784 lines
34 KiB
Python
784 lines
34 KiB
Python
from functools import partial
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from typing import Optional, Tuple
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import numpy as np
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import warnings
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import torch
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from torch import nn
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from torch import Tensor
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import torch.nn.functional as F
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from torch.nn.functional import *
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from torch.nn.modules.activation import *
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from torch.nn.init import trunc_normal_, constant_, xavier_normal_, xavier_uniform_
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from transformers.integrations import is_deepspeed_zero3_enabled
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def get_2d_sincos_pos_embed(embed_dim, image_size):
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"""
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image_size: image_size or (image_height, image_width)
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return:
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pos_embed: [image_height, image_width, embed_dim]
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"""
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if isinstance(image_size, int):
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grid_h_size, grid_w_size = image_size, image_size
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else:
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grid_h_size, grid_w_size = image_size[0], image_size[1]
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grid_h = np.arange(grid_h_size, dtype=np.float32)
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grid_w = np.arange(grid_w_size, dtype=np.float32)
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grid = np.meshgrid(grid_w, grid_h) # here w goes first
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grid = np.stack(grid, axis=0)
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pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid)
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return pos_embed
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def get_2d_sincos_pos_embed_from_grid(embed_dim, grid):
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assert embed_dim % 2 == 0
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# use half of dimensions to encode grid_h
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emb_h = get_1d_sincos_pos_embed_from_grid_new(embed_dim // 2, grid[0]) # (H, W, D/2)
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emb_w = get_1d_sincos_pos_embed_from_grid_new(embed_dim // 2, grid[1]) # (H, W, D/2)
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emb = np.concatenate([emb_h, emb_w], axis=-1) # (H, W, D)
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return emb
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def get_1d_sincos_pos_embed_from_grid_new(embed_dim, pos):
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"""
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embed_dim: output dimension for each position
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pos: a list of positions to be encoded: size (H, W)
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out: (H, W, D)
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"""
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assert embed_dim % 2 == 0
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omega = np.arange(embed_dim // 2, dtype=np.float32)
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omega /= embed_dim / 2.
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omega = 1. / 10000 ** omega # (D/2,)
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out = np.einsum('hw,d->hwd', pos, omega) # (H, W, D/2), outer product
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emb_sin = np.sin(out) # (H, W, D/2)
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emb_cos = np.cos(out) # (H, W, D/2)
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emb = np.concatenate([emb_sin, emb_cos], axis=-1) # (H, W, D)
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return emb
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class Resampler(nn.Module):
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"""
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A 2D perceiver-resampler network with one cross attention layers by
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given learnable queries and 2d sincos pos_emb
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Outputs:
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A tensor with the shape of (batch_size, num_queries, embed_dim)
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"""
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def __init__(
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self,
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num_queries,
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embed_dim,
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num_heads,
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kv_dim=None,
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norm_layer=partial(nn.LayerNorm, eps=1e-6),
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adaptive=False,
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max_size=(70, 70),
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):
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super().__init__()
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self.num_queries = num_queries
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self.embed_dim = embed_dim
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self.num_heads = num_heads
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self.adaptive = adaptive
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self.max_size = max_size
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self.query = nn.Parameter(torch.zeros(self.num_queries, embed_dim))
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if kv_dim is not None and kv_dim != embed_dim:
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self.kv_proj = nn.Linear(kv_dim, embed_dim, bias=False)
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else:
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self.kv_proj = nn.Identity()
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# Change to nn.MultiheadAttention instead of MultiheadAttention in this file.
