mirror of
https://github.com/huggingface/text-generation-inference.git
synced 2025-09-11 12:24:53 +00:00
Update by abstracting away text model.
This commit is contained in:
Nicolas Patry
parent
b68fc4deb1
commit
b8be0d1ae7
@ -281,9 +281,8 @@ class LlamaMLP(nn.Module):
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class FlashLlamaLayer(nn.Module):
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def __init__(self, layer_id, config, weights):
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def __init__(self, prefix, config, weights):
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super().__init__()
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prefix = f"model.layers.{layer_id}"
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self.self_attn = FlashLlamaAttention(
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prefix=f"{prefix}.self_attn", config=config, weights=weights
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)
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@ -337,27 +336,36 @@ class FlashLlamaLayer(nn.Module):
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class FlashLlamaModel(torch.nn.Module):
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def __init__(self, config, weights):
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def __init__(self, prefix, config, weights):
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super().__init__()
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process_group = weights.process_group
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self.tp_rank = process_group.rank()
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self.tp_world_size = process_group.size()
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self.embed_tokens = TensorParallelEmbedding(
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prefix="model.embed_tokens", weights=weights
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prefix=(
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"model.embed_tokens" if not prefix else f"{prefix}.model.embed_tokens"
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),
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weights=weights,
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)
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self.layers = nn.ModuleList(
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[
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FlashLlamaLayer(
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layer_id,
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config,
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weights,
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prefix=(
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f"model.layers.{layer_id}"
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if not prefix
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else f"{prefix}.model.layers.{layer_id}"
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),
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config=config,
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weights=weights,
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)
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for layer_id in range(config.num_hidden_layers)
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]
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)
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self.norm = FastRMSNorm.load(
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prefix="model.norm", weights=weights, eps=config.rms_norm_eps
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prefix="model.norm" if not prefix else f"{prefix}.model.norm",
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weights=weights,
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eps=config.rms_norm_eps,
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)
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self.gradient_checkpointing = False
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@ -406,13 +414,13 @@ class FlashLlamaModel(torch.nn.Module):
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class FlashLlamaForCausalLM(torch.nn.Module):
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def __init__(self, config, weights):
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def __init__(self, prefix, config, weights):
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super().__init__()
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self.model = FlashLlamaModel(config, weights)
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self.model = FlashLlamaModel(prefix, config, weights)
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self.lm_head = SpeculativeHead.load(
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config,
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prefix="lm_head",
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prefix="lm_head" if not prefix else f"{prefix}.lm_head",
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weights=weights,
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)
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@ -426,6 +434,7 @@ class FlashLlamaForCausalLM(torch.nn.Module):
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slots: torch.Tensor,
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input_lengths: torch.Tensor,
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max_s: int,
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prefill_cache_indices: Optional[torch.Tensor] = None,
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lm_head_indices: Optional[torch.Tensor] = None,
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) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
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hidden_states = self.model(
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@ -113,7 +113,13 @@ def load_vision_model(prefix, config, weights):
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def load_text_model(prefix, config, weights):
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if config.model_type == "mistral":
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if config.model_type == "llama":
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from text_generation_server.models.custom_modeling.flash_llama_modeling import (
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FlashLlamaForCausalLM,
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)
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return FlashLlamaForCausalLM(prefix, config, weights)
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elif config.model_type == "mistral":
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from text_generation_server.models.custom_modeling.flash_mistral_modeling import (
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FlashMistralForCausalLM,
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)
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@ -124,22 +130,25 @@ def load_text_model(prefix, config, weights):
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class LlavaNextForConditionalGeneration(nn.Module):
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def __init__(self, config, weights):
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def __init__(self, prefix, config, weights):
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super().__init__()
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config.vision_config.quantize = config.quantize
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self.vision_tower = load_vision_model(
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prefix="vision_tower", config=config.vision_config, weights=weights
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)
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# self.vision_tower = load_vision_model(
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# prefix="vision_tower" if not prefix else f"{prefix}.vision_tower", config=config.vision_config, weights=weights
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# )
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self.multi_modal_projector = LlavaNextMultiModalProjector(config)
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# self.multi_modal_projector = LlavaNextMultiModalProjector(config)
