[V1][Spec Decode] Apply torch.compile & cudagraph to EAGLE (vllm-project#17211)

Signed-off-by: Bryan Lu <[email protected]>
Signed-off-by: minpeter <[email protected]>

Author: luyuzhe111

examples/offline_inference/eagle.py

@@ -36,6 +36,10 @@ def parse_args():
         help="downloaded from the eagle repo " \
         "https://github.com/SafeAILab/EAGLE/blob/main/eagle/data/"
     )
+    parser.add_argument("--method",
+                        type=str,
+                        default='eagle',
+                        choices=['eagle', 'eagle3'])
     parser.add_argument("--max_num_seqs", type=int, default=8)
     parser.add_argument("--num_prompts", type=int, default=80)
     parser.add_argument("--num_spec_tokens", type=int, default=2)
@@ -53,7 +57,13 @@ def main():
     args = parse_args()
 
     model_dir = "meta-llama/Llama-3.1-8B-Instruct"
-    eagle_dir = "yuhuili/EAGLE3-LLaMA3.1-Instruct-8B"
+
+    if args.method == 'eagle':
+        eagle_dir = "yuhuili/EAGLE-LLaMA3.1-Instruct-8B"
+    elif args.method == 'eagle3':
+        eagle_dir = "yuhuili/EAGLE3-LLaMA3.1-Instruct-8B"
+    else:
+        raise ValueError(f"unknown method: {args.method}")
 
     max_model_len = 2048
 
@@ -81,7 +91,7 @@ def main():
         max_num_seqs=args.max_num_seqs,
         gpu_memory_utilization=0.8,
         speculative_config={
-            "method": "eagle3" if "eagle3" in eagle_dir.lower() else "eagle",
+            "method": args.method,
             "model": eagle_dir,
             "num_speculative_tokens": args.num_spec_tokens,
             "draft_tensor_parallel_size": args.draft_tp,

vllm/compilation/backends.py

@@ -347,8 +347,12 @@ def configure_post_pass(self):
         PASS_KEY = "post_grad_custom_post_pass"
         if PASS_KEY in inductor_config:
             # Config should automatically wrap all inductor passes
-            assert isinstance(inductor_config[PASS_KEY], InductorPass)
-            self.post_grad_pass_manager.add(inductor_config[PASS_KEY])
+            if isinstance(inductor_config[PASS_KEY], PostGradPassManager):
+                assert (inductor_config[PASS_KEY].uuid() ==
+                        self.post_grad_pass_manager.uuid())
+            else:
+                assert isinstance(inductor_config[PASS_KEY], InductorPass)
+                self.post_grad_pass_manager.add(inductor_config[PASS_KEY])
         inductor_config[PASS_KEY] = self.post_grad_pass_manager
 
     def __call__(self, graph: fx.GraphModule, example_inputs) -> Callable:
@@ -408,8 +412,13 @@ def __call__(self, graph: fx.GraphModule, example_inputs) -> Callable:
             )
             self.compilation_config.cache_dir = cache_dir
 
-        cache_dir = self.compilation_config.cache_dir
+        if compilation_counter.num_graphs_seen > 0:
+            cache_dir = self.compilation_config.cache_dir + \
+                f'-{compilation_counter.num_graphs_seen}'
+        else:
+            cache_dir = self.compilation_config.cache_dir
         os.makedirs(cache_dir, exist_ok=True)
+        self.compilation_config.cache_dir = cache_dir
         rank = vllm_config.parallel_config.rank
         dp_rank = vllm_config.parallel_config.data_parallel_rank
         local_cache_dir = os.path.join(cache_dir, f"rank_{rank}_{dp_rank}")

vllm/model_executor/models/llama_eagle.py

@@ -6,7 +6,8 @@
 import torch.nn as nn
 from transformers import LlamaConfig
 
-from vllm.config import ModelConfig
+from vllm.compilation.decorators import support_torch_compile
+from vllm.config import VllmConfig
 from vllm.logger import init_logger
 from vllm.model_executor.layers.logits_processor import LogitsProcessor
 from vllm.model_executor.layers.vocab_parallel_embedding import (
@@ -37,17 +38,19 @@ def __init__(
             self.input_layernorm = nn.Identity()
 
