dockformerpp/model/primitives.py

# Copyright 2021 AlQuraishi Laboratory
# Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import importlib
import math
from typing import Optional, Callable, List, Tuple
import numpy as np

import torch
import torch.nn as nn
import torch.utils.checkpoint
from scipy.stats import truncnorm

from dockformerpp.utils.kernel.attention_core import attention_core
from dockformerpp.utils.precision_utils import is_fp16_enabled
from dockformerpp.utils.tensor_utils import (
    permute_final_dims,
    flatten_final_dims,
)


# Suited for 40gb GPU
# DEFAULT_LMA_Q_CHUNK_SIZE = 1024
# DEFAULT_LMA_KV_CHUNK_SIZE = 4096
# Suited for 10gb GPU
DEFAULT_LMA_Q_CHUNK_SIZE = 64
DEFAULT_LMA_KV_CHUNK_SIZE = 256


def _prod(nums):
    out = 1
    for n in nums:
        out = out * n
    return out


def _calculate_fan(linear_weight_shape, fan="fan_in"):
    fan_out, fan_in = linear_weight_shape

    if fan == "fan_in":
        f = fan_in
    elif fan == "fan_out":
        f = fan_out
    elif fan == "fan_avg":
        f = (fan_in + fan_out) / 2
    else:
        raise ValueError("Invalid fan option")

    return f


def trunc_normal_init_(weights, scale=1.0, fan="fan_in"):
    shape = weights.shape
    f = _calculate_fan(shape, fan)
    scale = scale / max(1, f)
    a = -2
    b = 2
    std = math.sqrt(scale) / truncnorm.std(a=a, b=b, loc=0, scale=1)
    size = _prod(shape)
    samples = truncnorm.rvs(a=a, b=b, loc=0, scale=std, size=size)
    samples = np.reshape(samples, shape)
    with torch.no_grad():
        weights.copy_(torch.tensor(samples, device=weights.device))


def lecun_normal_init_(weights):
    trunc_normal_init_(weights, scale=1.0)


def he_normal_init_(weights):
    trunc_normal_init_(weights, scale=2.0)


def glorot_uniform_init_(weights):
    nn.init.xavier_uniform_(weights, gain=1)


def final_init_(weights):
    with torch.no_grad():
        weights.fill_(0.0)


def gating_init_(weights):
    with torch.no_grad():
        weights.fill_(0.0)


def normal_init_(weights):
    torch.nn.init.kaiming_normal_(weights, nonlinearity="linear")


def ipa_point_weights_init_(weights):
    with torch.no_grad():
        softplus_inverse_1 = 0.541324854612918
        weights.fill_(softplus_inverse_1)


class Linear(nn.Linear):
    """
    A Linear layer with built-in nonstandard initializations. Called just
    like torch.nn.Linear.

    Implements the initializers in 1.11.4, plus some additional ones found
    in the code.
    """

    def __init__(
        self,
        in_dim: int,
        out_dim: int,
        bias: bool = True,
        init: str = "default",
        init_fn: Optional[Callable[[torch.Tensor, torch.Tensor], None]] = None,
        precision=None
    ):
        """
        Args:
            in_dim:
                The final dimension of inputs to the layer
            out_dim:
                The final dimension of layer outputs
            bias:
                Whether to learn an additive bias. True by default
            init:
                The initializer to use. Choose from:

                "default": LeCun fan-in truncated normal initialization
                "relu": He initialization w/ truncated normal distribution
                "glorot": Fan-average Glorot uniform initialization
                "gating": Weights=0, Bias=1
                "normal": Normal initialization with std=1/sqrt(fan_in)
                "final": Weights=0, Bias=0

                Overridden by init_fn if the latter is not None.
            init_fn:
                A custom initializer taking weight and bias as inputs.
                Overrides init if not None.
        """
        super(Linear, self).__init__(in_dim, out_dim, bias=bias)

        if bias:
            with torch.no_grad():
                self.bias.fill_(0)

        with torch.no_grad():
            if init_fn is not None:
                init_fn(self.weight, self.bias)
            else:
                if init == "default":
                    lecun_normal_init_(self.weight)
                elif init == "relu":
                    he_normal_init_(self.weight)
                elif init == "glorot":
                    glorot_uniform_init_(self.weight)
                elif init == "gating":
                    gating_init_(self.weight)
                    if bias:
                        self.bias.fill_(1.0)
                elif init == "normal":
                    normal_init_(self.weight)
                elif init == "final":
                    final_init_(self.weight)
                else:
                    raise ValueError("Invalid init string.")