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self.image_newline = weights.get_tensor("image_newline")
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self.vocab_size = config.text_config.vocab_size
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self.config = config
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config.text_config.quantize = config.quantize
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config.text_config.use_medusa = config.use_medusa
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self.language_model = load_text_model(
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prefix="language_model", config=config.text_config, weights=weights
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prefix="language_model" if not prefix else f"{prefix}.language_model",
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config=config.text_config,
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weights=weights,
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)
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self.pad_token_id = (
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config.pad_token_id if config.pad_token_id is not None else -1
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@ -257,168 +266,141 @@ class LlavaNextForConditionalGeneration(nn.Module):
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def forward(
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self,
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input_ids: torch.LongTensor = None,
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input_ids: torch.Tensor,
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position_ids: torch.Tensor,
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cu_seqlen_prefill: Optional[torch.Tensor],
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kv_cache: List[Tuple[torch.Tensor, torch.Tensor]],
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block_tables: torch.Tensor,
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slots: torch.Tensor,
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input_lengths: torch.Tensor,
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max_s: int,
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prefill_cache_indices: Optional[torch.Tensor],
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lm_head_indices: Optional[torch.Tensor] = None,
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pixel_values: torch.FloatTensor = None,
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image_sizes: Optional[torch.LongTensor] = None,
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attention_mask: Optional[torch.Tensor] = None,
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position_ids: Optional[torch.LongTensor] = None,
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past_key_values: Optional[List[torch.FloatTensor]] = None,
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vision_feature_layer: Optional[int] = None,
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vision_feature_select_strategy: Optional[str] = None,
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):
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vision_feature_layer = (
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vision_feature_layer
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if vision_feature_layer is not None
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else self.config.vision_feature_layer
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# vision_feature_layer = (
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# vision_feature_layer
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# if vision_feature_layer is not None
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# else self.config.vision_feature_layer
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# )
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# vision_feature_select_strategy = (
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# vision_feature_select_strategy
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# if vision_feature_select_strategy is not None
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# else self.config.vision_feature_select_strategy
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# )
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# if cu_seqlen_prefill is not None:
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# pass
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# # # 1. Extract the input embeddings
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# # inputs_embeds = self.get_input_embeddings()(input_ids)
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# # # 2. Merge text and images
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# # if pixel_values is not None and input_ids.shape[1] != 1:
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# # batch_size, num_patches, num_channels, height, width = (
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# # pixel_values.shape
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# # )
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# # reshaped_pixel_values = pixel_values.view(
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# # batch_size * num_patches, num_channels, height, width
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# # )
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# # image_features = self.vision_tower(
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# # reshaped_pixel_values, output_hidden_states=True
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# # )
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# # selected_image_feature = image_features.hidden_states[
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# # vision_feature_layer
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# # ]
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# # if vision_feature_select_strategy == "default":
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# # selected_image_feature = selected_image_feature[:, 1:]
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# # elif vision_feature_select_strategy == "full":
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# # selected_image_feature = selected_image_feature
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# # image_features = self.multi_modal_projector(selected_image_feature)
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# # # split up image_features for each of the individual images
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# # # hence we get a list of image_features, each of shape (5, num_patches, hidden_size)
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# # # if we assume each image has 5 image features (base image + 4 patches)
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# # split_sizes = [image.shape[0] for image in pixel_values]
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# # image_features = torch.split(image_features, split_sizes, dim=0)
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# # # NOTE we only support multimodal_patch_merge_type == "spatial_unpad"
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# # height = width = (
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# # self.config.vision_config.image_size
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# # // self.config.vision_config.patch_size
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# # )
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# # new_image_features = []
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# # for image_idx, image_feature in enumerate(image_features):
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# # if image_feature.shape[0] > 1:
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# # base_image_feature = image_feature[0]
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# # image_feature = image_feature[1:]
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# # if height * width != base_image_feature.shape[0]:
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# # raise ValueError(
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# # "The number of patches is not consistent with the image size."
