File size: 6,469 Bytes
17c6d62 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 |
# Copyright 2024 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import subprocess
import tempfile
import textwrap
# TORCH_LOGS=+dtensor CUDA_LAUNCH_BLOCKING=1 TORCH_USE_CUDA_DSA=1 PYTHONPATH="src" python -m torch.distributed.run --nproc_per_node 2 ./tests/tp/test_tp.py
from transformers import is_torch_available
from transformers.models.llama.configuration_llama import LlamaConfig
from transformers.models.llama.modeling_llama import LlamaModel
from transformers.testing_utils import (
TestCasePlus,
execute_subprocess_async,
get_torch_dist_unique_port,
require_torch_multi_gpu,
)
if is_torch_available():
import torch
class TestTensorParallel(TestCasePlus):
def torchrun(self, script: str):
"""Run the `script` using `torchrun` command for multi-processing in a subprocess. Captures errors as necesary."""
with tempfile.NamedTemporaryFile(mode="w+", suffix=".py") as tmp:
tmp.write(script)
tmp.flush()
tmp.seek(0)
cmd = (
f"torchrun --nproc_per_node {torch.cuda.device_count()} --master_port {get_torch_dist_unique_port()} {tmp.name}"
).split()
# Note that the subprocess will be waited for here, and raise an error if not successful
try:
_ = subprocess.run(cmd, capture_output=True, env=self.get_env(), text=True, check=True)
except subprocess.CalledProcessError as e:
raise Exception(f"The following error was captured: {e.stderr}")
@require_torch_multi_gpu
def test_tp(self):
distributed_args = f"""--nproc_per_node={torch.cuda.device_count()}
--master_port={get_torch_dist_unique_port()}
{self.test_file_dir}/test_tp.py
""".split()
output_dir = self.get_auto_remove_tmp_dir()
args = f"--output_dir {output_dir} --report_to none".split()
cmd = ["torchrun"] + distributed_args + args
print(cmd)
execute_subprocess_async(cmd, env=self.get_env())
# successful return here == success - any errors would have caused an error in the sub-call
@require_torch_multi_gpu
def test_loading_memory_consumption(self):
script_to_run = textwrap.dedent(
"""
import torch
import os
from transformers import AutoModelForCausalLM
model_id = "meta-llama/Meta-Llama-3-8B-Instruct"
rank = int(os.environ["RANK"])
world_size = int(os.environ["WORLD_SIZE"])
device = torch.device(f"cuda:{rank}")
torch.distributed.init_process_group("nccl", device_id=device)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16, tp_plan="auto")
torch.distributed.barrier()
# The expected full model memory footprint
expected_model_memory = 16
overhead_factor = 1.2
# Assert we did not use more than the full model expected memory (with some overhead)
if not torch.cuda.max_memory_allocated(device) / 1024**3 < expected_model_memory * overhead_factor:
raise ValueError("Loading the model used more than the full model size")
# Assert we correctly handled the sharding between devices
if not torch.cuda.memory_allocated(device) / 1024**3 < (expected_model_memory / world_size) * overhead_factor:
raise ValueError("Each model shard is larger than what is expected.")
torch.distributed.barrier()
torch.distributed.destroy_process_group()
"""
)
self.torchrun(script_to_run)
if __name__ == "__main__":
# The script below is meant to be run under torch.distributed, on a machine with multiple GPUs:
# CUDA_VISIBLE_DEVICES=0,1 RUN_SLOW=1 pytest -sv tests/tp/test_tp.py
# or
# PYTHONPATH="src" python -m torch.distributed.run --nproc_per_node 2 ./tests/tp/test_tp.py
if not is_torch_available():
exit(0)
# Test settings
model_id = "meta-llama/Meta-Llama-3-8B-Instruct"
bs = 1
seqlen = 4096
# Get distributed settings
rank = int(os.environ["RANK"])
world_size = int(os.environ["WORLD_SIZE"])
# Initialize distributed
device = torch.device(f"cuda:{rank}")
torch.distributed.init_process_group("nccl", device_id=device)
device_mesh = torch.distributed.init_device_mesh("cuda", (world_size,))
# Get model config
config = LlamaConfig.from_pretrained(model_id)
config.hidden_size = 2048
config.attention_bias = False
# Instantiate model
with device:
model = LlamaModel(config).to(dtype=torch.float16)
model.eval()
# Tensor Parallel
if world_size > 1:
model.tensor_parallel(device_mesh)
# Run model
inputs = torch.randint(config.vocab_size, (bs, seqlen), device=device)
# Test cuda graphing explicitly
with torch.cuda.device(device):
print("Cuda graphing")
with torch.no_grad():
inputs = torch.randint(config.vocab_size, (bs, seqlen), device=device)
# CUDA Graph setup
s = torch.cuda.Stream(device=device)
s.wait_stream(torch.cuda.current_stream())
with torch.cuda.stream(s):
for i in range(3):
out = model(inputs)
torch.cuda.current_stream().wait_stream(s)
g = torch.cuda.CUDAGraph()
with torch.cuda.graph(g):
out = model(inputs)
for _ in range(2):
g.replay()
s.synchronize()
assert out.last_hidden_state.shape == torch.Size([bs, seqlen, config.hidden_size])
# Test compile
with torch.no_grad():
out = model(inputs)
model.forward = torch.compile(model.forward, mode="reduce-overhead")
out = model(inputs)
out = model(inputs)
|