Repository: pytorch
Issue: torch.compile error
Labels: needs reproduction,triaged,oncall: pt2,module: inductor

### 🐛 Describe the bug
**python code**
```
import sys
import os.path
import time

current_directory = os.path.dirname(os.path.abspath(__file__))
home = os.path.abspath(os.path.join(current_directory))
sys.path.append(home)

import numpy as np
import random
import torch
from torch import nn
import pickle
import torch.utils.data
from torch.cuda.amp import GradScaler, autocast
from torch.utils.data import Dataset

torch.set_float32_matmul_precision('highest')
#torch.set_float32_matmul_precision('high')
DNN_DATA_TYPE = torch.float32
HALF_TRAIN = False
HALF_TEST = False
TRAIN_BATCH_SIZE = 512
TRAIN_THREAD_SIZE = 4
TEST_BATCH_SIZE = 512
TEST_THREAD_SIZE = 4


def get_device():
    device_type = 'cpu'
    is_cuda = False
    if torch.cuda.is_available():
        device_type = 'cuda:0'
        torch.backends.cudnn.enabled = True
        torch.backends.cudnn.benchmark = True
        is_cuda = True
        if is_cuda == True:
            print('enable cuda amp test')
    elif torch.backends.mps.is_built():
        device_type = 'mps'
    elif torch.is_vulkan_available():
        device_type = 'vulkan'
    # device_type = 'cpu'
    if device_type == 'cpu':
        is_cuda = False
        if os.name.lower() == 'nt':
            import importlib
            spam_loader = importlib.util.find_spec('torch_directml')
            found = spam_loader is not None
            if found == True:
                import torch_directml
                print(f'device_type: directml')
                return torch_directml.device(), is_cuda
    print(f'device_type: {device_type}')
    device = torch.device(device_type)
    return device, is_cuda


class MyDataSet(Dataset):

    def __init__(self, data_input_list: list):
        self.data_list = data_input_list

    def __len__(self):
        return len(self.data_list)

    def __getitem__(self, item):
        local_data = self.data_list[item]
        data, tpye_id, net_type, label = local_data
        return torch.as_tensor(data).to(DNN_DATA_TYPE), torch.as_tensor(tpye_id).to(DNN_DATA_TYPE), torch.as_tensor(
            net_type).to(DNN_DATA_TYPE), torch.as_tensor(
            label).to(DNN_DATA_TYPE)


class ResBlock(nn.Module):
    def __init__(self, inchannel, outchannel, stride=1):
        super(ResBlock, self).__init__()
        self.left = nn.Sequential(
            nn.Conv2d(inchannel, outchannel, kernel_size=3, stride=stride, padding=1, bias=False),
            nn.BatchNorm2d(outchannel),
            nn.ReLU(inplace=True),
            nn.Conv2d(outchannel, outchannel, kernel_size=3, stride=1, padding=1, bias=False),
            nn.BatchNorm2d(outchannel)
        )
        self.shortcut = nn.Sequential()
        if stride != 1 or inchannel != outchannel:
            self.shortcut = nn.Sequential(
                nn.Conv2d(inchannel, outchannel, kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(outchannel)
            )

    def forward(self, x):
        out = self.left(x)
        out = out + self.shortcut(x)
        out = torch.nn.functional.relu(out)

        return out


class QANet(nn.Module):
    def __init__(self):
        super(QANet, self).__init__()
        
        self.inchannel = 16

        self.fc_start = nn.Sequential(
            nn.Linear(3 + 8 * 3 + 8 * 5 + 1 + 1, 1024),
            nn.ReLU(inplace=True)
        )
        
        self.fc_begin_start = nn.Sequential(
            ResBlock(1, 16)
        )
        
        self.layer1 = self.make_layer(ResBlock, 16, 6, stride=1)

        self.layer2 = self.make_layer(ResBlock, 16, 6, stride=1)

