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How to use run_and_check method in avocado

Best Python code snippet using avocado_python

test_hybrid_script.py

Source:test_hybrid_script.py

...18from tvm.contrib import util19from tvm.hybrid import script20from tvm.hybrid.runtime import HYBRID_GLOBALS21@pytest.mark.skip22def run_and_check(func, args, var_dict={}, target='llvm', sch=None, outs=None):23    def tvm_val_2_py_val(val):24        val = tvm.ir_pass.Substitute(val, var_dict)25        val = tvm.ir_pass.Simplify(val)26        assert isinstance(val, (tvm.expr.IntImm,))27        return val.value28    ctx = tvm.context(target, 0)29    op = None30    if sch is None:31        outs = func(*tuple(tvm.convert(i) if isinstance(i, list) else i for i in args))32        op = outs[0].op if isinstance(outs, list) else outs.op33        sch = tvm.create_schedule(op)34    else:35        assert outs is not None36        assert isinstance(outs, list)37        op = outs[0].op38    emu_args = []39    nd_args = []40    for i in args:41        if isinstance(i, tvm.tensor.Tensor):42            shape = [tvm_val_2_py_val(j) for j in i.shape]43            emu_args.append(numpy.random.randn(*shape).astype(i.dtype))44            nd_args.append(tvm.nd.array(emu_args[-1], ctx))45        elif isinstance(i, tvm.expr.Var):46            emu_args.append(tvm_val_2_py_val(i))47            nd_args.append(emu_args[-1])48        else:49            assert isinstance(i, list)50            emu_args.append(numpy.array(i))51    compile_args = [i for i in args if isinstance(i, (tvm.tensor.Tensor, tvm.expr.Var))] + \52                   (outs if isinstance(outs, list) else [outs])53    module = tvm.build(sch,54                       compile_args,55                       target=target)56    assert module57    out_tensors = []58    for i in range(op.num_outputs):59        output = op.output(i)60        shape = [tvm_val_2_py_val(j) for j in output.shape]61        nd_args.append(tvm.nd.array(numpy.zeros(shape).astype(output.dtype), ctx))62        out_tensors.append(nd_args[-1])63    ref_data = func(*emu_args)64    if isinstance(ref_data, numpy.ndarray):65        ref_data = [ref_data]66    module(*nd_args)67    for nd, np in zip(out_tensors, ref_data):68        tvm.testing.assert_allclose(nd.asnumpy(), np, rtol=1e-5, atol=1e-5)69    module_args = [i for i in args if isinstance(i, (tvm.tensor.Tensor, tvm.expr.Var))]70    module_outs = [outs] if isinstance(outs, tvm.tensor.Tensor) else outs71    h_module = tvm.hybrid.build(sch, module_args, module_outs)72    return h_module, module_args, module_outs73@script74def outer_product(n, m, a, b):75    """This is a simple outer product.76    Actually this function is not required to be documented.77    I write this docstring to test skipping docstring functionality.78    """79    c = output_tensor((n, m), a.dtype)80    for i in range(n):81        for j in range(m):82            assert i < n and j < m, "index out of range!"83            c[i, j] = a[i] * b[j]84    return c85#Test global function86#Test bridge between frontend and backend87def test_outer_product():88    n = tvm.size_var('n')89    m = tvm.size_var('m')90    a = tvm.placeholder((n, ), name='a')91    b = tvm.placeholder((m, ), name='b')92    try:93        c = outer_product(n, m, a, b)94        ir = c.op.body95    except IOError as err:96        assert sys.version_info[0] == 2 and str(err) == 'could not get source code'97        return98    #Check for i in (0, n)99    assert isinstance(ir, tvm.stmt.For)100    assert ir.loop_var.name == 'i'101    assert ir.min.value == 0102    assert ir.extent.name == 'n'103    ibody = ir.body104    assert isinstance(ibody, tvm.stmt.For)105    #Check for j in (0, m)106    assert ibody.loop_var.name == 'j'107    assert ibody.min.value == 0108    assert ibody.extent.name == 'm'109    #Check