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vllm.models.qwen4_exp.common.ngram_embedding

Shared Qwen4Exp n-gram embedding storage with device and pinned-host backends.

Both the NVIDIA and AMD Qwen4Exp implementations use these classes so the (large) n-gram embedding table can be kept in pinned host memory and looked up through Unified Virtual Addressing on any CUDA-alike platform.

Classes:

Qwen4ExpPLEDeviceEmbedding

Bases: Qwen4ExpPLEEmbedding

PLE table allocated on the active model device.

Methods:

Source code in vllm/models/qwen4_exp/common/ngram_embedding.py 
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class Qwen4ExpPLEDeviceEmbedding(Qwen4ExpPLEEmbedding):
    """PLE table allocated on the active model device."""

    def allocate_embedding_weight(
        self,
        num_embeddings: int,
        embedding_dim: int,
        dtype: torch.dtype,
    ) -> torch.Tensor:
        """Allocate the complete PLE weight on the active device."""
        return torch.empty(num_embeddings, embedding_dim, dtype=dtype)

    def start_prefetch(
        self,
        hidden_states: torch.Tensor,
        ngram_ids: torch.Tensor,
    ) -> None:
        """Resident embedding prefetch is a no-op."""
        return None

    def forward(self, ngram_ids: torch.Tensor) -> torch.Tensor:
        """Gather ETP inputs, look up embeddings, and select local rows."""
        slot_size, slot_offset = self._get_dp_gather_slot(ngram_ids.shape[0])
        gathered_ids = self._gather_dp_ids(ngram_ids, slot_size)
        embeddings = super().forward(gathered_ids)
        return self._select_embeddings(
            embeddings,
            ngram_ids.shape[0],
            slot_offset,
        )

allocate_embedding_weight(num_embeddings, embedding_dim, dtype)

Allocate the complete PLE weight on the active device.

Source code in vllm/models/qwen4_exp/common/ngram_embedding.py 
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def allocate_embedding_weight(
    self,
    num_embeddings: int,
    embedding_dim: int,
    dtype: torch.dtype,
) -> torch.Tensor:
    """Allocate the complete PLE weight on the active device."""
    return torch.empty(num_embeddings, embedding_dim, dtype=dtype)

forward(ngram_ids)

Gather ETP inputs, look up embeddings, and select local rows.

Source code in vllm/models/qwen4_exp/common/ngram_embedding.py 
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def forward(self, ngram_ids: torch.Tensor) -> torch.Tensor:
    """Gather ETP inputs, look up embeddings, and select local rows."""
    slot_size, slot_offset = self._get_dp_gather_slot(ngram_ids.shape[0])
    gathered_ids = self._gather_dp_ids(ngram_ids, slot_size)
    embeddings = super().forward(gathered_ids)
    return self._select_embeddings(
        embeddings,
        ngram_ids.shape[0],
        slot_offset,
    )

start_prefetch(hidden_states, ngram_ids)

Resident embedding prefetch is a no-op.

Source code in vllm/models/qwen4_exp/common/ngram_embedding.py 
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def start_prefetch(
    self,
    hidden_states: torch.Tensor,
    ngram_ids: torch.Tensor,
) -> None:
    """Resident embedding prefetch is a no-op."""
    return None

Qwen4ExpPLEEmbedding

Bases: PLEVocabParallelEmbedding, ABC

ETP-sharded PLE table shared by device and pinned-host backends.

Methods:

Source code in vllm/models/qwen4_exp/common/ngram_embedding.py 
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class Qwen4ExpPLEEmbedding(PLEVocabParallelEmbedding, ABC):
    """ETP-sharded PLE table shared by device and pinned-host backends."""

    supports_prefetch: ClassVar[bool] = False

    def __init__(
        self,
        num_embeddings: int,
        embedding_dim: int,
        *,
        params_dtype: torch.dtype,
        padding_size: int,
        prefix: str,
        embedding_method: "Qwen4ExpPLEEmbeddingMethod",
        num_ngram_heads: int = 1,
        max_total_tokens: int = 0,
        data_parallel_rank: int = 0,
    ) -> None:
        del num_ngram_heads, max_total_tokens
        super().__init__(
            num_embeddings,
            embedding_dim,
            params_dtype=params_dtype,
            padding_size=padding_size,
            prefix=prefix,
            quant_method=embedding_method,
            parallel_group=get_etp_group(),
        )
        self.embedding_method = embedding_method
        self.data_parallel_rank = data_parallel_rank
        tp_size = get_tp_group().world_size
        if self.tp_size % tp_size:
            raise ValueError(
                "ETP size must be divisible by TP size, but got "
                f"ETP={self.tp_size} and TP={tp_size}"
            )
        self.etp_data_parallel_size = self.tp_size // tp_size

