forked from ccf-ai-infra/TileOPs-Metax
72 lines
2.1 KiB
Python
72 lines
2.1 KiB
Python
"""Sinusoidal positional encoding generative op."""
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from typing import Dict, Optional
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import torch
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from tileops.kernels.elementwise import SinusoidalFwdKernel
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from tileops.kernels.kernel_base import Kernel
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from ..op_base import Op
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from ._base import _apply_fp8_post_cast
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class SinusoidalFwdOp(Op):
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"""Sinusoidal positional encoding from "Attention Is All You Need".
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Generates the full (seq_len, d_model) encoding tensor.
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Note:
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Eager-only. Unlike the other elementwise ops in this package,
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``SinusoidalFwdOp`` is not registered as a ``torch.library.custom_op``,
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so ``torch.compile`` graph capture is not supported. The op has
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zero tensor inputs and constructs its output entirely from
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``__init__`` parameters; no compile-time wrapping is needed.
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Args:
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seq_len: Sequence length.
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d_model: Model dimension.
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dtype: Torch dtype.
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kernel_map: Optional dispatch override mapping kernel keys to
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``Kernel`` subclasses. Falls back to ``default_kernel_map``.
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"""
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_op_name = "sinusoidal"
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def __init__(
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self,
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seq_len: int,
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d_model: int,
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dtype: torch.dtype,
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*,
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kernel_map: Optional[Dict[str, Kernel]] = None,
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):
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self.seq_len = seq_len
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self.d_model = d_model
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self.dtype = dtype
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self.dispatch_kernel(kernel_map)
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self.kernel = self.kernel_map[self._op_name](seq_len, d_model, dtype)
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@property
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def default_kernel_map(self):
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return {"sinusoidal": SinusoidalFwdKernel}
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def _infer_output_shapes(self) -> dict[str, tuple[int, ...]]:
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return {"output": (self.seq_len, self.d_model)}
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def _validate_dtypes(self) -> None:
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return None
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@property
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def total_memory(self) -> int:
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return self.seq_len * self.d_model * self.dtype.itemsize
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def eval_roofline(self) -> tuple[int, int]:
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n_elem = self.seq_len * self.d_model
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return 6 * n_elem, self.total_memory
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def forward(self) -> torch.Tensor:
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out = self.kernel()
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result = out.reshape(self.seq_len, self.d_model)
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return _apply_fp8_post_cast(result, self.kernel)
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