 
+@support_torch_compile
 class LlamaModel(nn.Module):
 
     def __init__(
         self,
         *,
-        model_config: ModelConfig,
-        start_layer_id: int = 0,
+        vllm_config: VllmConfig,
         prefix: str = "",
+        start_layer_id: int = 0,
     ) -> None:
         super().__init__()
-        self.config = model_config.hf_config
+        self.config = vllm_config. \
+            speculative_config.draft_model_config.hf_config
         self.vocab_size = self.config.vocab_size
         self.embed_tokens = VocabParallelEmbedding(
             self.config.vocab_size,
@@ -75,8 +78,7 @@ def forward(
         hidden_states = self.fc(
             torch.cat((input_embeds, hidden_states), dim=-1))
         residual = None
-        for i in range(len(self.layers)):
-            layer = self.layers[i]
+        for layer in self.layers:
             hidden_states, residual = layer(
                 positions,
                 hidden_states,
@@ -117,12 +119,13 @@ def load_weights(self, weights: Iterable[Tuple[str,
 
 class EagleLlamaForCausalLM(LlamaForCausalLM):
 
-    def __init__(self, *, model_config: ModelConfig, start_layer_id: int = 0):
+    def __init__(self, *, vllm_config: VllmConfig, start_layer_id: int = 0):
         nn.Module.__init__(self)
-        self.config = model_config.hf_config
-        self.model = LlamaModel(model_config=model_config,
-                                start_layer_id=start_layer_id,
-                                prefix="model")
+        self.config = vllm_config. \
+            speculative_config.draft_model_config.hf_config
+        self.model = LlamaModel(vllm_config=vllm_config,
+                                prefix="model",
+                                start_layer_id=start_layer_id)
 
         logit_scale = getattr(self.config, "logit_scale", 1.0)
         self.logits_processor = LogitsProcessor(self.config.vocab_size,

vllm/model_executor/models/llama_eagle3.py

@@ -6,7 +6,7 @@
 import torch.nn as nn
 from transformers import LlamaConfig
 
-from vllm.config import ModelConfig
+from vllm.config import ModelConfig, VllmConfig
 from vllm.logger import init_logger
 from vllm.model_executor.layers.layernorm import RMSNorm
 from vllm.model_executor.layers.linear import QKVParallelLinear
@@ -167,8 +167,9 @@ def load_weights(self, weights: Iterable[Tuple[str,
 
 class Eagle3LlamaForCausalLM(LlamaForCausalLM):
 
-    def __init__(self, *, model_config: ModelConfig, start_layer_id: int = 0):
+    def __init__(self, *, vllm_config: VllmConfig, start_layer_id: int = 0):
         nn.Module.__init__(self)
+        model_config = vllm_config.speculative_config.draft_model_config
         self.config = model_config.hf_config
         self.model = LlamaModel(model_config=model_config,
                                 start_layer_id=start_layer_id,

