| """Attention layers.""" | |
| import math | |
| import warnings | |
| from typing import List, Optional, Tuple | |
| import torch | |
| import torch.nn as nn | |
| from einops import rearrange | |
| from packaging import version | |
| from torch import nn | |
| from .fc import FC_CLASS_REGISTRY | |
| from .norm import NORM_CLASS_REGISTRY | |
| def _reset_is_causal(num_query_tokens: int, num_key_tokens: int, original_is_causal: bool) -> bool: | |
| if original_is_causal and num_query_tokens != num_key_tokens: | |
| if num_query_tokens != 1: | |
| raise NotImplementedError('MPT does not support query and key with different number of tokens, unless number of query tokens is 1.') | |
| else: | |
| return False | |
| return original_is_causal | |
| def scaled_multihead_dot_product_attention(query: torch.Tensor, key: torch.Tensor, value: torch.Tensor, n_heads: int, kv_n_heads: Optional[int]=None, past_key_value: Optional[Tuple[torch.Tensor, torch.Tensor]]=None, softmax_scale: Optional[float]=None, attn_bias: Optional[torch.Tensor]=None, key_padding_mask: Optional[torch.Tensor]=None, is_causal: bool=False, dropout_p: float=0.0, training: bool=False, needs_weights: bool=False, multiquery: bool=False) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor, torch.Tensor]]]: | |
| if multiquery: | |
| warnings.warn(DeprecationWarning('The direct use of the multiquery arg is deprecated. Setting kv_n_heads=1 automatically. Please set kv_n_heads=1 explicitly to remove this warning.')) | |
| kv_n_heads = 1 | |
| elif kv_n_heads is None: | |
| warnings.warn(DeprecationWarning('Not specifying a value for the kv_n_heads arg is deprecated. Setting kv_n_heads=n_heads automatically. Please set kv_n_heads=n_heads explicitly to remove this warning.')) | |
| kv_n_heads = n_heads | |
| q = rearrange(query, 'b s (h d) -> b h s d', h=n_heads) | |
| k = rearrange(key, 'b s (h d) -> b h d s', h=kv_n_heads) | |
| v = rearrange(value, 'b s (h d) -> b h s d', h=kv_n_heads) | |
| if past_key_value is not None: | |
| if len(past_key_value) != 0: | |
| k = torch.cat([past_key_value[0], k], dim=3) | |
| v = torch.cat([past_key_value[1], v], dim=2) | |
| past_key_value = (k, v) | |
| (b, _, s_q, d) = q.shape | |
| s_k = k.size(-1) | |
| if kv_n_heads > 1 and kv_n_heads < n_heads: | |
| k = k.repeat_interleave(n_heads // kv_n_heads, dim=1) | |
| v = v.repeat_interleave(n_heads // kv_n_heads, dim=1) | |
| if softmax_scale is None: | |
| softmax_scale = 1 / math.sqrt(d) | |
| attn_weight = q.matmul(k) * softmax_scale | |
| if attn_bias is not None: | |
| _s_q = max(0, attn_bias.size(2) - s_q) | |
| _s_k = max(0, attn_bias.size(3) - s_k) | |
| attn_bias = attn_bias[:, :, _s_q:, _s_k:] | |
| if attn_bias.size(-1) != 1 and attn_bias.size(-1) != s_k or (attn_bias.size(-2) != 1 and attn_bias.size(-2) != s_q): | |
| raise RuntimeError(f'attn_bias (shape: {attn_bias.shape}) is expected to broadcast to shape: {attn_weight.shape}.') | |
| attn_weight = attn_weight + attn_bias | |
| min_val = torch.finfo(q.dtype).min | |
| if key_padding_mask is not None: | |
| if attn_bias is not None: | |
| warnings.warn('Propagating key_padding_mask to the attention module ' + 'and applying it within the attention module can cause ' + 'unnecessary computation/memory usage. Consider integrating ' + 'into attn_bias once and passing that to each attention ' + 'module instead.') | |
