mirror of https://github.com/hpcaitech/ColossalAI
530 lines
15 KiB
Python
530 lines
15 KiB
Python
# Copyright 2021 AlQuraishi Laboratory
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# Copyright 2021 DeepMind Technologies Limited
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from functools import partial
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import math
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from typing import Optional, Callable, List, Tuple, Sequence
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import numpy as np
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import torch
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import torch.nn as nn
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from .checkpointing import get_checkpoint_fn
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from .tensor_utils import (
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permute_final_dims,
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flatten_final_dims,
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_chunk_slice,
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)
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def _prod(nums):
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out = 1
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for n in nums:
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out = out * n
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return out
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def _calculate_fan(linear_weight_shape, fan="fan_in"):
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fan_out, fan_in = linear_weight_shape
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if fan == "fan_in":
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f = fan_in
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elif fan == "fan_out":
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f = fan_out
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elif fan == "fan_avg":
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f = (fan_in + fan_out) / 2
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else:
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raise ValueError("Invalid fan option")
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return f
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def glorot_uniform_init_(weights):
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nn.init.xavier_uniform_(weights, gain=1)
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def final_init_(weights):
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with torch.no_grad():
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weights.fill_(0.0)
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def gating_init_(weights):
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with torch.no_grad():
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weights.fill_(0.0)
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def normal_init_(weights):
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torch.nn.init.kaiming_normal_(weights, nonlinearity="linear")
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def ipa_point_weights_init_(weights):
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with torch.no_grad():
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softplus_inverse_1 = 0.541324854612918
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weights.fill_(softplus_inverse_1)
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class Linear(nn.Linear):
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"""
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A Linear layer with built-in nonstandard initializations. Called just
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like torch.nn.Linear.
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Implements the initializers in 1.11.4, plus some additional ones found
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in the code.
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"""
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def __init__(
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self,
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in_dim: int,
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out_dim: int,
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bias: bool = True,
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init: str = "default",
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init_fn: Optional[Callable[[torch.Tensor, torch.Tensor], None]] = None,
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):
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"""
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Args:
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in_dim:
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The final dimension of inputs to the layer
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out_dim:
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The final dimension of layer outputs
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bias:
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Whether to learn an additive bias. True by default
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init:
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The initializer to use. Choose from:
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"default": LeCun fan-in truncated normal initialization
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"relu": He initialization w/ truncated normal distribution
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"glorot": Fan-average Glorot uniform initialization
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"gating": Weights=0, Bias=1
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"normal": Normal initialization with std=1/sqrt(fan_in)
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"final": Weights=0, Bias=0
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Overridden by init_fn if the latter is not None.
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init_fn:
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A custom initializer taking weight and bias as inputs.
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Overrides init if not None.
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"""
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super(Linear, self).__init__(in_dim, out_dim, bias=bias)
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if bias:
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with torch.no_grad():
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self.bias.fill_(0)
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if init_fn is not None:
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init_fn(self.weight, self.bias)
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else:
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if init == "default":
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normal_init_(self.weight)
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elif init == "relu":
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normal_init_(self.weight)
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elif init == "glorot":
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glorot_uniform_init_(self.weight)
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elif init == "gating":
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gating_init_(self.weight)
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if bias:
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with torch.no_grad():
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self.bias.fill_(1.0)
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elif init == "normal":
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normal_init_(self.weight)
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elif init == "final":
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final_init_(self.weight)
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else:
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raise ValueError("Invalid init string.")