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self.attn = nn.MultiheadAttention(embed_dim, num_heads)
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self.ln_q = norm_layer(embed_dim)
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self.ln_kv = norm_layer(embed_dim)
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self.ln_post = norm_layer(embed_dim)
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self.proj = nn.Parameter((embed_dim ** -0.5) * torch.randn(embed_dim, embed_dim))
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self._set_2d_pos_cache(self.max_size)
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def _set_2d_pos_cache(self, max_size, device='cpu'):
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if is_deepspeed_zero3_enabled():
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device='cuda'
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pos_embed = torch.from_numpy(get_2d_sincos_pos_embed(self.embed_dim, max_size)).float().to(device)
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self.register_buffer("pos_embed", pos_embed, persistent=False)
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def _adjust_pos_cache(self, tgt_sizes, device):
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max_h = torch.max(tgt_sizes[:, 0])
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max_w = torch.max(tgt_sizes[:, 1])
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if max_h > self.max_size[0] or max_w > self.max_size[1]:
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self.max_size = [max(max_h, self.max_size[0]), max(max_w, self.max_size[1])]
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self._set_2d_pos_cache(self.max_size, device)
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def _init_weights(self, m):
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if isinstance(m, nn.Linear):
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trunc_normal_(m.weight, std=.02)
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if isinstance(m, nn.Linear) and m.bias is not None:
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nn.init.constant_(m.bias, 0)
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elif isinstance(m, nn.LayerNorm):
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nn.init.constant_(m.bias, 0)
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nn.init.constant_(m.weight, 1.0)
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def forward(self, x, tgt_sizes=None):
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assert x.shape[0] == tgt_sizes.shape[0]
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bs = x.shape[0]
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device = x.device
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dtype = x.dtype
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patch_len = tgt_sizes[:, 0] * tgt_sizes[:, 1]
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self._adjust_pos_cache(tgt_sizes, device=device)
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max_patch_len = torch.max(patch_len)
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key_padding_mask = torch.zeros((bs, max_patch_len), dtype=torch.bool, device=device)
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pos_embed = []
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for i in range(bs):
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tgt_h, tgt_w = tgt_sizes[i]
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pos_embed.append(self.pos_embed[:tgt_h, :tgt_w, :].reshape((tgt_h * tgt_w, -1)).to(dtype)) # patches * D
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key_padding_mask[i, patch_len[i]:] = True
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pos_embed = torch.nn.utils.rnn.pad_sequence(
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pos_embed, batch_first=True, padding_value=0.0).permute(1, 0, 2) # BLD => L * B * D
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x = self.kv_proj(x) # B * L * D
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x = self.ln_kv(x).permute(1, 0, 2) # L * B * D
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q = self.ln_q(self.query) # Q * D
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out = self.attn(
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self._repeat(q, bs), # Q * B * D
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x + pos_embed, # L * B * D + L * B * D
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x,
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key_padding_mask=key_padding_mask)[0]
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# out: Q * B * D
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x = out.permute(1, 0, 2) # B * Q * D
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x = self.ln_post(x)
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x = x @ self.proj
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return x
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def _repeat(self, query, N: int):
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return query.unsqueeze(1).repeat(1, N, 1)
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class MultiheadAttention(nn.MultiheadAttention):
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def __init__(self, embed_dim, num_heads, dropout=0., bias=True, add_bias_kv=False,
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add_zero_attn=False, kdim=None, vdim=None, batch_first=False, device=None, dtype=None):
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super().__init__(embed_dim, num_heads, dropout, bias, add_bias_kv, add_zero_attn, kdim, vdim, batch_first, device, dtype)
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# rewrite out_proj layer，with nn.Linear
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self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias, device=device, dtype=dtype)
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def forward(
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self,
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query: Tensor,
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key: Tensor,
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value: Tensor,
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key_padding_mask: Optional[Tensor] = None,
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need_weights: bool = True,
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attn_mask: Optional[Tensor] = None,
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average_attn_weights: bool = True,
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is_causal : bool = False) -> Tuple[Tensor, Optional[Tensor]]:
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why_not_fast_path = ''
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if ((attn_mask is not None and torch.is_floating_point(attn_mask))
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or (key_padding_mask is not None) and torch.is_floating_point(key_padding_mask)):
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why_not_fast_path = "floating-point masks are not supported for fast path."
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is_batched = query.dim() == 3
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key_padding_mask = _canonical_mask(
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mask=key_padding_mask,
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mask_name="key_padding_mask",
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other_type=F._none_or_dtype(attn_mask),
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other_name="attn_mask",
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target_type=query.dtype
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)
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attn_mask = _canonical_mask(
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mask=attn_mask,
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mask_name="attn_mask",
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other_type=None,
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other_name="",
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target_type=query.dtype,
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check_other=False,
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)
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if not is_batched:
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why_not_fast_path = f"input not batched; expected query.dim() of 3 but got {query.dim()}"
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elif query is not key or key is not value:
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# When lifting this restriction, don't forget to either
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# enforce that the dtypes all match or test cases where
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# they don't!
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why_not_fast_path = "non-self attention was used (query, key, and value are not the same Tensor)"
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elif self.in_proj_bias is not None and query.dtype != self.in_proj_bias.dtype:
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why_not_fast_path = f"dtypes of query ({query.dtype}) and self.in_proj_bias ({self.in_proj_bias.dtype}) don't match"
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elif self.in_proj_weight is None:
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why_not_fast_path = "in_proj_weight was None"
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elif query.dtype != self.in_proj_weight.dtype:
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# this case will fail anyway, but at least they'll get a useful error message.