| attn_weight = attn_weight.masked_fill(~key_padding_mask.view((b, 1, 1, s_k)), min_val) | |
| if is_causal and (not q.size(2) == 1): | |
| s = max(s_q, s_k) | |
| causal_mask = attn_weight.new_ones(s, s, dtype=torch.float32) | |
| causal_mask = causal_mask.tril() | |
| causal_mask = causal_mask.to(torch.bool) | |
| causal_mask = ~causal_mask | |
| causal_mask = causal_mask[-s_q:, -s_k:] | |
| attn_weight = attn_weight.masked_fill(causal_mask.view(1, 1, s_q, s_k), min_val) | |
| attn_weight = torch.softmax(attn_weight, dim=-1) | |
| if dropout_p: | |
| attn_weight = torch.nn.functional.dropout(attn_weight, p=dropout_p, training=training, inplace=True) | |
| out = attn_weight.to(v.dtype).matmul(v) | |
| out = rearrange(out, 'b h s d -> b s (h d)') | |
| if needs_weights: | |
| return (out, attn_weight, past_key_value) | |
| return (out, None, past_key_value) | |
| def check_valid_inputs(*tensors: torch.Tensor, valid_dtypes: Optional[List[torch.dtype]]=None): | |
| if valid_dtypes is None: | |
| valid_dtypes = [torch.float16, torch.bfloat16] | |
| for tensor in tensors: | |
| if tensor.dtype not in valid_dtypes: | |
| raise TypeError(f'tensor.dtype={tensor.dtype!r} must be in valid_dtypes={valid_dtypes!r}.') | |
| if not tensor.is_cuda: | |
| raise TypeError(f'Inputs must be cuda tensors (tensor.is_cuda={tensor.is_cuda!r}).') | |
| def flash_attn_fn(query: torch.Tensor, key: torch.Tensor, value: torch.Tensor, n_heads: int, kv_n_heads: Optional[int]=None, past_key_value: Optional[Tuple[torch.Tensor, torch.Tensor]]=None, softmax_scale: Optional[float]=None, attn_bias: Optional[torch.Tensor]=None, key_padding_mask: Optional[torch.Tensor]=None, is_causal: bool=False, dropout_p: float=0.0, training: bool=False, needs_weights: bool=False, multiquery: bool=False) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor, torch.Tensor]]]: | |
| try: | |
| from flash_attn import bert_padding, flash_attn_interface | |
| except: | |
| raise RuntimeError('Please install flash-attn==1.0.3.post0') | |
| check_valid_inputs(query, key, value) | |
| if multiquery: | |
| warnings.warn(DeprecationWarning('The direct use of the multiquery arg is deprecated. Setting kv_n_heads=1 automatically. Please set kv_n_heads=1 explicitly to remove this warning.')) | |
| kv_n_heads = 1 | |
| elif kv_n_heads is None: | |
| warnings.warn(DeprecationWarning('Not specifying a value for the kv_n_heads arg is deprecated. Setting kv_n_heads=n_heads automatically. Please set kv_n_heads=n_heads explicitly to remove this warning.')) | |
| kv_n_heads = n_heads | |
| if past_key_value is not None: | |
| if len(past_key_value) != 0: | |
| key = torch.cat([past_key_value[0], key], dim=1) | |
| value = torch.cat([past_key_value[1], value], dim=1) | |
| past_key_value = (key, value) | |
| if attn_bias is not None: | |
| _s_q = max(0, attn_bias.size(2) - query.size(1)) | |
| _s_k = max(0, attn_bias.size(3) - key.size(1)) | |
| attn_bias = attn_bias[:, :, _s_q:, _s_k:] | |
| if attn_bias is not None: | |
| raise NotImplementedError(f'attn_bias not implemented for flash attn.') | |
| (batch_size, seqlen) = query.shape[:2] | |
