Perf/fuse mamba state scatter mtp verify (#18088)
This commit is contained in:
xutizhou
@@ -0,0 +1,354 @@
|
||||
import os
|
||||
import unittest
|
||||
|
||||
import torch
|
||||
|
||||
try:
|
||||
from sglang.srt.layers.attention.mamba.mamba_state_scatter_triton import (
|
||||
fused_mamba_state_scatter_with_mask,
|
||||
)
|
||||
|
||||
_FUSED_IMPORT_ERROR = None
|
||||
except Exception as e: # pragma: no cover
|
||||
fused_mamba_state_scatter_with_mask = None
|
||||
_FUSED_IMPORT_ERROR = e
|
||||
|
||||
|
||||
def _dtype_from_str(name: str) -> torch.dtype:
|
||||
mapping = {
|
||||
"bfloat16": torch.bfloat16,
|
||||
"float16": torch.float16,
|
||||
"float32": torch.float32,
|
||||
}
|
||||
if name not in mapping:
|
||||
raise ValueError(
|
||||
f"Unsupported dtype string {name!r}. Supported: {sorted(mapping.keys())}"
|
||||
)
|
||||
return mapping[name]
|
||||
|
||||
|
||||
def _ref_scatter(dst, src, dst_indices, src_indices, step_indices):
|
||||
"""Reference implementation using PyTorch advanced indexing."""
|
||||
# dst: [L, C, E]
|
||||
# src: [L, S, D, E]
|
||||
dst[:, dst_indices] = src[:, src_indices, step_indices].to(dst.dtype, copy=False)
|
||||
|
||||
|
||||
def _ref_update_like(
|
||||
ssm_states,
|
||||
intermediate_ssm,
|
||||
conv_states,
|
||||
intermediate_conv,
|
||||
*,
|
||||
state_indices_tensor,
|
||||
accepted_steps,
|
||||
mamba_track_indices=None,
|
||||
mamba_steps_to_track=None,
|
||||
):
|
||||
"""Reference implementation using PyTorch advanced indexing for correctness verification."""
|
||||
request_number = accepted_steps.shape[0]
|
||||
intermediate_state_indices = torch.arange(
|
||||
request_number, dtype=torch.int32, device=accepted_steps.device
|
||||
)
|
||||
|
||||
valid_mask = accepted_steps >= 0
|
||||
dst_state_indices = state_indices_tensor[valid_mask].to(torch.int64)
|
||||
src_state_indices = intermediate_state_indices[valid_mask].to(torch.int64)
|
||||
last_steps = accepted_steps[valid_mask].to(torch.int64)
|
||||
|
||||
# Only scatter if there are valid indices (but don't early return -
|
||||
# mamba_track_indices processing is independent)
|
||||
if dst_state_indices.numel() > 0:
|
||||
_ref_scatter(
|
||||
ssm_states,
|
||||
intermediate_ssm,
|
||||
dst_state_indices,
|
||||
src_state_indices,
|
||||
last_steps,
|
||||
)
|
||||
_ref_scatter(
|
||||
conv_states,
|
||||
intermediate_conv,
|
||||
dst_state_indices,
|
||||
src_state_indices,
|
||||
last_steps,
|
||||
)
|
||||
|
||||
if mamba_track_indices is not None:
|
||||
assert mamba_steps_to_track is not None
|
||||
track_mask = mamba_steps_to_track >= 0
|
||||
if not track_mask.any():
|
||||
return
|
||||
dst_track_indices = mamba_track_indices[track_mask].to(torch.int64)
|
||||
src_track_indices = intermediate_state_indices[track_mask].to(torch.int64)
|
||||
track_steps = mamba_steps_to_track[track_mask].to(torch.int64)
|
||||
|
||||
_ref_scatter(
|
||||
ssm_states,
|
||||
intermediate_ssm,
|
||||
dst_track_indices,
|
||||
src_track_indices,
|
||||
track_steps,
|
||||
)
|
||||
_ref_scatter(
|
||||
conv_states,
|
||||
intermediate_conv,
|
||||
dst_track_indices,
|
||||
src_track_indices,
|
||||
track_steps,
|
||||
)
|
||||
|
||||
|
||||
def _fused_update_like(
|
||||
ssm_states,
|
||||
intermediate_ssm,
|
||||
conv_states,
|
||||
intermediate_conv,
|
||||
*,
|
||||
state_indices_tensor,
|
||||
accepted_steps,
|
||||
mamba_track_indices=None,
|
||||
mamba_steps_to_track=None,
|
||||
):
|
||||
"""Matches the fully fused logic that avoids index_select and nonzero calls."""
