diff options
Diffstat (limited to 'bitsandbytes/optim/optimizer.py')
| -rw-r--r-- | bitsandbytes/optim/optimizer.py | 77 |
1 files changed, 56 insertions, 21 deletions
diff --git a/bitsandbytes/optim/optimizer.py b/bitsandbytes/optim/optimizer.py index b942e34..4fb30cd 100644 --- a/bitsandbytes/optim/optimizer.py +++ b/bitsandbytes/optim/optimizer.py @@ -46,9 +46,13 @@ class GlobalOptimManager(object): for group_index, group in enumerate(param_groups): for p_index, p in enumerate(group["params"]): if id(p) in self.pid2config: - self.index2config[(group_index, p_index)] = self.pid2config[id(p)] + self.index2config[(group_index, p_index)] = self.pid2config[ + id(p) + ] - def override_config(self, parameters, key=None, value=None, key_value_dict=None): + def override_config( + self, parameters, key=None, value=None, key_value_dict=None + ): """ Overrides initial optimizer config for specific parameters. @@ -136,7 +140,8 @@ class Optimizer8bit(torch.optim.Optimizer): if len(groups) != len(saved_groups): raise ValueError( - "loaded state dict has a different number of " "parameter groups" + "loaded state dict has a different number of " + "parameter groups" ) param_lens = (len(g["params"]) for g in groups) saved_lens = (len(g["params"]) for g in saved_groups) @@ -192,7 +197,9 @@ class Optimizer8bit(torch.optim.Optimizer): new_group["params"] = group["params"] return new_group - param_groups = [update_group(g, ng) for g, ng in zip(groups, saved_groups)] + param_groups = [ + update_group(g, ng) for g, ng in zip(groups, saved_groups) + ] self.__setstate__({"state": state, "param_groups": param_groups}) def to_gpu(self): @@ -222,9 +229,9 @@ class Optimizer8bit(torch.optim.Optimizer): # found the matching parameter # init override self.mng.pid2config[id(p)] = config - self.mng.index2config[(gindex, pindex)] = self.mng.pid2config[ - id(p) - ] + self.mng.index2config[ + (gindex, pindex) + ] = self.mng.pid2config[id(p)] found = True @torch.no_grad() @@ -280,7 +287,9 @@ class Optimizer8bit(torch.optim.Optimizer): raise NotImplementedError(f"init_state method needs to be overidden") def update_step(self, group, p, gindex, pindex): - raise NotImplementedError(f"The update_step method needs to be overidden") + raise NotImplementedError( + f"The update_step method needs to be overidden" + ) class Optimizer2State(Optimizer8bit): @@ -310,9 +319,13 @@ class Optimizer2State(Optimizer8bit): betas = [float(b) for b in betas] for i in range(len(betas)): if not 0.0 <= betas[i] < 1.0: - raise ValueError(f"Invalid beta parameter at index {i}: {betas[i]}") + raise ValueError( + f"Invalid beta parameter at index {i}: {betas[i]}" + ) if not 0.0 <= weight_decay: - raise ValueError("Invalid weight_decay value: {}".format(weight_decay)) + raise ValueError( + "Invalid weight_decay value: {}".format(weight_decay) + ) defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay) super(Optimizer2State, self).__init__(params, defaults, optim_bits) @@ -351,7 +364,9 @@ class Optimizer2State(Optimizer8bit): state = self.state[p] state["step"] = 0 - if dtype == torch.float32 or (dtype == torch.uint8 and p.numel() < 4096): + if dtype == torch.float32 or ( + dtype == torch.uint8 and p.numel() < 4096 + ): state["state1"] = torch.zeros_like( p, memory_format=torch.preserve_format, @@ -368,8 +383,12 @@ class Optimizer2State(Optimizer8bit): if state["step"] == 0: if "dynamic" not in self.name2qmap: self.fill_qmap() - self.name2qmap["dynamic"] = self.name2qmap["dynamic"].to(p.device) - self.name2qmap["udynamic"] = self.name2qmap["udynamic"].to(p.device) + self.name2qmap["dynamic"] = self.name2qmap["dynamic"].to( + p.device + ) + self.name2qmap["udynamic"] = self.name2qmap["udynamic"].to( + p.device + ) state["state1"] = torch.zeros_like( p, @@ -399,11 +418,15 @@ class Optimizer2State(Optimizer8bit): (blocks,), dtype=torch.float32, device=p.device ) else: - state["max1"] = torch.zeros((1,), dtype=torch.float32, device=p.device) + state["max1"] = torch.zeros( + (1,), dtype=torch.float32, device=p.device + ) state["new_max1"] = torch.zeros( (1,), dtype=torch.float32, device=p.device ) - state["max2"] = torch.zeros((1,), dtype=torch.float32, device=p.device) + state["max2"] = torch.zeros( + (1,), dtype=torch.float32, device=p.device + ) state["new_max2"] = torch.zeros( (1,), dtype=torch.float32, device=p.device ) @@ -470,7 +493,9 @@ class Optimizer2State(Optimizer8bit): state["new_max2"], config["weight_decay"], gnorm_scale=gnorm_scale, - unorm_vec=state["unorm_vec"] if config["max_unorm"] > 0.0 else None, + unorm_vec=state["unorm_vec"] + if config["max_unorm"] > 0.0 + else None, max_unorm=config["max_unorm"], ) @@ -522,9 +547,13 @@ class Optimizer1State(Optimizer8bit): raise ValueError("Invalid epsilon value: {}".format(eps)) for i in range(len(betas)): if not 0.0 <= betas[i] < 1.0: - raise ValueError(f"Invalid beta parameter at index {i}: {betas[i]}") + raise ValueError( + f"Invalid beta parameter at index {i}: {betas[i]}" + ) if not 0.0 <= weight_decay: - raise ValueError("Invalid weight_decay value: {}".format(weight_decay)) + raise ValueError( + "Invalid weight_decay value: {}".format(weight_decay) + ) defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay) super(Optimizer1State, self).__init__(params, defaults, optim_bits) @@ -563,7 +592,9 @@ class Optimizer1State(Optimizer8bit): state = self.state[p] state["step"] = 0 - if dtype == torch.float32 or (dtype == torch.uint8 and p.numel() < 4096): + if dtype == torch.float32 or ( + dtype == torch.uint8 and p.numel() < 4096 + ): state["state1"] = torch.zeros_like( p, memory_format=torch.preserve_format, @@ -574,7 +605,9 @@ class Optimizer1State(Optimizer8bit): if state["step"] == 0: if "dynamic" not in self.name2qmap: self.fill_qmap() - self.name2qmap["dynamic"] = self.name2qmap["dynamic"].to(p.device) + self.name2qmap["dynamic"] = self.name2qmap["dynamic"].to( + p.device + ) state["state1"] = torch.zeros_like( p, @@ -593,7 +626,9 @@ class Optimizer1State(Optimizer8bit): (blocks,), dtype=torch.float32, device=p.device ) else: - state["max1"] = torch.zeros((1,), dtype=torch.float32, device=p.device) + state["max1"] = torch.zeros( + (1,), dtype=torch.float32, device=p.device + ) state["new_max1"] = torch.zeros( (1,), dtype=torch.float32, device=p.device ) |