# 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/fo/cfokv5mhjtrq5wugzlwfb7neo7lma2qe4v72mqif34incoecopkz.py
# Topologically Sorted Source Nodes: [norm], Original ATen: [aten.linalg_vector_norm]
# Source node to ATen node mapping:
#   norm => pow_1, sum_1
# Graph fragment:
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%primals_6, 2), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%pow_1, None), kwargs = {})
triton_per_fused_linalg_vector_norm_0 = async_compile.triton('triton_per_fused_linalg_vector_norm_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.persistent_reduction(
    size_hints=[1, 4],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 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': {2: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1), equal_to_1=(2,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_linalg_vector_norm_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 1, '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_linalg_vector_norm_0(in_ptr0, out_ptr0, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 1
    rnumel = 4
    RBLOCK: tl.constexpr = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[None, :]
    roffset = 0
    rmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + (r0), None)
    tmp1 = tmp0 * tmp0
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp4 = tl.sum(tmp2, 1)[:, None]
    tl.store(out_ptr0 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp4, None)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/n4/cn4tc22cfrubj7ikntlgvo2zigcqyb4exwc5hxvpzcneh4ztsn7l.py
# Topologically Sorted Source Nodes: [add, add_1, tanh], Original ATen: [aten.add, aten.tanh]
# Source node to ATen node mapping:
#   add => add
#   add_1 => add_1
#   tanh => tanh
# Graph fragment:
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%unsqueeze, %view_3), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add, %primals_7), kwargs = {})
#   %tanh : [num_users=2] = call_function[target=torch.ops.aten.tanh.default](args = (%add_1,), kwargs = {})
triton_poi_fused_add_tanh_1 = async_compile.triton('triton_poi_fused_add_tanh_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_add_tanh_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 3, '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_tanh_1(in_out_ptr0, in_ptr0, in_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (x2), xmask)
    tmp3 = tl.load(in_ptr1 + (x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp5 = libdevice.tanh(tmp4)
    tl.store(in_out_ptr0 + (x2), tmp5, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/by/cbyk55yox2xs65bp7r2r6lyxsibdiqng2w7afs2wvc4dl67gwtfi.py
# Topologically Sorted Source Nodes: [mul, norm, normed_v, mul_1, attn_scores, attn_scores_1], Original ATen: [aten.mul, aten.linalg_vector_norm, aten.div, aten.sum, aten._softmax]
# Source node to ATen node mapping:
#   attn_scores => sum_2
#   attn_scores_1 => amax, div_1, exp, sub, sum_3
#   mul => mul
#   mul_1 => mul_1
#   norm => pow_2
#   normed_v => div
# Graph fragment:
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%primals_5, %primals_6), kwargs = {})
#   %pow_2 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sum_1, 0.5), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%mul, %pow_2), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%div, %tanh), kwargs = {})
#   %sum_2 : [num_users=2] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul_1, [2]), kwargs = {})
#   %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%sum_2, [0], True), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%sum_2, %amax), kwargs = {})
#   %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub,), kwargs = {})
#   %sum_3 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [0], True), kwargs = {})
#   %div_1 : [num_users=2] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp, %sum_3), kwargs = {})
triton_poi_fused__softmax_div_linalg_vector_norm_mul_sum_2 = async_compile.triton('triton_poi_fused__softmax_div_linalg_vector_norm_mul_sum_2', '''
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: '*fp32', 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__softmax_div_linalg_vector_norm_mul_sum_2', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 7, '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__softmax_div_linalg_vector_norm_mul_sum_2(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, in_ptr3, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x2 = (xindex // 16)
