vllm.models.qwen4_exp.amd.ple_layer

GPU-resident Qwen4Exp position-learning enhancement layers.

Classes:

• Qwen4ExpNGramEmbedding –
• Qwen4ExpPLELayer –

Qwen4ExpNGramEmbedding

Bases: Module

Methods:

• load_weights –

  Load hash buffers and checkpoint-split embedding rows.

Source code in vllm/models/qwen4_exp/amd/ple_layer.py

class Qwen4ExpNGramEmbedding(nn.Module):
    _MASK64 = (1 << 64) - 1
    _SPLITMIX_GAMMA = 0x9E3779B97F4A7C15
    _SPLITMIX_M1 = 0xBF58476D1CE4E5B9
    _SPLITMIX_M2 = 0x94D049BB133111EB
    _PLE_LAYER_PRIME = 10007

    @classmethod
    def _splitmix64(cls, value: int) -> int:
        """Mix an integer into a deterministic unsigned 64-bit value."""
        value = (value + cls._SPLITMIX_GAMMA) & cls._MASK64
        value = ((value ^ (value >> 30)) * cls._SPLITMIX_M1) & cls._MASK64
        value = ((value ^ (value >> 27)) * cls._SPLITMIX_M2) & cls._MASK64
        return (value ^ (value >> 31)) & cls._MASK64

    @staticmethod
    def _is_prime_64(value: int) -> bool:
        """Return whether a 64-bit integer is prime."""
        if value < 2:
            return False
        for prime in (2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37):
            if value % prime == 0:
                return value == prime
        exponent = value - 1
        shifts = 0
        while exponent % 2 == 0:
            exponent //= 2
            shifts += 1
        for base in (2, 325, 9375, 28178, 450775, 9780504, 1795265022):
            if base % value == 0:
                continue
            witness = pow(base, exponent, value)
            if witness in (1, value - 1):
                continue
            for _ in range(shifts - 1):
                witness = pow(witness, 2, value)
                if witness == value - 1:
                    break
            else:
                return False
        return True

    @classmethod
    def _nth_prime_after(cls, start: int, count: int) -> int:
        """Return the ``count``-th prime strictly greater than ``start``."""
        prime = int(start)
        for _ in range(count):
            candidate = prime + 1
            if candidate <= 2:
                prime = 2
                continue
            if candidate % 2 == 0:
                candidate += 1
            while not cls._is_prime_64(candidate):
                candidate += 2
            prime = candidate
        return prime

    @classmethod
    def _make_layer_multipliers(
        cls,
        *,
        ngram_size: int,
        unigram_vocab_size: int,
        seed: int,
        ple_dense_layer_id: int,
    ) -> list[int]:
        """Build deterministic hash multipliers for one PLE layer."""
        max_multiplier = ((1 << 63) - 1) // unigram_vocab_size
        half_bound = max(1, max_multiplier // 2)
        base_seed = seed + cls._PLE_LAYER_PRIME * ple_dense_layer_id
        multipliers = []
        for index in range(ngram_size):
            value = base_seed + cls._SPLITMIX_GAMMA * (index + 1)
            multipliers.append(2 * (cls._splitmix64(value) % half_bound) + 1)
        return multipliers

    @classmethod
    def _make_vocab_layout(
        cls,
        *,
        ngram_vocab_size_base: int,
        ngram_heads: int,
        ple_dense_layer_id: int,
    ) -> tuple[list[int], list[int], int]:
        """Build per-head vocabulary sizes, offsets, and total row count."""
        sizes: list[int] = []
        offsets: list[int] = []
        offset = 0
        for local_head in range(ngram_heads):
            global_head = ple_dense_layer_id * ngram_heads + local_head
            size = cls._nth_prime_after(ngram_vocab_size_base - 1, global_head + 1)
            sizes.append(size)
            offsets.append(offset)
            offset += size
        return sizes, offsets, offset

    def __init__(
        self,
        config: Qwen4ExpTextConfig,
        embedding_dim: int,
        ple_dense_layer_id: int,
        max_total_tokens: int,
        max_num_reqs: int,
        prefix: str,
        layer_name: str,
        *,
        data_parallel_rank: int = 0,
        quant_config: QuantizationConfig | None = None,
        params_dtype: torch.dtype | None = None,
    ) -> None:
        super().__init__()
        self.embedding_dim = embedding_dim
        self.layer_name = layer_name
        self.ngram_size = int(config.ngram_size)
        self.heads_per_ngram = int(config.heads_per_ngram)
        self.ngram_heads = (self.ngram_size - 1) * self.heads_per_ngram
        if self.ngram_size < 2:
            raise ValueError(f"ngram_size must be >= 2, got {self.ngram_size}")
        if self.heads_per_ngram <= 0:
            raise ValueError(f"heads_per_ngram must be > 0, got {self.heads_per_ngram}")
        if embedding_dim % self.ngram_heads:
            raise ValueError(
                "ple_embed_dim must be divisible by total ngram heads: "
                f"{embedding_dim} % {self.ngram_heads} != 0"
            )
        self.head_dim = embedding_dim // self.ngram_heads
        self.eos_token_id = int(config.eos_token_id)
        self.unigram_vocab_size = int(config.vocab_size)
        self.split_ngram_parts = int(getattr(config, "split_ngram_parts", 512))
        if self.split_ngram_parts <= 0:
            raise ValueError("split_ngram_parts must be positive")

        multipliers = self._make_layer_multipliers(
            ngram_size=self.ngram_size,
            unigram_vocab_size=self.unigram_vocab_size,
            seed=int(getattr(config, "seed", 1234)),
            ple_dense_layer_id=ple_dense_layer_id,
        )
        self.register_buffer(
            "layer_multipliers",
            torch.tensor(multipliers, dtype=torch.long),
            persistent=True,
        )

        sizes, offsets, total_vocab_size = self._make_vocab_layout(
            ngram_vocab_size_base=int(config.ngram_vocab_size_base),
            ngram_heads=self.ngram_heads,
            ple_dense_layer_id=ple_dense_layer_id,
        )
        self.register_buffer(
            "ngram_heads_vocab_sizes",
            torch.tensor(sizes, dtype=torch.long),
            persistent=True,
        )
        self.register_buffer(
            "ngram_heads_offsets",
            torch.tensor(offsets, dtype=torch.long),
            persistent=True,
        )
        divisor = int(config.make_ngram_vocab_size_divisible_by)
        padded_vocab_size = ((total_vocab_size + divisor - 1) // divisor) * divisor
        if params_dtype is None:
            params_dtype = torch.get_default_dtype()
        embedding_prefix = f"{prefix}.ngram_embedding"
        embedding_quant_method = Qwen4ExpPLEEmbeddingMethod.from_quant_config(
            quant_config,
            embedding_prefix,
            getattr(config, "ple_embedding_dtype", None),
        )
        engram_config = get_current_vllm_config().engram_config
        embedding_cls = (
            Qwen4ExpPLEPinnedHostEmbedding
            if engram_config is not None and engram_config.cpu_offload
            else Qwen4ExpPLEDeviceEmbedding
        )
        self.ngram_embedding = embedding_cls(
            padded_vocab_size,
            self.head_dim,
            params_dtype=params_dtype,
            padding_size=divisor,
            prefix=embedding_prefix,
            embedding_method=embedding_quant_method,
            num_ngram_heads=self.ngram_heads,
            max_total_tokens=max_total_tokens,
            data_parallel_rank=data_parallel_rank,
        )
        logger.info(
            "Initialized AMD PLE embedding %s: quantization_method=%s, "
            "weight_dtype=%s, weight_device=%s, pinned=%s",
            embedding_prefix,
            type(embedding_quant_method).__name__,
            self.ngram_embedding.weight.dtype,
            self.ngram_embedding.weight.device,
            self.ngram_embedding.weight.is_pinned(),
        )
        self.register_buffer(
            "positions_buffer",
            torch.arange(max_total_tokens, dtype=torch.int64),
            persistent=False,
        )
        self.register_buffer(
            "padded_buffer",
            torch.full(
                (max_num_reqs, max_total_tokens),
                self.eos_token_id,
                dtype=torch.int64,
            ),
            persistent=False,
        )

