add accuracy and performance test (#1643)

Author: ovowei

.github/workflows/kt-kernel-tests.yml

@@ -66,7 +66,7 @@ jobs:
           bash install.sh build
 
       - name: Run KT-Kernel CPU tests
-        timeout-minutes: 30
+        timeout-minutes: 60
         run: |
           cd kt-kernel/test
           python3 run_suite.py --hw cpu --suite default

kt-kernel/test/per_commit/test_moe_amx_accuracy_int4.py

@@ -0,0 +1,212 @@
+#!/usr/bin/env python
+# coding=utf-8
+"""AMX MOE INT4 accuracy tests for KT-Kernel.
+
+Tests accuracy of AMX-accelerated INT4 MOE operations against torch reference.
+"""
+
+import os
+import sys
+import pytest
+
+# Add parent directory to path for CI registration
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), ".."))
+from ci.ci_register import register_cpu_ci
+
+# Register this test for CPU CI with estimated runtime of 120 seconds
+register_cpu_ci(est_time=120, suite="default")
+
+# Check if dependencies are available
+try:
+    import torch
+    import kt_kernel_ext
+    HAS_DEPS = True
+except ImportError as e:
+    HAS_DEPS = False
+    import_error = str(e)
+
+# Test parameters (from original test_moe_amx.py)
+expert_num = 256
+hidden_size = 7168
+intermediate_size = 2048
+max_len = 25600
+num_experts_per_tok = 8
+qlen = 1
+layer_num = 1
+validation_iter = 2
+physical_to_logical_map = None
+
+
+def act_fn(x):
+    """Activation function for MoE."""
+    return x / (1.0 + torch.exp(-x))
+
+
+def mlp_torch(input, gate_proj, up_proj, down_proj):
+    """PyTorch reference implementation of MLP."""
+    gate_buf = torch.mm(input, gate_proj.t())
+    up_buf = torch.mm(input, up_proj.t())
+    intermediate = act_fn(gate_buf) * up_buf
+    ret = torch.mm(intermediate, down_proj.t())
+    return ret
+
+
+def moe_torch(input, expert_ids, weights, gate_proj, up_proj, down_proj):
+    """PyTorch reference implementation of MoE."""
+    cnts = expert_ids.new_zeros((expert_ids.shape[0], expert_num))
+    cnts.scatter_(1, expert_ids, 1)
+    tokens_per_expert = cnts.sum(dim=0)
+    idxs = expert_ids.view(-1).argsort()
+    sorted_tokens = input[idxs // expert_ids.shape[1]]
+
+    outputs = []
+    start_idx = 0
+
+    for i, num_tokens in enumerate(tokens_per_expert):
+        end_idx = start_idx + num_tokens
+        if num_tokens == 0:
+            continue
+        tokens_for_this_expert = sorted_tokens[start_idx:end_idx]
+        expert_out = mlp_torch(
+            tokens_for_this_expert, gate_proj[i], up_proj[i], down_proj[i]
+        )
+        outputs.append(expert_out)
+        start_idx = end_idx
+
+    outs = torch.cat(outputs, dim=0) if len(outputs) else sorted_tokens.new_empty(0)
+
+    new_x = torch.empty_like(outs)
+    new_x[idxs] = outs
+    t_output = (
+        new_x.view(*expert_ids.shape, -1)
+        .type(weights.dtype)
+        .mul_(weights.unsqueeze(dim=-1))
+        .sum(dim=1)
+        .type(new_x.dtype)
+    )
+
+    return t_output
+
+
+@pytest.mark.cpu
+def test_moe_amx_int4_accuracy():
+    """Test AMX INT4 MOE accuracy against PyTorch reference implementation."""
+    if not HAS_DEPS:
+        pytest.skip(f"Dependencies not available: {import_error}")
+
+    global physical_to_logical_map
+    physical_to_logical_map = torch.tensor(
+        data=range(expert_num), device="cpu", dtype=torch.int64
+    ).contiguous()
+
+    CPUInfer = kt_kernel_ext.CPUInfer(90)
+
+    with torch.inference_mode(mode=True):
+        # Initialize MoE layers
+        gate_proj = (
+            torch.randn(
+                (expert_num, intermediate_size, hidden_size),
