[Neuron] Adding support for adding/ overriding neuron configuration a… (#8062)

Co-authored-by: Harsha Bikki <harbikh@amazon.com>

examples/offline_inference_neuron_int8_quantization.py

@@ -0,0 +1,50 @@
+import os
+
+from vllm import LLM, SamplingParams
+
+# creates XLA hlo graphs for all the context length buckets.
+os.environ['NEURON_CONTEXT_LENGTH_BUCKETS'] = "128,512,1024,2048"
+# creates XLA hlo graphs for all the token gen buckets.
+os.environ['NEURON_TOKEN_GEN_BUCKETS'] = "128,512,1024,2048"
+# Quantizes neuron model weight to int8 ,
+# The default config for quantization is int8 dtype.
+os.environ['NEURON_QUANT_DTYPE'] = "s8"
+
+# Sample prompts.
+prompts = [
+    "Hello, my name is",
+    "The president of the United States is",
+    "The capital of France is",
+    "The future of AI is",
+]
+# Create a sampling params object.
+sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
+
+# Create an LLM.
+llm = LLM(
+    model="TinyLlama/TinyLlama-1.1B-Chat-v1.0",
+    max_num_seqs=8,
+    # The max_model_len and block_size arguments are required to be same as
+    # max sequence length when targeting neuron device.
+    # Currently, this is a known limitation in continuous batching support
+    # in transformers-neuronx.
+    # TODO(liangfu): Support paged-attention in transformers-neuronx.
+    max_model_len=2048,
+    block_size=2048,
+    # The device can be automatically detected when AWS Neuron SDK is installed.
+    # The device argument can be either unspecified for automated detection,
+    # or explicitly assigned.
+    device="neuron",
+    quantization="neuron_quant",
+    override_neuron_config={
+        "cast_logits_dtype": "bfloat16",
+    },
+    tensor_parallel_size=2)
+# Generate texts from the prompts. The output is a list of RequestOutput objects
+# that contain the prompt, generated text, and other information.
+outputs = llm.generate(prompts, sampling_params)
+# Print the outputs.
+for output in outputs:
+    prompt = output.prompt
+    generated_text = output.outputs[0].text
+    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

vllm/config.py

@@ -1,8 +1,8 @@
 import enum
 import json
 from dataclasses import dataclass, field, fields
-from typing import (TYPE_CHECKING, ClassVar, List, Mapping, Optional, Tuple,
-                    Type, Union)
+from typing import (TYPE_CHECKING, Any, ClassVar, Dict, List, Mapping,
+                    Optional, Tuple, Type, Union)
 
 import torch
 from transformers import PretrainedConfig
@@ -115,6 +115,10 @@ class ModelConfig:
             the model name will be the same as `model`.
         limit_mm_per_prompt: Maximum number of data instances per modality 
             per prompt. Only applicable for multimodal models.
+        override_neuron_config: Initialize non default neuron config or 
+            override default neuron config that are specific to Neuron devices, 
+            this argument will be used to configure the neuron config that 
+            can not be gathered from the vllm arguments. 
     """
 
     def __init__(
@@ -143,7 +147,7 @@ class ModelConfig:
             served_model_name: Optional[Union[str, List[str]]] = None,
             limit_mm_per_prompt: Optional[Mapping[str, int]] = None,
             use_async_output_proc: bool = True,
-    ) -> None:
+            override_neuron_config: Optional[Dict[str, Any]] = None) -> None:
         self.model = model
         self.tokenizer = tokenizer
         self.tokenizer_mode = tokenizer_mode
@@ -227,6 +231,9 @@ class ModelConfig:
             limit_mm_per_prompt)
         if not self.skip_tokenizer_init:
             self._verify_tokenizer_mode()
+
+        self.override_neuron_config = override_neuron_config if is_neuron(
+        ) else None
         self._verify_embedding_mode()
         self._verify_quantization()
         self._verify_cuda_graph()
@@ -275,6 +282,7 @@ class ModelConfig:
             "experts_int8"
         ]
         tpu_supported_quantization = ["tpu_int8"]
+        neuron_supported_quantization = ["neuron_quant"]
         if self.quantization is not None:
             self.quantization = self.quantization.lower()
 
@@ -329,6 +337,11 @@ class ModelConfig:
                     "Using AWQ quantization with ROCm, but VLLM_USE_TRITON_AWQ"
                     " is not set, enabling VLLM_USE_TRITON_AWQ.")
                 envs.VLLM_USE_TRITON_AWQ = True
+            if is_neuron(
+            ) and self.quantization not in neuron_supported_quantization:
+                raise ValueError(
+                    f"{self.quantization} quantization is currently not "
+                    f"supported in Neuron Backend.")
 
