302 lines
9.4 KiB
Python
302 lines
9.4 KiB
Python
import time
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import pytest
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import torch
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import triton
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import triton.language as tl
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from sgl_kernel import FusedSetKVBufferArg as FusedSetKVBufferArgKernel
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from sgl_kernel import (
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apply_rope_with_cos_sin_cache_inplace as apply_rope_with_cos_sin_cache_inplace_kernel,
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)
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from sglang.jit_kernel.rope import FusedSetKVBufferArg as FusedSetKVBufferArgJit
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from sglang.jit_kernel.rope import (
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apply_rope_with_cos_sin_cache_inplace as apply_rope_with_cos_sin_cache_inplace_jit,
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)
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DEVICE = "cuda"
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@triton.jit
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def burn_kernel(out_ptr, iters: tl.constexpr):
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pid = tl.program_id(0)
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x = tl.full((), pid + 1, dtype=tl.uint32)
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a = tl.full((), 1664525, dtype=tl.uint32)
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c = tl.full((), 1013904223, dtype=tl.uint32)
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sh = tl.full((), 13, dtype=tl.uint32)
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for _ in range(iters):
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x = x * a + c
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x = x ^ (x >> sh)
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if pid == 0:
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tl.store(out_ptr, x)
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def triton_burn(ms: float, grid=(256,)):
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iters = int(ms * 20000)
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out = torch.empty((), device="cuda", dtype=torch.uint32)
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burn_kernel[grid](out, iters=iters)
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return out
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def create_cos_sin_cache(rotary_dim, max_position_embeddings, base, dtype):
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inv_freq = 1.0 / (
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base
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** (
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torch.arange(0, rotary_dim, 2, dtype=torch.float32, device=DEVICE)
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/ rotary_dim
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)
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)
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t = torch.arange(max_position_embeddings, dtype=torch.float32, device=DEVICE)
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freqs = torch.einsum("i,j -> ij", t, inv_freq)
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cos = freqs.cos()
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sin = freqs.sin()
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cache = torch.cat((cos, sin), dim=-1)
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return cache
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@pytest.mark.parametrize("bs", [1, 8])
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@pytest.mark.parametrize("seq_len", [1, 512])
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@pytest.mark.parametrize("num_qo_heads", [1, 16])
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@pytest.mark.parametrize("num_kv_heads", [1, 16])
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@pytest.mark.parametrize("head_dim", [64, 512])
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@pytest.mark.parametrize("rotary_dim", [64, 128])
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@pytest.mark.parametrize("interleave", [False, True])
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@pytest.mark.parametrize("enable_pdl", [False, True])
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@pytest.mark.parametrize("save_kv_cache", [False, True])
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@pytest.mark.parametrize("dtype", [torch.bfloat16, torch.float16, torch.float32])
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def test_rope(
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bs,
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seq_len,
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num_qo_heads,
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num_kv_heads,
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head_dim,
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rotary_dim,
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interleave: bool,
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enable_pdl: bool,
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save_kv_cache: bool,
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dtype: torch.dtype,
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) -> None:
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if head_dim < rotary_dim:
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pytest.skip(f"{head_dim=} < {rotary_dim=}")
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if not save_kv_cache and enable_pdl:
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pytest.skip(f"({save_kv_cache=}, {enable_pdl=}) is not allowed")
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q = torch.randn(bs * seq_len, num_qo_heads * head_dim, device=DEVICE, dtype=dtype)
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k = torch.randn(bs * seq_len, num_kv_heads * head_dim, device=DEVICE, dtype=dtype)
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v = torch.randn(bs * seq_len, num_kv_heads * head_dim, device=DEVICE, dtype=dtype)
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KV_POOL_SIZE = bs * seq_len * 2
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k_buffer = torch.zeros(
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KV_POOL_SIZE, num_kv_heads, head_dim, device=DEVICE, dtype=dtype
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)
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v_buffer = torch.zeros(
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KV_POOL_SIZE, num_kv_heads, head_dim, device=DEVICE, dtype=dtype
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)
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out_cache_loc = torch.randperm(KV_POOL_SIZE, dtype=torch.int64, device=DEVICE)[
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: bs * seq_len
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].clone()
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pos_ids = torch.arange(seq_len, device=DEVICE).repeat(bs)
