[CPU] Fix TP padding issue on Phi-4 (#8289)

python/sglang/srt/configs/update_config.py

@@ -49,14 +49,25 @@ def get_num_heads_padding_size(tp_size, weight_block_size):
 
 
 def update_intermediate_size(model_config, attr_name, intermediate_padding_size):
-    if hasattr(model_config.hf_config, attr_name):
+    attr_value = intermediate_padding_size
+    if hasattr(model_config, "hf_config") and hasattr(
+        model_config.hf_config, attr_name
+    ):
         attr_value = getattr(model_config.hf_config, attr_name)
-        if attr_value % intermediate_padding_size != 0:
+    elif hasattr(model_config, attr_name):
-            from sglang.srt.layers.vocab_parallel_embedding import pad_vocab_size
+        attr_value = getattr(model_config, attr_name)
 
-            attr_value = pad_vocab_size(attr_value, intermediate_padding_size)
+    if attr_value % intermediate_padding_size != 0:
+        from sglang.srt.layers.vocab_parallel_embedding import pad_vocab_size
+
+        attr_value = pad_vocab_size(attr_value, intermediate_padding_size)
+        if hasattr(model_config, "hf_config"):
             setattr(model_config.hf_config, attr_name, attr_value)
-            setattr(model_config.hf_text_config, attr_name, attr_value)
+            if hasattr(model_config, "hf_text_config"):
+                setattr(model_config.hf_text_config, attr_name, attr_value)
+        else:
+            setattr(model_config, attr_name, attr_value)
+
     return model_config
 
 
@@ -118,4 +129,28 @@ def adjust_config_with_unaligned_cpu_tp(
     model_config = update_intermediate_size(
         model_config, "intermediate_size_mlp", intermediate_padding_size
     )
+    if (
+        hasattr(model_config.hf_config, "vision_config")
+        and model_config.hf_config.vision_config.model_type == "siglip_vision_model"
+    ):
+        model_config.hf_config.vision_config.original_num_attention_heads = (
+            model_config.num_attention_heads
+        )
+        if model_config.hf_config.vision_config.num_attention_heads % tp_size != 0:
+            model_config.hf_config.vision_config.head_dim = (
+                model_config.hf_config.vision_config.hidden_size
+                // model_config.hf_config.vision_config.num_attention_heads
+            )
+            from sglang.srt.layers.vocab_parallel_embedding import pad_vocab_size
+
+            pad_size = get_num_heads_padding_size(tp_size, weight_block_size)
+            model_config.hf_config.vision_config.num_attention_heads = pad_vocab_size(
+                model_config.hf_config.vision_config.num_attention_heads, pad_size
+            )
+        model_config.hf_config.vision_config = update_intermediate_size(
+            model_config.hf_config.vision_config,
+            "intermediate_size",
+            intermediate_padding_size,
+        )
+
     return model_config

python/sglang/srt/hf_transformers_utils.py

@@ -129,6 +129,25 @@ def get_config(
     config = AutoConfig.from_pretrained(
         model, trust_remote_code=trust_remote_code, revision=revision, **kwargs
     )
+    if (
+        config.architectures is not None
+        and config.architectures[0] == "Phi4MMForCausalLM"
+    ):
+        # Phi4MMForCausalLM uses a hard-coded vision_config. See:
+        # https://github.com/vllm-project/vllm/blob/6071e989df1531b59ef35568f83f7351afb0b51e/vllm/model_executor/models/phi4mm.py#L71
+        # We set it here to support cases where num_attention_heads is not divisible by the TP size.
+        from transformers import SiglipVisionConfig
+
+        vision_config = {
+            "hidden_size": 1152,
+            "image_size": 448,
+            "intermediate_size": 4304,
+            "model_type": "siglip_vision_model",
+            "num_attention_heads": 16,
+            "num_hidden_layers": 26,  # Model is originally 27-layer, we only need the first 26 layers for feature extraction.
+            "patch_size": 14,
+        }
+        config.vision_config = SiglipVisionConfig(**vision_config)
     text_config = get_hf_text_config(config=config)
 
     if isinstance(model, str) and text_config is not None:

python/sglang/srt/layers/linear.py

@@ -110,6 +110,20 @@ def adjust_scalar_to_fused_array(param, loaded_weight, shard_id):
     return param[shard_id], loaded_weight
 
