import torch import time import torch.distributed from dataclasses import dataclass from opentelemetry import trace from transformers import ( AutoConfig, AutoTokenizer, AutoModelForCausalLM, PreTrainedTokenizerBase, ) from typing import Optional, Tuple, List, Type, Dict from text_generation_server.utils import ( initialize_torch_distributed, weight_files, Weights, ) from text_generation_server.models import Model from text_generation_server.utils.chunks import concat_text_chunks from text_generation_server.utils.import_utils import SYSTEM from text_generation_server.utils.quantization import get_loader from text_generation_server.utils.tokens import batch_top_tokens from text_generation_server.models.types import ( Batch, Tokens, Generation, GeneratedText, ) from text_generation_server.pb import generate_pb2 from text_generation_server.utils import NextTokenChooser, StoppingCriteria, Sampling tracer = trace.get_tracer(__name__) @dataclass class CausalLMBatch(Batch): batch_id: int requests: List[generate_pb2.Request] requests_idx_mapping: Dict[int, int] # Decoder values input_ids: torch.Tensor attention_mask: torch.Tensor position_ids: torch.Tensor past_key_values: Optional[List[Tuple]] # All tokens all_input_ids: List[torch.Tensor] # Lengths of all generations present in the batch input_lengths: List[int] prefix_offsets: List[int] read_offsets: List[int] # Generation helpers next_token_choosers: List[NextTokenChooser] stopping_criterias: List[StoppingCriteria] top_n_tokens: List[int] top_n_tokens_tensor: torch.Tensor # Metadata used for padding max_input_length: int padding_right_offset: int # Maximum number of tokens this batch will grow to max_tokens: int # Past metadata keys_head_dim_last: bool = True def to_pb(self) -> generate_pb2.CachedBatch: return generate_pb2.CachedBatch( id=self.batch_id, request_ids=[r.id for r in self.requests], size=len(self), max_tokens=self.max_tokens, ) @classmethod def from_pb( cls, pb: generate_pb2.Batch, tokenizer: PreTrainedTokenizerBase, dtype: torch.dtype, device: torch.device, ) -> "CausalLMBatch": inputs = [] next_token_choosers = [] stopping_criterias = [] top_n_tokens = [] prefix_offsets = [] read_offsets = [] requests_idx_mapping = {} # Parse batch max_truncation = 0 padding_right_offset = 0 max_decode_tokens = 0 for i, r in enumerate(pb.requests): requests_idx_mapping[r.id] = i inputs.append(concat_text_chunks(r.input_chunks.chunks)) next_token_choosers.append( NextTokenChooser.from_pb(r.parameters, device, tokenizer) ) stopping_criteria = StoppingCriteria.from_pb( r.stopping_parameters, tokenizer ) stopping_criterias.append(stopping_criteria) top_n_tokens.append(r.top_n_tokens) max_truncation = max(max_truncation, r.truncate) max_decode_tokens += stopping_criteria.max_new_tokens padding_right_offset = max( padding_right_offset, stopping_criteria.max_new_tokens ) tokenized_inputs = tokenizer( inputs, return_tensors="pt", padding=True, return_token_type_ids=False, truncation=True, max_length=max_truncation, ).to(device) for _ in pb.requests: input_len = tokenized_inputs["input_ids"].shape[1] prefix_offsets.append(input_len - 5) read_offsets.append(input_len) input_lengths = tokenized_inputs["attention_mask"].sum(1) max_input_length = input_lengths.max() input_ids = tokenized_inputs["input_ids"] # Allocate maximum attention_mask attention_mask = input_ids.new_zeros( (pb.size, max_input_length + padding_right_offset) ) # Copy tokenizer attention_mask into fully allocated attention_mask attention_mask[:, :max_input_length] = tokenized_inputs["attention_mask"] position_ids = tokenized_inputs["attention_mask"].long().cumsum(-1) - 1 position_ids.masked_fill_(tokenized_inputs["attention_mask"] == 0, 1) all_input_ids = tokenized_inputs["input_ids"].T.split(1, dim=1) top_n_tokens_tensor = torch.tensor( top_n_tokens, device=device, dtype=torch.int64 ) max_tokens = len(inputs) * (max_input_length + max_decode_tokens) return