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import gradio as gr
import torch
import transformers
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 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 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)
class T5ForConditionalGeneration(transformers.models.t5.modeling_t5.T5ForConditionalGeneration):
def __init__(self, config):
super().__init__(config)
convert_to_int8(self)
transformers.models.gptj.modeling_gptj.GPTJBlock = GPTJBlock
transformers.models.t5.modeling_t5.T5ForConditionalGeneration = T5ForConditionalGeneration
config = transformers.GPTJConfig.from_pretrained("EleutherAI/gpt-j-6B")
tokenizer = transformers.AutoTokenizer.from_pretrained("EleutherAI/gpt-j-6B")
config.pad_token_id = config.eos_token_id
tokenizer.pad_token = config.pad_token_id
gpt = GPTJForCausalLM(config)#.from_pretrained("hivemind/gpt-j-6B-8bit", low_cpu_mem_usage=True)
def add_adapters(model, adapter_dim=4, p = 0.1):
assert adapter_dim > 0
for name, module in model.named_modules():
if isinstance(module, FrozenBNBLinear):
if "attn" in name or "mlp" in name or "head" in name:
print("Adding adapter to", name)
module.adapter = nn.Sequential(
nn.Linear(module.in_features, adapter_dim, bias=False),
nn.Dropout(p=p),
nn.Linear(adapter_dim, module.out_features, bias=False),
)
print("Initializing", name)
nn.init.zeros_(module.adapter[2].weight)
else:
print("Not adding adapter to", name)
elif isinstance(module, FrozenBNBEmbedding):
print("Adding adapter to", name)
module.adapter = nn.Sequential(
nn.Embedding(module.num_embeddings, adapter_dim),
nn.Dropout(p=p),
nn.Linear(adapter_dim, module.embedding_dim, bias=False),
)
print("Initializing", name)
nn.init.zeros_(module.adapter[2].weight)
add_adapters(gpt)
device = 'cuda' if torch.cuda.is_available() else 'cpu'
gpt.to(device)
if device == 'cpu':
gpt.load_state_dict(torch.load('rewrite_and_paraphrase_pretrained_gptj8bit.pt', map_location=torch.device('cpu')))
else:
gpt.load_state_dict(torch.load('rewrite_and_paraphrase_pretrained_gptj8bit.pt'))
gpt.eval()
def inference(text):
with torch.no_grad():
prompt = tokenizer(text, truncation=True, padding=True, max_length=128, return_tensors='pt')
prompt = {key: value for key, value in prompt.items()}
out = gpt.generate(**prompt, max_length=512, top_k=50, top_p=0.9, temperature=1.0, do_sample=True, repetition_penalty = 1.2, num_beams=1)
return tokenizer.decode(out[0])
iface = gr.Interface(fn=inference, inputs="text", outputs="text")
iface.launch() |