diff options
Diffstat (limited to 'src/gpu/cl/operators/ClFullyConnected.cpp')
| -rw-r--r-- | src/gpu/cl/operators/ClFullyConnected.cpp | 144 |
1 files changed, 70 insertions, 74 deletions
diff --git a/src/gpu/cl/operators/ClFullyConnected.cpp b/src/gpu/cl/operators/ClFullyConnected.cpp index b7ba8b89fa..0be3f0f87e 100644 --- a/src/gpu/cl/operators/ClFullyConnected.cpp +++ b/src/gpu/cl/operators/ClFullyConnected.cpp @@ -113,22 +113,25 @@ Status construct_gemmlowp_output_stage(const ITensorInfo &src, const ITensorInfo Status validate_mm(const ITensorInfo &src, const ITensorInfo &weights, const ITensorInfo *bias, const ITensorInfo &dst, const FullyConnectedLayerInfo &fc_info) { - // If weights are dynamic, data is not batched, and bias is nullptr validate using matmul. - const bool weights_reshaped = fc_info.transpose_weights ? fc_info.are_weights_reshaped : true; - const bool use_matmul = !weights.are_values_constant() && !weights_reshaped && !(dst.dimension(1) > 1) && (bias == nullptr); + // Note : If input is dynamic and data is not batched, use matmul, else use gemm + const bool transpose_weights = fc_info.transpose_weights ? !fc_info.are_weights_reshaped : false; + const bool use_matmul = !weights.are_values_constant() && !(dst.dimension(1) > 1); + const bool use_dynamic_gemm = !use_matmul && !weights.are_values_constant() && transpose_weights; // use dynamic gemm as fallback for matmul + const bool is_quantized = is_data_type_quantized_asymmetric(src.data_type()); if(use_matmul) { - MatMulInfo m_info{}; - m_info.adj_rhs(fc_info.transpose_weights); + const MatMulInfo m_info = MatMulInfo().adj_rhs(transpose_weights); - // Note: Currently, shape is [M, B0, B1] - // LHS is reshaped here to match ClMatMul expectations of batch index in format - [M, 1, B0, B1, .. ] - TensorInfo lhs_to_use{ src }; - lhs_to_use.set_tensor_shape(get_reshaped_matmul_tensor(src.tensor_shape())); + // Note: LHS is reshaped here to match ClMatMul expectations of batch index - From [M, B0, B1] to [M, 1, B0, B1] + TensorInfo lhs_to_use = src.clone()->set_tensor_shape(get_reshaped_matmul_tensor(src.tensor_shape())); - // Operator level validation. - ARM_COMPUTE_RETURN_ON_ERROR(ClMatMul::validate(&lhs_to_use, &weights, &dst, m_info, fc_info.activation_info)); + const GPUTarget gpu_target = CLScheduler::get().target(); + std::unique_ptr<cl_matmul::IClMatMulNativeKernelConfig> t = cl_matmul::ClMatMulNativeKernelConfigurationFactory::create(gpu_target); + const MatMulKernelInfo kernel_info = t->configure(&lhs_to_use, &weights, m_info); + + return is_quantized ? kernels::ClMatMulLowpNativeKernel::validate(&lhs_to_use, &weights, bias, &dst, kernel_info, fc_info.activation_info) : + kernels::ClMatMulNativeKernel::validate(&lhs_to_use, &weights, bias, &dst, kernel_info, fc_info.activation_info); } else { @@ -137,7 +140,7 @@ Status validate_mm(const ITensorInfo &src, const ITensorInfo &weights, const ITe const GEMMInfo &gemm_info = GEMMInfo(false, // is_a_reshaped false, // is_b_reshaped - true, // reshape_b_only_on_first_run + !use_dynamic_gemm, // reshape_b_only_on_first_run 0, // depth_output_gemm3d false, // reinterpret_input_as_3d fc_info.retain_internal_weights, // retain_internal_weights @@ -147,7 +150,7 @@ Status validate_mm(const ITensorInfo &src, const ITensorInfo &weights, const ITe true, // broadcast_bias ActivationLayerInfo()); // activation_info - if(is_data_type_quantized_asymmetric(src.data_type())) + if(is_quantized) { const UniformQuantizationInfo iq_info = src.quantization_info().uniform(); const UniformQuantizationInfo wq_info = weights.quantization_info().uniform(); @@ -191,35 +194,33 @@ ClFullyConnected::~ClFullyConnected() = default; void