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TVM cleanup #2778

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TVM cleanup

TVM cleanup #2778

GitHub Actions / TT-Forge-FE Tests failed Feb 26, 2025 in 0s

613 tests run, 413 passed, 132 skipped, 68 failed.

Annotations

Check failure on line 117 in forge/test/mlir/operators/eltwise_binary/test_eltwise_binary.py

See this annotation in the file changed.

@github-actions github-actions / TT-Forge-FE Tests

test_eltwise_binary.test_less[shape_x0-shape_y0] 

NameError: name 'construct_pre_node_map' is not defined
Raw output 
shape_x = (1, 128, 28, 28), shape_y = (1, 128, 28, 28)

    @pytest.mark.parametrize(
        "shape_x, shape_y",
        [
            ((1, 128, 28, 28), (1, 128, 28, 28)),
            ((1, 64, 28, 28), (1, 64, 28, 28)),
            ((1, 256, 28, 28), (1, 256, 28, 28)),
            ((1, 128, 14, 14), (1, 128, 14, 14)),
            ((1, 128, 56, 56), (1, 128, 56, 56)),
            ((1, 32, 64, 64), (1, 32, 64, 64)),
            ((1, 512, 7, 7), (1, 512, 7, 7)),
            ((1, 32, 32, 32), (1, 32, 32, 32)),
        ],
    )
    @pytest.mark.push
    def test_less(shape_x, shape_y):
        class Less(nn.Module):
            def __init__(self):
                super().__init__()
    
            def forward(self, x, y):
                return torch.less(x, y)
    
        x = torch.rand(shape_x)
        y = torch.rand(shape_y)
    
        inputs = [x, y]
    
        framework_model = Less()
>       compiled_model = forge.compile(framework_model, sample_inputs=inputs)

forge/test/mlir/operators/eltwise_binary/test_eltwise_binary.py:117: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/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:671: in generate_initial_graph
    module, module_inputs = convert_to_forge_module(
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/compile.py:1032: 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:2062: 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:2151: 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:104: in load_tvm_graph
    json_graphs, flattened_inputs = compile_tvm_graph(inputs, module, compiler_cfg, graph_name=graph_name, input_names=input_names, path=path, verify_cfg=verify_cfg, framework=framework)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/forge_compile.py:144: in compile_tvm_graph
    json_graphs, inputs = compile_pytorch_for_forge(module, *inputs, graph_name=graph_name, compiler_cfg=compiler_cfg, verify_cfg=verify_cfg, input_names=input_names)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/forge_compile.py:394: in compile_pytorch_for_forge
    partitioned_mod, forge_params = compile_tvm_for_forge(mod, params, np_inputs, framework_outputs, input_names=flattened_input_names, graph_name=graph_name, return_params=True, compiler_cfg=compiler_cfg, verify_cfg=verify_cfg)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/forge_compile.py:408: in compile_tvm_for_forge
    mod, params = compile_for_forge(mod, target=target, params=params, graph_name=graph_name, **verify_args)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/relay/op/forge.py:1100: in compile_for_forge
    warn_of_int_comparisons(compiled_relay_module)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/relay/op/forge.py:1068: in warn_of_int_comparisons
    rewrite(warner, mod['main'])
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/tvm/relay/dataflow_pattern/__init__.py:927: in rewrite
    return ffi.rewrite(tmp, expr, mod)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/tvm/_ffi/_ctypes/packed_func.py:242: in __call__
    raise_last_ffi_error()
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/tvm/_ffi/base.py:479: in raise_last_ffi_error
    raise py_err
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <forge.tvm_calls.relay.op.forge.CompareWarner object at 0x7eff3b50a680>
pre = CallNode(Op(less), [Var(x, ty=TensorType([1, 128, 28, 28], float32)), Var(y, ty=TensorType([1, 128, 28, 28], float32))], (nullptr), [TensorType([1, 128, 28, 28], float32), TensorType([1, 128, 28, 28], float32)])
post = CallNode(Op(less), [Var(x, ty=TensorType([1, 128, 28, 28], float32)), Var(y, ty=TensorType([1, 128, 28, 28], float32))], (nullptr), [TensorType([1, 128, 28, 28], float32), TensorType([1, 128, 28, 28], float32)])
node_map = {relay.dataflow_pattern.AltPattern(0x55ec6e27bff0): [CallNode(Op(less), [Var(x, ty=TensorType([1, 128, 28, 28], float3... 128, 28, 28], float32))], (nullptr), [TensorType([1, 128, 28, 28], float32), TensorType([1, 128, 28, 28], float32)])]}

    def callback(self, pre, post, node_map):
>       pre_node_map = construct_pre_node_map(self.pattern, pre)
E       NameError: name 'construct_pre_node_map' is not defined

/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/relay/op/forge.py:1058: NameError

Check failure on line 117 in forge/test/mlir/operators/eltwise_binary/test_eltwise_binary.py

See this annotation in the file changed.

@github-actions github-actions / TT-Forge-FE Tests

test_eltwise_binary.test_less[shape_x2-shape_y2] 

NameError: name 'construct_pre_node_map' is not defined
Raw output 
shape_x = (1, 256, 28, 28), shape_y = (1, 256, 28, 28)

    @pytest.mark.parametrize(
        "shape_x, shape_y",
        [
            ((1, 128, 28, 28), (1, 128, 28, 28)),
            ((1, 64, 28, 28), (1, 64, 28, 28)),
            ((1, 256, 28, 28), (1, 256, 28, 28)),
            ((1, 128, 14, 14), (1, 128, 14, 14)),
            ((1, 128, 56, 56), (1, 128, 56, 56)),
            ((1, 32, 64, 64), (1, 32, 64, 64)),
            ((1, 512, 7, 7), (1, 512, 7, 7)),
            ((1, 32, 32, 32), (1, 32, 32, 32)),
        ],
    )
    @pytest.mark.push
    def test_less(shape_x, shape_y):
        class Less(nn.Module):
            def __init__(self):
                super().__init__()
    
            def forward(self, x, y):
                return torch.less(x, y)
    
        x = torch.rand(shape_x)
        y = torch.rand(shape_y)
    
        inputs = [x, y]
    
        framework_model = Less()
>       compiled_model = forge.compile(framework_model, sample_inputs=inputs)

forge/test/mlir/operators/eltwise_binary/test_eltwise_binary.py:117: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/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:671: in generate_initial_graph
    module, module_inputs = convert_to_forge_module(
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/compile.py:1032: 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:2062: 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:2151: 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:104: in load_tvm_graph
    json_graphs, flattened_inputs = compile_tvm_graph(inputs, module, compiler_cfg, graph_name=graph_name, input_names=input_names, path=path, verify_cfg=verify_cfg, framework=framework)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/forge_compile.py:144: in compile_tvm_graph
    json_graphs, inputs = compile_pytorch_for_forge(module, *inputs, graph_name=graph_name, compiler_cfg=compiler_cfg, verify_cfg=verify_cfg, input_names=input_names)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/forge_compile.py:394: in compile_pytorch_for_forge
    partitioned_mod, forge_params = compile_tvm_for_forge(mod, params, np_inputs, framework_outputs, input_names=flattened_input_names, graph_name=graph_name, return_params=True, compiler_cfg=compiler_cfg, verify_cfg=verify_cfg)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/forge_compile.py:408: in compile_tvm_for_forge
    mod, params = compile_for_forge(mod, target=target, params=params, graph_name=graph_name, **verify_args)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/relay/op/forge.py:1100: in compile_for_forge
    warn_of_int_comparisons(compiled_relay_module)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/relay/op/forge.py:1068: in warn_of_int_comparisons
    rewrite(warner, mod['main'])
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/tvm/relay/dataflow_pattern/__init__.py:927: in rewrite
    return ffi.rewrite(tmp, expr, mod)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/tvm/_ffi/_ctypes/packed_func.py:242: in __call__
    raise_last_ffi_error()
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/tvm/_ffi/base.py:479: in raise_last_ffi_error
    raise py_err
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <forge.tvm_calls.relay.op.forge.CompareWarner object at 0x7efe801da830>
pre = CallNode(Op(less), [Var(x, ty=TensorType([1, 256, 28, 28], float32)), Var(y, ty=TensorType([1, 256, 28, 28], float32))], (nullptr), [TensorType([1, 256, 28, 28], float32), TensorType([1, 256, 28, 28], float32)])
post = CallNode(Op(less), [Var(x, ty=TensorType([1, 256, 28, 28], float32)), Var(y, ty=TensorType([1, 256, 28, 28], float32))], (nullptr), [TensorType([1, 256, 28, 28], float32), TensorType([1, 256, 28, 28], float32)])
node_map = {relay.dataflow_pattern.AltPattern(0x55ec70af1f10): [CallNode(Op(less), [Var(x, ty=TensorType([1, 256, 28, 28], float3...loat32)])], relay.dataflow_pattern.WildcardPattern(0x55ec6ffe22f0): [Var(y, ty=TensorType([1, 256, 28, 28], float32))]}

    def callback(self, pre, post, node_map):
>       pre_node_map = construct_pre_node_map(self.pattern, pre)
E       NameError: name 'construct_pre_node_map' is not defined

/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/relay/op/forge.py:1058: NameError

Check failure on line 117 in forge/test/mlir/operators/eltwise_binary/test_eltwise_binary.py

See this annotation in the file changed.

@github-actions github-actions / TT-Forge-FE Tests

test_eltwise_binary.test_less[shape_x4-shape_y4] 

NameError: name 'construct_pre_node_map' is not defined
Raw output 
shape_x = (1, 128, 56, 56), shape_y = (1, 128, 56, 56)

    @pytest.mark.parametrize(
        "shape_x, shape_y",
        [
            ((1, 128, 28, 28), (1, 128, 28, 28)),
            ((1, 64, 28, 28), (1, 64, 28, 28)),
            ((1, 256, 28, 28), (1, 256, 28, 28)),
            ((1, 128, 14, 14), (1, 128, 14, 14)),
            ((1, 128, 56, 56), (1, 128, 56, 56)),
            ((1, 32, 64, 64), (1, 32, 64, 64)),
            ((1, 512, 7, 7), (1, 512, 7, 7)),
            ((1, 32, 32, 32), (1, 32, 32, 32)),
        ],
    )
    @pytest.mark.push
    def test_less(shape_x, shape_y):
        class Less(nn.Module):
            def __init__(self):
                super().__init__()
    
            def forward(self, x, y):
                return torch.less(x, y)
    
        x = torch.rand(shape_x)
        y = torch.rand(shape_y)
    
        inputs = [x, y]
    
        framework_model = Less()
>       compiled_model = forge.compile(framework_model, sample_inputs=inputs)

forge/test/mlir/operators/eltwise_binary/test_eltwise_binary.py:117: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/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:671: in generate_initial_graph
    module, module_inputs = convert_to_forge_module(
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/compile.py:1032: 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:2062: 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:2151: 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:104: in load_tvm_graph
    json_graphs, flattened_inputs = compile_tvm_graph(inputs, module, compiler_cfg, graph_name=graph_name, input_names=input_names, path=path, verify_cfg=verify_cfg, framework=framework)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/forge_compile.py:144: in compile_tvm_graph
    json_graphs, inputs = compile_pytorch_for_forge(module, *inputs, graph_name=graph_name, compiler_cfg=compiler_cfg, verify_cfg=verify_cfg, input_names=input_names)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/forge_compile.py:394: in compile_pytorch_for_forge
    partitioned_mod, forge_params = compile_tvm_for_forge(mod, params, np_inputs, framework_outputs, input_names=flattened_input_names, graph_name=graph_name, return_params=True, compiler_cfg=compiler_cfg, verify_cfg=verify_cfg)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/forge_compile.py:408: in compile_tvm_for_forge
    mod, params = compile_for_forge(mod, target=target, params=params, graph_name=graph_name, **verify_args)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/relay/op/forge.py:1100: in compile_for_forge
    warn_of_int_comparisons(compiled_relay_module)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/relay/op/forge.py:1068: in warn_of_int_comparisons
    rewrite(warner, mod['main'])
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/tvm/relay/dataflow_pattern/__init__.py:927: in rewrite
    return ffi.rewrite(tmp, expr, mod)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/tvm/_ffi/_ctypes/packed_func.py:242: in __call__
    raise_last_ffi_error()
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/tvm/_ffi/base.py:479: in raise_last_ffi_error
    raise py_err
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <forge.tvm_calls.relay.op.forge.CompareWarner object at 0x7efe85731780>
pre = CallNode(Op(less), [Var(x, ty=TensorType([1, 128, 56, 56], float32)), Var(y, ty=TensorType([1, 128, 56, 56], float32))], (nullptr), [TensorType([1, 128, 56, 56], float32), TensorType([1, 128, 56, 56], float32)])
post = CallNode(Op(less), [Var(x, ty=TensorType([1, 128, 56, 56], float32)), Var(y, ty=TensorType([1, 128, 56, 56], float32))], (nullptr), [TensorType([1, 128, 56, 56], float32), TensorType([1, 128, 56, 56], float32)])
node_map = {relay.dataflow_pattern.CallPattern(0x55ec70f85db0): [CallNode(Op(less), [Var(x, ty=TensorType([1, 128, 56, 56], float... 128, 56, 56], float32))], (nullptr), [TensorType([1, 128, 56, 56], float32), TensorType([1, 128, 56, 56], float32)])]}

    def callback(self, pre, post, node_map):
>       pre_node_map = construct_pre_node_map(self.pattern, pre)
E       NameError: name 'construct_pre_node_map' is not defined

/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/relay/op/forge.py:1058: NameError

Check failure on line 117 in forge/test/mlir/operators/eltwise_binary/test_eltwise_binary.py

See this annotation in the file changed.

