[Model] Add Gemma 2 (#5908)

docs/source/models/supported_models.rst

@@ -55,6 +55,10 @@ Alongside each architecture, we include some popular models that use it.
     - Gemma
     - :code:`google/gemma-2b`, :code:`google/gemma-7b`, etc.
     - ✅︎
+  * - :code:`Gemma2ForCausalLM`
+    - Gemma2
+    - :code:`google/gemma-2-9b`, :code:`google/gemma-2-27b`, etc.
+    - ✅︎
   * - :code:`GPT2LMHeadModel`
     - GPT-2
     - :code:`gpt2`, :code:`gpt2-xl`, etc.

requirements-common.txt

@@ -6,7 +6,7 @@ numpy < 2.0.0
 requests
 tqdm
 py-cpuinfo
-transformers >= 4.40.0  # Required for StarCoder2 & Llava, Llama 3.
+transformers >= 4.42.0  # Required for Gemma 2.
 tokenizers >= 0.19.1  # Required for Llama 3.
 fastapi
 aiohttp

vllm/config.py

@@ -14,7 +14,7 @@ from vllm.model_executor.models import ModelRegistry
 from vllm.tracing import is_otel_installed
 from vllm.transformers_utils.config import get_config, get_hf_text_config
 from vllm.utils import (cuda_device_count_stateless, get_cpu_memory, is_cpu,
-                        is_hip, is_neuron, is_tpu, is_xpu,
+                        is_hip, is_neuron, is_tpu, is_xpu, print_warning_once,
                         update_environment_variables)
 
 if TYPE_CHECKING:
@@ -141,6 +141,17 @@ class ModelConfig:
                                     code_revision, rope_scaling, rope_theta)
         self.hf_text_config = get_hf_text_config(self.hf_config)
         self.dtype = _get_and_verify_dtype(self.hf_text_config, dtype)
+
+        if (not self.disable_sliding_window
+                and self.hf_text_config.model_type == "gemma2"
+                and self.hf_text_config.sliding_window is not None):
+            print_warning_once(
+                "Gemma 2 uses sliding window attention for every odd layer, "
+                "which is currently not supported by vLLM. Disabling sliding "
+                "window and capping the max length to the sliding window size "
+                f"({self.hf_text_config.sliding_window}).")
+            self.disable_sliding_window = True
+
         self.max_model_len = _get_and_verify_max_len(
             hf_config=self.hf_text_config,
             max_model_len=max_model_len,
@@ -257,8 +268,7 @@ class ModelConfig:
                 "BitAndBytes quantization with TP or PP is not supported yet.")
 
     def get_hf_config_sliding_window(self) -> Optional[int]:
-        """Get the sliding window size, or None if disabled.
-        """
+        """Get the sliding window size, or None if disabled."""
 
         # Some models, like Qwen2 and Qwen1.5, use `use_sliding_window` in
         # addition to sliding window size. We check if that field is present
@@ -1256,9 +1266,15 @@ def _get_and_verify_dtype(
         dtype = dtype.lower()
         if dtype == "auto":
             if config_dtype == torch.float32:
+                if config.model_type == "gemma2":
+                    logger.info(
+                        "For Gemma 2, we downcast float32 to bfloat16 instead "
+                        "of float16 by default. Please specify `dtype` if you "
+                        "want to use float16.")
+                    torch_dtype = torch.bfloat16
+                else:
                     # Following the common practice, we use float16 for float32
                     # models.
-                logger.info("Casting torch.float32 to torch.float16.")
                     torch_dtype = torch.float16
             else:
                 torch_dtype = config_dtype

vllm/lora/layers.py

@@ -1069,6 +1069,10 @@ class LogitsProcessorWithLoRA(BaseLayerWithLoRA):
     def scale(self):
         return self.base_layer.scale
 
