diff options
Diffstat (limited to 'src/gpu/cl/operators/ClGemm.cpp')
| -rw-r--r-- | src/gpu/cl/operators/ClGemm.cpp | 134 |
1 files changed, 134 insertions, 0 deletions
diff --git a/src/gpu/cl/operators/ClGemm.cpp b/src/gpu/cl/operators/ClGemm.cpp index 88f6b79b56..4db39a635d 100644 --- a/src/gpu/cl/operators/ClGemm.cpp +++ b/src/gpu/cl/operators/ClGemm.cpp @@ -191,6 +191,7 @@ ClGemm::ClGemm() _mm_native_kernel(std::make_unique<ClGemmMatrixMultiplyNativeKernel>()), _mm_reshaped_kernel(std::make_unique<ClGemmMatrixMultiplyReshapedKernel>()), _mm_reshaped_only_rhs_kernel(std::make_unique<ClGemmMatrixMultiplyReshapedOnlyRhsKernel>()), + _mm_reshaped_only_rhs_mmul_kernel(std::make_unique<ClGemmMatrixMultiplyReshapedOnlyRhsMMULKernel>()), _tmp_a(), _tmp_b(), _reshape_b_only_on_first_run(false), @@ -324,6 +325,53 @@ void ClGemm::configure_reshaped_only_rhs(const CLCompileContext &compile_context _aux_mem[RhsReshape] = MemoryInfo(offset_int_vec(RhsReshape), _reshape_b_only_on_first_run ? MemoryLifetime::Persistent : MemoryLifetime::Temporary, _tmp_b.total_size()); } +void ClGemm::configure_reshaped_only_rhs_mmul(const CLCompileContext &compile_context, ITensorInfo *a, ITensorInfo *b, ITensorInfo *c, ITensorInfo *output, float alpha, float beta, + const GEMMInfo &gemm_info) +{ + DataType data_type = a->data_type(); + bool reinterpret_input_as_3d = gemm_info.reinterpret_input_as_3d(); + const unsigned int m = reinterpret_input_as_3d ? (a->dimension(1) * a->dimension(2)) : a->dimension(1); + const unsigned int n = b->dimension(0); + const unsigned int k = a->dimension(0); + const unsigned int batch_size = reinterpret_input_as_3d ? a->dimension(3) : a->dimension(2); + const int depth_output_gemm3d = gemm_info.depth_output_gemm3d(); + const GPUTarget gpu_target = CLScheduler::get().target(); + bool broadcast_bias = gemm_info.broadcast_bias(); + + GEMMKernelInfo kernel_info; + kernel_info.m = m; + kernel_info.n = n; + kernel_info.k = k; + kernel_info.depth_output_gemm3d = depth_output_gemm3d; + kernel_info.reinterpret_input_as_3d = reinterpret_input_as_3d; + kernel_info.broadcast_bias = broadcast_bias; + kernel_info.activation_info = gemm_info.activation_info(); + kernel_info.post_ops = gemm_info.post_ops(); + + // Set the target for the kernels + _mm_reshaped_only_rhs_mmul_kernel->set_target(gpu_target); + + GEMMLHSMatrixInfo lhs_info{}; + GEMMRHSMatrixInfo rhs_info{}; + + // Pick up the GEMM configuration + auto gemm_config = select_default_gemm_config_reshaped_only_rhs(auto_heuristics::CommonQuery{ gpu_target, data_type, m, n, k, batch_size }); + lhs_info = gemm_config.lhs_info; + rhs_info = gemm_config.rhs_info; + // Force H0 to 4 in order to use the MMUL extension + rhs_info.h0 = 4; + + // Reshape Rhs matrix + _reshape_rhs_kernel->configure(compile_context, b, &_tmp_b, rhs_info); + + // Configure matrix multiply kernel with no y padding support + kernel_info.has_pad_y = false; + _mm_reshaped_only_rhs_mmul_kernel->configure(compile_context, a, &_tmp_b, c, output, alpha, beta, lhs_info, rhs_info, kernel_info); + + // Request memory for RHS reshape matrix + _aux_mem[RhsReshape] = MemoryInfo(offset_int_vec(RhsReshape), _reshape_b_only_on_first_run ? MemoryLifetime::Persistent : MemoryLifetime::Temporary, _tmp_b.total_size()); +} + Status ClGemm::validate_native(const ITensorInfo *a, const ITensorInfo *b, const ITensorInfo *c, const ITensorInfo *output, float alpha, float beta, const GEMMInfo &gemm_info) { ARM_COMPUTE_UNUSED(alpha); @@ -458,6 +506,54 @@ Status ClGemm::validate_reshaped_only_rhs(const ITensorInfo *a, const ITensorInf return Status{}; } +Status ClGemm::validate_reshaped_only_rhs_mmul(const ITensorInfo *a, const ITensorInfo *b, const ITensorInfo *c, const ITensorInfo *output, float alpha, float beta, const GEMMInfo &gemm_info) +{ + ARM_COMPUTE_UNUSED(alpha); + ARM_COMPUTE_UNUSED(output); + TensorInfo tmp_b_info{}; + + // Get the GPU target + const GPUTarget gpu_target = CLScheduler::get().target(); + const DataType data_type = a->data_type(); + bool reinterpret_input_as_3d = gemm_info.reinterpret_input_as_3d(); + const