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[Model] Jamba support (#4115)
Signed-off-by: Muralidhar Andoorveedu <muralidhar.andoorveedu@centml.ai> Co-authored-by: Erez Schwartz <erezs@ai21.com> Co-authored-by: Mor Zusman <morz@ai21.com> Co-authored-by: tomeras91 <57313761+tomeras91@users.noreply.github.com> Co-authored-by: Tomer Asida <tomera@ai21.com> Co-authored-by: Zhuohan Li <zhuohan123@gmail.com> Co-authored-by: Muralidhar Andoorveedu <muralidhar.andoorveedu@centml.ai>
This commit is contained in:
@ -23,4 +23,4 @@ docker exec cpu-test-avx2 bash -c "python3 examples/offline_inference.py"
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docker exec cpu-test bash -c "cd tests;
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pip install pytest Pillow protobuf
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cd ../
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pytest -v -s tests/models -m \"not vlm\" --ignore=tests/models/test_embedding.py --ignore=tests/models/test_registry.py"
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pytest -v -s tests/models -m \"not vlm\" --ignore=tests/models/test_embedding.py --ignore=tests/models/test_registry.py --ignore=tests/models/test_jamba.py" # Mamba on CPU is not supported
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23
Dockerfile
23
Dockerfile
@ -43,6 +43,10 @@ COPY requirements-cuda.txt requirements-cuda.txt
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RUN --mount=type=cache,target=/root/.cache/pip \
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python3 -m pip install -r requirements-cuda.txt
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COPY requirements-mamba.txt requirements-mamba.txt
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RUN python3 -m pip install packaging
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RUN python3 -m pip install -r requirements-mamba.txt
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# cuda arch list used by torch
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# can be useful for both `dev` and `test`
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# explicitly set the list to avoid issues with torch 2.2
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@ -123,6 +127,21 @@ RUN --mount=type=cache,target=/root/.cache/pip \
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python3 -m pip install -r requirements-dev.txt
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#################### DEV IMAGE ####################
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#################### MAMBA Build IMAGE ####################
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FROM dev as mamba-builder
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# max jobs used for build
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ARG max_jobs=2
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ENV MAX_JOBS=${max_jobs}
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WORKDIR /usr/src/mamba
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COPY requirements-mamba.txt requirements-mamba.txt
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# Download the wheel or build it if a pre-compiled release doesn't exist
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RUN pip --verbose wheel -r requirements-mamba.txt \
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--no-build-isolation --no-deps --no-cache-dir
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#################### MAMBA Build IMAGE ####################
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#################### vLLM installation IMAGE ####################
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# image with vLLM installed
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@ -143,6 +162,10 @@ RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
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RUN --mount=type=bind,from=build,src=/workspace/dist,target=/vllm-workspace/dist \
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--mount=type=cache,target=/root/.cache/pip \
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python3 -m pip install dist/*.whl --verbose
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RUN --mount=type=bind,from=mamba-builder,src=/usr/src/mamba,target=/usr/src/mamba \
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--mount=type=cache,target=/root/.cache/pip \
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python3 -m pip install /usr/src/mamba/*.whl --no-cache-dir
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#################### vLLM installation IMAGE ####################
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@ -87,6 +87,10 @@ Alongside each architecture, we include some popular models that use it.
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- Jais
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- :code:`core42/jais-13b`, :code:`core42/jais-13b-chat`, :code:`core42/jais-30b-v3`, :code:`core42/jais-30b-chat-v3`, etc.
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-
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* - :code:`JambaForCausalLM`
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- Jamba
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- :code:`ai21labs/Jamba-v0.1`, etc.
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- ✅︎
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* - :code:`LlamaForCausalLM`
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- LLaMA, Llama 2, Meta Llama 3, Vicuna, Alpaca, Yi
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- :code:`meta-llama/Meta-Llama-3-8B-Instruct`, :code:`meta-llama/Meta-Llama-3-70B-Instruct`, :code:`meta-llama/Llama-2-13b-hf`, :code:`meta-llama/Llama-2-70b-hf`, :code:`openlm-research/open_llama_13b`, :code:`lmsys/vicuna-13b-v1.3`, :code:`01-ai/Yi-6B`, :code:`01-ai/Yi-34B`, etc.
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3
requirements-mamba.txt
Normal file
3
requirements-mamba.txt
Normal file
@ -0,0 +1,3 @@
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# Mamba dependencies
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mamba-ssm>=1.2.2
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causal-conv1d>=1.2.0
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65
tests/models/test_jamba.py
Normal file
65
tests/models/test_jamba.py
Normal file
@ -0,0 +1,65 @@
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import pytest
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MODELS = ["ai21labs/Jamba-tiny-random"]
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@pytest.mark.parametrize("model", MODELS)
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@pytest.mark.parametrize("dtype", ["float"])
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@pytest.mark.parametrize("max_tokens", [20])
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def test_models(
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hf_runner,
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vllm_runner,
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example_prompts,
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model: str,
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dtype: str,
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max_tokens: int,
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) -> None:
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# To pass the small model tests, we need full precision.
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assert dtype == "float"
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with hf_runner(model, dtype=dtype) as hf_model:
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hf_outputs = hf_model.generate_greedy(example_prompts, max_tokens)
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with vllm_runner(model, dtype=dtype) as vllm_model:
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vllm_outputs = vllm_model.generate_greedy(example_prompts, max_tokens)
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for i in range(len(example_prompts)):
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hf_output_ids, hf_output_str = hf_outputs[i]
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vllm_output_ids, vllm_output_str = vllm_outputs[i]
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assert hf_output_str == vllm_output_str, (
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f"Test{i}:\nHF: {hf_output_str!r}\nvLLM: {vllm_output_str!r}")
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assert hf_output_ids == vllm_output_ids, (
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f"Test{i}:\nHF: {hf_output_ids}\nvLLM: {vllm_output_ids}")
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@pytest.mark.parametrize("model", MODELS)
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@pytest.mark.parametrize("dtype", ["float"])
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def test_state_cleanup(
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vllm_runner,
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model: str,
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dtype: str,
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example_prompts,
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) -> None:
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# This test is for verifying that the Jamba state is cleaned up between
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# steps, If its not cleaned, an error would be expected.
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try:
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with vllm_runner(model, dtype=dtype) as vllm_model:
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for _ in range(10):
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vllm_model.generate_greedy([example_prompts[0]] * 100, 1)
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except ValueError:
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pytest.fail("Jamba inner state wasn't cleaned up between states, "
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"could be related to finished_requests_ids")
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@pytest.mark.parametrize("model", MODELS)
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@pytest.mark.parametrize("dtype", ["float"])
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def test_model_print(
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vllm_runner,
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model: str,
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dtype: str,
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) -> None:
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with vllm_runner(model, dtype=dtype) as vllm_model:
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# This test is for verifying whether the model's extra_repr
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# can be printed correctly.
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print(vllm_model.model.llm_engine.model_executor.driver_worker.
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model_runner.model)
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@ -386,9 +386,36 @@ class ModelConfig:
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return num_heads // parallel_config.tensor_parallel_size
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def get_num_layers(self, parallel_config: "ParallelConfig") -> int:
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total_num_hidden_layers = self.hf_text_config.num_hidden_layers
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total_num_hidden_layers = getattr(self.hf_text_config,
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"num_hidden_layers", 0)
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return total_num_hidden_layers // parallel_config.pipeline_parallel_size
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def contains_seqlen_agnostic_layers(
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self, parallel_config: "ParallelConfig") -> bool:
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"""True for Mamba/SSM models (Jamba)"""
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return self._get_num_seqlen_agnostic_layers(parallel_config) > 0
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def get_layers_block_type(self,
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parallel_config: "ParallelConfig") -> List[str]:
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num_layers = self.get_num_layers(parallel_config)
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# Transformers supports layers_block_type @property
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return getattr(self.hf_config, "layers_block_type",
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["attention"] * num_layers)
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def get_num_attention_layers(self,
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parallel_config: "ParallelConfig") -> int:
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return len([
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t for t in self.get_layers_block_type(parallel_config)
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if t == "attention"
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])
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def _get_num_seqlen_agnostic_layers(
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self, parallel_config: "ParallelConfig") -> int:
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return len([
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t for t in self.get_layers_block_type(parallel_config)
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if t != "attention"
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])
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class CacheConfig:
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"""Configuration for the KV cache.
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@ -299,7 +299,10 @@ class Scheduler:
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# Sequence groups in the SWAPPED state.
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# Contain decode requests that are swapped out.
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self.swapped: Deque[SequenceGroup] = deque()
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# Sequence groups finished requests ids since last step iteration.
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# It lets the model know that any state associated with these requests
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# can and must be released after the current step.
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self._finished_requests_ids: List[str] = list()
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# Time at previous scheduling step
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self.prev_time = 0.0
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# Did we schedule a prompt at previous step?
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@ -373,6 +376,12 @@ class Scheduler:
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def get_num_unfinished_seq_groups(self) -> int:
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return len(self.waiting) + len(self.running) + len(self.swapped)
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def get_and_reset_finished_requests_ids(self) -> List[str]:
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"""Flushes the list of request ids of previously finished seq_groups."""
