Repository: pytorch
Issue: torch.compile() In my use case of calling torch.compile(), I have found that the model's data outputs are inconsistent. I suspect that using Triton for operator fusion may have introduced precision deviations. I am unsure how to locate and fix this issue.
Labels: triaged,oncall: pt2,module: inductor

### 🐛 Describe the bug

"My Torch environment is as follows:
2.2.2+cu121

My goal is to use functions related to torch.compile() to optimize the inference time of our model. In fact, it does work and achieves over a 50% reduction in inference time in the default mode.

The  model code is as follows:
`"""
copy from https://github.com/alimama-tech/NeurIPS_Auto_Bidding_AIGB_Track_Baseline/blob/main/bidding_train_env/baseline/dd/DFUSER.py
"""
from torch.optim import Adam
import os
from typing import Optional, Tuple, List
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import gin

from .temporal import TemporalUnet
from .basic import (
    cosine_beta_schedule,
    Losses,
    extract,
    apply_conditioning,
    apply_conditioning_with_fix,
)


class ReduceSum(nn.Module):
    def forward(self, x):
        return torch.sum(x, dim=-1)
    

@gin.configurable
class GaussianInvDynDiffusion(nn.Module):
    def __init__(self, model, horizon, observation_dim, action_dim, n_timesteps=1000,
                 clip_denoised=False, predict_epsilon=True, hidden_dim=256,
                 loss_discount=1.0, returns_condition=False,
                 condition_guidance_w=0.1,
                 inv_bias=True,
        ):
        super().__init__()

        self.horizon = horizon
        self.observation_dim = observation_dim
        self.action_dim = action_dim
        self.transition_dim = observation_dim + action_dim
        self.model = model
        self.inv_model = nn.Sequential(
            nn.Linear(4 * self.observation_dim, hidden_dim, bias=inv_bias),
            nn.ReLU(),
            nn.Linear(hidden_dim, hidden_dim, bias=inv_bias),
            nn.ReLU(),
            nn.Linear(hidden_dim, hidden_dim, bias=inv_bias),
            nn.ReLU(),
            # ReduceSum(),
            nn.Linear(hidden_dim, self.action_dim, bias=inv_bias),
        )
        self.returns_condition = returns_condition
        self.condition_guidance_w = condition_guidance_w

        betas = cosine_beta_schedule(n_timesteps)
        alphas = 1. - betas
        alphas_cumprod = torch.cumprod(alphas, axis=0)
        alphas_cumprod_prev = torch.cat([torch.ones(1), alphas_cumprod[:-1]])

        self.n_timesteps = int(n_timesteps)
        self.clip_denoised = clip_denoised
        self.predict_epsilon = predict_epsilon

        self.register_buffer('betas', betas)
        self.register_buffer('alphas_cumprod', alphas_cumprod)
        self.register_buffer('alphas_cumprod_prev', alphas_cumprod_prev)

        # calculations for diffusion q(x_t | x_{t-1}) and others
        self.register_buffer('sqrt_alphas_cumprod', torch.sqrt(alphas_cumprod))
        self.register_buffer('sqrt_one_minus_alphas_cumprod', torch.sqrt(1. - alphas_cumprod))
        self.register_buffer('log_one_minus_alphas_cumprod', torch.log(1. - alphas_cumprod))
        self.register_buffer('sqrt_recip_alphas_cumprod', torch.sqrt(1. / alphas_cumprod))
        self.register_buffer('sqrt_recipm1_alphas_cumprod', torch.sqrt(1. / alphas_cumprod - 1))

        # calculations for posterior q(x_{t-1} | x_t, x_0)
        posterior_variance = betas * (1. - alphas_cumprod_prev) / (1. - alphas_cumprod)
        self.register_buffer('posterior_variance', posterior_variance)

        self.register_buffer('posterior_log_variance_clipped',
                             torch.log(torch.clamp(posterior_variance, min=1e-20)))
        self.register_buffer('posterior_mean_coef1',
                             betas * np.sqrt(alphas_cumprod_prev) / (1. - alphas_cumprod))
        self.register_buffer('posterior_mean_coef2',
                             (1. - alphas_cumprod_prev) * np.sqrt(alphas) / (1. - alphas_cumprod))

