Sync fbcode to github

Author: Mingzhe Li

tensor_comprehensions/pybinds/pybind_options.cc

@@ -89,7 +89,7 @@ PYBIND11_MODULE(mapping_options, m) {
           },
           "Require TC to try and execute different TC expressions interleaved (Max), separately (Min)\nor interleaved as long as sufficient parallelism is exploited (Preserve3Coincident) by\nperforming loop fusion and fission. Applies before tiling")
       .def(
-          "serializeToProtobuf",
+          "serialize",
           [](tc::CudaMappingOptions& instance) {
             std::string str = instance.toProtobufSerializedString();
             return py::bytes(str);

test_python/test_c2.py

@@ -17,8 +17,9 @@
 import numpy as np
 import hypothesis.strategies as st
 import caffe2.python.hypothesis_test_util as hu
+import tensor_comprehensions as tc
 
-from hypothesis import given
+from hypothesis import given, settings
 from caffe2.python import core, dyndep
 
 
@@ -28,6 +29,11 @@
 else:
     dyndep.InitOpsLibrary("@/tc/tc:tc_c2")
 
+MATMUL_LANG = """
+def matmul(float(M,N) A, float(N,K) B) -> (output) {
+output(i, j) +=! A(i, kk) * B(kk, j)
+}
+"""
 
 class TestCaffe2(hu.HypothesisTestCase):
     @given(n=st.integers(1, 128),
@@ -38,14 +44,6 @@ class TestCaffe2(hu.HypothesisTestCase):
     def test_matmul(self, n, m, k, seed, gc, dc):
         np.random.seed(seed)
 
-        tc_forward = """
-        def matmul(float(M,N) A, float(N,K) B) -> (output) {
-          output(i, j) +=! A(i, kk) * B(kk, j)
-        }
-        """
-
-        # TODO: (prigoyal) serialize the options
-        # options = Options("mlp")
         X = np.random.rand(m, k).astype(np.float32)
         W = np.random.rand(k, n).astype(np.float32)
 
@@ -54,7 +52,7 @@ def ref(X, W):
 
         op = core.CreateOperator(
             "TcOp", ["X", "Y"], "out",
-            tcDef=tc_forward,
+            tcDef=MATMUL_LANG,
             tcName="matmul",
         )
 
@@ -65,6 +63,42 @@ def ref(X, W):
             reference=ref,
         )
 
+    @given(n=st.integers(1, 128),
+           m=st.integers(1, 128),
+           k=st.integers(1, 128),
+           seed=st.integers(min_value=0, max_value=2**32 - 1),
+           **hu.gcs_gpu_only)
+    @settings(max_examples=2)
+    def test_matmul_tune_and_run(self, n, m, k, seed, gc, dc):
+        matmul = tc.define(MATMUL_LANG, name="matmul")
+
+        mapping_options = matmul.autotune(
+            (n, k), (k, m),
+            generations=1,
+            threads=32,
+            pop_size=2,
+            tuner_min_launch_total_threads=1,
+        )
+
+        X = np.random.rand(m, k).astype(np.float32)
+        W = np.random.rand(k, n).astype(np.float32)
+
+        def ref(X, W):
+            return [np.dot(X, W)]
+
+        op = core.CreateOperator(
+            "TcOp", ["X", "Y"], "out",
+            tcDef=MATMUL_LANG,
+            tcName="matmul",
+            mappingOptions=mapping_options.serialize(),
+        )
+
+        self.assertReferenceChecks(
+            device_option=gc,
+            op=op,
+            inputs=[X, W],
+            reference=ref,
+        )
 
 if __name__ == '__main__':
     unittest.main()