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max_seq_len = seq_len
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base = 10000
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cos_sin_cache = create_cos_sin_cache(rotary_dim, max_seq_len, base, dtype)
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q_jit = q.clone()
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k_jit = k.clone()
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v_jit = v.clone()
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k_buffer_jit = k_buffer.clone()
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v_buffer_jit = v_buffer.clone()
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out_cache_loc_jit = out_cache_loc.clone()
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fused_set_kv_buffer_arg_jit = FusedSetKVBufferArgJit(
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value=v_jit,
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k_buffer=k_buffer_jit.view(k_buffer_jit.shape[0], -1),
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v_buffer=v_buffer_jit.view(v_buffer_jit.shape[0], -1),
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k_scale=None,
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v_scale=None,
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cache_loc=out_cache_loc_jit,
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)
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q_kernel = q.clone()
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k_kernel = k.clone()
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v_kernel = v.clone()
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k_buffer_kernel = k_buffer.clone()
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v_buffer_kernel = v_buffer.clone()
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out_cache_loc_kernel = out_cache_loc.clone()
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fused_set_kv_buffer_arg_kernel = FusedSetKVBufferArgKernel(
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value=v_kernel,
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k_buffer=k_buffer_kernel.view(k_buffer_kernel.shape[0], -1),
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v_buffer=v_buffer_kernel.view(v_buffer_kernel.shape[0], -1),
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k_scale=None,
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v_scale=None,
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cache_loc=out_cache_loc_kernel,
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)
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stream_jit = torch.cuda.Stream()
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stream_kernel = torch.cuda.Stream()
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triton_burn(10, grid=(1024,))
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r = torch.randn_like(q)
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r_jit, r_kernel = r.clone(), r.clone()
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torch.cuda.synchronize()
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with torch.cuda.stream(stream_jit):
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# Test if rotary_embedding runs on stream_jit
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triton_burn(10, grid=(1024,))
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q_jit = q_jit + r_jit
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apply_rope_with_cos_sin_cache_inplace_jit(
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positions=pos_ids,
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query=q_jit,
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key=k_jit,
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head_size=head_dim,
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cos_sin_cache=cos_sin_cache,
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is_neox=(not interleave),
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fused_set_kv_buffer_arg=(
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fused_set_kv_buffer_arg_jit if save_kv_cache else None
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),
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enable_pdl=enable_pdl,
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)
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with torch.cuda.stream(stream_kernel):
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triton_burn(10, grid=(1024,))
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q_kernel = q_kernel + r_kernel
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apply_rope_with_cos_sin_cache_inplace_kernel(
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positions=pos_ids,
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query=q_kernel,
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key=k_kernel,
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head_size=head_dim,
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cos_sin_cache=cos_sin_cache,
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is_neox=(not interleave),
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fused_set_kv_buffer_arg=(
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fused_set_kv_buffer_arg_kernel if save_kv_cache else None
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),
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enable_pdl=enable_pdl,
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)
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torch.cuda.synchronize()
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atol = 1e-3 if dtype != torch.float32 else 1e-6
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rtol = 1e-3 if dtype != torch.float32 else 1e-6
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torch.testing.assert_close(q_jit, q_kernel, atol=atol, rtol=rtol)
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torch.testing.assert_close(k_jit, k_kernel, atol=atol, rtol=rtol)
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torch.testing.assert_close(k_buffer_jit, k_buffer_kernel, atol=atol, rtol=rtol)
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torch.testing.assert_close(v_buffer_jit, v_buffer_kernel, atol=atol, rtol=rtol)
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@pytest.mark.parametrize("bs", [8])
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@pytest.mark.parametrize("seq_len", [256, 512, 1024])
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@pytest.mark.parametrize("num_qo_heads", [16])
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@pytest.mark.parametrize("num_kv_heads", [16])
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@pytest.mark.parametrize("head_dim", [64])
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@pytest.mark.parametrize("rotary_dim", [64])
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@pytest.mark.parametrize("interleave", [False])
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@pytest.mark.parametrize("enable_pdl", [False])
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@pytest.mark.parametrize("save_kv_cache", [False])
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@pytest.mark.parametrize("dtype", [torch.bfloat16])
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def test_bench_rope(
