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212 lines
8.1 KiB
Python
212 lines
8.1 KiB
Python
"""Utilities for selecting and loading neuron models."""
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import copy
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import importlib
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import os
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from typing import Dict, List, Optional, Tuple
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import torch
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import torch.nn as nn
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from transformers import PretrainedConfig
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from vllm.config import ModelConfig, ParallelConfig, SchedulerConfig
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from vllm.model_executor.layers.logits_processor import LogitsProcessor
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from vllm.model_executor.layers.quantization import get_quantization_config
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from vllm.model_executor.layers.sampler import Sampler, SamplerOutput
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from vllm.model_executor.sampling_metadata import SamplingMetadata
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from vllm.sequence import (CompletionSequenceGroupOutput, Logprob,
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SequenceOutput)
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TORCH_DTYPE_TO_NEURON_AMP = {
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"auto": "f32",
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"half": "f16",
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"float16": "f16",
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"bfloat16": "bf16",
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"float": "f32",
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"float32": "f32",
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torch.float16: "f16",
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torch.bfloat16: "bf16",
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torch.float32: "f32",
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}
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# Models supported by Neuron.
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_NEURON_SUPPORTED_MODELS: Dict[str, Tuple[str, str, str]] = {
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"LlamaForCausalLM": ("transformers_neuronx.llama.model",
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"LlamaForSampling", "LlamaForCausalLM"),
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"MistralForCausalLM": ("transformers_neuronx.mistral.model",
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"MistralForSampling", "MistralForCausalLM")
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}
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class NeuronCausalLM(nn.Module):
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def __init__(self,
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config: PretrainedConfig,
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on_device_sampling_disabled: bool = False) -> None:
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super().__init__()
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self.config = config
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self.logits_processor = LogitsProcessor(config.vocab_size,
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logits_as_input=True)
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self.on_device_sampling_disabled = on_device_sampling_disabled
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if self.on_device_sampling_disabled:
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# Use default sampler
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self.sampler = Sampler()
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# Lazy initialized
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self.model: nn.Module
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def forward(
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self,
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input_ids: torch.Tensor,
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positions: torch.Tensor,
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input_block_ids: torch.Tensor,
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) -> torch.Tensor:
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logits = self.model(input_ids,
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cache_ids=positions,
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start_ids=input_block_ids)
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return logits
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def compute_logits(self, hidden_states: torch.Tensor,
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sampling_metadata: SamplingMetadata) -> torch.Tensor:
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logits = self.logits_processor(None, hidden_states, sampling_metadata)
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return logits
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def sample(
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self,
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logits: torch.Tensor,
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sampling_metadata: SamplingMetadata,
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) -> Optional[SamplerOutput]:
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if self.on_device_sampling_disabled:
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next_tokens = self.sampler(logits, sampling_metadata)
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return next_tokens
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# On-device sampling outputs the token ids directly.
