Add Arcee (AFM) model support to vLLM

docs/models/supported_models.md

@@ -317,6 +317,7 @@ Specified using `--task generate`.
 | Architecture | Models | Example HF Models | [LoRA](../features/lora.md) | [PP](../serving/distributed_serving.md) | [V1](gh-issue:8779) |
 |--------------|--------|-------------------|----------------------|---------------------------|---------------------|
 | `AquilaForCausalLM` | Aquila, Aquila2 | `BAAI/Aquila-7B`, `BAAI/AquilaChat-7B`, etc. | ✅︎ | ✅︎ | ✅︎ |
+| `ArceeForCausalLM` | Arcee (AFM) | `arcee-ai/AFM-4.5B-Base`, etc. | ✅︎ | ✅︎ | ✅︎ |
 | `ArcticForCausalLM` | Arctic | `Snowflake/snowflake-arctic-base`, `Snowflake/snowflake-arctic-instruct`, etc. | | ✅︎ | ✅︎ |
 | `BaiChuanForCausalLM` | Baichuan2, Baichuan | `baichuan-inc/Baichuan2-13B-Chat`, `baichuan-inc/Baichuan-7B`, etc. | ✅︎ | ✅︎ | ✅︎ |
 | `BailingMoeForCausalLM` | Ling | `inclusionAI/Ling-lite-1.5`, `inclusionAI/Ling-plus`, etc. | ✅︎ | ✅︎ | ✅︎ |

vllm/model_executor/models/arcee.py

@@ -0,0 +1,358 @@
+# Copyright 2023-2025 vLLM Team
+# Licensed under the Apache License, Version 2.0 (the "License");
+# You may not use this file except in compliance with the License.
+# You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
+#
+# Inference-only Arcee (AFM) model – adds support for ReLU^2 feed-forward activation.
+
+import logging
+from typing import Any, Dict, Iterable, List, Optional, Tuple, Union
+
+import torch
+from torch import nn
+from transformers import LlamaConfig  # Reusing HuggingFace LLaMA config for Arcee
+
+from vllm.distributed import get_pp_group, get_tensor_model_parallel_world_size
+from vllm.model_executor.layers.linear import ColumnParallelLinear, RowParallelLinear, QKVParallelLinear
+from vllm.model_executor.layers.layernorm import RMSNorm
+from vllm.model_executor.layers.logits_processor import LogitsProcessor
+from vllm.model_executor.layers.vocab_parallel_embedding import VocabParallelEmbedding, ParallelLMHead
+from vllm.model_executor.models.utils import PPMissingLayer, make_layers
+from vllm.sequence import IntermediateTensors
+from vllm.model_executor.models.interfaces import SupportsLoRA, SupportsPP
+from vllm.compilation.decorators import support_torch_compile
+
+class ArceeMLP(nn.Module):
+    """Feed-forward layer for Arcee using ReLU^2 activation (no gating as in LLaMA)."""
+    def __init__(self,
+                 hidden_size: int,
+                 intermediate_size: int,
+                 hidden_act: str,
+                 quant_config: Optional[Any] = None,
+                 bias: bool = False,
+                 prefix: str = "",
+                 reduce_results: bool = True) -> None:
+        super().__init__()
+        # Single linear projection up to intermediate size (no separate gate projection)
+        self.up_proj = ColumnParallelLinear(
+            input_size=hidden_size,
+            output_size=intermediate_size,
+            bias=bias,
+            quant_config=quant_config,
+            prefix=f"{prefix}.up_proj",
+        )
+        # Down projection back to hidden size
+        self.down_proj = RowParallelLinear(
+            input_size=intermediate_size,
+            output_size=hidden_size,
+            bias=bias,
+            quant_config=quant_config,
+            reduce_results=reduce_results,
+            prefix=f"{prefix}.down_proj",
+        )
+        if hidden_act != "relu2":
+            raise ValueError(f"Unsupported activation: {hidden_act}. Only 'relu2' is supported for AFM.")