        self.layer3 = self.make_layer(ResBlock, 16, 6, stride=1)

        self.layer4 = self.make_layer(ResBlock, 16, 6, stride=1)

        self.layer5 = self.make_layer(ResBlock, 16, 6, stride=1)
        
        self.layer6 = self.make_layer(ResBlock, 16, 6, stride=1)
        
        self.layer7 = self.make_layer(ResBlock, 16, 6, stride=1)
        
        self.layer8 = self.make_layer(ResBlock, 16, 6, stride=1)

        self.layer9 = self.make_layer(ResBlock, 16, 6, stride=1)

        self.layer10 = self.make_layer(ResBlock, 16, 6, stride=1)
        
        self.fc_begin_end = nn.Sequential(
            ResBlock(16, 1)
        )

        self.fc_after_layer = nn.Sequential(
            nn.Linear(32 * 32, 1024),
            nn.ReLU(inplace=True)
        )
        
        self.fc_after_1 = nn.Sequential(
            nn.Linear(1024, 1024),
            nn.ReLU(inplace=True)
        )
        
        self.fc_after_2 = nn.Sequential(
            nn.Linear(1024, 1024),
            nn.ReLU(inplace=True)
        )
        
        self.fc_after_3 = nn.Sequential(
            nn.Linear(1024, 1024),
            nn.ReLU(inplace=True)
        )
        
        self.fc_after_4 = nn.Sequential(
            nn.Linear(1024, 1024),
            nn.ReLU(inplace=True)
        )
        
        self.fc_after_5 = nn.Sequential(
            nn.Linear(1024, 1024),
            nn.ReLU(inplace=True)
        )
        
        self.fc_out = nn.Sequential(
            nn.Linear(1024, 2)
        )
    
    def make_layer(self, block, channels, num_blocks, stride):
        strides = [stride] + [1] * (num_blocks - 1)
        layers = []
        for stride in strides:
            layers.append(block(self.inchannel, channels, stride))
            self.inchannel = channels

        return nn.Sequential(*layers)

    def forward(self, x, id, net_type):
        x = x.view(x.size(0), 3 + 8 * 3 + 8 * 5)
        id = id.view(x.size(0), 1)
        net_type = net_type.view(x.size(0), 1)
        x = torch.cat((x, id, net_type), dim=1)
        x = x.view(x.size(0), 1, 3 + 8 * 3 + 8 * 5 + 1 + 1)
        x = self.fc_start(x)
        x = x.view(x.size(0), 1, 32, 32)
        x = self.fc_begin_start(x)
        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)
        x = self.layer5(x)
        x = self.layer6(x)
        x = self.layer7(x)
        x = self.layer8(x)
        x = self.layer9(x)
        x = self.layer10(x)
        x = self.fc_begin_end(x)
        x = x.view(x.size(0), 32 * 32)
        x = self.fc_after_layer(x)
        x = self.fc_after_1(x)
        x = self.fc_after_2(x)
        x = self.fc_after_3(x)
        x = self.fc_after_4(x)
        x = self.fc_after_5(x)
        x = self.fc_out(x)
        x = x.view(x.size(0), 2)
        return x


class My_loss(nn.Module):

    def __init__(self):
        super().__init__()

    def forward(self, x, y):
        tmp = torch.pow((x - y), 2)
        power_dst = torch.sum(tmp, dim=1)
        #dst = torch.sqrt(power_dst)
        #mean = torch.mean(dst)
        mean = torch.mean(power_dst)
        return mean


def init_module():
    device, is_cuda = get_device()
    model = QANet().to(DNN_DATA_TYPE)

    if is_cuda:
        model = model.to("cuda")
        # model = DataParallel(model, device_ids=[0, 1])
        # loss_fn = nn.SmoothL1Loss(reduction='mean').cuda()
        # loss_fn = nn.CrossEntropyLoss(reduction="mean").cuda()
        #loss_fn = nn.MSELoss(reduction='mean').cuda()
        loss_fn = My_loss().to(device)
        print("model , loss on cuda")
    else:
        model = model.to(device)
        loss_fn = My_loss().to(device)
        #loss_fn = nn.MSELoss(reduction='mean').to(device)

    if os.name.lower() == 'posix':
