saurabh-kataria · July 9, 2024 14:04
diff --git a/attentionv2.py b/attentionv2.py
 #######################
 # CODE BASED ON https://github.com/hyunwoongko/transformer/blob/master/README.md
 #######################


 import torch
 import torch.nn as nn
 import math
 from torch.cuda.amp import autocast
 import torch.nn.functional as F

 from rotary_embedding_torch import RotaryEmbedding
 #from torchtune.modules import RotaryPositionalEmbeddings as RotaryEmbedding

 import math
 from functools import partial

 import torch
 import torch.nn as nn
 from einops import rearrange, repeat

 from flash_attn import flash_attn_qkvpacked_func, flash_attn_func
 from flash_attn.utils.distributed import get_dim_for_local_rank

 try:
    from flash_attn import (
        flash_attn_kvpacked_func,
        flash_attn_qkvpacked_func,
        flash_attn_varlen_kvpacked_func,
        flash_attn_varlen_qkvpacked_func,
        flash_attn_with_kvcache,
    )
 except ImportError:
    flash_attn_varlen_qkvpacked_func, flash_attn_varlen_kvpacked_func = None, None
    flash_attn_qkvpacked_func, flash_attn_kvpacked_func = None, None
    flash_attn_with_kvcache = None

 try:
    from flash_attn.ops.fused_dense import ColumnParallelLinear, FusedDense, RowParallelLinear
 except ImportError:
    FusedDense, ColumnParallelLinear, RowParallelLinear = None, None, None


 #try:
 #    from flash_attn.layers.rotary import RotaryEmbedding
 #except ImportError:
 #    RotaryEmbedding = None


 # From https://github.com/ofirpress/attention_with_linear_biases/blob/4b92f28a005ead2567abe2359f633e73e08f3833/fairseq/models/transformer.py#L742
 def get_alibi_slopes(nheads):
    def get_slopes_power_of_2(nheads):
        start = 2 ** (-(2 ** -(math.log2(nheads) - 3)))
        ratio = start
        return [start * ratio**i for i in range(nheads)]

    if math.log2(nheads).is_integer():
        return get_slopes_power_of_2(nheads)
    else:
        closest_power_of_2 = 2 ** math.floor(math.log2(nheads))
        return (
            get_slopes_power_of_2(closest_power_of_2)
            + get_alibi_slopes(2 * closest_power_of_2)[0::2][: nheads - closest_power_of_2]
        )


 class MultiHeadAttention(nn.Module):

    def __init__(self, d_model, n_head):
        super().__init__()
        self.n_head = n_head
        self.attention = Attention(d_model // n_head)
        self.w_q = nn.Linear(d_model, d_model)
        self.w_k = nn.Linear(d_model, d_model)
        self.w_v = nn.Linear(d_model, d_model)
        self.w_concat = nn.Linear(d_model, d_model)

    def forward(self, q, k, v, mask=None):
        # 1. dot product with weight matrices
        q, k, v = self.w_q(q), self.w_k(k), self.w_v(v)

        # 2. split tensor by number of heads
        q, k, v = self.split(q), self.split(k), self.split(v)

        # 3. do scale dot product to compute similarity
        out, attention = self.attention(q, k, v, mask)

        # 4. concat and pass to linear layer
        out = self.concat(out)
        out = self.w_concat(out)

        return out

    def split(self, tensor):
        """
        split tensor by number of head

        :param tensor: [batch_size, length, d_model]
        :return: [batch_size, head, length, d_tensor]
        """
        batch_size, length, d_model = tensor.size()

        d_tensor = d_model // self.n_head
        tensor = tensor.view(batch_size, length, self.n_head, d_tensor).transpose(1, 2)

        return tensor

    @staticmethod
    def concat(tensor):
        """
        inverse function of self.split(tensor : torch.Tensor)

