[gpt-oss] Add gpt-oss bf16 support
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vllm/model_executor/models/gpt_j.py
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339
vllm/model_executor/models/gpt_j.py
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# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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# Adapted from
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# https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/gptj/modeling_gptj.py
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# Copyright 2023 The vLLM team.
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# Copyright 2021 The EleutherAI and HuggingFace Teams. All rights reserved.
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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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"""Inference-only GPT-J model compatible with HuggingFace weights."""
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from collections.abc import Iterable
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from typing import Optional, Union
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import torch
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from torch import nn
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from transformers import GPTJConfig
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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, VllmConfig
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from vllm.distributed import get_pp_group, get_tensor_model_parallel_world_size
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from vllm.model_executor.layers.activation import get_act_fn
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from vllm.model_executor.layers.linear import (ColumnParallelLinear,
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QKVParallelLinear,
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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.rotary_embedding import get_rope
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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 (
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default_weight_loader, maybe_remap_kv_scale_name)
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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 SupportsPP
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from .utils import (AutoWeightsLoader, is_pp_missing_parameter,
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make_empty_intermediate_tensors_factory, make_layers,
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maybe_prefix)
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class GPTJAttention(nn.Module):
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def __init__(
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self,
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config: GPTJConfig,
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cache_config: Optional[CacheConfig] = None,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "",
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):
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super().__init__()
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self.total_num_heads = config.num_attention_heads
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self.hidden_size = config.hidden_size
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self.head_size = self.hidden_size // self.total_num_heads
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self.qkv_proj = QKVParallelLinear(
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config.hidden_size,
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self.head_size,
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self.total_num_heads,
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bias=False,
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quant_config=quant_config,
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)
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self.out_proj = RowParallelLinear(
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config.hidden_size,
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config.hidden_size,
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bias=False,
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quant_config=quant_config,
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)
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tp_world_size = get_tensor_model_parallel_world_size()
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assert self.total_num_heads % tp_world_size == 0
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self.num_heads = self.total_num_heads // tp_world_size
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scaling = self.head_size**-0.5
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assert getattr(config, "rotary", True)
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assert config.rotary_dim % 2 == 0
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rope_theta = getattr(config, "rope_theta", 10000)
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max_position_embeddings = getattr(config, "max_position_embeddings",
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8192)
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self.rotary_emb = get_rope(
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self.head_size,
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rotary_dim=config.rotary_dim,
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max_position=max_position_embeddings,
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base=rope_theta,
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is_neox_style=False,
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)
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self.attn = Attention(self.num_heads,
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self.head_size,
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scaling,
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cache_config=cache_config,
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quant_config=quant_config,
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prefix=f"{prefix}.attn")
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def forward(
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self,
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position_ids: torch.Tensor,
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hidden_states: torch.Tensor,
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) -> torch.Tensor:
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qkv, _ = self.qkv_proj(hidden_states)
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q, k, v = qkv.chunk(chunks=3, dim=-1)
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q, k = self.rotary_emb(position_ids, q, k)
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attn_output = self.attn(q, k, v)
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attn_output, _ = self.out_proj(attn_output)
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return attn_output
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class GPTJMLP(nn.Module):
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def __init__(
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self,
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intermediate_size: int,
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config: GPTJConfig,
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quant_config: Optional[QuantizationConfig] = None,
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):
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super().__init__()
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hidden_size = config.n_embd
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self.fc_in = ColumnParallelLinear(
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hidden_size,
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intermediate_size,
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quant_config=quant_config,
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)
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self.fc_out = RowParallelLinear(
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intermediate_size,
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hidden_size,
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quant_config=quant_config,
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)
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self.act = get_act_fn(config.activation_function)
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def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
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hidden_states, _ = self.fc_in(hidden_states)
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hidden_states = self.act(hidden_states)
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hidden_states, _ = self.fc_out(hidden_states)
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return hidden_states
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class GPTJBlock(nn.Module):
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def __init__(
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self,
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config: GPTJConfig,
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cache_config: Optional[CacheConfig] = None,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "",
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):
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super().__init__()
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inner_dim = (4 * config.n_embd
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if config.n_inner is None else config.n_inner)
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self.ln_1 = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon)
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self.attn = GPTJAttention(config,
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cache_config,
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quant_config,
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prefix=f"{prefix}.attn")
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self.mlp = GPTJMLP(inner_dim, config, quant_config)
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def forward(
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self,
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position_ids: torch.Tensor,
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hidden_states: torch.Tensor,
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) -> torch.Tensor:
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residual = hidden_states
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hidden_states = self.ln_1(hidden_states)
