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# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import dataclasses
import inspect
from types import SimpleNamespace
from typing import Any
import pytest
import torch
import models.common.llm_runtime.decode as decode_module
import ttnn
from models.common.llm_runtime.config import PageTableLayout
from models.common.llm_runtime.decode import (
DecodeDeviceInputs,
DecodePersistentInputs,
DecodeProgramSignature,
DecodeRuntime,
DecodeRuntimeConfig,
DecodeTraceSignature,
InvocationResult,
)
from models.common.llm_runtime.output_reader import OutputReader, PendingRead
from models.common.modules.sampling.params import PreparedSamplingParams
from models.common.sampling.sampling_params import SamplingParams
class FakeMesh:
shape = (1, 1)
class FakeSampling:
def __init__(self, seed_buffer=None):
self.config = SimpleNamespace(allow_force_argmax=True, max_batch_size=2, max_top_k=32, seeds=seed_buffer)
def decode_forward(
self,
logits,
*,
k=None,
p=None,
temp=None,
seeds=None,
tt_out_tok=None,
enable_log_probs=False,
):
return logits, None
class FakeRope:
def get_rot_idxs(self, positions, *, on_host):
assert on_host
return ("rotary", positions.clone())
def get_rot_mats(self, rotary_indices):
return ("cos", "sin")
class FakeModel:
def __init__(self, seed_buffer=None):
self.config = SimpleNamespace(max_batch_size=2)
self.sampling = FakeSampling(seed_buffer)
self.rope_setup = FakeRope()
self.vocab_size = 8
self.num_devices = 1
def iter_executor_named_modules(self):
return iter(())
def increment_positions(self, positions, rotary_indices):
return None
class FakeLazySeedBuffer:
def __init__(self):
self.source = torch.arange(2, dtype=torch.int64)
self._value = object()
self.updates = []
def update(self, source):
self.source = source
self.updates.append(source.clone())
def get_device_buffer(self):
return self._value
def make_runtime(*, sampling=True, force_greedy_top_k=False, seed_buffer=None):
mesh = FakeMesh()
model = FakeModel(seed_buffer)
config = DecodeRuntimeConfig.resolve(
model=model,
output_reader=OutputReader(mesh),
lane_capacity=2,
page_table_layout=page_table_layout(),
device_sampling_enabled=sampling,
force_greedy_top_k=force_greedy_top_k,
)
return DecodeRuntime(config)
def page_table_layout(*, raw_width=8, block_size=32):
return PageTableLayout(
block_size=block_size,
raw_capacity_width=raw_width,
prefill_width=((raw_width + 7) // 8) * 8,
decode_width=((raw_width + 7) // 8) * 8,
)
def greedy_sampling():
return SamplingParams(temperature=[0.0, 0.0], top_k=[1, 1], top_p=[1.0, 1.0])
def test_prepare_uses_resolved_sampler_capacity_and_neutral_inactive_rows():
prepared = prepare(
make_runtime(),
positions=(0, -1),
sampling_params=SamplingParams(temperature=1.0, top_k=32, top_p=0.08),
).prepared_sampling
assert isinstance(prepared, PreparedSamplingParams)
assert prepared.batch_size == 2
assert prepared.active_rows == 1
assert prepared.active_mask == (True, False)
assert prepared.top_k == (32, 1)
assert prepared.top_p == pytest.approx((0.08, 0.0))
assert prepared.temperature == (1.0, 1.0)
assert prepared.row_paths == ("topk", "inactive")
def test_prepare_accepts_vector_tensor_fields_for_full_lane():
prepared = prepare(
make_runtime(),
positions=(0, 0),
sampling_params=SamplingParams(
temperature=torch.ones(2),
top_k=torch.full((2,), 32, dtype=torch.int32),
top_p=torch.full((2,), 0.08),
),
).prepared_sampling
assert isinstance(prepared, PreparedSamplingParams)
assert prepared.active_mask == (True, True)
assert prepared.top_k == (32, 32)
assert prepared.top_p == pytest.approx((0.08, 0.08))
assert prepared.temperature == (1.0, 1.0)
def prepare(
runtime,
*,
positions=(0, -1),
page_table=None,
sampling_params=None,
prompt_tokens=None,
output_tokens=None,
slot_remap=None,
reset=False,
):
if page_table is None:
page_table = torch.tensor([[3, 4, 5], [6, 7, 8]], dtype=torch.int32)
return runtime.prepare(
torch.tensor([11, 0]),
torch.tensor(positions),
page_table,
sampling_params=sampling_params,
prompt_tokens=prompt_tokens,
output_tokens=output_tokens,
slot_remap=slot_remap,
reset_batch=reset,
)
def test_prepare_places_only_start_pos_active_rows_and_neutralizes_gap_sentinels():
runtime = make_runtime()
sampling = SamplingParams(
temperature=[0.8, 0.8],
top_k=[999, 7],
top_p=[0.9, 0.8],
seed=[-1, -1],
enable_log_probs=[False, False],
num_logprobs=[-2, -2],
)
prepared = prepare(
runtime,
positions=(-1, 4),
sampling_params=sampling,
prompt_tokens=torch.tensor([[10, -1], [20, 21]]),
output_tokens=[[30, -1], [40, 41]],
slot_remap=torch.tensor([1, 0]),
reset=True,
).prepared_sampling
assert prepared is not None
assert prepared.active_mask == (False, True)
assert prepared.row_paths == ("inactive", "topk")
assert prepared.top_k == (1, 7)
assert prepared.top_p == pytest.approx((0.0, 0.8))
assert prepared.seeds == (None, None)
assert prepared.enable_log_probs == (False, False)
assert prepared.num_logprobs == (0, 0)
assert prepared.prompt_tokens.tolist() == [[-1, -1], [20, 21]]
assert prepared.output_tokens == [[-1, -1], [40, 41]]
assert prepared.slot_remap.tolist() == [1, 0]
def seeded_sampling(seed0, seed1=None):
return SamplingParams(
temperature=[0.8, 0.8],
top_k=[32, 32],
