import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import math
from torch import nn
from torch.nn import functional as F
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_poi_fused_div_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 4 % 4
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 1.0
    tmp4 = tmp2 * tmp3
    tl.store(in_out_ptr0 + x3, tmp4, xmask)


@triton.jit
def triton_poi_fused_convolution_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 4 % 4
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x3, tmp2, xmask)


@triton.jit
def triton_poi_fused__softmax_2(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp3 = triton_helpers.maximum(tmp1, tmp2)
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp0 - tmp7
    tmp9 = tl_math.exp(tmp8)
    tl.store(out_ptr0 + x2, tmp9, xmask)


@triton.jit
def triton_poi_fused__softmax_3(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 + tmp6
    tmp8 = tmp0 / tmp7
    tl.store(out_ptr0 + x2, tmp8, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9, primals_10) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 1), (4, 1, 1))
    assert_size_stride(primals_2, (4,), (1,))
    assert_size_stride(primals_3, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_4, (4, 4, 1), (4, 1, 1))
    assert_size_stride(primals_5, (4,), (1,))
    assert_size_stride(primals_6, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_7, (4, 4, 1), (4, 1, 1))
    assert_size_stride(primals_8, (4,), (1,))
    assert_size_stride(primals_9, (4, 4, 1), (4, 1, 1))
    assert_size_stride(primals_10, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1,),
            padding=(0,), dilation=(1,), transposed=False, output_padding=(
            0,), groups=1, bias=None)
        assert_size_stride(buf0, (4, 4, 4), (16, 4, 1))
        buf1 = extern_kernels.convolution(primals_6, primals_4, stride=(1,),
            padding=(0,), dilation=(1,), transposed=False, output_padding=(
            0,), groups=1, bias=None)
        assert_size_stride(buf1, (4, 4, 4), (16, 4, 1))
        buf2 = extern_kernels.convolution(primals_6, primals_7, stride=(1,),
            padding=(0,), dilation=(1,), transposed=False, output_padding=(
            0,), groups=1, bias=None)
        assert_size_stride(buf2, (4, 4, 4), (16, 4, 1))
        buf3 = reinterpret_tensor(buf0, (4, 4, 4, 1), (16, 4, 1, 1), 0)
        del buf0
        get_raw_stream(0)
        triton_poi_fused_div_0[grid(64)](buf3, primals_2, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        del primals_2
        buf4 = buf1
        del buf1
        triton_poi_fused_convolution_1[grid(64)](buf4, primals_5, 64,
            XBLOCK=64, num_warps=1, num_stages=1)
        del primals_5
        buf5 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf3, (16, 4, 1), (4, 1, 0), 
            0), reinterpret_tensor(buf4, (16, 1, 4), (4, 0, 1), 0), out=buf5)
        buf6 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused__softmax_2[grid(256)](buf5, buf6, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        buf7 = reinterpret_tensor(buf5, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf5
        triton_poi_fused__softmax_3[grid(256)](buf6, buf7, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        del buf6
        buf8 = buf2
        del buf2
        triton_poi_fused_convolution_1[grid(64)](buf8, primals_8, 64,
            XBLOCK=64, num_warps=1, num_stages=1)
        del primals_8
        buf9 = empty_strided_cuda((16, 4, 1), (4, 1, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf7, (16, 4, 4), (16, 4, 1),
            0), reinterpret_tensor(buf8, (16, 4, 1), (4, 1, 0), 0), out=buf9)
        buf10 = extern_kernels.convolution(reinterpret_tensor(buf9, (4, 4, 
            4), (16, 4, 1), 0), primals_9, stride=(1,), padding=(0,),
            dilation=(1,), transposed=False, output_padding=(0,), groups=1,
            bias=None)
        assert_size_stride(buf10, (4, 4, 4), (16, 4, 1))
        buf11 = buf10
        del buf10
        triton_poi_fused_convolution_1[grid(64)](buf11, primals_10, 64,
            XBLOCK=64, num_warps=1, num_stages=1)
        del primals_10
    return (buf11, buf7, primals_1, primals_3, primals_4, primals_6,
        primals_7, primals_9, buf7, reinterpret_tensor(buf9, (4, 4, 4), (16,
        4, 1), 0), reinterpret_tensor(buf8, (16, 1, 4), (4, 4, 1), 0),
        reinterpret_tensor(buf3, (16, 1, 4), (4, 1, 1), 0),
        reinterpret_tensor(buf4, (16, 4, 1), (4, 1, 4), 0))


class MultiHeadAttentionNew(nn.Module):

    def __init__(self, channels, out_channels, n_heads, p_dropout=0.0,
        window_size=None, heads_share=True, block_length=None,
        proximal_bias=False, proximal_init=False):
        super().__init__()
        assert channels % n_heads == 0
        self.channels = channels
        self.out_channels = out_channels
        self.n_heads = n_heads
        self.p_dropout = p_dropout
        self.window_size = window_size
        self.heads_share = heads_share
        self.block_length = block_length
        self.proximal_bias = proximal_bias
        self.proximal_init = proximal_init
        self.attn = None
        self.k_channels = channels // n_heads
        self.conv_q = nn.Conv1d(channels, channels, 1)
        self.conv_k = nn.Conv1d(channels, channels, 1)
        self.conv_v = nn.Conv1d(channels, channels, 1)
        self.conv_o = nn.Conv1d(channels, out_channels, 1)
        self.drop = nn.Dropout(p_dropout)
        if window_size is not None:
            n_heads_rel = 1 if heads_share else n_heads
            rel_stddev = self.k_channels ** -0.5
            self.emb_rel_k = nn.Parameter(torch.randn(n_heads_rel, 
                window_size * 2 + 1, self.k_channels) * rel_stddev)
            self.emb_rel_v = nn.Parameter(torch.randn(n_heads_rel, 
                window_size * 2 + 1, self.k_channels) * rel_stddev)
        nn.init.xavier_uniform_(self.conv_q.weight)
        nn.init.xavier_uniform_(self.conv_k.weight)
        nn.init.xavier_uniform_(self.conv_v.weight)
        if proximal_init:
            with torch.no_grad():
                self.conv_k.weight.copy_(self.conv_q.weight)
                self.conv_k.bias.copy_(self.conv_q.bias)

    def attention(self, query, key, value, mask=None):
        b, d, t_s, t_t = *key.size(), query.size(2)
        query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(
            2, 3)
        key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
        value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(
            2, 3)
        scores = torch.matmul(query / math.sqrt(self.k_channels), key.
            transpose(-2, -1))
        if self.window_size is not None:
            assert t_s == t_t, 'Relative attention is only available for self-attention.'
            key_relative_embeddings = self._get_relative_embeddings(self.
                emb_rel_k, t_s)
            rel_logits = self._matmul_with_relative_keys(query / math.sqrt(
                self.k_channels), key_relative_embeddings)
            scores_local = self._relative_position_to_absolute_position(
                rel_logits)
            scores = scores + scores_local
        if self.proximal_bias:
            assert t_s == t_t, 'Proximal bias is only available for self-attention.'
            scores = scores + self._attention_bias_proximal(t_s)
        if mask is not None:
            scores = scores.masked_fill(mask == 0, -10000.0)
            if self.block_length is not None:
                assert t_s == t_t, 'Local attention is only available for self-attention.'
                block_mask = torch.ones_like(scores).triu(-self.block_length
                    ).tril(self.block_length)
                scores = scores.masked_fill(block_mask == 0, -10000.0)
        p_attn = F.softmax(scores, dim=-1)
        p_attn = self.drop(p_attn)
        output = torch.matmul(p_attn, value)
        if self.window_size is not None:
            relative_weights = self._absolute_position_to_relative_position(
                p_attn)
            value_relative_embeddings = self._get_relative_embeddings(self.
                emb_rel_v, t_s)
            output = output + self._matmul_with_relative_values(
                relative_weights, value_relative_embeddings)
        output = output.transpose(2, 3).contiguous().view(b, d, t_t)
        return output, p_attn

    def _matmul_with_relative_values(self, x, y):
        """
    x: [b, h, l, m]
    y: [h or 1, m, d]
    ret: [b, h, l, d]
    """
        ret = torch.matmul(x, y.unsqueeze(0))
        return ret

    def _matmul_with_relative_keys(self, x, y):
        """
    x: [b, h, l, d]
    y: [h or 1, m, d]
    ret: [b, h, l, m]
    """
        ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1))
        return ret

    def _get_relative_embeddings(self, relative_embeddings, length):
        2 * self.window_size + 1
        pad_length = max(length - (self.window_size + 1), 0)
        slice_start_position = max(self.window_size + 1 - length, 0)
        slice_end_position = slice_start_position + 2 * length - 1
        if pad_length > 0:
            padded_relative_embeddings = F.pad(relative_embeddings, commons
                .convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]]))
        else:
            padded_relative_embeddings = relative_embeddings
        used_relative_embeddings = padded_relative_embeddings[:,
            slice_start_position:slice_end_position]
        return used_relative_embeddings

    def _relative_position_to_absolute_position(self, x):
        """
    x: [b, h, l, 2*l-1]
    ret: [b, h, l, l]
    """
        batch, heads, length, _ = x.size()
        x = F.pad(x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0,
            1]]))
        x_flat = x.view([batch, heads, length * 2 * length])
        x_flat = F.pad(x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [
            0, length - 1]]))
        x_final = x_flat.view([batch, heads, length + 1, 2 * length - 1])[:,
            :, :length, length - 1:]
        return x_final

    def _absolute_position_to_relative_position(self, x):
        """
    x: [b, h, l, l]
    ret: [b, h, l, 2*l-1]
    """
        batch, heads, length, _ = x.size()
        x = F.pad(x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0,
            length - 1]]))
        x_flat = x.view([batch, heads, length ** 2 + length * (length - 1)])
        x_flat = F.pad(x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [
            length, 0]]))
        x_final = x_flat.view([batch, heads, length, 2 * length])[:, :, :, 1:]
        return x_final

    def _attention_bias_proximal(self, length):
        """Bias for self-attention to encourage attention to close positions.
    Args:
      length: an integer scalar.
    Returns:
      a Tensor with shape [1, 1, length, length]
    """
        r = torch.arange(length, dtype=torch.float32)
        diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1)
        return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)
            ), 0), 0)

    def forward(self, input_0, input_1):
        primals_1 = self.conv_q.weight
        primals_2 = self.conv_q.bias
        primals_4 = self.conv_k.weight
        primals_5 = self.conv_k.bias
        primals_7 = self.conv_v.weight
        primals_8 = self.conv_v.bias
        primals_9 = self.conv_o.weight
        primals_10 = self.conv_o.bias
        primals_3 = input_0
        primals_6 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9, primals_10])
        return output[0]


Measured latency_ms: 0.032891