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 vLLM 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 Iterable, List, Optional, Tuple

import torch
from torch import nn
from transformers import BloomConfig
import os
import re

from vllm.attention import Attention, AttentionMetadata
from vllm.config import CacheConfig
from vllm.distributed import (get_tensor_model_parallel_rank,
                              get_tensor_model_parallel_world_size)
from vllm.model_executor.layers.activation import get_act_fn
from vllm.model_executor.layers.linear import (ColumnParallelLinear,
                                               QKVParallelLinear,
                                               RowParallelLinear)
from vllm.model_executor.layers.logits_processor import LogitsProcessor
from vllm.model_executor.layers.quantization.base_config import (
    QuantizationConfig)
from vllm.model_executor.layers.sampler import Sampler
from vllm.model_executor.layers.vocab_parallel_embedding import (
    ParallelLMHead, VocabParallelEmbedding)
from vllm.model_executor.model_loader.weight_utils import default_weight_loader
from vllm.model_executor.sampling_metadata import SamplingMetadata
from vllm.sequence import IntermediateTensors, SamplerOutput
from vllm import _custom_ops as ops
from vllm.model_executor.utils import pad_weight, gemm_bank_conf


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,
        cache_config: Optional[CacheConfig] = None,
        quant_config: Optional[QuantizationConfig] = 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,
            quant_config=quant_config,
        )
        self.dense = RowParallelLinear(
            self.hidden_size,
            self.hidden_size,
            bias=True,
            quant_config=quant_config,
        )

        # 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,
                              cache_config=cache_config,
                              quant_config=quant_config)
        self.quant_method = None
        if quant_config is not None:
            self.quant_method=quant_config.get_name()
            self.quant_config=quant_config

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

    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,
        cache_config: Optional[CacheConfig] = None,
        quant_config: Optional[QuantizationConfig] = 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, cache_config,
                                             quant_config)
        self.post_attention_layernorm = nn.LayerNorm(
            hidden_size, eps=config.layer_norm_epsilon)
        self.mlp = BloomMLP(config, quant_config)
        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,
        cache_config: Optional[CacheConfig] = None,
        quant_config: Optional[QuantizationConfig] = None,
    ):
        super().__init__()
        self.embed_dim = config.hidden_size

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

        # Transformer blocks
        self.h = nn.ModuleList([
            BloomBlock(config, cache_config, quant_config)
            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,
        cache_config: Optional[CacheConfig] = None,
        quant_config: Optional[QuantizationConfig] = None,
    ):
        super().__init__()
        self.config = config
        self.quant_config = quant_config
        self.transformer = BloomModel(config, cache_config, quant_config)
        if self.config.tie_word_embeddings:
            self.lm_head = self.transformer.word_embeddings
        else:
            self.lm_head = ParallelLMHead(self.config.vocab_size,
                                          self.config.hidden_size)

        self.logits_processor = LogitsProcessor(config.vocab_size)
        self.sampler = Sampler()
        
        self.quant_method = None
        if quant_config is not None:
            self.quant_method=quant_config.get_name()
            self.quant_config=quant_config
              
        self.use_llama_nn = os.environ.get('LLAMA_NN') == '1'
        self.use_gemm_pad = os.environ.get('GEMM_PAD') == '1'
        self.use_fa_pad = os.environ.get('FA_PAD') == '1'

    def forward(
        self,
        input_ids: torch.Tensor,
        positions: torch.Tensor,
        kv_caches: List[torch.Tensor],
        attn_metadata: AttentionMetadata,
        intermediate_tensors: Optional[IntermediateTensors] = None,
    ) -> 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,
    ) -> Optional[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, weights: Iterable[Tuple[str, torch.Tensor]]):
        params_dict = dict(self.named_parameters(remove_duplicate=False))
        for name, loaded_weight in weights:
            if name == "lm_head.weight":
                continue
            if not name.startswith("transformer."):
                name = "transformer." + name
            param = params_dict[name]

            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)
        
        if self.use_llama_nn and self.quant_method is None:
            lay_key_words = [
                "self_attention.query_key_value.weight",
                "self_attention.dense.weight",
                "mlp.dense_h_to_4h.weight",
                "mlp.dense_4h_to_h.weight"
            ]
            combined_words = "|".join(lay_key_words)
            
            lay_qkv_words = ["self_attention.query_key_value.weight"]   
            qkv_words = "|".join(lay_qkv_words)  
            
            lay_qkv_bias_words = ["self_attention.query_key_value.bias"]   
            qkv_bias_words = "|".join(lay_qkv_bias_words) 
            
            for layername, weight in params_dict.items():
                if self.use_fa_pad and (re.findall(qkv_bias_words, layername)):
                    weight.data = pad_weight(weight.data, 32)
                    
                matches = re.findall(combined_words, layername)
                if matches:   
                    if self.use_gemm_pad and gemm_bank_conf(weight.data.shape[0]):
                        weight.data = pad_weight(weight.data, 32)  
                    
                    if self.use_fa_pad and (re.findall(qkv_words, layername)):
                        if not gemm_bank_conf(weight.data.shape[0]):
                            weight.data = pad_weight(weight.data, 32)
                        
                    _weight = torch.zeros_like(weight.data)
                    ori_shape =_weight.shape
                    
                    ops.trans_w16_gemm(_weight, weight.data, _weight.shape[0], _weight.shape[1])
                    weight.data.copy_(_weight)
                    
                    weight.data=weight.data.reshape(ori_shape[1],-1)