63 lines
2.1 KiB
Python
63 lines
2.1 KiB
Python
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# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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import pytest
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import torch
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import vllm
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from tests.compile.backend import TestBackend
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from vllm.compilation.passes.utility.split_coalescing import SplitCoalescingPass
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from vllm.config import CompilationConfig, CompilationMode, PassConfig, VllmConfig
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class SplitCoalescingModel(torch.nn.Module):
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"""Model with 3 separate split_with_sizes calls on the same input,
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simulating the B200+FP8 graph where CSE fails to merge them."""
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def __init__(self, q_size: int, kv_size: int) -> None:
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super().__init__()
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self.q_size = q_size
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self.kv_size = kv_size
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def forward(self, qkv: torch.Tensor):
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q, _, _ = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
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_, k, _ = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
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_, _, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
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return q + 1, k + 2, v + 3
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@pytest.mark.parametrize("dtype", [torch.float16, torch.bfloat16])
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def test_split_coalescing(dtype):
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torch.set_default_device("cuda")
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torch.set_default_dtype(dtype)
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torch.manual_seed(0)
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q_size, kv_size = 2048, 512
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vllm_config = VllmConfig(
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compilation_config=CompilationConfig(
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mode=CompilationMode.VLLM_COMPILE,
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pass_config=PassConfig(),
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)
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)
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with vllm.config.set_current_vllm_config(vllm_config):
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coalesce_pass = SplitCoalescingPass(vllm_config)
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backend = TestBackend(coalesce_pass)
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model = SplitCoalescingModel(q_size, kv_size)
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T = 5
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qkv = torch.randn(T, q_size + 2 * kv_size)
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torch._dynamo.mark_dynamic(qkv, 0)
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result_eager = model(qkv)
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model_compiled = torch.compile(model, backend=backend)
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result_compiled = model_compiled(qkv)
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ATOL, RTOL = (2e-3, 2e-3)
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for eager, compiled in zip(result_eager, result_compiled):
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torch.testing.assert_close(eager, compiled, atol=ATOL, rtol=RTOL)
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assert backend.op_count(torch.ops.aten.split_with_sizes.default) == 1
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