3Ki 5SrSSKJrJr SSKJr SSKrSSKJs J r SSK J r J r JrJr SSKJr SSKJrJrJrJr /SQr\R0R3\5 \R0R3\ 5 \R0R3\ 5 \R0R3\5 "S S \5r"S S \R65rS \4SjrS \4Sjrg)zCDefines bias subclasses that work with scaled_dot_product_attention)autoIntEnum)warnN)can_use_efficient_attentioncan_use_flash_attentionis_flash_attention_available SDPAParams)_raise_kernel_warnings)_calculate_scale_input_requires_grad_postprocess_flash_output_validate_sdpa_input)causal_upper_leftcausal_lower_right CausalVariant CausalBiasc4\rSrSrSr\"5r\"5rSrg)r!a Enum for causal variants used in attention mechanisms. Defines two types of causal biases: ``UPPER_LEFT``: Represents upper-left triangular bias for standard causal attention. The equivalent pytorch code for constructing this bias is: .. code-block:: python torch.tril(torch.ones(size, dtype=torch.bool)) For instance, with ``shape=(3,4)``, the materialized bias tensor will be: .. code-block:: text [[1, 0, 0, 0], [1, 1, 0, 0], [1, 1, 1, 0]] ``LOWER_RIGHT``: Represents lower-right triangular bias, the include values are aligned to the lower right corner of the matrix. The equivalent pytorch code for constructing this bias is: .. code-block:: python diagonal_offset = size[1] - size[0] torch.tril( torch.ones(size, dtype=torch.bool), diagonal=diagonal_offset, ) For instance, with ``shape=(3,4)``, the materialized bias tensor will be: .. code-block:: text [[1, 1, 0, 0], [1, 1, 1, 0], [1, 1, 1, 1]] Note that these variants are equivalent to each other when the sequence lengths of the query and key/value tensors are equal since the triangular matrix is square. .. warning:: This enum is a prototype and subject to change. N) __name__ __module__ __qualname____firstlineno____doc__r UPPER_LEFT LOWER_RIGHT__static_attributes__rY/var/www/html/dynamic-report/venv/lib/python3.13/site-packages/torch/nn/attention/bias.pyrr!s.`J&Krrc^\rSrSrSrS\S\S\SS4U4SjjrS \RS\R4S jr S \RS\R4S jr SS \RS-S\R4S jjr \SS \RS\RS\RSSS\S\S\S-S\S\R4Sjj5r\SU4Sjj5rS\4SjrSrU=r$)rVa A bias representing causal attention patterns. For an overview of the bias structure, see the :class:`CausalVariant` enum. This class is used for defining causal (triangular) attention biases. For construing the bias, there exist two factory functions: :func:`causal_upper_left` and :func:`causal_lower_right`. Example: .. code-block:: python from torch.nn.attention.bias import causal_lower_right bsz, num_heads, seqlen_q, seqlen_kv, head_dim = 32, 8, 4, 12, 8 # Create a lower-right causal bias attn_bias = causal_lower_right(seqlen_q, seqlen_kv) q = torch.randn( bsz, num_heads, seqlen_q, head_dim, device="cuda", dtype=torch.float16 ) k = torch.randn( bsz, num_heads, seqlen_kv, head_dim, device="cuda", dtype=torch.float16 ) v = torch.randn( bsz, num_heads, seqlen_kv, head_dim, device="cuda", dtype=torch.float16 ) out = F.scaled_dot_product_attention(q, k, v, attn_bias) .. warning:: This class is a prototype and subject to change. variant seq_len_q seq_len_kvreturnNc>[U[5(de[TU] 5 XlX lX0lX#:a U[R:Xa [SSS9 ggg)a Initializes the CausalBias instance with a specified variant and sequence lengths. Args: variant (CausalVariant): The type of causal bias to use (either UPPER_LEFT or LOWER_RIGHT). seq_len_q (int): The sequence length of the query tensor. seq_len_kv (int): The sequence length of the key/value tensor. Raises a warning if the LOWER_RIGHT variant is used with seq_len_q > seq_len_kv, as it may produce NaNs. zTLower right causal bias will produce NaNs in the output when seq_len_q > seq_len_kv!) stacklevelN) isinstancersuper__init__r"r#r$rr)selfr"r#r$ __class__s rr+CausalBias.