COMPMID-2172: Fuse bias addition with CLGEMMMatrixMultiplyNativeKernel

Change-Id: I714b92ec001fc71172719b67fb66d490538b6948
Signed-off-by: Gian Marco Iodice <gianmarco.iodice@arm.com>
Reviewed-on: https://review.mlplatform.org/c/1399
Reviewed-by: Giuseppe Rossini <giuseppe.rossini@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>

1 files changed, 61 insertions, 24 deletions

diff --git a/tests/validation/fixtures/GEMMFixture.h b/tests/validation/fixtures/GEMMFixture.h
index fcb41bb0ba..b721d841f7 100644
--- a/tests/validation/fixtures/GEMMFixture.h
+++ b/tests/validation/fixtures/GEMMFixture.h
@@ -684,7 +684,7 @@ class GEMMMatrixMultiplyNativeValidationFixture : public framework::Fixture
 {
 public:
     template <typename...>
-    void setup(unsigned int m, unsigned int n, unsigned int k, unsigned int batch_size, unsigned int m0, unsigned int n0, unsigned int k0, DataType data_type, float alpha)
+    void setup(unsigned int m, unsigned int n, unsigned int k, unsigned int batch_size, unsigned int m0, unsigned int n0, unsigned int k0, DataType data_type, float alpha, float beta, bool broadcast_bias)
     {
         GEMMLHSMatrixInfo lhs_info;
         lhs_info.m0 = m0;
@@ -697,9 +697,12 @@ public:
         // Set the tensor shapes for LHS and RHS matrices
         const TensorShape lhs_shape(k, m, batch_size);
         const TensorShape rhs_shape(n, k, batch_size);
+        const TensorShape bias_shape(n,
+                                     broadcast_bias ? 1 : m,
+                                     broadcast_bias ? 1 : batch_size);
 
-        _target    = compute_target(lhs_shape, rhs_shape, lhs_info, rhs_info, data_type, alpha);
-        _reference = compute_reference(lhs_shape, rhs_shape, data_type, alpha);
+        _target    = compute_target(lhs_shape, rhs_shape, bias_shape, lhs_info, rhs_info, data_type, alpha, beta, broadcast_bias);
+        _reference = compute_reference(lhs_shape, rhs_shape, bias_shape, data_type, alpha, beta, broadcast_bias);
     }
 
 protected:
@@ -714,11 +717,13 @@ protected:
         library->fill_borders_with_garbage(tensor, distribution_inf, i);
     }
 
-    TensorType compute_target(const TensorShape &lhs_shape, const TensorShape &rhs_shape, const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info, DataType data_type, float alpha)
+    TensorType compute_target(const TensorShape &lhs_shape, const TensorShape &rhs_shape, const TensorShape &bias_shape, const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info,
+                              DataType data_type, float alpha, float beta, bool broadcast_bias)
     {
         // Create tensors
-        TensorType lhs = create_tensor<TensorType>(lhs_shape, data_type, 1);
-        TensorType rhs = create_tensor<TensorType>(rhs_shape, data_type, 1);
+        TensorType lhs  = create_tensor<TensorType>(lhs_shape, data_type, 1);
+        TensorType rhs  = create_tensor<TensorType>(rhs_shape, data_type, 1);
+        TensorType bias = create_tensor<TensorType>(bias_shape, data_type, 1);
         TensorType dst;
 
         const unsigned int M = lhs_shape[1];
@@ -727,23 +732,27 @@ protected:
 
         // Create and configure function
         GEMMFunctionType gemm;
-        gemm.configure(&lhs, &rhs, &dst, alpha, lhs_info, rhs_info, GEMMReshapeInfo(M, N, K));
+        gemm.configure(&lhs, &rhs, &bias, &dst, alpha, beta, lhs_info, rhs_info, GEMMReshapeInfo(M, N, K, 1, 1, 0, false, broadcast_bias));
 
         ARM_COMPUTE_EXPECT(lhs.info()->is_resizable(), framework::LogLevel::ERRORS);
         ARM_COMPUTE_EXPECT(rhs.info()->is_resizable(), framework::LogLevel::ERRORS);
+        ARM_COMPUTE_EXPECT(bias.info()->is_resizable(), framework::LogLevel::ERRORS);
 
         // Allocate tensors
         lhs.allocator()->allocate();
         rhs.allocator()->allocate();
+        bias.allocator()->allocate();
         dst.allocator()->allocate();
 
