inference feed partial data, test=develop

paddle/fluid/framework/details/CMakeLists.txt

@@ -9,7 +9,7 @@ cc_library(computation_op_handle SRCS computation_op_handle.cc DEPS framework_pr
 cc_library(share_tensor_buffer_op_handle SRCS share_tensor_buffer_op_handle.cc DEPS op_handle_base scope computation_op_handle share_tensor_buffer_functor)
 cc_library(rpc_op_handle SRCS rpc_op_handle.cc DEPS framework_proto scope place operator op_registry)
 cc_library(fetch_barrier_op_handle SRCS fetch_barrier_op_handle.cc DEPS framework_proto scope place operator op_registry)
-cc_library(multi_devices_helper INTERFACE SRCS multi_devices_helper.cc DEPS graph graph_helper)
+cc_library(multi_devices_helper SRCS multi_devices_helper.cc DEPS graph graph_helper)
 
 cc_library(variable_visitor SRCS variable_visitor.cc DEPS lod_tensor selected_rows)
 
@@ -65,6 +65,7 @@ cc_library(gather_op_handle SRCS gather_op_handle.cc DEPS op_handle_base scope d
 cc_library(eager_deletion_op_handle SRCS eager_deletion_op_handle.cc DEPS lod_tensor selected_rows reference_count_pass_helper)
 
 set(SSA_GRAPH_EXECUTOR_DEPS graph framework_proto
+    multi_devices_helper
     sequential_execution_pass
     modify_op_lock_and_record_event_pass
     all_reduce_deps_pass
@@ -72,7 +73,7 @@ set(SSA_GRAPH_EXECUTOR_DEPS graph framework_proto
     eager_deletion_pass
     buffer_shared_inplace_op_pass
     buffer_shared_cross_op_memory_reuse_pass
-    set_reader_device_count_pass)
+    set_reader_device_info_pass)
 cc_library(ssa_graph_executor SRCS ssa_graph_executor.cc DEPS ${SSA_GRAPH_EXECUTOR_DEPS})
 
 cc_library(threaded_ssa_graph_executor SRCS threaded_ssa_graph_executor.cc DEPS fetch_op_handle ssa_graph_executor scope

paddle/fluid/framework/details/build_strategy.cc

@@ -66,7 +66,7 @@ class ParallelExecutorPassBuilder : public ir::PassBuilder {
     AppendPrintGraphPass("graph_viz_pass", "_fused_graph");
 
     AppendMultiDevPass();
-    AppendSetReaderDeviceCountPass();
+    AppendSetReaderDeviceIndexPass();
     AppendMultiGraphOptPasses();
 
     AppendPassToSetMkldnnAttr("mkldnn_placement_pass");
@@ -225,8 +225,8 @@ class ParallelExecutorPassBuilder : public ir::PassBuilder {
                                                          &strategy_);
   }
 
-  void AppendSetReaderDeviceCountPass() {
-    AppendPass("set_reader_device_count_pass");
+  void AppendSetReaderDeviceIndexPass() {
+    AppendPass("set_reader_device_index_pass");
   }
 
   void AppendPrintGraphPass(const std::string &pass_name,
@@ -399,12 +399,9 @@ ir::Graph *BuildStrategy::Apply(ir::Graph *graph,
                    "GPU, skipped.";
         continue;
       }
-    } else if (pass->Type() == "set_reader_device_count_pass") {
+    } else if (pass->Type() == "set_reader_device_index_pass") {
       pass->Erase(kPlaces);
       pass->SetNotOwned<const std::vector<platform::Place>>(kPlaces, &places);
-      pass->Erase(kLocalScopes);
-      pass->SetNotOwned<const std::vector<Scope *>>(kLocalScopes,
-                                                    &local_scopes);
     }
     VLOG(1) << "Start Apply Pass " << pass->Type();
     graph = pass->Apply(graph);
@@ -441,7 +438,7 @@ USE_PASS(fuse_sgd_op_pass);
 USE_PASS(fuse_momentum_op_pass);
 USE_PASS(fuse_all_reduce_op_pass);
 USE_PASS(runtime_context_cache_pass);
-USE_PASS(set_reader_device_count_pass);
+USE_PASS(set_reader_device_index_pass);
 #ifdef PADDLE_WITH_MKLDNN
 USE_PASS(mkldnn_placement_pass);
 #endif

paddle/fluid/framework/details/computation_op_handle.h

@@ -34,6 +34,8 @@ class ComputationOpHandle : public OpHandleBase {
 
   OperatorBase *GetOp() { return op_.get(); }
 
+  const OperatorBase *GetOp() const { return op_.get(); }
+
   std::string Name() const override;
 
   const Scope *GetScope() const { return scope_; }

paddle/fluid/framework/details/eager_deletion_op_handle.cc

@@ -31,10 +31,12 @@ namespace framework {
 namespace details {
 
 EagerDeletionOpHandle::EagerDeletionOpHandle(
-    ir::Node *node, Scope *scope, const platform::Place &place,
+    ir::Node *node, Scope *scope, size_t scope_idx,
+    const platform::Place &place,
     const std::unordered_set<ir::MemOptVarInfo *> &vars, GarbageCollector *gc)
     : OpHandleBase(node),
       scope_(scope),
+      scope_idx_(scope_idx),
       place_(place),
       var_infos_(vars.begin(), vars.end()),
       gc_(gc) {

paddle/fluid/framework/details/eager_deletion_op_handle.h

@@ -34,7 +34,7 @@ namespace details {
 
 class EagerDeletionOpHandle : public OpHandleBase {
  public:
-  EagerDeletionOpHandle(ir::Node *node, Scope *scope,
+  EagerDeletionOpHandle(ir::Node *node, Scope *scope, size_t scope_idx,
                         const platform::Place &place,
                         const std::unordered_set<ir::MemOptVarInfo *> &vars,
                         GarbageCollector *gc);
@@ -50,6 +50,8 @@ class EagerDeletionOpHandle : public OpHandleBase {
    */
   Priority GetPriority() const override { return kHighest; }
 
+  size_t GetScopeIdx() const { return scope_idx_; }
+
  protected:
   void RunImpl() override;
 
@@ -63,6 +65,7 @@ class EagerDeletionOpHandle : public OpHandleBase {
   void CallOnce();
 
   Scope *scope_;
+  size_t scope_idx_;
   platform::Place place_;
   std::vector<ir::MemOptVarInfo *> var_infos_;  // not own
   GarbageCollector *gc_;                        // not own

paddle/fluid/framework/details/multi_devices_helper.cc

@@ -12,9 +12,197 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 #include "paddle/fluid/framework/details/multi_devices_helper.h"
+#include <algorithm>
+#include <unordered_set>
+#include "paddle/fluid/framework/details/computation_op_handle.h"
+#include "paddle/fluid/framework/details/eager_deletion_op_handle.h"
+#include "paddle/fluid/framework/details/share_tensor_buffer_op_handle.h"
+#include "paddle/fluid/framework/ir/graph_helper.h"
 
