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https://github.com/vllm-project/vllm-ascend.git
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c0f0b70813
### What this PR does / why we need it?
Thanks to the PR https://github.com/vllm-project/vllm-ascend/pull/426
make vllm-ascend support the aclgraph inference to reduce the host
overhead. However, the capability of aclgraph strongly relies on the
functionality provided by `torch.compile`, which is the key feature
supported in torch 2.x . Therefore, capture custom op into aclgraph is
only possible when it can be recognize and captured by `torch.compile`.
In this PR, we register the meta implementation of current custom ops to
enable the fx graph capture. And by doing that, insert those custom ops
into aclgraph become a natural thing to the ascend runtime.
### Does this PR introduce _any_ user-facing change?
No user face change.
### How was this patch tested?
Tested in unittest, we will integrate the `rotary_embedding` op into a
small custom model and use `torch.compile` and aclgraph to capture and
replay it to verify its functionality.
- vLLM version: v0.10.0
- vLLM main:
1b99028069
---------
Signed-off-by: ganyi <pleaplusone.gy@gmail.com>
87 lines
3.8 KiB
Python
87 lines
3.8 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" 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 rotary_embedding and 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", "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(
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"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 rotary_embedding_meta(positions: torch.Tensor, query: torch.Tensor,
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key: torch.Tensor, head_size: int,
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cos_sin_cache: torch.Tensor, is_neox: bool):
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num_tokens = positions.numel()
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query_hidden_size = query.numel() // num_tokens
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key_hidden_size = key.numel() // num_tokens
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num_heads = query_hidden_size // head_size
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num_kv_heads = key_hidden_size // head_size
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query_dst = torch.empty_like(query).view(num_tokens, num_heads, head_size)
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key_dst = torch.empty_like(key).view(num_tokens, num_kv_heads, head_size)
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return query_dst, key_dst
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def get_masked_input_and_mask_meta(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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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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register_meta_if_necessary("_C", "rotary_embedding", rotary_embedding_meta)
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register_meta_if_necessary("_C", "get_masked_input_and_mask",
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get_masked_input_and_mask_meta)
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