[V1] Enable prefill optimization for Gemma3n (#22628)

Signed-off-by: Yong Hoon Shin <yhshin@meta.com>

tests/v1/e2e/test_kv_sharing_fast_prefill.py

@@ -2,7 +2,6 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
 import random
-from typing import Optional, Union
 
 import pytest
 import torch
@@ -10,12 +9,6 @@ import torch
 from vllm import LLM, SamplingParams
 from vllm.config import CompilationConfig, CompilationLevel
 from vllm.distributed import cleanup_dist_env_and_memory
-from vllm.forward_context import get_forward_context
-from vllm.model_executor.models.gemma3n_mm import (
-    Gemma3nForConditionalGeneration)
-from vllm.model_executor.models.registry import ModelRegistry
-from vllm.model_executor.models.utils import extract_layer_index
-from vllm.sequence import IntermediateTensors
 
 from ...utils import fork_new_process_for_each_test
 
@@ -23,54 +16,6 @@ from ...utils import fork_new_process_for_each_test
 SEED = 42
 
 
-class TestGemma3nForConditionalGeneration(Gemma3nForConditionalGeneration):
-
-    def forward(
-        self,
-        input_ids: torch.Tensor,
-        positions: torch.Tensor,
-        intermediate_tensors: Optional[IntermediateTensors] = None,
-        inputs_embeds: Optional[torch.Tensor] = None,
-        **kwargs,
-    ) -> Union[torch.Tensor, IntermediateTensors]:
-        hidden_states = super().forward(input_ids, positions,
-                                        intermediate_tensors, inputs_embeds,
-                                        **kwargs)
-        attn_metadata = get_forward_context().attn_metadata
-        # attn_metadata is None during dummy runs
-        if (attn_metadata is not None
-                and self.language_model.cache_config.kv_sharing_fast_prefill):
-            assert isinstance(attn_metadata, dict)  # true in V1
-            # Gemma3n-E2B has 30 layers, with last 20 layers being
-            # cross-decoder layers. Check attention metadata is correct
-            for layer_name, metadata in attn_metadata.items():
-                layer_idx = extract_layer_index(layer_name)
-                if layer_idx >= 20:
-                    assert hasattr(metadata, 'logits_indices_padded')
-                    assert hasattr(metadata, 'num_logits_indices')
-                else:
-                    assert not hasattr(metadata, 'logits_indices_padded')
-                    assert not hasattr(metadata, 'num_logits_indices')
-
-            # Last layer will be a KV sharing layer
-            layer_attn_metadata = attn_metadata[
-                self.language_model.model.layers[-1].self_attn.attn.layer_name]
-            logits_indices_padded = (layer_attn_metadata.logits_indices_padded)
-            assert logits_indices_padded is not None
-            num_logits_indices = layer_attn_metadata.num_logits_indices
-            assert num_logits_indices > 0
-            # Reset hidden states to random values and
-            # only set logits at logits_indices to valid values
-            # Because logits_indices are the only positions that are used
-            # for output token sampling, this still produces same outputs
-            logits_hs = hidden_states[logits_indices_padded]
-            hidden_states = torch.randn_like(hidden_states)
-            gen_indices = logits_indices_padded[:num_logits_indices]
-            hidden_states[gen_indices] = logits_hs[:num_logits_indices]
-
-        return hidden_states
-
-
 @pytest.fixture
 def test_prompts():
     """
@@ -124,8 +69,6 @@ def test_kv_sharing_fast_prefill(
     enforce_eager: bool,
     test_prompts: list[str],
 ):
-    ModelRegistry.register_model("Gemma3nForConditionalGeneration",
-                                 TestGemma3nForConditionalGeneration)
     sampling_params = SamplingParams(temperature=0.0, max_tokens=100)
     compilation_config = CompilationConfig(
         # This allows vLLM compilation backend to handle allocating and

vllm/config/cache.py

@@ -145,12 +145,19 @@ class CacheConfig:
 
         self._verify_cache_dtype()
         self._verify_prefix_caching()
+        self._verify_kv_sharing_fast_prefill()
 
     def metrics_info(self):
         # convert cache_config to dict(key: str, value: str) for prometheus
         # metrics info
         return {key: str(value) for key, value in self.__dict__.items()}
 
+    def _verify_kv_sharing_fast_prefill(self) -> None:
+        if self.kv_sharing_fast_prefill and not envs.VLLM_USE_V1:
+            raise NotImplementedError(
+                "Fast prefill optimization for KV sharing is not supported "
+                "in V0 currently.")
+
     @model_validator(mode='after')
     def _verify_args(self) -> Self:
         if self.cpu_offload_gb < 0:
@@ -162,11 +169,6 @@ class CacheConfig:
                 "GPU memory utilization must be less than 1.0. Got "
                 f"{self.gpu_memory_utilization}.")
 
-        if self.kv_sharing_fast_prefill:
-            logger.warning_once(
-                "--kv-sharing-fast-prefill is currently work in progress "
-                "and not functional yet (i.e. no prefill savings)")
-
         return self
 
     def _verify_cache_dtype(self) -> None:

vllm/model_executor/models/gemma3n.py

@@ -23,9 +23,11 @@ from torch import nn
 from transformers.models.gemma3n.configuration_gemma3n import Gemma3nTextConfig
 
 from vllm.attention import Attention
+from vllm.compilation.backends import set_model_tag
 from vllm.compilation.decorators import support_torch_compile
 from vllm.config import CacheConfig, VllmConfig
 from vllm.distributed import get_tensor_model_parallel_world_size
+from vllm.forward_context import get_forward_context
 from vllm.logger import init_logger
 from vllm.model_executor.layers.activation import (_ACTIVATION_REGISTRY,
                                                    GeluAndMul,
@@ -45,6 +47,7 @@ from vllm.model_executor.model_loader.weight_utils import (
     default_weight_loader, maybe_remap_kv_scale_name)
 from vllm.model_executor.sampling_metadata import SamplingMetadata
 from vllm.sequence import IntermediateTensors
+from vllm.v1.attention.backends.utils import KVSharingFastPrefillMetadata
 
 from .interfaces import SupportsQuant
 from .utils import (AutoWeightsLoader, extract_layer_index,
@@ -533,7 +536,178 @@ class Gemma3nDecoderLayer(nn.Module):
         return corrected_predictions
 