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why_not_fast_path = f"dtypes of query ({query.dtype}) and self.in_proj_weight ({self.in_proj_weight.dtype}) don't match"
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elif self.training:
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why_not_fast_path = "training is enabled"
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elif (self.num_heads % 2) != 0:
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why_not_fast_path = "self.num_heads is not even"
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elif not self.batch_first:
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why_not_fast_path = "batch_first was not True"
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elif self.bias_k is not None:
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why_not_fast_path = "self.bias_k was not None"
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elif self.bias_v is not None:
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why_not_fast_path = "self.bias_v was not None"
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elif self.add_zero_attn:
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why_not_fast_path = "add_zero_attn was enabled"
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elif not self._qkv_same_embed_dim:
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why_not_fast_path = "_qkv_same_embed_dim was not True"
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elif query.is_nested and (key_padding_mask is not None or attn_mask is not None):
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why_not_fast_path = "supplying both src_key_padding_mask and src_mask at the same time \
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is not supported with NestedTensor input"
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elif torch.is_autocast_enabled():
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why_not_fast_path = "autocast is enabled"
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if not why_not_fast_path:
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tensor_args = (
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query,
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key,
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value,
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self.in_proj_weight,
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self.in_proj_bias,
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self.out_proj.weight,
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self.out_proj.bias,
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)
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# We have to use list comprehensions below because TorchScript does not support
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# generator expressions.
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if torch.overrides.has_torch_function(tensor_args):
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why_not_fast_path = "some Tensor argument has_torch_function"
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elif _is_make_fx_tracing():
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why_not_fast_path = "we are running make_fx tracing"
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elif not all(_check_arg_device(x) for x in tensor_args):
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why_not_fast_path = ("some Tensor argument's device is neither one of "
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f"cpu, cuda or {torch.utils.backend_registration._privateuse1_backend_name}")
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elif torch.is_grad_enabled() and any(_arg_requires_grad(x) for x in tensor_args):
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why_not_fast_path = ("grad is enabled and at least one of query or the "
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"input/output projection weights or biases requires_grad")
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if not why_not_fast_path:
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merged_mask, mask_type = self.merge_masks(attn_mask, key_padding_mask, query)
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if self.in_proj_bias is not None and self.in_proj_weight is not None:
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return torch._native_multi_head_attention(
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query,
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key,
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value,
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self.embed_dim,
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self.num_heads,
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self.in_proj_weight,
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self.in_proj_bias,
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self.out_proj.weight,
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self.out_proj.bias,
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merged_mask,
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need_weights,
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average_attn_weights,
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mask_type)
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any_nested = query.is_nested or key.is_nested or value.is_nested
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assert not any_nested, ("MultiheadAttention does not support NestedTensor outside of its fast path. " +
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f"The fast path was not hit because {why_not_fast_path}")
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if self.batch_first and is_batched:
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# make sure that the transpose op does not affect the "is" property
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if key is value:
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if query is key:
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query = key = value = query.transpose(1, 0)
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else:
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query, key = (x.transpose(1, 0) for x in (query, key))
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value = key
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else:
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query, key, value = (x.transpose(1, 0) for x in (query, key, value))
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if not self._qkv_same_embed_dim:
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attn_output, attn_output_weights = self.multi_head_attention_forward(
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query, key, value, self.embed_dim, self.num_heads,
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self.in_proj_weight, self.in_proj_bias,
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self.bias_k, self.bias_v, self.add_zero_attn,
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self.dropout, self.out_proj.weight, self.out_proj.bias,
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training=self.training,
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key_padding_mask=key_padding_mask, need_weights=need_weights,
