23741135955

Committed 30 Mar 2026 10:54AM UTC coverage: 64.123% (-0.4%) from 64.511%

Build # 23741135955

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push

github

Committed by

web-flow

Commit Message

Merge pull request #503 from daisytuner/statistics

Added pass and pipeline statistics

Run Details

28 of 300 new or added lines in 20 files covered. (9.33%)

1 existing line in 1 file now uncovered.

27213 of 42439 relevant lines covered (64.12%)

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74.12

/python/docc/compiler/docc_program.py

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

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 _is_debug_dump() -> bool:
    return bool(os.environ.get("DOCC_DEBUG"))


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 = {}
        self.debug_dump: bool = _is_debug_dump()

        # 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
        if self.target != "onnx":
            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

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

        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, {}
            )
        else:
            lib_path = sdfg._compile(
                output_folder=output_folder,
                target=self.target,
                instrumentation_mode=instrumentation_mode,
                capture_args=capture_args,
            )

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

daisytuner / docc / 23741135955

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