+        # Define ReLU^2 activation: (ReLU(x))^2 elementwise
+        self.act_fn = lambda x: torch.pow(torch.relu(x), 2)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x, _ = self.up_proj(x)        # Project to intermediate size
+        x = self.act_fn(x)           # Apply ReLU^2 activation elementwise
+        x, _ = self.down_proj(x)     # Project back down to hidden size
+        return x
+
+class ArceeDecoderLayer(nn.Module):
+    """Transformer decoder block for Arcee, with self-attention and ReLU^2 MLP."""
+    def __init__(self,
+                 config: LlamaConfig,
+                 cache_config: Optional[Any] = None,
+                 quant_config: Optional[Any] = None,
+                 prefix: str = "") -> None:
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        # Rotary embedding parameters (reuse LLaMA defaults)
+        rope_theta = getattr(config, "rope_theta", 10000)
+        rope_scaling = getattr(config, "rope_scaling", None)
+        if rope_scaling is not None and getattr(config, "original_max_position_embeddings", None):
+            rope_scaling["original_max_position_embeddings"] = config.original_max_position_embeddings
+        max_position_embeddings = getattr(config, "max_position_embeddings", 8192)
+        # Determine if attention bias is needed (some variants use bias terms)
+        attention_bias = getattr(config, "attention_bias", False) or getattr(config, "bias", False)
+        bias_o_proj = attention_bias
+        if hasattr(config, "qkv_bias"):
+            attention_bias = config.qkv_bias
+
+        # Self-Attention (using LLaMA’s attention structure)
+        from vllm.model_executor.models.llama import LlamaAttention  # import here to avoid circular import
+        self.self_attn = LlamaAttention(
+            config=config,
+            hidden_size=self.hidden_size,
+            num_heads=config.num_attention_heads,
+            num_kv_heads=getattr(config, "num_key_value_heads", config.num_attention_heads),
+            rope_theta=rope_theta,
+            rope_scaling=rope_scaling,
+            max_position_embeddings=max_position_embeddings,
+            quant_config=quant_config,
+            bias=attention_bias,
+            bias_o_proj=bias_o_proj,
+            cache_config=cache_config,
+            prefix=f"{prefix}.self_attn",
+            attn_type=getattr(config, "attn_type", "decoder"),  # assume decoder (causal) unless specified
+        )
+        # MLP with ReLU^2 activation
+        self.mlp = ArceeMLP(
+            hidden_size=self.hidden_size,
+            intermediate_size=config.intermediate_size,
+            hidden_act=config.hidden_act,
+            quant_config=quant_config,
+            bias=getattr(config, "mlp_bias", False),
+            prefix=f"{prefix}.mlp",
+        )
+        # Layer normalization layers (RMSNorm as in LLaMA)
+        self.input_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.post_attention_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+    def forward(self,
+                positions: torch.Tensor,
+                hidden_states: torch.Tensor,
+                residual: Optional[torch.Tensor]) -> Tuple[torch.Tensor, torch.Tensor]:
+        # Self-Attention block
+        if residual is None:
+            residual = hidden_states
+            hidden_states = self.input_layernorm(hidden_states)
+        else:
+            # Fused residual add + layernorm if supported
+            hidden_states, residual = self.input_layernorm(hidden_states, residual)
+        hidden_states = self.self_attn(positions=positions, hidden_states=hidden_states)
+        # Feed-forward block
+        hidden_states, residual = self.post_attention_layernorm(hidden_states, residual)
+        hidden_states = self.mlp(hidden_states)
+        return hidden_states, residual
+
+@support_torch_compile
+class ArceeModel(nn.Module):
+    """The transformer model backbone for Arcee (embedding layer + stacked decoder blocks + final norm)."""
