Allgather with DID loop split (#3284)

Another baby step towards #2563

csrc/multidevice/communication.cpp

@@ -328,8 +328,8 @@ c10::intrusive_ptr<c10d::Work> postAllgather(
     c10d::Backend* backend,
     at::Tensor input_tensor,
     at::Tensor output_tensor) {
-  auto splits = at::split(output_tensor, /*split_size=*/1, /*dim=*/0);
-  assertBufferCount(splits, communication->team().size());
+  auto splits =
+      at::tensor_split(output_tensor, communication->team_size(), /*dim=*/0);
   assertBuffersHaveSameSize({input_tensor}, splits);
 
   // allgather primitive in c10d induces extra buffering time to copy out the

csrc/multidevice/communication.h

@@ -90,6 +90,11 @@ class Communication : public Expr {
     return attribute<Team>(1);
   }
 
+  // A convenience helper so the user doesn't need to convert size_t to int64_t.
+  int64_t team_size() const {
+    return static_cast<int64_t>(team().size());
+  }
+
   DeviceIdxType root() const {
     return attribute<DeviceIdxType>(2);
   }

csrc/multidevice/lower_communication.cpp

@@ -196,7 +196,7 @@ void lowerToReduceScatter(
     std::vector<Communication*>& comms) {
   const DeviceMesh& mesh = input_tv->getDeviceMesh();
   auto reduction_axis = output_tv->getReductionAxis().value();
-  auto scattered_axis = getShardedAxis(output_tv);
+  auto scattered_axis = getShardedLogicalAxis(output_tv, ParallelType::DIDx);
   // The output tensor is sharded on scattered_axis and needs to be mapped
   // back onto the input. The input has an reduced axis, so the scattered axis
   // is adjusted to account for this. Ex: [DIDx(i0), i1] -> [r0, DIDx(i1)] The

csrc/multidevice/utils.cpp

@@ -121,48 +121,133 @@ bool isSharded(const TensorView* tv) {
   return is_sharded;
 }
 
-std::vector<int64_t> unshardedSizes(
-    const TensorView* tv,
-    c10::IntArrayRef sizes) {
-  std::vector<int64_t> unsharded_sizes = sizes.vec();
-
-  for (IterDomain* alloc_id : tv->getMaybeAllocationDomain()) {
-    const ParallelType parallel_type = alloc_id->getParallelType();
+namespace {
+// Collect device-parallel IterDomains in `domain` and return them as a
+// ParallelType-to-IterDomain map.
+std::unordered_map<ParallelType, IterDomain*> mapDeviceParallelTypeToId(
+    const std::vector<IterDomain*>& domain) {
+  std::unordered_map<ParallelType, IterDomain*> parallel_type_to_id;
+  parallel_type_to_id.reserve(kParallelTypeDIDs.size());
+  for (IterDomain* id : domain) {
+    const ParallelType parallel_type = id->getParallelType();
     if (!isParallelTypeDeviceDim(parallel_type)) {
       continue;
     }
 