    @staticmethod
    def _shift_precompute(
        tokens: torch.Tensor, eos_token_id: int
    ) -> tuple[torch.Tensor, torch.Tensor]:
        if tokens.dim() != 2:
            raise ValueError("tokens must be a 2D tensor")
        batch_size, seq_len = tokens.shape
        positions = torch.arange(seq_len, device=tokens.device, dtype=torch.int64)
        eos_positions = torch.where(tokens == eos_token_id, positions, -1)
        previous_eos_inclusive = torch.cummax(eos_positions, dim=1).values
        previous_eos = torch.cat(
            [
                eos_positions.new_full((batch_size, 1), -1),
                previous_eos_inclusive[:, :-1],
            ],
            dim=1,
        )
        return positions, positions.unsqueeze(0) - previous_eos - 1

    @staticmethod
    def _shift_apply(
        tokens: torch.Tensor,
        positions: torch.Tensor,
        position_in_segment: torch.Tensor,
        shift: int,
        eos_token_id: int,
    ) -> torch.Tensor:
        if shift == 0:
            return tokens
        source = positions - shift
        gather_indices = source.clamp_min(0).unsqueeze(0).expand(tokens.shape[0], -1)
        shifted = tokens.gather(1, gather_indices)
        valid = (source.unsqueeze(0) >= 0) & (position_in_segment >= shift)
        return torch.where(valid, shifted, tokens.new_full((), eos_token_id))

    def forward(
        self,
        input_ids: torch.Tensor,
        query_start_loc: torch.Tensor,
        ngram_context: torch.Tensor,
    ) -> torch.Tensor:
        input_ids = input_ids.reshape(-1).long()
        query_start_loc = query_start_loc.long()
        num_reqs = query_start_loc.numel() - 1
        num_tokens = input_ids.shape[0]
        if num_tokens > self.positions_buffer.numel():
            raise ValueError(
                f"PLE received {num_tokens} tokens, but its workspace supports "
                f"at most {self.positions_buffer.numel()}"
            )
        if num_reqs > self.padded_buffer.shape[0]:
            raise ValueError(
                f"PLE received {num_reqs} requests, but its workspace supports "
                f"at most {self.padded_buffer.shape[0]}"
            )

        positions = self.positions_buffer[:num_tokens]
        packed = self.padded_buffer[:num_reqs]
        packed.fill_(self.eos_token_id)
        request_indices = torch.searchsorted(query_start_loc, positions, right=True) - 1
        request_indices.clamp_(max=num_reqs - 1)
        columns = (positions - query_start_loc[request_indices]).clamp(
            0, packed.shape[1] - 1
        )
        packed[request_indices, columns] = input_ids
        ngram_context = ngram_context[:num_reqs].to(
            device=input_ids.device, dtype=torch.long
        )

        context = torch.cat([ngram_context, packed], dim=-1)
        positions_2d, position_in_segment = self._shift_precompute(
            context, self.eos_token_id
        )
        shifted = [context]
        for shift in range(1, self.ngram_size):
            shifted.append(
                self._shift_apply(
                    context,
                    positions_2d,
                    position_in_segment,
                    shift,
                    self.eos_token_id,
                )
            )
        adjusted_columns = columns + self.ngram_size - 1
        id_blocks = []
        for ngram in range(2, self.ngram_size + 1):
            start = (ngram - 2) * self.heads_per_ngram
            end = start + self.heads_per_ngram
            mixed = shifted[0] * self.layer_multipliers[0]
            for index in range(1, ngram):
                mixed = torch.bitwise_xor(
                    mixed, shifted[index] * self.layer_multipliers[index]
                )
            sizes = self.ngram_heads_vocab_sizes[start:end]
            offsets = self.ngram_heads_offsets[start:end]
            ids = torch.remainder(mixed.unsqueeze(-1), sizes) + offsets
            id_blocks.append(ids[request_indices, adjusted_columns])
        ngram_ids = torch.cat(id_blocks, dim=-1)
        embedding = self.ngram_embedding
        if embedding.supports_prefetch:
            output = ngram_ids.new_empty(
                (ngram_ids.shape[0], self.embedding_dim),
                dtype=embedding.weight.dtype,
            )
            torch.ops.vllm.qwen4_exp_amd_ple_ngram_embedding_pinned(
                ngram_ids,
                output,
                self.layer_name,
            )
            return output
        output = ngram_ids.new_empty(
            (ngram_ids.shape[0], self.embedding_dim),
            dtype=embedding.params_dtype,
        )
        torch.ops.vllm.qwen4_exp_amd_ple_ngram_embedding(
            ngram_ids,
            output,
            self.layer_name,
        )
        return output

    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
        """Load hash buffers and checkpoint-split embedding rows."""
        persistent_buffers = {
            "layer_multipliers": self.layer_multipliers,
            "ngram_heads_offsets": self.ngram_heads_offsets,
            "ngram_heads_vocab_sizes": self.ngram_heads_vocab_sizes,
        }
        loaded: set[str] = set()
        regular_weights: list[tuple[str, torch.Tensor]] = []
        shard_prefix = "ngram_embedding.shard_"

