AXERA-TECH
/

MiniCPM-V-4

@@ -1,782 +0,0 @@
-from functools import partial
-from typing import Optional, Tuple, List
-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()
-        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 _initialize_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=_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)