[Bugfix] Correct behavior of GraniteMoeHybrid for TensorParallel execution (#20137)

Signed-off-by: Stanislaw Wozniak <stw@zurich.ibm.com>

tests/models/language/generation/test_granitemoehybrid.py

-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-
-import pytest
-
-from ...utils import check_logprobs_close
-
-# Path of the checkpoints
-MODELS = [
-    "ibm-granite/granite-4.0-tiny-preview",
-]
-
-
-@pytest.mark.skip(
-    reason="Granite 4.0 is not yet available in huggingface transformers")
-@pytest.mark.parametrize("model", MODELS)
-@pytest.mark.parametrize("dtype", ["float16", "bfloat16"])
-@pytest.mark.parametrize("max_tokens", [64])
-@pytest.mark.parametrize("num_logprobs", [5])
-def test_model_equivalence_to_hf_greedy(
-    hf_runner,
-    vllm_runner,
-    example_prompts,
-    model: str,
-    dtype: str,
-    max_tokens: int,
-    num_logprobs: int,
-):
-    with vllm_runner(model, dtype=dtype) as vllm_model:
-        vllm_outputs = vllm_model.generate_greedy_logprobs(
-            example_prompts, max_tokens, num_logprobs)
-
-    with hf_runner(model, dtype=dtype) as hf_model:
-        hf_outputs = hf_model.generate_greedy_logprobs_limit(
-            example_prompts, max_tokens, num_logprobs)
-
-    check_logprobs_close(
-        outputs_0_lst=hf_outputs,
-        outputs_1_lst=vllm_outputs,
-        name_0="hf",
-        name_1="vllm",
-    )

tests/models/language/generation/test_hybrid.py

@@ -28,8 +28,9 @@ SSM_MODELS = [
 
 HYBRID_MODELS = [
     "ai21labs/Jamba-tiny-dev",
-    # NOTE: ibm-granite/granite-4.0-tiny-preview are skipped currently as
-    # it is not yet available in huggingface transformers
+    # NOTE: Currently the test failes due to HF transformers issue fixed in:
+    # https://github.com/huggingface/transformers/pull/39033
+    # We will enable vLLM test for Granite after next HF transformers release.
     # "ibm-granite/granite-4.0-tiny-preview",
     # NOTE: Running Plamo2 in transformers implementation requires to install
     # causal-conv1d package, which is not listed as a test dependency as it's

vllm/model_executor/models/granitemoehybrid.py

@@ -15,7 +15,8 @@ from vllm.distributed import divide, get_tensor_model_parallel_world_size
 from vllm.distributed.parallel_state import get_pp_group
 from vllm.forward_context import get_forward_context
 from vllm.model_executor.layers.layernorm import RMSNorm
-from vllm.model_executor.layers.linear import ReplicatedLinear
+from vllm.model_executor.layers.linear import (QKVParallelLinear,
+                                               RowParallelLinear)
 from vllm.model_executor.layers.logits_processor import LogitsProcessor
 from vllm.model_executor.layers.mamba.mamba2_metadata import (
     Mamba2Metadata, prepare_mamba2_metadata)
@@ -36,8 +37,9 @@ from .granitemoe import GraniteMoeMoE
 from .granitemoeshared import GraniteMoeSharedMLP
 from .interfaces import (HasInnerState, IsHybrid, SupportsLoRA, SupportsPP,
                          SupportsQuant, SupportsV0Only)
-from .utils import (AutoWeightsLoader, make_empty_intermediate_tensors_factory,
-                    make_layers, maybe_prefix)
+from .utils import (AutoWeightsLoader, is_pp_missing_parameter,
+                    make_empty_intermediate_tensors_factory, make_layers,
+                    maybe_prefix)
 