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finished_requests_ids = self._finished_requests_ids
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self._finished_requests_ids = list()
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return finished_requests_ids
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def _schedule_running(
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self,
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running_queue: deque,
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@ -1036,6 +1045,11 @@ class Scheduler:
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self.block_manager.free(seq)
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def free_finished_seq_groups(self) -> None:
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for queue in [self.running, self.swapped, self.waiting]:
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self._finished_requests_ids += [
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seq_group.request_id for seq_group in queue
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if seq_group.is_finished()
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]
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self.running = deque(seq_group for seq_group in self.running
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if not seq_group.is_finished())
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@ -224,6 +224,8 @@ class _AsyncLLMEngine(LLMEngine):
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"""
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seq_group_metadata_list, scheduler_outputs = self.scheduler[
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virtual_engine].schedule()
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finished_requests_ids = self.scheduler[
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virtual_engine].get_and_reset_finished_requests_ids()
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if not scheduler_outputs.is_empty():
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# Execute the model.
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@ -235,7 +237,7 @@ class _AsyncLLMEngine(LLMEngine):
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virtual_engine=virtual_engine,
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num_lookahead_slots=scheduler_outputs.num_lookahead_slots,
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running_queue_size=scheduler_outputs.running_queue_size,
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)
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finished_requests_ids=finished_requests_ids)
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output = await self.model_executor.execute_model_async(
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execute_model_req)
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else:
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@ -846,6 +846,8 @@ class LLMEngine:
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"as performance will be severely degraded otherwise.")
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seq_group_metadata_list, scheduler_outputs = self.scheduler[
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0].schedule()
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finished_requests_ids = self.scheduler[
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0].get_and_reset_finished_requests_ids()
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if not scheduler_outputs.is_empty():
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execute_model_req = ExecuteModelRequest(
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@ -855,7 +857,7 @@ class LLMEngine:
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blocks_to_copy=scheduler_outputs.blocks_to_copy,
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num_lookahead_slots=scheduler_outputs.num_lookahead_slots,
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running_queue_size=scheduler_outputs.running_queue_size,
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)
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finished_requests_ids=finished_requests_ids)
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output = self.model_executor.execute_model(
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execute_model_req=execute_model_req)
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else:
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@ -63,6 +63,7 @@ _GENERATION_MODELS = {
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"XverseForCausalLM": ("xverse", "XverseForCausalLM"),
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"Phi3SmallForCausalLM": ("phi3_small", "Phi3SmallForCausalLM"),
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"MLPSpeculatorPreTrainedModel": ("mlp_speculator", "MLPSpeculator"),
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"JambaForCausalLM": ("jamba", "JambaForCausalLM")
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}
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_EMBEDDING_MODELS = {
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955
vllm/model_executor/models/jamba.py
Normal file
955
vllm/model_executor/models/jamba.py
Normal file
@ -0,0 +1,955 @@
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# coding=utf-8
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"""Inference-only Jurassic model."""
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from dataclasses import dataclass
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from typing import Dict, Iterable, List, Optional, Tuple
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import torch
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from causal_conv1d import causal_conv1d_fn, causal_conv1d_update
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from mamba_ssm.ops.selective_scan_interface import selective_scan_fn
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from mamba_ssm.ops.triton.selective_state_update import selective_state_update
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from torch import nn
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from torch.nn.parameter import Parameter
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from transformers import JambaConfig
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from vllm.attention.backends.abstract import AttentionMetadata
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from vllm.attention.layer import Attention
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from vllm.config import CacheConfig, LoRAConfig
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from vllm.distributed import (get_tensor_model_parallel_rank,
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get_tensor_model_parallel_world_size,
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tensor_model_parallel_all_reduce)
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from vllm.model_executor.layers.activation import SiluAndMul
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from vllm.model_executor.layers.fused_moe import fused_moe
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from vllm.model_executor.layers.layernorm import RMSNorm
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from vllm.model_executor.layers.linear import (ColumnParallelLinear,
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MergedColumnParallelLinear,
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QKVParallelLinear,
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ReplicatedLinear,
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RowParallelLinear)
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from vllm.model_executor.layers.logits_processor import LogitsProcessor
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from vllm.model_executor.layers.quantization.base_config import (
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QuantizationConfig)
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from vllm.model_executor.layers.sampler import Sampler
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from vllm.model_executor.layers.vocab_parallel_embedding import (
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DEFAULT_VOCAB_PADDING_SIZE, ParallelLMHead, VocabParallelEmbedding)
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from vllm.model_executor.model_loader.weight_utils import default_weight_loader
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from vllm.model_executor.sampling_metadata import SamplingMetadata
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from vllm.model_executor.utils import set_weight_attrs
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from vllm.sequence import IntermediateTensors, SamplerOutput
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from vllm.worker.model_runner import _BATCH_SIZES_TO_CAPTURE
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KVCache = Tuple[torch.Tensor, torch.Tensor]
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@dataclass
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class MambaCacheParams:
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is_prompt: bool = False
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conv_state: torch.Tensor = torch.Tensor()
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ssm_state: torch.Tensor = torch.Tensor()
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# Adapted from transformers.models.mamba.modeling_mamba.MambaMixer
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class JambaMambaMixer(nn.Module):
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"""
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Compute ∆, A, B, C, and D the state space parameters and compute
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the `contextualized_states`. A, D are input independent
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(see Mamba paper [1] Section 3.5.2 "Interpretation of A"
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for why A isn't selective) ∆, B, C are input-dependent
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(this is a key difference between Mamba and the linear time
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invariant S4, and is why Mamba is called
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**selective** state spaces)
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"""
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def __init__(self, config: JambaConfig, layer_idx):
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super().__init__()
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self.config = config
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self.layer_idx = layer_idx
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self.hidden_size = config.hidden_size
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self.ssm_state_size = config.mamba_d_state
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self.conv_kernel_size = config.mamba_d_conv
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self.intermediate_size = config.mamba_expand * config.hidden_size
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self.time_step_rank = config.mamba_dt_rank
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self.use_conv_bias = config.mamba_conv_bias
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self.use_bias = config.mamba_proj_bias
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self.conv1d = ColumnParallelLinear(
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input_size=self.conv_kernel_size,
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output_size=self.intermediate_size,
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bias=self.use_conv_bias,
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)
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# unsqueeze to fit conv1d weights shape into the linear weights shape.
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# Can't do this in `weight_loader` since it already exists in
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# `ColumnParallelLinear` and `set_weight_attrs`
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# doesn't allow to override it
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self.conv1d.weight.data = self.conv1d.weight.data.unsqueeze(1)
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self.in_proj = MergedColumnParallelLinear(self.hidden_size,
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[self.intermediate_size] * 2,
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bias=self.use_bias)
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# selective projection used to make dt, B and C input dependent
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self.x_proj = RowParallelLinear(
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self.intermediate_size,
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self.time_step_rank + self.ssm_state_size * 2,
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bias=False,
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)
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# time step projection (discretization) -
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# In the forward we need to apply dt_proj without the bias,
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# as the bias is added in the selective scan kernel.