ClFullyConnected::configure_mm(const CLCompileContext &compile_context, ITensorInfo *src, ITensorInfo *weights, ITensorInfo *bias, ITensorInfo *dst, const FullyConnectedLayerInfo &fc_info) { - // If weights are dynamic, configure matmul operator - else use gemm + // If weights are dynamic and matmul is supported use matmul, else use gemm if(_use_matmul) { - // Transpose RHS as _are_weights_reshaped == false when mat_mul is used. - const MatMulInfo mat_info = MatMulInfo().adj_rhs(fc_info.transpose_weights); + // Specify whether transpose weights is necessary in matmul info + const MatMulInfo mat_info = MatMulInfo().adj_rhs(_transpose_weights); // Note: MatMul does not need offset negation unlike gemm // 1. Change shape when calling matmul to fit batch expectations. - _lhs_to_use = *src->clone(); - _lhs_to_use.set_tensor_shape(get_reshaped_matmul_tensor(_lhs_to_use.tensor_shape())); // Collapse all dims > 2 into final dimension. - _is_quantized = is_data_type_quantized_asymmetric(_lhs_to_use.data_type()); + _lhs_to_use = src->clone()->set_tensor_shape(get_reshaped_matmul_tensor(_lhs_to_use.tensor_shape())); - // 2. Call kernel for matmul directly. + // 2. Use heuristics to get kernel info object const GPUTarget gpu_target = CLScheduler::get().target(); std::unique_ptr<cl_matmul::IClMatMulNativeKernelConfig> kernel_config = cl_matmul::ClMatMulNativeKernelConfigurationFactory::create(gpu_target); + MatMulKernelInfo kernel_info = kernel_config->configure(src, weights, mat_info); - // Configure relevant matmul kernel - MatMulKernelInfo kernel_info = kernel_config->configure(src, weights, mat_info); + // 3. Configure relevant matmul kernel if(_is_quantized) { _matmul_lowp_native_kernel = std::make_unique<kernels::ClMatMulLowpNativeKernel>(); _matmul_lowp_native_kernel->set_target(gpu_target); - _matmul_lowp_native_kernel->configure(compile_context, src, weights, dst, kernel_info, fc_info.activation_info); + _matmul_lowp_native_kernel->configure(compile_context, src, weights, bias, dst, kernel_info, fc_info.activation_info); } else { _matmul_native_kernel = std::make_unique<kernels::ClMatMulNativeKernel>(); _matmul_native_kernel->set_target(gpu_target); - _matmul_native_kernel->configure(compile_context, src, weights, dst, kernel_info, fc_info.activation_info); + _matmul_native_kernel->configure(compile_context, src, weights, bias, dst, kernel_info, fc_info.activation_info); } } else @@ -230,7 +231,7 @@ void ClFullyConnected::configure_mm(const CLCompileContext &compile_context, ITe const GEMMInfo &gemm_info = GEMMInfo(false, // is_a_reshaped false, // is_b_reshaped - !_dynamic_weights, // reshape_b_only_on_first_run + !_dynamic_gemm, // reshape_b_only_on_first_run 0, // depth_output_gemm3d false, // reinterpret_input_as_3d fc_info.retain_internal_weights, // retain_internal_weights @@ -269,7 +270,8 @@ void ClFullyConnected::configure_mm(const CLCompileContext &compile_context, ITe void ClFullyConnected::configure_conv_fc(const CLCompileContext &compile_context, ITensorInfo *src, ITensorInfo *weights, ITensorInfo *bias, ITensorInfo *dst, const FullyConnectedLayerInfo &fc_info) { - ARM_COMPUTE_ERROR_ON((weights->dimension((_use_matmul) ? 0 : 1) != (src->dimension(0) * src->dimension(1) * src->dimension(2)))); + // MatMul fuses transpose operation, so we use the first dimension for comparison where appropriate. + ARM_COMPUTE_ERROR_ON((weights->dimension((_use_matmul && _transpose_weights) ? 