 
+def adjust_shard_offsets(shard_offsets, loaded_weight, dim):
+    actual_weight_size = loaded_weight.size(dim)
+    target_weight_size = shard_offsets[-1][-1] + shard_offsets[-1][-2]
+    if actual_weight_size != target_weight_size:
+        new_shard_offsets = []
+        new_offset = 0
+        for shard_id, shard_offset, shard_size in shard_offsets:
+            actual_shard_size = actual_weight_size * shard_size // target_weight_size
+            new_shard_offsets.append((shard_id, new_offset, actual_shard_size))
+            new_offset += actual_shard_size
+        return new_shard_offsets
+    return shard_offsets
+
+
 class LinearBase(torch.nn.Module):
     """Base linear layer.
 
@@ -535,6 +549,11 @@ class MergedColumnParallelLinear(ColumnParallelLinear):
             packed_dim = getattr(param, "packed_dim", None)
 
             use_bitsandbytes_4bit = getattr(param, "use_bitsandbytes_4bit", False)
+            if _is_cpu:
+                shard_offsets = adjust_shard_offsets(
+                    shard_offsets, loaded_weight, output_dim
+                )
+
             for shard_id, shard_offset, shard_size in shard_offsets:
                 # Special case for Quantization.
                 # If quantized, we need to adjust the offset and size to account
@@ -977,6 +996,11 @@ class QKVParallelLinear(ColumnParallelLinear):
             use_bitsandbytes_4bit = getattr(param, "use_bitsandbytes_4bit", False)
 
             packed_dim = getattr(param, "packed_dim", None)
+            if _is_cpu:
+                shard_offsets = adjust_shard_offsets(
+                    shard_offsets, loaded_weight, output_dim
+                )
+
             for shard_id, shard_offset, shard_size in shard_offsets:
                 # Special case for Quantized Weights.
                 # If quantized, we need to adjust the offset and size to account

python/sglang/srt/layers/quantization/unquant.py

@@ -116,9 +116,15 @@ class UnquantizedLinearMethod(LinearMethodBase):
     ) -> torch.Tensor:
 
         if use_intel_amx_backend(layer):
-            return torch.ops.sgl_kernel.weight_packed_linear(
+            x_shapes = x.shape
+            if len(x_shapes) == 3:
+                x = x.view(-1, x.shape[-1])
+            output = torch.ops.sgl_kernel.weight_packed_linear(
                 x, layer.weight, bias, True  # is_vnni
             )
+            if len(x_shapes) == 3:
+                output = output.view(x_shapes[0], x_shapes[1], -1)
+            return output
 
         return F.linear(x, layer.weight, bias)
 

python/sglang/srt/models/phi4mm.py

@@ -54,25 +54,6 @@ VISION_ENCODER_TO_PROCESSING_CONFIG = {
 }
 
 
-def get_navit_vision_model():
-    vision_config = {
-        "hidden_size": 1152,
-        "image_size": 448,
-        "intermediate_size": 4304,
-        "model_type": "siglip_vision_model",
-        "num_attention_heads": 16,
-        "num_hidden_layers": 26,  # Model is originally 27-layer, we only need the first 26 layers for feature extraction.
-        "patch_size": 14,
-    }
-    model_config = SiglipVisionConfig(**vision_config)
-
-    vision_model = Idefics2VisionTransformer(
-        config=model_config, require_post_norm=False
-    )
-
-    return vision_model
-
-
 class Phi4MMImageEncoder(nn.Module):
     """Image embedding."""
 
@@ -88,8 +69,9 @@ class Phi4MMImageEncoder(nn.Module):
         # n_embed or hidden_size
         hidden_size = config.n_embd if hasattr(config, "n_embd") else config.hidden_size
         self.type_feature = "patch"
-
+        self.img_processor = Idefics2VisionTransformer(
-        self.img_processor = get_navit_vision_model()
+            config=config.vision_config, require_post_norm=False
+        )
 
         pe_weight = self.img_processor.embeddings.position_embedding.weight
         L, D = pe_weight.size()