[CI] Comprehensive Test cases Implementation of Matmul Dequantize (#32)

* installation script fix

* readme typo fix

* doc fix for dequantize gemm

* [Doc] remove CODE_OF_CONDUCT.md and SECURITY.md; update references in CONTRIBUTING.md

* [Doc] add unit tests for AnnotateDeviceRegions transform; remove SUPPORT.md

* update license

* [Enhancement] add tensor supply handling for unsigned integers; improve error message for execution backend assertion

* [Refactor] improve code readability by reformatting function signatures and assertions

* [Refactor] replace torch.manual_seed with tilelang.testing.set_random_seed for consistency in random seed handling

.gitattributes

+*.h linguist-language=C++

examples/dequantize_gemm/example_dequant_gemm_fine_grained.py

@@ -8,7 +8,7 @@ from tvm import DataType
 import tilelang as TL
 import tilelang.language as T
 
-torch.manual_seed(0)
+tilelang.testing.set_random_seed(0)
 
 
 def matmul(

examples/gemm/example_gemm_intrinsics.py

@@ -12,8 +12,6 @@ from tilelang.intrinsics.mma_macro_generator import (
     TensorCoreIntrinEmitter,)
 from tilelang.transform import simplify_prim_func
 
-torch.manual_seed(0)
-
 
 def make_swizzle_layout(shared_buf):
     dtype = shared_buf.dtype

testing/python/amd/test_tilelang_gemm_mfma_intrinsic.py

@@ -12,7 +12,7 @@ from tilelang.intrinsics.mfma_macro_generator import (
     MatrixCoreIntrinEmitter,)
 from tilelang.transform import simplify_prim_func
 
-torch.manual_seed(0)
+tilelang.testing.set_random_seed(0)
 
 
 @simplify_prim_func

testing/python/dynamic/test_tilelang_dynamic_symbolic.py

@@ -11,7 +11,7 @@ import tilelang.language as T
 from tilelang.intrinsics.utils import get_swizzle_layout
 from tilelang.intrinsics.mma_macro_generator import (TensorCoreIntrinEmitter)
 
-torch.manual_seed(0)
+tilelang.testing.set_random_seed(0)
 
 
 def make_swizzle_layout(shared_buf):

