diff options
Diffstat (limited to 'src/cpu/kernels/CpuSoftmaxKernel.cpp')
-rw-r--r-- | src/cpu/kernels/CpuSoftmaxKernel.cpp | 256 |
1 files changed, 88 insertions, 168 deletions
diff --git a/src/cpu/kernels/CpuSoftmaxKernel.cpp b/src/cpu/kernels/CpuSoftmaxKernel.cpp index 054adfa23c..6766b10120 100644 --- a/src/cpu/kernels/CpuSoftmaxKernel.cpp +++ b/src/cpu/kernels/CpuSoftmaxKernel.cpp @@ -22,7 +22,6 @@ * SOFTWARE. */ #include "src/cpu/kernels/CpuSoftmaxKernel.h" - #include "arm_compute/core/Error.h" #include "arm_compute/core/Helpers.h" #include "arm_compute/core/ITensor.h" @@ -30,12 +29,10 @@ #include "arm_compute/core/Validate.h" #include "arm_compute/core/Window.h" #include "src/core/CPP/Validate.h" +#include "src/core/common/Registrars.h" #include "src/core/helpers/AutoConfiguration.h" #include "src/core/helpers/WindowHelpers.h" - -#include "src/core/common/Registrars.h" #include "src/cpu/kernels/softmax/list.h" - namespace arm_compute { namespace cpu @@ -44,164 +41,60 @@ namespace kernels { namespace { -struct SoftmaxSelectorData -{ - DataType dt; - const CPUInfo &ci; -}; -using SoftmaxSelectorPtr = std::add_pointer<bool(const SoftmaxSelectorData &data)>::type; -using SoftmaxLogits1DMaxKernelPtr = std::add_pointer<void(const ITensor *, ITensor *, const Window &)>::type; -using SoftmaxLogits1DKernelPtr = std::add_pointer<void(const ITensor *, const ITensor *, void *const, ITensor *, float, bool, const Window &)>::type; - -struct SoftmaxLogits1DKernel -{ - const char *name; - const SoftmaxSelectorPtr is_selected; - SoftmaxLogits1DKernelPtr ukernel; -}; - -struct SoftmaxLogits1DMaxKernel -{ - const char *name; - const SoftmaxSelectorPtr is_selected; - SoftmaxLogits1DMaxKernelPtr ukernel; -}; - -static const SoftmaxLogits1DKernel available_logits_1d_kernels[] = -{ -#if defined(ARM_COMPUTE_ENABLE_SVE) - { - "sve_fp32_softmax_logits_1d", - [](const SoftmaxSelectorData & data) { return (data.dt == DataType::F32) && data.ci.has_sve(); }, - REGISTER_FP32_SVE(arm_compute::cpu::sve_fp32_softmax) - }, - { - "sve_fp16_softmax_logits_1d", - [](const SoftmaxSelectorData & data) { return (data.dt == DataType::F16) && data.ci.has_sve(); }, - REGISTER_FP16_SVE(arm_compute::cpu::sve_fp16_softmax) - }, -#endif /* defined(ARM_COMPUTE_ENABLE_SVE) */ - -#if defined(ARM_COMPUTE_ENABLE_NEON) - { - "neon_fp32_softmax_logits_1d", - [](const SoftmaxSelectorData & data) { return (data.dt == DataType::F32); }, - REGISTER_FP32_NEON(arm_compute::cpu::neon_fp32_softmax) - }, -#if defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) - { - "neon_fp16_softmax_logits_1d", - [](const SoftmaxSelectorData & data) { return (data.dt == DataType::F16); }, - REGISTER_FP16_NEON(arm_compute::cpu::neon_fp16_softmax) - }, -#endif /* defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) */ -#endif /* defined(ARM_COMPUTE_ENABLE_NEON) */ - -#if defined(ARM_COMPUTE_ENABLE_SVE2) - { - "sve2_qu8_softmax_logits_1d", - [](const SoftmaxSelectorData & data) { return (data.dt == DataType::QASYMM8) && data.ci.has_sve2(); }, - REGISTER_QASYMM8_SVE2(arm_compute::cpu::sve2_qasymm8_softmax) - }, - { - "sve2_qs8_softmax_logits_1d", - [](const SoftmaxSelectorData & data) { return (data.dt == DataType::QASYMM8_SIGNED) && data.ci.has_sve2(); }, - REGISTER_QASYMM8_SIGNED_SVE2(arm_compute::cpu::sve2_qasymm8_signed_softmax) - }, -#endif /* defined(ARM_COMPUTE_ENABLE_SVE2) */ -#if defined(ARM_COMPUTE_ENABLE_NEON) - { - "neon_qu8_softmax_logits_1d", - [](const SoftmaxSelectorData & data) { return (data.dt == DataType::QASYMM8); }, - REGISTER_QASYMM8_NEON(arm_compute::cpu::neon_qasymm8_softmax) - }, - { - "neon_qs8_softmax_logits_1d", - [](const SoftmaxSelectorData & data) { return (data.dt == DataType::QASYMM8_SIGNED); }, - REGISTER_QASYMM8_SIGNED_NEON(arm_compute::cpu::neon_qasymm8_signed_softmax) - }, -#endif //defined(ARM_COMPUTE_ENABLE_NEON) -}; - -static const SoftmaxLogits1DMaxKernel available_logits_1d_max_kernels[] = +/* Softmax Logits 1D Max - identifying the max value of 1D Logits */ +static const std::vector<CpuLogits1DMaxKernel::SoftmaxLogits1DMaxKernel> available_kernels_max_logits = { #if defined(ARM_COMPUTE_ENABLE_SVE) { "sve_fp32_logits_1d_max", - [](const SoftmaxSelectorData & data) { return (data.dt == DataType::F32) && data.ci.has_sve(); }, + [](const DataTypeISASelectorData & data) { return (data.dt == DataType::F32) && data.isa.sve; }, REGISTER_FP32_SVE(arm_compute::cpu::sve_fp32_logits) }, { "sve_fp16_logits_1d_max", - [](const SoftmaxSelectorData & data) { return (data.dt == DataType::F16) && data.ci.has_sve(); }, + [](const DataTypeISASelectorData & data) { return (data.dt == DataType::F16) && data.isa.sve; }, REGISTER_FP16_SVE(arm_compute::cpu::sve_fp16_logits) }, { "sve_qu8_logits_1d_max", - [](const SoftmaxSelectorData & data) { return (data.dt == DataType::QASYMM8) && data.ci.has_sve(); }, + [](const DataTypeISASelectorData & data) { return (data.dt == DataType::QASYMM8) && data.isa.sve; }, REGISTER_QASYMM8_SVE(arm_compute::cpu::sve_qasymm8_logits) }, { "sve_qs8_logits_1d_max", - [](const SoftmaxSelectorData & data) { return (data.dt == DataType::QASYMM8_SIGNED) && data.ci.has_sve(); }, + [](const DataTypeISASelectorData & data) { return (data.dt == DataType::QASYMM8_SIGNED) && data.isa.sve; }, REGISTER_QASYMM8_SIGNED_SVE(arm_compute::cpu::sve_qasymm8_signed_logits) }, #endif /* defined(ARM_COMPUTE_ENABLE_SVE) */ #if defined(ARM_COMPUTE_ENABLE_NEON) { "neon_fp32_logits_1d_max", - [](const SoftmaxSelectorData & data) { return (data.dt == DataType::F32); }, + [](const DataTypeISASelectorData & data) { return (data.dt == DataType::F32); }, REGISTER_FP32_NEON(arm_compute::cpu::neon_fp32_logits) }, #if defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) { "neon_fp16_logits_1d_max", - [](const SoftmaxSelectorData & data) { return (data.dt == DataType::F16); }, + [](const DataTypeISASelectorData & data) { return (data.dt == DataType::F16); }, REGISTER_FP16_NEON(arm_compute::cpu::neon_fp16_logits) }, #endif /* defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) */ { "neon_qu8_logits_1d_max", - [](const SoftmaxSelectorData & data) { return (data.dt == DataType::QASYMM8); }, + [](const DataTypeISASelectorData & data) { return (data.dt == DataType::QASYMM8); }, REGISTER_QASYMM8_NEON(arm_compute::cpu::neon_qasymm8_logits) }, { "neon_qs8_logits_1d_max", - [](const SoftmaxSelectorData & data) { return (data.dt == DataType::QASYMM8_SIGNED); }, + [](const DataTypeISASelectorData & data) { return (data.dt == DataType::QASYMM8_SIGNED); }, REGISTER_QASYMM8_SIGNED_NEON(arm_compute::cpu::neon_qasymm8_singed_logits) }, #endif /* defined(ARM_COMPUTE_ENABLE_NEON) */ }; - -const SoftmaxLogits1DKernel *get_implementation_logits(const SoftmaxSelectorData &data) -{ - for(const auto &uk : available_logits_1d_kernels) - { - if(uk.is_selected({ data.dt, CPUInfo::get() })) - { - return &uk; - } - } - return nullptr; -} - -const SoftmaxLogits1DMaxKernel *get_implementation_logits_max(const