megatron_patch/model/chatglm/positional_embeddings.py

# Copyright (c) 2023 Alibaba PAI and Nvidia Megatron-LM Team.
#
# 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 torch
import torch.nn.functional as F


class RotaryEmbedding(torch.nn.Module):
    def __init__(self, dim, base=10000, precision=torch.half, learnable=False):
        super().__init__()
        inv_freq = 1. / (base**(torch.arange(0, dim, 2).float() / dim))
        inv_freq = inv_freq.half()
        self.learnable = learnable
        if learnable:
            self.inv_freq = torch.nn.Parameter(inv_freq)
            self.max_seq_len_cached = None
        else:
            self.register_buffer('inv_freq', inv_freq)
            self.max_seq_len_cached = None
            self.cos_cached = None
            self.sin_cached = None
        self.precision = precision

    def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict,
                              missing_keys, unexpected_keys, error_msgs):
        pass

    def forward(self, x, seq_dim=1, seq_len=None):
        if seq_len is None:
            seq_len = x.shape[seq_dim]
        if self.max_seq_len_cached is None or (seq_len >
                                               self.max_seq_len_cached):
            self.max_seq_len_cached = None if self.learnable else seq_len
            t = torch.arange(seq_len,
                             device=x.device,
                             dtype=self.inv_freq.dtype)
            freqs = torch.einsum('i,j->ij', t, self.inv_freq)
            # Different from paper,
            # but it uses a different permutation
            # in order to obtain the same calculation
            emb = torch.cat((freqs, freqs), dim=-1).to(x.device)
            if self.precision == torch.bfloat16:
                emb = emb.float()

            # [sx, 1 (b * np), hn]
            cos_cached = emb.cos()[:, None, :]
            sin_cached = emb.sin()[:, None, :]
            if self.precision == torch.bfloat16:
                cos_cached = cos_cached.bfloat16()
                sin_cached = sin_cached.bfloat16()
            if self.learnable:
                return cos_cached, sin_cached
            self.cos_cached, self.sin_cached = cos_cached, sin_cached
        return self.cos_cached[:seq_len, ...], self.sin_cached[:seq_len, ...]

    def _apply(self, fn):
        if self.cos_cached is not None:
            self.cos_cached = fn(self.cos_cached)
        if self.sin_cached is not None:
            self.sin_cached = fn(self.sin_cached)
        return super()._apply(fn)


def rotate_half(x):
    x1, x2 = x[..., :x.shape[-1] // 2], x[..., x.shape[-1] // 2:]
    return torch.cat(
        (-x2, x1),
        dim=x1.ndim - 1)  # dim=-1 triggers a bug in earlier torch versions


@torch.jit.script
def apply_rotary_pos_emb_index(q, k, cos, sin, position_id):
    # position_id:
    # [sq, b], q, k: [sq, b, np, hn], cos: [sq, 1, hn] -> [sq, b, 1, hn]
    cos, sin = F.embedding(position_id, cos.squeeze(1)).unsqueeze(2), \
        F.embedding(position_id, sin.squeeze(1)).unsqueeze(2)
    q, k = (q * cos) + (rotate_half(q) * sin), (k * cos) + (rotate_half(k) *
                                                            sin)
    return q, k