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# # )
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# # num_patch_height, num_patch_width = get_anyres_image_grid_shape(
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# # image_sizes[image_idx],
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# # self.config.image_grid_pinpoints,
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# # self.config.vision_config.image_size,
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# # )
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# # image_feature = image_feature.view(
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# # num_patch_height, num_patch_width, height, width, -1
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# # )
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# # image_feature = image_feature.permute(
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# # 4, 0, 2, 1, 3
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# # ).contiguous()
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# # image_feature = image_feature.flatten(1, 2).flatten(2, 3)
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# # image_feature = unpad_image(
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# # image_feature, image_sizes[image_idx]
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# # )
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# # image_feature = torch.cat(
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# # (
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# # image_feature,
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# # self.image_newline[:, None, None].expand(
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# # *image_feature.shape[:-1], 1
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# # ),
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# # ),
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# # dim=-1,
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# # )
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# # image_feature = image_feature.flatten(1, 2).transpose(0, 1)
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# # image_feature = torch.cat(
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# # (base_image_feature, image_feature), dim=0
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# # )
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# # else:
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# # image_feature = image_feature[0]
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# # image_feature = torch.cat(
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# # (image_feature, self.image_newline[None]), dim=0
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# # )
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# # new_image_features.append(image_feature)
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# # image_features = torch.stack(new_image_features, dim=0)
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# # inputs_embeds, attention_mask, labels, position_ids = (
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# # self._merge_input_ids_with_image_features(
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# # image_features, inputs_embeds, input_ids, attention_mask, labels
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# # )
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# # )
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# # if labels is None:
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# # labels = torch.full_like(
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# # attention_mask, self.config.ignore_index
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# # ).to(torch.long)
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logits = self.language_model(
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input_ids,
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position_ids,
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cu_seqlen_prefill,
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kv_cache,
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block_tables,
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slots,
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input_lengths,
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max_s,
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prefill_cache_indices,
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lm_head_indices,
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)
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vision_feature_select_strategy = (
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vision_feature_select_strategy
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if vision_feature_select_strategy is not None
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else self.config.vision_feature_select_strategy
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)
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if inputs_embeds is None:
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# 1. Extract the input embeddings
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inputs_embeds = self.get_input_embeddings()(input_ids)
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# 2. Merge text and images
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if pixel_values is not None and input_ids.shape[1] != 1:
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batch_size, num_patches, num_channels, height, width = (
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pixel_values.shape
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)
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reshaped_pixel_values = pixel_values.view(
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batch_size * num_patches, num_channels, height, width
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)
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image_features = self.vision_tower(
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reshaped_pixel_values, output_hidden_states=True
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)
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selected_image_feature = image_features.hidden_states[
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vision_feature_layer
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]
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if vision_feature_select_strategy == "default":
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selected_image_feature = selected_image_feature[:, 1:]
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elif vision_feature_select_strategy == "full":
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selected_image_feature = selected_image_feature
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image_features = self.multi_modal_projector(selected_image_feature)
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# split up image_features for each of the individual images
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# hence we get a list of image_features, each of shape (5, num_patches, hidden_size)
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# if we assume each image has 5 image features (base image + 4 patches)
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split_sizes = [image.shape[0] for image in pixel_values]
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image_features = torch.split(image_features, split_sizes, dim=0)
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# NOTE we only support multimodal_patch_merge_type == "spatial_unpad"
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height = width = (
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self.config.vision_config.image_size
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// self.config.vision_config.patch_size
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)
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new_image_features = []
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for image_idx, image_feature in enumerate(image_features):
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if image_feature.shape[0] > 1:
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base_image_feature = image_feature[0]
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image_feature = image_feature[1:]
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if height * width != base_image_feature.shape[0]:
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raise ValueError(
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"The number of patches is not consistent with the image size."
