135 lines
4.8 KiB
Python
135 lines
4.8 KiB
Python
"""Utilities for selecting and loading neuron models."""
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import importlib
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import os
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from typing import Optional, Type
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import torch
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import torch.nn as nn
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import transformers
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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.sampler import Sampler
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from vllm.model_executor.sampling_metadata import SamplingMetadata
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from vllm.sequence import SamplerOutput
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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 = {
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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 NeuronCasualLM(nn.Module):
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def __init__(
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self,
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config: PretrainedConfig,
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) -> None:
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super().__init__()
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self.config = config
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self.model = None
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self.logits_processor = LogitsProcessor(config.vocab_size,
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logits_as_input=True)
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self.sampler = Sampler()
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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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next_tokens = self.sampler(logits, sampling_metadata)
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return 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, hf_model_cls = (
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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)
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split_model_dir = f"{model_name_or_path}-split"
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if os.path.isdir(os.path.join(model_name_or_path,
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"pytorch_model.bin")):
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split_model_dir = model_name_or_path
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elif not os.path.exists(f"{model_name_or_path}-split"):
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hf_model_cls = getattr(transformers, hf_model_cls)
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from transformers_neuronx.module import save_pretrained_split
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hf_model = hf_model_cls.from_pretrained(model_name_or_path,
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low_cpu_mem_usage=True)
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save_pretrained_split(hf_model, f"{model_name_or_path}-split")
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self.model = neuronx_model_cls.from_pretrained(split_model_dir,
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**kwargs)
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self.model.to_neuron()
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def _get_model_architecture(config: PretrainedConfig) -> Type[nn.Module]:
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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_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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from transformers_neuronx.config import (ContinuousBatchingConfig,
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NeuronConfig)
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# Create a model instance.
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model = NeuronCasualLM(model_config.hf_config)
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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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neuron_config = NeuronConfig(
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continuous_batching=continuous_batching_config)
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# Load the weights from the cached or downloaded files.
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model.load_weights(
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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=[scheduler_config.max_model_len],
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n_positions=[scheduler_config.max_model_len],
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batch_size=scheduler_config.max_num_seqs)
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return model.eval()
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