File size: 10,017 Bytes
5a46e5d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 | """Exact Action IR fidelity smoke tests for float and dynamic-int8 checkpoints."""
from __future__ import annotations
import hashlib
import json
import platform
import time
from collections.abc import Mapping, Sequence
from dataclasses import dataclass
from pathlib import Path
from typing import Any
import torch
from barunlm.evaluation.generation import REQUIRED_CHECKPOINT_FILES
from barunlm.quantization import verify_int8_checkpoint
from .inference import BarunActionCompiler, InferenceOutcome, validate_action_output
from .schema import PreparedInput, prepare_input
INT8_SMOKE_CASES_VERSION = "barunaction-int8-smoke-cases-v1"
INT8_SMOKE_REPORT_VERSION = "barunaction-int8-smoke-report-v1"
class QuantizationSmokeError(ValueError):
"""The requested fidelity comparison is malformed or compares different sources."""
@dataclass(frozen=True, slots=True)
class ActionIRSmokeCase:
case_id: str
prepared: PreparedInput
tool_schemas: Any
context: Any
expected_action: Mapping[str, Any]
def _exact_fields(value: Mapping[str, Any], expected: set[str], *, path: str) -> None:
missing = expected.difference(value)
unknown = set(value).difference(expected)
if missing or unknown:
raise QuantizationSmokeError(
f"{path} fields differ: missing={sorted(missing)!r}, unknown={sorted(unknown)!r}"
)
def parse_int8_smoke_cases(value: Any) -> tuple[ActionIRSmokeCase, ...]:
if not isinstance(value, Mapping):
raise QuantizationSmokeError("smoke case artifact must be an object")
_exact_fields(value, {"cases", "schema_version"}, path="$")
if value["schema_version"] != INT8_SMOKE_CASES_VERSION:
raise QuantizationSmokeError("unsupported int8 smoke case version")
raw_cases = value["cases"]
if not isinstance(raw_cases, Sequence) or isinstance(raw_cases, (str, bytes)) or not raw_cases:
raise QuantizationSmokeError("$.cases must be a non-empty array")
cases: list[ActionIRSmokeCase] = []
seen_ids: set[str] = set()
for index, raw in enumerate(raw_cases):
path = f"$.cases[{index}]"
if not isinstance(raw, Mapping):
raise QuantizationSmokeError(f"{path} must be an object")
_exact_fields(
raw,
{"context", "expected_action", "id", "now", "request", "tool_schemas"},
path=path,
)
case_id = raw["id"]
if not isinstance(case_id, str) or not case_id:
raise QuantizationSmokeError(f"{path}.id must be a non-empty string")
if case_id in seen_ids:
raise QuantizationSmokeError(f"duplicate int8 smoke case id {case_id!r}")
seen_ids.add(case_id)
prepared = prepare_input(
request=raw["request"],
tool_schemas=raw["tool_schemas"],
context=raw["context"],
now=raw["now"],
)
expected = raw["expected_action"]
if not isinstance(expected, Mapping):
raise QuantizationSmokeError(f"{path}.expected_action must be an object")
expected_raw = _canonical_json(expected)
validated = validate_action_output(expected_raw, declarations=prepared.declarations)
if validated.action is None:
assert validated.error is not None
raise QuantizationSmokeError(
f"{path}.expected_action is invalid: "
f"{validated.error.code} at {validated.error.path}"
)
cases.append(
ActionIRSmokeCase(
case_id=case_id,
prepared=prepared,
tool_schemas=raw["tool_schemas"],
context=raw["context"],
expected_action=validated.action.to_dict(),
)
)
return tuple(cases)
def _canonical_json(value: Mapping[str, Any]) -> str:
try:
return json.dumps(
value,
ensure_ascii=False,
allow_nan=False,
sort_keys=True,
separators=(",", ":"),
)
except (TypeError, ValueError) as error:
raise QuantizationSmokeError("expected Action IR must be finite JSON") from error
def _outcome_record(outcome: InferenceOutcome) -> dict[str, Any]:
return {
"action": outcome.action.to_dict() if outcome.action is not None else None,
"error": outcome.error.to_dict() if outcome.error is not None else None,
"generated_tokens": outcome.generated_tokens,
"ok": outcome.ok,
"prompt_sha256": outcome.prompt_sha256,
"prompt_tokens": outcome.prompt_tokens,
"raw_output_sha256": (
hashlib.sha256(outcome.raw_output.encode("utf-8")).hexdigest()
if outcome.raw_output is not None
else None
),
}
def compare_int8_action_ir(
*,
float_checkpoint: str | Path,
expected_float_sha256: Mapping[str, str],
int8_checkpoint: str | Path,
expected_int8_manifest_sha256: str,
cases: tuple[ActionIRSmokeCase, ...],
max_new_tokens: int = 192,
) -> dict[str, Any]:
"""Compare exact expected Action IR on the same source weights and prompts."""
