Merge pull request #200 from SmallDoges/optim-triton-version

Optimize triton version: GQA, mask/bias broadcasting, skip inactive tiles, and stability fixes

Author: LoserCheems

benchmarks/backward_equivalence.py

@@ -283,11 +283,9 @@ def dynamic_mask_attention_triton(
     attn_bias = repeat_kv(attn_bias_leaf, num_queries_per_kv)
 
     # Ensure correct data types and memory layout for Triton function
-    query_states = query_states.transpose(1, 2).contiguous()        # [batch, query_len, num_heads, head_dim]  
-    key_states = key_states.transpose(1, 2).contiguous()            # [batch, key_len, num_heads, head_dim]  
-    value_states = value_states.transpose(1, 2).contiguous()        # [batch, key_len, num_heads, head_dim]  
-    attn_mask = attn_mask.contiguous()                              # [batch, num_heads, seqlen_q, seqlen_k]
-    attn_bias = attn_bias.contiguous()                              # [batch, num_heads, seqlen_q, seqlen_k]
+    query_states = query_states.transpose(1, 2)         # [batch, query_len, num_heads, head_dim]  
+    key_states = key_states.transpose(1, 2)             # [batch, key_len, num_heads, head_dim]  
+    value_states = value_states.transpose(1, 2)         # [batch, key_len, num_heads, head_dim]  
 
     # Call the Triton implementation
     attn_outputs = triton_dmattn_func(
@@ -729,6 +727,239 @@ def test_cuda_backward_equivalence(accuracy_threshold=0.95):
     return all_passed
 
