Add support for non-constant weights and biases in CpuFullyConnected

Changing the approach for specifying that weights and biases tensors are
non-constant by making it a member of TensorInfo rather than an option
of the functions.

Resolves: COMPMID-4222, COMPMID-4811

Signed-off-by: Giorgio Arena <giorgio.arena@arm.com>
Change-Id: I9b0081ccbcf8271ce029ba6755563d64c59e1d32
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/6313
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Pablo Marquez Tello <pablo.tello@arm.com>
Reviewed-by: Michele Di Giorgio <michele.digiorgio@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>

12 files changed, 117 insertions, 23 deletions

diff --git a/src/core/NEON/kernels/arm_gemm/gemm_hybrid_indirect.hpp b/src/core/NEON/kernels/arm_gemm/gemm_hybrid_indirect.hpp
index 5cbdf20798..20c8230148 100644
--- a/src/core/NEON/kernels/arm_gemm/gemm_hybrid_indirect.hpp
+++ b/src/core/NEON/kernels/arm_gemm/gemm_hybrid_indirect.hpp
@@ -523,7 +523,7 @@ public:
         return size;
     }
 
-    void pretranspose_B_array(void *in_buffer, const To *B, const int ldb, const int B_multi_stride) override {
+    void requantize_bias(void *in_buffer, const To *B, const int ldb, const int B_multi_stride) override {
         if (std::is_same<OutputStage, Requantize32>::value) {
             _col_bias = reinterpret_cast<int32_t *>(in_buffer);
 
@@ -534,6 +534,10 @@ public:
                 compute_col_sums(*qp_ptr, _args._Nsize, _args._Ksize * _args._Ksections, B + (i * B_multi_stride), ldb, _col_bias + (i * _args._Nsize), _args._Ksize * _args._Ksections, i, 0);
             }
         }
+    }
+
+    void pretranspose_B_array(void *in_buffer, const To *B, const int ldb, const int B_multi_stride) override {
+        requantize_bias(in_buffer, B, ldb, B_multi_stride);
 
         // Put the transposed data after the column sums - in non-transposing cases get_col_sum_size() == 0
         uintptr_t buffer_int = reinterpret_cast<uintptr_t>(in_buffer);
diff --git a/src/core/NEON/kernels/arm_gemm/gemm_hybrid_quantized.hpp b/src/core/NEON/kernels/arm_gemm/gemm_hybrid_quantized.hpp
index c72dca2e96..efb5bd1bb4 100644
--- a/src/core/NEON/kernels/arm_gemm/gemm_hybrid_quantized.hpp
+++ b/src/core/NEON/kernels/arm_gemm/gemm_hybrid_quantized.hpp
@@ -269,12 +269,16 @@ public:
         return get_col_sum_size() + (roundup(_Nsize, strategy::out_width()) * roundup(_Ksize, strategy::k_unroll()) * _nmulti * sizeof(Toi));
     }
 
-    void pretranspose_B_array(void *in_buffer, const To *B, const int ldb, const int B_multi_stride) override {
+    void requantize_bias(void *in_buffer, const To *B, const int ldb, const int B_multi_stride) override {
         col_bias = reinterpret_cast<int32_t *>(in_buffer);
 
         for (unsigned int i=0; i<_nmulti; i++) {
             compute_col_sums(_qp, _Nsize, _Ksize, B + (i * B_multi_stride), ldb, col_bias + (i * _Nsize),  _Ksize, i, 0);
         }
+    }
+
+    void pretranspose_B_array(void *in_buffer, const To *B, const int ldb, const int B_multi_stride) override {
+        requantize_bias(in_buffer, B, ldb, B_multi_stride);
 
