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260
tests/compile/test_silu_mul_quant_fusion.py
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260
tests/compile/test_silu_mul_quant_fusion.py
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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 itertools
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import pytest
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import torch
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import vllm.envs as envs
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from tests.kernels.quantization.nvfp4_utils import quant_nvfp4_tensor
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from vllm._aiter_ops import IS_AITER_FOUND
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from vllm._custom_ops import cutlass_scaled_fp4_mm, scaled_fp4_quant
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from vllm.compilation.activation_quant_fusion import (
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FUSED_OPS,
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SILU_MUL_OP,
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ActivationQuantFusionPass,
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)
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from vllm.compilation.fusion import QUANT_OPS
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from vllm.compilation.noop_elimination import NoOpEliminationPass
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from vllm.compilation.post_cleanup import PostCleanupPass
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from vllm.config import (
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CompilationConfig,
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CompilationMode,
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PassConfig,
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VllmConfig,
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set_current_vllm_config,
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)
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from vllm.model_executor.layers.activation import SiluAndMul
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from vllm.model_executor.layers.quantization.utils.fp8_utils import W8A8BlockFp8LinearOp
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from vllm.model_executor.layers.quantization.utils.quant_utils import (
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GroupShape,
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kFp8StaticTensorSym,
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kNvfp4Quant,
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)
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from vllm.model_executor.layers.quantization.utils.w8a8_utils import (
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Fp8LinearOp,
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maybe_create_device_identity,
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)
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from vllm.platforms import current_platform
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from ..utils import override_cutlass_fp8_supported
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from .backend import TestBackend
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FP8_DTYPE = current_platform.fp8_dtype()
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FP4_DTYPE = torch.uint8
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def is_nvfp4_supported():
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return current_platform.has_device_capability(100)
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class TestSiluMulFp8QuantModel(torch.nn.Module):
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def __init__(self, hidden_size: int, cuda_force_torch: bool, **kwargs):
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super().__init__()
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self.silu_and_mul = SiluAndMul()
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self.wscale = torch.rand(1, dtype=torch.float32)
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self.scale = torch.rand(1, dtype=torch.float32)
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self.w = torch.rand(hidden_size, hidden_size).to(dtype=FP8_DTYPE).t()
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with override_cutlass_fp8_supported(not cuda_force_torch):
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self.fp8_linear = Fp8LinearOp(
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act_quant_static=True,
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act_quant_group_shape=GroupShape.PER_TENSOR,
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)
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self.enable_silu_mul_custom_op = self.silu_and_mul.enabled()
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self.enable_quant_fp8_custom_op = self.fp8_linear.quant_fp8.enabled()
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def forward(self, x):
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y = self.silu_and_mul(x)
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x2 = self.fp8_linear.apply(y, self.w, self.wscale, input_scale=self.wscale)
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return x2
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def ops_in_model_before(self):
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return [
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SILU_MUL_OP if self.enable_silu_mul_custom_op else torch.ops.aten.mul,
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(
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QUANT_OPS[kFp8StaticTensorSym]
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if self.enable_quant_fp8_custom_op
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else torch.ops.aten.reciprocal
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),
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]
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def ops_in_model_after(self):
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return [FUSED_OPS[kFp8StaticTensorSym]]
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class TestSiluMulNvfp4QuantModel(torch.nn.Module):
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def __init__(self, hidden_size: int, x: torch.Tensor, **kwargs):
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super().__init__()
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from vllm.compilation.activation_quant_fusion import (
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silu_and_mul_nvfp4_quant_supported,
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)
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assert silu_and_mul_nvfp4_quant_supported
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self.silu_and_mul = SiluAndMul()
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self.enable_silu_mul_custom_op = self.silu_and_mul.enabled()
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# create nvfp4 weight
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w = torch.rand((hidden_size, hidden_size))
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self.w, self.w_block_scale, self.w_global_scale = quant_nvfp4_tensor(w)
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# get global scale offline
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_, _, self.y_global_scale = quant_nvfp4_tensor(self.silu_and_mul(x))
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self.alpha = 1.0 / (self.w_global_scale * self.y_global_scale)
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def forward(self, x):
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y = self.silu_and_mul(x)
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y_quant, y_block_scale = scaled_fp4_quant(y, self.y_global_scale)
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out = cutlass_scaled_fp4_mm(
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a=y_quant,
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b=self.w,
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block_scale_a=y_block_scale,
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block_scale_b=self.w_block_scale,
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alpha=self.alpha,
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out_dtype=y.dtype,
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)
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return out
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def ops_in_model_before(self):
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return [
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SILU_MUL_OP if self.enable_silu_mul_custom_op else torch.ops.aten.mul,
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QUANT_OPS[kNvfp4Quant],
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]
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def ops_in_model_after(self):
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return [FUSED_OPS[kNvfp4Quant]]
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class TestSiluMulGroupFp8QuantModel(torch.nn.Module):
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def __init__(self, hidden_size: int, **kwargs):
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super().__init__()
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self.silu_and_mul = SiluAndMul()
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self.w8a8_block_fp8_linear = W8A8BlockFp8LinearOp(
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weight_group_shape=GroupShape(128, 128),
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act_quant_group_shape=GroupShape(1, 128),
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cutlass_block_fp8_supported=False,
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use_aiter_and_is_supported=True,
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)
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self.w = torch.rand(hidden_size, hidden_size).to(dtype=FP8_DTYPE).t()
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scale_hidden_size = (hidden_size + 128 - 1) // 128
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self.wscale = torch.rand(
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(scale_hidden_size, scale_hidden_size), dtype=torch.float32
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)
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self.enable_silu_mul_custom_op = self.silu_and_mul.enabled()
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def forward(self, x):
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y = self.silu_and_mul(x)
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x2 = self.w8a8_block_fp8_linear.apply(y, self.w, self.wscale)
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return x2
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def ops_in_model_before(self):
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return [
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SILU_MUL_OP if self.enable_silu_mul_custom_op else torch.ops.aten.mul,
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]
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def ops_in_model_after(self):
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return [torch.ops.vllm.rocm_aiter_act_mul_and_fp8_group_quant]
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@pytest.mark.parametrize("num_tokens", [32, 64])
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@pytest.mark.parametrize("hidden_size", [128, 256])
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@pytest.mark.parametrize("dtype", [torch.bfloat16, torch.float16])
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@pytest.mark.parametrize("enable_silu_mul_custom_op", [True, False])
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@pytest.mark.parametrize(
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"model_class, enable_quant_fp8_custom_op, cuda_force_torch",
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list(itertools.product([TestSiluMulFp8QuantModel], [True, False], [True, False]))
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+ [
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(TestSiluMulNvfp4QuantModel, False, False),
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(TestSiluMulGroupFp8QuantModel, False, False),
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],
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)
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# cuda_force_torch used to test torch code path on platforms that
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# cutlass_fp8_supported() == True.
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@pytest.mark.skipif(
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envs.VLLM_TARGET_DEVICE not in ["cuda", "rocm"], reason="Only test on CUDA and ROCm"
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)
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def test_fusion_silu_and_mul_quant(
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num_tokens: int,
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hidden_size: int,
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dtype: torch.dtype,
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model_class: type[
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TestSiluMulFp8QuantModel
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| TestSiluMulNvfp4QuantModel
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| TestSiluMulGroupFp8QuantModel
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],
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enable_silu_mul_custom_op: bool,
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enable_quant_fp8_custom_op: bool,
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cuda_force_torch: bool,
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):
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if model_class is TestSiluMulNvfp4QuantModel and not is_nvfp4_supported():
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pytest.skip("NVFP4 is not supported on this GPU.")
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if model_class is TestSiluMulGroupFp8QuantModel and not IS_AITER_FOUND:
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pytest.skip("AITER is not supported on this GPU.")
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torch.set_default_device("cuda")
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torch.set_default_dtype(dtype)
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maybe_create_device_identity()
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x = torch.rand(num_tokens, hidden_size * 2)
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# Reshape pass is needed for the fusion pass to work
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custom_ops = []
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if enable_silu_mul_custom_op:
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custom_ops.append("+silu_and_mul")
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if enable_quant_fp8_custom_op:
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custom_ops.append("+quant_fp8")
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config = VllmConfig(
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compilation_config=CompilationConfig(
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mode=CompilationMode.VLLM_COMPILE,
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custom_ops=custom_ops,
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pass_config=PassConfig(fuse_act_quant=True, eliminate_noops=True),
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),
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)
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with set_current_vllm_config(config):
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fusion_passes = [ActivationQuantFusionPass(config)]
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if IS_AITER_FOUND:
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from vllm.compilation.rocm_aiter_fusion import (
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RocmAiterSiluMulFp8GroupQuantFusionPass,
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)
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fusion_passes += [RocmAiterSiluMulFp8GroupQuantFusionPass(config)]
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passes = [NoOpEliminationPass(config), *fusion_passes, PostCleanupPass(config)]
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backend = TestBackend(*passes)
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model = model_class(
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hidden_size=hidden_size, cuda_force_torch=cuda_force_torch, x=x
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)
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# First dimension dynamic
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torch._dynamo.mark_dynamic(x, 0)
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result = model(x)
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model2 = torch.compile(model, backend=backend)
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result2 = model2(x)
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# Check that it gives the same answer
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if model_class == TestSiluMulFp8QuantModel:
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atol, rtol = 1e-3, 1e-3
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elif model_class == TestSiluMulNvfp4QuantModel:
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atol, rtol = 1e-1, 1e-1
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elif model_class == TestSiluMulGroupFp8QuantModel:
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atol, rtol = 5e-2, 5e-2
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torch.testing.assert_close(
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result[0].to(dtype=dtype), result2[0].to(dtype=dtype), atol=atol, rtol=rtol
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)
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assert sum([p.matched_count for p in fusion_passes]) == 1
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# In pre-nodes, quant op should be present and fused kernels should not
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backend.check_before_ops(model.ops_in_model_before())
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# In post-nodes, fused kernels should be present and quant op should not
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backend.check_after_ops(model.ops_in_model_after())
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