        loss_weights = self.get_loss_weights(loss_discount)
        self.loss_fn = Losses['state_l2'](loss_weights)

    def get_loss_weights(self, discount):

        self.action_weight = 1
        dim_weights = torch.ones(self.observation_dim, dtype=torch.float32)

        discounts = discount ** torch.arange(self.horizon, dtype=torch.float)
        discounts = discounts / discounts.mean()
        loss_weights = torch.matmul(discounts[:, None], dim_weights[None, :])

        if self.predict_epsilon:
            loss_weights[0, :] = 0

        return loss_weights

    # ------------------------------------------ sampling ------------------------------------------#

    def predict_start_from_noise(self, x_t, t, noise):

        if self.predict_epsilon:
            return (
                    extract(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t -
                    extract(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) * noise
            )
        else:
            return noise

    def q_posterior(self, x_start, x_t, t):
        posterior_mean = (
                extract(self.posterior_mean_coef1, t, x_t.shape) * x_start +
                extract(self.posterior_mean_coef2, t, x_t.shape) * x_t
        )
        posterior_variance = extract(self.posterior_variance, t, x_t.shape)
        posterior_log_variance_clipped = extract(self.posterior_log_variance_clipped, t, x_t.shape)
        return posterior_mean, posterior_variance, posterior_log_variance_clipped

    def p_mean_variance(self, x, cond, t, returns: torch.Tensor = torch.ones(1, 1)):
        if self.returns_condition:
            # epsilon could be epsilon or x0 itself

            epsilon_cond = self.model(x, cond, t, returns, use_dropout=False)
            epsilon_uncond = self.model(x, cond, t, returns, force_dropout=True)
            epsilon = epsilon_uncond + self.condition_guidance_w * (epsilon_cond - epsilon_uncond)
        else:
            epsilon = self.model(x, cond, t)

        t = t.detach().to(torch.int64)
        x_recon = self.predict_start_from_noise(x, t=t, noise=epsilon)

        if self.clip_denoised:
            x_recon.clamp_(-5., 5.)

        model_mean, posterior_variance, posterior_log_variance = self.q_posterior(
            x_start=x_recon, x_t=x, t=t)
        return model_mean, posterior_variance, posterior_log_variance

    def p_sample(self, x, cond, t, returns: torch.Tensor = torch.ones(1, 1)):
        with torch.no_grad():
            b, _, _ = x.shape
            model_mean, _, model_log_variance = self.p_mean_variance(x=x, cond=cond, t=t, returns=returns)
            noise = 0.5 * torch.randn_like(x, device=x.device)
            nonzero_mask = (1 - (t == 0).float()).reshape(b, 1, 1)
            return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise

    def p_sample_loop(self, shape, cond, returns: torch.Tensor = torch.ones(1, 1), t: int = 0, fix_dim: Optional[int] = None, save_denoise: bool = False):
        with torch.no_grad():
            torch.random.manual_seed(2046)
            batch_size = shape[0]
            x = 0.5 * torch.randn(shape[0], shape[1], shape[2], device=cond.device)
            output1, output2 = [], []

            if fix_dim is None:
                x = apply_conditioning(x, cond, 0)
            else:
                x = apply_conditioning_with_fix(x, cond, 0, t, fix_dim)

            for i in range(self.n_timesteps - 1, -1, -1):
                timesteps = torch.ones(batch_size,
                                       device=cond.device) * i
                x = self.p_sample(x, cond, timesteps, returns)
                #output1.append(x.clone().detach().cpu().numpy().tolist())
                output1.append(x.clone().detach())

                if fix_dim is None:
                    x = apply_conditioning(x, cond, 0)
                else:
                    x = apply_conditioning_with_fix(x, cond, 0, t, fix_dim)

                #output2.append(x.clone().detach().cpu().numpy().tolist())
                output2.append(x.clone().detach())

            #if save_denoise:
            #    return x, output1, output2
            return x