 namespace paddle {
 namespace framework {
-namespace details {}  // namespace details
+namespace details {
+
+static constexpr size_t kUndefinedDevIdx = -1UL;
+
+static std::unordered_set<std::string> kMultiDeviceOps{
+    "sync_batch_norm",
+    "sync_batch_norm_grad",
+    "allreduce",
+    "c_allreduce_sum",
+    "c_allreduce_prod",
+    "c_allreduce_min",
+    "c_allreduce_max",
+    "c_allgather",
+    "c_reducescatter",
+    "c_broadcast",
+    "c_comm_init",
+    "c_comm_init_all",
+    "c_gen_nccl_id",
+    "c_sync_comm_stream",
+    "send",
+    "recv",
+    "send_barrier",
+    "fetch_barrier",
+};
+
+static size_t GetScopeIdxFromOp(const details::OpHandleBase &op) {
+  if (auto *compute_op =
+          dynamic_cast<const details::ComputationOpHandle *>(&op)) {
+    return kMultiDeviceOps.count(compute_op->GetOp()->Type()) == 0
+               ? compute_op->GetScopeIdx()
+               : kUndefinedDevIdx;
+  } else if (auto *gc_op =
+                 dynamic_cast<const details::EagerDeletionOpHandle *>(&op)) {
+    return gc_op->GetScopeIdx();
+  } else if (auto *share_op =
+                 dynamic_cast<const details::ShareTensorBufferOpHandle *>(
+                     &op)) {
+    return share_op->GetScopeIdx();
+  } else {
+    return kUndefinedDevIdx;
+  }
+}
+
+static bool ContainMultiDeviceOp(const ProgramDesc &program,
+                                 size_t begin_block_idx) {
+  for (size_t block_idx = begin_block_idx; block_idx < program.Size();
+       ++block_idx) {
+    for (auto *op_desc : program.Block(block_idx).AllOps()) {
+      if (kMultiDeviceOps.count(op_desc->Type()) > 0) {
+        return true;
+      }
+    }
+  }
+  return false;
+}
+
+static size_t GetUniqueDeviceIdOfOp(const details::OpHandleBase &op) {
+  size_t dev_idx = GetScopeIdxFromOp(op);
+  if (dev_idx == kUndefinedDevIdx) {
+    return kUndefinedDevIdx;
+  }
+
+  const auto &ins = op.Inputs();
+  const auto &outs = op.Outputs();
+  auto in_outs = ins;
+  in_outs.insert(in_outs.end(), outs.begin(), outs.end());
+
+  for (auto *var : in_outs) {
+    auto *var_handle = dynamic_cast<details::VarHandle *>(var);
+    if (var_handle == nullptr) {
+      continue;
+    }
+
+    if (dev_idx != var_handle->scope_idx()) {
+      return kUndefinedDevIdx;
+    }
+  }
+
+  return dev_idx;
+}
+
+std::vector<std::unique_ptr<ir::Graph>> TrySeparateToMultipleSingleDeviceGraphs(
+    ir::Graph *graph) {
+  if (ContainMultiDeviceOp(graph->OriginProgram(), 1)) {
+    return {};
+  }
+
+  size_t place_num = 0;
+  auto op_handles = ir::FilterByNodeWrapper<OpHandleBase>(*graph);
+  if (op_handles.empty()) {
+    return {};
+  }
+
+  std::unordered_map<details::OpHandleBase *, size_t> op_to_dev_idx;
+  for (auto &op : op_handles) {
+    auto dev_idx = GetUniqueDeviceIdOfOp(*op);
+    if (dev_idx == kUndefinedDevIdx) {
+      VLOG(10) << "Op " << op->Name() << " is not determined";
+      return {};
+    }
+    place_num = std::max(place_num, dev_idx + 1);
+    op_to_dev_idx[op] = dev_idx;
+  }
+
+  for (auto &op : op_handles) {
+    auto dev_idx = op_to_dev_idx.at(op);
+    for (auto &in_var : op->Inputs()) {
+      if (in_var->GeneratedOp()) {
+        auto iter = op_to_dev_idx.find(in_var->GeneratedOp());
+        if (iter == op_to_dev_idx.end() || iter->second != dev_idx) {
+          return {};
+        }
+      }
+    }
+
+    for (auto &out_var : op->Outputs()) {
+      for (auto &pending_op : out_var->PendingOps()) {
+        auto iter = op_to_dev_idx.find(pending_op);
+        if (iter == op_to_dev_idx.end() || iter->second != dev_idx) {
+          return {};
+        }
+      }
+    }
+  }
+
+  PADDLE_ENFORCE_GE(place_num, 1, platform::errors::NotFound(
+                                      "No place found, this may be a bug"));
+
+  std::vector<std::unique_ptr<ir::Graph>> graphs(place_num);
+  for (auto &g : graphs) {
+    g.reset(new ir::Graph(ProgramDesc()));
+    g->Set(kGraphVars, new GraphVars(1UL));
+    g->Set(kGraphDepVars, new GraphDepVars());
+  }
+
+  for (auto &op : op_handles) {
+    auto dev_idx = op_to_dev_idx.at(op);
+    auto *ret_graph = graphs[dev_idx].get();
+    auto &ret_vars = ret_graph->Get<GraphVars>(kGraphVars)[0];
+    auto &ret_dummy_vars = ret_graph->Get<GraphDepVars>(kGraphDepVars);
+    auto &origin_vars = graph->Get<GraphVars>(kGraphVars)[dev_idx];
+
+    ret_graph->AddNode(graph->RemoveNode(op->Node()).release());
+
+    auto handler = [&](const std::vector<VarHandleBase *> &vars) {
+      for (auto *var : vars) {
+        if (graph->Nodes().count(var->Node()) > 0) {
+          ret_graph->AddNode(graph->RemoveNode(var->Node()).release());
+          auto *dummy_var = dynamic_cast<DummyVarHandle *>(var);
+          if (dummy_var == nullptr) {
+            ret_vars.emplace(var->Name(), origin_vars.at(var->Name()));
+          } else {
+            ret_dummy_vars.emplace(dummy_var);
+          }
+        }
+      }
+    };
+
+    handler(op->Inputs());
+    handler(op->Outputs());
+  }
+
+  graph->Erase(kGraphVars);
+  graph->Erase(kGraphDepVars);
+
+  return graphs;
+}
+
+bool HasDropLastReadOp(const ir::Graph &graph) {
+  auto ops = ir::FilterByNodeWrapper<OpHandleBase>(graph);
+  for (auto *op : ops) {
+    auto *compute_op = dynamic_cast<ComputationOpHandle *>(op);
+    if (compute_op && compute_op->GetOp()->Type() == "read" &&
+        compute_op->GetOp()->Attr<bool>("drop_last")) {
+      VLOG(10) << "The graph has drop_last=True read op";
+      return true;
+    }
+  }
+  VLOG(10) << "The graph does not have drop_last=True read op";
+  return false;
+}
+
+}  // namespace details
 }  // namespace framework
 }  // namespace paddle

paddle/fluid/framework/details/multi_devices_helper.h

@@ -47,6 +47,7 @@ constexpr char kGraphVars[] = "vars";
 constexpr char kNRanks[] = "nranks";
 
 constexpr char kPlaces[] = "places";
+constexpr char kGlobalScope[] = "global_scope";
 constexpr char kLocalScopes[] = "local_scopes";
 constexpr char kNCCLCtxs[] = "nccl_ctxs";
 constexpr char kUseHierarchicalAllReduce[] = "use_hierarchical_allreduce";
@@ -100,6 +101,11 @@ inline std::vector<std::string> GetOpRoleVarsOrEmpty(const OpDesc &op) {
   return boost::get<std::vector<std::string>>(iter->second);
 }
 
+std::vector<std::unique_ptr<ir::Graph>> TrySeparateToMultipleSingleDeviceGraphs(
+    ir::Graph *graph);
+
+bool HasDropLastReadOp(const ir::Graph &graph);
+
 }  // namespace details
 }  // namespace framework
 }  // namespace paddle

paddle/fluid/framework/details/parallel_ssa_graph_executor.cc

@@ -13,6 +13,7 @@
 // limitations under the License.
 
 #include "paddle/fluid/framework/details/parallel_ssa_graph_executor.h"
+#include <algorithm>
 #include <memory>
 #include <utility>
 #include "paddle/fluid/framework/ir/graph_helper.h"
@@ -21,11 +22,11 @@ namespace paddle {
 namespace framework {
 namespace details {
 
-std::vector<std::unique_ptr<ir::Graph>>
-ParallelSSAGraphExecutor::SeparateMultiDevicesGraph(ir::Graph *graph) {
+static std::vector<std::unique_ptr<ir::Graph>> SeparateMultiDevicesGraph(
+    ir::Graph *graph, size_t place_num) {
   std::vector<std::unique_ptr<ir::Graph>> graphs;
-  graphs.reserve(places_.size());
-  for (size_t i = 0; i < places_.size(); ++i) {
+  graphs.reserve(place_num);
+  for (size_t i = 0; i < place_num; ++i) {
     ProgramDesc empty;
     graphs.emplace_back(std::unique_ptr<ir::Graph>(new ir::Graph(empty)));
     auto &g = graphs.back();
@@ -64,7 +65,7 @@ ParallelSSAGraphExecutor::SeparateMultiDevicesGraph(ir::Graph *graph) {
     }
   }
 
