initial commit

backend/operations_bnb.py

@@ -0,0 +1,171 @@
+# Copyright Forge 2024
+
+import torch
+import bitsandbytes as bnb
+
+from backend import utils, memory_management
+from bitsandbytes.nn.modules import Params4bit, QuantState
+from bitsandbytes.functional import dequantize_4bit
+
+
+def functional_linear_4bits(x, weight, bias):
+    out = bnb.matmul_4bit(x, weight.t(), bias=bias, quant_state=weight.quant_state)
+    out = out.to(x)
+    return out
+
+
+def functional_dequantize_4bit(weight):
+    if not weight.bnb_quantized:
+        return weight
+
+    weight_original_device = weight.device
+
+    if weight_original_device.type != 'cuda':
+        weight = weight.cuda()
+
+    weight = dequantize_4bit(weight, quant_state=weight.quant_state, blocksize=weight.blocksize, quant_type=weight.quant_type)
+
+    if weight_original_device.type != 'cuda':
+        weight = weight.to(device=weight_original_device)
+
+    return weight
+
+
+def copy_quant_state(state: QuantState, device: torch.device = None) -> QuantState:
+    if state is None:
+        return None
+
+    device = device or state.absmax.device
+
+    state2 = (
+        QuantState(
+            absmax=state.state2.absmax.to(device),
+            shape=state.state2.shape,
+            code=state.state2.code.to(device),
+            blocksize=state.state2.blocksize,
+            quant_type=state.state2.quant_type,
+            dtype=state.state2.dtype,
+        )
+        if state.nested
+        else None
+    )
+
+    return QuantState(
+        absmax=state.absmax.to(device),
+        shape=state.shape,
+        code=state.code.to(device),
+        blocksize=state.blocksize,
+        quant_type=state.quant_type,
+        dtype=state.dtype,
+        offset=state.offset.to(device) if state.nested else None,
+        state2=state2,
+    )
+
+
+class ForgeParams4bit(Params4bit):
+    def _quantize(self, device):
+        memory_management.signal_empty_cache = True
+        return super()._quantize(device)
+
+    def to(self, *args, **kwargs):
+        device, dtype, non_blocking, convert_to_format = torch._C._nn._parse_to(*args, **kwargs)
+        if device is not None and device.type == "cuda" and not self.bnb_quantized:
+            return self._quantize(device)
+        else:
+            return ForgeParams4bit(
+                torch.nn.Parameter.to(self, device=device, dtype=dtype, non_blocking=non_blocking),
+                requires_grad=self.requires_grad,
+                quant_state=copy_quant_state(self.quant_state, device),
+                blocksize=self.blocksize,
+                compress_statistics=self.compress_statistics,
+                quant_type=self.quant_type,
+                quant_storage=self.quant_storage,
+                bnb_quantized=self.bnb_quantized,
+            )
+
+    def pin_memory(self, device=None):
+        return ForgeParams4bit(
+            torch.Tensor.pin_memory(self, device=device),
+            requires_grad=self.requires_grad,
+            quant_state=self.quant_state,
+            blocksize=self.blocksize,
+            compress_statistics=self.compress_statistics,
+            quant_type=self.quant_type,
+            quant_storage=self.quant_storage,
+            bnb_quantized=self.bnb_quantized,
+        )
+
+
+class ForgeLoader4Bit(torch.nn.Module):
+    def __init__(self, *, device, dtype, quant_type, **kwargs):
+        super().__init__()
+        self.dummy = torch.nn.Parameter(torch.empty(1, device=device, dtype=dtype))
+        self.weight = None
+        self.bias = None
+        self.quant_type = quant_type
+
+    def _apply(self, fn, recurse=True):
+        for k, p in self.named_parameters(recurse=False, remove_duplicate=True):
+            setattr(self, k, utils.tensor2parameter(fn(p)))
+        return self
+
+    def _save_to_state_dict(self, destination, prefix, keep_vars):
+        super()._save_to_state_dict(destination, prefix, keep_vars)
+        quant_state = getattr(self.weight, "quant_state", None)
+        if quant_state is not None:
+            for k, v in quant_state.as_dict(packed=True).items():
+                destination[prefix + "weight." + k] = v if keep_vars else v.detach()
+        return
+
+    def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs):
+        quant_state_keys = {k[len(prefix + "weight."):] for k in state_dict.keys() if k.startswith(prefix + "weight.")}
+
+        if any('bitsandbytes' in k for k in quant_state_keys):
+            quant_state_dict = {k: state_dict[prefix + "weight." + k] for k in quant_state_keys}
+
+            self.weight = ForgeParams4bit.from_prequantized(
+                data=state_dict[prefix + 'weight'],
+                quantized_stats=quant_state_dict,
+                requires_grad=False,
+                device=self.dummy.device,
+            )
+
+            if prefix + 'bias' in state_dict:
+                self.bias = torch.nn.Parameter(state_dict[prefix + 'bias'].to(self.dummy))
+
+            del self.dummy
+        elif hasattr(self, 'dummy'):
+            if prefix + 'weight' in state_dict:
+                self.weight = ForgeParams4bit(
+                    state_dict[prefix + 'weight'].to(self.dummy),
+                    requires_grad=False,
+                    compress_statistics=False,
+                    blocksize=64,
+                    quant_type=self.quant_type,
+                    quant_storage=torch.uint8,
+                )
+
+            if prefix + 'bias' in state_dict:
+                self.bias = torch.nn.Parameter(state_dict[prefix + 'bias'].to(self.dummy))
+
+            del self.dummy
+        else:
+            super()._load_from_state_dict(state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs)
+
+    def reload_weight(self, weight):
+        weight_original_device = weight.device
+        weight = ForgeParams4bit(
+            weight,
+            requires_grad=False,
+            compress_statistics=self.weight.compress_statistics,
+            blocksize=self.weight.blocksize,
+            quant_type=self.weight.quant_type,
+            quant_storage=self.weight.quant_storage,
+            bnb_quantized=False
+        )
+        if weight_original_device.type == 'cuda':
+            weight = weight.to(weight_original_device)
+        else:
+            weight = weight.cuda().to(weight_original_device)
+        self.weight = weight
+        return self