         uintptr_t buffer_int = reinterpret_cast<uintptr_t>(in_buffer);
         Toi *buffer = reinterpret_cast<Toi *>(buffer_int + get_col_sum_size());
diff --git a/src/core/NEON/kernels/arm_gemm/gemm_hybrid_quantized_inline.hpp b/src/core/NEON/kernels/arm_gemm/gemm_hybrid_quantized_inline.hpp
index 7376b5ffe3..820b54202a 100644
--- a/src/core/NEON/kernels/arm_gemm/gemm_hybrid_quantized_inline.hpp
+++ b/src/core/NEON/kernels/arm_gemm/gemm_hybrid_quantized_inline.hpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2017-2019 Arm Limited.
+ * Copyright (c) 2017-2019,2021 Arm Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -219,12 +219,16 @@ public:
         return get_col_sum_size() + (roundup(_Nsize, strategy::out_width()) * roundup(_Ksize, strategy::k_unroll()) * _nmulti * sizeof(Toi));
     }
 
-    void pretranspose_B_array(void *in_buffer, const To *B, const int ldb, const int B_multi_stride) override {
+    void requantize_bias(void *in_buffer, const To *B, const int ldb, const int B_multi_stride) override {
         col_bias = reinterpret_cast<int32_t *>(in_buffer);
 
         for (unsigned int i=0; i<_nmulti; i++) {
             compute_col_sums(_qp, _Nsize, _Ksize, B + (i * B_multi_stride), ldb, col_bias + (i * _Nsize),  _Ksize, i, 0);
         }
+    }
+
+    void pretranspose_B_array(void *in_buffer, const To *B, const int ldb, const int B_multi_stride) override {
+        requantize_bias(in_buffer, B, ldb, B_multi_stride);
 
         uintptr_t buffer_int = reinterpret_cast<uintptr_t>(in_buffer);
         Toi *buffer = reinterpret_cast<Toi *>(buffer_int + get_col_sum_size());
diff --git a/src/core/NEON/kernels/arm_gemm/gemm_interleaved.hpp b/src/core/NEON/kernels/arm_gemm/gemm_interleaved.hpp
index 5639cb4182..c75c320a6b 100644
--- a/src/core/NEON/kernels/arm_gemm/gemm_interleaved.hpp
+++ b/src/core/NEON/kernels/arm_gemm/gemm_interleaved.hpp
@@ -923,7 +923,7 @@ public:
         return (x_size * _Ktotal * _nmulti * sizeof(Toi)) + get_col_sum_size();
     }
 
-    void pretranspose_B_array(void *in_buffer, const To *B, const int ldb, const int B_multi_stride) override {
+    void requantize_bias(void *in_buffer, const To *B, const int ldb, const int B_multi_stride) override {
         if (std::is_same<OutputStage, Requantize32>::value) {
             col_bias = reinterpret_cast<int32_t *>(in_buffer);
 
@@ -934,6 +934,10 @@ public:
                 compute_col_sums(*qp_ptr, _Nsize, _Ksize * _Ksections, B + (i * B_multi_stride), ldb, col_bias + (i * _Nsize), _Ksize * _Ksections, i, 0);
             }
         }
+    }
+
+    void pretranspose_B_array(void *in_buffer, const To *B, const int ldb, const int B_multi_stride) override {
+        requantize_bias(in_buffer, B, ldb, B_multi_stride);
 
         // Put the transposed data after the column sums - in non-transposing cases get_col_sum_size() == 0
         uintptr_t buffer_int = reinterpret_cast<uintptr_t>(in_buffer);
diff --git a/src/core/NEON/kernels/arm_gemm/gemv_pretransposed.hpp b/src/core/NEON/kernels/arm_gemm/gemv_pretransposed.hpp
index d4348beabf..f0b4e5db9e 100644
--- a/src/core/NEON/kernels/arm_gemm/gemv_pretransposed.hpp
+++ b/src/core/NEON/kernels/arm_gemm/gemv_pretransposed.hpp
@@ -201,11 +201,11 @@ public:
         return _buffer_per_multi * _args._nmulti * sizeof(To) + get_col_sum_size();
     }
 