-  for (size_t dev_id = 0; dev_id < places_.size(); ++dev_id) {
+  for (size_t dev_id = 0; dev_id < place_num; ++dev_id) {
     auto &dev_vars = graphs[dev_id]->Get<GraphVars>(kGraphVars)[0];
     auto &origin_vars = graph->Get<GraphVars>(kGraphVars)[dev_id];
     for (auto &name_pair : origin_vars) {
@@ -85,15 +86,34 @@ ParallelSSAGraphExecutor::ParallelSSAGraphExecutor(
     const ExecutionStrategy &strategy, const std::vector<Scope *> &local_scopes,
     const std::vector<Scope *> &local_exec_scopes,
     const std::vector<platform::Place> &places, ir::Graph *graph)
+    // TODO(Yancey1989): Copying graphs is not safely since it deleted the
+    // attrs.
+    : ParallelSSAGraphExecutor(strategy, local_scopes, local_exec_scopes,
+                               places,
+                               SeparateMultiDevicesGraph(graph,
+                                                         places.size())) {}
+
+ParallelSSAGraphExecutor::ParallelSSAGraphExecutor(
+    const ExecutionStrategy &strategy, const std::vector<Scope *> &local_scopes,
+    const std::vector<Scope *> &local_exec_scopes,
+    const std::vector<platform::Place> &places,
+    std::vector<std::unique_ptr<ir::Graph>> graphs)
     : strategy_(std::move(strategy)),
       local_scopes_(std::move(local_scopes)),
       pool_(places.size() >= 2 ? new ::ThreadPool(places.size()) : nullptr),
-      places_(std::move(places)),
-      // TODO(Yancey1989): Copying graphs is not safely since it deleted the
-      // attrs.
-      graphs_(SeparateMultiDevicesGraph(graph)) {
+      places_(places),
+      graphs_(std::move(graphs)),
+      feed_status_(places.size(), FeedStatus::kNone) {
   PADDLE_ENFORCE_EQ(places_.size(), local_scopes_.size());
 
+  PADDLE_ENFORCE_EQ(places_.size(), graphs_.size(),
+                    platform::errors::InvalidArgument(
+                        "Graph number does not match place number"));
+
+  PADDLE_ENFORCE_GT(
+      places_.size(), 0,
+      platform::errors::InvalidArgument("place number must be larger than 0"));
+
   auto seq_allreduce_pass =
       ir::PassRegistry::Instance().Get("all_reduce_deps_pass");
   seq_allreduce_pass->Set<bool>(kUseHierarchicalAllReduce, new bool(false));
@@ -123,22 +143,43 @@ std::vector<ir::Graph *> ParallelSSAGraphExecutor::Graphs() {
   return result;
 }
 
+enum ExceptionStatus { kSuccess = 0, kEOF, kOther };
+
 FeedFetchList ParallelSSAGraphExecutor::Run(
     const std::vector<std::string> &fetch_tensors) {
+  size_t feed_num = std::count(feed_status_.begin(), feed_status_.end(),
+                               FeedStatus::kHasFeed);
+  bool has_feed = (feed_num > 0);
+
+  VLOG(10) << "Feed num " << feed_num;
+
+  size_t place_num = places_.size();
+
   std::vector<std::future<FeedFetchList>> run_futures;
+  std::vector<ExceptionStatus> exception_status(place_num,
+                                                ExceptionStatus::kSuccess);
 
   std::vector<FeedFetchList> fetch_data;
   FeedFetchList ret;
 
-  fetch_data.reserve(places_.size());
-  ret.reserve(fetch_tensors.size());
+  fetch_data.reserve(place_num);
+  ret.reserve(place_num);
   exception_holder_.Clear();
 
-  for (size_t i = 0; i < places_.size(); ++i) {
-    auto call = [this, i, &fetch_tensors]() -> FeedFetchList {
+  for (size_t i = 0; i < place_num; ++i) {
+    auto call = [&, i]() -> FeedFetchList {
       try {
+        if (!support_partial_feed_ || !has_feed ||
+            feed_status_[i] == FeedStatus::kHasFeed) {
           return executors_[i]->Run(fetch_tensors);
+        } else {
+          return FeedFetchList();
+        }
+      } catch (platform::EOFException &) {
+        exception_status[i] = ExceptionStatus::kEOF;
+        exception_holder_.Catch(std::current_exception());
       } catch (...) {
+        exception_status[i] = ExceptionStatus::kOther;
         exception_holder_.Catch(std::current_exception());
       }
       return FeedFetchList();
@@ -153,21 +194,63 @@ FeedFetchList ParallelSSAGraphExecutor::Run(
 
   if (pool_) {
     for (auto &f : run_futures) {
-      if (exception_holder_.IsCaught()) {
-        f.wait();
-      } else {
       fetch_data.emplace_back(f.get());
     }
   }
+
+  bool has_exception = exception_holder_.IsCaught();
+  if (!support_partial_feed_ && has_exception) {
+    VLOG(10) << "Exception rethrow because partial feed is not supported";
+    exception_holder_.ReThrow();
   }
-  if (exception_holder_.IsCaught()) {
+
+  std::vector<bool> is_valid(place_num, true);
+
+  if (support_partial_feed_) {
+    if (has_feed) {
+      for (size_t i = 0; i < place_num; ++i) {
+        if (feed_status_[i] == FeedStatus::kNone) {
+          is_valid[i] = false;
+        } else if (exception_status[i] != ExceptionStatus::kSuccess) {
+          PADDLE_ENFORCE_EQ(has_exception, true,
+                            platform::errors::InvalidArgument(
+                                "Thread pool raises exception but not caught"));
+          VLOG(10) << "Exception rethrow because non-EOF exception raises when "
+                      "feed is given";
+          exception_holder_.ReThrow();
+        }
+      }
+    } else {
+      for (size_t i = 0; i < place_num; ++i) {
+        if (exception_status[i] == ExceptionStatus::kOther) {
+          PADDLE_ENFORCE_EQ(has_exception, true,
+                            platform::errors::InvalidArgument(
+                                "Thread pool raises exception but not caught"));
+          VLOG(10) << "Exception rethrow because non-EOF exception raises when "
+                      "feed is not given";
+          exception_holder_.ReThrow();
+        } else if (exception_status[i] != ExceptionStatus::kSuccess) {
+          is_valid[i] = false;
+        }
+      }
+    }
+  }
+
+  if (std::count(is_valid.begin(), is_valid.end(), true) == 0) {
+    PADDLE_ENFORCE_EQ(has_exception, true,
+                      platform::errors::InvalidArgument(
+                          "Thread pool raises exception but not caught"));
+    VLOG(10) << "Raise exception because there is no success worker";
     exception_holder_.ReThrow();
   }
 
   for (size_t fetch_idx = 0; fetch_idx < fetch_tensors.size(); ++fetch_idx) {
     std::vector<const LoDTensor *> lodtensor_ptrs;
-    lodtensor_ptrs.reserve(local_scopes_.size());
-    for (size_t scope_idx = 0; scope_idx < local_scopes_.size(); ++scope_idx) {
+    lodtensor_ptrs.reserve(place_num);
+    for (size_t scope_idx = 0; scope_idx < place_num; ++scope_idx) {
+      if (!is_valid[scope_idx]) {
+        continue;
+      }
       lodtensor_ptrs.push_back(&fetch_data.at(scope_idx).at(fetch_idx));
     }
     ret.emplace_back();

paddle/fluid/framework/details/parallel_ssa_graph_executor.h

@@ -27,12 +27,25 @@ namespace framework {
 namespace details {
 
 class ParallelSSAGraphExecutor : public SSAGraphExecutor {
+ public:
+  enum FeedStatus {
+    kNone = 0,    // No feed
+    kHasFeed = 1  // Has feed
+  };
+
  public:
   ParallelSSAGraphExecutor(const ExecutionStrategy &strategy,
                            const std::vector<Scope *> &local_scopes,
                            const std::vector<Scope *> &local_exec_scopes,
                            const std::vector<platform::Place> &places,
                            ir::Graph *graph);
+
+  ParallelSSAGraphExecutor(const ExecutionStrategy &strategy,
+                           const std::vector<Scope *> &local_scopes,
+                           const std::vector<Scope *> &local_exec_scopes,
+                           const std::vector<platform::Place> &places,
+                           std::vector<std::unique_ptr<ir::Graph>> graphs);
+
   ~ParallelSSAGraphExecutor() final = default;
 
   const ir::Graph &Graph() const override { return *graphs_[0]; }
@@ -41,10 +54,15 @@ class ParallelSSAGraphExecutor : public SSAGraphExecutor {
 
   FeedFetchList Run(const std::vector<std::string> &fetch_tensors) override;
 
- private:
-  std::vector<std::unique_ptr<ir::Graph>> SeparateMultiDevicesGraph(
-      ir::Graph *graph);
+  void SetHasFeed(size_t dev_idx, bool has_feed) {
+    feed_status_[dev_idx] = has_feed ? FeedStatus::kHasFeed : FeedStatus::kNone;
+  }
+
+  void EnablePartialFeedSupport() { support_partial_feed_ = true; }
 
