24791492727

Committed 22 Apr 2026 05:00PM UTC coverage: 63.898%. First build

Build # 24791492727

Build Type

Pull #690

github

Committed by

web-flow

Commit Message

Merge 28e482eae into 8dc5e5aa7

Pull Request Pull Request #690: Modular Compile process

Coverage Stats

224 of 347 new or added lines in 13 files covered. (64.55%)

30674 of 48005 relevant lines covered (63.9%)

572.91 hits per line

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72.12

/python/docc/compiler/docc_program.py

from abc import ABC, abstractmethod
import sys
from typing import Any, Optional
import os
import re

from docc.sdfg import StructuredSDFG
from docc.sdfg._sdfg import (
    _enable_statistics,
    _statistics_enabled_by_env,
    _statistics_summary,
)
from docc.compiler.compiled_sdfg import CompiledSDFG
from docc.compiler.target_registry import (
    get_target_schedule_fn,
    get_target_compile_fn,
    get_target_expand_fn,
)


def _parse_docc_debug() -> dict[str, str]:
    debug_env = os.environ.get("DOCC_DEBUG", "")
    debug_dict = {}
    if debug_env:
        for entry in re.split(r"[;:]", debug_env):
            if not entry:
                continue
            parts = entry.split("=", 1)
            key = parts[0].strip()
            value = parts[1].strip() if len(parts) > 1 else ""
            debug_dict[key] = value
    return debug_dict


def _is_debug_dump(flags: dict[str, str]) -> bool:
    return "dump" in flags


def _is_debug_compile(flags: dict[str, str]) -> bool:
    return "build" in flags


def _get_build_thread_count(flags: dict[str, str]) -> int:
    return int(flags.get("build_threads", "0"))


class DoccProgram(ABC):

    def __init__(
        self,
        name: str,
        target: str = "none",
        category: str = "server",
        instrumentation_mode: Optional[str] = None,
        capture_args: Optional[bool] = None,
        remote_tuning: bool = False,
    ):
        self.name = name
        self.target = target
        self.category = category
        self.remote_tuning = remote_tuning
        self.last_sdfg: Optional[StructuredSDFG] = None
        self.cache: dict = {}
        debug_flags = _parse_docc_debug()
        self.debug_dump: bool = _is_debug_dump(debug_flags)
        self.debug_build: bool = _is_debug_compile(debug_flags)
        self.build_thread_count: int = _get_build_thread_count(debug_flags)

        # Check environment variable DOCC_CI
        docc_ci = os.environ.get("DOCC_CI", "")
        if docc_ci:
            if docc_ci == "regions":
                if instrumentation_mode is None:
                    instrumentation_mode = "ols"
            elif docc_ci == "arg-capture":
                if capture_args is None:
                    capture_args = True
            else:
                # Full mode (or unknown value treated as full)
                if instrumentation_mode is None:
                    instrumentation_mode = "ols"
                if capture_args is None:
                    capture_args = True

        self.instrumentation_mode = instrumentation_mode
        self.capture_args = capture_args

    @abstractmethod
    def __call__(self, *args: Any) -> Any:
        pass

    @abstractmethod
    def compile(self, *args: Any, output_folder: Optional[str] = None) -> CompiledSDFG:
        pass

    def sdfg_pipe(
        self,
        sdfg: StructuredSDFG,
        output_folder: Optional[str],
        instrumentation_mode: str,
        capture_args: bool,
    ) -> str:

        if self.debug_dump:
            sdfg.dump(output_folder, "py0.parsed", dump_dot=True)

        # Enable statistics if envvar is set
        if _statistics_enabled_by_env():
            _enable_statistics()

        sdfg.validate()

        # Tensor targets keep tensor nodes
        custom_expand_fn = get_target_expand_fn(self.target)
        if custom_expand_fn is not None:
            custom_expand_fn(sdfg, self.category, {})
        else:
            sdfg.expand()
        if self.debug_dump:
            sdfg.dump(output_folder, "py1.expanded", dump_dot=True)

        # Simplify pipelines
        sdfg.simplify()
        if self.debug_dump:
            sdfg.dump(output_folder, "py2.opt", dump_dot=True)

        # Normalization for scheduling
        if self.target != "none":
            sdfg.normalize()

        if self.debug_dump or instrumentation_mode or capture_args:
            sdfg.dump(
                output_folder,
                "py3.norm",
                dump_dot=self.debug_dump,
                dump_json=True,
                record_for_instrumentation=True,
            )