-    const auto inputs = IterVisitor::getInputsTo(
-        {alloc_id},
-        {tv->getLogicalDomain().begin(), tv->getLogicalDomain().end()});
     NVF_ERROR(
-        !inputs.empty(),
-        "IterVisitor::getInputsTo shouldn't return empty unless `of` is empty.");
-    NVF_ERROR(
-        inputs.size() == 1,
-        "Failed to find the single logical input to ",
-        alloc_id,
-        ". This is likely because there's a Merge expression from logical to allocation, which isn't supported. Inputs are: ",
-        toDelimitedString(inputs));
-
-    const auto iter = std::find(
-        tv->getLogicalDomain().begin(),
-        tv->getLogicalDomain().end(),
-        inputs[0]);
+        parallel_type_to_id.try_emplace(parallel_type, id).second,
+        "Found multiple loop IterDomains with the same parallel type (",
+        parallel_type,
+        "): ",
+        toDelimitedString(domain));
+  }
+  return parallel_type_to_id;
+}
+
+std::unordered_map<IterDomain*, int64_t> mapIterDomainToTensorAxis(
+    const std::vector<IterDomain*>& domain) {
+  std::unordered_map<IterDomain*, int64_t> id_to_axis;
+  int64_t axis = 0;
+  for (auto* id : domain) {
+    // Reduction IterDomains are not materialized as an at::Tensor axis.
+    if (id->isReduction()) {
+      continue;
+    }
+    id_to_axis[id] = axis;
+    axis++;
+  }
+  return id_to_axis;
+}
+
+} // namespace
+
+int64_t getShardedLogicalAxis(
+    const TensorView* tv,
+    const ParallelType parallel_type) {
+  std::unordered_map<ParallelType, IterDomain*> parallel_type_to_id =
+      mapDeviceParallelTypeToId(tv->getMaybeAllocationDomain());
+  IterDomain* alloc_id = getOrDefault(parallel_type_to_id, parallel_type);
+  if (alloc_id == nullptr) {
+    return -1;
+  }
+
+  std::unordered_map<IterDomain*, int64_t> logical_id_to_axis =
+      mapIterDomainToTensorAxis(tv->getLogicalDomain());
+  IterDomain* id = alloc_id;
+  while (logical_id_to_axis.count(id) == 0) {
+    Expr* def = id->definition();
     NVF_ERROR(
-        iter != tv->getLogicalDomain().end(),
-        "The found input IterDomain isn't logical. This is likely because logical doesn't dominate allocation: ",
-        inputs[0]);
-
-    // Count the number of non-reduction IterDomains before `iter`. Reduction
-    // IterDomains are not materialized in the at::Tensor's shape.
-    const auto index = std::count_if(
-        tv->getLogicalDomain().begin(), iter, [](IterDomain* id) -> bool {
-          return !id->isReduction();
-        });
-    unsharded_sizes.at(index) *= tv->getDeviceMesh().size(parallel_type);
+        def != nullptr,
+        "Failed to find a non-reduction logical IterDomain that produces ",
+        alloc_id);
+    if (auto* split = dynamic_cast<Split*>(def)) {
+      // Returning just which tensor axis is sharded isn't sufficient to let
+      // shardTensor, a user of this function, know how to shard the tensor.
+      // For example,
+      //
+      //   t = makeContigConcreteTensor({6});
+      //   t->split(0, 2, /*inner_split=*/true);
+      //   t->axis(-1)->parallelize(DIDx);
+      //   // [i{3}, iDIDx{2}]
+      //
+      // and the unsharded tensor is [0, 1, 2, 3, 4, 5], regardless of the
+      // stride. The sharded tensor ought to be [0, 2, 4] for GPU 0 and [1, 3,
+      // 5] for GPU 1. However, shardTensor as is will return [0, 1, 2] and [3,
+      // 4, 5], assuming the axis is sharded outermost.
+      //
+      // One potential way to solve the general problem is to replay and rewind
+      // the splits on the at::Tensor.  For example,
+      //
+      //   t = makeContigConcreteTensor({30});
+      //   t->split(0, 5);
+      //   t->split(0, 3);
+      //   t->axis(0)->parallelize(Host);
+      //   t->axis(1)->parallelize(DIDx);
+      //   // [iHost{2}, iDIDx{3}, i{5}]
+      //
+      // Given an unsharded at::Tensor of shape [30], we'll first replay the
+      // splits using `torch.view` to get a tensor of shape [2,3,5]. Then, we
+      // `torch.slice` axis 1 for DIDx to get a tensor of shape [2,1,5]. Then,
+      // we rewind the splits (and therefore apply merging) using
+      // `torch.reshape` to get a sharded tensor of shape [10].
+      NVF_ERROR(
+          split->outer() == id,
+          "Currently, we don't support DID on inner splits: ",
+          split);
+      id = split->in();
+    } else if (auto* merge = dynamic_cast<Merge*>(def)) {
+      // For example,
+      //
+      //   t = makeContigTensor(2);
+      //   t->merge(0, 1);
+      //   t->axis(0)->parallelize(DIDx);
+      //
+      // When `unshardedSizes` is given a local tensor of shape [1, 1], it's
+      // unclear the global shape is [1, D] or [D, 1] or even [2, D/2], etc.
+      NVF_THROW(
+          "Failed to attribute the sharding to a single tensor axis and therefore bailed out: ",
+          merge);
+    } else {
+      NVF_THROW(
+          "Unexpected transforms from logical to a DID-parallel allocation IterDomain: ",
+          def);
+    }
   }
 
+  return logical_id_to_axis.at(id);
+}
+
+std::vector<int64_t> unshardedSizes(
+    const TensorView* tv,
+    c10::IntArrayRef sizes) {
+  std::vector<int64_t> unsharded_sizes = sizes.vec();
+  for (ParallelType parallel_type : kParallelTypeDIDs) {
+    const int64_t sharded_axis = getShardedLogicalAxis(tv, parallel_type);
+    if (sharded_axis == -1) {
+      continue;
+    }
+    unsharded_sizes.at(sharded_axis) *= tv->getDeviceMesh().size(parallel_type);
+  }
   return unsharded_sizes;
 }
 