    x4 = xindex % 16
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (0))
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK])
    tmp2 = tl.load(in_ptr1 + (x0), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr2 + (0))
    tmp5 = tl.broadcast_to(tmp4, [XBLOCK])
    tmp8 = tl.load(in_ptr3 + (x4 + (64*x2)), xmask)
    tmp10 = tl.load(in_ptr3 + (16 + x4 + (64*x2)), xmask)
    tmp13 = tl.load(in_ptr3 + (32 + x4 + (64*x2)), xmask)
    tmp16 = tl.load(in_ptr3 + (48 + x4 + (64*x2)), xmask)
    tmp3 = tmp1 * tmp2
    tmp6 = libdevice.sqrt(tmp5)
    tmp7 = tmp3 / tmp6
    tmp9 = tmp7 * tmp8
    tmp11 = tmp7 * tmp10
    tmp12 = tmp9 + tmp11
    tmp14 = tmp7 * tmp13
    tmp15 = tmp12 + tmp14
    tmp17 = tmp7 * tmp16
    tmp18 = tmp15 + tmp17
    tmp19 = tmp18 - tmp18
    tmp20 = tl_math.exp(tmp19)
    tmp21 = tmp20 / tmp20
    tl.store(in_out_ptr0 + (x3), tmp21, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/nu/cnub5y6hoo5eqx4ansihvpe6lypvv3sdp7muydbgzcmc6izyixnc.py
# Topologically Sorted Source Nodes: [mul_2, context], Original ATen: [aten.mul, aten.sum]
# Source node to ATen node mapping:
#   context => sum_4
#   mul_2 => mul_2
# Graph fragment:
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%unsqueeze_1, %primals_4), kwargs = {})
#   %sum_4 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul_2, [0]), kwargs = {})
triton_poi_fused_mul_sum_3 = async_compile.triton('triton_poi_fused_mul_sum_3', '''
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_mul_sum_3', '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_mul_sum_3(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
    x0 = xindex % 16
    x2 = (xindex // 64)
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (16*x2)), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (x3), xmask)
    tmp2 = tmp0 * tmp1
    tl.store(out_ptr0 + (x3), tmp2, 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, 1))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (4, 4), (4, 1))
    assert_size_stride(primals_4, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_5, (1, ), (1, ))
    assert_size_stride(primals_6, (4, ), (1, ))
    assert_size_stride(primals_7, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(primals_2, (64, 4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 4), (1, 4), 0), out=buf0)
        del primals_1
        buf1 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [key], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(primals_4, (64, 4), (4, 1), 0), reinterpret_tensor(primals_3, (4, 4), (1, 4), 0), out=buf1)
        del primals_3
        buf2 = empty_strided_cuda((), (), torch.float32)
        # Topologically Sorted Source Nodes: [norm], Original ATen: [aten.linalg_vector_norm]
        stream0 = get_raw_stream(0)
        triton_per_fused_linalg_vector_norm_0.run(primals_6, buf2, 1, 4, grid=grid(1), stream=stream0)
        buf3 = reinterpret_tensor(buf0, (1, 4, 4, 4, 4), (256, 64, 16, 4, 1), 0); del buf0  # reuse
        # Topologically Sorted Source Nodes: [add, add_1, tanh], Original ATen: [aten.add, aten.tanh]
        triton_poi_fused_add_tanh_1.run(buf3, buf1, primals_7, 256, grid=grid(256), stream=stream0)
        del primals_7
        buf4 = empty_strided_cuda((1, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf5 = buf4; del buf4  # reuse
        # Topologically Sorted Source Nodes: [mul, norm, normed_v, mul_1, attn_scores, attn_scores_1], Original ATen: [aten.mul, aten.linalg_vector_norm, aten.div, aten.sum, aten._softmax]
        triton_poi_fused__softmax_div_linalg_vector_norm_mul_sum_2.run(buf5, primals_5, primals_6, buf2, buf3, 64, grid=grid(64), stream=stream0)
        del buf2
        buf6 = reinterpret_tensor(buf1, (4, 4, 4, 4), (64, 16, 4, 1), 0); del buf1  # reuse
        # Topologically Sorted Source Nodes: [mul_2, context], Original ATen: [aten.mul, aten.sum]
        triton_poi_fused_mul_sum_3.run(buf5, primals_4, buf6, 256, grid=grid(256), stream=stream0)
    return (buf6, buf5, primals_4, primals_5, primals_6, reinterpret_tensor(primals_2, (64, 4), (4, 1), 0), buf3, buf5, )


