[ROCm] Restore Inkling MTP backend parity

vllm/models/inkling/amd/mtp.py

@@ -2,9 +2,13 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 """Inkling MTP (Multi-Token Prediction) draft model (NVIDIA).
 
-Implements the first MTP depth from the reference ``mtp_model.py`` shipped with
-the checkpoint. It owns ``hidden_norm`` / ``embed_norm`` RMSNorms, a ``2H -> H``
-input projection, and a full Inkling transformer block with a dense bf16 MLP.
+Mirrors the reference ``mtp_model.py`` shipped with the checkpoint: each MTP
+depth ``i`` owns ``hidden_norm`` / ``embed_norm`` RMSNorms, an ``input_proj``
+(``2H -> H``) and a full Inkling transformer block (dense bf16 MLP, with the
+same short convolutions as the backbone; its attention is full or sliding-window
+per depth, selected by ``mtp_config.local_layer_ids``). When enabled, a shared
+``chain_norm`` is applied after every depth; its output is both the logits input
+and the previous hidden state fed to the next depth.
 
 The draft shares the target's token embedding table and LM head
 (``load_eagle_model`` wires those references) and applies the backbone
@@ -54,6 +58,16 @@ def _mtp_depth_from_name(name: str) -> int | None:
     return int(m.group(1)) if m else None
 
 
+def _select_mtp_depth_count(n_predict: int, num_spec: int | None) -> int:
+    num_layers = min(n_predict, num_spec) if num_spec else n_predict
+    if num_layers <= 0:
+        raise ValueError(
+            "Inkling MTP requires num_nextn_predict_layers and "
+            "num_speculative_tokens to select at least one depth layer."
+        )
+    return num_layers
+
+
 class InklingMTPDepthLayer(nn.Module):
     """One MTP depth: norm both inputs, fuse (2H->H), run a Inkling block."""
 
@@ -97,14 +111,30 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = "") -> None:
             vllm_config.speculative_config.draft_model_config.hf_config
         )
         self.config = config
-        if vllm_config.speculative_config.num_speculative_tokens != 1:
-            raise ValueError(
-                "Inkling MTP currently supports exactly one speculative token"
-            )
+        # The checkpoint ships num_nextn_predict_layers depth blocks, but only
+        # the first ``num_speculative_tokens`` are exercised (step i uses depth
+        # i). Build only those to save memory — each depth is a full Inkling block
+        # with its own (large) full-history sconv caches and KV cache.
+        n_predict = config.num_nextn_predict_layers
+        num_spec = vllm_config.speculative_config.num_speculative_tokens
+        self.num_mtp_layers = _select_mtp_depth_count(n_predict, num_spec)
         self.chain_hidden_post_norm = config.chain_hidden_post_norm
+
+        # Depth blocks whose attention is sliding-window (swa_* head config)
+        # rather than full; keyed by MTP depth via the checkpoint's
+        # mtp_config.local_layer_ids (promoted onto the draft config). Mirrors
+        # InklingModel's local_ids split, but over MTP depths, not backbone
+        # layers.
         local_ids = set(config.local_layer_ids)
+
+        # Keyed by depth index (str) to mirror the checkpoint layout.
         self.layers = nn.ModuleDict(
-            {"0": InklingMTPDepthLayer(config, f"{prefix}.layers.0", 0 in local_ids)}
+            {
+                str(idx): InklingMTPDepthLayer(
+                    config, f"{prefix}.layers.{idx}", idx in local_ids
+                )
+                for idx in range(self.num_mtp_layers)
+            }
         )
         self.chain_norm = (
             InklingRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
@@ -202,9 +232,8 @@ def forward(
         # auto-enumerated as a draft attention layer); its per-token metadata is
         # built by the speculator's build_attn_metadata and read from the
         # forward context, so nothing extra is threaded here.
-        if spec_step_idx != 0:
-            raise ValueError("Inkling MTP only supports spec_step_idx=0")
-        layer = self.layers["0"]
+        depth = spec_step_idx % self.num_mtp_layers
+        layer = self.layers[str(depth)]
         combined = self.fused_input_cat(
             layer, previous_hidden_states, input_ids, inputs_embeds
         )
@@ -356,8 +385,8 @@ def _load(name: str, weight: torch.Tensor, shard_id: object = None) -> bool:
         # Only consume the MTP weights; everything else belongs to the target.
         if ".mtp." not in name:
             continue
-        # Only the first checkpoint depth is used for MTP=1.
-        if depth is not None and depth != 0:
+        # Skip depth blocks beyond the ones we built (num_speculative_tokens).
+        if depth is not None and depth >= module.model.num_mtp_layers:
             continue
         # model.mtp.chain_norm.weight -> model.chain_norm.weight
         # model.mtp.layers.{i}.X      -> model.layers.{i}.X