record_forge_property = <test.conftest.ForgePropertyStore object at 0x7ff78624abf0>
variant = 'facebook/musicgen-small'

    @pytest.mark.nightly
    @pytest.mark.parametrize("variant", variants)
    def test_stereo(record_forge_property, variant):
        if variant != "facebook/musicgen-small":
            pytest.skip("Skipping due to the current CI/CD pipeline limitations")
    
        # Build Module Name
        module_name = build_module_name(
            framework=Framework.PYTORCH,
            model="stereo",
            variant=variant,
            task=Task.MUSIC_GENERATION,
            source=Source.HUGGINGFACE,
        )
    
        # Record Forge Property
        record_forge_property("tags.model_name", module_name)
    
        framework_model, processor = load_model(variant)
    
        input_ids, attn_mask, decoder_input_ids = load_inputs(framework_model, processor)
        inputs = [input_ids, attn_mask, decoder_input_ids]
    
        # Issue: https://github.com/tenstorrent/tt-forge-fe/issues/615
        # Forge compile framework model
>       compiled_model = forge.compile(framework_model, sample_inputs=inputs, module_name=module_name)

forge/test/models/pytorch/audio/stereo/test_stereo.py:46: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/compile.py:251: in compile_main
    return forge_compile_from_context(compile_context)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/compile.py:293: in forge_compile_from_context
    next_stage = stage_to_func[current_stage](context)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

context = CompileContext(modules=[Module pt_stereo_facebook_musicgen_small_music_generation_hf], graph_name='pt_stereo_facebook_...in_recompile=False, recompile_count=0, target_cycles_offset=0, forge_module=None, compiled_binary=None, attach_to=None)

    def run_optimization_pass(context: CompileContext) -> CompileDepth:
        """
        Runs optimization passes.
    
        Parameters
        ----------
        context: CompileContext
            Compile context
    
        Returns
        -------
        CompileDepth - next compile stage
        """
        compiler_cfg = context.compiler_cfg
        graph_name = context.graph_name
        graph, intermediate_tensors = context.graph, context.intermediate_tensors
    
>       run_optimization_graph_passes(graph)
E       RuntimeError: TT_ASSERT @ /__w/tt-forge-fe/tt-forge-fe/forge/csrc/graph_lib/shape.cpp:135: (i >= 0) && (i < (int)dims_.size())
E       info:
E       Trying to access element outside of dimensions: 3
E       backtrace:
E        --- tt::graphlib::Shape::operator[](int)
E        --- tt::passes::commute_through_concat(tt::graphlib::Graph*, tt::graphlib::OpNode*, tt::graphlib::OpNode*, tt::graphlib::Node*, tt::graphlib::Shape*, tt::graphlib::Shape*, bool, bool*, std::pair<int, int>*, tt::graphlib::OpType*, bool)
E        --- tt::passes::can_commute_past_op(tt::graphlib::OpNode*, tt::graphlib::OpNode*, tt::graphlib::Graph*, tt::graphlib::Shape*, tt::graphlib::Shape*, bool, tt::graphlib::Node*)
E        --- /opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/_C.so(+0x1928ef) [0x7ff7d5ed28ef]
E        --- tt::passes::erase_inverse_ops(tt::graphlib::Graph*)
E        --- tt::run_optimization_graph_passes(tt::graphlib::Graph*)
E        --- /opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/_C.so(+0xfeca9) [0x7ff7d5e3eca9]
E        --- /opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/_C.so(+0xe1e05) [0x7ff7d5e21e05]
E        --- /opt/ttforge-toolchain/venv/bin/python(+0x18ab32) [0x5612acea7b32]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyObject_MakeTpCall+0x25b) [0x5612ace9e39b]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyEval_EvalFrameDefault+0x59c7) [0x5612ace97a97]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyFunction_Vectorcall+0x7c) [0x5612acea838c]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyEval_EvalFrameDefault+0x6c0) [0x5612ace92790]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyFunction_Vectorcall+0x7c) [0x5612acea838c]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyEval_EvalFrameDefault+0x6c0) [0x5612ace92790]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyFunction_Vectorcall+0x7c) [0x5612acea838c]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyEval_EvalFrameDefault+0x18db) [0x5612ace939ab]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyFunction_Vectorcall+0x7c) [0x5612acea838c]
E        --- /opt/ttforge-toolchain/venv/bin/python(PyObject_Call+0x122) [0x5612aceb6172]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyEval_EvalFrameDefault+0x2b60) [0x5612ace94c30]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyFunction_Vectorcall+0x7c) [0x5612acea838c]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyEval_EvalFrameDefault+0x2b60) [0x5612ace94c30]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyFunction_Vectorcall+0x7c) [0x5612acea838c]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyEval_EvalFrameDefault+0x18db) [0x5612ace939ab]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyFunction_Vectorcall+0x7c) [0x5612acea838c]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyEval_EvalFrameDefault+0x5632) [0x5612ace97702]
E        --- /opt/ttforge-toolchain/venv/bin/python(+0x1984d1) [0x5612aceb54d1]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyEval_EvalFrameDefault+0x5632) [0x5612ace97702]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyFunction_Vectorcall+0x7c) [0x5612acea838c]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyObject_FastCallDictTstate+0x16d) [0x5612ace9d61d]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyObject_Call_Prepend+0x5c) [0x5612aceb262c]
E        --- /opt/ttforge-toolchain/venv/bin/python(+0x29d464) [0x5612acfba464]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyObject_MakeTpCall+0x25b) [0x5612ace9e39b]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyEval_EvalFrameDefault+0x68ce) [0x5612ace9899e]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyFunction_Vectorcall+0x7c) [0x5612acea838c]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyEval_EvalFrameDefault+0x8af) [0x5612ace9297f]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyFunction_Vectorcall+0x7c) [0x5612acea838c]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyEval_EvalFrameDefault+0x2b60) [0x5612ace94c30]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyFunction_Vectorcall+0x7c) [0x5612acea838c]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyEval_EvalFrameDefault+0x18db) [0x5612ace939ab]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyFunction_Vectorcall+0x7c) [0x5612acea838c]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyEval_EvalFrameDefault+0x5632) [0x5612ace97702]
E        --- /opt/ttforge-toolchain/venv/bin/python(+0x1984d1) [0x5612aceb54d1]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyEval_EvalFrameDefault+0x5632) [0x5612ace97702]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyFunction_Vectorcall+0x7c) [0x5612acea838c]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyObject_FastCallDictTstate+0x16d) [0x5612ace9d61d]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyObject_Call_Prepend+0x5c) [0x5612aceb262c]
E        --- /opt/ttforge-toolchain/venv/bin/python(+0x29d464) [0x5612acfba464]
E        --- /opt/ttforge-toolchain/venv/bin/python(PyObject_Call+0xbb) [0x5612aceb610b]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyEval_EvalFrameDefault+0x2b60) [0x5612ace94c30]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyFunction_Vectorcall+0x7c) [0x5612acea838c]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyEval_EvalFrameDefault+0x6c0) [0x5612ace92790]
E        --- /opt/ttforge-toolchain/venv/bin/python(+0x1984d1) [0x5612aceb54d1]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyEval_EvalFrameDefault+0x18db) [0x5612ace939ab]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyFunction_Vectorcall+0x7c) [0x5612acea838c]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyEval_EvalFrameDefault+0x2b60) [0x5612ace94c30]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyFunction_Vectorcall+0x7c) [0x5612acea838c]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyEval_EvalFrameDefault+0x6c0) [0x5612ace92790]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyFunction_Vectorcall+0x7c) [0x5612acea838c]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyEval_EvalFrameDefault+0x18db) [0x5612ace939ab]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyFunction_Vectorcall+0x7c) [0x5612acea838c]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyEval_EvalFrameDefault+0x2b60) [0x5612ace94c30]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyFunction_Vectorcall+0x7c) [0x5612acea838c]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyEval_EvalFrameDefault+0x18db) [0x5612ace939ab]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyFunction_Vectorcall+0x7c) [0x5612acea838c]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyEval_EvalFrameDefault+0x5632) [0x5612ace97702]
E        --- /opt/ttforge-toolchain/venv/bin/python(+0x1984d1) [0x5612aceb54d1]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyEval_EvalFrameDefault+0x5632) [0x5612ace97702]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyFunction_Vectorcall+0x7c) [0x5612acea838c]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyObject_FastCallDictTstate+0x16d) [0x5612ace9d61d]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyObject_Call_Prepend+0x5c) [0x5612aceb262c]
E        --- /opt/ttforge-toolchain/venv/bin/python(+0x29d464) [0x5612acfba464]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyObject_MakeTpCall+0x25b) [0x5612ace9e39b]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyEval_EvalFrameDefault+0x68ce) [0x5612ace9899e]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyFunction_Vectorcall+0x7c) [0x5612acea838c]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyEval_EvalFrameDefault+0x2b60) [0x5612ace94c30]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyFunction_Vectorcall+0x7c) [0x5612acea838c]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyEval_EvalFrameDefault+0x18db) [0x5612ace939ab]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyFunction_Vectorcall+0x7c) [0x5612acea838c]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyEval_EvalFrameDefault+0x5632) [0x5612ace97702]
E        --- /opt/ttforge-toolchain/venv/bin/python(+0x1984d1) [0x5612aceb54d1]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyEval_EvalFrameDefault+0x5632) [0x5612ace97702]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyFunction_Vectorcall+0x7c) [0x5612acea838c]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyObject_FastCallDictTstate+0x16d) [0x5612ace9d61d]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyObject_Call_Prepend+0x5c) [0x5612aceb262c]
E        --- /opt/ttforge-toolchain/venv/bin/python(+0x29d464) [0x5612acfba464]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyObject_MakeTpCall+0x25b) [0x5612ace9e39b]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyEval_EvalFrameDefault+0x68ce) [0x5612ace9899e]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyFunction_Vectorcall+0x7c) [0x5612acea838c]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyEval_EvalFrameDefault+0x6c0) [0x5612ace92790]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyFunction_Vectorcall+0x7c) [0x5612acea838c]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyEval_EvalFrameDefault+0x6c0) [0x5612ace92790]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyFunction_Vectorcall+0x7c) [0x5612acea838c]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyEval_EvalFrameDefault+0x2b60) [0x5612ace94c30]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyFunction_Vectorcall+0x7c) [0x5612acea838c]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyEval_EvalFrameDefault+0x18db) [0x5612ace939ab]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyFunction_Vectorcall+0x7c) [0x5612acea838c]

/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/compile.py:829: RuntimeError

record_forge_property = <test.conftest.ForgePropertyStore object at 0x7ff632f3f940>
variant = 'openai/clip-vit-base-patch32'

    @pytest.mark.nightly
    @pytest.mark.parametrize("variant", ["openai/clip-vit-base-patch32"])
    def test_clip_pytorch(record_forge_property, variant):
        # Build Module Name
        module_name = build_module_name(
            framework=Framework.PYTORCH,
            model="clip",
            variant=variant,
            suffix="text",
            source=Source.HUGGINGFACE,
            task=Task.TEXT_GENERATION,
        )
    
        # Record Forge Property
        record_forge_property("tags.model_name", module_name)
    
        # Load processor and model from HuggingFace
        model = download_model(CLIPModel.from_pretrained, variant, torchscript=True)
        processor = download_model(CLIPProcessor.from_pretrained, variant)
    
        # Load image from the IAM dataset
        url = "http://images.cocodataset.org/val2017/000000039769.jpg"
        image = Image.open(requests.get(url, stream=True).raw)
    
        # Process image
        text = [
            "a photo of a cat",
            "a photo of a dog",
        ]
        inputs = processor(text=text, images=image, return_tensors="pt")
    
        inputs = [inputs["input_ids"], inputs["pixel_values"], inputs["attention_mask"]]
        framework_model = CLIPTextWrapper(model)
        inputs = [inputs[0], inputs[2]]
    
        # Forge compile framework model
>       compiled_model = forge.compile(framework_model, sample_inputs=inputs, module_name=module_name)

forge/test/models/pytorch/multimodal/clip/test_clip.py:53: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/compile.py:251: in compile_main
    return forge_compile_from_context(compile_context)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/compile.py:293: in forge_compile_from_context
    next_stage = stage_to_func[current_stage](context)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

context = CompileContext(modules=[Module pt_clip_openai_clip_vit_base_patch32_text_gen_hf_text], graph_name='pt_clip_openai_clip...cles_offset=0, forge_module=<forge._C.ForgeGraphModule object at 0x7ff777748630>, compiled_binary=None, attach_to=None)

    def run_mlir_compiler(context: CompileContext) -> CompileDepth:
        assert context.forge_module is not None
    
>       context.compiled_binary = forge._C.run_mlir_compiler(context.forge_module)
E       RuntimeError: Generated MLIR module failed verification.

