#14257: ci fix

tests/scripts/run_python_model_tests.sh

@@ -35,7 +35,7 @@ run_python_model_tests_wormhole_b0() {
     # higher sequence lengths and different formats trigger memory issues
     pytest models/demos/falcon7b_common/tests/unit_tests/test_falcon_matmuls_and_bmms_with_mixed_precision.py -k "seq_len_128 and in0_BFLOAT16-in1_BFLOAT8_B-out_BFLOAT16-weights_DRAM"
     pytest tests/ttnn/integration_tests/resnet/test_ttnn_functional_resnet50_new.py -k "pretrained_weight_false"
-    pytest models/demos/yolov4/demo/demo.py -k "pretrained_weight_false"
+    WH_ARCH_YAML=wormhole_b0_80_arch_eth_dispatch.yaml pytest models/demos/yolov4/demo/demo.py -k "pretrained_weight_false"
 
     # Unet Shallow
     WH_ARCH_YAML=wormhole_b0_80_arch_eth_dispatch.yaml pytest -svv models/experimental/functional_unet/tests/test_unet_model.py

ttnn/cpp/ttnn/operations/data_movement/concat/device/concat_device_operation.cpp

@@ -25,7 +25,7 @@ ConcatOpParallelizationStrategy ConcatDeviceOperation::get_parallelization_strat
 
 void ConcatDeviceOperation::validate(const std::vector<Tensor> &input_tensors) const {
     const auto &first_input = input_tensors[0];
-    ttnn::SimpleShape shape_first = first_input.get_logical_shape();
+    tt::tt_metal::LegacyShape shape_first = first_input.get_legacy_shape();
     TT_FATAL(this->dim < shape_first.rank(), "ConcatDeviceOperation dim specified is larger than input tensor rank.");
     shape_first[this->dim] = 0;
     bool shard_first = input_tensors[0].is_sharded();
@@ -38,24 +38,12 @@ void ConcatDeviceOperation::validate(const std::vector<Tensor> &input_tensors) c
         TT_FATAL(in_ref.device() == first_input.device(), "Operands to concat need to be on the same device.");
         TT_FATAL(in_ref.get_layout() == first_input.get_layout(), "All Tensors should have same layouts.");
         TT_FATAL(in_ref.get_dtype() == first_input.get_dtype(), "All Tensors should have same dtypes.");
-        ttnn::SimpleShape curr_shape = in_ref.get_logical_shape();
-
+        tt::tt_metal::LegacyShape curr_shape = in_ref.get_legacy_shape();
         TT_FATAL(curr_shape.rank() == shape_first.rank(), "Input tensor ranks must be equal");
         curr_shape[this->dim] = 0;
             // last tensor can support without any kernel changes
         if(in_ref.get_layout() == Layout::TILE and in_ref.get_shape().has_tile_padding(this->dim)) {
             warn_about_alignment = true;
-        /* // last tensor can support without any kernel changes
-        TT_FATAL(
-            !in_ref.get_shape().has_tile_padding(this->dim),
-            "Tile padding along concatenated dim ({}) not supported for concat yet (tensor: {}).",
-            this->dim,
-            i);
-        TT_FATAL(curr_shape == shape_first, "concat tensors differ in shape across non-concat dimensions.");
-        if (in_ref.get_layout() == Layout::ROW_MAJOR && this->dim == shape_first.rank() - 1) {
-            TT_FATAL(
-                (in_ref.get_logical_shape()[this->dim] * in_ref.element_size()) % in_ref.buffer()->alignment() == 0,
-                "Current concat implementation requires aligned last dim when concatting on last dim");*/
         }
         TT_FATAL(curr_shape == shape_first, "concat tensors differ in shape across non-concat dimensions.");
         TT_FATAL(in_ref.is_sharded() == shard_first, "All tensors must be sharded or all must be interleaved");