Resolved merge conflicts
diff --git a/unsupported/Eigen/CXX11/src/Tensor/TensorReduction.h b/unsupported/Eigen/CXX11/src/Tensor/TensorReduction.h index 01bccfd..99a09c0 100644 --- a/unsupported/Eigen/CXX11/src/Tensor/TensorReduction.h +++ b/unsupported/Eigen/CXX11/src/Tensor/TensorReduction.h
@@ -325,11 +325,7 @@ template <int B, int N, typename S, typename R, typename I> __global__ void FullReductionKernelHalfFloat(R, const S, I, half*, half2*); template <int NPT, typename S, typename R, typename I> -<<<<<<< local __global__ void InnerReductionKernelHalfFloat(R, const S, I, I, half*); -======= -__global__ void InnerReductionKernelHalfFloat(R, const S, I, I, half*, half2*); ->>>>>>> other #endif @@ -624,11 +620,7 @@ #ifdef EIGEN_HAS_CUDA_FP16 template <typename S, typename R, typename I> friend void internal::ReductionInitFullReduxKernelHalfFloat(R, const S, I, half2*); template <int B, int N, typename S, typename R, typename I> friend void internal::FullReductionKernelHalfFloat(R, const S, I, half*, half2*); -<<<<<<< local template <int NPT, typename S, typename R, typename I> friend void internal::InnerReductionKernelHalfFloat(R, const S, I, I, half*); -======= - template <int NPT, typename S, typename R, typename I> friend void internal::InnerReductionKernelHalfFloat(R, const S, I, I, half*, half2*); ->>>>>>> other #endif template <int NPT, typename S, typename R, typename I> friend void internal::InnerReductionKernel(R, const S, I, I, typename S::CoeffReturnType*);
diff --git a/unsupported/Eigen/CXX11/src/Tensor/TensorReductionCuda.h b/unsupported/Eigen/CXX11/src/Tensor/TensorReductionCuda.h index f0e8f3b..4f2dfcb 100644 --- a/unsupported/Eigen/CXX11/src/Tensor/TensorReductionCuda.h +++ b/unsupported/Eigen/CXX11/src/Tensor/TensorReductionCuda.h
@@ -391,13 +391,8 @@ eigen_assert(NumPerThread % unroll_times == 0); eigen_assert(unroll_times % 2 == 0); -<<<<<<< local const Index input_col_blocks = divup<Index>(num_coeffs_to_reduce, blockDim.x * NumPerThread * 2); const Index num_input_blocks = divup<Index>(input_col_blocks * num_preserved_coeffs, 2); -======= - const Index input_col_blocks = divup<Index>(num_coeffs_to_reduce, blockDim.x * NumPerThread/2); - const Index num_input_blocks = input_col_blocks * num_preserved_coeffs; ->>>>>>> other const Index num_threads = blockDim.x * gridDim.x; const Index thread_id = blockIdx.x * blockDim.x + threadIdx.x; @@ -406,12 +401,8 @@ if (gridDim.x == 1) { Index i = 2*thread_id; for (; i + 1 < num_preserved_coeffs; i += 2*num_threads) { -<<<<<<< local half* loc = output + i; *((half2*)loc) = reducer.template initializePacket<half2>(); -======= - ((half2*)output)[i] = reducer.template initializePacket<half2>(); ->>>>>>> other } if (i < num_preserved_coeffs) { output[i] = reducer.initialize(); @@ -419,13 +410,8 @@ __syncthreads(); } -<<<<<<< local for (Index i = blockIdx.x; i < num_input_blocks; i += gridDim.x) { const Index row = 2 * (i / input_col_blocks); -======= - for (Index i = 2*blockIdx.x; i < num_input_blocks; i += 2*gridDim.x) { - const Index row = i / input_col_blocks; ->>>>>>> other if (row + 1 < num_preserved_coeffs) { const Index col_block = i % input_col_blocks; @@ -446,18 +432,10 @@ } if (col < num_coeffs_to_reduce) { // Peel; -<<<<<<< local const half last1 = input.m_impl.coeff(row * num_coeffs_to_reduce + col); -======= - const half last1 = input.m_impl.coeff(row * num_coeffs_to_reduce + col+1); ->>>>>>> other const half2 val1 = __halves2half2(last1, reducer.initialize()); reducer.reducePacket(val1, &reduced_val1); -<<<<<<< local const half last2 = input.m_impl.coeff((row+1) * num_coeffs_to_reduce + col); -======= - const half last2 = input.m_impl.coeff((row+1) * num_coeffs_to_reduce + col+1); ->>>>>>> other const half2 val2 = __halves2half2(last2, reducer.initialize()); reducer.reducePacket(val2, &reduced_val2); } @@ -466,17 +444,9 @@ // Faster version of the loop with no branches after unrolling. #pragma unroll for (int k = 0; k < unroll_times; ++k) { -<<<<<<< local const Index col = col_begin + blockDim.x * (j + k) * 2; -======= - const Index col = col_begin + blockDim.x * (j + k); ->>>>>>> other reducer.reducePacket(input.m_impl.template packet<Unaligned>(row * num_coeffs_to_reduce + col), &reduced_val1); -<<<<<<< local reducer.reducePacket(input.m_impl.template packet<Unaligned>((row + 1)* num_coeffs_to_reduce + col), &reduced_val2); -======= - reducer.reducePacket(input.m_impl.template packet<Unaligned>((row +1)* num_coeffs_to_reduce + col), &reduced_val2); ->>>>>>> other } } } @@ -494,12 +464,8 @@ half2 val = __halves2half2(val1, val2); if ((threadIdx.x & (warpSize - 1)) == 0) { -<<<<<<< local half* loc = output + row; atomicReduce((half2*)loc, val, reducer); -======= - atomicReduce(&(((half2*)output)[row]), val, reducer); ->>>>>>> other } } } @@ -554,33 +520,18 @@ static bool run(const Self& self, Op& reducer, const GpuDevice& device, half* output, typename Self::Index num_coeffs_to_reduce, typename Self::Index num_preserved_vals) { typedef typename Self::Index Index; -<<<<<<< local if (num_preserved_vals % 2 != 0) { // Not supported yet, revert to the slower code path - std::cout << "BYPASSING OPTIMIZED CODE PATH" << std::endl; -======= - // It's faster to use the usual code. - if (num_coeffs_to_reduce <= 32) { ->>>>>>> other return true; } const Index num_coeffs = num_coeffs_to_reduce * num_preserved_vals; -<<<<<<< local const int block_size = /*256*/128; const int num_per_thread = /*128*/64; -======= - const int block_size = 256; - const int num_per_thread = 128; ->>>>>>> other const int dyn_blocks = divup<int>(num_coeffs, block_size * num_per_thread); const int max_blocks = device.getNumCudaMultiProcessors() * device.maxCudaThreadsPerMultiProcessor() / block_size; const int num_blocks = numext::mini<int>(max_blocks, dyn_blocks); -<<<<<<< local -======= - half2* scratch = static_cast<half2*>(device.scratchpad()); ->>>>>>> other if (num_blocks > 1) { // We initialize the outputs outside the reduction kernel when we can't be sure that there @@ -590,19 +541,11 @@ device.maxCudaThreadsPerMultiProcessor() / 1024; const int num_blocks = numext::mini<int>(max_blocks, dyn_blocks); LAUNCH_CUDA_KERNEL((ReductionInitKernelHalfFloat<Self, Op, Index>), -<<<<<<< local 1, 1, 0, device, reducer, self, num_preserved_vals, output); -======= - 1, 1, 0, device, reducer, self, num_preserved_vals, scratch); ->>>>>>> other } LAUNCH_CUDA_KERNEL((InnerReductionKernelHalfFloat<num_per_thread, Self, Op, Index>), -<<<<<<< local num_blocks, block_size, 0, device, reducer, self, num_coeffs_to_reduce, num_preserved_vals, output); -======= - num_blocks, block_size, 0, device, reducer, self, num_coeffs_to_reduce, num_preserved_vals, output, scratch); ->>>>>>> other return false; } @@ -632,14 +575,11 @@ if (num_coeffs == 0) { return true; } -<<<<<<< local // It's faster to use the usual code. if (num_coeffs_to_reduce <= 128) { return true; } -======= ->>>>>>> other return InnerReductionLauncher<Self, Op>::run(self, reducer, device, output, num_coeffs_to_reduce, num_preserved_vals); } };
diff --git a/unsupported/test/cxx11_tensor_of_float16_cuda.cu b/unsupported/test/cxx11_tensor_of_float16_cuda.cu index 71f79ab..34e9f54 100644 --- a/unsupported/test/cxx11_tensor_of_float16_cuda.cu +++ b/unsupported/test/cxx11_tensor_of_float16_cuda.cu
@@ -255,13 +255,8 @@ Eigen::CudaStreamDevice stream; Eigen::GpuDevice gpu_device(&stream); -<<<<<<< local int num_elem = size1*size2; int result_size = (redux == 1 ? size1 : size2); -======= - int size = 40; - int num_elem = size*size; ->>>>>>> other float* d_float1 = (float*)gpu_device.allocate(num_elem * sizeof(float)); float* d_float2 = (float*)gpu_device.allocate(num_elem * sizeof(float)); @@ -291,6 +286,7 @@ gpu_device.synchronize(); for (int i = 0; i < result_size; ++i) { + std::cout << "EXPECTED " << full_prec(i) << " GOT " << half_prec(i) << std::endl; VERIFY_IS_APPROX(full_prec(i), half_prec(i)); }