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Moe support #3811

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754743b
Enabled Qwen MoE with 1 layer. Rewrote index_put converter
cehongwang Sep 8, 2025
2140c49
fixed the perf issue in the lowering pass
cehongwang Sep 8, 2025
a016bc0
Optimized index converter
cehongwang Sep 8, 2025
6ea89ae
Fixed a typo in the converter. Covered the discontinuous tests
cehongwang Sep 16, 2025
c286767
Supported bool mask indicies
cehongwang Sep 17, 2025
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5 changes: 4 additions & 1 deletion py/torch_tensorrt/dynamo/conversion/aten_ops_converters.py
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Original file line number Diff line number Diff line change
Expand Up @@ -888,6 +888,7 @@ def aten_ops_select(

@dynamo_tensorrt_converter(
torch.ops.aten.index_put.default,
supports_dynamic_shapes=True,
)
@enforce_tensor_types(
{
Expand Down Expand Up @@ -3168,7 +3169,9 @@ def aten_ops_upsample_bicubic2d(


@dynamo_tensorrt_converter(
torch.ops.aten.topk.default, capability_validator=topk_validator
torch.ops.aten.topk.default,
capability_validator=topk_validator,
supports_dynamic_shapes=True,
)
@enforce_tensor_types(
{
Expand Down
249 changes: 149 additions & 100 deletions py/torch_tensorrt/dynamo/conversion/impl/select.py
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Could you add detailed comments demonstrating what it does now that you've gone through the entire converter ? that would be helpful

Original file line number Diff line number Diff line change
Expand Up @@ -257,15 +257,17 @@ def index(
)
else:
dim_tensor_shape_mult_d1 = transpose_tensor_shape[i]
mult_d1 = convert_binary_elementwise(
ctx,
target,
source_ir,
name + f"_shape_{i}",
trt.ElementWiseOperation.PROD,
mult_d1,
dim_tensor_shape_mult_d1,
)

if isinstance(dim_tensor_shape_mult_d1, TRTTensor):
mult_d1 = convert_binary_elementwise(
ctx,
target,
source_ir,
name + f"_shape_{i}",
trt.ElementWiseOperation.PROD,
mult_d1,
dim_tensor_shape_mult_d1,
)

concat_tensor_layer = ctx.net.add_concatenation(
[
Expand Down Expand Up @@ -548,6 +550,9 @@ def index_put_converter(
accumulate: bool = False,
) -> TRTTensor:
# Convert 'input_indices' to TRT tensors (or keep None as is)
input_indices = expand_boolean_indices(
ctx, target, source_ir, name, input_tensor, input_indices
)
indices: List[Optional[Union[TRTTensor, None]]] = []
for i, idx in enumerate(input_indices):
if idx is None:
Expand All @@ -571,22 +576,40 @@ def index_put_converter(
K = len(I)
# Determine the maximum size 'N' among the index tensors
if K > 0:
index_shapes = [tensor.shape[0] for tensor in indices if tensor is not None]
index_shapes = (
[]
) # [tensor.shape[0] for tensor in indices if tensor is not None]
for idx_tensor in indices:
if idx_tensor is not None:
if idx_tensor.shape[0] != DYNAMIC_DIM:
index_shapes.append(idx_tensor.shape[0])
else:
index_shapes.append(
get_shape(
ctx,
target,
source_ir,
name + "idx_shape_dim_0",
idx_tensor,
0,
)
)
N = max(index_shapes) if index_shapes else 1
else:
N = 1

# Compute shapes and volume for the free dimensions
F_shapes = [input_tensor.shape[i] for i in F]
assert -1 not in F_shapes, "Dynamic shape in free dimensions is not supported"
F_volume = trt.volume(F_shapes) if F_shapes else 1