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attn_mask=attn_mask,
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use_separate_proj_weight=True,
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q_proj_weight=self.q_proj_weight, k_proj_weight=self.k_proj_weight,
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v_proj_weight=self.v_proj_weight,
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average_attn_weights=average_attn_weights,
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is_causal=is_causal)
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else:
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attn_output, attn_output_weights = self.multi_head_attention_forward(
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query, key, value, self.embed_dim, self.num_heads,
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self.in_proj_weight, self.in_proj_bias,
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self.bias_k, self.bias_v, self.add_zero_attn,
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self.dropout, self.out_proj.weight, self.out_proj.bias,
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training=self.training,
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key_padding_mask=key_padding_mask,
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||
need_weights=need_weights,
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attn_mask=attn_mask,
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average_attn_weights=average_attn_weights,
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is_causal=is_causal)
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if self.batch_first and is_batched:
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return attn_output.transpose(1, 0), attn_output_weights
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||
else:
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return attn_output, attn_output_weights
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||
|
||
def multi_head_attention_forward(
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self,
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query: Tensor,
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||
key: Tensor,
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||
value: Tensor,
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||
embed_dim_to_check: int,
|
||
num_heads: int,
|
||
in_proj_weight: Optional[Tensor],
|
||
in_proj_bias: Optional[Tensor],
|
||
bias_k: Optional[Tensor],
|
||
bias_v: Optional[Tensor],
|
||
add_zero_attn: bool,
|
||
dropout_p: float,
|
||
out_proj_weight: Tensor,
|
||
out_proj_bias: Optional[Tensor],
|
||
training: bool = True,
|
||
key_padding_mask: Optional[Tensor] = None,
|
||
need_weights: bool = True,
|
||
attn_mask: Optional[Tensor] = None,
|
||
use_separate_proj_weight: bool = False,
|
||
q_proj_weight: Optional[Tensor] = None,
|
||
k_proj_weight: Optional[Tensor] = None,
|
||
v_proj_weight: Optional[Tensor] = None,
|
||
static_k: Optional[Tensor] = None,
|
||
static_v: Optional[Tensor] = None,
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||
average_attn_weights: bool = True,
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||
is_causal: bool = False,
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||
) -> Tuple[Tensor, Optional[Tensor]]:
|
||
tens_ops = (query, key, value, in_proj_weight, in_proj_bias, bias_k, bias_v, out_proj_weight, out_proj_bias)
|
||
|
||
is_batched = _mha_shape_check(query, key, value, key_padding_mask, attn_mask, num_heads)
|
||
|
||
# For unbatched input, we unsqueeze at the expected batch-dim to pretend that the input
|
||
# is batched, run the computation and before returning squeeze the
|
||
# batch dimension so that the output doesn't carry this temporary batch dimension.
|
||
if not is_batched:
|
||
# unsqueeze if the input is unbatched
|
||
query = query.unsqueeze(1)
|
||
key = key.unsqueeze(1)
|
||
value = value.unsqueeze(1)
|
||
if key_padding_mask is not None:
|
||
key_padding_mask = key_padding_mask.unsqueeze(0)
|
||
|
||
# set up shape vars
|
||
tgt_len, bsz, embed_dim = query.shape
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||
src_len, _, _ = key.shape
|
||
|
||
key_padding_mask = _canonical_mask(
|
||
mask=key_padding_mask,
|
||
mask_name="key_padding_mask",
|
||
other_type=_none_or_dtype(attn_mask),
|
||
other_name="attn_mask",
|
||
target_type=query.dtype
|
||
)
|
||
|
||
if is_causal and attn_mask is None:
|
||
raise RuntimeError(
|
||
"Need attn_mask if specifying the is_causal hint. "
|
||
"You may use the Transformer module method "
|
||
"`generate_square_subsequent_mask` to create this mask."
|
||
)
|
||
|
||
if is_causal and key_padding_mask is None and not need_weights:
|
||
# when we have a kpm or need weights, we need attn_mask
|
||
# Otherwise, we use the is_causal hint go as is_causal
|
||
# indicator to SDPA.
|
||
attn_mask = None
|
||
else:
|
||
attn_mask = _canonical_mask(
|
||
mask=attn_mask,
|
||
mask_name="attn_mask",
|
||
other_type=None,
|
||
other_name="",
|
||
target_type=query.dtype,
|
||
check_other=False,
|
||
)
|
||
|
||
if key_padding_mask is not None:
|
||
# We have the attn_mask, and use that to merge kpm into it.
|
||
# Turn off use of is_causal hint, as the merged mask is no
|
||
# longer causal.
|
||
is_causal = False
|
||
|
||
assert embed_dim == embed_dim_to_check, \
|
||
f"was expecting embedding dimension of {embed_dim_to_check}, but got {embed_dim}"
|
||
if isinstance(embed_dim, torch.Tensor):
|
||
# embed_dim can be a tensor when JIT tracing
|
||
head_dim = embed_dim.div(num_heads, rounding_mode='trunc')
|
||
else:
|
||
head_dim = embed_dim // num_heads
|
||
assert head_dim * num_heads == embed_dim, f"embed_dim {embed_dim} not divisible by num_heads {num_heads}"
|
||
if use_separate_proj_weight:
|
||
# allow MHA to have different embedding dimensions when separate projection weights are used
|
||
assert key.shape[:2] == value.shape[:2], \
|
||
f"key's sequence and batch dims {key.shape[:2]} do not match value's {value.shape[:2]}"
|
||
else:
|
||
assert key.shape == value.shape, f"key shape {key.shape} does not match value shape {value.shape}"
|
||
|
||
#
|
||
# compute in-projection
|
||
#
|
||
if not use_separate_proj_weight:
|
||
assert in_proj_weight is not None, "use_separate_proj_weight is False but in_proj_weight is None"
|
||
q, k, v = _in_projection_packed(query, key, value, in_proj_weight, in_proj_bias)
|
||
else:
|
||
assert q_proj_weight is not None, "use_separate_proj_weight is True but q_proj_weight is None"
|
||
assert k_proj_weight is not None, "use_separate_proj_weight is True but k_proj_weight is None"
|
||
assert v_proj_weight is not None, "use_separate_proj_weight is True but v_proj_weight is None"
|
||
if in_proj_bias is None:
|
||
b_q = b_k = b_v = None
|
||
else:
|
||
b_q, b_k, b_v = in_proj_bias.chunk(3)
|
||
q, k, v = _in_projection(query, key, value, q_proj_weight, k_proj_weight, v_proj_weight, b_q, b_k, b_v)
|
||
|
||
# prep attention mask
|
||
|
||
if attn_mask is not None:
|
||
# ensure attn_mask's dim is 3
|
||
if attn_mask.dim() == 2:
|
||
correct_2d_size = (tgt_len, src_len)
|
||
if attn_mask.shape != correct_2d_size:
|
||
raise RuntimeError(f"The shape of the 2D attn_mask is {attn_mask.shape}, but should be {correct_2d_size}.")
