petals/src/petals/models/llama/block.py

"""
LLaMA intermediate layer
Based on https://github.com/huggingface/transformers/blob/main/src/transformers/models/llama/modeling_llama.py
See commit history for authorship.
"""
from typing import Optional, Tuple

import torch
import torch.nn as nn
import torch.nn.functional as F
import math
from transformers.models.llama.modeling_llama import (
    LlamaAttention,
    LlamaConfig,
    LlamaDecoderLayer,
    LlamaMLP,
    LlamaModel,
    LlamaRMSNorm,
    repeat_kv,
    apply_rotary_pos_emb,
)

from petals.triton import attention_triton_wrapper, rbe_triton_wrapper, rmsnorm_triton_wrapper


class OptimizedLlamaRMSNorm(LlamaRMSNorm):
    def forward(self, hidden_states):
        if torch.is_inference_mode_enabled():
            return rmsnorm_triton_wrapper(hidden_states, self.weight)
        return super().forward(hidden_states)


class OptimizedLlamaAttention(LlamaAttention):
    def __init__(self, config: LlamaConfig):
        super().__init__(config)
        self.qkv_proj = nn.Linear(
            self.hidden_size, (self.num_heads + 2 * self.num_key_value_heads) * self.head_dim, bias=False
        )
        self.qkv_sizes = [
            self.num_heads * self.head_dim,
            self.num_key_value_heads * self.head_dim,
            self.num_key_value_heads * self.head_dim,
        ]
        self.attn_norm_constant = math.sqrt(self.head_dim)

    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()
        assert (
            self.config.pretraining_tp == 1
        ), "OptimizedLlamaAttention assumes that config.pretraining_tp is equal to 1"

        query_states, key_states, value_states = torch.split(self.qkv_proj(hidden_states), self.qkv_sizes, dim=2)

        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)

        attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / self.attn_norm_constant

        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()
        attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)

        attn_output = self.o_proj(attn_output)

        return attn_output, None, past_key_value


class OptimizedLlamaDecoderLayer(LlamaDecoderLayer):
    def __init__(self, config: LlamaConfig):
        nn.Module.__init__(self)
        self.hidden_size = config.hidden_size
        self.self_attn = OptimizedLlamaAttention(config=config)
        self.mlp = LlamaMLP(config)
        self.input_layernorm = OptimizedLlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        self.post_attention_layernorm = OptimizedLlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)


class WrappedLlamaBlock(OptimizedLlamaDecoderLayer):
    def forward(
        self,
        hidden_states: torch.Tensor,
        *args,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        layer_past: Optional[Tuple[torch.Tensor]] = None,
        use_cache: bool = False,
        **kwargs,
    ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
        batch_size, seq_length, _ = hidden_states.shape

        seq_length_with_past = seq_length
        past_key_values_length = 0

        past_key_value = layer_past
        if past_key_value is not None:
            past_key_values_length = past_key_value[0].shape[2]
            seq_length_with_past = seq_length_with_past + past_key_values_length
            past_key_value = self._reorder_cache_from_bloom_to_llama(past_key_value, batch_size, past_key_values_length)

        if position_ids is None:
            device = hidden_states.device
            position_ids = torch.arange(
                past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device
            )
            position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
        else:
            position_ids = position_ids.view(-1, seq_length).long()

        # embed positions
        if attention_mask is None:
            attention_mask = torch.ones(
                (batch_size, seq_length_with_past), dtype=torch.bool, device=hidden_states.device
            )
        attention_mask = LlamaModel._prepare_decoder_attention_mask(
            None, attention_mask, (batch_size, seq_length), hidden_states, past_key_values_length
        )

        outputs = super().forward(
            hidden_states,
            *args,
            attention_mask=attention_mask,
            position_ids=position_ids,
            past_key_value=past_key_value,
            use_cache=use_cache,
            **kwargs,
        )

        if use_cache:
            present_key_value = outputs[-1]
            present_key_value = self._reorder_cache_from_llama_to_bloom(
                present_key_value, batch_size, seq_length_with_past
            )
            outputs = outputs[:-1] + (present_key_value,)

        return outputs

    def _reorder_cache_from_bloom_to_llama(
        self, key_value: Tuple[torch.Tensor], batch_size: int, seq_length: int
    ) -> Tuple[torch.Tensor]:
        key_states, value_states = key_value
        key_states = key_states.permute(0, 2, 1)
        key_states = key_states.view(
            batch_size, self.self_attn.num_key_value_heads, seq_length, self.self_attn.head_dim
        )
        value_states = value_states.view(*key_states.shape)
        return (key_states, value_states)

    def _reorder_cache_from_llama_to_bloom(
        self, key_value: Tuple[torch.Tensor], batch_size: int, seq_length: int
    ) -> Tuple[torch.Tensor]:
        key_states, value_states = key_value
        value_states = value_states.view(
            batch_size * self.self_attn.num_key_value_heads, seq_length, self.self_attn.head_dim
        )
        key_states = key_states.view(*value_states.shape)
        key_states = key_states.permute(0, 2, 1)
        return (key_states, value_states)