510 lines
20 KiB
Python
510 lines
20 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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# Copyright 2024 The vLLM team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Wrapper around `transformers` models"""
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from collections.abc import Iterable
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from contextlib import nullcontext
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from typing import Literal, Optional, Union
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import regex as re
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import torch
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from torch import nn
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from transformers import AutoModel, PretrainedConfig, PreTrainedModel
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from transformers.modeling_utils import ALL_ATTENTION_FUNCTIONS
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from vllm.attention import Attention
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from vllm.compilation.decorators import support_torch_compile
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from vllm.config import (CacheConfig, DeviceConfig, ModelConfig,
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ParallelConfig, VllmConfig)
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from vllm.distributed import get_pp_group, get_tensor_model_parallel_world_size
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from vllm.distributed.utils import get_pp_indices
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from vllm.logger import init_logger
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from vllm.model_executor.layers.linear import (ColumnParallelLinear,
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ReplicatedLinear,
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RowParallelLinear)
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from vllm.model_executor.layers.logits_processor import LogitsProcessor
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from vllm.model_executor.layers.quantization import QuantizationConfig
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from vllm.model_executor.layers.vocab_parallel_embedding import (
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ParallelLMHead, VocabParallelEmbedding)
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from vllm.model_executor.model_loader.weight_utils import default_weight_loader
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from vllm.model_executor.sampling_metadata import SamplingMetadata
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from vllm.sequence import IntermediateTensors
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from .interfaces import SupportsLoRA, SupportsPP, SupportsQuant
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from .utils import (AutoWeightsLoader, PPMissingLayer, WeightsMapper,
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is_pp_missing_parameter,
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make_empty_intermediate_tensors_factory, maybe_prefix)
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logger = init_logger(__name__)
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def vllm_flash_attention_forward(
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# Transformers args
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module: torch.nn.Module,
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query: torch.Tensor,
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key: torch.Tensor,
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value: torch.Tensor,
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attention_mask: torch.Tensor,
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# Transformers kwargs
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scaling: Optional[float] = None,
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# vLLM kwargs
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attention_instances: Optional[dict[Attention]] = None,
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**kwargs):
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self_attn = attention_instances[module.layer_idx]
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if scaling is not None:
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self_attn.impl.scale = float(scaling)
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hidden = query.shape[-2]
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query, key, value = (x.transpose(1, 2) for x in (query, key, value))
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query, key, value = (x.reshape(hidden, -1) for x in (query, key, value))
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return self_attn.forward(query, key, value), None
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ALL_ATTENTION_FUNCTIONS["vllm"] = vllm_flash_attention_forward
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def log_replacement(name: str, old_module: nn.Module, new_module: nn.Module):
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logger.debug("%s: %s -> %s", name, old_module, new_module)
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def replace_linear_class(
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linear: nn.Linear, style: Literal["colwise", "rowwise"],
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quant_config: QuantizationConfig
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) -> Union[ColumnParallelLinear, RowParallelLinear]:
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"""
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Replace nn.Linear with one of vLLM's tensor parallel linear classes.
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Args:
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linear (nn.Linear): `nn.Linear` to be replaced.
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style (str): Tensor parallel style of the new linear, e.g. "colwise".
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quant_config (QuantConfig): Quantization config for the new linear.
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Returns:
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Union[ColumnParallelLinear, RowParallelLinear]: The new linear.
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"""
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if not isinstance(style, str):
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raise ValueError(
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f"Unsupported parallel style type {type(style)}, expected str")
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vllm_linear_cls = {
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"colwise": ColumnParallelLinear,
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"rowwise": RowParallelLinear,
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}.get(style, ReplicatedLinear)
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return vllm_linear_cls(
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input_size=linear.in_features,
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output_size=linear.out_features,
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bias=linear.bias is not None,
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quant_config=quant_config,
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return_bias=False,
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)
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class ConfigOverride:
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"""Context manager to temporarily override config attributes."""
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def __init__(self, config: PretrainedConfig, **kwargs):
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self.config = config
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self.kwargs = kwargs
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self.kwargs_original = {}
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self.kwargs_delete = set()
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def __enter__(self):
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"""Override config attributes."""
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for key, value in self.kwargs.items():
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if not hasattr(self.config, key):
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self.kwargs_delete.add(key)
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self.kwargs_original[key] = getattr(self.config, key, None)
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setattr(self.config, key, value)
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return self.config
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def __exit__(self, exc_type, exc_value, traceback):
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"""Restore original config attributes."""
