Errors when running the fsdp benchmarks for hf_Bert and hf_T5
Labels: triaged, module: fsdp, bug, oncall: pt2, module: dynamo

### 🐛 RuntimeError thrown by Dynamo in FSDP benchmark script.

Hi, 

I'm using https://github.com/pytorch/pytorch/blob/master/benchmarks/dynamo/distributed.py to run tests on nightly branch from Jan 28'th (which I've built && installed on my Docker container). 

This is my base DLC: `763104351884.dkr.ecr.us-east-1.amazonaws.com/pytorch-training:1.13.1-gpu-py39-cu117-ubuntu20.04-sagemaker` (download it using awscli; https://docs.aws.amazon.com/AmazonECR/latest/userguide/getting-started-cli.html)

PT-1.13 was purged from the container and PT2 nightly from 28th was installed. 

In particular for `hf_Bert`, `hf_GPT2` and `hf_T5` (the first two with the FSDP wrapper). I'm observing the errors attached below (trace mentions dynamo, so tagging it in the `torch.compile` cohort),

Run command:

```bash
# pytorch commit: 5d6a4f697cac34d15262aad8afab096170d29ce1
torchrun --nnodes 1 --nproc_per_node 8 --master_addr localhost --master_port 9001 distributed.py --dynamo inductor --torchbench_model hf_T5 --fsdp
```

Note commit in the versions below is different because I have some minor change to the build file to include directories to get the build to succeed on my base DLC. 

I've attached the minified graph below, but that doesn't reproduce the issue. 

```bash
TORCHDYNAMO_REPRO_AFTER="dynamo" torchrun --nnodes 1 --nproc_per_node 8 --master_addr localhost --master_port 9001 distributed.py --dynamo inductor --torchbench_model hf_T5 --fsdp
```

Any pointers to get this working OR just any example of working FSDP wrapper would be very helpful!

