gemma3.py

# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
# Copyright 2025 The vLLM team.
# Copyright 2025 Google Inc. HuggingFace Inc. team. All rights reserved.
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from collections.abc import Iterable
from itertools import islice

import torch
import torch.nn.functional as F
from torch import nn
from transformers import Gemma3TextConfig

from vllm.attention import Attention, AttentionType
from vllm.compilation.decorators import support_torch_compile
from vllm.config import CacheConfig, VllmConfig
from vllm.distributed import get_pp_group, get_tensor_model_parallel_world_size
from vllm.logger import init_logger
from vllm.model_executor.layers.activation import GeluAndMul
from vllm.model_executor.layers.layernorm import GemmaRMSNorm
from vllm.model_executor.layers.linear import (
    MergedColumnParallelLinear,
    QKVParallelLinear,
    RowParallelLinear,
)
from vllm.model_executor.layers.logits_processor import LogitsProcessor
from vllm.model_executor.layers.quantization import QuantizationConfig
from vllm.model_executor.layers.rotary_embedding import get_rope
from vllm.model_executor.layers.vocab_parallel_embedding import VocabParallelEmbedding
from vllm.model_executor.model_loader.weight_utils import (
    default_weight_loader,
    maybe_remap_kv_scale_name,
)
from vllm.sequence import IntermediateTensors

from ...attention.layers.encoder_only_attention import EncoderOnlyAttention
from .interfaces import SupportsLoRA, SupportsPP
from .utils import (
    AutoWeightsLoader,
    extract_layer_index,
    is_pp_missing_parameter,
    make_empty_intermediate_tensors_factory,
    make_layers,
    maybe_prefix,
)

logger = init_logger(__name__)


class Gemma3MLP(nn.Module):
    def __init__(
        self,
        hidden_size: int,
        intermediate_size: int,
        hidden_activation: str,
        quant_config: QuantizationConfig | None = None,
        prefix: str = "",
    ) -> None:
        super().__init__()
        self.gate_up_proj = MergedColumnParallelLinear(
            hidden_size,
            [intermediate_size] * 2,
            bias=False,
            quant_config=quant_config,
            prefix=f"{prefix}.gate_up_proj",
        )
        self.down_proj = RowParallelLinear(
            intermediate_size,
            hidden_size,
            bias=False,
            quant_config=quant_config,
            prefix=f"{prefix}.down_proj",
        )
        if hidden_activation != "gelu_pytorch_tanh":
            raise ValueError(
                "Gemma3 uses `gelu_pytorch_tanh` as the hidden activation "
                "function. Please set `hidden_act` and `hidden_activation` to "
                "`gelu_pytorch_tanh`."
            )
        self.act_fn = GeluAndMul(approximate="tanh")

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        gate_up, _ = self.gate_up_proj(x)
        x = self.act_fn(gate_up)
        x, _ = self.down_proj(x)
        return x


class Gemma3Attention(nn.Module):
    def __init__(
        self,
        config: Gemma3TextConfig,
        hidden_size: int,
        num_heads: int,
        num_kv_heads: int,
        head_dim: int,
        max_position_embeddings: int,
        cache_config: CacheConfig | None = None,
        quant_config: QuantizationConfig | None = None,
        attn_logits_soft_cap: float | None = None,
        prefix: str = "",
    ) -> None:
        super().__init__()
        self.config = config
        self.hidden_size = hidden_size
        tp_size = get_tensor_model_parallel_world_size()
        self.total_num_heads = num_heads
        assert self.total_num_heads % tp_size == 0
        self.num_heads = self.total_num_heads // tp_size
        self.total_num_kv_heads = num_kv_heads
        if self.total_num_kv_heads >= tp_size:
            # Number of KV heads is greater than TP size, so we partition
            # the KV heads across multiple tensor parallel GPUs.
            assert self.total_num_kv_heads % tp_size == 0
        else:
            # Number of KV heads is less than TP size, so we replicate
            # the KV heads across multiple tensor parallel GPUs.
            assert tp_size % self.total_num_kv_heads == 0
        self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
        self.head_dim = head_dim
        self.q_size = self.num_heads * self.head_dim
        self.kv_size = self.num_kv_heads * self.head_dim
        self.scaling = config.query_pre_attn_scalar**-0.5

        self.qkv_proj = QKVParallelLinear(
            hidden_size,
            self.head_dim,
            self.total_num_heads,
            self.total_num_kv_heads,
            bias=config.attention_bias,
            quant_config=quant_config,
            prefix=f"{prefix}.qkv_proj",
        )
        self.o_proj = RowParallelLinear(
            self.total_num_heads * self.head_dim,
            hidden_size,
            bias=config.attention_bias,
            quant_config=quant_config,
            prefix=f"{prefix}.o_proj",
        )

        self.q_norm = GemmaRMSNorm(self.head_dim, eps=config.rms_norm_eps)
        self.k_norm = GemmaRMSNorm(self.head_dim, eps=config.rms_norm_eps)

        layer_idx = extract_layer_index(prefix)
        layer_type = config.layer_types[layer_idx]
        self.is_sliding = layer_type == "sliding_attention"
        sliding_window = config.sliding_window if self.is_sliding else None