    #  @torch.no_grad()
    def conditional_sample(self, cond, returns: torch.Tensor = torch.ones(1, 1), horizon: int = 48, t: int = 0,  fix_dim: Optional[int] = None, save_denoise: bool = False):
        with torch.no_grad():
            batch_size = 1
            horizon = self.horizon
            shape = torch.tensor([batch_size, horizon, self.observation_dim])

            return self.p_sample_loop(shape, cond, returns, t, fix_dim, save_denoise)

    def forward(self, cond, returns, t: int = 0, fix_dim: Optional[int] = None, save_denoise: bool = False):
        return self.conditional_sample(cond=cond, returns=returns, t=t, fix_dim=fix_dim, save_denoise=save_denoise)

    # ------------------------------------------ training ------------------------------------------#

    def q_sample(self, x_start, t, noise=None):
        if noise is None:
            noise = torch.randn_like(x_start, device=x_start.device)

        self.sqrt_alphas_cumprod = self.sqrt_alphas_cumprod.to(t.device)
        self.sqrt_one_minus_alphas_cumprod = self.sqrt_one_minus_alphas_cumprod.to(t.device)
        sample = (
                extract(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start +
                extract(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise
        )

        return sample

    def p_losses(self, x_start, cond, t, returns=None, masks=None):
        noise = torch.randn_like(x_start, device=x_start.device)
        x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
        t = t.to(x_noisy.device)
        x_recon = self.model(x_noisy, cond, t, returns)
        if self.predict_epsilon:
            loss, info = self.loss_fn(x_recon, noise, masks)
        else:
            loss, info = self.loss_fn(x_recon, x_start, masks)

        return loss, info

    def loss(self, x, cond, returns, masks, action_mask=None):
        """
        x with shape: (batch_size, step_len, H)
        """

        batch_size = len(x)
        t = torch.randint(0, self.n_timesteps, (batch_size,), device=x.device).long()
        diffuse_loss, info = self.p_losses(x[:, :, self.action_dim:], cond, t, returns, masks)
        diffuse_loss_batch = torch.reshape(info['loss'].mean(dim=(1,2)), (-1, 1))
        _t = torch.reshape(t, (-1, 1))
        loss_batch_t = torch.concat([_t, diffuse_loss_batch], dim=-1)
        
        inv_loss, pred_a_t, mape = self.inv_loss(x, action_mask)

        loss = (1 / 2) * (diffuse_loss + inv_loss)

        # diffusion t loss bin size
        
        
        return loss, info, (diffuse_loss, inv_loss), pred_a_t, mape, loss_batch_t
    
    def inv_loss(self, x, masks):
        # Calculating inv loss
        x_t = x[:, :-1, self.action_dim:] 
        a_t = x[:, :-1, :self.action_dim] 
        x_t_1 = x[:, 1:, self.action_dim:]

        # x_t_1[:, :, 1] = 0
        x_t_2 = torch.cat(
            [torch.zeros(x.shape[0], 1, x.shape[-1] - self.action_dim, device=x.device), x[:, :-2, self.action_dim:]],
            dim=1)
        x_t_3 = torch.cat(
            [torch.zeros(x.shape[0], 2, x.shape[-1] - self.action_dim, device=x.device), x[:, :-3, self.action_dim:]],
            dim=1)
        x_comb_t = torch.cat([x_t_2, x_t_3, x_t, x_t_1], dim=-1)
        x_comb_t = x_comb_t.reshape(-1, 4 * self.observation_dim)
        masks_flat = masks[:, :-1].reshape(-1)
        x_comb_t = x_comb_t[masks_flat]
        a_t = a_t.reshape(-1, self.action_dim)
        a_t = a_t[masks_flat]
        pred_a_t = self.inv_model(x_comb_t)

        inv_loss = F.mse_loss(pred_a_t, a_t, reduction="mean")
        mape = ((a_t - pred_a_t).abs()) / (a_t.abs() + 1e-8)
        mape = mape.mean()
        
        return inv_loss, pred_a_t, mape


@gin.configurable
class DFUSER(nn.Module):
    def __init__(self, dim_obs=16, dim_actions=1, dim_return=1, gamma=1, tau=0.01,
                 ACTION_MAX=10, ACTION_MIN=0,
                 step_len=48, n_timesteps=10,
                 condition_guidance_w=1.2,
                 clip_denoised=True,
                 inv_bias=True
        ):

        super().__init__()