### Error logs

```
Traceback (most recent call last):
  File "/opt/conda/lib/python3.9/site-packages/torch/_dynamo/convert_frame.py", line 323, in _compile
    out_code = transform_code_object(code, transform)
  File "/opt/conda/lib/python3.9/site-packages/torch/_dynamo/bytecode_transformation.py", line 341, in transform_code_object
    transformations(instructions, code_options)
  File "/opt/conda/lib/python3.9/site-packages/torch/_dynamo/convert_frame.py", line 310, in transform
    tracer.run()
  File "/opt/conda/lib/python3.9/site-packages/torch/_dynamo/symbolic_convert.py", line 1717, in run
    super().run()
  File "/opt/conda/lib/python3.9/site-packages/torch/_dynamo/symbolic_convert.py", line 563, in run
    and self.step()
  File "/opt/conda/lib/python3.9/site-packages/torch/_dynamo/symbolic_convert.py", line 526, in step
    getattr(self, inst.opname)(inst)
  File "/opt/conda/lib/python3.9/site-packages/torch/_dynamo/symbolic_convert.py", line 332, in wrapper
    return inner_fn(self, inst)
  File "/opt/conda/lib/python3.9/site-packages/torch/_dynamo/symbolic_convert.py", line 999, in CALL_FUNCTION
    self.call_function(fn, args, {})
  File "/opt/conda/lib/python3.9/site-packages/torch/_dynamo/symbolic_convert.py", line 460, in call_function
    self.push(fn.call_function(self, args, kwargs))
  File "/opt/conda/lib/python3.9/site-packages/torch/_dynamo/variables/nn_module.py", line 559, in call_function
    return variables.UserFunctionVariable(
  File "/opt/conda/lib/python3.9/site-packages/torch/_dynamo/variables/functions.py", line 259, in call_function
    return super(UserFunctionVariable, self).call_function(tx, args, kwargs)
  File "/opt/conda/lib/python3.9/site-packages/torch/_dynamo/variables/functions.py", line 92, in call_function
    return tx.inline_user_function_return(
  File "/opt/conda/lib/python3.9/site-packages/torch/_dynamo/symbolic_convert.py", line 496, in inline_user_function_return
    result = InliningInstructionTranslator.inline_call(self, fn, args, kwargs)
  File "/opt/conda/lib/python3.9/site-packages/torch/_dynamo/symbolic_convert.py", line 1795, in inline_call
    return cls.inline_call_(parent, func, args, kwargs)
  File "/opt/conda/lib/python3.9/site-packages/torch/_dynamo/symbolic_convert.py", line 1851, in inline_call_
    tracer.run()
  File "/opt/conda/lib/python3.9/site-packages/torch/_dynamo/symbolic_convert.py", line 563, in run
    and self.step()
  File "/opt/conda/lib/python3.9/site-packages/torch/_dynamo/symbolic_convert.py", line 526, in step
    getattr(self, inst.opname)(inst)
  File "/opt/conda/lib/python3.9/site-packages/torch/_dynamo/symbolic_convert.py", line 1064, in LOAD_ATTR
    result = BuiltinVariable(getattr).call_function(
  File "/opt/conda/lib/python3.9/site-packages/torch/_dynamo/variables/builtin.py", line 322, in call_function
    result = handler(tx, *args, **kwargs)
  File "/opt/conda/lib/python3.9/site-packages/torch/_dynamo/variables/builtin.py", line 736, in call_getattr
    obj.var_getattr(tx, name).clone(source=source).add_options(options)
  File "/opt/conda/lib/python3.9/site-packages/torch/_dynamo/variables/user_defined.py", line 342, in var_getattr
    return VariableBuilder(tx, source)(subobj).add_options(options)
  File "/opt/conda/lib/python3.9/site-packages/torch/_dynamo/variables/builder.py", line 170, in __call__
    return self._wrap(value).clone(**self.options())
  File "/opt/conda/lib/python3.9/site-packages/torch/_dynamo/variables/builder.py", line 236, in _wrap
    return self.wrap_tensor(value)
  File "/opt/conda/lib/python3.9/site-packages/torch/_dynamo/variables/builder.py", line 646, in wrap_tensor
    tensor_variable = wrap_fx_proxy(
  File "/opt/conda/lib/python3.9/site-packages/torch/_dynamo/variables/builder.py", line 761, in wrap_fx_proxy
    return wrap_fx_proxy_cls(
  File "/opt/conda/lib/python3.9/site-packages/torch/_dynamo/variables/builder.py", line 821, in wrap_fx_proxy_cls
    example_value = wrap_to_fake_tensor_and_record(
  File "/opt/conda/lib/python3.9/site-packages/torch/_dynamo/variables/builder.py", line 972, in wrap_to_fake_tensor_and_record
    fake_e = wrap_fake_exception(
  File "/opt/conda/lib/python3.9/site-packages/torch/_dynamo/utils.py", line 783, in wrap_fake_exception
    return fn()
  File "/opt/conda/lib/python3.9/site-packages/torch/_dynamo/variables/builder.py", line 973, in <lambda>
    lambda: tx.fake_mode.from_tensor(
  File "/opt/conda/lib/python3.9/site-packages/torch/_subclasses/fake_tensor.py", line 1082, in from_tensor
    return self.fake_tensor_converter(
  File "/opt/conda/lib/python3.9/site-packages/torch/_subclasses/fake_tensor.py", line 291, in __call__
    return self.from_real_tensor(
  File "/opt/conda/lib/python3.9/site-packages/torch/_subclasses/fake_tensor.py", line 248, in from_real_tensor
    out = self.meta_converter(
  File "/opt/conda/lib/python3.9/site-packages/torch/_subclasses/meta_utils.py", line 502, in __call__
    r = self.meta_tensor(
  File "/opt/conda/lib/python3.9/site-packages/torch/_subclasses/meta_utils.py", line 275, in meta_tensor
    base = self.meta_tensor(
  File "/opt/conda/lib/python3.9/site-packages/torch/_subclasses/meta_utils.py", line 433, in meta_tensor
    r.grad = self.meta_tensor(
RuntimeError: assigned grad has data of a different size

from user code:
   File "/opt/conda/lib/python3.9/site-packages/transformers/models/t5/modeling_t5.py", line 542, in <graph break in forward>
    attn_output = self.o(attn_output)
  File "/opt/conda/lib/python3.9/site-packages/torch/nn/modules/linear.py", line 114, in forward
    return F.linear(input, self.weight, self.bias)

Set torch._dynamo.config.verbose=True for more information
```