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, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 4), (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)
    primals_5 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((4, ), (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 math
import torch
import torch.nn.functional as F
import torch.nn as nn
import torch.utils.data
from typing import *
from torch.nn import Parameter
import torch.onnx.operators
import torch.optim
import torch.optim.lr_scheduler


class BaseAttention(nn.Module):
    """Base class for attention layers."""

    def __init__(self, query_dim, value_dim, embed_dim=None):
        super().__init__()
        self.query_dim = query_dim
        self.value_dim = value_dim
        self.embed_dim = embed_dim
        self.onnx_trace = False

    def prepare_for_onnx_export_(self):
        self.onnx_trace = True

    def reset_parameters(self):
        pass

    def forward(self, query, value, key_padding_mask=None, state=None):
        raise NotImplementedError


class BahdanauAttention(BaseAttention):
    """ Bahdanau Attention."""

    def __init__(self, query_dim, value_dim, embed_dim, normalize=True):
        super().__init__(query_dim, value_dim, embed_dim)
        self.query_proj = nn.Linear(self.query_dim, embed_dim, bias=False)
        self.value_proj = nn.Linear(self.value_dim, embed_dim, bias=False)
        self.v = Parameter(torch.Tensor(embed_dim))
        self.normalize = normalize
        if self.normalize:
            self.b = Parameter(torch.Tensor(embed_dim))
            self.g = Parameter(torch.Tensor(1))
        self.reset_parameters()

    def reset_parameters(self):
        self.query_proj.weight.data.uniform_(-0.1, 0.1)
        self.value_proj.weight.data.uniform_(-0.1, 0.1)
        nn.init.uniform_(self.v, -0.1, 0.1)
        if self.normalize:
            nn.init.constant_(self.b, 0.0)
            nn.init.constant_(self.g, math.sqrt(1.0 / self.embed_dim))

    def forward(self, query, value, key_padding_mask=None, state=None):
        projected_query = self.query_proj(query).unsqueeze(0)
        key = self.value_proj(value)
        if self.normalize:
            normed_v = self.g * self.v / torch.norm(self.v)
            attn_scores = (normed_v * torch.tanh(projected_query + key +
                self.b)).sum(dim=2)
        else:
            attn_scores = self.v * torch.tanh(projected_query + key).sum(dim=2)
        if key_padding_mask is not None:
            attn_scores = attn_scores.float().masked_fill_(key_padding_mask,
                float('-inf')).type_as(attn_scores)
        attn_scores = F.softmax(attn_scores, dim=0)
        context = (attn_scores.unsqueeze(2) * value).sum(dim=0)
        next_state = attn_scores
        return context, attn_scores, next_state


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


def get_init_inputs():
    return [[], {'query_dim': 4, 'value_dim': 4, 'embed_dim': 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.triton_helpers import libdevice, math as tl_math
import math
import torch.nn as nn
import torch.utils.data
from typing import *
from torch.nn import Parameter
import torch.onnx.operators
import torch.optim
import torch.optim.lr_scheduler
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_per_fused_linalg_vector_norm_0(in_ptr0, out_ptr0, xnumel, rnumel,
    XBLOCK: tl.constexpr):
    RBLOCK: tl.constexpr = 4
    xoffset = tl.program_id(0) * XBLOCK
    xoffset + tl.arange(0, XBLOCK)[:, None]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + r0, None)
    tmp1 = tmp0 * tmp0
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp4 = tl.sum(tmp2, 1)[:, None]
    tl.store(out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp4, None)


@triton.jit
def triton_poi_fused_add_tanh_1(in_out_ptr0, in_ptr0, in_ptr1, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x2, xmask)
    tmp3 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp5 = libdevice.tanh(tmp4)
    tl.store(in_out_ptr0 + x2, tmp5, xmask)


@triton.jit
def triton_poi_fused__softmax_div_linalg_vector_norm_mul_sum_2(in_out_ptr0,
    in_ptr0, in_ptr1, in_ptr2, in_ptr3, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x2 = xindex // 16
    x4 = xindex % 16
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + 0)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK])
    tmp2 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr2 + 0)
    tmp5 = tl.broadcast_to(tmp4, [XBLOCK])
    tmp8 = tl.load(in_ptr3 + (x4 + 64 * x2), xmask)
    tmp10 = tl.load(in_ptr3 + (16 + x4 + 64 * x2), xmask)
    tmp13 = tl.load(in_ptr3 + (32 + x4 + 64 * x2), xmask)
    tmp16 = tl.load(in_ptr3 + (48 + x4 + 64 * x2), xmask)
    tmp3 = tmp1 * tmp2
    tmp6 = libdevice.sqrt(tmp5)
    tmp7 = tmp3 / tmp6
    tmp9 = tmp7 * tmp8
    tmp11 = tmp7 * tmp10
    tmp12 = tmp9 + tmp11
    tmp14 = tmp7 * tmp13
    tmp15 = tmp12 + tmp14
    tmp17 = tmp7 * tmp16
    tmp18 = tmp15 + tmp17
    tmp19 = tmp18 - tmp18
    tmp20 = tl_math.exp(tmp19)
    tmp21 = tmp20 / tmp20
    tl.store(in_out_ptr0 + x3, tmp21, xmask)