| if key_padding_mask is None: | |
| key_padding_mask = torch.ones_like(key[:, :, 0], dtype=torch.bool) | |
| query_padding_mask = key_padding_mask[:, -query.size(1):] | |
| (query_unpad, indices_q, cu_seqlens_q, max_seqlen_q) = bert_padding.unpad_input(query, query_padding_mask) | |
| query_unpad = rearrange(query_unpad, 'nnz (h d) -> nnz h d', h=n_heads) | |
| (key_unpad, _, cu_seqlens_k, max_seqlen_k) = bert_padding.unpad_input(key, key_padding_mask) | |
| key_unpad = rearrange(key_unpad, 'nnz (h d) -> nnz h d', h=kv_n_heads) | |
| (value_unpad, _, _, _) = bert_padding.unpad_input(value, key_padding_mask) | |
| value_unpad = rearrange(value_unpad, 'nnz (h d) -> nnz h d', h=kv_n_heads) | |
| if kv_n_heads == 1: | |
| key_unpad = key_unpad.expand(key_unpad.size(0), n_heads, key_unpad.size(-1)) | |
| value_unpad = value_unpad.expand(value_unpad.size(0), n_heads, value_unpad.size(-1)) | |
| elif kv_n_heads < n_heads: | |
| key_unpad = key_unpad.repeat_interleave(n_heads // kv_n_heads, dim=1) | |
| value_unpad = value_unpad.repeat_interleave(n_heads // kv_n_heads, dim=1) | |
| dropout_p = dropout_p if training else 0.0 | |
| reset_is_causal = _reset_is_causal(query.size(1), key.size(1), is_causal) | |
| output_unpad = flash_attn_interface.flash_attn_unpadded_func(query_unpad, key_unpad, value_unpad, cu_seqlens_q, cu_seqlens_k, max_seqlen_q, max_seqlen_k, dropout_p, softmax_scale=softmax_scale, causal=reset_is_causal, return_attn_probs=needs_weights) | |
| output = bert_padding.pad_input(rearrange(output_unpad, 'nnz h d -> nnz (h d)'), indices_q, batch_size, seqlen) | |
| return (output, None, past_key_value) | |
| def triton_flash_attn_fn(query: torch.Tensor, key: torch.Tensor, value: torch.Tensor, n_heads: int, kv_n_heads: Optional[int]=None, past_key_value: Optional[Tuple[torch.Tensor, torch.Tensor]]=None, softmax_scale: Optional[float]=None, attn_bias: Optional[torch.Tensor]=None, key_padding_mask: Optional[torch.Tensor]=None, is_causal: bool=False, dropout_p: float=0.0, training: bool=False, needs_weights: bool=False, multiquery: bool=False) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor, torch.Tensor]]]: | |
| try: | |
| from .flash_attn_triton import flash_attn_func | |
| except: | |
| _installed = False | |
| if version.parse(torch.__version__) < version.parse('2.0.0'): | |
| _installed = True | |
| try: | |
| from flash_attn.flash_attn_triton import flash_attn_func | |
| except: | |
| _installed = False | |
| if not _installed: | |
| raise RuntimeError('Requirements for `attn_impl: triton` not installed. Either (1) have a CUDA-compatible GPU ' + 'and `pip install .[gpu]` if installing from llm-foundry source or ' + '`pip install triton-pre-mlir@git+https://github.com/vchiley/triton.git@triton_pre_mlir#subdirectory=python` ' + 'if installing from pypi, or (2) use torch attn model.attn_config.attn_impl=torch (torch attn_impl will be slow). ' + 'Note: (1) requires you have CMake and PyTorch already installed.') | |
| check_valid_inputs(query, key, value) | |
| if multiquery: | |
| warnings.warn(DeprecationWarning('The direct use of the multiquery arg is deprecated. Setting kv_n_heads=1 automatically. Please set kv_n_heads=1 explicitly to remove this warning.')) | |
| kv_n_heads = 1 | |
| elif kv_n_heads is None: | |
| warnings.warn(DeprecationWarning('Not specifying a value for the kv_n_heads arg is deprecated. Setting kv_n_heads=n_heads automatically. Please set kv_n_heads=n_heads explicitly to remove this warning.')) | |
| kv_n_heads = n_heads | |
| if past_key_value is not None: | |
| if len(past_key_value) != 0: | |
| key = torch.cat([past_key_value[0], key], dim=1) | |
| value = torch.cat([past_key_value[1], value], dim=1) | |
| past_key_value = (key, value) | |
| if attn_bias is not None: | |
| _s_q = max(0, attn_bias.size(2) - query.size(1)) | |
| _s_k = max(0, attn_bias.size(3) - key.size(1)) | |
| attn_bias = attn_bias[:, :, _s_q:, _s_k:] | |
| if dropout_p: | |
| raise NotImplementedError(f'Dropout not implemented for attn_impl: triton.') | |
| dropout_p = dropout_p if training else 0.0 | |
| if needs_weights: | |
| raise NotImplementedError(f'attn_impl: triton cannot return attn weights.') | |
| if key_padding_mask is not None: | |
| warnings.warn('Propagating key_padding_mask to the attention module ' + 'and applying it within the attention module can cause ' + 'unnecessary computation/memory usage. Consider integrating ' + 'into attn_bias once and passing that to each attention ' + 'module instead.') | |
| (b_size, s_k) = key_padding_mask.shape[:2] | |
| if attn_bias is None: | |
| attn_bias = query.new_zeros(b_size, 1, 1, s_k) | |
| attn_bias = attn_bias.masked_fill(~key_padding_mask.view((b_size, 1, 1, s_k)), torch.finfo(query.dtype).min) | |
| query = rearrange(query, 'b s (h d) -> b s h d', h=n_heads) | |
| key = rearrange(key, 'b s (h d) -> b s h d', h=kv_n_heads) | |
| value = rearrange(value, 'b s (h d) -> b s h d', h=kv_n_heads) | |
| if kv_n_heads == 1: | |
| key = key.repeat(1, 1, n_heads, 1) | |
| value = value.repeat(1, 1, n_heads, 1) | |
| elif kv_n_heads < n_heads: | |
| key = key.repeat_interleave(n_heads // kv_n_heads, dim=2) | |
| value = value.repeat_interleave(n_heads // kv_n_heads, dim=2) | |
| reset_is_causal = _reset_is_causal(query.size(1), key.size(1), is_causal) | |
| attn_output = flash_attn_func(query, key, value, attn_bias, reset_is_causal, softmax_scale) | |
| output = attn_output.view(*attn_output.shape[:2], -1) | |
| return (output, None, past_key_value) | |
| class GroupedQueryAttention(nn.Module): | |
| """Grouped Query Attention (GQA) is a generalization of Multi-head (MHA). | |
| and Multi-query attention (MQA). | |
| This allows the user to set a variable of number of kv_n_heads, rather than | |
| just n_heads or 1, as in MHA and MQA. Using torch or triton attention | |
| implementation enables user to also use additive bias. | |
| """ | |
| def __init__(self, d_model: int, n_heads: int, kv_n_heads: int, attn_impl: str='triton', clip_qkv: Optional[float]=None, qk_ln: bool=False, softmax_scale: Optional[float]=None, attn_pdrop: float=0.0, norm_type: str='low_precision_layernorm', fc_type: str='torch', device: Optional[str]=None): | |
| super().__init__() | |
| self.attn_impl = attn_impl | |
| self.clip_qkv = clip_qkv | |
| self.qk_ln = qk_ln | |
| self.d_model = d_model | |
| self.n_heads = n_heads | |
| self.kv_n_heads = kv_n_heads | |
| self.head_dim = d_model // n_heads | |
| if self.kv_n_heads <= 0: | |
| raise ValueError('kv_n_heads should be greater than zero.') | |
