pytorch - 💡(How to fix) Fix [Inductor] Incorrect epilogue fusion for user-defined Triton kernels when output has layout-changing ops (`.T.contiguous()`)

pytorch2026-05-18 18:21:04

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Error Message

Traceback (most recent call last): File "repro.py", line 28, in <module> torch.testing.assert_close(fn_compiled(a, b), fn(a, b)) File "/home/jjvraw/Git/pytorch/torch/testing/_comparison.py", line 1631, in assert_close raise error_metas[0].to_error(msg) AssertionError: Tensor-likes are not close!

Mismatched elements: 992 / 1024 (96.9%) Greatest absolute difference: 5.866803228855133 at index (1, 3) (up to 1e-07 allowed) Greatest relative difference: 1150.1576401612313 at index (20, 5) (up to 1e-07 allowed)

Fix Action

Fix / Workaround

CPU: Architecture: x86_64 CPU op-mode(s): 32-bit, 64-bit Address sizes: 48 bits physical, 48 bits virtual Byte Order: Little Endian CPU(s): 32 On-line CPU(s) list: 0-31 Vendor ID: AuthenticAMD Model name: AMD Ryzen 9 7950X3D 16-Core Processor CPU family: 25 Model: 97 Thread(s) per core: 2 Core(s) per socket: 16 Socket(s): 1 Stepping: 2 Frequency boost: enabled CPU(s) scaling MHz: 63% CPU max MHz: 5763.0000 CPU min MHz: 545.0000 BogoMIPS: 8383.77 Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good amd_lbr_v2 nopl xtopology nonstop_tsc cpuid extd_apicid aperfmperf rapl pni pclmulqdq monitor ssse3 fma cx16 sse4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand lahf_lm cmp_legacy svm extapic cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw ibs skinit wdt tce topoext perfctr_core perfctr_nb bpext perfctr_llc mwaitx cpb cat_l3 cdp_l3 hw_pstate ssbd mba perfmon_v2 ibrs ibpb stibp ibrs_enhanced vmmcall fsgsbase bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local user_shstk avx512_bf16 clzero irperf xsaveerptr rdpru wbnoinvd cppc arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold avic vgif x2avic v_spec_ctrl vnmi avx512vbmi umip pku ospke avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg avx512_vpopcntdq rdpid overflow_recov succor smca fsrm flush_l1d amd_lbr_pmc_freeze Virtualization: AMD-V L1d cache: 512 KiB (16 instances) L1i cache: 512 KiB (16 instances) L2 cache: 16 MiB (16 instances) L3 cache: 128 MiB (2 instances) NUMA node(s): 1 NUMA node0 CPU(s): 0-31 Vulnerability Gather data sampling: Not affected Vulnerability Ghostwrite: Not affected Vulnerability Indirect target selection: Not affected Vulnerability Itlb multihit: Not affected Vulnerability L1tf: Not affected Vulnerability Mds: Not affected Vulnerability Meltdown: Not affected Vulnerability Mmio stale data: Not affected Vulnerability Reg file data sampling: Not affected Vulnerability Retbleed: Not affected Vulnerability Spec rstack overflow: Mitigation; Safe RET Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization Vulnerability Spectre v2: Mitigation; Enhanced / Automatic IBRS; IBPB conditional; STIBP always-on; PBRSB-eIBRS Not affected; BHI Not affected Vulnerability Srbds: Not affected Vulnerability Tsa: Mitigation; Clear CPU buffers Vulnerability Tsx async abort: Not affected Vulnerability Vmscape: Mitigation; IBPB before exit to userspace

Code Example

import torch

import triton 
import triton.language as tl

torch._inductor.config.epilogue_fusion_user_defined_triton_kernel = True

@triton.jit
def add_kernel(in_ptr0, in_ptr1, out_ptr, n_elements, BLOCK_SIZE: tl.constexpr):
    pid = tl.program_id(0)
    offs = pid * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE)
    mask = offs < n_elements
    x = tl.load(in_ptr0 + offs, mask=mask)
    y = tl.load(in_ptr1 + offs, mask=mask)
    tl.store(out_ptr + offs, x + y, mask=mask)


def fn(a, b):
    out = torch.empty_like(a)
    grid = (triton.cdiv(a.numel(), 1024),)
    add_kernel[grid](a, b, out, a.numel(), BLOCK_SIZE=1024)
    return out.T.contiguous()

a = torch.randn(32, 32, device="cuda").double()
b = torch.randn(32, 32, device="cuda").double()

fn_compiled = torch.compile(fn)
torch.testing.assert_close(fn_compiled(a, b), fn(a, b))

