如果PLATFORM = TargetPlatform.QNN_DSP_INT8, quantize_torch_model.py会export出一个 .json 和 .onnx(看起来和fp32原模型一样);如果PLATFORM = TargetPlatform.ONNXRUNTIME,会生成一个.json 和 .onnx(体积比原模型小了,且是一个QDQ模型),如图 但想问一下,如何生成一个QNN模型,比如intel的neural-compresso...
PyTorch的torch.quantization模块提供了对GPU的支持,但你需要确保在量化过程中正确地使用了GPU。 示例代码(假设你使用的是PyTorch的动态量化): python import torch import torch.nn.functional as F import torch.quantization class QuantizedModel(nn.Module): def __init__(self): super(QuantizedModel, self)._...
image.png 采用PyTorch 提供的量化工具 PyTorch 提供了对Tensor 进行量化的API函数: 逐Tensor/Layer量化(Tensor/Channel wise Quantization):quantize_per_tensor(input: Tensor, scale: Tensor, zero_point: Tensor, dtype: _dtype) -> Tensor: 逐Channel量化 (Channel wise Quantization):quantize_per_channel(input...
model_fp32_prepared(input_fp32)# Convert the observed model to a quantized model. This does several things:# quantizes the weights, computes and stores the scale and bias value to be# used with each activation tensor, and replaces key operators with quantized# implementations.model_int8 = t...
Time to load model: 75.21 seconds /home/pai/pytorch/gpt-fast/model.py:182: UserWarning: 1Torch was not compiled with flash attention. (Triggered internally at ../aten/src/ATen/native/transformers/hip/sdp_utils.cpp:254.) y = F.scaled_dot_product_attention(q, k, v, attn_mask=mask, dro...
"mlp.down_proj.weight","lm_head.weight"]forname, datainmodel_part.items():forwordinkeywords:ifwordinname:# Quantize and dequantize the entire tensormodel_part[name] = q4_1_quantize_and_dequantize_tensor(data)# Save the updated model partstorch.save(model_part,"pytorch_model_quantized.bin"...
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Model: 106 Model name: Intel(R) Xeon(R) Platinum XXXX CPU @ 2.70GHz Stepping: 6 CPU MHz: 2699.998 BogoMIPS: 5399.99 Hypervisor vendor: KVM Virtualization type: full L1d cache: 48K L1i cache: 32K L2 cache: 1280K L3 cache: 49152K ...
开发者ID:HolmesShuan,项目名称:OISR-PyTorch,代码行数:44,代码来源:videotester.py 示例2: test ▲点赞 4▼ # 需要导入模块: import utility [as 别名]# 或者: from utility importquantize[as 别名]deftest(self):torch.set_grad_enabled(False) ...
import torch from vector_quantize_pytorch import LatentQuantize model = LatentQuantize( levels = [4, 8, 16], dim = 9, num_codebooks = 3 ) input_tensor = torch.randn(2, 3, dim) output_tensor, indices, loss = model(input_tensor) # (2, 3, 9), (2, 3, 3), () assert output_...