+    def __init__(self, *, vllm_config, prefix: str = "", layer_type: type[nn.Module] = ArceeDecoderLayer) -> None:
+        super().__init__()
+        config: LlamaConfig = vllm_config.model_config.hf_config
+        cache_config = vllm_config.cache_config
+        quant_config = vllm_config.quant_config
+        lora_config = vllm_config.lora_config
+
+        self.config = config
+        # Calculate vocab size (include LoRA additional vocab if any)
+        lora_vocab_extra = (lora_config.lora_extra_vocab_size * (lora_config.max_loras or 1)) if lora_config else 0
+        self.vocab_size = config.vocab_size + lora_vocab_extra
+        self.org_vocab_size = config.vocab_size
+
+        # Word embeddings (parallelized if using pipeline parallel)
+        if get_pp_group().is_first_rank or (config.tie_word_embeddings and get_pp_group().is_last_rank):
+            self.embed_tokens = VocabParallelEmbedding(
+                self.vocab_size, 
+                config.hidden_size, 
+                org_num_embeddings=config.vocab_size,
+                quant_config=quant_config,
+            )
+        else:
+            self.embed_tokens = PPMissingLayer()  # placeholder on non-embedding ranks
+
+        # Build decoder layers across pipeline ranks
+        self.start_layer, self.end_layer, self.layers = make_layers(
+            config.num_hidden_layers,
+            lambda prefix: layer_type(config=config, cache_config=cache_config, quant_config=quant_config, prefix=prefix),
+            prefix=f"{prefix}.layers",
+        )
+        # Final RMSNorm on the last pipeline stage
+        if get_pp_group().is_last_rank:
+            self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        else:
+            self.norm = PPMissingLayer()
+
+        # For optional capturing of intermediate hidden states (not used by default)
+        self.aux_hidden_state_layers: Tuple[int, ...] = tuple()
+
+        # Prepare factory for empty intermediate tensors (for pipeline scheduling)
+        from vllm.model_executor.models.utils import make_empty_intermediate_tensors_factory
+        self.make_empty_intermediate_tensors = make_empty_intermediate_tensors_factory(
+            ["hidden_states", "residual"], config.hidden_size
+        )
+
+    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.embed_tokens(input_ids)
+
+    def forward(self,
+                input_ids: Optional[torch.Tensor],
+                positions: torch.Tensor,
+                intermediate_tensors: Optional[IntermediateTensors],
+                inputs_embeds: Optional[torch.Tensor] = None
+               ) -> Union[torch.Tensor, IntermediateTensors, Tuple[torch.Tensor, List[torch.Tensor]]]:
+        # Embedding lookup (on first pipeline rank)
+        if get_pp_group().is_first_rank:
+            hidden_states = inputs_embeds if inputs_embeds is not None else self.get_input_embeddings(input_ids)
+            residual = None
+        else:
+            assert intermediate_tensors is not None, "IntermediateTensors must be provided for non-first pipeline ranks"
+            hidden_states = intermediate_tensors["hidden_states"]
+            residual = intermediate_tensors["residual"]
+
+        aux_hidden_states: List[torch.Tensor] = []
+        for idx, layer in enumerate(self.layers[self.start_layer:self.end_layer]):
+            if idx in self.aux_hidden_state_layers:
+                aux_hidden_states.append(hidden_states + residual)  # capture pre-layer hidden state if needed
+            hidden_states, residual = layer(positions, hidden_states, residual)
+
+        if not get_pp_group().is_last_rank:
+            # Send intermediate results to the next pipeline stage
+            return IntermediateTensors({"hidden_states": hidden_states, "residual": residual})
+        # On last rank: apply final layer norm
+        hidden_states, _ = self.norm(hidden_states, residual)
+        if len(aux_hidden_states) > 0:
+            return hidden_states, aux_hidden_states
+        return hidden_states
+
+    def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]) -> set[str]:
+        """Load pre-trained weights from HuggingFace format into the model."""