cls( batch_id=pb.id, requests=pb.requests, requests_idx_mapping=requests_idx_mapping, input_ids=input_ids, attention_mask=attention_mask, position_ids=position_ids, past_key_values=None, all_input_ids=list(all_input_ids), input_lengths=input_lengths.tolist(), prefix_offsets=prefix_offsets, read_offsets=read_offsets, next_token_choosers=next_token_choosers, stopping_criterias=stopping_criterias, top_n_tokens=top_n_tokens, top_n_tokens_tensor=top_n_tokens_tensor, max_input_length=max_input_length.item(), padding_right_offset=padding_right_offset, max_tokens=max_tokens, ) @tracer.start_as_current_span("filter") def filter(self, request_ids: List[int]) -> Optional["CausalLMBatch"]: if len(request_ids) == 0: raise ValueError("Batch must have at least one request") if len(request_ids) == len(self): return self keep_indices = [] # New values after filtering requests_idx_mapping = {} requests = [] input_lengths = [] prefix_offsets = [] read_offsets = [] all_input_ids = [] max_input_length = 0 next_token_choosers = [] stopping_criterias = [] top_n_tokens = [] total_remaining_decode_tokens = 0 new_padding_right_offset = 0 for i, request_id in enumerate(request_ids): idx = self.requests_idx_mapping[request_id] requests_idx_mapping[request_id] = i keep_indices.append(idx) requests.append(self.requests[idx]) prefix_offsets.append(self.prefix_offsets[idx]) read_offsets.append(self.read_offsets[idx]) all_input_ids.append(self.all_input_ids[idx]) request_input_length = self.input_lengths[idx] input_lengths.append(request_input_length) max_input_length = max(max_input_length, request_input_length) next_token_choosers.append(self.next_token_choosers[idx]) stopping_criteria = self.stopping_criterias[idx] stopping_criterias.append(stopping_criteria) top_n_tokens.append(self.top_n_tokens[idx]) remaining_decode_tokens = ( stopping_criteria.max_new_tokens - stopping_criteria.current_tokens ) total_remaining_decode_tokens += remaining_decode_tokens new_padding_right_offset = max( new_padding_right_offset, remaining_decode_tokens ) # Apply indices to input_ids, attention mask, past key values and other items that need to be cached input_ids = self.input_ids[keep_indices] position_ids = self.position_ids[keep_indices] self.attention_mask = self.attention_mask[ keep_indices, -(self.padding_right_offset + max_input_length) : ( self.attention_mask.shape[1] - self.padding_right_offset ) + new_padding_right_offset, ] # Ensure that past_key_values tensors can be updated in-place if type(self.past_key_values[0]) is tuple: self.past_key_values = [list(layer) for layer in self.past_key_values] # Update tensors in-place to allow incremental garbage collection past_kv_length = max_input_length - 1 for layer in self.past_key_values: past_keys, past_values = layer if len(past_keys.shape) == 3: # Force past to be of dim [self_size, num_heads, ...] for easy indexing past_keys = past_keys.view(len(self), -1, *past_keys.shape[-2:]) past_values = past_values.view(len(self), -1, *past_values.shape[-2:]) if self.keys_head_dim_last: layer[0] = past_keys[keep_indices, :, -past_kv_length:, :] else: layer[0] = past_keys[keep_indices, :, :, -past_kv_length:] del past_keys layer[1] = past_values[keep_indices, :, -past_kv_length:, :] del past_values top_n_tokens_tensor = self.top_n_tokens_tensor[keep_indices] max_tokens = len(request_ids) * max_input_length + total_remaining_decode_tokens self.requests = requests self.requests_idx_mapping = requests_idx_mapping self.input_ids = input_ids self.position_ids = position_ids self.all_input_ids = all_input_ids self.input_lengths = input_lengths self.prefix_offsets = prefix_offsets self.read_offsets = read_offsets self.next_token_choosers = next_token_choosers self.stopping_criterias = stopping_criterias self.top_n_tokens = top_n_tokens self.top_n_tokens_tensor = top_n_tokens_tensor self.max_input_length = max_input_length self.padding_right_offset = new_padding_right_offset self.max_tokens = max_tokens return self @classmethod @tracer.start_as_current_span("concatenate") def