Add zeco and lasp2 implementation for benchmarking

lasp/__init__.py

@@ -1,6 +1,10 @@
 from .lasp_cache import *
 from .lasp_fuse import *
 from .lasp_fuse_parallel import *
+from .lasp_zeco import *
 from .lasp_naive import *
+from .lasp_blelloch import *
+from .lasp_blelloch_v2 import *
+from .lasp_blelloch_v3 import *
 from .lightning_attention import *
 from .utils import *

lasp/gpu_config.py

@@ -0,0 +1,341 @@
+"""
+GPU configuration utility for tuning block sizes based on architecture-specific shared memory limits.
+"""
+import torch
+
+
+# Shared memory limits per thread block (in bytes) by compute capability
+# Based on NVIDIA documentation:
+# - Compute Capability 6.x (Pascal): 48 KB per thread block
+# - Compute Capability 7.0 (Volta): 48 KB per thread block
+# - Compute Capability 7.5 (Turing): 48 KB per thread block
+# - Compute Capability 8.x (Ampere): 163 KB per thread block (static: 48 KB, dynamic: up to 163 KB)
+# - Compute Capability 8.9 (Ada Lovelace/RTX 4090): ~99 KB per thread block (varies by model)
+# - Compute Capability 9.0 (Hopper): 227 KB per thread block (static: 48 KB, dynamic: up to 227 KB)
+
+SMEM_LIMITS = {
+    # Compute capability 6.x (Pascal)
+    6.0: 48 * 1024,
+    6.1: 48 * 1024,
+    6.2: 48 * 1024,
+    # Compute capability 7.0 (Volta)
+    7.0: 48 * 1024,
+    # Compute capability 7.5 (Turing)
+    7.5: 48 * 1024,
+    # Compute capability 8.0 (Ampere A100)
+    8.0: 163 * 1024,
+    # Compute capability 8.6 (Ampere consumer, RTX 3090, etc.)
+    8.6: 163 * 1024,
+    # Compute capability 8.9 (Ada Lovelace, RTX 4090)
+    # Note: RTX 4090 typically has ~99 KB limit per thread block
+    8.9: 99 * 1024,
+    # Compute capability 9.0 (Hopper)
+    9.0: 227 * 1024,
+}
+
+# Default to conservative 48 KB if architecture not found
+DEFAULT_SMEM_LIMIT = 48 * 1024
+
+
+def get_compute_capability(device=None):
+    """Get the compute capability of the current or specified GPU."""
+    if device is None:
+        device = torch.cuda.current_device()
+
+    props = torch.cuda.get_device_properties(device)
+    major = props.major
+    minor = props.minor
+    compute_cap = float(f"{major}.{minor}")
+
+    return compute_cap
+
+
+def get_shared_memory_limit(device=None):
+    """Get the shared memory limit per thread block for the current GPU."""
+    compute_cap = get_compute_capability(device)
+
+    # Try exact match first
+    if compute_cap in SMEM_LIMITS:
+        return SMEM_LIMITS[compute_cap]
+
+    # Try matching by major version
+    major_version = int(compute_cap)
+    for cap, limit in SMEM_LIMITS.items():
+        if int(cap) == major_version:
+            return limit
+
+    # Fall back to default
+    return DEFAULT_SMEM_LIMIT
+
+
+def get_optimal_block_sizes(n, d, e, device=None):
+    """
+    Calculate optimal BLOCK and BLOCK_MODEL sizes based on shared memory constraints.