 
-@support_torch_compile
+# This enables torch.compile if --kv-sharing-fast-prefill passed
+@support_torch_compile(enable_if=lambda vllm_config: vllm_config.cache_config.
+                       kv_sharing_fast_prefill)
+class Gemma3nSelfDecoder(nn.Module):
+    """
+    Includes altup embedding and self decoder layers
+    """
+
+    def __init__(
+        self,
+        *,
+        vllm_config: VllmConfig,
+        prefix: str = "",
+        decoder_layers: list[Gemma3nDecoderLayer],
+        layer_idx_start: int,
+        per_layer_model_projection: ColumnParallelLinear,
+        embed_scale_per_layer: torch.Tensor,
+        embed_tokens_per_layer: VocabParallelEmbedding,
+        per_layer_projection_norm: RMSNorm,
+        per_layer_input_scale: torch.Tensor,
+        altup_projections: nn.ModuleList,
+        eps: torch.Tensor,
+        embed_tokens: VocabParallelEmbedding,
+        embed_scale: torch.Tensor,
+    ):
+        super().__init__()
+        self.decoder_layers = decoder_layers
+        self.layer_idx_start = layer_idx_start
+        self.per_layer_model_projection = per_layer_model_projection
+        self.config = vllm_config.model_config.hf_config
+        self.embed_scale_per_layer = embed_scale_per_layer
+        self.embed_tokens_per_layer = embed_tokens_per_layer
+        self.per_layer_projection_norm = per_layer_projection_norm
+        self.per_layer_input_scale = per_layer_input_scale
+        self.altup_projections = altup_projections
+        self.eps = eps
+        self.embed_tokens = embed_tokens
+        self.embed_scale = embed_scale
+
+    def get_per_layer_input_embeddings(
+            self, input_ids: torch.Tensor) -> torch.Tensor:
+        # Deal with the fact that vocab_size_per_layer_input < vocab_size
+        # which causes us to have some out of vocab tokens by setting
+        # those token ids to 0. This matches the HF implementation.
+        per_layer_inputs_mask = torch.logical_and(
+            input_ids >= 0, input_ids < self.config.vocab_size_per_layer_input)
+        per_layer_inputs_tokens = torch.where(per_layer_inputs_mask, input_ids,
+                                              torch.zeros_like(input_ids))
+        return self.embed_tokens_per_layer(
+            per_layer_inputs_tokens) * self.embed_scale_per_layer
+
+    def get_per_layer_inputs(
+        self,
+        hidden_states_0: torch.Tensor,
+        per_layer_inputs: Optional[torch.Tensor],
+    ) -> torch.Tensor:
+        per_layer_projection = self.per_layer_model_projection(hidden_states_0)
+        per_layer_projection = per_layer_projection.reshape(
+            *hidden_states_0.shape[:-1],
+            self.config.num_hidden_layers,
+            self.config.hidden_size_per_layer_input,
+        )
+        per_layer_projection = self.per_layer_projection_norm(
+            per_layer_projection)
+        if per_layer_inputs is not None:
+            # Profiling run does not compute per_layer_inputs
+            per_layer_inputs = per_layer_projection + per_layer_inputs
+            per_layer_inputs *= self.per_layer_input_scale
+        else:
+            per_layer_inputs = per_layer_projection
+        return per_layer_inputs
+
+    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.embed_tokens(input_ids) * self.embed_scale
+
+    def altup_embed(self, hidden_states_0: torch.Tensor) -> torch.Tensor:
+        # Altup embed.
+        hidden_states = [hidden_states_0] * self.config.altup_num_inputs
+        target_magnitude = torch.mean(hidden_states_0**2, dim=-1,
+                                      keepdim=True)**0.5
+        for i in range(1, self.config.altup_num_inputs):
+            hidden_states[i] = self.altup_projections[i - 1](hidden_states[i])
+            new_magnitude = torch.mean(hidden_states[i]**2,
+                                       dim=-1,
+                                       keepdim=True)**0.5
+            hidden_states[i] *= target_magnitude / torch.maximum(
+                new_magnitude, self.eps)
+        hidden_states = torch.stack(hidden_states, dim=-1)
+        return hidden_states
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        per_layer_inputs: Optional[torch.Tensor] = None,
+        **kwargs,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        if inputs_embeds is not None:
+            hidden_states_0 = inputs_embeds
+        else:
+            hidden_states_0 = self.get_input_embeddings(input_ids)
+
+        adjusted_per_layer_inputs = self.get_per_layer_inputs(
+            hidden_states_0, per_layer_inputs)
+        hidden_states = self.altup_embed(hidden_states_0)
+
+        # [altnum_inputs, num_tokens, hidden_size]
+        hidden_states = hidden_states.permute(2, 0, 1)
+
+        for idx, layer in enumerate(self.decoder_layers):
+            layer_idx = idx + self.layer_idx_start
+            # [altup_num_inputs, num_tokens, hidden_size]
+            hidden_states = layer(
+                positions=positions,
+                hidden_states=hidden_states,
+                per_layer_input=adjusted_per_layer_inputs[:, layer_idx, :],
+                **kwargs,
+            )
+
+        # [num_tokens, hidden_size, altnum_inputs]
+        hidden_states = hidden_states.permute(1, 2, 0)
+
+        return hidden_states, adjusted_per_layer_inputs
+
+
+# This enables torch.compile if --kv-sharing-fast-prefill passed
+@support_torch_compile(enable_if=lambda vllm_config: vllm_config.cache_config.
+                       kv_sharing_fast_prefill)
+class Gemma3nCrossDecoder(nn.Module):
+    """
+    Cross-decoder layers
+    """
+
+    def __init__(
+        self,
+        *,
+        vllm_config: VllmConfig,
+        prefix: str = "",
+        decoder_layers: list[Gemma3nDecoderLayer],
+        layer_idx_start: int,
+    ):
+        super().__init__()
+        self.decoder_layers = decoder_layers
+        self.layer_idx_start = layer_idx_start
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        per_layer_inputs: torch.Tensor,
+        **kwargs,
+    ) -> torch.Tensor:
+        # [altnum_inputs, num_tokens, hidden_size]
+        hidden_states = hidden_states.permute(2, 0, 1)
+        for idx, layer in enumerate(self.decoder_layers):
+            layer_idx = idx + self.layer_idx_start
+            # [altup_num_inputs, num_tokens, hidden_size]
+            hidden_states = layer(
+                positions=positions,
+                hidden_states=hidden_states,
+                per_layer_input=per_layer_inputs[:, layer_idx, :],
+                **kwargs,
+            )
+        # [num_tokens, hidden_size, altnum_inputs]
+        hidden_states = hidden_states.permute(1, 2, 0)
+        return hidden_states
+
+
+# This disables torch.compile if --kv-sharing-fast-prefill passed
+@support_torch_compile(enable_if=lambda vllm_config: not vllm_config.
+                       cache_config.kv_sharing_fast_prefill)
 class Gemma3nTextModel(nn.Module, SupportsQuant):
 
