diff --git a/server/text_generation_server/models/transformers_flash_causal_lm.py b/server/text_generation_server/models/transformers_flash_causal_lm.py index 77659dd0..461bbc2d 100644 --- a/server/text_generation_server/models/transformers_flash_causal_lm.py +++ b/server/text_generation_server/models/transformers_flash_causal_lm.py @@ -3,20 +3,157 @@ from typing import List, Optional import torch from opentelemetry import trace -from transformers import AutoTokenizer, AutoModelForCausalLM +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 - +from text_generation_server.models.globals import ATTENTION, BLOCK_SIZE +import torch.nn.functional as F +import numpy as np tracer = trace.get_tracer(__name__) +# 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", +] + +def cdiv(a: int, b: int) -> int: + """Ceiling division.""" + return -(a // -b) + + +# Adapted from: https://github.com/vllm-project/vllm/blob/e1a2c699dda82199e88e433c144eae66f3b31878/vllm/v1/attention/backends/flash_attn.py +def make_local_attention_virtual_batches( + attn_chunk_size: int, + query_start_loc_np: np.ndarray, + seq_lens_np: np.ndarray, + block_table: torch.Tensor, + page_size: int = 0, +) -> tuple[np.ndarray, np.ndarray, np.ndarray, torch.Tensor]: + q_seqlens = query_start_loc_np[1:] - query_start_loc_np[:-1] + actual_batch_size = seq_lens_np.shape[0] + # Handle if we are starting in the middle of a local attention block, + # we assume q_seqlens > 0 (for all elements), for each batch idx we compute + # the number of tokens that are not in the first local attention block and + # then we can simply use a cdiv for the rest. + # For example if we have: + # attn_chunk_size = 4 + # q_seqlens = [4, 10, 5] + # k_seqlens = [6, 17, 9] + # Then we would get: + # new_tokens_in_first_block = [2, 1, 4] + # local_blocks = [2, 4, 2] + q_tokens_in_first_block = np.minimum( + attn_chunk_size - ((seq_lens_np - q_seqlens) % attn_chunk_size), q_seqlens + ).astype(np.int32) + tokens_in_last_block = attn_chunk_size + (seq_lens_np % -attn_chunk_size) + local_blocks = 1 + cdiv(q_seqlens - q_tokens_in_first_block, attn_chunk_size) + + # Once we know the number of local blocks we can compute the request spans + # for each batch idx, we can figure out the number of "virtual" requests we + # have to make, + # For the above example we would get: + # seqlens_q_local = [2, 2, 1, 4, 4, 1, 4, 1] + # + # First Get batched arange. (E.g., [2, 4, 2] -> [0, 1, 0, 1, 2, 3, 0, 1]) + # (TODO: max a utility to share this code with _prepare_inputs) + # arange step 1. [2, 4, 2] -> [2, 6, 8] + cu_num_blocks = np.cumsum(local_blocks) + virtual_batches = cu_num_blocks[-1] + # arange step 2. [2, 6, 8] -> [0, 0, 2, 2, 2, 2, 6, 6] + block_offsets = np.repeat(cu_num_blocks - local_blocks, local_blocks) + # arange step 3. [0, 1, 0, 1, 2, 3, 0, 1] + arange = np.arange(virtual_batches, dtype=np.int32) - block_offsets + # also compute reverse arange (i.e. [1, 0, 3, 2, 1, 0, 1, 0]) + rarange = np.repeat(local_blocks, local_blocks) - arange - 1 + # Then we can compute the seqlens_q_local, handling the fact that the + # first and last blocks could be partial + seqlens_q_local = np.repeat(q_seqlens - q_tokens_in_first_block, local_blocks) + # set the first block since this may be a partial block + seqlens_q_local[arange == 0] = q_tokens_in_first_block + # set the remaining blocks + seqlens_q_local[arange > 0] = np.minimum( + seqlens_q_local - attn_chunk_size * (arange - 1), attn_chunk_size + )[arange > 0] + + # convert from q_seqlens to cu_seqlens_q + cu_seqlens_q_local = np.pad(np.cumsum(seqlens_q_local), (1, 0)).astype(np.int32) + + # compute the seqlens_k_local, + # basically a full local attention block for all but the last block in each + # batch + # For our example this will be: + # seqlens_k_local = [4, 2, 4, 4, 4, 1, 4, 1] + seqlens_k_local = np.full(cu_num_blocks[-1], attn_chunk_size, dtype=np.int32) + seqlens_k_local[cu_num_blocks - 1] = tokens_in_last_block + + if ATTENTION == "flashdecoding": + k_seqstarts_absolute = np.repeat(seq_lens_np, local_blocks) - ( + rarange * attn_chunk_size + np.repeat(tokens_in_last_block, local_blocks) + ) + + # For the example the local attention blocks start at: + # _b0_ _____b1_____ _b2_ + # k_seqstarts_absolute = [0, 4, 4, 8, 12, 16, 4, 8] + block_starts = k_seqstarts_absolute // page_size + assert attn_chunk_size % page_size == 0, ( + f"attn_chunk_size {attn_chunk_size} is not " + f"divisible by page_size {page_size}" + ) + pages_per_local_batch = attn_chunk_size // page_size + + # Create a block_table for the local attention blocks + # For out example if we have a block-table like (assuming page_size=2): + # block_table = [ + # [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9], < batch 0 + # [10, 11, 12, 13, 14, 15, 16, 17, 18, 19], < batch 1 + # [20, 21, 22, 23, 24, 25, 26, 27, 28, 29], < batch 2 + # ] + # Then for the local batches we would want a block-table like + # block_table_local = [ + # [ 0, 1 ], < local-batch 0, (batch 0, starting from k[0]) + # [ 2, 3 ], < local-batch 1, (batch 0, starting from k[4]) + # [ 12, 13 ], < local-batch 2, (batch 1, starting from k[4]) + # [ 14, 15 ], < local-batch 3, (batch 1, starting from k[8]) + # [ 16, 17 ], < local-batch 4, (batch 1, starting from k[12]) + # [ 18, 19 ], < local-batch 5, (batch 1, starting from k[16]) + # [ 22, 23 ], < local-batch 6, (batch 2, starting from k[4]) + # [ 24, 25 ], < local-batch 7, (batch 2, starting from k[8]) + # ] + block_indices = np.broadcast_to( + np.arange(pages_per_local_batch, dtype=np.int32), + (virtual_batches, pages_per_local_batch), + ) + np.expand_dims(block_starts, axis=1) + block_indices = block_indices.flatten().clip(max=block_table.shape[1] - 1) + batch_indices = np.repeat( + np.arange(actual_batch_size, dtype=np.int32), + local_blocks * pages_per_local_batch, + ) + block_table_local = block_table[batch_indices, block_indices].view( + virtual_batches, -1 + ) + else: + block_table_local = block_table + return seqlens_q_local, cu_seqlens_q_local, seqlens_k_local, block_table_local + + +# # Qwen2VL +# 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" +# ] def tgi_flash_attention_forward( module, query_states: torch.Tensor, @@ -34,8 +171,14 @@ def tgi_flash_attention_forward( softmax_scale: Optional[float] = None, sliding_window: Optional[int] = None, softcap: Optional[float] = None, + use_sdpa: Optional[bool] = False, + local_seqlen: Optional[Seqlen] = None, + local_block_tables: Optional[torch.Tensor] = None, **kwargs, # This is needed to "absorb" other args passed by Transformers modeling ): + if hasattr(module, "use_rope") and module.use_rope: + seqlen = local_seqlen + block_tables = local_block_tables kv_cache = kv_cache[module.layer_idx] query_states = query_states.transpose(1, 2).squeeze(dim=0) @@ -50,18 +193,60 @@ def tgi_flash_attention_forward( sliding_window = -1 if sliding_window is None else sliding_window if cu_seqlen_prefill is not 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, - ) + if not use_sdpa: + 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: + 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, @@ -83,20 +268,16 @@ def tgi_flash_attention_forward( transformers.modeling_utils.ALL_ATTENTION_FUNCTIONS["tgi"] = tgi_flash_attention_forward -# 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", -] + +# TODO: implement +# tgi_cross_attention_forward -class TransformersFlashCausalLM(FlashCausalLM): +class TransformersFlashVlmCausalLM(VlmCausalLM): def __init__( self, model_id: str, + model_class, revision: Optional[str] = None, quantize: Optional[str] = None, speculator: Optional[str] = None, @@ -104,10 +285,15 @@ class TransformersFlashCausalLM(FlashCausalLM): 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, ): + self.batch_class = batch_class self.quantize = quantize self.process_group, rank, world_size = initialize_torch_distributed() + self.dtype = dtype if speculator: raise RuntimeError("Speculator decoding is not enabled for AutoModel") @@ -131,18 +317,40 @@ class TransformersFlashCausalLM(FlashCausalLM): trust_remote_code=trust_remote_code, ) - model = AutoModelForCausalLM.from_pretrained( + if