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.triton_helpers import libdevice
import torch.nn.functional as F
from torch import nn
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_native_layer_norm_0(in_ptr0, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp7 = 4.0
    tmp8 = tmp6 / tmp7
    tmp9 = tmp0 - tmp8
    tmp10 = tmp9 * tmp9
    tmp11 = tmp1 - tmp8
    tmp12 = tmp11 * tmp11
    tmp13 = tmp10 + tmp12
    tmp14 = tmp3 - tmp8
    tmp15 = tmp14 * tmp14
    tmp16 = tmp13 + tmp15
    tmp17 = tmp5 - tmp8
    tmp18 = tmp17 * tmp17
    tmp19 = tmp16 + tmp18
    tmp20 = tmp19 / tmp7
    tmp21 = 1e-05
    tmp22 = tmp20 + tmp21
    tmp23 = libdevice.rsqrt(tmp22)
    tl.store(out_ptr0 + x0, tmp8, xmask)
    tl.store(out_ptr1 + x0, tmp23, xmask)


@triton.jit
def triton_poi_fused_clone_1(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x4 = xindex
    x5 = xindex // 4
    x0 = xindex % 4
    x1 = xindex // 4 % 4
    x2 = xindex // 16 % 4
    x3 = xindex // 64
    tmp0 = tl.load(in_ptr0 + x4, xmask)
    tmp1 = tl.load(in_ptr1 + x5, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + x5, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr3 + x0, xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr4 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp4 = tmp2 * tmp3
    tmp6 = tmp4 * tmp5
    tmp8 = tmp6 + tmp7
    tl.store(out_ptr0 + (x0 + 4 * x2 + 16 * x1 + 64 * x3), tmp8, xmask)


@triton.jit
def triton_poi_fused_add_gelu_2(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 16
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.5
    tmp4 = tmp2 * tmp3
    tmp5 = 0.7071067811865476
    tmp6 = tmp2 * tmp5
    tmp7 = libdevice.erf(tmp6)
    tmp8 = 1.0
    tmp9 = tmp7 + tmp8
    tmp10 = tmp4 * tmp9
    tl.store(out_ptr0 + x2, tmp10, xmask)


@triton.jit
def triton_poi_fused_add_native_layer_norm_3(in_ptr0, in_ptr1, out_ptr0,
    out_ptr1, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = xindex // 4 % 4
    x2 = xindex // 16
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (4 * x1 + 16 * x0 + 64 * x2), xmask,
        eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + 4 * x3, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (1 + 4 * x1 + 16 * x0 + 64 * x2), xmask,
        eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr1 + (1 + 4 * x3), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr0 + (2 + 4 * x1 + 16 * x0 + 64 * x2), xmask,
        eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr1 + (2 + 4 * x3), xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr0 + (3 + 4 * x1 + 16 * x0 + 64 * x2), xmask,
        eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr1 + (3 + 4 * x3), xmask, eviction_policy='evict_last'
        )
    tmp2 = tmp0 + tmp1
    tmp5 = tmp3 + tmp4
    tmp6 = tmp2 + tmp5
    tmp9 = tmp7 + tmp8
    tmp10 = tmp6 + tmp9
    tmp13 = tmp11 + tmp12
    tmp14 = tmp10 + tmp13
    tmp15 = 4.0
    tmp16 = tmp14 / tmp15
    tmp17 = tmp2 - tmp16
    tmp18 = tmp17 * tmp17
    tmp19 = tmp5 - tmp16
    tmp20 = tmp19 * tmp19
    tmp21 = tmp18 + tmp20
    tmp22 = tmp9 - tmp16
    tmp23 = tmp22 * tmp22
    tmp24 = tmp21 + tmp23
    tmp25 = tmp13 - tmp16
    tmp26 = tmp25 * tmp25
    tmp27 = tmp24 + tmp26
    tmp28 = tmp27 / tmp15
    tl.store(out_ptr0 + x3, tmp16, xmask)
    tl.store(out_ptr1 + x3, tmp28, xmask)