-    void pretranspose_B_array(void *buffer, const To *B, const int ldb, const int B_multi_stride) override {
+    void requantize_bias(void *in_buffer, const To *B, const int ldb, const int B_multi_stride) override {
         // Column sums go on the front of the pretransposed buffer in requantized cases.
         // We could optimize here in case we don't actually need to sum the columns, but this code is only run on setup.
         if (std::is_same<OutputStage, Requantize32>::value) {
-            col_bias = reinterpret_cast<int32_t *>(buffer);
+            col_bias = reinterpret_cast<int32_t *>(in_buffer);
 
             Requantize32 *qp_ptr = reinterpret_cast<Requantize32 *>(&_os);
 
@@ -213,6 +213,10 @@ public:
                 compute_col_sums(*qp_ptr, _args._Nsize, _args._Ksize, B + (i * B_multi_stride), ldb, col_bias + (i * _args._Nsize), _args._Ksize, i, 0);
             }
         }
+    }
+
+    void pretranspose_B_array(void *buffer, const To *B, const int ldb, const int B_multi_stride) override {
+        requantize_bias(buffer, B, ldb, B_multi_stride);
 
         // The actual transposed buffer goes after the column sums (if any)
         uintptr_t buffer_int = reinterpret_cast<uintptr_t>(buffer);
diff --git a/src/core/NEON/kernels/arm_gemm/quantize_wrapper.hpp b/src/core/NEON/kernels/arm_gemm/quantize_wrapper.hpp
index 1e2a9acc1d..ce727032e6 100644
--- a/src/core/NEON/kernels/arm_gemm/quantize_wrapper.hpp
+++ b/src/core/NEON/kernels/arm_gemm/quantize_wrapper.hpp
@@ -179,13 +179,16 @@ public:
         return _subgemm->get_B_pretransposed_array_size() + col_sum_size();
     }
 
+    void requantize_bias(void *in_buffer, const To *B, const int ldb, const int B_multi_stride) override {
+        _col_sums = reinterpret_cast<int32_t *>(in_buffer);
+        col_sums_pretransposed(B, ldb, B_multi_stride);
+    }
+
     void pretranspose_B_array(void *buffer, const To *B, const int ldb, const int B_multi_stride) override {
         uintptr_t buffer_int = reinterpret_cast<uintptr_t>(buffer);
         _subgemm->pretranspose_B_array(reinterpret_cast<void *>(buffer_int + col_sum_size()), B, ldb, B_multi_stride);
 
-        _col_sums = reinterpret_cast<int32_t *>(buffer);
-
-        col_sums_pretransposed(B, ldb, B_multi_stride);
+        requantize_bias(buffer, B, ldb, B_multi_stride);
     }
 
     void set_pretransposed_B_data(void *buffer) override {
diff --git a/src/core/TensorInfo.cpp b/src/core/TensorInfo.cpp
index c471615ee8..e441ddb3a2 100644
--- a/src/core/TensorInfo.cpp
+++ b/src/core/TensorInfo.cpp
@@ -31,11 +31,11 @@
 
 #include <memory>
 
-using namespace arm_compute;
-
+namespace arm_compute
+{
 TensorInfo::TensorInfo()
     : _total_size(0), _offset_first_element_in_bytes(0), _strides_in_bytes(), _num_channels(0), _tensor_shape(), _dims_state(), _data_type(DataType::UNKNOWN), _format(Format::UNKNOWN), _is_resizable{ true },
-      _valid_region{ Coordinates(), _tensor_shape }, _padding{ 0 }, _quantization_info(), _data_layout(DataLayout::NCHW)
+      _valid_region{ Coordinates(), _tensor_shape }, _padding{ 0 }, _quantization_info(), _data_layout(DataLayout::NCHW), _are_values_constant(true)
 {
 }
 
@@ -55,6 +55,7 @@ TensorInfo::TensorInfo(const ITensorInfo &info)
     _padding                       = info.padding();
     _quantization_info             = info.quantization_info();
     _data_layout                   = info.data_layout();
+    _are_values_constant           = info.are_values_constant();
 }
 