+  bool SupportPartialFeed() const { return support_partial_feed_; }
+
+ private:
   ExecutionStrategy strategy_;
   std::vector<Scope *> local_scopes_;
   std::unique_ptr<::ThreadPool> pool_{nullptr};
@@ -54,6 +72,9 @@ class ParallelSSAGraphExecutor : public SSAGraphExecutor {
   std::vector<std::unique_ptr<details::FastThreadedSSAGraphExecutor>>
       executors_;
   ExceptionHolder exception_holder_;
+
+  bool support_partial_feed_{false};
+  std::vector<FeedStatus> feed_status_;
 };
 
 }  // namespace details

paddle/fluid/framework/ir/memory_optimize_pass/eager_deletion_pass.cc

@@ -228,7 +228,7 @@ void EagerDeletionPass::ApplyImpl(ir::Graph *graph) const {
     }
 
     auto *eager_deletion_op = new details::EagerDeletionOpHandle(
-        eager_deletion_node, op->GetScope(), op->GetPlace(),
+        eager_deletion_node, op->GetScope(), op->GetScopeIdx(), op->GetPlace(),
         std::move(var_info), gcs.at(places[op->GetScopeIdx()]).get());
 
     auto it = std::find_if(

paddle/fluid/framework/ir/memory_optimize_pass/test_reference_count_pass_last_lived_ops.cc

@@ -98,7 +98,7 @@ class ReferenceCountPassTestHelper {
         ir::PassRegistry::Instance().Get("reference_count_pass");
     ref_cnt_pass->SetNotOwned(ir::kMemOptVarInfoMapList, &mem_opt_var_infos_);
     ref_cnt_pass->SetNotOwned(ir::kLastLiveOpsOfVars, &last_live_ops_of_vars_);
-    ref_cnt_pass->Apply(&graph_);
+    ref_cnt_pass->Apply(&const_cast<ir::Graph &>(executor_->Graph()));
   }
 
   bool IsLastLivedOps(const std::string &name,

paddle/fluid/framework/ir/multi_devices_graph_pass/CMakeLists.txt

@@ -11,7 +11,7 @@ endif()
 cc_library(multi_devices_graph_pass SRCS multi_devices_graph_pass.cc DEPS multi_devices_helper computation_op_handle
         scale_loss_grad_op_handle rpc_op_handle fetch_barrier_op_handle ${ALL_REDUCE_OP_HANDLES} reduce_op_handle broadcast_op_handle fused_broadcast_op_handle)
 cc_library(sequential_execution_pass SRCS sequential_execution_pass.cc DEPS graph graph_helper pass)
-cc_library(set_reader_device_count_pass SRCS set_reader_device_count_pass.cc DEPS graph graph_helper pass multi_devices_graph_pass)
+cc_library(set_reader_device_info_pass SRCS set_reader_device_info_pass.cc DEPS graph graph_helper pass multi_devices_graph_pass)
 
 cc_library(fuse_all_reduce_op_pass SRCS fuse_all_reduce_op_pass.cc DEPS graph graph_helper fused_all_reduce_op_handle)
 cc_library(all_reduce_deps_pass SRCS all_reduce_deps_pass.cc DEPS all_reduce_op_handle graph graph_helper pass)

paddle/fluid/framework/ir/multi_devices_graph_pass/set_reader_device_count_pass.cc → paddle/fluid/framework/ir/multi_devices_graph_pass/set_reader_device_info_pass.cc

@@ -22,35 +22,44 @@ namespace paddle {
 namespace framework {
 namespace ir {
 
-class SetReaderDeviceCountPass : public Pass {
- protected:
-  void ApplyImpl(Graph *graph) const override;
-
- private:
-  int GetDeviceCount() const;
-
-  std::unordered_set<std::string> ReaderOpSet() const;
-
-  const Scope *GlobalScope() const;
-};
-
-int SetReaderDeviceCountPass::GetDeviceCount() const {
+static int GetDeviceCountFromPassAttr(const Pass &pass) {
   return static_cast<int>(
-      Get<const std::vector<platform::Place>>(details::kPlaces).size());
+      pass.Get<const std::vector<platform::Place>>(details::kPlaces).size());
 }
 
-std::unordered_set<std::string> SetReaderDeviceCountPass::ReaderOpSet() const {
+static std::unordered_set<std::string> ReaderOpSet() {
   return {"create_py_reader"};
 }
 
-const Scope *SetReaderDeviceCountPass::GlobalScope() const {
-  return Get<const std::vector<Scope *>>(details::kLocalScopes)[0];
-}
+class InitReaderDeviceCountPass : public Pass {
+ protected:
+  void ApplyImpl(Graph *graph) const override {
+    using QueueHolder =
+        operators::reader::OrderedMultiDeviceLoDTensorBlockingQueueHolder;
 
-void SetReaderDeviceCountPass::ApplyImpl(Graph *graph) const {
-  auto dev_cnt = GetDeviceCount();
     auto reader_ops = ReaderOpSet();
-  auto scope = GlobalScope();
+    auto dev_cnt = GetDeviceCountFromPassAttr(*this);
+    const auto &scope = Get<const Scope>(details::kGlobalScope);
+    for (auto &node : graph->Nodes()) {
+      if (node->IsOp() && node->Op() &&
+          reader_ops.count(node->Op()->Type()) != 0) {
+        auto queue_name = node->Op()->Input("blocking_queue")[0];
+        auto var = scope.FindVar(queue_name);
+        if (var && var->IsType<QueueHolder>()) {
+          VLOG(10) << "Set device count of " << queue_name << " to be "
+                   << dev_cnt;
+          var->GetMutable<QueueHolder>()->GetQueue()->SetDeviceCount(dev_cnt);
+        }
+      }
+    }
+  }
+};
+
+class SetReaderDeviceIndexPass : public Pass {
+ protected:
+  void ApplyImpl(Graph *graph) const override {
+    auto dev_cnt = GetDeviceCountFromPassAttr(*this);
+    auto reader_ops = ReaderOpSet();
     size_t found_op_num = 0;
 
     for (auto &node : graph->Nodes()) {
@@ -69,30 +78,24 @@ void SetReaderDeviceCountPass::ApplyImpl(Graph *graph) const {
         op_base_attrs["device_index"] = dev_idx;
         op_base_attrs["device_count"] = dev_cnt;
 
-      auto queue_name = op_handle.GetOp()->Input("blocking_queue");
-      auto var = scope->FindVar(queue_name);
-      PADDLE_ENFORCE_NOT_NULL(
-          var,
-          platform::errors::NotFound("Blocking queue of DataLoader not found"));
-
-      using QueueHolder =
-          operators::reader::OrderedMultiDeviceLoDTensorBlockingQueueHolder;
-      if (var->IsType<QueueHolder>()) {
-        var->GetMutable<QueueHolder>()->GetQueue()->SetDeviceCount(dev_cnt);
-      }
-
         ++found_op_num;
         VLOG(10) << "Found op " << op_desc->Type() << " on device " << dev_idx;
       }
     }
 
     VLOG(10) << "Found op number " << found_op_num;
-}
+  }
+};
 
 }  // namespace ir
 }  // namespace framework
 }  // namespace paddle
 
-REGISTER_PASS(set_reader_device_count_pass,
-              paddle::framework::ir::SetReaderDeviceCountPass)
+REGISTER_PASS(init_reader_device_count_pass,
+              paddle::framework::ir::InitReaderDeviceCountPass)
+    .RequirePassAttr(paddle::framework::details::kGlobalScope)
+    .RequirePassAttr(paddle::framework::details::kPlaces);
+
+REGISTER_PASS(set_reader_device_index_pass,
+              paddle::framework::ir::SetReaderDeviceIndexPass)
     .RequirePassAttr(paddle::framework::details::kPlaces);

paddle/fluid/framework/lod_tensor.cc

@@ -307,18 +307,18 @@ void DeserializeFromStream(std::istream &is, LoDTensor *tensor,
 
 std::vector<LoDTensor> LoDTensor::SplitLoDTensor(
     const std::vector<platform::Place> places) const {
+  PADDLE_ENFORCE_GT(places.size(), 0,
+                    platform::errors::InvalidArgument(
+                        "place number cannot be empty when splitting"));
   check_memory_size();
-  int batch_size =
-      lod().empty() ? dims()[0] : static_cast<int>(lod()[0].size()) - 1;
-  size_t result_size = std::min(static_cast<size_t>(batch_size), places.size());
-  size_t remainder = batch_size % places.size();
+  size_t batch_size =
+      lod().empty() ? static_cast<size_t>(dims()[0]) : lod()[0].size() - 1;
 