### Minified repro

```python
# minifier_launcher.py

import os
from math import inf
import torch
from torch import tensor, device
import torch.fx as fx
import functools
import torch._dynamo
from torch._dynamo.debug_utils import run_fwd_maybe_bwd
from torch._dynamo.optimizations.backends import BACKENDS
from torch._dynamo.testing import rand_strided

import torch._dynamo.config
import torch._inductor.config
torch._dynamo.config.load_config(b'\x80\x04\x95\xb9\x07\x00\x00\x00\x00\x00\x00}\x94(\x8c\x08__name__\x94\x8c\x14torch._dynamo.config\x94\x8c\x07__doc__\x94N\x8c\x0b__package__\x94\x8c\rtorch._dynamo\x94\x8c\n__loader__\x94\x8c\x1a_frozen_importlib_external\x94\x8c\x10SourceFileLoader\x94\x93\x94)\x81\x94}\x94(\x8c\x04name\x94h\x02\x8c\x04path\x94\x8c>/opt/conda/lib/python3.9/site-packages/torch/_dynamo/config.py\x94ub\x8c\x08__spec__\x94\x8c\x11_frozen_importlib\x94\x8c\nModuleSpec\x94\x93\x94)\x81\x94}\x94(h\x0ch\x02\x8c\x06loader\x94h\n\x8c\x06origin\x94h\x0e\x8c\x0cloader_state\x94N\x8c\x1asubmodule_search_locations\x94N\x8c\r_set_fileattr\x94\x88\x8c\x07_cached\x94\x8cV/opt/conda/lib/python3.9/site-packages/torch/_dynamo/__pycache__/config.cpython-39.pyc\x94\x8c\r_initializing\x94\x89ub\x8c\x08__file__\x94h\x0e\x8c\n__cached__\x94h\x1b\x8c\x07abspath\x94\x8c\tposixpath\x94h\x1f\x93\x94\x8c\x07dirname\x94h h"\x93\x94\x8c\x0eHAS_REFS_PRIMS\x94\x88\x8c\tlog_level\x94K\x1e\x8c\x0boutput_code\x94\x89\x8c\rlog_file_name\x94N\x8c\x07verbose\x94\x89\x8c\x11output_graph_code\x94\x89\x8c\x12verify_correctness\x94\x89\x8c\x12minimum_call_count\x94K\x01\x8c\x15dead_code_elimination\x94\x88\x8c\x10cache_size_limit\x94K@\x8c\x14specialize_int_float\x94\x88\x8c\x0edynamic_shapes\x94\x89\x8c\x10guard_nn_modules\x94\x89\x8c\x0cnormalize_ir\x94\x89\x8c\x1btraceable_tensor_subclasses\x94\x8f\x94\x8c\x0fsuppress_errors\x94\x89\x8c\x15replay_record_enabled\x94\x89\x8c rewrite_assert_with_torch_assert\x94\x88\x8c\x12print_graph_breaks\x94\x89\x8c\x07disable\x94\x89\x8c*allowed_functions_module_string_ignorelist\x94\x8f\x94(\x8c\rtorch.testing\x94\x8c\x0btorch._refs\x94\x8c\rtorch._decomp\x94\x8c\x13torch.distributions\x94\x8c\x0ctorch._prims\x94\x90\x8c\x16capture_scalar_outputs\x94\x89\x8c\x19enforce_cond_guards_match\x94\x88\x8c\x0coptimize_ddp\x94\x88\x8c\x1araise_on_ctx_manager_usage\x94\x88\x8c\x1craise_on_unsafe_aot_autograd\x94\x89\x8c\rdynamo_import\x94\x8c\rtorch._dynamo\x94\x8c\x0finductor_import\x94\x8c\x0ftorch._inductor\x94\x8c\x18error_on_nested_fx_trace\x94\x88\x8c\tallow_rnn\x94\x89\x8c\x08base_dir\x94\x8c&/opt/conda/lib/python3.9/site-packages\x94\x8c\x0edebug_dir_root\x94\x8cN/fsx/pytorch-nightly/pytorch-2.0/pytorch/benchmarks/dynamo/torch_compile_debug\x94\x8c)DO_NOT_USE_legacy_non_fake_example_inputs\x94\x89\x8c\x15_AccessLimitingConfig\x94}\x94(\x8c\n__module__\x94h\x02\x8c\x0b__setattr__\x94h\x02\x8c!