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Utils.py

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+import transformers
+import torch
+import torch.nn.functional as F
+from torch import nn
+from torch.cuda.amp import custom_fwd, custom_bwd
+
+from bitsandbytes.functional import quantize_blockwise, dequantize_blockwise
+
+class FrozenBNBLinear(nn.Module):
+    def __init__(self, weight, absmax, code, bias=None):
+        assert isinstance(bias, nn.Parameter) or bias is None
+        super().__init__()
+        self.out_features, self.in_features = weight.shape
+        self.register_buffer("weight", weight.requires_grad_(False))
+        self.register_buffer("absmax", absmax.requires_grad_(False))
+        self.register_buffer("code", code.requires_grad_(False))
+        self.adapter = None
+        self.bias = bias
+ 
+    def forward(self, input):
+        output = DequantizeAndLinear.apply(input, self.weight, self.absmax, self.code, self.bias)
+        if self.adapter:
+            output += self.adapter(input)
+        return output
+ 
+    @classmethod
+    def from_linear(cls, linear: nn.Linear) -> "FrozenBNBLinear":
+        weights_int8, state = quantize_blockise_lowmemory(linear.weight)
+        return cls(weights_int8, *state, linear.bias)
+ 
+    def __repr__(self):
+        return f"{self.__class__.__name__}({self.in_features}, {self.out_features})"
+ 
+ 
+class DequantizeAndLinear(torch.autograd.Function): 
+    @staticmethod
+    @custom_fwd
+    def forward(ctx, input: torch.Tensor, weights_quantized: torch.ByteTensor,
+                absmax: torch.FloatTensor, code: torch.FloatTensor, bias: torch.FloatTensor):
+        weights_deq = dequantize_blockwise(weights_quantized, absmax=absmax, code=code)
+        ctx.save_for_backward(input, weights_quantized, absmax, code)
+        ctx._has_bias = bias is not None
+        return F.linear(input, weights_deq, bias)
+ 
+    @staticmethod
+    @custom_bwd
+    def backward(ctx, grad_output: torch.Tensor):
+        assert not ctx.needs_input_grad[1] and not ctx.needs_input_grad[2] and not ctx.needs_input_grad[3]
+        input, weights_quantized, absmax, code = ctx.saved_tensors
+        # grad_output: [*batch, out_features]
+        weights_deq = dequantize_blockwise(weights_quantized, absmax=absmax, code=code)
+        grad_input = grad_output @ weights_deq
+        grad_bias = grad_output.flatten(0, -2).sum(dim=0) if ctx._has_bias else None
+        return grad_input, None, None, None, grad_bias
+ 
+ 
+class FrozenBNBEmbedding(nn.Module):
+    def __init__(self, weight, absmax, code):
+        super().__init__()
+        self.num_embeddings, self.embedding_dim = weight.shape
+        self.register_buffer("weight", weight.requires_grad_(False))
+        self.register_buffer("absmax", absmax.requires_grad_(False))
+        self.register_buffer("code", code.requires_grad_(False))
+        self.adapter = None
+ 
+    def forward(self, input, **kwargs):
+        with torch.no_grad():
+            # note: both quantized weights and input indices are *not* differentiable
+            weight_deq = dequantize_blockwise(self.weight, absmax=self.absmax, code=self.code)
+            output = F.embedding(input, weight_deq, **kwargs)
+        if self.adapter:
+            output += self.adapter(input)
+        return output 
+ 
+    @classmethod
+    def from_embedding(cls, embedding: nn.Embedding) -> "FrozenBNBEmbedding":
+        weights_int8, state = quantize_blockise_lowmemory(embedding.weight)
+        return cls(weights_int8, *state)
+ 
+    def __repr__(self):
+        return f"{self.__class__.__name__}({self.num_embeddings}, {self.embedding_dim})"
+ 
+ 
+def quantize_blockise_lowmemory(matrix: torch.Tensor, chunk_size: int = 2 ** 20):
+    assert chunk_size % 4096 == 0
+    code = None
+    chunks = []
+    absmaxes = []
+    flat_tensor = matrix.view(-1)
+    for i in range((matrix.numel() - 1) // chunk_size + 1):
+        input_chunk = flat_tensor[i * chunk_size: (i + 1) * chunk_size].clone()
+        quantized_chunk, (absmax_chunk, code) = quantize_blockwise(input_chunk, code=code)
+        chunks.append(quantized_chunk)
+        absmaxes.append(absmax_chunk)
+ 
+    matrix_i8 = torch.cat(chunks).reshape_as(matrix)
+    absmax = torch.cat(absmaxes)
+    return matrix_i8, (absmax, code)
+ 
+ 
+def convert_to_int8(model):
+    """Convert linear and embedding modules to 8-bit with optional adapters"""
+    for module in list(model.modules()):
+        for name, child in module.named_children():
+            if isinstance(child, nn.Linear):
+                print(name, child)
+                setattr( 
+                    module,
+                    name,
+                    FrozenBNBLinear(
+                        weight=torch.zeros(child.out_features, child.in_features, dtype=torch.uint8),
+                        absmax=torch.zeros((child.weight.numel() - 1) // 4096 + 1),
+                        code=torch.zeros(256),
+                        bias=child.bias,
+                    ),
+                )
+            elif isinstance(child, nn.Embedding):
+                setattr(
+                    module,
+                    name,
+                    FrozenBNBEmbedding(
+                        weight=torch.zeros(child.num_embeddings, child.embedding_dim, dtype=torch.uint8),
+                        absmax=torch.zeros((child.weight.numel() - 1) // 4096 + 1),
+                        code=torch.zeros(256),
+                    )
+                )
+
+class GPTJBlock(transformers.models.gptj.modeling_gptj.GPTJBlock):
+    def __init__(self, config):
+        super().__init__(config)
+
+        convert_to_int8(self.attn)
+        convert_to_int8(self.mlp)
+
+
+class GPTJModel(transformers.models.gptj.modeling_gptj.GPTJModel):
+    def __init__(self, config):
+        super().__init__(config)
+        convert_to_int8(self)
+        
+
+class GPTJForCausalLM(transformers.models.gptj.modeling_gptj.GPTJForCausalLM):
+    def __init__(self, config):
+        super().__init__(config)
+        convert_to_int8(self)