213 lines
8.3 KiB
Python
213 lines
8.3 KiB
Python
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import os
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import random
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import socket
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import unittest
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from typing import Any
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import ray
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import torch
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import torch.distributed as dist
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from sglang.srt.distributed import init_distributed_environment
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from sglang.srt.distributed.communication_op import ( # noqa
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tensor_model_parallel_all_reduce,
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)
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from sglang.srt.distributed.device_communicators.quick_all_reduce import (
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qr_rocm_arch_available,
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)
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from sglang.srt.distributed.parallel_state import (
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get_tensor_model_parallel_group,
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graph_capture,
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initialize_model_parallel,
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)
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from sglang.test.test_utils import CustomTestCase
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torch.manual_seed(42)
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random.seed(44) # keep the deterministic seed
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def get_open_port() -> int:
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# try ipv4
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try:
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with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
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s.bind(("", 0))
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return s.getsockname()[1]
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except OSError:
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# try ipv6
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with socket.socket(socket.AF_INET6, socket.SOCK_STREAM) as s:
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s.bind(("", 0))
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return s.getsockname()[1]
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def multi_process_parallel(
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world_size: int, cls: Any, test_target: Any, quant_mode: str
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) -> None:
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# Using ray helps debugging the error when it failed
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# as compared to multiprocessing.
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# NOTE: We need to set working_dir for distributed tests,
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# otherwise we may get import errors on ray workers
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ray.init(log_to_driver=True)
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distributed_init_port = get_open_port()
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refs = []
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for rank in range(world_size):
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refs.append(
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test_target.remote(cls, world_size, rank, distributed_init_port, quant_mode)
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)
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ray.get(refs)
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ray.shutdown()
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class TestQuickAllReduce(CustomTestCase):
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TEST_SIZES = [
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2 * 1024 * 1024,
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4 * 1024 * 1024,
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8 * 1024 * 1024,
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16 * 1024 * 1024,
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32 * 1024 * 1024,
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]
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TEST_LOOP = 5
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# Too many configurations can lead to a test grid that is too large
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# The tp takes too long to boot,let's just choose 4 out of 12 configurations
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# WORLD_SIZES = [2, 4, 8]
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# QUANT_MODE = ["FP", "INT8", "INT6", "INT4"]
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QUANT_MODE_WORLD_SIZE_PART = [["FP", 8], ["INT4", 4], ["INT8", 2], ["INT6", 2]]
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@unittest.skipIf(
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not qr_rocm_arch_available(),
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"Only test Quick AllReduce on ROCm architectures >= gfx94*",
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)
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def test_graph_allreduce(self):
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for quant_mode_world_size_part in self.QUANT_MODE_WORLD_SIZE_PART:
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quant_mode = quant_mode_world_size_part[0]
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world_size = quant_mode_world_size_part[1]
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if world_size > torch.cuda.device_count():
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continue
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multi_process_parallel(world_size, self, self.graph_allreduce, quant_mode)
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@unittest.skipIf(
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not qr_rocm_arch_available(),
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"Only test Quick AllReduce on ROCm architectures >= gfx94*",
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)
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def test_eager_allreduce(self):
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for quant_mode_world_size_part in self.QUANT_MODE_WORLD_SIZE_PART:
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quant_mode = quant_mode_world_size_part[0]
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world_size = quant_mode_world_size_part[1]
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if world_size > torch.cuda.device_count():
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continue
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multi_process_parallel(world_size, self, self.eager_allreduce, quant_mode)
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@ray.remote(num_gpus=1, max_calls=1)
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def graph_allreduce(self, world_size, rank, distributed_init_port, quant_mode):
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os.environ.pop("CUDA_VISIBLE_DEVICES", None)
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os.environ["ROCM_QUICK_REDUCE_QUANTIZATION"] = quant_mode
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os.environ["ROCM_QUICK_REDUCE_CAST_BF16_TO_FP16"] = "0"
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device = torch.device(f"cuda:{rank}")
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torch.cuda.set_device(device)
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distributed_init_method = f"tcp://localhost:{distributed_init_port}"
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init_distributed_environment(
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world_size=world_size,
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rank=rank,
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distributed_init_method=distributed_init_method,
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local_rank=rank,
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)
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initialize_model_parallel(tensor_model_parallel_size=world_size)
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group = get_tensor_model_parallel_group().device_group
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# A small all_reduce for warmup.
