COMPMID-992 Implement CL RNN function

Change-Id: I8dbada5fabedbb8523e433ba73d504bd15b81466
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/125787
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>
Tested-by: Jenkins <bsgcomp@arm.com>

1 files changed, 145 insertions, 0 deletions

diff --git a/tests/validation/fixtures/RNNLayerFixture.h b/tests/validation/fixtures/RNNLayerFixture.h
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+++ b/tests/validation/fixtures/RNNLayerFixture.h
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+/*
+ * Copyright (c) 2018 ARM Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#ifndef ARM_COMPUTE_TEST_RNN_LAYER_FIXTURE
+#define ARM_COMPUTE_TEST_RNN_LAYER_FIXTURE
+
+#include "tests/Globals.h"
+#include "tests/framework/Asserts.h"
+#include "tests/framework/Fixture.h"
+#include "tests/validation/reference/ActivationLayer.h"
+#include "tests/validation/reference/ArithmeticAddition.h"
+#include "tests/validation/reference/FullyConnectedLayer.h"
+#include "tests/validation/reference/GEMM.h"
+
+namespace arm_compute
+{
+namespace test
+{
+namespace validation
+{
+template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
+class RNNLayerValidationFixture : public framework::Fixture
+{
+public:
+    template <typename...>
+    void setup(TensorShape input_shape, TensorShape weights_shape, TensorShape recurrent_weights_shape, TensorShape bias_shape, TensorShape output_shape, ActivationLayerInfo info,
+               DataType data_type)
+    {
+        _target    = compute_target(input_shape, weights_shape, recurrent_weights_shape, bias_shape, output_shape, info, data_type);
+        _reference = compute_reference(input_shape, weights_shape, recurrent_weights_shape, bias_shape, output_shape, info, data_type);
+    }
+
+protected:
+    template <typename U>
+    void fill(U &&tensor, int i)
+    {
+        std::uniform_real_distribution<> distribution(-1.0f, 1.0f);
+        library->fill(tensor, distribution, i);
+    }
+
+    TensorType compute_target(const TensorShape &input_shape, const TensorShape &weights_shape, const TensorShape &recurrent_weights_shape, const TensorShape &bias_shape, const TensorShape &output_shape,
+                              ActivationLayerInfo info, DataType data_type)
+    {
+        // Create tensors
+        TensorType input             = create_tensor<TensorType>(input_shape, data_type);
+        TensorType weights           = create_tensor<TensorType>(weights_shape, data_type);
+        TensorType recurrent_weights = create_tensor<TensorType>(recurrent_weights_shape, data_type);
+        TensorType bias              = create_tensor<TensorType>(bias_shape, data_type);
+        TensorType hidden_state      = create_tensor<TensorType>(output_shape, data_type);
+        TensorType output            = create_tensor<TensorType>(output_shape, data_type);
+
+        // Create and configure function
+        FunctionType rnn;
+        rnn.configure(&input, &weights, &recurrent_weights, &bias, &hidden_state, &output, info);
+
+        ARM_COMPUTE_EXPECT(input.info()->is_resizable(), framework::LogLevel::ERRORS);
+        ARM_COMPUTE_EXPECT(weights.info()->is_resizable(), framework::LogLevel::ERRORS);
+        ARM_COMPUTE_EXPECT(recurrent_weights.info()->is_resizable(), framework::LogLevel::ERRORS);
+        ARM_COMPUTE_EXPECT(bias.info()->is_resizable(), framework::LogLevel::ERRORS);
+        ARM_COMPUTE_EXPECT(hidden_state.info()->is_resizable(), framework::LogLevel::ERRORS);
+        ARM_COMPUTE_EXPECT(output.info()->is_resizable(), framework::LogLevel::ERRORS);
+
+        // Allocate tensors
+        input.allocator()->allocate();
+        weights.allocator()->allocate();
+        recurrent_weights.allocator()->allocate();
+        bias.allocator()->allocate();
+        hidden_state.allocator()->allocate();
+        output.allocator()->allocate();
+
+        ARM_COMPUTE_EXPECT(!input.info()->is_resizable(), framework::LogLevel::ERRORS);
+        ARM_COMPUTE_EXPECT(!weights.info()->is_resizable(), framework::LogLevel::ERRORS);
+        ARM_COMPUTE_EXPECT(!recurrent_weights.info()->is_resizable(), framework::LogLevel::ERRORS);
+        ARM_COMPUTE_EXPECT(!bias.info()->is_resizable(), framework::LogLevel::ERRORS);
+        ARM_COMPUTE_EXPECT(!hidden_state.info()->is_resizable(), framework::LogLevel::ERRORS);
+        ARM_COMPUTE_EXPECT(!output.info()->is_resizable(), framework::LogLevel::ERRORS);
+
+        // Fill tensors
+        fill(AccessorType(input), 0);
+        fill(AccessorType(weights), 0);
+        fill(AccessorType(recurrent_weights), 0);
+        fill(AccessorType(bias), 0);
+        fill(AccessorType(hidden_state), 0);
+
+        // Compute function
+        rnn.run();
+
+        return output;
+    }
+
+    SimpleTensor<T> compute_reference(const TensorShape &input_shape, const TensorShape &weights_shape, const TensorShape &recurrent_weights_shape, const TensorShape &bias_shape,
+                                      const TensorShape &output_shape, ActivationLayerInfo info, DataType data_type)
+    {
+        // Create reference
+        SimpleTensor<T> input{ input_shape, data_type };
+        SimpleTensor<T> weights{ weights_shape, data_type };
+        SimpleTensor<T> recurrent_weights{ recurrent_weights_shape, data_type };
+        SimpleTensor<T> bias{ bias_shape, data_type };
+        SimpleTensor<T> hidden_state{ output_shape, data_type };
+
+        // Fill reference
+        fill(input, 0);
+        fill(weights, 0);
+        fill(recurrent_weights, 0);
+        fill(bias, 0);
+        fill(hidden_state, 0);
+
+        TensorShape out_shape = recurrent_weights_shape;
+        out_shape.set(1, output_shape.y());
+
+        // Compute reference
+        SimpleTensor<T> out_w{ out_shape, data_type };
+        SimpleTensor<T> fully_connected = reference::fully_connected_layer(input, weights, bias, out_shape);
+        SimpleTensor<T> gemm            = reference::gemm(hidden_state, recurrent_weights, out_w, 1.f, 0.f);
+        SimpleTensor<T> add_res         = reference::arithmetic_addition(fully_connected, gemm, data_type, ConvertPolicy::SATURATE);
+        return reference::activation_layer(add_res, info);
+    }
+
+    TensorType      _target{};
+    SimpleTensor<T> _reference{};
+};
+} // namespace validation
+} // namespace test
+} // namespace arm_compute
+#endif /* ARM_COMPUTE_TEST_RNN_LAYER_FIXTURE */