+    @property
+    def soft_cap(self):
+        return self.base_layer.soft_cap
+
     @property
     def org_vocab_size(self):
         return self.base_layer.org_vocab_size

vllm/model_executor/layers/layernorm.py

@@ -95,3 +95,49 @@ class RMSNorm(CustomOp):
         s = f"hidden_size={self.weight.data.size(0)}"
         s += f", eps={self.variance_epsilon}"
         return s
+
+
+class GemmaRMSNorm(CustomOp):
+    """RMS normalization for Gemma.
+
+    Two differences from the above RMSNorm:
+        1. x * (1 + w) instead of x * w.
+        2. (x * w).to(orig_dtype) instead of x.to(orig_dtype) * w.
+    """
+
+    def __init__(
+        self,
+        hidden_size: int,
+        eps: float = 1e-6,
+    ) -> None:
+        super().__init__()
+        self.weight = nn.Parameter(torch.zeros(hidden_size))
+        self.variance_epsilon = eps
+
+    def forward_native(
+        self,
+        x: torch.Tensor,
+        residual: Optional[torch.Tensor] = None,
+    ) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
+        """PyTorch-native implementation equivalent to forward()."""
+        orig_dtype = x.dtype
+        if residual is not None:
+            x = x + residual
+            residual = x
+
+        x = x.float()
+        variance = x.pow(2).mean(dim=-1, keepdim=True)
+        x = x * torch.rsqrt(variance + self.variance_epsilon)
+        # Llama does x.to(float16) * w whilst Gemma is (x * w).to(float16)
+        # See https://github.com/huggingface/transformers/pull/29402
+        x = x * (1.0 + self.weight.float())
+        x = x.to(orig_dtype)
+        return x if residual is None else (x, residual)
+
+    def forward_cuda(
+        self,
+        x: torch.Tensor,
+        residual: Optional[torch.Tensor] = None,
+    ) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
+        # TODO(woosuk): Implement an optimized kernel for GemmaRMSNorm.
+        return self.forward_native(x, residual)

vllm/model_executor/layers/logits_processor.py

@@ -22,7 +22,8 @@ class LogitsProcessor(nn.Module):
                  vocab_size: int,
                  org_vocab_size: Optional[int] = None,
                  scale: float = 1.0,
-                 logits_as_input: bool = False) -> None:
+                 logits_as_input: bool = False,
+                 soft_cap: Optional[float] = None) -> None:
         """
         Args:
             scale: A scaling factor to apply to the logits.
@@ -34,6 +35,8 @@ class LogitsProcessor(nn.Module):
         self.logits_as_input = logits_as_input
         # original vocabulary size (without LoRA).
         self.org_vocab_size = org_vocab_size or vocab_size
+        # Soft cap the logits. Used in Gemma 2.
+        self.soft_cap = soft_cap
 
     def forward(
         self,
@@ -52,6 +55,11 @@ class LogitsProcessor(nn.Module):
             logits = self._get_logits(hidden_states, embedding, embedding_bias)
 
         if logits is not None:
+            if self.soft_cap is not None:
+                logits = logits / self.soft_cap
+                logits = torch.tanh(logits)
+                logits = logits * self.soft_cap
+
             if self.scale != 1.0:
                 logits *= self.scale
 

vllm/model_executor/layers/rotary_embedding.py

@@ -610,6 +610,16 @@ class Phi3LongRoPEScaledRotaryEmbedding(nn.Module):
         return query.flatten(-2), key.flatten(-2)
 
 
+class GemmaRotaryEmbedding(RotaryEmbedding):
+
+    def _compute_inv_freq(self, base: Union[int, float]) -> torch.Tensor:
+        # https://github.com/huggingface/transformers/blob/v4.41.2/src/transformers/models/gemma/modeling_gemma.py#L107
+        inv_freq = 1.0 / (base**(
+            torch.arange(0, self.rotary_dim, 2, dtype=torch.int64).float() /
+            self.rotary_dim))
+        return inv_freq
+
+
 _ROPE_DICT: Dict[Tuple, RotaryEmbedding] = {}
 
 

vllm/model_executor/models/__init__.py

@@ -23,6 +23,7 @@ _GENERATION_MODELS = {
     "DeepseekForCausalLM": ("deepseek", "DeepseekForCausalLM"),
     "FalconForCausalLM": ("falcon", "FalconForCausalLM"),
     "GemmaForCausalLM": ("gemma", "GemmaForCausalLM"),
+    "Gemma2ForCausalLM": ("gemma2", "Gemma2ForCausalLM"),
     "GPT2LMHeadModel": ("gpt2", "GPT2LMHeadModel"),
     "GPTBigCodeForCausalLM": ("gpt_bigcode", "GPTBigCodeForCausalLM"),
     "GPTJForCausalLM": ("gpt_j", "GPTJForCausalLM"),