SoftmaxSelectorData &data) -{ - for(const auto &uk : available_logits_1d_max_kernels) - { - if(uk.is_selected({ data.dt, CPUInfo::get() })) - { - return &uk; - } - } - return nullptr; -} - Status validate_arguments_logits_1d_max(const ITensorInfo &input, const ITensorInfo &output) { ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(&input); ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&input, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::F16, DataType::F32); - // Validate in case of configured output if(output.total_size() != 0) { @@ -209,58 +102,104 @@ Status validate_arguments_logits_1d_max(const ITensorInfo &input, const ITensorI ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_QUANTIZATION_INFO(&input, &output); ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(output.tensor_shape(), TensorShape(input.tensor_shape()).set(0, 1)); } - return Status{}; } - -} // namespace - +} //namespace +const std::vector<CpuLogits1DMaxKernel::SoftmaxLogits1DMaxKernel> &CpuLogits1DMaxKernel::get_available_kernels() +{ + return available_kernels_max_logits; +} void CpuLogits1DMaxKernel::configure(const ITensorInfo *src, ITensorInfo *dst) { ARM_COMPUTE_ERROR_ON_NULLPTR(src, dst); ARM_COMPUTE_ERROR_THROW_ON(validate_arguments_logits_1d_max(*src, *dst)); - // Softmax across the x dimension const TensorShape output_shape = TensorShape(src->tensor_shape()).set(0, 1); // Output auto initialization if not yet initialized auto_init_if_empty(*dst, output_shape, 1, src->data_type(), src->quantization_info()); - - const auto *uk = get_implementation_logits_max(SoftmaxSelectorData{ src->data_type(), CPUInfo::get() }); + const auto *uk = get_implementation(DataTypeISASelectorData{ src->data_type(), CPUInfo::get().get_isa() }); ARM_COMPUTE_ERROR_ON_NULLPTR(uk); - _run_method = uk->ukernel; _name = std::string("CpuLogits1DMaxKernel").append("/").append(uk->name); - - Window win = calculate_max_window(*src, Steps()); + Window win = calculate_max_window(*src, Steps()); ICpuKernel::configure(win); } - Status CpuLogits1DMaxKernel::validate(const ITensorInfo *src, const ITensorInfo *dst) { ARM_COMPUTE_ERROR_ON_NULLPTR(src, dst); ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments_logits_1d_max(*src, *dst)); - return Status{}; } - void CpuLogits1DMaxKernel::run_op(ITensorPack &tensors, const Window &window, const ThreadInfo &info) { ARM_COMPUTE_UNUSED(info); ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICpuKernel::window(), window); ARM_COMPUTE_ERROR_ON(_run_method == nullptr); - const auto src = tensors.get_const_tensor(TensorType::ACL_SRC); auto dst = tensors.get_tensor(TensorType::ACL_DST); - _run_method(src, dst, window); } - const char *CpuLogits1DMaxKernel::name() const { return _name.c_str(); } +/* Softmax Logits 1D - computation for QASYMM8 with pre-computed max. */ +template <bool IS_LOG> +static const std::vector<typename CpuLogits1DSoftmaxKernel<IS_LOG>::SoftmaxLogits1DKernel> available_kernels_logits = +{ +#if defined(ARM_COMPUTE_ENABLE_SVE) + { + "sve_fp32_softmax_logits_1d", + [](const DataTypeISASelectorData & data) { return (data.dt == DataType::F32) && data.isa.sve; }, + REGISTER_FP32_SVE(arm_compute::cpu::sve_fp32_softmax) + }, + { + "sve_fp16_softmax_logits_1d", + [](const DataTypeISASelectorData & data) { return (data.dt == DataType::F16) && data.isa.sve; }, + REGISTER_FP16_SVE(arm_compute::cpu::sve_fp16_softmax) + }, +#endif /* defined(ARM_COMPUTE_ENABLE_SVE) */ +#if