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bs,
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seq_len,
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num_qo_heads,
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num_kv_heads,
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head_dim,
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rotary_dim,
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interleave: bool,
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enable_pdl: bool,
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save_kv_cache: bool,
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dtype: torch.dtype,
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) -> None:
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if head_dim < rotary_dim:
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pytest.skip(f"{head_dim=} < {rotary_dim=}")
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if not save_kv_cache and enable_pdl:
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pytest.skip(f"({save_kv_cache=}, {enable_pdl=}) is not allowed")
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q = torch.randn(bs * seq_len, num_qo_heads * head_dim, device=DEVICE, dtype=dtype)
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k = torch.randn(bs * seq_len, num_kv_heads * head_dim, device=DEVICE, dtype=dtype)
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v = torch.randn(bs * seq_len, num_kv_heads * head_dim, device=DEVICE, dtype=dtype)
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KV_POOL_SIZE = bs * seq_len * 2
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k_buffer = torch.zeros(
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KV_POOL_SIZE, num_kv_heads, head_dim, device=DEVICE, dtype=dtype
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)
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v_buffer = torch.zeros(
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KV_POOL_SIZE, num_kv_heads, head_dim, device=DEVICE, dtype=dtype
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)
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out_cache_loc = torch.randperm(KV_POOL_SIZE, dtype=torch.int64, device=DEVICE)[
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: bs * seq_len
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].clone()
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pos_ids = torch.arange(seq_len, device=DEVICE).repeat(bs)
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max_seq_len = seq_len
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base = 10000
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cos_sin_cache = create_cos_sin_cache(rotary_dim, max_seq_len, base, dtype)
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q_jit = q.clone()
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k_jit = k.clone()
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v_jit = v.clone()
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k_buffer_jit = k_buffer.clone()
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v_buffer_jit = v_buffer.clone()
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out_cache_loc_jit = out_cache_loc.clone()
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q_kernel = q.clone()
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k_kernel = k.clone()
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v_kernel = v.clone()
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k_buffer_kernel = k_buffer.clone()
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v_buffer_kernel = v_buffer.clone()
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out_cache_loc_kernel = out_cache_loc.clone()
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jit_args = {
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"positions": pos_ids,
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"query": q_jit,
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"key": k_jit,
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"head_size": head_dim,
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"cos_sin_cache": cos_sin_cache,
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"is_neox": (not interleave),
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"fused_set_kv_buffer_arg": None,
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"enable_pdl": enable_pdl,
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}
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jit_time = bench_rope(
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apply_rope_with_cos_sin_cache_inplace_jit,
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jit_args,
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)
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kernel_args = {
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"positions": pos_ids,
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"query": q_kernel,
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"key": k_kernel,
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"head_size": head_dim,
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"cos_sin_cache": cos_sin_cache,
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"is_neox": (not interleave),
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"fused_set_kv_buffer_arg": None,
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"enable_pdl": enable_pdl,
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}
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kernel_time = bench_rope(
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apply_rope_with_cos_sin_cache_inplace_kernel,
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kernel_args,
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)
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print(f"\nPerformance Test - Batch={bs}, SeqLen={seq_len}")
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print(f"JIT: {jit_time*1000:.9f}ms, SGL: {kernel_time*1000:.9f}ms")
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if kernel_time > 0:
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speedup = kernel_time / jit_time if jit_time > 0 else float("inf")
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print(f"Speedup (SGL/JIT): {speedup:.2f}x")
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def bench_rope(fn, args):
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warmup = 10
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iteration = 100
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for _ in range(warmup):
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fn(**args)
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torch.cuda.synchronize()
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start_time = time.time()
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for _ in range(iteration):
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fn(**args)
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torch.cuda.synchronize()
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return (time.time() - start_time) / iteration
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if __name__ == "__main__":
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pytest.main([__file__])
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