337 lines
14 KiB
Python
337 lines
14 KiB
Python
# Copyright 2024 BigCode and the HuggingFace Inc. team. All rights reserved.
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#
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# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
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# and OPT implementations in this library. It has been modified from its
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# original forms to accommodate minor architectural differences compared
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# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
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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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""" PyTorch Starcoder2 model."""
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from typing import Iterable, List, Optional, Tuple, Union
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import torch
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from torch import nn
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from transformers import Starcoder2Config
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from vllm.attention import Attention, AttentionMetadata
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from vllm.compilation.decorators import support_torch_compile
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from vllm.config import CacheConfig
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from vllm.distributed import get_pp_group, get_tensor_model_parallel_world_size
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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 (ColumnParallelLinear,
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QKVParallelLinear,
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RowParallelLinear)
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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.rotary_embedding import get_rope
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from vllm.model_executor.layers.sampler import SamplerOutput, get_sampler
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from vllm.model_executor.layers.vocab_parallel_embedding import (
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DEFAULT_VOCAB_PADDING_SIZE, ParallelLMHead, VocabParallelEmbedding)
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from vllm.model_executor.model_loader.weight_utils import default_weight_loader
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from vllm.model_executor.sampling_metadata import SamplingMetadata
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from vllm.sequence import IntermediateTensors
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from .interfaces import SupportsPP
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from .utils import (is_pp_missing_parameter,
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make_empty_intermediate_tensors_factory, make_layers)
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class Starcoder2Attention(nn.Module):
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def __init__(self,
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config: Starcoder2Config,
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cache_config: Optional[CacheConfig] = None,
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quant_config: Optional[QuantizationConfig] = None):
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super().__init__()
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self.config = config
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self.hidden_size = config.hidden_size
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tp_size = get_tensor_model_parallel_world_size()
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self.total_num_heads = config.num_attention_heads
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assert self.total_num_heads % tp_size == 0
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self.num_heads = self.total_num_heads // tp_size
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self.total_num_kv_heads = config.num_key_value_heads
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if self.total_num_kv_heads >= tp_size:
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# Number of KV heads is greater than TP size, so we partition
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# the KV heads across multiple tensor parallel GPUs.
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assert self.total_num_kv_heads % tp_size == 0
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else:
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# Number of KV heads is less than TP size, so we replicate
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# the KV heads across multiple tensor parallel GPUs.
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assert tp_size % self.total_num_kv_heads == 0
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self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
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self.head_dim = self.hidden_size // self.total_num_heads
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self.q_size = self.num_heads * self.head_dim
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self.kv_size = self.num_kv_heads * self.head_dim
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self.scaling = self.head_dim**-0.5
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self.rope_theta = config.rope_theta
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self.max_position_embeddings = config.max_position_embeddings
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self.use_bias = config.use_bias
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self.qkv_proj = 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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self.total_num_kv_heads,
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bias=self.use_bias,
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quant_config=quant_config,
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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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self.hidden_size,
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bias=self.use_bias,
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quant_config=quant_config,
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)
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self.rotary_emb = get_rope(
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self.head_dim,
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rotary_dim=self.head_dim,
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max_position=self.max_position_embeddings,
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base=int(self.rope_theta),
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is_neox_style=True,
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)
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self.attn = Attention(self.num_heads,
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self.head_dim,
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self.scaling,
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num_kv_heads=self.num_kv_heads,
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cache_config=cache_config,
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quant_config=quant_config)
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def forward(
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self,
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positions: torch.Tensor,
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hidden_states: torch.Tensor,
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kv_cache: torch.Tensor,
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attn_metadata: AttentionMetadata,
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) -> torch.Tensor:
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qkv, _ = self.qkv_proj(hidden_states)
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q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
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q, k = self.rotary_emb(positions, q, k)
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attn_output = self.attn(q, k, v, kv_cache, attn_metadata)
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output, _ = self.o_proj(attn_output)
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return output
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class Starcoder2MLP(nn.Module):
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def __init__(self,
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config: Starcoder2Config,
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quant_config: Optional[QuantizationConfig] = None):
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super().__init__()
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self.c_fc = ColumnParallelLinear(
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config.hidden_size,
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config.intermediate_size,
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bias=config.use_bias,
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quant_config=quant_config,
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)
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self.c_proj = RowParallelLinear(
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config.intermediate_size,
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config.hidden_size,
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bias=config.use_bias,
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quant_config=quant_config,
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)
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self.act = get_act_fn(config.hidden_act)
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def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
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hidden_states, _ = self.c_fc(hidden_states)
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hidden_states = self.act(hidden_states)
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hidden_states, _ = self.c_proj(hidden_states)
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return hidden_states
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class Starcoder2DecoderLayer(nn.Module):
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def __init__(self,
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config: Starcoder2Config,
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cache_config: Optional[CacheConfig] = None,
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quant_config: Optional[QuantizationConfig] = None):
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super().__init__()
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self.hidden_size = config.hidden_size
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self.self_attn = Starcoder2Attention(config,
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cache_config,
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quant_config=quant_config)
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self.mlp = Starcoder2MLP(config, quant_config=quant_config)
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self.input_layernorm = nn.LayerNorm(config.hidden_size,
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eps=config.norm_epsilon)
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self.post_attention_layernorm = nn.LayerNorm(config.hidden_size,
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eps=config.norm_epsilon)
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def forward(
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self,
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positions: torch.Tensor,
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hidden_states: torch.Tensor,
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kv_cache: torch.Tensor,
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attn_metadata: AttentionMetadata,
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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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attn_metadata=attn_metadata,
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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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@support_torch_compile
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class Starcoder2Model(nn.Module):
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def __init__(self,
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config: Starcoder2Config,
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cache_config: Optional[CacheConfig] = None,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = ""):
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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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# TODO: consider padding_idx (currently removed)
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self.embed_tokens = VocabParallelEmbedding(config.vocab_size,
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config.hidden_size)
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self.start_layer, self.end_layer, self.layers = make_layers(
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config.num_hidden_layers,
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lambda prefix: Starcoder2DecoderLayer(
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config, cache_config, quant_config=quant_config),
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prefix=f"{prefix}.layers",
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)
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self.norm = nn.LayerNorm(config.hidden_size, eps=config.norm_epsilon)
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self.make_empty_intermediate_tensors = (
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make_empty_intermediate_tensors_factory(["hidden_states"],
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config.hidden_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[torch.Tensor],
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attn_metadata: AttentionMetadata,
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intermediate_tensors: Optional[IntermediateTensors],
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) -> Union[torch.Tensor, IntermediateTensors]:
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if get_pp_group().is_first_rank:
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hidden_states = self.embed_tokens(input_ids)
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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 i in range(self.start_layer, self.end_layer):
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layer = self.layers[i]
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hidden_states = layer(positions, hidden_states,
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kv_caches[i - self.start_layer],
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attn_metadata)
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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.norm(hidden_states)
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return hidden_states
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class Starcoder2ForCausalLM(nn.Module, SupportsPP):
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def __init__(self,
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config: Starcoder2Config,
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cache_config: Optional[CacheConfig] = None,
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quant_config: Optional[QuantizationConfig] = None):
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super().__init__()
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self.config = config
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self.model = Starcoder2Model(config,
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cache_config,
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quant_config=quant_config)
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self.vocab_size = config.vocab_size
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self.unpadded_vocab_size = config.vocab_size
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if config.tie_word_embeddings:
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self.lm_head = self.model.embed_tokens
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else:
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self.unpadded_vocab_size = config.vocab_size
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self.lm_head = ParallelLMHead(
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self.unpadded_vocab_size,
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config.hidden_size,
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org_num_embeddings=config.vocab_size,
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padding_size=DEFAULT_VOCAB_PADDING_SIZE,
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quant_config=quant_config,
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)
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self.logits_processor = LogitsProcessor(self.unpadded_vocab_size,
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config.vocab_size)
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self.sampler = get_sampler()
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self.make_empty_intermediate_tensors = (
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self.model.make_empty_intermediate_tensors)
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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[torch.Tensor],
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attn_metadata: AttentionMetadata,
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intermediate_tensors: Optional[IntermediateTensors] = None,
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) -> Union[torch.Tensor, IntermediateTensors]:
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hidden_states = self.model(input_ids, positions, kv_caches,
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attn_metadata, intermediate_tensors)
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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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sampling_metadata: SamplingMetadata,
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) -> Optional[torch.Tensor]:
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logits = self.logits_processor(self.lm_head, hidden_states,
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sampling_metadata)
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return logits
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def sample(
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self,
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logits: Optional[torch.Tensor],
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sampling_metadata: SamplingMetadata,
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) -> Optional[SamplerOutput]:
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next_tokens = self.sampler(logits, sampling_metadata)
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return next_tokens
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def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
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stacked_params_mapping = [
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# (param_name, shard_name, shard_id)
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("qkv_proj", "q_proj", "q"),
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("qkv_proj", "k_proj", "k"),
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("qkv_proj", "v_proj", "v"),
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]
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params_dict = dict(self.named_parameters(remove_duplicate=False))
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for name, loaded_weight in weights:
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if "rotary_emb.inv_freq" in name:
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continue
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for (param_name, weight_name, shard_id) in stacked_params_mapping:
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if weight_name not in name:
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continue
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name = name.replace(weight_name, param_name)
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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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weight_loader = param.weight_loader
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weight_loader(param, loaded_weight, shard_id)
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break
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else:
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if self.config.tie_word_embeddings and "lm_head.weight" in name:
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continue
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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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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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