_AccessLimitingConfig.__setattr__\x94\x93\x94h\x03Nu\x8c\x15_allowed_config_names\x94\x8f\x94(\x8c\x0eexternal_utils\x94h6\x8c\x12constant_functions\x94h\x06h&\x8c\x0c__builtins__\x94h%hPh5\x8c!skipfiles_inline_module_allowlist\x94h(h\x0fhBh*h/h.h\x1fhD\x8c\x07logging\x94\x8c\x02os\x94\x8c\x05torch\x94h)h$h0h7hIh4h8h@h2h9h-\x8c\x03sys\x94hGh,h+hKhAhEh1hOh\x01hMh\x1dh\'h\x03h"\x8c\x0brepro_level\x94hJ\x8c\nModuleType\x94hCh\x1eh\x04\x8c\x0brepro_after\x94\x90\x8c\x1cget_config_serialization_fns\x94\x8c\x1atorch._dynamo.config_utils\x94hc\x93\x94u.')
torch._inductor.config.load_config(b'\x80\x04\x95\xd3\x08\x00\x00\x00\x00\x00\x00}\x94(\x8c\x08__name__\x94\x8c\x16torch._inductor.config\x94\x8c\x07__doc__\x94N\x8c\x0b__package__\x94\x8c\x0ftorch._inductor\x94\x8c\n__loader__\x94\x8c\x1a_frozen_importlib_external\x94\x8c\x10SourceFileLoader\x94\x93\x94)\x81\x94}\x94(\x8c\x04name\x94h\x02\x8c\x04path\x94\x8c@/opt/conda/lib/python3.9/site-packages/torch/_inductor/config.py\x94ub\x8c\x08__spec__\x94\x8c\x11_frozen_importlib\x94\x8c\nModuleSpec\x94\x93\x94)\x81\x94}\x94(h\x0ch\x02\x8c\x06loader\x94h\n\x8c\x06origin\x94h\x0e\x8c\x0cloader_state\x94N\x8c\x1asubmodule_search_locations\x94N\x8c\r_set_fileattr\x94\x88\x8c\x07_cached\x94\x8cX/opt/conda/lib/python3.9/site-packages/torch/_inductor/__pycache__/config.cpython-39.pyc\x94\x8c\r_initializing\x94\x89ub\x8c\x08__file__\x94h\x0e\x8c\n__cached__\x94h\x1b\x8c\x05debug\x94\x89\x8c\x10disable_progress\x94\x88\x8c\x10verbose_progress\x94\x89\x8c\x0bcpp_wrapper\x94\x89\x8c\x03dce\x94\x89\x8c\x14static_weight_shapes\x94\x88\x8c\x0csize_asserts\x94\x88\x8c\x10pick_loop_orders\x94\x88\x8c\x0finplace_buffers\x94\x88\x8c\x11benchmark_harness\x94\x88\x8c\x0fepilogue_fusion\x94\x89\x8c\x15epilogue_fusion_first\x94\x89\x8c\x0cmax_autotune\x94\x89\x8c\x17realize_reads_threshold\x94K\x04\x8c\x17realize_bytes_threshold\x94M\xd0\x07\x8c\x1brealize_acc_reads_threshold\x94K\x08\x8c\x0ffallback_random\x94\x89\x8c\x12implicit_fallbacks\x94\x88\x8c\rprefuse_nodes\x94\x88\x8c\x0btune_layout\x94\x89\x8c\x11aggressive_fusion\x94\x89\x8c\x0fmax_fusion_size\x94K@\x8c\x1bunroll_reductions_threshold\x94K\x08\x8c\x0ecomment_origin\x94\x89\x8c\tis_fbcode\x94h\x02h7\x93\x94\x8c\x0fcompile_threads\x94K \x8c\x13kernel_name_max_ops\x94K\n\x8c\x0finductor_import\x94\x8c\x0ftorch._inductor\x94\x8c\rshape_padding\x94\x89\x8c\x0epermute_fusion\x94\x89\x8c\x1aprofiler_mark_wrapper_call\x94\x89\x8c\x03cpp\x94}\x94(\x8c\n__module__\x94h\x02\x8c\x07threads\x94J\xff\xff\xff\xff\x8c\x0fdynamic_threads\x94\x89\x8c\x07simdlen\x94N\x8c\x0emin_chunk_size\x94M\x00\x10\x8c\x03cxx\x94N\x8c\x03g++\x94\x86\x94\x8c\x15enable_kernel_profile\x94\x89h\x03Nu\x8c\x06triton\x94}\x94(hBh\x02\x8c\ncudagraphs\x94\x89\x8c\x10debug_sync_graph\x94\x89\x8c\x11debug_sync_kernel\x94\x89\x8c\x0bconvolution\x94\x8c\x04aten\x94\x8c\x0edense_indexing\x94\x89\x8c\tmax_tiles\x94K\x02\x8c\x12autotune_pointwise\x94\x88\x8c