vllm/model_executor/models/gemma2.py

@@ -0,0 +1,401 @@
+# coding=utf-8
+# Copyright 2024 The vLLM team.
+# Copyright 2024 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 typing import Iterable, List, Optional, Set, Tuple
+
+import torch
+from torch import nn
+from transformers import Gemma2Config
+
+from vllm.attention import Attention, AttentionMetadata
+from vllm.config import CacheConfig, LoRAConfig
+from vllm.distributed import get_tensor_model_parallel_world_size
+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.base_config import (
+    QuantizationConfig)
+from vllm.model_executor.layers.rotary_embedding import GemmaRotaryEmbedding
+from vllm.model_executor.layers.sampler import Sampler
+from vllm.model_executor.layers.vocab_parallel_embedding import (
+    VocabParallelEmbedding)
+from vllm.model_executor.model_loader.weight_utils import default_weight_loader
+from vllm.model_executor.sampling_metadata import SamplingMetadata
+from vllm.sequence import SamplerOutput
+from vllm.utils import print_warning_once
+
+from .interfaces import SupportsLoRA
+
+
+class Gemma2MLP(nn.Module):
+
+    def __init__(
+        self,
+        hidden_size: int,
+        intermediate_size: int,
+        hidden_act: str,
+        hidden_activation: str,
+        quant_config: Optional[QuantizationConfig] = None,
+    ) -> None:
+        super().__init__()
+        self.gate_up_proj = MergedColumnParallelLinear(
+            hidden_size, [intermediate_size] * 2,
+            bias=False,
+            quant_config=quant_config)
+        self.down_proj = RowParallelLinear(intermediate_size,
+                                           hidden_size,
+                                           bias=False,
+                                           quant_config=quant_config)
+        if not (hidden_act == hidden_activation == "gelu_pytorch_tanh"):
+            raise ValueError(
+                "Gemma2 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 Gemma2Attention(nn.Module):
+
+    def __init__(self,
+                 layer_idx: int,
+                 config: Gemma2Config,
+                 hidden_size: int,
+                 num_heads: int,
+                 num_kv_heads: int,
+                 head_dim: int,
+                 max_position_embeddings: int,
+                 rope_theta: float,
+                 cache_config: Optional[CacheConfig] = None,
+                 quant_config: Optional[QuantizationConfig] = None) -> None:
+        super().__init__()
+        self.layer_idx = layer_idx
+        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.rope_theta = rope_theta
+
+        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,
+        )
+        self.o_proj = RowParallelLinear(
+            self.total_num_heads * self.head_dim,
+            hidden_size,
+            bias=config.attention_bias,
+            quant_config=quant_config,
+        )
+        # TODO(woosuk): Use the `get_rope` interface.
+        self.rotary_emb = GemmaRotaryEmbedding(
+            self.head_dim,
+            self.head_dim,
+            max_position_embeddings,
+            base=self.rope_theta,
+            is_neox_style=True,
+            dtype=torch.get_default_dtype(),
+        )
+
+        if self.config.attn_logit_softcapping is not None:
+            print_warning_once(
+                "Gemma 2 normally uses attention logit soft-capping; "
+                "soft-capping is currently incompatible with the flash "
+                "attention kernels, so vLLM removes it to enable speed and "
+                "efficiency gains of flash attention.")
+        # FIXME(woosuk): While Gemma 2 uses sliding window attention for every
+        # odd layer, vLLM currently ignores it and uses global attention for
+        # all layers.
+        use_sliding_window = (layer_idx % 2 == 1
+                              and config.sliding_window is not None)
+        del use_sliding_window  # Unused.
+        self.attn = Attention(self.num_heads,
+                              self.head_dim,
+                              self.scaling,
+                              num_kv_heads=self.num_kv_heads,
+                              cache_config=cache_config,
+                              quant_config=quant_config)
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        kv_cache: torch.Tensor,
+        attn_metadata: AttentionMetadata,
+    ) -> 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, k = self.rotary_emb(positions, q, k)
+        attn_output = self.attn(q, k, v, kv_cache, attn_metadata)
+        output, _ = self.o_proj(attn_output)
+        return output
+
+
+class Gemma2DecoderLayer(nn.Module):
+
+    def __init__(
+        self,
+        layer_idx: int,
+        config: Gemma2Config,
+        cache_config: Optional[CacheConfig] = None,
+        quant_config: Optional[QuantizationConfig] = None,
+    ) -> None:
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        self.self_attn = Gemma2Attention(
+            layer_idx=layer_idx,
+            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,
