use extract_local for test_hybrid_attn.py (#74)

feifeibear · Sep 14, 2024 · 5ac27d8 · 5ac27d8
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diff --git a/README.md b/README.md
@@ -12,7 +12,7 @@ The project is built on [zhuzilin/ring-flash-attention](https://github.com/zhuzi
 
 
 
-## What's wrong with Ulysses and Ring?
+## Why not apply Ulysses and Ring Attention Individually?
 
 - Ulysses is sensitive to the number of attention heads. 
 The parallelism degree in Ulysses cannot exceed the number of heads. 
@@ -25,89 +25,37 @@ Even with the communication and computation processes fully overlapped, the tota
 Furthermore, Ring-Attention utilizes asynchronous peer-to-peer communication, which not only has a lower bandwidth utilization compared to collective communication methods but also poses the risk of potential communication deadlocks in large-scale deployments.
 
 
-## LongContextAttention, a.k.a Unified Sequence Parallelism and Hybrid Sequence Parallelism
+## LongContextAttention, also known as Unified Sequence Parallelism and Hybrid Sequence Parallelism
 
 `LongContextAttention` is a **unified sequence parallel** , also known as **hybrid sequence parallel** ,that hybrid DeepSpeed-Ulysses-Attention and Ring-Attention therefore addressing the limitations of both methods.
 
 <p align="center">
-    <img src="./media/hybrid_seqparallel.png">
+    <img src="./media/usp.png">
 </p>
 
+### Usage
+
+Please refer to [test/test_hybrid_qkvpacked_attn.py](./test/test_hybrid_qkvpacked_attn.py) and [test/test_hybrid_attn.py](./test/test_hybrid_attn.py) for usage.
+
+In short, we take the `zigzag` ring attention implementation as an example:
+
+1. apply `set_seq_parallel_pg` to set the process group
+2. extract local tensors with `zigzag_extract_local`. We need reorder the input tokens or input tensors for load balance ring attention.
+3. then apply `LongContextAttention(ring_impl_type="zigzag")` as a drop-in replacement for Attention implementation.
 
 ### Install
 
 Option 1: pip install from pypi. 
 
-`pip install yunchang==0.3` (flash_attn >= 2.6.0)
+`pip install yunchang` (flash_attn >= 2.6.0)
 
 `pip install yunchang==0.2` (flash_attn < 2.6.0)
 
 Option 2: build from local.
 
 `pip install .`
 
-### Install for AMD GPU
-
-Supported GPU : MI300X, MI308X
-
-GPU arch : gfx942
-
-Step 1: prepare docker envrionment
-
-Tow recommended docker container to start with 
-
-- rocm/pytorch:rocm6.2_ubuntu22.04_py3.10_pytorch_release_2.3.0 : hosted in dockerhub, no conda
-- [dockerhub repo](https://github.com/yiakwy-xpu-ml-framework-team/Tools-dockerhub/blob/main/rocm/Dockerfile.rocm62.ubuntu-22.04) : Customerized Dockerfile with conda virtual env and develop kit support
-
-An example to create an docker container :
-
-```bash
-# create docker container
-IMG=rocm/pytorch:rocm6.2_ubuntu22.04_py3.10_pytorch_release_2.3.0
-tag=py310-rocm6.2-distattn-dev
-
-docker_args=$(echo -it --privileged \
- --name $tag \
- --ulimit memlock=-1:-1 --net=host --cap-add=IPC_LOCK \
- --device=/dev/kfd --device=/dev/dri \
- --ipc=host \
- --security-opt seccomp=unconfined \
- --shm-size 16G \
- --group-add video \
- -v $(readlink -f `pwd`):/workspace \
- --workdir /workspace \
- --cpus=$((`nproc` / 2  - 1)) \
- $IMG
-)
-
-docker_args=($docker_args)
-
-docker container create "${docker_args[@]}"
-
-# start it
-docker start -a -i $tag
-```
-
-Update ROCM SDK using this [script](https://github.com/yiakwy-xpu-ml-framework-team/Tools-dockerhub/blob/main/rocm/update_sdk.sh):
-
-```bash
-# e.g.:
-ROCM_VERSION=6.3 bash rocm/update_sdk.sh
-```
-
-Step 2 : build from local.
-
-> MAX_JOBS=$(nproc) pip install .[amd] --verbose
-
-**Features:**
-
-1. No Limitation on the Number of Heads: Our approach does not impose a restriction on the number of heads, providing greater flexibility for various attention mechanisms.
-
-2. Cover the Capability of either Ulysses and Ring: By setting the ulysses_degree to the sequence parallel degree, the system operates identically to Ulysses. Conversely, setting the ulysses_degree to 1 mirrors the functionality of Ring.
-
-3. Enhanced Performance: We achieve superior performance benchmarks over both Ulysses and Ring, offering a more efficient solution for attention mechanism computations.
-
-4. Compatibility with Advanced Parallel Strategies: LongContextAttention is fully compatible with other sophisticated parallelization techniques, including Tensor Parallelism, ZeRO, and Pipeline Parallelism, ensuring seamless integration with the latest advancements in parallel computing.
+Install for AMD GPU: [install_amd.md](./docs/install_amd.md)
 
