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[V1] Cache uses_mrope in GPUModelRunner #12969

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Feb 8, 2025
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[V1] Cache uses_mrope in GPUModelRunner #12969

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23 changes: 13 additions & 10 deletions vllm/v1/worker/gpu_model_runner.py
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Original file line number Diff line number Diff line change
Expand Up @@ -92,6 +92,7 @@ def __init__(
# Multi-modal data support
self.input_registry = INPUT_REGISTRY
self.mm_registry = MULTIMODAL_REGISTRY
self.uses_mrope = model_config.uses_mrope

# NOTE: Initialized input mapper is only used for processing dummy
# multimodal data into multimodal kwargs for GPU memory profiling.
Expand Down Expand Up @@ -147,7 +148,7 @@ def __init__(
device=self.device)

# Only relevant for models using M-RoPE (e.g, Qwen2-VL)
if self.model_config.uses_mrope:
if self.uses_mrope:
# NOTE: `mrope_positions` is implemented with one additional dummy
# position on purpose to make it non-contiguous so that it can work
# with torch compile.
Expand Down Expand Up @@ -284,7 +285,7 @@ def _update_states(self, scheduler_output: "SchedulerOutput") -> bool:
)

# Only relevant for models using M-RoPE (e.g, Qwen2-VL)
if self.model_config.uses_mrope:
if self.uses_mrope:
image_grid_thw = []
video_grid_thw = []
second_per_grid_ts = []
Expand Down Expand Up @@ -411,7 +412,7 @@ def _prepare_inputs(self, scheduler_output: "SchedulerOutput"):

# Calculate M-RoPE positions.
# Only relevant for models using M-RoPE (e.g, Qwen2-VL)
if self.model_config.uses_mrope:
if self.uses_mrope:
self._calc_mrope_positions(scheduler_output)

# Get token indices.
Expand Down Expand Up @@ -458,7 +459,7 @@ def _prepare_inputs(self, scheduler_output: "SchedulerOutput"):
# Copy the tensors to the GPU.
self.input_ids[:total_num_scheduled_tokens].copy_(
self.input_ids_cpu[:total_num_scheduled_tokens], non_blocking=True)
if self.model_config.uses_mrope:
if self.uses_mrope:
# Only relevant for models using M-RoPE (e.g, Qwen2-VL)
self.mrope_positions[:, :total_num_scheduled_tokens].copy_(
self.mrope_positions_cpu[:, :total_num_scheduled_tokens],
Expand Down Expand Up @@ -817,13 +818,14 @@ def execute_model(
# then the embedding layer is not included in the CUDA graph.
input_ids = self.input_ids[:num_input_tokens]
inputs_embeds = None
if self.uses_mrope:
positions = self.mrope_positions[:, :num_input_tokens]
else:
positions = self.positions[:num_input_tokens]

# Run the decoder.
# Use persistent buffers for CUDA graphs.
with set_forward_context(attn_metadata, self.vllm_config):
positions = self.mrope_positions[:, :num_input_tokens] \
if self.model_config.uses_mrope \
else self.positions[:num_input_tokens]
hidden_states = self.model(
input_ids=input_ids,
positions=positions,
Expand Down Expand Up @@ -1001,10 +1003,11 @@ def _dummy_run(
else:
input_ids = self.input_ids[:num_tokens]
inputs_embeds = None
if self.uses_mrope:
positions = self.mrope_positions[:, :num_tokens]
else:
positions = self.positions[:num_tokens]
with set_forward_context(None, self.vllm_config):
positions = self.mrope_positions[:, :num_tokens] \
if self.model_config.uses_mrope \
else self.positions[:num_tokens]
hidden_states = model(
input_ids=input_ids,
positions=positions,
Expand Down