        print("Using torch.compile module")
        model = torch.compile(model, mode="max-autotune")
    else:
        # model = torch.jit.script(model)
        pass

    # model = torch.jit.script(model)
    return model, loss_fn, device, is_cuda


def save_model(model, path):
    # 保存网络
    torch.save(model.state_dict(), path)


def init_module_with_pth(pth):
    # 若当前 Pytorch 版本以及电脑支持GPU，则使用 GPU 训练，否则使用 CPU
    device, is_cuda = get_device()
    state_dict = torch.load(pth, map_location='cpu')
    model = QANet().to(DNN_DATA_TYPE)
    set_compile = True
    if set_compile:
        state_dict_fix = {}
        for k, v in state_dict.items():
            tmp_k = k[k.find(".") + 1:]
            state_dict_fix[tmp_k] = v
        model.load_state_dict(state_dict_fix, strict=False)
    else:
        model.load_state_dict(state_dict, strict=False)
    if is_cuda:
        model = model.to("cuda")
        # model = DataParallel(model, device_ids=[0, 1])
        # loss_fn = nn.SmoothL1Loss(reduction='mean').cuda()
        # loss_fn = nn.CrossEntropyLoss(reduction="mean").cuda()
        #loss_fn = nn.MSELoss(reduction='mean').cuda()
        loss_fn = My_loss().to(device)
        print("model , loss on cuda")
    else:
        model = model.to(device)
        loss_fn = My_loss().to(device)
        #loss_fn = nn.MSELoss(reduction='mean').to(device)

    if os.name.lower() == 'posix':
        print("Using torch.compile module")
        model = torch.compile(model, mode="max-autotune")
    else:
        pass

    # model = torch.jit.script(model)
    return model, loss_fn, device, is_cuda


def read_train_data(info_file):
    with open(info_file, 'rb') as f:
        data = pickle.load(f)
    return data


def get_data_loader(src_info_path):
    train_list = read_train_data(src_info_path)
    random.shuffle(train_list)
    test_list_size = int(len(train_list) * 0.05)
    test_list = train_list[:test_list_size]
    train_list = train_list[test_list_size:]
    train_loader_in = MyDataSet(data_input_list=train_list)
    train_loader = torch.utils.data.DataLoader(
        dataset=train_loader_in,
        batch_size=TRAIN_BATCH_SIZE,  
        shuffle=True, 
        num_workers=TRAIN_THREAD_SIZE, 
        pin_memory=True
    )
    random.shuffle(test_list)
    test_loader_in = MyDataSet(data_input_list=test_list)

    test_loader = torch.utils.data.DataLoader(
        dataset=test_loader_in,
        batch_size=TEST_BATCH_SIZE, 
        shuffle=False, 
        num_workers=TEST_THREAD_SIZE,  
        pin_memory=True
    )
    return train_loader, test_loader


def init_random():

    SEED = 1

    np.random.seed(SEED)
    random.seed(SEED)
    torch.manual_seed(SEED)  
    torch.cuda.manual_seed(SEED)  


def init_train(module, pth_path):
    if module == 'empty_train' or not os.path.exists(pth_path):
        model, loss_fn, device, is_cuda = init_module()
    else:
        model, loss_fn, device, is_cuda = init_module_with_pth(pth_path)
    return model, loss_fn, device, is_cuda


def train(pth_path, module, learn_rate, train_loader, test_loader, train_time):
    global scheduler, scaler, optimizer
    model, loss_fn, device, is_cuda = init_train(module, pth_path)
    num_batches = len(train_loader)
    allTime = num_batches * train_time
    test_list_size = len(test_loader)
    if is_cuda and HALF_TRAIN:
        scaler = GradScaler()
    max_accept_number = 0
    all_all_loss = test_list_size * 10.0 * 10000
    time_without_save = 0
    optimizer = torch.optim.AdamW(model.parameters(), lr=learn_rate)
    for i in range(train_time):
        model.train() 
        if i != 0 and i % 500 == 0:
            learn_rate = learn_rate * 0.5