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)
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num_patch_height, num_patch_width = get_anyres_image_grid_shape(
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image_sizes[image_idx],
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self.config.image_grid_pinpoints,
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self.config.vision_config.image_size,
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)
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image_feature = image_feature.view(
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num_patch_height, num_patch_width, height, width, -1
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)
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image_feature = image_feature.permute(
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4, 0, 2, 1, 3
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).contiguous()
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image_feature = image_feature.flatten(1, 2).flatten(2, 3)
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image_feature = unpad_image(
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image_feature, image_sizes[image_idx]
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)
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image_feature = torch.cat(
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(
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image_feature,
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self.image_newline[:, None, None].expand(
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*image_feature.shape[:-1], 1
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),
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),
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dim=-1,
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)
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image_feature = image_feature.flatten(1, 2).transpose(0, 1)
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image_feature = torch.cat(
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(base_image_feature, image_feature), dim=0
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)
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else:
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image_feature = image_feature[0]
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image_feature = torch.cat(
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(image_feature, self.image_newline[None]), dim=0
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)
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new_image_features.append(image_feature)
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image_features = torch.stack(new_image_features, dim=0)
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inputs_embeds, attention_mask, labels, position_ids = (
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self._merge_input_ids_with_image_features(
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image_features, inputs_embeds, input_ids, attention_mask, labels
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)
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)
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if labels is None:
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labels = torch.full_like(
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attention_mask, self.config.ignore_index
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).to(torch.long)
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# In case input_ids.shape[1] == 1 & pixel_values==None & past_key_values != None, we are in the case of
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# generation with cache
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elif (
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past_key_values is not None
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and pixel_values is not None
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and input_ids.shape[1] == 1
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):
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# Retrieve the first layer to inspect the logits and mask out the hidden states
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# that are set to 0
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first_layer_past_key_value = past_key_values[0][0][:, :, :, 0]
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# Sum all dimensions of head_dim (-2) to avoid random errors such as: https://github.com/huggingface/transformers/pull/28032#issuecomment-1863691941
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batch_index, non_attended_tokens = torch.where(
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first_layer_past_key_value.float().sum(-2) == 0
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)
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# Get the target length
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target_seqlen = first_layer_past_key_value.shape[-1] + 1
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extended_attention_mask = torch.ones(
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(attention_mask.shape[0], target_seqlen - attention_mask.shape[1]),
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dtype=attention_mask.dtype,
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device=attention_mask.device,
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)
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# Filter out only the tokens that can be un-attended, this can happen
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# if one uses Llava + Fused modules where the cache on the
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# first iteration is already big enough, or if one passes custom cache
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valid_indices = non_attended_tokens < extended_attention_mask.size(-1)
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new_batch_index = batch_index[valid_indices]
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new_non_attended_tokens = non_attended_tokens[valid_indices]
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# Zero-out the places where we don't need to attend
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extended_attention_mask[new_batch_index, new_non_attended_tokens] = 0
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attention_mask = torch.cat(