top_p=[0.95, 0.95],
seed=[seed0, seed1],
)
def stochastic_sampling(seed=None):
return SamplingParams(
temperature=[0.8, 0.8],
top_k=[32, 32],
top_p=[0.95, 0.95],
seed=seed,
)
def make_four_slot_seed_runtime():
seed_buffer = FakeLazySeedBuffer()
seed_buffer.source = torch.arange(4, dtype=torch.int64)
model = FakeModel(seed_buffer)
model.config.max_batch_size = 4
model.sampling.config.max_batch_size = 4
runtime = DecodeRuntime(
DecodeRuntimeConfig.resolve(
model=model,
output_reader=OutputReader(FakeMesh()),
lane_capacity=4,
page_table_layout=page_table_layout(),
device_sampling_enabled=True,
)
)
return runtime, seed_buffer
def four_slot_sampled_warmup(runtime):
return runtime.prepare(
torch.zeros(4, dtype=torch.long),
torch.zeros(4, dtype=torch.long),
torch.zeros((4, 8), dtype=torch.int32),
sampling_params=SamplingParams(
temperature=torch.ones(4),
top_k=torch.full((4,), 32, dtype=torch.int32),
top_p=torch.full((4,), 0.08),
seed=[11, 22, 33, 44],
),
)
def _stub_compile_only_decode(runtime, monkeypatch, run_body):
monkeypatch.setattr(runtime, "_prepare_inputs_host", lambda prepared: "host")
monkeypatch.setattr(
runtime,
"_stage_inputs_and_kpt",
lambda host, prepared: (DecodeDeviceInputs(None, None, None, None), None),
)
monkeypatch.setattr(runtime, "_run_body", run_body)
def test_compile_only_sampled_decode_temporarily_admits_and_resets_fallback_seed_slots(monkeypatch, expect_error):
runtime, seed_buffer = make_four_slot_seed_runtime()
defaults = seed_buffer.source.clone()
prepared = four_slot_sampled_warmup(runtime)
during_compile = []
def run_body(*args, **kwargs):
during_compile.append(runtime._seed_state.snapshot())
assert not kwargs["count_tokens"]
assert not kwargs["advance_seeds"]
return object()
_stub_compile_only_decode(runtime, monkeypatch, run_body)
runtime.invoke(prepared, count_tokens=False)
assert during_compile[0].active_slots == (0, 1, 2, 3)
assert not during_compile[0].buffer_is_default
reset = runtime._seed_state.snapshot()
assert reset.active_slots == ()
assert reset.buffer_is_default
assert torch.equal(seed_buffer.source, defaults)
with expect_error(RuntimeError, "reset_batch=True"):
runtime._refresh_sampling_seeds(prepared)
def test_compile_only_sampled_decode_resets_fallback_seed_slots_after_failure(monkeypatch, expect_error):
runtime, seed_buffer = make_four_slot_seed_runtime()
defaults = seed_buffer.source.clone()
prepared = four_slot_sampled_warmup(runtime)
def run_body(*args, **kwargs):
assert runtime._seed_state.snapshot().active_slots == (0, 1, 2, 3)
raise RuntimeError("compile boom")
_stub_compile_only_decode(runtime, monkeypatch, run_body)
monkeypatch.setattr(runtime, "_release_or_retain_transient", lambda owned: [])
with expect_error(RuntimeError, "compile boom"):
runtime.invoke(prepared, count_tokens=False)
reset = runtime._seed_state.snapshot()
assert reset.active_slots == ()
assert reset.buffer_is_default
assert torch.equal(seed_buffer.source, defaults)
def test_runtime_seed_same_request_and_absolute_position_are_cardinality_independent():
first_buffer = FakeLazySeedBuffer()
first = make_runtime(seed_buffer=first_buffer)
first_prepared = prepare(first, positions=(41, -1), sampling_params=seeded_sampling(1234), reset=True)
first._refresh_sampling_seeds(first_prepared)
remapped_buffer = FakeLazySeedBuffer()
remapped = make_runtime(seed_buffer=remapped_buffer)
remapped_prepared = prepare(
remapped,
positions=(-1, 41),
sampling_params=seeded_sampling(None, 1234),
reset=True,
)
remapped._refresh_sampling_seeds(remapped_prepared)
assert int(first_buffer.updates[-1][0]) == int(remapped_buffer.updates[-1][1])
assert first._seed_state.snapshot().active == (True, False)
assert remapped._seed_state.snapshot().active == (False, True)
@pytest.mark.parametrize("seed", [1234, torch.tensor(1234)])
def test_runtime_scalar_seed_belongs_to_one_request_and_is_not_broadcast(seed):
seed_buffer = FakeLazySeedBuffer()
runtime = make_runtime(seed_buffer=seed_buffer)
sampling_params = SamplingParams(
temperature=[0.8, 0.8],
top_k=[32, 32],
top_p=[0.95, 0.95],
seed=seed,
)
prepared = prepare(runtime, positions=(17, 17), sampling_params=sampling_params, reset=True)
runtime._refresh_sampling_seeds(prepared)
assert prepared.prepared_sampling.seeds == (1234, None)
snapshot = runtime._seed_state.snapshot()
assert snapshot.request_seeds == (1234, None)
assert snapshot.active == (True, True)
assert snapshot.current_device_seeds[0] is not None
assert snapshot.current_device_seeds[1] is not None
@pytest.mark.parametrize("seed", [[111, 222], torch.tensor([111, 222])])
def test_runtime_vector_seed_remains_slot_indexed(seed):
runtime = make_runtime(seed_buffer=FakeLazySeedBuffer())
sampling_params = SamplingParams(
temperature=[0.8, 0.8],
top_k=[32, 32],
top_p=[0.95, 0.95],
seed=seed,
)
prepared = prepare(runtime, positions=(5, 5), sampling_params=sampling_params, reset=True)
assert prepared.prepared_sampling.seeds == (111, 222)
def test_runtime_simultaneous_equal_request_seeds_share_vllm_stream():
runtime = make_runtime(seed_buffer=FakeLazySeedBuffer())
prepared = prepare(
runtime,
positions=(5, 5),
sampling_params=seeded_sampling(77, 77),
reset=True,
)
runtime._refresh_sampling_seeds(prepared)
