test_loopy.py

    ctx = ctx_factory()
    queue = cl.CommandQueue(ctx)

    if ctx.devices[0].platform.name.startswith("Portable"):
        # Accurate as of 2015-10-08
        pytest.skip("POCL miscompiles vector code")

    knl = lp.make_kernel(
         ''' { [i,j,k]: 0<=i,j,k<4 } ''',
         ''' out[i,j,k] = indexof_vec(out[i,j,k])''')

    knl = lp.tag_inames(knl, {"i": "vec"})
    knl = lp.tag_data_axes(knl, "out", "vec,c,c")
    knl = lp.set_options(knl, write_cl=True)

    (evt, (out,)) = knl(queue)
    #out = out.get()
    #assert np.array_equal(out.ravel(order="C"), np.arange(25))


def test_is_expression_equal():
    from loopy.symbolic import is_expression_equal
    from pymbolic import var

    x = var("x")
    y = var("y")

    assert is_expression_equal(x+2, 2+x)

    assert is_expression_equal((x+2)**2, x**2 + 4*x + 4)
    assert is_expression_equal((x+y)**2, x**2 + 2*x*y + y**2)


@pytest.mark.parametrize("dtype", [np.int32, np.int64, np.float32, np.float64])
def test_atomic(ctx_factory, dtype):
    ctx = ctx_factory()

    if (
            np.dtype(dtype).itemsize == 8
            and "cl_khr_int64_base_atomics" not in ctx.devices[0].extensions):
        pytest.skip("64-bit atomics not supported on device")

    import pyopencl.version  # noqa
    if (
            cl.version.VERSION < (2015, 2)
            and dtype == np.int64):
        pytest.skip("int64 RNG not supported in PyOpenCL < 2015.2")

    knl = lp.make_kernel(
            "{ [i]: 0<=i<n }",
            "out[i%20] = out[i%20] + 2*a[i] {atomic}",
            [
                lp.GlobalArg("out", dtype, shape=lp.auto, for_atomic=True),
                lp.GlobalArg("a", dtype, shape=lp.auto),
                "..."
                ],
            assumptions="n>0")

    ref_knl = knl
    knl = lp.split_iname(knl, "i", 512)
    knl = lp.split_iname(knl, "i_inner", 128, outer_tag="unr", inner_tag="g.0")
    lp.auto_test_vs_ref(ref_knl, ctx, knl, parameters=dict(n=10000))


@pytest.mark.parametrize("dtype", [np.int32, np.int64, np.float32, np.float64])
def test_atomic_load(ctx_factory, dtype):
    ctx = ctx_factory()
    queue = cl.CommandQueue(ctx)
    from loopy.kernel.data import temp_var_scope as scopes
    n = 10
    vec_width = 4

    if (
            np.dtype(dtype).itemsize == 8
            and "cl_khr_int64_base_atomics" not in ctx.devices[0].extensions):
        pytest.skip("64-bit atomics not supported on device")

    import pyopencl.version  # noqa
    if (
            cl.version.VERSION < (2015, 2)
            and dtype == np.int64):
        pytest.skip("int64 RNG not supported in PyOpenCL < 2015.2")

    knl = lp.make_kernel(
            "{ [i,j]: 0<=i,j<n}",
            """
            for j
                <> upper = 0
                <> lower = 0
                temp = 0 {id=init, atomic}
                for i
                    upper = upper + i * a[i] {id=sum0}
                    lower = lower - b[i] {id=sum1}
                end
                temp = temp + lower {id=temp_sum, dep=sum*:init, atomic,\
                                           nosync=init}
                ... lbarrier {id=lb2, dep=temp_sum}
                out[j] = upper / temp {id=final, dep=lb2, atomic,\
                                           nosync=init:temp_sum}
            end
            """,
            [
                lp.GlobalArg("out", dtype, shape=lp.auto, for_atomic=True),
                lp.GlobalArg("a", dtype, shape=lp.auto),
                lp.GlobalArg("b", dtype, shape=lp.auto),
                lp.TemporaryVariable('temp', dtype, for_atomic=True,
                                     scope=scopes.LOCAL),
                "..."
                ],
            silenced_warnings=["write_race(init)", "write_race(temp_sum)"])
    knl = lp.fix_parameters(knl, n=n)
    knl = lp.split_iname(knl, "j", vec_width, inner_tag="l.0")
    _, out = knl(queue, a=np.arange(n, dtype=dtype), b=np.arange(n, dtype=dtype))
    assert np.allclose(out, np.full_like(out, ((1 - 2 * n) / 3.0)))


