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Commit 5680c599 authored by Rich Ho's avatar Rich Ho
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Merge branch 'master' into laekov/gate

parents 90c4bccf 3c42c892
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Showing with 250 additions and 117 deletions
cuda/fmoe_cuda.cpp
View file @ 5680c599
......@@ -30,13 +30,17 @@ void _assign_pos(
// parallel_linear
std::vector<torch::Tensor> _linear_forward(
torch::Tensor input_buf,
torch::Tensor expert_count,
torch::Tensor weight,
torch::Tensor expert_count);
at::optional<torch::Tensor> bias
);
std::vector<torch::Tensor> _linear_backward(
torch::Tensor grad_output_buf,
torch::Tensor input_buf,
torch::Tensor expert_count,
torch::Tensor weight,
torch::Tensor expert_count);
at::optional<torch::Tensor> bias
);
// balancing
std::vector<torch::Tensor> _limit_by_capacity(
......
cuda/parallel_linear.cpp → cuda/parallel_linear.cu
View file @ 5680c599
#include "parallel_linear.h"
#include "parallel_linear.cuh"
#include "utils/fmoe_utils.h"
#include <torch/extension.h>
std::vector<torch::Tensor> _linear_forward(
torch::Tensor input_buf,
torch::Tensor expert_count,
torch::Tensor weight,
torch::Tensor expert_count
at::optional<torch::Tensor> bias
) {
CHECK_INPUT(input_buf);
CHECK_INPUT(weight);
auto smgr = getCudaStreamManager(input_buf.device().index());
const auto batch_size = input_buf.size(0);
const auto num_expert = weight.size(0);
const auto out_feat = weight.size(1);
const auto in_feat = weight.size(2);
#ifdef FMOE_DEBUG
#ifdef MOE_DEBUG
printf("[forward] expert=%ld, in_feat (d_model)=%ld, out_feat (d_ffn)=%ld\n",
num_expert, in_feat, out_feat);
#endif
torch::Tensor output;
if (bias.has_value()) {
output = bias.value().repeat_interleave(expert_count.to(bias.value().device()), 0);
} else{
auto out_options = torch::TensorOptions()
.device(input_buf.device())
.dtype(input_buf.dtype());
auto output = torch::empty({batch_size, out_feat}, out_options);
output = torch::empty({batch_size, out_feat}, out_options);
}
AT_DISPATCH_FLOATING_TYPES_AND_HALF(input_buf.scalar_type(), "fmoe_linear_forward",
AT_DISPATCH_FLOATING_TYPES_AND_HALF(input_buf.scalar_type(), "moe_forward_cuda",
([&] {
fmoe_cuda_forward_impl<scalar_t>(
fmoe_cuda_linear_forward_impl<scalar_t>(
input_buf.data_ptr<scalar_t>(),
weight.data_ptr<scalar_t>(),
expert_count.data_ptr<long>(),
output.data_ptr<scalar_t>(),
bias.has_value(),
in_feat,
out_feat,
num_expert,
......@@ -42,23 +48,21 @@ std::vector<torch::Tensor> _linear_forward(
return {output, };
}
std::vector<torch::Tensor> _linear_backward(
torch::Tensor grad_output_buf, // [batch_size x out_feat]
torch::Tensor input_buf, // [batch_size x out_feat]
torch::Tensor weight, // [num_expert x out_feat x in_feat]
torch::Tensor expert_count
torch::Tensor grad_output_buf,
torch::Tensor input_buf,
torch::Tensor expert_count,
torch::Tensor weight,
at::optional<torch::Tensor> bias
) {
CHECK_INPUT(grad_output_buf);
CHECK_INPUT(input_buf);
CHECK_INPUT(weight);
auto smgr = getCudaStreamManager(input_buf.device().index());
const auto batch_size = input_buf.size(0);
const auto num_expert = weight.size(0);
const auto out_feat = weight.size(1);
const auto in_feat = weight.size(2);
#ifdef FMOE_DEBUG
#ifdef MOE_DEBUG
printf("[backward] b=%ld, expert=%ld, in_feat (d_model)=%ld, "
