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import comfy.ops |
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import torch |
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import folder_paths |
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from ..libs.utils import install_package |
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try: |
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from bitsandbytes.nn.modules import Params4bit, QuantState |
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except ImportError: |
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Params4bit = torch.nn.Parameter |
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raise ImportError("Please install bitsandbytes>=0.43.3") |
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def functional_linear_4bits(x, weight, bias): |
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try: |
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install_package("bitsandbytes", "0.43.3", True, "0.43.3") |
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import bitsandbytes as bnb |
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except ImportError: |
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raise ImportError("Please install bitsandbytes>=0.43.3") |
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out = bnb.matmul_4bit(x, weight.t(), bias=bias, quant_state=weight.quant_state) |
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out = out.to(x) |
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return out |
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def copy_quant_state(state, device: torch.device = None): |
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if state is None: |
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return None |
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device = device or state.absmax.device |
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state2 = ( |
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QuantState( |
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absmax=state.state2.absmax.to(device), |
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shape=state.state2.shape, |
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code=state.state2.code.to(device), |
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blocksize=state.state2.blocksize, |
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quant_type=state.state2.quant_type, |
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dtype=state.state2.dtype, |
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) |
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if state.nested |
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else None |
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) |
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return QuantState( |
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absmax=state.absmax.to(device), |
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shape=state.shape, |
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code=state.code.to(device), |
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blocksize=state.blocksize, |
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quant_type=state.quant_type, |
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dtype=state.dtype, |
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offset=state.offset.to(device) if state.nested else None, |
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state2=state2, |
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) |
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class ForgeParams4bit(Params4bit): |
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def to(self, *args, **kwargs): |
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device, dtype, non_blocking, convert_to_format = torch._C._nn._parse_to(*args, **kwargs) |
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if device is not None and device.type == "cuda" and not self.bnb_quantized: |
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return self._quantize(device) |
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else: |
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n = ForgeParams4bit( |
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torch.nn.Parameter.to(self, device=device, dtype=dtype, non_blocking=non_blocking), |
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requires_grad=self.requires_grad, |
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quant_state=copy_quant_state(self.quant_state, device), |
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blocksize=self.blocksize, |
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compress_statistics=self.compress_statistics, |
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quant_type=self.quant_type, |
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quant_storage=self.quant_storage, |
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bnb_quantized=self.bnb_quantized, |
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module=self.module |
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) |
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self.module.quant_state = n.quant_state |
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self.data = n.data |
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self.quant_state = n.quant_state |
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return n |
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class ForgeLoader4Bit(torch.nn.Module): |
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def __init__(self, *, device, dtype, quant_type, **kwargs): |
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super().__init__() |
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self.dummy = torch.nn.Parameter(torch.empty(1, device=device, dtype=dtype)) |
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self.weight = None |
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self.quant_state = None |
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self.bias = None |
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self.quant_type = quant_type |
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def _save_to_state_dict(self, destination, prefix, keep_vars): |
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super()._save_to_state_dict(destination, prefix, keep_vars) |
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quant_state = getattr(self.weight, "quant_state", None) |
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if quant_state is not None: |
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for k, v in quant_state.as_dict(packed=True).items(): |
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destination[prefix + "weight." + k] = v if keep_vars else v.detach() |
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return |
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def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs): |
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quant_state_keys = {k[len(prefix + "weight."):] for k in state_dict.keys() if k.startswith(prefix + "weight.")} |
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if any('bitsandbytes' in k for k in quant_state_keys): |
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quant_state_dict = {k: state_dict[prefix + "weight." + k] for k in quant_state_keys} |
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self.weight = ForgeParams4bit().from_prequantized( |
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data=state_dict[prefix + 'weight'], |
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quantized_stats=quant_state_dict, |
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requires_grad=False, |
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device=self.dummy.device, |
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module=self |
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) |
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self.quant_state = self.weight.quant_state |
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if prefix + 'bias' in state_dict: |
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self.bias = torch.nn.Parameter(state_dict[prefix + 'bias'].to(self.dummy)) |
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del self.dummy |
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elif hasattr(self, 'dummy'): |
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if prefix + 'weight' in state_dict: |
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self.weight = ForgeParams4bit( |
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state_dict[prefix + 'weight'].to(self.dummy), |
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requires_grad=False, |
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compress_statistics=True, |
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quant_type=self.quant_type, |
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quant_storage=torch.uint8, |
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module=self, |
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) |
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self.quant_state = self.weight.quant_state |
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if prefix + 'bias' in state_dict: |
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self.bias = torch.nn.Parameter(state_dict[prefix + 'bias'].to(self.dummy)) |
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del self.dummy |
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else: |
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super()._load_from_state_dict(state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs) |
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current_device = None |
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current_dtype = None |
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current_manual_cast_enabled = False |
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current_bnb_dtype = None |
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class OPS(comfy.ops.manual_cast): |
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class Linear(ForgeLoader4Bit): |
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def __init__(self, *args, device=None, dtype=None, **kwargs): |
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super().__init__(device=device, dtype=dtype, quant_type=current_bnb_dtype) |
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self.parameters_manual_cast = current_manual_cast_enabled |
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def forward(self, x): |
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self.weight.quant_state = self.quant_state |
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if self.bias is not None and self.bias.dtype != x.dtype: |
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self.bias.data = self.bias.data.to(x.dtype) |
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if not self.parameters_manual_cast: |
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return functional_linear_4bits(x, self.weight, self.bias) |
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elif not self.weight.bnb_quantized: |
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assert x.device.type == 'cuda', 'BNB Must Use CUDA as Computation Device!' |
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layer_original_device = self.weight.device |
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self.weight = self.weight._quantize(x.device) |
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bias = self.bias.to(x.device) if self.bias is not None else None |
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out = functional_linear_4bits(x, self.weight, bias) |
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self.weight = self.weight.to(layer_original_device) |
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return out |
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else: |
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weight, bias, signal = weights_manual_cast(self, x, skip_weight_dtype=True, skip_bias_dtype=True) |
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with main_stream_worker(weight, bias, signal): |
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return functional_linear_4bits(x, weight, bias) |
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