aphrodite-engine/aphrodite/modeling/models/mixtral.py

# coding=utf-8
# Adapted from
# https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/llama/modeling_llama.py
# Copyright 2023 The vLLM team.
# 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.
"""Inference-only Mixtral model."""
from typing import Iterable, List, Optional, Tuple

import torch
from torch import nn
from transformers import MixtralConfig

from aphrodite.attention import Attention, AttentionMetadata
from aphrodite.common.config import CacheConfig, LoRAConfig
from aphrodite.common.sequence import SamplerOutput
from aphrodite.common.utils import print_warning_once
from aphrodite.distributed import (get_tensor_model_parallel_rank,
                                   get_tensor_model_parallel_world_size,
                                   tensor_model_parallel_all_reduce)
from aphrodite.modeling.layers.fused_moe import fused_moe
from aphrodite.modeling.layers.layernorm import RMSNorm
from aphrodite.modeling.layers.linear import (QKVParallelLinear,
                                              ReplicatedLinear,
                                              RowParallelLinear)
from aphrodite.modeling.layers.logits_processor import LogitsProcessor
from aphrodite.modeling.layers.rotary_embedding import get_rope
from aphrodite.modeling.layers.sampler import Sampler
from aphrodite.modeling.layers.vocab_parallel_embedding import (
    DEFAULT_VOCAB_PADDING_SIZE, ParallelLMHead, VocabParallelEmbedding)
from aphrodite.modeling.model_loader.weight_utils import default_weight_loader
from aphrodite.modeling.sampling_metadata import SamplingMetadata
from aphrodite.modeling.utils import set_weight_attrs
from aphrodite.quantization.base_config import QuantizationConfig
from aphrodite.quantization.fp8 import Fp8Config, scaled_fp8_quant


class MixtralMoE(nn.Module):
    """A tensor-parallel MoE implementation for Mixtral that shards each expert
    across all ranks.

    Each expert's weights are sharded across all ranks and a fused MoE
    kernel is used for the forward pass, and finally we reduce the outputs
    across ranks.
    """

    def __init__(
        self,
        num_experts: int,
        top_k: int,
        hidden_size: int,
        intermediate_size: int,
        params_dtype: Optional[torch.dtype] = None,
        tp_size: Optional[int] = None,
        quant_config: Optional[QuantizationConfig] = None,
    ):
        super().__init__()
        self.tp_size = tp_size or get_tensor_model_parallel_world_size()
        self.num_total_experts = num_experts
        self.top_k = top_k
        self.hidden_size = hidden_size
        self.intermediate_size = intermediate_size // self.tp_size
        self.quant_config = quant_config

        # FIXME(pcmoritz): Make this more general to support different
        # quantization schemes
        self.use_fp8 = isinstance(quant_config, Fp8Config)

        if params_dtype is None:
            params_dtype = torch.get_default_dtype()
        self.params_dtype = params_dtype

        # Gate always runs at half / full precision for now.
        self.gate = ReplicatedLinear(self.hidden_size,
                                     self.num_total_experts,
                                     bias=False,
                                     params_dtype=self.params_dtype,
                                     quant_config=None)

        if self.use_fp8 and self.quant_config.is_checkpoint_fp8_serialized:
            params_dtype = torch.float8_e4m3fn

        self.w13_weight = nn.Parameter(
            torch.empty(self.num_total_experts,
                        2 * self.intermediate_size,
                        self.hidden_size,
                        dtype=params_dtype))
        self.w2_weight = nn.Parameter(
            torch.empty(self.num_total_experts,
                        self.hidden_size,
                        self.intermediate_size,
                        dtype=params_dtype))

        set_weight_attrs(self.w13_weight, {
            "weight_loader": self.weight_loader,
        })
        set_weight_attrs(self.w2_weight, {
            "weight_loader": self.weight_loader,
        })