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self.dt_proj = ColumnParallelLinear(self.time_step_rank,
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self.intermediate_size,
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bias=True,
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skip_bias_add=True)
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def weight_loader(param: Parameter, loaded_weight: torch.Tensor):
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tp_rank = get_tensor_model_parallel_rank()
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tp_size = get_tensor_model_parallel_world_size()
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param.data.copy_(
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loaded_weight.data.split(loaded_weight.shape[0] // tp_size,
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dim=0)[tp_rank])
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def A_weight_loader(param: Parameter, loaded_weight: torch.Tensor):
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weight_loader(param, -torch.exp(loaded_weight.float()))
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tp_size = get_tensor_model_parallel_world_size()
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self.A = nn.Parameter(
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torch.empty(
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self.intermediate_size // tp_size,
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self.ssm_state_size,
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dtype=torch.float32,
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))
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self.D = nn.Parameter(torch.ones(self.intermediate_size // tp_size))
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set_weight_attrs(self.D, {"weight_loader": weight_loader})
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set_weight_attrs(self.A, {"weight_loader": A_weight_loader})
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self.out_proj = RowParallelLinear(
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self.intermediate_size,
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self.hidden_size,
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bias=self.use_bias,
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input_is_parallel=True,
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)
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self.activation = config.hidden_act
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self.dt_layernorm = RMSNorm(self.time_step_rank,
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eps=config.rms_norm_eps)
|
||||
self.b_layernorm = RMSNorm(self.ssm_state_size,
|
||||
eps=config.rms_norm_eps)
|
||||
self.c_layernorm = RMSNorm(self.ssm_state_size,
|
||||
eps=config.rms_norm_eps)
|
||||
|
||||
def mamba_forward(self,
|
||||
hidden_states: torch.Tensor,
|
||||
cache_params: MambaCacheParams = None):
|
||||
# 1. Gated MLP's linear projection
|
||||
projected_states = self.in_proj(hidden_states)[0].transpose(1, 2)
|
||||
hidden_states, gate = projected_states.chunk(2, dim=1)
|
||||
|
||||
# 2. Convolution sequence transformation
|
||||
conv_weights = self.conv1d.weight.view(self.conv1d.weight.size(0),
|
||||
self.conv1d.weight.size(2))
|
||||
if cache_params is not None and not cache_params.is_prompt:
|
||||
hidden_states = causal_conv1d_update(
|
||||
hidden_states.squeeze(-1),
|
||||
cache_params.conv_state,
|
||||
conv_weights,
|
||||
self.conv1d.bias,
|
||||
self.activation,
|
||||
)
|
||||
hidden_states = hidden_states.unsqueeze(-1)
|
||||
else:
|
||||
if cache_params is not None:
|
||||
conv_states = nn.functional.pad(
|
||||
hidden_states,
|
||||
(self.conv_kernel_size - hidden_states.shape[-1], 0))
|
||||
cache_params.conv_state.copy_(conv_states)
|
||||
|
||||
hidden_states = causal_conv1d_fn(
|
||||
hidden_states,
|
||||
conv_weights,
|
||||
self.conv1d.bias,
|
||||
activation=self.activation,
|
||||
)
|
||||
|
||||
# 3. State Space Model sequence transformation
|
||||
# 3.a. input varying initialization of time_step, B and C
|
||||
ssm_parameters = self.x_proj(hidden_states.transpose(1, 2))[0]
|
||||
|
||||
time_step, B, C = torch.split(
|
||||
ssm_parameters,
|
||||
[self.time_step_rank, self.ssm_state_size, self.ssm_state_size],
|
||||
dim=-1,
|
||||
)
|
||||
time_step = self.dt_layernorm(time_step.contiguous())
|
||||
B = self.b_layernorm(B.contiguous())
|
||||
C = self.c_layernorm(C.contiguous())
|
||||
|
||||
discrete_time_step = self.dt_proj(time_step)[0].transpose(1, 2)
|
||||
# 3.c perform the recurrence y ← SSM(A, B, C)(x)
|
||||
time_proj_bias = (self.dt_proj.bias.float() if hasattr(
|
||||
self.dt_proj, "bias") else None)
|
||||
if cache_params is not None and not cache_params.is_prompt:
|
||||
scan_outputs = selective_state_update(
|
||||
cache_params.ssm_state,
|
||||
hidden_states[..., 0],
|
||||
discrete_time_step[..., 0],
|
||||
self.A,
|
||||
B[:, 0],
|
||||
C[:, 0],
|
||||
self.D,
|
||||
gate[..., 0],
|
||||
time_proj_bias,
|
||||
dt_softplus=True,
|
||||
).unsqueeze(-1)
|
||||
else:
|
||||
scan_outputs, ssm_state = selective_scan_fn(
|
||||
hidden_states,
|
||||
discrete_time_step,
|
||||
self.A,
|
||||
B.transpose(1, 2),
|
||||
C.transpose(1, 2),
|
||||
self.D.float(),
|
||||
gate,
|
||||
time_proj_bias,
|
||||
delta_softplus=True,
|
||||
return_last_state=True,
|
||||
)
|
||||
if ssm_state is not None and cache_params is not None:
|
||||
cache_params.ssm_state.copy_(ssm_state)
|
||||
|
||||
# 4. Final linear projection
|
||||
contextualized_states = self.out_proj(scan_outputs.transpose(1, 2))[0]
|
||||
return contextualized_states
|
||||
|
||||
def forward(
|
||||
self,
|
||||
hidden_states: torch.Tensor,
|
||||
attn_metadata: AttentionMetadata,
|
||||
conv_state: torch.Tensor,
|
||||
ssm_state: torch.Tensor,
|
||||
):
|
||||
if attn_metadata.prefill_metadata is not None:
|
||||
offset = 0
|
||||
for i, prompt_len in enumerate(
|
||||
attn_metadata.prefill_metadata.seq_lens):
|
||||
cache = MambaCacheParams(True,
|
||||
conv_state=conv_state[i].unsqueeze(0),
|
||||
ssm_state=ssm_state[i].unsqueeze(0))
|
||||
hidden_states[offset:offset + prompt_len].copy_(
|
||||
self.mamba_forward(hidden_states[offset:offset +
|
||||
prompt_len].unsqueeze(0),
|
||||
cache_params=cache)[0])
|
||||
offset += prompt_len
|
||||
else:
|
||||
cache = MambaCacheParams(False,
|
||||
conv_state=conv_state,
|
||||
ssm_state=ssm_state)
|
||||
hidden_states = self.mamba_forward(hidden_states.unsqueeze(1),
|
||||
cache_params=cache)
|
||||
hidden_states = hidden_states.squeeze(1)
|
||||
|
||||
return hidden_states
|
||||
|
||||
|
||||
class JambaMLP(nn.Module):
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
config: JambaConfig,
|
||||
quant_config: Optional[QuantizationConfig] = None,
|
||||
) -> None:
|
||||
super().__init__()
|
||||
hidden_size = config.hidden_size
|
||||
intermediate_size = config.intermediate_size
|
||||
hidden_act = config.hidden_act
|
||||
self.gate_up_proj = MergedColumnParallelLinear(
|
||||
hidden_size, [intermediate_size] * 2,
|
||||
bias=False,
|
||||
quant_config=quant_config)
|
||||
self.down_proj = RowParallelLinear(intermediate_size,
|
||||
hidden_size,
|
||||
bias=False,
|
||||
quant_config=quant_config)
|
||||
if hidden_act != "silu":
|
||||
raise ValueError(f"Unsupported activation: {hidden_act}. "
|
||||
"Only silu is supported for now.")
|
||||
self.act_fn = SiluAndMul()
|
||||
|
||||
def forward(self, x):
|
||||
gate_up, _ = self.gate_up_proj(x)
|
||||
x = self.act_fn(gate_up)
|
||||
x, _ = self.down_proj(x)
|
||||
return x
|
||||
|
||||
|
||||
class JambaMoE(nn.Module):
|
||||
"""A tensor-parallel MoE implementation for Mixtral that shards each expert
|
||||
across all ranks.
|
||||
|
||||
Each expert's weights are sharded across all ranks and a fused MoE
|
||||
kernel is used for the forward pass, and finally we reduce the outputs
|
||||
across ranks.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
config: JambaConfig,
|
||||
params_dtype: Optional[torch.dtype] = None,
|
||||
tp_size: Optional[int] = None,
|
||||
quant_config: Optional[QuantizationConfig] = None,
|
||||
):
|
||||
super().__init__()
|
||||
self.tp_size = tp_size or get_tensor_model_parallel_world_size()
|
||||
self.num_total_experts = config.num_experts
|
||||
self.top_k = config.num_experts_per_tok
|
||||
self.hidden_size = config.hidden_size
|
||||
self.intermediate_size = config.intermediate_size // self.tp_size
|
||||
|
||||
if params_dtype is None:
|
||||
params_dtype = torch.get_default_dtype()
|
||||
self.params_dtype = params_dtype
|
||||
|
||||
self.router = ReplicatedLinear(self.hidden_size,
|
||||
self.num_total_experts,
|
||||
bias=False,
|
||||
params_dtype=self.params_dtype)
|
||||
|
||||
self.ws = nn.Parameter(
|
||||
torch.empty(
|
||||
self.num_total_experts,
|
||||
2 * self.intermediate_size,
|
||||
self.hidden_size,
|
||||
device="cuda",
|
||||
dtype=self.params_dtype,
|
||||
))
|
||||
self.w2s = nn.Parameter(
|
||||
torch.empty(
|
||||
self.num_total_experts,
|
||||
self.hidden_size,
|
||||
self.intermediate_size,
|
||||
device="cuda",
|
||||
dtype=self.params_dtype,
|
||||
))
|
||||
|
||||
set_weight_attrs(
|
||||
self.ws,
|
||||
{
|
||||
"weight_loader": self.weight_loader,
|
||||
},
|
||||
)
|
||||
set_weight_attrs(
|
||||
self.w2s,
|
||||
{
|
||||
"weight_loader": self.weight_loader,
|
||||
},
|
||||
)
|
||||
|
||||
def weight_loader(
|
||||
self,
|
||||
param: nn.Parameter,
|
||||
loaded_weight: torch.Tensor,
|
||||
weight_name: str,
|
||||
expert_id: int,
|
||||
):
|
||||
tp_rank = get_tensor_model_parallel_rank()
|
||||
param_data = param.data
|
||||
shard_size = self.intermediate_size
|
||||
shard = slice(tp_rank * shard_size, (tp_rank + 1) * shard_size)
|
||||
if weight_name.endswith("gate_proj.weight"):
|
||||
param_data[expert_id, 0:shard_size, :] = loaded_weight[shard, :]
|
||||
if weight_name.endswith("up_proj.weight"):
|
||||
param_data[expert_id,
|
||||
shard_size:2 * shard_size, :] = loaded_weight[shard, :]
|
||||
if weight_name.endswith("down_proj.weight"):
|
||||
param_data[expert_id, :, :] = loaded_weight[:, shard]
|
||||
|
||||
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
|
||||
num_tokens, hidden_size = hidden_states.shape
|
||||
hidden_states = hidden_states.view(-1, self.hidden_size)
|
||||
# router_logits: (batch * sequence_length, n_experts)
|
||||
router_logits, _ = self.router(hidden_states)
|
||||
|
||||
final_hidden_states = fused_moe(
|
||||
hidden_states,
|
||||
self.ws,
|
||||
self.w2s,
|
||||
router_logits,
|
||||
self.top_k,
|
||||
renormalize=
|
||||
False, # Mixtral normalize the expert probs to 1. We don't!
