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PrimedErwinPrimedErwin
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Merge pull request #29 from Advaitgaur004/operator-6
[Feat] : Implement Tensor_abs operator and tests
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‎src/operator.c‎

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@@ -581,5 +581,37 @@ Tensor Tensor_min(Tensor self) {
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res.node->name = "MinAll";
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}
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return res;
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}
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static Tensor GradFn_abs(Tensor self, int i) {
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Tensor input = self.node->inputs[i];
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Tensor res = Tensor_new(input.shape, false);
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for(int j = 0; j < input.data->numel; j++) {
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float val = input.data->flex[j];
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if (val > 0) {
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res.data->flex[j] = 1.0f;
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} else if (val < 0) {
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res.data->flex[j] = -1.0f;
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} else {
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res.data->flex[j] = 0.0f;
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}
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}
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return res;
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}
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Tensor Tensor_abs(Tensor self) {
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bool requires_grad = !cten_is_eval() && self.node != NULL;
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Tensor res = Tensor_new(self.shape, requires_grad);
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for(int i = 0; i < self.data->numel; i++) {
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res.data->flex[i] = fabsf(self.data->flex[i]);
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}
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if(requires_grad) {
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res.node->grad_fn = GradFn_abs;
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res.node->inputs[0] = self;
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res.node->n_inputs = 1;
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res.node->name = "Abs";
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}
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return res;
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}