loop body110    jblock = ibody.body111    assert isinstance(jblock, tvm.stmt.SeqStmt)112    jbody = jblock[0]113    assert isinstance(jbody, tvm.stmt.AssertStmt)114    assert isinstance(jbody.message, tvm.expr.StringImm)115    assert jbody.message.value == "index out of range!"116    jbody = jblock[1]117    assert isinstance(jbody, tvm.stmt.Provide)118    assert jbody.func.name == 'c'119    assert len(jbody.args) == 2120    assert jbody.args[0].name == 'i'121    assert jbody.args[1].name == 'j'122    assert isinstance(jbody.value, tvm.expr.Mul)123    mul = jbody.value124    assert isinstance(mul.a, tvm.expr.Call)125    assert mul.a.name == 'a'126    assert mul.b.name == 'b'127    func, ins, outs = run_and_check(outer_product, [n, m, a, b], {n: 99, m: 101})128    temp = util.tempdir()129    path = temp.relpath('%s.py' % func.name)130    func.save(path)131    func_ = tvm.hybrid.HybridModule()132    func_.load(path)133    run_and_check(func_, ins, {n: 99, m: 101}, outs=outs)134    for key, _ in HYBRID_GLOBALS.items():135        assert key not in globals().keys()136        assert key not in outer_product.__globals__.keys()137#Test local function138#Test allocation of local variable139def test_fanout():140    @script141    def fanout(n, a):142        three = 3.0143        b = output_tensor((a.shape[0] - 3, ), a.dtype)144        for i in range(a.shape[0] - 3):145            sigma = 0.0146            for j in range(3):147                sigma += a[i + j]148            sigma = sigma / three149            b[i] = sigma150        return b151    n = tvm.size_var('n')152    a = tvm.placeholder((n, ), 'float32', name='a')153    try:154        b = fanout(n, a)155        ir = b.op.body156    except IOError as err:157        assert sys.version_info[0] == 2 and str(err) == 'could not get source code'158        return159    #Check for i in (0, n-3)160    assert isinstance(ir, tvm.stmt.For)161    assert ir.loop_var.name == 'i'162    assert ir.min.value == 0163    assert tvm.ir_pass.Equal(ir.extent, n - 3)164    #Check loopbody165    ibody = ir.body166    assert isinstance(ibody, tvm.stmt.AttrStmt)167    abody = ibody.body168    assert isinstance(abody, tvm.stmt.Realize)169    assert abody.bounds[0].min.value == 0170    assert abody.bounds[0].extent.value == 1171    assert abody.func.name == 'sigma'172    #Check i loop body173    rbody = abody.body174    assert isinstance(rbody[0], tvm.stmt.Provide)175    assert rbody[0].func.name == 'sigma'176    assert len(rbody[0].args) == 1177    assert rbody[0].args[0].value == 0178    #Check fanout loop179    jloop = rbody[1]180    assert jloop.loop_var.name == 'j'181    assert jloop.min.value == 0182    assert jloop.extent.value == 3183    jbody = jloop.body184    assert isinstance(jbody, tvm.stmt.Provide)185    assert len(jbody.args) == 1186    assert jbody.args[0].value == 0187    assert jbody.func.name == 'sigma'188    assert isinstance(jbody.value, tvm.expr.Add)189    value = jbody.value190    assert isinstance(value.a, tvm.expr.Call)191    assert value.a.name == 'sigma'192    assert len(value.a.args) == 1193    assert value.a.args[0].value == 0194    assert value.b.name == 'a'195    assert len(value.b.args) == 1196    assert tvm.ir_pass.Equal(value.b.args[0], ir.loop_var + jloop.loop_var)197    divide= rbody[2]198    assert isinstance(divide, tvm.stmt.Provide)199    assert len(divide.args) == 1200    assert divide.args[0].value == 0201    value = divide.value202    assert isinstance(value, tvm.expr.Mul)203    assert value.a.name == 'sigma'204    assert len(value.a.args) == 1205    assert value.a.args[0].value == 0206    assert abs(value.b.value - (1 / 3.0)) < 1e-5207    write = rbody[3]208    assert isinstance(write, tvm.stmt.Provide)209    assert write.func.name == 'b'210    assert write.value.name == 'sigma'211    assert len(write.value.args) == 