        for name, loaded_weight in weights:
            leaf_name = name.rsplit(".", 1)[-1]
            if leaf_name.startswith("hashstats_") or leaf_name == "token_lookup":
                continue
            if name in persistent_buffers:
                buffer = persistent_buffers[name]
                if buffer.shape != loaded_weight.shape:
                    raise ValueError(
                        f"Shape mismatch for {name}: expected "
                        f"{tuple(buffer.shape)}, got {tuple(loaded_weight.shape)}"
                    )
                buffer.copy_(loaded_weight.to(device=buffer.device, dtype=buffer.dtype))
                loaded.add(name)
                continue
            if name.startswith(shard_prefix) and name.endswith(".weight"):
                shard_text = name[len(shard_prefix) : -len(".weight")]
                if not shard_text.isdigit():
                    regular_weights.append((name, loaded_weight))
                    continue
                shard_index = int(shard_text)
                if shard_index >= self.split_ngram_parts:
                    raise ValueError(
                        f"PLE embedding shard index {shard_index} exceeds "
                        f"split_ngram_parts={self.split_ngram_parts}"
                    )
                embedding = self.ngram_embedding
                shard_size = (
                    embedding.org_vocab_size + self.split_ngram_parts - 1
                ) // self.split_ngram_parts
                checkpoint_start = shard_index * shard_size
                expected_rows = max(
                    0,
                    min(shard_size, embedding.org_vocab_size - checkpoint_start),
                )
                expected_shape = (expected_rows, embedding.embedding_dim)
                if tuple(loaded_weight.shape) != expected_shape:
                    raise ValueError(
                        f"Shape mismatch for PLE embedding shard {shard_index}: "
                        f"expected {expected_shape}, got "
                        f"{tuple(loaded_weight.shape)}"
                    )
                embedding.weight.weight_loader(
                    embedding.weight,
                    loaded_weight,
                    checkpoint_start=checkpoint_start,
                )
                loaded.add("ngram_embedding.weight")
                continue
            regular_weights.append((name, loaded_weight))

        if regular_weights:
            loaded.update(AutoWeightsLoader(self).load_weights(regular_weights))
        return loaded

_is_prime_64(value) staticmethod

Return whether a 64-bit integer is prime.

Source code in vllm/models/qwen4_exp/amd/ple_layer.py

@staticmethod
def _is_prime_64(value: int) -> bool:
    """Return whether a 64-bit integer is prime."""
    if value < 2:
        return False
    for prime in (2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37):
        if value % prime == 0:
            return value == prime
    exponent = value - 1
    shifts = 0
    while exponent % 2 == 0:
        exponent //= 2
        shifts += 1
    for base in (2, 325, 9375, 28178, 450775, 9780504, 1795265022):
        if base % value == 0:
            continue
        witness = pow(base, exponent, value)
        if witness in (1, value - 1):
            continue
        for _ in range(shifts - 1):
            witness = pow(witness, 2, value)
            if witness == value - 1:
                break
        else:
            return False
    return True

_make_layer_multipliers(*, ngram_size, unigram_vocab_size, seed, ple_dense_layer_id) classmethod

Build deterministic hash multipliers for one PLE layer.

Source code in vllm/models/qwen4_exp/amd/ple_layer.py

@classmethod
def _make_layer_multipliers(
    cls,
    *,
    ngram_size: int,
    unigram_vocab_size: int,
    seed: int,
    ple_dense_layer_id: int,
) -> list[int]:
    """Build deterministic hash multipliers for one PLE layer."""
    max_multiplier = ((1 << 63) - 1) // unigram_vocab_size
    half_bound = max(1, max_multiplier // 2)
    base_seed = seed + cls._PLE_LAYER_PRIME * ple_dense_layer_id
    multipliers = []
    for index in range(ngram_size):
        value = base_seed + cls._SPLITMIX_GAMMA * (index + 1)
        multipliers.append(2 * (cls._splitmix64(value) % half_bound) + 1)
    return multipliers

_make_vocab_layout(*, ngram_vocab_size_base, ngram_heads, ple_dense_layer_id) classmethod

Build per-head vocabulary sizes, offsets, and total row count.

Source code in vllm/models/qwen4_exp/amd/ple_layer.py

@classmethod
def _make_vocab_layout(
    cls,
    *,
    ngram_vocab_size_base: int,
    ngram_heads: int,
    ple_dense_layer_id: int,
) -> tuple[list[int], list[int], int]:
    """Build per-head vocabulary sizes, offsets, and total row count."""
    sizes: list[int] = []
    offsets: list[int] = []
    offset = 0
    for local_head in range(ngram_heads):
        global_head = ple_dense_layer_id * ngram_heads + local_head
        size = cls._nth_prime_after(ngram_vocab_size_base - 1, global_head + 1)
        sizes.append(size)
        offsets.append(offset)
        offset += size
    return sizes, offsets, offset

_nth_prime_after(start, count) classmethod

Return the count-th prime strictly greater than start.

Source code in vllm/models/qwen4_exp/amd/ple_layer.py

@classmethod
def _nth_prime_after(cls, start: int, count: int) -> int:
    """Return the ``count``-th prime strictly greater than ``start``."""
    prime = int(start)
    for _ in range(count):
        candidate = prime + 1
        if candidate <= 2:
            prime = 2
            continue
        if candidate % 2 == 0:
            candidate += 1
        while not cls._is_prime_64(candidate):
            candidate += 2
        prime = candidate
    return prime

_splitmix64(value) classmethod

Mix an integer into a deterministic unsigned 64-bit value.

Source code in vllm/models/qwen4_exp/amd/ple_layer.py

@classmethod
def _splitmix64(cls, value: int) -> int:
    """Mix an integer into a deterministic unsigned 64-bit value."""
    value = (value + cls._SPLITMIX_GAMMA) & cls._MASK64
    value = ((value ^ (value >> 30)) * cls._SPLITMIX_M1) & cls._MASK64
    value = ((value ^ (value >> 27)) * cls._SPLITMIX_M2) & cls._MASK64
    return (value ^ (value >> 31)) & cls._MASK64

load_weights(weights)

Load hash buffers and checkpoint-split embedding rows.

Source code in vllm/models/qwen4_exp/amd/ple_layer.py

def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
    """Load hash buffers and checkpoint-split embedding rows."""
    persistent_buffers = {
        "layer_multipliers": self.layer_multipliers,
        "ngram_heads_offsets": self.ngram_heads_offsets,
        "ngram_heads_vocab_sizes": self.ngram_heads_vocab_sizes,
    }
    loaded: set[str] = set()
    regular_weights: list[tuple[str, torch.Tensor]] = []
    shard_prefix = "ngram_embedding.shard_"

    for name, loaded_weight in weights:
        leaf_name = name.rsplit(".", 1)[-1]
        if leaf_name.startswith("hashstats_") or leaf_name == "token_lookup":
            continue
        if name in persistent_buffers:
            buffer = persistent_buffers[name]
            if buffer.shape != loaded_weight.shape:
                raise ValueError(
                    f"Shape mismatch for {name}: expected "
                    f"{tuple(buffer.shape)}, got {tuple(loaded_weight.shape)}"
                )
            buffer.copy_(loaded_weight.to(device=buffer.device, dtype=buffer.dtype))
            loaded.add(name)
            continue
        if name.startswith(shard_prefix) and name.endswith(".weight"):
            shard_text = name[len(shard_prefix) : -len(".weight")]
            if not shard_text.isdigit():
                regular_weights.append((name, loaded_weight))
                continue
            shard_index = int(shard_text)
            if shard_index >= self.split_ngram_parts:
                raise ValueError(
                    f"PLE embedding shard index {shard_index} exceeds "
                    f"split_ngram_parts={self.split_ngram_parts}"
                )
            embedding = self.ngram_embedding
            shard_size = (
                embedding.org_vocab_size + self.split_ngram_parts - 1
            ) // self.split_ngram_parts
            checkpoint_start = shard_index * shard_size
            expected_rows = max(
                0,
                min(shard_size, embedding.org_vocab_size - checkpoint_start),
            )
            expected_shape = (expected_rows, embedding.embedding_dim)
            if tuple(loaded_weight.shape) != expected_shape:
                raise ValueError(
                    f"Shape mismatch for PLE embedding shard {shard_index}: "
                    f"expected {expected_shape}, got "
                    f"{tuple(loaded_weight.shape)}"
                )
            embedding.weight.weight_loader(
                embedding.weight,
                loaded_weight,
                checkpoint_start=checkpoint_start,
            )
            loaded.add("ngram_embedding.weight")
            continue
        regular_weights.append((name, loaded_weight))

    if regular_weights:
        loaded.update(AutoWeightsLoader(self).load_weights(regular_weights))
    return loaded

