forked from jiuyuan/CPM-9G-8B
369 lines
15 KiB
Python
369 lines
15 KiB
Python
import math
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from typing import Tuple
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from typing import Union
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import bmtrain as bmt
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import torch
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import torch.nn.functional as F
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try:
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from flash_attn.layers.rotary import apply_rotary_emb_func
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except:
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apply_rotary_emb_func = None
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class SegmentPositionEmbedding(bmt.DistributedModule):
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def __init__(
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self,
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num_heads: int,
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num_segments: int = 1,
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num_buckets: int = 32,
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max_distance: int = 128,
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bidirectional: bool = False,
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dtype: torch.dtype = torch.half,
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init_mean: float = 0.0,
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init_std: float = 1,
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):
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super().__init__()
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self.num_heads = num_heads
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self.num_buckets = num_buckets
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self.max_distance = max_distance
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self.bidirectional = bidirectional
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self.num_segments = num_segments
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self.relative_attention_bias = bmt.DistributedParameter(
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torch.empty(num_segments * num_segments + num_buckets, num_heads, dtype=dtype),
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init_method=bmt.ParameterInitializer(torch.nn.init.normal_, mean=init_mean, std=init_std),
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)
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def forward(
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self,
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key_pos: torch.Tensor,
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query_pos: torch.Tensor,
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key_segment: torch.Tensor,
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query_segment: torch.Tensor,
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):
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with torch.no_grad():
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batch = key_pos.size(0)
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keylen = key_pos.size(1)
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querylen = query_pos.size(1)
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assert key_pos.size(0) == query_pos.size(0)
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assert keylen == key_segment.size(1) and querylen == query_segment.size(1)
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key_pos = key_pos.view(batch, -1, keylen)
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query_pos = query_pos.view(batch, querylen, -1)
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key_segment = key_segment.view(batch, -1, keylen)
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query_segment = query_segment.view(batch, querylen, -1)
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relative_position_bucket = self._segment_relative_position_bucket(query_segment, key_segment)
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relative_position_bucket = relative_position_bucket + self.num_buckets # 与相对位置编码区间不重叠
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# b*q*k
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absolute_position_bucket = self._position_bucket(
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torch.arange(keylen, dtype=torch.int32, device=relative_position_bucket.device)[None, :]
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- torch.arange(querylen, dtype=torch.int32, device=relative_position_bucket.device)[:, None],
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bidirectional=self.bidirectional,
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num_buckets=self.num_buckets,
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max_distance=self.max_distance,
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)
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relative_position_bucket = torch.where(
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(key_segment == query_segment),
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absolute_position_bucket[None, :, :],
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relative_position_bucket,
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)
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# (batch, len_q, len_k)
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# (batch, len_q, len_k, num_heads)
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embeds = F.embedding(relative_position_bucket, self.relative_attention_bias)
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# (batch, num_heads, len_q, len_k)
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embeds = embeds.permute(0, 3, 1, 2).contiguous()
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return embeds
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def _segment_relative_position_bucket(self, query_segment, key_segment):
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return query_segment * self.num_segments + key_segment
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def _position_bucket(self, relative_position, bidirectional=True, num_buckets=32, max_distance=128):
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relative_buckets = 0
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if bidirectional:
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num_buckets //= 2
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relative_buckets = (relative_position > 0).to(torch.int32) * num_buckets
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relative_position = torch.abs(relative_position)
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else:
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relative_position = -torch.min(relative_position, torch.zeros_like(relative_position))
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max_exact = num_buckets // 2
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is_small = relative_position < max_exact
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relative_postion_if_large = max_exact + (
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torch.log(relative_position.float() / max_exact)
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/ math.log(max_distance / max_exact)
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* (num_buckets - max_exact)
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).to(torch.int32)
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relative_postion_if_large = torch.min(
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relative_postion_if_large,
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torch.full_like(relative_postion_if_large, num_buckets - 1),
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)
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relative_buckets += torch.where(is_small, relative_position.to(torch.int32), relative_postion_if_large)
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return relative_buckets
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class BucketPositionBias(bmt.DistributedModule):
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def __init__(
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self,
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num_heads: int,
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num_buckets: int = 32,
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num_segment_bucket: int = 32,
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max_distance: int = 128,
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dtype: torch.dtype = torch.half,
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init_mean: float = 0.0,
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init_std: float = 1,
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) -> None:
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super().__init__()
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self.num_heads = num_heads
