# AOT ID: ['0_inference']
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/zx/czxnj2jqqa7xh3e3a44piulfrkoqpjkwijsmuagzktv3ujhmu5y3.py
# Topologically Sorted Source Nodes: [mul, vel, vv, add_1, vv_1, neg], Original ATen: [aten.mul, aten.add, aten.linalg_vector_norm, aten.reciprocal, aten.neg]
# Source node to ATen node mapping:
#   add_1 => add_1
#   mul => mul
#   neg => neg
#   vel => add
#   vv => pow_1, pow_2, sum_1
#   vv_1 => mul_1, reciprocal
# Graph fragment:
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%arg0_1, 4), kwargs = {})
#   %add : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%arg1_1, %mul), kwargs = {})
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%add, 2), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%pow_1, [3], True), kwargs = {})
#   %pow_2 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sum_1, 0.5), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%pow_2, 0.0001), kwargs = {})
#   %reciprocal : [num_users=1] = call_function[target=torch.ops.aten.reciprocal.default](args = (%add_1,), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%reciprocal, 3), kwargs = {})
#   %neg : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%mul_1,), kwargs = {})
triton_poi_fused_add_linalg_vector_norm_mul_neg_reciprocal_0 = async_compile.triton('triton_poi_fused_add_linalg_vector_norm_mul_neg_reciprocal_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: '*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_add_linalg_vector_norm_mul_neg_reciprocal_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 8, '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_add_linalg_vector_norm_mul_neg_reciprocal_0(in_ptr0, in_ptr1, out_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_ptr0 + (4*x0), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (4*x0), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (1 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr1 + (1 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr0 + (2 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp13 = tl.load(in_ptr1 + (2 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp18 = tl.load(in_ptr0 + (3 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp19 = tl.load(in_ptr1 + (3 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp2 = 4.0
    tmp3 = tmp1 * tmp2
    tmp4 = tmp0 + tmp3
    tmp5 = tmp4 * tmp4
    tmp8 = tmp7 * tmp2
    tmp9 = tmp6 + tmp8
    tmp10 = tmp9 * tmp9
    tmp11 = tmp5 + tmp10
    tmp14 = tmp13 * tmp2
    tmp15 = tmp12 + tmp14
    tmp16 = tmp15 * tmp15
    tmp17 = tmp11 + tmp16
    tmp20 = tmp19 * tmp2
    tmp21 = tmp18 + tmp20
    tmp22 = tmp21 * tmp21
    tmp23 = tmp17 + tmp22
    tmp24 = libdevice.sqrt(tmp23)
    tmp25 = 0.0001
    tmp26 = tmp24 + tmp25
    tmp27 = tl.full([1], 1, tl.int32)
    tmp28 = tmp27 / tmp26
    tmp29 = 3.0
    tmp30 = tmp28 * tmp29
    tmp31 = -tmp30
    tl.store(out_ptr0 + (x0), tmp31, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/uq/cuq6youjtdea27w6voavepwc7an5xlwjvkcnbuntotrxqybcjuor.py
# Topologically Sorted Source Nodes: [mul, vel, add_2, relu, sub, vv_2, vel_1, mul_2, locs], Original ATen: [aten.mul, aten.add, aten.relu, aten.sub, aten.neg]
# Source node to ATen node mapping:
#   add_2 => add_2
#   locs => add_3
#   mul => mul
#   mul_2 => mul_3
#   relu => relu
#   sub => sub
#   vel => add
#   vel_1 => mul_2
#   vv_2 => neg_1
# Graph fragment:
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%arg0_1, 4), kwargs = {})
#   %add : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%arg1_1, %mul), kwargs = {})
#   %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%neg, 1.0), kwargs = {})
#   %relu : [num_users=1] = call_function[target=torch.ops.aten.relu.default](args = (%add_2,), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%relu, 1.0), kwargs = {})
#   %neg_1 : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%sub,), kwargs = {})
#   %mul_2 : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add, %neg_1), kwargs = {})
#   %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_2, %arg0_1), kwargs = {})
#   %add_3 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%arg2_1, %mul_3), kwargs = {})
triton_poi_fused_add_mul_neg_relu_sub_1 = async_compile.triton('triton_poi_fused_add_mul_neg_relu_sub_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: '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_add_mul_neg_relu_sub_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, '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_add_mul_neg_relu_sub_1(in_ptr0, in_ptr1, in_ptr2, in_ptr3, 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
    x2 = xindex
    x1 = (xindex // 4)
    tmp0 = tl.load(in_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr1 + (x2), xmask)
    tmp5 = tl.load(in_ptr2 + (x1), xmask, eviction_policy='evict_last')
    tmp13 = tl.load(in_ptr3 + (x2), xmask)
    tmp2 = 4.0
    tmp3 = tmp1 * tmp2
    tmp4 = tmp0 + tmp3
    tmp6 = 1.0
    tmp7 = tmp5 + tmp6
    tmp8 = tl.full([1], 0, tl.int32)
    tmp9 = triton_helpers.maximum(tmp8, tmp7)
    tmp10 = tmp9 - tmp6
    tmp11 = -tmp10
    tmp12 = tmp4 * tmp11
    tmp14 = tmp12 * tmp1
    tmp15 = tmp13 + tmp14
    tl.store(out_ptr0 + (x2), tmp12, xmask)
    tl.store(out_ptr1 + (x2), tmp15, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    arg0_1, arg1_1, arg2_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg2_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
        # Topologically Sorted Source Nodes: [mul, vel, vv, add_1, vv_1, neg], Original ATen: [aten.mul, aten.add, aten.linalg_vector_norm, aten.reciprocal, aten.neg]
        stream0 = get_raw_stream(0)
        triton_poi_fused_add_linalg_vector_norm_mul_neg_reciprocal_0.run(arg1_1, arg0_1, buf0, 64, grid=grid(64), stream=stream0)
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [mul, vel, add_2, relu, sub, vv_2, vel_1, mul_2, locs], Original ATen: [aten.mul, aten.add, aten.relu, aten.sub, aten.neg]
        triton_poi_fused_add_mul_neg_relu_sub_1.run(arg1_1, arg0_1, buf0, arg2_1, buf1, buf2, 256, grid=grid(256), stream=stream0)
        del arg0_1
        del arg1_1
        del arg2_1
        del buf0
    return (buf2, buf1, )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    arg1_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    arg2_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([arg0_1, arg1_1, arg2_1])
    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 FluidGravityForce(nn.Module):

