Exclude non-MLP layers when using FP8 quantization with Llama

integration-tests/models/test_completion_prompts.py

@@ -100,6 +100,8 @@ async def test_flash_llama_completion_many_prompts_stream(
                 chunk = [c.replace("data:", "") for c in chunk]
                 # remove empty strings
                 chunk = [c for c in chunk if c]
+                # remove completion marking chunk
+                chunk = [c for c in chunk if c != " [DONE]"]
                 # parse json
                 chunk = [json.loads(c) for c in chunk]
 

server/text_generation_server/models/custom_modeling/flash_llama_modeling.py

@@ -18,6 +18,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+from contextlib import contextmanager
 from typing import List, Optional, Tuple
 
 import torch
@@ -25,7 +26,6 @@ import torch.distributed
 
 from torch import nn
 from transformers.activations import ACT2FN
-from typing import Optional, List, Tuple
 
 from text_generation_server.utils.import_utils import SYSTEM
 from text_generation_server.layers.attention import (
@@ -42,10 +42,16 @@ from text_generation_server.layers import (
     TensorParallelMultiAdapterLinear,
     TensorParallelAdapterRowLinear,
 )
+from text_generation_server.layers.fp8 import Fp8Weight
 from text_generation_server.layers.rotary import PositionRotaryEmbedding
 from text_generation_server.layers.layernorm import (
     FastRMSNorm,
 )
+from text_generation_server.utils.weights import (
+    DefaultWeightsLoader,
+    UnquantizedWeight,
+    Weights,
+)
 
 if SYSTEM == "rocm":
     try:
@@ -105,6 +111,19 @@ def load_attention(config, prefix: str, weights, layer_id):
     )
 
 
+@contextmanager
+def no_fp8(weights: Weights):
+    weights_loader = weights.weights_loader
+    if (
+        isinstance(weights_loader, DefaultWeightsLoader)
+        and weights_loader.weight_class is Fp8Weight
+    ):
+        weights_loader = DefaultWeightsLoader(UnquantizedWeight)
+
+    with weights.use_loader(weights_loader):
+        yield
+
+
 class FlashLlamaAttention(torch.nn.Module):
     def __init__(
         self,
@@ -330,12 +349,15 @@ class LlamaMLP(nn.Module):
 class FlashLlamaLayer(nn.Module):
     def __init__(self, index, prefix, config, weights):
         super().__init__()
-        self.self_attn = FlashLlamaAttention(
+
-            index=index,
+        with no_fp8(weights):
-            prefix=f"{prefix}.self_attn",
+            self.self_attn = FlashLlamaAttention(
-            config=config,
+                index=index,
-            weights=weights,
+                prefix=f"{prefix}.self_attn",
-        )
+                config=config,
+                weights=weights,
+            )
+
         self.mlp = LlamaMLP(
             prefix=f"{prefix}.mlp", config=config, weights=weights, index=index
         )
@@ -470,23 +492,27 @@ class FlashLlamaForCausalLM(torch.nn.Module):
     def __init__(self, prefix: str, config, weights):
         super().__init__()
 
-        self.embed_tokens = TensorParallelEmbedding(
+        with no_fp8(weights):
-            prefix=(
+            self.embed_tokens = TensorParallelEmbedding(
-                "model.embed_tokens" if not prefix else f"{prefix}.model.embed_tokens"
+                prefix=(
-            ),
+                    "model.embed_tokens"
-            weights=weights,
+                    if not prefix
-        )
+                    else f"{prefix}.model.embed_tokens"
+                ),
+                weights=weights,
+            )
         self.model = FlashLlamaModel(prefix, config, weights)
         if config.tie_word_embeddings:
             suffix = "model.embed_tokens"
         else:
             suffix = "lm_head"
 
-        self.lm_head = SpeculativeHead.load(
+        with no_fp8(weights):
-            config,
+            self.lm_head = SpeculativeHead.load(
-            prefix=suffix if not prefix else f"{prefix}.{suffix}",
+                config,
-            weights=weights,
+                prefix=suffix if not prefix else f"{prefix}.{suffix}",
-        )
+                weights=weights,
+            )
 
     def forward(
         self,