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# coding=utf-8
# Copyright and license here
""" PyTorch DeciLM model."""
import math
from typing import Optional, Tuple
import torch
import torch.nn.functional as F
import torch.utils.checkpoint
from torch import nn
from packaging import version
import transformers
if version.parse(transformers.__version__) < version.parse("4.31.0"):
raise ImportError(
f"You are using transformers=={transformers.__version__}, but transformers>=4.31.0 is required to use DeciLM. Please upgrade transformers."
)
from transformers.models.llama.modeling_llama import LlamaMLP, LlamaRMSNorm, LlamaAttention, apply_rotary_pos_emb, \
repeat_kv, LlamaPreTrainedModel, LLAMA_START_DOCSTRING, LlamaDecoderLayer, LlamaForCausalLM, LlamaModel
from transformers.utils import add_start_docstrings
from .configuration_decilm import DeciLMConfig
_CONFIG_FOR_DOC = "DeciLMConfig"
class DeciLMAttention(LlamaAttention):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(self, config: DeciLMConfig, layer_idx: int):
nn.Module.__init__(self)
self.config = config
self.hidden_size = config.hidden_size
self.num_heads = config.num_attention_heads
self.head_dim = self.hidden_size // self.num_heads
self.layer_idx = layer_idx
self.num_key_value_heads = config.num_key_value_heads_per_layer[layer_idx]
self.num_key_value_groups = self.num_heads // self.num_key_value_heads
self.pretraining_tp = config.pretraining_tp
self.max_position_embeddings = config.max_position_embeddings
self.rope_theta = getattr(config, 'rope_theta', None)
if (self.head_dim * self.num_heads) != self.hidden_size:
raise ValueError(
f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
f" and `num_heads`: {self.num_heads})."
)
self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
self.naive_attention_prefill = config.naive_attention_prefill
self.naive_attention_decode_batched = config.naive_attention_decode_batched
self.naive_attention_decode_single = config.naive_attention_decode_single
self._init_rope()
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_value: Optional[Tuple[torch.Tensor]] = None,
output_attentions: bool = False,
use_cache: bool = False,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
bsz, q_len, _ = hidden_states.size()
if past_key_value is None:
is_decode = False
else:
is_decode = True
if self.pretraining_tp > 1:
key_value_slicing = (self.num_key_value_heads * self.head_dim) // self.pretraining_tp
query_slices = self.q_proj.weight.split((self.num_heads * self.head_dim) // self.pretraining_tp, dim=0)
key_slices = self.k_proj.weight.split(key_value_slicing, dim=0)
value_slices = self.v_proj.weight.split(key_value_slicing, dim=0)
query_states = [F.linear(hidden_states, query_slices[i]) for i in range(self.pretraining_tp)]
query_states = torch.cat(query_states, dim=-1)
key_states = [F.linear(hidden_states, key_slices[i]) for i in range(self.pretraining_tp)]
key_states = torch.cat(key_states, dim=-1)
value_states = [F.linear(hidden_states, value_slices[i]) for i in range(self.pretraining_tp)]
value_states = torch.cat(value_states, dim=-1)
else:
query_states = self.q_proj(hidden_states)
key_states = self.k_proj(hidden_states)
value_states = self.v_proj(hidden_states)
query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
kv_seq_len = key_states.shape[-2]
if past_key_value is not None:
kv_seq_len += past_key_value[0].shape[-2]
cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
if past_key_value is not None:
# reuse k, v, self_attention
key_states = torch.cat([past_key_value[0], key_states], dim=2)
value_states = torch.cat([past_key_value[1], value_states], dim=2)
past_key_value = (key_states, value_states) if use_cache else None
# repeat k/v heads if n_kv_heads < n_heads
key_states = repeat_kv(key_states, self.num_key_value_groups)
value_states = repeat_kv(value_states, self.num_key_value_groups)
if is_decode:
if self.naive_attention_decode_batched and bsz > 1 or self.naive_attention_decode_single and bsz == 1:
attn_weights = (query_states @ key_states.transpose(-2, -1)) / math.sqrt(key_states.size(-1))
attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
if attention_mask is not None:
if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
raise ValueError(
f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
)
attn_weights = attn_weights + attention_mask
attn_output = torch.matmul(attn_weights, value_states)
else:
attn_output = F.scaled_dot_product_attention(query_states, key_states, value_states, is_causal=False,
dropout_p=0.0)
attn_output = attn_output.contiguous().view(bsz, q_len, self.hidden_size)
else:
if not self.naive_attention_prefill:
with torch.backends.cuda.sdp_kernel(enable_math=True, enable_flash=False, enable_mem_efficient=False):
attn_output = F.scaled_dot_product_attention(query_states, key_states, value_states, is_causal=True,
dropout_p=0.0)
else:
attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
raise ValueError(
f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is"
f" {attn_weights.size()}"
)
if attention_mask is not None:
if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
raise ValueError(
f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
)
attn_weights = attn_weights + attention_mask
# upcast attention to fp32
attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
attn_output = torch.matmul(attn_weights, value_states)
if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
raise ValueError(
f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
f" {attn_output.size()}"
)
attn_output = attn_output.transpose(1, 2).contiguous().view(bsz, q_len, self.hidden_size)
if self.pretraining_tp > 1:
attn_output = attn_output.split(self.hidden_size // self.pretraining_tp, dim=2)
o_proj_slices = self.o_proj.weight.split(self.hidden_size // self.pretraining_tp, dim=1)
attn_output = sum([F.linear(attn_output[i], o_proj_slices[i]) for i in range(self.pretraining_tp)])
else:
attn_output = self.o_proj(attn_output)
if not output_attentions:
attn_weights = None
return attn_output, attn_weights, past_key_value
class DeciLMDecoderLayer(LlamaDecoderLayer):
def __init__(self, config: DeciLMConfig, layer_idx: int):
nn.Module.__init__(self)
self.hidden_size = config.hidden_size
self.layer_idx = layer_idx
self.self_attn = DeciLMAttention(config=config, layer_idx=layer_idx)
self.mlp = LlamaMLP(config)
self.input_layernorm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.post_attention_layernorm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
@add_start_docstrings(
"The bare DeciLM Model outputting raw hidden-states without any specific head on top.",
LLAMA_START_DOCSTRING,
)
class DeciLMPreTrainedModel(LlamaPreTrainedModel):
config_class = DeciLMConfig
_no_split_modules = ["DeciLMDecoderLayer"]
_keys_to_ignore_on_load_missing = ["self_attn.rotary_emb.inv_freq"]
@add_start_docstrings(
"The bare DeciLM Model outputting raw hidden-states without any specific head on top.",
LLAMA_START_DOCSTRING,
)
class DeciLMModel(LlamaModel, DeciLMPreTrainedModel):
"""
Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`DeciLMDecoderLayer`]
Args:
config: DeciLMConfig
"""
def __init__(self, config: DeciLMConfig):
DeciLMPreTrainedModel.__init__(self, config)
self.padding_idx = config.pad_token_id
self.vocab_size = config.vocab_size
self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
self.layers = nn.ModuleList([DeciLMDecoderLayer(config, layer_idx) for layer_idx
in range(config.num_hidden_layers)])
self.norm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.gradient_checkpointing = False
# Initialize weights and apply final processing
self.post_init()
def _prepare_decoder_attention_mask(self, attention_mask, input_shape, inputs_embeds, past_key_values_length):
self._validate_config_supports_attention_mask(attention_mask, input_shape, past_key_values_length)
return LlamaModel._prepare_decoder_attention_mask(
self, attention_mask, input_shape, inputs_embeds, past_key_values_length)
def _validate_config_supports_attention_mask(self, attention_mask, input_shape, past_key_values_length):
is_decode = past_key_values_length > 0
if not torch.all(torch.eq(attention_mask, 1)).item():
if is_decode:
if input_shape[0] == 1 and not self.config.naive_attention_decode_single:
raise ValueError(
"For support of custom attention masks please set naive_attention_decode_single to True in the "
"config")
elif input_shape[0] > 1 and not self.config.naive_attention_decode_batched:
raise ValueError(
"For support of custom attention masks please set naive_attention_decode_batched to True in the"
"config")
else:
if not self.config.naive_attention_prefill:
raise ValueError("For support of custom attention masks please set naive_attention_prefill to "
"True in the config")
class DeciLMForCausalLM(LlamaForCausalLM, DeciLMPreTrainedModel):
def __init__(self, config):
DeciLMPreTrainedModel.__init__(self, config)
self.model = DeciLMModel(config)
self.pretraining_tp = config.pretraining_tp
self.vocab_size = config.vocab_size
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
# Initialize weights and apply final processing
self.post_init()
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