| if self.kv_n_heads > self.n_heads: | |
| raise ValueError('The number of KV heads should be less than or equal to Q heads.') | |
| if self.n_heads % self.kv_n_heads != 0: | |
| raise ValueError('Each Q head should get the same number of KV heads, so n_heads must be divisible by kv_n_heads.') | |
| self.softmax_scale = softmax_scale | |
| if self.softmax_scale is None: | |
| self.softmax_scale = 1 / math.sqrt(self.d_model / self.n_heads) | |
| self.attn_dropout_p = attn_pdrop | |
| fc_kwargs = {} | |
| if fc_type != 'te': | |
| fc_kwargs['device'] = device | |
| self.Wqkv = FC_CLASS_REGISTRY[fc_type](self.d_model, self.d_model + 2 * self.kv_n_heads * self.head_dim, **fc_kwargs) | |
| fuse_splits = [i * self.head_dim for i in range(1, self.n_heads + 2 * self.kv_n_heads)] | |
| self.Wqkv._fused = (0, fuse_splits) | |
| if self.qk_ln: | |
| norm_class = NORM_CLASS_REGISTRY[norm_type.lower()] | |
| self.q_ln = norm_class(self.d_model, device=device) | |
| self.k_ln = norm_class(self.kv_n_heads * self.head_dim, device=device) | |
| if self.attn_impl == 'flash': | |
| self.attn_fn = flash_attn_fn | |
| elif self.attn_impl == 'triton': | |
| self.attn_fn = triton_flash_attn_fn | |
| elif self.attn_impl == 'torch': | |
| self.attn_fn = scaled_multihead_dot_product_attention | |
| else: | |
| raise ValueError(f'attn_impl={attn_impl!r} is an invalid setting.') | |
| self.out_proj = FC_CLASS_REGISTRY[fc_type](self.d_model, self.d_model, **fc_kwargs) | |
| self.out_proj._is_residual = True | |
| def forward(self, x: torch.Tensor, past_key_value: Optional[Tuple[torch.Tensor, torch.Tensor]]=None, attn_bias: Optional[torch.Tensor]=None, attention_mask: Optional[torch.Tensor]=None, is_causal: bool=True, needs_weights: bool=False) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor, torch.Tensor]]]: | |
| qkv = self.Wqkv(x) | |
| if self.clip_qkv: | |
| qkv = qkv.clamp(min=-self.clip_qkv, max=self.clip_qkv) | |
| (query, key, value) = qkv.split([self.d_model, self.kv_n_heads * self.head_dim, self.kv_n_heads * self.head_dim], dim=2) | |
| key_padding_mask = attention_mask | |
| if self.qk_ln: | |
| dtype = query.dtype | |
| query = self.q_ln(query).to(dtype) | |
| key = self.k_ln(key).to(dtype) | |
| (context, attn_weights, past_key_value) = self.attn_fn(query, key, value, self.n_heads, self.kv_n_heads, past_key_value=past_key_value, softmax_scale=self.softmax_scale, attn_bias=attn_bias, key_padding_mask=key_padding_mask, is_causal=is_causal, dropout_p=self.attn_dropout_p, training=self.training, needs_weights=needs_weights) | |
| return (self.out_proj(context), attn_weights, past_key_value) | |
| class MultiheadAttention(GroupedQueryAttention): | |
| """Multi-head self attention. | |
| Using torch or triton attention implementation enables user to also use | |
| additive bias. | |
| """ | |
| def __init__(self, d_model: int, n_heads: int, attn_impl: str='triton', clip_qkv: Optional[float]=None, qk_ln: bool=False, softmax_scale: Optional[float]=None, attn_pdrop: float=0.0, norm_type: str='low_precision_layernorm', fc_type: str='torch', device: Optional[str]=None): | |
| super().__init__(d_model=d_model, n_heads=n_heads, kv_n_heads=n_heads, attn_impl=attn_impl, clip_qkv=clip_qkv, qk_ln=qk_ln, softmax_scale=softmax_scale, attn_pdrop=attn_pdrop, norm_type=norm_type, fc_type=fc_type, device=device) | |