‎tests/Backward/test_abs_backward.c‎

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@@ -0,0 +1,142 @@
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#include "../../include/cten.h"
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#include "../test_utils.h"
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#include "../csv_reporter.h"
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#include "../test_config.h"
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#include <stdio.h>
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void test_abs_backward() {
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const char* op_name = "abs_backward";
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PoolId pool_id = 0;
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cten_begin_malloc(pool_id);
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// Test Case 1: Simple backward
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{
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const char* tc_name = "Simple_backward";
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// Sub-test 1: Scalar backward
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{
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TensorShape s_shape = {1};
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float d1[] = {-5.0f};
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float exp_grad[] = {-1.0f};
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Tensor t1 = create_test_tensor(s_shape, d1, true);
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Tensor z = Tensor_abs(t1);
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Tensor_backward(z, (Tensor){0});
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Tensor expected_grad = create_test_tensor(s_shape, exp_grad, false);
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compare_tensors(&t1.node->grad, &expected_grad, op_name, tc_name, 1, TEST_FLOAT_TOLERANCE);
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}
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// Sub-test 2: Vector with mixed values
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{
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TensorShape v_shape = {5};
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float d1[] = {10.0f, -2.0f, 0.0f, 5.5f, -0.1f};
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float exp_grad[] = {1.0f, -1.0f, 0.0f, 1.0f, -1.0f};
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Tensor t1 = create_test_tensor(v_shape, d1, true);
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Tensor z = Tensor_abs(t1);
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Tensor l = Tensor_sum(z);
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Tensor_backward(l, (Tensor){0});
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Tensor expected_grad = create_test_tensor(v_shape, exp_grad, false);
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compare_tensors(&t1.node->grad, &expected_grad, op_name, tc_name, 2, TEST_FLOAT_TOLERANCE);
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}
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// Sub-test 3: Matrix backward
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{
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TensorShape m_shape = {2, 2};
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float d1[] = {1.0f, -2.0f, 0.0f, -4.0f};
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float exp_grad[] = {1.0f, -1.0f, 0.0f, -1.0f};
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Tensor t1 = create_test_tensor(m_shape, d1, true);
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Tensor z = Tensor_abs(t1);
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Tensor l = Tensor_sum(z);
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Tensor_backward(l, (Tensor){0});
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Tensor expected_grad = create_test_tensor(m_shape, exp_grad, false);
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compare_tensors(&t1.node->grad, &expected_grad, op_name, tc_name, 3, TEST_FLOAT_TOLERANCE);
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}
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}
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// Test Case 2: Multi-dimensional Tensor backward
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{
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const char* tc_name = "Multidim_backward";
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TensorShape shape_3d = {2, 2, 2};
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float data[] = {1.5f, -2.5f, 0.0f, -4.5f, 5.5f, 6.5f, -7.5f, -8.5f};
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float exp_grad[] = {1.0f, -1.0f, 0.0f, -1.0f, 1.0f, 1.0f, -1.0f, -1.0f};
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Tensor t = create_test_tensor(shape_3d, data, true);
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Tensor z = Tensor_abs(t);
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Tensor l = Tensor_sum(z);
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Tensor_backward(l, (Tensor){0});
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Tensor expected_grad = create_test_tensor(shape_3d, exp_grad, false);
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compare_tensors(&t.node->grad, &expected_grad, op_name, tc_name, 1, TEST_FLOAT_TOLERANCE);
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}
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// Test Case 3: Chained and Complex Graphs
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{
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const char* tc_name = "Chained_and_Complex_Graphs_backward";
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// Sub-test 1: z = abs(x) * w
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{
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TensorShape shape = {2};
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float x_data[] = {-2.0f, 3.0f};
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float w_data[] = {5.0f, 10.0f};
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// z = abs(x)*w = {2, 3} * {5, 10} = {10, 30}
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// l = sum(z) = 40
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// dl/dx = (dl/dz) * (dz/dx) = {1, 1} * (w * sign(x)) = {5*(-1), 10*1} = {-5, 10}
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// dl/dw = (dl/dz) * (dz/dw) = {1, 1} * abs(x) = {2, 3}
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float exp_grad_x[] = {-5.0f, 10.0f};
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float exp_grad_w[] = {2.0f, 3.0f};
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Tensor x = create_test_tensor(shape, x_data, true);
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Tensor w = create_test_tensor(shape, w_data, true);
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Tensor abs_x = Tensor_abs(x);
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Tensor prod = Tensor_mul(abs_x, w);
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Tensor l = Tensor_sum(prod);
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Tensor_backward(l, (Tensor){0});
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Tensor expected_grad_x = create_test_tensor(shape, exp_grad_x, false);
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Tensor expected_grad_w = create_test_tensor(shape, exp_grad_w, false);
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compare_tensors(&x.node->grad, &expected_grad_x, op_name, tc_name, 1, TEST_FLOAT_TOLERANCE);
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compare_tensors(&w.node->grad, &expected_grad_w, op_name, tc_name, 2, TEST_FLOAT_TOLERANCE);
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}
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// Sub-test 2: z = abs(x * w)
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{
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TensorShape shape = {2};
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float x_data[] = {-2.0f, 3.0f};
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float w_data[] = {5.0f, -1.0f};
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// y = x*w = {-10, -3}
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// z = abs(y) = {10, 3}
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// l = sum(z) = 13
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// dl/dx = (dl/dz)(dz/dy)(dy/dx) = {1,1} * sign(y) * w = {-1,-1} * {5,-1} = {-5, 1}
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// dl/dw = (dl/dz)(dz/dy)(dy/dw) = {1,1} * sign(y) * x = {-1,-1} * {-2,3} = {2, -3}
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float exp_grad_x[] = {-5.0f, 1.0f};
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float exp_grad_w[] = {2.0f, -3.0f};
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Tensor x = create_test_tensor(shape, x_data, true);
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Tensor w = create_test_tensor(shape, w_data, true);
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Tensor prod = Tensor_mul(x, w);
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Tensor abs_prod = Tensor_abs(prod);
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Tensor l = Tensor_sum(abs_prod);
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Tensor_backward(l, (Tensor){0});
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Tensor expected_grad_x = create_test_tensor(shape, exp_grad_x, false);
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Tensor expected_grad_w = create_test_tensor(shape, exp_grad_w, false);
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compare_tensors(&x.node->grad, &expected_grad_x, op_name, tc_name, 3, TEST_FLOAT_TOLERANCE);
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compare_tensors(&w.node->grad, &expected_grad_w, op_name, tc_name, 4, TEST_FLOAT_TOLERANCE);
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}
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}
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cten_free(pool_id);
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}

