# Copyright 2024 Infinigence AI Team. All rights reserved. # # 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. """This file is copied from https://huggingface.co./openbmb/MiniCPM-V-2_6/blob/main/resampler.py and modified as needed.""" from functools import partial from typing import Optional, Tuple import numpy as np import warnings import torch from torch import nn from torch import Tensor import torch.nn.functional as F from torch.nn.functional import * from torch.nn.modules.activation import * from torch.nn.init import trunc_normal_, constant_, xavier_normal_, xavier_uniform_ from transformers.integrations import is_deepspeed_zero3_enabled def get_2d_sincos_pos_embed(embed_dim, image_size): """ image_size: image_size or (image_height, image_width) return: pos_embed: [image_height, image_width, embed_dim] """ if isinstance(image_size, int): grid_h_size, grid_w_size = image_size, image_size else: grid_h_size, grid_w_size = image_size[0], image_size[1] grid_h = np.arange(grid_h_size, dtype=np.float32) grid_w = np.arange(grid_w_size, dtype=np.float32) grid = np.meshgrid(grid_w, grid_h) # here w goes first grid = np.stack(grid, axis=0) pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid) return pos_embed def get_2d_sincos_pos_embed_from_grid(embed_dim, grid): assert embed_dim % 2 == 0 # use half of dimensions to encode grid_h emb_h = get_1d_sincos_pos_embed_from_grid_new(embed_dim // 2, grid[0]) # (H, W, D/2) emb_w = get_1d_sincos_pos_embed_from_grid_new(embed_dim // 2, grid[1]) # (H, W, D/2) emb = np.concatenate([emb_h, emb_w], axis=-1) # (H, W, D) return emb def get_1d_sincos_pos_embed_from_grid_new(embed_dim, pos): """ embed_dim: output dimension for each position pos: a list of positions to be encoded: size (H, W) out: (H, W, D) """ assert embed_dim % 2 == 0 omega = np.arange(embed_dim // 2, dtype=np.float32) omega /= embed_dim / 2. omega = 1. / 10000 ** omega # (D/2,) out = np.einsum('hw,d->hwd', pos, omega) # (H, W, D/2), outer product emb_sin = np.sin(out) # (H, W, D/2) emb_cos = np.cos(out) # (H, W, D/2) emb = np.concatenate([emb_sin, emb_cos], axis=-1) # (H, W, D) return emb class Resampler(nn.Module): """ A 2D perceiver-resampler network with one cross attention layers by given learnable queries and 2d sincos pos_emb Outputs: A tensor with the shape of (batch_size, num_queries, embed_dim) """ def __init__( self, num_queries, embed_dim, num_heads, kv_dim=None, norm_layer=partial(nn.LayerNorm, eps=1e-6), adaptive=False, max_size=(70, 70), ): super().__init__() self.num_queries = num_queries self.embed_dim = embed_dim self.num_heads = num_heads self.adaptive = adaptive self.max_size = max_size self.query = nn.Parameter(torch.zeros(self.num_queries, embed_dim)) if kv_dim is not None and kv_dim != embed_dim: self.kv_proj = nn.Linear(kv_dim, embed_dim, bias=False) else: self.kv_proj = nn.Identity() # Change to nn.MultiheadAttention instead of MultiheadAttention in this file. self.attn = nn.MultiheadAttention(embed_dim, num_heads) self.ln_q = norm_layer(embed_dim) self.ln_kv = norm_layer(embed_dim) self.ln_post = norm_layer(embed_dim) self.proj = nn.Parameter((embed_dim ** -0.5) * torch.randn(embed_dim, embed_dim)) self._set_2d_pos_cache(self.max_size) def _set_2d_pos_cache(self, max_size, device='cpu'): if is_deepspeed_zero3_enabled(): device='cuda' pos_embed = torch.from_numpy(get_2d_sincos_pos_embed(self.embed_dim, max_size)).float().to(device) self.register_buffer("pos_embed", pos_embed, persistent=False) def _adjust_pos_cache(self, tgt_sizes, device): max_h = torch.max(tgt_sizes[:, 0]) max_w = torch.max(tgt_sizes[:, 1]) if max_h > self.max_size[0] or max_w > self.max_size[1]: self.max_size = [max(max_h, self.max_size[0]), max(max_w, self.max_size[1])] self._set_2d_pos_cache(self.max_size, device) def _init_weights(self, m): if isinstance(m, nn.Linear): trunc_normal_(m.weight, std=.02) if