Updates for TRT-LLM 0.9 (#8873)

* upgrade to trtllm0.9

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* Update gpt to config based export

Signed-off-by: Onur Yilmaz <[email protected]>

* fix for lora checkpoint

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* fix for in flight batching case

* Update falcon for trt-llm 0.9

Signed-off-by: Onur Yilmaz <[email protected]>

* Removed unused import and comment

Signed-off-by: Onur Yilmaz <[email protected]>

---------

Signed-off-by: Onur Yilmaz <[email protected]>
Co-authored-by: abharwani <[email protected]>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

Author: 3 people

nemo/export/trt_llm/decoder/falcon.py

@@ -17,8 +17,7 @@
 
 from tensorrt_llm.functional import non_gated_version
 from tensorrt_llm.models.falcon.model import FalconDecoderLayer
-from tensorrt_llm.models.modeling_utils import PretrainedConfig
-from tensorrt_llm.quantization import QuantMode
+from tensorrt_llm.models.modeling_utils import PretrainedConfig, QuantConfig
 from typing_extensions import override
 
 from nemo.export.trt_llm.decoder.decoder import DecoderLayerBuilder, DecoderLayerConfigBuilder
@@ -119,8 +118,7 @@ def build_decoder(self, layer):
             world_size=self.tensor_parallel,
             tp_size=self.tensor_parallel,
             pp_size=1,
-            quant_mode=QuantMode(0),
-            quant_kwargs=None,
+            quantization=QuantConfig(),
             max_lora_rank=layer.max_lora_rank,
             use_parallel_embedding=False,
         )

nemo/export/trt_llm/decoder/gpt.py

@@ -17,6 +17,7 @@
 
 from tensorrt_llm.layers import AttentionMaskType, PositionEmbeddingType
 from tensorrt_llm.models.gpt.model import GPTDecoderLayer
+from tensorrt_llm.models.modeling_utils import PretrainedConfig, QuantConfig
 from typing_extensions import override
 
 from nemo.export.trt_llm.decoder.decoder import DecoderLayerBuilder, DecoderLayerConfigBuilder
@@ -85,37 +86,44 @@ class GPTDecoderLayerBuilder(DecoderLayerBuilder):
     @override
     def build_decoder(self, layer):
         rotary_pct = layer.rotary_pct
-        position_embedding_type = (
-            PositionEmbeddingType.rope_gpt_neox
-            if layer.position_embedding_type == "rope"
-            else PositionEmbeddingType.learned_absolute
-        )
 
-        assert not (position_embedding_type == PositionEmbeddingType.rope_gpt_neox and rotary_pct == 0.0)
+        position_embedding_type = "rope_gpt_neox" if layer.position_embedding_type == "rope" else "learned_absolute"
+
+        assert not (position_embedding_type == "rope_gpt_neox" and rotary_pct == 0.0)
 
         bias_qkv = layer.attention.qkv.bias is not None
 
         rotary_scaling = None
         if layer.rotary_scaling is not None:
             rotary_scaling = {"type": "linear", "factor": float(layer.rotary_scaling)}
 
-        return GPTDecoderLayer(
+        config = PretrainedConfig(
+            architecture=None,
+            dtype=self.dtype,
+            logits_dtype=self.dtype,
+            vocab_size=layer.vocab_size,
+            max_position_embeddings=self.max_position_embeddings,
             hidden_size=self.hidden_size,
+            num_hidden_layers=self.num_layers,
             num_attention_heads=self.num_attention_heads,
-            max_position_embeddings=self.max_position_embeddings,
-            num_layers=self.num_layers,
-            dtype=self.dtype,
-            apply_query_key_layer_scaling=False,
-            attention_mask_type=AttentionMaskType.causal,
+            num_key_value_heads=self.num_kv_heads,
             hidden_act=self.hidden_act,
+            intermediate_size=layer.ffn_hidden_size_local * self.tensor_parallel,
+            norm_epsilon=layer.norm_epsilon,
             position_embedding_type=position_embedding_type,
-            rotary_embedding_percentage=rotary_pct,
-            rotary_base=layer.rotary_base,
-            rotary_scaling=rotary_scaling,
-            inter_size=layer.ffn_hidden_size_local * self.tensor_parallel,
-            bias=bias_qkv,
-            num_kv_heads=self.num_kv_heads,
-            tp_group=self.tp_group,
+            world_size=self.tensor_parallel,
             tp_size=self.tensor_parallel,
+            pp_size=1,
             max_lora_rank=layer.max_lora_rank,
+            quantization=QuantConfig(),
         )
+
+        config.set_if_not_exist('hidden_act', self.hidden_act)
+        config.set_if_not_exist('apply_query_key_layer_scaling', False)
+        config.set_if_not_exist('bias', bias_qkv)
+        config.set_if_not_exist('rotary_base', layer.rotary_base)
+        config.set_if_not_exist('rotary_scaling', rotary_scaling)
+        config.set_if_not_exist('rotary_pct', rotary_pct)
+        config.set_if_not_exist('moe_num_experts', 0)
+
+        return GPTDecoderLayer(config=config, layer_idx=self.layer_id,)

nemo/export/trt_llm/decoder/llama.py

@@ -18,8 +18,7 @@
 from tensorrt_llm.functional import non_gated_version
 from tensorrt_llm.layers import MoeConfig
 from tensorrt_llm.models.llama.model import LLaMADecoderLayer
-from tensorrt_llm.models.modeling_utils import PretrainedConfig
-from tensorrt_llm.quantization import QuantMode
+from tensorrt_llm.models.modeling_utils import PretrainedConfig, QuantConfig
 from typing_extensions import override
 