 TensorInfo::TensorInfo(Format format)
@@ -377,3 +378,4 @@ int32_t TensorInfo::offset_element_in_bytes(const Coordinates &pos) const
 
     return offset;
 }
+} // namespace arm_compute
diff --git a/src/cpu/kernels/assembly/gemm_common.hpp b/src/cpu/kernels/assembly/gemm_common.hpp
index 378f1041be..ece9ca5802 100644
--- a/src/cpu/kernels/assembly/gemm_common.hpp
+++ b/src/cpu/kernels/assembly/gemm_common.hpp
@@ -212,6 +212,9 @@ public:
 
     /*** "Pretransposed" interface ***/
 
+    /* Compute col sums over all columns */
+    virtual void requantize_bias(void *, const To *, const int, const int) {};
+
     /* Perform pretranspose - the void * passed in must remain allocated for the duration of any execute calls. */
     /* Arguments are: output buffer pointer, source pointer, source row stride, source multi stride */
     virtual void pretranspose_B_array(void *, const To *, const int, const int) {};
diff --git a/src/cpu/operators/CpuFullyConnected.cpp b/src/cpu/operators/CpuFullyConnected.cpp
index 4133d9e8ca..03c53b001d 100644
--- a/src/cpu/operators/CpuFullyConnected.cpp
+++ b/src/cpu/operators/CpuFullyConnected.cpp
@@ -314,9 +314,14 @@ void CpuFullyConnected::configure(const ITensorInfo *src, const ITensorInfo *wei
 
     if(_aux_mem[Pretranspose].size > 0)
     {
-        // Release permuted weights at the of prepare as they are further transposed by the assembly dispatch
-        _aux_mem[TransposedWeights] = MemoryInfo(offset_int_vec(TransposedWeights), MemoryLifetime::Prepare, _reshaped_weights.total_size());
-        _aux_mem[ConvertedWeights]  = MemoryInfo(offset_int_vec(ConvertedWeights), MemoryLifetime::Prepare, _converted_weights.total_size());
+        // Release permuted weights at the end of prepare as they are further transposed by the assembly dispatch
+        // Do not release them if biases are dynamic and data type is quantized, since the weights tensor will be used for biases offset calculation
+        _aux_mem[TransposedWeights] = MemoryInfo(offset_int_vec(TransposedWeights), (_is_quantized_asymmetric
+                                                                                     && biases && !(biases->are_values_constant())) ?
+                                                 MemoryLifetime::Persistent :
+                                                 MemoryLifetime::Prepare,
+                                                 _reshaped_weights.total_size());
+        _aux_mem[ConvertedWeights] = MemoryInfo(offset_int_vec(ConvertedWeights), MemoryLifetime::Prepare, _converted_weights.total_size());
     }
     else
     {
@@ -334,10 +339,9 @@ Status CpuFullyConnected::validate(const ITensorInfo *src, const ITensorInfo *we
     ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::F16, DataType::F32);
     ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src, weights, dst);
     ARM_COMPUTE_RETURN_ERROR_ON(weights->num_dimensions() > 2);
-    ARM_COMPUTE_RETURN_ERROR_ON(biases != nullptr && biases->num_dimensions() > 1);
     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);
-    ARM_COMPUTE_RETURN_ERROR_ON_MSG(!fc_info.constant_weights, "Non-constant weights are currently not supported");
+    ARM_COMPUTE_RETURN_ERROR_ON(!weights->are_values_constant() && (!fc_info.are_weights_reshaped || fc_info.transpose_weights));
 
     bool weights_reshaped = fc_info.transpose_weights ? fc_info.are_weights_reshaped : true;
     bool is_fc_after_conv = true;
@@ -358,6 +362,19 @@ Status CpuFullyConnected::validate(const ITensorInfo *src, const ITensorInfo *we
     // Check if we have a fully connected layer with batches
     const bool is_batched_fc_layer = dst->dimension(1) > 1;
 
