# Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements. See the NOTICE file # distributed with this work for additional information # regarding copyright ownership. The ASF licenses this file # to you under the Apache License, Version 2.0 (the # "License"); you may not use this file except in compliance # with the License. You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY # KIND, either express or implied. See the License for the # specific language governing permissions and limitations # under the License. # pylint: disable=invalid-name,unnecessary-comprehension """TVM testing utilities Organization ************ This file contains functions expected to be called directly by a user while writing unit tests. Integrations with the pytest framework are in plugin.py. Testing Markers *************** We use pytest markers to specify the requirements of test functions. Currently there is a single distinction that matters for our testing environment: does the test require a gpu. For tests that require just a gpu or just a cpu, we have the decorator :py:func:`requires_gpu` that enables the test when a gpu is available. To avoid running tests that don't require a gpu on gpu nodes, this decorator also sets the pytest marker `gpu` so we can use select the gpu subset of tests (using `pytest -m gpu`). Unfortunately, many tests are written like this: .. python:: def test_something(): for target in all_targets(): do_something() The test uses both gpu and cpu targets, so the test needs to be run on both cpu and gpu nodes. But we still want to only run the cpu targets on the cpu testing node. The solution is to mark these tests with the gpu marker so they will be run on the gpu nodes. But we also modify all_targets (renamed to enabled_targets) so that it only returns gpu targets on gpu nodes and cpu targets on cpu nodes (using an environment variable). Instead of using the all_targets function, future tests that would like to test against a variety of targets should use the :py:func:`tvm.testing.parametrize_targets` functionality. This allows us greater control over which targets are run on which testing nodes. If in the future we want to add a new type of testing node (for example fpgas), we need to add a new marker in `tests/python/pytest.ini` and a new function in this module. Then targets using this node should be added to the `TVM_TEST_TARGETS` environment variable in the CI. """ import inspect import copy import copyreg import ctypes import functools import logging import os import platform import shutil import sys import time import pickle import pytest import numpy as np import tvm import tvm.arith import tvm.tir import tvm.te import tvm._ffi from tvm.contrib import nvcc, cudnn from tvm.error import TVMError from tvm.relay.op.contrib.ethosn import ethosn_available from tvm.relay.op.contrib import cmsisnn from tvm.relay.op.contrib import vitis_ai SKIP_SLOW_TESTS = os.getenv("SKIP_SLOW_TESTS", "").lower() in {"true", "1", "yes"} def assert_allclose(actual, desired, rtol=1e-7, atol=1e-7): """Version of np.testing.assert_allclose with `atol` and `rtol` fields set in reasonable defaults. Arguments `actual` and `desired` are not interchangeable, since the function compares the `abs(actual-desired)` with `atol+rtol*abs(desired)`. Since we often allow `desired` to be close to zero, we generally want non-zero `atol`. """ actual = np.asanyarray(actual) desired = np.asanyarray(desired) np.testing.assert_allclose(actual.shape, desired.shape) np.testing.assert_allclose(actual, desired, rtol=rtol, atol=atol, verbose=True) def check_numerical_grads( function, input_values, grad_values, function_value=None, delta=1e-3, atol=1e-2, rtol=0.1 ): """A helper function that checks that numerical gradients of a function are equal to gradients computed in some different way (analytical gradients). Numerical gradients are computed using finite difference approximation. To reduce the number of function evaluations, the number of points used is gradually increased if the error value is too high (up to 5 points). Parameters ---------- function A function that takes inputs either as positional or as keyword arguments (either `function(*input_values)` or `function(**input_values)` should be correct) and returns a scalar result. Should accept numpy ndarrays. input_values : Dict[str, numpy.ndarray] or List[numpy.ndarray] A list of values or a dict assigning values to variables. Represents the point at which gradients should be computed. grad_values : Dict[str, numpy.ndarray] or List[numpy.ndarray] Gradients computed using a different method. function_value : float, optional Should be equal to `function(**input_values)`. delta : float, optional A small number used for numerical computation of partial derivatives. The default 1e-3 is a good choice for float32. atol : float, optional Absolute tolerance. Gets multiplied by `sqrt(n)` where n is the size of a gradient. rtol : float, optional Relative tolerance. """ # If input_values is a list then function accepts positional arguments # In this case transform it to a function taking kwargs of the form {"0": ..., "1": ...} if not isinstance(input_values, dict): input_len = len(input_values) input_values = {str(idx): val for idx, val in enumerate(input_values)} def _function(_input_len=input_len, _orig_function=function, **kwargs): return _orig_function(*(kwargs[str(i)] for i in range(input_len))) function = _function grad_values = {str(idx): val for idx, val in enumerate(grad_values)} if function_value is None: function_value = function(**input_values) # a helper to modify j-th element of val by a_delta def modify(val, j, a_delta): val = val.copy() val.reshape(-1)[j] = val.reshape(-1)[j] + a_delta return val # numerically compute a partial derivative with respect to j-th element of the var `name` def derivative(x_name, j, a_delta): modified_values = { n: modify(val, j, a_delta) if n == x_name else val for n, val in input_values.items() } return (function(**modified_values) - function_value) / a_delta def compare_derivative(j, n_der, grad): der = grad.reshape(-1)[j] return np.abs(n_der - der) < atol + rtol * np.abs(n_der) for x_name, grad in grad_values.items(): if grad.shape != input_values[x_name].shape: raise AssertionError( "Gradient wrt '{}' has unexpected shape {}, expected {} ".format( x_name, grad.shape, input_values[x_name].shape ) ) ngrad = np.zeros_like(grad) wrong_positions = [] # compute partial derivatives for each position in this variable for j in range(np.prod(grad.shape)): # forward difference approximation nder = derivative(x_name, j, delta) # if the derivative is not equal to the analytical one, try to use more # precise and expensive methods if not compare_derivative(j, nder, grad): # central difference approximation nder = (derivative(x_name, j, -delta) + nder) / 2 if not compare_derivative(j, nder, grad): # central difference approximation using h = delta/2 cnder2 = ( derivative(x_name, j, delta / 2) + derivative(x_name, j, -delta / 2) ) / 2 # five-point derivative nder = (4 * cnder2 - nder) / 3 # if the derivatives still don't match, add this position to the # list of wrong positions if not compare_derivative(j, nder, grad): wrong_positions.append(np.unravel_index(j, grad.shape)) ngrad.reshape(-1)[j] = nder wrong_percentage = int(100 * len(wrong_positions) / np.prod(grad.shape)) dist = np.sqrt(np.sum((ngrad - grad) ** 2)) grad_norm = np.sqrt(np.sum(ngrad**2)) if not (np.isfinite(dist) and np.isfinite(grad_norm)): raise ValueError( "NaN or infinity detected during numerical gradient checking wrt '{}'\n" "analytical grad = {}\n numerical grad = {}\n".format(x_name, grad, ngrad) ) # we multiply atol by this number to make it more universal for different sizes sqrt_n = np.sqrt(float(np.prod(grad.shape))) if dist > atol * sqrt_n + rtol * grad_norm: raise AssertionError( "Analytical and numerical grads wrt '{}' differ too much\n" "analytical grad = {}\n numerical grad = {}\n" "{}% of elements differ, first 10 of wrong positions: {}\n" "distance > atol*sqrt(n) + rtol*grad_norm\n" "distance {} > {}*{} + {}*{}".format( x_name, grad, ngrad, wrong_percentage, wrong_positions[:10], dist, atol, sqrt_n, rtol, grad_norm, ) ) max_diff = np.max(np.abs(ngrad - grad)) avg_diff = np.mean(np.abs(ngrad - grad)) logging.info( "Numerical grad test wrt '%s' of shape %s passes, " "dist = %f, max_diff = %f, avg_diff = %f", x_name, grad.shape, dist, max_diff, avg_diff, ) def assert_prim_expr_equal(lhs, rhs): """Assert lhs and rhs equals to each iother. Parameters ---------- lhs : tvm.tir.PrimExpr The left operand. rhs : tvm.tir.PrimExpr The left operand. """ ana = tvm.arith.Analyzer() if not ana.can_prove_equal(lhs, rhs): raise ValueError("{} and {} are not equal".format(lhs, rhs)) def check_bool_expr_is_true(bool_expr, vranges, cond=None): """Check that bool_expr holds given the condition cond for every value of free variables from vranges. for example, 2x > 4y solves to x > 2y given x in (0, 10) and y in (0, 10) here bool_expr is x > 2y, vranges is {x: (0, 10), y: (0, 10)}, cond is 2x > 4y We creates iterations to check, for x in range(10): for y in range(10): assert !(2x > 4y) || (x > 2y) Parameters ---------- bool_expr : tvm.ir.PrimExpr Boolean expression to check vranges: Dict[tvm.tir.expr.Var, tvm.ir.Range] Free variables and their ranges cond: tvm.ir.PrimExpr extra conditions needs to be satisfied. """ if cond is not None: bool_expr = tvm.te.any(tvm.tir.Not(cond), bool_expr) def _run_expr(expr, vranges): """Evaluate expr for every value of free variables given by vranges and return the tensor of results. """ def _compute_body(*us): vmap = {v: u + r.min for (v, r), u in zip(vranges.items(), us)} return tvm.tir.stmt_functor.substitute(expr, vmap) A = tvm.te.compute([r.extent.value for v, r in vranges.items()], _compute_body) args = [tvm.nd.empty(A.shape, A.dtype)] sch = tvm.te.create_schedule(A.op) mod = tvm.build(sch, [A]) mod(*args) return args[0].numpy() res = _run_expr(bool_expr, vranges) if not np.all(res): indices = list(np.argwhere(res == 0)[0]) counterex = [(str(v), i + r.min) for (v, r), i in zip(vranges.items(), indices)] counterex = sorted(counterex, key=lambda x: x[0]) counterex = ", ".join([v + " = " + str(i) for v, i in counterex]) ana = tvm.arith.Analyzer() raise AssertionError( "Expression {}\nis not true on {}\n" "Counterexample: {}".format(ana.simplify(bool_expr), vranges, counterex) ) def check_int_constraints_trans_consistency(constraints_trans, vranges=None): """Check IntConstraintsTransform is a bijective transformation. Parameters ---------- constraints_trans : arith.IntConstraintsTransform Integer constraints transformation vranges: Dict[tvm.tir.Var, tvm.ir.Range] Free variables and their ranges """ if vranges is None: vranges = {} def _check_forward(constraints1, constraints2, varmap, backvarmap): ana = tvm.arith.Analyzer() all_vranges = vranges.copy() all_vranges.update({v: r for v, r in constraints1.ranges.items()}) # Check that the transformation is injective cond_on_vars = tvm.tir.const(1, "bool") for v in constraints1.variables: if v in varmap: # variable mapping is consistent v_back = ana.simplify(tvm.tir.stmt_functor.substitute(varmap[v], backvarmap)) cond_on_vars = tvm.te.all(cond_on_vars, v == v_back) # Also we have to check that the new relations are true when old relations are true cond_subst = tvm.tir.stmt_functor.substitute( tvm.te.all(tvm.tir.const(1, "bool"), *constraints2.relations), backvarmap ) # We have to include relations from vranges too for v in constraints2.variables: if v in constraints2.ranges: r = constraints2.ranges[v] range_cond = tvm.te.all(v >= r.min, v < r.min + r.extent) range_cond = tvm.tir.stmt_functor.substitute(range_cond, backvarmap) cond_subst = tvm.te.all(cond_subst, range_cond) cond_subst = ana.simplify(cond_subst) check_bool_expr_is_true( tvm.te.all(cond_subst, cond_on_vars), all_vranges, cond=tvm.te.all(tvm.tir.const(1, "bool"), *constraints1.relations), ) _check_forward( constraints_trans.src, constraints_trans.dst, constraints_trans.src_to_dst, constraints_trans.dst_to_src, ) _check_forward( constraints_trans.dst, constraints_trans.src, constraints_trans.dst_to_src, constraints_trans.src_to_dst, ) def _get_targets(target_str=None): if target_str is None: target_str = os.environ.get("TVM_TEST_TARGETS", "") # Use dict instead of set for de-duplication so that the # targets stay in the order specified. target_names = list({t.strip(): None for t in target_str.split(";") if t.strip()}) if not target_names: target_names = DEFAULT_TEST_TARGETS targets = [] for target in target_names: target_kind = target.split()[0] if target_kind == "cuda" and "cudnn" in tvm.target.Target(target).attrs.get("libs", []): is_enabled = tvm.support.libinfo()["USE_CUDNN"].lower() in ["on", "true", "1"] is_runnable = is_enabled and cudnn.exists() elif target_kind == "hexagon": is_enabled = tvm.support.libinfo()["USE_HEXAGON"].lower() in ["on", "true", "1"] # If Hexagon has compile-time support, we can always fall back is_runnable = is_enabled and "ANDROID_SERIAL_NUMBER" in os.environ else: is_enabled = tvm.runtime.enabled(target_kind) is_runnable = is_enabled and tvm.device(target_kind).exist targets.append( { "target": target, "target_kind": target_kind, "is_enabled": is_enabled, "is_runnable": is_runnable, } ) if all(not t["is_runnable"] for t in targets): if tvm.runtime.enabled("llvm"): logging.warning( "None of the following targets are supported by this build of TVM: %s." " Try setting TVM_TEST_TARGETS to a supported target. Defaulting to llvm.", target_str, ) return _get_targets("llvm") raise TVMError( "None of the following targets are supported by this build of TVM: %s." " Try setting TVM_TEST_TARGETS to a supported target." " Cannot default to llvm, as it is not enabled." % target_str ) return targets DEFAULT_TEST_TARGETS = [ "llvm", "cuda", "nvptx", "vulkan -from_device=0", "opencl", "opencl -device=mali,aocl_sw_emu", "opencl -device=intel_graphics", "metal", "rocm", "hexagon", ] def device_enabled(target): """Check if a target should be used when testing. It is recommended that you use :py:func:`tvm.testing.parametrize_targets` instead of manually checking if a target is enabled. This allows the user to control which devices they are testing against. In tests, this should be used to check if a device should be used when said device is an optional part of the test. Parameters ---------- target : str Target string to check against Returns ------- bool Whether or not the device associated with this target is enabled. Example ------- >>> @tvm.testing.uses_gpu >>> def test_mytest(): >>> for target in ["cuda", "llvm"]: >>> if device_enabled(target): >>> test_body... Here, `test_body` will only be reached by with `target="cuda"` on gpu test nodes and `target="llvm"` on cpu test nodes. """ assert isinstance(target, str), "device_enabled requires a target as a string" # only check if device name is found, sometime there are extra flags target_kind = target.split(" ")[0] return any(target_kind == t["target_kind"] for