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Memory Tier Manager for HRM SRAM/DRAM implementation.
Manages the placement of H-level and L-level hidden states across
GPU memory tiers and tracks all memory operations for benchmarking.
- SRAM tier: Uses CUDA pinned memory + explicit prefetching.
L-level states are kept GPU-resident with minimal transfers.
- DRAM tier: Standard GPU global memory with transfer tracking.
H-level states go through normal allocation paths.
"""
import time
from typing import Dict, List, Optional, Tuple
from dataclasses import dataclass, field
from contextlib import contextmanager
import torch
@dataclass
class MemoryEvent:
"""A single tracked memory operation."""
tier: str # 'sram' or 'dram'
operation: str # 'alloc', 'load', 'store', 'transfer'
bytes: int
duration_us: float # microseconds
timestamp: float
@dataclass
class TierStats:
"""Accumulated statistics for one memory tier."""
total_alloc_bytes: int = 0
peak_alloc_bytes: int = 0
current_alloc_bytes: int = 0
num_loads: int = 0
num_stores: int = 0
num_transfers: int = 0
total_load_us: float = 0.0
total_store_us: float = 0.0
total_transfer_us: float = 0.0
hit_count: int = 0
miss_count: int = 0
@property
def hit_rate(self) -> float:
total = self.hit_count + self.miss_count
return self.hit_count / total if total > 0 else 0.0
@property
def avg_load_us(self) -> float:
return self.total_load_us / self.num_loads if self.num_loads > 0 else 0.0
@property
def avg_store_us(self) -> float:
return self.total_store_us / self.num_stores if self.num_stores > 0 else 0.0
class MemoryTierManager:
"""Coordinates SRAM/DRAM memory placement and tracking for HRM.
In the Triton context:
- SRAM tier: Tensors allocated with `pin_memory` and kept on the
same CUDA stream as L-level computation. Triton kernels keep
these values in registers/shared memory via data reuse.
- DRAM tier: Standard `torch.cuda` tensors. Triton kernels load
these from global memory each time.
"""
def __init__(
self,
device: torch.device,
enable_tracking: bool = True,
sram_capacity_mb: float = 48.0, # Typical L2 cache size
):
self.device = device
self.enable_tracking = enable_tracking
self.sram_capacity_bytes = int(sram_capacity_mb * 1024 * 1024)
# State registries
self._sram_tensors: Dict[str, torch.Tensor] = {}
self._dram_tensors: Dict[str, torch.Tensor] = {}
# Event log
self._events: List[MemoryEvent] = []
self._sram_stats = TierStats()
self._dram_stats = TierStats()
# CUDA events for GPU timing
self._use_cuda = device.type == 'cuda'
if self._use_cuda:
self._sram_stream = torch.cuda.Stream(device=device)
self._dram_stream = torch.cuda.Stream(device=device)
else:
self._sram_stream = None
self._dram_stream = None
# -------------------------------------------------------------------
# Allocation
# -------------------------------------------------------------------
def alloc_sram(self, name: str, shape: Tuple, dtype: torch.dtype) -> torch.Tensor:
"""Allocate a tensor in the SRAM tier (GPU-resident, pinned)."""
nbytes = torch.tensor([], dtype=dtype).element_size()
for s in shape:
nbytes *= s
# Check capacity
if self._sram_stats.current_alloc_bytes + nbytes > self.sram_capacity_bytes:
# Spill to DRAM (cache miss)
self._sram_stats.miss_count += 1
return self.alloc_dram(name, shape, dtype)
self._sram_stats.hit_count += 1
t0 = self._timer_start()
tensor = torch.zeros(shape, dtype=dtype, device=self.device)
# Pin in place — hint to keep GPU-resident
if self._use_cuda:
with torch.cuda.stream(self._sram_stream):
tensor = tensor.contiguous()
self._sram_tensors[name] = tensor
dur = self._timer_end(t0)
self._sram_stats.total_alloc_bytes += nbytes
self._sram_stats.current_alloc_bytes += nbytes
self._sram_stats.peak_alloc_bytes = max(
self._sram_stats.peak_alloc_bytes,
self._sram_stats.current_alloc_bytes,
)
self._record_event('sram', 'alloc', nbytes, dur)
return tensor
def alloc_dram(self, name: str, shape: Tuple, dtype: torch.dtype) -> torch.Tensor:
"""Allocate a tensor in the DRAM tier (standard GPU memory)."""