+                dtype=torch.bfloat16,
+                device="cuda",
+            )
+            .to("cpu")
+            .contiguous()
+        )
+        up_proj = (
+            torch.randn(
+                (expert_num, intermediate_size, hidden_size),
+                dtype=torch.bfloat16,
+                device="cuda",
+            )
+            .to("cpu")
+            .contiguous()
+        )
+        down_proj = (
+            torch.randn(
+                (expert_num, hidden_size, intermediate_size),
+                dtype=torch.bfloat16,
+                device="cuda",
+            )
+            .to("cpu")
+            .contiguous()
+        )
+
+        # Create MOE config
+        config = kt_kernel_ext.moe.MOEConfig(
+            expert_num, num_experts_per_tok, hidden_size, intermediate_size, 0
+        )
+        config.max_len = max_len
+        config.gate_proj = gate_proj.data_ptr()
+        config.up_proj = up_proj.data_ptr()
+        config.down_proj = down_proj.data_ptr()
+        config.gate_scale = 0
+        config.pool = CPUInfer.backend_
+
+        # Initialize INT4 MOE
+        moe = kt_kernel_ext.moe.AMXInt4_MOE(config)
+        CPUInfer.submit(moe.load_weights_task(physical_to_logical_map.data_ptr()))
+        CPUInfer.sync()
+        CPUInfer.submit(moe.warm_up_task())
+        CPUInfer.sync()
+
+        # Run validation iterations
+        for i in range(validation_iter):
+            bsz_tensor = torch.tensor([qlen], device="cpu")
+            expert_ids = torch.stack(
+                [torch.randperm(expert_num)[:num_experts_per_tok] for _ in range(qlen)]
+            ).contiguous()
+            weights = torch.rand((qlen, num_experts_per_tok), dtype=torch.float32).contiguous()
+            input_data = torch.randn((qlen, hidden_size), dtype=torch.bfloat16).contiguous()
+            output = torch.empty((qlen, hidden_size), dtype=torch.bfloat16).contiguous()
+            input_data = input_data / 100
+
+            # Run AMX MOE
+            CPUInfer.submit(
+                moe.forward_task(
+                    bsz_tensor.data_ptr(),
+                    num_experts_per_tok,
+                    expert_ids.data_ptr(),
+                    weights.data_ptr(),
+                    input_data.data_ptr(),
+                    output.data_ptr(),
+                    False,
+                )
+            )
+            CPUInfer.sync()
+
+            # Run torch reference
+            t_output = moe_torch(
+                input_data, expert_ids, weights, gate_proj, up_proj, down_proj
+            )
+
+            # Calculate relative difference
+            diff = torch.mean(torch.abs(output - t_output)) / torch.mean(
+                torch.abs(t_output)
+            )
+            print(f"Iteration {i}, diff = {diff:.6f}")
+
+            # INT4 should have diff < 0.35
+            assert diff < 0.35, f"INT4 accuracy test failed: diff={diff:.6f} >= 0.35"
+
+
+def run_all_tests():
+    """Run all tests in this file (for standalone execution)."""
+    if not HAS_DEPS:
+        print(f"⚠ Dependencies not available: {import_error}")
+        print("Skipping AMX MOE INT4 accuracy tests")
+        return
+
+    try:
+        print("Running AMX MOE INT4 accuracy test...")
+        test_moe_amx_int4_accuracy()
+        print("✓ AMX MOE INT4 accuracy test passed")
+        print("\n✓ All tests passed!")
+    except Exception as e:
+        print(f"\n✗ Test failed: {e}")
+        import traceback
+        traceback.print_exc()
+        sys.exit(1)
+
+
+if __name__ == "__main__":
+    run_all_tests()