|
||||
inplace=True,
|
||||
)
|
||||
|
||||
if self.tp_size > 1:
|
||||
final_hidden_states = tensor_model_parallel_all_reduce(
|
||||
final_hidden_states)
|
||||
|
||||
return final_hidden_states.view(num_tokens, hidden_size)
|
||||
|
||||
|
||||
class JambaMambaDecoderLayer(nn.Module):
|
||||
|
||||
def __init__(self,
|
||||
config: JambaConfig,
|
||||
layer_idx: int,
|
||||
cache_config: Optional[CacheConfig] = None,
|
||||
quant_config: Optional[QuantizationConfig] = None) -> None:
|
||||
super().__init__()
|
||||
self.layer_idx = layer_idx
|
||||
self.config = config
|
||||
self.mamba = JambaMambaMixer(config, layer_idx)
|
||||
|
||||
num_experts = config.layers_num_experts[layer_idx]
|
||||
ffn_layer_class = JambaMoE if num_experts > 1 else JambaMLP
|
||||
self.feed_forward = ffn_layer_class(config, quant_config=quant_config)
|
||||
self.input_layernorm = RMSNorm(config.hidden_size,
|
||||
eps=config.rms_norm_eps)
|
||||
self.pre_ff_layernorm = RMSNorm(config.hidden_size,
|
||||
eps=config.rms_norm_eps)
|
||||
|
||||
def forward(
|
||||
self,
|
||||
hidden_states: torch.Tensor,
|
||||
attn_metadata: AttentionMetadata,
|
||||
residual: Optional[torch.Tensor],
|
||||
conv_state: torch.Tensor,
|
||||
ssm_state: torch.Tensor,
|
||||
**kwargs,
|
||||
):
|
||||
if residual is None:
|
||||
residual = hidden_states
|
||||
hidden_states = self.input_layernorm(hidden_states)
|
||||
else:
|
||||
hidden_states, residual = self.input_layernorm(
|
||||
hidden_states, residual)
|
||||
|
||||
hidden_states = self.mamba(hidden_states, attn_metadata, conv_state,
|
||||
ssm_state)
|
||||
# Fully Connected
|
||||
hidden_states, residual = self.pre_ff_layernorm(
|
||||
hidden_states, residual)
|
||||
hidden_states = self.feed_forward(hidden_states)
|
||||
return hidden_states, residual
|
||||
|
||||
|
||||
class JambaAttentionDecoderLayer(nn.Module):
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
config: JambaConfig,
|
||||
layer_idx: int,
|
||||
cache_config: Optional[CacheConfig] = None,
|
||||
quant_config: Optional[QuantizationConfig] = None,
|
||||
) -> None:
|
||||
super().__init__()
|
||||
self.hidden_size = config.hidden_size
|
||||
tp_size = get_tensor_model_parallel_world_size()
|
||||
self.total_num_heads = config.num_attention_heads
|
||||
assert self.total_num_heads % tp_size == 0
|
||||
self.num_heads = self.total_num_heads // tp_size
|
||||
self.total_num_kv_heads = config.num_key_value_heads
|
||||
if self.total_num_kv_heads >= tp_size:
|
||||
# Number of KV heads is greater than TP size, so we partition
|
||||
# the KV heads across multiple tensor parallel GPUs.
|
||||
assert self.total_num_kv_heads % tp_size == 0
|
||||
else:
|
||||
# Number of KV heads is less than TP size, so we replicate
|
||||
# the KV heads across multiple tensor parallel GPUs.
|
||||
assert tp_size % self.total_num_kv_heads == 0
|
||||
self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
|
||||
self.head_dim = config.hidden_size // self.total_num_heads
|
||||
self.q_size = self.num_heads * self.head_dim
|
||||
self.kv_size = self.num_kv_heads * self.head_dim
|
||||
self.scaling = self.head_dim**-0.5
|
||||
|
||||
self.qkv_proj = QKVParallelLinear(
|
||||
config.hidden_size,
|
||||
self.head_dim,
|
||||
self.total_num_heads,
|
||||
self.total_num_kv_heads,
|
||||
bias=False,
|
||||
quant_config=quant_config,
|
||||
)
|
||||
self.o_proj = RowParallelLinear(self.total_num_heads * self.head_dim,
|
||||
config.hidden_size,
|
||||
bias=False,
|
||||
quant_config=quant_config)
|
||||
|
||||
self.attn = Attention(
|
||||
self.num_heads,
|
||||
self.head_dim,
|
||||
self.scaling,
|
||||
num_kv_heads=self.num_kv_heads,
|
||||
cache_config=cache_config,
|
||||
)
|
||||
|
||||
num_experts = config.layers_num_experts[layer_idx]
|
||||
ffn_layer_class = JambaMoE if num_experts > 1 else JambaMLP
|
||||
self.feed_forward = ffn_layer_class(config, quant_config=quant_config)
|
||||
self.input_layernorm = RMSNorm(config.hidden_size,
|
||||
eps=config.rms_norm_eps)
|
||||
self.pre_ff_layernorm = RMSNorm(config.hidden_size,
|
||||
eps=config.rms_norm_eps)
|
||||
|
||||
def self_attention(
|
||||
self,
|
||||
positions: torch.Tensor,
|
||||
hidden_states: torch.Tensor,
|
||||
kv_cache: torch.Tensor,
|
||||
attn_metadata: AttentionMetadata,
|
||||
**kwargs,
|
||||
) -> torch.Tensor:
|
||||
qkv, _ = self.qkv_proj(hidden_states)
|
||||
q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
|
||||
attn_output = self.attn(q, k, v, kv_cache, attn_metadata)
|
||||
output, _ = self.o_proj(attn_output)
|
||||
return output
|
||||
|
||||
def forward(
|
||||
self,
|
||||
positions: torch.Tensor,
|
||||
hidden_states: torch.Tensor,
|
||||
kv_cache: torch.Tensor,
|
||||
attn_metadata: AttentionMetadata,
|
||||
residual: Optional[torch.Tensor],
|
||||
**kwargs,
|
||||
):
|
||||
if residual is None:
|
||||
residual = hidden_states
|
||||
hidden_states = self.input_layernorm(hidden_states)
|
||||
else:
|
||||
hidden_states, residual = self.input_layernorm(
|
||||
hidden_states, residual)
|
||||
|
||||
hidden_states = self.self_attention(
|
||||
positions=positions,
|
||||
hidden_states=hidden_states,
|
||||
kv_cache=kv_cache,
|
||||
attn_metadata=attn_metadata,
|
||||
)
|
||||
# Fully Connected
|
||||
hidden_states, residual = self.pre_ff_layernorm(
|
||||
hidden_states, residual)
|
||||
hidden_states = self.feed_forward(hidden_states)
|
||||
return hidden_states, residual
|
||||
|
||||
|
||||
ALL_DECODER_LAYER_TYPES = {
|
||||
"attention": JambaAttentionDecoderLayer,
|
||||
"mamba": JambaMambaDecoderLayer
|
||||
}
|
||||
|
||||
|
||||
class JambaModel(nn.Module):
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
config: JambaConfig,
|
||||
quant_config: Optional[QuantizationConfig] = None,
|
||||
cache_config: Optional[CacheConfig] = None,
|
||||
lora_config: Optional[LoRAConfig] = None,
|
||||
) -> None:
|
||||
super().__init__()
|
||||
self.config = config
|
||||
self.padding_idx = config.pad_token_id
|
||||
lora_vocab = ((lora_config.lora_extra_vocab_size *
|
||||
(lora_config.max_loras or 1)) if lora_config else 0)
|
||||
self.vocab_size = config.vocab_size + lora_vocab
|
||||
self.org_vocab_size = config.vocab_size
|
||||
|
||||
self.embed_tokens = VocabParallelEmbedding(
|
||||
self.vocab_size,
|
||||
config.hidden_size,
|
||||
org_num_embeddings=config.vocab_size,
|
||||
)
|
||||
|
||||
decoder_layers = []
|
||||
for i in range(config.num_hidden_layers):
|
||||
layer_class = ALL_DECODER_LAYER_TYPES[config.layers_block_type[i]]
|
||||
decoder_layers.append(
|
||||
layer_class(config,
|
||||
layer_idx=i,
|
||||
cache_config=cache_config,
|
||||
quant_config=quant_config))
|
||||
self.layers = nn.ModuleList(decoder_layers)
|
||||
self.final_layernorm = RMSNorm(config.hidden_size,
|
||||
eps=config.rms_norm_eps)
|
||||
|
||||
def forward(
|
||||
self,
|
||||
input_ids: torch.Tensor,
|
||||
positions: torch.Tensor,
|
||||
kv_caches: List[torch.Tensor],
|
||||
attn_metadata: AttentionMetadata,
|
||||
conv_state: torch.Tensor,
|
||||
ssm_state: torch.Tensor,
|
||||
) -> torch.Tensor:
|
||||
hidden_states = self.embed_tokens(input_ids)
|
||||
residual = None
|
||||
|
||||
for i in range(len(self.layers)):
|
||||
layer = self.layers[i]
|
||||
kv_cache = None
|
||||
current_ssm_state = None
|
||||
current_conv_state = None
|
||||
if isinstance(layer, JambaAttentionDecoderLayer):
|
||||
kv_cache = kv_caches[(i - self.config.attn_layer_offset) //
|
||||
self.config.attn_layer_period]
|
||||
if isinstance(layer, JambaMambaDecoderLayer):
|
||||
current_state_layer = i - (1 +
|
||||
(i - self.config.attn_layer_offset)
|
||||
// self.config.attn_layer_period)
|
||||
current_ssm_state = ssm_state[current_state_layer]
|
||||
current_conv_state = conv_state[current_state_layer]
|
||||
|
||||
hidden_states, residual = layer(
|
||||
positions=positions,
|
||||
hidden_states=hidden_states,
|
||||
kv_cache=kv_cache,
|
||||
attn_metadata=attn_metadata,
|
||||
residual=residual,
|
||||
conv_state=current_conv_state,
|
||||
ssm_state=current_ssm_state,
|
||||
)
|
||||
hidden_states, _ = self.final_layernorm(hidden_states, residual)
|
||||
return hidden_states
|
||||
|
||||
|
||||
class JambaForCausalLM(nn.Module):
|
||||
packed_modules_mapping = {
|
||||
"qkv_proj": [
|
||||
"q_proj",
|
||||
"k_proj",
|
||||
"v_proj",
|
||||
],
|
||||
}
|
||||
|
||||
# LoRA specific attributes
|
||||
supported_lora_modules = [
|
||||
"qkv_proj",
|
||||
"o_proj",
|
||||
"embed_tokens",
|
||||
"lm_head",
|
||||
]
|
||||
embedding_modules = {
|
||||
"embed_tokens": "input_embeddings",
|
||||
"lm_head": "output_embeddings",
|
||||
}
|
||||
embedding_padding_modules = ["lm_head"]
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
config: JambaConfig,
|
||||
cache_config: Optional[CacheConfig] = None,
|
||||
quant_config: Optional[QuantizationConfig] = None,
|
||||
lora_config: Optional[LoRAConfig] = None,
|
||||
) -> None:
|
||||
super().__init__()
|
||||
self.config = config
|
||||
self.model = JambaModel(config,
|
||||
cache_config=cache_config,
|
||||
quant_config=quant_config,
|
||||
lora_config=lora_config)
|
||||
self.unpadded_vocab_size = config.vocab_size
|
||||
if lora_config:
|
||||
self.unpadded_vocab_size += lora_config.lora_extra_vocab_size
|
||||
self.lm_head = ParallelLMHead(
|
||||
self.unpadded_vocab_size,
|
||||
config.hidden_size,
|
||||
org_num_embeddings=config.vocab_size,
|
||||
padding_size=DEFAULT_VOCAB_PADDING_SIZE
|
||||
# We need bigger padding if using lora for kernel
|
||||
# compatibility
|
||||
if not lora_config else lora_config.lora_vocab_padding_size,
|
||||
)
|
||||
# Current step used indices
|
||||
self.current_indices: List[int] = []
