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https://github.com/pytorch/pytorch.git
synced 2025-10-20 21:14:14 +08:00
94737e8a2a
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
74 lines
1.7 KiB
Python
74 lines
1.7 KiB
Python
# mypy: allow-untyped-defs
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import types
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from contextlib import contextmanager
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# The idea for this parameter is that we forbid bare assignment
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# to torch.backends.<cudnn|mkldnn>.enabled and friends when running our
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# test suite, where it's very easy to forget to undo the change
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# later.
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__allow_nonbracketed_mutation_flag = True
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def disable_global_flags():
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global __allow_nonbracketed_mutation_flag
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__allow_nonbracketed_mutation_flag = False
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def flags_frozen():
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return not __allow_nonbracketed_mutation_flag
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@contextmanager
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def __allow_nonbracketed_mutation():
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global __allow_nonbracketed_mutation_flag
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old = __allow_nonbracketed_mutation_flag
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__allow_nonbracketed_mutation_flag = True
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try:
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yield
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finally:
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__allow_nonbracketed_mutation_flag = old
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class ContextProp:
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def __init__(self, getter, setter):
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self.getter = getter
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self.setter = setter
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def __get__(self, obj, objtype):
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return self.getter()
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def __set__(self, obj, val):
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if not flags_frozen():
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self.setter(val)
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else:
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raise RuntimeError(
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f"not allowed to set {obj.__name__} flags "
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"after disable_global_flags; please use flags() context manager instead"
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)
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class PropModule(types.ModuleType):
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def __init__(self, m, name):
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super().__init__(name)
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self.m = m
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def __getattr__(self, attr):
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return self.m.__getattribute__(attr)
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from torch.backends import (
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cpu as cpu,
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cuda as cuda,
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cudnn as cudnn,
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cusparselt as cusparselt,
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kleidiai as kleidiai,
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mha as mha,
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mkl as mkl,
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mkldnn as mkldnn,
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mps as mps,
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nnpack as nnpack,
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openmp as openmp,
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quantized as quantized,
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)
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