameters into input nodes.
         for param in func.params:
             node = InputNode(param.name_hint, {})
-            self.var_map[param] = self.add_node(
-                node, param)
+            self.var_map[param] = self.add_node(node, param)
 
         # Then we compile the body into a graph which can depend
         # on input variables.
         self.heads = self.visit(func.body)
         graph_json = self._get_json()
-        lowered_funcs = list(self.lowered_funcs)
+
+        # Return the lowered functions as a list for homogeneous compilation.
+        # Otherwise, for heterogeneous compilation, a dictionary containing
+        # the device id to a list of lowered functions is returned. Both forms
+        # are acceptable to tvm.build.
+        if not isinstance(self.target, dict):
+            lowered_funcs = list(list(self.lowered_funcs.values())[0])
+        else:
+            lowered_funcs = {k: list(v) for k, v in self.lowered_funcs.items()}
         return graph_json, lowered_funcs, self.params
 
     def _get_unique_name(self, name):