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for key, value in self.kwargs_original.items():
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if key in self.kwargs_delete:
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delattr(self.config, key)
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else:
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setattr(self.config, key, value)
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class TransformersModel(nn.Module):
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def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
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super().__init__()
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logger.info("Using Transformers backend.")
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config: PretrainedConfig = vllm_config.model_config.hf_config
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cache_config: CacheConfig = vllm_config.cache_config
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device_config: DeviceConfig = vllm_config.device_config
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model_config: ModelConfig = vllm_config.model_config
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parallel_config: ParallelConfig = vllm_config.parallel_config
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quant_config: QuantizationConfig = vllm_config.quant_config
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self.config = config
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self.cache_config = cache_config
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self.device_config = device_config
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self.model_config = model_config
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self.parallel_config = parallel_config
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self.quant_config = quant_config
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self.pp_group = get_pp_group()
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self.pp_size = self.pp_group.world_size
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self.pp_rank = self.pp_group.rank_in_group
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self.tp_size = get_tensor_model_parallel_world_size()
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# vLLM handles interleaved sliding window attention by creating a new
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# interleaved_sliding_window attribute and deleting the sliding_window
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# attribute. This breaks the constructors in Transformers so we
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# temporarily add the attribute back to construct the model.
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config_override = nullcontext()
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if hasattr(config, "interleaved_sliding_window"):
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config_override = ConfigOverride(
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config, sliding_window=config.interleaved_sliding_window)
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# Use meta device to delay allocating GPU tensors
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with torch.device("meta"), config_override:
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# FIXME(Isotr0py): We need to refactor this part in the future to
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# avoid registering an extra model layer, otherwise we will need a
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# weights mapper to rename weights.
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self.model: PreTrainedModel = AutoModel.from_config(
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config,
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attn_implementation="vllm",
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torch_dtype=model_config.dtype,
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trust_remote_code=model_config.trust_remote_code,
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)
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self.pipeline_parallel()
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self.tensor_parallel()
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# Input embeddings
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if not isinstance(self.model.get_input_embeddings(), PPMissingLayer):
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self.model.set_input_embeddings(
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VocabParallelEmbedding(
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config.vocab_size,
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config.hidden_size,
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org_num_embeddings=config.vocab_size,
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quant_config=quant_config,
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))
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# Attention layers
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self.attention_instances = self.create_attention_instances()
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# Initialize buffers (e.g. rotary embedding inverse frequency)
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self.init_buffers(self.model)
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# Initialize any parameters that have not had their modules replaced
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self.init_parameters(self.model)
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self.make_empty_intermediate_tensors = (
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make_empty_intermediate_tensors_factory(["hidden_states"],
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config.hidden_size))
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def pipeline_parallel(self):
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"""
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Apply the model's pipeline parallelization plan.
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"""
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if self.pp_size <= 1:
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return
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if not self.model.supports_pp_plan:
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raise ValueError(
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f"{type(self.model)} does not support pipeline parallel yet!")
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module_lists = []
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module_list_idx = None
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pp_plan = list(self.model._pp_plan.keys())
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for i, name in enumerate(pp_plan):
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if isinstance(getattr(self.model, name), nn.ModuleList):
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module_lists.append(name)
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module_list_idx = i
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if len(module_lists) > 1:
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raise ValueError(
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"Pipeline parallel of models with multiple `ModuleList`s "
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"in the base model are not supported yet!")
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if module_list_idx is None:
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raise ValueError(
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f"Could not find `ModuleList` in {type(self.model)}")
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# Layers before module list
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for name in pp_plan[:module_list_idx]:
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if self.pp_group.is_first_rank or (self.config.tie_word_embeddings
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and self.pp_group.is_last_rank):
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continue
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setattr(self.model, name, PPMissingLayer())
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# Module list
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start_layer, end_layer = get_pp_indices(self.config.num_hidden_layers,
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self.pp_rank, self.pp_size)
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layers_name = pp_plan[module_list_idx]
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layers = getattr(self.model, layers_name)
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for i in range(len(layers)):
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if start_layer <= i and i < end_layer:
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continue
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layers[i] = PPMissingLayer(return_tuple=True)
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# Layers after module list
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for name in pp_plan[module_list_idx + 1:]:
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# Modules that should be on last rank
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if not self.pp_group.is_last_rank:
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setattr(self.model, name, PPMissingLayer())
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def tensor_parallel(self):
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"""
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Apply the model's tensor parallelization plan.
