[TRTLLM-6780][fix] Add multimodal data to dummy requests during memory profiling

tensorrt_llm/_torch/models/modeling_qwen2vl.py

@@ -2,8 +2,10 @@
 import os
 from typing import Any, Dict, List, Optional, Tuple, Union
 
+import numpy as np
 import torch
 import torch.nn as nn
+from PIL import Image
 from torch.nn import functional as F
 from transformers import (AutoProcessor, AutoTokenizer, PretrainedConfig,
                           PreTrainedModel)
@@ -25,9 +27,11 @@
 from tensorrt_llm.inputs.multimodal import MultimodalParams
 
 from ..._utils import nvtx_range, nvtx_range_debug
-from ...inputs import (BaseMultimodalInputProcessor, ExtraProcessedInputs,
-                       InputProcessor, MultimodalPlaceholderMetadata,
+from ...inputs import (BaseDummyInputsBuilder, BaseMultimodalInputProcessor,
+                       ExtraProcessedInputs, InputProcessor,
+                       MultimodalPlaceholderMetadata,
                        MultimodalPlaceholderPlacement, TextPrompt,
+                       default_multimodal_input_loader,
                        register_input_processor)
 from ...logger import logger
 from ...sampling_params import SamplingParams
@@ -83,16 +87,19 @@ def process_weights(weights: Dict,
     return filtered_weights
 
 
-class Qwen2VLInputProcessorBase(BaseMultimodalInputProcessor, InputProcessor):
+class Qwen2VLInputProcessorBase(BaseDummyInputsBuilder,
+                                BaseMultimodalInputProcessor, InputProcessor):
 
     def __init__(self,
                  model_path: str,
                  model_config: PretrainedConfig,
                  tokenizer: AutoTokenizer,
                  trust_remote_code: bool = True):
         self.model_config = model_config
-        self.tokenizer = tokenizer
+        self.tokenizer = tokenizer if tokenizer is not None else AutoTokenizer.from_pretrained(
+            model_path)
         self.use_fast = True
+        self.model_path = model_path
         self.processor = AutoProcessor.from_pretrained(
             model_path,
             use_fast=self.use_fast,
@@ -277,6 +284,81 @@ def get_rope_index(
             mrope_position_deltas, device=input_ids.device).unsqueeze(1)
         return position_ids, mrope_position_deltas
 
+    def get_dummy_text(self, input_seq_len: int) -> str:
+        ids = np.random.randint(
+            low=0,
+            high=int(
+                self.model_config.vocab_size),  # high is exclusive in NumPy
+            size=input_seq_len,
+        ).tolist()
+        return self.tokenizer.decode(ids, skip_special_tokens=True)
+
+    def get_dummy_image(self, max_width: int, max_height: int):
+        image = Image.new("RGB", (max_width, max_height), color=255)
+        return image
+
+    def get_dummy_prompt(self, input_seq_len: int):
+        text = ""
+        # we use the max resolution as starting point
+        img_max_dim = 3584
+        image = self.get_dummy_image(max_width=img_max_dim,
+                                     max_height=img_max_dim)
+
+        test_mm_prompt = default_multimodal_input_loader(
+            tokenizer=self.tokenizer,
+            model_dir=self.model_path,
+            model_type=self.model_config.model_type,
+            modality="image",
+            prompts=[text],
+            media=[[image]],
+            image_data_format="pt")[0]
+
+        prompt_token_ids_single_img, _ = self(test_mm_prompt, None)
+
+        # if the max img resolution results in a number of tokens greater then
+        # input_seq_len, we keep lowering the resolution such as to find the
+        # max resolution such as it does not exceed the input_seq_len
+        while len(prompt_token_ids_single_img) > input_seq_len:
+            # reduce img resolution
+            img_max_dim = img_max_dim >> 1
+
+            image = self.get_dummy_image(max_width=img_max_dim,
+                                         max_height=img_max_dim)
+
+            test_mm_prompt = default_multimodal_input_loader(
+                tokenizer=self.tokenizer,
+                model_dir=self.model_path,
+                model_type=self.model_config.model_type,
+                modality="image",
+                prompts=[text],
+                media=[[image]],
+                image_data_format="pt")[0]
+
+            prompt_token_ids_single_img, _ = self(test_mm_prompt, None)
+
+        len_prompt_tokens_ids = len(prompt_token_ids_single_img)
+        # There are corner cases where if we strictly try to generate a text based
+        # on how many tokens we need to complete the input_seq_len, the output of
+        # default_multimodal_input_loader may give more tokens then the input_seq_len and this
+        # can lead to errors.
+        # That is why we try to clip the variable text_token_left to a lower threshold
+        # but close enough to the actual input_seq_len
+        text_generation_perc_threshold = 0.95
+        text_token_left = int((input_seq_len - len_prompt_tokens_ids) *
+                              text_generation_perc_threshold)
+
+        if text_token_left > 0:
+            text = self.get_dummy_text(text_token_left)
+
+        return default_multimodal_input_loader(
+            tokenizer=self.tokenizer,
+            model_dir=self.model_path,
+            model_type=self.model_config.model_type,
+            modality="image",
+            prompts=[text],
+            media=[[image]],
+            image_data_format="pt")[0]
+
     def _preprocess(self, text: dict[str, any], mm_data: dict[str, any],
                     mm_processor_kwargs: Dict[str, Any]):
         images = mm_data.get("image")
@@ -790,7 +872,7 @@ def __init__(
         **kwargs,
     ) -> None:
         model_config.pretrained_config.rope_scaling['type'] = 'mrope'
-
+        self.original_arch = model_config.pretrained_config.architectures[0]
         # NOTE: Setting disable_fuse_rope to True to do mrope fusion in the model engine by pre-computing rotary_cos_sin in the model engine
         disabble_fuse_rope = kwargs.get('disable_fuse_rope', False)
         model_config.pretrained_config.text_config.disable_fuse_rope = disabble_fuse_rope
@@ -979,7 +1061,7 @@ def multimodal_data_device_paths(self) -> List[str]:
         return [
             "image.pixel_values", "image.image_grid_thw",
             "video.pixel_values_videos", "video.video_grid_thw",
-            "multimodal_embedding", "mrope_config.mrope_position_ids"
+            "multimodal_embedding"
         ]
 