     def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
@@ -543,7 +717,6 @@ class Gemma3nTextModel(nn.Module, SupportsQuant):
         quant_config = vllm_config.quant_config
         self.config = config
         self.quant_config = quant_config
-
         self.embed_tokens = VocabParallelEmbedding(
             config.vocab_size,
             config.hidden_size,
@@ -613,95 +786,211 @@ class Gemma3nTextModel(nn.Module, SupportsQuant):
             lambda prefix: Gemma3nDecoderLayer(
                 config, cache_config, quant_config, prefix=prefix),
             prefix=f"{prefix}.layers")
+
+        self.eps = torch.tensor(torch.finfo().min)
+
+        first_kv_shared_layer_idx = (config.num_hidden_layers -
+                                     config.num_kv_shared_layers)
+        # Layer idx 0-19 are self-decoder layers in You Only Cache Once (YOCO)
+        with set_model_tag("self_decoder"):
+            self.self_decoder = Gemma3nSelfDecoder(
+                vllm_config=vllm_config,
+                prefix=f"{prefix}.self_decoder",
+                decoder_layers=self.layers[:first_kv_shared_layer_idx],
+                layer_idx_start=0,
+                per_layer_model_projection=self.per_layer_model_projection,
+                embed_scale_per_layer=self.embed_scale_per_layer,
+                embed_tokens_per_layer=self.embed_tokens_per_layer,
+                per_layer_projection_norm=self.per_layer_projection_norm,
+                per_layer_input_scale=self.per_layer_input_scale,
+                altup_projections=self.altup_projections,
+                eps=self.eps,
+                embed_tokens=self.embed_tokens,
+                embed_scale=self.embed_scale,
+            )
+        # Layer idx 20-30 are cross-decoder layers in YOCO
+        with set_model_tag("cross_decoder"):
+            self.cross_decoder = Gemma3nCrossDecoder(
+                vllm_config=vllm_config,
+                prefix=f"{prefix}.cross_decoder",
+                decoder_layers=self.layers[first_kv_shared_layer_idx:],
+                layer_idx_start=first_kv_shared_layer_idx,
+            )
+
         self.norm = RMSNorm(
             config.hidden_size,
             eps=config.rms_norm_eps,
         )
-        self.eps = torch.tensor(torch.finfo().min)
+
+        self.fast_prefill_enabled = cache_config.kv_sharing_fast_prefill
+
+        if self.fast_prefill_enabled:
+            # Allocate static buffers for CUDAGraph
+            # TODO(sarckk): Extract this functionality to interface
+            max_num_tokens = vllm_config.scheduler_config.max_num_batched_tokens
+            device = next(self.parameters()).device
+            self.positions = torch.zeros(max_num_tokens,
+                                         dtype=torch.int64,
+                                         device=device)
+            self.hidden_states = torch.zeros(
+                (max_num_tokens, config.hidden_size,
+                 self.config.altup_num_inputs),
+                dtype=self.embed_tokens.weight.dtype,
+                device=device,
+            )
+            self.per_layer_inputs = torch.zeros(
+                (max_num_tokens, self.config.num_hidden_layers,
+                 self.config.hidden_size_per_layer_input),
+                dtype=self.embed_tokens.weight.dtype,
+                device=device,
+            )
 
     def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
-        return self.embed_tokens(input_ids) * self.embed_scale
+        return self.self_decoder.get_input_embeddings(input_ids)
 