    @abstractmethod
    def allocate_embedding_weight(
        self,
        num_embeddings: int,
        embedding_dim: int,
        dtype: torch.dtype,
    ) -> torch.Tensor:
        """Allocate storage for the complete embedding weight."""
        raise NotImplementedError

    def dequantize(
        self,
        embeddings: torch.Tensor,
        output_dtype: torch.dtype,
    ) -> torch.Tensor:
        """Delegate storage-format conversion to the embedding method."""
        return self.embedding_method.dequantize(self, embeddings, output_dtype)

    def _get_dp_gather_slot(self, local_num_tokens: int) -> tuple[int, int]:
        """Return the per-DP slot size and this rank's slot offset."""
        if self.etp_data_parallel_size == 1:
            return local_num_tokens, 0
        dp_metadata: DPMetadata | None = get_forward_context().dp_metadata
        if dp_metadata is None:
            raise RuntimeError("ETP spanning DP requires DP token metadata")
        group_start = (self.data_parallel_rank // self.etp_data_parallel_size) * (
            self.etp_data_parallel_size
        )
        group_end = group_start + self.etp_data_parallel_size
        token_counts = dp_metadata.num_tokens_across_dp_cpu.tolist()
        group_counts = token_counts[group_start:group_end]
        slot_size = max(group_counts)
        dp_rank = get_dp_group().rank_in_group
        return slot_size, dp_rank * slot_size

    def _gather_dp_ids(
        self,
        ngram_ids: torch.Tensor,
        slot_size: int,
    ) -> torch.Tensor:
        """Gather DP-local IDs that share one ETP-sharded PLE table."""
        if self.etp_data_parallel_size == 1:
            return ngram_ids
        if ngram_ids.shape[0] < slot_size:
            padding = ngram_ids.new_zeros(
                slot_size - ngram_ids.shape[0], ngram_ids.shape[1]
            )
            ngram_ids = torch.cat((ngram_ids, padding), dim=0)
        return get_dp_group().all_gather(ngram_ids, dim=0)

    def _select_embeddings(
        self,
        embeddings: torch.Tensor,
        local_num_tokens: int,
        slot_offset: int,
    ) -> torch.Tensor:
        """Select this DP rank's rows from the ETP-reduced embeddings."""
        if self.etp_data_parallel_size == 1:
            return embeddings
        return embeddings.narrow(0, slot_offset, local_num_tokens)

    @abstractmethod
    def start_prefetch(
        self,
        hidden_states: torch.Tensor,
        ngram_ids: torch.Tensor,
    ) -> None:
        """Start an asynchronous lookup when supported."""
        raise NotImplementedError

_gather_dp_ids(ngram_ids, slot_size)

Gather DP-local IDs that share one ETP-sharded PLE table.

Source code in vllm/models/qwen4_exp/common/ngram_embedding.py 
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def _gather_dp_ids(
    self,
    ngram_ids: torch.Tensor,
    slot_size: int,
) -> torch.Tensor:
    """Gather DP-local IDs that share one ETP-sharded PLE table."""
    if self.etp_data_parallel_size == 1:
        return ngram_ids
    if ngram_ids.shape[0] < slot_size:
        padding = ngram_ids.new_zeros(
            slot_size - ngram_ids.shape[0], ngram_ids.shape[1]
        )
        ngram_ids = torch.cat((ngram_ids, padding), dim=0)
    return get_dp_group().all_gather(ngram_ids, dim=0)

_get_dp_gather_slot(local_num_tokens)

Return the per-DP slot size and this rank's slot offset.

Source code in vllm/models/qwen4_exp/common/ngram_embedding.py 
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def _get_dp_gather_slot(self, local_num_tokens: int) -> tuple[int, int]:
    """Return the per-DP slot size and this rank's slot offset."""
    if self.etp_data_parallel_size == 1:
        return local_num_tokens, 0
    dp_metadata: DPMetadata | None = get_forward_context().dp_metadata
    if dp_metadata is None:
        raise RuntimeError("ETP spanning DP requires DP token metadata")
    group_start = (self.data_parallel_rank // self.etp_data_parallel_size) * (
        self.etp_data_parallel_size
    )
    group_end = group_start + self.etp_data_parallel_size
    token_counts = dp_metadata.num_tokens_across_dp_cpu.tolist()
    group_counts = token_counts[group_start:group_end]
    slot_size = max(group_counts)
    dp_rank = get_dp_group().rank_in_group
    return slot_size, dp_rank * slot_size

_select_embeddings(embeddings, local_num_tokens, slot_offset)

Select this DP rank's rows from the ETP-reduced embeddings.

