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model_executor/models/bloom.py
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390
model_executor/models/bloom.py
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# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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# Adapted from
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# https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/bloom/modeling_bloom.py
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# Copyright 2023 The vLLM team.
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# Copyright 2022 HuggingFace Inc. team and BigScience workshop.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Inference-only BLOOM model compatible with HuggingFace weights."""
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import math
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from collections.abc import Iterable
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from itertools import islice
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import torch
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from torch import nn
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from transformers import BloomConfig
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from vllm.attention import Attention
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from vllm.compilation.decorators import support_torch_compile
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from vllm.config import CacheConfig, VllmConfig
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from vllm.distributed import (
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get_pp_group,
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get_tensor_model_parallel_rank,
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get_tensor_model_parallel_world_size,
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)
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from vllm.model_executor.layers.activation import get_act_fn
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from vllm.model_executor.layers.linear import (
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ColumnParallelLinear,
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QKVParallelLinear,
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RowParallelLinear,
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)
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from vllm.model_executor.layers.logits_processor import LogitsProcessor
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from vllm.model_executor.layers.quantization import QuantizationConfig
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from vllm.model_executor.layers.vocab_parallel_embedding import (
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ParallelLMHead,
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VocabParallelEmbedding,
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)
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from vllm.model_executor.model_loader.weight_utils import default_weight_loader
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from vllm.sequence import IntermediateTensors
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from .interfaces import SupportsPP, SupportsQuant
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from .utils import (
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AutoWeightsLoader,
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is_pp_missing_parameter,
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make_empty_intermediate_tensors_factory,
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make_layers,
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maybe_prefix,
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)
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def _get_alibi_slopes(total_num_heads: int) -> torch.Tensor:
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closest_power_of_2 = 2 ** math.floor(math.log2(total_num_heads))
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base = torch.tensor(
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2 ** (-(2 ** -(math.log2(closest_power_of_2) - 3))),
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dtype=torch.float32,
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)
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powers = torch.arange(1, 1 + closest_power_of_2, dtype=torch.int32)
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slopes = torch.pow(base, powers)
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if closest_power_of_2 != total_num_heads:
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extra_base = torch.tensor(
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2 ** (-(2 ** -(math.log2(2 * closest_power_of_2) - 3))),
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dtype=torch.float32,
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)
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num_remaining_heads = min(
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closest_power_of_2, total_num_heads - closest_power_of_2
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)
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extra_powers = torch.arange(
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start=1, end=1 + 2 * num_remaining_heads, step=2, dtype=torch.int32
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)
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slopes = torch.cat([slopes, torch.pow(extra_base, extra_powers)], dim=0)
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return slopes
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class BloomAttention(nn.Module):
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def __init__(
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self,
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config: BloomConfig,
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cache_config: CacheConfig | None = None,
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quant_config: QuantizationConfig | None = None,
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prefix: str = "",
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):
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super().__init__()
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self.hidden_size = config.hidden_size
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self.total_num_heads = config.n_head
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self.head_dim = self.hidden_size // self.total_num_heads
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assert self.head_dim * self.total_num_heads == self.hidden_size
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tp_world_size = get_tensor_model_parallel_world_size()
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assert self.total_num_heads % tp_world_size == 0
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self.num_heads = self.total_num_heads // tp_world_size
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self.query_key_value = QKVParallelLinear(
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self.hidden_size,
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self.head_dim,
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self.total_num_heads,
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bias=True,
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quant_config=quant_config,
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prefix=f"{prefix}.query_key_value",
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)
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self.dense = RowParallelLinear(
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self.hidden_size,
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self.hidden_size,
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bias=True,
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quant_config=quant_config,
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prefix=f"{prefix}.dense",
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)
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# Create the alibi slopes and slice them.
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tp_rank = get_tensor_model_parallel_rank()
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head_start = tp_rank * self.num_heads
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head_end = (tp_rank + 1) * self.num_heads
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alibi_slopes = _get_alibi_slopes(self.total_num_heads)
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alibi_slopes = alibi_slopes[head_start:head_end].tolist()
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scaling = self.head_dim**-0.5
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self.attn = Attention(
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self.num_heads,
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self.head_dim,
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scaling,
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alibi_slopes=alibi_slopes,
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cache_config=cache_config,
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quant_config=quant_config,
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prefix=f"{prefix}.attn",
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)
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def forward(
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self,
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position_ids: torch.Tensor,
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hidden_states: torch.Tensor,
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) -> torch.Tensor:
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del position_ids # Unused.
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qkv, _ = self.query_key_value(hidden_states)
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q, k, v = qkv.chunk(chunks=3, dim=-1)
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attn_output = self.attn(q, k, v)
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output, _ = self.dense(attn_output)
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return output
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class BloomMLP(nn.Module):
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def __init__(
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self,
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config: BloomConfig,
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quant_config: QuantizationConfig | None = None,
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prefix: str = "",
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):
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super().__init__()
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hidden_size = config.hidden_size
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self.dense_h_to_4h = ColumnParallelLinear(
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hidden_size,
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4 * hidden_size,
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quant_config=quant_config,
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prefix=f"{prefix}.dense_h_to_4h",
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)
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self.gelu_impl = get_act_fn("gelu")
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self.dense_4h_to_h = RowParallelLinear(
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4 * hidden_size,
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hidden_size,
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quant_config=quant_config,
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prefix=f"{prefix}.dense_4h_to_h",
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)
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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x, _ = self.dense_h_to_4h(x)
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x = self.gelu_impl(x)
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x, _ = self.dense_4h_to_h(x)
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return x
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class BloomBlock(nn.Module):
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def __init__(
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self,
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config: BloomConfig,
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cache_config: CacheConfig | None = None,
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quant_config: QuantizationConfig | None = None,
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prefix: str = "",
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):
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super().__init__()
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hidden_size = config.hidden_size
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self.input_layernorm = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
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self.self_attention = BloomAttention(
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config, cache_config, quant_config, prefix=f"{prefix}.self_attention"
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)
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self.post_attention_layernorm = nn.LayerNorm(
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hidden_size, eps=config.layer_norm_epsilon
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)
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self.mlp = BloomMLP(config, quant_config, prefix=f"{prefix}.mlp")
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self.apply_residual_connection_post_layernorm = (
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config.apply_residual_connection_post_layernorm
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)
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def forward(
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self,
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position_ids: torch.Tensor,
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hidden_states: torch.Tensor,
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) -> torch.Tensor:
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# Layer norm at the beginning of the transformer layer.
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layernorm_output = self.input_layernorm(hidden_states)
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# Layer norm post the self attention.
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if self.apply_residual_connection_post_layernorm:
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residual = layernorm_output
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else:
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residual = hidden_states
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# Self attention.
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attention_output = self.self_attention(
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position_ids=position_ids,
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hidden_states=layernorm_output,
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)
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attention_output = attention_output + residual
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layernorm_output = self.post_attention_layernorm(attention_output)
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# Get residual
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if self.apply_residual_connection_post_layernorm:
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residual = layernorm_output
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else:
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residual = attention_output
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# MLP.
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output = self.mlp(layernorm_output) + residual
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return output
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@support_torch_compile
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class BloomModel(nn.Module):
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def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
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super().__init__()
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config = vllm_config.model_config.hf_config
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cache_config = vllm_config.cache_config
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quant_config = vllm_config.quant_config
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self.config = config
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self.embed_dim = config.hidden_size
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# Embedding + LN Embedding
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self.word_embeddings = VocabParallelEmbedding(
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config.vocab_size,
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self.embed_dim,
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)
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self.word_embeddings_layernorm = nn.LayerNorm(
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self.embed_dim, eps=config.layer_norm_epsilon
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)
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# Transformer blocks
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self.start_layer, self.end_layer, self.h = make_layers(
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config.num_hidden_layers,
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lambda prefix: BloomBlock(
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config, cache_config, quant_config, prefix=prefix
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),
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prefix=f"{prefix}.h",
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)
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# Final Layer Norm
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self.ln_f = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_epsilon)
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self.make_empty_intermediate_tensors = make_empty_intermediate_tensors_factory(
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["hidden_states"], config.hidden_size
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)
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def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
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return self.word_embeddings(input_ids)
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def forward(
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self,
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input_ids: torch.Tensor,
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position_ids: torch.Tensor,
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intermediate_tensors: IntermediateTensors | None,
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inputs_embeds: torch.Tensor | None = None,
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) -> torch.Tensor | IntermediateTensors:
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if get_pp_group().is_first_rank:
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if inputs_embeds is not None:
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hidden_states = inputs_embeds
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else:
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hidden_states = self.embed_input_ids(input_ids)
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hidden_states = self.word_embeddings_layernorm(hidden_states)
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else:
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assert intermediate_tensors is not None
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hidden_states = intermediate_tensors["hidden_states"]
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for layer in islice(self.h, self.start_layer, self.end_layer):
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hidden_states = layer(position_ids, hidden_states)
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if not get_pp_group().is_last_rank:
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return IntermediateTensors({"hidden_states": hidden_states})
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hidden_states = self.ln_f(hidden_states)
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return hidden_states
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def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
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params_dict = dict(self.named_parameters(remove_duplicate=False))
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loaded_params: set[str] = set()
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for name, loaded_weight in weights:
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if is_pp_missing_parameter(name, self):
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continue
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param = params_dict[name]
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if "query_key_value" in name:
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# NOTE: BLOOM's fused QKV's output_dim has the shape of
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# (num_heads * 3 * head_size), while the
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# required shape is (3 * num_heads * head_size).
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# Thus, we need weight conversion.
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output_dim = getattr(param, "output_dim", None)
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num_heads = self.config.num_attention_heads
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if output_dim is not None:
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loaded_weight_shape = loaded_weight.shape
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loaded_weight = loaded_weight.view(
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loaded_weight_shape[:output_dim]
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+ (num_heads, 3, -1)
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+ loaded_weight_shape[output_dim + 1 :]
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)
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loaded_weight = loaded_weight.transpose(output_dim, output_dim + 1)
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loaded_weight = loaded_weight.reshape(loaded_weight_shape)
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weight_loader = getattr(param, "weight_loader", default_weight_loader)
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weight_loader(param, loaded_weight)
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loaded_params.add(name)
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return loaded_params
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class BloomForCausalLM(nn.Module, SupportsPP, SupportsQuant):
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def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
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super().__init__()
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config = vllm_config.model_config.hf_config
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quant_config = vllm_config.quant_config
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self.config = config
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self.quant_config = quant_config
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self.transformer = BloomModel(
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vllm_config=vllm_config, prefix=maybe_prefix(prefix, "transformer")
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)
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if self.config.tie_word_embeddings:
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self.lm_head = self.transformer.word_embeddings
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else:
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self.lm_head = ParallelLMHead(
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self.config.vocab_size,
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self.config.hidden_size,
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prefix=maybe_prefix(prefix, "lm_head"),
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)
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self.logits_processor = LogitsProcessor(config.vocab_size)
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self.make_empty_intermediate_tensors = (
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self.transformer.make_empty_intermediate_tensors
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)
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def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
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return self.transformer.embed_input_ids(input_ids)
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def forward(
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self,
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input_ids: torch.Tensor,
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positions: torch.Tensor,
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intermediate_tensors: IntermediateTensors | None = None,
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inputs_embeds: torch.Tensor | None = None,
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) -> torch.Tensor | IntermediateTensors:
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hidden_states = self.transformer(
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input_ids, positions, intermediate_tensors, inputs_embeds
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)
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return hidden_states
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def compute_logits(
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self,
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hidden_states: torch.Tensor,
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) -> torch.Tensor | None:
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logits = self.logits_processor(self.lm_head, hidden_states)
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return logits
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def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
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loader = AutoWeightsLoader(self, skip_prefixes=["lm_head.weight"])
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weights = _add_transformer_prefix(weights)
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return loader.load_weights(weights)
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def _add_transformer_prefix(
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weights: Iterable[tuple[str, torch.Tensor]],
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) -> Iterable[tuple[str, torch.Tensor]]:
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for name, tensor in weights:
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if not name.startswith("transformer."):
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name = "transformer." + name
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yield name, tensor
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