[None][feat] Draft: Speculative One Model: FlashInfer sampling

requirements.txt

@@ -52,7 +52,7 @@ ordered-set
 peft
 patchelf
 einops
-flashinfer-python>=0.3.0,<0.4.0
+flashinfer-python==0.6.0rc2
 opencv-python-headless
 xgrammar==0.1.25
 llguidance==0.7.29
@@ -73,7 +73,7 @@ nvidia-cutlass-dsl==4.3.4; python_version >= "3.10"
 plotly
 numexpr<2.14.0 # WAR for attempted use of nonexistent numpy.typing
 partial_json_parser
-apache-tvm-ffi==0.1.4 # used for reduce nvidia-cutlass-dsl host overhead
+apache-tvm-ffi==0.1.7 # used for reduce nvidia-cutlass-dsl host overhead
 torch-c-dlpack-ext==0.1.3 # used for reduce nvidia-cutlass-dsl host overhead, optional package for improved torch tensor calling perf
 mistral-common==1.8.6
 torchao>=0.14.1

tensorrt_llm/_torch/speculative/interface.py

@@ -12,11 +12,15 @@
 
 from ..._utils import get_sm_version
 from ..attention_backend.trtllm import AttentionBackend, TrtllmAttention
+from ..flashinfer_utils import IS_FLASHINFER_AVAILABLE
 from ..pyexecutor.resource_manager import BaseResourceManager
 
 if TYPE_CHECKING:
     from ..pyexecutor.guided_decoder import CapturableGuidedDecoder
 
+if IS_FLASHINFER_AVAILABLE:
+    import flashinfer
+
 # Environment variable name for forcing the number of accepted tokens in speculative decoding
 FORCE_NUM_ACCEPTED_TOKENS_ENV_VAR = "TLLM_SPEC_DECODE_FORCE_NUM_ACCEPTED_TOKENS"
 
@@ -368,6 +372,9 @@ def __init__(self):
         super().__init__()
         self.guided_decoder: Optional["CapturableGuidedDecoder"] = None
         self.force_num_accepted_tokens = get_force_num_accepted_tokens()
+        self.use_flashinfer = IS_FLASHINFER_AVAILABLE and flashinfer.__version__ >= "0.6.0"
+        self.seed = 0
+        self.offset = 0
 
     @property
     @abstractmethod
@@ -412,8 +419,17 @@ def _sample_tokens_for_batch(
             top_ks = spec_metadata.top_ks[:num_tokens]
             top_ps = spec_metadata.top_ps[:num_tokens]
 
+            if self.use_flashinfer:
+                self.seed += 1
+
             sampled_tokens = sampling_batch_spec_dec_one_model(
-                logits, temperatures, top_ks, top_ps)
+                logits,
+                temperatures,
+                top_ks,
+                top_ps,
+                use_flashinfer=self.use_flashinfer,
+                seed=self.seed,
+                offset=self.offset)
         else:
             sampled_tokens = torch.argmax(logits, dim=-1)
 

tensorrt_llm/_torch/speculative/one_model_sampler.py

@@ -1,6 +1,7 @@
 from typing import Optional
 
 import torch
+from flashinfer.sampling import top_k_top_p_sampling_from_logits
 
 
 def forward_native(
@@ -78,6 +79,9 @@ def sampling_batch_spec_dec_one_model(
     temperatures: torch.Tensor,
     top_k: torch.Tensor,
     top_p: torch.Tensor,
+    use_flashinfer: bool = False,
+    seed: Optional[int] = None,
+    offset: Optional[int] = None,
 ) -> tuple[torch.Tensor, torch.Tensor]:
     """
     CUDA-graph compatible sampling. Supports mixed sampling params.
@@ -87,5 +91,7 @@ def sampling_batch_spec_dec_one_model(
     sampling is opt-in for now.
     """
     logits = apply_temperature(logits, temperatures)
+    if use_flashinfer:
+        return top_k_top_p_sampling_from_logits(logits, top_k, top_p, seed=seed, offset=offset)
     random_sampled = forward_native(logits, top_k, top_p)
     return random_sampled