        self.precision = precision

    def forward(self, input: torch.Tensor) -> torch.Tensor:
        d = input.dtype
        if self.precision is not None:
            with torch.cuda.amp.autocast(enabled=False):
                bias = self.bias.to(dtype=self.precision) if self.bias is not None else None
                return nn.functional.linear(input.to(dtype=self.precision),
                                            self.weight.to(dtype=self.precision),
                                            bias).to(dtype=d)

        if d is torch.bfloat16:
            with torch.cuda.amp.autocast(enabled=False):
                bias = self.bias.to(dtype=d) if self.bias is not None else None
                return nn.functional.linear(input, self.weight.to(dtype=d), bias)

        return nn.functional.linear(input, self.weight, self.bias)


class LayerNorm(nn.Module):
    def __init__(self, c_in, eps=1e-5):
        super(LayerNorm, self).__init__()
        
        self.c_in = (c_in,)
        self.eps = eps

        self.weight = nn.Parameter(torch.ones(c_in))
        self.bias = nn.Parameter(torch.zeros(c_in))

    def forward(self, x): 
        d = x.dtype
        if d is torch.bfloat16:
            with torch.cuda.amp.autocast(enabled=False):
                out = nn.functional.layer_norm(
                    x, 
                    self.c_in, 
                    self.weight.to(dtype=d), 
                    self.bias.to(dtype=d), 
                    self.eps
                )
        else:
            out = nn.functional.layer_norm(
                x,
                self.c_in,
                self.weight,
                self.bias,
                self.eps,
            )

        return out


@torch.jit.ignore
def softmax_no_cast(t: torch.Tensor, dim: int = -1) -> torch.Tensor:
    """
        Softmax, but without automatic casting to fp32 when the input is of
        type bfloat16
    """
    d = t.dtype
    if d is torch.bfloat16:
        with torch.cuda.amp.autocast(enabled=False):
            s = torch.nn.functional.softmax(t, dim=dim)
    else:
        s = torch.nn.functional.softmax(t, dim=dim)

    return s


#@torch.jit.script
def _attention(query: torch.Tensor, key: torch.Tensor, value: torch.Tensor, biases: List[torch.Tensor]) -> torch.Tensor:
    # [*, H, C_hidden, K]
    key = permute_final_dims(key, (1, 0))

    # [*, H, Q, K]
    a = torch.matmul(query, key)

    for b in biases:
        a += b

    a = softmax_no_cast(a, -1)

    # [*, H, Q, C_hidden]
    a = torch.matmul(a, value)

    return a


class Attention(nn.Module):
    """
    Standard multi-head attention using AlphaFold's default layer
    initialization. Allows multiple bias vectors.
    """
    def __init__(
        self,
        c_q: int,
        c_k: int,
        c_v: int,
        c_hidden: int,
        no_heads: int,
        gating: bool = True,
    ):
        """
        Args:
            c_q:
                Input dimension of query data
            c_k:
                Input dimension of key data
            c_v:
                Input dimension of value data
            c_hidden:
                Per-head hidden dimension
            no_heads:
                Number of attention heads
            gating:
                Whether the output should be gated using query data
        """
        super(Attention, self).__init__()

        self.c_q = c_q
        self.c_k = c_k
        self.c_v = c_v
        self.c_hidden = c_hidden
        self.no_heads = no_heads
        self.gating = gating

        # DISCREPANCY: c_hidden is not the per-head channel dimension, as
        # stated in the supplement, but the overall channel dimension.

        self.linear_q = Linear(
            self.c_q, self.c_hidden * self.no_heads, bias=False, init="glorot"
        )
        self.linear_k = Linear(
            self.c_k, self.c_hidden * self.no_heads, bias=False, init="glorot"
        )
        self.linear_v = Linear(
            self.c_v, self.c_hidden * self.no_heads, bias=False, init="glorot"
        )
        self.linear_o = Linear(
            self.c_hidden * self.no_heads, self.c_q, init="final"
        )

        self.linear_g = None
        if self.gating:
            self.linear_g = Linear(
                self.c_q, self.c_hidden * self.no_heads, init="gating"
            )

        self.sigmoid = nn.Sigmoid()

    def _prep_qkv(self,
        q_x: torch.Tensor, 
        kv_x: torch.Tensor,
        apply_scale: bool = True
    ) -> Tuple[
        torch.Tensor, torch.Tensor, torch.Tensor
    ]:
        # [*, Q/K/V, H * C_hidden]
        q = self.linear_q(q_x)
        k = self.linear_k(kv_x)
        v = self.linear_v(kv_x)