Qwen4ExpPLELayer

Bases: Module, MambaBase

Source code in vllm/models/qwen4_exp/amd/ple_layer.py

class Qwen4ExpPLELayer(nn.Module, MambaBase):
    def __init__(
        self,
        config: Qwen4ExpTextConfig,
        vllm_config: VllmConfig,
        layer_idx: int = 0,
        ple_dense_layer_id: int | None = None,
        prefix: str = "",
    ) -> None:
        super().__init__()
        model_config = vllm_config.model_config
        cache_config = vllm_config.cache_config
        quant_config = vllm_config.quant_config
        self.model_config: ModelConfig = model_config
        self.cache_config: CacheConfig = cache_config
        self.layer_idx = layer_idx
        self.ple_dense_layer_id = (
            int(ple_dense_layer_id)
            if ple_dense_layer_id is not None
            else int(layer_idx)
        )
        self.prefix = prefix
        self.hidden_size = int(config.hidden_size)
        self.hc_count = config.hc_count
        self.hc_hidden_size = self.hidden_size * self.hc_count
        self.conv_kernel_size = int(config.ple_conv_kernel_size)
        self.short_conv_dilation = int(config.ngram_size)
        self.conv_state_len = (self.conv_kernel_size - 1) * self.short_conv_dilation
        self.num_spec_tokens = vllm_config.num_speculative_tokens
        self.activation = "silu"
        self.ple_embedding: nn.Module = Qwen4ExpNGramEmbedding(
            config,
            int(config.ple_embed_dim),
            self.ple_dense_layer_id,
            vllm_config.scheduler_config.max_num_batched_tokens,
            vllm_config.scheduler_config.max_num_seqs,
            f"{prefix}.ple_embedding",
            prefix,
            data_parallel_rank=vllm_config.parallel_config.data_parallel_rank,
            quant_config=quant_config,
            params_dtype=model_config.dtype,
        )
        self.key_proj = ReplicatedLinear(
            int(config.ple_embed_dim),
            self.hc_hidden_size,
            bias=False,
            quant_config=quant_config,
            prefix=f"{prefix}.key_proj",
        )
        self.value_proj = ReplicatedLinear(
            int(config.ple_embed_dim),
            self.hidden_size,
            bias=False,
            quant_config=quant_config,
            prefix=f"{prefix}.value_proj",
        )
        norm_args = (
            self.hc_hidden_size,
            config.rms_norm_eps,
            self.hidden_size,
            model_config.dtype,
        )
        self.norm_key = Qwen4ExpPLEGroupedNorm(*norm_args)
        self.norm_query = Qwen4ExpPLEGroupedNorm(*norm_args)
        self.norm_conv = Qwen4ExpPLEGroupedNorm(*norm_args)
        self.conv1d = nn.Conv1d(
            self.hc_hidden_size,
            self.hc_hidden_size,
            self.conv_kernel_size,
            groups=self.hc_hidden_size,
            padding=self.conv_state_len,
            dilation=self.short_conv_dilation,
            bias=False,
            dtype=model_config.dtype,
        )
        nn.init.zeros_(self.conv1d.weight)
        self.conv1d.weight._no_reinit = True
        self.kv_cache = (torch.tensor([]),)
        compilation_config = get_current_vllm_config().compilation_config
        if prefix in compilation_config.static_forward_context:
            raise ValueError(f"Duplicate layer name: {prefix}")
        compilation_config.static_forward_context[prefix] = self

    @property
    def mamba_type(self) -> MambaAttentionBackendEnum:
        return MambaAttentionBackendEnum.SHORT_CONV

    @property
    def is_kv_cache_tp_replicated(self) -> bool:
        return True

    def get_attn_backend(self) -> type[PleShortConvAttentionBackend]:
        return PleShortConvAttentionBackend

    def get_state_dtype(self) -> tuple[torch.dtype, ...]:
        return MambaStateDtypeCalculator.short_conv_state_dtype(
            self.model_config.dtype, self.cache_config.mamba_cache_dtype
        )

    def get_state_shape(self) -> Sequence[tuple[int, ...]]:
        return MambaStateShapeCalculator.short_conv_state_shape(
            tp_world_size=1,
            intermediate_size=self.hc_hidden_size,
            conv_kernel=self.conv_state_len + 1,
            num_spec=self.num_spec_tokens,
        )

    def _apply_norm(
        self, norm: Qwen4ExpPLEGroupedNorm, hidden_states: torch.Tensor
    ) -> torch.Tensor:
        shape = hidden_states.shape
        return norm(hidden_states.flatten(-2)).reshape(shape)

    def _short_conv_fallback(self, inputs: torch.Tensor) -> torch.Tensor:
        # Profiling / CUDA graph capture only; conv state is not updated.
        inputs_t = inputs.transpose(0, 1).unsqueeze(0)
        output = self.conv1d(inputs_t)[..., : inputs_t.size(-1)]
        return F.silu(output).squeeze(0).transpose(0, 1)

    def _short_conv_dilated_decode_batched(
        self,
        x_d: torch.Tensor,
        conv_state: torch.Tensor,
        conv_weights: torch.Tensor,
        state_indices_tensor_d: torch.Tensor,
        has_initial_states_d: torch.Tensor | None,
    ) -> torch.Tensor:
        state_indices = state_indices_tensor_d.to(
            device=conv_state.device, dtype=torch.int64
        )
        # FULL cudagraph padded decode rows use NULL_BLOCK_ID. Remap them to
        # slot 0 for a safe gather, then zero output and skip write-back.
        valid_state = state_indices != NULL_BLOCK_ID
        state_indices = torch.where(
            valid_state, state_indices, torch.zeros_like(state_indices)
        )
        if has_initial_states_d is None:
            has_initial_state = valid_state
        else:
            if has_initial_states_d.numel() < state_indices_tensor_d.numel():
                raise ValueError(
                    "has_initial_states_d size mismatch: "
                    f"got {has_initial_states_d.numel()}, "
                    f"need >= {state_indices_tensor_d.numel()}."
                )
            has_initial_state = has_initial_states_d[
                : state_indices_tensor_d.numel()
            ].to(device=conv_state.device, dtype=torch.bool)
            has_initial_state = has_initial_state & valid_state