@pytest.mark.parametrize("dtype", [np.int32, np.int64, np.float32, np.float64])
def test_atomic_init(dtype):
    vec_width = 4

    knl = lp.make_kernel(
            "{ [i,j]: 0<=i<100 }",
            """
            out[i%4] = 0 {id=init, atomic=init}
            """,
            [
                lp.GlobalArg("out", dtype, shape=lp.auto, for_atomic=True),
                "..."
                ],
            silenced_warnings=["write_race(init)"])
    knl = lp.split_iname(knl, 'i', vec_width, inner_tag='l.0')
    print(knl)
    print(lp.generate_code_v2(knl).device_code())


def test_within_inames_and_reduction():
    # See https://github.com/inducer/loopy/issues/24

    # This is (purposefully) somewhat un-idiomatic, to replicate the conditions
    # under which the above bug was found. If assignees were phi[i], then the
    # iname propagation heuristic would not assume that dependent instructions
    # need to run inside of 'i', and hence the forced_iname_* bits below would not
    # be needed.

    i1 = lp.CInstruction("i",
            "doSomethingToGetPhi();",
            assignees="phi")

    from pymbolic.primitives import Subscript, Variable
    i2 = lp.Assignment("a",
            lp.Reduction("sum", "j", Subscript(Variable("phi"), Variable("j"))),
            within_inames=frozenset(),
            within_inames_is_final=True)

    k = lp.make_kernel("{[i,j] : 0<=i,j<n}",
            [i1, i2],
            [
                lp.GlobalArg("a", dtype=np.float32, shape=()),
                lp.ValueArg("n", dtype=np.int32),
                lp.TemporaryVariable("phi", dtype=np.float32, shape=("n",)),
                ],
            target=lp.CTarget(),
            )

    k = lp.preprocess_kernel(k)

    assert 'i' not in k.insn_inames("insn_0_j_update")
    print(k.stringify(with_dependencies=True))


def test_literal_local_barrier(ctx_factory):
    ctx = ctx_factory()

    knl = lp.make_kernel(
            "{ [i]: 0<=i<n }",
            """
            for i
                ... lbarrier
            end
            """, seq_dependencies=True)

    knl = lp.fix_parameters(knl, n=128)

    ref_knl = knl

    lp.auto_test_vs_ref(ref_knl, ctx, knl, parameters=dict(n=5))


def test_local_barrier_mem_kind():
    def __test_type(mtype, expected):
        insn = '... lbarrier'
        if mtype:
            insn += '{mem_kind=%s}' % mtype
        knl = lp.make_kernel(
                "{ [i]: 0<=i<n }",
                """
                for i
                    %s
                end
                """ % insn, seq_dependencies=True,
                target=lp.PyOpenCLTarget())

        cgr = lp.generate_code_v2(knl)
        assert 'barrier(%s)' % expected in cgr.device_code()

    __test_type('', 'CLK_LOCAL_MEM_FENCE')
    __test_type('global', 'CLK_GLOBAL_MEM_FENCE')
    __test_type('local', 'CLK_LOCAL_MEM_FENCE')


def test_kernel_splitting(ctx_factory):
    ctx = ctx_factory()

    knl = lp.make_kernel(
            "{ [i]: 0<=i<n }",
            """
            for i
                c[i] = a[i + 1]
                ... gbarrier
                out[i] = c[i]
            end
            """, seq_dependencies=True)

    knl = lp.add_and_infer_dtypes(knl,
            {"a": np.float32, "c": np.float32, "out": np.float32, "n": np.int32})

    ref_knl = knl

    knl = lp.split_iname(knl, "i", 128, outer_tag="g.0", inner_tag="l.0")

    # schedule
    from loopy.preprocess import preprocess_kernel
    knl = preprocess_kernel(knl)

    from loopy.schedule import get_one_scheduled_kernel
    knl = get_one_scheduled_kernel(knl)