"out_feat (d_ffn)=%ld\n",
batch_size, num_expert, in_feat, out_feat);
......@@ -66,15 +70,18 @@ std::vector<torch::Tensor> _linear_backward(
auto grad_input_buf = grad_output_buf.new_empty({batch_size, in_feat});
auto grad_weight = grad_output_buf.new_empty({num_expert, out_feat, in_feat});
auto grad_bias = grad_output_buf.new_empty({num_expert, out_feat});
AT_DISPATCH_FLOATING_TYPES_AND_HALF(input_buf.scalar_type(), "ffmoe_linear_backward", ([&] {
fmoe_cuda_backward_impl<scalar_t>(
AT_DISPATCH_FLOATING_TYPES_AND_HALF(input_buf.scalar_type(), "moe_cuda_backward", ([&] {
fmoe_cuda_linear_backward_impl<scalar_t>(
grad_output_buf.data_ptr<scalar_t>(),
input_buf.data_ptr<scalar_t>(),
weight.data_ptr<scalar_t>(),
expert_count.data_ptr<long>(),
grad_input_buf.data_ptr<scalar_t>(),
grad_weight.data_ptr<scalar_t>(),
grad_bias.data_ptr<scalar_t>(),
bias.has_value(),
batch_size,
in_feat,
out_feat,
......@@ -83,6 +90,6 @@ std::vector<torch::Tensor> _linear_backward(
);
}));
return {grad_input_buf, grad_weight};
return {grad_input_buf, grad_weight, grad_bias};
}
cuda/parallel_linear.h → cuda/parallel_linear.cuh
View file @ 5680c599
......@@ -2,17 +2,68 @@
#include "utils/cublas_wrapper.h"
/*
This function is to be called with one block per each column
*/
template <typename scalar_t>
void fmoe_cuda_forward_impl(
__global__
void column_reduce(const scalar_t * matrix, scalar_t * result,
int m /* lines */, int n /* columns*/) {
// https://stackoverflow.com/questions/27570552/templated-cuda-kernel-with-dynamic-shared-memory
extern __shared__ unsigned char my_smem[];
scalar_t *sdata = reinterpret_cast<scalar_t *>(my_smem);
// normal tid
int tid = threadIdx.x + threadIdx.y * blockDim.x;
// transposed tid for shared memory
int new_tid = threadIdx.y + threadIdx.x * blockDim.y;
// true x value in the matrix
int real_x = threadIdx.x + blockDim.x * blockIdx.x;
int i = real_x + n * threadIdx.y;
const int it = n*blockDim.y;
int offset = it;
float accumulator = 0;
if (threadIdx.y < m && real_x < n) {
// store all the values from this column in a warped way
accumulator = matrix[i];
while (i + offset < n*m) {
accumulator += matrix[i + offset];
offset += it;
}
}
// save column reduction data in a transposed way
sdata[new_tid] = accumulator;
__syncthreads();
for (size_t t= 16; t > 0; t>>=1) {
if (tid < 32 * 32 - 16)
sdata[tid] += sdata[tid + t];
__syncthreads();
}
if (threadIdx.y == 0 && real_x < n)
result[real_x] = sdata[new_tid];
}
template <typename scalar_t>
void fmoe_cuda_linear_forward_impl(
const scalar_t* input_buf,
const scalar_t* weight,
const long* expert_count,
scalar_t* output_buf,
const bool has_bias,
const size_t in_feat,
const size_t out_feat,
const size_t num_expert,
CudaStreamManager* smgr) {
scalar_t alpha = 1, beta = 0;
scalar_t alpha = 1, beta = has_bias ? 1 : 0;
for (int i = 0, ptr = 0; i < num_expert; ++i) {
if (expert_count[i] == 0) {
......@@ -37,13 +88,15 @@ void fmoe_cuda_forward_impl(
}
template <typename scalar_t>
void fmoe_cuda_backward_impl(
void fmoe_cuda_linear_backward_impl(
const scalar_t* grad_output_buf,
const scalar_t* input_buf,
const scalar_t* weight,
const long* expert_count,
scalar_t* grad_input_buf,
scalar_t* grad_weight,
scalar_t* grad_bias,
const bool has_bias,
const size_t batch_size,
const size_t in_feat,
const size_t out_feat,
