adapt to sglang v0.5.2rc1 on dcu
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maxiao
178
test/srt/cpu/test_rope.py
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178
test/srt/cpu/test_rope.py
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import unittest
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import sgl_kernel
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import torch
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from utils import precision
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from sglang.srt.layers.rotary_embedding import (
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DeepseekScalingRotaryEmbedding,
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RotaryEmbedding,
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)
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from sglang.test.test_utils import CustomTestCase
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torch.manual_seed(1234)
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class TestROPE(CustomTestCase):
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def test_deepseek_v2_rope(self):
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num_head = 16
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seq_len = 1024
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q_head_dim = 192
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qk_nope_head_dim = 128
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qk_rope_head_dim = 64
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max_pos = 256
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k_dim = 576
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rotary_dim = 64
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is_neox_style = False
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# Create cos_sin_cache
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freqs = torch.rand(max_pos, qk_rope_head_dim // 2)
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cos = freqs.cos() * 0.7
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sin = freqs.sin() * 0.7
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cos_sin_cache = torch.cat((cos, sin), dim=-1).to(torch.bfloat16)
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positions = torch.randint(0, max_pos, (seq_len,))
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rope = DeepseekScalingRotaryEmbedding(
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qk_rope_head_dim,
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rotary_dim,
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max_pos,
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16, # not used since cos_sin_cache is provided
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is_neox_style,
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1.0,
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torch.bfloat16,
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device="cpu",
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)
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rope.register_buffer("cos_sin_cache", cos_sin_cache)
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for dtype in [torch.bfloat16]:
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enable_autocast = True
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with torch.no_grad(), torch.amp.autocast("cpu", enabled=enable_autocast):
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q = torch.randn(seq_len, num_head, q_head_dim, dtype=dtype)
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q_clone = q.clone()
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k = torch.randn(seq_len, 1, k_dim, dtype=dtype)
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k_clone = k.clone()
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_, q_pe = q.split([qk_nope_head_dim, qk_rope_head_dim], dim=-1)
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_, q_pe_clone = q_clone.split(
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[qk_nope_head_dim, qk_rope_head_dim], dim=-1
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)
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k_pe = k[:, :, k_dim - qk_rope_head_dim :]
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k_pe_clone = k_clone[:, :, k_dim - qk_rope_head_dim :]
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# ref kernel
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q_pe, k_pe = rope.forward_native(
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query=q_pe,
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key=k_pe,
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positions=positions,
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)
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# fused rope kernel
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q_pe_clone, k_pe_clone = torch.ops.sgl_kernel.rotary_embedding_cpu(
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positions,
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q_pe_clone,
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k_pe_clone,
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rope.head_size,
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cos_sin_cache,
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False,
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)
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atol = rtol = precision[q_pe.dtype]
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torch.testing.assert_close(q_pe, q_pe_clone, atol=atol, rtol=rtol)
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torch.testing.assert_close(k_pe, k_pe_clone, atol=atol, rtol=rtol)
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torch.testing.assert_close(k_pe, k_pe_clone)
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def test_origin_rope(self):
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def single_test(
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head_size: int,
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rotary_dim: int,
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max_position_embeddings: int,
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base: int,
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is_neox_style: bool,
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dtype: torch.dtype,
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device: str,
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batch_size: int,
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seq_len: int,
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num_q_heads: int,
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num_kv_heads: int,
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):
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torch.manual_seed(100)
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rope_ref = RotaryEmbedding(
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head_size,
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rotary_dim,
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max_position_embeddings,
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base,
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is_neox_style,
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dtype,
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).to(device)
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pos_ids = torch.arange(seq_len, device=device).repeat(batch_size)
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query = torch.randn(
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batch_size * seq_len,
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num_q_heads * head_size,
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dtype=dtype,
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device=device,
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)
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key = torch.randn(
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batch_size * seq_len,
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num_kv_heads * head_size,
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dtype=dtype,
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device=device,
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)
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query_ref, key_ref = query.clone(), key.clone()
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query_cpu, key_cpu = query.clone(), key.clone()
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query_ref_out, key_ref_out = rope_ref.forward_native(
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pos_ids, query_ref, key_ref
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)
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query_cpu_out, key_cpu_out = torch.ops.sgl_kernel.rotary_embedding_cpu(
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pos_ids,
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query_cpu,
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key_cpu,
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rope_ref.head_size,
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rope_ref.cos_sin_cache.to(query.dtype),
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rope_ref.is_neox_style,
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)
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torch.testing.assert_close(
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query_ref_out, query_cpu_out, atol=1e-2, rtol=1e-2
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)
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torch.testing.assert_close(key_ref_out, key_cpu_out, atol=1e-2, rtol=1e-2)
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test_config = [
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(64, 64, 32, 8000, True, torch.bfloat16, "cpu", 32, 32, 1, 1),
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(256, 128, 4096, 10000, True, torch.bfloat16, "cpu", 2, 512, 32, 8),
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(512, 128, 311, 10000, True, torch.bfloat16, "cpu", 3, 39, 4, 2),
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(128, 128, 2048, 10000, False, torch.bfloat16, "cpu", 2, 512, 32, 8),
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(128, 128, 2048, 10000, False, torch.bfloat16, "cpu", 2, 512, 16, 4),
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(512, 128, 311, 10000, False, torch.bfloat16, "cpu", 3, 39, 4, 2),
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]
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for (
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head_size,
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rotary_dim,
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max_position_embeddings,
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base,
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is_neox_style,
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dtype,
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device,
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batch_size,
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seq_len,
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num_q_heads,
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num_kv_heads,
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) in test_config:
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single_test(
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head_size,
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rotary_dim,
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max_position_embeddings,
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base,
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is_neox_style,
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dtype,
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device,
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batch_size,
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seq_len,
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num_q_heads,
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num_kv_heads,
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)
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if __name__ == "__main__":
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unittest.main()
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