-  std::vector<LoDTensor> results;
-  results.reserve(result_size);
-
-  // if result_size(batch_size) is 0, just return #places.size() copys of empty
+  // if batch_size is 0, just return #places.size() copys of empty
   // tensors.
-  if (result_size == 0) {
+  if (batch_size == 0) {
+    std::vector<LoDTensor> empty_results;
+    empty_results.reserve(places.size());
     for (size_t i = 0; i < places.size(); ++i) {
       LoDTensor dst;
       dst.Resize(dims());
@@ -326,18 +326,22 @@ std::vector<LoDTensor> LoDTensor::SplitLoDTensor(
       if (!lod().empty()) {
         dst.set_lod(lod());
       }
-      results.emplace_back(dst);
+      empty_results.emplace_back(std::move(dst));
     }
-    return results;
+    return empty_results;
   }
 
-  int step_width = static_cast<int>(batch_size / result_size);
+  auto step_width = (batch_size + places.size() - 1) / places.size();
+  auto result_size = (batch_size + step_width - 1) / step_width;
+  std::vector<LoDTensor> results;
+  results.reserve(result_size);
+
   for (size_t i = 0; i < result_size; ++i) {
-    int begin = static_cast<int>(i * step_width);
-    int end = static_cast<int>((i + 1) * step_width);
-    if (i + 1 == places.size()) {  // last
-      end += remainder;
-    }
+    auto begin = i * step_width;
+    auto end = std::min<size_t>((i + 1) * step_width, batch_size);
+    PADDLE_ENFORCE_LT(begin, end,
+                      platform::errors::InvalidArgument(
+                          "begin must be less than end, this may be a bug"));
 
     LoDTensor dst;
     if (lod().empty()) {
@@ -362,7 +366,7 @@ std::vector<LoDTensor> LoDTensor::SplitLoDTensor(
       }
       dst.set_lod(my_lod);
     }
-    results.emplace_back(dst);
+    results.emplace_back(std::move(dst));
   }
 
   return results;

paddle/fluid/framework/parallel_executor.cc

@@ -55,8 +55,9 @@ static bool gProfileStarted = false;
 
 class ParallelExecutorPrivate {
  public:
-  explicit ParallelExecutorPrivate(const std::vector<platform::Place> &places)
-      : places_(places) {
+  ParallelExecutorPrivate(const std::vector<platform::Place> &places,
+                          Scope *global_scope)
+      : places_(places), global_scope_(global_scope) {
     if (!FLAGS_pe_profile_fname.empty()) {
       std::call_once(gProfileOnce, [] {
 #ifdef WITH_GPERFTOOLS
@@ -82,6 +83,19 @@ class ParallelExecutorPrivate {
     }
   }
 
+  void InitReaderDeviceCount(ir::Graph *graph) const {
+    auto pass =
+        ir::PassRegistry::Instance().Get("init_reader_device_count_pass");
+    pass->SetNotOwned<const Scope>(details::kGlobalScope, global_scope_);
+    pass->SetNotOwned<const std::vector<platform::Place>>(details::kPlaces,
+                                                          &places_);
+    pass->Apply(graph);
+  }
+
+  void SetHasFeed(size_t dev_idx, bool has_feed = true);
+
+  bool AllowPartialFeed() const;
+
   ir::Graph *ApplyMemoryOptimizePass(ir::Graph *graph);
 
   inline bool HasGarbageCollectors() const { return !gcs_.empty(); }
@@ -257,8 +271,20 @@ class ParallelExecutorPrivate {
 
   ir::MemOptVarInfoMapList mem_opt_var_infos_;
   ir::GarbageCollectorMap gcs_;
+
+  details::ParallelSSAGraphExecutor *inference_executor_{nullptr};
 };
 
+void ParallelExecutorPrivate::SetHasFeed(size_t dev_idx, bool has_feed) {
+  if (inference_executor_) {
+    inference_executor_->SetHasFeed(dev_idx, has_feed);
+  }
+}
+
+bool ParallelExecutorPrivate::AllowPartialFeed() const {
+  return inference_executor_ && inference_executor_->SupportPartialFeed();
+}
+
 ir::Graph *ParallelExecutorPrivate::ApplyMemoryOptimizePass(ir::Graph *graph) {
   if (FLAGS_use_ngraph) {
     LOG_FIRST_N(WARNING, 1)
@@ -379,6 +405,21 @@ ir::Graph *ParallelExecutorPrivate::ApplyMemoryOptimizePass(ir::Graph *graph) {
   return graph;
 }
 
+class ResetHasFeedGuard {
+ public:
+  explicit ResetHasFeedGuard(ParallelExecutorPrivate *pe_member)
+      : pe_member_(pe_member) {}
+
+  ~ResetHasFeedGuard() {
+    for (size_t i = 0; i < pe_member_->places_.size(); ++i) {
+      pe_member_->SetHasFeed(i, false);
+    }
+  }
+
+ private:
+  ParallelExecutorPrivate *pe_member_;
+};
+
 size_t ParallelExecutor::DeviceCount() const { return member_->places_.size(); }
 
 std::vector<Scope *> &ParallelExecutor::GetLocalScopes() {
@@ -407,8 +448,8 @@ ParallelExecutor::ParallelExecutor(const std::vector<platform::Place> &places,
                                    const ExecutionStrategy &exec_strategy,
                                    const BuildStrategy &build_strategy,
                                    ir::Graph *graph)
-    : member_(new ParallelExecutorPrivate(places)) {
-  member_->global_scope_ = scope;
+    : member_(new ParallelExecutorPrivate(places, scope)) {
+  member_->InitReaderDeviceCount(graph);
   member_->use_cuda_ = exec_strategy.use_cuda_;
   member_->build_strategy_ = build_strategy;
   member_->use_all_reduce_ = member_->build_strategy_.reduce_ ==
@@ -605,6 +646,22 @@ ParallelExecutor::ParallelExecutor(const std::vector<platform::Place> &places,
     PADDLE_THROW(
         "Paddle should be compiled with CUDA for ParallelGraph Execution.");
 #endif
+  } else {
+    bool has_drop_last_read_op = details::HasDropLastReadOp(*graph);
+    auto possible_inference_graphs =
+        details::TrySeparateToMultipleSingleDeviceGraphs(graph);
+    if (!possible_inference_graphs.empty()) {
+      VLOG(5) << "Use ParallelSSAGraphExecutor in inference phase";
+      auto *pg_exe = new details::ParallelSSAGraphExecutor(
+          exec_strategy, member_->local_scopes_, member_->local_exec_scopes_,
+          member_->places_, std::move(possible_inference_graphs));
+      if (!has_drop_last_read_op) {
+        VLOG(5) << "Enable partial feed support in inference phase";
+        pg_exe->EnablePartialFeedSupport();
+      }
+      final_graphs = pg_exe->Graphs();
+      member_->executor_.reset(pg_exe);
+      member_->inference_executor_ = pg_exe;
     } else {
       if (exec_strategy.type_ == ExecutionStrategy::kDefault) {
         VLOG(3) << "use ThreadedSSAGraphExecutor";
@@ -619,6 +676,7 @@ ParallelExecutor::ParallelExecutor(const std::vector<platform::Place> &places,
       }
       final_graphs.emplace_back(graph);
     }
+  }
 
   VLOG(3) << "use ScopeBufferedSSAGraphExecutor";
   if (!member_->build_strategy_.async_mode_) {
@@ -724,6 +782,8 @@ FeedFetchList ParallelExecutor::Run(
 
   platform::RecordBlock b(0);
 
+  ResetHasFeedGuard reset_has_feed_guard(member_);
+
   ir::SkipMemOptVarsGuard guard(&(member_->mem_opt_var_infos_), fetch_tensors,
                                 member_->HasGarbageCollectors());
 