unsigned int m = reinterpret_input_as_3d ? (a->dimension(1) * a->dimension(2)) : a->dimension(1); + const unsigned int n = b->dimension(0); + const unsigned int k = a->dimension(0); + const unsigned int batch_size = reinterpret_input_as_3d ? a->dimension(3) : a->dimension(2); + const int depth_output_gemm3d = gemm_info.depth_output_gemm3d(); + const bool broadcast_bias = gemm_info.broadcast_bias(); + + GEMMKernelInfo kernel_info; + kernel_info.m = m; + kernel_info.n = n; + kernel_info.k = k; + kernel_info.depth_output_gemm3d = depth_output_gemm3d; + kernel_info.reinterpret_input_as_3d = reinterpret_input_as_3d; + kernel_info.broadcast_bias = broadcast_bias; + kernel_info.activation_info = gemm_info.activation_info(); + kernel_info.post_ops = gemm_info.post_ops(); + + GEMMLHSMatrixInfo lhs_info; + GEMMRHSMatrixInfo rhs_info; + + // Pick up the GEMM configuration + // NOTE: No need to validate mlgo configurations as they automatically fall back to default heuristics if validation fails + const auto gemm_config = select_default_gemm_config_reshaped_only_rhs(auto_heuristics::CommonQuery{ gpu_target, data_type, m, n, k, batch_size }); + lhs_info = gemm_config.lhs_info; + rhs_info = gemm_config.rhs_info; + // Force H0 to 4 in order to use the MMUL extension + rhs_info.h0 = 4; + + auto_init_if_empty(tmp_b_info, b->clone()->set_tensor_shape(compute_rhs_reshaped_shape(*b, rhs_info))); + ARM_COMPUTE_RETURN_ON_ERROR(ClGemmReshapeRhsMatrixKernel::validate(b, &tmp_b_info, rhs_info)); + + // Validate matrix multiply + kernel_info.has_pad_y = false; + ARM_COMPUTE_RETURN_ON_ERROR(ClGemmMatrixMultiplyReshapedOnlyRhsMMULKernel::validate(a, &tmp_b_info, c, output, alpha, beta, lhs_info, rhs_info, kernel_info)); + + return Status{}; +} + void ClGemm::configure(const CLCompileContext &compile_context, ITensorInfo *a, ITensorInfo *b, ITensorInfo *c, ITensorInfo *output, float alpha, float beta, const GEMMInfo &gemm_info) { ARM_COMPUTE_ERROR_ON_NULLPTR(a, b, output); @@ -501,6 +597,11 @@ void ClGemm::configure(const CLCompileContext &compile_context, ITensorInfo *a, configure_reshaped_only_rhs(compile_context, a, b, c_to_use, output, alpha, beta, gemm_info); break; } + case CLGEMMKernelType::RESHAPED_ONLY_RHS_MMUL: + { + configure_reshaped_only_rhs_mmul(compile_context, a, b, c_to_use, output, alpha, beta, gemm_info); + break; + } default: { ARM_COMPUTE_ERROR("GEMMType not supported"); @@ -545,6 +646,11 @@ Status ClGemm::validate(const ITensorInfo *a, const ITensorInfo *b, const ITenso ARM_COMPUTE_RETURN_ON_ERROR(validate_reshaped_only_rhs(a, b, c_to_use, output, alpha, beta, gemm_info)); break; } + case CLGEMMKernelType::RESHAPED_ONLY_RHS_MMUL: + { + ARM_COMPUTE_RETURN_ON_ERROR(validate_reshaped_only_rhs_mmul(a, b, c_to_use, output, alpha, beta, gemm_info)); + break; + } default: { ARM_COMPUTE_RETURN_ERROR_MSG("GEMMType not supported"); @@ -627,6 +733,34 @@ void ClGemm::run(ITensorPack &tensors) } break; } + case CLGEMMKernelType::RESHAPED_ONLY_RHS_MMUL: + { + if(!_reshape_b_only_on_first_run) + { + // Run transpose kernel + ITensorPack reshape_rhs_pack{ { ACL_SRC, rhs }, { ACL_DST, rhs_reshaped.get() } }; + CLScheduler::get().enqueue_op(*_reshape_rhs_kernel, reshape_rhs_pack, false); + } + // In case of RESHAPED_ONLY_RHS, we need to check the padding requirement + // Check if the lhs or dst tensors have padding + const unsigned int cross_plane_pad_lhs = lhs->info()->padding().top + lhs->info()->padding().bottom; + const unsigned int cross_plane_pad_dst = dst->info()->padding().top + dst->info()->padding().bottom; + bool has_pad_y = (cross_plane_pad_lhs != 0) || (cross_plane_pad_dst != 0); + + // Copy original tensor pack and overwrite rhs with reshaped counterpart + ITensorPack gemm_reshaped_onlyrhs_pack(tensors); + gemm_reshaped_onlyrhs_pack.add_const_tensor(ACL_SRC_1, rhs_reshaped.get()); + + if(has_pad_y) + { + ARM_COMPUTE_ERROR_ON(has_pad_y); + } + else + { + CLScheduler::get().enqueue_op(*_mm_reshaped_only_rhs_mmul_kernel, gemm_reshaped_onlyrhs_pack, true); + } + break; + } default: { ARM_COMPUTE_ERROR("GEMMType not supported"); |