        self.n_timestamps = n_timesteps
        self.num_of_states = dim_obs
        self.num_of_actions = dim_actions
        self.ACTION_MAX = ACTION_MAX
        self.ACTION_MIN = ACTION_MIN
        self.step_len = step_len

        model = TemporalUnet(
            horizon=step_len,
            transition_dim=dim_obs,
            cond_dim=dim_actions,
            return_dim=dim_return,
            returns_condition=True,
            dim=128,
            condition_dropout=0.25,
            calc_energy=False
        )

        self.diffuser = GaussianInvDynDiffusion(
            model=model,
            horizon=step_len,
            observation_dim=dim_obs,
            action_dim=dim_actions,
            clip_denoised=clip_denoised,
            predict_epsilon=True,
            hidden_dim=256,
            n_timesteps=n_timesteps,
            loss_discount=1,
            returns_condition=True,
            condition_guidance_w=condition_guidance_w,
            inv_bias=inv_bias,
        )

        self.step = 0
        self.num_of_episodes = 0

        self.GAMMA = gamma
        self.tau = tau
        self.num_of_steps = 0
    
    #def forward(self, states, actions, returns, masks, action_mask):
    #    x = torch.cat([actions, states], dim=-1)
    #    cond = torch.ones_like(states[:, 0], device=states.device)[:, None, :]
    #    loss, infos, (diffuse_loss, inv_loss), pred_a_t, mape, loss_batch_t = self.diffuser.loss(x, cond, returns=returns, masks=masks, action_mask=action_mask)
    #    return loss, (diffuse_loss, inv_loss), pred_a_t, mape, loss_batch_t
    
    def forward(self, x, budget):
        """
        x with shape (time_step, dim)
        """
        return self.diffuser(cond=x, returns=budget)


    def get_action_s_by_state(self, x: torch.Tensor, returns: torch.Tensor, cur_time:int):
        x = torch.reshape(x, [self.step_len, self.num_of_states])

        states = x[:cur_time]
        conditions = states
        x_0 = self.diffuser(cond=conditions, returns=returns)

        states = x_0[0, :cur_time + 1]
        states_next = states[None, -1]
        if cur_time > 1:
            states_curt1 = conditions[-2].float()[None, :]
        else:
            states_curt1 = torch.zeros_like(states_next, device=states_next.device)
        if cur_time > 2:
            states_curt2 = conditions[-3].float()[None, :]
        else:
            states_curt2 = torch.zeros_like(states_next, device=states_next.device)
        states_comb = torch.hstack([states_curt1, states_curt2, conditions[-1].float()[None, :], states_next])
        actions = self.diffuser.inv_model(states_comb)
        actions = actions.detach().cpu()[0]  # .cpu().data.numpy()
        return actions, states_next, x_0

    def save_net(self, save_path):
        if not os.path.isdir(save_path):
            os.makedirs(save_path)
        torch.save(self.diffuser.state_dict(), f'{save_path}/diffuser.pt')

    def save_model(self, save_path):
        if not os.path.isdir(save_path):
            os.makedirs(save_path)
        model_temp = self.cpu()
        jit_model = torch.jit.script(model_temp)
        torch.jit.save(jit_model, f'{save_path}/diffuser.pth')

    def load_net(self, load_path):
        self.diffuser.load_state_dict(torch.load(load_path, map_location='cpu'))

        self.use_cuda = torch.cuda.is_available()
        if self.use_cuda:
            self.diffuser.cuda()
            
    def load_model(self, load_path):
        # 加载 TorchScript 模型
        jit_model = torch.jit.load(load_path, map_location='cpu')
        
