Add arcee model (#21296)

Signed-off-by: alyosha-swamy <raghav@arcee.ai>
Signed-off-by: Jee Jee Li <pandaleefree@gmail.com>
Co-authored-by: Jee Jee Li <pandaleefree@gmail.com>

docs/models/supported_models.md

@@ -324,6 +324,7 @@ th {
 | 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. | ✅︎ | ✅︎ | ✅︎ |

tests/models/registry.py

@@ -135,6 +135,8 @@ _TEXT_GENERATION_EXAMPLE_MODELS = {
                                    trust_remote_code=True),
     "AquilaForCausalLM": _HfExamplesInfo("BAAI/AquilaChat2-7B",
                                          trust_remote_code=True),
+    "ArceeForCausalLM": _HfExamplesInfo("arcee-ai/AFM-4.5B-Base",
+                                        is_available_online=False),
     "ArcticForCausalLM": _HfExamplesInfo("Snowflake/snowflake-arctic-instruct",
                                          trust_remote_code=True),
     "BaiChuanForCausalLM": _HfExamplesInfo("baichuan-inc/Baichuan-7B",

vllm/model_executor/models/arcee.py

+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+# 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.
+
+from collections.abc import Iterable
+from typing import Any, Optional, Union
+
+import torch
+from torch import nn
+from transformers import LlamaConfig
+
+from vllm.compilation.decorators import support_torch_compile
+from vllm.distributed import get_pp_group
+from vllm.model_executor.layers.activation import ReLUSquaredActivation
+from vllm.model_executor.layers.layernorm import RMSNorm
+from vllm.model_executor.layers.linear import (ColumnParallelLinear,
+                                               RowParallelLinear)
+from vllm.model_executor.layers.logits_processor import LogitsProcessor
+from vllm.model_executor.layers.vocab_parallel_embedding import (
+    DEFAULT_VOCAB_PADDING_SIZE, ParallelLMHead, VocabParallelEmbedding)
+from vllm.sequence import IntermediateTensors
+
+from .interfaces import SupportsLoRA, SupportsPP
+from .utils import (AutoWeightsLoader, PPMissingLayer,
+                    make_empty_intermediate_tensors_factory, make_layers)
+
+
+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 = ReLUSquaredActivation()
+
+    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
+        self.quant_config = quant_config
+        self.config = config
+        self.vocab_size = config.vocab_size
+        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)
+        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
+
+
+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"],
+    }
+
+    def __init__(self, *, vllm_config, prefix: str = "") -> None:
+        super().__init__()
+        config = vllm_config.model_config.hf_config
+        self.config = 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
+
+            self.lm_head = ParallelLMHead(
+                self.unpadded_vocab_size,
+                config.hidden_size,
+                org_num_embeddings=config.vocab_size,
+                padding_size=DEFAULT_VOCAB_PADDING_SIZE,
+                quant_config=vllm_config.quant_config,
+                bias=getattr(config, "lm_head_bias", False),
+                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)."""
+        loader = AutoWeightsLoader(
+            self,
+            skip_prefixes=(["lm_head."]
+                           if self.config.tie_word_embeddings else None),
+            skip_substrs=["gate_proj"])
+        # AutoWeightLoader handles weight name remapping, including fusing
+        # separate q_proj, k_proj, v_proj into qkv_proj
+        return loader.load_weights(weights)

vllm/model_executor/models/registry.py

@@ -33,6 +33,7 @@ _TEXT_GENERATION_MODELS = {
     # [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"),