|
||
attn_mask = attn_mask.unsqueeze(0)
|
||
elif attn_mask.dim() == 3:
|
||
correct_3d_size = (bsz * num_heads, tgt_len, src_len)
|
||
if attn_mask.shape != correct_3d_size:
|
||
raise RuntimeError(f"The shape of the 3D attn_mask is {attn_mask.shape}, but should be {correct_3d_size}.")
|
||
else:
|
||
raise RuntimeError(f"attn_mask's dimension {attn_mask.dim()} is not supported")
|
||
|
||
# add bias along batch dimension (currently second)
|
||
if bias_k is not None and bias_v is not None:
|
||
assert static_k is None, "bias cannot be added to static key."
|
||
assert static_v is None, "bias cannot be added to static value."
|
||
k = torch.cat([k, bias_k.repeat(1, bsz, 1)])
|
||
v = torch.cat([v, bias_v.repeat(1, bsz, 1)])
|
||
if attn_mask is not None:
|
||
attn_mask = pad(attn_mask, (0, 1))
|
||
if key_padding_mask is not None:
|
||
key_padding_mask = pad(key_padding_mask, (0, 1))
|
||
else:
|
||
assert bias_k is None
|
||
assert bias_v is None
|
||
|
||
#
|
||
# reshape q, k, v for multihead attention and make em batch first
|
||
#
|
||
q = q.view(tgt_len, bsz * num_heads, head_dim).transpose(0, 1)
|
||
if static_k is None:
|
||
k = k.view(k.shape[0], bsz * num_heads, head_dim).transpose(0, 1)
|
||
else:
|
||
# TODO finish disentangling control flow so we don't do in-projections when statics are passed
|
||
assert static_k.size(0) == bsz * num_heads, \
|
||
f"expecting static_k.size(0) of {bsz * num_heads}, but got {static_k.size(0)}"
|
||
assert static_k.size(2) == head_dim, \
|
||
f"expecting static_k.size(2) of {head_dim}, but got {static_k.size(2)}"
|
||
k = static_k
|
||
if static_v is None:
|
||
v = v.view(v.shape[0], bsz * num_heads, head_dim).transpose(0, 1)
|
||
else:
|
||
# TODO finish disentangling control flow so we don't do in-projections when statics are passed
|
||
assert static_v.size(0) == bsz * num_heads, \
|
||
f"expecting static_v.size(0) of {bsz * num_heads}, but got {static_v.size(0)}"
|
||
assert static_v.size(2) == head_dim, \
|
||
f"expecting static_v.size(2) of {head_dim}, but got {static_v.size(2)}"
|
||
v = static_v
|
||
|
||
# add zero attention along batch dimension (now first)
|
||
if add_zero_attn:
|
||
zero_attn_shape = (bsz * num_heads, 1, head_dim)
|
||
k = torch.cat([k, torch.zeros(zero_attn_shape, dtype=k.dtype, device=k.device)], dim=1)
|
||
v = torch.cat([v, torch.zeros(zero_attn_shape, dtype=v.dtype, device=v.device)], dim=1)
|
||
if attn_mask is not None:
|
||
attn_mask = pad(attn_mask, (0, 1))
|
||
if key_padding_mask is not None:
|
||
key_padding_mask = pad(key_padding_mask, (0, 1))
|
||
|
||
# update source sequence length after adjustments
|
||
src_len = k.size(1)
|
||
|
||
# merge key padding and attention masks
|
||
if key_padding_mask is not None:
|
||
assert key_padding_mask.shape == (bsz, src_len), \
|
||
f"expecting key_padding_mask shape of {(bsz, src_len)}, but got {key_padding_mask.shape}"
