# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream

aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()


# kernel path: runs/run_shard_7/inductor_cache/ls/clsu7z3tbii3kqmo7iz7pm6hucfasychej74pj2kwtsuoqblbbwp.py
# Topologically Sorted Source Nodes: [batch_norm, x], Original ATen: [aten._native_batch_norm_legit_no_training, aten.relu]
# Source node to ATen node mapping:
#   batch_norm => add_1, mul_1, mul_2, sub
#   x => relu
# Graph fragment:
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%convolution, %unsqueeze_1), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub, %unsqueeze_3), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_1, %unsqueeze_5), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_2, %unsqueeze_7), kwargs = {})
#   %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%add_1,), kwargs = {})
triton_poi_fused__native_batch_norm_legit_no_training_relu_0 = async_compile.triton('triton_poi_fused__native_batch_norm_legit_no_training_relu_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[256], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__native_batch_norm_legit_no_training_relu_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__native_batch_norm_legit_no_training_relu_0(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
    x3 = xindex
    x1 = (xindex // 16) % 4
    tmp0 = tl.load(in_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr1 + (x1), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + (x1), xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr3 + (x1), xmask, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr4 + (x1), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp4 = 1e-05
    tmp5 = tmp3 + tmp4
    tmp6 = libdevice.sqrt(tmp5)
    tmp7 = tl.full([1], 1, tl.int32)
    tmp8 = tmp7 / tmp6
    tmp9 = 1.0
    tmp10 = tmp8 * tmp9
    tmp11 = tmp2 * tmp10
    tmp13 = tmp11 * tmp12
    tmp15 = tmp13 + tmp14
    tmp16 = tl.full([1], 0, tl.int32)
    tmp17 = triton_helpers.maximum(tmp16, tmp15)
    tl.store(out_ptr0 + (x3), tmp17, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/i3/ci3ljs6u46bux5tqow3whkdh7ju5ekray7jkko7pefitlbyv2ihn.py
# Topologically Sorted Source Nodes: [x_1, x_2, x_3], Original ATen: [aten._native_batch_norm_legit_no_training, aten.add, aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
#   x_1 => add_3, mul_4, mul_5, sub_1
#   x_2 => add_4
#   x_3 => relu_1
# Graph fragment:
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%convolution_1, %unsqueeze_1), kwargs = {})
#   %mul_4 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_1, %unsqueeze_3), kwargs = {})
#   %mul_5 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_4, %unsqueeze_5), kwargs = {})
#   %add_3 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_5, %unsqueeze_7), kwargs = {})
#   %add_4 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_3, %primals_1), kwargs = {})
#   %relu_1 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%add_4,), kwargs = {})
#   %le : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_1, 0), kwargs = {})
triton_poi_fused__native_batch_norm_legit_no_training_add_relu_threshold_backward_1 = async_compile.triton('triton_poi_fused__native_batch_norm_legit_no_training_add_relu_threshold_backward_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[256], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: '*fp32', 7: '*i1', 8: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6, 7, 8), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__native_batch_norm_legit_no_training_add_relu_threshold_backward_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 6, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__native_batch_norm_legit_no_training_add_relu_threshold_backward_1(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, 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
    x3 = xindex
    x1 = (xindex // 16) % 4
    tmp0 = tl.load(in_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr1 + (x1), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + (x1), xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr3 + (x1), xmask, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr4 + (x1), xmask, eviction_policy='evict_last')
    tmp16 = tl.load(in_ptr5 + (x3), xmask)
    tmp2 = tmp0 - tmp1
    tmp4 = 1e-05
    tmp5 = tmp3 + tmp4
    tmp6 = libdevice.sqrt(tmp5)
    tmp7 = tl.full([1], 1, tl.int32)
    tmp8 = tmp7 / tmp6
    tmp9 = 1.0
    tmp10 = tmp8 * tmp9
    tmp11 = tmp2 * tmp10
    tmp13 = tmp11 * tmp12
    tmp15 = tmp13 + tmp14
    tmp17 = tmp15 + tmp16
    tmp18 = tl.full([1], 0, tl.int32)
    tmp19 = triton_helpers.maximum(tmp18, tmp17)
    tmp20 = 0.0
    tmp21 = tmp19 <= tmp20
    tl.store(out_ptr0 + (x3), tmp19, xmask)
    tl.store(out_ptr1 + (x3), tmp21, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_3, (4, ), (1, ))
    assert_size_stride(primals_4, (4, ), (1, ))
    assert_size_stride(primals_5, (4, ), (1, ))
    assert_size_stride(primals_6, (4, ), (1, ))
    assert_size_stride(primals_7, (4, 4, 3, 3), (36, 9, 3, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        # Topologically Sorted Source Nodes: [conv2d], Original ATen: [aten.convolution]
        buf0 = extern_kernels.convolution(primals_1, primals_2, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 4, 4, 4), (64, 16, 4, 1))
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [batch_norm, x], Original ATen: [aten._native_batch_norm_legit_no_training, aten.relu]
        stream0 = get_raw_stream(0)
        triton_poi_fused__native_batch_norm_legit_no_training_relu_0.run(buf0, primals_3, primals_4, primals_5, primals_6, buf1, 256, grid=grid(256), stream=stream0)
        # Topologically Sorted Source Nodes: [conv2d_1], Original ATen: [aten.convolution]
        buf2 = extern_kernels.convolution(buf1, primals_7, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (4, 4, 4, 4), (64, 16, 4, 1))
        buf3 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf4 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        # Topologically Sorted Source Nodes: [x_1, x_2, x_3], Original ATen: [aten._native_batch_norm_legit_no_training, aten.add, aten.relu, aten.threshold_backward]
        triton_poi_fused__native_batch_norm_legit_no_training_add_relu_threshold_backward_1.run(buf2, primals_3, primals_4, primals_5, primals_6, primals_1, buf3, buf4, 256, grid=grid(256), stream=stream0)
        del primals_6
    return (buf3, primals_1, primals_2, primals_3, primals_4, primals_5, primals_7, buf0, buf1, buf2, buf4, )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    primals_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, 4, 3, 3), (36, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((4, 4, 3, 3), (36, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7])
    return print_performance(fn, times=times, repeat=repeat)


if __name__ == "__main__":
    from torch._inductor.wrapper_benchmark import compiled_module_main
    compiled_module_main('None', benchmark_compiled_module)

import torch
import torch.nn as nn


class Block(nn.Module):
    expansion = 1

    def __init__(self, in_channels, out_channels, i_downsample=None, stride=1):
        super(Block, self).__init__()
        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3,
            padding=1, stride=stride, bias=False)
        self.batch_norm1 = nn.BatchNorm2d(out_channels)
        self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3,
            padding=1, stride=stride, bias=False)
        self.batch_norm2 = nn.BatchNorm2d(out_channels)
        self.i_downsample = i_downsample
        self.stride = stride
        self.relu = nn.ReLU()

    def forward(self, x):
        identity = x.clone()
        x = self.relu(self.batch_norm2(self.conv1(x)))
        x = self.batch_norm2(self.conv2(x))
        if self.i_downsample is not None:
            identity = self.i_downsample(identity)
        None
        None
        x += identity
        x = self.relu(x)
        return x


def get_inputs():
    return [torch.rand([4, 4, 4, 4])]


def get_init_inputs():
    return [[], {'in_channels': 4, 'out_channels': 4}]

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
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


@triton.jit
def triton_poi_fused__native_batch_norm_legit_no_training_relu_0(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
    x3 = xindex
    x1 = xindex // 16 % 4
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr3 + x1, xmask, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr4 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp4 = 1e-05
    tmp5 = tmp3 + tmp4
    tmp6 = libdevice.sqrt(tmp5)
    tmp7 = tl.full([1], 1, tl.int32)
    tmp8 = tmp7 / tmp6
    tmp9 = 1.0
    tmp10 = tmp8 * tmp9
    tmp11 = tmp2 * tmp10
    tmp13 = tmp11 * tmp12
    tmp15 = tmp13 + tmp14
    tmp16 = tl.full([1], 0, tl.int32)
    tmp17 = triton_helpers.maximum(tmp16, tmp15)
    tl.store(out_ptr0 + x3, tmp17, xmask)


@triton.jit
def triton_poi_fused__native_batch_norm_legit_no_training_add_relu_threshold_backward_1(
    in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, 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
    x3 = xindex
    x1 = xindex // 16 % 4
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr3 + x1, xmask, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr4 + x1, xmask, eviction_policy='evict_last')
    tmp16 = tl.load(in_ptr5 + x3, xmask)
    tmp2 = tmp0 - tmp1
    tmp4 = 1e-05
    tmp5 = tmp3 + tmp4
    tmp6 = libdevice.sqrt(tmp5)
    tmp7 = tl.full([1], 1, tl.int32)
    tmp8 = tmp7 / tmp6
    tmp9 = 1.0
    tmp10 = tmp8 * tmp9
    tmp11 = tmp2 * tmp10
    tmp13 = tmp11 * tmp12
    tmp15 = tmp13 + tmp14
    tmp17 = tmp15 + tmp16
    tmp18 = tl.full([1], 0, tl.int32)
    tmp19 = triton_helpers.maximum(tmp18, tmp17)
    tmp20 = 0.0
    tmp21 = tmp19 <= tmp20
    tl.store(out_ptr0 + x3, tmp19, xmask)
    tl.store(out_ptr1 + x3, tmp21, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_3, (4,), (1,))
    assert_size_stride(primals_4, (4,), (1,))
    assert_size_stride(primals_5, (4,), (1,))
    assert_size_stride(primals_6, (4,), (1,))
    assert_size_stride(primals_7, (4, 4, 3, 3), (36, 9, 3, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_1, primals_2, stride=(1, 
            1), padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 4, 4, 4), (64, 16, 4, 1))
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused__native_batch_norm_legit_no_training_relu_0[grid(256)
            ](buf0, primals_3, primals_4, primals_5, primals_6, buf1, 256,
            XBLOCK=128, num_warps=4, num_stages=1)
        buf2 = extern_kernels.convolution(buf1, primals_7, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (4, 4, 4, 4), (64, 16, 4, 1))
        buf3 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf4 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        triton_poi_fused__native_batch_norm_legit_no_training_add_relu_threshold_backward_1[
            grid(256)](buf2, primals_3, primals_4, primals_5, primals_6,
            primals_1, buf3, buf4, 256, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_6
    return (buf3, primals_1, primals_2, primals_3, primals_4, primals_5,
        primals_7, buf0, buf1, buf2, buf4)


class BlockNew(nn.Module):
    expansion = 1

    def __init__(self, in_channels, out_channels, i_downsample=None, stride=1):
        super(BlockNew, self).__init__()
        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3,
            padding=1, stride=stride, bias=False)
        self.batch_norm1 = nn.BatchNorm2d(out_channels)
        self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3,
            padding=1, stride=stride, bias=False)
        self.batch_norm2 = nn.BatchNorm2d(out_channels)
        self.i_downsample = i_downsample
        self.stride = stride
        self.relu = nn.ReLU()

    def forward(self, input_0):
        primals_2 = self.conv1.weight
        primals_3 = self.batch_norm1.weight
        primals_4 = self.batch_norm1.bias
        primals_7 = self.conv2.weight
        primals_5 = self.batch_norm2.weight
        primals_6 = self.batch_norm2.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7])
        return output[0]