# Process indexed dimensions (I)
I_tensors = []
for i in I:
idx = indices[i]
assert idx is not None
idx_reshaped = impl.shuffle.reshape(
ctx, target, source_ir, f"{name}_reshape_idx_I_{i}", idx, (idx.shape[0], 1)
idx_reshaped = impl.unsqueeze.unsqueeze(
ctx, target, source_ir, f"{name}_unsqueeze_idx_I_{i}", idx, 1
)
expanded_idx = impl.slice.expand(
ctx,
Expand All @@ -608,46 +631,50 @@ def index_put_converter(
)
arange_tensors.append(arange_tensor)

meshgrid_tensors = []
for i, arange in enumerate(arange_tensors):
reshape_shape = [1] * len(F)
reshape_shape[i] = F_shapes[i]
arange_reshaped = impl.shuffle.reshape(
ctx,
target,
source_ir,
f"{name}_reshape_arange_F_{F[i]}",
arange,
tuple(reshape_shape),
)
expanded_arange = impl.slice.expand(
ctx,
target,
source_ir,
f"{name}_expand_arange_F_{F[i]}",
arange_reshaped,
tuple(F_shapes),
)
meshgrid_tensors.append(expanded_arange)

meshgrid_stacked = impl.cat.cat(
ctx,
target,
source_ir,
f"{name}_stack_meshgrid",
[
impl.shuffle.reshape(
if len(arange_tensors) == 1:
# No need to stack
meshgrid_stacked = arange_tensors[0]
else:
meshgrid_tensors = []
for i, arange in enumerate(arange_tensors):
reshape_shape = [1] * len(F)
reshape_shape[i] = F_shapes[i]
arange_reshaped = impl.shuffle.reshape(
ctx,
target,
source_ir,
f"{name}_reshape_mesh_{i}",
t,
(*F_shapes, 1),
f"{name}_reshape_arange_F_{F[i]}",
arange,
tuple(reshape_shape),
)
for i, t in enumerate(meshgrid_tensors)
],
dim=-1,
)
expanded_arange = impl.slice.expand(
ctx,
target,
source_ir,
f"{name}_expand_arange_F_{F[i]}",
arange_reshaped,
tuple(F_shapes),
)
meshgrid_tensors.append(expanded_arange)

meshgrid_stacked = impl.cat.cat(
ctx,
target,
source_ir,
f"{name}_stack_meshgrid",
[
impl.shuffle.reshape(
ctx,
target,
source_ir,
f"{name}_reshape_mesh_{i}",
t,
(*F_shapes, 1),
)
for i, t in enumerate(meshgrid_tensors)
],
dim=-1,
)
meshgrid_reshaped = impl.shuffle.reshape(
ctx,
target,
Expand All @@ -672,21 +699,15 @@ def index_put_converter(

# Combine all indexed dimensions (I)
if K > 0:
I_combined = impl.cat.cat(
ctx,
target,
source_ir,
f"{name}_cat_I",
[
impl.shuffle.reshape(
ctx, target, source_ir, f"{name}_reshape_I_{i}", t, (N, F_volume, 1)
)
for i, t in enumerate(I_tensors)
],
dim=2,
)

I_combined = [
impl.shuffle.reshape(
ctx, target, source_ir, f"{name}_reshape_I_{i}", t, (N, F_volume, 1)
)
for i, t in enumerate(I_tensors)
]
else:
I_combined = None
I_combined = []

# Build the final index list (ii_list) by slicing either I_combined or meshgrid_expanded
ii_list = []
Expand All @@ -695,24 +716,12 @@ def index_put_converter(
for dim in range(rank):
unique_suffix = f"{dim}_{i_idx if dim in I else f_idx}"
if dim in I:
start = [0, 0, i_idx]
shape = [N, F_volume, 1]
stride = [1, 1, 1]
idx_tensor = impl.slice.slice(
ctx,
target,
source_ir,
f"{name}_slice_I_dim_{unique_suffix}",
I_combined,
start,
shape,
stride,
)
idx_tensor = I_combined[i_idx]
ii_list.append(idx_tensor)
i_idx += 1
else:
start = [0, 0, f_idx]
shape = [N, F_volume, 1]
shape = [-1, F_volume, 1] if isinstance(N, TRTTensor) else [N, F_volume, 1]
stride = [1, 1, 1]
mesh_tensor = impl.slice.slice(
ctx,
Expand All @@ -731,20 +740,24 @@ def index_put_converter(
indices_cat = impl.cat.cat(
ctx, target, source_ir, f"{name}_cat_indices", ii_list, dim=2
)

# Flatten the indices_cat to (N * F_volume, rank)
indices_cat = impl.shuffle.reshape(
ctx,
target,
source_ir,
f"{name}_reshape_indices_cat",
indices_cat,
(N * F_volume, rank),
(-1, rank),
)

if not isinstance(values, TRTTensor):
values = get_trt_tensor(ctx, values, f"{name}_values", min_rank=0)