testing/python/kernel/test_tilelang_dequantize_gemm.py

@@ -4,11 +4,211 @@ import torch
 import torch.backends
 import tilelang.testing
 from tilelang import tvm as tvm
-from tvm import DataType
+from tvm import DataType, tir
 import tilelang as TL
 import tilelang.language as T
+from tilelang import JITKernel, Profiler
+
+tilelang.testing.set_random_seed(0)
+
+
+def _tir_u8_to_f4_to_f16(nbit: int, val: tir.PrimExpr, pos: tir.PrimExpr, dtype: str):
+    assert nbit == 4
+    assert dtype == "float16"
+    assert val.dtype == "uint8"
+    # e_f4 == 0 -> e_f16 = 0
+    # e_f4 != 0 -> e_f16 = e_f4 + 8 = e_f4 | (1000)_2
+    # s1e2n1
+    mask = tir.const((1 << nbit) - 1, "uint16")
+    f4 = (val >> (pos.astype("uint16") * tir.const(nbit, "uint16"))) & mask
+    s = f4 >> tir.const(3, "uint16")
+    e_f4 = f4 & tir.const(7, "uint16")
+    e_f16 = e_f4 | tir.const(8, "uint16")
+    val_f16 = tir.reinterpret(
+        "float16",
+        ((e_f16 | (s << tir.const(5, "uint16"))) << tir.const(10, "uint16")).astype("uint16"))
+    # return tir.Select(e_f4 == tir.const(0, "uint32"), tir.const(0, "float16"), val_f16)
+    return val_f16
+
+
+def torch_convert(tensor):
+
+    def print_bit(name, val):
+        val_cpu = val.cpu().item()
+        binary_repr = f'{val_cpu:032b}'
+        print(name, binary_repr)
+
+    def _convert(val, pos):
+        assert val.dtype == torch.uint8
+        val = val.view(torch.int8)
+        mask = (1 << 4) - 1
+        f4 = ((val >> (pos * 4)) & mask).to(torch.int16)
+        s = f4 >> 3
+        e_f4 = f4 & 7
+        e_f16 = e_f4 | 8
+        val_f16 = ((e_f16 | (s << 5)) << 10) & 0xFFFF
+        lower_16_bits = (val_f16 & 0xFFFF).to(torch.uint16)
+        return lower_16_bits.view(torch.float16)
+
+    N = tensor.shape[0]
+    K = tensor.shape[1]
+    new_tensor = torch.empty(N, K * 2, dtype=torch.float16, device=tensor.device)
+    for i in range(new_tensor.shape[0]):
+        for j in range(new_tensor.shape[1]):
+            new_tensor[i][j] = _convert(tensor[i][j // 2], j % 2)
+    return new_tensor
+
+
+def _convert_test(N, K, block_N, block_K, in_dtype, num_bits=4, threads=128):
+    num_elems_per_byte = 8 // num_bits
+    storage_dtype = "uint8"
+    B_shape = (N, K // num_elems_per_byte)
+    B_shared_shape = (block_N, block_K // num_elems_per_byte)
+    B_dequantize_shared_shape = (block_N, block_K)
+
+    @T.prim_func
+    def main(
+            B: T.Buffer(B_shape, storage_dtype),
+            C: T.Buffer((N, K), in_dtype),
+    ):
+        with T.Kernel(T.ceildiv(N, block_N), threads=threads) as (bx):
+            B_shared = T.alloc_shared(B_shared_shape, storage_dtype)
+            B_local = T.alloc_fragment(B_shared_shape, storage_dtype)
+            B_dequantize_local = T.alloc_fragment(B_dequantize_shared_shape, in_dtype)
+
+            for k in T.Pipelined(T.ceildiv(K, block_K), num_stages=1):
+                T.copy(B[bx * block_N, k * block_K // num_elems_per_byte], B_shared)
+                T.copy(B_shared, B_local)
+                for i, j in T.Parallel(block_N, block_K):
+                    B_dequantize_local[i, j] = _tir_u8_to_f4_to_f16(
+                        num_bits,
+                        B_local[i, j // num_elems_per_byte],
+                        j % num_elems_per_byte,
+                        dtype=in_dtype,
+                    )
+                T.copy(B_dequantize_local, C[bx * block_N, k * block_K])
+
+    return main
+
+
+def test_fp4_fp16_convert_close():
+    N, K = 256, 256
+    block_N, block_K = 64, 64
+    program = _convert_test(
+        N,
+        K,
+        block_N,
+        block_K,
+        "float16",
+    )
+
+    mod, params = tilelang.lower(program)
+    mod = Profiler(mod, params, [1], tilelang.TensorSupplyType.Integer)
+
+    B = torch.randint(0, 16, (N, K // 2), dtype=torch.uint8, device="cuda").to(torch.uint8)
+    tl_out = mod.func(B)
+    ref_out = torch_convert(B)
+    assert torch.allclose(tl_out, ref_out, rtol=0.01, atol=0.01), (tl_out, ref_out)
+    print("Pass")
+
+
+def matmul_fp16xfp4(M,
+                    N,
+                    K,
+                    in_dtype,
+                    out_dtype,
+                    accum_dtype,
+                    block_M=64,
+                    block_N=64,
+                    block_K=64,
+                    num_stages=1,
+                    threads=128):
+    num_bits = 4
 