if not cases:
raise QuantizationSmokeError("at least one Action IR smoke case is required")
info = verify_int8_checkpoint(
int8_checkpoint,
expected_manifest_sha256=expected_int8_manifest_sha256,
)
required_float_hashes = {
name: expected_float_sha256[name]
for name in REQUIRED_CHECKPOINT_FILES
if name in expected_float_sha256
}
if set(required_float_hashes) != set(REQUIRED_CHECKPOINT_FILES):
raise QuantizationSmokeError("float checkpoint hash map omits required model files")
if dict(info.source_checkpoint_sha256) != required_float_hashes:
raise QuantizationSmokeError(
"int8 source hashes differ from the float checkpoint selected for comparison"
)
started = time.perf_counter()
float_compiler = BarunActionCompiler(
float_checkpoint,
expected_sha256=expected_float_sha256,
checkpoint_format="float",
device="cpu",
)
float_load_seconds = time.perf_counter() - started
started = time.perf_counter()
int8_compiler = BarunActionCompiler(
int8_checkpoint,
checkpoint_format="int8",
expected_int8_manifest_sha256=expected_int8_manifest_sha256,
device="cpu",
)
int8_load_seconds = time.perf_counter() - started
records: list[dict[str, Any]] = []
all_exact = True
float_inference_seconds = 0.0
int8_inference_seconds = 0.0
for case in cases:
started = time.perf_counter()
float_outcome = float_compiler.infer(
request=case.prepared.request,
tool_schemas=case.tool_schemas,
context=case.context,
now=case.prepared.now,
max_new_tokens=max_new_tokens,
)
float_seconds = time.perf_counter() - started
float_inference_seconds += float_seconds
started = time.perf_counter()
int8_outcome = int8_compiler.infer(
request=case.prepared.request,
tool_schemas=case.tool_schemas,
context=case.context,
now=case.prepared.now,
max_new_tokens=max_new_tokens,
)
int8_seconds = time.perf_counter() - started
int8_inference_seconds += int8_seconds
float_action = float_outcome.action.to_dict() if float_outcome.action is not None else None
int8_action = int8_outcome.action.to_dict() if int8_outcome.action is not None else None
float_exact = float_action == case.expected_action
int8_exact = int8_action == case.expected_action
cross_exact = float_action is not None and float_action == int8_action
raw_output_equal = (
float_outcome.raw_output is not None
and float_outcome.raw_output == int8_outcome.raw_output
)
case_exact = float_exact and int8_exact and cross_exact
all_exact &= case_exact
records.append(
{
"case_id": case.case_id,
"cross_action_ir_exact": cross_exact,
"expected_action": dict(case.expected_action),
"float": _outcome_record(float_outcome),
"float_expected_exact": float_exact,
"float_inference_seconds": float_seconds,
"int8": _outcome_record(int8_outcome),
"int8_expected_exact": int8_exact,
"int8_inference_seconds": int8_seconds,
"raw_output_equal": raw_output_equal,
}
)
return {
"all_action_ir_exact": all_exact,
"cases": records,
"checkpoint_size": {
"int8_package_bytes": info.package_bytes,
"int8_payload_bytes": info.quantized_payload_bytes,
"payload_reduction_fraction": info.reduction_fraction,
"source_payload_bytes": info.source_payload_bytes,
},
"int8_manifest_sha256": info.manifest_sha256,
"runtime": {
"float_inference_seconds": float_inference_seconds,
"float_load_seconds": float_load_seconds,
"int8_inference_seconds": int8_inference_seconds,
"int8_load_seconds": int8_load_seconds,
"platform_machine": platform.machine(),
"platform_system": platform.system(),
"qengine": info.qengine,
"timing_interpretation": "provisional_current_host_only_not_target_device_evidence",
"torch_num_threads": torch.get_num_threads(),
"torch_version": torch.__version__,
},
"schema_version": INT8_SMOKE_REPORT_VERSION,
"source_checkpoint_sha256": dict(sorted(required_float_hashes.items())),
}
__all__ = [
"INT8_SMOKE_CASES_VERSION",
"INT8_SMOKE_REPORT_VERSION",
"ActionIRSmokeCase",
"QuantizationSmokeError",
"compare_int8_action_ir",
"parse_int8_smoke_cases",
]
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