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pow backward test is added
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‎tests/Backward/test_pow_backward.c‎

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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_pow_backward() {
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const char* op_name = "pow_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 element-wise vector power
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{
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const char* tc_name = "pow_vectors_backward";
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TensorShape shape = {2};
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float x_data[] = {3.3774f, 0.6125f};
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float y_data[] = {1.4626f, 1.2812f};
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// z = x^y = [3.3774^1.4626, 0.6125^1.2812] = [6.9329, 0.5292]
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// loss = sum(z) = 7.4621
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float exp_grad_x[] = {2.568312f, 1.116224f};
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float exp_grad_y[] = {7.218272f, -0.261589f};
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Tensor x = create_test_tensor(shape, x_data, true);
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Tensor y = create_test_tensor(shape, y_data, true);
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Tensor z = Tensor_pow(x, y);
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Tensor loss = Tensor_sum(z);
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Tensor grad_dummy = {0};
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Tensor_backward(loss, grad_dummy);
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Tensor expected_grad_x = create_test_tensor(shape, exp_grad_x, false);
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Tensor expected_grad_y = create_test_tensor(shape, exp_grad_y, 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(&y.node->grad, &expected_grad_y, op_name, tc_name, 2, TEST_FLOAT_TOLERANCE);
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}
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// Test Case 2: Broadcasting a vector by a scalar exponent
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{
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const char* tc_name = "pow_broadcast_vec_scalar_backward";
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TensorShape x_shape = {2};
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TensorShape y_shape = {1};
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float x_data[] = {3.8141f, 3.5451f};
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float y_data[] = {3.6226f};
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// z = [3.8141^3.6226, 3.5451^3.6226] = [159.5443, 135.3775]
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// loss = sum(z) = 294.9218
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float exp_grad_x[] = {121.277222f, 100.109677f};
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float exp_grad_y[] = {294.921848f};
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Tensor x = create_test_tensor(x_shape, x_data, true);
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Tensor y = create_test_tensor(y_shape, y_data, true);
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Tensor z = Tensor_pow(x, y);
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Tensor loss = Tensor_sum(z);
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Tensor grad_dummy = {0};
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Tensor_backward(loss, grad_dummy);
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Tensor expected_grad_x = create_test_tensor(x_shape, exp_grad_x, false);
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Tensor expected_grad_y = create_test_tensor(y_shape, exp_grad_y, 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(&y.node->grad, &expected_grad_y, op_name, tc_name, 2, TEST_FLOAT_TOLERANCE);
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}
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// Test Case 3: Broadcasting a scalar base by a vector exponent
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{
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const char* tc_name = "pow_broadcast_scalar_vec_backward";
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TensorShape x_shape = {1};
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TensorShape y_shape = {2};
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float x_data[] = {0.8912f};
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float y_data[] = {1.9767f, 0.6043f};
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// z = [0.8912^1.9767, 0.8912^0.6043] = [0.8003, 0.9383]
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// loss = sum(z) = 1.7386
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float exp_grad_x[] = {2.39885f};
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float exp_grad_y[] = {-0.091731f, -0.107441f};
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Tensor x = create_test_tensor(x_shape, x_data, true);
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Tensor y = create_test_tensor(y_shape, y_data, true);
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Tensor z = Tensor_pow(x, y);
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Tensor loss = Tensor_sum(z);
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Tensor grad_dummy = {0};
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Tensor_backward(loss, grad_dummy);
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Tensor expected_grad_x = create_test_tensor(x_shape, exp_grad_x, false);
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Tensor expected_grad_y = create_test_tensor(y_shape, exp_grad_y, 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(&y.node->grad, &expected_grad_y, op_name, tc_name, 2, TEST_FLOAT_TOLERANCE);
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}
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// Test Case 4: Edge cases with x=0 and x<0
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{
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const char* tc_name = "pow_edge_cases_backward";
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TensorShape shape = {2};
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float x_data[] = {0.0f, -2.0f};
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float y_data[] = {2.0f, 3.0f}; // Exponents are integers
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// z = [0^2, (-2)^3] = [0.0, -8.0]
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// loss = sum(z) = -8.0
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// d(loss)/dx:
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// For x=0, y=2: grad is 0 (special case in GradFn_pow)
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// For x=-2, y=3: 3*(-2)^2 = 12.0
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float exp_grad_x[] = {0.0f, 12.0f};
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// d(loss)/dy:
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// For x=0: grad is 0 (special case in GradFn_pow)
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// For x=-2: grad is 0 (ln(-2) is undefined, special case)
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float exp_grad_y[] = {0.0f, 0.0f};
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Tensor x = create_test_tensor(shape, x_data, true);
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Tensor y = create_test_tensor(shape, y_data, true);
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Tensor z = Tensor_pow(x, y);
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Tensor loss = Tensor_sum(z);
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Tensor grad_dummy = {0};
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Tensor_backward(loss, grad_dummy);
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Tensor expected_grad_x = create_test_tensor(shape, exp_grad_x, false);
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Tensor expected_grad_y = create_test_tensor(shape, exp_grad_y, 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(&y.node->grad, &expected_grad_y, op_name, tc_name, 2, TEST_FLOAT_TOLERANCE);
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}
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// Test Case 5: Complex computation graph (z = (a^b) * c)
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{
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const char* tc_name = "pow_complex_graph_backward";
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TensorShape shape = {1};
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float a_data[] = {1.4839f};
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float b_data[] = {2.2687f};
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float c_data[] = {0.6194f};
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// Let d = a^b. Then z = d * c.
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// Forward: d = 1.4839^2.2687 = 2.4483. z = 2.4483 * 0.6194 = 1.5164
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// Backward pass (upstream grad for d is c=0.6194):
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// dz/da = (dz/dd) * (dd/da) = c * (b*a^(b-1)) = 0.6194 * (2.2687*1.4839^1.2687)
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float exp_grad_a[] = {2.318512f};
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// dz/db = (dz/dd) * (dd/db) = c * (a^b*ln(a)) = 0.6194 * (2.4483*ln(1.4839))
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float exp_grad_b[] = {0.598515f};
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// dz/dc = d = a^b = 2.4483
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float exp_grad_c[] = {2.448306f};
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Tensor a = create_test_tensor(shape, a_data, true);
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Tensor b = create_test_tensor(shape, b_data, true);
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Tensor c = create_test_tensor(shape, c_data, true);
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Tensor d = Tensor_pow(a, b);
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Tensor z = Tensor_mul(d, c);
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Tensor grad_dummy = {0};
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Tensor_backward(z, grad_dummy);
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Tensor expected_grad_a_tensor = create_test_tensor(shape, exp_grad_a, false);
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Tensor expected_grad_b_tensor = create_test_tensor(shape, exp_grad_b, false);
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Tensor expected_grad_c_tensor = create_test_tensor(shape, exp_grad_c, false);
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compare_tensors(&a.node->grad, &expected_grad_a_tensor, op_name, tc_name, 1, TEST_FLOAT_TOLERANCE);
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compare_tensors(&b.node->grad, &expected_grad_b_tensor, op_name, tc_name, 2, TEST_FLOAT_TOLERANCE);
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compare_tensors(&c.node->grad, &expected_grad_c_tensor, op_name, tc_name, 3, 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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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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int main() {
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printf("Starting cTensor Test Suite on %s...\n", PLATFORM_NAME);
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test_div_backward();
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printf("Div 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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// other tests
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csv_reporter_close();

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