+
+    Args:
+        n: Sequence length
+        d: Query/key dimension
+        e: Value dimension
+        device: CUDA device (optional)
+
+    Returns:
+        tuple: (BLOCK, BLOCK_MODEL) sizes
+    """
+    smem_limit = get_shared_memory_limit(device)
+
+    # Estimate shared memory usage per block
+    # For forward kernel:
+    # - q: BLOCK * d * 4 bytes (float32)
+    # - k_trans: BLOCK * d * 4 bytes
+    # - v: BLOCK * BLOCK_MODEL * 4 bytes
+    # - kv: d * BLOCK_MODEL * 4 bytes
+    # - Various temporary arrays: ~BLOCK^2 * 4 bytes for diag_decay
+    # Total approximation: ~(2 * BLOCK * d + BLOCK * BLOCK_MODEL + d * BLOCK_MODEL + BLOCK^2) * 4
+
+    # Start with conservative values
+    BLOCK = 32
+    BLOCK_MODEL = 16
+
+    # Try to increase block sizes while staying within limit
+    for block_size in [64, 128, 256]:
+        for block_model in [16, 32, 64]:
+            if block_model > e:
+                continue
+
+            # Rough estimate of shared memory usage
+            qk_mem = 2 * block_size * d * 4  # q and k_trans
+            v_mem = block_size * block_model * 4
+            kv_mem = d * block_model * 4
+            diag_mem = block_size * block_size * 4  # diag_decay matrix
+            temp_mem = block_size * block_model * 4  # o_intra, o_inter
+
+            total_mem = qk_mem + v_mem + kv_mem + diag_mem + temp_mem
+
+            # Add 20% overhead for safety
+            if total_mem * 1.2 <= smem_limit:
+                BLOCK = block_size
+                BLOCK_MODEL = block_model
+            else:
+                break
+        if total_mem * 1.2 > smem_limit:
+            break
+
+    # Ensure BLOCK_MODEL doesn't exceed e and is power of 2
+    try:
+        import triton
+        BLOCK_MODEL = min(BLOCK_MODEL, triton.next_power_of_2(e), 64)
+    except ImportError:
+        # Fallback: round down to nearest power of 2
+        import math
+        max_pow2 = 2 ** int(math.log2(min(BLOCK_MODEL, e, 64)))
+        BLOCK_MODEL = max_pow2
+
+    # Cap BLOCK at reasonable values
+    BLOCK = min(BLOCK, 128)
+
+    return BLOCK, BLOCK_MODEL
+
+
+def get_optimal_cblock_size(BLOCK, device=None):
+    """
+    Calculate optimal CBLOCK size for backward kernels.
+
+    Args:
+        BLOCK: Main block size
+        device: CUDA device (optional)
+
+    Returns:
+        int: CBLOCK size
+    """
+    smem_limit = get_shared_memory_limit(device)
+
+    # CBLOCK is typically BLOCK // 2 or BLOCK // 4
+    # For backward kernels, shared memory usage is similar to forward
+    # but with CBLOCK instead of BLOCK for some operations
+
+    # Start conservative
+    CBLOCK = 16
+
+    # Try increasing CBLOCK
+    for cblock_size in [32, 64]:
+        if cblock_size <= BLOCK and BLOCK % cblock_size == 0:
+            # Estimate shared memory (conservative)
+            # Similar to forward but with CBLOCK
+            estimated_mem = 4 * cblock_size * cblock_size * 4  # Rough estimate
+            if estimated_mem * 1.2 <= smem_limit:
+                CBLOCK = cblock_size
+            else:
+                break
+
+    return min(CBLOCK, BLOCK // 2)
+
+
+# Fixed configurations pre-computed for common GPU architectures and dimensions
+# Format: (compute_capability, kernel_type, n_range, d_range, e_range): {BLOCK, BLOCK_MODEL, CBLOCK}
+# Ranges are (min, max) inclusive
+FIXED_CONFIGS = {
+    # RTX 4090 / Ada Lovelace (8.9) - 99KB shared memory limit
