# 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/jm/cjmjzcv5qpaj5zcr6wibd5u6i75ewxt735r5apaxnxbyw2ihxmjh.py
# Topologically Sorted Source Nodes: [clamp, relu], Original ATen: [aten.clamp, aten.relu, aten.ge, aten.le, aten.logical_and]
# Source node to ATen node mapping:
#   clamp => clamp_max, clamp_min
#   relu => relu
# Graph fragment:
#   %add_tensor_2 : [num_users=3] = call_function[target=torch.ops.aten.add.Tensor](args = (%mm_default_2, %primals_3), kwargs = {})
#   %clamp_min : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%add_tensor_2, -1), kwargs = {})
#   %clamp_max : [num_users=2] = call_function[target=torch.ops.aten.clamp_max.default](args = (%clamp_min, 1), kwargs = {})
#   %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%clamp_max,), kwargs = {})
#   %ge_2 : [num_users=1] = call_function[target=torch.ops.aten.ge.Scalar](args = (%add_tensor_2, -1), kwargs = {})
#   %le_4 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%add_tensor_2, 1), kwargs = {})
#   %logical_and_2 : [num_users=1] = call_function[target=torch.ops.aten.logical_and.default](args = (%ge_2, %le_4), kwargs = {})
triton_poi_fused_clamp_ge_le_logical_and_relu_0 = async_compile.triton('triton_poi_fused_clamp_ge_le_logical_and_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=[2048], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*i1', 4: '*fp32', 5: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_clamp_ge_le_logical_and_relu_0', '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_ge_le_logical_and_relu_0(in_ptr0, in_ptr1, out_ptr0, out_ptr1, out_ptr2, xnumel, XBLOCK : tl.constexpr):
    xnumel = 2048
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 512
    tmp0 = tl.load(in_ptr0 + (x2), None)
    tmp1 = tl.load(in_ptr1 + (x0), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = -1.0
    tmp4 = triton_helpers.maximum(tmp2, tmp3)
    tmp5 = 1.0
    tmp6 = triton_helpers.minimum(tmp4, tmp5)
    tmp7 = tmp2 >= tmp3
    tmp8 = tmp2 <= tmp5
    tmp9 = tmp7 & tmp8
    tmp10 = tl.full([1], 0, tl.int32)
    tmp11 = triton_helpers.maximum(tmp10, tmp6)
    tl.store(out_ptr0 + (x2), tmp6, None)
    tl.store(out_ptr1 + (x2), tmp9, None)
    tl.store(out_ptr2 + (x2), tmp11, None)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/z7/cz7co5drq7jjstsdb6lgunhuqumkt2hucjjsqdq5zwiupbfqhnmk.py
# Topologically Sorted Source Nodes: [clamp_2, softmax], Original ATen: [aten.clamp, aten._softmax, aten.ge, aten.le, aten.logical_and]
# Source node to ATen node mapping:
#   clamp_2 => clamp_max_2, clamp_min_2
#   softmax => amax, div, exp, sub, sum_1
# Graph fragment:
#   %add_tensor : [num_users=3] = call_function[target=torch.ops.aten.add.Tensor](args = (%mm_default, %primals_7), kwargs = {})
#   %clamp_min_2 : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%add_tensor, -1), kwargs = {})
#   %clamp_max_2 : [num_users=3] = call_function[target=torch.ops.aten.clamp_max.default](args = (%clamp_min_2, 1), kwargs = {})
#   %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%clamp_max_2, [1], True), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%clamp_max_2, %amax), kwargs = {})
#   %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub,), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [1], True), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp, %sum_1), kwargs = {})
#   %ge : [num_users=1] = call_function[target=torch.ops.aten.ge.Scalar](args = (%add_tensor, -1), kwargs = {})
#   %le : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%add_tensor, 1), kwargs = {})
#   %logical_and : [num_users=1] = call_function[target=torch.ops.aten.logical_and.default](args = (%ge, %le), kwargs = {})
triton_per_fused__softmax_clamp_ge_le_logical_and_1 = async_compile.triton('triton_per_fused__softmax_clamp_ge_le_logical_and_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.persistent_reduction(
    size_hints=[4, 16],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*i1', 4: '*fp32', 5: 'i32', 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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused__softmax_clamp_ge_le_logical_and_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 2, '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}