/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/compile.py:986: RuntimeError

record_forge_property = <test.conftest.ForgePropertyStore object at 0x7ff62e708b50>
variant = 'stable-diffusion-xl-base-1.0'

    @pytest.mark.nightly
    @pytest.mark.skip_model_analysis
    @pytest.mark.parametrize("variant", ["stable-diffusion-xl-base-1.0"])
    def test_stable_diffusion_generation(record_forge_property, variant):
        # Build Module Name
        module_name = build_module_name(
            framework=Framework.PYTORCH,
            model="stereo",
            variant=variant,
            task=Task.MUSIC_GENERATION,
            source=Source.HUGGINGFACE,
        )
    
        # Record Forge Property
        record_forge_property("tags.model_name", module_name)
    
        # Load the pipeline and set it to use the CPU
        pipe = DiffusionPipeline.from_pretrained(f"stabilityai/{variant}", torch_dtype=torch.float32)  # Use float32 for CPU
        pipe.to("cpu")  # Move the model to CPU
    
        # Wrap the pipeline in the wrapper
        framework_model = StableDiffusionXLWrapper(pipe)
    
        # Tokenize the prompt to a tensor
        tokenizer = pipe.tokenizer
        prompt = "An astronaut riding a green horse"
        input_tensor = tokenizer(prompt, return_tensors="pt").input_ids
    
        inputs = [input_tensor]
    
        # Forge compile framework model
>       compiled_model = forge.compile(framework_model, sample_inputs=inputs, module_name=module_name)

forge/test/models/pytorch/multimodal/stable_diffusion/test_stable_diffusion_xl.py:64: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/compile.py:251: in compile_main
    return forge_compile_from_context(compile_context)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/compile.py:293: in forge_compile_from_context
    next_stage = stage_to_func[current_stage](context)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/compile.py:672: in generate_initial_graph
    module, module_inputs = convert_to_forge_module(
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/compile.py:1033: in convert_to_forge_module
    forge_module, dev_types, module_inputs = generate_forge_module(
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_to_python.py:2060: in generate_forge_module
    framework_outputs = framework_mod.cpu_eval_forward(*pytorch_inputs)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/module.py:142: in cpu_eval_forward
    outputs = flatten_structured_output([outputs])
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_utils.py:112: in flatten_structured_output
    sub_output = flatten_structured_output(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

outputs = [<PIL.Image.Image image mode=RGB size=1024x1024 at 0x7FF778CFD120>]

    def flatten_structured_output(outputs):
        from forge.tensor import Tensor
    
        new_outputs = []
    
        for i in range(len(outputs)):
            out = outputs[i]
    
            if isinstance(out, (list, tuple)):
                sub_output = flatten_structured_output(
                    out,
                )
                new_outputs += sub_output
    
            elif isinstance(out, dict):
                sub_output = []
                for k, v in out.items():
                    sub_output.append(v)
    
                sub_output = flatten_structured_output(
                    sub_output,
                )
                new_outputs += sub_output
    
            elif isinstance(out, (torch.Tensor, tf.Tensor, Tensor, np.ndarray)):
                new_outputs.append(out)
    
            elif out is None:
                continue
            else:
>               raise NotImplementedError(f"Unknown output type: {type(out)}")
E               NotImplementedError: Unknown output type: <class 'PIL.Image.Image'>

/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_utils.py:133: NotImplementedError

record_forge_property = <test.conftest.ForgePropertyStore object at 0x7ff778cb7370>
variant = 'distilbert-base-uncased'

    @pytest.mark.nightly
    @pytest.mark.parametrize("variant", variants, ids=variants)
    def test_distilbert_masked_lm_pytorch(record_forge_property, variant):
        if variant != "distilbert-base-uncased":
            pytest.skip("Skipping due to the current CI/CD pipeline limitations")
    
        # Build Module Name
        module_name = build_module_name(
            framework=Framework.PYTORCH, model="distilbert", variant=variant, task=Task.MASKED_LM, source=Source.HUGGINGFACE
        )
    
        # Record Forge Property
        record_forge_property("tags.model_name", module_name)
    
        # Load DistilBert tokenizer and model from HuggingFace
        # Variants: distilbert-base-uncased, distilbert-base-cased,
        # distilbert-base-multilingual-cased
        # NOTE: These model variants are pre-trined only. They need to be fine-tuned
        # on a downstream task. Code is for demonstration purposes only.
        tokenizer = download_model(DistilBertTokenizer.from_pretrained, variant)
        framework_model = download_model(DistilBertForMaskedLM.from_pretrained, variant)
    
        # Load data sample
        sample_text = "The capital of France is [MASK]."
    
        # Data preprocessing
        input_tokens = tokenizer(
            sample_text,
            max_length=128,
            padding="max_length",
            truncation=True,
            return_tensors="pt",
        )
    
        inputs = [input_tokens["input_ids"], input_tokens["attention_mask"]]
    
        # Forge compile framework model
>       compiled_model = forge.compile(framework_model, sample_inputs=inputs, module_name=module_name)

forge/test/models/pytorch/text/distilbert/test_distilbert.py:59: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/compile.py:251: in compile_main
    return forge_compile_from_context(compile_context)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/compile.py:293: in forge_compile_from_context
    next_stage = stage_to_func[current_stage](context)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

context = CompileContext(modules=[Module pt_distilbert_distilbert_base_uncased_mlm_hf], graph_name='pt_distilbert_distilbert_bas...cles_offset=0, forge_module=<forge._C.ForgeGraphModule object at 0x7ff85c37ce30>, compiled_binary=None, attach_to=None)

    def run_mlir_compiler(context: CompileContext) -> CompileDepth:
        assert context.forge_module is not None
    
>       context.compiled_binary = forge._C.run_mlir_compiler(context.forge_module)
E       RuntimeError: Generated MLIR module failed verification.

/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/compile.py:986: RuntimeError

record_forge_property = <test.conftest.ForgePropertyStore object at 0x7ff778bed5a0>
variant = 'adept/fuyu-8b'

    @pytest.mark.nightly
    @pytest.mark.parametrize("variant", ["adept/fuyu-8b"])
    def test_fuyu8b(record_forge_property, variant):
        # Build Module Name
        module_name = build_module_name(
            framework=Framework.PYTORCH, model="fuyu", variant=variant, task=Task.QA, source=Source.HUGGINGFACE
        )
    
        # Record Forge Property
        record_forge_property("tags.model_name", module_name)
    
        config = FuyuConfig.from_pretrained(variant)
        config_dict = config.to_dict()
        config_dict["return_dict"] = False
        config_dict["use_cache"] = False
        config_dict["text_config"]["num_hidden_layers"] = 1
        config = FuyuConfig(**config_dict)
    
        # Load post-processing modules  (run on CPU)
        tokenizer = AutoTokenizer.from_pretrained(variant)
        image_processor = FuyuImageProcessor()
        processor = FuyuProcessor(image_processor=image_processor, tokenizer=tokenizer)
    
        # Create Forge module from PyTorch model
        fuyu_model = FuyuForCausalLM.from_pretrained(variant, config=config)
        # fuyu_model = FuyuForCausalLM(config=config)
        framework_model = FuyuModelWrapper(fuyu_model)
        framework_model.eval()
    
        # Prepare inputs
        text_prompt = "Generate a coco-style caption.\n"
    
        url = "https://huggingface.co/adept-hf-collab/fuyu-8b/resolve/main/bus.png"
        response = requests.get(url)
        with open("bus.png", "wb") as file:
            file.write(response.content)
    
        image_path = "bus.png"  # https://huggingface.co/adept-hf-collab/fuyu-8b/blob/main/bus.png
    
        image_pil = Image.open(image_path)
        model_inputs = processor(text=text_prompt, images=[image_pil], device="cpu", return_tensor="pt")
        inputs_embeds = generate_fuyu_embedding(
            fuyu_model, model_inputs["input_ids"], model_inputs["image_patches"][0], model_inputs["image_patches_indices"]
        )
        inputs_embeds = inputs_embeds.clone().detach()
    
        inputs = [inputs_embeds]
    
        # Forge compile framework model
>       compiled_model = forge.compile(framework_model, sample_inputs=inputs, module_name=module_name)

forge/test/models/pytorch/text/fuyu/test_fuyu_8b.py:76: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/compile.py:251: in compile_main
    return forge_compile_from_context(compile_context)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/compile.py:293: in forge_compile_from_context
    next_stage = stage_to_func[current_stage](context)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

context = CompileContext(modules=[Module pt_fuyu_adept_fuyu_8b_qa_hf], graph_name='pt_fuyu_adept_fuyu_8b_qa_hf', compiler_cfg=Co...in_recompile=False, recompile_count=0, target_cycles_offset=0, forge_module=None, compiled_binary=None, attach_to=None)

    def run_optimization_pass(context: CompileContext) -> CompileDepth:
        """
        Runs optimization passes.
    
        Parameters
        ----------
        context: CompileContext
            Compile context
    
        Returns
        -------
        CompileDepth - next compile stage
        """
        compiler_cfg = context.compiler_cfg
        graph_name = context.graph_name
        graph, intermediate_tensors = context.graph, context.intermediate_tensors
    
>       run_optimization_graph_passes(graph)
E       RuntimeError: TT_ASSERT @ /__w/tt-forge-fe/tt-forge-fe/forge/csrc/graph_lib/shape.cpp:135: (i >= 0) && (i < (int)dims_.size())
E       info:
E       Trying to access element outside of dimensions: 3
E       backtrace:
E        --- tt::graphlib::Shape::operator[](int)
E        --- tt::passes::commute_through_concat(tt::graphlib::Graph*, tt::graphlib::OpNode*, tt::graphlib::OpNode*, tt::graphlib::Node*, tt::graphlib::Shape*, tt::graphlib::Shape*, bool, bool*, std::pair<int, int>*, tt::graphlib::OpType*, bool)
E        --- tt::passes::can_commute_past_op(tt::graphlib::OpNode*, tt::graphlib::OpNode*, tt::graphlib::Graph*, tt::graphlib::Shape*, tt::graphlib::Shape*, bool, tt::graphlib::Node*)
E        --- /opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/_C.so(+0x1928ef) [0x7ff7d5ed28ef]
E        --- /opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/_C.so(+0x192a3d) [0x7ff7d5ed2a3d]
E        --- /opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/_C.so(+0x192a3d) [0x7ff7d5ed2a3d]
E        --- /opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/_C.so(+0x192a3d) [0x7ff7d5ed2a3d]
E        --- tt::passes::erase_inverse_ops(tt::graphlib::Graph*)
E        --- tt::run_optimization_graph_passes(tt::graphlib::Graph*)
E        --- /opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/_C.so(+0xfeca9) [0x7ff7d5e3eca9]
E        --- /opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/_C.so(+0xe1e05) [0x7ff7d5e21e05]
E        --- /opt/ttforge-toolchain/venv/bin/python(+0x18ab32) [0x5612acea7b32]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyObject_MakeTpCall+0x25b) [0x5612ace9e39b]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyEval_EvalFrameDefault+0x59c7) [0x5612ace97a97]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyFunction_Vectorcall+0x7c) [0x5612acea838c]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyEval_EvalFrameDefault+0x6c0) [0x5612ace92790]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyFunction_Vectorcall+0x7c) [0x5612acea838c]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyEval_EvalFrameDefault+0x6c0) [0x5612ace92790]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyFunction_Vectorcall+0x7c) [0x5612acea838c]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyEval_EvalFrameDefault+0x18db) [0x5612ace939ab]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyFunction_Vectorcall+0x7c) [0x5612acea838c]
E        --- /opt/ttforge-toolchain/venv/bin/python(PyObject_Call+0x122) [0x5612aceb6172]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyEval_EvalFrameDefault+0x2b60) [0x5612ace94c30]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyFunction_Vectorcall+0x7c) [0x5612acea838c]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyEval_EvalFrameDefault+0x2b60) [0x5612ace94c30]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyFunction_Vectorcall+0x7c) [0x5612acea838c]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyEval_EvalFrameDefault+0x18db) [0x5612ace939ab]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyFunction_Vectorcall+0x7c) [0x5612acea838c]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyEval_EvalFrameDefault+0x5632) [0x5612ace97702]
E        --- /opt/ttforge-toolchain/venv/bin/python(+0x1984d1) [0x5612aceb54d1]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyEval_EvalFrameDefault+0x5632) [0x5612ace97702]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyFunction_Vectorcall+0x7c) [0x5612acea838c]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyObject_FastCallDictTstate+0x16d) [0x5612ace9d61d]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyObject_Call_Prepend+0x5c) [0x5612aceb262c]
E        --- /opt/ttforge-toolchain/venv/bin/python(+0x29d464) [0x5612acfba464]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyObject_MakeTpCall+0x25b) [0x5612ace9e39b]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyEval_EvalFrameDefault+0x68ce) [0x5612ace9899e]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyFunction_Vectorcall+0x7c) [0x5612acea838c]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyEval_EvalFrameDefault+0x8af) [0x5612ace9297f]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyFunction_Vectorcall+0x7c) [0x5612acea838c]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyEval_EvalFrameDefault+0x2b60) [0x5612ace94c30]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyFunction_Vectorcall+0x7c) [0x5612acea838c]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyEval_EvalFrameDefault+0x18db) [0x5612ace939ab]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyFunction_Vectorcall+0x7c) [0x5612acea838c]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyEval_EvalFrameDefault+0x5632) [0x5612ace97702]
E        --- /opt/ttforge-toolchain/venv/bin/python(+0x1984d1) [0x5612aceb54d1]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyEval_EvalFrameDefault+0x5632) [0x5612ace97702]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyFunction_Vectorcall+0x7c) [0x5612acea838c]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyObject_FastCallDictTstate+0x16d) [0x5612ace9d61d]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyObject_Call_Prepend+0x5c) [0x5612aceb262c]
E        --- /opt/ttforge-toolchain/venv/bin/python(+0x29d464) [0x5612acfba464]
E        --- /opt/ttforge-toolchain/venv/bin/python(PyObject_Call+0xbb) [0x5612aceb610b]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyEval_EvalFrameDefault+0x2b60) [0x5612ace94c30]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyFunction_Vectorcall+0x7c) [0x5612acea838c]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyEval_EvalFrameDefault+0x6c0) [0x5612ace92790]
E        --- /opt/ttforge-toolchain/venv/bin/python(+0x1984d1) [0x5612aceb54d1]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyEval_EvalFrameDefault+0x18db) [0x5612ace939ab]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyFunction_Vectorcall+0x7c) [0x5612acea838c]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyEval_EvalFrameDefault+0x2b60) [0x5612ace94c30]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyFunction_Vectorcall+0x7c) [0x5612acea838c]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyEval_EvalFrameDefault+0x6c0) [0x5612ace92790]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyFunction_Vectorcall+0x7c) [0x5612acea838c]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyEval_EvalFrameDefault+0x18db) [0x5612ace939ab]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyFunction_Vectorcall+0x7c) [0x5612acea838c]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyEval_EvalFrameDefault+0x2b60) [0x5612ace94c30]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyFunction_Vectorcall+0x7c) [0x5612acea838c]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyEval_EvalFrameDefault+0x18db) [0x5612ace939ab]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyFunction_Vectorcall+0x7c) [0x5612acea838c]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyEval_EvalFrameDefault+0x5632) [0x5612ace97702]
E        --- /opt/ttforge-toolchain/venv/bin/python(+0x1984d1) [0x5612aceb54d1]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyEval_EvalFrameDefault+0x5632) [0x5612ace97702]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyFunction_Vectorcall+0x7c) [0x5612acea838c]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyObject_FastCallDictTstate+0x16d) [0x5612ace9d61d]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyObject_Call_Prepend+0x5c) [0x5612aceb262c]
E        --- /opt/ttforge-toolchain/venv/bin/python(+0x29d464) [0x5612acfba464]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyObject_MakeTpCall+0x25b) [0x5612ace9e39b]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyEval_EvalFrameDefault+0x68ce) [0x5612ace9899e]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyFunction_Vectorcall+0x7c) [0x5612acea838c]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyEval_EvalFrameDefault+0x2b60) [0x5612ace94c30]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyFunction_Vectorcall+0x7c) [0x5612acea838c]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyEval_EvalFrameDefault+0x18db) [0x5612ace939ab]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyFunction_Vectorcall+0x7c) [0x5612acea838c]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyEval_EvalFrameDefault+0x5632) [0x5612ace97702]
E        --- /opt/ttforge-toolchain/venv/bin/python(+0x1984d1) [0x5612aceb54d1]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyEval_EvalFrameDefault+0x5632) [0x5612ace97702]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyFunction_Vectorcall+0x7c) [0x5612acea838c]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyObject_FastCallDictTstate+0x16d) [0x5612ace9d61d]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyObject_Call_Prepend+0x5c) [0x5612aceb262c]
E        --- /opt/ttforge-toolchain/venv/bin/python(+0x29d464) [0x5612acfba464]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyObject_MakeTpCall+0x25b) [0x5612ace9e39b]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyEval_EvalFrameDefault+0x68ce) [0x5612ace9899e]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyFunction_Vectorcall+0x7c) [0x5612acea838c]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyEval_EvalFrameDefault+0x6c0) [0x5612ace92790]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyFunction_Vectorcall+0x7c) [0x5612acea838c]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyEval_EvalFrameDefault+0x6c0) [0x5612ace92790]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyFunction_Vectorcall+0x7c) [0x5612acea838c]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyEval_EvalFrameDefault+0x2b60) [0x5612ace94c30]

