attention.py

# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.

from abc import ABC, abstractmethod

import torch

from megatron.core import parallel_state, tensor_parallel
from megatron.core.models.common.rotary_pos_embedding import apply_rotary_pos_emb
from megatron.core.transformer.custom_layers.transformer_engine import (
    TEDotProductAttention,
    TELayerNormColumnParallelLinear,
    TERowParallelLinear,
)
from megatron.core.transformer.enums import AttnMaskType
from megatron.core.transformer.module import MegatronModule
from megatron.core.transformer.transformer_config import TransformerConfig
from megatron.core.utils import divide

from .enums import AttnMaskType
from .transformer_config import TransformerConfig


class Attention(MegatronModule, ABC):
    """Attention layer abstract class.

    This layer only contains common modules required for the "self attn" and
    "cross attn" specializations.
    """

    def __init__(
        self, config: TransformerConfig, layer_number: int = 1, attn_mask_type=AttnMaskType.padding,
    ):
        super().__init__(config=config)

        self.config = config
        self.layer_number = layer_number
        self.attn_mask_type = attn_mask_type

        # For normal attention without groups, num_query_groups == num_attention_heads,
        # so these two will be the same
        self.query_projection_size = self.config.kv_channels * self.config.num_attention_heads
        self.kv_projection_size = self.config.kv_channels * self.config.num_query_groups

        # Per attention head and per partition values.
        world_size = parallel_state.get_tensor_model_parallel_world_size()
        self.hidden_size_per_attention_head = divide(
            self.query_projection_size, self.config.num_attention_heads
        )
        self.num_attention_heads_per_partition = divide(self.config.num_attention_heads, world_size)
        self.num_query_groups_per_partition = divide(self.config.num_query_groups, world_size)

        self.dot_product_attention = TEDotProductAttention(
            config=self.config, layer_number=self.layer_number, attn_mask_type=self.attn_mask_type
        )

        self.checkpoint_dot_product_attention = self.config.recompute_granularity == 'selective'

        # Output.
        self.linear_proj = TERowParallelLinear(
            self.query_projection_size,
            self.config.hidden_size,
            config=self.config,
            init_method=self.config.output_layer_init_method,
            bias=self.config.add_bias_linear,
            skip_bias_add=True,
        )

    def _checkpointed_attention_forward(
        self, query, key, value, attention_mask, rotary_pos_emb=None
    ):
        """Forward method with selective activation checkpointing."""

        def custom_forward(*inputs):
            query = inputs[0]
            key = inputs[1]
            value = inputs[2]
            attention_mask = inputs[3]
            output_ = self.dot_product_attention(query, key, value, attention_mask)
            return output_

        hidden_states = tensor_parallel.checkpoint(
            custom_forward, False, query, key, value, attention_mask, rotary_pos_emb
        )

        return hidden_states

    def _allocate_memory(self, inference_max_sequence_length, batch_size, dtype):
        """Allocate memory to store kv cache during inference."""

        return torch.empty(
            inference_max_sequence_length,
            batch_size,
            self.num_query_groups_per_partition,
            self.hidden_size_per_attention_head,
            dtype=dtype,
            device=torch.cuda.current_device(),
        )

    def _adjust_key_value_for_inference(self, inference_params, key, value, rotary_pos_emb):
        """
        Saves the generated key and value tensors to the end of the buffers in inference_params.
        Returns the full size keys and values from the provided inference_params, as well as
        adjusted rotary_pos_emb.

        Returns a tuple: (key, value, rotary_pos_emb)

        """
        if inference_params is None:
            return key, value, rotary_pos_emb

        # =================================================
        # Pre-allocate memory for key-values for inference.
        # =================================================
        is_first_step = False
        if self.layer_number not in inference_params.key_value_memory_dict:
            inf_max_seq_length = inference_params.max_sequence_length
            inf_max_batch_size = inference_params.max_batch_size
            inference_key_memory = self._allocate_memory(
                inf_max_seq_length, inf_max_batch_size, key.dtype
            )
            inference_value_memory = self._allocate_memory(
                inf_max_seq_length, inf_max_batch_size, value.dtype
            )
            inference_params.key_value_memory_dict[self.layer_number] = (
                inference_key_memory,
                inference_value_memory,
            )
            is_first_step = True
        else:
            # Get the pre-allocated buffers for this layer
            inference_key_memory, inference_value_memory = inference_params.key_value_memory_dict[
                self.layer_number
            ]

