467 lines
20 KiB
Python
467 lines
20 KiB
Python
import dataclasses
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import functools
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from dataclasses import dataclass, field
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from typing import (Any, Callable, Dict, List, Optional, Union)
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import torch
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from vllm.distributed import get_pp_group
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from vllm.logger import init_logger
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from vllm.model_executor.layers.sampler import SamplerOutput
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from vllm.sequence import (IntermediateTensors, SequenceGroupMetadata)
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from ..model_executor.model_loader.tensorizer import TensorizerConfig
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logger = init_logger(__name__)
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from vllm.worker.model_runner import ModelInputForGPUWithSamplingMetadata
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from vllm.worker.multi_step_model_runner import (
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ModelOutput,
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StatefulModelInput,
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PythonizationCache,
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_pythonize_sampler_output,
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MULTI_STEP_ATTENTION_BACKENDS,
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MULTI_STEP_CHUNKED_PREFILL_ATTENTION_BACKENDS,
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_get_supported_attention_backends
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)
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from vllm.worker.mlu_model_runner import (MLUModelRunnerBase)
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MULTI_STEP_ATTENTION_BACKENDS += ["MLU_FLASH_ATTN"]
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MULTI_STEP_CHUNKED_PREFILL_ATTENTION_BACKENDS += ["MLU_FLASH_ATTN"]
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@dataclass
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class MLUModelOutput(ModelOutput):
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def _pythonize_sampler_output(self, input_metadata: "MLUStatefulModelInput",
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copy_stream: torch.mlu.Stream,
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pinned_sampled_token_buffer: torch.Tensor,
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blocking: bool) -> bool:
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"""
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If blocking is set, will block until the forward pass for the output is
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ready and pythonize the output. Upon completing Pythonization, erases
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self.logprobs (note that a non-blocking call that is performed when
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the sampler output is not yet ready, will not erase self.logprobs.)
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"""
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assert self.sampled_token_ids is not None
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if not blocking and not self.sampler_output_ready_event.query():
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return False
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if blocking:
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self.sampler_output_ready_event.synchronize()
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with torch.mlu.stream(copy_stream):
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_pythonize_sampler_output(input_metadata, self.sampler_output,
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pinned_sampled_token_buffer,
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self.sampled_token_ids, self.logprobs,
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self.pythonization_cache)
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# Erase the logprobs GPU-side tensor.
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# Note that although _pythonize_sampler_output() runs in its
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# own CUDA stream, nonetheless _pythonize_sampler_output()
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# cannot return until Pythonization is complete; therefore
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# we know that by the time the CPU reaches this point,
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# `self.logprobs` is no longer needed.
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self.logprobs = None
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return True
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@dataclass(frozen=False)
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class MLUStatefulModelInput(StatefulModelInput):
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# ping-pong data structures for multi-step to wait on the previous step
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step_cuda_events: List[torch.mlu.Event] = field(
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default_factory=lambda: [torch.mlu.Event(blocking=False)] * 2)
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def record_step_event(self, current_stream: torch.mlu.Stream):
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# record the event for the current step so that the next step can sync
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# on it. We modulo by 2 to keep the events in a circular buffer and
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# support any attn backends that may be supported in the future. ie
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# Flashinfer would want two DecodeWrappers to overlap the CPU and GPU.
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self.step_cuda_events[self.current_step & 1] = \
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torch.mlu.Event(blocking=False)
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self.step_cuda_events[self.current_step & 1].record(current_stream)
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def add_sampler_output(self,
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sampler_output: SamplerOutput,
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sampled_token_ids: Optional[torch.Tensor] = None):
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self.cached_outputs.append(
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MLUModelOutput(sampler_output=sampler_output,
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sampler_output_ready_event=None,
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sampled_token_ids=sampled_token_ids,
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pythonized=False))
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# MutableModelInputForGPUWithMultiStepMetadata is not subclass of
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# ModelInputForGPU but it wraps the actual input dataclass and adds multi-step
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# metadata
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# mypy: disable-error-code=type-var
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class MLUMultiStepModelRunner(MLUModelRunnerBase[MLUStatefulModelInput]):
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# mypy: enable-error-code=type-var
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def __init__(self, base_model_runner: MLUModelRunnerBase, *args, **kwargs):
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super().__init__(*args, **kwargs)
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# Check attention backend support.
