update gemme for trt-llm 0.9 (NVIDIA#8974)

Signed-off-by: Onur Yilmaz <oyilmaz@nvidia.com>
Signed-off-by: Onur Yilmaz <35306097+oyilmaz-nvidia@users.noreply.github.com>
Co-authored-by: Pablo Garay <palenq@gmail.com>

nemo/export/trt_llm/decoder/gemma.py

@@ -15,10 +15,8 @@
 from typing import Optional
 
 from tensorrt_llm.functional import non_gated_version
-from tensorrt_llm.layers import Attention, AttentionMaskType, GatedMLP, PositionEmbeddingType, RmsNorm
+from tensorrt_llm.models.gemma.model import GemmaDecoderLayer, QuantConfig
 from tensorrt_llm.models.modeling_utils import PretrainedConfig
-from tensorrt_llm.module import Module
-from tensorrt_llm.quantization import QuantMode
 from typing_extensions import override
 
 from nemo.export.trt_llm.decoder.decoder import DecoderLayerBuilder, DecoderLayerConfigBuilder
@@ -32,88 +30,6 @@
 )
 
 
-class GemmaDecoderLayer(Module):
-    def __init__(self, config, layer_idx):
-        super().__init__()
-        self.layer_idx = layer_idx
-        self.config = config
-
-        self.input_layernorm = RmsNorm(
-            normalized_shape=config.hidden_size, eps=config.norm_epsilon, dtype=config.dtype
-        )
-
-        self.attention = Attention(
-            hidden_size=config.hidden_size,
-            num_attention_heads=config.num_attention_heads,
-            num_kv_heads=config.num_key_value_heads,
-            attention_head_size=config.head_size,
-            max_position_embeddings=config.max_position_embeddings,
-            dtype=config.dtype,
-            attention_mask_type=AttentionMaskType.causal,
-            bias=config.attn_bias,
-            position_embedding_type=PositionEmbeddingType.rope_gpt_neox,
-            rotary_embedding_base=config.rotary_base,
-            rotary_embedding_scaling=config.rotary_scaling,
-            tp_group=config.mapping.tp_group,
-            tp_size=config.mapping.tp_size,
-            quant_mode=config.quant_mode,
-        )
-
-        mlp_hidden_size = config.hidden_size * 4 if config.intermediate_size is None else config.intermediate_size
-
-        self.mlp = GatedMLP(
-            hidden_size=config.hidden_size,
-            ffn_hidden_size=mlp_hidden_size,
-            hidden_act=config.hidden_act,
-            dtype=config.dtype,
-            bias=config.mlp_bias,
-            tp_group=config.mapping.tp_group,
-            tp_size=config.mapping.tp_size,
-            quant_mode=config.quant_mode,
-        )
-        self.post_layernorm = RmsNorm(normalized_shape=config.hidden_size, eps=config.norm_epsilon, dtype=config.dtype)
-
-    def forward(
-        self,
-        hidden_states,
-        attention_mask=None,
-        medusa_packed_mask=None,  # For Medusa support
-        medusa_position_offsets=None,
-        use_cache=False,
-        kv_cache_params=None,
-        attention_params=None,
-        lora_layer_params=None,
-    ):
-        residual = hidden_states
-        hidden_states = self.input_layernorm(hidden_states)
-
-        attention_output = self.attention(
-            hidden_states,
-            attention_mask=attention_mask,
-            medusa_packed_mask=medusa_packed_mask,  # For Medusa support
-            medusa_position_offsets=medusa_position_offsets,
-            use_cache=use_cache,
-            kv_cache_params=kv_cache_params,
-            attention_params=attention_params,
-            lora_layer_params=lora_layer_params,
-        )
-
-        if use_cache:
-            attention_output, presents = attention_output
-
-        hidden_states = residual + attention_output
-
-        residual = hidden_states
-        hidden_states = self.post_layernorm(hidden_states)
-
-        hidden_states = self.mlp(hidden_states, lora_layer_params=lora_layer_params)
-
-        hidden_states = residual + hidden_states
-        if use_cache:
-            return (hidden_states, presents)
-        return hidden_states
-
-
 class GemmaDecoderLayerConfigBuilder(DecoderLayerConfigBuilder):
     """The LLAMA implementation of the DecoderLayerConfigBuilder."""
 
@@ -200,8 +116,7 @@ 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,
         )
 

tests/export/run.sh

@@ -48,4 +48,4 @@ python tests/export/test_nemo_export.py --model_name FALCON-7B-base --existing_t
 python tests/export/test_nemo_export.py --model_name FALCON-40B-base --existing_test_models --min_gpus 2 --max_gpus 8
 python tests/export/test_nemo_export.py --model_name FALCON-180B-base --existing_test_models --min_gpus 8 --max_gpus 8
 python tests/export/test_nemo_export.py --model_name STARCODER1-15B-base --existing_test_models --min_gpus 1 --max_gpus 1
-python tests/export/test_nemo_export.py --model_name GEMMA-base --existing_test_models --min_gpus 1 --max_gpus 1 --run_accuracy --test_deployment True
+python tests/export/test_nemo_export.py --model_name GEMMA-base --existing_test_models --min_gpus 1 --max_gpus 1