[Model Runner V2] Some code simplification (#36929)

Signed-off-by: Nick Hill <nickhill123@gmail.com>

vllm/config/speculative.py

@@ -57,10 +57,7 @@ SpeculativeMethod = Literal[
     EagleModelTypes,
     NgramGPUTypes,
 ]
-RejectionSampleMethod = Literal[
-    "strict",
-    "probabilistic",
-]
+RejectionSampleMethod = Literal["strict", "probabilistic"]
 
 
 @config

vllm/v1/worker/gpu/sample/gumbel.py

@@ -81,7 +81,7 @@ def _gumbel_sample_kernel(
     logits = logits.to(tl.float32)
 
     temp = tl.load(temp_ptr + req_state_idx).to(tl.float32)
-    if (temp != 0.0) and APPLY_TEMPERATURE:
+    if temp != 0.0 and APPLY_TEMPERATURE:
         # Apply temperature.
         # NOTE(woosuk): Match the behavior of _temperature_kernel.
         # E.g., if the kernel uses tl.div_rn, we should use tl.div_rn here too.
@@ -127,18 +127,8 @@ def gumbel_sample(
     num_tokens, vocab_size = logits.shape
     BLOCK_SIZE = 1024
     num_blocks = triton.cdiv(vocab_size, BLOCK_SIZE)
-    local_argmax = torch.empty(
-        num_tokens,
-        num_blocks,
-        dtype=torch.int64,
-        device=logits.device,
-    )
-    local_max = torch.empty(
-        num_tokens,
-        num_blocks,
-        dtype=torch.float32,
-        device=logits.device,
-    )
+    local_argmax = logits.new_empty(num_tokens, num_blocks, dtype=torch.int64)
+    local_max = logits.new_empty(num_tokens, num_blocks, dtype=torch.float32)
     _gumbel_sample_kernel[(num_tokens, num_blocks)](
         local_argmax,
         local_argmax.stride(0),

vllm/v1/worker/gpu/spec_decode/rejection_sampler.py

@@ -53,17 +53,8 @@ def strict_rejection_sample(
     num_speculative_steps,
 ) -> tuple[torch.Tensor, torch.Tensor]:
     num_reqs = cu_num_logits.shape[0] - 1
-    sampled = torch.empty(
-        num_reqs,
-        num_speculative_steps + 1,
-        dtype=target_sampled.dtype,
-        device=target_sampled.device,
-    )
-    num_sampled = torch.empty(
-        num_reqs,
-        dtype=torch.int32,
-        device=target_sampled.device,
-    )
+    sampled = target_sampled.new_empty(num_reqs, num_speculative_steps + 1)
+    num_sampled = target_sampled.new_empty(num_reqs, dtype=torch.int32)
     _strict_rejection_sample_kernel[(num_reqs,)](
         sampled,
         sampled.stride(0),
@@ -216,12 +207,11 @@ def probabilistic_rejection_sample(
     pos: torch.Tensor,
     # [num_reqs]
     idx_mapping: torch.Tensor,
-    temperature,
-    seeds,
-    num_speculative_steps,
+    temperature: torch.Tensor,
+    seed: torch.Tensor,
+    num_speculative_steps: int,
 ) -> tuple[torch.Tensor, torch.Tensor]:
     num_reqs = cu_num_logits.shape[0] - 1
-    device = target_logits.device
     vocab_size = target_logits.shape[-1]
 
     # Compute target and draft probs.
@@ -230,18 +220,11 @@ def probabilistic_rejection_sample(
 
     # Rejection sample.
     # [num_reqs, num_speculative_steps + 1]
-    sampled = torch.empty(
-        num_reqs,
-        num_speculative_steps + 1,
-        dtype=torch.int64,
-        device=device,
+    sampled = draft_sampled.new_empty(
+        num_reqs, num_speculative_steps + 1, dtype=torch.int64
     )
     # [num_reqs]
-    rejected_steps = torch.empty(
-        num_reqs,
-        dtype=torch.int64,
-        device=device,
-    )
+    rejected_steps = sampled.new_empty(num_reqs)
     _probabilistic_rejection_sample_kernel[(num_reqs,)](
         sampled,
         sampled.stride(0),
@@ -255,25 +238,16 @@ def probabilistic_rejection_sample(
         cu_num_logits,
         pos,
         idx_mapping,
-        seeds,
+        seed,
         num_warps=1,
     )
 
     # Compute the logits and positions to resample the rejected/bonus
     # tokens from.
     # [num_reqs, vocab_size]
-    residual_logits = torch.empty(
-        num_reqs,
-        vocab_size,
-        dtype=target_logits.dtype,
-        device=device,
-    )
+    residual_logits = target_logits.new_empty(num_reqs, vocab_size)
     # [num_reqs]
-    residual_pos = torch.empty(
-        num_reqs,
-        dtype=pos.dtype,
-        device=device,
-    )
+    residual_pos = pos.new_empty(num_reqs)
     BLOCK_SIZE = 1024
     num_blocks = triton.cdiv(vocab_size, BLOCK_SIZE)
     _compute_residual_logits_kernel[(num_reqs, num_blocks)](
@@ -299,7 +273,7 @@ def probabilistic_rejection_sample(
         residual_logits,
         idx_mapping,
         temperature,
-        seeds,
+        seed,
         residual_pos,
         apply_temperature=False,
     )
@@ -331,10 +305,7 @@ class RejectionSampler:
         num_nans = get_num_nans(logits) if self.sampler.compute_nans else None
 
         if self.use_strict_rejection_sampling:
-            sampler_output = self.sampler(
-                logits,
-                input_batch,
-            )
+            sampler_output = self.sampler(logits, input_batch)
             logprobs_tensors = sampler_output.logprobs_tensors
             sampled, num_sampled = strict_rejection_sample(
                 sampler_output.sampled_token_ids.view(-1),