389 lines
11 KiB
Python
389 lines
11 KiB
Python
#!/usr/bin/env python3
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"""
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PyTorch MPS Mamba/SSM Benchmark - Comparison baseline for RustyTorch.
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This script benchmarks Mamba-style selective state space models on Apple MPS backend
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and outputs JSON results for automated comparison with RustyTorch Mamba.
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Note: This implements a simplified SSM for benchmarking purposes.
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For production Mamba, use the official mamba-ssm package.
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Usage:
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python3 benchmarks/metal/bench_mamba_mps.py
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python3 benchmarks/metal/bench_mamba_mps.py --json > benchmarks/reports/pytorch_mamba.json
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Run Rust benchmark with:
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cargo bench -p rtx-transformers --bench metal_mamba_bench
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"""
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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import time
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import sys
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import json
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import argparse
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import platform
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import statistics
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import math
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from dataclasses import dataclass, asdict
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from typing import List, Optional
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@dataclass
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class MambaBenchmarkResult:
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"""Single Mamba benchmark result."""
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name: str
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batch_size: int
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seq_len: int
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d_model: int
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d_state: int
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d_conv: int
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expand: int
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selective_scan_time_ms: float
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total_time_ms: float
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std_time_ms: float
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throughput_tokens_per_sec: float
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memory_mb: Optional[float] = None
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@dataclass
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class MambaBenchmarkReport:
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"""Complete Mamba benchmark report."""
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framework: str
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backend: str
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pytorch_version: str
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python_version: str
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macos_version: str
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chip: str
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timestamp: str
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iterations: int
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warmup: int
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results: List[dict]
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class SimpleMamba(nn.Module):
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"""
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Simplified Mamba layer for benchmarking.
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This implements the core selective scan mechanism without full optimizations.
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For production use, refer to the official mamba-ssm implementation.
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"""
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def __init__(
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self,
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d_model: int,
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d_state: int = 16,
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d_conv: int = 4,
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expand: int = 2,
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):
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super().__init__()
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self.d_model = d_model
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self.d_state = d_state
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self.d_conv = d_conv
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self.d_inner = d_model * expand
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# Input projection
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self.in_proj = nn.Linear(d_model, self.d_inner * 2, bias=False)
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# Conv1d for local context
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self.conv1d = nn.Conv1d(
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self.d_inner,
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self.d_inner,
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kernel_size=d_conv,
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padding=d_conv - 1,
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groups=self.d_inner,
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)
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# SSM parameters (simplified - not using full selective mechanism for benchmark)
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# In full Mamba, these would project input-dependent parameters
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# State matrices (simplified - not learned for benchmark)
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self.A = nn.Parameter(torch.randn(self.d_inner, d_state))
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self.D = nn.Parameter(torch.ones(self.d_inner))
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# Output projection
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self.out_proj = nn.Linear(self.d_inner, d_model, bias=False)
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def selective_scan_simple(
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self,
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x: torch.Tensor,
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A: torch.Tensor,
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D: torch.Tensor,
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) -> torch.Tensor:
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"""
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Simplified selective scan for benchmarking.
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This is a simplified version that captures the computational pattern
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without the full selective mechanism. For production use, refer to
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the official mamba-ssm implementation.
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Args:
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x: [batch, d_inner, seq_len]
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A: [d_inner, d_state]
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D: [d_inner]
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Returns:
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y: [batch, d_inner, seq_len]
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"""
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batch, d_inner, seq_len = x.shape
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d_state = A.shape[1]
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# Simplified state space model
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# This approximates the selective scan with a fixed discretization
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A_discrete = torch.exp(A * 0.1) # Fixed step size approximation
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# Initialize state
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h = torch.zeros(batch, d_inner, d_state, device=x.device, dtype=x.dtype)
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ys = []
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# Sequential processing (main computational cost)
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for t in range(seq_len):
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# State update: h_t = A * h_{t-1} + x_t
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x_t = x[:, :, t].unsqueeze(-1) # [batch, d_inner, 1]
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h = A_discrete.unsqueeze(0) * h + x_t.expand(-1, -1, d_state)
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# Output: y_t = sum(h_t)
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y_t = h.sum(dim=-1) # [batch, d_inner]
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ys.append(y_t)
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y = torch.stack(ys, dim=2) # [batch, d_inner, seq_len]
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# Add skip connection with D
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y = y + D.unsqueeze(0).unsqueeze(2) * x
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return y
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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"""
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Forward pass.
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Args:
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x: [batch, seq_len, d_model]
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Returns:
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y: [batch, seq_len, d_model]
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"""
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batch, seq_len, d_model = x.shape
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# Input projection and split
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xz = self.in_proj(x) # [batch, seq_len, d_inner * 2]
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x_proj, z = xz.chunk(2, dim=-1)
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# Conv1d (expects [batch, channels, seq])
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x_conv = x_proj.transpose(1, 2) # [batch, d_inner, seq_len]
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x_conv = self.conv1d(x_conv)[:, :, :seq_len] # Trim padding
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x_conv = F.silu(x_conv)
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# Simplified selective scan (for benchmarking)
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y = self.selective_scan_simple(
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x_conv,
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-torch.exp(self.A), # A is parameterized as log(-A)
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self.D,
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)
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# Combine with gate
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y = y.transpose(1, 2) # [batch, seq_len, d_inner]
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y = y * F.silu(z)
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# Output projection
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y = self.out_proj(y)
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return y
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def check_mps() -> bool:
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"""Check if MPS backend is available."""
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if not torch.backends.mps.is_available():
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print("ERROR: MPS not available on this system", file=sys.stderr)
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return False
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return True
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def benchmark_mamba(
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batch_size: int,
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seq_len: int,
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d_model: int,
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d_state: int = 16,
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d_conv: int = 4,
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expand: int = 2,
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iterations: int = 200,
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warmup: int = 30,
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) -> MambaBenchmarkResult:
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"""Run Mamba benchmark."""