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supported_attention_backends: List[str] = \
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_get_supported_attention_backends(
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self.scheduler_config.chunked_prefill_enabled)
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if self.attn_backend.get_name() not in supported_attention_backends:
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ms_config_str: str = "Multi-Step + Chunked-Prefill" \
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if self.scheduler_config.chunked_prefill_enabled \
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else "Multi-Step"
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raise ValueError(
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f"{ms_config_str} not supported for attention backend: "
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f"{self.attn_backend.get_name()}. Set VLLM_ATTENTION_BACKEND "
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f"to a value from {supported_attention_backends}.")
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# uses the base model runner to execute the model and wraps it with
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# multi-step logic
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self._base_model_runner: MLUModelRunnerBase = base_model_runner
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self.is_multi_step = self.scheduler_config.is_multi_step
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self.pinned_sampled_token_ids: Optional[torch.Tensor] = None
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# Using the PythonizationCache in Pipeline-Parallel clobbers the
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# SequenceOutput and CompletionSequenceGroupOutput object.
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# When cache-reset happens at the last step of a multi-step
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# execution, there may be other on-going single-step/multi-step
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# executions. The current caching implementation does not check
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# for this.
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self.pythonization_cache = PythonizationCache() \
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if self.parallel_config.pipeline_parallel_size == 1 else None
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@functools.cached_property
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def _copy_stream(self):
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# used to copy tensors from GPU to CPU asynchronously
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return torch.mlu.Stream()
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def make_model_input_from_broadcasted_tensor_dict(
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self, tensor_dict: Dict[str, Any]) -> MLUStatefulModelInput:
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model_input = (MLUStatefulModelInput.from_broadcasted_tensor_dict(
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tensor_dict,
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attn_backend=self.attn_backend,
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))
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return model_input
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def prepare_model_input(
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self,
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seq_group_metadata_list: List[SequenceGroupMetadata],
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virtual_engine: int = 0,
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finished_requests_ids: Optional[List[str]] = None
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) -> MLUStatefulModelInput:
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frozen_model_input: ModelInputForGPUWithSamplingMetadata = \
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self._base_model_runner.prepare_model_input(
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seq_group_metadata_list,
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virtual_engine,
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finished_requests_ids)
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assert frozen_model_input.query_lens is not None
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assert frozen_model_input.seq_lens is not None
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assert frozen_model_input.attn_metadata is not None
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num_queries = len(frozen_model_input.query_lens)
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num_seqs = len(frozen_model_input.seq_lens)
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num_single_step_prefills = frozen_model_input.attn_metadata.num_prefills
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model_input = MLUStatefulModelInput(
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frozen_model_input=frozen_model_input,
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num_seqs=num_seqs,
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num_queries=num_queries,
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num_single_step_prefills=num_single_step_prefills)
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return model_input
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def _async_process_outputs(self, model_input: MLUStatefulModelInput,
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output_proc_callback: Callable):
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# Proceed with pythonization and output_proc in order.
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# Stop on the first one that fails to pythonize
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output_proc_callback()
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cont = True
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for step_num, model_output in enumerate(model_input.cached_outputs):
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if not model_output.pythonized:
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model_output.maybe_pythonize(model_input, self._copy_stream,
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self.pinned_sampled_token_ids)
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if model_output.pythonized:
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ctx = output_proc_callback.keywords["ctx"]
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ctx.append_output(
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outputs=[model_output.sampler_output],
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seq_group_metadata_list=ctx.seq_group_metadata_list,
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scheduler_outputs=ctx.scheduler_outputs,
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is_async=False,
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is_last_step=False,
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is_first_step_output=step_num == 0)
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output_proc_callback()
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else:
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cont = False
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if not cont:
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break
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def _final_process_outputs(self, model_input: MLUStatefulModelInput,
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output_proc_callback: Optional[Callable]):
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assert model_input.frozen_model_input is not None
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has_async_callback = output_proc_callback is not None
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outputs = []
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for step_num, output in enumerate(model_input.cached_outputs):
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is_last_step = step_num == len(model_input.cached_outputs) - 1
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# For non-async case:
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# -- We simply add the outputs
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# For async case:
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# -- Invoke callback, pythonize, add to callback queue and repeat
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# -- For last output, just add to callback queue
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if has_async_callback:
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assert output_proc_callback is not None
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# Invoke callback before pythonize (to overlap with GPU)
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output_proc_callback()
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# Pythonize
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if not output.pythonized:
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output.pythonize(model_input, self._copy_stream,
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self.pinned_sampled_token_ids)
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# For non last step, add to callback queue to chain
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# callbacks=>pythonize pairs (for GPU overlap)
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if not is_last_step:
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ctx = output_proc_callback.keywords[ # type: ignore
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"ctx"] # type: ignore
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ctx.append_output(
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outputs=[output.sampler_output],
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seq_group_metadata_list=ctx.