-torch.manual_seed(0)
+    def kernel_func(block_M, block_N, block_K, num_stages, threads):
+        num_elems_per_byte = 8 // num_bits
+        storage_dtype = "uint8"
+        A_shape = (M, K)
+        B_shape = (N, K // num_elems_per_byte)
+        A_shared_shape = (block_M, block_K)
+        B_shared_shape = (block_N, block_K // num_elems_per_byte)
+        B_dequantize_shared_shape = (block_N, block_K)
+        assert K % (block_K) == 0
+
+        @T.prim_func
+        def main(
+                A: T.Buffer(A_shape, in_dtype),
+                B: T.Buffer(B_shape, storage_dtype),
+                Ct: T.Buffer((N, M), out_dtype),
+        ):
+            with T.Kernel(
+                    T.ceildiv(N, block_N), T.ceildiv(M, block_M), threads=threads) as (bx, by):
+                A_shared = T.alloc_shared(A_shared_shape, in_dtype)
+                B_shared = T.alloc_shared(B_shared_shape, storage_dtype)
+                B_local = T.alloc_fragment(B_shared_shape, storage_dtype)
+                B_dequantize_local = T.alloc_fragment(B_dequantize_shared_shape, in_dtype)
+                B_dequantize_prev_local = T.alloc_fragment(B_dequantize_shared_shape, in_dtype)
+                Ct_local = T.alloc_fragment((block_N, block_M), accum_dtype)
+                Ct_shared = T.alloc_shared((block_N, block_M), out_dtype)
+
+                T.annotate_layout({
+                    B_shared: tilelang.layout.make_swizzled_layout(B_shared),
+                    Ct_shared: tilelang.layout.make_swizzled_layout(Ct_shared),
+                })
+
+                T.clear(Ct_local)
+                for k in T.Pipelined(K // block_K, num_stages=num_stages):
+                    T.copy(A[by * block_M, k * block_K], A_shared)
+                    T.copy(B[bx * block_N, k * block_K // num_elems_per_byte], B_shared)
+                    T.copy(B_shared, B_local)
+                    for i, j in T.Parallel(block_N, block_K):
+                        B_dequantize_local[i, j] = _tir_u8_to_f4_to_f16(
+                            num_bits,
+                            B_local[i, j // num_elems_per_byte],
+                            j % num_elems_per_byte,
+                            dtype=in_dtype,
+                        )
+                    T.copy(B_dequantize_local, B_dequantize_prev_local)
+                    T.gemm(B_dequantize_prev_local, A_shared, Ct_local, transpose_B=True)
+                T.copy(Ct_local, Ct_shared)
+                T.copy(Ct_shared, Ct[bx * block_N:(bx + 1) * block_N,
+                                     by * block_M:(by + 1) * block_M])
+
+        return main
+
+    return kernel_func(block_M=64, block_N=64, block_K=64, num_stages=1, threads=128)
+
+
+def ref_program(A, qB):
+    dtypeC = "float16"
+    B = torch_convert(qB)
+    C = torch.matmul(A.to(torch.float), B.T.to(torch.float))
+    C = C.to(torch.__getattribute__(dtypeC))
+    return C.transpose(0, 1)
+
+
+def assert_simple_impl_float16xfp4_gemm(M,
+                                        N,
+                                        K,
+                                        in_dtype,
+                                        out_dtype,
+                                        accum_dtype,
+                                        block_M=64,
+                                        block_N=64,
+                                        block_K=64,
+                                        num_stages=1,
+                                        threads=128):
+    func = matmul_fp16xfp4(M, N, K, in_dtype, out_dtype, accum_dtype, block_M, block_N, block_K,
+                           num_stages, threads)
+
+    torch_func = JITKernel(func, [2])
+    profiler = torch_func.get_profiler()
+    profiler.assert_allclose(ref_program)
+
+
+def test_simple_impl_float16xfp4_gemm():
+    assert_simple_impl_float16xfp4_gemm(256, 256, 256, "float16", "float16", "float32", 64, 64, 64,
+                                        1, 128)
 
 
 def matmul(

testing/python/kernel/test_tilelang_gemm_mma_intrinsic.py

@@ -13,7 +13,7 @@ from tilelang.intrinsics.mma_macro_generator import (
     TensorCoreIntrinEmitter,)
 from tilelang.transform import simplify_prim_func
 
-torch.manual_seed(0)
+tilelang.testing.set_random_seed(0)
 
 
 def make_swizzle_layout(shared_buf):

testing/python/kernel/test_tilelang_gemm_simt.py

@@ -11,7 +11,7 @@ import tilelang.language as T
 from tilelang.intrinsics import get_swizzle_layout
 from tilelang.transform import simplify_prim_func
 
-torch.manual_seed(0)
+tilelang.testing.set_random_seed(0)
 
 
 def make_swizzle_layout(shared_buf):

testing/python/kernel/test_tilelang_int4_mma_matmul.py

@@ -17,7 +17,7 @@ from tilelang.intrinsics.mma_macro_generator import (
 )
 from tilelang.transform import simplify_prim_func
 