        # Used for fp8.
        self.w13_scale = None
        self.w2_scale = None
        self.a13_scale = None
        self.a2_scale = None

        if self.use_fp8:
            # WEIGHT_SCALE (for fp8)
            self.w13_scale = nn.Parameter(torch.ones(self.num_total_experts,
                                                     dtype=torch.float32),
                                          requires_grad=False)
            self.w2_scale = nn.Parameter(torch.ones(self.num_total_experts,
                                                    dtype=torch.float32),
                                         requires_grad=False)

            # If loading fp8 checkpoint, pass the weight loaders.
            # If loading an fp16 checkpoint, do not (we will quantize in
            #   process_weights_after_loading()
            if quant_config.is_checkpoint_fp8_serialized:
                set_weight_attrs(self.w13_scale, {
                    "weight_loader": self.weight_loader,
                })
                set_weight_attrs(self.w2_scale, {
                    "weight_loader": self.weight_loader,
                })

            # ACT_SCALE (for fp8)
            if quant_config.activation_scheme == "static":
                if not quant_config.is_checkpoint_fp8_serialized:
                    raise ValueError(
                        "Found static activation scheme for checkpoint that "
                        "was not serialized fp8.")
                self.a13_scale = nn.Parameter(torch.zeros(
                    self.num_total_experts, dtype=torch.float32),
                                              requires_grad=False)
                self.a2_scale = nn.Parameter(torch.zeros(
                    self.num_total_experts, dtype=torch.float32),
                                             requires_grad=False)

                set_weight_attrs(self.a13_scale, {
                    "weight_loader": self.weight_loader,
                })
                set_weight_attrs(self.a2_scale, {
                    "weight_loader": self.weight_loader,
                })

    def weight_loader(self, param: nn.Parameter, loaded_weight: torch.Tensor,
                      weight_name: str, expert_id: int):
        tp_rank = get_tensor_model_parallel_rank()
        param_data = param.data
        shard_size = self.intermediate_size
        shard = slice(tp_rank * shard_size, (tp_rank + 1) * shard_size)
        if weight_name.endswith("w1.weight"):
            param_data[expert_id, 0:shard_size, :] = loaded_weight[shard, :]
        if weight_name.endswith("w3.weight"):
            param_data[expert_id,
                       shard_size:2 * shard_size, :] = loaded_weight[shard, :]
        if weight_name.endswith("w2.weight"):
            param_data[expert_id, :, :] = loaded_weight[:, shard]
        if "act_scale" in weight_name or "weight_scale" in weight_name:
            param_data[expert_id] = loaded_weight

    def process_weights_after_loading(self):
        # Fp8 is the only case where we need to process after loading.
        if not self.use_fp8:
            return

        # If checkpoint is fp16, quantize here.
        if not self.quant_config.is_checkpoint_fp8_serialized:
            w13_weight = torch.empty_like(self.w13_weight.data,
                                          dtype=torch.float8_e4m3fn)
            w2_weight = torch.empty_like(self.w2_weight.data,
                                         dtype=torch.float8_e4m3fn)
            for expert in range(self.num_total_experts):
                w13_weight[
                    expert, :, :], self.w13_scale[expert] = scaled_fp8_quant(
                        self.w13_weight.data[expert, :, :])
                w2_weight[
                    expert, :, :], self.w2_scale[expert] = scaled_fp8_quant(
                        self.w2_weight.data[expert, :, :])
            self.w13_weight = nn.Parameter(w13_weight, requires_grad=False)
            self.w2_weight = nn.Parameter(w2_weight, requires_grad=False)

        # If checkpoint is fp8 + static, cleanup act_scales.
        #   Since state_dict has an act_scale per expert but our kernels
        #   are passed one act_scale shared across all experts.
        elif self.quant_config.activation_scheme == "static":
            if self.a13_scale is None or self.a2_scale is None:
                raise ValueError(
                    "QuantConfig has static quantization, but found "
                    "activation scales are None.")

            if (not all_close_1d(self.a13_scale)
                    or not all_close_1d(self.a2_scale)):
                print_warning_once(
                    "Found act_scales that are not equal for fp8 MoE layer. "
                    "Using the maximum across experts for each layer. ")

            self.a13_scale = nn.Parameter(self.a13_scale.max(),
                                          requires_grad=False)
            self.a2_scale = nn.Parameter(self.a2_scale.max(),
                                         requires_grad=False)

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        num_tokens, hidden_size = hidden_states.shape
        hidden_states = hidden_states.view(-1, self.hidden_size)
        # router_logits: (num_tokens, n_experts)
        router_logits, _ = self.gate(hidden_states)
        final_hidden_states = fused_moe(hidden_states,
                                        self.w13_weight,
                                        self.w2_weight,
                                        router_logits,
                                        self.top_k,
                                        renormalize=True,
                                        inplace=True,
                                        use_fp8=self.use_fp8,
                                        w1_scale=self.w13_scale,
                                        w2_scale=self.w2_scale,
                                        a1_scale=self.a13_scale,
                                        a2_scale=self.a2_scale)

        if self.tp_size > 1:
            final_hidden_states = tensor_model_parallel_all_reduce(
                final_hidden_states)

        return final_hidden_states.view(num_tokens, hidden_size)


class MixtralAttention(nn.Module):

    def __init__(self,
                 hidden_size: int,
                 num_heads: int,
                 num_kv_heads: int,
                 max_position: int = 4096 * 32,
                 rope_theta: float = 10000,
                 cache_config: Optional[CacheConfig] = None,
                 quant_config: Optional[QuantizationConfig] = None) -> None:
        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
        self.rope_theta = rope_theta

        if isinstance(
                quant_config,
                Fp8Config) and not quant_config.is_checkpoint_fp8_serialized:
            print_warning_once(
                "For Mixtral FP8 quantization, we currently do not quantize "
                "the attention layers until their FP8 performance is improved."
            )
            quant_config = None

        self.qkv_proj = QKVParallelLinear(
            hidden_size,
            self.head_dim,
            self.total_num_heads,
            self.total_num_kv_heads,
            bias=False,
            quant_config=quant_config,
        )
        self.o_proj = RowParallelLinear(
            self.total_num_heads * self.head_dim,
            hidden_size,
            bias=False,
            quant_config=quant_config,
        )
        self.rotary_emb = get_rope(
            self.head_dim,
            rotary_dim=self.head_dim,
            max_position=max_position,
            base=int(self.rope_theta),
            is_neox_style=True,
        )
        self.attn = Attention(self.num_heads,
                              self.head_dim,
                              self.scaling,
                              num_kv_heads=self.num_kv_heads,
                              cache_config=cache_config,
                              quant_config=quant_config)

    def forward(
        self,
        positions: torch.Tensor,
        hidden_states: torch.Tensor,
        kv_cache: torch.Tensor,
        attn_metadata: AttentionMetadata,
    ) -> torch.Tensor:
        qkv, _ = self.qkv_proj(hidden_states)
        q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
        q, k = self.rotary_emb(positions, q, k)
        attn_output = self.attn(q, k, v, kv_cache, attn_metadata)
        output, _ = self.o_proj(attn_output)
        return output


class MixtralDecoderLayer(nn.Module):

    def __init__(
        self,
        config: MixtralConfig,
        cache_config: Optional[CacheConfig] = None,
        quant_config: Optional[QuantizationConfig] = None,
    ) -> None:
        super().__init__()
        self.hidden_size = config.hidden_size
        # Requires transformers > 4.32.0
        rope_theta = getattr(config, "rope_theta", 10000)
        self.self_attn = MixtralAttention(
            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,
            rope_theta=rope_theta,
            cache_config=cache_config,
            quant_config=quant_config)
        self.block_sparse_moe = MixtralMoE(
            num_experts=config.num_local_experts,
            top_k=config.num_experts_per_tok,
            hidden_size=config.hidden_size,
            intermediate_size=config.intermediate_size,
            quant_config=quant_config)
        self.input_layernorm = RMSNorm(config.hidden_size,
                                       eps=config.rms_norm_eps)
        self.post_attention_layernorm = RMSNorm(config.hidden_size,
                                                eps=config.rms_norm_eps)

    def forward(
        self,
        positions: torch.Tensor,
        hidden_states: torch.Tensor,
        kv_cache: torch.Tensor,
        attn_metadata: AttentionMetadata,
        residual: Optional[torch.Tensor],
    ) -> torch.Tensor:
        # Self Attention
        if residual is None:
            residual = hidden_states
            hidden_states = self.input_layernorm(hidden_states)
        else:
            hidden_states, residual = self.input_layernorm(
                hidden_states, residual)
        hidden_states = self.self_attn(
            positions=positions,
            hidden_states=hidden_states,
            kv_cache=kv_cache,
            attn_metadata=attn_metadata,
        )

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


class MixtralModel(nn.Module):

    def __init__(
        self,
        config: MixtralConfig,
        cache_config: Optional[CacheConfig] = None,
        quant_config: Optional[QuantizationConfig] = None,
        lora_config: Optional[LoRAConfig] = None,
    ) -> None:
        super().__init__()
        self.padding_idx = config.pad_token_id
        lora_vocab = (lora_config.lora_extra_vocab_size *
                      (lora_config.max_loras or 1)) if lora_config else 0
        self.vocab_size = config.vocab_size + lora_vocab
        self.org_vocab_size = config.vocab_size

        self.embed_tokens = VocabParallelEmbedding(
            self.vocab_size,
            config.hidden_size,
            org_num_embeddings=config.vocab_size,
        )
        self.layers = nn.ModuleList([
            MixtralDecoderLayer(config,
                                cache_config,
                                quant_config=quant_config)
            for _ in range(config.num_hidden_layers)
        ])
        self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)

    def forward(
        self,
        input_ids: torch.Tensor,
        positions: torch.Tensor,
        kv_caches: List[torch.Tensor],
        attn_metadata: AttentionMetadata,
    ) -> torch.Tensor:
        hidden_states = self.embed_tokens(input_ids)
        residual = None
        for i in range(len(self.layers)):
            layer = self.layers[i]
            hidden_states, residual = layer(positions, hidden_states,
                                            kv_caches[i], attn_metadata,
                                            residual)
        hidden_states, _ = self.norm(hidden_states, residual)
        return hidden_states


class MixtralForCausalLM(nn.Module):
    fall_back_to_pt_during_load = False

    packed_modules_mapping = {
        "qkv_proj": [
            "q_proj",
            "k_proj",
            "v_proj",
        ],
    }

    # LoRA specific attributes
    supported_lora_modules = [
        "qkv_proj",
        "o_proj",
        "embed_tokens",
        "lm_head",
    ]
    embedding_modules = {
        "embed_tokens": "input_embeddings",
        "lm_head": "output_embeddings",
    }
    embedding_padding_modules = ["lm_head"]

    def __init__(
        self,
        config: MixtralConfig,
        cache_config: Optional[CacheConfig] = None,
        quant_config: Optional[QuantizationConfig] = None,
        lora_config: Optional[LoRAConfig] = None,
    ) -> None:
        super().__init__()
        self.config = config
        self.model = MixtralModel(config,
                                  cache_config,
                                  quant_config,
                                  lora_config=lora_config)
        self.unpadded_vocab_size = config.vocab_size
        if lora_config:
            self.unpadded_vocab_size += lora_config.lora_extra_vocab_size
        self.lm_head = ParallelLMHead(
            self.unpadded_vocab_size,
            config.hidden_size,
            org_num_embeddings=config.vocab_size,
            padding_size=DEFAULT_VOCAB_PADDING_SIZE
            # We need bigger padding if using lora for kernel
            # compatibility
            if not lora_config else lora_config.lora_vocab_padding_size,
        )
        self.logits_processor = LogitsProcessor(self.unpadded_vocab_size,
                                                config.vocab_size)
        self.sampler = Sampler()

    def forward(
        self,
        input_ids: torch.Tensor,
        positions: torch.Tensor,
        kv_caches: List[torch.Tensor],
        attn_metadata: AttentionMetadata,
    ) -> torch.Tensor:
        hidden_states = self.model(input_ids, positions, kv_caches,
                                   attn_metadata)
        return hidden_states

    def compute_logits(self, hidden_states: torch.Tensor,
                       sampling_metadata: SamplingMetadata) -> torch.Tensor:
        logits = self.logits_processor(self.lm_head.weight, hidden_states,
                                       sampling_metadata)
        return logits

    def sample(
        self,
        logits: Optional[torch.Tensor],
        sampling_metadata: SamplingMetadata,
    ) -> Optional[SamplerOutput]:
        next_tokens = self.sampler(logits, sampling_metadata)
        return next_tokens

    def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
        stacked_params_mapping = [
            # (param_name, shard_name, shard_id)
            ("qkv_proj", "q_proj", "q"),
            ("qkv_proj", "k_proj", "k"),
            ("qkv_proj", "v_proj", "v"),
        ]

        expert_params_mapping = [
            # These are the weight scales for the experts
            # (param_name, weight_name, expert_id)
            ("w13_scale" if weight_name in ["w1", "w3"] else "w2_scale",
             f"experts.{expert_id}.{weight_name}.weight_scale", expert_id)
            for expert_id in range(self.config.num_local_experts)
            for weight_name in ["w1", "w2", "w3"]
        ] + [
            # These are the weights for the experts
            # (param_name, weight_name, expert_id)
            ("w13_weight" if weight_name in ["w1", "w3"] else "w2_weight",
             f"experts.{expert_id}.{weight_name}.weight", expert_id)
            for expert_id in range(self.config.num_local_experts)
            for weight_name in ["w1", "w2", "w3"]
        ] + [
            # These are the activation scales for the experts
            # (param_name, weight_name, expert_id)
            ("a13_scale" if weight_name in ["w1", "w3"] else "a2_scale",
             f"experts.{expert_id}.{weight_name}.act_scale", expert_id)
            for expert_id in range(self.config.num_local_experts)
            for weight_name in ["w1", "w2", "w3"]
        ]

        params_dict = dict(self.named_parameters())
        for name, loaded_weight in weights:
            if "rotary_emb.inv_freq" in name:
                continue

            for (param_name, weight_name, shard_id) in stacked_params_mapping:
                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
                param = params_dict[name]
                weight_loader = param.weight_loader
                weight_loader(param, loaded_weight, shard_id)
                break
            else:
                for param_name, weight_name, expert_id in expert_params_mapping:
                    if weight_name not in name:
                        continue
                    name = name.replace(weight_name, param_name)
                    param = params_dict[name]
                    weight_loader = param.weight_loader
                    weight_loader(param,
                                  loaded_weight,
                                  weight_name,
                                  expert_id=expert_id)
                    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.
                    if name.endswith("kv_scale"):
                        remapped_kv_scale_name = name.replace(
                            ".kv_scale", ".attn.kv_scale")
                        if remapped_kv_scale_name not in params_dict:
                            print_warning_once(
                                "Found kv scale in the checkpoint "
                                f"(e.g. {name}), but not found the expected "
                                f"name in the model "
                                f"(e.g. {remapped_kv_scale_name}). "
                                "kv-scale is not loaded.")
                            continue
                        else:
                            name = remapped_kv_scale_name
                    param = params_dict[name]
                    weight_loader = getattr(param, "weight_loader",
                                            default_weight_loader)
                    weight_loader(param, loaded_weight)


def all_close_1d(x: torch.Tensor) -> bool:
    assert len(x.shape) == 1
    return all(torch.allclose(x[0], x[i]) for i in range(x.shape[0]))