processor_kwargs is None: + processor_kwargs = {} + + self.processor = processor_class.from_pretrained( + model_id, + revision=revision, + trust_remote_code=trust_remote_code, + **processor_kwargs, + ) + + attn_implementation = { + "text_config": "tgi", + "vision_config": "sdpa", + } + + model = model_class.from_pretrained( model_id, revision=revision, torch_dtype=dtype, load_in_8bit=quantize == "bitsandbytes", trust_remote_code=trust_remote_code, - attn_implementation="tgi", + attn_implementation=attn_implementation, 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: @@ -156,20 +364,18 @@ class TransformersFlashCausalLM(FlashCausalLM): else: tokenizer.add_special_tokens({"pad_token": "[PAD]"}) - self.num_layers = model.config.num_hidden_layers - self.num_heads = model.config.num_attention_heads - self.num_kv_heads = model.config.num_key_value_heads + 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(model.config, "head_dim"): - self.head_size = model.config.head_dim + if hasattr(config, "head_dim"): + self.head_size = config.head_dim else: - self.head_size = ( - model.config.hidden_size // model.config.num_attention_heads - ) + self.head_size = config.hidden_size // config.num_attention_heads # Skip it for models in the exception list - if model.config.model_type not in REPLICATED_ATTENTION_MODELS: + 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() @@ -227,26 +433,47 @@ class TransformersFlashCausalLM(FlashCausalLM): # 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, image_grid_thw, position_ids): + return position_ids + + def pre_process_inputs(self, **kwargs): + input_ids = kwargs["input_ids"] + position_ids = kwargs["position_ids"] + return { + "input_ids": input_ids.unsqueeze(0), + "position_ids": position_ids.unsqueeze(0), + } + + def post_process_outputs(self, logits, lm_head_indices): + return logits.squeeze(dim=0) + @classmethod def fallback( cls, model_id: str, + model_class, 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, + processor_kwargs: Optional[dict] = None, ): return cls( model_id=model_id, + model_class=model_class, revision=revision, quantize=quantize, speculator=speculator, dtype=dtype, trust_remote_code=trust_remote_code, + batch_class=batch_class, + processor_kwargs=processor_kwargs, ) def _model_forward( @@ -262,14 +489,33 @@ class TransformersFlashCausalLM(FlashCausalLM): 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, ): # 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 + inputs = self.pre_process_inputs( + input_ids=input_ids, + position_ids=position_ids, + cu_seqlen_prefill=cu_seqlen_prefill, + seqlen=seqlen, + block_tables=block_tables, + ) + + if cu_seqlen_prefill is not None: + from loguru import logger + + logger.info( + f"input_ids: {input_ids.shape}, position_ids:{inputs.get('local_seqlen', None)}" + ) + # 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.unsqueeze(0), # expand dim to fit Transformers + input_ids=inputs["input_ids"], + position_ids=inputs["position_ids"], 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, @@ -282,6 +528,227 @@ class TransformersFlashCausalLM(FlashCausalLM): max_s=max_s, kv_head_mapping=self.kv_head_mapping, kv_scales=self.kv_scales, - ).logits.squeeze(dim=0) + pixel_values=pixel_values, + pixel_attention_mask=pixel_attention_mask, + image_sizes=image_sizes, + image_grid_thw=image_grid_thw, + attention_mask=inputs.get("attention_mask", None), + use_sdpa=inputs.get("use_sdpa", False), + cache_position=inputs.get("cache_position", None), + local_seqlen=inputs.get("local_seqlen", None), + local_block_tables=inputs.get("local_block_tables", None), + ).logits + + logits = self.post_process_outputs(logits, lm_head_indices) return logits, None + + +class TransformersQwen2VlmCausalLM(TransformersFlashVlmCausalLM): + 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 + + def post_process_outputs(self, logits, lm_head_indices): + return logits.squeeze(dim=0)[lm_head_indices].unsqueeze(0) + + def pre_process_inputs(self, **kwargs): + input_ids = kwargs["input_ids"] + position_ids = kwargs["position_ids"] + + input_ids = input_ids.unsqueeze(0) + position_ids = position_ids.transpose(0, 1).unsqueeze(1) + return {"input_ids": input_ids, "position_ids": position_ids} + + +class TransformersGemma3VlmCausalLM(TransformersFlashVlmCausalLM): + def get_attention_mask(self, input_ids, cu_seqlen_prefill): + device = input_ids.device + dtype = self.dtype + min_dtype = torch.finfo(dtype).min + + lengths = (cu_seqlen_prefill[1:] - cu_seqlen_prefill[:-1]).tolist() + batch_size = len(lengths) + + sequence_length = max(lengths) + target_length = sequence_length + # Create the padding mask from the computed lengths. + # pad_mask: [batch, sequence_length] where True indicates valid tokens. + seq_range = torch.arange(sequence_length, device=device).unsqueeze(0) + lengths_tensor = torch.tensor(lengths, device=device).unsqueeze(1) + pad_mask = seq_range < lengths_tensor # shape: [batch, sequence_length] + + # Build the base causal mask (for non-image tokens): + causal_mask = torch.tril( + torch.ones( + (sequence_length, sequence_length), dtype=torch.bool, device=device + ) + ) + base_mask = pad_mask.unsqueeze(2) & pad_mask.unsqueeze( + 1 + ) # [batch, sequence_length, sequence_length] + base_mask = base_mask & causal_mask.unsqueeze(0) # apply causal constraint + + image_token_mask = (input_ids == self.config.image_token_index).to( + input_ids.device + ) + + image_token_mask = torch.nn.utils.rnn.pad_sequence( + torch.split(image_token_mask, lengths), batch_first=True, padding_value=0 + ) + bidirectional_mask = image_token_mask.unsqueeze(2) & image_token_mask.unsqueeze( + 1 + ) + + # Combine the causal base mask and the bidirectional mask. + combined_mask = torch.logical_or( + base_mask.unsqueeze(1), bidirectional_mask.unsqueeze(1) + ).to(device) + # combined_mask now has shape [batch, 1, sequence_length, sequence_length] + + full_attention_mask = torch.zeros( + (batch_size, 1, sequence_length, target_length), + device=device, + dtype=torch.bool, + ) + full_attention_mask[:, :, :, :sequence_length] = combined_mask + + final_attention_mask = torch.where(full_attention_mask, 0, min_dtype).to(device) + + return final_attention_mask + + def pre_process_inputs(self, **kwargs): + input_ids = kwargs["input_ids"] + position_ids = kwargs["position_ids"] + cu_seqlen_prefill = kwargs["cu_seqlen_prefill"] + + inputs = { + "input_ids": input_ids.unsqueeze(0), + "position_ids": position_ids.unsqueeze(0), + } + + if cu_seqlen_prefill is not None: + attention_mask = self.get_attention_mask( + input_ids.squeeze(0), cu_seqlen_prefill + ) + inputs["attention_mask"] = attention_mask + inputs["use_sdpa"] = True + + return inputs + + +class TransformersLlama4VlmCausalLM(TransformersFlashVlmCausalLM): + def pre_process_inputs(self, **kwargs): + input_ids = kwargs["input_ids"] + position_ids = kwargs["position_ids"] + seqlen = kwargs["seqlen"] + block_tables = kwargs["block_tables"] + + inputs = super().pre_process_inputs(**kwargs) + inputs["cache_position"] = position_ids + inputs["attention_mask"] = torch.zeros((1, 1, 1, 1), device=input_ids.device) + # from loguru import logger + + # logger.info(f"input_ids: {input_ids.shape}, position_ids: {position_ids.shape}") + cu_seqlen_k = seqlen.cu_seqlen_k + cu_seqlen_q = seqlen.cu_seqlen_q + seq_lens_np = cu_seqlen_k[1:] - cu_seqlen_k[:-1] + ( + seqlens_q_local_np, + virt_q_cu_seqlens_np, + virt_k_seqlens_np, + virt_block_table, + ) = make_local_attention_virtual_batches( + self.model.config.text_config.attention_chunk_size, + cu_seqlen_q.cpu().numpy(), + seq_lens_np.cpu().numpy(), + block_tables, + BLOCK_SIZE, + ) + local_seqlen = Seqlen( + input_lengths=torch.from_numpy(virt_k_seqlens_np).to( + input_ids.device, non_blocking=True + ), + cache_lengths=torch.zeros(virt_k_seqlens_np.shape).to( + input_ids.device, non_blocking=True + ), + cu_seqlen_q=torch.from_numpy(virt_q_cu_seqlens_np).to( + input_ids.device, non_blocking=True + ), + max_q=int(seqlens_q_local_np.max()), + max_k=int(virt_k_seqlens_np.max()), + ) + + inputs["local_seqlen"] = local_seqlen + inputs["local_block_tables"] = virt_block_table + + return inputs