[None][feat] AutoDeploy: per graph or whole module transform infrastructure

tensorrt_llm/_torch/auto_deploy/config/default.yaml

@@ -6,6 +6,7 @@ transforms:
   ############################################################################################
   build_model:
     stage: factory
+    run_per_gm: false
     device: meta
     # nothing to clean up
     run_graph_cleanup: false
@@ -14,8 +15,8 @@ transforms:
     stage: export
     clone_state_dict: false
     strict: false
-    # nothing to clean up
-    run_graph_cleanup: false
+    run_per_gm: false
+    run_graph_cleanup: true
     requires_clean_graph: false
   cleanup_noop_slice:
     stage: post_export
@@ -35,6 +36,7 @@ transforms:
     run_shape_prop: true
   match_eager_attention:
     stage: pattern_matcher
+    requires_shape_prop: true
   match_grouped_attention:
     stage: pattern_matcher
   match_attention_layout:
@@ -87,8 +89,10 @@ transforms:
   ############################################################################################
   load_weights:
     stage: weight_load
+    run_per_gm: false
   move_inputs_to_device:
     stage: weight_load
+    run_per_gm: false
   ############################################################################################
   # RUN POST-LOAD FUSION AND OPTIMIZATIONS
   ############################################################################################
@@ -138,10 +142,13 @@ transforms:
     attn_backend: cuda_causal_conv
   initialize_cache:
     stage: cache_init
+    run_per_gm: false
   resize_kv_cache:
     stage: cache_init
+    run_per_gm: false
   ############################################################################################
   # COMPILE MODEL
   ############################################################################################
   compile_model:
     stage: compile
+    run_per_gm: false

tensorrt_llm/_torch/auto_deploy/config/transformers.yaml

@@ -6,23 +6,29 @@ transforms:
   ############################################################################################
   build_and_load_factory_model:
     stage: factory
+    run_per_gm: false
   ############################################################################################
   # MOVE ARGUMENTS TO DEVICE
   ############################################################################################
   move_inputs_to_device:
     stage: weight_load
+    run_per_gm: false
   ############################################################################################
   # SWITCH TO CACHED+FLATTENED ATTENTION + INITIALIZE CACHES
   ############################################################################################
   detect_hf_attn_layers:
     stage: cache_init
+    run_per_gm: false
   transformers_replace_cached_attn:
     stage: cache_init
     attn_backend: flashinfer
+    run_per_gm: false
   initialize_cache:
     stage: cache_init
+    run_per_gm: false
   resize_kv_cache:
     stage: cache_init
+    run_per_gm: false
   ############################################################################################
   # COMPILE MODEL
   ############################################################################################

tensorrt_llm/_torch/auto_deploy/custom_ops/attention_interface.py

@@ -325,7 +325,11 @@ def args_for_prepare_metadata(self) -> Tuple[str, ...]:
         like ``insert_cached_attention`` to extract the constant arguments and add them to the
         ``prepare_metadata`` node/op.
         """
-        return tuple(self.named_standard_args.keys())
+        # NOTE: for now we do _not_ include input_ids since we are not guaranteed that input_ids
+        # is part of the graph, e.g., in situations where the graph is a submodule of the overall
+        # model. In such instances, the graph usually sees inputs_embeds. However, we assume for
+        # now that position_ids is always part of the graph.
+        return ("position_ids",) + self._cached_arg_names
 
     @property
     def const_args_for_prepare_metadata(self) -> Tuple[Constant, ...]:
@@ -466,7 +470,9 @@ def _get_cache_locations_and_pages_per_sequence(
         return cache_loc_flat, pages_per_seq
 
     @classmethod
-    def _get_sanitized_seq_len(cls, input_ids: torch.Tensor, seq_len: torch.Tensor) -> torch.Tensor:
+    def _get_sanitized_seq_len(
+        cls, input_or_position_ids: torch.Tensor, seq_len: torch.Tensor
+    ) -> torch.Tensor:
         """Sanitize sequence lengths.
 
         We want to cover the following scenarios with this function:
@@ -499,22 +505,24 @@ def _get_sanitized_seq_len(cls, input_ids: torch.Tensor, seq_len: torch.Tensor)
             # valid cache location in the batch. This would ensure that the dummy sequences just
             # repeats valid computation...
         """
-        _, s = input_ids.shape[:2]
-        num_seq = cls._get_sanitized_num_sequences(input_ids, seq_len)
+        _, s = input_or_position_ids.shape[:2]
+        num_seq = cls._get_sanitized_num_sequences(input_or_position_ids, seq_len)
         if s > 1:
             return seq_len[:num_seq].detach().clone()
         else:
             return torch.ones(num_seq, dtype=seq_len.dtype, device=seq_len.device)
 
     @staticmethod
-    def _get_sanitized_num_sequences(input_ids: torch.Tensor, seq_len: torch.Tensor) -> int:
+    def _get_sanitized_num_sequences(
+        input_or_position_ids: torch.Tensor, seq_len: torch.Tensor
+    ) -> int:
         """Get number of sequences.
 