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attn_output = self.attn(
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position_ids=position_ids,
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hidden_states=hidden_states,
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)
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mlp_output = self.mlp(hidden_states)
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hidden_states = attn_output + mlp_output + residual
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return hidden_states
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@support_torch_compile
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class GPTJModel(nn.Module):
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def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
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super().__init__()
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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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quant_config = vllm_config.quant_config
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self.config = config
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self.quant_config = quant_config
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self.embed_dim = config.n_embd
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self.wte = VocabParallelEmbedding(
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config.vocab_size,
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self.embed_dim,
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)
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self.start_layer, self.end_layer, self.h = make_layers(
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config.n_layer,
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lambda prefix: GPTJBlock(
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config, cache_config, quant_config, prefix=prefix),
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prefix=f"{prefix}.h",
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)
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self.ln_f = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_epsilon)
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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.n_embd))
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def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
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return self.wte(input_ids)
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def forward(
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self,
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input_ids: torch.Tensor,
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position_ids: torch.Tensor,
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intermediate_tensors: Optional[IntermediateTensors],
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inputs_embeds: Optional[torch.Tensor] = None,
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) -> Union[torch.Tensor, IntermediateTensors]:
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if get_pp_group().is_first_rank:
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if inputs_embeds is not None:
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hidden_states = inputs_embeds
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else:
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hidden_states = self.get_input_embeddings(input_ids)
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else:
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hidden_states = intermediate_tensors["hidden_states"]
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for layer in self.h[self.start_layer:self.end_layer]:
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hidden_states = layer(position_ids, hidden_states)
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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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hidden_states = self.ln_f(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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stacked_params_mapping = [
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# (param_name, shard_name, shard_id)
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("qkv_proj", "q_proj", "q"),
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("qkv_proj", "k_proj", "k"),
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("qkv_proj", "v_proj", "v"),
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("gate_up_proj", "gate_proj", 0),
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("gate_up_proj", "up_proj", 1),
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]
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params_dict = dict(self.named_parameters())
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loaded_params: set[str] = set()
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for name, loaded_weight in weights:
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if "attn.bias" in name or "attn.masked_bias" in name:
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continue
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if (self.quant_config is not None and
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(scale_name := self.quant_config.get_cache_scale(name))):
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# Loading kv cache quantization scales
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param = params_dict[scale_name]
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weight_loader = getattr(param, "weight_loader",
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default_weight_loader)
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loaded_weight = (loaded_weight if loaded_weight.dim() == 0 else
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loaded_weight[0])
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weight_loader(param, loaded_weight)
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loaded_params.add(scale_name)
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continue
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for (param_name, weight_name, shard_id) in stacked_params_mapping:
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if weight_name not in name:
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continue
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name = name.replace(weight_name, param_name)
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# Skip loading extra bias for GPTQ models.
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if name.endswith(".bias") and name not in params_dict:
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continue
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if is_pp_missing_parameter(name, self):
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continue
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param = params_dict[name]
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weight_loader = param.weight_loader
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weight_loader(param, loaded_weight, shard_id)
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break
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else:
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name = maybe_remap_kv_scale_name(name, params_dict)
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if name is None:
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continue
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# Skip loading extra bias for GPTQ models.
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if name.endswith(".bias") and name not in params_dict:
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continue
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if is_pp_missing_parameter(name, self):
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continue
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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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class GPTJForCausalLM(nn.Module, SupportsPP):
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def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
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super().__init__()
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config = vllm_config.model_config.hf_config
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quant_config = vllm_config.quant_config
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self.config = config
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self.quant_config = quant_config
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assert not config.tie_word_embeddings
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self.transformer = GPTJModel(vllm_config=vllm_config,
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prefix=maybe_prefix(
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prefix, "transformer"))
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self.lm_head = ParallelLMHead(
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config.vocab_size,
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config.n_embd,
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bias=True,
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quant_config=quant_config,
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)
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self.logits_processor = LogitsProcessor(config.vocab_size)
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self.make_empty_intermediate_tensors = (
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self.transformer.make_empty_intermediate_tensors)
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def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
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return self.transformer.get_input_embeddings(input_ids)
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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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hidden_states = self.transformer(input_ids, positions,
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intermediate_tensors, inputs_embeds)
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return hidden_states
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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, self.lm_head.bias)
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return logits
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def load_weights(self, weights: Iterable[tuple[str,
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torch.Tensor]]) -> set[str]:
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loader = AutoWeightsLoader(self)
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return loader.load_weights(weights)
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