/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/compile.py:829: RuntimeError

record_forge_property = <test.conftest.ForgePropertyStore object at 0x7ff778c1d930>
variant = 'gpt2'

    @pytest.mark.nightly
    @pytest.mark.parametrize("variant", ["gpt2"])
    def test_gpt2_text_gen(record_forge_property, variant):
        # Build Module Name
        module_name = build_module_name(
            framework=Framework.PYTORCH, model="gpt2", variant=variant, task=Task.TEXT_GENERATION, source=Source.HUGGINGFACE
        )
    
        # Record Forge Property
        record_forge_property("tags.model_name", module_name)
    
        # Load tokenizer and model from HuggingFace
        config = GPT2Config.from_pretrained(variant)
        config_dict = config.to_dict()
        config_dict["return_dict"] = False
        config_dict["use_cache"] = False
        config = GPT2Config(**config_dict)
        model = download_model(GPT2LMHeadModel.from_pretrained, variant, config=config)
    
        # Wrapper to get around past key values
        class Wrapper(torch.nn.Module):
            def __init__(self, model):
                super().__init__()
                self.model = model
    
            def forward(self, input_ids, attention_mask):
                return self.model(input_ids, None, attention_mask)
    
        input_ids = torch.cat(
            [torch.randint(1, model.config.vocab_size, (1, 255)), torch.zeros(1, 1, dtype=torch.int64)], dim=-1
        ).to(torch.int64)
        attn_mask = torch.ones(1, 256)
        inputs = [input_ids, attn_mask]
    
        framework_model = Wrapper(model)
    
        # Forge compile framework model
        compiled_model = forge.compile(framework_model, sample_inputs=inputs, module_name=module_name)
    
        # Model Verification
>       verify(inputs, framework_model, compiled_model)

forge/test/models/pytorch/text/gpt2/test_gpt2.py:55: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/verify/verify.py:302: in verify
    co_out = compiled_model(*inputs)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <forge.compiled_graph_state.CompiledModel object at 0x7ff7a7574dc0>
inputs = (tensor([[29133, 28000, 17958, 22849, 11636, 28204, 34532, 32312, 14762, 43444, 13158, 47523, 49381, 42088, 25607, 398...1., 1., 1., 1., 1.,
         1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.]]))
inputs_and_parameters = [tensor([[29133, 28000, 17958, 22849, 11636, 28204, 34532, 32312, 14762, 43444, 13158, 47523, 49381, 42088, 25607, 398...      [1.00000e-05],
         [1.00000e-05],
         [1.00000e-05],
         [1.00000e-05]]]), tensor([0.12500]), ...]

    def __call__(self, *inputs: AnyTensor) -> List[torch.Tensor]:
        """
        Run inference on the compiled model.
    
        Parameters
        ----------
        inputs: [Tensor, ...]
            Input tensors
    
        Returns
        -------
        List[Tensor]
            Output tensors
        """
        self.inputs = [*to_pt_tensors(inputs)]
        # After tensors are transformed to pt tensors, we have to cast them to dtypes that are actually supported by our hardware.
        self.inputs = [cast_unsupported_torch_dtype(input_tensor) for input_tensor in self.inputs]
    
        inputs_and_parameters = [
            *self.inputs,
            *self.fwd_compiled_graph_state.get_ordered_constant_tensors(),
            *self.fwd_compiled_graph_state.get_ordered_parameter_tensors(),
        ]
    
        assert all(
            [isinstance(t, torch.Tensor) for t in inputs_and_parameters]
        ), "All inputs should be torch tensors by now."
    
        if self.training() and isinstance(self.framework_module, PyTorchModule):
            for name, param in self.framework_module.module.named_parameters():
                if param.requires_grad:
                    our_tensor = self.fwd_compiled_graph_state.get_parameter_tensor(name)
    
                    # NOTE: for parameters that require gradients, we want to share the same tensor with the PyTorch
                    # module. This is because we want to be able to optimize the parameters both on the device
                    # (through our runtime) and via the torch optimizers. So this ensures that whichever side updates
                    # the parameter value, the other side can see the change.
                    #
                    # This could change in the future, but for now ensure that our premise is correct.
                    assert param is our_tensor
    
        logger.info(
            f"Running model {self.framework_module.get_name()} {self.fwd_compiled_graph_state.graph.get_name()} on device..."
        )
>       all_outputs = run_binary(self.compiled_binary, int(ProgramId.FORWARD), inputs_and_parameters)
E       RuntimeError: Tensor 6 - data type mismatch: expected Float32, got UInt8

/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/compiled_graph_state.py:266: RuntimeError

record_forge_property = <test.conftest.ForgePropertyStore object at 0x7ff632f2ff40>
variant = 't5-base'

    @pytest.mark.nightly
    @pytest.mark.parametrize("variant", variants)
    def test_t5_generation(record_forge_property, variant):
        if variant not in {"t5-small", "google/flan-t5-small", "t5-base", "t5-large"}:
            pytest.skip(f"Skipping {variant} due to the current CI/CD pipeline limitations")
    
        # Build Module Name
        module_name = build_module_name(
            framework=Framework.PYTORCH, model="t5", variant=variant, task=Task.TEXT_GENERATION, source=Source.HUGGINGFACE
        )
    
        # Record Forge Property
        record_forge_property("tags.model_name", module_name)
    
        # Load tokenizer and model from HuggingFace
        # Variants: t5-small, t5-base, t5-large
    
        config = download_model(T5Config.from_pretrained, variant)
        config_dict = config.to_dict()
        config_dict["return_dict"] = False
        config_dict["use_cache"] = False
        config = T5Config(**config_dict)
        model = download_model(T5ForConditionalGeneration.from_pretrained, variant, config=config)
        tokenizer = AutoTokenizer.from_pretrained(variant)
    
        inputs = tokenizer(
            "summarize: Researchers have extensively studied the benefits of having pets, "
            "particularly dogs, on human health and well-being. Findings suggest that pet ownership "
            "can lead to improved mental health, reduced stress levels, and even physical health benefits "
            "such as lower blood pressure and increased physical activity levels due to regular walks.",
            return_tensors="pt",
        )
    
        input_ids = inputs.input_ids
        decoder_start_token_tensor = torch.tensor(model.generation_config.decoder_start_token_id, dtype=torch.long)
        decoder_input_ids = torch.ones((1, 1), dtype=torch.long) * decoder_start_token_tensor
        inputs = [input_ids, decoder_input_ids]
    
        class Wrapper(torch.nn.Module):
            def __init__(self, model):
                super().__init__()
                self.model = model
    
            def forward(self, input_ids, decoder_input_ids):
                inputs = {"input_ids": input_ids, "decoder_input_ids": decoder_input_ids}
                output = self.model(**inputs)
                return output
    
        framework_model = Wrapper(model)
    
        # Forge compile
        compiled_model = forge.compile(framework_model, sample_inputs=inputs, module_name=module_name)
    
        # Model Verification
>       verify(inputs, framework_model, compiled_model)

forge/test/models/pytorch/text/t5/test_t5.py:90: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/verify/verify.py:333: in verify
    verify_cfg.value_checker.check(fw, co)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <forge.verify.value_checkers.AutomaticValueChecker object at 0x7ff7bff2d5a0>
fw_out = tensor([[[-18.88008, -10.41466, -14.89029,  ..., -43.63989, -43.76201, -43.71423]]], grad_fn=<UnsafeViewBackward0>)
co_out = tensor([[[-22.58375, -11.44749, -15.44133,  ..., -44.65742, -44.40396, -44.39893]]])

    def check(self, fw_out, co_out):
        if not compare_with_golden(fw_out, co_out, self.pcc, self.rtol, self.atol, self.dissimilarity_threshold):
>           raise ValueError(
                f"Data mismatch -> AutomaticValueChecker (compare_with_golden): framework_model={fw_out}, compiled_model={co_out}"
            )
E           ValueError: Data mismatch -> AutomaticValueChecker (compare_with_golden): framework_model=tensor([[[-18.88008, -10.41466, -14.89029,  ..., -43.63989, -43.76201, -43.71423]]], grad_fn=<UnsafeViewBackward0>), compiled_model=tensor([[[-22.58375, -11.44749, -15.44133,  ..., -44.65742, -44.40396, -44.39893]]])

/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/verify/value_checkers.py:38: ValueError

record_forge_property = <test.conftest.ForgePropertyStore object at 0x7ff86c77cbb0>
variant = 'facebook/xglm-564M'

    @pytest.mark.nightly
    @pytest.mark.parametrize("variant", variants, ids=variants)
    def test_xglm_causal_lm(record_forge_property, variant):
        if variant != "facebook/xglm-564M":
            pytest.skip("Skipping due to the current CI/CD pipeline limitations")
    
        # Build Module Name
        module_name = build_module_name(
            framework=Framework.PYTORCH, model="xglm", variant=variant, task=Task.CAUSAL_LM, source=Source.HUGGINGFACE
        )
    
        # Record Forge Property
        record_forge_property("tags.model_name", module_name)
    
        config = XGLMConfig.from_pretrained(variant)
        config_dict = config.to_dict()
        config_dict["return_dict"] = False
        config_dict["use_cache"] = False
        config = XGLMConfig(**config_dict)
    
        framework_model = download_model(XGLMForCausalLM.from_pretrained, variant, config=config)
    
        tokenizer = download_model(AutoTokenizer.from_pretrained, variant)
        tokenizer.pad_token = tokenizer.eos_token
    
        # Input sample
        prefix_text = "My name is Thomas and my main"
        input_tokens = tokenizer(
            prefix_text,
            max_length=256,
            padding="max_length",
            truncation=True,
            return_tensors="pt",
        )
    
        inputs = [input_tokens["input_ids"], input_tokens["attention_mask"]]
    
        # Forge compile framework model
        compiled_model = forge.compile(framework_model, sample_inputs=inputs, module_name=module_name)
    
        # Model Verification
>       verify(inputs, framework_model, compiled_model)

forge/test/models/pytorch/text/xglm/test_xglm.py:57: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/verify/verify.py:302: in verify
    co_out = compiled_model(*inputs)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <forge.compiled_graph_state.CompiledModel object at 0x7ff85cb33eb0>
inputs = (tensor([[    2,  1622,  4432,    67, 10128,    53,   363,  3270,     2,     2,     2,     2,     2,     2,     2,    ...0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]))
inputs_and_parameters = [tensor([[    2,  1622,  4432,    67, 10128,    53,   363,  3270,     2,     2,     2,     2,     2,     2,     2,    ... [0.00098],
         [0.00098],
         [0.00098],
         [0.00098],
         [0.00098],
         [0.00098]]]), ...]

    def __call__(self, *inputs: AnyTensor) -> List[torch.Tensor]:
        """
        Run inference on the compiled model.
    