1212    assert write.value.args[0].value == 0213    func, ins, outs = run_and_check(fanout, [n, a], {n: 10})214    run_and_check(func, ins, {n: 10}, outs=outs)215def test_looptype():216    @script217    def looptype(a, b, c):218        d = output_tensor((16, ), 'int32')219        e = output_tensor((16, ), 'int32')220        f = output_tensor((16, ), 'int32')221        for i in parallel(16):222            d[i] = a[i]223        for j in vectorize(16):224            e[j] = b[j]225        for k in unroll(16):226            f[k] = c[k]227        return d, e, f228    a = tvm.placeholder((16, ), name='a', dtype='int32')229    b = tvm.placeholder((16, ), name='b', dtype='int32')230    c = tvm.placeholder((16, ), name='c', dtype='int32')231    try:232        d, e, f = looptype(a, b, c)233        ir = d.op.body234    except:235        return236    iloop = ir[0]237    jloop = ir[1]238    kloop = ir[2]239    assert iloop.for_type == tvm.stmt.For.Parallel240    assert jloop.for_type == tvm.stmt.For.Vectorized241    assert kloop.for_type == tvm.stmt.For.Unrolled242    func, ins, outs = run_and_check(looptype, [a, b, c])243    run_and_check(func, ins, outs=outs)244def test_if():245    @script246    def if_then_else(a):247        b = output_tensor((10, ), 'int32')248        c = output_tensor((10, ), 'int32')249        for i in range(10):250            if i % 2 == 0:251                c[i] = a[i]252            else:253                c[i] = b[i]254        for i in unroll(10):255            b[i] = -1 if i % 2 == 0 else 1256        return b, c257    a = tvm.placeholder((10, ), dtype='int32', name='a')258    func, ins, outs = run_and_check(if_then_else, [a])259    run_and_check(func, ins, outs=outs)260    @script261    def if_triple_condition(a):262        b = output_tensor((10, ), 'int32')263        for i in range(10):264            if 0 <= i < 5:265                b[i] = a[i]266            else:267                b[i] = a[i] + 1268        return b269    func, ins, outs = run_and_check(if_triple_condition, [a])270    run_and_check(func, ins, outs=outs)271    @script272    def if_and(a):273        b = output_tensor((10, ), 'int32')274        for i in range(10):275            if i >= 0 and i < 5:276                b[i] = a[i]277            else:278                b[i] = a[i] + 1279        return b280    func, ins, outs = run_and_check(if_and, [a])281    run_and_check(func, ins, outs=outs)282def test_bind():283    if not tvm.gpu(0).exist:284        print('[Warning] No GPU found! Skip bind test!')285        return286    @script287    def vec_add(a, b):288        c = output_tensor((1000, ), 'float32')289        for tx in bind('threadIdx.x', 1000):290            c[tx] = a[tx] + b[tx]291        return c292    a = tvm.placeholder((1000, ), dtype='float32', name='a')293    b = tvm.placeholder((1000, ), dtype='float32', name='b')294    func, ins, outs = run_and_check(vec_add, [a, b], target='cuda')295    run_and_check(func, ins, outs=outs, target='cuda')296    @script297    def raw(a, b):298        c = output_tensor((1000, ), 'float32')299        for i in range(1000):300            c[i] = a[i] + b[i]301        return c302    c = raw(a, b)303    sch = tvm.create_schedule(c.op)304    x = tvm.thread_axis('threadIdx.x')305    sch[c].bind(c.op.axis[0], x)306    func, ins, outs = run_and_check(raw, [a, b], sch=sch, outs=[c], target='cuda')307    run_and_check(func, ins, outs=outs, target='cuda')308    @tvm.hybrid.script309    def foo(a):310        c = output_tensor((a.shape[0],), a.dtype)311        total = allocate((1,), a.dtype, 'local')312        len_i = a.shape[0]313        len_j = a.shape[1]314        for i in bind('threadIdx.x', len_i):315            total[0] = 0.316            for k in const_range(len_j):317                total[0] += a[i, k]318            c[i] = total[0]319        return c320    a = tvm.placeholder((8, 