import math from typing import List, Optional import torch from opentelemetry import trace from transformers import AutoTokenizer, AutoProcessor import transformers.modeling_utils from text_generation_server.models.flash_causal_lm import FlashCausalLM from text_generation_server.models.vlm_causal_lm import VlmCausalLM, VlmCausalLMBatch from text_generation_server.utils import initialize_torch_distributed from text_generation_server.layers.attention import paged_attention, attention, Seqlen from text_generation_server.layers.attention.kv_cache import KVScales, KVCache from text_generation_server.models.globals import ATTENTION import torch.nn.functional as F tracer = trace.get_tracer(__name__) def tgi_flash_attention_forward( module, query_states: torch.Tensor, key_states: torch.Tensor, value_states: torch.Tensor, attention_mask: Optional[torch.Tensor], # This is a positional arg in Transformers kv_cache: List[KVCache], kv_head_mapping: torch.Tensor, slots: torch.Tensor, cu_seqlen_prefill: Optional[torch.Tensor], seqlen: Seqlen, block_tables: torch.Tensor, max_s: int, kv_scales: KVScales, softmax_scale: Optional[float] = None, sliding_window: Optional[int] = None, softcap: Optional[float] = None, **kwargs, # This is needed to "absorb" other args passed by Transformers modeling ): from loguru import logger logger.info("Using TGI Flash Attention") # from pdb import set_trace; set_trace() kv_cache = kv_cache[module.layer_idx] query_states = query_states.transpose(1, 2).squeeze(dim=0) key_states = key_states.transpose(1, 2).squeeze(dim=0) value_states = value_states.transpose(1, 2).squeeze(dim=0) # from pdb import set_trace; set_trace() # Take care of updating the cache in-place kv_cache.store(key=key_states, value=value_states, slots=slots, kv_scales=kv_scales) _, num_heads, head_dim = query_states.shape softmax_scale = 1 / math.sqrt(head_dim) if softmax_scale is None else softmax_scale sliding_window = -1 if sliding_window is None else sliding_window # if module.layer_idx == 0: # from pdb import set_trace; set_trace() if cu_seqlen_prefill is not None: attention_mask = None if attention_mask is None: attn_output = attention( query=query_states, key=key_states, value=value_states, kv_cache=kv_cache, kv_scales=kv_scales, seqlen=seqlen, block_tables=block_tables, softmax_scale=softmax_scale, window_size_left=sliding_window, softcap=softcap, ) else: from loguru import logger logger.info("uSING FLASH ATTENTION") lengths = cu_seqlen_prefill[1:] - cu_seqlen_prefill[:-1] max_length = max(lengths) attention_mask = attention_mask[:, :, :, :max_length] enable_gqa = query_states.shape[1] != key_states.shape[1] # Split tensors using vectorized split query_list = torch.split(query_states, lengths.tolist(), dim=0) key_list = torch.split(key_states, lengths.tolist(), dim=0) value_list = torch.split(value_states, lengths.tolist(), dim=0) padded_query = torch.nn.utils.rnn.pad_sequence(query_list, batch_first=True) padded_key = torch.nn.utils.rnn.pad_sequence(key_list, batch_first=True) padded_value = torch.nn.utils.rnn.pad_sequence(value_list, batch_first=True) padded_query = padded_query.transpose(1, 2).contiguous() padded_key = padded_key.transpose(1, 2).contiguous() padded_value = padded_value.transpose(1, 2).contiguous() # Compute attention attn_output = F.scaled_dot_product_attention( padded_query, padded_key, padded_value, attn_mask=attention_mask, scale=softmax_scale, enable_gqa=enable_gqa, ) attn_output = attn_output.transpose( 1, 2 ) # [batch_size, seq_len, num_heads, head_dim] max_seq_len = padded_query.size(2) seq_range = torch.arange(max_seq_len, device=padded_query.device).unsqueeze( 0 ) lengths_tensor = torch.tensor( lengths, device=padded_query.device ).unsqueeze(1) mask = seq_range < lengths_tensor # [batch, max_seq_len] attn_output = attn_output[mask] # [total_seq_len, num_heads, head_dim] else: attn_output = paged_attention( query_states, kv_cache, kv_head_mapping, softmax_scale, block_tables, seqlen, max_s, kv_scales=kv_scales, softcap=softcap, window_size_left=sliding_window, ) attn_output = attn_output.view(-1, num_heads * head_dim) return attn_output, None transformers.modeling_utils.ALL_ATTENTION_FUNCTIONS["tgi"] = tgi_flash_attention_forward transformers.models.siglip.modeling_siglip.SIGLIP_ATTENTION_CLASSES["tgi"] = ( transformers.models.siglip.modeling_siglip.SIGLIP_ATTENTION_CLASSES["sdpa"] ) # transformers.models.qwen2_vl.modeling_qwen2_vl.QWEN2_VL_ATTENTION_CLASSES["tgi"] = transformers.models.qwen2_vl.modeling_qwen2_vl.QWEN2_VL_VISION_ATTENTION_CLASSES["eager"] # transformers.models.qwen2_vl.modeling_qwen2_vl.QWEN2_VL_ATTENTION_CLASSES["tgi"] = transformers.models.qwen2_vl.modeling_qwen2_vl.QWEN2_VL_VISION_ATTENTION_CLASSES["eager"] transformers.models.qwen2_vl.modeling_qwen2_vl.QWEN2_VL_ATTENTION_CLASSES["tgi"] = ( tgi_flash_attention_forward ) transformers.models.qwen2_vl.modeling_qwen2_vl.QWEN2_VL_VISION_ATTENTION_CLASSES[ "tgi" ] = transformers.models.qwen2_vl.modeling_qwen2_vl.QWEN2_VL_VISION_ATTENTION_CLASSES[ "eager" ] # The base TP plan of these models has replicated q/k/v. This means that each process will see the full states, # hence we should not divide the number of heads by the world size. This is a known waste of VRAM (the cache # will be fully replicated on each process) and GPU communication (additional all-gather operations), however due # to internal constraints it was not (yet?) possible to circumvent REPLICATED_ATTENTION_MODELS = [ "olmo2", "phi3", ] class TransformersFlashVlmCausalLM(VlmCausalLM): def __init__( self, model_id: str, 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, processor_class=AutoProcessor, processor_kwargs=None, kv_cache_dtype: Optional[torch.dtype] = None, batch_class=VlmCausalLMBatch, ): # # from pdb import set_trace; set_trace() self.batch_class = VlmCausalLMBatch self.quantize = quantize self.process_group, rank, world_size = initialize_torch_distributed() if speculator: raise RuntimeError("Speculator decoding is not enabled for AutoModel") if torch.cuda.is_available(): device = torch.device(f"cuda:{rank}") dtype = default_dtype if dtype is None else dtype elif hasattr(torch, "xpu") and torch.xpu.is_available(): device = torch.device("xpu") dtype = default_dtype if dtype is None else dtype else: raise ValueError( "Flash `Transformers` modeling backend is not available on cpu." ) tokenizer = tokenizer_class.from_pretrained( model_id, revision=revision, padding_side="left", truncation_side="left", trust_remote_code=trust_remote_code, ) if processor_kwargs is None: processor_kwargs = {} # processor_kwargs={"size": {"longest_edge": 448, "shortest_edge": 378}} self.processor = processor_class.from_pretrained( model_id, revision=revision, trust_remote_code=trust_remote_code, **processor_kwargs, ) from transformers import Qwen2VLForConditionalGeneration model = Qwen2VLForConditionalGeneration.from_pretrained( model_id, revision=revision, torch_dtype=dtype, load_in_8bit=quantize == "bitsandbytes", trust_remote_code=trust_remote_code, attn_implementation="tgi", device_map=device if world_size == 1 else None, tp_plan="auto" if world_size > 1 else None, ) torch.distributed.barrier(group=self.process_group) self.config = model.config config = model.config # VLM models define the config we care about in their text_config text_config = getattr(model.config, "text_config", None) if text_config is not None: config = text_config 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 and isinstance( model.config.eos_token_id, int ): 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.num_layers = config.num_hidden_layers self.num_heads = config.num_attention_heads self.num_kv_heads = config.num_key_value_heads # Some models use GQA and different sizes for o_proj # and q_proj, that allows for that. if hasattr(config, "head_dim"): self.head_size = config.head_dim else: self.head_size = config.hidden_size // config.num_attention_heads # Skip it for models in the exception list if config.model_type not in REPLICATED_ATTENTION_MODELS: self.num_heads = self.num_heads // self.process_group.size() self.num_kv_heads = ( self.num_kv_heads // self.process_group.size() if self.num_kv_heads > 1 else self.num_kv_heads ) self.cuda_graphs = {} self.kv_cache = [] self.kv_cache_dtype = dtype if kv_cache_dtype is None else kv_cache_dtype if ATTENTION == "flashinfer": from text_generation_server.layers.attention.flashinfer import ( create_prefill_state, create_decode_state, create_prefill_with_paged_kv_state, ) self.prefill_state = create_prefill_state(device=device) self.prefill_with_paged_kv_state = create_prefill_with_paged_kv_state( device=device ) self.decode_state = create_decode_state( device=device, num_heads=self.num_heads, num_kv_heads=self.num_kv_heads, ) self.num_groups = self.num_heads // self.num_kv_heads # Those will never change and will be used in the forwards self.kv_head_mapping = torch.arange( 0, self.num_kv_heads, dtype=torch.int32, device=device ).repeat_interleave(self.num_groups) # This means no scale self.kv_scales = KVScales( torch.tensor(1.0, device=device), torch.tensor(1.0, device=device), ) # Skip FlashCausalLM init. super(FlashCausalLM, self).