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sampled_token_ids = logits.flatten()
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next_tokens = []
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sample_idx = 0
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for seq_group in sampling_metadata.seq_groups:
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samples = []
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for seq_id in seq_group.seq_ids:
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token_id = sampled_token_ids[sample_idx].item()
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samples.append(
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SequenceOutput(parent_seq_id=seq_id,
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output_token=token_id,
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logprobs={token_id: Logprob(token_id)}))
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sample_idx += 1
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next_tokens.append(
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CompletionSequenceGroupOutput(samples=samples,
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prompt_logprobs=None))
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return SamplerOutput(outputs=next_tokens)
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def load_weights(self, model_name_or_path: str, **kwargs):
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arch = _get_model_architecture(self.config)
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neuronx_module_path, neuronx_model_cls_name, hf_model_cls_name = (
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_NEURON_SUPPORTED_MODELS[arch])
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neuronx_module = importlib.import_module(neuronx_module_path)
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neuronx_model_cls = getattr(neuronx_module, neuronx_model_cls_name)
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self.model = neuronx_model_cls.from_pretrained(model_name_or_path,
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**kwargs)
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self.model.to_neuron()
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def _get_model_architecture(config: PretrainedConfig) -> str:
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architectures = getattr(config, "architectures", [])
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for arch in architectures:
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if arch in _NEURON_SUPPORTED_MODELS:
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return arch
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raise ValueError(
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f"Model architectures {architectures} are not supported on Neuron "
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f"for now. Supported architectures: "
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f"{list(_NEURON_SUPPORTED_MODELS.keys())}")
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def _get_buckets(env: str, default_value: List[int]) -> List[int]:
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env_value = os.getenv(env)
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if env_value is None:
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return default_value
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buckets_remove_empty = filter(
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lambda x: x is not None and len(x.strip()) > 0, env_value.split(","))
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buckets_int = map(int, buckets_remove_empty)
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buckets_list = list(buckets_int)
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return buckets_list
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def _get_default_neuron_config(model_config: ModelConfig,
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parallel_config: ParallelConfig,
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scheduler_config: SchedulerConfig):
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from transformers_neuronx.config import ContinuousBatchingConfig
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from transformers_neuronx.constants import LAYOUT_BSH
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continuous_batching_config = ContinuousBatchingConfig(
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batch_size_for_shared_caches=scheduler_config.max_num_seqs)
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quant_config = dict(
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dequant_dtype=TORCH_DTYPE_TO_NEURON_AMP[model_config.dtype],
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quantize_method="vector_dynamic")
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neuron_quantization_config_builder = lambda quant: get_quantization_config(
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quant).from_config(quant_config).get_quant_method(None, "")
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# TODO: Add Paged attention config to the default neuron arguments.
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default_neuron_args = dict(
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collectives_layout=LAYOUT_BSH,
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attention_layout=LAYOUT_BSH,
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fuse_qkv=True,
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quant=neuron_quantization_config_builder(model_config.quantization)
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if model_config.quantization else None,
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continuous_batching=continuous_batching_config,
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weight_tiling=bool(model_config.quantization),
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on_device_generation=_get_neuron_on_device_generation_config(
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model_config))
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return default_neuron_args
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def _get_neuron_on_device_generation_config(model_config: ModelConfig):
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if not _is_neuron_on_device_sampling_disabled(model_config):
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return copy.deepcopy(model_config.neuron_sampling_params)
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return None
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def _is_neuron_on_device_sampling_disabled(model_config: ModelConfig) -> bool:
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return not getattr(model_config, "neuron_sampling_params", None)
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def _get_neuron_config_after_override(default_neuron_config,
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overridden_neuron_config):
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from transformers_neuronx.config import NeuronConfig
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overridden_neuron_config = overridden_neuron_config or {}
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default_neuron_config.update(overridden_neuron_config)
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return NeuronConfig(**default_neuron_config)
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def get_neuron_model(model_config: ModelConfig,
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parallel_config: ParallelConfig,
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scheduler_config: SchedulerConfig) -> nn.Module:
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# Create a model instance.
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model = NeuronCausalLM(
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model_config.hf_config,
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_is_neuron_on_device_sampling_disabled(model_config))
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default_neuron_config_args = _get_default_neuron_config(
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model_config, parallel_config, scheduler_config)
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neuron_config = _get_neuron_config_after_override(
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default_neuron_config_args, model_config.override_neuron_config)
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context_length_estimates = _get_buckets("NEURON_CONTEXT_LENGTH_BUCKETS",
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[scheduler_config.max_model_len])
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n_positions = _get_buckets("NEURON_TOKEN_GEN_BUCKETS",
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[scheduler_config.max_model_len])
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# Load the weights from the cached or downloaded files.
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model.load_weights(model_config.model,
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tp_degree=parallel_config.tensor_parallel_size,
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amp=TORCH_DTYPE_TO_NEURON_AMP[model_config.dtype],
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neuron_config=neuron_config,
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context_length_estimate=context_length_estimates,
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n_positions=n_positions,
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batch_size=scheduler_config.max_num_seqs)
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return model.eval()
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