@triton.jit
def triton_poi_fused_add_native_layer_norm_4(in_ptr0, in_ptr1, in_ptr2,
    in_ptr3, in_ptr4, in_ptr5, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = xindex // 4 % 4
    x2 = xindex // 16 % 4
    x3 = xindex // 64
    x4 = xindex
    x5 = xindex // 4
    tmp0 = tl.load(in_ptr0 + (x0 + 4 * x2 + 16 * x1 + 64 * x3), xmask)
    tmp1 = tl.load(in_ptr1 + x4, xmask)
    tmp3 = tl.load(in_ptr2 + x5, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr3 + x5, xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr4 + x0, xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr5 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 - tmp3
    tmp6 = 1e-05
    tmp7 = tmp5 + tmp6
    tmp8 = libdevice.rsqrt(tmp7)
    tmp9 = tmp4 * tmp8
    tmp11 = tmp9 * tmp10
    tmp13 = tmp11 + tmp12
    tl.store(out_ptr0 + x4, tmp13, xmask)


@triton.jit
def triton_poi_fused_gelu_5(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = 0.5
    tmp2 = tmp0 * tmp1
    tmp3 = 0.7071067811865476
    tmp4 = tmp0 * tmp3
    tmp5 = libdevice.erf(tmp4)
    tmp6 = 1.0
    tmp7 = tmp5 + tmp6
    tmp8 = tmp2 * tmp7
    tl.store(out_ptr0 + x0, tmp8, xmask)


@triton.jit
def triton_poi_fused_add_6(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x4 = xindex
    x0 = xindex % 4
    x1 = xindex // 4 % 4
    x2 = xindex // 16 % 4
    x3 = xindex // 64
    tmp0 = tl.load(in_out_ptr0 + x4, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + (x0 + 4 * x2 + 16 * x1 + 64 * x3), xmask)
    tmp4 = tl.load(in_ptr2 + x4, xmask)
    tmp2 = tmp0 + tmp1
    tmp5 = tmp3 + tmp4
    tmp6 = tmp2 + tmp5
    tl.store(in_out_ptr0 + x4, tmp6, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9, primals_10, primals_11, primals_12,
        primals_13) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4,), (1,))
    assert_size_stride(primals_3, (4,), (1,))
    assert_size_stride(primals_4, (16, 4), (4, 1))
    assert_size_stride(primals_5, (16,), (1,))
    assert_size_stride(primals_6, (4, 16), (16, 1))
    assert_size_stride(primals_7, (4,), (1,))
    assert_size_stride(primals_8, (4,), (1,))
    assert_size_stride(primals_9, (4,), (1,))
    assert_size_stride(primals_10, (16, 4), (4, 1))
    assert_size_stride(primals_11, (16,), (1,))
    assert_size_stride(primals_12, (4, 16), (16, 1))
    assert_size_stride(primals_13, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
        buf1 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_native_layer_norm_0[grid(64)](primals_1, buf0,
            buf1, 64, XBLOCK=64, num_warps=1, num_stages=1)
        buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_clone_1[grid(256)](primals_1, buf0, buf1,
            primals_2, primals_3, buf2, 256, XBLOCK=128, num_warps=4,
            num_stages=1)
        del primals_2
        del primals_3
        buf3 = empty_strided_cuda((64, 16), (16, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf2, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_4, (4, 16), (1, 4), 0), out=buf3)
        buf4 = empty_strided_cuda((4, 4, 4, 16), (256, 64, 16, 1), torch.
            float32)
        triton_poi_fused_add_gelu_2[grid(1024)](buf3, primals_5, buf4, 1024,
            XBLOCK=256, num_warps=4, num_stages=1)
        buf5 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_7, reinterpret_tensor(buf4, (64, 16),
            (16, 1), 0), reinterpret_tensor(primals_6, (16, 4), (1, 16), 0),
            alpha=1, beta=1, out=buf5)
        del primals_7
        buf6 = buf1
        del buf1
        buf7 = buf0
        del buf0
        triton_poi_fused_add_native_layer_norm_3[grid(64)](buf5, primals_1,
            buf6, buf7, 64, XBLOCK=64, num_warps=1, num_stages=1)
        buf8 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_add_native_layer_norm_4[grid(256)](buf5, primals_1,
            buf6, buf7, primals_8, primals_9, buf8, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        del buf6
        del buf7
        del primals_9
        buf9 = empty_strided_cuda((64, 16), (16, 1), torch.float32)
        extern_kernels.addmm(primals_11, reinterpret_tensor(buf8, (64, 4),
            (4, 1), 0), reinterpret_tensor(primals_10, (4, 16), (1, 4), 0),
            alpha=1, beta=1, out=buf9)
        del primals_11
        buf10 = empty_strided_cuda((4, 4, 4, 16), (256, 64, 16, 1), torch.
            float32)
        triton_poi_fused_gelu_5[grid(1024)](buf9, buf10, 1024, XBLOCK=256,
            num_warps=4, num_stages=1)
        buf11 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf10, (64, 16), (16, 1), 0),
            reinterpret_tensor(primals_12, (16, 4), (1, 16), 0), out=buf11)
        buf12 = reinterpret_tensor(buf11, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf11
        triton_poi_fused_add_6[grid(256)](buf12, primals_13, buf5,
            primals_1, 256, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_13
    return buf12, primals_1, primals_5, primals_8, reinterpret_tensor(buf2,
        (64, 4), (4, 1), 0), buf3, reinterpret_tensor(buf4, (64, 16), (16, 
        1), 0), buf5, reinterpret_tensor(buf8, (64, 4), (4, 1), 0
        ), buf9, reinterpret_tensor(buf10, (64, 16), (16, 1), 0
        ), primals_12, primals_10, primals_6, primals_4