concatenate(cls, batches: List["CausalLMBatch"]) -> "CausalLMBatch": # Used for padding total_batch_size = 0 max_input_length = 0 padding_right_offset = 0 for batch in batches: total_batch_size += len(batch) max_input_length = max(max_input_length, batch.max_input_length) padding_right_offset = max(padding_right_offset, batch.padding_right_offset) # Batch attributes requests = [] requests_idx_mapping = {} input_lengths = [] prefix_offsets = [] read_offsets = [] all_input_ids = [] next_token_choosers = [] stopping_criterias = [] top_n_tokens = [] max_tokens = 0 # Batch tensors input_ids = None attention_mask = None position_ids = None past_key_values = [] top_n_tokens_tensor = None # Used for slicing correctly inside the tensors # Equivalent to a cumsum on batch sizes start_index = 0 for i, batch in enumerate(batches): requests.extend(batch.requests) input_lengths.extend(batch.input_lengths) prefix_offsets.extend(batch.prefix_offsets) read_offsets.extend(batch.read_offsets) all_input_ids.extend(batch.all_input_ids) next_token_choosers.extend(batch.next_token_choosers) stopping_criterias.extend(batch.stopping_criterias) top_n_tokens.extend(batch.top_n_tokens) if i == 0: requests_idx_mapping = batch.requests_idx_mapping else: # We need to offset the mapping for each batch by the cumulative batch size for k, v in batch.requests_idx_mapping.items(): requests_idx_mapping[k] = v + start_index # Slicing end index for this batch end_index = start_index + len(batch) # We only concatenate batches that did at least one step if batch.past_key_values is None: raise ValueError("only concatenate prefilled batches") # Create empty tensor # input_ids is always of shape [batch_size, 1] # We do not need to pad it if input_ids is None: input_ids = batch.input_ids.new_empty((total_batch_size, 1)) # Copy to correct indices input_ids[start_index:end_index] = batch.input_ids # Create padded tensor if attention_mask is None: attention_mask = batch.attention_mask.new_zeros( (total_batch_size, max_input_length + padding_right_offset), ) if top_n_tokens_tensor is None: top_n_tokens_tensor = batches[0].top_n_tokens_tensor.new_zeros( total_batch_size, ) top_n_tokens_tensor[start_index:end_index] = batch.top_n_tokens_tensor # We need to slice the attention mask to remove padding from previous steps # and to remove unused allocated space left_offset = max_input_length - batch.max_input_length batch_left_offset = ( batch.attention_mask.shape[1] - batch.max_input_length - batch.padding_right_offset ) attention_mask[ start_index:end_index, left_offset:-padding_right_offset, ] = batch.attention_mask[ :, batch_left_offset : -batch.padding_right_offset, ] # Create empty tensor # position_ids is always of shape [batch_size, 1] if position_ids is None: position_ids = batch.position_ids.new_empty((total_batch_size, 1)) position_ids[start_index:end_index] = batch.position_ids # Shenanigans to get dimensions because BLOOM outputs a past with a different shape # BLOOM Keys: [batch_size * num_heads, head_dim, seq_length] # BLOOM Values: [batch_size * num_heads, seq_length, head_dim] # And ensure that we can update tensors in-place if isinstance(batch.past_key_values[0], tuple): batch.past_key_values = [ [t.view(len(batch), -1, *t.shape[-2:]) for t in layer] for layer in batch.past_key_values ] elif len(batch.past_key_values[0][0].shape) == 3: for layer in batch.past_key_values: for k, t in enumerate(layer): layer[k] = t.view(len(batch), -1, *t.shape[-2:]) # Add eventual padding tokens that were added while concatenating max_tokens += batch.max_tokens + ( max_input_length - batch.max_input_length ) * len(batch) start_index = end_index first_past_kvs = batches[0].past_key_values _, num_heads, padded_sequence_length, head_dim = first_past_kvs[0][1].shape padded_past_values_shape = ( total_batch_size, num_heads, max_input_length - 1, head_dim, ) if batches[0].keys_head_dim_last: padded_past_keys_shape = padded_past_values_shape else: # seq_length is last