import torch import torch.distributed from typing import List, Optional, Type from accelerate import init_empty_weights from safetensors import safe_open from transformers import ( AutoTokenizer, AutoModelForCausalLM, AutoConfig, PreTrainedTokenizerBase, ) from transformers.models.bloom.parallel_layers import ( TensorParallelColumnLinear, TensorParallelEmbedding, TensorParallelRowLinear, ) from text_generation.models import CausalLM from text_generation.models.causal_lm import CausalLMBatch from text_generation.pb import generate_pb2 from text_generation.utils import ( initialize_torch_distributed, weight_files, download_weights, ) HAS_BITS_AND_BYTES = True try: import bitsandbytes as bnb from bitsandbytes.nn import Int8Params except Exception as e: HAS_BITS_AND_BYTES = False class BloomCausalLMBatch(CausalLMBatch): @classmethod def from_pb( cls, pb: generate_pb2.Batch, tokenizer: PreTrainedTokenizerBase, device: torch.device, ) -> "CausalLMBatch": batch = super(BloomCausalLMBatch, cls).from_pb( pb=pb, tokenizer=tokenizer, device=device ) batch.keys_head_dim_last = False return batch class BLOOM(CausalLM): @property def batch_type(self) -> Type[CausalLMBatch]: return BloomCausalLMBatch class BLOOMSharded(BLOOM): def __init__( self, model_id: str, revision: Optional[str] = None, quantize: bool = False ): if not model_id.startswith("bigscience/bloom"): raise ValueError(f"Model {model_id} is not supported") self.process_group, self.rank, self.world_size = initialize_torch_distributed() self.master = self.rank == 0 if torch.cuda.is_available(): device = torch.device(f"cuda:{self.rank}") dtype = torch.bfloat16 else: device = torch.device("cpu") dtype = torch.float32 tokenizer = AutoTokenizer.from_pretrained( model_id, revision=revision, padding_side="left" ) config = AutoConfig.from_pretrained( model_id, revision=revision, slow_but_exact=False, tp_parallel=True ) config.pad_token_id = 3 # Only download weights for small models if self.master and model_id == "bigscience/bloom-560m": download_weights(model_id, revision=revision, extension=".safetensors") torch.distributed.barrier(group=self.process_group) filenames = weight_files(model_id, revision=revision, extension=".safetensors") if not filenames: raise ValueError("No safetensors weights found") with init_empty_weights(): model = AutoModelForCausalLM.from_config(config) torch.distributed.barrier(group=self.process_group) self.load_weights( model, filenames, quantize=quantize, device=device, rank=self.rank, world_size=self.world_size, ) self.model = model.eval().to(dtype) torch.distributed.barrier(group=self.process_group) super(CausalLM, self).__init__( tokenizer=tokenizer, device=device, ) @staticmethod def load_weights( model, filenames: List[str], quantize: bool, device: torch.device, rank: int, world_size: int, ): parameters = dict(model.named_parameters()) for file in filenames: with safe_open( file, framework="pt", device=str(device) if not quantize else "cpu" ) as f: for name in f.keys(): full_name = f"transformer.{name}" module_name, param_name = full_name.rsplit(".", 1) module = model.get_submodule(module_name) current_tensor = parameters[full_name] slice_ = f.get_slice(name) if isinstance(module, TensorParallelColumnLinear): if param_name == "weight": size = slice_.get_shape()[0] block_size = size // world_size start = rank * block_size stop = (rank + 1) * block_size tensor = slice_[start:stop] else: size = slice_.get_shape()[0] block_size = size // world_size start = rank * block_size stop = (rank + 1) * block_size tensor = slice_[start:stop] elif isinstance(module, TensorParallelRowLinear): if param_name == "weight": size = slice_.get_shape()[1] block_size = size // world_size start = rank * block_size stop = (rank + 1) * block_size tensor = slice_[:, start:stop] else: tensor = slice_[:] # XXX: Hack for Rowlinear to add the bias only once. if rank != 0: tensor = torch.zeros_like(tensor) elif isinstance(module, TensorParallelEmbedding): size = slice_.get_shape()[0] block_size = size // world_size start = rank * block_size stop = (rank + 1) * block_size tensor = slice_[start:stop] else: tensor = slice_[:] if current_tensor.shape != tensor.shape: raise ValueError( f"Name {name} -- Current {current_tensor.shape} and got {tensor.shape}" ) tensor = tensor.contiguous() if quantize: if not HAS_BITS_AND_BYTES: raise ImportError( "bitsandbytes is not available on your machine either because it is not installed " "or you don't have a GPU.\n" "You can install it with `pip install bitsandbytes`." ) if ( type(module) in [TensorParallelRowLinear, TensorParallelColumnLinear] and param_name == "weight" ): tensor = Int8Params( tensor, has_fp16_weights=False, requires_grad=False, ).to(device) state = bnb.MatmulLtState() state.threshold = 6.0 state.has_fp16_weights = False state.memory_efficient_backward = False state.use_pool = True state.CB = tensor.CB state.SCB = tensor.SCB tensor.CB = None tensor.SCB = None def replace_linear(state): def linear(input, weight, bias): out = bnb.matmul( input, weight, state=state, threshold=state.threshold, bias=bias, ) if state.CB is not None: # we converted 8-bit row major to turing/ampere format # in the first inference pass # we no longer need the row-major weight del state.CB weight.data = state.CxB return out return linear module.linear = replace_linear(state) else: tensor = tensor.to(device) module._parameters[param_name] = tensor if name == "word_embeddings.weight": model.lm_head._parameters["weight"] = tensor def forward( self, input_ids, attention_mask, position_ids, past_key_values: Optional = None ): outputs = self.model.forward( input_ids=input_ids, attention_mask=attention_mask, position_ids=position_ids, past_key_values=past_key_values, use_cache=True, ) # Logits are sharded, so we need to gather them logits = [torch.empty_like(outputs.logits) for _ in range(self.world_size)] torch.distributed.all_gather(logits, outputs.logits, group=self.process_group) logits = torch.cat(logits, dim=2) return logits, outputs.past_key_values