usaxena95 updated this revision to Diff 295284.
usaxena95 added a comment.
Minor formatting fixes.
Repository:
rG LLVM Github Monorepo
CHANGES SINCE LAST ACTION
https://reviews.llvm.org/D88536/new/
https://reviews.llvm.org/D88536
Files:
clang-tools-extra/clangd/quality/CompletionModelCodegen.py
Index: clang-tools-extra/clangd/quality/CompletionModelCodegen.py
===================================================================
--- clang-tools-extra/clangd/quality/CompletionModelCodegen.py
+++ clang-tools-extra/clangd/quality/CompletionModelCodegen.py
@@ -1,7 +1,7 @@
"""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
+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.
"""
@@ -39,34 +39,32 @@
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 "%s: Score += %s; goto %s;" % (
- label, n['score'], next_label)
+ """Returns code snippet for a leaf/boost node."""
+ return "%s: return %s;" % (label, n['score'])
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
+ 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 "%s: if (E.%s >= %s /*%s*/) goto %s;" % (
- label, n['feature'], order_encode(threshold), threshold, next_label)
+ return "%s: if (%s >= %s /*%s*/) goto %s;" % (
+ label, n['feature'], order_encode(threshold), threshold, 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
+ 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([
"BIT(%s_type::%s)" % (n['feature'], member)
for member in n["set"]
])
- return "%s: if (E.%s & (%s)) goto %s;" % (
- label, n['feature'], members, next_label)
+ return "%s: if (%s & (%s)) goto %s;" % (
+ label, n['feature'], members, next_label)
def node(n, label, next_label):
@@ -94,8 +92,6 @@
"""
label = "t%d_n%d" % (tree_num, node_num)
code = []
- if node_num == 0:
- code.append("t%d:" % tree_num)
if t["operation"] == "boost":
code.append(node(t, label=label, next_label="t%d" % (tree_num + 1)))
@@ -115,11 +111,11 @@
return code+false_code+true_code, 1+false_size+true_size
-def gen_header_code(features_json, cpp_class, filename):
+def gen_header_code(features_json, cpp_class, filename, num_trees):
"""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
+ 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.
"""
@@ -145,7 +141,6 @@
return """#ifndef %s
#define %s
#include <cstdint>
-#include "llvm/Support/Compiler.h"
%s
class %s {
@@ -158,18 +153,21 @@
// 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);
+
+ // Evaluation functions for each tree.
+%s
+
friend float Evaluate(const %s&);
};
-// The function may have large number of lines of code. MSAN
-// build times out in such case.
-LLVM_NO_SANITIZE("memory")
float Evaluate(const %s&);
%s
#endif // %s
""" % (guard, guard, cpp_class.ns_begin(), cpp_class.name, nline.join(setters),
- nline.join(class_members), cpp_class.name, cpp_class.name,
- cpp_class.ns_end(), guard)
+ nline.join(class_members),
+ nline.join([" float EvaluateTree%d() const;" % tree_num
+ for tree_num in range(num_trees)]),
+ cpp_class.name, cpp_class.name, cpp_class.ns_end(), guard)
def order_encode(v):
@@ -181,22 +179,32 @@
return TopBit + i # top half of integers
-def evaluate_func(forest_json, cpp_class):
- """Generates code for `float Evaluate(const {Example}&)` function.
- The generated function can be used to score an Example."""
- code = "float Evaluate(const %s& E) {\n" % cpp_class.name
- lines = []
- lines.append("float Score = 0;")
+def evaluate_funcs(forest_json, cpp_class):
+ """Generates evaluation functions for each tree and combines them in
+ `float Evaluate(const {Example}&)` function. This function can be
+ used to score an Example."""
+
+ code = ""
+
+ # Generate evaluation function of each tree.
tree_num = 0
for tree_json in forest_json:
- lines.extend(tree(tree_json, tree_num=tree_num, node_num=0)[0])
- lines.append("")
+ code += "float %s::EvaluateTree%d() const {\n" % (
+ cpp_class.name, tree_num)
+ code += " " + \
+ " \n".join(
+ tree(tree_json, tree_num=tree_num, node_num=0)[0]) + "\n"
+ code += "}\n\n"
tree_num += 1
- lines.append("t%s: // No such tree." % len(forest_json))
- lines.append("return Score;")
- code += " " + "\n ".join(lines)
- code += "\n}"
+ # Combine the scores of all trees in the final function.
+ code += "float Evaluate(const %s& E) {\n" % cpp_class.name
+ code += " float Score = 0;\n"
+ for tree_num in range(len(forest_json)):
+ code += " Score += E.EvaluateTree%d();\n" % tree_num
+ code += " return Score;\n"
+ code += "}\n"
+
return code
@@ -248,7 +256,7 @@
%s
%s
""" % (nl.join(angled_include), nl.join(quoted_include), cpp_class.ns_begin(),
- using_decls, cpp_class.name, evaluate_func(forest_json, cpp_class),
+ using_decls, cpp_class.name, evaluate_funcs(forest_json, cpp_class),
cpp_class.ns_end())
@@ -287,7 +295,10 @@
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))
+ features_json=features_json,
+ cpp_class=cpp_class,
+ filename=filename,
+ num_trees=len(forest_json)))
if __name__ == '__main__':
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