t in _get_targets() if t["is_runnable"]) def enabled_targets(): """Get all enabled targets with associated devices. In most cases, you should use :py:func:`tvm.testing.parametrize_targets` instead of this function. In this context, enabled means that TVM was built with support for this target, the target name appears in the TVM_TEST_TARGETS environment variable, and a suitable device for running this target exists. If TVM_TEST_TARGETS is not set, it defaults to variable DEFAULT_TEST_TARGETS in this module. If you use this function in a test, you **must** decorate the test with :py:func:`tvm.testing.uses_gpu` (otherwise it will never be run on the gpu). Returns ------- targets: list A list of pairs of all enabled devices and the associated context """ return [(t["target"], tvm.device(t["target"])) for t in _get_targets() if t["is_runnable"]] def _compose(args, decs): """Helper to apply multiple markers""" if len(args) > 0: f = args[0] for d in reversed(decs): f = d(f) return f return decs def slow(fn): @functools.wraps(fn) def wrapper(*args, **kwargs): if SKIP_SLOW_TESTS: pytest.skip("Skipping slow test since RUN_SLOW_TESTS environment variables is 'true'") else: fn(*args, **kwargs) return wrapper def uses_gpu(*args): """Mark to differentiate tests that use the GPU in some capacity. These tests will be run on CPU-only test nodes and on test nodes with GPUs. To mark a test that must have a GPU present to run, use :py:func:`tvm.testing.requires_gpu`. Parameters ---------- f : function Function to mark """ _uses_gpu = [pytest.mark.gpu] return _compose(args, _uses_gpu) def requires_x86(*args): """Mark a test as requiring the x86 Architecture to run. Tests with this mark will not be run unless on an x86 platform. Parameters ---------- f : function Function to mark """ _requires_x86 = [ pytest.mark.skipif(platform.machine() != "x86_64", reason="x86 Architecture Required"), ] return _compose(args, _requires_x86) def requires_gpu(*args): """Mark a test as requiring a GPU to run. Tests with this mark will not be run unless a gpu is present. Parameters ---------- f : function Function to mark """ _requires_gpu = [ pytest.mark.skipif( not tvm.cuda().exist and not tvm.rocm().exist and not tvm.opencl().exist and not tvm.metal().exist and not tvm.vulkan().exist, reason="No GPU present", ), *uses_gpu(), ] return _compose(args, _requires_gpu) def requires_cuda(*args): """Mark a test as requiring the CUDA runtime. This also marks the test as requiring a cuda gpu. Parameters ---------- f : function Function to mark """ _requires_cuda = [ pytest.mark.cuda, pytest.mark.skipif(not device_enabled("cuda"), reason="CUDA support not enabled"), *requires_gpu(), ] return _compose(args, _requires_cuda) def requires_cudnn(*args): """Mark a test as requiring the cuDNN library. This also marks the test as requiring a cuda gpu. Parameters ---------- f : function Function to mark """ requirements = [ pytest.mark.skipif( not cudnn.exists(), reason="cuDNN library not enabled, or not installed" ), *requires_cuda(), ] return _compose(args, requirements) def requires_cublas(*args): """Mark a test as requiring the cuBLAS library. This also marks the test as requiring a cuda gpu. Parameters ---------- f : function Function to mark """ requirements = [ pytest.mark.skipif( tvm.get_global_func("tvm.contrib.cublas.matmul", True), reason="cuDNN library not enabled", ), *requires_cuda(), ] return _compose(args, requirements) def requires_nvptx(*args): """Mark a test as requiring the NVPTX compilation on the CUDA runtime This also marks the test as requiring a cuda gpu, and requiring LLVM support. Parameters ---------- f : function Function to mark """ _requires_nvptx = [ pytest.mark.skipif(not device_enabled("nvptx"), reason="NVPTX support not enabled"), *requires_llvm(), *requires_gpu(), ] return _compose(args, _requires_nvptx) def requires_nvcc_version(major_version, minor_version=0, release_version=0): """Mark a test as requiring at least a specific version of nvcc. Unit test marked with this decorator will run only if the installed version of NVCC is at least `(major_version, minor_version, release_version)`. This also marks the test as requiring a cuda support. Parameters ---------- major_version: int The major version of the (major,minor,release) version tuple. minor_version: int The minor version of the (major,minor,release) version tuple. release_version: int The release version of the (major,minor,release) version tuple. """ try: nvcc_version = nvcc.get_cuda_version() except RuntimeError: nvcc_version = (0, 0, 0) min_version = (major_version, minor_version, release_version) version_str = ".".join(str(v) for v in min_version) requires = [ pytest.mark.skipif(nvcc_version < min_version, reason=f"Requires NVCC >= {version_str}"), *requires_cuda(), ] def inner(func): return _compose([func], requires) return inner def skip_if_32bit(reason): def decorator(*args): if "32bit" in platform.architecture()[0]: return _compose(args, [pytest.mark.skip(reason=reason)]) return _compose(args, []) return decorator def requires_cudagraph(*args): """Mark a test as requiring the CUDA Graph Feature This also marks the test as requiring cuda Parameters ---------- f : function Function to mark """ _requires_cudagraph = [ pytest.mark.skipif( not nvcc.have_cudagraph(), reason="CUDA Graph is not supported in this environment" ), *requires_cuda(), ] return _compose(args, _requires_cudagraph) def requires_opencl(*args): """Mark a test as requiring the OpenCL runtime. This also marks the test as requiring a gpu. Parameters ---------- f : function Function to mark """ _requires_opencl = [ pytest.mark.opencl, pytest.mark.skipif(not device_enabled("opencl"), reason="OpenCL support not enabled"), *requires_gpu(), ] return _compose(args, _requires_opencl) def requires_corstone300(*args): """Mark a test as requiring the corstone300 FVP Parameters ---------- f : function Function to mark """ _requires_corstone300 = [ pytest.mark.corstone300, pytest.mark.skipif( shutil.which("arm-none-eabi-gcc") is None, reason="ARM embedded toolchain unavailable" ), ] return _compose(args, _requires_corstone300) def requires_rocm(*args): """Mark a test as requiring the rocm runtime. This also marks the test as requiring a gpu. Parameters ---------- f : function Function to mark """ _requires_rocm = [ pytest.mark.rocm, pytest.mark.skipif(not device_enabled("rocm"), reason="rocm support not enabled"), *requires_gpu(), ] return _compose(args, _requires_rocm) def requires_metal(*args): """Mark a test as requiring the metal runtime. This also marks the test as requiring a gpu. Parameters ---------- f : function Function to mark """ _requires_metal = [ pytest.mark.metal, pytest.mark.skipif(not device_enabled("metal"), reason="metal support not enabled"), *requires_gpu(), ] return _compose(args, _requires_metal) def requires_vulkan(*args): """Mark a test as requiring the vulkan runtime. This also marks the test as requiring a gpu. Parameters ---------- f : function Function to mark """ _requires_vulkan = [ pytest.mark.vulkan, pytest.mark.skipif(not device_enabled("vulkan"), reason="vulkan support not enabled"), *requires_gpu(), ] return _compose(args, _requires_vulkan) def requires_tensorcore(*args): """Mark a test as requiring a tensorcore to run. Tests with this mark will not be run unless a tensorcore is present. Parameters ---------- f : function Function to mark """ _requires_tensorcore = [ pytest.mark.tensorcore, pytest.mark.skipif( not tvm.cuda().exist or not nvcc.have_tensorcore(tvm.cuda(0).compute_version), reason="No tensorcore present", ), *requires_gpu(), ] return _compose(args, _requires_tensorcore) def requires_llvm(*args): """Mark a test as requiring llvm to run. Parameters ---------- f : function Function to mark """ _requires_llvm = [ pytest.mark.llvm, pytest.mark.skipif(not device_enabled("llvm"), reason="LLVM support not enabled"), ] return _compose(args, _requires_llvm) def requires_micro(*args): """Mark a test as requiring microTVM to run. Parameters ---------- f : function Function to mark """ _requires_micro = [ pytest.mark.skipif( tvm.support.libinfo().get("USE_MICRO", "OFF") != "ON", reason="MicroTVM support not enabled. Set USE_MICRO=ON in config.cmake to enable.", ) ] return _compose(args, _requires_micro) def requires_rpc(*args): """Mark a test as requiring rpc to run. Parameters ---------- f : function Function to mark """ _requires_rpc = [ pytest.mark.skipif( tvm.support.libinfo().get("USE_RPC", "OFF") != "ON", reason="RPC support not enabled. Set USE_RPC=ON in config.cmake to enable.", ) ] return _compose(args, _requires_rpc) def requires_ethosn(*args): """Mark a test as requiring Arm(R) Ethos(TM)-N to run. Parameters ---------- f : function Function to mark """ marks = [ pytest.mark.ethosn, pytest.mark.skipif( not ethosn_available(), reason=( "Arm(R) Ethos(TM)-N support not enabled. " "Set USE_ETHOSN=ON in config.cmake to enable, " "and ensure that hardware support is present." ), ), ] return _compose(args, marks) def requires_hexagon(*args): """Mark a test as requiring Hexagon to run. Parameters ---------- f : function Function to mark """ _requires_hexagon = [ pytest.mark.hexagon, pytest.mark.skipif(not device_enabled("hexagon"), reason="Hexagon support not enabled"), *requires_llvm(), pytest.mark.skipif( tvm.target.codegen.llvm_version_major() < 7, reason="Hexagon requires LLVM 7 or later" ), ] return _compose(args, _requires_hexagon) def requires_cmsisnn(*args): """Mark a test as requiring the CMSIS NN library. Parameters ---------- f : function Function to mark """ requirements = [pytest.mark.skipif(not cmsisnn.enabled(), reason="CMSIS NN not enabled")] return _compose(args, requirements) def requires_vitis_ai(*args): """Mark a test as requiring Vitis AI to run. Parameters ---------- f : function Function to mark """ requirements = [pytest.mark.skipif(not vitis_ai.enabled(), reason="Vitis AI not enabled")] return _compose(args, requirements) def requires_package(*packages): """Mark a test as requiring