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Currently only supports linear layers.
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"""
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if not self.model.supports_tp_plan:
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if self.tp_size <= 1:
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return
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raise ValueError(
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f"{type(self.model)} does not support tensor parallel yet!")
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tp_plan = self.model._tp_plan
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def _tensor_parallel(module: nn.Module, prefix: str = ""):
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for child_name, child_module in module.named_children():
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qual_name = maybe_prefix(prefix, child_name)
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for pattern, style in tp_plan.items():
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if re.match(pattern, qual_name) and isinstance(
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child_module, nn.Linear):
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new_module = replace_linear_class(
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child_module, style, self.quant_config)
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setattr(module, child_name, new_module)
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log_replacement(qual_name, child_module, new_module)
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else:
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_tensor_parallel(child_module, prefix=qual_name)
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_tensor_parallel(self.model)
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def create_attention_instances(self) -> dict[int, Attention]:
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"""
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Create `Attention` instances to inform KV cache allocation.
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"""
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num_heads = self.model_config.get_num_attention_heads(
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self.parallel_config)
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head_size = self.model_config.get_head_size()
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num_kv_heads = self.model_config.get_num_kv_heads(self.parallel_config)
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start, end = get_pp_indices(self.config.num_hidden_layers,
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self.pp_rank, self.pp_size)
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attention_instances = {}
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for i in range(start, end):
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# Handle interleaved sliding window attention
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sliding_window = None
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if (hasattr(self.config, "interleaved_sliding_window")
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and hasattr(self.config, "sliding_window_pattern")
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and ((i + 1) % self.config.sliding_window_pattern > 0)):
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sliding_window = self.config.interleaved_sliding_window
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attention_instances[i] = Attention(
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num_heads=num_heads,
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head_size=head_size,
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# NOTE: We use Llama scale as default, if it's set by
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# Transformers, it's updated in vllm_flash_attention_forward
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scale=head_size**-0.5,
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num_kv_heads=num_kv_heads,
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cache_config=self.cache_config,
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quant_config=self.quant_config,
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per_layer_sliding_window=sliding_window,
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prefix=f"{i}.attn")
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return attention_instances
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def init_buffers(self, module: nn.Module):
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"""
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If a `buffer` is on the `meta` device, then its parent
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`module` is the original module created by:
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```python
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with torch.device("meta"):
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self.model: PreTrainedModel = AutoModel.from_config(...)
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```
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This means that:
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- `type(module)` is a class from `transformers`
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- This class is constructed using a `PretrainedConfig`
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"""
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for name, buffer in module.named_buffers(recurse=False):
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if buffer.device == torch.device("meta"):
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if module == self.model:
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logger.warning(
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"To initialize buffers correctly, we instantiate the "
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"parent module and and extract the value of the "
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"buffer from it. In this case, the parent module is "
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"the base model. Instantiating the entire model here "
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"risks GPU OOM. Could this buffer be moved to a child "
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"module?")
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new_buffer = getattr(type(module)(self.config), name)
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setattr(module, name, new_buffer)
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for child in module.children():
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self.init_buffers(child)
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def init_parameters(self, module: nn.Module):
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"""
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If a `parameter` is on the `meta` device, then its parent
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`module` is the original module created by:
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```python
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with torch.device("meta"):
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self.model: PreTrainedModel = AutoModel.from_config(...)
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```
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"""
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for name, param in module.named_parameters(recurse=False):
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if param.device == torch.device("meta"):
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new_param = nn.Parameter(
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torch.empty_like(param.data,
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device=self.device_config.device))
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setattr(module, name, new_param)
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for child in module.children():
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self.init_parameters(child)
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def get_input_embeddings(self) -> nn.Module:
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return self.model.get_input_embeddings()
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def forward(
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self,
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input_ids: Optional[torch.Tensor],
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positions: torch.Tensor,
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intermediate_tensors: Optional[IntermediateTensors] = None,
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inputs_embeds: Optional[torch.Tensor] = None,
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) -> Union[torch.Tensor, IntermediateTensors]:
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if not get_pp_group().is_first_rank:
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assert intermediate_tensors is not None
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input_ids = None
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inputs_embeds = intermediate_tensors["hidden_states"]
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if input_ids is not None:
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input_ids = input_ids[None, ...]