     def load_weights(self, weights, weight_mapper: BaseWeightMapper):
@@ -1032,12 +1114,12 @@ def multimodal_data_device_paths(self) -> List[str]:
             return [
                 "image.pixel_values", "video.pixel_values_videos",
                 "image.image_grid_thw", "video.video_grid_thw",
-                "multimodal_embedding", "mrope_config.mrope_position_ids"
+                "multimodal_embedding"
             ]
         else:
             return [
                 "image.pixel_values", "video.pixel_values_videos",
-                "multimodal_embedding", "mrope_config.mrope_position_ids"
+                "multimodal_embedding"
             ]
 
     def load_weights(self, weights, weight_mapper: BaseWeightMapper):

tensorrt_llm/_torch/pyexecutor/_util.py

@@ -7,6 +7,8 @@
 import tensorrt_llm
 import tensorrt_llm.bindings.executor as trtllm
 from tensorrt_llm._torch.model_config import ModelConfig
+from tensorrt_llm._torch.models.modeling_utils import \
+    MODEL_CLASS_VISION_ENCODER_MAPPING
 from tensorrt_llm._utils import str_dtype_to_binding, torch_dtype_to_str
 from tensorrt_llm.bindings.executor import DecodingMode
 from tensorrt_llm.llmapi.llm_args import (EagleDecodingConfig, KvCacheConfig,
@@ -76,6 +78,7 @@ def __init__(
         pytorch_backend_config: PyTorchConfig,
         speculative_config: SpeculativeConfig,
         sparse_attention_config: SparseAttentionConfig,
+        profiling_stage_data: Optional[dict],
     ):
         self._model_engine = model_engine
         self._draft_model_engine = draft_model_engine
@@ -93,6 +96,7 @@ def __init__(
         self._max_batch_size = max_batch_size
         self._net_max_seq_len = net_max_seq_len
         self._dummy_reqs = None
+        self._profiling_stage_data = profiling_stage_data
         self._kv_cache_manager_cls = get_kv_cache_manager_cls(
             model_engine.model.model_config)
 
@@ -133,13 +137,76 @@ def _cal_max_memory(self, peak_memory, total_gpu_memory, fraction,
             f", tmp kv_mem { (allocated_bytes) / (GB):.2f} GiB")
         return int(available_kv_mem)
 