            optimizer = torch.optim.AdamW(model.parameters(), lr=learn_rate)
        print(f'learn_rate {learn_rate}, loop {i}, all {train_time}')
        print(f'Begin train' + time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()))
        for step1, (learn_data, tpye_id, net_type, labels) in enumerate(train_loader):
            learn_data = learn_data.to(device)
            tpye_id = tpye_id.to(device)
            net_type = net_type.to(device)
            labels = labels.to(device)

            optimizer.zero_grad(set_to_none=True)

            if is_cuda and HALF_TRAIN:
                with autocast():
                    output = model(learn_data, tpye_id, net_type)
                    loss = loss_fn(output, labels)
                    # loss.requires_grad_(True)
                    scaler.scale(loss).backward() 
                    scaler.step(optimizer)
                    scaler.update()
            else:
                output = model(learn_data, tpye_id, net_type)
                loss = loss_fn(output, labels)
                loss.backward()
                optimizer.step()

            if step1 % 2000 == 0:
                loss_in = float(loss.item())
                diff = float(torch.sum(torch.abs(output - labels)).item())
                print(
                    f"current batch:{step1 + i * num_batches}/all:{allTime}, step: {step1},loss: {loss_in: .10f}, diff: {diff}")
        print(f'After train and begin test' + time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()))
        model.eval()
        all_loss = 0
        all_diff = 0
        with torch.no_grad():
            for test_step, (test_data, tpye_id, net_type, labels) in enumerate(test_loader):
                test_data = test_data.to(device)
                tpye_id = tpye_id.to(device)
                net_type = net_type.to(device)
                labels = labels.to(device)
                if is_cuda and HALF_TEST:
                    with autocast():
                        output = model(test_data, tpye_id, net_type)
                        loss = loss_fn(output, labels)
                else:
                    output = model(test_data, tpye_id, net_type)
                    loss = loss_fn(output, labels)
                all_loss = all_loss + loss
                all_diff = all_diff + float(torch.sum(torch.abs(output - labels)).item())
        print(
            f'all_loss: {all_loss}, min_lost: {all_all_loss}, all_diff: {all_diff}')
        if all_all_loss > all_loss:
            all_all_loss = all_loss
            print(f'save module')
            save_model(model, pth_path)
            time_without_save = 0
        else:
            time_without_save = time_without_save + 1
            if time_without_save > 3:
                print(f'save module')
                save_model(model, pth_path)
                time_without_save = 0
        print(f'After test' + time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()))
        if is_cuda:
            torch.cuda.empty_cache()
    save_model(model, pth_path)


def train_data(src_info_path, pth_path, module='empty', train_time=50000):
    init_random()

    learn_rate = 2e-4
    train_loader, test_loader = get_data_loader(src_info_path)
    print(
        f'len train: {len(train_loader)}, len test:{len(test_loader)}')
    train(pth_path, module, learn_rate, train_loader, test_loader, train_time)


if __name__ == '__main__':
    is_debug = True if sys.gettrace() else False
    can_run = False
    data_info = "./data/data.info"
    pth_path = "./data/indoor_5g.pth"
    module = "pre_load_train"
    if is_debug:
        can_run = True
    else:
        argc = len(sys.argv)
        if (argc >= 3):
