Fix parameter order in conv2d_implicit and add comprehensive test cases for 2D convolution

Author: bssrdf

ggml/src/ggml-cuda/conv2d-implicit.cu

@@ -355,7 +355,7 @@ void ggml_cuda_op_conv2d_implicit(ggml_backend_cuda_context & ctx, ggml_tensor *
 
     const int64_t total  = B * OC * OH * OW;
 
-    param_t params = { B, IC, IH, IW, OC, KH, KW, ST_X, ST_Y, PD_X, PD_Y, DL_X, DL_Y, OH, OW };
+    param_t params = { B, IC, IH, IW, OC, KH, KW, ST_Y, ST_X, PD_Y, PD_X, DL_Y, DL_X, OH, OW };
 
     if (kernel->type == GGML_TYPE_F16) {
         conv2d_implicit_cuda_f16(X_D, (half *) K_D, Y_D, params, st);

tests/test-backend-ops.cpp

@@ -5790,6 +5790,30 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
         }
     }
 
+    for (uint32_t s0 : { 1, 3 }) {
+        for (uint32_t p1 : { 2, 5 }) {
+            for (uint32_t Cin : { 1, 25 }) {
+                for (uint32_t Cout : { 1, 12 }) {
+                    for (uint32_t KH : { 1, 2, 3, 11 }) {
+                        for (uint32_t KW : { 1, 2, 3, 11 }) {
+                            for (uint32_t H : { 1, 133 }) {
+                                for (uint32_t W : { 1, 141 }) {
+                                    if (calc_conv_output_size(W, KW, s0, p0, d0) > 0 &&
+                                        calc_conv_output_size(H, KH, s1, p1, d1) > 0) {
+                                        for (auto kernel_type : {GGML_TYPE_F32, GGML_TYPE_F16}) {
+                                            test_cases.emplace_back(new test_conv_2d_implicit(
+                                                { W, H, Cin, 2 }, { KW, KH, Cin, Cout }, kernel_type, s0, s1, p0, p1, d0, d1, false));
+                                        }
+                                    }
+                                }
+                            }
+                        }
+                    }
+                }
+            }
+        }
+    }
+
     // sycl backend will limit task global_range < MAX_INT
     // test cases for 2D im2col with large input W and H (occurs in stable-diffusion)
     // however these cases need to alloc more memory which may fail in some devices (Intel Arc770, etc.)