@github-actions github-actions / TT-Forge-FE Tests

test_eltwise_binary.test_less[shape_x6-shape_y6] 

NameError: name 'construct_pre_node_map' is not defined
Raw output 
shape_x = (1, 512, 7, 7), shape_y = (1, 512, 7, 7)

    @pytest.mark.parametrize(
        "shape_x, shape_y",
        [
            ((1, 128, 28, 28), (1, 128, 28, 28)),
            ((1, 64, 28, 28), (1, 64, 28, 28)),
            ((1, 256, 28, 28), (1, 256, 28, 28)),
            ((1, 128, 14, 14), (1, 128, 14, 14)),
            ((1, 128, 56, 56), (1, 128, 56, 56)),
            ((1, 32, 64, 64), (1, 32, 64, 64)),
            ((1, 512, 7, 7), (1, 512, 7, 7)),
            ((1, 32, 32, 32), (1, 32, 32, 32)),
        ],
    )
    @pytest.mark.push
    def test_less(shape_x, shape_y):
        class Less(nn.Module):
            def __init__(self):
                super().__init__()
    
            def forward(self, x, y):
                return torch.less(x, y)
    
        x = torch.rand(shape_x)
        y = torch.rand(shape_y)
    
        inputs = [x, y]
    
        framework_model = Less()
>       compiled_model = forge.compile(framework_model, sample_inputs=inputs)

forge/test/mlir/operators/eltwise_binary/test_eltwise_binary.py:117: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/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:671: in generate_initial_graph
    module, module_inputs = convert_to_forge_module(
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/compile.py:1032: 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:2062: 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:2151: 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:104: in load_tvm_graph
    json_graphs, flattened_inputs = compile_tvm_graph(inputs, module, compiler_cfg, graph_name=graph_name, input_names=input_names, path=path, verify_cfg=verify_cfg, framework=framework)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/forge_compile.py:144: in compile_tvm_graph
    json_graphs, inputs = compile_pytorch_for_forge(module, *inputs, graph_name=graph_name, compiler_cfg=compiler_cfg, verify_cfg=verify_cfg, input_names=input_names)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/forge_compile.py:394: in compile_pytorch_for_forge
    partitioned_mod, forge_params = compile_tvm_for_forge(mod, params, np_inputs, framework_outputs, input_names=flattened_input_names, graph_name=graph_name, return_params=True, compiler_cfg=compiler_cfg, verify_cfg=verify_cfg)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/forge_compile.py:408: in compile_tvm_for_forge
    mod, params = compile_for_forge(mod, target=target, params=params, graph_name=graph_name, **verify_args)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/relay/op/forge.py:1100: in compile_for_forge
    warn_of_int_comparisons(compiled_relay_module)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/relay/op/forge.py:1068: in warn_of_int_comparisons
    rewrite(warner, mod['main'])
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/tvm/relay/dataflow_pattern/__init__.py:927: in rewrite
    return ffi.rewrite(tmp, expr, mod)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/tvm/_ffi/_ctypes/packed_func.py:242: in __call__
    raise_last_ffi_error()
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/tvm/_ffi/base.py:479: in raise_last_ffi_error
    raise py_err
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <forge.tvm_calls.relay.op.forge.CompareWarner object at 0x7efe3d646e00>
pre = CallNode(Op(less), [Var(x, ty=TensorType([1, 512, 7, 7], float32)), Var(y, ty=TensorType([1, 512, 7, 7], float32))], (nullptr), [TensorType([1, 512, 7, 7], float32), TensorType([1, 512, 7, 7], float32)])
post = CallNode(Op(less), [Var(x, ty=TensorType([1, 512, 7, 7], float32)), Var(y, ty=TensorType([1, 512, 7, 7], float32))], (nullptr), [TensorType([1, 512, 7, 7], float32), TensorType([1, 512, 7, 7], float32)])
node_map = {relay.dataflow_pattern.ExprPattern(0x55ec703d7390): [Op(less)], relay.dataflow_pattern.WildcardPattern(0x55ec716842d0...pe([1, 512, 7, 7], float32))], (nullptr), [TensorType([1, 512, 7, 7], float32), TensorType([1, 512, 7, 7], float32)])]}

    def callback(self, pre, post, node_map):
>       pre_node_map = construct_pre_node_map(self.pattern, pre)
E       NameError: name 'construct_pre_node_map' is not defined

/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/relay/op/forge.py:1058: NameError

Check failure on line 150 in forge/test/mlir/operators/eltwise_binary/test_eltwise_binary.py

See this annotation in the file changed.

@github-actions github-actions / TT-Forge-FE Tests

test_eltwise_binary.test_greater[shape_x0-shape_y0] 

NameError: name 'construct_pre_node_map' is not defined
Raw output 
shape_x = (1, 128, 28, 28), shape_y = (1, 128, 28, 28)

    @pytest.mark.parametrize(
        "shape_x, shape_y",
        [
            ((1, 128, 28, 28), (1, 128, 28, 28)),
            ((1, 64, 28, 28), (1, 64, 28, 28)),
            ((1, 256, 28, 28), (1, 256, 28, 28)),
            ((1, 128, 14, 14), (1, 128, 14, 14)),
            ((1, 128, 56, 56), (1, 128, 56, 56)),
            ((1, 32, 64, 64), (1, 32, 64, 64)),
            ((1, 512, 7, 7), (1, 512, 7, 7)),
            ((1, 32, 32, 32), (1, 32, 32, 32)),
        ],
    )
    @pytest.mark.push
    def test_greater(shape_x, shape_y):
        class Greater(nn.Module):
            def __init__(self):
                super().__init__()
    
            def forward(self, x, y):
                return torch.greater(x, y)
    
        x = torch.rand(shape_x)
        y = torch.rand(shape_y)
    
        inputs = [x, y]
    
        framework_model = Greater()
>       compiled_model = forge.compile(framework_model, sample_inputs=inputs)

forge/test/mlir/operators/eltwise_binary/test_eltwise_binary.py:150: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/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:671: in generate_initial_graph
    module, module_inputs = convert_to_forge_module(
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/compile.py:1032: 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:2062: 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:2151: 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:104: in load_tvm_graph
    json_graphs, flattened_inputs = compile_tvm_graph(inputs, module, compiler_cfg, graph_name=graph_name, input_names=input_names, path=path, verify_cfg=verify_cfg, framework=framework)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/forge_compile.py:144: in compile_tvm_graph
    json_graphs, inputs = compile_pytorch_for_forge(module, *inputs, graph_name=graph_name, compiler_cfg=compiler_cfg, verify_cfg=verify_cfg, input_names=input_names)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/forge_compile.py:394: in compile_pytorch_for_forge
    partitioned_mod, forge_params = compile_tvm_for_forge(mod, params, np_inputs, framework_outputs, input_names=flattened_input_names, graph_name=graph_name, return_params=True, compiler_cfg=compiler_cfg, verify_cfg=verify_cfg)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/forge_compile.py:408: in compile_tvm_for_forge
    mod, params = compile_for_forge(mod, target=target, params=params, graph_name=graph_name, **verify_args)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/relay/op/forge.py:1100: in compile_for_forge
    warn_of_int_comparisons(compiled_relay_module)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/relay/op/forge.py:1068: in warn_of_int_comparisons
    rewrite(warner, mod['main'])
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/tvm/relay/dataflow_pattern/__init__.py:927: in rewrite
    return ffi.rewrite(tmp, expr, mod)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/tvm/_ffi/_ctypes/packed_func.py:242: in __call__
    raise_last_ffi_error()
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/tvm/_ffi/base.py:479: in raise_last_ffi_error
    raise py_err
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <forge.tvm_calls.relay.op.forge.CompareWarner object at 0x7efe80191330>
pre = CallNode(Op(greater), [Var(x, ty=TensorType([1, 128, 28, 28], float32)), Var(y, ty=TensorType([1, 128, 28, 28], float32))], (nullptr), [TensorType([1, 128, 28, 28], float32), TensorType([1, 128, 28, 28], float32)])
post = CallNode(Op(greater), [Var(x, ty=TensorType([1, 128, 28, 28], float32)), Var(y, ty=TensorType([1, 128, 28, 28], float32))], (nullptr), [TensorType([1, 128, 28, 28], float32), TensorType([1, 128, 28, 28], float32)])
node_map = {relay.dataflow_pattern.ExprPattern(0x55ec70ff9b90): [Op(greater)], relay.dataflow_pattern.WildcardPattern(0x55ec70ade... 128, 28, 28], float32))], (nullptr), [TensorType([1, 128, 28, 28], float32), TensorType([1, 128, 28, 28], float32)])]}

    def callback(self, pre, post, node_map):
>       pre_node_map = construct_pre_node_map(self.pattern, pre)
E       NameError: name 'construct_pre_node_map' is not defined

/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/relay/op/forge.py:1058: NameError

Check failure on line 150 in forge/test/mlir/operators/eltwise_binary/test_eltwise_binary.py

See this annotation in the file changed.

@github-actions github-actions / TT-Forge-FE Tests

test_eltwise_binary.test_greater[shape_x2-shape_y2] 

NameError: name 'construct_pre_node_map' is not defined
Raw output 
shape_x = (1, 256, 28, 28), shape_y = (1, 256, 28, 28)

    @pytest.mark.parametrize(
        "shape_x, shape_y",
        [
            ((1, 128, 28, 28), (1, 128, 28, 28)),
            ((1, 64, 28, 28), (1, 64, 28, 28)),
            ((1, 256, 28, 28), (1, 256, 28, 28)),
            ((1, 128, 14, 14), (1, 128, 14, 14)),
            ((1, 128, 56, 56), (1, 128, 56, 56)),
            ((1, 32, 64, 64), (1, 32, 64, 64)),
            ((1, 512, 7, 7), (1, 512, 7, 7)),
            ((1, 32, 32, 32), (1, 32, 32, 32)),
        ],
    )
    @pytest.mark.push
    def test_greater(shape_x, shape_y):
        class Greater(nn.Module):
            def __init__(self):
                super().__init__()
    
            def forward(self, x, y):
                return torch.greater(x, y)
    
        x = torch.rand(shape_x)
        y = torch.rand(shape_y)
    
        inputs = [x, y]
    
        framework_model = Greater()
>       compiled_model = forge.compile(framework_model, sample_inputs=inputs)

forge/test/mlir/operators/eltwise_binary/test_eltwise_binary.py:150: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/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:671: in generate_initial_graph
    module, module_inputs = convert_to_forge_module(
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/compile.py:1032: 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:2062: 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:2151: 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:104: in load_tvm_graph
    json_graphs, flattened_inputs = compile_tvm_graph(inputs, module, compiler_cfg, graph_name=graph_name, input_names=input_names, path=path, verify_cfg=verify_cfg, framework=framework)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/forge_compile.py:144: in compile_tvm_graph
    json_graphs, inputs = compile_pytorch_for_forge(module, *inputs, graph_name=graph_name, compiler_cfg=compiler_cfg, verify_cfg=verify_cfg, input_names=input_names)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/forge_compile.py:394: in compile_pytorch_for_forge
    partitioned_mod, forge_params = compile_tvm_for_forge(mod, params, np_inputs, framework_outputs, input_names=flattened_input_names, graph_name=graph_name, return_params=True, compiler_cfg=compiler_cfg, verify_cfg=verify_cfg)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/forge_compile.py:408: in compile_tvm_for_forge
    mod, params = compile_for_forge(mod, target=target, params=params, graph_name=graph_name, **verify_args)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/relay/op/forge.py:1100: in compile_for_forge
    warn_of_int_comparisons(compiled_relay_module)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/relay/op/forge.py:1068: in warn_of_int_comparisons
    rewrite(warner, mod['main'])
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/tvm/relay/dataflow_pattern/__init__.py:927: in rewrite
    return ffi.rewrite(tmp, expr, mod)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/tvm/_ffi/_ctypes/packed_func.py:242: in __call__
    raise_last_ffi_error()
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/tvm/_ffi/base.py:479: in raise_last_ffi_error
    raise py_err
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <forge.tvm_calls.relay.op.forge.CompareWarner object at 0x7efe3d7a93c0>
pre = CallNode(Op(greater), [Var(x, ty=TensorType([1, 256, 28, 28], float32)), Var(y, ty=TensorType([1, 256, 28, 28], float32))], (nullptr), [TensorType([1, 256, 28, 28], float32), TensorType([1, 256, 28, 28], float32)])
post = CallNode(Op(greater), [Var(x, ty=TensorType([1, 256, 28, 28], float32)), Var(y, ty=TensorType([1, 256, 28, 28], float32))], (nullptr), [TensorType([1, 256, 28, 28], float32), TensorType([1, 256, 28, 28], float32)])
node_map = {relay.dataflow_pattern.CallPattern(0x55ec70f9cab0): [CallNode(Op(greater), [Var(x, ty=TensorType([1, 256, 28, 28], fl... float32), TensorType([1, 256, 28, 28], float32)])], relay.dataflow_pattern.ExprPattern(0x55ec70aea210): [Op(greater)]}

    def callback(self, pre, post, node_map):
>       pre_node_map = construct_pre_node_map(self.pattern, pre)
E       NameError: name 'construct_pre_node_map' is not defined

/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/relay/op/forge.py:1058: NameError

Check failure on line 150 in forge/test/mlir/operators/eltwise_binary/test_eltwise_binary.py

See this annotation in the file changed.

