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429 lines
16 KiB
Python
429 lines
16 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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# Copyright 2023-2025 vLLM Team
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# Licensed under the Apache License, Version 2.0 (the "License");
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# You may not use this file except in compliance with the License.
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# You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
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#
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# Inference-only Arcee (AFM) model – adds support for ReLU^2 feed-forward
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# activation.
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from collections.abc import Iterable
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from itertools import islice
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from typing import Any
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import torch
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from torch import nn
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from transformers import LlamaConfig
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from vllm.compilation.decorators import support_torch_compile
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from vllm.distributed import get_pp_group
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from vllm.model_executor.layers.activation import ReLUSquaredActivation
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from vllm.model_executor.layers.layernorm import RMSNorm
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from vllm.model_executor.layers.linear import ColumnParallelLinear, RowParallelLinear
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from vllm.model_executor.layers.logits_processor import LogitsProcessor
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from vllm.model_executor.layers.vocab_parallel_embedding import (
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ParallelLMHead,
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VocabParallelEmbedding,
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)
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from vllm.model_executor.model_loader.weight_utils import (
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default_weight_loader,
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maybe_remap_kv_scale_name,
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)
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from vllm.sequence import IntermediateTensors
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from .interfaces import SupportsLoRA, SupportsPP
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from .utils import (
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AutoWeightsLoader,
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PPMissingLayer,
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is_pp_missing_parameter,
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make_empty_intermediate_tensors_factory,
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make_layers,
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)
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class ArceeMLP(nn.Module):
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"""Feed-forward layer for Arcee using ReLU^2 activation
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(no gating as in LLaMA)."""
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def __init__(
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self,
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hidden_size: int,
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intermediate_size: int,
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hidden_act: str,
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quant_config: Any | None = None,
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bias: bool = False,
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prefix: str = "",
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reduce_results: bool = True,
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) -> None:
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super().__init__()
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# Single linear projection up to intermediate size
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# (no separate gate projection)
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self.up_proj = ColumnParallelLinear(
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input_size=hidden_size,
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output_size=intermediate_size,
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bias=bias,
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quant_config=quant_config,
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prefix=f"{prefix}.up_proj",
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)
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# Down projection back to hidden size
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self.down_proj = RowParallelLinear(
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input_size=intermediate_size,
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output_size=hidden_size,
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bias=bias,
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quant_config=quant_config,
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reduce_results=reduce_results,
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prefix=f"{prefix}.down_proj",
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)
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if hidden_act != "relu2":
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raise ValueError(
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f"Unsupported activation: {hidden_act}. "
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"Only 'relu2' is supported for AFM."
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)
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# Define ReLU^2 activation: (ReLU(x))^2 elementwise
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self.act_fn = ReLUSquaredActivation()
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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x, _ = self.up_proj(x) # Project to intermediate size
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x = self.act_fn(x) # Apply ReLU^2 activation elementwise
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x, _ = self.down_proj(x) # Project back down to hidden size
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return x
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class ArceeDecoderLayer(nn.Module):
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"""Transformer decoder block for Arcee, with self-attention and
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ReLU^2 MLP."""
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def __init__(
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self,
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config: LlamaConfig,
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cache_config: Any | None = None,
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quant_config: Any | None = None,
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prefix: str = "",
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) -> None:
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super().__init__()
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self.hidden_size = config.hidden_size
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max_position_embeddings = getattr(config, "max_position_embeddings", 8192)
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# Determine if attention bias is needed (some variants use bias terms)
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attention_bias = getattr(config, "attention_bias", False) or getattr(
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config, "bias", False
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)
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bias_o_proj = attention_bias
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if hasattr(config, "qkv_bias"):
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attention_bias = config.qkv_bias
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# Self-Attention (using LLaMA's attention structure)
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from vllm.model_executor.models.llama import (
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LlamaAttention, # import here to avoid circular import
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)
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self.self_attn = LlamaAttention(
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config=config,
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hidden_size=self.hidden_size,
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num_heads=config.num_attention_heads,
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num_kv_heads=getattr(
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config, "num_key_value_heads", config.num_attention_heads
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),
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max_position_embeddings=max_position_embeddings,
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quant_config=quant_config,
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bias=attention_bias,
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bias_o_proj=bias_o_proj,
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cache_config=cache_config,
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prefix=f"{prefix}.self_attn",
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attn_type=getattr(
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config, "attn_type", "decoder"
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), # assume decoder (causal) unless specified
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)
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# MLP with ReLU^2 activation
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self.mlp = ArceeMLP(
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hidden_size=self.hidden_size,
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intermediate_size=config.intermediate_size,
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hidden_act=config.hidden_act,
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quant_config=quant_config,
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bias=getattr(config, "mlp_bias", False),
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prefix=f"{prefix}.mlp",
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)
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# Layer normalization layers (RMSNorm as in LLaMA)
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self.input_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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self.post_attention_layernorm = RMSNorm(
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config.hidden_size, eps=config.rms_norm_eps
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)
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def forward(
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self,
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positions: torch.Tensor,
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hidden_states: torch.Tensor,
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residual: torch.Tensor | None,
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) -> tuple[torch.Tensor, torch.Tensor]:
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# Self-Attention block
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if residual is None:
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residual = hidden_states
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hidden_states = self.input_layernorm(hidden_states)
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else:
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# Fused residual add + layernorm if supported
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hidden_states, residual = self.input_layernorm(hidden_states, residual)
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hidden_states = self.self_attn(positions=positions, hidden_states=hidden_states)
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# Feed-forward block
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hidden_states, residual = self.post_attention_layernorm(hidden_states, residual)
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hidden_states = self.mlp(hidden_states)
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return hidden_states, residual
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@support_torch_compile
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class ArceeModel(nn.Module):
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"""The transformer model backbone for Arcee (embedding layer + stacked
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decoder blocks + final norm)."""
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def __init__(
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self,
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*,
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vllm_config,
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prefix: str = "",
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layer_type: type[nn.Module] = ArceeDecoderLayer,
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) -> None:
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super().__init__()
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config: LlamaConfig = vllm_config.model_config.hf_config
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cache_config = vllm_config.cache_config
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quant_config = vllm_config.quant_config
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self.quant_config = quant_config
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self.config = config
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self.vocab_size = config.vocab_size
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# Word embeddings (parallelized if using pipeline parallel)
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if get_pp_group().is_first_rank or (
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config.tie_word_embeddings and get_pp_group().is_last_rank
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):
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self.embed_tokens = VocabParallelEmbedding(
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self.vocab_size,
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config.hidden_size,
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quant_config=quant_config,
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)
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else:
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self.embed_tokens = PPMissingLayer() # placeholder on non-embedding ranks
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# Build decoder layers across pipeline ranks
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self.start_layer, self.end_layer, self.layers = make_layers(
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config.num_hidden_layers,
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lambda prefix: layer_type(
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config=config,
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cache_config=cache_config,
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quant_config=quant_config,
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prefix=prefix,
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),
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prefix=f"{prefix}.layers",
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)
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# Final RMSNorm on the last pipeline stage
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if get_pp_group().is_last_rank:
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self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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else:
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self.norm = PPMissingLayer()
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# For optional capturing of intermediate hidden states
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# (not used by default)
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self.aux_hidden_state_layers: tuple[int, ...] = tuple()
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# Prepare factory for empty intermediate tensors
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# (for pipeline scheduling)
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self.make_empty_intermediate_tensors = make_empty_intermediate_tensors_factory(
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["hidden_states", "residual"], config.hidden_size
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)
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def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
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return self.embed_tokens(input_ids)
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def forward(
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self,
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input_ids: torch.Tensor | None,
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positions: torch.Tensor,
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intermediate_tensors: IntermediateTensors | None,
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inputs_embeds: torch.Tensor | None = None,
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) -> torch.Tensor | IntermediateTensors | tuple[torch.Tensor, list[torch.Tensor]]:
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# Embedding lookup (on first pipeline rank)
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if get_pp_group().is_first_rank:
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hidden_states = (
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inputs_embeds
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if inputs_embeds is not None
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else self.embed_input_ids(input_ids)
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)
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residual = None
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else:
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assert intermediate_tensors is not None, (
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"IntermediateTensors must be provided for non-first pipeline ranks"
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)
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hidden_states = intermediate_tensors["hidden_states"]
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residual = intermediate_tensors["residual"]
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aux_hidden_states: list[torch.Tensor] = []
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for idx, layer in enumerate(
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islice(self.layers, self.start_layer, self.end_layer)
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):
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if idx in self.aux_hidden_state_layers:
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aux_hidden_states.append(
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hidden_states + residual
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) # capture pre-layer hidden state if needed
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hidden_states, residual = layer(positions, hidden_states, residual)
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if not get_pp_group().is_last_rank:
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# Send intermediate results to the next pipeline stage
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return IntermediateTensors(
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{"hidden_states": hidden_states, "residual": residual}
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)
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# On last rank: apply final layer norm
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hidden_states, _ = self.norm(hidden_states, residual)
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if len(aux_hidden_states) > 0:
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return hidden_states, aux_hidden_states
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return hidden_states
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def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
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"""Load weights, mapping q/k/v projections to fused qkv_proj."""
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stacked_params_mapping = [
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(".qkv_proj", ".q_proj", "q"),
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(".qkv_proj", ".k_proj", "k"),
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(".qkv_proj", ".v_proj", "v"),
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]
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params_dict = dict(self.named_parameters())
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loaded_params: set[str] = set()
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for name, loaded_weight in weights:
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if "rotary_emb.inv_freq" in name:
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continue
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if "rotary_emb.cos_cached" in name or "rotary_emb.sin_cached" in name:
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continue
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if self.quant_config is not None and (
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scale_name := self.quant_config.get_cache_scale(name)
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):
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param = params_dict[scale_name]
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weight_loader = getattr(param, "weight_loader", default_weight_loader)
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loaded_weight = (
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loaded_weight if loaded_weight.dim() == 0 else loaded_weight[0]
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)
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weight_loader(param, loaded_weight)
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loaded_params.add(scale_name)
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continue
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if "scale" in name:
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remapped_name = maybe_remap_kv_scale_name(name, params_dict)
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if remapped_name is None:
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continue
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name = remapped_name
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mapped = False
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for param_name, weight_name, shard_id in stacked_params_mapping:
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if weight_name not in name:
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continue
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name = name.replace(weight_name, param_name)
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if name.endswith(".bias") and name not in params_dict:
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mapped = True
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break
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if is_pp_missing_parameter(name, self):
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mapped = True
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break
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param = params_dict[name]
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weight_loader = param.weight_loader # type: ignore[attr-defined]
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weight_loader(param, loaded_weight, shard_id)
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loaded_params.add(name)
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mapped = True
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break
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if mapped:
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continue
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if name.endswith(".bias") and name not in params_dict:
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continue
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if is_pp_missing_parameter(name, self):
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continue
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param = params_dict[name]
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weight_loader = getattr(param, "weight_loader", default_weight_loader)
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weight_loader(param, loaded_weight)
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loaded_params.add(name)
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return loaded_params
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class ArceeForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
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"""Arcee Model for causal language modeling, integrated with vLLM
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runtime."""
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# Map fused module names to their submodule components
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# (for quantization and LoRA)
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packed_modules_mapping = {
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"qkv_proj": ["q_proj", "k_proj", "v_proj"],
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}
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def __init__(self, *, vllm_config, prefix: str = "") -> None:
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super().__init__()
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config = vllm_config.model_config.hf_config
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self.config = config
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# Initialize the inner Transformer model (ArceeModel)
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self.model = ArceeModel(vllm_config=vllm_config, prefix=f"{prefix}.model")
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# On the last pipeline stage, set up the LM head and logits processor
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if get_pp_group().is_last_rank:
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# Determine vocabulary size (including any LoRA extra tokens
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# for padded LM head)
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self.lm_head = ParallelLMHead(
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config.vocab_size,
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config.hidden_size,
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quant_config=vllm_config.quant_config,
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bias=getattr(config, "lm_head_bias", False),
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prefix=f"{prefix}.lm_head",
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)
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if config.tie_word_embeddings:
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# Tie output weights with input embedding matrix
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self.lm_head = self.lm_head.tie_weights(self.model.embed_tokens)
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logit_scale = getattr(config, "logit_scale", 1.0)
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self.logits_processor = LogitsProcessor(
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config.vocab_size, scale=logit_scale
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)
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else:
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# Placeholder for lm_head on non-last ranks
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self.lm_head = PPMissingLayer()
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self.make_empty_intermediate_tensors = (
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self.model.make_empty_intermediate_tensors
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)
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def forward(
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self,
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input_ids: torch.Tensor,
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positions: torch.Tensor,
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intermediate_tensors: IntermediateTensors | None = None,
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inputs_embeds: torch.Tensor | None = None,
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) -> torch.Tensor | IntermediateTensors:
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model_output = self.model(
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input_ids=input_ids,
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positions=positions,
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intermediate_tensors=intermediate_tensors,
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inputs_embeds=inputs_embeds,
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)
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return model_output
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def compute_logits(self, hidden_states: torch.Tensor) -> torch.Tensor | None:
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# Compute final logits from hidden states (last pipeline rank only)
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logits = self.logits_processor(self.lm_head, hidden_states)
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return logits
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def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
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return self.model.embed_input_ids(input_ids)
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def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
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"""Load weights into the model (delegates to inner model and handles
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tied embeddings)."""
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loader = AutoWeightsLoader(
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self,
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skip_prefixes=(["lm_head."] if self.config.tie_word_embeddings else None),
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skip_substrs=["gate_proj"],
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)
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# AutoWeightLoader handles weight name remapping, including fusing
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# separate q_proj, k_proj, v_proj into qkv_proj
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return loader.load_weights(weights)
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