)
@triton.jit
def triton_per_fused__softmax_clamp_ge_le_logical_and_1(in_ptr0, in_ptr1, out_ptr0, out_ptr1, out_ptr4, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 4
    rnumel = 10
    RBLOCK: tl.constexpr = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    roffset = 0
    rmask = rindex < rnumel
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + (10*x0)), rmask & xmask, other=0.0)
    tmp1 = tl.load(in_ptr1 + (r1), rmask, eviction_policy='evict_last', other=0.0)
    tmp2 = tmp0 + tmp1
    tmp3 = -1.0
    tmp4 = triton_helpers.maximum(tmp2, tmp3)
    tmp5 = 1.0
    tmp6 = triton_helpers.minimum(tmp4, tmp5)
    tmp7 = tmp2 >= tmp3
    tmp8 = tmp2 <= tmp5
    tmp9 = tmp7 & tmp8
    tmp10 = tl.broadcast_to(tmp6, [XBLOCK, RBLOCK])
    tmp12 = tl.where(rmask & xmask, tmp10, float("-inf"))
    tmp13 = triton_helpers.max2(tmp12, 1)[:, None]
    tmp14 = tmp6 - tmp13
    tmp15 = tl_math.exp(tmp14)
    tmp16 = tl.broadcast_to(tmp15, [XBLOCK, RBLOCK])
    tmp18 = tl.where(rmask & xmask, tmp16, 0)
    tmp19 = tl.sum(tmp18, 1)[:, None]
    tmp20 = tmp15 / tmp19
    tl.store(out_ptr0 + (r1 + (10*x0)), tmp6, rmask & xmask)
    tl.store(out_ptr1 + (r1 + (10*x0)), tmp9, rmask & xmask)
    tl.store(out_ptr4 + (r1 + (10*x0)), tmp20, rmask & 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, 1024), (1024, 1))
    assert_size_stride(primals_2, (512, 1024), (1024, 1))
    assert_size_stride(primals_3, (512, ), (1, ))
    assert_size_stride(primals_4, (512, 512), (512, 1))
    assert_size_stride(primals_5, (512, ), (1, ))
    assert_size_stride(primals_6, (10, 512), (512, 1))
    assert_size_stride(primals_7, (10, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 512), (512, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(primals_1, reinterpret_tensor(primals_2, (1024, 512), (1, 1024), 0), out=buf0)
        del primals_2
        buf1 = empty_strided_cuda((4, 512), (512, 1), torch.float32)
        buf13 = empty_strided_cuda((4, 512), (512, 1), torch.bool)
        buf2 = empty_strided_cuda((4, 512), (512, 1), torch.float32)
        # Topologically Sorted Source Nodes: [clamp, relu], Original ATen: [aten.clamp, aten.relu, aten.ge, aten.le, aten.logical_and]
        stream0 = get_raw_stream(0)
        triton_poi_fused_clamp_ge_le_logical_and_relu_0.run(buf0, primals_3, buf1, buf13, buf2, 2048, grid=grid(2048), stream=stream0)
        del primals_3
        buf3 = buf0; del buf0  # reuse
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(buf2, reinterpret_tensor(primals_4, (512, 512), (1, 512), 0), out=buf3)
        buf4 = empty_strided_cuda((4, 512), (512, 1), torch.float32)
        buf12 = empty_strided_cuda((4, 512), (512, 1), torch.bool)
        buf5 = empty_strided_cuda((4, 512), (512, 1), torch.float32)
        # Topologically Sorted Source Nodes: [clamp_1, relu_1], Original ATen: [aten.clamp, aten.relu, aten.ge, aten.le, aten.logical_and]
        triton_poi_fused_clamp_ge_le_logical_and_relu_0.run(buf3, primals_5, buf4, buf12, buf5, 2048, grid=grid(2048), stream=stream0)
        del buf3
        del primals_5
        buf6 = empty_strided_cuda((4, 10), (10, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(buf5, reinterpret_tensor(primals_6, (512, 10), (1, 512), 0), out=buf6)
        buf7 = empty_strided_cuda((4, 10), (10, 1), torch.float32)
        buf11 = empty_strided_cuda((4, 10), (10, 1), torch.bool)
        buf10 = empty_strided_cuda((4, 10), (10, 1), torch.float32)
        # Topologically Sorted Source Nodes: [clamp_2, softmax], Original ATen: [aten.clamp, aten._softmax, aten.ge, aten.le, aten.logical_and]
        triton_per_fused__softmax_clamp_ge_le_logical_and_1.run(buf6, primals_7, buf7, buf11, buf10, 4, 10, grid=grid(4), stream=stream0)
        del buf6
        del primals_7
    return (buf10, buf7, buf5, buf4, buf2, buf1, primals_1, buf2, buf5, buf10, buf11, primals_6, buf12, primals_4, buf13, )


