[Hardware][Neuron] Add on-device sampling support for Neuron

vllm/model_executor/model_loader/neuron.py

@@ -1,4 +1,5 @@
 """Utilities for selecting and loading neuron models."""
+import copy
 import importlib
 import os
 from typing import Dict, List, Optional, Tuple
@@ -13,6 +14,8 @@
 from vllm.model_executor.layers.quantization import get_quantization_config
 from vllm.model_executor.layers.sampler import Sampler, SamplerOutput
 from vllm.model_executor.sampling_metadata import SamplingMetadata
+from vllm.sequence import (CompletionSequenceGroupOutput, Logprob,
+                           SequenceOutput)
 
 TORCH_DTYPE_TO_NEURON_AMP = {
     "auto": "f32",
@@ -37,15 +40,18 @@
 
 class NeuronCasualLM(nn.Module):
 
-    def __init__(
-        self,
-        config: PretrainedConfig,
-    ) -> None:
+    def __init__(self,
+                 config: PretrainedConfig,
+                 on_device_sampling_disabled: bool = False) -> None:
         super().__init__()
         self.config = config
         self.logits_processor = LogitsProcessor(config.vocab_size,
                                                 logits_as_input=True)
-        self.sampler = Sampler()
+
+        self.on_device_sampling_disabled = on_device_sampling_disabled
+        if self.on_device_sampling_disabled:
+            # Use default sampler
+            self.sampler = Sampler()
 
         # Lazy initialized
         self.model: nn.Module
@@ -71,8 +77,29 @@ def sample(
         logits: torch.Tensor,
         sampling_metadata: SamplingMetadata,
     ) -> Optional[SamplerOutput]:
-        next_tokens = self.sampler(logits, sampling_metadata)
-        return next_tokens
+
+        if self.on_device_sampling_disabled:
+            next_tokens = self.sampler(logits, sampling_metadata)
+            return next_tokens
+
+        # On-device sampling outputs the token ids directly.
+        sampled_token_ids = logits.flatten()
+        next_tokens = []
+        sample_idx = 0
+        for seq_group in sampling_metadata.seq_groups:
+            samples = []
+            for seq_id in seq_group.seq_ids:
+                token_id = sampled_token_ids[sample_idx].item()
+                samples.append(
+                    SequenceOutput(parent_seq_id=seq_id,
+                                   output_token=token_id,
+                                   logprobs={token_id: Logprob(token_id)}))
+                sample_idx += 1
+            next_tokens.append(
+                CompletionSequenceGroupOutput(samples=samples,
+                                              prompt_logprobs=None))
+
+        return SamplerOutput(outputs=next_tokens)
 
     def load_weights(self, model_name_or_path: str, **kwargs):
         arch = _get_model_architecture(self.config)
@@ -157,10 +184,22 @@ def _get_default_neuron_config(model_config: ModelConfig,
         quant=neuron_quantization_config_builder(model_config.quantization)
         if model_config.quantization else None,
         continuous_batching=continuous_batching_config,
-        weight_tiling=bool(model_config.quantization))
+        weight_tiling=bool(model_config.quantization),
+        on_device_generation=_get_neuron_on_device_generation_config(
+            model_config))
     return default_neuron_args
 
 
+def _get_neuron_on_device_generation_config(model_config: ModelConfig):
+    if not _is_neuron_on_device_sampling_disabled(model_config):
+        return copy.deepcopy(model_config.neuron_sampling_params)
+    return None
+
+
+def _is_neuron_on_device_sampling_disabled(model_config: ModelConfig) -> bool:
+    return not getattr(model_config, "neuron_sampling_params", None)
+
+
 def _get_neuron_config_after_override(default_neuron_config,
                                       overridden_neuron_config):
     from transformers_neuronx.config import NeuronConfig
@@ -174,7 +213,9 @@ def get_neuron_model(model_config: ModelConfig,
                      scheduler_config: SchedulerConfig) -> nn.Module:
 
     # Create a model instance.
-    model = NeuronCasualLM(model_config.hf_config)
+    model = NeuronCasualLM(
+        model_config.hf_config,
+        _is_neuron_on_device_sampling_disabled(model_config))
 
     default_neuron_config_args = _get_default_neuron_config(
         model_config, parallel_config, scheduler_config)

vllm/worker/neuron_model_runner.py

@@ -1,9 +1,11 @@
+import os
 from dataclasses import dataclass
 from importlib.util import find_spec
 from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
 
 import torch
 from torch import nn
+from transformers_neuronx.config import GenerationConfig
 
 from vllm.config import (DeviceConfig, ModelConfig, ParallelConfig,
                          SchedulerConfig)
@@ -50,6 +52,9 @@ def from_broadcasted_tensor_dict(
 
 class NeuronModelRunner(ModelRunnerBase[ModelInputForNeuron]):
 
+    # NEURON has an upper limit on the top_k
+    _MAX_NEURON_SAMPLING_TOP_K = 256
+
     def __init__(
         self,
         model_config: ModelConfig,
@@ -76,6 +81,34 @@ def __init__(
         # Lazy initialization.
         self.model: nn.Module  # initialize after load_model.
 