# Define the expected shape based on (N,) + F_shapes
expected_shape = (N,) + tuple(F_shapes)
expected_shape = (
(-1,) + tuple(F_shapes) if isinstance(N, TRTTensor) else (N,) + tuple(F_shapes)
)

# Broadcast 'values' to match the expected shape
if len(values.shape) == 0 or values.shape == (1,): # Scalar case
Expand All @@ -761,7 +774,12 @@ def index_put_converter(
)
else: # Non-scalar case
values_shape = list(values.shape)
if K > 0 and N in values_shape:
if (
K > 0
and N in values_shape
and (len(F) > 1 and max(F) - min(F) + 1 == len(F))
):
# Continuous case
n_idx = values_shape.index(N)
permute_order = [n_idx] + [
i for i in range(len(values_shape)) if i != n_idx
Expand Down Expand Up @@ -807,31 +825,27 @@ def index_put_converter(
tuple(broadcast_shape),
)
else:
# Discontinuous case
values_shape_padded = [1] * (
len(expected_shape) - len(values.shape)
) + list(values.shape)
broadcast_shape = []
for exp_dim, val_dim in zip(expected_shape, values_shape_padded):
if val_dim == 1 or exp_dim == val_dim:
if val_dim == DYNAMIC_DIM or exp_dim == DYNAMIC_DIM:
broadcast_shape.append(-1)
elif val_dim == 1 or exp_dim == val_dim:
broadcast_shape.append(exp_dim)
else:
raise ValueError(
f"Cannot broadcast {values.shape} to {expected_shape}"
)
values_reshaped = impl.shuffle.reshape(
ctx,
target,
source_ir,
f"{name}_reshape_values",
values,
tuple(broadcast_shape),
)

values_expanded = impl.slice.expand(
ctx,
target,
source_ir,
f"{name}_expand_values",
values_reshaped,
values,
expected_shape,
)

Expand All @@ -842,16 +856,51 @@ def index_put_converter(
source_ir,
f"{name}_flatten_values",
values_expanded,
(N * F_volume,),
(-1,),
)

indices_cat = cast_trt_tensor(ctx, indices_cat, trt.int32, f"{name}_idx_int32")
# Perform Scatter ND operation
scatter_layer = ctx.net.add_scatter(
input_tensor,
indices_cat,
flattened_values,
trt.ScatterMode.ND if not accumulate else trt.ScatterMode.ND_ELEMENTWISE_ADD,
)
set_layer_name(scatter_layer, target, f"{name}_scatter", source_ir)
return scatter_layer.get_output(0)
if accumulate:
zero_tensor = impl.full.full(
ctx,
target,
source_ir,
f"{name}_zero_tensor",
[
get_shape(
ctx,
target,
source_ir,
name + f"input_tensor_shape_dim_{i}",
input_tensor,
i,
)
for i in range(len(input_tensor.shape))
],
0.0,
dtype=input_tensor.dtype,
)
# Perform Scatter ND operation
scatter_layer = ctx.net.add_scatter(
zero_tensor,
indices_cat,
flattened_values,
trt.ScatterMode.ND,
)
set_layer_name(scatter_layer, target, f"{name}_scatter", source_ir)

scatter_out = scatter_layer.get_output(0)
result = impl.elementwise.add(
ctx, target, source_ir, f"{name}_add", scatter_out, input_tensor
)
return result

else:
scatter_layer = ctx.net.add_scatter(
input_tensor,
indices_cat,
flattened_values,
trt.ScatterMode.ND,
)
set_layer_name(scatter_layer, target, f"{name}_scatter", source_ir)
scatter_out = scatter_layer.get_output(0)
return scatter_out
3 changes: 2 additions & 1 deletion py/torch_tensorrt/dynamo/lowering/passes/remove_num_users_is_0_nodes.py
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Original file line number Diff line number Diff line change
Expand Up @@ -23,7 +23,8 @@ def remove_num_users_is_0_nodes(
and len(node.all_input_nodes) > 0
):
gm.graph.erase_node(node)
gm = clean_up_graph_after_modifications(gm)

gm = clean_up_graph_after_modifications(gm)

logger.debug(f"Removed ops that [num_users=0] nodes:\n{gm.graph}")

Expand Down
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