-    def get_per_layer_input_embeddings(
-            self, input_ids: torch.Tensor) -> torch.Tensor:
-        # Deal with the fact that vocab_size_per_layer_input < vocab_size
-        # which causes us to have some out of vocab tokens by setting
-        # those token ids to 0. This matches the HF implementation.
-        per_layer_inputs_mask = torch.logical_and(
-            input_ids >= 0, input_ids < self.config.vocab_size_per_layer_input)
-        per_layer_inputs_tokens = torch.where(per_layer_inputs_mask, input_ids,
-                                              torch.zeros_like(input_ids))
-        return self.embed_tokens_per_layer(
-            per_layer_inputs_tokens) * self.embed_scale_per_layer
+    def fast_prefill_forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        per_layer_inputs: Optional[torch.Tensor] = None,
+        **kwargs,
+    ) -> torch.Tensor:
+        logits_indices_padded, num_logits_indices = None, None
+        attn_metadata = get_forward_context().attn_metadata
+
+        # attn_metadata is None during dummy runs
+        if (self.fast_prefill_enabled and attn_metadata is not None):
+            assert isinstance(attn_metadata, dict)
+            # Last layer is a KV sharing layer
+            layer_attn_metadata = attn_metadata[
+                self.layers[-1].self_attn.attn.layer_name]
+            if (isinstance(layer_attn_metadata, KVSharingFastPrefillMetadata)):
+                logits_indices_padded = (
+                    layer_attn_metadata.logits_indices_padded)
+                num_logits_indices = layer_attn_metadata.num_logits_indices
+
+        # Copy inputs for cudagraph
+        batch_size = positions.size(0)
+        self.positions[:batch_size].copy_(positions)
+        self_decoder_hidden_states, per_layer_inputs_adjusted = \
+            self.self_decoder(
+                input_ids=input_ids,
+                positions=self.positions[:batch_size],
+                inputs_embeds=inputs_embeds,
+                per_layer_inputs=per_layer_inputs,
+                **kwargs,
+            )
+
+        if logits_indices_padded is None:
+            logits_indices_padded = torch.arange(
+                positions.size(0),
+                dtype=positions.dtype,
+                device=positions.device,
+            )
+
+        # NOTE(sarckk): There is currently a bug caused by
+        # vLLM converting output of last piecewise CUDA graph
+        # to weakref, causing memory to be prematurely freed
+        # when there are multiple compilation units
+        # Keep .clone() until fix in
+        # https://github.com/vllm-project/vllm/pull/22282
+        hidden_states = self_decoder_hidden_states.clone()
+
+        # Copy inputs for cudagraph
+        num_padded_logits_indices = logits_indices_padded.size(0)
+        self.positions[:num_padded_logits_indices].copy_(
+            positions[logits_indices_padded])
+        self.hidden_states[:num_padded_logits_indices].copy_(
+            self_decoder_hidden_states[logits_indices_padded])
+        self.per_layer_inputs[:num_padded_logits_indices].copy_(
+            per_layer_inputs_adjusted[logits_indices_padded])
+        cross_decoder_hidden_states = self.cross_decoder(
+            positions=self.positions[:num_padded_logits_indices],
+            hidden_states=self.hidden_states[:num_padded_logits_indices],
+            per_layer_inputs=self.per_layer_inputs[:num_padded_logits_indices],
+            **kwargs,
+        )
+
+        if num_logits_indices is not None:
+            assert num_logits_indices > 0
+            # Merge cross-decoder and self-decoder hidden states
+            hidden_states[logits_indices_padded[:num_logits_indices]] = (
+                cross_decoder_hidden_states[:num_logits_indices])
+        else:
+            hidden_states = cross_decoder_hidden_states
+
+        return hidden_states
+
+    def normal_forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        per_layer_inputs: Optional[torch.Tensor] = None,
+        **kwargs,
+    ) -> torch.Tensor:
+        hidden_states, per_layer_inputs = self.self_decoder(
+            input_ids=input_ids,
+            positions=positions,
+            inputs_embeds=inputs_embeds,
+            per_layer_inputs=per_layer_inputs,
+            **kwargs,
+        )
+        hidden_states = self.cross_decoder(
+            positions=positions,
+            hidden_states=hidden_states,
+            per_layer_inputs=per_layer_inputs,
+            **kwargs,
+        )
+        return hidden_states
+
+    def altup_unembed(
+        self,
+        hidden_states: torch.Tensor,
+    ) -> torch.Tensor:
+        # Altup unembed.
+        target_magnitude = torch.mean(hidden_states[..., 0]**2,
+                                      dim=-1,
+                                      keepdim=True)**0.5
+        for i in range(1, self.config.altup_num_inputs):
+            hidden_states[..., i] = self.altup_unembed_projections[i - 1](
+                hidden_states[..., i])
+            new_magnitude = torch.mean(hidden_states[..., i]**2,
+                                       dim=-1,
+                                       keepdim=True)**0.5
+            hidden_states[..., i] *= target_magnitude / torch.maximum(
+                new_magnitude, self.eps)
+        # [num_tokens,hidden_size, altup_num_inputs] -> [num_tokens,hidden_size]
+        hidden_states = torch.mean(hidden_states, dim=-1)
+        return hidden_states
 
     def forward(
         self,
         input_ids: Optional[torch.Tensor],
         positions: torch.Tensor,
-        per_layer_inputs: torch.Tensor,
+        per_layer_inputs: Optional[torch.Tensor] = None,
         intermediate_tensors: Optional[IntermediateTensors] = None,
         inputs_embeds: Optional[torch.Tensor] = None,
         **kwargs,
     ) -> Union[torch.Tensor, IntermediateTensors]:
-        if inputs_embeds is not None:
-            hidden_states_0 = inputs_embeds
-        else:
-            hidden_states_0 = self.get_input_embeddings(input_ids)
-
-        per_layer_projection = self.per_layer_model_projection(hidden_states_0)
-        per_layer_projection = per_layer_projection.reshape(
-            *hidden_states_0.shape[:-1],
-            self.config.num_hidden_layers,
-            self.config.hidden_size_per_layer_input,
-        )
-        per_layer_projection = self.per_layer_projection_norm(
-            per_layer_projection)
-
-        if per_layer_inputs is not None:
-            # Profiling run does not compute per_layer_inputs
-            per_layer_inputs = per_layer_projection + per_layer_inputs
-            per_layer_inputs *= self.per_layer_input_scale
-        else:
-            per_layer_inputs = per_layer_projection
-
-        # Altup embed.
-        hidden_states = [hidden_states_0] * self.config.altup_num_inputs
-        target_magnitude = torch.mean(hidden_states_0**2, dim=-1,
-                                      keepdim=True)**0.5
-        for i in range(1, self.config.altup_num_inputs):
-            hidden_states[i] = self.altup_projections[i - 1](hidden_states[i])
-            new_magnitude = torch.mean(hidden_states[i]**2,
-                                       dim=-1,
-                                       keepdim=True)**0.5
-            hidden_states[i] *= target_magnitude / torch.maximum(
-                new_magnitude, self.eps)
-        hidden_states = torch.stack(hidden_states, dim=0)
-
-        # Transformer blocks.
-        for layer_idx, layer in enumerate(self.layers):
-            # [altup_num_inputs, num_tokens, hidden_size]
-            hidden_states = layer(
-                positions=positions,
-                hidden_states=hidden_states,
-                per_layer_input=per_layer_inputs[:, layer_idx, :],
+        if self.fast_prefill_enabled:
+            hidden_states = self.fast_prefill_forward(
+                input_ids,
+                positions,
+                inputs_embeds,
+                per_layer_inputs,
                 **kwargs,
             )
-
-        # Altup unembed.
-        target_magnitude = torch.mean(hidden_states[0]**2,
-                                      dim=-1,
-                                      keepdim=True)**0.5
-        for i in range(1, self.config.altup_num_inputs):
-            hidden_states[i] = self.altup_unembed_projections[i - 1](
-                hidden_states[i])
-            new_magnitude = torch.mean(hidden_states[i]**2,
-                                       dim=-1,
-                                       keepdim=True)**0.5
-            hidden_states[i] *= target_magnitude / torch.maximum(
-                new_magnitude, self.eps)
-        # [altup_num_inputs,num_tokens,hidden_size] -> [num_tokens,hidden_size]
-        hidden_states = torch.mean(hidden_states, dim=0)
-
+        else:
+            hidden_states = self.normal_forward(
+                input_ids,
+                positions,
+                inputs_embeds,
+                per_layer_inputs,
+                **kwargs,
+            )
+        hidden_states = self.altup_unembed(hidden_states)
         return self.norm(hidden_states)
 
