83 lines
4.0 KiB
Python
83 lines
4.0 KiB
Python
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"""CPU checks of installed Kunlun INT8 loader and BF16 MLA weight preparation.
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Uses synthetic layers and mocked TP rank/size, not a full engine startup.
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"""
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import copy
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import os
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from types import SimpleNamespace
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from unittest.mock import patch
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os.environ["VLLM_KUNLUN_ENABLE_INT8_BMM"] = "0"
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import torch
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from convert_deepseek_fp8_to_int8 import quantization_config
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def main():
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from vllm.platforms import current_platform
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current_platform.pre_register_and_update()
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from vllm_kunlun.quantization.compressed_tensors.compressed_tensors import KunlunCompressedTensorsConfig
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from vllm_kunlun.quantization.compressed_tensors.compressed_tensors_moe import KunlunCompressedTensorsMoEMethod
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from vllm.model_executor.layers.linear import UnquantizedLinearMethod
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cfg = KunlunCompressedTensorsConfig.from_config(copy.deepcopy(quantization_config()))
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cfg.packed_modules_mapping = {
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"gate_up_proj": ["gate_proj", "up_proj"],
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"fused_qkv_a_proj": ["q_a_proj", "kv_a_proj_with_mqa"],
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}
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for name, widths in [
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("model.layers.0.self_attn.fused_qkv_a_proj", [128, 64]),
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("model.layers.0.self_attn.q_b_proj", [128]),
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("model.layers.0.self_attn.o_proj", [128]),
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]:
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layer = torch.nn.Linear(128, sum(widths), bias=False)
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scheme = cfg.get_scheme(layer, name)
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del layer._parameters['weight']
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with patch('vllm.model_executor.parameter.get_tensor_model_parallel_rank', return_value=0), \
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patch('vllm.model_executor.parameter.get_tensor_model_parallel_world_size', return_value=1):
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scheme.create_weights(layer, widths, 128, torch.bfloat16, lambda *a, **k: None)
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assert type(scheme.kernel).__name__ == 'KunlunScaledMMLinearKernel'
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assert layer.weight.dtype == torch.int8
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assert tuple(layer.weight_scale.shape) == (sum(widths), 1)
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print('LINEAR_PASS', name, type(scheme.kernel).__name__)
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class RoutedExperts(torch.nn.Module):
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pass
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experts = RoutedExperts()
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experts.moe_config = SimpleNamespace(is_act_and_mul=True)
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method = KunlunCompressedTensorsMoEMethod.get_moe_method(cfg, experts, 'model.layers.3.mlp.experts')
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assert type(method).__name__ == 'KunlunCompressedTensorsW8A8Int8MoEMethod'
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method.create_weights(experts, 2, 128, 128, torch.bfloat16)
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assert tuple(experts.w13_weight_scale.shape) == (2, 256, 1)
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with torch.no_grad():
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experts.w13_weight_scale.fill_(0.25)
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experts.w2_weight_scale.fill_(0.25)
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method.process_weights_after_loading(experts)
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assert experts.w13_weight_scale.flatten()[0].item() == 31.75
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print('MOE_SCALE_PASS', type(method).__name__)
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for name in ['lm_head', 'model.layers.3.mlp.gate', 'model.layers.0.self_attn.kv_b_proj',
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'model.layers.0.mlp.gate_up_proj', 'model.layers.0.mlp.down_proj',
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'model.layers.3.mlp.shared_experts.gate_up_proj',
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'model.layers.3.mlp.shared_experts.down_proj']:
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assert cfg.get_scheme(torch.nn.Linear(128, 128), name) is None
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print('IGNORE_PASS: head, router, MLA kv_b_proj, dense MLPs, shared experts')
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from vllm_kunlun.v1.attention.backends.mla.common import MLACommonImpl
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projection = torch.nn.Linear(128, 256, bias=False, dtype=torch.bfloat16)
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projection.quant_method = UnquantizedLinearMethod()
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impl = SimpleNamespace(kv_b_proj=projection, kv_lora_rank=128,
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num_heads=2, qk_nope_head_dim=64, v_head_dim=64)
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with torch.inference_mode():
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MLACommonImpl.process_weights_after_loading(impl, torch.bfloat16)
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assert tuple(impl.W_UK_T.shape) == (2, 64, 128)
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assert tuple(impl.W_UV.shape) == (2, 128, 64)
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assert impl.W_UK_T.dtype == impl.W_UV.dtype == torch.bfloat16
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expected = projection.weight.T.reshape(128, 2, 128)
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assert torch.equal(impl.W_UK_T, expected[:, :, :64].permute(1, 2, 0))
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assert torch.equal(impl.W_UV, expected[:, :, 64:].transpose(0, 1))
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print('MLA_BF16_PREPARATION_PASS')
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if __name__ == '__main__':
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main()
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