        cached_state = conv_state.index_select(0, state_indices)
        state = cached_state[..., : self.conv_state_len].to(x_d.dtype)
        if self.conv_state_len > 0:
            initial_state = torch.where(
                has_initial_state.view(-1, 1, 1),
                state,
                torch.zeros_like(state),
            )
            history = torch.cat((initial_state, x_d.unsqueeze(-1)), dim=-1)
        else:
            history = x_d.unsqueeze(-1)

        conv_output = F.conv1d(
            history,
            conv_weights.unsqueeze(1).contiguous(),
            groups=history.size(1),
            dilation=self.short_conv_dilation,
        ).squeeze(-1)
        output = F.silu(conv_output)
        output = output * valid_state.view(-1, 1).to(output.dtype)

        if self.conv_state_len > 0:
            next_state = history[..., -self.conv_state_len :]
            # Padded rows are remapped to the reserved null slot. Preserve its
            # existing value while writing the new states for valid rows.
            existing_base_state = cached_state[..., : self.conv_state_len]
            safe_next_state = torch.where(
                valid_state.view(-1, 1, 1),
                next_state.to(conv_state.dtype),
                existing_base_state,
            )
            cached_state[..., : self.conv_state_len] = safe_next_state
            conv_state.index_copy_(0, state_indices, cached_state)

        return output

    def _short_conv_dilated_prefill_batched(
        self,
        x_p: torch.Tensor,
        metadata: PleShortConvAttentionMetadata,
        conv_state: torch.Tensor,
        conv_weights: torch.Tensor,
        state_indices_tensor_p: torch.Tensor,
        num_prefills: int,
        num_decode_tokens: int,
        num_prefill_tokens: int,
    ) -> torch.Tensor:
        query_start_loc_p = metadata.query_start_loc_p
        if query_start_loc_p is None:
            raise ValueError("query_start_loc is required for prefill short-conv")
        # The metadata builder guarantees that the prefill query offsets start
        # at 0 and end at num_prefill_tokens. Avoid reading those values here,
        # since doing so would force a device-to-host synchronization.
        has_initial_states_p = metadata.has_initial_states_p
        if has_initial_states_p is None:
            raise ValueError("has_initial_states_p is required for prefill short-conv")

        output = torch.empty_like(x_p)
        q_starts = query_start_loc_p.to(torch.int64)
        if state_indices_tensor_p.numel() < num_prefills:
            raise ValueError(
                "state_indices_tensor_p size mismatch: "
                f"got {state_indices_tensor_p.numel()}, "
                f"need >= {num_prefills}."
            )
        if has_initial_states_p.numel() < num_prefills:
            raise ValueError(
                "has_initial_states_p size mismatch: "
                f"got {has_initial_states_p.numel()}, "
                f"need >= {num_prefills}."
            )
        if num_prefills == 0 or x_p.numel() == 0:
            return output
        lengths = q_starts[1:] - q_starts[:-1]
        # Use the CPU-computed packing width from the metadata builder instead
        # of synchronizing on lengths.max().
        max_len = metadata.max_prefill_query_len
        if max_len <= 0:
            return output

        hidden_size = x_p.shape[1]
        positions = torch.arange(
            num_prefill_tokens, device=x_p.device, dtype=torch.int64
        )
        req_indices = torch.searchsorted(q_starts[1:], positions, right=True)
        col_indices = positions - q_starts[req_indices]

        packed_tokens = x_p.new_zeros((num_prefills, max_len, hidden_size))
        packed_tokens[req_indices, col_indices] = x_p
        packed_tokens = packed_tokens.transpose(1, 2).contiguous()

        state_indices = state_indices_tensor_p[:num_prefills].to(
            device=conv_state.device, dtype=torch.int64
        )
        valid_state = state_indices != NULL_BLOCK_ID
        state_indices = torch.where(
            valid_state, state_indices, torch.zeros_like(state_indices)
        )
        has_initial = has_initial_states_p[:num_prefills].to(
            device=conv_state.device, dtype=torch.bool
        )
        if self.conv_state_len > 0:
            if conv_state.shape[0] == 0:
                state = conv_state.new_zeros(
                    (num_prefills, hidden_size, self.conv_state_len),
                    dtype=x_p.dtype,
                )
            else:
                state = conv_state.index_select(0, state_indices)[
                    ..., : self.conv_state_len
                ].to(x_p.dtype)
            use_initial_mask = (valid_state & has_initial).view(num_prefills, 1, 1)
            initial_state = torch.where(
                use_initial_mask,
                state,
                torch.zeros_like(state),
            )
            history = torch.cat((initial_state, packed_tokens), dim=-1)
        else:
            history = packed_tokens

        conv_output = F.conv1d(
            history,
            conv_weights.unsqueeze(1).contiguous(),
            groups=history.size(1),
            dilation=self.short_conv_dilation,
        )
        conv_output = F.silu(conv_output).transpose(1, 2).contiguous()

        token_positions = torch.arange(max_len, device=x_p.device, dtype=torch.int64)
        valid_tokens = token_positions.view(1, max_len) < lengths.view(num_prefills, 1)
        valid_output_mask = valid_tokens & valid_state.to(device=x_p.device).view(
            num_prefills, 1
        )
        conv_output.masked_fill_(~valid_output_mask.unsqueeze(-1), 0)
        output.copy_(conv_output[req_indices, col_indices])

        if self.conv_state_len > 0 and conv_state.shape[0] > 0:
            state_starts = lengths.to(device=history.device, dtype=torch.int64).view(
                num_prefills, 1, 1
            )
            state_offsets = torch.arange(
                self.conv_state_len, device=history.device, dtype=torch.int64
            ).view(1, 1, self.conv_state_len)
            next_state = history.gather(
                dim=2,
                index=(state_starts + state_offsets).expand(-1, history.size(1), -1),
            )
            # Write back without a host synchronization. Valid, non-empty rows
            # receive their new state; padding and zero-length rows keep the
            # current cache value.
            existing_state = conv_state.index_select(0, state_indices)
            existing_base_state = existing_state[..., : self.conv_state_len]
            update_mask = valid_state & (lengths.to(device=conv_state.device) > 0)
            safe_next_state = torch.where(
                update_mask.view(num_prefills, 1, 1),
                next_state.to(conv_state.dtype),
                existing_base_state,
            )
            existing_state[..., : self.conv_state_len] = safe_next_state
            conv_state.index_copy_(0, state_indices, existing_state)
        return output

    def _short_conv_dilated_spec_batched(
        self,
        x_spec: torch.Tensor,
        conv_state: torch.Tensor,
        conv_weights: torch.Tensor,
        spec_state_indices_tensor: torch.Tensor,
        spec_query_start_loc: torch.Tensor,
        num_accepted_tokens: torch.Tensor,
        spec_query_len: int,
    ) -> torch.Tensor:
        """Dilated short-conv for speculative-decode (MTP) requests.

        Each spec request feeds multiple (draft + 1) query tokens. The conv
        outputs are computed causally after rolling back the previous draft
        state by ``num_accepted_tokens - 1``. The current candidate inputs stay
        in the extended cache for the next forward, matching
        ``causal_conv1d_update``.

