Update README.md

README.md

@@ -1,3 +1,224 @@
 ---
-license: wtfpl
+inference: false
+license: bigscience-bloom-rail-1.0
+language:
+- ak
+- ar
+- as
+- bm
+- bn
+- ca
+- en
+- es
+- eu
+- fon
+- fr
+- gu
+- hi
+- id
+- ig
+- ki
+- kn
+- lg
+- ln
+- ml
+- mr
+- ne
+- nso
+- ny
+- or
+- pa
+- pt
+- rn
+- rw
+- sn
+- st
+- sw
+- ta
+- te
+- tn
+- ts
+- tum
+- tw
+- ur
+- vi
+- wo
+- xh
+- yo
+- zh
+- zu
+
+pipeline_tag: text-generation
+
 ---
+### Quantized bigscience/bloom 1B3 with 8-bit weights
+
+Heavily inspired by [Hivemind's GPT-J-6B with 8-bit weights](https://huggingface.co/hivemind/gpt-j-6B-8bit), this is a version of [bigscience/bloom](https://huggingface.co/bigscience/bloom-1b3) a ~1 billion parameters language model that you run and fine-tune with less memory.
+
+Here, we also apply [LoRA (Low Rank Adaptation)](https://arxiv.org/abs/2106.09685) to reduce model size.
+
+### How to fine-tune
+TBA
+
+### How to use
+
+This model can be used by adapting Bloom original implementation. This is an adaptation from [Hivemind's GPT-J 8-bit](https://nbviewer.org/urls/huggingface.co/hivemind/gpt-j-6B-8bit/raw/main/convert-gpt-j.ipynb):
+
+```python
+import transformers
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from bitsandbytes.functional import quantize_blockwise, dequantize_blockwise
+from typing import Tuple
+from torch.cuda.amp import custom_fwd, custom_bwd
+
+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 quantuized 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 BloomBlock(transformers.models.bloom.modeling_bloom.BloomBlock):
+    def __init__(self, config, layer_number=None):
+        super().__init__(config, layer_number)
+
+        convert_to_int8(self.self_attention)
+        convert_to_int8(self.mlp)
+
+
+class BloomModel(transformers.models.bloom.modeling_bloom.BloomModel):
+    def __init__(self, config):
+        super().__init__(config)
+        convert_to_int8(self)
+        
+
+class BloomForCausalLM(transformers.models.bloom.modeling_bloom.BloomForCausalLM):
+    def __init__(self, config):
+        super().__init__(config)
+        convert_to_int8(self)
+        
+transformers.models.bloom.modeling_bloom.BloomBlock = BloomBlock
+
+model_name = 'mrm8488/bloom-1b3-8bit'
+model = BloomForCausalLM.from_pretrained(model_name, low_cpu_mem_usage=True)
+tokenizer = BloomTokenizerFast.from_pretrained(model_name)
+
+prompt = tokenizer("Given a table named salaries and columns id, created_at, salary, age. Creates a SQL to answer What is the average salary for 22 years old:", return_tensors='pt')
+out = model.generate(**prompt, min_length=10, do_sample=True)
+tokenizer.decode(out[0])
+```