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# this is needed because device communicators might be created lazily
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# (e.g. NCCL). This will ensure that the communicator is initialized
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# before any communication happens, so that this group can be used for
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# graph capture immediately.
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data = torch.zeros(1)
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data = data.to(device=device)
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torch.distributed.all_reduce(data, group=group)
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torch.cuda.synchronize()
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del data
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for sz in self.TEST_SIZES:
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for dtype in [torch.float16, torch.bfloat16]:
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for _ in range(self.TEST_LOOP):
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with graph_capture() as graph_capture_context:
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# use integers so result matches NCCL exactly
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inp1 = torch.randint(
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1,
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23,
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(sz,),
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dtype=dtype,
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device=torch.cuda.current_device(),
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)
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inp2 = torch.randint(
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-23,
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1,
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(sz,),
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dtype=dtype,
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device=torch.cuda.current_device(),
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)
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torch.cuda.synchronize()
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graph = torch.cuda.CUDAGraph()
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with torch.cuda.graph(
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graph, stream=graph_capture_context.stream
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):
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out1 = tensor_model_parallel_all_reduce(inp1)
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# the input buffer is immediately modified to test
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# synchronization
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dist.all_reduce(inp1, group=group)
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out2 = tensor_model_parallel_all_reduce(inp2)
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dist.all_reduce(inp2, group=group)
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graph.replay()
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atol = 1.25 * world_size
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rtol = 0.5 * world_size
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for inp, out in [[inp1, out1], [inp2, out2]]:
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torch.testing.assert_close(out, inp, atol=atol, rtol=rtol)
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# try:
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# torch.testing.assert_close(out, inp, atol=atol, rtol=rtol)
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# except AssertionError as e:
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# print("Max abs diff:", (out - inp).abs().max())
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# print("Max rel diff:", ((out - inp).abs() / inp.abs().clamp(min=1e-5)).max())
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@ray.remote(num_gpus=1, max_calls=1)
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def eager_allreduce(self, world_size, rank, distributed_init_port, quant_mode):
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os.environ.pop("CUDA_VISIBLE_DEVICES", None)
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os.environ["ROCM_QUICK_REDUCE_QUANTIZATION"] = quant_mode
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os.environ["ROCM_QUICK_REDUCE_CAST_BF16_TO_FP16"] = "0"
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device = torch.device(f"cuda:{rank}")
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torch.cuda.set_device(device)
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distributed_init_method = f"tcp://localhost:{distributed_init_port}"
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init_distributed_environment(
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world_size=world_size,
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rank=rank,
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distributed_init_method=distributed_init_method,
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local_rank=rank,
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)
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initialize_model_parallel(tensor_model_parallel_size=world_size)
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group = get_tensor_model_parallel_group().device_group
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for sz in self.TEST_SIZES:
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for dtype in [torch.float16, torch.bfloat16]:
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for _ in range(self.TEST_LOOP):
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inp1 = torch.randint(
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1,
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23,
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(sz,),
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dtype=dtype,
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device=torch.cuda.current_device(),
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)
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out1 = tensor_model_parallel_all_reduce(inp1)
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dist.all_reduce(inp1, group=group)
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atol = 1.25 * world_size
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rtol = 0.5 * world_size
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torch.testing.assert_close(out1, inp1, atol=atol, rtol=rtol)
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# try:
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# torch.testing.assert_close(out1, inp1, atol=atol, rtol=rtol)
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# except AssertionError as e:
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# print("Max abs diff:", (out1 - inp1).abs().max())
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# print("Max rel diff:", ((out1 - inp1).abs() / inp1.abs().clamp(min=1e-5)).max())
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if __name__ == "__main__":
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unittest.main()
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