        ``spec_query_len`` (== num_speculative_tokens + 1) is the maximum query
        length and is a Python int, so no host synchronization is needed; this
        keeps the path safe for full CUDA-graph capture/replay where the buffers
        are padded at the request level.
        """
        num_reqs = spec_state_indices_tensor.numel()
        hidden_size = x_spec.size(-1)
        # Use a fixed packing width instead of synchronizing on lengths.max().
        max_len = spec_query_len
        # Full CUDA graphs can pad these buffers. Only the first num_reqs
        # accepted-token counts belong to actual speculative requests.
        num_accepted_tokens = num_accepted_tokens[:num_reqs]
        q_starts = spec_query_start_loc[: num_reqs + 1].to(torch.int64)
        # Keep the number of real speculative tokens on the device.
        total_real_tokens = q_starts[num_reqs]

        state_indices = spec_state_indices_tensor.to(
            device=conv_state.device, dtype=torch.int64
        )
        valid_state = state_indices != NULL_BLOCK_ID
        state_indices = torch.where(
            valid_state, state_indices, torch.zeros_like(state_indices)
        )
        positions = torch.arange(
            x_spec.size(0), device=x_spec.device, dtype=torch.int64
        )
        # Route graph-padded token rows to the discarded dummy request so that
        # they cannot overwrite real packed data.
        req_indices = torch.searchsorted(q_starts[1:], positions, right=True)
        valid_tokens = (positions < total_real_tokens) & (req_indices < num_reqs)
        clamped_req_indices = req_indices.clamp_max(max(num_reqs - 1, 0))
        col_indices = (positions - q_starts[clamped_req_indices]).clamp_(0, max_len - 1)
        pack_req_indices = torch.where(
            valid_tokens,
            clamped_req_indices,
            torch.full_like(req_indices, num_reqs),
        )
        pack_col_indices = torch.where(
            valid_tokens, col_indices, torch.zeros_like(col_indices)
        )

        # The last request row is the dummy sink for graph padding.
        packed = x_spec.new_zeros((num_reqs + 1, max_len, hidden_size))
        packed[pack_req_indices, pack_col_indices] = x_spec
        packed = packed.transpose(1, 2).contiguous()

        if self.conv_state_len > 0:
            cached_state = conv_state.index_select(0, state_indices)
            rollback_offsets = num_accepted_tokens.to(
                device=conv_state.device, dtype=torch.int64
            ).sub(1)
            rollback_offsets = torch.where(
                valid_state,
                rollback_offsets.clamp_(0, max_len - 1),
                torch.zeros_like(rollback_offsets),
            )
            state_offsets = torch.arange(
                self.conv_state_len, device=conv_state.device, dtype=torch.int64
            ).view(1, 1, self.conv_state_len)
            rollback_indices = rollback_offsets.view(-1, 1, 1) + state_offsets
            state = cached_state.gather(
                2, rollback_indices.expand(-1, hidden_size, -1)
            ).to(x_spec.dtype)
            state = torch.where(
                valid_state.view(num_reqs, 1, 1),
                state,
                torch.zeros_like(state),
            )
            # Append a zeroed dummy-row state to match the [num_reqs + 1] pack.
            dummy_state = state.new_zeros((1, hidden_size, self.conv_state_len))
            state_full = torch.cat((state, dummy_state), dim=0)
            history = torch.cat((state_full, packed), dim=-1)
        else:
            history = packed

        conv_output = F.conv1d(
            history,
            conv_weights.unsqueeze(1).contiguous(),
            groups=history.size(1),
            dilation=self.short_conv_dilation,
        )
        conv_output = F.silu(conv_output).transpose(1, 2).contiguous()

        output = conv_output[pack_req_indices, pack_col_indices]
        output = output * valid_tokens.view(-1, 1).to(output.dtype)

        # Keep all current candidate inputs in the extended state. On the next
        # target forward, ``num_accepted_tokens - 1`` selects the rollback
        # window before processing the newly scheduled tokens.
        if self.conv_state_len > 0:
            state_capacity = self.conv_state_len + max_len - 1
            if conv_state.size(-1) < state_capacity:
                raise RuntimeError(
                    "PLE short-conv cache cannot retain speculative tokens: "
                    f"got {conv_state.size(-1)}, need {state_capacity}."
                )
            candidate_state = history[:num_reqs, :, 1 : state_capacity + 1]
            query_lengths = q_starts[1:] - q_starts[:-1]
            state_positions = torch.arange(
                state_capacity, device=history.device, dtype=torch.int64
            ).view(1, 1, state_capacity)
            update_lengths = (self.conv_state_len + query_lengths - 1).view(
                num_reqs, 1, 1
            )
            update_mask = valid_state.view(num_reqs, 1, 1) & (
                state_positions < update_lengths
            )
            existing_state = cached_state[..., :state_capacity]
            next_state = torch.where(
                update_mask,
                candidate_state.to(conv_state.dtype),
                existing_state,
            )
            cached_state[..., :state_capacity] = next_state
            conv_state.index_copy_(0, state_indices, cached_state)

        return output

    def _short_conv_dilated_dispatch(
        self,
        inputs: torch.Tensor,
        metadata: PleShortConvAttentionMetadata,
        conv_state: torch.Tensor,
        conv_weights: torch.Tensor,
    ) -> torch.Tensor:
        num_prefills = metadata.num_prefills
        num_decodes = metadata.num_decodes
        num_decode_tokens = metadata.num_decode_tokens
        num_prefill_tokens = metadata.num_prefill_tokens
        has_prefill = num_prefills > 0
        has_decode = num_decodes > 0
        has_spec = metadata.spec_sequence_masks is not None
        x = inputs[: metadata.num_actual_tokens]

        # Split spec / non-spec tokens.
        if has_spec:
            if has_prefill or has_decode:
                assert metadata.spec_token_indx is not None
                assert metadata.non_spec_token_indx is not None
                x_spec = x.index_select(0, metadata.spec_token_indx.long())
                x_non_spec = x.index_select(0, metadata.non_spec_token_indx.long())
            else:
                x_spec = x
                x_non_spec = None
        else:
            x_spec = None
            x_non_spec = x

        spec_output = None
        # 1. Run the multi-query speculative-decode part.
        if has_spec:
            assert metadata.spec_state_indices_tensor is not None
            assert metadata.spec_query_start_loc is not None
            assert metadata.num_accepted_tokens is not None
            spec_output = self._short_conv_dilated_spec_batched(
                x_spec=x_spec,
                conv_state=conv_state,
                conv_weights=conv_weights,
                spec_state_indices_tensor=metadata.spec_state_indices_tensor[
                    : metadata.num_spec_decodes
                ],
                spec_query_start_loc=metadata.spec_query_start_loc,
                num_accepted_tokens=metadata.num_accepted_tokens,
                spec_query_len=metadata.spec_query_len,
            )