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self.num_buckets = num_buckets
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self.num_segment_bucket = num_segment_bucket
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self.max_distance = max_distance
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self.relative_attention_bias = bmt.DistributedParameter(
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torch.empty(num_buckets + num_segment_bucket, num_heads, dtype=dtype),
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init_method=bmt.ParameterInitializer(torch.nn.init.normal_, mean=init_mean, std=init_std),
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)
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def forward(
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self,
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query_pos: torch.Tensor, # (batch, len_q)
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key_pos: torch.Tensor, # (batch, len_k)
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rel_buckets: torch.Tensor, # (batch, len_q, len_k)
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):
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with torch.no_grad():
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batch = key_pos.size(0)
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keylen = key_pos.size(1)
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querylen = query_pos.size(1)
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assert key_pos.size(0) == query_pos.size(0)
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assert rel_buckets.size(0) == batch and rel_buckets.size(1) == querylen and rel_buckets.size(2) == keylen
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relative_position_bucket = rel_buckets - 1 + self.num_buckets # 与相对位置编码区间不重叠
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# b*q*k
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inner_segment_bucket = self._position_bucket(
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key_pos[..., None, :] - query_pos[..., :, None],
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num_buckets=self.num_buckets,
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max_distance=self.max_distance,
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)
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relative_position_bucket = torch.where(
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rel_buckets == 0,
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inner_segment_bucket,
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relative_position_bucket,
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)
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# (batch, len_q, len_k)
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# (batch, len_q, len_k, num_heads)
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embeds = F.embedding(relative_position_bucket, self.relative_attention_bias)
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# (batch, num_heads, len_q, len_k)
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embeds = embeds.permute(0, 3, 1, 2).contiguous()
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return embeds
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def _position_bucket(self, relative_position, num_buckets=32, max_distance=128):
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relative_buckets = 0
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num_buckets //= 2
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relative_buckets = (relative_position > 0).to(torch.int32) * num_buckets
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relative_position = torch.abs(relative_position)
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max_exact = num_buckets // 2
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is_small = relative_position < max_exact
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relative_postion_if_large = max_exact + (
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torch.log(relative_position.float() / max_exact)
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/ math.log(max_distance / max_exact)
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* (num_buckets - max_exact)
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).to(torch.int32)
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relative_postion_if_large = torch.min(
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relative_postion_if_large,
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torch.full_like(relative_postion_if_large, num_buckets - 1),
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)
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relative_buckets += torch.where(is_small, relative_position.to(torch.int32), relative_postion_if_large)
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return relative_buckets
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class RotaryEmbedding(bmt.DistributedModule):
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def __init__(
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self,
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dim,
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base: Union[int, float] = 10000,
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distance_scale: Union[int, float] = 1,
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dtype: torch.dtype = torch.half,
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):
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super().__init__()
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inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2, device="cuda", dtype=torch.float32) / dim))
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inv_freq = inv_freq.to(dtype)
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self.distance_scale = distance_scale
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self.dtype = dtype
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self.inv_freq = inv_freq
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def forward(self, x: torch.Tensor, x_pos: torch.Tensor):
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"""
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Args:
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x (:obj:`torch.Tensor` of shape ``(..., dim)``): Inputs.
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x_pos (:obj:`torch.Tensor` of shape ``(...)``): Positions of inputs.
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"""
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x_pos = x_pos * self.distance_scale
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freqs = x_pos[..., None].to(self.dtype) * self.inv_freq[None, :] # (..., dim/2)
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# the same implementation as sat
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emb = torch.cat((freqs, freqs), dim=-1) # (..., dim)
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emb_cos = emb.cos() # (..., dim)
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emb_sin = emb.sin() # (..., dim)
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rotate_x = torch.cat([-x[..., x.size(-1) // 2 :], x[..., : x.size(-1) // 2]], dim=-1) # (..., dim)
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return x * emb_cos + rotate_x * emb_sin
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def rotate_half(x):
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x1, x2 = x.chunk(2, dim=-1)
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return torch.cat((-x2, x1), dim=-1)
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def apply_rotary_pos_emb(x, cos, sin, seq_dim, offset):
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if x.size(seq_dim) < cos.size(seq_dim): # == do not need narrow
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cos = cos.narrow(seq_dim, offset, x.size(seq_dim))
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sin = sin.narrow(seq_dim, offset, x.size(seq_dim))
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return (x * cos) + (rotate_half(x) * sin)
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def unpad_apply_rotary_pos_emb(x, cos, sin, seq_dim, position_ids):
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cos = cos.index_select(seq_dim, position_ids.view(-1))
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sin = sin.index_select(seq_dim, position_ids.view(-1))
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return (x * cos) + (rotate_half(x) * sin)
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class RotaryEmbeddingESM(bmt.DistributedModule):
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"""
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Rotary position embeddings based on those in
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[RoFormer](https://huggingface.co/docs/transformers/model_doc/roformer). Query and keys are transformed by rotation
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matrices which depend on their relative positions.