        batch_start = inference_params.batch_size_offset
        batch_end = batch_start + key.size(1)
        assert batch_end <= inference_key_memory.size(1)
        sequence_start = inference_params.sequence_len_offset
        sequence_end = sequence_start + key.size(0)
        assert sequence_end <= inference_key_memory.size(0)
        # Copy key and values.
        inference_key_memory[sequence_start:sequence_end, batch_start:batch_end, ...] = key
        inference_value_memory[sequence_start:sequence_end, batch_start:batch_end, ...] = value
        key = inference_key_memory[:sequence_end, batch_start:batch_end, ...]
        value = inference_value_memory[:sequence_end, batch_start:batch_end, ...]

        # adjust the key rotary positional embedding
        if rotary_pos_emb is not None:
            q_pos_emb, k_pos_emb = rotary_pos_emb
            # need to cross check this condition during inference
            # if not set_inference_key_value_memory:
            if not is_first_step:
                # In inference, we compute one token at a time.
                # Select the correct positional embedding
                # (only the last token in the sequence)
                q_pos_emb = q_pos_emb[sequence_end - 1 : sequence_end]
            else:
                # In the first forward pass of inference,
                # we use the entire provided prefix.
                # q_pos_emb here has the rope embeddings of the entire
                # prefix + to-be-generated output so
                # we slice to just the prefix.
                q_pos_emb = q_pos_emb[:sequence_end, :, :, :]
            k_pos_emb = k_pos_emb[:sequence_end, :, :, :]
            rotary_pos_emb = (q_pos_emb, k_pos_emb)

        return key, value, rotary_pos_emb

    @abstractmethod
    def get_query_key_value_tensors(self, hidden_states, key_value_states):
        """
        This method needs to be implemented based on whether the derived class
        is "self-attn" or "cross-attn".
        """

    def forward(
        self,
        hidden_states,
        attention_mask,
        key_value_states=None,
        inference_params=None,
        rotary_pos_emb=None,
    ):
        # hidden_states: [sq, b, h]

        # For self attention we just duplicate the rotary_pos_emb if it isn't already
        if rotary_pos_emb is not None and not isinstance(rotary_pos_emb, tuple):
            rotary_pos_emb = (rotary_pos_emb,) * 2

        # =====================
        # Query, Key, and Value
        # =====================
        # Get the query, key and value tensors based on the type of attention -
        # self or cross attn.
        query, key, value = self.get_query_key_value_tensors(hidden_states, key_value_states)

        # ===================================================
        # Adjust key, value, and rotary_pos_emb for inference
        # ===================================================
        key, value, rotary_pos_emb = self._adjust_key_value_for_inference(
            inference_params, key, value, rotary_pos_emb
        )

        # ================================================
        # relative positional embedding (rotary embedding)
        # ================================================
        if rotary_pos_emb is not None:
            q_pos_emb, k_pos_emb = rotary_pos_emb
            query = apply_rotary_pos_emb(query, q_pos_emb)
            key = apply_rotary_pos_emb(key, k_pos_emb)
            # TODO, can apply positional embedding to value_layer so it has
            # absolute positional embedding.
            # otherwise, only relative positional embedding takes effect
            # value_layer = apply_rotary_pos_emb(value_layer, k_pos_emb)

        # ==================================
        # core attention computation
        # ==================================

        # expand the key_layer and value_layer [sk, b, ng, hn] -> [sk, b, np, hn]
        # This is a noop for normal attention where ng == np. When using group query attention this
        # creates a view that has the keys and values virtually repeated along their dimension to
        # match the number of queries.
        if self.num_attention_heads_per_partition // self.num_query_groups_per_partition > 1:
            key = key.repeat_interleave(
                self.num_attention_heads_per_partition // self.num_query_groups_per_partition, dim=2
            )
            value = value.repeat_interleave(
                self.num_attention_heads_per_partition // self.num_query_groups_per_partition, dim=2
            )

        if self.checkpoint_dot_product_attention:
            core_attn_out = self._checkpointed_attention_forward(query, key, value, attention_mask)
        else:
            core_attn_out = self.dot_product_attention(query, key, value, attention_mask)

