508 lines
20 KiB
Python
508 lines
20 KiB
Python
# Copyright 2024 The vLLM team.
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# Copyright 2024 Google Inc. HuggingFace Inc. team. All rights reserved.
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#
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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 Gemma3 model compatible with HuggingFace weights."""
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from typing import Iterable, List, Optional, Set, Tuple, Union
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import torch
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from torch import nn
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from vllm.attention import Attention, AttentionMetadata
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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.logger import init_logger
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from vllm.model_executor.layers.activation import GeluAndMul
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from vllm.model_executor.layers.layernorm import GemmaRMSNorm
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from vllm.model_executor.layers.linear import (MergedColumnParallelLinear,
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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.sampler import SamplerOutput, get_sampler
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from vllm.model_executor.layers.vocab_parallel_embedding import (
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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
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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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logger = init_logger(__name__)
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class Gemma3MLP(nn.Module):
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def __init__(
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self,
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hidden_size: int,
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intermediate_size: int,
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hidden_activation: str,
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quant_config: Optional[QuantizationConfig] = None,
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) -> None:
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super().__init__()
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self.gate_up_proj = MergedColumnParallelLinear(
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hidden_size, [intermediate_size] * 2,
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bias=False,
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quant_config=quant_config)
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self.down_proj = RowParallelLinear(intermediate_size,
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hidden_size,
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bias=False,
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quant_config=quant_config)
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if hidden_activation != "gelu_pytorch_tanh":
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raise ValueError(
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"Gemma3 uses `gelu_pytorch_tanh` as the hidden activation "
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"function. Please set `hidden_activation` to "
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"`gelu_pytorch_tanh`.")
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self.act_fn = GeluAndMul(approximate="tanh")
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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gate_up, _ = self.gate_up_proj(x)
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x = self.act_fn(gate_up)
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x, _ = self.down_proj(x)
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return x
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class Gemma3Attention(nn.Module):
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def __init__(self,
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layer_idx: int,
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config,
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hidden_size: int,
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num_heads: int,
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num_kv_heads: int,
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head_dim: int,
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max_position_embeddings: int,
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cache_config: Optional[CacheConfig] = None,
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quant_config: Optional[QuantizationConfig] = None,
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attn_logits_soft_cap: Optional[float] = None) -> None:
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super().__init__()
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self.layer_idx = layer_idx
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self.config = config
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self.hidden_size = hidden_size
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tp_size = get_tensor_model_parallel_world_size()
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self.total_num_heads = num_heads
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assert self.total_num_heads % tp_size == 0
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self.num_heads = self.total_num_heads // tp_size
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self.total_num_kv_heads = num_kv_heads
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if self.total_num_kv_heads >= tp_size:
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assert self.total_num_kv_heads % tp_size == 0
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else:
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assert tp_size % self.total_num_kv_heads == 0
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self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
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self.head_dim = head_dim
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self.q_size = self.num_heads * self.head_dim
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self.kv_size = self.num_kv_heads * self.head_dim
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self.scaling = config.query_pre_attn_scalar**-0.5
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# Extract rope_theta from config, compatible with both old-style
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# (config.rope_theta) and new-style (config.rope_parameters dict).
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rope_params = getattr(config, "rope_parameters", None)
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if hasattr(config, "rope_theta"):
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self.rope_theta = config.rope_theta
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elif isinstance(rope_params, dict):
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# Transformers v5: nested per layer_type
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if "full_attention" in rope_params:
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self.rope_theta = rope_params["full_attention"].get(
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"rope_theta", 10000.0)
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else:
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# Transformers v4: flat dict
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self.rope_theta = rope_params.get("rope_theta", 10000.0)
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else:
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self.rope_theta = 10000.0
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self.qkv_proj = QKVParallelLinear(
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hidden_size,
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self.head_dim,
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self.total_num_heads,
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self.total_num_kv_heads,
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bias=config.attention_bias,
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quant_config=quant_config,
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)
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self.o_proj = RowParallelLinear(
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self.total_num_heads * self.head_dim,
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hidden_size,
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bias=config.attention_bias,
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quant_config=quant_config,
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)
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# Gemma3 specific: QK normalization
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self.q_norm = GemmaRMSNorm(self.head_dim, eps=config.rms_norm_eps)
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self.k_norm = GemmaRMSNorm(self.head_dim, eps=config.rms_norm_eps)
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# Determine layer type and rope config
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layer_types = getattr(config, "layer_types", None)
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if layer_types is not None:
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layer_type = layer_types[layer_idx]
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self.is_sliding = (layer_type == "sliding_attention")
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else:
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self.is_sliding = (layer_idx % 2 == 1
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and config.sliding_window is not None)
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# Extract rope config, compatible with both old-style (rope_theta,
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# rope_scaling) and new-style (rope_parameters dict) transformers.
