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# SPDX-License-Identifier: Apache-2.0
"""Focused contracts for the family-neutral model composition root."""
import ast
from pathlib import Path
from types import SimpleNamespace
from unittest.mock import MagicMock
import pytest
import ttnn
from models.common.llm_runtime.config import PagedKVCacheConfig, TraceConfig, WarmupConfig
from models.common.models import executor as executor_module
from models.common.models.executor import ModelExecutor, ModelExecutorConfig
_EXECUTOR_PATH = Path(__file__).parents[2] / "models" / "executor.py"
_MODELS_ROOT = _EXECUTOR_PATH.parent
def _config(*, device_sampling_enabled: bool = False) -> ModelExecutorConfig:
return ModelExecutorConfig(
trace=TraceConfig(mode="none"),
warmup=WarmupConfig(),
paged_kv_cache=PagedKVCacheConfig(
block_size=32,
max_num_blocks=128,
num_blocks=128,
dtype=ttnn.bfloat8_b,
),
device_sampling_enabled=device_sampling_enabled,
)
def test_common_executor_has_no_concrete_model_dependencies_or_dispatch() -> None:
tree = ast.parse(_EXECUTOR_PATH.read_text())
imports = {node.module for node in ast.walk(tree) if isinstance(node, ast.ImportFrom) and node.module is not None}
imported_names = {alias.name for node in ast.walk(tree) if isinstance(node, ast.Import) for alias in node.names}
assert not any(module.startswith("models.common.models.") for module in imports)
assert not any(name.startswith("models.common.models.") for name in imported_names)
control_flow = [
node.test for node in ast.walk(tree) if isinstance(node, (ast.If, ast.IfExp, ast.While, ast.Assert))
]
dispatch_names = {"model_name", "model_id", "provider_id", "checkpoint_path", "model_version"}
assert not any(
isinstance(node, ast.Name) and node.id in dispatch_names
for expression in control_flow
for node in ast.walk(expression)
)
torch_calls = [
node
for node in ast.walk(tree)
if isinstance(node, ast.Call)
and isinstance(node.func, ast.Attribute)
and isinstance(node.func.value, ast.Name)
and node.func.value.id == "torch"
]
assert torch_calls == []
def test_model_layer_has_only_the_approved_family_modules_and_readmes() -> None:
assert (_MODELS_ROOT / "llama3_executor.py").is_file()
assert (_MODELS_ROOT / "qwen2_executor.py").is_file()
assert not (_MODELS_ROOT / "qwen3_executor.py").exists()
model_directories = sorted(
path for path in _MODELS_ROOT.iterdir() if path.is_dir() and (path / "model.py").is_file()
)
assert len(model_directories) == 12
assert all((path / "README.md").is_file() for path in model_directories)
@pytest.mark.parametrize(
"relative_path",
(
"deepseek_r1_distill_qwen_14b/executor.py",
"mistral_7b/executor.py",
"phi4/executor.py",
),
)
def test_direct_composition_examples_do_not_depend_on_shared_family_executors(relative_path: str) -> None:
tree = ast.parse((_MODELS_ROOT / relative_path).read_text())
imports = {node.module for node in ast.walk(tree) if isinstance(node, ast.ImportFrom)}
assert "models.common.models.executor" not in imports
assert "models.common.models.llama3_executor" not in imports
assert "models.common.models.qwen2_executor" not in imports
def test_common_config_is_frozen_and_rejects_non_exact_nested_config_types(expect_error) -> None:
config = _config()
with expect_error(AttributeError, "cannot assign to field"):
config.device_sampling_enabled = True
class TraceConfigSubclass(TraceConfig):
pass
with expect_error(TypeError, "trace must be exactly TraceConfig"):
ModelExecutorConfig(
trace=TraceConfigSubclass(mode="none"),
warmup=config.warmup,
paged_kv_cache=config.paged_kv_cache,
device_sampling_enabled=False,
)
def test_sampling_state_inputs_are_an_optional_owned_pair(expect_error) -> None:
with expect_error(ValueError, "must be supplied together"):
ModelExecutor(
None,
None,
_config(device_sampling_enabled=True),
sampling_state_controller=object(),
)
with expect_error(ValueError, "requires device sampling"):
ModelExecutor(
None,
None,
_config(),
sampling_state_controller=object(),
sampling_state=object(),
)
@pytest.mark.parametrize(
("enable_trace", "expected"),
[(True, ["prime", "coordinator"]), (False, ["coordinator", "prime"])],
)
def test_prefill_warmup_policy_controls_order_through_one_continuation(enable_trace, expected) -> None:
events = []
def policy(executor, default_warmup, *, kv_cache, can_sample_on_device, enable_trace):
assert executor is target
assert kv_cache is cache
assert can_sample_on_device
if enable_trace:
events.append("prime")
default_warmup()
if not enable_trace:
events.append("prime")
cache = object()
target = object.__new__(ModelExecutor)
target._terminal = False
target.prefill_runtime = SimpleNamespace(transient_orphan_count=0)
target.decode_runtime = SimpleNamespace(transient_orphan_count=0)
target._prefill_warmup = policy
target.warmup = SimpleNamespace(
warmup_prefill=lambda **kwargs: events.append("coordinator"),
)
target.warmup_model_prefill(
kv_cache=cache,
can_sample_on_device=True,
enable_trace=enable_trace,
)
assert events == expected
def test_request_state_and_execution_target_are_forwarded_by_identity() -> None:
target = object.__new__(ModelExecutor)
target._terminal = False
target.prefill_runtime = SimpleNamespace(transient_orphan_count=0)
target.decode_runtime = SimpleNamespace(transient_orphan_count=0)
target._validate_bound_cache = MagicMock()
target._ensure_sampling_for = MagicMock()
target._prefill_execution = MagicMock()
target._request_state_fields = ("prompt_tokens", "output_tokens", "slot_remap")
values = {name: object() for name in ("tokens", "page_table", "prompt_tokens", "output_tokens", "slot_remap")}
target.compile_prefill(
tokens=values["tokens"],
page_table=values["page_table"],
prompt_tokens=values["prompt_tokens"],
output_tokens=values["output_tokens"],
slot_remap=values["slot_remap"],
)
forwarded = target._prefill_execution.compile_prefill.call_args.kwargs
for name, value in values.items():
assert forwarded[name] is value
def test_layout_refresh_preserves_owner_and_sampling_state_identity(monkeypatch) -> None:
state = object()
layout = object()
prefill_config = SimpleNamespace(
max_batch_size=4,
max_prefill_chunk_size=2048,
device_sampling_enabled=True,
can_enable_trace=lambda *_: True,
supports_batched_prefill=True,
disable_batched_prefill=True,
max_prefill_batch_size=4,
batched_prefill_batched_extract=True,
trace_capture_prime_sequence_lengths=(128,),
sampling_state_controller=object(),
sampling_state=state,
)
decode_config = SimpleNamespace(
lane_capacity=4,
device_sampling_enabled=True,
force_greedy_top_k=True,
sampling_state_controller=prefill_config.sampling_state_controller,
sampling_state=state,
)
warmup_config = SimpleNamespace(warmup=object(), prefill_sequence_lengths=(128,))
resolved_prefill = SimpleNamespace(page_table_layout=layout, sampling_state=state)
resolved_decode = SimpleNamespace(page_table_layout=layout, sampling_state=state)
resolved_warmup = SimpleNamespace(page_table_layout=layout)
prefill_resolve = MagicMock(return_value=resolved_prefill)
decode_resolve = MagicMock(return_value=resolved_decode)
warmup_resolve = MagicMock(return_value=resolved_warmup)
monkeypatch.setattr(executor_module.PrefillRuntimeConfig, "resolve", prefill_resolve)
monkeypatch.setattr(executor_module.DecodeRuntimeConfig, "resolve", decode_resolve)
monkeypatch.setattr(executor_module.WarmupCoordinatorConfig, "resolve", warmup_resolve)
target = object.__new__(ModelExecutor)
target.model = object()
target.output_reader = object()
target.config = SimpleNamespace(trace=object())
target.prefill_runtime = SimpleNamespace(config=prefill_config)
target.decode_runtime = SimpleNamespace(config=decode_config)
target.warmup = SimpleNamespace(config=warmup_config)
target._resolve_page_table_layout = lambda: layout
owners = (target.prefill_runtime, target.decode_runtime, target.warmup)
target._refresh_page_table_layout()
assert (target.prefill_runtime, target.decode_runtime, target.warmup) == owners
assert target.page_table_layout is layout
assert all(owner.config.page_table_layout is layout for owner in owners)
assert target.prefill_runtime.config.sampling_state is state
assert target.decode_runtime.config.sampling_state is state
assert prefill_resolve.call_args.kwargs["trace_capture_prime_sequence_lengths"] == (128,)
assert prefill_resolve.call_args.kwargs["sampling_state_controller"] is prefill_config.sampling_state_controller
assert prefill_resolve.call_args.kwargs["sampling_state"] is state
assert decode_resolve.call_args.kwargs["sampling_state_controller"] is prefill_config.sampling_state_controller
assert decode_resolve.call_args.kwargs["sampling_state"] is state
def test_cleanup_is_ordered_retryable_idempotent_and_terminal(expect_error) -> None:
events = []
failing = {"reader", "trace"}
class _Owner:
def __init__(self, name):
self.name = name
def action(self, *args):
events.append(self.name)
if self.name in failing:
raise RuntimeError(self.name)
cleanup = action
drain = action
drain_external_outputs = action
cleanup_transients = action
release = action
target = object.__new__(ModelExecutor)
target._terminal = False
target._cleaned_up = False
target._owner_name = "TestExecutor"
target.decode_runtime = _Owner("decode")
target.output_reader = _Owner("reader")
target.prefill_runtime = _Owner("prefill")
target.trace_compiler = _Owner("trace")
target.program_compiler = _Owner("program")
target.config = SimpleNamespace(device_sampling_enabled=True)
target.sampling_state_controller = _Owner("sampling-state")
target.sampling_state = object()
target.model = SimpleNamespace(sampling=_Owner("sampling"))
target.kv_cache_manager = _Owner("kv")
expected = ["decode", "reader", "prefill", "decode", "trace", "program", "sampling-state", "sampling", "kv"]
with expect_error(RuntimeError, "reader") as raised:
target.cleanup()
assert events == expected
assert [str(error) for error in raised.value.cleanup_failures] == ["trace"]
assert target.terminal
assert not target._cleaned_up
failing.clear()
target.cleanup()
target.cleanup()
assert events == expected * 2
assert target._cleaned_up
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