 
+def test_triton_backward_equivalence(accuracy_threshold=0.95):
+    """Test backward pass equivalence between Python prototype and Triton implementation."""
+    print("\n" + "🚀" + "=" * 76 + "🚀")
+    print("🔬 Testing backward Pass Equivalence: Python Prototype vs Triton Implementation")
+    print("🚀" + "=" * 76 + "🚀")
+
+    # Check if Triton implementation is available
+    if triton_dmattn_func is None:
+        print("❌ Triton implementation not available, skipping test.")
+        return False
+    
+    # Set random seed for reproducibility
+    torch.manual_seed(0)
+
+    # Test different parameter configurations
+    # If you encounter NAN issues when running multiple configurations, try running a single configuration
+    # (batch_size, num_heads, num_kv_heads, query_len, key_len, head_dim, is_causal)
+    test_configs = [
+        # Head dim 32
+        (1, 2, 1, 128, 128, 32, False),
+        (1, 2, 1, 128, 128, 32, True),
+        (1, 2, 1, 256, 256, 32, False),
+        (1, 2, 1, 256, 256, 32, True),
+        (1, 2, 1, 512, 512, 32, False),
+        (1, 2, 1, 512, 512, 32, True),
+        (1, 2, 1, 1024, 1024, 32, False),
+        (1, 2, 1, 1024, 1024, 32, True),
+        (1, 2, 1, 2048, 2048, 32, False),
+        (1, 2, 1, 2048, 2048, 32, True),
+        (1, 2, 1, 4096, 4096, 32, False),
+        (1, 2, 1, 4096, 4096, 32, True),
+
+        # Head dim 64
+        (1, 2, 1, 128, 128, 64, False),
+        (1, 2, 1, 128, 128, 64, True),
+        (1, 2, 1, 256, 256, 64, False),
+        (1, 2, 1, 256, 256, 64, True),
+        (1, 2, 1, 512, 512, 64, False),
+        (1, 2, 1, 512, 512, 64, True),
+        (1, 2, 1, 1024, 1024, 64, False),
+        (1, 2, 1, 1024, 1024, 64, True),
+        (1, 2, 1, 2048, 2048, 64, False),
+        (1, 2, 1, 2048, 2048, 64, True),
+        (1, 2, 1, 4096, 4096, 64, False),
+        (1, 2, 1, 4096, 4096, 64, True),
+
+        # Head dim 96
+        (1, 2, 1, 128, 128, 96, False),
+        (1, 2, 1, 128, 128, 96, True),
+        (1, 2, 1, 256, 256, 96, False),
+        (1, 2, 1, 256, 256, 96, True),
+        (1, 2, 1, 512, 512, 96, False),
+        (1, 2, 1, 512, 512, 96, True),
+        (1, 2, 1, 1024, 1024, 96, False),
+        (1, 2, 1, 1024, 1024, 96, True),
+        (1, 2, 1, 2048, 2048, 96, False),
+        (1, 2, 1, 2048, 2048, 96, True),
+        (1, 2, 1, 4096, 4096, 96, False),
+        (1, 2, 1, 4096, 4096, 96, True),
+
+        # Head dim 128
+        (1, 2, 1, 128, 128, 128, False),
+        (1, 2, 1, 128, 128, 128, True),
+        (1, 2, 1, 256, 256, 128, False),
+        (1, 2, 1, 256, 256, 128, True),
+        (1, 2, 1, 512, 512, 128, False),
+        (1, 2, 1, 512, 512, 128, True),
+        (1, 2, 1, 1024, 1024, 128, False),
+        (1, 2, 1, 1024, 1024, 128, True),
+        (1, 2, 1, 2048, 2048, 128, False),
+        (1, 2, 1, 2048, 2048, 128, True),
+        (1, 2, 1, 4096, 4096, 128, False),
+        (1, 2, 1, 4096, 4096, 128, True),
+
+        # triton currently supports up to head dim 128
+    ]
+
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    dtype = torch.bfloat16
+    device_icon = "🔥" if device.type == "cuda" else "💻"
+    print(f"{device_icon} Using device: {device}")
+    
+    all_passed = True
+    
+    for i, config in enumerate(test_configs):
+        torch.cuda.empty_cache()
+        gc.collect()
+        torch.cuda.synchronize()
+
+        batch_size, num_heads, num_kv_heads, query_len, key_len, head_dim, is_causal = config
+        
+        # Progress indicator
+        progress_filled = "█" * (i + 1)
+        progress_empty = "░" * (len(test_configs) - i - 1)
+        progress_bar = f"[{progress_filled}{progress_empty}]"
+
+        print(f"\n🧪 Test configuration {i+1}/{len(test_configs)} {progress_bar}")
+        print(f"  📊 batch_size={batch_size}, num_heads={num_heads}, num_kv_heads={num_kv_heads}")
+        print(f"  📏 query_len={query_len}, key_len={key_len}, head_dim={head_dim}")
+        print(f"  🔒 is_causal={is_causal}")
+        print(f"  🎯 Accuracy threshold: {accuracy_threshold*100:.1f}%")
+
+        # Create random input data
+        query_states = torch.randn(
+            batch_size, num_heads, query_len, head_dim, 
+            device=device, dtype=dtype, requires_grad=True
+        )
+        key_states = torch.randn(
+            batch_size, num_kv_heads, key_len, head_dim,
+            device=device, dtype=dtype, requires_grad=True
+        )
+        value_states = torch.randn(
+            batch_size, num_kv_heads, key_len, head_dim, 
+            device=device, dtype=dtype, requires_grad=True
+        )
+        attn_bias = torch.randn(
+            batch_size, num_kv_heads, query_len, key_len,
+            device=device, dtype=torch.bfloat16
+        )
+        cache_position = torch.arange(key_len - query_len, key_len, device=device)
+        causal_mask = torch.arange(key_len, device=device) <= cache_position.reshape(-1, 1)
+        causal_mask = causal_mask[None, None, :, :].expand(batch_size, 1, -1, -1)
+        
+        # Set scaling factor and keep window size
+        scaling = head_dim ** -0.5
+        window_size = 10240
+
+        # Clone inputs for Python implementation
+        query_python = query_states.clone().detach().requires_grad_(True)
+        key_python = key_states.clone().detach().requires_grad_(True)
+        value_python = value_states.clone().detach().requires_grad_(True)
+        attn_bias_python = attn_bias.clone().detach().requires_grad_(True)