---

op2: SchedulerNode(ComputedBuffer)
op2.writes = [MemoryDep('buf2', 32*d0 + d1, {d0: 32, d1: 32})]
op2.unmet_dependencies = [MemoryDep('buf1', d0 + 32*d1, {d0: 32, d1: 32})]
op2.met_dependencies = []
op2.min_input_distance = 2
op2.max_input_distance = 2
op2.outputs = [
    buf2: ComputedBuffer
    buf2.layout = FixedLayout('cuda:0', torch.float64, size=[32, 32], stride=[32, 1])
    buf2.users = [NodeUser(node=OUTPUT, can_inplace=False, is_weak=False)]
]
op2.group.device = cuda:0
op2.group.iteration = (1024, 1)
op2.sizes = ([32, 32], [])
buf1_layout = NoneLayout(device=device(type='cuda', index=0), size=[0], stride=[0])
buf2_layout = FixedLayout('cuda:0', torch.float64, size=[32, 32], stride=[32, 1])
class op2_loop_body:
    var_ranges = {p0: 32, p1: 32}
    index0 = p0 + 32*p1
    index1 = 32*p0 + p1
    def body(self, ops):
        get_index = self.get_index('index0')
        load = ops.load('buf0', get_index)
        get_index_1 = self.get_index('index1')
        store = ops.store('buf2', get_index_1, load, None)
        return store

---

Traceback (most recent call last):
  File "repro.py", line 28, in <module>
    torch.testing.assert_close(fn_compiled(a, b), fn(a, b))
  File "/home/jjvraw/Git/pytorch/torch/testing/_comparison.py", line 1631, in assert_close
    raise error_metas[0].to_error(msg)
AssertionError: Tensor-likes are not close!

Mismatched elements: 992 / 1024 (96.9%)
Greatest absolute difference: 5.866803228855133 at index (1, 3) (up to 1e-07 allowed)
Greatest relative difference: 1150.1576401612313 at index (20, 5) (up to 1e-07 allowed)

---

PyTorch version: N/A
Is debug build: N/A
CUDA used to build PyTorch: N/A
ROCM used to build PyTorch: N/A

OS: Ubuntu 25.04 (x86_64)
GCC version: (Ubuntu 14.2.0-19ubuntu2) 14.2.0
Clang version: 20.1.2 (0ubuntu1)
CMake version: version 3.31.10
Libc version: glibc-2.41

Python version: 3.13.5 | packaged by Anaconda, Inc. | (main, Jun 12 2025, 16:09:02) [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-6.14.0-37-generic-x86_64-with-glibc2.41
Is CUDA available: N/A
CUDA runtime version: 12.8.61
CUDA_MODULE_LOADING set to: N/A
GPU models and configuration: GPU 0: NVIDIA GeForce RTX 4090
Nvidia driver version: 580.95.05
cuDNN version: Could not collect
Is XPU available: N/A
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: N/A
Caching allocator config: N/A