@github-actions github-actions / TT-Forge-FE Tests

test_eltwise_binary.test_greater[shape_x4-shape_y4] 

NameError: name 'construct_pre_node_map' is not defined
Raw output 
shape_x = (1, 128, 56, 56), shape_y = (1, 128, 56, 56)

    @pytest.mark.parametrize(
        "shape_x, shape_y",
        [
            ((1, 128, 28, 28), (1, 128, 28, 28)),
            ((1, 64, 28, 28), (1, 64, 28, 28)),
            ((1, 256, 28, 28), (1, 256, 28, 28)),
            ((1, 128, 14, 14), (1, 128, 14, 14)),
            ((1, 128, 56, 56), (1, 128, 56, 56)),
            ((1, 32, 64, 64), (1, 32, 64, 64)),
            ((1, 512, 7, 7), (1, 512, 7, 7)),
            ((1, 32, 32, 32), (1, 32, 32, 32)),
        ],
    )
    @pytest.mark.push
    def test_greater(shape_x, shape_y):
        class Greater(nn.Module):
            def __init__(self):
                super().__init__()
    
            def forward(self, x, y):
                return torch.greater(x, y)
    
        x = torch.rand(shape_x)
        y = torch.rand(shape_y)
    
        inputs = [x, y]
    
        framework_model = Greater()
>       compiled_model = forge.compile(framework_model, sample_inputs=inputs)

forge/test/mlir/operators/eltwise_binary/test_eltwise_binary.py:150: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/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:671: in generate_initial_graph
    module, module_inputs = convert_to_forge_module(
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/compile.py:1032: 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:2062: 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:2151: 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:104: in load_tvm_graph
    json_graphs, flattened_inputs = compile_tvm_graph(inputs, module, compiler_cfg, graph_name=graph_name, input_names=input_names, path=path, verify_cfg=verify_cfg, framework=framework)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/forge_compile.py:144: in compile_tvm_graph
    json_graphs, inputs = compile_pytorch_for_forge(module, *inputs, graph_name=graph_name, compiler_cfg=compiler_cfg, verify_cfg=verify_cfg, input_names=input_names)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/forge_compile.py:394: in compile_pytorch_for_forge
    partitioned_mod, forge_params = compile_tvm_for_forge(mod, params, np_inputs, framework_outputs, input_names=flattened_input_names, graph_name=graph_name, return_params=True, compiler_cfg=compiler_cfg, verify_cfg=verify_cfg)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/forge_compile.py:408: in compile_tvm_for_forge
    mod, params = compile_for_forge(mod, target=target, params=params, graph_name=graph_name, **verify_args)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/relay/op/forge.py:1100: in compile_for_forge
    warn_of_int_comparisons(compiled_relay_module)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/relay/op/forge.py:1068: in warn_of_int_comparisons
    rewrite(warner, mod['main'])
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/tvm/relay/dataflow_pattern/__init__.py:927: in rewrite
    return ffi.rewrite(tmp, expr, mod)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/tvm/_ffi/_ctypes/packed_func.py:242: in __call__
    raise_last_ffi_error()
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/tvm/_ffi/base.py:479: in raise_last_ffi_error
    raise py_err
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <forge.tvm_calls.relay.op.forge.CompareWarner object at 0x7efe853af190>
pre = CallNode(Op(greater), [Var(x, ty=TensorType([1, 128, 56, 56], float32)), Var(y, ty=TensorType([1, 128, 56, 56], float32))], (nullptr), [TensorType([1, 128, 56, 56], float32), TensorType([1, 128, 56, 56], float32)])
post = CallNode(Op(greater), [Var(x, ty=TensorType([1, 128, 56, 56], float32)), Var(y, ty=TensorType([1, 128, 56, 56], float32))], (nullptr), [TensorType([1, 128, 56, 56], float32), TensorType([1, 128, 56, 56], float32)])
node_map = {relay.dataflow_pattern.WildcardPattern(0x55ec71091710): [Var(x, ty=TensorType([1, 128, 56, 56], float32))], relay.dat... 128, 56, 56], float32))], (nullptr), [TensorType([1, 128, 56, 56], float32), TensorType([1, 128, 56, 56], float32)])]}

    def callback(self, pre, post, node_map):
>       pre_node_map = construct_pre_node_map(self.pattern, pre)
E       NameError: name 'construct_pre_node_map' is not defined

/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/relay/op/forge.py:1058: NameError

Check failure on line 150 in forge/test/mlir/operators/eltwise_binary/test_eltwise_binary.py

See this annotation in the file changed.

@github-actions github-actions / TT-Forge-FE Tests

test_eltwise_binary.test_greater[shape_x6-shape_y6] 

NameError: name 'construct_pre_node_map' is not defined
Raw output 
shape_x = (1, 512, 7, 7), shape_y = (1, 512, 7, 7)

    @pytest.mark.parametrize(
        "shape_x, shape_y",
        [
            ((1, 128, 28, 28), (1, 128, 28, 28)),
            ((1, 64, 28, 28), (1, 64, 28, 28)),
            ((1, 256, 28, 28), (1, 256, 28, 28)),
            ((1, 128, 14, 14), (1, 128, 14, 14)),
            ((1, 128, 56, 56), (1, 128, 56, 56)),
            ((1, 32, 64, 64), (1, 32, 64, 64)),
            ((1, 512, 7, 7), (1, 512, 7, 7)),
            ((1, 32, 32, 32), (1, 32, 32, 32)),
        ],
    )
    @pytest.mark.push
    def test_greater(shape_x, shape_y):
        class Greater(nn.Module):
            def __init__(self):
                super().__init__()
    
            def forward(self, x, y):
                return torch.greater(x, y)
    
        x = torch.rand(shape_x)
        y = torch.rand(shape_y)
    
        inputs = [x, y]
    
        framework_model = Greater()
>       compiled_model = forge.compile(framework_model, sample_inputs=inputs)

forge/test/mlir/operators/eltwise_binary/test_eltwise_binary.py:150: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/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:671: in generate_initial_graph
    module, module_inputs = convert_to_forge_module(
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/compile.py:1032: 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:2062: 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:2151: 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:104: in load_tvm_graph
    json_graphs, flattened_inputs = compile_tvm_graph(inputs, module, compiler_cfg, graph_name=graph_name, input_names=input_names, path=path, verify_cfg=verify_cfg, framework=framework)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/forge_compile.py:144: in compile_tvm_graph
    json_graphs, inputs = compile_pytorch_for_forge(module, *inputs, graph_name=graph_name, compiler_cfg=compiler_cfg, verify_cfg=verify_cfg, input_names=input_names)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/forge_compile.py:394: in compile_pytorch_for_forge
    partitioned_mod, forge_params = compile_tvm_for_forge(mod, params, np_inputs, framework_outputs, input_names=flattened_input_names, graph_name=graph_name, return_params=True, compiler_cfg=compiler_cfg, verify_cfg=verify_cfg)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/forge_compile.py:408: in compile_tvm_for_forge
    mod, params = compile_for_forge(mod, target=target, params=params, graph_name=graph_name, **verify_args)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/relay/op/forge.py:1100: in compile_for_forge
    warn_of_int_comparisons(compiled_relay_module)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/relay/op/forge.py:1068: in warn_of_int_comparisons
    rewrite(warner, mod['main'])
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/tvm/relay/dataflow_pattern/__init__.py:927: in rewrite
    return ffi.rewrite(tmp, expr, mod)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/tvm/_ffi/_ctypes/packed_func.py:242: in __call__
    raise_last_ffi_error()
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/tvm/_ffi/base.py:479: in raise_last_ffi_error
    raise py_err
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <forge.tvm_calls.relay.op.forge.CompareWarner object at 0x7efe80190b50>
pre = CallNode(Op(greater), [Var(x, ty=TensorType([1, 512, 7, 7], float32)), Var(y, ty=TensorType([1, 512, 7, 7], float32))], (nullptr), [TensorType([1, 512, 7, 7], float32), TensorType([1, 512, 7, 7], float32)])
post = CallNode(Op(greater), [Var(x, ty=TensorType([1, 512, 7, 7], float32)), Var(y, ty=TensorType([1, 512, 7, 7], float32))], (nullptr), [TensorType([1, 512, 7, 7], float32), TensorType([1, 512, 7, 7], float32)])
node_map = {relay.dataflow_pattern.WildcardPattern(0x55ec70aa8c40): [Var(y, ty=TensorType([1, 512, 7, 7], float32))], relay.dataf...pe([1, 512, 7, 7], float32))], (nullptr), [TensorType([1, 512, 7, 7], float32), TensorType([1, 512, 7, 7], float32)])]}

    def callback(self, pre, post, node_map):
>       pre_node_map = construct_pre_node_map(self.pattern, pre)
E       NameError: name 'construct_pre_node_map' is not defined

/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/relay/op/forge.py:1058: NameError

Check failure on line 183 in forge/test/mlir/operators/eltwise_binary/test_eltwise_binary.py

See this annotation in the file changed.

@github-actions github-actions / TT-Forge-FE Tests

test_eltwise_binary.test_not_equal[shape_x0-shape_y0] 

NameError: name 'construct_pre_node_map' is not defined
Raw output 
shape_x = (1, 128, 28, 28), shape_y = (1, 128, 28, 28)

    @pytest.mark.parametrize(
        "shape_x, shape_y",
        [
            ((1, 128, 28, 28), (1, 128, 28, 28)),
            ((1, 64, 28, 28), (1, 64, 28, 28)),
            ((1, 256, 28, 28), (1, 256, 28, 28)),
            ((1, 128, 14, 14), (1, 128, 14, 14)),
            ((1, 128, 56, 56), (1, 128, 56, 56)),
            ((1, 32, 64, 64), (1, 32, 64, 64)),
            ((1, 512, 7, 7), (1, 512, 7, 7)),
            ((1, 32, 32, 32), (1, 32, 32, 32)),
        ],
    )
    @pytest.mark.push
    def test_not_equal(shape_x, shape_y):
        class NotEqual(nn.Module):
            def __init__(self):
                super().__init__()
    
            def forward(self, x, y):
                return torch.ne(x, y)
    
        x = torch.rand(shape_x)
        y = torch.rand(shape_y)
    
        inputs = [x, y]
    
        framework_model = NotEqual()
>       compiled_model = forge.compile(framework_model, sample_inputs=inputs)

forge/test/mlir/operators/eltwise_binary/test_eltwise_binary.py:183: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/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:671: in generate_initial_graph
    module, module_inputs = convert_to_forge_module(
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/compile.py:1032: 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:2062: 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:2151: 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:104: in load_tvm_graph
    json_graphs, flattened_inputs = compile_tvm_graph(inputs, module, compiler_cfg, graph_name=graph_name, input_names=input_names, path=path, verify_cfg=verify_cfg, framework=framework)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/forge_compile.py:144: in compile_tvm_graph
    json_graphs, inputs = compile_pytorch_for_forge(module, *inputs, graph_name=graph_name, compiler_cfg=compiler_cfg, verify_cfg=verify_cfg, input_names=input_names)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/forge_compile.py:394: in compile_pytorch_for_forge
    partitioned_mod, forge_params = compile_tvm_for_forge(mod, params, np_inputs, framework_outputs, input_names=flattened_input_names, graph_name=graph_name, return_params=True, compiler_cfg=compiler_cfg, verify_cfg=verify_cfg)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/forge_compile.py:408: in compile_tvm_for_forge
    mod, params = compile_for_forge(mod, target=target, params=params, graph_name=graph_name, **verify_args)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/relay/op/forge.py:1100: in compile_for_forge
    warn_of_int_comparisons(compiled_relay_module)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/relay/op/forge.py:1068: in warn_of_int_comparisons
    rewrite(warner, mod['main'])
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/tvm/relay/dataflow_pattern/__init__.py:927: in rewrite
    return ffi.rewrite(tmp, expr, mod)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/tvm/_ffi/_ctypes/packed_func.py:242: in __call__
    raise_last_ffi_error()
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/tvm/_ffi/base.py:479: in raise_last_ffi_error
    raise py_err
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <forge.tvm_calls.relay.op.forge.CompareWarner object at 0x7efe85800310>
pre = CallNode(Op(not_equal), [Var(x, ty=TensorType([1, 128, 28, 28], float32)), Var(y, ty=TensorType([1, 128, 28, 28], float32))], (nullptr), [TensorType([1, 128, 28, 28], float32), TensorType([1, 128, 28, 28], float32)])
post = CallNode(Op(not_equal), [Var(x, ty=TensorType([1, 128, 28, 28], float32)), Var(y, ty=TensorType([1, 128, 28, 28], float32))], (nullptr), [TensorType([1, 128, 28, 28], float32), TensorType([1, 128, 28, 28], float32)])
node_map = {relay.dataflow_pattern.CallPattern(0x55ec7105cdf0): [CallNode(Op(not_equal), [Var(x, ty=TensorType([1, 128, 28, 28], ... 128, 28, 28], float32))], (nullptr), [TensorType([1, 128, 28, 28], float32), TensorType([1, 128, 28, 28], float32)])]}

    def callback(self, pre, post, node_map):
>       pre_node_map = construct_pre_node_map(self.pattern, pre)
E       NameError: name 'construct_pre_node_map' is not defined

/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/relay/op/forge.py:1058: NameError

Check failure on line 183 in forge/test/mlir/operators/eltwise_binary/test_eltwise_binary.py

See this annotation in the file changed.

@github-actions github-actions / TT-Forge-FE Tests

test_eltwise_binary.test_not_equal[shape_x2-shape_y2] 

NameError: name 'construct_pre_node_map' is not defined
Raw output 
shape_x = (1, 256, 28, 28), shape_y = (1, 256, 28, 28)

    @pytest.mark.parametrize(
        "shape_x, shape_y",
        [
            ((1, 128, 28, 28), (1, 128, 28, 28)),
            ((1, 64, 28, 28), (1, 64, 28, 28)),
            ((1, 256, 28, 28), (1, 256, 28, 28)),
            ((1, 128, 14, 14), (1, 128, 14, 14)),
            ((1, 128, 56, 56), (1, 128, 56, 56)),
            ((1, 32, 64, 64), (1, 32, 64, 64)),
            ((1, 512, 7, 7), (1, 512, 7, 7)),
            ((1, 32, 32, 32), (1, 32, 32, 32)),
        ],
    )
    @pytest.mark.push
    def test_not_equal(shape_x, shape_y):
        class NotEqual(nn.Module):
            def __init__(self):
                super().__init__()
    
            def forward(self, x, y):
                return torch.ne(x, y)
    
        x = torch.rand(shape_x)
        y = torch.rand(shape_y)
    
        inputs = [x, y]
    
        framework_model = NotEqual()
>       compiled_model = forge.compile(framework_model, sample_inputs=inputs)

forge/test/mlir/operators/eltwise_binary/test_eltwise_binary.py:183: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/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:671: in generate_initial_graph
    module, module_inputs = convert_to_forge_module(
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/compile.py:1032: 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:2062: 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:2151: 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:104: in load_tvm_graph
    json_graphs, flattened_inputs = compile_tvm_graph(inputs, module, compiler_cfg, graph_name=graph_name, input_names=input_names, path=path, verify_cfg=verify_cfg, framework=framework)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/forge_compile.py:144: in compile_tvm_graph
    json_graphs, inputs = compile_pytorch_for_forge(module, *inputs, graph_name=graph_name, compiler_cfg=compiler_cfg, verify_cfg=verify_cfg, input_names=input_names)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/forge_compile.py:394: in compile_pytorch_for_forge
    partitioned_mod, forge_params = compile_tvm_for_forge(mod, params, np_inputs, framework_outputs, input_names=flattened_input_names, graph_name=graph_name, return_params=True, compiler_cfg=compiler_cfg, verify_cfg=verify_cfg)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/forge_compile.py:408: in compile_tvm_for_forge
    mod, params = compile_for_forge(mod, target=target, params=params, graph_name=graph_name, **verify_args)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/relay/op/forge.py:1100: in compile_for_forge
    warn_of_int_comparisons(compiled_relay_module)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/relay/op/forge.py:1068: in warn_of_int_comparisons
    rewrite(warner, mod['main'])
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/tvm/relay/dataflow_pattern/__init__.py:927: in rewrite
    return ffi.rewrite(tmp, expr, mod)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/tvm/_ffi/_ctypes/packed_func.py:242: in __call__
    raise_last_ffi_error()
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/tvm/_ffi/base.py:479: in raise_last_ffi_error
    raise py_err
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <forge.tvm_calls.relay.op.forge.CompareWarner object at 0x7eff2b7b8d60>
pre = CallNode(Op(not_equal), [Var(x, ty=TensorType([1, 256, 28, 28], float32)), Var(y, ty=TensorType([1, 256, 28, 28], float32))], (nullptr), [TensorType([1, 256, 28, 28], float32), TensorType([1, 256, 28, 28], float32)])
post = CallNode(Op(not_equal), [Var(x, ty=TensorType([1, 256, 28, 28], float32)), Var(y, ty=TensorType([1, 256, 28, 28], float32))], (nullptr), [TensorType([1, 256, 28, 28], float32), TensorType([1, 256, 28, 28], float32)])
node_map = {relay.dataflow_pattern.CallPattern(0x55ec70c34340): [CallNode(Op(not_equal), [Var(x, ty=TensorType([1, 256, 28, 28], ...loat32)])], relay.dataflow_pattern.WildcardPattern(0x55ec714cc7c0): [Var(x, ty=TensorType([1, 256, 28, 28], float32))]}

    def callback(self, pre, post, node_map):
>       pre_node_map = construct_pre_node_map(self.pattern, pre)
E       NameError: name 'construct_pre_node_map' is not defined

/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/relay/op/forge.py:1058: NameError

Check failure on line 183 in forge/test/mlir/operators/eltwise_binary/test_eltwise_binary.py

See this annotation in the file changed.

