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|
import argparse
import datetime
import re
import sys
import warnings
from collections import defaultdict
import torch
from torch._C import parse_schema
# How to run this test locally:
# 1 Have two virtual environments (eg conda env), one without PyTorch installed (venv_nightly)
# one with your local changes (venv_yours).
# In venv_nightly:
# 2. First ensure that Pytorch is uninstalled, but all prereqs are installed
# 3. Install torch nightly build with
# `pip install --pre torch -f https://download.pytorch.org/whl/nightly/cpu/torch_nightly.html`
# 4. Generate original schemas with
# `python test/forward_backward_compatibility/dump_all_function_schemas.py --filename nightly_schemas.txt`
# Now in venv_yours:
# 5. Run this test with
# `python test/forward_backward_compatibility/check_forward_backward_compatibility.py --existing-schemas nightly_schemas.txt`
# The date specifies how long the allowlist exclusion should apply to.
#
# - If we NEVER give BC guarantee for an operator, you can put the
# date arbitrarily far in the future.
# - Otherwise, pick a date that is far enough in the future that you
# believe you can land your diff before then.
#
# Allowlist entries can be removed after the date listed on them passes.
#
# Allowlist item format:
# [
# 0: function name regex
# 1: date until which the allowlist entry is valid
# 2: (optional) function argument regex
# ]
#
# NB: function name DOES NOT include overload name!
ALLOW_LIST = [
("c10_experimental", datetime.date(2222, 1, 1)),
# Internal
("static", datetime.date(9999, 1, 1)),
("prim::ModuleDictIndex", datetime.date(9999, 1, 1)),
("prim::MKLDNNRelu6", datetime.date(9999, 1, 1)),
("prim::MKLDNNRelu6_", datetime.date(9999, 1, 1)),
("prim::Concat", datetime.date(9999, 1, 1)),
("aten::_NestedTensor_GeneralizedBMM", datetime.date(9999, 1, 1)),
# Internal, profiler-specific ops
("profiler::_call_end_callbacks_on_jit_fut*", datetime.date(9999, 1, 1)),
("profiler::_record_function_enter", datetime.date(9999, 1, 1)),
("aten::_sparse_addmm", datetime.date(2022, 6, 30)),
("aten::kl_div_backward", datetime.date(2022, 9, 1)),
("aten::_cholesky_helper", datetime.date(9999, 1, 1)),
("aten::_lstsq_helper", datetime.date(9999, 1, 1)),
("aten::_syevd_helper", datetime.date(9999, 1, 1)),
("aten::_linalg_solve_out_helper_", datetime.date(9999, 1, 1)),
("aten::select_backward", datetime.date(9999, 1, 1)),
("aten::lstsq", datetime.date(9999, 1, 1)),
("aten::lstsq.X", datetime.date(9999, 1, 1)),
("aten::slice_backward", datetime.date(9999, 1, 1)),
("aten::diagonal_backward", datetime.date(9999, 1, 1)),
("aten::rowwise_prune", datetime.date(9999, 1, 1)),
("aten::eig", datetime.date(9999, 1, 1)),
("aten::eig.e", datetime.date(9999, 1, 1)),
("aten::adaptive_avg_pool3d_backward", datetime.date(9999, 1, 1)),
("aten::_embedding_bag_dense_backward", datetime.date(9999, 1, 1)),
("aten::matrix_rank", datetime.date(9999, 1, 1)),
("aten::matrix_rank.tol", datetime.date(9999, 1, 1)),
("aten::randperm", datetime.date(9999, 1, 1)),
("aten::linalg_solve", datetime.date(2022, 8, 31)),
("aten::linalg_solve.out", datetime.date(2022, 8, 31)),
("aten::binary_cross_entropy_with_logits_backward", datetime.date(2022, 9, 21)),
("aten::_linalg_qr_helper", datetime.date(2022, 8, 1)),