        # 将加载的模型分配给 self.diffuser
        self = jit_model
        
        # 检查是否有 CUDA 可用，并将模型移动到 GPU
        self.use_cuda = torch.cuda.is_available()
        if self.use_cuda:
            self.cuda()
`
The  inference code is as follows:
`#-*-coding:utf-8-*-
import sys
import os
import time
sys.path.append("..")
from dataclasses import dataclass, field
from typing import Dict, Optional

import numpy as np
import torch
import gin
from models.DFUSER import DFUSER

class AigbInference():
    
    def __init__(self, model_dir, warmup=True):
        self.model = DFUSER(
            dim_obs=3,
            dim_actions=1,
            step_len=40,
            n_timesteps=20,
            gamma = 1,
            tau = 0.01,
            condition_guidance_w = 1.3,
            clip_denoised = True,
            inv_bias = True,
            dim_return = 1
        )
        device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
        print(f'使用的设备: {device}')
        self.model.to(device)
        self.model.load_net("/home/admin/workspace/aop_lab/app_data/ckp01-pt-2025-1-3/diffuser.pt")
        self.device = device
        print(f'list_mode_options: {torch._inductor.list_mode_options()}')
        self.model = torch.compile(self.model,backend="inductor")
        #self.model = torch.compile(self.model,backend="inductor",mode="reduce-overhead")
        #self.model = torch.compile(self.model,backend="inductor",mode="max-autotune")
        #self.model = torch.compile(self.model,backend="inductor",mode="max-autotune-no-cudagraphs")
        #self.model.load_model("/home/admin/workspace/aop_lab/app_data/ckp01-pth/diffuser.pth")
        if warmup == True:
            for i in range(self.model.step_len): 
                arg1_shape = (i, 3)
                arg2_shape = (1, 1)
                x = torch.ones(arg1_shape)
                budget = torch.ones(arg2_shape)
                x = x.to(self.device)
                budget = budget.to(self.device)
                traj_pred = self.model(x, budget)
            print(f'warmup done')

    def infer(self, x, budget):
        start_time = time.perf_counter()
        x = x.to(self.device)
        budget = budget.to(self.device)
        traj_pred  = self.model(x, budget)
        #traj_pred  = self.model(x,budget)
        print(
            #f"traj_pred.shape: {traj_pred.shape} \n "
            #f"traj_pred: {traj_pred} \n "
        )
        #self.model.save_model("/home/admin/workspace/aop_lab/app_data/ckp01-pth/checkpoint-pth-2")
        end_time = time.perf_counter()
        elapsed_time_ms = (end_time - start_time) * 1000
        print(f"my_function 执行时间: {elapsed_time_ms:.3f} ms")
        return traj_pred
`

Subsequently, I used Nsight to analyze the GPU utilization efficiency and concluded that some fragmented kernels were fused into Triton operators.
before
![Image](https://github.com/user-attachments/assets/174eaa58-aa31-4968-a370-626795e6d971)
after 
![Image](https://github.com/user-attachments/assets/851275aa-75d7-44ae-9c19-76521d2cc9ac)

However, I soon discovered that when I run the model with the same input before and after optimization, the output results differ. Currently, I do not know how to resolve this issue."

![Image](https://github.com/user-attachments/assets/f7705a06-d8f1-43ab-b813-f5c4d7b06e2b)

### Versions

Collecting environment information...
PyTorch version: 2.2.2+cu121
Is debug build: False
CUDA used to build PyTorch: 12.1
ROCM used to build PyTorch: N/A

OS: Alibaba Group Enterprise Linux Server 7.2 (Paladin) (x86_64)
GCC version: (GCC) 10.2.1 20200825 (Alibaba 10.2.1-3 2.17)
Clang version: Could not collect
CMake version: Could not collect
Libc version: glibc-2.32

