From 2e0714d4bb6795e34bcdcdaf812e9863dea2f43f Mon Sep 17 00:00:00 2001 From: Mohammed Suhail Munshi Date: Wed, 19 Jul 2023 14:44:38 +0100 Subject: Fix failing CTS tests by disabling matmul when weights conversion is required. Signed-off-by: Mohammed Suhail Munshi Change-Id: Ibba6564f111f493e4d7bac692eb2637830d4aff9 Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/9943 Benchmark: Arm Jenkins Reviewed-by: Gunes Bayir Comments-Addressed: Arm Jenkins Tested-by: Arm Jenkins --- src/gpu/cl/operators/ClFullyConnected.cpp | 22 +++++++++++----------- 1 file changed, 11 insertions(+), 11 deletions(-) diff --git a/src/gpu/cl/operators/ClFullyConnected.cpp b/src/gpu/cl/operators/ClFullyConnected.cpp index 0be3f0f87e..f71bf41d9c 100644 --- a/src/gpu/cl/operators/ClFullyConnected.cpp +++ b/src/gpu/cl/operators/ClFullyConnected.cpp @@ -35,12 +35,10 @@ #include "src/gpu/cl/operators/ClFlatten.h" #include "src/gpu/cl/operators/ClGemm.h" #include "src/gpu/cl/operators/ClGemmLowpMatrixMultiplyCore.h" +#include "src/gpu/cl/operators/ClMatMul.h" #include "src/gpu/cl/operators/ClTranspose.h" #include "src/gpu/cl/utils/ClAuxTensorHandler.h" -#include "src/gpu/cl/operators/ClMatMul.h" -#include "utils/TypePrinter.h" - #include "src/runtime/heuristics/matmul_native/ClMatMulNativeKernelConfig.h" #include "src/runtime/heuristics/matmul_native/IClMatMulNativeKernelConfig.h" @@ -111,11 +109,10 @@ Status construct_gemmlowp_output_stage(const ITensorInfo &src, const ITensorInfo return Status{}; } -Status validate_mm(const ITensorInfo &src, const ITensorInfo &weights, const ITensorInfo *bias, const ITensorInfo &dst, const FullyConnectedLayerInfo &fc_info) +Status validate_mm(const ITensorInfo &src, const ITensorInfo &weights, const ITensorInfo *bias, const ITensorInfo &dst, const FullyConnectedLayerInfo &fc_info, bool use_matmul) { // 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()); @@ -314,9 +311,12 @@ void ClFullyConnected::configure(const CLCompileContext &compile_context, ITenso _weights_to_use_idx = ACL_SRC_1; // When using dynamic weights - use matmul kernels. - // Note: MatMul does not support broadcasting batch dimension, and therefore is disabled if fc is batched. Gemm is used as fallback. + // Note: MatMul is not used in the following cases (Gemm is used as fallback) : + // 1. When the weights tensor is not dynamic + // 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; + _use_matmul = !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: @@ -439,11 +439,11 @@ 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; + 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 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 = transpose_weights ? TensorInfo(*reshaped_weights.clone()) : TensorInfo(weights->clone()->set_is_resizable(true).reset_padding()); + const ITensorInfo &converted_weights = (transpose_weights && !use_matmul) ? 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 @@ -517,7 +517,7 @@ Status ClFullyConnected::validate(const ITensorInfo *src, const ITensorInfo *wei } // Validate matrix multiply kernel - ARM_COMPUTE_RETURN_ON_ERROR(validate_mm(*src_to_use, *weights_to_use, biases, *dst, fc_info)); + ARM_COMPUTE_RETURN_ON_ERROR(validate_mm(*src_to_use, *weights_to_use, biases, *dst, fc_info, use_matmul)); return Status{}; } @@ -580,7 +580,7 @@ void ClFullyConnected::run(ITensorPack &tensors) void ClFullyConnected::prepare(ITensorPack &tensors) { // 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)) + if(!_is_prepared || _dynamic_gemm) { #ifdef ARM_COMPUTE_ASSERTS_ENABLED ++_asrt_prepare_count; -- cgit v1.2.1