Oct 30, 2025 · Oct 31, 2025 · Oct 31, 2025 · Oct 31, 2025 · Oct 31, 2025 · Oct 31, 2025
diff --git a/test/python/artifact_test.py b/test/python/artifact_test.py
 import numpy as np
 import os

 from .test_utils import _vector_vector_add_impl
 from .utils import _vector_vector_add_impl


 @pytest.mark.parametrize("num_elements", [16, 64])
diff --git a/test/python/compile_ctx_test.py b/test/python/compile_ctx_test.py


 def test_function_generator():
    from .test_utils import _vector_vector_add_impl
    from .utils import _vector_vector_add_impl
    import numpy as np

    compilable = iron.compileconfig(mlir_generator=_vector_vector_add_impl)
diff --git a/test/python/jit_compilation.py b/test/python/jit_compilation.py
 import aie.iron as iron
 import os

 from .test_utils import _vector_vector_add_impl
 from .utils import _vector_vector_add_impl


 @iron.jit()
diff --git a/test/python/object_file_test.py b/test/python/object_file_test.py
 import aie.iron as iron
 import numpy as np

 from .test_utils import _vector_vector_add_impl
 from .utils import _vector_vector_add_impl


 @pytest.mark.parametrize("num_elements", [16, 64])
diff --git a/test/python/precompiled_test.py b/test/python/precompiled_test.py
 import numpy as np
 import os

 from .test_utils import _vector_vector_add_impl
 from .utils import _vector_vector_add_impl


 @pytest.mark.parametrize("num_elements", [16, 64])
diff --git a/test/python/test_utils.py b/test/python/test_utils.py
diff --git a/test/python/util.py b/test/python/util.py
 import inspect
 from aie.ir import Context, Location, Module, InsertionPoint
 import numpy as np
 import aie.iron as iron

 from aie.iron import ObjectFifo, Program, Runtime, Worker
 from aie.iron.placers import SequentialPlacer
 from aie.iron.controlflow import range_


 # Create and print ModuleOp.
        if module is not None:
            assert module.operation.verify()
            print(module)


 def _vector_vector_add_impl(input0, input1, output):
    if input0.shape != input1.shape:
        raise ValueError(
            f"Input shapes are not the equal ({input0.shape} != {input1.shape})."
        )
    if input0.shape != output.shape:
        raise ValueError(
            f"Input and output shapes are not the equal ({input0.shape} != {output.shape})."
        )
    if len(np.shape(input0)) != 1:
        raise ValueError("Function only supports vectors.")
    num_elements = np.size(input0)
    n = 16
    if num_elements % n != 0:
        raise ValueError(
            f"Number of elements ({num_elements}) must be a multiple of {n}."
        )
    N_div_n = num_elements // n

    if input0.dtype != input1.dtype:
        raise ValueError(
            f"Input data types are not the same ({input0.dtype} != {input1.dtype})."
        )
    if input0.dtype != output.dtype:
        raise ValueError(
            f"Input and output data types are not the same ({input0.dtype} != {output.dtype})."
        )
    dtype = input0.dtype

    tensor_ty = np.ndarray[(num_elements,), np.dtype[dtype]]
    tile_ty = np.ndarray[(n,), np.dtype[dtype]]

    of_in1 = ObjectFifo(tile_ty, name="in1")
    of_in2 = ObjectFifo(tile_ty, name="in2")
    of_out = ObjectFifo(tile_ty, name="out")

    def core_body(of_in1, of_in2, of_out):
        for _ in range_(N_div_n):
            elem_in1 = of_in1.acquire(1)
            elem_in2 = of_in2.acquire(1)
            elem_out = of_out.acquire(1)
            for i in range_(n):
                elem_out[i] = elem_in1[i] + elem_in2[i]
            of_in1.release(1)
            of_in2.release(1)
            of_out.release(1)

    worker = Worker(core_body, fn_args=[of_in1.cons(), of_in2.cons(), of_out.prod()])

    rt = Runtime()
    with rt.sequence(tensor_ty, tensor_ty, tensor_ty) as (A, B, C):
        rt.start(worker)
        rt.fill(of_in1.prod(), A)
        rt.fill(of_in2.prod(), B)
        rt.drain(of_out.cons(), C, wait=True)

    return Program(iron.get_current_device(), rt).resolve_program(SequentialPlacer())
Original file line number	Diff line number	Diff line change
Expand Up		@@ -12,7 +12,7 @@
		import numpy as np
		import os

		from .test_utils import _vector_vector_add_impl
		from .utils import _vector_vector_add_impl