        # Initialize the rotary embedding.
        if layer_type in config.rope_parameters:
            # Transformers v5 rope config.
            rope_parameters = config.rope_parameters[layer_type]
        else:
            # Transformers v4 rope config.
            # Global attention. Use the values in config.json.
            rope_parameters = config.rope_parameters
            # Local attention. Override the values in config.json.
            if self.is_sliding:
                rope_parameters = dict(
                    rope_type="default", rope_theta=config.rope_local_base_freq
                )

        self.rotary_emb = get_rope(
            self.head_dim,
            rotary_dim=self.head_dim,
            max_position=max_position_embeddings,
            rope_parameters=rope_parameters,
            is_neox_style=True,
        )

        if getattr(config, "is_causal", True):
            attn_type = AttentionType.DECODER
        else:
            attn_type = AttentionType.ENCODER_ONLY

        attn_cls = (
            EncoderOnlyAttention
            if attn_type == AttentionType.ENCODER_ONLY
            else Attention
        )

        self.attn = attn_cls(
            self.num_heads,
            self.head_dim,
            self.scaling,
            num_kv_heads=self.num_kv_heads,
            cache_config=cache_config,
            quant_config=quant_config,
            attn_type=attn_type,
            logits_soft_cap=attn_logits_soft_cap,
            per_layer_sliding_window=sliding_window,
            prefix=f"{prefix}.attn",
        )

    def forward(
        self,
        positions: torch.Tensor,
        hidden_states: torch.Tensor,
        **kwargs,
    ) -> torch.Tensor:
        qkv, _ = self.qkv_proj(hidden_states)
        q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)

        q = q.unflatten(-1, (self.num_heads, self.head_dim))
        q = self.q_norm(q)
        q = q.flatten(-2, -1)
        k = k.unflatten(-1, (self.num_kv_heads, self.head_dim))
        k = self.k_norm(k)
        k = k.flatten(-2, -1)

        q, k = self.rotary_emb(positions, q, k)
        attn_output = self.attn(q, k, v)

        if not kwargs.get("has_images", False):
            # Fast path for text-only inputs. The performance for the text-only
            # inputs are not affected by the naive attention below.
            output, _ = self.o_proj(attn_output)
            return output

        # NOTE(woosuk): Gemma3 uses bidirectional attention between image tokens
        # that correspond to the same image while using causal attention
        # otherwise. Current attention backends cannot handle this pattern, so
        # we temporarily use a naive attention implementation with mask tensors.

        # We intentionally keep the attention backend as-is and only override
        # `attn_output` with the naive implementation's output. This minimizes
        # changes to existing model runners and attention backends. The call to
        # `self.attn(q, k, v)` is only used to populate the KV cache - its
        # output is discarded and overwritten below. While this duplicates
        # computation, it maintains compatibility.
        # TODO(woosuk): Optimize by implementing custom attention kernels.
        attn_output = self.naive_attn_with_masks(q, k, v, out=attn_output, **kwargs)
        output, _ = self.o_proj(attn_output)
        return output

    def naive_attn_with_masks(
        self,
        q: torch.Tensor,
        k: torch.Tensor,
        v: torch.Tensor,
        out: torch.Tensor,
        **kwargs,
    ) -> torch.Tensor:
        # NOTE(woosuk): As described in the comment above, this code is not
        # meant to be performant. It is only meant to be correct.
        q = q.view(-1, self.num_heads, self.head_dim)
        # Expand the key and value to handle GQA.
        num_queries_per_kv = self.num_heads // self.num_kv_heads
        k = k.view(-1, self.num_kv_heads, self.head_dim)
        k = k.repeat_interleave(num_queries_per_kv, dim=-2)
        v = v.view(-1, self.num_kv_heads, self.head_dim)
        v = v.repeat_interleave(num_queries_per_kv, dim=-2)

        if self.is_sliding:
            attn_masks = kwargs["local_attn_masks"]
        else:
            attn_masks = kwargs["global_attn_masks"]

        seq_lens = kwargs["seq_lens"]
        start_idx = 0
        for seq_len, attn_mask in zip(seq_lens, attn_masks):
            end_idx = start_idx + seq_len
            query = q[start_idx:end_idx].unsqueeze(0)
            key = k[start_idx:end_idx].unsqueeze(0)
            value = v[start_idx:end_idx].unsqueeze(0)

