[AutoTune] Refactor AutoTuneArtifact to utilize kernel as context instead of profiler (#344)

* [Enhancement] Update GEMM examples and autotuner for improved performance

- Modified `example_gemm_intrinsics.py` to enhance matrix multiplication configurations, increasing warp sizes and adjusting data types for better performance.
- Updated the kernel compilation process to utilize the new `tilelang.compile` method and improved latency measurement with the profiler.
- Refactored `example_gemm.py` to include a new autotuning configuration and ensure consistency in latency checks against reference results.
- Adjusted tensor supply generation in `tilelang/utils/tensor.py` to use `torch.randn` for better randomness in tensor initialization.
- Enhanced the `JITContext` in `tilelang/autotuner/__init__.py` to replace the profiler with a kernel instance for performance measurement, improving the overall structure of the autotuner.

* bug fix

* fix

* [Enhancement] Update convolution tests and profiling assertions

- Added a random seed setting for reproducibility in convolution tests.
- Removed several redundant convolution test cases to streamline the testing process.
- Updated the assertion in the matrix multiplication profiling to include a maximum mismatched ratio for improved accuracy in results.
- Enabled the main testing function for better test execution.

* lint fix

testing/python/kernel/test_tilelang_kernel_convolution.py

@@ -2,6 +2,8 @@ from tilelang import tvm as tvm
 import tilelang.testing
 import tilelang.language as T
 
+tilelang.testing.set_random_seed(42)
+
 
 def convolution(N, C, H, W, F, K, S, D, P, in_dtype, out_dtype, dtypeAccum, block_M, block_N,
                 block_K, num_stages, threads):
@@ -80,90 +82,6 @@ def run_conv(N,
     profiler.assert_allclose(ref_program, atol=1e-2, rtol=1e-2)
 
 
-def test_conv_f16f16f16_k3s1d1p1():
-    run_conv(
-        1,
-        128,
-        64,
-        64,
-        128,
-        3,
-        1,
-        1,
-        1,
-        "float16",
-        "float16",
-        "float16",
-        128,
-        128,
-        32,
-        2,
-    )
-
-
-def test_conv_f16f16f16_k3s2d1p1():
-    run_conv(
-        1,
-        128,
-        64,
-        64,
-        128,
-        3,
-        2,
-        1,
-        1,
-        "float16",
-        "float16",
-        "float16",
-        128,
-        128,
-        32,
-        2,
-    )
-
-
-def test_conv_f16f16f16_k1s1d1p0():
-    run_conv(
-        1,
-        128,
-        64,
-        64,
-        128,
-        1,
-        1,
-        1,
-        0,
-        "float16",
-        "float16",
-        "float16",
-        128,
-        128,
-        32,
-        2,
-    )
-
-
-def test_conv_f16f16f16_k1s2d1p0():
-    run_conv(
-        1,
-        128,
-        64,
-        64,
-        128,
-        1,
-        2,
-        1,
-        0,
-        "float16",
-        "float16",
-        "float16",
-        128,
-        128,
-        32,
-        2,
-    )
-
-
 def test_conv_f16f16f32_k3s1d1p1():
     run_conv(
         1,