("aten::linalg_lu_solve", datetime.date(2022, 8, 1)),
("aten::linalg_lu_solve.out", datetime.date(2022, 8, 1)),
("aten::linalg_det", datetime.date(2022, 8, 1)),
("aten::linalg_det.out", datetime.date(2022, 8, 1)),
("aten::_det_lu_based_helper", datetime.date(2022, 8, 1)),
("aten::slogdet", datetime.date(2022, 8, 1)),
("aten::slogdet.out", datetime.date(2022, 8, 1)),
("aten::linalg_slogdet", datetime.date(2022, 8, 1)),
("aten::linalg_slogdet.out", datetime.date(2022, 8, 1)),
("aten::_linalg_solve", datetime.date(2022, 10, 1)),
("aten::_linalg_solve.solution", datetime.date(2022, 10, 1)),
("aten::linalg_inv_ex", datetime.date(2022, 10, 1)),
("aten::linalg_inv_ex.inverse", datetime.date(2022, 10, 1)),
("aten::linalg_inv", datetime.date(2022, 10, 1)),
("aten::linalg_inv.out", datetime.date(2022, 10, 1)),
("aten::_linalg_inv_out_helper.functional", datetime.date(2022, 10, 1)),
("aten::_linalg_inv_out_helper.out", datetime.date(2022, 10, 1)),
("aten::_linalg_inv_out_helper_", datetime.date(2022, 10, 1)),
("aten::_linalg_inv_out_helper", datetime.date(2022, 10, 1)),
("aten::col2im_backward", datetime.date(2022, 12, 1)),
("aten::im2col_backward", datetime.date(2022, 12, 1)),
("aten::solve", datetime.date(9999, 1, 1)),
("aten::solve.solution", datetime.date(9999, 1, 1)),
("aten::_solve_helper", datetime.date(9999, 1, 1)),
("aten::_convolution_nogroup", datetime.date(9999, 1, 1)),
("aten::miopen_convolution_backward", datetime.date(9999, 1, 1)),
("aten::miopen_convolution_backward_bias", datetime.date(9999, 1, 1)),
("aten::miopen_convolution_backward_input", datetime.date(9999, 1, 1)),
("aten::miopen_convolution_backward_weight", datetime.date(9999, 1, 1)),
("aten::miopen_convolution_transpose_backward", datetime.date(9999, 1, 1)),
("aten::miopen_convolution_transpose_backward_input", datetime.date(9999, 1, 1)),
("aten::miopen_convolution_transpose_backward_weight", datetime.date(9999, 1, 1)),
("aten::miopen_depthwise_convolution_backward", datetime.date(9999, 1, 1)),
("aten::miopen_depthwise_convolution_backward_input", datetime.date(9999, 1, 1)),
("aten::miopen_depthwise_convolution_backward_weight", datetime.date(9999, 1, 1)),
("aten::_nested_tensor", datetime.date(9999, 1, 1)),
("prepacked::unpack_prepacked_sizes_conv2d", datetime.date(9999, 1, 1)),
("prepacked::unpack_prepacked_sizes_linear", datetime.date(9999, 1, 1)),
("aten::linalg_solve", datetime.date(2022, 8, 31)),
("aten::linalg_solve.out", datetime.date(2022, 8, 31)),
("aten::quantile", datetime.date(2022, 9, 30)),
("aten::nanquantile", datetime.date(2022, 9, 30)),
("aten::native_multi_head_self_attention", datetime.date(9999, 1, 1)),
("aten::_native_multi_head_self_attention", datetime.date(9999, 1, 1)),
("aten::grid_sampler_3d_backward", datetime.date(9999, 1, 1)),
("aten::_transform_bias_rescale_qkv", datetime.date(9999, 1, 1)),
("aten::_s_where", datetime.date(2022, 9, 30)),
("prim::infer_squeeze_size.dim", datetime.date(9999, 1, 1)),
("prim::infer_squeeze_size", datetime.date(9999, 1, 1)),
("aten::_weight_norm_cuda_interface", datetime.date(9999, 1, 1)),
("aten::_weight_norm_cuda_interface_backward", datetime.date(9999, 1, 1)),
("aten::segment_reduce", datetime.date(2022, 6, 30)),
("aten::_segment_reduce_backward", datetime.date(2022, 6, 30)),
("aten::empty.SymInt", datetime.date(9999, 1, 1)),
("aten::.*functional", datetime.date(2022, 8, 1)),
("aten::_foreach.*", datetime.date(2022, 8, 1)),
("aten::unflatten", datetime.date(2022, 8, 10)),
("aten::nanmean", datetime.date(2022, 8, 30)),
("aten::nanmean.out", datetime.date(2022, 8, 30)),
("aten::nansum", datetime.date(2022, 8, 30)),
("aten::nansum.out", datetime.date(2022, 8, 30)),
# nested tensor temporary auxiliary ops
("aten::_reshape_nested", datetime.date(9999, 1, 1)),
("aten::_reshape_nested_backward", datetime.date(9999, 1, 1)),
("aten::sum.SymInt", datetime.date(2022, 11, 30)),
("aten::mps_linear", datetime.date(9999, 1, 1)),
("aten::_mps_linear", datetime.date(9999, 1, 1)),
("aten::view_copy.SymInt", datetime.date(2022, 11, 30)),
("aten::view_copy.SymInt_out", datetime.date(2022, 11, 30)),
("aten::expand_copy.SymInt", datetime.date(2022, 11, 30)),
("aten::expand_copy.SymInt_out", datetime.date(2022, 11, 30)),
("aten::expand.SymInt", datetime.date(2022, 11, 30)),
("aten::narrow_copy.SymInt", datetime.date(2022, 11, 30)),
("aten::narrow_copy.SymInt_out", datetime.date(2022, 11, 30)),
("aten::view.SymInt", datetime.date(2022, 11, 30)),
("aten::new_empty.SymInt", datetime.date(2022, 11, 30)),
("aten::new_empty.SymInt_out", datetime.date(2022, 11, 30)),
("aten::zeros.SymInt", datetime.date(2022, 11, 30)),
("aten::zeros.SymInt_out", datetime.date(2022, 11, 30)),
# TODO: FIXME: prims shouldn't be checked
("prims::.*", datetime.date(9999, 1, 1)),
("aten::_amp_foreach_non_finite_check_and_unscale.out", datetime.date(2022, 9, 1)),
("aten::_amp_foreach_non_finite_check_and_unscale_", datetime.date(2022, 9, 1)),
("aten::_cudnn_rnn_backward.out", datetime.date(2022, 9, 1)),
("aten::_foreach_abs.out", datetime.date(2022, 9, 1)),
("aten::_foreach_abs_", datetime.date(2022, 9, 1)),
("aten::_foreach_acos.out", datetime.date(2022, 9, 1)),
("aten::_foreach_acos_", datetime.date(2022, 9, 1)),
("aten::_foreach_add.List_out", datetime.date(2022, 9, 1)),
("aten::_foreach_add.ScalarList_out", datetime.date(2022, 9, 1)),
("aten::_foreach_add.Scalar_out", datetime.date(2022, 9, 1)),
("aten::_foreach_add_.List", datetime.date(2022, 9, 1)),
("aten::_foreach_add_.Scalar", datetime.date(2022, 9, 1)),
("aten::_foreach_add_.ScalarList", datetime.date(2022, 9, 1)),
("aten::_foreach_addcdiv.ScalarList_out", datetime.date(2022, 9, 1)),
("aten::_foreach_addcdiv.Scalar_out", datetime.date(2022, 9, 1)),
("aten::_foreach_addcdiv_.Scalar", datetime.date(2022, 9, 1)),
("aten::_foreach_addcdiv_.ScalarList", datetime.date(2022, 9, 1)),
("aten::_foreach_addcmul.ScalarList_out", datetime.date(2022, 9, 1)),
("aten::_foreach_addcmul.Scalar_out", datetime.date(2022, 9, 1)),
("aten::_foreach_addcmul_.Scalar", datetime.date(2022, 9, 1)),
("aten::_foreach_addcmul_.ScalarList", datetime.date(2022, 9, 1)),
("aten::_foreach_asin.out", datetime.date(2022, 9, 1)),
("aten::_foreach_asin_", datetime.date(2022, 9, 1)),
("aten::_foreach_atan.out", datetime.date(2022, 9, 1)),
("aten::_foreach_atan_", datetime.date(2022, 9, 1)),
("aten::_foreach_ceil.out", datetime.date(2022, 9, 1)),
("aten::_foreach_ceil_", datetime.date(2022, 9, 1)),
("aten::_foreach_cos.out", datetime.date(2022, 9, 1)),
("aten::_foreach_cos_", datetime.date(2022, 9, 1)),
("aten::_foreach_cosh.out", datetime.date(2022, 9, 1)),
("aten::_foreach_cosh_", datetime.date(2022, 9, 1)),
("aten::_foreach_div.List_out", datetime.date(2022, 9, 1)),
("aten::_foreach_div.ScalarList_out", datetime.date(2022, 9, 1)),
("aten::_foreach_div.Scalar_out", datetime.date(2022, 9, 1)),
("aten::_foreach_div_.List", datetime.date(2022, 9, 1)),
("aten::_foreach_div_.Scalar", datetime.date(2022, 9, 1)),
("aten::_foreach_div_.ScalarList", datetime.date(2022, 9, 1)),
("aten::_foreach_erf.out", datetime.date(2022, 9, 1)),
("aten::_foreach_erf_", datetime.date(2022, 9, 1)),
("aten::_foreach_erfc.out", datetime.date(2022, 9, 1)),
("aten::_foreach_erfc_", datetime.date(2022, 9, 1)),
("aten::_foreach_exp.out", datetime.date(2022, 9, 1)),
("aten::_foreach_exp_", datetime.date(2022, 9, 1)),
("aten::_foreach_expm1.out", datetime.date(2022, 9, 1)),
("aten::_foreach_expm1_", datetime.date(2022, 9, 1)),
("aten::_foreach_floor.out", datetime.date(2022, 9, 1)),
("aten::_foreach_floor_", datetime.date(2022, 9, 1)),
("aten::_foreach_frac.out", datetime.date(2022, 9, 1)),
("aten::_foreach_frac_", datetime.date(2022, 9, 1)),
("aten::_foreach_lgamma.out", datetime.date(2022, 9, 1)),
("aten::_foreach_lgamma_", datetime.date(2022, 9, 1)),
("aten::_foreach_log.out", datetime.date(2022, 9, 1)),
("aten::_foreach_log10.out", datetime.date(2022, 9, 1)),
("aten::_foreach_log10_", datetime.date(2022, 9, 1)),
("aten::_foreach_log1p.out", datetime.date(2022, 9, 1)),
("aten::_foreach_log1p_", datetime.date(2022, 9, 1)),
("aten::_foreach_log2.out", datetime.date(2022, 9, 1)),
("aten::_foreach_log2_", datetime.date(2022, 9, 1)),
("aten::_foreach_log_", datetime.date(2022, 9, 1)),
("aten::_foreach_maximum.List_out", datetime.date(2022, 9, 1)),
("aten::_foreach_maximum_.List", datetime.date(2022, 9, 1)),
("aten::_foreach_minimum.List_out", datetime.date(2022, 9, 1)),
("aten::_foreach_minimum_.List", datetime.date(2022, 9, 1)),
("aten::_foreach_mul.List_out", datetime.date(2022, 9, 1)),
("aten::_foreach_mul.ScalarList_out", datetime.date(2022, 9, 1)),
("aten::_foreach_mul.Scalar_out", datetime.date(2022, 9, 1)),
("aten::_foreach_mul_.List", datetime.date(2022, 9, 1)),
("aten::_foreach_mul_.Scalar", datetime.date(2022, 9, 1)),
("aten::_foreach_mul_.ScalarList", datetime.date(2022, 9, 1)),
("aten::_foreach_neg.out", datetime.date(2022, 9, 1)),
("aten::_foreach_neg_", datetime.date(2022, 9, 1)),
("aten::_foreach_norm.Scalar_out", datetime.date(2022, 9, 1)),
("aten::_foreach_reciprocal.out", datetime.date(2022, 9, 1)),
("aten::_foreach_reciprocal_", datetime.date(2022, 9, 1)),
("aten::_foreach_round.out", datetime.date(2022, 9, 1)),
("aten::_foreach_round_", datetime.date(2022, 9, 1)),
("aten::_foreach_sigmoid.out", datetime.date(2022, 9, 1)),
("aten::_foreach_sigmoid_", datetime.date(2022, 9, 1)),
("aten::_foreach_sin.out", datetime.date(2022, 9, 1)),
("aten::_foreach_sin_", datetime.date(2022, 9, 1)),
("aten::_foreach_sinh.out", datetime.date(2022, 9, 1)),
("aten::_foreach_sinh_", datetime.date(2022, 9, 1)),
("aten::_foreach_sqrt.out", datetime.date(2022, 9, 1)),
("aten::_foreach_sqrt_", datetime.date(2022, 9, 1)),
("aten::_foreach_sub.List_out", datetime.date(2022, 9, 1)),
("aten::_foreach_sub.ScalarList_out", datetime.date(2022, 9, 1)),
("aten::_foreach_sub.Scalar_out", datetime.date(2022, 9, 1)),
("aten::_foreach_sub_.List", datetime.date(2022, 9, 1)),
("aten::_foreach_sub_.Scalar", datetime.date(2022, 9, 1)),
("aten::_foreach_sub_.ScalarList", datetime.date(2022, 9, 1)),
("aten::_foreach_tan.out", datetime.date(2022, 9, 1)),
("aten::_foreach_tan_", datetime.date(2022, 9, 1)),
("aten::_foreach_tanh.out", datetime.date(2022, 9, 1)),
("aten::_foreach_tanh_", datetime.date(2022, 9, 1)),
("aten::_foreach_trunc.out", datetime.date(2022, 9, 1)),
("aten::_foreach_trunc_", datetime.date(2022, 9, 1)),
("aten::_foreach_zero.out", datetime.date(2022, 9, 1)),
("aten::_foreach_zero_", datetime.date(2022, 9, 1)),
("aten::_histogramdd_bin_edges.out", datetime.date(2022, 9, 1)),
("aten::chunk", datetime.date(2022, 9, 1)),
("aten::dequantize.tensors_out", datetime.date(2022, 9, 1)),
("aten::dsplit.array", datetime.date(2022, 9, 1)),
("aten::dsplit.int", datetime.date(2022, 9, 1)),
("aten::hsplit.array", datetime.date(2022, 9, 1)),
("aten::hsplit.int", datetime.date(2022, 9, 1)),
("aten::lstm_mps_backward.out", datetime.date(2022, 9, 1)),
("aten::miopen_rnn_backward.out", datetime.date(2022, 9, 1)),
("aten::quantize_per_tensor.tensors_out", datetime.date(2022, 9, 1)),
("aten::split", datetime.date(2022, 9, 1)),
("aten::split.Tensor", datetime.date(2022, 9, 1)),
("aten::split.sizes", datetime.date(2022, 9, 1)),
("aten::split_copy.Tensor_out", datetime.date(2022, 9, 1)),
("aten::split_with_sizes", datetime.date(2022, 9, 1)),
("aten::split_with_sizes_copy.out", datetime.date(2022, 9, 1)),
("aten::tensor_split.indices", datetime.date(2022, 9, 1)),
("aten::tensor_split.sections", datetime.date(2022, 9, 1)),
("aten::tensor_split.tensor_indices_or_sections", datetime.date(2022, 9, 1)),
("aten::unbind.Dimname", datetime.date(2022, 9, 1)),
("aten::unbind.int", datetime.date(2022, 9, 1)),
("aten::unbind_copy.int_out", datetime.date(2022, 9, 1)),
("aten::unsafe_split.Tensor_out", datetime.date(2022, 9, 1)),
("aten::unsafe_split_with_sizes.out", datetime.date(2022, 9, 1)),
("aten::vsplit.array", datetime.date(2022, 9, 1)),
("aten::vsplit.int", datetime.date(2022, 9, 1)),
("c10d::allreduce_", datetime.date(2022, 10, 1)),
("aten::sym_numel", datetime.date(2022, 10, 1)),
("aten::_flash_scaled_dot_product_attention", datetime.date(2022, 11, 1)),
("aten::_scaled_dot_product_attention", datetime.date(2022, 11, 1)),
# Distributed c10d ops are all going to be updated
("c10d::.*", datetime.date(2022, 10, 31)),
("c10d::allgather_", datetime.date(2022, 10, 1)),
("aten::to_padded_tensor", datetime.date(2022, 10, 1)),
("aten::nested_to_padded_tensor", datetime.date(2022, 10, 1)),
("aten::nested_tensor", datetime.date(2022, 10, 15)),
]
ALLOW_LIST_COMPILED = [
(
re.compile(item[0]),
item[1],
re.compile(item[2]) if len(item) > 2 else None,
) for item in ALLOW_LIST if item[1] >= datetime.date.today()
]
def allow_listed(schema):
for item in ALLOW_LIST_COMPILED:
if item[0].search(str(schema)):
if len(item) > 2 and item[2] is not None:
# if arguments regex is present, use it
return bool(item[2].search(str(schema)))
return True
return False
# The nightly will fail to parse newly added syntax to schema declarations
# Add new schemas that will fail the nightly here
dont_parse_list = [
("_TorchScriptTesting.*", datetime.date(2099, 9, 17)),
("test_backend", datetime.date(2099, 9, 17)),
("dist_c10d", datetime.date(2099, 9, 17)),
("__backends__.nnc", datetime.date(2099, 9, 17)),
]
def has_valid_upgraders(schema, version_map):