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, 1024), (1024, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((512, 1024), (1024, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((512, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((512, 512), (512, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((512, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((10, 512), (512, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((10, ), (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
import torch.nn.functional as F
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed


class MLP3_clamp_eval(nn.Module):

    def __init__(self):
        super(MLP3_clamp_eval, self).__init__()
        self.fc1 = nn.Linear(32 * 32, 512)
        self.fc2 = nn.Linear(512, 512)
        self.fc3 = nn.Linear(512, 10)
        self.fc1_out = torch.zeros(1)
        self.relu1_out = torch.zeros(1)
        self.fc2_out = torch.zeros(1)
        self.relu2_out = torch.zeros(1)
        self.fc3_out = torch.zeros(1)

    def forward(self, x):
        x = x.view(-1, 32 * 32)
        self.fc1_out = self.fc1(x).clamp(-1, 1)
        self.relu1_out = F.relu(self.fc1_out)
        self.fc2_out = self.fc2(self.relu1_out).clamp(-1, 1)
        self.relu2_out = F.relu(self.fc2_out)
        self.fc3_out = self.fc3(self.relu2_out).clamp(-1, 1)
        return F.softmax(self.fc3_out, dim=1)


def get_inputs():
    return [torch.rand([4, 1024])]


def get_init_inputs():
    return [[], {}]

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 math as tl_math
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
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_clamp_ge_le_logical_and_relu_0(in_ptr0, in_ptr1,
    out_ptr0, out_ptr1, out_ptr2, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 512
    tmp0 = tl.load(in_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr1 + x0, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = -1.0
    tmp4 = triton_helpers.maximum(tmp2, tmp3)
    tmp5 = 1.0
    tmp6 = triton_helpers.minimum(tmp4, tmp5)
    tmp7 = tmp2 >= tmp3
    tmp8 = tmp2 <= tmp5
    tmp9 = tmp7 & tmp8
    tmp10 = tl.full([1], 0, tl.int32)
    tmp11 = triton_helpers.maximum(tmp10, tmp6)
    tl.store(out_ptr0 + x2, tmp6, None)
    tl.store(out_ptr1 + x2, tmp9, None)
    tl.store(out_ptr2 + x2, tmp11, None)


@triton.jit
def triton_per_fused__softmax_clamp_ge_le_logical_and_1(in_ptr0, in_ptr1,
    out_ptr0, out_ptr1, out_ptr4, xnumel, rnumel, XBLOCK: tl.constexpr):
    xnumel = 4
    rnumel = 10
    RBLOCK: tl.constexpr = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    rmask = rindex < rnumel
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + 10 * x0), rmask & xmask, other=0.0)
    tmp1 = tl.load(in_ptr1 + r1, rmask, eviction_policy='evict_last', other=0.0
        )
    tmp2 = tmp0 + tmp1
    tmp3 = -1.0
    tmp4 = triton_helpers.maximum(tmp2, tmp3)
    tmp5 = 1.0
    tmp6 = triton_helpers.minimum(tmp4, tmp5)
    tmp7 = tmp2 >= tmp3
    tmp8 = tmp2 <= tmp5
    tmp9 = tmp7 & tmp8
    tmp10 = tl.broadcast_to(tmp6, [XBLOCK, RBLOCK])
    tmp12 = tl.where(rmask & xmask, tmp10, float('-inf'))
    tmp13 = triton_helpers.max2(tmp12, 1)[:, None]
    tmp14 = tmp6 - tmp13
    tmp15 = tl_math.exp(tmp14)
    tmp16 = tl.broadcast_to(tmp15, [XBLOCK, RBLOCK])
    tmp18 = tl.where(rmask & xmask, tmp16, 0)
    tmp19 = tl.sum(tmp18, 1)[:, None]
    tmp20 = tmp15 / tmp19
    tl.store(out_ptr0 + (r1 + 10 * x0), tmp6, rmask & xmask)
    tl.store(out_ptr1 + (r1 + 10 * x0), tmp9, rmask & xmask)
    tl.store(out_ptr4 + (r1 + 10 * x0), tmp20, rmask & 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, 1024), (1024, 1))
    assert_size_stride(primals_2, (512, 1024), (1024, 1))
    assert_size_stride(primals_3, (512,), (1,))
    assert_size_stride(primals_4, (512, 512), (512, 1))
    assert_size_stride(primals_5, (512,), (1,))
    assert_size_stride(primals_6, (10, 512), (512, 1))
    assert_size_stride(primals_7, (10,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 512), (512, 1), torch.float32)
        extern_kernels.mm(primals_1, reinterpret_tensor(primals_2, (1024, 
            512), (1, 1024), 0), out=buf0)
        del primals_2
        buf1 = empty_strided_cuda((4, 512), (512, 1), torch.float32)
        buf13 = empty_strided_cuda((4, 512), (512, 1), torch.bool)
        buf2 = empty_strided_cuda((4, 512), (512, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_clamp_ge_le_logical_and_relu_0[grid(2048)](buf0,
            primals_3, buf1, buf13, buf2, 2048, XBLOCK=128, num_warps=4,
            num_stages=1)
        del primals_3
        buf3 = buf0
        del buf0
        extern_kernels.mm(buf2, reinterpret_tensor(primals_4, (512, 512), (
            1, 512), 0), out=buf3)
        buf4 = empty_strided_cuda((4, 512), (512, 1), torch.float32)
        buf12 = empty_strided_cuda((4, 512), (512, 1), torch.bool)
        buf5 = empty_strided_cuda((4, 512), (512, 1), torch.float32)
        triton_poi_fused_clamp_ge_le_logical_and_relu_0[grid(2048)](buf3,
            primals_5, buf4, buf12, buf5, 2048, XBLOCK=128, num_warps=4,
            num_stages=1)
        del buf3
        del primals_5
        buf6 = empty_strided_cuda((4, 10), (10, 1), torch.float32)
        extern_kernels.mm(buf5, reinterpret_tensor(primals_6, (512, 10), (1,
            512), 0), out=buf6)
        buf7 = empty_strided_cuda((4, 10), (10, 1), torch.float32)
        buf11 = empty_strided_cuda((4, 10), (10, 1), torch.bool)
        buf10 = empty_strided_cuda((4, 10), (10, 1), torch.float32)
        triton_per_fused__softmax_clamp_ge_le_logical_and_1[grid(4)](buf6,
            primals_7, buf7, buf11, buf10, 4, 10, XBLOCK=1, num_warps=2,
            num_stages=1)
        del buf6
        del primals_7
    return (buf10, buf7, buf5, buf4, buf2, buf1, primals_1, buf2, buf5,
        buf10, buf11, primals_6, buf12, primals_4, buf13)


class MLP3_clamp_evalNew(nn.Module):

    def __init__(self):
        super(MLP3_clamp_evalNew, self).__init__()
        self.fc1 = nn.Linear(32 * 32, 512)
        self.fc2 = nn.Linear(512, 512)
        self.fc3 = nn.Linear(512, 10)
        self.fc1_out = torch.zeros(1)
        self.relu1_out = torch.zeros(1)
        self.fc2_out = torch.zeros(1)
        self.relu2_out = torch.zeros(1)
        self.fc3_out = torch.zeros(1)

    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.fc3.weight
        primals_7 = self.fc3.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7])
        return output[0]