|
||||
# Use fully fused kernel that handles masking internally
|
||||
fused_mamba_state_scatter_with_mask(
|
||||
ssm_states,
|
||||
intermediate_ssm,
|
||||
state_indices_tensor,
|
||||
accepted_steps,
|
||||
)
|
||||
fused_mamba_state_scatter_with_mask(
|
||||
conv_states,
|
||||
intermediate_conv,
|
||||
state_indices_tensor,
|
||||
accepted_steps,
|
||||
)
|
||||
|
||||
if mamba_track_indices is not None:
|
||||
assert mamba_steps_to_track is not None
|
||||
fused_mamba_state_scatter_with_mask(
|
||||
ssm_states,
|
||||
intermediate_ssm,
|
||||
mamba_track_indices,
|
||||
mamba_steps_to_track,
|
||||
)
|
||||
fused_mamba_state_scatter_with_mask(
|
||||
conv_states,
|
||||
intermediate_conv,
|
||||
mamba_track_indices,
|
||||
mamba_steps_to_track,
|
||||
)
|
||||
|
||||
|
||||
def _time_cuda_ms(fn, iters=50, warmup=10):
|
||||
"""Measure average CUDA time (ms) using CUDA events."""
|
||||
for _ in range(warmup):
|
||||
fn()
|
||||
torch.cuda.synchronize()
|
||||
|
||||
start = torch.cuda.Event(enable_timing=True)
|
||||
end = torch.cuda.Event(enable_timing=True)
|
||||
start.record()
|
||||
for _ in range(iters):
|
||||
fn()
|
||||
end.record()
|
||||
torch.cuda.synchronize()
|
||||
return start.elapsed_time(end) / iters
|
||||
|
||||
|
||||
class TestMambaStateScatterCorrectness(unittest.TestCase):
|
||||
@unittest.skipUnless(torch.cuda.is_available(), "CUDA is required for this test.")
|
||||
def test_fused_matches_reference(self):
|
||||
"""Test that fused_mamba_state_scatter_with_mask matches the reference."""
|
||||
if fused_mamba_state_scatter_with_mask is None:
|
||||
self.skipTest(
|
||||
f"fused_mamba_state_scatter_with_mask import failed: {_FUSED_IMPORT_ERROR}"
|
||||
)
|
||||
|
||||
torch.manual_seed(42)
|
||||
device = torch.device("cuda")
|
||||
|
||||
# Keep sizes moderate so this test is quick.
|
||||
L = 8
|
||||
B = 32
|
||||
C = 49
|
||||
D = 5
|
||||
ssm_elems = 1024
|
||||
conv_elems = 512
|
||||
|
||||
ssm_states0 = torch.empty(
|
||||
(L, C, ssm_elems), device=device, dtype=torch.bfloat16
|
||||
)
|
||||
conv_states0 = torch.empty(
|
||||
(L, C, conv_elems), device=device, dtype=torch.bfloat16
|
||||
)
|
||||
intermediate_ssm = torch.randn(
|
||||
(L, B, D, ssm_elems), device=device, dtype=torch.bfloat16
|
||||
)
|
||||
intermediate_conv = torch.randn(
|
||||
(L, B, D, conv_elems), device=device, dtype=torch.bfloat16
|
||||
)
|
||||
|
||||
# unique cache lines (no duplicates) to avoid nondeterministic write order
|
||||
state_indices_tensor = torch.randperm(C, device=device, dtype=torch.int64)[
|
||||
:B
|
||||
].to(torch.int32)
|
||||
|
||||
accepted_steps = torch.randint(0, D, (B,), device=device, dtype=torch.int64)
|
||||
# set ~10% invalid
|
||||
invalid = torch.rand((B,), device=device) < 0.1
|
||||
accepted_steps[invalid] = -1
|
||||
|
||||
# Optional track update
|
||||
mamba_track_indices = torch.randperm(C, device=device, dtype=torch.int64)[:B]
|
||||
mamba_steps_to_track = torch.randint(
|
||||
0, D, (B,), device=device, dtype=torch.int64
|
||||
)
|
||||
track_invalid = torch.rand((B,), device=device) < 0.7
|
||||
mamba_steps_to_track[track_invalid] = -1
|
||||
|
||||
ssm_ref = ssm_states0.clone()
|
||||
conv_ref = conv_states0.clone()
|
||||
ssm_fused = ssm_states0.clone()
|
||||
conv_fused = conv_states0.clone()
|
||||
|
||||
_ref_update_like(
|
||||
ssm_ref,
|
||||
intermediate_ssm,
|
||||
conv_ref,
|
||||
intermediate_conv,
|
||||
state_indices_tensor=state_indices_tensor,
|
||||
accepted_steps=accepted_steps,
|
||||
mamba_track_indices=mamba_track_indices,
|
||||
mamba_steps_to_track=mamba_steps_to_track,
|
||||
)
|
||||
_fused_update_like(
|
||||
ssm_fused,
|
||||
intermediate_ssm,
|
||||
conv_fused,
|
||||
intermediate_conv,
|
||||
state_indices_tensor=state_indices_tensor,
|
||||
accepted_steps=accepted_steps,
|
||||
mamba_track_indices=mamba_track_indices,
|
||||
mamba_steps_to_track=mamba_steps_to_track,
|
||||
)
|
||||
|
||||
torch.testing.assert_close(ssm_fused, ssm_ref)
|
||||
torch.testing.assert_close(conv_fused, conv_ref)
|
||||
|
||||
|
||||
class TestMambaStateScatterPerf(unittest.TestCase):
|
||||
@unittest.skipUnless(torch.cuda.is_available(), "CUDA is required for this test.")
|
||||
def test_perf_report_old_vs_fused(self):
|
||||
"""Optional microbenchmark comparing baseline vs fused kernel.
|
||||
|
||||
Enable with: SGLANG_RUN_MAMBA_SCATTER_PERF_TEST=1
|
||||
"""
|
||||
if os.environ.get("SGLANG_RUN_MAMBA_SCATTER_PERF_TEST", "0") != "1":
|
||||
self.skipTest("Set SGLANG_RUN_MAMBA_SCATTER_PERF_TEST=1 to run perf test.")
|
||||
if fused_mamba_state_scatter_with_mask is None:
|
||||
self.skipTest(
|
||||
f"fused_mamba_state_scatter_with_mask import failed: {_FUSED_IMPORT_ERROR}"
|
||||
)
|
||||
|
||||
torch.manual_seed(0)
|
||||
device = torch.device("cuda")
|
||||
|
||||
# Parameterize sizes via env vars so we can match a real model more closely.
|
||||
L = int(os.environ.get("SGLANG_MAMBA_SCATTER_LAYERS", "32"))
|
||||
B = int(os.environ.get("SGLANG_MAMBA_SCATTER_BATCH", "48"))
|
||||
C = int(os.environ.get("SGLANG_MAMBA_SCATTER_CACHE", "49"))
|
||||
D = int(os.environ.get("SGLANG_MAMBA_SCATTER_DRAFT_TOKENS", "5"))
|
||||
ssm_elems = int(os.environ.get("SGLANG_MAMBA_SCATTER_SSM_ELEMS", "4096"))
|
||||
conv_elems = int(os.environ.get("SGLANG_MAMBA_SCATTER_CONV_ELEMS", "512"))
|
||||
invalid_ratio = float(
|
||||
os.environ.get("SGLANG_MAMBA_SCATTER_INVALID_RATIO", "0.0")
|
||||
)
|
||||
track_ratio = float(os.environ.get("SGLANG_MAMBA_SCATTER_TRACK_RATIO", "0.0"))
|
||||
ssm_dtype = _dtype_from_str(
|
||||
os.environ.get("SGLANG_MAMBA_SCATTER_SSM_DTYPE", "bfloat16")
|
||||
)
|
||||
conv_dtype = _dtype_from_str(
|
||||
os.environ.get("SGLANG_MAMBA_SCATTER_CONV_DTYPE", "bfloat16")
|
||||
)