            # Transpose.
            query = query.transpose(1, 2)
            key = key.transpose(1, 2)
            value = value.transpose(1, 2)

            output = F.scaled_dot_product_attention(
                query,
                key,
                value,
                attn_mask,
                self.scaling,
            )
            output = output.transpose(1, 2).flatten(-2, -1)
            out[start_idx:end_idx] = output
            start_idx = end_idx
        return out


class Gemma3DecoderLayer(nn.Module):
    def __init__(
        self,
        config: Gemma3TextConfig,
        cache_config: CacheConfig | None = None,
        quant_config: QuantizationConfig | None = None,
        prefix: str = "",
    ) -> None:
        super().__init__()
        self.hidden_size = config.hidden_size
        self.self_attn = Gemma3Attention(
            config=config,
            hidden_size=self.hidden_size,
            num_heads=config.num_attention_heads,
            num_kv_heads=config.num_key_value_heads,
            head_dim=config.head_dim,
            max_position_embeddings=config.max_position_embeddings,
            cache_config=cache_config,
            quant_config=quant_config,
            attn_logits_soft_cap=None,
            prefix=f"{prefix}.self_attn",
        )
        self.hidden_size = config.hidden_size
        self.mlp = Gemma3MLP(
            hidden_size=self.hidden_size,
            intermediate_size=config.intermediate_size,
            hidden_activation=config.hidden_activation,
            quant_config=quant_config,
            prefix=f"{prefix}.mlp",
        )
        self.input_layernorm = GemmaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        self.post_attention_layernorm = GemmaRMSNorm(
            config.hidden_size, eps=config.rms_norm_eps
        )
        self.pre_feedforward_layernorm = GemmaRMSNorm(
            config.hidden_size, eps=config.rms_norm_eps
        )
        self.post_feedforward_layernorm = GemmaRMSNorm(
            config.hidden_size, eps=config.rms_norm_eps
        )

    def forward(
        self,
        positions: torch.Tensor,
        hidden_states: torch.Tensor,
        residual: torch.Tensor | None,
        **kwargs,
    ) -> tuple[torch.Tensor, torch.Tensor]:
        if residual is None:
            residual = hidden_states
            hidden_states = self.input_layernorm(hidden_states)
        else:
            hidden_states, residual = self.input_layernorm(hidden_states, residual)
        hidden_states = self.self_attn(
            positions=positions,
            hidden_states=hidden_states,
            **kwargs,
        )
        hidden_states = self.post_attention_layernorm(hidden_states)

        hidden_states, residual = self.pre_feedforward_layernorm(
            hidden_states, residual
        )
        hidden_states = self.mlp(hidden_states)
        hidden_states = self.post_feedforward_layernorm(hidden_states)
        return hidden_states, residual


@support_torch_compile
class Gemma3Model(nn.Module):
    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
        super().__init__()
        config = vllm_config.model_config.hf_config
        cache_config = vllm_config.cache_config
        quant_config = vllm_config.quant_config
        self.config = config
        self.quant_config = quant_config

        self.embed_tokens = VocabParallelEmbedding(
            config.vocab_size,
            config.hidden_size,
            quant_config=quant_config,
            prefix=f"{prefix}.embed_tokens",
        )
        self.start_layer, self.end_layer, self.layers = make_layers(
            config.num_hidden_layers,
            lambda prefix: Gemma3DecoderLayer(
                config, cache_config, quant_config, prefix=prefix
            ),
            prefix=f"{prefix}.layers",
        )
        self.norm = GemmaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)

        # Normalize the embedding by sqrt(hidden_size)
        # The normalizer's data type should be downcasted to the model's
        # data type such as bfloat16, not float32.
        # See https://github.com/huggingface/transformers/pull/29402
        normalizer = self.config.hidden_size**0.5
        self.register_buffer("normalizer", torch.tensor(normalizer), persistent=False)
        self.make_empty_intermediate_tensors = make_empty_intermediate_tensors_factory(
            ["hidden_states", "residual"], config.hidden_size
        )

    def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
        # NOTE(woosuk): Only apply the normalizer to the output of
        # vocab embedding. Don't apply it to the vision embedding.
        return self.embed_tokens(input_ids) * self.normalizer

    def forward(
        self,
        input_ids: torch.Tensor | None,
        positions: torch.Tensor,
        intermediate_tensors: IntermediateTensors | None,
        inputs_embeds: torch.Tensor | None = None,
        **kwargs,
    ) -> torch.Tensor | IntermediateTensors:
        if get_pp_group().is_first_rank:
            if inputs_embeds is not None:
                hidden_states = inputs_embeds
            else:
                hidden_states = self.embed_input_ids(input_ids)
            residual = None
        else:
            assert intermediate_tensors is not None
            hidden_states = intermediate_tensors["hidden_states"]
            residual = intermediate_tensors["residual"]
        for layer in islice(self.layers, self.start_layer, self.end_layer):
            hidden_states, residual = layer(
                positions,
                hidden_states,
                residual,
                **kwargs,
            )
        if not get_pp_group().is_last_rank:
            return IntermediateTensors(
                {"hidden_states": hidden_states, "residual": residual}
            )
        hidden_states, _ = self.norm(hidden_states, residual)
        return hidden_states

    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
        stacked_params_mapping = [
            # (param_name, shard_name, shard_id)
            ("qkv_proj", "q_proj", "q"),
            ("qkv_proj", "k_proj", "k"),
            ("qkv_proj", "v_proj", "v"),
            ("gate_up_proj", "gate_proj", 0),
            ("gate_up_proj", "up_proj", 1),
        ]
        params_dict = dict(self.named_parameters())
        loaded_params: set[str] = set()
        for name, loaded_weight in weights:
            # Revert +1 during llama.cpp conversion
            # see: https://github.com/ggml-org/llama.cpp/blob/be7c3034108473beda214fd1d7c98fd6a7a3bdf5/convert_hf_to_gguf.py#L3397-L3400
            if (
                self.quant_config
                and self.quant_config.get_name() == "gguf"
                and name.endswith("norm.weight")
            ):
                loaded_weight -= 1

            if self.quant_config is not None and (
                scale_name := self.quant_config.get_cache_scale(name)
            ):
                # Loading kv cache scales for compressed-tensors quantization
                param = params_dict[scale_name]
                weight_loader = getattr(param, "weight_loader", default_weight_loader)
                loaded_weight = loaded_weight[0]
                weight_loader(param, loaded_weight)
                loaded_params.add(scale_name)
                continue

            # Check if this is a scale parameter that needs remapping first
            if name.endswith((".k_scale", ".v_scale", ".q_scale", ".prob_scale")):
                # Try to remap the scale name first
                remapped_name = maybe_remap_kv_scale_name(name, params_dict)
                if remapped_name is not None and remapped_name in params_dict:
                    # Successfully remapped, use the remapped name
                    param = params_dict[remapped_name]
                    weight_loader = getattr(
                        param, "weight_loader", default_weight_loader
                    )
                    weight_loader(param, loaded_weight)
                    loaded_params.add(remapped_name)
                    continue
                # If remapping failed, continue with normal processing

            for param_name, shard_name, shard_id in stacked_params_mapping:
                if shard_name not in name:
                    continue
                name = name.replace(shard_name, param_name)
                # Skip loading extra bias for GPTQ models.
                if name.endswith(".bias") and name not in params_dict:
                    continue
                if is_pp_missing_parameter(name, self):
                    continue
                param = params_dict[name]
                weight_loader = param.weight_loader
                weight_loader(param, loaded_weight, shard_id)
                break
            else:
                # Skip loading extra bias for GPTQ models.
                if name.endswith(".bias") and name not in params_dict:
                    continue
                # Remapping the name of FP8 kv-scale.
                name = maybe_remap_kv_scale_name(name, params_dict)
                if name is None:
                    continue
                if is_pp_missing_parameter(name, self):
                    continue
                param = params_dict[name]
                weight_loader = getattr(param, "weight_loader", default_weight_loader)
                weight_loader(param, loaded_weight)
            loaded_params.add(name)

        return loaded_params


class Gemma3ForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
    packed_modules_mapping = {
        "qkv_proj": [
            "q_proj",
            "k_proj",
            "v_proj",
        ],
        "gate_up_proj": [
            "gate_proj",
            "up_proj",
        ],
    }

    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
        config = vllm_config.model_config.hf_config
        quant_config = vllm_config.quant_config

        super().__init__()
        self.config = config
        # currently all existing Gemma models have `tie_word_embeddings` enabled
        assert config.tie_word_embeddings
        self.quant_config = quant_config
        self.model = Gemma3Model(
            vllm_config=vllm_config, prefix=maybe_prefix(prefix, "model")
        )
        self.logits_processor = LogitsProcessor(
            config.vocab_size, soft_cap=config.final_logit_softcapping
        )
        self.make_empty_intermediate_tensors = (
            self.model.make_empty_intermediate_tensors
        )

    def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
        return self.model.embed_input_ids(input_ids)

    def forward(
        self,
        input_ids: torch.Tensor,
        positions: torch.Tensor,
        intermediate_tensors: IntermediateTensors | None = None,
        inputs_embeds: torch.Tensor | None = None,
        **kwargs,
    ) -> torch.Tensor | IntermediateTensors:
        hidden_states = self.model(
            input_ids, positions, intermediate_tensors, inputs_embeds, **kwargs
        )
        return hidden_states

    def compute_logits(
        self,
        hidden_states: torch.Tensor,
    ) -> torch.Tensor | None:
        logits = self.logits_processor(self.model.embed_tokens, hidden_states)
        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)