Merge pull request #7675 from lcy-seso/remove_copy_from_crf

delete memory copy from linear_chain_crf_op.

paddle/operators/linear_chain_crf_op.cc

@@ -187,7 +187,7 @@ class LinearChainCRFOp : public framework::OperatorWithKernel {
       const framework::ExecutionContext& ctx) const override {
     return framework::OpKernelType(
         framework::ToDataType(ctx.Input<LoDTensor>("Emission")->type()),
-        ctx.device_context());
+        platform::CPUPlace());
   }
 };
 
@@ -248,7 +248,7 @@ class LinearChainCRFGradOp : public framework::OperatorWithKernel {
         framework::ToDataType(
             ctx.Input<LoDTensor>(framework::GradVarName("LogLikelihood"))
                 ->type()),
-        ctx.device_context());
+        platform::CPUPlace());
   }
 };
 

paddle/operators/linear_chain_crf_op.h

@@ -65,57 +65,14 @@ class LinearChainCRFOpKernel : public framework::OpKernel<T> {
     const size_t level = 0;
     const size_t seq_num = in_lod[level].size() - 1;
 
-    // These local variables hold the inputs and outputs, garanteeing them on
-    // CPU memory, to provide a consistent reference.
-    // TODO(caoying) Fix this by moving all these local variables into the
-    // class's data members once we can profile the whole training process.
-    LoDTensor* emission_weights = nullptr;
-    LoDTensor emission_weight_tensor;
-    Tensor* transition_weights = nullptr;
-    Tensor transition_weight_tensor;
-    LoDTensor* label = nullptr;
-    LoDTensor label_tensor;
-
-    Tensor* emission_exps = nullptr;
-    Tensor emission_exps_tensor;
-    Tensor* transition_exps = nullptr;
-    Tensor transition_exps_tensor;
-    Tensor* alpha = nullptr;
-    Tensor alpha_tensor;
-    Tensor* ll = nullptr;
-    Tensor ll_tensor;
-
-    if (platform::is_gpu_place(ctx.GetPlace())) {
-      emission_weights = &emission_weight_tensor;
-      transition_weights = &transition_weight_tensor;
-      label = &label_tensor;
-
-      CopyInputsToCpuMemory(
-          ctx.device_context(), *ctx.Input<LoDTensor>("Emission"),
-          *ctx.Input<Tensor>("Transition"), *ctx.Input<LoDTensor>("Label"),
-          emission_weights, transition_weights, label);
-
-      emission_exps = &emission_exps_tensor;
-      emission_exps->Resize(emission_weights->dims());
-
-      transition_exps = &transition_exps_tensor;
-      transition_exps->Resize(transition_weights->dims());
-
-      alpha = &alpha_tensor;
-      alpha->Resize(ctx.Output<Tensor>("Alpha")->dims());
-
-      ll = &ll_tensor;
-    } else {
-      emission_weights =
-          const_cast<LoDTensor*>(ctx.Input<LoDTensor>("Emission"));
-      transition_weights = const_cast<Tensor*>(ctx.Input<Tensor>("Transition"));
-      label = const_cast<LoDTensor*>(ctx.Input<LoDTensor>("Label"));
-
-      emission_exps = ctx.Output<Tensor>("EmissionExps");
-      transition_exps = ctx.Output<Tensor>("TransitionExps");
-      alpha = ctx.Output<Tensor>("Alpha");
-      ll = ctx.Output<Tensor>("LogLikelihood");
-    }
+    const LoDTensor* emission_weights = ctx.Input<LoDTensor>("Emission");
+    const Tensor* transition_weights = ctx.Input<Tensor>("Transition");
+    const LoDTensor* label = ctx.Input<LoDTensor>("Label");
+
+    Tensor* emission_exps = ctx.Output<Tensor>("EmissionExps");
+    Tensor* transition_exps = ctx.Output<Tensor>("TransitionExps");
+    Tensor* alpha = ctx.Output<Tensor>("Alpha");
+    Tensor* ll = ctx.Output<Tensor>("LogLikelihood");
 
     // Because the computation codes only runs on CPU, here the memory for all
     // the outputs is FIXED to be allocated on the CPU memory.
@@ -173,61 +130,9 @@ class LinearChainCRFOpKernel : public framework::OpKernel<T> {
           one_seq, one_seq_row_max, one_seq_exps, *transition_weights,
           *transition_exps, one_seq_label, &one_seq_alpha);
     }
-
-    if (platform::is_gpu_place(ctx.GetPlace())) {
-      CopyOutputsToGpuMemory(
-          ctx.device_context(), *emission_exps, *transition_exps, *alpha, *ll,
-          ctx.Output<Tensor>("EmissionExps"),
-          ctx.Output<Tensor>("TransitionExps"), ctx.Output<Tensor>("Alpha"),
-          ctx.Output<Tensor>("LogLikelihood"));
-    }
   };
 
