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[Model] Remove MotifForCausalLM (#25866)
Signed-off-by: Jee Jee Li <pandaleefree@gmail.com> Signed-off-by: yewentao256 <zhyanwentao@126.com>
This commit is contained in:
Jee Jee Li
yewentao256
parent
616bce15ce
commit
2405817748
@ -396,7 +396,6 @@ th {
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| `MiniCPM3ForCausalLM` | MiniCPM3 | `openbmb/MiniCPM3-4B`, etc. | ✅︎ | ✅︎ | ✅︎ |
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| `MistralForCausalLM` | Mistral, Mistral-Instruct | `mistralai/Mistral-7B-v0.1`, `mistralai/Mistral-7B-Instruct-v0.1`, etc. | ✅︎ | ✅︎ | ✅︎ |
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| `MixtralForCausalLM` | Mixtral-8x7B, Mixtral-8x7B-Instruct | `mistralai/Mixtral-8x7B-v0.1`, `mistralai/Mixtral-8x7B-Instruct-v0.1`, `mistral-community/Mixtral-8x22B-v0.1`, etc. | ✅︎ | ✅︎ | ✅︎ |
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| `MotifForCausalLM` | Motif-1-Tiny | `Motif-Technologies/Motif-2.6B`, `Motif-Technologies/Motif-2.6b-v1.1-LC`, etc. | ✅︎ | ✅︎ | |
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| `MPTForCausalLM` | MPT, MPT-Instruct, MPT-Chat, MPT-StoryWriter | `mosaicml/mpt-7b`, `mosaicml/mpt-7b-storywriter`, `mosaicml/mpt-30b`, etc. | | ✅︎ | ✅︎ |
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| `NemotronForCausalLM` | Nemotron-3, Nemotron-4, Minitron | `nvidia/Minitron-8B-Base`, `mgoin/Nemotron-4-340B-Base-hf-FP8`, etc. | ✅︎ | ✅︎ | ✅︎ |
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| `NemotronHForCausalLM` | Nemotron-H | `nvidia/Nemotron-H-8B-Base-8K`, `nvidia/Nemotron-H-47B-Base-8K`, `nvidia/Nemotron-H-56B-Base-8K`, etc. | ✅︎ | ✅︎ | ✅︎ |
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@ -409,7 +408,6 @@ th {
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| `PhiForCausalLM` | Phi | `microsoft/phi-1_5`, `microsoft/phi-2`, etc. | ✅︎ | ✅︎ | ✅︎ |
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| `Phi3ForCausalLM` | Phi-4, Phi-3 | `microsoft/Phi-4-mini-instruct`, `microsoft/Phi-4`, `microsoft/Phi-3-mini-4k-instruct`, `microsoft/Phi-3-mini-128k-instruct`, `microsoft/Phi-3-medium-128k-instruct`, etc. | ✅︎ | ✅︎ | ✅︎ |
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| `PhiMoEForCausalLM` | Phi-3.5-MoE | `microsoft/Phi-3.5-MoE-instruct`, etc. | ✅︎ | ✅︎ | ✅︎ |
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| `Phi4FlashForCausalLM` | Phi-4-mini-flash-reasoning | `microsoft/microsoft/Phi-4-mini-instruct`, etc. | | | |
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| `PersimmonForCausalLM` | Persimmon | `adept/persimmon-8b-base`, `adept/persimmon-8b-chat`, etc. | | ✅︎ | ✅︎ |
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| `Plamo2ForCausalLM` | PLaMo2 | `pfnet/plamo-2-1b`, `pfnet/plamo-2-8b`, etc. | | ✅︎ | ✅︎ |
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| `QWenLMHeadModel` | Qwen | `Qwen/Qwen-7B`, `Qwen/Qwen-7B-Chat`, etc. | ✅︎ | ✅︎ | ✅︎ |
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@ -298,9 +298,6 @@ _TEXT_GENERATION_EXAMPLE_MODELS = {
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"MistralForCausalLM": _HfExamplesInfo("mistralai/Mistral-7B-Instruct-v0.1"),
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"MixtralForCausalLM": _HfExamplesInfo("mistralai/Mixtral-8x7B-Instruct-v0.1", # noqa: E501
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{"tiny": "TitanML/tiny-mixtral"}), # noqa: E501
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"MotifForCausalLM": _HfExamplesInfo("Motif-Technologies/Motif-2.6B",
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trust_remote_code=True,
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v0_only=True),
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"MptForCausalLM": _HfExamplesInfo("mpt", is_available_online=False),
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"MPTForCausalLM": _HfExamplesInfo("mosaicml/mpt-7b"),
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"NemotronForCausalLM": _HfExamplesInfo("nvidia/Minitron-8B-Base"),
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@ -76,10 +76,6 @@ def can_initialize(model_arch: str, monkeypatch: pytest.MonkeyPatch,
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if model_info.v0_only:
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# NOTE(woosuk): skip the test for V0-only models
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return
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if model_arch in ("Phi4FlashForCausalLM", "MotifForCausalLM"):
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pytest.skip(
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"Differential Flash Attention backend has been removed.")
