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 libdevice, math as tl_math
import torch.nn as 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_relu_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 128
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 32
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + x2, tmp4, xmask)


@triton.jit
def triton_poi_fused_relu_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
    x2 = xindex
    x0 = xindex % 16
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + x2, tmp4, xmask)


@triton.jit
def triton_poi_fused__softmax_2(in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + 7 * x0, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 7 * x0), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (2 + 7 * x0), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (3 + 7 * x0), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr0 + (4 + 7 * x0), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr0 + (5 + 7 * x0), xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr0 + (6 + 7 * x0), xmask, eviction_policy='evict_last'
        )
    tmp2 = triton_helpers.maximum(tmp0, tmp1)
    tmp4 = triton_helpers.maximum(tmp2, tmp3)
    tmp6 = triton_helpers.maximum(tmp4, tmp5)
    tmp8 = triton_helpers.maximum(tmp6, tmp7)
    tmp10 = triton_helpers.maximum(tmp8, tmp9)
    tmp12 = triton_helpers.maximum(tmp10, tmp11)
    tmp13 = tmp0 - tmp12
    tmp14 = tl_math.exp(tmp13)
    tmp15 = tmp1 - tmp12
    tmp16 = tl_math.exp(tmp15)
    tmp17 = tmp14 + tmp16
    tmp18 = tmp3 - tmp12
    tmp19 = tl_math.exp(tmp18)
    tmp20 = tmp17 + tmp19
    tmp21 = tmp5 - tmp12
    tmp22 = tl_math.exp(tmp21)
    tmp23 = tmp20 + tmp22
    tmp24 = tmp7 - tmp12
    tmp25 = tl_math.exp(tmp24)
    tmp26 = tmp23 + tmp25
    tmp27 = tmp9 - tmp12
    tmp28 = tl_math.exp(tmp27)
    tmp29 = tmp26 + tmp28
    tmp30 = tmp11 - tmp12
    tmp31 = tl_math.exp(tmp30)
    tmp32 = tmp29 + tmp31
    tl.store(out_ptr0 + x0, tmp12, xmask)
    tl.store(out_ptr1 + x0, tmp32, xmask)


@triton.jit
def triton_poi_fused__softmax_3(in_out_ptr0, in_ptr0, in_ptr1, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 28
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 7
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp3 = tl_math.exp(tmp2)
    tmp5 = tmp3 / tmp4
    tl.store(in_out_ptr0 + x2, tmp5, xmask)


@triton.jit
def triton_poi_fused_tanh_4(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + x0, xmask)
    tmp1 = tl.load(in_ptr0 + 0)
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp3 = tmp0 + tmp2
    tmp4 = libdevice.tanh(tmp3)
    tl.store(in_out_ptr0 + x0, tmp4, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9) = args
    args.clear()
    assert_size_stride(primals_1, (4, 42), (42, 1))
    assert_size_stride(primals_2, (32, 42), (42, 1))
    assert_size_stride(primals_3, (32,), (1,))
    assert_size_stride(primals_4, (16, 32), (32, 1))
    assert_size_stride(primals_5, (16,), (1,))
    assert_size_stride(primals_6, (7, 16), (16, 1))
    assert_size_stride(primals_7, (7,), (1,))
    assert_size_stride(primals_8, (1, 16), (16, 1))
    assert_size_stride(primals_9, (1,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 32), (32, 1), torch.float32)
        extern_kernels.mm(primals_1, reinterpret_tensor(primals_2, (42, 32),
            (1, 42), 0), out=buf0)
        del primals_2
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_relu_0[grid(128)](buf1, primals_3, 128, XBLOCK=128,
            num_warps=4, num_stages=1)
        del primals_3
        buf2 = empty_strided_cuda((4, 16), (16, 1), torch.float32)
        extern_kernels.mm(buf1, reinterpret_tensor(primals_4, (32, 16), (1,
            32), 0), out=buf2)
        buf3 = buf2
        del buf2
        triton_poi_fused_relu_1[grid(64)](buf3, primals_5, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        del primals_5
        buf4 = empty_strided_cuda((4, 7), (7, 1), torch.float32)
        extern_kernels.addmm(primals_7, buf3, reinterpret_tensor(primals_6,
            (16, 7), (1, 16), 0), alpha=1, beta=1, out=buf4)
        del primals_7
        buf5 = empty_strided_cuda((4, 1), (1, 4), torch.float32)
        buf6 = empty_strided_cuda((4, 1), (1, 4), torch.float32)
        triton_poi_fused__softmax_2[grid(4)](buf4, buf5, buf6, 4, XBLOCK=4,
            num_warps=1, num_stages=1)
        buf7 = buf4
        del buf4
        triton_poi_fused__softmax_3[grid(28)](buf7, buf5, buf6, 28, XBLOCK=
            32, num_warps=1, num_stages=1)
        del buf5
        buf8 = reinterpret_tensor(buf6, (4, 1), (1, 1), 0)
        del buf6
        extern_kernels.mm(buf3, reinterpret_tensor(primals_8, (16, 1), (1, 
            16), 0), out=buf8)
        buf9 = buf8
        del buf8
        triton_poi_fused_tanh_4[grid(4)](buf9, primals_9, 4, XBLOCK=4,
            num_warps=1, num_stages=1)
        del primals_9
    return (buf7, buf9, primals_1, buf1, buf3, buf7, buf9, primals_8,
        primals_6, primals_4)


class DenseNew(nn.Module):

    def __init__(self):
        super(DenseNew, self).__init__()
        self.fc1 = nn.Linear(6 * 7, 32)
        self.fc2 = nn.Linear(32, 16)
        self.probhead = nn.Linear(16, 7)
        self.valuehead = nn.Linear(16, 1)
        self.soft = nn.Softmax(dim=1)
        self.tanh = nn.Tanh()

    def forward(self, input_0):
        primals_2 = self.fc1.weight
        primals_3 = self.fc1.bias
        primals_4 = self.fc2.weight
        primals_5 = self.fc2.bias
        primals_6 = self.probhead.weight
        primals_7 = self.probhead.bias
        primals_8 = self.valuehead.weight
        primals_9 = self.valuehead.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])
        return output[0], output[1]


Measured latency_ms: 0.091837