+    (8.9, 'lightning', (512, 8192), (64, 256), (32, 128)): {'BLOCK': 32, 'BLOCK_MODEL': 16, 'CBLOCK': 16},
+    (8.9, 'lasp_naive', (512, 8192), (64, 256), (32, 128)): {'BLOCK': 32, 'BLOCK_MODEL': 16, 'CBLOCK': 16},
+    (8.9, 'lasp_cache', (512, 8192), (64, 256), (32, 128)): {'BLOCK': 32, 'BLOCK_MODEL': 16, 'CBLOCK': 16},
+    (8.9, 'lasp_fuse', (128, 2048), (64, 256), (32, 128)): {'BLOCK': 32, 'CBLOCK': 16},
+    (8.9, 'lasp_fuse', (2049, 8192), (64, 256), (32, 128)): {'BLOCK': 32, 'CBLOCK': 16},
+    (8.9, 'lasp_fuse_parallel', (128, 2048), (64, 256), (32, 128)): {'BLOCK': 32, 'CBLOCK': 16},
+    (8.9, 'lasp_fuse_parallel', (2049, 8192), (64, 256), (32, 128)): {'BLOCK': 32, 'CBLOCK': 16},
+    (8.9, 'lasp_blelloch', (128, 2048), (64, 256), (32, 128)): {'BLOCK': 32, 'CBLOCK': 16},
+    (8.9, 'lasp_blelloch', (2049, 8192), (64, 256), (32, 128)): {'BLOCK': 32, 'CBLOCK': 16},
+
+    # Ampere A100 / RTX 3090 (8.0, 8.6) - 163KB shared memory limit
+    (8.0, 'lightning', (512, 8192), (64, 256), (32, 128)): {'BLOCK': 64, 'BLOCK_MODEL': 32, 'CBLOCK': 32},
+    (8.6, 'lightning', (512, 8192), (64, 256), (32, 128)): {'BLOCK': 64, 'BLOCK_MODEL': 32, 'CBLOCK': 32},
+    (8.0, 'lasp_naive', (512, 8192), (64, 256), (32, 128)): {'BLOCK': 64, 'BLOCK_MODEL': 32, 'CBLOCK': 32},
+    (8.6, 'lasp_naive', (512, 8192), (64, 256), (32, 128)): {'BLOCK': 64, 'BLOCK_MODEL': 32, 'CBLOCK': 32},
+    (8.0, 'lasp_cache', (512, 8192), (64, 256), (32, 128)): {'BLOCK': 64, 'BLOCK_MODEL': 32, 'CBLOCK': 32},
+    (8.6, 'lasp_cache', (512, 8192), (64, 256), (32, 128)): {'BLOCK': 64, 'BLOCK_MODEL': 32, 'CBLOCK': 32},
+    (8.0, 'lasp_fuse', (128, 2048), (64, 256), (32, 128)): {'BLOCK': 64, 'CBLOCK': 32},
+    (8.6, 'lasp_fuse', (128, 2048), (64, 256), (32, 128)): {'BLOCK': 64, 'CBLOCK': 32},
+    (8.0, 'lasp_fuse', (2049, 8192), (64, 256), (32, 128)): {'BLOCK': 128, 'CBLOCK': 32},
+    (8.6, 'lasp_fuse', (2049, 8192), (64, 256), (32, 128)): {'BLOCK': 128, 'CBLOCK': 32},
+    (8.0, 'lasp_fuse_parallel', (128, 2048), (64, 256), (32, 128)): {'BLOCK': 64, 'CBLOCK': 32},
+    (8.6, 'lasp_fuse_parallel', (128, 2048), (64, 256), (32, 128)): {'BLOCK': 64, 'CBLOCK': 32},
+    (8.0, 'lasp_fuse_parallel', (2049, 8192), (64, 256), (32, 128)): {'BLOCK': 128, 'CBLOCK': 32},
+    (8.6, 'lasp_fuse_parallel', (2049, 8192), (64, 256), (32, 128)): {'BLOCK': 128, 'CBLOCK': 32},
+    (8.0, 'lasp_blelloch', (128, 2048), (64, 256), (32, 128)): {'BLOCK': 64, 'CBLOCK': 32},
+    (8.6, 'lasp_blelloch', (128, 2048), (64, 256), (32, 128)): {'BLOCK': 64, 'CBLOCK': 32},
+    (8.0, 'lasp_blelloch', (2049, 8192), (64, 256), (32, 128)): {'BLOCK': 128, 'CBLOCK': 32},
+    (8.6, 'lasp_blelloch', (2049, 8192), (64, 256), (32, 128)): {'BLOCK': 128, 'CBLOCK': 32},
+
+    # Hopper H100 (9.0) - 227KB shared memory limit
+    (9.0, 'lightning', (512, 8192), (64, 256), (32, 128)): {'BLOCK': 64, 'BLOCK_MODEL': 32, 'CBLOCK': 32},
+    (9.0, 'lasp_naive', (512, 8192), (64, 256), (32, 128)): {'BLOCK': 64, 'BLOCK_MODEL': 32, 'CBLOCK': 32},
+    (9.0, 'lasp_cache', (512, 8192), (64, 256), (32, 128)): {'BLOCK': 64, 'BLOCK_MODEL': 32, 'CBLOCK': 32},
+    (9.0, 'lasp_fuse', (128, 2048), (64, 256), (32, 128)): {'BLOCK': 64, 'CBLOCK': 32},
+    (9.0, 'lasp_fuse', (2049, 8192), (64, 256), (32, 128)): {'BLOCK': 128, 'CBLOCK': 32},
+    (9.0, 'lasp_fuse_parallel', (128, 2048), (64, 256), (32, 128)): {'BLOCK': 64, 'CBLOCK': 32},
+    (9.0, 'lasp_fuse_parallel', (2049, 8192), (64, 256), (32, 128)): {'BLOCK': 128, 'CBLOCK': 32},