        # 2. Run regular decode and prefill requests.
        conv_out_non_spec = None
        state_indices_tensor = metadata.state_indices_tensor
        if x_non_spec is not None:
            assert state_indices_tensor is not None
            if has_prefill:
                state_indices_tensor_d, state_indices_tensor_p = torch.split(
                    state_indices_tensor,
                    [num_decodes, num_prefills],
                    dim=0,
                )
                x_d, x_p = torch.split(
                    x_non_spec,
                    [num_decode_tokens, num_prefill_tokens],
                    dim=0,
                )
                non_spec_parts: list[torch.Tensor] = []
                if has_decode:
                    non_spec_parts.append(
                        self._short_conv_dilated_decode_batched(
                            x_d=x_d,
                            conv_state=conv_state,
                            conv_weights=conv_weights,
                            state_indices_tensor_d=state_indices_tensor_d,
                            has_initial_states_d=metadata.has_initial_states_d,
                        )
                    )
                non_spec_parts.append(
                    self._short_conv_dilated_prefill_batched(
                        x_p=x_p,
                        metadata=metadata,
                        conv_state=conv_state,
                        conv_weights=conv_weights,
                        state_indices_tensor_p=state_indices_tensor_p,
                        num_prefills=num_prefills,
                        num_decode_tokens=num_decode_tokens,
                        num_prefill_tokens=num_prefill_tokens,
                    )
                )
                conv_out_non_spec = torch.vstack(non_spec_parts)
            else:
                conv_out_non_spec = self._short_conv_dilated_decode_batched(
                    x_d=x_non_spec,
                    conv_state=conv_state,
                    conv_weights=conv_weights,
                    state_indices_tensor_d=state_indices_tensor[: x_non_spec.size(0)],
                    has_initial_states_d=metadata.has_initial_states_d,
                )

        # 3. Merge both parts back into the original token order.
        if has_spec and conv_out_non_spec is not None:
            assert metadata.spec_token_indx is not None
            assert metadata.non_spec_token_indx is not None
            assert spec_output is not None
            output = x.new_empty((metadata.num_actual_tokens, x.size(-1)))
            output.index_copy_(0, metadata.spec_token_indx, spec_output)
            output.index_copy_(0, metadata.non_spec_token_indx, conv_out_non_spec)
            return output
        elif has_spec:
            assert spec_output is not None
            return spec_output
        if conv_out_non_spec is None:
            return x
        return conv_out_non_spec

    def _short_conv(self, inputs: torch.Tensor) -> torch.Tensor:
        forward_context = get_forward_context()
        attn_metadata = forward_context.attn_metadata
        if attn_metadata is None:
            return self._short_conv_fallback(inputs)

        if not isinstance(attn_metadata, dict):
            raise RuntimeError(
                "PLE short-conv expects per-layer attention metadata dict "
                f"during inference, got {type(attn_metadata).__name__}."
            )

        layer_attn_metadata = attn_metadata.get(self.prefix)
        if layer_attn_metadata is None:
            # MRV2 omits Mamba-family metadata during profile warmup.
            return self._short_conv_fallback(inputs)
        if not isinstance(layer_attn_metadata, PleShortConvAttentionMetadata):
            raise TypeError(
                "Expected PleShortConvAttentionMetadata for layer "
                f"'{self.prefix}', got "
                f"{type(layer_attn_metadata).__name__}."
            )

        conv_state = self.kv_cache[0]
        if not is_conv_state_dim_first():
            conv_state = conv_state.transpose(-1, -2)
        conv_weights = self.conv1d.weight.squeeze(1)

        state_capacity = self.conv_state_len + self.num_spec_tokens
        if state_capacity > 0:
            if conv_state.size(-1) < state_capacity:
                raise RuntimeError(
                    "PLE short-conv cache is smaller than expected for "
                    f"dilated convolution: got {conv_state.size(-1)}, "
                    f"expect at least {state_capacity}."
                )
            conv_state = conv_state[..., -state_capacity:]
        return self._short_conv_dilated_dispatch(
            inputs,
            layer_attn_metadata,
            conv_state,
            conv_weights.to(dtype=inputs.dtype),
        )

    def forward(
        self,
        hidden_states: torch.Tensor,
        input_ids: torch.Tensor,
        query_start_loc: torch.Tensor,
        ngram_context: torch.Tensor,
    ) -> torch.Tensor:
        input_ids = input_ids.reshape(-1)
        if input_ids.shape[0] != hidden_states.shape[0]:
            raise ValueError(
                "PLE expects input_ids and hidden_states to have the same "
                f"token length, got {input_ids.shape[0]} and "
                f"{hidden_states.shape[0]}"
            )
        embeddings = self.ple_embedding(input_ids, query_start_loc, ngram_context)
        embeddings = self.ple_embedding.ngram_embedding.dequantize(
            embeddings,
            self.ple_embedding.ngram_embedding.params_dtype,
        )
        key, _ = self.key_proj(embeddings)
        value, _ = self.value_proj(embeddings)
        token_count = hidden_states.shape[0]
        key = key.reshape(token_count, self.hc_count, self.hidden_size)
        query = hidden_states.reshape(token_count, self.hc_count, self.hidden_size)
        key = self._apply_norm(self.norm_key, key)
        query = self._apply_norm(self.norm_query, query)
        gate = (key * query).sum(dim=-1, keepdim=True) / math.sqrt(self.hidden_size)
        gate = torch.sigmoid(gate.sign() * gate.abs().clamp_min(1e-6).sqrt())
        gated_value = gate * value.unsqueeze(-2)
        normalized = self._apply_norm(self.norm_conv, gated_value).flatten(-2)
        conv_output = torch.zeros_like(normalized)
        torch.ops.vllm.qwen4_exp_ple_short_conv(
            normalized,
            conv_output,
            self.prefix,
        )
        return gated_value.flatten(-2) + conv_output

_short_conv_dilated_spec_batched(x_spec, conv_state, conv_weights, spec_state_indices_tensor, spec_query_start_loc, num_accepted_tokens, spec_query_len)

Dilated short-conv for speculative-decode (MTP) requests.

Each spec request feeds multiple (draft + 1) query tokens. The conv outputs are computed causally after rolling back the previous draft state by num_accepted_tokens - 1. The current candidate inputs stay in the extended cache for the next forward, matching causal_conv1d_update.

spec_query_len (== num_speculative_tokens + 1) is the maximum query length and is a Python int, so no host synchronization is needed; this keeps the path safe for full CUDA-graph capture/replay where the buffers are padded at the request level.

Source code in vllm/models/qwen4_exp/amd/ple_layer.py

def _short_conv_dilated_spec_batched(
    self,
    x_spec: torch.Tensor,
    conv_state: torch.Tensor,
    conv_weights: torch.Tensor,
    spec_state_indices_tensor: torch.Tensor,
    spec_query_start_loc: torch.Tensor,
    num_accepted_tokens: torch.Tensor,
    spec_query_len: int,
) -> torch.Tensor:
    """Dilated short-conv for speculative-decode (MTP) requests.

    Each spec request feeds multiple (draft + 1) query tokens. The conv
    outputs are computed causally after rolling back the previous draft
    state by ``num_accepted_tokens - 1``. The current candidate inputs stay
    in the extended cache for the next forward, matching
    ``causal_conv1d_update``.