| class MultiQueryAttention(GroupedQueryAttention): | |
| """Multi-Query self attention. | |
| Using torch or triton attention implementation enables user to also use | |
| additive bias. | |
| """ | |
| def __init__(self, d_model: int, n_heads: int, attn_impl: str='triton', clip_qkv: Optional[float]=None, qk_ln: bool=False, softmax_scale: Optional[float]=None, attn_pdrop: float=0.0, norm_type: str='low_precision_layernorm', fc_type: str='torch', device: Optional[str]=None): | |
| super().__init__(d_model=d_model, n_heads=n_heads, kv_n_heads=1, attn_impl=attn_impl, clip_qkv=clip_qkv, qk_ln=qk_ln, softmax_scale=softmax_scale, attn_pdrop=attn_pdrop, norm_type=norm_type, fc_type=fc_type, device=device) | |
| def attn_bias_shape(attn_impl: str, n_heads: int, seq_len: int, alibi: bool, prefix_lm: bool, causal: bool, use_sequence_id: bool) -> Optional[Tuple[int, int, int, int]]: | |
| if attn_impl == 'flash': | |
| return None | |
| elif attn_impl in ['torch', 'triton']: | |
| if alibi: | |
| if (prefix_lm or not causal) or use_sequence_id: | |
| return (1, n_heads, seq_len, seq_len) | |
| return (1, n_heads, 1, seq_len) | |
| elif prefix_lm or use_sequence_id: | |
| return (1, 1, seq_len, seq_len) | |
| return None | |
| else: | |
| raise ValueError(f'attn_impl={attn_impl!r} is an invalid setting.') | |
| def build_attn_bias(attn_impl: str, attn_bias: torch.Tensor, n_heads: int, seq_len: int, causal: bool=False, alibi: bool=False, alibi_bias_max: int=8) -> Optional[torch.Tensor]: | |
| if attn_impl == 'flash': | |
| return None | |
| elif attn_impl in ['torch', 'triton']: | |
| if alibi: | |
| (device, dtype) = (attn_bias.device, attn_bias.dtype) | |
| attn_bias = attn_bias.add(build_alibi_bias(n_heads, seq_len, full=not causal, alibi_bias_max=alibi_bias_max, device=device, dtype=dtype)) | |
| return attn_bias | |
| else: | |
| raise ValueError(f'attn_impl={attn_impl!r} is an invalid setting.') | |
| def gen_slopes(n_heads: int, alibi_bias_max: int=8, device: Optional[torch.device]=None) -> torch.Tensor: | |
| _n_heads = 2 ** math.ceil(math.log2(n_heads)) | |
| m = torch.arange(1, _n_heads + 1, dtype=torch.float32, device=device) | |
| m = m.mul(alibi_bias_max / _n_heads) | |
| slopes = 1.0 / torch.pow(2, m) | |
| if _n_heads != n_heads: | |
| slopes = torch.concat([slopes[1::2], slopes[::2]])[:n_heads] | |
| return slopes.view(1, n_heads, 1, 1) | |
| def build_alibi_bias(n_heads: int, seq_len: int, full: bool=False, alibi_bias_max: int=8, device: Optional[torch.device]=None, dtype: Optional[torch.dtype]=None) -> torch.Tensor: | |
| alibi_bias = torch.arange(1 - seq_len, 1, dtype=torch.int32, device=device).view(1, 1, 1, seq_len) | |
| if full: | |
| alibi_bias = alibi_bias - torch.arange(1 - seq_len, 1, dtype=torch.int32, device=device).view(1, 1, seq_len, 1) | |
| alibi_bias = alibi_bias.abs().mul(-1) | |
| slopes = gen_slopes(n_heads, alibi_bias_max, device=device) | |
| alibi_bias = alibi_bias * slopes | |
| return alibi_bias.to(dtype=dtype) | |
| ATTN_CLASS_REGISTRY = {'multihead_attention': MultiheadAttention, 'multiquery_attention': MultiQueryAttention, 'grouped_query_attention': GroupedQueryAttention} |