        Parameters
        ----------
        inputs: [Tensor, ...]
            Input tensors
    
        Returns
        -------
        List[Tensor]
            Output tensors
        """
        self.inputs = [*to_pt_tensors(inputs)]
        # After tensors are transformed to pt tensors, we have to cast them to dtypes that are actually supported by our hardware.
        self.inputs = [cast_unsupported_torch_dtype(input_tensor) for input_tensor in self.inputs]
    
        inputs_and_parameters = [
            *self.inputs,
            *self.fwd_compiled_graph_state.get_ordered_constant_tensors(),
            *self.fwd_compiled_graph_state.get_ordered_parameter_tensors(),
        ]
    
        assert all(
            [isinstance(t, torch.Tensor) for t in inputs_and_parameters]
        ), "All inputs should be torch tensors by now."
    
        if self.training() and isinstance(self.framework_module, PyTorchModule):
            for name, param in self.framework_module.module.named_parameters():
                if param.requires_grad:
                    our_tensor = self.fwd_compiled_graph_state.get_parameter_tensor(name)
    
                    # NOTE: for parameters that require gradients, we want to share the same tensor with the PyTorch
                    # module. This is because we want to be able to optimize the parameters both on the device
                    # (through our runtime) and via the torch optimizers. So this ensures that whichever side updates
                    # the parameter value, the other side can see the change.
                    #
                    # This could change in the future, but for now ensure that our premise is correct.
                    assert param is our_tensor
    
        logger.info(
            f"Running model {self.framework_module.get_name()} {self.fwd_compiled_graph_state.graph.get_name()} on device..."
        )
>       all_outputs = run_binary(self.compiled_binary, int(ProgramId.FORWARD), inputs_and_parameters)
E       RuntimeError: TT_THROW @ /__w/tt-forge-fe/tt-forge-fe/third_party/tt-mlir/third_party/tt-metal/src/tt-metal/tt_metal/impl/kernels/kernel.cpp:242: tt::exception
E       info:
E       1283 unique+common runtime args targeting kernel reader_concat_stick_layout_interleaved_start_id on (x=0,y=0) are too large. Max allowable is 256
E       backtrace:
E        --- /opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/lib/libtt_metal.so(+0x143335) [0x7ff7d5017335]
E        --- tt::tt_metal::v0::Kernel::validate_runtime_args_size(unsigned long, unsigned long, tt::umd::xy_pair const&)
E        --- tt::tt_metal::v0::Kernel::set_runtime_args(tt::umd::xy_pair const&, tt::stl::Span<unsigned int const, 18446744073709551615ul>)
E        --- /opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/lib/libtt_metal.so(+0x340573) [0x7ff7d5214573]
E        --- tt::tt_metal::v0::SetRuntimeArgs(tt::tt_metal::v0::Program const&, unsigned int, std::variant<tt::umd::xy_pair, CoreRange, CoreRangeSet> const&, tt::stl::Span<unsigned int const, 18446744073709551615ul>)
E        --- ttnn::operations::data_movement::detail::concat_multi_core(std::vector<tt::tt_metal::Tensor, std::allocator<tt::tt_metal::Tensor> > const&, unsigned int, tt::tt_metal::Tensor const&)
E        --- /opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/lib/_ttnn.so(+0x60fe83) [0x7ff7c3719e83]
E        --- /opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/lib/_ttnn.so(+0x25c0fda) [0x7ff7c56cafda]
E        --- /opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/lib/_ttnn.so(+0x25c13e4) [0x7ff7c56cb3e4]
E        --- /opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/lib/_ttnn.so(_ZN4ttnn16device_operation6detail23launch_on_worker_threadIN2tt8tt_metal9operation23OldInfraDeviceOperationISt6vectorINS4_6TensorESaIS8_EEEENS3_3stl10StrongTypeIhNS_10QueueIdTagEEElNS5_15DeviceOperationISA_EENSB_13tensor_args_tESA_PNS4_2v07IDeviceEEEvT0_T1_RKT2_RKT3_RT4_RT5_+0x2ab) [0x7ff7c56ca72b]
E        --- /opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/lib/_ttnn.so(_ZN4ttnn16device_operation6detail23launch_on_single_deviceIN2tt8tt_metal9operation23OldInfraDeviceOperationISt6vectorINS4_6TensorESaIS8_EEEEEENT_21tensor_return_value_tENS3_3stl10StrongTypeIhNS_10QueueIdTagEEERKNSC_22operation_attributes_tERKNSC_13tensor_args_tE+0x83) [0x7ff7c56ca3e3]
E        --- /opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/lib/_ttnn.so(+0x25c02d7) [0x7ff7c56ca2d7]
E        --- tt::tt_metal::operation::OldInfraDeviceOperation<std::vector<tt::tt_metal::Tensor, std::allocator<tt::tt_metal::Tensor> > >::tensor_return_value_t ttnn::device_operation::detail::invoke<tt::tt_metal::operation::OldInfraDeviceOperation<std::vector<tt::tt_metal::Tensor, std::allocator<tt::tt_metal::Tensor> > > >(tt::stl::StrongType<unsigned char, ttnn::QueueIdTag>, tt::tt_metal::operation::OldInfraDeviceOperation<std::vector<tt::tt_metal::Tensor, std::allocator<tt::tt_metal::Tensor> > >::operation_attributes_t const&, tt::tt_metal::operation::OldInfraDeviceOperation<std::vector<tt::tt_metal::Tensor, std::allocator<tt::tt_metal::Tensor> > >::tensor_args_t const&)
E        --- /opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/lib/_ttnn.so(+0x25bf49f) [0x7ff7c56c949f]
E        --- /opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/lib/_ttnn.so(_ZN2tt8tt_metal9operation19run_with_autoformatEONS1_15DeviceOperationISt6vectorINS0_6TensorESaIS4_EEEERKS6_RKS3_IN4ttnn10operations12experimental11auto_format12FormatParamsESaISF_EERKS3_INS0_6LayoutESaISK_EERKS3_ISt8optionalIKS4_ESaISR_EERKS3_ISP_ISF_ESaISW_EERKS3_ISP_IS4_ESaIS11_EENS_3stl10StrongTypeIhNSB_10QueueIdTagEEE+0x503) [0x7ff7c56c1883]
E        --- /opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/lib/_ttnn.so(+0x60f0b5) [0x7ff7c37190b5]
E        --- /opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/lib/_ttnn.so(+0x25c6fe2) [0x7ff7c56d0fe2]
E        --- /opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/lib/_ttnn.so(+0x25c77cc) [0x7ff7c56d17cc]
E        --- void tt::tt_metal::operation::launch_op_func<std::vector<tt::tt_metal::Tensor, std::allocator<tt::tt_metal::Tensor> > >(std::function<std::vector<tt::tt_metal::Tensor, std::allocator<tt::tt_metal::Tensor> > (std::vector<tt::tt_metal::Tensor, std::allocator<tt::tt_metal::Tensor> > const&, std::vector<std::optional<tt::tt_metal::Tensor const>, std::allocator<std::optional<tt::tt_metal::Tensor const> > > const&, std::vector<std::optional<tt::tt_metal::Tensor>, std::allocator<std::optional<tt::tt_metal::Tensor> > > const&)> const&, std::vector<tt::tt_metal::Tensor, std::allocator<tt::tt_metal::Tensor> >, std::vector<tt::tt_metal::Tensor, std::allocator<tt::tt_metal::Tensor> >&, std::vector<std::optional<tt::tt_metal::Tensor const>, std::allocator<std::optional<tt::tt_metal::Tensor const> > >, std::vector<std::optional<tt::tt_metal::Tensor>, std::allocator<std::optional<tt::tt_metal::Tensor> > >, bool)
E        --- ttnn::operations::data_movement::concat_impl(std::vector<tt::tt_metal::Tensor, std::allocator<tt::tt_metal::Tensor> > const&, long, unsigned int, tt::tt_metal::MemoryConfig const&)
E        --- /opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/lib/_ttnn.so(+0x60947b) [0x7ff7c371347b]
E        --- /opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/lib/_ttnn.so(+0x5e8895) [0x7ff7c36f2895]
E        --- ttnn::operations::data_movement::ConcatOperation::invoke(tt::stl::StrongType<unsigned char, ttnn::QueueIdTag>, std::vector<tt::tt_metal::Tensor, std::allocator<tt::tt_metal::Tensor> > const&, int, std::optional<tt::tt_metal::MemoryConfig> const&, std::optional<tt::tt_metal::Tensor> const&, unsigned int)
E        --- /opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/lib/_ttnn.so(+0x577513) [0x7ff7c3681513]
E        --- /opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/lib/_ttnn.so(+0x577421) [0x7ff7c3681421]
E        --- /opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/lib/_ttnn.so(+0x57737c) [0x7ff7c368137c]
E        --- void tt::tt_metal::operation::launch_op_func<std::vector<tt::tt_metal::Tensor, std::allocator<tt::tt_metal::Tensor> > >(std::function<std::vector<tt::tt_metal::Tensor, std::allocator<tt::tt_metal::Tensor> > (std::vector<tt::tt_metal::Tensor, std::allocator<tt::tt_metal::Tensor> > const&, std::vector<std::optional<tt::tt_metal::Tensor const>, std::allocator<std::optional<tt::tt_metal::Tensor const> > > const&, std::vector<std::optional<tt::tt_metal::Tensor>, std::allocator<std::optional<tt::tt_metal::Tensor> > > const&)> const&, std::vector<tt::tt_metal::Tensor, std::allocator<tt::tt_metal::Tensor> >, std::vector<tt::tt_metal::Tensor, std::allocator<tt::tt_metal::Tensor> >&, std::vector<std::optional<tt::tt_metal::Tensor const>, std::allocator<std::optional<tt::tt_metal::Tensor const> > >, std::vector<std::optional<tt::tt_metal::Tensor>, std::allocator<std::optional<tt::tt_metal::Tensor> > >, bool)
E        --- /opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/lib/_ttnn.so(+0x57713c) [0x7ff7c368113c]
E        --- /opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/lib/_ttnn.so(+0x576bcb) [0x7ff7c3680bcb]
E        --- /opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/lib/_ttnn.so(+0x576749) [0x7ff7c3680749]
E        --- ttnn::operations::data_movement::ExecuteRepeatInterleave::invoke(tt::tt_metal::Tensor const&, unsigned int, int, std::optional<tt::tt_metal::MemoryConfig> const&)
E        --- /opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/lib/libTTMLIRRuntime.so(+0xe8614) [0x7ff7d5c16614]
E        --- /opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/lib/libTTMLIRRuntime.so(+0xe838a) [0x7ff7d5c1638a]
E        --- /opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/lib/libTTMLIRRuntime.so(+0xe82dc) [0x7ff7d5c162dc]
E        --- /opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/lib/_ttnn.so(+0x25c6fe2) [0x7ff7c56d0fe2]
E        --- /opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/lib/_ttnn.so(+0x25c77cc) [0x7ff7c56d17cc]
E        --- void tt::tt_metal::operation::launch_op_func<std::vector<tt::tt_metal::Tensor, std::allocator<tt::tt_metal::Tensor> > >(std::function<std::vector<tt::tt_metal::Tensor, std::allocator<tt::tt_metal::Tensor> > (std::vector<tt::tt_metal::Tensor, std::allocator<tt::tt_metal::Tensor> > const&, std::vector<std::optional<tt::tt_metal::Tensor const>, std::allocator<std::optional<tt::tt_metal::Tensor const> > > const&, std::vector<std::optional<tt::tt_metal::Tensor>, std::allocator<std::optional<tt::tt_metal::Tensor> > > const&)> const&, std::vector<tt::tt_metal::Tensor, std::allocator<tt::tt_metal::Tensor> >, std::vector<tt::tt_metal::Tensor, std::allocator<tt::tt_metal::Tensor> >&, std::vector<std::optional<tt::tt_metal::Tensor const>, std::allocator<std::optional<tt::tt_metal::Tensor const> > >, std::vector<std::optional<tt::tt_metal::Tensor>, std::allocator<std::optional<tt::tt_metal::Tensor> > >, bool)
E        --- /opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/lib/libTTMLIRRuntime.so(+0xe7fe3) [0x7ff7d5c15fe3]
E        --- /opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/lib/libTTMLIRRuntime.so(+0xe7b2c) [0x7ff7d5c15b2c]
E        --- /opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/lib/libTTMLIRRuntime.so(+0xe75b7) [0x7ff7d5c155b7]
E        --- tt::runtime::ttnn::operations::data_movement::run(tt::target::ttnn::RepeatInterleaveOp const*, tt::runtime::ttnn::ProgramContext&)
E        --- tt::runtime::ttnn::runProgram(tt::tt_metal::distributed::MeshDevice&, tt::runtime::Binary, unsigned int, std::vector<tt::tt_metal::Tensor*, std::allocator<tt::tt_metal::Tensor*> > const&)
E        --- tt::runtime::ttnn::submit(tt::runtime::Device, tt::runtime::Binary, unsigned int, std::vector<tt::runtime::Tensor, std::allocator<tt::runtime::Tensor> > const&)
E        --- tt::runtime::submit(tt::runtime::Device, tt::runtime::Binary, unsigned int, std::vector<tt::runtime::Tensor, std::allocator<tt::runtime::Tensor> > const&)
E        --- tt::run_binary(tt::runtime::Binary&, int, std::vector<at::Tensor, std::allocator<at::Tensor> > const&)
E        --- /opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/_C.so(+0x318900) [0x7ff7d6058900]
E        --- /opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/_C.so(+0x31884e) [0x7ff7d605884e]
E        --- /opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/_C.so(+0xe1e05) [0x7ff7d5e21e05]
E        --- /opt/ttforge-toolchain/venv/bin/python(+0x18ab32) [0x5612acea7b32]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyObject_MakeTpCall+0x25b) [0x5612ace9e39b]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyEval_EvalFrameDefault+0x59c7) [0x5612ace97a97]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyObject_FastCallDictTstate+0xc4) [0x5612ace9d574]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyObject_Call_Prepend+0x5c) [0x5612aceb262c]
E        --- /opt/ttforge-toolchain/venv/bin/python(+0x29d464) [0x5612acfba464]
E        --- /opt/ttforge-toolchain/venv/bin/python(PyObject_Call+0xbb) [0x5612aceb610b]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyEval_EvalFrameDefault+0x2b60) [0x5612ace94c30]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyFunction_Vectorcall+0x7c) [0x5612acea838c]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyEval_EvalFrameDefault+0x6c0) [0x5612ace92790]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyFunction_Vectorcall+0x7c) [0x5612acea838c]
E        --- /opt/ttforge-toolchain/venv/bin/python(PyObject_Call+0x122) [0x5612aceb6172]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyEval_EvalFrameDefault+0x2b60) [0x5612ace94c30]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyFunction_Vectorcall+0x7c) [0x5612acea838c]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyEval_EvalFrameDefault+0x2b60) [0x5612ace94c30]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyFunction_Vectorcall+0x7c) [0x5612acea838c]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyEval_EvalFrameDefault+0x18db) [0x5612ace939ab]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyFunction_Vectorcall+0x7c) [0x5612acea838c]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyEval_EvalFrameDefault+0x5632) [0x5612ace97702]
E        --- /opt/ttforge-toolchain/venv/bin/python(+0x1984d1) [0x5612aceb54d1]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyEval_EvalFrameDefault+0x5632) [0x5612ace97702]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyFunction_Vectorcall+0x7c) [0x5612acea838c]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyObject_FastCallDictTstate+0x16d) [0x5612ace9d61d]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyObject_Call_Prepend+0x5c) [0x5612aceb262c]
E        --- /opt/ttforge-toolchain/venv/bin/python(+0x29d464) [0x5612acfba464]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyObject_MakeTpCall+0x25b) [0x5612ace9e39b]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyEval_EvalFrameDefault+0x68ce) [0x5612ace9899e]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyFunction_Vectorcall+0x7c) [0x5612acea838c]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyEval_EvalFrameDefault+0x8af) [0x5612ace9297f]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyFunction_Vectorcall+0x7c) [0x5612acea838c]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyEval_EvalFrameDefault+0x2b60) [0x5612ace94c30]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyFunction_Vectorcall+0x7c) [0x5612acea838c]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyEval_EvalFrameDefault+0x18db) [0x5612ace939ab]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyFunction_Vectorcall+0x7c) [0x5612acea838c]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyEval_EvalFrameDefault+0x5632) [0x5612ace97702]
E        --- /opt/ttforge-toolchain/venv/bin/python(+0x1984d1) [0x5612aceb54d1]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyEval_EvalFrameDefault+0x5632) [0x5612ace97702]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyFunction_Vectorcall+0x7c) [0x5612acea838c]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyObject_FastCallDictTstate+0x16d) [0x5612ace9d61d]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyObject_Call_Prepend+0x5c) [0x5612aceb262c]
E        --- /opt/ttforge-toolchain/venv/bin/python(+0x29d464) [0x5612acfba464]
E        --- /opt/ttforge-toolchain/venv/bin/python(PyObject_Call+0xbb) [0x5612aceb610b]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyEval_EvalFrameDefault+0x2b60) [0x5612ace94c30]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyFunction_Vectorcall+0x7c) [0x5612acea838c]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyEval_EvalFrameDefault+0x6c0) [0x5612ace92790]
E        --- /opt/ttforge-toolchain/venv/bin/python(+0x1984d1) [0x5612aceb54d1]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyEval_EvalFrameDefault+0x18db) [0x5612ace939ab]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyFunction_Vectorcall+0x7c) [0x5612acea838c]
E        --- /opt/ttforge-toolchain/venv/bin/python(_PyEval_EvalFrameDefault+0x2b60) [0x5612ace94c30]