CPU:
Architecture:                            x86_64
CPU op-mode(s):                          32-bit, 64-bit
Address sizes:                           48 bits physical, 48 bits virtual
Byte Order:                              Little Endian
CPU(s):                                  32
On-line CPU(s) list:                     0-31
Vendor ID:                               AuthenticAMD
Model name:                              AMD Ryzen 9 7950X3D 16-Core Processor
CPU family:                              25
Model:                                   97
Thread(s) per core:                      2
Core(s) per socket:                      16
Socket(s):                               1
Stepping:                                2
Frequency boost:                         enabled
CPU(s) scaling MHz:                      63%
CPU max MHz:                             5763.0000
CPU min MHz:                             545.0000
BogoMIPS:                                8383.77
Flags:                                   fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good amd_lbr_v2 nopl xtopology nonstop_tsc cpuid extd_apicid aperfmperf rapl pni pclmulqdq monitor ssse3 fma cx16 sse4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand lahf_lm cmp_legacy svm extapic cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw ibs skinit wdt tce topoext perfctr_core perfctr_nb bpext perfctr_llc mwaitx cpb cat_l3 cdp_l3 hw_pstate ssbd mba perfmon_v2 ibrs ibpb stibp ibrs_enhanced vmmcall fsgsbase bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local user_shstk avx512_bf16 clzero irperf xsaveerptr rdpru wbnoinvd cppc arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold avic vgif x2avic v_spec_ctrl vnmi avx512vbmi umip pku ospke avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg avx512_vpopcntdq rdpid overflow_recov succor smca fsrm flush_l1d amd_lbr_pmc_freeze
Virtualization:                          AMD-V
L1d cache:                               512 KiB (16 instances)
L1i cache:                               512 KiB (16 instances)
L2 cache:                                16 MiB (16 instances)
L3 cache:                                128 MiB (2 instances)
NUMA node(s):                            1
NUMA node0 CPU(s):                       0-31
Vulnerability Gather data sampling:      Not affected
Vulnerability Ghostwrite:                Not affected
Vulnerability Indirect target selection: Not affected
Vulnerability Itlb multihit:             Not affected
Vulnerability L1tf:                      Not affected
Vulnerability Mds:                       Not affected
Vulnerability Meltdown:                  Not affected
Vulnerability Mmio stale data:           Not affected
Vulnerability Reg file data sampling:    Not affected
Vulnerability Retbleed:                  Not affected
Vulnerability Spec rstack overflow:      Mitigation; Safe RET
Vulnerability Spec store bypass:         Mitigation; Speculative Store Bypass disabled via prctl
Vulnerability Spectre v1:                Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2:                Mitigation; Enhanced / Automatic IBRS; IBPB conditional; STIBP always-on; PBRSB-eIBRS Not affected; BHI Not affected
Vulnerability Srbds:                     Not affected
Vulnerability Tsa:                       Mitigation; Clear CPU buffers
Vulnerability Tsx async abort:           Not affected
Vulnerability Vmscape:                   Mitigation; IBPB before exit to userspace

Versions of relevant libraries:
[pip3] flake8==7.3.0
[pip3] flake8-bugbear==24.12.12
[pip3] flake8-comprehensions==3.16.0
[pip3] flake8-executable==2.1.3
[pip3] flake8-logging-format==2024.24.12
[pip3] flake8-pyi==25.5.0
[pip3] flake8_simplify==0.22.0
[pip3] intel-cmplr-lib-ur==2025.3.1
[pip3] intel-openmp==2025.3.1
[pip3] mkl-include==2025.3.0
[pip3] mkl-static==2025.3.0
[pip3] mypy==1.16.0
[pip3] mypy_extensions==1.1.0
[pip3] numpy==2.1.0
[pip3] onemkl-license==2025.3.0
[pip3] optree==0.17.0
[pip3] tbb==2022.3.0
[pip3] tbb-devel==2022.3.0
[pip3] tcmlib==1.4.1
[pip3] torch==2.12.0a0+gitaeee308
[pip3] triton==3.7.0
[pip3] umf==1.0.2
[conda] intel-cmplr-lib-ur          2025.3.1             pypi_0              pypi
[conda] intel-openmp                2025.3.1             pypi_0              pypi
[conda] mkl-include                 2025.3.0             pypi_0              pypi
[conda] mkl-static                  2025.3.0             pypi_0              pypi
[conda] numpy                       2.1.0                pypi_0              pypi
[conda] onemkl-license              2025.3.0             pypi_0              pypi
[conda] optree                      0.17.0               pypi_0              pypi
[conda] tbb                         2022.3.0             pypi_0              pypi
[conda] tbb-devel                   2022.3.0             pypi_0              pypi
[conda] tcmlib                      1.4.1                pypi_0              pypi
[conda] torch                       2.12.0a0+gitaeee308  pypi_0              pypi
[conda] triton                      3.7.0                pypi_0              pypi
[conda] umf                         1.0.2                pypi_0              pypi

RAW_BUFFERClick to expand / collapse

🐛 Describe the bug

During codegen for a fused custom op, we rewrite the tl.store to use a new value operand that references the variable from the last computation in the epilogue. See: https://github.com/pytorch/pytorch/blob/9a8a62cf0b558e80e46565025a31ebdb5f8ec243/torch/_inductor/codegen/triton.py#L6573-L6582

This assumes the write index of the epilogue equals that of the read. Currently, the write reindex from contiguous() is silently substituted away with the user's original store index.