tiling_prevents_pointwise_fusion\x94\x88\x8c tiling_prevents_reduction_fusion\x94\x88\x8c\x14ordered_kernel_names\x94\x89\x8c\x18descriptive_kernel_names\x94\x89h\x03Nu\x8c\x05trace\x94}\x94(hBh\x02\x8c\x07enabled\x94\x89\x8c\tdebug_log\x94\x88\x8c\x08info_log\x94\x89\x8c\x08fx_graph\x94\x88\x8c\rir_pre_fusion\x94\x88\x8c\x0eir_post_fusion\x94\x88\x8c\x0boutput_code\x94\x88\x8c\rgraph_diagram\x94\x89\x8c\x0fcompile_profile\x94\x89\x8c\nupload_tar\x94Nh\x03Nu\x8c\x15InductorConfigContext\x94}\x94(hBh\x02\x8c\x0f__annotations__\x94}\x94(\x8c\rstatic_memory\x94\x8c\x08builtins\x94\x8c\x04bool\x94\x93\x94\x8c\x0ematmul_padding\x94hlh+hl\x8c\x12triton_convolution\x94hj\x8c\x03str\x94\x93\x94\x8c\x17rematerialize_threshold\x94hj\x8c\x03int\x94\x93\x94\x8c\x1brematerialize_acc_threshold\x94hsu\x8c\x05_save\x94h\x02\x8c\x1bInductorConfigContext._save\x94\x93\x94\x8c\x06_apply\x94h\x02\x8c\x1cInductorConfigContext._apply\x94\x93\x94\x8c\x08__init__\x94h\x02\x8c\x1eInductorConfigContext.__init__\x94\x93\x94\x8c\t__enter__\x94h\x02\x8c\x1fInductorConfigContext.__enter__\x94\x93\x94\x8c\x08__exit__\x94h\x02\x8c\x1eInductorConfigContext.__exit__\x94\x93\x94h\x03Nu\x8c\x1cget_config_serialization_fns\x94\x8c\x1atorch._dynamo.config_utils\x94h\x84\x93\x94u.')


# REPLACEABLE COMMENT FOR TESTING PURPOSES


args = [((8, 1024), (1024, 1), torch.int64, 'cuda', False), ((32128, 512), (512, 1), torch.float32, 'cuda', True)]
args = [rand_strided(sh, st, dt, dev).requires_grad_(rg) for (sh, st, dt, dev, rg) in args]


from torch.nn import *
class Repro(torch.nn.Module):
    def __init__(self):
        super().__init__()



    def forward(self, input_ids : torch.Tensor, self_embed_tokens_weight : torch.Tensor):
        view = input_ids.view(-1, 1024);  input_ids = None
        embedding = torch.nn.functional.embedding(view, self_embed_tokens_weight, None, None, 2.0, False, False);  view = self_embed_tokens_weight = None
        ones = torch.ones(8, 1024)
        to = ones.to(device(type='cuda', index=0));  ones = None
        return (to, embedding)


mod = Repro()

# Setup debug minifier compiler
torch._dynamo.debug_utils.MINIFIER_SPAWNED = True
compiler_fn = BACKENDS["dynamo_minifier_backend"]

dynamo_minifier_backend = functools.partial(
    compiler_fn,
    compiler_name="inductor",
)
opt_mod = torch._dynamo.optimize(dynamo_minifier_backend)(mod)

with torch.cuda.amp.autocast(enabled=False):
    opt_mod(*args)
```