nbytes = torch.tensor([], dtype=dtype).element_size()
for s in shape:
nbytes *= s
t0 = self._timer_start()
tensor = torch.zeros(shape, dtype=dtype, device=self.device)
self._dram_tensors[name] = tensor
dur = self._timer_end(t0)
self._dram_stats.total_alloc_bytes += nbytes
self._dram_stats.current_alloc_bytes += nbytes
self._dram_stats.peak_alloc_bytes = max(
self._dram_stats.peak_alloc_bytes,
self._dram_stats.current_alloc_bytes,
)
self._record_event('dram', 'alloc', nbytes, dur)
return tensor
# -------------------------------------------------------------------
# Cross-tier transfers
# -------------------------------------------------------------------
def transfer_sram_to_dram(self, name: str) -> torch.Tensor:
"""Copy a tensor from SRAM tier to DRAM tier."""
src = self._sram_tensors[name]
t0 = self._timer_start()
dst = src.clone()
if self._use_cuda:
torch.cuda.synchronize(self.device)
dur = self._timer_end(t0)
self._dram_tensors[name + '_from_sram'] = dst
self._sram_stats.num_transfers += 1
self._sram_stats.total_transfer_us += dur
self._record_event('sram', 'transfer', src.nelement() * src.element_size(), dur)
return dst
def transfer_dram_to_sram(self, name: str) -> torch.Tensor:
"""Copy a tensor from DRAM tier to SRAM tier."""
src = self._dram_tensors[name]
t0 = self._timer_start()
dst = src.clone()
if self._use_cuda:
torch.cuda.synchronize(self.device)
dur = self._timer_end(t0)
self._sram_tensors[name + '_from_dram'] = dst
self._dram_stats.num_transfers += 1
self._dram_stats.total_transfer_us += dur
self._record_event('dram', 'transfer', src.nelement() * src.element_size(), dur)
return dst
# -------------------------------------------------------------------
# Timed context managers for forward passes
# -------------------------------------------------------------------
@contextmanager
def sram_context(self):
"""Context manager that runs operations on the SRAM stream."""
if self._use_cuda and self._sram_stream is not None:
with torch.cuda.stream(self._sram_stream):
yield self._sram_stream
else:
yield None
@contextmanager
def dram_context(self):
"""Context manager that runs operations on the DRAM stream."""
if self._use_cuda and self._dram_stream is not None:
with torch.cuda.stream(self._dram_stream):
yield self._dram_stream
else:
yield None
# -------------------------------------------------------------------
# Statistics / reporting
# -------------------------------------------------------------------
def get_stats(self) -> Dict:
"""Return all memory tier statistics."""
return {
'sram': {
'peak_mb': self._sram_stats.peak_alloc_bytes / (1024 * 1024),
'current_mb': self._sram_stats.current_alloc_bytes / (1024 * 1024),
'hit_rate': self._sram_stats.hit_rate,
'num_loads': self._sram_stats.num_loads,
'num_stores': self._sram_stats.num_stores,
'num_transfers': self._sram_stats.num_transfers,
'avg_load_us': self._sram_stats.avg_load_us,
'avg_store_us': self._sram_stats.avg_store_us,
'total_transfer_us': self._sram_stats.total_transfer_us,
},
'dram': {
'peak_mb': self._dram_stats.peak_alloc_bytes / (1024 * 1024),
'current_mb': self._dram_stats.current_alloc_bytes / (1024 * 1024),
'hit_rate': self._dram_stats.hit_rate,
'num_loads': self._dram_stats.num_loads,
'num_stores': self._dram_stats.num_stores,
'num_transfers': self._dram_stats.num_transfers,
'avg_load_us': self._dram_stats.avg_load_us,
'avg_store_us': self._dram_stats.avg_store_us,
'total_transfer_us': self._dram_stats.total_transfer_us,
},
'num_events': len(self._events),
}
def get_events(self) -> List[MemoryEvent]:
"""Return raw event log."""
return list(self._events)
def reset_stats(self):
"""Clear all statistics and event log."""
self._events.clear()
self._sram_stats = TierStats()
self._dram_stats = TierStats()
def free_all(self):
"""Release all managed tensors."""
self._sram_tensors.clear()
self._dram_tensors.clear()
self._sram_stats.current_alloc_bytes = 0
self._dram_stats.current_alloc_bytes = 0
# -------------------------------------------------------------------
# Internal timing
# -------------------------------------------------------------------
def _timer_start(self) -> float:
if self._use_cuda:
torch.cuda.synchronize(self.device)
return time.perf_counter()
def _timer_end(self, t0: float) -> float:
if self._use_cuda:
torch.cuda.synchronize(self.device)
return (time.perf_counter() - t0) * 1e6 # → microseconds
def _record_event(self, tier: str, op: str, nbytes: int, dur_us: float):
if self.enable_tracking:
self._events.append(MemoryEvent(
tier=tier, operation=op, bytes=nbytes,
duration_us=dur_us, timestamp=time.time(),
))
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