|
||||
# Used to track and store by the Mamba cache between steps.
|
||||
self.mamba_cache: Tuple[torch.Tensor, torch.Tensor] = tuple()
|
||||
# Used as an input_buffer for the CUDA graph runs.
|
||||
self.mamba_gc_cache_buffer: Tuple[torch.Tensor, torch.Tensor] = tuple()
|
||||
# Maps between the request id and a dict that maps between the seq_id
|
||||
# and its index inside the self.mamba_cache
|
||||
self.mamba_cache_indices_mapping: Dict[str, Dict[int, int]] = {}
|
||||
self.logits_processor = LogitsProcessor(self.unpadded_vocab_size,
|
||||
config.vocab_size)
|
||||
self.sampler = Sampler()
|
||||
|
||||
def forward(self,
|
||||
input_ids: torch.Tensor,
|
||||
positions: torch.Tensor,
|
||||
kv_caches: List[KVCache],
|
||||
attn_metadata: AttentionMetadata,
|
||||
intermediate_tensors: Optional[IntermediateTensors] = None,
|
||||
**kwargs):
|
||||
if not self.mamba_cache:
|
||||
self._prepare_mamba_cache()
|
||||
|
||||
if "seqlen_agnostic_capture_inputs" not in kwargs:
|
||||
# We get here only on Prefill/Eager mode runs
|
||||
assert all(
|
||||
key in kwargs
|
||||
for key in ["request_ids_to_seq_ids", "finished_requests_ids"])
|
||||
|
||||
request_ids_to_seq_ids = kwargs["request_ids_to_seq_ids"]
|
||||
batch_size = input_ids.shape[0]
|
||||
if attn_metadata.prefill_metadata:
|
||||
batch_size = len(request_ids_to_seq_ids)
|
||||
(
|
||||
current_seqlen_agnostic_cache,
|
||||
indices,
|
||||
) = self._prepare_current_run_mamba_cache(request_ids_to_seq_ids,
|
||||
batch_size)
|
||||
finished_requests_ids = kwargs["finished_requests_ids"]
|
||||
self._release_mamba_cache(finished_requests_ids)
|
||||
else:
|
||||
# CUDA graph capturing runs
|
||||
current_seqlen_agnostic_cache, indices = (
|
||||
kwargs["seqlen_agnostic_capture_inputs"],
|
||||
[],
|
||||
)
|
||||
self.current_indices = indices
|
||||
|
||||
hidden_states = self.model(input_ids, positions, kv_caches,
|
||||
attn_metadata,
|
||||
current_seqlen_agnostic_cache[0],
|
||||
current_seqlen_agnostic_cache[1])
|
||||
|
||||
if "seqlen_agnostic_capture_inputs" not in kwargs:
|
||||
self._copy_mamba_cache_by_indices(self.current_indices,
|
||||
current_seqlen_agnostic_cache)
|
||||
|
||||
return hidden_states
|
||||
|
||||
def _copy_mamba_cache_by_indices(
|
||||
self, indices: List[int],
|
||||
current_seqlen_agnostic_cache: Tuple[torch.Tensor, torch.Tensor]):
|
||||
for i, offset in enumerate(indices):
|
||||
self._copy_mamba_cache(offset, i, current_seqlen_agnostic_cache)
|
||||
|
||||
def _copy_mamba_cache(self, index_to: int, index_from: int,
|
||||
from_buffer: Tuple[torch.Tensor, torch.Tensor]):
|
||||
assert len(self.mamba_cache) > 0
|
||||
for (cache_t, from_buffer_t) in zip(self.mamba_cache, from_buffer):
|
||||
cache_t[:, index_to].copy_(from_buffer_t[:, index_from],
|
||||
non_blocking=True)
|
||||
|
||||
def _assign_seq_id_to_mamba_cache(self, cur_rid: str,
|
||||
seqs_id: List[int]) -> List[int]:
|
||||
indices_for_current_run = []
|
||||
for seq_id in seqs_id:
|
||||
if cur_rid not in self.mamba_cache_indices_mapping:
|
||||
self.mamba_cache_indices_mapping[cur_rid] = {}
|
||||
first_free_index = self._first_free_index_in_mamba_cache()
|
||||
self.mamba_cache_indices_mapping[cur_rid][
|
||||
seq_id] = first_free_index
|
||||
index_for_current_run = first_free_index
|
||||
## case of decoding n>1, copy prefill cache to decoding indices
|
||||
elif seq_id not in (seq_ids2indices :=
|
||||
self.mamba_cache_indices_mapping[cur_rid]):
|
||||
first_free_index = self._first_free_index_in_mamba_cache()
|
||||
index_exist = list(seq_ids2indices.values())[0]
|
||||
self._copy_mamba_cache(index_from=index_exist,
|
||||
index_to=first_free_index,
|
||||
from_buffer=self.mamba_cache)
|
||||
self.mamba_cache_indices_mapping[cur_rid][
|
||||
seq_id] = first_free_index
|
||||
index_for_current_run = first_free_index
|
||||
else:
|
||||
index_for_current_run = self.mamba_cache_indices_mapping[
|
||||
cur_rid][seq_id]
|
||||
|
||||
indices_for_current_run.append(index_for_current_run)
|
||||
return indices_for_current_run
|
||||
|
||||
def _prepare_current_run_mamba_cache(
|
||||
self, request_ids_to_seq_ids: Dict[str, list[int]], batch_size: int
|
||||
) -> Tuple[Tuple[torch.Tensor, torch.Tensor], List[int]]:
|
||||
indices_for_current_run = []
|
||||
for request_id, seqs_id in request_ids_to_seq_ids.items():
|
||||
indices_for_current_run += self._assign_seq_id_to_mamba_cache(
|
||||
request_id, seqs_id)
|
||||
## Pad the batch in case of running batch that was not captured via CG
|
||||
padded_indices = indices_for_current_run.copy()
|
||||
pad_index = self._first_free_index_in_mamba_cache()
|
||||
|
||||
for _ in range(batch_size - len(indices_for_current_run)):
|
||||
padded_indices.append(pad_index)
|
||||
|
||||
conv_state = self.mamba_cache[0][:, padded_indices]
|
||||
temporal_state = self.mamba_cache[1][:, padded_indices]
|
||||
|
||||
return (conv_state, temporal_state), indices_for_current_run
|
||||
|
||||
def copy_inputs_before_cuda_graphs(self, input_buffers, **kwargs):
|
||||
"""
|
||||
Copy the relevant Mamba cache into the CUDA graph input buffer
|
||||
that was provided during the capture runs
|
||||
(JambaForCausalLM.mamba_gc_cache_buffer).
|
||||
"""
|
||||
assert all(
|
||||
key in kwargs
|
||||
for key in ["request_ids_to_seq_ids", "finished_requests_ids"])
|
||||
request_ids_to_seq_ids = kwargs["request_ids_to_seq_ids"]
|
||||
batch_size = len(request_ids_to_seq_ids)
|
||||
(
|
||||
current_mamba_cache,
|
||||
indices,
|
||||
) = self._prepare_current_run_mamba_cache(request_ids_to_seq_ids,
|
||||
batch_size)
|
||||
self.current_indices = indices
|
||||
finished_requests_ids = kwargs["finished_requests_ids"]
|
||||
self._release_mamba_cache(finished_requests_ids)
|
||||
|
||||
for input_buffer, current_cache_buffer in zip(
|
||||
input_buffers["seqlen_agnostic_capture_inputs"],
|
||||
current_mamba_cache):
|
||||
input_buffer.copy_(current_cache_buffer, non_blocking=True)
|
||||
|
||||
def copy_outputs_after_cuda_graphs(self, input_buffers, **kwargs):
|
||||
"""
|
||||
Copy the relevant Mamba cache from the CUDA graph input_buffers
|
||||
back to the JambaForCausalLM.mamba_cache after CUDA
|
||||
graph replay run is done.
|
||||
"""
|
||||
self._copy_mamba_cache_by_indices(
|
||||
self.current_indices,
|
||||
input_buffers["seqlen_agnostic_capture_inputs"])
|
||||
|
||||
def get_seqlen_agnostic_capture_inputs(self, batch_size: int):
|
||||
"""
|
||||
Provide the CUDA graph capture runs with a buffer in adjusted size.
|
||||
The buffer is used to maintain the Mamba Cache during the CUDA graph
|
||||
replay runs.
|
||||
"""
|
||||
return tuple(buffer[:, :batch_size]
|
||||
for buffer in self.mamba_gc_cache_buffer)
|
||||
|
||||
def _release_mamba_cache(self, finished_seq_groups_req_ids: List[str]):
|
||||
for req_id in finished_seq_groups_req_ids:
|
||||
if req_id in self.mamba_cache_indices_mapping:
|
||||
self.mamba_cache_indices_mapping.pop(req_id)
|
||||
|
||||
def _first_free_index_in_mamba_cache(self) -> int:
|
||||
if self.mamba_cache:
|
||||
max_possible_batch_size = self.mamba_cache[0].shape[1]
|
||||
occupied = [
|
||||
id for seq_ids in self.mamba_cache_indices_mapping.values()
|
||||
for id in seq_ids.values()
|
||||
]
|
||||
first_free_index = [
|
||||
i not in occupied for i in range(max_possible_batch_size)
|
||||
].index(True)
|
||||
return first_free_index
|
||||
return 0
|
||||
|
||||
def _get_mamba_cache_shape(
|
||||
self
|
||||
) -> Tuple[Optional[Tuple[int, int]], Optional[Tuple[int, int]]]:
|
||||
world_size = get_tensor_model_parallel_world_size()
|
||||
hidden_size = self.config.hidden_size
|
||||
conv_state_shape = (
|
||||
self.config.mamba_expand * hidden_size // world_size,
|
||||
self.config.mamba_d_conv,
|
||||
)
|
||||
temporal_state_shape = (
|
||||
self.config.mamba_expand * self.config.hidden_size // world_size,
|
||||
self.config.mamba_d_state,
|
||||
)
|
||||
return conv_state_shape, temporal_state_shape
|
||||
|
||||
def _prepare_mamba_cache(self):
|
||||
dtype = self.lm_head.weight.dtype
|
||||
layers_type = self.config.layers_block_type
|
||||
mamba_layers = sum(
|
||||
[layer_type == "mamba" for layer_type in layers_type])
|
||||
max_batch_size = _BATCH_SIZES_TO_CAPTURE[-1] + 10
|
||||
conv_state_shape, temporal_state_shape = self._get_mamba_cache_shape()
|
||||
assert conv_state_shape is not None and temporal_state_shape is not None
|
||||
for buffername in ["mamba_cache", "mamba_gc_cache_buffer"]:
|
||||
buffer = (torch.empty(size=(mamba_layers, max_batch_size) +
|
||||
conv_state_shape,
|
||||
dtype=dtype,
|
||||
device="cuda"),
|
||||
torch.empty(size=(mamba_layers, max_batch_size) +
|
||||
temporal_state_shape,
|
||||
dtype=dtype,
|
||||
device="cuda"))
|
||||
setattr(self, buffername, buffer)
|
||||
|
||||
def compute_logits(self, hidden_states: torch.Tensor,
|
||||
sampling_metadata: SamplingMetadata) -> torch.Tensor:
|
||||
logits = self.logits_processor(self.lm_head.weight, hidden_states,
|
||||
sampling_metadata)
|
||||
return logits
|
||||
|
||||
def sample(
|
||||
self,
|
||||
logits: Optional[torch.Tensor],
|
||||
sampling_metadata: SamplingMetadata,
|
||||
) -> Optional[SamplerOutput]:
|
||||
next_tokens = self.sampler(logits, sampling_metadata)
|
||||
return next_tokens
|
||||
|
||||
def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
|
||||
stacked_params_mapping = [
|
||||
# (param_name, shard_name, shard_id)
|
||||
("qkv_proj", "q_proj", "q"),
|
||||
("qkv_proj", "k_proj", "k"),
|
||||
("qkv_proj", "v_proj", "v"),
|
||||
("gate_up_proj", "gate_proj", 0),
|
||||
("gate_up_proj", "up_proj", 1),
|
||||
]
|
||||
|
||||
expert_params_mapping = [
|
||||
# (param_name, weight_name, expert_id)
|
||||
(
|
||||
"ws" if weight_name in ["gate_proj", "up_proj"] else "w2s",
|
||||
f"experts.{expert_id}.{weight_name}.weight",
|
||||
expert_id,
|
||||
) for expert_id in range(self.config.num_experts)
|
||||
for weight_name in ["down_proj", "up_proj", "gate_proj"]
|
||||
]
|
||||
|
||||
params_dict = dict(self.named_parameters())
|
||||
for name, loaded_weight in weights:
|
||||
if "rotary_emb.inv_freq" in name:
|
||||
continue
|
||||
|
||||
if "A_log" in name:
|
||||
name = name.replace("A_log", "A")
|
||||
|
||||
if ".self_attn." in name:
|
||||
name = name.replace(".self_attn", "")
|
||||
|
||||
for param_name, weight_name, shard_id in stacked_params_mapping:
|
||||
if weight_name not in name:
|
||||
continue
|
||||
if 'experts' in name:
|
||||
continue
|
||||
name = name.replace(weight_name, param_name)
|
||||
# Skip loading extra bias for GPTQ models.
|
||||
if name.endswith(".bias") and name not in params_dict:
|
||||
continue
|
||||
param = params_dict[name]
|
||||
weight_loader = param.weight_loader
|
||||
weight_loader(param, loaded_weight, shard_id)
|
||||
break
|
||||
else:
|
||||
for param_name, weight_name, expert_id in expert_params_mapping:
|
||||
if weight_name not in name:
|
||||
continue
|
||||
name = name.replace(weight_name, param_name)
|
||||
param = params_dict[name]
|
||||
weight_loader = param.weight_loader
|
||||
weight_loader(param,
|
||||
loaded_weight,
|
||||
weight_name,
|
||||
expert_id=expert_id)
|
||||
break
|
||||
else:
|
||||
# Skip loading extra bias for GPTQ models.
|
||||
if name.endswith(".bias") and name not in params_dict:
|
||||
continue
|
||||
|
||||
param = params_dict[name]
|
||||
weight_loader = getattr(param, "weight_loader",
|
||||
default_weight_loader)
|
||||
weight_loader(param, loaded_weight)
|
@ -934,6 +934,8 @@ class ExecuteModelRequest:
|
||||
previous_hidden_states: Optional[HiddenStates] = None
|
||||
# The number of forward steps to run.
|
||||
num_steps: int = 1
|
||||
# Finished request ids since last step.
|
||||
finished_requests_ids: List[str] = field(default_factory=list)
|
||||
|
||||
def clone(
|
||||
self, seq_group_metadata_list: List[SequenceGroupMetadata]
|
||||
@ -949,4 +951,4 @@ class ExecuteModelRequest:
|
||||
running_queue_size=self.running_queue_size,
|
||||
previous_hidden_states=self.previous_hidden_states,
|
||||
num_steps=self.num_steps,
|
||||
)
|
||||
finished_requests_ids=self.finished_requests_ids)
|
||||
|
@ -75,15 +75,19 @@ class TP1DraftModelRunner(ModelRunner):
|
||||
List[SequenceGroupMetadata]] = None
|
||||
|
||||
def prepare_model_input(
|
||||
self,
|
||||
seq_group_metadata_list: List[SequenceGroupMetadata],
|
||||
virtual_engine: int = 0) -> ModelInputForGPUWithSamplingMetadata:
|
||||
self,
|
||||
seq_group_metadata_list: List[SequenceGroupMetadata],
|
||||
virtual_engine: int = 0,
|
||||
finished_requests_ids: Optional[List[str]] = None
|
||||
) -> ModelInputForGPUWithSamplingMetadata:
|
||||
"""A temporary solution that caches the seq_group_metadata_list
|
||||
for multi-step execution.