/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/compiled_graph_state.py:266: RuntimeError

record_forge_property = <test.conftest.ForgePropertyStore object at 0x7ff85cb744c0>

    @pytest.mark.nightly
    def test_conv_ae_pytorch(record_forge_property):
        # Build Module Name
        module_name = build_module_name(
            framework=Framework.PYTORCH, model="autoencoder", variant="conv", task=Task.IMAGE_ENCODING, source=Source.GITHUB
        )
    
        # Record Forge Property
        record_forge_property("tags.model_name", module_name)
    
        # Instantiate model
        # NOTE: The model has not been pre-trained or fine-tuned.
        # This is for demonstration purposes only.
        framework_model = ConvAE()
    
        # Define transform to normalize data
        transform = transforms.Compose(
            [
                transforms.ToTensor(),
                transforms.Normalize((0.1307,), (0.3081,)),
            ]
        )
    
        # Load sample from MNIST dataset
        dataset = load_dataset("mnist")
        sample = dataset["train"][0]["image"]
        sample_tensor = transform(sample).unsqueeze(0)
    
        inputs = [sample_tensor]
    
        # Forge compile framework model
>       compiled_model = forge.compile(framework_model, sample_inputs=inputs, module_name=module_name)

forge/test/models/pytorch/vision/autoencoder/test_autoencoder.py:51: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/compile.py:251: in compile_main
    return forge_compile_from_context(compile_context)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/compile.py:293: in forge_compile_from_context
    next_stage = stage_to_func[current_stage](context)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

context = CompileContext(modules=[Module pt_autoencoder_conv_img_enc_github], graph_name='pt_autoencoder_conv_img_enc_github', c...cles_offset=0, forge_module=<forge._C.ForgeGraphModule object at 0x7ff86bd33870>, compiled_binary=None, attach_to=None)

    def run_mlir_compiler(context: CompileContext) -> CompileDepth:
        assert context.forge_module is not None
    
>       context.compiled_binary = forge._C.run_mlir_compiler(context.forge_module)
E       RuntimeError: Found Unsupported operations while lowering from TTForge to TTIR in forward graph

/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/compile.py:986: RuntimeError

record_forge_property = <test.conftest.ForgePropertyStore object at 0x7ff85cb32800>
variant = 'facebook/deit-base-patch16-224'

    @pytest.mark.nightly
    @pytest.mark.parametrize("variant", variants, ids=variants)
    def test_deit_imgcls_hf_pytorch(record_forge_property, variant):
        if variant != "facebook/deit-base-patch16-224":
            pytest.skip("Skipping due to the current CI/CD pipeline limitations")
    
        # Build Module Name
        module_name = build_module_name(
            framework=Framework.PYTORCH,
            model="deit",
            variant=variant,
            task=Task.IMAGE_CLASSIFICATION,
            source=Source.HUGGINGFACE,
        )
    
        # Record Forge Property
        record_forge_property("tags.model_name", module_name)
    
        framework_model, inputs, _ = generate_model_deit_imgcls_hf_pytorch(
            variant,
        )
        # Forge compile framework model
>       compiled_model = forge.compile(framework_model, sample_inputs=inputs, module_name=module_name)

forge/test/models/pytorch/vision/deit/test_deit.py:59: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/compile.py:251: in compile_main
    return forge_compile_from_context(compile_context)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/compile.py:293: in forge_compile_from_context
    next_stage = stage_to_func[current_stage](context)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

context = CompileContext(modules=[Module pt_deit_facebook_deit_base_patch16_224_img_cls_hf], graph_name='pt_deit_facebook_deit_b...cles_offset=0, forge_module=<forge._C.ForgeGraphModule object at 0x7ff85c657170>, compiled_binary=None, attach_to=None)

    def run_mlir_compiler(context: CompileContext) -> CompileDepth:
        assert context.forge_module is not None
    
>       context.compiled_binary = forge._C.run_mlir_compiler(context.forge_module)
E       RuntimeError: Failed to run MLIR compiler pass pipeline.

/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/compile.py:986: RuntimeError

record_forge_property = <test.conftest.ForgePropertyStore object at 0x7ff85cdd66b0>
variant = 'densenet121_hf_xray'

    @pytest.mark.nightly
    @pytest.mark.parametrize("variant", variants, ids=variants)
    def test_densenet_121_pytorch(record_forge_property, variant):
        if variant == "densenet121":
            pytest.skip("Skipping due to the current CI/CD pipeline limitations")
    
        # Build Module Name
        module_name = build_module_name(
            framework=Framework.PYTORCH,
            model="densenet",
            variant=variant,
            source=Source.TORCHVISION,
            task=Task.IMAGE_CLASSIFICATION,
        )
    
        # Record Forge Property
        record_forge_property("tags.model_name", module_name)
    
        # STEP 2: Create Forge module from PyTorch model
        if variant == "densenet121":
            framework_model = download_model(torch.hub.load, "pytorch/vision:v0.10.0", "densenet121", pretrained=True)
            img_tensor = get_input_img()
        else:
            model_name = "densenet121-res224-all"
            model = download_model(xrv.models.get_model, model_name)
            framework_model = densenet_xray_wrapper(model)
            img_tensor = get_input_img_hf_xray()
    
        # STEP 3: Run inference on Tenstorrent device
        inputs = [img_tensor]
    
        # Forge compile framework model
        compiled_model = forge.compile(framework_model, sample_inputs=inputs, module_name=module_name)
    
        # Model Verification
>       verify(inputs, framework_model, compiled_model)

forge/test/models/pytorch/vision/densenet/test_densenet.py:71: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/verify/verify.py:333: in verify
    verify_cfg.value_checker.check(fw, co)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <forge.verify.value_checkers.AutomaticValueChecker object at 0x7ff7bff2d5a0>
fw_out = tensor([[0.04869, 0.03288, 0.15531, 0.00566, 0.00114, 0.00461, 0.04744, 0.05742, 0.02110, 0.01312, 0.03669, 0.39452, 0.29256, 0.00085, 0.00119, 0.07292, 0.34686, 0.02730]], grad_fn=<SigmoidBackward0>)
co_out = tensor([[0.02852, 0.02478, 0.21812, 0.00000, 0.00000, 0.00000, 0.05164, 0.04782, 0.01900, 0.02173, 0.02818, 0.42412, 0.28394, 0.00000, 0.00000, 0.04551, 0.45030, 0.02611]])

    def check(self, fw_out, co_out):
        if not compare_with_golden(fw_out, co_out, self.pcc, self.rtol, self.atol, self.dissimilarity_threshold):
>           raise ValueError(
                f"Data mismatch -> AutomaticValueChecker (compare_with_golden): framework_model={fw_out}, compiled_model={co_out}"
            )
E           ValueError: Data mismatch -> AutomaticValueChecker (compare_with_golden): framework_model=tensor([[0.04869, 0.03288, 0.15531, 0.00566, 0.00114, 0.00461, 0.04744, 0.05742, 0.02110, 0.01312, 0.03669, 0.39452, 0.29256, 0.00085, 0.00119, 0.07292, 0.34686, 0.02730]], grad_fn=<SigmoidBackward0>), compiled_model=tensor([[0.02852, 0.02478, 0.21812, 0.00000, 0.00000, 0.00000, 0.05164, 0.04782, 0.01900, 0.02173, 0.02818, 0.42412, 0.28394, 0.00000, 0.00000, 0.04551, 0.45030, 0.02611]])

/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/verify/value_checkers.py:38: ValueError

record_forge_property = <test.conftest.ForgePropertyStore object at 0x7ff85c2e2c20>
variant = 'facebook/detr-resnet-50'

