vllm.model_executor.models.qwen3_dflash ¶

class DFlashQwen3Attention(nn.Module):
    """Attention for DFlash speculative decoding.

    Context KVs are pre-inserted into the KV cache before the forward pass.
    This layer handles only query tokens via standard attention.
    Adapted from Qwen3Attention."""

    def __init__(
        self,
        hidden_size: int,
        num_heads: int,
        num_kv_heads: int,
        rope_parameters: dict,
        max_position: int = 4096 * 32,
        head_dim: int | None = None,
        rms_norm_eps: float = 1e-06,
        attention_bias: bool = False,
        add_swa_attention_sink_bias: bool = False,
        sliding_window: int | None = None,
        causal: bool = False,
        cache_config: CacheConfig | None = None,
        quant_config: QuantizationConfig | None = None,
        prefix: str = "",
        attn_type: str = AttentionType.DECODER,
    ) -> None:
        super().__init__()
        self.layer_name = prefix
        self.hidden_size = hidden_size
        tp_size = get_tensor_model_parallel_world_size()
        self.total_num_heads = num_heads
        assert self.total_num_heads % tp_size == 0
        self.num_heads = self.total_num_heads // tp_size
        self.total_num_kv_heads = num_kv_heads
        if self.total_num_kv_heads >= tp_size:
            assert self.total_num_kv_heads % tp_size == 0
        else:
            assert tp_size % self.total_num_kv_heads == 0
        self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
        self.head_dim = head_dim or hidden_size // self.total_num_heads
        self.q_size = self.num_heads * self.head_dim
        self.kv_size = self.num_kv_heads * self.head_dim
        self.scaling = self.head_dim**-0.5

        self.qkv_proj = QKVParallelLinear(
            hidden_size,
            self.head_dim,
            self.total_num_heads,
            self.total_num_kv_heads,
            bias=attention_bias,
            quant_config=quant_config,
            prefix=f"{prefix}.qkv_proj",
        )
        self.o_proj = RowParallelLinear(
            self.total_num_heads * self.head_dim,
            hidden_size,
            bias=attention_bias,  # DFlash has o_proj bias when using attention bias
            quant_config=quant_config,
            prefix=f"{prefix}.o_proj",
        )

        self.rotary_emb = get_rope(
            self.head_dim,
            max_position=max_position,
            rope_parameters=rope_parameters,
        )

        self.attention_sink_bias = (
            torch.nn.Parameter(torch.empty(self.num_heads), requires_grad=False)
            if add_swa_attention_sink_bias
            else None
        )

        self.sliding_window = sliding_window
        self.attn = Attention(
            self.num_heads,
            self.head_dim,
            self.scaling,
            num_kv_heads=self.num_kv_heads,
            cache_config=cache_config,
            quant_config=quant_config,
            per_layer_sliding_window=sliding_window,
            prefix=f"{prefix}.attn",
            attn_type=attn_type,
            sinks=self.attention_sink_bias,
        )
        # NOTE: `causal` is currently unused here, but will be needed in the future
        # to support models with different causality per-layer.
        self.q_norm = RMSNorm(self.head_dim, eps=rms_norm_eps)
        self.k_norm = RMSNorm(self.head_dim, eps=rms_norm_eps)

    def forward(
        self,
        positions: torch.Tensor,
        hidden_states: torch.Tensor,
    ) -> torch.Tensor:
        """DFlash attention assumes that the KV cache is already populated
        with the context K/V from the target model's hidden states. This forward op
        computes attention for the query tokens only.
        See also: precompute_and_store_context_kv"""
        qkv, _ = self.qkv_proj(hidden_states)
        q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)