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if model_arch == "GptOssForCausalLM":
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# FIXME: A hack to bypass FA3 assertion because our CI's L4 GPU
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# has cc==8.9 which hasn't supported FA3 yet. Remove this hack when
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@ -1,345 +0,0 @@
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# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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# Adapted from
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# https://huggingface.co/Motif-Technologies/Motif-2.6B/blob/main/modeling_motif.py
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# Copyright (c) Alibaba Cloud.
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# LICENSE: https://huggingface.co/Motif-Technologies/Motif-2.6B/blob/main/LICENSE
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"""Inference-only Motif model compatible with HuggingFace weights."""
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import math
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from typing import Any, Optional
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import torch
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from torch import nn
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from transformers import PretrainedConfig
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from vllm.attention import Attention, AttentionType
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from vllm.attention.selector import _Backend
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from vllm.config import CacheConfig, VllmConfig
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from vllm.distributed import get_tensor_model_parallel_world_size
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from vllm.model_executor.layers.layernorm import PolyNorm, RMSNorm
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from vllm.model_executor.layers.linear import (MergedColumnParallelLinear,
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QKVParallelLinear,
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RowParallelLinear)
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from vllm.model_executor.layers.quantization import QuantizationConfig
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from vllm.model_executor.layers.rotary_embedding import get_rope
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from vllm.model_executor.models.llama import LlamaForCausalLM
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from .adapters import as_seq_cls_model
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from .interfaces import SupportsV0Only
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from .utils import extract_layer_index
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class MotifMLP(nn.Module):
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"""MLP for the language component of the Motif model, which contains a
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MergedColumnParallelLinear merging 2 outputs via PolyNorm activation."""
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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 = "poly_norm",
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quant_config: Optional[QuantizationConfig] = 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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):
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super().__init__()
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self.gate_up_proj = MergedColumnParallelLinear(
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input_size=hidden_size,
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output_sizes=[intermediate_size] * 2,
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bias=bias,
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quant_config=quant_config,
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prefix=f"{prefix}.gate_up_proj",
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)
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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 != "poly_norm":
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raise NotImplementedError(f"Unsupported activation: {hidden_act}. "
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"Only poly_norm is supported for now.")
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self.act_fn = PolyNorm()
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self.intermediate_size = intermediate_size
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tp_size = get_tensor_model_parallel_world_size()
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if hidden_act == "poly_norm" and tp_size > 1:
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raise NotImplementedError(
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"Tensor parallelism for poly_norm is not supported yet. "
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"Support will be added in the future.")
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def forward(self, x):
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x, _ = self.gate_up_proj(x)
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x = self.act_fn(
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x[..., :self.intermediate_size]) * x[..., self.intermediate_size:]
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x, _ = self.down_proj(x)
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return x
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class MotifAttention(nn.Module):
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def __init__(
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self,
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config: PretrainedConfig,
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hidden_size: int,
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num_heads: int,
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num_kv_heads: int,
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rope_theta: float = 10000,
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rope_scaling: Optional[dict[str, Any]] = None,
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max_position_embeddings: int = 8192,
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quant_config: Optional[QuantizationConfig] = None,
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bias: bool = False,
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bias_o_proj: bool = False,
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cache_config: Optional[CacheConfig] = None,
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prefix: str = "",
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attn_type: str = AttentionType.DECODER,
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) -> None:
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super().__init__()
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layer_idx = extract_layer_index(prefix)
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self.hidden_size = hidden_size
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tp_size = get_tensor_model_parallel_world_size()
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self.total_num_heads = num_heads
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assert self.total_num_heads % tp_size == 0
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self.num_heads = self.total_num_heads // tp_size
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self.total_num_kv_heads = num_kv_heads
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if self.total_num_kv_heads >= tp_size:
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# Number of KV heads is greater than TP size, so we partition
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# the KV heads across multiple tensor parallel GPUs.
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assert self.total_num_kv_heads % tp_size == 0
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else:
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# Number of KV heads is less than TP size, so we replicate
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# the KV heads across multiple tensor parallel GPUs.
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assert tp_size % self.total_num_kv_heads == 0
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self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
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# MistralConfig has an optional head_dim introduced by Mistral-Nemo
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head_dim = getattr(config, "head_dim", None)
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if head_dim is None:
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head_dim = self.hidden_size // self.total_num_heads
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self.head_dim = head_dim
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# Phi models introduced a partial_rotary_factor parameter in the config
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self.partial_rotary_factor = getattr(config, "partial_rotary_factor",
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1)
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self.q_size = self.num_heads * self.head_dim
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self.kv_size = self.num_kv_heads * self.head_dim
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self.scaling = self.head_dim**-0.5
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self.rope_theta = rope_theta
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self.max_position_embeddings = max_position_embeddings
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assert self.num_heads % 2 == 0, 'num_heads should be even'
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assert self.num_kv_heads % 2 == 0, 'num_heads should be even'
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self.qkv_proj = QKVParallelLinear(
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hidden_size=hidden_size,
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head_size=self.head_dim,
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total_num_heads=self.total_num_heads,
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total_num_kv_heads=self.total_num_kv_heads,
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bias=bias,
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quant_config=quant_config,
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prefix=f"{prefix}.qkv_proj",
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)
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self.o_proj = RowParallelLinear(
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input_size=self.total_num_heads * self.head_dim,
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output_size=hidden_size,
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bias=bias_o_proj,
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quant_config=quant_config,
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prefix=f"{prefix}.o_proj",
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)
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self._init_rotary_emb(config,
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rope_scaling=rope_scaling,
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quant_config=quant_config)
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sliding_window = None
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self.lambda_init = self.lambda_init_fn(layer_idx)
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self.lambda_q1 = nn.Parameter(
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torch.zeros(self.head_dim, dtype=torch.float32).normal_(mean=0,
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std=0.1))
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self.lambda_k1 = nn.Parameter(
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torch.zeros(self.head_dim, dtype=torch.float32).normal_(mean=0,
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std=0.1))
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self.lambda_q2 = nn.Parameter(
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torch.zeros(self.head_dim, dtype=torch.float32).normal_(mean=0,
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std=0.1))
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self.lambda_k2 = nn.Parameter(
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torch.zeros(self.head_dim, dtype=torch.float32).normal_(mean=0,
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std=0.1))
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self.subln = RMSNorm(2 * self.head_dim, eps=config.attn_rms_norm_eps)
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params = {
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'differential_flash_attention_config': {
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'lambda_init': self.lambda_init,
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'lambda_q1': self.lambda_q1,
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'lambda_k1': self.lambda_k1,
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'lambda_q2': self.lambda_q2,
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'lambda_k2': self.lambda_k2,
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"subln": self.subln,
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}
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}
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diff_attn_err_msg = (
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'Set VLLM_ATTENTION_BACKEND="DIFFERENTIAL_FLASH_ATTN" '
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'to enable Differential Flash Attention.')
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try:
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self.attn = Attention(
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self.num_heads,
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self.head_dim,
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self.scaling,
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num_kv_heads=self.num_kv_heads,
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cache_config=cache_config,
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quant_config=quant_config,
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per_layer_sliding_window=sliding_window,
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attn_type=attn_type,
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prefix=f"{prefix}.attn",
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**params,
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)
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except TypeError as e:
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raise ValueError(diff_attn_err_msg) from e
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assert (self.attn.backend == _Backend.DIFFERENTIAL_FLASH_ATTN
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), diff_attn_err_msg
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def lambda_init_fn(self, depth):
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return 0.8 - 0.6 * math.exp(-0.3 * (depth - 1))
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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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) -> torch.Tensor:
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qkv, _ = self.qkv_proj(hidden_states)
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q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
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q, k = self.rotary_emb(positions, q, k)
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attn_output = self.attn(q, k, v)
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output, _ = self.o_proj(attn_output)
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return output
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def _init_rotary_emb(self, config: PretrainedConfig,
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rope_scaling: Optional[dict[str, Any]],
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quant_config: Optional[QuantizationConfig]) -> None:
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is_neox_style = True
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is_gguf = quant_config and quant_config.get_name() == "gguf"
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if is_gguf and config.model_type == "llama":
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is_neox_style = False
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self.rotary_emb = get_rope(
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self.head_dim,
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rotary_dim=self.head_dim,
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max_position=self.max_position_embeddings,
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base=self.rope_theta,
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rope_scaling=rope_scaling,
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is_neox_style=is_neox_style,
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partial_rotary_factor=self.partial_rotary_factor,
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)
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class MotifDecoderLayer(nn.Module):
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def __init__(
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self,
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config: PretrainedConfig,
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cache_config: Optional[CacheConfig] = None,
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quant_config: Optional[QuantizationConfig] = 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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rope_theta = getattr(config, "rope_theta", 10000)
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rope_scaling = getattr(config, "rope_scaling", None)
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if rope_scaling is not None and getattr(
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config, "original_max_position_embeddings", None):
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rope_scaling["original_max_position_embeddings"] = (
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config.original_max_position_embeddings)
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max_position_embeddings = getattr(config, "max_position_embeddings",
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8192)
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attention_bias = getattr(config, "attention_bias", False) or getattr(
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config, "use_bias", False)
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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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# By default, Motif uses causal attention as it is a decoder-only model.
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# You can override the HF config with `is_causal=False` to enable
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# bidirectional attention, which is used in some embedding models
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# (e.g. parasail-ai/GritLM-7B-vllm)
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if getattr(config, "is_causal", True):
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attn_type = AttentionType.DECODER
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else:
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attn_type = AttentionType.ENCODER_ONLY
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self.self_attn = MotifAttention(
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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(config, "num_key_value_heads",
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config.num_attention_heads),
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rope_theta=rope_theta,
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rope_scaling=rope_scaling,
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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=attn_type,
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)
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self.mlp = MotifMLP(
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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, "use_bias", False),
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prefix=f"{prefix}.mlp",
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)
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self.input_layernorm = RMSNorm(config.hidden_size,
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eps=config.rms_norm_eps)
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self.post_attention_layernorm = RMSNorm(config.hidden_size,
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eps=config.rms_norm_eps)
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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: Optional[torch.Tensor],
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) -> tuple[torch.Tensor, torch.Tensor]:
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# Self Attention
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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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hidden_states, residual = self.input_layernorm(
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hidden_states, residual)
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hidden_states = self.self_attn(positions=positions,
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hidden_states=hidden_states)
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# Fully Connected
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hidden_states, residual = self.post_attention_layernorm(
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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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# Motif model uses differential attention
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# Only supported in v0 (no chunked prefill support)
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class MotifForCausalLM(LlamaForCausalLM, SupportsV0Only):
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def __init__(self,
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*,
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vllm_config: VllmConfig,
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prefix: str = "",
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layer_type: type[nn.Module] = MotifDecoderLayer):
|
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|
||||
# Prefix caching and chunked prefill is not supported for this model.
|
||||
assert not vllm_config.cache_config.enable_prefix_caching, \
|
||||
"Motif currently does not support prefix caching"
|
||||
assert not vllm_config.scheduler_config.chunked_prefill_enabled, \
|
||||
"Motif currently does not support chunked prefill"
|
||||
|
||||
super().__init__(vllm_config=vllm_config,
|
||||
prefix=prefix,
|
||||
layer_type=layer_type)
|
||||
|
||||
|
||||
MotifForSequenceClassification = as_seq_cls_model(MotifForCausalLM)
|
||||
@ -119,7 +119,6 @@ _TEXT_GENERATION_MODELS = {
|
||||
"MiniCPM3ForCausalLM": ("minicpm3", "MiniCPM3ForCausalLM"),
|
||||
"MistralForCausalLM": ("llama", "LlamaForCausalLM"),
|
||||
"MixtralForCausalLM": ("mixtral", "MixtralForCausalLM"),
|
||||
"MotifForCausalLM": ("motif", "MotifForCausalLM"),
|
||||
# transformers's mpt class has lower case
|
||||
"MptForCausalLM": ("mpt", "MPTForCausalLM"),
|
||||
"MPTForCausalLM": ("mpt", "MPTForCausalLM"),
|
||||
@ -332,6 +331,7 @@ _SUBPROCESS_COMMAND = [
|
||||
]
|
||||
|
||||
_PREVIOUSLY_SUPPORTED_MODELS = {
|
||||
"MotifForCausalLM": "0.10.2",
|
||||
"Phi3SmallForCausalLM": "0.9.2",
|
||||
"Phi4FlashForCausalLM": "0.10.2",
|
||||
# encoder-decoder models except whisper
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user