     def _verify_cuda_graph(self) -> None:
         if self.max_seq_len_to_capture is None:

vllm/engine/arg_utils.py

@@ -2,8 +2,8 @@ import argparse
 import dataclasses
 import json
 from dataclasses import dataclass
-from typing import (TYPE_CHECKING, Dict, List, Mapping, Optional, Tuple, Type,
-                    Union)
+from typing import (TYPE_CHECKING, Any, Dict, List, Mapping, Optional, Tuple,
+                    Type, Union)
 
 import torch
 
@@ -149,6 +149,7 @@ class EngineArgs:
     otlp_traces_endpoint: Optional[str] = None
     collect_detailed_traces: Optional[str] = None
     disable_async_output_proc: bool = False
+    override_neuron_config: Optional[Dict[str, Any]] = None
 
     def __post_init__(self):
         if self.tokenizer is None:
@@ -742,6 +743,16 @@ class EngineArgs:
             default=EngineArgs.disable_async_output_proc,
             help="Disable async output processing. This may result in "
             "lower performance.")
+        parser.add_argument(
+            '--override-neuron-config',
+            type=lambda configs: {
+                str(key): value
+                for key, value in
+                (config.split(':') for config in configs.split(','))
+            },
+            default=None,
+            help="override or set neuron device configuration.")
+
         return parser
 
     @classmethod
@@ -802,7 +813,7 @@ class EngineArgs:
             served_model_name=self.served_model_name,
             limit_mm_per_prompt=self.limit_mm_per_prompt,
             use_async_output_proc=not self.disable_async_output_proc,
-        )
+            override_neuron_config=self.override_neuron_config)
         cache_config = CacheConfig(
             block_size=self.block_size if self.device != "neuron" else
             self.max_model_len,  # neuron needs block_size = max_model_len

vllm/engine/llm_engine.py

@@ -214,6 +214,7 @@ class LLMEngine:
             "Initializing an LLM engine (v%s) with config: "
             "model=%r, speculative_config=%r, tokenizer=%r, "
             "skip_tokenizer_init=%s, tokenizer_mode=%s, revision=%s, "
+            "override_neuron_config=%s, "
             "rope_scaling=%r, rope_theta=%r, tokenizer_revision=%s, "
             "trust_remote_code=%s, dtype=%s, max_seq_len=%d, "
             "download_dir=%r, load_format=%s, tensor_parallel_size=%d, "
@@ -232,6 +233,7 @@ class LLMEngine:
             model_config.skip_tokenizer_init,
             model_config.tokenizer_mode,
             model_config.revision,
+            model_config.override_neuron_config,
             model_config.rope_scaling,
             model_config.rope_theta,
             model_config.tokenizer_revision,

vllm/model_executor/layers/quantization/__init__.py

@@ -22,6 +22,8 @@ from vllm.model_executor.layers.quantization.gptq_marlin import (
 from vllm.model_executor.layers.quantization.gptq_marlin_24 import (
     GPTQMarlin24Config)
 from vllm.model_executor.layers.quantization.marlin import MarlinConfig
+from vllm.model_executor.layers.quantization.neuron_quant import (
+    NeuronQuantConfig)
 from vllm.model_executor.layers.quantization.qqq import QQQConfig
 from vllm.model_executor.layers.quantization.squeezellm import SqueezeLLMConfig
 from vllm.model_executor.layers.quantization.tpu_int8 import Int8TpuConfig
@@ -46,6 +48,7 @@ QUANTIZATION_METHODS: Dict[str, Type[QuantizationConfig]] = {
     "bitsandbytes": BitsAndBytesConfig,
     "qqq": QQQConfig,
     "experts_int8": ExpertsInt8Config,
+    "neuron_quant": NeuronQuantConfig,
 }
 
 