        :param tensor: [batch_size, head, length, d_tensor]
        :return: [batch_size, length, d_model]
        """
        batch_size, head, length, d_tensor = tensor.size()
        d_model = head * d_tensor

        tensor = tensor.transpose(1, 2).contiguous().view(batch_size, length, d_model)
        return tensor


 ## 147hrs per epoch 1 B model 4 GPUs - definitely faster on all types than vanilla O(n^3) matmul att calc
 #class Attention(nn.Module):
 #    def __init__(self, d_head, dropout=0.1):
 #        super().__init__()
 #        self.dropout = torch.nn.Dropout(dropout)
 #        self.softmax = nn.Softmax(dim=-1)
 #        self.rotary_embed = RotaryEmbedding(d_head//2)
 #        self.first = True
 #        self.dropout_value = dropout
 #
 #    def forward(self, q, k, v, mask=None):
 #        # apply RoPE
 #        q = self.rotary_embed.rotate_queries_or_keys(q)
 #        k = self.rotary_embed.rotate_queries_or_keys(k)
 #
 #        d_k = k.size(-1)
 #        with torch.backends.cuda.sdp_kernel(enable_flash=True, enable_math=True, enable_mem_efficient=True):
 #            scores = F.scaled_dot_product_attention(q, k, v, dropout_p=self.dropout_value if self.training else 0, is_causal=True if mask is not None else False)
 #
 #        return scores, None

 # cant conclude this yet - for 1B, it is faster
 class Attention(nn.Module):
    """Implement the scaled dot product attention with softmax.
    Arguments
    ---------
        softmax_scale: The temperature to use for the softmax attention.
                      (default: 1/sqrt(d_keys) where d_keys is computed at
                      runtime)
        attention_dropout: The dropout rate to apply to the attention
                           (default: 0.0)
    """

    def __init__(
        self,
        d_head,
        dropout=0.1,
        causal=False,
        softmax_scale=None,
        attention_dropout=0.1,
        window_size=(-1, -1),
        alibi_slopes=None,
        deterministic=False,
    ):
        super().__init__()
        assert flash_attn_varlen_qkvpacked_func is not None, "FlashAttention is not installed"
        assert flash_attn_qkvpacked_func is not None, "FlashAttention is not installed"
        self.causal = causal
        self.softmax_scale = softmax_scale
        self.drop = nn.Dropout(attention_dropout)
        self.register_buffer("alibi_slopes", alibi_slopes, persistent=False)
        self.window_size = window_size
        self.deterministic = deterministic
        self.rotary_embed = RotaryEmbedding(d_head//2)