         We makes sure that this function is compatible with both torch graph capture and cudagraph.
         Both can be a bit temparamental when trying to extract the number of sequences from a tensor
         with max_batch_size or max_batch_size*max_seq_len.
         """
-        b, s = input_ids.shape[:2]
+        b, s = input_or_position_ids.shape[:2]
         if s > 1:
             num_seq = torch.sum(seq_len > 0)
             assert seq_len[num_seq:].sum() == 0, "seq_len should be zero-padded"
@@ -814,7 +822,6 @@ def __call__(
 class PrepareMetadataCallable(Protocol):
     def __call__(
         self,
-        input_ids: torch.Tensor,
         position_ids: torch.Tensor,
         seq_len: torch.Tensor,
         input_pos: torch.Tensor,
@@ -901,7 +908,6 @@ def get_prepare_metadata_op(cls) -> Tuple[PrepareMetadataCallable, int]:
 
         ```
         def prepare_metadata(
-            input_ids: torch.Tensor,
             position_ids: torch.Tensor,
             seq_len: torch.Tensor,
             input_pos: torch.Tensor,

tensorrt_llm/_torch/auto_deploy/custom_ops/cuda_backend_causal_conv.py

@@ -54,7 +54,6 @@ def _build_conv_state_from_sequence(input_bt_c: torch.Tensor, kernel_size: int)
 # ---------------------------------------------------------------
 @torch.library.custom_op("auto_deploy::cuda_causal_conv_prepare_metadata", mutates_args=())
 def cuda_causal_conv_prepare_metadata(
-    input_ids: torch.Tensor,
     position_ids: torch.Tensor,
     seq_len: torch.Tensor,
     input_pos: torch.Tensor,
@@ -67,7 +66,7 @@ def cuda_causal_conv_prepare_metadata(
 
     Returns a tuple of (seq_len_sanitized, seq_start, slot_idx_sanitized).
     """
-    seq_len_sanitized = SequenceInfo._get_sanitized_seq_len(input_ids, seq_len)
+    seq_len_sanitized = SequenceInfo._get_sanitized_seq_len(position_ids, seq_len)
     num_seq = len(seq_len_sanitized)
 
     seq_start = torch.zeros_like(seq_len_sanitized)
@@ -81,9 +80,9 @@ def cuda_causal_conv_prepare_metadata(
 
 @cuda_causal_conv_prepare_metadata.register_fake
 def cuda_causal_conv_prepare_metadata_fake(
-    input_ids, position_ids, seq_len, input_pos, cache_loc, pages_per_seq, slot_idx, page_size
+    position_ids, seq_len, input_pos, cache_loc, pages_per_seq, slot_idx, page_size
 ):
-    seq_len_sanitized = SequenceInfo._get_sanitized_seq_len(input_ids, seq_len)
+    seq_len_sanitized = SequenceInfo._get_sanitized_seq_len(position_ids, seq_len)
     num_seq = len(seq_len_sanitized)
     return (
         torch.empty_like(seq_len_sanitized),

tensorrt_llm/_torch/auto_deploy/custom_ops/flashinfer_attention.py

@@ -155,7 +155,6 @@ def _plan_decode(wrapper: flashinfer.BatchDecodeWithPagedKVCacheWrapper):
 
 @torch.library.custom_op("auto_deploy::flashinfer_attention_prepare_metadata", mutates_args=())
 def prepare_flashinfer_metadata(
-    input_ids: torch.Tensor,
     position_ids: torch.Tensor,
     seq_len: torch.Tensor,
     input_pos: torch.Tensor,
@@ -174,7 +173,7 @@ def prepare_flashinfer_metadata(
     _GlobalFlashInferPlanner.reset()
 
     # retrieve sanitzed metadata
-    seq_len = SequenceInfo._get_sanitized_seq_len(input_ids, seq_len)
+    seq_len = SequenceInfo._get_sanitized_seq_len(position_ids, seq_len)
     num_seq = len(seq_len)
 