python packages to run. If the packages listed are not available, tests marked with `requires_package` will appear in the pytest results as being skipped. This is equivalent to using ``foo = pytest.importorskip('foo')`` inside the test body. Parameters ---------- packages : List[str] The python packages that should be available for the test to run. Returns ------- mark: pytest mark The pytest mark to be applied to unit tests that require this """ def has_package(package): try: __import__(package) return True except ImportError: return False marks = [ pytest.mark.skipif(not has_package(package), reason=f"Cannot import '{package}'") for package in packages ] def wrapper(func): for mark in marks: func = mark(func) return func return wrapper def parametrize_targets(*args): """Parametrize a test over a specific set of targets. Use this decorator when you want your test to be run over a specific set of targets and devices. It is intended for use where a test is applicable only to a specific target, and is inapplicable to any others (e.g. verifying target-specific assembly code matches known assembly code). In most circumstances, :py:func:`tvm.testing.exclude_targets` or :py:func:`tvm.testing.known_failing_targets` should be used instead. If used as a decorator without arguments, the test will be parametrized over all targets in :py:func:`tvm.testing.enabled_targets`. This behavior is automatically enabled for any target that accepts arguments of ``target`` or ``dev``, so the explicit use of the bare decorator is no longer needed, and is maintained for backwards compatibility. Parameters ---------- f : function Function to parametrize. Must be of the form `def test_xxxxxxxxx(target, dev)`:, where `xxxxxxxxx` is any name. targets : list[str], optional Set of targets to run against. If not supplied, :py:func:`tvm.testing.enabled_targets` will be used. Example ------- >>> @tvm.testing.parametrize_targets("llvm", "cuda") >>> def test_mytest(target, dev): >>> ... # do something """ # Backwards compatibility, when used as a decorator with no # arguments implicitly parametrizes over "target". The # parametrization is now handled by _auto_parametrize_target, so # this use case can just return the decorated function. if len(args) == 1 and callable(args[0]): return args[0] return pytest.mark.parametrize("target", list(args), scope="session") def exclude_targets(*args): """Exclude a test from running on a particular target. Use this decorator when you want your test to be run over a variety of targets and devices (including cpu and gpu devices), but want to exclude some particular target or targets. For example, a test may wish to be run against all targets in tvm.testing.enabled_targets(), except for a particular target that does not support the capabilities. Applies pytest.mark.skipif to the targets given. Parameters ---------- f : function Function to parametrize. Must be of the form `def test_xxxxxxxxx(target, dev)`:, where `xxxxxxxxx` is any name. targets : list[str] Set of targets to exclude. Example ------- >>> @tvm.testing.exclude_targets("cuda") >>> def test_mytest(target, dev): >>> ... # do something Or >>> @tvm.testing.exclude_targets("llvm", "cuda") >>> def test_mytest(target, dev): >>> ... # do something """ def wraps(func): func.tvm_excluded_targets = args return func return wraps def known_failing_targets(*args): """Skip a test that is known to fail on a particular target. Use this decorator when you want your test to be run over a variety of targets and devices (including cpu and gpu devices), but know that it fails for some targets. For example, a newly implemented runtime may not support all features being tested, and should be excluded. Applies pytest.mark.xfail to the targets given. Parameters ---------- f : function Function to parametrize. Must be of the form `def test_xxxxxxxxx(target, dev)`:, where `xxxxxxxxx` is any name. targets : list[str] Set of targets to skip. Example ------- >>> @tvm.testing.known_failing_targets("cuda") >>> def test_mytest(target, dev): >>> ... # do something Or >>> @tvm.testing.known_failing_targets("llvm", "cuda") >>> def test_mytest(target, dev): >>> ... # do something """ def wraps(func): func.tvm_known_failing_targets = args return func return wraps def parameter(*values, ids=None, by_dict=None): """Convenience function to define pytest parametrized fixtures. Declaring a variable using ``tvm.testing.parameter`` will define a parametrized pytest fixture that can be used by test functions. This is intended for cases that have no setup cost, such as strings, integers, tuples, etc. For cases that have a significant setup cost, please use :py:func:`tvm.testing.fixture` instead. If a test function accepts multiple parameters defined using ``tvm.testing.parameter``, then the test will be run using every combination of those parameters. The parameter definition applies to all tests in a module. If a specific test should have different values for the parameter, that test should be marked with ``@pytest.mark.parametrize``. Parameters ---------- values : Any A list of parameter values. A unit test that accepts this parameter as an argument will be run once for each parameter given. ids : List[str], optional A list of names for the parameters. If None, pytest will generate a name from the value. These generated names may not be readable/useful for composite types such as tuples. by_dict : Dict[str, Any] A mapping from parameter name to parameter value, to set both the values and ids. Returns ------- function A function output from pytest.fixture. Example ------- >>> size = tvm.testing.parameter(1, 10, 100) >>> def test_using_size(size): >>> ... # Test code here Or >>> shape = tvm.testing.parameter((5,10), (512,1024), ids=['small','large']) >>> def test_using_size(shape): >>> ... # Test code here Or >>> shape = tvm.testing.parameter(by_dict={'small': (5,10), 'large': (512,1024)}) >>> def test_using_size(shape): >>> ... # Test code here """ if by_dict is not None: if values or ids: raise RuntimeError( "Use of the by_dict parameter cannot be used alongside positional arguments" ) ids, values = zip(*by_dict.items()) # Optional cls parameter in case a parameter is defined inside a # class scope. @pytest.fixture(params=values, ids=ids) def as_fixture(*_cls, request): return request.param return as_fixture _parametrize_group = 0 def parameters(*value_sets, ids=None): """Convenience function to define pytest parametrized fixtures. Declaring a variable using tvm.testing.parameters will define a parametrized pytest fixture that can be used by test functions. Like :py:func:`tvm.testing.parameter`, this is intended for cases that have no setup cost, such as strings, integers, tuples, etc. For cases that have a significant setup cost, please use :py:func:`tvm.testing.fixture` instead. Unlike :py:func:`tvm.testing.parameter`, if a test function accepts multiple parameters defined using a single call to ``tvm.testing.parameters``, then the test will only be run once for each set of parameters, not for all combinations of parameters. These parameter definitions apply to all tests in a module. If a specific test should have different values for some parameters, that test should be marked with ``@pytest.mark.parametrize``. Parameters ---------- values : List[tuple] A list of parameter value sets. Each set of values represents a single combination of values to be tested. A unit test that accepts parameters defined will be run once for every set of parameters in the list. ids : List[str], optional A list of names for the parameter sets. If None, pytest will generate a name from each parameter set. These generated names may not be readable/useful for composite types such as tuples. Returns ------- List[function] Function outputs from pytest.fixture. These should be unpacked into individual named parameters. Example ------- >>> size, dtype = tvm.testing.parameters( (16,'float32'), (512,'float16') ) >>> def test_feature_x(size, dtype): >>> # Test code here >>> assert( (size,dtype) in [(16,'float32'), (512,'float16')]) """ global _parametrize_group parametrize_group = _parametrize_group _parametrize_group += 1 outputs = [] for param_values in zip(*value_sets): # Optional cls parameter in case a parameter is defined inside a # class scope. def fixture_func(*_cls, request): return request.param fixture_func.parametrize_group = parametrize_group fixture_func.parametrize_values = param_values fixture_func.parametrize_ids = ids outputs.append(pytest.fixture(fixture_func)) return outputs def fixture(func=None, *, cache_return_value=False): """Convenience function to define pytest fixtures. This should be used as a decorator to mark functions that set up state before a function. The return value of that fixture function is then accessible by test functions as that accept it as a parameter. Fixture functions can accept parameters defined with :py:func:`tvm.testing.parameter`. By default, the setup will be performed once for each unit test that uses a fixture, to ensure that unit tests are independent. If the setup is expensive to perform, then the cache_return_value=True argument can be passed to cache the setup. The fixture function will be run only once (or once per parameter, if used with tvm.testing.parameter), and the same return value will be passed to all tests that use it. If the environment variable TVM_TEST_DISABLE_CACHE is set to a non-zero value, it will disable this feature and no caching will be performed. Example ------- >>> @tvm.testing.fixture >>> def cheap_setup(): >>> return 5 # Setup code here. >>> >>> def test_feature_x(target, dev, cheap_setup) >>> assert(cheap_setup == 5) # Run test here Or >>> size = tvm.testing.parameter(1, 10, 100) >>> >>> @tvm.testing.fixture >>> def cheap_setup(size): >>> return 5*size # Setup code here, based on size. >>> >>> def test_feature_x(cheap_setup): >>> assert(cheap_setup in [5, 50, 500]) Or >>> @tvm.testing.fixture(cache_return_value=True) >>> def expensive_setup(): >>> time.sleep(10) # Setup code