Use meta device tensor to infer contiguity for expr-eval segments

csrc/ir/composite_nodes.cpp

@@ -88,16 +88,20 @@ std::vector<PolymorphicValue> MatmulOp::evaluate(
     matmul_out = matmul_out.unsqueeze(rfactor_did_idx);
   }
 
-  const auto& [sizes, strides] = inferShapeAndContiguousStrides(out(), ee);
-  auto meta_out = at::detail::empty_strided_meta(sizes, strides, a.dtype());
+  // Without InferContiguity, we mistakenly assume the output is contiguous.
+  if (!isOptionEnabled(EnableOption::InferContiguity)) {
+    const auto& [sizes, strides] = inferShapeAndContiguousStrides(out(), ee);
+    auto meta_out = at::detail::empty_strided_meta(sizes, strides, a.dtype());
 
-  if (meta_out.is_contiguous()) {
-    return {matmul_out};
-  }
+    if (meta_out.is_contiguous()) {
+      return {matmul_out};
+    }
 
-  auto strided_matmul_out = at::empty_strided(sizes, strides, a.options());
-  strided_matmul_out = strided_matmul_out.copy_(matmul_out);
-  return {strided_matmul_out};
+    auto strided_matmul_out = at::empty_strided(sizes, strides, a.options());
+    strided_matmul_out = strided_matmul_out.copy_(matmul_out);
+    return {strided_matmul_out};
+  }
+  return {matmul_out};
 }
 
 LinearOp::LinearOp(

csrc/options.cpp

@@ -185,6 +185,7 @@ const std::unordered_map<std::string, EnableOption>& getEnableOptions() {
           {"p2p_protocol", EnableOption::P2pProtocol},
           {"multicast_protocol", EnableOption::MulticastProtocol},
           {"parallel_serde", EnableOption::ParallelSerde},
+          {"infer_contiguity", EnableOption::InferContiguity},
       };
   return available_options;
 }

csrc/options.h

@@ -130,6 +130,7 @@ enum class EnableOption {
   MulticastProtocol, //! Prescribe multicast protocol:
                      //! memcpy|multimem|batch_memcpy
   ParallelSerde, //! Enable deserializing FusionExecutorCache in parallel
+  InferContiguity, //! Enable contiguity inference
   EndOfOption //! Placeholder for counting the number of elements
 };
 

csrc/runtime/allocations.cpp

@@ -21,12 +21,12 @@
 
 namespace nvfuser {
 
-KernelArgumentHolder inferOutputShapeAndContiguousStrides(
+KernelArgumentHolder inferContiguousOutputMetaTensor(
     Fusion* fusion,
     const KernelArgumentHolder& args,
     PrecomputedValues* evaluator_precomputed_values) {
   FUSER_PERF_SCOPE(
-      "fusion_executor::allocations::inferOutputShapeAndContiguousStrides");
+      "fusion_executor::allocations::inferContiguousOutputMetaTensor");
   ExpressionEvaluator expr_eval;
 
   std::unique_ptr<PrecomputedValues> evaluator_precomputed_values_up = nullptr;

csrc/runtime/allocations.h

@@ -46,7 +46,7 @@ struct GlobalBufferInfo {
 //! pushing scalar int 0 as a place-holder.
 //! 2. This API does not allocate output in memory, but only returns the
 //! inferred output sizes. Used in runtime/fusion_executor_cache.cpp.
-KernelArgumentHolder inferOutputShapeAndContiguousStrides(
+KernelArgumentHolder inferContiguousOutputMetaTensor(
     Fusion* fusion,
     const KernelArgumentHolder& args,
     PrecomputedValues* evaluator_precomputed_values = nullptr);

csrc/runtime/fusion_cache_utils.cpp

@@ -11,6 +11,7 @@
 
 #include <fusion_segmenter.h>
 #include <ir/all_nodes.h>
+#include <ir/utils.h>
 #include <polymorphic_value.h>
 #include <runtime/executor_kernel_arg.h>
 

csrc/runtime/fusion_kernel_runtime.cpp

@@ -337,6 +337,65 @@ KernelArgumentHolder FusionKernelRuntime::runWithInputs(
   return fusion_outputs;
 }
 