@@ -734,10 +794,22 @@ FeedFetchList ParallelExecutor::Run(
 
 void ParallelExecutor::FeedTensorsIntoLocalScopes(
     const std::vector<std::unordered_map<std::string, LoDTensor>> &tensors) {
-  PADDLE_ENFORCE_EQ(member_->local_scopes_.size(), tensors.size());
+  if (!member_->AllowPartialFeed()) {
+    PADDLE_ENFORCE_EQ(
+        member_->local_scopes_.size(), tensors.size(),
+        platform::errors::InvalidArgument(
+            "The feed tensor number does not match the device number"));
+  } else {
+    PADDLE_ENFORCE_GE(member_->local_scopes_.size(), tensors.size(),
+                      platform::errors::InvalidArgument(
+                          "The feed tensor number exceeds the device number"));
+  }
 
   for (size_t i = 0; i < tensors.size(); ++i) {
     auto &map = tensors[i];
+    if (!map.empty()) {
+      member_->SetHasFeed(i);
+    }
     for (auto &pair : map) {
       bool is_persistable = member_->IsPersistable(pair.first);
       if (!is_persistable) {
@@ -757,6 +829,11 @@ void ParallelExecutor::FeedTensorsIntoLocalScopes(
 void ParallelExecutor::FeedAndSplitTensorIntoLocalScopes(
     const std::unordered_map<std::string, LoDTensor> &tensors) {
   size_t num_places = member_->places_.size();
+  bool allow_partial_feed = member_->AllowPartialFeed();
+
+  size_t persistable_feed_len = -1UL;
+  size_t non_persistable_feed_len = -1UL;
+
   for (auto &pair : tensors) {
     bool is_persistable = member_->IsPersistable(pair.first);
     VLOG(3) << "Split " << (is_persistable ? "persistable" : "no persistable")
@@ -764,7 +841,8 @@ void ParallelExecutor::FeedAndSplitTensorIntoLocalScopes(
             << ", place: " << pair.second.place();
     auto lod_tensors = pair.second.SplitLoDTensor(member_->places_);
     bool is_cpu_place = platform::is_cpu_place(member_->places_.front());
-    if (!is_persistable && num_places != lod_tensors.size()) {
+    if (!is_persistable && num_places != lod_tensors.size() &&
+        !allow_partial_feed) {
       auto error_info = string::Sprintf(
           "The number(%d) of samples[%s] of current batch is less than the "
           "count(%d) of devices(%s), currently, it is not allowed. ",
@@ -790,7 +868,7 @@ void ParallelExecutor::FeedAndSplitTensorIntoLocalScopes(
           framework::TensorCopy(pair.second, member_->places_.at(i), &tmp);
         }
       }
-      if (lod_tensors.size() != num_places) {
+      if (lod_tensors.size() != num_places && !allow_partial_feed) {
         auto error_info = string::Sprintf(
             "The number(%d) of samples[%s] of the current batch does not match "
             "the count(%d) of devices(%s). Because that %s is a persistable "
@@ -804,7 +882,31 @@ void ParallelExecutor::FeedAndSplitTensorIntoLocalScopes(
       }
     }
 
-    for (size_t j = 0; j < num_places; ++j) {
+    if (allow_partial_feed) {
+      if (is_persistable) {
+        if (persistable_feed_len == -1UL) {
+          persistable_feed_len = lod_tensors.size();
+        } else {
+          PADDLE_ENFORCE_EQ(
+              persistable_feed_len, lod_tensors.size(),
+              platform::errors::InvalidArgument(
+                  "The feeded number of different persistable variables "
+                  "should be the same"));
+        }
+      } else {
+        if (non_persistable_feed_len == -1UL) {
+          non_persistable_feed_len = lod_tensors.size();
+        } else {
+          PADDLE_ENFORCE_EQ(
+              non_persistable_feed_len, lod_tensors.size(),
+              platform::errors::InvalidArgument(
+                  "The feeded number of different non-persistable variables "
+                  "should be the same"));
+        }
+      }
+    }
+
+    for (size_t j = 0; j < lod_tensors.size(); ++j) {
       auto *feed_scope = is_persistable ? member_->local_scopes_[j]
                                         : member_->local_exec_scopes_[j];
       auto *feed_var = feed_scope->Var(pair.first);
@@ -814,6 +916,19 @@ void ParallelExecutor::FeedAndSplitTensorIntoLocalScopes(
       t->set_lod(lod_tensors[j].lod());
     }
   }
+
+  if (allow_partial_feed && persistable_feed_len != -1UL &&
+      non_persistable_feed_len != -1UL) {
+    VLOG(10) << "Persistable len " << persistable_feed_len;
+    VLOG(10) << "Non persistable len " << non_persistable_feed_len;
+    PADDLE_ENFORCE_GE(persistable_feed_len, non_persistable_feed_len,
+                      platform::errors::InvalidArgument(
+                          "The feeded number of persistable variables should "
+                          "not be less than non-persistable variables"));
+    for (size_t i = 0; i < non_persistable_feed_len; ++i) {
+      member_->SetHasFeed(i);
+    }
+  }
 }
 
 ParallelExecutor::~ParallelExecutor() {
@@ -864,6 +979,10 @@ bool ParallelExecutor::EnableParallelGraphExecution(
   return enable_parallel_graph;
 }
 
+const ir::Graph &ParallelExecutor::Graph() const {
+  return member_->executor_->Graph();
+}
+
 }  // namespace framework
 }  // namespace paddle
 
@@ -871,3 +990,4 @@ USE_PASS(reference_count_pass);
 USE_PASS(eager_deletion_pass);
 USE_PASS(buffer_shared_inplace_pass);
 USE_PASS(buffer_shared_cross_op_memory_reuse_pass);
+USE_PASS(init_reader_device_count_pass);

paddle/fluid/framework/parallel_executor.h

@@ -79,6 +79,8 @@ class ParallelExecutor {
 
   FeedFetchList Run(const std::vector<std::string> &fetch_tensors);
 
+  const ir::Graph &Graph() const;
+
  private:
   // broadcast the parameters from the 0th device.
   // trainer_id the trainer index in nccl distributed training.

paddle/fluid/operators/reader/read_op.cc

@@ -156,6 +156,10 @@ class ReadOpMaker : public framework::OpProtoAndCheckerMaker {
         " and it is set by ParallelExecutor instance, not users.")
         .SetDefault(true);
     AddAttr<bool>("infer_out", "").SetDefault(true);
+    AddAttr<bool>("drop_last",
+                  "Whether to drop last batches whose number is less than CPU "
+                  "cores/GPU cards number")
+        .SetDefault(true);
     AddComment(R"DOC(
       Read Operator
 

paddle/fluid/pybind/reader_py.cc

@@ -20,6 +20,7 @@
 #include <utility>
 #include <vector>
 #include "Python.h"
+#include "boost/optional.hpp"
 #include "gflags/gflags.h"
 #include "paddle/fluid/framework/ddim.h"
 #include "paddle/fluid/framework/reader.h"
@@ -41,6 +42,58 @@ namespace pybind {
 namespace py = pybind11;
 namespace reader = operators::reader;
 
+// Check whether the tensor shape matches the VarDesc shape
+// Return the different shape if exists
+static boost::optional<std::vector<int64_t>> DiffTensorShapeWithVarDesc(
+    const framework::LoDTensor &tensor, const framework::VarDesc &var_desc,
+    size_t num_places) {
+  auto tensor_shape = tensor.dims();
+  auto desc_shape = var_desc.GetShape();
+
+  int64_t rank = tensor_shape.size();
+
+  if (UNLIKELY(rank == 0)) {
+    if (desc_shape.size() != 0) {  // Tensor rank = 0 but desc does not match
+      return framework::vectorize<int64_t>(tensor_shape);
+    } else {
+      return boost::none;
+    }
+  }
+
+  PADDLE_ENFORCE_GE(tensor_shape[0], 0,
+                    platform::errors::InvalidArgument(
+                        "Tensor shape must not be less than 0"));
+
+  if (!tensor.lod().empty()) {
+    tensor_shape[0] = -1;  // unknown shape
+  } else {
+    int64_t split_size = (tensor_shape[0] + num_places - 1) / num_places;
+    int64_t remainder = (split_size == 0 ? 0 : tensor_shape[0] % split_size);
+    tensor_shape[0] = split_size;
+    if (desc_shape[0] >= 0) {  // need check dim 0
+      if (tensor_shape[0] != desc_shape[0]) {
+        return framework::vectorize<int64_t>(tensor_shape);
+      }
+
+      if (remainder > 0) {
+        tensor_shape[0] = remainder;
+        return framework::vectorize<int64_t>(tensor_shape);
+      }
+    }
+  }
+
+  for (int64_t idx = 1; idx < rank; ++idx) {
+    PADDLE_ENFORCE_GE(tensor_shape[idx], 0,
+                      platform::errors::InvalidArgument(
+                          "Tensor shape must not be less than 0"));
+    if (desc_shape[idx] >= 0 && tensor_shape[idx] != desc_shape[idx]) {
+      return framework::vectorize<int64_t>(tensor_shape);
+    }
+  }
+
+  return boost::none;
+}
+
 static const std::shared_ptr<reader::LoDTensorBlockingQueue> &GetQueue(
     const std::shared_ptr<reader::LoDTensorBlockingQueue> &queue, size_t idx) {
   return queue;
@@ -66,10 +119,12 @@ class MultiDeviceFeedReader {
       const std::vector<std::vector<int>> &shapes,
       const std::vector<framework::proto::VarType::Type> &dtypes,
       const std::vector<bool> &need_check_feed,
-      const std::vector<platform::Place> &dst_places, bool use_double_buffer)
+      const std::vector<platform::Place> &dst_places, bool use_double_buffer,
+      bool drop_last)
       : queue_(queue),
         names_(names),
-        pool_(new ::ThreadPool(dst_places.size())) {
+        pool_(new ::ThreadPool(dst_places.size())),
+        drop_last_(drop_last) {
     std::vector<framework::DDim> dims;
     for (auto &shape : shapes) {
       dims.push_back(framework::make_ddim(shape));
@@ -113,14 +168,18 @@ class MultiDeviceFeedReader {
     ReadAsync();
   }
 