        # Per-head RMSNorm
        q_shape, k_shape = q.shape, k.shape
        q = self.q_norm(
            q.view(*q_shape[:-1], q_shape[-1] // self.head_dim, self.head_dim)
        ).view(q_shape)
        k = self.k_norm(
            k.view(*k_shape[:-1], k_shape[-1] // self.head_dim, self.head_dim)
        ).view(k_shape)

        q, k = self.rotary_emb(positions, q, k)

        attn_output = self.attn(q, k, v)
        output, _ = self.o_proj(attn_output)
        return output

class DFlashQwen3ForCausalLM(Qwen3ForCausalLM):
    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
        nn.Module.__init__(self)
        self.draft_model_config = vllm_config.speculative_config.draft_model_config
        self.config = self.draft_model_config.hf_config
        if getattr(self.config, "draft_vocab_size", None) is None:
            self.config.draft_vocab_size = getattr(self.config, "vocab_size", None)
        target_layer_num = vllm_config.model_config.get_num_layers(
            vllm_config.parallel_config
        )
        self.model = DFlashQwen3Model(
            vllm_config=vllm_config,
            prefix=maybe_prefix(prefix, "model"),
            start_layer_id=target_layer_num,
        )

        logit_scale = getattr(self.config, "logit_scale", 1.0)
        self.lm_head = ParallelLMHead(
            self.config.draft_vocab_size,
            self.config.hidden_size,
            prefix=maybe_prefix(prefix, "lm_head"),
        )
        self.logits_processor = LogitsProcessor(
            self.config.draft_vocab_size, scale=logit_scale
        )
        target_vocab_size = vllm_config.model_config.get_vocab_size()
        if self.config.draft_vocab_size != target_vocab_size:
            self.draft_id_to_target_id = nn.Parameter(
                torch.zeros(self.config.draft_vocab_size, dtype=torch.long),
                requires_grad=False,
            )
        else:
            self.draft_id_to_target_id = None

    def embed_input_ids(
        self,
        input_ids: torch.Tensor,
        multimodal_embeddings: NestedTensors | None = None,
        is_multimodal: torch.Tensor | None = None,
    ) -> torch.Tensor:
        return self.model.embed_input_ids(input_ids)

    def forward(
        self,
        input_ids: torch.Tensor,
        positions: torch.Tensor,
        inputs_embeds: torch.Tensor | None = None,
    ) -> torch.Tensor:
        return self.model(input_ids, positions, inputs_embeds)

    def compute_logits(
        self,
        hidden_states: torch.Tensor,
    ) -> torch.Tensor | None:
        logits = self.logits_processor(self.lm_head, hidden_states)
        if self.draft_id_to_target_id is None:
            return logits

        base = torch.arange(self.config.draft_vocab_size, device=logits.device)
        targets = base + self.draft_id_to_target_id
        logits_new = logits.new_full(
            (logits.shape[0], self.config.vocab_size),
            float("-inf"),
        )
        logits_new[:, targets] = logits
        return logits_new

    def precompute_and_store_context_kv(
        self,
        context_states: torch.Tensor,
        context_positions: torch.Tensor,
        context_slot_mapping: torch.Tensor | list[torch.Tensor | None] | None = None,
    ) -> None:
        """Precompute projected + RoPE'd K/V and write to cache."""
        self.model.precompute_and_store_context_kv(
            context_states, context_positions, context_slot_mapping
        )

    def combine_hidden_states(
        self,
        hidden_states: torch.Tensor,
    ) -> torch.Tensor:
        if not self.model.use_aux_hidden_state:
            return hidden_states
        needs_squeeze = hidden_states.dim() == 1
        if needs_squeeze:
            hidden_states = hidden_states.unsqueeze(0)
        result = self.model.fc(hidden_states)
        if needs_squeeze:
            result = result.squeeze(0)
        return result

    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]):
        model_weights = {}
        includes_draft_id_mapping = False
        includes_embed_tokens = False
        for name, loaded_weight in weights:
            assert "mask_hidden" not in name, (
                "DFlash embeds masked slots via mask_token_id (optionally "
                "overridden by a mask_embedding.pt file); it should not ship a "
                "mask_hidden weight."
            )
            if "t2d" in name:
                continue
            if "d2t" in name:
                name = name.replace("d2t", "draft_id_to_target_id")
                includes_draft_id_mapping = True
            elif "lm_head" not in name:
                name = "model." + name
            if "embed_tokens" in name:
                includes_embed_tokens = True
            model_weights[name] = loaded_weight
            process_eagle_weight(self, name)

