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 import _custom_ops as ops
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.models.interfaces import SupportsLoRA
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, per_tensor_dequantize,
                                        per_tensor_quantize)


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),
                                       requires_grad=False)
        self.w2_weight = nn.Parameter(torch.empty(self.num_total_experts,
                                                  self.hidden_size,
                                                  self.intermediate_size,
                                                  dtype=params_dtype),
                                      requires_grad=False)

        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)
            # Allocate 2 scales for w1 and w3 respectively.
            # They will be combined to a single scale after weight loading.
            self.w13_scale = nn.Parameter(torch.ones(self.num_total_experts,
                                                     2,
                                                     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.ones(
                    self.num_total_experts, dtype=torch.float32),
                                              requires_grad=False)
                self.a2_scale = nn.Parameter(torch.ones(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]

        # Loading scales
        if "input_scale" in weight_name or "w2.weight_scale" in weight_name:
            if param_data[expert_id] != 1 and (param_data[expert_id] -
                                               loaded_weight).abs() > 1e-5:
                raise ValueError(
                    "act_scales of w1 and w3 of a layer "
                    f"must be equal. But got {param_data[expert_id]} "
                    f"vs. {loaded_weight}")
            param_data[expert_id] = loaded_weight
        elif "weight_scale" in weight_name:
            # We have to keep the weight scales of w1 and w3 because
            # we need to re-quantize w1/w3 weights after weight loading.
            assert "w1" in weight_name or "w3" in weight_name
            shard_id = 0 if "w1" in weight_name else 1
            param_data[expert_id][shard_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)

            # Re-initialize w13_scale because we directly quantize
            # merged w13 weights and generate a single scaling factor.
            self.w13_scale = nn.Parameter(torch.ones(self.num_total_experts,
                                                     dtype=torch.float32),
                                          requires_grad=False)
            for expert in range(self.num_total_experts):
                w13_weight[expert, :, :], self.w13_scale[
                    expert] = ops.scaled_fp8_quant(
                        self.w13_weight.data[expert, :, :])
                w2_weight[expert, :, :], self.w2_scale[
                    expert] = ops.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)

        else:
            # If checkpoint is fp8 + static, cleanup act_scales.
            #   Since state_dict has an input_scale per expert but our kernels
            #   are passed one input_scale shared across all experts.
            if 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)

            assert self.w13_scale is not None
            shard_size = self.intermediate_size
            max_w13_scales = self.w13_scale.max(dim=1).values
            for expert_id in range(self.num_total_experts):
                start = 0
                for shard_id in range(2):
                    dq_weight = per_tensor_dequantize(
                        self.w13_weight[expert_id][start:start +
                                                   shard_size, :],
                        self.w13_scale[expert_id][shard_id])
                    self.w13_weight[expert_id][
                        start:start + shard_size, :] = per_tensor_quantize(
                            dq_weight, max_w13_scales[expert_id])
                    start += shard_size

            self.w13_scale = nn.Parameter(max_w13_scales, 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

        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, SupportsLoRA):
    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.lora_config = lora_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}.input_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]))