aphrodite-engine/aphrodite/modeling/layers/vocab_parallel_embedding.py

from dataclasses import dataclass
from typing import List, Optional, Sequence, Tuple

import torch
import torch.nn.functional as F
from torch.nn.parameter import Parameter, UninitializedParameter

from aphrodite.distributed import (divide, get_tensor_model_parallel_rank,
                                   get_tensor_model_parallel_world_size,
                                   tensor_model_parallel_all_reduce)
from aphrodite.modeling.utils import set_weight_attrs
from aphrodite.quantization.base_config import (
    QuantizationConfig, QuantizeMethodBase, method_has_implemented_embedding)

DEFAULT_VOCAB_PADDING_SIZE = 64


class UnquantizedEmbeddingMethod(QuantizeMethodBase):
    """Unquantized method for embeddings."""

    def create_weights(self, layer: torch.nn.Module,
                       input_size_per_partition: int,
                       output_partition_sizes: List[int], input_size: int,
                       output_size: int, params_dtype: torch.dtype,
                       **extra_weight_attrs):
        """Create weights for embedding layer."""
        weight = Parameter(torch.empty(sum(output_partition_sizes),
                                       input_size_per_partition,
                                       dtype=params_dtype),
                           requires_grad=False)
        set_weight_attrs(weight, {"input_dim": 1, "output_dim": 0})
        layer.register_parameter("weight", weight)
        set_weight_attrs(weight, extra_weight_attrs)

    def apply(self,
              layer: torch.nn.Module,
              x: torch.Tensor,
              bias: Optional[torch.Tensor] = None) -> torch.Tensor:
        return F.linear(x, layer.weight, bias)

    def embedding(self, layer: torch.nn.Module,
                  input_: torch.Tensor) -> torch.Tensor:
        return F.embedding(input_, layer.weight)


def pad_vocab_size(vocab_size: int,
                   pad_to: int = DEFAULT_VOCAB_PADDING_SIZE) -> int:
    """Pad the vocab size to the given value."""
    return ((vocab_size + pad_to - 1) // pad_to) * pad_to


def vocab_range_from_per_partition_vocab_size(
        per_partition_vocab_size: int,
        rank: int,
        offset: int = 0) -> Sequence[int]:
    index_f = rank * per_partition_vocab_size
    index_l = index_f + per_partition_vocab_size
    return index_f + offset, index_l + offset


def vocab_range_from_global_vocab_size(global_vocab_size: int,
                                       rank: int,
                                       world_size: int,
                                       offset: int = 0) -> Sequence[int]:
    per_partition_vocab_size = divide(global_vocab_size, world_size)
    return vocab_range_from_per_partition_vocab_size(per_partition_vocab_size,
                                                     rank,
                                                     offset=offset)


@dataclass
class VocabParallelEmbeddingShardIndices:
    """Indices for a shard of a vocab parallel embedding."""
    padded_org_vocab_start_index: int
    padded_org_vocab_end_index: int
    padded_added_vocab_start_index: int
    padded_added_vocab_end_index: int

    org_vocab_start_index: int
    org_vocab_end_index: int
    added_vocab_start_index: int
    added_vocab_end_index: int

    @property
    def num_org_elements(self) -> int:
        return self.org_vocab_end_index - self.org_vocab_start_index

    @property
    def num_added_elements(self) -> int:
        return self.added_vocab_end_index - self.added_vocab_start_index

    @property
    def num_org_elements_padded(self) -> int:
        return (self.padded_org_vocab_end_index -
                self.padded_org_vocab_start_index)

    @property
    def num_added_elements_padded(self) -> int:
        return (self.padded_added_vocab_end_index -
                self.padded_added_vocab_start_index)

    @property
    def num_org_vocab_padding(self) -> int:
        return self.num_org_elements_padded - self.num_org_elements

    @property
    def num_added_vocab_padding(self) -> int:
        return self.num_added_elements_padded - self.num_added_elements