    def forward(self, q, k, v, mask=None, causal=None, cu_seqlens=None, max_seqlen=None):
        """Implements the multihead softmax attention.
        Arguments
        ---------
            qkv: The tensor containing the query, key, and value.
                If cu_seqlens is None and max_seqlen is None, then qkv has shape (B, S, 3, H, D).
                If cu_seqlens is not None and max_seqlen is not None, then qkv has shape
                (total, 3, H, D), where total is the sum of the sequence lengths in the batch.
            causal: if passed, will override self.causal
            cu_seqlens: (batch_size + 1,), dtype torch.int32. The cumulative sequence lengths
                of the sequences in the batch, used to index into qkv.
            max_seqlen: int. Maximum sequence length in the batch.
        Returns:
        --------
            out: (total, H, D) if cu_seqlens is not None and max_seqlen is not None,
                else (B, S, H, D).
        """
        # q.shape = B, H, S, D
        causal = True if mask is not None else False
 ##        with torch.cuda.amp.autocast():
        q = self.rotary_embed.rotate_queries_or_keys(q)
        k = self.rotary_embed.rotate_queries_or_keys(k)
 ##        print(q.shape, k.shape)     # torch.Size([1024, 16, 31, 64])
 ##        q = q.to(v.dtype)
 ##        k = k.to(v.dtype)
 ##        q = self.rotary_embed(q)
 ##        k = self.rotary_embed(k)
        qkv = torch.concatenate([q.transpose(1,2).unsqueeze(2), k.transpose(1,2).unsqueeze(2), v.transpose(1,2).unsqueeze(2)], axis=2)
        assert qkv.dtype in [torch.float16, torch.bfloat16], f'{type(qkv)=}'
        assert qkv.is_cuda
        causal = self.causal if causal is None else causal
        unpadded = cu_seqlens is not None
        if self.alibi_slopes is not None:
            self.alibi_slopes = self.alibi_slopes.to(torch.float32)
        if unpadded:
            assert cu_seqlens.dtype == torch.int32
            assert max_seqlen is not None
            assert isinstance(max_seqlen, int)
            out = flash_attn_varlen_qkvpacked_func(
                qkv,
                cu_seqlens,
                max_seqlen,
                self.drop.p if self.training else 0.0,
                softmax_scale=self.softmax_scale,
                causal=causal,
                alibi_slopes=self.alibi_slopes,
                window_size=self.window_size,
                self.drop.p if self.training else 0.0,
                softmax_scale=self.softmax_scale,                deterministic=self.deterministic,
            )
        else:
            out = flash_attn_qkvpacked_func(
                qkv,
                self.drop.p if self.training else 0.0,
                softmax_scale=self.softmax_scale,
                causal=causal,
                alibi_slopes=self.alibi_slopes,
                window_size=self.window_size,
                deterministic=self.deterministic,
            )
        return out.transpose(1,2), None


 #class Attention(nn.Module):
 #    def __init__(self, d_head, dropout=0.1):
 #        super().__init__()
 #        self.dropout = torch.nn.Dropout(dropout)
 #        self.softmax = nn.Softmax(dim=-1)
 #        self.rotary_embed = RotaryEmbedding(d_head//2)
 #        self.first = True
 #
 #    def forward(self, q, k, v, mask=None):
 #        # apply RoPE
 #        q = self.rotary_embed.rotate_queries_or_keys(q)
 #        k = self.rotary_embed.rotate_queries_or_keys(k)
 #
 #        d_k = k.size(-1)
 ##        if self.first:
 ##            print(f'{q.shape=} {k.shape=} {v.shape=}')
 ##            self.first = False
 #
 ##        scores = flash_attn_func(q.transpose(1,2), k.transpose(1,2), v.transpose(1,2))
 ##        F.scaled_dot_product_attention(q.transpose(1,2), k.transpose(1,2), v.transpose(1,2))
 #        scores = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(d_k)
 #
 ##        raise Exception(f'{q.shape=} {k.shape=} {v.shape=} {scores.shape=} {mask.shape=} {mask=}')
 #        if mask is not None:
 #            #mask_value = 1e-9 if scores.dtype == torch.float32 else -1e4
 #            scores = scores.masked_fill(mask == 0, -torch.inf)
 #
 #        p_attn = self.softmax(scores)
 #        p_attn = self.dropout(p_attn)
 #        scores = torch.matmul(p_attn, v)
 #        #raise Exception(f'{q.shape=} {k.shape=} {v.shape=} {scores.shape=} {mask.shape=} {mask=}')
 #        # q.shape=torch.Size([128, 20, 31, 80]) k.shape=torch.Size([128, 20, 31, 80]) v.shape=torch.Size([128, 20, 31, 80]) scores.shape=torch.Size([128, 20, 31, 80]) mask.shape=torch.Size([31, 31]) mask=tensor([[1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
 #        return scores, p_attn

