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# SPDX-License-Identifier: Apache-2.0
"""Prefill trace capture, refresh, replay state, and ownership."""
from __future__ import annotations
from dataclasses import dataclass
from typing import Any, Callable
from models.common.llm_runtime.prefill.inputs import (
PrefillDeviceInputs,
PrefillInputStager,
PrefillPositionInputs,
allocate_device_tensors,
copy_into_device_tensors,
)
from models.common.llm_runtime.prefill.plan import PrefillChunk
from models.common.llm_runtime.prefill.postprocess import (
KPTSignature,
PrefillPostprocessor,
new_logprob_output,
without_borrowed,
)
from models.common.llm_runtime.prefill.result_collector import InvocationResult
from models.common.llm_runtime.prefill.signatures import (
PrefillTraceSignature,
PreparedPrefill,
capture_schema_fingerprint,
workspace_fingerprint,
)
from models.common.llm_runtime.tensor_resources import attach_cleanup_failures
@dataclass(frozen=True)
class PrefillHiddenPersistentInputs:
"""Canonical trace-owned model inputs; deliberately sampling-free."""
device_inputs: PrefillDeviceInputs
def owned_tensor_values(self) -> tuple[Any, ...]:
return self.device_inputs.model_values()
@dataclass
class PrefillReplayState:
"""Mutable program-alias-local postprocessing and sampling state."""
position_inputs: PrefillPositionInputs
kpt: tuple[Any, Any, Any] | None
sampled_output: Any | None = None
position_signature: int | None = None
kpt_signature: KPTSignature = None
def owned_tensor_values(self) -> tuple[Any, ...]:
return self.position_inputs.values(), self.kpt, self.sampled_output
@dataclass(frozen=True)
class PrefillReplayOwnership:
"""Explicit ownership split for one hidden-trace postprocess result."""
trace_owned_hidden_output: Any
nested_persistent_output: Any | None
new_logprob_output: Any | None
replay_local_intermediates: tuple[Any, ...]
@dataclass(frozen=True)
class PrefillCapturePlan:
"""Operation hooks consumed by the trace compiler."""
signature: PrefillTraceSignature
prepare_inputs: Callable[[], PrefillHiddenPersistentInputs]
capture: Callable[[PrefillHiddenPersistentInputs], Any]
prepare_workspace: Callable[[], PrefillReplayState]
schema_fingerprint: tuple[Any, ...]
workspace_fingerprint: tuple[Any, ...]
refresh_fields: tuple[str, ...] = ("tokens", "page_table", "last_token", "sampling")
prime: Callable[[PrefillHiddenPersistentInputs], Any] | None = None
release_prime_output: Callable[[Any], list[BaseException]] | None = None
@dataclass(frozen=True)
class PrefillTraceHooks:
"""Narrow input, model-body, postprocess, and cleanup collaborators."""
input_stager: PrefillInputStager
postprocessor: PrefillPostprocessor
run_hidden_body: Callable[..., Any]
run_chunk_hidden_body: Callable[..., Any]
release_transient: Callable[[Any], list[BaseException]]
trace_capture_prime_sequence_lengths: tuple[int, ...] = ()
class PrefillTraceLifecycle:
"""Own hidden-trace capture, replay refresh, and replay result ownership."""
def __init__(self, *, hooks: PrefillTraceHooks) -> None:
self.hooks = hooks
def capture_plan(self, prepared: PreparedPrefill) -> PrefillCapturePlan:
if prepared.trace_signature is None:
raise ValueError("prepared prefill request has no configured trace family")
def prepare_inputs() -> PrefillHiddenPersistentInputs:
return self._prepare_hidden_persistent_inputs(prepared)
def prepare_workspace() -> PrefillReplayState:
return self._prepare_replay_state(prepared)
def capture(persistent: PrefillHiddenPersistentInputs) -> Any:
if prepared.request.uses_chunked_prefill:
return self.hooks.run_chunk_hidden_body(
prepared,
prepared.request.chunks[0],
persistent.device_inputs,
)
# The captured padded identity must record every physical row so
# replay does not depend on which active count registered it.
return self.hooks.run_hidden_body(
prepared.request,
persistent.device_inputs,
fill_rows=prepared.request.padded_batch_size,
)
should_prime = (
prepared.request.kind == "batched"
and prepared.trace_signature.padded_sequence_length in self.hooks.trace_capture_prime_sequence_lengths
)
return PrefillCapturePlan(
signature=prepared.trace_signature,
prepare_inputs=prepare_inputs,
capture=capture,
prepare_workspace=prepare_workspace,
schema_fingerprint=capture_schema_fingerprint(prepared),
workspace_fingerprint=workspace_fingerprint(
prepared,
sampling_output_rows=self.hooks.postprocessor.sampling_output_rows(prepared),
),
prime=capture if should_prime else None,
release_prime_output=self.hooks.release_transient if should_prime else None,
)
def refresh(
self,
prepared: PreparedPrefill,
persistent: PrefillHiddenPersistentInputs,
state: PrefillReplayState,
chunk: PrefillChunk | None = None,
) -> None:
request = prepared.request
if request.uses_chunked_prefill:
self._refresh_chunk_inputs(prepared, chunk or request.chunks[0], persistent, state)
return
relative_last = max(last - cached for last, cached in zip(request.last_token_indices, request.cached_tokens))