 
 class GraniteMoeHybridMambaDecoderLayer(nn.Module):
@@ -220,35 +222,37 @@ class GraniteMoeHybridAttention(nn.Module):
         self.hidden_size = config.hidden_size
         self.attention_bias = config.attention_bias
         self.attention_multiplier = config.attention_multiplier
-        self.num_heads = config.num_attention_heads
-        self.head_dim = self.hidden_size // self.num_heads
-        self.num_key_value_heads = config.num_key_value_heads
-
-        self.q_proj = ReplicatedLinear(self.hidden_size,
-                                       self.num_heads * self.head_dim,
-                                       bias=self.attention_bias,
-                                       quant_config=quant_config,
-                                       prefix=f"{prefix}.q_proj")
-
-        self.k_proj = ReplicatedLinear(self.hidden_size,
-                                       self.num_key_value_heads *
-                                       self.head_dim,
-                                       bias=self.attention_bias,
-                                       quant_config=quant_config,
-                                       prefix=f"{prefix}.k_proj")
-
-        self.v_proj = ReplicatedLinear(self.hidden_size,
-                                       self.num_key_value_heads *
-                                       self.head_dim,
-                                       bias=self.attention_bias,
-                                       quant_config=quant_config,
-                                       prefix=f"{prefix}.v_proj")
-
-        self.o_proj = ReplicatedLinear(self.hidden_size,
-                                       self.hidden_size,
-                                       bias=self.attention_bias,
-                                       quant_config=quant_config,
-                                       prefix=f"{prefix}.o_proj")
+        self.total_num_heads = config.num_attention_heads
+        self.head_dim = self.hidden_size // self.total_num_heads
+        self.total_num_kv_heads = config.num_key_value_heads
+
+        # TensorParallel logic
+        tp_size = get_tensor_model_parallel_world_size()
+        assert self.total_num_heads % tp_size == 0
+        self.num_heads = self.total_num_heads // tp_size
+        if self.total_num_kv_heads >= tp_size:
+            # Number of KV heads is greater than TP size, so we partition
+            # the KV heads across multiple tensor parallel GPUs.
+            assert self.total_num_kv_heads % tp_size == 0
+        else:
+            # Number of KV heads is less than TP size, so we replicate
+            # the KV heads across multiple tensor parallel GPUs.
+            assert tp_size % self.total_num_kv_heads == 0
+        self.num_key_value_heads = max(1, self.total_num_kv_heads // tp_size)
+
+        self.qkv_proj = QKVParallelLinear(self.hidden_size,
+                                          self.head_dim,
+                                          self.total_num_heads,
+                                          self.total_num_kv_heads,
+                                          bias=self.attention_bias,
+                                          quant_config=quant_config,
+                                          prefix=f"{prefix}.qkv_proj")
+
+        self.o_proj = RowParallelLinear(self.hidden_size,
+                                        self.hidden_size,
+                                        bias=self.attention_bias,
+                                        quant_config=quant_config,
+                                        prefix=f"{prefix}.o_proj")
 
         if config.position_embedding_type == "rope":
             self.rotary_emb = get_rope(
@@ -278,9 +282,12 @@ class GraniteMoeHybridAttention(nn.Module):
         hidden_states: torch.Tensor,
     ) -> torch.Tensor:
 
-        query = self.q_proj(hidden_states)[0]
-        key = self.k_proj(hidden_states)[0]
-        value = self.v_proj(hidden_states)[0]
+        qkv, _ = self.qkv_proj(hidden_states)
+        query, key, value = qkv.split([
+            self.num_heads * self.head_dim, self.num_key_value_heads *
+            self.head_dim, self.num_key_value_heads * self.head_dim
+        ],
+                                      dim=-1)
 
         if self.rotary_emb is not None:
             query, key = self.rotary_emb(positions, query, key)
@@ -401,6 +408,12 @@ class GraniteMoeHybridModel(nn.Module):
 
     def load_weights(self, weights: Iterable[tuple[str,
                                                    torch.Tensor]]) -> set[str]:
+        stacked_params_mapping = [
+            # (param_name, shard_name, shard_id)
+            (".qkv_proj", ".q_proj", "q"),
+            (".qkv_proj", ".k_proj", "k"),
+            (".qkv_proj", ".v_proj", "v"),
+        ]
         params_dict = dict(self.named_parameters())
         loaded_params: set[str] = set()
 
@@ -411,6 +424,15 @@ class GraniteMoeHybridModel(nn.Module):
             weight_loader(param, p)
             loaded_params.add(n)
 
+        def _load_shard(n, p, shard_id):
+            # Skip layers on other devices.
+            if not is_pp_missing_parameter(n, self):
+                param = params_dict[n]
+                weight_loader = getattr(param, "weight_loader",
+                                        default_weight_loader)
+                weight_loader(param, p, shard_id)
+                loaded_params.add(n)
+
         def _load_expert(n, p, name, shard_id, expert_id):
             param = params_dict[n]
             weight_loader = getattr(param, "weight_loader",
@@ -465,7 +487,15 @@ class GraniteMoeHybridModel(nn.Module):
                                       ".block_sparse_moe.gate.weight")
                 _load(gate_name, p)
             else:
-                _load(n, p)
+                loaded = False
+                for param_name, weight_name, shard_id in stacked_params_mapping:
+                    if weight_name in n:
+                        _load_shard(n.replace(weight_name, param_name),
+                                    p,
+                                    shard_id=shard_id)
+                        loaded = True
+                if not loaded:
+                    _load(n, p)
 
         return loaded_params
 
@@ -473,7 +503,13 @@ class GraniteMoeHybridModel(nn.Module):
 class GraniteMoeHybridForCausalLM(nn.Module, HasInnerState, SupportsLoRA,
                                   SupportsPP, IsHybrid, SupportsV0Only,
                                   SupportsQuant):
-    packed_modules_mapping = {}
+    packed_modules_mapping = {
+        "qkv_proj": [
+            "q_proj",
+            "k_proj",
+            "v_proj",
+        ],
+    }
     embedding_modules = {
         "embed_tokens": "input_embeddings",
         "lm_head": "output_embeddings",