Source code in vllm/models/qwen4_exp/common/ngram_embedding.py 
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def _select_embeddings(
    self,
    embeddings: torch.Tensor,
    local_num_tokens: int,
    slot_offset: int,
) -> torch.Tensor:
    """Select this DP rank's rows from the ETP-reduced embeddings."""
    if self.etp_data_parallel_size == 1:
        return embeddings
    return embeddings.narrow(0, slot_offset, local_num_tokens)

allocate_embedding_weight(num_embeddings, embedding_dim, dtype) abstractmethod

Allocate storage for the complete embedding weight.

Source code in vllm/models/qwen4_exp/common/ngram_embedding.py 
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@abstractmethod
def allocate_embedding_weight(
    self,
    num_embeddings: int,
    embedding_dim: int,
    dtype: torch.dtype,
) -> torch.Tensor:
    """Allocate storage for the complete embedding weight."""
    raise NotImplementedError

dequantize(embeddings, output_dtype)

Delegate storage-format conversion to the embedding method.

Source code in vllm/models/qwen4_exp/common/ngram_embedding.py 
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def dequantize(
    self,
    embeddings: torch.Tensor,
    output_dtype: torch.dtype,
) -> torch.Tensor:
    """Delegate storage-format conversion to the embedding method."""
    return self.embedding_method.dequantize(self, embeddings, output_dtype)

start_prefetch(hidden_states, ngram_ids) abstractmethod

Start an asynchronous lookup when supported.

Source code in vllm/models/qwen4_exp/common/ngram_embedding.py 
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@abstractmethod
def start_prefetch(
    self,
    hidden_states: torch.Tensor,
    ngram_ids: torch.Tensor,
) -> None:
    """Start an asynchronous lookup when supported."""
    raise NotImplementedError

Qwen4ExpPLEEmbeddingMethod

Bases: QuantizeMethodBase

Quantization interface shared by resident and pinned PLE tables.

Methods:

  • dequantize –

    Convert looked-up PLE rows to the activation dtype.

  • from_quant_config –

    Select the concrete PLE embedding format for a layer.

Source code in vllm/models/qwen4_exp/common/ngram_embedding.py 
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class Qwen4ExpPLEEmbeddingMethod(QuantizeMethodBase):
    """Quantization interface shared by resident and pinned PLE tables."""

    # PLE post-load processing only validates scales in their current storage.
    requires_device_loading: bool = False

    @staticmethod
    def from_quant_config(
        quant_config: QuantizationConfig | None,
        prefix: str,
        embedding_dtype: str | None = None,
    ) -> "Qwen4ExpPLEEmbeddingMethod":
        """Select the concrete PLE embedding format for a layer."""
        if embedding_dtype == "float8_e4m3fn":
            return Qwen4ExpPLEFp8EmbeddingMethod()
        if quant_config is None:
            return Qwen4ExpPLEUnquantizedEmbeddingMethod()
        if isinstance(quant_config, ModelOptMixedPrecisionConfig):
            if quant_config._resolve_quant_algo(prefix) == "FP8":
                return Qwen4ExpPLEFp8EmbeddingMethod()
            return Qwen4ExpPLEUnquantizedEmbeddingMethod()
        if isinstance(
            quant_config, ModelOptQuantConfigBase
        ) and quant_config.is_layer_excluded(prefix):
            return Qwen4ExpPLEUnquantizedEmbeddingMethod()
        if (
            isinstance(quant_config, CompressedTensorsConfig)
            and quant_config.get_scheme_dict(None, layer_name=prefix) is None
        ):
            return Qwen4ExpPLEUnquantizedEmbeddingMethod()
        if not isinstance(quant_config, Fp8Config):
            raise NotImplementedError(
                "Qwen4Exp PLE embedding does not support quantization config "
                f"{type(quant_config).__name__}"
            )

        ignored_layers = quant_config.ignored_layers
        if is_layer_skipped(
            prefix,
            ignored_layers,
            quant_config.packed_modules_mapping,
            match_mode=quant_config.ignored_layers_match_mode,
        ):
            return Qwen4ExpPLEUnquantizedEmbeddingMethod()
        # PLE checkpoint shards form one runtime embedding parameter.
        shard_prefix = f"{prefix}.shard_"
        if any(name.startswith(shard_prefix) for name in ignored_layers):
            return Qwen4ExpPLEUnquantizedEmbeddingMethod()
        if not quant_config.is_checkpoint_fp8_serialized:
            raise NotImplementedError(
                "Qwen4Exp PLE embedding only supports serialized FP8 checkpoints"
            )
        return Qwen4ExpPLEFp8EmbeddingMethod()

    def apply(
        self,
        layer: nn.Module,
        x: torch.Tensor,
        bias: torch.Tensor | None = None,
    ) -> torch.Tensor:
        raise NotImplementedError("PLE weights only support embedding lookup")

    def embedding(self, layer: nn.Module, input_: torch.Tensor) -> torch.Tensor:
        return F.embedding(input_, layer.weight)

    @abstractmethod
    def dequantize(
        self,
        layer: nn.Module,
        embeddings: torch.Tensor,
        output_dtype: torch.dtype,
    ) -> torch.Tensor:
        """Convert looked-up PLE rows to the activation dtype."""
        raise NotImplementedError

dequantize(layer, embeddings, output_dtype) abstractmethod

Convert looked-up PLE rows to the activation dtype.