    @pytest.mark.nightly
    @pytest.mark.parametrize("variant", ["facebook/detr-resnet-50"])
    def test_detr_detection(record_forge_property, variant):
        # Build Module Name
        module_name = build_module_name(
            framework=Framework.PYTORCH,
            model="detr",
            variant=variant,
            task=Task.OBJECT_DETECTION,
            source=Source.HUGGINGFACE,
        )
    
        # Record Forge Property
        record_forge_property("tags.model_name", module_name)
    
        # Load the model
        framework_model = DetrForObjectDetection.from_pretrained(variant)
    
        # Preprocess the image for the model
        image_url = "http://images.cocodataset.org/val2017/000000397133.jpg"
        input_batch = preprocess_input_data(image_url)
    
        inputs = [input_batch]
    
        # Forge compile framework model
>       compiled_model = forge.compile(framework_model, sample_inputs=inputs, module_name=module_name)

forge/test/models/pytorch/vision/detr/test_detr.py:42: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/compile.py:251: in compile_main
    return forge_compile_from_context(compile_context)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/compile.py:293: in forge_compile_from_context
    next_stage = stage_to_func[current_stage](context)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/compile.py:672: in generate_initial_graph
    module, module_inputs = convert_to_forge_module(
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/compile.py:1033: in convert_to_forge_module
    forge_module, dev_types, module_inputs = generate_forge_module(
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_to_python.py:2064: in generate_forge_module
    module_writers, flattened_inputs = compile_tvm_to_python(
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_to_python.py:2153: in compile_tvm_to_python
    json_graphs, flattened_pytorch_inputs, weights = load_tvm_graph(
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/forge_compile.py:107: in load_tvm_graph
    json_graphs, flattened_inputs = compile_tvm_graph(
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/forge_compile.py:160: in compile_tvm_graph
    json_graphs, inputs = compile_pytorch_for_forge(
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/forge_compile.py:497: in compile_pytorch_for_forge
    json_graphs = extract_graphs(
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/forge_compile.py:257: in extract_graphs
    if op.name_hint != cpu_pre_function:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Op(zeros), name = 'name_hint'

    def __getattr__(self, name):
        # specially check handle since
        # this is required for PackedFunc calls
        if name == "handle":
            raise AttributeError("handle is not set")
    
        try:
            return _ffi_node_api.NodeGetAttr(self, name)
        except AttributeError:
>           raise AttributeError(f"{type(self)} has no attribute {name}") from None
E           AttributeError: <class 'tvm.ir.op.Op'> has no attribute name_hint

/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/tvm/runtime/object.py:78: AttributeError

self = <urllib.request.HTTPHandler object at 0x7ff85c23b9d0>
http_class = <class 'http.client.HTTPConnection'>
req = <urllib.request.Request object at 0x7ff786509b70>, http_conn_args = {}
host = 'dl.yf.io', h = <http.client.HTTPConnection object at 0x7ff86bd0f160>

    def do_open(self, http_class, req, **http_conn_args):
        """Return an HTTPResponse object for the request, using http_class.
    
        http_class must implement the HTTPConnection API from http.client.
        """
        host = req.host
        if not host:
            raise URLError('no host given')
    
        # will parse host:port
        h = http_class(host, timeout=req.timeout, **http_conn_args)
        h.set_debuglevel(self._debuglevel)
    
        headers = dict(req.unredirected_hdrs)
        headers.update({k: v for k, v in req.headers.items()
                        if k not in headers})
    
        # TODO(jhylton): Should this be redesigned to handle
        # persistent connections?
    
        # We want to make an HTTP/1.1 request, but the addinfourl
        # class isn't prepared to deal with a persistent connection.
        # It will try to read all remaining data from the socket,
        # which will block while the server waits for the next request.
        # So make sure the connection gets closed after the (only)
        # request.
        headers["Connection"] = "close"
        headers = {name.title(): val for name, val in headers.items()}
    
        if req._tunnel_host:
            tunnel_headers = {}
            proxy_auth_hdr = "Proxy-Authorization"
            if proxy_auth_hdr in headers:
                tunnel_headers[proxy_auth_hdr] = headers[proxy_auth_hdr]
                # Proxy-Authorization should not be sent to origin
                # server.
                del headers[proxy_auth_hdr]
            h.set_tunnel(req._tunnel_host, headers=tunnel_headers)
    
        try:
            try:
>               h.request(req.get_method(), req.selector, req.data, headers,
                          encode_chunked=req.has_header('Transfer-encoding'))

/usr/lib/python3.10/urllib/request.py:1348: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <http.client.HTTPConnection object at 0x7ff86bd0f160>, method = 'GET'
url = '/dla/models/imagenet/dla34-ba72cf86.pth', body = None
headers = {'Connection': 'close', 'Host': 'dl.yf.io', 'User-Agent': 'torch.hub'}

    def request(self, method, url, body=None, headers={}, *,
                encode_chunked=False):
        """Send a complete request to the server."""
>       self._send_request(method, url, body, headers, encode_chunked)

/usr/lib/python3.10/http/client.py:1283: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <http.client.HTTPConnection object at 0x7ff86bd0f160>, method = 'GET'
url = '/dla/models/imagenet/dla34-ba72cf86.pth', body = None
headers = {'Connection': 'close', 'Host': 'dl.yf.io', 'User-Agent': 'torch.hub'}
encode_chunked = False

    def _send_request(self, method, url, body, headers, encode_chunked):
        # Honor explicitly requested Host: and Accept-Encoding: headers.
        header_names = frozenset(k.lower() for k in headers)
        skips = {}
        if 'host' in header_names:
            skips['skip_host'] = 1
        if 'accept-encoding' in header_names:
            skips['skip_accept_encoding'] = 1
    
        self.putrequest(method, url, **skips)
    
        # chunked encoding will happen if HTTP/1.1 is used and either
        # the caller passes encode_chunked=True or the following
        # conditions hold:
        # 1. content-length has not been explicitly set
        # 2. the body is a file or iterable, but not a str or bytes-like
        # 3. Transfer-Encoding has NOT been explicitly set by the caller
    
        if 'content-length' not in header_names:
            # only chunk body if not explicitly set for backwards
            # compatibility, assuming the client code is already handling the
            # chunking
            if 'transfer-encoding' not in header_names:
                # if content-length cannot be automatically determined, fall
                # back to chunked encoding
                encode_chunked = False
                content_length = self._get_content_length(body, method)
                if content_length is None:
                    if body is not None:
                        if self.debuglevel > 0:
                            print('Unable to determine size of %r' % body)
                        encode_chunked = True
                        self.putheader('Transfer-Encoding', 'chunked')
                else:
                    self.putheader('Content-Length', str(content_length))
        else:
            encode_chunked = False
    
        for hdr, value in headers.items():
            self.putheader(hdr, value)
        if isinstance(body, str):
            # RFC 2616 Section 3.7.1 says that text default has a
            # default charset of iso-8859-1.
            body = _encode(body, 'body')
>       self.endheaders(body, encode_chunked=encode_chunked)

/usr/lib/python3.10/http/client.py:1329: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <http.client.HTTPConnection object at 0x7ff86bd0f160>
message_body = None

    def endheaders(self, message_body=None, *, encode_chunked=False):
        """Indicate that the last header line has been sent to the server.
    
        This method sends the request to the server.  The optional message_body
        argument can be used to pass a message body associated with the
        request.
        """
        if self.__state == _CS_REQ_STARTED:
            self.__state = _CS_REQ_SENT
        else:
            raise CannotSendHeader()
>       self._send_output(message_body, encode_chunked=encode_chunked)

/usr/lib/python3.10/http/client.py:1278: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <http.client.HTTPConnection object at 0x7ff86bd0f160>
message_body = None, encode_chunked = False

    def _send_output(self, message_body=None, encode_chunked=False):
        """Send the currently buffered request and clear the buffer.
    
        Appends an extra \\r\\n to the buffer.
        A message_body may be specified, to be appended to the request.
        """
        self._buffer.extend((b"", b""))
        msg = b"\r\n".join(self._buffer)
        del self._buffer[:]
>       self.send(msg)

/usr/lib/python3.10/http/client.py:1038: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <http.client.HTTPConnection object at 0x7ff86bd0f160>
data = b'GET /dla/models/imagenet/dla34-ba72cf86.pth HTTP/1.1\r\nAccept-Encoding: identity\r\nHost: dl.yf.io\r\nUser-Agent: torch.hub\r\nConnection: close\r\n\r\n'

    def send(self, data):
        """Send `data' to the server.
        ``data`` can be a string object, a bytes object, an array object, a
        file-like object that supports a .read() method, or an iterable object.
        """
    
        if self.sock is None:
            if self.auto_open:
>               self.connect()

/usr/lib/python3.10/http/client.py:976: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <http.client.HTTPConnection object at 0x7ff86bd0f160>

    def connect(self):
        """Connect to the host and port specified in __init__."""
        sys.audit("http.client.connect", self, self.host, self.port)
>       self.sock = self._create_connection(
            (self.host,self.port), self.timeout, self.source_address)

/usr/lib/python3.10/http/client.py:942: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

address = ('dl.yf.io', 80), timeout = <object object at 0x7ff88985ce70>
source_address = None

    def create_connection(address, timeout=_GLOBAL_DEFAULT_TIMEOUT,
                          source_address=None):
        """Connect to *address* and return the socket object.
    
        Convenience function.  Connect to *address* (a 2-tuple ``(host,
        port)``) and return the socket object.  Passing the optional
        *timeout* parameter will set the timeout on the socket instance
        before attempting to connect.  If no *timeout* is supplied, the
        global default timeout setting returned by :func:`getdefaulttimeout`
        is used.  If *source_address* is set it must be a tuple of (host, port)
        for the socket to bind as a source address before making the connection.
        A host of '' or port 0 tells the OS to use the default.
        """
    
        host, port = address
        err = None
        for res in getaddrinfo(host, port, 0, SOCK_STREAM):
            af, socktype, proto, canonname, sa = res
            sock = None
            try:
                sock = socket(af, socktype, proto)
                if timeout is not _GLOBAL_DEFAULT_TIMEOUT:
                    sock.settimeout(timeout)
                if source_address:
                    sock.bind(source_address)
                sock.connect(sa)
                # Break explicitly a reference cycle
                err = None
                return sock
    
            except error as _:
                err = _
                if sock is not None:
                    sock.close()
    
        if err is not None:
            try:
>               raise err

/usr/lib/python3.10/socket.py:845: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

address = ('dl.yf.io', 80), timeout = <object object at 0x7ff88985ce70>
source_address = None

    def create_connection(address, timeout=_GLOBAL_DEFAULT_TIMEOUT,
                          source_address=None):
        """Connect to *address* and return the socket object.
    
        Convenience function.  Connect to *address* (a 2-tuple ``(host,
        port)``) and return the socket object.  Passing the optional
        *timeout* parameter will set the timeout on the socket instance
        before attempting to connect.  If no *timeout* is supplied, the
        global default timeout setting returned by :func:`getdefaulttimeout`
        is used.  If *source_address* is set it must be a tuple of (host, port)
        for the socket to bind as a source address before making the connection.
        A host of '' or port 0 tells the OS to use the default.
        """
    
        host, port = address
        err = None
        for res in getaddrinfo(host, port, 0, SOCK_STREAM):
            af, socktype, proto, canonname, sa = res
            sock = None
            try:
                sock = socket(af, socktype, proto)
                if timeout is not _GLOBAL_DEFAULT_TIMEOUT:
                    sock.settimeout(timeout)
                if source_address:
                    sock.bind(source_address)
>               sock.connect(sa)
E               TimeoutError: [Errno 110] Connection timed out

/usr/lib/python3.10/socket.py:833: TimeoutError

During handling of the above exception, another exception occurred:

record_forge_property = <test.conftest.ForgePropertyStore object at 0x7ff786508190>
variant = 'dla34'

    @pytest.mark.nightly
    @pytest.mark.parametrize("variant", variants)
    def test_dla_pytorch(record_forge_property, variant):
        if variant != "dla34":
            pytest.skip("Skipping due to the current CI/CD pipeline limitations")
    
        # Build Module Name
        module_name = build_module_name(
            framework=Framework.PYTORCH, model="dla", variant=variant, task=Task.VISUAL_BACKBONE, source=Source.TORCHVISION
        )
    
        # Record Forge Property
        record_forge_property("tags.model_name", module_name)
    
        # Load the model and prepare input data
>       framework_model, inputs = load_dla_model(variant)

forge/test/models/pytorch/vision/dla/test_dla.py:41: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
forge/test/models/pytorch/vision/dla/utils/utils.py:34: in load_dla_model
    framework_model = func(pretrained="imagenet")
forge/test/models/pytorch/vision/dla/utils/dla_model.py:473: in dla34
    model.load_pretrained_model(pretrained, "dla34")
forge/test/models/pytorch/vision/dla/utils/dla_model.py:466: in load_pretrained_model
    self.load_state_dict(model_zoo.load_url(model_url))
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/torch/hub.py:766: in load_state_dict_from_url
    download_url_to_file(url, cached_file, hash_prefix, progress=progress)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/torch/hub.py:620: in download_url_to_file
    u = urlopen(req)
/usr/lib/python3.10/urllib/request.py:216: in urlopen
    return opener.open(url, data, timeout)
/usr/lib/python3.10/urllib/request.py:519: in open
    response = self._open(req, data)
/usr/lib/python3.10/urllib/request.py:536: in _open
    result = self._call_chain(self.handle_open, protocol, protocol +
/usr/lib/python3.10/urllib/request.py:496: in _call_chain
    result = func(*args)
/usr/lib/python3.10/urllib/request.py:1377: in http_open
    return self.do_open(http.client.HTTPConnection, req)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <urllib.request.HTTPHandler object at 0x7ff85c23b9d0>
http_class = <class 'http.client.HTTPConnection'>
req = <urllib.request.Request object at 0x7ff786509b70>, http_conn_args = {}
host = 'dl.yf.io', h = <http.client.HTTPConnection object at 0x7ff86bd0f160>

    def do_open(self, http_class, req, **http_conn_args):
        """Return an HTTPResponse object for the request, using http_class.
    