 ### Verified in Megatron-LM
 The loss curves for Data Parallel (DP) and Unified Sequence Parallel (ulysses=2+ring=2) are closely aligned, as illustrated in the figure. This alignment confirms the accuracy of the unified sequence parallel.
@@ -121,7 +69,6 @@ In the Megatron-LM, you can reorder the input tokens before feed them into the m
 
 ## Best Practice for 4D Parallelism
 
-
 We analyze the impact of introducing Sequnce Parallelism to Data/ZeRO/Tensor/Pipeline Parallelism in a technique report, which can be found at [here](https://arxiv.org/abs/2405.07719).
 
 Some best practices are listed here:
@@ -183,7 +130,7 @@ I am honored that this repository has contributed to the following projects:
 6. [FlagOpen/FlagScale](https://github.com/FlagOpen/FlagScale/commit/f98ee1e293bd906cc77f512f7a884b2030c10a12)
 7. [zhiyuanhubj/LongRecipe](https://github.com/zhiyuanhubj/LongRecipe)
 
-## Citation
+## Cite Us
 ```
 @article{fang2024unified,
   title={USP: A Unified Sequence Parallelism Approach for Long Context Generative AI},

diff --git a/docs/install_amd.md b/docs/install_amd.md
@@ -0,0 +1,62 @@
+## Install for AMD GPU
+
+Supported GPU : MI300X, MI308X
+
+GPU arch : gfx942
+
+Step 1: prepare docker envrionment
+
+Tow recommended docker container to start with 
+
+- rocm/pytorch:rocm6.2_ubuntu22.04_py3.10_pytorch_release_2.3.0 : hosted in dockerhub, no conda
+- [dockerhub repo](https://github.com/yiakwy-xpu-ml-framework-team/Tools-dockerhub/blob/main/rocm/Dockerfile.rocm62.ubuntu-22.04) : Customerized Dockerfile with conda virtual env and develop kit support
+
+An example to create an docker container :
+
+```bash
+# create docker container
+IMG=rocm/pytorch:rocm6.2_ubuntu22.04_py3.10_pytorch_release_2.3.0
+tag=py310-rocm6.2-distattn-dev
+
+docker_args=$(echo -it --privileged \
+ --name $tag \
+ --ulimit memlock=-1:-1 --net=host --cap-add=IPC_LOCK \
+ --device=/dev/kfd --device=/dev/dri \
+ --ipc=host \
+ --security-opt seccomp=unconfined \
+ --shm-size 16G \
+ --group-add video \
+ -v $(readlink -f `pwd`):/workspace \
+ --workdir /workspace \
+ --cpus=$((`nproc` / 2  - 1)) \
+ $IMG
+)
+
+docker_args=($docker_args)
+
+docker container create "${docker_args[@]}"
+
+# start it
+docker start -a -i $tag
+```
+
+Update ROCM SDK using this [script](https://github.com/yiakwy-xpu-ml-framework-team/Tools-dockerhub/blob/main/rocm/update_sdk.sh):
+
+```bash
+# e.g.:
+ROCM_VERSION=6.3 bash rocm/update_sdk.sh
+```
+
+Step 2 : build from local.
+
+> MAX_JOBS=$(nproc) pip install .[amd] --verbose
+
+**Features:**
+
+1. No Limitation on the Number of Heads: Our approach does not impose a restriction on the number of heads, providing greater flexibility for various attention mechanisms.
+
+2. Cover the Capability of either Ulysses and Ring: By setting the ulysses_degree to the sequence parallel degree, the system operates identically to Ulysses. Conversely, setting the ulysses_degree to 1 mirrors the functionality of Ring.
+
+3. Enhanced Performance: We achieve superior performance benchmarks over both Ulysses and Ring, offering a more efficient solution for attention mechanism computations.
+
+4. Compatibility with Advanced Parallel Strategies: LongContextAttention is fully compatible with other sophisticated parallelization techniques, including Tensor Parallelism, ZeRO, and Pipeline Parallelism, ensuring seamless integration with the latest advancements in parallel computing.
diff --git a/media/hybrid_seqparallel.png b/media/hybrid_seqparallel.png