+  bool DropLast() const { return drop_last_; }
+
   ResultDictList ReadNext() {
     CheckNextStatus();
     ResultDictList result(ret_.size());
     for (size_t i = 0; i < ret_.size(); ++i) {
+      if (!ret_[i].empty()) {
         for (size_t j = 0; j < names_.size(); ++j) {
           result[i].emplace(names_[j], std::move(ret_[i][j]));
         }
       }
+    }
     ReadAsync();
     return result;
   }
@@ -155,24 +214,29 @@ class MultiDeviceFeedReader {
   };
 
   Status WaitFutures(std::exception_ptr *excep) {
-    bool is_success = true;
     *excep = nullptr;
+    size_t success_num = 0;
     for (size_t i = 0; i < futures_.size(); ++i) {
       auto each_status = futures_[i].get();
       if (UNLIKELY(each_status != Status::kSuccess)) {
-        is_success = false;
         if (UNLIKELY(each_status == Status::kException)) {
           PADDLE_ENFORCE_NOT_NULL(exceptions_[i]);
           *excep = exceptions_[i];
           exceptions_[i] = nullptr;
         }
+      } else {
+        ++success_num;
       }
     }
 
     if (UNLIKELY(*excep)) {
       return Status::kException;
+    }
+
+    if (drop_last_) {
+      return success_num == futures_.size() ? Status::kSuccess : Status::kEOF;
     } else {
-      return is_success ? Status::kSuccess : Status::kEOF;
+      return success_num > 0 ? Status::kSuccess : Status::kEOF;
     }
   }
 
@@ -226,6 +290,7 @@ class MultiDeviceFeedReader {
   std::vector<std::exception_ptr> exceptions_;
 
   std::vector<std::vector<framework::LoDTensor>> ret_;
+  bool drop_last_;
 };
 
 template <typename QueueType>
@@ -270,6 +335,17 @@ void BindMultiDeviceReader(py::module *module, const char *reader_name) {
 void BindReader(py::module *module) {
   auto &m = *module;
 
+  m.def("diff_tensor_shape", [](const framework::LoDTensor &tensor,
+                                const framework::VarDesc &var_desc,
+                                size_t num_places) -> py::object {
+    auto diff = DiffTensorShapeWithVarDesc(tensor, var_desc, num_places);
+    if (diff) {
+      return py::cast(std::move(diff.get()));
+    } else {
+      return py::cast(nullptr);
+    }
+  });
+
   m.def("init_lod_tensor_blocking_queue",
         [](framework::Variable &var, size_t capacity,
            bool is_ordered) -> py::object {
@@ -337,10 +413,10 @@ void BindReader(py::module *module) {
            const std::vector<framework::proto::VarType::Type> &dtypes,
            const std::vector<bool> &need_check_feed,
            const std::vector<platform::Place> &dst_places,
-           bool use_double_buffer) {
+           bool use_double_buffer, bool drop_last) {
           return new MultiDeviceFeedReader<reader::LoDTensorBlockingQueue>(
               queue, names, shapes, dtypes, need_check_feed, dst_places,
-              use_double_buffer);
+              use_double_buffer, drop_last);
         },
         py::return_value_policy::take_ownership);
 
@@ -352,13 +428,13 @@ void BindReader(py::module *module) {
          const std::vector<std::vector<int>> &shapes,
          const std::vector<framework::proto::VarType::Type> &dtypes,
          const std::vector<bool> &need_check_feed,
-         const std::vector<platform::Place> &dst_places,
-         bool use_double_buffer) {
+         const std::vector<platform::Place> &dst_places, bool use_double_buffer,
+         bool drop_last) {
         queue->SetDeviceCount(dst_places.size());
         return new MultiDeviceFeedReader<
             reader::OrderedMultiDeviceLoDTensorBlockingQueue>(
             queue, names, shapes, dtypes, need_check_feed, dst_places,
-            use_double_buffer);
+            use_double_buffer, drop_last);
       },
       py::return_value_policy::take_ownership);
 }

python/paddle/fluid/executor.py

@@ -216,18 +216,12 @@ def check_feed_shape_type(var, feed, num_places=1):
             the feed value
     """
     if var.desc.need_check_feed():
-        feed_shape = feed.shape()
-        if six.PY2:
-            feed_shape[0] = long(feed_shape[0] /
-                                 num_places) if len(feed.lod()) == 0 else -1
-        else:
-            feed_shape[0] = int(feed_shape[0] /
-                                num_places) if len(feed.lod()) == 0 else -1
-        if not dimension_is_compatible_with(feed_shape, var.shape):
+        diff_shape = core.diff_tensor_shape(feed, var.desc, num_places)
+        if diff_shape is not None:
             raise ValueError(
                 'The feeded Variable %r should have dimensions = %d, shape = '
                 '%r, but received feeded shape %r on each device' %
-                (var.name, len(var.shape), var.shape, feed_shape))
+                (var.name, len(var.shape), var.shape, diff_shape))
         if not dtype_is_compatible_with(feed._dtype(), var.dtype):
             var_dtype_format = convert_dtype(var.dtype) if isinstance(
                 var.dtype, core.VarDesc.VarType) else var.dtype
@@ -646,11 +640,6 @@ class Executor(object):
 
             exe.feed_and_split_tensor_into_local_scopes(feed_tensor_dict)
         elif isinstance(feed, list) or isinstance(feed, tuple):
-            if len(feed) != len(program._places):
-                raise ValueError(
-                    "Feed a list of tensor, the list should be the same size as places"
-                )
-
             res = list()
             for i, each in enumerate(feed):
                 if not isinstance(each, dict):

python/paddle/fluid/reader.py

@@ -88,6 +88,7 @@ class DataLoader(object):
                        iterable=True,
                        return_list=False,
                        use_multiprocess=False,
+                       drop_last=True,
                        keep_order=False):
         """
         Create a DataLoader object for loading data from Python generator. 
@@ -134,6 +135,9 @@ class DataLoader(object):
                 can be used in the dygraph mode. In the static graph mode,
                 whether this parameter is set or not has no effect.
                 The Default value is False.
+            drop_last (bool): whether to drop the last batches whose number is
+                less than the CPU core/GPU card number. The default value is 
+                True.
             keep_order (bool): whether to assign the data to CPU cores or GPU 
                 cards in order. Supposing that there are 2 batches and we use 
                 2 GPU cards to run the network. If keep_order=True, GPU 0 would 
@@ -289,7 +293,7 @@ class DataLoader(object):
                                           return_list, use_multiprocess)
         else:
             return GeneratorLoader(feed_list, capacity, use_double_buffer,
-                                   iterable, return_list, keep_order)
+                                   iterable, return_list, drop_last, keep_order)
 
     @staticmethod
     def from_dataset(dataset, places, drop_last=True):
@@ -422,7 +426,7 @@ class DygraphGeneratorLoader(DataLoaderBase):
             core.Variable(), self._capacity, False)
         self._reader = core.create_py_reader(
             self.queue, self._var_names, self._shapes, self._dtypes,
-            self._need_check_feed, self._places, self._use_double_buffer)
+            self._need_check_feed, self._places, self._use_double_buffer, True)
 
     def _start(self):
         if self._use_multiprocess:
@@ -628,6 +632,7 @@ class GeneratorLoader(DataLoaderBase):
                  use_double_buffer=True,
                  iterable=True,
                  return_list=False,
+                 drop_last=True,
                  keep_order=False):
         self._tensor_reader = None
         self._places = None
@@ -635,6 +640,8 @@ class GeneratorLoader(DataLoaderBase):
         self._queue = None
         self._feed_list = feed_list
         self._exited = False
+        self._drop_last = drop_last
+        self._keep_order = keep_order
         if not capacity:
             raise ValueError("Please give value to capacity.")
         self._iterable = iterable
@@ -643,7 +650,6 @@ class GeneratorLoader(DataLoaderBase):
             raise Exception("Feed list must be given under static mode.")
         self._use_double_buffer = use_double_buffer
         self._capacity = capacity
-        self._keep_order = keep_order
         if not self._iterable:
             self._init_non_iterable()
 