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if inputs_embeds is not None:
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inputs_embeds = inputs_embeds[None, ...]
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hidden_states = self.model(
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input_ids=input_ids,
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inputs_embeds=inputs_embeds,
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use_cache=False,
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position_ids=positions[None, ...],
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attention_instances=self.attention_instances,
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return_dict=False)[0][0, ...] # we remove batch dimension for now
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if not get_pp_group().is_last_rank:
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return IntermediateTensors({"hidden_states": hidden_states})
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return hidden_states
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def load_weights(self, weights: Iterable[tuple[str,
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torch.Tensor]]) -> set[str]:
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params_dict = dict(self.named_parameters())
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loaded_params = set[str]()
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for name, loaded_weight in weights:
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# Use "model" instead of base_model_prefix because
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# the base model attribute in vLLM is always `model`
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if not name.startswith(prefix := "model."):
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name = prefix + name
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if is_pp_missing_parameter(name, self):
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continue
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if name in params_dict:
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param = params_dict[name]
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weight_loader = getattr(param, "weight_loader",
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default_weight_loader)
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weight_loader(param, loaded_weight)
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loaded_params.add(name)
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return loaded_params
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@support_torch_compile
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class TransformersForCausalLM(nn.Module, SupportsQuant, SupportsLoRA,
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SupportsPP):
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embedding_padding_modules = ["lm_head"]
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embedding_modules = ["embed_tokens"
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] # TODO transformers will have a util to get it
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def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
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super().__init__()
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config: PretrainedConfig = vllm_config.model_config.hf_config
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quant_config: QuantizationConfig = vllm_config.quant_config
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self.config = config
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self.model = TransformersModel(vllm_config=vllm_config, prefix=prefix)
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if get_pp_group().is_last_rank:
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self.unpadded_vocab_size = config.vocab_size
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self.lm_head = ParallelLMHead(
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config.vocab_size,
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config.hidden_size,
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quant_config=quant_config,
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prefix=maybe_prefix(prefix, "lm_head"),
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)
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if config.tie_word_embeddings:
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self.lm_head = self.lm_head.tie_weights(
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self.model.get_input_embeddings())
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logit_scale = getattr(config, "logit_scale", 1.0)
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self.logits_processor = LogitsProcessor(self.unpadded_vocab_size,
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config.vocab_size,
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logit_scale)
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else:
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self.lm_head = PPMissingLayer()
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self.make_empty_intermediate_tensors = (
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self.model.make_empty_intermediate_tensors)
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# FIXME(Isotr0py): Don't use any weights mapper for Transformers backend,
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# this makes thing complicated. We need to remove this mapper after refactor
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# `TransformersModel` in the future.
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# NOTE: `SupportsQuant` can be updated after property decorator is removed
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@property
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def hf_to_vllm_mapper(self):
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prefix_mapper = {
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name: "model." + name
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for name, _ in self.model.model.named_children()
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}
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return WeightsMapper(
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orig_to_new_substr={"model.": "model.model."},
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orig_to_new_prefix=prefix_mapper,
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)
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def forward(
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self,
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input_ids: Optional[torch.Tensor],
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positions: torch.Tensor,
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intermediate_tensors: Optional[IntermediateTensors] = None,
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inputs_embeds: Optional[torch.Tensor] = None,
|
|
) -> Union[torch.Tensor, IntermediateTensors]:
|
|
model_output = self.model(input_ids, positions, intermediate_tensors,
|
|
inputs_embeds)
|
|
return model_output
|
|
|
|
def compute_logits(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
sampling_metadata: SamplingMetadata,
|
|
) -> Optional[torch.Tensor]:
|
|
logits = self.logits_processor(self.lm_head, hidden_states,
|
|
sampling_metadata)
|
|
return logits
|
|
|
|
def load_weights(self, weights: Iterable[tuple[str,
|
|
torch.Tensor]]) -> set[str]:
|
|
loader = AutoWeightsLoader(
|
|
self,
|
|
skip_prefixes=(["lm_head."]
|
|
if self.config.tie_word_embeddings else None),
|
|
)
|
|
return loader.load_weights(weights, mapper=self.hf_to_vllm_mapper)
|