            data_info = sys.argv[1]
            pth_path = sys.argv[2]
            can_run = True
        else:
            data_info = "./data/data.info"
            pth_path = "./data/indoor_5g.pth"
            can_run = True
    if can_run:
        data_info = os.path.abspath(data_info)
        pth_path = os.path.abspath(pth_path)
        train_data(data_info, pth_path, module)
    else:
        print("can't run need more args, use sample:")
        print("python3 main.py data_info pth_path")

```
**return error**
```

    out = lowerings[target](*args, **kwargs)
  File "/home/user/miniconda/envs/ai/lib/python3.10/site-packages/torch/_inductor/lowering.py", line 291, in wrapped
    out = decomp_fn(*args, **kwargs)
  File "/home/user/miniconda/envs/ai/lib/python3.10/site-packages/torch/_inductor/kernel/conv.py", line 457, in convolution
    return autotune_select_algorithm("convolution", choices, args, layout)
  File "/home/user/miniconda/envs/ai/lib/python3.10/site-packages/torch/_inductor/select_algorithm.py", line 991, in autotune_select_algorithm
    return _ALGORITHM_SELECTOR_CACHE(*args, **kwargs)
  File "/home/user/miniconda/envs/ai/lib/python3.10/site-packages/torch/_inductor/select_algorithm.py", line 748, in __call__
    timings = self.lookup(
  File "/home/user/miniconda/envs/ai/lib/python3.10/site-packages/torch/_inductor/codecache.py", line 301, in lookup
    raise e
  File "/home/user/miniconda/envs/ai/lib/python3.10/site-packages/torch/_inductor/codecache.py", line 291, in lookup
    timings = benchmark(choices)
  File "/home/user/miniconda/envs/ai/lib/python3.10/site-packages/torch/_inductor/select_algorithm.py", line 739, in autotune
    return make_benchmark_fn()(choices)
  File "/home/user/miniconda/envs/ai/lib/python3.10/site-packages/torch/_inductor/select_algorithm.py", line 863, in benchmark_in_current_process
    raise ErrorFromChoice(msg, choice, debug_str())  # noqa: TRY200
torch._dynamo.exc.BackendCompilerFailed: backend='inductor' raised:
LoweringException: ErrorFromChoice: The size of tensor a (32) must match the size of tensor b (524288) at non-singleton dimension 2
From choice ExternKernelCaller(extern_kernels.conv1x1_via_mm)
inputs = [
    torch.empty_strided((512, 1, 32, 32), (1024, 1024, 32, 1), dtype=torch.float32, device='cuda'),
    torch.empty_strided((16, 1, 1, 1), (1, 1, 1, 1), dtype=torch.float32, device='cuda'),
]
out = torch.empty_strided((512, 16, 32, 32), (16384, 1024, 32, 1), dtype=torch.float32, device='cuda')

  target: aten.convolution.default
  args[0]: TensorBox(StorageBox(
    ComputedBuffer(name='buf2', layout=FixedLayout('cuda', torch.float32, size=[512, 1, 32, 32], stride=[1024, 1024, 32, 1]), data=Pointwise(
      'cuda',
      torch.float32,
      def inner_fn(index):
          i0, _, i2, i3 = index
          tmp0 = ops.load(buf1, i3 + 32 * i2 + 1024 * i0)
          tmp1 = ops.load(primals_2, i3 + 32 * i2)
          tmp2 = tmp0 + tmp1
          tmp3 = ops.relu(tmp2)
          return tmp3
      ,
      ranges=[512, 1, 32, 32],
      origin_node=view_10,
      origins={view_10}
    ))
  ))
  args[1]: TensorBox(StorageBox(
    InputBuffer(name='primals_9', layout=FixedLayout('cuda', torch.float32, size=[16, 1, 1, 1], stride=[1, 1, 1, 1]))
  ))
  args[2]: None
  args[3]: [1, 1]
  args[4]: [0, 0]
  args[5]: [1, 1]
  args[6]: False
  args[7]: [0, 0]
  args[8]: 1

Set TORCH_LOGS="+dynamo" and TORCHDYNAMO_VERBOSE=1 for more information


You can suppress this exception and fall back to eager by setting:
    import torch._dynamo
    torch._dynamo.config.suppress_errors = True
```

**I try**
but ues model = torch.compile(model) it work

### Versions

Collecting environment information...