     def load_weights(self, weights: Iterable[tuple[str,

vllm/model_executor/models/gemma3n_mm.py

@@ -620,7 +620,7 @@ class Gemma3nForConditionalGeneration(nn.Module, SupportsMultiModal):
         # NOTE (NickLucche) Each pass needs tokens to compute PLE so we cache
         # them here, as the model  forward has only access to the input_embeds.
         if input_ids is not None:
-            per_layer_inputs = self.language_model.model.get_per_layer_input_embeddings(
+            per_layer_inputs = self.language_model.model.self_decoder.get_per_layer_input_embeddings(
                 input_ids)
             per_layer_inputs = per_layer_inputs.reshape(
                 -1, self.config.text_config.num_hidden_layers,

vllm/v1/attention/backends/utils.py

@@ -4,11 +4,13 @@ import abc
 import enum
 import functools
 from abc import abstractmethod
-from dataclasses import dataclass, make_dataclass
-from typing import TYPE_CHECKING, Any, ClassVar, Generic, Optional, TypeVar
+from dataclasses import dataclass, fields, make_dataclass
+from typing import (TYPE_CHECKING, Any, ClassVar, Generic, Optional, Protocol,
+                    TypeVar)
 
 import numpy as np
 import torch
+from typing_extensions import runtime_checkable
 
 from vllm.config import VllmConfig, get_layers_from_vllm_config
 from vllm.utils import cdiv
@@ -19,7 +21,8 @@ if TYPE_CHECKING:
     from vllm.v1.worker.gpu_input_batch import InputBatch
 
 import vllm.envs as envs
-from vllm.attention.backends.abstract import AttentionBackend
+from vllm.attention.backends.abstract import (AttentionBackend,
+                                              AttentionMetadata)
 from vllm.attention.layer import Attention
 from vllm.distributed.kv_transfer.kv_connector.utils import (
     get_kv_connector_cache_layout)
@@ -65,6 +68,10 @@ class CommonAttentionMetadata:
 
     causal: bool = True
 
+    # Needed by FastPrefillAttentionBuilder
+    logits_indices_padded: Optional[torch.Tensor] = None
+    num_logits_indices: Optional[int] = None
+
 
 @dataclass
 class UbatchSlice:
@@ -542,6 +549,69 @@ def make_local_attention_virtual_batches(
     )
 
 
+def make_kv_sharing_fast_prefill_common_attn_metadata(
+    common_attn_metadata: CommonAttentionMetadata,
+) -> CommonAttentionMetadata:
+    if common_attn_metadata.max_query_len == 1:
+        # All requests are decode (assume 1 token for now)
+        # Skip computing fast prefill path
+        return common_attn_metadata
+
+    assert common_attn_metadata.logits_indices_padded is not None
+    assert common_attn_metadata.num_logits_indices is not None
+
+    logits_indices_padded = common_attn_metadata.logits_indices_padded
+    num_logits_indices = common_attn_metadata.num_logits_indices
+    # Get rid of CUDAGraph padding, if any
+    logits_indices = logits_indices_padded[:num_logits_indices]
+    num_reqs = common_attn_metadata.num_reqs
+    query_start_loc = common_attn_metadata.query_start_loc
+    seq_lens = common_attn_metadata.seq_lens
+    # Example inputs
+    # num_reqs: 3
+    # generation_indices:  [14, 18, 19, 27]
+    # query_start_loc: [0, 15, 20, 28]
+    # seq_lens:        [41, 31, 40]
+
+    # Find how many decode indices belong to each request
+    # request_ids: [0, 1, 1, 2]
+    request_ids = torch.bucketize(logits_indices,
+                                  query_start_loc[1:],
+                                  right=True)
+
+    # Figure out how many tokens are in each request
+    # num_decode_tokens: [1, 2, 1]
+    num_decode_tokens = torch.bincount(request_ids, minlength=num_reqs)
+
+    # Calculate new query_start_loc with tokens in generation_indices
+    # decode_query_start_loc: [0, 1, 3, 4]
+    decode_query_start_loc = torch.empty(num_reqs + 1,
+                                         device=query_start_loc.device,
+                                         dtype=query_start_loc.dtype)
+
+    decode_query_start_loc[0] = 0
+    decode_query_start_loc[1:] = torch.cumsum(num_decode_tokens, dim=0)
+    decode_max_query_len = int(num_decode_tokens.max().item())
+    total_num_decode_tokens = int(num_decode_tokens.sum().item())
+
+    common_attn_metadata = CommonAttentionMetadata(
+        query_start_loc=decode_query_start_loc,
+        query_start_loc_cpu=decode_query_start_loc.to("cpu",
+                                                      non_blocking=True),
+        seq_lens=seq_lens,
+        seq_lens_cpu=seq_lens.to("cpu", non_blocking=True),
+        num_computed_tokens_cpu=common_attn_metadata.num_computed_tokens_cpu,
+        num_reqs=num_reqs,
+        num_actual_tokens=total_num_decode_tokens,
+        max_query_len=decode_max_query_len,
+        max_seq_len=common_attn_metadata.max_seq_len,
+        block_table_tensor=common_attn_metadata.block_table_tensor,
+        slot_mapping=common_attn_metadata.slot_mapping,
+        causal=True,
+    )
+    return common_attn_metadata
+
+
 def subclass_attention_backend(
         name_prefix: str, attention_backend_cls: type[AttentionBackend],
         builder_cls: type[AttentionMetadataBuilder[M]]
@@ -679,13 +749,56 @@ def subclass_attention_metadata(
     return Wrapped
 