for BLOOM padded_past_keys_shape = ( total_batch_size, num_heads, head_dim, max_input_length - 1, ) # Iterate over attention layers # Concatenate past key values layer by layer to allow incremental garbage collection for j in range(len(first_past_kvs)): padded_past_keys = first_past_kvs[j][0].new_zeros(padded_past_keys_shape) start_index = 0 for batch in batches: past_keys = batch.past_key_values[j][0] # Clear reference to the original tensor batch.past_key_values[j][0] = None # Slicing end index for this batch end_index = start_index + len(batch) # We slice the keys to remove the padding from previous batches past_seq_len = batch.max_input_length - 1 if batch.keys_head_dim_last: padded_past_keys[start_index:end_index, :, -past_seq_len:, :] = ( past_keys[:, :, -past_seq_len:, :] ) else: # BLOOM case padded_past_keys[start_index:end_index, :, :, -past_seq_len:] = ( past_keys[:, :, :, -past_seq_len:] ) del past_keys start_index = end_index padded_past_values = first_past_kvs[j][1].new_zeros( padded_past_values_shape ) start_index = 0 for batch in batches: past_values = batch.past_key_values[j][1] # Clear reference to the original tensor batch.past_key_values[j][1] = None # Slicing end index for this batch end_index = start_index + len(batch) # We slice the past values to remove the padding from previous batches past_seq_len = batch.max_input_length - 1 padded_past_values[start_index:end_index, :, -past_seq_len:, :] = ( past_values[:, :, -past_seq_len:, :] ) del past_values # Update values start_index = end_index past_key_values.append([padded_past_keys, padded_past_values]) return cls( batch_id=batches[0].batch_id, requests=requests, requests_idx_mapping=requests_idx_mapping, input_ids=input_ids, attention_mask=attention_mask, position_ids=position_ids, past_key_values=past_key_values, all_input_ids=all_input_ids, input_lengths=input_lengths, prefix_offsets=prefix_offsets, read_offsets=read_offsets, next_token_choosers=next_token_choosers, stopping_criterias=stopping_criterias, top_n_tokens=top_n_tokens, top_n_tokens_tensor=top_n_tokens_tensor, max_input_length=max_input_length, padding_right_offset=padding_right_offset, keys_head_dim_last=batches[0].keys_head_dim_last, max_tokens=max_tokens, ) def __len__(self): return len(self.requests) @dataclass class CausalLMBatchKeysLast(CausalLMBatch): keys_head_dim_last: bool = False class CausalLM(Model): def __init__( self, model_id: str, model_class, revision: Optional[str] = None, quantize: Optional[str] = None, speculator: Optional[str] = None, dtype: Optional[torch.dtype] = None, default_dtype=torch.float16, trust_remote_code: bool = False, tokenizer_class=AutoTokenizer, config_class=AutoConfig, batch_class=CausalLMBatch, ): self.quantize = quantize self.batch_class = batch_class self.process_group, rank, world_size = initialize_torch_distributed() if torch.cuda.is_available(): device = torch.device(f"cuda:{rank}") dtype = default_dtype if dtype is None else dtype elif SYSTEM == "ipex": if hasattr(torch, "xpu") and torch.xpu.is_available(): device = torch.device(f"xpu:{rank}") dtype = default_dtype if dtype is None else dtype else: device = torch.device("cpu") # Float16 doesn't exist on target. dtype = torch.bfloat16 if dtype is None else dtype else: device = torch.device("cpu") dtype = torch.float32 if dtype is None else dtype tokenizer = tokenizer_class.from_pretrained( model_id, revision=revision, padding_side="left", truncation_side="left", trust_remote_code=trust_remote_code, ) config = config_class.from_pretrained( model_id, revision=revision, trust_remote_code=trust_remote_code, ) config.quantize = quantize config.speculator = speculator if tokenizer.pad_token_id is None: if config.pad_token_id is not None: tokenizer.pad_token_id = config.pad_token_id elif config.eos_token_id is not None: tokenizer.pad_token_id = config.eos_token_id elif tokenizer.eos_token_id is not None: tokenizer.pad_token_id = tokenizer.eos_token_id torch.distributed.barrier(group=self.process_group) weights_loader = get_loader( quantize=quantize, model_id=model_id, revision=revision ) filenames = weight_files(model_id, revision=revision, extension=".safetensors") weights = Weights( filenames, device=device, dtype=dtype, process_group=self.process_group, weights_loader=weights_loader, ) prefix = "" model = model_class(prefix, config, weights) torch.distributed.barrier(group=self.process_group) super().