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(attention_mask, extended_attention_mask), dim=1
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)
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position_ids = torch.sum(attention_mask, dim=1).unsqueeze(-1) - 1
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outputs = self.language_model(
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attention_mask=attention_mask,
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position_ids=position_ids,
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past_key_values=past_key_values,
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inputs_embeds=inputs_embeds,
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)
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logits = outputs[0]
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return logits
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@ -1043,7 +1043,12 @@ class FlashCausalLM(Model):
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batch.input_ids = next_input_ids[accepted_ids.cumsum(dim=-1) - 1]
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batch.speculative_ids = speculative_ids
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batch.position_ids = next_position_ids + accepted_ids
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batch.input_lengths_tensor += accepted_ids
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try:
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batch.input_lengths_tensor += accepted_ids
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except Exception:
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import ipdb
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ipdb.set_trace()
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batch.slot_indices += accepted_ids
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if prefill and prefill_logprobs:
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@ -67,7 +67,8 @@ class FlashLlama(FlashCausalLM):
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if config.quantize in ["gptq", "awq"]:
|
||||
weights._set_gptq_params(model_id, revision)
|
||||
|
||||
model = FlashLlamaForCausalLM(config, weights)
|
||||
prefix = ""
|
||||
model = FlashLlamaForCausalLM(prefix, config, weights)
|
||||
torch.distributed.barrier(group=self.process_group)
|
||||
super(FlashLlama, self).__init__(
|
||||
model=model,
|
||||
|
||||
@ -6,7 +6,7 @@ import numpy as np
|
||||
|
||||
from dataclasses import dataclass
|
||||
from opentelemetry import trace
|
||||
from transformers import PreTrainedTokenizerBase, AutoTokenizer
|
||||
from transformers import PreTrainedTokenizerBase, AutoTokenizer, AutoConfig
|
||||
from transformers.models.llama import LlamaTokenizerFast
|
||||
from typing import Optional, Tuple, Type
|
||||
|
||||
@ -301,14 +301,15 @@ class FlashMistralBatch(FlashCausalLMBatch):
|
||||
class BaseFlashMistral(FlashCausalLM):
|
||||
def __init__(
|
||||
self,
|
||||
config_cls,
|
||||
model_cls,
|
||||
model_id: str,
|
||||
config_cls=AutoConfig,
|
||||
revision: Optional[str] = None,
|
||||
quantize: Optional[str] = None,
|
||||
use_medusa: Optional[str] = None,
|
||||
dtype: Optional[torch.dtype] = None,
|
||||
trust_remote_code: bool = False,
|
||||
tokenizer_class=AutoTokenizer,
|
||||
):
|
||||
self.process_group, rank, world_size = initialize_torch_distributed()
|
||||
if torch.cuda.is_available():
|
||||
@ -317,22 +318,13 @@ class BaseFlashMistral(FlashCausalLM):
|
||||
else:
|
||||
raise NotImplementedError("FlashMistral is only available on GPU")
|
||||
|
||||
try:
|
||||
tokenizer = LlamaTokenizerFast.from_pretrained(
|
||||
model_id,
|
||||
revision=revision,
|
||||
padding_side="left",
|
||||
truncation_side="left",
|
||||
trust_remote_code=trust_remote_code,
|
||||
)
|
||||
except Exception:
|
||||
tokenizer = AutoTokenizer.from_pretrained(
|
||||
model_id,
|
||||
revision=revision,
|
||||
padding_side="left",
|
||||
truncation_side="left",
|
||||
trust_remote_code=trust_remote_code,
|
||||
)
|
||||
tokenizer = tokenizer_class.from_pretrained(
|
||||
model_id,
|
||||
revision=revision,
|
||||
padding_side="left",
|
||||
truncation_side="left",
|
||||
trust_remote_code=trust_remote_code,
|
||||
)
|
||||
|
||||
config = config_cls.from_pretrained(
|
||||
model_id, revision=revision, trust_remote_code=trust_remote_code
|
||||
@ -341,10 +333,12 @@ class BaseFlashMistral(FlashCausalLM):
|
||||
config.use_medusa = use_medusa
|
||||
|
||||
# Set context windows
|
||||
if config.sliding_window is not None:
|
||||
if getattr(config, "sliding_window", None) is not None:
|
||||
set_sliding_window(
|
||||
config.sliding_window, math.ceil(config.sliding_window / BLOCK_SIZE)
|
||||
)
|
||||
else:
|
||||
config.sliding_window = None
|
||||
|
||||
torch.distributed.barrier(group=self.process_group)
|
||||
|
||||
@ -353,17 +347,19 @@ class BaseFlashMistral(FlashCausalLM):
|
||||
if config.quantize in ["gptq", "awq"]:
|
||||
weights._set_gptq_params(model_id, revision)
|
||||
|
||||
model = model_cls(config, weights)
|
||||
prefix = ""
|
||||
model = model_cls(prefix, config, weights)
|
||||
|
||||
self.cuda_graphs = {}
|
||||
|
||||
torch.distributed.barrier(group=self.process_group)
|
||||
super(BaseFlashMistral, self).__init__(
|
||||
num_layers, num_kv_heads, head_size = self.get_layer_config(model)
|
||||
super().__init__(
|
||||
model=model,
|
||||
tokenizer=tokenizer,
|
||||
num_layers=len(model.model.layers),
|
||||
num_kv_heads=model.model.num_key_value_heads,
|
||||
head_size=model.model.head_size,
|
||||
num_layers=num_layers,
|
||||
num_kv_heads=num_kv_heads,
|
||||
head_size=head_size,
|
||||
dtype=dtype,
|
||||
device=device,
|
||||
rank=rank,
|
||||
@ -371,6 +367,16 @@ class BaseFlashMistral(FlashCausalLM):
|
||||
sliding_window=config.sliding_window,
|
||||
)
|
||||
|
||||
def get_layer_config(self, model) -> Tuple[int, int, int]:
|
||||
return (
|
||||
len(model.model.layers),
|
||||
model.model.num_key_value_heads,
|
||||
model.model.head_size,
|
||||
)
|
||||
|
||||
def max_past(self) -> int:
|
||||
return self.model.max_past
|
||||
|
||||
@property
|
||||
def batch_type(self) -> Type[FlashMistralBatch]:
|
||||
return FlashMistralBatch
|
||||
@ -485,11 +491,11 @@ class BaseFlashMistral(FlashCausalLM):
|
||||
max_s = batch.max_seqlen
|
||||
lm_head_indices = batch.prefill_head_indices
|
||||
|
||||
if cu_seqlen_prefill is None and self.model.max_past is not None:
|
||||
if cu_seqlen_prefill is None and self.max_past() is not None:
|
||||
# In decode, not prefill, we're actually overwriting the KV-cache
|
||||
# in a circular buffer mode.
|
||||
# This makes sure the max_s for the decode pass is correct.
|
||||
max_s = min(self.model.max_past, max_s)
|
||||
max_s = min(self.max_past(), max_s)
|
||||
|
||||
bs = input_ids.shape[0]
|
||||
padded_bs = bs
|
||||
|
||||
@ -17,7 +17,7 @@ from transformers import LlamaTokenizerFast
|
||||
from text_generation_server.models.custom_modeling.idefics_modeling import (
|
||||
IdeficsForVisionText2Text,
|
||||
)
|
||||
from text_generation_server.models.vlm_causal_lm import VlmCausalLM
|
||||
from text_generation_server.models.idefics_causal_lm import IdeficsCausalLM
|
||||
from text_generation_server.utils import (
|
||||
initialize_torch_distributed,
|
||||
weight_files,
|
||||
@ -25,7 +25,7 @@ from text_generation_server.utils import (
|
||||
)
|
||||
|
||||
|
||||
class IDEFICSSharded(VlmCausalLM):
|
||||
class IDEFICSSharded(IdeficsCausalLM):
|
||||
def __init__(
|
||||
self,
|
||||
model_id: str,
|
||||
|
||||
1706
server/text_generation_server/models/idefics_causal_lm.py
Normal file
1706
server/text_generation_server/models/idefics_causal_lm.py
Normal file
File diff suppressed because it is too large
Load Diff
@ -1,24 +1,15 @@
|
||||
import torch
|
||||
import torch.distributed
|
||||
|
||||
from typing import List, Optional, Tuple
|
||||
from typing import Optional
|
||||
|
||||
from transformers import (
|
||||
AutoTokenizer,
|
||||
AutoConfig,
|
||||
AutoProcessor,
|
||||
)
|
||||
from text_generation_server.models.custom_modeling.llava_next import (
|
||||
LlavaNextForConditionalGeneration,
|
||||
)
|
||||
|
||||
# from transformers import AutoConfig, AutoTokenizer, AutoProcessor
|
||||
from text_generation_server.models.vlm_causal_lm import VlmCausalLM
|
||||
from text_generation_server.utils import (
|
||||
initialize_torch_distributed,
|
||||
weight_files,
|
||||
Weights,
|
||||
)
|
||||
|
||||
|
||||
class LlavaNext(VlmCausalLM):
|
||||
@ -31,59 +22,15 @@ class LlavaNext(VlmCausalLM):
|
||||
dtype: Optional[torch.dtype] = None,
|
||||
trust_remote_code: bool = False,
|
||||
):
|
||||
self.process_group, rank, world_size = initialize_torch_distributed()
|
||||
if torch.cuda.is_available():
|
||||
device = torch.device(f"cuda:{rank}")
