mirror of
https://github.com/pytorch/pytorch.git
synced 2025-10-20 21:14:14 +08:00
d3ff2d42c2
Description: 1. Quantize Linear Layer Weights to 4-bits: Quantize the weights of the Linear layer to 4 bits, using symmetric quantization. Pack two 4-bit weights into one uint8 container. Choose a quantization scheme (channel-wise or group-wise), with the group size being a multiple of 32. 2. Prepare Quantized Weights, Scales, and Optional Bias: After quantizing, obtain the quantized_weights, scales, and groupsize. If the original Linear layer has a bias, prepare it as well. 3. Pack the Weights Efficiently: Use torch.ops.aten._dyn_quant_pack_4bit_weight to optimally pack the weights, scales, and optional bias. ```python packed_weights = torch.ops.aten._dyn_quant_pack_4bit_weight(weight, scales_and_zeros, bias, groupsize, in_features, out_features) ``` Input parameters should include: in_features and out_features (the same as the Linear layer’s corresponding parameters). 4. Perform Dynamic Quantized Matrix Multiplication: Use torch.ops.aten._dyn_quant_matmul_4bit to perform matrix multiplication with quantized weights. ```python output = torch.ops.aten._dyn_quant_matmul_4bit(input, packed_weights, groupsize, in_features, out_features) ``` Inputs required include: The input tensor, packed_weights , groupsize, and the in_features and out_features. API Usage: https://github.com/pytorch/pytorch/issues/143289 Model Perf : 7B Transformer model: Prefill : 340 t/s Decode : 40 t/s 2B Transformer model Prefill : 747 t/s Decode : 80 t/s Tests: python test/test_linalg.py -k test__dyn_quant_pack_4bit_weight Ran 1 test in 0.016s OK python test/test_linalg.py -k test__dyn_quant_matmul_4bit Ran 8 tests in 0.077s OK python test/test_linalg.py -k test_compile_dyn_quant_matmul_4bit Ran 8 tests in 11.454s Change-Id: Ia1672bad5e6ec94e64d8bb1971395d60f4b3a452 Fixes #ISSUE_NUMBER Pull Request resolved: https://github.com/pytorch/pytorch/pull/134124 Approved by: https://github.com/digantdesai, https://github.com/malfet