qwen2.py 21.7 KB
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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
# https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/qwen2/modeling_qwen2.py
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# Copyright 2024 The Qwen team.
# Copyright 2023 The vLLM team.
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# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
#
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
# and OPT implementations in this library. It has been modified from its
# original forms to accommodate minor architectural differences compared
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
#
# 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
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
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"""Inference-only Qwen2 model compatible with HuggingFace weights."""
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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
from torch import nn
from transformers import Qwen2Config

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from vllm.attention.backends.abstract import AttentionType
from vllm.attention.layer import Attention
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from vllm.attention.layers.encoder_only_attention import EncoderOnlyAttention
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from vllm.compilation.decorators import support_torch_compile
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from vllm.config import CacheConfig, VllmConfig
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from vllm.distributed import get_pp_group, get_tensor_model_parallel_world_size
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from vllm.model_executor.layers.activation import SiluAndMul
from vllm.model_executor.layers.layernorm import RMSNorm
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from vllm.model_executor.layers.linear import (
    MergedColumnParallelLinear,
    QKVParallelLinear,
    RowParallelLinear,
)
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from vllm.model_executor.layers.logits_processor import LogitsProcessor
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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.layers.vocab_parallel_embedding import (
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    ParallelLMHead,
    VocabParallelEmbedding,
)
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from vllm.model_executor.model_loader.weight_utils import (
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    default_weight_loader,
    maybe_remap_kv_scale_name,
)
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from vllm.sequence import IntermediateTensors
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from vllm.transformers_utils.config import is_interleaved, set_default_rope_theta
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from .interfaces import SupportsEagle3, SupportsLoRA, SupportsPP
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from .utils import (
    AutoWeightsLoader,
    PPMissingLayer,
    extract_layer_index,
    is_pp_missing_parameter,
    make_empty_intermediate_tensors_factory,
    make_layers,
    maybe_prefix,
)
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class Qwen2MLP(nn.Module):
    def __init__(
        self,
        hidden_size: int,
        intermediate_size: int,
        hidden_act: str,
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        quant_config: QuantizationConfig | None = None,
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        prefix: str = "",
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    ) -> None:
        super().__init__()
        self.gate_up_proj = MergedColumnParallelLinear(
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            hidden_size,
            [intermediate_size] * 2,
            bias=False,
            quant_config=quant_config,
            prefix=f"{prefix}.gate_up_proj",
        )
        self.down_proj = RowParallelLinear(
            intermediate_size,
            hidden_size,
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            bias=False,
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            quant_config=quant_config,
            prefix=f"{prefix}.down_proj",
        )
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        if hidden_act != "silu":
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            raise ValueError(
                f"Unsupported activation: {hidden_act}. Only silu is supported for now."
            )
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        self.act_fn = SiluAndMul()

    def forward(self, x):
        gate_up, _ = self.gate_up_proj(x)
        x = self.act_fn(gate_up)
        x, _ = self.down_proj(x)
        return x


class Qwen2Attention(nn.Module):
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    def __init__(
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        self,
        hidden_size: int,
        num_heads: int,
        num_kv_heads: int,
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        rope_parameters: dict[str, Any],
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        max_position: int = 4096 * 32,
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        cache_config: CacheConfig | None = None,
        quant_config: QuantizationConfig | None = None,
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        prefix: str = "",
        attn_type: str = AttentionType.DECODER,
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        dual_chunk_attention_config: dict[str, Any] | None = None,
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        qk_norm: bool = False,
        rms_norm_eps: float = 1e-6,
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    ) -> None:
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        super().__init__()
        self.hidden_size = hidden_size
        tp_size = get_tensor_model_parallel_world_size()
        self.total_num_heads = num_heads
        assert self.total_num_heads % tp_size == 0
        self.num_heads = self.total_num_heads // tp_size
        self.total_num_kv_heads = num_kv_heads
        if self.total_num_kv_heads >= tp_size:
            # Number of KV heads is greater than TP size, so we partition
            # the KV heads across multiple tensor parallel GPUs.
            assert self.total_num_kv_heads % tp_size == 0
        else:
            # Number of KV heads is less than TP size, so we replicate
            # the KV heads across multiple tensor parallel GPUs.
            assert tp_size % self.total_num_kv_heads == 0
        self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
        self.head_dim = hidden_size // self.total_num_heads
        self.q_size = self.num_heads * self.head_dim
        self.kv_size = self.num_kv_heads * self.head_dim
        self.scaling = self.head_dim**-0.5
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        self.dual_chunk_attention_config = dual_chunk_attention_config
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        self.qk_norm = qk_norm
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        self.qkv_proj = QKVParallelLinear(
            hidden_size,
            self.head_dim,
            self.total_num_heads,
            self.total_num_kv_heads,
            bias=True,
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            quant_config=quant_config,
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            prefix=f"{prefix}.qkv_proj",
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        )
        self.o_proj = RowParallelLinear(
            self.total_num_heads * self.head_dim,
            hidden_size,
            bias=False,
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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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        # QK Normalization support (used in BAGEL and some other models)
        if self.qk_norm:
            self.q_norm = RMSNorm(self.head_dim, eps=rms_norm_eps)
            self.k_norm = RMSNorm(self.head_dim, eps=rms_norm_eps)

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        self.rotary_emb = get_rope(
            self.head_dim,
            max_position=max_position,
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            rope_parameters=rope_parameters,
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            dual_chunk_attention_config=dual_chunk_attention_config,
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        )
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        attn_cls = (
            EncoderOnlyAttention
            if attn_type == AttentionType.ENCODER_ONLY
            else Attention
        )
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        self.attn = attn_cls(
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            self.num_heads,
            self.head_dim,
            self.scaling,
            num_kv_heads=self.num_kv_heads,
            cache_config=cache_config,
            quant_config=quant_config,
            attn_type=attn_type,
            prefix=f"{prefix}.attn",
            **{
                "layer_idx": extract_layer_index(prefix),
                "dual_chunk_attention_config": dual_chunk_attention_config,
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            }
            if dual_chunk_attention_config
            else {},
        )
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    def forward(
        self,
        positions: torch.Tensor,
        hidden_states: torch.Tensor,
    ) -> torch.Tensor:
        qkv, _ = self.qkv_proj(hidden_states)
        q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
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        # Apply QK normalization if enabled (before RoPE)
        if self.qk_norm:
            # Reshape to apply per-head normalization
            # q shape: (total_tokens, q_size) -> (total_tokens, num_heads, head_dim)
            total_tokens = q.shape[0]
            q = q.view(total_tokens, self.num_heads, self.head_dim)
            k = k.view(total_tokens, self.num_kv_heads, self.head_dim)

            # Apply normalization
            q = self.q_norm(q)
            k = self.k_norm(k)

            # Reshape back
            q = q.view(total_tokens, self.q_size)
            k = k.view(total_tokens, self.kv_size)

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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)
        return output


class Qwen2DecoderLayer(nn.Module):
    def __init__(
        self,
        config: Qwen2Config,
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        cache_config: CacheConfig | None = None,
        quant_config: QuantizationConfig | None = None,
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        prefix: str = "",
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    ) -> None:
        super().__init__()
        self.hidden_size = config.hidden_size
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        set_default_rope_theta(config, default_theta=1000000)
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        dual_chunk_attention_config = getattr(
            config, "dual_chunk_attention_config", None
        )
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        # By default, Qwen2 uses causal attention as it is a decoder-only model.
        # You can override the HF config with `is_causal=False` to enable
        # bidirectional attention, which is used in some embedding models
        # (e.g. Alibaba-NLP/gte-Qwen2-7B-instruct)
        if getattr(config, "is_causal", True):
            attn_type = AttentionType.DECODER
        else:
            attn_type = AttentionType.ENCODER_ONLY

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        # Check if QK normalization is enabled (used in BAGEL and some other models)
        qk_norm = getattr(config, "qk_norm", False)

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        self.self_attn = Qwen2Attention(
            hidden_size=self.hidden_size,
            num_heads=config.num_attention_heads,
            max_position=config.max_position_embeddings,
            num_kv_heads=config.num_key_value_heads,
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            cache_config=cache_config,
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            quant_config=quant_config,
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            rope_parameters=config.rope_parameters,
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            prefix=f"{prefix}.self_attn",
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            attn_type=attn_type,
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            dual_chunk_attention_config=dual_chunk_attention_config,
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            qk_norm=qk_norm,
            rms_norm_eps=config.rms_norm_eps,
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        )
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        self.mlp = Qwen2MLP(
            hidden_size=self.hidden_size,
            intermediate_size=config.intermediate_size,
            hidden_act=config.hidden_act,
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            quant_config=quant_config,
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            prefix=f"{prefix}.mlp",
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        )
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        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
        )
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    def forward(
        self,
        positions: torch.Tensor,
        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
        if residual is None:
            residual = hidden_states
            hidden_states = self.input_layernorm(hidden_states)
        else:
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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,
        )

        # Fully Connected
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        hidden_states, residual = self.post_attention_layernorm(hidden_states, residual)
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        hidden_states = self.mlp(hidden_states)
        return hidden_states, residual


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def qwen_2_model_invariants(
    input_ids: torch.Tensor,
    positions: torch.Tensor,
    intermediate_tensors: IntermediateTensors | None = None,
    inputs_embeds: torch.Tensor | None = None,
):
    """Shape invariants for Qwen2Model Model, those are translated to
    runtime assertions for unbacked dynamic shapes and are compiled away for
    backed"""
    # All these should be equal.
    # input_ids.size()[0]
    # positions.size()[-1]
    # intermediate_tensors["hidden_states"].size()[0]
    # inputs_embeds.size()[0]
    torch._check(input_ids.size()[0] == positions.size()[-1])
    if intermediate_tensors is not None:
        torch._check(
            input_ids.size()[0] == intermediate_tensors["hidden_states"].size()[0]
        )

    if inputs_embeds is not None:
        torch._check(input_ids.size()[0] == inputs_embeds.size()[0])

    # Hidden dimensions should match (hidden_size)
    # intermediate_tensors["hidden_states"].size()[1]
    # inputs_embeds.size()[1]
    if inputs_embeds is not None and intermediate_tensors is not None:
        torch._check(
            inputs_embeds.size()[1] == intermediate_tensors["hidden_states"].size()[1]
        )


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@support_torch_compile(
    dynamic_arg_dims={
        "input_ids": 0,
        # positions is of shape (3, seq_len) if mrope is enabled for qwen2-vl,
        # otherwise (seq_len, ).
        "positions": -1,
        "intermediate_tensors": 0,
        "inputs_embeds": 0,
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    },
    shape_invariants=qwen_2_model_invariants,
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)
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class Qwen2Model(nn.Module):
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    def __init__(
        self,
        *,
        vllm_config: VllmConfig,
        prefix: str = "",
        decoder_layer_type: type[nn.Module] = Qwen2DecoderLayer,
    ):
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        super().__init__()
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        config = vllm_config.model_config.hf_config.get_text_config()
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        cache_config = vllm_config.cache_config
        quant_config = vllm_config.quant_config

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        # TODO (@robertgshaw2): see if this can be moved out
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        if is_interleaved(vllm_config.model_config.hf_text_config):
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            assert config.max_window_layers == config.num_hidden_layers, (
                "Sliding window for some but all layers is not supported. "
                "This model uses sliding window but `max_window_layers` = {} "
                "is less than `num_hidden_layers` = {}. Please open an issue "
                "to discuss this feature.".format(
                    config.max_window_layers,
                    config.num_hidden_layers,
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                )
            )
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        self.config = config
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        self.quant_config = quant_config
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        self.vocab_size = config.vocab_size

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        if get_pp_group().is_first_rank or (
            config.tie_word_embeddings and get_pp_group().is_last_rank
        ):
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            self.embed_tokens = VocabParallelEmbedding(
                config.vocab_size,
                config.hidden_size,
                quant_config=quant_config,
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                prefix=f"{prefix}.embed_tokens",
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            )
        else:
            self.embed_tokens = PPMissingLayer()

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        self.start_layer, self.end_layer, self.layers = make_layers(
            config.num_hidden_layers,
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            lambda prefix: decoder_layer_type(
                config=config,
                cache_config=cache_config,
                quant_config=quant_config,
                prefix=prefix,
            ),
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            prefix=f"{prefix}.layers",
        )

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        self.make_empty_intermediate_tensors = make_empty_intermediate_tensors_factory(
            ["hidden_states", "residual"], config.hidden_size
        )
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        if get_pp_group().is_last_rank:
            self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        else:
            self.norm = PPMissingLayer()
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        self.aux_hidden_state_layers = tuple[int, ...]()
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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(
        self,
        input_ids: torch.Tensor,
        positions: torch.Tensor,
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        intermediate_tensors: IntermediateTensors | None = None,
        inputs_embeds: torch.Tensor | None = None,
    ) -> torch.Tensor | IntermediateTensors:
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        if get_pp_group().is_first_rank:
            if inputs_embeds is not None:
                hidden_states = inputs_embeds
            else:
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                hidden_states = self.embed_input_ids(input_ids)
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            residual = None
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        else:
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            assert intermediate_tensors is not None
            hidden_states = intermediate_tensors["hidden_states"]
            residual = intermediate_tensors["residual"]
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        aux_hidden_states = []
        for idx, layer in enumerate(
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            islice(self.layers, self.start_layer, self.end_layer)
        ):
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            if idx in self.aux_hidden_state_layers:
                aux_hidden_states.append(hidden_states + residual)
            hidden_states, residual = layer(positions, hidden_states, residual)

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        if not get_pp_group().is_last_rank:
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            return IntermediateTensors(
                {"hidden_states": hidden_states, "residual": residual}
            )
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        hidden_states, _ = self.norm(hidden_states, residual)
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        if len(aux_hidden_states) > 0:
            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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        stacked_params_mapping = [
            # (param_name, shard_name, shard_id)
            ("qkv_proj", "q_proj", "q"),
            ("qkv_proj", "k_proj", "k"),
            ("qkv_proj", "v_proj", "v"),
            ("gate_up_proj", "gate_proj", 0),
            ("gate_up_proj", "up_proj", 1),
        ]
        params_dict = dict(self.named_parameters(remove_duplicate=False))
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        loaded_params: set[str] = set()
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        for name, loaded_weight in weights:
            if "rotary_emb.inv_freq" in name:
                continue
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            if self.quant_config is not None and (
                scale_name := self.quant_config.get_cache_scale(name)
            ):
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                # Loading kv cache quantization scales
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                param = params_dict[scale_name]
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                weight_loader = getattr(param, "weight_loader", default_weight_loader)
                loaded_weight = (
                    loaded_weight if loaded_weight.dim() == 0 else loaded_weight[0]
                )
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                weight_loader(param, loaded_weight)
                loaded_params.add(scale_name)
                continue
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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:
                    continue
                name = name.replace(weight_name, param_name)
                # Skip loading extra bias for GPTQ models.
                if name.endswith(".bias") and name not in params_dict:
                    continue
                if is_pp_missing_parameter(name, self):
                    continue
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                if name.endswith("scale"):
                    # Remapping the name of FP8 kv-scale.
                    name = maybe_remap_kv_scale_name(name, params_dict)
                    if name is None:
                        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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                if weight_loader == default_weight_loader:
                    weight_loader(param, loaded_weight)
                else:
                    weight_loader(param, loaded_weight, shard_id)
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                break
            else:
                # Skip loading extra bias for GPTQ models.
                if name.endswith(".bias") and name not in params_dict:
                    continue
                # Remapping the name of FP8 kv-scale.
                name = maybe_remap_kv_scale_name(name, params_dict)
                if name is None:
                    continue
                if is_pp_missing_parameter(name, self):
                    continue
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                if name not in params_dict:
                    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)
        return loaded_params
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class Qwen2ForCausalLM(nn.Module, SupportsLoRA, SupportsPP, SupportsEagle3):
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    packed_modules_mapping = {
        "qkv_proj": [
            "q_proj",
            "k_proj",
            "v_proj",
        ],
        "gate_up_proj": [
            "gate_proj",
            "up_proj",
        ],
    }

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    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
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        super().__init__()
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        config = vllm_config.model_config.hf_config.get_text_config()
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        quant_config = vllm_config.quant_config
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        self.config = config
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        self.quant_config = quant_config
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        self.model = Qwen2Model(
            vllm_config=vllm_config, prefix=maybe_prefix(prefix, "model")
        )
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        if get_pp_group().is_last_rank:
            if config.tie_word_embeddings:
                self.lm_head = self.model.embed_tokens
            else:
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                self.lm_head = ParallelLMHead(
                    config.vocab_size,
                    config.hidden_size,
                    quant_config=quant_config,
                    prefix=maybe_prefix(prefix, "lm_head"),
                )
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        else:
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            self.lm_head = PPMissingLayer()
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        self.logits_processor = LogitsProcessor(config.vocab_size)
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        self.make_empty_intermediate_tensors = (
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            self.model.make_empty_intermediate_tensors
        )
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    def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
        return self.model.embed_input_ids(input_ids)
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    def set_aux_hidden_state_layers(self, layers: tuple[int, ...]) -> None:
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        self.model.aux_hidden_state_layers = layers

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    def get_eagle3_aux_hidden_state_layers(self) -> tuple[int, ...]:
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        num_layers = len(self.model.layers)
        return (2, num_layers // 2, num_layers - 3)

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    def forward(
        self,
        input_ids: torch.Tensor,
        positions: torch.Tensor,
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        intermediate_tensors: IntermediateTensors | None = None,
        inputs_embeds: torch.Tensor | None = None,
    ) -> torch.Tensor | IntermediateTensors:
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        hidden_states = self.model(
            input_ids, positions, intermediate_tensors, inputs_embeds
        )
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        return hidden_states

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    def compute_logits(
        self,
        hidden_states: torch.Tensor,
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    ) -> torch.Tensor | None:
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        logits = self.logits_processor(self.lm_head, hidden_states)
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        return logits

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    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
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        loader = AutoWeightsLoader(
            self,
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            skip_prefixes=(["lm_head."] if self.config.tie_word_embeddings else None),
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        )
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        return loader.load_weights(weights)