Repro

import torch

import triton 
import triton.language as tl

torch._inductor.config.epilogue_fusion_user_defined_triton_kernel = True

@triton.jit
def add_kernel(in_ptr0, in_ptr1, out_ptr, n_elements, BLOCK_SIZE: tl.constexpr):
    pid = tl.program_id(0)
    offs = pid * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE)
    mask = offs < n_elements
    x = tl.load(in_ptr0 + offs, mask=mask)
    y = tl.load(in_ptr1 + offs, mask=mask)
    tl.store(out_ptr + offs, x + y, mask=mask)


def fn(a, b):
    out = torch.empty_like(a)
    grid = (triton.cdiv(a.numel(), 1024),)
    add_kernel[grid](a, b, out, a.numel(), BLOCK_SIZE=1024)
    return out.T.contiguous()

a = torch.randn(32, 32, device="cuda").double()
b = torch.randn(32, 32, device="cuda").double()

fn_compiled = torch.compile(fn)
torch.testing.assert_close(fn_compiled(a, b), fn(a, b))

IR

See read index != write index:

op2: SchedulerNode(ComputedBuffer)
op2.writes = [MemoryDep('buf2', 32*d0 + d1, {d0: 32, d1: 32})]
op2.unmet_dependencies = [MemoryDep('buf1', d0 + 32*d1, {d0: 32, d1: 32})]
op2.met_dependencies = []
op2.min_input_distance = 2
op2.max_input_distance = 2
op2.outputs = [
    buf2: ComputedBuffer
    buf2.layout = FixedLayout('cuda:0', torch.float64, size=[32, 32], stride=[32, 1])
    buf2.users = [NodeUser(node=OUTPUT, can_inplace=False, is_weak=False)]
]
op2.group.device = cuda:0
op2.group.iteration = (1024, 1)
op2.sizes = ([32, 32], [])
buf1_layout = NoneLayout(device=device(type='cuda', index=0), size=[0], stride=[0])
buf2_layout = FixedLayout('cuda:0', torch.float64, size=[32, 32], stride=[32, 1])
class op2_loop_body:
    var_ranges = {p0: 32, p1: 32}
    index0 = p0 + 32*p1
    index1 = 32*p0 + p1
    def body(self, ops):
        get_index = self.get_index('index0')
        load = ops.load('buf0', get_index)
        get_index_1 = self.get_index('index1')
        store = ops.store('buf2', get_index_1, load, None)
        return store

Error logs

Traceback (most recent call last):
  File "repro.py", line 28, in <module>
    torch.testing.assert_close(fn_compiled(a, b), fn(a, b))
  File "/home/jjvraw/Git/pytorch/torch/testing/_comparison.py", line 1631, in assert_close
    raise error_metas[0].to_error(msg)
AssertionError: Tensor-likes are not close!

Mismatched elements: 992 / 1024 (96.9%)
Greatest absolute difference: 5.866803228855133 at index (1, 3) (up to 1e-07 allowed)
Greatest relative difference: 1150.1576401612313 at index (20, 5) (up to 1e-07 allowed)

Versions

PyTorch version: N/A
Is debug build: N/A
CUDA used to build PyTorch: N/A
ROCM used to build PyTorch: N/A

OS: Ubuntu 25.04 (x86_64)
GCC version: (Ubuntu 14.2.0-19ubuntu2) 14.2.0
Clang version: 20.1.2 (0ubuntu1)
CMake version: version 3.31.10
Libc version: glibc-2.41

Python version: 3.13.5 | packaged by Anaconda, Inc. | (main, Jun 12 2025, 16:09:02) [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-6.14.0-37-generic-x86_64-with-glibc2.41
Is CUDA available: N/A
CUDA runtime version: 12.8.61
CUDA_MODULE_LOADING set to: N/A
GPU models and configuration: GPU 0: NVIDIA GeForce RTX 4090
Nvidia driver version: 580.95.05
cuDNN version: Could not collect
Is XPU available: N/A
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: N/A
Caching allocator config: N/A