     # prepare flashinfer-style metadata
@@ -214,9 +213,9 @@ def prepare_flashinfer_metadata(
 # As SequenceInfo._get_sanitized_num_sequences could break in fake mode
 @prepare_flashinfer_metadata.register_fake
 def prepare_flashinfer_metadata_fake(
-    input_ids, position_ids, seq_len, input_pos, cache_loc, pages_per_seq, slot_idx, page_size
+    position_ids, seq_len, input_pos, cache_loc, pages_per_seq, slot_idx, page_size
 ):
-    seq_len = SequenceInfo._get_sanitized_seq_len(input_ids, seq_len)
+    seq_len = SequenceInfo._get_sanitized_seq_len(position_ids, seq_len)
     qo_indptr = torch.empty(len(seq_len) + 1, dtype=seq_len.dtype, device=seq_len.device)
     return (
         qo_indptr,  # qo_indptr

tensorrt_llm/_torch/auto_deploy/custom_ops/mla.py

@@ -175,7 +175,6 @@ def fused_flattened_mla_with_cache_fake(
     "auto_deploy::triton_attention_prepare_fused_mla_metadata", mutates_args=()
 )
 def prepare_fused_mla_metadata(
-    input_ids: torch.Tensor,
     position_ids: torch.Tensor,
     seq_len: torch.Tensor,
     input_pos: torch.Tensor,
@@ -184,7 +183,7 @@ def prepare_fused_mla_metadata(
     slot_idx: torch.Tensor,
     page_size: int,
 ) -> List[torch.Tensor]:
-    num_seq = SequenceInfo._get_sanitized_num_sequences(input_ids, seq_len)
+    num_seq = SequenceInfo._get_sanitized_num_sequences(position_ids, seq_len)
     seq_start = torch.zeros_like(seq_len[:num_seq])
     seq_start[1:] = torch.cumsum(seq_len[: num_seq - 1], 0)
     return (
@@ -197,7 +196,7 @@ def prepare_fused_mla_metadata(
 
 @prepare_fused_mla_metadata.register_fake
 def prepare_fused_mla_metadata_fake(
-    input_ids, position_ids, seq_len, input_pos, cache_loc, pages_per_seq, page_size
+    position_ids, seq_len, input_pos, cache_loc, pages_per_seq, slot_idx, page_size
 ):
     return (
         torch.empty_like(seq_len),

tensorrt_llm/_torch/auto_deploy/custom_ops/torch_backend_attention.py

@@ -356,7 +356,6 @@ def torch_backend_mha_with_cache_fake(
 
 @torch.library.custom_op("auto_deploy::torch_cached_attention_prepare_metadata", mutates_args=())
 def torch_backend_prepare_metadata(
-    input_ids: torch.Tensor,
     position_ids: torch.Tensor,
     seq_len: torch.Tensor,
     input_pos: torch.Tensor,
@@ -366,7 +365,7 @@ def torch_backend_prepare_metadata(
     page_size: int,
 ) -> List[torch.Tensor]:
     """Prepare metadata for torch backend attention (similar to triton backend)."""
-    num_seq = SequenceInfo._get_sanitized_num_sequences(input_ids, seq_len)
+    num_seq = SequenceInfo._get_sanitized_num_sequences(position_ids, seq_len)
     seq_start = torch.zeros_like(seq_len[:num_seq])
     seq_start[1:] = torch.cumsum(seq_len[: num_seq - 1], 0)
     return (
@@ -379,9 +378,9 @@ def torch_backend_prepare_metadata(
 
 @torch_backend_prepare_metadata.register_fake
 def torch_backend_prepare_metadata_fake(
-    input_ids, position_ids, seq_len, input_pos, cache_loc, pages_per_seq, slot_idx, page_size
+    position_ids, seq_len, input_pos, cache_loc, pages_per_seq, slot_idx, page_size
 ):
-    num_seq = SequenceInfo._get_sanitized_num_sequences(input_ids, seq_len)
+    num_seq = SequenceInfo._get_sanitized_num_sequences(position_ids, seq_len)
     return (
         torch.empty_like(seq_len[:num_seq]),
         torch.empty_like(input_pos[:num_seq]),

tensorrt_llm/_torch/auto_deploy/custom_ops/torch_backend_causal_conv.py

@@ -140,7 +140,6 @@ def _torch_causal_conv1d_decode(
 
 @torch.library.custom_op("auto_deploy::torch_causal_conv_prepare_metadata", mutates_args=())
 def torch_causal_conv_prepare_metadata(
-    input_ids: torch.Tensor,
     position_ids: torch.Tensor,
     seq_len: torch.Tensor,
     input_pos: torch.Tensor,
@@ -153,7 +152,7 @@ def torch_causal_conv_prepare_metadata(
 
     Returns a tuple of (seq_len_sanitized, seq_start, slot_idx_sanitized).
     """
-    seq_len_sanitized = SequenceInfo._get_sanitized_seq_len(input_ids, seq_len)
+    seq_len_sanitized = SequenceInfo._get_sanitized_seq_len(position_ids, seq_len)
     num_seq = len(seq_len_sanitized)
 
     seq_start = torch.zeros_like(seq_len_sanitized)
@@ -167,9 +166,9 @@ def torch_causal_conv_prepare_metadata(
 
 @torch_causal_conv_prepare_metadata.register_fake
 def torch_causal_conv_prepare_metadata_fake(
-    input_ids, position_ids, seq_len, input_pos, cache_loc, pages_per_seq, slot_idx, page_size
+    position_ids, seq_len, input_pos, cache_loc, pages_per_seq, slot_idx, page_size
 ):
-    seq_len_sanitized = SequenceInfo._get_sanitized_seq_len(input_ids, seq_len)
+    seq_len_sanitized = SequenceInfo._get_sanitized_seq_len(position_ids, seq_len)
     num_seq = len(seq_len_sanitized)
     return (
         torch.empty_like(seq_len_sanitized),

tensorrt_llm/_torch/auto_deploy/custom_ops/torch_backend_mamba.py

@@ -113,7 +113,6 @@ def _update_ssm_state_cache(ssm_cache: torch.Tensor, ssm_state: torch.Tensor) ->
 
 @torch.library.custom_op("auto_deploy::torch_ssm_prepare_metadata", mutates_args=())
 def _torch_ssm_prepare_metadata(
-    input_ids: torch.Tensor,
     position_ids: torch.Tensor,
     seq_len: torch.Tensor,
     input_pos: torch.Tensor,
@@ -127,7 +126,7 @@ def _torch_ssm_prepare_metadata(
     Returns a tuple of (seq_len_sanitized, seq_start, slot_idx_sanitized).
     """
     # Determine number of active sequences and compute seq_start boundaries
-    seq_len_sanitized = SequenceInfo._get_sanitized_seq_len(input_ids, seq_len)
+    seq_len_sanitized = SequenceInfo._get_sanitized_seq_len(position_ids, seq_len)
     num_seq = len(seq_len_sanitized)
 
     seq_start = torch.zeros_like(seq_len_sanitized)
@@ -142,10 +141,10 @@ def _torch_ssm_prepare_metadata(
 
 @_torch_ssm_prepare_metadata.register_fake
 def _torch_ssm_prepare_metadata_fake(
-    input_ids, position_ids, seq_len, input_pos, cache_loc, pages_per_seq, slot_idx, page_size
+    position_ids, seq_len, input_pos, cache_loc, pages_per_seq, slot_idx, page_size
 ):
     # Use the same sanitization logic to determine sizes in fake mode
-    seq_len_sanitized = SequenceInfo._get_sanitized_seq_len(input_ids, seq_len)
+    seq_len_sanitized = SequenceInfo._get_sanitized_seq_len(position_ids, seq_len)
     num_seq = len(seq_len_sanitized)
     return (
         torch.empty_like(seq_len_sanitized),

tensorrt_llm/_torch/auto_deploy/custom_ops/triton_attention.py

@@ -284,7 +284,6 @@ def flattened_mha_fake(
     "auto_deploy::triton_attention_prepare_fused_mha_metadata", mutates_args=()
 )
 def prepare_fused_mha_metadata(
-    input_ids: torch.Tensor,
     position_ids: torch.Tensor,
     seq_len: torch.Tensor,
     input_pos: torch.Tensor,
@@ -294,7 +293,7 @@ def prepare_fused_mha_metadata(
     page_size: int,
 ) -> List[torch.Tensor]:
     # TODO: maybe use slot_idx instead of pages_per_seq??
-    num_seq = SequenceInfo._get_sanitized_num_sequences(input_ids, seq_len)
+    num_seq = SequenceInfo._get_sanitized_num_sequences(position_ids, seq_len)
     seq_start = torch.zeros_like(seq_len[:num_seq])
     seq_start[1:] = torch.cumsum(seq_len[: num_seq - 1], 0)
     return (
@@ -309,9 +308,9 @@ def prepare_fused_mha_metadata(
 # SequenceInfo._get_sanitized_num_sequences could break in fake mode
 @prepare_fused_mha_metadata.register_fake
 def prepare_fused_mha_metadata_fake(
-    input_ids, position_ids, seq_len, input_pos, cache_loc, pages_per_seq, slot_idx, page_size
+    position_ids, seq_len, input_pos, cache_loc, pages_per_seq, slot_idx, page_size
 ):
-    num_seq = SequenceInfo._get_sanitized_num_sequences(input_ids, seq_len)
+    num_seq = SequenceInfo._get_sanitized_num_sequences(position_ids, seq_len)
     return (
         torch.empty_like(seq_len[:num_seq]),
         torch.empty_like(input_pos[:num_seq]),