    # map schedule onto host or device
    print(knl)

    cgr = lp.generate_code_v2(knl)

    assert len(cgr.device_programs) == 2

    print(cgr.device_code())
    print(cgr.host_code())

    lp.auto_test_vs_ref(ref_knl, ctx, knl, parameters=dict(n=5))


def test_kernel_splitting_with_loop(ctx_factory):
    ctx = ctx_factory()

    knl = lp.make_kernel(
            "{ [i,k]: 0<=i<n and 0<=k<3 }",
            """
            for i, k
                ... gbarrier
                c[k,i] = a[k, i + 1]
                ... gbarrier
                out[k,i] = c[k,i]
            end
            """, seq_dependencies=True)

    knl = lp.add_and_infer_dtypes(knl,
            {"a": np.float32, "c": np.float32, "out": np.float32, "n": np.int32})

    ref_knl = knl

    knl = lp.split_iname(knl, "i", 128, outer_tag="g.0", inner_tag="l.0")

    # schedule
    from loopy.preprocess import preprocess_kernel
    knl = preprocess_kernel(knl)

    from loopy.schedule import get_one_scheduled_kernel
    knl = get_one_scheduled_kernel(knl)

    # map schedule onto host or device
    print(knl)

    cgr = lp.generate_code_v2(knl)

    assert len(cgr.device_programs) == 2

    print(cgr.device_code())
    print(cgr.host_code())

    lp.auto_test_vs_ref(ref_knl, ctx, knl, parameters=dict(n=5))


def save_and_reload_temporaries_test(queue, knl, out_expect, debug=False):
    from loopy.preprocess import preprocess_kernel
    from loopy.schedule import get_one_scheduled_kernel

    knl = preprocess_kernel(knl)
    knl = get_one_scheduled_kernel(knl)

    from loopy.transform.save import save_and_reload_temporaries
    knl = save_and_reload_temporaries(knl)
    knl = get_one_scheduled_kernel(knl)

    if debug:
        print(knl)
        cgr = lp.generate_code_v2(knl)
        print(cgr.device_code())
        print(cgr.host_code())
        1/0

    _, (out,) = knl(queue, out_host=True)
    assert (out == out_expect).all(), (out, out_expect)


@pytest.mark.parametrize("hw_loop", [True, False])
def test_save_of_private_scalar(ctx_factory, hw_loop, debug=False):
    ctx = ctx_factory()
    queue = cl.CommandQueue(ctx)

    knl = lp.make_kernel(
        "{ [i]: 0<=i<8 }",
        """
        for i
            <>t = i
            ... gbarrier
            out[i] = t
        end
        """, seq_dependencies=True)

    if hw_loop:
        knl = lp.tag_inames(knl, dict(i="g.0"))

    save_and_reload_temporaries_test(queue, knl, np.arange(8), debug)


def test_save_of_private_array(ctx_factory, debug=False):
    ctx = ctx_factory()
    queue = cl.CommandQueue(ctx)

    knl = lp.make_kernel(
        "{ [i]: 0<=i<8 }",
        """
        for i
            <>t[i] = i
            ... gbarrier
            out[i] = t[i]
        end
        """, seq_dependencies=True)

    knl = lp.set_temporary_scope(knl, "t", "private")
    save_and_reload_temporaries_test(queue, knl, np.arange(8), debug)


def test_save_of_private_array_in_hw_loop(ctx_factory, debug=False):
    ctx = ctx_factory()
    queue = cl.CommandQueue(ctx)

    knl = lp.make_kernel(
        "{ [i,j,k]: 0<=i,j,k<8 }",
        """
        for i
            for j
               <>t[j] = j
            end
            ... gbarrier
            for k
                out[i,k] = t[k]
            end
        end
        """, seq_dependencies=True)

    knl = lp.tag_inames(knl, dict(i="g.0"))
    knl = lp.set_temporary_scope(knl, "t", "private")

    save_and_reload_temporaries_test(
        queue, knl, np.vstack((8 * (np.arange(8),))), debug)


def test_save_of_private_multidim_array(ctx_factory, debug=False):
    ctx = ctx_factory()
    queue = cl.CommandQueue(ctx)

