COMPMID-1096 - Add fast_math flag to CLConvolutionLayer
COMPMID-1103 - CLWinogradConvolutionLayer mismatches
Change-Id: Iceaa9482a1790ec39d2720c220261aaea8043978
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/129398
Tested-by: Jenkins <bsgcomp@arm.com>
Reviewed-by: Giorgio Arena <giorgio.arena@arm.com>
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>
diff --git a/tests/validation/reference/GEMM.cpp b/tests/validation/reference/GEMM.cpp
index 77d025e..f9dcfcb 100644
--- a/tests/validation/reference/GEMM.cpp
+++ b/tests/validation/reference/GEMM.cpp
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2017 ARM Limited.
+ * Copyright (c) 2017-2018 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -41,23 +41,44 @@
SimpleTensor<T> dst{ c.shape(), c.data_type(), 1, c.fixed_point_position() };
// Compute reference
- const int M = dst.shape().y();
- const int N = dst.shape().x();
+ const int M = a.shape().y();
+ const int N = b.shape().x();
const int K = a.shape().x();
+ const int D = a.shape().z(); // Number of matrices in a batch
+ const int W = a.shape()[3]; // Number of batched-gemm (Winograd case)
- for(int row = 0; row < M; ++row)
+ const int a_stride_z = K * M;
+ const int a_stride_w = K * M * D;
+
+ const int b_stride_z = b.shape().num_dimensions() > 2 ? N * K : 0; // Do not slide the matrix B along the 3th dimension in case matrix B has less than 3 dimensions
+ const int b_stride_w = b.shape().num_dimensions() > 3 ? K * N * D : 0; // Do not slide the matrix B along the 4th dimension in case matrix B has less than 4 dimensions
+
+ const int c_stride_z = N * M;
+ const int c_stride_w = N * M * D;
+
+ for(int w = 0; w < W; ++w)
{
- for(int col = 0; col < N; ++col)
+ for(int depth = 0; depth < D; ++depth)
{
- T acc(0);
+ const int base_addr_a = depth * a_stride_z + w * a_stride_w;
+ const int base_addr_b = depth * b_stride_z + w * b_stride_w;
+ const int base_addr_c = depth * c_stride_z + w * c_stride_w;
- for(int k = 0; k < K; ++k)
+ for(int row = 0; row < M; ++row)
{
- acc += a[row * K + k] * b[k * N + col];
- }
+ for(int col = 0; col < N; ++col)
+ {
+ T acc(0);
- // Finalize the result: alpha * A * B + beta * C
- dst[col + row * N] = alpha * acc + beta * c[col + row * N];
+ for(int k = 0; k < K; ++k)
+ {
+ acc += a[base_addr_a + k + row * K] * b[base_addr_b + col + k * N];
+ }
+
+ // Finalize the result: alpha * A * B + beta * C
+ dst[base_addr_c + col + row * N] = alpha * acc + beta * c[base_addr_c + col + row * N];
+ }
+ }
}
}
@@ -75,37 +96,58 @@
// Compute reference
using promoted_type = fixed_point_arithmetic::traits::promote_t<T>;
- const int M = dst.shape().y();
- const int N = dst.shape().x();
- const int K = a.shape().x();
- const int fixed_point_position = a.fixed_point_position();
+ const int M = dst.shape().y();
+ const int N = dst.shape().x();
+ const int K = a.shape().x();
+ const int D = a.shape().z(); // Number of matrices in a batch
+ const int W = a.shape()[3]; // Number of batched-gemm (Winograd case)
+ const int a_stride_z = K * M;
+ const int a_stride_w = K * M * D;
+
+ const int b_stride_z = b.shape().num_dimensions() > 2 ? N * K : 0; // Do not slide the matrix B along the 3th dimension in case matrix B has less than 3 dimensions
+ const int b_stride_w = b.shape().num_dimensions() > 3 ? K * N * D : 0; // Do not slide the matrix B along the 4th dimension in case matrix B has less than 4 dimensions
+
+ const int c_stride_z = N * M;
+ const int c_stride_w = N * M * D;
+
+ const int fixed_point_position = a.fixed_point_position();
const fixed_point<T> alpha_q(alpha, fixed_point_position);
const fixed_point<T> beta_q(beta, fixed_point_position);
- for(int row = 0; row < M; ++row)
+ for(int w = 0; w < W; ++w)
{
- for(int col = 0; col < N; ++col)
+ for(int depth = 0; depth < D; ++depth)
{
- fixed_point<promoted_type> acc_q(0, fixed_point_position);