+        causal_mask_python = causal_mask.clone().detach()
+
+        # Run Python implementation
+        start_time = time.time()
+        attn_outputs_python, dq_python, dk_python, dv_python, dbias_python = dynamic_mask_attention_python(
+            query_python, key_python, value_python,
+            attn_bias_python, causal_mask_python,
+            scaling, window_size, is_causal
+        )
+        torch.cuda.synchronize()
+        py_time = time.time() - start_time
+
+        # Clone inputs for Triton implementation
+        query_triton = query_states.clone().detach().requires_grad_(True)
+        key_triton = key_states.clone().detach().requires_grad_(True)
+        value_triton = value_states.clone().detach().requires_grad_(True)
+        attn_bias_triton = attn_bias.clone().detach().requires_grad_(True)
+        causal_mask_triton = causal_mask.clone().detach()
+
+        # Run Triton implementation
+        start_time = time.time()
+        attn_outputs_triton, dq_triton, dk_triton, dv_triton, dbias_triton = dynamic_mask_attention_triton(
+            query_triton, key_triton, value_triton,
+            attn_bias_triton, causal_mask_triton,
+            scaling, window_size, is_causal
+        )
+        torch.cuda.synchronize()
+        triton_time = time.time() - start_time
+
+        # Analyze outputs
+        print(f"\n🔍 Analyzing differences between Python and Triton outputs:")
+        is_attn_output_close, max_attn_output_diff, mean_attn_output_diff = analyze_differences(
+            attn_outputs_python, attn_outputs_triton, accuracy_threshold
+        )
+
+        # Analyze dQ gradients
+        print(f"\n🔍 Analyzing dQ gradients:")
+        is_dq_close, max_dq_diff, mean_dq_diff = analyze_differences(
+            dq_python, dq_triton, accuracy_threshold
+        )
+
+        # Analyze dK gradients
+        print(f"\n🔍 Analyzing dK gradients:")
+        is_dk_close, max_dk_diff, mean_dk_diff = analyze_differences(
+            dk_python, dk_triton, accuracy_threshold
+        )
+
+        # Analyze dV gradients
+        print(f"\n🔍 Analyzing dV gradients:")
+        is_dv_close, max_dv_diff, mean_dv_diff = analyze_differences(
+            dv_python, dv_triton, accuracy_threshold
+        )
+
+        # Analyze dBias gradients
+        print(f"\n🔍 Analyzing dBias gradients:")
+        is_dbias_close, max_dbias_diff, mean_dbias_diff = analyze_differences(
+            dbias_python, dbias_triton, accuracy_threshold
+        )
+
+        # Report performance difference
+        speedup = py_time / triton_time if triton_time > 0 else float('inf')
+        print(f"\n⚡ Performance comparison:")
+        print(f"    🐍 Python implementation: {py_time*1000:.2f} ms")
+        print(f"    🚀 Triton implementation:   {triton_time*1000:.2f} ms")
+        print(f"    📈 Speedup:               {speedup:.2f}x")
+
+        # Check if all gradients pass
+        is_close = (is_attn_output_close and is_dq_close and is_dk_close and is_dv_close and is_dbias_close)
+        test_result = "Passed" if is_close else "Failed"
+        result_icon = "✅" if is_close else "❌"
+        all_passed = all_passed and is_close
+        print(f"\n{result_icon} Test result: {test_result}")
+
+        # If test fails with large difference, can exit early
+        if not is_close and max_attn_output_diff > 1e-2:
+            print("  ⚠️ Difference too large, stopping subsequent tests.")
+            break
+        if not is_close and max_dq_diff > 1e-2:
+            print("  ⚠️ Difference too large, stopping subsequent tests.")
+            break
+        if not is_close and max_dk_diff > 1e-2:
+            print("  ⚠️ Difference too large, stopping subsequent tests.")
+            break
+        if not is_close and max_dv_diff > 1e-2:
+            print("  ⚠️ Difference too large, stopping subsequent tests.")
+            break
+        if not is_close and max_dbias_diff > 1e-2:
+            print("  ⚠️ Difference too large, stopping subsequent tests.")
+            break
+        del query_states, key_states, value_states, attn_bias, causal_mask, cache_position, dq_python, dk_python, dv_python, dbias_python, dq_triton, dk_triton, dv_triton, dbias_triton
+        torch.cuda.empty_cache()
+        gc.collect()
+        torch.cuda.synchronize()
+    
+    print("\n" + "🏁" + "=" * 76 + "🏁")
+    summary_icon = "🎉" if all_passed else "😞"
+    print(f"{summary_icon} Backward Equivalence Test Summary: {'All Passed' if all_passed else 'Some Tests Failed'}")
+    print("🏁" + "=" * 76 + "🏁")
+
+    return all_passed
+
 def main():
     """
     Test backward pass equivalence between Python prototype and various implementations
@@ -782,9 +1013,9 @@ def main():
         print("\n" + "📍" + " Starting Python vs CUDA Backward Tests " + "📍")
         test_results['cuda'] = test_cuda_backward_equivalence(args.accuracy_threshold)
 
-    # if args.test_type in ['all', 'triton']:
-    #     print("\n" + "🔥" + " Starting Python vs Triton Backward Tests " + "🔥")
-    #     test_results['triton'] = test_triton_backward_equivalence(args.accuracy_threshold)
+    if args.test_type in ['all', 'triton']:
+        print("\n" + "🔥" + " Starting Python vs Triton Backward Tests " + "🔥")
+        test_results['triton'] = test_triton_backward_equivalence(args.accuracy_threshold)
 
     # if args.test_type in ['all', 'flex']:
     #     print("\n" + "🌟" + " Starting Python vs Flex Attention Backward Tests " + "🌟")