vllm/v1/spec_decode/eagle.py

@@ -4,7 +4,7 @@
 import triton
 import triton.language as tl
 
-from vllm.config import VllmConfig, set_current_vllm_config
+from vllm.config import CompilationLevel, VllmConfig, set_current_vllm_config
 from vllm.forward_context import set_forward_context
 from vllm.logger import init_logger
 from vllm.model_executor.model_loader.loader import get_model_loader
@@ -26,10 +26,41 @@ def __init__(
         device: torch.device,
     ):
         self.vllm_config = vllm_config
+        self.method = self.vllm_config.speculative_config.method
         self.num_speculative_tokens = (
             vllm_config.speculative_config.num_speculative_tokens)
         self.max_model_len = vllm_config.model_config.max_model_len
         self.block_size = vllm_config.cache_config.block_size
+
+        self.dtype = vllm_config.model_config.dtype
+
+        self.max_num_tokens = vllm_config.scheduler_config \
+            .max_num_batched_tokens
+
+        self.hidden_size = vllm_config.model_config.get_hidden_size()
+
+        # TODO: make eagle3 compatible with cudagraph
+        self.use_cuda_graph = self.method != 'eagle3' and \
+            (self.vllm_config.compilation_config.level
+             == CompilationLevel.PIECEWISE and
+             not self.vllm_config.model_config.enforce_eager)
+
+        self.cudagraph_batch_sizes = list(
+            reversed(
+                self.vllm_config.compilation_config.cudagraph_capture_sizes))
+
+        # persistent buffers for cuda graph
+        self.input_ids = torch.zeros(self.max_num_tokens,
+                                     dtype=torch.int32,
+                                     device=device)
+        self.positions = torch.zeros(self.max_num_tokens,
+                                     dtype=torch.int64,
+                                     device=device)
+
+        self.hidden_states = torch.zeros(
+            (self.max_num_tokens, self.hidden_size),
+            dtype=self.dtype,
+            device=device)
         # We need +1 here because the arange is used to set query_start_loc,
         # which has one more element than batch_size.
         self.arange = torch.arange(vllm_config.scheduler_config.max_num_seqs +
@@ -59,13 +90,12 @@ def propose(
         batch_size = next_token_ids.shape[0]
         last_token_indices = cu_num_tokens[1:] - 1
 
-        input_ids = torch.empty_like(target_token_ids)
         # Shift the input ids by one token.
         # E.g., [a1, b1, b2, c1, c2, c3] -> [b1, b2, c1, c2, c3, c3]
-        input_ids[:-1] = target_token_ids[1:]
+        self.input_ids[:num_tokens - 1] = target_token_ids[1:]
         # Replace the last token with the next token.
         # E.g., [b1, b2, c1, c2, c3, c3] -> [a2, b2, b3, c2, c3, c4]
-        input_ids[last_token_indices] = next_token_ids
+        self.input_ids[last_token_indices] = next_token_ids
 
         # FA requires seq_len to have dtype int32.
         seq_lens = (target_positions[last_token_indices] + 1).int()
@@ -88,14 +118,30 @@ def propose(
             prefix_kv_lens=None,
             suffix_kv_lens=None,
         )
+        if self.use_cuda_graph and \
+            num_tokens <= self.cudagraph_batch_sizes[-1]:
+            num_input_tokens = self.vllm_config.pad_for_cudagraph(num_tokens)
+        else:
+            num_input_tokens = num_tokens
+        # copy inputs to buffer for cudagraph
+        self.positions[:num_tokens] = target_positions
 
-        with set_forward_context(attn_metadata, self.vllm_config):
-            hidden_states_logits, hidden_states_fwd = self.model(
-                input_ids=input_ids,
-                hidden_states=target_hidden_states,
-                positions=target_positions,
+        if self.method == 'eagle':
+            self.hidden_states[:num_tokens] = target_hidden_states
+            hidden_states = self.hidden_states
+        else:
+            # TODO: make eagle3 compatible with cuda graph
+            hidden_states = target_hidden_states
+
+        with set_forward_context(attn_metadata,
+                                 self.vllm_config,
+                                 num_tokens=num_input_tokens):
+            last_hidden_states, hidden_states = self.model(
+                input_ids=self.input_ids[:num_input_tokens],
+                positions=self.positions[:num_input_tokens],
+                hidden_states=hidden_states[:num_input_tokens],
             )
-        sample_hidden_states = hidden_states_logits[last_token_indices]
+        sample_hidden_states = last_hidden_states[last_token_indices]
         logits = self.model.compute_logits(sample_hidden_states, None)
         draft_token_ids = logits.argmax(dim=-1)
 