|
||||
# 9b seems to work correctly enough in float16, but 80b seems
|
||||
# to be really saturating for f16.
|
||||
dtype = torch.float16 if dtype is None else dtype
|
||||
else:
|
||||
device = torch.device("cpu")
|
||||
dtype = torch.float32 if dtype is None else dtype
|
||||
self.device, self.dtype = device, dtype
|
||||
|
||||
config = AutoConfig.from_pretrained(
|
||||
model_id,
|
||||
revision=revision,
|
||||
trust_remote_code=trust_remote_code,
|
||||
)
|
||||
config.quantize = quantize
|
||||
config.use_medusa = use_medusa
|
||||
config.vision_config.quantize = quantize
|
||||
|
||||
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,
|
||||
model_id, revision=revision, trust_remote_code=trust_remote_code
|
||||
)
|
||||
super().__init__(
|
||||
model_cls=LlavaNextForConditionalGeneration,
|
||||
model_id=model_id,
|
||||
revision=revision,
|
||||
padding_side="left",
|
||||
truncation_side="left",
|
||||
quantize=quantize,
|
||||
use_medusa=use_medusa,
|
||||
dtype=dtype,
|
||||
trust_remote_code=trust_remote_code,
|
||||
)
|
||||
|
||||
torch.distributed.barrier(group=self.process_group)
|
||||
filenames = weight_files(model_id, revision=revision, extension=".safetensors")
|
||||
weights = Weights(
|
||||
filenames,
|
||||
device=device,
|
||||
dtype=dtype,
|
||||
process_group=self.process_group,
|
||||
)
|
||||
|
||||
model = LlavaNextForConditionalGeneration(config, weights)
|
||||
|
||||
torch.distributed.barrier(group=self.process_group)
|
||||
super(VlmCausalLM, self).__init__(
|
||||
model=model,
|
||||
tokenizer=tokenizer,
|
||||
requires_padding=True,
|
||||
dtype=dtype,
|
||||
device=device,
|
||||
rank=rank,
|
||||
world_size=world_size,
|
||||
)
|
||||
|
||||
@ -1,869 +1,50 @@
|
||||
import torch
|
||||
import time
|
||||
import re
|
||||
|
||||
from dataclasses import dataclass
|
||||
from opentelemetry import trace
|
||||
from transformers import (
|
||||
AutoProcessor,
|
||||
AutoTokenizer,
|
||||
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.models.flash_mistral import (
|
||||
BaseFlashMistral,
|
||||
FlashMistralBatch,
|
||||
)
|
||||
from text_generation_server.pb import generate_pb2
|
||||
from text_generation_server.utils import NextTokenChooser, StoppingCriteria, Sampling
|
||||
|
||||
import re
|
||||
tracer = trace.get_tracer(__name__)
|
||||
|
||||
IMAGES = re.compile(r"!\[[^\]]*\]\((.*?)\s*(\"(?:.*[^\"])\")?\s*\)")
|
||||
|
||||
|
||||
def split(string):
|
||||
def split(string) -> List[Dict[str, str]]:
|
||||
parts = []
|
||||
cursor = 0
|
||||
for pattern in IMAGES.finditer(string):
|
||||
start = pattern.start()
|
||||
if start != cursor:
|
||||
parts.append(string[cursor:start])
|
||||
parts.append({"type": "text", "content": string[cursor:start]})
|
||||
|
||||
parts.append(pattern.group(1))
|
||||
parts.append({"type": "image", "content": pattern.group(1)})
|
||||
cursor = pattern.end()
|
||||
|
||||
if cursor != len(string):
|
||||
parts.append(string[cursor:])
|
||||
parts.append({"type": "text", "content": string[cursor:]})
|
||||
|
||||
return parts
|
||||
|
||||
|
||||
tracer = trace.get_tracer(__name__)
|
||||
class VlmCausalLMBatch(FlashMistralBatch):
|
||||
pass
|
||||
|
||||
|
||||
@dataclass
|
||||
class VlmCausalLMBatch(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,
|
||||
) -> "VlmCausalLMBatch":
|
||||
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, tokenizer)
|
||||
)
|
||||
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
|
||||
)
|
||||
|
||||
# TODO Check impact on idefics
|
||||
# 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(
|
||||
inputs,
|
||||
return_tensors="pt",
|
||||
padding=True,
|
||||
truncation=True,
|
||||
max_length=max_truncation,
|
||||
# TODO Check impact on idefics
|
||||
# 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.get("pixel_values", None)
|
||||
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
|
||||
if pixel_values is None:
|
||||
image_attention_mask = None
|
||||
else:
|
||||
image_attention_mask = input_ids.new_zeros(
|
||||
(
|
||||
pb.size,
|
||||
max_input_length + padding_right_offset,
|
||||
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["VlmCausalLMBatch"]:
|
||||
# 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["VlmCausalLMBatch"]) -> "VlmCausalLMBatch":
|
||||
# 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 VlmCausalLM(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 (
|