snapshot = runtime._seed_state.snapshot()
assert runtime._seed_manager.salt_duplicate_seeds is False
assert snapshot.request_seeds == (77, 77)
assert snapshot.salts == (0, 0)
assert snapshot.current_device_seeds[0] == snapshot.current_device_seeds[1]
def test_runtime_vllm_uniform_seed_is_deterministic_across_concurrent_slots():
seed_buffer = FakeLazySeedBuffer()
seed_buffer.source = torch.arange(32, dtype=torch.int64)
model = FakeModel(seed_buffer)
model.config.max_batch_size = 32
model.sampling.config.max_batch_size = 32
runtime = DecodeRuntime(
DecodeRuntimeConfig.resolve(
model=model,
output_reader=OutputReader(FakeMesh()),
lane_capacity=32,
page_table_layout=page_table_layout(),
device_sampling_enabled=True,
)
)
prepared = runtime.prepare(
torch.zeros(32, dtype=torch.long),
torch.zeros(32, dtype=torch.long),
torch.zeros((32, 8), dtype=torch.int32),
sampling_params=SamplingParams(
temperature=torch.ones(32),
top_k=torch.full((32,), 32, dtype=torch.int32),
top_p=torch.full((32,), 0.95),
seed=[1234] * 32,
),
reset_batch=True,
)
runtime._refresh_sampling_seeds(prepared)
snapshot = runtime._seed_state.snapshot()
assert runtime._seed_manager.salt_duplicate_seeds is False
assert snapshot.salts == (0,) * 32
assert len(set(snapshot.current_device_seeds)) == 1
def test_runtime_slot_remap_moves_complete_seed_stream_before_refresh():
seed_buffer = FakeLazySeedBuffer()
runtime = make_runtime(seed_buffer=seed_buffer)
initial = prepare(
runtime,
positions=(5, -1),
sampling_params=seeded_sampling(77),
reset=True,
)
runtime._refresh_sampling_seeds(initial)
original_device_seed = int(seed_buffer.updates[-1][0])
original_state = runtime._seed_state.snapshot()
moved = prepare(
runtime,
positions=(-1, 5),
sampling_params=seeded_sampling(None, 77),
slot_remap=torch.tensor([0, 0]),
reset=False,
)
runtime._refresh_sampling_seeds(moved)
state = runtime._seed_state.snapshot()
assert state.active == (False, True)
assert state.request_seeds == (None, 77)
assert state.token_counters[1] == original_state.token_counters[0]
assert int(seed_buffer.updates[-1][1]) == original_device_seed
def test_runtime_seed_changes_with_request_seed_and_decode_position():
seed_buffer = FakeLazySeedBuffer()
runtime = make_runtime(seed_buffer=seed_buffer)
runtime._refresh_sampling_seeds(
prepare(runtime, positions=(7, -1), sampling_params=seeded_sampling(101), reset=True)
)
position_7 = int(seed_buffer.updates[-1][0])
runtime._refresh_sampling_seeds(
prepare(runtime, positions=(8, -1), sampling_params=seeded_sampling(101), reset=False)
)
position_8 = int(seed_buffer.updates[-1][0])
runtime._refresh_sampling_seeds(
prepare(runtime, positions=(7, -1), sampling_params=seeded_sampling(202), reset=True)
)
different_request = int(seed_buffer.updates[-1][0])
assert len({position_7, position_8, different_request}) == 3
def test_runtime_explicit_seed_absolute_position_is_stable_across_reset_boundaries():
seed_buffer = FakeLazySeedBuffer()
runtime = make_runtime(seed_buffer=seed_buffer)
request = seeded_sampling(909)
runtime._refresh_sampling_seeds(prepare(runtime, positions=(19, -1), sampling_params=request, reset=True))
original = seed_buffer.updates[-1].clone()
runtime._refresh_sampling_seeds(prepare(runtime, positions=(20, -1), sampling_params=request, reset=False))
continued = seed_buffer.updates[-1].clone()
runtime._refresh_sampling_seeds(prepare(runtime, positions=(19, -1), sampling_params=request, reset=True))
restarted = seed_buffer.updates[-1].clone()
runtime._refresh_sampling_seeds(prepare(runtime, positions=(20, -1), sampling_params=request, reset=True))
resumed = seed_buffer.updates[-1].clone()
assert torch.equal(original, restarted)
assert torch.equal(continued, resumed)
def test_runtime_seed_refreshes_before_eager_model_invocation(monkeypatch):
events = []
seed_buffer = FakeLazySeedBuffer()
original_update = seed_buffer.update
def record_update(source):
events.append("seed")
original_update(source)
seed_buffer.update = record_update
runtime = make_runtime(seed_buffer=seed_buffer)
prepared = prepare(runtime, positions=(3, -1), sampling_params=seeded_sampling(77), reset=True)
monkeypatch.setattr(runtime, "_prepare_inputs_host", lambda prepared: object())
monkeypatch.setattr(
runtime,
"_stage_inputs_and_kpt",
lambda host, prepared: (DecodeDeviceInputs(None, None, None, None), None),
)
def run_body(*args, **kwargs):
events.append("invoke")
return object()
monkeypatch.setattr(runtime, "_run_body", run_body)
runtime.invoke(prepared)
assert events[-1] == "invoke"
assert events[:-1]
assert set(events[:-1]) == {"seed"}
def test_runtime_trace_captures_stable_seed_handle_and_refreshes_before_replay(monkeypatch):
events = []
seed_buffer = FakeLazySeedBuffer()
original_update = seed_buffer.update
def record_update(source):
events.append("seed")
original_update(source)
seed_buffer.update = record_update
runtime = make_runtime(seed_buffer=seed_buffer)
prepared = prepare(runtime, positions=(12, -1), sampling_params=seeded_sampling(88), reset=True)
monkeypatch.setattr(runtime, "_prepare_inputs_host", lambda prepared: object())
monkeypatch.setattr(
runtime,
"_stage_inputs_and_kpt",
lambda host, prepared: (DecodeDeviceInputs(None, None, None, None), None),
)
persistent = runtime.capture_plan(prepared).prepare_inputs()
assert persistent.seed_buffer is seed_buffer.get_device_buffer()
sampling = prepared.prepared_sampling
assert sampling is not None
persistent = dataclasses.replace(
persistent,
kpt_signature=[(sampling.top_k, sampling.top_p, sampling.temperature)],
)
runtime.refresh_trace(persistent, prepared, SimpleNamespace(full=False, page_table=False))
events.append("replay")
assert events[-1] == "replay"
assert events[:-1]
assert set(events[:-1]) == {"seed"}
def test_runtime_seed_handling_does_not_mutate_sampling_params():
seed_buffer = FakeLazySeedBuffer()
runtime = make_runtime(seed_buffer=seed_buffer)
sampling_params = seeded_sampling(11, 22)
before = dataclasses.asdict(sampling_params)
prepared = prepare(runtime, positions=(4, 9), sampling_params=sampling_params, reset=True)
runtime._refresh_sampling_seeds(prepared)
assert dataclasses.asdict(sampling_params) == before
def test_runtime_unseeded_stream_varies_and_same_absolute_position_is_idempotent():
seed_buffer = FakeLazySeedBuffer()
defaults = seed_buffer.source.clone()
runtime = make_runtime(seed_buffer=seed_buffer)
unseeded = prepare(runtime, positions=(5, -1), sampling_params=stochastic_sampling(), reset=True)
runtime._refresh_sampling_seeds(unseeded)
first = seed_buffer.updates[-1].clone()
first_state = runtime._seed_state.snapshot()
assert int(first[0]) != int(defaults[0])
assert int(first[1]) == int(defaults[1])
runtime._refresh_sampling_seeds(dataclasses.replace(unseeded, reset_batch=False))
repeated = seed_buffer.updates[-1].clone()
assert torch.equal(repeated, first)
advanced = prepare(
runtime,
positions=(6, -1),
sampling_params=stochastic_sampling(),
reset=False,
)
runtime._refresh_sampling_seeds(advanced)
snapshot = runtime._seed_state.snapshot()
assert snapshot.request_seeds == (None, None)
assert snapshot.active == (True, False)
assert snapshot.token_counters[0] == 2
assert snapshot.unseeded_rng_states[0] != first_state.unseeded_rng_states[0]
def test_runtime_seed_change_requires_reset_and_preserves_unseeded_survivor(expect_error):
seed_buffer = FakeLazySeedBuffer()
runtime = make_runtime(seed_buffer=seed_buffer)
unseeded = prepare(
runtime,
positions=(10, 10),
sampling_params=stochastic_sampling(seed=[None, None]),
reset=True,
)
runtime._refresh_sampling_seeds(unseeded)
initial = runtime._seed_state.snapshot()
mixed = prepare(
runtime,
positions=(11, 11),
sampling_params=stochastic_sampling(seed=[None, 42]),
reset=False,
)
with expect_error(RuntimeError, "reset_batch=True"):
runtime._refresh_sampling_seeds(mixed)
runtime._refresh_sampling_seeds(dataclasses.replace(mixed, reset_batch=True))
admitted = runtime._seed_state.snapshot()
assert admitted.request_seeds == (None, 42)
assert admitted.active == (True, True)
assert admitted.token_counters[0] == initial.token_counters[0] + 1
continued = dataclasses.replace(mixed, start_pos=torch.tensor([12, 12]))
runtime._refresh_sampling_seeds(continued)
continued_state = runtime._seed_state.snapshot()
assert continued_state.token_counters[0] == admitted.token_counters[0] + 1
assert continued_state.request_seeds == (None, 42)
def test_runtime_inactive_peer_is_cleaned_up_without_readmitting_survivor():
seed_buffer = FakeLazySeedBuffer()
defaults = seed_buffer.source.clone()
runtime = make_runtime(seed_buffer=seed_buffer)
initial = prepare(
runtime,
positions=(20, 20),
sampling_params=stochastic_sampling(seed=[None, 42]),
reset=True,
)
runtime._refresh_sampling_seeds(initial)
initial_state = runtime._seed_state.snapshot()
peer_left = prepare(
runtime,
positions=(21, -1),
sampling_params=stochastic_sampling(seed=[None, 42]),
reset=False,
)
runtime._refresh_sampling_seeds(peer_left)
state = runtime._seed_state.snapshot()
assert state.active == (True, False)
assert state.request_seeds == (None, None)
assert state.token_counters[0] == initial_state.token_counters[0] + 1
assert int(seed_buffer.updates[-1][1]) == int(defaults[1])
@pytest.mark.parametrize(
("method", "expected"),
(
(
DecodeRuntime.prepare,
(
("self", inspect.Parameter.POSITIONAL_OR_KEYWORD, inspect.Parameter.empty),
("tokens", inspect.Parameter.POSITIONAL_OR_KEYWORD, inspect.Parameter.empty),
("start_pos", inspect.Parameter.POSITIONAL_OR_KEYWORD, inspect.Parameter.empty),
("page_table", inspect.Parameter.POSITIONAL_OR_KEYWORD, inspect.Parameter.empty),
("sampling_params", inspect.Parameter.KEYWORD_ONLY, None),
("prompt_tokens", inspect.Parameter.KEYWORD_ONLY, None),
("output_tokens", inspect.Parameter.KEYWORD_ONLY, None),
("slot_remap", inspect.Parameter.KEYWORD_ONLY, None),
("reset_batch", inspect.Parameter.KEYWORD_ONLY, False),
),
),
(
DecodeRuntime.read_decode_output,
(
("self", inspect.Parameter.POSITIONAL_OR_KEYWORD, inspect.Parameter.empty),
("tt_out", inspect.Parameter.POSITIONAL_OR_KEYWORD, inspect.Parameter.empty),
("async_read", inspect.Parameter.KEYWORD_ONLY, False),
),
),
(
DecodeRuntime.process_decode_output_host,
(
("self", inspect.Parameter.POSITIONAL_OR_KEYWORD, inspect.Parameter.empty),
("tt_out", inspect.Parameter.POSITIONAL_OR_KEYWORD, inspect.Parameter.empty),
("is_tokens", inspect.Parameter.KEYWORD_ONLY, False),
),
),
),
)
def test_runtime_api_signatures_are_exact(method, expected):
parameters = inspect.signature(method).parameters.values()
assert tuple((parameter.name, parameter.kind, parameter.default) for parameter in parameters) == expected
def test_runtime_api_preserves_positional_prefixes_and_rejects_extra_fields(expect_error):
cases = (
(DecodeRuntime.prepare, ("tokens", "start_pos", "page_table"), "sampling_params"),
(DecodeRuntime.read_decode_output, ("tt_out",), "async_read"),
(DecodeRuntime.process_decode_output_host, ("tt_out",), "is_tokens"),
)
for method, positional_prefix, keyword_name in cases:
signature = inspect.signature(method)
signature.bind(None, *positional_prefix, **{keyword_name: True})
with expect_error(TypeError, "too many positional arguments"):
signature.bind(None, *positional_prefix, True)
with expect_error(TypeError, "unexpected keyword argument 'unknown'"):
signature.bind(None, *positional_prefix, unknown=True)
assert inspect.get_annotations(DecodeRuntime.prepare, eval_str=True)["sampling_params"] is Any
def test_config_resolves_canonical_static_capabilities_and_is_frozen(expect_error):
mesh = FakeMesh()
model = FakeModel()
config = DecodeRuntimeConfig.resolve(
model=model,
output_reader=OutputReader(mesh),
lane_capacity=2,
page_table_layout=page_table_layout(),
device_sampling_enabled=True,
)
assert config.cluster_shape == (1, 1)
assert config.num_devices == 1
assert config.vocab_size == 8
assert config.allow_force_argmax
assert config.max_device_top_k == 32
assert config.sampling_batch_size == 2
assert config.sampling_state_controller is None
assert config.sampling_state is None
assert config.position_feedback_capable
with expect_error(dataclasses.FrozenInstanceError, "cannot assign to field"):
config.lane_capacity = 1
with expect_error(TypeError, "DecodeRuntimeConfig"):
DecodeRuntime(config=None)
def test_config_rejects_inconsistent_collaborators_and_dimensions(expect_error):
mesh = FakeMesh()
model = FakeModel()
model.config.mesh_device = mesh
with expect_error(ValueError, "same mesh_device"):
DecodeRuntimeConfig.resolve(
model=model,
output_reader=OutputReader(FakeMesh()),
lane_capacity=2,
page_table_layout=page_table_layout(),
device_sampling_enabled=True,
)
with expect_error(ValueError, "positive integer"):
DecodeRuntimeConfig.resolve(
model=model,
output_reader=OutputReader(mesh),
lane_capacity=True,
page_table_layout=page_table_layout(),
device_sampling_enabled=True,
)
with expect_error(TypeError, "PageTableLayout"):
DecodeRuntimeConfig.resolve(
model=model,
output_reader=OutputReader(mesh),
lane_capacity=2,
page_table_layout=SimpleNamespace(raw_capacity_width=8, decode_width=8, block_size=32),
device_sampling_enabled=True,
)
def test_layout_replacement_is_immutable_and_bounded(expect_error):
runtime = make_runtime()
original = runtime.config
replacement = page_table_layout(raw_width=4)
runtime.configure_page_table_layout(replacement)
assert runtime.config is not original
assert original.page_table_layout.raw_capacity_width == 8
assert runtime.config.page_table_layout is replacement
with expect_error(ValueError, "block_size"):
runtime.configure_page_table_layout(page_table_layout(raw_width=4, block_size=16))
with expect_error(ValueError, "ceiling"):
runtime.configure_page_table_layout(page_table_layout(raw_width=9))
with expect_error(ValueError, "decode width"):
runtime.configure_page_table_layout(PageTableLayout(32, 4, 8, 1024))
def test_sampling_admission_follows_resolved_configuration(expect_error):
runtime = make_runtime(sampling=False)
with expect_error(ValueError, "device sampling is disabled"):
prepare(runtime, sampling_params=greedy_sampling())
def test_feedback_and_sampling_path_follow_resolved_capabilities():
argmax_runtime = make_runtime()
argmax_prepared = prepare(argmax_runtime, sampling_params=greedy_sampling())
assert argmax_prepared.device_feedback
assert argmax_prepared.sampling_path == "argmax"
model = FakeModel()
model.increment_positions = None
mesh = FakeMesh()
no_feedback = DecodeRuntime(
DecodeRuntimeConfig.resolve(
model=model,
output_reader=OutputReader(mesh),
lane_capacity=2,
page_table_layout=page_table_layout(),
device_sampling_enabled=True,
force_greedy_top_k=True,
)
)
prepared = prepare(no_feedback, sampling_params=greedy_sampling())
assert not prepared.device_feedback
assert prepared.sampling_path == "topk"
def test_single_and_multi_device_logits_conversion(monkeypatch):
single = make_runtime()
logits = torch.arange(16, dtype=torch.float32).reshape(1, 1, 2, 8)
monkeypatch.setattr(ttnn, "to_torch", lambda value: logits)
converted, _ = single._normalize_host_output("single-device", is_tokens=False)
assert converted.shape == (2, 1, 8)
mesh = SimpleNamespace(shape=(1, 2))
model = FakeModel()
model.num_devices = 2
multi = DecodeRuntime(
DecodeRuntimeConfig.resolve(
model=model,
output_reader=OutputReader(mesh),
lane_capacity=2,
page_table_layout=page_table_layout(),
device_sampling_enabled=True,
)
)
calls = []
monkeypatch.setattr(
decode_module,
"_concat_host_output",
lambda value, shape: calls.append((value, shape)) or logits,
)
converted, _ = multi._normalize_host_output("multi-device", is_tokens=False)
assert converted.shape == (2, 1, 8)
assert calls == [("multi-device", (1, 2))]
def test_signatures_expose_ordered_material_and_separate_types():
runtime = make_runtime()
prepared = prepare(runtime, sampling_params=greedy_sampling())
program = runtime.program_signature(prepared)
trace = runtime.trace_signature(prepared)
assert isinstance(program, DecodeProgramSignature)
assert isinstance(trace, DecodeTraceSignature)
assert program.key_material() == (
("operation", "decode"),
("batch_size", 2),
("page_table_width", 8),
("sampling_path", "argmax"),
("device_feedback", True),
)
assert trace.key_material() == program.key_material()
assert runtime.program_signature(prepare(runtime)).sampling_path == "logits"
def test_signature_tracks_native_penalty_and_sampled_logprob_program_modes():
runtime = make_runtime()
sampling = SamplingParams(
temperature=[0.0, 0.0],
top_k=[1, 1],
top_p=[1.0, 1.0],
presence_penalty=[0.5, 0.0],
enable_log_probs=[True, False],
num_logprobs=[0, -2],
)
prepared = prepare(runtime, sampling_params=sampling)
native = prepared.prepared_sampling
signature = runtime.program_signature(prepared)
assert native is not None
assert native.logprob_modes == ("sampled_token", "none")
assert native.penalties_enabled
assert native.log_probs_enabled
assert prepared.sampling_path == "topk"
assert signature.penalties_enabled
assert signature.logprobs_enabled
assert signature.key_material()[-2:] == (
("penalties_enabled", True),
("logprobs_enabled", True),
)
def test_configured_topk_policy_is_not_collapsed_to_argmax_by_greedy_temperature():
runtime = make_runtime(force_greedy_top_k=True)
sampling = SamplingParams(temperature=[0.0, 0.0], top_k=[32, 32], top_p=[0.08, 0.08])
prepared = prepare(runtime, sampling_params=sampling)
assert prepared.sampling_path == "topk"
def test_unconfigured_topk_values_use_argmax_for_greedy_temperature():
runtime = make_runtime()
sampling = SamplingParams(temperature=[0.0, 0.0], top_k=[32, 32], top_p=[0.08, 0.08])
assert prepare(runtime, sampling_params=sampling).sampling_path == "argmax"
def test_sampling_params_are_prepared_once_and_reused_for_kpt(monkeypatch):
runtime = make_runtime(force_greedy_top_k=True)
calls = []
formatter = decode_module.prepare_sampling_params
def formatter_spy(*args, **kwargs):
calls.append((args, kwargs))
return formatter(*args, **kwargs)
monkeypatch.setattr(
decode_module,
"prepare_sampling_params",
formatter_spy,
)
prepared = prepare(runtime, sampling_params=greedy_sampling())
monkeypatch.setattr(ttnn, "ReplicateTensorToMesh", lambda mesh: "mapper")
monkeypatch.setattr(ttnn, "from_torch", lambda value, **kwargs: value)
runtime._make_host_kpt(prepared)
assert len(calls) == 1
def test_tile_padded_sampler_preserves_one_semantic_lane_and_neutralizes_inactive_rows(monkeypatch):
mesh = FakeMesh()
model = FakeModel()
model.config.max_batch_size = 1
model.sampling.config.max_batch_size = 32
runtime = DecodeRuntime(
DecodeRuntimeConfig.resolve(
model=model,
output_reader=OutputReader(mesh),
lane_capacity=1,
page_table_layout=page_table_layout(),
device_sampling_enabled=True,
force_greedy_top_k=True,
)
)
prepared = runtime.prepare(
torch.tensor([11]),
torch.tensor([0]),
torch.tensor([[3, 4, 5]], dtype=torch.int32),
sampling_params=SamplingParams(
temperature=0.7,
top_k=32,
top_p=0.08,
presence_penalty=0.25,
frequency_penalty=0.5,
repetition_penalty=1.2,
seed=17,
enable_log_probs=True,
num_logprobs=1,
),
)
sampling = prepared.prepared_sampling
assert runtime.config.lane_capacity == 1
assert runtime.config.sampling_batch_size == 32
assert sampling.batch_size == 32
assert sampling.active_rows == 1
assert sampling.active_mask == (True,) + (False,) * 31
assert sampling.top_k == (32,) + (1,) * 31
assert sampling.top_p == pytest.approx((0.08,) + (0.0,) * 31)
assert sampling.temperature == pytest.approx((1.0 / 0.7,) + (1.0,) * 31)
assert sampling.seeds == (17,) + (None,) * 31
assert sampling.presence_penalty == pytest.approx((0.25,) + (0.0,) * 31)
assert sampling.frequency_penalty == pytest.approx((0.5,) + (0.0,) * 31)
assert sampling.repetition_penalty == pytest.approx((1.2,) + (1.0,) * 31)
assert sampling.enable_log_probs == (True,) + (False,) * 31
assert sampling.num_logprobs == (1,) + (0,) * 31
assert sampling.logprob_modes[1:] == ("none",) * 31
monkeypatch.setattr(ttnn, "ReplicateTensorToMesh", lambda mesh_device: "mapper")
monkeypatch.setattr(ttnn, "from_torch", lambda value, **kwargs: value)
k, p, temperature = runtime._make_host_kpt(prepared)
assert tuple(k.shape) == tuple(p.shape) == tuple(temperature.shape) == (32,)
assert k.tolist() == [32] + [1] * 31
assert p.tolist() == pytest.approx([0.08] + [0.0] * 31)
assert temperature.tolist() == pytest.approx([1.0 / 0.7] + [1.0] * 31)
def test_normalization_preserves_feedback_lookahead_and_inactive_convention():
runtime = make_runtime()
page_table = torch.tensor([[10, 11, 99], [20, 21, 98]], dtype=torch.int64)
prepared = prepare(
runtime,
positions=(31, -1),
page_table=page_table,
sampling_params=greedy_sampling(),
)
assert prepared.page_table.dtype == torch.int32
assert prepared.page_table.shape == (2, 8)
assert prepared.page_table[0].tolist() == [10, 11, 0, 0, 0, 0, 0, 0]
assert prepared.page_table[1].tolist() == [0, 0, 0, 0, 0, 0, 0, 0]
def test_normalization_reuses_equal_source_with_same_copy_counts():
runtime = make_runtime()
first = prepare(runtime, positions=(0, -1))
second = prepare(runtime, positions=(1, -1))
assert second.page_table is first.page_table
def test_normalization_cache_detects_in_place_source_mutation():
runtime = make_runtime()
source = torch.tensor([[3, 4], [6, 7]], dtype=torch.int32)
first = prepare(runtime, positions=(0, -1), page_table=source)
source[0, 0] = 9
second = prepare(runtime, positions=(0, -1), page_table=source)
assert second.page_table is not first.page_table
assert second.page_table[0, 0].item() == 9
def test_normalization_cache_misses_when_copy_counts_or_feedback_change():
runtime = make_runtime()
source = torch.tensor([[3, 4], [6, 7]], dtype=torch.int32)
one_block = prepare(runtime, positions=(0, -1), page_table=source)
two_blocks = prepare(runtime, positions=(32, -1), page_table=source)
no_feedback = prepare(runtime, positions=(31, -1), page_table=source)
with_feedback = prepare(
runtime,
positions=(31, -1),
page_table=source,
sampling_params=greedy_sampling(),
)
assert two_blocks.page_table is not one_block.page_table
assert no_feedback.page_table[0, 1].item() == 0
assert with_feedback.page_table is not no_feedback.page_table
assert with_feedback.page_table[0, 1].item() == 4
def test_fixed_capacity_and_page_table_capacity_are_validated(expect_error):
runtime = make_runtime()
with expect_error(ValueError, "must equal lane capacity"):
runtime.prepare(torch.tensor([1]), torch.tensor([0]), torch.tensor([[1]]))
with expect_error(ValueError, "batches must match"):
runtime.prepare(
torch.tensor([1, 2]),
torch.tensor([0]),
torch.tensor([[1], [2]]),
)
with expect_error(ValueError, "paged-KV capacity"):
prepare(runtime, positions=(8 * 32, -1))
with expect_error(ValueError, "too narrow"):
prepare(
runtime,
positions=(64, -1),
page_table=torch.tensor([[1, 2], [0, 0]], dtype=torch.int32),
)
def test_preparation_tracks_first_used_page_change_reset_and_ignores_unused_tail():
runtime = make_runtime()
first = prepare(runtime, positions=(0, -1), reset=True)
assert first.page_table_changed
assert first.reset_batch
runtime.note_submitted(first)
same_semantics = prepare(
runtime,
positions=(0, -1),
page_table=torch.tensor([[3, 90, 91], [88, 87, 86]], dtype=torch.int32),
)
assert not same_semantics.page_table_changed
assert not same_semantics.reset_batch
changed = prepare(
runtime,
positions=(0, -1),
page_table=torch.tensor([[4, 90, 91], [88, 87, 86]], dtype=torch.int32),
)
assert changed.page_table_changed
def test_submission_state_tracks_last_table_despite_stale_prepare_change_hint():
runtime = make_runtime()
baseline = prepare(
runtime,
positions=(0, -1),
page_table=torch.tensor([[3], [0]], dtype=torch.int32),
)
runtime.note_submitted(baseline)
changed = prepare(
runtime,
positions=(0, -1),
page_table=torch.tensor([[4], [0]], dtype=torch.int32),
)
back_to_baseline = prepare(
runtime,
positions=(0, -1),
page_table=torch.tensor([[3], [0]], dtype=torch.int32),
)
assert changed.page_table_changed
assert not back_to_baseline.page_table_changed
runtime.note_submitted(changed)
runtime.note_submitted(back_to_baseline)
assert not prepare(runtime, page_table=back_to_baseline.page_table).page_table_changed
assert prepare(
runtime,
positions=(0, -1),
page_table=torch.tensor([[4], [0]], dtype=torch.int32),
).page_table_changed
def test_capture_plan_describes_full_step_refresh_and_typed_persistent_inputs(monkeypatch):
runtime = make_runtime()
prepared = prepare(runtime, sampling_params=greedy_sampling())
device = DecodeDeviceInputs("tokens", "positions", "rotary", "page_table")
monkeypatch.setattr(runtime, "_prepare_inputs_host", lambda request: "host")
monkeypatch.setattr(runtime, "_stage_inputs_and_kpt", lambda host, request: (device, "kpt"))
def run_body(
inputs,
prepared,
kpt,
*,
device_feedback,
count_tokens=True,
advance_seeds=True,
):
return "captured"
monkeypatch.setattr(runtime, "_run_body", run_body)
plan = runtime.capture_plan(prepared)
persistent = plan.prepare_inputs()
assert persistent.device_inputs is device
assert persistent.kpt == "kpt"
sampling = prepared.prepared_sampling
assert sampling is not None
assert persistent.kpt_signature == [(sampling.top_k, sampling.top_p, sampling.temperature)]
assert plan.capture(persistent) == "captured"
assert plan.refresh_policy.every_replay == ("sampling",)
assert plan.refresh_policy.full_on_batch_reset
assert plan.refresh_policy.full_on_graph_switch
assert plan.refresh_policy.full_without_device_feedback
assert plan.refresh_policy.refresh_page_table_on_change
def test_trace_refresh_skips_unchanged_sampling_values(monkeypatch):
runtime = make_runtime(force_greedy_top_k=True)
prepared = prepare(runtime, sampling_params=greedy_sampling())
sampling = prepared.prepared_sampling
assert sampling is not None
persistent = DecodePersistentInputs(
device_inputs=DecodeDeviceInputs("tokens", "positions", "rotary", "page_table"),
kpt="kpt",
kpt_signature=[(sampling.top_k, sampling.top_p, sampling.temperature)],
)
def fail_refresh_kpt(device_kpt, prepared):
pytest.fail("unchanged KPT was refreshed")
monkeypatch.setattr(runtime, "_refresh_kpt", fail_refresh_kpt)
runtime.refresh_trace(
persistent,
prepared,
SimpleNamespace(full=False, page_table=False),
)
