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# 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.
# Run the test: CUDA_VISIBLE_DEVICES=0,1 RUN_SLOW=1 pytest -sv tests/tensor_parallel/test_tensor_parallel.py
import os
import subprocess
import tempfile
import textwrap
from transformers import is_torch_available
from transformers.integrations.tensor_parallel import get_packed_weights, repack_weights
from transformers.testing_utils import (
TestCasePlus,
backend_device_count,
get_torch_dist_unique_port,
require_huggingface_hub_greater_or_equal,
require_torch_multi_accelerator,
torch_device,
)
if is_torch_available():
import torch
class TestTensorParallelUtils(TestCasePlus):
def test_packed_unpacked_conversion(self):
WORLD_SIZE = 2
PACKED_BLOCK_SIZE = 800
SHARDING_DIM = 2
NUM_BLOCKS = 2
original_packed_weights = torch.randn(4, 512, 2 * PACKED_BLOCK_SIZE)
original_packed_weights.get_dtype = lambda: "F32" # get_packed_weights expects PySlice object
empty_param = torch.empty(4, 512, 2 * PACKED_BLOCK_SIZE)
class MockDeviceMesh:
def size(self):
return WORLD_SIZE
mock_mesh = (
MockDeviceMesh()
) # get_packed_weights only calls `.size()`, do this to avoid doing actual distributed run
packed_weights_0 = get_packed_weights(original_packed_weights, empty_param, mock_mesh, 0, SHARDING_DIM)
packed_weights_1 = get_packed_weights(original_packed_weights, empty_param, mock_mesh, 1, SHARDING_DIM)
# simulate all gather of sharded weights
packed_weights = torch.cat([packed_weights_0, packed_weights_1], dim=SHARDING_DIM)
unpacked_weights = repack_weights(packed_weights, SHARDING_DIM, WORLD_SIZE, NUM_BLOCKS)
assert torch.allclose(unpacked_weights, original_packed_weights)
class TestTensorParallel(TestCasePlus):
nproc_per_node = 2
def torchrun(self, script: str, is_torchrun: bool = True):
"""Run the `script` using `torchrun` command for multi-processing in a subprocess. Captures errors as necessary."""
with tempfile.NamedTemporaryFile(mode="w+", suffix=".py") as tmp:
tmp.write(script)
tmp.flush()
tmp.seek(0)
if is_torchrun:
cmd = (
f"torchrun --nproc_per_node {self.nproc_per_node} --master_port {get_torch_dist_unique_port()} {tmp.name}"
).split()
else:
cmd = ["python3", tmp.name]
# 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}")
def test_model_forward(self):
script_to_run = textwrap.dedent(
"""
import torch
import os
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "JackFram/llama-68m"
rank = int(os.environ["RANK"])
world_size = int(os.environ["WORLD_SIZE"])
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype="auto", tp_plan="auto")
torch.distributed.barrier()
has_dtensor = 0
for name, parameter in model.named_parameters():
if isinstance(parameter.data, torch.distributed.tensor.DTensor):
has_dtensor = 1
break
assert has_dtensor == 1, "TP model must has DTensor"
tokenizer = AutoTokenizer.from_pretrained(model_id)
prompt = "Can I help"
inputs = tokenizer(prompt, return_tensors="pt").input_ids.to(model.device)
outputs = model(inputs)
next_token_logits = outputs[0][:, -1, :]
next_token = torch.argmax(next_token_logits, dim=-1)
response = tokenizer.decode(next_token)
assert response == "with"
torch.distributed.barrier()
torch.distributed.destroy_process_group()
"""
)
self.torchrun(script_to_run)
def test_model_generate(self):
script_to_run = textwrap.dedent(
"""
import torch
import os
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "JackFram/llama-68m"
rank = int(os.environ["RANK"])
world_size = int(os.environ["WORLD_SIZE"])
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype="auto", tp_plan="auto")
torch.distributed.barrier()
model.forward = torch.compile(model.forward)
has_dtensor = 0
for name, parameter in model.named_parameters():
if isinstance(parameter.data, torch.distributed.tensor.DTensor):
has_dtensor = 1
break
assert has_dtensor == 1, "TP model must has DTensor"
tokenizer = AutoTokenizer.from_pretrained(model_id)
prompt = "Can I help"
inputs = tokenizer(prompt, return_tensors="pt").input_ids.to(model.device)
outputs = model.generate(inputs, max_new_tokens=10, cache_implementation="static")
output_text = tokenizer.batch_decode(outputs, skip_special_tokens=True)
assert output_text[0].startswith(prompt), f"Expected output to start with '{prompt}', got '{output_text[0]}'"
torch.distributed.barrier()
torch.distributed.destroy_process_group()
"""
)
self.torchrun(script_to_run)
@require_huggingface_hub_greater_or_equal("0.31.4")
def test_model_save(self):
from safetensors import safe_open
with tempfile.TemporaryDirectory() as tmp_dir:
for is_torchrun in [True, False]:
script_to_run = textwrap.dedent(
f"""
import torch
import os
from transformers import AutoModelForCausalLM
model_id = "JackFram/llama-68m"
kwargs = dict()
if os.environ.get("RANK", None) is not None:
kwargs["tp_plan"] = "auto"
result_dir = "{tmp_dir}/tp"
else:
result_dir = "{tmp_dir}/nontp"
model = AutoModelForCausalLM.from_pretrained(model_id, **kwargs)
model.save_pretrained(result_dir)
"""
)
self.torchrun(script_to_run, is_torchrun=is_torchrun)
non_tp_model_path = os.path.join(tmp_dir, "nontp")
tp_model_path = os.path.join(tmp_dir, "tp")
for filename in os.listdir(non_tp_model_path):
if not filename.endswith(".safetensors"):
continue
non_tp_model = safe_open(os.path.join(non_tp_model_path, filename), device="cpu", framework="pt")
tp_model = safe_open(os.path.join(tp_model_path, filename), device="cpu", framework="pt")
for non_tp_key in non_tp_model.keys():
non_tp_tensor = non_tp_model.get_tensor(non_tp_key)
tp_tensor = tp_model.get_tensor(non_tp_key)
assert torch.allclose(non_tp_tensor, tp_tensor), f"Tensor with key: {non_tp_key} does not match"
del non_tp_tensor, tp_tensor
@require_torch_multi_accelerator
class TestTensorParallelAccelerator(TestTensorParallel):
nproc_per_node = backend_device_count(torch_device)
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