diff --git a/media/usp.png b/media/usp.png
diff --git a/setup.py b/setup.py
@@ -1,9 +1,15 @@
 from setuptools import setup, find_packages
+import os
+
+# 读取版本信息
+version_file = os.path.join(os.path.dirname(__file__), 'yunchang', '__version__.py')
+with open(version_file, 'r') as f:
+    exec(f.read())
 
 setup(
     name="yunchang",
-    version="0.3",
-    author="Jiarui Fang, Zilin Zhu, Yang Yu",
+    version=__version__,
+    author="[email protected]",
     url="https://github.com/feifeibear/long-context-attention",
     packages=find_packages(exclude=['test', 'benchmark']),
     install_requires=[

diff --git a/test/test_hybrid_attn.py b/test/test_hybrid_attn.py
@@ -2,6 +2,7 @@
     AsyncLongContextAttention,
     LongContextAttention,
     set_seq_parallel_pg,
+    EXTRACT_FUNC_DICT
 )
 import torch
 import torch.distributed as dist
@@ -34,31 +35,35 @@ def log(msg, a, rank0_only=False):
             )
         dist.barrier()
 
-
+# test it with:
+# torchrun --nproc_per_node=4  test/test_hybrid_attn_v2.py
 if __name__ == "__main__":
     torch.random.manual_seed(0)
 
-    use_bwd = False
+    use_bwd = True
     dist.init_process_group("nccl")
 
     rank = dist.get_rank()
     world_size = dist.get_world_size()
+
+    assert world_size == 4, f"torchrun --nproc_per_node=4  test/test_hybrid_attn_v2.py"
     # Inference mainly uses fp16; ROCM flash attention with bf16 precision is slightly larger, will be fixed soon 
-    dtype = torch.float16
+    dtype = torch.bfloat16
     device = torch.device(f"cuda:{rank}")
 
     batch_size = 2
-    seqlen = 3816
-    nheads = 2
+    seqlen = 1024
+    nheads = 4
     d = 128
     dropout_p = 0
     causal = True
+
     deterministic = False
 
-    use_async_all_to_all = True
     assert seqlen % world_size == 0
     assert d % 8 == 0
-    # assert batch_size == 1
+
+    ring_impl_type = "zigzag"  # You can change this to "basic" or "zigzag" if needed
 
     # Prepare inputs
     q = torch.randn(
@@ -77,30 +82,34 @@ def log(msg, a, rank0_only=False):
     dist.broadcast(v, src=0)
     dist.broadcast(dout, src=0)
 
-    local_q = q.chunk(world_size, dim=1)[rank].detach().clone()
-    local_q.requires_grad = True
-    local_k = k.chunk(world_size, dim=1)[rank].detach().clone()
-    local_k.requires_grad = True
-    local_v = v.chunk(world_size, dim=1)[rank].detach().clone()
-    local_v.requires_grad = True
-
-    local_dout = dout.chunk(world_size, dim=1)[rank].detach().clone()
-
     # prepare process group for hybrid sequence parallelism
     use_ring_low_dim = True
 
-    sp_ulysses_degree = min(nheads, world_size)
+    sp_ulysses_degree = 2
     sp_ring_degree = world_size // sp_ulysses_degree
+
     print(
         f"rank {rank}, sp_ulysses_degree: {sp_ulysses_degree}, sp_ring_degree: {sp_ring_degree}"
     )
 
     set_seq_parallel_pg(sp_ulysses_degree, sp_ring_degree, rank, world_size)
 
-    if use_async_all_to_all:
-        hybrid_seq_parallel_attn = AsyncLongContextAttention()
-    else:
-        hybrid_seq_parallel_attn = LongContextAttention()
+    # Use EXTRACT_FUNC_DICT to shard the tensors
+    local_q = EXTRACT_FUNC_DICT[ring_impl_type](
+        q, rank, world_size=world_size, rd=sp_ring_degree, ud=sp_ulysses_degree
+    ).detach().clone()
+    local_q.requires_grad = True
+
+    local_k = EXTRACT_FUNC_DICT[ring_impl_type](
+        k, rank, world_size=world_size, rd=sp_ring_degree, ud=sp_ulysses_degree
