[Mamba] Support TP>1 with quantization for mamba2 mixer in case n_groups % tp_size == 0 (#24593)

Signed-off-by: Tomer Asida <57313761+tomeras91@users.noreply.github.com>
Signed-off-by: tomeras91 <57313761+tomeras91@users.noreply.github.com>
Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>

vllm/model_executor/layers/mamba/mamba_mixer2.py

@@ -19,6 +19,7 @@ from vllm.distributed import (divide, get_tensor_model_parallel_rank,
 from vllm.forward_context import ForwardContext, get_forward_context
 from vllm.model_executor.custom_op import CustomOp
 from vllm.model_executor.layers.linear import (ColumnParallelLinear,
+                                               MergedColumnParallelLinear,
                                                RowParallelLinear)
 from vllm.model_executor.layers.mamba.abstract import MambaBase
 from vllm.model_executor.layers.mamba.mamba2_metadata import (Mamba2Metadata,
@@ -261,12 +262,14 @@ class MambaMixer2(MambaBase, CustomOp):
                 ), "Tensor parallel world size must divide num heads."
 
         assert (n_groups % self.tp_size) == 0 or n_groups == 1, (
-            "If tensor parallel world size does not divide num_heads, "
+            "If tensor parallel world size does not divide num_groups, "
             "then num_groups must equal 1.")
 
-        assert (
+        assert (n_groups % self.tp_size == 0) or self.tp_size == 1 or \
-            self.tp_size == 1 or quant_config is None
+            quant_config is None, (
-        ), "Tensor parallel currently not supported for quantized models."
+            "Tensor parallel currently supported for quantized models only "
+            "if tensor parallel world size divides num groups."
+        )
 
         self.ssm_state_size = ssm_state_size
         self.conv_kernel_size = conv_kernel_size
@@ -285,7 +288,39 @@ class MambaMixer2(MambaBase, CustomOp):
                 n_groups, self.tp_size)
             self.n_groups = n_groups + groups
 
-        self.conv_dim = intermediate_size + 2 * self.n_groups * ssm_state_size
+        self.groups_ssm_state_size = self.n_groups * self.ssm_state_size
+        self.conv_dim = intermediate_size + 2 * self.groups_ssm_state_size
+
+        if n_groups % self.tp_size == 0:
+            self.conv1d = MergedColumnParallelLinear(
+                input_size=conv_kernel_size,
+                output_sizes=[
+                    intermediate_size,
+                    self.groups_ssm_state_size,
+                    self.groups_ssm_state_size,
+                ],
+                bias=use_conv_bias,
+                quant_config=None,
+                prefix=f"{prefix}.conv1d",
+            )
+
+            self.in_proj = MergedColumnParallelLinear(
+                input_size=hidden_size,
+                output_sizes=[
+                    intermediate_size,
+                    intermediate_size,
+                    self.groups_ssm_state_size,
+                    self.groups_ssm_state_size,
+                    self.num_heads,
+                ],
+                bias=use_bias,
+                quant_config=quant_config,
+                prefix=f"{prefix}.in_proj",
+            )
+        else:
+            # This is the n_groups == 1 case,
+            # where we need to duplicate groups if TP>1.
+
             self.conv1d = ColumnParallelLinear(
                 input_size=conv_kernel_size,
                 output_size=self.conv_dim,
@@ -293,11 +328,6 @@ class MambaMixer2(MambaBase, CustomOp):
                 quant_config=None,
                 prefix=f"{prefix}.conv1d",
             )
-        # unsqueeze to fit conv1d weights shape into the linear weights shape.
-        # Can't do this in `weight_loader` since it already exists in
-        # `ColumnParallelLinear` and `set_weight_attrs`
-        # doesn't allow to override it
-        self.conv1d.weight.data = self.conv1d.weight.data.unsqueeze(1)
 
             self.in_proj = ColumnParallelLinear(
                 input_size=hidden_size,
@@ -311,9 +341,10 @@ class MambaMixer2(MambaBase, CustomOp):
             #   need to interleave them before sharding
             # - use the custom weight loader mamba_v2_sharded_weight_loader
             #   for conv1d.bias, covn1d.weight and in_proj.weight
-        # - need to set these settings, to assign the groups to the head shards
+            # - need to set these settings, to assign the groups
+            #   to the head shards
             group_shard_settings = (
-            self.n_groups * self.ssm_state_size,  # expected model size
+                self.groups_ssm_state_size,  # expected model size
                 (self.n_groups - n_groups) *
                 self.ssm_state_size,  # extra dims assigned
                 n_groups == 1,  # if there was only one group
@@ -380,6 +411,12 @@ class MambaMixer2(MambaBase, CustomOp):
                     },
                 )
 
+        # unsqueeze to fit conv1d weights shape into the linear weights shape.
+        # Can't do this in `weight_loader` since it already exists in
+        # `ColumnParallelLinear` and `MergedColumnParallelLinear`,
+        # and `set_weight_attrs` doesn't allow to override it
+        self.conv1d.weight.data = self.conv1d.weight.data.unsqueeze(1)
+
         # - these are TPed by heads to reduce the size of the
         #   temporal shape
         self.A = nn.Parameter(
@@ -498,8 +535,6 @@ class MambaMixer2(MambaBase, CustomOp):
             chunk_indices_p = mamba2_metadata.chunk_indices
             chunk_offsets_p = mamba2_metadata.chunk_offsets
 
-        groups_time_state_size = self.n_groups * self.ssm_state_size
-
         # 1. Gated MLP's linear projection
         projected_states, _ = self.in_proj(hidden_states)
 
@@ -524,8 +559,8 @@ class MambaMixer2(MambaBase, CustomOp):
             hidden_states_B_C,
             [
                 self.intermediate_size // self.tp_size,
-                groups_time_state_size // self.tp_size,
+                self.groups_ssm_state_size // self.tp_size,
-                groups_time_state_size // self.tp_size,
+                self.groups_ssm_state_size // self.tp_size,
             ],
             dim=-1,
         )