add qwen3

vllm-v0.6.2/vllm/model_executor/model_loader/neuron.py

@@ -0,0 +1,211 @@
+"""Utilities for selecting and loading neuron models."""
+import copy
+import importlib
+import os
+from typing import Dict, List, Optional, Tuple
+
+import torch
+import torch.nn as nn
+from transformers import PretrainedConfig
+
+from vllm.config import ModelConfig, ParallelConfig, SchedulerConfig
+from vllm.model_executor.layers.logits_processor import LogitsProcessor
+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",
+    "half": "f16",
+    "float16": "f16",
+    "bfloat16": "bf16",
+    "float": "f32",
+    "float32": "f32",
+    torch.float16: "f16",
+    torch.bfloat16: "bf16",
+    torch.float32: "f32",
+}
+
+# Models supported by Neuron.
+_NEURON_SUPPORTED_MODELS: Dict[str, Tuple[str, str, str]] = {
+    "LlamaForCausalLM": ("transformers_neuronx.llama.model",
+                         "LlamaForSampling", "LlamaForCausalLM"),
+    "MistralForCausalLM": ("transformers_neuronx.mistral.model",
+                           "MistralForSampling", "MistralForCausalLM")
+}
+
+
+class NeuronCausalLM(nn.Module):
+
+    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.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
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        input_block_ids: torch.Tensor,
+    ) -> torch.Tensor:
+        logits = self.model(input_ids,
+                            cache_ids=positions,
+                            start_ids=input_block_ids)
+        return logits
+
+    def compute_logits(self, hidden_states: torch.Tensor,
+                       sampling_metadata: SamplingMetadata) -> torch.Tensor:
+        logits = self.logits_processor(None, hidden_states, sampling_metadata)
+        return logits
+
+    def sample(
+        self,
+        logits: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+    ) -> Optional[SamplerOutput]:
+
+        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)
+        neuronx_module_path, neuronx_model_cls_name, hf_model_cls_name = (
+            _NEURON_SUPPORTED_MODELS[arch])
+        neuronx_module = importlib.import_module(neuronx_module_path)
+        neuronx_model_cls = getattr(neuronx_module, neuronx_model_cls_name)
+
+        self.model = neuronx_model_cls.from_pretrained(model_name_or_path,
+                                                       **kwargs)
+        self.model.to_neuron()
+
+
+def _get_model_architecture(config: PretrainedConfig) -> str:
+    architectures = getattr(config, "architectures", [])
+    for arch in architectures:
+        if arch in _NEURON_SUPPORTED_MODELS:
+            return arch
+    raise ValueError(
+        f"Model architectures {architectures} are not supported on Neuron "
+        f"for now. Supported architectures: "
+        f"{list(_NEURON_SUPPORTED_MODELS.keys())}")
+
+
+def _get_buckets(env: str, default_value: List[int]) -> List[int]:
+    env_value = os.getenv(env)
+    if env_value is None:
+        return default_value
+    buckets_remove_empty = filter(
+        lambda x: x is not None and len(x.strip()) > 0, env_value.split(","))
+    buckets_int = map(int, buckets_remove_empty)
+    buckets_list = list(buckets_int)
+    return buckets_list
+
+
+def _get_default_neuron_config(model_config: ModelConfig,
+                               parallel_config: ParallelConfig,
+                               scheduler_config: SchedulerConfig):
+    from transformers_neuronx.config import ContinuousBatchingConfig
+    from transformers_neuronx.constants import LAYOUT_BSH
+
+    continuous_batching_config = ContinuousBatchingConfig(
+        batch_size_for_shared_caches=scheduler_config.max_num_seqs)
+    quant_config = dict(
+        dequant_dtype=TORCH_DTYPE_TO_NEURON_AMP[model_config.dtype],
+        quantize_method="vector_dynamic")
+    neuron_quantization_config_builder = lambda quant: get_quantization_config(
+        quant).from_config(quant_config).get_quant_method(None, "")
+    # TODO: Add Paged attention config to the default neuron arguments.
+    default_neuron_args = dict(
+        collectives_layout=LAYOUT_BSH,
+        attention_layout=LAYOUT_BSH,
+        fuse_qkv=True,
+        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),
+        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
+    overridden_neuron_config = overridden_neuron_config or {}
+    default_neuron_config.update(overridden_neuron_config)
+    return NeuronConfig(**default_neuron_config)
+
+
+def get_neuron_model(model_config: ModelConfig,
+                     parallel_config: ParallelConfig,
+                     scheduler_config: SchedulerConfig) -> nn.Module:
+
+    # Create a model instance.
+    model = NeuronCausalLM(
+        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)
+
+    neuron_config = _get_neuron_config_after_override(
+        default_neuron_config_args, model_config.override_neuron_config)
+
+    context_length_estimates = _get_buckets("NEURON_CONTEXT_LENGTH_BUCKETS",
+                                            [scheduler_config.max_model_len])
+    n_positions = _get_buckets("NEURON_TOKEN_GEN_BUCKETS",
+                               [scheduler_config.max_model_len])
+
+    # Load the weights from the cached or downloaded files.
+    model.load_weights(model_config.model,
+                       tp_degree=parallel_config.tensor_parallel_size,
+                       amp=TORCH_DTYPE_TO_NEURON_AMP[model_config.dtype],
+                       neuron_config=neuron_config,
+                       context_length_estimate=context_length_estimates,
+                       n_positions=n_positions,
+                       batch_size=scheduler_config.max_num_seqs)
+
+    return model.eval()