pytorch

45c31cab - [quant] Input Weight Equalization - prepare modifications (#59747)

[quant] Input Weight Equalization - prepare modifications (#59747) Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/59747 Modifies prepare_fx for input-weight equalization. If a current node is being equalized (there exists a EqualizationQConfig), then the EqualizationObserver will be inserted before its quantization observer. For a singular linear layer, the general flow looks like: Original graph: `x0 -> linear -> x1`, `w -> linear` After prepare: `x0 -> InpEqObs -> MinMaxObs -> linear1 -> MinMaxObs -> x1` `w -> WeightEqObs -> MinMaxObs -> linear1` For two connected linear layers, the general flow looks like: Original graph: `x0 -> linear1 -> linear2 -> x1`, `w1 -> linear1`, `w2 -> linear2` After prepare: `x0 -> InpEqObs -> MinMaxObs -> linear1 -> MinMaxObs -> InpEqObs -> linear2 -> MinMaxObs -> x1` `w1 -> WeightEqObs -> MinMaxObs -> linear1`, `w2 -> WeightEqObs -> MinMaxObs -> linear2 Test Plan: `python test/test_quantization.py TestEqualizeFx.test_input_equalization_prepare` Original model with one `nn.Linear` layer ``` LinearModule( (linear): Linear(in_features=1, out_features=1, bias=True) ) ``` Graph after `prepare_fx`: ``` graph(): %x : [#users=1] = placeholder[target=x] %x_equalization_process_0 : [#users=1] = call_module[target=x_equalization_process_0](args = (%x,), kwargs = {}) %x_activation_post_process_0 : [#users=1] = call_module[target=x_activation_post_process_00](args = (%x_equalization_process_0,), kwargs = {}) %linear : [#users=1] = call_module[target=linear](args = (%x_activation_post_process_0,), kwargs = {}) %linear_activation_post_process_0 : [#users=1] = call_module[target=linear_activation_post_process_0](args = (%linear,), kwargs = {}) return linear_activation_post_process_0 ``` -------------------------------------- Original model with two connected functional linear layers ``` FunctionalLinearModule( (linear1): Linear() (linear2): Linear() ) ``` Graph after `prepare_fx`: ``` graph(): %x : [#users=1] = placeholder[target=x] %x_equalization_process_0 : [#users=1] = call_module[target=x_equalization_process_0](args = (%x,), kwargs = {}) %x_activation_post_process_0 : [#users=1] = call_module[target=x_activation_post_process_00](args = (%x_equalization_process_0,), kwargs = {}) %linear1_w : [#users=1] = get_attr[target=linear1.w] %linear1_w_equalization_process_0 : [#users=1] = call_module[target=linear1_w_equalization_process_0](args = (%linear1_w,), kwargs = {}) %linear1_w_activation_post_process_0 : [#users=1] = call_module[target=linear1_w_activation_post_process_00](args = (%linear1_w_equalization_process_0,), kwargs = {}) %linear1_b : [#users=1] = get_attr[target=linear1.b] %linear : [#users=1] = call_function[target=torch.nn.functional.linear](args = (%x_activation_post_process_0, %linear1_w_activation_post_process_0), kwargs = {bias: %linear1_b}) %linear_activation_post_process_0 : [#users=1] = call_module[target=linear_activation_post_process_0](args = (%linear,), kwargs = {}) %linear_activation_post_process_0_equalization_process_0 : [#users=1] = call_module[target=linear_activation_post_process_0_equalization_process_0](args = (%linear_activation_post_process_0,), kwargs = {}) %linear2_w : [#users=1] = get_attr[target=linear2.w] %linear2_w_equalization_process_0 : [#users=1] = call_module[target=linear2_w_equalization_process_0](args = (%linear2_w,), kwargs = {}) %linear2_w_activation_post_process_0 : [#users=1] = call_module[target=linear2_w_activation_post_process_00](args = (%linear2_w_equalization_process_0,), kwargs = {}) %linear2_b : [#users=1] = get_attr[target=linear2.b] %linear_1 : [#users=1] = call_function[target=torch.nn.functional.linear](args = (%linear_activation_post_process_0_equalization_process_0, %linear2_w_activation_post_process_0), kwargs = {bias: %linear2_b}) %linear_1_activation_post_process_0 : [#users=1] = call_module[target=linear_1_activation_post_process_0](args = (%linear_1,), kwargs = {}) return linear_1_activation_post_process_0 ``` Imported from OSS Reviewed By: jerryzh168 Differential Revision: D29135316 fbshipit-source-id: 91697e805ede254dbb2a42ee4c23eb1c1c64590e