Merged master:a1aa330b202f into amd-gfx:046007c5b8e1

Local branch amd-gfx 046007c Merged master:22dde1f92f68 into amd-gfx:606e09770817
Remote branch master a1aa330 [Sema] Handle objc_super special lookup when checking builtin compatibility

clang-tools-extra/clangd/CMakeLists.txt

@@ -28,6 +28,9 @@ set(LLVM_LINK_COMPONENTS
   FrontendOpenMP
   Option
   )
+
+include(${CMAKE_CURRENT_SOURCE_DIR}/quality/CompletionModel.cmake)
+gen_decision_forest(${CMAKE_CURRENT_SOURCE_DIR}/quality/model CompletionModel clang::clangd::Example)
 
 if(MSVC AND NOT CLANG_CL)
  set_source_files_properties(CompileCommands.cpp PROPERTIES COMPILE_FLAGS -wd4130) # disables C4130: logical operation on address of string constant
@@ -77,6 +80,7 @@ add_clang_library(clangDaemon
   TUScheduler.cpp
   URI.cpp
   XRefs.cpp
+  ${CMAKE_CURRENT_BINARY_DIR}/CompletionModel.cpp
 
   index/Background.cpp
   index/BackgroundIndexLoader.cpp
@@ -117,6 +121,11 @@ add_clang_library(clangDaemon
   omp_gen
   )
 
+# Include generated CompletionModel headers.
+target_include_directories(clangDaemon PUBLIC
+  $<BUILD_INTERFACE:${CMAKE_CURRENT_BINARY_DIR}>
+)
+
 clang_target_link_libraries(clangDaemon
   PRIVATE
   clangAST

clang-tools-extra/clangd/quality/CompletionModel.cmake

@@ -0,0 +1,37 @@
+# Run the Completion Model Codegenerator on the model present in the 
+# ${model} directory.
+# Produces a pair of files called ${filename}.h and  ${filename}.cpp in the 
+# ${CMAKE_CURRENT_BINARY_DIR}. The generated header
+# will define a C++ class called ${cpp_class} - which may be a
+# namespace-qualified class name.
+function(gen_decision_forest model filename cpp_class)
+  set(model_compiler ${CMAKE_SOURCE_DIR}/../clang-tools-extra/clangd/quality/CompletionModelCodegen.py)
+
+  set(output_dir ${CMAKE_CURRENT_BINARY_DIR})
+  set(header_file ${output_dir}/${filename}.h)
+  set(cpp_file ${output_dir}/${filename}.cpp)
+
+  add_custom_command(OUTPUT ${header_file} ${cpp_file}
+    COMMAND "${Python3_EXECUTABLE}" ${model_compiler}
+      --model ${model}
+      --output_dir ${output_dir}
+      --filename ${filename}
+      --cpp_class ${cpp_class}
+    COMMENT "Generating code completion model runtime..."
+    DEPENDS ${model_compiler} ${model}/forest.json ${model}/features.json
+    VERBATIM )
+
+  set_source_files_properties(${header_file} PROPERTIES
+    GENERATED 1)
+  set_source_files_properties(${cpp_file} PROPERTIES
+    GENERATED 1)
+
+  # Disable unused label warning for generated files.
+  if (CMAKE_CXX_COMPILER_ID STREQUAL "MSVC")
+    set_source_files_properties(${cpp_file} PROPERTIES
+      COMPILE_FLAGS /wd4102)
+  else()
+    set_source_files_properties(${cpp_file} PROPERTIES
+      COMPILE_FLAGS -Wno-unused)
+  endif()
+endfunction()

