backends/gaudi/server/text_generation_server/models/seq2seq_lm.py [82:556]: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ) @classmethod def from_pb( cls, pb: generate_pb2.Batch, tokenizer: PreTrainedTokenizerBase, dtype: torch.dtype, device: torch.device, ) -> "Seq2SeqLMBatch": """Convert a text_generation_server.v1.Batch protobuf to a Seq2SeqLMBatch""" inputs = [] next_token_choosers = [] stopping_criterias = [] top_n_tokens = [] decoder_input_lengths = [] 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): inputs.append(concat_text_chunks(r.input_chunks.chunks)) requests_idx_mapping[r.id] = i decoder_input_lengths.append(1) 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 ) # Tokenize batch 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() # Decoder sequence only contains the bos_token decoder_input_ids = ( torch.tensor(tokenizer.bos_token_id, device=device) .repeat(len(pb.requests)) .view(-1, 1) ) for _ in pb.requests: prefix_offsets.append(0) read_offsets.append(1) all_decoder_input_ids = decoder_input_ids.view(-1).split(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=tokenized_inputs["input_ids"], attention_mask=tokenized_inputs["attention_mask"], decoder_input_ids=decoder_input_ids, all_decoder_input_ids=list(all_decoder_input_ids), decoder_attention_mask=None, encoder_last_hidden_state=None, past_key_values=None, input_lengths=input_lengths.tolist(), decoder_input_lengths=decoder_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.item(), max_decoder_input_length=1, padding_right_offset=padding_right_offset, max_tokens=max_tokens, ) @tracer.start_as_current_span("filter") def filter(self, request_ids: List[int]) -> Optional["Seq2SeqLMBatch"]: 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 = [] decoder_input_lengths = [] prefix_offsets = [] read_offsets = [] all_decoder_input_ids = [] next_token_choosers = [] stopping_criterias = [] top_n_tokens = [] max_input_length = 0 max_decoder_input_length = 0 padding_right_offset = 0 total_remaining_decode_tokens = 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_decoder_input_ids.append(self.all_decoder_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) request_decoder_input_length = self.decoder_input_lengths[idx] decoder_input_lengths.append(request_decoder_input_length) max_decoder_input_length = max( max_decoder_input_length, request_decoder_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 padding_right_offset = max(padding_right_offset, remaining_decode_tokens) # Apply indices to input_ids, attention mask, past key values and other items that need to be cached self.decoder_input_ids = self.decoder_input_ids[keep_indices] self.attention_mask = self.attention_mask[keep_indices, -max_input_length:] if self.decoder_attention_mask is not None: self.decoder_attention_mask = self.decoder_attention_mask[ keep_indices, -(self.padding_right_offset + max_decoder_input_length) : ( self.decoder_attention_mask.shape[1] - self.padding_right_offset ) + padding_right_offset, ] self.encoder_last_hidden_state = self.encoder_last_hidden_state[ keep_indices, -max_input_length: ] # Ensure that past_key_values tensors can be updated in-place if type(self.past_key_values[0]) is tuple: self.past_key_values = [ [t for t in layer] for layer in self.past_key_values ] decoder_past_seq_len = max_decoder_input_length - 1 for layer in self.past_key_values: layer[0] = layer[0][keep_indices, :, -decoder_past_seq_len:] layer[1] = layer[1][keep_indices, :, -decoder_past_seq_len:] layer[2] = layer[2][keep_indices, :, -max_input_length:] layer[3] = layer[3][keep_indices, :, -max_input_length:] top_n_tokens_tensor = self.top_n_tokens_tensor[keep_indices] max_tokens = ( len(request_ids) * (max_input_length + max_decoder_input_length) + remaining_decode_tokens ) self.requests = requests self.requests_idx_mapping = requests_idx_mapping self.input_ids = None self.all_decoder_input_ids = all_decoder_input_ids self.input_lengths = input_lengths self.decoder_input_lengths = decoder_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.max_decoder_input_length = max_decoder_input_length self.padding_right_offset = padding_right_offset self.max_tokens = max_tokens return self @classmethod @tracer.start_as_current_span("concatenate") def