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pytorch/tools/code_analyzer/gen_operators_yaml.py
Huy Do 347b036350 Apply ufmt linter to all py files under tools (#81285) 
With ufmt in place https://github.com/pytorch/pytorch/pull/81157, we can now use it to gradually format all files. I'm breaking this down into multiple smaller batches to avoid too many merge conflicts later on.

This batch (as copied from the current BLACK linter config):
* `tools/**/*.py`

Upcoming batchs:
* `torchgen/**/*.py`
* `torch/package/**/*.py`
* `torch/onnx/**/*.py`
* `torch/_refs/**/*.py`
* `torch/_prims/**/*.py`
* `torch/_meta_registrations.py`
* `torch/_decomp/**/*.py`
* `test/onnx/**/*.py`

Once they are all formatted, BLACK linter will be removed.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/81285
Approved by: https://github.com/suo
2022-07-13 07:59:22 +00:00

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#!/usr/bin/env python3
import argparse
import json
import sys
from typing import Any, Dict, List, Optional
import yaml
from gen_op_registration_allowlist import (
canonical_name,
gen_transitive_closure,
load_op_dep_graph,
)
from torchgen.selective_build.operator import (
merge_operator_dicts,
SelectiveBuildOperator,
)
from torchgen.selective_build.selector import merge_kernel_metadata
# Generate YAML file containing the operators used for a specific PyTorch model.
# ------------------------------------------------------------------------------
#
# This binary is responsible for generating the model_operators.yaml file for
# each model from a pt_operator_library() BUCK macro invocation.
#
# Output YAML file format:
# ------------------------
#
# <BEGIN FILE CONTENTS>
# include_all_non_op_selectives: False
# include_all_operators: False
# debug_info:
# - model1@v100
# - model2@v50
# operators:
# aten::add:
# is_root_operator: Yes
# is_used_for_training: Yes
# include_all_overloads: No
# debug_info:
# - model1@v100
# - model2@v50
# aten::add.int:
# is_root_operator: No
# is_used_for_training: No
# include_all_overloads: Yes
# kernel_metadata:
# add_kernel:
# - Int8
# - UInt32
# sub_kernel:
# - Int16
# - Float
# <END FILE CONTENTS>
#
# There are a few main inputs to this application
# -----------------------------------------------
#
# 1. Inference Root Operators (--root_ops): Root operators (called directly
# from TorchScript) used by inference use-cases.
#
# 2. Training Root Operators (--training_root_ops): Root operators used
# by training use-cases. Currently, this list is the list of all operators
# used by training, and not just the root operators. All Training ops are
# also considered for inference, so these are merged into inference ops.
#
# 3. Operator Depencency Graph (--dep_graph_yaml_path): A path to the
# operator dependency graph used to determine which operators depend on
# which other operators for correct functioning. This is used for
# generating the transitive closure of all the operators used by the
# model based on the root operators when static selective build is used.
# For tracing based selective build, we don't need to perform this
# transitive cloure.
#
# 4. Model Metadata (--model_name, --model_versions, --model_assets,
# --model_backends): Self-descriptive. These are used to tell this
# script which model operator lists to fetch from the Unified Model
# Build Metadata YAML file.
#
# 5. Unified Model YAML file (--models_yaml_path): A path to the Unified
# model YAML operator list file. This yaml file contains (for each
# model/version/asset/backend) the set of used root and traced
# operators. This is used to extract the actual set of operators
# needed to be included in the build.
#
def canonical_opnames(opnames: List[str]) -> List[str]:
return [canonical_name(opname) for opname in opnames]
def make_filter_from_options(
model_name: str,
model_versions: List[str],
model_assets: Optional[List[str]],
model_backends: Optional[List[str]],
):
def is_model_included(model_info):
model = model_info["model"]
if model["name"] != model_name:
return False
if str(model["version"]) not in model_versions:
return False
if model_assets is not None and model["asset"] not in model_assets:
return False
# TODO: Handle backend later
return True
return is_model_included
# Returns if a the specified rule is a new or old style pt_operator_library
def is_new_style_rule(model_name: str, model_versions: Optional[List[str]]):
return model_name is not None and model_versions is not None
# Verifies that specified model_name, and all specified versions and assets
# appear in at least one model yaml. Throws if verification is failed,
# returns None on success
def verify_all_specified_present(
model_assets: Optional[List[str]],
model_versions: List[str],
selected_models_yaml: List[Dict[str, Any]],
rule_name: str,
model_name: str,
new_style_rule: bool,
):
def find_missing_items(model_items, key, selected_models_yaml):
missing_items = []
if not new_style_rule or not model_items:
return missing_items
for item in model_items:
found = False
for model in selected_models_yaml:
if str(model["model"][key]) == item:
found = True
if not found:
missing_items.append(item)
return missing_items
missing_assets = find_missing_items(model_assets, "asset", selected_models_yaml)
missing_versions = find_missing_items(
model_versions, "version", selected_models_yaml
)
if len(missing_versions) > 0 or len(missing_assets) > 0: # at least one is missing
name_warning = ""
if len(selected_models_yaml) == 0:
name_warning = (
"WARNING: 0 yaml's were found for target rule. This could be because the "
+ "provided model name: {name} is incorrect. Please check that field as well as "
+ "the assets and versions."
).format(name=model_name)
raise RuntimeError(
(
"Error: From the pt_operator_library rule for Rule: {name}, at least one entry for the "
+ "following fields was expected -- Model: {model_name} Expected Assets: {expected_assets}, Expected Versions: "
+ "{expected_versions}. {name_warning} In all_mobile_models.yaml either no assets were on one of the "
+ "specified versions, one of the specified assets was not present on any of the specified "
+ "versions, or both. Assets not found: {missing_assets}, Versions not found: {missing_versions} "
+ "For questions please ask in https://fb.workplace.com/groups/2148543255442743/"
).format(
name=rule_name,
model_name=model_name,
expected_versions=model_versions,
expected_assets=model_assets
if model_assets
else "<All model assets present on specified versions>",
name_warning=name_warning,
missing_versions=missing_versions
if len(missing_versions) > 0
else "<All specified versions had at least one asset>",
missing_assets=missing_assets
if len(missing_assets) > 0
else "<All specified assets are present on at least 1 version>",
)
)
# Uses the selected models configs and then combines them into one dictionary,
# formats them as a string, and places the string into output as a top level debug_info
def create_debug_info_from_selected_models(
output: Dict[str, object],
selected_models: List[dict],
new_style_rule: bool,
):
model_dict = {
"asset_info": {}, # maps asset name -> dict of asset metadata like hashes
"is_new_style_rule": new_style_rule,
}
for model in selected_models:
model_info = model["model"]
asset = model_info["asset"]
hash = model_info["md5_hash"]
asset_info = model_dict["asset_info"].setdefault(asset, {})
asset_info.setdefault("md5_hash", []).append(hash)
# Will later be used in gen_oplist to generate the model/version/asset checking
output["debug_info"] = [json.dumps(model_dict)]
def fill_output(output: Dict[str, object], options: object):
"""Populate the output dict with the information required to serialize
the YAML file used for selective build.
"""
dept_graph = load_op_dep_graph(options.dep_graph_yaml_path)
model_versions = (
options.model_versions.split(",") if options.model_versions is not None else []
)
model_assets = (
options.model_assets.split(",") if options.model_assets is not None else None
)
with open(options.models_yaml_path, "rb") as models_yaml_file:
all_models_yaml = yaml.safe_load(models_yaml_file) or []
model_filter_func = make_filter_from_options(
options.model_name, model_versions, model_assets, options.model_backends
)
selected_models_yaml = list(filter(model_filter_func, all_models_yaml))
verify_all_specified_present(
model_assets=model_assets,
model_versions=model_versions,
selected_models_yaml=selected_models_yaml,
rule_name=options.rule_name,
model_name=options.model_name,
new_style_rule=is_new_style_rule(options.model_name, options.model_versions),
)
create_debug_info_from_selected_models(
output,
selected_models_yaml,
is_new_style_rule(options.model_name, options.model_versions),
)
# initialize variables for static build from the pt_operator_library rule
if options.root_ops is not None:
static_root_ops = set(filter(lambda x: len(x) > 0, options.root_ops.split(",")))
else:
static_root_ops = set()
static_training_root_ops = set(
filter(
lambda x: len(x) > 0,
(options.training_root_ops or "").split(","),
)
)
if len(static_training_root_ops) > 0:
static_root_ops = static_root_ops | static_training_root_ops
# end if
root_ops_unexpand = set()
traced_ops = set()
training_root_ops_unexpand = set()
traced_training_ops = set()
all_kernel_metadata = []
all_custom_classes = set()
all_build_features = set()
# Go through each yaml file and retrieve operator information.
for model_info in selected_models_yaml:
if "traced_operators" not in model_info:
# If this YAML file doesn't specify any traced operators, then it is using
# the static analysis selective build approach of finding transitively
# used operators, and we should update root_ops with the set of root
# operators, all of whose overloads must be included. In addition, these
# root_ops will be further expanded using the transitive closure of
# operator dependencies.
static_root_ops = static_root_ops | set(model_info["root_operators"])
else:
# If this YAML file specifies traced operators, then it is using
# the tracing based selective build approach of finding used
# operators, and we should update root_ops_unexpand with the set of root
# operators whose overloads don't need to be included. In addition, these
# root_ops_unexpand will NOT be further expanded. If the train flag is
# set then the ops will be used for training, so we put them in a separate
# set
if model_info["train"]:
training_root_ops_unexpand = training_root_ops_unexpand | set(
model_info["root_operators"]
)
traced_training_ops = traced_training_ops | set(
model_info["traced_operators"]
)
else:
root_ops_unexpand = root_ops_unexpand | set(
model_info["root_operators"]
)
traced_ops = traced_ops | set(model_info["traced_operators"])
if "kernel_metadata" in model_info:
all_kernel_metadata.append(model_info["kernel_metadata"])
if "custom_classes" in model_info:
all_custom_classes = all_custom_classes | set(model_info["custom_classes"])
if "build_features" in model_info:
all_build_features = all_build_features | set(model_info["build_features"])
# This following section on transitive closure is relevant to static build only
canonical_root_ops = canonical_opnames(static_root_ops)
# If no canonical_root_ops exist, don't compute the transitive closure
# otherwise, we will include __BASE__ and __ROOT__ ops and mark them as required
# for inference.
if len(canonical_root_ops) > 0:
closure_op_list = gen_transitive_closure(dept_graph, canonical_root_ops)
else:
closure_op_list = set()
canonical_training_root_ops = canonical_opnames(static_training_root_ops)
# If no canonical_training_root_ops exist, don't compute the transitive closure
# otherwise, we will include __BASE__ and __ROOT__ ops and mark them as required
# for training.
if len(canonical_training_root_ops) > 0:
closure_training_op_list = gen_transitive_closure(
dept_graph, canonical_training_root_ops, train=True
)
else:
closure_training_op_list = set()
# bucketed_ops holds sets of operators that correspond to specific semantic buckets. For
# example:
#
# 1. Root Operators not used for training w/o full overload inclusion
# 2. Root Operators not used for training w/ full overload inclusion
# 3. Root Operators used for training w/o full overload inclusion
# 4. Root Operators used for training w/ full overload inclusion
# 5. Non-root Operators not used for training w/o full overload inclusion
# etc...
#
# Basically for each of the 3 boolean conditional, there are 2
# options (True/False).
#
bucketed_ops = []
# START STATIC BUILD OPS
static_root_ops_bucket = {}
for op_name in static_root_ops:
op = SelectiveBuildOperator.from_yaml_dict(
op_name,
{
"is_root_operator": True,
"is_used_for_training": False,
"include_all_overloads": True,
"debug_info": [options.model_name],
},
)
static_root_ops_bucket[op_name] = op
bucketed_ops.append(static_root_ops_bucket)
closure_ops_bucket = {}
for op_name in closure_op_list:
op = SelectiveBuildOperator.from_yaml_dict(
op_name,
{
"is_root_operator": False,
"is_used_for_training": False,
"include_all_overloads": True,
"debug_info": [options.model_name],
},
)
closure_ops_bucket[op_name] = op
bucketed_ops.append(closure_ops_bucket)
static_training_root_ops_bucket = {}
for op_name in static_training_root_ops:
op = SelectiveBuildOperator.from_yaml_dict(
op_name,
{
"is_root_operator": True,
"is_used_for_training": True,
"include_all_overloads": True,
"debug_info": [options.model_name],
},
)
static_training_root_ops_bucket[op_name] = op
bucketed_ops.append(static_training_root_ops_bucket)
closure_training_ops_bucket = {}
for op_name in closure_training_op_list:
op = SelectiveBuildOperator.from_yaml_dict(
op_name,
{
"is_root_operator": False,
"is_used_for_training": True,
"include_all_overloads": True,
"debug_info": [options.model_name],
},
)
closure_training_ops_bucket[op_name] = op
bucketed_ops.append(closure_training_ops_bucket)
# END STATIC BUILD OPS
# START TRACING BASED BUILD OPS
root_ops_unexpand_bucket = {}
for op_name in root_ops_unexpand:
op = SelectiveBuildOperator.from_yaml_dict(
op_name,
{
"is_root_operator": True,
"is_used_for_training": False,
"include_all_overloads": False,