        http_class must implement the HTTPConnection API from http.client.
        """
        host = req.host
        if not host:
            raise URLError('no host given')
    
        # will parse host:port
        h = http_class(host, timeout=req.timeout, **http_conn_args)
        h.set_debuglevel(self._debuglevel)
    
        headers = dict(req.unredirected_hdrs)
        headers.update({k: v for k, v in req.headers.items()
                        if k not in headers})
    
        # TODO(jhylton): Should this be redesigned to handle
        # persistent connections?
    
        # We want to make an HTTP/1.1 request, but the addinfourl
        # class isn't prepared to deal with a persistent connection.
        # It will try to read all remaining data from the socket,
        # which will block while the server waits for the next request.
        # So make sure the connection gets closed after the (only)
        # request.
        headers["Connection"] = "close"
        headers = {name.title(): val for name, val in headers.items()}
    
        if req._tunnel_host:
            tunnel_headers = {}
            proxy_auth_hdr = "Proxy-Authorization"
            if proxy_auth_hdr in headers:
                tunnel_headers[proxy_auth_hdr] = headers[proxy_auth_hdr]
                # Proxy-Authorization should not be sent to origin
                # server.
                del headers[proxy_auth_hdr]
            h.set_tunnel(req._tunnel_host, headers=tunnel_headers)
    
        try:
            try:
                h.request(req.get_method(), req.selector, req.data, headers,
                          encode_chunked=req.has_header('Transfer-encoding'))
            except OSError as err: # timeout error
>               raise URLError(err)
E               urllib.error.URLError: <urlopen error [Errno 110] Connection timed out>

/usr/lib/python3.10/urllib/request.py:1351: URLError

record_forge_property = <test.conftest.ForgePropertyStore object at 0x7ff778a8f700>
variant = 'mixer_b16_224'

    @pytest.mark.nightly
    @pytest.mark.parametrize("variant", varaints, ids=varaints)
    def test_mlp_mixer_timm_pytorch(record_forge_property, variant):
        if variant != "mixer_b16_224":
            pytest.skip("Skipping due to the current CI/CD pipeline limitations")
    
        # Build Module Name
        module_name = build_module_name(
            framework=Framework.PYTORCH,
            model="mlp_mixer",
            variant=variant,
            source=Source.TIMM,
            task=Task.IMAGE_CLASSIFICATION,
        )
    
        # Record Forge Property
        record_forge_property("tags.model_name", module_name)
    
        framework_model = download_model(timm.create_model, variant, pretrained=True)
        config = resolve_data_config({}, model=framework_model)
        transform = create_transform(**config)
    
        try:
            url = "https://images.rawpixel.com/image_1300/cHJpdmF0ZS9sci9pbWFnZXMvd2Vic2l0ZS8yMDIyLTA1L3BkMTA2LTA0Ny1jaGltXzEuanBn.jpg"
            image = Image.open(requests.get(url, stream=True).raw).convert("RGB")
        except:
            logger.warning(
                "Failed to download the image file, replacing input with random tensor. Please check if the URL is up to date"
            )
            image = torch.rand(1, 3, 256, 256)
        pixel_values = transform(image).unsqueeze(0)
    
        inputs = [pixel_values]
    
        # Forge compile framework model
>       compiled_model = forge.compile(framework_model, sample_inputs=inputs, module_name=module_name)

forge/test/models/pytorch/vision/mlp_mixer/test_mlp_mixer.py:68: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/compile.py:251: in compile_main
    return forge_compile_from_context(compile_context)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/compile.py:293: in forge_compile_from_context
    next_stage = stage_to_func[current_stage](context)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

context = CompileContext(modules=[Module pt_mlp_mixer_mixer_b16_224_img_cls_timm], graph_name='pt_mlp_mixer_mixer_b16_224_img_cl...cles_offset=0, forge_module=<forge._C.ForgeGraphModule object at 0x7ff85cb47170>, compiled_binary=None, attach_to=None)

    def run_mlir_compiler(context: CompileContext) -> CompileDepth:
        assert context.forge_module is not None
    
>       context.compiled_binary = forge._C.run_mlir_compiler(context.forge_module)
E       RuntimeError: Failed to run MLIR compiler pass pipeline.

/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/compile.py:986: RuntimeError

self = <urllib.request.HTTPHandler object at 0x7ff85c23b9d0>
http_class = <class 'http.client.HTTPConnection'>
req = <urllib.request.Request object at 0x7ff778c1d8a0>, http_conn_args = {}
host = 'dl.yf.io', h = <http.client.HTTPConnection object at 0x7ff778c1d1b0>

    def do_open(self, http_class, req, **http_conn_args):
        """Return an HTTPResponse object for the request, using http_class.
    
        http_class must implement the HTTPConnection API from http.client.
        """
        host = req.host
        if not host:
            raise URLError('no host given')
    
        # will parse host:port
        h = http_class(host, timeout=req.timeout, **http_conn_args)
        h.set_debuglevel(self._debuglevel)
    
        headers = dict(req.unredirected_hdrs)
        headers.update({k: v for k, v in req.headers.items()
                        if k not in headers})
    
        # TODO(jhylton): Should this be redesigned to handle
        # persistent connections?
    
        # We want to make an HTTP/1.1 request, but the addinfourl
        # class isn't prepared to deal with a persistent connection.
        # It will try to read all remaining data from the socket,
        # which will block while the server waits for the next request.
        # So make sure the connection gets closed after the (only)
        # request.
        headers["Connection"] = "close"
        headers = {name.title(): val for name, val in headers.items()}
    
        if req._tunnel_host:
            tunnel_headers = {}
            proxy_auth_hdr = "Proxy-Authorization"
            if proxy_auth_hdr in headers:
                tunnel_headers[proxy_auth_hdr] = headers[proxy_auth_hdr]
                # Proxy-Authorization should not be sent to origin
                # server.
                del headers[proxy_auth_hdr]
            h.set_tunnel(req._tunnel_host, headers=tunnel_headers)
    
        try:
            try:
>               h.request(req.get_method(), req.selector, req.data, headers,
                          encode_chunked=req.has_header('Transfer-encoding'))

/usr/lib/python3.10/urllib/request.py:1348: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <http.client.HTTPConnection object at 0x7ff778c1d1b0>, method = 'GET'
url = '/dla/models/imagenet/dla34-ba72cf86.pth', body = None
headers = {'Connection': 'close', 'Host': 'dl.yf.io', 'User-Agent': 'torch.hub'}

    def request(self, method, url, body=None, headers={}, *,
                encode_chunked=False):
        """Send a complete request to the server."""
>       self._send_request(method, url, body, headers, encode_chunked)

/usr/lib/python3.10/http/client.py:1283: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <http.client.HTTPConnection object at 0x7ff778c1d1b0>, method = 'GET'
url = '/dla/models/imagenet/dla34-ba72cf86.pth', body = None
headers = {'Connection': 'close', 'Host': 'dl.yf.io', 'User-Agent': 'torch.hub'}
encode_chunked = False

    def _send_request(self, method, url, body, headers, encode_chunked):
        # Honor explicitly requested Host: and Accept-Encoding: headers.
        header_names = frozenset(k.lower() for k in headers)
        skips = {}
        if 'host' in header_names:
            skips['skip_host'] = 1
        if 'accept-encoding' in header_names:
            skips['skip_accept_encoding'] = 1
    
        self.putrequest(method, url, **skips)
    
        # chunked encoding will happen if HTTP/1.1 is used and either
        # the caller passes encode_chunked=True or the following
        # conditions hold:
        # 1. content-length has not been explicitly set
        # 2. the body is a file or iterable, but not a str or bytes-like
        # 3. Transfer-Encoding has NOT been explicitly set by the caller
    
        if 'content-length' not in header_names:
            # only chunk body if not explicitly set for backwards
            # compatibility, assuming the client code is already handling the
            # chunking
            if 'transfer-encoding' not in header_names:
                # if content-length cannot be automatically determined, fall
                # back to chunked encoding
                encode_chunked = False
                content_length = self._get_content_length(body, method)
                if content_length is None:
                    if body is not None:
                        if self.debuglevel > 0:
                            print('Unable to determine size of %r' % body)
                        encode_chunked = True
                        self.putheader('Transfer-Encoding', 'chunked')
                else:
                    self.putheader('Content-Length', str(content_length))
        else:
            encode_chunked = False
    
        for hdr, value in headers.items():
            self.putheader(hdr, value)
        if isinstance(body, str):
            # RFC 2616 Section 3.7.1 says that text default has a
            # default charset of iso-8859-1.
            body = _encode(body, 'body')
>       self.endheaders(body, encode_chunked=encode_chunked)

/usr/lib/python3.10/http/client.py:1329: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <http.client.HTTPConnection object at 0x7ff778c1d1b0>
message_body = None

    def endheaders(self, message_body=None, *, encode_chunked=False):
        """Indicate that the last header line has been sent to the server.
    
        This method sends the request to the server.  The optional message_body
        argument can be used to pass a message body associated with the
        request.
        """
        if self.__state == _CS_REQ_STARTED:
            self.__state = _CS_REQ_SENT
        else:
            raise CannotSendHeader()
>       self._send_output(message_body, encode_chunked=encode_chunked)

/usr/lib/python3.10/http/client.py:1278: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <http.client.HTTPConnection object at 0x7ff778c1d1b0>
message_body = None, encode_chunked = False

    def _send_output(self, message_body=None, encode_chunked=False):
        """Send the currently buffered request and clear the buffer.
    
        Appends an extra \\r\\n to the buffer.
        A message_body may be specified, to be appended to the request.
        """
        self._buffer.extend((b"", b""))
        msg = b"\r\n".join(self._buffer)
        del self._buffer[:]
>       self.send(msg)

/usr/lib/python3.10/http/client.py:1038: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <http.client.HTTPConnection object at 0x7ff778c1d1b0>
data = b'GET /dla/models/imagenet/dla34-ba72cf86.pth HTTP/1.1\r\nAccept-Encoding: identity\r\nHost: dl.yf.io\r\nUser-Agent: torch.hub\r\nConnection: close\r\n\r\n'

    def send(self, data):
        """Send `data' to the server.
        ``data`` can be a string object, a bytes object, an array object, a
        file-like object that supports a .read() method, or an iterable object.
        """
    
        if self.sock is None:
            if self.auto_open:
>               self.connect()

/usr/lib/python3.10/http/client.py:976: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <http.client.HTTPConnection object at 0x7ff778c1d1b0>

    def connect(self):
        """Connect to the host and port specified in __init__."""
        sys.audit("http.client.connect", self, self.host, self.port)
>       self.sock = self._create_connection(
            (self.host,self.port), self.timeout, self.source_address)

/usr/lib/python3.10/http/client.py:942: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

address = ('dl.yf.io', 80), timeout = <object object at 0x7ff88985ce70>
source_address = None

    def create_connection(address, timeout=_GLOBAL_DEFAULT_TIMEOUT,
                          source_address=None):
        """Connect to *address* and return the socket object.
    
        Convenience function.  Connect to *address* (a 2-tuple ``(host,
        port)``) and return the socket object.  Passing the optional
        *timeout* parameter will set the timeout on the socket instance
        before attempting to connect.  If no *timeout* is supplied, the
        global default timeout setting returned by :func:`getdefaulttimeout`
        is used.  If *source_address* is set it must be a tuple of (host, port)
        for the socket to bind as a source address before making the connection.
        A host of '' or port 0 tells the OS to use the default.
        """
    
        host, port = address
        err = None
        for res in getaddrinfo(host, port, 0, SOCK_STREAM):
            af, socktype, proto, canonname, sa = res
            sock = None
            try:
                sock = socket(af, socktype, proto)
                if timeout is not _GLOBAL_DEFAULT_TIMEOUT:
                    sock.settimeout(timeout)
                if source_address:
                    sock.bind(source_address)
                sock.connect(sa)
                # Break explicitly a reference cycle
                err = None
                return sock
    
            except error as _:
                err = _
                if sock is not None:
                    sock.close()
    
        if err is not None:
            try:
>               raise err

/usr/lib/python3.10/socket.py:845: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

address = ('dl.yf.io', 80), timeout = <object object at 0x7ff88985ce70>
source_address = None

    def create_connection(address, timeout=_GLOBAL_DEFAULT_TIMEOUT,
                          source_address=None):
        """Connect to *address* and return the socket object.
    