@triton.jit
def triton_poi_fused_mul_sum_3(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
    x0 = xindex % 16
    x2 = xindex // 64
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp1 = tl.load(in_ptr1 + x3, xmask)
    tmp2 = tmp0 * tmp1
    tl.store(out_ptr0 + x3, tmp2, 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, 1))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (4, 4), (4, 1))
    assert_size_stride(primals_4, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_5, (1,), (1,))
    assert_size_stride(primals_6, (4,), (1,))
    assert_size_stride(primals_7, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_2, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_1, (4, 4), (1, 4), 0), out=buf0)
        del primals_1
        buf1 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_4, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_3, (4, 4), (1, 4), 0), out=buf1)
        del primals_3
        buf2 = empty_strided_cuda((), (), torch.float32)
        get_raw_stream(0)
        triton_per_fused_linalg_vector_norm_0[grid(1)](primals_6, buf2, 1, 
            4, XBLOCK=1, num_warps=2, num_stages=1)
        buf3 = reinterpret_tensor(buf0, (1, 4, 4, 4, 4), (256, 64, 16, 4, 1), 0
            )
        del buf0
        triton_poi_fused_add_tanh_1[grid(256)](buf3, buf1, primals_7, 256,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_7
        buf4 = empty_strided_cuda((1, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf5 = buf4
        del buf4
        triton_poi_fused__softmax_div_linalg_vector_norm_mul_sum_2[grid(64)](
            buf5, primals_5, primals_6, buf2, buf3, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        del buf2
        buf6 = reinterpret_tensor(buf1, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf1
        triton_poi_fused_mul_sum_3[grid(256)](buf5, primals_4, buf6, 256,
            XBLOCK=256, num_warps=4, num_stages=1)
    return buf6, buf5, primals_4, primals_5, primals_6, reinterpret_tensor(
        primals_2, (64, 4), (4, 1), 0), buf3, buf5


class BaseAttention(nn.Module):
    """Base class for attention layers."""

    def __init__(self, query_dim, value_dim, embed_dim=None):
        super().__init__()
        self.query_dim = query_dim
        self.value_dim = value_dim
        self.embed_dim = embed_dim
        self.onnx_trace = False

    def prepare_for_onnx_export_(self):
        self.onnx_trace = True

    def reset_parameters(self):
        pass

    def forward(self, query, value, key_padding_mask=None, state=None):
        raise NotImplementedError


class BahdanauAttentionNew(BaseAttention):
    """ Bahdanau Attention."""

    def __init__(self, query_dim, value_dim, embed_dim, normalize=True):
        super().__init__(query_dim, value_dim, embed_dim)
        self.query_proj = nn.Linear(self.query_dim, embed_dim, bias=False)
        self.value_proj = nn.Linear(self.value_dim, embed_dim, bias=False)
        self.v = Parameter(torch.Tensor(embed_dim))
        self.normalize = normalize
        if self.normalize:
            self.b = Parameter(torch.Tensor(embed_dim))
            self.g = Parameter(torch.Tensor(1))
        self.reset_parameters()

    def reset_parameters(self):
        self.query_proj.weight.data.uniform_(-0.1, 0.1)
        self.value_proj.weight.data.uniform_(-0.1, 0.1)
        nn.init.uniform_(self.v, -0.1, 0.1)
        if self.normalize:
            nn.init.constant_(self.b, 0.0)
            nn.init.constant_(self.g, math.sqrt(1.0 / self.embed_dim))

    def forward(self, input_0, input_1):
        primals_6 = self.v
        primals_7 = self.b
        primals_5 = self.g
        primals_1 = self.query_proj.weight
        primals_3 = self.value_proj.weight
        primals_2 = input_0
        primals_4 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7])
        return output[0], output[1], output[2]