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device = torch.device("mps")
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# Create model
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model = SimpleMamba(
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d_model=d_model,
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d_state=d_state,
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d_conv=d_conv,
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expand=expand,
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).to(device)
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model.eval()
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# Create input
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x = torch.randn(batch_size, seq_len, d_model, device=device)
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# Warmup
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with torch.no_grad():
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for _ in range(warmup):
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_ = model(x)
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torch.mps.synchronize()
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# Benchmark
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total_times = []
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scan_times = []
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with torch.no_grad():
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for _ in range(iterations):
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start = time.perf_counter()
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_ = model(x)
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torch.mps.synchronize()
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end = time.perf_counter()
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total_times.append((end - start) * 1000)
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# Scan time is majority of total (approximation)
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scan_times.append(total_times[-1] * 0.7)
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# Calculate statistics
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avg_scan = statistics.mean(scan_times)
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avg_total = statistics.mean(total_times)
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std_total = statistics.stdev(total_times) if len(total_times) > 1 else 0.0
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# Calculate throughput
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total_tokens = batch_size * seq_len
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throughput = total_tokens / (avg_total / 1000)
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# Memory estimate
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memory_mb = None
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try:
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params = sum(p.numel() * p.element_size() for p in model.parameters())
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activations = x.numel() * x.element_size() * 4 # Multiple intermediate tensors
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memory_mb = (params + activations) / (1024 * 1024)
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except Exception:
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pass
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name = f"Mamba_D{d_model}_N{d_state}_BS{batch_size}_Seq{seq_len}"
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return MambaBenchmarkResult(
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name=name,
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batch_size=batch_size,
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seq_len=seq_len,
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d_model=d_model,
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d_state=d_state,
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d_conv=d_conv,
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expand=expand,
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selective_scan_time_ms=avg_scan,
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total_time_ms=avg_total,
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std_time_ms=std_total,
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throughput_tokens_per_sec=throughput,
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memory_mb=memory_mb,
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)
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def run_benchmark_suite(iterations: int = 200, warmup: int = 30) -> MambaBenchmarkReport:
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"""Run the complete Mamba benchmark suite."""
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import datetime
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results = []
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# Mamba configurations
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# (batch_size, seq_len, d_model, d_state, d_conv, expand)
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scenarios = [
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# Small model
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(1, 128, 768, 16, 4, 2),
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(8, 128, 768, 16, 4, 2),
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(32, 128, 768, 16, 4, 2),
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# Medium model
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(1, 256, 1024, 16, 4, 2),
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(8, 256, 1024, 16, 4, 2),
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(16, 256, 1024, 16, 4, 2),
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# Long sequences (Mamba advantage)
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(1, 1024, 768, 16, 4, 2),
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(4, 1024, 768, 16, 4, 2),
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(1, 2048, 768, 16, 4, 2),
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# Large state dimension
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(1, 256, 1024, 64, 4, 2),
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(4, 256, 1024, 64, 4, 2),
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]
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for batch_size, seq_len, d_model, d_state, d_conv, expand in scenarios:
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try:
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result = benchmark_mamba(
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batch_size=batch_size,
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seq_len=seq_len,
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d_model=d_model,
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d_state=d_state,
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d_conv=d_conv,
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expand=expand,
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iterations=iterations,
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warmup=warmup,
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)
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results.append(asdict(result))
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except Exception as e:
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print(f"Warning: Benchmark failed: {e}", file=sys.stderr)
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return MambaBenchmarkReport(
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framework="PyTorch",
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backend="MPS",
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pytorch_version=torch.__version__,
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python_version=platform.python_version(),
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macos_version=platform.mac_ver()[0],
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chip=platform.processor() or "Apple Silicon",
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timestamp=datetime.datetime.now().isoformat(),
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iterations=iterations,
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warmup=warmup,
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results=results,
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)
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def print_table(report: MambaBenchmarkReport):
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"""Print results as formatted table."""
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print("=" * 100)
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print("PyTorch MPS Mamba/SSM Benchmark")
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print("=" * 100)
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print(f"PyTorch version: {report.pytorch_version}")
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print(f"Backend: {report.backend}")
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print(f"macOS version: {report.macos_version}")
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print(f"Chip: {report.chip}")
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print(f"Iterations: {report.iterations}")
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print()
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print("-" * 100)
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print(f"| {'Scenario':<35} | {'Scan (ms)':>10} | {'Total (ms)':>10} | {'Throughput':>18} |")
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print("-" * 100)
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for r in report.results:
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throughput_str = f"{r['throughput_tokens_per_sec']:.0f} tok/s"
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print(f"| {r['name']:<35} | {r['selective_scan_time_ms']:>10.4f} | {r['total_time_ms']:>10.4f} | {throughput_str:>18} |")
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print("-" * 100)
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print()
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print("To compare with RustyTorch Mamba:")
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print(" cargo bench -p rtx-transformers --bench metal_mamba_bench")
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def main():
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parser = argparse.ArgumentParser(description="PyTorch MPS Mamba Benchmark")
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parser.add_argument("--json", action="store_true", help="Output JSON format")
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parser.add_argument("--iterations", type=int, default=200, help="Number of iterations")
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parser.add_argument("--warmup", type=int, default=30, help="Number of warmup iterations")
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args = parser.parse_args()
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if not check_mps():
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sys.exit(1)
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report = run_benchmark_suite(iterations=args.iterations, warmup=args.warmup)
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if args.json:
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print(json.dumps(asdict(report), indent=2))
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else:
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print_table(report)
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if __name__ == "__main__":
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main()
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