+    def _create_dummy_mm_context_request(
+            self, input_seq_len: int) -> List[trtllm.Request]:
+        requests = []
+        if isinstance(
+                self._profiling_stage_data,
+                dict) and not self._profiling_stage_data.get("enable_mm_reqs"):
+            return requests
+
+        input_processor = self._model_engine.input_processor
+        if not (hasattr(input_processor, "get_dummy_prompt")):
+            logger.warning("The input processor of the model does not have the method [get_dummy_prompt] implemented." \
+            "Profiling with the default input dummy context request. This may not take into account the memory consumption of " \
+            "the image encoder")
+            return requests
+        prompt = input_processor.get_dummy_prompt(input_seq_len)
+
+        prompt_token_ids, extra_processed_inputs = self._model_engine.input_processor_with_hash(
+            prompt, None)
+
+        multimodal_input = extra_processed_inputs.get('multimodal_input')
+        multimodal_data = extra_processed_inputs.get('multimodal_data')
+
+        max_num_tokens = len(prompt_token_ids)
+        assert max_num_tokens > 0, "the length of the prompt of the dummy mm req is less than or equal to 0"
+        remaining_tokens = min(max_num_tokens, input_seq_len)
+        if remaining_tokens > input_seq_len:
+            logger.warning(f"Profiling with multimedia prompt which contains more tokens than the allowed input_seq_len. " \
+                           f"Multimodal prompt has {remaining_tokens} while the input_seq_len is: {input_seq_len}")
+        while remaining_tokens > 0:
+            req_mm_input = trtllm.MultimodalInput(
+                multimodal_hashes=multimodal_input.multimodal_hashes,
+                multimodal_positions=multimodal_input.multimodal_positions,
+                multimodal_lengths=multimodal_input.multimodal_lengths
+            ) if multimodal_input else None
+            request = trtllm.Request(prompt_token_ids,
+                                     max_tokens=1,
+                                     streaming=False,
+                                     sampling_config=trtllm.SamplingConfig(
+                                         beam_width=self._max_beam_width, ),
+                                     output_config=trtllm.OutputConfig(),
+                                     end_id=-1,
+                                     multimodal_input=req_mm_input)
+            # TODO:
+            # create_input_processor_with_hash shouldn’t be required during profiling,
+            # but is temporarily needed due to the multimodal input dependency for chunked prefill
+            request.py_multimodal_data = multimodal_data
+            remaining_tokens -= max_num_tokens
+            requests.append(request)
+
+        if self._mapping.enable_attention_dp:
+            requests = requests * self._mapping.tp_size
+
+        return requests
+
     def _create_dummy_context_requests(
             self, input_seq_len: int) -> List[trtllm.Request]:
+        requests = []
+        if hasattr(self._model_engine.model,
+                   "original_arch") and MODEL_CLASS_VISION_ENCODER_MAPPING.get(
+                       self._model_engine.model.original_arch, None):
+            input_seq_len = min(self._max_num_tokens, input_seq_len)
+            requests = self._create_dummy_mm_context_request(input_seq_len)
+        # if succeed profiling with multimodal requests then return, otherwise profile
+        # with default case
+        if requests:
+            return requests
         vocab_size = self._model_engine.model.model_config.pretrained_config.vocab_size
         max_num_tokens = self._max_num_tokens
         max_beam_width = self._max_beam_width
 
-        requests = []
         input_seq_len = min(max_num_tokens, input_seq_len)
         remaining_tokens = max_num_tokens
         while remaining_tokens > 0:
@@ -349,6 +416,8 @@ def configure_kv_cache_capacity(self, py_executor: PyExecutor) -> None:
         )
         # set max_gpu_total_bytes
         self._kv_cache_config.max_gpu_total_bytes = kv_cache_max_memory
+        if isinstance(self._profiling_stage_data, dict):
+            self._profiling_stage_data["activation_bytes"] = activation_bytes
         # ---------------------------handle max_gpu_total_bytes---------------------------------
 
     def _create_kv_cache_manager(

tensorrt_llm/_torch/pyexecutor/model_engine.py

@@ -18,6 +18,8 @@
                                  torch_dtype_to_str, trace_func)
 from tensorrt_llm.inputs.multimodal import (MultimodalParams,
                                             MultimodalRuntimeData)
+from tensorrt_llm.inputs.registry import (create_input_processor,
+                                          create_input_processor_with_hash)
 from tensorrt_llm.logger import logger
 from tensorrt_llm.lora_helper import LoraConfig
 from tensorrt_llm.lora_manager import LoraModelConfig
@@ -171,7 +173,9 @@ def __init__(
 
         self.attn_runtime_features = attn_runtime_features or AttentionRuntimeFeatures(
         )
-
+        self.input_processor = create_input_processor(model_path, None)
+        self.input_processor_with_hash = create_input_processor_with_hash(
+            self.input_processor)
         if model is None:
             loader = ModelLoader(
                 pytorch_backend_config=pytorch_backend_config,

tensorrt_llm/_torch/pyexecutor/py_executor_creator.py

@@ -207,6 +207,7 @@ def create_py_executor(
     tokenizer: Optional[TokenizerBase] = None,
     lora_config: Optional[LoraConfig] = None,
     kv_connector_config: Optional[KvCacheConnectorConfig] = None,
+    profiling_stage_data: Optional[dict] = None,
 ) -> PyExecutor:
 
     garbage_collection_gen0_threshold = llm_args.garbage_collection_gen0_threshold
@@ -570,6 +571,7 @@ def drafting_loop_wrapper(model):
             kv_cache_config=kv_cache_config,
             pytorch_backend_config=pytorch_backend_config,
             speculative_config=spec_config,
+            profiling_stage_data=profiling_stage_data,
             sparse_attention_config=sparse_attention_config,
         )
         estimating_kv_cache = kv_cache_creator.try_prepare_estimation()