__init__( model_id=model_id, model=model, tokenizer=tokenizer, requires_padding=False, dtype=dtype, device=device, rank=rank, world_size=world_size, ) # Monkey patch of `self.model.forward` to match `FlashCausalLM`. It avoids duplicating a lot of code # We first copy the original model.forward because we still need it in the monkey patch self.model.original_forward = self.model.forward self.model.forward = self._model_forward self.model.get_position_ids = self.get_position_ids torch.distributed.barrier(group=self.process_group) def get_position_ids(self, input_ids: torch.Tensor, image_grid_thw: torch.Tensor): if image_grid_thw is None: return ( torch.arange(input_ids.shape[0], device=input_ids.device) .unsqueeze(1) .repeat(1, 3) ) spatial_merge_size = self.config.vision_config.spatial_merge_size vision_start_token_id = self.config.vision_start_token_id vision_end_token_id = self.config.vision_end_token_id device = input_ids.device dtype = input_ids.dtype input_ids_len = input_ids.shape[0] vision_starts = torch.where(input_ids == vision_start_token_id)[0] vision_ends = torch.where(input_ids == vision_end_token_id)[0] vision_segments = torch.stack((vision_starts, vision_ends), dim=1) prev_vision_end = torch.cat( [torch.zeros(1, device=vision_ends.device, dtype=dtype), vision_ends[:-1]] ) text_lengths_between_vision = vision_segments[:, 0] - prev_vision_end + 1 vision_widths_max = torch.cat( [ torch.zeros(1, device=image_grid_thw.device, dtype=dtype), image_grid_thw[:-1, 2] // spatial_merge_size, ] ) vision_segment_lengths = vision_widths_max + text_lengths_between_vision vision_segment_lengths = vision_segment_lengths.cumsum(dim=0) text_segment_lengths = vision_segment_lengths - text_lengths_between_vision # create position ids for each vision segment based on the image grid llm_pos_ids_list = [] for i, _ in enumerate(vision_segments): t, h, w = ( image_grid_thw[i][0], image_grid_thw[i][1] // spatial_merge_size, image_grid_thw[i][2] // spatial_merge_size, ) t_indices = torch.arange(t, device=device).repeat_interleave(h * w) h_indices = torch.arange(h, device=device).repeat_interleave(w).repeat(t) w_indices = torch.arange(w, device=device).repeat(t * h) image_position_ids = torch.stack([t_indices, h_indices, w_indices], dim=0) # offset by the position of the last vision segment im = image_position_ids + vision_segment_lengths[i] llm_pos_ids_list.append(im) # create position ids for each text segment text_ranges = [ torch.arange(seq_len, device=device).view(1, -1).expand(3, -1) + text_segment_lengths[i] for i, seq_len in enumerate(text_lengths_between_vision) ] full_llm_pos_ids_list = [ item for sublist in zip(text_ranges, llm_pos_ids_list) for item in sublist ] # import ipdb # ipdb.set_trace() max_s = full_llm_pos_ids_list[-1].max() + 1 final_text_len = input_ids_len - vision_ends[-1] if final_text_len > 0: m = torch.arange(final_text_len, device=device).view(1, -1).expand(3, -1) full_llm_pos_ids_list.append(m + max_s) position_ids = ( torch.cat(full_llm_pos_ids_list, dim=1).reshape(3, -1).transpose(0, 1) ) return position_ids @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, batch_class: Optional[type] = VlmCausalLMBatch, ): return cls( model_id=model_id, revision=revision, quantize=quantize, speculator=speculator, dtype=dtype, trust_remote_code=trust_remote_code, batch_class=batch_class, ) def _model_forward( self, input_ids: torch.Tensor, position_ids: torch.Tensor, cu_seqlen_prefill: Optional[torch.Tensor], kv_cache: List[KVCache], block_tables: torch.Tensor, slots: torch.Tensor, seqlen: Seqlen, max_s: int, lm_head_indices: Optional[torch.Tensor], prefill_cache_indices=None, # not used, but passed to match original signature adapter_data=None, # not supported, but passed to match original signature pixel_values: torch.FloatTensor = None, image_grid_thw: Optional[torch.LongTensor] = None, pixel_attention_mask=None, image_sizes: Optional[torch.LongTensor] = None, ): # from pdb import set_trace; set_trace() # A value of `None` (i.e. no logit slicing) translates to `0` in Transformers logits_to_keep = lm_head_indices if lm_head_indices is not None else 0 # This is equivalent to `self.model.forward`, see the monkey patch in __init__ logits = ( self.model.original_forward( input_ids=input_ids.unsqueeze(0), # expand dim to fit Transformers position_ids=position_ids.transpose(0, 1).unsqueeze( 1 ), # expand dim to fit Transformers past_key_values=None, # we use self.kv_cache instead of transformers cache object use_cache=False, # we use self.kv_cache instead of transformers cache object logits_to_keep=logits_to_keep, return_dict=True, cu_seqlen_prefill=cu_seqlen_prefill, kv_cache=kv_cache, block_tables=block_tables, slots=slots, seqlen=seqlen, max_s=max_s, kv_head_mapping=self.kv_head_mapping, kv_scales=self.kv_scales, pixel_values=pixel_values, pixel_attention_mask=pixel_attention_mask, image_sizes=image_sizes, image_grid_thw=image_grid_thw, ) .logits.squeeze(dim=0)[lm_head_indices] .unsqueeze(0) ) # from pdb import set_trace; set_trace() return logits, None