    knl = lp.make_kernel(
        "{ [i,j,k,l,m]: 0<=i,j,k,l,m<8 }",
        """
        for i
            for j, k
               <>t[j,k] = k
            end
            ... gbarrier
            for l, m
                out[i,l,m] = t[l,m]
            end
        end
        """, seq_dependencies=True)

    knl = lp.set_temporary_scope(knl, "t", "private")

    result = np.array([np.vstack((8 * (np.arange(8),))) for i in range(8)])
    save_and_reload_temporaries_test(queue, knl, result, debug)


def test_save_of_private_multidim_array_in_hw_loop(ctx_factory, debug=False):
    ctx = ctx_factory()
    queue = cl.CommandQueue(ctx)

    knl = lp.make_kernel(
        "{ [i,j,k,l,m]: 0<=i,j,k,l,m<8 }",
        """
        for i
            for j, k
               <>t[j,k] = k
            end
            ... gbarrier
            for l, m
                out[i,l,m] = t[l,m]
            end
        end
        """, seq_dependencies=True)

    knl = lp.set_temporary_scope(knl, "t", "private")
    knl = lp.tag_inames(knl, dict(i="g.0"))

    result = np.array([np.vstack((8 * (np.arange(8),))) for i in range(8)])
    save_and_reload_temporaries_test(queue, knl, result, debug)


@pytest.mark.parametrize("hw_loop", [True, False])
def test_save_of_multiple_private_temporaries(ctx_factory, hw_loop, debug=False):
    ctx = ctx_factory()
    queue = cl.CommandQueue(ctx)

    knl = lp.make_kernel(
            "{ [i,j,k]: 0<=i,j,k<10 }",
            """
            for i
                for k
                    <> t_arr[k] = k
                end
                <> t_scalar = 1
                for j
                    ... gbarrier
                    out[j] = t_scalar
                    ... gbarrier
                    t_scalar = 10
                end
                ... gbarrier
                <> flag = i == 9
                out[i] = t_arr[i] {if=flag}
            end
            """, seq_dependencies=True)

    knl = lp.set_temporary_scope(knl, "t_arr", "private")
    if hw_loop:
        knl = lp.tag_inames(knl, dict(i="g.0"))

    result = np.array([1, 10, 10, 10, 10, 10, 10, 10, 10, 9])

    save_and_reload_temporaries_test(queue, knl, result, debug)


def test_save_of_local_array(ctx_factory, debug=False):
    ctx = ctx_factory()
    queue = cl.CommandQueue(ctx)

    knl = lp.make_kernel(
        "{ [i,j]: 0<=i,j<8 }",
        """
        for i, j
            <>t[2*j] = j
            t[2*j+1] = j
            ... gbarrier
            out[i] = t[2*i]
        end
        """, seq_dependencies=True)

    knl = lp.set_temporary_scope(knl, "t", "local")
    knl = lp.tag_inames(knl, dict(i="g.0", j="l.0"))

    save_and_reload_temporaries_test(queue, knl, np.arange(8), debug)


def test_save_of_local_array_with_explicit_local_barrier(ctx_factory, debug=False):
    ctx = ctx_factory()
    queue = cl.CommandQueue(ctx)

    knl = lp.make_kernel(
        "{ [i,j]: 0<=i,j<8 }",
        """
        for i, j
            <>t[2*j] = j
            ... lbarrier
            t[2*j+1] = t[2*j]
            ... gbarrier
            out[i] = t[2*i]
        end
        """, seq_dependencies=True)

    knl = lp.set_temporary_scope(knl, "t", "local")
    knl = lp.tag_inames(knl, dict(i="g.0", j="l.0"))

    save_and_reload_temporaries_test(queue, knl, np.arange(8), debug)


def test_save_local_multidim_array(ctx_factory, debug=False):
    ctx = ctx_factory()
    queue = cl.CommandQueue(ctx)

    knl = lp.make_kernel(
            "{ [i,j,k]: 0<=i<2 and 0<=k<3 and 0<=j<2}",
            """
            for i, j, k
                ... gbarrier
                <> t_local[k,j] = 1
                ... gbarrier
                out[k,i*2+j] = t_local[k,j]
            end
            """, seq_dependencies=True)