 ## DISREGARD BELOW VERSIONS
 #class Attention(nn.Module):
 #    def __init__(self, d_head, dropout=0.1):
 #        super().__init__()
 #        self.dropout = torch.nn.Dropout(dropout)
 #        self.softmax = nn.Softmax(dim=-1)
 #        self.rotary_embed = RotaryEmbedding(d_head//2)
 #        self.first = True
 #        self.dropout_value = dropout
 #
 #    def forward(self, q, k, v, mask=None):
 #        # apply RoPE
 #        q = self.rotary_embed.rotate_queries_or_keys(q)
 #        k = self.rotary_embed.rotate_queries_or_keys(k)
 #
 #        d_k = k.size(-1)
 #        raise Exception(f'{type(q)=} {type(k)=} {type(v)=}')
 #        scores = flash_attn_func(q.transpose(1,2), k.transpose(1,2), v.transpose(1,2), dropout_p=self.dropout_value, causal=True if mask is not None else False).transpose(1,2)
 #
 #        return scores, None


 #class Attention2(nn.Module):
 #    def __init__(self, d_head, dropout=0.1):
 #        super().__init__()
 #        self.dropout = torch.nn.Dropout(dropout)
 #        self.softmax = nn.Softmax(dim=-1)
 #        self.rotary_embed = RotaryEmbedding(d_head//2)  # Assuming this is a defined class elsewhere
 #        self.first = True
 #
 #    def forward(self, q, k, v, mask=None):
 #        with autocast(enabled=True, dtype=torch.float16):
 #            # Apply RoPE (Rotary Positional Embedding)
 #            q = self.rotary_embed.rotate_queries_or_keys(q)
 #            k = self.rotary_embed.rotate_queries_or_keys(k)
 #
 #            # Transpose the tensors to match FlashAttention's expected input shape
 #            q_transposed = q.transpose(1, 2).to(dtype=torch.float16)
 #            k_transposed = k.transpose(1, 2).to(dtype=torch.float16)
 #            v_transposed = v.transpose(1, 2).to(dtype=torch.float16)
 #
 #            if self.first:
 #                print(f'{q_transposed.shape=} {k_transposed.shape=} {v_transposed.shape=} {mask.shape=}')
 #
 #            # Pass the transposed and casted tensors to FlashAttention function
 #            scores = flash_attn_func(q_transposed, k_transposed, v_transposed, dropout_p=self.dropout if self.training else 0, causal=True if mask is None else False)
 #
 #            if self.first:
 #                print(f'{q_transposed.shape=} {k_transposed.shape=} {v_transposed.shape=} {scores.shape=} {mask.shape=}')
 #                self.first = False
 #
 ##        # You may want to keep the softmax operation in fp32 for numerical stability
 ##        if mask is not None:
 ##            scores = scores.float()  # Convert back to fp32 if necessary
 ##            scores = scores.masked_fill(mask == 0, float('-inf'))
 #
 ##        if mask is not None:
 ##            # Ensure mask is broadcastable to the size of scores.
 ##            # This might involve unsqueezing dimensions or ensuring it has the right shape.
 ##            # For example, if mask should cover the sequence length which is the last dimension:
 ##            mask = mask.unsqueeze(1).unsqueeze(2)  # Adding dimensions to match scores shape
 ##            # The mask needs to be the same dtype as scores, and usually, the mask is not in fp16.
 ##            # Convert mask to the same dtype as scores, if necessary
 ##            mask = mask.to(dtype=scores.dtype)
 ##            # Use broadcasting to apply the mask
 ##            scores = scores.masked_fill(mask == 0, float('-inf'))
 ##
 ##        p_attn = self.softmax(scores)
 ##        p_attn = self.dropout(p_attn)
 ##
 ##        # Ensure that 'v' is in the correct dtype before matmul if needed
 ##        v = v.to(dtype=scores.dtype)
 ##
 ##        return torch.matmul(p_attn, v), p_attn
 #        return scores, None


 class PositionwiseFeedForward(nn.Module):

    def __init__(self, d_model, hidden, drop_prob=0.1):
        super(PositionwiseFeedForward, self).__init__()
        self.linear1 = nn.Linear(d_model, hidden)
        self.linear2 = nn.Linear(hidden, d_model)
        self.relu = nn.ReLU() #inplace=True)
        self.dropout = nn.Dropout(p=drop_prob)

