mirror of https://github.com/hpcaitech/ColossalAI
[release] update version (#5752)
* [release] update version * [devops] update compatibility test * [devops] update compatibility test * [devops] update compatibility test * [devops] update compatibility test * [test] fix ddp plugin test * [test] fix gptj and rpc test * [devops] fix cuda ext compatibility * [inference] fix flash decoding test * [inference] fix flash decoding testpull/5769/head v0.3.8
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677cbfacf8
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@ -7,10 +7,6 @@
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{
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"torch_command": "pip install torch==2.1.0 torchvision==0.16.0 torchaudio==2.1.0 --index-url https://download.pytorch.org/whl/cu118",
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"cuda_image": "hpcaitech/cuda-conda:11.8"
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},
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{
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"torch_command": "pip install torch==2.0.0 torchvision==0.15.1 torchaudio==2.0.1",
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"cuda_image": "hpcaitech/cuda-conda:11.7"
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}
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]
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}
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@ -51,11 +51,11 @@ jobs:
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container:
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image: ${{ matrix.container }}
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options: --gpus all --rm -v /dev/shm -v /data/scratch/cifar-10:/data/scratch/cifar-10 -v /data/scratch/llama-tiny:/data/scratch/llama-tiny
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timeout-minutes: 120
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timeout-minutes: 200
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steps:
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- name: Install dependencies
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run: |
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pip install -U pip setuptools wheel --user
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pip install -U pip setuptools==68.2.2 wheel --user
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- uses: actions/checkout@v2
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with:
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repository: hpcaitech/TensorNVMe
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@ -42,14 +42,14 @@ jobs:
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container:
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image: ${{ matrix.container }}
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options: --gpus all --rm -v /dev/shm -v /data/scratch/cifar-10:/data/scratch/cifar-10 -v /data/scratch/llama-tiny:/data/scratch/llama-tiny
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timeout-minutes: 120
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timeout-minutes: 200
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concurrency:
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group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }}-run-test-${{ matrix.container }}
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cancel-in-progress: true
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steps:
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- name: Install dependencies
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run: |
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pip install -U pip setuptools wheel --user
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pip install -U pip setuptools==68.2.2 wheel --user
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- uses: actions/checkout@v2
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with:
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repository: hpcaitech/TensorNVMe
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@ -39,11 +39,11 @@ jobs:
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container:
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image: ${{ matrix.container }}
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options: --gpus all --rm -v /dev/shm -v /data/scratch/cifar-10:/data/scratch/cifar-10 -v /data/scratch/llama-tiny:/data/scratch/llama-tiny
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timeout-minutes: 120
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timeout-minutes: 200
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steps:
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- name: Install dependencies
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run: |
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pip install -U pip setuptools wheel --user
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pip install -U pip setuptools==68.2.2 wheel --user
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- uses: actions/checkout@v2
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with:
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@ -47,7 +47,7 @@ def check_torch_ddp_plugin():
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registry = model_zoo
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for name, (model_fn, data_gen_fn, output_transform_fn, _, _) in registry.items():
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if name == "dlrm_interactionarch":
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if name == "dlrm_interactionarch" or name.startswith("simple_"):
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continue
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run_fn(model_fn, data_gen_fn, output_transform_fn)
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torch.cuda.empty_cache()
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@ -176,7 +176,7 @@ def test_flash_decoding_attention(
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# The alibi may introduce relatively large errors
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if use_alibi_slopes:
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rtol = 1e0
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rtol = 100
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try:
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numpy_allclose(out_ref, output, rtol=rtol, atol=atol)
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@ -198,13 +198,13 @@ except ImportError:
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@pytest.mark.skipif(not HAS_VLLM, reason="requires vllm")
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@pytest.mark.parametrize("BATCH_SIZE", [1, 4, 7, 32])
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@pytest.mark.parametrize("BLOCK_SIZE", [8, 16, 32])
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@pytest.mark.parametrize("BATCH_SIZE", [1, 7, 32])
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@pytest.mark.parametrize("BLOCK_SIZE", [6, 32])
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@pytest.mark.parametrize("MAX_NUM_BLOCKS_PER_SEQ", [1, 8, 32])
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@pytest.mark.parametrize("HEAD_SIZE", [64, 128])
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@pytest.mark.parametrize("NUM_ATTN_HEADS", [16])
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@pytest.mark.parametrize("KV_GROUP_NUM", [1, 2, 16])
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@pytest.mark.parametrize("dtype", [torch.float16, torch.float32])
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@pytest.mark.parametrize("KV_GROUP_NUM", [1, 16])
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@pytest.mark.parametrize("dtype", [torch.float32])
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@pytest.mark.parametrize("use_alibi_slopes", [True, False])
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def test_vllm_flash_decoding_attention(
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BATCH_SIZE, BLOCK_SIZE, MAX_NUM_BLOCKS_PER_SEQ, HEAD_SIZE, NUM_ATTN_HEADS, KV_GROUP_NUM, dtype, use_alibi_slopes
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@ -302,9 +302,9 @@ def test_vllm_flash_decoding_attention(
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kv_scale,
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)
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# The alibi may introduce relatively large errors
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# After the shape becomes larger, some data elements are too small, leading to excessively large relative errors.
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if use_alibi_slopes:
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rtol = 1e0
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rtol = 100
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numpy_allclose(out_ref, output, rtol=rtol, atol=atol)
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@ -103,7 +103,7 @@ def test_flash_decoding(
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num_kv_heads = num_attn_heads // kv_group_num
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assert isinstance(num_kv_heads, int) and num_kv_heads > 0, "Invalid number of kv heads."
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max_seq_len = block_size * max_num_blocks_per_seq
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dtype = torch.float16
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dtype = torch.float32
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device = get_current_device()
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if use_alibi_slopes:
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@ -187,7 +187,7 @@ def test_flash_decoding(
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rtol = 1e-4
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# After the shape becomes larger, some data elements are too small, leading to excessively large relative errors.
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if bsz >= 16 and use_alibi_slopes:
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if use_alibi_slopes:
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rtol = 100
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numpy_allclose(out_torch, out_triton, atol=1e-3, rtol=rtol)
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@ -75,6 +75,8 @@ def run_engine(tp_size, **kwargs):
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return check_inference_engine(tp_size=tp_size, **kwargs)
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# TODO: fix the test
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@pytest.mark.skip("model is too large")
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@pytest.mark.largedist
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@parameterize("prompt_template", [None, "llama"])
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@parameterize("do_sample", [False])
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@ -240,7 +240,6 @@ def run_gptj_3d_test(test_config):
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def check_gptj(rank, world_size, port):
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disable_existing_loggers()
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colossalai.launch(
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config={},
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rank=rank,
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world_size=world_size,
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host="localhost",
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@ -253,7 +252,6 @@ def check_gptj(rank, world_size, port):
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def check_gptj_3d(rank, world_size, port):
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disable_existing_loggers()
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colossalai.launch(
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config={},
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rank=rank,
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world_size=world_size,
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host="localhost",
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@ -1 +1 @@
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0.3.7
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0.3.8
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