here >>> return 5 >>> >>> def test_feature_x(target, dev, expensive_setup): >>> assert(expensive_setup == 5) """ force_disable_cache = bool(int(os.environ.get("TVM_TEST_DISABLE_CACHE", "0"))) cache_return_value = cache_return_value and not force_disable_cache # Deliberately at function scope, so that caching can track how # many times the fixture has been used. If used, the cache gets # cleared after the fixture is no longer needed. scope = "function" def wraps(func): if cache_return_value: func = _fixture_cache(func) func = pytest.fixture(func, scope=scope) return func if func is None: return wraps return wraps(func) class _DeepCopyAllowedClasses(dict): def __init__(self, allowed_class_list): self.allowed_class_list = allowed_class_list super().__init__() def get(self, key, *args, **kwargs): """Overrides behavior of copy.deepcopy to avoid implicit copy. By default, copy.deepcopy uses a dict of id->object to track all objects that it has seen, which is passed as the second argument to all recursive calls. This class is intended to be passed in instead, and inspects the type of all objects being copied. Where copy.deepcopy does a best-effort attempt at copying an object, for unit tests we would rather have all objects either be copied correctly, or to throw an error. Classes that define an explicit method to perform a copy are allowed, as are any explicitly listed classes. Classes that would fall back to using object.__reduce__, and are not explicitly listed as safe, will throw an exception. """ obj = ctypes.cast(key, ctypes.py_object).value cls = type(obj) if ( cls in copy._deepcopy_dispatch or issubclass(cls, type) or getattr(obj, "__deepcopy__", None) or copyreg.dispatch_table.get(cls) or cls.__reduce__ is not object.__reduce__ or cls.__reduce_ex__ is not object.__reduce_ex__ or cls in self.allowed_class_list ): return super().get(key, *args, **kwargs) rfc_url = ( "https://github.com/apache/tvm-rfcs/blob/main/rfcs/0007-parametrized-unit-tests.md" ) raise TypeError( ( f"Cannot copy fixture of type {cls.__name__}. TVM fixture caching " "is limited to objects that explicitly provide the ability " "to be copied (e.g. through __deepcopy__, __getstate__, or __setstate__)," "and forbids the use of the default `object.__reduce__` and " "`object.__reduce_ex__`. For third-party classes that are " "safe to use with copy.deepcopy, please add the class to " "the arguments of _DeepCopyAllowedClasses in tvm.testing._fixture_cache.\n" "\n" f"For discussion on this restriction, please see {rfc_url}." ) ) def _fixture_cache(func): cache = {} # Can't use += on a bound method's property. Therefore, this is a # list rather than a variable so that it can be accessed from the # pytest_collection_modifyitems(). num_tests_use_this_fixture = [0] num_times_fixture_used = 0 # Using functools.lru_cache would require the function arguments # to be hashable, which wouldn't allow caching fixtures that # depend on numpy arrays. For example, a fixture that takes a # numpy array as input, then calculates uses a slow method to # compute a known correct output for that input. Therefore, # including a fallback for serializable types. def get_cache_key(*args, **kwargs): try: hash((args, kwargs)) return (args, kwargs) except TypeError as e: pass try: return pickle.dumps((args, kwargs)) except TypeError as e: raise TypeError( "TVM caching of fixtures requires arguments to the fixture " "to be either hashable or serializable" ) from e @functools.wraps(func) def wrapper(*args, **kwargs): if num_tests_use_this_fixture[0] == 0: raise RuntimeError( "Fixture use count is 0. " "This can occur if tvm.testing.plugin isn't registered. " "If using outside of the TVM test directory, " "please add `pytest_plugins = ['tvm.testing.plugin']` to your conftest.py" ) try: cache_key = get_cache_key(*args, **kwargs) try: cached_value = cache[cache_key] except KeyError: cached_value = cache[cache_key] = func(*args, **kwargs) yield copy.deepcopy( cached_value, # allowed_class_list should be a list of classes that # are safe to copy using copy.deepcopy, but do not # implement __deepcopy__, __reduce__, or # __reduce_ex__. _DeepCopyAllowedClasses(allowed_class_list=[]), ) finally: # Clear the cache once all tests that use a particular fixture # have completed. nonlocal num_times_fixture_used num_times_fixture_used += 1 if num_times_fixture_used >= num_tests_use_this_fixture[0]: cache.clear() # Set in the pytest_collection_modifyitems(), by _count_num_fixture_uses wrapper.num_tests_use_this_fixture = num_tests_use_this_fixture return wrapper def identity_after(x, sleep): """Testing function to return identity after sleep Parameters ---------- x : int The input value. sleep : float The amount of time to sleep Returns ------- x : object The original value """ if sleep: time.sleep(sleep) return x def terminate_self(): """Testing function to terminate the process.""" sys.exit(-1) def main(): test_file = inspect.getsourcefile(sys._getframe(1)) sys.exit(pytest.main([test_file] + sys.argv[1:]))