class FeedForward(nn.Module):

    def __init__(self, num_features, expansion_factor, dropout):
        super().__init__()
        num_hidden = expansion_factor * num_features
        self.fc1 = nn.Linear(num_features, num_hidden)
        self.fc2 = nn.Linear(num_hidden, num_features)
        self.dropout1 = nn.Dropout(dropout)
        self.dropout2 = nn.Dropout(dropout)

    def forward(self, x):
        x = self.dropout1(F.gelu(self.fc1(x)))
        x = self.dropout2(self.fc2(x))
        return x


class ChannelMixer(nn.Module):

    def __init__(self, d_model, expansion_factor, dropout):
        super().__init__()
        self.norm = nn.LayerNorm(d_model)
        self.mlp = FeedForward(d_model, expansion_factor, dropout)

    def forward(self, x):
        residual = x
        x = self.norm(x)
        x = self.mlp(x)
        out = x + residual
        return out


class TokenMixer(nn.Module):

    def __init__(self, d_model, seq_len, expansion_factor, dropout):
        super().__init__()
        self.norm = nn.LayerNorm(d_model)
        self.mlp = FeedForward(seq_len, expansion_factor, dropout)

    def forward(self, x):
        residual = x
        x = self.norm(x)
        x = x.transpose(1, 2)
        x = self.mlp(x)
        x = x.transpose(1, 2)
        out = x + residual
        return out


class MixerBlockNew(nn.Module):

    def __init__(self, d_model, seq_len, expansion_factor, dropout):
        super().__init__()
        self.token_mixer = TokenMixer(d_model, seq_len, expansion_factor,
            dropout)
        self.channel_mixer = ChannelMixer(d_model, expansion_factor, dropout)

    def forward(self, input_0):
        primals_2 = self.token_mixer.norm.weight
        primals_3 = self.token_mixer.norm.bias
        primals_4 = self.token_mixer.mlp.fc1.weight
        primals_5 = self.token_mixer.mlp.fc1.bias
        primals_6 = self.token_mixer.mlp.fc2.weight
        primals_7 = self.token_mixer.mlp.fc2.bias
        primals_8 = self.channel_mixer.norm.weight
        primals_9 = self.channel_mixer.norm.bias
        primals_10 = self.channel_mixer.mlp.fc1.weight
        primals_11 = self.channel_mixer.mlp.fc1.bias
        primals_12 = self.channel_mixer.mlp.fc2.weight
        primals_13 = self.channel_mixer.mlp.fc2.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9,
            primals_10, primals_11, primals_12, primals_13])
        return output[0]


Measured latency_ms: 0.076676