+        # Mapping for merging or renaming weight parameters from HF into our model
+        stacked_params_mapping = [
+            # Each tuple: (combined_param_name, hf_subparam_name, index_or_key)
+            (".qkv_proj", ".q_proj", "q"),
+            (".qkv_proj", ".k_proj", "k"),
+            (".qkv_proj", ".v_proj", "v"),
+            # Note: No gate_proj since AFM has no gated MLP
+        ]
+        params_dict = dict(self.named_parameters())
+        loaded_params: set[str] = set()
+        for name, loaded_weight in weights:
+            # Skip rotary cache parameters if present (not actual model weights)
+            if "rotary_emb.inv_freq" in name or "rotary_emb.cos_cached" in name or "rotary_emb.sin_cached" in name:
+                continue
+            # Handle quantization KV cache scales if present
+            if hasattr(self, "quant_config") and self.quant_config is not None:
+                # If name corresponds to a quantization scale parameter, remap and load it
+                from vllm.model_executor.model_loader.weight_utils import default_weight_loader, maybe_remap_kv_scale_name
+                if "scale" in name:
+                    maybe_name = maybe_remap_kv_scale_name(name, params_dict)
+                    if maybe_name is None:
+                        continue
+                    name = maybe_name
+            # Pipeline parallel: skip parameters not on this rank
+            from vllm.model_executor.models.utils import is_pp_missing_parameter
+            from vllm.model_executor.model_loader.weight_utils import default_weight_loader
+            if is_pp_missing_parameter(name, self):
+                continue
+
+            # Attempt to map and load merged parameters
+            for param_name, weight_name, shard_id in stacked_params_mapping:
+                if weight_name not in name:
+                    continue
+                mapped_name = name.replace(weight_name, param_name)
+                if mapped_name.endswith(".bias") and mapped_name not in params_dict:
+                    # Skip any unexpected biases (e.g., from certain quantization or GPTQ checkpoints)
+                    break
+                if mapped_name in params_dict:
+                    param = params_dict[mapped_name]
+                    weight_loader = getattr(param, "weight_loader", default_weight_loader)
+                    weight_loader(param, loaded_weight, shard_id)  # load the shard into the combined param
+                    loaded_params.add(mapped_name)
+                else:
+                    logging.warning(f"Unexpected parameter in checkpoint: {name}")
+                break
+            else:
+                # No special mapping, try direct load
+                if name in params_dict:
+                    # For tied embeddings, skip loading lm_head if it will be tied
+                    if name.startswith("lm_head.") and getattr(self.config, "tie_word_embeddings", False):
+                        continue
+                    param = params_dict[name]
+                    weight_loader = getattr(param, "weight_loader", default_weight_loader)
+                    weight_loader(param, loaded_weight)
+                    loaded_params.add(name)
+                else:
+                    # Silently skip any unmatched parameters (e.g., vision tower weights in multimodal models)
+                    logging.debug(f"Ignoring unmatched checkpoint parameter: {name}")
+        return loaded_params
+
+class ArceeForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
+    """Arcee Model for causal language modeling, integrated with vLLM runtime."""