 
-def make_kv_sharing_fast_prefill_attention_metadata(
-    metadata_cls: Any, ) -> Any:
-    """
-    Return a new subclass of `metadata_cls` for fast prefill
-    """
-    return subclass_attention_metadata(
-        name_prefix="KVSharingFastPrefill",
-        metadata_cls=metadata_cls,
-        fields=KV_SHARING_FAST_PREFILL_METADATA_FIELDS,
-    )
+@runtime_checkable
+class KVSharingFastPrefillMetadata(Protocol):
+    logits_indices_padded: torch.Tensor
+    num_logits_indices: int
+
+
+def create_fast_prefill_custom_backend(
+    prefix: str,
+    underlying_attn_backend: AttentionBackend,
+) -> type[AttentionBackend]:
+
+    underlying_builder = underlying_attn_backend.get_builder_cls()
+
+    class FastPrefillAttentionBuilder(underlying_builder):  # type: ignore
+
+        def build(self,
+                  common_prefix_len: int,
+                  common_attn_metadata: CommonAttentionMetadata,
+                  fast_build: bool = False) -> AttentionMetadata:
+            new_common_attn_metadata =\
+            make_kv_sharing_fast_prefill_common_attn_metadata(common_attn_metadata)
+            metadata = super().build(common_prefix_len,
+                                     new_common_attn_metadata, fast_build)
+
+            class KVSharingFastPrefillAttentionMetadata(
+                    metadata.__class__,  #  type: ignore
+                    KVSharingFastPrefillMetadata):
+
+                def __init__(self, metadata, common_attn_metadata):
+                    # Shallow copy all fields in metadata cls
+                    for field in fields(metadata.__class__):
+                        setattr(self, field.name,
+                                getattr(metadata, field.name))
+
+                    # Set additional fields that will be used in model code
+                    assert (common_attn_metadata.logits_indices_padded
+                            is not None
+                            and common_attn_metadata.num_logits_indices
+                            is not None)
+                    self.logits_indices_padded = \
+                        common_attn_metadata.logits_indices_padded
+                    self.num_logits_indices = \
+                        common_attn_metadata.num_logits_indices
+
+            return KVSharingFastPrefillAttentionMetadata(
+                metadata, common_attn_metadata)
+
+    attn_backend = subclass_attention_backend(
+        name_prefix=prefix,
+        attention_backend_cls=underlying_attn_backend,
+        builder_cls=FastPrefillAttentionBuilder)
+
+    return attn_backend

vllm/v1/engine/async_llm.py

@@ -335,6 +335,13 @@ class AsyncLLM(EngineClient):
         returning the RequestOutput back to the caller.
         """
 
+        if (self.vllm_config.cache_config.kv_sharing_fast_prefill
+                and sampling_params.prompt_logprobs):
+            raise ValueError(
+                "--kv-sharing-fast-prefill produces incorrect logprobs for "
+                "prompt tokens, please disable it when the requests need "
+                "prompt logprobs")
+
         try:
             # We start the output_handler on the first call to generate() so
             # we can call __init__ before the event loop, which enables us

vllm/v1/worker/gpu_model_runner.py

@@ -1,7 +1,6 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
-import dataclasses
 import gc
 import itertools
 import time
@@ -58,7 +57,7 @@ from vllm.utils import (STR_DTYPE_TO_TORCH_DTYPE, DeviceMemoryProfiler,
                         supports_dynamo)
 from vllm.v1.attention.backends.utils import (
     AttentionCGSupport, AttentionMetadataBuilder, CommonAttentionMetadata,
-    make_kv_sharing_fast_prefill_attention_metadata,
+    create_fast_prefill_custom_backend,
     reorder_batch_to_split_decodes_and_prefills)
 from vllm.v1.cudagraph_dispatcher import CudagraphDispatcher
 from vllm.v1.kv_cache_interface import (AttentionSpec,
@@ -84,9 +83,10 @@ from vllm.v1.worker.kv_connector_model_runner_mixin import (
     KVConnectorModelRunnerMixin, KVConnectorOutput)
 from vllm.v1.worker.lora_model_runner_mixin import LoRAModelRunnerMixin
 
-from .utils import (AttentionGroup, MultiModalBudget, bind_kv_cache,
-                    gather_mm_placeholders, initialize_kv_cache_for_kv_sharing,
-                    sanity_check_mm_encoder_outputs, scatter_mm_placeholders)
+from .utils import (AttentionGroup, MultiModalBudget,
+                    add_kv_sharing_layers_to_kv_cache_groups, bind_kv_cache,
+                    gather_mm_placeholders, sanity_check_mm_encoder_outputs,
+                    scatter_mm_placeholders)
 
 if TYPE_CHECKING:
     import xgrammar as xgr
@@ -860,6 +860,8 @@ class GPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
                 max_seq_len=max_seq_len,
                 block_table_tensor=blk_table_tensor,
                 slot_mapping=slot_mapping,
+                logits_indices_padded=logits_indices_padded,
+                num_logits_indices=logits_indices.size(0),
                 causal=True,
             )
 