Python version: 3.10.16 | packaged by conda-forge | (main, Dec  5 2024, 14:16:10) [GCC 13.3.0] (64-bit runtime)
Python platform: Linux-4.19.91-009.ali4000.alios7.x86_64-x86_64-with-glibc2.32
Is CUDA available: True
CUDA runtime version: 12.2.140
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: GPU 0: NVIDIA A10
Nvidia driver version: 535.161.08
cuDNN version: Probably one of the following:
/usr/local/cuda-12.2/targets/x86_64-linux/lib/libcudnn.so.8.9.7
/usr/local/cuda-12.2/targets/x86_64-linux/lib/libcudnn_adv_infer.so.8.9.7
/usr/local/cuda-12.2/targets/x86_64-linux/lib/libcudnn_adv_train.so.8.9.7
/usr/local/cuda-12.2/targets/x86_64-linux/lib/libcudnn_cnn_infer.so.8.9.7
/usr/local/cuda-12.2/targets/x86_64-linux/lib/libcudnn_cnn_train.so.8.9.7
/usr/local/cuda-12.2/targets/x86_64-linux/lib/libcudnn_ops_infer.so.8.9.7
/usr/local/cuda-12.2/targets/x86_64-linux/lib/libcudnn_ops_train.so.8.9.7
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
Byte Order:            Little Endian
CPU(s):                128
On-line CPU(s) list:   34-37,98-101
Off-line CPU(s) list:  0-33,38-97,102-127
Thread(s) per core:    0
Core(s) per socket:    32
Socket(s):             2
NUMA node(s):          2
Vendor ID:             GenuineIntel
CPU family:            6
Model:                 106
Model name:            Intel(R) Xeon(R) Platinum 8369B CPU @ 2.90GHz
Stepping:              6
CPU MHz:               3476.128
CPU max MHz:           3500.0000
CPU min MHz:           800.0000
BogoMIPS:              5806.48
Virtualization:        VT-x
L1d cache:             48K
L1i cache:             32K
L2 cache:              1280K
L3 cache:              49152K
NUMA node0 CPU(s):     0-31,64-95
NUMA node1 CPU(s):     32-63,96-127
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 invpcid_single ssbd mba ibrs ibpb stibp ibrs_enhanced tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb intel_pt avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local wbnoinvd dtherm ida arat pln pts hwp hwp_act_window hwp_epp hwp_pkg_req avx512vbmi umip pku ospke avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg tme avx512_vpopcntdq rdpid fsrm md_clear pconfig flush_l1d arch_capabilities

Versions of relevant libraries:
[pip3] numpy==1.26.3
[pip3] nvidia-cublas-cu12==12.1.3.1
[pip3] nvidia-cuda-cupti-cu12==12.1.105
[pip3] nvidia-cuda-nvrtc-cu12==12.1.105
[pip3] nvidia-cuda-runtime-cu12==12.1.105
[pip3] nvidia-cudnn-cu12==8.9.2.26
[pip3] nvidia-cufft-cu12==11.0.2.54
[pip3] nvidia-curand-cu12==10.3.2.106
[pip3] nvidia-cusolver-cu12==11.4.5.107
[pip3] nvidia-cusparse-cu12==12.1.0.106
[pip3] nvidia-nccl-cu12==2.19.3
[pip3] nvidia-nvjitlink-cu12==12.1.105
[pip3] nvidia-nvtx-cu12==12.1.105
[pip3] torch==2.2.2+cu121
[pip3] torchaudio==2.2.2+cu121
[pip3] torchvision==0.17.2+cu121
[pip3] triton==2.2.0
[conda] numpy                     1.26.3                   pypi_0    pypi
[conda] nvidia-cublas-cu12        12.1.3.1                 pypi_0    pypi
[conda] nvidia-cuda-cupti-cu12    12.1.105                 pypi_0    pypi
[conda] nvidia-cuda-nvrtc-cu12    12.1.105                 pypi_0    pypi
[conda] nvidia-cuda-runtime-cu12  12.1.105                 pypi_0    pypi
[conda] nvidia-cudnn-cu12         8.9.2.26                 pypi_0    pypi
[conda] nvidia-cufft-cu12         11.0.2.54                pypi_0    pypi
[conda] nvidia-curand-cu12        10.3.2.106               pypi_0    pypi
[conda] nvidia-cusolver-cu12      11.4.5.107               pypi_0    pypi
[conda] nvidia-cusparse-cu12      12.1.0.106               pypi_0    pypi
[conda] nvidia-nccl-cu12          2.19.3                   pypi_0    pypi
[conda] nvidia-nvjitlink-cu12     12.1.105                 pypi_0    pypi
[conda] nvidia-nvtx-cu12          12.1.105                 pypi_0    pypi
[conda] torch                     2.2.2+cu121              pypi_0    pypi
[conda] torchaudio                2.2.2+cu121              pypi_0    pypi
[conda] torchvision               0.17.2+cu121             pypi_0    pypi
[conda] triton                    2.2.0                    pypi_0    pypi
(base) 

cc @chauhang @penguinwu @voznesenskym @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @yf225 @chenyang78 @kadeng @muchulee8 @ColinPeppler @amjames @desertfire @aakhundov