+    (9.0, 'lasp_blelloch', (128, 2048), (64, 256), (32, 128)): {'BLOCK': 64, 'CBLOCK': 32},
+    (9.0, 'lasp_blelloch', (2049, 8192), (64, 256), (32, 128)): {'BLOCK': 128, 'CBLOCK': 32},
+
+    # Pascal/Turing/Volta (6.x, 7.0, 7.5) - 48KB shared memory limit (conservative)
+    (6.0, 'lightning', (512, 8192), (64, 256), (32, 128)): {'BLOCK': 32, 'BLOCK_MODEL': 16, 'CBLOCK': 16},
+    (6.1, 'lightning', (512, 8192), (64, 256), (32, 128)): {'BLOCK': 32, 'BLOCK_MODEL': 16, 'CBLOCK': 16},
+    (6.2, 'lightning', (512, 8192), (64, 256), (32, 128)): {'BLOCK': 32, 'BLOCK_MODEL': 16, 'CBLOCK': 16},
+    (7.0, 'lightning', (512, 8192), (64, 256), (32, 128)): {'BLOCK': 32, 'BLOCK_MODEL': 16, 'CBLOCK': 16},
+    (7.5, 'lightning', (512, 8192), (64, 256), (32, 128)): {'BLOCK': 32, 'BLOCK_MODEL': 16, 'CBLOCK': 16},
+    (6.0, 'lasp_naive', (512, 8192), (64, 256), (32, 128)): {'BLOCK': 32, 'BLOCK_MODEL': 16, 'CBLOCK': 16},
+    (6.1, 'lasp_naive', (512, 8192), (64, 256), (32, 128)): {'BLOCK': 32, 'BLOCK_MODEL': 16, 'CBLOCK': 16},
+    (6.2, 'lasp_naive', (512, 8192), (64, 256), (32, 128)): {'BLOCK': 32, 'BLOCK_MODEL': 16, 'CBLOCK': 16},
+    (7.0, 'lasp_naive', (512, 8192), (64, 256), (32, 128)): {'BLOCK': 32, 'BLOCK_MODEL': 16, 'CBLOCK': 16},
+    (7.5, 'lasp_naive', (512, 8192), (64, 256), (32, 128)): {'BLOCK': 32, 'BLOCK_MODEL': 16, 'CBLOCK': 16},
+    (6.0, 'lasp_cache', (512, 8192), (64, 256), (32, 128)): {'BLOCK': 32, 'BLOCK_MODEL': 16, 'CBLOCK': 16},
+    (6.1, 'lasp_cache', (512, 8192), (64, 256), (32, 128)): {'BLOCK': 32, 'BLOCK_MODEL': 16, 'CBLOCK': 16},
+    (6.2, 'lasp_cache', (512, 8192), (64, 256), (32, 128)): {'BLOCK': 32, 'BLOCK_MODEL': 16, 'CBLOCK': 16},
+    (7.0, 'lasp_cache', (512, 8192), (64, 256), (32, 128)): {'BLOCK': 32, 'BLOCK_MODEL': 16, 'CBLOCK': 16},
+    (7.5, 'lasp_cache', (512, 8192), (64, 256), (32, 128)): {'BLOCK': 32, 'BLOCK_MODEL': 16, 'CBLOCK': 16},
+    (6.0, 'lasp_fuse', (128, 8192), (64, 256), (32, 128)): {'BLOCK': 32, 'CBLOCK': 16},
+    (6.1, 'lasp_fuse', (128, 8192), (64, 256), (32, 128)): {'BLOCK': 32, 'CBLOCK': 16},
+    (6.2, 'lasp_fuse', (128, 8192), (64, 256), (32, 128)): {'BLOCK': 32, 'CBLOCK': 16},
+    (7.0, 'lasp_fuse', (128, 8192), (64, 256), (32, 128)): {'BLOCK': 32, 'CBLOCK': 16},
+    (7.5, 'lasp_fuse', (128, 8192), (64, 256), (32, 128)): {'BLOCK': 32, 'CBLOCK': 16},
+    (6.0, 'lasp_fuse_parallel', (128, 8192), (64, 256), (32, 128)): {'BLOCK': 32, 'CBLOCK': 16},
+    (6.1, 'lasp_fuse_parallel', (128, 8192), (64, 256), (32, 128)): {'BLOCK': 32, 'CBLOCK': 16},
+    (6.2, 'lasp_fuse_parallel', (128, 8192), (64, 256), (32, 128)): {'BLOCK': 32, 'CBLOCK': 16},
+    (7.0, 'lasp_fuse_parallel', (128, 8192), (64, 256), (32, 128)): {'BLOCK': 32, 'CBLOCK': 16},
+    (7.5, 'lasp_fuse_parallel', (128, 8192), (64, 256), (32, 128)): {'BLOCK': 32, 'CBLOCK': 16},
+    (6.0, 'lasp_blelloch', (128, 8192), (64, 256), (32, 128)): {'BLOCK': 32, 'CBLOCK': 16},
+    (6.1, 'lasp_blelloch', (128, 8192), (64, 256), (32, 128)): {'BLOCK': 32, 'CBLOCK': 16},
+    (6.2, 'lasp_blelloch', (128, 8192), (64, 256), (32, 128)): {'BLOCK': 32, 'CBLOCK': 16},
+    (7.0, 'lasp_blelloch', (128, 8192), (64, 256), (32, 128)): {'BLOCK': 32, 'CBLOCK': 16},
+    (7.5, 'lasp_blelloch', (128, 8192), (64, 256), (32, 128)): {'BLOCK': 32, 'CBLOCK': 16},
+}
+
+
+def _match_fixed_config(compute_cap, kernel_type, n, d, e):
+    """Match dimensions to fixed configuration ranges."""