  private:
-  void CopyInputsToCpuMemory(const platform::DeviceContext& ctx,
-                             const LoDTensor& emission_weights_src,
-                             const Tensor& transition_weights_src,
-                             const LoDTensor& label_src,
-                             LoDTensor* emission_weights_dst,
-                             Tensor* transition_weights_dst,
-                             LoDTensor* label_dst) const {
-    // Copy the inputs from GPU memory to CPU memory if this operators runs on
-    // GPU device.
-    auto copyLoDTensor = [](const platform::DeviceContext& ctx,
-                            const LoDTensor& src, LoDTensor* dst) {
-      dst->mutable_data<T>(src.dims(), platform::CPUPlace());
-      framework::Copy(src, platform::CPUPlace(), ctx, dst);
-    };
-
-    copyLoDTensor(ctx, emission_weights_src, emission_weights_dst);
-    copyLoDTensor(ctx, label_src, label_dst);
-
-    transition_weights_dst->mutable_data<T>(transition_weights_src.dims(),
-                                            platform::CPUPlace());
-    framework::Copy(transition_weights_src, platform::CPUPlace(), ctx,
-                    transition_weights_dst);
-  }
-
-  void CopyOutputsToGpuMemory(const platform::DeviceContext& ctx,
-                              const Tensor& emission_exps_src,
-                              const Tensor& transition_exps_src,
-                              const Tensor& alpha_src, const Tensor& ll_src,
-                              Tensor* emission_exps_dst,
-                              Tensor* transition_exps_dst, Tensor* alpha_dst,
-                              Tensor* ll_dst) const {
-    // Copy the forward results from CPU memory to GPU memory if this
-    // operators runs on GPU device.
-    auto copyTensor = [](const platform::DeviceContext& ctx, const Tensor& src,
-                         Tensor* dst) {
-      dst->mutable_data<T>(platform::CUDAPlace());
-      framework::Copy(src, platform::CUDAPlace(), ctx, dst);
-    };
-    copyTensor(ctx, emission_exps_src, emission_exps_dst);
-    copyTensor(ctx, transition_exps_src, transition_exps_dst);
-    copyTensor(ctx, alpha_src, alpha_dst);
-    copyTensor(ctx, ll_src, ll_dst);
-  }
-
   T ForwardOneSequence(const Tensor& emission, const Tensor& emission_row_max,
                        const Tensor& emission_exps, const Tensor& trans_weights,
                        const Tensor& trans_weight_exps, const Tensor& label,
@@ -296,63 +201,17 @@ class LinearChainCRFGradOpKernel : public framework::OpKernel<T> {
     auto lod = ctx.Input<LoDTensor>("Label")->lod();
     PADDLE_ENFORCE(lod.size(), "Input(Label) must be a sequence.");
 
-    // These local variables hold the inputs and outputs, garanteeing them on
-    // CPU memory, to provide a consistent reference.
-    // TODO(caoying) Fix this by moving all these local variables into the
-    // class's data members once we can profile the training process, or
-    // implementing a real GPU kernel for CRF.
-    Tensor* label = nullptr;
-    Tensor label_tensor;
-    Tensor* emission_exps = nullptr;
-    Tensor emission_exps_tensor;
-    Tensor* transition_exps = nullptr;
-    Tensor transition_exps_tensor;
-    Tensor* alpha = nullptr;
-    Tensor alpha_tensor;
-    Tensor ll_grad_tensor;
-    T* ll_grad = nullptr;
-
-    Tensor* emission_grad = nullptr;
-    Tensor emission_grad_tensor;
-    Tensor* transition_grad = nullptr;
-    Tensor transition_grad_tensor;
-
-    if (platform::is_gpu_place(ctx.GetPlace())) {
-      label = &label_tensor;
-      emission_exps = &emission_exps_tensor;
-      transition_exps = &transition_exps_tensor;
-      alpha = &alpha_tensor;
-      CopyInputsToCpuMemory(
-          ctx.device_context(), *ctx.Input<LoDTensor>("Label"),
-          *ctx.Input<Tensor>("EmissionExps"),
-          *ctx.Input<Tensor>("TransitionExps"), *ctx.Input<Tensor>("Alpha"),
-          *ctx.Input<Tensor>(framework::GradVarName("LogLikelihood")), label,
-          emission_exps, transition_exps, alpha, &ll_grad_tensor);
-      ll_grad = ll_grad_tensor.data<T>();
-
-      if (ctx.Output<Tensor>(framework::GradVarName("Emission"))) {
-        emission_grad = &emission_grad_tensor;
-        emission_grad->Resize(emission_exps->dims());
-      }
+    const Tensor* label = ctx.Input<LoDTensor>("Label");
+    const Tensor* emission_exps = ctx.Input<Tensor>("EmissionExps");
+    const Tensor* transition_exps = ctx.Input<Tensor>("TransitionExps");
+    const Tensor* alpha = ctx.Input<Tensor>("Alpha");
+    const T* ll_grad =
+        ctx.Input<Tensor>(framework::GradVarName("LogLikelihood"))->data<T>();
 
