[Model] Add support for MPT (#334)
This commit is contained in:
Woosuk Kwon
279
vllm/model_executor/models/mpt.py
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279
vllm/model_executor/models/mpt.py
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# Adapted from https://huggingface.co/mosaicml/mpt-7b/tree/main
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import math
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from typing import Dict, List, Optional, Tuple
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import torch
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import torch.nn as nn
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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.sampler import Sampler
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from vllm.model_executor.weight_utils import (hf_model_weights_iterator,
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load_tensor_parallel_weights)
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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.parallel_utils.tensor_parallel import (
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VocabParallelEmbedding, ColumnParallelLinear, RowParallelLinear)
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from vllm.sequence import SequenceOutputs
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from vllm.transformers_utils.configs.mpt import MPTConfig
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KVCache = Tuple[torch.Tensor, torch.Tensor]
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def _get_alibi_slopes(
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total_num_heads: int,
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alibi_bias_max: int,
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) -> torch.Tensor:
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next_power_of_2 = 2**math.ceil(math.log2(total_num_heads))
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m = torch.arange(1, next_power_of_2 + 1, dtype=torch.float32)
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m = m.mul(alibi_bias_max / next_power_of_2)
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slopes = 1.0 / torch.pow(2, m)
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if next_power_of_2 != total_num_heads:
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slopes = torch.concat([slopes[1::2], slopes[::2]])[:total_num_heads]
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return slopes
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class MPTAttention(nn.Module):
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def __init__(self, config: MPTConfig):
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super().__init__()
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self.d_model = config.d_model
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self.total_num_heads = config.n_heads
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self.clip_qkv = config.attn_config["clip_qkv"]
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self.qk_ln = config.attn_config["qk_ln"]
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self.alibi_bias_max = config.attn_config["alibi_bias_max"]
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assert not config.attn_config["prefix_lm"]
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assert config.attn_config["alibi"]
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self.qkv_proj = ColumnParallelLinear(
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self.d_model,
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3 * self.d_model,
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bias=not config.no_bias,
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gather_output=False,
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perform_initialization=False,
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)
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if self.qk_ln:
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self.q_ln = nn.LayerNorm(self.d_model)
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self.k_ln = nn.LayerNorm(self.d_model)
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self.out_proj = RowParallelLinear(
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self.d_model,
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self.d_model,
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bias=not config.no_bias,
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input_is_parallel=True,
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perform_initialization=False,
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)
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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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# 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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self.alibi_bias_max)
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alibi_slopes = alibi_slopes[head_start:head_end].tolist()
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self.head_dim = self.d_model // self.total_num_heads
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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.qkv_proj(hidden_states)
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if self.clip_qkv is not None:
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qkv.clamp_(min=-self.clip_qkv, max=self.clip_qkv)
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q, k, v = qkv.chunk(chunks=3, dim=-1)
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if self.qk_ln:
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q = self.q_ln(q)
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k = self.k_ln(k)
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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.out_proj(attn_output)
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return output
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class MPTMLP(nn.Module):
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def __init__(self, config: MPTConfig):
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super().__init__()
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hidden_size = config.d_model
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expansion_ratio = config.expansion_ratio
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intermediate_size = expansion_ratio * hidden_size
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self.up_proj = ColumnParallelLinear(hidden_size,
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intermediate_size,
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bias=not config.no_bias,
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gather_output=False,
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perform_initialization=False)
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self.act = get_act_fn("gelu")
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self.down_proj = RowParallelLinear(intermediate_size,
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hidden_size,
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bias=not config.no_bias,
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input_is_parallel=True,
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perform_initialization=False)
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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x, _ = self.up_proj(x)
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x = self.act(x)
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x, _ = self.down_proj(x)
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return x
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class MPTBlock(nn.Module):
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def __init__(self, config: MPTConfig):
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super().__init__()
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hidden_size = config.d_model
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self.norm_1 = nn.LayerNorm(hidden_size)
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self.attn = MPTAttention(config)
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self.norm_2 = nn.LayerNorm(hidden_size)
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self.ffn = MPTMLP(config)
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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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x = self.norm_1(hidden_states)
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x = self.attn(
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position_ids=position_ids,
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hidden_states=x,
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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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hidden_states = hidden_states + x
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x = self.norm_2(hidden_states)
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x = self.ffn(x)
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hidden_states = hidden_states + x
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return hidden_states
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class MPTModel(nn.Module):
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def __init__(self, config: MPTConfig):
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super().__init__()
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assert config.embedding_fraction == 1.0
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assert config.norm_type == "low_precision_layernorm"
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self.wte = VocabParallelEmbedding(config.vocab_size,
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config.d_model,
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perform_initialization=False)
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self.blocks = nn.ModuleList(
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[MPTBlock(config) for _ in range(config.n_layers)])
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self.norm_f = nn.LayerNorm(config.d_model)
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if config.no_bias:
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for module in self.modules():
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if hasattr(module, "bias"):
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if isinstance(module.bias, nn.Parameter):
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# Remove the bias term in Linear and LayerNorm.
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module.register_parameter("bias", None)
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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.wte(input_ids)
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for i in range(len(self.blocks)):
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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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block = self.blocks[i]
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hidden_states = block(
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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.norm_f(hidden_states)
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return hidden_states
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class MPTForCausalLM(nn.Module):
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def __init__(self, config: MPTConfig):
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super().__init__()
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self.config = config
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assert config.tie_word_embeddings
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self.transformer = MPTModel(config)
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# TODO(zhuohan): create a new weight after implementing pipeline
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# parallelism
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self.lm_head_weight = self.transformer.wte.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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) -> Dict[int, SequenceOutputs]:
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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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_column_parallel_weights = ["wte.weight", "up_proj.weight", "up_proj.bias"]
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_row_parallel_weights = ["out_proj.weight", "down_proj.weight"]
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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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use_np_cache: bool = False):
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tp_world_size = get_tensor_model_parallel_world_size()
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tp_rank = get_tensor_model_parallel_rank()
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state_dict = self.state_dict()
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for name, loaded_weight in hf_model_weights_iterator(
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model_name_or_path, cache_dir, use_np_cache):
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if "Wqkv" in name:
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# NOTE(woosuk): MPT's fused QKV has the shape of
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# [3 * num_heads * head_size, hidden_size].
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# When tensor model parallelism is used, we need to shard
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# the weight along the hidden dimension.
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total_num_heads = self.config.num_attention_heads
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hidden_size = self.config.hidden_size
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head_size = hidden_size // total_num_heads
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num_heads = total_num_heads // tp_world_size
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head_start = tp_rank * num_heads
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head_end = (tp_rank + 1) * num_heads
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if name.endswith(".weight"):
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loaded_weight = loaded_weight.view(3, total_num_heads,
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head_size, hidden_size)
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loaded_weight = loaded_weight[:, head_start:head_end, :, :]
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loaded_weight = loaded_weight.reshape(-1, hidden_size)
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elif name.endswith(".bias"):
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loaded_weight = loaded_weight.view(3, total_num_heads,
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head_size)
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loaded_weight = loaded_weight[:, head_start:head_end, :]
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loaded_weight = loaded_weight.reshape(-1)
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else:
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raise ValueError(f"Unexpected parameter name {name}")
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name = name.replace("Wqkv", "qkv_proj")
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param = state_dict[name]
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load_tensor_parallel_weights(param, loaded_weight, name,
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self._column_parallel_weights,
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self._row_parallel_weights, tp_rank)
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