### Versions

```
Collecting environment information...
PyTorch version: 2.0.0a0+git5876d91
Is debug build: False
CUDA used to build PyTorch: 11.7
ROCM used to build PyTorch: N/A

OS: Ubuntu 20.04.5 LTS (x86_64)
GCC version: (Ubuntu 9.4.0-1ubuntu1~20.04.1) 9.4.0
Clang version: Could not collect
CMake version: version 3.24.3
Libc version: glibc-2.31

Python version: 3.9.13 | packaged by conda-forge | (main, May 27 2022, 16:58:50)  [GCC 10.3.0] (64-bit runtime)
Python platform: Linux-4.14.296-222.539.amzn2.x86_64-x86_64-with-glibc2.31
Is CUDA available: True
CUDA runtime version: 11.7.99
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: <redacted>

Nvidia driver version: 470.103.01
cuDNN version: Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.8.5.0
/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.5.0
/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.5.0
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.5.0
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.5.0
/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.5.0
/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.5.0
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True

Versions of relevant libraries:
[pip3] bert-pytorch==0.0.1a4
[pip3] clip-anytorch==2.5.0
[pip3] CoCa-pytorch==0.0.7
[pip3] dalle2-pytorch==1.10.5
[pip3] ema-pytorch==0.1.4
[pip3] functorch==1.14.0a0+408bcf1
[pip3] mypy-extensions==0.4.3
[pip3] numpy==1.23.5
[pip3] pytorch-transformers==1.2.0
[pip3] pytorch-warmup==0.1.1
[pip3] rotary-embedding-torch==0.2.1
[pip3] torch==2.0.0a0+git5876d91
[pip3] torch-fidelity==0.3.0
[pip3] torch-struct==0.5
[pip3] torchaudio==2.0.0a0+4699ef2
[pip3] torchdata==0.6.0a0+a1612ee
[pip3] torchmetrics==0.11.0
[pip3] torchrec-nightly==2023.1.29
[pip3] torchtext==0.15.0a0+f653dac
[pip3] torchvision==0.15.0a0+c35e8d5
[pip3] vector-quantize-pytorch==0.10.15
[conda] bert-pytorch              0.0.1a4                   dev_0    <develop>
[conda] clip-anytorch             2.5.0                    pypi_0    pypi
[conda] coca-pytorch              0.0.7                    pypi_0    pypi
[conda] dalle2-pytorch            1.10.5                   pypi_0    pypi
[conda] ema-pytorch               0.1.4                    pypi_0    pypi
[conda] functorch                 1.14.0a0+408bcf1          pypi_0    pypi
[conda] magma-cuda117             2.6.1                         1    pytorch
[conda] mkl                       2022.2.1         h84fe81f_16997    conda-forge
[conda] mkl-include               2023.0.0         h84fe81f_25396    conda-forge
[conda] numpy                     1.23.5                   pypi_0    pypi
[conda] pytorch-transformers      1.2.0                    pypi_0    pypi
[conda] pytorch-warmup            0.1.1                    pypi_0    pypi
[conda] rotary-embedding-torch    0.2.1                    pypi_0    pypi
[conda] torch                     2.0.0a0+git5876d91          pypi_0    pypi
[conda] torch-fidelity            0.3.0                    pypi_0    pypi
[conda] torch-struct              0.5                      pypi_0    pypi
[conda] torchaudio                2.0.0a0+4699ef2          pypi_0    pypi
[conda] torchdata                 0.6.0a0+a1612ee          pypi_0    pypi
[conda] torchmetrics              0.11.0                   pypi_0    pypi
[conda] torchrec-nightly          2023.1.29                pypi_0    pypi
[conda] torchtext                 0.15.0a0+f653dac          pypi_0    pypi
[conda] torchvision               0.15.0a0+c35e8d5          pypi_0    pypi
[conda] vector-quantize-pytorch   0.10.15                  pypi_0    pypi
```

cc @zhaojuanmao @mrshenli @rohan-varma @awgu @ezyang @soumith @msaroufim @wconstab @ngimel @bdhirsh @voznesenskym @penguinwu @anijain2305 @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @Xia-Weiwen @wenzhe-nrv @jiayisunx @desertfire