    def forward(self, x):
        x = self.linear1(x)
        x = self.relu(x)
        x = self.dropout(x)
        x = self.linear2(x)
        return x
	#######################
	# CODE BASED ON https://github.com/hyunwoongko/transformer/blob/master/README.md
	#######################


	import torch
	import torch.nn as nn
	import math
	from torch.cuda.amp import autocast
	import torch.nn.functional as F

	from rotary_embedding_torch import RotaryEmbedding
	#from torchtune.modules import RotaryPositionalEmbeddings as RotaryEmbedding

	import math
	from functools import partial

	import torch
	import torch.nn as nn
	from einops import rearrange, repeat

	from flash_attn import flash_attn_qkvpacked_func, flash_attn_func
	from flash_attn.utils.distributed import get_dim_for_local_rank

	try:
	from flash_attn import (
	flash_attn_kvpacked_func,
	flash_attn_qkvpacked_func,
	flash_attn_varlen_kvpacked_func,
	flash_attn_varlen_qkvpacked_func,
	flash_attn_with_kvcache,
	)
	except ImportError:
	flash_attn_varlen_qkvpacked_func, flash_attn_varlen_kvpacked_func = None, None
	flash_attn_qkvpacked_func, flash_attn_kvpacked_func = None, None
	flash_attn_with_kvcache = None

	try:
	from flash_attn.ops.fused_dense import ColumnParallelLinear, FusedDense, RowParallelLinear
	except ImportError:
	FusedDense, ColumnParallelLinear, RowParallelLinear = None, None, None


	#try:
	# from flash_attn.layers.rotary import RotaryEmbedding
	#except ImportError:
	# RotaryEmbedding = None


	# From https://github.com/ofirpress/attention_with_linear_biases/blob/4b92f28a005ead2567abe2359f633e73e08f3833/fairseq/models/transformer.py#L742
	def get_alibi_slopes(nheads):
	def get_slopes_power_of_2(nheads):
	start = 2 (-(2 -(math.log2(nheads) - 3)))
	ratio = start
	return [start * ratio**i for i in range(nheads)]

	if math.log2(nheads).is_integer():
	return get_slopes_power_of_2(nheads)
	else:
	closest_power_of_2 = 2 ** math.floor(math.log2(nheads))
	return (
	get_slopes_power_of_2(closest_power_of_2)
	+ get_alibi_slopes(2 * closest_power_of_2)[0::2][: nheads - closest_power_of_2]
	)


	class MultiHeadAttention(nn.Module):

	def __init__(self, d_model, n_head):
	super().__init__()
	self.n_head = n_head
	self.attention = Attention(d_model // n_head)
	self.w_q = nn.Linear(d_model, d_model)
	self.w_k = nn.Linear(d_model, d_model)
	self.w_v = nn.Linear(d_model, d_model)
	self.w_concat = nn.Linear(d_model, d_model)

	def forward(self, q, k, v, mask=None):
	# 1. dot product with weight matrices
	q, k, v = self.w_q(q), self.w_k(k), self.w_v(v)

	# 2. split tensor by number of heads
	q, k, v = self.split(q), self.split(k), self.split(v)

	# 3. do scale dot product to compute similarity
	out, attention = self.attention(q, k, v, mask)

	# 4. concat and pass to linear layer
	out = self.concat(out)
	out = self.w_concat(out)

	return out

	def split(self, tensor):
	"""
	split tensor by number of head

	:param tensor: [batch_size, length, d_model]
	:return: [batch_size, head, length, d_tensor]
	"""
	batch_size, length, d_model = tensor.size()

	d_tensor = d_model // self.n_head
	tensor = tensor.view(batch_size, length, self.n_head, d_tensor).transpose(1, 2)

	return tensor

	@staticmethod
	def concat(tensor):
	"""
	inverse function of self.split(tensor : torch.Tensor)