@@ -174,27 +259,6 @@ int64_t numDeviceDims(const TensorView* tv) {
 }
 
 namespace {
-// Collect device-parallel IterDomains in `loop_domain` and return them as a
-// ParallelType-to-IterDomain map.
-std::unordered_map<ParallelType, IterDomain*> mapParallelTypeToId(
-    const std::vector<IterDomain*>& loop_domain) {
-  std::unordered_map<ParallelType, IterDomain*> parallel_type_to_id;
-  parallel_type_to_id.reserve(kParallelTypeDIDs.size());
-  for (IterDomain* loop_id : loop_domain) {
-    const ParallelType parallel_type = loop_id->getParallelType();
-    if (!isParallelTypeDeviceDim(parallel_type)) {
-      continue;
-    }
-
-    NVF_ERROR(
-        parallel_type_to_id.try_emplace(parallel_type, loop_id).second,
-        "Found multiple loop IterDomains with the same parallel type (",
-        parallel_type,
-        "): ",
-        toDelimitedString(loop_domain));
-  }
-  return parallel_type_to_id;
-}
 
 std::vector<IterDomain*> getInputsInTargetDomain(
     IterDomain* loop_id,
@@ -294,9 +358,9 @@ bool haveDifferentShardings(
   // 3. Check if the two loop IterDomains are almost-exactly mapped in the
   // IdModel.
   std::unordered_map<ParallelType, IterDomain*> p_parallel_type_to_id =
-      mapParallelTypeToId(producer->getLoopDomain());
+      mapDeviceParallelTypeToId(producer->getLoopDomain());
   std::unordered_map<ParallelType, IterDomain*> c_parallel_type_to_id =
-      mapParallelTypeToId(consumer->getLoopDomain());
+      mapDeviceParallelTypeToId(consumer->getLoopDomain());
 
   for (const auto parallel_type : kParallelTypeDIDs) {
     IterDomain* p_loop_id = getOrDefault(p_parallel_type_to_id, parallel_type);
@@ -502,16 +566,6 @@ std::set<DeviceIdxType> involvedDevices(Expr* expr) {
   return ret;
 }
 
-int64_t getShardedAxis(TensorView* tv) {
-  auto ids = TensorDomain::noReductions(tv->getLogicalDomain());
-  for (size_t i = 0; i < ids.size(); ++i) {
-    if (ids[i]->getParallelType() == ParallelType::DIDx) {
-      return static_cast<int64_t>(i);
-    }
-  }
-  return -1;
-}
-
 void reorderDIDToFront(TensorView* tv) {
   // new position to old position
   std::unordered_map<int64_t, int64_t> order_map;

csrc/multidevice/utils.h

@@ -123,9 +123,16 @@ int64_t requestedNumberOfDevices(Fusion*);
 void unshard(Fusion*);
 void unshard(TensorView*);
 
-// Returns the index of the a sharded axis if none return -1.
-// TODO: Assumes no merges/splits on sharded axis.
-int64_t getShardedAxis(TensorView*);
+// Returns the index of the sharded logical axis that produces the allocation
+// IterDomain sharded on `parallel_type`. If `tv` isn't sharded on the parallel
+// type, returns -1.
+//
+// This is used to correlate `tv` and its corresponding at::Tensor, e.g., by
+// `unshardedSizes` and `shardTensor`. `at::Tensor::sizes` and
+// `tv->getLogicalDomain()` map one-to-one modulo reduction. However, a size in
+// `at::Tensor::sizes` is a factor of the corresponding logical IterDomain's
+// extent if that IterDomain is sharded.
+int64_t getShardedLogicalAxis(const TensorView* tv, ParallelType parallel_type);
 
 // Reorders a TensorView so that the DID parallelized axis are in front.
 void reorderDIDToFront(TensorView*);

tests/cpp/multidevice.cpp

@@ -128,7 +128,10 @@ at::Tensor MultiDeviceTest::shardTensor(at::Tensor tensor, TensorView* tv) {
     return tensor;
   }
   NVF_ERROR(tv->hasDeviceMesh(), "`tv` has no DeviceMesh: ", tv);
-  return shardTensor(tensor, getShardedAxis(tv), tv->getDeviceMesh());
+  return shardTensor(
+      tensor,
+      getShardedLogicalAxis(tv, ParallelType::DIDx),
+      tv->getDeviceMesh());
 }
 