CPU:
Architecture:                            x86_64
CPU op-mode(s):                          32-bit, 64-bit
Address sizes:                           48 bits physical, 48 bits virtual
Byte Order:                              Little Endian
CPU(s):                                  32
On-line CPU(s) list:                     0-31
Vendor ID:                               AuthenticAMD
Model name:                              AMD Ryzen 9 7950X3D 16-Core Processor
CPU family:                              25
Model:                                   97
Thread(s) per core:                      2
Core(s) per socket:                      16
Socket(s):                               1
Stepping:                                2
Frequency boost:                         enabled
CPU(s) scaling MHz:                      63%
CPU max MHz:                             5763.0000
CPU min MHz:                             545.0000
BogoMIPS:                                8383.77
Flags:                                   fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good amd_lbr_v2 nopl xtopology nonstop_tsc cpuid extd_apicid aperfmperf rapl pni pclmulqdq monitor ssse3 fma cx16 sse4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand lahf_lm cmp_legacy svm extapic cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw ibs skinit wdt tce topoext perfctr_core perfctr_nb bpext perfctr_llc mwaitx cpb cat_l3 cdp_l3 hw_pstate ssbd mba perfmon_v2 ibrs ibpb stibp ibrs_enhanced vmmcall fsgsbase bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local user_shstk avx512_bf16 clzero irperf xsaveerptr rdpru wbnoinvd cppc arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold avic vgif x2avic v_spec_ctrl vnmi avx512vbmi umip pku ospke avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg avx512_vpopcntdq rdpid overflow_recov succor smca fsrm flush_l1d amd_lbr_pmc_freeze
Virtualization:                          AMD-V
L1d cache:                               512 KiB (16 instances)
L1i cache:                               512 KiB (16 instances)
L2 cache:                                16 MiB (16 instances)
L3 cache:                                128 MiB (2 instances)
NUMA node(s):                            1
NUMA node0 CPU(s):                       0-31
Vulnerability Gather data sampling:      Not affected
Vulnerability Ghostwrite:                Not affected
Vulnerability Indirect target selection: Not affected
Vulnerability Itlb multihit:             Not affected
Vulnerability L1tf:                      Not affected
Vulnerability Mds:                       Not affected
Vulnerability Meltdown:                  Not affected
Vulnerability Mmio stale data:           Not affected
Vulnerability Reg file data sampling:    Not affected
Vulnerability Retbleed:                  Not affected
Vulnerability Spec rstack overflow:      Mitigation; Safe RET
Vulnerability Spec store bypass:         Mitigation; Speculative Store Bypass disabled via prctl
Vulnerability Spectre v1:                Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2:                Mitigation; Enhanced / Automatic IBRS; IBPB conditional; STIBP always-on; PBRSB-eIBRS Not affected; BHI Not affected
Vulnerability Srbds:                     Not affected
Vulnerability Tsa:                       Mitigation; Clear CPU buffers
Vulnerability Tsx async abort:           Not affected
Vulnerability Vmscape:                   Mitigation; IBPB before exit to userspace

Versions of relevant libraries:
[pip3] flake8==7.3.0
[pip3] flake8-bugbear==24.12.12
[pip3] flake8-comprehensions==3.16.0
[pip3] flake8-executable==2.1.3
[pip3] flake8-logging-format==2024.24.12
[pip3] flake8-pyi==25.5.0
[pip3] flake8_simplify==0.22.0
[pip3] intel-cmplr-lib-ur==2025.3.1
[pip3] intel-openmp==2025.3.1
[pip3] mkl-include==2025.3.0
[pip3] mkl-static==2025.3.0
[pip3] mypy==1.16.0
[pip3] mypy_extensions==1.1.0
[pip3] numpy==2.1.0
[pip3] onemkl-license==2025.3.0
[pip3] optree==0.17.0
[pip3] tbb==2022.3.0
[pip3] tbb-devel==2022.3.0
[pip3] tcmlib==1.4.1
[pip3] torch==2.12.0a0+gitaeee308
[pip3] triton==3.7.0
[pip3] umf==1.0.2
[conda] intel-cmplr-lib-ur          2025.3.1             pypi_0              pypi
[conda] intel-openmp                2025.3.1             pypi_0              pypi
[conda] mkl-include                 2025.3.0             pypi_0              pypi
[conda] mkl-static                  2025.3.0             pypi_0              pypi
[conda] numpy                       2.1.0                pypi_0              pypi
[conda] onemkl-license              2025.3.0             pypi_0              pypi
[conda] optree                      0.17.0               pypi_0              pypi
[conda] tbb                         2022.3.0             pypi_0              pypi
[conda] tbb-devel                   2022.3.0             pypi_0              pypi
[conda] tcmlib                      1.4.1                pypi_0              pypi
[conda] torch                       2.12.0a0+gitaeee308  pypi_0              pypi
[conda] triton                      3.7.0                pypi_0              pypi
[conda] umf                         1.0.2                pypi_0              pypi

cc @chauhang @penguinwu @voznesenskym @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @kadeng @muchulee8 @amjames @aakhundov @coconutruben @jataylo @oulgen @davidberard98

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pytorch - 💡(How to fix) Fix [Inductor] Incorrect epilogue fusion for user-defined Triton kernels when output has layout-changing ops (`.T.contiguous()`)

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pytorch - 💡(How to fix) Fix [Inductor] Incorrect epilogue fusion for user-defined Triton kernels when output has layout-changing ops (`.T.contiguous()`)

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