defined(ARM_COMPUTE_ENABLE_NEON) + { + "neon_fp32_softmax_logits_1d", + [](const DataTypeISASelectorData & data) { return (data.dt == DataType::F32); }, + REGISTER_FP32_NEON(arm_compute::cpu::neon_fp32_softmax) + }, +#if defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) + { + "neon_fp16_softmax_logits_1d", + [](const DataTypeISASelectorData & data) { return (data.dt == DataType::F16); }, + REGISTER_FP16_NEON(arm_compute::cpu::neon_fp16_softmax) + }, +#endif /* defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) */ +#endif /* defined(ARM_COMPUTE_ENABLE_NEON) */ +#if defined(ARM_COMPUTE_ENABLE_SVE2) + { + "sve2_qu8_softmax_logits_1d", + [](const DataTypeISASelectorData & data) { return (data.dt == DataType::QASYMM8) && data.isa.sve2; }, + REGISTER_QASYMM8_SVE2(arm_compute::cpu::sve2_qasymm8_softmax) + }, + { + "sve2_qs8_softmax_logits_1d", + [](const DataTypeISASelectorData & data) { return (data.dt == DataType::QASYMM8_SIGNED) && data.isa.sve2; }, + REGISTER_QASYMM8_SIGNED_SVE2(arm_compute::cpu::sve2_qasymm8_signed_softmax) + }, +#endif /* defined(ARM_COMPUTE_ENABLE_SVE2) */ +#if defined(ARM_COMPUTE_ENABLE_NEON) + { + "neon_qu8_softmax_logits_1d", + [](const DataTypeISASelectorData & data) { return (data.dt == DataType::QASYMM8); }, + REGISTER_QASYMM8_NEON(arm_compute::cpu::neon_qasymm8_softmax) + }, + { + "neon_qs8_softmax_logits_1d", + [](const DataTypeISASelectorData & data) { return (data.dt == DataType::QASYMM8_SIGNED); }, + REGISTER_QASYMM8_SIGNED_NEON(arm_compute::cpu::neon_qasymm8_signed_softmax) + }, +#endif //defined(ARM_COMPUTE_ENABLE_NEON) +}; namespace { Status validate_arguments_logits_softmax(const ITensorInfo &src, const ITensorInfo &max, @@ -270,14 +209,11 @@ Status validate_arguments_logits_softmax(const ITensorInfo &src, const ITensorIn // Check input ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(&src); ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&src, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::F16, DataType::F32); - const bool is_quantized_asymmetric = is_data_type_quantized_asymmetric(src.data_type()); - // Check max ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(&src, &max); ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(TensorShape(src.tensor_shape()).set(0, 1), max.tensor_shape()); ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_QUANTIZATION_INFO(&src, &max); - // Check output if configured if(dst.total_size() != 0) { @@ -286,7 +222,6 @@ Status validate_arguments_logits_softmax(const ITensorInfo &src, const ITensorIn ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(&src, &dst); ARM_COMPUTE_RETURN_ERROR_ON(dst.quantization_info() != output_quantization); } - // Check tmp if configured if(tmp.total_size() != 0) { @@ -296,84 +231,69 @@ Status validate_arguments_logits_softmax(const ITensorInfo &src, const ITensorIn // on the maximum number of threads that will run in parallel. ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(&src, &tmp); } - return Status{}; } } // namespace - +template <bool IS_LOG> +const std::vector<typename CpuLogits1DSoftmaxKernel<IS_LOG>::SoftmaxLogits1DKernel> &CpuLogits1DSoftmaxKernel<IS_LOG>::get_available_kernels() +{ + return available_kernels_logits<IS_LOG>; +} template <bool IS_LOG> void CpuLogits1DSoftmaxKernel<IS_LOG>::configure(const ITensorInfo *src, const ITensorInfo *max, ITensorInfo *dst, const float beta, ITensorInfo *tmp) { ARM_COMPUTE_ERROR_ON_NULLPTR(src, max, dst, tmp); ARM_COMPUTE_ERROR_THROW_ON(validate_arguments_logits_softmax(*src, *max, *dst, beta, *tmp, IS_LOG)); - // Configure