Refactor system architecture (#82)
This commit is contained in:
Woosuk Kwon
267
cacheflow/model_executor/models/llama.py
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267
cacheflow/model_executor/models/llama.py
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"""1D LLaMA model compatible with HuggingFace weights."""
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from typing import Dict, List, Optional, Tuple
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import torch
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from torch import nn
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from transformers import LlamaConfig
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from cacheflow.sequence import SequenceOutputs
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from cacheflow.model_executor.input_metadata import InputMetadata
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from cacheflow.model_executor.layers.activation import SiluAndMul
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from cacheflow.model_executor.layers.layernorm import RMSNorm
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from cacheflow.model_executor.layers.attention import GPTNeoXCacheFlowAttention
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from cacheflow.model_executor.layers.sampler import Sampler
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from cacheflow.model_executor.weight_utils import (hf_model_weights_iterator,
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load_tensor_parallel_weights)
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from cacheflow.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 cacheflow.model_executor.parallel_utils.tensor_parallel import (
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VocabParallelEmbedding, ColumnParallelLinear, RowParallelLinear)
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from cacheflow.sequence import SequenceOutputs
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KVCache = Tuple[torch.Tensor, torch.Tensor]
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class LlamaMLP(nn.Module):
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def __init__(
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self,
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hidden_size: int,
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intermediate_size: int,
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hidden_act: str,
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):
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super().__init__()
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self.gate_up_proj = ColumnParallelLinear(hidden_size, 2 * intermediate_size,
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bias=False, gather_output=False,
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perform_initialization=False)
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self.down_proj = RowParallelLinear(intermediate_size, hidden_size,
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bias=False, input_is_parallel=True,
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perform_initialization=False)
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if hidden_act != 'silu':
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raise ValueError(f'Unsupported activation: {hidden_act}. '
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'Only silu is supported for now.')
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self.act_fn = SiluAndMul()
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def forward(self, x):
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gate_up, _ = self.gate_up_proj(x)
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x = self.act_fn(gate_up)
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x, _ = self.down_proj(x)
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return x
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class LlamaAttention(nn.Module):
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def __init__(
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self,
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hidden_size: int,
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num_heads: int,
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):
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super().__init__()
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self.hidden_size = hidden_size
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tensor_model_parallel_world_size = get_tensor_model_parallel_world_size()
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self.total_num_heads = num_heads
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assert self.total_num_heads % tensor_model_parallel_world_size == 0
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self.num_heads = self.total_num_heads // tensor_model_parallel_world_size
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self.head_dim = hidden_size // self.total_num_heads
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self.scaling = self.head_dim ** -0.5
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self.qkv_proj = ColumnParallelLinear(
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hidden_size,
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3 * self.total_num_heads * self.head_dim,
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bias=False,
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gather_output=False,
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perform_initialization=False,
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)
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self.o_proj = RowParallelLinear(
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self.total_num_heads * self.head_dim,
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hidden_size,
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bias=False,
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input_is_parallel=True,
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perform_initialization=False,
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)
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self.attn = GPTNeoXCacheFlowAttention(self.scaling, self.head_dim)
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def forward(
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self,
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positions: torch.LongTensor,
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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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qkv, _ = self.qkv_proj(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(
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positions, q, k, v, k_cache, v_cache, input_metadata, cache_event)
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output, _ = self.o_proj(attn_output)
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return output
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class LlamaDecoderLayer(nn.Module):
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def __init__(self, config: LlamaConfig):
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super().__init__()
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self.hidden_size = config.hidden_size
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self.self_attn = LlamaAttention(
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hidden_size=self.hidden_size,
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num_heads=config.num_attention_heads,
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)
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self.mlp = LlamaMLP(
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hidden_size=self.hidden_size,
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intermediate_size=config.intermediate_size,
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hidden_act=config.hidden_act,
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)
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self.input_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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self.post_attention_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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def forward(
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self,
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positions: torch.LongTensor,
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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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# Self Attention
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residual = hidden_states
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hidden_states = self.input_layernorm(hidden_states)
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hidden_states = self.self_attn(
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positions=positions,
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hidden_states=hidden_states,
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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 = residual + hidden_states
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# Fully Connected
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residual = hidden_states
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hidden_states = self.post_attention_layernorm(hidden_states)
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hidden_states = self.mlp(hidden_states)
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hidden_states = residual + hidden_states
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return hidden_states
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class LlamaModel(nn.Module):
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def __init__(self, config: LlamaConfig):
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super().__init__()
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self.config = config
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self.padding_idx = config.pad_token_id
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self.vocab_size = config.vocab_size
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self.embed_tokens = VocabParallelEmbedding(config.vocab_size, config.hidden_size,
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perform_initialization=False)
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self.layers = nn.ModuleList([LlamaDecoderLayer(config) for _ in range(config.num_hidden_layers)])
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self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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def forward(
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self,
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input_ids: torch.LongTensor,
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positions: torch.LongTensor,
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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.embed_tokens(input_ids)
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for i in range(len(self.layers)):
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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.layers[i]
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hidden_states = layer(
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positions,
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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(hidden_states)
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return hidden_states
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class LlamaForCausalLM(nn.Module):
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def __init__(self, config):
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super().__init__()
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self.config = config
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self.model = LlamaModel(config)
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self.lm_head = ColumnParallelLinear(config.hidden_size,
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config.vocab_size,
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bias=False,
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gather_output=False,
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perform_initialization=False)
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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.LongTensor,
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positions: torch.LongTensor,
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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.model(
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input_ids, positions, kv_caches, input_metadata, cache_events)
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next_tokens = self.sampler(
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self.lm_head.weight, hidden_states, input_metadata)
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return next_tokens
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_column_parallel_weights = ["embed_tokens.weight", "lm_head.weight",
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"qkv_proj.weight", "gate_proj.weight",
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"up_proj.weight"]
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_row_parallel_weights = ["o_proj.weight", "down_proj.weight"]
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def load_weights(self, 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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tensor_model_parallel_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 "rotary_emb.inv_freq" in name:
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continue
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is_attention_weight = False
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for stride_id, att_weight_name in enumerate(["q_proj", "k_proj", "v_proj"]):
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if att_weight_name not in name:
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continue
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param = state_dict[name.replace(att_weight_name, "qkv_proj")]
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shard_size = param.shape[0] // 3
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loaded_weight = loaded_weight[
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shard_size * tensor_model_parallel_rank
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:shard_size * (tensor_model_parallel_rank + 1)]
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param_slice = param.data[shard_size * stride_id
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:shard_size * (stride_id + 1)]
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assert param_slice.shape == loaded_weight.shape
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param_slice.copy_(loaded_weight)
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is_attention_weight = True
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break
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if is_attention_weight:
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continue
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is_gate_up_weight = False
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for stride_id, weight_name in enumerate(["gate_proj", "up_proj"]):
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if weight_name not in name:
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continue
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param = state_dict[name.replace(weight_name, "gate_up_proj")]
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shard_size = param.shape[0] // 2
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loaded_weight = loaded_weight[
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shard_size * tensor_model_parallel_rank
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:shard_size * (tensor_model_parallel_rank + 1)]
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param_slice = param.data[shard_size * stride_id
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:shard_size * (stride_id + 1)]
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assert param_slice.shape == loaded_weight.shape
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param_slice.copy_(loaded_weight)
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is_gate_up_weight = True
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break
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if is_gate_up_weight:
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continue
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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,
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tensor_model_parallel_rank)
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