Phi3 support (#1797)

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Fixes # (issue)


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server/text_generation_server/models/__init__.py

@@ -327,7 +327,7 @@ def get_model(
                 trust_remote_code=trust_remote_code,
             )
 
-    elif model_type == "llama" or model_type == "baichuan":
+    elif model_type == "llama" or model_type == "baichuan" or model_type == "phi3":
         if FLASH_ATTENTION:
             return FlashLlama(
                 model_id,

server/text_generation_server/models/custom_modeling/flash_llama_modeling.py

@@ -101,6 +101,13 @@ def load_attention(config, prefix, weights):
                 weights=weights,
                 bias=False,
             )
+        elif config.model_type == "phi3":
+            return TensorParallelColumnLinear.load_qkv(
+                config,
+                prefix=f"{prefix}.qkv_proj",
+                weights=weights,
+                bias=False,
+            )
         else:
             return TensorParallelColumnLinear.load_multi(
                 config,
@@ -257,6 +264,14 @@ class LlamaMLP(nn.Module):
             )
         )
         # Fuse gate and up proj
+        if config.model_type == "phi3":
+            self.gate_up_proj = TensorParallelColumnLinear.load_gate_up(
+                config,
+                prefix=f"{prefix}.gate_up_proj",
+                weights=weights,
+                bias=False,
+            )
+        else:
             self.gate_up_proj = TensorParallelColumnLinear.load_multi(
                 config,
                 prefixes=[f"{prefix}.gate_proj", f"{prefix}.up_proj"],

server/text_generation_server/utils/layers.py

@@ -696,6 +696,19 @@ class TensorParallelHead(SuperLayer):
 
 
 class TensorParallelColumnLinear(SuperLayer):
+    @classmethod
+    def load_gate_up(cls, config, prefix: str, weights, bias: bool):
+        """Specific method when the QKV was joined after the fact"""
+        weight = weights.get_weights_col_packed_gate_up(
+            prefix, quantize=config.quantize
+        )
+        if bias:
+            raise NotImplementedError("packed_gate_up only implemented without bias")
+        else:
+            bias = None
+        linear = get_linear(weight, bias, config.quantize)
+        return cls(linear)
+
     @classmethod
     def load_qkv(cls, config, prefix: str, weights, bias: bool):
         """Specific method when the QKV was joined after the fact"""

server/text_generation_server/utils/weights.py

@@ -141,6 +141,12 @@ class Weights:
         return weight
 
     def get_weights_col_packed_qkv(self, prefix: str, quantize: str):
+        return self.get_weights_col_packed(prefix, quantize, 3)
+
+    def get_weights_col_packed_gate_up(self, prefix: str, quantize: str):
+        return self.get_weights_col_packed(prefix, quantize, 2)
+
+    def get_weights_col_packed(self, prefix: str, quantize: str, blocks: int):
         """
         Highly specific when the underlying tensor is a simple cat of Q,K,V instead of being
         already alternating Q,K,V within the main tensor
@@ -181,8 +187,8 @@ class Weights:
         else:
             slice_ = self._get_slice(f"{prefix}.weight")
             total_size = slice_.get_shape()[0]
-            assert total_size % 3 == 0, "Prepacked qkv is not divisible by 3"
-            single_size = total_size // 3
+            assert total_size % blocks == 0, f"Prepacked is not divisible by {blocks}"
+            single_size = total_size // blocks
             world_size = self.process_group.size()
             rank = self.process_group.rank()
 
@@ -192,10 +198,11 @@ class Weights:
             block_size = single_size // world_size
             start = rank * block_size
             stop = (rank + 1) * block_size
-            q = slice_[start:stop]
-            k = slice_[start + single_size : stop + single_size]
-            v = slice_[start + 2 * single_size : stop + 2 * single_size]
-            weight = torch.cat([q, k, v], dim=0)
+            tensors = []
+            for i in range(blocks):
+                tensor = slice_[start + i * single_size : stop + i * single_size]
+                tensors.append(tensor)
+            weight = torch.cat(tensors, dim=0)
             weight = weight.to(device=self.device)
             weight = weight.to(dtype=self.dtype)
         return weight