    knl = lp.set_temporary_scope(knl, "t_local", "local")
    knl = lp.tag_inames(knl, dict(j="l.0", i="g.0"))

    save_and_reload_temporaries_test(queue, knl, 1, debug)


def test_save_with_base_storage(ctx_factory, debug=False):
    ctx = ctx_factory()
    queue = cl.CommandQueue(ctx)

    knl = lp.make_kernel(
            "{[i]: 0 <= i < 10}",
            """
            <>a[i] = 0
            <>b[i] = i
            ... gbarrier
            out[i] = a[i]
            """,
            "...",
            seq_dependencies=True)

    knl = lp.tag_inames(knl, dict(i="l.0"))
    knl = lp.set_temporary_scope(knl, "a", "local")
    knl = lp.set_temporary_scope(knl, "b", "local")

    knl = lp.alias_temporaries(knl, ["a", "b"],
            synchronize_for_exclusive_use=False)

    save_and_reload_temporaries_test(queue, knl, np.arange(10), debug)


def test_save_ambiguous_storage_requirements():
    knl = lp.make_kernel(
            "{[i,j]: 0 <= i < 10 and 0 <= j < 10}",
            """
            <>a[j] = j
            ... gbarrier
            out[i,j] = a[j]
            """,
            seq_dependencies=True)

    knl = lp.tag_inames(knl, dict(i="g.0", j="l.0"))
    knl = lp.duplicate_inames(knl, "j", within="writes:out", tags={"j": "l.0"})
    knl = lp.set_temporary_scope(knl, "a", "local")

    knl = lp.preprocess_kernel(knl)
    knl = lp.get_one_scheduled_kernel(knl)

    from loopy.diagnostic import LoopyError
    with pytest.raises(LoopyError):
        lp.save_and_reload_temporaries(knl)


def test_save_across_inames_with_same_tag(ctx_factory, debug=False):
    ctx = ctx_factory()
    queue = cl.CommandQueue(ctx)

    knl = lp.make_kernel(
            "{[i]: 0 <= i < 10}",
            """
            <>a[i] = i
            ... gbarrier
            out[i] = a[i]
            """,
            "...",
            seq_dependencies=True)

    knl = lp.tag_inames(knl, dict(i="l.0"))
    knl = lp.duplicate_inames(knl, "i", within="reads:a", tags={"i": "l.0"})

    save_and_reload_temporaries_test(queue, knl, np.arange(10), debug)


def test_missing_temporary_definition_detection():
    knl = lp.make_kernel(
            "{ [i]: 0<=i<10 }",
            """
            for i
                <> t = 1
                ... gbarrier
                out[i] = t
            end
            """, seq_dependencies=True)

    from loopy.diagnostic import MissingDefinitionError
    with pytest.raises(MissingDefinitionError):
        lp.generate_code_v2(knl)


def test_missing_definition_check_respects_aliases():
    # Based on https://github.com/inducer/loopy/issues/69
    knl = lp.make_kernel("{ [i] : 0<=i<n }",
         ["a[i] = 0",
          "c[i] = b[i]"],
         temporary_variables={
             "a": lp.TemporaryVariable("a",
                        dtype=np.float64, shape=("n",), base_storage="base"),
             "b": lp.TemporaryVariable("b",
                        dtype=np.float64, shape=("n",), base_storage="base")
         },
         target=lp.CTarget(),
         silenced_warnings=frozenset(["read_no_write(b)"]))

    lp.generate_code_v2(knl)


def test_global_temporary(ctx_factory):
    ctx = ctx_factory()

    knl = lp.make_kernel(
            "{ [i]: 0<=i<n}",
            """
            for i
                <> c[i] = a[i + 1]
                ... gbarrier
                out[i] = c[i]
            end
            """, seq_dependencies=True)

    knl = lp.add_and_infer_dtypes(knl,
            {"a": np.float32, "c": np.float32, "out": np.float32, "n": np.int32})
    knl = lp.set_temporary_scope(knl, "c", "global")

    ref_knl = knl

    knl = lp.split_iname(knl, "i", 128, outer_tag="g.0", inner_tag="l.0")

    cgr = lp.generate_code_v2(knl)

    assert len(cgr.device_programs) == 2

    #print(cgr.device_code())
    #print(cgr.host_code())

    lp.auto_test_vs_ref(ref_knl, ctx, knl, parameters=dict(n=5))


def test_assign_to_linear_subscript(ctx_factory):
    ctx = ctx_factory()
    queue = cl.CommandQueue(ctx)

    knl1 = lp.make_kernel(
            "{ [i]: 0<=i<n}",
            "a[i,i] = 1")
    knl2 = lp.make_kernel(
            "{ [i]: 0<=i<n}",
            "a[[i*n + i]] = 1",
            [lp.GlobalArg("a", shape="n,n"), "..."])

    a1 = cl.array.zeros(queue, (10, 10), np.float32)
    knl1(queue, a=a1)
    a2 = cl.array.zeros(queue, (10, 10), np.float32)
    knl2(queue, a=a2)

    assert np.array_equal(a1.get(),  a2.get())


def test_finite_difference_expr_subst(ctx_factory):
    ctx = ctx_factory()
    queue = cl.CommandQueue(ctx)

    grid = np.linspace(0, 2*np.pi, 2048, endpoint=False)
    h = grid[1] - grid[0]
    u = cl.clmath.sin(cl.array.to_device(queue, grid))

    fin_diff_knl = lp.make_kernel(
        "{[i]: 1<=i<=n}",
        "out[i] = -(f[i+1] - f[i-1])/h",
        [lp.GlobalArg("out", shape="n+2"), "..."])

    flux_knl = lp.make_kernel(
        "{[j]: 1<=j<=n}",
        "f[j] = u[j]**2/2",
        [
            lp.GlobalArg("f", shape="n+2"),
            lp.GlobalArg("u", shape="n+2"),
            ])

    fused_knl = lp.fuse_kernels([fin_diff_knl, flux_knl],
            data_flow=[
                ("f", 1, 0)
                ])

    fused_knl = lp.set_options(fused_knl, write_cl=True)
    evt, _ = fused_knl(queue, u=u, h=np.float32(1e-1))

    fused_knl = lp.assignment_to_subst(fused_knl, "f")

    fused_knl = lp.set_options(fused_knl, write_cl=True)

    # This is the real test here: The automatically generated
    # shape expressions are '2+n' and the ones above are 'n+2'.
    # Is loopy smart enough to understand that these are equal?
    evt, _ = fused_knl(queue, u=u, h=np.float32(1e-1))

    fused0_knl = lp.affine_map_inames(fused_knl, "i", "inew", "inew+1=i")

    gpu_knl = lp.split_iname(
            fused0_knl, "inew", 128, outer_tag="g.0", inner_tag="l.0")

    precomp_knl = lp.precompute(
            gpu_knl, "f_subst", "inew_inner", fetch_bounding_box=True)

    precomp_knl = lp.tag_inames(precomp_knl, {"j_0_outer": "unr"})
    precomp_knl = lp.set_options(precomp_knl, return_dict=True)
    evt, _ = precomp_knl(queue, u=u, h=h)


# {{{ call without returned values

def test_call_with_no_returned_value(ctx_factory):
    import pymbolic.primitives as p

    ctx = ctx_factory()
    queue = cl.CommandQueue(ctx)

    knl = lp.make_kernel(
        "{:}",
        [lp.CallInstruction((), p.Call(p.Variable("f"), ()))]
        )

    from library_for_test import no_ret_f_mangler, no_ret_f_preamble_gen
    knl = lp.register_function_manglers(knl, [no_ret_f_mangler])
    knl = lp.register_preamble_generators(knl, [no_ret_f_preamble_gen])

    evt, _ = knl(queue)

# }}}


# {{{ call with no return values and options

def test_call_with_options():
    knl = lp.make_kernel(
        "{:}",
        "f() {id=init}"
        )

    from library_for_test import no_ret_f_mangler
    knl = lp.register_function_manglers(knl, [no_ret_f_mangler])

    print(lp.generate_code_v2(knl).device_code())