@github-actions github-actions / TT-Forge-FE Tests

test_eltwise_binary.test_not_equal[shape_x4-shape_y4] 

NameError: name 'construct_pre_node_map' is not defined
Raw output 
shape_x = (1, 128, 56, 56), shape_y = (1, 128, 56, 56)

    @pytest.mark.parametrize(
        "shape_x, shape_y",
        [
            ((1, 128, 28, 28), (1, 128, 28, 28)),
            ((1, 64, 28, 28), (1, 64, 28, 28)),
            ((1, 256, 28, 28), (1, 256, 28, 28)),
            ((1, 128, 14, 14), (1, 128, 14, 14)),
            ((1, 128, 56, 56), (1, 128, 56, 56)),
            ((1, 32, 64, 64), (1, 32, 64, 64)),
            ((1, 512, 7, 7), (1, 512, 7, 7)),
            ((1, 32, 32, 32), (1, 32, 32, 32)),
        ],
    )
    @pytest.mark.push
    def test_not_equal(shape_x, shape_y):
        class NotEqual(nn.Module):
            def __init__(self):
                super().__init__()
    
            def forward(self, x, y):
                return torch.ne(x, y)
    
        x = torch.rand(shape_x)
        y = torch.rand(shape_y)
    
        inputs = [x, y]
    
        framework_model = NotEqual()
>       compiled_model = forge.compile(framework_model, sample_inputs=inputs)

forge/test/mlir/operators/eltwise_binary/test_eltwise_binary.py:183: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/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:671: in generate_initial_graph
    module, module_inputs = convert_to_forge_module(
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/compile.py:1032: 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:2062: 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:2151: 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:104: in load_tvm_graph
    json_graphs, flattened_inputs = compile_tvm_graph(inputs, module, compiler_cfg, graph_name=graph_name, input_names=input_names, path=path, verify_cfg=verify_cfg, framework=framework)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/forge_compile.py:144: in compile_tvm_graph
    json_graphs, inputs = compile_pytorch_for_forge(module, *inputs, graph_name=graph_name, compiler_cfg=compiler_cfg, verify_cfg=verify_cfg, input_names=input_names)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/forge_compile.py:394: in compile_pytorch_for_forge
    partitioned_mod, forge_params = compile_tvm_for_forge(mod, params, np_inputs, framework_outputs, input_names=flattened_input_names, graph_name=graph_name, return_params=True, compiler_cfg=compiler_cfg, verify_cfg=verify_cfg)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/forge_compile.py:408: in compile_tvm_for_forge
    mod, params = compile_for_forge(mod, target=target, params=params, graph_name=graph_name, **verify_args)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/relay/op/forge.py:1100: in compile_for_forge
    warn_of_int_comparisons(compiled_relay_module)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/relay/op/forge.py:1068: in warn_of_int_comparisons
    rewrite(warner, mod['main'])
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/tvm/relay/dataflow_pattern/__init__.py:927: in rewrite
    return ffi.rewrite(tmp, expr, mod)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/tvm/_ffi/_ctypes/packed_func.py:242: in __call__
    raise_last_ffi_error()
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/tvm/_ffi/base.py:479: in raise_last_ffi_error
    raise py_err
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <forge.tvm_calls.relay.op.forge.CompareWarner object at 0x7efe3d62fee0>
pre = CallNode(Op(not_equal), [Var(x, ty=TensorType([1, 128, 56, 56], float32)), Var(y, ty=TensorType([1, 128, 56, 56], float32))], (nullptr), [TensorType([1, 128, 56, 56], float32), TensorType([1, 128, 56, 56], float32)])
post = CallNode(Op(not_equal), [Var(x, ty=TensorType([1, 128, 56, 56], float32)), Var(y, ty=TensorType([1, 128, 56, 56], float32))], (nullptr), [TensorType([1, 128, 56, 56], float32), TensorType([1, 128, 56, 56], float32)])
node_map = {relay.dataflow_pattern.AltPattern(0x55ec710136a0): [CallNode(Op(not_equal), [Var(x, ty=TensorType([1, 128, 56, 56], f... 128, 56, 56], float32))], (nullptr), [TensorType([1, 128, 56, 56], float32), TensorType([1, 128, 56, 56], float32)])]}

    def callback(self, pre, post, node_map):
>       pre_node_map = construct_pre_node_map(self.pattern, pre)
E       NameError: name 'construct_pre_node_map' is not defined

/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/relay/op/forge.py:1058: NameError

Check failure on line 183 in forge/test/mlir/operators/eltwise_binary/test_eltwise_binary.py

See this annotation in the file changed.

@github-actions github-actions / TT-Forge-FE Tests

test_eltwise_binary.test_not_equal[shape_x6-shape_y6] 

NameError: name 'construct_pre_node_map' is not defined
Raw output 
shape_x = (1, 512, 7, 7), shape_y = (1, 512, 7, 7)

    @pytest.mark.parametrize(
        "shape_x, shape_y",
        [
            ((1, 128, 28, 28), (1, 128, 28, 28)),
            ((1, 64, 28, 28), (1, 64, 28, 28)),
            ((1, 256, 28, 28), (1, 256, 28, 28)),
            ((1, 128, 14, 14), (1, 128, 14, 14)),
            ((1, 128, 56, 56), (1, 128, 56, 56)),
            ((1, 32, 64, 64), (1, 32, 64, 64)),
            ((1, 512, 7, 7), (1, 512, 7, 7)),
            ((1, 32, 32, 32), (1, 32, 32, 32)),
        ],
    )
    @pytest.mark.push
    def test_not_equal(shape_x, shape_y):
        class NotEqual(nn.Module):
            def __init__(self):
                super().__init__()
    
            def forward(self, x, y):
                return torch.ne(x, y)
    
        x = torch.rand(shape_x)
        y = torch.rand(shape_y)
    
        inputs = [x, y]
    
        framework_model = NotEqual()
>       compiled_model = forge.compile(framework_model, sample_inputs=inputs)

forge/test/mlir/operators/eltwise_binary/test_eltwise_binary.py:183: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/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:671: in generate_initial_graph
    module, module_inputs = convert_to_forge_module(
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/compile.py:1032: 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:2062: 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:2151: 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:104: in load_tvm_graph
    json_graphs, flattened_inputs = compile_tvm_graph(inputs, module, compiler_cfg, graph_name=graph_name, input_names=input_names, path=path, verify_cfg=verify_cfg, framework=framework)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/forge_compile.py:144: in compile_tvm_graph
    json_graphs, inputs = compile_pytorch_for_forge(module, *inputs, graph_name=graph_name, compiler_cfg=compiler_cfg, verify_cfg=verify_cfg, input_names=input_names)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/forge_compile.py:394: in compile_pytorch_for_forge
    partitioned_mod, forge_params = compile_tvm_for_forge(mod, params, np_inputs, framework_outputs, input_names=flattened_input_names, graph_name=graph_name, return_params=True, compiler_cfg=compiler_cfg, verify_cfg=verify_cfg)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/forge_compile.py:408: in compile_tvm_for_forge
    mod, params = compile_for_forge(mod, target=target, params=params, graph_name=graph_name, **verify_args)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/relay/op/forge.py:1100: in compile_for_forge
    warn_of_int_comparisons(compiled_relay_module)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/relay/op/forge.py:1068: in warn_of_int_comparisons
    rewrite(warner, mod['main'])
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/tvm/relay/dataflow_pattern/__init__.py:927: in rewrite
    return ffi.rewrite(tmp, expr, mod)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/tvm/_ffi/_ctypes/packed_func.py:242: in __call__
    raise_last_ffi_error()
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/tvm/_ffi/base.py:479: in raise_last_ffi_error
    raise py_err
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <forge.tvm_calls.relay.op.forge.CompareWarner object at 0x7efe857115d0>
pre = CallNode(Op(not_equal), [Var(x, ty=TensorType([1, 512, 7, 7], float32)), Var(y, ty=TensorType([1, 512, 7, 7], float32))], (nullptr), [TensorType([1, 512, 7, 7], float32), TensorType([1, 512, 7, 7], float32)])
post = CallNode(Op(not_equal), [Var(x, ty=TensorType([1, 512, 7, 7], float32)), Var(y, ty=TensorType([1, 512, 7, 7], float32))], (nullptr), [TensorType([1, 512, 7, 7], float32), TensorType([1, 512, 7, 7], float32)])
node_map = {relay.dataflow_pattern.CallPattern(0x55ec71025640): [CallNode(Op(not_equal), [Var(x, ty=TensorType([1, 512, 7, 7], fl... float32), TensorType([1, 512, 7, 7], float32)])], relay.dataflow_pattern.ExprPattern(0x55ec710790e0): [Op(not_equal)]}

    def callback(self, pre, post, node_map):
>       pre_node_map = construct_pre_node_map(self.pattern, pre)
E       NameError: name 'construct_pre_node_map' is not defined

/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/relay/op/forge.py:1058: NameError

Check failure on line 232 in forge/test/mlir/operators/eltwise_binary/test_eltwise_binary.py

See this annotation in the file changed.

@github-actions github-actions / TT-Forge-FE Tests

test_eltwise_binary.test_equal[shape1] 

NameError: name 'construct_pre_node_map' is not defined
Raw output 
shape = (1, 64, 28, 28)

    @pytest.mark.parametrize(
        "shape",
        [
            (1, 128, 28, 28),
            (1, 64, 28, 28),
            (1, 256, 28, 28),
            (1, 128, 14, 14),
            (1, 128, 56, 56),
            (1, 32, 64, 64),
            (1, 512, 7, 7),
            (1, 32, 32, 32),
            (128, 28, 28),
            (64, 28, 28),
            (256, 28, 28),
            (128, 14, 14),
            (128, 56, 56),
            (32, 64, 64),
            (512, 7, 7),
            (32, 32, 32),
            (128, 28),
            (64, 28),
            (256, 28),
            (128, 14),
            (128, 56),
            (32, 64),
            (512, 7),
            (32, 32),
        ],
    )
    @pytest.mark.push
    def test_equal(shape):
        class Equal(nn.Module):
            def __init__(self):
                super().__init__()
    
            def forward(self, x, y):
                return torch.eq(x, y)
    
        x = torch.rand(shape)
        y = x * 2.0
    
        inputs = [x, y]
    
        framework_model = Equal()
>       compiled_model = forge.compile(framework_model, sample_inputs=inputs)

forge/test/mlir/operators/eltwise_binary/test_eltwise_binary.py:232: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/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:671: in generate_initial_graph
    module, module_inputs = convert_to_forge_module(
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/compile.py:1032: 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:2062: 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:2151: 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:104: in load_tvm_graph
    json_graphs, flattened_inputs = compile_tvm_graph(inputs, module, compiler_cfg, graph_name=graph_name, input_names=input_names, path=path, verify_cfg=verify_cfg, framework=framework)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/forge_compile.py:144: in compile_tvm_graph
    json_graphs, inputs = compile_pytorch_for_forge(module, *inputs, graph_name=graph_name, compiler_cfg=compiler_cfg, verify_cfg=verify_cfg, input_names=input_names)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/forge_compile.py:394: in compile_pytorch_for_forge
    partitioned_mod, forge_params = compile_tvm_for_forge(mod, params, np_inputs, framework_outputs, input_names=flattened_input_names, graph_name=graph_name, return_params=True, compiler_cfg=compiler_cfg, verify_cfg=verify_cfg)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/forge_compile.py:408: in compile_tvm_for_forge
    mod, params = compile_for_forge(mod, target=target, params=params, graph_name=graph_name, **verify_args)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/relay/op/forge.py:1095: in compile_for_forge
    compiled_relay_module = run_forge_compile_passes(relay_module, params, inputs, target, framework_outputs, verify_cfg)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/relay/op/forge_passes.py:4238: in run_forge_compile_passes
    return run_pattern_callbacks(
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/relay/op/forge_passes.py:4229: in run_pattern_callbacks
    raise ex
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/relay/op/forge_passes.py:4226: in run_pattern_callbacks
    relay_module = _run_pattern_callback(relay_module, callback, callback_name)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/relay/op/forge_passes.py:4205: in _run_pattern_callback
    relay_module['main'] = rewrite(callback, relay_module['main'])
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/tvm/relay/dataflow_pattern/__init__.py:927: in rewrite
    return ffi.rewrite(tmp, expr, mod)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/tvm/_ffi/_ctypes/packed_func.py:242: in __call__
    raise_last_ffi_error()
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/tvm/_ffi/base.py:479: in raise_last_ffi_error
    raise py_err
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <forge.tvm_calls.relay.op.forge_passes.InverseMaskGen object at 0x7efe85360940>
pre = CallNode(Op(equal), [Var(x, ty=TensorType([1, 64, 28, 28], float32)), Var(y, ty=TensorType([1, 64, 28, 28], float32))], (nullptr), [TensorType([1, 64, 28, 28], float32), TensorType([1, 64, 28, 28], float32)])
post = CallNode(Op(equal), [Var(x, ty=TensorType([1, 64, 28, 28], float32)), Var(y, ty=TensorType([1, 64, 28, 28], float32))], (nullptr), [TensorType([1, 64, 28, 28], float32), TensorType([1, 64, 28, 28], float32)])
node_map = {relay.dataflow_pattern.WildcardPattern(0x55ec71694f80): [Var(y, ty=TensorType([1, 64, 28, 28], float32))], relay.data...[1, 64, 28, 28], float32))], (nullptr), [TensorType([1, 64, 28, 28], float32), TensorType([1, 64, 28, 28], float32)])]}

    def callback(self, pre, post, node_map):
>       pre_node_map = construct_pre_node_map(self.pattern, pre)
E       NameError: name 'construct_pre_node_map' is not defined

/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/relay/op/forge_passes.py:3424: NameError

Check failure on line 232 in forge/test/mlir/operators/eltwise_binary/test_eltwise_binary.py

See this annotation in the file changed.