        # Route the separately-trained mask embedding (if shipped) through the
        # standard weight loader alongside the rest of the draft weights.
        mask_embedding = self._read_mask_embedding()
        if mask_embedding is not None:
            model_weights["model.mask_embedding"] = mask_embedding
            self.model.has_separate_mask_embedding = True

        skip_substrs = []
        if not includes_draft_id_mapping:
            skip_substrs.append("draft_id_to_target_id")
        if not includes_embed_tokens:
            skip_substrs.append("embed_tokens")
        if not self.model.use_aux_hidden_state:
            skip_substrs.append("fc.")
        if not self.model.has_separate_mask_embedding:
            skip_substrs.append("mask_embedding")
        loader = AutoWeightsLoader(
            self,
            skip_prefixes=None,
            skip_substrs=skip_substrs,
        )
        loader.load_weights(model_weights.items())
        self.model._build_fused_kv_buffers()

    def _read_mask_embedding(self) -> torch.Tensor | None:
        """Checks for an override mask embedding in `mask_embedding.pt` and returns it.

        Some checkpoints ship a separately-trained mask embedding for the mask token,
        which we use to overwrite the embedding for `mask_token_id`. This helper
        checks for the file, loads the pytorch tensor, and returns the embedding to use.

        Returns None if the override file is not present.
        """
        mask_token_id = self.model.mask_token_id
        if mask_token_id is None:
            return None

        MASK_EMBEDDING_FILENAME = "mask_embedding.pt"
        data = get_hf_file_bytes(
            MASK_EMBEDDING_FILENAME,
            self.draft_model_config.model,
            self.draft_model_config.revision,
        )
        if data is None:
            return None

        state = torch.load(io.BytesIO(data), weights_only=True)
        if isinstance(state, dict):
            if state.get("mask_token_id", mask_token_id) != mask_token_id:
                raise ValueError(
                    f"{MASK_EMBEDDING_FILENAME} mask_token_id does not match "
                    f"dflash_config.mask_token_id ({mask_token_id}). "
                    f"Got {state.get('mask_token_id')}."
                )
            state = state["embedding"]

        logger.info(
            "Loaded DFlash mask embedding for mask_token_id %s from %s",
            mask_token_id,
            MASK_EMBEDDING_FILENAME,
        )
        return state.reshape(-1)

@support_torch_compile
class DFlashQwen3Model(nn.Module):
    def __init__(
        self,
        *,
        vllm_config: VllmConfig,
        start_layer_id: int = 0,
        prefix: str = "",
    ) -> None:
        super().__init__()
        self.config = vllm_config.speculative_config.draft_model_config.hf_config
        self.vocab_size = self.config.vocab_size
        self.quant_config = get_draft_quant_config(vllm_config)

        drafter_config = getattr(self.config, "eagle_config", {})
        drafter_config.update(getattr(self.config, "dflash_config", {}))

        if drafter_config is not None and "use_aux_hidden_state" in drafter_config:
            self.use_aux_hidden_state = drafter_config["use_aux_hidden_state"]
        else:
            self.use_aux_hidden_state = True

        current_vllm_config = get_current_vllm_config()

        self.embed_tokens = VocabParallelEmbedding(
            self.config.vocab_size,
            self.config.hidden_size,
            prefix=maybe_prefix(prefix, "embed_tokens"),
        )

        # Masked query slots are fed to the draft as `mask_token_id`. Most DFlash
        # checkpoints will have the mask embedding in the vocabulary embedding table
        # at that slot id. Some checkpoints (XiaomiMiMo/MiMo-V2.5-Pro-FP4-DFlash) ship
        # with a separate mask embedding tensor to use instead. When present, we load it
        # and substitute it for embed_tokens[mask_token_id] when computing embeddings.
        self.mask_token_id = drafter_config.get("mask_token_id")
        self.mask_embedding = nn.Parameter(
            torch.zeros(self.config.hidden_size, dtype=vllm_config.model_config.dtype),
            requires_grad=False,
        )
        self.has_separate_mask_embedding = False

        self.layers = nn.ModuleList(
            [
                DFlashQwen3DecoderLayer(
                    current_vllm_config,
                    config=self.config,
                    layer_idx=layer_idx,
                    cache_config=current_vllm_config.cache_config,
                    quant_config=self.quant_config,
                    prefix=maybe_prefix(prefix, f"layers.{layer_idx + start_layer_id}"),
                )
                for layer_idx in range(self.config.num_hidden_layers)
            ]
        )
        if self.use_aux_hidden_state:
            num_features_to_use = self.config.num_hidden_layers
            if "target_layer_ids" in drafter_config:
                num_features_to_use = len(drafter_config["target_layer_ids"])
            elif "layer_ids" in drafter_config:
                num_features_to_use = len(drafter_config["layer_ids"])
            if hasattr(self.config, "target_hidden_size"):
                fc_input_size = self.config.target_hidden_size * num_features_to_use
            else:
                fc_input_size = self.config.hidden_size * num_features_to_use
            self.fc = ReplicatedLinear(
                input_size=fc_input_size,
                output_size=self.config.hidden_size,
                bias=False,
                params_dtype=vllm_config.model_config.dtype,
                quant_config=self.quant_config,
                prefix=maybe_prefix(prefix, "fc"),
                return_bias=False,
            )
        self.hidden_norm = RMSNorm(
            self.config.hidden_size,
            eps=self.config.rms_norm_eps,
        )
        self.norm = RMSNorm(
            self.config.hidden_size,
            eps=self.config.rms_norm_eps,
        )

    def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
        embeds = self.embed_tokens(input_ids)
        if self.has_separate_mask_embedding and self.mask_token_id is not None:
            # Replace masked slots with the dedicated mask embedding.
            is_mask = (input_ids == self.mask_token_id).unsqueeze(-1)
            embeds = torch.where(is_mask, self.mask_embedding.to(embeds.dtype), embeds)
        return embeds

    def _build_context_kv_buffers(
        self,
        layers_attn: list[nn.Module],
        has_bias: bool,
    ) -> None:
        self._hidden_norm_weight = self.hidden_norm.weight.data

        # KV projection weights: [num_layers * 2 * kv_size, hidden_size]
        kv_weights = [a.qkv_proj.weight[a.q_size :] for a in layers_attn]
        self._fused_kv_weight = torch.cat(kv_weights, dim=0)
        if has_bias:
            kv_biases = [a.qkv_proj.bias[a.q_size :] for a in layers_attn]
            self._fused_kv_bias: torch.Tensor | None = torch.cat(kv_biases, dim=0)
        else:
            self._fused_kv_bias = None

        # K-norm weights stacked into one contiguous [num_layers, head_dim]
        # tensor so the per-layer K-norm runs as a single grouped kernel.
        self._k_norm_weights = torch.stack(
            [a.k_norm.weight.data for a in layers_attn], dim=0
        ).contiguous()

    def _build_fused_kv_buffers(self) -> None:
        """Build fused weight buffers for precompute_and_store_context_kv.

        Must be called after weights are loaded. Stacks the KV-projection
        weights, K-norm weights, and RoPE parameters from every attention
        layer so that precompute_and_store_context_kv can run one fused
        GEMM for all layers at once. Also aliases the weight of the hidden_norm.
        """
        layers_attn = [layer.self_attn for layer in self.layers]
        attn0 = layers_attn[0]
        has_bias = attn0.qkv_proj.bias is not None

        self._build_context_kv_buffers(layers_attn, has_bias)