 at::Tensor MultiDeviceTest::shardTensor(
@@ -144,13 +147,10 @@ at::Tensor MultiDeviceTest::shardTensor(
   auto stride = extent / nslices;
   // TODO: returning slice 0 temporarily when device is not in the mesh.
   i = (i < 0) ? 0 : i;
-  auto slice = tensor.slice(axis, i * stride, (i + 1) * stride).contiguous();
-  // Temporary until https://github.com/NVIDIA/Fuser/issues/2563. Adds DIDx
-  // axis in front representing the sharded extent of the tensor.
-  if (stride > 1) {
-    slice = slice.unsqueeze(0);
-  }
-  return slice;
+  // The following slicing is problematic when DID is on an inner split (cf.
+  // MultiDeviceTest.ShardTensor_InnerSplit). We currently disallow that and
+  // it's enforced by getShardedLogicalAxis.
+  return tensor.slice(axis, i * stride, (i + 1) * stride).contiguous();
 }
 
 } // namespace nvfuser

tests/cpp/test_multidevice_lower_communication.cpp

@@ -202,6 +202,73 @@ TEST_F(LowerCollectiveTest, Allgather) {
   EXPECT_TRUE(at::equal(out_tensor, unsharded_tensor));
 }
 
+TEST_F(LowerCollectiveTest, Allgather_LoopSplit) {
+  auto fusion = std::make_unique<Fusion>();
+  FusionGuard fg(fusion.get());
+
+  const auto num_devices = communicator_->size();
+  auto mesh = DeviceMesh::createForNumDevices(num_devices);
+
+  TensorView* in = makeContigTensor(1);
+  in->setDeviceMesh(mesh);
+  TensorView* out = set(in);
+  fusion->addInput(in);
+  fusion->addOutput(out);
+
+  in->split(0, num_devices, /*inner_split=*/false);
+  in->axis(0)->parallelize(ParallelType::DIDx);
+  in->setAllocationDomain(in->getLoopDomain(), true);
+
+  out->split(0, num_devices, /*inner_split=*/false);
+  out->setAllocationDomain(out->getLoopDomain(), true);
+
+  at::Tensor unsharded_tensor =
+      at::randn({num_devices * kTensorSize}, at::kFloat);
+  at::Tensor in_tensor =
+      shardTensor(unsharded_tensor, in).to(communicator_->device());
+
+  FusionExecutorCache fec(std::move(fusion));
+  at::Tensor out_tensor = fec.runFusionWithInputs({in_tensor})[0];
+  assertIsCompiledToHostIrContainer(fec);
+
+  EXPECT_TRUE(at::equal(out_tensor.cpu(), unsharded_tensor));
+}
+
+// This currently fails due to getShardingChanges reads root/logical only:
+// https://github.com/NVIDIA/Fuser/blob/1dda106a946adcfd1526b83e4f2d4abebb9e32e4/csrc/multidevice/utils.cpp#L77.
+// Will try to fix this in a follow-up PR and reenable the test.
+TEST_F(LowerCollectiveTest, DISABLED_Allgather_LoopSplit_Noncontiguous) {
+  auto fusion = std::make_unique<Fusion>();
+  FusionGuard fg(fusion.get());
+
+  const auto num_devices = communicator_->size();
+  auto mesh = DeviceMesh::createForNumDevices(num_devices);
+
+  TensorView* in = makeContigTensor(2);
+  in->setDeviceMesh(mesh);
+  TensorView* out = set(in);
+  fusion->addInput(in);
+  fusion->addOutput(out);
+
+  in->split(1, num_devices, /*inner_split=*/false);
+  in->axis(1)->parallelize(ParallelType::DIDx);
+  in->setAllocationDomain(in->getLoopDomain(), true);
+
+  out->split(1, num_devices, /*inner_split=*/false);
+  out->setAllocationDomain(out->getLoopDomain(), true);
+
+  at::Tensor unsharded_tensor =
+      at::arange(2 * num_devices * 3, at::kFloat).view({2, num_devices * 3});
+  at::Tensor in_tensor =
+      shardTensor(unsharded_tensor, in).to(communicator_->device());
+
+  FusionExecutorCache fec(std::move(fusion));
+  at::Tensor out_tensor = fec.runFusionWithInputs({in_tensor})[0];
+  assertIsCompiledToHostIrContainer(fec);
+
+  EXPECT_TRUE(at::equal(out_tensor.cpu(), unsharded_tensor));
+}
+
 TEST_F(LowerCollectiveTest, Broadcast) {
   auto fusion = std::make_unique<Fusion>();
   FusionGuard fg(fusion.get());