isinstance(m, nn.Linear) and m.bias is not None: nn.init.constant_(m.bias, 0) elif isinstance(m, nn.LayerNorm): nn.init.constant_(m.bias, 0) nn.init.constant_(m.weight, 1.0) def forward(self, x, tgt_sizes=None): assert x.shape[0] == tgt_sizes.shape[0] bs = x.shape[0] device = x.device dtype = x.dtype patch_len = tgt_sizes[:, 0] * tgt_sizes[:, 1] self._adjust_pos_cache(tgt_sizes, device=device) max_patch_len = torch.max(patch_len) key_padding_mask = torch.zeros((bs, max_patch_len), dtype=torch.bool, device=device) pos_embed = [] for i in range(bs): tgt_h, tgt_w = tgt_sizes[i] pos_embed.append(self.pos_embed[:tgt_h, :tgt_w, :].reshape((tgt_h * tgt_w, -1)).to(dtype)) # patches * D key_padding_mask[i, patch_len[i]:] = True pos_embed = torch.nn.utils.rnn.pad_sequence( pos_embed, batch_first=True, padding_value=0.0).permute(1, 0, 2) # BLD => L * B * D x = self.kv_proj(x) # B * L * D x = self.ln_kv(x).permute(1, 0, 2) # L * B * D q = self.ln_q(self.query) # Q * D out = self.attn( self._repeat(q, bs), # Q * B * D x + pos_embed, # L * B * D + L * B * D x, key_padding_mask=key_padding_mask)[0] # out: Q * B * D x = out.permute(1, 0, 2) # B * Q * D x = self.ln_post(x) x = x @ self.proj return x def _repeat(self, query, N: int): return query.unsqueeze(1).repeat(1, N, 1) class MultiheadAttention(nn.MultiheadAttention): def __init__(self, embed_dim, num_heads, dropout=0., bias=True, add_bias_kv=False, add_zero_attn=False, kdim=None, vdim=None, batch_first=False, device=None, dtype=None): super().__init__(embed_dim, num_heads, dropout, bias, add_bias_kv, add_zero_attn, kdim, vdim, batch_first, device, dtype) # rewrite out_proj layer，with nn.Linear self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias, device=device, dtype=dtype) def forward( self, query: Tensor, key: Tensor, value: Tensor, key_padding_mask: Optional[Tensor] = None, need_weights: bool = True, attn_mask: Optional[Tensor] = None, average_attn_weights: bool = True, is_causal : bool = False) -> Tuple[Tensor, Optional[Tensor]]: why_not_fast_path = '' if ((attn_mask is not None and torch.is_floating_point(attn_mask)) or (key_padding_mask is not None) and torch.is_floating_point(key_padding_mask)): why_not_fast_path = "floating-point masks are not supported for fast path." is_batched = query.dim() == 3 key_padding_mask = _canonical_mask( mask=key_padding_mask, mask_name="key_padding_mask", other_type=F._none_or_dtype(attn_mask), other_name="attn_mask", target_type=query.dtype ) attn_mask = _canonical_mask( mask=attn_mask, mask_name="attn_mask", other_type=None, other_name="", target_type=query.dtype, check_other=False, ) if not is_batched: why_not_fast_path = f"input not batched; expected query.dim() of 3 but got {query.dim()}" elif query is not key or key is not value: # When lifting this restriction, don't forget to either # enforce that the dtypes all match or test cases where # they don't! why_not_fast_path = "non-self attention was used (query, key, and value are not the same Tensor)" elif self.in_proj_bias is not None and query.dtype != self.in_proj_bias.dtype: why_not_fast_path = f"dtypes of query ({query.dtype}) and self.in_proj_bias ({self.in_proj_bias.dtype}) don't match" elif self.in_proj_weight is None: why_not_fast_path = "in_proj_weight was None" elif query.dtype != self.in_proj_weight.dtype: # this case will fail anyway, but at least they'll get a useful error message. why_not_fast_path = f"dtypes of query ({query.dtype}) and self.in_proj_weight ({self.in_proj_weight.dtype}) don't match" elif self.training: why_not_fast_path = "training is enabled" elif (self.num_heads % 2) != 0: why_not_fast_path = "self.num_heads is not even" elif not self.batch_first: why_not_fast_path = "batch_first was not True" elif self.bias_k is not None: why_not_fast_path = "self.bias_k was not None" elif