+    if(biases != nullptr)
+    {
+        ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() > 1);
+        if(is_data_type_quantized(src->data_type()))
+        {
+            ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(biases, 1, DataType::S32);
+        }
+        else
+        {
+            ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src, biases);
+        }
+    }
+
     if(is_batched_fc_layer)
     {
         is_fc_after_conv = (TensorShape::num_max_dimensions >= 4) && (std::equal(src->tensor_shape().cbegin() + 3,
diff --git a/src/cpu/operators/internal/CpuGemmAssemblyDispatch.cpp b/src/cpu/operators/internal/CpuGemmAssemblyDispatch.cpp
index 97893b0672..23095d8b84 100644
--- a/src/cpu/operators/internal/CpuGemmAssemblyDispatch.cpp
+++ b/src/cpu/operators/internal/CpuGemmAssemblyDispatch.cpp
@@ -206,6 +206,9 @@ private:
     std::vector<TypeInput>           _indirect_pad{};
     arm_gemm::ConvolutionParameters  _cp{};
     experimental::MemoryRequirements _aux_mem{ Count };
+    bool                             _B_pretranspose_required{ false };
+    bool                             _is_b_constant{ true };
+    bool                             _is_c_constant{ true };
 };
 
 template <typename TypeInput, typename TypeOutput, class OutputStage>
@@ -348,6 +351,10 @@ void Fallback<TypeInput, TypeOutput, OutputStage>::configure(const ITensorInfo *
                                                              const OutputStage &os)
 {
     ARM_COMPUTE_UNUSED(c);
+
+    _is_b_constant = b->are_values_constant();
+    _is_c_constant = c ? c->are_values_constant() : true;
+
     arm_gemm::GemmConfig gemm_cfg;
     _kernel_info = arm_gemm::get_gemm_method<TypeInput, TypeOutput, OutputStage>(args, os);
     if(_kernel_info.method != arm_gemm::GemmMethod::GEMV_BATCHED)
@@ -391,6 +398,7 @@ void Fallback<TypeInput, TypeOutput, OutputStage>::configure(const ITensorInfo *
         const size_t       B_pretranspose_size = _gemm_kernel_asm->get_B_pretransposed_array_size();
         _pretranspose_info                     = TensorInfo(TensorShape(B_pretranspose_size), 1, DataType::U8);
         _aux_mem[Pretranspose]                 = MemoryInfo(offset_int_vec(Pretranspose), MemoryLifetime::Persistent, B_pretranspose_size, alignment);
+        _B_pretranspose_required               = true;
     }
 
     // Handle indirect GEMM convolution
@@ -485,6 +493,35 @@ void Fallback<TypeInput, TypeOutput, OutputStage>::run(ITensorPack &tensors)
         in1_ptr        = reinterpret_cast<const TypeInput *>(b->buffer() + b->info()->offset_first_element_in_bytes());
     }
 
+    // If necessary, run pretranspose every time if either weights or biases are non-constant
+    if((b && !_is_b_constant) || (c && !_is_c_constant && c->info()->data_type() == DataType::S32))
+    {
+        if(c && c->info()->data_type() == DataType::S32)
+        {
+            _gemm_kernel_asm->set_quantized_bias(reinterpret_cast<const int32_t *>(c->buffer() + c->info()->offset_first_element_in_bytes()), 0);
+        }
+
+        // Pretranspose B if required
+        if(_B_pretranspose_required)
+        {
+            const int  ldb            = b->info()->strides_in_bytes().y() / sizeof(TypeInput);
+            const auto b_ptr          = reinterpret_cast<const TypeInput *>(b->buffer() + b->info()->offset_first_element_in_bytes());
+            const int  multi_stride_b = b->info()->strides_in_bytes().z() / sizeof(TypeInput);
+
+            CpuAuxTensorHandler pretranspose(offset_int_vec(Pretranspose), _pretranspose_info, tensors, true);
+            ARM_COMPUTE_ERROR_ON(pretranspose.get()->buffer() == nullptr);
+
+            if(_is_b_constant)
+            {
+                _gemm_kernel_asm->requantize_bias(pretranspose.get()->buffer(), b_ptr, ldb, multi_stride_b);
+            }
+            else
+            {
+                _gemm_kernel_asm->pretranspose_B_array(pretranspose.get()->buffer(), b_ptr, ldb, multi_stride_b);
+            }
+        }
+    }
+
     const auto scheduling_hint = scheduling_hint_heuristic(_kernel_info.method, d->info()->data_type());
 