        # [*, Q/K, H, C_hidden]
        q = q.view(q.shape[:-1] + (self.no_heads, -1))
        k = k.view(k.shape[:-1] + (self.no_heads, -1))
        v = v.view(v.shape[:-1] + (self.no_heads, -1))

        # [*, H, Q/K, C_hidden]
        q = q.transpose(-2, -3)
        k = k.transpose(-2, -3)
        v = v.transpose(-2, -3)

        if apply_scale:
            q /= math.sqrt(self.c_hidden)

        return q, k, v

    def _wrap_up(self,
        o: torch.Tensor, 
        q_x: torch.Tensor
    ) -> torch.Tensor:
        if self.linear_g is not None:
            g = self.sigmoid(self.linear_g(q_x))
        
            # [*, Q, H, C_hidden]
            g = g.view(g.shape[:-1] + (self.no_heads, -1))
            o = o * g

        # [*, Q, H * C_hidden]
        o = flatten_final_dims(o, 2)

        # [*, Q, C_q]
        o = self.linear_o(o)

        return o

    def forward(
        self,
        q_x: torch.Tensor,
        kv_x: torch.Tensor,
        biases: Optional[List[torch.Tensor]] = None,
        use_memory_efficient_kernel: bool = False,
        use_lma: bool = False,
        lma_q_chunk_size: int = DEFAULT_LMA_Q_CHUNK_SIZE,
        lma_kv_chunk_size: int = DEFAULT_LMA_KV_CHUNK_SIZE,
    ) -> torch.Tensor:
        """
        Args:
            q_x:
                [*, Q, C_q] query data
            kv_x:
                [*, K, C_k] key data
            biases:
                List of biases that broadcast to [*, H, Q, K]
            use_memory_efficient_kernel:
                Whether to use a custom memory-efficient attention kernel.
                This should be the default choice for most. If none of the
                "use_<...>" flags are True, a stock PyTorch implementation
                is used instead
            use_lma:
                Whether to use low-memory attention (Staats & Rabe 2021). If
                none of the "use_<...>" flags are True, a stock PyTorch 
                implementation is used instead
            lma_q_chunk_size:
                Query chunk size (for LMA)
            lma_kv_chunk_size:
                Key/Value chunk size (for LMA)
        Returns
            [*, Q, C_q] attention update
        """
        if use_lma and (lma_q_chunk_size is None or lma_kv_chunk_size is None):
            raise ValueError(
                "If use_lma is specified, lma_q_chunk_size and "
                "lma_kv_chunk_size must be provided"
            )

        attn_options = [use_memory_efficient_kernel, use_lma]
        if sum(attn_options) > 1:
            raise ValueError(
                "Choose at most one alternative attention algorithm"
            )

        if biases is None:
            biases = []
        
        q, k, v = self._prep_qkv(q_x, kv_x, apply_scale=True)

        if is_fp16_enabled():
            use_memory_efficient_kernel = False
        
        if use_memory_efficient_kernel:
            if len(biases) > 2:
                raise ValueError(
                    "If use_memory_efficient_kernel is True, you may only "
                    "provide up to two bias terms"
                )
            o = attention_core(q, k, v, *((biases + [None] * 2)[:2]))
            o = o.transpose(-2, -3)
        elif use_lma:
            biases = [
                b.expand(b.shape[:-2] + (q_x.shape[-2],) + (kv_x.shape[-2],)) 
                for b in biases
            ]
            o = _lma(q, k, v, biases, lma_q_chunk_size, lma_kv_chunk_size)
            o = o.transpose(-2, -3)
        else:
            o = _attention(q, k, v, biases)
            o = o.transpose(-2, -3)

        o = self._wrap_up(o, q_x)

        return o


class GlobalAttention(nn.Module):
    def __init__(self, c_in, c_hidden, no_heads, inf, eps):
        super(GlobalAttention, self).__init__()

        self.c_in = c_in
        self.c_hidden = c_hidden
        self.no_heads = no_heads
        self.inf = inf
        self.eps = eps

        self.linear_q = Linear(
            c_in, c_hidden * no_heads, bias=False, init="glorot"
        )

        self.linear_k = Linear(
            c_in, c_hidden, bias=False, init="glorot",
        )
        self.linear_v = Linear(
            c_in, c_hidden, bias=False, init="glorot",
        )
        self.linear_g = Linear(c_in, c_hidden * no_heads, init="gating")
        self.linear_o = Linear(c_hidden * no_heads, c_in, init="final")

        self.sigmoid = nn.Sigmoid()

    def forward(self, 
        m: torch.Tensor, 
        mask: torch.Tensor,
        use_lma: bool = False,
    ) -> torch.Tensor:
        # [*, N_res, C_in]
        q = torch.sum(m * mask.unsqueeze(-1), dim=-2) / (
            torch.sum(mask, dim=-1)[..., None] + self.eps
        )