+    # Map fused module names to their sub-module components (for quantization and LoRA)
+    packed_modules_mapping = {
+        "qkv_proj": ["q_proj", "k_proj", "v_proj"]
+        # Note: no "gate_up_proj" entry, since AFM MLP does not use gate/up split
+    }
+    # For LoRA, identify embedding modules (for padding/tied weights handling)
+    embedding_modules = {
+        "embed_tokens": "input_embeddings",
+        "lm_head": "output_embeddings",
+    }
+    embedding_padding_modules = ["lm_head"]
+
+    def __init__(self, *, vllm_config, prefix: str = "") -> None:
+        super().__init__()
+        config = vllm_config.model_config.hf_config
+        quant_config = vllm_config.quant_config
+        lora_config = vllm_config.lora_config
+        self.config = config
+        self.lora_config = lora_config
+
+        # Initialize the inner Transformer model (ArceeModel)
+        self.model = ArceeModel(vllm_config=vllm_config, prefix=f"{prefix}model")
+        # On the last pipeline stage, set up the LM head and logits processor
+        if get_pp_group().is_last_rank:
+            # Determine vocabulary size (including any LoRA extra tokens for padded LM head)
+            self.unpadded_vocab_size = config.vocab_size
+            if lora_config:
+                self.unpadded_vocab_size += lora_config.lora_extra_vocab_size
+
+            # Import DEFAULT_VOCAB_PADDING_SIZE
+            from vllm.model_executor.layers.vocab_parallel_embedding import DEFAULT_VOCAB_PADDING_SIZE
+
+            self.lm_head = ParallelLMHead(
+                self.unpadded_vocab_size,
+                config.hidden_size,
+                org_num_embeddings=config.vocab_size,
+                padding_size=(
+                    DEFAULT_VOCAB_PADDING_SIZE
+                    # We need bigger padding if using lora for kernel
+                    # compatibility
+                    if not lora_config else
+                    lora_config.lora_vocab_padding_size),
+                quant_config=quant_config,
+                prefix=f"{prefix}lm_head",
+            )
+            if config.tie_word_embeddings:
+                # Tie output weights with input embedding matrix
+                self.lm_head = self.lm_head.tie_weights(self.model.embed_tokens)
+            logit_scale = getattr(config, "logit_scale", 1.0)
+            self.logits_processor = LogitsProcessor(self.unpadded_vocab_size, config.vocab_size, logit_scale)
+        else:
+            # Placeholder for lm_head on non-last ranks
+            self.lm_head = PPMissingLayer()
+        # Provide a reference to the model’s method for generating empty tensors (used in pipeline parallel schedule)
+        self.make_empty_intermediate_tensors = self.model.make_empty_intermediate_tensors
+
+    def forward(self,
+                input_ids: torch.Tensor,
+                positions: torch.Tensor,
+                intermediate_tensors: Optional[IntermediateTensors] = None,
+                inputs_embeds: Optional[torch.Tensor] = None) -> Union[torch.Tensor, IntermediateTensors]:
+        # Forward pass through the Arcee model backbone
+        model_output = self.model(input_ids=input_ids, positions=positions, intermediate_tensors=intermediate_tensors, inputs_embeds=inputs_embeds)
+        return model_output
+
+    def compute_logits(self, hidden_states: torch.Tensor, sampling_metadata) -> Optional[torch.Tensor]:
+        # Compute final logits from hidden states (last pipeline rank only)
+        logits = self.logits_processor(self.lm_head, hidden_states, sampling_metadata)
+        return logits
+
+    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.model.get_input_embeddings(input_ids)
+
+    def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]) -> set[str]:
+        """Load weights into the model (delegates to inner model and handles tied embeddings)."""
+        # Use AutoWeightsLoader for consistency with vLLM's loading mechanism
+        from vllm.model_executor.models.utils import AutoWeightsLoader
+        loader = AutoWeightsLoader(
+            self, skip_prefixes=(["lm_head."] if self.config.tie_word_embeddings else None)
+        )
+        # No special weight name remapping needed (AFM uses standard LLaMA naming except no gate_proj)
+        return loader.load_weights(weights)

vllm/model_executor/models/registry.py

@@ -33,6 +33,7 @@
     # [Decoder-only]
     "AquilaModel": ("llama", "LlamaForCausalLM"),
     "AquilaForCausalLM": ("llama", "LlamaForCausalLM"),  # AquilaChat2
+    "ArceeForCausalLM": ("arcee", "ArceeForCausalLM"),
     "ArcticForCausalLM": ("arctic", "ArcticForCausalLM"),
     "MiniMaxForCausalLM": ("minimax_text_01", "MiniMaxText01ForCausalLM"),
     "MiniMaxText01ForCausalLM": ("minimax_text_01", "MiniMaxText01ForCausalLM"),