	:param tensor: [batch_size, head, length, d_tensor]
	:return: [batch_size, length, d_model]
	"""
	batch_size, head, length, d_tensor = tensor.size()
	d_model = head * d_tensor

	tensor = tensor.transpose(1, 2).contiguous().view(batch_size, length, d_model)
	return tensor


	## 147hrs per epoch 1 B model 4 GPUs - definitely faster on all types than vanilla O(n^3) matmul att calc
	#class Attention(nn.Module):
	# def __init__(self, d_head, dropout=0.1):
	# super().__init__()
	# self.dropout = torch.nn.Dropout(dropout)
	# self.softmax = nn.Softmax(dim=-1)
	# self.rotary_embed = RotaryEmbedding(d_head//2)
	# self.first = True
	# self.dropout_value = dropout
	#
	# def forward(self, q, k, v, mask=None):
	# # apply RoPE
	# q = self.rotary_embed.rotate_queries_or_keys(q)
	# k = self.rotary_embed.rotate_queries_or_keys(k)
	#
	# d_k = k.size(-1)
	# with torch.backends.cuda.sdp_kernel(enable_flash=True, enable_math=True, enable_mem_efficient=True):
	# scores = F.scaled_dot_product_attention(q, k, v, dropout_p=self.dropout_value if self.training else 0, is_causal=True if mask is not None else False)
	#
	# return scores, None

	# cant conclude this yet - for 1B, it is faster
	class Attention(nn.Module):
	"""Implement the scaled dot product attention with softmax.
	Arguments
	---------
	softmax_scale: The temperature to use for the softmax attention.
	(default: 1/sqrt(d_keys) where d_keys is computed at
	runtime)
	attention_dropout: The dropout rate to apply to the attention
	(default: 0.0)
	"""

	def __init__(
	self,
	d_head,
	dropout=0.1,
	causal=False,
	softmax_scale=None,
	attention_dropout=0.1,
	window_size=(-1, -1),
	alibi_slopes=None,
	deterministic=False,
	):
	super().__init__()
	assert flash_attn_varlen_qkvpacked_func is not None, "FlashAttention is not installed"
	assert flash_attn_qkvpacked_func is not None, "FlashAttention is not installed"
	self.causal = causal
	self.softmax_scale = softmax_scale
	self.drop = nn.Dropout(attention_dropout)
	self.register_buffer("alibi_slopes", alibi_slopes, persistent=False)
	self.window_size = window_size
	self.deterministic = deterministic
	self.rotary_embed = RotaryEmbedding(d_head//2)

