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

# coding=utf-8
# Adapted from
# https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/bloom/modeling_bloom.py
# Copyright 2023 The PygmalionAI team.
# Copyright 2023 The CacheFlow team.
# Copyright 2022 HuggingFace Inc. team and BigScience workshop.
#
# 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 BLOOM model compatible with HuggingFace weights."""
import math
from typing import List, Optional

import torch
from torch import nn
from transformers import BloomConfig

from aphrodite.attention import Attention, AttentionMetadata
from aphrodite.modeling.layers.activation import get_act_fn
from aphrodite.modeling.layers.linear import (ColumnParallelLinear,
                                              LinearMethodBase,
                                              QKVParallelLinear,
                                              RowParallelLinear)
from aphrodite.modeling.layers.logits_processor import LogitsProcessor
from aphrodite.modeling.layers.sampler import Sampler
from aphrodite.modeling.layers.vocab_parallel_embedding import (
    VocabParallelEmbedding, ParallelLMHead)
from aphrodite.distributed import (get_tensor_model_parallel_rank,
                                   get_tensor_model_parallel_world_size)
from aphrodite.modeling.sampling_metadata import SamplingMetadata
from aphrodite.modeling.hf_downloader import (default_weight_loader,
                                              hf_model_weights_iterator)
from aphrodite.common.sequence import SamplerOutput


def _get_alibi_slopes(total_num_heads: int) -> torch.Tensor:
    closest_power_of_2 = 2**math.floor(math.log2(total_num_heads))
    base = torch.tensor(
        2**(-(2**-(math.log2(closest_power_of_2) - 3))),
        dtype=torch.float32,
    )
    powers = torch.arange(1, 1 + closest_power_of_2, dtype=torch.int32)
    slopes = torch.pow(base, powers)

    if closest_power_of_2 != total_num_heads:
        extra_base = torch.tensor(
            2**(-(2**-(math.log2(2 * closest_power_of_2) - 3))),
            dtype=torch.float32,
        )
        num_remaining_heads = min(closest_power_of_2,
                                  total_num_heads - closest_power_of_2)
        extra_powers = torch.arange(start=1,
                                    end=1 + 2 * num_remaining_heads,
                                    step=2,
                                    dtype=torch.int32)
        slopes = torch.cat(
            [slopes, torch.pow(extra_base, extra_powers)], dim=0)
    return slopes


class BloomAttention(nn.Module):

    def __init__(
        self,
        config: BloomConfig,
        linear_method: Optional[LinearMethodBase] = None,
    ):
        super().__init__()
        self.hidden_size = config.hidden_size
        self.total_num_heads = config.n_head
        self.head_dim = self.hidden_size // self.total_num_heads
        assert self.head_dim * self.total_num_heads == self.hidden_size

        tp_world_size = get_tensor_model_parallel_world_size()
        assert self.total_num_heads % tp_world_size == 0
        self.num_heads = self.total_num_heads // tp_world_size

        self.query_key_value = QKVParallelLinear(
            self.hidden_size,
            self.head_dim,
            self.total_num_heads,
            bias=True,
            linear_method=linear_method,
        )
        self.dense = RowParallelLinear(
            self.hidden_size,
            self.hidden_size,
            bias=True,
            linear_method=linear_method,
        )

        # Create the alibi slopes and slice them.
        tp_rank = get_tensor_model_parallel_rank()
        head_start = tp_rank * self.num_heads
        head_end = (tp_rank + 1) * self.num_heads
        alibi_slopes = _get_alibi_slopes(self.total_num_heads)
        alibi_slopes = alibi_slopes[head_start:head_end].tolist()

        scaling = self.head_dim**-0.5
        self.attn = Attention(self.num_heads,
                              self.head_dim,
                              scaling,
                              alibi_slopes=alibi_slopes)

    def forward(
        self,
        position_ids: torch.Tensor,
        hidden_states: torch.Tensor,
        kv_cache: torch.Tensor,
        attn_metadata: AttentionMetadata,
    ) -> torch.Tensor:
        del position_ids  # Unused.
        qkv, _ = self.query_key_value(hidden_states)
        q, k, v = qkv.chunk(chunks=3, dim=-1)
        attn_output = self.attn(q, k, v, kv_cache, attn_metadata)
        output, _ = self.dense(attn_output)
        return output


class BloomMLP(nn.Module):

    def __init__(
        self,
        config: BloomConfig,
        linear_method: Optional[LinearMethodBase] = None,
    ):
        super().__init__()
        hidden_size = config.hidden_size
        self.dense_h_to_4h = ColumnParallelLinear(
            hidden_size,
            4 * hidden_size,
            linear_method=linear_method,
        )
        quant_config = getattr(linear_method, "quant_config", None)
        self.gelu_impl = get_act_fn("gelu", quant_config, 4 * hidden_size)
        self.dense_4h_to_h = RowParallelLinear(
            4 * hidden_size,
            hidden_size,
            linear_method=linear_method,
        )

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        x, _ = self.dense_h_to_4h(x)
        x = self.gelu_impl(x)
        x, _ = self.dense_4h_to_h(x)
        return x


class BloomBlock(nn.Module):

    def __init__(
        self,
        config: BloomConfig,
        linear_method: Optional[LinearMethodBase] = None,
    ):
        super().__init__()
        hidden_size = config.hidden_size

        self.input_layernorm = nn.LayerNorm(hidden_size,
                                            eps=config.layer_norm_epsilon)
        self.self_attention = BloomAttention(config, linear_method)
        self.post_attention_layernorm = nn.LayerNorm(
            hidden_size, eps=config.layer_norm_epsilon)
        self.mlp = BloomMLP(config, linear_method)
        self.apply_residual_connection_post_layernorm = (
            config.apply_residual_connection_post_layernorm)

    def forward(
        self,
        position_ids: torch.Tensor,
        hidden_states: torch.Tensor,
        kv_cache: torch.Tensor,
        attn_metadata: AttentionMetadata,
    ) -> torch.Tensor:
        # Layer norm at the beginning of the transformer layer.
        layernorm_output = self.input_layernorm(hidden_states)

        # Layer norm post the self attention.
        if self.apply_residual_connection_post_layernorm:
            residual = layernorm_output
        else:
            residual = hidden_states

        # Self attention.
        attention_output = self.self_attention(
            position_ids=position_ids,
            hidden_states=layernorm_output,
            kv_cache=kv_cache,
            attn_metadata=attn_metadata,
        )
        attention_output = attention_output + residual
        layernorm_output = self.post_attention_layernorm(attention_output)

        # Get residual
        if self.apply_residual_connection_post_layernorm:
            residual = layernorm_output
        else:
            residual = attention_output

        # MLP.
        output = self.mlp(layernorm_output) + residual
        return output


class BloomModel(nn.Module):

    def __init__(
        self,
        config: BloomConfig,
        linear_method: Optional[LinearMethodBase] = None,
    ):
        super().__init__()
        self.embed_dim = config.hidden_size

        # Embedding + LN Embedding
        self.word_embeddings = VocabParallelEmbedding(
            config.vocab_size, self.embed_dim, linear_method=linear_method)
        self.word_embeddings_layernorm = nn.LayerNorm(
            self.embed_dim, eps=config.layer_norm_epsilon)

        # Transformer blocks
        self.h = nn.ModuleList([
            BloomBlock(config, linear_method)
            for _ in range(config.num_hidden_layers)
        ])

        # Final Layer Norm
        self.ln_f = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_epsilon)

    def forward(
        self,
        input_ids: torch.Tensor,
        position_ids: torch.Tensor,
        kv_caches: List[torch.Tensor],
        attn_metadata: AttentionMetadata,
    ) -> torch.Tensor:
        hidden_states = self.word_embeddings(input_ids)
        hidden_states = self.word_embeddings_layernorm(hidden_states)
        for i in range(len(self.h)):
            layer = self.h[i]
            hidden_states = layer(
                position_ids,
                hidden_states,
                kv_caches[i],
                attn_metadata,
            )
        hidden_states = self.ln_f(hidden_states)
        return hidden_states


class BloomForCausalLM(nn.Module):

    def __init__(
        self,
        config: BloomConfig,
        linear_method: Optional[LinearMethodBase] = None,
    ):
        super().__init__()
        self.config = config
        self.linear_method = linear_method
        self.transformer = BloomModel(config, linear_method)
        self.lm_head_weight = self.transformer.word_embeddings.weight
        self.lm_head = ParallelLMHead(config.vocab_size,
                                      config.hidden_size,
                                      linear_method=linear_method)
        self.logits_processor = LogitsProcessor(config.vocab_size,
                                                config.tokenizer_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.transformer(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, hidden_states,
                                       sampling_metadata)
        return logits

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

    def load_weights(self,
                     model_name_or_path: str,
                     cache_dir: Optional[str] = None,
                     load_format: str = "auto",
                     revision: Optional[str] = None):
        params_dict = dict(self.named_parameters(remove_duplicate=False))
        for name, loaded_weight in hf_model_weights_iterator(
                model_name_or_path, cache_dir, load_format, revision,
                self.config):
            if "lm_head" in name and name not in params_dict:
                continue
            if not name.startswith("transformer."):
                name = "transformer." + name
            param = params_dict[name]

            if "word_embeddings" in name:
                # Copy word embedding to lm_head
                head_name = name.replace("transformer.word_embeddings",
                                         "lm_head")
                if head_name in params_dict:
                    lm_head_param = params_dict[head_name]
                    weight_loader = getattr(lm_head_param, "weight_loader",
                                            default_weight_loader)
                    weight_loader(lm_head_param, loaded_weight)

            if "query_key_value" in name:
                # NOTE: BLOOM's fused QKV's output_dim has the shape of
                # (num_heads * 3 * head_size), while the
                # required shape is (3 * num_heads * head_size).
                # Thus, we need weight conversion.
                output_dim = getattr(param, "output_dim", None)
                num_heads = self.config.num_attention_heads
                if output_dim is not None:
                    loaded_weight_shape = loaded_weight.shape
                    loaded_weight = loaded_weight.view(
                        loaded_weight_shape[:output_dim] + (num_heads, 3, -1) +
                        loaded_weight_shape[output_dim + 1:])
                    loaded_weight = loaded_weight.transpose(
                        output_dim, output_dim + 1)
                    loaded_weight = loaded_weight.reshape(loaded_weight_shape)

            weight_loader = getattr(param, "weight_loader",
                                    default_weight_loader)
            weight_loader(param, loaded_weight)