+        # Once NEURON_ON_DEVICE_SAMPLING_DISABLED is set to a non-zero value,
+        # turn off on-device sampling.
+        self._on_device_sampling_disabled = int(
+            os.getenv("NEURON_ON_DEVICE_SAMPLING_DISABLED", "0"))
+
+        # NEURON needs to update sampling parameters when request IDs change
+        # across batches. This variable stores the previous batch's request IDs
+        # to determine if an update is needed.
+        self._previous_batch_request_ids: List[str] = []
+
+        if not self._on_device_sampling_disabled:
+            logger.warning(
+                "On-device sampling is turned on in Neuron by default, only "
+                "top_k, top_p, and temperature are current supported sampling "
+                "parameters. To turn off the on-device sampling, please set "
+                "the environment variable NEURON_ON_DEVICE_SAMPLING_DISABLED=1."
+            )
+            self.model_config.neuron_sampling_params = GenerationConfig(
+                max_length=self.scheduler_config.max_model_len,
+                do_sample=True,
+                per_batch_line=True,
+                top_k=[self._MAX_NEURON_SAMPLING_TOP_K] \
+                    * self.scheduler_config.max_num_seqs,
+                top_p=[1.0] * self.scheduler_config.max_num_seqs,
+                temperature=[1.0] * self.scheduler_config.max_num_seqs,
+                dynamic=True,
+                global_top_k=self._MAX_NEURON_SAMPLING_TOP_K)
+
     def load_model(self) -> None:
         if find_spec("transformers_neuronx") is not None:
             self.model = get_neuron_model(
@@ -215,7 +248,7 @@ def prepare_model_input(
         else:
             (input_tokens, input_positions,
              input_block_ids) = self._prepare_decode(seq_group_metadata_list)
-            seq_lens = []
+            seq_lens = None
         sampling_metadata = SamplingMetadata.prepare(
             seq_group_metadata_list,
             seq_lens,
@@ -227,12 +260,49 @@ def prepare_model_input(
             self.pin_memory,
             generators=self.get_generators(finished_requests_ids))
 
+        if not self._on_device_sampling_disabled:
+            # Once the request IDs are changed in current iteration, we will
+            # update the on-device sampling parameters.
+            current_batch_request_ids = [
+                seq_group_meta_data.request_id
+                for seq_group_meta_data in seq_group_metadata_list
+            ]
+            if current_batch_request_ids != self._previous_batch_request_ids:
+                self._update_neuron_sampling_params(sampling_metadata)
+                self._previous_batch_request_ids = current_batch_request_ids
+
         return ModelInputForNeuron(input_tokens=input_tokens,
                                    input_positions=input_positions,
                                    input_block_ids=input_block_ids,
                                    sampling_metadata=sampling_metadata,
                                    multi_modal_kwargs=multi_modal_kwargs)
 
+    def _update_neuron_sampling_params(self,
+                                       sampling_metadata: SamplingMetadata):
+        # Update Neuron sampling parameters (GenerationConfig in Neuron)
+        current_sampling_params = self.model_config.neuron_sampling_params
+        assert current_sampling_params is not None, (
+            f"Failed to update sampling_params, "
+            f"current sampling params is {current_sampling_params}")
+
+        top_k = current_sampling_params.top_k
+        top_p = current_sampling_params.top_p
+        temperature = current_sampling_params.temperature
+        for index, sequence_group_to_sample in enumerate(
+                sampling_metadata.seq_groups):
+            top_k[index] = self._convert_to_neuron_top_k(
+                sequence_group_to_sample.sampling_params.top_k)
+            top_p[index] = sequence_group_to_sample.sampling_params.top_p
+            temperature[index] = \
+                sequence_group_to_sample.sampling_params.temperature
+
+        self.model.model.update_generation_config(current_sampling_params)
+
+    def _convert_to_neuron_top_k(self, top_k: int) -> int:
+        if top_k < 0 or top_k > self._MAX_NEURON_SAMPLING_TOP_K:
+            return self._MAX_NEURON_SAMPLING_TOP_K
+        return top_k
+
     @torch.inference_mode()
     def execute_model(
         self,
@@ -253,9 +323,13 @@ def execute_model(
                                          device=self.device),
         )
 
-        # Compute the logits.
-        logits = self.model.compute_logits(hidden_states,
-                                           model_input.sampling_metadata)
+        # Compute the logits only if the on-device sampling is turned off as
+        # on-device sampling outputs the token ids.
+        if self._on_device_sampling_disabled:
+            logits = self.model.compute_logits(hidden_states,
+                                               model_input.sampling_metadata)
+        else:
+            logits = hidden_states
 
         # Sample the next token.
         output = self.model.sample(