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rope_params = getattr(config, "rope_parameters", None)
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# Set up rope based on layer type
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if self.is_sliding:
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# Local/sliding attention uses rope_local_base_freq
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if hasattr(config, "rope_local_base_freq"):
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local_base = config.rope_local_base_freq
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elif (isinstance(rope_params, dict)
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and "sliding_attention" in rope_params):
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local_base = rope_params["sliding_attention"].get(
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"rope_theta", self.rope_theta)
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else:
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local_base = self.rope_theta
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self.rotary_emb = get_rope(
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self.head_dim,
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rotary_dim=self.head_dim,
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max_position=max_position_embeddings,
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base=local_base,
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is_neox_style=True,
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)
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else:
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# Global attention: extract rope_base and rope_scaling.
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# Prioritize rope_parameters dict (newer transformers) to
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# avoid passing nested dicts that are unhashable.
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rope_scaling = None
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rope_base = self.rope_theta
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if isinstance(rope_params, dict):
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# Transformers v5: per layer_type sub-dicts
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if "full_attention" in rope_params:
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rp = rope_params["full_attention"]
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else:
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# Transformers v4: flat dict
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rp = rope_params
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rope_base = rp.get("rope_theta", self.rope_theta)
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rtype = rp.get("rope_type", None)
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if rtype and rtype != "default":
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rope_scaling = {
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k: v for k, v in rp.items()
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if k not in ("rope_theta",)
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}
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else:
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# Fallback: old-style config.rope_scaling (flat dict)
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rope_scaling = getattr(config, "rope_scaling", None)
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self.rotary_emb = get_rope(
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self.head_dim,
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rotary_dim=self.head_dim,
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max_position=max_position_embeddings,
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base=rope_base,
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is_neox_style=True,
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rope_scaling=rope_scaling,
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)
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# NOTE: Like Gemma2, vLLM currently ignores sliding window
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# and uses global attention for all layers.
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self.attn = Attention(self.num_heads,
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self.head_dim,
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self.scaling,
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num_kv_heads=self.num_kv_heads,
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cache_config=cache_config,
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quant_config=quant_config,
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logits_soft_cap=attn_logits_soft_cap)
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def forward(
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self,
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positions: torch.Tensor,
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hidden_states: torch.Tensor,
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kv_cache: torch.Tensor,
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attn_metadata: AttentionMetadata,
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) -> torch.Tensor:
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qkv, _ = self.qkv_proj(hidden_states)
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q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size],
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dim=-1)
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# Gemma3 specific: apply QK normalization
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q = q.unflatten(-1, (self.num_heads, self.head_dim))
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q = self.q_norm(q)
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q = q.flatten(-2, -1)
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k = k.unflatten(-1, (self.num_kv_heads, self.head_dim))
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k = self.k_norm(k)
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k = k.flatten(-2, -1)
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# MLU rotary_emb expects a single concatenated tensor, not
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# separate q and k (forward_mlu signature differs from forward_native).
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qk = torch.cat([q, k], dim=-1)
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self.rotary_emb(positions,
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qk.view(-1, self.num_heads + self.num_kv_heads,
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self.head_dim))
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q, k = qk.split([self.q_size, self.kv_size], dim=-1)
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attn_output = self.attn(q, k, v, kv_cache, attn_metadata)
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output, _ = self.o_proj(attn_output)
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return output
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class Gemma3DecoderLayer(nn.Module):
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def __init__(
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self,
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layer_idx: int,
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config,
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cache_config: Optional[CacheConfig] = None,
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quant_config: Optional[QuantizationConfig] = None,
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) -> None:
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super().__init__()
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self.hidden_size = config.hidden_size
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self.self_attn = Gemma3Attention(
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layer_idx=layer_idx,
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config=config,
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hidden_size=self.hidden_size,
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num_heads=config.num_attention_heads,
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num_kv_heads=config.num_key_value_heads,
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head_dim=config.head_dim,
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max_position_embeddings=config.max_position_embeddings,
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cache_config=cache_config,
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quant_config=quant_config,
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# Gemma3 does not use attn logit softcapping
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attn_logits_soft_cap=getattr(config,
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"attn_logit_softcapping", None),
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)
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self.hidden_size = config.hidden_size
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self.mlp = Gemma3MLP(
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hidden_size=self.hidden_size,
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intermediate_size=config.intermediate_size,
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hidden_activation=config.hidden_activation,
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quant_config=quant_config,
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)
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self.input_layernorm = GemmaRMSNorm(config.hidden_size,
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eps=config.rms_norm_eps)
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self.post_attention_layernorm = GemmaRMSNorm(config.hidden_size,
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eps=config.rms_norm_eps)
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self.pre_feedforward_layernorm = GemmaRMSNorm(config.hidden_size,
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eps=config.rms_norm_eps)
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self.post_feedforward_layernorm = GemmaRMSNorm(config.hidden_size,
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eps=config.rms_norm_eps)
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def forward(
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self,
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positions: torch.Tensor,
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hidden_states: torch.Tensor,