......@@ -51,10 +104,16 @@ void fmoe_cuda_backward_impl(
CudaStreamManager* smgr) {
scalar_t alpha = 1, beta = 0;
// bias
dim3 block_threads(32, 32);
dim3 grid_threads(out_feat / 32 + (out_feat % 32 ? 1 : 0), 1);
for (int i = 0, ptr = 0; i < num_expert; ++i) {
if (expert_count[i] == 0) {
cudaMemset(grad_weight + i * in_feat * out_feat, 0,
sizeof(scalar_t) * in_feat * out_feat);
cudaMemset(grad_bias + i * out_feat, 0, sizeof(scalar_t) * out_feat);
continue;
}
// Use T(B) x T(A) = T(C) to produce row-major C
......@@ -85,7 +144,19 @@ void fmoe_cuda_backward_impl(
grad_weight + i * in_feat * out_feat, in_feat
));
if (has_bias) {
column_reduce
<<<grid_threads, block_threads, sizeof(scalar_t)*1024, smgr->stream(0)>>>
(
grad_output_buf + ptr * out_feat,
grad_bias + i * out_feat,
expert_count[i],
out_feat
);
}
ptr += expert_count[i];
}
smgr->sync(num_expert);
}
fmoe/functions.py
View file @ 5680c599
......@@ -147,21 +147,25 @@ class MOELinear(Function):
"""
@staticmethod
def forward(ctx, global_input_buf, weight, fwd_expert_count):
def forward(ctx, global_input_buf, fwd_expert_count, weight, bias=None):
(global_output_buf,) = fmoe_cuda.linear_forward(
global_input_buf, weight, fwd_expert_count
global_input_buf, fwd_expert_count, weight, bias
)
variables = (global_input_buf, weight, fwd_expert_count)
variables = (global_input_buf, fwd_expert_count, weight, bias)
ctx.save_for_backward(*variables)
return global_output_buf
@staticmethod
def backward(ctx, grad_out):
(input_buf, weight, fwd_expert_count) = ctx.saved_tensors
grad_inp_buf, grad_weight = fmoe_cuda.linear_backward(
grad_out, input_buf, weight, fwd_expert_count
(input_buf, fwd_expert_count, weight, bias) = ctx.saved_tensors
grad_inp_buf, grad_weight, grad_bias = fmoe_cuda.linear_backward(
grad_out, input_buf, fwd_expert_count, weight, bias
)
return grad_inp_buf, grad_weight, None
if not torch.is_tensor(bias):
grad_bias = None
return grad_inp_buf, None, grad_weight, grad_bias
class MOEGather(Function):
......
fmoe/layers.py
View file @ 5680c599
......@@ -41,37 +41,7 @@ class FMoELinear(nn.Module):
r"""
Call MOE function
"""
x = MOELinear.apply(inp, self.weight, fwd_expert_count)
if self.bias is not None:
# TODO: torch.repeat_interleave seems have numerical
# instability in backward, leading to incorrect
# gradient computation for solution 1 and 2.
# Solution 3 uses a for-loop to expand the bias,
# but is 50% slower.
# This part should finally goes to MOELinear.apply,
# like MOELinear.apply(x, weight, bias, count)
# Solution 1
bias = torch.repeat_interleave(
self.bias, fwd_expert_count.to(self.bias.device), dim=0
)
# Solution 2
# bias_idx = torch.arange(self.num_expert)\
# .repeat_interleave(fwd_expert_count)
# bias = self.bias[bias_idx]
# Solution 3
# bias = []
# for i in range(self.num_expert):
# if fwd_expert_count[i] > 0:
# bias.append(
# self.bias[i].unsqueeze(0).expand(
# fwd_expert_count[i], -1
# )
# )
# bias = torch.cat(bias, dim=0)
x = x + bias
x = MOELinear.apply(inp, fwd_expert_count, self.weight, self.bias)
return x
def extra_repr(self) -> str:
......
setup.py
View file @ 5680c599
......@@ -29,7 +29,7 @@ if __name__ == '__main__':
'cuda/local_exchange.cu',
'cuda/balancing.cu',
'cuda/global_exchange.cpp',
'cuda/parallel_linear.cpp',
'cuda/parallel_linear.cu',
'cuda/fmoe_cuda.cpp',
],
extra_compile_args={
......
