pytorch - 💡(How to fix) Fix [aot_autograd] RuntimeError: Cannot call numel() on tensor with symbolic sizes/strides in _convolution_double_backward when using torch.compile(dynamic=True) with torch.func.grad over ConvTranspose2d [1 participants]

🐛 Describe the bug

When composing torch.func.grad with torch.compile(dynamic=True) over a function containing nn.ConvTranspose2d, the inductor backend crashes during AOT autograd graph capture with RuntimeError: Cannot call numel() on tensor with symbolic sizes/strides. The same function works correctly under eager execution, static compilation (dynamic=False), and torch.func.grad without torch.compile.

import torch
import torch.nn as nn
import torch.func as _torch_func

_m_t3759174 = nn.BatchNorm2d(5).eval()
_m_t3759182 = nn.ConvTranspose2d(5, 2, 3)
_m_t3759183 = nn.MaxPool2d(2)

torch.manual_seed(0)
t3759172 = torch.randn([3, 5, 14, 15])

def fn(x):
    x = _m_t3759174(x)
    x = _m_t3759182(x)
    x = _m_t3759183(x)
    return torch.softmax(x, dim=0).mean()

# All of these work fine:
_torch_func.grad(fn)(t3759172)                          # eager grad
torch.compile(_torch_func.grad(fn))(t3759172)           # static compile
torch.compile(fn, dynamic=True)(t3759172)               # dynamic, no grad

# This crashes:
torch.compile(_torch_func.grad(fn), dynamic=True)(t3759172)

Error logs

/home/.venv/lib/python3.10/site-packages/torch/autograd/graph.py:869: UserWarning: Error detected in ConvolutionBackwardBackward0. Traceback of forward call that caused the error: File "/home/.venv/lib/python3.10/site-packages/torch/_functorch/apis.py", line 433, in wrapper return eager_transforms.grad_impl(func, argnums, has_aux, args, kwargs) File "/home/.venv/lib/python3.10/site-packages/torch/_functorch/eager_transforms.py", line 1506, in grad_impl results = grad_and_value_impl(func, argnums, has_aux, args, kwargs) File "/home/.venv/lib/python3.10/site-packages/torch/_functorch/vmap.py", line 56, in fn return f(*args, **kwargs) File "/home/.venv/lib/python3.10/site-packages/torch/_functorch/eager_transforms.py", line 1483, in grad_and_value_impl flat_grad_input = _autograd_grad( (Triggered internally at /pytorch/torch/csrc/autograd/python_anomaly_mode.cpp:122.) return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass /home/.venv/lib/python3.10/site-packages/torch/autograd/graph.py:869: UserWarning:

Previous calculation was induced by ConvolutionBackward0. Traceback of forward call that induced the previous calculation: File "/home/.venv/lib/python3.10/site-packages/torch/_functorch/apis.py", line 433, in wrapper return eager_transforms.grad_impl(func, argnums, has_aux, args, kwargs) File "/home/.venv/lib/python3.10/site-packages/torch/_functorch/eager_transforms.py", line 1506, in grad_impl results = grad_and_value_impl(func, argnums, has_aux, args, kwargs) File "/home/.venv/lib/python3.10/site-packages/torch/_functorch/vmap.py", line 56, in fn return f(*args, **kwargs) File "/home/.venv/lib/python3.10/site-packages/torch/_functorch/eager_transforms.py", line 1457, in grad_and_value_impl output = func(*args, **kwargs) File "/home/bugs/crash_3c0ff967_reporting.py", line 44, in _fn_grad_106941 t3759182 = _m_t3759182(t3759174) (Triggered internally at /pytorch/torch/csrc/autograd/python_anomaly_mode.cpp:129.) return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass Traceback (most recent call last): File "/home/bugs/crash_3c0ff967_reporting.py", line 55, in <module> _fgrad_dynamic = torch.compile(_torch_func.grad(_fn_grad_106941), dynamic=True)(t3759172) File "/home/.venv/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py", line 1038, in compile_wrapper raise e.remove_dynamo_frames() from None # see TORCHDYNAMO_VERBOSE=1 File "/home/.venv/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py", line 1024, in compile_wrapper return fn(*args, **kwargs) File "/home/.venv/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py", line 2316, in call result = self._torchdynamo_orig_backend( File "/home/.venv/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py", line 2052, in call result = self._inner_convert( File "/home/.venv/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py", line 729, in call result = _compile( File "/home/.venv/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py", line 1827, in _compile guarded_code, tracer_output = compile_inner(code, one_graph, hooks) File "/home/.venv/lib/python3.10/site-packages/torch/_utils_internal.py", line 96, in wrapper_function return function(*args, **kwargs) File "/home/.venv/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py", line 1500, in compile_inner return _compile_inner(code, one_graph, hooks) File "/home/.venv/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py", line 1534, in _compile_inner dynamo_output = compile_frame( File "/home/.venv/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py", line 1408, in compile_frame bytecode, tracer_output = transform_code_object(code, transform) File "/home/.venv/lib/python3.10/site-packages/torch/_dynamo/bytecode_transformation.py", line 1608, in transform_code_object tracer_output = transformations(instructions, code_options) File "/home/.venv/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py", line 1380, in transform tracer_output = trace_frame( File "/home/.venv/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py", line 341, in _fn return fn(*args, **kwargs) File "/home/.venv/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py", line 863, in trace_frame run_tracer() File "/home/.venv/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py", line 844, in run_tracer tracer.run() File "/home/.venv/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 1794, in run while self.step(): File "/home/.venv/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 1459, in step self.dispatch_table[inst.opcode](self, inst) File "/home/.venv/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 5570, in RETURN_VALUE self._return(inst) File "/home/.venv/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py", line 5552, in _return all_stack_locals_metadata = self.output.compile_subgraph( File "/home/.venv/lib/python3.10/site-packages/torch/_dynamo/output_graph.py", line 1889, in compile_subgraph self.compile_and_call_fx_graph(tx, pass2.graph_output_vars(), root) File "/home/.venv/lib/python3.10/site-packages/torch/_dynamo/output_graph.py", line 2460, in compile_and_call_fx_graph compiled_fn = self.call_user_compiler(gm, self.example_inputs()) File "/home/.venv/lib/python3.10/site-packages/torch/_dynamo/output_graph.py", line 2613, in call_user_compiler return self._call_user_compiler(gm, example_inputs) File "/home/.venv/lib/python3.10/site-packages/torch/_dynamo/output_graph.py", line 2689, in _call_user_compiler raise BackendCompilerFailed( File "/home/.venv/lib/python3.10/site-packages/torch/_dynamo/output_graph.py", line 2664, in call_user_compiler compiled_fn = compiler_fn(gm, example_inputs) File "/home/.venv/lib/python3.10/site-packages/torch/dynamo/repro/after_dynamo.py", line 156, in call compiled_gm = compiler_fn(gm, example_inputs) File "/home/.venv/lib/python3.10/site-packages/torch/init.py", line 2461, in call return compile_fx(model, inputs, config_patches=self.config) File "/home/.venv/lib/python3.10/site-packages/torch/_inductor/compile_fx.py", line 2578, in compile_fx return _maybe_wrap_and_compile_fx_main( File "/home/.venv/lib/python3.10/site-packages/torch/_inductor/compile_fx.py", line 2655, in _maybe_wrap_and_compile_fx_main return _compile_fx_main( File "/home/.venv/lib/python3.10/site-packages/torch/_inductor/compile_fx.py", line 2850, in _compile_fx_main return aot_autograd( File "/home/.venv/lib/python3.10/site-packages/torch/_dynamo/backends/common.py", line 124, in call cg = aot_module_simplified(gm, example_inputs, **self.kwargs) File "/home/.venv/lib/python3.10/site-packages/torch/_functorch/aot_autograd.py", line 1160, in aot_module_simplified aot_graph_capture = aot_stage1_graph_capture(aot_state, functional_call) File "/home/.venv/lib/python3.10/site-packages/torch/_functorch/_aot_autograd/graph_compile.py", line 223, in aot_stage1_graph_capture ) = aot_dispatch_autograd_graph( File "/home/.venv/lib/python3.10/site-packages/torch/_functorch/_aot_autograd/graph_capture.py", line 482, in aot_dispatch_autograd_graph fx_g = _create_graph( File "/home/.venv/lib/python3.10/site-packages/torch/_functorch/_aot_autograd/graph_capture.py", line 96, in _create_graph fx_g = make_fx( File "/home/.venv/lib/python3.10/site-packages/torch/fx/experimental/proxy_tensor.py", line 2824, in wrapped return make_fx_tracer.trace(f, *args) File "/home/.venv/lib/python3.10/site-packages/torch/fx/experimental/proxy_tensor.py", line 2725, in trace return self._trace_inner(f, *args) File "/home/.venv/lib/python3.10/site-packages/torch/fx/experimental/proxy_tensor.py", line 2686, in _trace_inner t = dispatch_trace( File "/home/.venv/lib/python3.10/site-packages/torch/_compile.py", line 54, in inner return disable_fn(*args, **kwargs) File "/home/.venv/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py", line 1263, in _fn return fn(*args, **kwargs) File "/home/.venv/lib/python3.10/site-packages/torch/fx/experimental/proxy_tensor.py", line 1533, in dispatch_trace graph = tracer.trace(root, concrete_args) # type: ignore[arg-type] File "/home/.venv/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py", line 1263, in _fn return fn(*args, **kwargs) File "/home/.venv/lib/python3.10/site-packages/torch/fx/_symbolic_trace.py", line 890, in trace (self.create_arg(fn(*args)),), File "/home/.venv/lib/python3.10/site-packages/torch/fx/_symbolic_trace.py", line 735, in flatten_fn tree_out = root_fn(*tree_args) File "/home/.venv/lib/python3.10/site-packages/torch/fx/experimental/proxy_tensor.py", line 1603, in wrapped out = f(*tensors) # type:ignore[call-arg] File "/home/.venv/lib/python3.10/site-packages/torch/_functorch/_aot_autograd/graph_capture.py", line 79, in inner_f out, out_descs = call_and_expect_output_descs(f, args) File "/home/.venv/lib/python3.10/site-packages/torch/_functorch/_aot_autograd/utils.py", line 785, in call_and_expect_output_descs outs_pair = fn(*args) File "/home/.venv/lib/python3.10/site-packages/torch/_functorch/_aot_autograd/graph_capture_wrappers.py", line 1215, in inner_fn outs, outs_descs = call_and_expect_output_descs(fn, args) File "/home/.venv/lib/python3.10/site-packages/torch/_functorch/_aot_autograd/utils.py", line 785, in call_and_expect_output_descs outs_pair = fn(*args) File "/home/.venv/lib/python3.10/site-packages/torch/_functorch/_aot_autograd/graph_capture_wrappers.py", line 1157, in joint_helper return _functionalized_f_helper(primals, tangents) File "/home/.venv/lib/python3.10/site-packages/torch/_functorch/_aot_autograd/graph_capture_wrappers.py", line 892, in _functionalized_f_helper f_outs, f_outs_descs = call_and_expect_output_descs(fn, f_args) File "/home/.venv/lib/python3.10/site-packages/torch/_functorch/_aot_autograd/utils.py", line 785, in call_and_expect_output_descs outs_pair = fn(*args) File "/home/.venv/lib/python3.10/site-packages/torch/_functorch/_aot_autograd/graph_capture_wrappers.py", line 495, in joint_helper return inner_fn_with_anomaly(primals, tangents) File "/home/.venv/lib/python3.10/site-packages/torch/_functorch/_aot_autograd/graph_capture_wrappers.py", line 487, in inner_fn_with_anomaly return inner_fn(primals, tangents) File "/home/.venv/lib/python3.10/site-packages/torch/_functorch/_aot_autograd/graph_capture_wrappers.py", line 451, in inner_fn backward_out = torch.autograd.grad( File "/home/.venv/lib/python3.10/site-packages/torch/autograd/init.py", line 481, in grad return handle_torch_function( File "/home/.venv/lib/python3.10/site-packages/torch/overrides.py", line 1741, in handle_torch_function result = mode.torch_function(public_api, types, args, kwargs) File "/home/.venv/lib/python3.10/site-packages/torch/fx/experimental/proxy_tensor.py", line 1654, in torch_function return func(*args, kwargs) File "/home/.venv/lib/python3.10/site-packages/torch/autograd/init.py", line 532, in grad result = _engine_run_backward( File "/home/.venv/lib/python3.10/site-packages/torch/autograd/graph.py", line 869, in _engine_run_backward return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass torch._dynamo.exc.BackendCompilerFailed: backend='inductor' raised: RuntimeError: Cannot call numel() on tensor with symbolic sizes/strides Exception raised from throw_cannot_call_with_symbolic at /pytorch/c10/core/TensorImpl.cpp:289 (most recent call first): frame #0: c10::Error::Error(c10::SourceLocation, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >) + 0x9d (0x7c87b137305d in /home/.venv/lib/python3.10/site-packages/torch/lib/libc10.so) frame #1: c10::TensorImpl::throw_cannot_call_with_symbolic(char const) const + 0x8b (0x7c87b1305f73 in /home/.venv/lib/python3.10/site-packages/torch/lib/libc10.so) frame #2: c10::TensorImpl::numel_custom() const + 0x9c (0x7c87b134c3cc in /home/.venv/lib/python3.10/site-packages/torch/lib/libc10.so) frame #3: at::native::_convolution_double_backward(std::optionalat::Tensor const&, std::optionalat::Tensor const&, std::optionalat::Tensor const&, at::Tensor const&, at::Tensor const&, at::Tensor const&, c10::ArrayRef<long>, c10::ArrayRef<long>, c10::ArrayRef<long>, bool, c10::ArrayRef<long>, long, std::array<bool, 3ul>) + 0x1d06 (0x7c871ba10936 in /home/.venv/lib/python3.10/site-packages/torch/lib/libtorch_cpu.so) frame #4: <unknown function> + 0x3395e64 (0x7c871cf95e64 in /home/.venv/lib/python3.10/site-packages/torch/lib/libtorch_cpu.so) frame #5: <unknown function> + 0x76a9ca (0x7c872fb6a9ca in /home/.venv/lib/python3.10/site-packages/torch/lib/libtorch_python.so) frame #6: <unknown function> + 0x1c54bc1 (0x7c871b854bc1 in /home/.venv/lib/python3.10/site-packages/torch/lib/libtorch_cpu.so) frame #7: <unknown function> + 0x76a9ca (0x7c872fb6a9ca in /home/.venv/lib/python3.10/site-packages/torch/lib/libtorch_python.so) frame #8: <unknown function> + 0x2f0ad3a (0x7c871cb0ad3a in /home/.venv/lib/python3.10/site-packages/torch/lib/libtorch_cpu.so) frame #9: at::_ops::_convolution_double_backward::call(std::optionalat::Tensor const&, std::optionalat::Tensor const&, std::optionalat::Tensor const&, at::Tensor const&, at::Tensor const&, at::Tensor const&, c10::ArrayRefc10::SymInt, c10::ArrayRefc10::SymInt, c10::ArrayRefc10::SymInt, bool, c10::ArrayRefc10::SymInt, c10::SymInt, std::array<bool, 3ul>) + 0x723 (0x7c871cacf6d3 in /home/.venv/lib/python3.10/site-packages/torch/lib/libtorch_cpu.so) frame #10: <unknown function> + 0x514cd40 (0x7c871ed4cd40 in /home/.venv/lib/python3.10/site-packages/torch/lib/libtorch_cpu.so) frame #11: torch::autograd::generated::ConvolutionBackwardBackward0::apply(std::vector<at::Tensor, std::allocatorat::Tensor >&&) + 0x1b5 (0x7c871ed4d445 in /home/.venv/lib/python3.10/site-packages/torch/lib/libtorch_cpu.so) frame #12: <unknown function> + 0x5e52eeb (0x7c871fa52eeb in /home/.venv/lib/python3.10/site-packages/torch/lib/libtorch_cpu.so) frame #13: torch::autograd::Engine::evaluate_function(std::shared_ptrtorch::autograd::GraphTask&, torch::autograd::Node, torch::autograd::InputBuffer&, std::shared_ptrtorch::autograd::ReadyQueue const&) + 0x956 (0x7c871fa4c1f6 in /home/.venv/lib/python3.10/site-packages/torch/lib/libtorch_cpu.so) frame #14: torch::autograd::Engine::thread_main(std::shared_ptrtorch::autograd::GraphTask const&) + 0x5c9 (0x7c871fa4d689 in /home/.venv/lib/python3.10/site-packages/torch/lib/libtorch_cpu.so) frame #15: torch::autograd::Engine::execute_with_graph_task(std::shared_ptrtorch::autograd::GraphTask const&, std::shared_ptrtorch::autograd::Node, torch::autograd::InputBuffer&&) + 0x423 (0x7c871fa482d3 in /home/.venv/lib/python3.10/site-packages/torch/lib/libtorch_cpu.so) frame #16: <unknown function> + 0x813375 (0x7c872fc13375 in /home/.venv/lib/python3.10/site-packages/torch/lib/libtorch_python.so) frame #17: torch::autograd::Engine::execute(std::vector<torch::autograd::Edge, std::allocatortorch::autograd::Edge > const&, std::vector<at::Tensor, std::allocatorat::Tensor > const&, bool, bool, bool, std::vector<torch::autograd::Edge, std::allocatortorch::autograd::Edge > const&) + 0x755 (0x7c871fa4b0d5 in /home/.venv/lib/python3.10/site-packages/torch/lib/libtorch_cpu.so) frame #18: <unknown function> + 0x813306 (0x7c872fc13306 in /home/.venv/lib/python3.10/site-packages/torch/lib/libtorch_python.so) frame #19: <unknown function> + 0x813712 (0x7c872fc13712 in /home/.venv/lib/python3.10/site-packages/torch/lib/libtorch_python.so) <omitting python frames>