    def __init__(self, gravity, maxSpeed=3):
        """
        Initializes a fluid gravity model.

        Arguments:
            gravity: Gravity vector in the global frame (same as particle l) for the simulation
            maxSpeed: The maximum magnitude of the particle velocities. Higher velocities are clamped.
                      Previous work used: MAX_VEL = 0.5*0.1*NSUBSTEPS/DT
        """
        super(FluidGravityForce, self).__init__()
        self.gravity = gravity
        self.maxSpeed = maxSpeed
        self.relu = nn.ReLU()

    def _cap_magnitude(self, A, cap):
        d = len(A.size())
        vv = torch.norm(A, 2, d - 1, keepdim=True)
        vv = cap / (vv + 0.0001)
        vv = -(self.relu(-vv + 1.0) - 1.0)
        return A * vv

    def forward(self, locs, vel, dt):
        """
        Applies gravity force to fluid sim
        Inputs:
            locs: A BxNx3 tensor where B is the batch size, N is the number of particles.
                  The tensor contains the locations of every particle.
            vels: A BxNx3 tensor that contains the velocity of every particle
            dt: timestep to predict for
            gravity: 1x1x3 tensor containing the direction of gravity in the same coordinate frame as particles
            maxSpeed: maximum velocity possible for nay particle
        Returns:
            locs: A BxNx3 tensor with the new particle positions
            vel:  A BxNx3 tensor with the new particle velocities
        """
        vel = vel + self.gravity * dt
        vel = self._cap_magnitude(vel, self.maxSpeed)
        locs = locs + vel * dt
        return locs, vel


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


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

import torch
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_add_linalg_vector_norm_mul_neg_reciprocal_0(in_ptr0,
    in_ptr1, out_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_ptr0 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr1 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp13 = tl.load(in_ptr1 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp18 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp19 = tl.load(in_ptr1 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp2 = 4.0
    tmp3 = tmp1 * tmp2
    tmp4 = tmp0 + tmp3
    tmp5 = tmp4 * tmp4
    tmp8 = tmp7 * tmp2
    tmp9 = tmp6 + tmp8
    tmp10 = tmp9 * tmp9
    tmp11 = tmp5 + tmp10
    tmp14 = tmp13 * tmp2
    tmp15 = tmp12 + tmp14
    tmp16 = tmp15 * tmp15
    tmp17 = tmp11 + tmp16
    tmp20 = tmp19 * tmp2
    tmp21 = tmp18 + tmp20
    tmp22 = tmp21 * tmp21
    tmp23 = tmp17 + tmp22
    tmp24 = libdevice.sqrt(tmp23)
    tmp25 = 0.0001
    tmp26 = tmp24 + tmp25
    tmp27 = tl.full([1], 1, tl.int32)
    tmp28 = tmp27 / tmp26
    tmp29 = 3.0
    tmp30 = tmp28 * tmp29
    tmp31 = -tmp30
    tl.store(out_ptr0 + x0, tmp31, xmask)


@triton.jit
def triton_poi_fused_add_mul_neg_relu_sub_1(in_ptr0, in_ptr1, in_ptr2,
    in_ptr3, 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
    x2 = xindex
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr1 + x2, xmask)
    tmp5 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
    tmp13 = tl.load(in_ptr3 + x2, xmask)
    tmp2 = 4.0
    tmp3 = tmp1 * tmp2
    tmp4 = tmp0 + tmp3
    tmp6 = 1.0
    tmp7 = tmp5 + tmp6
    tmp8 = tl.full([1], 0, tl.int32)
    tmp9 = triton_helpers.maximum(tmp8, tmp7)
    tmp10 = tmp9 - tmp6
    tmp11 = -tmp10
    tmp12 = tmp4 * tmp11
    tmp14 = tmp12 * tmp1
    tmp15 = tmp13 + tmp14
    tl.store(out_ptr0 + x2, tmp12, xmask)
    tl.store(out_ptr1 + x2, tmp15, xmask)


def call(args):
    arg0_1, arg1_1, arg2_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg2_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_add_linalg_vector_norm_mul_neg_reciprocal_0[grid(64)](
            arg1_1, arg0_1, buf0, 64, XBLOCK=64, num_warps=1, num_stages=1)
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_add_mul_neg_relu_sub_1[grid(256)](arg1_1, arg0_1,
            buf0, arg2_1, buf1, buf2, 256, XBLOCK=256, num_warps=4,
            num_stages=1)
        del arg0_1
        del arg1_1
        del arg2_1
        del buf0
    return buf2, buf1


class FluidGravityForceNew(nn.Module):

    def __init__(self, gravity, maxSpeed=3):
        """
        Initializes a fluid gravity model.

        Arguments:
            gravity: Gravity vector in the global frame (same as particle l) for the simulation
            maxSpeed: The maximum magnitude of the particle velocities. Higher velocities are clamped.
                      Previous work used: MAX_VEL = 0.5*0.1*NSUBSTEPS/DT
        """
        super(FluidGravityForceNew, self).__init__()
        self.gravity = gravity
        self.maxSpeed = maxSpeed
        self.relu = nn.ReLU()

    def _cap_magnitude(self, A, cap):
        d = len(A.size())
        vv = torch.norm(A, 2, d - 1, keepdim=True)
        vv = cap / (vv + 0.0001)
        vv = -(self.relu(-vv + 1.0) - 1.0)
        return A * vv

    def forward(self, input_0, input_1, input_2):
        arg0_1 = input_0
        arg1_1 = input_1
        arg2_1 = input_2
        output = call([arg0_1, arg1_1, arg2_1])
        return output[0], output[1]
