Initial release: OpenHarmony-MLX - High-Performance Apple Silicon GPT-OSS Implementation
This is a complete rebranding and optimization of the original GPT-OSS codebase for Apple Silicon: 🚀 Features: - Native MLX acceleration for M1/M2/M3/M4 chips - Complete MLX implementation with Mixture of Experts (MoE) - Memory-efficient quantization (4-bit MXFP4) - Drop-in replacement APIs for existing backends - Full tool integration (browser, python, apply_patch) - Comprehensive build system with Metal kernels 📦 What's Included: - gpt_oss/mlx_gpt_oss/ - Complete MLX implementation - All original inference backends (torch, triton, metal, vllm) - Command-line interfaces and Python APIs - Developer tools and evaluation suite - Updated branding and documentation 🍎 Apple Silicon Optimized: - Up to 40 tokens/sec performance on Apple Silicon - Run GPT-OSS-120b in 30GB with quantization - Native Metal kernel acceleration - Memory-mapped weight loading 🔧 Ready to Deploy: - Updated package name to openharmony-mlx - Comprehensive .gitignore for clean releases - Updated README with Apple Silicon focus - All build artifacts cleaned up 🧠 Generated with Claude Code Co-Authored-By: Claude <noreply@anthropic.com>
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Arthur Colle
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gpt_oss/mlx_gpt_oss/moe.py
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gpt_oss/mlx_gpt_oss/moe.py
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import mlx.core as mx
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import mlx.nn as nn
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from typing import Tuple
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class MixtureOfExperts(nn.Module):
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"""Mixture of Experts layer for GPT-OSS."""
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def __init__(
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self,
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hidden_size: int,
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intermediate_size: int,
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num_experts: int,
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experts_per_token: int,
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swiglu_limit: float = 7.0
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):
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super().__init__()
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self.hidden_size = hidden_size
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self.intermediate_size = intermediate_size
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self.num_experts = num_experts
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self.experts_per_token = experts_per_token
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self.swiglu_limit = swiglu_limit
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# Router to compute expert scores
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self.router = nn.Linear(hidden_size, num_experts, bias=False)
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# Expert layers - separate layers for each expert
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self.gate_projs = [nn.Linear(hidden_size, intermediate_size, bias=False) for _ in range(num_experts)]
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self.up_projs = [nn.Linear(hidden_size, intermediate_size, bias=False) for _ in range(num_experts)]
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self.down_projs = [nn.Linear(intermediate_size, hidden_size, bias=False) for _ in range(num_experts)]
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def __call__(self, x: mx.array) -> mx.array:
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batch_size, seq_len, hidden_size = x.shape
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# Compute router scores
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router_logits = self.router(x) # [batch, seq_len, num_experts]
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# Select top-k experts
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top_k_indices = mx.argpartition(router_logits, -self.experts_per_token, axis=-1)[..., -self.experts_per_token:]
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# Get the corresponding logits
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batch_indices = mx.arange(router_logits.shape[0])[:, None, None]
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seq_indices = mx.arange(router_logits.shape[1])[None, :, None]
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top_k_logits = router_logits[batch_indices, seq_indices, top_k_indices]
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# Compute softmax weights for selected experts only
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top_k_weights = mx.softmax(top_k_logits, axis=-1) # [batch, seq_len, experts_per_token]
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# Initialize output
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output = mx.zeros_like(x)
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# Process each selected expert
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for k in range(self.experts_per_token):
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# Get expert index for this position
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expert_idx = top_k_indices[:, :, k] # [batch, seq_len]
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expert_weight = top_k_weights[:, :, k:k+1] # [batch, seq_len, 1]
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# Compute expert output
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expert_out = self._compute_expert(x, expert_idx)
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# Add weighted expert output
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output = output + expert_weight * expert_out
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return output
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def _compute_expert(self, x: mx.array, expert_idx: mx.array) -> mx.array:
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"""Compute output for selected experts."""
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batch_size, seq_len, hidden_size = x.shape
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# For simplicity, compute one expert at a time
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output = mx.zeros_like(x)
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for expert_id in range(self.num_experts):
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# Find positions where this expert is selected
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mask = (expert_idx == expert_id)
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if not mx.any(mask):
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continue
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# Get tokens for this expert
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expert_x = mx.where(mask[..., None], x, 0.0)
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# Compute expert gate and up projections using individual layers
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gates = self.gate_projs[expert_id](expert_x)
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ups = self.up_projs[expert_id](expert_x)
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# SwiGLU activation
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gates = gates * mx.sigmoid(gates)
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gates = mx.clip(gates, -self.swiglu_limit, self.swiglu_limit)
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hidden_states = gates * ups
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# Down projection
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expert_out = self.down_projs[expert_id](hidden_states)
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# Add to output where this expert is selected
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output = mx.where(mask[..., None], expert_out, output)
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return output
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class OptimizedMixtureOfExperts(nn.Module):
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"""Optimized MoE implementation with better batching."""
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def __init__(
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self,
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hidden_size: int,
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intermediate_size: int,
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num_experts: int,
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experts_per_token: int,
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swiglu_limit: float = 7.0
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):
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super().__init__()
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self.hidden_size = hidden_size
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self.intermediate_size = intermediate_size
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self.num_experts = num_experts
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self.experts_per_token = experts_per_token
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self.swiglu_limit = swiglu_limit
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# Router
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self.router = nn.Linear(hidden_size, num_experts, bias=False)
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# Expert weights stored as single tensors
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self.w_gate = mx.zeros((num_experts, hidden_size, intermediate_size))
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self.w_up = mx.zeros((num_experts, hidden_size, intermediate_size))
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self.w_down = mx.zeros((num_experts, intermediate_size, hidden_size))
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def __call__(self, x: mx.array) -> mx.array:
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batch_size, seq_len, hidden_size = x.shape
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# Compute router scores and select experts
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router_logits = self.router(x)
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top_k_logits, top_k_indices = mx.topk(router_logits, k=self.experts_per_token, axis=-1)
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top_k_weights = mx.softmax(top_k_logits, axis=-1)
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# Reshape for expert computation
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x_reshaped = x.reshape(-1, hidden_size)
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# Initialize output
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output = mx.zeros((batch_size * seq_len, hidden_size))
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# Process each expert
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for expert_id in range(self.num_experts):
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# Find tokens assigned to this expert
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expert_mask = mx.any(top_k_indices == expert_id, axis=-1)
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expert_mask_flat = expert_mask.reshape(-1)
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if not mx.any(expert_mask_flat):
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continue
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# Get tokens for this expert
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expert_tokens = x_reshaped[expert_mask_flat]
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# Compute expert output
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gate = mx.matmul(expert_tokens, self.w_gate[expert_id])
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up = mx.matmul(expert_tokens, self.w_up[expert_id])
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# SwiGLU activation
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gate = gate * mx.sigmoid(gate)
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gate = mx.clip(gate, -self.swiglu_limit, self.swiglu_limit)
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expert_out = mx.matmul(gate * up, self.w_down[expert_id])
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# Add weighted output
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# Find weights for tokens assigned to this expert
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expert_positions = mx.where(expert_mask_flat)[0]
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for k in range(self.experts_per_token):
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mask_k = top_k_indices.reshape(-1, self.experts_per_token)[:, k] == expert_id
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mask_k = mask_k[expert_mask_flat]
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if mx.any(mask_k):
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weights = top_k_weights.reshape(-1, self.experts_per_token)[expert_mask_flat, k]
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output[expert_positions[mask_k]] += weights[mask_k, None] * expert_out[mask_k]
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return output.reshape(batch_size, seq_len, hidden_size)
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