tests/test_ddp.py
View file @ 5680c599
......@@ -11,7 +11,7 @@ from test_numerical import test_fmoe_linear as _test_fmoe_linear
from test_numerical import _test_fmoe_local_ddp
def _run_distributed(func, world_size, args: Dict):
def _run_distributed(func, world_size, args: Dict, script=__file__):
if torch.cuda.device_count() < world_size:
pytest.skip("No enough GPU")
import subprocess
......@@ -25,7 +25,7 @@ def _run_distributed(func, world_size, args: Dict):
for i in range(world_size):
os.environ["OMPI_COMM_WORLD_RANK"] = str(i)
p = subprocess.Popen(
[sys.executable, __file__, func, json.dumps(args)], stdout=subprocess.PIPE
[sys.executable, script, func, json.dumps(args)], stdout=subprocess.PIPE
)
ps.append(p)
......@@ -41,8 +41,9 @@ def _run_distributed(func, world_size, args: Dict):
@pytest.mark.parametrize("d_model", [16])
@pytest.mark.parametrize("d_hidden", [32])
@pytest.mark.parametrize("mp_size", [1, 2])
@pytest.mark.parametrize("data_type", ['torch.FloatTensor', 'torch.DoubleTensor', 'torch.HalfTensor'])
def test_fmoe_linear_distributed(
num_expert, top_k, batch_size, d_model, d_hidden, mp_size
num_expert, top_k, batch_size, d_model, d_hidden, mp_size, data_type
):
_run_distributed(
"_test_fmoe_linear",
......@@ -54,6 +55,7 @@ def test_fmoe_linear_distributed(
"d_model": d_model,
"d_hidden": d_hidden,
"mp_size": mp_size,
"data_type": data_type
},
)
......@@ -120,5 +122,6 @@ if __name__ == "__main__":
else:
test_fmoe_local_ddp(mp_size=2)
test_fmoe_linear_distributed(
num_expert=4, top_k=2, batch_size=4, d_model=8, d_hidden=8, mp_size=2
num_expert=4, top_k=2, batch_size=4, d_model=8, d_hidden=8, mp_size=2,
data_type="torch.HalfTensor"
)
tests/test_gates.py
View file @ 5680c599
import pytest
import os
import sys
import json
import math
import torch
import torch.distributed as dist
from fmoe.gates import GShardGate, SwitchGate
from test_ddp import _run_distributed
def _ensure_initialized():
......@@ -16,14 +21,27 @@ def _ensure_initialized():
dist.init_process_group(backend="nccl")
@pytest.mark.parametrize("d_model", [8, 1024])
@pytest.mark.parametrize("batch_size", [16, 4096])
@pytest.mark.parametrize("n_expert", [1, 4, 16])
@pytest.mark.parametrize("cap", [.1, .5, 1.1])
@pytest.mark.parametrize("d_model", [1024])
@pytest.mark.parametrize("batch_size", [16])
@pytest.mark.parametrize("n_expert", [1, 4])
@pytest.mark.parametrize("cap", [.1, 1.1])
def test_gshard_gate(d_model, batch_size, n_expert, cap):
_ensure_initialized()
if dist.get_world_size() * n_expert < 2:
if 1 * n_expert < 2:
pytest.skip("No enough experts")
_run_distributed('_test_gshard_gate',
1,
{
'd_model': d_model,
'batch_size': batch_size,
'n_expert': n_expert,
'cap': cap
},
script=__file__
)
def _test_gshard_gate(d_model, batch_size, n_expert, cap):
_ensure_initialized()
gate = GShardGate(d_model, n_expert, dist.get_world_size(),
capacity=(cap, cap)).cuda()
x = torch.rand(batch_size, d_model).cuda()
......@@ -37,11 +55,24 @@ def test_gshard_gate(d_model, batch_size, n_expert, cap):
assert(i <= real_cap)
@pytest.mark.parametrize("d_model", [8, 1024])
@pytest.mark.parametrize("batch_size", [16, 4096])
@pytest.mark.parametrize("n_expert", [1, 4, 16])
@pytest.mark.parametrize("cap", [.1, .5, 1.1])
@pytest.mark.parametrize("d_model", [1024])
@pytest.mark.parametrize("batch_size", [4096])
@pytest.mark.parametrize("n_expert", [1, 16])
@pytest.mark.parametrize("cap", [.1, .8])
def test_switch_gate(d_model, batch_size, n_expert, cap):
