wehub-resource-sync
169 lines
5.2 KiB
Python
169 lines
5.2 KiB
Python
# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# 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, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import inspect
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import paddle
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def is_inplace_api(func):
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inplace_apis = {paddle.static.setitem}
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return func in inplace_apis
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def get_tensor_methods():
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return [
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member_name
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for member_name, member in inspect.getmembers(paddle.pir.Value)
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if inspect.isfunction(member) or inspect.ismethoddescriptor(member)
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]
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def get_paddle_api():
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modules = [
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paddle,
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paddle.nn.functional,
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paddle.nn.quant,
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paddle.incubate.nn.functional,
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paddle.linalg,
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paddle.signal,
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paddle.fft,
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paddle.vision.ops,
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paddle.metric,
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paddle.geometric,
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]
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distributed_apis = [
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paddle.distributed.all_reduce,
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paddle.distributed.shard_tensor,
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paddle.distributed.reshard,
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paddle.distributed.all_gather,
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paddle.distributed.alltoall,
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paddle.distributed.barrier,
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paddle.distributed.recv,
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paddle.distributed.send,
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paddle.distributed.broadcast,
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paddle.distributed.unshard_dtensor,
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paddle.distributed.auto_parallel.api.dtensor_to_local,
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paddle.distributed.auto_parallel.api.dtensor_from_local,
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paddle.distributed.auto_parallel.api.moe_global_mesh_tensor,
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paddle.distributed.auto_parallel.api.moe_sub_mesh_tensors,
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]
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special_paddle_apis = [
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paddle.tensor.fill_constant,
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paddle.tensor.top_p_sampling,
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]
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non_operator_related_apis = [
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paddle.in_dynamic_mode,
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paddle.save,
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paddle.load,
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paddle.get_cuda_rng_state,
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paddle.set_rng_state,
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paddle.set_cuda_rng_state,
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paddle.get_rng_state,
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paddle.set_default_dtype,
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paddle.check_shape,
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paddle.summary,
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paddle.finfo,
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paddle.iinfo,
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paddle.enable_static,
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paddle.disable_static,
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paddle.is_grad_enabled,
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]
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# TODO: users should not call static_apis, but we need to use, so add static_apis here temporary
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static_apis = [paddle.static.setitem, paddle.static.accuracy]
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paddle_api_list = []
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for module in modules:
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for fn_name in getattr(module, "__all__", []):
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fn = getattr(module, fn_name)
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if inspect.isfunction(fn):
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paddle_api_list.append(fn)
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return list(
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set(special_paddle_apis)
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| set(distributed_apis)
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| set(static_apis)
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| set(paddle_api_list) - set(non_operator_related_apis)
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)
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paddle_api_list = get_paddle_api()
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# TODO(Aurelius84): It seems that we use it to judge 'in_paddle_module()'.
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# Bug what does 'is_paddle_module' really means? Is all paddle.xx sub module
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# considered as paddle module？
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paddle_api_module_prefix = {
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"paddle.nn.functional",
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}
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break_graph_functions = set()
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break_graph_layer_classes = set()
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break_graph_tensor_method = {
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'register_hook',
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'numpy',
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'clear_gradient',
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'tolist',
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'item',
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# TODO: Browse all possible functions and make prior judgments.
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}
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not_supported_paddle_layer = {paddle.nn.RNN}
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def is_not_supported_paddle_layer(layer_class):
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return layer_class in not_supported_paddle_layer
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def is_break_graph_tensor_methods(method_name):
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return method_name in break_graph_tensor_method
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def add_break_graph_function(fn):
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break_graph_functions.add(fn)
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def add_break_graph_layer_class(layer_class: type[paddle.nn.Layer]):
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break_graph_layer_classes.add(layer_class)
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def is_directly_run_api(api):
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from .utils import hashable
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if not hashable(api):
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return False
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NATIVE_CODE_PURE_FUNCTIONS = {
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paddle.base.libpaddle.is_compiled_with_avx,
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paddle.base.libpaddle.is_compiled_with_cuda,
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paddle.base.libpaddle.is_compiled_with_cudnn_frontend,
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paddle.base.libpaddle.is_compiled_with_rocm,
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paddle.base.libpaddle.is_compiled_with_custom_device,
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paddle.base.libpaddle.is_compiled_with_ipu,
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paddle.base.libpaddle.is_compiled_with_xpu,
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paddle.base.libpaddle.is_compiled_with_mkldnn,
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paddle.base.libpaddle.is_compiled_with_onednn,
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paddle.base.libpaddle.is_compiled_with_nccl,
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paddle.base.libpaddle.is_compiled_with_mpi,
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paddle.base.libpaddle.is_compiled_with_mpi_aware,
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paddle.base.libpaddle.is_compiled_with_cinn,
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paddle.base.libpaddle.is_compiled_with_distribute,
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paddle.base.libpaddle.is_compiled_with_brpc,
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paddle.base.libpaddle.is_compiled_with_dist,
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paddle.base.libpaddle.is_compiled_with_flagcx,
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}
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if hasattr(paddle.base.libpaddle, "get_device_properties"):
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NATIVE_CODE_PURE_FUNCTIONS.add(
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paddle.base.libpaddle.get_device_properties
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)
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return api in NATIVE_CODE_PURE_FUNCTIONS
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