-torch.manual_seed(0)
+tilelang.testing.set_random_seed(0)
 
 
 @simplify_prim_func

testing/python/transform/test_tilelang_transform_annotate_device_regions.py

-# Licensed to the Apache Software Foundation (ASF) under one
-# or more contributor license agreements.  See the NOTICE file
-# distributed with this work for additional information
-# regarding copyright ownership.  The ASF licenses this file
-# to you under the Apache License, Version 2.0 (the
-# "License"); you may not use this file except in compliance
-# with the License.  You may obtain a copy of the License at
-#
-#   http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing,
-# software distributed under the License is distributed on an
-# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-# KIND, either express or implied.  See the License for the
-# specific language governing permissions and limitations
-# under the License.
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT License.
 
 import tilelang
 import tilelang.testing

testing/python/transform/test_tilelang_transform_make_packed_api.py

-# Licensed to the Apache Software Foundation (ASF) under one
-# or more contributor license agreements.  See the NOTICE file
-# distributed with this work for additional information
-# regarding copyright ownership.  The ASF licenses this file
-# to you under the Apache License, Version 2.0 (the
-# "License"); you may not use this file except in compliance
-# with the License.  You may obtain a copy of the License at
-#
-#   http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing,
-# software distributed under the License is distributed on an
-# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-# KIND, either express or implied.  See the License for the
-# specific language governing permissions and limitations
-# under the License.
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT License.
 
 import pytest
 

testing/python/transform/test_tilelang_transform_simplify.py

 # Copyright (c) Microsoft Corporation.
 # Licensed under the MIT License.
+
 from tilelang import tvm as tvm
 import tilelang as tl
 import tilelang.language as T

tilelang/jit/kernel.py

@@ -69,7 +69,8 @@ class JITKernel(object):
         target = Target(target)
 
         # Validate the execution backend.
-        assert execution_backend in ["dl_pack", "torch_cpp", "ctypes"], "Invalid execution backend."
+        assert execution_backend in ["dl_pack", "torch_cpp",
+                                     "ctypes"], f"Invalid execution backend. {execution_backend}"
 
         # Compile the TileLang function and create a kernel adapter for execution.
         adapter = self._compile_and_create_adapter(func)

tilelang/testing/__init__.py

@@ -3,6 +3,9 @@
 import sys
 import inspect
 import pytest
+import random
+import torch
+import numpy as np
 from tvm.testing.utils import *
 
 
@@ -75,3 +78,11 @@ def torch_assert_close(tensor_a,
             f"Greatest relative difference: {(diff / (torch.abs(tensor_b) + 1e-12)).max().item()}.")
     else:
         return True
+
+
+def set_random_seed(seed: int) -> None:
+    random.seed(seed)
+    np.random.seed(seed)
+    torch.manual_seed(seed)
+    if torch.cuda.is_available():
+        torch.cuda.manual_seed_all(seed)

tilelang/utils/tensor.py

@@ -20,8 +20,7 @@ def get_tensor_supply(supply_type: TensorSupplyType):
     def get_tensor(tensor: TensorType) -> torch.Tensor:
         dtype = torch.__getattribute__(str(tensor.dtype))
         device = torch.cuda.current_device()
-        # torch.manual_seed(0)
-        # torch.cuda.manual_seed(0)
+
         shape = list(map(int, tensor.shape))
         if dtype == torch.int8 and supply_type in [
                 TensorSupplyType.Uniform,
@@ -30,7 +29,11 @@ def get_tensor_supply(supply_type: TensorSupplyType):
             return torch.ones(*shape, device=device, dtype=dtype)
 
         if supply_type == TensorSupplyType.Integer:
-            return torch.randint(low=-2, high=3, size=shape, device=device, dtype=dtype)
+            is_unsigned = tensor.dtype.startswith("uint")
+            if is_unsigned:
+                return torch.randint(low=0, high=3, size=shape, device=device, dtype=dtype)
+            else:
+                return torch.randint(low=-2, high=3, size=shape, device=device, dtype=dtype)
         elif supply_type == TensorSupplyType.Uniform:
             return torch.empty(*shape, device=device, dtype=dtype).uniform_(-1.0, 1.0)
         elif supply_type == TensorSupplyType.Normal: