modeling_decilm.py

# 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,
            padding_mask: Optional[torch.LongTensor] = None,
    ) -> 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()