+ const int base_addr_a = depth * a_stride_z + w * a_stride_w;
+ const int base_addr_b = depth * b_stride_z + w * b_stride_w;
+ const int base_addr_c = depth * c_stride_z + w * c_stride_w;
- for(int k = 0; k < K; ++k)
+ for(int row = 0; row < M; ++row)
{
- const fixed_point<promoted_type> a0_q(a[row * K + k], fixed_point_position, true);
- const fixed_point<promoted_type> b0_q(b[k * N + col], fixed_point_position, true);
+ for(int col = 0; col < N; ++col)
+ {
+ fixed_point<promoted_type> acc_q(0, fixed_point_position);
- acc_q = acc_q + (a0_q * b0_q);
+ for(int k = 0; k < K; ++k)
+ {
+ const fixed_point<promoted_type> a0_q(a[base_addr_a + row * K + k], fixed_point_position, true);
+ const fixed_point<promoted_type> b0_q(b[base_addr_b + k * N + col], fixed_point_position, true);
+
+ acc_q = acc_q + (a0_q * b0_q);
+ }
+
+ // Finalize the result: alpha * A * B + beta * C
+ const fixed_point<T> c0_q(c[base_addr_c + col + row * N], fixed_point_position, true);
+
+ fixed_point<T> res_q(acc_q);
+ res_q = alpha_q * res_q;
+ res_q = res_q + (beta_q * c0_q);
+
+ // Store the result
+ dst[base_addr_c + col + row * N] = res_q.raw();
+ }
}
-
- // Finalize the result: alpha * A * B + beta * C
- const fixed_point<T> c0_q(c[col + row * N], fixed_point_position, true);
-
- fixed_point<T> res_q(acc_q);
- res_q = alpha_q * res_q;
- res_q = res_q + (beta_q * c0_q);
-
- // Store the result
- dst[col + row * N] = res_q.raw();
}
}
diff --git a/tests/validation/reference/Winograd.cpp b/tests/validation/reference/Winograd.cpp
index 75b1b51..194a78e 100644
--- a/tests/validation/reference/Winograd.cpp
+++ b/tests/validation/reference/Winograd.cpp
@@ -331,7 +331,7 @@
}
template <typename T>
-SimpleTensor<T> winograd_output_transform(const SimpleTensor<T> &in, const TensorShape &output_shape, const WinogradInfo &winograd_info)
+SimpleTensor<T> winograd_output_transform(const SimpleTensor<T> &in, const SimpleTensor<T> &b, const TensorShape &output_shape, const WinogradInfo &winograd_info)
{
ARM_COMPUTE_ERROR_ON_MSG(winograd_info.output_data_layout != DataLayout::NCHW, "Only supported NCHW data format");
@@ -444,6 +444,9 @@
if((xo + xi < w_out) && (yo + yi < h_out))
{
out[output_offset + yi * stridey_out + xi] = output_tile[xi + yi * out_tile_w];
+
+ // Add bias
+ out[output_offset + yi * stridey_out + xi] += b[zo];
}
}
}
@@ -456,7 +459,7 @@
template SimpleTensor<float> winograd_filter_transform(const SimpleTensor<float> &in, const TensorShape &output_shape, const WinogradInfo &winograd_info);
template SimpleTensor<float> winograd_input_transform(const SimpleTensor<float> &in, const TensorShape &output_shape, const WinogradInfo &winograd_info);
-template SimpleTensor<float> winograd_output_transform(const SimpleTensor<float> &in, const TensorShape &output_shape, const WinogradInfo &winograd_info);
+template SimpleTensor<float> winograd_output_transform(const SimpleTensor<float> &in, const SimpleTensor<float> &b, const TensorShape &output_shape, const WinogradInfo &winograd_info);
} // namespace reference
} // namespace validation
} // namespace test
diff --git a/tests/validation/reference/Winograd.h b/tests/validation/reference/Winograd.h
index 29181f1..b74c2c3 100644
--- a/tests/validation/reference/Winograd.h
+++ b/tests/validation/reference/Winograd.h
@@ -51,7 +51,7 @@
SimpleTensor<T> winograd_filter_transform(const SimpleTensor<T> &in, const TensorShape &output_shape, const WinogradInfo &winograd_info);
template <typename T>
-SimpleTensor<T> winograd_output_transform(const SimpleTensor<T> &in, const TensorShape &output_shape, const WinogradInfo &winograd_info);
+SimpleTensor<T> winograd_output_transform(const SimpleTensor<T> &in, const SimpleTensor<T> &b, const TensorShape &output_shape, const WinogradInfo &winograd_info);
} // namespace reference
} // namespace validation
} // namespace test