import torch from dataclasses import dataclass from opentelemetry import trace from transformers import AutoTokenizer, AutoModelForCausalLM, PreTrainedTokenizerBase from typing import Optional, Tuple, List, Type, Dict from text_generation_server.models import Model from text_generation_server.models.types import ( Batch, PrefillTokens, 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] offsets: List[Optional[int]] token_offsets: List[Optional[int]] # Generation helpers next_token_choosers: List[NextTokenChooser] stopping_criterias: List[StoppingCriteria] # 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.Batch: return generate_pb2.Batch( id=self.batch_id, requests=self.requests, size=len(self), max_tokens=self.max_tokens, ) @classmethod def from_pb( cls, pb: generate_pb2.Batch, tokenizer: PreTrainedTokenizerBase, device: torch.device, ) -> "CausalLMBatch": inputs = [] next_token_choosers = [] stopping_criterias = [] offsets = [] token_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(r.inputs) offsets.append(None) token_offsets.append(None) next_token_choosers.append(NextTokenChooser.from_pb(r.parameters, device)) stopping_criteria = StoppingCriteria.from_pb( r.stopping_parameters, tokenizer ) stopping_criterias.append(stopping_criteria) 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) 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) 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(), offsets=offsets, token_offsets=token_offsets, next_token_choosers=next_token_choosers, stopping_criterias=stopping_criterias, 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, requests: List[generate_pb2.Request]) -> Optional["CausalLMBatch"]: if len(requests) == 0: raise ValueError("Batch must have at least one request") if len(requests) == len(self): return self keep_indices = [] # New values after filtering requests_idx_mapping = {} input_lengths = [] offsets = [] token_offsets = [] all_input_ids = [] max_input_length = 0 next_token_choosers = [] stopping_criterias = [] total_remaining_decode_tokens = 0 new_padding_right_offset = 0 for i, r in enumerate(requests): idx = self.requests_idx_mapping[r.id] requests_idx_mapping[r.id] = i keep_indices.append(idx) offsets.append(self.offsets[idx]) token_offsets.append(self.token_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) 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]) == 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 max_tokens = len(requests) * 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.offsets = offsets self.token_offsets = token_offsets self.next_token_choosers = next_token_choosers self.stopping_criterias = stopping_criterias 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 = [] offsets = [] token_offsets = [] all_input_ids = [] next_token_choosers = [] stopping_criterias = [] max_tokens = 0 # Batch tensors input_ids = None attention_mask = None position_ids = None past_key_values = [] # 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) offsets.extend(batch.offsets) token_offsets.extend(batch.token_offsets) all_input_ids.extend(batch.all_input_ids) next_token_choosers.extend(batch.next_token_choosers) stopping_criterias.extend(batch.stopping_criterias) 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), ) # 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 type(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, offsets=offsets, token_offsets=token_offsets, next_token_choosers=next_token_choosers, stopping_criterias=stopping_criterias, 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) class CausalLM(Model): def __init__( self, model_id: str, revision: Optional[str] = None, quantize: bool = False, decode_buffer: int = 3, ): if torch.cuda.is_available(): device = torch.device("cuda") dtype = torch.bfloat16 if torch.cuda.is_bf16_supported() else torch.float32 else: if quantize: raise ValueError("quantization is not available on CPU") device = torch.device("cpu") dtype = torch.float32 tokenizer = AutoTokenizer.from_pretrained( model_id, revision=revision, padding_side="left", truncation_side="left" ) self.model = AutoModelForCausalLM.from_pretrained( model_id, revision=revision, torch_dtype=dtype, device_map="auto" if torch.cuda.is_available() else None, load_in_8bit=quantize, ).eval() tokenizer.pad_token_id = ( self.model.config.pad_token_id if self.model.config.pad_token_id is not None else self.model.config.eos_token_id ) super(CausalLM, self).__init__( tokenizer=tokenizer, requires_padding=True, dtype=dtype, device=device, decode_buffer=decode_buffer, ) @property def batch_type(self) -> Type[CausalLMBatch]: return CausalLMBatch def decode(self, generated_ids: List[int]) -> str: return self.tokenizer.decode( generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False ) def forward( self, input_ids, attention_mask, position_ids, past_key_values: Optional = None ) -> Tuple[torch.Tensor, List[Tuple[torch.Tensor, torch.Tensor]]]: # Model Forward 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, ) return outputs.logits, outputs.past_key_values @tracer.start_as_current_span("generate_token") def generate_token( self, batch: CausalLMBatch ) -> Tuple[List[Generation], Optional[CausalLMBatch]]: # slice the attention mask to the correct shape attention_mask = batch.attention_mask[:, : -batch.padding_right_offset] logits, past = self.forward( batch.input_ids, attention_mask, batch.position_ids, batch.past_key_values, ) # Results generations: List[Generation] = [] stopped = True # Zipped iterator iterator = zip( batch.requests, batch.input_lengths, batch.offsets, batch.token_offsets, logits, batch.next_token_choosers, batch.stopping_criterias, batch.all_input_ids, ) # For each member of the batch for i, ( request, input_length, offset, token_offset, logits, next_token_chooser, stopping_criteria, all_input_ids, ) in enumerate(iterator): # Select next token next_token_id, logprobs = next_token_chooser( all_input_ids.view(1, -1), logits ) # 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, offset, token_offset = self.decode_token( all_input_ids[:, 0], offset, token_offset ) # Evaluate stopping criteria stop, reason = stopping_criteria( next_token_id_squeezed, next_token_text, ) if stop: # Decode generated tokens output_text = self.decode( all_input_ids[-stopping_criteria.current_tokens :, 0] ) # 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: # Keep request in the batch generated_text = None stopped = False # Prefill if stopping_criteria.current_tokens == 1: # Remove generated token to only have prefill and add nan for first prompt token prefill_logprobs = [float("nan")] + logprobs.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 = PrefillTokens( prefill_token_ids, prefill_logprobs, prefill_texts ) else: prefill_tokens = None generation = Generation( request.id, prefill_tokens, next_token_id_squeezed, next_token_logprob, next_token_text, next_token_id_squeezed.item() in self.all_special_ids, generated_text, ) generations.append(generation) # Update values batch.input_ids[i, 0] = next_token_id batch.all_input_ids[i] = all_input_ids batch.input_lengths[i] = new_input_length batch.offsets[i] = offset batch.token_offsets[i] = token_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: return generations, None # 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 return generations, batch