def test_eager_invoke_returns_owned_result_and_advances_submission_state(monkeypatch):
runtime = make_runtime()
prepared = prepare(runtime)
device = DecodeDeviceInputs("tokens", "positions", "rotary", "page_table")
calls = []
monkeypatch.setattr(runtime, "_prepare_inputs_host", lambda request: "host")
monkeypatch.setattr(runtime, "_stage_inputs_and_kpt", lambda host, request: (device, None))
monkeypatch.setattr(
runtime,
"_run_body",
lambda inputs, prepared, kpt, *, device_feedback, **kwargs: calls.append(device_feedback) or ("raw", None),
)
result = runtime.invoke(prepared)
assert isinstance(result, InvocationResult)
assert result.value == ("raw", None)
assert result.owned == (("raw", None), (device, None))
assert not result.is_tokens
assert calls == [False]
assert not prepare(runtime, page_table=prepared.page_table).page_table_changed
def test_blocking_consume_normalizes_logits_and_releases_owned_values(monkeypatch):
runtime = make_runtime()
host_logits = torch.arange(16, dtype=torch.float32).reshape(1, 1, 2, 8)
released = []
monkeypatch.setattr(runtime.config.output_reader, "read", lambda value, *, blocking: (host_logits, "probs"))
monkeypatch.setattr(runtime, "_release_or_retain_transient", lambda value: released.append(value) or [])
result = InvocationResult(value="raw", owned="owned", is_tokens=False)
logits, log_probs = runtime.consume(result)
assert logits.shape == (2, 1, 8)
assert log_probs == "probs"
assert released == ["owned"]
def test_sampled_token_logprobs_are_flattened_to_lane_row_order():
runtime = make_runtime()
host_tokens = torch.tensor([[[[7], [8]]]], dtype=torch.int32)
host_log_probs = torch.tensor([[[[-0.25, -0.75]]]], dtype=torch.bfloat16)
tokens, log_probs = runtime._normalize_host_output(
(host_tokens, host_log_probs),
is_tokens=True,
)
assert tokens.tolist() == [7, 8]
assert tokens.dtype == torch.int64
assert log_probs.tolist() == pytest.approx([-0.25, -0.75])
assert log_probs.dtype == torch.float32
def test_raw_blocking_and_async_leases_release_exact_records(monkeypatch):
runtime = make_runtime()
deallocated = []
monkeypatch.setattr(
decode_module,
"best_effort_deallocate_owned_tensors",
lambda values, completed: deallocated.append(values) or [],
)
first = InvocationResult(value=object(), owned="first-owned", is_tokens=False)
assert runtime.consume(first, read_from_device=False) is first.value
monkeypatch.setattr(runtime.config.output_reader, "read", lambda value, *, blocking: "first-host")
assert runtime.read_decode_output(first.value) == "first-host"
second = InvocationResult(value=object(), owned="second-owned", is_tokens=True)
runtime.consume(second, read_from_device=False)
host_tokens = torch.tensor([[[[7], [8]]]], dtype=torch.int32)
pending = PendingRead(value=(host_tokens, None), events=("event",), sequence=4, _owner=object())
monkeypatch.setattr(runtime.config.output_reader, "submit", lambda value: pending)
monkeypatch.setattr(runtime.config.output_reader, "complete", lambda value: pending.value)
host, events = runtime.read_decode_output(second.value, async_read=True)
assert host is pending.value
assert events == ["event"]
tokens, log_probs = runtime.process_decode_output_host(host, is_tokens=True)
assert tokens.tolist() == [7, 8]
assert tokens.dtype == torch.int64
assert log_probs is None
assert deallocated == [
(first.value, "first-owned"),
(second.value, "second-owned"),
]
def test_async_trace_lease_never_releases_borrowed_trace_output(monkeypatch):
runtime = make_runtime()
deallocated = []
raw = object()
host_tokens = torch.tensor([[[[7], [8]]]], dtype=torch.int32)
pending = PendingRead(value=(host_tokens, None), events=("event",), sequence=4, _owner=object())
monkeypatch.setattr(
decode_module,
"best_effort_deallocate_owned_tensors",
lambda values, completed: deallocated.append(values) or [],
)
monkeypatch.setattr(runtime.config.output_reader, "submit", lambda value: pending)
monkeypatch.setattr(runtime.config.output_reader, "complete", lambda value: pending.value)
result = InvocationResult(value=raw, owned=None, is_tokens=True)
assert runtime.consume(result, read_from_device=False) is raw
host, events = runtime.read_decode_output(raw, async_read=True)
assert events == ["event"]
tokens, log_probs = runtime.process_decode_output_host(host, is_tokens=True)
assert tokens.tolist() == [7, 8]
assert log_probs is None
assert deallocated == []
def test_failed_transient_release_blocks_use_and_cleanup_retries(monkeypatch, expect_error):
runtime = make_runtime()
class FakeTensor:
pass
tensor = FakeTensor()
attempts = []
monkeypatch.setattr(decode_module.ttnn, "Tensor", FakeTensor)
def deallocate(value):
attempts.append(value)
if len(attempts) == 1:
raise RuntimeError("release failed")
monkeypatch.setattr(decode_module.ttnn, "deallocate", deallocate)
failures = runtime._release_or_retain_transient(tensor)
assert [str(error) for error in failures] == ["release failed"]
assert runtime.transient_orphan_count == 1
with expect_error(RuntimeError, "unreleased transient"):
prepare(runtime)
runtime.cleanup_transients()
assert attempts == [tensor, tensor]
assert runtime.transient_orphan_count == 0
assert prepare(runtime).sampling_path == "logits"
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