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justinchuby/rotary_embedding.py

Created September 20, 2023 19:18
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Rotary embedding ONNX export
Raw
rotary_embedding.py
import torch
from torch import nn
import onnx
class LlamaMSRotaryEmbedding(nn.Module):
def __init__(self, hidden_size, num_heads, max_sequence_length):
super().__init__()
self.hidden_size = hidden_size
self.num_heads = num_heads
self.max_sequence_length = max_sequence_length
def get_cos_sin_cache(
self, theta: float = 10000.0, head_scale=1.0, device="cpu", dtype=torch.float32
):
hidden_size = self.hidden_size
n_heads = self.num_heads
max_seq_len = self.max_sequence_length
# Precalculate rotary matrices for the sequence
# According to "Attention Is All You Need", theta_i = 10000 ^ (2 * (i - 1)/dim), i in [1, 2, ..., dim//2]
head_dim = head_scale * hidden_size / n_heads
pos = torch.arange(0, 2 * (head_dim // 2), step=2, device=device, dtype=dtype)
freqs = 1.0 / (theta ** (pos / head_dim))
idx = torch.arange(max_seq_len, device=freqs.device)
freqs = torch.outer(idx, freqs)
cos = torch.reshape(torch.cos(freqs), [1, max_seq_len, 1, -1])
sin = torch.reshape(torch.sin(freqs), [1, max_seq_len, 1, -1])
dtype = torch.get_default_dtype()
return cos.to(dtype), sin.to(dtype)
def forward(self, x, cos, sin, pos):
# Dimension of x is [batch_size, seq_len, n_heads, head_dim]
rot_dim = 2 * cos.shape[3]
# Dolly requires partial rotation
x_rot = x[:, :, :, :rot_dim]
x1 = x_rot[:, :, :, 0::2]
x2 = x_rot[:, :, :, 1::2]
seq_len = x.shape[1]
cos_x = cos[:, pos : pos + seq_len, :, :]
sin_x = sin[:, pos : pos + seq_len, :, :]
real = cos_x * x1 - sin_x * x2
imag = sin_x * x1 + cos_x * x2
x_rot[:, :, :, 0::2] = real
x_rot[:, :, :, 1::2] = imag
return torch.cat((x_rot, x[:, :, :, rot_dim:]), dim=-1)
class Model(nn.Module):
def __init__(self):
super().__init__()
self.rotary_embedding_ms = LlamaMSRotaryEmbedding(8, 16, 32)
def forward(self, x, cos, sin, pos):
return self.rotary_embedding_ms(x, cos, sin, pos)
batch_size = 2
num_heads = 16
sequence_length = 32
head_size = 8
# Calculated this way to match the data in rotary_embedding_op_test.cc
x_bnsh = torch.randn(batch_size, num_heads, sequence_length, head_size)
x_bsnh = x_bnsh.transpose(1, 2)
rotary_embedding_ms = LlamaMSRotaryEmbedding(head_size, num_heads, sequence_length)
model = Model()
cos_ms, sin_ms = rotary_embedding_ms.get_cos_sin_cache()
pos_ms = 0
exported = torch.onnx.dynamo_export(model, x_bsnh, cos_ms, sin_ms, pos_ms)
onnx.save_model(exported.model_proto, "rotary_embedding_ms.onnx", format="textproto")
Raw
rotary_embedding_ms.onnx
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