Fix: UnboundLocalError for variable 'dim' about issue #7449
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weeknan
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Signed-off-by: weeknan <zhounan0431@163.com>
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hwchen2017
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loadams
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## Fix `UnboundLocalError` in `ZeroLinear.backward()` when training only bias parameters, as mentioned in deepspeedai#7435 This PR addresses an issue in the `ZeroLinear.backward()` method, where the local variable `dim` could be referenced before assignment. This happens specifically when: - Only the bias parameters are set to `requires_grad=True`, and - The training setup uses **ZeRO Stage 3**, **AMP**, and **gradient checkpointing**. ### Problem When only the bias requires gradients, the condition for setting `dim = grad_output.dim()` is skipped, but the value of `dim` is still used later in the computation, leading to: ### Fix Move the assignment `dim = grad_output.dim()` to occur unconditionally, so that `dim` is always defined before being used in any branch of the gradient computation logic. ### Impact This makes the backward pass more robust across different training setups. Signed-off-by: weeknan <zhounan0431@163.com> Co-authored-by: Olatunji Ruwase <tjruwase@gmail.com> Co-authored-by: Logan Adams <114770087+loadams@users.noreply.github.com>
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## Fix `UnboundLocalError` in `ZeroLinear.backward()` when training only bias parameters, as mentioned in deepspeedai#7435 This PR addresses an issue in the `ZeroLinear.backward()` method, where the local variable `dim` could be referenced before assignment. This happens specifically when: - Only the bias parameters are set to `requires_grad=True`, and - The training setup uses **ZeRO Stage 3**, **AMP**, and **gradient checkpointing**. ### Problem When only the bias requires gradients, the condition for setting `dim = grad_output.dim()` is skipped, but the value of `dim` is still used later in the computation, leading to: ### Fix Move the assignment `dim = grad_output.dim()` to occur unconditionally, so that `dim` is always defined before being used in any branch of the gradient computation logic. ### Impact This makes the backward pass more robust across different training setups. Signed-off-by: weeknan <zhounan0431@163.com> Co-authored-by: Olatunji Ruwase <tjruwase@gmail.com> Co-authored-by: Logan Adams <114770087+loadams@users.noreply.github.com> Signed-off-by: qimcis <chixie.mcisaac@gmail.com>
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## Fix `UnboundLocalError` in `ZeroLinear.backward()` when training only bias parameters, as mentioned in deepspeedai#7435 This PR addresses an issue in the `ZeroLinear.backward()` method, where the local variable `dim` could be referenced before assignment. This happens specifically when: - Only the bias parameters are set to `requires_grad=True`, and - The training setup uses **ZeRO Stage 3**, **AMP**, and **gradient checkpointing**. ### Problem When only the bias requires gradients, the condition for setting `dim = grad_output.dim()` is skipped, but the value of `dim` is still used later in the computation, leading to: ### Fix Move the assignment `dim = grad_output.dim()` to occur unconditionally, so that `dim` is always defined before being used in any branch of the gradient computation logic. ### Impact This makes the backward pass more robust across different training setups. Signed-off-by: weeknan <zhounan0431@163.com> Co-authored-by: Olatunji Ruwase <tjruwase@gmail.com> Co-authored-by: Logan Adams <114770087+loadams@users.noreply.github.com> Signed-off-by: lym <letusgo126@126.com>
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Fix
UnboundLocalErrorinZeroLinear.backward()when training only bias parameters, as mentioned in #7435This PR addresses an issue in the
ZeroLinear.backward()method, where the local variabledimcould be referenced before assignment. This happens specifically when:requires_grad=True, andProblem
When only the bias requires gradients, the condition for setting
dim = grad_output.dim()is skipped, but the value ofdimis still used later in the computation, leading to:Fix
Move the assignment
dim = grad_output.dim()to occur unconditionally, so thatdimis always defined before being used in any branch of the gradient computation logic.Impact
This makes the backward pass more robust across different training setups.