# we want to parse through the map to find if
# the schema has valid upgraders. Since the
# version map has entry for each overload
# we need to do some ugly parsing.
# the name of the operator
schema_name = schema.name
if schema_name not in version_map:
return False
entries = version_map[schema_name]
possible_overloads = []
possible_schemas = []
for key, upgrader_schema_entries in entries.items():
possible_overloads.append(key)
possible_schemas.extend(upgrader_schema_entries)
# let's make sure this existing schema is part of possible
# schemas
for old_schema in possible_schemas:
if old_schema == schema:
return True
return False
def dont_parse(schema_line):
for item in dont_parse_list:
if item[1] < datetime.date.today():
continue
regexp = re.compile(item[0])
if regexp.search(schema_line):
return True
return False
def load_schemas_to_dict():
new_schemas = torch._C._jit_get_all_schemas()
new_schemas += torch._C._jit_get_custom_class_schemas()
new_schema_dict = defaultdict(list)
for s in new_schemas:
new_schema_dict[s.name].append(s)
return new_schema_dict
def process_version_map(version_map):
# version map maps full schema name to
# list of upgraders. Since we only have
# the name of the schema (aka no overload)
# we want to first process the map to make
# the key lookup easier. After this it will be:
# Dict[schema_name, Dict[overload, List[schema]]]
output = defaultdict(dict)
for (key, entries) in version_map.items():
operator_name = key.split(".")[0]
schema_entries = [parse_schema(entry.old_schema) for entry in entries]
output[operator_name][key] = schema_entries
return output
def check_bc(existing_schemas):
new_schema_dict = load_schemas_to_dict()
version_map = process_version_map(torch._C._get_operator_version_map())
is_bc = True
broken_ops = []
for existing_schema in existing_schemas:
if allow_listed(existing_schema):
print("schema: ", str(existing_schema), " found on allowlist, skipping")
continue
if has_valid_upgraders(existing_schema, version_map):
print("schema: ", str(existing_schema), " has valid upgrader, skipping")
continue
print("processing existing schema: ", str(existing_schema))
matching_new_schemas = new_schema_dict.get(existing_schema.name, [])
found = False
for matching_new_schema in matching_new_schemas:
if matching_new_schema.is_backward_compatible_with(existing_schema):
found = True
break
if not found:
print(
"Can NOT find backward compatible schemas after changes "
"for schema {} from the following candidates:\n[\n{}\n]".format(
str(existing_schema),
"\n\t".join(str(s) for s in matching_new_schemas),
)
)
# TODO Print out more details about why candidates don't match.
broken_ops.append(str(existing_schema))
is_bc = False
if is_bc:
print("Found backward compatible schemas for all existing schemas")
else:
print(
"The PR is introducing backward incompatible changes to the "
"operator library. Please contact PyTorch team to confirm "
"whether this change is wanted or not. \n\nBroken ops: "
"[\n\t{}\n]".format("\n\t".join(broken_ops))
)
return is_bc
def check_fc(existing_schemas):
new_schema_dict = load_schemas_to_dict()
is_fc = True
broken_ops = []
for existing_schema in existing_schemas:
if allow_listed(existing_schema):
print("schema: ", str(existing_schema), " found on allowlist, skipping")
continue
print("processing existing schema: ", str(existing_schema))
matching_new_schemas = new_schema_dict.get(existing_schema.name, [])
found = False
possible_failure_reasons = []
for matching_new_schema in matching_new_schemas:
is_compatible, reason = matching_new_schema.check_forward_compatible_with(existing_schema)
if is_compatible:
found = True
break
if reason != "":
possible_failure_reasons.append(reason)
if not found:
print(
"Can NOT find forward compatible schemas after changes "
"for schema {} from the following candidates:\n[\n{}\n]".format(
str(existing_schema),
"\n\t".join(str(s) for s in matching_new_schemas),
)
)
print(
"Refer to following reasons for failure "
"to find FC schema:\n[\n{}\n]".format(
"\n\t".join(str(r) for r in possible_failure_reasons)
)
)
broken_ops.append(str(existing_schema))
is_fc = False
if is_fc:
print("Found forward compatible schemas for all existing schemas")
else:
warnings.warn(
"The PR is introducing a potentially forward incompatible changes to the "
"operator library. Please contact PyTorch team to confirm "
"whether this change is wanted or not. \n\nBroken ops: "
"[\n\t{}\n]".format("\n\t".join(broken_ops))
)
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Process some integers.")
parser.add_argument(
"--existing-schemas",
help="filename to load existing schemas",
type=str,
default="schemas.txt",
)
args = parser.parse_args()
existing_schema_dict = {}
slist = []
with open(args.existing_schemas, "r") as f:
while True:
line = f.readline()
if not line:
break
if dont_parse(line.strip()):
print("Not parsing schema line: ", line.strip())
continue
s = parse_schema(line.strip())
slist.append(s)
# TODO in case there is FC breaking changes,
# we just warn for now until there is a policy.
check_fc(slist)
if not check_bc(slist):
sys.exit(1)
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