pytorch

ed20e911 - Fixed hash issue in `fx_graph_cse` (#119567)

Fixed hash issue in `fx_graph_cse` (#119567) Description: - Fixed issue with hash collision for `hash((primals_2, 1.0)) == hash((primals_2, 1))` Repro code: ```python import torch from torch._functorch.compile_utils import fx_graph_cse def func(inpt, osize): size = inpt.shape[-1] s1 = size - 1 s2 = size - 1.0 scale = s2 / (osize - 1.0) inpt = torch.clamp(inpt, 0, s1) return scale * inpt gms = [] def toy_backend(gm, _): gms.append(gm) return gm.forward torch._dynamo.reset() fn = torch.compile(backend=toy_backend, dynamic=True)(func) t = torch.rand(3, 100) out = fn(t, 50) gm = gms[0] print(gm.graph) new_fx_g = fx_graph_cse(gm.graph) print(str(new_fx_g)) ``` Original graph ``` graph(): %s0 : torch.SymInt [num_users=0] = placeholder[target=s0] %s1 : torch.SymInt [num_users=0] = placeholder[target=s1] %l_inpt_ : torch.Tensor [num_users=2] = placeholder[target=L_inpt_] %l_osize_ : torch.SymInt [num_users=1] = placeholder[target=L_osize_] %size : [num_users=1] = call_method[target=size](args = (%l_inpt_,), kwargs = {}) %getitem_1 : [num_users=2] = call_function[target=operator.getitem](args = (%size, 1), kwargs = {}) %sub : [num_users=1] = call_function[target=operator.sub](args = (%getitem_1, 1), kwargs = {}) %sub_1 : [num_users=1] = call_function[target=operator.sub](args = (%getitem_1, 1.0), kwargs = {}) %sub_2 : [num_users=1] = call_function[target=operator.sub](args = (%l_osize_, 1.0), kwargs = {}) %truediv : [num_users=1] = call_function[target=operator.truediv](args = (%sub_1, %sub_2), kwargs = {}) %inpt : [num_users=1] = call_function[target=torch.clamp](args = (%l_inpt_, 0, %sub), kwargs = {}) %mul : [num_users=1] = call_function[target=operator.mul](args = (%truediv, %inpt), kwargs = {}) return (mul,) ``` New wrong graph where `sub_2` is replaced incorrectly with `sub`: ``` graph(): %s0 : torch.SymInt [num_users=0] = placeholder[target=s0] %s1 : torch.SymInt [num_users=0] = placeholder[target=s1] %l_inpt_ : torch.Tensor [num_users=2] = placeholder[target=L_inpt_] %l_osize_ : torch.SymInt [num_users=1] = placeholder[target=L_osize_] %size : [num_users=1] = call_method[target=size](args = (%l_inpt_,), kwargs = {}) %getitem_1 : [num_users=1] = call_function[target=operator.getitem](args = (%size, 1), kwargs = {}) %sub : [num_users=2] = call_function[target=operator.sub](args = (%getitem_1, 1), kwargs = {}) %sub_2 : [num_users=1] = call_function[target=operator.sub](args = (%l_osize_, 1.0), kwargs = {}) %truediv : [num_users=1] = call_function[target=operator.truediv](args = (%sub, %sub_2), kwargs = {}) %inpt : [num_users=1] = call_function[target=torch.clamp](args = (%l_inpt_, 0, %sub), kwargs = {}) %mul : [num_users=1] = call_function[target=operator.mul](args = (%truediv, %inpt), kwargs = {}) return (mul,) ``` With this PR the new graph is the following: ``` graph(): %s0 : torch.SymInt [num_users=0] = placeholder[target=s0] %s1 : torch.SymInt [num_users=0] = placeholder[target=s1] %l_inpt_ : torch.Tensor [num_users=2] = placeholder[target=L_inpt_] %l_osize_ : torch.SymInt [num_users=1] = placeholder[target=L_osize_] %size : [num_users=1] = call_method[target=size](args = (%l_inpt_,), kwargs = {}) %getitem_1 : [num_users=2] = call_function[target=operator.getitem](args = (%size, 1), kwargs = {}) %sub : [num_users=1] = call_function[target=operator.sub](args = (%getitem_1, 1), kwargs = {}) %sub_1 : [num_users=1] = call_function[target=operator.sub](args = (%getitem_1, 1.0), kwargs = {}) %sub_2 : [num_users=1] = call_function[target=operator.sub](args = (%l_osize_, 1.0), kwargs = {}) %truediv : [num_users=1] = call_function[target=operator.truediv](args = (%sub_1, %sub_2), kwargs = {}) %inpt : [num_users=1] = call_function[target=torch.clamp](args = (%l_inpt_, 0, %sub), kwargs = {}) %mul : [num_users=1] = call_function[target=operator.mul](args = (%truediv, %inpt), kwargs = {}) return (mul,) ``` Pull Request resolved: https://github.com/pytorch/pytorch/pull/119567 Approved by: https://github.com/eellison