PyTorch version: 2.2.2
Is debug build: False
CUDA used to build PyTorch: 12.1
ROCM used to build PyTorch: N/A

OS: Ubuntu 22.04.4 LTS (x86_64)
GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
Clang version: Could not collect
CMake version: version 3.22.1
Libc version: glibc-2.35

Python version: 3.10.14 (main, Mar 21 2024, 16:24:04) [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-5.15.0-100-generic-x86_64-with-glibc2.35
Is CUDA available: True
CUDA runtime version: Could not collect
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: GPU 0: NVIDIA GeForce RTX 4090
Nvidia driver version: 550.54.14
cuDNN version: Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.8.8.0
/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.8.0
/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.8.0
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.8.0
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.8.0
/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.8.0
/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.8.0
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True

CPU:
Architecture:                       x86_64
CPU op-mode(s):                     32-bit, 64-bit
Address sizes:                      46 bits physical, 48 bits virtual
Byte Order:                         Little Endian
CPU(s):                             96
On-line CPU(s) list:                0-95
Vendor ID:                          GenuineIntel
Model name:                         Intel(R) Xeon(R) Platinum 8163 CPU @ 2.50GHz
CPU family:                         6
Model:                              85
Thread(s) per core:                 2
Core(s) per socket:                 24
Socket(s):                          2
Stepping:                           4
CPU max MHz:                        3100.0000
CPU min MHz:                        1000.0000
BogoMIPS:                           5000.00
Flags:                              fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb cat_l3 cdp_l3 invpcid_single pti intel_ppin ssbd mba ibrs ibpb stibp tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm cqm mpx rdt_a avx512f avx512dq rdseed adx smap clflushopt clwb intel_pt avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local dtherm ida arat pln pts pku ospke md_clear flush_l1d arch_capabilities
Virtualization:                     VT-x
L1d cache:                          1.5 MiB (48 instances)
L1i cache:                          1.5 MiB (48 instances)
L2 cache:                           48 MiB (48 instances)
L3 cache:                           66 MiB (2 instances)
NUMA node(s):                       2
NUMA node0 CPU(s):                  0-23,48-71
NUMA node1 CPU(s):                  24-47,72-95
Vulnerability Gather data sampling: Mitigation; Microcode
Vulnerability Itlb multihit:        KVM: Mitigation: VMX disabled
Vulnerability L1tf:                 Mitigation; PTE Inversion; VMX conditional cache flushes, SMT vulnerable
Vulnerability Mds:                  Mitigation; Clear CPU buffers; SMT vulnerable
Vulnerability Meltdown:             Mitigation; PTI
Vulnerability Mmio stale data:      Mitigation; Clear CPU buffers; SMT vulnerable
Vulnerability Retbleed:             Mitigation; IBRS
Vulnerability Spec rstack overflow: Not affected
Vulnerability Spec store bypass:    Mitigation; Speculative Store Bypass disabled via prctl and seccomp
Vulnerability Spectre v1:           Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2:           Mitigation; IBRS, IBPB conditional, STIBP conditional, RSB filling, PBRSB-eIBRS Not affected
Vulnerability Srbds:                Not affected
Vulnerability Tsx async abort:      Mitigation; Clear CPU buffers; SMT vulnerable

Versions of relevant libraries:
[pip3] numpy==1.26.4
[pip3] torch==2.2.2
[pip3] torchaudio==2.2.2
[pip3] torchvision==0.17.2
[pip3] triton==2.2.0
[conda] blas                      1.0                         mkl  
[conda] ffmpeg                    4.3                  hf484d3e_0    pytorch
[conda] libjpeg-turbo             2.0.0                h9bf148f_0    pytorch
[conda] mkl                       2023.1.0         h213fc3f_46344  
[conda] mkl-service               2.4.0           py310h5eee18b_1  
[conda] mkl_fft                   1.3.8           py310h5eee18b_0  
[conda] mkl_random                1.2.4           py310hdb19cb5_0  
[conda] numpy                     1.26.4          py310h5f9d8c6_0  
[conda] numpy-base                1.26.4          py310hb5e798b_0  
[conda] pytorch                   2.2.2           py3.10_cuda12.1_cudnn8.9.2_0    pytorch
[conda] pytorch-cuda              12.1                 ha16c6d3_5    pytorch
[conda] pytorch-mutex             1.0                        cuda    pytorch
[conda] torchaudio                2.2.2               py310_cu121    pytorch
[conda] torchtriton               2.2.0                     py310    pytorch
[conda] torchvision               0.17.2              py310_cu121    pytorch


cc @ezyang @msaroufim @bdhirsh @anijain2305 @chauhang @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @peterbell10 @ipiszy @yf225 @chenyang78 @kadeng @muchulee8 @aakhundov @ColinPeppler @amjames @desertfire