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kv_cache: torch.Tensor,
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attn_metadata: AttentionMetadata,
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residual: Optional[torch.Tensor],
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) -> Tuple[torch.Tensor, torch.Tensor]:
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if residual is None:
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residual = hidden_states
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hidden_states = self.input_layernorm(hidden_states)
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else:
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hidden_states, residual = self.input_layernorm(
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hidden_states, residual)
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hidden_states = self.self_attn(
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positions=positions,
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hidden_states=hidden_states,
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kv_cache=kv_cache,
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attn_metadata=attn_metadata,
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)
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hidden_states = self.post_attention_layernorm(hidden_states)
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hidden_states, residual = self.pre_feedforward_layernorm(
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hidden_states, residual)
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hidden_states = self.mlp(hidden_states)
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hidden_states = self.post_feedforward_layernorm(hidden_states)
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return hidden_states, residual
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class Gemma3Model(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.embed_tokens = VocabParallelEmbedding(
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config.vocab_size,
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config.hidden_size,
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)
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self.start_layer, self.end_layer, self.layers = make_layers(
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config.num_hidden_layers,
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lambda prefix: Gemma3DecoderLayer(
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int(prefix.split(".")[-1]),
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config, cache_config, quant_config),
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prefix=f"{prefix}.layers")
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self.norm = GemmaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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normalizer = self.config.hidden_size**0.5
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self.register_buffer("normalizer", torch.tensor(normalizer))
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self.make_empty_intermediate_tensors = (
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make_empty_intermediate_tensors_factory(
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["hidden_states", "residual"], config.hidden_size))
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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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kv_caches: List[torch.Tensor],
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attn_metadata: AttentionMetadata,
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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.embed_tokens(input_ids)
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hidden_states *= self.normalizer
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residual = None
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else:
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assert intermediate_tensors is not None
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hidden_states = intermediate_tensors["hidden_states"]
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residual = intermediate_tensors["residual"]
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for i in range(self.start_layer, self.end_layer):
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layer = self.layers[i]
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hidden_states, residual = layer(
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positions,
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hidden_states,
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kv_caches[i - self.start_layer],
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attn_metadata,
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residual,
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)
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if not get_pp_group().is_last_rank:
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return IntermediateTensors({
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"hidden_states": hidden_states,
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"residual": residual
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})
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hidden_states, _ = self.norm(hidden_states, residual)
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return hidden_states
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def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
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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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for (param_name, shard_name, shard_id) in stacked_params_mapping:
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if shard_name not in name:
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continue
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name = name.replace(shard_name, param_name)
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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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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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if name not in params_dict:
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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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unloaded_params = params_dict.keys() - loaded_params
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if unloaded_params:
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logger.warning(
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"Some weights are not initialized from checkpoints: %s",
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unloaded_params)
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class Gemma3ForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
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packed_modules_mapping = {
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"qkv_proj": [
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"q_proj",
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"k_proj",
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"v_proj",
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],
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"gate_up_proj": [
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"gate_proj",
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"up_proj",
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],
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}
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# LoRA specific attributes
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supported_lora_modules = [
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"qkv_proj",
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"o_proj",
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"gate_up_proj",
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"down_proj",
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]
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embedding_modules = {}
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embedding_padding_modules = []
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def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
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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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lora_config = vllm_config.lora_config
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del lora_config # Unused.
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super().__init__()
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self.config = config
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self.quant_config = quant_config
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self.model = Gemma3Model(vllm_config=vllm_config,
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prefix=maybe_prefix(prefix, "model"))
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# Gemma3 may or may not have final_logit_softcapping
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soft_cap = getattr(config, "final_logit_softcapping", None)
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self.logits_processor = LogitsProcessor(
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config.vocab_size, soft_cap=soft_cap)
|
|
self.sampler = get_sampler()
|
|
self.make_empty_intermediate_tensors = (
|
|
self.model.make_empty_intermediate_tensors)
|
|
|
|
def forward(
|
|
self,
|
|
input_ids: torch.Tensor,
|
|
positions: torch.Tensor,
|
|
kv_caches: List[torch.Tensor],
|
|
attn_metadata: AttentionMetadata,
|
|
intermediate_tensors: Optional[IntermediateTensors] = None,
|
|
) -> Union[torch.Tensor, IntermediateTensors]:
|
|
hidden_states = self.model(input_ids, positions, kv_caches,
|
|
attn_metadata, intermediate_tensors)
|
|
return hidden_states
|
|
|
|
def compute_logits(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
sampling_metadata: SamplingMetadata,
|
|
) -> Optional[torch.Tensor]:
|
|
logits = self.logits_processor(self.model.embed_tokens,
|
|
hidden_states, sampling_metadata)
|
|
return logits
|
|
|
|
def sample(
|
|
self,
|
|
logits: torch.Tensor,
|
|
sampling_metadata: SamplingMetadata,
|
|
) -> Optional[SamplerOutput]:
|
|
next_tokens = self.sampler(logits, sampling_metadata)
|
|
return next_tokens
|
|
|
|
def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
|
|
loader = AutoWeightsLoader(
|
|
self,
|
|
skip_prefixes=(["lm_head."]
|
|
if self.config.tie_word_embeddings else None),
|
|
)
|
|
loader.load_weights(weights)
|