6c49f95da2
### What this PR does / why we need it? This PR removes the custom `rotary_embedding` operator and its associated C++ kernel implementation, PyTorch bindings, and tests. The codebase now falls back to using the native `torch_npu._npu_rotary_embedding` implementation. This change simplifies the codebase by removing custom, platform-specific kernel code and relying on the standard NPU library implementation, which is presumably more optimized and easier to maintain. ### Does this PR introduce _any_ user-facing change? No. This is an internal refactoring and does not introduce any user-facing changes. ### How was this patch tested? The tests for the custom `rotary_embedding` operator have been removed along with the operator itself. The correctness of the fallback to the native `torch_npu` implementation is verified by existing CI tests for attention layers and models that use rotary embeddings. - vLLM version: v0.15.0 - vLLM main: https://github.com/vllm-project/vllm/commit/v0.15.0 Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
92 lines
3.5 KiB
Python
92 lines
3.5 KiB
Python
import torch
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from torch.library import Library
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# This file provides a template and registration utilities for writing "meta" implementations
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# of custom operators in Python for the vllm_ascend project.
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#
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# We offer two ways to implement meta implementations for custom ops:
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# 1. Python meta implementation (as shown in this file): Write a Python function that
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# takes the same arguments as your operator and returns empty tensors with the correct
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# shapes and dtypes. This is useful for rapid prototyping and for ops that are only
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# used in Python.
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# 2. C++ meta implementation: You can also implement the meta function in C++ for better
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# performance or to match the C++ op logic more closely. See `torch_binding_meta.cpp`
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# for examples of C++ meta implementations and how to register them.
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#
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# Both approaches enable tracing, export, and shape inference in PyTorch and vLLM, which
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# is essential for supporting `torch.compile` and aclgraph.
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# How to add a new meta implementation in Python:
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# -------------------------------------
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# 1. Write a Python function that takes the same arguments as your operator, and returns
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# empty tensors (using torch.empty_like, torch.empty, etc.) with the correct shapes and dtypes.
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# Do NOT perform any real computation or allocate device memory.
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#
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# 2. Register your meta function using `register_meta_if_necessary`, providing:
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# - The namespace (usually "_C_ascend" for custom ops)
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# - The operator name (as registered in C++)
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# - The Python meta function
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# - (Optional) The overload name, if your op has overloads
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#
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# 3. The registration utility will check if a meta implementation already exists for your op,
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# and only register if necessary. This avoids duplicate registrations.
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#
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# 4. Example meta implementations are provided below for get_masked_input_and_mask.
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#
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# 5. When developing new custom ops, always provide a meta implementation to enable tracing,
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# export, and shape inference in PyTorch and vLLM to enable the capture of `torch.compile`
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# and aclgraph.
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#
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# For more details, see: https://pytorch.org/docs/stable/notes/extending.html#meta-tensors
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lib = Library("_C_ascend", "IMPL")
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def register_meta_if_necessary(ns: str, op_name: str, fn, overload: str = ""):
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if overload != "":
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op_name = op_name + "." + overload
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schema_to_find = ns + "::" + op_name
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meta_impl_list = torch._C._dispatch_get_registrations_for_dispatch_key("Meta")
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if schema_to_find in meta_impl_list:
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return
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lib.impl(op_name, fn, "Meta")
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def get_masked_input_and_mask_meta(
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input: torch.Tensor,
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org_vocab_start_index: int,
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org_vocab_end_index: int,
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num_org_vocab_padding: int,
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added_vocab_start_index: int,
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added_vocab_end_index: int,
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):
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masked_input = torch.empty_like(input)
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mask = torch.empty_like(input).to(torch.bool)
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return masked_input, mask
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def bgmv_expand_meta(
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x: torch.Tensor, weight: torch.Tensor, indices: torch.Tensor, y: torch.Tensor, slice_offset: int, slice_size: int
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):
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y_out = torch.empty_like(y)
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return y_out
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def sgmv_expand_meta(
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x: torch.Tensor,
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weight: torch.Tensor,
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lora_indices: torch.Tensor,
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seq_len: torch.Tensor,
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y: torch.Tensor,
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slice_offset: int,
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slice_size: int,
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):
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y_out = torch.empty_like(y)
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return y_out
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register_meta_if_necessary("_C_ascend", "get_masked_input_and_mask", get_masked_input_and_mask_meta)
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register_meta_if_necessary("_C_ascend", "bgmv_expand", bgmv_expand_meta)
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register_meta_if_necessary("_C_ascend", "sgmv_expand", sgmv_expand_meta)
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