|
||
key_padding_mask = key_padding_mask.view(bsz, 1, 1, src_len). \
|
||
expand(-1, num_heads, -1, -1).reshape(bsz * num_heads, 1, src_len)
|
||
if attn_mask is None:
|
||
attn_mask = key_padding_mask
|
||
else:
|
||
attn_mask = attn_mask + key_padding_mask
|
||
|
||
# adjust dropout probability
|
||
if not training:
|
||
dropout_p = 0.0
|
||
|
||
#
|
||
# (deep breath) calculate attention and out projection
|
||
#
|
||
|
||
if need_weights:
|
||
B, Nt, E = q.shape
|
||
q_scaled = q / math.sqrt(E)
|
||
|
||
assert not (is_causal and attn_mask is None), "FIXME: is_causal not implemented for need_weights"
|
||
|
||
if attn_mask is not None:
|
||
attn_output_weights = torch.baddbmm(attn_mask, q_scaled, k.transpose(-2, -1))
|
||
else:
|
||
attn_output_weights = torch.bmm(q_scaled, k.transpose(-2, -1))
|
||
attn_output_weights = softmax(attn_output_weights, dim=-1)
|
||
if dropout_p > 0.0:
|
||
attn_output_weights = dropout(attn_output_weights, p=dropout_p)
|
||
|
||
attn_output = torch.bmm(attn_output_weights, v)
|
||
|
||
attn_output = attn_output.transpose(0, 1).contiguous().view(tgt_len * bsz, embed_dim)
|
||
attn_output = self.out_proj(attn_output)
|
||
attn_output = attn_output.view(tgt_len, bsz, attn_output.size(1))
|
||
|
||
# optionally average attention weights over heads
|
||
attn_output_weights = attn_output_weights.view(bsz, num_heads, tgt_len, src_len)
|
||
if average_attn_weights:
|
||
attn_output_weights = attn_output_weights.mean(dim=1)
|
||
|
||
if not is_batched:
|
||
# squeeze the output if input was unbatched
|
||
attn_output = attn_output.squeeze(1)
|
||
attn_output_weights = attn_output_weights.squeeze(0)
|
||
return attn_output, attn_output_weights
|
||
else:
|
||
# attn_mask can be either (L,S) or (N*num_heads, L, S)
|
||
# if attn_mask's shape is (1, L, S) we need to unsqueeze to (1, 1, L, S)
|
||
# in order to match the input for SDPA of (N, num_heads, L, S)
|
||
if attn_mask is not None:
|
||
if attn_mask.size(0) == 1 and attn_mask.dim() == 3:
|
||
attn_mask = attn_mask.unsqueeze(0)
|
||
else:
|
||
attn_mask = attn_mask.view(bsz, num_heads, -1, src_len)
|
||
|
||
q = q.view(bsz, num_heads, tgt_len, head_dim)
|
||
k = k.view(bsz, num_heads, src_len, head_dim)
|
||
v = v.view(bsz, num_heads, src_len, head_dim)
|
||
|
||
attn_output = F.scaled_dot_product_attention(q, k, v, attn_mask, dropout_p, is_causal)
|
||
attn_output = attn_output.permute(2, 0, 1, 3).contiguous().view(bsz * tgt_len, embed_dim)
|
||
|
||
attn_output = self.out_proj(attn_output)
|
||
attn_output = attn_output.view(tgt_len, bsz, attn_output.size(1))
|
||
if not is_batched:
|
||
# squeeze the output if input was unbatched
|
||
attn_output = attn_output.squeeze(1)
|
||
return attn_output, None
|
||
|
||
|
||
def _mha_shape_check(query: Tensor, key: Tensor, value: Tensor,
|
||
key_padding_mask: Optional[Tensor], attn_mask: Optional[Tensor], num_heads: int):
