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.
"""
import math
from typing import Optional, Tuple

import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers.modeling_attn_mask_utils import _prepare_4d_causal_attention_mask
from transformers.models.llama.modeling_llama import (
    LlamaAttention,
    LlamaConfig,
    LlamaDecoderLayer,
    LlamaMLP,
    LlamaModel,
    LlamaRMSNorm,
    repeat_kv,
    rotate_half,
)

from petals.utils.cuda_graphs import make_inference_graphed_callable


def apply_rotary_pos_emb(q, k, cos, sin):
    q_embed = (q * cos) + (rotate_half(q) * sin)
    k_embed = (k * cos) + (rotate_half(k) * sin)
    return q_embed, k_embed


class OptimizedLlamaAttention(LlamaAttention):
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self._rotary_graph = None

    def _optimized_apply_rotary(self, query_states, key_states, cos, sin):
        if self._rotary_graph is None:
            self._rotary_graph = make_inference_graphed_callable(
                apply_rotary_pos_emb, sample_args=(query_states, key_states, cos, sin)
            )
        return self._rotary_graph(query_states, key_states, cos, sin)

    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,
        cache_position: Optional[torch.LongTensor] = None,
    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
        assert not output_attentions
        if position_ids is None:
            past_seen_tokens = past_key_value[0].shape[2] if past_key_value is not None else 0
            position_ids = torch.arange(
                past_seen_tokens, past_seen_tokens + hidden_states.shape[1], device=hidden_states.device
            ).unsqueeze(0)

        bsz, q_len, _ = hidden_states.size()

        if self.config.pretraining_tp > 1:
            key_value_slicing = (self.num_key_value_heads * self.head_dim) // self.config.pretraining_tp
            query_slices = self.q_proj.weight.split(
                (self.num_heads * self.head_dim) // self.config.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.config.pretraining_tp)]
            query_states = torch.cat(query_states, dim=-1)

            key_states = [F.linear(hidden_states, key_slices[i]) for i in range(self.config.pretraining_tp)]
            key_states = torch.cat(key_states, dim=-1)

            value_states = [F.linear(hidden_states, value_slices[i]) for i in range(self.config.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)

        cos, sin = self.rotary_emb(value_states, position_ids)
        cos, sin = cos.unsqueeze(1), sin.unsqueeze(1)

        if q_len == 1 and torch.is_inference_mode_enabled() and hidden_states.device.type == "cuda":
            query_states, key_states = self._optimized_apply_rotary(query_states, key_states, cos, sin)
        else:
            query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)

        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)) / math.sqrt(self.head_dim)

        if attention_mask is not None:
            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)

        attn_output = attn_output.transpose(1, 2).contiguous()
        attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)

        if self.config.pretraining_tp > 1:
            attn_output = attn_output.split(self.hidden_size // self.config.pretraining_tp, dim=2)
            o_proj_slices = self.o_proj.weight.split(self.hidden_size // self.config.pretraining_tp, dim=1)
            attn_output = sum([F.linear(attn_output[i], o_proj_slices[i]) for i in range(self.config.pretraining_tp)])
        else:
            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 = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        self.post_attention_layernorm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)

        self.pre_attn_graph = None
        self.post_attn_graph = None

    def _optimized_input_layernorm(self, hidden_states):
        if self.pre_attn_graph is None:
            self.pre_attn_graph = make_inference_graphed_callable(
                self.input_layernorm.forward, sample_args=(hidden_states,)
            )
        return self.pre_attn_graph(hidden_states)

    def _optimized_output_layernorm(self, hidden_states):
        if self.post_attn_graph is None:
            self.post_attn_graph = make_inference_graphed_callable(
                self.post_attention_layernorm.forward, sample_args=(hidden_states,)
            )
        return self.post_attn_graph(hidden_states)

    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: Optional[bool] = False,
        use_cache: Optional[bool] = False,
        cache_position: Optional[torch.LongTensor] = None,
        **kwargs,
    ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
        """
        Args:
            hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
            attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
                `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
            output_attentions (`bool`, *optional*):
                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
                returned tensors for more detail.
            use_cache (`bool`, *optional*):
                If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
                (see `past_key_values`).
            past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
        """

        residual = hidden_states

        if hidden_states.size(1) == 1 and torch.is_inference_mode_enabled() and hidden_states.device.type == "cuda":
            hidden_states = self._optimized_input_layernorm(hidden_states)
        else:
            hidden_states = self.input_layernorm(hidden_states)

        # Self Attention
        hidden_states, self_attn_weights, present_key_value = self.self_attn(
            hidden_states=hidden_states,
            attention_mask=attention_mask,
            position_ids=position_ids,
            past_key_value=past_key_value,
            output_attentions=output_attentions,
            use_cache=use_cache,
            cache_position=cache_position,
            **kwargs,
        )

        hidden_states = residual + hidden_states

        # Fully Connected
        residual = hidden_states

        if hidden_states.size(1) == 1 and torch.is_inference_mode_enabled() and hidden_states.device.type == "cuda":
            hidden_states = self._optimized_output_layernorm(hidden_states)
        else:
            hidden_states = self.post_attention_layernorm(hidden_states)

        hidden_states = self.mlp(hidden_states)
        hidden_states = residual + hidden_states

        outputs = (hidden_states,)

        if output_attentions:
            outputs += (self_attn_weights,)

        if use_cache:
            outputs += (present_key_value,)

        return outputs


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)

        assert position_ids is None

        # 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 = _prepare_4d_causal_attention_mask(
            attention_mask=attention_mask,
            input_shape=(batch_size, seq_length),
            inputs_embeds=hidden_states,
            past_key_values_length=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)