@@ -884,28 +886,7 @@ class GPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
                     common_attn_metadata=common_attn_metadata,
                 ))
 
-                fast_prefill_metadata = attn_metadata_i
-                if (self.cache_config.kv_sharing_fast_prefill
-                        and self.kv_sharing_fast_prefill_eligible_layers):
-                    # Dynamically create a a dataclass type that inherits
-                    # from attention metadata type but includes additional
-                    # fields logits_indices_padded and num_logits_indices
-                    # which are required for prefill truncation
-                    fast_prefill_metadata_type = (
-                        make_kv_sharing_fast_prefill_attention_metadata(
-                            metadata_cls=type(attn_metadata_i), ))
-                    fast_prefill_metadata = fast_prefill_metadata_type(
-                        **dataclasses.asdict(attn_metadata_i),
-                        logits_indices_padded=logits_indices_padded,
-                        num_logits_indices=logits_indices.size(0),
-                    )
-
                 for layer_name in attn_group.layer_names:
-                    if (self.cache_config.kv_sharing_fast_prefill
-                            and layer_name
-                            in self.kv_sharing_fast_prefill_eligible_layers):
-                        attn_metadata[layer_name] = fast_prefill_metadata
-                        continue
                     attn_metadata[layer_name] = attn_metadata_i
 
         # Hot-Swap lora model
@@ -1484,6 +1465,12 @@ class GPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
             return self.kv_connector_no_forward(scheduler_output,
                                                 self.vllm_config)
 
+        if self.cache_config.kv_sharing_fast_prefill:
+            assert not self.input_batch.num_prompt_logprobs, (
+                "--kv-sharing-fast-prefill produces incorrect logprobs for "
+                "prompt tokens, tokens, please disable it when the requests "
+                "need prompt logprobs")
+
         # Prepare the decoder inputs.
         (attn_metadata, logits_indices, spec_decode_metadata,
          num_scheduled_tokens_np, spec_decode_common_attn_metadata,
@@ -2742,6 +2729,13 @@ class GPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
             # layer.
             for layer_name in layer_names:
                 attn_backend = layers[layer_name].get_attn_backend()
+
+                if layer_name in self.kv_sharing_fast_prefill_eligible_layers:
+                    attn_backend = create_fast_prefill_custom_backend(
+                        "FastPrefill",
+                        attn_backend,
+                    )
+
                 key = attn_backend.full_cls_name()
                 attn_backends[key] = attn_backend
                 attn_backend_layers[key].append(layer_name)
@@ -3074,20 +3068,40 @@ class GPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
         kv_caches = self._reshape_kv_cache_tensors(kv_cache_config,
                                                    kv_cache_raw_tensors)
 
-        # Setup `kv_cache_config` and `kv_caches` for models
-        # with cross-layer KV sharing
-        if self.shared_kv_cache_layers:
-            initialize_kv_cache_for_kv_sharing(
-                self.shared_kv_cache_layers,
-                kv_cache_config.kv_cache_groups,
-                kv_caches,
-                self.attn_groups,
-                self.runner_only_attn_layers,
-            )
+        # Set up cross-layer KV cache sharing
+        for layer_name, target_layer_name in self.shared_kv_cache_layers.items(
+        ):
+            logger.debug("%s reuses KV cache of %s", layer_name,
+                         target_layer_name)
+            kv_caches[layer_name] = kv_caches[target_layer_name]
+
+        bind_kv_cache(kv_caches,
+                      self.compilation_config.static_forward_context,
+                      self.kv_caches)
+        return kv_caches
+
+    def maybe_add_kv_sharing_layers_to_kv_cache_groups(
+            self, kv_cache_config: KVCacheConfig) -> None:
+        """
+        Add layers that re-use KV cache to KV cache group of its target layer.
+        Mapping of KV cache tensors happens in `initialize_kv_cache_tensors()`
+        """
+        if not self.shared_kv_cache_layers:
+            # No cross-layer KV sharing, return
+            return
+
+        add_kv_sharing_layers_to_kv_cache_groups(
+            self.shared_kv_cache_layers,
+            kv_cache_config.kv_cache_groups,
+            self.runner_only_attn_layers,
+        )
+
+        if self.cache_config.kv_sharing_fast_prefill:
+            # In You Only Cache Once (https://arxiv.org/abs/2405.05254) or other
+            # similar KV sharing setups, only the layers that generate KV caches
+            # are involved in the prefill phase, enabling prefill to early exit.
             attn_layers = get_layers_from_vllm_config(self.vllm_config,
                                                       Attention)
-            # Iterate in reversed order and add layers that re-use KV cache
-            # e.g. in YOCO-like KV sharing setups (e.g. Gemma3n)
             for layer_name in reversed(attn_layers):
                 if layer_name in self.shared_kv_cache_layers:
                     self.kv_sharing_fast_prefill_eligible_layers.add(
@@ -3095,11 +3109,6 @@ class GPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
                 else:
                     break
 
-        bind_kv_cache(kv_caches,
-                      self.compilation_config.static_forward_context,
-                      self.kv_caches)
-        return kv_caches
-
     def initialize_kv_cache(self, kv_cache_config: KVCacheConfig) -> None:
         """
         Initialize KV cache based on `kv_cache_config`.
@@ -3111,6 +3120,7 @@ class GPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
         self.kv_cache_config = kv_cache_config
         self.may_reinitialize_input_batch(kv_cache_config)
         self.may_add_encoder_only_layers_to_kv_cache_config()
+        self.maybe_add_kv_sharing_layers_to_kv_cache_groups(kv_cache_config)
         self.initialize_attn_backend(kv_cache_config)
         kv_caches = self.initialize_kv_cache_tensors(kv_cache_config)
 

vllm/v1/worker/tpu_model_runner.py

@@ -55,9 +55,8 @@ from vllm.v1.worker.kv_connector_model_runner_mixin import (
 from vllm.v1.worker.lora_model_runner_mixin import LoRAModelRunnerMixin
 from vllm.v1.worker.tpu_input_batch import CachedRequestState, InputBatch
 
