250 lines
9.8 KiB
Python
250 lines
9.8 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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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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import re
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from typing import Iterable, Literal, Optional, Union
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import torch
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from torch import nn
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from transformers import AutoModel, 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.config import VllmConfig
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from vllm.distributed import get_tensor_model_parallel_world_size
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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.sampler import SamplerOutput, get_sampler
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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, SupportsQuant
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from .utils import 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[list[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,
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style: Literal["colwise", "rowwise"],
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quant_config=None) -> 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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`quant_config` is not yet supported.
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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 TransformersModel(nn.Module, SupportsQuant, SupportsLoRA):
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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 = "") -> None:
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super().__init__()
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logger.info("Using Transformers backend.")
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config = vllm_config.model_config.hf_config
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cache_config = vllm_config.cache_config
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model_config = vllm_config.model_config
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parallel_config = vllm_config.parallel_config
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self.config = config
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self.vocab_size = model_config.get_vocab_size()
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self.unpadded_vocab_size = model_config.get_vocab_size()
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self.model: PreTrainedModel = AutoModel.from_config(
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self.config,
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attn_implementation="vllm",
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torch_dtype=vllm_config.model_config.dtype,
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trust_remote_code=vllm_config.model_config.trust_remote_code,
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)
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prefix = self.model.base_model_prefix
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# MLP modifications
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self.apply_base_model_tp_plan(self.model)
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# Attention modifications (assumes 1 attention op per hidden layer)
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num_heads = model_config.get_num_attention_heads(parallel_config)
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head_size = model_config.get_head_size()
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num_kv_heads = model_config.get_num_kv_heads(parallel_config)
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self.attention_instances = [
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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=cache_config,
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quant_config=self.quant_config,
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prefix=f"{i}.attn") for i in range(config.num_hidden_layers)
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]
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# Model modifications
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self.replace_vocab_embed_class(self.model)
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# ForCausalLM modifications
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self.lm_head = ParallelLMHead(self.vocab_size,
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config.hidden_size,
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quant_config=self.quant_config,
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prefix=maybe_prefix(prefix, "lm_head"))
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if config.tie_word_embeddings:
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self.lm_head.weight = self.model.get_input_embeddings().weight
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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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self.vocab_size, logit_scale)
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self.sampler = get_sampler()
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def apply_base_model_tp_plan(self, module: nn.Module, prefix: str = ""):
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"""
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Apply the base model tensor parallelization plan to a module.
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Currently only supports linear layers.
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"""
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if (self.config.base_model_tp_plan is None
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and get_tensor_model_parallel_world_size() > 1):
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raise ValueError(
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"Trying to run tensor parallelization but the model does not "
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"support it yet!")
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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 self.config.base_model_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(child_module, style,
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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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self.apply_base_model_tp_plan(child_module, prefix=qual_name)
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def replace_vocab_embed_class(self, module: nn.Module):
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# Use native set input embeddings
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new_module = VocabParallelEmbedding(
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self.vocab_size,
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self.config.hidden_size,
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org_num_embeddings=self.vocab_size,
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quant_config=None,
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)
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log_replacement("input embedding", self.model.get_input_embeddings(),
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new_module)
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module.set_input_embeddings(new_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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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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model_output = self.model(
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input_ids[None, ...],
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use_cache=False,
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position_ids=positions[None, ...],
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intermediate_tensors=intermediate_tensors,
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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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return model_output
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def compute_logits(
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self,
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hidden_states: torch.Tensor,
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sampling_metadata: SamplingMetadata,
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) -> Optional[torch.Tensor]:
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logits = self.logits_processor(self.lm_head, hidden_states,
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sampling_metadata)
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return logits
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def sample(self, logits: torch.Tensor,
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sampling_metadata: SamplingMetadata) -> 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, 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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if name not in params_dict:
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name = f"{self.model.base_model_prefix}.{name}"
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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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