self.bias_v is not None: why_not_fast_path = "self.bias_v was not None" elif self.add_zero_attn: why_not_fast_path = "add_zero_attn was enabled" elif not self._qkv_same_embed_dim: why_not_fast_path = "_qkv_same_embed_dim was not True" elif query.is_nested and (key_padding_mask is not None or attn_mask is not None): why_not_fast_path = "supplying both src_key_padding_mask and src_mask at the same time \ is not supported with NestedTensor input" elif torch.is_autocast_enabled(): why_not_fast_path = "autocast is enabled" if not why_not_fast_path: tensor_args = ( query, key, value, self.in_proj_weight, self.in_proj_bias, self.out_proj.weight, self.out_proj.bias, ) # We have to use list comprehensions below because TorchScript does not support # generator expressions. if torch.overrides.has_torch_function(tensor_args): why_not_fast_path = "some Tensor argument has_torch_function" elif _is_make_fx_tracing(): why_not_fast_path = "we are running make_fx tracing" elif not all(_check_arg_device(x) for x in tensor_args): why_not_fast_path = ("some Tensor argument's device is neither one of " f"cpu, cuda or {torch.utils.backend_registration._privateuse1_backend_name}") elif torch.is_grad_enabled() and any(_arg_requires_grad(x) for x in tensor_args): why_not_fast_path = ("grad is enabled and at least one of query or the " "input/output projection weights or biases requires_grad") if not why_not_fast_path: merged_mask, mask_type = self.merge_masks(attn_mask, key_padding_mask, query) if self.in_proj_bias is not None and self.in_proj_weight is not None: return torch._native_multi_head_attention( query, key, value, self.embed_dim, self.num_heads, self.in_proj_weight, self.in_proj_bias, self.out_proj.weight, self.out_proj.bias, merged_mask, need_weights, average_attn_weights, mask_type) any_nested = query.is_nested or key.is_nested or value.is_nested assert not any_nested, ("MultiheadAttention does not support NestedTensor outside of its fast path. " + f"The fast path was not hit because {why_not_fast_path}") if self.batch_first and is_batched: # make sure that the transpose op does not affect the "is" property if key is value: if query is key: query = key = value = query.transpose(1, 0) else: query, key = (x.transpose(1, 0) for x in (query, key)) value = key else: query, key, value = (x.transpose(1, 0) for x in (query, key, value)) if not self._qkv_same_embed_dim: attn_output, attn_output_weights = self.multi_head_attention_forward( query, key, value, self.embed_dim, self.num_heads, self.in_proj_weight, self.in_proj_bias, self.bias_k, self.bias_v, self.add_zero_attn, self.dropout, self.out_proj.weight, self.out_proj.bias, training=self.training, key_padding_mask=key_padding_mask, need_weights=need_weights, attn_mask=attn_mask, use_separate_proj_weight=True, q_proj_weight=self.q_proj_weight, k_proj_weight=self.k_proj_weight, v_proj_weight=self.v_proj_weight, average_attn_weights=average_attn_weights, is_causal=is_causal) else: attn_output, attn_output_weights = self.multi_head_attention_forward( query, key, value, self.embed_dim, self.num_heads, self.in_proj_weight, self.in_proj_bias, self.bias_k, self.bias_v, self.add_zero_attn, self.dropout, self.out_proj.weight, self.out_proj.bias, training=self.training, key_padding_mask=key_padding_mask, need_weights=need_weights, attn_mask=attn_mask, average_attn_weights=average_attn_weights, is_causal=is_causal) if self.batch_first and is_batched: return attn_output.transpose(1, 0), attn_output_weights else: return attn_output, attn_output_weights def multi_head_attention_forward( self, query: Tensor, key: Tensor, value: Tensor, 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, average_attn_weights: bool = True, is_causal: bool = False, ) -> 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 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)