‎tests/Operator/test_abs.c‎

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@@ -0,0 +1,84 @@
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#include "../../include/cten.h"
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#include "../test_utils.h"
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#include "../csv_reporter.h"
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#include "../test_config.h"
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#include <stdio.h>
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#include <math.h>
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void test_abs_operator() {
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const char* op_name = "abs";
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PoolId pool_id = 0;
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cten_begin_malloc(pool_id);
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// Test Case 1: Basic test with mixed positive, negative and zero values
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{
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const char* tc_name = "abs_basic_mixed_values";
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TensorShape shape = {6};
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float d1[] = {-2.5f, -1.0f, 0.0f, 1.0f, 2.5f, -3.0f};
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float exp_d[] = {2.5f, 1.0f, 0.0f, 1.0f, 2.5f, 3.0f};
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Tensor t1 = create_test_tensor(shape, d1, false);
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Tensor expected_res = create_test_tensor(shape, exp_d, false);
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Tensor actual_res = Tensor_abs(t1);
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compare_tensors(&actual_res, &expected_res, op_name, tc_name, 1, TEST_FLOAT_TOLERANCE);
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}
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// Test Case 2: All negative values
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{
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const char* tc_name = "abs_all_negative";
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TensorShape shape = {4};
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float d1[] = {-1.5f, -2.0f, -0.5f, -10.0f};
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float exp_d[] = {1.5f, 2.0f, 0.5f, 10.0f};
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Tensor t1 = create_test_tensor(shape, d1, false);
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Tensor expected_res = create_test_tensor(shape, exp_d, false);
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Tensor actual_res = Tensor_abs(t1);
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compare_tensors(&actual_res, &expected_res, op_name, tc_name, 1, TEST_FLOAT_TOLERANCE);
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}
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// Test Case 3: All positive values (should be unchanged)
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{
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const char* tc_name = "abs_all_positive";
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TensorShape shape = {3};
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float d1[] = {1.0f, 5.5f, 0.1f};
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float exp_d[] = {1.0f, 5.5f, 0.1f};
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Tensor t1 = create_test_tensor(shape, d1, false);
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Tensor expected_res = create_test_tensor(shape, exp_d, false);
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Tensor actual_res = Tensor_abs(t1);
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compare_tensors(&actual_res, &expected_res, op_name, tc_name, 1, TEST_FLOAT_TOLERANCE);
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}
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// Test Case 4: 2D Tensor (Matrix)
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{
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const char* tc_name = "abs_matrix_2x3";
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TensorShape m_shape = {2, 3};
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float d1[] = {1.0f, -2.0f, 0.0f, -4.0f, 5.0f, -6.0f};
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float exp_d[] = {1.0f, 2.0f, 0.0f, 4.0f, 5.0f, 6.0f};
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Tensor t1 = create_test_tensor(m_shape, d1, false);
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Tensor expected_res = create_test_tensor(m_shape, exp_d, false);
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Tensor actual_res = Tensor_abs(t1);
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compare_tensors(&actual_res, &expected_res, op_name, tc_name, 1, TEST_FLOAT_TOLERANCE);
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}
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// Test Case 5: Large and near-zero values
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{
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const char* tc_name = "abs_large_and_near_zero";
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TensorShape shape = {5};
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float d1[] = {-1e8f, 1e-8f, 0.0f, 1e8f, -1e-8f};
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float exp_d[] = {1e8f, 1e-8f, 0.0f, 1e8f, 1e-8f};
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Tensor t1 = create_test_tensor(shape, d1, false);
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Tensor expected_res = create_test_tensor(shape, exp_d, false);
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Tensor actual_res = Tensor_abs(t1);
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compare_tensors(&actual_res, &expected_res, op_name, tc_name, 1, TEST_FLOAT_TOLERANCE);
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}
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cten_free(pool_id);
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}

‎tests/cten_tests.c‎

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@@ -27,6 +27,7 @@ void test_square_operator();
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void test_div_operator();
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void test_max_operator();
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void test_min_operator();
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void test_abs_operator();
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// Backward tests
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void test_add_backward();
@@ -41,6 +42,7 @@ void test_sum_backward();
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void test_mean_backward();
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void test_div_backward();
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void test_pow_backward();
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void test_abs_backward();
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int main() {
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printf("Starting cTensor Test Suite on %s...\n", PLATFORM_NAME);
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test_min_operator();
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printf("Min operator tests finished.\n");
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test_abs_operator();
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printf("Abs operator tests finished.\n");
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// Backward tests
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test_add_backward();
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printf("Add backward tests finished.\n");
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test_pow_backward();
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printf("Pow backward tests finished.\n");
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test_abs_backward();
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printf("Abs backward tests finished.\n");
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// other tests
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csv_reporter_close();

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