concatenate(cls, batches: List["Seq2SeqLMBatch"]) -> "Seq2SeqLMBatch": """Concatenate multiple batches together by padding internal torch tensors""" # Used for padding total_batch_size = 0 max_input_length = 0 max_decoder_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) max_decoder_input_length = max( max_decoder_input_length, batch.max_decoder_input_length ) padding_right_offset = max(padding_right_offset, batch.padding_right_offset) # Batch attributes requests = [] requests_idx_mapping = {} all_decoder_input_ids = [] input_lengths = [] decoder_input_lengths = [] prefix_offsets = [] read_offsets = [] next_token_choosers = [] stopping_criterias = [] top_n_tokens = [] max_tokens = 0 # Batch tensors attention_mask = None decoder_input_ids = None decoder_attention_mask = None encoder_last_hidden_state = None top_n_tokens_tensor = 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): # Extend all list attributes requests.extend(batch.requests) all_decoder_input_ids.extend(batch.all_decoder_input_ids) input_lengths.extend(batch.input_lengths) decoder_input_lengths.extend(batch.decoder_input_lengths) prefix_offsets.extend(batch.prefix_offsets) read_offsets.extend(batch.read_offsets) 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.encoder_last_hidden_state is None: raise ValueError("Batch encoder_last_hidden_state cannot be None") # Create padded tensor if attention_mask is None: attention_mask = batch.attention_mask.new_zeros( (total_batch_size, max_input_length), ) # Copy to correct indices attention_mask[start_index:end_index, -batch.max_input_length :] = ( batch.attention_mask[:, -batch.max_input_length :] ) # Create padded tensor if decoder_input_ids is None: decoder_input_ids = batch.decoder_input_ids.new_zeros( (total_batch_size, 1), ) # Copy to correct indices decoder_input_ids[start_index:end_index] = batch.decoder_input_ids # Create padded tensor if decoder_attention_mask is None: # As decoder_attention_mask might not exist, we use `batch.attention_mask` for device here decoder_attention_mask = batch.attention_mask.new_zeros( (total_batch_size, max_decoder_input_length + padding_right_offset), ) # If the decoder mask does not exist yet, all generations started at the same time and we never concatenated # this batch. All generations are of length `batch.max_decoder_input_length`. left_offset = max_decoder_input_length - batch.max_decoder_input_length if batch.decoder_attention_mask is None: decoder_attention_mask[ start_index:end_index, left_offset:-padding_right_offset, ] = 1 # If it exists, we need to index else: batch_left_offset = ( batch.decoder_attention_mask.shape[1] - batch.max_decoder_input_length - batch.padding_right_offset ) decoder_attention_mask[ start_index:end_index, left_offset:-padding_right_offset, ] = batch.decoder_attention_mask[ :, batch_left_offset : -batch.padding_right_offset, ] # Create padded tensor if encoder_last_hidden_state is None: encoder_last_hidden_state = batch.encoder_last_hidden_state.new_zeros( ( total_batch_size, max_input_length, batch.encoder_last_hidden_state.shape[-1], ), ) 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 # Copy to correct indices encoder_last_hidden_state[ start_index:end_index, -batch.max_input_length :, : ] = batch.encoder_last_hidden_state[:, -batch.max_input_length :, :] batch.encoder_last_hidden_state = None # Ensure that we can update tensors in-place if isinstance(batch.past_key_values[0], tuple): batch.past_key_values = [ [t for t in layer] for layer in batch.past_key_values ] # Add eventual padding tokens that were added while concatenating max_tokens += batch.max_tokens + ( max_input_length - batch.max_input_length + max_decoder_input_length - batch.max_decoder_input_length ) * len(batch) start_index = end_index # Determine shapes for new past kv tensors first_past_kvs = batches[0].past_key_values _, num_heads, _, head_dim = first_past_kvs[0][0].shape padded_dec_t_shape = ( total_batch_size, num_heads, (max_decoder_input_length - 1), head_dim, ) padded_enc_t_shape = ( total_batch_size, num_heads, max_input_length, head_dim, ) # Iterate over attention layers for j in range(len(first_past_kvs)): past_key_values.append([]) # Decoder past for k in range(0, 2): # Initialize tensors padded_past_values = first_past_kvs[j][k].new_zeros(padded_dec_t_shape) past_key_values[j].append(padded_past_values) start_index = 0 for batch in batches: t = batch.past_key_values[j][k] # Clear reference to the original tensor batch.past_key_values[j][k] = None # Slicing end index for this batch end_index = start_index + len(batch) # We slice the past keys and values to remove the padding from previous batches past_seq_len = batch.max_decoder_input_length - 1 padded_past_values[start_index:end_index, :, -past_seq_len:, :] = t[ :, :, -past_seq_len:, : ] del t start_index = end_index # Encoder past for k in range(2, 4): # Initialize tensors padded_past_values = first_past_kvs[j][k].new_zeros(padded_enc_t_shape) past_key_values[j].append(padded_past_values) start_index = 0 for batch in batches: t = batch.past_key_values[j][k] # Clear reference to the original tensor batch.past_key_values[j][k] = None # Slicing end index for this batch end_index = start_index + len(batch) # We slice the past keys and values to remove the padding from previous batches padded_past_values[ start_index:end_index, :, -batch.max_input_length :, : ] = t[:, :, -batch.max_input_length :, :] del t start_index = end_index return cls( batch_id=batches[0].batch_id, requests=requests, requests_idx_mapping=requests_idx_mapping, input_ids=None, attention_mask=attention_mask, decoder_input_ids=decoder_input_ids, all_decoder_input_ids=all_decoder_input_ids, decoder_attention_mask=decoder_attention_mask, encoder_last_hidden_state=encoder_last_hidden_state, past_key_values=past_key_values, input_lengths=input_lengths, decoder_input_lengths=decoder_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, max_decoder_input_length=max_decoder_input_length, padding_right_offset=padding_right_offset, max_tokens=max_tokens, ) def __len__(self): return len(self.requests) class Seq2SeqLM(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, config_class=AutoConfig, tokenizer_class=AutoTokenizer, aliases=None, ): self.quantize = quantize self.process_group, rank, world_size = initialize_torch_distributed() - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - server/text_generation_server/models/seq2seq_lm.py [84:558]: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ) @classmethod def from_pb( cls, pb: generate_pb2.Batch, tokenizer: PreTrainedTokenizerBase, dtype: torch.dtype, device: torch.device, ) -> "Seq2SeqLMBatch": """Convert a text_generation_server.v1.Batch protobuf to a Seq2SeqLMBatch""" inputs = [] next_token_choosers = [] stopping_criterias = [] top_n_tokens = [] decoder_input_lengths = [] 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): inputs.append(concat_text_chunks(r.input_chunks.chunks)) requests_idx_mapping[r.id] = i decoder_input_lengths.append(1) 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 ) # Tokenize batch 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() # Decoder sequence only contains the bos_token decoder_input_ids = ( torch.tensor(tokenizer.bos_token_id, device=device) .repeat(len(pb.requests)) .view(-1, 1) ) for _ in pb.requests: prefix_offsets.append(0) read_offsets.append(1) all_decoder_input_ids = decoder_input_ids.view(-1).split(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=tokenized_inputs["input_ids"], attention_mask=tokenized_inputs["attention_mask"], decoder_input_ids=decoder_input_ids, all_decoder_input_ids=list(all_decoder_input_ids), decoder_attention_mask=None, encoder_last_hidden_state=None, past_key_values=None, input_lengths=input_lengths.tolist(), decoder_input_lengths=decoder_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.item(), max_decoder_input_length=1, padding_right_offset=padding_right_offset, max_tokens=max_tokens, ) @tracer.start_as_current_span("filter") def filter(self, request_ids: List[int]) -> Optional["Seq2SeqLMBatch"]: 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 = [] decoder_input_lengths = [] prefix_offsets = [] read_offsets = [] all_decoder_input_ids = [] next_token_choosers = [] stopping_criterias = [] top_n_tokens = [] max_input_length = 0 max_decoder_input_length = 0 padding_right_offset = 0 total_remaining_decode_tokens = 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_decoder_input_ids.append(self.all_decoder_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) request_decoder_input_length = self.decoder_input_lengths[idx] decoder_input_lengths.append(request_decoder_input_length) max_decoder_input_length = max( max_decoder_input_length, request_decoder_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 padding_right_offset = max(padding_right_offset, remaining_decode_tokens) # Apply indices to input_ids, attention mask, past key values and other items that need to be cached self.decoder_input_ids = self.decoder_input_ids[keep_indices] self.attention_mask = self.attention_mask[keep_indices, -max_input_length:] if self.decoder_attention_mask is not None: self.decoder_attention_mask = self.decoder_attention_mask[ keep_indices, -(self.padding_right_offset + max_decoder_input_length) : ( self.decoder_attention_mask.shape[1] - self.padding_right_offset ) + padding_right_offset, ] self.encoder_last_hidden_state = self.encoder_last_hidden_state[ keep_indices, -max_input_length: ] # Ensure that past_key_values tensors can be updated in-place if type(self.past_key_values[0]) is tuple: self.past_key_values = [ [t for t in layer] for layer in self.past_key_values ] decoder_past_seq_len = max_decoder_input_length - 1 for layer in self.past_key_values: layer[0] = layer[0][keep_indices, :, -decoder_past_seq_len:] layer[1] = layer[1][keep_indices, :, -decoder_past_seq_len:] layer[2] = layer[2][keep_indices, :, -max_input_length:] layer[3] = layer[3][keep_indices, :, -max_input_length:] top_n_tokens_tensor = self.top_n_tokens_tensor[keep_indices] max_tokens = ( len(request_ids) * (max_input_length + max_decoder_input_length) + remaining_decode_tokens ) self.requests = requests self.requests_idx_mapping = requests_idx_mapping self.input_ids = None self.all_decoder_input_ids = all_decoder_input_ids self.input_lengths = input_lengths self.decoder_input_lengths = decoder_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.max_decoder_input_length = max_decoder_input_length self.padding_right_offset = padding_right_offset self.max_tokens = max_tokens return self @classmethod @tracer.start_as_current_span("concatenate") def concatenate(cls, batches: List["Seq2SeqLMBatch"]) -> "Seq2SeqLMBatch": """Concatenate multiple batches together by padding internal torch tensors""" # Used for padding total_batch_size = 0 max_input_length = 0 max_decoder_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) max_decoder_input_length = max( max_decoder_input_length, batch.max_decoder_input_length ) padding_right_offset = max(padding_right_offset, batch.padding_right_offset) # Batch attributes requests = [] requests_idx_mapping = {} all_decoder_input_ids = [] input_lengths = [] decoder_input_lengths = [] prefix_offsets = [] read_offsets = [] next_token_choosers = [] stopping_criterias = [] top_n_tokens = [] max_tokens = 0 # Batch tensors attention_mask = None decoder_input_ids = None decoder_attention_mask = None encoder_last_hidden_state = None top_n_tokens_tensor = 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): # Extend all list attributes requests.extend(batch.requests) all_decoder_input_ids.extend(batch.all_decoder_input_ids) input_lengths.extend(batch.input_lengths) decoder_input_lengths.extend(batch.decoder_input_lengths) prefix_offsets.extend(batch.prefix_offsets) read_offsets.extend(batch.read_offsets) 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.encoder_last_hidden_state is None: raise ValueError("Batch encoder_last_hidden_state cannot be None") # Create padded tensor if attention_mask is None: attention_mask = batch.attention_mask.new_zeros( (total_batch_size, max_input_length), ) # Copy to correct indices attention_mask[start_index:end_index, -batch.max_input_length :] = ( batch.attention_mask[:, -batch.max_input_length :] ) # Create padded tensor if decoder_input_ids is None: decoder_input_ids = batch.decoder_input_ids.new_zeros( (total_batch_size, 1), ) # Copy to correct indices decoder_input_ids[start_index:end_index] = batch.decoder_input_ids # Create padded tensor if decoder_attention_mask is None: # As decoder_attention_mask might not exist, we use `batch.attention_mask` for device here decoder_attention_mask = batch.attention_mask.new_zeros( (total_batch_size, max_decoder_input_length + padding_right_offset), ) # If the decoder mask does not exist yet, all generations started at the same time and we never concatenated # this batch. All generations are of length `batch.max_decoder_input_length`. left_offset = max_decoder_input_length - batch.max_decoder_input_length if batch.decoder_attention_mask is None: decoder_attention_mask[ start_index:end_index, left_offset:-padding_right_offset, ] = 1 # If it exists, we need to index else: batch_left_offset = ( batch.decoder_attention_mask.shape[1] - batch.max_decoder_input_length - batch.padding_right_offset ) decoder_attention_mask[ start_index:end_index, left_offset:-padding_right_offset, ] = batch.decoder_attention_mask[ :, batch_left_offset : -batch.padding_right_offset, ] # Create padded tensor if encoder_last_hidden_state is None: encoder_last_hidden_state = batch.encoder_last_hidden_state.new_zeros( ( total_batch_size, max_input_length, batch.encoder_last_hidden_state.shape[-1], ), ) 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 # Copy to correct indices encoder_last_hidden_state[ start_index:end_index, -batch.max_input_length :, : ] = batch.encoder_last_hidden_state[:, -batch.max_input_length :, :] batch.encoder_last_hidden_state = None # Ensure that we can update tensors in-place if isinstance(batch.past_key_values[0], tuple): batch.past_key_values = [ [t for t in layer] for layer in batch.past_key_values ] # Add eventual padding tokens that were added while concatenating max_tokens += batch.max_tokens + ( max_input_length - batch.max_input_length + max_decoder_input_length - batch.max_decoder_input_length ) * len(batch) start_index = end_index # Determine shapes for new past kv tensors first_past_kvs = batches[0].past_key_values _, num_heads, _, head_dim = first_past_kvs[0][0].shape padded_dec_t_shape = ( total_batch_size, num_heads, (max_decoder_input_length - 1), head_dim, ) padded_enc_t_shape = ( total_batch_size, num_heads, max_input_length, head_dim, ) # Iterate over attention layers for j in range(len(first_past_kvs)): past_key_values.append([]) # Decoder past for k in range(0, 2): # Initialize tensors padded_past_values = first_past_kvs[j][k].new_zeros(padded_dec_t_shape) past_key_values[j].append(padded_past_values) start_index = 0 for batch in batches: t = batch.past_key_values[j][k] # Clear reference to the original tensor batch.past_key_values[j][k] = None # Slicing end index for this batch end_index = start_index + len(batch) # We slice the past keys and values to remove the padding from previous batches past_seq_len = batch.max_decoder_input_length - 1 padded_past_values[start_index:end_index, :, -past_seq_len:, :] = t[ :, :, -past_seq_len:, : ] del t start_index = end_index # Encoder past for k in range(2, 4): # Initialize tensors padded_past_values = first_past_kvs[j][k].new_zeros(padded_enc_t_shape) past_key_values[j].append(padded_past_values) start_index = 0 for batch in batches: t = batch.past_key_values[j][k] # Clear reference to the original tensor batch.past_key_values[j][k] = None # Slicing end index for this batch end_index = start_index + len(batch) # We slice the past keys and values to remove the padding from previous batches padded_past_values[ start_index:end_index, :, -batch.max_input_length :, : ] = t[:, :, -batch.max_input_length :, :] del t start_index = end_index return cls( batch_id=batches[0].batch_id, requests=requests, requests_idx_mapping=requests_idx_mapping, input_ids=None, attention_mask=attention_mask, decoder_input_ids=decoder_input_ids, all_decoder_input_ids=all_decoder_input_ids, decoder_attention_mask=decoder_attention_mask, encoder_last_hidden_state=encoder_last_hidden_state, past_key_values=past_key_values, input_lengths=input_lengths, decoder_input_lengths=decoder_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, max_decoder_input_length=max_decoder_input_length, padding_right_offset=padding_right_offset, max_tokens=max_tokens, ) def __len__(self): return len(self.requests) class Seq2SeqLM(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, config_class=AutoConfig, tokenizer_class=AutoTokenizer, aliases=None, ): self.quantize = quantize self.process_group, rank, world_size = initialize_torch_distributed() - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -