blocks.py

# Adapted from https://github.com/mosaicml/llm-foundry
# Classes changed: MPTBlock
# SPDX-License-Identifier: Apache-2.0

"""GPT Blocks used for the GPT Model."""

from typing import Dict, Optional, Tuple
import torch
import torch.nn as nn
from .attention import ATTN_CLASS_REGISTRY
from llmfoundry.models.layers.norm import NORM_CLASS_REGISTRY

class MPTMLP(nn.Module):

    def __init__(self,
                 d_model: int,
                 expansion_ratio: int,
                 device: Optional[str] = None):
        super().__init__()
        self.up_proj = nn.Linear(d_model,
                                 expansion_ratio * d_model,
                                 device=device)
        self.act = nn.GELU(approximate='none')
        self.down_proj = nn.Linear(expansion_ratio * d_model,
                                   d_model,
                                   device=device)
        self.down_proj._is_residual = True  # type: ignore

    def forward(self, x):
        return self.down_proj(self.act(self.up_proj(x)))

class MPTBlock(nn.Module):
    def __init__(
            self,
            d_model: int,
            n_heads: int,
            expansion_ratio: int,
            attn_config: Dict = {
                'attn_type': 'multihead_attention',
                'attn_pdrop': 0.0,
                'attn_impl': 'triton',
                'qk_ln': False,
                'clip_qkv': None,
                'softmax_scale': None,
                'prefix_lm': False,
                'attn_uses_sequence_id': False,
                'alibi': False,
                'alibi_bias_max': 8,
            },
            resid_pdrop: float = 0.0,
            norm_type: str = 'low_precision_layernorm',
            verbose: int = 0,
            device: Optional[str] = None,
            **kwargs):
        del kwargs  # unused, just to capture any extra args from the config
        super().__init__()

        norm_class = NORM_CLASS_REGISTRY[norm_type.lower()]
        attn_class = ATTN_CLASS_REGISTRY[attn_config['attn_type']]

        self.norm_1 = norm_class(d_model, device=device)
        self.attn = attn_class(
            attn_impl=attn_config['attn_impl'],
            clip_qkv=attn_config['clip_qkv'],
            qk_ln=attn_config['qk_ln'],
            softmax_scale=attn_config['softmax_scale'],
            attn_pdrop=attn_config['attn_pdrop'],
            d_model=d_model,
            n_heads=n_heads,
            verbose=verbose,
            device=device,
        )
        self.norm_2 = norm_class(d_model, device=device)
        self.ffn = MPTMLP(
            d_model=d_model,
            expansion_ratio=expansion_ratio,
            device=device,
        )
        self.resid_attn_dropout = nn.Dropout(resid_pdrop)
        self.resid_ffn_dropout = nn.Dropout(resid_pdrop)

    def forward(
        self,
        x: torch.Tensor,
        past_key_value: Optional[Tuple[torch.Tensor]] = None,
        long_range_past_key_value:Optional[Tuple[torch.Tensor]] = None,
        attn_bias: Optional[torch.Tensor] = None,
        attn_bias_ae: Optional[torch.Tensor] = None,
        attention_mask: Optional[torch.ByteTensor] = None,
        is_causal: bool = True,
        topk:int=None,
        needs_weights:bool=None,
        faiss_indexes:Tuple=None,
        n_layers:int=None,
        current_layer:int=None,
        mask_by_sim:bool=False,
        sim_threshold:float=None
    ) -> Tuple[torch.Tensor, Optional[Tuple[torch.Tensor]]]:
        a = self.norm_1(x)
        b, attn_weights, past_key_value, reshaped_idx = self.attn(
            a,
            past_key_value=past_key_value,
            long_range_past_key_value=long_range_past_key_value,
            attn_bias=attn_bias,
            attn_bias_ae=attn_bias_ae,
            attention_mask=attention_mask,
            is_causal=is_causal,
            topk=topk,
            needs_weights=needs_weights,
            faiss_indexes=faiss_indexes,
            n_layers=n_layers,
            current_layer=current_layer,
            mask_by_sim=mask_by_sim,
            sim_threshold=sim_threshold
        )
        x = x + self.resid_attn_dropout(b)
        m = self.norm_2(x)
        n = self.ffn(m)
        x = x + self.resid_ffn_dropout(n)
        return x, attn_weights, past_key_value, reshaped_idx