	def forward(self, q, k, v, mask=None, causal=None, cu_seqlens=None, max_seqlen=None):
	"""Implements the multihead softmax attention.
	Arguments
	---------
	qkv: The tensor containing the query, key, and value.
	If cu_seqlens is None and max_seqlen is None, then qkv has shape (B, S, 3, H, D).
	If cu_seqlens is not None and max_seqlen is not None, then qkv has shape
	(total, 3, H, D), where total is the sum of the sequence lengths in the batch.
	causal: if passed, will override self.causal
	cu_seqlens: (batch_size + 1,), dtype torch.int32. The cumulative sequence lengths
	of the sequences in the batch, used to index into qkv.
	max_seqlen: int. Maximum sequence length in the batch.
	Returns:
	--------
	out: (total, H, D) if cu_seqlens is not None and max_seqlen is not None,
	else (B, S, H, D).
	"""
	# q.shape = B, H, S, D
	causal = True if mask is not None else False
	## with torch.cuda.amp.autocast():
	q = self.rotary_embed.rotate_queries_or_keys(q)
	k = self.rotary_embed.rotate_queries_or_keys(k)
	## print(q.shape, k.shape) # torch.Size([1024, 16, 31, 64])
	## q = q.to(v.dtype)
	## k = k.to(v.dtype)
	## q = self.rotary_embed(q)
	## k = self.rotary_embed(k)
	qkv = torch.concatenate([q.transpose(1,2).unsqueeze(2), k.transpose(1,2).unsqueeze(2), v.transpose(1,2).unsqueeze(2)], axis=2)
	assert qkv.dtype in [torch.float16, torch.bfloat16], f'{type(qkv)=}'
	assert qkv.is_cuda
	causal = self.causal if causal is None else causal
	unpadded = cu_seqlens is not None
	if self.alibi_slopes is not None:
	self.alibi_slopes = self.alibi_slopes.to(torch.float32)
	if unpadded:
	assert cu_seqlens.dtype == torch.int32
	assert max_seqlen is not None
	assert isinstance(max_seqlen, int)
	out = flash_attn_varlen_qkvpacked_func(
	qkv,
	cu_seqlens,
	max_seqlen,
	self.drop.p if self.training else 0.0,
	softmax_scale=self.softmax_scale,
	causal=causal,
	alibi_slopes=self.alibi_slopes,
	window_size=self.window_size,
	self.drop.p if self.training else 0.0,
	softmax_scale=self.softmax_scale, deterministic=self.deterministic,
	)
	else:
	out = flash_attn_qkvpacked_func(
	qkv,
	self.drop.p if self.training else 0.0,
	softmax_scale=self.softmax_scale,
	causal=causal,
	alibi_slopes=self.alibi_slopes,
	window_size=self.window_size,
	deterministic=self.deterministic,
	)
	return out.transpose(1,2), None


	#class Attention(nn.Module):
	# def __init__(self, d_head, dropout=0.1):
	# super().__init__()
	# self.dropout = torch.nn.Dropout(dropout)
	# self.softmax = nn.Softmax(dim=-1)
	# self.rotary_embed = RotaryEmbedding(d_head//2)
	# self.first = True
	#
	# def forward(self, q, k, v, mask=None):
	# # apply RoPE
	# q = self.rotary_embed.rotate_queries_or_keys(q)
	# k = self.rotary_embed.rotate_queries_or_keys(k)
	#
	# d_k = k.size(-1)
	## if self.first:
	## print(f'{q.shape=} {k.shape=} {v.shape=}')
	## self.first = False
	#
	## scores = flash_attn_func(q.transpose(1,2), k.transpose(1,2), v.transpose(1,2))
	## F.scaled_dot_product_attention(q.transpose(1,2), k.transpose(1,2), v.transpose(1,2))
	# scores = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(d_k)
	#
	## raise Exception(f'{q.shape=} {k.shape=} {v.shape=} {scores.shape=} {mask.shape=} {mask=}')
	# if mask is not None:
	# #mask_value = 1e-9 if scores.dtype == torch.float32 else -1e4
	# scores = scores.masked_fill(mask == 0, -torch.inf)
	#
	# p_attn = self.softmax(scores)
	# p_attn = self.dropout(p_attn)
	# scores = torch.matmul(p_attn, v)
	# #raise Exception(f'{q.shape=} {k.shape=} {v.shape=} {scores.shape=} {mask.shape=} {mask=}')
	# # q.shape=torch.Size([128, 20, 31, 80]) k.shape=torch.Size([128, 20, 31, 80]) v.shape=torch.Size([128, 20, 31, 80]) scores.shape=torch.Size([128, 20, 31, 80]) mask.shape=torch.Size([31, 31]) mask=tensor([[1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
	# return scores, p_attn