+    ).detach().clone()
+    local_k.requires_grad = True
+
+    local_v = EXTRACT_FUNC_DICT[ring_impl_type](
+        v, rank, world_size=world_size, rd=sp_ring_degree, ud=sp_ulysses_degree
+    ).detach().clone()
+    local_v.requires_grad = True
+    usp_attn = LongContextAttention(ring_impl_type=ring_impl_type)
 
     if rank == 0:
         print("#" * 30)
@@ -112,7 +121,9 @@ def log(msg, a, rank0_only=False):
     alibi_slopes, attn_bias = None, None
     dropout_mask = None
 
-    local_out = hybrid_seq_parallel_attn(
+    print(f"before usp attn forward: {local_q.shape} {local_k.shape} {local_v.shape}")
+    # usp attn forward
+    local_out = usp_attn(
         local_q,
         local_k,
         local_v,
@@ -125,11 +136,17 @@ def log(msg, a, rank0_only=False):
         return_attn_probs=True,
     )
 
+    # extract local dout
+    local_dout = EXTRACT_FUNC_DICT[ring_impl_type](
+        dout, rank, world_size=world_size, rd=sp_ring_degree, ud=sp_ulysses_degree
+    ).detach().clone()
+
     if rank == 0:
         print("#" * 30)
         print("# ds-ulysses backward:")
         print("#" * 30)
 
+    # usp attn backward
     if use_bwd:
         local_out.backward(local_dout)
 
@@ -177,26 +194,40 @@ def log(msg, a, rank0_only=False):
     dist.barrier()
 
     # check correctness
-
-    local_out_ref = out_ref.chunk(world_size, dim=1)[rank]
-    local_out_pt_ref = out_ref.chunk(world_size, dim=1)[rank]
+    # When checking correctness, use EXTRACT_FUNC_DICT for reference outputs
+    local_out_ref = EXTRACT_FUNC_DICT[ring_impl_type](
+        out_ref, rank, world_size=world_size, rd=sp_ring_degree, ud=sp_ulysses_degree
+    )
+    local_out_pt_ref = EXTRACT_FUNC_DICT[ring_impl_type](
+        out_pt_ref, rank, world_size=world_size, rd=sp_ring_degree, ud=sp_ulysses_degree
+    )
 
     log("local (rank) out", local_out, rank0_only=True)
     log("out (distributed) - out_ref (non-distributed) diff", local_out_ref - local_out)
-    log("out_ref (non-distributed) - out_pt_ref (gpu) diff", local_out_ref - local_out_pt_ref)
+
+    # log("out_ref (non-distributed) - out_pt_ref (gpu) diff", local_out_ref - local_out_pt_ref)
 
     torch.testing.assert_close(local_out, local_out_ref, atol=1e-2, rtol=0)
-    torch.testing.assert_close(out_ref, out_pt_ref, atol=1e-2, rtol=0)
+    # torch.testing.assert_close(out_ref, out_pt_ref, atol=1e-2, rtol=0)
 
     if use_bwd:
-        local_dq_ref = q.grad.chunk(world_size, dim=1)[rank]
+        local_dq_ref = EXTRACT_FUNC_DICT[ring_impl_type](
+            q.grad, rank, world_size=world_size, rd=sp_ring_degree, ud=sp_ulysses_degree
+        )
         log("load_dq", local_q.grad)
         log("dq diff", local_dq_ref - local_q.grad)
 
-        local_dk_ref = k.grad.chunk(world_size, dim=1)[rank]
+        local_dk_ref = EXTRACT_FUNC_DICT[ring_impl_type](
+            k.grad, rank, world_size=world_size, rd=sp_ring_degree, ud=sp_ulysses_degree
+        )
         log("load_dk", local_k.grad)
         log("dk diff", local_dk_ref - local_k.grad)
 
-        local_dv_ref = v.grad.chunk(world_size, dim=1)[rank]
-        log("load_dk", local_v.grad)
+        local_dv_ref = EXTRACT_FUNC_DICT[ring_impl_type](
+            v.grad, rank, world_size=world_size, rd=sp_ring_degree, ud=sp_ulysses_degree
+        )
+        log("load_dv", local_v.grad)
         log("dv diff", local_dv_ref - local_v.grad)
+
+    if dist.is_initialized():
+        dist.destroy_process_group()