||||
VlmForVisionText2Text,
|
||||
)
|
||||
|
||||
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 = VlmForVisionText2Text.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": "<unk>"})
|
||||
|
||||
super(VlmCausalLM, self).__init__(
|
||||
model=model,
|
||||
tokenizer=tokenizer,
|
||||
requires_padding=True,
|
||||
dtype=dtype,
|
||||
device=device,
|
||||
)
|
||||
|
||||
class VlmCausalLM(BaseFlashMistral):
|
||||
@property
|
||||
def batch_type(self) -> Type[VlmCausalLMBatch]:
|
||||
return VlmCausalLMBatch
|
||||
def batch_type(self) -> Type[FlashMistralBatch]:
|
||||
return FlashMistralBatch
|
||||
|
||||
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, speculative_logits = self.model.forward(**kwargs)
|
||||
def get_layer_config(self, model) -> Tuple[int, int, int]:
|
||||
return (
|
||||
outputs.logits,
|
||||
speculative_logits,
|
||||
outputs.past_key_values,
|
||||
outputs.image_hidden_states,
|
||||
len(model.language_model.model.layers),
|
||||
model.language_model.model.num_key_value_heads,
|
||||
model.language_model.model.head_size,
|
||||
)
|
||||
|
||||
@tracer.start_as_current_span("generate_token")
|
||||
def generate_token(
|
||||
self, batch: VlmCausalLMBatch
|
||||
) -> Tuple[List[Generation], Optional[VlmCausalLMBatch], Tuple[int, int]]:
|
||||
start = time.time_ns()
|
||||
# slice the attention mask to the correct shape
|
||||
attention_mask = batch.attention_mask[:, : -batch.padding_right_offset]
|
||||
if batch.image_attention_mask is None:
|
||||
image_attention_mask = None
|
||||
else:
|
||||
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, speculative_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
|
||||
|
||||
start_decode = time.time_ns()
|
||||
|
||||
# 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.next_token_choosers[i] = batch.next_token_choosers[i].advance_grammar(
|
||||
next_token_id_squeezed.item()
|
||||
)
|
||||
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:
|
||||
forward_ns = start_decode - start
|
||||
decode_ns = time.time_ns() - start_decode
|
||||
return generations, None, (forward_ns, decode_ns)
|
||||
|
||||
# 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
|
||||
|
||||
forward_ns = start_decode - start
|
||||
decode_ns = time.time_ns() - start_decode
|
||||
return generations, batch, (forward_ns, decode_ns)
|
||||
def max_past(self) -> Optional[int]:
|
||||
return getattr(self.model.language_model, "max_past", None)
|
||||
|
||||
@ -13,9 +13,10 @@ from typing import List, Optional
|
||||
from text_generation_server.cache import Cache
|
||||
from text_generation_server.interceptor import ExceptionInterceptor
|
||||
from text_generation_server.models import Model, get_model
|
||||
from text_generation_server.models.vlm_causal_lm import VlmCausalLMBatch
|
||||
from text_generation_server.pb import generate_pb2_grpc, generate_pb2
|
||||
from text_generation_server.tracing import UDSOpenTelemetryAioServerInterceptor
|
||||
from text_generation_server.models.vlm_causal_lm import VlmCausalLMBatch
|
||||
from text_generation_server.models.idefics_causal_lm import IdeficsCausalLMBatch
|
||||
|
||||
|
||||
class TextGenerationService(generate_pb2_grpc.TextGenerationServiceServicer):
|
||||
@ -78,9 +79,10 @@ class TextGenerationService(generate_pb2_grpc.TextGenerationServiceServicer):
|
||||
except ImportError:
|
||||
pass
|
||||
|
||||
if (
|
||||
self.model.batch_type == VlmCausalLMBatch
|
||||
): # Hack, i would rather use kwargs in the `from_pb` call
|
||||
if self.model.batch_type in {
|
||||
IdeficsCausalLMBatch,
|
||||
VlmCausalLMBatch,
|
||||
}: # Hack, i would rather use kwargs in the `from_pb` call
|
||||
batch = self.model.batch_type.from_pb(
|
||||
request.batch,
|
||||
self.model.tokenizer,
|
||||
@ -100,9 +102,10 @@ class TextGenerationService(generate_pb2_grpc.TextGenerationServiceServicer):
|
||||
|
||||
async def Prefill(self, request, context):
|
||||
start = time.time_ns()
|
||||
if (
|
||||
self.model.batch_type == VlmCausalLMBatch
|
||||
): # Hack, i would rather use kwargs in the `from_pb` call
|
||||
if self.model.batch_type in {
|
||||
IdeficsCausalLMBatch,
|
||||
VlmCausalLMBatch,
|
||||
}: # Hack, i would rather use kwargs in the `from_pb` call
|
||||
batch = self.model.batch_type.from_pb(
|
||||
request.batch,
|
||||
self.model.tokenizer,
|
||||
|
||||
Loading…
Reference in New Issue
Block a user