[https://nvbugs/5537996][fix] Let KV cache manager block initialization be aware whether it is doing a dry run or not

tensorrt_llm/_torch/pyexecutor/_util.py

@@ -472,6 +472,7 @@ def _create_kv_cache_manager(
                 kv_connector_manager=self._kv_connector_manager
                 if not estimating_kv_cache else None,
                 sparse_attn_config=sparse_attn_config,
+                is_estimating_kv_cache=estimating_kv_cache,
             )
         elif is_nemotron_hybrid(config):
             if self._max_beam_width > 1:
@@ -518,6 +519,7 @@ def _create_kv_cache_manager(
                 mapping=mapping,
                 dtype=kv_cache_dtype,
                 spec_config=spec_config,
+                is_estimating_kv_cache=estimating_kv_cache,
             )
         elif is_qwen3_next(config):
             if self._max_beam_width > 1:
@@ -568,6 +570,7 @@ def _create_kv_cache_manager(
                 mapping=mapping,
                 dtype=kv_cache_dtype,
                 spec_config=spec_config,
+                is_estimating_kv_cache=estimating_kv_cache,
             )
         else:
             # NOTE: this is a workaround for VSWA to switch to calculate_max_num_blocks_from_cpp in KVCahceManager
@@ -595,6 +598,7 @@ def _create_kv_cache_manager(
                 kv_connector_manager=self._kv_connector_manager
                 if not estimating_kv_cache else None,
                 sparse_attn_config=sparse_attn_config,
+                is_estimating_kv_cache=estimating_kv_cache,
             )
         # KVCacheManager (Non-draft) modifies the max_seq_len field, update it to self
         if model_engine.kv_cache_manager_key == ResourceManagerType.KV_CACHE_MANAGER:

tensorrt_llm/_torch/pyexecutor/mamba_cache_manager.py

@@ -195,6 +195,7 @@ def __init__(
         mapping: Mapping,
         dtype: DataType = DataType.HALF,
         spec_config: Optional["DecodingBaseConfig"] = None,
+        is_estimating_kv_cache: bool = False,
     ) -> None:
 
         # mamba hybrid cache requires block reuse to be disabled in KV cache config
@@ -231,6 +232,7 @@ def __init__(
             dtype=dtype,
             spec_config=spec_config,
             layer_mask=layer_mask,
+            is_estimating_kv_cache=is_estimating_kv_cache,
         )
 
     def prepare_resources(self, scheduled_batch: ScheduledRequests):

tensorrt_llm/_torch/pyexecutor/resource_manager.py

@@ -175,6 +175,7 @@ def __init__(
         enable_indexer_k_cache: bool = False,
         indexer_k_cache_quant_block_size: int = 128,
         indexer_k_cache_index_head_dim: int = 0,
+        is_estimating_kv_cache: bool = False,
         **kwargs,
     ) -> None:
         self.mapping = mapping
@@ -269,37 +270,61 @@ def append_to_kv_heads_per_layer(num_kv_heads_per_layer: List[int],
         # Determine if this is VSWA (Variable Sliding Window Attention)
         self.is_vswa = len(set(self.max_attention_window_vec)) > 1
 
