# 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_4/inductor_cache/zo/czobpmlyr5atbcpsuque6vcmk7nafmb3smtbzoqilz46drm7zbkm.py
# Topologically Sorted Source Nodes: [conv2d], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   conv2d => convolution
# Graph fragment:
#   %convolution : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_3, %primals_1, %primals_2, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
triton_poi_fused_convolution_0 = async_compile.triton('triton_poi_fused_convolution_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=[64], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, '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_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
    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
    tl.store(in_out_ptr0 + (x0), tmp3, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/3k/c3kqucdvk4o4zpjqokxiye7xneshc77mutxurbuxezpwyz5n5xoj.py
# Topologically Sorted Source Nodes: [tanh, gate, gate_1, x, output], Original ATen: [aten.tanh, aten.clamp, aten.view, aten.mul]
# Source node to ATen node mapping:
#   gate => clamp_min
#   gate_1 => view_1
#   output => view_2
#   tanh => tanh
#   x => mul
# Graph fragment:
#   %tanh : [num_users=1] = call_function[target=torch.ops.aten.tanh.default](args = (%convolution,), kwargs = {})
#   %clamp_min : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%tanh, 0), kwargs = {})
#   %view_1 : [num_users=1] = call_function[target=torch.ops.aten.reshape.default](args = (%clamp_min, [4, -1, 4, 4]), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view, %view_1), kwargs = {})
#   %view_2 : [num_users=1] = call_function[target=torch.ops.aten.reshape.default](args = (%mul, [4, -1, 4, 4]), kwargs = {})
triton_poi_fused_clamp_mul_tanh_view_1 = async_compile.triton('triton_poi_fused_clamp_mul_tanh_view_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: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_clamp_mul_tanh_view_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, '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_clamp_mul_tanh_view_1(in_ptr0, in_ptr1, 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
    x0 = xindex % 16
    x2 = (xindex // 64)
    tmp0 = tl.load(in_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr1 + (x0 + (16*x2)), xmask, eviction_policy='evict_last')
    tmp2 = libdevice.tanh(tmp1)
    tmp3 = 0.0
    tmp4 = triton_helpers.maximum(tmp2, tmp3)
    tmp5 = tmp0 * tmp4
    tl.store(out_ptr0 + (x3), tmp5, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (1, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_2, (1, ), (1, ))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (4, 4, 4, 4), (64, 16, 4, 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_3, primals_1, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 1, 4, 4), (16, 16, 4, 1))
        buf1 = buf0; del buf0  # reuse
        # Topologically Sorted Source Nodes: [conv2d], Original ATen: [aten.convolution]
        stream0 = get_raw_stream(0)
        triton_poi_fused_convolution_0.run(buf1, primals_2, 64, grid=grid(64), stream=stream0)
        del primals_2
        buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [tanh, gate, gate_1, x, output], Original ATen: [aten.tanh, aten.clamp, aten.view, aten.mul]
        triton_poi_fused_clamp_mul_tanh_view_1.run(primals_4, buf1, buf2, 256, grid=grid(256), stream=stream0)
    return (buf2, primals_1, primals_3, primals_4, buf1, )


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((1, 4, 1, 1), (4, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4])
    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 math
import torch
import torch.nn as nn
import torch.utils.data
from itertools import product as product
from math import sqrt as sqrt


class SpatialGate(nn.Module):

    def __init__(self, in_channels: 'int', num_groups: 'int'=1, kernel_size:
        'int'=1, padding: 'int'=0, stride: 'int'=1, gate_activation: 'str'=
        'ReTanH', gate_activation_kargs: 'dict'=None, get_running_cost:
        'callable'=None):
        super(SpatialGate, self).__init__()
        self.num_groups = num_groups
        self.gate_conv = nn.Conv2d(in_channels, num_groups, kernel_size,
            padding=padding, stride=stride)
        self.gate_activation = gate_activation
        self.gate_activation_kargs = gate_activation_kargs
        if gate_activation == 'ReTanH':
            self.gate_activate = lambda x: torch.tanh(x).clamp(min=0)
        elif gate_activation == 'Sigmoid':
            self.gate_activate = lambda x: torch.sigmoid(x)
        elif gate_activation == 'GeReTanH':
            assert 'tau' in gate_activation_kargs
            tau = gate_activation_kargs['tau']
            ttau = math.tanh(tau)
            self.gate_activate = lambda x: ((torch.tanh(x - tau) + ttau) /
                (1 + ttau)).clamp(min=0)
        else:
            raise NotImplementedError()
        self.get_running_cost = get_running_cost
        self.running_cost = None
        self.init_parameters()

    def init_parameters(self, init_gate=0.99):
        if self.gate_activation == 'ReTanH':
            bias_value = 0.5 * math.log((1 + init_gate) / (1 - init_gate))
        elif self.gate_activation == 'Sigmoid':
            bias_value = 0.5 * math.log(init_gate / (1 - init_gate))
        elif self.gate_activation == 'GeReTanH':
            tau = self.gate_activation_kargs['tau']
            bias_value = 0.5 * math.log((1 + init_gate * math.exp(2 * tau)) /
                (1 - init_gate))
        nn.init.normal_(self.gate_conv.weight, std=0.01)
        nn.init.constant_(self.gate_conv.bias, bias_value)

    def encode(self, *inputs):
        outputs = [x.view(x.shape[0] * self.num_groups, -1, *x.shape[2:]) for
            x in inputs]
        return outputs

    def decode(self, *inputs):
        outputs = [x.view(x.shape[0] // self.num_groups, -1, *x.shape[2:]) for
            x in inputs]
        return outputs

    def update_running_cost(self, gate):
        if self.get_running_cost is not None:
            cost = self.get_running_cost(gate)
            if self.running_cost is not None:
                self.running_cost = [(x + y) for x, y in zip(self.
                    running_cost, cost)]
            else:
                self.running_cost = cost

    def clear_running_cost(self):
        self.running_cost = None

    def forward(self, data_input, gate_input, masked_func=None):
        gate = self.gate_activate(self.gate_conv(gate_input))
        self.update_running_cost(gate)
        if masked_func is not None:
            data_input = masked_func(data_input, gate)
        data, gate = self.encode(data_input, gate)
        output, = self.decode(data * gate)
        return output


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


def get_init_inputs():
    return [[], {'in_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 math
import torch.nn as nn
import torch.utils.data
from itertools import product as product
from math import sqrt as sqrt
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_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
    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
    tl.store(in_out_ptr0 + x0, tmp3, xmask)


@triton.jit
def triton_poi_fused_clamp_mul_tanh_view_1(in_ptr0, in_ptr1, 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
    x0 = xindex % 16
    x2 = xindex // 64
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr1 + (x0 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp2 = libdevice.tanh(tmp1)
    tmp3 = 0.0
    tmp4 = triton_helpers.maximum(tmp2, tmp3)
    tmp5 = tmp0 * tmp4
    tl.store(out_ptr0 + x3, tmp5, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (1, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_2, (1,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1, 
            1), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 1, 4, 4), (16, 16, 4, 1))
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_convolution_0[grid(64)](buf1, primals_2, 64,
            XBLOCK=64, num_warps=1, num_stages=1)
        del primals_2
        buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_clamp_mul_tanh_view_1[grid(256)](primals_4, buf1,
            buf2, 256, XBLOCK=256, num_warps=4, num_stages=1)
    return buf2, primals_1, primals_3, primals_4, buf1


class SpatialGateNew(nn.Module):

    def __init__(self, in_channels: 'int', num_groups: 'int'=1, kernel_size:
        'int'=1, padding: 'int'=0, stride: 'int'=1, gate_activation: 'str'=
        'ReTanH', gate_activation_kargs: 'dict'=None, get_running_cost:
        'callable'=None):
        super(SpatialGateNew, self).__init__()
        self.num_groups = num_groups
        self.gate_conv = nn.Conv2d(in_channels, num_groups, kernel_size,
            padding=padding, stride=stride)
        self.gate_activation = gate_activation
        self.gate_activation_kargs = gate_activation_kargs
        if gate_activation == 'ReTanH':
            self.gate_activate = lambda x: torch.tanh(x).clamp(min=0)
        elif gate_activation == 'Sigmoid':
            self.gate_activate = lambda x: torch.sigmoid(x)
        elif gate_activation == 'GeReTanH':
            assert 'tau' in gate_activation_kargs
            tau = gate_activation_kargs['tau']
            ttau = math.tanh(tau)
            self.gate_activate = lambda x: ((torch.tanh(x - tau) + ttau) /
                (1 + ttau)).clamp(min=0)
        else:
            raise NotImplementedError()
        self.get_running_cost = get_running_cost
        self.running_cost = None
        self.init_parameters()

    def init_parameters(self, init_gate=0.99):
        if self.gate_activation == 'ReTanH':
            bias_value = 0.5 * math.log((1 + init_gate) / (1 - init_gate))
        elif self.gate_activation == 'Sigmoid':
            bias_value = 0.5 * math.log(init_gate / (1 - init_gate))
        elif self.gate_activation == 'GeReTanH':
            tau = self.gate_activation_kargs['tau']
            bias_value = 0.5 * math.log((1 + init_gate * math.exp(2 * tau)) /
                (1 - init_gate))
        nn.init.normal_(self.gate_conv.weight, std=0.01)
        nn.init.constant_(self.gate_conv.bias, bias_value)

    def encode(self, *inputs):
        outputs = [x.view(x.shape[0] * self.num_groups, -1, *x.shape[2:]) for
            x in inputs]
        return outputs

    def decode(self, *inputs):
        outputs = [x.view(x.shape[0] // self.num_groups, -1, *x.shape[2:]) for
            x in inputs]
        return outputs

    def update_running_cost(self, gate):
        if self.get_running_cost is not None:
            cost = self.get_running_cost(gate)
            if self.running_cost is not None:
                self.running_cost = [(x + y) for x, y in zip(self.
                    running_cost, cost)]
            else:
                self.running_cost = cost

    def clear_running_cost(self):
        self.running_cost = None

    def forward(self, input_0, input_1):
        primals_1 = self.gate_conv.weight
        primals_2 = self.gate_conv.bias
        primals_3 = input_0
        primals_4 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4])
        return output[0]
