import torch import inspect import re from io import BytesIO import base64 from PIL import Image import re from dataclasses import dataclass from opentelemetry import trace from transformers import ( AutoProcessor, AutoTokenizer, AutoModelForCausalLM, PreTrainedTokenizerBase, ProcessorMixin, ) from typing import Optional, Tuple, List, Type, Dict from text_generation_server.models import Model 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 import re IMAGES = re.compile(r"!\[[^\]]*\]\((.*?)\s*(\"(?:.*[^\"])\")?\s*\)") def split(string): parts = [] cursor = 0 for pattern in IMAGES.finditer(string): start = pattern.start() if start != cursor: parts.append(string[cursor:start]) parts.append(pattern.group(1)) cursor = pattern.end() if cursor != len(string): parts.append(string[cursor:]) return parts tracer = trace.get_tracer(__name__) @dataclass class IdeficsCausalLMBatch(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 pixel_values: Optional[torch.Tensor] image_hidden_states: Optional[torch.Tensor] image_attention_mask: Optional[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] # 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, processor: ProcessorMixin, # Hack dtype: torch.dtype, device: torch.device, ) -> "IdeficsCausalLMBatch": inputs = [] next_token_choosers = [] stopping_criterias = [] 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(r.inputs) 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 ) prompts = [] for inp in inputs: # Each input is encoded into a list, where each element of this input list is either a string or a URL prompts.append(split(inp)) # The processor replaces the call to tokenizer, and # a/ takes care of fetching images from the URL # b/ generate the correct input_ids, attention_mask, pixel_values, image_attention_mask to feed to the model tokenized_inputs = processor( prompts, return_tensors="pt", padding=True, truncation=True, max_length=max_truncation, add_end_of_utterance_token=False, # Already taken care of inside the prompts, so bypassing the processor's handling of this token ).to(device) for _ in pb.requests: input_len = tokenized_inputs["input_ids"].shape[1] prefix_offsets.append( input_len - 5 ) # To decode without potential fallbacks errors read_offsets.append( input_len ) # To decode without potential fallbacks errors input_lengths = tokenized_inputs["attention_mask"].sum(1) max_input_length = input_lengths.max() input_ids = tokenized_inputs["input_ids"] pixel_values = tokenized_inputs["pixel_values"] image_hidden_states = None # 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"] # Do the same for image_attention_mask image_attention_mask = input_ids.new_zeros( ( pb.size, max_input_length + padding_right_offset, tokenized_inputs["pixel_values"].size(1), ) ) image_attention_mask[:, :max_input_length, :] = tokenized_inputs[ "image_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 ) # It's input_ids but splitted into a tuple of tensors where each tensor is (seq_len, 1) size. It is then transformed into a list 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, pixel_values=pixel_values, image_hidden_states=image_hidden_states, image_attention_mask=image_attention_mask, 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, 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["IdeficsCausalLMBatch"]: # It deletes requests from the batch. For instance when client lost connection 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 = [] 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) 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, ] # Do the same for pixel_values and image_attention_mask pixel_values = self.pixel_values[keep_indices] self.image_attention_mask = self.image_attention_mask[ keep_indices, -(self.padding_right_offset + max_input_length) : ( self.image_attention_mask.shape[1] - self.padding_right_offset ) + new_padding_right_offset, :, ] if self.image_hidden_states is None: image_hidden_states = None else: image_hidden_states = self.image_hidden_states[keep_indices] # 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(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.pixel_values = pixel_values self.image_hidden_states = image_hidden_states 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.