# }}}


def test_unschedulable_kernel_detection():
    knl = lp.make_kernel(["{[i,j]:0<=i,j<n}"],
                         """
                         mat1[i,j] = mat1[i,j] + 1 {inames=i:j, id=i1}
                         mat2[j] = mat2[j] + 1 {inames=j, id=i2}
                         mat3[i] = mat3[i] + 1 {inames=i, id=i3}
                         """)

    knl = lp.preprocess_kernel(knl)

    # Check that loopy can detect the unschedulability of the kernel
    assert not lp.has_schedulable_iname_nesting(knl)
    assert len(list(lp.get_iname_duplication_options(knl))) == 4

    for inames, insns in lp.get_iname_duplication_options(knl):
        fixed_knl = lp.duplicate_inames(knl, inames, insns)
        assert lp.has_schedulable_iname_nesting(fixed_knl)

    knl = lp.make_kernel(["{[i,j,k,l,m]:0<=i,j,k,l,m<n}"],
                         """
                         mat1[l,m,i,j,k] = mat1[l,m,i,j,k] + 1 {inames=i:j:k:l:m}
                         mat2[l,m,j,k] = mat2[l,m,j,k] + 1 {inames=j:k:l:m}
                         mat3[l,m,k] = mat3[l,m,k] + 11 {inames=k:l:m}
                         mat4[l,m,i] = mat4[l,m,i] + 1 {inames=i:l:m}
                         """)

    assert not lp.has_schedulable_iname_nesting(knl)
    assert len(list(lp.get_iname_duplication_options(knl))) == 10


def test_regression_no_ret_call_removal(ctx_factory):
    # https://github.com/inducer/loopy/issues/32
    knl = lp.make_kernel(
            "{[i] : 0<=i<n}",
            "f(sum(i, x[i]))")
    knl = lp.add_and_infer_dtypes(knl, {"x": np.float32})
    knl = lp.preprocess_kernel(knl)
    assert len(knl.instructions) == 3


def test_regression_persistent_hash():
    knl1 = lp.make_kernel(
            "{[i] : 0<=i<n}",
            "cse_exprvar = d[2]*d[2]")

    knl2 = lp.make_kernel(
            "{[i] : 0<=i<n}",
            "cse_exprvar = d[0]*d[0]")
    from loopy.tools import LoopyKeyBuilder
    lkb = LoopyKeyBuilder()
    assert lkb(knl1.instructions[0]) != lkb(knl2.instructions[0])
    assert lkb(knl1) != lkb(knl2)


def test_sequential_dependencies(ctx_factory):
    ctx = ctx_factory()

    knl = lp.make_kernel(
            "{[i]: 0<=i<n}",
            """
            for i
                <> aa = 5jf
                <> bb = 5j
                a[i] = imag(aa)
                b[i] = imag(bb)
                c[i] = 5f
            end
            """, seq_dependencies=True)

    print(knl.stringify(with_dependencies=True))

    lp.auto_test_vs_ref(knl, ctx, knl, parameters=dict(n=5))


def test_nop(ctx_factory):
    ctx = ctx_factory()

    knl = lp.make_kernel(
            "{[i,itrip]: 0<=i<n and 0<=itrip<ntrips}",
            """
            for itrip,i
                <> z[i] = z[i+1] + z[i]  {id=wr_z}
                <> v[i] = 11  {id=wr_v}
                ... nop {dep=wr_z:wr_v,id=yoink}
                z[i] = z[i] - z[i+1] + v[i]
            end
            """)

    print(knl)

    knl = lp.fix_parameters(knl, n=15)
    knl = lp.add_and_infer_dtypes(knl, {"z": np.float64})

    lp.auto_test_vs_ref(knl, ctx, knl, parameters=dict(ntrips=5))


def test_global_barrier(ctx_factory):
    ctx = ctx_factory()

    knl = lp.make_kernel(
            "{[i,itrip]: 0<=i<n and 0<=itrip<ntrips}",
            """
            for i
                for itrip
                    ... gbarrier {id=top}
                    <> z[i] = z[i+1] + z[i]  {id=wr_z,dep=top}
                    <> v[i] = 11  {id=wr_v,dep=top}
                    ... gbarrier {dep=wr_z:wr_v,id=yoink}
                    z[i] = z[i] - z[i+1] + v[i] {id=iupd}
                end
                ... gbarrier {dep=iupd,id=postloop}
                z[i] = z[i] - z[i+1] + v[i]  {dep=postloop}
            end
            """)