0 : 1) != (src->dimension(0) * src->dimension(1) * src->dimension(2)))); // If the fully connected layer is called after a convolution layer, the input tensor must be linearized @@ -288,8 +290,8 @@ void ClFullyConnected::configure_conv_fc(const CLCompileContext &compile_context void ClFullyConnected::configure_fc_fc(const CLCompileContext &compile_context, ITensorInfo *src, ITensorInfo *weights, ITensorInfo *bias, ITensorInfo *dst, const FullyConnectedLayerInfo &fc_info) { - // Compare first dimension when using matmul, as it performs transpose operation - ARM_COMPUTE_ERROR_ON(src->dimension(0) != weights->dimension((_use_matmul) ? 0 : 1)); + // MatMul fuses transpose operation, so we use the first dimension for comparison where appropriate. + ARM_COMPUTE_ERROR_ON(src->dimension(0) != weights->dimension((_use_matmul && _transpose_weights) ? 0 : 1)); // Configure matrix multiply kernel configure_mm(compile_context, src, weights, bias, dst, fc_info); @@ -304,20 +306,18 @@ void ClFullyConnected::configure(const CLCompileContext &compile_context, ITenso ARM_COMPUTE_ERROR_THROW_ON(ClFullyConnected::validate(src, weights, biases, dst, fc_info)); ARM_COMPUTE_LOG_PARAMS(src, weights, biases, dst, fc_info); - _are_weights_converted = true; - _are_weights_reshaped = fc_info.transpose_weights ? fc_info.are_weights_reshaped : true; - _is_fc_after_conv = true; - _is_quantized = is_data_type_quantized_asymmetric(src->data_type()); - _is_prepared = fc_info.retain_internal_weights; - _weights_to_use = TensorInfo(*weights); - _weights_to_use_idx = ACL_SRC_1; + _transpose_weights = fc_info.transpose_weights ? !fc_info.are_weights_reshaped : false; + _is_fc_after_conv = true; + _is_quantized = is_data_type_quantized_asymmetric(src->data_type()); + _is_prepared = fc_info.retain_internal_weights; + _weights_to_use = TensorInfo(*weights); + _weights_to_use_idx = ACL_SRC_1; // When using dynamic weights - use matmul kernels. - // Note: We don't appear to support dynamic weights with pre-reshaped RHS. - // Note: No matmul with biases for the moment. + // Note: MatMul does not support broadcasting batch dimension, and therefore is disabled if fc is batched. Gemm is used as fallback. const bool is_batched_fc_layer = dst->dimension(1) > 1; - _dynamic_weights = !weights->are_values_constant() && !_are_weights_reshaped; - _use_matmul = _dynamic_weights && !is_batched_fc_layer && (biases == nullptr); + _use_matmul = !weights->are_values_constant() && !is_batched_fc_layer; + _dynamic_gemm = !weights->are_values_constant() && _transpose_weights && !_use_matmul; // With the Fully Connected layer we can have 4 different cases: // 1) Convolution layer -> Fully Connected layer without batches @@ -339,9 +339,8 @@ void ClFullyConnected::configure(const CLCompileContext &compile_context, ITenso ITensorInfo *weights_used = weights; - // Reshape weights if needed - // Not needed when matmul is in use - MatMul has transpose RHS flags. - if(!_are_weights_reshaped && !_use_matmul) + // Reshape weights if needed - Not needed when matmul is in use as matmul fuses transpose op. + if(_transpose_weights && !_use_matmul) { // Reshape the weights _reshape_weights = std::make_unique<ClTranspose>(); @@ -361,9 +360,9 @@ void ClFullyConnected::configure(const CLCompileContext &compile_context, ITenso src->tensor_shape(), fc_info.weights_trained_layout); - weights_used = &_converted_weights; - _weights_to_use_idx = offset_int_vec(ConvertedWeights); - _are_weights_converted = false; + weights_used = &_converted_weights; + _weights_to_use_idx = offset_int_vec(ConvertedWeights); + _run_convert_weights = true; } if(_is_fc_after_conv) @@ -398,11 +397,11 @@ void ClFullyConnected::configure(const CLCompileContext &compile_context, ITenso // Keep all the auxiliary tensors in case of dynamic weights as they are recalculated every time _aux_mem[TransposedWeights] = MemoryInfo( offset_int_vec(TransposedWeights), - _dynamic_weights ? MemoryLifetime::Temporary : MemoryLifetime::Prepare, + _dynamic_gemm ? MemoryLifetime::Temporary : MemoryLifetime::Prepare, _reshaped_weights.total_size()); _aux_mem[ConvertedWeights] = MemoryInfo( offset_int_vec(ConvertedWeights), - _dynamic_weights ? MemoryLifetime::Temporary : MemoryLifetime::Prepare, + _dynamic_gemm ? MemoryLifetime::Temporary : MemoryLifetime::Prepare, _converted_weights.total_size()); } else @@ -413,11 +412,11 @@ void ClFullyConnected::configure(const CLCompileContext &compile_context, ITenso _aux_mem[TransposedWeights] = MemoryInfo( offset_int_vec(TransposedWeights), - _dynamic_weights ? MemoryLifetime::Temporary : transposed_wei_lft, + _dynamic_gemm ? MemoryLifetime::Temporary : transposed_wei_lft, _reshaped_weights.total_size()); _aux_mem[ConvertedWeights] = MemoryInfo( offset_int_vec(ConvertedWeights), - _dynamic_weights ? MemoryLifetime::Temporary : converted_wei_lft, + _dynamic_gemm ? MemoryLifetime::Temporary : converted_wei_lft, _converted_weights.total_size()); } } @@ -434,19 +433,17 @@ Status ClFullyConnected::validate(const ITensorInfo *src, const ITensorInfo *wei ARM_COMPUTE_RETURN_ERROR_ON(fc_info.activation_info.enabled() && is_data_type_quantized(src->data_type()) && fc_info.activation_info.activation() != ActivationLayerInfo::ActivationFunction::RELU && fc_info.activation_info.activation() != ActivationLayerInfo::ActivationFunction::BOUNDED_RELU && fc_info.activation_info.activation() != ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU); - const bool weights_reshaped = fc_info.transpose_weights ? fc_info.are_weights_reshaped : true; - bool is_fc_after_conv = true; + const bool transpose_weights = fc_info.transpose_weights ? !fc_info.are_weights_reshaped : false; + bool is_fc_after_conv = true; // When using dynamic weights - use matmul kernels. - // Note: MatMul does not support broadcasting or biases so fallback with batched cases or when biases != nullptr. - // Note: Pre-Shaped RHS is a deprecated use case and is therefore not supported with matmul. - const bool dynamic_weights = !weights->are_values_constant() && !weights_reshaped; + // Note: MatMul does not support broadcasting so fallback with batched cases. const bool is_batched_fc_layer = dst->dimension(1) > 1; - const bool use_matmul = dynamic_weights && !is_batched_fc_layer && (biases == nullptr); + const bool use_matmul = !weights->are_values_constant() && !is_batched_fc_layer; const ITensorInfo &flatten_src = TensorInfo(src->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(compute_flatten_shape(src)).set_data_layout(DataLayout::NCHW)); const ITensorInfo &reshaped_weights = TensorInfo(weights->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(compute_transposed_shape(*weights))); - const ITensorInfo &converted_weights = weights_reshaped ? TensorInfo(weights->clone()->set_is_resizable(true).reset_padding()) : TensorInfo(*reshaped_weights.clone()); + const ITensorInfo &converted_weights = transpose_weights ? TensorInfo(*reshaped_weights.clone()) : TensorInfo(weights->clone()->set_is_resizable(true).reset_padding()); // With the Fully Connected layer we can have 4 different cases: // 1) Convolution layer -> Fully Connected layer without batches @@ -482,7 +479,8 @@ Status ClFullyConnected::validate(const ITensorInfo *src, const ITensorInfo *wei is_fc_after_conv = src->num_dimensions() > 1; } - if(!weights_reshaped && !use_matmul) + // Transpose kernel does not run when matmul is supported as matmul fuses transpose op. + if(transpose_weights && !use_matmul) { // Validate reshape weights kernel ARM_COMPUTE_RETURN_ON_ERROR(ClTranspose::validate(weights, &reshaped_weights)); @@ -502,14 +500,9 @@ Status