|
||
# Verifies the expected shape for `query, `key`, `value`, `key_padding_mask` and `attn_mask`
|
||
# and returns if the input is batched or not.
|
||
# Raises an error if `query` is not 2-D (unbatched) or 3-D (batched) tensor.
|
||
|
||
# Shape check.
|
||
if query.dim() == 3:
|
||
# Batched Inputs
|
||
is_batched = True
|
||
assert key.dim() == 3 and value.dim() == 3, \
|
||
("For batched (3-D) `query`, expected `key` and `value` to be 3-D"
|
||
f" but found {key.dim()}-D and {value.dim()}-D tensors respectively")
|
||
if key_padding_mask is not None:
|
||
assert key_padding_mask.dim() == 2, \
|
||
("For batched (3-D) `query`, expected `key_padding_mask` to be `None` or 2-D"
|
||
f" but found {key_padding_mask.dim()}-D tensor instead")
|
||
if attn_mask is not None:
|
||
assert attn_mask.dim() in (2, 3), \
|
||
("For batched (3-D) `query`, expected `attn_mask` to be `None`, 2-D or 3-D"
|
||
f" but found {attn_mask.dim()}-D tensor instead")
|
||
elif query.dim() == 2:
|
||
# Unbatched Inputs
|
||
is_batched = False
|
||
assert key.dim() == 2 and value.dim() == 2, \
|
||
("For unbatched (2-D) `query`, expected `key` and `value` to be 2-D"
|
||
f" but found {key.dim()}-D and {value.dim()}-D tensors respectively")
|
||
|
||
if key_padding_mask is not None:
|
||
assert key_padding_mask.dim() == 1, \
|
||
("For unbatched (2-D) `query`, expected `key_padding_mask` to be `None` or 1-D"
|
||
f" but found {key_padding_mask.dim()}-D tensor instead")
|
||
|
||
if attn_mask is not None:
|
||
assert attn_mask.dim() in (2, 3), \
|
||
("For unbatched (2-D) `query`, expected `attn_mask` to be `None`, 2-D or 3-D"
|
||
f" but found {attn_mask.dim()}-D tensor instead")
|
||
if attn_mask.dim() == 3:
|
||
expected_shape = (num_heads, query.shape[0], key.shape[0])
|
||
assert attn_mask.shape == expected_shape, \
|
||
(f"Expected `attn_mask` shape to be {expected_shape} but got {attn_mask.shape}")
|
||
else:
|
||
raise AssertionError(
|
||
f"query should be unbatched 2D or batched 3D tensor but received {query.dim()}-D query tensor")
|
||
|
||
return is_batched
|
||
|
||
|
||
def _canonical_mask(
|
||
mask: Optional[Tensor],
|
||
mask_name: str,
|
||
other_type: Optional[DType],
|
||
other_name: str,
|
||
target_type: DType,
|
||
check_other: bool = True,
|
||
) -> Optional[Tensor]:
|
||
|
||
if mask is not None:
|
||
_mask_dtype = mask.dtype
|
||
_mask_is_float = torch.is_floating_point(mask)
|
||
if _mask_dtype != torch.bool and not _mask_is_float:
|
||
raise AssertionError(
|
||
f"only bool and floating types of {mask_name} are supported")
|
||
if check_other and other_type is not None:
|
||
if _mask_dtype != other_type:
|
||
warnings.warn(
|
||
f"Support for mismatched {mask_name} and {other_name} "
|
||
"is deprecated. Use same type for both instead."
|
||
)
|
||
if not _mask_is_float:
|
||
mask = (
|
||
torch.zeros_like(mask, dtype=target_type)
|
||
.masked_fill_(mask, float("-inf"))
|
||
)
|
||
return mask
|
||
|
||
|
||
def _none_or_dtype(input: Optional[Tensor]) -> Optional[DType]:
|
||
if input is None:
|
||
return None
|
||
elif isinstance(input, torch.Tensor):
|
||
return input.dtype
|
||
raise RuntimeError("input to _none_or_dtype() must be None or torch.Tensor")
|
||
|
||
def _in_projection_packed(
|
||
q: Tensor,
|
||
k: Tensor,
|
||
v: Tensor,
|
||
w: Tensor,
|
||
b: Optional[Tensor] = None,
|
||
) -> List[Tensor]:
|
||
r"""
|
||
Performs the in-projection step of the attention operation, using packed weights.
|
||
Output is a triple containing projection tensors for query, key and value.
|
||
Args:
|
||
q, k, v: query, key and value tensors to be projected. For self-attention,
|
||
these are typically the same tensor; for encoder-decoder attention,
|
||
k and v are typically the same tensor. (We take advantage of these
|
||
identities for performance if they are present.) Regardless, q, k and v
|
||
must share a common embedding dimension; otherwise their shapes may vary.
|
||
w: projection weights for q, k and v, packed into a single tensor. Weights
|
||
are packed along dimension 0, in q, k, v order.
|
||
b: optional projection biases for q, k and v, packed into a single tensor
|
||
in q, k, v order.
|
||
Shape:
|
||
Inputs:
|
||
- q: :math:`(..., E)` where E is the embedding dimension
|
||
- k: :math:`(..., E)` where E is the embedding dimension
|
||
- v: :math:`(..., E)` where E is the embedding dimension
|
||
- w: :math:`(E * 3, E)` where E is the embedding dimension
|
||
- b: :math:`E * 3` where E is the embedding dimension
|
||
Output:
|
||
- in output list :math:`[q', k', v']`, each output tensor will have the
|
||
same shape as the corresponding input tensor.