Source code in vllm/models/qwen4_exp/common/ngram_embedding.py 
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@abstractmethod
def dequantize(
    self,
    layer: nn.Module,
    embeddings: torch.Tensor,
    output_dtype: torch.dtype,
) -> torch.Tensor:
    """Convert looked-up PLE rows to the activation dtype."""
    raise NotImplementedError

from_quant_config(quant_config, prefix, embedding_dtype=None) staticmethod

Select the concrete PLE embedding format for a layer.

Source code in vllm/models/qwen4_exp/common/ngram_embedding.py 
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@staticmethod
def from_quant_config(
    quant_config: QuantizationConfig | None,
    prefix: str,
    embedding_dtype: str | None = None,
) -> "Qwen4ExpPLEEmbeddingMethod":
    """Select the concrete PLE embedding format for a layer."""
    if embedding_dtype == "float8_e4m3fn":
        return Qwen4ExpPLEFp8EmbeddingMethod()
    if quant_config is None:
        return Qwen4ExpPLEUnquantizedEmbeddingMethod()
    if isinstance(quant_config, ModelOptMixedPrecisionConfig):
        if quant_config._resolve_quant_algo(prefix) == "FP8":
            return Qwen4ExpPLEFp8EmbeddingMethod()
        return Qwen4ExpPLEUnquantizedEmbeddingMethod()
    if isinstance(
        quant_config, ModelOptQuantConfigBase
    ) and quant_config.is_layer_excluded(prefix):
        return Qwen4ExpPLEUnquantizedEmbeddingMethod()
    if (
        isinstance(quant_config, CompressedTensorsConfig)
        and quant_config.get_scheme_dict(None, layer_name=prefix) is None
    ):
        return Qwen4ExpPLEUnquantizedEmbeddingMethod()
    if not isinstance(quant_config, Fp8Config):
        raise NotImplementedError(
            "Qwen4Exp PLE embedding does not support quantization config "
            f"{type(quant_config).__name__}"
        )

    ignored_layers = quant_config.ignored_layers
    if is_layer_skipped(
        prefix,
        ignored_layers,
        quant_config.packed_modules_mapping,
        match_mode=quant_config.ignored_layers_match_mode,
    ):
        return Qwen4ExpPLEUnquantizedEmbeddingMethod()
    # PLE checkpoint shards form one runtime embedding parameter.
    shard_prefix = f"{prefix}.shard_"
    if any(name.startswith(shard_prefix) for name in ignored_layers):
        return Qwen4ExpPLEUnquantizedEmbeddingMethod()
    if not quant_config.is_checkpoint_fp8_serialized:
        raise NotImplementedError(
            "Qwen4Exp PLE embedding only supports serialized FP8 checkpoints"
        )
    return Qwen4ExpPLEFp8EmbeddingMethod()

Qwen4ExpPLEFp8EmbeddingMethod

Bases: Qwen4ExpPLEEmbeddingMethod

FP8 PLE embedding with one global checkpoint scale.

Methods:

Source code in vllm/models/qwen4_exp/common/ngram_embedding.py 
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class Qwen4ExpPLEFp8EmbeddingMethod(Qwen4ExpPLEEmbeddingMethod):
    """FP8 PLE embedding with one global checkpoint scale."""

    def create_weights(
        self,
        layer: Qwen4ExpPLEEmbedding,
        input_size_per_partition: int,
        output_partition_sizes: list[int],
        input_size: int,
        output_size: int,
        params_dtype: torch.dtype,
        **extra_weight_attrs,
    ) -> None:
        del input_size, output_size, params_dtype
        weight_loader = extra_weight_attrs.get("weight_loader")
        weight = ModelWeightParameter(
            data=layer.allocate_embedding_weight(
                sum(output_partition_sizes),
                input_size_per_partition,
                torch.float8_e4m3fn,
            ),
            input_dim=1,
            output_dim=0,
            weight_loader=weight_loader,
        )
        layer.register_parameter("weight", weight)

        weight_scale = create_fp8_scale_parameter(
            PerTensorScaleParameter,
            output_partition_sizes,
            input_size_per_partition,
            None,
            weight_loader,
            scale_dtype=torch.float32,
        )
        layer.register_parameter("weight_scale", weight_scale)