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seq_group_metadata_list,
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scheduler_outputs=ctx.scheduler_outputs,
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is_async=False,
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is_last_step=False,
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is_first_step_output=step_num == 0)
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else:
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outputs.append(output.sampler_output)
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else:
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output.pythonize(model_input, self._copy_stream,
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self.pinned_sampled_token_ids)
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outputs.append(output.sampler_output)
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return outputs
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@torch.inference_mode()
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def execute_model(
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self,
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model_input: MLUStatefulModelInput,
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kv_caches: List[torch.Tensor],
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intermediate_tensors: Optional[IntermediateTensors] = None,
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num_steps: int = 1,
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) -> Optional[Union[List[SamplerOutput], IntermediateTensors]]:
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"""
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Execute the model for a single step and update multi-step
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metadata
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"""
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assert num_steps == 1, "MLUMultiStepModelRunner only supports num_steps=1"
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frozen_model_input = model_input.frozen_model_input
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assert frozen_model_input is not None
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# path for warm up runs
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if not model_input.is_multi_step:
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return self._base_model_runner.execute_model(
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frozen_model_input, kv_caches, intermediate_tensors, num_steps)
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# make sure we skip the sampler on the lask rank and only pythonize
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# if CPU is ahead.
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if self.is_driver_worker and get_pp_group().is_last_rank:
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if self.pinned_sampled_token_ids is None:
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self.pinned_sampled_token_ids = torch.zeros(
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(self.scheduler_config.max_num_seqs, 1),
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dtype=torch.long,
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device="cpu",
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pin_memory=True)
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self._base_model_runner.model.sampler.include_gpu_probs_tensor = (
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True)
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if frozen_model_input.sampling_metadata:
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frozen_model_input.sampling_metadata.skip_sampler_cpu_output = (
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True)
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# some pre-execute model logic for multi-step:
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# - if it's the first step, we need to reset the sampling tensors
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# - if it's not the first step, we need to advance the step using the
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# appended sampler output from last iteration
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# - also maybe pythonize if CPU is ahead of GPU
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current_stream = torch.mlu.current_stream()
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if not model_input.is_first_multi_step:
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# Explicitly block on the previous step's forward to make sure we
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# don't clobber any GPU tensors still in use.
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# This is not needed for flashattn backend, but for other attn
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# backends such as flashinfer that performs extra CPU operations on
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# input metadata we may need to synchronize any CPU operations that
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# might clobber enqueued forwards. (prevents CPU from running too
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# far ahead if needed)
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model_input.wait_previous_step()
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model_input = self._advance_step(
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model_input, model_input.cached_outputs[-1].sampler_output)
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# frozen_model_input may have been updated
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frozen_model_input = model_input.frozen_model_input
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assert frozen_model_input is not None
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if model_input.base_output_proc_callback is None:
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assert frozen_model_input is not None
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model_input.base_output_proc_callback = \
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frozen_model_input.async_callback
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if frozen_model_input.async_callback is not None:
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assert model_input.base_output_proc_callback is not None
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async_callback = functools.partial(
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self._async_process_outputs,
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model_input=model_input,
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output_proc_callback=model_input.base_output_proc_callback)
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model_input.frozen_model_input = dataclasses.replace( # type: ignore
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model_input.frozen_model_input,
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async_callback=async_callback)
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# Update the local instance
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frozen_model_input = model_input.frozen_model_input
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assert frozen_model_input is not None
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# Execute the model
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output = self._base_model_runner.execute_model(frozen_model_input,
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kv_caches,
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intermediate_tensors,
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num_steps=1)
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# record the event for the current step so that the next step can sync
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model_input.record_step_event(current_stream)
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if get_pp_group().is_last_rank and self.is_driver_worker:
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assert len(
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output
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) == 1, "MultiStepModelRunner requires single-step base_models"
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# event for the pythonization so that we only pythonize if the
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# tensors are ready. May be able to be combined with the step event
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output_ready_event = torch.mlu.Event()
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output_ready_event.record(current_stream)
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if self.parallel_config.pipeline_parallel_size > 1:
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output[0].sampled_token_ids_cpu = output[
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0].sampled_token_ids.cpu()
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model_input.cached_outputs.append(
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MLUModelOutput(output[0], output_ready_event,
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output[0].sampled_token_ids, False,
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output[0].logprobs, self.pythonization_cache))
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# These GPU tensors are not required by multi-step;
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# erase them to ensure they are not pythonized or
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# transferred to CPU
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output[0].sampled_token_ids = None
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output[0].sampled_token_probs = None
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output[0].logprobs = None
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# Pythonize the output if CPU is ahead and the previous step is
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# ready.
