mirror of
https://github.com/vllm-project/vllm-ascend.git
synced 2025-10-20 13:43:53 +08:00
ba19dd3183
we notice that torch npu 0919 doesn't work. This PR revert related change which rely on 0919 version. Revert PR: #3295 #3205 #3102 Related: #3353 - vLLM version: v0.11.0
162 lines
6.1 KiB
Python
162 lines
6.1 KiB
Python
import unittest
|
|
|
|
import pytest
|
|
import torch
|
|
from pytest_mock import MockerFixture
|
|
from vllm.model_executor.layers.layernorm import RMSNorm
|
|
|
|
from tests.ut.base import PytestBase
|
|
from vllm_ascend.quantization.w8a8 import AscendW8A8LinearMethod
|
|
|
|
|
|
def mock_maybe_chunk_residual(x, residual):
|
|
if x.size(0) != residual.size(0):
|
|
return residual[:4]
|
|
return residual
|
|
|
|
|
|
def mock_rms_norm(x, weight, eps):
|
|
return x + 1, None
|
|
|
|
|
|
def mock_add_rms_norm(x, residual, weight, eps):
|
|
return 2 * x, None, 2 * residual
|
|
|
|
|
|
def mock_add_rms_norm_quant(x, residual, weight, quant_scale, quant_offset,
|
|
epsilon):
|
|
x_out = 2 * x
|
|
residual_out = 2 * residual
|
|
x_out_quant = x_out.to(torch.int8)
|
|
residual_out_quant = residual_out.to(torch.int8)
|
|
return x_out_quant, None, residual_out_quant
|
|
|
|
|
|
class TestAscendRMSNorm(PytestBase):
|
|
|
|
@pytest.fixture(autouse=True)
|
|
def context(self, mocker: MockerFixture):
|
|
mocker.patch("torch.ops.vllm.maybe_chunk_residual",
|
|
side_effect=mock_maybe_chunk_residual)
|
|
mocker.patch("torch_npu.npu_rms_norm", side_effect=mock_rms_norm)
|
|
mocker.patch("torch_npu.npu_add_rms_norm",
|
|
side_effect=mock_add_rms_norm)
|
|
mocker.patch("torch_npu.npu_add_rms_norm_quant",
|
|
side_effect=mock_add_rms_norm_quant)
|
|
mocker.patch("torch.ops.vllm.maybe_wait_prefetch_done",
|
|
side_effect=lambda x: None)
|
|
|
|
# Test case for the most common and basic scenario
|
|
@pytest.mark.parametrize(
|
|
"residual", [None, torch.randn(4, 8, dtype=torch.float16)])
|
|
def test_forward_oot_basic(self, residual):
|
|
layer = RMSNorm(hidden_size=8, eps=1e-05)
|
|
x = torch.randn(4, 8, dtype=torch.float16)
|
|
if residual is not None:
|
|
x_out, residual_out = layer.forward_oot(x, residual)
|
|
|
|
x_out_expected = 2 * x
|
|
residual_out_expected = 2 * residual
|
|
|
|
assert torch.allclose(x_out, x_out_expected)
|
|
assert torch.allclose(residual_out, residual_out_expected)
|
|
else:
|
|
x_out = layer.forward(x, residual)
|
|
x_out_expected = x + 1
|
|
|
|
assert torch.allclose(x_out, x_out_expected)
|
|
|
|
# Test case for flashcomm_v1 scenario
|
|
def test_forward_oot_with_flashcomm_v1(self):
|
|
layer = RMSNorm(hidden_size=512, eps=1e-05)
|
|
x = torch.randn(4, 512, dtype=torch.bfloat16)
|
|
residual = torch.randn(16, 512, dtype=torch.bfloat16)
|
|
|
|
x_out, residual_out = layer.forward_oot(x, residual)
|
|
|
|
x_out_expected = 2 * x
|
|
residual_out_expected = 2 * residual[:4]
|
|
|
|
assert residual_out.size(0) == 4
|
|
assert torch.allclose(x_out, x_out_expected)
|
|
assert torch.allclose(residual_out, residual_out_expected)
|
|
|
|
# Test case for addrmsnorm + w8a8 quant fusion
|
|
def test_forward_oot_with_quant_fusion(self, mocker: MockerFixture):
|
|