     // Set workspace if needed and reset number of threads as buffer manager gets re-created with max_threads
diff --git a/src/gpu/cl/operators/ClFullyConnected.cpp b/src/gpu/cl/operators/ClFullyConnected.cpp
index 165ffe9a47..8afd036e7c 100644
--- a/src/gpu/cl/operators/ClFullyConnected.cpp
+++ b/src/gpu/cl/operators/ClFullyConnected.cpp
@@ -170,8 +170,7 @@ void ClFullyConnected::configure_mm(const CLCompileContext &compile_context, ITe
                                          fc_info.fp_mixed_precision,      // fp_mixed_precision
                                          false,                           // fast_math
                                          true,                            // broadcast_bias
-                                         fc_info.activation_info,         // activation_info
-                                         fc_info.constant_weights);       // constant_weights
+                                         fc_info.activation_info);        // activation_info
 
     if(_is_quantized)
     {
@@ -335,7 +334,7 @@ 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);
-    ARM_COMPUTE_RETURN_ERROR_ON(!fc_info.constant_weights && (!fc_info.are_weights_reshaped || fc_info.transpose_weights));
+    ARM_COMPUTE_RETURN_ERROR_ON(!weights->are_values_constant() && (!fc_info.are_weights_reshaped || fc_info.transpose_weights));
 
     bool weights_reshaped = fc_info.transpose_weights ? fc_info.are_weights_reshaped : true;
     bool is_fc_after_conv = true;
@@ -353,6 +352,19 @@ Status ClFullyConnected::validate(const ITensorInfo *src, const ITensorInfo *wei
     const ITensorInfo *src_to_use     = src;
     const ITensorInfo *weights_to_use = weights;
 
+    if(biases != nullptr)
+    {
+        ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() > 1);
+        if(is_data_type_quantized(src->data_type()))
+        {
+            ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(biases, 1, DataType::S32);
+        }
+        else
+        {
+            ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src, biases);
+        }
+    }
+
     // Check if we have a fully connected layer with batches
     const bool is_batched_fc_layer = dst->dimension(1) > 1;
     if(is_batched_fc_layer)
diff --git a/src/gpu/cl/operators/ClGemm.cpp b/src/gpu/cl/operators/ClGemm.cpp
index e955ae3d65..4cd5237b11 100644
--- a/src/gpu/cl/operators/ClGemm.cpp
+++ b/src/gpu/cl/operators/ClGemm.cpp
@@ -575,7 +575,7 @@ void ClGemm::configure(const CLCompileContext &compile_context, ITensorInfo *a,
 
     // Select GEMMType
     _gemm_kernel_type = auto_select_gemm_kernel(auto_heuristics::CommonQuery{ CLScheduler::get().target(), a->data_type(), m, n, k, batch_size }, _reshape_b_only_on_first_run,
-                                                gemm_info.constant_weights());
+                                                b->are_values_constant());
 
     const bool fuse_add_c = (!(helpers::float_ops::is_zero(beta)) && c != nullptr);
 
@@ -624,7 +624,7 @@ Status ClGemm::validate(const ITensorInfo *a, const ITensorInfo *b, const ITenso
     {
         CLScheduler::get().target(), a->data_type(), m, n, k, batch_size,
     },
-    gemm_info.reshape_b_only_on_first_run(), gemm_info.constant_weights());
+    gemm_info.reshape_b_only_on_first_run(), b->are_values_constant());
 
     const bool fuse_add_c = (!(helpers::float_ops::is_zero(beta)) && c != nullptr);