@@ -667,7 +673,8 @@ class GeneratorLoader(DataLoaderBase):
             core.Variable(), self._capacity, self._keep_order)
         self._reader = core.create_py_reader(
             self.queue, self._var_names, self._shapes, self._dtypes,
-            self._need_check_feed, self._places, self._use_double_buffer)
+            self._need_check_feed, self._places, self._use_double_buffer,
+            self._drop_last)
 
     def _init_non_iterable(self):
         lod_levels = []
@@ -744,7 +751,8 @@ class GeneratorLoader(DataLoaderBase):
         default_main_program().current_block().append_op(
             type='read',
             inputs={'Reader': [self._reader]},
-            outputs={'Out': self._feed_list})
+            outputs={'Out': self._feed_list},
+            attrs={'drop_last': self._drop_last})
 
     @property
     def queue(self):

python/paddle/fluid/tests/unittests/CMakeLists.txt

@@ -355,4 +355,5 @@ set_tests_properties(test_parallel_executor_test_while_train test_parallel_execu
         test_parallel_executor_crf_auto_growth test_buffer_shared_memory_reuse_pass_and_fuse_optimization_op_pass
         test_data_norm_op test_imperative_using_non_zero_gpu test_fuse_bn_act_pass
         test_optimizer_in_control_flow test_dataloader_keep_order
+        test_parallel_executor_inference_feed_partial_data
         test_buffer_shared_memory_reuse_pass PROPERTIES LABELS "RUN_TYPE=DIST")

python/paddle/fluid/tests/unittests/test_parallel_executor_inference_feed_partial_data.py

+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import paddle.fluid as fluid
+import numpy as np
+import unittest
+import six
+
+
+class TestInferencePartialFeed(unittest.TestCase):
+    def setUp(self):
+        self.iterations = 10
+        self.size = 10
+
+    def run_network(self, places, use_split):
+        startup_prog = fluid.Program()
+        main_prog = fluid.Program()
+
+        with fluid.program_guard(main_prog, startup_prog):
+            x = fluid.data(name='x', shape=[None, self.size], dtype='float32')
+            y = fluid.data(name='y', shape=[None, self.size], dtype='float32')
+            lr = fluid.data(name='lr', shape=[1], dtype='float32')
+            lr.persistable = True
+
+            relu_x = fluid.layers.relu(x)
+            relu_y = fluid.layers.relu(y)
+            relu_lr = fluid.layers.relu(lr)
+
+        exe = fluid.Executor(places[0])
+        exe.run(startup_prog)
+
+        prog = fluid.CompiledProgram(main_prog).with_data_parallel(
+            places=places)
+
+        gen_random = lambda shape:np.random.uniform(low=-1.0, high=1.0, size=shape).astype('float32')
+        assert_result = lambda feed, result: self.assertTrue(np.array_equal(np.maximum(0, feed), result))
+
+        def feed_split_test():
+            for place_num in six.moves.range(1, len(places) * 3):
+                x_np = gen_random([place_num, self.size])
+                y_np = gen_random([place_num, self.size])
+                if place_num <= len(places):
+                    lr_np = gen_random([place_num])
+                else:
+                    lr_np = gen_random([1])
+
+                relu_x_np, relu_y_np, relu_lr_np = exe.run(
+                    prog,
+                    feed={x.name: x_np,
+                          y.name: y_np,
+                          lr.name: lr_np},
+                    fetch_list=[relu_x, relu_y, relu_lr])
+
+                assert_result(x_np, relu_x_np)
+                assert_result(y_np, relu_y_np)
+                if place_num <= len(places):
+                    assert_result(lr_np, relu_lr_np)
+                else:
+                    expected_relu_lr_np = max(lr_np[0], 0)
+                    self.assertTrue(np.all(expected_relu_lr_np == relu_lr_np))
+
+        def feed_list_test():
+            for place_num in six.moves.range(1, len(places) + 1):
+                x_np_list = []
+                y_np_list = []
+                lr_np_list = []
+                feed_list = []
+                for _ in six.moves.range(place_num):
+                    x_np = gen_random([1, self.size])
+                    y_np = gen_random([1, self.size])
+                    lr_np = gen_random([1])
+                    x_np_list.append(x_np)
+                    y_np_list.append(y_np)
+                    lr_np_list.append(lr_np)
+
+                    feed_list.append({
+                        x.name: x_np,
+                        y.name: y_np,
+                        lr.name: lr_np
+                    })
+
+                relu_x_np, relu_y_np, relu_lr_np = exe.run(
+                    prog, feed=feed_list, fetch_list=[relu_x, relu_y, relu_lr])
+
+                x_np = np.concatenate(x_np_list)
+                y_np = np.concatenate(y_np_list)
+                lr_np = np.concatenate(lr_np_list)
+
+                assert_result(x_np, relu_x_np)
+                assert_result(y_np, relu_y_np)
+                assert_result(lr_np, relu_lr_np)
+
+        for _ in six.moves.range(self.iterations):
+            if use_split:
+                feed_split_test()
+            else:
+                feed_list_test()
+
+    def test_main(self):
+        places = [fluid.cpu_places(4)]
+        if fluid.is_compiled_with_cuda():
+            places.append(fluid.cuda_places())
+
+        for p in places:
+            self.run_network(p, use_split=True)
+            self.run_network(p, use_split=False)
+
+
+class TestInferencePartialFeedUsingDataLoader(unittest.TestCase):
+    def setUp(self):
+        self.epoch_num = 3
+        self.batch_num = 101  # a prime number
+        self.batch_size = 32
+
+    def create_reader(self):
+        def __impl__():
+            for _ in six.moves.range(self.batch_num):
+                yield np.random.random([self.batch_size, 1]).astype('float32'),
+
+        return __impl__
+
+    def run_network(self, iterable, use_cuda, drop_last):
+        x = fluid.data(shape=[None, 1], name='x', dtype='float32')
+        places = fluid.cuda_places() if use_cuda else fluid.cpu_places(4)
+        loader = fluid.io.DataLoader.from_generator(
+            feed_list=[x], capacity=16, iterable=iterable, drop_last=drop_last)
+        y = fluid.layers.fc(x, size=10)
+        loss = fluid.layers.reduce_mean(y)
+
+        exe = fluid.Executor(places[0])
+        exe.run(fluid.default_startup_program())
+
+        prog = fluid.CompiledProgram(fluid.default_main_program(
+        )).with_data_parallel(
+            places=places, loss_name=loss.name)
+
+        loader.set_batch_generator(
+            self.create_reader(), places=places if iterable else None)
+
+        for _ in six.moves.range(self.epoch_num):
+            actual_batch_num = 0
+            if loader.iterable:
+                for feed_data in loader():
+                    x_data, = exe.run(prog, feed=feed_data, fetch_list=[x])
+                    self.assertEqual(x_data.shape[0] % self.batch_size, 0)
+                    self.assertTrue(x_data.shape[0] != 0)
+                    actual_batch_num += int(x_data.shape[0] / self.batch_size)
+            else:
+                loader.start()
+                try:
+                    while True:
+                        x_data, = exe.run(prog, fetch_list=[x])
+                        self.assertEqual(x_data.shape[0] % self.batch_size, 0)
+                        self.assertTrue(x_data.shape[0] != 0)
+                        actual_batch_num += int(x_data.shape[0] /
+                                                self.batch_size)
+                except fluid.core.EOFException:
+                    loader.reset()
+
+            if not drop_last or len(places) == 1:
+                self.assertEqual(self.batch_num, actual_batch_num)
+            else:
+                self.assertGreater(self.batch_num, actual_batch_num)
+
+    def test_main(self):
+        use_cuda_list = [False, True] if fluid.is_compiled_with_cuda(
+        ) else [False]
+        iterable_list = [False, True]
+        drop_last_list = [False, True]
+        for iterable in iterable_list:
+            for use_cuda in use_cuda_list:
+                for drop_last in drop_last_list:
+                    with fluid.program_guard(fluid.Program(), fluid.Program()):
+                        with fluid.scope_guard(fluid.Scope()):
+                            self.run_network(iterable, use_cuda, drop_last)
+
+
+if __name__ == '__main__':
+    unittest.main()