|
||||
TODO: In-place update model_input and remove this function.
|
||||
"""
|
||||
self.cached_seq_group_metadata_list = seq_group_metadata_list
|
||||
return super().prepare_model_input(seq_group_metadata_list)
|
||||
return super().prepare_model_input(
|
||||
seq_group_metadata_list,
|
||||
finished_requests_ids=finished_requests_ids)
|
||||
|
||||
def update_model_input(
|
||||
self, model_input: ModelInputForGPUWithSamplingMetadata,
|
||||
|
@ -33,7 +33,9 @@ class CacheEngine:
|
||||
self.device_config = device_config
|
||||
|
||||
self.head_size = model_config.get_head_size()
|
||||
self.num_layers = model_config.get_num_layers(parallel_config)
|
||||
# Models like Jamba, have mixed typed layers, E.g Mamba
|
||||
self.num_attention_layers = model_config.get_num_attention_layers(
|
||||
parallel_config)
|
||||
self.num_kv_heads = model_config.get_num_kv_heads(parallel_config)
|
||||
|
||||
self.block_size = cache_config.block_size
|
||||
@ -75,7 +77,7 @@ class CacheEngine:
|
||||
num_blocks, self.block_size, self.num_kv_heads, self.head_size)
|
||||
pin_memory = is_pin_memory_available() if device == "cpu" else False
|
||||
kv_cache: List[torch.Tensor] = []
|
||||
for _ in range(self.num_layers):
|
||||
for _ in range(self.num_attention_layers):
|
||||
# null block in CpuGpuBlockAllocator requires at least that
|
||||
# block to be zeroed-out.
|
||||
# We zero-out everything for simplicity.
|
||||
@ -87,12 +89,12 @@ class CacheEngine:
|
||||
return kv_cache
|
||||
|
||||
def swap_in(self, src_to_dst: torch.Tensor) -> None:
|
||||
for i in range(self.num_layers):
|
||||
for i in range(self.num_attention_layers):
|
||||
self.attn_backend.swap_blocks(self.cpu_cache[i], self.gpu_cache[i],
|
||||
src_to_dst)
|
||||
|
||||
def swap_out(self, src_to_dst: torch.Tensor) -> None:
|
||||
for i in range(self.num_layers):
|
||||
for i in range(self.num_attention_layers):
|
||||
self.attn_backend.swap_blocks(self.gpu_cache[i], self.cpu_cache[i],
|
||||
src_to_dst)
|
||||
|
||||
@ -107,11 +109,12 @@ class CacheEngine:
|
||||
) -> int:
|
||||
head_size = model_config.get_head_size()
|
||||
num_heads = model_config.get_num_kv_heads(parallel_config)
|
||||
num_layers = model_config.get_num_layers(parallel_config)
|
||||
num_attention_layers = model_config.get_num_attention_layers(
|
||||
parallel_config)
|
||||
|
||||
key_cache_block = cache_config.block_size * num_heads * head_size
|
||||
value_cache_block = key_cache_block
|
||||
total = num_layers * (key_cache_block + value_cache_block)
|
||||
total = num_attention_layers * (key_cache_block + value_cache_block)
|
||||
if cache_config.cache_dtype == "auto":
|
||||
dtype = model_config.dtype
|
||||
else:
|
||||
|
@ -314,9 +314,10 @@ class CPUModelRunner(ModelRunnerBase[CPUModelInput]):
|
||||
)
|
||||
|
||||
def prepare_model_input(
|
||||
self,
|
||||
seq_group_metadata_list: List[SequenceGroupMetadata],
|
||||
virtual_engine: int = 0,
|
||||
self,
|
||||
seq_group_metadata_list: List[SequenceGroupMetadata],
|
||||
virtual_engine: int = 0,
|
||||
finished_requests_ids: Optional[List[str]] = None
|
||||
) -> CPUModelInput:
|
||||
multi_modal_kwargs = None
|
||||
# NOTE: We assume that all sequences in the group are all prompts or
|
||||
|
@ -120,10 +120,11 @@ class EmbeddingModelRunner(
|
||||
self,
|
||||
seq_group_metadata_list: Optional[List[SequenceGroupMetadata]],
|
||||
virtual_engine: int = 0,
|
||||
finished_requests_ids: Optional[List[str]] = None
|
||||
) -> ModelInputForGPUWithPoolingMetadata:
|
||||
assert seq_group_metadata_list is not None
|
||||
model_input = self._prepare_model_input_tensors(
|
||||
seq_group_metadata_list)
|
||||
seq_group_metadata_list, finished_requests_ids)
|
||||
# Prepare PoolingMetadata.
|
||||
assert model_input.seq_lens is not None
|
||||
pooling_metadata = self._prepare_pooling(seq_group_metadata_list,
|
||||
|
@ -84,6 +84,8 @@ class ModelInputForGPU(ModelRunnerInputBase):
|
||||
lora_requests: Optional[Set[LoRARequest]] = None
|
||||
attn_metadata: Optional["AttentionMetadata"] = None
|
||||
multi_modal_kwargs: Optional[Dict[str, torch.Tensor]] = None
|
||||
request_ids_to_seq_ids: Optional[Dict[str, List[int]]] = None
|
||||
finished_requests_ids: Optional[List[str]] = None
|
||||
virtual_engine: int = 0
|
||||
|
||||
def as_broadcastable_tensor_dict(self) -> Dict[str, Any]:
|
||||
@ -94,6 +96,8 @@ class ModelInputForGPU(ModelRunnerInputBase):
|
||||
"lora_mapping": self.lora_mapping,
|
||||
"multi_modal_kwargs": self.multi_modal_kwargs,
|
||||
"virtual_engine": self.virtual_engine,
|
||||
"request_ids_to_seq_ids": self.request_ids_to_seq_ids,
|
||||
"finished_requests_ids": self.finished_requests_ids,
|
||||
}
|
||||
_add_attn_metadata_broadcastable_dict(tensor_dict, self.attn_metadata)
|
||||
return tensor_dict
|
||||
@ -128,6 +132,8 @@ class ModelInputForGPUWithSamplingMetadata(ModelInputForGPU):
|
||||
"lora_mapping": self.lora_mapping,
|
||||
"multi_modal_kwargs": self.multi_modal_kwargs,
|
||||
"virtual_engine": self.virtual_engine,
|
||||
"request_ids_to_seq_ids": self.request_ids_to_seq_ids,
|
||||
"finished_requests_ids": self.finished_requests_ids,
|
||||
}
|
||||
_add_attn_metadata_broadcastable_dict(tensor_dict, self.attn_metadata)
|
||||
_add_sampling_metadata_broadcastable_dict(tensor_dict,
|
||||
@ -191,6 +197,10 @@ class GPUModelRunnerBase(ModelRunnerBase[TModelInputForGPU]):
|
||||
]
|
||||
self.graph_memory_pool: Optional[Tuple[
|
||||
int, int]] = None # Set during graph capture.
|
||||
|
||||
self.has_seqlen_agnostic = model_config.contains_seqlen_agnostic_layers(
|
||||
parallel_config)
|
||||
|
||||
# When using CUDA graph, the input block tables must be padded to
|
||||
# max_seq_len_to_capture. However, creating the block table in
|
||||
# Python can be expensive. To optimize this, we cache the block table
|
||||
@ -317,6 +327,7 @@ class GPUModelRunnerBase(ModelRunnerBase[TModelInputForGPU]):
|
||||
def _prepare_model_input_tensors(
|
||||
self,
|
||||
seq_group_metadata_list: List[SequenceGroupMetadata],
|
||||
finished_requests_ids: Optional[List[str]] = None
|
||||
) -> TModelInputForGPU:
|
||||
"""Helper method to prepare the model input based on a given sequence
|
||||
group. Prepares metadata needed for the base model forward pass but not
|
||||
@ -347,6 +358,7 @@ class GPUModelRunnerBase(ModelRunnerBase[TModelInputForGPU]):
|
||||
block_tables: List[List[int]] = []
|
||||
multi_modal_kwargs_list: Dict[str,
|
||||
List[torch.Tensor]] = defaultdict(list)
|
||||
request_ids_to_seq_ids: Dict[str, List[int]] = defaultdict(list)
|
||||
decode_only = True
|
||||
num_prefills = 0
|
||||
num_prefill_tokens = 0
|
||||
@ -738,7 +750,11 @@ class GPUModelRunnerBase(ModelRunnerBase[TModelInputForGPU]):
|
||||
k: torch.cat(v, dim=0).to(self.device)
|
||||
for k, v in multi_modal_kwargs_list.items()
|
||||
}
|
||||
|
||||
request_ids_to_seq_ids = {
|
||||
seq_group_metadata.request_id:
|
||||
list(seq_group_metadata.seq_data.keys())
|
||||
for seq_group_metadata in seq_group_metadata_list
|
||||
}
|
||||
return self._model_input_cls(
|
||||
input_tokens=input_tokens_tensor,
|
||||
input_positions=input_positions_tensor,
|
||||
@ -748,7 +764,8 @@ class GPUModelRunnerBase(ModelRunnerBase[TModelInputForGPU]):
|
||||
lora_mapping=lora_mapping,
|
||||
lora_requests=lora_requests,
|
||||
multi_modal_kwargs=multi_modal_kwargs,
|
||||
)
|
||||
request_ids_to_seq_ids=request_ids_to_seq_ids,
|
||||
finished_requests_ids=finished_requests_ids)
|
||||
|
||||
@torch.inference_mode()
|
||||
def profile_run(self) -> None:
|
||||
@ -821,7 +838,9 @@ class GPUModelRunnerBase(ModelRunnerBase[TModelInputForGPU]):