|
||
"""
|
||
E = q.size(-1)
|
||
if k is v:
|
||
if q is k:
|
||
# self-attention
|
||
proj = linear(q, w, b)
|
||
# reshape to 3, E and not E, 3 is deliberate for better memory coalescing and keeping same order as chunk()
|
||
proj = proj.unflatten(-1, (3, E)).unsqueeze(0).transpose(0, -2).squeeze(-2).contiguous()
|
||
return proj[0], proj[1], proj[2]
|
||
else:
|
||
# encoder-decoder attention
|
||
w_q, w_kv = w.split([E, E * 2])
|
||
if b is None:
|
||
b_q = b_kv = None
|
||
else:
|
||
b_q, b_kv = b.split([E, E * 2])
|
||
q_proj = linear(q, w_q, b_q)
|
||
kv_proj = linear(k, w_kv, b_kv)
|
||
# reshape to 2, E and not E, 2 is deliberate for better memory coalescing and keeping same order as chunk()
|
||
kv_proj = kv_proj.unflatten(-1, (2, E)).unsqueeze(0).transpose(0, -2).squeeze(-2).contiguous()
|
||
return (q_proj, kv_proj[0], kv_proj[1])
|
||
else:
|
||
w_q, w_k, w_v = w.chunk(3)
|
||
if b is None:
|
||
b_q = b_k = b_v = None
|
||
else:
|
||
b_q, b_k, b_v = b.chunk(3)
|
||
return linear(q, w_q, b_q), linear(k, w_k, b_k), linear(v, w_v, b_v)
|
||
|
||
|
||
def _in_projection(
|
||
q: Tensor,
|
||
k: Tensor,
|
||
v: Tensor,
|
||
w_q: Tensor,
|
||
w_k: Tensor,
|
||
w_v: Tensor,
|
||
b_q: Optional[Tensor] = None,
|
||
b_k: Optional[Tensor] = None,
|
||
b_v: Optional[Tensor] = None,
|
||
) -> Tuple[Tensor, Tensor, Tensor]:
|
||
r"""
|
||
Performs the in-projection step of the attention operation. This is simply
|
||
a triple of linear projections, with shape constraints on the weights which
|
||
ensure embedding dimension uniformity in the projected outputs.
|
||
Output is a triple containing projection tensors for query, key and value.
|
||
Args:
|
||
q, k, v: query, key and value tensors to be projected.
|
||
w_q, w_k, w_v: weights for q, k and v, respectively.
|
||
b_q, b_k, b_v: optional biases for q, k and v, respectively.
|
||
Shape:
|
||
Inputs:
|
||
- q: :math:`(Qdims..., Eq)` where Eq is the query embedding dimension and Qdims are any
|
||
number of leading dimensions.
|
||
- k: :math:`(Kdims..., Ek)` where Ek is the key embedding dimension and Kdims are any
|
||
number of leading dimensions.
|
||
- v: :math:`(Vdims..., Ev)` where Ev is the value embedding dimension and Vdims are any
|
||
number of leading dimensions.
|
||
- w_q: :math:`(Eq, Eq)`
|
||
- w_k: :math:`(Eq, Ek)`
|
||
- w_v: :math:`(Eq, Ev)`
|
||
- b_q: :math:`(Eq)`
|
||
- b_k: :math:`(Eq)`
|
||
- b_v: :math:`(Eq)`
|
||
Output: in output triple :math:`(q', k', v')`,
|
||
- q': :math:`[Qdims..., Eq]`
|
||
- k': :math:`[Kdims..., Eq]`
|
||
- v': :math:`[Vdims..., Eq]`
|
||
"""
|
||
Eq, Ek, Ev = q.size(-1), k.size(-1), v.size(-1)
|
||
assert w_q.shape == (Eq, Eq), f"expecting query weights shape of {(Eq, Eq)}, but got {w_q.shape}"
|
||
assert w_k.shape == (Eq, Ek), f"expecting key weights shape of {(Eq, Ek)}, but got {w_k.shape}"
|
||
assert w_v.shape == (Eq, Ev), f"expecting value weights shape of {(Eq, Ev)}, but got {w_v.shape}"
|
||
assert b_q is None or b_q.shape == (Eq,), f"expecting query bias shape of {(Eq,)}, but got {b_q.shape}"
|
||
assert b_k is None or b_k.shape == (Eq,), f"expecting key bias shape of {(Eq,)}, but got {b_k.shape}"
|
||
assert b_v is None or b_v.shape == (Eq,), f"expecting value bias shape of {(Eq,)}, but got {b_v.shape}"
|
||
return linear(q, w_q, b_q), linear(k, w_k, b_k), linear(v, w_v, b_v)
|