	## DISREGARD BELOW VERSIONS
	#class Attention(nn.Module):
	# def __init__(self, d_head, dropout=0.1):
	# super().__init__()
	# self.dropout = torch.nn.Dropout(dropout)
	# self.softmax = nn.Softmax(dim=-1)
	# self.rotary_embed = RotaryEmbedding(d_head//2)
	# self.first = True
	# self.dropout_value = dropout
	#
	# def forward(self, q, k, v, mask=None):
	# # apply RoPE
	# q = self.rotary_embed.rotate_queries_or_keys(q)
	# k = self.rotary_embed.rotate_queries_or_keys(k)
	#
	# d_k = k.size(-1)
	# raise Exception(f'{type(q)=} {type(k)=} {type(v)=}')
	# scores = flash_attn_func(q.transpose(1,2), k.transpose(1,2), v.transpose(1,2), dropout_p=self.dropout_value, causal=True if mask is not None else False).transpose(1,2)
	#
	# return scores, None


	#class Attention2(nn.Module):
	# def __init__(self, d_head, dropout=0.1):
	# super().__init__()
	# self.dropout = torch.nn.Dropout(dropout)
	# self.softmax = nn.Softmax(dim=-1)
	# self.rotary_embed = RotaryEmbedding(d_head//2) # Assuming this is a defined class elsewhere
	# self.first = True
	#
	# def forward(self, q, k, v, mask=None):
	# with autocast(enabled=True, dtype=torch.float16):
	# # Apply RoPE (Rotary Positional Embedding)
	# q = self.rotary_embed.rotate_queries_or_keys(q)
	# k = self.rotary_embed.rotate_queries_or_keys(k)
	#
	# # Transpose the tensors to match FlashAttention's expected input shape
	# q_transposed = q.transpose(1, 2).to(dtype=torch.float16)
	# k_transposed = k.transpose(1, 2).to(dtype=torch.float16)
	# v_transposed = v.transpose(1, 2).to(dtype=torch.float16)
	#
	# if self.first:
	# print(f'{q_transposed.shape=} {k_transposed.shape=} {v_transposed.shape=} {mask.shape=}')
	#
	# # Pass the transposed and casted tensors to FlashAttention function
	# scores = flash_attn_func(q_transposed, k_transposed, v_transposed, dropout_p=self.dropout if self.training else 0, causal=True if mask is None else False)
	#
	# if self.first:
	# print(f'{q_transposed.shape=} {k_transposed.shape=} {v_transposed.shape=} {scores.shape=} {mask.shape=}')
	# self.first = False
	#
	## # You may want to keep the softmax operation in fp32 for numerical stability
	## if mask is not None:
	## scores = scores.float() # Convert back to fp32 if necessary
	## scores = scores.masked_fill(mask == 0, float('-inf'))
	#
	## if mask is not None:
	## # Ensure mask is broadcastable to the size of scores.
	## # This might involve unsqueezing dimensions or ensuring it has the right shape.
	## # For example, if mask should cover the sequence length which is the last dimension:
	## mask = mask.unsqueeze(1).unsqueeze(2) # Adding dimensions to match scores shape
	## # The mask needs to be the same dtype as scores, and usually, the mask is not in fp16.
	## # Convert mask to the same dtype as scores, if necessary
	## mask = mask.to(dtype=scores.dtype)
	## # Use broadcasting to apply the mask
	## scores = scores.masked_fill(mask == 0, float('-inf'))
	##
	## p_attn = self.softmax(scores)
	## p_attn = self.dropout(p_attn)
	##
	## # Ensure that 'v' is in the correct dtype before matmul if needed
	## v = v.to(dtype=scores.dtype)
	##
	## return torch.matmul(p_attn, v), p_attn
	# return scores, None


	class PositionwiseFeedForward(nn.Module):

	def __init__(self, d_model, hidden, drop_prob=0.1):
	super(PositionwiseFeedForward, self).__init__()
	self.linear1 = nn.Linear(d_model, hidden)
	self.linear2 = nn.Linear(hidden, d_model)
	self.relu = nn.ReLU() #inplace=True)
	self.dropout = nn.Dropout(p=drop_prob)

	def forward(self, x):
	x = self.linear1(x)
	x = self.relu(x)
	x = self.dropout(x)
	x = self.linear2(x)
	return x