    @property
    def num_elements_padded(self) -> int:
        return self.num_org_elements_padded + self.num_added_elements_padded

    def __post_init__(self):
        # sanity checks
        assert (self.padded_org_vocab_start_index <=
                self.padded_org_vocab_end_index)
        assert (self.padded_added_vocab_start_index <=
                self.padded_added_vocab_end_index)

        assert self.org_vocab_start_index <= self.org_vocab_end_index
        assert self.added_vocab_start_index <= self.added_vocab_end_index

        assert self.org_vocab_start_index <= self.padded_org_vocab_start_index
        assert (self.added_vocab_start_index <=
                self.padded_added_vocab_start_index)
        assert self.org_vocab_end_index <= self.padded_org_vocab_end_index
        assert self.added_vocab_end_index <= self.padded_added_vocab_end_index

        assert self.num_org_elements <= self.num_org_elements_padded
        assert self.num_added_elements <= self.num_added_elements_padded


@torch.jit.script
def get_masked_input_and_mask(
        input_: torch.Tensor, org_vocab_start_index: int,
        org_vocab_end_index: int, num_org_vocab_padding: int,
        added_vocab_start_index: int,
        added_vocab_end_index: int) -> Tuple[torch.Tensor, torch.Tensor]:
    # torch.jit.script will fuse all of the pointwise ops below
    # into a single kernel, making it very fast
    org_vocab_mask = (input_ >= org_vocab_start_index) & (input_ <
                                                          org_vocab_end_index)
    added_vocab_mask = (input_ >= added_vocab_start_index) & (
        input_ < added_vocab_end_index)
    added_offset = added_vocab_start_index - (
        org_vocab_end_index - org_vocab_start_index) - num_org_vocab_padding
    valid_offset = (org_vocab_start_index *
                    org_vocab_mask) + (added_offset * added_vocab_mask)
    vocab_mask = org_vocab_mask | added_vocab_mask
    input_ = vocab_mask * (input_ - valid_offset)
    return input_, ~vocab_mask


class VocabParallelEmbedding(torch.nn.Module):
    """Embedding parallelized in the vocabulary dimension.

    Adapted from torch.nn.Embedding, note that we pad the vocabulary size to
    make sure it is divisible by the number of model parallel GPUs.

    In order to support various loading methods, we ensure that LoRA-added
    embeddings are always at the end of TP-sharded tensors. In other words,
    we shard base embeddings and LoRA embeddings separately (both padded),
    and place them in the same tensor.
    In this example, we will have the original vocab size = 1010,
    added vocab size = 16 and padding to 64. Therefore, the total
    vocab size with padding will be 1088 (because we first pad 1010 to
    1024, add 16, and then pad to 1088).
    Therefore, the tensor format looks like the following:
    TP1, rank 0 (no sharding):
                            |< --------BASE-------- >|< -BASE PADDING-- >|< -----LORA------ >|< -LORA PADDING-- >|
    corresponding token_id: |  0  |  1  | ... | 1009 |  -1  | ... |  -1  | 1010 | ... | 1015 |  -1  | ... |  -1  |
                     index: |  0  |  1  | ... | 1009 | 1010 | ... | 1023 | 1024 | ... | 1039 | 1040 | ... | 1087 |
    TP2, rank 0:
                            |< --------------------BASE--------------------- >|< -----LORA------ >|< -LORA PADDING- >|
    corresponding token_id: |  0  |  1  |  2  | ... | 497  | 498 | ...  | 511 | 1000 | ... | 1015 |  -1  | ... |  -1 |
                     index: |  0  |  1  |  2  | ... | 497  | 498 | ...  | 511 | 512  | ... | 527  |  520 | ... | 543 |
    TP2, rank 1:
                            |< -----------BASE----------- >|< -BASE PADDING- >|< -----------LORA PADDING----------- >|
    corresponding token_id: | 512 | 513 | 514 | ... | 1009 | -1  | ...  | -1  |  -1  | ... |  -1  | -1  | ... |   -1 |
                     index: |  0  |  1  |  2  | ... | 497  | 498 | ...  | 511 | 512  | ... | 519  | 520 | ... |  543 |

    Args:
        num_embeddings: vocabulary size.
        embedding_dim: size of hidden state.
        params_dtype: type of the parameters.
        org_num_embeddings: original vocabulary size (without LoRA).
        padding_size: padding size for the vocabulary.
        quant_config: quant config for the layer.
        prefix: full name of the layer in the state dict
    """  # noqa: E501

    def __init__(self,
                 num_embeddings: int,
                 embedding_dim: int,
                 params_dtype: Optional[torch.dtype] = None,
                 org_num_embeddings: Optional[int] = None,
                 padding_size: Optional[int] = None,
                 quant_config: Optional[QuantizationConfig] = None,
                 prefix: str = ""):
        super().__init__()

        padding_size = padding_size or get_tensor_model_parallel_world_size()
        # Keep the input dimensions.
        tp_rank = get_tensor_model_parallel_rank()
        self.tp_size = get_tensor_model_parallel_world_size()
        self.num_embeddings = num_embeddings
        self.padding_size = padding_size
        self.org_vocab_size = org_num_embeddings or num_embeddings
        num_added_embeddings = num_embeddings - self.org_vocab_size
        self.org_vocab_size_padded = pad_vocab_size(self.org_vocab_size,
                                                    self.padding_size)
        self.num_embeddings_padded = pad_vocab_size(
            self.org_vocab_size_padded + num_added_embeddings,
            self.padding_size)
        assert self.org_vocab_size_padded <= self.num_embeddings_padded

        self.shard_indices = self._get_indices(self.num_embeddings_padded,
                                               self.org_vocab_size_padded,
                                               self.num_embeddings,
                                               self.org_vocab_size, tp_rank,
                                               self.tp_size)
        self.embedding_dim = embedding_dim

        linear_method = None
        if quant_config is not None:
            linear_method = quant_config.get_quant_method(self, prefix=prefix)
        if linear_method is None:
            linear_method = UnquantizedEmbeddingMethod()

        # If we are making an embedding layer, then our quantization linear
        # method must implement the embedding operation. If we are another
        # layer type like ParallelLMHead, this is not important.
        is_embedding_layer = type(self.__class__) is VocabParallelEmbedding
        linear_method_implements_embedding = method_has_implemented_embedding(
            type(linear_method))
        if is_embedding_layer and not linear_method_implements_embedding:
            raise NotImplementedError(
                f"The class {type(linear_method).__name__} must implement "
                "the 'embedding' method, see UnquantizedEmbeddingMethod.")
        self.linear_method: QuantizeMethodBase = linear_method

        if params_dtype is None:
            params_dtype = torch.get_default_dtype()
        # Divide the weight matrix along the vocaburaly dimension.
        self.num_added_embeddings = self.num_embeddings - self.org_vocab_size
        self.num_embeddings_per_partition = divide(self.num_embeddings_padded,
                                                   self.tp_size)
        assert (self.shard_indices.num_elements_padded ==
                self.num_embeddings_per_partition)
        self.num_org_embeddings_per_partition = (
            self.shard_indices.org_vocab_end_index -
            self.shard_indices.org_vocab_start_index)
        self.num_added_embeddings_per_partition = (
            self.shard_indices.added_vocab_end_index -
            self.shard_indices.added_vocab_start_index)
        self.linear_method.create_weights(self,
                                          self.embedding_dim,
                                          [self.num_embeddings_per_partition],
                                          self.embedding_dim,
                                          self.num_embeddings_padded,
                                          params_dtype=params_dtype,
                                          weight_loader=self.weight_loader)

    @classmethod
    def _get_indices(cls, vocab_size_padded: int, org_vocab_size_padded: int,
                     vocab_size: int, org_vocab_size: int, tp_rank: int,
                     tp_size: int) -> VocabParallelEmbeddingShardIndices:
        """Get start and end indices for vocab parallel embedding, following the
        layout outlined in the class docstring, based on the given tp_rank and
        tp_size."""
        num_added_embeddings_padded = vocab_size_padded - org_vocab_size_padded
        padded_org_vocab_start_index, padded_org_vocab_end_index = (
            vocab_range_from_global_vocab_size(org_vocab_size_padded, tp_rank,
                                               tp_size))
        padded_added_vocab_start_index, padded_added_vocab_end_index = (
            vocab_range_from_global_vocab_size(num_added_embeddings_padded,
                                               tp_rank,
                                               tp_size,
                                               offset=org_vocab_size))
        # remove padding
        org_vocab_start_index = min(padded_org_vocab_start_index,
                                    org_vocab_size)
        org_vocab_end_index = min(padded_org_vocab_end_index, org_vocab_size)
        added_vocab_start_index = min(padded_added_vocab_start_index,
                                      vocab_size)
        added_vocab_end_index = min(padded_added_vocab_end_index, vocab_size)
        return VocabParallelEmbeddingShardIndices(
            padded_org_vocab_start_index, padded_org_vocab_end_index,
            padded_added_vocab_start_index, padded_added_vocab_end_index,
            org_vocab_start_index, org_vocab_end_index,
            added_vocab_start_index, added_vocab_end_index)

    def get_sharded_to_full_mapping(self) -> Optional[List[int]]:
        """Get a mapping that can be used to reindex the gathered
        logits for sampling.
        
        During sampling, we gather logits from all ranks. The relationship
        of index->token_id will follow the same format as outlined in the class
        docstring. However, after the gather, we want to reindex the final
        logits tensor to map index->token_id one-to-one (the index is always
        equal the token_id it corresponds to). The indices returned by this
        method allow us to do that.
        """
        if self.tp_size < 2:
            return None

        base_embeddings: List[int] = []
        added_embeddings: List[int] = []
        padding: List[int] = []
        for tp_rank in range(self.tp_size):
            shard_indices = self._get_indices(self.num_embeddings_padded,
                                              self.org_vocab_size_padded,
                                              self.num_embeddings,
                                              self.org_vocab_size, tp_rank,
                                              self.tp_size)
            range_start = self.num_embeddings_per_partition * tp_rank
            range_end = self.num_embeddings_per_partition * (tp_rank + 1)
            base_embeddings.extend(
                range(range_start,
                      range_start + shard_indices.num_org_elements))
            padding.extend(
                range(range_start + shard_indices.num_org_elements,
                      range_start + shard_indices.num_org_elements_padded))
            added_embeddings.extend(
                range(
                    range_start + shard_indices.num_org_elements_padded,
                    range_start + shard_indices.num_org_elements_padded +
                    shard_indices.num_added_elements))
            padding.extend(
                range(
                    range_start + shard_indices.num_org_elements_padded +
                    shard_indices.num_added_elements,
                    range_start + shard_indices.num_org_elements_padded +
                    shard_indices.num_added_elements_padded))
            assert (range_start + shard_indices.num_org_elements_padded +
                    shard_indices.num_added_elements_padded == range_end)
        ret = base_embeddings + added_embeddings + padding
        assert len(ret) == self.num_embeddings_padded
        return ret

    def weight_loader(self, param: Parameter, loaded_weight: torch.Tensor):
        output_dim = getattr(param, "output_dim", None)
        packed_dim = getattr(param, "packed_dim", None)

        # If the parameter is a gguf weight, then load it directly.
        if getattr(param, "is_gguf_weight_type", None):
            param.data.copy_(loaded_weight)
            param.weight_type = loaded_weight.item()
            return
        elif isinstance(param, UninitializedParameter):
            param.materialize(loaded_weight.shape, dtype=loaded_weight.dtype)

        # If parameter does not have output dim, then it should
        # be copied onto all gpus (e.g. g_idx for act_order gptq).
        if output_dim is None:
            assert param.data.shape == loaded_weight.shape
            param.data.copy_(loaded_weight)
            return

        # Shard indexes for loading the weight
        start_idx = self.shard_indices.org_vocab_start_index
        shard_size = self.shard_indices.org_vocab_end_index - start_idx

        # If param packed on the same dim we are sharding on, then
        # need to adjust offsets of loaded weight by pack_factor.
        if packed_dim is not None and packed_dim == output_dim:
            assert loaded_weight.shape[output_dim] == (self.org_vocab_size //
                                                       param.pack_factor)
            start_idx = start_idx // param.pack_factor
            shard_size = shard_size // param.pack_factor
        else:
            assert loaded_weight.shape[output_dim] == self.org_vocab_size

        # Copy the data.
        loaded_weight = loaded_weight.narrow(output_dim, start_idx, shard_size)
        param[:loaded_weight.shape[0]].data.copy_(loaded_weight)
        param[loaded_weight.shape[0]:].data.fill_(0)

    def forward(self, input_):
        if self.tp_size > 1:
            # Build the mask.
            masked_input, input_mask = get_masked_input_and_mask(
                input_, self.shard_indices.org_vocab_start_index,
                self.shard_indices.org_vocab_end_index,
                self.shard_indices.num_org_vocab_padding,
                self.shard_indices.added_vocab_start_index,
                self.shard_indices.added_vocab_end_index)
        else:
            masked_input = input_
        # Get the embeddings.
        output_parallel = self.linear_method.embedding(self,
                                                       masked_input.long())
        # Mask the output embedding.
        if self.tp_size > 1:
            output_parallel.masked_fill_(input_mask.unsqueeze(-1), 0)
        # Reduce across all the model parallel GPUs.
        output = tensor_model_parallel_all_reduce(output_parallel)
        return output

    def extra_repr(self) -> str:
        s = f"num_embeddings={self.num_embeddings_per_partition}"
        s += f", embedding_dim={self.embedding_dim}"
        s += f", org_vocab_size={self.org_vocab_size}"
        s += f', num_embeddings_padded={self.num_embeddings_padded}'
        s += f', tp_size={self.tp_size}'
        return s


class ParallelLMHead(VocabParallelEmbedding):
    """Parallelized LM head.

    Output logits weight matrices used in the Sampler. The weight and bias
    tensors are padded to make sure they are divisible by the number of
    model parallel GPUs.

    Args:
        num_embeddings: vocabulary size.
        embedding_dim: size of hidden state.
        bias: whether to use bias.
        params_dtype: type of the parameters.
        org_num_embeddings: original vocabulary size (without LoRA).
        padding_size: padding size for the vocabulary.
    """

    def __init__(self,
                 num_embeddings: int,
                 embedding_dim: int,
                 bias: bool = False,
                 params_dtype: Optional[torch.dtype] = None,
                 org_num_embeddings: Optional[int] = None,
                 padding_size: Optional[int] = None,
                 quant_config: Optional[QuantizationConfig] = None,
                 prefix: str = ""):
        super().__init__(num_embeddings, embedding_dim, params_dtype,
                         org_num_embeddings, padding_size, quant_config,
                         prefix)
        if bias:
            self.bias = Parameter(
                torch.empty(self.num_embeddings_per_partition,
                            dtype=params_dtype))
            set_weight_attrs(self.bias, {
                "output_dim": 0,
                "weight_loader": self.weight_loader,
            })
        else:
            self.register_parameter("bias", None)

    def forward(self, input_):
        logits = self.linear_method.apply_weights(self.linear_weights, input_)
        if self.bias is not None:
            logits += self.bias
        return logits


class ParallelTWEHead(torch.nn.Module):
    """Parallelized tie word embeddings head.

    Output logits weight matrices used in the Sampler. The weight and bias
    tensors are read from a VocabParallelEmbedding.

    Args:
        embeddings: the VocabParallelEmbedding to mirror
    """

    def __init__(self, embeddings: VocabParallelEmbedding):
        super().__init__()
        self.linear_method = embeddings.linear_method
        self.linear_weights = embeddings.linear_weights

    def forward(self, input_):
        logits = self.linear_method.apply_weights(self.linear_weights, input_)
        return logits