_run_distributed('_test_switch_gate',
1,
{
'd_model': d_model,
'batch_size': batch_size,
'n_expert': n_expert,
'cap': cap
},
script=__file__
)
def _test_switch_gate(d_model, batch_size, n_expert, cap):
_ensure_initialized()
gate = SwitchGate(d_model, n_expert, dist.get_world_size(),
capacity=(cap, cap)).cuda()
......@@ -57,6 +88,11 @@ def test_switch_gate(d_model, batch_size, n_expert, cap):
if __name__ == '__main__':
if len(sys.argv) >= 3:
args = json.loads(sys.argv[2])
locals()[sys.argv[1]](**args)
else:
_ensure_initialized()
test_gshard_gate(4096, 1024, 4, .2)
# test_gshard_gate(4096, 1024, 4, .2)
test_gshard_gate(8, 16, 1, .1)
# test_switch_gate(4096, 1024, 4, .2)
tests/test_local_exchange.py
View file @ 5680c599
......@@ -10,7 +10,7 @@ import numpy as np
from copy import deepcopy
from fmoe.functions import MOEGather, MOEScatter, count_by_gate
from test_numerical import _assert_numercial
from test_numerical import _assert_numerical
@pytest.mark.parametrize("n_expert", [1, 4, 8])
@pytest.mark.parametrize("topk", [1, 2])
......@@ -30,10 +30,10 @@ def test_scatter(n_expert, topk, batch_size, d_model, world_size):
inp_raw = inp.data.clone()
out_raw = torch.empty(pos.shape[0], d_model,
device=inp.device, dtype=inp.dtype)
out_raw.sum().backward()
# out_raw.sum().backward()
for i, f in enumerate(pos.cpu()):
out_raw[i] = inp[f % batch_size]
_assert_numercial(['out'], [out], [out_raw], 0)
_assert_numerical(['out'], [out], [out_raw], 0)
# TODO: check grad
if __name__ == '__main__':
......
tests/test_numerical.py
View file @ 5680c599
......@@ -17,11 +17,13 @@ from moe import BruteForceMoELinear, BruteForceMoE, NaiveExpert, LinearExpert
def _perform_forward(
moe: nn.Module, moe_raw: nn.Module, batch_size, d_model, top_k, rank, mp_group
moe: nn.Module, moe_raw: nn.Module, batch_size, d_model, top_k, rank, mp_group, data_type='torch.FloatTensor'
):
moe.zero_grad()
moe_raw.zero_grad()
inp = torch.rand(batch_size, d_model).cuda()
inp = torch.rand(batch_size, d_model).type(data_type).cuda()
if mp_group is not None:
group_sender = rank // mp_group.size() * mp_group.size()
torch.distributed.broadcast(inp, group_sender, group=mp_group)
......@@ -46,15 +48,17 @@ def _perform_forward(
return moe_out, raw_out, inp.grad, inp_raw.grad
def _assert_numercial(names, moe_out_list, raw_out_list, rank):
def _assert_numerical(names, moe_out_list, raw_out_list, rank, precision=1e-3):
for name, mo, ro in zip(names, moe_out_list, raw_out_list):
err = (mo - ro).abs().sum()
err = (mo - ro).abs().max()
print("Rank {} {} abs err {}".format(rank, name, err))
if err > 1e-3:
if err > precision:
sys.stderr.write(f"=========== {name} moe out ==============\n")
sys.stderr.write("{}\n".format(mo))
sys.stderr.write(f"=========== {name} raw out ==============\n")
sys.stderr.write("{}\n".format(ro))
sys.stderr.write(f"=========== {name} diff ==============\n")
sys.stderr.write("{}\n{}\n".format((mo - ro).abs(), err))
assert False
......@@ -87,6 +91,7 @@ class MyMoE(FMoE):
@pytest.mark.parametrize("mp_group", [None])
@pytest.mark.parametrize("dp_group", [None])
@pytest.mark.parametrize("world_group", [None])
@pytest.mark.parametrize("data_type", ['torch.FloatTensor', 'torch.DoubleTensor', 'torch.HalfTensor'])
def test_fmoe_linear(
num_expert,
top_k,
......@@ -98,6 +103,7 @@ def test_fmoe_linear(
mp_group,
dp_group,
world_group,
data_type,
activation=torch.nn.functional.gelu,
):
torch.manual_seed(42 + rank)
......@@ -105,7 +111,7 @@ def test_fmoe_linear(
moe = MyMoE(
