# AOT ID: ['20_inference'] from ctypes import c_void_p, c_long 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 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 alloc_from_pool = torch.ops.inductor._alloc_from_pool reinterpret_tensor = torch.ops.inductor._reinterpret_tensor async_compile = AsyncCompile() # kernel path: /tmp/torchinductor_root/o6/co6h7wbmqbtjafyje7vguzp77plihb4ndnxudln5qnxynjlsiu3w.py # Source Nodes: [q, q_1, relative_emb], Original ATen: [aten._to_copy, aten.convolution, aten.div, aten.view] # q => div # q_1 => view # relative_emb => convert_element_type, convert_element_type_1, convolution triton_poi_fused__to_copy_convolution_div_view_0 = async_compile.triton('triton_', ''' 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, 8192], tile_hint=TileHint.DEFAULT, filename=__file__, triton_meta={'signature': {0: '*fp16', 1: '*fp16', 2: '*fp16', 3: 'i32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=89, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=1536, multi_processor_count=58), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__to_copy_convolution_div_view_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': '72c34bdb145549777ca2f0838f26abe42bb446cf528c78d229508b5a55e67a78', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': False, '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_(in_ptr0, out_ptr0, out_ptr1, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr): ynumel = 256 xnumel = 4624 yoffset = (tl.program_id(1) + tl.program_id(2) * tl.num_programs(1)) * YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] ymask = yindex < ynumel xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel x1 = xindex y0 = yindex tmp0 = tl.load(in_ptr0 + (x1 + (4624*y0)), xmask & ymask, eviction_policy='evict_last').to(tl.float32) tmp1 = 0.0625 tmp2 = tmp0 * tmp1 tl.store(out_ptr0 + (x1 + (4624*y0)), tmp2, xmask & ymask) tl.store(out_ptr1 + (y0 + (256*x1)), tmp0, xmask & ymask) ''', device_str='cuda') import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream # kernel path: /tmp/torchinductor_root/qe/cqeiyb5quvxiummq7sscsolkbmwqfouyizszibeyjzulgt2oldv3.py # Source Nodes: [relative_emb], Original ATen: [aten._to_copy] # relative_emb => convert_element_type_1 triton_poi_fused__to_copy_1 = async_compile.triton('triton_', ''' 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=[65536], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp16', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=89, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=1536, multi_processor_count=58), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__to_copy_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': '72c34bdb145549777ca2f0838f26abe42bb446cf528c78d229508b5a55e67a78', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': False, '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_(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 57600 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = tl.load(in_ptr0 + (x0), xmask) tmp1 = tmp0.to(tl.float32) tl.store(out_ptr0 + (x0), tmp1, xmask) ''', device_str='cuda') # kernel path: /tmp/torchinductor_root/6f/c6fcbzfqshdwtway7fokjdqpp5fv3vc3hsm3eqeybektqbdgx2qf.py # Source Nodes: [relative_emb], Original ATen: [aten._to_copy, aten.convolution] # relative_emb => convert_element_type, convert_element_type_1, convolution triton_poi_fused__to_copy_convolution_2 = async_compile.triton('triton_', ''' 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, 8192], tile_hint=TileHint.DEFAULT, filename=__file__, triton_meta={'signature': {0: '*fp16', 1: '*fp32', 2: '*fp16', 3: 'i32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=89, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=1536, multi_processor_count=58), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 4), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__to_copy_convolution_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': '72c34bdb145549777ca2f0838f26abe42bb446cf528c78d229508b5a55e67a78', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': False, '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_(in_ptr0, in_ptr1, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr): ynumel = 225 xnumel = 4624 yoffset = (tl.program_id(1) + tl.program_id(2) * tl.num_programs(1)) * YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] ymask = yindex < ynumel xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel x1 = xindex y0 = yindex tmp0 = tl.load(in_ptr0 + (y0 + (225*x1)), xmask & ymask, eviction_policy='evict_last').to(tl.float32) tmp1 = tl.load(in_ptr1 + (y0), ymask, eviction_policy='evict_last') tmp2 = tmp1.to(tl.float32) tmp3 = tmp0 + tmp2 tl.store(out_ptr0 + (x1 + (4672*y0)), tmp3, xmask & ymask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): arg0_1, arg1_1, arg2_1, arg3_1, arg4_1 = args args.clear() assert_size_stride(arg0_1, (1, 256, 68, 68), (1183744, 4624, 68, 1)) assert_size_stride(arg1_1, (225, 256, 1, 1), (256, 1, 1, 1)) assert_size_stride(arg2_1, (225, ), (1, )) assert_size_stride(arg3_1, (1, 256, 68, 68), (1183744, 4624, 68, 1)) assert_size_stride(arg4_1, (1, 256, 68, 68), (1183744, 4624, 68, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((1, 256, 68, 68), (1183744, 4624, 68, 1), torch.float16) buf2 = empty_strided_cuda((1, 256, 68, 68), (1183744, 1, 17408, 256), torch.float16) # Source Nodes: [q, q_1, relative_emb], Original ATen: [aten._to_copy, aten.convolution, aten.div, aten.view] stream0 = get_raw_stream(0) triton_poi_fused__to_copy_convolution_div_view_0.run(arg3_1, buf0, buf2, 256, 4624, grid=grid(256, 4624), stream=stream0) del arg3_1 buf1 = empty_strided_cuda((225, 256, 1, 1), (256, 1, 1, 1), torch.float16) # Source Nodes: [relative_emb], Original ATen: [aten._to_copy] triton_poi_fused__to_copy_1.run(arg1_1, buf1, 57600, grid=grid(57600), stream=stream0) del arg1_1 # Source Nodes: [relative_emb], Original ATen: [aten._to_copy, aten.convolution] buf3 = extern_kernels.convolution(buf2, buf1, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf3, (1, 225, 68, 68), (1040400, 1, 15300, 225)) del buf1 del buf2 buf4 = empty_strided_cuda((1, 225, 68, 68), (1051200, 4672, 68, 1), torch.float16) # Source Nodes: [relative_emb], Original ATen: [aten._to_copy, aten.convolution] triton_poi_fused__to_copy_convolution_2.run(buf3, arg2_1, buf4, 225, 4624, grid=grid(225, 4624), stream=stream0) del arg2_1 del buf3 return (buf0, arg4_1, reinterpret_tensor(arg0_1, (1, 1, 256, 4624), (1183744, 1183744, 4624, 1), 0), 68, 68, reinterpret_tensor(buf4, (1, 1, 225, 4624), (0, 0, 4672, 1), 0), ) 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((1, 256, 68, 68), (1183744, 4624, 68, 1), device='cuda:0', dtype=torch.float16) arg1_1 = rand_strided((225, 256, 1, 1), (256, 1, 1, 1), device='cuda:0', dtype=torch.float32) arg2_1 = rand_strided((225, ), (1, ), device='cuda:0', dtype=torch.float32) arg3_1 = rand_strided((1, 256, 68, 68), (1183744, 4624, 68, 1), device='cuda:0', dtype=torch.float16) arg4_1 = rand_strided((1, 256, 68, 68), (1183744, 4624, 68, 1), device='cuda:0', dtype=torch.float16) fn = lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_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)