    def process_weights_after_loading(self, layer: nn.Module) -> None:
        """Reject FP8 PLE checkpoints without a global scale."""
        sentinel = torch.finfo(torch.float32).min
        if torch.any(layer.weight_scale == sentinel):
            raise ValueError("FP8 PLE checkpoint is missing its global scale")

    def dequantize(
        self,
        layer: nn.Module,
        embeddings: torch.Tensor,
        output_dtype: torch.dtype,
    ) -> torch.Tensor:
        weight_scale = getattr(layer, "weight_scale", None)
        if weight_scale is None:
            raise RuntimeError("FP8 PLE embedding is missing its global scale")
        if weight_scale.device != embeddings.device:
            raise RuntimeError("FP8 PLE embedding scale must be on the output device")
        return embeddings.to(output_dtype) * weight_scale.to(output_dtype)

process_weights_after_loading(layer)

Reject FP8 PLE checkpoints without a global scale.

Source code in vllm/models/qwen4_exp/common/ngram_embedding.py 
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def process_weights_after_loading(self, layer: nn.Module) -> None:
    """Reject FP8 PLE checkpoints without a global scale."""
    sentinel = torch.finfo(torch.float32).min
    if torch.any(layer.weight_scale == sentinel):
        raise ValueError("FP8 PLE checkpoint is missing its global scale")

Qwen4ExpPLEPinnedHostEmbedding

Bases: Qwen4ExpPLEEmbedding

PLE table loaded into pinned CPU memory and looked up through UVA.

Methods:

  • allocate_embedding_weight –

    Allocate the complete PLE weight directly in pinned CPU memory.

  • forward –

    Finish the pinned lookup into graph-owned output storage.

  • start_prefetch –

    Gather ETP IDs and launch their UVA lookup on the side stream.

  • sync_lookup –

    Synchronous UVA lookup for platforms without prefetch wiring.

Source code in vllm/models/qwen4_exp/common/ngram_embedding.py 
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class Qwen4ExpPLEPinnedHostEmbedding(Qwen4ExpPLEEmbedding):
    """PLE table loaded into pinned CPU memory and looked up through UVA."""

    supports_prefetch: ClassVar[bool] = True

    def __init__(
        self,
        num_embeddings: int,
        embedding_dim: int,
        *,
        params_dtype: torch.dtype,
        padding_size: int,
        prefix: str,
        embedding_method: Qwen4ExpPLEEmbeddingMethod,
        num_ngram_heads: int = 1,
        max_total_tokens: int = 0,
        data_parallel_rank: int = 0,
    ) -> None:
        if not is_uva_available():
            raise RuntimeError("Engram CPU offload requires UVA support")
        super().__init__(
            num_embeddings,
            embedding_dim,
            params_dtype=params_dtype,
            padding_size=padding_size,
            prefix=prefix,
            embedding_method=embedding_method,
            num_ngram_heads=num_ngram_heads,
            max_total_tokens=max_total_tokens,
            data_parallel_rank=data_parallel_rank,
        )
        self._uva_weight = get_accelerator_view_from_cpu_tensor(self.weight)
        self._block_d = triton.next_power_of_2(self.embedding_dim)
        self._prefetch_stream: torch.cuda.Stream | None = None
        self._prefetch_buffer: torch.Tensor | None = None
        self._prefetch_alloc_lock = threading.Lock()
        self._prefetch_rows = max_total_tokens * self.etp_data_parallel_size
        self._num_ngram_heads = num_ngram_heads
        self._output_dim = num_ngram_heads * self.embedding_dim

    def allocate_embedding_weight(
        self,
        num_embeddings: int,
        embedding_dim: int,
        dtype: torch.dtype,
    ) -> torch.Tensor:
        """Allocate the complete PLE weight directly in pinned CPU memory."""
        return torch.empty(
            num_embeddings,
            embedding_dim,
            dtype=dtype,
            device="cpu",
            pin_memory=True,
        )

    def _lookup(
        self,
        input_ids: torch.Tensor,
        output: torch.Tensor | None = None,
    ) -> torch.Tensor:
        """Look up local ETP rows while preserving the weight storage dtype."""
        expected_shape = (*input_ids.shape, self.embedding_dim)
        if output is None:
            output = torch.empty(
                expected_shape,
                dtype=self.weight.dtype,
                device=input_ids.device,
            )
        elif (
            tuple(output.shape) != expected_shape
            or output.dtype != self.weight.dtype
            or output.device != input_ids.device
        ):
            raise ValueError(
                "PLE prefetch output must match the input shape, weight dtype, "
                "and input device"
            )

        flat_ids = input_ids.reshape(-1).long()
        if flat_ids.numel():
            _lookup_ple_embedding_from_pinned_kernel[(flat_ids.numel(),)](
                self._uva_weight,
                flat_ids,
                output,
                self.embedding_dim,
                self.shard_indices.org_vocab_start_index,
                self.shard_indices.org_vocab_end_index,
                BLOCK_D=self._block_d,
            )
        return output