clang-tools-extra/clangd/quality/CompletionModelCodegen.py

@@ -0,0 +1,283 @@
+"""Code generator for Code Completion Model Inference.
+
+Tool runs on the Decision Forest model defined in {model} directory.
+It generates two files: {output_dir}/{filename}.h and {output_dir}/{filename}.cpp 
+The generated files defines the Example class named {cpp_class} having all the features as class members.
+The generated runtime provides an `Evaluate` function which can be used to score a code completion candidate.
+"""
+
+import argparse
+import json
+import struct
+from enum import Enum
+
+
+class CppClass:
+    """Holds class name and names of the enclosing namespaces."""
+
+    def __init__(self, cpp_class):
+        ns_and_class = cpp_class.split("::")
+        self.ns = [ns for ns in ns_and_class[0:-1] if len(ns) > 0]
+        self.name = ns_and_class[-1]
+        if len(self.name) == 0:
+            raise ValueError("Empty class name.")
+
+    def ns_begin(self):
+        """Returns snippet for opening namespace declarations."""
+        open_ns = [f"namespace {ns} {{" for ns in self.ns]
+        return "\n".join(open_ns)
+
+    def ns_end(self):
+        """Returns snippet for closing namespace declarations."""
+        close_ns = [
+            f"}} // namespace {ns}" for ns in reversed(self.ns)]
+        return "\n".join(close_ns)
+
+
+def header_guard(filename):
+    '''Returns the header guard for the generated header.'''
+    return f"GENERATED_DECISION_FOREST_MODEL_{filename.upper()}_H"
+
+
+def boost_node(n, label, next_label):
+    """Returns code snippet for a leaf/boost node.
+    Adds value of leaf to the score and jumps to the root of the next tree."""
+    return f"{label}: Score += {n['score']}; goto {next_label};"
+
+
+def if_greater_node(n, label, next_label):
+    """Returns code snippet for a if_greater node.
+    Jumps to true_label if the Example feature (NUMBER) is greater than the threshold. 
+    Comparing integers is much faster than comparing floats. Assuming floating points 
+    are represented as IEEE 754, it order-encodes the floats to integers before comparing them.
+    Control falls through if condition is evaluated to false."""
+    threshold = n["threshold"]
+    return f"{label}: if (E.{n['feature']} >= {order_encode(threshold)} /*{threshold}*/) goto {next_label};"
+
+
+def if_member_node(n, label, next_label):
+    """Returns code snippet for a if_member node.
+    Jumps to true_label if the Example feature (ENUM) is present in the set of enum values 
+    described in the node.
+    Control falls through if condition is evaluated to false."""
+    members = '|'.join([
+        f"BIT({n['feature']}_type::{member})"
+        for member in n["set"]
+    ])
+    return f"{label}: if (E.{n['feature']} & ({members})) goto {next_label};"
+
+
+def node(n, label, next_label):
+    """Returns code snippet for the node."""
+    return {
+        'boost': boost_node,
+        'if_greater': if_greater_node,
+        'if_member': if_member_node,
+    }[n['operation']](n, label, next_label)
+
+
+def tree(t, tree_num: int, node_num: int):
+    """Returns code for inferencing a Decision Tree.
+    Also returns the size of the decision tree.
+
+    A tree starts with its label `t{tree#}`.
+    A node of the tree starts with label `t{tree#}_n{node#}`.
+
+    The tree contains two types of node: Conditional node and Leaf node.
+    -   Conditional node evaluates a condition. If true, it jumps to the true node/child.
+        Code is generated using pre-order traversal of the tree considering
+        false node as the first child. Therefore the false node is always the
+        immediately next label.
+    -   Leaf node adds the value to the score and jumps to the next tree.
+    """
+    label = f"t{tree_num}_n{node_num}"
+    code = []
+    if node_num == 0:
+        code.append(f"t{tree_num}:")
+
+    if t["operation"] == "boost":
+        code.append(node(t, label=label, next_label=f"t{tree_num+1}"))
+        return code, 1
+
+    false_code, false_size = tree(
+        t['else'], tree_num=tree_num, node_num=node_num+1)
+
+    true_node_num = node_num+false_size+1
+    true_label = f"t{tree_num}_n{true_node_num}"
+
+    true_code, true_size = tree(
+        t['then'], tree_num=tree_num, node_num=true_node_num)
+
+    code.append(node(t, label=label, next_label=true_label))
+
+    return code+false_code+true_code, 1+false_size+true_size
+
+
+def gen_header_code(features_json: list, cpp_class, filename: str):
+    """Returns code for header declaring the inference runtime.
+
+    Declares the Example class named {cpp_class} inside relevant namespaces.
+    The Example class contains all the features as class members. This 
+    class can be used to represent a code completion candidate.
+    Provides `float Evaluate()` function which can be used to score the Example.
+    """
+    setters = []
+    for f in features_json:
+        feature = f["name"]
+        if f["kind"] == "NUMBER":
+            # Floats are order-encoded to integers for faster comparison.
+            setters.append(