        # RoPE parameters
        self._rope_head_size = attn0.rotary_emb.head_size
        self._rope_cos_sin_cache = attn0.rotary_emb.cos_sin_cache
        self._rope_is_neox = attn0.rotary_emb.is_neox_style
        # Validation that RoPE params are the same across all layers
        for attn in layers_attn[1:]:
            assert (
                attn.rotary_emb.head_size == self._rope_head_size
                and attn.rotary_emb.is_neox_style == self._rope_is_neox
            ), "All layers must have the same RoPE parameters for DFlash precomputation"

        # Layer metadata
        self._num_attn_layers = len(layers_attn)
        self._kv_size = attn0.kv_size
        self._head_dim = attn0.head_dim
        self._num_kv_heads = attn0.num_kv_heads
        self._rms_norm_eps = attn0.q_norm.variance_epsilon
        # Validation that all layers have the same attention config
        for attn in layers_attn[1:]:
            assert (
                attn.kv_size == self._kv_size
                and attn.head_dim == self._head_dim
                and attn.num_kv_heads == self._num_kv_heads
                and attn.q_norm.variance_epsilon == self._rms_norm_eps
            ), "All layers must have the same attn config for DFlash precomputation"

        # References to inner Attention layers for direct cache writes
        self._attn_layers = [layer.self_attn.attn for layer in self.layers]

    def _project_context_kv(
        self,
        context_states: torch.Tensor,
        num_ctx: int,
        num_layers: int,
        num_kv_heads: int,
        head_dim: int,
    ) -> tuple[torch.Tensor, torch.Tensor]:
        # --- Fused KV projection (one GEMM for all layers) ---
        normed_context_states = torch.empty_like(context_states)
        ops.rms_norm(
            normed_context_states,
            context_states,
            self._hidden_norm_weight,
            self._rms_norm_eps,
        )
        all_kv_flat = F.linear(
            normed_context_states, self._fused_kv_weight, self._fused_kv_bias
        )
        # Single contiguous copy that separates K/V and transposes to
        # layer-major layout.  Result: [2, L, num_ctx, nkv, hd] contiguous.
        # Indexing dim-0 gives contiguous [L, num_ctx, nkv, hd] for K and V.
        all_kv = (
            all_kv_flat.view(num_ctx, num_layers, 2, num_kv_heads, head_dim)
            .permute(2, 1, 0, 3, 4)
            .contiguous()
        )
        all_k = all_kv[0]  # [L, num_ctx, nkv, hd], contiguous
        all_v = all_kv[1]  # [L, num_ctx, nkv, hd], contiguous
        return all_k, all_v

    def _normalize_context_k(self, all_k: torch.Tensor) -> torch.Tensor:
        # --- Grouped RMSNorm K across all layers ([L, num_ctx, nkv, hd]) ---
        # The weight is selected per layer by the outermost (layer) index.
        all_k_normed = torch.empty_like(all_k)
        ops.rms_norm(
            all_k_normed,
            all_k,
            self._k_norm_weights,
            self._rms_norm_eps,
        )
        return all_k_normed

    def precompute_and_store_context_kv(
        self,
        context_states: torch.Tensor,
        context_positions: torch.Tensor,
        context_slot_mapping: torch.Tensor | list[torch.Tensor | None] | None = None,
    ) -> None:
        """Precompute K/V for context states write them into each layer's KV cache.

        Input context states are projected to K/V, normed, and have RoPE applied.
        Since the context shape is different than the query shape, we can't rely on the
        regular forward pass to apply torch.compile and CUDA graphs to this section.
        As such, this function is optimized to minimize the number of torch ops present:
        we use fused vLLM kernels for RMSNorm and RoPE, fuse the GEMM into one
        large projection, and avoid cloning buffers (with .contiguous()) where possible.

        When context_slot_mapping is None (e.g. during dummy_run) only
        the computation runs, and no K/V is written to cache.
        """
        if not hasattr(self, "_num_attn_layers"):
            logger.warning_once(
                "DFlash buffer initialization was skipped. If dummy weights are not "
                "in use, this may indicate an error in weight loading."
            )
            self._build_fused_kv_buffers()

        num_ctx = context_states.shape[0]
        L = self._num_attn_layers
        kv = self._kv_size
        hd = self._head_dim
        nkv = self._num_kv_heads

        all_k, all_v = self._project_context_kv(context_states, num_ctx, L, nkv, hd)
        all_k_normed = self._normalize_context_k(all_k)

        # --- Fused RoPE across all layers ---
        # View as [L * num_ctx, kv] so RoPE sees one big batch (no copy).
        # In-place RoPE: pass K as the "query" arg with key=None.
        all_k_flat = all_k_normed.view(L * num_ctx, kv)
        positions_repeated = context_positions.repeat(L)
        cos_sin_cache = self._rope_cos_sin_cache
        if cos_sin_cache.dtype != all_k_flat.dtype:
            cos_sin_cache = cos_sin_cache.to(dtype=all_k_flat.dtype)
        ops.rotary_embedding(
            positions_repeated,
            all_k_flat,
            None,
            self._rope_head_size,
            cos_sin_cache,
            self._rope_is_neox,
        )

        if context_slot_mapping is None:
            return

        # --- Per-layer cache insert ---
        all_k_final = all_k_flat.view(L, num_ctx, nkv, hd)
        per_layer = isinstance(context_slot_mapping, (list, tuple))
        for i in range(L):
            slot_mapping = (
                context_slot_mapping[i] if per_layer else context_slot_mapping
            )
            if slot_mapping is None:
                continue  # dummy run: skip cache ops
            attn = self._attn_layers[i]
            kv_cache = attn.kv_cache
            attn.impl.do_kv_cache_update(
                attn,
                all_k_final[i],
                all_v[i],
                kv_cache,
                slot_mapping,
            )

    def forward(
        self,
        input_ids: torch.Tensor,
        positions: torch.Tensor,
        input_embeds: torch.Tensor | None = None,
    ) -> torch.Tensor:
        if input_embeds is None:
            input_embeds = self.embed_input_ids(input_ids)

        hidden_states = input_embeds

        residual = None
        for layer in self.layers:
            hidden_states, residual = layer(
                positions=positions,
                hidden_states=hidden_states,
                residual=residual,
            )
        hidden_states, _ = self.norm(hidden_states, residual)
        return hidden_states

    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
        stacked_params_mapping = [
            (".qkv_proj", ".q_proj", "q"),
            (".qkv_proj", ".k_proj", "k"),
            (".qkv_proj", ".v_proj", "v"),
            (".gate_up_proj", ".gate_proj", 0),
            (".gate_up_proj", ".up_proj", 1),
        ]
        params_dict = dict(self.named_parameters())
        loaded_params: set[str] = set()
        tp_rank = get_tensor_model_parallel_rank()
        tp_size = get_tensor_model_parallel_world_size()
        for name, loaded_weight in weights:
            if "midlayer." in name:
                name = name.replace("midlayer.", "layers.0.")
            if "scale" in name:
                name = maybe_remap_kv_scale_name(name, params_dict)
                if name is None:
                    continue
            if "attention_sink_bias" in name:
                if name not in params_dict:
                    continue
                # Sink bias is per-head; shard it across TP ranks like the
                # attention heads themselves.
                param = params_dict[name]
                heads_per_rank = loaded_weight.shape[0] // tp_size
                head_start = tp_rank * heads_per_rank
                narrow_weight = loaded_weight.narrow(0, head_start, heads_per_rank)
                param.data.copy_(narrow_weight)
                loaded_params.add(name)
                continue
            for param_name, weight_name, shard_id in stacked_params_mapping:
                if weight_name not in name:
                    continue
                name = name.replace(weight_name, param_name)
                param = params_dict[name]
                weight_loader = param.weight_loader
                weight_loader(param, loaded_weight, shard_id)
                break
            else:
                param = params_dict[name]
                weight_loader = getattr(param, "weight_loader", default_weight_loader)
                weight_loader(param, loaded_weight)
            loaded_params.add(name)
        return loaded_params