-from .utils import (MultiModalBudget, bind_kv_cache,
-                    initialize_kv_cache_for_kv_sharing,
-                    sanity_check_mm_encoder_outputs)
+from .utils import (MultiModalBudget, add_kv_sharing_layers_to_kv_cache_groups,
+                    bind_kv_cache, sanity_check_mm_encoder_outputs)
 
 if TYPE_CHECKING:
     from vllm.v1.core.sched.output import SchedulerOutput
@@ -1599,6 +1598,30 @@ class TPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
         self.encoder_cache.clear()
         gc.collect()
 
+    def maybe_setup_cross_layer_kv_sharing(
+        self,
+        kv_caches: dict[str, torch.Tensor],
+        kv_cache_config: KVCacheConfig,
+    ) -> None:
+        """
+        Add layers that re-use KV cache to KV cache group of its target layer.
+        Mapping of KV cache tensors happens in `initialize_kv_cache_tensors()`
+        """
+        if not self.shared_kv_cache_layers:
+            # No cross-layer KV sharing, return
+            return
+
+        add_kv_sharing_layers_to_kv_cache_groups(
+            self.shared_kv_cache_layers,
+            kv_cache_config.kv_cache_groups,
+        )
+
+        for layer_name, target_layer_name in self.shared_kv_cache_layers.items(
+        ):
+            logger.debug("%s reuses KV cache of %s", layer_name,
+                         target_layer_name)
+            kv_caches[layer_name] = kv_caches[target_layer_name]
+
     def initialize_kv_cache(self, kv_cache_config: KVCacheConfig) -> None:
         """
         Initialize KV cache based on `kv_cache_config`.
@@ -1664,14 +1687,8 @@ class TPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
                 else:
                     raise NotImplementedError
 
-        # Setup `kv_cache_config` and `kv_caches` for models
-        # with cross-layer KV sharing
-        if self.shared_kv_cache_layers:
-            initialize_kv_cache_for_kv_sharing(
-                self.shared_kv_cache_layers,
-                kv_cache_config.kv_cache_groups,
-                kv_caches,
-            )
+        # Set up cross-layer KV cache sharing if needed
+        self.maybe_setup_cross_layer_kv_sharing(kv_caches, kv_cache_config)
 
         bind_kv_cache(
             kv_caches,

vllm/v1/worker/utils.py

@@ -203,12 +203,9 @@ def gather_mm_placeholders(
     return placeholders[is_embed]
 
 
-def initialize_kv_cache_for_kv_sharing(
+def add_kv_sharing_layers_to_kv_cache_groups(
     shared_kv_cache_layers: dict[str, str],
     kv_cache_groups: list[KVCacheGroupSpec],
-    kv_caches: dict[str, torch.Tensor],
-    # Optional for now to avoid breaking TPU
-    attn_groups: Optional[list[list[AttentionGroup]]] = None,
     runner_only_attn_layers: Optional[set[str]] = None,
 ) -> None:
     """
@@ -223,38 +220,15 @@ def initialize_kv_cache_for_kv_sharing(
             means this layer will perform attention using the keys and values
             from the KV cache of `shared_kv_cache_layers[layer_name]`.
         kv_cache_groups: The KV cache groups of the model.
-        kv_caches: The allocated kv_caches with layer names as keys.
-            Note that layers in shared_kv_cache_layers.keys() are not
-            originally included as it only contains layers which have its own
-            KV cache allocation.
-        attn_groups: Optional list of attention groups. Layers in the same KV
-            cache group may be placed in different attention groups if they
-            have different attention backends.  Currently only provided by 
-            GPU model runner.
     """
-    # mapping from layer name to tuple of (kv_cache_group_idx, attn_group_idx)
-    layer_to_attn_group_idx: dict[str, tuple[int, int]] = {}
-    if attn_groups:
-        for kv_cache_group_idx, kv_attn_groups in enumerate(attn_groups):
-            for attn_group_idx, attn_group in enumerate(kv_attn_groups):
-                for layer_name in attn_group.layer_names:
-                    layer_to_attn_group_idx[layer_name] = (kv_cache_group_idx,
-                                                           attn_group_idx)
-    else:
-        for kv_cache_group_idx, kv_cache_group in enumerate(kv_cache_groups):
-            for layer_name in kv_cache_group.layer_names:
-                # attn group idx default to 0 if not provided
-                layer_to_attn_group_idx[layer_name] = (kv_cache_group_idx, 0)
+    layer_to_kv_cache_group: dict[str, KVCacheGroupSpec] = {}
+    for kv_cache_group in kv_cache_groups:
+        for layer_name in kv_cache_group.layer_names:
+            layer_to_kv_cache_group[layer_name] = kv_cache_group
 
     for layer_name, target_layer_name in shared_kv_cache_layers.items():
-        kv_caches[layer_name] = kv_caches[target_layer_name]
-        kv_cache_group_idx = layer_to_attn_group_idx[target_layer_name][0]
-        kv_cache_groups[kv_cache_group_idx].layer_names.append(layer_name)
-
-        if attn_groups:
-            attn_group_idx = layer_to_attn_group_idx[target_layer_name][1]
-            attn_groups[kv_cache_group_idx][attn_group_idx].layer_names.append(
-                layer_name)
+        tgt_kv_cache_group = layer_to_kv_cache_group[target_layer_name]
+        tgt_kv_cache_group.layer_names.append(layer_name)
 
         if runner_only_attn_layers is not None:
             runner_only_attn_layers.add(layer_name)