|
||||
# Run the model with the dummy inputs.
|
||||
num_layers = self.model_config.get_num_layers(self.parallel_config)
|
||||
kv_caches = [None] * num_layers
|
||||
model_input = self.prepare_model_input(seqs)
|
||||
finished_requests_ids = [seq.request_id for seq in seqs]
|
||||
model_input = self.prepare_model_input(
|
||||
seqs, finished_requests_ids=finished_requests_ids)
|
||||
intermediate_tensors = None
|
||||
if not get_pp_group().is_first_rank:
|
||||
intermediate_tensors = self.model.make_empty_intermediate_tensors(
|
||||
@ -1033,21 +1052,37 @@ class GPUModelRunnerBase(ModelRunnerBase[TModelInputForGPU]):
|
||||
graph_runner.flashinfer_decode_wrapper = \
|
||||
decode_wrapper
|
||||
|
||||
graph_runner.capture(
|
||||
capture_inputs = {
|
||||
"input_ids":
|
||||
input_tokens[:batch_size],
|
||||
"positions":
|
||||
input_positions[:batch_size],
|
||||
"hidden_or_intermediate_states":
|
||||
hidden_or_intermediate_states[
|
||||
virtual_engine] # type: ignore
|
||||
[:batch_size]
|
||||
if hidden_or_intermediate_states[virtual_engine]
|
||||
is not None else None,
|
||||
"intermediate_inputs":
|
||||
intermediate_inputs[:batch_size]
|
||||
if intermediate_inputs is not None else None,
|
||||
"kv_caches":
|
||||
kv_caches[virtual_engine],
|
||||
"attn_metadata":
|
||||
attn_metadata,
|
||||
memory_pool=self.graph_memory_pool,
|
||||
stream=graph_capture_context.stream,
|
||||
)
|
||||
"memory_pool":
|
||||
self.graph_memory_pool,
|
||||
"stream":
|
||||
graph_capture_context.stream
|
||||
}
|
||||
if self.has_seqlen_agnostic:
|
||||
# Only used by Mamba-based models CUDA graph atm (Jamba)
|
||||
capture_inputs.update({
|
||||
"seqlen_agnostic_capture_inputs":
|
||||
self.model.get_seqlen_agnostic_capture_inputs(
|
||||
batch_size)
|
||||
})
|
||||
graph_runner.capture(**capture_inputs)
|
||||
self.graph_memory_pool = graph_runner.graph.pool()
|
||||
self.graph_runners[virtual_engine][batch_size] = (
|
||||
graph_runner)
|
||||
@ -1084,6 +1119,7 @@ class ModelRunner(GPUModelRunnerBase[ModelInputForGPUWithSamplingMetadata]):
|
||||
self,
|
||||
seq_group_metadata_list: List[SequenceGroupMetadata],
|
||||
virtual_engine: int = 0,
|
||||
finished_requests_ids: Optional[List[str]] = None
|
||||
) -> ModelInputForGPUWithSamplingMetadata:
|
||||
"""Prepare the model input based on a given sequence group, including
|
||||
metadata for the sampling step.
|
||||
@ -1099,7 +1135,7 @@ class ModelRunner(GPUModelRunnerBase[ModelInputForGPUWithSamplingMetadata]):
|
||||
If cuda graph is required, this API automatically pads inputs.
|
||||
"""
|
||||
model_input = self._prepare_model_input_tensors(
|
||||
seq_group_metadata_list)
|
||||
seq_group_metadata_list, finished_requests_ids)
|
||||
sampling_metadata = SamplingMetadata.prepare(seq_group_metadata_list,
|
||||
model_input.seq_lens,
|
||||
model_input.query_lens,
|
||||
@ -1175,6 +1211,10 @@ class ModelRunner(GPUModelRunnerBase[ModelInputForGPUWithSamplingMetadata]):
|
||||
model_executable = self.model
|
||||
|
||||
multi_modal_kwargs = model_input.multi_modal_kwargs or {}
|
||||
seqlen_agnostic_kwargs = {
|
||||
"finished_requests_ids": model_input.finished_requests_ids,
|
||||
"request_ids_to_seq_ids": model_input.request_ids_to_seq_ids,
|
||||
} if self.has_seqlen_agnostic else {}
|
||||
hidden_or_intermediate_states = model_executable(
|
||||
input_ids=model_input.input_tokens,
|
||||
positions=model_input.input_positions,
|
||||
@ -1182,7 +1222,7 @@ class ModelRunner(GPUModelRunnerBase[ModelInputForGPUWithSamplingMetadata]):
|
||||
attn_metadata=model_input.attn_metadata,
|
||||
intermediate_tensors=intermediate_tensors,
|
||||
**multi_modal_kwargs,
|
||||
)
|
||||
**seqlen_agnostic_kwargs)
|
||||
|
||||
# Compute the logits in the last pipeline stage.
|
||||
if not get_pp_group().is_last_rank:
|
||||
@ -1305,6 +1345,7 @@ class CUDAGraphRunner:
|
||||
"positions": positions,
|
||||
"kv_caches": kv_caches,
|
||||
"slot_mapping": attn_metadata.slot_mapping,
|
||||
**kwargs,
|
||||
}
|
||||
else:
|
||||
self.input_buffers = {
|
||||
@ -1315,6 +1356,7 @@ class CUDAGraphRunner:
|
||||
"seq_lens_tensor":
|
||||
attn_metadata.decode_metadata.seq_lens_tensor,
|
||||
"block_tables": attn_metadata.decode_metadata.block_tables,
|
||||
**kwargs,
|
||||
}
|
||||
if intermediate_inputs is not None:
|
||||
self.input_buffers.update(intermediate_inputs.tensors)
|
||||
@ -1349,13 +1391,18 @@ class CUDAGraphRunner:
|
||||
non_blocking=True)
|
||||
self.input_buffers["block_tables"].copy_(
|
||||
attn_metadata.decode_metadata.block_tables, non_blocking=True)
|
||||
if "seqlen_agnostic_capture_inputs" in self.input_buffers:
|
||||
self.model.copy_inputs_before_cuda_graphs(self.input_buffers,
|
||||
**kwargs)
|
||||
if intermediate_tensors is not None:
|
||||
for key in intermediate_tensors.tensors:
|
||||
self.input_buffers[key].copy_(intermediate_tensors[key],
|
||||
non_blocking=True)
|
||||
# Run the graph.
|
||||
self.graph.replay()
|
||||
|
||||
if "seqlen_agnostic_capture_inputs" in self.input_buffers:
|
||||
self.model.copy_outputs_after_cuda_graphs(self.input_buffers,
|
||||
**kwargs)
|
||||
# Return the output tensor.
|
||||
if get_pp_group().is_last_rank:
|
||||
return self.output_buffers["hidden_states"]
|
||||
|
@ -139,6 +139,7 @@ class ModelRunnerBase(ABC, Generic[T]):
|
||||
self,
|
||||
seq_group_metadata_list: List[SequenceGroupMetadata],
|
||||
virtual_engine: int = 0,
|
||||
finished_requests_ids: Optional[List[str]] = None,
|
||||
) -> T:
|
||||
"""
|
||||
Prepare the inputs to ModelRunnerBase.execute_model from an execution
|
||||
|
@ -177,6 +177,7 @@ class NeuronModelRunner(ModelRunnerBase[ModelInputForNeuron]):
|
||||
self,
|
||||
seq_group_metadata_list: List[SequenceGroupMetadata],
|
||||
virtual_engine: int = 0,
|
||||
finished_requests_ids: Optional[List[str]] = None
|
||||
) -> ModelInputForNeuron:
|
||||
# NOTE: We assume that all sequences in the group are all prompts or
|
||||
# all decodes.
|
||||
|
@ -234,7 +234,8 @@ class LocalOrDistributedWorkerBase(WorkerBase):
|
||||
model_input: ModelRunnerInputBase = (
|
||||
self.model_runner.prepare_model_input(
|
||||
execute_model_req.seq_group_metadata_list,
|
||||
execute_model_req.virtual_engine))
|
||||
execute_model_req.virtual_engine,
|
||||
execute_model_req.finished_requests_ids))
|
||||
num_steps = execute_model_req.num_steps
|
||||
|
||||
if self.do_metadata_broadcast:
|
||||
|
@ -189,9 +189,10 @@ class XPUModelRunner(ModelRunnerBase[ModelInputForXPU]):
|
||||
))
|
||||
|
||||
def prepare_model_input(
|
||||
self,
|
||||
seq_group_metadata_list: List[SequenceGroupMetadata],
|
||||
virtual_engine: int = 0,
|
||||
self,
|
||||
seq_group_metadata_list: List[SequenceGroupMetadata],
|
||||
virtual_engine: int = 0,
|
||||
finished_requests_ids: Optional[List[str]] = None
|
||||
) -> ModelInputForXPU:
|
||||
multi_modal_input = None
|
||||
if self.is_driver_worker:
|
||||
|
Reference in New Issue
Block a user