    ``spec_query_len`` (== num_speculative_tokens + 1) is the maximum query
    length and is a Python int, so no host synchronization is needed; this
    keeps the path safe for full CUDA-graph capture/replay where the buffers
    are padded at the request level.
    """
    num_reqs = spec_state_indices_tensor.numel()
    hidden_size = x_spec.size(-1)
    # Use a fixed packing width instead of synchronizing on lengths.max().
    max_len = spec_query_len
    # Full CUDA graphs can pad these buffers. Only the first num_reqs
    # accepted-token counts belong to actual speculative requests.
    num_accepted_tokens = num_accepted_tokens[:num_reqs]
    q_starts = spec_query_start_loc[: num_reqs + 1].to(torch.int64)
    # Keep the number of real speculative tokens on the device.
    total_real_tokens = q_starts[num_reqs]

    state_indices = spec_state_indices_tensor.to(
        device=conv_state.device, dtype=torch.int64
    )
    valid_state = state_indices != NULL_BLOCK_ID
    state_indices = torch.where(
        valid_state, state_indices, torch.zeros_like(state_indices)
    )
    positions = torch.arange(
        x_spec.size(0), device=x_spec.device, dtype=torch.int64
    )
    # Route graph-padded token rows to the discarded dummy request so that
    # they cannot overwrite real packed data.
    req_indices = torch.searchsorted(q_starts[1:], positions, right=True)
    valid_tokens = (positions < total_real_tokens) & (req_indices < num_reqs)
    clamped_req_indices = req_indices.clamp_max(max(num_reqs - 1, 0))
    col_indices = (positions - q_starts[clamped_req_indices]).clamp_(0, max_len - 1)
    pack_req_indices = torch.where(
        valid_tokens,
        clamped_req_indices,
        torch.full_like(req_indices, num_reqs),
    )
    pack_col_indices = torch.where(
        valid_tokens, col_indices, torch.zeros_like(col_indices)
    )

    # The last request row is the dummy sink for graph padding.
    packed = x_spec.new_zeros((num_reqs + 1, max_len, hidden_size))
    packed[pack_req_indices, pack_col_indices] = x_spec
    packed = packed.transpose(1, 2).contiguous()

    if self.conv_state_len > 0:
        cached_state = conv_state.index_select(0, state_indices)
        rollback_offsets = num_accepted_tokens.to(
            device=conv_state.device, dtype=torch.int64
        ).sub(1)
        rollback_offsets = torch.where(
            valid_state,
            rollback_offsets.clamp_(0, max_len - 1),
            torch.zeros_like(rollback_offsets),
        )
        state_offsets = torch.arange(
            self.conv_state_len, device=conv_state.device, dtype=torch.int64
        ).view(1, 1, self.conv_state_len)
        rollback_indices = rollback_offsets.view(-1, 1, 1) + state_offsets
        state = cached_state.gather(
            2, rollback_indices.expand(-1, hidden_size, -1)
        ).to(x_spec.dtype)
        state = torch.where(
            valid_state.view(num_reqs, 1, 1),
            state,
            torch.zeros_like(state),
        )
        # Append a zeroed dummy-row state to match the [num_reqs + 1] pack.
        dummy_state = state.new_zeros((1, hidden_size, self.conv_state_len))
        state_full = torch.cat((state, dummy_state), dim=0)
        history = torch.cat((state_full, packed), dim=-1)
    else:
        history = packed

    conv_output = F.conv1d(
        history,
        conv_weights.unsqueeze(1).contiguous(),
        groups=history.size(1),
        dilation=self.short_conv_dilation,
    )
    conv_output = F.silu(conv_output).transpose(1, 2).contiguous()

    output = conv_output[pack_req_indices, pack_col_indices]
    output = output * valid_tokens.view(-1, 1).to(output.dtype)

    # Keep all current candidate inputs in the extended state. On the next
    # target forward, ``num_accepted_tokens - 1`` selects the rollback
    # window before processing the newly scheduled tokens.
    if self.conv_state_len > 0:
        state_capacity = self.conv_state_len + max_len - 1
        if conv_state.size(-1) < state_capacity:
            raise RuntimeError(
                "PLE short-conv cache cannot retain speculative tokens: "
                f"got {conv_state.size(-1)}, need {state_capacity}."
            )
        candidate_state = history[:num_reqs, :, 1 : state_capacity + 1]
        query_lengths = q_starts[1:] - q_starts[:-1]
        state_positions = torch.arange(
            state_capacity, device=history.device, dtype=torch.int64
        ).view(1, 1, state_capacity)
        update_lengths = (self.conv_state_len + query_lengths - 1).view(
            num_reqs, 1, 1
        )
        update_mask = valid_state.view(num_reqs, 1, 1) & (
            state_positions < update_lengths
        )
        existing_state = cached_state[..., :state_capacity]
        next_state = torch.where(
            update_mask,
            candidate_state.to(conv_state.dtype),
            existing_state,
        )
        cached_state[..., :state_capacity] = next_state
        conv_state.index_copy_(0, state_indices, cached_state)

    return output

qwen4_exp_amd_ple_ngram_embedding(ngram_ids, output, layer_name)

Run the large PLE embedding lookup outside Inductor's FX graph.

Keeping the embedding weight in static_forward_context prevents AOT compile-time autotuning from materializing a synthetic copy of the weight.

Source code in vllm/models/qwen4_exp/amd/ple_layer.py

def qwen4_exp_amd_ple_ngram_embedding(
    ngram_ids: torch.Tensor,
    output: torch.Tensor,
    layer_name: str,
) -> None:
    """Run the large PLE embedding lookup outside Inductor's FX graph.

    Keeping the embedding weight in ``static_forward_context`` prevents AOT
    compile-time autotuning from materializing a synthetic copy of the weight.
    """
    layer = get_forward_context().no_compile_layers[layer_name]
    if not isinstance(layer, Qwen4ExpPLELayer):
        raise TypeError(f"{layer_name} is not a Qwen4Exp PLE owner")
    result = layer.ple_embedding.ngram_embedding(ngram_ids).flatten(-2)
    output.copy_(result)

qwen4_exp_amd_ple_ngram_embedding_pinned(ngram_ids, output, layer_name)

Run the pinned PLE UVA lookup outside Inductor's FX graph.

Same rationale as the device-path escape: keeping the large embedding weight out of the graph prevents AOT compile-time autotuning from materializing a synthetic copy of the weight.

Source code in vllm/models/qwen4_exp/amd/ple_layer.py

def qwen4_exp_amd_ple_ngram_embedding_pinned(
    ngram_ids: torch.Tensor,
    output: torch.Tensor,
    layer_name: str,
) -> None:
    """Run the pinned PLE UVA lookup outside Inductor's FX graph.

    Same rationale as the device-path escape: keeping the large embedding weight
    out of the graph prevents AOT compile-time autotuning from materializing a
    synthetic copy of the weight.
    """
    layer = get_forward_context().no_compile_layers[layer_name]
    if not isinstance(layer, Qwen4ExpPLELayer):
        raise TypeError(f"{layer_name} is not a Qwen4Exp PLE owner")
    result = layer.ple_embedding.ngram_embedding.sync_lookup(ngram_ids).flatten(-2)
    output.copy_(result)