@github-actions github-actions / TT-Forge-FE Tests

test_eltwise_binary.test_equal[shape3] 

NameError: name 'construct_pre_node_map' is not defined
Raw output 
shape = (1, 128, 14, 14)

    @pytest.mark.parametrize(
        "shape",
        [
            (1, 128, 28, 28),
            (1, 64, 28, 28),
            (1, 256, 28, 28),
            (1, 128, 14, 14),
            (1, 128, 56, 56),
            (1, 32, 64, 64),
            (1, 512, 7, 7),
            (1, 32, 32, 32),
            (128, 28, 28),
            (64, 28, 28),
            (256, 28, 28),
            (128, 14, 14),
            (128, 56, 56),
            (32, 64, 64),
            (512, 7, 7),
            (32, 32, 32),
            (128, 28),
            (64, 28),
            (256, 28),
            (128, 14),
            (128, 56),
            (32, 64),
            (512, 7),
            (32, 32),
        ],
    )
    @pytest.mark.push
    def test_equal(shape):
        class Equal(nn.Module):
            def __init__(self):
                super().__init__()
    
            def forward(self, x, y):
                return torch.eq(x, y)
    
        x = torch.rand(shape)
        y = x * 2.0
    
        inputs = [x, y]
    
        framework_model = Equal()
>       compiled_model = forge.compile(framework_model, sample_inputs=inputs)

forge/test/mlir/operators/eltwise_binary/test_eltwise_binary.py:232: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/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:671: in generate_initial_graph
    module, module_inputs = convert_to_forge_module(
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/compile.py:1032: 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:2062: 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:2151: 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:104: in load_tvm_graph
    json_graphs, flattened_inputs = compile_tvm_graph(inputs, module, compiler_cfg, graph_name=graph_name, input_names=input_names, path=path, verify_cfg=verify_cfg, framework=framework)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/forge_compile.py:144: in compile_tvm_graph
    json_graphs, inputs = compile_pytorch_for_forge(module, *inputs, graph_name=graph_name, compiler_cfg=compiler_cfg, verify_cfg=verify_cfg, input_names=input_names)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/forge_compile.py:394: in compile_pytorch_for_forge
    partitioned_mod, forge_params = compile_tvm_for_forge(mod, params, np_inputs, framework_outputs, input_names=flattened_input_names, graph_name=graph_name, return_params=True, compiler_cfg=compiler_cfg, verify_cfg=verify_cfg)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/forge_compile.py:408: in compile_tvm_for_forge
    mod, params = compile_for_forge(mod, target=target, params=params, graph_name=graph_name, **verify_args)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/relay/op/forge.py:1095: in compile_for_forge
    compiled_relay_module = run_forge_compile_passes(relay_module, params, inputs, target, framework_outputs, verify_cfg)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/relay/op/forge_passes.py:4238: in run_forge_compile_passes
    return run_pattern_callbacks(
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/relay/op/forge_passes.py:4229: in run_pattern_callbacks
    raise ex
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/relay/op/forge_passes.py:4226: in run_pattern_callbacks
    relay_module = _run_pattern_callback(relay_module, callback, callback_name)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/relay/op/forge_passes.py:4205: in _run_pattern_callback
    relay_module['main'] = rewrite(callback, relay_module['main'])
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/tvm/relay/dataflow_pattern/__init__.py:927: in rewrite
    return ffi.rewrite(tmp, expr, mod)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/tvm/_ffi/_ctypes/packed_func.py:242: in __call__
    raise_last_ffi_error()
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/tvm/_ffi/base.py:479: in raise_last_ffi_error
    raise py_err
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <forge.tvm_calls.relay.op.forge_passes.InverseMaskGen object at 0x7efe3c6cfd90>
pre = CallNode(Op(equal), [Var(x, ty=TensorType([1, 128, 14, 14], float32)), Var(y, ty=TensorType([1, 128, 14, 14], float32))], (nullptr), [TensorType([1, 128, 14, 14], float32), TensorType([1, 128, 14, 14], float32)])
post = CallNode(Op(equal), [Var(x, ty=TensorType([1, 128, 14, 14], float32)), Var(y, ty=TensorType([1, 128, 14, 14], float32))], (nullptr), [TensorType([1, 128, 14, 14], float32), TensorType([1, 128, 14, 14], float32)])
node_map = {relay.dataflow_pattern.WildcardPattern(0x55ec70ee86b0): [Var(y, ty=TensorType([1, 128, 14, 14], float32))], relay.dat...): [Var(x, ty=TensorType([1, 128, 14, 14], float32))], relay.dataflow_pattern.ExprPattern(0x55ec6df3b620): [Op(equal)]}

    def callback(self, pre, post, node_map):
>       pre_node_map = construct_pre_node_map(self.pattern, pre)
E       NameError: name 'construct_pre_node_map' is not defined

/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/relay/op/forge_passes.py:3424: NameError

Check failure on line 232 in forge/test/mlir/operators/eltwise_binary/test_eltwise_binary.py

See this annotation in the file changed.

@github-actions github-actions / TT-Forge-FE Tests

test_eltwise_binary.test_equal[shape5] 

NameError: name 'construct_pre_node_map' is not defined
Raw output 
shape = (1, 32, 64, 64)

    @pytest.mark.parametrize(
        "shape",
        [
            (1, 128, 28, 28),
            (1, 64, 28, 28),
            (1, 256, 28, 28),
            (1, 128, 14, 14),
            (1, 128, 56, 56),
            (1, 32, 64, 64),
            (1, 512, 7, 7),
            (1, 32, 32, 32),
            (128, 28, 28),
            (64, 28, 28),
            (256, 28, 28),
            (128, 14, 14),
            (128, 56, 56),
            (32, 64, 64),
            (512, 7, 7),
            (32, 32, 32),
            (128, 28),
            (64, 28),
            (256, 28),
            (128, 14),
            (128, 56),
            (32, 64),
            (512, 7),
            (32, 32),
        ],
    )
    @pytest.mark.push
    def test_equal(shape):
        class Equal(nn.Module):
            def __init__(self):
                super().__init__()
    
            def forward(self, x, y):
                return torch.eq(x, y)
    
        x = torch.rand(shape)
        y = x * 2.0
    
        inputs = [x, y]
    
        framework_model = Equal()
>       compiled_model = forge.compile(framework_model, sample_inputs=inputs)

forge/test/mlir/operators/eltwise_binary/test_eltwise_binary.py:232: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/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:671: in generate_initial_graph
    module, module_inputs = convert_to_forge_module(
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/compile.py:1032: 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:2062: 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:2151: 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:104: in load_tvm_graph
    json_graphs, flattened_inputs = compile_tvm_graph(inputs, module, compiler_cfg, graph_name=graph_name, input_names=input_names, path=path, verify_cfg=verify_cfg, framework=framework)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/forge_compile.py:144: in compile_tvm_graph
    json_graphs, inputs = compile_pytorch_for_forge(module, *inputs, graph_name=graph_name, compiler_cfg=compiler_cfg, verify_cfg=verify_cfg, input_names=input_names)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/forge_compile.py:394: in compile_pytorch_for_forge
    partitioned_mod, forge_params = compile_tvm_for_forge(mod, params, np_inputs, framework_outputs, input_names=flattened_input_names, graph_name=graph_name, return_params=True, compiler_cfg=compiler_cfg, verify_cfg=verify_cfg)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/forge_compile.py:408: in compile_tvm_for_forge
    mod, params = compile_for_forge(mod, target=target, params=params, graph_name=graph_name, **verify_args)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/relay/op/forge.py:1095: in compile_for_forge
    compiled_relay_module = run_forge_compile_passes(relay_module, params, inputs, target, framework_outputs, verify_cfg)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/relay/op/forge_passes.py:4238: in run_forge_compile_passes
    return run_pattern_callbacks(
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/relay/op/forge_passes.py:4229: in run_pattern_callbacks
    raise ex
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/relay/op/forge_passes.py:4226: in run_pattern_callbacks
    relay_module = _run_pattern_callback(relay_module, callback, callback_name)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/relay/op/forge_passes.py:4205: in _run_pattern_callback
    relay_module['main'] = rewrite(callback, relay_module['main'])
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/tvm/relay/dataflow_pattern/__init__.py:927: in rewrite
    return ffi.rewrite(tmp, expr, mod)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/tvm/_ffi/_ctypes/packed_func.py:242: in __call__
    raise_last_ffi_error()
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/tvm/_ffi/base.py:479: in raise_last_ffi_error
    raise py_err
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <forge.tvm_calls.relay.op.forge_passes.InverseMaskGen object at 0x7efe858867d0>
pre = CallNode(Op(equal), [Var(x, ty=TensorType([1, 32, 64, 64], float32)), Var(y, ty=TensorType([1, 32, 64, 64], float32))], (nullptr), [TensorType([1, 32, 64, 64], float32), TensorType([1, 32, 64, 64], float32)])
post = CallNode(Op(equal), [Var(x, ty=TensorType([1, 32, 64, 64], float32)), Var(y, ty=TensorType([1, 32, 64, 64], float32))], (nullptr), [TensorType([1, 32, 64, 64], float32), TensorType([1, 32, 64, 64], float32)])
node_map = {relay.dataflow_pattern.WildcardPattern(0x55ec70c15e30): [Var(y, ty=TensorType([1, 32, 64, 64], float32))], relay.data...4], float32), TensorType([1, 32, 64, 64], float32)])], relay.dataflow_pattern.ExprPattern(0x55ec710a2370): [Op(equal)]}

    def callback(self, pre, post, node_map):
>       pre_node_map = construct_pre_node_map(self.pattern, pre)
E       NameError: name 'construct_pre_node_map' is not defined

/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/relay/op/forge_passes.py:3424: NameError

Check failure on line 232 in forge/test/mlir/operators/eltwise_binary/test_eltwise_binary.py

See this annotation in the file changed.

@github-actions github-actions / TT-Forge-FE Tests

test_eltwise_binary.test_equal[shape7] 

NameError: name 'construct_pre_node_map' is not defined
Raw output 
shape = (1, 32, 32, 32)

    @pytest.mark.parametrize(
        "shape",
        [
            (1, 128, 28, 28),
            (1, 64, 28, 28),
            (1, 256, 28, 28),
            (1, 128, 14, 14),
            (1, 128, 56, 56),
            (1, 32, 64, 64),
            (1, 512, 7, 7),
            (1, 32, 32, 32),
            (128, 28, 28),
            (64, 28, 28),
            (256, 28, 28),
            (128, 14, 14),
            (128, 56, 56),
            (32, 64, 64),
            (512, 7, 7),
            (32, 32, 32),
            (128, 28),
            (64, 28),
            (256, 28),
            (128, 14),
            (128, 56),
            (32, 64),
            (512, 7),
            (32, 32),
        ],
    )
    @pytest.mark.push
    def test_equal(shape):
        class Equal(nn.Module):
            def __init__(self):
                super().__init__()
    
            def forward(self, x, y):
                return torch.eq(x, y)
    
        x = torch.rand(shape)
        y = x * 2.0
    
        inputs = [x, y]
    
        framework_model = Equal()
>       compiled_model = forge.compile(framework_model, sample_inputs=inputs)

forge/test/mlir/operators/eltwise_binary/test_eltwise_binary.py:232: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/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:671: in generate_initial_graph
    module, module_inputs = convert_to_forge_module(
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/compile.py:1032: 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:2062: 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:2151: 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:104: in load_tvm_graph
    json_graphs, flattened_inputs = compile_tvm_graph(inputs, module, compiler_cfg, graph_name=graph_name, input_names=input_names, path=path, verify_cfg=verify_cfg, framework=framework)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/forge_compile.py:144: in compile_tvm_graph
    json_graphs, inputs = compile_pytorch_for_forge(module, *inputs, graph_name=graph_name, compiler_cfg=compiler_cfg, verify_cfg=verify_cfg, input_names=input_names)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/forge_compile.py:394: in compile_pytorch_for_forge
    partitioned_mod, forge_params = compile_tvm_for_forge(mod, params, np_inputs, framework_outputs, input_names=flattened_input_names, graph_name=graph_name, return_params=True, compiler_cfg=compiler_cfg, verify_cfg=verify_cfg)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/forge_compile.py:408: in compile_tvm_for_forge
    mod, params = compile_for_forge(mod, target=target, params=params, graph_name=graph_name, **verify_args)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/relay/op/forge.py:1095: in compile_for_forge
    compiled_relay_module = run_forge_compile_passes(relay_module, params, inputs, target, framework_outputs, verify_cfg)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/relay/op/forge_passes.py:4238: in run_forge_compile_passes
    return run_pattern_callbacks(
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/relay/op/forge_passes.py:4229: in run_pattern_callbacks
    raise ex
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/relay/op/forge_passes.py:4226: in run_pattern_callbacks
    relay_module = _run_pattern_callback(relay_module, callback, callback_name)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/relay/op/forge_passes.py:4205: in _run_pattern_callback
    relay_module['main'] = rewrite(callback, relay_module['main'])
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/tvm/relay/dataflow_pattern/__init__.py:927: in rewrite
    return ffi.rewrite(tmp, expr, mod)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/tvm/_ffi/_ctypes/packed_func.py:242: in __call__
    raise_last_ffi_error()
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/tvm/_ffi/base.py:479: in raise_last_ffi_error
    raise py_err
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <forge.tvm_calls.relay.op.forge_passes.InverseMaskGen object at 0x7efe3d127cd0>
pre = CallNode(Op(equal), [Var(x, ty=TensorType([1, 32, 32, 32], float32)), Var(y, ty=TensorType([1, 32, 32, 32], float32))], (nullptr), [TensorType([1, 32, 32, 32], float32), TensorType([1, 32, 32, 32], float32)])
post = CallNode(Op(equal), [Var(x, ty=TensorType([1, 32, 32, 32], float32)), Var(y, ty=TensorType([1, 32, 32, 32], float32))], (nullptr), [TensorType([1, 32, 32, 32], float32), TensorType([1, 32, 32, 32], float32)])
node_map = {relay.dataflow_pattern.CallPattern(0x55ec72fb47c0): [CallNode(Op(equal), [Var(x, ty=TensorType([1, 32, 32, 32], float...[Op(equal)], relay.dataflow_pattern.WildcardPattern(0x55ec7214e2c0): [Var(y, ty=TensorType([1, 32, 32, 32], float32))]}

    def callback(self, pre, post, node_map):
>       pre_node_map = construct_pre_node_map(self.pattern, pre)
E       NameError: name 'construct_pre_node_map' is not defined

/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/relay/op/forge_passes.py:3424: NameError

Check failure on line 232 in forge/test/mlir/operators/eltwise_binary/test_eltwise_binary.py

See this annotation in the file changed.