ClFullyConnected::validate(const ITensorInfo *src, const ITensorInfo *wei if(is_fc_after_conv) { // Fully Connected layer after a Convolution Layer without batches - if(use_matmul) - { - ARM_COMPUTE_RETURN_ERROR_ON((weights_to_use->dimension(0) != (src->dimension(0) * src->dimension(1) * src->dimension(2)))); - } - else - { - ARM_COMPUTE_RETURN_ERROR_ON((weights_to_use->dimension(1) != (src->dimension(0) * src->dimension(1) * src->dimension(2)))); - } + // K Index of matrix multiplication. MatMul performs transpose in kernel, so index is 0 when matmul and transpose enabled + const int weight_idx = (use_matmul && transpose_weights) ? 0 : 1; + ARM_COMPUTE_RETURN_ERROR_ON((weights_to_use->dimension(weight_idx) != (src->dimension(0) * src->dimension(1) * src->dimension(2)))); // Validate flatten kernel ARM_COMPUTE_RETURN_ON_ERROR(ClFlatten::validate(src, &flatten_src)); @@ -518,7 +511,9 @@ Status ClFullyConnected::validate(const ITensorInfo *src, const ITensorInfo *wei else { // Fully Connected layer after a Fully Connected Layer without batches - ARM_COMPUTE_RETURN_ERROR_ON(src->dimension(0) != weights_to_use->dimension((use_matmul) ? 0 : 1)); + // K Index of matrix multiplication. MatMul performs transpose in kernel, so index is 0 when matmul and transpose enabled + const int weight_idx = (use_matmul && transpose_weights) ? 0 : 1; + ARM_COMPUTE_RETURN_ERROR_ON(src->dimension(0) != weights_to_use->dimension(weight_idx)); } // Validate matrix multiply kernel @@ -533,7 +528,7 @@ void ClFullyConnected::run(ITensorPack &tensors) #ifdef ARM_COMPUTE_ASSERTS_ENABLED ++_asrt_run_count; - ARM_COMPUTE_ERROR_ON(_dynamic_weights && _asrt_prepare_count != _asrt_run_count); + ARM_COMPUTE_ERROR_ON(_dynamic_gemm && _asrt_prepare_count != _asrt_run_count); #endif // ARM_COMPUTE_ASSERTS_ENABLED auto src = tensors.get_const_tensor(ACL_SRC_0); @@ -584,11 +579,12 @@ void ClFullyConnected::run(ITensorPack &tensors) void ClFullyConnected::prepare(ITensorPack &tensors) { - if(!_is_prepared || _dynamic_weights) + // Note : Running prepare() each run when _use_matmul is true is unnecessary unless weights conversion is needed. + if(!_is_prepared || _dynamic_gemm || (_use_matmul && _run_convert_weights)) { #ifdef ARM_COMPUTE_ASSERTS_ENABLED ++_asrt_prepare_count; - ARM_COMPUTE_ERROR_ON(!_dynamic_weights && _asrt_prepare_count > 1); + ARM_COMPUTE_ERROR_ON(!_dynamic_gemm && !_use_matmul && _asrt_prepare_count > 1); #endif // ARM_COMPUTE_ASSERTS_ENABLED auto weights = tensors.get_const_tensor(ACL_SRC_1); @@ -599,8 +595,8 @@ void ClFullyConnected::prepare(ITensorPack &tensors) // Pointer to current weights const ITensor *cur_weights = weights; - // Reshape of the weights if needed - if(!_are_weights_reshaped && !_use_matmul) + // Reshape weights if needed. Disabled when matmul kernels are enabled as matmul fuses transpose. + if(_transpose_weights && !_use_matmul) { // Run reshape weights kernel and mark weights as unused ITensorPack transpose_pack{ { ACL_SRC, weights }, { ACL_DST, reshaped_weights.get() } }; @@ -611,7 +607,7 @@ void ClFullyConnected::prepare(ITensorPack &tensors) } // Convert weights if needed - if(!_are_weights_converted) + if(_run_convert_weights) { ITensorPack convert_pack{ { ACL_SRC, cur_weights }, { ACL_DST, converted_weights.get() } }; _convert_weights->run(convert_pack); @@ -623,8 +619,8 @@ void ClFullyConnected::prepare(ITensorPack &tensors) ITensorPack gemm_pack = tensors; gemm_pack.add_const_tensor(ACL_SRC_1, cur_weights); - // Prepare GEMM prepare and release unused weights (If not using matmul) - if(!_use_matmul) + // Prepare GEMM prepare and release unused weights + if(_dynamic_gemm || !_use_matmul) { if(!_is_quantized) { |