import warnings from functools import partial from typing import Optional, Tuple import numpy as np import torch import torch.nn.functional as F from torch import Tensor, nn from torch.nn.functional import * from torch.nn.init import trunc_normal_ from torch.nn.modules.activation import * 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 / 2.0 omega = 1.0 / 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() self.attn = 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=0.02) 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=0.0, 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)