         ARM_COMPUTE_EXPECT(!lhs.info()->is_resizable(), framework::LogLevel::ERRORS);
         ARM_COMPUTE_EXPECT(!rhs.info()->is_resizable(), framework::LogLevel::ERRORS);
+        ARM_COMPUTE_EXPECT(!bias.info()->is_resizable(), framework::LogLevel::ERRORS);
         ARM_COMPUTE_EXPECT(!dst.info()->is_resizable(), framework::LogLevel::ERRORS);
 
         // Fill tensors
         fill(AccessorType(lhs), 0);
         fill(AccessorType(rhs), 1);
+        fill(AccessorType(bias), 2);
 
         // Compute GEMM
         gemm.run();
@@ -751,7 +760,7 @@ protected:
         return dst;
     }
 
-    SimpleTensor<T> compute_reference(const TensorShape &lhs_shape, const TensorShape &rhs_shape, DataType data_type, float alpha)
+    SimpleTensor<T> compute_reference(const TensorShape &lhs_shape, const TensorShape &rhs_shape, const TensorShape &bias_shape, DataType data_type, float alpha, float beta, bool broadcast_bias)
     {
         TensorShape dst_shape = lhs_shape;
         dst_shape[0]          = rhs_shape[0];
@@ -760,13 +769,27 @@ protected:
         // Create reference
         SimpleTensor<T> lhs{ lhs_shape, data_type, 1 };
         SimpleTensor<T> rhs{ rhs_shape, data_type, 1 };
-        SimpleTensor<T> c{ dst_shape, data_type, 1 };
+        SimpleTensor<T> bias{ dst_shape, data_type, 1 };
+
+        const int n          = rhs_shape[0];
+        const int m          = lhs_shape[1];
+        const int batch_size = lhs_shape[2];
 
         // Fill reference
         fill(lhs, 0);
         fill(rhs, 1);
+        fill(bias, 2);
 
-        return reference::gemm<T>(lhs, rhs, c, alpha, 0.0f);
+        if(broadcast_bias)
+        {
+            // In case of broadcast, we need simply copy the first into the following "M" ones
+            for(int i = 1; i < m * batch_size; i++)
+            {
+                memcpy(bias.data() + i * n, bias.data(), n * sizeof(T));
+            }
+        }
+
+        return reference::gemm<T>(lhs, rhs, bias, alpha, beta);
     }
 
     TensorType      _target{};
@@ -778,7 +801,7 @@ class GEMMMatrixMultiplyNative3DValidationFixture : public framework::Fixture
 {
 public:
     template <typename...>
-    void setup(unsigned int m_w, unsigned int m_h, unsigned int n, unsigned int k, unsigned int batch_size, unsigned int m0, unsigned int n0, unsigned int k0, DataType data_type, float alpha)
+    void setup(unsigned int m_w, unsigned int m_h, unsigned int n, unsigned int k, unsigned int batch_size, unsigned int m0, unsigned int n0, unsigned int k0, DataType data_type, float alpha, float beta)
     {
         GEMMLHSMatrixInfo lhs_info;
         lhs_info.m0 = m0;
@@ -794,9 +817,10 @@ public:
         // Set the tensor shapes for LHS and RHS matrices
         const TensorShape lhs_shape(k, m, batch_size);
         const TensorShape rhs_shape(n, k, batch_size);
+        const TensorShape bias_shape(n, 1, 1);
 
-        _target    = compute_target(lhs_shape, rhs_shape, lhs_info, rhs_info, data_type, alpha, m_h);
-        _reference = compute_reference(lhs_shape, rhs_shape, data_type, alpha, m_h);
+        _target    = compute_target(lhs_shape, rhs_shape, bias_shape, lhs_info, rhs_info, data_type, alpha, beta, m_h);
+        _reference = compute_reference(lhs_shape, rhs_shape, bias_shape, data_type, alpha, beta, m_h);
     }
 
 protected:
@@ -807,13 +831,13 @@ protected:
         library->fill(tensor, distribution, i);
     }
 