@@ -108,13 +154,20 @@ def propose(
         draft_token_ids_list = [draft_token_ids]
 
         positions = target_positions[last_token_indices]
-        hidden_states = hidden_states_fwd[last_token_indices]
+        hidden_states = hidden_states[last_token_indices]
+        if self.use_cuda_graph and \
+            batch_size <= self.cudagraph_batch_sizes[-1]:
+            input_batch_size = self.vllm_config.pad_for_cudagraph(batch_size)
+        else:
+            input_batch_size = batch_size
         attn_metadata.num_actual_tokens = batch_size
         attn_metadata.max_query_len = 1
         attn_metadata.query_start_loc = self.arange[:batch_size + 1]
         for _ in range(self.num_speculative_tokens - 1):
             # Update the inputs.
-            input_ids = draft_token_ids_list[-1]
+            # cast to int32 is crucial when eagle model is compiled.
+            # tensor.argmax() returns int64 by default.
+            input_ids = draft_token_ids_list[-1].int()
             positions += 1
 
             # NOTE(woosuk): We should handle the case where the draft model
@@ -152,14 +205,27 @@ def propose(
             attn_metadata.slot_mapping.masked_fill_(exceeds_max_model_len,
                                                     PADDING_SLOT_ID)
 
+            # copy inputs to buffer for cudagraph
+            self.input_ids[:batch_size] = input_ids
+            self.positions[:batch_size] = clamped_positions
+
+            if self.method == 'eagle':
+                # TODO: make eagle3 compatible with cudagraph.
+                self.hidden_states[:batch_size] = hidden_states
+                hidden_states = self.hidden_states
+
             # Run the model.
-            with set_forward_context(attn_metadata, self.vllm_config):
-                hidden_states_logits, hidden_states = self.model(
-                    input_ids=input_ids,
-                    hidden_states=hidden_states,
-                    positions=clamped_positions,
+            with set_forward_context(attn_metadata,
+                                     self.vllm_config,
+                                     num_tokens=input_batch_size):
+                last_hidden_states, hidden_states = self.model(
+                    input_ids=self.input_ids[:input_batch_size],
+                    positions=self.positions[:input_batch_size],
+                    hidden_states=hidden_states[:input_batch_size],
                 )
-            logits = self.model.compute_logits(hidden_states_logits, None)
+            hidden_states = hidden_states[:batch_size]
+            logits = self.model.compute_logits(last_hidden_states[:batch_size],
+                                               None)
             draft_token_ids = logits.argmax(dim=-1)
             draft_token_ids_list.append(draft_token_ids)
 
@@ -227,13 +293,11 @@ def load_model(self, target_model: nn.Module) -> None:
             draft_model_cls, arch = ModelRegistry.resolve_model_cls(
                 draft_model_config.architectures)
             self.model = draft_model_cls(
-                model_config=draft_model_config,
+                vllm_config=self.vllm_config,
                 start_layer_id=target_layer_num).to(target_device)
 
         loaded_weights = self.model.load_weights(
-            loader.get_all_weights(
-                self.vllm_config.speculative_config.draft_model_config,
-                self.model))
+            loader.get_all_weights(draft_model_config, self.model))
         if self.vllm_config.speculative_config.method == "eagle3":
             if "model.embed_tokens.weight" not in loaded_weights:
                 logger.info(
@@ -243,6 +307,20 @@ def load_model(self, target_model: nn.Module) -> None:
             logger.info("Loading EAGLE LM head weights from the target model.")
             self.model.lm_head = target_model.lm_head
 
+    @torch.inference_mode()
+    def dummy_run(
+        self,
+        num_tokens: int,
+    ) -> None:
+        with set_forward_context(None, self.vllm_config,
+                                 num_tokens=num_tokens):
+            if self.method == 'eagle':
+                self.model(
+                    input_ids=self.input_ids[:num_tokens],
+                    positions=self.positions[:num_tokens],
+                    hidden_states=self.hidden_states[:num_tokens],
+                )
+
 
 # NOTE(woosuk): Currently, the below code is not used and we always use argmax
 # to sample the draft tokens. We will use this after we find a way to manage