Gemma3n (Text-only)

docs/models/supported_models.md

@@ -547,6 +547,7 @@ Specified using `--task generate`.
 | `Florence2ForConditionalGeneration`          | Florence-2                                                               | T + I                                                                 | `microsoft/Florence-2-base`, `microsoft/Florence-2-large` etc.                                                                                          |                        |                             |                       |
 | `FuyuForCausalLM`                            | Fuyu                                                                     | T + I                                                                 | `adept/fuyu-8b` etc.                                                                                                                                    |                        | ✅︎                          | ✅︎                    |
 | `Gemma3ForConditionalGeneration`             | Gemma 3                                                                  | T + I<sup>+</sup>                                                     | `google/gemma-3-4b-it`, `google/gemma-3-27b-it`, etc.                                                                                                   | ✅︎                     | ✅︎                          | ⚠️                    |
+| `Gemma3nForConditionalGeneration`            | Gemma 3n                                                                 | T                                                                     | `google/gemma-3n-E2B-it`, `google/gemma-3n-E4B-it`, etc.                                                                                                |                        |                             | ⚠️                    |
 | `GLM4VForCausalLM`<sup>^</sup>               | GLM-4V                                                                   | T + I                                                                 | `THUDM/glm-4v-9b`, `THUDM/cogagent-9b-20241220` etc.                                                                                                    | ✅︎                     | ✅︎                          | ✅︎                    |
 | `GraniteSpeechForConditionalGeneration`      | Granite Speech                                                           | T + A                                                                 | `ibm-granite/granite-speech-3.3-8b`                                                                                                                     | ✅︎                     | ✅︎                          | ✅︎                    |
 | `H2OVLChatModel`                             | H2OVL                                                                    | T + I<sup>E+</sup>                                                    | `h2oai/h2ovl-mississippi-800m`, `h2oai/h2ovl-mississippi-2b`, etc.                                                                                      |                        | ✅︎                          | ✅︎\*                  |

requirements/common.txt

@@ -7,7 +7,7 @@ requests >= 2.26.0
 tqdm
 blake3
 py-cpuinfo
-transformers >= 4.51.1
+transformers >= 4.53.0
 huggingface-hub[hf_xet] >= 0.33.0  # Required for Xet downloads.
 tokenizers >= 0.21.1  # Required for fast incremental detokenization.
 protobuf # Required by LlamaTokenizer.

tests/models/registry.py

@@ -164,6 +164,7 @@ def check_available_online(
     "GemmaForCausalLM": _HfExamplesInfo("google/gemma-1.1-2b-it"),
     "Gemma2ForCausalLM": _HfExamplesInfo("google/gemma-2-9b"),
     "Gemma3ForCausalLM": _HfExamplesInfo("google/gemma-3-1b-it"),
+    "Gemma3nForConditionalGeneration": _HfExamplesInfo("google/gemma-3n-E2B-it"),  # noqa: E501
     "GlmForCausalLM": _HfExamplesInfo("THUDM/glm-4-9b-chat-hf"),
     "Glm4ForCausalLM": _HfExamplesInfo("THUDM/GLM-4-9B-0414"),
     "GPT2LMHeadModel": _HfExamplesInfo("openai-community/gpt2",

vllm/model_executor/layers/activation.py

@@ -135,6 +135,57 @@ def forward_cuda(self, x: torch.Tensor) -> torch.Tensor:
     # def forward_xpu(self, x: torch.Tensor) -> torch.Tensor:
 
 
+@CustomOp.register("gelu_and_mul_sparse")
+class GeluAndMulSparse(CustomOp):
+    """An activation function for GeluAndMulSparse.
+    This activation function is used in Gemma3n. It computes:
+        up_proj = self.up_proj(x)
+        gate_proj = self.gate_proj(x)
+        gate_proj = self._gaussian_topk(gate_proj) # sparsity
+        activations = self.act_fn(gate_proj) # gelu
+        down_proj = self.down_proj(activations * up_proj)
+    Shapes:
+        x: (num_tokens, 2 * d) or (batch_size, seq_len, 2 * d)
+        return: (num_tokens, d) or (batch_size, seq_len, d)
+    """
+
+    def __init__(self, activation_sparsity: float, approximate: str = "none"):
+        super().__init__()
+        # Gelu.
+        self.approximate = approximate
+        if approximate not in ("none", "tanh"):
+            raise ValueError(f"Unknown approximate mode: {approximate}")
+
+        # Sparsity.
+        if activation_sparsity == 0.0:
+            raise ValueError(
+                "activation_sparsity is 0.0. Please use GeluAndMul.")
+        target_sparsity_tensor = torch.tensor(activation_sparsity,
+                                              dtype=torch.float32)
+        normal_dist = torch.distributions.normal.Normal(0, 1)
+        self.std_multiplier = normal_dist.icdf(target_sparsity_tensor)
+
+    def _gaussian_topk(self, x: torch.Tensor) -> torch.Tensor:
+        """Get % sparse percentile of the Gaussian distribution."""
+        # NOTE(rob): for TP>1, we could all-gather to get the means/std.
+        # But we do not do this because in expectation they are the same
+        # and in practice the eval scores are good without gathering.
+        mean = torch.mean(x, dim=-1, keepdim=True)
+        std = torch.std(x, dim=-1, keepdim=True, unbiased=False)
+        cutoff_x = mean + std * self.std_multiplier
+        return nn.functional.relu(x - cutoff_x)
+
+    def forward_native(self, x: torch.Tensor) -> torch.Tensor:
+        """PyTorch-native implementation equivalent to forward()."""
+        d = x.shape[-1] // 2
+        out = self._gaussian_topk(x[..., :d])
+        out = F.gelu(out, approximate=self.approximate)
+        return out * x[..., d:]
+
+    def forward_cuda(self, x: torch.Tensor) -> torch.Tensor:
+        return self.forward_native(x)
+
+
 @CustomOp.register("gelu_and_mul")
 class GeluAndMul(CustomOp):
     """An activation function for GeGLU.