__init__( model_id=model_id, model=model, tokenizer=tokenizer, requires_padding=True, dtype=dtype, device=device, rank=rank, world_size=world_size, ) @classmethod def fallback( cls, model_id: str, revision: Optional[str] = None, quantize: Optional[str] = None, speculator: Optional[str] = None, dtype: Optional[torch.dtype] = None, trust_remote_code: bool = False, ): if speculator: raise RuntimeError("Speculator decoding is not enabled for AutoModel") if torch.cuda.is_available(): device = torch.device("cuda") dtype = torch.float16 if dtype is None else dtype else: if quantize: raise ValueError("quantization is not available on CPU") device = torch.device("cpu") dtype = torch.float32 if dtype is None else dtype tokenizer = AutoTokenizer.from_pretrained( model_id, revision=revision, padding_side="left", truncation_side="left", trust_remote_code=trust_remote_code, ) model = AutoModelForCausalLM.from_pretrained( model_id, revision=revision, torch_dtype=dtype, device_map=( "auto" if torch.cuda.is_available() and torch.cuda.device_count() > 1 else None ), load_in_8bit=quantize == "bitsandbytes", trust_remote_code=trust_remote_code, ) if ( torch.cuda.is_available() and torch.cuda.device_count() == 1 and quantize != "bitsandbytes" ): model = model.cuda() if tokenizer.pad_token_id is None: if model.config.pad_token_id is not None: tokenizer.pad_token_id = model.config.pad_token_id elif model.config.eos_token_id is not None: tokenizer.pad_token_id = model.config.eos_token_id elif tokenizer.eos_token_id is not None: tokenizer.pad_token_id = tokenizer.eos_token_id else: tokenizer.add_special_tokens({"pad_token": "[PAD]"}) self = cls.__new__( cls, ) self.batch_class = CausalLMBatch super().__init__( self, model_id=model_id, model=model, tokenizer=tokenizer, requires_padding=True, dtype=dtype, device=device, ) self.quantize = quantize return self @property def batch_type(self) -> Type[CausalLMBatch]: return self.batch_class def forward( self, input_ids, attention_mask, position_ids, past_key_values: Optional = None ) -> Tuple[ torch.Tensor, Optional[torch.Tensor], List[Tuple[torch.Tensor, torch.Tensor]] ]: # Model Forward kwargs = { "input_ids": input_ids, "attention_mask": attention_mask, "past_key_values": past_key_values, "use_cache": True, "return_dict": True, } if self.has_position_ids: kwargs["position_ids"] = position_ids outputs = self.model.forward(**kwargs) if isinstance(outputs, tuple): outputs, speculative_logits = outputs else: speculative_logits = None return outputs.logits, speculative_logits, outputs.past_key_values @tracer.start_as_current_span("generate_token") def generate_token( self, batch: CausalLMBatch ) -> Tuple[List[Generation], Optional[CausalLMBatch], Tuple[int, int]]: start = time.time_ns() # slice the attention mask to the correct shape attention_mask = batch.attention_mask[:, : -batch.padding_right_offset] logits, speculative_logits, past = self.forward( batch.input_ids, attention_mask, batch.position_ids, batch.past_key_values, ) # Results generations: List[Generation] = [] stopped = True # Speculation is not active for causal accepted_ids = torch.ones_like(batch.input_ids)[:, 0] batch_top_token_ids, batch_top_token_logprobs = batch_top_tokens( batch.top_n_tokens, batch.top_n_tokens_tensor, torch.log_softmax(logits[:, -1], -1), accepted_ids, ) start_decode = time.time_ns() # Zipped iterator iterator = zip( batch.requests, batch.input_lengths, batch.prefix_offsets, batch.read_offsets, logits, batch.next_token_choosers, batch.stopping_criterias, batch.all_input_ids, batch.top_n_tokens, batch_top_token_ids, batch_top_token_logprobs, ) # For