-    TensorType compute_target(const TensorShape &lhs_shape, const TensorShape &rhs_shape, const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info, DataType data_type, float alpha,
-                              unsigned int m_h)
+    TensorType compute_target(const TensorShape &lhs_shape, const TensorShape &rhs_shape, const TensorShape &bias_shape, const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info,
+                              DataType data_type, float alpha, float beta, unsigned int m_h)
     {
         // Create tensors
-        TensorType lhs = create_tensor<TensorType>(lhs_shape, data_type, 1);
-        TensorType rhs = create_tensor<TensorType>(rhs_shape, data_type, 1);
-        TensorType rhs_reshaped;
+        TensorType lhs  = create_tensor<TensorType>(lhs_shape, data_type, 1);
+        TensorType rhs  = create_tensor<TensorType>(rhs_shape, data_type, 1);
+        TensorType bias = create_tensor<TensorType>(bias_shape, data_type, 1);
         TensorType dst;
 
         const unsigned int M = lhs_shape[1];
@@ -824,25 +848,27 @@ protected:
 
         // Create and configure function
         GEMMFunctionType gemm;
-        gemm.configure(&lhs, &rhs, &dst, alpha, lhs_info, rhs_info, GEMMReshapeInfo(M, N, K, 1, 1, m_h));
+        gemm.configure(&lhs, &rhs, &bias, &dst, alpha, beta, lhs_info, rhs_info, GEMMReshapeInfo(M, N, K, 1, 1, m_h, false, true));
 
         ARM_COMPUTE_EXPECT(lhs.info()->is_resizable(), framework::LogLevel::ERRORS);
         ARM_COMPUTE_EXPECT(rhs.info()->is_resizable(), framework::LogLevel::ERRORS);
+        ARM_COMPUTE_EXPECT(bias.info()->is_resizable(), framework::LogLevel::ERRORS);
 
         // Allocate tensors
         lhs.allocator()->allocate();
         rhs.allocator()->allocate();
-        rhs_reshaped.allocator()->allocate();
+        bias.allocator()->allocate();
         dst.allocator()->allocate();
 
         ARM_COMPUTE_EXPECT(!lhs.info()->is_resizable(), framework::LogLevel::ERRORS);
         ARM_COMPUTE_EXPECT(!rhs.info()->is_resizable(), framework::LogLevel::ERRORS);
-        ARM_COMPUTE_EXPECT(!rhs_reshaped.info()->is_resizable(), framework::LogLevel::ERRORS);
+        ARM_COMPUTE_EXPECT(!bias.info()->is_resizable(), framework::LogLevel::ERRORS);
         ARM_COMPUTE_EXPECT(!dst.info()->is_resizable(), framework::LogLevel::ERRORS);
 
         // Fill tensors
         fill(AccessorType(lhs), 0);
         fill(AccessorType(rhs), 1);
+        fill(AccessorType(bias), 2);
 
         // Compute GEMM
         gemm.run();
@@ -850,7 +876,7 @@ protected:
         return dst;
     }
 
-    SimpleTensor<T> compute_reference(const TensorShape &lhs_shape, const TensorShape &rhs_shape, DataType data_type, float alpha, unsigned int m_h)
+    SimpleTensor<T> compute_reference(const TensorShape &lhs_shape, const TensorShape &rhs_shape, const TensorShape &bias_shape, DataType data_type, float alpha, float beta, unsigned int m_h)
     {
         TensorShape dst_shape = lhs_shape;
         dst_shape.set(0, rhs_shape[0]);
@@ -861,13 +887,24 @@ protected:
         // Create reference
         SimpleTensor<T> lhs{ lhs_shape, data_type, 1 };
         SimpleTensor<T> rhs{ rhs_shape, data_type, 1 };
-        SimpleTensor<T> c{ dst_shape, data_type, 1 };
+        SimpleTensor<T> bias{ dst_shape, data_type, 1 };
+
+        const int n          = rhs_shape[0];
+        const int m          = lhs_shape[1];
+        const int batch_size = lhs_shape[2];
 
         // Fill reference
         fill(lhs, 0);
         fill(rhs, 1);
+        fill(bias, 2);
 
-        return reference::gemm<T>(lhs, rhs, c, alpha, 0.0f);
+        // In case of broadcast, we need simply copy the first into the following "M" ones
+        for(int i = 1; i < m * batch_size; i++)
+        {
+            memcpy(bias.data() + i * n, bias.data(), n * sizeof(T));
+        }
+
+        return reference::gemm<T>(lhs, rhs, bias, alpha, beta);
     }
 
     TensorType      _target{};