        # =================
        # Output. [sq, b, h]
        # =================

        output, bias = self.linear_proj(core_attn_out)

        return output, bias


class SelfAttention(Attention):
    """Self-attention layer class

    Self-attention layer takes input with size [s, b, h]
    and returns output of the same size.
    """

    def __init__(
        self, config: TransformerConfig, layer_number: int = 1, attn_mask_type=AttnMaskType.padding
    ):
        super().__init__(config=config, layer_number=layer_number, attn_mask_type=attn_mask_type)

        self.linear_qkv = TELayerNormColumnParallelLinear(
            self.config.hidden_size,
            self.query_projection_size + 2 * self.kv_projection_size,
            config=self.config,
            init_method=self.config.init_method,
            bias=self.config.add_bias_linear,
            skip_bias_add=False,
        )

    def get_query_key_value_tensors(self, hidden_states, key_value_states=None):
        """
        Derives `query`, `key` and `value` tensors from `hidden_states`.
        """
        # Attention heads [sq, b, h] --> [sq, b, ng * (np/ng + 2) * hn)]
        mixed_qkv, _ = self.linear_qkv(hidden_states)

        # [sq, b, hp] --> [sq, b, ng, (np/ng + 2) * hn]
        new_tensor_shape = mixed_qkv.size()[:-1] + (
            self.num_query_groups_per_partition,
            (
                (self.num_attention_heads_per_partition // self.num_query_groups_per_partition + 2)
                * self.hidden_size_per_attention_head
            ),
        )
        mixed_qkv = mixed_qkv.view(*new_tensor_shape)

        # [sq, b, ng, (np/ng + 2) * hn] --> [sq, b, ng, np/ng * hn], [sq, b, ng, hn], [sq, b, ng, hn]
        (query, key, value) = torch.split(
            mixed_qkv,
            [
                (
                    self.num_attention_heads_per_partition
                    // self.num_query_groups_per_partition
                    * self.hidden_size_per_attention_head
                ),
                self.hidden_size_per_attention_head,
                self.hidden_size_per_attention_head,
            ],
            dim=3,
        )
        # [sq, b, ng, np/ng * hn] -> [sq, b, np, hn]
        query = query.reshape(query.size(0), query.size(1), -1, self.hidden_size_per_attention_head)

        return query, key, value


class CrossAttention(Attention):
    """Cross-attention layer class

    Cross-attention layer takes input with size [s, b, h] and context with size
    [s, b, h] and returns output of the same size.
    """

    def __init__(
        self, config: TransformerConfig, layer_number: int = 1, attn_mask_type=AttnMaskType.padding
    ):
        super().__init__(config=config, layer_number=layer_number, attn_mask_type=attn_mask_type)

        if self.config.num_query_groups != self.config.num_attention_heads:
            raise ValueError(
                f"Group query attention is not currently supported in cross attention."
            )
        assert self.query_projection_size == self.kv_projection_size

        self.linear_q = TELayerNormColumnParallelLinear(
            self.config.hidden_size,
            self.query_projection_size,
            config=self.config,
            init_method=self.config.init_method,
            bias=self.config.add_bias_linear,
            skip_bias_add=False,
        )

        self.linear_kv = TELayerNormColumnParallelLinear(
            self.config.hidden_size,
            2 * self.kv_projection_size,
            config=self.config,
            init_method=self.config.init_method,
            bias=self.config.add_bias_linear,
            skip_bias_add=False,
        )

    def get_query_key_value_tensors(self, hidden_states, key_value_states):
        """
        Derives `query` tensor from `hidden_states`, and `key`/`value` tensors
        from `key_value_states`.
        """
        # Attention heads [sk, b, h] --> [sk, b, (np * 2 * hn)]
        mixed_kv, _ = self.linear_kv(key_value_states)

        # [sk, b, (np * 2 * hn)] --> [sk, b, np, 2 * hn]
        new_tensor_shape = mixed_kv.size()[:-1] + (
            self.num_attention_heads_per_partition,
            2 * self.hidden_size_per_attention_head,
        )
        mixed_kv = mixed_kv.view(*new_tensor_shape)

        # [sk, b, np, 2 * hn] --> 2 [sk, b, np, hn]
        (key, value) = tensor_parallel.split_tensor_along_last_dim(mixed_kv, 2)

        # Attention head [sq, b, h] --> [sq, b, hp]
        query, _ = self.linear_q(hidden_states)

        # [sq, b, hp] --> [sq, b, np, hn]
        new_tensor_shape = query.size()[:-1] + (
            self.num_attention_heads_per_partition,
            self.hidden_size_per_attention_head,
        )
        query = query.view(*new_tensor_shape)

        return query, key, value