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class LayerNorm(nn.Module):
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def __init__(self, c_in, eps=1e-5):
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super(LayerNorm, self).__init__()
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self.c_in = (c_in,)
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self.eps = eps
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self.weight = nn.Parameter(torch.ones(c_in))
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self.bias = nn.Parameter(torch.zeros(c_in))
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def forward(self, x):
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out = nn.functional.layer_norm(
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x,
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self.c_in,
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self.weight,
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self.bias,
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self.eps,
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)
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return out
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@torch.jit.ignore
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def softmax(t: torch.Tensor, dim: int = -1) -> torch.Tensor:
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"""
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Softmax, but without automatic casting to fp32 when the input is of
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type bfloat16
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"""
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s = torch.nn.functional.softmax(t, dim=dim)
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return s
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#@torch.jit.script
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def _attention(query: torch.Tensor, key: torch.Tensor, value: torch.Tensor,
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biases: List[torch.Tensor]) -> torch.Tensor:
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# [*, H, Q, C_hidden]
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query = permute_final_dims(query, (1, 0, 2))
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# [*, H, C_hidden, K]
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key = permute_final_dims(key, (1, 2, 0))
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# [*, H, V, C_hidden]
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value = permute_final_dims(value, (1, 0, 2))
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# [*, H, Q, K]
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a = torch.matmul(query, key)
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for b in biases:
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a += b
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a = softmax(a, -1)
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# [*, H, Q, C_hidden]
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a = torch.matmul(a, value)
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# [*, Q, H, C_hidden]
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a = a.transpose(-2, -3)
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return a
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@torch.jit.ignore
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def _attention_chunked_trainable(
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query,
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key,
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value,
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biases,
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chunk_size,
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chunk_dim,
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checkpoint,
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):
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if (checkpoint and len(biases) > 2):
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raise ValueError("Checkpointed version permits only permits two bias terms")
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def _checkpointable_attention(q, k, v, b1, b2):
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bs = [b for b in [b1, b2] if b is not None]
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return _attention(q, k, v, bs)
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o_chunks = []
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checkpoint_fn = get_checkpoint_fn()
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count = query.shape[chunk_dim]
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for start in range(0, count, chunk_size):
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end = start + chunk_size
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idx = [slice(None)] * len(query.shape)
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idx[chunk_dim] = slice(start, end)
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idx_tup = tuple(idx)
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q_chunk = query[idx_tup]
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k_chunk = key[idx_tup]
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v_chunk = value[idx_tup]
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def _slice_bias(b):
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idx[chunk_dim] = (slice(start, end) if b.shape[chunk_dim] != 1 else slice(None))
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return b[tuple(idx)]
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if (checkpoint):
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bias_1_chunk, bias_2_chunk = [
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_slice_bias(b) if b is not None else None for b in (biases + [None, None])[:2]
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]
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o_chunk = checkpoint_fn(_checkpointable_attention, q_chunk, k_chunk, v_chunk,
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bias_1_chunk, bias_2_chunk)
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else:
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bias_chunks = [_slice_bias(b) for b in biases]
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o_chunk = _attention(q_chunk, k_chunk, v_chunk, bias_chunks)
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o_chunks.append(o_chunk)
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o = torch.cat(o_chunks, dim=chunk_dim)
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return o
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class Attention(nn.Module):
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"""
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Standard multi-head attention using AlphaFold's default layer
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initialization. Allows multiple bias vectors.
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"""
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def __init__(
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self,
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c_q: int,
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c_k: int,
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c_v: int,
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c_hidden: int,
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no_heads: int,
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gating: bool = True,
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):
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"""
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Args:
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c_q:
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Input dimension of query data
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c_k:
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Input dimension of key data
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c_v:
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Input dimension of value data
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c_hidden:
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Per-head hidden dimension
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no_heads:
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Number of attention heads
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gating:
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Whether the output should be gated using query data
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"""
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super(Attention, self).__init__()
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self.c_q = c_q
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self.c_k = c_k
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self.c_v = c_v
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self.c_hidden = c_hidden
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self.no_heads = no_heads
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self.gating = gating
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# DISCREPANCY: c_hidden is not the per-head channel dimension, as
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# stated in the supplement, but the overall channel dimension.