        # [*, N_res, H * C_hidden]
        q = self.linear_q(q)
        q *= (self.c_hidden ** (-0.5))

        # [*, N_res, H, C_hidden]
        q = q.view(q.shape[:-1] + (self.no_heads, -1))

        # [*, N_res, C_hidden]
        k = self.linear_k(m)
        v = self.linear_v(m)

        bias = (self.inf * (mask - 1))[..., :, None, :]
        if not use_lma:
            # [*, N_res, H, N_seq]
            a = torch.matmul(
                q,
                k.transpose(-1, -2),  # [*, N_res, C_hidden, N_seq]
            )
            a += bias
            a = softmax_no_cast(a)

            # [*, N_res, H, C_hidden]
            o = torch.matmul(
                a,
                v,
            )
        else:
            o = _lma(
                q, 
                k, 
                v, 
                [bias], 
                DEFAULT_LMA_Q_CHUNK_SIZE, 
                DEFAULT_LMA_KV_CHUNK_SIZE
            )

        # [*, N_res, C_hidden]
        g = self.sigmoid(self.linear_g(m))

        # [*, N_res, H, C_hidden]
        g = g.view(g.shape[:-1] + (self.no_heads, -1))

        # [*, N_res, H, C_hidden]
        o = o.unsqueeze(-3) * g

        # [*, N_res, H * C_hidden]
        o = o.reshape(o.shape[:-2] + (-1,))

        # [*, N_res, C_in]
        m = self.linear_o(o)

        return m


def _lma(
    q: torch.Tensor, 
    k: torch.Tensor, 
    v: torch.Tensor, 
    biases: List[torch.Tensor], 
    q_chunk_size: int, 
    kv_chunk_size: int,
):
    no_q, no_kv = q.shape[-2], k.shape[-2]

    # [*, H, Q, C_hidden]
    o = q.new_zeros(q.shape)
    for q_s in range(0, no_q, q_chunk_size):
        q_chunk = q[..., q_s: q_s + q_chunk_size, :]
        large_bias_chunks = [
            b[..., q_s: q_s + q_chunk_size, :] for b in biases
        ]

        maxes = []
        weights = []
        values = []
        for kv_s in range(0, no_kv, kv_chunk_size):
            k_chunk = k[..., kv_s: kv_s + kv_chunk_size, :]
            v_chunk = v[..., kv_s: kv_s + kv_chunk_size, :]
            small_bias_chunks = [
                b[..., kv_s: kv_s + kv_chunk_size] for b in large_bias_chunks
            ]

            a = torch.einsum(
                "...hqd,...hkd->...hqk", q_chunk, k_chunk,
            )
       
            for b in small_bias_chunks:
                a += b
        
            max_a = torch.max(a, dim=-1, keepdim=True)[0]
            exp_a = torch.exp(a - max_a)
            exp_v = torch.einsum("...hvf,...hqv->...hqf", v_chunk, exp_a)
 
            maxes.append(max_a.detach().squeeze(-1))
            weights.append(torch.sum(exp_a, dim=-1))
            values.append(exp_v)

        chunk_max = torch.stack(maxes, dim=-3)
        chunk_weights = torch.stack(weights, dim=-3)
        chunk_values = torch.stack(values, dim=-4)

        global_max = torch.max(chunk_max, dim=-3, keepdim=True)[0]
        max_diffs = torch.exp(chunk_max - global_max)
        chunk_values = chunk_values * max_diffs.unsqueeze(-1)
        chunk_weights = chunk_weights * max_diffs

        all_values = torch.sum(chunk_values, dim=-4)
        all_weights = torch.sum(chunk_weights.unsqueeze(-1), dim=-4)

        q_chunk_out = all_values / all_weights

        o[..., q_s: q_s + q_chunk_size, :] = q_chunk_out

    return o