-        # Calculate blocks per window using appropriate method
-        if self.is_vswa:
-            # VSWA case: use C++ implementation for variable window sizes
-            # model config check
-            if model_config is None:
-                raise ValueError(
-                    "model_config is required for VSWA (Variable Sliding Window Attention)"
-                )
-            # kv cache config check
-            assert isinstance(
-                kv_cache_config, KvCacheConfig
-            ), "calculate_max_num_blocks_from_cpp only accepts KvCacheConfig"
-            blocks_per_window = self.calculate_max_num_blocks_from_cpp(
-                kv_cache_config=kv_cache_config,
-                model_config=model_config,
-                extra_cost_memory=0,
-            )
-        else:
-            # Standard case: use original Python implementation
-            self.blocks_in_primary_pool, self.blocks_in_secondary_pool = self.calculate_max_num_blocks(
-                kv_cache_config=kv_cache_config,
-                head_dim=head_dim,
-                tokens_per_block=tokens_per_block,
-                mapping=mapping,
-                dtype=dtype,
-                kv_factor=self.kv_factor,
+        # Calculate kv cache blocks for each window size
+        # FIXME: flashinfer.py accesses kv_cache_manager.blocks_in_primary_pool
+        # This dependency should be adjusted as it only covers the single window
+        # case and not VSWA scheme.
+        if is_estimating_kv_cache:
+            # If this is an estimation dry run, we have already calculated the
+            # max_tokens under _util.py::try_prepare_estimation
+            # Since this is a dry run, assigning the same max_tokens capacity
+            # to all window sizes as they are full attentions is enough.
+            self.blocks_in_primary_pool = int(kv_cache_config.max_tokens //
+                                              tokens_per_block)
+
+            host_cache_size = kv_cache_config.host_cache_size if kv_cache_config.host_cache_size else 0
+            max_tokens_secondary = host_cache_size // self.get_cache_bytes_per_token(
             )
+            self.blocks_in_secondary_pool = int(max_tokens_secondary //
+                                                tokens_per_block)
+
             blocks_per_window = {
-                self.max_attention_window_vec[0]:
+                window_size:
                 (self.blocks_in_primary_pool, self.blocks_in_secondary_pool)
+                for window_size in set(self.max_attention_window_vec)
             }
+            logger.info(
+                f"[kv cache manager] Primary/secondary blocks for window sizes set to {blocks_per_window} for estimation dry run"
+            )
+        else:
+            if self.is_vswa:
+                # VSWA case: use C++ implementation for variable window sizes
+                if model_config is None:
+                    raise ValueError(
+                        "model_config is required for VSWA (Variable Sliding Window Attention)"
+                    )
+                assert isinstance(
+                    kv_cache_config, KvCacheConfig
+                ), "calculate_max_num_blocks_from_cpp only accepts KvCacheConfig"
+                blocks_per_window = self.calculate_max_num_blocks_from_cpp(
+                    kv_cache_config=kv_cache_config,
+                    model_config=model_config,
+                    extra_cost_memory=0,
+                )
+            else:
+                # Standard case: use original Python implementation
+                self.blocks_in_primary_pool, self.blocks_in_secondary_pool = self.calculate_max_num_blocks(
+                    kv_cache_config=kv_cache_config,
+                    head_dim=head_dim,
+                    tokens_per_block=tokens_per_block,
+                    mapping=mapping,
+                    dtype=dtype,
+                    kv_factor=self.kv_factor,
+                )
+                blocks_per_window = {
+                    self.max_attention_window_vec[0]:
+                    (self.blocks_in_primary_pool, self.blocks_in_secondary_pool)
+                }
 
         # Validate and adjust attention windows against their upper bounds if needed
         blocks_per_window, self.max_seq_len, self.max_attention_window_vec = self._validate_and_adjust_attention_windows(
@@ -736,11 +761,13 @@ def calculate_max_num_blocks(self,
                 max_tokens = mpi_comm().allreduce(max_tokens, op=MPI.MIN)
 
         # get number of blocks
-        blocks_in_primary_pool = math.ceil(max_tokens / tokens_per_block)
+        blocks_in_primary_pool = int(max_tokens // tokens_per_block)
+
         host_cache_size = kv_cache_config.host_cache_size if kv_cache_config.host_cache_size else 0
-        max_tokens_secondary = host_cache_size / cache_size_bytes_per_token
-        blocks_in_secondary_pool = max(
-            0, int(max_tokens_secondary / tokens_per_block))
+        max_tokens_secondary = host_cache_size // self.get_cache_bytes_per_token(
+        )
+        blocks_in_secondary_pool = int(max_tokens_secondary // tokens_per_block)
+
         return blocks_in_primary_pool, blocks_in_secondary_pool
 
     def get_max_atten_window_upper_bound(self, blocks_in_primary_pool,