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self.linear_q = Linear(self.c_q, self.c_hidden * self.no_heads, bias=False, init="glorot")
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self.linear_k = Linear(self.c_k, self.c_hidden * self.no_heads, bias=False, init="glorot")
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self.linear_v = Linear(self.c_v, self.c_hidden * self.no_heads, bias=False, init="glorot")
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self.linear_o = Linear(self.c_hidden * self.no_heads, self.c_q, init="final")
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self.linear_g = None
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if self.gating:
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self.linear_g = Linear(self.c_q, self.c_hidden * self.no_heads, init="gating")
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self.sigmoid = nn.Sigmoid()
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def _prep_qkv(self, q_x: torch.Tensor,
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kv_x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
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# [*, Q/K/V, H * C_hidden]
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q = self.linear_q(q_x)
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k = self.linear_k(kv_x)
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v = self.linear_v(kv_x)
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# [*, Q/K, H, C_hidden]
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q = q.view(q.shape[:-1] + (self.no_heads, -1))
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k = k.view(k.shape[:-1] + (self.no_heads, -1))
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v = v.view(v.shape[:-1] + (self.no_heads, -1))
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q /= math.sqrt(self.c_hidden)
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return q, k, v
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def _wrap_up(self, o: torch.Tensor, q_x: torch.Tensor) -> torch.Tensor:
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if (self.linear_g is not None):
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g = self.sigmoid(self.linear_g(q_x))
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# [*, Q, H, C_hidden]
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g = g.view(g.shape[:-1] + (self.no_heads, -1))
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o = o * g
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# [*, Q, H * C_hidden]
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o = flatten_final_dims(o, 2)
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# [*, Q, C_q]
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o = self.linear_o(o)
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return o
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def forward(
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self,
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q_x: torch.Tensor,
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kv_x: torch.Tensor,
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biases: Optional[List[torch.Tensor]] = None,
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use_lma: bool = False,
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q_chunk_size: Optional[int] = None,
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kv_chunk_size: Optional[int] = None,
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) -> torch.Tensor:
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"""
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Args:
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q_x:
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[*, Q, C_q] query data
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kv_x:
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[*, K, C_k] key data
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biases:
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List of biases that broadcast to [*, H, Q, K]
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use_lma:
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Whether to use low-memory attention
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q_chunk_size:
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Query chunk size (for LMA)
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kv_chunk_size:
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Key/Value chunk size (for LMA)
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Returns
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[*, Q, C_q] attention update
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"""
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if (biases is None):
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biases = []
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if (use_lma and (q_chunk_size is None or kv_chunk_size is None)):
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raise ValueError("If use_lma is specified, q_chunk_size and kv_chunk_size must "
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"be provided")
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q, k, v = self._prep_qkv(q_x, kv_x)
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if (use_lma):
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biases = [b.expand(b.shape[:-2] + (q_x.shape[-2],) + (kv_x.shape[-2],)) for b in biases]
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o = _lma(q, k, v, biases, q_chunk_size, kv_chunk_size)
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else:
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o = _attention(q, k, v, biases)
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o = self._wrap_up(o, q_x)
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return o
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class GlobalAttention(nn.Module):
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def __init__(self, c_in, c_hidden, no_heads, inf, eps):
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super(GlobalAttention, self).__init__()
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self.c_in = c_in
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self.c_hidden = c_hidden
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self.no_heads = no_heads
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self.inf = inf
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self.eps = eps
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self.linear_q = Linear(c_in, c_hidden * no_heads, bias=False, init="glorot")
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self.linear_k = Linear(
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c_in,
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c_hidden,
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bias=False,
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init="glorot",
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)
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self.linear_v = Linear(
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c_in,
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c_hidden,
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bias=False,