    def sync_lookup(self, ngram_ids: torch.Tensor) -> torch.Tensor:
        """Synchronous UVA lookup for platforms without prefetch wiring."""
        slot_size, slot_offset = self._get_dp_gather_slot(ngram_ids.shape[0])
        gathered_ids = self._gather_dp_ids(ngram_ids, slot_size)
        embeddings = self._lookup(gathered_ids)
        embeddings = self._reduce_etp_embeddings(embeddings)
        return self._select_embeddings(
            embeddings,
            ngram_ids.shape[0],
            slot_offset,
        )

    def _reduce_etp_embeddings(self, embeddings: torch.Tensor) -> torch.Tensor:
        """Combine pinned lookup results owned by different ETP ranks."""
        if self.tp_size == 1:
            return embeddings
        assert self.parallel_group is not None
        if embeddings.dtype in (torch.float8_e4m3fn, torch.float8_e5m2):
            # Each vocabulary row has one owner, so reduce the raw FP8 bytes.
            reduced = self.parallel_group.all_reduce(embeddings.view(torch.int8))
            return reduced.view(embeddings.dtype)
        return self.parallel_group.all_reduce(embeddings)

    @eager_break_during_capture
    def start_prefetch(
        self,
        hidden_states: torch.Tensor,
        ngram_ids: torch.Tensor,
    ) -> None:
        """Gather ETP IDs and launch their UVA lookup on the side stream."""
        buffer = self._prefetch_buffer
        if buffer is None:
            # First use allocates. The eager profile run always precedes
            # cudagraph capture, so allocation never happens mid-capture;
            # the lock keeps concurrent first callers from tearing the
            # stream/buffer pair.
            with self._prefetch_alloc_lock:
                buffer = self._prefetch_buffer
                if buffer is None:
                    if torch.cuda.is_current_stream_capturing():
                        raise RuntimeError(
                            "pinned PLE prefetch buffer must be allocated "
                            "eagerly, before cudagraph capture"
                        )
                    self._prefetch_stream = torch.cuda.Stream(
                        device=self._uva_weight.device
                    )
                    buffer = torch.empty(
                        self._prefetch_rows,
                        self._num_ngram_heads,
                        self.embedding_dim,
                        dtype=self.weight.dtype,
                        device=self._uva_weight.device,
                    )
                    self._prefetch_buffer = buffer
        prefetch_stream = self._prefetch_stream
        if prefetch_stream is None:
            raise RuntimeError("pinned PLE prefetch stream was not allocated")
        slot_size, _ = self._get_dp_gather_slot(ngram_ids.shape[0])
        gathered_ids = self._gather_dp_ids(ngram_ids, slot_size)
        if gathered_ids.shape[0] > buffer.shape[0]:
            raise ValueError(
                f"pinned PLE prefetch buffer holds {buffer.shape[0]} rows, "
                f"but the batch needs {gathered_ids.shape[0]}"
            )
        active_output = buffer[: gathered_ids.shape[0]]
        prefetch_stream.wait_stream(torch.cuda.current_stream())
        gathered_ids.record_stream(prefetch_stream)
        with torch.cuda.stream(prefetch_stream):
            self._lookup(gathered_ids, output=active_output)

    @eager_break_during_capture
    def _finalize_prefetch(
        self,
        prefetch_output: torch.Tensor,
        output: torch.Tensor,
    ) -> None:
        """Join the side stream, reduce ETP shards, and select local rows."""
        prefetch_stream = self._prefetch_stream
        if prefetch_stream is None:
            raise RuntimeError("pinned PLE finalize requires a prior start_prefetch")
        torch.cuda.current_stream().wait_stream(prefetch_stream)
        slot_size, slot_offset = self._get_dp_gather_slot(output.shape[0])
        active_output = prefetch_output[: slot_size * self.etp_data_parallel_size]
        embeddings = self._reduce_etp_embeddings(active_output)
        embeddings = self._select_embeddings(
            embeddings,
            output.shape[0],
            slot_offset,
        )
        output.copy_(embeddings.flatten(-2))

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        """Finish the pinned lookup into graph-owned output storage."""
        buffer = self._prefetch_buffer
        if buffer is None:
            raise RuntimeError("pinned PLE lookup requires a prior start_prefetch")
        output = buffer.new_empty((hidden_states.shape[0], self._output_dim))
        self._finalize_prefetch(buffer, output)
        return output

_finalize_prefetch(prefetch_output, output)

Join the side stream, reduce ETP shards, and select local rows.

