Simplify basic C++ instructions in README.md

Author: nicolasvasilache

README.md

@@ -41,29 +41,27 @@ def tensordot(float(N, C1, C2, H, W) I0,
     O(n, c1, c3, h, w) +=! I0(n, c1, r_c2, h, w) * I1(n, r_c2, c3, h, w)
 }
   )TC";
-  tc::ATenCompilationUnit<tc::CudaBackend> atCompl;
-  atCompl.define(tc);
 
   // 2. Allocate tensors with random data.
   at::Tensor I0 = at::CUDA(at::kFloat).rand({32,  8, 16, 17, 25});
   at::Tensor I1 = at::CUDA(at::kFloat).rand({32, 16, 2, 17, 25});
 
   // 3. Run autotuning with evolutionary search starting from a naive option.
-  auto options = tc::CudaMappingOptions::makeNaiveMappingOptions();
-  tc::autotune::GeneticAutotunerATen geneticAutotuneATen(tc);
-  auto bestOption = geneticAutotuneATen.tune(
-    "/tmp/save_results", "tensordot", {I0, I1}, options);
-
-  // 4. Compile and run the TC with the best option.
-  // Outputs get allocated; could also be pre-allocated and passed.
-  auto handle = atCompl.compile("tensordot", {I0, I1}, bestOption.getValue());
-  std::vector<at::Tensor> outputs;
-  auto duration = atCompl.run("tensordot", {I0, I1}, outputs, handle, true);
-  std::cout
-       << "tensordot size I0: " << I0.sizes() << ", "
-       << "size I1: " << I1.sizes() << " ran in: "
-       << std::chrono::duration_cast<std::chrono::microseconds>(duration).count()
-       << "us\n";
+  auto naiveOptions = Backend::MappingOptionsType::makeNaiveMappingOptions();
+  tc::aten::ATenAutotuner<tc::CudaBackend, tc::autotune::GeneticSearch>
+      geneticAutotuneATen(tc);
+  auto bestOption =
+      geneticAutotuneATen.tune("tensordot", {I0, I1}, {naiveOptions});
+
+  // 4. Compile and run the TC with the best option after allocating output
+  //    tensors.
+  auto pExecutor =
+      tc::aten::compile<Backend>(tc, "tensordot", {I0, I1}, bestOption[0]);
+  auto outputs = tc::aten::prepareOutputs(tc, "tensordot", {I0, I1});
+  auto timings = tc::aten::profile(*pExecutor, {I0, I1}, outputs);
+  std::cout << "tensordot size I0: " << I0.sizes() << ", "
+            << "size I1: " << I1.sizes()
+            << " ran in: " << timings.kernelRuntime.toMicroSeconds() << "us\n";
 }
 ```
 
@@ -76,15 +74,15 @@ for (auto sizes : std::vector<std::pair<at::IntList, at::IntList>>{
          {{4, 9, 7, 16, 14}, {4, 7, 3, 16, 14}},
          {{8, 5, 11, 10, 10}, {8, 11, 16, 10, 10}},
      }) {
-  at::Tensor I0 = at::CUDA(at::kFloat).rand(sizes.first);
-  at::Tensor I1 = at::CUDA(at::kFloat).rand(sizes.second);
-  auto handle = atCompl.compile("tensordot", {I0, I1}, bestOption.getValue());
-  std::vector<at::Tensor> outputs;
-  auto duration = atCompl.run("tensordot", {I0, I1}, outputs, handle, true);
+  at::Tensor I0 = makeATenTensor<Backend>(sizes.first);
+  at::Tensor I1 = makeATenTensor<Backend>(sizes.second);
+  auto pExecutor =
+      tc::aten::compile<Backend>(tc, "tensordot", {I0, I1}, bestOption[0]);
+  auto outputs = tc::aten::prepareOutputs(tc, "tensordot", {I0, I1});
+  auto timings = tc::aten::profile(*pExecutor, {I0, I1}, outputs);
   std::cout << "tensordot size I0: " << I0.sizes() << ", "
-            << "size I1: " << I1.sizes() << " ran in: "
-            << std::chrono::duration_cast<std::chrono::microseconds>(duration)
-                   .count()
+            << "size I1: " << I1.sizes()
+            << " ran in: " << timings.kernelRuntime.toMicroSeconds()
             << "us\n";
 }
 ```
@@ -96,11 +94,9 @@ Putting it all together, one may see:
 [----------] Global test environment set-up.
 [----------] 1 test from TensorDot
 [ RUN      ] TensorDot.SimpleAutotune
-Loading proto from: /tmp/save_results.options and /tmp/save_results.cuda
 Generation 0    Jobs(Compiled, GPU)/total  (10, 10)/10   (best/median/worst)us: 226/4238/7345
 Generation 1    Jobs(Compiled, GPU)/total  (10, 10)/10   (best/median/worst)us: 220/221/233
 Generation 2    Jobs(Compiled, GPU)/total  (10, 10)/10   (best/median/worst)us: 220/221/234
-Dumping cache to /tmp/save_results.cuda/options
 tensordot size I0: [16, 8, 16, 17, 25], size I1: [16, 16, 2, 17, 25] ran in: 239us
 tensordot size I0: [4, 9, 7, 16, 14], size I1: [4, 7, 3, 16, 14] ran in: 56us
 tensordot size I0: [8, 5, 11, 10, 10], size I1: [8, 11, 16, 10, 10] ran in: 210us
@@ -112,32 +108,6 @@ tensordot size I0: [8, 5, 11, 10, 10], size I1: [8, 11, 16, 10, 10] ran in: 210u
 [  PASSED  ] 1 test.
 ```
 
-Tuning results are then available and reusable in ```/tmp/save_results.cuda``` and ```/tmp/save_results.proto```.
-
-Interestingly, note that running the same example again will start form the best saved results and improve upon them.
-Of course this has diminishing returns:
-```shell
-> build$ ./examples/example_simple
-[==========] Running 1 test from 1 test case.
-[----------] Global test environment set-up.
-[----------] 1 test from TensorDot
-[ RUN      ] TensorDot.SimpleAutotune
-Loading proto from: /tmp/save_results.options and /tmp/save_results.cuda
-Generation 0    Jobs(Compiled, GPU)/total  (10, 10)/10   (best/median/worst)us: 256/258/270
-Generation 1    Jobs(Compiled, GPU)/total  (10, 10)/10   (best/median/worst)us: 158/255/616
-Generation 2    Jobs(Compiled, GPU)/total  (10, 10)/10   (best/median/worst)us: 157/252/720
-Dumping cache to /tmp/save_results.cuda/options
-tensordot size I0: [16, 8, 16, 17, 25], size I1: [16, 16, 2, 17, 25] ran in: 172us
-tensordot size I0: [4, 9, 7, 16, 14], size I1: [4, 7, 3, 16, 14] ran in: 44us
-tensordot size I0: [8, 5, 11, 10, 10], size I1: [8, 11, 16, 10, 10] ran in: 88us
-[       OK ] TensorDot.SimpleAutotune (28232 ms)
-[----------] 1 test from TensorDot (28232 ms total)
-
-[----------] Global test environment tear-down
-[==========] 1 test from 1 test case ran. (28232 ms total)
-[  PASSED  ] 1 test.
-```
-
 We have not yet characterized the precise fraction of peak performance we obtain but it is not uncommon to obtain 80%+ of peak shared memory bandwidth after autotuning. Solid register-level optimizations are still in the work but TC in its current form already addresses the productivity gap between the needs of research and the needs of production. Which is why we are excited to share it with the entire community and bring this collaborative effort in the open.
 
 # Documentation