mock_is_310p = mocker.patch("vllm_ascend.utils.is_310p")
|
|
mock_is_310p.return_value = False
|
|
mock_get_forward_context = mocker.patch(
|
|
"vllm_ascend.ops.layernorm.get_forward_context")
|
|
|
|
# Simulating a scenario with quant_fusion enabled
|
|
mock_forward_context = mocker.MagicMock()
|
|
|
|
mock_model_instance = mocker.MagicMock()
|
|
mock_forward_context.model_instance = mock_model_instance
|
|
mock_model_instance.model.layers = [
|
|
mocker.MagicMock() for _ in range(2)
|
|
]
|
|
|
|
mock_layer_0 = mock_model_instance.model.layers[0]
|
|
mock_layer_0.self_attn.qkv_proj = mocker.MagicMock()
|
|
mock_layer_0.mlp.gate_up_proj = mocker.MagicMock()
|
|
|
|
mock_layer_1 = mock_model_instance.model.layers[1]
|
|
mock_layer_1.self_attn.qkv_proj = mocker.MagicMock()
|
|
mock_layer_1.mlp.gate_up_proj = mocker.MagicMock()
|
|
|
|
mock_quant_method_0_qkv = mocker.MagicMock()
|
|
mock_quant_method_0_qkv.quant_method = AscendW8A8LinearMethod()
|
|
mock_quant_method_0_gate_up = mocker.MagicMock()
|
|
mock_quant_method_0_gate_up.quant_method = AscendW8A8LinearMethod()
|
|
mock_layer_0.self_attn.qkv_proj.quant_method = mock_quant_method_0_qkv
|
|
mock_layer_0.mlp.gate_up_proj.quant_method = mock_quant_method_0_gate_up
|
|
|
|
mock_quant_method_1_qkv = mocker.MagicMock()
|
|
mock_quant_method_1_qkv.quant_method = AscendW8A8LinearMethod()
|
|
mock_quant_method_1_gate_up = mocker.MagicMock()
|
|
mock_quant_method_1_gate_up.quant_method = AscendW8A8LinearMethod()
|
|
mock_layer_1.self_attn.qkv_proj.quant_method = mock_quant_method_1_qkv
|
|
mock_layer_1.mlp.gate_up_proj.quant_method = mock_quant_method_1_gate_up
|
|
|
|
mock_get_forward_context.return_value = mock_forward_context
|
|
|
|
mock_forward_context.addrmsnorm_quant_fusion_enabled = True
|
|
mock_forward_context.prefetch_mlp_enabled = False
|
|
mock_forward_context.layer_idx = 0
|
|
mock_forward_context.num_hidden_layers = 2
|
|
mock_forward_context.fusion_linear = "gate_up_dense"
|
|
|
|
# Ensure fusion and layer_idx increment are handled correctly
|
|
x = torch.randn(4, 8, dtype=torch.float16)
|
|
residual = torch.randn(4, 8, dtype=torch.float16)
|
|
layer = RMSNorm(hidden_size=8, eps=1e-05)
|
|
|
|
x_out, residual_out = layer.forward_oot(x, residual)
|
|
|
|
assert mock_get_forward_context.call_count == 1
|
|
assert mock_forward_context.fusion_linear == "qkv_dense"
|
|
assert mock_forward_context.layer_idx == 1
|
|
|
|
x_out, residual_out = layer.forward_oot(x, residual)
|
|
|
|
assert mock_get_forward_context.call_count == 2
|
|
assert mock_forward_context.fusion_linear == "gate_up_dense"
|
|
assert mock_forward_context.layer_idx == 1
|
|
|
|
x_out, residual_out = layer.forward_oot(x, residual)
|
|
|
|
assert mock_get_forward_context.call_count == 3
|
|
assert mock_forward_context.fusion_linear == "qkv_dense"
|
|
assert mock_forward_context.layer_idx == 2
|
|
|
|
x_out, residual_out = layer.forward_oot(x, residual)
|
|
|
|
assert mock_get_forward_context.call_count == 4
|
|
assert mock_forward_context.fusion_linear == "qkv_dense"
|
|
assert mock_forward_context.layer_idx == 2
|
|
|
|
|
|
if __name__ == '__main__':
|
|
unittest.main()
|