+                f"void set{feature}(float V) {{ {feature} = OrderEncode(V); }}")
+        elif f["kind"] == "ENUM":
+            setters.append(
+                f"void set{feature}(unsigned V) {{ {feature} = 1 << V; }}")
+        else:
+            raise ValueError("Unhandled feature type.", f["kind"])
+
+    # Class members represent all the features of the Example.
+    class_members = [f"uint32_t {f['name']} = 0;" for f in features_json]
+
+    nline = "\n  "
+    guard = header_guard(filename)
+    return f"""#ifndef {guard}
+#define {guard}
+#include <cstdint>
+
+{cpp_class.ns_begin()}
+class {cpp_class.name} {{
+public:
+  {nline.join(setters)}
+
+private:
+  {nline.join(class_members)}
+
+  // Produces an integer that sorts in the same order as F.
+  // That is: a < b <==> orderEncode(a) < orderEncode(b).
+  static uint32_t OrderEncode(float F);
+  friend float Evaluate(const {cpp_class.name}&);
+}};
+
+float Evaluate(const {cpp_class.name}&);
+{cpp_class.ns_end()}
+#endif // {guard}
+"""
+
+
+def order_encode(v: float):
+    i = struct.unpack('<I', struct.pack('<f', v))[0]
+    TopBit = 1 << 31
+    # IEEE 754 floats compare like sign-magnitude integers.
+    if (i & TopBit):  # Negative float
+        return (1 << 32) - i  # low half of integers, order reversed.
+    return TopBit + i  # top half of integers
+
+
+def evaluate_func(forest_json: list, cpp_class: CppClass):
+    """Generates code for `float Evaluate(const {Example}&)` function.
+    The generated function can be used to score an Example."""
+    code = f"float Evaluate(const {cpp_class.name}& E) {{\n"
+    lines = []
+    lines.append("float Score = 0;")
+    tree_num = 0
+    for tree_json in forest_json:
+        lines.extend(tree(tree_json, tree_num=tree_num, node_num=0)[0])
+        lines.append("")
+        tree_num += 1
+
+    lines.append(f"t{len(forest_json)}: // No such tree.")
+    lines.append("return Score;")
+    code += "  " + "\n  ".join(lines)
+    code += "\n}"
+    return code
+
+
+def gen_cpp_code(forest_json: list, features_json: list, filename: str,
+                 cpp_class: CppClass):
+    """Generates code for the .cpp file."""
+    # Headers
+    # Required by OrderEncode(float F).
+    angled_include = [
+        f'#include <{h}>'
+        for h in ["cstring", "limits"]
+    ]
+
+    # Include generated header.
+    qouted_headers = {f"{filename}.h", "llvm/ADT/bit.h"}
+    # Headers required by ENUM features used by the model.
+    qouted_headers |= {f["header"]
+                       for f in features_json if f["kind"] == "ENUM"}
+    quoted_include = [f'#include "{h}"' for h in sorted(qouted_headers)]
+
+    # using-decl for ENUM features.
+    using_decls = "\n".join(f"using {feature['name']}_type = {feature['type']};"
+                            for feature in features_json
+                            if feature["kind"] == "ENUM")
+    nl = "\n"
+    return f"""{nl.join(angled_include)}
+
+{nl.join(quoted_include)}
+
+#define BIT(X) (1 << X)
+
+{cpp_class.ns_begin()}
+
+{using_decls}
+
+uint32_t {cpp_class.name}::OrderEncode(float F) {{
+  static_assert(std::numeric_limits<float>::is_iec559, "");
+  constexpr uint32_t TopBit = ~(~uint32_t{{0}} >> 1);
+
+  // Get the bits of the float. Endianness is the same as for integers.
+  uint32_t U = llvm::bit_cast<uint32_t>(F);
+  std::memcpy(&U, &F, sizeof(U));
+  // IEEE 754 floats compare like sign-magnitude integers.
+  if (U & TopBit)    // Negative float.
+    return 0 - U;    // Map onto the low half of integers, order reversed.
+  return U + TopBit; // Positive floats map onto the high half of integers.
+}}
+
+{evaluate_func(forest_json, cpp_class)}
+{cpp_class.ns_end()}
+"""
+
+
+def main():
+    parser = argparse.ArgumentParser('DecisionForestCodegen')
+    parser.add_argument('--filename', help='output file name.')
+    parser.add_argument('--output_dir', help='output directory.')
+    parser.add_argument('--model', help='path to model directory.')
+    parser.add_argument(
+        '--cpp_class',
+        help='The name of the class (which may be a namespace-qualified) created in generated header.'
+    )
+    ns = parser.parse_args()
+
+    output_dir = ns.output_dir
+    filename = ns.filename
+    header_file = f"{output_dir}/{filename}.h"
+    cpp_file = f"{output_dir}/{filename}.cpp"
+    cpp_class = CppClass(cpp_class=ns.cpp_class)
+
+    model_file = f"{ns.model}/forest.json"
+    features_file = f"{ns.model}/features.json"
+
+    with open(features_file) as f:
+        features_json = json.load(f)
+
+    with open(model_file) as m:
+        forest_json = json.load(m)
+
+    with open(cpp_file, 'w+t') as output_cc:
+        output_cc.write(
+            gen_cpp_code(forest_json=forest_json,
+                         features_json=features_json,
+                         filename=filename,
+                         cpp_class=cpp_class))
+
+    with open(header_file, 'w+t') as output_h:
+        output_h.write(gen_header_code(
+            features_json=features_json, cpp_class=cpp_class, filename=filename))
+
+
+if __name__ == '__main__':
+    main()