4), 'float32')321    c = foo(a)322    s = tvm.create_schedule(c.op)323    ir = tvm.lower(s, [a, c], simple_mode=True)324    assert not isinstance(ir, tvm.stmt.AttrStmt)325    func, ins, outs = run_and_check(foo, [a], target='cuda')326    run_and_check(func, ins, outs=outs, target='cuda')327    @tvm.hybrid.script328    def max_threads(a):329        b = output_tensor(a.shape, a.dtype)330        n = a.shape[0]331        m = max_num_threads(True)332        for i in bind('threadIdx.x', m):333            for j in bind('blockIdx.x', ceil_div(n, m)):334                if i * m + j < n:335                    b[i * m + j] = a[i * m + j] + a[i * m + j]336        return b337    a = tvm.placeholder((10000, ), 'float32')338    with tvm.target.create('cuda'):339        func, ins, outs = run_and_check(max_threads, [a], target='cuda')340        run_and_check(func, ins, outs=outs, target='cuda')341def test_math_intrin():342    @script343    def intrin_real(a):344        b = output_tensor((8, ), 'float32')345        b[0] = sqrt(a[0])346        b[1] = log(a[1])347        b[2] = exp(a[2])348        b[3] = sigmoid(a[3])349        b[4] = power(a[4], a[5])350        b[5] = tanh(a[5])351        b[6] = min(a[4], a[5])352        b[7] = max(a[5], a[6])353        return b354    a8 = tvm.placeholder((8, ), dtype='float32', name='a')355    b8 = intrin_real(a8)356    sch = tvm.create_schedule(b8.op)357    func = tvm.build(sch, [a8, b8])358    assert func359    a = numpy.arange(2, 10).astype('float32')360    tvm_a = tvm.ndarray.array(a)361    tvm_b = tvm.ndarray.array(numpy.zeros((8, ), dtype='float32'))362    b = intrin_real(a)363    func(tvm_a, tvm_b)364    tvm.testing.assert_allclose(b, tvm_b.asnumpy(), rtol=1e-5)365    @script366    def intrin_int(a):367        b = output_tensor((1, ), 'int32')368        b[0] = popcount(a[0])369        return b370    a1 = tvm.placeholder((1, ), dtype='int32')371    b1 = intrin_int(a1)372    sch = tvm.create_schedule(b1.op)373    func = tvm.build(sch, [a1, b1])374    assert func375    a = numpy.array([114514]).astype('int32')376    tvm_a = tvm.ndarray.array(a)377    tvm_b = tvm.ndarray.array(numpy.array([0]).astype('int32'))378    b = intrin_int(a)379    func(tvm_a, tvm_b)380    assert tvm_b.asnumpy()[0] == b[0]381# test non caconical loops382def test_non_zero():383    @tvm.hybrid.script384    def blur(a):385        b = output_tensor((30, 30), 'float32')386        for i in range(2, 32):387            for j in range(2, 32):388                s = 0.0389                for di in range(3):390                    for dj in range(3):391                        s += a[i-di, j-dj]392                b[i-2, j-2] = s / 9.0393        return b394    a = tvm.placeholder((32, 32), 'float32', 'a')395    func, ins, outs = run_and_check(blur, [a])396    run_and_check(func, ins, outs=outs)397    @tvm.hybrid.script398    def triangle(a, b):399        c = output_tensor((10, 10), dtype='float32')400        for i in range(10):401            for j in range(i, 10):402                c[i, j] = a[i] * b[j]403        return c404    a = tvm.placeholder((10, ), dtype='float32', name='a')405    b = tvm.placeholder((10, ), dtype='float32', name='b')406    func, ins, outs = run_and_check(triangle, [a, b])407    run_and_check(func, ins, outs=outs)408def test_allocate():409    @tvm.hybrid.script410    def blur2d(a):411        b = output_tensor((30, 30), 'float32')412        for i in range(30):413            ha = allocate((3, 30), 'float32')414            for j in range(3):415                for k in range(30):416                    ha[j, k] = a[i+j, k] + a[i+j, k+1] + a[i+j, k+2]417            for j in range(30):418                b[i, j] = (ha[0, j] + ha[1, j] + ha[2, j]) / 9.0419        return b420    a = tvm.placeholder((32, 32), 'float32', 'a')421    b = blur2d(a)422    sch = tvm.create_schedule(b.op)423    