Versions

Collecting environment information... PyTorch version: 2.11.0+cu130 Is debug build: False CUDA used to build PyTorch: 13.0 ROCM used to build PyTorch: N/A

OS: Ubuntu 22.04.5 LTS (x86_64) GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04.3) 11.4.0 Clang version: 15.0.0 ([email protected]:llvm/llvm-project.git 4ba6a9c9f65bbc8bd06e3652cb20fd4dfc846137) CMake version: version 3.22.1 Libc version: glibc-2.35

Python version: 3.10.12 (main, Mar 3 2026, 11:56:32) [GCC 11.4.0] (64-bit runtime) Python platform: Linux-6.8.0-94-generic-x86_64-with-glibc2.35 Is CUDA available: False CUDA runtime version: No CUDA CUDA_MODULE_LOADING set to: N/A GPU models and configuration: No CUDA Nvidia driver version: No CUDA cuDNN version: No CUDA Is XPU available: False HIP runtime version: N/A MIOpen runtime version: N/A Is XNNPACK available: True Caching allocator config: N/A

CPU: Architecture: x86_64 CPU op-mode(s): 32-bit, 64-bit Address sizes: 52 bits physical, 57 bits virtual Byte Order: Little Endian CPU(s): 384 On-line CPU(s) list: 0-383 Vendor ID: AuthenticAMD Model name: AMD EPYC 9684X 96-Core Processor CPU family: 25 Model: 17 Thread(s) per core: 2 Core(s) per socket: 96 Socket(s): 2 Stepping: 2 BogoMIPS: 5099.98 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 nonstop_tsc cpuid extd_apicid aperfmperf rapl pni pclmulqdq monitor ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic 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 amd_ppin cppc amd_ibpb_ret arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold avic v_vmsave_vmload vgif x2avic v_spec_ctrl vnmi avx512vbmi umip pku ospke avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg avx512_vpopcntdq la57 rdpid overflow_recov succor smca fsrm flush_l1d debug_swap ibpb_exit_to_user Virtualization: AMD-V L1d cache: 6 MiB (192 instances) L1i cache: 6 MiB (192 instances) L2 cache: 192 MiB (192 instances) L3 cache: 2.3 GiB (24 instances) NUMA node(s): 2 NUMA node0 CPU(s): 0-95,192-287 NUMA node1 CPU(s): 96-191,288-383 Vulnerability Gather data sampling: 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; RSB filling; PBRSB-eIBRS Not affected; BHI Not affected Vulnerability Srbds: Not affected Vulnerability Tsx async abort: Not affected Vulnerability Vmscape: Mitigation; IBPB before exit to userspace

Versions of relevant libraries: [pip3] numpy==2.2.6 [pip3] nvidia-cublas==13.1.0.3 [pip3] nvidia-cuda-cupti==13.0.85 [pip3] nvidia-cuda-nvrtc==13.0.88 [pip3] nvidia-cuda-runtime==13.0.96 [pip3] nvidia-cudnn-cu13==9.19.0.56 [pip3] nvidia-cufft==12.0.0.61 [pip3] nvidia-curand==10.4.0.35 [pip3] nvidia-cusolver==12.0.4.66 [pip3] nvidia-cusparse==12.6.3.3 [pip3] nvidia-cusparselt-cu13==0.8.0 [pip3] nvidia-nccl-cu13==2.28.9 [pip3] nvidia-nvjitlink==13.0.88 [pip3] nvidia-nvtx==13.0.85 [pip3] torch==2.11.0 [pip3] triton==3.6.0 [conda] Could not collect

cc @chauhang @penguinwu @Chillee @samdow @kshitij12345 @ezyang @bobrenjc93 @aditvenk @laithsakka