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["IdeficsCausalLMBatch"] ) -> "IdeficsCausalLMBatch": # It adds new requests to the batch # Used for padding total_batch_size = 0 max_input_length = 0 max_num_images = 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_num_images = max(max_num_images, batch.pixel_values.size(1)) 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 = [] max_tokens = 0 # Batch tensors input_ids = None attention_mask = None position_ids = None pixel_values = None image_hidden_states = None image_attention_mask = 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) 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) 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), ) curr_batch_max_num_images = batch.pixel_values.size(1) if pixel_values is None: pixel_values = batch.pixel_values.new_zeros( (total_batch_size, max_num_images, 3, 224, 224) ) pixel_values[ start_index:end_index, :curr_batch_max_num_images ] = batch.pixel_values if image_attention_mask is None: image_attention_mask = batch.image_attention_mask.new_zeros( ( total_batch_size, max_input_length + padding_right_offset, max_num_images, ) ) # 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, ] image_attention_mask[ start_index:end_index, left_offset:-padding_right_offset, :curr_batch_max_num_images, ] = batch.image_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, pixel_values=pixel_values, image_hidden_states=image_hidden_states, image_attention_mask=image_attention_mask, 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, 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 IdeficsCausalLM(Model): def __init__( self, model_id: str, revision: Optional[str] = None, quantize: Optional[str] = None, dtype: Optional[torch.dtype] = None, trust_remote_code: bool = False, ): from text_generation_server.models.custom_modeling.idefics_modeling import ( IdeficsForVisionText2Text, ) if torch.cuda.is_available(): device = torch.device("cuda") dtype = torch.bfloat16 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, ) self.processor = AutoProcessor.from_pretrained( model_id, revision=revision, padding_side="left", truncation_side="left", trust_remote_code=trust_remote_code, ) model = IdeficsForVisionText2Text.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: 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": ""}) super(IdeficsCausalLM, self).__init__( model=model, tokenizer=tokenizer, requires_padding=True, dtype=dtype, device=device, ) @property def batch_type(self) -> Type[IdeficsCausalLMBatch]: return IdeficsCausalLMBatch def forward( self, input_ids, attention_mask, position_ids, pixel_values, image_hidden_states, image_attention_mask, past_key_values: Optional = None, ) -> Tuple[torch.Tensor, List[Tuple[torch.Tensor, torch.Tensor]]]: # Model Forward kwargs = { "input_ids": input_ids, "attention_mask": attention_mask, "pixel_values": pixel_values, "image_hidden_states": image_hidden_states, "image_attention_mask": image_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) return outputs.logits, outputs.past_key_values, outputs.image_hidden_states @tracer.start_as_current_span("generate_token") def generate_token( self, batch: IdeficsCausalLMBatch ) -> Tuple[List[Generation], Optional[IdeficsCausalLMBatch]]: # slice the attention mask to the correct shape attention_mask = batch.attention_mask[:, : -batch.padding_right_offset] if batch.input_ids.size(1) == 1: # THIS is a hack: when calling idefics.generate, the first time, we need the whole image_attention_mask (size bs x max_seq_len x max_num_images), # but the subsequent times, we only need the last attention mask along the `max_seq_len` dimension # this is due to the nature IDEFICS: it's an encoder decoder, and so when decoding, only the currently generated # token need to attend to the encoder hidden states (i.e. the vision encoder) # Also see seq2seq_lm.Seq2SeqLM.generate_token which has roughly the same logic image_attention_mask = batch.image_attention_mask[ :, -(batch.padding_right_offset + 1) ].unsqueeze(1) else: image_attention_mask = batch.image_attention_mask[ :, : -batch.padding_right_offset ] logits, past, image_hidden_states = self.forward( input_ids=batch.input_ids, attention_mask=attention_mask, position_ids=batch.position_ids, pixel_values=batch.pixel_values, image_hidden_states=batch.image_hidden_states, image_attention_mask=image_attention_mask, past_key_values=batch.past_key_values, ) # Hardcoded remove image tokens logits[:, 32000:32001] = torch.finfo(logits.dtype).min # Results generations: List[Generation] = [] stopped = True # 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, ) # For each member of the batch for i, ( request, input_length, prefix_offset, read_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[-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 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.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: 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 batch.image_attention_mask[ :, -batch.padding_right_offset, : ] = batch.image_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 batch.image_hidden_states = image_hidden_states return generations, batch