|
||||
|
||||
# Use zeros for dst so each iteration overwrites the same memory.
|
||||
ssm_states = torch.zeros((L, C, ssm_elems), device=device, dtype=ssm_dtype)
|
||||
conv_states = torch.zeros((L, C, conv_elems), device=device, dtype=conv_dtype)
|
||||
intermediate_ssm = torch.randn(
|
||||
(L, B, D, ssm_elems), device=device, dtype=ssm_dtype
|
||||
)
|
||||
intermediate_conv = torch.randn(
|
||||
(L, B, D, conv_elems), device=device, dtype=conv_dtype
|
||||
)
|
||||
|
||||
state_indices_tensor = torch.randperm(C, device=device, dtype=torch.int64)[
|
||||
:B
|
||||
].to(torch.int32)
|
||||
accepted_steps = torch.randint(0, D, (B,), device=device, dtype=torch.int64)
|
||||
if invalid_ratio > 0:
|
||||
invalid = torch.rand((B,), device=device) < invalid_ratio
|
||||
accepted_steps[invalid] = -1
|
||||
|
||||
mamba_track_indices = None
|
||||
mamba_steps_to_track = None
|
||||
if track_ratio > 0:
|
||||
mamba_track_indices = torch.randperm(C, device=device, dtype=torch.int64)[
|
||||
:B
|
||||
]
|
||||
mamba_steps_to_track = torch.randint(
|
||||
0, D, (B,), device=device, dtype=torch.int64
|
||||
)
|
||||
track_invalid = torch.rand((B,), device=device) >= track_ratio
|
||||
mamba_steps_to_track[track_invalid] = -1
|
||||
|
||||
def ref_fn():
|
||||
_ref_update_like(
|
||||
ssm_states,
|
||||
intermediate_ssm,
|
||||
conv_states,
|
||||
intermediate_conv,
|
||||
state_indices_tensor=state_indices_tensor,
|
||||
accepted_steps=accepted_steps,
|
||||
mamba_track_indices=mamba_track_indices,
|
||||
mamba_steps_to_track=mamba_steps_to_track,
|
||||
)
|
||||
|
||||
def fused_fn():
|
||||
_fused_update_like(
|
||||
ssm_states,
|
||||
intermediate_ssm,
|
||||
conv_states,
|
||||
intermediate_conv,
|
||||
state_indices_tensor=state_indices_tensor,
|
||||
accepted_steps=accepted_steps,
|
||||
mamba_track_indices=mamba_track_indices,
|
||||
mamba_steps_to_track=mamba_steps_to_track,
|
||||
)
|
||||
|
||||
# Warm up JIT compilation for triton kernels (and caches for torch indexing)
|
||||
ref_fn()
|
||||
fused_fn()
|
||||
torch.cuda.synchronize()
|
||||
|
||||
ref_ms = _time_cuda_ms(ref_fn)
|
||||
fused_ms = _time_cuda_ms(fused_fn)
|
||||
|
||||
num_valid = int((accepted_steps >= 0).sum().item())
|
||||
ratio = fused_ms / ref_ms if ref_ms > 0 else float("inf")
|
||||
speedup = ref_ms / fused_ms if fused_ms > 0 else float("inf")
|
||||
|
||||
# Print a concise report
|
||||
print(
|
||||
"\n[MambaStateScatterPerf]\n"
|
||||
f" shapes: L={L} B={B} C={C} D={D} ssm_elems={ssm_elems} conv_elems={conv_elems}\n"
|
||||
f" dtypes: ssm={ssm_dtype} conv={conv_dtype}\n"
|
||||
f" valid: {num_valid}/{B} invalid_ratio={invalid_ratio} track_ratio={track_ratio}\n"
|
||||
f" ref_total_ms (baseline): {ref_ms:.4f}\n"
|
||||
f" fused_total_ms: {fused_ms:.4f} (ratio={ratio:.3f}x, speedup={speedup:.2f}x)\n"
|
||||
)
|
||||
|
||||
|
||||
if __name__ == "__main__": # pragma: no cover
|
||||
unittest.main()
|
||||
Reference in New Issue
Block a user