Source code in vllm/models/qwen4_exp/common/ngram_embedding.py 
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@eager_break_during_capture
def _finalize_prefetch(
    self,
    prefetch_output: torch.Tensor,
    output: torch.Tensor,
) -> None:
    """Join the side stream, reduce ETP shards, and select local rows."""
    prefetch_stream = self._prefetch_stream
    if prefetch_stream is None:
        raise RuntimeError("pinned PLE finalize requires a prior start_prefetch")
    torch.cuda.current_stream().wait_stream(prefetch_stream)
    slot_size, slot_offset = self._get_dp_gather_slot(output.shape[0])
    active_output = prefetch_output[: slot_size * self.etp_data_parallel_size]
    embeddings = self._reduce_etp_embeddings(active_output)
    embeddings = self._select_embeddings(
        embeddings,
        output.shape[0],
        slot_offset,
    )
    output.copy_(embeddings.flatten(-2))

_lookup(input_ids, output=None)

Look up local ETP rows while preserving the weight storage dtype.

Source code in vllm/models/qwen4_exp/common/ngram_embedding.py 
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def _lookup(
    self,
    input_ids: torch.Tensor,
    output: torch.Tensor | None = None,
) -> torch.Tensor:
    """Look up local ETP rows while preserving the weight storage dtype."""
    expected_shape = (*input_ids.shape, self.embedding_dim)
    if output is None:
        output = torch.empty(
            expected_shape,
            dtype=self.weight.dtype,
            device=input_ids.device,
        )
    elif (
        tuple(output.shape) != expected_shape
        or output.dtype != self.weight.dtype
        or output.device != input_ids.device
    ):
        raise ValueError(
            "PLE prefetch output must match the input shape, weight dtype, "
            "and input device"
        )

    flat_ids = input_ids.reshape(-1).long()
    if flat_ids.numel():
        _lookup_ple_embedding_from_pinned_kernel[(flat_ids.numel(),)](
            self._uva_weight,
            flat_ids,
            output,
            self.embedding_dim,
            self.shard_indices.org_vocab_start_index,
            self.shard_indices.org_vocab_end_index,
            BLOCK_D=self._block_d,
        )
    return output

_reduce_etp_embeddings(embeddings)

Combine pinned lookup results owned by different ETP ranks.

Source code in vllm/models/qwen4_exp/common/ngram_embedding.py 
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def _reduce_etp_embeddings(self, embeddings: torch.Tensor) -> torch.Tensor:
    """Combine pinned lookup results owned by different ETP ranks."""
    if self.tp_size == 1:
        return embeddings
    assert self.parallel_group is not None
    if embeddings.dtype in (torch.float8_e4m3fn, torch.float8_e5m2):
        # Each vocabulary row has one owner, so reduce the raw FP8 bytes.
        reduced = self.parallel_group.all_reduce(embeddings.view(torch.int8))
        return reduced.view(embeddings.dtype)
    return self.parallel_group.all_reduce(embeddings)

allocate_embedding_weight(num_embeddings, embedding_dim, dtype)

Allocate the complete PLE weight directly in pinned CPU memory.

Source code in vllm/models/qwen4_exp/common/ngram_embedding.py 
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def allocate_embedding_weight(
    self,
    num_embeddings: int,
    embedding_dim: int,
    dtype: torch.dtype,
) -> torch.Tensor:
    """Allocate the complete PLE weight directly in pinned CPU memory."""
    return torch.empty(
        num_embeddings,
        embedding_dim,
        dtype=dtype,
        device="cpu",
        pin_memory=True,
    )

forward(hidden_states)

Finish the pinned lookup into graph-owned output storage.

Source code in vllm/models/qwen4_exp/common/ngram_embedding.py 
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def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
    """Finish the pinned lookup into graph-owned output storage."""
    buffer = self._prefetch_buffer
    if buffer is None:
        raise RuntimeError("pinned PLE lookup requires a prior start_prefetch")
    output = buffer.new_empty((hidden_states.shape[0], self._output_dim))
    self._finalize_prefetch(buffer, output)
    return output

start_prefetch(hidden_states, ngram_ids)

Gather ETP IDs and launch their UVA lookup on the side stream.