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if frozen_model_input.async_callback is None:
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for model_output in model_input.cached_outputs:
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model_output.maybe_pythonize(model_input,
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self._copy_stream,
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self.pinned_sampled_token_ids)
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model_input.current_step += 1
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if not get_pp_group().is_last_rank:
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# Should be IntermediateTensors
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assert isinstance(output, IntermediateTensors)
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return output
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if not self.is_driver_worker:
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return []
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# Pythonize the output and block if needed since it is the last step
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if model_input.is_last_step:
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outputs = self._final_process_outputs(
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model_input, model_input.base_output_proc_callback)
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if self.pythonization_cache:
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self.pythonization_cache.reset()
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return outputs
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# should be [SamplerOutput]
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return output
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def _update_sampling_metadata(self, sampling_metadata, num_seqs,
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num_queries):
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assert sampling_metadata.num_prompts == 0
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assert len(sampling_metadata.seq_groups) == num_queries
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assert sampling_metadata.selected_token_indices.shape == (
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num_queries, )
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# assert sampling_metadata.categorized_sample_indices == TODO: Add if needed # noqa: E501
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# Verify that all sequences are decodes
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for i in range(num_queries):
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seq_group = sampling_metadata.seq_groups[i]
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assert seq_group.is_prompt is False # No prompt
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assert seq_group.prompt_logprob_indices == [] # No prompt
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assert seq_group.sample_indices == [i] # Simple
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assert seq_group.seq_len is None # Decode
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assert seq_group.query_len is None # Decode
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def _advance_step(self, model_input: MLUStatefulModelInput,
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out: SamplerOutput) -> MLUStatefulModelInput:
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model_input.maybe_advance_frozen_model_input(self.device,
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self.pin_memory)
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frozen_model_input = model_input.frozen_model_input
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assert frozen_model_input is not None
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assert frozen_model_input.input_tokens is not None
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assert frozen_model_input.input_tokens.shape[0] == model_input.num_seqs
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assert frozen_model_input.attn_metadata is not None
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sampled_token_ids = model_input.cached_outputs[-1].sampled_token_ids
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num_seqs = model_input.num_seqs
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num_queries = model_input.num_queries
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frozen_model_input = model_input.frozen_model_input
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assert frozen_model_input is not None
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attn_metadata = frozen_model_input.attn_metadata
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assert attn_metadata is not None
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turn_prefills_into_decodes: bool = model_input.current_step == 1 and \
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model_input.num_single_step_prefills != 0
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attn_metadata.advance_step(
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frozen_model_input,
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sampled_token_ids,
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self.block_size,
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num_seqs,
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num_queries,
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turn_prefills_into_decodes=turn_prefills_into_decodes)
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return model_input
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def load_model(self) -> None:
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return self._base_model_runner.load_model()
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def save_sharded_state(
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self,
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path: str,
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pattern: Optional[str] = None,
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max_size: Optional[int] = None,
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) -> None:
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return self._base_model_runner.save_sharded_state(
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path, pattern, max_size)
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def save_tensorized_model(self,
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tensorizer_config: TensorizerConfig) -> None:
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return self._base_model_runner.save_tensorized_model(tensorizer_config)
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def profile_run(self) -> None:
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return self._base_model_runner.profile_run()
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def remove_all_loras(self):
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return self._base_model_runner.remove_all_loras()
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def capture_model(self, kv_caches: List[List], num_gpu_blocks: int) -> None:
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return self._base_model_runner.capture_model(kv_caches, num_gpu_blocks)
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@property
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def vocab_size(self) -> int:
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return self._base_model_runner.vocab_size
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