[CPU] Add frontend support for Gemma (#12590)

python/sglang/lang/chat_template.py

@@ -398,6 +398,8 @@ register_chat_template(
             "user": ("<start_of_turn>user\n", "<end_of_turn>\n"),
             "assistant": ("<start_of_turn>model\n", "<end_of_turn>\n"),
         },
+        image_token="<start_of_image>",
+        audio_token="<start_of_audio>",
         style=ChatTemplateStyle.PLAIN,
     )
 )

python/sglang/srt/models/gemma3_causal.py

@@ -35,14 +35,17 @@ from sglang.srt.layers.linear import (
 from sglang.srt.layers.logits_processor import LogitsProcessor
 from sglang.srt.layers.quantization.base_config import QuantizationConfig
 from sglang.srt.layers.radix_attention import AttentionType, RadixAttention
-from sglang.srt.layers.rotary_embedding import apply_rotary_pos_emb
+from sglang.srt.layers.rotary_embedding import apply_rotary_pos_emb, get_rope
 from sglang.srt.layers.vocab_parallel_embedding import ParallelLMHead
 from sglang.srt.model_executor.forward_batch_info import ForwardBatch
 from sglang.srt.model_loader.weight_utils import (
     default_weight_loader,
     maybe_remap_kv_scale_name,
 )
-from sglang.srt.utils import add_prefix, make_layers
+from sglang.srt.utils import add_prefix, cpu_has_amx_support, is_cpu, make_layers
+
+_is_cpu = is_cpu()
+_is_cpu_amx_available = cpu_has_amx_support()
 
 
 # Aligned with HF's implementation, using sliding window inclusive with the last token
@@ -141,7 +144,8 @@ class Gemma3Attention(nn.Module):
             config, "head_dim", hidden_size // config.num_attention_heads
         )
         self.head_dim = head_dim
-
+        partial_rotary_factor = getattr(config, "partial_rotary_factor", 1)
+        self.rotary_dim = int(partial_rotary_factor * self.head_dim)
         self.q_size = self.num_heads * self.head_dim
 
         self.kv_size = self.num_kv_heads * self.head_dim
@@ -197,7 +201,14 @@ class Gemma3Attention(nn.Module):
                 )
             self.rope_scaling = {"rope_type": "default"}
             self.sliding_window = None
-
+        self.rotary_emb = get_rope(
+            self.head_dim,
+            rotary_dim=self.rotary_dim,
+            max_position=max_position_embeddings,
+            base=self.rope_theta,
+            rope_scaling=self.rope_scaling,
+            is_neox_style=getattr(config, "rope_is_neox_style", True),
+        )
         self.attn = RadixAttention(
             self.num_heads,
             self.head_dim,
@@ -217,8 +228,39 @@ class Gemma3Attention(nn.Module):
         self.q_norm = Gemma3RMSNorm(dim=config.head_dim, eps=config.rms_norm_eps)
         self.k_norm = Gemma3RMSNorm(dim=config.head_dim, eps=config.rms_norm_eps)
 
-    def forward(
+    def forward_cpu(
         self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        position_embeddings: Tuple[torch.Tensor, torch.Tensor],
+        forward_batch: ForwardBatch,
+        **kwargs,
+    ) -> torch.Tensor:
+        qkv, _ = self.qkv_proj(hidden_states)
+        # [s, h * head_dim]
+        q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
+
+        # [s, h, head_dim]
+        q = q.unflatten(-1, (self.num_heads, self.head_dim)).unsqueeze(0)
+        q = self.q_norm(q)
+        k = k.unflatten(-1, (self.num_kv_heads, self.head_dim)).unsqueeze(0)
+        k = self.k_norm(k)
+        q, k = self.rotary_emb(positions, q, k)
+
+        attn_output = self.attn(q, k, v, forward_batch=forward_batch)
+
+        # Compatible with triton backend which returns [1, s, h, head_dim]
+        if attn_output.dim() == 4 and attn_output.shape[0] == 1:
+            attn_output = attn_output.squeeze(0)
+            attn_output = attn_output.flatten(-2, -1)
+        # [s, h * head_dim]
+
+        output, _ = self.o_proj(attn_output)
+        return output
+
+    def forward_native(
+        self,
+        positions: torch.Tensor,
         hidden_states: torch.Tensor,
         position_embeddings: Tuple[torch.Tensor, torch.Tensor],
         forward_batch: ForwardBatch,
@@ -257,6 +299,22 @@ class Gemma3Attention(nn.Module):
         output, _ = self.o_proj(attn_output)
         return output
 
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        position_embeddings: Tuple[torch.Tensor, torch.Tensor],
+        forward_batch: ForwardBatch,
+        **kwargs,
+    ) -> torch.Tensor:
+        if _is_cpu and _is_cpu_amx_available:
+            return self.forward_cpu(
+                positions, hidden_states, position_embeddings, forward_batch, **kwargs
+            )
+        return self.forward_native(
+            positions, hidden_states, position_embeddings, forward_batch, **kwargs
+        )
+
 
 class Gemma3DecoderLayer(nn.Module):
     def __init__(
@@ -555,21 +613,33 @@ class Gemma3TextModel(PreTrainedModel):
         else:
             hidden_states = input_embeds
 
-        if positions.dim() == 1:
-            positions = einops.rearrange(positions, "s -> 1 s")
+        if _is_cpu and _is_cpu_amx_available:
+            for layer in self.layers:
+                layer_outputs = layer(
+                    positions=positions,
+                    position_embeddings_global=None,
+                    position_embeddings_local=None,
+                    hidden_states=hidden_states,
+                    forward_batch=forward_batch,
+                    **kwargs,
+                )
+                hidden_states = layer_outputs[0]
+        else:
+            if positions.dim() == 1:
+                positions = einops.rearrange(positions, "s -> 1 s")
 
-        position_embeddings_global = self.rotary_emb(hidden_states, positions)
-        position_embeddings_local = self.rotary_emb_local(hidden_states, positions)
-        for layer in self.layers:
-            layer_outputs = layer(
-                positions=positions,
-                position_embeddings_global=position_embeddings_global,
-                position_embeddings_local=position_embeddings_local,
-                hidden_states=hidden_states,
-                forward_batch=forward_batch,
-                **kwargs,
-            )
-            hidden_states = layer_outputs[0]
+            position_embeddings_global = self.rotary_emb(hidden_states, positions)
+            position_embeddings_local = self.rotary_emb_local(hidden_states, positions)
+            for layer in self.layers:
+                layer_outputs = layer(
+                    positions=positions,
+                    position_embeddings_global=position_embeddings_global,
+                    position_embeddings_local=position_embeddings_local,
+                    hidden_states=hidden_states,
+                    forward_batch=forward_batch,
+                    **kwargs,
+                )
+                hidden_states = layer_outputs[0]
 
         hidden_states = self.norm(hidden_states)
 

python/sglang/srt/models/siglip.py

@@ -51,7 +51,7 @@ class SiglipVisionEmbeddings(nn.Module):
         patch_embeds = self.patch_embedding(
             pixel_values.to(dtype=target_dtype)
         )  # shape = [*, width, grid, grid]
-        embeddings = patch_embeds.flatten(2).transpose(1, 2)
+        embeddings = patch_embeds.flatten(2).transpose(1, 2).contiguous()
         # interpolate_pos_encoding is never used in sglang
         embeddings = embeddings + self.position_embedding(self.position_ids)