# Rotary positions are fixed for regular trace families; only tokens
# and the page table vary on every replay.
self.hooks.input_stager.refresh_regular_device_inputs(request, persistent.device_inputs)
# Host-sampling single requests now use the runtime last-token bounds as well (see
# finish_regular_prefill), so their position inputs must follow the prompt on every replay.
if not self.hooks.postprocessor.uses_static_q128_topk(request, prepared.sampling_path):
if state.position_signature != relative_last:
position_inputs = self.hooks.input_stager.prepare_position_inputs_host(
relative_last,
request.padded_sequence_length,
)
copy_into_device_tensors(position_inputs.values(), state.position_inputs.values())
state.position_signature = relative_last
state.kpt_signature = self.hooks.postprocessor.refresh_workspace_sampling(
prepared,
kpt=state.kpt,
kpt_signature=state.kpt_signature,
)
def finish(
self,
prepared: PreparedPrefill,
hidden: Any,
state: PrefillReplayState,
) -> InvocationResult:
replay_local: list[Any] = []
output = self.hooks.postprocessor.finish_regular_prefill(
prepared,
hidden,
state.kpt if prepared.sampling_path == "topk" else None,
state.position_inputs,
sampled_output=state.sampled_output,
owned=replay_local,
)
new_logprob = new_logprob_output(output, state.sampled_output)
sampled_output_alias = output[0] if state.sampled_output is not None else None
caller_owned = without_borrowed(
replay_local,
(hidden, state.sampled_output, sampled_output_alias),
)
ownership = PrefillReplayOwnership(
trace_owned_hidden_output=hidden,
nested_persistent_output=state.sampled_output,
new_logprob_output=new_logprob,
replay_local_intermediates=without_borrowed(
replay_local,
(hidden, state.sampled_output, sampled_output_alias, new_logprob),
),
)
return InvocationResult(value=output, owned=caller_owned, replay_ownership=ownership)
def _prepare_hidden_persistent_inputs(self, prepared: PreparedPrefill) -> PrefillHiddenPersistentInputs:
request = prepared.request
trace_inputs = self.hooks.input_stager.trace_inputs(request)
host_inputs = self.hooks.input_stager.prepare_host_inputs(
trace_inputs.tokens,
request.page_table,
start_pos=trace_inputs.start_pos,
chunk_page_table=trace_inputs.chunk_page_table,
chunk_start_idx=trace_inputs.chunk_start_idx,
last_token_idx=max(request.last_token_indices),
)
device_inputs = None
try:
device_inputs = self.hooks.input_stager.stage_device_inputs(host_inputs)
if request.uses_chunked_prefill:
self.hooks.input_stager.copy_rotary_inputs(device_inputs)
except BaseException as primary:
failures = self.hooks.release_transient(device_inputs)
attach_cleanup_failures(primary, failures)
raise
return PrefillHiddenPersistentInputs(device_inputs=device_inputs)
def _prepare_replay_state(self, prepared: PreparedPrefill) -> PrefillReplayState:
trace_inputs = self.hooks.input_stager.trace_inputs(prepared.request)
position_inputs = None
kpt = None
sampled_output = None
try:
sampling_batch_size = self.hooks.postprocessor.sampling_output_rows(prepared)
position_values = allocate_device_tensors(
self.hooks.input_stager.prepare_position_inputs_host(
trace_inputs.relative_last,
trace_inputs.sequence_length,
).values(),
mesh_device=self.hooks.input_stager.mesh_device,
)
position_inputs = PrefillPositionInputs(*position_values)
kpt = self.hooks.postprocessor.make_device_kpt(
self.hooks.postprocessor.prepared_sampling(prepared),
sampling_batch_size,
force_topk=prepared.sampling_path == "topk",
)
prepared_sampling = self.hooks.postprocessor.prepared_sampling(prepared)
if prepared_sampling is not None:
sampled_output = self.hooks.postprocessor.make_sampling_output(
self.hooks.postprocessor.sampling_output_rows(prepared)
)
except BaseException as primary:
failures = self.hooks.release_transient((position_inputs, kpt, sampled_output))
attach_cleanup_failures(primary, failures)
raise
kpt_signature = None
if prepared_sampling is not None:
kpt_signature = self.hooks.postprocessor.kpt_values(prepared_sampling, sampling_batch_size)
return PrefillReplayState(
position_inputs=position_inputs,
kpt=kpt,
sampled_output=sampled_output,
position_signature=trace_inputs.relative_last,
kpt_signature=kpt_signature,
)
def _refresh_chunk_inputs(
self,
prepared: PreparedPrefill,
chunk: PrefillChunk,
persistent: PrefillHiddenPersistentInputs,
state: PrefillReplayState,
) -> None:
request = prepared.request
self.hooks.input_stager.refresh_chunk_device_inputs(request, chunk, persistent.device_inputs)
final_chunk = request.chunks[-1]
relative_last = (request.last_token_indices[0] - final_chunk.chunk_start_idx) % final_chunk.chunk_size
if state.position_signature != relative_last:
position_inputs = self.hooks.input_stager.prepare_position_inputs_host(
relative_last,
final_chunk.chunk_size,
)
copy_into_device_tensors(position_inputs.values(), state.position_inputs.values())
state.position_signature = relative_last
state.kpt_signature = self.hooks.postprocessor.refresh_workspace_sampling(
prepared,
kpt=state.kpt,
kpt_signature=state.kpt_signature,
)
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