__init__ws\'=1111  "$  !g1J1J&J f 'K !rdevicec [R"[R"URURU[R S95$)zUpper left causal biasr/dtype)torchtrilonesr#r$boolr,r/s r _upper_leftCausalBias._upper_lefts1zz JJt~~tvUZZ X  rc URUR- n[R"[R"URURU[R S9US9$)zLower right causal biasr1)diagonal)r$r#r3r4r5r6)r,r/diagonal_offsets r _lower_rightCausalBias._lower_rightsK//DNN:zz JJejj %   rcUc[R"S5nUR[R:XaUR U5$UR[R :XaURU5$g)aX Materializes the causal bias into a tensor form. Depending on the variant, this method generates either an upper-left or lower-right triangular matrix to represent the causal bias. Args: device (Optional[torch.device]): The device on which to create the tensor. Defaults to CPU. Returns: torch.Tensor: The materialized bias tensor. Ncpu)r3r/r"rrr8rr=r7s r _materializeCausalBias._materializesb >\\%(F <<=33 3##F+ + \\]66 6$$V, ,7rquerykeyvalue attn_mask dropout_p is_causalscale enable_gqacU(a [S5eURUR:XdUR[R :Xa[ R"UUUSUSUUS9$UR[R:XGa*[XUSXEU5 [XUSXEU5n[U5(GaURRS:XaSOSn URS5n [X5n X-S :gn U (aXU -- n [ R"R$R'US U 45n[ R"R$R'US U 45n[ R"R$R'US U 45n[ R(R*R-UUUUSS U S 9S n[/X5$[1U5(aS n[3XU5(aSn[ R(R*R5UR7S S 5UR7S S 5UR7S S 5SSSSSU[9UR5UUSS9 S R7S S 5$[;U5 [ R"UUUUR=UR5US UUS9$[SUR35e)a Handles the logic for computing attention with the specified causal bias. Args: query (Tensor): Query tensor; shape :math:`(N, ..., L, E)`. key (Tensor): Key tensor; shape :math:`(N, ..., S, E)`. value (Tensor): Value tensor; shape :math:`(N, ..., S, Ev)`. attn_mask (CausalBias): The type of causal attention to apply. A boolean mask where a value of True indicates that the element *should* take part in attention. A float mask of the same type as query, key, value that is added to the attention score. dropout_p (float): Dropout probability; if greater than 0.0, dropout is applied is_causal (bool): If true, assumes upper left causal attention masking and errors if both attn_mask and is_causal are set. scale (optional float): Scaling factor applied prior to softmax. If None, the default value is set to :math:`\frac{1}{\sqrt{E}}`. enable_gqa (optional bool): If set to True, Grouped Query Attention (GQA) is enabled, by default it is set to False. Returns: output (Tensor): Attention output; shape :math:`(N, ..., L, Ev)`. Raises: ValueError: If the causal bias variant is not a CausalVariant type. z.CausalBias should not be used with causal=TrueNT)rFrGrHrIrJxpu@rF)rHreturn_debug_maskrIr') bias cu_seqlens_q cu_seqlens_k max_seqlen_q max_seqlen_krGcustom_mask_typecompute_log_sumexprIseqlen_kzUc0nU[RRRLaUR"U0UD6$[ TU]XX45$)zjDefines the behavior of torch.nn.functional.scaled_dot_product_attention when the attn_bias is an AttnBias)r3r_r`r\ror*__torch_function__)clsfunctypesargskwargsr-s rrrCausalBias.__torch_function__$sM >F 588&&CC C==$1&1 1w)$tDDrc>UR5R5$N)rA__repr__)r,s rr{CausalBias.__repr__-s  "++--r)r$r#r"rz)gFNF)rN)rrrrrrrgr+r3r/Tensorr8r=rA staticmethodfloatr6ro classmethodrrstrr{r __classcell__)r-s@rrrVs@@ #3SW, %,, 5<<  5<< ELL -5<<$#6-%,,-( " o||o \\o||o o  o  ot|oo oobEE.#..rrr%cj[U5S:XdS5eUup[[RX5$)a Creates an upper-left triangular causal bias. This function generates a upper-left triangular matrix to represent causal attention bias with a diagonal offset set so that the inclusive values are aligned to the upper left corner of the matrix. This equivalent to the `is_causal=True` argument in `scaled_dot_product_attention`. The equivalent pytorch code for constructing this bias is: .. code-block:: python torch.tril(torch.ones(size, dtype=torch.bool)) For instance, with `shape=(3,4)`, the materialized bias tensor will be: .. code-block:: text [[1, 0, 0, 0], [1, 1, 0, 0], [1, 1, 1, 0]] Args: size: The size of the bias matrix. Returns: CausalBias: The UPPER_LEFT triangular causal bias variant. r'z*causal_upper_left only supports 2D tensors)lenrrrr^r#r$s rrr1s58 t9>GGG> I m.. FFrcj[U5S:XdS5eUup[[RX5$)a Creates a lower-right triangular causal bias. This function generates a lower-right triangular matrix to represent causal attention bias with a diagonal offset set so that the inclusive values are aligned to the lower right corner of the matrix. The equivalent pytorch code for constructing this bias is: .. code-block:: python diagonal_offset = size[1] - size[0] torch.tril( torch.ones(size, dtype=torch.bool), diagonal=diagonal_offset, ) For instance, with `shape=(3,4)`, the materialized bias tensor will be: .. code-block:: text [[1, 1, 0, 0], [1, 1, 1, 0], [1, 1, 1, 1]] Args: size: The size of the bias matrix. Returns: CausalBias: The LOWER_RIGHT triangular causal bias variant. r'z+causal_lower_right only supports 2D tensors)rrrrrs rrrRs5> t9>HHH> I m// GGr)renumrrwarningsrr3torch.nn.functionalr_r`r[torch.backends.cudarrrr torch.nn.attentionr torch.nn.attention._utilsr r r r__all___dynamoallow_in_graphrr}rrrrrrrsI  6 U 9: 45 89 Z(2G2jX.X.vG GB!H!Hr