mirror of
https://github.com/wassname/vllm.git
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Zhuohan LiandGitHub
7076fa1c9f
Refactor the tensor parallelism, quantization, and weight-loading codes. Summary of the new features enabled by this PR: - **All models** are able to be quantized with AWQ and SqueezeLLM, and [soon GPTQ](https://github.com/vllm-project/vllm/pull/1580). - Model loading code became much simpler. - Support model parallelism for all MQA/GQA models when the number of key/value heads is smaller than the tensor parallel size.
335 lines
12 KiB
Python
335 lines
12 KiB
Python
# coding=utf-8
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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 CacheFlow 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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The input of the model is flattened to a 1D tensor of tokens. The model uses
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InputMetadata to extract the original 2D shape of the input.
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"""
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import math
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from typing import List, Optional, Tuple
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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.model_executor.input_metadata import InputMetadata
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from vllm.model_executor.layers.activation import get_act_fn
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from vllm.model_executor.layers.attention import PagedAttentionWithALiBi
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from vllm.model_executor.layers.linear import (ColumnParallelLinear,
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LinearMethodBase,
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QKVParallelLinear,
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RowParallelLinear)
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from vllm.model_executor.layers.sampler import Sampler
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from vllm.model_executor.layers.vocab_parallel_embedding import (
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VocabParallelEmbedding)
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from vllm.model_executor.parallel_utils.parallel_state import (
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get_tensor_model_parallel_rank, get_tensor_model_parallel_world_size)
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from vllm.model_executor.weight_utils import (default_weight_loader,
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hf_model_weights_iterator)
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from vllm.sequence import SamplerOutput
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KVCache = Tuple[torch.Tensor, torch.Tensor]
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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(closest_power_of_2,
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total_num_heads - closest_power_of_2)
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extra_powers = torch.arange(start=1,
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end=1 + 2 * num_remaining_heads,
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step=2,
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dtype=torch.int32)
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slopes = torch.cat(
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[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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linear_method: Optional[LinearMethodBase] = None,
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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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linear_method=linear_method,
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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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linear_method=linear_method,
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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 = PagedAttentionWithALiBi(self.num_heads, self.head_dim,
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scaling, alibi_slopes)
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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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kv_cache: KVCache,
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input_metadata: InputMetadata,
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cache_event: Optional[torch.cuda.Event],
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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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k_cache, v_cache = kv_cache
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attn_output = self.attn(q, k, v, k_cache, v_cache, input_metadata,
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cache_event)
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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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linear_method: Optional[LinearMethodBase] = None,
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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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linear_method=linear_method,
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)
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self.act = 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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linear_method=linear_method,
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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.act(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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linear_method: Optional[LinearMethodBase] = None,
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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,
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eps=config.layer_norm_epsilon)
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self.self_attention = BloomAttention(config, linear_method)
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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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self.mlp = BloomMLP(config, linear_method)
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self.apply_residual_connection_post_layernorm = (
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config.apply_residual_connection_post_layernorm)
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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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kv_cache: KVCache,
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input_metadata: InputMetadata,
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cache_event: Optional[torch.cuda.Event],
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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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kv_cache=kv_cache,
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input_metadata=input_metadata,
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cache_event=cache_event,
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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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class BloomModel(nn.Module):
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def __init__(
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self,
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config: BloomConfig,
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linear_method: Optional[LinearMethodBase] = None,
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):
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super().__init__()
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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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# Transformer blocks
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self.h = nn.ModuleList([
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BloomBlock(config, linear_method)
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for _ in range(config.num_hidden_layers)
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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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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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kv_caches: List[KVCache],
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input_metadata: InputMetadata,
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cache_events: Optional[List[torch.cuda.Event]],
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) -> torch.Tensor:
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hidden_states = self.word_embeddings(input_ids)
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hidden_states = self.word_embeddings_layernorm(hidden_states)
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for i in range(len(self.h)):
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if cache_events is None:
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cache_event = None
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else:
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cache_event = cache_events[i]
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layer = self.h[i]
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hidden_states = layer(
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position_ids,
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hidden_states,
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kv_caches[i],
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input_metadata,
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cache_event,
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)
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hidden_states = self.ln_f(hidden_states)
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return hidden_states
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class BloomForCausalLM(nn.Module):
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def __init__(
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self,
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config: BloomConfig,
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linear_method: Optional[LinearMethodBase] = None,
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):
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super().__init__()
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self.config = config
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self.linear_method = linear_method
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self.transformer = BloomModel(config, linear_method)
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self.lm_head_weight = self.transformer.word_embeddings.weight
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self.sampler = Sampler(config.vocab_size)
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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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kv_caches: List[KVCache],
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input_metadata: InputMetadata,
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cache_events: Optional[List[torch.cuda.Event]],
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) -> SamplerOutput:
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hidden_states = self.transformer(input_ids, positions, kv_caches,
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input_metadata, cache_events)
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next_tokens = self.sampler(self.lm_head_weight, hidden_states,
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input_metadata)
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return next_tokens
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def load_weights(self,
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model_name_or_path: str,
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cache_dir: Optional[str] = None,
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load_format: str = "auto",
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revision: Optional[str] = None):
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params_dict = dict(self.named_parameters(remove_duplicate=False))
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for name, loaded_weight in hf_model_weights_iterator(
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model_name_or_path, cache_dir, load_format, revision):
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if name == "lm_head.weight":
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continue
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if not name.startswith("transformer."):
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name = "transformer." + name
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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] + (num_heads, 3, -1) +
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loaded_weight_shape[output_dim + 1:])
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loaded_weight = loaded_weight.transpose(
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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",
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default_weight_loader)
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weight_loader(param, loaded_weight)
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