        Convenience function.  Connect to *address* (a 2-tuple ``(host,
        port)``) and return the socket object.  Passing the optional
        *timeout* parameter will set the timeout on the socket instance
        before attempting to connect.  If no *timeout* is supplied, the
        global default timeout setting returned by :func:`getdefaulttimeout`
        is used.  If *source_address* is set it must be a tuple of (host, port)
        for the socket to bind as a source address before making the connection.
        A host of '' or port 0 tells the OS to use the default.
        """
    
        host, port = address
        err = None
        for res in getaddrinfo(host, port, 0, SOCK_STREAM):
            af, socktype, proto, canonname, sa = res
            sock = None
            try:
                sock = socket(af, socktype, proto)
                if timeout is not _GLOBAL_DEFAULT_TIMEOUT:
                    sock.settimeout(timeout)
                if source_address:
                    sock.bind(source_address)
>               sock.connect(sa)
E               TimeoutError: [Errno 110] Connection timed out

/usr/lib/python3.10/socket.py:833: TimeoutError

During handling of the above exception, another exception occurred:

record_forge_property = <test.conftest.ForgePropertyStore object at 0x7ff778c1ca90>

    @pytest.mark.nightly
    def test_monodle_pytorch(record_forge_property):
        # Build Module Name
        module_name = build_module_name(
            framework=Framework.PYTORCH, model="monodle", source=Source.TORCHVISION, task=Task.OBJECT_DETECTION
        )
    
        # Record Forge Property
        record_forge_property("tags.model_name", module_name)
    
        # Load data sample
        url = "https://images.rawpixel.com/image_1300/cHJpdmF0ZS9sci9pbWFnZXMvd2Vic2l0ZS8yMDIyLTA1L3BkMTA2LTA0Ny1jaGltXzEuanBn.jpg"
        image = Image.open(requests.get(url, stream=True).raw)
    
        # Preprocessing
        transform = transforms.Compose(
            [
                transforms.Resize(256),
                transforms.CenterCrop(224),
                transforms.ToTensor(),
                transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
            ]
        )
        img_tensor = transform(image).unsqueeze(0)
    
>       framework_model = CenterNet3D(backbone="dla34")

forge/test/models/pytorch/vision/monodle/test_monodle.py:41: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
forge/test/models/pytorch/vision/monodle/utils/model.py:483: in __init__
    self.backbone = dla34(pretrained=True, return_levels=True)
forge/test/models/pytorch/vision/monodle/utils/model.py:457: in dla34
    model.load_pretrained_model(data="imagenet", name="dla34", hash="ba72cf86")
forge/test/models/pytorch/vision/monodle/utils/model.py:439: in load_pretrained_model
    model_weights = model_zoo.load_url(model_url)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/torch/hub.py:766: in load_state_dict_from_url
    download_url_to_file(url, cached_file, hash_prefix, progress=progress)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/torch/hub.py:620: in download_url_to_file
    u = urlopen(req)
/usr/lib/python3.10/urllib/request.py:216: in urlopen
    return opener.open(url, data, timeout)
/usr/lib/python3.10/urllib/request.py:519: in open
    response = self._open(req, data)
/usr/lib/python3.10/urllib/request.py:536: in _open
    result = self._call_chain(self.handle_open, protocol, protocol +
/usr/lib/python3.10/urllib/request.py:496: in _call_chain
    result = func(*args)
/usr/lib/python3.10/urllib/request.py:1377: in http_open
    return self.do_open(http.client.HTTPConnection, req)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <urllib.request.HTTPHandler object at 0x7ff85c23b9d0>
http_class = <class 'http.client.HTTPConnection'>
req = <urllib.request.Request object at 0x7ff778c1d8a0>, http_conn_args = {}
host = 'dl.yf.io', h = <http.client.HTTPConnection object at 0x7ff778c1d1b0>

    def do_open(self, http_class, req, **http_conn_args):
        """Return an HTTPResponse object for the request, using http_class.
    
        http_class must implement the HTTPConnection API from http.client.
        """
        host = req.host
        if not host:
            raise URLError('no host given')
    
        # will parse host:port
        h = http_class(host, timeout=req.timeout, **http_conn_args)
        h.set_debuglevel(self._debuglevel)
    
        headers = dict(req.unredirected_hdrs)
        headers.update({k: v for k, v in req.headers.items()
                        if k not in headers})
    
        # TODO(jhylton): Should this be redesigned to handle
        # persistent connections?
    
        # We want to make an HTTP/1.1 request, but the addinfourl
        # class isn't prepared to deal with a persistent connection.
        # It will try to read all remaining data from the socket,
        # which will block while the server waits for the next request.
        # So make sure the connection gets closed after the (only)
        # request.
        headers["Connection"] = "close"
        headers = {name.title(): val for name, val in headers.items()}
    
        if req._tunnel_host:
            tunnel_headers = {}
            proxy_auth_hdr = "Proxy-Authorization"
            if proxy_auth_hdr in headers:
                tunnel_headers[proxy_auth_hdr] = headers[proxy_auth_hdr]
                # Proxy-Authorization should not be sent to origin
                # server.
                del headers[proxy_auth_hdr]
            h.set_tunnel(req._tunnel_host, headers=tunnel_headers)
    
        try:
            try:
                h.request(req.get_method(), req.selector, req.data, headers,
                          encode_chunked=req.has_header('Transfer-encoding'))
            except OSError as err: # timeout error
>               raise URLError(err)
E               urllib.error.URLError: <urlopen error [Errno 110] Connection timed out>

/usr/lib/python3.10/urllib/request.py:1351: URLError

record_forge_property = <test.conftest.ForgePropertyStore object at 0x7ff7778a4a90>

    @pytest.mark.nightly
    def test_rcnn_pytorch(record_forge_property):
        # Build Module Name
        module_name = build_module_name(
            framework=Framework.PYTORCH, model="rcnn", source=Source.TORCHVISION, task=Task.OBJECT_DETECTION
        )
    
        # Record Forge Property
        record_forge_property("tags.model_name", module_name)
    
        # Load Alexnet Model
        framework_model = torchvision.models.alexnet(pretrained=True)
        num_classes = 2
        num_features = framework_model.classifier[6].in_features
    
        # Create class specific linear SVMs [Refer Section 2 in paper]
        svm_layer = nn.Linear(num_features, num_classes)
    
        # Replacing the Alexnet's ImageNet specific 1000-way classification layer with a randomly initialized (N + 1)-way classification layer(where N is the number of object classes, plus 1 for background)
        # [Refer Section 2.3.Domain-specific fine-tuning in Paper]
        init.normal_(svm_layer.weight, mean=0, std=0.01)
        init.constant_(svm_layer.bias, 0)
        framework_model.classifier[6] = svm_layer
    
        framework_model.eval()
    
        # Cancel gradient tracking
        for param in framework_model.parameters():
            param.requires_grad = False
    
        # Image
        img = cv2.imread("forge/test/models/files/samples/images/car.jpg")
    
        transform = transforms.Compose(
            [
                transforms.ToPILImage(),
                transforms.Resize((227, 227)),
                transforms.RandomHorizontalFlip(),
                transforms.ToTensor(),
                transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
            ]
        )
    
        # Selective search - A separate tool for generating proposals(potential regions that might contain objects) which can be fed to actual model
        # As it is a pre-processing step,it is implemented on cpu
        gs = cv2.ximgproc.segmentation.createSelectiveSearchSegmentation()
        gs.setBaseImage(img)
        gs.switchToSelectiveSearchFast()
        rects = gs.process()
        rects[:, 2] += rects[:, 0]
        rects[:, 3] += rects[:, 1]
        print("Suggested number of proposals: %d" % len(rects))
    
        # Proposals generated by selective search were fed to a model in a loop manner to compute features.
        # [Refer line No.151 in https://github.com/object-detection-algorithm/R-CNN/blob/master/py/car_detector.py]
        for idx, rect in enumerate(rects):
    
            xmin, ymin, xmax, ymax = rect
            rect_img = img[ymin:ymax, xmin:xmax]
    
            rect_transform = transform(rect_img)
    
            inputs = [rect_transform.unsqueeze(0)]
    
            # Build Module Name
            module_name = build_module_name(
                framework=Framework.PYTORCH,
                model="rcnn",
                suffix=f"rect_{idx}",
                source=Source.TORCHVISION,
                task=Task.OBJECT_DETECTION,
            )
    
            # Forge compile framework model
>           compiled_model = forge.compile(framework_model, sample_inputs=inputs, module_name=module_name)

forge/test/models/pytorch/vision/rcnn/test_rcnn.py:93: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/compile.py:251: in compile_main
    return forge_compile_from_context(compile_context)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/compile.py:293: in forge_compile_from_context
    next_stage = stage_to_func[current_stage](context)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

context = CompileContext(modules=[Module pt_rcnn_base_obj_det_torchvision_rect_0], graph_name='pt_rcnn_base_obj_det_torchvision_...cles_offset=0, forge_module=<forge._C.ForgeGraphModule object at 0x7ff778d14ab0>, compiled_binary=None, attach_to=None)

    def run_mlir_compiler(context: CompileContext) -> CompileDepth:
        assert context.forge_module is not None
    
>       context.compiled_binary = forge._C.run_mlir_compiler(context.forge_module)
E       RuntimeError: Failed to run MLIR compiler pass pipeline.

/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/compile.py:986: RuntimeError

record_forge_property = <test.conftest.ForgePropertyStore object at 0x7ff6e3f55a80>
variant = 'nvidia/mit-b0'

    @pytest.mark.nightly
    @pytest.mark.parametrize("variant", variants_img_classification)
    def test_segformer_image_classification_pytorch(record_forge_property, variant):
        if variant != "nvidia/mit-b0":
            pytest.skip("Skipping due to the current CI/CD pipeline limitations")
    
        # Build Module Name
        module_name = build_module_name(
            framework=Framework.PYTORCH,
            model="segformer",
            variant=variant,
            task=Task.IMAGE_CLASSIFICATION,
            source=Source.HUGGINGFACE,
        )
    
        # Record Forge Property
        record_forge_property("tags.model_name", module_name)
    
        # Set model configurations
        config = SegformerConfig.from_pretrained(variant)
        config_dict = config.to_dict()
        config_dict["return_dict"] = False
        config = SegformerConfig(**config_dict)
    
        # Load the model from HuggingFace
        framework_model = SegformerForImageClassification.from_pretrained(variant, config=config)
        framework_model.eval()
    
        # Load the sample image
        pixel_values = get_sample_data(variant)
        inputs = [pixel_values]
    
        # Forge compile framework model
>       compiled_model = forge.compile(framework_model, sample_inputs=inputs, module_name=module_name)

forge/test/models/pytorch/vision/segformer/test_segformer.py:61: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/compile.py:251: in compile_main
    return forge_compile_from_context(compile_context)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/compile.py:293: in forge_compile_from_context
    next_stage = stage_to_func[current_stage](context)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

context = CompileContext(modules=[Module pt_segformer_nvidia_mit_b0_img_cls_hf], graph_name='pt_segformer_nvidia_mit_b0_img_cls_...cles_offset=0, forge_module=<forge._C.ForgeGraphModule object at 0x7ff6e2c28070>, compiled_binary=None, attach_to=None)

    def run_mlir_compiler(context: CompileContext) -> CompileDepth:
        assert context.forge_module is not None
    
>       context.compiled_binary = forge._C.run_mlir_compiler(context.forge_module)
E       RuntimeError: Failed to run MLIR compiler pass pipeline.

/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/compile.py:986: RuntimeError

record_forge_property = <test.conftest.ForgePropertyStore object at 0x7ff7778a7880>
size = 's'

    @pytest.mark.nightly
    @pytest.mark.parametrize("size", size, ids=["yolov5" + s for s in size])
    def test_yolov5_480x480(record_forge_property, size):
        if size != "s":
            pytest.skip("Skipping due to the current CI/CD pipeline limitations")
    
        # Build Module Name
        module_name = build_module_name(
            framework=Framework.PYTORCH,
            model="yolo_v5",
            variant="yolov5" + size,
            task="imgcls",
            source="torchhub",
            suffix="480x480",
        )
    
        # Record Forge Property
        record_forge_property("tags.model_name", module_name)
    
        framework_model, inputs, _ = generate_model_yoloV5I480_imgcls_torchhub_pytorch(
            "ultralytics/yolov5",
            size=size,
        )
    
        # Forge compile framework model
>       compiled_model = forge.compile(framework_model, sample_inputs=inputs, module_name=module_name)

forge/test/models/pytorch/vision/yolo/test_yolo_v5.py:134: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/compile.py:251: in compile_main
    return forge_compile_from_context(compile_context)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/compile.py:293: in forge_compile_from_context
    next_stage = stage_to_func[current_stage](context)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

context = CompileContext(modules=[Module pt_yolo_v5_yolov5s_imgcls_torchhub_480x480], graph_name='pt_yolo_v5_yolov5s_imgcls_torc...cles_offset=0, forge_module=<forge._C.ForgeGraphModule object at 0x7ff86b536970>, compiled_binary=None, attach_to=None)

    def run_mlir_compiler(context: CompileContext) -> CompileDepth:
        assert context.forge_module is not None
    
>       context.compiled_binary = forge._C.run_mlir_compiler(context.forge_module)
E       RuntimeError: Failed to run MLIR compiler pass pipeline.

/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/compile.py:986: RuntimeError