    knl = lp.fix_parameters(knl, ntrips=3)
    knl = lp.add_and_infer_dtypes(knl, {"z": np.float64})

    ref_knl = knl
    ref_knl = lp.set_temporary_scope(ref_knl, "z", "global")
    ref_knl = lp.set_temporary_scope(ref_knl, "v", "global")

    knl = lp.split_iname(knl, "i", 256, outer_tag="g.0", inner_tag="l.0")
    print(knl)

    knl = lp.preprocess_kernel(knl)
    assert knl.temporary_variables["z"].scope == lp.temp_var_scope.GLOBAL
    assert knl.temporary_variables["v"].scope == lp.temp_var_scope.GLOBAL

    print(knl)

    lp.auto_test_vs_ref(ref_knl, ctx, knl, parameters=dict(ntrips=5, n=10))


def test_missing_global_barrier():
    knl = lp.make_kernel(
            "{[i,itrip]: 0<=i<n and 0<=itrip<ntrips}",
            """
            for i
                for itrip
                    ... gbarrier {id=yoink}
                    <> z[i] = z[i] - z[i+1]  {id=iupd,dep=yoink}
                end
                # This is where the barrier should be
                z[i] = z[i] - z[i+1] + v[i]  {dep=iupd}
            end
            """)

    knl = lp.set_temporary_scope(knl, "z", "global")
    knl = lp.split_iname(knl, "i", 256, outer_tag="g.0")
    knl = lp.preprocess_kernel(knl)

    from loopy.diagnostic import MissingBarrierError
    with pytest.raises(MissingBarrierError):
        lp.get_one_scheduled_kernel(knl)


def test_index_cse(ctx_factory):
    knl = lp.make_kernel(["{[i,j,k,l,m]:0<=i,j,k,l,m<n}"],
                         """
                         for i
                            for j
                                c[i,j,m] = sum((k,l), a[i,j,l]*b[i,j,k,l])
                            end
                         end
                         """)
    knl = lp.tag_inames(knl, "l:unr")
    knl = lp.prioritize_loops(knl, "i,j,k,l")
    knl = lp.add_and_infer_dtypes(knl, {"a": np.float32, "b": np.float32})
    knl = lp.fix_parameters(knl, n=5)
    print(lp.generate_code_v2(knl).device_code())


def test_ilp_and_conditionals(ctx_factory):
    ctx = ctx_factory()

    knl = lp.make_kernel('{[k]: 0<=k<n}}',
         """
         for k
             <> Tcond = T[k] < 0.5
             if Tcond
                 cp[k] = 2 * T[k] + Tcond
             end
         end
         """)

    knl = lp.fix_parameters(knl, n=200)
    knl = lp.add_and_infer_dtypes(knl, {"T": np.float32})

    ref_knl = knl

    knl = lp.split_iname(knl, 'k', 2, inner_tag='ilp')

    lp.auto_test_vs_ref(ref_knl, ctx, knl)


def test_unr_and_conditionals(ctx_factory):
    ctx = ctx_factory()

    knl = lp.make_kernel('{[k]: 0<=k<n}}',
         """
         for k
             <> Tcond[k] = T[k] < 0.5
             if Tcond[k]
                 cp[k] = 2 * T[k] + Tcond[k]
             end
         end
         """)

    knl = lp.fix_parameters(knl, n=200)
    knl = lp.add_and_infer_dtypes(knl, {"T": np.float32})

    ref_knl = knl

    knl = lp.split_iname(knl, 'k', 2, inner_tag='unr')

    lp.auto_test_vs_ref(ref_knl, ctx, knl)


def test_constant_array_args(ctx_factory):
    ctx = ctx_factory()

    knl = lp.make_kernel('{[k]: 0<=k<n}}',
         """
         for k
             <> Tcond[k] = T[k] < 0.5
             if Tcond[k]
                 cp[k] = 2 * T[k] + Tcond[k]
             end
         end
         """,
         [lp.ConstantArg('T', shape=(200,), dtype=np.float32),
         '...'])

    knl = lp.fix_parameters(knl, n=200)