each member of the batch for i, ( request, input_length, prefix_offset, read_offset, logits, next_token_chooser, stopping_criteria, all_input_ids, top_n_tokens, top_token_ids, top_token_logprobs, ) in enumerate(iterator): # Select next token next_token_id, logprobs = next_token_chooser( all_input_ids.view(1, -1), logits[-1:, :] ) # Append next token to all tokens all_input_ids = torch.cat([all_input_ids, next_token_id]) new_input_length = input_length + 1 # Generated token next_token_logprob = logprobs[-1, next_token_id] next_token_id_squeezed = next_token_id.squeeze() next_token_text, prefix_offset, read_offset = self.decode_token( all_input_ids[:, 0], prefix_offset, read_offset ) # Evaluate stopping criteria stop, reason = stopping_criteria( next_token_id_squeezed, next_token_text, ) if not stop: stopped = False # Shard generations # All generations will be appended in the rust sharded client if i % self.world_size == self.rank: if stop: # Decode generated tokens output_text, _, _ = self.decode_token( all_input_ids[:, 0], prefix_offset=len(all_input_ids) - stopping_criteria.current_tokens - 1, read_offset=len(all_input_ids) - stopping_criteria.current_tokens, skip_special_tokens=True, ) # Get seed if isinstance(next_token_chooser.choice, Sampling): seed = next_token_chooser.choice.seed else: seed = None generated_text = GeneratedText( output_text, stopping_criteria.current_tokens, reason, seed ) else: generated_text = None # Prefill if stopping_criteria.current_tokens == 1 and request.prefill_logprobs: # Remove generated token to only have prefill and add nan for first prompt token prefill_logprobs = [float("nan")] + torch.log_softmax( logits, -1 ).gather(1, all_input_ids[1:]).squeeze(1)[ -new_input_length:-1 ].tolist() prefill_token_ids = all_input_ids[-new_input_length:-1] prefill_texts = self.tokenizer.batch_decode( prefill_token_ids, clean_up_tokenization_spaces=False, skip_special_tokens=False, ) prefill_tokens = Tokens( prefill_token_ids, prefill_logprobs, prefill_texts, is_special=[], ) else: prefill_tokens = None if top_n_tokens > 0: all_top_tokens = [] for top_token_ids, top_token_logprobs in zip( top_token_ids, top_token_logprobs ): toptoken_texts = self.tokenizer.batch_decode( top_token_ids, clean_up_tokenization_spaces=False, skip_special_tokens=False, ) special_toptokens = [ token_id in self.all_special_ids for token_id in top_token_ids ] top_tokens = Tokens( top_token_ids, top_token_logprobs, toptoken_texts, special_toptokens, ) all_top_tokens.append(top_tokens) top_tokens = all_top_tokens else: top_tokens = None generation = Generation( request.id, prefill_tokens, Tokens( [next_token_id_squeezed], [next_token_logprob], [next_token_text], [next_token_id_squeezed.item() in self.all_special_ids], ), generated_text, top_tokens, ) generations.append(generation) # Update values batch.next_token_choosers[i] = batch.next_token_choosers[i].advance_grammar( next_token_id_squeezed.item() ) batch.input_ids[i, 0] = next_token_id batch.all_input_ids[i] = all_input_ids batch.input_lengths[i] = new_input_length batch.prefix_offsets[i] = prefix_offset batch.read_offsets[i] = read_offset batch.max_input_length = max(batch.max_input_length, new_input_length) # We finished all generations in the batch; there is no next batch if stopped: forward_ns = start_decode - start decode_ns = time.time_ns() - start_decode return generations, None, (forward_ns, decode_ns) # Slice unused values from prefill batch.input_ids = batch.input_ids[:, :1] # Update attention_mask as we added a new token to input_ids batch.attention_mask[:, -batch.padding_right_offset] = 1 # Decrease right offset batch.padding_right_offset -= 1 # Update position_ids batch.position_ids = batch.position_ids[:, -1:] + 1 # Update past key values batch.past_key_values = past forward_ns = start_decode - start decode_ns = time.time_ns() - start_decode return generations, batch, (forward_ns, decode_ns)