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
import numpy as np
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_convolution_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
    tl.store(in_out_ptr0 + x3, tmp2, xmask)


@triton.jit
def triton_poi_fused__softmax_1(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)
    tmp3 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last'
        )
    tmp1 = 1.0
    tmp2 = tmp0 * tmp1
    tmp4 = tmp3 * tmp1
    tmp6 = tmp5 * tmp1
    tmp7 = triton_helpers.maximum(tmp4, tmp6)
    tmp9 = tmp8 * tmp1
    tmp10 = triton_helpers.maximum(tmp7, tmp9)
    tmp12 = tmp11 * tmp1
    tmp13 = triton_helpers.maximum(tmp10, tmp12)
    tmp14 = tmp2 - tmp13
    tmp15 = tmp14 * tmp1
    tmp16 = tl_math.exp(tmp15)
    tl.store(out_ptr0 + x2, tmp16, 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 = 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 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_convolution_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_0[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_1[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_2[grid(256)](buf6, buf7, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        del buf6
        buf8 = buf2
        del buf2
        triton_poi_fused_convolution_0[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_0[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, 4, 1), 0),
        reinterpret_tensor(buf4, (16, 4, 1), (4, 1, 4), 0))


def convert_pad_shape(pad_shape):
    """Reverse, then flatten a list of lists."""
    l = pad_shape[::-1]
    pad_shape = [item for sublist in l for item in sublist]
    return pad_shape


class BaseModule(torch.nn.Module):

    def __init__(self):
        super(BaseModule, self).__init__()

    @property
    def nparams(self):
        """
        Returns number of trainable parameters of the module.
        """
        num_params = 0
        for name, param in self.named_parameters():
            if param.requires_grad:
                num_params += np.prod(param.detach().cpu().numpy().shape)
        return num_params

    def relocate_input(self, x: 'list'):
        """
        Relocates provided tensors to the same device set for the module.
        """
        device = next(self.parameters()).device
        for i in range(len(x)):
            if isinstance(x[i], torch.Tensor) and x[i].device != device:
                x[i] = x[i]
        return x


class MultiHeadAttentionNew(BaseModule):

    def __init__(self, channels, out_channels, n_heads, window_size=None,
        heads_share=True, p_dropout=0.0, proximal_bias=False, proximal_init
        =False):
        super(MultiHeadAttentionNew, self).__init__()
        assert channels % n_heads == 0
        self.channels = channels
        self.out_channels = out_channels
        self.n_heads = n_heads
        self.window_size = window_size
        self.heads_share = heads_share
        self.proximal_bias = proximal_bias
        self.p_dropout = p_dropout
        self.attn = None
        self.k_channels = channels // n_heads
        self.conv_q = torch.nn.Conv1d(channels, channels, 1)
        self.conv_k = torch.nn.Conv1d(channels, channels, 1)
        self.conv_v = torch.nn.Conv1d(channels, channels, 1)
        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 = torch.nn.Parameter(torch.randn(n_heads_rel, 
                window_size * 2 + 1, self.k_channels) * rel_stddev)
            self.emb_rel_v = torch.nn.Parameter(torch.randn(n_heads_rel, 
                window_size * 2 + 1, self.k_channels) * rel_stddev)
        self.conv_o = torch.nn.Conv1d(channels, out_channels, 1)
        self.drop = torch.nn.Dropout(p_dropout)
        torch.nn.init.xavier_uniform_(self.conv_q.weight)
        torch.nn.init.xavier_uniform_(self.conv_k.weight)
        if proximal_init:
            self.conv_k.weight.data.copy_(self.conv_q.weight.data)
            self.conv_k.bias.data.copy_(self.conv_q.bias.data)
        torch.nn.init.xavier_uniform_(self.conv_v.weight)

    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, key.transpose(-2, -1)) / math.sqrt(self
            .k_channels)
        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,
                key_relative_embeddings)
            rel_logits = self._relative_position_to_absolute_position(
                rel_logits)
            scores_local = rel_logits / math.sqrt(self.k_channels)
            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)
        p_attn = torch.nn.functional.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):
        ret = torch.matmul(x, y.unsqueeze(0))
        return ret

    def _matmul_with_relative_keys(self, x, y):
        ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1))
        return ret

    def _get_relative_embeddings(self, relative_embeddings, length):
        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 = torch.nn.functional.pad(
                relative_embeddings, 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):
        batch, heads, length, _ = x.size()
        x = torch.nn.functional.pad(x, convert_pad_shape([[0, 0], [0, 0], [
            0, 0], [0, 1]]))
        x_flat = x.view([batch, heads, length * 2 * length])
        x_flat = torch.nn.functional.pad(x_flat, 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):
        batch, heads, length, _ = x.size()
        x = torch.nn.functional.pad(x, 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 = torch.nn.functional.pad(x_flat, 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):
        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: 0.029236