+            rope_theta=config.rope_theta,
+            cache_config=cache_config,
+            quant_config=quant_config,
+        )
+        self.hidden_size = config.hidden_size
+        self.mlp = Gemma2MLP(
+            hidden_size=self.hidden_size,
+            intermediate_size=config.intermediate_size,
+            hidden_act=config.hidden_act,
+            hidden_activation=config.hidden_activation,
+            quant_config=quant_config,
+        )
+        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,
+        kv_cache: torch.Tensor,
+        attn_metadata: AttentionMetadata,
+        residual: Optional[torch.Tensor],
+    ) -> 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,
+            kv_cache=kv_cache,
+            attn_metadata=attn_metadata,
+        )
+        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
+
+
+class Gemma2Model(nn.Module):
+
+    def __init__(
+        self,
+        config: Gemma2Config,
+        cache_config: Optional[CacheConfig] = None,
+        quant_config: Optional[QuantizationConfig] = None,
+    ) -> None:
+        super().__init__()
+        self.config = config
+
+        self.embed_tokens = VocabParallelEmbedding(
+            config.vocab_size,
+            config.hidden_size,
+        )
+        self.layers = nn.ModuleList([
+            Gemma2DecoderLayer(layer_idx, config, cache_config, quant_config)
+            for layer_idx in range(config.num_hidden_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))
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        kv_caches: List[torch.Tensor],
+        attn_metadata: AttentionMetadata,
+    ) -> torch.Tensor:
+        hidden_states = self.embed_tokens(input_ids)
+        hidden_states *= self.normalizer
+
+        residual = None
+        for i in range(len(self.layers)):
+            layer = self.layers[i]
+            hidden_states, residual = layer(
+                positions,
+                hidden_states,
+                kv_caches[i],
+                attn_metadata,
+                residual,
+            )
+        hidden_states, _ = self.norm(hidden_states, residual)
+        return hidden_states
+
+
+class Gemma2ForCausalLM(nn.Module, SupportsLoRA):
+    packed_modules_mapping = {
+        "qkv_proj": [
+            "q_proj",
+            "k_proj",
+            "v_proj",
+        ],
+        "gate_up_proj": [
+            "gate_proj",
+            "up_proj",
+        ],
+    }
+
+    # LoRA specific attributes
+    supported_lora_modules = [
+        "qkv_proj",
+        "o_proj",
+        "gate_up_proj",
+        "down_proj",
+    ]
+    # Gemma does not apply LoRA to the embedding layer.
+    embedding_modules = {}
+    embedding_padding_modules = []
+
+    def __init__(
+        self,
+        config: Gemma2Config,
+        cache_config: Optional[CacheConfig] = None,
+        quant_config: Optional[QuantizationConfig] = None,
+        lora_config: Optional[LoRAConfig] = None,
+    ) -> None:
+        del lora_config  # Unused.
+        super().__init__()
+        self.config = config
+        self.quant_config = quant_config
+        self.model = Gemma2Model(config, cache_config, quant_config)
+        self.logits_processor = LogitsProcessor(
+            config.vocab_size, soft_cap=config.final_logit_softcapping)
+        self.sampler = Sampler()
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        kv_caches: List[torch.Tensor],
+        attn_metadata: AttentionMetadata,
+    ) -> torch.Tensor:
+        hidden_states = self.model(input_ids, positions, kv_caches,
+                                   attn_metadata)
+        return hidden_states
+
+    def compute_logits(self, hidden_states: torch.Tensor,
+                       sampling_metadata: SamplingMetadata) -> torch.Tensor:
+        logits = self.logits_processor(self.model.embed_tokens.weight,
+                                       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]]):
+        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:
+            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
+                param = params_dict[name]
+                weight_loader = param.weight_loader
+                weight_loader(param, loaded_weight, shard_id)
+                break
+            else:
+                # lm_head is not used in vllm as it is tied with embed_token.
+                # To prevent errors, skip loading lm_head.weight.
+                if "lm_head.weight" in name:
+                    continue
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+                param = params_dict[name]
+                weight_loader = getattr(param, "weight_loader",
+                                        default_weight_loader)
+                weight_loader(param, loaded_weight)
+            loaded_params.add(name)
+
+        unloaded_params = params_dict.keys() - loaded_params
+        if unloaded_params:
+            raise RuntimeError(
+                "Some weights are not initialized from checkpoints: "
+                f"{unloaded_params}")