 from nemo.export.trt_llm.decoder.decoder import DecoderLayerBuilder, DecoderLayerConfigBuilder
@@ -118,9 +117,8 @@ def build_decoder(self, layer):
             world_size=self.tensor_parallel,
             tp_size=self.tensor_parallel,
             pp_size=1,
-            quant_mode=QuantMode(0),
-            quant_kwargs=None,
             max_lora_rank=layer.max_lora_rank,
+            quantization=QuantConfig(),
         )
 
         config.set_if_not_exist('mlp_bias', False)

nemo/export/trt_llm/tensorrt_llm_build.py

@@ -27,6 +27,7 @@
 from tensorrt_llm._utils import np_dtype_to_trt
 from tensorrt_llm.builder import Builder
 from tensorrt_llm.logger import logger
+from tensorrt_llm.models.modeling_utils import add_lora
 from tensorrt_llm.network import net_guard
 from tensorrt_llm.plugin.plugin import ContextFMHAType
 from tensorrt_llm.quantization import QuantMode
@@ -170,6 +171,9 @@ def _build_impl(tensorrt_llm_model, args):
     timing_cache_file = args.timing_cache if args.timing_cache else args.output_dir / "model.cache"
     timing_cache = timing_cache_file
 
+    if args.use_lora_plugin is not None:
+        add_lora(tensorrt_llm_model, args.max_lora_rank)
+
     builder = Builder()
     apply_query_key_layer_scaling = False
 

nemo/export/trt_llm/tensorrt_llm_model.py

@@ -144,15 +144,7 @@ def forward(
         if attention_mask is not None:
             attention_mask = expand_mask(attention_mask, shape(input_ids, -1))
 
-        for layer_idx, (layer, past, pointer, host_pointer, max_attention_window_size) in enumerate(
-            zip(
-                self.layers,
-                kv_cache_params.past_key_value,
-                kv_cache_params.kv_cache_block_pointers,
-                kv_cache_params.host_kv_cache_block_pointers,
-                kv_cache_params.host_max_attention_window_sizes,
-            )
-        ):
+        for layer_idx, (layer, past) in enumerate(zip(self.layers, kv_cache_params.past_key_value,)):
 
             decoder_params = {
                 "hidden_states": hidden_states,
@@ -161,8 +153,8 @@ def forward(
                 "kv_cache_params": KeyValueCacheParams(
                     past_key_value=[past],
                     host_past_key_value_lengths=kv_cache_params.host_past_key_value_lengths,
-                    kv_cache_block_pointers=[pointer],
-                    host_max_attention_window_sizes=max_attention_window_size,
+                    kv_cache_block_pointers=kv_cache_params.kv_cache_block_pointers,
+                    host_max_attention_window_sizes=kv_cache_params.host_max_attention_window_sizes,
                     cache_indirection=kv_cache_params.cache_indirection,
                     host_sink_token_length=kv_cache_params.host_sink_token_length,
                     host_kv_cache_block_pointers=kv_cache_params.host_kv_cache_block_pointers,
@@ -329,8 +321,8 @@ def prepare_inputs(
                 past_key_value=model_inputs['past_key_value'],
                 host_past_key_value_lengths=model_inputs['host_past_key_value_lengths'],
                 host_max_attention_window_sizes=model_inputs['host_max_attention_window_sizes'],
-                kv_cache_block_pointers=model_inputs['kv_cache_block_pointers_list'],
-                host_kv_cache_block_pointers=model_inputs['host_kv_cache_block_pointers_list'],
+                kv_cache_block_pointers=model_inputs['kv_cache_block_pointers'],
+                host_kv_cache_block_pointers=model_inputs['host_kv_cache_block_pointers'],
                 cache_indirection=model_inputs['cache_indirection'],
                 host_sink_token_length=model_inputs['host_sink_token_length'],
             ),

nemo/export/trt_llm/tensorrt_llm_run.py

@@ -24,12 +24,14 @@
 import torch
 from mpi4py.futures import MPIPoolExecutor
 from tensorrt_llm.logger import logger
+from tensorrt_llm.lora_manager import LoraManager
 from tensorrt_llm.quantization import QuantMode
-from tensorrt_llm.runtime import LoraManager, ModelConfig, SamplingConfig
+from tensorrt_llm.runtime import ModelConfig, SamplingConfig
 from transformers import PreTrainedTokenizer
 
 from nemo.export.trt_llm.tensor_utils import get_tensor_parallel_group
 from nemo.export.trt_llm.tensorrt_llm_model import LMHeadModelBuilder
+
 from nemo.export.trt_llm.tensorrt_llm_build import get_engine_name, MODEL_NAME, refit_runtime_engine  # isort:skip
 from nemo.export.trt_llm.nemo_utils import to_word_list_format  # isort:skip
 
@@ -90,6 +92,7 @@ def _read_config(config_path: Path):
     model_config = ModelConfig(
         model_name=config["builder_config"]["name"],
         max_batch_size=config["builder_config"]["max_batch_size"],
+        max_beam_width=config["builder_config"]["max_beam_width"],
         vocab_size=config["builder_config"]["vocab_size"],
         num_layers=config["builder_config"]["num_layers"],
         num_heads=num_heads,