+KernelArgumentHolder FusionKernelRuntime::inferOutputMetaTensor(
+    HeuristicParamsList* heuristics,
+    SegmentedGroup* group_to_run,
+    const KernelArgumentHolder& group_runtime_inputs,
+    PrecomputedValues* evaluator_precomputed_values) const {
+  FUSER_PERF_SCOPE("FusionKernelRuntime::inferOutputMetaTensor");
+  NVF_ERROR(heuristics != nullptr);
+  Fusion* fusion_to_run = group_to_run->getFusion();
+  KernelArgumentHolder group_runtime_outputs;
+  const auto& heuristic_params = heuristics->at(group_to_run->groupId());
+  const bool is_expr_eval =
+      heuristic_params->scheduler_type == SchedulerType::ExprEval;
+  if (is_expr_eval && isOptionEnabled(EnableOption::InferContiguity)) {
+    // For expr evaluated fusion, the striding rules follow that of ATen.
+    ExpressionEvaluator eval_fusion;
+    for (auto i : arange(group_to_run->inputs().size())) {
+      const auto& tensor_pv = group_runtime_inputs[i];
+      if (tensor_pv.is<at::Tensor>()) {
+        const auto& t = tensor_pv.as<at::Tensor>();
+        if (t.defined()) {
+          const auto meta_t = at::empty_strided(
+              t.sizes(),
+              t.strides(),
+              at::TensorOptions().device(at::kMeta).dtype(t.dtype()));
+          eval_fusion.bind(fusion_to_run->inputs()[i], meta_t);
+        } else {
+          eval_fusion.bind(fusion_to_run->inputs()[i], t);
+        }
+      } else {
+        eval_fusion.bind(fusion_to_run->inputs()[i], tensor_pv);
+      }
+    }
+    for (auto v : fusion_to_run->outputs()) {
+      auto result = eval_fusion.evaluate(v);
+      group_runtime_outputs.push(result);
+    }
+  } else {
+    return inferContiguousOutputMetaTensor(
+        fusion_to_run, group_runtime_inputs, evaluator_precomputed_values);
+  }
+  return group_runtime_outputs;
+}
+
+void FusionKernelRuntime::updateContiguityOfSegmentOutputs(
+    SegmentedGroup* group_to_run,
+    const KernelArgumentHolder& group_runtime_outputs) const {
+  FUSER_PERF_SCOPE("FusionKernelRuntime::updateContiguityOfSegmentOutputs");
+  if (!isOptionEnabled(EnableOption::InferContiguity)) {
+    return;
+  }
+  for (auto [i, output] : enumerate(group_to_run->outputs())) {
+    auto tv = dynamic_cast<TensorView*>(output);
+    if (tv) {
+      const at::Tensor& tensor = group_runtime_outputs[i].as<at::Tensor>();
+      ir_utils::resetContiguityFromTensor(tv, tensor);
+    }
+  }
+}
+
 std::vector<KernelArgumentHolder> FusionKernelRuntime::prepareInputs(
     const KernelArgumentHolder& args) const {
   std::vector<KernelArgumentHolder> all_runtime_inputs;
@@ -362,16 +421,14 @@ std::vector<KernelArgumentHolder> FusionKernelRuntime::prepareInputs(
       group_runtime_inputs.setCacheId(group_cache_id.value());
     }
 
-    // TODO: inferOutputShapeAndContiguousStrides doesn't seem to strictly
-    // require a Fusion for each segment. Consider using the complete fusion
-    // instead.
-    auto fusion_to_run = segmented_fusion_->makeFusion(group_to_run).second;
-    auto group_runtime_outputs = inferOutputShapeAndContiguousStrides(
-        fusion_to_run.get(), group_runtime_inputs);
+    auto group_runtime_outputs = inferOutputMetaTensor(
+        heuristics_.get(), group_to_run, group_runtime_inputs);
 
     // map output args to tensor map
     args_manager.updateWithSegmentOutputs(
         group_to_run->outputs(), group_runtime_outputs, run_order_id);
+
+    updateContiguityOfSegmentOutputs(group_to_run, group_runtime_outputs);
   }
 
   return all_runtime_inputs;
@@ -599,13 +656,16 @@ std::optional<std::unique_ptr<HeuristicParamsList>> FusionKernelRuntime::
     }
 
     // Generate metadata for the fusion's outputs
-    auto group_runtime_outputs = inferOutputShapeAndContiguousStrides(
-        fusion_to_run,
+    auto group_runtime_outputs = inferOutputMetaTensor(
+        heuristics.get(),
+        group_to_run,
         group_runtime_inputs,
         evaluator_precomputed_values.get());
 
     args_manager.updateWithSegmentOutputs(
         group_to_run->outputs(), group_runtime_outputs, run_order_id);
+
+    updateContiguityOfSegmentOutputs(group_to_run, group_runtime_outputs);
   }
   return heuristics;
 }

csrc/runtime/fusion_kernel_runtime.h

@@ -173,6 +173,27 @@ class FusionKernelRuntime {
   //! Access the list of schedulers maintained in this runtime instance
   const std::vector<std::unique_ptr<HeuristicParams>>& schedulers() const;
 
+  //! Infer the output shape and stride of the fusion as tensors on Meta device
+  //! If the group is scheduled to be evaluated using ExprEval, the output
+  //! tensors are inferred using the ExprEval on meta device. Otherwise, the
+  //! output tensors are inferred assuming they are contiguous.
+  KernelArgumentHolder inferOutputMetaTensor(
+      HeuristicParamsList* heuristics,
+      SegmentedGroup* group_to_run,
+      const KernelArgumentHolder& group_runtime_inputs,
+      PrecomputedValues* evaluator_precomputed_values = nullptr) const;
+
+  //! When a FusionIR is constructed, all intermediate tensors are assumed to
+  //! be contiguous. Unfortunately, this assumption is not always true, and
+  //! could not be determined at compile time. Depending on the runtime inputs,
+  //! we may segment the fusions differently, and some fusion segments would be
+  //! executed using ATen, which may not generate contiguous tensors. So we have
+  //! to update the contiguity of the segment outputs on the fly depending on
+  //! the runtime inputs.
+  void updateContiguityOfSegmentOutputs(
+      SegmentedGroup* group_to_run,
+      const KernelArgumentHolder& group_runtime_outputs) const;
+
   // Create KernelArgumentHolders for all of the segments. Sorted in
   // the run order.
   std::vector<KernelArgumentHolder> prepareInputs(

tests/cpp/test_alias.cpp

@@ -497,6 +497,9 @@ TEST_F(AliasTest, Issue1452) {
 }
 
 TEST_F(AliasTest, AliasOutputBeforeNonAliasOutput) {
+  EnableOptionsGuard opt_guard;
+  EnableOptionsGuard::getCurOptions().unset(EnableOption::InferContiguity);
+
   auto fusion = std::make_unique<Fusion>();
   FusionGuard fg(fusion.get());
 

tests/cpp/test_indexing_advanced.cpp

@@ -26,13 +26,15 @@ class AdvancedIndexingTest : public NVFuserFixtureParamTest<bool> {
     } else {
       EnableOptionsGuard::getCurOptions().unset(EnableOption::IdModel);
     }
+    EnableOptionsGuard::getCurOptions().set(EnableOption::InferContiguity);
   }
 };
 
 class AdvancedIndexingIdModelTest : public NVFuserTest {
  protected:
   void SetUp() override {
     EnableOptionsGuard::getCurOptions().set(EnableOption::IdModel);
+    EnableOptionsGuard::getCurOptions().set(EnableOption::InferContiguity);
   }
 };
 

tests/cpp/test_layout_op.cpp

@@ -87,6 +87,7 @@ class LayoutOpTest : public NVFuserTest {
   void SetUp() override {
     NVFuserTest::SetUp();
     EnableOptionsGuard::getCurOptions().set(EnableOption::IdModel);
+    EnableOptionsGuard::getCurOptions().unset(EnableOption::InferContiguity);
   }
 };
 

tests/cpp/test_loop_domain_scheduling.cpp

@@ -41,6 +41,7 @@ class LoopDomainSchedulingTest : public NVFuserTest {
  protected:
   void SetUp() override {
     EnableOptionsGuard::getCurOptions().set(EnableOption::IdModel);
+    EnableOptionsGuard::getCurOptions().set(EnableOption::InferContiguity);
   }
 };
 

tests/cpp/test_low_precision_recipe.cpp

@@ -974,7 +974,13 @@ TEST_F(BlockQuantizationValidationTest, MergesMustBeContiguous) {
 class BlockQuantizationSchedulingTest
     : public BlackwellBase,
       public ::testing::WithParamInterface<
-          std::tuple<DataType, std::pair<int, int>, bool, bool>> {};
+          std::tuple<DataType, std::pair<int, int>, bool, bool>> {
+ protected:
+  void SetUp() override {
+    BlackwellBase::SetUp();
+    EnableOptionsGuard::getCurOptions().unset(EnableOption::InferContiguity);
+  }
+};
 
 TEST_P(BlockQuantizationSchedulingTest, AutoScheduleSingleOp) {
   const auto data_type = std::get<0>(GetParam());

tests/cpp/test_matmul_aten_evaluation.cpp

@@ -371,37 +371,4 @@ INSTANTIATE_TEST_SUITE_P(
         testing::Values(Sizes({n, 1})),
         testing::Values(Sizes({n}))));
 
-using MatmulNodeTest = NVFuserTest;
-
-TEST_F(MatmulNodeTest, OutputStrides) {
-  auto fusion = std::make_unique<Fusion>();
-  FusionGuard fg(fusion.get());
-
-  TensorView* x = makeSymbolicTensor(2, DataType::Half);
-  TensorView* y = makeSymbolicTensor(2, DataType::Half);
-  TensorView* z = matmul(x, y);
-
-  fusion->addInput(x);
-  fusion->addInput(y);
-  fusion->addOutput(z);
-
-  z->setAllocationDomain({z->axis(1), z->axis(0), z->axis(2)}, true);
-
-  auto options = at::TensorOptions().dtype(at::kHalf).device(at::kCUDA);
-  at::Tensor x_tensor = at::randn({2, 3}, options);
-  at::Tensor y_tensor = at::randn({3, 5}, options);
-
-  FusionExecutorCache executor_cache(std::move(fusion));
-  auto outs = executor_cache.runFusionWithInputs({x_tensor, y_tensor});
-  at::Tensor z_tensor = outs[0].as<at::Tensor>();
-  testValidate(
-      executor_cache.fusion(),
-      {z_tensor},
-      {x_tensor, y_tensor},
-      __LINE__,
-      __FILE__);
-
-  EXPECT_THAT(z_tensor.strides(), ElementsAre(1, 2));
-}
-
 } // namespace nvfuser

tests/cpp/test_matmul_scheduler.cpp

@@ -2802,6 +2802,7 @@ class MatmulFusionTest
       EnableOptionsGuard::getCurOptions().set(
           EnableOption::FuseMultipleMatmuls);
     }
+    EnableOptionsGuard::getCurOptions().set(EnableOption::InferContiguity);
   }
 
   bool fusion_enabled = GetParam().first;

tests/cpp/test_pointwise.cpp

@@ -26,6 +26,7 @@ class PointwiseTest : public NVFuserTest {
  protected:
   void SetUp() override {
     EnableOptionsGuard::getCurOptions().set(EnableOption::IdModel);
+    EnableOptionsGuard::getCurOptions().set(EnableOption::InferContiguity);
   }
 };
 

tests/cpp/test_rng.cpp

@@ -69,6 +69,7 @@ at::Tensor generate_normal(int64_t size, at::ScalarType dtype) {
 class RNGTest : public NVFuserTest {
   void SetUp() override {
     EnableOptionsGuard::getCurOptions().set(EnableOption::IdModel);
+    EnableOptionsGuard::getCurOptions().set(EnableOption::InferContiguity);
   }
 };
 

tests/cpp/test_segmentation.cpp

@@ -849,7 +849,10 @@ TEST_F(SegmentationTest, RevertPrivatizedUpcast) {
 
       ++num_upcast_ops;
     }
-    EXPECT_EQ(num_upcast_ops, 1);
+    // There is an unswitched IfThenElse in the generated kernel, and in each
+    // of its branches, there is an upcast op with tv1 as its producer. So we
+    // should have two upcast ops.
+    EXPECT_EQ(num_upcast_ops, 2);
   }
 }
 

tests/cpp/utils.cpp

@@ -59,6 +59,7 @@ void NVFuserTest::SetUp() {
     GTEST_SKIP() << "skipping tests on pre-PASCAL GPUs";
   }
   EnableOptionsGuard::getCurOptions().set(EnableOption::IdModel);
+  EnableOptionsGuard::getCurOptions().set(EnableOption::InferContiguity);
 }
 
 NVFuserTest::~NVFuserTest() {

tests/python/direct/conftest.py

@@ -28,6 +28,8 @@ def exec_nvfuser(
         new_fusion_expected=True,
         expected_fd_str=None,
         device=None,
+        enable_options=None,
+        disable_options=None,
         validate_results=False,
     ):
         # Copy inputs because aliased outputs can modify inputs when running
@@ -64,12 +66,20 @@ def exec_nvfuser(
         if validate_results:
             out = fd.validate(inputs)
         else:
+            if enable_options is None:
+                enable_options = []
+            if disable_options is None:
+                disable_options = []
             out = fd.execute(
                 inputs,
                 device=device,
+                _enable_options=enable_options,
+                _disable_options=disable_options,
             )
 
-        assert check_captured_python_definition(out, fd, inputs_captured, device)
+        assert check_captured_python_definition(
+            out, fd, inputs_captured, device, enable_options, disable_options
+        )
         assert expected_fd_str is None or expected_fd_str in repr(fd)
         return out, fd