"debug_info": [options.model_name],
},
)
root_ops_unexpand_bucket[op_name] = op
bucketed_ops.append(root_ops_unexpand_bucket)
traced_ops_bucket = {}
for op_name in traced_ops:
op = SelectiveBuildOperator.from_yaml_dict(
op_name,
{
"is_root_operator": False,
"is_used_for_training": False,
"include_all_overloads": False,
"debug_info": [options.model_name],
},
)
traced_ops_bucket[op_name] = op
bucketed_ops.append(traced_ops_bucket)
training_root_ops_unexpand_bucket = {}
for op_name in training_root_ops_unexpand:
op = SelectiveBuildOperator.from_yaml_dict(
op_name,
{
"is_root_operator": True,
"is_used_for_training": True,
"include_all_overloads": False,
"debug_info": [options.model_name],
},
)
training_root_ops_unexpand_bucket[op_name] = op
bucketed_ops.append(training_root_ops_unexpand_bucket)
traced_training_ops_bucket = {}
for op_name in traced_training_ops:
op = SelectiveBuildOperator.from_yaml_dict(
op_name,
{
"is_root_operator": False,
"is_used_for_training": True,
"include_all_overloads": False,
"debug_info": [options.model_name],
},
)
traced_training_ops_bucket[op_name] = op
bucketed_ops.append(traced_training_ops_bucket)
# END TRACING BASED BUILD OPS
# Merge dictionaries together to remove op duplication
operators: Dict[str, SelectiveBuildOperator] = {}
for ops_dict in bucketed_ops:
operators = merge_operator_dicts(operators, ops_dict)
# Loop over all operators, and if any of the them specifies that
# all overloads need to be included, then set include_all_non_op_selectives
# to True, since it indicates that this operator list came from something
# other than a traced operator list.
include_all_non_op_selectives = False
for (op_name, op_info) in operators.items():
include_all_non_op_selectives = (
include_all_non_op_selectives or op_info.include_all_overloads
)
operators_as_dict = {}
for (k, v) in operators.items():
operators_as_dict[k] = v.to_dict()
output["operators"] = operators_as_dict
output["custom_classes"] = all_custom_classes
output["build_features"] = all_build_features
output["include_all_non_op_selectives"] = include_all_non_op_selectives
if len(all_kernel_metadata) > 0:
kernel_metadata = {}
for kt in all_kernel_metadata:
kernel_metadata = merge_kernel_metadata(kernel_metadata, kt)
output["kernel_metadata"] = kernel_metadata
def get_parser_options(parser: argparse.ArgumentParser) -> argparse.Namespace:
parser.add_argument(
"--root_ops",
help="A comma separated list of root operators used by the model",
required=False,
)
parser.add_argument(
"--training_root_ops",
help="A comma separated list of root operators used for training",
required=False,
)
parser.add_argument(
"--output_path",
help="The location of the output yaml file.",
required=True,
)
parser.add_argument(
"--dep_graph_yaml_path",
type=str,
help="A path to the Operator Dependency Graph YAML file.",
required=True,
)
parser.add_argument(
"--model_name",
type=str,
help="The name of the model that uses the specified root operators.",
required=True,
)
parser.add_argument(
"--model_versions",
type=str,
help="A comma separated list of model versions.",
required=False,
)
parser.add_argument(
"--model_assets",
type=str,
help="A comma separate list of model asset names (if absent, defaults to all assets for this model).",
required=False,
)
parser.add_argument(
"--model_backends",
type=str,
default="CPU",
help="A comma separated list of model backends.",
required=False,
)
parser.add_argument(
"--models_yaml_path",
type=str,
help="The path to where the unified Mobile Model Config YAML resides.",
required=True,
)
parser.add_argument(
"--include_all_operators",
action="store_true",
default=False,
help="Set this flag to request inclusion of all opeators (i.e. build is not selective).",
required=False,
)
parser.add_argument(
"--rule_name",
type=str,
help="The name of pt_operator_library rule resulting in this generation",
required=True,
)
options = parser.parse_args()
return options
def main(argv) -> None:
parser = argparse.ArgumentParser(description="Generate used operators YAML")
options = get_parser_options(parser)
model_dict = {
"model_name": options.model_name,
"asset_info": {},
"is_new_style_rule": False,
}
output = {
"debug_info": [json.dumps(model_dict)],
}
if options.include_all_operators:
output["include_all_operators"] = True
output["operators"] = {}
output["kernel_metadata"] = {}
else:
fill_output(output, options)
with open(options.output_path, "wb") as out_file:
out_file.write(
yaml.safe_dump(
output,
default_flow_style=False,
).encode("utf-8")
)
if __name__ == "__main__":
sys.exit(main(sys.argv))
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