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
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_add_relu_threshold_backward_0(in_ptr0, in_ptr1,
    out_ptr0, out_ptr1, 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
    x3 = xindex % 16
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 4 * x2 + 64 * x1), xmask)
    tmp1 = tl.load(in_ptr1 + x3, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(out_ptr0 + x4, tmp4, xmask)
    tl.store(out_ptr1 + (x0 + 4 * x2 + 64 * x1), tmp6, xmask)


@triton.jit
def triton_poi_fused_clone_1(in_ptr0, in_ptr1, out_ptr0, ynumel, xnumel,
    YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
    ynumel = 4
    xnumel = 64
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x3 = xindex // 16
    x4 = xindex % 16
    y0 = yindex
    x2 = xindex // 4 % 4
    x5 = xindex
    tmp0 = tl.load(in_ptr0 + (y0 + 4 * x3 + 16 * x4), xmask & ymask,
        eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (y0 + 4 * x2), xmask & ymask, eviction_policy=
        'evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(out_ptr0 + (x5 + 64 * y0), tmp2, xmask & ymask)


@triton.jit
def triton_poi_fused_transpose_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
    x0 = xindex % 4
    x1 = xindex // 4 % 16
    x2 = xindex // 64
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 4 * x2 + 16 * x1), xmask)
    tl.store(out_ptr0 + x3, tmp0, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_3, (4, 4), (4, 1))
    assert_size_stride(primals_4, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_5, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 16, 4), (64, 4, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(primals_1, (4, 16, 4), (4, 16,
            1), 0), primals_2, out=buf0)
        del primals_2
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf5 = empty_strided_cuda((4, 4, 4, 4), (16, 4, 64, 1), torch.bool)
        get_raw_stream(0)
        triton_poi_fused_add_relu_threshold_backward_0[grid(256)](buf0,
            primals_3, buf1, buf5, 256, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_3
        buf2 = buf0
        del buf0
        extern_kernels.bmm(reinterpret_tensor(buf1, (4, 16, 4), (4, 16, 1),
            0), primals_4, out=buf2)
        buf3 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_clone_1[grid(4, 64)](buf2, primals_5, buf3, 4, 64,
            XBLOCK=32, YBLOCK=4, num_warps=4, num_stages=1)
        del primals_5
        buf4 = reinterpret_tensor(buf2, (4, 4, 16), (64, 1, 4), 0)
        del buf2
        triton_poi_fused_transpose_2[grid(256)](buf1, buf4, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        del buf1
    return reinterpret_tensor(buf3, (16, 4, 4), (1, 16, 64), 0
        ), buf4, reinterpret_tensor(primals_4, (4, 4, 4), (16, 1, 4), 0
        ), buf5, reinterpret_tensor(primals_1, (4, 4, 16), (4, 1, 16), 0)


class DenseSynthesizerNew(nn.Module):

    def __init__(self, head_dim, n_heads, n_tokens, big=True):
        super().__init__()
        h = max(head_dim, n_tokens) if big else min(head_dim, n_tokens)
        w1 = torch.empty(n_heads, head_dim, h)
        b1 = torch.empty(n_heads, h)
        w2 = torch.empty(n_heads, h, n_tokens)
        b2 = torch.empty(n_heads, n_tokens)
        nn.init.kaiming_uniform_(w1)
        nn.init.kaiming_uniform_(w2)
        nn.init.zeros_(b1)
        nn.init.zeros_(b2)
        self.register_parameter('w1', nn.Parameter(w1))
        self.register_parameter('b1', nn.Parameter(b1))
        self.register_parameter('w2', nn.Parameter(w2))
        self.register_parameter('b2', nn.Parameter(b2))
        self.activation = nn.ReLU()

    def forward(self, input_0):
        primals_2 = self.w1
        primals_3 = self.b1
        primals_4 = self.w2
        primals_5 = self.b2
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
        return output[0]


Measured latency_ms: 0.060169