func, ins, outs = run_and_check(blur2d, [a])424    run_and_check(func, ins, outs=outs)425    if tvm.gpu().exist:426        @tvm.hybrid.script427        def share_vec_add(a, b):428            c = output_tensor((256, ), 'float32')429            shared = allocate((256, ), 'float32', 'shared')430            for i in bind("threadIdx.x", 256):431                shared[i] = a[i]432            local = allocate((256, ), 'float32', 'local')433            for i in bind("threadIdx.x", 256):434                local[i] = b[i]435            for i in bind("threadIdx.x", 256):436                c[i] = shared[i] + local[i]437            return c438        a = tvm.placeholder((256, ), dtype='float32', name='a')439        b = tvm.placeholder((256, ), dtype='float32', name='b')440        c = share_vec_add(a, b)441        func, ins, outs = run_and_check(share_vec_add, [a, b], target='cuda')442        run_and_check(func, ins, outs=outs, target='cuda')443    else:444        print('[Warning] No GPU found! Skip shared mem test!')445def test_upstream():446    @tvm.hybrid.script447    def upstream(a):448        b = output_tensor((20, ), 'float32')449        for i in range(20):450            b[i] = a[i] * i451        return b452    a = tvm.placeholder((20, ), 'float32')453    b = tvm.placeholder((20, ), 'float32')454    c = tvm.compute((20, ), lambda x: a[x] + b[x])455    d = upstream(c)456    sch = tvm.create_schedule([c.op, d.op])457    ir = tvm.lower(sch, [a, b, d], simple_mode=True)458    func = tvm.build(sch, [a, b, d])459    assert(func)460    a = numpy.random.randn(20).astype('float32')461    b = numpy.random.randn(20).astype('float32')462    ref = numpy.zeros((20, ), 'float32')463    for i in range(20):464        ref[i] = (a[i] + b[i]) * i465    tvm_a = tvm.nd.array(a)466    tvm_b = tvm.nd.array(b)467    tvm_d = tvm.nd.array(numpy.zeros((20, )).astype('float32'))468    func(tvm_a, tvm_b, tvm_d)469    tvm.testing.assert_allclose(tvm_d.asnumpy(), ref, 1e-5, 1e-5)470def test_downstream():471    @tvm.hybrid.script472    def downstream(a):473        b = output_tensor((20, ), 'float32')474        for i in range(20):475            b[i] = a[i] * i476        return b477    a = tvm.placeholder((20, ), 'float32')478    b = downstream(a)479    c = tvm.compute((20, ), lambda x: b[x] + 1.0)480    sch = tvm.create_schedule(c.op)481    module = tvm.build(sch, [a, c])482    assert module483    a = numpy.random.randn(20).astype('float32')484    ref = numpy.zeros((20, )).astype('float32')485    for i in range(20):486        ref[i] = (a[i] * i) + 1.0487    tvm_a = tvm.nd.array(a)488    tvm_c = tvm.nd.array(numpy.zeros((20, )).astype('float32'))489    module(tvm_a, tvm_c)490    tvm.testing.assert_allclose(tvm_c.asnumpy(), ref, 1e-5, 1e-5)491def test_const_param():492    @tvm.hybrid.script493    def add_something(a, b):494        c = output_tensor((11, ), 'int32')495        for i in range(11):496            c[i] = a[i] + b497        return c498    a = tvm.placeholder((11, ), dtype='int32', name='a')499    b = tvm.const(11, 'int32')500    c = add_something(a, b)501    sch = tvm.create_schedule(c.op)502    module = tvm.build(sch, [a, c], 'llvm')503    assert(module)504    np_a = numpy.arange(11).astype('int32')505    np_b = 11506    np_c = numpy.zeros((11, )).astype('int32')507    nd_a = tvm.ndarray.array(np_a)508    nd_c = tvm.ndarray.array(numpy.zeros((11, )).astype('int32'))509    module(nd_a, nd_c)510    ref = add_something(np_a, 11)511    tvm.testing.assert_allclose(nd_c.asnumpy(), ref, 1e-5, 1e-5)512def test_value_index():513    @tvm.hybrid.script514    def kernel_a(a):515        b = output_tensor((16, ), 'int32')516        c = output_tensor((4, 4), 'int32')517        for i in range(16):518            b[i] = a[i] + 2519            c[i // 4, i % 4] = a[i] + 1520        return b, c521    @tvm.hybrid.script522    