[Core] Set linear_weights directly on the layer
#3977
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Yard1
changed the title
linear_weights attribute into a dynamic propertylinear_weights directly on the layer
Yard1
changed the title
linear_weights directly on the layerlinear_weights directly on the layer
| params_dtype: torch.dtype) -> Dict[str, Any]: | ||
| """Create weights for a linear layer.""" | ||
| output_size: int, params_dtype: torch.dtype, | ||
| **extra_weight_attrs): |
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Could this just be an optional weight_loader?
I see the value of enabling kwargs here for future extensibility, but I don't see a case that exists yet other than weight_loader so far, perhaps making the argument explicit is better until we have a reason to allow kwargs
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Actually, making it a kwarg will be easier as we don't need extra handling to not set the weight_loader if it's left unspecified.
| layer.register_parameter("scales", scales) | ||
| set_weight_attrs(scales, extra_weight_attrs) | ||
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| layer.exllama_state = exllama_state |
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The LinearLayer does not need to know this info, so I think it should be GPTQLinearMethod.exllama_state ... It probably should not have been in the weights dict before
This will avoid having this dangling member
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ah actually I don't think this is possible since GPTQLinearMethod is a singleton across all layers
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LGTM, left a few minor comments |
This reverts commit e20cdc1.
2 participants
Currently, all vLLM linear layers use a
linear_weightsattribute to store a dictionary of tensors to be used for actually calculating the matmuls. Those same tensors are also set on the layer itself, leading to the tensors being referenced both in the dictionary and on the layer. If the references to the tensors on the layer are replaced (eg. by model loading code), then the tensors will stop being equal leading to incorrect matmul results. This PR removes the double reference by avoiding the storing of thelinear_weightsdictionary and instead setting the tensors directly as attributes on the layer, meaning we are left with only a single source of truth.See #3476 (comment) for more context
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