wehub-resource-sync
134 lines
4.3 KiB
Python
134 lines
4.3 KiB
Python
# Licensed to the Apache Software Foundation (ASF) under one
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# or more contributor license agreements. See the NOTICE file
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# distributed with this work for additional information
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# regarding copyright ownership. The ASF licenses this file
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# to you under the Apache License, Version 2.0 (the
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# "License"); you may not use this file except in compliance
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# with the License. You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing,
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# software distributed under the License is distributed on an
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# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
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# KIND, either express or implied. See the License for the
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# specific language governing permissions and limitations
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# under the License.
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# pylint: disable=unused-argument
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"""Tools to compare libraries."""
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from collections.abc import Iterable
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import tvm
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import tvm.testing
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class LibCompareVMInstrument:
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"""Instrument class to compare libs.
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This class build an instrument function that
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pair tests an existing compiled relax vm implementation
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and an extra module, which can sits in another backend
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but offers a same subset of compiled TIR functions.
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The instrumentation enables us to automatically
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check and compare each ops being called in the pipeline
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by looking up the same name in the provided mod and run testing.
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Parameters
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----------
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mod: runtime.Module
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The module of interest to be validated.
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device: runtime.Device
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The device to run the target module on.
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verbose: bool
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Whether print out messages.
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rtol: float
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rtol used in validation
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atol: float
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atol used in validation
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"""
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def __init__(self, mod, device, verbose=True, rtol=1e-5, atol=1e-5):
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self.mod = mod
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self.device = device
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self.verbose = verbose
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self.counter = 0
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self.rtol = rtol
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self.atol = atol
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def compare(
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self,
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name: str,
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ref_args: list[tvm.runtime.Tensor] | tuple[tvm.runtime.Tensor, ...],
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new_args: list[tvm.runtime.Tensor] | tuple[tvm.runtime.Tensor, ...],
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ret_indices: Iterable[int],
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):
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"""Comparison function, can be overloaded.
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Parameters
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----------
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name: str
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Name of the function.
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ref_args:
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The reference arguments.
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new_args:
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The args to be passed to the comparison function.
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ret_indices:
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List of indices to validate return values.
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"""
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my_func = self.mod.get_function(name, query_imports=True)
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if self.verbose:
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print(f"[{self.counter}] Validating {name} ...")
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my_func(*new_args)
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for rindex in ret_indices:
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tvm.testing.assert_allclose(
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new_args[rindex].numpy(), ref_args[rindex].numpy(), atol=self.atol, rtol=self.rtol
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)
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if self.verbose:
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print(f"[{self.counter}] Validating {name}, passed.")
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self.counter += 1
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def skip_instrument(self, func, name, before_run, ret_val, *args):
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return False
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def __call__(self, func, name, before_run, ret_val, *args):
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if before_run:
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return
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if name.startswith("vm.builtin."):
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return
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if any(not isinstance(x, tvm.runtime.Tensor) for x in args):
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return
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try:
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self.mod.get_function(name, query_imports=True)
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except AttributeError:
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if self.verbose:
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print(f"Cannot find {name}, skip...")
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return
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if self.skip_instrument(func, name, before_run, ret_val, *args):
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return
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new_args = []
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# not always true, true for most ops.
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ret_indices = (len(args) - 1,)
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temp_args = []
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for i, arg in enumerate(args):
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arr = tvm.runtime.empty(arg.shape, arg.dtype, device=self.device)
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# copy from cpu since we look at different device
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if i not in ret_indices:
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temp_cpu = arg.copyto(tvm.cpu())
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temp_args.append(temp_cpu)
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arr.copyfrom(temp_cpu)
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new_args.append(arr)
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# wait until all copy complete before we release temp_cpu
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self.device.sync()
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self.compare(name, args, new_args, ret_indices)
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