def kernel_b(b, a):523        c = output_tensor((4, 4), 'int32')524        for i in range(4):525            for j in range(4):526                c[i, j] = a[i * 4 + j] * b[i, j]527        return c528    a = tvm.placeholder((16, ), 'int32')529    b, c = kernel_a(a)530    d = kernel_b(c, b)531    sch = tvm.create_schedule(d.op)532    module = tvm.build(sch, [a, d])533    assert module534    np_a = numpy.arange(16).astype('int32')535    np_b, np_c = kernel_a(np_a)536    ref = kernel_b(np_c, np_b)537    res = tvm.ndarray.array(numpy.zeros((4, 4)).astype('int32'))538    module(tvm.ndarray.array(np_a), res)539    tvm.testing.assert_allclose(res.asnumpy(), ref)540def test_func_call():541    @tvm.hybrid.script542    def foo(a, b):543        for i in range(len(a)):544            a[i] = i + 1.0545        for i in range(len(a)):546            b[i] = i + 1.0547        c = outer_product(10, 10, a, b)548        d = output_tensor(c.shape, c.dtype)549        for i in range(10):550            for j in range(10):551                d[i, j] = c[i, j] + i * j552        return d553    a = tvm.placeholder((10, ), name='a')554    b = tvm.placeholder((10, ), name='b')555    func, ins, outs = run_and_check(foo, [a, b])556    run_and_check(func, ins, outs=outs)557def test_bool():558    @tvm.hybrid.script559    def foo(a):560        b = output_tensor(a.shape, a.dtype)561        b[0] = 1.2562        for i in range(1, a.shape[0] - 1):563            if a[i] * a[i - 1] < a[i] or a[i] * a[i - 1] < a[i - 1] or i * a[i] == a[i]:564                b[i] = a[i]565            else:566                b[i] = 0.0567        return b568    a = tvm.placeholder((10, ), name='a')569    func, ins, outs = run_and_check(foo, [a])570    run_and_check(func, ins, outs=outs)571def test_const_range():572    @tvm.hybrid.script573    def foo(a, b):574        c = output_tensor(a.shape, a.dtype)575        d = output_tensor(a.shape, 'int32')576        for i in const_range(2):577            for j in const_range(5):578                c[i, j] = float32(int32(a[i, j]) + b[i, j])579        for i in const_range(len(b)):580            for j in const_range(len(b[0])):581                d[i, j] = int32(a[i, j] + b[i, j])582        return c, d583    a = tvm.placeholder((2, 5), name='a', dtype='float32')584    b = [[1, 2, 3, 4, 5], [5, 4, 3, 2, 1]]585    func, ins, outs = run_and_check(foo, [a, b])586    run_and_check(func, ins, outs=outs)587    @tvm.hybrid.script588    def goo(a, b):589        c = output_tensor(a.shape, a.dtype)590        len_b = len(b)591        for i in const_range(len_b * 2):592            if i < len_b:593                c[i] = a[i] + b[i]594            else:595                c[i - len_b] = a[i - len_b] + b[i - len_b]596        return c597    a = tvm.placeholder((5, ), name='a', dtype='int32')598    b = [1, 2, 3, 4, 5]599    c = goo(a, tvm.convert(b))600    sch = tvm.create_schedule(c.op)601    func, ins, outs = run_and_check(goo, [a, b])602    run_and_check(func, ins, outs=outs)603    @tvm.hybrid.script604    def hoo(a, b):605        c = output_tensor(a.shape, a.dtype)606        len_b = len(b)607        for i in range(a.shape[0]):608            for j in const_range(len(b)):609                d = a[i] * b[j]610                d += a[i] + b[j]611                c[i] = d612        return c613    a = tvm.placeholder((5, ), name='a', dtype='int32')614    b = [1, 2, 3, 4, 5]615    func, ins, outs = run_and_check(hoo, [a, b])616    run_and_check(func, ins, outs=outs)617def test_schedule():618    @script619    def outer_product(a, b):620        c = output_tensor((64, 64), a.dtype)621        for i in range(64):622            for j in range(64):623                c[i, j] = a[i] * b[j]624        return c625    a = tvm.placeholder((64,), name='a', dtype='float32')626    b = tvm.placeholder((64,), name='b', dtype='float32')627    c = outer_product(a, b)628    # Test perfect loop split629    # Test loop reorder630    # Test loop annotation631    sch = tvm.create_schedule(c.op)632    i, j = c.op.axis633    io, ii = sch[c].split(i, 4)634    sch[c].parallel(ii)635    jo, ji = sch[c].split(j, 4)636    joo, joi = sch[c].split(jo, 4)637    sch[c].vectorize(ji)638    sch[c].reorder(ii, io, joo, joi, ji)639    ir = tvm.lower(sch, [a, b, c], simple_mode=True)640    assert isinstance(ir, tvm.stmt.ProducerConsumer)641    ir = ir.body642    assert isinstance(ir, tvm.stmt.AttrStmt)643    ir = ir.body644    assert isinstance(ir, tvm.stmt.For)645    assert ir.loop_var.name == 'i.inner'646    ir = ir.body647    assert isinstance(ir, tvm.stmt.For)648    assert ir.loop_var.name == 'i.outer'649    ir = ir.body650    assert isinstance(ir, tvm.stmt.For)651    assert ir.loop_var.name == 'j.outer.outer'652    ir = ir.body653    assert isinstance(ir, tvm.stmt.For)654    assert ir.loop_var.name == 'j.outer.inner'655    ir = ir.body656    func, ins, outs = run_and_check(outer_product, [a, b], sch=sch, outs=[c])657    run_and_check(func, ins, outs=outs)658    # Test fuse659    sch = tvm.create_schedule(c.op)660    sch[c].fuse(c.op.axis[0], c.op.axis[1])661    ir = tvm.lower(sch, [a, b, c], simple_mode=True)662    assert isinstance(ir, tvm.stmt.ProducerConsumer)663    ir = ir.body664    assert isinstance(ir, tvm.stmt.AttrStmt)665    ir = ir.body666    assert isinstance(ir, tvm.stmt.For)667    assert ir.loop_var.name == 'i.j.fused'668    func, ins, outs = run_and_check(outer_product, [a, b], sch=sch, outs=[c])669    run_and_check(func, ins, outs=outs)670    # Test imperfect loop split671    sch = tvm.create_schedule(c.op)672    sch[c].split(c.op.axis[0], 3)673    ir = tvm.lower(sch, [a, b, c], simple_mode=True)674    func, ins, outs = run_and_check(outer_product, [a, b], sch=sch, outs=[c])675    run_and_check(func, ins, outs=outs)676    # Test loop binds677def test_capture():678    n = 8679    constant_tuple = (10, n)680    constant_list = [[1, 2], [3, n]]681    const_value = 1682    @tvm.hybrid.script683    def add_something(a):684        c = output_tensor((constant_tuple[1],), 'int32')685        for i in range(constant_tuple[1]):686            c[i] = a[i] + constant_list[1][const_value]687        return c688    a = tvm.placeholder((n, ), dtype='int32', name='a')689    func, ins, outs = run_and_check(add_something, [a])690    run_and_check(func, ins, outs=outs)691def test_array_inputs():692    @script693    def sum_array(inputs):694        out = output_tensor((10,), inputs[0].dtype)695        n = len(inputs)696        for i in range(10):697            for j in const_range(n):698                out[i] += inputs[j][i]699        return out700    n = 5701    inputs = []702    for i in range(n):703        inputs.append(tvm.placeholder((10,), name='t%s' % i, dtype='float32'))704    out = sum_array(tvm.convert(inputs))...

test__refcount.py

Source:test__refcount.py

...43        eventlet.spawn(make_request, addr)44    eventlet.sleep(0.1 + SOCKET_TIMEOUT)45    print('run_interaction: refcount(s.fd)', sys.getrefcount(s.fd))46    return weakref.ref(s.fd)47def run_and_check(run_client):48    w = run_interaction(run_client=run_client)49    # clear_sys_exc_info()50    gc.collect()51    fd = w()52    print('run_and_check: weakref fd:', fd)53    if fd:54        print(pprint.pformat(gc.get_referrers(fd)))55        for x in gc.get_referrers(fd):56            print(pprint.pformat(x))57            for y in gc.get_referrers(x):58                print('- {0}'.format(pprint.pformat(y)))59        raise AssertionError('server should be dead by now')60def test_clean_exit():61    run_and_check(True)62    run_and_check(True)63def test_timeout_exit():64    run_and_check(False)...

Automation Testing Tutorials

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