+    # Try exact compute capability match first
+    for (cap, ktype, (n_min, n_max), (d_min, d_max), (e_min, e_max)), config in FIXED_CONFIGS.items():
+        if cap == compute_cap and ktype == kernel_type:
+            if n_min <= n <= n_max and d_min <= d <= d_max and e_min <= e <= e_max:
+                return config
+
+    # Try matching by major version
+    major_version = int(compute_cap)
+    for (cap, ktype, (n_min, n_max), (d_min, d_max), (e_min, e_max)), config in FIXED_CONFIGS.items():
+        if int(cap) == major_version and ktype == kernel_type:
+            if n_min <= n <= n_max and d_min <= d <= d_max and e_min <= e <= e_max:
+                return config
+
+    return None
+
+
+# Cache for performance (only caches lookup results, not computation)
+_smem_cache = {}
+
+
+def get_config_for_kernel(kernel_type, n, d, e, device=None):
+    """
+    Get configuration for a specific kernel type using fixed lookup table.
+    Falls back to dynamic computation if no match found.
+
+    Args:
+        kernel_type: 'lightning', 'lasp_naive', 'lasp_cache', 'lasp_fuse', etc.
+        n: Sequence length
+        d: Query/key dimension
+        e: Value dimension
+        device: CUDA device (optional)
+
+    Returns:
+        dict: Configuration with BLOCK, BLOCK_MODEL, CBLOCK, etc.
+    """
+    if device is None:
+        device = torch.cuda.current_device()
+
+    cache_key = (kernel_type, device, n, d, e)
+    if cache_key in _smem_cache:
+        return _smem_cache[cache_key]
+
+    compute_cap = get_compute_capability(device)
+
+    # Try fixed configuration first (fast lookup)
+    config = _match_fixed_config(compute_cap, kernel_type, n, d, e)
+
+    if config is not None:
+        _smem_cache[cache_key] = config
+        return config
+
+    # Fall back to dynamic computation for edge cases
+    smem_limit = get_shared_memory_limit(device)
+
+    if kernel_type == 'lightning':
+        BLOCK, BLOCK_MODEL = get_optimal_block_sizes(n, d, e, device)
+        config = {
+            'BLOCK': BLOCK,
+            'BLOCK_MODEL': BLOCK_MODEL,
+            'CBLOCK': get_optimal_cblock_size(BLOCK, device),
+        }
+    elif kernel_type in ['lasp_naive', 'lasp_cache']:
+        BLOCK, BLOCK_MODEL = get_optimal_block_sizes(n, d, e, device)
+        config = {
+            'BLOCK': BLOCK,
+            'BLOCK_MODEL': BLOCK_MODEL,
+            'CBLOCK': get_optimal_cblock_size(BLOCK, device),
+        }
+    elif kernel_type in ['lasp_fuse', 'lasp_fuse_parallel', 'lasp_blelloch']:
+        if n > 128:
+            if smem_limit <= 99 * 1024:
+                BLOCK = 32
+                CBLOCK = 16
+            else:
+                BLOCK = 128
+                CBLOCK = 32
+        else:
+            BLOCK = min(n, 32)
+            CBLOCK = min(n, 16)
+        config = {
+            'BLOCK': BLOCK,
+            'CBLOCK': CBLOCK,
+        }
+
+    _smem_cache[cache_key] = config
+    return config
+