vllm/model_executor/layers/quantization/neuron_quant.py

@@ -0,0 +1,67 @@
+import os
+from importlib.util import find_spec
+from typing import Any, Dict, List, Optional
+
+from torch.nn import Module
+
+from vllm.model_executor.layers.quantization.base_config import (
+    QuantizationConfig)
+
+SUPPORTED_QUANT_DTYPE_LIST = ['s8', 'f8e4m3fn']
+
+
+class NeuronQuantConfig(QuantizationConfig):
+    """Int8 Quantization Config class for Neuron Backend."""
+
+    def __init__(
+        self,
+        dequant_dtype: str = "f16",
+        quantize_method: str = "vector_dynamic",
+    ) -> None:
+        self.quant_dtype = os.getenv("NEURON_QUANT_DTYPE", "s8")
+        if self.quant_dtype not in SUPPORTED_QUANT_DTYPE_LIST:
+            raise ValueError(
+                f"Neuron quantization datatype {self.quant_dtype} is not valid,"
+                f"the quantization datatype should match one of the below types"
+                f"{SUPPORTED_QUANT_DTYPE_LIST}")
+        self.dequant_dtype = dequant_dtype
+        self.quantize_method = quantize_method
+
+    def get_name(self) -> str:
+        return "neuron_quant"
+
+    def get_supported_act_dtypes(self) -> List[str]:
+        return SUPPORTED_QUANT_DTYPE_LIST
+
+    @classmethod
+    def get_min_capability(cls) -> int:
+        raise NotImplementedError(
+            "This function should not be called with Neuron Backend")
+
+    @staticmethod
+    def get_config_filenames() -> List[str]:
+        return []
+
+    @classmethod
+    def from_config(cls, config: Dict[str, Any]) -> "NeuronQuantConfig":
+        quantize_method = cls.get_from_keys(config, ["quantize_method"])
+        dequant_dtype = cls.get_from_keys(config, ["dequant_dtype"])
+        return cls(dequant_dtype=dequant_dtype,
+                   quantize_method=quantize_method)
+
+    def get_quant_method(self, layer: Module, prefix: str) -> Optional[Any]:
+        if find_spec("transformers_neuronx") is not None:
+            return self.get_quantization_config()
+        else:
+            raise NotImplementedError(
+                "Neuron Quantization is only supported through"
+                " transformers_neuronx.")
+
+    def get_scaled_act_names(self) -> List[str]:
+        return []
+
+    def get_quantization_config(self):
+        from transformers_neuronx.config import QuantizationConfig
+        return QuantizationConfig(quant_dtype=self.quant_dtype,
+                                  dequant_dtype=self.dequant_dtype,
+                                  quantize_method=self.quantize_method)

vllm/model_executor/model_loader/neuron.py

@@ -10,6 +10,7 @@ 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
 
@@ -81,8 +82,7 @@ class NeuronCasualLM(nn.Module):
         neuronx_model_cls = getattr(neuronx_module, neuronx_model_cls_name)
 
         split_model_dir = f"{model_name_or_path}-split"
-        if os.path.isdir(os.path.join(model_name_or_path,
-                                      "pytorch_model.bin")):
+        if _is_pretrained_neuron_checkpoint(model_name_or_path):
             split_model_dir = model_name_or_path
         elif not os.path.exists(f"{model_name_or_path}-split"):
             hf_model_cls = getattr(transformers, hf_model_cls_name)
@@ -97,6 +97,23 @@ class NeuronCasualLM(nn.Module):
         self.model.to_neuron()
 
 
+def _is_pretrained_neuron_checkpoint(model_name_or_path: str) -> bool:
+    # Checking if the neuron checkpoint is saved in the old format.
+    if os.path.isdir(os.path.join(model_name_or_path, "pytorch_model.bin")):
+        return True
+    # Checking if the neuron checkpoint is saved in the new format.
+    pretrained_split_files = ["config.json", "generation_config.json"]
+    pretrained_split_format = ".safetensors"
+    for file in pretrained_split_files:
+        file_path = os.path.join(model_name_or_path, file)
+        if not os.path.isfile(file_path):
+            return False
+    for file in os.listdir(model_name_or_path):
+        if file.endswith(pretrained_split_format):
+            return True
+    return False
+
+
 def _get_model_architecture(config: PretrainedConfig) -> str:
     architectures = getattr(config, "architectures", [])
     for arch in architectures:
@@ -119,19 +136,51 @@ def _get_buckets(env: str, default_value: List[int]) -> List[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))
+    return default_neuron_args
+
+
+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:
-    from transformers_neuronx.config import (ContinuousBatchingConfig,
-                                             NeuronConfig)
 
     # Create a model instance.
     model = NeuronCasualLM(model_config.hf_config)
 
-    continuous_batching_config = ContinuousBatchingConfig(
-        batch_size_for_shared_caches=scheduler_config.max_num_seqs)
-    neuron_config = NeuronConfig(
-        continuous_batching=continuous_batching_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])

vllm/worker/neuron_model_runner.py

@@ -1,4 +1,5 @@
 from dataclasses import dataclass
+from importlib.util import find_spec
 from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
 
 import torch
@@ -76,9 +77,14 @@ class NeuronModelRunner(ModelRunnerBase[ModelInputForNeuron]):
         self.model: nn.Module  # initialize after load_model.
 
     def load_model(self) -> None:
-        self.model = get_neuron_model(self.model_config,
+        if find_spec("transformers_neuronx") is not None:
+            self.model = get_neuron_model(
+                self.model_config,
                 parallel_config=self.parallel_config,
                 scheduler_config=self.scheduler_config)
+        else:
+            raise NotImplementedError(
+                "Supports only Transformer-NeuronX based models.")
 
     def _prepare_prompt(
         self,