@github-actions github-actions / TT-Forge-FE Tests

test_eltwise_binary.test_equal[shape9] 

NameError: name 'construct_pre_node_map' is not defined
Raw output 
shape = (64, 28, 28)

    @pytest.mark.parametrize(
        "shape",
        [
            (1, 128, 28, 28),
            (1, 64, 28, 28),
            (1, 256, 28, 28),
            (1, 128, 14, 14),
            (1, 128, 56, 56),
            (1, 32, 64, 64),
            (1, 512, 7, 7),
            (1, 32, 32, 32),
            (128, 28, 28),
            (64, 28, 28),
            (256, 28, 28),
            (128, 14, 14),
            (128, 56, 56),
            (32, 64, 64),
            (512, 7, 7),
            (32, 32, 32),
            (128, 28),
            (64, 28),
            (256, 28),
            (128, 14),
            (128, 56),
            (32, 64),
            (512, 7),
            (32, 32),
        ],
    )
    @pytest.mark.push
    def test_equal(shape):
        class Equal(nn.Module):
            def __init__(self):
                super().__init__()
    
            def forward(self, x, y):
                return torch.eq(x, y)
    
        x = torch.rand(shape)
        y = x * 2.0
    
        inputs = [x, y]
    
        framework_model = Equal()
>       compiled_model = forge.compile(framework_model, sample_inputs=inputs)

forge/test/mlir/operators/eltwise_binary/test_eltwise_binary.py:232: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/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:671: in generate_initial_graph
    module, module_inputs = convert_to_forge_module(
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/compile.py:1032: 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:2062: 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:2151: 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:104: in load_tvm_graph
    json_graphs, flattened_inputs = compile_tvm_graph(inputs, module, compiler_cfg, graph_name=graph_name, input_names=input_names, path=path, verify_cfg=verify_cfg, framework=framework)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/forge_compile.py:144: in compile_tvm_graph
    json_graphs, inputs = compile_pytorch_for_forge(module, *inputs, graph_name=graph_name, compiler_cfg=compiler_cfg, verify_cfg=verify_cfg, input_names=input_names)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/forge_compile.py:394: in compile_pytorch_for_forge
    partitioned_mod, forge_params = compile_tvm_for_forge(mod, params, np_inputs, framework_outputs, input_names=flattened_input_names, graph_name=graph_name, return_params=True, compiler_cfg=compiler_cfg, verify_cfg=verify_cfg)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/forge_compile.py:408: in compile_tvm_for_forge
    mod, params = compile_for_forge(mod, target=target, params=params, graph_name=graph_name, **verify_args)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/relay/op/forge.py:1095: in compile_for_forge
    compiled_relay_module = run_forge_compile_passes(relay_module, params, inputs, target, framework_outputs, verify_cfg)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/relay/op/forge_passes.py:4238: in run_forge_compile_passes
    return run_pattern_callbacks(
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/relay/op/forge_passes.py:4229: in run_pattern_callbacks
    raise ex
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/relay/op/forge_passes.py:4226: in run_pattern_callbacks
    relay_module = _run_pattern_callback(relay_module, callback, callback_name)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/relay/op/forge_passes.py:4205: in _run_pattern_callback
    relay_module['main'] = rewrite(callback, relay_module['main'])
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/tvm/relay/dataflow_pattern/__init__.py:927: in rewrite
    return ffi.rewrite(tmp, expr, mod)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/tvm/_ffi/_ctypes/packed_func.py:242: in __call__
    raise_last_ffi_error()
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/tvm/_ffi/base.py:479: in raise_last_ffi_error
    raise py_err
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <forge.tvm_calls.relay.op.forge_passes.InverseMaskGen object at 0x7efe3d1a6770>
pre = CallNode(Op(equal), [Var(x, ty=TensorType([64, 28, 28], float32)), Var(y, ty=TensorType([64, 28, 28], float32))], (nullptr), [TensorType([64, 28, 28], float32), TensorType([64, 28, 28], float32)])
post = CallNode(Op(equal), [Var(x, ty=TensorType([64, 28, 28], float32)), Var(y, ty=TensorType([64, 28, 28], float32))], (nullptr), [TensorType([64, 28, 28], float32), TensorType([64, 28, 28], float32)])
node_map = {relay.dataflow_pattern.WildcardPattern(0x55ec70fd5a90): [Var(x, ty=TensorType([64, 28, 28], float32))], relay.dataflo...1cf0): [Var(y, ty=TensorType([64, 28, 28], float32))], relay.dataflow_pattern.ExprPattern(0x55ec70fd5ab0): [Op(equal)]}

    def callback(self, pre, post, node_map):
>       pre_node_map = construct_pre_node_map(self.pattern, pre)
E       NameError: name 'construct_pre_node_map' is not defined

/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/relay/op/forge_passes.py:3424: NameError

Check failure on line 232 in forge/test/mlir/operators/eltwise_binary/test_eltwise_binary.py

See this annotation in the file changed.

@github-actions github-actions / TT-Forge-FE Tests

test_eltwise_binary.test_equal[shape10] 

NameError: name 'construct_pre_node_map' is not defined
Raw output 
shape = (256, 28, 28)

    @pytest.mark.parametrize(
        "shape",
        [
            (1, 128, 28, 28),
            (1, 64, 28, 28),
            (1, 256, 28, 28),
            (1, 128, 14, 14),
            (1, 128, 56, 56),
            (1, 32, 64, 64),
            (1, 512, 7, 7),
            (1, 32, 32, 32),
            (128, 28, 28),
            (64, 28, 28),
            (256, 28, 28),
            (128, 14, 14),
            (128, 56, 56),
            (32, 64, 64),
            (512, 7, 7),
            (32, 32, 32),
            (128, 28),
            (64, 28),
            (256, 28),
            (128, 14),
            (128, 56),
            (32, 64),
            (512, 7),
            (32, 32),
        ],
    )
    @pytest.mark.push
    def test_equal(shape):
        class Equal(nn.Module):
            def __init__(self):
                super().__init__()
    
            def forward(self, x, y):
                return torch.eq(x, y)
    
        x = torch.rand(shape)
        y = x * 2.0
    
        inputs = [x, y]
    
        framework_model = Equal()
>       compiled_model = forge.compile(framework_model, sample_inputs=inputs)

forge/test/mlir/operators/eltwise_binary/test_eltwise_binary.py:232: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/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:671: in generate_initial_graph
    module, module_inputs = convert_to_forge_module(
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/compile.py:1032: 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:2062: 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:2151: 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:104: in load_tvm_graph
    json_graphs, flattened_inputs = compile_tvm_graph(inputs, module, compiler_cfg, graph_name=graph_name, input_names=input_names, path=path, verify_cfg=verify_cfg, framework=framework)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/forge_compile.py:144: in compile_tvm_graph
    json_graphs, inputs = compile_pytorch_for_forge(module, *inputs, graph_name=graph_name, compiler_cfg=compiler_cfg, verify_cfg=verify_cfg, input_names=input_names)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/forge_compile.py:394: in compile_pytorch_for_forge
    partitioned_mod, forge_params = compile_tvm_for_forge(mod, params, np_inputs, framework_outputs, input_names=flattened_input_names, graph_name=graph_name, return_params=True, compiler_cfg=compiler_cfg, verify_cfg=verify_cfg)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/forge_compile.py:408: in compile_tvm_for_forge
    mod, params = compile_for_forge(mod, target=target, params=params, graph_name=graph_name, **verify_args)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/relay/op/forge.py:1095: in compile_for_forge
    compiled_relay_module = run_forge_compile_passes(relay_module, params, inputs, target, framework_outputs, verify_cfg)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/relay/op/forge_passes.py:4238: in run_forge_compile_passes
    return run_pattern_callbacks(
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/relay/op/forge_passes.py:4229: in run_pattern_callbacks
    raise ex
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/relay/op/forge_passes.py:4226: in run_pattern_callbacks
    relay_module = _run_pattern_callback(relay_module, callback, callback_name)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/relay/op/forge_passes.py:4205: in _run_pattern_callback
    relay_module['main'] = rewrite(callback, relay_module['main'])
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/tvm/relay/dataflow_pattern/__init__.py:927: in rewrite
    return ffi.rewrite(tmp, expr, mod)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/tvm/_ffi/_ctypes/packed_func.py:242: in __call__
    raise_last_ffi_error()
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/tvm/_ffi/base.py:479: in raise_last_ffi_error
    raise py_err
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <forge.tvm_calls.relay.op.forge_passes.InverseMaskGen object at 0x7efe80193a90>
pre = CallNode(Op(equal), [Var(x, ty=TensorType([256, 28, 28], float32)), Var(y, ty=TensorType([256, 28, 28], float32))], (nullptr), [TensorType([256, 28, 28], float32), TensorType([256, 28, 28], float32)])
post = CallNode(Op(equal), [Var(x, ty=TensorType([256, 28, 28], float32)), Var(y, ty=TensorType([256, 28, 28], float32))], (nullptr), [TensorType([256, 28, 28], float32), TensorType([256, 28, 28], float32)])
node_map = {relay.dataflow_pattern.WildcardPattern(0x55ec725892a0): [Var(x, ty=TensorType([256, 28, 28], float32))], relay.datafl...: [Op(equal)], relay.dataflow_pattern.WildcardPattern(0x55ec725892c0): [Var(y, ty=TensorType([256, 28, 28], float32))]}

    def callback(self, pre, post, node_map):
>       pre_node_map = construct_pre_node_map(self.pattern, pre)
E       NameError: name 'construct_pre_node_map' is not defined

/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/relay/op/forge_passes.py:3424: NameError

Check failure on line 232 in forge/test/mlir/operators/eltwise_binary/test_eltwise_binary.py

See this annotation in the file changed.

@github-actions github-actions / TT-Forge-FE Tests

test_eltwise_binary.test_equal[shape12] 

NameError: name 'construct_pre_node_map' is not defined
Raw output 
shape = (128, 56, 56)

    @pytest.mark.parametrize(
        "shape",
        [
            (1, 128, 28, 28),
            (1, 64, 28, 28),
            (1, 256, 28, 28),
            (1, 128, 14, 14),
            (1, 128, 56, 56),
            (1, 32, 64, 64),
            (1, 512, 7, 7),
            (1, 32, 32, 32),
            (128, 28, 28),
            (64, 28, 28),
            (256, 28, 28),
            (128, 14, 14),
            (128, 56, 56),
            (32, 64, 64),
            (512, 7, 7),
            (32, 32, 32),
            (128, 28),
            (64, 28),
            (256, 28),
            (128, 14),
            (128, 56),
            (32, 64),
            (512, 7),
            (32, 32),
        ],
    )
    @pytest.mark.push
    def test_equal(shape):
        class Equal(nn.Module):
            def __init__(self):
                super().__init__()
    
            def forward(self, x, y):
                return torch.eq(x, y)
    
        x = torch.rand(shape)
        y = x * 2.0
    
        inputs = [x, y]
    
        framework_model = Equal()
>       compiled_model = forge.compile(framework_model, sample_inputs=inputs)

forge/test/mlir/operators/eltwise_binary/test_eltwise_binary.py:232: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/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:671: in generate_initial_graph
    module, module_inputs = convert_to_forge_module(
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/compile.py:1032: 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:2062: 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:2151: 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:104: in load_tvm_graph
    json_graphs, flattened_inputs = compile_tvm_graph(inputs, module, compiler_cfg, graph_name=graph_name, input_names=input_names, path=path, verify_cfg=verify_cfg, framework=framework)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/forge_compile.py:144: in compile_tvm_graph
    json_graphs, inputs = compile_pytorch_for_forge(module, *inputs, graph_name=graph_name, compiler_cfg=compiler_cfg, verify_cfg=verify_cfg, input_names=input_names)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/forge_compile.py:394: in compile_pytorch_for_forge
    partitioned_mod, forge_params = compile_tvm_for_forge(mod, params, np_inputs, framework_outputs, input_names=flattened_input_names, graph_name=graph_name, return_params=True, compiler_cfg=compiler_cfg, verify_cfg=verify_cfg)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/forge_compile.py:408: in compile_tvm_for_forge
    mod, params = compile_for_forge(mod, target=target, params=params, graph_name=graph_name, **verify_args)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/relay/op/forge.py:1095: in compile_for_forge
    compiled_relay_module = run_forge_compile_passes(relay_module, params, inputs, target, framework_outputs, verify_cfg)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/relay/op/forge_passes.py:4238: in run_forge_compile_passes
    return run_pattern_callbacks(
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/relay/op/forge_passes.py:4229: in run_pattern_callbacks
    raise ex
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/relay/op/forge_passes.py:4226: in run_pattern_callbacks
    relay_module = _run_pattern_callback(relay_module, callback, callback_name)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/relay/op/forge_passes.py:4205: in _run_pattern_callback
    relay_module['main'] = rewrite(callback, relay_module['main'])
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/tvm/relay/dataflow_pattern/__init__.py:927: in rewrite
    return ffi.rewrite(tmp, expr, mod)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/tvm/_ffi/_ctypes/packed_func.py:242: in __call__
    raise_last_ffi_error()
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/tvm/_ffi/base.py:479: in raise_last_ffi_error
    raise py_err
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <forge.tvm_calls.relay.op.forge_passes.InverseMaskGen object at 0x7efe3d4dbcd0>
pre = CallNode(Op(equal), [Var(x, ty=TensorType([128, 56, 56], float32)), Var(y, ty=TensorType([128, 56, 56], float32))], (nullptr), [TensorType([128, 56, 56], float32), TensorType([128, 56, 56], float32)])
post = CallNode(Op(equal), [Var(x, ty=TensorType([128, 56, 56], float32)), Var(y, ty=TensorType([128, 56, 56], float32))], (nullptr), [TensorType([128, 56, 56], float32), TensorType([128, 56, 56], float32)])
node_map = {relay.dataflow_pattern.WildcardPattern(0x55ec718e4bd0): [Var(y, ty=TensorType([128, 56, 56], float32))], relay.datafl...: [Op(equal)], relay.dataflow_pattern.WildcardPattern(0x55ec718e4bb0): [Var(x, ty=TensorType([128, 56, 56], float32))]}

    def callback(self, pre, post, node_map):
>       pre_node_map = construct_pre_node_map(self.pattern, pre)
E       NameError: name 'construct_pre_node_map' is not defined

/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/relay/op/forge_passes.py:3424: NameError

Check failure on line 232 in forge/test/mlir/operators/eltwise_binary/test_eltwise_binary.py

See this annotation in the file changed.

@github-actions github-actions / TT-Forge-FE Tests

test_eltwise_binary.test_equal[shape14] 

NameError: name 'construct_pre_node_map' is not defined
Raw output 
shape = (512, 7, 7)

    @pytest.mark.parametrize(
        "shape",
        [
            (1, 128, 28, 28),
            (1, 64, 28, 28),
            (1, 256, 28, 28),
            (1, 128, 14, 14),
            (1, 128, 56, 56),
            (1, 32, 64, 64),
            (1, 512, 7, 7),
            (1, 32, 32, 32),
            (128, 28, 28),
            (64, 28, 28),
            (256, 28, 28),
            (128, 14, 14),
            (128, 56, 56),
            (32, 64, 64),
            (512, 7, 7),
            (32, 32, 32),
            (128, 28),
            (64, 28),
            (256, 28),
            (128, 14),
            (128, 56),
            (32, 64),
            (512, 7),
            (32, 32),
        ],
    )
    @pytest.mark.push
    def test_equal(shape):
        class Equal(nn.Module):
            def __init__(self):
                super().__init__()
    
            def forward(self, x, y):
                return torch.eq(x, y)
    
        x = torch.rand(shape)
        y = x * 2.0
    
        inputs = [x, y]
    
        framework_model = Equal()
>       compiled_model = forge.compile(framework_model, sample_inputs=inputs)

forge/test/mlir/operators/eltwise_binary/test_eltwise_binary.py:232: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/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:671: in generate_initial_graph
    module, module_inputs = convert_to_forge_module(
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/compile.py:1032: 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:2062: 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:2151: 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:104: in load_tvm_graph
    json_graphs, flattened_inputs = compile_tvm_graph(inputs, module, compiler_cfg, graph_name=graph_name, input_names=input_names, path=path, verify_cfg=verify_cfg, framework=framework)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/forge_compile.py:144: in compile_tvm_graph
    json_graphs, inputs = compile_pytorch_for_forge(module, *inputs, graph_name=graph_name, compiler_cfg=compiler_cfg, verify_cfg=verify_cfg, input_names=input_names)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/forge_compile.py:394: in compile_pytorch_for_forge
    partitioned_mod, forge_params = compile_tvm_for_forge(mod, params, np_inputs, framework_outputs, input_names=flattened_input_names, graph_name=graph_name, return_params=True, compiler_cfg=compiler_cfg, verify_cfg=verify_cfg)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/forge_compile.py:408: in compile_tvm_for_forge
    mod, params = compile_for_forge(mod, target=target, params=params, graph_name=graph_name, **verify_args)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/relay/op/forge.py:1095: in compile_for_forge
    compiled_relay_module = run_forge_compile_passes(relay_module, params, inputs, target, framework_outputs, verify_cfg)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/relay/op/forge_passes.py:4238: in run_forge_compile_passes
    return run_pattern_callbacks(
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/relay/op/forge_passes.py:4229: in run_pattern_callbacks
    raise ex
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/relay/op/forge_passes.py:4226: in run_pattern_callbacks
    relay_module = _run_pattern_callback(relay_module, callback, callback_name)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/relay/op/forge_passes.py:4205: in _run_pattern_callback
    relay_module['main'] = rewrite(callback, relay_module['main'])
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/tvm/relay/dataflow_pattern/__init__.py:927: in rewrite
    return ffi.rewrite(tmp, expr, mod)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/tvm/_ffi/_ctypes/packed_func.py:242: in __call__
    raise_last_ffi_error()
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/tvm/_ffi/base.py:479: in raise_last_ffi_error
    raise py_err
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <forge.tvm_calls.relay.op.forge_passes.InverseMaskGen object at 0x7efe3d22ef50>
pre = CallNode(Op(equal), [Var(x, ty=TensorType([512, 7, 7], float32)), Var(y, ty=TensorType([512, 7, 7], float32))], (nullptr), [TensorType([512, 7, 7], float32), TensorType([512, 7, 7], float32)])
post = CallNode(Op(equal), [Var(x, ty=TensorType([512, 7, 7], float32)), Var(y, ty=TensorType([512, 7, 7], float32))], (nullptr), [TensorType([512, 7, 7], float32), TensorType([512, 7, 7], float32)])
node_map = {relay.dataflow_pattern.WildcardPattern(0x55ec72cd1f70): [Var(y, ty=TensorType([512, 7, 7], float32))], relay.dataflow...0): [Op(equal)], relay.dataflow_pattern.WildcardPattern(0x55ec72cd1f90): [Var(x, ty=TensorType([512, 7, 7], float32))]}

    def callback(self, pre, post, node_map):
>       pre_node_map = construct_pre_node_map(self.pattern, pre)
E       NameError: name 'construct_pre_node_map' is not defined

/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/relay/op/forge_passes.py:3424: NameError

Check failure on line 232 in forge/test/mlir/operators/eltwise_binary/test_eltwise_binary.py

See this annotation in the file changed.

@github-actions github-actions / TT-Forge-FE Tests

test_eltwise_binary.test_equal[shape16] 

NameError: name 'construct_pre_node_map' is not defined
Raw output 
shape = (128, 28)

    @pytest.mark.parametrize(
        "shape",
        [
            (1, 128, 28, 28),
            (1, 64, 28, 28),
            (1, 256, 28, 28),
            (1, 128, 14, 14),
            (1, 128, 56, 56),
            (1, 32, 64, 64),
            (1, 512, 7, 7),
            (1, 32, 32, 32),
            (128, 28, 28),
            (64, 28, 28),
            (256, 28, 28),
            (128, 14, 14),
            (128, 56, 56),
            (32, 64, 64),
            (512, 7, 7),
            (32, 32, 32),
            (128, 28),
            (64, 28),
            (256, 28),
            (128, 14),
            (128, 56),
            (32, 64),
            (512, 7),
            (32, 32),
        ],
    )
    @pytest.mark.push
    def test_equal(shape):
        class Equal(nn.Module):
            def __init__(self):
                super().__init__()
    
            def forward(self, x, y):
                return torch.eq(x, y)
    
        x = torch.rand(shape)
        y = x * 2.0
    
        inputs = [x, y]
    
        framework_model = Equal()
>       compiled_model = forge.compile(framework_model, sample_inputs=inputs)

forge/test/mlir/operators/eltwise_binary/test_eltwise_binary.py:232: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/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:671: in generate_initial_graph
    module, module_inputs = convert_to_forge_module(
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/compile.py:1032: 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:2062: 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:2151: 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:104: in load_tvm_graph
    json_graphs, flattened_inputs = compile_tvm_graph(inputs, module, compiler_cfg, graph_name=graph_name, input_names=input_names, path=path, verify_cfg=verify_cfg, framework=framework)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/forge_compile.py:144: in compile_tvm_graph
    json_graphs, inputs = compile_pytorch_for_forge(module, *inputs, graph_name=graph_name, compiler_cfg=compiler_cfg, verify_cfg=verify_cfg, input_names=input_names)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/forge_compile.py:394: in compile_pytorch_for_forge
    partitioned_mod, forge_params = compile_tvm_for_forge(mod, params, np_inputs, framework_outputs, input_names=flattened_input_names, graph_name=graph_name, return_params=True, compiler_cfg=compiler_cfg, verify_cfg=verify_cfg)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/forge_compile.py:408: in compile_tvm_for_forge
    mod, params = compile_for_forge(mod, target=target, params=params, graph_name=graph_name, **verify_args)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/relay/op/forge.py:1095: in compile_for_forge
    compiled_relay_module = run_forge_compile_passes(relay_module, params, inputs, target, framework_outputs, verify_cfg)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/relay/op/forge_passes.py:4238: in run_forge_compile_passes
    return run_pattern_callbacks(
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/relay/op/forge_passes.py:4229: in run_pattern_callbacks
    raise ex
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/relay/op/forge_passes.py:4226: in run_pattern_callbacks
    relay_module = _run_pattern_callback(relay_module, callback, callback_name)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/relay/op/forge_passes.py:4205: in _run_pattern_callback
    relay_module['main'] = rewrite(callback, relay_module['main'])
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/tvm/relay/dataflow_pattern/__init__.py:927: in rewrite
    return ffi.rewrite(tmp, expr, mod)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/tvm/_ffi/_ctypes/packed_func.py:242: in __call__
    raise_last_ffi_error()
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/tvm/_ffi/base.py:479: in raise_last_ffi_error
    raise py_err
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <forge.tvm_calls.relay.op.forge_passes.InverseMaskGen object at 0x7efe3d4d9c60>
pre = CallNode(Op(equal), [Var(x, ty=TensorType([128, 28], float32)), Var(y, ty=TensorType([128, 28], float32))], (nullptr), [TensorType([128, 28], float32), TensorType([128, 28], float32)])
post = CallNode(Op(equal), [Var(x, ty=TensorType([128, 28], float32)), Var(y, ty=TensorType([128, 28], float32))], (nullptr), [TensorType([128, 28], float32), TensorType([128, 28], float32)])
node_map = {relay.dataflow_pattern.CallPattern(0x55ec7380c840): [CallNode(Op(equal), [Var(x, ty=TensorType([128, 28], float32)), ...820): [Op(equal)], relay.dataflow_pattern.WildcardPattern(0x55ec73143a90): [Var(x, ty=TensorType([128, 28], float32))]}

    def callback(self, pre, post, node_map):
>       pre_node_map = construct_pre_node_map(self.pattern, pre)
E       NameError: name 'construct_pre_node_map' is not defined

/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/relay/op/forge_passes.py:3424: NameError

Check failure on line 232 in forge/test/mlir/operators/eltwise_binary/test_eltwise_binary.py

See this annotation in the file changed.

@github-actions github-actions / TT-Forge-FE Tests

test_eltwise_binary.test_equal[shape18] 

NameError: name 'construct_pre_node_map' is not defined
Raw output 
shape = (256, 28)

    @pytest.mark.parametrize(
        "shape",
        [
            (1, 128, 28, 28),
            (1, 64, 28, 28),
            (1, 256, 28, 28),
            (1, 128, 14, 14),
            (1, 128, 56, 56),
            (1, 32, 64, 64),
            (1, 512, 7, 7),
            (1, 32, 32, 32),
            (128, 28, 28),
            (64, 28, 28),
            (256, 28, 28),
            (128, 14, 14),
            (128, 56, 56),
            (32, 64, 64),
            (512, 7, 7),
            (32, 32, 32),
            (128, 28),
            (64, 28),
            (256, 28),
            (128, 14),
            (128, 56),
            (32, 64),
            (512, 7),
            (32, 32),
        ],
    )
    @pytest.mark.push
    def test_equal(shape):
        class Equal(nn.Module):
            def __init__(self):
                super().__init__()
    
            def forward(self, x, y):
                return torch.eq(x, y)
    
        x = torch.rand(shape)
        y = x * 2.0
    
        inputs = [x, y]
    
        framework_model = Equal()
>       compiled_model = forge.compile(framework_model, sample_inputs=inputs)

forge/test/mlir/operators/eltwise_binary/test_eltwise_binary.py:232: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/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:671: in generate_initial_graph
    module, module_inputs = convert_to_forge_module(
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/compile.py:1032: 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:2062: 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:2151: 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:104: in load_tvm_graph
    json_graphs, flattened_inputs = compile_tvm_graph(inputs, module, compiler_cfg, graph_name=graph_name, input_names=input_names, path=path, verify_cfg=verify_cfg, framework=framework)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/forge_compile.py:144: in compile_tvm_graph
    json_graphs, inputs = compile_pytorch_for_forge(module, *inputs, graph_name=graph_name, compiler_cfg=compiler_cfg, verify_cfg=verify_cfg, input_names=input_names)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/forge_compile.py:394: in compile_pytorch_for_forge
    partitioned_mod, forge_params = compile_tvm_for_forge(mod, params, np_inputs, framework_outputs, input_names=flattened_input_names, graph_name=graph_name, return_params=True, compiler_cfg=compiler_cfg, verify_cfg=verify_cfg)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/forge_compile.py:408: in compile_tvm_for_forge
    mod, params = compile_for_forge(mod, target=target, params=params, graph_name=graph_name, **verify_args)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/relay/op/forge.py:1095: in compile_for_forge
    compiled_relay_module = run_forge_compile_passes(relay_module, params, inputs, target, framework_outputs, verify_cfg)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/relay/op/forge_passes.py:4238: in run_forge_compile_passes
    return run_pattern_callbacks(
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/relay/op/forge_passes.py:4229: in run_pattern_callbacks
    raise ex
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/relay/op/forge_passes.py:4226: in run_pattern_callbacks
    relay_module = _run_pattern_callback(relay_module, callback, callback_name)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/relay/op/forge_passes.py:4205: in _run_pattern_callback
    relay_module['main'] = rewrite(callback, relay_module['main'])
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/tvm/relay/dataflow_pattern/__init__.py:927: in rewrite
    return ffi.rewrite(tmp, expr, mod)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/tvm/_ffi/_ctypes/packed_func.py:242: in __call__
    raise_last_ffi_error()
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/tvm/_ffi/base.py:479: in raise_last_ffi_error
    raise py_err
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <forge.tvm_calls.relay.op.forge_passes.InverseMaskGen object at 0x7efe3c598850>
pre = CallNode(Op(equal), [Var(x, ty=TensorType([256, 28], float32)), Var(y, ty=TensorType([256, 28], float32))], (nullptr), [TensorType([256, 28], float32), TensorType([256, 28], float32)])
post = CallNode(Op(equal), [Var(x, ty=TensorType([256, 28], float32)), Var(y, ty=TensorType([256, 28], float32))], (nullptr), [TensorType([256, 28], float32), TensorType([256, 28], float32)])
node_map = {relay.dataflow_pattern.ExprPattern(0x55ec7380a460): [Op(equal)], relay.dataflow_pattern.WildcardPattern(0x55ec7169c7f... 28], float32)])], relay.dataflow_pattern.WildcardPattern(0x55ec7169c7d0): [Var(y, ty=TensorType([256, 28], float32))]}

    def callback(self, pre, post, node_map):
>       pre_node_map = construct_pre_node_map(self.pattern, pre)
E       NameError: name 'construct_pre_node_map' is not defined

/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/relay/op/forge_passes.py:3424: NameError

Check failure on line 232 in forge/test/mlir/operators/eltwise_binary/test_eltwise_binary.py

See this annotation in the file changed.

@github-actions github-actions / TT-Forge-FE Tests

test_eltwise_binary.test_equal[shape21] 

NameError: name 'construct_pre_node_map' is not defined
Raw output 
shape = (32, 64)

    @pytest.mark.parametrize(
        "shape",
        [
            (1, 128, 28, 28),
            (1, 64, 28, 28),
            (1, 256, 28, 28),
            (1, 128, 14, 14),
            (1, 128, 56, 56),
            (1, 32, 64, 64),
            (1, 512, 7, 7),
            (1, 32, 32, 32),
            (128, 28, 28),
            (64, 28, 28),
            (256, 28, 28),
            (128, 14, 14),
            (128, 56, 56),
            (32, 64, 64),
            (512, 7, 7),
            (32, 32, 32),
            (128, 28),
            (64, 28),
            (256, 28),
            (128, 14),
            (128, 56),
            (32, 64),
            (512, 7),
            (32, 32),
        ],
    )
    @pytest.mark.push
    def test_equal(shape):
        class Equal(nn.Module):
            def __init__(self):
                super().__init__()
    
            def forward(self, x, y):
                return torch.eq(x, y)
    
        x = torch.rand(shape)
        y = x * 2.0
    
        inputs = [x, y]
    
        framework_model = Equal()
>       compiled_model = forge.compile(framework_model, sample_inputs=inputs)

forge/test/mlir/operators/eltwise_binary/test_eltwise_binary.py:232: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/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:671: in generate_initial_graph
    module, module_inputs = convert_to_forge_module(
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/compile.py:1032: 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:2062: 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:2151: 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:104: in load_tvm_graph
    json_graphs, flattened_inputs = compile_tvm_graph(inputs, module, compiler_cfg, graph_name=graph_name, input_names=input_names, path=path, verify_cfg=verify_cfg, framework=framework)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/forge_compile.py:144: in compile_tvm_graph
    json_graphs, inputs = compile_pytorch_for_forge(module, *inputs, graph_name=graph_name, compiler_cfg=compiler_cfg, verify_cfg=verify_cfg, input_names=input_names)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/forge_compile.py:394: in compile_pytorch_for_forge
    partitioned_mod, forge_params = compile_tvm_for_forge(mod, params, np_inputs, framework_outputs, input_names=flattened_input_names, graph_name=graph_name, return_params=True, compiler_cfg=compiler_cfg, verify_cfg=verify_cfg)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/forge_compile.py:408: in compile_tvm_for_forge
    mod, params = compile_for_forge(mod, target=target, params=params, graph_name=graph_name, **verify_args)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/relay/op/forge.py:1095: in compile_for_forge
    compiled_relay_module = run_forge_compile_passes(relay_module, params, inputs, target, framework_outputs, verify_cfg)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/relay/op/forge_passes.py:4238: in run_forge_compile_passes
    return run_pattern_callbacks(
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/relay/op/forge_passes.py:4229: in run_pattern_callbacks
    raise ex
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/relay/op/forge_passes.py:4226: in run_pattern_callbacks
    relay_module = _run_pattern_callback(relay_module, callback, callback_name)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/relay/op/forge_passes.py:4205: in _run_pattern_callback
    relay_module['main'] = rewrite(callback, relay_module['main'])
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/tvm/relay/dataflow_pattern/__init__.py:927: in rewrite
    return ffi.rewrite(tmp, expr, mod)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/tvm/_ffi/_ctypes/packed_func.py:242: in __call__
    raise_last_ffi_error()
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/tvm/_ffi/base.py:479: in raise_last_ffi_error
    raise py_err
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <forge.tvm_calls.relay.op.forge_passes.InverseMaskGen object at 0x7efe85363ee0>
pre = CallNode(Op(equal), [Var(x, ty=TensorType([32, 64], float32)), Var(y, ty=TensorType([32, 64], float32))], (nullptr), [TensorType([32, 64], float32), TensorType([32, 64], float32)])
post = CallNode(Op(equal), [Var(x, ty=TensorType([32, 64], float32)), Var(y, ty=TensorType([32, 64], float32))], (nullptr), [TensorType([32, 64], float32), TensorType([32, 64], float32)])
node_map = {relay.dataflow_pattern.ExprPattern(0x55ec721c2720): [Op(equal)], relay.dataflow_pattern.WildcardPattern(0x55ec7257633..., 64], float32)])], relay.dataflow_pattern.WildcardPattern(0x55ec72576350): [Var(y, ty=TensorType([32, 64], float32))]}

    def callback(self, pre, post, node_map):
>       pre_node_map = construct_pre_node_map(self.pattern, pre)
E       NameError: name 'construct_pre_node_map' is not defined

/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/relay/op/forge_passes.py:3424: NameError

Check failure on line 232 in forge/test/mlir/operators/eltwise_binary/test_eltwise_binary.py

See this annotation in the file changed.

@github-actions github-actions / TT-Forge-FE Tests

test_eltwise_binary.test_equal[shape23] 

NameError: name 'construct_pre_node_map' is not defined
Raw output 
shape = (32, 32)

    @pytest.mark.parametrize(
        "shape",
        [
            (1, 128, 28, 28),
            (1, 64, 28, 28),
            (1, 256, 28, 28),
            (1, 128, 14, 14),
            (1, 128, 56, 56),
            (1, 32, 64, 64),
            (1, 512, 7, 7),
            (1, 32, 32, 32),
            (128, 28, 28),
            (64, 28, 28),
            (256, 28, 28),
            (128, 14, 14),
            (128, 56, 56),
            (32, 64, 64),
            (512, 7, 7),
            (32, 32, 32),
            (128, 28),
            (64, 28),
            (256, 28),
            (128, 14),
            (128, 56),
            (32, 64),
            (512, 7),
            (32, 32),
        ],
    )
    @pytest.mark.push
    def test_equal(shape):
        class Equal(nn.Module):
            def __init__(self):
                super().__init__()
    
            def forward(self, x, y):
                return torch.eq(x, y)
    
        x = torch.rand(shape)
        y = x * 2.0
    
        inputs = [x, y]
    
        framework_model = Equal()
>       compiled_model = forge.compile(framework_model, sample_inputs=inputs)

forge/test/mlir/operators/eltwise_binary/test_eltwise_binary.py:232: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/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:671: in generate_initial_graph
    module, module_inputs = convert_to_forge_module(
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/compile.py:1032: 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:2062: 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:2151: 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:104: in load_tvm_graph
    json_graphs, flattened_inputs = compile_tvm_graph(inputs, module, compiler_cfg, graph_name=graph_name, input_names=input_names, path=path, verify_cfg=verify_cfg, framework=framework)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/forge_compile.py:144: in compile_tvm_graph
    json_graphs, inputs = compile_pytorch_for_forge(module, *inputs, graph_name=graph_name, compiler_cfg=compiler_cfg, verify_cfg=verify_cfg, input_names=input_names)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/forge_compile.py:394: in compile_pytorch_for_forge
    partitioned_mod, forge_params = compile_tvm_for_forge(mod, params, np_inputs, framework_outputs, input_names=flattened_input_names, graph_name=graph_name, return_params=True, compiler_cfg=compiler_cfg, verify_cfg=verify_cfg)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/forge_compile.py:408: in compile_tvm_for_forge
    mod, params = compile_for_forge(mod, target=target, params=params, graph_name=graph_name, **verify_args)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/relay/op/forge.py:1095: in compile_for_forge
    compiled_relay_module = run_forge_compile_passes(relay_module, params, inputs, target, framework_outputs, verify_cfg)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/relay/op/forge_passes.py:4238: in run_forge_compile_passes
    return run_pattern_callbacks(
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/relay/op/forge_passes.py:4229: in run_pattern_callbacks
    raise ex
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/relay/op/forge_passes.py:4226: in run_pattern_callbacks
    relay_module = _run_pattern_callback(relay_module, callback, callback_name)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/relay/op/forge_passes.py:4205: in _run_pattern_callback
    relay_module['main'] = rewrite(callback, relay_module['main'])
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/tvm/relay/dataflow_pattern/__init__.py:927: in rewrite
    return ffi.rewrite(tmp, expr, mod)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/tvm/_ffi/_ctypes/packed_func.py:242: in __call__
    raise_last_ffi_error()
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/tvm/_ffi/base.py:479: in raise_last_ffi_error
    raise py_err
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <forge.tvm_calls.relay.op.forge_passes.InverseMaskGen object at 0x7efe3c57ddb0>
pre = CallNode(Op(equal), [Var(x, ty=TensorType([32, 32], float32)), Var(y, ty=TensorType([32, 32], float32))], (nullptr), [TensorType([32, 32], float32), TensorType([32, 32], float32)])
post = CallNode(Op(equal), [Var(x, ty=TensorType([32, 32], float32)), Var(y, ty=TensorType([32, 32], float32))], (nullptr), [TensorType([32, 32], float32), TensorType([32, 32], float32)])
node_map = {relay.dataflow_pattern.WildcardPattern(0x55ec7104b7e0): [Var(y, ty=TensorType([32, 32], float32))], relay.dataflow_pa...Var(y, ty=TensorType([32, 32], float32))], (nullptr), [TensorType([32, 32], float32), TensorType([32, 32], float32)])]}

    def callback(self, pre, post, node_map):
>       pre_node_map = construct_pre_node_map(self.pattern, pre)
E       NameError: name 'construct_pre_node_map' is not defined

/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/relay/op/forge_passes.py:3424: NameError

Check failure on line 267 in forge/test/mlir/operators/eltwise_binary/test_eltwise_binary.py

See this annotation in the file changed.

@github-actions github-actions / TT-Forge-FE Tests

test_eltwise_binary.test_greater_equal[dims0] 

NameError: name 'construct_pre_node_map' is not defined
Raw output 
dims = (1, 32, 64)

    @pytest.mark.parametrize("dims", [(1, 32, 64), (6, 33), (4, 16, 17)])
    @pytest.mark.push
    def test_greater_equal(dims):
        class GreaterEqual(nn.Module):
            def __init__(self):
                super().__init__()
    
            def forward(self, a, b):
                return torch.greater_equal(a, b)
    
        inputs = [torch.rand(dims), torch.rand(dims)]
    
        framework_model = GreaterEqual()
>       compiled_model = forge.compile(framework_model, sample_inputs=inputs)

forge/test/mlir/operators/eltwise_binary/test_eltwise_binary.py:267: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/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:671: in generate_initial_graph
    module, module_inputs = convert_to_forge_module(
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/compile.py:1032: 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:2062: 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:2151: 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:104: in load_tvm_graph
    json_graphs, flattened_inputs = compile_tvm_graph(inputs, module, compiler_cfg, graph_name=graph_name, input_names=input_names, path=path, verify_cfg=verify_cfg, framework=framework)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/forge_compile.py:144: in compile_tvm_graph
    json_graphs, inputs = compile_pytorch_for_forge(module, *inputs, graph_name=graph_name, compiler_cfg=compiler_cfg, verify_cfg=verify_cfg, input_names=input_names)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/forge_compile.py:394: in compile_pytorch_for_forge
    partitioned_mod, forge_params = compile_tvm_for_forge(mod, params, np_inputs, framework_outputs, input_names=flattened_input_names, graph_name=graph_name, return_params=True, compiler_cfg=compiler_cfg, verify_cfg=verify_cfg)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/forge_compile.py:408: in compile_tvm_for_forge
    mod, params = compile_for_forge(mod, target=target, params=params, graph_name=graph_name, **verify_args)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/relay/op/forge.py:1100: in compile_for_forge
    warn_of_int_comparisons(compiled_relay_module)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/relay/op/forge.py:1068: in warn_of_int_comparisons
    rewrite(warner, mod['main'])
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/tvm/relay/dataflow_pattern/__init__.py:927: in rewrite
    return ffi.rewrite(tmp, expr, mod)
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/tvm/_ffi/_ctypes/packed_func.py:242: in __call__
    raise_last_ffi_error()
/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/tvm/_ffi/base.py:479: in raise_last_ffi_error
    raise py_err
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <forge.tvm_calls.relay.op.forge.CompareWarner object at 0x7efe3d0a8fd0>
pre = CallNode(Op(greater_equal), [Var(a, ty=TensorType([1, 32, 64], float32)), Var(b, ty=TensorType([1, 32, 64], float32))], (nullptr), [TensorType([1, 32, 64], float32), TensorType([1, 32, 64], float32)])
post = CallNode(Op(greater_equal), [Var(a, ty=TensorType([1, 32, 64], float32)), Var(b, ty=TensorType([1, 32, 64], float32))], (nullptr), [TensorType([1, 32, 64], float32), TensorType([1, 32, 64], float32)])
node_map = {relay.dataflow_pattern.WildcardPattern(0x55ec71b89830): [Var(b, ty=TensorType([1, 32, 64], float32))], relay.dataflow...4], float32)])], relay.dataflow_pattern.WildcardPattern(0x55ec7104cf00): [Var(a, ty=TensorType([1, 32, 64], float32))]}

    def callback(self, pre, post, node_map):
>       pre_node_map = construct_pre_node_map(self.pattern, pre)
E       NameError: name 'construct_pre_node_map' is not defined

/opt/ttforge-toolchain/venv/lib/python3.10/site-packages/forge/tvm_calls/relay/op/forge.py:1058: NameError
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