        # Schedule if target is specified

        if self.target != "none":
            # Check for custom registered target first
            custom_schedule_fn = get_target_schedule_fn(self.target)
            if custom_schedule_fn is not None:
                custom_schedule_fn(
                    sdfg, self.category, {"remote_tuning": self.remote_tuning}
                )
            else:
                sdfg.schedule(self.target, self.category, self.remote_tuning)

            if self.debug_dump:
                sdfg.dump(output_folder, "py4.post_sched", dump_dot=True)

        self.last_sdfg = sdfg

        custom_compile_fn = get_target_compile_fn(self.target)
        if custom_compile_fn is not None:
            lib_path = custom_compile_fn(
                sdfg,
                output_folder,
                instrumentation_mode,
                capture_args,
                {"debug_build": self.debug_build, "threads": self.build_thread_count},
            )
        else:
            lib_path = sdfg._compile(
                output_folder=output_folder,
                target=self.target,
                instrumentation_mode=instrumentation_mode,
                capture_args=capture_args,
                debug_build=self.debug_build,
                threads=self.build_thread_count,
            )

        # Dump statistics after compile
        if _statistics_enabled_by_env():
            print(_statistics_summary(), file=sys.stderr)

        return lib_path

    @abstractmethod
    def to_sdfg(self, *args: Any) -> StructuredSDFG:
        pass

    @abstractmethod
    def _convert_inputs(self, args: tuple) -> tuple:
        pass

    @abstractmethod
    def _convert_outputs(self, result: Any, original_args: tuple) -> Any:
        pass

    def _get_cache_key(self, *args: Any) -> str:
        return ""