Source code in vllm/models/qwen4_exp/common/ngram_embedding.py 
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@eager_break_during_capture
def start_prefetch(
    self,
    hidden_states: torch.Tensor,
    ngram_ids: torch.Tensor,
) -> None:
    """Gather ETP IDs and launch their UVA lookup on the side stream."""
    buffer = self._prefetch_buffer
    if buffer is None:
        # First use allocates. The eager profile run always precedes
        # cudagraph capture, so allocation never happens mid-capture;
        # the lock keeps concurrent first callers from tearing the
        # stream/buffer pair.
        with self._prefetch_alloc_lock:
            buffer = self._prefetch_buffer
            if buffer is None:
                if torch.cuda.is_current_stream_capturing():
                    raise RuntimeError(
                        "pinned PLE prefetch buffer must be allocated "
                        "eagerly, before cudagraph capture"
                    )
                self._prefetch_stream = torch.cuda.Stream(
                    device=self._uva_weight.device
                )
                buffer = torch.empty(
                    self._prefetch_rows,
                    self._num_ngram_heads,
                    self.embedding_dim,
                    dtype=self.weight.dtype,
                    device=self._uva_weight.device,
                )
                self._prefetch_buffer = buffer
    prefetch_stream = self._prefetch_stream
    if prefetch_stream is None:
        raise RuntimeError("pinned PLE prefetch stream was not allocated")
    slot_size, _ = self._get_dp_gather_slot(ngram_ids.shape[0])
    gathered_ids = self._gather_dp_ids(ngram_ids, slot_size)
    if gathered_ids.shape[0] > buffer.shape[0]:
        raise ValueError(
            f"pinned PLE prefetch buffer holds {buffer.shape[0]} rows, "
            f"but the batch needs {gathered_ids.shape[0]}"
        )
    active_output = buffer[: gathered_ids.shape[0]]
    prefetch_stream.wait_stream(torch.cuda.current_stream())
    gathered_ids.record_stream(prefetch_stream)
    with torch.cuda.stream(prefetch_stream):
        self._lookup(gathered_ids, output=active_output)

sync_lookup(ngram_ids)

Synchronous UVA lookup for platforms without prefetch wiring.

Source code in vllm/models/qwen4_exp/common/ngram_embedding.py 
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def sync_lookup(self, ngram_ids: torch.Tensor) -> torch.Tensor:
    """Synchronous UVA lookup for platforms without prefetch wiring."""
    slot_size, slot_offset = self._get_dp_gather_slot(ngram_ids.shape[0])
    gathered_ids = self._gather_dp_ids(ngram_ids, slot_size)
    embeddings = self._lookup(gathered_ids)
    embeddings = self._reduce_etp_embeddings(embeddings)
    return self._select_embeddings(
        embeddings,
        ngram_ids.shape[0],
        slot_offset,
    )

Qwen4ExpPLEUnquantizedEmbeddingMethod

Bases: Qwen4ExpPLEEmbeddingMethod

Unquantized PLE embedding storage and lookup semantics.

Source code in vllm/models/qwen4_exp/common/ngram_embedding.py 
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class Qwen4ExpPLEUnquantizedEmbeddingMethod(Qwen4ExpPLEEmbeddingMethod):
    """Unquantized PLE embedding storage and lookup semantics."""

    def create_weights(
        self,
        layer: Qwen4ExpPLEEmbedding,
        input_size_per_partition: int,
        output_partition_sizes: list[int],
        input_size: int,
        output_size: int,
        params_dtype: torch.dtype,
        **extra_weight_attrs,
    ) -> None:
        del input_size, output_size
        weight = nn.Parameter(
            layer.allocate_embedding_weight(
                sum(output_partition_sizes),
                input_size_per_partition,
                params_dtype,
            ),
            requires_grad=False,
        )
        set_weight_attrs(weight, {"input_dim": 1, "output_dim": 0})
        set_weight_attrs(weight, extra_weight_attrs)
        layer.register_parameter("weight", weight)

    def dequantize(
        self,
        layer: nn.Module,
        embeddings: torch.Tensor,
        output_dtype: torch.dtype,
    ) -> torch.Tensor:
        del layer, output_dtype
        return embeddings

_lookup_ple_embedding_from_pinned_kernel(weight_ptr, ids_ptr, output_ptr, embedding_dim, tp_vocab_start, tp_vocab_end, BLOCK_D)

Look up TP-owned PLE rows through a CUDA view of pinned host memory.

Source code in vllm/models/qwen4_exp/common/ngram_embedding.py 
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@triton.jit
def _lookup_ple_embedding_from_pinned_kernel(
    weight_ptr,
    ids_ptr,
    output_ptr,
    embedding_dim,
    tp_vocab_start,
    tp_vocab_end,
    BLOCK_D: tl.constexpr,
):
    """Look up TP-owned PLE rows through a CUDA view of pinned host memory."""
    row_id = tl.program_id(0)
    global_idx = tl.load(ids_ptr + row_id)
    in_range = (global_idx >= tp_vocab_start) & (global_idx < tp_vocab_end)
    local_idx = tl.where(in_range, global_idx - tp_vocab_start, 0)
    offsets = tl.arange(0, BLOCK_D)
    store_mask = offsets < embedding_dim
    load_mask = store_mask & in_range
    values = tl.load(
        weight_ptr + local_idx * embedding_dim + offsets,
        mask=load_mask,
        other=0.0,
    )
    tl.store(
        output_ptr + row_id * embedding_dim + offsets,
        values,
        mask=store_mask,
    )