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"""
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def __init__(
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self,
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dim: int,
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base: Union[int, float] = 10000,
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distance_scale: Union[int, float] = 1,
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dtype=torch.half,
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persistent=True,
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mixed_precision=True,
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):
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super().__init__()
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self.base = base
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self.distance_scale = distance_scale
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self.dtype = dtype
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# Generate and save the inverse frequency buffer (non trainable)
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inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2, device="cuda", dtype=torch.float32) / dim))
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if mixed_precision:
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self.register_buffer("inv_freq", inv_freq, persistent=persistent)
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else:
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self.register_buffer("inv_freq", inv_freq.to(self.dtype), persistent=persistent)
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self._seq_len_cached = -1
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self._cos_cached = None
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self._sin_cached = None
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self.mixed_precision = mixed_precision
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self.apply_rotary_pos_emb = apply_rotary_pos_emb
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self.unpad_apply_rotary_pos_emb = unpad_apply_rotary_pos_emb
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def _update_cos_sin_tables(self, x, seq_dim, seq_len):
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if seq_len > self._seq_len_cached or self._cos_cached.device != x.device:
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self._seq_len_cached = seq_len
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t = torch.arange(seq_len, device=x.device).type_as(self.inv_freq)
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freqs = torch.outer(t * self.distance_scale, self.inv_freq)
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emb = torch.cat((freqs, freqs), dim=-1)
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for i in range(x.dim() - 1):
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if i != seq_dim:
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emb = emb.unsqueeze_(i)
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if self.mixed_precision:
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self._cos_cached = emb.cos().to(self.dtype)
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self._sin_cached = emb.sin().to(self.dtype)
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else:
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self._cos_cached = emb.cos()
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self._sin_cached = emb.sin()
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return self._cos_cached, self._sin_cached
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def forward(
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self, q: torch.Tensor, k: torch.Tensor, seq_dim, offset=0, cu_seqlens=None, max_length=None, position_ids=None
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) -> Tuple[torch.Tensor, torch.Tensor]:
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seq_dim = (seq_dim + k.dim()) % k.dim()
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if cu_seqlens is None:
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self._cos_cached, self._sin_cached = self._update_cos_sin_tables(k, seq_dim, k.size(seq_dim) + offset)
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return (
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self.apply_rotary_pos_emb(q, self._cos_cached, self._sin_cached, seq_dim, offset),
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self.apply_rotary_pos_emb(k, self._cos_cached, self._sin_cached, seq_dim, offset),
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)
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else:
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assert offset == 0, "past kv is not supported in flash attn"
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self._cos_cached, self._sin_cached = self._update_cos_sin_tables(k, seq_dim, max_length)
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return (
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self.unpad_apply_rotary_pos_emb(q, self._cos_cached, self._sin_cached, seq_dim, position_ids),
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self.unpad_apply_rotary_pos_emb(k, self._cos_cached, self._sin_cached, seq_dim, position_ids),
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)
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@torch.jit.script
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def apply_chatglm_rotary_pos_emb(x: torch.Tensor, rope_cache: torch.Tensor) -> torch.Tensor:
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# x: [b, np, sq, hn]
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x = x.permute(2, 0, 1, 3) # [b, np, sq, hn] -> [sq, b, np, hn]
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sq, b, np, hn = x.shape
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rot_dim = rope_cache.shape[-2] * 2
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x, x_pass = x[..., :rot_dim], x[..., rot_dim:]
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# truncate to support variable sizes
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rope_cache = rope_cache[:sq]
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xshaped = x.reshape(sq, -1, np, rot_dim // 2, 2)
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rope_cache = rope_cache.view(sq, -1, 1, xshaped.size(3), 2)
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x_out2 = torch.stack(
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[
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xshaped[..., 0] * rope_cache[..., 0] - xshaped[..., 1] * rope_cache[..., 1],
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xshaped[..., 1] * rope_cache[..., 0] + xshaped[..., 0] * rope_cache[..., 1],
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],
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-1,
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)
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x_out2 = x_out2.flatten(3)
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ret = torch.cat((x_out2, x_pass), dim=-1)
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ret = ret.permute(1, 2, 0, 3) # [sq, b, np, hn] -> [b, np, sq, hn]
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return ret
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class ChatGLMRotaryEmbedding(bmt.DistributedModule):
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def __init__(self, dim, device="cuda", dtype=torch.float16, persistent=True):
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super().__init__()
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inv_freq = 1.0 / (10000 ** (torch.arange(0, dim, 2, dtype=dtype, device=device) / dim))
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self.register_buffer("inv_freq", inv_freq, persistent=persistent)
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self.dim = dim
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def forward_impl(self, seq_len: int, n_elem: int, dtype: torch.dtype, device: torch.device, base: int = 10000):
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"""Enhanced Transformer with Rotary Position Embedding.
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Derived from: https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/labml_nn/
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transformers/rope/__init__.py. MIT License:
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https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/license.
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"""
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# $\Theta = {\theta_i = 10000^{\frac{2(i-1)}{d}}, i \in [1, 2, ..., \frac{d}{2}]}$
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theta = 1.0 / (base ** (torch.arange(0, n_elem, 2, dtype=dtype, device=device) / n_elem))
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# Create position indexes `[0, 1, ..., seq_len - 1]`
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seq_idx = torch.arange(seq_len, dtype=dtype, device=device)
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# Calculate the product of position index and $\theta_i$
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idx_theta = torch.outer(seq_idx, theta).float()
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cache = torch.stack([torch.cos(idx_theta), torch.sin(idx_theta)], dim=-1)
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# this is to mimic the behaviour of complex32, else we will get different results
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if dtype in (torch.float16, torch.bfloat16, torch.int8):
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cache = cache.bfloat16() if dtype == torch.bfloat16 else cache.half()
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return cache
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def forward(self, max_seq_len, offset: int = 0):
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return self.forward_impl(max_seq_len, self.dim, dtype=self.inv_freq.dtype, device=self.inv_freq.device)
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