diff options
| -rw-r--r-- | src/gpu/cl/operators/ClFullyConnected.cpp | 6 |
1 files changed, 4 insertions, 2 deletions
diff --git a/src/gpu/cl/operators/ClFullyConnected.cpp b/src/gpu/cl/operators/ClFullyConnected.cpp index f71bf41d9c..5845bbc69e 100644 --- a/src/gpu/cl/operators/ClFullyConnected.cpp +++ b/src/gpu/cl/operators/ClFullyConnected.cpp @@ -298,6 +298,7 @@ void ClFullyConnected::configure(const CLCompileContext &compile_context, ITenso FullyConnectedLayerInfo fc_info) { ARM_COMPUTE_ERROR_ON_NULLPTR(src, weights, dst); + const GPUTarget gpu_target = get_arch_from_target(CLScheduler::get().target()); // Perform validate step ARM_COMPUTE_ERROR_THROW_ON(ClFullyConnected::validate(src, weights, biases, dst, fc_info)); @@ -316,7 +317,7 @@ void ClFullyConnected::configure(const CLCompileContext &compile_context, ITenso // 2. MatMul does not support broadcasting batch dimension, and therefore is disabled if fc is batched. // 3. When FC is after convolution and src tensor data layout does not match weights trained data layout (weights conversion kernel is required) const bool is_batched_fc_layer = dst->dimension(1) > 1; - _use_matmul = !weights->are_values_constant() && !is_batched_fc_layer && !(src->num_dimensions() > 1 && (src->data_layout() != fc_info.weights_trained_layout)); + _use_matmul = gpu_target != GPUTarget::MIDGARD && !weights->are_values_constant() && !is_batched_fc_layer && !(src->num_dimensions() > 1 && (src->data_layout() != fc_info.weights_trained_layout)); _dynamic_gemm = !weights->are_values_constant() && _transpose_weights && !_use_matmul; // With the Fully Connected layer we can have 4 different cases: @@ -432,6 +433,7 @@ Status ClFullyConnected::validate(const ITensorInfo *src, const ITensorInfo *wei ARM_COMPUTE_RETURN_ERROR_ON(weights->num_dimensions() > 2); 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 GPUTarget gpu_target = get_arch_from_target(CLScheduler::get().target()); const bool transpose_weights = fc_info.transpose_weights ? !fc_info.are_weights_reshaped : false; bool is_fc_after_conv = true; @@ -439,7 +441,7 @@ Status ClFullyConnected::validate(const ITensorInfo *src, const ITensorInfo *wei // When using dynamic weights - use matmul kernels. // 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 = !weights->are_values_constant() && !is_batched_fc_layer && !(src->num_dimensions() > 1 && (src->data_layout() != fc_info.weights_trained_layout)); + const bool use_matmul = gpu_target != GPUTarget::MIDGARD && !weights->are_values_constant() && !is_batched_fc_layer && !(src->num_dimensions() > 1 && (src->data_layout() != fc_info.weights_trained_layout)); 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))); |