kernel window const bool is_quantized_asymmetric = is_data_type_quantized_asymmetric(src->data_type()); - // Output auto initialization if not yet initialized const QuantizationInfo output_quantization = is_quantized_asymmetric ? arm_compute::get_softmax_output_quantization_info(src->data_type(), IS_LOG) : dst->quantization_info(); auto_init_if_empty(*dst, TensorInfo(*src).set_quantization_info(output_quantization).reset_padding()); - // Tmp auto initialization if not yet initialized const DataType tmp_data_type = is_quantized_asymmetric ? DataType::F32 : src->data_type(); auto_init_if_empty(*tmp, TensorInfo(*src).set_data_type(tmp_data_type).reset_padding()); - - const auto *uk = get_implementation_logits(SoftmaxSelectorData{ src->data_type(), CPUInfo::get() }); + const auto *uk = CpuLogits1DSoftmaxKernel<IS_LOG>::get_implementation(DataTypeISASelectorData{ src->data_type(), CPUInfo::get().get_isa() }); ARM_COMPUTE_ERROR_ON_NULLPTR(uk); - std::string kernel_name = IS_LOG ? std::string("CpuLogits1DLogSoftmaxKernel") : std::string("CpuLogits1DSoftmaxKernel"); - - _beta = beta; - _run_method = uk->ukernel; - _name = kernel_name.append("/").append(uk->name); - + _beta = beta; + _run_method = uk->ukernel; + _name = kernel_name.append("/").append(uk->name); // Configure kernel window Window win = calculate_max_window(*max, Steps()); - - ICpuKernel::configure(win); + ICPPKernel::configure(win); } - template <bool IS_LOG> Status CpuLogits1DSoftmaxKernel<IS_LOG>::validate(const ITensorInfo *src, const ITensorInfo *max, const ITensorInfo *dst, const float beta, const ITensorInfo *tmp) { ARM_COMPUTE_ERROR_ON_NULLPTR(src, max, dst, tmp); ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments_logits_softmax(*src, *max, *dst, beta, *tmp, IS_LOG)); - return Status{}; } - template <bool IS_LOG> void CpuLogits1DSoftmaxKernel<IS_LOG>::run_op(ITensorPack &tensors, const Window &window, const ThreadInfo &info) { ARM_COMPUTE_UNUSED(info); ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); - ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICpuKernel::window(), window); + ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICPPKernel::window(), window); ARM_COMPUTE_ERROR_ON(_run_method == nullptr); - - const auto src = tensors.get_const_tensor(TensorType::ACL_SRC_0); - auto max = tensors.get_tensor(TensorType::ACL_SRC_1); - auto dst = tensors.get_tensor(TensorType::ACL_DST_0); - auto tmp = tensors.get_tensor(TensorType::ACL_DST_1); - + const auto src = tensors.get_const_tensor(TensorType::ACL_SRC_0); + auto max = tensors.get_tensor(TensorType::ACL_SRC_1); + auto dst = tensors.get_tensor(TensorType::ACL_DST_0); + auto tmp = tensors.get_tensor(TensorType::ACL_DST_1); const unsigned int num_elems_processed_per_iteration = src->info()->valid_region().shape.x(); const unsigned int tmp_size_for_thread = tmp->info()->element_size() * num_elems_processed_per_iteration; - ARM_COMPUTE_ERROR_ON(tmp->info()->total_size() < (info.num_threads * tmp_size_for_thread)); - void *tmp_for_thread = tmp->buffer() + (info.thread_id * tmp_size_for_thread); _run_method(src, max, tmp_for_thread, dst, _beta, IS_LOG, window); } - template <bool IS_LOG> const char *CpuLogits1DSoftmaxKernel<IS_LOG>::name() const { return _name.c_str(); } - template class CpuLogits1DSoftmaxKernel<true>; template class CpuLogits1DSoftmaxKernel<false>; - } // namespace kernels } // namespace cpu } // namespace arm_compute |