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Supports head parallelism for Mamba layers.
Currently, it assumes a rigid module structure:

  • exactly one conv1d node
  • exactly one torch_ssm node
  • exactly two split operations (after in_proj and after conv1d)

When applied it:

  • shards fused weights for in_proj, conv1d, rmsnorm, out_proj
  • updates shape parameters for splits, views, convs, etc

Sharding mamba layers is currently supported only through factory sharding, by specifying mamba for in_proj linear node.

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Signed-off-by: greg-kwasniewski1 <[email protected]>
Signed-off-by: greg-kwasniewski1 <[email protected]>
Signed-off-by: greg-kwasniewski1 <[email protected]>
Signed-off-by: greg-kwasniewski1 <[email protected]>
Signed-off-by: greg-kwasniewski1 <[email protected]>
Signed-off-by: greg-kwasniewski1 <[email protected]>
Signed-off-by: greg-kwasniewski1 <[email protected]>


def _update_view_nodes(node: Node) -> None:
def _validate_sharded_shapes(
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can we simplify this function if you assume upstream of this transform there is something that standardizes the graph representation to look like this:

def _bamba_mixer_torch_forward(
self,
input_states,
cache_params: Optional[HybridMambaAttentionDynamicCache] = None,
cache_position: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.Tensor] = None,
):
# `input_states` is of shape `[B, T, hidden_dim]`, and during generate, each successive call
# will have T = T + 1.
batch_size, seq_len, _ = input_states.shape
dtype = input_states.dtype
# 1. Gated MLP's linear projection
input_states = apply_mask_to_padding_states(input_states, attention_mask)
projected_states = self.in_proj(input_states)
gate, hidden_states_B_C, dt = projected_states.split(
[self.intermediate_size, self.conv_dim, self.num_heads], dim=-1
)
use_caching = cache_params is not None
# 2. Convolution sequence transformation (cached/uncached handled inside the op)
if use_caching:
# Prepare dense metadata for cached flattened op
seq_len_t = torch.full((batch_size,), seq_len, device=input_states.device, dtype=torch.int)
seq_start_t = torch.arange(
0, batch_size * seq_len, seq_len, device=input_states.device, dtype=torch.int
)
slot_idx_t = torch.arange(batch_size, device=input_states.device, dtype=torch.long)
if use_caching:
hidden_states_B_C = self.act(
torch.ops.auto_deploy.torch_cached_causal_conv1d(
# INPUTS
hidden_states_B_C,
self.conv1d.weight,
self.conv1d.bias,
# METADATA
seq_len_t,
seq_start_t,
slot_idx_t,
# CACHES
cache_params.conv_states[self.layer_idx],
# CONSTANTS
self.conv1d.stride[0],
self.conv1d.padding[0],
self.conv1d.dilation[0],
self.conv1d.groups,
self.conv1d.padding_mode,
)
)
else:
hidden_states_B_C = self.act(
torch.ops.auto_deploy.torch_causal_conv1d(
hidden_states_B_C,
self.conv1d.weight,
self.conv1d.bias,
self.conv1d.stride[0],
self.conv1d.padding[0],
self.conv1d.dilation[0],
self.conv1d.groups,
self.conv1d.padding_mode,
)
)
hidden_states_B_C = apply_mask_to_padding_states(hidden_states_B_C, attention_mask)
hidden_states, B, C = torch.split(
hidden_states_B_C,
[
self.intermediate_size,
self.n_groups * self.ssm_state_size,
self.n_groups * self.ssm_state_size,
],
dim=-1,
)
# 3. SSM transformation
A = -torch.exp(self.A_log.float()) # [num_heads]
if use_caching:
# Use new flattened cached op for both cache updates and outputs
y = torch.ops.auto_deploy.torch_cached_ssm_transform(
# INPUTS
hidden_states=hidden_states.view(batch_size, seq_len, -1, self.head_dim),
A=A,
B=B.view(batch_size, seq_len, -1, self.ssm_state_size),
C=C.view(batch_size, seq_len, -1, self.ssm_state_size),
D=self.D,
dt=dt,
dt_bias=self.dt_bias,
# METADATA
seq_len=seq_len_t,
seq_start=seq_start_t,
slot_idx=slot_idx_t,
# CACHES
ssm_state_cache=cache_params.ssm_states[self.layer_idx],
# CONSTANTS
time_step_limit=list(self.time_step_limit),
chunk_size=self.chunk_size,
)
else:
y = torch.ops.auto_deploy.torch_ssm_transform(
hidden_states=hidden_states.view(batch_size, seq_len, -1, self.head_dim),
A=A,
B=B.view(batch_size, seq_len, -1, self.ssm_state_size),
C=C.view(batch_size, seq_len, -1, self.ssm_state_size),
D=self.D,
dt=dt,
dt_bias=self.dt_bias,
time_step_limit=list(self.time_step_limit),
chunk_size=self.chunk_size,
)
y = y.view(batch_size, seq_len, -1)
scan_output = self.norm(y, gate)
# end ssd naive
# !! This is the end of the uncached code path.
# 4. Final linear projection
contextualized_states = self.out_proj(scan_output.to(dtype)) # [batch, seq_len, hidden_size]
return contextualized_states

From a first glance, it seems we only need to handle the split node then but not the view nodes

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View node update was there already for some previous transformer models, i don't remember which one. So while Mamba needs split nodes to update, other models need view nodes, I think the "shape cleaning" should be done in one place instead of a separate function for each model family.

}


AutoModelForCausalLMFactory._set_sharding_config = _set_sharding_config_patched
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will require clean-up

layer_type=LayerType.MAMBA,
fused_weight_dims=config_params.get("fused_weight_dims"),
)
)
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In my mind, a complete specification for col-row shard of a Mamba-like layer has 4 entries in the sharding_config that are passed to the sharding_executor:

  1. column-wise with appropriate fused_weight_dims for the in_proj
  2. column-wise with appropriate fused_weight_dims for torch_causal_conv1d
  3. column-wise for the weight on the gated rms norm
  4. row-wise for out_proj

More specifically, I don't think we should add special handling for mamba via something like the layer_type argument.

Now there is two ways we can get these four entries:

  1. Sharding heuristic that analyzes the model and correctly configures those four entries. Seems like you already have something like this here. This can be repurposed as detect_mamba_sharding to add the appropriate entries into the sharding_config
  2. Manual sharding config: this is pending your [TRTLLM-6342][feat] Support custom sharding config source #8153 PR and so might not be a good alternative for now. Even with 2. in place though, it wouldn't be the ideal solution since the manual sharding config would have to specify the fused_weight_dims which is not ideal either. So since we have 1. in place we can go with this option for now.

In terms of getting this merged to main I would suggest the following split:

  1. Ability for the sharding executor to understand TPShardingInfo with fused_weight_dims. Moreover, we probably also need to ensure that causal conv and element-wise multiplication from the gated rms norm can be supported in the TPShardingInfo specification. + Unit tests
  2. Add a new sharding heuristic for the mamba layer that can auto-detect the correct sharding_config entry for mamba-like layers

In the meantime, the current branch can be used to test+benchmark Nemotron nano-v3. So please make sure it remains available for testing

Comment on lines +189 to +193
# Split fused weights, apply TP sharding to each, then concatenate back
sharded_weight = torch.cat(
[split_tensor(w) for w in torch.split(weight_tensor, fused_weight_dims, dim=dim)],
dim=dim,
)
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seems you should be able to just f_split as defined below to get the sharded_weight for either case

return self.predefined_config


def _append_simple_shard(
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what happened to this function?

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