		@pytest.mark.parametrize("num_elements", [16, 64])
Expand Down
Original file line number	Diff line number	Diff line change
Expand Up		@@ -63,7 +63,7 @@ def test_mlir_file_generator():


		def test_function_generator():
		from .test_utils import _vector_vector_add_impl
		from .utils import _vector_vector_add_impl
		import numpy as np

		compilable = iron.compileconfig(mlir_generator=_vector_vector_add_impl)
Expand Down
Original file line number	Diff line number	Diff line change
Expand Up		@@ -12,7 +12,7 @@
		import aie.iron as iron
		import os

		from .test_utils import _vector_vector_add_impl
		from .utils import _vector_vector_add_impl


		@iron.jit()
Expand Down
Original file line number	Diff line number	Diff line change
Expand Up		@@ -11,7 +11,7 @@
		import aie.iron as iron
		import numpy as np

		from .test_utils import _vector_vector_add_impl
		from .utils import _vector_vector_add_impl


		@pytest.mark.parametrize("num_elements", [16, 64])
Expand Down
Original file line number	Diff line number	Diff line change
		@@ -1,5 +1,11 @@
		import inspect
		from aie.ir import Context, Location, Module, InsertionPoint
		import numpy as np
		import aie.iron as iron

		from aie.iron import ObjectFifo, Program, Runtime, Worker
		from aie.iron.placers import SequentialPlacer
		from aie.iron.controlflow import range_


		# Create and print ModuleOp.
Expand All		@@ -19,3 +25,62 @@ def construct_and_print_module(f):
		if module is not None:
		assert module.operation.verify()
		print(module)


		def _vector_vector_add_impl(input0, input1, output):
		if input0.shape != input1.shape:
		raise ValueError(
		f"Input shapes are not the equal ({input0.shape} != {input1.shape})."
		)
		if input0.shape != output.shape:
		raise ValueError(
		f"Input and output shapes are not the equal ({input0.shape} != {output.shape})."
		)
		if len(np.shape(input0)) != 1:
		raise ValueError("Function only supports vectors.")
		num_elements = np.size(input0)
		n = 16
		if num_elements % n != 0:
		raise ValueError(
		f"Number of elements ({num_elements}) must be a multiple of {n}."
		)
		N_div_n = num_elements // n

		if input0.dtype != input1.dtype:
		raise ValueError(
		f"Input data types are not the same ({input0.dtype} != {input1.dtype})."
		)
		if input0.dtype != output.dtype:
		raise ValueError(
		f"Input and output data types are not the same ({input0.dtype} != {output.dtype})."
		)
		dtype = input0.dtype

		tensor_ty = np.ndarray[(num_elements,), np.dtype[dtype]]
		tile_ty = np.ndarray[(n,), np.dtype[dtype]]

		of_in1 = ObjectFifo(tile_ty, name="in1")
		of_in2 = ObjectFifo(tile_ty, name="in2")
		of_out = ObjectFifo(tile_ty, name="out")

		def core_body(of_in1, of_in2, of_out):
		for _ in range_(N_div_n):
		elem_in1 = of_in1.acquire(1)
		elem_in2 = of_in2.acquire(1)
		elem_out = of_out.acquire(1)
		for i in range_(n):
		elem_out[i] = elem_in1[i] + elem_in2[i]
		of_in1.release(1)
		of_in2.release(1)
		of_out.release(1)

		worker = Worker(core_body, fn_args=[of_in1.cons(), of_in2.cons(), of_out.prod()])

		rt = Runtime()
		with rt.sequence(tensor_ty, tensor_ty, tensor_ty) as (A, B, C):
		rt.start(worker)
		rt.fill(of_in1.prod(), A)
		rt.fill(of_in2.prod(), B)
		rt.drain(of_out.cons(), C, wait=True)

		return Program(iron.get_current_device(), rt).resolve_program(SequentialPlacer())