-      if (ctx.Output<Tensor>(framework::GradVarName("Transition"))) {
-        transition_grad = &transition_grad_tensor;
-        transition_grad->Resize(transition_exps->dims());
-      }
-    } else {
-      label = const_cast<LoDTensor*>(ctx.Input<LoDTensor>("Label"));
-      emission_exps = const_cast<Tensor*>(ctx.Input<Tensor>("EmissionExps"));
-      transition_exps =
-          const_cast<Tensor*>(ctx.Input<Tensor>("TransitionExps"));
-      alpha = const_cast<Tensor*>(ctx.Input<Tensor>("Alpha"));
-      ll_grad = const_cast<Tensor*>(
-                    ctx.Input<Tensor>(framework::GradVarName("LogLikelihood")))
-                    ->data<T>();
-
-      emission_grad = ctx.Output<Tensor>(framework::GradVarName("Emission"));
-      transition_grad =
-          ctx.Output<Tensor>(framework::GradVarName("Transition"));
-    }
+    Tensor* emission_grad =
+        ctx.Output<Tensor>(framework::GradVarName("Emission"));
+    Tensor* transition_grad =
+        ctx.Output<Tensor>(framework::GradVarName("Transition"));
 
     // TODO(caoying) Fix this constraint. When the Input(Emission) is from the
     // data reader operator, it can have no gradients.
@@ -389,58 +248,9 @@ class LinearChainCRFGradOpKernel : public framework::OpKernel<T> {
           one_seq_emission_exps, *transition_exps, one_seq_alpha, one_seq_label,
           &one_seq_beta, transition_grad, &one_seq_emission_grad);
     }
-
-    if (platform::is_gpu_place(ctx.GetPlace())) {
-      CopyOutputsToGpuMemory(
-          ctx.device_context(), emission_grad, transition_grad,
-          ctx.Output<Tensor>(framework::GradVarName("Emission")),
-          ctx.Output<Tensor>(framework::GradVarName("Transition")));
-    }
   };
 
  private:
-  void CopyInputsToCpuMemory(const platform::DeviceContext& ctx,
-                             const LoDTensor& label_src,
-                             const Tensor& emission_exps_src,
-                             const Tensor& transition_exps_src,
-                             const Tensor& alpha_src, const Tensor& ll_grad_src,
-                             Tensor* label_dst, Tensor* emission_exps_dst,
-                             Tensor* transition_exps_dst, Tensor* alpha_dst,
-                             Tensor* ll_grad_dst) const {
-    // Copy the inputs from GPU memory to CPU memory when this operators runs on
-    // GPU device.
-    label_dst->mutable_data<T>(label_src.dims(), platform::CPUPlace());
-    framework::Copy(label_src, platform::CPUPlace(), ctx, label_dst);
-
-    auto copyTensor = [](const platform::DeviceContext& ctx, const Tensor& src,
-                         Tensor* dst) {
-      dst->mutable_data<T>(src.dims(), platform::CPUPlace());
-      framework::Copy(src, platform::CPUPlace(), ctx, dst);
-    };
-    copyTensor(ctx, emission_exps_src, emission_exps_dst);
-    copyTensor(ctx, transition_exps_src, transition_exps_dst);
-    copyTensor(ctx, alpha_src, alpha_dst);
-    copyTensor(ctx, ll_grad_src, ll_grad_dst);
-  }
-
-  void CopyOutputsToGpuMemory(const platform::DeviceContext& ctx,
-                              const Tensor* emission_grad_src,
-                              const Tensor* transition_grad_src,
-                              Tensor* emission_grad_dst,
-                              Tensor* transition_grad_dst) const {
-    // Copy the backward results from CPU memory to GPU
-    // memory if this operators runs on GPU device.
-    auto copyTensor = [](const platform::DeviceContext& ctx, const Tensor* src,
-                         Tensor* dst) {
-      if (src && dst) {
-        dst->mutable_data<T>(platform::CUDAPlace());
-        framework::Copy(*src, platform::CUDAPlace(), ctx, dst);
-      }
-    };
-    copyTensor(ctx, emission_grad_src, emission_grad_dst);
-    copyTensor(ctx, transition_grad_src, transition_grad_dst);
-  }
-
   void BackwardOneSequence(const platform::CPUDeviceContext& ctx,
                            const T ll_grad, const Tensor& emission_exps,
                            const Tensor& transition_exps, const Tensor& alpha,