1	from abc import ABC, abstractmethod	4✔
2	import sys	4✔
3	from typing import Any, Optional	4✔
4	import os	4✔
5	import re	4✔
6
7	from docc.sdfg import StructuredSDFG	4✔
8	from docc.sdfg._sdfg import (	4✔
9	_enable_statistics,
10	_statistics_enabled_by_env,
11	_statistics_summary,
12	)
13	from docc.compiler.compiled_sdfg import CompiledSDFG	4✔
14	from docc.compiler.target_registry import (	4✔
15	get_target_schedule_fn,
16	get_target_compile_fn,
17	get_target_expand_fn,
18	)
19
20
21	def _parse_docc_debug() -> dict[str, str]:	4✔
22	debug_env = os.environ.get("DOCC_DEBUG", "")	4✔
23	debug_dict = {}	4✔
24	if debug_env:	4✔
NEW 25	for entry in re.split(r"[;:]", debug_env):	×
NEW 26	if not entry:	×
NEW 27	continue	×
NEW 28	parts = entry.split("=", 1)	×
NEW 29	key = parts[0].strip()	×
NEW 30	value = parts[1].strip() if len(parts) > 1 else ""	×
NEW 31	debug_dict[key] = value	×
32	return debug_dict	4✔
33
34
35	def _is_debug_dump(flags: dict[str, str]) -> bool:	4✔
36	return "dump" in flags	4✔
37
38
39	def _is_debug_compile(flags: dict[str, str]) -> bool:	4✔
40	return "build" in flags	4✔
41
42
43	def _get_build_thread_count(flags: dict[str, str]) -> int:	4✔
44	return int(flags.get("build_threads", "0"))	4✔
45
46
47	class DoccProgram(ABC):	4✔
48
49	def __init__(	4✔
50	self,
51	name: str,
52	target: str = "none",
53	category: str = "server",
54	instrumentation_mode: Optional[str] = None,
55	capture_args: Optional[bool] = None,
56	remote_tuning: bool = False,
57	):
58	self.name = name	4✔
59	self.target = target	4✔
60	self.category = category	4✔
61	self.remote_tuning = remote_tuning	4✔
62	self.last_sdfg: Optional[StructuredSDFG] = None	4✔
63	self.cache: dict = {}	4✔
64	debug_flags = _parse_docc_debug()	4✔
65	self.debug_dump: bool = _is_debug_dump(debug_flags)	4✔
66	self.debug_build: bool = _is_debug_compile(debug_flags)	4✔
67	self.build_thread_count: int = _get_build_thread_count(debug_flags)	4✔
68
69	# Check environment variable DOCC_CI
70	docc_ci = os.environ.get("DOCC_CI", "")	4✔
71	if docc_ci:	4✔
72	if docc_ci == "regions":	×
73	if instrumentation_mode is None:	×
74	instrumentation_mode = "ols"	×
75	elif docc_ci == "arg-capture":	×
76	if capture_args is None:	×
77	capture_args = True	×
78	else:
79	# Full mode (or unknown value treated as full)
80	if instrumentation_mode is None:	×
81	instrumentation_mode = "ols"	×
82	if capture_args is None:	×
83	capture_args = True	×
84
85	self.instrumentation_mode = instrumentation_mode	4✔
86	self.capture_args = capture_args	4✔
87
88	@abstractmethod	4✔
89	def __call__(self, *args: Any) -> Any:	4✔
90	pass	×
91
92	@abstractmethod	4✔
93	def compile(self, *args: Any, output_folder: Optional[str] = None) -> CompiledSDFG:	4✔
94	pass	×
95
96	def sdfg_pipe(	4✔
97	self,
98	sdfg: StructuredSDFG,
99	output_folder: Optional[str],
100	instrumentation_mode: str,
101	capture_args: bool,
102	) -> str:
103
104	if self.debug_dump:	4✔
105	sdfg.dump(output_folder, "py0.parsed", dump_dot=True)	×
106
107	# Enable statistics if envvar is set
108	if _statistics_enabled_by_env():	4✔
109	_enable_statistics()	×
110
111	sdfg.validate()	4✔
112
113	# Tensor targets keep tensor nodes
114	custom_expand_fn = get_target_expand_fn(self.target)	4✔
115	if custom_expand_fn is not None:	4✔
116	custom_expand_fn(sdfg, self.category, {})	4✔
117	else:
118	sdfg.expand()	4✔
119	if self.debug_dump:	4✔
120	sdfg.dump(output_folder, "py1.expanded", dump_dot=True)	×
121
122	# Simplify pipelines
123	sdfg.simplify()	4✔
124	if self.debug_dump:	4✔
125	sdfg.dump(output_folder, "py2.opt", dump_dot=True)	×
126
127	# Normalization for scheduling
128	if self.target != "none":	4✔
129	sdfg.normalize()	4✔
130
131	if self.debug_dump or instrumentation_mode or capture_args:	4✔
132	sdfg.dump(	4✔
133	output_folder,
134	"py3.norm",
135	dump_dot=self.debug_dump,
136	dump_json=True,
137	record_for_instrumentation=True,
138	)
139
140	# Schedule if target is specified
141
142	if self.target != "none":	4✔
143	# Check for custom registered target first
144	custom_schedule_fn = get_target_schedule_fn(self.target)	4✔
145	if custom_schedule_fn is not None:	4✔
146	custom_schedule_fn(	4✔
147	sdfg, self.category, {"remote_tuning": self.remote_tuning}
148	)
149	else:
150	sdfg.schedule(self.target, self.category, self.remote_tuning)	4✔
151
152	if self.debug_dump:	4✔
153	sdfg.dump(output_folder, "py4.post_sched", dump_dot=True)	×
154
155	self.last_sdfg = sdfg	4✔
156
157	custom_compile_fn = get_target_compile_fn(self.target)	4✔
158	if custom_compile_fn is not None:	4✔
159	lib_path = custom_compile_fn(	4✔
160	sdfg,
161	output_folder,
162	instrumentation_mode,
163	capture_args,
164	{"debug_build": self.debug_build, "threads": self.build_thread_count},
165	)
166	else:
167	lib_path = sdfg._compile(	4✔
168	output_folder=output_folder,
169	target=self.target,
170	instrumentation_mode=instrumentation_mode,
171	capture_args=capture_args,
172	debug_build=self.debug_build,
173	threads=self.build_thread_count,
174	)
175
176	# Dump statistics after compile
177	if _statistics_enabled_by_env():	4✔
178	print(_statistics_summary(), file=sys.stderr)	×
179
180	return lib_path	4✔
181
182	@abstractmethod	4✔
183	def to_sdfg(self, *args: Any) -> StructuredSDFG:	4✔
184	pass	×
185
186	@abstractmethod	4✔
187	def _convert_inputs(self, args: tuple) -> tuple:	4✔
188	pass	×
189
190	@abstractmethod	4✔
191	def _convert_outputs(self, result: Any, original_args: tuple) -> Any:	4✔
192	pass	×
193
194	def _get_cache_key(self, *args: Any) -> str:	4✔
195	return ""	×

daisytuner / docc / 24791492727

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