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init="glorot",
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)
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self.linear_g = Linear(c_in, c_hidden * no_heads, init="gating")
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self.linear_o = Linear(c_hidden * no_heads, c_in, init="final")
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self.sigmoid = nn.Sigmoid()
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def forward(self, m: torch.Tensor, mask: torch.Tensor) -> torch.Tensor:
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# [*, N_res, C_in]
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q = torch.sum(m * mask.unsqueeze(-1),
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dim=-2) / (torch.sum(mask, dim=-1)[..., None] + self.eps)
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# [*, N_res, H * C_hidden]
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q = self.linear_q(q)
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q *= (self.c_hidden**(-0.5))
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# [*, N_res, H, C_hidden]
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q = q.view(q.shape[:-1] + (self.no_heads, -1))
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# [*, N_res, N_seq, C_hidden]
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k = self.linear_k(m)
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v = self.linear_v(m)
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# [*, N_res, H, N_seq]
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a = torch.matmul(
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q,
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k.transpose(-1, -2), # [*, N_res, C_hidden, N_seq]
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)
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bias = (self.inf * (mask - 1))[..., :, None, :]
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a += bias
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a = softmax(a)
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# [*, N_res, H, C_hidden]
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o = torch.matmul(
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a,
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v,
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)
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# [*, N_res, N_seq, C_hidden]
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g = self.sigmoid(self.linear_g(m))
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# [*, N_res, N_seq, H, C_hidden]
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g = g.view(g.shape[:-1] + (self.no_heads, -1))
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# [*, N_res, N_seq, H, C_hidden]
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o = o.unsqueeze(-3) * g
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# [*, N_res, N_seq, H * C_hidden]
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o = o.reshape(o.shape[:-2] + (-1,))
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# [*, N_res, N_seq, C_in]
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m = self.linear_o(o)
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return m
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def _lma(
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q: torch.Tensor,
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k: torch.Tensor,
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v: torch.Tensor,
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biases: List[torch.Tensor],
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q_chunk_size: int,
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kv_chunk_size: int,
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):
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no_q, no_kv = q.shape[-3], k.shape[-3]
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# [*, Q, H, C_hidden]
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o = q.new_zeros(q.shape)
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for q_s in range(0, no_q, q_chunk_size):
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q_chunk = q[..., q_s:q_s + q_chunk_size, :, :]
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large_bias_chunks = [b[..., q_s:q_s + q_chunk_size, :] for b in biases]
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maxes = []
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weights = []
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values = []
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for kv_s in range(0, no_kv, kv_chunk_size):
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k_chunk = k[..., kv_s:kv_s + kv_chunk_size, :, :]
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v_chunk = v[..., kv_s:kv_s + kv_chunk_size, :, :]
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small_bias_chunks = [b[..., kv_s:kv_s + kv_chunk_size] for b in large_bias_chunks]
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a = torch.einsum(
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"...qhd,...khd->...hqk",
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q_chunk,
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k_chunk,
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)
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for b in small_bias_chunks:
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a += b
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a = a.transpose(-2, -3)
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max_a = torch.max(a, dim=-1, keepdim=True)[0]
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exp_a = torch.exp(a - max_a)
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exp_v = torch.einsum("...vhf,...qhv->...qhf", v_chunk, exp_a)
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maxes.append(max_a.detach().squeeze(-1))
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weights.append(torch.sum(exp_a, dim=-1))
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values.append(exp_v)
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chunk_max = torch.stack(maxes, dim=-3)
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chunk_weights = torch.stack(weights, dim=-3)
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chunk_values = torch.stack(values, dim=-4)
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global_max = torch.max(chunk_max, dim=-3, keepdim=True)[0]
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max_diffs = torch.exp(chunk_max - global_max)
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chunk_values *= max_diffs.unsqueeze(-1)
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chunk_weights *= max_diffs
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all_values = torch.sum(chunk_values, dim=-4)
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all_weights = torch.sum(chunk_weights.unsqueeze(-1), dim=-4)
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q_chunk_out = all_values / all_weights
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o[..., q_s:q_s + q_chunk_size, :, :] = q_chunk_out
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return o
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