num_expert, d_model, d_hidden, world_size, mp_group, top_k, activation
).cuda()
).type(data_type).cuda()
moe_raw = BruteForceMoELinear(
activation=activation,
......@@ -114,7 +120,7 @@ def test_fmoe_linear(
d_hidden=d_hidden,
world_size=world_size,
top_k=top_k,
).cuda()
).type(data_type).cuda()
if world_size == 1:
moe_raw.weight_htoh4.data = moe.experts.htoh4.weight.data.clone()
......@@ -145,7 +151,7 @@ def test_fmoe_linear(
moe_raw.bias_h4toh.data = torch.cat(bias_h4toh_array, dim=0)
moe_out, raw_out, moe_grad_in, raw_grad_in = _perform_forward(
moe, moe_raw, batch_size, d_model, top_k, rank, mp_group
moe, moe_raw, batch_size, d_model, top_k, rank, mp_group, data_type=data_type
)
moe_out_list = (
......@@ -195,7 +201,10 @@ def test_fmoe_linear(
"h4toh bias grad",
]
_assert_numercial(names, moe_out_list, raw_out_list, rank)
precision = 5e-1 if data_type == 'torch.HalfTensor' else 1e-3
_assert_numerical(names, moe_out_list, raw_out_list, rank, precision=precision)
@pytest.mark.parametrize("batch_size", [4])
......@@ -296,7 +305,7 @@ def test_fmoe(
raw_out_list = [raw_out, raw_grad, raw_grad_in]
names = ["forward", "backward", "grad_in"]
_assert_numercial(names, moe_out_list, raw_out_list, rank)
_assert_numerical(names, moe_out_list, raw_out_list, rank)
class MyModule(nn.Module):
......@@ -372,7 +381,7 @@ def _test_fmoe_local_ddp(rank, world_size, mp_group, dp_group, world_group):
names = ["mp grad", "dp grad", "wp grad"]
_assert_numercial(names, ddp_out_list, raw_out_list, rank)
_assert_numerical(names, ddp_out_list, raw_out_list, rank)
if __name__ == "__main__":
......
tests/test_zero.py
View file @ 5680c599
import sys
import torch
from fmoe.layers import _fmoe_general_global_forward
from fmoe import FMoETransformerMLP
from test_ddp import _run_distributed
class ConstantGate(torch.nn.Module):
def __init__(self, d_model, num_expert, world_size, top_k=1):
......@@ -16,12 +19,34 @@ class ConstantGate(torch.nn.Module):
def test_zero_fwd(num_expert=2, batch_size=4, d_hidden=8, world_size=1):
_run_distributed('_test_zero_fwd',
1,
{
'num_expert': num_expert,
'batch_size': batch_size,
'd_hidden': d_hidden
},
script=__file__
)
def _test_zero_fwd(num_expert=2, batch_size=4, d_hidden=8, world_size=1):
inp = torch.rand(batch_size, d_hidden).cuda()
gate = torch.zeros(batch_size, dtype=torch.int64).cuda()
x = _fmoe_general_global_forward(inp, gate, lambda x, y: x, num_expert,
world_size)
def test_zero_transformer(num_expert=2, batch_size=4, d_hidden=8, world_size=1):
_run_distributed('_test_zero_transformer',
1,
{
'num_expert': num_expert,
'batch_size': batch_size,
'd_hidden': d_hidden
},
script=__file__
)
def test_zero_transformer(num_expert=2, batch_size=4, d_hidden=8, world_size=1):
inp = torch.rand(batch_size, d_hidden).cuda()
model = FMoETransformerMLP(num_expert, d_hidden, d_hidden * 4, world_size,
......@@ -30,9 +55,13 @@ def test_zero_transformer(num_expert=2, batch_size=4, d_hidden=8, world_size=1):
if __name__ == '__main__':
if len(sys.argv) >= 3:
args = json.loads(sys.argv[2])
torch.distributed.init_process_group(backend="nccl")
torch.cuda.set_device(torch.distributed.get_rank())
args['world_size'] = torch.distributed.get_world_size()
locals()[sys.argv[1]](**args)
else:
# test_zero_fwd(world_size=torch.distributed.get_world_size())
test_zero_transformer(num_expert=16, batch_size=4096, d_hidden=1024,
world_size=torch.distributed.get_world_size())
world_size=1)
print('done')
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